Estimates of the Fraction 

of the Fleet with High 

Evaporative Emissions 

based on the 

Ken Caryl Station 

(Denver, Colorado) 

Field Study 

Report 

Version 6 

Prepared for: 

U.S. Environmental Protection 

Agency 

September 25, 2010 
T:\Library\EPA-100925.docx 

ERG: 0218.04.004.001 

Estimates of the Fraction of the Fleet with High Evaporative Emissions 

based on the Ken Caryl Station (Denver, Colorado) Field Study 

REPORT 

Version 6 

as part of 

Determine Percentage of High Evaporative Emission Vehicles in the On-Road Fleet of Motor 

Vehicle Passenger Car and Light Trucks Pilot Program 

Work Assignment 4-4 

Submitted to: 

Constance Hart 

U.S. Environmental Protection Agency 

2565 Plymouth Road 

Ann Arbor, MI 48105 

Submitted by: 

Timothy H. DeFries 

Cindy Palacios 

Sandeep Kishan 

Eastern Research Group, Inc. 

3508 Far West Blvd., Suite 210 

Austin, TX 78731 

September 25, 2010
i 

Table of Contents 

1.0 Introduction ............................................................................................................... 1-1

1.1 Report Structure ............................................................................................................ 1-1

2.0 Background ............................................................................................................... 2-1

3.0 PSHED and LSHED Hot-Soak Emission Measurement Characteristics ..................... 3-1

4.0 Estimated High-PSHED Fraction of the Ken Caryl IM Station Fleet Using EI23 Bin DeStratification ............................................................................................................... 4-1

4.1 RSD and PSHED Testing at the Ken Caryl IM Station .................................... 4-2

4.2 The 175-Vehicle Stratified Set .......................................................................... 4-3

4.3 The 5830-Vehicle Random Set ....................................................................... 4-22

4.4 De-Stratifying the Ken Caryl Stratified Sample According to EI23 Bin ........ 4-24

4.5 Proposed Methods for Quantification of Uncertainties and Biases in the High 

PSHED Fraction of the Ken Caryl Fleet for EI23 Bin De-Stratification ........ 4-29

5.0 Estimated High-PSHED Fraction of the Denver On-Road Fleet from De-Stratifications 

Based on Advanced RSD Evaporative Emissions Indices ........................................... 5-1

6.0 References ............................................................................................................... 6-1

List of Tables 

Table 3-1. CDPHE Data Comparing LSHED and PSHED 15-Minute Hot-Soak Results .......... 3-3

Table 3-2. Estimated Equivalent LSHED and PSHED Hot-Soak Values ................................... 3-8

Table 4-1. Descriptions of the 175 Participating Vehicles ......................................................... 4-6

Table 4-2. Participating Vehicle PSHED and Measurement RSD Results .............................. 4-11

Table 4-3. Distribution of 12-mph Measurement RSDs in the Stratified Sample by 

EI23 Bin ......................................................................................................................... 4-21

Table 4-4. Number and Percent of High PSHEDs in the Stratified Set for Different HighPSHED Definitions ........................................................................................................ 4-22

Table 4-5. Distribution of Selection RSDs in the Random Sample .......................................... 4-24

Table 4-6. De-Stratification Calculation for High-PSHED Definition = 2 g/Qhr .................... 4-25

Table 4-7. Results for Ken Caryl EI23 Bin De-Stratifications for Various High-PSHED 

Definitions...................................................................................................................... 4-26

Table 4-8. High-PSHED Fraction of the Ken Caryl Fleet for Various High-PSHED Definitions 

by Model Year Group .................................................................................................... 4-27

Figure 4-3. Estimated Distribution of PSHEDs in the Ken Caryl Fleet by Model 

Year Group..................................................................................................................... 4-28

Table 4-9. Distribution of Measurement RSDs in the Stratified Sample by EI23 Bin and 

Model Year Group ......................................................................................................... 4-28

Table 4-10. Sources of Error in the Ken Caryl High-PSHED Fraction .................................... 4-30

Table 4-11. Contribution of the Size of the Stratified Sample to the Uncertainty in the HighPSHED Fraction............................................................................................................. 4-30
ii 

List of Figures 

Figure 3-1. PSHED vs LSHED Hot-Soaks when the LSHED is after the PSHED ................. 3-6

Figure 3-2. PSHED vs LSHED Hot-Soaks when the LSHED is before the PSHED .............. 3-7

Figure 3-3. PSHED vs LSHED Hot-Soaks .............................................................................. 3-8

Figure 3-4. Hot-Soak Variability in PSHED ......................................................................... 3-10

Figure 3-5. Hot-Soak Variability in LSHED ......................................................................... 3-11

Figure 3-6. Hot-Soak Variability in SHED............................................................................ 3-13

Figure 4-1. PSHED vs. EI23 for the Stratified Set ................................................................ 4-20

Figure 4-2. Estimated Distribution of PSHEDs in the Ken Caryl Fleet ................................ 4-26
1-1 

1.0 Introduction 

EPA is further developing the MOVES mobile sources inventory model. As part of that 

effort, EPA needs to be able to model the evaporative emissions of gasoline-powered on-road 

vehicles. Evaporative emissions occur when volatile components of gasoline are emitted or when 

raw gasoline leaks from the fuel system and the evaporative emissions control system. To meet 

the evaporative emissions modeling needs of the MOVES model, EPA has been involved with 

an ongoing investigation to quantify fleet evaporative emissions. Ultimately, EPA would like to 

know the distribution of the mass of evaporative emissions across all vehicles in the fleet. 

The purpose of this report is to develop fractions of various levels of high evaporative 

emissions across the Denver fleet utilizing the most robust data collected to date. This report is 

one of a series in a work assignment which supports the EPA’s National Portable Emissions 

Measurement System (PEMS) Deployment contract. Innovative methods for measuring and 

testing real-world evaporative emissions were studied, procedures were developed, and data 

collected over two summers in Denver, CO with the support of the Colorado Department of 

Public Health and Environment (CDPHE) through a Cooperative Research and Development 

Agreement (CRADA). The Coordinating Research Council (CRC) supported the project during 

the first summer, 2008, as CRC project E-77-3. 

1.1 Report Structure 

Section 2 provides some background on evaporative emissions and briefly describes the 

CRC E-77 and E-77-2 studies and the Lipan Station study. Those studies precede the Ken Caryl 

study, which provides the data for this report. 

Section 3 provides an in-depth comparison of the measurement of hot-soak emissions by 

laboratory SHED and by portable SHED (PSHED). Data collected by CDPHE is the basis of the 

analysis. 

The goal of Section 4 is to use the Ken Caryl data to estimate the high-PSHED fraction of 

the fleet of vehicles that patronize the Ken Caryl IM station. First, Section 4.1 describes the 

vehicle selection and testing at Ken Caryl. There are two data sets that are used to perform the 

analysis. The first is the set of 175 vehicles that were participants in the study and therefore 

received RSD measurements and PSHED measurements. This data is described in Section 4.2. 

The second is the set of all 5830 vehicles that entered the station driveway during the study and 

represent the fleet of vehicles that patronize Ken Caryl. Most of these vehicles were not 

participants in the study and these therefore have only RSD measurements but no PSHED 
1-2 

measurements. The real investigation in this study happens in Section 4.4 where we de-stratify 

the PSHED values of the 175 participants to estimate the distribution of PSHED values of the 

5830 vehicles that entered Ken Caryl during the study period. In this section the de-stratifying 

variable is the EI23. The results give an estimate of the fraction of high-PSHED vehicles for 

different definitions of high PSHED. Section 4.5 begins a quantification of the different factors 

that influence uncertainties and biases in the estimate of fraction of high-PSHED in the Ken 

Caryl fleet. 

Sections 5 briefly summarizes the datasets that are available for use in estimating the 

high-PSHED fraction of the Denver on-road fleet. Analyses on these datasets using new 

evaporative emissions indices and new index/PSHED relationships can help addresses the issue 

that the Ken Caryl fleet is probably somewhat different from the Denver on-road fleet. 

Nevertheless, an estimate of the characteristics of even the Ken Caryl fleet provides insight into 

the prevalence of elevated evaporative emissions in the Denver fleet. 
2-1 

2.0 Background 

Evaporative emissions from gasoline vehicles have been evaluated and regulated since 

the early 1970s. Gasoline vehicles have evaporative emissions control systems that control 

excessive evaporative emissions, which are usually gasoline vapors but can also be evaporated 

liquid gasoline if liquid leaks are present. When these systems or the gasoline delivery system of 

a vehicle malfunction, excessive evaporative emissions can be emitted. The mass of evaporative 

emissions from individual vehicles has been quantified in previous studies, [1, 2, 3] but the 

frequency of vehicles in such a state in the general population has only been estimated based on 

limited data. [1, 3, 4] 

The Coordinating Research Council Real World Group has been conducting permeation 

evaporative emission testing in the E-77 and E-77-2 programs. In the pilot E-77 program the 

permeation test procedure was refined before using it in a larger program. Most of the vehicles 

were specifically recruited as aging enhanced vehicles, model years 1996-2000. Three preenhanced vehicles and one newer, near-zero vehicle were also tested. A focus was on nonethanol fuel looking at both 7 and 9 RVP. The program also looked at 24-hour diurnals at the 

traditional temperature range of 65-105 F and a higher temperature diurnal of 85-120F. A 

subset of the vehicles was tested for 72-hour diurnals. One of the aging enhanced vehicles had an 

implanted leak. The series of tests were run both before and after implanting the leak. The 

magnitude of the leak was the minimum detectable size necessary for an OBD code to flag. The 

results show the resulting hydrocarbon emissions to be several orders of magnitude larger with 

the implanted leak. This indicates a major impact for inventory, establishing the need to define 

the rate of occurrence of “leakers” in the in-use fleet. Vapor leaks can have an impact on the 

inventory. Traditional evaporative emissions testing, as well as the current E-77 testing, has 

shown a range of evaporative emissions, particularly with aging vehicles. The higher emission 

levels (but lower than the gross liquid “leakers”) are associated with slow vapor leaks introduced 

as the system ages. The newer technology for evaporative systems is now starting to age, and we 

do not have information on the number of these higher emission vehicles in the fleet. It is 

anticipated to be much lower than in the past, but they do occur. 

Previous E-77 testing has confirmed that leaks, both liquid and vapor, can be a significant 

part of any hydrocarbon inventory. The missing piece of information is how often the leaking 

vehicles occur. A comprehensive program for finding the number of these vehicles in the 

existing fleet has not been attempted since the American Petroleum Institute “Raw Fuel Leak 

Survey in I/M Lanes” in 1998[1] or the California Bureau of Automotive Repair “Evaporative 
2-2 

Emission Impact of Smog Check” in 2000.[3] The current fleet consists of aging enhanced 

evaporative emissions vehicles which were not part of or still too new in the older studies. 

Further laboratory testing in the E-77-2 and E-77-2c programs implanted similar size 

leaks, not only at the gas cap but in other locations which were indicated as high occurrence in 

the initial field testing work the summer of 2008, E-77-3. The results indicate a difference in 

magnitude for evaporative emissions dependant on location of the leak. If a vehicle is equipped 

with ORVR, then most of the leak effects were mitigated. Leaks implanted at the top of the tank 

and the connection to the canister were found to be much higher. The implanted leak data 

indicates there may be significant effects from small leaks which can multiply in the inventory. 

Using RSD data from the California RSD pilot study, our initial estimate was that “High 

Evaps” make up on the order of 1% of the gasoline-fueled vehicles in the fleet.[5] As we 

proceeded in this study over the past few years there has been evidence that this was lower than 

what is occurring in the real world. 

During the summer of 2008 CDPHE provided access to the Lipan IM Station as well as 

staff and remote sensing equipment for the project. At Lipan, the exhaust HC emission 

concentrations of vehicles entering the IM station driveway were screened by an RSD unit. 

Exhaust HC emission concentration was used to screen vehicles for evaporative emissions 

because the California RSD Pilot study indicated that vehicles with elevated evaporative 

emissions sometimes appeared to have elevated exhaust HC emissions when measured by RSD. 

A stratified sample of 1981 model year and newer vehicles were offered participation in 

intensive evaporative emissions testing, which consisted of a 15-minute hot-soak test in a 

portable SHED (PSHED), a visual, olfactory, and electronic sniffer examination of vehicle fuel 

metering and evaporative emissions control system components, and additional RSD 

measurements at 12 mph in the IM station driveway. Measurements for a comparison of PSHED 

results with laboratory SHED were also made on selected participating vehicles. Overall, the 

Lipan study [6] demonstrated a connection between RSD and PSHED values for real-world 

light-duty gasoline vehicles. 

During the fall of 2008, EPA and CDPHE attempted to collect data in a non-IM, low 

altitude location. CDPHE drove their RSD van to San Antonio where EPA rented warehouse 

space to set up a PSHED and evaluated respondents to the study.[7] Unfortunately, due to delays 

in the process, the seasonal benefits of summer faded as the fall temperatures set in. The study 

was not as successful as Denver in the summer months. 
2-3 

During the following summer, 2009, CDPHE continued to gather information with the 

Regional Air Quality Council (RAQC) with EPA in a consultant role. This study performed at 

the Ken Caryl IM station in Denver built upon the Lipan experience. Vehicles entering the Ken 

Caryl station driveway were screened by an RSD unit using an evaporative index known as EI23. 

A stratified sample of 1961 model year and newer vehicles were offered participation in 

intensive evaporative emissions testing, which consisted of the PSHED hot-soak test, the visual, 

olfactory, and electronic sniffer examination of the vehicle, and additional RSD measurements. 

In the Ken Caryl study, participating vehicles received duplicate RSD measurements at 12, 34, 

and 55 mph. Overall, the Ken Caryl study reinforced the connection between RSD and PSHED 

values for real-world light-duty gasoline vehicles with testing of a wider range of model years 

and RSD vehicle speeds. 

This report uses the Ken Caryl data to estimate the fraction of elevated evaporative 

emissions vehicles in the mix of vehicles that patronized the Ken Caryl station during the 

summer of 2009. 
3-1 

3.0 PSHED and LSHED Hot-Soak Emission Measurement 

Characteristics 

From June through October 2009, the Colorado Department of Public Health and 

Environment (CDPHE) undertook testing to compare the hot-soak emissions measurements in a 

portable SHED (PSHED) and in its laboratory SHED (LSHED).[8] During this period, CDPHE 

measured evaporative emissions on 15 vehicles. The results of this testing are shown in Table 3-

1. The table shows the results of hot-soak measurements and heat-build measurements. The 

heat-build measurements will not be discussed but are shown in the table for completeness 

The objective of the testing was to compare hot-soak results from LSHED and PSHED 

tests in back-to-back testing. To put the PSHED and LSHED testing on the same time basis, 

testing in the PSHED and LSHED were both conducted using a 15-minute hot-soak. The results 

reported in Table 3-1 are for 15 minutes both for the PSHED and LSHED. 

The LSHED used for the testing is located at CDPHE’s Aurora test facility near Denver, 

Colorado. The PSHED was also set up at the Aurora Facility. As in the Lipan I/M Station 

testing, the PSHED for the Ken Caryl I/M Station testing consisted of a 10 x 20 x 8 foot 

enclosure, which was sealed using sheet plastic and duct tape. The details and performance of 

the PSHED enclosure have been discussed earlier.[9] Hydrocarbon concentrations inside the 

PSHED were measured using a lab-grade flame ionization detector analytical system with a 10 

point calibration. 

Colorado has a program to test and repair vehicles identified using its on-going on-road 

RSD measurement program. Vehicles with elevated RSD values are required to bring their 

vehicles to CDPHE for tailpipe testing and visual inspection. A subset of these vehicles were 

selected for the LSHED/PSHED comparison test. Specifically, vehicles that initially passed the 

IM240 exhaust emissions test, and then during the evaluation of the vehicle, CDPHE staff 

technicians believed that the vehicle could possibly be a high evaporative emitter because the 

vehicle either failed the intrusive pressure test, produced a strong odor or visible leak, or had 

OBD evaporative codes, were sent to LSHED/PSHED comparison testing. 

For the LSHED/PSHED comparison testing, the following test sequence was used. The 

vehicle was temperature stabilized overnight while its evaporative emissions canister was bench 

purged. The next day, with the purged canister back in the vehicle, the vehicle underwent a onehour diurnal test. Next, the vehicle was conditioned for the first SHED test. The conditioning 

involved driving for five minutes at 55 mph on the dynamometer and then driving the test 
3-2 

sequence for the Hot505. This same conditioning sequence was used prior to each LSHED or 

PSHED test. 

Table 3-1 shows the sequence of testing for each vehicle according to the test date and 

test time. The table shows that this sequence was always performed: PSHED, LSHED, PSHED, 

LSHED. Most vehicles received four SHED tests made up of two PSHED tests and two LSHED 

tests with the sequence given by the column named CDPHE Test ID: 1P, 1L, 2P, 2L. In most 

cases the vehicles received the PSHED and LSHED tests in their as-received condition, but in a 

few cases they were also tested after repair. 

In the analysis in this section we will compare the back-to-back measurements made by 

the PSHED against those made by the LSHED. In addition, we will examine the repeatability of 

duplicate PSHED measurements and the repeatability of LSHED measurements. While the 

“back-to-back” SHED measurements were made as close as possible to each other, those 

measurements were not made simultaneously. Accordingly, the state of the vehicle and its 

evaporative emissions control system are likely to be different for any pair of measurements that 

we compare in spite of the conditioning procedure. For example, in the test sequence 1P, 1L, 2P, 

2L, the 1P and 2P are nominally duplicates of each other. However, there may be a systematic 

bias between 1P and 2P that arises from the fact that the vehicle has been driven more 

cumulative miles before the 2P test than the cumulative miles before the 1P test. This difference 

could cause the 2P hot-soak to have a higher value than the 1P hot-soak because the tank 

temperature was higher for the 2P test. Alternatively, the extra driving could cause the 2P hotsoak test to be lower than the 1P hot-soak test because the evaporative emissions control canister 

experienced more purging for 2P than for 1P. The same argument could be made for comparison 

of 1L and 2L or for comparisons of 1P and 1L, or 2P and 2L tests. The point is that these socalled duplicate tests are not truly duplicates, and the influences of differences in vehicle state 

produced by differences in cumulative vehicle conditioning potentially contribute to the 

observed differences in the measured SHED hot-soak values. 

