United States

Environmental Protection

Agency

Air and Radiation EPA420-R-02-003

January 2002

M6.EXH.002

Analysis of Emissions

Deterioration Using

Ohio and Wisconsin

IM240 Data

 Printed on Recycled

Paper
EPA420-R-02-003

January 2002

Analysis of Emissions Deterioration

Using Ohio and Wisconsin IM240 Data

M6.EXH.002

Phil Enns

Ed Glover

Penny Carey

Assessment and Modeling Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

NOTICE

This technical report does not necessarily represent final EPA decisions or positions.

It is intended to present technical analysis of issues using data that are currently available.

The purpose in the release of such reports is to facilitate the exchange of

technical information and to inform the public of technical developments which

may form the basis for a final EPA decision, position, or regulatory action.
1

Enns, P., E. Glover, P. Carey, and M. Sklar, “Determination of Running Emissions

as a Function of Mileage for 1981-1993 Model Year Light-Duty Cars,” Report No.

M6.EXH.001.

2

Glover, E. and P. Carey, “Determination of Start Emissions as a Function of Mileage and

Soak Time for 1981-1993 Model Year Light-Duty Vehicles,” Report No. M6.STE.003.

2 

1.0 INTRODUCTION

This report documents the collection and analysis of IM240 data conducted in support of

EPA’s study of in-use deterioration and the revision of the MOBILE6 emissions inventory model.

IM240 data have certain strengths and weaknesses relative to other emissions data. After extensively

comparing results based on various data sources, IM240 data from Ohio were used to correct

deterioration estimates computed from FTP data. These results and a description of the analysis are

reported elsewhere.1

 In addition to this limited application, the IM240 test data have added

considerably to the understanding of vehicle emissions in the overall fleet.

A substantial effort was devoted to collecting and studying data from state inspection and

maintenance (I/M) programs that employ the IM240 test cycle. This cycle was developed to provide

a relatively short (239 second) test that captures the essential features of the well-established Federal

Test Procedure (FTP), which has long served as the standard for exhaust emissions testing. IM240

data offer two principle advantages. First, very large samples of data are available since state I/M

programs aim to test all or most registered vehicles. Second, as a result of this comprehensive

testing, the samples approach a census of all vehicles and are thus relatively unbiased.

There are several disadvantages to using IM240 data when compared with FTP testing. The

test is shorter than the FTP and is therefore considered less representative of real driving. In

particular, the IM240 test contains no cold start portion. Vehicle preconditioning and ambient

temperature conditions are uncontrolled in IM240 testing. Data recording at IM test lanes is generally

less thorough and accurate, especially with regard to odometer readings. Finally, it is necessary to

make several transformations of the IM240 data to obtain FTP-comparable results. Thus, from the

outset, IM240 data were considered less satisfactory than FTP data, and regarded as supplementary

rather than primary. 

In MOBILE6, vehicle exhaust emissions will be allocated between engine start (start

emissions) and travel (running emissions). This split enables the separate characterization of start

and running emissions for correction factors such as fuel effects and ambient temperature. It also

allows a more precise weighting of these two aspects of exhaust emissions for particular situations

such as morning commute, parking lots and freeways. Because the IM240 test does not contain a

cold start, data from that test are appropriate only for the study of running emissions; start emissions

are considered in a separate document.2

Section 2 describes the data obtained from Colorado, Arizona, Ohio, and Wisconsin, and

provides a brief summary of the I/M programs in each state. Section 3 defines important terms and
3 

describes the methodology and analysis of the data. Section 4 presents results and conclusions

obtained from the analysis. Data from Phoenix, Arizona and Colorado were analyzed by an EPA

contractor. The results from that work are described briefly, with details found in referenced reports.

 Data from Dayton, Ohio, are discussed more thoroughly in this paper. The Ohio data received

special attention because that location had no previous I/M program; as a result, these data were

considered more representative of intrinsic emissions deterioration.

