Environ. Sci. Technol. 1996, 30, 1392-1397
Effect of Chemical Product Yield Uncertainties on Reactivities of VOCs and Emissions from Reformulated Gasolines and Methanol Fuels YUEH-JIUN YANG,† WILLIAM R. STOCKWELL,‡ AND J A N A B . M I L F O R D * ,§ Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, Fraunhofer Institute for Atmospheric Environmental Research (IFU), 82467 Garmisch-Partenkirchen, Germany, and Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309
To account for differences in exhaust composition that arise with the use of fuels other than conventional gasoline, California regulations apply reactivity adjustment factors (RAFs) to emissions standards for new motor vehicles. The RAFs are based on estimates of the sensitivity of ozone formation to each of the individual organic compounds in the exhaust. In this study, uncertainties have been estimated for the incremental reactivities of individual organic compounds and for RAFs for motor vehicle exhaust, accounting for uncertainties in chemical rate parameters and product yields and for variability in exhaust composition. Uncertainties (1σ) in incremental reactivities of individual compounds range from about 25 to 75% of mean estimates and are typically about 10% higher than previous estimates obtained by considering independent rate parameters as the only source of uncertainty in the chemical mechanism. The incremental reactivities of relatively rapidly reacting compounds are sensitive to the peroxy radical yields in their primary oxidation reactions. RAF values of 0.87 ( 0.11 (1σ) and 0.42 ( 0.06, respectively, are calculated for exhaust emissions from a test gasoline with low aromatics and low olefins content, and from an 85% methanol, 15% gasoline blend. The RAF values show little sensitivity to product yield uncertainties.
Introduction Estimates of the relative reactivities of different organic compounds with respect to ozone formation can provide * Phone: 303-492-5542; fax: 303-492-2863; e-mail address:
[email protected]. † Carnegie Mellon University. ‡ Fraunhofer Institute for Atmospheric Environmental Research. § University of Colorado.
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a useful basis for developing control strategies for reducing ambient ozone levels (1). In 1990, the California Air Resources Board (CARB) adopted new motor vehicle exhaust regulations that included a reactivity adjustment to allowable organic compound emissions levels (2). For vehicle/fuel combinations that produce exhaust emissions with a volatile organic compound (VOC) composition different from vehicles fueled on conventional gasoline, a reactivity adjustment factor (RAF) is applied to the emissions standards to equalize the expected impact on ozone. The RAFs are calculated from the maximum incremental reactivities (MIRs) of the individual organic compounds in the exhaust, using the scale developed by Carter (3). In previous studies (4, 5), uncertainties were estimated for incremental reactivities of individual organic compounds and RAFs of exhaust emissions from vehicle operation on seven test fuels from the Auto/Oil Air Quality Improvement Research Program (6), including reformulated gasolines and M85 (85% methanol, 15% gasoline blend). The uncertainty estimates accounted for uncertainties in the rate parameters of the SAPRC90 (7) chemical mechanism used to calculate the incremental reactivities and for variability in the exhaust composition data. A limitation of the previous studies was that rate parameters were the only source of uncertainty considered in the chemical mechanism, whereas the product yields of many reactions of organic compounds are also uncertain. A second limitation was that the rate parameters were assumed to be independent. However, some mechanism parameters might be considered to be correlated because they are estimated from the same kinetic data or because of constraints on product yields (e.g., conservation of carbon). This paper addresses these two limitations of the previous work. Uncertainties associated with product yields in the SAPRC90 chemical mechanism are considered in addition to uncertainties in rate parameters, and correlations across some parameters are incorporated as opposed to treating all mechanism parameters as independent. Uncertainties are calculated for two incremental reactivity scales (maximum incremental reactivities and maximum ozone incremental reactivities) (2, 3) and for MIR-based RAFs of motor vehicle exhaust emissions associated with a test gasoline and a methanol blend (6). The results presented here represent a thorough treatment of uncertainties in the parameters of the SAPRC90 chemical mechanism. However, incremental reactivities may also be affected by uncertainties in mechanism formulation and in the representativeness of the simulation conditions for which they are calculated (1). RAFs may also be influenced by additional uncertainties in the exhaust emissions data, since the vehicles and test conditions relied on here are not fully representative of the on-road vehicle population.