Comparison of PSHED hot-soaks with LSHED hot-soaks – The data in Table 3-1 

contains 33 pairs of a PSHED and an LSHED measurement. For the purposes of this analysis, 

we have paired the data so that they are closest in time for the same vehicle. Thus, we pair 1P 

with 1L, 2P with 2L, 3P with 3L, etc. However, it is important to recognize that in every 

instance the LSHED measurement follows the PSHED measurement and, therefore, time effects 

may influence any comparisons that are made between the PSHED and LSHED results. 
3-3 

Table 3-1. CDPHE Data Comparing LSHED and PSHED 15-Minute Hot-Soak Resu

Vehicle 

ID Year Make Model VIN 

Test 

Date 

Test 

Time (end) Odometer 

CDPHE 

Test ID 

SHED 

Used 

Test 

Type 

HE-2279 1994 Ford Ranger 1FTCR15X6RPA02449 

6/16/09 10:30:01 96761 HB(HB1) Lab Heat Buil6/16/09 13:47:34 96769 1P Portable HotSoak 6/16/09 14:37:17 96778 1L Lab HotSoak 6/16/09 16:00:15 96794 2P Portable HotSoak 6/16/09 16:31:37 96798 2L Lab HotSoak 6/18/09 9:49:39 96798 3L(HB2) Lab Heat Buil6/18/09 12:39:27 96805 3P Portable HotSoak 6/18/09 13:26:51 96812 3L Lab HotSoak 6/18/09 14:06:30 96820 4P Portable HotSoak 6/18/09 14:47:49 96827 4L Lab HotSoak 

HE-2969 1995 Saturn SL2 1G8ZJ5270SZ170783 

6/23/09 15:35:26 219834 1L(HB1) Lab Heat Buil6/24/09 9:50:02 219841 1P Portable HotSoak 6/24/09 10:29:12 219848 1L Lab HotSoak 6/24/09 11:05:21 219855 2P Portable HotSoak 6/24/09 11:58:39 219862 2L Lab HotSoak 6/25/09 9:28:49 219882 4L(HB2) Lab Heat Buil6/25/09 11:26:34 219869 3P Portable HotSoak 6/25/09 12:05:00 219875 3L Lab HotSoak 6/25/09 12:51:32 219881 4P Portable HotSoak 6/25/09 13:42:11 219888 4L Lab HotSoak 6/26/09 11:35:54 219888 7L(HB3) Lab Heat Buil

HE-3091 1990 Buick Century 1G4AH54NXL6429451 

6/30/09 9:15:52 245472 1L(HB1) Lab Heat Buil6/30/09 12:40:54 245707 1P Portable HotSoak 6/30/09 13:14:48 245715 1L Lab HotSoak 6/30/09 13:50:42 245723 2P Portable HotSoak 6/30/09 14:26:06 245730 2L Lab HotSoak 

7/1/09 13:48:27 2454731 4L(HB2) Lab Heat Buil
3-4 

Vehicle 

ID Year Make Model VIN 

Test 

Date 

Test 

Time (end) Odometer 

CDPHE 

Test ID 

SHED 

Used 

Tes

Typ

720PHW 

(R) 1996 Honda Passport 4S6CM58V2T4408241 

7/15/09 9:56:27 181810 HB1 Lab Heat B

7/15/09 10:52:19 181817 1P Portable HotSo7/15/09 11:26:08 181826 1L Lab HotSo7/15/09 13:46:53 181831 2P Portable HotSo7/15/09 14:20:46 181844 2L Lab HotSo

HE-3394 1993 Oldsmobile Cutlass 1G3AG54NXP6346986 

7/21/09 9:29:04 95439 HB1 Lab Heat B

7/21/09 10:22:01 95445 1P Portable HotSo7/21/09 10:55:35 95450 1L Lab HotSo7/21/09 11:26:58 95454 2P Portable HotSo7/21/09 12:01:19 95462 2L Lab HotSo7/23/09 9:39:54 95476 HB2 Lab Heat B

HE-3790 1995 Ford Ranger 1FTDR15X3SPA00748 

8/18/09 9:23:45 163650 HB1 Lab Heat B

8/18/09 9:59:16 163657 1P Portable HotSo8/18/09 10:31:09 163664 1L Lab HotSo8/18/09 11:01:23 163671 2P Portable HotSo8/18/09 11:36:37 163678 2L Lab HotSo8/20/09 9:43:52 163682 HB2 Lab Heat B

HE-3555 1996 SAAB 900SE YS3DF58N8T2038204 

8/26/09 9:14:21 193145 HB1 Lab Heat B

8/26/09 13:56:16 193149 1P Portable HotSo8/26/09 14:30:24 193156 1L Lab HotSo8/26/09 15:01:12 193161 2P Portable HotSo8/26/09 15:37:01 193167 2L Lab HotSo8/31/09 14:32:33 193145 HB2 Lab Heat B

631-SWU 1996 Toyota Camry JT2BG12KXT0374251 

9/1/09 11:01:26 248058 HB1 Lab Heat B

9/1/09 13:49:59 248065 1P Portable HotSo9/1/09 14:27:17 248074 1L Lab HotSo9/1/09 15:03:00 248080 2P Portable HotSo9/1/09 15:36:51 248090 2L Lab HotSo

9/10/09 9:59:35 248095 HB2 Lab Heat B

HE-3863 1994 Ford Explorer 1FMDU34X5RUE11796 

9/22/09 9:51:56 284456 HB1 Lab Heat B

9/22/09 10:39:24 284462 1P Portable HotSo9/22/09 11:01:25 284469 1L Lab HotSo9/22/09 14:16:18 284477 2P Portable HotSo9/22/09 14:43:11 284484 2L Lab HotSo9/24/09 10:12:57 0 HB2 Lab Heat B

HE-3649 1987 Ford Thunderbird 1FABP64W6HH224477 

9/23/09 9:40:27 118616 HB1 Lab Heat B

9/23/09 10:29:36 118622 1P Portable HotSo9/23/09 11:05:37 118629 1L Lab HotSo
3-5 

Vehicle 

ID Year Make Model VIN 

Test 

Date 

Test 

Time (end) Odometer 

CDPHE 

Test ID 

SHED 

Used 

Tes

Typ

9/23/09 13:29:12 118634 2P Portable HotSo9/23/09 14:05:29 118641 2L Lab HotSo

XXX3400 1992 Dodge Caravan 2P4GH2539NR643400 

7/14/09 10:28:00 209454 HB1 Lab Heat B

7/14/09 13:47:44 209462 1P Portable HotSo7/14/09 14:25:34 209468 1L Lab HotSo7/14/09 15:11:56 209475 2P Portable HotSo7/14/09 15:46:01 209484 2L Lab HotSo

HE-3358 1994 Mercury Villager 4M2DV11W7RDJ40266 

8/12/09 8:39:45 217852 HB1 Lab Heat B

8/13/09 9:29:56 217859 1P Portable HotSo8/13/09 9:53:06 217866 1L Lab HotSo8/13/09 10:24:50 217872 2P Portable HotSo8/13/09 11:00:02 217879 2L Lab HotSo8/17/09 9:54:40 218292 HB2 Lab Heat B

HE-3491 1994 Cadillac Seville 1G6KS52Y8RU805899 

8/25/09 11:45:56 143333 HB1 Lab Heat B

8/25/09 14:21:11 144715 1P Portable HotSo8/25/09 14:55:05 144720 1L Lab HotSo8/26/09 11:25:01 144726 2P Portable HotSo8/26/09 12:03:27 144731 2L Lab HotSo

9/2/09 9:59:28 144740 HB2 Lab Heat B

HE-4006 1996 Ford Ranger 1FTCR14U9TPA38196 

9/16/09 9:22:26 96363 HB1 Lab Heat B

9/16/09 10:15:05 96370 1P Portable HotSo9/16/09 10:40:24 96376 1L Lab HotSo9/16/09 13:34:31 96382 2P Portable HotSo9/16/09 13:59:37 96387 2L Lab HotSo

5702 1992 Oldsmobile Eighty Eight 1G3HN53L5NH315702 6/8/09 15:42:33 168568 1L(HB1) Lab Heat B

6/11/09 8:10:01 168594 1P Portable HotSo6/11/09 10:02:42 168602 2L(1L) Lab HotSo
3-6 

Figure 3-1 shows a plot of the PSHED versus the LSHED measurements on a log-log 

scale. One reason that we use the log scales is that the PSHED measurements for the test 

vehicles in the study cover three orders of magnitude. In addition, the scatter of the data points 

in the plot near the parity line indicate that the variability of the PSHED measurements on these 

vehicles appears to be better described by a constant relative variability than by a constant 

absolute variability. The plot indicates that there is a tendency for the LSHED values to be 

higher than the PSHED values since more points are to the right of the parity than are to the left 

of the line. This could arise either because the LSHED measurements always occur after the 

PSHED measurements in the pairs or because of a real difference in the accuracy of PSHED 

measurements relative to LSHED measurements. 

Figure 3-1. PSHED vs LSHED Hot-Soaks when the LSHED is after the PSHED 

To determine whether the apparent difference in PSHED versus LSHED values in Figure 

3-1 arise from a bias between the PSHED and LSHED tests or whether the difference is 

produced by a time effect, we pair the LSHED and PSHED data values in a different way. For 

this analysis, we pair the values for each vehicle such that the LSHED is tested before the 

PSHED measurement. For example, the 2Ls are paired with the 1Ps. When this new repairing 

of the data is plotted, the result is Figure 3-2. This plot uses the same scales as Figure 3-1. The 
3-7 

plot shows that just like Figure 3-1, there is a tendency of the PSHEDs to have lower values than 

the LSHEDs even though, in this case, the LSHED test came before the PSHED test. When 

considered together, Figures 3-1 and 3-2 show that the differences between LSHED and PSHED 

measurements in this dataset are not caused by the order of the LSHED and PSHED tests but are 

instead caused by a real bias between the LSHED and PSHED measurements. Specifically, the 

PSHED values tend to be smaller than the LSHED values for the same vehicle. Power law 

regression lines for the data points are shown as the thin blue and red lines in Figures 3-1 and 3-2. 

The location of these two lines are quite similar to each other. 

Figure 3-2. PSHED vs LSHED Hot-Soaks when the LSHED is before the PSHED 

If we combine the two datasets that produced Figures 3-1 and 3-2, and produce a 

combined plot, the result is Figure 3-3. The overall regression line through this data is given by: 

PSHED = 0.7871 * LSHED0.8207

where: PSHED and LSHED are in the units of g/15min. 
3-8 

Figure 3-3. PSHED vs LSHED Hot-Soaks 

A comparison of the equivalent PSHED and LSHED values based on this equation is 

shown in Table 3-2. We do not know why the PSHED values tend to be lower than the LSHED 

values given that the test procedures and the equipment set up for both the PSHED and the 

LSHED are fundamentally the same. 

Table 3-2. Estimated Equivalent LSHED and PSHED Hot-Soak Values 

Approximate

Equivalent 

LSHED 

(g/Qhr) 

Approximate

Equivalent 

PSHED 

(g/Qhr) 

0.035 0.050

0.050 0.067

0.081 0.100

0.100 0.119

0.188 0.200

0.200 0.210

0.50 0.45

0.58 0.50

1.00 0.79

1.34 1.00

2.00 1.39
3-9 

Approximate

Equivalent 

LSHED 

(g/Qhr) 

Approximate

Equivalent 

PSHED 

(g/Qhr) 

3.1 2.00

5.0 2.9

9.5 5.0

10.0 5.2

20.0 9.2

22 10.0

50 19.5

PSHED and LSHED hot-soak emission variability – The analysis in the previous 

discussion demonstrated that the PSHED measurements tended to produce hot-soak values 

somewhat lower than LSHED measurements as shown in Figure 3-3. However, that figure also 

showed a large scatter of the individual data points. In this discussion, we examine the degree of 

scatter produced by replicate SHED measurements. 

Figures 3-4 and 3-5 show plots of duplicate PSHED and LSHED measurements, 

respectively. For both plots, the horizontal axis is the first SHED measurement, for example, 1P, 

and the vertical axis is the second SHED measurement, for example, 2P. In all cases, the first 

and second SHED measurements are separated by a period of time during which a SHED 

measurement of the other type was performed. The sequence of tests for each vehicle can be 

seen in Table 3-1. 
3-10 

Figure 3-4. Hot-Soak Variability in PSHED 

0.01

0.10

1.00

10.00

100.00

0.01 0.10 1.00 10.00 100.00

First PSHED (g/Qhr)

Second PSHED (g/Qhr)
3-11 

Figure 3-5. Hot-Soak Variability in LSHED 

0.01

0.10

1.00

10.00

100.00

0.01 0.10 1.00 10.00 100.00

First LSHED (g/Qhr)

Second LSHED (g/Qhr)

Both Figures 3-4 and 3-5 show a similar scatter of points about the parity line for both 

duplicate PSHED tests and duplicate LSHED tests. First, in both figures the scatter of points 

about the parity line is relatively symmetrical which indicates that the order of testing for the 

dataset as a whole did not influence the measured value. This is consistent with the same finding 

from the analysis of the comparison of LSHED and PSHED values discussed earlier. 

The second important feature of Figures 3-4 and 3-5 is that scatter of points about the 

parity line for the two plots is quite similar – at least from a visual comparison of the two plots. 

A statistical comparison of the variances of the PSHED and LSHED measurements indicate a 

statistically significant difference in the variances at the 95% confidence level. However, when 

the single data point in the upper left corner of Figure 3-4 was dropped from the dataset, the 

variances for the PSHED and LSHED replicates were found to be not statistically different. This 

single data point was generated by Vehicle HE-3555. Table 3-1 shows that the SHED emissions 

of this vehicle in chronological order were 0.0, 4.7, 9.9, and 55.5 g/Qhr. We suspect that this 

vehicle produced higher and higher hot-soak emissions as additional SHED tests were performed. 

The time dependence of the hot-soak emissions for this vehicle was much stronger than for any 
3-12 

of the other vehicles in the dataset and, therefore, caused the PSHED variability to be larger than 

the LSHED variability. 

The third feature of Figures 3-4 and 3-5 that is notable is the relatively homogeneous 

scatter of the data points about the parity line across the three orders of magnitude range of the 

SHED data. This homogeneous scatter supports the notion that the hot-soak variability of 

vehicles tends to be proportional to the value of the hot-soak measurements. That is, the 

variability is a percent of the hot-soak measurement value. 

We believe that the similar hot-soak variabilities in the PSHED and in the LSHED are 

consistent with the notion that the variabilities are dominated by the hot-soak emission 

variability of the vehicles themselves. The measurement variability due to the performance of 

the test in either the PSHED or the LSHED is a small component of the total variability of the 

measured values as demonstrated by the propane recovery and retention tests performed in the 

PSHED during the Lipan Street IM station testing.[9] 

Given all of the considerations about the variability in the PSHED and LSHED from this 

dataset, we believe it is appropriate to combine the data from Figures 3-4 and 3-5 into one 

dataset which are plotted in Figure 3-6. This figure can be viewed primarily as the 

demonstration of the hot-soak variability of the vehicles in this dataset regardless of which 

SHED the measurements were made in. 

The data from Vehicle HE-3555 greatly influence the estimated hot-soak variability of 

the SHED measurements. The HE-3555 measurements were real measurements and, therefore, 

represent vehicles in the fleet that have hot-soak emissions with strong time dependences. 

However, the particularly strong time dependence of the successive hot-soak emissions for that 

vehicle is not expected to be representative of 1 vehicle in 15 of the fleet vehicles. Thus, there is 

a case for not including the Vehicle HE-3555 data when estimating hot-soak emissions 

variability. 

The analysis of the data, as shown in Figure 3-6, which includes the HE-3555 data, 

indicates that the standard deviation of the natural log of a hot-soak measurement in either the 

LSHED or the PSHED is 0.922. The anti-log of 0.922 is 2.51. Thus, repeated SHED hot-soak 

measurements for a vehicle would fall between 40% (= 1/2.51) and 251% of the vehicle’s 

average (characteristic) hot-soak value 68% of the time. 
3-13 

Figure 3-6. Hot-Soak Variability in SHED 

0.01

0.10

1.00

10.00

100.00

0.01 0.10 1.00 10.00 100.00

First SHED (g/Qhr)

Second SHED (g/Qhr)

LSHED

1:1

PSHED

If the HE-3555 data is not included, the analysis indicates that the standard deviation of 

the natural log of a hot-soak measurement in either the LSHED or the PSHED is 0.581. The 

anti-log of 0.581 is 1.79. Thus, repeated SHED hot-soak measurements for a vehicle would fall 

between 56% (= 1/1.79) and 179% of the vehicle’s average (characteristic) hot-soak value 68% 

of the time. 

A easy-to-remember compromise between the two different estimates of hot-soak 

variability, which could be used as a rule of thumb, might be that repeated SHED hot-soak 

measurements for a vehicle would fall between 50% and 200% of the vehicle’s average 

(characteristic) hot-soak value 68% of the time. 

Summary of LSHED and PSHED results – This brief analysis of the CDPHE 

PSHED/LSHED test results indicates that: 

 The PSHED tends to produce lower hot-soak emission measurement results than 

the LSHED and that the relative differences are larger for higher hot-soaks. For 

vehicles that have low hot-soak values, the PSHED and LSHED results are very 

similar. 
3-14 

 The variability of the hot-soak measurements for this dataset as a whole was 

independent of the order of the measurements. However, individual vehicles had 

different time dependence trends. The hot-soaks for some vehicles decrease 

somewhat with time. Others increase somewhat with time and one vehicle’s hotsoak emissions increased greatly with time. 

 The relative hot-soak emissions variability was independent of whether the 

measurements were taken in the PSHED or LSHED and were relatively constant 

over three orders of magnitude. 

For the determination of the fraction of the fleet that has elevated PSHED emissions, the 

size of the variability of vehicle hot-soak emissions is an important factor. For example, if the 

hot-soak variability were small, which it is not, then the measurement hot-soak emissions values 

of a sample of vehicles would closely resemble the characteristic hot-soak emissions of those 

vehicles. Consequently, the fraction of the fleet that has high hot-soak emissions would closely 

match the fraction of the fleet that would have high hot-soak emissions based on their 

characteristic hot-soak emissions. However, if the emissions variabilities of vehicles is large, 

which has been shown here, then a single hot-soak measurement on each vehicle in a sample of 

the fleet would indicate that a larger fraction of the vehicles have high hot-soak emissions than 

the fraction of vehicles with high hot-soak emissions based on the characteristic hot-soak 

emissions would indicate. 
4-1 

4.0 Estimated High-PSHED Fraction of the Ken Caryl IM Station Fleet 

Using EI23 Bin De-Stratification 

The objective of this report is to provide estimates of the high evaporative emissions 

fraction of the Denver fleet, based on the PSHED data. One estimate can be made using the data 

collected at the Ken Caryl IM station during the summer of 2009. This estimate can be made by 

de-stratifying the PSHED measurements according to the distribution of EI23 Bin values 

observed in the sample of Denver vehicles that patronized the Ken Caryl IM station during the 

study period. There are reasons to believe that the high-PSHED fraction calculated for the Ken 

Caryl fleet may be somewhat different from the high-PSHED fraction of the Denver fleet. 

Nevertheless, the Ken Caryl estimate may be one of the best estimates that can currently be made. 

Colorado uses Denver-wide “clean screening” of the on-road vehicles using RSD. 

Thereby, the state exempts about 40% of registered vehicles that have low RSD readings from 

emissions inspection at I/M stations. Accordingly, the vehicles that receive I/M inspections are 

likely to have higher emissions (exhaust and/or evap1

) than vehicles in the fleet as a whole. The 

RSD monitors are typically located where vehicles are traveling at higher speeds. We believe 

that RSD instruments have a more difficult time “seeing” evaporative emissions at higher speeds 

because of the more rapid dispersion of the evaporative emissions plume. Other than because of 

vehicle age and differences in general emission control technologies, there has been no evidence 

to suggest that high exhaust emission vehicles are correlated with high evaporative emissions 

vehicles. On the other hand, the Ken Caryl station is located in a higher income part of Denver 

and, therefore, the vehicles that go to this I/M station for their inspections may have lower 

emissions than vehicles in the Denver fleet as a whole. While these two effects influence the 

accuracy of any high-PSHED fraction estimated using Ken Caryl vehicles, the estimate still 

provides insight into the approximate fleet high-PSHED fraction. 

The subsections below describe the RSD and PSHED testing performed in Summer 2009. 