2.0 IM240 DATA

2.1 Colorado and Phoenix

IM240 data collected by the state of Colorado between April and August, 1995, were

supplied to an EPA contractor, Systems Application International, Inc. (SAI) for evaluation of

emissions deterioration. The Colorado program uses a fast-pass procedure whereby most passing

vehicles are not required to complete the full 240-second test. However, a special study was

conducted on a random sample of vehicles that were required to complete the full test. These full test

results comprised the data used by SAI. 

A significant issue in working with IM240 test data concerns the accuracy of reported

odometer readings. State I/M programs record odometer incidentally, and under closer scrutiny,

numerous anomalies often are revealed. The most common problem is the occurrence of

unrealistically low mileage given the vehicle’s age. In addition, a few cases of exorbitantly high

mileages were observed. The best explanation for many of these observations is that the recorder

miscopied the left-most digit of the odometer, e.g., a car with 121,300 miles is recorded as having

only 21,300 miles. For older vehicles, the odometer may only include five digits, precluding accurate

recording of mileages greater than 99,999.

SAI identified problems with the reported odometer readings, and developed a procedure for

modifying these values so that they would better conform with known properties of vehicle mileage

accumulation. Their approach corrects the odometer based on a probabilistic model of the relation

between vehicle age and mileage. Based on the results from its application to the Arizona and

Colorado data, this methodology appears to have shortcomings. It produces corrected data sets with

certain unnatural features of mileage distribution. The best explanation is that the method’s

assumptions are simply not sophisticated enough to describe the true error patterns in the data. A

more satisfactory method was not developed, so this model was used in SAI’s subsequent analyses

and was also applied, in modified form, to EPA’s study of Ohio IM240 data.

SAI also developed a statistical technique for identifying emission value outliers for the

purpose of deleting unusually large or small values in the analyses of deterioration. A small

percentage of emission values were adjusted on this basis. Extensive summary statistics were
3

 Cohen, J.P., R.K. Iwamiya, and R.E. Looker, “In-Use Deterioration Data Analysis: Task

1, Initial Data Analysis and Quality Review of Colorado IM240 Data,” SYSAPP-96/72d, Draft

Report, Systems Application International, Inc., November, 1996.

4

 Cohen, J.P., R.K. Iwamiya, and R.E. Looker, “In-Use Deterioration Data Analysis: Task

1, Initial Data Analysis and Quality Review of Phoenix IM240 Data,” SYSAPP-96/76d, Draft

Report, Systems Application International, Inc., November, 1996.

4 

prepared for both the edited and unedited data.3

Data from the Phoenix, Arizona I/M program collected from January to June, 1996, were also

analyzed by SAI. The Arizona program uses a fast-pass/fast-fail (FPFF) algorithm which can pass

or fail the vehicle prior to the end of the IM240 sequence. The state also conducts full IM240 tests

without using the FPFF algorithm on a randomly-selected two percent of the vehicle population.

These random full IM240 test results comprised the data used by SAI. These data contained

odometer anomalies of a somewhat different nature, so the contractor modified its correction

software accordingly. It also applied the emission outlier screening procedure developed for the

Colorado data to produce a modified Phoenix data set. Summary statistics were again generated for

the raw and screened data.4

2.2 Ohio

IM240 data collected in Dayton, Ohio were of special interest in this study. This city had no

previous I/M experience, so there is reason to believe that deterioration of measured emissions would

be more “natural” than in regions with earlier I/M programs. Like Colorado, Ohio employs a fastpass algorithm to speed up the testing process, but random full IM240 tests are not conducted. As

a result, it is necessary to estimate full 240-second values from the fast-pass results. These estimates

were developed using a regression model developed with data from the Wisconsin I/M program.

Both data sets are discussed below.

