Environ. Sci. Technol. 1985, 19, 821-828
Diffusion and Reaction of Pollutants in Stratus Clouds: Application to Nocturnal Acid Formation in Plumes Christian Seigneur* and Pradeep Saxena
Systems Applications, Inc., 101 Lucas Valley Road, San Rafael, California 94903 Vince A. Mirabella
Research and Development, Southern California Edison, Rosemead, California 9 1770
A mathematical model is presented that describes the transport, turbulent diffusion, and chemical reactions of air pollutants in stratus clouds. The chemical kinetic mechanism treats 97 gaseous, heterogeneous, and aqueous reactions between 54 species. The dispersion and nighttime chemistry of a power plant plume in a stratus cloud is simulated. The contributions of various chemical pathways to the formation of sulfate and nitrate, the differences between plume and background concentrations, and the effect of reduced primary emissions on secondary pollutants are discussed. Calculated sulfate and nitrate concentrations are commensurate with measured atmospheric concentrations. ( I ) Introduction The chemistry of pollutants in clouds is of particular interest because of its relevance to the formation of sulfate and nitrate species. These species contribute to several adverse environmental phenomena such as acid deposition, high ambient concentrations of inhalable particulate matter, and atmospheric visibility degradation. Development of efficient control strategies for the improvement of air quality requires determination of the pathways that lead to sulfate and nitrate formation and the quantification of the relationships between the concentration levels of secondary pollutants such as sulfate and nitrate and those of their precursors, i.e., sulfur dioxide (SO,) and nitrogen oxides (NO,), respectively. Such relationships are complex because of the nonlinearity of the atmospheric chemistry of their pollutants. Seigneur and Saxena (1)used a detailed gas-phase/liquid-phase chemical kinetic mechanism to investigate the pathways that are predominant in sulfate and nitrate formation in different environments. Their chemical kinetic simulations showed that the importance of various pathways varies with ambient conditions. Seigneur et al. (2) used the same chemical kinetic mechanism to study the effect of reductions in SOz, NO,, and reactive hydrocarbons on sulfate and nitrate concentrations. Their model calculations suggested that, in the gas phase, a certain percentage reduction in SOz and NO, concentrations leads to a nearly linear reduction, i.e., a nearly equal percentage reduction, in sulfate and nitrate concentrations, respectively. However, in clouds or fog, reductions in SOz and NO, concentrations lead to less-than-linear reductions in sulfate and nitrate concentrations, respectively. This nonlinearity of the cloud chemistry of sulfate and nitrate formation is an important aspect of any emission control strategy since it affects the magnitude of reductions in sulfate and nitrate concentrations that would result from reductions in SOz and NO, emissions. The analysis of Seigneur and co-workers (I,2) focused on the chemistry of sulfate and nitrate formation, and no consideration was given to atmospheric diffusion processes. Since the availability of chemical species to react depends 0013-936X/85/0919-0821$01.50/0
on both diffusion and chemical reaction, diffusion is likely to affect not only the relative importance of the various chemical pathways leading to acid species formation but also the relationships between precursors and acid species. The study of the reaction and diffusion processes of acid species formation in clouds is, therefore, a critical element of our understanding of the relationships between the concentrations of such species and their precursor concentrations. In this paper we extend the analysis of the atmospheric chemistry of sulfate and nitrate formation to incorporate simultaneous diffusion and chemical reactions in clouds. We have developed a mathematical model that describes the turbulent diffusion and reaction processes that govern the atmospheric concentrations of chemical species in clouds. Because the modeling of cumulus clouds would require additional treatment of cloud dynamics such as entrainment of air into the clouds and droplet formation by condensing water vapor, the model presented here applies primarily to stratus clouds. The model incorporates a detailed treatment of gas-phase and liquid-phase atmospheric chemistry, and turbulent diffusion processes. Section 2 presents the formulation of the mathematical model. In section 3, the model simulates the chemistry of a power plant plume released in a stratus cloud layer, and the importance of various chemical pathways and diffusion processes involved in the formation of secondary pollutants is discussed. The effect on secondary pollutants of controlling precursor emissions is investigated in section 4. Concluding remarks are presented in section 5.