The RSD and PSHED results on the 175-vehicle stratified sample of vehicles entering Ken Caryl 

will be used to establish a relationship between the EI23 evaporative emissions index and hotsoak emissions as measured by the PSHED test. This relationship will then be applied to the 

 

1

 RSD HC by Method A, which is used by the Denver-wide on-road RSD instruments to clean-screen vehicles, is 

influenced by both exhaust HC and evaporative HC emissions. (See Section 4.2 for a description of Methods A and 

B.) However, RSD monitors are typically located where vehicles are traveling at higher speeds. Because of its use of 

CO2 as a tracer for exhaust emissions, RSD can measure exhaust HC concentrations without bias over a wide range 

of speeds. However, evaporative HC emissions do not have a tracer. The evaporative HC emissions signal seen by 

the RSD instrument is probably approximately inversely proportional to vehicle speed. Therefore, at higher speeds 

the evaporative HC signal is smaller than at low speeds. As a consequence, the RSD instrument is less likely to 

“see” or detect evaporative emissions at higher speeds. 
4-2 

5830-vehicle random set, which is made up of most vehicles that entered the I/M station 

driveway during the study period. Standard de-stratification techniques are used to estimate the 

fraction of the Ken Caryl fleet that is expected to have PSHEDs greater than various definitions 

of a high PSHED. The de-stratification technique is also applied to the Ken Caryl fleet as a 

function of model year group. A Monte Carlo simulation provides a means of estimating the 

influence of various uncertainties in the Ken Caryl data on the uncertainty of the calculated highPSHED fraction. An estimate of the uncertainty is critical to understanding the quality of the 

calculated high-PSHED fraction results. This, in turn, provides important guidance to EPA for 

using the results of the calculations for MOVES. 

4.1 RSD and PSHED Testing at the Ken Caryl IM Station 

The goal of the testing at the Ken Caryl IM station was to obtain RSD and evaporative 

emissions data on a stratified sample of vehicles to determine the relationship between RSD and 

evaporative emissions for two purposes: 

1) To be able to use RSD to characterize the evaporative emissions of a large fleet of 

vehicles, and 

2) To be able to use RSD to identify individual vehicles that are likely to have 

elevated evaporative emissions. 

Because only a small fraction of the fleet is believed to be made up of vehicles with 

elevated evaporative emissions, a stratified sample that was enriched in vehicles with elevated 

evaporative emissions was desirable. Accordingly, an RSD evaporative emissions index, known 

as EI23, was developed for the Ken Caryl study to provide a means of stratifying the vehicles 

that enter the I/M station driveway. 

The EI23 RSD evaporative emissions index was developed from the pretesting data that 

was collected on test vehicles in the summer of 2008. All of that pretesting data was collected at 

low speeds near 12 mph (range = 7 to 17 mph). Accordingly, EI23 is applicable only to vehicles 

that receive RSDs at speeds near 12 mph. Analysis of the EI23 index indicated that it contained 

small but important dependences on the exhaust HC emissions of vehicles. Therefore, to allow 

the EI23 to be less dependent on an exhaust HC emissions, we developed EI23 Bin. Appendix A 

describes the calculation of EI23 and EI23 Bin. EI23 Bin has integer values of 1 through 7 with 

low values indicating low running loss emissions and high values indicating high running loss 

emissions. ESP developed the code to calculate EI23 and EI23 Bin and embedded the code in 

the operating system of the entrance driveway RSD unit at the Ken Caryl IM station. This 
4-3 

provided a near instantaneous indication of the EI23 Bin value as vehicles entered the driveway 

of the station. 

The evaporative emission study at the Ken Caryl IM station took place from June 29 

through September 4, 2009. Vehicles entering the station drove past the driveway RSD unit and 

received a first RSD measurement, which was called the Selection RSD. Vehicles were assigned 

to an EI23 Bin based on the Selection RSD. Within each bin, vehicles were randomly selected to 

be solicited for participation in the study. To achieve stratification, a higher fraction of vehicles 

with high EI23 Bin values were randomly selected than those vehicles with low EI23 Bin values. 

Note that the stratified sampling scheme that was used to identify vehicles for solicitation did not 

consider the model year of the vehicle. 

At this point, the owners of targeted vehicles were approached for participation in the 

study. The vehicles of owners who agreed to participate received six additional RSD 

measurements, known as Measurement RSDs, and a single PSHED test, which measured hotsoak emissions during a 15-minute soak that followed a conditioning drive. 

The six Measurement RSDs were made up of two RSDs on the highway at 55 mph, two 

RSDs on the highway at 34 mph, and two RSDs in the station driveway at 12 mph. For this 

analysis, we are concerned with the two Measurement RSDs in the driveway at 12 mph because 

EI23 is currently meaningful only for RSD beam blocks taken at speeds near 12 mph. These two 

RSDs were measured on the same RSD instrument as the Selection RSDs. 

After the vehicle received the six Measurement RSDs, the vehicle received the PSHED 

test in which the hot-soak emissions of the vehicles was measured during a 15-minute soak. The 

study participants also received other testing including the standard I/M inspection and the 

Modified California Method which included olfactory, visual, and electronic HC sniffer 

examination of various components on the vehicle. 

4.2 The 175-Vehicle Stratified Set 

Table 4-1 shows the descriptions of the 175 vehicles that were participants in the study. 

These vehicles make up the stratified set. Table 4-2 shows the results of the PSHED tests and 

the 12-mph Measurement RSD tests on the participants. 173 of the vehicles in the stratified set 

received two 12-mph Measurement RSDs, one vehicle (Packet ID = 365) received three 12-mph 

Measurement RSDs, and one vehicle (Vehicle ID = 69) received zero Measurement RSDs. In all, 

the 175-vehicle stratified set received 349 12-mph Measurement RSDs. Table 4-2 shows the 

EI23 Bin values for each of these 349 Measurement RSDs as well as the standard RSD-
4-4 

calculated values (Methods A and B) for tailpipe concentrations. Note that the reported tailpipe 

HC concentrations may be biased by the presence of evaporative emissions. 

RSD Methods A and B are ESP-proprietary methods for converting the 50 10ms 

concentration-pathlength measurements obtained from every valid RSD beam block into 

estimates of exhaust concentrations. Method B was used on ESP 3000 series instruments, and 

Method A was used on ESP 4000 and 4600 series instruments. Since the methods are proprietary, 

we do not know exactly how they work. However, in general we believe that Method B 

calculates the slope of regression lines through HC, CO, or NO concentration-pathlength values 

against CO2 concentration-pathlength values for each beam block. The slopes of these three 

regressions are then used with the balanced combustion equation to arrive at estimates of exhaust 

concentrations. In contrast, in general we believe that Method A calculates the slope of the 

regression line that connects the centroid of the HC, CO, or NO concentration-pathlength values 

against CO2 concentration-pathlength values with the origin. Method A slopes are also used with 

chemical stoichiometry to calculate estimated exhaust concentrations. ESP informed us that they 

believed that Method A was responsive to both exhaust and evaporative emissions, while 

Method B was responsive to primarily exhaust emissions. Since we know that exhaust HC 

interferes with the RSD detection of evaporative emissions, we chose to use Method B as a 

clearer measure of the quantity that produces the interference. Accordingly, for these studies, 

ESP added code to the 4000 and 4600 series instruments that estimate the Method B emissions 

concentrations that would have been produced on a 3000 series instrument. 

The PSHED results in Table 4-2 are for the vehicle as received, that is, before any 

evaporative emissions repairs were made. Accordingly, these PSHED measurements were taken 

with the vehicle under the same nominal condition as for the Measurement RSDs. Examination 

of the PSHED values in the table show that the hot-soak values span three orders of magnitude. 

The stratified set, which is made up of data from the 175 participants in the Ken Caryl 

study, can be used to establish the relationship between RSD measurements and evaporative 

emissions. In this analysis, the relationship specifically examined will be the relationship 

between the 12-mph Measurement RSD EI23 Bin values and the PSHED 15-minute hot-soak 

values, which have units of grams per 15 minutes (g/Qhr). 

Note that the relationship between EI23 Bin and PSHED must not use the Selection RSD 

data. The Selection RSD values are biased by the statistical effect known as regression toward 

the mean. In general, regression toward the mean occurs whenever a sample is selected using a 

variable that contains error. Since we know that EI23 contains a large amount of error, we 
4-5 

expect and have shown [10] that the EI23s from the Selection RSDs are biased with respect to 

the Measurement RSDs because of regression toward the mean. However, the relationship 

between the Measurement RSDs and their EI23s and Bin values is not biased. Accordingly, an 

analysis of the relationship between PSHED and the Measurement RSD EI23 Bin values is 

capable of providing the true relationship between these variables. 
4-6 

Table 4-1. Descriptions of the 175 Participating Vehicles 

Combined 

Packet 

ID 

(unique to 

vehicle) VIN 

Model 

Year Make Model 

Fuel Metering Type 

(Carburetted or FuelInjected) VECI Engine Family VECI Evap Family Trans. Type 

Fue

Gau

(1.0

0.70.50.2

0.00

7 1998 Ford Explorer F WFMXT04.0HAA WFMXE0120BAE A 09 1992 Saturn SL F N4G1.9V5JPH5 NAO-4B M 0

17 1993 Mercury Grand Marquis F PFM4.6V5FDF2 F3AE-9C485-J A 18 2003 VW Passat F 3ADXV01.8342 3ADXR0140232 A 020 1990 Nissan Pathfinder F LNS3.0T5FCF2 FI6-3 . 21 1991 Jeep Wrangler F MCR2.5T5FEL3 MT-2.5M-1P M 25 2000 Audi A6 F YADXV02.8334 YADXR0140233 A 027 1992 Jeep Wrangler F NCR2.5T5FEL4 

NT-2.5J-1S or NT-2.5M-1P M 0

28 1989 Dodge/Mitsubishi Ram/Raider F MMT3.0T5FB16 1 M 034 1995 Ford Ranger F SFM2.318GFEA 2.3L-SFM1045 M 39 1994 Jeep Grand Cherokee F RCR5.288GAEA RCR1058AYPOA A 042 1987 Dodge Power Ram C HCR5.2T2HEM8 HCRTX; HCRTY M 44 1989 Chevrolet Caprice C KIG5.7V5NEA4 KB0-1P A 45 1997 Ford F150 F VFM5.458GFEK VFM1160AYMFD A 46 1990 Ford Taurus F LFM3.0V5FXG5 F0AE-9C485UK A 048 1994 Chevrolet Camaro F R1G3.4V8GAEA R1G1058AYMOA M 049 1994 Mazda 929 F RTK3.0VJGFEA RTK1078BYM03 A 053 1997 Pontiac Grand Am F VGM2.4VJGKEK VGM1095AYMEA A 57 1996 Ford Explorer XLT F TFM4.028GKFK 4.0L-TFM1120 M 062 2003 Toyota Tundra F 3TYXTG4.7HBY 3TYXR0190A30 A 64 1976 Oldsmobile Omega C . . A 68 2001 Jeep Wrangler F 1CRXT04.0200 1CRXE0101GCS A 69 2002 Land Rover Freelander F 2LRXTO2.5001 2LRXR0124002 A 0

75.096 1986 Toyota MR2 F GTY1.6V5FBB9 EV-E M 0

77 1987 Saab 900 Turbo F HSA2.0V5FTBX . M 079 1988 Chevrolet 1500 Pickup C J3G5.7T5TYA2 JFO-3C A 082 1993 Cadillac El Dorado F P1G4.6V8X8B9 PCO-1A A 085 1996 Dodge Ram 1500 F TCR36OH8G1EK TCR1073AYPOB A 091 1977 Chevrolet Blazer C . . A 092 1997 Ford F-150 F VFM1160AYMFD VFM5.458GFEK A 093 2003 Dodge Durango F 3CRXT04.75BO 3CRXE0101GDH A 095 1998 Nissan Quest F WNSXT03.0A4A WNSXE0057MAA A 

100 1984 Chevrolet Suburban C EGM05.7ABB8 . A 0

103.122 1988 Toyota Camry LE F JTY2.0V5FBB8 EV-E A 

108 1995 Cadillac SLS F S1G4.6VJGFEA S1G1089AYPOA A 109 1995 Toyota Avalon F STY3.0VJGFEK 200201MZ-FE A 110 2005 Toyota Avalon F 5TYXV03.5PEA 5TYXR0130A11 . 
4-7 

Combined 

Packet 

ID 

(unique to 

vehicle) VIN 

Model 

Year Make Model 

Fuel Metering Type 

(Carburetted or FuelInjected) VECI Engine Family VECI Evap Family Trans. Type 

Fue

Gau

(1.0

0.70.50.2

0.00

112 1986 Ford LTD F GFM3.8V5HHF9 E6AE9C485AER A 0120 1989 Chevrolet Camaro F M1G5.7VANTA8 MBO-1A A 0121 1994 Isuzu Amigo F RSZ2.678GAFA RSZ1046BYM00 M 127 1993 Jeep Cherokee F PCR4.0T5FGA5 PTATR or PTA A 130 1995 Ford Explorer F SFM4.028GFEA 4.0L-SFM1120 M 0131 1997 Subaru Outback F VFJ2.5VJGKEK VFJ1030BYMA3 M 0134 2003 Chevrolet Impala F 3GMXV03.8044 3GMXR0124919 A 135 2001 Chrysler Sebring F 1DSXV03.0GNG 1DSXR0165A1F M 0138 2002 Suzuki Vitara F 2SKXT1.59LC1 2SKXE0089S56 M 139 2002 Pontiac Sunfire F . . A 156 1999 Ford Explorer F 4.0L.XFMXE01 XFMXT04.02GF A 157 1982 Ford F-150 Explorer C CFM5.0T2AAF4 2DP A 158 2005 Chevrolet Cobalt F 5GMXV02.2026 5GMXR0124919 A 159 1990 Mazda MX-6 F LTK2.2V5FFG3 J M 161 2001 Saturn SC-1 F 1GMXV01.9002 1GMXR0080902 M 

162.332 1995 Jeep Wrangler F SCR2.578GAEA 

SCR1058AYMON and SCR1058AYPON M 0

163 2002 Volvo S60 F B15244T 2VVXR0133AAA A 168 1994 Saturn SL2 F R4G1.9VHGBEA R4G1035AGPOC A 177 2004 Dodge Ram 1500 F 4CRXT05.75J0 4CRXR0218GDH M 178 1969 VW CP - Dunebuggy C . . M 184 1998 VW Jetta F . . M 185 2002 Toyota Echo F 2TYXV01.5FFA 2TYXR0075AK1 A 186 1995 Ford F-150 F SFM1045AYMOA SFM5.888GBJA A 188 2005 Lexus GX-470 F 5TYXT04.7PKX 5TYXR0190P30 A 0189 1993 GMC Safari . P3G4.3T5TAA6 PFO-3A A 0190 2001 Ford Expedition F 1FMXT05.4RF8 1FMXE0155BBG A 192 1991 Infiniti Q45 F MNS4.5V5FAA9 F18-1 A 193 2003 Ford Focus F 3FMXV02.0VH1 3FMXR0080BBE M 194 1994 Geo Prizm F RNT1.6VHGAFA RNT1047DYM00 M 197 2000 Honda Accord F YHNXV03.0FA3 YHNXR0130AAA A 

203.205 1991 Jeep Wrangler F MCR4.0T5FED1 MT-4.0M-1P M 0

212 1977 Ford Econoline C . . A 0213 1979 Dodge D-150 C 9TD-360-2CP 9K-7 A 0214 2000 Chevrolet S-10 F YGMXT04.3182 YGMXE0095904 A 217 1994 Ford Ranger F RFM4.077GAEA RFM1045AYPOA M 218 1990 VW Cabriolet F LVW1.8V5FWD7 LWC2 M 0219 1991 Lexus LS400 F MTY4.0V5FBB5 EV-SE A 220 1994 Toyota Tacoma F RTY3.087GAFA RTY1047DYM00 M 221 1994 Ford Bronco F RFM5.888GBJA RFM1045AYMOA A 232 1992 Ford Explorer F NFM4.0T5FYH8 F2AE-9C485-H A 0
4-8 

Combined 

Packet 

ID 

(unique to 

vehicle) VIN 

Model 

Year Make Model 

Fuel Metering Type 

(Carburetted or FuelInjected) VECI Engine Family VECI Evap Family Trans. Type 

Fue

Gau

(1.0

0.70.50.2

0.00

238 1993 Jeep Wrangler F PCR4.0T5FGA5 PTAPR OR PTA M 246 2003 Chevrolet Suburban F 3GMXT05.3175 3GMXE0133916 A 249 1997 BMW 328i F VBM2.8VJGKEK VBM1156AYPEO A 0252 2002 Toyota Tacoma F 2TYXT03.4FFP 2TYXR0135AKO A 255 2004 Lexus RX330 F 4TYXT03.3PEM 4TYXR0165P21 A 0261 1994 GMC Suburban F R3G5.785GAEB R3G1085AYMOA A 262 1984 Nissan Datsun C ENS2.4T2AAFO 4CAB-3 M 0263 1997 Honda Accord F VHN2.2VJGKFK VHN1090AYMEA M 266 2002 Chevrolet Tahoe F 2GMXT05.3188 2GMXE0133915 A 271 1997 GMC Sierra F VGM5.75PGFEK VGM1098AYMBA A 0278 2002 Dodge Dakota F 2CRXT04.72D1 2CRXE0101GCS A 285 1998 Honda CRV F WHNXT02.0UF1 WHNXE0080AAB A 290 2005 Acura TSX F 5HNXV02.4KBP 5HNXR0140BBA A 294 1985 Ford F-150 C FFM5.8T2HGG1 5FAE-9C4G5 A 295 1999 Mazda 626 F XTKXV02.0VBA XTKXR0125BFA A 0301 1981 GMC Sierra K1500 C 18L4HANA 1D4D-8 A 0302 1989 Dodge Pickup F KCR3.9T5HFM9 KCRTD M 305 1997 Dodge Ram 1500 F VCR5.968GFEK VCR1073AYPBB A 306 1967 Chevrolet Chevelle C . . A 309 2001 Nissan Sentra F 1NSXV01.8D1A 1NSXR0085RCA A 311 2002 VW Passat F 2ADXV02.8334 2ADXR0140233 A 0316 2003 Hyundai Accent F 3HYXV01.6BLS 3HYXR0105PEA M 320 2004 Chevrolet Cavalier F 4GMXV02.2025 4GMXR0124919 A 321 1997 Chevrolet S-10 F VGM2.218G1EK VGM1095AYMEA . 0325 2000 Chevrolet Prizm F YNTXV01.8FFA YNTXR0115AK1 M 327 1981 BMW 320i F . . M 329 1969 Jeep Commando C . . M 0331 1994 Chevrolet Blazer F R3G4.329GFEA R3G1058AYMON A 337 2001 Nissan Xterra F 1NSXT03.3C5A 1NSXR0120RCA M 339 1994 Toyota 4Runner F RTY3.087GAFA RTY1047DYM00 M 343 1991 VW Golf F MVW1.8V5FWB6 VAP M 0

348.432 1990 Dodge Ram Charger C . . A 0

350 2005 Subaru Outback F 5FJXX02.5MJS 5FJXR01253CJ A 352 2001 Chevrolet Astro F 1GMXT04.3181 1GMXE0212924 A 0354 1989 Jeep Cherokee F KAM242T5LND8 KT-242H-1S A 355 1993 Toyota Corolla F PTY1.8V5FFD9 EV-E M 0357 1997 Nissan Pathfinder F VNS3.328G2EK VNS1110AYMEA A 361 1995 VW Golf F SVW2.0V8GFEA SVW1045BYPOV A 362 2001 Audi TT Quatro F 1ADXV01.8336 1ADXR0130242 M 364 1995 Buick Roadmaster F S1G5.7V8GAEA S1G1058AYPOF A 365 2002 Ford Mustang F 2FMXV03.8VFB 2FMXR0105BAE A 
4-9 

