The literature (13) suggests that an increase in salt concentrations would provide a competitive advantage for cyanophytes. Therefore, salt inputs from the development of oil shale could provide a competitive advantage for blue-green algae. The increased presence of cyanophytes would represent a change of species composition of Lake Powell. At present no cyanophytes are common in Lake Powell. Also, it is suggested in the literature review ( 1 0 ) that lower Pearsall ion ratios ()symbol and no percentage difference in time to IOalml. IS reported e Initial ratio of [propylene] to (]propylene] [+butane/) 0 15 In expt 97 0 13 In expts 99 106 113 and 116 0 14 in expt 114 0 07 in expt 115 ‘Fxperiments 231-247 used three varieties of hydrocarbon mixtures I191
+
percentages of bonds in the four categories in the mechanism. However, the number of assumptions required to use it with minimum data is less than for other generalized mechanisms of comparable complexity. The carbon-bond mechanism is based on the approximations that (1)the reactions at a carbon atom (or double bond) are independent of reactions elsewhere in the molecule containing it, and (2) carbon atoms with similar bonding react similarly, and a t similar rates. The first approximation contradicts the stepwise oxidation process, shown in Figure 1, in which the oxidation of an aldehyde converts the adjacent carbon atom into an aldehyde, thus activating it for further reaction. Nevertheless, the first approximation is valid in many applications, principally because of an apparent insensitivity of photochemical kinetic mechanisms to the rate of aldehyde oxidation. Hydroxyl radicals can react with aldehydes to form oxidizing radicals. Thus, increasing the rate of that reaction would be expected to increase ozone formation. However, it also reduces the aldehyde concentration, which in turn lowers the production of radicals from aldehyde photolysis and tends to reduce ozone 696
Environmental Science & Technology
formation. The two effects often cancel in simulations of mixtures of hydrocarbons. For simulating experiments with a single initial hydrocarbon, particularly acetaldehyde, the approximation is not very good. Similarly, the second approximation is most accurate in simulations of a broad mixture of hydrocarbons. For a single hydrocarbon, such as propylene or 2-butene, treating its initial reactions explicitly by using the appropriate rate constants is necessary for accurate results. For urban applications the CBM treats four types of carbon atoms as reactants: PAR (all single-bonded carbon atoms, including those in paraffins, alkyl side chains of aromatics, and so on, but excluding methane and ethane); OLE (all atoms in carbon-carbon double bonds, treated in pairs of carbon atoms, except those in ethylene and aromatic rings); ARO (all atoms in carbon-carbon double bonds in ethylene and aromatic rings); and CAR (carbonyl carbon atoms, whether in aldehydes or ketones). This treatment seems to account adequately for the types of carbon atoms that are important in photochemical smog; carbon atoms in triple bonds, amines, alcohols, and other bonds appear to be unimportant because
-B
0.54
-
0.36
-
n
1.20
---OBSERVED
-
0 c
-n
--c
E n
.r
IJ m U L
E U
U E
0.18
OBSERVED
E
-
sm
E
-PREDICTED
0.90
0.6C
E V
0
I
0
50
100
150
200
250
300
350
400
s
0.3(
Time ( m i n u t e s )
Figure 4. Observed ozone concentrations and predictions of the carbon-bond mechanism for UCR propylene-NO, run EC-16
oL-..0
T i m e (minutes)
-
‘.‘Or
PREDICTED
50
17-
100
: ’
150
200
(
Figure 6. Observed propylene concentrations and predictions of the carbon-bond mechanism for UCR propylene-NO, run EC-16
I
I
I
1
250
300
350
400
Time ( m i n u t e s )
Figure 5. Observed NO and NO2 concentrations and predictions of the carbon-bond mechanism for UCR propylene-NO, run EC-16
few of them are emitted (18). In some specialized applications more or less condensation of the smog mechanism may be appropriate. We have mentioned that a somewhat less condensed mechanism could be formulated by treating primary, secondary, and tertiary single-bonded carbon atoms separately. We have already used a less condensed :set of olefin groupings to model some of the University of North Carolina outdoor smog chamber experiments. These will be described briefly in the next section. In the other direction, we have experimented with some success on a more condensed mechanism that involves only one hydrocarbon and one carbonyl species. We intend to address these topics more fully a t a later date. Validation and Application
