A Predictive Model to Correlate Fuel Specifications with On-Road

Feb 13, 2006 - ... del Petróleo, Estudios Ambientales, Eje Central Lázaro Cárdenas No. 152, San ... Isaac Schifter , Luis Díaz , Rene Rodríguez , Luci...
0 downloads 0 Views 191KB Size
Download PDF
Environ. Sci. Technol. 2006, 40, 1270-1279

A Predictive Model to Correlate Fuel Specifications with On-Road Vehicles Emissions in Mexico I. SCHIFTER,* L. DIAZ, AND E. LO ´ PEZ-SALINAS Instituto Mexicano del Petro´leo, Estudios Ambientales, Eje Central La´zaro Ca´rdenas No. 152, San Bartolo Atepehuacan, Me´xico, D.F., 07730, Mexico

TABLE 1. Main Air Pollutants in the Metropolitan Area of Mexico City pollutant SO2 ozone NO2 CO PM10

Mexico is currently in the process of implementing its third air management program, which includes control measures targeting emissions reductions from mobile, point, and area sources. Achieving the program goals will require changes in the composition and in physical properties of gasoline and implementing an emissions reduction schedule. For that purpose a study was undertaken to support understanding of the effect of gasoline fuel parameters on exhaust emissions. Specifically, the relative impacts of Reid vapor pressure, distillation parameters, oxygen, sulfur, olefins, and aromatic contents on the exhaust emissions of in-use vehicles of the metropolitan area of Mexico City were investigated. The results were used to develop a model to predict CO, nitrogen oxides, total hydrocarbons, and toxic emissions such as benzene, 1,3-butadiene, formaldehyde, and acetaldehyde. Also a statistical model that predicts evaporative emissions was built. Results of the present model are compared with those obtained using the complex model of the United States Environmental Protection Agency.

Introduction Due to complex socio-political, economic, and geographical conditions motor vehicles are the major sources of air polluting emissions in several Mexican cities. An example of such problems is the metropolitan area of Mexico City (MAMC) with nearly 20 million inhabitants and 3.5 million motor vehicles, which consume more than 40 million liters of fuel each day. These numbers combined with the geographic conditions have granted MAMC the status of a highly contaminated city (1-2). The most important air pollutants in the MAMC are ozone, sulfur oxides, nitrogen oxides, and particulate matter. To illustrate the MAMC air pollution problem, Mexican air quality standards, the number of days of the year that standards are exceeded, and the estimated contribution of vehicles to the total inventory of these pollutants are presented in Table 1 and were reported elsewhere (3). In the case of volatile organic compounds (VOCs), in 1999 it was estimated that vehicles in the MAMC contribute 23% of the total emissions, exclusively related to sales and distribution of gasoline, while VOC/NOx ratios measured in 2004 ranged between 20.2 and 21.5 (ppm C ppm-1) (4-5). Relating the national trend in ozone to the trends in ozone precursor emissions is very complex because of such * Corresponding author phone: (+52) 9175-8507; fax: (+52) 91758484; e-mail: [email protected]. 1270

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 40, NO. 4, 2006

PM 2.5 a

Mexican air quality standard

percentage of estimated vehicle days above air contribution to quality standard total inventory (%)

0.13 ppm average 24 hours 0.11 ppb average 1 hour 0.21 ppm average 1 hour 11 ppm average 8 hours 150 µg m-3 average 24 hours no standard

0.5

25

88

naa

10

65

1

98

94

52

na

72

Not available.

important influences as variations in precursor reactivity, nonuniformity in the geographical distribution of precursors, and meteorological effects. The evolution of ozone daily average data for the most populated metropolitan areas of Mexico suggests that ozone concentrations in the MAMC decreased between 1990 and 1998, despite of increasing fuel demand, but there is no clear trend for 1999-2002 (3). At present, Mexico’s government is in the process of implementing its third air quality management program (2002-2010). The first one started in 1990 and had several major accomplishments, including the introduction of catalytic converters, the phaseout of leaded gasoline, and the establishment of vehicle emissions standards. The second one (1995-2000) introduced oxygenated compounds in gasoline, restrictions in aromatic content, and sulfur reduction in industrial fuel. Among the specific provisions for the near future, car manufacturers and environmental authorities agreed that only vehicles meeting tier 2 emission standards (1.31 g CO km-1, 0.03 g nonmethane hydrocarbons km-1, 0.04 g NOx km-1, with an emissions durability certificate of 192 000 km) will be marketed from the year 2008 onward. As a consequence and under tier 2 standards, the Mexican refinery system needs to remove additional sulfur from the gasoline that they are currently producing, and therefore the present gasoline has to be reformulated. To produce the required fuels, the flexibility of the refineries has to be ensured by specifying fuel properties only where a clear link to vehicle performance or emissions is demonstrated (6). Two types of regular unleaded grade gasoline and one of premium grade are available throughout Mexico, all manufactured by the state-owned oil company. In ozone nonattainment areas (i.e., the MAMC, Guadalajara, and Monterrey), from 1998 regular oxygenated gasoline (1.2 wt % oxygen as methyl-tert-butyl ether, MTBE) of about 350 ppm S is available, while in the rest of the country regular gasoline averages 750 ppm S and with an oxygen content of about 0.4 wt % as MTBE. Premium gasoline is available nationwide and represents approximately 12% of total sales. A key principle first manifested in the concept of a gasoline reformulation program is that a vehicle and its fuel are an integrated system for which emissions controls should be fashioned to derive the optimum benefit from each of the system’s components. In acknowledgment of this principle, the auto and oil industries launched research programs in the United States and in Europe that have served as the cornerstone for the design of strategies concerning fuel formulation (7-11). 10.1021/es0503884 CCC: $33.50

 2006 American Chemical Society Published on Web 02/13/2006

37 ( 3.33 56.9 ( 4.01 112.6 ( 5.32 180.1 ( 3.44 217 ( 4.78 37.9 ( 1.13 36.7 ( 2.01 59.5 ( 4.99 55.5 ( 6.34 100.7 ( 3.63 95.8 ( 7.77 163.4 ( 2.96 168 ( 3.22 204.6 ( 9.42 209.4 ( 3.33 37.8 ( 2.61 66.6 ( 2.57 105.3 ( 1.77 160.4 ( 1.89 199.6 ( 1.99