Combined 

Packet 

ID 

(unique to 

vehicle) VIN 

Model 

Year Make Model 

Fuel Metering Type 

(Carburetted or FuelInjected) VECI Engine Family VECI Evap Family Trans. Type 

Fue

Gau

(1.0

0.70.50.2

0.00

367 1993 Chevrolet Van 20 F P3G5.7T5TYA8 PF0-3C A 0370 2003 Land Rover Discovery F 3LRXT04.6001 3LRXE0124001 A 371 1996 Toyota Corolla F TTY1.8VJGFFK TTY1047DYMAO A 375 2005 Ford Focus F 5FMXV02.31D4 5FMXR0120GAK A 0379 1993 Subaru Legacy F . . A 381 1998 Jeep Cherokee F WCRXT0242220 WCRXE0101G2S A 0383 1994 Chevrolet S-10 C R3G4.375GAEA R3G1108WYMOC M 385 2001 Dodge Dakota F 1CRXT04.72D2 1CRXE0101GCH A 0387 2000 Jeep Grand Cherokee F . . A 391 1999 Isuzu Rodeo F XSZXT03.52EK XSZXT0095ME0 A 392 1999 Honda Accord F XHNXV03.0FF1 XHNXR0130AAA A 0393 2001 Toyota Solara F . . A 0395 1997 Chevrolet S-10 F VGM4.31PGKEK VGM1095AYMEA A 396 2000 Mercury Mystique F YFMXV02.5VBC YFMXR0115BBE A 397 2005 Ford Freestyle F 5FMXV03.02EC 5FMXR0185GBK A 0401 1995 Lexus SC 300 F STY3.0VJGAFA STY1080DYM00 A 402 2004 Hyundai Elantra F 4HYXV02.0XW4 4HYXR0148PDX M 404 1998 Isuzu Rodeo F WSZXT03.52EK WSZXT0095ME0 A 0405 1996 Chevrolet Cavalier F TGM2.2V8GKEK TGM1089AYMEA A 0416 1999 Mazda Protege F XTKXV01.6VBA XTKXR0125BFB A 0420 1996 Jeep Grand Cherokee F TCR4.028GKEK TCR1073AYPBP A 0424 1970 VW Beetle C . . M 425 1998 Mitsubishi Eclipse F . . M 0427 1984 Toyota Land Cruiser C ETY4.2T2AFF4 EV-F M 428 1975 Chevrolet C-10 C 350 cid; 2bbl . A 0430 2002 Jeep Wrangler . 2CRXT04.02D2 2CRXE0101GCS M 0431 1993 Mitsubishi 3000 GT F PMT3.0V5FF26 1F M 0437 1991 Chevrolet S-10 C M3G4.3T5XEB2 MBO-3E A 438 1975 Chevrolet C-10 C GM113 350 ci . A 439 1998 Pontiac Grand Prix F WGMXV03.1041 WGMXR0133918 A 0454 1988 BMW M6 F JBM3.5V5FMS4 EV50 M 0460 2003 Dodge Durango F 3CRXTO5.95B2 3CRXE0101GDH A 466 2003 Acura RSX F 3HNXV02.0XKC 3HNXR0099AAA A 468 2004 Subaru Outback F 4FJXV02.5NKR 4FJXR01254CE M 469 2000 Nissan Pathfinder F YNSXT03.325A YNSXE0110MBA A 475 2000 Chevrolet Astro AWD F YGMXT04.3183 YGMXE0111911 A 481 2003 Nissan Frontier F 3NSXT03.3C6A 3NSXR0120MAA A 482 1992 Nissan Stanza F NNS2.4V5FAAX F14-2 A 486 1993 Jeep Cherokee F PCR4.0T5FGA5 PTAPR A 0489 1991 Ford Bronco F . . M 494 1995 Mazda MX-6 F STK2.0VJ6FEA STK1065BYP02 M 
4-10 

Combined 

Packet 

ID 

(unique to 

vehicle) VIN 

Model 

Year Make Model 

Fuel Metering Type 

(Carburetted or FuelInjected) VECI Engine Family VECI Evap Family Trans. Type 

Fue

Gau

(1.0

0.70.50.2

0.00

497 1994 Buick Century F R1G3.4V8GAEA R1G1058AYM0A A 0498 1998 Honda Accord F WHNXV02.3PA3 WHNXR0130AAA M 504 1992 Ford F-150 F NFM5.8T5HZC1 F2AE-9C485 . 507 2001 Toyota Sequoia F 1TYXT04.7JBW 1TYXE0190AF0 A 0512 2005 Hyundai Santa Fe F 5HYXT03.5MM5 5HYXR0175PES . 0517 1999 Chevrolet Lumina F XGMXV03.4041 XGMXE0095904 A 521 1994 Nissan Sentra . RNS1.6VJGFEA RNS1030BYM0A . 527 1995 Eagle Talon F SDS2.0VJGFEA SDS1062AYM0H A 0540 1998 Honda Accord F . . A 542 1986 Subaru GL10 F GFJ1.8V5HCNX MU M 546 1996 Geo Prizm F TNT1.8VJGFFK TNT1047DYMAO A 547 1975 Ford Ranger F-250 C . . M 550 1992 Ford Taurus F NFM3.8V5FJF9 . A 559 1989 Oldsmobile Regency F K2G3.8V8XEBI KBO-2D A 0563 1998 Ford Taurus F WFMXV03.0NAA WFMXE0115BAE A 0568 1997 Saturn SL1 F VGM1.9VJGKEK VGM1035AYPAA A 
4-11 

Table 4-2. Participating Vehicle PSHED and Measurement RSD Results 

PSHED, Before-Repair Measurement RSD, Before-Repair 

Combined 

Packet 

ID 

(unique to 

vehicle) 

Seal DateTime 

Seal 

Temperature 

(F) 

Seal 

Barometric 

Pressure 

(in. Hg) 

Measured 

PSHED 

HC at 15 

Minute 

Soak 

(grams) DateTime 

Speed 

(mph) 

Temperature 

(F) VDF EI23 

EI23 

Bin 

HC 

Method 

A 

(ppmC3) 

7 29JUN09:11:42:00 90 24.39 0.176 29JUN09:11:42:38 10 89 180 125 3 24 7 29JUN09:11:42:00 90 24.39 0.176 29JUN09:11:45:02 10 89 183 80 2 2 9 29JUN09:13:43:00 92 24.36 5.039 29JUN09:13:43:42 10 94 278 14595 2 9868 9 29JUN09:13:43:00 92 24.36 5.039 29JUN09:13:45:36 10 94 280 1098 6 4832 

17 29JUN09:15:35:00 93 24.34 56.424 29JUN09:15:37:29 10 96 361 5071 7 10304 17 29JUN09:15:35:00 93 24.34 56.424 29JUN09:15:39:17 9 96 363 2227 7 7710 18 29JUN09:17:20:00 94 24.32 0.099 29JUN09:17:20:36 10 95 434 84 2 37 18 29JUN09:17:20:00 94 24.32 0.099 29JUN09:17:22:58 12 95 437 74 2 83 20 30JUN09:10:37:00 88 24.38 0.234 30JUN09:10:38:36 14 88 103 80 2 26 20 30JUN09:10:37:00 88 24.38 0.234 30JUN09:10:41:21 13 88 107 64 2 39 21 30JUN09:11:52:00 93 24.38 1.241 30JUN09:11:30:18 10 92 144 110 3 545 21 30JUN09:11:52:00 93 24.38 1.241 30JUN09:11:32:09 9 92 149 489 5 1875 25 30JUN09:13:54:00 94 24.36 0.266 30JUN09:13:54:41 12 100 235 112 3 374 25 30JUN09:13:54:00 94 24.36 0.266 30JUN09:13:57:10 12 99 236 92 2 449 27 30JUN09:15:42:00 96 24.34 5.939 30JUN09:15:42:12 10 99 312 770 7 4442 27 30JUN09:15:42:00 96 24.34 5.939 30JUN09:15:44:31 14 99 315 561 6 1635 28 30JUN09:17:16:00 94 24.32 19.380 30JUN09:17:17:48 10 93 375 251 4 760 28 30JUN09:17:16:00 94 24.32 19.380 30JUN09:17:19:58 9 93 376 272 5 609 34 01JUL09:13:21:00 96 24.31 2.996 01JUL09:13:23:10 13 100 169 200 4 551 34 01JUL09:13:21:00 96 24.31 2.996 01JUL09:13:24:42 11 100 171 346 5 649 39 02JUL09:09:40:00 80 24.48 0.194 02JUL09:09:39:31 11 78 50 75 2 96 39 02JUL09:09:40:00 80 24.48 0.194 02JUL09:09:41:48 10 78 54 74 2 123 42 06JUL09:12:38:00 88 24.36 24.117 06JUL09:12:31:05 13 92 203 325 5 996 42 06JUL09:12:38:00 88 24.36 24.117 06JUL09:12:35:37 12 93 210 127 3 398 44 06JUL09:16:01:00 90 24.29 14.015 06JUL09:15:52:49 13 91 359 373 5 1540 44 06JUL09:16:01:00 90 24.29 14.015 06JUL09:15:54:22 13 91 363 268 4 2057 45 06JUL09:14:35:00 90 24.30 0.043 06JUL09:14:31:04 11 95 307 46 1 10 45 06JUL09:14:35:00 90 24.30 0.043 06JUL09:14:32:32 11 91 310 101 3 39 46 06JUL09:16:33:00 92 24.28 3.058 06JUL09:16:26:59 12 87 386 404 5 825 46 06JUL09:16:33:00 92 24.28 3.058 06JUL09:16:27:50 12 87 388 320 5 636 48 06JUL09:17:27:00 86 24.30 6.667 06JUL09:17:23:03 12 82 428 96 3 35 48 06JUL09:17:27:00 86 24.30 6.667 06JUL09:17:23:58 12 82 430 347 5 513 49 07JUL09:11:28:00 92 24.27 15.588 07JUL09:11:21:34 12 92 110 458 5 1059 49 07JUL09:11:28:00 92 24.27 15.588 07JUL09:11:22:31 13 92 112 1448 7 1656 53 07JUL09:09:49:00 84 24.27 0.258 07JUL09:09:19:14 13 76 36 127 3 131 53 07JUL09:09:49:00 84 24.27 0.258 07JUL09:09:21:08 11 76 38 135 3 281 57 07JUL09:12:12:00 90 24.28 0.033 07JUL09:12:06:38 14 91 143 69 2 8 57 07JUL09:12:12:00 90 24.28 0.033 07JUL09:12:07:49 12 91 145 75 2 33 
4-12 

PSHED, Before-Repair Measurement RSD, Before-Repair 

Combined 

Packet 

ID 

(unique to 

vehicle) 

Seal DateTime 

Seal 

Temperature 

(F) 

Seal 

Barometric 

Pressure 

(in. Hg) 

Measured 

PSHED 

HC at 15 

Minute 

Soak 

(grams) DateTime 

Speed 

(mph) 

Temperature 

(F) VDF EI23 

EI23 

Bin 

HC 

Method 

A 

(ppmC3) 

62 07JUL09:17:29:00 91 24.24 0.011 07JUL09:17:25:16 12 87 302 54 1 -19 62 07JUL09:17:29:00 91 24.24 0.011 07JUL09:17:26:55 11 87 305 64 2 4 64 08JUL09:13:41:00 95 24.24 15.649 08JUL09:13:38:33 12 104 192 3835 7 2608 64 08JUL09:13:41:00 95 24.24 15.649 08JUL09:13:39:37 11 104 194 1209 7 3287 68 09JUL09:10:51:00 77 24.40 0.087 09JUL09:10:48:24 10 78 105 94 3 106 68 09JUL09:10:51:00 77 24.40 0.087 09JUL09:10:49:36 11 78 108 91 3 45 

75.096 09JUL09:15:48:00 85 24.35 0.249 09JUL09:15:44:33 12 95 242 195 4 1004 75.096 09JUL09:15:48:00 85 24.35 0.249 09JUL09:15:46:34 12 95 244 222 4 1003 

77 09JUL09:17:43:00 83 24.35 3.906 09JUL09:17:39:56 12 85 275 369 5 1450 77 09JUL09:17:43:00 83 24.35 3.906 09JUL09:17:41:05 11 85 277 595 6 1339 79 10JUL09:13:59:00 92 24.46 18.250 10JUL09:13:54:50 12 100 192 1519 7 3871 79 10JUL09:13:59:00 92 24.46 18.250 10JUL09:13:56:22 11 100 196 2673 7 3636 82 10JUL09:15:26:00 95 24.41 0.473 10JUL09:15:23:05 12 102 240 96 3 32 82 10JUL09:15:26:00 95 24.41 0.473 10JUL09:15:24:38 11 102 243 98 3 79 85 16JUL09:12:32:00 91 24.54 6.077 16JUL09:12:28:03 12 94 120 265 4 366 85 16JUL09:12:32:00 91 24.54 6.077 16JUL09:12:29:29 10 94 122 404 5 413 91 15JUL09:10:58:00 76 24.52 15.176 15JUL09:10:54:55 12 76 58 323 5 710 91 15JUL09:10:58:00 76 24.52 15.176 15JUL09:10:55:55 12 76 60 901 6 1280 92 14JUL09:11:08:00 87 24.35 0.022 14JUL09:11:05:14 11 85 86 73 2 31 92 14JUL09:11:08:00 87 24.35 0.022 14JUL09:11:06:20 10 85 89 96 3 22 93 14JUL09:13:21:00 93 24.32 0.051 14JUL09:13:17:57 12 95 155 87 2 9 93 14JUL09:13:21:00 93 24.32 0.051 14JUL09:13:19:05 11 95 158 79 2 2 95 15JUL09:09:19:00 74 24.53 0.451 15JUL09:09:15:11 13 72 32 76 2 226 95 15JUL09:09:19:00 74 24.53 0.451 15JUL09:09:16:30 13 72 35 199 4 157 

100 15JUL09:12:08:00 82 24.50 10.130 15JUL09:12:05:12 12 86 85 1926 7 833 100 15JUL09:12:08:00 82 24.50 10.130 15JUL09:12:06:02 13 86 87 2432 7 1680 

103.122 16JUL09:10:49:00 88 24.55 5.011 16JUL09:10:46:39 8 88 53 218 4 517 103.122 16JUL09:10:49:00 88 24.55 5.011 16JUL09:10:47:40 11 88 56 277 5 424 

108 16JUL09:14:11:00 91 24.53 22.449 16JUL09:14:08:23 13 97 174 6713 7 3493 108 16JUL09:14:11:00 91 24.53 22.449 16JUL09:14:09:19 12 97 177 2031 7 2753 109 20JUL09:15:13:00 95 24.32 2.352 20JUL09:15:09:30 15 103 229 147 3 153 109 20JUL09:15:13:00 95 24.32 2.352 20JUL09:15:11:06 14 104 232 276 5 142 110 16JUL09:16:27:00 92 24.52 0.038 16JUL09:16:24:24 11 99 243 86 2 6 110 16JUL09:16:27:00 92 24.52 0.038 16JUL09:16:25:21 12 99 245 95 3 -3 112 16JUL09:15:47:00 92 24.51 9.116 16JUL09:15:41:41 11 99 224 2091 7 4672 112 16JUL09:15:47:00 92 24.51 9.116 16JUL09:15:42:30 11 96 226 626 6 2932 120 17JUL09:12:00:00 90 24.56 20.322 17JUL09:11:50:44 12 89 130 298 5 432 120 17JUL09:12:00:00 90 24.56 20.322 17JUL09:11:53:01 11 89 134 177 4 518 121 17JUL09:12:40:00 92 24.56 0.115 17JUL09:12:37:12 12 92 163 101 3 47 121 17JUL09:12:40:00 92 24.56 0.115 17JUL09:12:38:04 13 92 165 81 2 24 127 17JUL09:17:00:00 93 24.50 26.376 17JUL09:16:58:08 11 96 297 852 6 3715 
4-13 

PSHED, Before-Repair Measurement RSD, Before-Repair 

Combined 

Packet 

ID 

(unique to 

vehicle) 

Seal DateTime 

Seal 

Temperature 

(F) 

Seal 

Barometric 

Pressure 

(in. Hg) 

Measured 

PSHED 

HC at 15 

Minute 

Soak 

(grams) DateTime 

Speed 

(mph) 

Temperature 

(F) VDF EI23 

EI23 

Bin 

HC 

Method 

A 

(ppmC3) 

127 17JUL09:17:00:00 93 24.50 26.376 17JUL09:16:59:03 11 96 299 310 5 3146 130 20JUL09:11:16:00 90 24.38 0.049 20JUL09:11:11:22 17 94 83 38 1 10 130 20JUL09:11:16:00 90 24.38 0.049 20JUL09:11:14:16 12 94 86 45 1 -27 131 20JUL09:12:30:00 91 24.37 0.100 20JUL09:12:27:05 15 97 122 49 1 2 131 20JUL09:12:30:00 91 24.37 0.100 20JUL09:12:28:42 14 97 124 68 2 6 134 20JUL09:13:54:00 92 24.36 0.024 20JUL09:13:51:17 15 94 170 64 2 -14 134 20JUL09:13:54:00 92 24.36 0.024 20JUL09:13:52:26 15 94 172 75 2 -9 135 21JUL09:10:32:00 75 24.48 0.047 21JUL09:10:28:18 14 77 74 52 1 17 135 21JUL09:10:32:00 75 24.48 0.047 21JUL09:10:29:32 11 77 76 82 2 0 138 21JUL09:09:16:00 74 24.51 0.020 21JUL09:09:11:06 14 73 41 70 2 -8 138 21JUL09:09:16:00 74 24.51 0.020 21JUL09:09:14:44 13 73 45 45 1 30 139 20JUL09:17:10:00 90 24.35 0.027 20JUL09:17:07:14 14 90 287 66 2 -50 139 20JUL09:17:10:00 90 24.35 0.027 20JUL09:17:08:12 13 90 289 56 1 -7 156 22JUL09:11:34:00 83 24.45 0.015 22JUL09:11:30:41 13 90 89 91 3 29 156 22JUL09:11:34:00 83 24.45 0.015 22JUL09:11:32:29 13 90 93 136 3 38 157 22JUL09:14:54:00 94 24.42 5.074 22JUL09:14:11:46 13 99 156 366 5 779 157 22JUL09:14:54:00 94 24.42 5.074 22JUL09:14:13:13 14 99 159 169 4 479 158 22JUL09:12:52:00 85 24.44 0.015 22JUL09:12:48:39 14 95 116 56 1 5 158 22JUL09:12:52:00 85 24.44 0.015 22JUL09:12:49:42 14 95 118 57 2 1 159 23JUL09:09:35:00 81 24.42 0.728 23JUL09:09:31:45 13 79 39 267 4 419 159 23JUL09:09:35:00 81 24.42 0.728 23JUL09:09:32:46 13 79 41 501 5 702 161 23JUL09:11:01:00 89 24.40 0.015 23JUL09:10:54:07 13 88 69 90 2 132 161 23JUL09:11:01:00 89 24.40 0.015 23JUL09:10:55:27 13 88 70 63 2 55 

162.332 11AUG09:14:21:00 91 24.50 0.166 11AUG09:14:18:09 13 103 160 82 2 49 162.332 11AUG09:14:21:00 91 24.50 0.166 11AUG09:14:19:25 14 103 162 77 2 220 