(2) Formulation of the Model The diffusion and reaction of chemical species in stratus clouds are treated by a Lagrangian mathematical model based on a two-dimensional grid that follows the mean wind flow. The grid size, which can be set as desired, Le., with columns of various widths and layers of various depths, remains constant throughout a simulation. This type of grid structure is particularly useful in simulating the dispersion of stack plumes in the atmosphere. The structure of the model as it was used in the present study is based on equidistant columns and grids and is illustrated in Figure 1. The chemical species concentrations are calculated as an ensemble average for each grid cell from the atmospheric diffusion equation (3):
where Ci is the ensemble and spatially averaged concentration of species i, N is the total number of species modeled, K,, and K, are the horizontal and vertical turbulent diffusion coefficients, respectively, Ri is the reaction rate of species i and depends on other species concentra-
0 1985 American Chemical Society
Environ. Scl. Technol., Vol. 19, No. 9, 1985
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Stratus Cloud
Figure 1. Schematic description of the dlffusion/reaction plume model in a stratus cloud,
tions and on kinetic rate constants kl,..., kM, Tiis the diffusion-limited mass-transfer rate of species i and depends on the concentration of species i and the cloud droplet size distribution n(r),where r is the radius of the cloud droplet, and Si is the emission rate of species i. In addition to the continuity equation (eq l),thermodynamic equilibrium is assumed to be maintained across the interface of the interstitial gas phase and cloud droplet liquid phase and within the liquid phase: i = 1, ..., N’ (2) Ci = Ei(C1, ..., CN,,K1, ..., KE) where Ei represents the thermodynamic equilibrium, i.e., Henry’s law for gas/liquid-phase equilibrium or chemical equilibrium in the liquid phase, and K j values, j = 1, ..., E, are the equilibrium constants. The equilibrium equation (eq 2) is solved for N’-soluble gas-phase species and liquid-phase species involved in chemical equilibria. In the numerical solution of the model equations, the transport and chemical kinetics are treated first for all species (eq 1);thermodynamic equilibrium is then calculated (eq 2). Finally, the system is determined by its initial and boundary conditions: Ci = C? at t = 0 i = 1, ..., N
Ci = C p a t y = kY,z = 2 aCi/az = 0 a t z = -2 (cloud base)
i = 1,..., N i = 1, ..., N
(3)
where f Y and &Z represent the boundaries of the grid domain. The boundary conditions for the top and sides of the grid state that the species concentrations are equal to the background concentrations. These boundary conditions determine the flux through the grid boundaries according to the gradient between the boundary concentrations and the upper and side grid cell concentrations. The lower boundary condition states that the strong temperature inversion at the cloud base precludes vertical C are selected from atmospheric diffusion. The values of : data. The values of C;b are calculated as function of time 822
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by the model. This calculation is based on eq 1 (without the diffusion terms) and eq 2. Equations 1-3 are the basic equations representing a gas-phaselliquid-phase system undergoing reaction and diffusion processes. I t is assumed that the system is conserved by the mean wind flow, i.e., there are no significant wind shear, convergence, or divergence effects. The treatment of turbulent diffusion in this two-dimensional grid model is based on the model of Godden and Lurmann (4). In order to mathematically describe the diffusion of a plume in a two-dimensional grid system, it is appropriate to relate the Lagrangian and Eulerian equations of the diffusion of a slender plume (Le., when axial diffusion is neglected) with Gaussian concentration fields. These relationships between the eddy-diffusion coefficients, K y and K , (Eulerian atmospheric diffusion equation) and the standard deviations cry and u, of the plume spread (Lagrangian Gaussian diffusion equation) are expressed as follows (e.g., see ref 5 and 6): (4)
These relationships hold for time scales larger than the Lagrangian integral time scale that is on the order of 100 s. The horizontal and vertical eddy-diffusion coefficients were determined for this particular application of the model from the dispersion coefficients of Pasquill-Gifford-Turner (7), where cry and (T, are the horizontal and vertical dispersion coefficients, which are functions of atmospheric stability and distance from the source. Stable atmospheric conditions are assumed for stratus clouds. The chemical kinetic mechanism describes the chemistry of nitrogen oxides (NO,), reactive hydrocarbons (RHC), and sulfur dioxide (SO,). The mechanism includes a total of 97 chemical reactions occurring in the gas phase, at the surface of cloud droplets, and in the liquid phase. The gas-phase chemical mechanism is based on the carbon-bond mechanism (8). The liquid-phase chemical mechanism has been presented and discussed in detail