D-86 Distillation

33.5 ( 1.49 40.6 ( 3.54 34.6 ( 1.48 34.5 ( 1.05 40.5 ( 1.70 39.2 ( 3.92 45.9 ( 1.07 55.2 ( 10.94 47.1 ( 3.02 46.5 ( 1.20 68.4 ( 5.76 66.4 ( 3.07 92.8 ( 15.22 111.9 ( 2.97 91.8 ( 16.25 98.4 ( 11.99 104.4 ( 1.47 106.3 ( 1.38 162.7 ( 4.07 162.4 ( 1.18 161 ( 3.24 163.2 ( 4.01 168.3 ( 6.72 160.8 ( 0.46 200.8 ( 1.24 204.2 ( 4.10 203.2 ( 0.54 204.9 ( 3.16 204.8 ( 1.46 200.8 ( 1.87 35.6 ( 1.37 55.4 ( 4.77 100.7 ( 6.45 161.6 ( 1.80 203.8 ( 2.94 42.6 ( 1.08 61.7 ( 3.56 108.3 ( 1.49 163.2 ( 2.95 202 ( 2.05 39.6 ( 0.62 63.7 ( 2.96 102.5 ( 2.67 162.3 ( 2.48 202 ( 3.01 38.2 ( 1.26 64.9 ( 2.94 105.5 ( 2.67 162 ( 2.59 202.9 ( 2.28 38.3 ( 1.37 66.8 ( 4.75 106.5 ( 1.77 162.8 ( 1.12 202.5 ( 2.87

VOL. 40, NO. 4, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

a

EtOH.

35.94 ( 1.54 57.92 ( 9.04 97.38 ( 7.71 166.52 ( 3.46 203.12 ( 2.48 IBP, °C 10%, °C 50%, °C 90%, °C EP%, °C

0.89 ( 0.65

540 ( 12.03 8.3 ( 1.08 92.6 ( 1.09 82 ( 1.22 87.3 ( 1.34 340 ( 13.71 7.9 ( 0.69 92.3 ( 0.37 82.1 ( 1.89 87.2 ( 1.44

1 ( 0.67 0.99 ( 0.26

403 ( 9.66 7.66 ( 0.13 91.2 ( 0.13 83.5 ( 0.27 87.4 ( 0.08 817 ( 26.20 6.6 ( 0.28 91.6 ( 0.19 83.5 ( 0.33 87.6 ( 0.11

0.75 ( 0.14 0.79 ( 0.12

209 ( 10.43 6.7 ( 0.25 91.2 ( 0.26 84.1 ( 0.35 87.7 ( 0.16 89 ( 3.31 6.62 ( 0.28 91.3 ( 0.22 83.7 ( 0.49 87.5 ( 0.27

0.59 ( 0.08 1.25 ( 0.08 1.31 ( 0.11

402 ( 10.50 415 ( 18.03 403 ( 14.05 6.62 ( 0.41 10.92 ( 0.16 10.81 ( 0.09 91 ( 0.49 91.3 ( 0.33 92.2 ( 0.76 83.5 ( 0.50 83.7 ( 0.22 83.4 ( 0.28 87.5 ( 0.23 87.5 ( 0.15 87.9 ( 0.24

2.26 ( 0.37 0.98 ( 0.12 1.15 ( 0.16

423 ( 11.14 387 ( 12.74 8.64 ( 0.29 10.68 ( 0.44 91.4 ( 0.18 91.5 ( 0.40 83.7 ( 0.50 84.1 ( 0.34 87.5 ( 0.27 87.8 ( 0.07 386 ( 23.40 7.41 ( 0.19 91.4 ( 0.43 83.8 ( 0.17 87.6 ( 0.22

1.13 ( 0.17 0.9 ( 0.02

406 ( 22.96 6.88 ( 0.22 91.7 ( 0.42 83.7 ( 0.19 87.7 ( 0.27 411 ( 26.73 6.85 ( 0.16 91.5 ( 0.20 83.5 ( 0.22 87.5 ( 0.07 724 ( 17.80 6.87 ( 0.11 91.6 ( 0.19 84.6 ( 0.46 88.1 ( 0.27

412 ( 17.77 6.83 ( 0.22 91.5 ( 0.29 83.5 ( 0.30 86.9 ( 1.11

0.86 ( 0.06 1.14 ( 0.08

1.09 ( 0.17

0.24 ( 0.77

18.7 ( 1.43 7.63 ( 1.38

1.45 ( 0.13 1.21 ( 0.05

8.98 ( 1.62 6.12 ( 1.19

1.05 ( 0.07 0.98 ( 0.01

7.41 ( 1.44 4.97 ( 0.96

1.03 ( 0.08 1.06 ( 0.05

15.48 ( 0.15 15.04 ( 0.36

1.07 ( 0.09 0.98 ( 0.01 1.03 ( 0.07 1 ( 0.03

6.92 ( 0.29 6.88 ( 0.57 6.62 ( 0.40 6.7 ( 0.52

2.10 ( 0.03a 2.03 ( 0.03

6.54 ( 0.46 6.64 ( 0.59

0.98 ( 0.01 0.00

6.65 ( 0.44 13.51 ( 1.03

0.34 ( 0.016

fuel B

31.8 ( 2.01 25.3 ( 1.41

fuel A MA

24.14 ( 1.98 19.77 ( 1.21

H-S M-S L-S

19.84 ( 1.85 19.95 ( 0.83 35.8 ( 1.13

H-olef/arom H-olef/RVP

20.72 ( 1.28 40.18 ( 1.20

H-arom H-RVP

18.69 ( 0.77 18.95 ( 0.56

M-RVP EtOH

20.62 ( 1.34 18.84 ( 0.68

H-MTBE L-MTBE 0 MTBE

19.14 ( 1.24 27.95 ( 0.41

19.31 ( 0.83

fuels variables

aromatics, vol % olefins, vol % oxygen, as MTBE, wt % benzene, vol % sulfur, ppm RVP, psi RON MON (RON + MON)/2

Test Fuels. Fuels can be characterized in terms of a number of different sets of fuel parameters. The predictive model includes the effects of fuel benzene (for benzene emission only), aromatics, olefins, RVP, sulfur, oxygen content, and oxygenate type. The model incorporates also the fuel percentage that had evaporated at 90 °C. On average the ranges covered were olefins from 6% to 19% v/v, aromatics from 19% to 40% v/v, RVP from 6.6 to 10.9 psi, oxygen from 0 to 2 wt %, and sulfur from 90 to 820 ppm. Fuels were formulated from currently available blending stocks from Mexican refinery streams with primary consideration given to hydrocarbon composition. The blending process followed prescriptions reported by Pahl (14). The inspection data for the gasoline components was performed following American Society for Testing Materials (ASTM) procedures, described elsewhere (15). In the course of this study five batches of each fuel were prepared, each one approximately every 3 months. Fuels were blended based on

individual average

Experimental Section

TABLE 2. Confidence Intervals (95%) of Test Fuel Properties from the Five Batches of Gasolines