Methods Incremental reactivities of organic compounds are calculated in computer simulations designed to represent typical conditions leading to high ozone concentrations in urban areas. The incremental reactivities are defined as the local sensitivities of ozone to the initial concentrations of the organic compounds (4), calculated in a box model for a
0013-936X/96/0930-1392$12.00/0
1996 American Chemical Society
single set of “typical” simulation conditions. The simulation conditions used for this study are the same as those described previously (4). The MIR scale adopted by CARB is calculated using the SAPRC90 mechanism for simulation conditions with NOx inputs adjusted to maximize the reactivity of the base mixture of VOCs. The second scale considered here is the MOIR scale, which is calculated for simulation conditions with NOx inputs adjusted to maximize the peak ozone produced with the base mixture of VOCs. Uncertainties in MIRs and MOIRs have been estimated for 28 explicit organic compounds and five lumped classes, with the explicit compounds selected primarily because of their prevalence in motor vehicle exhaust emissions. Uncertainty estimates for the incremental reactivities are calculated by propagating probability distributions for SAPRC90 chemical parameters through the box model simulations, using Monte Carlo analysis with Latin hypercube sampling. A more detailed description of the methodology used for the incremental reactivity and uncertainty calculations has been presented elsewhere (4, 5). The results described here were obtained from Monte Carlo calculations using a sample size of 400, with a total of 214 SAPRC90 parameters (including 73 rate constants and 141 product yields) treated as random variables. The uncertain rate parameters are assumed to be log-normally distributed. Uncertain product yields are treated as uniformly distributed random variables. The uncertainty estimates for the rate parameters were documented previously (4), except that correlations between some parameters were introduced for this analysis. The correlations and uncertainty estimates for the product yields are discussed below. Correlations between Mechanism Parameters. In the previous studies (4, 5), uncertain rate parameters in the SAPRC90 mechanism were assumed to vary independently. However, some parameters in the mechanism are assigned values based on analogy with the reactions of other species. In cases in which values of pairs of parameters have been derived from the same kinetic data, or using the same absorption cross sections, the uncertainties in the parameters can be viewed as correlated. Correlations were introduced in this study for the rate parameters of the reactions listed in Table 1. Since it is difficult to evaluate the degree of the correlations between parameters, a perfect correlation (i.e., correlation coefficient, F ) 1) is assumed in each case. Results with all random variables treated as independent and results with perfect correlations bound those that would be obtained with intermediate correlations that might be more realistic. Uncertainty Estimates of Product Yields. Product yields of significant reactions of atmospheric inorganic species are fairly well-known. For many reactions of organic species, however, the product yields are relatively uncertain due to lack of data or to the lumping procedures used to condense mechanisms. In this study, uncertainty estimates for product yields in the SAPRC mechanism are assigned based on evaluation of the quality of experimental determinations of the particular product species (8). For example, irrespective of the reaction in which they occur, HO2 yields are assigned uncertainty bounds of 30% for nominal yields less than 0.7 and 20% for yields above 0.7. These relatively high uncertainties correspond to the fact that peroxy radical yields are inferred from experimental results and not measured directly. Because the SAPRC mechanism includes hundreds of product yields, it is not feasible to treat all of them as
TABLE 1
Rate Constants for Which Correlations Are Included in the Simulations reactions HO2 + HO2 + H2O f NO3 + HO2 + H2O f RO2 + NO f RO2R + NO f CCO-O2 + NO f C2CO-O2 + NO f CCO-O2 + NO2 f HCOCO-O2 + NO2 f CCO-O2 + HO2 f C2CO-O2 + HO2 f CCO-O2 + RO2 f C2CO-O2 + RO2 f NO3 + hν f NO2 + O NO3 + hν f NO + O2 HCHO + hν f 2HO2 + CO HCHO + hν f H2 + CO AFG1 + hν f AFG2 + hν f
k(300 K)a 10-1
1.34 × 1.34 × 10-1 1.13 × 104 1.13 × 104 1.46 × 104 1.46 × 104 1.07 × 104 1.07 × 104 7.19 × 103 7.19 × 103 1.60 × 104 1.60 × 104 photolytic photolytic photolytic photolytic photolytic photolytic
1σ 4.74 × 10-2 4.74 × 10-2 8.55 × 103 8.55 × 103 1.10 × 104 1.10 × 104 7.22 × 103 7.22 × 103 5.48 × 103 5.48 × 103 1.21 × 104 1.21 × 104
a Rate constants and standard deviations in ppm min units as appropriate to the order of reactions.