elsewhere (1,9). In its present formulation, the model does not include temperature dependence, and kinetic and thermodynamic parameter values are for the reference temperature of 25 "C. Such secondaqy pollutants as ozone, sulfate, and nitrate are formed from the chemical interactions of the precursors NO,, RHC, and SO2. Ozone and other photochemical oxidants such as hydrogen peroxide and peroxyacetyl nitrate (PAN) are formed from the gas-phase photochemistry of NO, and RHC. Sulfate is formed in clouds primarily from the oxidation of SO2by OH radicals in the interstitial gas phase and by the aqueous oxidation of S(1V) by H202, O2 (catalyzed by Mn2+ and Fe3+), and 03. Nitrate is formed slowly in the liquid phase from the autooxidation of NO2 and in the gas phase via the oxidation of NOzby OH radicals and by the reaction of NO2 with 03, which leads to NO3 radicals. These NO3 radicals may then react with NO2to form N205,which is subsequently hydrolyzed to HN03 at the droplet surface. The NO3 radicals may also be scavenged by droplets or react in the gas phase with phenols and aldehydes to form nitrate. The relative importance of these chemical pathways to the formation of acid species varies according to the ambient conditions (1). The mass transfer of species from the gas phase to the liquid phase is treated as a diffusion-limited process for radicals and H2S04and appears as the term Ti in eq 1. The gas and liquid phases are assumed to be well mixed. This assumption for the liquid phase is valid for droplets with radii smaller than 10 pm (10, 11). The scavenging of radicals is thought to be a reversible process (12). However, because of a lack of data for Henry's law equilibrium of radicals except for H 0 2and because of the large uncertainty associated with the efficiency of the scavenging process, which is assumed to be 1%in our model, we did not pursue a more detailed treatment of radical scavenging and assumed that radical scavenging was irreversible. A monodispersed cloud droplet distribution is assumed. This hypothesis primarily affects the scavenging rate of radicals and has no effect on the equilibrium concentrations of other chemical species. Thus, if necessary, the cloud droplet distribution can be easily included in the term T,in eq 1. However, because of uncertainties in the efficiency of the scavenging process, the use of a polydispersed cloud droplet population is not justified for this model. The thermodynamic equilibrium across the gas/liquid interface is treated according to the constraints of eq 2. Henry's law is applied to 11 gases (HN03, H202,HCHO, NH3, HN02,CH3C002N02,SO2, C02, 03,NO2,and NO). Twenty-two liquid-phase chemical equilibria are also treated according to eq 2. In addition, electroneutrality of the liquid phase is satisfied. The differential equations are solved by means of a numerical technique suitable for stiff systems (13). The thermodynamic equilibrium and electroneutrality equations are solved iteratively by using Newton's method. (3) Application of the Model The diffusion/reaction model was used to investigate the formation of oxidants and acid species, namely, 03, OH, H202,NO3, sulfate, and nitrate, in a power plant plume released in a stratus cloud. Experimental measurements of stratus cloud chemical composition conducted in the Los Angeles basin can be used as model input data and, to a certain extent, to evaluate the model calculations. Since these measurements were performed at night, we conducted simulations for nighttime conditions. The duration of each simulation was 3 h (midnight to 3:OO a.m.).
Table I. Key Parameters for Power Plant Plume Simulations parameter
value
background concentrations' so29 PPb NO,, PPb 0 3 , PPb "3, ppb HzOz, PPb RHC, P P ~ COD PPm sulfate, ~ g . m - ~ nitrate, ~ g - m - ~ ammonium, ~ g - m - ~ Fe3t, ~ g - m - ~ Mn2+,~ g m - ~ MgZt, Nat, ~ g . m - ~ Cl-, ~ g - m - ~ emissiong (base case) SOz t-year-l NO,, t-year-l cloud water content, gm-3 droplet radius, Pm temperature,* "C accommodation coefficient wind speed, m-sd
5 30 70 0.1 1 1700 320 12 30 6.6 1.1 0.033 0.42 0.67 1.79
104 104 0.2 5.0 25 0.01 5
"Richards et al. (14). bReference temperature for the kinetic and thermodvnamic Darameters. 50
40
0
i
I
I. 0
3
2
1
Tlma (houri)
Figure 2. Evolution of sulfate concentrations in the plume (model central cell). (-) Total sulfate; (*e*) Initial sulfate: I---) sulfate formed via SO,(g) OH(g); (---) sulfate formed via S(IV)(aq) H O,(aq); (-) sulfate formed via S(1VXaq) 4- O,(aq) (with Mn2+and Fe3$ catalysis).
+
+
The plume was released in the central cell of the grid. This grid, depicted in Figure 1,consists of three layers that are 50 m thick and five columns that are 500 m wide. Concentrations selected are typical of those observed in the Los Angeles basin. Table I presents background concentrations, precursor emission rates, and other parameters used in this study. Background concentrations are derived from the measurements of Richards et al. (14), and emission rates are typical of those from a large power plant in the Los Angeles area. The results of these simulations illustrate the range of relevant information that this model is capable of providing, particularly in regard to the interacting effects of diffusion and reaction, and the relative contributions of various chemical pathways to the formation of secondary pollutants. We first discuss the concentrations of sulfate, nitrate, and the pH value calculated for the plume center. We then extend our discussion to the chemistry of the plume relative to the ambient background chemistry by studying the concentrations of sulfate and nitrate in the plume in excess of the background and the relative importance of Envlron. Sci. Technol., Vol. 19, No. 9, 1985
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9
2.8
2.6
2.4
I
2.2
Flgure 3. Evolution of nitrate concentrations In the plume (model central cell). (-) Total nitrate; ( e *) initial nitrate: (- --) nitrate formed H,O(I); (.-) via NO,(g) 4- OH(g); (- -) nitrate formed vla N,O,(g) Nitrate formed via NO,(g) 4- PHEN(g); (- -) nitrate formed via NO,(g) 4- H,O(I).