The Ohio data includes IM240 test results on all of the 1981 and later registered cars and

light-duty trucks scheduled to be tested from April, 1996 through March, 1997. Since the program

testing frequency is biennial, the sample of vehicles represents approximately half of the overall

vehicle population. Also, because the first month is April, the data set does not contain testing

results collected during early startup in 1995 and the beginning of 1996. The original database

contained more than one million vehicles from three separate Ohio cities (Cleveland, Akron/Canton,

and Dayton/Springfield). Only the data from Dayton/Springfield (referred to as the “Dayton” data

elsewhere in this report) were used in the analysis, and these data were further restricted to the valid

initial tests (no post-repair retests were used). The sample was restricted to Dayton because it had

never implemented any I/M or anti-tampering (ATP) program in the past. The other two sites had

implemented decentralized I/M or ATP programs in the past. These programs may have had some

influence on the prior condition of the vehicles, and thus potentially bias emissions in the sample

from those cities. 
5 

The sample contained both cars and trucks, with significant numbers of data points for all

model years from 1981 through 1995. A vehicle identification number (VIN) decoder developed by

Radian Corp. was used to determine each vehicle’s model year, vehicle type (car or truck), and fuel

metering type (fuel injected or carbureted). The VIN decoder was believed to have achieved a high

rate of proper decoding; nevertheless, because of undecodable VINs, voided or suspicious emission

tests (unusually low or high CO2 emissions, i.e., less than 100 g/mi or greater than 1500 g/mi), or

missing data in key fields, the sample size was reduced to approximately 211,000 vehicles from

Dayton/Springfield. Table 1 provides a breakdown of the Dayton sample by model year, vehicle

type, and technology (fuel delivery type).

The State of Ohio does not conduct full IM240 tests on all its vehicles or on a random sample

of vehicles. A small fraction of vehicles take the full 239 seconds to pass. All failures take the full

239 seconds. The vehicles displaying low emissions during the beginning of the test have their test

terminated early under what is called a fast-pass (FP) procedure. The purpose of the FP test is to

speed up the testing process and increase the lane volume so as to more efficiently utilize fixed

testing resources. The FP test is not a standardized test in terms of length, because it can be

terminated as early as 30 seconds into the cycle and as late as 238 seconds. 

The FP results are also a function of the specific FP algorithm used to determine whether an

individual vehicle passes out of the test early (fast-passes) or stays in the test the full 239 seconds.

The algorithm is expressed as a large table of cut points (pass/fail standards) that is applied

simultaneously for all three pollutants at each second of the test. The four states which conduct fastpass IM240 testing use slightly different FP algorithms, which makes comparison between the states

somewhat difficult. Ohio performs its fast-pass (FP) testing in accordance with an EPA fast-pass

algorithm recommended in the “EPA High Tech I/M Guidance Document.”

As with the Arizona and Colorado data, the mileage recorded in the Ohio data is of

questionable quality for many of the individual vehicles. An odometer correction was judged to be

necessary because of two systematic problems. First, in about 200 cases, the odometer value was

found to be unrealistically high, i.e., over 300,000. The most likely explanation for these outliers is

the accidental recording of the tenth’s digit, with the effect of multiplying mileage by ten. For

example, under this scenario, a proper reading of 61,000 miles becomes an unreasonable 610,000

miles. This problem was addressed by eliminating all readings over 300,000 miles. However, for

vehicles with less than 300,000 miles it was difficult to determine whether improper recording of

the tenth’s digit occurred.

 As with the Colorado and Arizona IM240 data, inspection of the Dayton, Ohio, data set

suggests the second type of problem, odometer rollover. This problem was considered far more

widespread and difficult to rectify. One reason for odometer rollovers is that many vehicle odometers

in earlier model years were not designed to record mileage greater than 99,999, and frequently during

the I/M process the inspector merely records the displayed mileage. Moreover, the collection of

accurate odometer data by an I/M program usually is not an important priority, giving rise to

concerns about the values even in newer vehicles. Consequently, in a substantial number of vehicles,

mileage is unrealistically low for the given vehicle’s age. Initially, a correction for this problem was

attempted using a modified version of the methodology created by SAI for use with the data from
5

 Cohen, J.P., R.K. Iwamiya, and R.E. Looker, “Analysis of In-Use Deterioration of

Emissions Using I/M 240 Data,” SYSAPP-97/06d, Draft Final Report, Systems Application

International, Inc., February, 1997. 