uncertain parameters. Product yields of inorganic reactions are treated as known. Furthermore, although the product yields from many organic reactions are uncertain, only those likely to be influential are treated as random variables. Previous results showed that the rate constants of the primary oxidation reactions of organic compounds are influential to incremental reactivity uncertainties (4). Correspondingly, for 30 organic compounds explicitly included in the mechanism, the uncertain product yields of reactions with HO and O3 (where applicable) are included in the uncertainty analysis. Uncertainties in product yields of photolysis of MEK, methyl glyoxal (MGLY), and the carbonyl products of aromatics oxidation (AFG1 and AFG2) are also included. The primary oxidation reactions of methane and formaldehyde are relatively well understood, so the product yields in their reactions are treated as known. Both the MIR and MOIR scales are defined for urban conditions in which the dominant chemical pathway for peroxy radicals is reaction with nitric oxide. Thus, peroxy radical-peroxy radical reactions generally have little influence on the uncertainty of incremental reactivities. Uncertainties in product yields of peroxy radical-peroxy radical reactions are also neglected, with the exception of those from the reaction C2CO-O2 + HO2. They are included because MIRs of alkenes and acetaldehyde were found to be somewhat sensitive to the rate parameter of this reaction (4). To further reduce the number of parameters to be treated as random variables, a few additional simplifications are made. Uncertainties in yields of H2O, CO2, and -C (used to balance carbon in reactants and products), are assumed to be negligible. Uncertainties in nominally small fractional yields of acetone, MEK, glyoxal, methylglyoxal, benzaldehyde, phenol, and cresol are also neglected. Similarly, it is assumed that uncertainties in low fractional yields of peroxy radicals are less important than uncertainties in higher yields of other peroxy radicals in the same reactions. Only the peroxy radicals with higher yields are treated as uncertain. Finally, yields of some products of simple or well-known reactions, such as the HO2 yield in the reaction
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TABLE 2
Upper and Lower Bound Uncertainties Associated with Product Yields uncertainty bounds of product yieldsb
reactionsa AFG1 + hν f AFG2 + hν f CCHO + HO f RCHO + HO f ACET + HO f MEK + HO f MEK + hν f MGLY + hν f C2COO2 + HO2 f ethene + HO f ethene + O3 f ethane + HO f butane + HO f 2MEC5 + HO f BENZEN + HO f TOLUEN + HO f C2BENZ + HO f OXYL + HO f MPXYL + HO f 124TMB + HO f MEOH + HO f ETOH + HO f MTBE + HO f ETBE + HO f 224TMC5 + HO f MECYC5 + HO f propene + HO f propene + O3 f 13BUTD + HO f 13BUTD + O3 f 2M1BUTE + HO f 2M1BUTE + O3 f 2M2BUTE + HO f 2M2BUTE + O3 f 3MCYCPNTE + HO f 3MCYCPNTE + O3 f AAR1 + HO f AAR2 + HO f AAR3 + HO f OLE1 + HO f OLE1 + O3 f OLE2 + HO f OLE2 + O3 f
[0.80, 1.20] HO2, [0.80, 1.20] HCOCOO2, [0.80, 1.20] RCO3 [0.80, 1.20] HO2, [0.80, 1.20] CCOO2, [0.80, 1.20] RCO3 [0.80, 1.00] CCOO2, [0.80, 1.00] RCO3 [0.80, 1.00] C2COO2, [0.80, 1.00] RCO3 [0.68, 0.92] MGLY [0.43, 0.58] CCHO, [1.05, 1.95] R2O2, [1.05, 1.95] RO2 [0.80, 1.20] CCOO2, [0.85, 1.00] CCHO, [0.70, 1.00] RO2R, [0.80, 1.20] RCO3, [0.70, 1.00] RO2 [0.80, 1.20] HO2, [0.80, 1.20] CCOO2, [0.80, 1.20] RCO3 [0.70, 1.00] OOH, [0.85, 1.15] CCHO [1.23, 1.56] HCHO, [0.70, 1.00] RO2R, [0.70, 1.00] RO2 [0.85, 1.15] HCHO, [0.10, 1.40] HO2 [0.85, 1.00] CCHO [0.28, 0.52] R2O2, [0.49, 0.66] CCHO, [0.45, 0.61] MEK, [1.28, 1.52] RO2 [0.52, 0.97] R2O2, [0.46, 0.63] RCHO, [0.62, 0.83] MEK, [1.42, 2.08] RO2 [0.34, 0.64] AFG1, [0.54, 0.76] RO2R, [0.54, 0.76] RO2 [0.29, 0.53] AFG2, [0.52, 0.74] RO2R, [0.52, 0.74] RO2 [0.29, 0.53] AFG2, [0.52, 0.74] RO2R, [0.52, 0.74] RO2 [0.31, 0.43] MGLY, [0.47, 0.87] AFG2, [0.57, 0.82] RO2R, [0.57, 0.82] RO2 [0.31, 0.43] MGLY, [0.47, 0.87] AFG2, [0.57, 0.82] RO2R, [0.57, 0.82] RO2 [0.42, 0.78] AFG2, [0.57, 0.82] RO2R, [0.53, 0.71] MGLY, [0.57, 0.82] RO2 [0.80, 1.00] HCHO [0.78, 0.92] CCHO [0.33, 0.45] HCHO, [0.35, 0.47] MEK, [0.26, 0.48] R2O2, [1.26, 1.48] RO2 [0.99, 1.33] HCHO, [0.48, 0.66] MEK, [0.81, 1.27] R2O2, [1.81, 2.51] RO2 [0.62, 1.14] R2O2, [0.63, 0.86] RCHO, [0.49, 0.66] MEK, [1.61, 2.14] RO2 [0.24, 0.33] HCHO, [0.49, 0.91] RCHO, [1.39, 2.57] R2O2, [0.42, 0.56] MEK, [2.39, 3.57] RO2 [0.70, 