The carbon-bond mechanism was developed and validated with the Predictions of explicit mechanisms for smog chamber experiments performed a t the University of California a t Riverside during 1975 and 1976. Experiments were performed with propylene, butane, 1-butene, and a mixture of propylene and butane as the initial hydrocarbon species. The differential equations from the application of the relevant explicit mechanism and the carbon-bond mechanism were solved using the comput.er program CHEMK (27),which treats dilution during sampling in smog chambers and includes a plotting routine. In addition to comparisons with the explicit simulations of propylene, butane, and 1-butene, the carbon-bond mechanism was used to simulate a number of toluene experiments for which no explicit simulation mechanisms yet exist. Thus, the “slow double bond” hydrocarbon grouping is more properly an empirical mechanism than a condensed one. We hope that present work on explicit mechanisms for aromatics and ethylene will support validation of a condensed mechanism for those species. The carbon-bond mechanism is intended to produce predictions similar t o those of explicit mechanisms in less computing time. Consequently, the carbon-bond mechanism is properly validated with data from simulations of smog
chamber experiments using explicit mechanisms rather than with data from the smog chamber experiment itself. In other words, the standard for judging the carbon-bond mechanism is the explicit mechanism. A fit between the predictions of the carbon-bond mechanism and experimental data that is closer than the corresponding fit for an explicit mechanism must be regarded as fortuitous. Of course, the carbon-bond mechanism was developed for use in air quality models, so the final test of its value is how well its predictions fit atmospheric data. Below we discuss validation of the mechanism with both smog chamber studies and atmospheric data. Simulation Results. The use of the carbon-bond mechanism to model smog chamber experiments initiated with a single hydrocarbon presents a paradoxical situation. The best rate constants for simulating a mixture of initial hydrocarbons may not be the best for simulating one hydrocarbon at a time. Furthermore, the optimal rate parameters for simulating smog chamber experiments may not be optimal for application to the atmosphere because the hydrocarbon mixes may differ. The CAR photolysis rate constant used in the following simulations with the carbon-bond mechanism was derived from the constants in the corresponding simulations with explicit kinetic mechanisms as follows. The photolysis rate constant for each carbonyl was multiplied by its simulated concentration a t the time of the highest total carbonyl concentration. These quantities were then added and divided by the highest total carbonyl concentration to obtain the CAR photolysis rate constant. The same initial concentrations of HNOz were used in carbon-bond mechanism simulations and the corresponding simulations with explicit mechanisms. The results of these simulations are presented and discussed in detail elsewhere ( 1 ) and summarized in Table 11. A typical result, showing ozone, NO,, PAN, and hydrocarbon behavior, is given in Figures 4 through 7. The overall results ( 1 ) indicate that the poorest fits to smog chamber data for both the explicit mechanisms and the carbon-bond mechanism are for low HC/NO, ratios, where the rate of hydrocarbon oxidation is low. Figures 8 and 9 show the absolute and percentage differences between the measured maximum 1-h-average ozone concentrations and those predicted by the explicit and carbon-bond mechanisms, respectively. All UCR systems except toluene-NO, are included. These figures show that the average absolute difference and the average percentage difference are small and positive. The indicated positive bias is not statistically significant for two reasons: The number of experiments is small, and the standard deviation is much larger than the average difference. Figure 10 shows the absolute and percentage differences between the maximum 1-h-average ozone concentrations predicted by the carbon-bond mechanism and those predicted Volume 14, Number 6, June 1980
