# 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, 2010T:\Library\EPA-100925.docx ERG: 0218.04.004.001



### Estimates of the Fraction of the Fl eet 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 Emi ssion Vehicles in the On-Road Fleet of Motor Vehicle Passenger Car and Li ght 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, 2010i 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 Ca ryl 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 Evaporat ive Emissions Indices ........................................... 5-1 6.0 References ............................................................................................................... 6-1

List of Tables

Table 3-1. CDPHE Data Comparing LSHED an d PSHED 15-Minute Hot-Soak Results .......... 3-3 Table 3-2. Estimated Equivalent LSHE D 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 Percen t 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 Cary l Fleet for Various High-PSHED Definitions by Model Year Group .................................................................................................... 4-27 Figure 4-3. Estimated Distri bution of PSHEDs in the Ken Caryl Fleet by Model Year Group..................................................................................................................... 4-28 Table 4-9. Distribution of M easurement RSDs in the Stratified Sample by EI23 Bin and Model Year Group ......................................................................................................... 4-28 Table 4-10. Sources of Error in th e 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............................................................................................................. 4-3 0ii List of Figures

Figure 3-1. PSHED vs LSHED Hot-Soaks wh en 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 Vari ability in PSHED ......................................................................... 3-10 Figure 3-5. Hot-Soak Variability in LSHED ......................................................................... 3-11 Figure 3-6. Hot-Soak Va riability in SHED............................................................................ 3-13 Figure 4-1. PSHED vs. EI23 fo r the Stratified Set ................................................................ 4-20 Figure 4-2. Estimated Dist ribution of PSHEDs in the Ken Caryl Fleet ................................ 4-261-1 

### 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 evap orative emissions of gasoline-powered on-road vehicles. Evaporative emissions occur when volat ile 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 th e MOVES model, EPA has been involved with an ongoing investigation to quantify fleet evapor ative emissions. Ultimately, EPA would like to know the distribution of the mass of evaporative emissions across all vehi cles in the fleet.

The purpose of this report is to develop frac tions 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 cont ract. Innovative methods for measuring and testing real-world evaporative emissions were studied, procedures were developed, and data collected over two summers in Denver, CO w ith 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 evapor ative emissions and briefly describes the
CRC E-77 and E-77-2 studies and th e Lipan Station study. Those studies precede the Ken Caryl study, which provides the da ta 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 hi gh-PSHED fraction of the fleet of vehicles that patr onize the Ken Caryl IM station. First, Section 4.1 describes the vehicle selection and testing at Ke n Caryl. There are two data sets that are used to perform the analysis. The first is the set of 175 vehicles that were particip ants in the study and therefore
received RSD measurements and PSHED measurem ents. This data is described in Section 4.2.
The second is the set of all 5830 vehicles that entered the station driv eway 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 RS D measurements but no PSHED1-2 measurements. The real investigation in this st udy happens in Section 4.4 where we de-stratify the PSHED values of the 175 partic ipants to estimate the distri bution of PSHED values of the 5830 vehicles that entered Ken Caryl during the st udy period. In this sect ion the de-stratifying variable is the EI23. The results give an estima te of the fraction of high-PSHED vehicles for different definitions of high PSH ED. Section 4.5 begins a quantific ation of the different factors
that influence uncertainties a nd biases in the estimate of fr action 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 flee t. Analyses on these datasets using new evaporative emissions indices a nd new index/PSHED relationships can help addresses the issue that the Ken Caryl fleet is probably somewhat different fr om 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 eva porative emissions contro l 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 emi ssions can be emitted. The mass of evaporative emissions from individual vehicles has been qua ntified in previous stud ies, [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 usi ng it in a larger program. Most of the vehicles were specifically recruited as aging enha nced 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 l ooked 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 diur nals. One of the aging en hanced 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 hydrocar bon emissions to be several orde rs of magnitude larger with the implanted leak. This indicates a major impact for inventory, establishing the need to define
the rate of occurrence of “leaker s” in the in-use fleet. Vapor l eaks can have an impact on the inventory. Traditional evaporative emissions te sting, as well as the cu rrent E-77 testing, has shown a range of evaporative emissions, particul arly 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 eva porative systems is now st arting 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 pi ece 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 Califor nia Bureau of Automotiv e Repair “Evaporative2-2 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 i ndicate 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 sm all leaks which can multiply in the inventory.

Using RSD data from the California RSD pilo t study, our initial estimate was that “High Evaps” make up on the order of 1% of the gaso line-fueled vehicles in the fleet.[5] As we proceeded in this study over the past few years th ere has been evidence that this was lower than what is occurring in the real world.

During the summer of 2008 CDPHE provided acce ss 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 st ation driveway were sc reened by an RSD unit.
Exhaust HC emission concentration was used to screen vehicles for evaporative emissions because the California RSD Pilot study indicate d that vehicles with elevated evaporative emissions sometimes appeared to have elevated exhaust HC emissions when measured by RSD. A stratified sample of 1981 m odel year and newer vehicles we re 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 contro l system components, and additional RSD measurements at 12 mph in the IM station driv eway. 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 PSH ED values for real-world light-duty gasoline vehicles.

During the fall of 2008, EPA and CDPHE attemp ted 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 De nver in the summer months.2-3 During the following summer, 2009, CDPHE conti nued 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 sc reened by an RSD unit using an evaporative index known as EI23. A stratified sample of 1961 m odel year and newer vehicles we re 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, an d additional RSD measurements. In the Ken Caryl study, partic ipating vehicles re ceived duplicate RSD measurements at 12, 34, and 55 mph. Overall, the Ken Caryl study rein forced 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 th at patronized the Ken Ca ryl station during the
summer of 2009.3-1 

### 3.0 PSHED and LSHED Hot-S oak Emission Measurement Characteristics

From June through October 2009, the Colora do Department of Public Health and Environment (CDPHE) undertook testing to compar e 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. Th e results of this testin g are shown in Table 3-
1. The table shows the results of hot-soak meas urements 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 compar e hot-soak results from LSHED and PSHED
tests in back-to-back testing. To put the PSHED and LSHE D testing on the same time basis, testing in the PSHED and LSHE D 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 se t up at the Aurora Facility. As in the Lipan I/M Station testing, the PSHED for the Ken Caryl I/M Statio n 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 i onization detector analytical system with a 10 point calibration.

Colorado has a program to test and repair ve hicles identified usi ng its on-going on-road RSD measurement program. Vehicles with elev ated RSD values are required to bring their
vehicles to CDPHE for ta ilpipe testing and visual inspection. A subset of these vehicles were selected for the LSHED/PSHED comparison test. Specifically, vehicles th at initially passed the IM240 exhaust emissions test, and then during the evaluation of the vehicle, CDPHE staff technicians believed that the vehicle could possi bly be a high evaporative emitter because the
vehicle either failed the intrusiv e pressure test, produced a str ong odor or visible leak, or had OBD evaporative codes, were sent to LSHED/PSHED comparison testing.