163 23JUL09:13:44:00 94 24.39 0.052 23JUL09:13:40:24 14 106 153 53 1 42 163 23JUL09:13:44:00 94 24.39 0.052 23JUL09:13:42:07 15 106 156 44 1 14 168 23JUL09:15:03:00 96 24.36 25.565 23JUL09:14:59:55 13 107 189 1754 7 2613 168 23JUL09:15:03:00 96 24.36 25.565 23JUL09:15:01:09 12 107 191 799 6 1799 177 24JUL09:16:28:00 100 24.35 0.034 24JUL09:16:24:43 14 102 214 93 3 18 177 24JUL09:16:28:00 100 24.35 0.034 24JUL09:16:25:52 15 102 216 60 2 59 178 24JUL09:13:15:00 99 24.37 1.975 24JUL09:13:11:29 14 100 132 217 4 1153 178 24JUL09:13:15:00 99 24.37 1.975 24JUL09:13:13:30 14 100 135 160 3 1676 184 27JUL09:09:44:00 77 24.50 0.137 27JUL09:09:40:24 12 78 63 45 1 28 184 27JUL09:09:44:00 77 24.50 0.137 27JUL09:09:41:51 12 78 69 75 2 12 185 27JUL09:10:50:00 82 24.49 0.018 27JUL09:10:46:12 12 85 111 65 2 -12 185 27JUL09:10:50:00 82 24.49 0.018 27JUL09:10:47:42 11 85 112 85 2 7 186 27JUL09:11:58:00 85 24.47 0.145 27JUL09:11:53:44 12 91 158 101 3 49 186 27JUL09:11:58:00 85 24.47 0.145 27JUL09:11:55:12 11 91 161 48 1 262 188 27JUL09:13:05:00 88 24.45 0.015 27JUL09:13:02:15 12 93 215 138 3 11 188 27JUL09:13:05:00 88 24.45 0.015 27JUL09:13:03:37 12 93 217 108 3 -5 
4-14 

PSHED, Before-Repair Measurement RSD, Before-Repair 

Combined 

Packet 

ID 

(unique to 

vehicle) 

Seal DateTime 

Seal 

Temperature 

(F) 

Seal 

Barometric 

Pressure 

(in. Hg) 

Measured 

PSHED 

HC at 15 

Minute 

Soak 

(grams) DateTime 

Speed 

(mph) 

Temperature 

(F) VDF EI23 

EI23 

Bin 

HC 

Method 

A 

(ppmC3) 

189 27JUL09:14:44:00 87 24.41 0.282 27JUL09:14:40:22 11 95 283 88 2 74 189 27JUL09:14:44:00 87 24.41 0.282 27JUL09:14:42:07 10 95 286 80 2 292 190 28JUL09:10:26:00 72 24.44 0.045 28JUL09:10:22:42 12 67 72 118 3 13 190 28JUL09:10:26:00 72 24.44 0.045 28JUL09:10:23:50 12 67 76 91 3 4 192 28JUL09:08:56:00 71 24.44 0.043 28JUL09:08:52:18 13 69 21 161 4 39 192 28JUL09:08:56:00 71 24.44 0.043 28JUL09:08:53:47 13 69 24 63 2 32 193 28JUL09:09:34:00 74 24.45 0.053 28JUL09:09:27:47 14 72 38 45 1 -1 193 28JUL09:09:34:00 74 24.45 0.053 28JUL09:09:28:58 13 70 39 55 1 -11 194 28JUL09:11:27:00 73 24.44 0.039 28JUL09:11:24:06 14 68 130 34 1 53 194 28JUL09:11:27:00 73 24.44 0.039 28JUL09:11:24:49 13 68 132 33 1 373 197 28JUL09:12:54:00 74 24.43 0.025 28JUL09:12:51:06 13 72 185 59 2 1 197 28JUL09:12:54:00 74 24.43 0.025 28JUL09:12:52:05 13 72 187 52 1 10 

203.205 28JUL09:14:45:00 76 24.40 13.730 28JUL09:14:42:44 11 72 273 347 5 390 203.205 28JUL09:14:45:00 76 24.40 13.730 28JUL09:14:43:44 10 72 275 285 5 460 

212 28JUL09:16:58:00 77 24.37 7.620 28JUL09:16:54:42 12 72 346 168 4 924 212 28JUL09:16:58:00 77 24.37 7.620 28JUL09:16:56:00 13 72 348 254 4 1106 213 29JUL09:09:46:00 75 24.35 3.830 29JUL09:09:38:05 10 70 38 382 4 7108 213 29JUL09:09:46:00 75 24.35 3.830 29JUL09:09:39:15 11 70 40 422 4 6361 214 29JUL09:11:21:00 76 24.36 0.025 29JUL09:11:16:23 12 74 105 88 2 62 214 29JUL09:11:21:00 76 24.36 0.025 29JUL09:11:17:22 12 74 107 144 3 65 217 29JUL09:13:46:00 73 24.36 0.841 29JUL09:13:43:32 13 72 179 192 4 141 217 29JUL09:13:46:00 73 24.36 0.841 29JUL09:13:44:34 14 72 181 755 6 404 218 29JUL09:10:42:00 77 24.36 0.325 29JUL09:10:39:23 14 76 80 68 2 146 218 29JUL09:10:42:00 77 24.36 0.325 29JUL09:10:40:32 12 76 81 64 2 152 219 29JUL09:14:45:00 75 24.33 0.100 29JUL09:14:41:23 13 73 213 72 2 5 219 29JUL09:14:45:00 75 24.33 0.100 29JUL09:14:43:15 12 73 219 91 3 12 220 29JUL09:15:47:00 75 24.37 0.044 29JUL09:15:43:45 12 75 256 47 1 -5 220 29JUL09:15:47:00 75 24.37 0.044 29JUL09:15:44:54 14 75 258 79 2 15 221 29JUL09:12:20:00 73 24.35 0.041 29JUL09:12:16:12 13 71 141 100 3 203 221 29JUL09:12:20:00 73 24.35 0.041 29JUL09:12:17:19 11 71 143 93 3 4 232 30JUL09:13:36:00 65 24.47 0.164 30JUL09:13:32:56 14 61 147 113 3 71 232 30JUL09:13:36:00 65 24.47 0.164 30JUL09:13:34:12 12 61 149 181 4 116 238 30JUL09:17:15:00 70 24.42 0.287 30JUL09:17:11:35 11 72 242 130 3 99 238 30JUL09:17:15:00 70 24.42 0.287 30JUL09:17:12:44 12 72 244 158 4 94 246 31JUL09:10:51:00 84 24.37 0.024 31JUL09:10:47:23 12 83 76 67 2 33 246 31JUL09:10:51:00 84 24.37 0.024 31JUL09:10:48:36 12 85 79 124 3 18 249 31JUL09:13:18:00 87 24.34 0.141 31JUL09:13:13:59 12 86 172 116 3 24 249 31JUL09:13:18:00 87 24.34 0.141 31JUL09:13:15:04 12 86 174 82 2 16 252 31JUL09:16:27:00 83 24.35 0.036 31JUL09:16:23:54 11 79 272 142 3 52 252 31JUL09:16:27:00 83 24.35 0.036 31JUL09:16:24:59 10 79 273 136 3 29 255 31JUL09:17:11:00 82 24.38 0.018 31JUL09:17:08:34 12 78 285 116 3 21 
4-15 

PSHED, Before-Repair Measurement RSD, Before-Repair 

Combined 

Packet 

ID 

(unique to 

vehicle) 

Seal DateTime 

Seal 

Temperature 

(F) 

Seal 

Barometric 

Pressure 

(in. Hg) 

Measured 

PSHED 

HC at 15 

Minute 

Soak 

(grams) DateTime 

Speed 

(mph) 

Temperature 

(F) VDF EI23 

EI23 

Bin 

HC 

Method 

A 

(ppmC3) 

255 31JUL09:17:11:00 82 24.38 0.018 31JUL09:17:09:32 12 78 286 180 4 25 261 03AUG09:12:58:00 34 24.38 0.403 03AUG09:12:53:14 12 95 160 174 4 331 261 03AUG09:12:58:00 34 24.38 0.403 03AUG09:12:54:26 15 95 161 126 3 121 262 03AUG09:14:07:00 93 24.36 1.603 03AUG09:14:03:02 11 98 196 55 1 555 262 03AUG09:14:07:00 93 24.36 1.603 03AUG09:14:04:40 12 98 199 220 4 538 263 03AUG09:11:48:00 95 24.39 0.030 03AUG09:11:43:18 14 95 125 39 1 22 263 03AUG09:11:48:00 95 24.39 0.030 03AUG09:11:44:32 13 95 127 49 1 -23 266 12AUG09:10:19:00 86 24.51 0.037 12AUG09:10:15:56 14 82 43 93 3 11 266 12AUG09:10:19:00 86 24.51 0.037 12AUG09:10:17:03 16 82 45 77 2 15 271 04AUG09:08:58:00 78 24.48 1.283 04AUG09:08:54:36 13 72 34 98 3 57 271 04AUG09:08:58:00 78 24.48 1.283 04AUG09:08:55:37 14 73 36 109 3 51 278 04AUG09:11:28:00 87 24.46 0.060 04AUG09:11:24:29 13 86 112 94 3 17 278 04AUG09:11:28:00 87 24.46 0.060 04AUG09:11:25:26 13 86 113 71 2 35 285 04AUG09:15:57:00 94 24.42 0.022 04AUG09:15:53:07 14 97 180 67 2 -2 285 04AUG09:15:57:00 94 24.42 0.022 04AUG09:15:54:18 16 97 183 77 2 -18 290 05AUG09:13:04:00 88 24.50 0.017 05AUG09:13:00:38 15 93 134 95 3 2 290 05AUG09:13:04:00 88 24.50 0.017 05AUG09:13:01:41 15 93 135 91 3 0 294 05AUG09:14:59:00 91 24.48 3.788 05AUG09:14:55:11 13 101 183 56 1 509 294 05AUG09:14:59:00 91 24.48 3.788 05AUG09:14:56:15 14 101 185 63 2 555 295 05AUG09:16:30:00 92 24.43 0.033 05AUG09:16:26:43 15 110 218 70 2 -27 295 05AUG09:16:30:00 92 24.43 0.033 05AUG09:16:27:55 13 110 220 61 2 -10 301 07AUG09:09:11:00 85 24.24 16.107 07AUG09:09:07:42 12 77 42 150 3 1103 301 07AUG09:09:11:00 85 24.24 16.107 07AUG09:09:08:45 12 77 43 95 2 1612 302 06AUG09:15:47:00 85 24.31 0.156 06AUG09:15:43:15 15 79 195 118 3 184 302 06AUG09:15:47:00 85 24.31 0.156 06AUG09:15:45:00 14 79 197 95 3 193 305 07AUG09:10:58:00 89 24.24 0.107 07AUG09:10:54:19 14 85 99 72 2 3 305 07AUG09:10:58:00 89 24.24 0.107 07AUG09:10:55:42 15 86 103 101 3 65 306 06AUG09:16:34:00 81 24.31 21.643 06AUG09:16:31:15 14 74 222 368 5 1473 306 06AUG09:16:34:00 81 24.31 21.643 06AUG09:16:32:10 17 74 224 152 3 1303 309 07AUG09:14:57:00 96 24.16 2.264 07AUG09:14:54:53 16 110 231 506 5 859 309 07AUG09:14:57:00 96 24.16 2.264 07AUG09:14:55:48 13 110 234 251 4 388 311 07AUG09:16:12:00 94 24.14 0.165 07AUG09:16:09:26 15 110 271 107 3 1 311 07AUG09:16:12:00 94 24.14 0.165 07AUG09:16:10:17 14 110 272 110 3 18 316 10AUG09:09:36:00 74 24.54 0.691 10AUG09:09:33:02 10 71 31 167 4 244 316 10AUG09:09:36:00 74 24.54 0.691 10AUG09:09:34:15 9 71 33 802 5 . 320 10AUG09:11:02:00 79 24.53 0.034 10AUG09:10:58:31 14 76 71 107 3 16 320 10AUG09:11:02:00 79 24.53 0.034 10AUG09:10:59:51 13 76 76 70 2 -1 321 10AUG09:12:45:00 86 24.50 12.329 10AUG09:12:41:00 15 86 124 785 6 2266 321 10AUG09:12:45:00 86 24.50 12.329 10AUG09:12:42:00 15 88 126 1151 7 1671 325 11AUG09:09:40:00 79 24.55 0.034 11AUG09:09:37:20 13 75 34 84 2 10 325 11AUG09:09:40:00 79 24.55 0.034 11AUG09:09:38:17 14 75 35 66 2 2 
4-16 

PSHED, Before-Repair Measurement RSD, Before-Repair 

Combined 

Packet 

ID 

(unique to 

vehicle) 

Seal DateTime 

Seal 

Temperature 

(F) 

Seal 

Barometric 

Pressure 

(in. Hg) 

Measured 

PSHED 

HC at 15 

Minute 

Soak 

(grams) DateTime 

Speed 

(mph) 

Temperature 

(F) VDF EI23 

EI23 

Bin 

HC 

Method 

A 

(ppmC3) 

327 10AUG09:17:28:00 86 24.46 1.565 10AUG09:17:25:35 10 94 266 208 4 571 327 10AUG09:17:28:00 86 24.46 1.565 10AUG09:17:26:47 11 94 268 234 4 755 329 11AUG09:11:00:00 87 24.55 7.535 11AUG09:10:55:53 13 84 64 1987 7 1530 329 11AUG09:11:00:00 87 24.55 7.535 11AUG09:10:56:56 15 84 66 1708 7 1709 331 11AUG09:12:44:00 90 24.52 4.608 11AUG09:12:39:23 13 93 116 753 6 1206 331 11AUG09:12:44:00 90 24.52 4.608 11AUG09:12:40:36 13 93 117 817 6 1443 337 11AUG09:17:16:00 94 24.46 0.066 11AUG09:17:12:59 14 118 248 53 1 4 337 11AUG09:17:16:00 94 24.46 0.066 11AUG09:17:14:03 14 118 249 63 2 -4 339 12AUG09:11:47:00 91 24.48 2.236 12AUG09:11:44:06 14 93 102 56 1 81 339 12AUG09:11:47:00 91 24.48 2.236 12AUG09:11:44:58 14 93 103 109 3 18 343 12AUG09:13:22:00 93 24.45 0.963 12AUG09:13:19:22 16 104 147 582 6 888 343 12AUG09:13:22:00 93 24.45 0.963 12AUG09:13:20:05 13 104 148 962 6 720 

348.432 12AUG09:14:43:00 91 24.43 0.567 12AUG09:14:40:04 12 101 181 115 3 470 348.432 12AUG09:14:43:00 91 24.43 0.567 12AUG09:14:40:55 12 101 183 166 4 408 

350 12AUG09:16:17:00 93 24.40 0.022 12AUG09:16:11:52 14 115 229 51 1 54 350 12AUG09:16:17:00 93 24.40 0.022 12AUG09:16:14:05 13 115 232 79 2 -9 352 13AUG09:09:45:00 84 24.42 0.565 13AUG09:09:41:11 15 81 32 197 4 67 352 13AUG09:09:45:00 84 24.42 0.565 13AUG09:09:42:42 12 81 35 109 3 65 354 13AUG09:13:25:00 90 24.40 133.986 13AUG09:13:21:57 13 91 152 9350 7 11162 354 13AUG09:13:25:00 90 24.40 133.986 13AUG09:13:23:01 13 91 154 575 6 2754 355 13AUG09:11:37:00 91 24.40 2.216 13AUG09:11:33:43 15 90 103 899 5 . 355 13AUG09:11:37:00 91 24.40 2.216 13AUG09:11:35:17 13 90 106 427 5 2899 357 21AUG09:15:52:00 87 24.45 2.323 21AUG09:15:49:31 16 102 292 816 6 624 357 21AUG09:15:52:00 87 24.45 2.323 21AUG09:15:50:44 14 102 294 307 5 314 361 14AUG09:10:29:00 80 24.34 0.171 14AUG09:10:25:19 17 74 72 99 3 169 361 14AUG09:10:29:00 80 24.34 0.171 14AUG09:10:26:31 15 75 74 94 3 163 362 14AUG09:12:02:00 81 24.33 0.127 14AUG09:11:57:02 13 81 125 46 1 29 362 14AUG09:12:02:00 81 24.33 0.127 14AUG09:12:00:31 15 81 130 65 2 73 364 14AUG09:13:51:00 82 24.30 1.209 14AUG09:13:47:34 18 83 186 375 5 1088 364 14AUG09:13:51:00 82 24.30 1.209 14AUG09:13:48:58 14 83 188 386 5 939 365 14AUG09:16:52:00 82 24.28 0.024 14AUG09:16:47:40 16 81 280 70 2 -25 365 14AUG09:16:52:00 82 24.28 0.024 14AUG09:16:48:56 4 81 282 109 3 11 365 14AUG09:16:52:00 82 24.28 0.024 14AUG09:16:50:36 16 81 283 80 2 26 367 14AUG09:15:31:00 85 24.27 14.561 14AUG09:15:26:58 13 94 244 769 6 1160 367 14AUG09:15:31:00 85 24.27 14.561 14AUG09:15:28:53 12 94 247 1339 7 1882 370 17AUG09:10:08:00 72 24.48 0.015 17AUG09:10:04:47 13 67 61 137 3 24 370 17AUG09:10:08:00 72 24.48 0.015 17AUG09:10:06:22 13 67 63 118 3 16 371 17AUG09:13:32:00 78 24.46 0.087 17AUG09:13:29:03 15 84 164 87 2 17 371 17AUG09:13:32:00 78 24.46 0.087 17AUG09:13:29:49 15 84 166 76 2 45 375 17AUG09:12:15:00 77 24.47 0.052 17AUG09:12:11:31 15 76 131 98 3 23 375 17AUG09:12:15:00 77 24.47 0.052 17AUG09:12:13:14 14 76 133 72 2 7 
4-17 

PSHED, Before-Repair Measurement RSD, Before-Repair 

Combined 

Packet 

ID 

(unique to 

vehicle) 

Seal DateTime 

Seal 

Temperature 

(F) 

Seal 

Barometric 

Pressure 

(in. Hg) 

Measured 

PSHED 

HC at 15 

Minute 

Soak 

(grams) DateTime 

Speed 

(mph) 

Temperature 

(F) VDF EI23 

EI23 

Bin 

HC 

Method 

A 

(ppmC3) 