In the U. S., for each batch of gasoline being supplied from the refinery, the producer can comply with the air pollutants standards for motor gasoline in one of three ways. First, for a given property, each producer may choose to meet either the flat limit or the averaging limit. When choosing the flat limits, a producer may not exceed the flat limits for any gasoline sold. Whereas under the averaging limits, the volume-weighted average value of individual gasoline properties cannot exceed the averaging limits. A second compliance option allows producers the use of a predictive model to identify other sets of property limits (flat, averaging, or mixed) that may be more optimal for refiners. A third compliance option allows for certification of alternative gasoline formulations based on the results of vehicle emission testing. Currently, most of the gasoline sold in the U. S. complies with the Environmental Protection Agency regulations through the use of a predictive model, also known as the complex model. The complex model is basically a set of equations relating gasoline properties to vehicle emissions that are used to identify alternative limits that correspond to better exhaust emissions than a reference fuel (i.e., 1990 U. S. average properties) tested with 1987-1992 model year (MY) vehicles. The model performs a number of calculations to predict emissions of HC and NOx from the candidate fuel and compares these emissions with those predicted for the reference fuel to determine if the candidate fuel is acceptable or not (12). In Mexico, environmental regulation mandates fuels to be certified by flat limits, but scarce data exist to relate fuel properties with vehicle emissions testing results; therefore in view of future fuel modifications, it is crucial to develop a predictive model that provides reliable information about fuel properties and vehicles emissions. The application of the complex model in Mexico is not straightforward because of differences in fuel formulation, vehicle fleet characteristics, and climatic conditions (13). Thus, an emissions test was conducted to collect data for a generalized mathematical model able to predict regulated emissions (e.g., CO, THC, and NOx) and benzene (Bz), 1,3butadiene (Bd), formaldehyde (Fd), and acetaldehyde (Ac) emissions using data obtained with 14 fuels with different Reid vapor pressure (RVP), distillation parameters, oxygen, sulfur, olefin, and aromatic contents. Additionally, an experimental program was designed to derive fuel-nonexhaust compositional relationships. Nonexhaust emissions produced during diurnal, hot soak, and refueling events were obtained for different technological groups of vehicles available in Mexico using fuels with increasing RVP values.

1271

intervals for the fuel properties from the five batches of gasoline are shown in Table 2. Test Vehicles. The required number of testing vehicles was reduced by stratified sampling, i.e., different ranges of model year (MY) were used, acting as a surrogate for different emission control technologies. Thirty in-use units were randomly recruited in areas of the MAMC of different socioeconomic indicators. The vehicles were driven to our facilities directly from their daily operation. Then, an inspection was performed to determine if repairs were necessary to safely test the vehicle on the dynamometer. These repairs were performed only to correct defective brakes, replace worn tires, correct wheel alignment, and fix leaking exhaust systems. Preliminary tests were carried out to confirm that the vehicles were normal emitters (e.g., defined as two times below the corresponding certification emission for CO, THC, or NOx standards, in brand new vehicles). A description of the main characteristics of the fleet is shown in Table 3. For the purpose of developing the predictive model, older vehicles (pre-1990) were not considered. This technology group is significant for not having emission control systems such as catalytic converters, oxygen sensor feedback, or fuel injection and therefore is difficult to characterize through sampling a small subset of the total population (9). Vehicles were classified into two different technological groups: The “tier 0” fleet refers to 12 1993-1998 MY vehicles complying with emission limits of 2.1 g km-1 for CO, 0.25 g km-1 for THC, and 0.62 g km-1 for NOx. The “tier 1” fleet is made up of 14 1999-2002 MY vehicles meeting certification emission standards of 2.1 g km-1 for CO, 0.156 g km-1 for nonmethane hydrocarbons (NMHC), and 0.25 g km-1 for NOx. This fleet was not equipped with an on board diagnosis system, and no emissions durability certificate was issued, which now is mandated by the Mexican Environmental Agency. Chassis Dynamometer Emissions Testing. The objective of the test program was to define a sound and repeatable way of measuring short-term effects of fuels on regulated emissions; thus the vehicles were tested on the exhaust emission certification procedures and tolerances of the U. S. Federal Test Procedure (FTP-75) (16). The FTP is a test cycle that is used in Mexico to certify emission performance standards for new light-duty gasoline vehicles. The vehicles are placed on a chassis dynamometer and are operated over the FTP-75 standardized driving cycle. A Horiba ECDM-48 electric dynamometer, coupled with a Horiba constantvolume sampler, CVS-45, was used throughout the study

TABLE 3. Test Vehicles technological group

tier 0

tier 1

maker

model year

GM Chrysler Nissan GM Chrysler VW GM Nissan GM Ford Ford Chrysler Nissan Chrysler GM Chrysler Dodge Nissan VW Nissan GM Ford GM Ford VW VW

‘93 Cutlass ‘93 Spirit ‘93 Tsuru ‘94 Cavalier ‘96 Neon ‘97 Sedan ‘97 Cavalier ‘98 Sentra ‘98 Monza ‘98 pickup ‘98 Escort ‘98 Stratus ‘99 Tsuru ‘99 Neon ‘99 Chevy ‘99 Stratus ‘99 pickup ‘00 pickup ‘00 Pointer ‘01 Sentra ‘01 Monza ‘02 Fiesta ‘02 Astra ‘01 Focus ‘01 Jetta ‘02 Sedan

fuel displacement supply odometer (L) system (km) 2.8 2.5 1.6 3.1 2 1.6 2.2 1.6 1.6 5 2 2.2 1.6 2 1.4 2.2 3.5 2.4 1.8 1.8 1.6 1.6 1.6 2 2 1.6

MPFIa MPFI MPFI MPFI MPFI MPFI MPFI MPFI TBIb MPFI MPFI MPFI MPFI MPFI TBI MPFI MPFI MPFI MPFI MPFI MPFI MPFI MPFI MPFI MPFI MPFI