1.00] RO2R, [0.85, 0.93] CCHO, [0.85, 1.15] HCHO, [0.70, 1.00] RO2 [0.55, 0.75] HCHO, [0.43, 0.58] CCHO [0.70, 1.00] RO2R, [0.85, 1.15] HCHO, [0.85, 0.95] RCHO, [0.70, 1.00] RO2 [0.43, 0.58] HCHO, [0.43, 0.58] RCHO [0.70, 1.00] RO2R, [0.85, 1.15] HCHO, [0.85, 0.96] MEK, [0.70, 1.00] RO2 [0.47, 0.63] HCHO, [0.77, 1.04] MEK [0.70, 1.00] RO2R, [0.85, 0.90] ACET, [0.85, 1.15] CCHO, [0.70, 1.00] RO2 [0.43, 0.58] CCHO, [0.46, 0.62] MEK, [0.43, 0.58] ACET [0.66, 0.89] CCHO, [0.66, 0.89] RCHO, [0.54, 1.01] RO2R [0.19, 0.35] RO2R, [0.55, 0.75] CCHO, [0.43, 0.58] RCHO, [0.30, 0.40] MEK, [0.19, 0.35] RO2 [0.12, 0.16] HCHO, [0.27, 0.36] CCHO, [0.14, 0.19] RCHO, [0.23, 0.43] R2O2, [1.20, 1.40] RO2 [0.44, 0.83] R2O2, [0.15, 0.20] CCHO, [0.17, 0.24] RCHO, [0.26, 0.35] MEK, [1.38, 1.77] RO2 [0.31, 0.41] MGLY, [0.34, 0.62] AFG2, [0.26, 0.35] MEK [0.61, 1.13] RO2R, [0.74, 1.00] HCHO, [0.22, 0.29] CCHO, [0.52, 0.71] RCHO, [0.74, 1.26] RO2 [0.46, 0.63] HCHO, [0.22, 0.29] CCHO, [0.30, 0.41] RCHO [0.65, 1.21] RO2R, [0.27, 0.37] HCHO, [0.55, 0.74] CCHO, [0.51, 0.70] RCHO, [0.72, 1.28] RO2 [0.24, 0.33] HCHO, [0.39, 0.52] CCHO, [0.28, 0.37] RCHO, [0.31, 0.42] MEK
a Key: ACET, acetone; MGLY, methylglyoxal; 2MEC5, 2-methylpentane; 224TMC5, 2,2,4-trimethylpentane; C2BENZ, ethylbenzene; oxyl, o-xylene; MPXYL, m,p-xylene; MECYC5, methylcyclopentane; MEOH, methanol; ETOH, ethanol; 13BUTD, 1,3-butadiene; 124TMB, 1,2,4-trimethylbenzene; 2M1BUTE, 2-methyl-1-butene; 2M2BUTE, 2-methyl-2-butene; 3MCYCPNTE, 3-methylcyclopentene; AARn, lumped alkane classes; OLEn, lumped alkene classes. b The notation [lower bound, upper bound] is intended to distinguish these values from (1σ uncertainties used for rate constants and model results (MIRs, RAFs, etc.).
of methanol with HO, are assumed to be known. Table 2 shows the product yields treated as uniformly distributed random variables in the Monte Carlo simulations, with associated uncertainties shown as lower and upper bounds. Correlations between yields of different products in a given reaction exist because of constraints that balance radicals and carbon (8). In addition, yields of total peroxy radical operators are constrained to equal the sum of the yields of the radicals they represent. For example, RCO3 represents the sum of HCOCO-O2, CCO-O2, and C2COO2. The yields of the explicit peroxy acyl radicals are treated as random variables, and the RCO3 yield is treated as a dependent variable that is calculated for each sample as the sum of the other randomly drawn yields. Exhaust Emission Compositions and RAF Calculations. For comparison with the results of the previous study (5), uncertainty estimates incorporating product yield uncertainties and correlations across chemical parameters have been calculated for the RAFs of exhaust emissions associated
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with vehicle operation on M85 and on a test gasoline. Exhaust composition data from the Auto/Oil Air Quality Improvement Research Program’s (6) tests of “AMOTA” (industry average gasoline in conventional vehicles), “AMOTF” (a test gasoline with a Reid vapor pressure of 8.8 psia and with reduced aromatics, olefins, and 90% distillation temperature) and “METHZ” (M85 in prototype flexible or variable-fueled vehicles) have been used in the RAF calculations. Note that AMOTF does not contain an oxygenate and was not designed to satisfy either California or federal criteria for designation as a reformulated gasoline. However, as shown previously (5), uncertainty results for AMOTF exhaust reactivity are highly representative of those for other gasoline-based test fuels evaluated by the AQIRP, including those with lower RVP and those with oxygenates. A total of 28 organic compounds from the emissions are treated explicitly in the mechanism, with the remaining species identified in the exhaust tests represented either by surrogate assignments (3) or by one of five lumped classes
TABLE 3
Uncertainty Apportionment for Average Ozone Concentrations by Multivariate Linear Regression Analysis reactions or product yields
FIGURE 1. Predicted ozone concentrations with 1σ uncertainties for MIR and MOIR simulation conditions from this study (solid) in comparison with results from ref 4, which only incorporated uncertainties of independent rate constants (open). Although the results are offset for clarity of presentation, the symbols are all shown for the same simulation times (hourly).