For the LSHED/PSHED comparison testing, th e following test sequence was used. The vehicle was temperature stabilized overnight wh ile 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 conditio ned for the first SHED test. The conditioning involved driving for five minut es at 55 mph on the dynamometer and then driving the test3-2 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 sequen ce 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 name d CDPHE Test ID: 1P, 1L, 2P, 2L. In most
cases the vehicles received the PSHED and LSHE D 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 repeatab ility 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 te st sequence 1P, 1L, 2P, 2L, the 1P and 2P are nominally duplicates of eac h 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. Alternat ively, the extra driving could cause the 2P hotsoak test to be lower than the 1P hot-soak test because the evaporative em issions 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, or2P 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 a nd 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. Howeve r, it is important to recognize that in every
instance the LSHED measurement follows the PSHE D measurement and, therefore, time effects may influence any comparisons that are made between the PSHED and LSHED results.3-3Table 3-1. CDPHE Data Comparing LSHED and PSHED 15-Minute Hot-Soak ResultsVehicleID Year Make Model VINTestDateTestTime(end) OdometerCDPHETest IDSHEDUsedTestTypeEvapEmissions(g/Qhr)CDPHECommentsHE-2279 1994 Ford Ranger 1FTCR15X6RPA024496/16/09 10:30:01 96761 HB(HB1) Lab Heat Build 28.300 As received6/16/09 13:47:34 967691P Portable HotSoak5.997 As received6/16/09 14:37:17 967781L Lab HotSoak12.257 As received6/16/09 16:00:15 967942P Portable HotSoak1.839 As received6/16/09 16:31:37 967982L Lab HotSoak3.094 As received6/18/09 9:49:39 96798 3L(HB2) Lab Heat Build 0.846 After repair6/18/09 12:39:27 968053P Portable HotSoak0.441Correlation afterI/M240 & preps6/18/09 13:26:51 968123L Lab HotSoak5.899 Hot-soak6/18/09 14:06:30 968204P Portable HotSoak6.783 Correlation6/18/09 14:47:49 968274L Lab HotSoak10.244 Hot-soakHE-2969 1995 Saturn SL2 1G8ZJ5270SZ1707836/23/09 15:35:26 219834 1L(HB1) Lab Heat Build 18.561 As received6/24/09 9:50:02 2198411P Portable HotSoak1.073 As received6/24/09 10:29:12 2198481L Lab HotSoak1.131 As received6/24/09 11:05:21 2198552P Portable HotSoak0.888 As received6/24/09 11:58:39 2198622L Lab HotSoak0.219 As received6/25/09 9:28:49 219882 4L(HB2) Lab Heat Build 13.590 After repair6/25/09 11:26:34 2198693P Portable HotSoak0.163After repair +auxiliarycanister6/25/09 12:05:00 2198753L Lab HotSoak0.242After repair +auxiliarycanister6/25/09 12:51:32 2198814P Portable HotSoak0.280After repair +auxiliarycanister6/25/09 13:42:11 2198884L Lab HotSoak0.242After repair +auxiliarycanister6/26/09 11:35:54 219888 7L(HB3) Lab Heat Build 0.678After repairnew canisterHE-3091 1990 Buick Century 1G4AH54NXL64294516/30/09 9:15:52 245472 1L(HB1) Lab Heat Build 29.516 As received6/30/09 12:40:54 2457071P Portable HotSoak3.502 As received6/30/09 13:14:48 2457151L Lab HotSoak7.721 As received6/30/09 13:50:42 2457232P Portable HotSoak2.611 As received6/30/09 14:26:06 2457302L Lab HotSoak6.919 As received7/1/09 13:48:272454731 4L(HB2) Lab Heat Build 3.190 After repair3-4VehicleID Year Make Model VINTestDateTestTime(end) OdometerCDPHETest IDSHEDUsedTestTypeEvapEmissions(g)CDPHEComments720PHW(R)1996 Honda Passport 4S6CM58V2T44082417/15/09 9:56:27 181810 HB1 Lab Heat Build 40.319 As received7/15/09 10:52:19 1818171P Portable HotSoak3.148 As received7/15/09 11:26:08 1818261L Lab HotSoak3.982 As received7/15/09 13:46:53 1818312P Portable HotSoak4.472 As received7/15/09 14:20:46 1818442L Lab HotSoak4.632 As receivedHE-3394 1993 Oldsmobile Cutlass 1G3AG54NXP63469867/21/09 9:29:04 95439 HB1 Lab Heat Build 32.159 As received7/21/09 10:22:01 954451P Portable HotSoak1.459 As received7/21/09 10:55:35 954501L Lab HotSoak3.713 As received7/21/09 11:26:58 954542P Portable HotSoak3.258 As received7/21/09 12:01:19 954622L Lab HotSoak5.736 As received7/23/09 9:39:54 95476 HB2 Lab Heat Build 0.210 After repairHE-3790 1995 Ford Ranger 1FTDR15X3SPA007488/18/09 9:23:45 163650 HB1 Lab Heat Build 10.090 As received8/18/09 9:59:16 1636571P Portable HotSoak2.999 As received8/18/09 10:31:09 1636641L Lab HotSoak5.429 As received8/18/09 11:01:23 1636712P Portable HotSoak6.979 As received8/18/09 11:36:37 1636782L Lab HotSoak10.265 As received8/20/09 9:43:52 163682 HB2 Lab Heat Build 0.475 After repairHE-3555 1996 SAAB 900SE YS3DF58N8T20382048/26/09 9:14:21 193145 HB1 Lab Heat Build 93.708 As received8/26/09 13:56:16 1931491P Portable HotSoak0.000 As received8/26/09 14:30:24 1931561L Lab HotSoak4.659 As received8/26/09 15:01:12 1931612P Portable HotSoak9.945 As received8/26/09 15:37:01 1931672L Lab HotSoak55.507 As received8/31/09 14:32:33 193145 HB2 Lab Heat Build 1.406 After repair631-SWU 1996 Toyota Camry JT2BG12KXT03742519/1/09 11:01:26 248058 HB1 Lab Heat Build 7.278 As received9/1/09 13:49:59 2480651P Portable HotSoak0.000 As received9/1/09 14:27:17 2480741L Lab HotSoak0.041 As received9/1/09 15:03:00 2480802P Portable HotSoak0.069 As received9/1/09 15:36:51 2480902L Lab HotSoak0.041 As received9/10/09 9:59:35 248095 HB2 Lab Heat Build 0.039 Junker CorrHE-3863 1994 Ford Explorer 1FMDU34X5RUE117969/22/09 9:51:56 284456 HB1 Lab Heat Build 0.978 As received9/22/09 10:39:24 2844621P Portable HotSoak0.369 As received9/22/09 11:01:25 2844691L Lab HotSoak0.614 As received9/22/09 14:16:18 2844772P Portable HotSoak0.413 As received9/22/09 14:43:11 2844842L Lab HotSoak1.033 As received9/24/09 10:12:570 HB2 Lab Heat Build 1.017 After repairHE-3649 1987 Ford Thunderbird 1FABP64W6HH2244779/23/09 9:40:27 118616 HB1 Lab Heat Build 90.936 As received9/23/09 10:29:36 1186221P Portable HotSoak0.926 As received9/23/09 11:05:37 1186291L Lab HotSoak1.348 As received3-5VehicleID Year Make Model VINTestDateTestTime(end) OdometerCDPHETest IDSHEDUsedTestTypeEvapEmissions(g)CDPHEComments9/23/09 13:29:12 1186342P Portable HotSoak0.733 As received9/23/09 14:05:29 1186412L Lab HotSoak1.621 As receivedXXX3400 1992 Dodge Caravan 2P4GH2539NR6434007/14/09 10:28:00 209454 HB1 Lab Heat Build 40.261 As received7/14/09 13:47:44 2094621P Portable HotSoak26.671 As received7/14/09 14:25:34 2094681L Lab HotSoak12.655 As received7/14/09 15:11:56 2094752P Portable HotSoak10.721 As received7/14/09 15:46:01 2094842L Lab HotSoak3.651 As receivedHE-3358 1994 Mercury Villager 4M2DV11W7RDJ402668/12/09 8:39:45 217852 HB1 Lab Heat Build 74.551 As received8/13/09 9:29:56 2178591P Portable HotSoak3.814 As received8/13/09 9:53:06 2178661L Lab HotSoak5.900 As received8/13/09 10:24:50 2178722P Portable HotSoak3.122 As received8/13/09 11:00:02 2178792L Lab HotSoak10.398 As received8/17/09 9:54:40 218292 HB2 Lab Heat Build 0.213 After repairHE-3491 1994 Cadillac Seville 1G6KS52Y8RU8058998/25/09 11:45:56 143333 HB1 Lab Heat Build 19.588 As received8/25/09 14:21:11 1447151P Portable HotSoak9.469 As received8/25/09 14:55:05 1447201L Lab HotSoak29.125 As received8/26/09 11:25:01 1447262P Portable HotSoak10.450 As received8/26/09 12:03:27 1447312L Lab HotSoak18.244 As received9/2/09 9:59:28 144740 HB2 Lab Heat Build 3.091 After repairHE-4006 1996 Ford Ranger 1FTCR14U9TPA381969/16/09 9:22:26 96363 HB1 Lab Heat Build 27.520 As received9/16/09 10:15:05 963701P Portable HotSoak0.419 As received9/16/09 10:40:24 963761L Lab HotSoak0.396 As received9/16/09 13:34:31 963822P Portable HotSoak0.210 As received9/16/09 13:59:37 963872L Lab HotSoak0.306 As received5702 1992 Oldsmobile Eighty Eight 1G3HN53L5NH3157026/8/09 15:42:33 168568 1L(HB1) Lab Heat Build 0.144As received -retest6/11/09 8:10:01 1685941P Portable HotSoak0.140 As received6/11/09 10:02:42 1686022L(1L) Lab HotSoak0.172 As received3-6 Figure 3-1 shows a plot of the PSHED vers us 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 magnit ude. In addition, the scatter of the data points in the plot near the parity line indicate that the variability of the PSH ED measurements on these vehicles appears to be better de scribed by a constant relative variability than by a constant absolute variability. The plot indicates that there is a tendenc y 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 a nd LSHED tests or whether the difference is produced by a time effect, we pair the LSHED an d PSHED data values in a different way. For this analysis, we pair the valu es for each vehicle such that the LSHED is tested before the PSHED measurement. For example, the 2Ls are pa ired with the 1Ps. When this new repairing of the data is plotted, the result is Figure 3-2. Th is plot uses the same scales as Figure 3-1. The3-7 plot shows that just like Figure 3-1, there is a tendency of the PSH EDs to have lower values than the LSHEDs even though, in this case, the LS HED 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 LSHE D 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 re gression line through this data is given by:

PSHED = 0.7871 * LSHED 0.8207

where: PSHED and LSHED ar e 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 pr ocedures 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 1.393-9 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 meas urements tended to produce hot-soak values
somewhat lower than LSHED measurements as s hown in Figure 3-3. However, that figure also showed a large scatter of the individual data point s. In this discussion, we examine the degree of scatter produced by replicat e SHED measurements.

Figures 3-4 and 3-5 show plots of dup licate 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 exam ple, 2P. In all cases, the first and second SHED measurements are separate d 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 scat ter 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 relative ly symmetrical which indicates th at the order of testing for the
dataset as a whole did not influence the measured va lue. This is consiste nt with the same finding from the analysis of the comparison of LS HED and PSHED values discussed earlier.

The second important feature of Figures 3-4 a nd 3-5 is that scatter of points about the parity line for the two plots is qu ite 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-35 55. 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 emissi ons as additional SHED tests were performed. The time dependence of the hot-soak emissions for this vehicle was much stronger than for any3-12 of the other vehicles in the data set 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 poin ts 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 valu e of the hot-soak measurements. That is, the variability is a per cent of the hot-soak measurement value.

We believe that the similar hot-soak variab ilities in the PSHED and in the LSHED are
consistent with the notion that the variab ilities are dominated by the hot-soak emission variability of the vehicles themselves. The m easurement 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 th e PSHED and LSHED from this dataset, we believe it is appropriate to combin e the data from Figures 3-4 and 3-5 into one dataset which are plotted in Figure 3-6. Th is figure can be viewed primarily as the
demonstration of the hot-soak vari ability of the vehicles in this dataset regardless of which
SHED the measurements were made in.

The data from Vehicle HE-3555 greatly influe nce 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 re presentative 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 devi ation 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 st andard 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 th e 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 betw een 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-s oak 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 measurem ents for this dataset as a whole was independent of the order of the measurem ents. 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 variabili ty 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 fr action 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 th e emissions variabilities of vehicles is large, which has been shown here, then a single hot-soa k measurement on each vehi cle in a sample of
the fleet would indicate that a la rger fraction of the ve hicles 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 th e summer of 2009. This estimate can be made by de-stratifying the PSHED meas urements according to the di stribution of EI23 Bin values observed in the sample of Denver vehicles that patronized the Ken Cary l 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 statio ns. Accordingly, the vehicles th at receive I/M inspections are likely to have higher emissions (exhaust and/or evap 1 ) than vehicles in the fleet as a whole. The RSD monitors are typically locat ed 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 evap orative emissions plume. Other than because of vehicle age and differences in general emission control technologies, ther e has been no evidence to suggest that high exhaust em ission 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 approxi mate fleet high-PSHED fraction.

The subsections below describe the RSD and PSHED testing performe d 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 betweenthe EI23 evaporative emissions index and hotsoak emissions as measured by the PSHED test. Th is 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 CO 2 as a tracer for exhaust emissions, RSD can measure exha ust 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 pro portional to vehicle speed. Th erefore, 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 em issions at higher speeds.4-2 5830-vehicle random set, which is made up of mo st vehicles that en tered the I/M station driveway during the study period. Standard de-stratification techni ques 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-stratifi cation technique is also applie d 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 Cary l data on the uncertainty of the calculated highPSHED fraction. An estimate of th e uncertainty is critical to understanding the quality of the calculated high-PSHED fraction resu lts. 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 indi vidual 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 desira ble. Accordingly, an RSD evaporative emissions index, known as EI23, was developed for the Ken Caryl study to provide a means of st ratifying the vehicles that enter the I/M station driveway.

The EI23 RSD evaporative emissions index wa s developed from the pretesting data that
was collected on test vehicles in the summer of 2 008. All of that pretesting data was collected at low speeds near 12 mph (range = 7 to 17 mph). Acco rdingly, EI23 is applicable only to vehicles that receive RSDs at speeds near 12 mph. Analysis of the EI23 i ndex 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 runni ng loss emissions and high values indicating high running loss emissions. ESP developed the code to calcula te EI23 and EI23 Bin and embedded the code in the operating system of the entr ance driveway RSD unit at the Ken Caryl IM station. This4-3 provided a near instantaneous indication of the EI 23 Bin value as vehicles entered the driveway
of the station.