697
,-:I I
0 . 3 0 ~
OBSERVE0 PREDICTED
I
AVERAGE DIFFERENCE =
1
'S 0.15 m
42 S U
s
o0
200 250 Time ( m i n u t e s )
150
100
50
300
350
4
400
TANDARD DEVIATION = k7%
Figure 7. Observed peroxyacetyl nitrate (PAN) concentrations and predictions of the carbon-bond mechanism for UCR propylene-NO, run EC-16 10 I
1
st
-60
I
-50
-40
-30
-20 -10
0
10
20
Difference (percent)
I AVERAGE DIFFERENCE
a
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=
dlLlQsl
(a)
I
Percentage D i f f e r e n c e s
'1
30
:
40
50
a
AVERAGE DIFFERENCE =
I
STANDARD *DEVIATION = 20%
n
I
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I
I I
L-5 0-50
-40
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)
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I I 10
0
30
40
50
60
Difference (percent) (a)
x
:5
0
% L
2 =J
4-
STANDARD DEVIATION = 0.047 ppm
3-
- 3
-
2-
1
-
-
Percentage D i f f e r e n c e s
10
D i f f e r e n c e (ppm)
I AVERAGE DIFFERENCE = t0.015 ppm
9-
-
(b)
Differences i n Concentration
0-
Figure 9. Differences between predictions of carbon-bond mechanism
f -7-
and measurements of maximum 1-h-average ozone concentrations: (a) percentage differences; (b) differences in concentration
w W
k
6-
0"
5-
0.053 ppm
-
c
D i f f e r e n c e (ppm) (b)
Differences i n Concentration
Figure 8. Differences between predictions of explicit mechanisms and measurements of maximum 1-h-average ozone concentrations: (a) percentage differences; (b) differences in concentration
by the explicit mechanisms. Again, toluene-NO, systems are not included. The small differences and standard deviations in Figure 10 suggest that the carbon-bond mechanism does retain the significant features of the explicit mechanisms even though it requires only 10 t o 20% as much computing time.
Component Runs Results of the simulation of a set of 11 experiments using a mixture of hydrocarbons are also reported in Table 11. The nonupdated carbon-bond mechanism was used except that reaction rate constants 27 and 30 were reduced to 1.1and 50 ppm-' min-' to reflect the high percentage of ethylene in the slow double bond category. Ethylene reacts with NO3 very slowly and does not make PAN. Internal olefins were treated as carbonyls to reflect their high reactivity and rapid decay. 698
Environmental Science & Technology
UNC Outdoor Smog Chamber. The outdoor smog chamber a t the University of North Carolina represents a somewhat better test of an atmospheric kinetic mechanism than does a chamber with an artificial light source such as UCR. We report the June 12-13,1954, experiment, continued for 36 h, involving a high HC and a low HC precursor mix (the UNC chamber has two compartments). Because of the high proportion of internal olefins in the UNC experiment mix, internal and terminal olefins were treated separately and an internal olefin-NO3 reaction was included (rate constant 9000 ppm-l min-I). The rate constant of the chamber-dependent N205 + H20 reaction was lowered to be consistent with prior experience with the UNC chamber. Otherwise, the carbonbond mechanism in Table I was used. The results are presented in Figures 11 and 12. Atmospheric Studies. The use of a kinetic mechanism in a complex air quality model for an urban airshed presents problems of validation that are much greater than those of smog chamber simulations. An airshed model represents the complex interactions among emissions, meteorology, and kinetics. Errors in any phase of the simulation may invalidate the exercise; compensating errors may sometimes give rise to a fallacious validation. Realizing the uncertainties, we report here the results of airshed modeling of three days in Denver, Colo. (28) and two days in Los Angeles (29). Full reporting of these modeling projects in this paper is impractical. Thus we confine ourselves to reporting averages of pollutant measurements at all monitoring stations in the two areas and the corresponding regional average predicted concentrations. We feel that these