92 784 125 500 143 348 152 788 122 321 60 325 80 658 26 070 69 047 26 558 42 347 43 198 27 386 74 654 54 639 31 574 87 151 56 301 32 000 36 554 7483 15 064 3034 54 471 19 320 23 000

a Multipoint injection. b Throttle-body injection, all vehicles equipped with three-way catalysts.

high (H), medium (M), and low (L) values of the design variables and in some cases as the combined effects. L-MTBE was designated as the base gasoline from which other fuel parameters were varied and was computed by averaging together all brand composites with proportional weighting. Fuel individual average was blended to have refinery average levels of sulfur, benzene, RVP, aromatics, olefins, and distillation properties of the year 2000 gasoline and was calculated by averaging together all brand composites with proportional weighting. Fuel MA simulates average properties of gasoline sold in nonattainment cities of Mexico. Doping M-S with thiophene yielded the sulfur level of fuel H-S. Fuels A and B were used later for validation of our predictive model. Fuel A is a commercial gasoline sold in the MAMC obtained from the Tula Refinery, while Fuel B was obtained from the Cadereyta Refinery, and it represents gasoline available outside of the nonattainment areas. The 95% confidence

TABLE 4. Experimental Mean Exhaust Emissions for the Average of Tier 0 and 1 Fleets tier 0 pollutant

individual average

0 MTBE

CO (g km-1) THC (g km-1) NOx (g km-1) Bz (mg km-1) Bd (mg km-1) Fd (mg km-1) Ac (mg km-1)

2.9168 0.2538 0.2229 16.3057 1.6807 2.5069 1.3524

2.5701 2.4950 2.4288 2.2777 2.4767 2.7261 2.7151 0.2425 0.2280 0.2226 0.2292 0.2141 0.2288 0.2718 0.2126 0.1999 0.1952 0.2008 0.1879 0.2011 0.2383 13.2508 13.0200 11.4580 10.1994 10.6275 12.0780 24.1665 1.8144 1.5532 1.3015 1.1842 1.2299 1.0210 1.6637 2.5887 2.7243 3.2658 2.9146 2.5268 2.5095 2.6591 1.2775 1.2926 1.3678 2.4190 1.3631 1.4094 1.5977

L-MTBE

H-MTBE

EtOH

M-RVP

H-RVP

H-arom

H-olef/RVP H-olef/arom 2.8138 0.2313 0.2030 13.3312 1.5063 2.7276 1.4623

3.0081 0.2298 0.2022 18.0540 1.4127 2.5091 1.2062

L-S

M-S

H-S

MA

2.0915 2.1813 2.4358 2.5803 0.2146 0.2202 0.2490 0.2379 0.1872 0.1931 0.2189 0.2090 12.8442 11.6728 12.7069 14.7325 1.5605 1.4213 1.6473 1.7710 2.6688 2.5019 2.8663 2.8420 1.2135 1.2200 1.3162 1.3370

tier 1 pollutant km-1)

CO (g THC (g km-1) NOx (g km-1) Bz (mg km-1) Bd (mg km-1) Fd (mg km-1) Ac (mg km-1)

1272

9

individual average

0 MTBE

L-MTBE

H-MTBE

EtOH

M-RVP

H-RVP

H-arom

H-olef/RVP

H-olef/arom

L-S

M-S

H-S

MA

2.3546 0.1807 0.1571 13.0626 1.2095 2.5023 1.0900

2.1063 0.1788 0.1537 8.1754 1.3830 2.3196 0.9952

2.1154 0.1833 0.1586 9.5892 1.3198 2.3359 0.9840

1.8464 0.1607 0.1366 8.1337 1.0586 3.0534 1.1060

1.8480 0.1714 0.1473 8.3736 0.8770 2.3727 1.7217

2.0925 0.1663 0.1419 8.8973 0.7948 2.0521 1.0116

2.3764 0.1690 0.1457 10.3658 0.9757 2.0779 0.9652

2.3938 0.2106 0.1875 18.0031 1.1388 2.0787 1.1367

2.1387 0.1634 0.1396 9.7380 0.8938 2.3500 1.1942

2.2519 0.1586 0.1369 11.5256 1.2544 2.1163 0.9989

1.8575 0.1548 0.1346 10.4676 0.6594 2.2563 0.8931

2.0551 0.1712 0.1455 7.4773 0.8080 2.1965 0.8766

2.0586 0.1817 0.1549 8.8320 0.7191 2.1997 0.9298

2.0353 0.1679 0.1441 10.4238 1.1351 2.3114 0.9523

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 40, NO. 4, 2006

FIGURE 1. Average CO + THC + NOx and total toxic emissions from tier 0 and tier 1 fleets. along with a Horiba analytical bench system, which provided emissions analysis for CO, THC, CH4, CO2, and NOx. All vehicles received an LA-4 cycle (preconditioning for FTP) followed by a 12 h soak (no diurnal heat build) before testing. The FTP composite emission rate is a weighted combination of the three measured bags designed to represent two trips. The FTP incorporates a cold start segment (bag 1), a hot stabilized segment (bag 2), and a hot start segment (bag 3). Composite emissions were averaged over the entire test cycle. The criterion to determine whether repeat test was necessary was similar to the one applied when developing the AQIRP and CONCAWE programs (17, 18). Retest was performed when the FTP composite emissions ratio between the larger values divided by the smaller one was greater than

1.175 for THC, 1.336 for CO, and 1.40 for NOx. It was anticipated that this degree of replication would render differences in fleet-average emissions of approximately 7% between the two levels of each design variable statistically significant at 95% confidence. The AQIRP fuel change procedure was used prior to each emissions test (8). The procedure is designed to minimize fuel carryover effects by thoroughly flushing the fuel delivery system and also to precondition the canister to the next fuel. These steps are followed by an urban dynamometer driving sequence preconditioning drive (to allow the vehicle’s computer to “learn” the new fuel) and a key off/idle sequence prior to re-entry into the standard FTP preconditioning. VOL. 40, NO. 4, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

1273

FIGURE 2. Percent change in emissions for the regulated pollutants, when using industrial average fuel as a baseline.