-
-
+
various pathways for sulfate and nitrate formation in the plume and in the background. Figure 2 presents the sulfate concentrations in the central cell of the model, i.e., plume center, as a function of time. The contributions of the various pathways for oxidation of SOz to sulfate are also presented. The total sulfate plume concentration increases from an initial value of 12 to 28 rg-m-, during the first few hours when sulfate formation occurs rapidly and overtakes the dispersion process. Sulfate formation and dispersion nearly balance each other during the second hour, and the plume sulfate concentration remains nearly constant. After 2 h, the plume dispersion becomes more dominant than sulfate formation, and the plume sulfate concentration decreases to 26 pgm-, after 3 h of simulation. The major chemical pathways leading to sulfate formation in the plume are the gas-phasereaction of SOzwith OH and the liquid-phase reactions of S(IV)(aq) with H20z(aq)and with Oz(aq) catalyzed by Mn2+and Fe3+. The reaction of SOz with OH is an important pathway despite the nighttime conditions that prevent photochemical activity, because OH radicals are produced via the thermal decomposition of peroxyacetyl nitrate (PAN). The decomposition of PAN leads to OH formation if NO concentration levels are sufficiently high (e.e., see ref 1 and 15). High concentration levels of OH radicals have been reported in the atmosphere during nighttime (16). In our model simulations, OH radical concentrations in the plume central cell were calculated to be 7 X 1.5 X and 2X ppm after 1 , 2 , and 3 h of simulation, respectively. The OH concentrations decrease as the plume is dispersed, and NO concentrations decrease. The oxidation of S(IV)(aq)by H202(aq)and 02(aq)catalyzed by trace metals occurs rapidly. The rates of these reactions are fairly high at low pH values, and the pH in the plume decreases from 2.75 to 2.2 during the 3-h simulation. Oxidation of S(IV)(aq)by 03(aq) is not important because this reaction is slow at these pH values. Figure 3 presents the nitrate concentrations in the central cell of the model, as a function of time. The total nitrate concentration increases from 30 to 80 pgm-, during the first 1.5 h. This increase is due primarily to the gasphase reaction of NO2with OH, which is produced through the thermal decomposition of PAN in the presence of NO. Other chemical pathways that lead to nitrate formation involve NO, radicals, which are important in the nighttime chemistry of HNO, (17). NO, radicals may react with NOz to form N205(which is subsequently hydrolyzed to "OB) or with phenolic compounds to form HNO,; the NO3 radicals may also be oxidized in solution to form nitrate 824
Environ. Sci. Technol., Vol. 19,
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2.0
.
1.8
-
1.6
0
1
2
Time (hours)
Flgure 4. Evolution of cloud water pH. (.-) background.
(-e-)
Plume (model central cell);
ions. After 1.5 h, plume dispersion balances the formation of nitrate in the plume, and plume nitrate concentrations remain nearly constant during the rest of the simulation. As plume dispersion becomes significant, the relative contribution of the reaction of NOz with OH decreases, whereas the contribution of the reactions involving the NO, radical increases. This phenomenon is discussed later when we compare the formation of nitrate in the plume and in the background. The pH of cloud water is shown in Figure 4. Since the acidity level is directly related to the level of sulfate and nitrate concentrations in the plume, the pH drops rapidly near the emission source but decreases more slowly after about 1 h of simulation due to plume dilution. The pH difference between the plume center and the background is largest at 80 min (0.23 pH unit); after this time, dilution of the plume leads to a decrease in sulfate and nitrate concentrations that is not completely compensated by the faster formation rate of these species in the plume than in the background. The pH values calculated by the model range from 2.8 to 2.3 for the background and from 2.8 to 2.2 for the plume center. Richards et al. (14) have reported pH values as low as 2.4 in stratus clouds. The lower calculated values, at the end of the simulation, result from the fact that the model initial concentrations were typical of those reported by Richards et al. (14) and reactions yielding more sulfate and nitrate occurred during the 3-h simulation period. Figure 5 presents the sulfate formed in the central cell of the plume in excess of the background for the total sulfate concentration and the relative contributions of the various sulfate formation pathways. Figure 6 presents similar information for nitrate. The amount of sulfate formed in the plume in excess of the background levels is at most 12 pgm-,. This value is commensurate with measurements reported by Richards et al. (14).In airborne flights around Fontana, Richards et al. (14) reported sulfate concentrations in a plume of ~ background levels. about 15-60 y g ~ m -above As the discussion of Figure 2 showed, sulfate concentrations in the plume increase primarily during the first hour of simulation when the rate of sulfate formation is
Table 11. Reactions of the Chemical Kinetic Mechanism Involved in NO9 Chemistryu
reaction no. 1
2 3 4 5 6 7 8 9 10
uncertainty in rate parameter (multiplicative factor)
reaction
rate
NO + O3 NOz + O2 NO + NO3 2NOZ NOz + O3 NO3 + O2 NO3 NO,NO2 + NOS Nz06 Nz06E& 2HN03(aq) PHOC+ HN03 PHEN' + NO, CARB' + NO3 HN03 + HOz + CO
27 ppm-l mi& 2.8 X lo4 ppm-' m i d ppm-' min-' 4.8 X 0.43 m i d 1280 ppm-' 0.25 min-' 5000 ppm-' min-l 1.76 ppm-' min-' 1.32 min-' 10.8 min-Id
--
*
+
NO3 k NO Oz NO3F!. NO^ + 0
ref
1.2 3 1.15 10 1.7
18 18 18 19 18 17 8 18, 20 18 18
10 5
5
"Rate parameters are given for a temperature of 25 OC. bFor a cloud water content of 0.2 pm-3 and droplet radius of 5 pm. 'PHEN = phenolic species; PHO = phenoxy radical; CARB = carbonyl group. dValue at noon. These photolytic reactions are included in OUT mechanism, but their rates were zero in our nighttime simulations.