The evaporative emission study at the Ken Caryl IM station t ook place from June 29 through September 4, 2009. Vehicles entering the st ation drove past the driveway RSD unit and received a first RSD measurement, which was call ed the Selection RSD. Vehicles were assigned
to an EI23 Bin based on the Selection RSD. With in each bin, vehicles were randomly selected to be solicited for participation in the study. To ach ieve stratification, a high er 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 vehicl es were approached for participation in the study. The vehicles of owners who agreed to participate received six additional RSDmeasurements, 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 Measur ement RSDs in the driveway at 12 mph because EI23 is currently meaningful only for RSD beam bl ocks 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 Measurem ent RSDs, the vehicle received the PSHED
test in which the hot-soak emissions of the ve hicles 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 whic h 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 vehi cles that were partic ipants in the study. These vehicles make up the stratified set. Tabl e 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 vehi cle (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-mp h Measurement RSDs. Table 4-2 shows the
EI23 Bin values for each of these 349 Measur ement 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-proprietar y methods for converting the 50 10ms concentration-pathlength measurements obtaine d 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 inst ruments. Since the methods are proprietary, we do not know exactly how they work. Howeve r, in general we believe that Method B calculates the slope of regression lines through HC, CO, or NO concentration-pathlength values against CO 2 concentration-pathlength values for each beam block. The slopes of these three regressions are then used with the balanced com bustion equation to arrive at estimates of exhaust concentrations. In contrast, in general we be lieve that Method A calcu lates the slope of the regression line that connects the centroid of the HC, CO, or NO concentration-pathlength values against CO 2 concentration-pathlength values with the origin. Method A slopes are also used with chemical stoichiometry to calculate estimated exha ust 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 evapora tive emissions, we chose to use Method B as a clearer measure of the quantity that produces th e interference. Accordingly, for these studies,
ESP added code to the 4000 and 4600 series inst ruments 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 we re made. Accordingly, these PSH ED measurements were taken
with the vehicle under the same nominal conditio n as for the Measurement RSDs. Examination
of the PSHED values in the table show that the ho t-soak values span three orders of magnitude.