379 17AUG09:16:13:00 79 24.44 0.068 17AUG09:16:03:01 14 80 252 81 2 59 379 17AUG09:16:13:00 79 24.44 0.068 17AUG09:16:04:03 12 79 254 126 3 43 381 17AUG09:16:55:00 77 24.45 0.050 17AUG09:16:52:34 14 75 266 142 3 62 381 17AUG09:16:55:00 77 24.45 0.050 17AUG09:16:53:23 14 75 268 112 3 52 383 18AUG09:09:38:00 73 24.40 2.161 18AUG09:09:34:31 12 68 46 89 2 36 383 18AUG09:09:38:00 73 24.40 2.161 18AUG09:09:36:05 13 68 48 145 3 114 385 18AUG09:11:27:00 82 24.37 0.022 18AUG09:11:24:44 13 81 94 110 3 20 385 18AUG09:11:27:00 82 24.37 0.022 18AUG09:11:25:38 14 81 96 70 2 -2 387 18AUG09:13:03:00 78 24.35 0.018 18AUG09:12:59:34 13 78 132 76 2 5 387 18AUG09:13:03:00 78 24.35 0.018 18AUG09:13:00:47 15 78 134 67 2 13 391 19AUG09:11:53:00 83 24.27 0.080 19AUG09:11:49:50 12 81 95 77 2 65 391 19AUG09:11:53:00 83 24.27 0.080 19AUG09:11:50:51 13 81 98 81 2 22 392 19AUG09:14:31:00 86 24.27 0.046 19AUG09:14:28:20 15 94 164 78 2 -3 392 19AUG09:14:31:00 86 24.27 0.046 19AUG09:14:29:32 13 94 167 95 3 -2 393 19AUG09:15:33:00 85 24.26 0.464 19AUG09:15:30:23 13 100 191 125 3 124 393 19AUG09:15:33:00 85 24.26 0.464 19AUG09:15:32:16 12 100 195 584 6 286 395 20AUG09:09:49:00 73 24.49 0.718 20AUG09:09:45:58 13 66 37 177 4 257 395 20AUG09:09:49:00 73 24.49 0.718 20AUG09:09:47:06 13 66 38 312 5 194 396 19AUG09:17:00:00 85 24.26 0.038 19AUG09:16:55:17 14 103 224 74 2 -25 396 19AUG09:17:00:00 85 24.26 0.038 19AUG09:16:58:18 12 104 231 90 3 -27 397 20AUG09:11:43:00 80 24.48 0.027 20AUG09:11:40:28 16 77 110 53 1 -2 397 20AUG09:11:43:00 80 24.48 0.027 20AUG09:11:41:30 14 77 111 75 2 -2 401 20AUG09:12:33:00 82 24.48 0.349 20AUG09:12:30:14 14 82 128 100 3 1 401 20AUG09:12:33:00 82 24.48 0.349 20AUG09:12:31:15 13 82 129 121 3 12 402 20AUG09:14:10:00 83 24.47 0.015 20AUG09:14:07:00 15 92 174 93 3 -20 402 20AUG09:14:10:00 83 24.47 0.015 20AUG09:14:08:07 13 92 178 86 2 -7 404 20AUG09:17:06:00 85 24.44 11.063 20AUG09:17:03:14 13 103 242 83 2 6 404 20AUG09:17:06:00 85 24.44 11.063 20AUG09:17:04:19 12 103 243 85 2 38 405 20AUG09:15:47:00 84 24.43 0.040 20AUG09:15:43:32 12 99 209 82 2 10 405 20AUG09:15:47:00 84 24.43 0.040 20AUG09:15:44:37 12 99 211 118 3 63 416 21AUG09:11:59:00 81 24.52 0.015 21AUG09:11:56:27 14 79 150 83 2 -4 416 21AUG09:11:59:00 81 24.52 0.015 21AUG09:11:57:25 13 79 151 94 3 16 420 21AUG09:14:13:00 86 24.49 2.260 21AUG09:14:09:50 13 96 234 118 3 156 420 21AUG09:14:13:00 86 24.49 2.260 21AUG09:14:10:54 14 96 235 103 3 112 424 21AUG09:16:36:00 88 24.45 1.935 21AUG09:16:32:40 13 107 300 90 2 1249 424 21AUG09:16:36:00 88 24.45 1.935 21AUG09:16:33:43 11 107 301 176 4 1010 425 24AUG09:10:23:00 84 24.38 0.008 24AUG09:10:19:24 14 80 54 65 2 3 425 24AUG09:10:23:00 84 24.38 0.008 24AUG09:10:20:20 14 80 57 73 2 7 427 24AUG09:13:54:00 85 24.38 26.900 24AUG09:13:49:48 12 87 180 871 6 2422 427 24AUG09:13:54:00 85 24.38 26.900 24AUG09:13:50:55 14 87 181 3625 7 2857 428 24AUG09:15:24:00 81 24.41 7.751 24AUG09:15:20:03 13 80 227 1305 7 4576 
4-18 

PSHED, Before-Repair Measurement RSD, Before-Repair 

Combined 

Packet 

ID 

(unique to 

vehicle) 

Seal DateTime 

Seal 

Temperature 

(F) 

Seal 

Barometric 

Pressure 

(in. Hg) 

Measured 

PSHED 

HC at 15 

Minute 

Soak 

(grams) DateTime 

Speed 

(mph) 

Temperature 

(F) VDF EI23 

EI23 

Bin 

HC 

Method 

A 

(ppmC3) 

428 24AUG09:15:24:00 81 24.41 7.751 24AUG09:15:21:22 13 80 230 895 6 4490 430 24AUG09:12:09:00 85 24.38 0.073 24AUG09:12:05:54 13 85 116 94 3 21 430 24AUG09:12:09:00 85 24.38 0.073 24AUG09:12:07:00 12 85 119 82 2 -2 431 24AUG09:16:59:00 80 24.40 0.054 24AUG09:16:55:41 15 76 273 66 2 180 431 24AUG09:16:59:00 80 24.40 0.054 24AUG09:16:57:09 13 76 275 107 3 143 437 25AUG09:14:37:00 76 24.50 0.133 25AUG09:14:34:19 13 71 209 104 3 47 437 25AUG09:14:37:00 76 24.50 0.133 25AUG09:14:35:21 14 69 211 73 2 19 438 25AUG09:16:49:00 77 24.47 6.933 25AUG09:16:46:01 12 75 280 531 5 3412 438 25AUG09:16:49:00 77 24.47 6.933 25AUG09:16:47:07 13 75 281 1391 7 2717 439 25AUG09:16:03:00 78 24.48 0.018 25AUG09:15:57:23 13 79 263 42 1 11 439 25AUG09:16:03:00 78 24.48 0.018 25AUG09:15:58:31 14 79 264 112 3 16 454 25AUG09:15:32:00 74 24.48 0.455 25AUG09:15:27:03 13 73 236 140 3 20 454 25AUG09:15:32:00 74 24.48 0.455 25AUG09:15:30:09 10 73 240 202 4 21 460 26AUG09:11:16:00 81 24.52 0.032 26AUG09:11:12:52 11 80 89 159 4 87 460 26AUG09:11:16:00 81 24.52 0.032 26AUG09:11:13:57 11 81 90 133 3 76 466 27AUG09:13:53:00 88 24.56 0.018 27AUG09:13:46:22 14 91 150 100 3 13 466 27AUG09:13:53:00 88 24.56 0.018 27AUG09:13:47:28 15 91 152 78 2 -1 468 27AUG09:15:39:00 86 24.55 0.022 27AUG09:15:35:40 14 99 207 71 2 15 468 27AUG09:15:39:00 86 24.55 0.022 27AUG09:15:36:47 12 99 208 77 2 -22 469 27AUG09:14:24:00 88 24.56 0.052 27AUG09:14:20:47 12 95 172 92 3 36 469 27AUG09:14:24:00 88 24.56 0.052 27AUG09:14:21:53 12 95 173 90 2 30 475 27AUG09:17:00:00 85 24.54 2.454 27AUG09:16:55:13 13 102 243 3560 7 1444 475 27AUG09:17:00:00 85 24.54 2.454 27AUG09:16:57:54 13 102 246 457 5 731 481 28AUG09:09:55:00 77 24.65 0.036 28AUG09:09:51:27 13 70 49 115 3 35 481 28AUG09:09:55:00 77 24.65 0.036 28AUG09:09:52:26 13 70 51 86 2 20 482 28AUG09:11:14:00 80 24.64 0.195 28AUG09:11:09:53 15 74 101 144 3 82 482 28AUG09:11:14:00 80 24.64 0.195 28AUG09:11:11:22 13 74 103 85 2 19 486 28AUG09:12:53:00 82 24.62 0.277 28AUG09:12:49:15 15 81 157 129 3 192 486 28AUG09:12:53:00 82 24.62 0.277 28AUG09:12:50:34 3 81 160 110 3 249 489 28AUG09:14:46:00 87 24.57 28.800 28AUG09:14:43:57 11 98 238 8618 7 4132 489 28AUG09:14:46:00 87 24.57 28.800 28AUG09:14:45:12 11 98 241 5404 7 8811 494 28AUG09:16:13:00 86 24.54 0.190 28AUG09:16:09:57 17 105 285 72 2 478 494 28AUG09:16:13:00 86 24.54 0.190 28AUG09:16:11:10 14 105 287 116 3 572 497 31AUG09:10:38:00 73 24.39 0.126 31AUG09:10:33:10 12 70 120 109 3 266 497 31AUG09:10:38:00 73 24.39 0.126 31AUG09:10:34:10 13 70 121 90 2 178 498 31AUG09:11:40:00 79 24.37 0.027 31AUG09:11:38:39 15 77 168 71 2 1 498 31AUG09:11:40:00 79 24.37 0.027 31AUG09:11:39:30 15 77 170 72 2 29 504 31AUG09:14:28:00 84 24.33 0.132 31AUG09:14:24:37 12 85 292 89 2 167 504 31AUG09:14:28:00 84 24.33 0.132 31AUG09:14:28:18 13 85 298 127 3 103 507 31AUG09:13:08:00 82 24.36 0.064 31AUG09:13:04:45 14 85 227 78 2 -23 507 31AUG09:13:08:00 82 24.36 0.064 31AUG09:13:05:49 15 85 230 72 2 -3 
4-19 

PSHED, Before-Repair Measurement RSD, Before-Repair 

Combined 

Packet 

ID 

(unique to 

vehicle) 

Seal DateTime 

Seal 

Temperature 

(F) 

Seal 

Barometric 

Pressure 

(in. Hg) 

Measured 

PSHED 

HC at 15 

Minute 

Soak 

(grams) DateTime 

Speed 

(mph) 

Temperature 

(F) VDF EI23 

EI23 

Bin 

HC 

Method 

A 

(ppmC3) 

512 31AUG09:16:52:00 84 24.31 0.020 31AUG09:16:49:10 15 81 371 94 3 -5 512 31AUG09:16:52:00 84 24.31 0.020 31AUG09:16:50:07 15 81 373 87 2 4 517 01SEP09:10:23:00 82 24.45 0.033 01SEP09:10:20:48 14 77 68 105 3 7 517 01SEP09:10:23:00 82 24.45 0.033 01SEP09:10:21:50 14 77 69 103 3 35 521 31AUG09:15:58:00 85 24.30 0.248 31AUG09:15:56:22 13 86 345 87 2 328 521 31AUG09:15:58:00 85 24.30 0.248 31AUG09:15:57:23 12 86 348 84 2 316 527 01SEP09:11:55:00 87 24.43 2.202 01SEP09:11:51:45 13 86 112 267 4 1103 527 01SEP09:11:55:00 87 24.43 2.202 01SEP09:11:53:14 13 86 116 566 6 770 540 01SEP09:14:58:00 86 24.40 2.098 01SEP09:14:55:43 14 100 196 334 5 933 540 01SEP09:14:58:00 86 24.40 2.098 01SEP09:14:56:44 15 100 199 1050 7 694 542 01SEP09:17:28:00 85 24.39 0.130 01SEP09:17:25:26 13 90 254 51 1 -8 542 01SEP09:17:28:00 85 24.39 0.130 01SEP09:17:26:22 15 90 255 87 2 -10 546 02SEP09:12:10:00 81 24.52 0.622 02SEP09:12:07:05 14 84 121 460 5 1745 546 02SEP09:12:10:00 81 24.52 0.622 02SEP09:12:08:06 14 84 122 526 6 1219 547 02SEP09:10:47:00 78 24.52 6.837 02SEP09:10:45:53 14 76 81 82 2 1161 547 02SEP09:10:47:00 78 24.52 6.837 02SEP09:10:46:59 12 76 83 85 2 1300 550 02SEP09:15:41:00 88 24.46 14.696 02SEP09:15:38:37 12 102 207 2959 7 3192 550 02SEP09:15:41:00 88 24.46 14.696 02SEP09:15:39:44 10 102 209 2839 7 3000 559 03SEP09:13:37:00 82 24.46 4.127 03SEP09:13:34:26 14 92 132 351 5 704 559 03SEP09:13:37:00 82 24.46 4.127 03SEP09:13:35:24 15 92 133 361 5 299 563 03SEP09:16:21:00 82 24.42 0.050 03SEP09:16:17:32 13 89 207 86 2 -8 563 03SEP09:16:21:00 82 24.42 0.050 03SEP09:16:18:56 13 89 209 105 3 -9 568 04SEP09:11:53:00 83 24.43 0.387 04SEP09:11:50:44 12 83 117 4456 7 2848 568 04SEP09:11:53:00 83 24.43 0.387 04SEP09:11:51:10 14 83 118 439 5 . 
4-20 

Figure 4-1 shows a scatter plot of the PSHEDs versus the 12-mph Measurement RSD 

EI23 values for the stratified set. The plot shows that high EI23 values are associated with high 

PSHED values. This relationship gives us some confidence that the EI23 index is capable of 

picking up some aspect of evaporative emissions that the PSHED test measures. 

Figure 4-1. PSHED vs. EI23 for the Stratified Set 

As mentioned earlier, EI23 has an artifact that causes it to be influenced by exhaust HC 

concentrations. Therefore, Figure 4-1 does not provide an accurate picture of the relationship 

between PSHED and that portion of EI23 that detects evaporative emissions. To determine that 

relationship, the EI23 Bin values, which have been created to attempt to minimize the exhaust 

HC artifact, need to be considered. 

Table 4-3 shows the distribution of the EI23 Bin values for the stratified set. 
4-21 

Table 4-3. Distribution of 12-mph Measurement RSDs2

 

in the Stratified Sample3 by EI23 Bin 

EI23 Bin

1 2 3 4 5 6 7 Total 

 

30 105 94 34 36 21 29 349 

Within each EI23 Bin, the number of PSHED values that are regarded as elevated can be 

counted and compared with the total number of PSHED values in the same EI23 Bin. Table 4-4 

shows the number of high PSHEDs within each EI23 Bin for different definitions of high 

PSHED. For example, the second row of Table 4-4 shows the case where the definition of high 

PSHED is 2 g/Qhr. The percent of PSHEDs that are above 2 g/Qhr is determined by dividing the 

high-PSHED count for a particular EI23 Bin by the corresponding total count from Table 4-3. 

For example, according to Table 4-4, 16 PSHEDs were larger than 2 g/Qhr for EI23 Bin = 6, and 

according to Table 4-3, there were 21 PSHEDs tested for EI23 Bin = 6. Therefore, 76% (= 

16/21) of PSHEDs for EI23 Bin = 6 were larger than 2 g/Qhr. 

Other definitions of high PSHED can be used to further describe the relationship between 

PSHED and EI23 Bin. These are shown by other rows in Table 4-4 which use high-PSHED 

definitions of 1, 5, 10, 20, 50, and 100 g/Qhr. 

The table shows that RSDs with high EI23 Bin values are much more likely to have highPSHED values than those with low EI23 Bin values. 

When de-stratifications are performed on all of these distributions using the random 

dataset population values for EI23 Bin, the result will be an estimate of the distribution of 

PSHED values for the Ken Caryl fleet. 

 

2

 Measurements RSDs were taken after the vehicle’s owner agreed to be a participant. These RSDs were taken at a 

nominal speed of 12 mph in the station entrance driveway. 173 vehicles received two 12-mph Measurement RSDs, 

1 vehicle received three 12-mph Measurement RSDs, and 1 vehicle received zero 12-mph Measurement RSDs. 3

 The stratified sample of Ken Caryl vehicles is made up of 175 vehicles that were sampled according to a stratified 

random scheme based on the EI23 Bin obtained from the Selection RSD, which was obtained before the vehicle 

owner was solicited for participation in the evaporative emissions testing. 
4-22 

Table 4-4. Number and Percent of High PSHEDs in the Stratified Set 

for Different High-PSHED Definitions 

 EI23 Bin 

 1 2 3 4 5 6 7 

 High-PSHED Definition (g/Qhr) 

1 3 9 13 18 31 16 28 

10% 9% 14% 53% 86% 76% 97% 

 

2 2 8 9 13 28 16 28 

7% 8% 10% 38% 78% 76% 97% 

 

5 0 6 4 8 15 11 26 

0% 6% 4% 24% 42% 52% 90% 

 

10 

0 3 3 3 10 7 20 

0% 3% 3% 9% 28% 33% 69% 

 

20 0 0 2 1 4 4 9 

0% 0% 2% 3% 11% 19% 31% 

 

50 0 0 0 0 0 1 3 

0% 0% 0% 0% 0% 5% 10% 

 

100 0 0 0 0 0 1 1 

0% 0% 0% 0% 0% 5% 3% 

4.3 The 5830-Vehicle Random Set 

In the process of selecting vehicles for participation in the Ken Caryl study, RSDs were 

obtained in the entrance driveway to the I/M station on thousands of vehicles. These RSDs were 

taken under the same speed conditions and by the same RSD instrument as those that were used 

for the 12-mph Measurement RSDs of the participants. Thus, these Selection RSDs can be used 

to de-stratify the stratified set and provide an estimate of the high-PSHED fraction of the fleet of 

vehicles that entered the Ken Caryl IM station during the study. As mentioned at the beginning 

of this section, the estimate of the high-PSHED fraction of the Ken Caryl fleet is not an unbiased 

measure of the high-PSHED fraction of the Denver-wide fleet because the vehicles entering the 

Ken Caryl IM station are not randomly selected from the Denver fleet. 

The random set includes the stratified set of study participants, as well as vehicles that 

were solicited but refused to participate, and the set of vehicles that were not identified for 

solicitation. To create a clean, random set of vehicles and their Selection RSD values, a number 
4-23 

of filters were applied to the set of all RSDs on all vehicles entering the station. We examined 

all RSDs taken by the IM station driveway RSD unit during this study. For the RSD to be useful, 

the model year of the vehicle was required. We obtained the model year for each RSD beam 

block that had an RSD photo with a legible license plate that could be tied to the Colorado 

registration database. If the model year of the vehicle for which an RSD was available could not 

be obtained, then that RSD measurement was not included in the dataset. Because EI23 and 

EI23 Bin are calculated from each RSD beam block’s 50 10ms data points and the RSDcalculated HC Method B concentration, an RSD invalid flag did not cause an RSD measurement 

to be deleted from the dataset. 

The resulting set of RSDs sometimes had more than one RSD available for a vehicle. 

This was a consequence of some vehicles entering the IM station driveway multiple times. Since 

only a single EI23 Bin value was required to characterize each vehicle, in those cases where 

more than one EI23 Bin value was available for a given vehicle, we chose just a single value. If 

the vehicle was one of the 175 participant vehicles, then we simply used the Selection RSD EI23 

Bin value. For those vehicles that were non-participants, we chose the first RSD EI23 Bin value. 

The resulting dataset contained 5,830 EI23 Bin values on 5,830 vehicles that entered the 

Ken Caryl IM station driveway during the study period. Table 4-5 shows the distribution of the 

vehicles in the random set as a function of model year group and EI23 Bin. The table shows that 

most vehicles are in the newer model year groups and are in the lower EI23 Bins. The table also 

shows that old model year vehicles are not found in the lowest EI23 Bin and the newest vehicles 

are not frequently found in the highest EI23 Bins. The distribution of vehicles in the random 

dataset can be used to de-stratify the PSHED values from the stratified dataset as will be shown 

in the next subsection. 
4-24 

Table 4-5. Distribution of Selection RSDs4

 in the Random Sample5

 EI23 Bin 

 1 2 3 4 5 6 7 Total

 Model Year Group

1961-1970 0 3 4 5 4 2 2 20 

1971-1980 5 14 14 16 7 6 9 71 

1981-1995 227 482 395 150 67 33 30 1384 

1996-2003 761 1425 911 187 19 19 8 3330 

2004-2010 247 456 285 33 4 0 0 1025 

 

Total 1240 2380 1609 391 101 60 49 5830 

4.4 De-Stratifying the Ken Caryl Stratified Sample According to EI23 Bin 

The distribution of vehicles in the Ken Caryl 5830-vehicle random set described in SubSection 4.3 can be used to de-stratify the 175-vehicle stratified set that was described in Section 

4.2. The de-stratification calculations follow the standard method which is briefly described in 

Appendix B. Stratified samples are usually de-stratified with a variable that contains no error. 