(5). The uncertainty in the exhaust composition for each fuel is estimated from the variances and covariances in the emissions rates of each species across the vehicles tested on the fuel. Monte Carlo analysis is used to calculate the RAF distribution for each fuel, combining the MIR uncertainties of individual compounds/classes and the variability of exhaust compositions. The calculation procedures are identical to those used previously (5) except that the revised MIR uncertainty estimates are used.
Results and Discussion Figure 1 shows uncertainties in time-varying O3 concentrations predicted for MIR and MOIR simulation conditions. Results from this study, in which 214 rate constants and product yields are treated as uncertain, are compared to results from the previous study (4), which only considered rate constant uncertainties. Introduction of product yield uncertainties and correlations between parameters increased the uncertainty in peak ozone concentrations slightly, from (30 (4) (1σ relative to the mean) to (31% for MIR conditions and from (20 (4) to (21% for MOIR conditions. Table 3 presents the parameters that contribute the most to the uncertainties in predicted time-averaged ozone concentrations. These parameters were identified by applying multivariate linear regression to the Monte Carlo results. Table 3 indicates that the top contributions to uncertainty in ozone are associated with rate parameters, rather than product stoichiometric coefficients. The dominant contributions are associated with the rate parameters for the HNO3 formation and NO2 photolysis reactions. Other parameters that strongly influence the uncertainty in ozone concentrations include the rate constants for photolysis of ozone, formaldehyde, AFG1, and AFG2 (these two are correlated), for the reactions of O3 + NO, AAR3 + HO, and for the formation and decomposition of PAN (and the correlated reactions of its analogs). In general, the influential parameters in Table 3 are similar to those found in the previous study (4) in which only independent rate parameters were considered as sources of uncertainty. The main differences are due to the treatment of some rate parameters as perfectly correlated. For 28 organic compounds and five lumped classes, the estimated uncertainties (1σ) in MIRs from Monte Carlo simulations ranged from (25 to (66% of the mean
σ/µa
reg coeff
% UCb
MIR Case (R 2 ) 0.91) 0.27 HO + NO2 f 0.26 NO2 + hν f 0.19 O3 + NO f 0.75 CCO-O2 + NO f c 0.67 CCO-O2 + NO2 f d 0.34 O3 + hν f 0.34 HCHO + hν f 2HO2 + COd 0.27 AAR3 + HO f 1.20 AFG1 + hν f f 0.24 O 1D + M f
-0.34 0.30 -0.29 0.07 -0.07 0.13 0.10 0.11 0.02 -0.12
23.15 17.11 8.09 7.14 6.55 4.83 3.20 2.23 2.13 2.12
MOIR case (R 2 ) 0.90) 0.27 HO + NO2 f 0.26 NO2 + hν f 0.75 CCO-O2 + NO f c 0.67 CCO-O2 + NO2 f d 0.19 O3 + NO f 0.34 HCHO + hν f 2HO2 + COe 1.20 AFG1 + hν f f 0.34 O3 + hν f PAN f 0.70 AAR3 + HO f 0.27
-0.27 -0.25 0.07 -0.08 -0.23 0.08 0.02 0.08 0.04 0.08
19.81 16.51 11.76 10.00 7.40 2.62 2.62 2.50 2.29 1.96
a Uncertainties of normalized parameters. b Uncertainty contribution. Rate constant correlated with that for C2COO2 + NO f. d Rate constant correlated with that for HCOCOO2 + NO2 f. e Rate constant correlated with that for HCHO + hν f H2 + CO. f Rate constant correlated with that for AFG2 + hν f. c