6 

Colorado and Phoenix. In the modified approach, the odometer for a vehicle with low mileage may

be incremented by 100,000 miles if it fits a particular profile. In effect, this procedure adds 100K

miles to selected vehicles; the proportion of vehicles so adjusted grows with age. The choice of

vehicles assigned a new mileage is made probabilistically according to distributions fitted to the data

under certain assumptions underlying the SAI methodology. While not an ideal solution, it was felt

that this method yielded an improvement to the uncorrected odometer values in the raw data. After

reviewing the corrected odometers, however, a decision was made to use region-specific mileage

accumulations instead for subsequent analysis.

2.3 Wisconsin Second-by-Second Data

Raw emission values from the Ohio data are not directly comparable to one another because

they correspond to varying test durations. To address this concern, a model for predicting the full

239-second emissions rate from a partial fast-pass test score was developed. This model was fitted

using second-by-second data from the Wisconsin IM240 test program. These data were collected as

a random sample over three different months (December, 1995, April, 1996, and October, 1996).

It contained data on 3,148 cars and 1,192 light trucks with a range of model years from 1981 through

1995. The Wisconsin IM240 data were chosen over data from the other two IM240 states (Arizona

and Colorado) with second-by-second data because of the geographic, demographic, and

meteorological similarities between Ohio and Wisconsin. Furthermore, both states use the same

testing contractor, so analyzers and specific test procedures are likely to be similar. Results from the

Wisconsin data analysis are reported in the next section.

3.0 METHODOLOGY AND ANALYSES

3.1 Colorado and Phoenix

SAI performed an extensive analysis of emissions deterioration for the combined

Phoenix/Colorado data set. A variety of regression models was investigated, and several are

discussed in detail.5

 They focus on the logarithmic transformation of emissions, which tends to give

better model fits than the untransformed raw data. Model coefficients estimated in this way provide

multiplicative adjustments to a baseline zero-mile emission rate. In general, the models do not fit the

data especially well due to the lack of good baseline values for individual vehicles. Highlights of

SAI’s findings include the following:

1. As expected, emissions deteriorate with increasing mileage.

2. The rate of deterioration of emissions is less at higher mileages and in older model year
7 

vehicles. 

3. CO and HC emissions in Colorado tend to be higher than in Arizona; with NOx, the

opposite effect is observed.

3.2 Fast-Pass to Full IM240 Conversion

3.2.1 EPA Approach

As noted earlier, the Ohio IM240 test uses a fast pass algorithm, i.e., the test is terminated

prematurely for vehicles displaying low emissions at an elapsed time of as little as 30 seconds.

Partial and full test emissions are all measured in grams per mile. Nevertheless, examination of

second-by-second data gives ample evidence that emissions from tests of varying duration are not

directly comparable, since the speed-acceleration mix changes over the cycle. Therefore, estimation

of a “simulated” full IM240 test score was undertaken for all passing vehicles for which only a fast

pass score was available.

From the Wisconsin second-by-second data, regression models were constructed in which

the full IM240 emissions are predicted based on several independent variables using only the tests

from passing vehicles. The natural logarithm of emissions was used as the independent variable.

Model regressors include vehicle type (car or truck), fuel metering type, model year, and simulated

length of test (in seconds). The simulated test length was determined by applying the fast-pass

algorithm used in Ohio to the second-by-second emissions. These models give good fits, with Rsquared values ranging from 70% to 82%. Table 2 reports the coefficients for these models.

The coefficients from these models were then used to predict full IM240 scores for each Ohio

fast pass test. Because the models fit the natural logarithm of emissions, the antilog transformation

was employed to obtain values in IM240 space.

3.2.2 RFF Approach

An alternative approach to estimating full IM240 scores from fast-pass scores was proposed

by Resources for the Future (RFF). This methodology involves regressing the IM240 emissions

against emissions at a given time point in the test using all tests. The RFF model includes the model

year variable (but not vehicle type or fuel metering system). This approach produces a different

equation for every test duration between 30 and 239 seconds. This approach avoids the problem of

correlation between the regressor and error terms that may affect the EPA models. However, there

is concern that it may be inappropriate to use tests from failing cars with high emissions in fitting

an equation intended to represent a fast pass (i.e., low-emission) outcome.