TABLE 5. Regression Coefficients, Tailpipe Emissions, Tier 0 Vehicles fuel term

CO

THC

NOx

Bz

Bd

Fd

Ac

intercept aromatics MTBE ethanol olefins RVP sulfur ASTM T90 point MTBE-sulfur benzene R2 standard error

0.921880 0.005372 -0.013692* -0.017404 0.000431 0.031620 0.000203 0.001920 0.000022

-1.490062 0.008792 -0.024694 -0.008312 0.000032 -0.001731 0.000219 0.004220

-0.797120 0.001627 0.020758 -0.009757 0.003906 0.003556 0.000237* 0.004025

2.576423 0.026292a -0.009702 -0.060219* -0.002484 -0.005185 0.000068 0.016721

0.361091 1.478 × 10-7 -0.128470 -0.219024* 0.030131 -0.094596 0.000138 0.032033

1.047979 -0.003111 0.113655a 0.042452 0.000053 0.001372

0.299621 0.008319 0.019862 0.305368* 0.000185 0.003119 0.000095 0.008254

0.806450 0.045770

0.025104 0.893640 0.145780

0.640570 0.098470

0.864780 0.103560

a

0.824710 0.064890

0.709430 0.054710

0.732640 0.136430

Significant at the 95% confidence level.

TABLE 6. Regression Coefficients, Tailpipe Emissions for Tier 1 Vehicles fuel term intercept aromatics MTBE ethanol olefins RVP sulfur ASTM T90 point MTBE-sulfur MTBE-T 90 point benzene R2 standard error a

CO

THC

NOx

0.610059 0.002378 -0.054111 -0.068802 0.001899 0.015189 0.000231 0.007638 0.000159

-1.869092 0.005874 -0.043478 -0.018368 -0.001931 -0.004972 0.000301 0.003856 0.000081

-1.496747 0.000859 0.022916 0.023824 0.002887 0.001294 0.000366 0.006713 0.000170 0.000812

0.794850 0.064870

0.801780 0.058760

0.871240 0.044580

Bz 2.266079 0.028376 -0.020608 -0.064337 -0.000235 -0.006298 0.000231 0.024551 0.017233 0.909470 0.081670

Bd 10-1

2.528 × 1.035 × 10-7 -8.993 × 10-2 -1.533 × 10-1a 2.109 × 10-2 -6.622 × 10-2 9.670 × 10-5 2.242 × 10-2

0.756264 0.123474

Fd

Ac

0.606299 -0.002047 0.081614 -0.020441 0.002644 -0.002568 0.000254 0.005691

-0.008004 0.007457 0.042081 0.289030 0.002565 0.002511 0.000114 0.014579

0.676450 0.161470

0.935680 0.082450

Significant at the 95% confidence level.

The fuels were tested in a different randomized order, and repeat tests on a fuel were not conducted back-to-back to ensure that the results were truly independent and to minimize the risk of fuel effects becoming compromised by any drift in vehicle performance or other time-related effects. Whenever a fuel having different sulfur content was introduced in the testing sequence for a particular vehicle, an extended sulfur purge was performed, as described elsewhere (19). The principle of sulfur purging was to make the vehicle transiently run rich at a high catalyst temperature to remove accumulated sulfur. 1274

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 40, NO. 4, 2006

Speciation/Analytical Procedures. The procedure to quantify VOCs has been described elsewhere (20). Briefly, Tedlar bags containing sample tailpipe gases are collected during the FTP, and then the bags are transferred to the chemical laboratory and analyzed using four Varian Star 3400 gas chromatographs and three HP 6890 chromatographs equipped with flame ionization detectors. Over 170 C1-C12 species are identified and quantified using this procedure. Carbonyls (aldehydes and ketones) were collected by diverting a small amount of diluted exhaust gas through a Horiba collection system to trap the carbonyls that were later

TABLE 7. Percentage Change in Emissions: Experimental versus Prediction reference ) fuel individual average

reference ) fuel individual average

fuel A tier 0 (% change)

fuel B tier 0 (% change)

fuel A tier 1 (% change)

fuel B tier 1 (% change)

complex model

fuel A

fuel B

pollutant

experimental

model

experimental

model

experimental

model

experimental

model

(% change)

(% change)

CO HC exhaust NOx benzene 1,3-butadiene formaldehyde acetaldehyde

-8.5 -5.9 -3.3 -1.7 -6.3 1.7 -8.4

-11.2 -11.9 -8.9 -7.2 -10.8 12.3 -2.8

-3.3 -3.2 -1.7 -20.0 -27.9 -2.2 -14.0

-10.5 -2.6 -9.0 -14.6 -11.8 -0.6 -1.3

2.5 9.7 11.6 57.7 115.0 -2.8 22.5

0.6 5.6 3.4 33.8 88.7 -1.7 13.2

16.1 16.1 6.8 29.5 77.7 -8.2 30.8

11.7 9.8 4.8 47.3 86.1 -3.0 23.8

-9.86 -0.98 -28.54 -15.28 15.34 -2.75

7.39 11.53 -11.13 52.73 -5.85 14.53

analyzed by high-performance liquid chromatography (HPLC) on a Hewlett-Packard 1100 chromatograph equipped with a UV-vis detector. The above analyses were pooled to yield a single hydrocarbon speciation for each exhaust sample. Aldehydes were collected in silica gel cartridges with 2,4dinitrophenylhydrazine as the derivative reagent, extracted with acetonitrile, and later analyzed in the HPLC equipment. Evaporative Emissions. Since evaporative emissions represent a substantial fraction of the total VOC emissions from light-duty gasoline vehicles, the model also comprises three nonexhaust emissions equations for VOCs: diurnal, hot soak, and refueling emissions. Diurnal and hot soak evaporative emissions were evaluated according to the FTP75 procedure by placing the vehicles in a sealed housing for evaporative determination, which captures all vapor emitted from the vehicle during the diurnal and hot soak. During the test, total hydrocarbons are monitored with a flame ionization detector. Diurnal and hot soak emissions data were collected using the H-MTBE, EtOH, M-RVP, and H-RVP fuels (RVP ranging from 6.60 to 10.68 psi). The results of these tests are generally expressed in grams of hydrocarbons per test and later transformed to grams of hydrocarbons per kilometer, according to the procedure described by Stebar et al. (21). Vehicles having at least one event of diurnal soak and three of hot soak events were considered. The average traveled distance per day was predetermined as 50 km day-1. Evaporative emissions data on vehicle refueling were incorporated in the model following the procedure of the California Environmental Protection Agency (22), performed on a service station located within our facilities (4). The equipment-adjusted flow was measured with the dispenser tip in its largest vertical position for at least 1 min. Refueling was carried out using the fastest refueling speed and stopped when 80% of the tank capacity was reached. Vapor recovery efficiency was calculated with five commercial vapor recovery units employed in gas stations of the MAMC (4).