The stratified set, which is ma de up of data from the 175 participants in the Ken Caryl study, can be used to establish the relationshi p between RSD measurements and evaporative emissions. In this analysis, the relationship sp ecifically examined will be the relationship between the 12-mph Measurement RSD EI23 Bi n values and the PSHED 15-minute hot-soak values, which have units of gr ams 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 th e statistical effect know n 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, we4-5 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 a nd 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 rela tionship between these variables.4-6Table 4-1. Descriptions ofthe 175 Participating VehiclesCombinedPacketID(unique tovehicle) VINModelYear Make ModelFuel Metering Type(Carburetted or FuelInjected)VECI EngineFamily VECI Evap FamilyTrans.TypeFuelGauge(1.000.750.500.250.00)OdometerReading(miles)OdometerResolution7 1998 Ford Explorer F WFMXT04.0HAA WFMXE0120BAE A 0.25 150,752 69 1992 Saturn SL F N4G1.9V5JPH5 NAO-4B M 0.75 183,866 617 1993 Mercury Grand Marquis F PFM4.6V5FDF2 F3AE-9C485-J A 0 123,479 618 2003 VW Passat F 3ADXV01.8342 3ADXR0140232 A 0.75 90,785 620 1990 Nissan Pathfinder F LNS3.0T5FCF2 FI6-3 . 0 131,594 621 1991 Jeep Wrangler F MCR2.5T5FEL3 MT-2.5M-1P M 0.5 186,399 625 2000 Audi A6 F YADXV02.8334 YADXR0140233 A 0.75 170,206 627 1992 Jeep Wrangler F NCR2.5T5FEL4NT-2.5J-1Sor NT-2.5M-1P M 0.25 123,991 628 1989 Dodge/Mitsubishi Ram/Raider F MMT3.0T5FB16 1 M 0.25 270,017 634 1995 Ford Ranger F SFM2.318GFEA 2.3L-SFM1045 M 1 250,342 639 1994 Jeep Grand Cherokee F RCR5.288GAEA RCR1058AYPOA A 0.25 180,985 642 1987 Dodge Power Ram C HCR5.2T2HEM8 HCRTX; HCRTY M . 22,091 544 1989 Chevrolet Caprice C KIG5.7V5NEA4 KB0-1P A 0.5 23,960 545 1997 Ford F150 F VFM5.458GFEK VFM1160AYMFD A 1 81,284 646 1990 Ford Taurus F LFM3.0V5FXG5 F0AE-9C485UK A 0.25 61,972 548 1994 Chevrolet Camaro F R1G3.4V8GAEA R1G1058AYMOA M 0.25 127,080 649 1994 Mazda 929 F RTK3.0VJGFEA RTK1078BYM03 A 0.25 172,864 653 1997 Pontiac Grand Am F VGM2.4VJGKEK VGM1095AYMEA A 0 151,915 657 1996 Ford Explorer XLT F TFM4.028GKFK 4.0L-TFM1120 M 0.25 146,781 662 2003 Toyota Tundra F 3TYXTG4.7HBY 3TYXR0190A30 A 1 91,529 664 1976 Oldsmobile Omega C . . A 1 27,334 568 2001 Jeep Wrangler F 1CRXT04.0200 1CRXE0101GCS A 0.5 117,117 669 2002 Land Rover Freelander F 2LRXTO2.5001 2LRXR0124002 A 0.75 51,650 675.096 1986 Toyota MR2 F GTY1.6V5FBB9 EV-E M 0.25 164,392 677 1987 Saab 900 Turbo F HSA2.0V5FTBX . M 0.75 167,729 679 1988 Chevrolet 1500 Pickup C J3G5.7T5TYA2 JFO-3C A 0.25 108,501 682 1993 Cadillac El Dorado F P1G4.6V8X8B9 PCO-1A A 0.75 113,203 685 1996 Dodge Ram 1500 F TCR36OH8G1EK TCR1073AYPOB A 0.75 215,632 691 1977 Chevrolet Blazer C . . A 0.75 48,390 592 1997 Ford F-150 F VFM1160AYMFD VFM5.458GFEK A 0.75 97,618 693 2003 Dodge Durango F 3CRXT04.75BO 3CRXE0101GDH A 0.75 136,907 695 1998 Nissan Quest F WNSXT03.0A4A WNSXE0057MAA A 0.5 203,363 6100 1984 Chevrolet Suburban C EGM05.7ABB8 . A 0.25 9,014 5103.122 1988 Toyota Camry LE F JTY2.0V5FBB8 EV-E A 0.5 192,553 6108 1995 Cadillac SLS F S1G4.6VJGFEA S1G1089AYPOA A 0.5 117,743 6109 1995 Toyota Avalon F STY3.0VJGFEK 200201MZ-FE A 1 216,382 6110 2005 Toyota Avalon F 5TYXV03.5PEA 5TYXR0130A11 . 0.5 33,990 64-7CombinedPacketID(unique tovehicle) VINModelYear Make ModelFuel Metering Type(Carburetted or FuelInjected)VECI EngineFamily VECI Evap FamilyTrans.TypeFuelGauge(1.000.750.500.250.00)OdometerReading(miles)OdometerResolution112 1986 Ford LTD F GFM3.8V5HHF9 E6AE9C485AER A 0.25 22,403 5120 1989 Chevrolet Camaro F M1G5.7VANTA8 MBO-1A A 0.25 62,617 5121 1994 Isuzu Amigo F RSZ2.678GAFA RSZ1046BYM00 M 1 151,681 6127 1993 Jeep Cherokee F PCR4.0T5FGA5 PTATR or PTA A 0.5 119,955 6130 1995 Ford Explorer F SFM4.028GFEA 4.0L-SFM1120 M 0.75 161,106 6131 1997 Subaru Outback F VFJ2.5VJGKEK VFJ1030BYMA3 M 0.75 106,677 6134 2003 Chevrolet Impala F 3GMXV03.8044 3GMXR0124919 A 0 61,112 5135 2001 Chrysler Sebring F 1DSXV03.0GNG 1DSXR0165A1F M 0.75 72,977 6138 2002 Suzuki Vitara F 2SKXT1.59LC1 2SKXE0089S56 M 1 89,695 6139 2002 Pontiac Sunfire F . . A 0.5 50,442 6156 1999 Ford Explorer F 4.0L.XFMXE01 XFMXT04.02GF A 0.5 141,863 6157 1982 Ford F-150 Explorer C CFM5.0T2AAF4 2DP A 0 80,147 5158 2005 Chevrolet Cobalt F 5GMXV02.2026 5GMXR0124919 A 0.5 50,434 6159 1990 Mazda MX-6 F LTK2.2V5FFG3 J M 0 190,264 6161 2001 Saturn SC-1 F 1GMXV01.9002 1GMXR0080902 M 0.5 94,138 6162.332 1995 Jeep Wrangler F SCR2.578GAEASCR1058AYMONand SCR1058AYPON M 0.75 110,145 6163 2002 Volvo S60 F B15244T 2VVXR0133AAA A 1 58,198 6168 1994 Saturn SL2 F R4G1.9VHGBEA R4G1035AGPOC A 0 126,153 6177 2004 Dodge Ram 1500 F 4CRXT05.75J0 4CRXR0218GDH M 1 51,576 6178 1969 VW CP - Dunebuggy C . . M . 71,185 5184 1998 VW Jetta F . . M 0 53,494 6185 2002 Toyota Echo F 2TYXV01.5FFA 2TYXR0075AK1 A 0.5 68,028 6186 1995 Ford F-150 F SFM1045AYMOA SFM5.888GBJA A 0.5 147,811 6188 2005 Lexus GX-470 F 5TYXT04.7PKX 5TYXR0190P30 A 0.75 35,753 .189 1993 GMC Safari . P3G4.3T5TAA6 PFO-3A A 0.75 212,226 6190 2001 Ford Expedition F 1FMXT05.4RF8 1FMXE0155BBGA 0.5 164,049 6192 1991 Infiniti Q45 F MNS4.5V5FAA9 F18-1 A 0.5 202,128 6193 2003 Ford Focus F 3FMXV02.0VH1 3FMXR0080BBE M 0.5 94,337 .194 1994 Geo Prizm F RNT1.6VHGAFA RNT1047DYM00 M 1 162,187 6197 2000 Honda Accord F YHNXV03.0FA3 YHNXR0130AAA A 0 115,971 6203.205 1991 Jeep Wrangler F MCR4.0T5FED1 MT-4.0M-1P M 0.25 108,432 6212 1977 Ford Econoline C . . A 0.25 9,900 5213 1979 Dodge D-150 C 9TD-360-2CP 9K-7 A 0.25 91,164 5214 2000 Chevrolet S-10 F YGMXT04.3182 YGMXE0095904 A 1 76,888 .217 1994 Ford Ranger F RFM4.077GAEA RFM1045AYPOA M 1 70,206 6218 1990 VW Cabriolet F LVW1.8V5FWD7 LWC2 M 0.25 159,869 6219 1991 Lexus LS400 F MTY4.0V5FBB5 EV-SE A 0 96,587 6220 1994 Toyota Tacoma F RTY3.087GAFA RTY1047DYM00 M 1 78,987 6221 1994 Ford Bronco F RFM5.888GBJA RFM1045AYMOA A 0.5 163,745 6232 1992 Ford Explorer F NFM4.0T5FYH8 F2AE-9C485-H A 0.25 45,600 54-8CombinedPacketID(unique tovehicle) VINModelYear Make ModelFuel Metering Type(Carburetted or FuelInjected)VECI EngineFamily VECI Evap FamilyTrans.TypeFuelGauge(1.000.750.500.250.00)OdometerReading(miles)OdometerResolution238 1993 Jeep Wrangler F PCR4.0T5FGA5 PTAPR OR PTA M 0.5 48,277 6246 2003 Chevrolet Suburban F 3GMXT05.3175 3GMXE0133916 A 0.5 126,891 6249 1997 BMW 328i F VBM2.8VJGKEK VBM1156AYPEO A 0.75 115,006 6252 2002 Toyota Tacoma F 2TYXT03.4FFP 2TYXR0135AKO A 0.5 100,762 6255 2004 Lexus RX330 F 4TYXT03.3PEM 4TYXR0165P21 A 0.75 51,333 6261 1994 GMC Suburban F R3G5.785GAEB R3G1085AYMOA A 0.5 163,611 6262 1984 Nissan Datsun C ENS2.4T2AAFO 4CAB-3 M 0.25 116,994 6263 1997 Honda Accord F VHN2.2VJGKFK VHN1090AYMEA M 0 91,510 6266 2002 Chevrolet Tahoe F 2GMXT05.3188 2GMXE0133915 A 1 145,201 6271 1997 GMC Sierra F VGM5.75PGFEK VGM1098AYMBA A 0.25 132,808 6278 2002 Dodge Dakota F 2CRXT04.72D1 2CRXE0101GCS A 0.5 153,682 6285 1998 Honda CRV F WHNXT02.0UF1 WHNXE0080AAB A 0.5 166,819 6290 2005 Acura TSX F 5HNXV02.4KBP 5HNXR0140BBA A 0.5 70,166 6294 1985 Ford F-150 C FFM5.8T2HGG1 5FAE-9C4G5 A 0.5 93,586 5295 1999 Mazda 626 F XTKXV02.0VBA XTKXR0125BFA A 0.75 108,547 6301 1981 GMC Sierra K1500 C 18L4HANA 1D4D-8 A 0.25 81,898 5302 1989 Dodge Pickup F KCR3.9T5HFM9 KCRTD M 0.5 184,015 6305 1997 Dodge Ram 1500 F VCR5.968GFEK VCR1073AYPBB A 1 147,531 6306 1967 Chevrolet Chevelle C . . A 0.5 44,500 5309 2001 Nissan Sentra F 1NSXV01.8D1A 1NSXR0085RCA A 1 101,164 6311 2002 VW Passat F 2ADXV02.8334 2ADXR0140233 A 0.25 64,049 .316 2003 Hyundai Accent F 3HYXV01.6BLS 3HYXR0105PEA M 0.5 104,439 6320 2004 Chevrolet Cavalier F 4GMXV02.2025 4GMXR0124919 A 0.5 62,915 .321 1997 Chevrolet S-10 F VGM2.218G1EK VGM1095AYMEA . 