 RFF tested its method on second-by-second data from Arizona, with acceptable results.

When applied to the Ohio data, the EPA and RFF models produced similar mean estimates of full

IM240 scores (see Table 3). Therefore, it was decided to use the values generated by the EPA

approach.
6

 Brzezinski, D., E. Glover and P. Enns , “The Determination of Hot Running Emissions

From FTP Bag Emissions,” Report No. M6.STE.002.

8 

3.3 IM240 to Running LA4 Conversion

The next step involved estimating running LA4 scores for each Ohio vehicle. (The Running

LA4 is a test cycle comprised of the 1372-second LA4 trace that underlies the FTP, with no start

component. Unlike previous versions of MOBILE, MOBILE6 will treat start and running emissions

separately.1) This estimation was achieved using two sets of regression models. The first set of

models was developed from a sample of 77 tests conducted for the purpose of estimating a Running

LA4 from a conventional three-bag FTP. This test program and its analysis are described in a

separate report.6

 The results of this work were then used with a sample of FTP and IM240 paired

tests conducted on vehicles chosen from I/M lanes in Hammond, Indiana and Phoenix, Arizona. This

sample was comprised of 997 vehicles, of which 938 are from model year 1981 or later. Coefficients

from the 78-vehicle study were used to convert the FTP bag scores to Running LA4 values for

subsequent correlation with matching IM240 scores.

The IM240 tests at the inspection lane were, of course, based on the vehicle’s tank fuel.

When moved to the lab, each vehicle’s fuel was replaced with Indolene in accordance with standard

test protocol. Then, in addition to the FTP, a lab IM240 was conducted. For this analysis, however,

the IM240-to-FTP conversion was made between the lane IM240 (tank fuel) and the lab FTP

(Indolene), since IM240 data from Ohio is based on tank fuel tests. 

Table 4 shows the results of modeling the log of running LA4 emissions as a function of log

of IM240 plus dummy variables representing vehicle fuel metering technology and model year group

(defined in the table). The coefficients from these models were then applied to the Ohio data to

produce fitted Running LA4 scores for that much larger data set. The modeling approach is similar

to that used to simulate full IM240 scores from fast pass scores. The fitted values of the natural log

of Running LA4 are converted to gram per mile space using the antilog transformation. 

4.0 RESULTS AND CONCLUSIONS

Inspection and maintenance programs provide large samples of emission test data. These data

are not subject to the types of recruitment bias found in samples collected for other purposes such

as the EPA emissions factor program. The IM240 test is designed to better emulate actual on-road

driving than older I/M procedures. Therefore, these data offer a number of possibilities for improved

modeling of emissions deterioration and other types of behavior. Despite these benefits, as this report

explains, currently available IM240 test data also suffer from shortcomings that need to be addressed

before the data can be used directly in modeling emissions. 

For MOBILE6, the Ohio IM240 data are used indirectly to modify emission rates derived

from FTP data. In this application, FTP data are employed to determine running LA4 deterioration

for Tier 0 cars. The resulting emission rates are then adjusted by applying a high emitter correction
9 

factor based on the Ohio IM240 data. These correction factors were judged necessary to adjust for

possible bias in the samples of FTP test data collected by EPA and the vehicle manufacturers. This

bias is attributed to questions concerning vehicle owner willingness to participate in emissions

testing programs. The Ohio IM240 scores were used to develop the high emitter correction factors,

but regional-specific annual average odometer readings by vehicle age were substituted for the

reported odometers. This approach takes advantage of the Ohio IM240 data set, which represents

a large sample of vehicles not subject to a previous I/M program, while also limiting one of the

major shortcomings of the Ohio IM240 data, i.e., the reported odometer readings. The methodology

used to develop the high emitter correction factors is described in the paper cited earlier.1
10 