estimates, compared to a range of (27 to (68% when only rate parameter uncertainties were considered (4). MIR uncertainty results for an illustrative set of compounds are shown in Table 4. For most of the compounds studied, the MIR uncertainties increased slightly when product yield uncertainties and correlations were considered. The largest differences occurred for alkenes, for which including product yield uncertainties increased MIR uncertainty estimates by 12-35% over the uncertainty estimates calculated by only considering rate constant uncertainties. For formaldehyde, the estimated uncertainty is reduced from (32 (4) to (25% due to the assumed correlation between the rates of the two photolysis reactions, HCHO + hν f 2HO2 + CO and HCHO + hν f H2 + CO. Photolysis rates for these two reactions share the same absorption cross sections. The rate parameters and product yields that have the most influence on uncertainties in calculated MIRs of selected compounds are listed in Table 5, based on regression analysis of the Monte Carlo results. The table shows that parameters contributing the most to MIR uncertainties are similar to those found previously (4). Compounds with relatively slow HO reaction rates exhibit high sensitivity to the rate constants of their primary oxidation reactions. In addition, however, MIRs for relatively fast reacting compounds, such as alkenes and aldehydes, are sensitive to the peroxy radical yields in their reactions with HO. MIRs for aromatic compounds are also sensitive to the yields of AFG2 in their oxidation pathways, as illustrated in Table 5 for m,p-xylene. As shown in Table 4 for selected compounds, accounting for correlations and uncertainties in product yields changed the 1σ uncertainties estimated for the MOIRs of most
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TABLE 4
TABLE 5
MIR and MOIR Uncertainty Estimates for Selected VOCs
Apportionment of Uncertainty in MIRs
compound
reactions or product yields
meana (ppm O3/ppm C) (SD/mean)%a (SD/mean)%b
formaldehyde 1,3-butadiene propene acetaldehyde ethene m,p-xylene 2-methyl-1-butene toluene MEK methanol butane MTBE
MIR 3.78 2.47 2.19 1.88 1.70 1.44 1.24 0.49 0.35 0.31 0.26 0.20
24.7 34.6 36.6 41.1 42.1 42.9 36.2 52.0 49.2 46.7 53.7 49.3
32.1 28.6 32.5 40.2 35.6 37.4 26.7 45.7 46.5 49.1 50.6 47.3
formaldehyde 1,3-butadiene propene ethene acetaldehyde m,p-xylene 2-methyl-1-butene toluene MEK methanol butane MTBE
MOIR 1.98 1.37 1.30 1.12 1.04 0.74 0.73 0.22 0.22 0.23 0.20 0.17
54.7 47.7 46.1 40.4 53.4 62.5 47.9 83.0 60.3 35.7 49.5 33.8
57.7 44.7 41.6 33.1 53.2 57.5 39.8 72.7 53.0 34.4 42.5 36.2
a
Including uncertainties of product yields and parameter correlations. Considering only the uncertainties of rate constants and treating them as independent (4).
b
compounds by 5-15%. The largest changes are for ethene, for which the uncertainty increased from (33 (4) to (40%, and 2-methyl-1-butene, for which the uncertainty increased from (40 (4) to (48%. In both cases the increased uncertainty was due to contributions from the product yields of the primary oxidation reactions. The inclusion of correlations between the HCHO photolysis rates caused a decrease in the uncertainty of the formaldehyde MOIR from (58 (4) to (54%. Uncertainty attribution results for the MOIRs of selected compounds are shown in Table 6. As with the MIRs, the influential parameters are similar to the results found previously, except that MOIRs for relatively fast reacting compounds such as the alkenes are sensitive to the peroxy radical yields of their reactions with HO. For relatively well studied species such as ethene, propene, and acetaldehyde, uncertainties in MIR and MOIR values are arguably lower than the estimates presented here. This is because uncertainties in their radical yields and rate constant values are constrained by theory and by environmental chamber experiments to be less than indicated by evaluations of kinetic data or measurement capabilities. Another possible source of bias toward overestimation that would affect most of the MIR and MOIR uncertainties is the treatment of the rate parameters for the CCO-O2 + NO and CCO-O2 + NO2 reactions as independent variables. Because they are competing reactions, accounting for the likelihood that the ratio of these rate parameters is better known than their absolute values would reduce output uncertainties. Finally, as discussed previously (4), when different sources of uncertainty estimates for rate parameters were in conflict, the larger estimate was conservatively assigned for this study. All of these factors suggest that the results provide conservative estimates of the effect of