0.25 130,259 6325 2000 Chevrolet Prizm F YNTXV01.8FFA YNTXR0115AK1 M 0.5 122,120 6327 1981 BMW 320i F . . M 0 342,673 6329 1969 Jeep Commando C . . M 0.75 88,099 5331 1994 Chevrolet Blazer F R3G4.329GFEA R3G1058AYMON A 0 195,091 6337 2001 Nissan Xterra F 1NSXT03.3C5A 1NSXR0120RCA M 0 100,174 6339 1994 Toyota 4Runner F RTY3.087GAFA RTY1047DYM00 M 0 213,353 6343 1991 VW Golf F MVW1.8V5FWB6 VAP M 0.25 251,793 6348.432 1990 Dodge Ram Charger C . . A 0.25 217,996 6350 2005 Subaru Outback F 5FJXX02.5MJS 5FJXR01253CJ A 1 40,203 6352 2001 Chevrolet Astro F 1GMXT04.3181 1GMXE0212924 A 0.75 108,973 6354 1989 Jeep Cherokee F KAM242T5LND8 KT-242H-1S A 1 171,562 6355 1993 Toyota Corolla F PTY1.8V5FFD9 EV-E M 0.25 164,799 6357 1997 Nissan Pathfinder F VNS3.328G2EK VNS1110AYMEA A 1 166,353 6361 1995 VW Golf F SVW2.0V8GFEA SVW1045BYPOV A 1 90,050 .362 2001 Audi TT Quatro F 1ADXV01.8336 1ADXR0130242 M 0.5 75,014 .364 1995 Buick Roadmaster F S1G5.7V8GAEA S1G1058AYPOF A 1 58,162 6365 2002 Ford Mustang F 2FMXV03.8VFB 2FMXR0105BAE A 0 95,551 64-9CombinedPacketID(unique tovehicle) VINModelYear Make ModelFuel Metering Type(Carburetted or FuelInjected)VECI EngineFamily VECI Evap FamilyTrans.TypeFuelGauge(1.000.750.500.250.00)OdometerReading(miles)OdometerResolution367 1993 Chevrolet Van 20 F P3G5.7T5TYA8 PF0-3C A 0.75 193,004 6370 2003 Land Rover Discovery F 3LRXT04.6001 3LRXE0124001 A 0.5 97,938 .371 1996 Toyota Corolla F TTY1.8VJGFFK TTY1047DYMAO A 1 115,405 6375 2005 Ford Focus F 5FMXV02.31D4 5FMXR0120GAK A 0.25 74,792 6379 1993 Subaru Legacy F . . A 0 146,307 6381 1998 Jeep Cherokee F WCRXT0242220 WCRXE0101G2S A 0.25 119,882 6383 1994 Chevrolet S-10 C R3G4.375GAEA R3G1108WYMOC M 0.5 155,277 6385 2001 Dodge Dakota F 1CRXT04.72D2 1CRXE0101GCH A 0.75 114,101 6387 2000 Jeep Grand Cherokee F . . A 0.5 113,318 6391 1999 Isuzu Rodeo F XSZXT03.52EK XSZXT0095ME0 A 1 146,398 6392 1999 Honda Accord F XHNXV03.0FF1 XHNXR0130AAA A 0.25 102,999 6393 2001 Toyota Solara F . . A 0.25 85,136 .395 1997 Chevrolet S-10 F VGM4.31PGKEK VGM1095AYMEA A 0.5 135,474 6396 2000 Mercury Mystique F YFMXV02.5VBC YFMXR0115BBE A 1 107,568 6397 2005 Ford Freestyle F 5FMXV03.02EC 5FMXR0185GBK A 0.75 58,994 6401 1995 Lexus SC 300 F STY3.0VJGAFA STY1080DYM00 A 0.5 97,225 6402 2004 Hyundai Elantra F 4HYXV02.0XW4 4HYXR0148PDX M 0 97,587 6404 1998 Isuzu Rodeo F WSZXT03.52EK WSZXT0095ME0 A 0.25 133,367 6405 1996 Chevrolet Cavalier F TGM2.2V8GKEK TGM1089AYMEA A 0.75 137,833 6416 1999 Mazda Protege F XTKXV01.6VBA XTKXR0125BFB A 0.25 117,953 6420 1996 Jeep Grand Cherokee F TCR4.028GKEK TCR1073AYPBP A 0.75 162,205 6424 1970 VW Beetle C . . M . 3,141 5425 1998 Mitsubishi Eclipse F . . M 0.25 108,341 6427 1984 Toyota Land Cruiser C ETY4.2T2AFF4 EV-F M 0 167,153 6428 1975 Chevrolet C-10 C 350 cid; 2bbl . A 0.75 63,242 5430 2002 Jeep Wrangler . 2CRXT04.02D2 2CRXE0101GCS M 0.25 56,704 .431 1993 Mitsubishi 3000 GT F PMT3.0V5FF26 1F M 0.25 115,444 6437 1991 Chevrolet S-10 C M3G4.3T5XEB2 MBO-3E A 0.5 205,451 6438 1975 Chevrolet C-10 C GM113 350 ci . A 0 42,455 5439 1998 Pontiac Grand Prix F WGMXV03.1041 WGMXR0133918 A 0.75 151,899 6454 1988 BMW M6 F JBM3.5V5FMS4 EV50 M 0.75 23,065 6460 2003 Dodge Durango F 3CRXTO5.95B2 3CRXE0101GDH A 0.5 83,160 6466 2003 Acura RSX F 3HNXV02.0XKC 3HNXR0099AAA A 0.5 29,398 6468 2004 Subaru Outback F 4FJXV02.5NKR 4FJXR01254CE M 0 71,196 6469 2000 Nissan Pathfinder F YNSXT03.325A YNSXE0110MBA A 0.5 101,775 6475 2000 Chevrolet Astro AWD F YGMXT04.3183 YGMXE0111911 A 0 131,985 6481 2003 Nissan Frontier F 3NSXT03.3C6A 3NSXR0120MAA A 0 57,875 .482 1992 Nissan Stanza F NNS2.4V5FAAX F14-2 A 1 127,769 6486 1993 Jeep Cherokee F PCR4.0T5FGA5 PTAPR A 0.25 106,403 6489 1991 Ford Bronco F . . M 0.5 49,350 5494 1995 Mazda MX-6 F STK2.0VJ6FEA STK1065BYP02 M 0.5 204,521 64-10CombinedPacketID(unique tovehicle) VINModelYear Make ModelFuel Metering Type(Carburetted or FuelInjected)VECI EngineFamily VECI Evap FamilyTrans.TypeFuelGauge(1.000.750.500.250.00)OdometerReading(miles)OdometerResolution497 1994 Buick Century F R1G3.4V8GAEA R1G1058AYM0A A 0.25 107,759 6498 1998 Honda Accord F WHNXV02.3PA3 WHNXR0130AAA M 0.5 197,136 6504 1992 Ford F-150 F NFM5.8T5HZC1 F2AE-9C485 . 1 189,852 6507 2001 Toyota Sequoia F 1TYXT04.7JBW 1TYXE0190AF0 A 0.25 129,155 6512 2005 Hyundai Santa Fe F 5HYXT03.5MM5 5HYXR0175PES . 0.25 93,921 6517 1999 Chevrolet Lumina F XGMXV03.4041 XGMXE0095904 A 1 110,145 6521 1994 Nissan Sentra . RNS1.6VJGFEA RNS1030BYM0A . 0.5 125,478 6527 1995 Eagle Talon F SDS2.0VJGFEA SDS1062AYM0H A 0.25 143,162 6540 1998 Honda Accord F . . A 1 146,347 6542 1986 Subaru GL10 F GFJ1.8V5HCNX MU M 1 157,417 6546 1996 Geo Prizm F TNT1.8VJGFFK TNT1047DYMAO A 1 103,065 6547 1975 Ford Ranger F-250 C . . M 1 6,780 5550 1992 Ford Taurus F NFM3.8V5FJF9 . A 1 70,615 5559 1989 Oldsmobile Regency F K2G3.8V8XEBI KBO-2D A 0.75 348,697 6563 1998 Ford Taurus F WFMXV03.0NAA WFMXE0115BAE A 0.75 48,551 6568 1997 Saturn SL1 F VGM1.9VJGKEK VGM1035AYPAA A 0 104,441 64-11Table 4-2. Participating Vehicle PSHED and Measurement RSD ResultsPSHED, Before-Repair Measurement RSD, Before-RepairCombinedPacketID(unique tovehicle)Seal DateTimeSealTemperature(F)SealBarometricPressure(in. Hg)MeasuredPSHEDHC at 15MinuteSoak(grams) DateTimeSpeed(mph)Temperature(F) VDF EI23EI23BinHCMethodA(ppmC3)HCMethodB(ppmC3)CO(%)NO(ppm)CO2(%)7 29JUN09:11:42:00 90 24.39 0.17629JUN09:11:42:38 10 89 180 125 3 24 6 0.02 127 15.037 29JUN09:11:42:00 90 24.39 0.176 29JUN09:11:45:02 10 89 183 80 2 2 9 0.01 297 15.049 29JUN09:13:43:00 92 24.36 5.03929JUN09:13:43:42 10 94 278 14595 2 9868 -7130 0.55 1051 14.329 29JUN09:13:43:00 92 24.36 5.03929JUN09:13:45:36 10 94 280 1098 6 4832 5350 0.68 521 14.4117 29JUN09:15:35:00 93 24.34 56.424 29JUN09:15:37:29 10 96 361 5071 7 10304 3749 2.31 686 13.0617 29JUN09:15:35:00 93 24.34 56.424 29JUN09:15:39:17 9 96 363 2227 7 7710 5520 2.69 1106 12.8518 29JUN09:17:20:00 94 24.32 0.099 29JUN09:17:20:36 10 95 434 84 2 37 31 0.04 31 15.0218 29JUN09:17:20:00 94 24.32 0.099 29JUN09:17:22:58 12 95 437 74 2 83 16 0.05 0 15.0120 30JUN09:10:37:00 88 24.38 0.234 30JUN09:10:38:36 14 88 103 80 2 26 -28 0.08 86 14.9920 30JUN09:10:37:00 88 24.38 0.234 30JUN09:10:41:21 13 88 107 64 2 39 -9 0.10 84 14.9821 30JUN09:11:52:00 93 24.38 1.241 30JUN09:11:30:18 10 92 144 110 3 545 240 0.11 271 14.9521 30JUN09:11:52:00 93 24.38 1.241 30JUN09:11:32:09 9 92 149 489 5 1875 990 0.06 1146 14.9225 30JUN09:13:54:00 94 24.36 0.266 30JUN09:13:54:41 12 100 235 112 3 374 78 0.03 23 15.0225 30JUN09:13:54:00 94 24.36 0.266 30JUN09:13:57:10 12 99 236 92 2 449 295 0.06 11 15.0027 30JUN09:15:42:00 96 24.34 5.93930JUN09:15:42:12 10 99 312 770 7 4442 -4988 0.55 415 14.5127 30JUN09:15:42:00 96 24.34 5.93930JUN09:15:44:31 14 99 315 561 6 1635 -1427 0.30 1327 14.7428 30JUN09:17:16:00 94 24.32 19.380 30JUN09:17:17:48 10 93 375 251 4 760 255 0.31 231 14.8028 30JUN09:17:16:00 94 24.32 19.380 30JUN09:17:19:58 9 93 376 272 5 609 -191 0.02 1094 14.9834 01JUL09:13:21:00 96 24.31 2.996 01JUL09:13:23:10 13 100 169 200 4 551 461 0.59 43 14.6134 01JUL09:13:21:00 96 24.31 2.996 01JUL09:13:24:42 11 100 171 346 5 649 576 0.04 36 15.0139 02JUL09:09:40:00 80 24.48 0.194 02JUL09:09:39:31 11 78 50 75 2 96 67 0.23 771 14.8639 02JUL09:09:40:00 80 24.48 0.194 02JUL09:09:41:48 10 78 54 74 2 123 81 0.13 384 14.9442 06JUL09:12:38:00 88 24.36 24.117 06JUL09:12:31:05 13 92 203 325 5 996 939 10.86 186 7.2342 06JUL09:12:38:00 88 24.36 24.117 06JUL09:12:35:37 12 93 210 127 3 398 280 2.47 821 13.2444 06JUL09:16:01:00 90 24.29 14.015 06JUL09:15:52:49 13 91 359 373 5 1540 1024 0.21 939 14.8244 06JUL09:16:01:00 90 24.29 14.015 06JUL09:15:54:22 13 91 363 268 4 2057 636 0.15 1190 14.8445 06JUL09:14:35:00 90 24.30 0.043 06JUL09:14:31:04 11 95 307 46 1 10 -124 0.05 107 15.0245 06JUL09:14:35:00 90 24.30 0.043 06JUL09:14:32:32 11 91 310 101 3 39 132 0.15 365 14.9346 06JUL09:16:33:00 92 24.28 3.058 06JUL09:16:26:59 12 87 386 404 5 825 992 0.09 8 14.9646 06JUL09:16:33:00 92 24.28 3.058 06JUL09:16:27:50 12 87 388 320 5 636 520 0.06 -23 14.9948 06JUL09:17:27:00 86 24.30 6.667 06JUL09:17:23:03 12 82 428 