Table 1

Distribution of Dayton, Ohio IM240 Data

------------------------------------------------------------------------------------

| | VEHICLE | |

| |-----------------------------------------------| |

| | CAR | TRUCK | ALL |

| |-----------------------+-----------------------+-----------------------|

| | FUEL METERING | | FUEL METERING | | FUEL METERING | |

| |---------------| |---------------| |---------------| |

| | CARB | FI | ALL | CARB | FI | ALL | CARB | FI | ALL |

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|MODEL YEAR| | | | | | | | | |

|----------| | | | | | | | | |

|1981 | 924| 140| 1,064| 158| 7| 165| 1,082| 147| 1,229|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1982 | 2,767| 882| 3,649| 862| 19| 881| 3,629| 901| 4,530|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1983 | 2,791| 998| 3,789| 739| 5| 744| 3,530| 1,003| 4,533|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1984 | 7,105| 4,146| 11,251| 2,182| 87| 2,269| 9,287| 4,233| 13,520|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1985 | 4,329| 4,542| 8,871| 1,749| 316| 2,065| 6,078| 4,858| 10,936|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1986 | 6,771| 11,207| 17,978| 1,805| 2,873| 4,678| 8,576| 14,080| 22,656|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1987 | 2,777| 8,041| 10,818| 626| 2,278| 2,904| 3,403| 10,319| 13,722|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1988 | 3,092| 16,367| 19,459| 443| 5,236| 5,679| 3,535| 21,603| 25,138|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1989 | 1,399| 10,117| 11,516| 82| 3,231| 3,313| 1,481| 13,348| 14,829|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1990 | 267| 16,606| 16,873| 119| 4,271| 4,390| 386| 20,877| 21,263|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1991 | 7| 9,519| 9,526| 6| 3,074| 3,080| 13| 12,593| 12,606|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1992 | .| 16,604| 16,604| .| 5,289| 5,289| .| 21,893| 21,893|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1993 | .| 10,646| 10,646| .| 3,517| 3,517| .| 14,163| 14,163|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1994 | .| 13,740| 13,740| .| 5,061| 5,061| .| 18,801| 18,801|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1995 | .| 7,895| 7,895| .| 2,528| 2,528| .| 10,423| 10,423|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|1996 | .| 783| 783| 8| 230| 238| 8| 1,013| 1,021|

|----------+-------+-------+-------+-------+-------+-------+-------+-------+-------|

|ALL | 32,229|132,233|164,462| 8,779| 38,022| 46,801| 41,008|170,255|211,263|

------------------------------------------------------------------------------------
11 

Table 2

Fast-Pass to Full IM240 Regression Models

Using Wisconsin Second-by-Second IM240 Data

These models use the following independent variables:

LFxx = Natural Log(fast past gram/mile value of pollutant xx)

F=0 (carbureted fuel metering), =1 (fuel injected)

V=0 (truck), =1 (car)

Di=0 (not model year i), =1 (model year i) , i=1981 to 1994

Di

*LFxx = Crossproduct to capture slope change with model year

The Di and crossproduct coefficients are not shown.

The dependent variable is Lxx = Natural Log(240-second gram/mile value of xx).

Dependent Variable: LCO - Log(IM240 CO)

 Root MSE 0.68473 R-square 0.7034

 Dep Mean 1.17901 Adj R-sq 0.7013

 C.V. 58.07704

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 -0.042493 0.04651906 -0.913 0.3611

 LFCO 1 0.497165 0.01568796 31.691 0.0001

 F 1 -0.238492 0.03735494 -6.384 0.0001

 V 1 0.070382 0.02377776 2.960 0.0031

 FSEC 1 0.003180 0.00019780 16.075 0.0001
12 

Table 2 (Continued)

Dependent Variable: LHC - Log(IM240 HC)

 Root MSE 0.50372 R-square 0.8189

 Dep Mean -1.37212 Adj R-sq 0.8176

 C.V. -36.71100

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 -1.594231 0.04709904 -33.848 0.0001

 LFHC 1 0.529476 0.01496448 35.382 0.0001

 F 1 -0.109558 0.02765615 -3.961 0.0001

 V 1 0.161715 0.01776551 9.103 0.0001

 FSEC 1 0.003870 0.00014103 27.444 0.0001

Dependent Variable: LNOX - Log(IM240 NOX)