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σ/µa
reg coeff
% UCb
Formaldehyde (MIR ) 3.78 ( 0.93, R 2 ) 0.82) 0.34 0.26 HCHO + hν f 2HO2 + COc 0.75 0.08 CCO-O2 + NO f d 0.67 -0.08 CCO-O2 + NO2 f HO + CO f 0.28 0.15 0.34 0.11 O3 + hν f PAN f 0.70 0.05 0.26 0.12 NO2 + hν f AAR3 + HO f 0.27 -0.11
22.91 10.02 7.51 5.06 4.08 2.90 2.71 2.42
Propene (MIR ) 2.19 ( 0.80, R 2 ) 0.84) 0.17 0.68 propene + HO:RO2R, RO2 0.75 0.13 CCO-O2 + NO f d e 0.67 -0.13 CCO-O2 + NO2 f 0.26 0.26 NO2 + hν f PAN f 0.70 0.09 0.27 -0.24 HO + NO2 f c 0.34 0.16 HCHO + hν f 2HO2 + CO 0.19 -0.25 O3 + NO f
19.51 13.52 9.81 6.65 5.92 5.47 4.27 3.32
Butane (MIR ) 0.26 ( 0.14, R 2 ) 0.83) 0.75 0.22 CCO-O2 + NO f 0.27 -0.55 HO + NO2 f 0.26 0.47 NO2 + hν f 0.67 -0.18 CCO-O2 + NO2 f e butane + HO f 0.19 0.65 0.34 0.28 O3 + hν f PAN f 0.70 0.12 0.19 -0.40 O3 + NO f
15.49 12.92 9.04 8.90 8.21 5.05 4.01 3.41
Acetaldehyde (MIR ) 1.88 ( 0.77, R 2 ) 0.82) 0.75 0.16 CCO-O2 + NO f d 0.67 -0.16 CCO-O2 + NO2 f e PAN f 0.70 0.12 0.06 1.23 CCHO + HO:CCO-O2, RCO3 0.34 0.19 CCHO + hν f 0.26 0.23 NO2 + hν f CCHO + HO f 0.36 0.16 0.19 -0.25 O3 + NO f
21.05 15.43 10.32 7.11 5.88 5.08 4.72 3.24
m,p-xylene (MIR ) 1.44 ( 0.62, R 2 ) 0.80) 0.75 0.14 CCO-O2 + NO f d MPXYL + HO f 0.31 0.27 0.67 -0.12 CCO-O2 + NO2 f e 1.20 0.06 AFG1 + hν ff 0.26 0.26 NO2 + hν f 0.26 0.26 HO + NO2 f MPXYL + HO:AFG2 0.17 0.36 PAN f 0.70 0.08
15.81 9.75 9.23 7.37 6.52 5.62 5.41 3.85
Methanol (MIR ) 0.31 ( 0.14, R 2 ) 0.85) 0.27 -0.61 HO + NO2 f MEOH + HO f 0.20 0.68 0.26 0.40 NO2 + hν f 0.34 0.28 HCHO + hν f 2HO2 + COc 0.34 0.26 O3 + hν f 0.75 0.10 CCO-O2 + NOf d 0.67 -0.10 CCO-O2 + NO2 f e 0.19 -0.34 O3 + NO f
21.33 14.23 8.63 6.95 6.24 4.11 3.80 3.31
a Normalized uncertainty of parameters. b Uncertainty contribution. Rate constant correlated with HCHO + hν f H2 + CO. d Rate constant correlated with C2CO-O2 + NO f. e Rate constant correlated with HCOCO-O2 + NO2 f. f Rate constant correlated with AFG2 + hν f. c
uncertainties in rate parameters and product yields on MIR and MOIR values. To illustrate the effect of the MIR uncertainties on RAF values, Figure 2 presents cumulative distribution functions for the RAFs of exhaust from vehicle operation on AMOTF and METHZ, with AMOTA as the base fuel. RAF values of 0.87 (0.11 (mean (1σ) and 0.42 ( 0.06, respectively, are calculated for exhaust emissions from vehicles fueled on AMOTF and METHZ. In Figure 2, results from this study
TABLE 6
Apportionment of Uncertainty in MOIRs reactions or product yields
σ/µa
reg coeff
% UCb
Formaldehyde (MOIR ) 1.98 ( 1.08, R 2 ) 0.86) 0.34 -1.20 O3 + hν f 0.24 -1.04 O1D + H2O f 0.24 1.02 O1D + M f 0.27 0.70 HO + NO2 f 1.20 -0.09 AFG1 + hν f c 0.34 -0.30 RCHO + hν f AAR3 + HO f 0.27 -0.34 e 0.75 -0.11 CCO-O2 + NO f
33.65 12.28 11.83 7.33 2.57 2.13 1.76 1.41
Propene (MOIR ) 1.30 ( 0.60, R 2 ) 0.87) 0.34 -0.75 O3 + hν f 0.17 1.14 propene + HO:RO2R, RO2 1 0.24 -0.67 O D + H2O f 0.24 0.58 O1D + M f PAN f 0.70 0.15 0.26 0.38 NO2 + hν f e 0.67 -0.13 CCO-O2 + NO2 f 0.75 0.10 CCO-O2 + NO f d
26.03 15.74 10.09 7.57 4.21 3.97 2.99 2.63
Butane (MOIR ) 0.20 ( 0.10, R 2 ) 0.84) 0.75 0.30 CCO-O2 + NO f d 0.67 -0.30 CCO-O2 + NO2 f e butane + HO f 0.19 0.86 0.26 0.59 NO2 + hν f PAN f 0.70 0.21 0.19 -0.46 O3 + NO f HO + CO f 0.28 -0.27 0.34 -0.19 O3 + hν f
19.98 16.61 10.62 10.45 9.24 3.37 2.35 1.97
Acetaldehyde (MOIR ) 1.04 ( 0.56, R 2 ) 0.86) 0.34 -0.56 O3 + hν f PAN f 0.70 0.23 0.67 -0.24 CCO-O2 + NO2 f e 0.75 0.18 CCO-O2 + NO f d 0.24 -0.52 O1D + H2O f 1 0.24 0.45 O D+Mf 0.26 0.39 NO2 + hν f 0.06 1.62 CCHO + HO:CCO-O2, RCO3