96 3 35 9 0.21 18 14.9048 06JUL09:17:27:00 86 24.30 6.667 06JUL09:17:23:58 12 82 430 347 5 513 -360 1.20 39 14.1849 07JUL09:11:28:00 92 24.27 15.588 07JUL09:11:21:34 12 92 110 458 5 1059 650 0.61 1471 14.5349 07JUL09:11:28:00 92 24.27 15.588 07JUL09:11:22:31 13 92 112 1448 7 1656 793 0.64 2256 14.4653 07JUL09:09:49:00 84 24.27 0.258 07JUL09:09:19:14 13 76 36 127 3 131 72 0.09 404 14.9753 07JUL09:09:49:00 84 24.27 0.258 07JUL09:09:21:08 11 76 38 135 3 281 144 0.12 299 14.9557 07JUL09:12:12:00 90 24.28 0.033 07JUL09:12:06:38 14 91 143 69 2 8 15 0.00 -7 15.0557 07JUL09:12:12:00 90 24.28 0.033 07JUL09:12:07:49 12 91 145 75 2 33 34 0.13 -6 14.964-12PSHED, Before-Repair Measurement RSD, Before-RepairCombinedPacketID(unique tovehicle)Seal DateTimeSealTemperature(F)SealBarometricPressure(in. Hg)MeasuredPSHEDHC at 15MinuteSoak(grams) DateTimeSpeed(mph)Temperature(F) VDF EI23EI23BinHCMethodA(ppmC3)HCMethodB(ppmC3)CO(%)NO(ppm)CO2(%)62 07JUL09:17:29:00 91 24.24 0.011 07JUL09:17:25:16 12 87 302 54 1 -19 -5 0.12 590 14.9562 07JUL09:17:29:00 91 24.24 0.011 07JUL09:17:26:55 11 87 305 64 2 4 -29 -0.01 464 15.0464 08JUL09:13:41:00 95 24.24 15.649 08JUL09:13:38:33 12 104 192 3835 7 2608 1467 0.16 71 14.8664 08JUL09:13:41:00 95 24.24 15.649 08JUL09:13:39:37 11 104 194 1209 7 3287 1249 0.15 456 14.8368 09JUL09:10:51:00 77 24.40 0.087 09JUL09:10:48:24 10 78 105 94 3 106 128 0.10 -2 14.9868 09JUL09:10:51:00 77 24.40 0.087 09JUL09:10:49:36 11 78 108 91 3 45 -2 0.02 1 15.0475.096 09JUL09:15:48:00 85 24.35 0.249 09JUL09:15:44:33 12 95 242 195 4 1004 906 3.87 835 12.2275.096 09JUL09:15:48:00 85 24.35 0.249 09JUL09:15:46:34 12 95 244 222 4 1003 943 3.08 1178 12.7777 09JUL09:17:43:00 83 24.35 3.906 09JUL09:17:39:56 12 85 275 369 5 1450 995 0.21 1396 14.8177 09JUL09:17:43:00 83 24.35 3.906 09JUL09:17:41:05 11 85 277 595 6 1339 1361 0.19 1159 14.8379 10JUL09:13:59:00 92 24.46 18.250 10JUL09:13:54:50 12 100 192 1519 7 3871 -1673 0.61 927 14.4779 10JUL09:13:59:00 92 24.46 18.250 10JUL09:13:56:22 11 100 196 2673 7 3636 -1624 0.25 995 14.7382 10JUL09:15:26:00 95 24.41 0.473 10JUL09:15:23:05 12 102 240 96 3 32 35 0.19 161 14.9182 10JUL09:15:26:00 95 24.41 0.473 10JUL09:15:24:38 11 102 243 98 3 79 36 0.42 29 14.7585 16JUL09:12:32:00 91 24.54 6.077 16JUL09:12:28:03 12 94 120 265 4 366 145 0.04 49 15.0185 16JUL09:12:32:00 91 24.54 6.077 16JUL09:12:29:29 10 94 122 404 5 413 -51 0.12 326 14.9491 15JUL09:10:58:00 76 24.52 15.176 15JUL09:10:54:55 12 76 58 323 5 710 737 1.63 2422 13.7891 15JUL09:10:58:00 76 24.52 15.176 15JUL09:10:55:55 12 76 60 901 6 1280 1206 2.13 2267 13.4192 14JUL09:11:08:00 87 24.35 0.022 14JUL09:11:05:14 11 85 86 73 2 31 -88 0.02 86 15.0492 14JUL09:11:08:00 87 24.35 0.022 14JUL09:11:06:20 10 85 89 96 3 22 -95 0.01 574 15.0293 14JUL09:13:21:00 93 24.32 0.051 14JUL09:13:17:57 12 95 155 87 2 9 5 0.01 -3 15.0493 14JUL09:13:21:00 93 24.32 0.051 14JUL09:13:19:05 11 95 158 79 2 2 -37 0.01 26 15.0495 15JUL09:09:19:00 74 24.53 0.451 15JUL09:09:15:11 13 72 32 76 2 226 60 0.00 2470 14.9595 15JUL09:09:19:00 74 24.53 0.451 15JUL09:09:16:30 13 72 35 199 4 157 58 0.06 1701 14.94100 15JUL09:12:08:00 82 24.50 10.130 15JUL09:12:05:12 12 86 85 1926 7 833 531 3.48 598 12.51100 15JUL09:12:08:00 82 24.50 10.130 15JUL09:12:06:02 13 86 87 2432 7 1680 2035 4.57 578 11.70103.122 16JUL09:10:49:00 88 24.55 5.011 16JUL09:10:46:39 8 88 53 218 4 517 227 0.22 1432 14.83103.122 16JUL09:10:49:00 88 24.55 5.011 16JUL09:10:47:40 11 88 56 277 5 424 267 0.25 1815 14.80108 16JUL09:14:11:00 91 24.53 22.449 16JUL09:14:08:23 13 97 174 6713 7 3493 1942 0.09 87 14.88108 16JUL09:14:11:00 91 24.53 22.449 16JUL09:14:09:19 12 97 177 2031 7 2753 1414 0.02 729 14.93109 20JUL09:15:13:00 95 24.32 2.352 20JUL09:15:09:30 15 103 229 147 3 153 -136 0.04 36 15.02109 20JUL09:15:13:00 95 24.32 2.352 20JUL09:15:11:06 14 104 232 276 5 142 -40 0.03 43 15.03110 16JUL09:16:27:00 92 24.52 0.038 16JUL09:16:24:24 11 99 243 86 2 6 -3 0.02 21 15.04110 16JUL09:16:27:00 92 24.52 0.038 16JUL09:16:25:21 12 99 245 95 3 -3 -8 0.00 12 15.05112 16JUL09:15:47:00 92 24.51 9.116 16JUL09:15:41:41 11 99 224 2091 7 4672 2155 0.00 1479 14.86112 16JUL09:15:47:00 92 24.51 9.116 16JUL09:15:42:30 11 96 226 626 6 2932 -1101 0.19 1933 14.76120 17JUL09:12:00:00 90 24.56 20.322 17JUL09:11:50:44 12 89 130 298 5 432 309 1.04 889 14.26120 17JUL09:12:00:00 90 24.56 20.322 17JUL09:11:53:01 11 89 134 177 4 518 365 0.96 1027 14.31121 17JUL09:12:40:00 92 24.56 0.115 17JUL09:12:37:12 12 92 163 101 3 47 -94 0.15 625 14.92121 17JUL09:12:40:00 92 24.56 0.115 17JUL09:12:38:04 13 92 165 81 2 24 -5 0.13 852 14.93127 17JUL09:17:00:00 93 24.50 26.376 17JUL09:16:58:08 11 96 297 852 6 3715 606 0.09 119 14.874-13PSHED, Before-Repair Measurement RSD, Before-RepairCombinedPacketID(unique tovehicle)Seal DateTimeSealTemperature(F)SealBarometricPressure(in. Hg)MeasuredPSHEDHC at 15MinuteSoak(grams) DateTimeSpeed(mph)Temperature(F) VDF EI23EI23BinHCMethodA(ppmC3)HCMethodB(ppmC3)CO(%)NO(ppm)CO2(%)127 17JUL09:17:00:00 93 24.50 26.376 17JUL09:16:59:03 11 96 299 310 5 3146 -507 0.02 205 14.94130 20JUL09:11:16:00 90 24.38 0.049 20JUL09:11:11:22 17 94 83 38 1 10 34 1.31 47 14.11130 20JUL09:11:16:00 90 24.38 0.049 20JUL09:11:14:16 12 94 86 45 1 -27 -40 0.06 258 15.01131 20JUL09:12:30:00 91 24.37 0.100 20JUL09:12:27:05 15 97 122 49 1 2 -11 0.12 263 14.96131 20JUL09:12:30:00 91 24.37 0.100 20JUL09:12:28:42 14 97 124 68 2 6 9 0.10 41 14.98134 20JUL09:13:54:00 92 24.36 0.024 20JUL09:13:51:17 15 94 170 64 2 -14 -28 0.05 -5 15.02134 20JUL09:13:54:00 92 24.36 0.024 20JUL09:13:52:26 15 94 172 75 2 -9 -3 0.04 21 15.02135 21JUL09:10:32:00 75 24.48 0.047 21JUL09:10:28:18 14 77 74 52 1 17 11 0.02 -5 15.04135 21JUL09:10:32:00 75 24.48 0.047 21JUL09:10:29:32 11 77 76 82 2 0 -2 0.11 11 14.97138 21JUL09:09:16:00 74 24.51 0.020 21JUL09:09:11:06 14 73 41 70 2 -8 -8 0.01 48 15.05138 21JUL09:09:16:00 74 24.51 0.020 21JUL09:09:14:44 13 73 45 45 1 30 27 0.10 -10 14.98139 20JUL09:17:10:00 90 24.35 0.027 20JUL09:17:07:14 14 90 287 66 2 -50 -9 0.00 16 15.05139 20JUL09:17:10:00 90 24.35 0.027 20JUL09:17:08:12 13 90 289 56 1 -7 -6 0.05 -2 15.02156 22JUL09:11:34:00 83 24.45 0.015 22JUL09:11:30:41 13 90 89 91 3 29 -2 0.04 59 15.02156 22JUL09:11:34:00 83 24.45 0.015 22JUL09:11:32:29 13 90 93 136 3 38 -29 0.03 69 15.03157 22JUL09:14:54:00 94 24.42 5.074 22JUL09:14:11:46 13 99 156 366 5 779 264 0.19 1883 14.82157 22JUL09:14:54:00 94 24.42 5.074 22JUL09:14:13:13 14 99 159 169 4 479 248 0.57 1531 14.58158 22JUL09:12:52:00 85 24.44 0.015 22JUL09:12:48:39 14 95 116 56 1 5 9 0.01 13 15.04158 22JUL09:12:52:00 85 24.44 0.015 22JUL09:12:49:42 14 95 118 57 2 1 7 0.01 5 15.05159 23JUL09:09:35:00 81 24.42 0.728 23JUL09:09:31:45 13 79 39 267 4 419 273 0.24 115 14.87159 23JUL09:09:35:00 81 24.42 0.728 23JUL09:09:32:46 13 79 41 501 5 702 622 0.14 130 14.92161 23JUL09:11:01:00 89 24.40 0.015 23JUL09:10:54:07 13 88 69 90 2 132 111 0.26 1577 14.81161 23JUL09:11:01:00 89 24.40 0.015 23JUL09:10:55:27 13 88 70 63 2 55 111 0.04 609 15.00162.332 11AUG09:14:21:00 91 24.50 0.166 11AUG09:14:18:09 13 103 160 82 2 49 21 0.22 510 14.88162.332 11AUG09:14:21:00 91 24.50 0.166 11AUG09:14:19:25 14 103 162 77 2 220 104 0.74 243 14.51163 23JUL09:13:44:00 94 24.39 0.052 23JUL09:13:40:24 14 106 153 53 1 42 9 0.12 1 14.96163 23JUL09:13:44:00 94 24.39 0.052 23JUL09:13:42:07 15 106 156 44 1 14 23 0.07 -12 15.01168 23JUL09:15:03:00 96 24.36 25.565 23JUL09:14:59:55 13 107 189 1754 7 2613 -76 0.19 45 14.84168 23JUL09:15:03:00 96 24.36 25.565 23JUL09:15:01:09 12 107 191 799 6 1799 144 0.97 118 14.30177 24JUL09:16:28:00 100 24.35 0.034 24JUL09:16:24:43 14 102 214 93 3 18 -24 0.21 -4 14.91177 24JUL09:16:28:00 100 24.35 0.034 24JUL09:16:25:52 15 102 216 60 2 59 21 0.32 10 14.82178 24JUL09:13:15:00 99 24.37 1.975 24JUL09:13:11:29 14 100 132 217 4 1153 687 5.42 1349 11.09178 24JUL09:13:15:00 99 24.37 1.975 24JUL09:13:13:30 14 100 135 160 3 1676 687 5.52 