.yl
W
E
L 3 U
0 U
16 -
(AVERAGE DIFFERENCE
14
1
12 -
= -8%
-
10 -
Lc
8-
L W
n
6-
J
rSTANOARD D E V I A T I O N = 10%
0
-
z
4-
-
-
-
-
2-
Figure 13. Time variations over all stations of observed and predicted average ozone concentrations in the Denver metropolitan area W VI E
2
3 U 0 U
LC
10 lot 8-
L a n
6-
z
4-
5
I
I STANDARD D E V I A T I O N = * 0.03 ppm
- I
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-8.404.354.304.254.20 -8.40 4 ~ 3 E ~ 3 G ! z E . ' 2 0 -o'.nc!.io -0.15-0.10-0.05 -d.05' D i f f e r e n c e (ppm) (b)
I
l
l
-
PRLDiCTLD
+
I
-
I
-
dT,bG.\o o
0.05 0.10 0.15 0.i5
Differences i n Concentration
Figure 10. Differences between the maximum 1-h-average ozone concentrations predicted by the carbon-bond mechanism and by the explicit mechanisms: (a) percentage differences; (b) differences in concentration
-
0.4
-
5
1
9
13
17
I
I
I
21
I
I
1 5 Time (hour)
13
9
I 1
I 17
Figure 11. Simulation of results from a 36-h outdoor smog chamber experiment at UNC-high HC precursor mix
0.4
e
I
I
I
-..-
I
I
I
I
03
-03 (SIMULATED) -.-
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NO2 + PAN
NO2 + PAN (SIMULATED)
0
.-
" 0 5
9
13
17
21
1
5
9
13
17
Time ( h o u r )
Figure 12. Simulation of results from a 36-h outdoor smog chamber experiment at UNC-low HC precursor mix
comparisons represent the best test of the kinetic mechanism as opposed to the treatment of meterological variables. Figure 13 shows the average results for the Denver simulations. I t is clear that one could not hope for a better agreement (the underprediction at low concentrations is an inevitable product of the grid averaging assumption, which eliminates the effect of subgrid concentration variations on 1-h-average concentrations (30)). We emphasize that no "tuning" of the carbon-bond mechanism was used to obtain these results. Figures 14 and 15 show the basin-averaged results for the Los Angeles studies. Air quality modeling of Los Angeles is more difficult than modeling of Denver; the wind fields in Los Angeles are more complex and the initial conditions (representing pollutants still remaining from the previous day) are more important. Two sets of wind fields and initial conditions were prepared for each day. The same version of the carbonbond mechanism, however, was used in all simulations. We believe that it is reasonable to conclude that chemistry is apparently not the present limiting factor in air quality modeling of urban airsheads. Summary Remarks Much work remains to be done in the elucidation of the important features of smog photochemistry, notably in the areas of aromatic hydrocarbon oxidation and the inciusion of temperature effects. However, we believe that presently
Volume
14, Number 6, June 1980
699
-
PREDICTED
Hour
(Local Daylight
Time)
Figure 15. Basin-wide average ozone concentrations in Los Angeles predicted by the carbon-bond mechanism (for two sets of initial pollutant concentrations) and measured on August 4, 1975
available photochemical kinetic mechanisms are sufficiently developed for most practical applications to the atmosphere. T o this end, we believe that the carbon-bond mechanism offers advantages in realism and ease of application. These advantages include carbon mass conservation, elimination of assumptions concerning average molecular weights, reduction of the span of rate constants covered by each grouped species, and reduced computing requirements. Whatever the particular needs of any modeling application, these are factors that should a t least be considered. Acknowledgment
We thank Marcia C. Dodge of the Environmental Protection Agency for many helpful discussions and advice in the review of this manuscript. We are also indebted to Mark Meldgin and Caroline Cochran for their editorial assistance. L i t e r a t u r e Cited (1) Whitten, G. Z.; Hogo, H. “Mathematical Modeling of Simulated Photochemical Smog”, Systems Applications, Inc., San Rafael, Calif., EPA-600/3-77-011. (2) Benson, S. W. “Thermochemical Kinetics”; Wiley: New York, 1968. (3) Greiner, N. R. J . Chem. Phys. 1970,53, 1070-1076. (4) Hecht, T. A.; Seinfeld, J. H.; Dodge, M. C. Enuiron. Sci. Technol.