15.60 11.15 10.48 7.95 6.57 4.80 4.45 3.69
m,p-Xylene (MOIR ) 0.74 ( 0.46, R 2 ) 0.87) 0.34 -1.04 O3 + hν f 0.24 -0.94 O1D + H2O f 0.24 0.87 O1D + M f MPXYL + HO f 0.31 0.54 0.26 0.41 NO2 + hν f 0.34 -0.31 HO + NO2 f 0.27 0.39 HCHO + hν f 2HO2 + COf MPXYL + HO:AFG2 0.17 0.59
30.11 12.00 10.25 6.64 2.90 2.72 2.67 2.52
Methanol (MOIR ) 0.23 ( 0.08, R 2 ) 0.90) MEOH + HO f 0.20 1.03 0.26 0.56 NO2 + hν f 0.34 -0.43 O3 + hν f 0.24 -0.40 O1D + H2O f 0.19 -0.45 O3 + NO f 0.27 -0.31 HO + NO2 f 0.24 0.33 O1D + M f 0.24 0.30 HO2 + NO f
25.57 13.39 13.33 5.64 4.68 4.39 3.69 3.16
a Normalized uncertainty of parameters. b Uncertainty contribution. Rate constant correlated with AFG2 + hν f. d Rate constant correlated with C2CO-O2 + NO f. e Rate constant correlated with HCOCO-O2 + NO2 f. f Rate constant correlated with HCHO + hν f H2 + CO.
FIGURE 2. Mass-based cumulative distribution functions for the RAFs of exhaust emissions associated with a reformulated gasoline (AMOTF) and M85 (METHZ). Results are compared from this study (solid line) and a previous study (5) that neglected product yield uncertainties and correlations between rate parameters (dashed line). The mean and standard deviation of the results from the current study are given in the upper left corner of each plot. The median value is shown on the horizontal axis. including correlations and product yield uncertainties in the analysis does not significantly change the uncertainty distribution for the AMOTF RAF. The uncertainty in the METHZ RAF value was reduced slightly from (0.07 (5) to (0.06, due to the reduced uncertainties in the MIRs for methanol and formaldehyde, which are the dominant species in the exhaust emissions. As demonstrated before (5), uncertainties in RAF values are small compared to uncertainties calculated for the incremental reactivities of individual organic compounds, because uncertainties in chemical parameters have directionally similar effects on MIRs of many organic species.
Acknowledgments Financial support for this project was provided by the U.S. Department of Energy’s National Renewable Energy Laboratory. The authors thank Michelle Bergin and Armistead Russell for helpful discussions.
Literature Cited (1) Russell, A.; Milford, J.; Bergin, M. S.; McBride, S.; McNair, L.; Yang, Y.; Stockwell, W. R.; Croes, B. Science 1995, 269, 491-495. (2) California Air Resources Board (CARB) Proposed Regulations for Low-Emissions Vehicles and Clean Fuels: Staff Report and Technical Support Document. State of California Air Resources Board, Sacramento, CA, August 1990. (3) Carter, W. P. L. J. Air Waste Manage. Assoc. 1994, 44, 881-899. (4) Yang, Y.-J.; Stockwell, W. R; Milford, J. B. Environ. Sci. Techol. 1995, 29, 1336-1345. (5) Yang, Y.-J.; Milford, J. B. Environ. Sci. Technol. 1996, 30, 196203. (6) Auto/Oil Air Quality Improvement Research Program (AQIRP), Technical Bulletin Nos. 6 and 7, Coordinating Research Council, Atlanta, GA, 1992. (7) Carter, W. P. L. Atmos. Environ. 1990, 24A, 481-518. (8) Bergin, M. S.; Russell, A. G.; Yang, Y.-J.; Milford, J. B.; Kirchner, F.; Stockwell, W. R. Effects of Uncertainty in SAPRC90 Rate Constants and Product Yields on Reactivity Adjustment Factors for Alternatively Fueled Vehicle Emissions. Draft final report prepared for the National Renewable Energy Laboratory, Contract No. 13013-1, Golden, CO, June 1995.
c
are superimposed on previous results from calculations that neglected product yield uncertainties and correlations between parameters (5). The comparison shows that
Received for review September 21, 1995. Revised manuscript received December 11, 1995. Accepted December 11, 1995.X ES950707Z X
Abstract published in Advance ACS Abstracts, February 15, 1996.
VOL. 30, NO. 4, 1996 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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