1107 11.01184 27JUL09:09:44:00 77 24.50 0.137 27JUL09:09:40:24 12 78 63 45 1 28 30 0.06 7 15.01184 27JUL09:09:44:00 77 24.50 0.137 27JUL09:09:41:51 12 78 69 75 2 12 8 0.04 19 15.03185 27JUL09:10:50:00 82 24.49 0.018 27JUL09:10:46:12 12 85 111 65 2 -12 -26 0.09 49 14.99185 27JUL09:10:50:00 82 24.49 0.018 27JUL09:10:47:42 11 85 112 85 2 7 36 0.08 138 14.99186 27JUL09:11:58:00 85 24.47 0.145 27JUL09:11:53:44 12 91 158 101 3 49 21 0.11 541 14.95186 27JUL09:11:58:00 85 24.47 0.145 27JUL09:11:55:12 11 91 161 48 1 262 233 4.91 69 11.52188 27JUL09:13:05:00 88 24.45 0.015 27JUL09:13:02:15 12 93 215 138 3 11 -10 0.03 5 15.03188 27JUL09:13:05:00 88 24.45 0.015 27JUL09:13:03:37 12 93 217 108 3 -5 -38 0.03 -6 15.034-14PSHED, Before-Repair Measurement RSD, Before-RepairCombinedPacketID(unique tovehicle)Seal DateTimeSealTemperature(F)SealBarometricPressure(in. Hg)MeasuredPSHEDHC at 15MinuteSoak(grams) DateTimeSpeed(mph)Temperature(F) VDF EI23EI23BinHCMethodA(ppmC3)HCMethodB(ppmC3)CO(%)NO(ppm)CO2(%)189 27JUL09:14:44:00 87 24.41 0.282 27JUL09:14:40:22 11 95 283 88 2 74 7 0.12 262 14.96189 27JUL09:14:44:00 87 24.41 0.282 27JUL09:14:42:07 10 95 286 80 2 292 302 1.70 113 13.82190 28JUL09:10:26:00 72 24.44 0.045 28JUL09:10:22:42 12 67 72 118 3 13 -22 0.04 -1 15.02190 28JUL09:10:26:00 72 24.44 0.045 28JUL09:10:23:50 12 67 76 91 3 4 -3 0.01 1 15.04192 28JUL09:08:56:00 71 24.44 0.043 28JUL09:08:52:18 13 69 21 161 4 39 32 0.09 100 14.99192 28JUL09:08:56:00 71 24.44 0.043 28JUL09:08:53:47 13 69 24 63 2 32 38 0.08 150 14.99193 28JUL09:09:34:00 74 24.45 0.053 28JUL09:09:27:47 14 72 38 45 1 -1 6 0.03 30 15.03193 28JUL09:09:34:00 74 24.45 0.053 28JUL09:09:28:58 13 70 39 55 1 -11 -7 0.02 67 15.04194 28JUL09:11:27:00 73 24.44 0.039 28JUL09:11:24:06 14 68 130 34 1 53 100 0.16 291 14.93194 28JUL09:11:27:00 73 24.44 0.039 28JUL09:11:24:49 13 68 132 33 1 373 396 1.92 339 13.66197 28JUL09:12:54:00 74 24.43 0.025 28JUL09:12:51:06 13 72 185 59 2 1 -2 0.01 21 15.05197 28JUL09:12:54:00 74 24.43 0.025 28JUL09:12:52:05 13 72 187 52 1 10 12 0.06 -15 15.01203.205 28JUL09:14:45:00 76 24.40 13.730 28JUL09:14:42:44 11 72 273 347 5 390 236 0.14 43 14.94203.205 28JUL09:14:45:00 76 24.40 13.730 28JUL09:14:43:44 10 72 275 285 5 460 205 0.14 58 14.94212 28JUL09:16:58:00 77 24.37 7.620 28JUL09:16:54:42 12 72 346 168 4 924 578 2.36 924 13.30212 28JUL09:16:58:00 77 24.37 7.620 28JUL09:16:56:00 13 72 348 254 4 1106 759 2.36 939 13.29213 29JUL09:09:46:00 75 24.35 3.830 29JUL09:09:38:05 10 70 38 382 4 7108 6092 4.86 1595 11.30213 29JUL09:09:46:00 75 24.35 3.830 29JUL09:09:39:15 11 70 40 422 4 6361 6236 2.96 1449 12.69214 29JUL09:11:21:00 76 24.36 0.025 29JUL09:11:16:23 12 74 105 88 2 62 46 0.04 -16 15.02214 29JUL09:11:21:00 76 24.36 0.025 29JUL09:11:17:22 12 74 107 144 3 65 18 0.03 -4 15.03217 29JUL09:13:46:00 73 24.36 0.841 29JUL09:13:43:32 13 72 179 192 4 141 168 0.27 689 14.83217 29JUL09:13:46:00 73 24.36 0.841 29JUL09:13:44:34 14 72 181 755 6 404 309 0.15 755 14.91218 29JUL09:10:42:00 77 24.36 0.325 29JUL09:10:39:23 14 76 80 68 2 146 -55 0.25 338 14.86218 29JUL09:10:42:00 77 24.36 0.325 29JUL09:10:40:32 12 76 81 64 2 152 6 0.12 56 14.96219 29JUL09:14:45:00 75 24.33 0.100 29JUL09:14:41:23 13 73 213 72 2 5 14 0.04 263 15.01219 29JUL09:14:45:00 75 24.33 0.100 29JUL09:14:43:15 12 73 219 91 3 12 12 0.09 386 14.98220 29JUL09:15:47:00 75 24.37 0.044 29JUL09:15:43:45 12 75 256 47 1 -5 -12 0.02 45 15.04220 29JUL09:15:47:00 75 24.37 0.044 29JUL09:15:44:54 14 75 258 79 2 15 28 0.01 96 15.05221 29JUL09:12:20:00 73 24.35 0.041 29JUL09:12:16:12 13 71 141 100 3 203 208 1.46 136 14.00221 29JUL09:12:20:00 73 24.35 0.041 29JUL09:12:17:19 11 71 143 93 3 4 -79 0.08 235 14.99232 30JUL09:13:36:00 65 24.47 0.164 30JUL09:13:32:56 14 61 147 113 3 71 12 0.06 190 15.00232 30JUL09:13:36:00 65 24.47 0.164 30JUL09:13:34:12 12 61 149 181 4 116 -6 0.11 2330 14.89238 30JUL09:17:15:00 70 24.42 0.287 30JUL09:17:11:35 11 72 242 130 3 99 24 0.08 95 14.99238 30JUL09:17:15:00 70 24.42 0.287 30JUL09:17:12:44 12 72 244 158 4 94 43 0.18 147 14.92246 31JUL09:10:51:00 84 24.37 0.024 31JUL09:10:47:23 12 83 76 67 2 33 -29 0.19 58 14.91246 31JUL09:10:51:00 84 24.37 0.024 31JUL09:10:48:36 12 85 79 124 3 18 -32 0.30 56 14.84249 31JUL09:13:18:00 87 24.34 0.141 31JUL09:13:13:59 12 86 172 116 3 24 1 0.12 32 14.97249 31JUL09:13:18:00 87 24.34 0.141 31JUL09:13:15:04 12 86 174 82 2 16 33 0.21 38 14.90252 31JUL09:16:27:00 83 24.35 0.036 31JUL09:16:23:54 11 79 272 142 3 52 -12 0.06 37 15.01252 31JUL09:16:27:00 83 24.35 0.036 31JUL09:16:24:59 10 79 273 136 3 29 -53 0.03 59 15.03255 31JUL09:17:11:00 82 24.38 0.018 31JUL09:17:08:34 12 78 285 116 3 21 -9 0.00 15 15.054-15PSHED, Before-Repair Measurement RSD, Before-RepairCombinedPacketID(unique tovehicle)Seal DateTimeSealTemperature(F)SealBarometricPressure(in. Hg)MeasuredPSHEDHC at 15MinuteSoak(grams) DateTimeSpeed(mph)Temperature(F) VDF EI23EI23BinHCMethodA(ppmC3)HCMethodB(ppmC3)CO(%)NO(ppm)CO2(%)255 31JUL09:17:11:00 82 24.38 0.018 31JUL09:17:09:32 12 78 286 180 4 25 -34 0.01 -10 15.05261 03AUG09:12:58:00 34 24.38 0.403 03AUG09:12:53:14 12 95 160 174 4 331 246 5.36 145 11.19261 03AUG09:12:58:00 34 24.38 0.403 03AUG09:12:54:26 15 95 161 126 3 121 37 0.66 25 14.58262 03AUG09:14:07:00 93 24.36 1.603 03AUG09:14:03:02 11 98 196 55 1 555 -21 0.33 593 14.78262 03AUG09:14:07:00 93 24.36 1.603 03AUG09:14:04:40 12 98 199 220 4 538 -164 0.75 373 14.48263 03AUG09:11:48:00 95 24.39 0.030 03AUG09:11:43:18 14 95 125 39 1 22 -7 0.12 54 14.96263 03AUG09:11:48:00 95 24.39 0.030 03AUG09:11:44:32 13 95 127 49 1 -23 -21 0.13 113 14.96266 12AUG09:10:19:00 86 24.51 0.037 12AUG09:10:15:56 14 82 43 93 3 11 -7 0.04 543 15.01266 12AUG09:10:19:00 86 24.51 0.037 12AUG09:10:17:03 16 82 45 77 2 15 9 0.11 111 14.97271 04AUG09:08:58:00 78 24.48 1.283 04AUG09:08:54:36 13 72 34 98 3 57 -13 0.07 243 14.99271 04AUG09:08:58:00 78 24.48 1.283 04AUG09:08:55:37 14 73 36 109 3 51 -14 0.06 308 14.99278 04AUG09:11:28:00 87 24.46 0.060 04AUG09:11:24:29 13 86 112 94 3 17 84 0.03 48 15.03278 04AUG09:11:28:00 87 24.46 0.060 04AUG09:11:25:26 13 86 113 71 2 35 -26 0.04 98 15.02285 04AUG09:15:57:00 94 24.42 0.022 04AUG09:15:53:07 14 97 180 67 2 -2 -6 0.21 56 14.90285 04AUG09:15:57:00 94 24.42 0.022 04AUG09:15:54:18 16 97 183 77 2 -18 -28 0.81 36 14.47290 05AUG09:13:04:00 88 24.50 0.017 05AUG09:13:00:38 15 93 134 95 3 2 4 0.05 3 15.02290 05AUG09:13:04:00 88 24.50 0.017 05AUG09:13:01:41 15 93 135 91 3 0 3 0.02 -8 15.04294 05AUG09:14:59:00 91 24.48 3.788 05AUG09:14:55:11 13 101 183 56 1 509 513 4.80 661 11.58294 05AUG09:14:59:00 91 24.48 3.788 05AUG09:14:56:15 14 101 185 63 2 555 493 3.79 1064 12.28295 05AUG09:16:30:00 92 24.43 0.033 05AUG09:16:26:43 15 110 218 70 2 -27 -3 0.03 78 15.03295 05AUG09:16:30:00 92 24.43 0.033 05AUG09:16:27:55 13 110 220 61 2 -10 16 0.02 1192 15.00301 07AUG09:09:11:00 85 24.24 16.107 07AUG09:09:07:42 12 77 42 150 3 1103 808 9.46 336 8.22301 07AUG09:09:11:00 85 24.24 16.107 07AUG09:09:08:45 12 77 43 95 2 1612 762 8.19 138 9.13302 06AUG09:15:47:00 85 24.31 0.156 06AUG09:15:43:15 15 79 195 118 3 184 182 3.24 817 12.69302 06AUG09:15:47:00 85 24.31 0.156 06AUG09:15:45:00 14 79 197 95 3 193 207 4.41 693 11.86305 07AUG09:10:58:00 89 24.24 0.107 07AUG09:10:54:19 14 85 99 72 2 3 -8 0.00 2910 14.95305 07AUG09:10:58:00 89 24.24 0.107 07AUG09:10:55:42 15 86 103 101 3 65 100 0.34 2335 14.73306 06AUG09:16:34:00 81 24.31 21.643 06AUG09:16:31:15 14 74 222 368 5 1473 1313 7.64 614 9.51306 06AUG09:16:34:00 81 24.31 21.643 06AUG09:16:32:10 17 74 224 152 3 1303 1156 8.19 693 9.12309 07AUG09:14:57:00 96 24.16 2.264 07AUG09:14:54:53 16 110 231 506 5 859 302 0.12 4098 14.79309 07AUG09:14:57:00 96 24.16 2.264 07AUG09:14:55:48 13 110 234 251 4 388 219 0.09 3307 14.86311 07AUG09:16:12:00 94 24.14 0.165 07AUG09:16:09:26 15 110 271 107 3 1 -12 0.17 -4 14.93311 07AUG09:16:12:00 94 24.14 0.165 07AUG09:16:10:17 14 110 272 110 3 18 37 0.06 9 15.01316 