Results and Discussion Experimental Exhaust Emissions Results. Simple arithmetic means were used to summarize the experimental exhaust emissions results in Table 4for the tier 0 and tier 1 fleets, while (CO+ THC + NOx) results and total toxic emissions of tested fuels in both fleets were plotted in Figure 1. Addition of MTBE to the fuels decreased CO, NOx, and THC emissions in both fleets to 5% and 8% for tier 0 vehicles and 10-12% for the tier 1 fleet. Accordingly, the auto/oil industry study found that adding oxygenate at 2.7 wt % oxygen reduces CO emissions by 11-14% in 1983-1985 MY vehicles (13). Higher RVP, aromatics, and olefin contents raise CO, THC, and NOx tailpipe emissions in both fleets as reported by Hochhauser et al. (24). In the case of sulfur impact, in tier 0 vehicles decreases of 13.8%, 14.5%, and 14% in THC, NOx, and CO emissions

were found when sulfur content was lowered from 817 to 89 ppm, while in tier 1 vehicles these pollutants decreased 10%, 15%, and 13%, respectively. Sulfur impacts for tier 0 vehicles, on the basis of analysis of results collected from several U. S. test programs, estimated that HC, NOx, and CO reductions were 13%, 6.6%, and 15.4%, respectively, when sulfur is lowered from 450 to 50 ppm. For certified tier 1 vehicles, two sulfur concentrations were tested (30 and 330 ppm), and the results indicate that the percent increases in emissions were 24.2%, 20.8%, and 10% for NMHC, CO, and NOx, respectively (25). Benzene shows the highest emission rates among the toxic compounds in both fleets. The highest increase in exhaust emission of this compound corresponds to H-arom fuel followed by that of the H-olef/arom fuel. When ethanol is used instead of MTBE, (H-MTBE vs EtOH fuels), formaldehyde emissions decreased 11% in the tier 0 fleet, while acetaldehyde increased 43%. For tier 1 vehicles, a 22% decrease in formaldehyde and a 36% increase in acetaldehyde were observed. These results agree with our previous results for a set of 1993-1998 MY vehicles using a 6 vol % ethanol fuel relative to a 5% MTBE base gasoline (23). Predictive Model Development. The procedures and statistical methodology used to build the predictive model are similar to the techniques used to construct the complex models for exhaust VOC and NOx emissions (26-27). Each emission was examined on a vehicle-by-vehicle basis taking into consideration the physical and chemical properties of each of the five batches prepared for the 14 fuels tested. The complete data set was examined for outliers by inspecting Student’s t-test residuals (residuals divided by their standard errors). Analysis of covariance techniques were then used to detect and adjust for systematic trends in the data. Whenever a consistent trend was found, which was significant at P < 1%, the averages and standard errors were adjusted to eliminate any bias that might be caused by that trend. In practice, however, the adjustments had relatively little effect on mean emissions owing to the robustness of the experimental design. In short, the statistical approach used in this study included the use of averaged values for each fuel parameter as their centered value. The variable regressed is thus the true value of the parameter in question minus the averaged or centering value (28). The centering value for each fuel parameter simply represents the average of the fuel parameter in the database. The model was built by considering the two technological groups separately. On the basis of input from the U. S. Environmental Protection Agency, the natural logarithm of gross emissions was used as the dependent variable for all pollutants. Modeling in log space, in which the dependent variable is the natural logarithm of mass emissions per kilometer, has the advantage that it increases the explanatory power of the VOL. 40, NO. 4, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

1275

FIGURE 3. Comparison of percent change in emissions of THC and NOx predicted by our model with those predicted by the complex model. model by increasing the correlation coefficient between emissions and fuel parameters. Moreover, models in log space have more normally distributed residuals (the variation in the dependent variable that cannot be explained by the model) than models that were not logarithmically transformed. Following EPA procedures, once the regression equations were determined, the model was refined by eliminating any terms that did not significantly affect its behavior. That is, by ranking the terms in each pollutant-specific model on the basis of their contribution to the model’s explanatory power. Furthermore a least-squares regression on the database was applied to generate equations that contain all of the original terms except those that cumulatively accounted for only a small (generally less than 1%) portion of the model’s explanatory power. Eleven separate exhaust submodels were developed for the six pollutants and the two vehicle emissions control technology classes. The exhaust emissions of the reference fuel specifications and the candidate fuel specifications for each technological class of vehicles are predicted by the submodel of the predictive model. The differences between the predicted exhaust emissions for the reference fuel specifications and the candidate fuel specifications are combined to yield technological-classweighted vehicle predicted emissions differences. The general 1276

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 40, NO. 4, 2006

equation can be described in the following way

E ) exp[a0 + a1*(A) + a2*(OX) + a3*(OL) + a4*(P) + a5*(AZ)+ a6*(T) + a7*(AOX) + a8*(AOL) + a9*(AP) + a10*(AAZ)+ a11*(AT) + a12*(OXOL) + a13*(OXP) + a14*(OXAZ)+ a15*(OXT) + a16*(OLP) + a17*(OLAZ) + a18*(OLT)+ a19*(PAZ) + a20*(PT) + a21*(AZT) + a22 *(Bz)] where E is THC, NOx, CO, 1,3-butadiene, formaldehyde, or acetaldehyde in g km-1, A is aromatics, OX is oxygen (MTBE or EtOH), OL is olefins, P is RVP, AZ is sulfur, Bz is benzene, and T ) 90 °C. The variable OX allows selection of the oxygenate source of the reference fuel depending whether the oxygen originates from the use of MTBE or EtOH (28). To estimate the exhaust benzene emission, a coefficient was incorporated that refers to the amount of benzene contained in the fuel and therefore was not considered as a independent variable in the model. Once the raw model was developed, it was evaluated using the random balance approach to determine the relative contribution of individual model terms to the predictive power of the raw model. Terms retained after using an approximate cut-off point of 99% were then pooled, and a backward regression was run on those terms to determine