 Root MSE 0.43297 R-square 0.7359

 Dep Mean -0.26214 Adj R-sq 0.7340

 C.V. -165.16871

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 -0.681601 0.03277202 -20.798 0.0001

 LFNO 1 0.480133 0.01264385 37.974 0.0001

 F 1 -0.023815 0.02363659 -1.008 0.3137

 V 1 0.082967 0.01511348 5.490 0.0001

 FSEC 1 0.001471 0.00011878 12.382 0.0001
M6.EXH.002 13

Table 3

Comparison of EPA and RFF Full 240-Second Means by Model Year

Carbureted Vehicles

 -------------------------------------------------------------------------------------------------- | | RFF | EPA | RFF | EPA | RFF | EP | | FP IM240 | FP IM240 | FP IM240 | FP IM240 | FP IM240 | FP  |MODEL | HC HC | HC HC | CO CO | CO CO | NOX NOX | NOX |  |YEAR +-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |--------| | | | | | | | | | | |  |1980 | 1.42| 1.22| 1.42| 1.32| 21.15| 20.25| 21.16| 22.66| 1.85| 1.91| 1.85|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1981 | 1.84| 1.74| 1.85| 1.79| 29.69| 29.69| 29.70| 31.23| 2.13| 2.27| 2.14|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1982 | 1.88| 1.76| 1.89| 1.95| 27.73| 27.52| 27.73| 34.48| 2.22| 2.33| 2.22|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1983 | 1.67| 1.52| 1.67| 1.55| 23.44| 23.14| 23.44| 22.71| 2.13| 2.25| 2.14|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1984 | 1.55| 1.38| 1.56| 1.47| 23.90| 23.19| 23.91| 25.68| 1.93| 2.02| 1.93|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1985 | 1.57| 1.37| 1.57| 1.46| 22.91| 22.06| 22.92| 23.03| 1.95| 2.00| 1.95|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1986 | 1.28| 1.07| 1.29| 1.17| 18.97| 18.07| 18.97| 21.70| 1.79| 1.83| 1.79|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1987 | 1.13| 0.90| 1.13| 1.00| 17.24| 15.81| 17.24| 16.83| 1.68| 1.69| 1.68|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1988 | 0.89| 0.64| 0.89| 0.77| 13.10| 11.14| 13.10| 13.50| 1.35| 1.36| 1.35|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1989 | 0.75| 0.51| 0.75| 0.56| 12.30| 10.01| 12.30| 11.08| 1.28| 1.22| 1.28|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1990 | 0.96| 0.62| 0.96| 0.61| 11.92| 10.05| 11.93| 10.51| 1.02| 0.99| 1.02|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1991 | 0.81| 0.72| 0.82| 0.78| 13.71| 12.98| 13.72| 13.81| 1.11| 1.08| 1.11|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1996 | 0.40| 0.11| 0.41| 0.20| 4.93| 1.59| 4.94| 3.15| 0.45| 0.38| 0.46|  --------------------------------------------------------------------------------------------------
M6.EXH.002 14

Table 3 (continued)