*---. /'
12
...'.
'.
/' / I
.
/
/ I
'\
I
10
3
E 2 8 c 0
2
I
C
"
B
/
6
0
d 2 $
4
2
0 0
1
2
3
0
2
1
Time (hours)
Time (hours)
Figure 5. Sulfate concentrations in the plume (model central cell) in excess of the background. (-) Total sulfate; (- -) sulfate formed via SO,(g) 4- OH(g); (---) sulfate formed via S(IV)(aq) 4- H 02(aq); (-e) sulfate formed via S(IV)(aq) O,(aq) (with Mn2+ and Fe3' catalysis).
Figure 6. Nitrate concentrations in the plume (model central cell) in excess of the background. (-) Total nitrate; (- -) nitrate formed via NO,(g) OH(g); (- -) nitrate formed via N,O,(g) H,O(I); nitrate PHEN(g); (- -) nitrate formed via NO,(g)+ H,O(I). formed via NO,(g)
faster than that of plume sulfate dispersion. This phenomenon is exemplified in Figure 5 as the plume excess sulfate concentration reaches its maximum value at 70 min. As the plume disperses, this peak gradually erodes and the plume sulfate concentration tends to revert to background levels. The gas-phase reaction of SO2 with OH is the most important pathway for formation of plume sulfate in excess of background sulfate because OH concentration levels are higher in the plume than in the background. Thus, OH production is greater in the plume, where NO concentiation levels are higher than in the background. For exampprn in the plume ple, OH concentrations were 7 X central cell after 1h of simulation compared to 9 X ppm in the background. Another mechanism that contributes appreciably to the total amount of sulfate formation is the liquid-phase oxidation of S(IV)(aq)by 02(aq) catalyzed by trace metals such as Mn2+and Fe3+. As SO2 from the plume mixes with H202from the background, this pathway becomes important, and its effect is evident in the larger increase in excess sulfate formed through the reaction of S(1V)(as) with H20z(aq)that becomes appreciable after 45 min of simulation.
Simulation results for nitrate concentrations are shown in Figure 6. The amount of nitrate formed in the plume in excess of the background concentrations is at most 20 p~g'm-~. It should be noted, however, that nitrate concentrations are calculated to be as much as 3 ~ g - mlower - ~ in the plume than in the background at the beginning of the simulation. Richards et al. (14) reported nitrate concen- ~ or 6 ~ g - m higher -~ than trations to be 20 ~ g - m lower background values, depending on background samples. Therefore, our model calculations are qualitatively consistent with currently available data. Oxidation of NOz by OH radicals in the gas phase contributes most significantly to the total amount of plume excess nitrate formation because higher OH concentrations are present in the plume than in the background. Nitrate formation due to NO3 radical reactions occurs more rapidly in the background than in the plume, leading to the negative excess concentrations shown in Figure 6. This phenomenon can be understood by reviewing the NO3 reactions presented in Table 11. NO is oxidized by O3 and NO3 at a significantly faster rate than i s NO2. High NO concentrations in the plume
-
+
+
-
+
-
+
(-e)
Environ. Sci. Technol., Vol. 19, No. 9, 1985
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100
J
--___
_ / - - - -
-I
Tima (hours)
Time (houri)
Figure 7. Effect of emission reductions on sulfate concentrations in the plume (model central cell). (-) Plume concentration (base case); (- -) plume concentration (50 % SO, emission reduction); (.-I plume concentration (50% NO, emisslon reduction); (- -) background concentration.
-
-
deplete not only 0,that is available for NO, radical formation, but also NO,, by converting it back to NO2. Therefore, since NO, concentrations are higher in the background atmosphere than in the plume, most nitrate formation mechanisms involving NO, radicals such as reactions 4-6 and 8 occur at a faster rate in the background than in the plume. The phenol concentration level is affected by OH radical concentrations since phenols are a product of the oxidation of toluene by OH radicals (21-23). Thus, phenol concentrations are higher in the plume than in the background. Since the formation of nitrate by reaction 7 depends on the level of phenol and NO, concentrations, there is a compensating effect between the phenol and NO, concentrations, which are, respectively, higher and lower in the plume than in the background. Reaction 7 becomes more important in the plume than in the background because the excess in plume phenol concentrations exceeds the deficit in plume NO, concentrations. ( 4 ) Sensitivity of Acid Formation to Emission Rates The effect of emission reductions on acid formation is a major consideration in the acid deposition issue; therefore, it is of interest to investigate the relationship between sulfate and nitrate formation and emission levels. Seigneur et al. (2) studied this relationship using a box model to simulate typical conditions in the Midwestern-Northeastern United States. They concluded that some nonlinearities exist between SOz and NO, emissions and sulfate and nitrate formation, respectively, when cloud chemistry takes place. In this section we present the results of power plant plume simulations in which SO2 and NO, emissions from the point source were independently reduced by 50%. Figures 7 and 8 present the concentrations of sulfate and nitrate in the plume center and in the background. Results are shown for the base case simulation, the simulation with 50% SO2 emission reduction, and the Simulation with 50% NO, emission reduction. Background concentration levels are the same for all three simulations. Fifty Percent Reduction of SO2 Emissions. A 50% reduction of SO2emissions results in reductions of 32, 26, and 20% in the amount of sulfate formed in the plume after 1, 2, and 3 h of simulation, respectively. This nonlinear relationship exists because the aqueous reaction of S(IV)(aq) with H202(aq)is a major pathway for the oxidation of SO2 to sulfate; 45% of sulfate formed in the plume after 3 h of simulation results from this reaction. This process is limited by the amount of H202present initially in the plume and later in the background as the 826
Environ. Sci. Technol., Vol. 19, No. 9, 1985
Figure 8. Effect of emission reductions on nitrate concentrations in the plume (model central cell). (-) Plume concentration (base case); plume concentration with 50 % SO2 emission reduction coincides with the base case value; (.e.) plume concentration (50% NO, emission reduction); (- -) background concentration.