FIGURE 4. Comparison of percent change in emissions of the four toxics predicted by our model with those predicted by the complex model. the coefficients shown in Tables 5 and 6 for tier 0 and tier 1 vehicles, respectively. Predicted Emissions Using Individual Average Fuel as a Baseline. Percent variations in emissions predicted by our model for the regulated pollutants are shown in Figure 2. The values refer to average emissions on both fleets. The combination of high RVP, olefins, and aromatics content in the fuels increases THC emissions when the individual average fuel is taken as a baseline. It is obvious that the qualitative and quantitative change in emissions varies if other fuel is taken as a baseline, but the results suggest that much improvement can be achieved if the individual average fuel is adopted as a baseline to define targets of emissions reductions on the basis of a fuel specification-vehicle technology couple. Validation of the Predictive Model. Because of the wide range of fuel parameters tested, measured exhaust emissions were compared with those predicted by our model. For validation of the model with fuels A and B, 15 vehicles were selected randomly from the entire fleet, and the FTP-75 protocol was performed. A comparison of the experimental percent change in emissions and the results predicted by our model (individual average fuel as a baseline) is shown in Table 7, which includes, as a reference, the percent changes in emissions calculated with the complex model. The discrepancies between actual test results and model prediction are on the same order of magnitude as those described

by the EPA (29) and by Korotney et al. (30) in the development of the complex model. The qualitative agreement between both models is fairly good, taking into consideration the differences in vehicle technologies. Comparison of the Predictive Model with the Complex Model. The percent changes in THC-NOx emissions, as forecasted by our model with those calculated using the complex model, are shown in Figure 3. The results with the predictive model are for the average of the two fleets of vehicles, with the individual average fuel used as a baseline. In a similar manner, a comparison of toxic emissions predicted with both models is shown in Figure 4. In both cases, the prediction of both models agrees qualitatively quite well despite the differences between physical and chemical properties of fuels used as a baseline and vehicle technologies. Evaporative Emissions Model Results. Evaporative emissions results for diurnal and hot soak and refueling measurements are shown in Figure 5 for the average of both fleets. The regulated emission standard for evaporative emissions has not changed in Mexico since 1993, when multipoint injection was introduced without fuel tank pressure sensors coupled to computer control systems. In general, vehicle control systems work properly from 6.60 to 8.64 psi RVP values. Further increases in RVP make the evaporative control system malfunction, and a 2-fold increase in emissions is observed during the hot soak test. VOL. 40, NO. 4, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

1277

(2) (3)

(4) (5)

FIGURE 5. Diurnal, hot soak and refueling emissions as a function of RVP.

(6)

TABLE 8. Possible Formulation for Lower Sulfur Fuels

(7)

parameter

individual average

aromatics, vol % olefins, vol % oxygen, as MTBE, wt % benzene, vol % RVP, psi sulfur, ppm T ) 90, °C

28.0 13.5 0.34 1.14 8.9 724 167

fuel C

fuel D

fuel E

28.0 30.0 35.0 15.0 15.0 15.0 1.00 1.00 1.00 1.10 1.10 1.10 7.2 7.0 7.0 300 100 50 163 163 163

(8) (9)

(10)

TABLE 9. Predicted Change in Emissions for the Lower Sulfur Fuels Using Individual Average Fuel as a Baselinea fuel C pollutant

tier 0

tier 1

CO -14.1 -14.6 THC exhaust -11.6 -14 NOx -9.8 -12.3 VOC -19.5 -19.5 Bz -9.3 -18.1 Bd -6.3 -5.5 Fd 4.8 -6.6 Ac -6.3 -7.6 a

fuel D

fuel E

tier 0

tier 1

tier 0

tier 1

-17.2 -13.9 -13.8 -21.8 -5.6 -7.1 3.1 -6.6

-18.5 -18.1 -18.6 -21.8 -17.2 -4.1 -11.5 -8.4

-15.8 -11 -14.1 -21.8 7.3 -4.7 1.2 -3.1

-18.5 -17 -19.7 -21.8 -5.6 -6.8 -13.5 -5.5

(11)

(12) (13)

The values that are negative represent decreases.

(14)

Application of the Predictive Model to Fuel Formulation in Mexico. It is estimated that in the year 2008 the number of light-duty gasoline vehicles in Mexico will be about 13.9 millions units, 71% of which will be tier 1 and 6% tier 0 technologies, the rest being mostly vehicles without emissions control systems (20%). Hence, the unavoidable reduction in sulfur to introduce tier 2 vehicles must take into account a change in gasoline parameters and a possible impact on these technologies. Three possible formulations are presented in Table 8, considering the need to eliminate more sulfur from fluidized catalytic cracking (FCC) streams available in Mexico’s refineries. Predicted variations in emissions for both technological groups of vehicles, using individual average fuel as a baseline, are shown in Table 9. It is possible then to increase aromatic content of gasoline up to 35% and olefins up to 15 vol %, if sulfur content is lowered, allowing a gain in flexibility of the refinery for future formulations.

(15) (16) (17) (18)

(19) (20)

(21)

Literature Cited (1) Edgerton, S. A.; Bian, X.; Doran, J. C.; Fast, J. D.; Hubbe, J. M.; Malone, E. L.; Shaw, W. J.; Whiteman, C. D.; Zhong, S.; Arriaga, J. L.; Ortiz, E.; Ruiz, M.; Sosa, G.; Vega, E.; Limon, T.; Guzman, 1278

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 40, NO. 4, 2006

(22)