1974,8, 327-339. ( 5 ) Demerjian, K. L.; Kerr, J. A,; Calvert, J. G. In “Advances in Environmental Science Technology”, Pitts, J. N., Metcalf, R. L., Eds.; Wiley: New York, 1974. (6) Niki, H.; Daby, E. E.; Weinstock, B. Adu. Chem. Ser. 1972, No. 113. (7) Chameides, W. L.; Walker, J. C. G. J . Geophys. Res. 1973, 78, 8751-8760. (8) Graedel. T. E.: Farrow. L. A.: Weber. T. A. Enuiron. Sci. Technol. 1977,11, 690-694. (9) Hampson, R. F.; Garvin, D. Nutl. Bur. Stand. ( U S . ) ,Tech. Note 1975. No. 866. (10) Hknpson,R. F.;Garvin,D. Natl. Bur. Stand. (U.S.),Spec. Publ. 1978, No. 513. (11) Nlki, H., et al. Chem. Phys. Lett. 1977,45, 564-566. (12) Graham, R. A.; Winer, A. M.; Pitts, J. N., Jr. Chem. Phys. Lett. 1977,51, 215-220. (13) Cox, R. A. J . Photochem. 1974,3, 291-304. (14) Cox, R. A. Int. J . Chem. Kinet. 1975, Symp. No. 1, 379-398. (15) Atkinson, R.; Hansen, D. A,; Pitts, J. N., Jr. J . Chem. Phys. 1975, 62, 3284-3288. (16) Cox, R. A.; Derwent, R. G. J . Photochem. 1975,4, 139-153. (17) Hamilton, E. J., Jr.; Lii, R. R. Int. J . Chem. Kinet. 1977, 9, 875-885. (18) Calvert, J. G. Enuiron. Sci. Technol. 1976,10, 256. (19) Whitten, G. Z., et al. “Modeling of Simulated Photochemical Smog with Kinetic Mechanisms”, Vol. I and 11,Systems Applications, Inc., San Rafael, Calif., 1979, EPA-600/3-79-001a, NTIS No. PB-290 507, PB-290 508. (20) Davis, D. D., private communication with Dr. M. Dodge of EPA, 1976. (21) Davis, D. D.; Heaps, W.; McGee, T. Geophys. Res. Lett. 1976, 3, 331-333. (22) Herron, J. T.; Penzhorn, R. D. J . Phys. Chem. 1969, 73, 191196. (23) Morris, E. D., Jr.; Niki, H. J . Chem. Phys. 1971, 55, 19911992. (24) Volman, D. H.; Gorse, R. A. “Photochemistry of the Gaseous Hydrogen Peroxide-Carbon Monoxide Systems: Rate Constants for Hydroxyl Radical Reactions by Competitive Kinetics”, University of California, Davis, Calif., 1972, Final Report, Project 5143-2. (25) Chan, W. H., et al. Enuiron. Sci. Technol. 1976,10, 674-682. (26) Finlayson, B. J.; Pitts, J. N., Jr. Science 1976,192, 111-119. (27) Whitten, G. Z.; Meyer, J. P. “CHEMK: A Computer Modeling Scheme for Chemical Kinetics”. Svstems ADDlications.~, Inc.. San Rafael, Calif., 1976, CS75-70. (28) Anderson, G. E.. et al. “Air Qualitv in the Denver MetroDolitan Region: 1974-2000”, Systems Applications, Inc., San Rafael; Calif., 1977, EPA-908/1-77-002. (29) Tesche. T. W.: Burton. C. S. “Simulated ImDact of Alternative Emissions Control Strategies on Photochemical Oxidants in Los Angeles”, Systems Applications, Inc., San Rafael, Calif.. 1978. EF78-22R. (30) Seinfeld, J. Enuiron. Sci. Technol. 1977,11, 248. I