However, since EI23 Bin has a large uncertainty, the standard de-stratification calculations using 

it may produce errors in the calculated high-PSHED fraction of the Ken Caryl fleet. The size of 

these errors as well as an estimation of uncertainty and biases in the high-PSHED fraction will be 

discussed in Section 4.5. Nevertheless, in this section, we will get started by performing the 

standard de-stratification calculations with the assumption that EI23 Bin values have no error. 

After the uncertainties and biases discussed in Section 4.5 are quantified, the Ken Caryl highPSHED fraction values will be updated and the size of the uncertainty in the fraction will be 

estimated. 

Calculation of the overall high-PSHED fraction for the Ken Caryl fleet – The 

discussion begins with a calculation of the overall high-PSHED fraction which is calculated by 

de-stratifying the PSHED results of the stratified set (see Table 4-4) according to the observed 

EI23 Bin value distribution in the Ken Caryl random dataset (see Table 4-5). The stratifying 

variable is EI23 Bin. 

 

4

 Counts are for one RSD per vehicle. For the 174 study participants, the RSD used was the Selection RSD. For 

non-participants, the RSD used was the first RSD. These RSDs were taken at a nominal speed of 12 mph in the 

station entrance driveway. 

5

 The random sample of Ken Caryl vehicles includes most vehicles entering the Ken Caryl IM station during the 

study period, including 174 of the vehicles that participated in the evaporative emissions testing. 
4-25 

An example de-stratification calculation for the case where the high-PSHED definition is 

2 g/Qhr is shown in Table 4-6. The first column of the table shows the EI23 Bins. Column 2 

shows the distribution of EI23 Bin values for the stratified sample from Table 4-3. Column 3 

shows the number of PSHEDs that were greater than 2 g/Qhr from the second row of Table 4-4. 

The fourth and fifth columns of the table show the fraction of elevated PSHEDs and their 

standard errors which are calculated from Columns 2 and 3. Column 6 shows the distribution of 

EI23 Bin values in the 5830-vehicle random sample, and Column 7 shows the fraction of 

vehicles in the random sample of the total number of vehicles. The result of the de-stratification 

is given at the bottom of Column 8. It is calculated, as described in Appendix B, by the linear 

combination of the fraction of elevated PSHEDs in the stratified sample (Column 4) and the 

fraction of vehicles in the random sample (Column 7). The result indicates that about 12.7% of 

the 5,830 vehicles entering the Ken Caryl station would have PSHEDs greater than 2 g/Qhr. 

Table 4-6. De-Stratification Calculation for High-PSHED Definition = 2 g/Qhr 

Selection 

RSD 

EI23 Bin 

Stratified Sample Random Sample 

Number 

of 

Selection 

RSDs 

(Nh) 

Number 

of 

Elevated 

PSHEDs 

Fraction 

of 

Elevated 

PSHEDs

(ph) 

Std Error of 

Fraction 

of 

Elevated 

PSHEDs 

(sh/sqrt(Nh))

Number 

of 

Vehicles

Fraction 

of 

Vehicles 

(Wh) 

Estimated 

Fraction 

of 

Elevated 

PSHEDs 

 

1 30 2 0.067 0.046 1240 0.213 

2 105 8 0.076 0.026 2380 0.408 

3 94 9 0.096 0.030 1609 0.276 

4 34 13 0.382 0.083 391 0.067 

5 36 28 0.778 0.069 101 0.017 

6 21 16 0.762 0.093 60 0.010 

7 29 28 0.966 0.034 49 0.008 

 

Overall 349 104 5830 1.000 0.127

The same sort of calculation can be made using other high-PSHED definitions. The 

calculation differs by using different numbers of elevated PSHEDs in Column 3 of Table 4-6 as 

obtained for different rows from Table 4-4, which correspond to different high-PSHED 

definitions. The results of those calculations are shown in Table 4-7. The table shows that when 

the high-PSHED definition is high, the estimated high-PSHED fraction of the Ken Caryl fleet is 

low. When the high-PSHED definition is low, then a much larger fraction of the Ken Caryl fleet 

is expected to have high PSHEDs. Figure 4-2 shows a plot of the values from Table 4-7. 
4-26 

Table 4-7. Results for Ken Caryl EI23 Bin De-Stratifications 

for Various High-PSHED Definitions 

HighPSHED 

Definition 

(g/Qhr) 

Estimated 

Ken Caryl 

Fleet 

HighPSHED 

Fraction 

 

100 0.0008

50 0.0014

20 0.0143

10 0.040

5 0.071

2 0.127

1 0.161

Figure 4-2. Estimated Distribution of PSHEDs in the Ken Caryl Fleet 

0.00

0.05

0.10

0.15

0.20

1 10 100

High PSHED Definition (g/Qhr)

Estimated Fraction of Ken Caryl Fleet 

with High PSHED
4-27 

Calculation of the Ken Caryl high-PSHED fraction by model year group – The 

estimate of the high-PSHED fraction of different model year groups within the Ken Caryl 

random dataset can be estimated in a similar manner. The approach is to first break up the 

distribution of EI23 Bin values into several model year groups and then to de-stratify the 

stratified sample results using the EI23 Bin distributions of each of the model year groups. 

The high-PSHED fractions of the Ken Caryl fleet by model year group are obtained by 

de-stratifying the stratified dataset for the different high-PSHED definitions (shown in Table 4-4) 

using the random dataset EI23 Bin distributions for the five model year groups (shown in Table 

4-5). The results are shown in Table 4-8 and are plotted in Figure 4-3. 

Table 4-8. High-PSHED Fraction of the Ken Caryl Fleet 

for Various High-PSHED Definitions 

by Model Year Group 

Model Year Group 

HighPSHED 

Definition 

(g/Qhr) 1961-1970 1971-1980 1981-1995 1996-2003 2004-2010 

All Model 

Years 

 

100 0.0082 0.0084 0.0019 0.0004 0.0000 0.0008

50 0.0151 0.0171 0.0034 0.0005 0.0000 0.0014

20 0.0839 0.0772 0.0259 0.0099 0.0073 0.0143

10 0.1906 0.1748 0.0650 0.0311 0.0255 0.0404

5 0.3013 0.2717 0.1096 0.0568 0.0465 0.0710

2 0.4545 0.3882 0.1830 0.1066 0.0919 0.1268

1 0.5178 0.4422 0.2239 0.1387 0.1211 0.1608

It is important to understand that this de-stratification approach makes the assumption 

that the relationship between the fraction of PSHED values that are designated high and the EI23 

Bin values is independent of model year group. Whether this assumption is appropriate requires 

additional evaluation. If the assumption proves incorrect, then the relationship between PSHED 

and EI23 Bin values may differ among model year groups, which may affect the high PSHED 

fractions presented above. 

We know the model years of the 175 vehicles in the stratified set; they are given in Table 

4-9. 
4-28 

Figure 4-3. Estimated Distribution of PSHEDs in the Ken Caryl Fleet 

by Model Year Group 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

1 10 100

High-PSHED Definition (g/Qhr)

Estimated Fraction of Ken Caryl Fleet 

with High PSHED

1961-1970

1971-1980

1981-1995

1996-2003

2004-2010

Table 4-9. Distribution of Measurement RSDs in the Stratified Sample 

by EI23 Bin and Model Year Group6

 EI23 Bin 

 1 2 3 4 5 6 7 Total

 Model Year Group

1961-1970 0 1 2 2 1 0 2 8 

1971-1980 0 2 0 4 2 2 4 14 

1981-1995 10 28 34 20 24 15 19 150 

1996-2003 17 63 47 7 9 4 4 151 

2004-2010 3 11 11 1 0 0 0 26 

 

Total 30 105 94 34 36 21 29 349 

 

6

 Model year or model year group was not used to stratify the sample. 
4-29 

4.5 Proposed Methods for Quantification of Uncertainties and Biases in the 

High PSHED Fraction of the Ken Caryl Fleet for EI23 Bin De-Stratification 

The de-stratification results reported in Subsection 4.4 were calculated and presented 

without any regard for the quality of the final value. We already mentioned that the highPSHED fraction of the Ken Caryl fleet is an approximation of the high-PSHED fraction of the 

Denver fleet. In this subsection we discuss an analysis method that could be used in the future to 

quantify uncertainties and biases in the high-PSHED fraction. If we quantified the effects of 

these errors in the Ken Caryl high-PSHED fraction, we could update the values reported in 

Section 4.4 to provide values with less bias and with known uncertainty. These values would 

provide an improvement on the current estimates. 

Table 4-10 shows the sources of uncertainty and bias that we expect in the calculated Ken 

Caryl high-PSHED fraction for de-stratification using EI23 Bin. The top of the table identifies 

seven sources of error. Five of them are uncertainties and two of them are biases. Uncertainties, 

in general, can be reduced by obtaining larger sample sizes. However, biases are more 

troublesome because increasing sample size does not reduce the size of the biases. Therefore, if 

biases are somehow present in the dataset, the calculated high-PSHED fraction of the Ken Caryl 

fleet will also be biased. 

The first source of error (shown in Column 1 of Table 4-10) is the uncertainty of the 

high-PSHED fraction within each EI23 Bin in the stratified sample. The cause of this error is the 

small number of vehicles that are sampled in each of the EI23 Bins. This effect can be 

demonstrated by examining the sample de-stratification in Table 4-6. For EI23 Bin = 1, 30 

PSHED measurements were used. Of these, two had PSHEDs greater than 2 g/Qhr for a highPSHED fraction for the Bin of 0.067. The two elevated PSHEDs could easily have been 1 or 3 

elevated PSHEDs by chance alone. Accordingly, there is uncertainty in the value of 0.067. If 

300 PSHEDs had been obtained for EI23 Bin = 1 instead, then the uncertainty in the fraction of 

PSHEDs that were elevated would be considerably less. This same uncertainty concept applies 

to all seven of the EI23 Bins and contributes to the uncertainty of the high-PSHED fraction that 

is calculated during de-stratification. This source of error is a part of the standard destratification technique and is presented in Appendix B. Table 4-11 shows the size of this source 

of error for the Ken Caryl fleet overall high-PSHED fraction for various high-PSHED definitions. 
4-30 

Table 4-10. Sources of Error in the Ken Caryl High-PSHED Fraction 

 1 2 3 4 5 6 7 

Sources of 

Error: 

Uncertainty 

of the 

HighPSHED 

fraction for 

each EI23 

Bin in the 

Stratified 

Sample 

Vehicle HotSoak 

Emissions 

Variability + 

PSHED 

Measurement 

Error 

Uncertainty 

of PSHED 

at low 

PSHED 

levels due 

to PSHED 

Detection 

Limit 

Uncertainty 

of EI23 Bin 

Assignments 

in Stratified 

Sample 

Uncertainty 

of EI23 Bin 

Assignments 

in Random 

Sample 

Bias at low 

Running 

Loss 

levels due 

to RSD 

Evap 

Detection 

Limit in 

Stratified 

Set 

Bias at 

low 

Running 

Loss 

levels due 

to RSD 

Evap 

Detection 

Limit in 

Random 

Set 

Method of 

quantifying 

the 

underlying 

variable 

error that 

causes 

error in the 

fleet highPSHED 

fraction 

Standard 

error of the 

fraction of 

Elevated 

PSHEDs in 

each EI23 

Bin in the 

stratified 

sample 

Analysis of 

duplicate 

SHED 

measurements 

Examination 

of PSHED 

test HC time 

series 

during the 

baseline 

period 

Duplicate 

EI23 Bins 

from 

Measurement 

RSDs on the 

175-vehicle 

stratified set 

Duplicate 

EI23 Bins 

from 

Measurement 

RSDs on the 

175-vehicle 

stratified set 

Analysis of 

PreTesting 

data 

Analysis of 

PreTesting 

data 

Size of 

error in 

underlying 

variable (1 

sigma) 

See 

examples in 

Column 5 of 

Table 4-6 

Natural log of 

measured 

PSHEDs vary 

with a sigma 

of 0.7 

0.0040 

g/Qhr at low 

PSHED 

values 

[Estimated to 

be 1 bin unit] 

[Estimated to 

be 1 bin unit] 

[Estimated 

EI23 Bin 

detection 

limit of ?] 

[Estimated 

EI23 Bin 

detection 

limit of ?] 

Table 4-11. Contribution of the Size of the Stratified Sample to the Uncertainty in 

the High-PSHED Fraction 

High 

PSHED 

Definition 

(g/Qhr) 

Estimated 

Ken Caryl 

Fleet 

HighPSHED 

Fraction 

Size of 

Error due 

to 

StratifiedSample 

Size (1 

sigma) 

 

100 0.0008 0.0006

50 0.0014 0.0007

20 0.0143 0.0048

10 0.040 0.013

5 0.071 0.017

2 0.127 0.025

1 0.161 0.028
4-31 

The second source of error (shown in Column 2 of Table 4-10) is the uncertainty in the 

measured PSHEDs for the 175 vehicles in the stratified dataset. This uncertainty is made up of 

two parts. The first part is the measurement error of the PSHED technique. We know that the 

measurement error is quite small as indicated by the propane retention and recovery tests 

(PSHED HC recovery was 97.6% and retention was 95.7%) [9] that were used daily in the Ken 

Caryl study. The second contribution to the PSHED uncertainty is much larger and is caused by 

the variability of the hot-soak emissions of each individual vehicle. The Section 3.0 analysis of 

CDPHE’s duplicate SHED measurements quantifies the sum of these two contributions to 

PSHED uncertainty. Those results indicated that the plus and minus one standard deviation 

limits of PSHED uncertainties were at about +200% and -50% of the reported PSHED value. 

The influence of this error in measured PSHED values in the stratified dataset on the highPSHED fraction will be discussed later in this section. 

The PSHED values are also affected by noise in the second-by-second HC concentrations 

during each stratified dataset PSHED test. This uncertainty is presented in the Column 3 of Table 

4-10. During initial testing at the Lipan Street Station, our personnel who operated the PSHED 

indicated that they expected the detection limit of the PSHED method was about 0.1 g/Qhr. This 

estimate was based on the variability of the PSHED HC concentrations on the display of the 

PSHED flame ionization detector before the vehicle was put in the PSHED and for the 

variability in concentrations in the PSHED for quite new vehicles which presumably had zero 

hot-soak emissions. On the other hand, we note that all but one of the reported PSHED values 

for the 175-vehicle stratified set were greater than 0.01 g/Qhr (see Figure 4-1). The noise in 

PSHED values can be determined more precisely by examination of the PSHED HC 

concentration time series for the PSHED measurements of the 175 participants in the Ken Caryl 

study. In the period just before the door to the PSHED was sealed, any drift or variability in 

baseline PSHED HC values will define the PSHED detection limit. Examination of the HC time 

trace for the vehicle with the lowest PSHED value (0.008 g/Qhr) clearly shows increasing HC 

concentrations that noisily rise above a noisy baseline after the vehicle was sealed in the PSHED. 

Thus, the actual detection limit of the PSHED technique is much lower than 0.1 g/Qhr. The 

standard deviations of the PSHED emission rate for the 175 PSHED tests for 60 seconds of 

baseline just before each vehicle entered the PSHED ranged from 0.0015 to 0.0040 g/Qhr. From 

these values, the pooled standard deviation of the baseline noise was found to be 0.0028 g/Qhr. 

This noise contributes twice to the reported PSHED values because the PSHED values are 

calculated as the increase from PSHED seal time to 900 seconds later. Therefore, this source of 

noise causes an uncertainty in all PSHED values with a standard deviation of 0.0040 g/Qhr (= 
4-32 

SQRT(2) * 0.0028 g/Qhr) since the noise in the baseline is uncorrelated with the noise 900 

seconds later. 

Just as the measured PSHED values have uncertainty, the measured EI23 Bin values also 

have uncertainty. The uncertainty in EI23 Bin value affects the EI23 Bin assignments of 

individual RSD measurements for both the stratified sample (shown in Column 4 of Table 4-10) 

and for the 5830-vehicle random sample (shown in Column 5 of Table 4-10). The uncertainty in 

the bin assignments affects the uncertainty in the high-PSHED fraction calculated during this destratification. We expect that the uncertainty in the stratified sample will have a much larger 

influence on the calculated high-PSHED fraction than the uncertainty in the random sample will. 

The characteristic uncertainty in an EI23 Bin value can be determined from the duplicate 12-mph 

measurement RSDs taken on the 175-vehicle stratified dataset. We expect that one standard 

deviation of the error in EI23 Bins is approximately equal to one EI23 Bin unit (see Appendix A). 

To determine the value more precisely across the full range of EI23 Bins, we analyzed the 

duplicate data on EI23 Bin values before the values are rounded. 

A bias that might affect the calculated high-PSHED fraction arises because of the 

detection limit of evaporative emissions as detected by the RSD EI23 evaporative emissions 

index. The bias would affect both the EI23 Bin values in the stratified set and in the random set. 

These sources of error are shown in Table 4-10 in Columns 6 and 7. We know from the Summer 

2008 pre-testing data that under the best conditions, artificial evaporative emissions can be 

detected with about 90% reliability for PSHED-equivalent running loss levels of about 185 

g/Qhr. However, the reliability dropped to about 50% for running loss emission levels of about 

19 g/Qhr. For running loss levels of about 2 g/Qhr, the reliability of RSD EI23 Bin values to 

detect evaporative emissions was only at about 5%. If the large amount of noise in EI23 Bin 

values causes a detection limit problem, then we suspect that there may be a bias in the EI23 

values for both the stratified and random datasets. This bias would tend to elevate the highPSHED fraction calculated during de-stratification. We can analyze the size of the detection 

limit by analyzing the pre-testing data taken on test vehicles in Summer 2009. This pre-testing 

data was taken using simulated evaporative emissions on vehicles with real, that is not simulated, 

exhaust. We believe that determination of the RSD EI23 Bin detection limit should be evaluated 

on vehicles with natural exhaust because variability in the exhaust HC concentrations degrades 

the detection limit of RSD EI23. 

The influences of uncertainties and biases on the high-PSHED fraction using a 

Monte Carlo simulation – The above discussions explain the reasons for sources of error in 

PSHED measurements and EI23 Bin assignments. However, their influence on the calculated 
4-33 

Ken Caryl high-PSHED fraction also has to be determined. These influences are critical to 

defining the quality of the calculated high-PSHED fraction values. Specifically, we suspect that 

the biases and uncertainties cause biases for low values of high-PSHED definitions and 

potentially cause large uncertainties in the high-PSHED fraction at all levels of high-PSHED 

definitions. The relationship between these sources of error and their influence on the highPSHED fraction is complicated. Because of the complex nature of the influences of these 

sources of error, we could use a Monte Carlo simulation approach to estimate the influence on 

the de-stratified value of high-PSHED fraction as a function of the high-PSHED definition. The 

approach is described below. 

Starting with the measured PSHED and EI23 Bin values for the stratified dataset and the 

measured EI23 Bin values for the random dataset, we estimate the true values of these variables 

by subtracting the estimated detection limit biases for the EI23 Bin variables. While this clearly 

can only be done in an approximate manner, the resulting approximate true EI23 Bin values give 

us the opportunity to perturb the uncertainties in PSHED and EI23 Bin and the bias in EI23 Bin 

and then to de-stratify the results using standard techniques. The degree to which the curve of 

high-PSHED fraction versus high-PSHED definition (for example, see Figure 4-2) “jumps 

around” during perturbation will be an indication of the amount of variability that the sources of 

error introduce into the de-stratification. In the Monte Carlo simulation, additional sets of trials 

can be calculated by varying the sizes of the uncertainties and biases in PSHED and EI23 Bin. 

The results would be an indication of the influences of the sources of error on the de-stratified 

value for high-PSHED fraction of the Ken Caryl fleet. 