-
plume becomes more dilute. A simulation conducted without dispersion indicates that the SOz/sulfate relationship is more nonlinear when the plume is diluted in the background than when it is not diluted since, after 3 h of plume dispersion, the sulfate concentration was reduced by 20% in the former case and by 29% in the latter case. This greater nonlinearity occurs because the aqueous reaction of S(IV)(aq) with H202(aq)becomes more important to total sulfate formation as the plume is diluted with the background. A reduction in SO2 emissions has an insignificant effect on nitrate formation because SOz chemistry has little effect on NO,/RHC chemistry. The gas-phase reaction of SOz with OH yields H2S04 and HOz;if the H 0 2radical is then converted to an OH radical, the effect of SOz on OH concentrations is negligible. It has been shown that, at concentration levels typical of those found in Midwestern United States, SO2 has a small effect on OH radical concentrations (2). At the NO, and RHC concentration levels present in the Los Angeles basin, OH radical concentrations are even less affected by changes in SOz concentrations. Fifty Percent Reduction of NO, Emissions. The effect of a 50% reduction of NO, emissions on nitrate formation is shown in Figure 8. A 50% reduction of NO, emissions yields varying results as the plume is transported downwind. At night, OH radical formation occurs in the plume because of the decomposition of PAN in the presence of NO. Thus, lower NO, concentrations yield lower OH radical concentrations and a lower rate of oxidation of NOz by OH radicals. On the other hand, lower NO, concentrations yield higher 0, concentrations in the plume and, accordingly, higher NO, concentrations. Thus, the rate of oxidation of NO2by NO, radicals and of other oxidation pathways involving NO, radicals is higher. Thus, reducing NO, emissions produces opposite effects on different pathways to nitrate formation, yielding a 38 and 30% reduction in nitrate formation after the first and second hours of simulation, respectively, and an 11% increase in nitrate formation after 3 h of simulation. The effect of NO, emission reduction on nitrate concentration is therefore complex and varies according to the relative importance of various pathways involving OH and NO, radicals for nitrate formation. Since the gas-phase oxidation of SO2 by OH radicals in a major pathway for sulfate formation at night, and because OH concentration levels in the plume are lower when NO concentrations are lower, NO, emission reduction tends to reduce sulfate formation in the plume at night.
(5) Conclusion We have presented a mathematical model that describes the emission, transport, turbulent diffusion, and gas- and liquid-phase chemistries of atmospheric trace gases advected along an air parcel trajectory. This model is particularly useful in studying the diffusion and chemical reaction of pollutants in cloud layers. The model was applied to the study of the chemistry of a power plant plume released in a stratus cloud layer under typical nighttime conditions in the Los Angeles basin. Although, at the moment, there exists no data base to evaluate the model performance, the results of the model simulations were in qualitative agreement with ambient measurements of pollutant concentration levels in stratus clouds (14). Our analysis of the simulation results focused primarily on the formation of sulfate and nitrate species and the chemistry of related oxidant species. Because of relatively low H202concentrations at night, the aqueous-phaseoxidation of SO2 by H202is not the only important pathway of sulfate formation. The gas-phase reaction of SO2 with OH in the plume is important because PAN thermal decomposition in the presence of NO leads to OH concentration levels as high as lo-’ ppm in the plume. Aqueous-phase oxidation of SO2 catalyzed by trace metals is also important for sulfate formation. For nitrate formation, the gas-phase oxidation of NO2 with OH radicals in the plume is important because of the presence of PAN and high NO concentrations. Reactions involving NO3 radicals are important at night; however, they are less important in the plume than in the background because O3 levels are lower in the plume, and the reaction of NOz with O3is the major source of NO3 radicals. Simulations were conducted with reduced SO2 and NO, emissions to evaluate the potential effect of emission controls on acid species formation. These simulations indicate that a 50% reduction in SO2 emissions yields a less-than-linear reduction in sulfate formation-about 20-30% reduction. The S02/sulfate relationship is more nonlinear when plume dilution is taken into account than it is when dilution is not considered, such