F.; Archuleta, J.; Bossert, J. E.; Elliot, S.; Lee, J. T.; McNeir, L. A.; Chow, J. C.; Watson, J. G.; Coutler, R. L.; Doskey, P. V.; Gafney, J. S.; Marley, N. A.; Neff, W.; Petty, R. J. Air Waste Manage. Assoc. 1999, 49, 1221. Smith, F. A.; Elliot, S.; Blake, D.; Rowland, F. S. Environ. Sci. Policy 2002, issue 6, 449. McKinley, G.; Zuk, M.; Jojer, M.; Avalos, M.; Gonza´lez, I.; Herna´ndez, M.; Iniestra, R.; Laguna, I.; Martinez, M.; Osnaya, P.; Reynales, L.; Valde´s, R.; Martı´nez, J. The Local Benefits of Global Air Pollution Control in Mexico City; Instituto Nacional de Ecologı´a: Mexico City, 2003. http://www.epa.gov/ies/ documents/mexico/maxicofinal.pdf. Schifter, I.; Magdaleno, M.; Dı´az, L.; Krueger, B.; Leo´n, J.; Palmerı´n, M. E.; Casas, R.; Melgarejo, A.; Lo´pez-Salinas, E. J. Air Waste Manage. Assoc. 2002, 52, 174. Arriaga-Colin, J. L.; Jason West, J.; Sosa, G.; Escalona, S. S.; Ordun ˜ ez, R. M.; Cervantes, A. D. M. Atmos. Environ. 2004, 38, 2523-2533. United States Environmental Protection Agency, Office of Mobile Sources. EPA Staff Paper on Gasoline Sulfur Issues; Report No. EPA-420R98-005; Environmental Protection Agency: Washington, DC, 1998. Auto/Oil Air Quality Improvement Research Program. Predicted Effects of Reformulated Gasoline T50, T90, Sulfur and Oxygen Content on Air Quality in Years 2000 and 2010; Technical Bulletin 21; Coordinating Research Council: Atlanta, GA, 1997. Auto/Oil Air Quality Improvement Research Program. Program Final Report; Auto/Oil Air Quality Improvement Research Program: Detroit, MI, 1997. Burns, V. R.; Benson, J. D.; Hochhauser, A. M.; Koehl, W. J.; Dreucher, W. M.; Reuter, P. M. Description of the Auto/Oil Air Quality Improvement Research Program. In Auto/Oil Air Quality Improvement Research Program, SAE SP-920; SAE Technical Paper 912320; Society of Automotive Engineers: Warrendale, PA, 1992; pp 1-28 . Burns, V. R.; Hochhauser, A. M.; Reuter R. M.; Rapp, L. A.; Knepper, J. C.; Rippon, B.; Koehl, W. J.; Leppard, W. R.; Rutherford, J. A.; Benson, J. D.; Painter, L. J. Gasoline reformulation and vehicle technology effects on emissionssAuto/ Oil Air Quality Improvement Research Program. In Auto/Oil Air Quality Improvement Research Program, SAE SP-1117; Society of Automotive Engineers: Warrendale, PA, 1995; Vol. 3, pp 143165. Air Quality Report: Report of the Working Group I on Environmental Objectives, Auto Oil II Programme final version 7.1.; European Commission. Office for Official Publications of the European Communities. Directorate General for the Environment, 2000, Luxembourg. http://europa.eu.int./comm/ environment/sitemap_en.htm. Code of Federal Regulations, Subpart D 80.40, Reformulated Gasoline; Office of the Federal Register: Washington, DC, 1994. Schifter, I.; Dı´az, L.; Vera, M.; Castillo, M.; Ramos, F.; Avalos, S.; Lopez-Salinas, E. Environ. Sci. Technol. 2000, 34, 2663. Pahl, R. H.; McNally, M. J. Fuel Blending and Analysis for the Auto/Oil Air Quality Improvement Research Program; Paper No. 902098; Society of Automotive Engineers: Warrendale, PA, 1990. American Society for Testing Materials. Standard Specification for Spark-Ignition Engine Fuel; D-4814-01a; American Society for Testing Materials: West Conshohocken, PA, 2001. Code of Federal Regulations, Title 40, Part 86, Office of the Federal Register; Washington, DC, 1993. Painter, L.; Rutherford, J. A. In Auto/Oil Quality Improvement Research Program; Paper No. 920319; Society of Automotive Engineers: Warrendale, PA, 1992. Special Task Force FE/STF-20, CONCAWE Fuels Quality and Emissions Management Group. Fuel Effects on Emissions from Modern Gasoline Vehicles: Part 1sSulphur Effects; CONCAWE: Brussels, Belgium, 2003. Schifter, I.; Dı´az, L.; Vera, M.; Guzman, E.; Lo´pez-Salinas, E. Fuel 2004, 83, 2065. Bjordal, S. D.; Goodfellow, C. L.; Bennett, P.; Beckwith, P. Relative effects of catalyst and fuel formulation on gasoline vehicle exhaust emissions. In Proceedings of the International Fall Fuels and Lubricants Meeting and Exposition, San Antonio, TX, Oct 14-17, 1996; Paper No. 961902; Society of Automotive Engineers: Warrendale, PA, 1996. Stebar, R. F.; Benson, J. D.; Brinkman, N. D.; Dunker, A. M.; Sapre, A. R.; Schwing, A. R.; Martens, S. W. Gasoline Vapor Pressure Reduction-an Option for Cleaner Air; Paper No. 852132; Society of Automotive Engineers: Warrendale, PA, 1985. Air Resources Board, California Environmental Protection Agency. Vapor Recovery Test Procedure TP-201.2c, Spillage from

(23) (24)

(25) (26) (27) (28)

Phase II Systems; California Environmental Protection Agency: Sacramento, CA, 1996. Schifter, I.; Vera, M.; Dı´az, L.; Guzma´n, E.; Ramos, F.; LopezSalinas, E. Environ. Sci. Technol. 2001, 35, 1893. Hochhauser, A. M.; Benson J. D.; Burns, V.; Gorse, R. A.; Koehl, W. J.; Painter, L. J.; Rippon, B. H.; Reuter, R. M.; Rutherford, J. A. In Auto/Oil Quality Improvement Research Program; Paper No. 912322; Society of Automotive Engineers: Warrendale, PA, 1992. United States Environmental Protection Agency. Fuel Sulfur Effects on Exhaust Emissions; Report Number M6.Ful.001; Environmental Protection Agency: Washington, DC, 2001. Regulations of fuels and fuels additives; Standards for reformulated and conventional gasoline: Final rule. Fed. Regist. 1994, 59 (32). Rao, V. Development of an Exhaust Carbon Monoxide Emissions Model; Paper No. 961214; Society of Automotive Engineers: Warrendale, PA, 1996. Air Resources Board, California Environmental Protection Agency. California Procedures for Evaluating Alternative Speci-

fications for Phase 3 Reformulated Gasolines. Using the California Predictive Model; California Environmental Protection Agency: Sacramento, CA, 2000. (29) Korotney, D. J.; Rao, V.; Lindhjem, C. E.; Sklar, M. Reformulated Gasoline Effects on Exhaust Emissions: Phase III; Investigation on the Effects of Sulfur, Olefins, Volatility, and Aromatics and the Interactions Between Olefins and Volatility or Sulfur; Paper No. 950782; Society of Automotive Engineers: Warrendale, PA, 1995. (30) Environmental Protection Agency. Final Regulatory Impact Analysis for Reformulated Gasoline; Environmental Protection Agency: Washington, DC, 1993.

Received for review February 24, 2005. Revised manuscript received October 20, 2005. Accepted November 22, 2005. ES0503884

VOL. 40, NO. 4, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

1279