Overall, we expect that several features of the Ken Caryl stratified data (EI23/PSHED 

pairs) and random data (EI23s) cause uncertainties and biases in the estimated high-PSHED 

fraction of the fleet. The sources of these uncertainties and biases are discussed above and 

include the small number of vehicles (175) in the Ken Caryl study, PSHED measurement error, 

variability in the hot-soak emissions of individual vehicles, noise in the PSHED HC 

concentration time series, uncertainty in RSD EI23 values, and the high evaporative emissions 

detection limit of the EI23 method. The size of the influences of these uncertainties on the 

estimated high-PSHED fraction of the fleet might be able to be determined using a Monte Carlo 

simulation using the Ken Caryl data. 
5-1 

5.0 Estimated High-PSHED Fraction of the Denver On-Road Fleet 

from De-Stratifications Based on Advanced RSD Evaporative 

Emissions Indices 

The objective of the analyses in this report is to determine the high-PSHED fraction of 

the Denver on-road fleet. We want to estimate this fraction for different high-PSHED definitions. 

We also want to determine the high-PSHED fraction for the overall fleet and also for the 

different model year groups that make up the fleet. Finally, it is important to also estimate the 

quality of any high-PSHED value that is calculated so that EPA can make a judgment about the 

usefulness of the reported high-PSHED fractions. The quality of the reported value can be 

determined from estimates of the uncertainties and any biases that might be present in the 

calculated high-PSHED fraction. 

The analysis in Section 4.0 took a first step by calculating the distribution PSHED values 

for the Ken Caryl fleet, which is a subset of the Denver fleet. Since the Ken Caryl study was 

completed, additional data has been collected and new evaporative emissions indices have been 

developed. These variously take into account vehicle speed, exhaust HC, and use multiple RSDs 

to arrive at a single comprehensive index for each vehicle. We need to find a way to use this new 

information to make improved estimates of the high-PSHED fraction of the Denver on-road fleet. 

We begin with a review of the data that are available. 

The Ken Caryl 175-vehicle stratified dataset – This is the dataset that was de-stratified 

in Section 4.0. In general, each of the 175 participating vehicles received one PSHED 

measurement, and two RSDs at each of 12, 34, and 55 mph. The big advantage of this dataset is 

that it can be used to create a link (a relationship) between any RSD evap index and a PSHED 

hot-soak emission rate. It does not matter that the vehicles in the stratified dataset were selected 

using EI23 Bin values. Once the stratified dataset is created, any relationship between any 

variables on the 175 vehicles will represent the relationship between the same variables on any 

set of vehicles – including the Denver fleet. This means that we can use the Ken Caryl stratified 

dataset to determine the relationship between PSHED and any evap index and be confident that 

the same relationship between PSHED and the evap index will hold for Denver fleet. 

PreTesting RSD data from Summer and Fall 2008 and Summer 2009 – CDPHE 

collected a large amount of RSD data on multiple runs of test vehicles with simulated and real 

tailpipe exhaust and simulated evaporative emissions. The advantage of this data is that the 

evaporative emissions rate is known since we produced it artificially by metering propane or 
5-2 

liquid gasoline. Also, up to 20 replicate RSD runs were made on each test condition. These data 

can be used to develop a wide variety of evap indices. 

RSDs of the Denver fleet – Colorado has an on-going program to measure the RSDs of a 

significant portion of the Denver on-road fleet. We have hundreds of thousands of Denver RSDs 

taken between January 2009 and September 2010. Most vehicles have many replicate RSDs, 

which serve to better characterize each vehicle’s emissions, as well as provide emissions 

measurements for each vehicle under a variety of operating conditions such as speed (20 to 

60mph), acceleration, road grade, ambient temperature, and fuel volatility. The RSD dataset 

contains the license plates of the vehicles. The model years of many vehicles can therefore be 

determined via the registration database. The dataset also has the 50 10ms data points determined 

during each RSD beam block. A wide variety of RSD evap indices can be calculated for each 

RSD using these data. 

CDPHE PSHED data from Summer 2010 – The development of new evap indices is 

the primary benefit of the Summer 2010 study toward estimating the high-PSHED fraction of the 

fleet. Vehicles in the Summer 2010 study received on-road RSDs routinely as part of the 

Colorado on-going RSD program. Because we had to select vehicles using their on-road RSDs as 

the only source of information, we developed several new RSD evap indices that were intended 

to work over a wide range of vehicle speeds – not just near 12 mph. The various evap indices 

were used to attempt to identify vehicles with the highest chance of being high evaporative 

emitting vehicles. Vehicle owners were then requested to participate in the study. Participants 

received hot-soak emissions tests by either PSHED at the West Tech Center or by LSHED at 

Aurora. The problem with this set of recruited vehicles (as far as de-stratification to determine 

fleet high-PSHED fraction is concerned) is that the vehicles were not selected using a stratified 

random design. Because the dataset does not represent the Denver fleet and is not a stratified 

sample, it cannot be de-stratified to determine the high-PSHED fraction of the fleet. Specifically, 

from this data we can calculate the fraction of high-PSHEDs for vehicles that have high evap 

indices, but we cannot calculate the fraction of high-PSHEDs for vehicles that have low evap 

indices since low evap index vehicles were not solicited for participation. 
6-1 

6.0 References 

1. D. McClement, “Raw Fuel Leak Survey in I/M Lanes,” prepared for American Petroleum 

Institute and Coordinating Research Council, prepared by Automotive Testing 

Laboratories, Mesa, Arizona, June 10, 1998. 

2. H.M. Haskew, T.F. Liberty, D. McClement, “Fuel Permeation from Automotive 

Systems,” CRC Project No. E-65, prepared for California Air Resources Board and 

Coordinating Research Council, prepared by Harold Haskew & Associates and 

Automotive Testing Laboratories, September 2004. 

3. D. Amlin, R. Carlisle, S. Kishan, R.F. Klausmeier, H. Haskew, “Evaporative Emissions 

Impact of Smog Check,” prepared for California Bureau of Automotive Repair, prepared 

by California Bureau of Automotive Repair, Eastern Research Group, de la Torre 

Klausmeier Consulting, Harold Haskew & Associates, September 15, 2000. 

4. L.C. Landman, “Evaporative Emissions of Gross Liquid Leakers in MOBILE6,” 

M6.EVP.009, U.S. Environmental Protection Agency, EPA420-R-01-024, April 2001. 

5. T.H. DeFries, S. Kishan, “Draft Input for ICR PartB,” submitted to U.S. Environmental 

Protection Agency, EPA-080508b, May 8, 2008. 

6. T.H. DeFries, J.H. Lindner, S. Kishan, C.F. Palacios, “Investigation of Techniques for 

High Evaporative Emissions Vehicle Detection: Denver Summer 2008 Pilot Study at 

Lipan Street Station, CRC E-77-3,” report, Version 2, submitted to U.S. Environmental 

Protection Agency, EPA-091207, December 7, 2009. 

7. T.H. DeFries, S. Kishan, “Investigation of Techniques for High Evaporative Emissions 

Vehicle Detection: November/December 2008 San Antonio Pilot Study,” report, Version 

1, submitted to U.S. Environmental Protection Agency, EPA-101109, November 9, 2010. 

8. J.M. Kemper, “Lab SHED / Portable SHED Comparison,” presented at High Evap Field 

Study Project Meeting, February 4, 2010, Denver, Colorado. Contact Jim Kemper at 

CDPHE for detailed information on testing. 

9. J.H. Lindner, T.H. DeFries, “Evaluation of Portable SHED Characteristics,” Version 1, 

prepared for U.S. Environmental Protection Agency, EPA-090209, February 9, 2009. 

10. T.H. DeFries, H.J. Williamson, “Issues and Intermediate Results Regarding the Use of 

RSD Measurements to Assess the Fraction of the Fleet with High Evaporative 

Emissions,” EPA-091001, October 1, 2009. 
Appendix A 

Definition of EI23 and EI23 Bin 
A-1 

Calculation of RSD Evap Index 23 (EI23) 

a) The RSD Evap Index 23 is calculated from the 50 10ms HC and CO2 concentration-pathlength 

values obtained by the ESP 4000 or 4600 series instrument. 

b) Collect the 50 10ms HC and CO2 data for the beam block. The observations are numbered 0 

through 49. 

c) Delete observations 0, 1, 2, and 3. 

d) Delete all observations that have a flag of asterisk (*). Note: An asterisk flag occurs when the 

plume is truncated and no subsequent 10ms information is available for that plume. This can 

occur, for example, when a tailgater truncates data acquisition of the plume from the vehicle in 

front of it. 

e) Delete all observations that have a flag of hyphen (-). Note: A hyphen flag occurs when data 

acquisition of the plume is temporarily interrupted but more 10ms information is available for 

that plume after the interruption. 

f) Count the number N of observations that remain after these deletions are made. 

f) Perform a linear ordinary least squares regression of HC (ppm-cm) on CO2 of the remaining N 

observations of the form: HC = a + b * CO2, where a and b are constants. 

g) Calculate the residual for each of the N observations in the regression. The residual is defined 

as the observed (or measured) HC minus the predicted (or fitted) HC. 

i) Divide N (the number of remaining observations) by 10. Round the result to the nearest whole 

number and call the result X. 

h) Sort the N residuals from the largest to the smallest. (Note: -6 is smaller than -4) 

j) Find the Xth largest residual. 

k) The value of the Xth largest residual is the value of EI23. 

EI23 Dependences 

Generally, EI23 seems to be linear with the evaporative HC mass emissions flow rate. However, 

EI23 also has a small, but important, dependence on exhaust HC in two ways and a dependence 

on vehicle speed in at least one way. First, EI23 seems to linearly increase with the exhaust HC 

concentration, but the dependence is relatively weak since most of the dependence is removed by 

the method used to calculate EI23. Nevertheless, when exhaust HC is high and evaporative 

emissions are low, the presence of the exhaust HC can fool EI23 into “thinking” that moderately 

high evaporative emissions are present. Second, exhaust HC causes the uncertainty of EI23 to be 
A-2 

larger than when no exhaust HC is present. This means that we are even less certain of the 

amount of evaporative emissions. 

The value of EI23 decreases with vehicle speed. This is a consequence of the increased 

dispersion of the evaporative emissions plume at higher speeds. If the speed dependence of the 

dispersion of evaporative emissions from a vehicle is like the wind speed dependence of the 

dispersion of large plumes from stationary sources, the dependence of the RSD evaporative 

emissions signal might be proportional to the inverse of the vehicle speed. The speeds of vehicles 

during the Selection RSDs in this study were around 12 mph since the RSD instrument was in 

the driveway of the IM station. Accordingly, the dependence of EI23 on speed was not used to 

select vehicles. 

Assignment of a Beam Block to an EI23 Bin 

We have not found a reliable way to correct the dependences of EI23 on exhaust HC 

concentration, but we believe we do need to account for these effects so that the assignment of 

vehicles to EI23 Bins is not systematically biased. So, instead of correcting the values of EI23 

themselves, we bin the values of EI23 using bin definitions that are dependent on the observed 

exhaust HC (as measured by RSD Method B). 

a) For the beam block, obtain the Method B HC concentration (HC_MethodB_ppmC3) as 

determined by the same algorithm used at Lipan Street station in August 2008. This is the 

approximate algorithm that is used on the 4600 series instrument. 

b) Use the following algorithm, the EI23 (ppm-cm), and HC_MethodB (ppmC3) to assign the 

beam block to an EI23 Bin. Because EI23 has a small but important dependence on the exhaust 

HC concentration, we need to take this dependence into account when assigning an EI23 value to 

an EI23 Bin. Otherwise, low evap beam blocks that happen to have high exhaust HC will get 

assigned to the wrong EI23 Bin: 

For each beam block, calculate the lowest and highest expected value of lnln_ei23: 

bottom_lnlnei23 

= ln(ln( 78.536 + 79.005 * 0.00 + 0.014181 * HC_MethodB)) - 2*0.091 

top_lnlnei23 

= ln(ln( 78.536 + 79.005 * 15.0 + 0.014181 * HC_MethodB)) + 2*0.091 

Divide the range of expected lnln_ei23 into 8 bands. The size of the increment in lnlnei23 space 

(assuming 8 increments between the lower and upper ends of the range is given by: 

increment = (top_lnlnei23_range - bottom_lnlnei23_range) / 8 

Calculate the values of ei23 that divide the bins: 

Divider_1 = exp(exp( bottom_lnlnei23 + 1 * increment )) 
A-3 

Divider_2 = exp(exp( bottom_lnlnei23 + 2 * increment )) 

Divider_3 = exp(exp( bottom_lnlnei23 + 3 * increment )) 

Divider_4 = exp(exp( bottom_lnlnei23 + 4 * increment )) 

Divider_5 = exp(exp( bottom_lnlnei23 + 5 * increment )) 

Divider_6 = exp(exp( bottom_lnlnei23 + 6 * increment )) 

Divider_7 = exp(exp( bottom_lnlnei23 + 7 * increment )) 

Now, assign the value of EI23 to the appropriate bin: 

 if -9999999 LT ei23 LE Divider_1 then ei23bin = 1 

else if Divider_1 LT ei23 LE Divider_2 then ei23bin = 2 

else if Divider_2 LT ei23 LE Divider_3 then ei23bin = 3 

else if Divider_3 LT ei23 LE Divider_4 then ei23bin = 4 

else if Divider_4 LT ei23 LE Divider_5 then ei23bin = 5 

else if Divider_5 LT ei23 LE Divider_6 then ei23bin = 6 

else if Divider_6 LT ei23 LE Divider_7 then ei23bin = 7 

 else if Divider_7 LT ei23 then ei23bin = 8 

Because there are so few beam blocks above Divider_7, we combine EI23 Bins 7 and 8: 

if ei23bin = 8 then ei23bin = 7 

Interpretation of EI23 Bins 

Vehicles that fall into the higher numbered bins tend to have higher evaporative emissions than 

those that fall into lower numbered bins. However, the EI23 Bins should not be interpreted as if 

the evaporative emissions are proportional to the bin number. For example, vehicles in Bin 6 do 

not have twice the evaporative emissions of vehicles in Bin 3. 

The width of each EI23 Bin represents approximately one standard deviation of the variability of 

a single EI23 measurement after accounting for the effects of the exhaust HC concentration on 

EI23. Thus, for a single RSD beam block, EI23 Bin differences of two or more bins can be 

thought of as a statistically significant difference. Thus, a beam block that produces a Bin 5 is 

significantly higher than a Bin 3 and lower than a Bin 7. 

Another way to think of the EI23 Bins is derived from their distribution in a sample of vehicles 

from the fleet. When we calculated the EI23 Bins for the 1916 beam blocks collected in August 

2008 at the Lipan Street station that had speeds between 8.7 and 19.5 mph, with known model 

years, and with measured Method B HC concentrations, we found the following distribution: 

 Bin % in Bin Cumulative De-Cumulative 

 7 0.94 % 0.94 % 100 % 

 6 0.99 % 1.93 % 99.06 % 

 5 2.61 % 4.54 % 98.07 % 

 4 8.30 % 12.84 % 95.46 % 
A-4 

 3 25.1 % 37.9 % 87.2 % 

 2 44.1 % 82.0 % 62.1 % 

 1 18.0 % 100 % 18.0 % 

Thus, most (~87%) vehicles in the fleet will likely be in Bins 1, 2, and 3. Vehicles in Bins 4 and 

5 will tend to be marginal evaporative emitters, although because of the large variability in EI23, 

some low evaporative and some high evaporative emitters will also be in Bins 4 and 5. Vehicles 

that fall into Bins 6 and 7 have the largest probability of being high evaporative emitters. For 

example, of the Lipan vehicles that received PSHED measurements, 5 of 8 (63%) Bin 6 vehicles 

and 9 of 11 (82%) Bin 7 vehicles had PSHED evaporative emissions greater than 3 grams / 15 

minutes. On the other hand, it is important to note that 9 of 117 (8%) of the Bin 1, 2, and 3 

vehicles also had PSHED evaporative emissions greater than 3 grams / 15 minutes. 

Desired Solicitation Fraction by EI23 Bin 

The following method was used to randomly solicit approximately 6% of Bin 1, 2, and 3 vehicles, 

30% of Bin 4 vehicles, and 100% of Bin 5, 6, and 7 vehicles. Each of the seven EI23 Bins will 

have a “Desired Solicitation Fraction (DSF)” between 0 and 1 assigned to it. The table below 

gives the values of the DSF used throughout the Ken Caryl project. 

EI23 Bin DSF 

1 0.06 

2 0.06 

3 0.06 

4 0.30 

5 1.00 

6 1.00 

7 1.00 

When an eligible beam block occurred, the program assigned a random number from the uniform 

(0,1) distribution to the beam block. If the random number is less than or equal to the DSF, the 

vehicle associated with the beam block will be designated “Yes” – meaning: Solicit this vehicle. 

Otherwise, it will be designated “No” – meaning: Do not solicit this vehicle. 
Appendix B 

Stratified Sampling 
B-1 

The equations pertaining to stratified sampling discussed in this section are presented by 

Gilbert7. The goal is to determine the number of observations to be taken from each of several 

strata so that the variability of the mean is minimized. The equation for the optimal sample size 

for a given stratum is: 





 h h

L

h=1

h h

h

W

n W

n =

where 

nh = the sample size in stratum number h, 

n = the total sample size for all strata, 

Wh = the fraction of the actual population that falls in stratum h, 

L = the number of strata, and 

σh = the standard deviation of the distribution from which the individual data values in 

stratum h are sampled. 

This equation follows conceptual guidelines. The number of points taken from a stratum 

is directly proportional to the fraction of the population comprised of that stratum (the fraction is 

Wh). Also, the number of points from a stratum is directly proportional to σh, which is a measure 

of the variability in the stratum. 

The estimate of the population mean, xpop , is the weighted mean of the stratum means, 

x h :

The point here is that the strata are not sampled proportionately to their actual 

representation in the population. If a simple arithmetic average of the complete stratified sample 

were calculated, the different strata would be weighted disproportionately to their representation 

in the population, and a biased average would result. The weighting scheme in the calculation of 

xpop accounts for the nature of the sample and produces an unbiased estimate of the population 

mean. The formulation here produces the unbiased estimate of the population mean with the 

 

7

 Gilbert, R.O., Statistical Methods for Environmental Pollution Monitoring, Van Nostrand Reinhold, New York, 

1987. 

x = Wh xh

L

h=1

pop 
B-2 

minimum error variance, given the total sample size, n. The standard error of the mean is the 

square root of its error variance. The standard error of this weighted mean estimate is as follows: 

where fh is the number of data points in stratum h divided by the population size of this 

stratum. 

The factor (1-fh) accounts for the finitude of the population in stratum h. If the sample 

sizes are small compared to the sizes of the strata in the population, this factor can be ignored. 

The result is somewhat conservative (larger) estimates of the standard errors for the stratified 

results. The factor (1-fh) has been ignored (set to 1) in the calculations presented below, so the 

standard errors for the stratified analysis are somewhat conservative. 

In practice the true standard deviations, σh, are not known and are estimated on the basis 

of historical data that exist before the planned stratified sampling effort. The sample standard 

deviation, sh, based on a sample, xh,i, i=1 to m, is: 

where x h is the arithmetic mean. 

When the individual data values are dichotomous, for example, 1 for a vehicle with high 

evap and 0 for a vehicle with low evap, then the standard deviation can also be expressed using 

the probability ph that the vehicle has high evap: 

sh = ph qh

where: 

 ph is the probability that a vehicle in stratum h has high evap, and 

qh is the probability that a vehicle in stratum h has low evap (qh = 1- ph). 

(1-f )

n = W h

h

2

2 h

h

L

h=1

xpop



 

m-1

(x - x )

s =

2

h,i h

m

i=1

h