as in a box model, because the nonlinearity of the S02/sulfate relationship results mainly from the reaction of S(IV)(aq)with HzOz(aq)when this reaction is oxidant limited. In these simulations, the oxidation of SO2 to sulfate is to some extent oxidant limited, and the background air is a source of oxidant for the plume SO2. It should be noted, however, that the S02/sulfate relationship depends on the oxidation pathways leading to sulfate formation, and the results obtained in these simulations should not be generalized to other conditions. Reduction of SO2 emissions has little effect on nitrate formation. A 50% reduction in NO, emissions leads to a reduction in nitrate formation near the stack but to an increase in nitrate formation further downwind as the plume becomes diluted. These changes in nitrate level, due to NO, emission reduction, vary because changes in NO, concentrations either increase or decrease OH concentrations, depending on the NO,/RHC ratio, and also affect NO3 concentrations. Sulfate formation tends to be reduced when NO, emissions are reduced, because of the change in OH concentration levels. The results of these simulations point out the nonlinearities of atmospheric chemistry and diffusion, as well as the need for detailed analytical tools to understand the chemistry of acid formation in the atmosphere and the effect of precursor emissions on secondary pollutants. This work focused on the study of a single power plant plume for nighttime conditions. Further work should investigate
the contributions of other emission sources and the chemistry of acid formation over an entire airshed and other conditions such as daytime and coastal environments. Studies of the model sensitivity to various atmospheric parameters such as cloud temperature and cloud water content would be of particular interest. The effect of reduction in RHC emissions should also be examined since these primary pollutants contribute to the formation of oxidants such as H202,OH, 03,and NO3 through photochemical smog chemistry and are therefore involved in atmospheric acid chemistry.
Acknowledgments We sincerely thank A. B. Hudischewskyjfor performing the model simulations and J. Rodich for her excellent technical editing. We are also grateful to J. H. Seinfeld for providing useful comments on the manuscript. Registry No. SOz, 7446-09-5; NO,, 11104-93-1.
Literature Cited Seigneur, C.; Saxena, P. Atmos. Enuiron. 1984, 18, 2109-2124.
Seigneur,C.; Saxena, P.; Roth, P. M. Science (Washington, D.c.) 1984,225,1028-1030. Seinfeld, J. H. “Air Pollution: Physical and Chemical Fundamentals”; McGraw-Hill: New York, NY 1975; pp 1-523.
Godden, D.; Lurmann, F. W. “Development of the PLMSTAR Model and Its Application to Ozone Episode Conditions in the South Coast Air Basin” Environmental Research and Technology, Newbury Park, CA, 1983, PA702-200.
Csanady, G. T. “Turbulent Diffusion in the Environment”; D. Reidel Publishing Co.: Dordrecht, The Netherlands, 1973; pp 1-248. Seinfeld, J. H. Adv. Chem. Eng. 1983, 12, 209-299. Turner, D. B. “Workbook of Atmospheric Dispersion Estimates”; US.Environmental Protection Agency: Research Triangle Park, NC, 1979; No. AP-26. Killus,J. P.; Whitten, G. Z. “Technical Discussions Relating to the Use of the Carbon-Bond Mechanism in OZIPM/ EKMA”;U.S.Environmental Protection Agency, Research Triangle Park, NC, 1984; EPA-450/4-84-009. Seigneur, C.; Saxena, P. Water Air Soil Pollut. 1985,24, 419-429.
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Schwartz, S. E. In “Chemistry of Multiphase Atmospheric Systems”; Jaeschke, W., Ed.; Springer: Heidelberg, West Germany; in press. Schwartz, S. E. J. Geophys. Res. 1984,89, 11589-11598. Young, T. R.; Boris, J. P. J. Phys. Chem. 1977, 81, 2424-2427.
Richards, L. W.; Anderson, J. A.; Blumenthal, D. L.; Duckhorn, S. L.; McDonald, J. A. “Characterization of Reactants, Reaction Mechanisms, and Reaction Products Leading to Existence of Acid Rain and Acid Aerosol Conditions in Southern California”; California Air Resources Board: Sacramento, CA, 1983. Killus, J. P.; Whitten, G. Z. J. Geophys. Res. 1985, 90, 2430-2432.
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Chameides, W. L.; Davis, D. D. In “Precipitation, Scavenging, Dry Deposition,and Resuspension”;Pruppacher, H. R.; Semonin, R. G.; Slinn, W. G. N., Eds.; Elsevier Science Publishing Co.: New York, NY, 1983; Vol. I, pp 431-443. Environ. Sci. Technol., Vol. 19, No. 9, 1985
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Morris, E. D.; Niki, H. J. Phys. Chem. 1974,78,1337-1338. Atkinson, R.; Carter, W. P. L.; Darnall, K. R.; Winer, A. M.; Pith, J. N., Jr. Int. J . Chem. Kinet. 1980,12,179-837. Killus, J. P.; Whitten, G. 2.Atmos. Environ. 1982, 16, 1973-1988.
