United States

Environmental Protection

Agency

Air and Radiation EPA420-P-99-013

March 1999

M6.EXH.002

Analysis of Emissions

Deterioration Using Ohio

and Wisconsin IM240

Data

DRAFT

 Printed on Recycled Paper
EPA420-P-99-013

March 1999

Analysis of Emissions Deterioration Using

Ohio and Wisconsin IM240 Data

M6.EXH.002

DRAFT

Phil Enns

Ed Glover

Penny Carey

Michael Sklar

Assessment and Modeling Division

Office of Mobile Sources

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 which 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.
M6.EXH.002 DRAFT 10/20/98

EPA420-P-99-013

- Draft -

Analysis of Emissions Deterioration

Using Ohio and Wisconsin IM240 Data

Report Number M6.EXH.002

October 20, 1998

Phil Enns

Ed Glover

Penny Carey

Michael Sklar

U.S.EPA Assessment and Modeling Division

NOTICE

This technical report does not necessarily represent final EPA decisions or positions. It is intended to present technical

analysis of issues using data which 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.

M6.EXH.002 2 DRAFT 10/20/98

- Draft -

Analysis of Emissions Deterioration

Using Ohio and Wisconsin IM240 Data

Report Number M6.EXH.002

October 20, 1998

Phil Enns

Ed Glover

Penny Carey

Michael Sklar

U.S.EPA Assessment and Modeling Division

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 wellestablished 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. 
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.

M6.EXH.002 3 DRAFT 10/20/98

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 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
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.

M6.EXH.002 4 DRAFT 10/20/98

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

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 fast-pass 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
M6.EXH.002 5 DRAFT 10/20/98

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. 

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 fast-pass 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
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. 

M6.EXH.002 6 DRAFT 10/20/98

correction for this problem was attempted using a modified version of the methodology created

by SAI for use with the data from 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.
M6.EXH.002 7 DRAFT 10/20/98

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

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 R-squared 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.

M6.EXH.002 8 DRAFT 10/20/98

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 78 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 factor based on the Ohio IM240 data. These correction factors were judged
M6.EXH.002 9 DRAFT 10/20/98

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
M6.EXH.002 10 DRAFT 10/20/98

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|

------------------------------------------------------------------------------------
M6.EXH.002 11 DRAFT 10/20/98

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
M6.EXH.002 12 DRAFT 10/20/98

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|  --------------------------------------------------------------------------------------------------
M6.EXH.002 15 DRAFT 10/20/98

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
M6.EXH.002 16 DRAFT 10/20/98

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