Fuel Injected Vehicles

 -------------------------------------------------------------------------------------------------- | | RFF | EPA | RFF | EPA | RFF | EP | | FP IM240 | FP IM240 | FP IM240 | FP IM240 | FP IM240 | FP  |MODEL | HC HC | HC HC | CO CO | CO CO | NOX NOX | NOX |  |YEAR +-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |--------| | | | | | | | | | | |  |1980 | 0.74| 0.48| 0.75| 0.53| 9.27| 7.36| 9.28| 7.80| 1.19| 1.15| 1.20|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1981 | 1.69| 1.53| 1.70| 1.54| 23.37| 23.73| 23.37| 23.21| 1.87| 2.09| 1.88|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1982 | 1.42| 1.30| 1.43| 1.40| 17.21| 17.53| 17.21| 21.42| 2.70| 2.83| 2.71|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1983 | 1.35| 1.18| 1.36| 1.15| 16.19| 16.09| 16.19| 14.60| 2.42| 2.55| 2.43|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1984 | 1.34| 1.13| 1.34| 1.14| 15.88| 15.38| 15.89| 14.98| 2.33| 2.43| 2.33|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1985 | 1.37| 1.10| 1.37| 1.12| 15.58| 15.65| 15.58| 14.02| 1.96| 2.05| 1.96|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1986 | 1.25| 0.93| 1.26| 0.98| 13.11| 12.18| 13.11| 12.91| 1.56| 1.66| 1.57|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1987 | 1.10| 0.79| 1.11| 0.85| 12.16| 10.99| 12.17| 10.66| 1.64| 1.67| 1.64|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1988 | 0.99| 0.65| 1.00| 0.75| 10.86| 8.98| 10.86| 9.56| 1.42| 1.43| 1.42|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1989 | 0.90| 0.56| 0.91| 0.59| 10.76| 8.36| 10.77| 8.10| 1.37| 1.32| 1.37|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1990 | 0.75| 0.43| 0.76| 0.40| 10.13| 7.29| 10.13| 6.79| 1.26| 1.16| 1.27|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1991 | 0.59| 0.38| 0.59| 0.42| 8.24| 6.49| 8.25| 7.00| 1.17| 1.04| 1.17|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1992 | 0.50| 0.29| 0.51| 0.39| 7.50| 5.46| 7.51| 7.39| 0.96| 0.84| 0.96|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1993 | 0.43| 0.21| 0.43| 0.26| 6.51| 4.32| 6.52| 4.65| 0.90| 0.75| 0.91|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1994 | 0.31| 0.11| 0.31| 0.20| 5.03| 2.86| 5.03| 3.78| 0.57| 0.52| 0.58|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1995 | 0.23| 0.06| 0.24| 0.12| 4.21| 2.02| 4.22| 2.25| 0.46| 0.42| 0.47|  |--------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ |1996 | 0.16| 0.03| 0.17| 0.09| 2.58| 0.95| 2.58| 1.53| 0.33| 0.32| 0.34|  --------------------------------------------------------------------------------------------------
15 

Table 4

Regression models: Running LA4 vs. IM240

These models use the following independent variables:

LFxx = Natural Log(fast past gram/mile value of pollutant xx)

FI=0 (carbureted fuel metering), =1 (fuel injected)

M1=1 (model years 1981-82), =0 (otherwise)

M2=1 (model years 1983-87), =0 (otherwise)

The dependent variable is

 

LxxRUN = Natural Log(Running LA4 gram/mile value of xx).

Dependent Variable: LHCRUN - Log(Running LA4 HC)

 Root MSE 0.79242 R-square 0.7068

 Dep Mean -1.00124 Adj R-sq 0.7055

 C.V. -79.14334

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 -0.750184 0.09554650 -7.852 0.0001

 LHCIM 1 0.948738 0.02344691 40.463 0.0001

 FI 1 -0.006354 0.08638848 -0.074 0.9414

 M1 1 0.827859 0.12575290 6.583 0.0001

 M2 1 0.383417 0.05923913 6.472 0.0001
16 

Table 4 (continued)

Dependent Variable: LCORUN - Log(Running LA4 CO)

 Root MSE 0.91602 R-square 0.6434

 Dep Mean 1.65384 Adj R-sq 0.6419

 C.V. 55.38721

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 -0.700927 0.11976277 -5.853 0.0001

 LCOIM 1 0.956897 0.02533007 37.777 0.0001

 FI 1 0.173259 0.09985916 1.735 0.0831

 M1 1 0.770544 0.14457986 5.330 0.0001

 M2 1 0.193573 0.06678826 2.898 0.0038

Dependent Variable: LNOXRUN - Log(Running LA4 NOX)

 Root MSE 0.57696 R-square 0.6533

 Dep Mean -0.34445 Adj R-sq 0.6518

 C.V. -167.50296

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 -0.623836 0.06824633 -9.141 0.0001

 LNOXIM 1 0.849380 0.02276379 37.313 0.0001

 FI 1 -0.089006 0.06267262 -1.420 0.1559

 M1 1 0.280035 0.09190557 3.047 0.0024

 M2 1 0.209848 0.04307749 4.871 0.0001