(23) Leone, J. A.; Seinfeld, J. H. Int. J. Chem. Kinet. 1984,16, 159-193. Received for review August 10,1984. Revised manuscript received January 4, 1985. Accepted March 19, 1985.
Variability of Aluminum Concentrations in Organs and Whole Bodies of Smallmouth Bass (Mieropferus do/omleui) William G. Brumbaugh“ and Donald A. Kane Columbia National Fisheries Research Laboratory, U.S. Fish and Wildlife Service, Columbia, Missouri 65201
w Variability of aluminum concentrations in smallmouth bass (Micropterus dolomieui) was evaluated by analysis with graphite furnace atomic absorption spectrophotometry. Fish analyzed as whole bodies were compared to fish which had selected organs analyzed individually and separately from the carcass. Gastrointestinal tract contents contained highly variable amounts of aluminum and caused bias and increased variability when included in whole-body samples. Since aluminum concentrations in tissues of stomach and intestine were similar to those in the whole body (less gastrointestinal tract contents), the entire gastrointestinal tract and contents could be removed to reduce bias and variability without measurably altering the “true” whole-body aluminum concentrations. Of the organs analyzed, gill filaments had the highest and most variable aluminum concentrations and may have contributed to within-fish whole-body variability because of incomplete homogenization.
Introduction High soluble concentrations of metals in lakes and streams are often associated with low pH levels as a result of the mobilization or dissolution from sediments or terrestrial soils. As for most metals, solubility of aluminum exhibits a strong inverse relation to pH (1,2). The presence of elevated soluble aluminum concentrations at low pH can adversely affect aquatic organisms. The most thoroughly investigated biological consequence of elevated aluminum concentrations has been the toxicity to fish and sublethal effects on fish (3, 4). Exposure to aluminum and low pH may result in fish mortality because of respiratory stress caused by gill damage and mucous clogging of the gill membrane and to the loss of sodium and chloride ions (5, 6). Sublethal concentrations of aluminum have been shown to cause histopathological changes in the liver, kidney, skin, muscle, and gills and interfere with reproductive physiology in trout (6, 7). These sublethal effects and the influence of aluminum bioaccumulation are gaining interest among researchers. Bioaccumulation of contaminants is often used as an indicator of the ”health” of fish populations (8) and is usually measured in whole-body fish. Accurate assessment of a particular contaminant may require special considerations and can be affected by collection methods, sample preparation procedures, and analysis technique. Analysis of whole fish for aluminum presents many problems. A representative, uncontaminated sample is difficult to obtain, and measurement must be made in a relatively nonuniform and complex matrix. Results from a round robin survey for metals in water samples showed aluminum measurements to be in greatest error (9),which exemplifies the difficulty of measuring low levels of this 828
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element. Since aluminum is the third most abundant element in the earth’s crust, caution must be exercised to avoid dust contamination and excessive sample handling. Aluminum also is a major component of most laboratory glassware and can be leached out by certain reagents (10). Fusion of aluminum from glassware during dry ashing of samples can cause positive errors for which blanks may not give proper correction (11). Aluminum may exhibit anomalous behavior when determined by graphite furnace atomic absorption, although the use of improved graphite materials has greatly reduced this problem (12). The primary objective in the present study was to ascertain potential field and analytical sources of variability and error when whole-body fish samples are analyzed for aluminum.
Experimental Section Sample Collection. Smallmouth bass (Micropterus dolomieui) were collected from Chatuge Reservoir, which is located on the border of Georgia and North Carolina. The reservoir receives runoff from forested watersheds that are poorly buffered (13),and there are no known sources of significant contamination from agriculture within the watershed. Mean alkalinity in the reservoir is about 140 pequiv/L, and average pH is 6.3 for 52 measurements taken over a 6-year period. Aluminum concentrations are widely variable, ranging from 0.06 to 0.94 mg/L (14). After collection by gill netting, electrofishing, and rotenone application, fish were placed in large plastic bags, frozen, and shipped to the Columbia National Fisheries Research Laboratory. Sample Preparation. All dissection tools and homogenization equipment were rinsed with dilute HC1 and then ultrapure water (15-18 Ma-cm specific resistivity) before each sample was processed. Frozen fish used for wholebody analysis were sectioned with a band saw and passed twice through a small meat grinder. Fish which were dissected before analysis were placed on a clean polyethylene sheet, and the liver, kidney, gill filaments, and gastrointestinal tract were removed with stainless steel scalpels and surgical scissors. The gastrointestinal tract was sliced open, and the contents were flushed into preleached beakers with ultrapure water. Large, intact food items were removed and not included in the analysis of the “gut” contents. Analysis of gut tissue included the stomach, intestine, and pyloric caeca. The remaining “carcass”was homogenized by chopping it into sections on a Teflon cutting board and then passing it 3 times through a small meat grinder. Sample Digestion. Digestion glassware was washed sequentially with 16 M HNO,, 12 M HC1, and ultrapure H20. Polyethylene sample bottles were leached for 48 h with a 6 M HN03-2 M HC1 solution, rinsed, and filled with US. Copyright. Published 1985 by the American Chemical Society