10AUG09:09:36:00 74 24.54 0.691 10AUG09:09:33:02 10 71 31 167 4 244 -772 0.44 306 14.72316 10AUG09:09:36:00 74 24.54 0.691 10AUG09:09:34:15 9 71 33 802 5 . -8466 . . .320 10AUG09:11:02:00 79 24.53 0.034 10AUG09:10:58:31 14 76 71 107 3 16 -47 0.06 7 15.01320 10AUG09:11:02:00 79 24.53 0.034 10AUG09:10:59:51 13 76 76 70 2 -1 12 0.02 -32 15.04321 10AUG09:12:45:00 86 24.50 12.329 10AUG09:12:41:00 15 86 124 785 6 2266 1175 0.41 1187 14.65321 10AUG09:12:45:00 86 24.50 12.329 10AUG09:12:42:00 15 88 126 1151 7 1671 1802 0.45 975 14.64325 11AUG09:09:40:00 79 24.55 0.034 11AUG09:09:37:20 13 75 34 84 2 10 21 0.11 155 14.97325 11AUG09:09:40:00 79 24.55 0.034 11AUG09:09:38:17 14 75 35 66 2 2 4 0.03 160 15.024-16PSHED, Before-Repair Measurement RSD, Before-RepairCombinedPacketID(unique tovehicle)Seal DateTimeSealTemperature(F)SealBarometricPressure(in. Hg)MeasuredPSHEDHC at 15MinuteSoak(grams) DateTimeSpeed(mph)Temperature(F) VDF EI23EI23BinHCMethodA(ppmC3)HCMethodB(ppmC3)CO(%)NO(ppm)CO2(%)327 10AUG09:17:28:00 86 24.46 1.565 10AUG09:17:25:35 10 94 266 208 4 571 326 0.33 1115 14.76327 10AUG09:17:28:00 86 24.46 1.565 10AUG09:17:26:47 11 94 268 234 4 755 16 0.37 1241 14.72329 11AUG09:11:00:00 87 24.55 7.535 11AUG09:10:55:53 13 84 64 1987 7 1530 623 5.20 592 11.26329 11AUG09:11:00:00 87 24.55 7.535 11AUG09:10:56:56 15 84 66 1708 7 1709 373 4.48 744 11.76331 11AUG09:12:44:00 90 24.52 4.608 11AUG09:12:39:23 13 93 116 753 6 1206 1067 0.04 82 14.98331 11AUG09:12:44:00 90 24.52 4.608 11AUG09:12:40:36 13 93 117 817 6 1443 967 0.03 269 14.98337 11AUG09:17:16:00 94 24.46 0.066 11AUG09:17:12:59 14 118 248 53 1 4 -12 0.14 136 14.95337 11AUG09:17:16:00 94 24.46 0.066 11AUG09:17:14:03 14 118 249 63 2 -4 -6 0.23 11 14.89339 12AUG09:11:47:00 91 24.48 2.236 12AUG09:11:44:06 14 93 102 56 1 81 25 0.37 79 14.78339 12AUG09:11:47:00 91 24.48 2.236 12AUG09:11:44:58 14 93 103 109 3 18 -28 0.05 1853 14.95343 12AUG09:13:22:00 93 24.45 0.963 12AUG09:13:19:22 16 104 147 582 6 888 562 0.18 841 14.86343 12AUG09:13:22:00 93 24.45 0.963 12AUG09:13:20:05 13 104 148 962 6 720 594 0.18 384 14.89348.432 12AUG09:14:43:00 91 24.43 0.567 12AUG09:14:40:04 12 101 181 115 3 470 406 1.39 1240 14.00348.432 12AUG09:14:43:00 91 24.43 0.567 12AUG09:14:40:55 12 101 183 166 4 408 334 0.79 1702 14.41350 12AUG09:16:17:00 93 24.40 0.022 12AUG09:16:11:52 14 115 229 51 1 54 -51 0.15 23 14.94350 12AUG09:16:17:00 93 24.40 0.022 12AUG09:16:14:05 13 115 232 79 2 -9 -59 0.10 -3 14.98352 13AUG09:09:45:00 84 24.42 0.565 13AUG09:09:41:11 15 81 32 197 4 67 88 0.10 2062 14.90352 13AUG09:09:45:00 84 24.42 0.565 13AUG09:09:42:42 12 81 35 109 3 65 28 0.10 575 14.96354 13AUG09:13:25:00 90 24.40 133.986 13AUG09:13:21:57 13 91 152 9350 7 11162 9613 0.30 2309 14.42354 13AUG09:13:25:00 90 24.40 133.986 13AUG09:13:23:01 13 91 154 575 6 2754 1066 0.53 2424 14.50355 13AUG09:11:37:00 91 24.40 2.216 13AUG09:11:33:43 15 90 103 899 5 . 23662 . . .355 13AUG09:11:37:00 91 24.40 2.216 13AUG09:11:35:17 13 90 106 427 5 2899 -953 0.45 1129 14.61357 21AUG09:15:52:00 87 24.45 2.323 21AUG09:15:49:31 16 102 292 816 6 624 487 0.07 756 14.96357 21AUG09:15:52:00 87 24.45 2.323 21AUG09:15:50:44 14 102 294 307 5 314 66 0.06 544 14.98361 14AUG09:10:29:00 80 24.34 0.171 14AUG09:10:25:19 17 74 72 99 3 169 145 0.52 2344 14.60361 14AUG09:10:29:00 80 24.34 0.171 14AUG09:10:26:31 15 75 74 94 3 163 111 0.49 2418 14.61362 14AUG09:12:02:00 81 24.33 0.127 14AUG09:11:57:02 13 81 125 46 1 29 21 0.21 42 14.90362 14AUG09:12:02:00 81 24.33 0.127 14AUG09:12:00:31 15 81 130 65 2 73 24 0.44 59 14.74364 14AUG09:13:51:00 82 24.30 1.209 14AUG09:13:47:34 18 83 186 375 5 1088 183 0.02 40 15.01364 14AUG09:13:51:00 82 24.30 1.209 14AUG09:13:48:58 14 83 188 386 5 939 137 0.02 280 15.00365 14AUG09:16:52:00 82 24.28 0.024 14AUG09:16:47:40 16 81 280 70 2 -25 -62 0.00 27 15.05365 14AUG09:16:52:00 82 24.28 0.024 14AUG09:16:48:56 4 81 282 109 3 11 -83 0.00 1781 14.99365 14AUG09:16:52:00 82 24.28 0.024 14AUG09:16:50:36 16 81 283 80 2 26 18 0.03 12 15.03367 14AUG09:15:31:00 85 24.27 14.561 14AUG09:15:26:58 13 94 244 769 6 1160 1833 0.82 430 14.41367 14AUG09:15:31:00 85 24.27 14.561 14AUG09:15:28:53 12 94 247 1339 7 1882 3890 0.49 457 14.63370 17AUG09:10:08:00 72 24.48 0.015 17AUG09:10:04:47 13 67 61 137 3 24 -33 0.00 20 15.05370 17AUG09:10:08:00 72 24.48 0.015 17AUG09:10:06:22 13 67 63 118 3 16 15 0.02 13 15.04371 17AUG09:13:32:00 78 24.46 0.087 17AUG09:13:29:03 15 84 164 87 2 17 27 0.00 1086 15.01371 17AUG09:13:32:00 78 24.46 0.087 17AUG09:13:29:49 15 84 166 76 2 45 -5 0.10 589 14.96375 17AUG09:12:15:00 77 24.47 0.052 17AUG09:12:11:31 15 76 131 98 3 23 -3 0.03 -3 15.03375 17AUG09:12:15:00 77 24.47 0.052 17AUG09:12:13:14 14 76 133 72 2 7 1 0.03 1260 14.994-17PSHED, Before-Repair Measurement RSD, Before-RepairCombinedPacketID(unique tovehicle)Seal DateTimeSealTemperature(F)SealBarometricPressure(in. Hg)MeasuredPSHEDHC at 15MinuteSoak(grams) DateTimeSpeed(mph)Temperature(F) VDF EI23EI23BinHCMethodA(ppmC3)HCMethodB(ppmC3)CO(%)NO(ppm)CO2(%)379 17AUG09:16:13:00 79 24.44 0.068 17AUG09:16:03:01 14 80 252 81 2 59 14 0.14 496 14.94379 17AUG09:16:13:00 79 24.44 0.068 17AUG09:16:04:03 12 79 254 126 3 43 29 0.10 520 14.96381 17AUG09:16:55:00 77 24.45 0.050 17AUG09:16:52:34 14 75 266 142 3 62 45 0.37 1853 14.72381 17AUG09:16:55:00 77 24.45 0.050 17AUG09:16:53:23 14 75 268 112 3 52 -18 0.35 1978 14.73383 18AUG09:09:38:00 73 24.40 2.161 18AUG09:09:34:31 12 68 46 89 2 36 20 0.05 1749 14.95383 18AUG09:09:38:00 73 24.40 2.161 18AUG09:09:36:05 13 68 48 145 3 114 66 0.30 257 14.83385 18AUG09:11:27:00 82 24.37 0.022 18AUG09:11:24:44 13 81 94 110 3 20 -18 0.01 -7 15.04385 18AUG09:11:27:00 82 24.37 0.022 18AUG09:11:25:38 14 81 96 70 2 -2 5 0.03 4 15.04387 18AUG09:13:03:00 78 24.35 0.018 18AUG09:12:59:34 13 78 132 76 2 5 2 -0.01 919 15.03387 18AUG09:13:03:00 78 24.35 0.018 18AUG09:13:00:47 15 78 134 67 2 13 17 0.00 1120 15.01391 19AUG09:11:53:00 83 24.27 0.080 19AUG09:11:49:50 12 81 95 77 2 65 -24 0.07 173 15.00391 19AUG09:11:53:00 83 24.27 0.080 19AUG09:11:50:51 13 81 98 81 2 22 -23 0.12 189 14.96392 19AUG09:14:31:00 86 24.27 0.046 19AUG09:14:28:20 15 94 164 78 2 -3 12 0.05 20 15.01392 19AUG09:14:31:00 86 24.27 0.046 19AUG09:14:29:32 13 94 167 95 3 -2 2 0.03 26 15.03393 19AUG09:15:33:00 85 24.26 0.464 19AUG09:15:30:23 13 100 191 125 3 124 97 0.01 1003 15.00393 19AUG09:15:33:00 85 24.26 0.464 19AUG09:15:32:16 12 100 195 584 6 286 254 0.01 3518 14.91395 20AUG09:09:49:00 73 24.49 0.718 20AUG09:09:45:58 13 66 37 177 4 257 -328 0.02 165 15.02395 20AUG09:09:49:00 73 24.49 0.718 20AUG09:09:47:06 13 66 38 312 5 194 62 0.10 82 14.97396 19AUG09:17:00:00 85 24.26 0.038 19AUG09:16:55:17 14 103 224 74 2 -25 -80 0.20 -12 14.91396 19AUG09:17:00:00 85 24.26 0.038 19AUG09:16:58:18 12 104 231 90 3 -27 -115 0.22 38 14.90397 20AUG09:11:43:00 80 24.48 0.027 20AUG09:11:40:28 16 77 110 53 1 -2 -156 -0.02 -61 15.07397 20AUG09:11:43:00 80 24.48 0.027 20AUG09:11:41:30 14 77 111 75 2 -2 -15 0.07 -17 15.00401 20AUG09:12:33:00 82 24.48 0.349 20AUG09:12:30:14 14 82 128 100 3 1 -5 0.01 40 15.04401 20AUG09:12:33:00 82 24.48 0.349 20AUG09:12:31:15 13 82 129 121 3 12 10 0.01 21 15.04402 20AUG09:14:10:00 83 24.47 0.015 20AUG09:14:07:00 15 92 174 93 3 -20 -91 0.04 15 15.03402 20AUG09:14:10:00 83 24.47 0.015 20AUG09:14:08:07 13 92 178 86 2 -7 65 0.06 -69 15.01404 20AUG09:17:06:00 85 24.44 11.063 20AUG09:17:03:14 13 103 242 83 2 6 12 0.07 234 14.99404 20AUG09:17:06:00 85 24.44 11.063 20AUG09:17:04:19 12 103 243 85 2 38 42 0.11 210 14.97405 20AUG09:15:47:00 84 24.43 0.040 20AUG09:15:43:32 12 99 209 82 2 10 -8 0.08 -13 15.00405 20AUG09:15:47:00 84 24.43 0.040 20AUG09:15:44:37 12 99 211 118 3 63 71 0.11 295 14.97416 21AUG09:11:59:00 81 24.52 0.015 21AUG09:11:56:27 14 79 150 83 2 -4 -16 0.07 941 14.97416 21AUG09:11:59:00 81 24.52 0.015 21AUG09:11:57:25 13 79 151 94 3 16 16 0.19 349 14.91420 21AUG09:14:13:00 86 24.49 2.260 21AUG09:14:09:50 13 96 234 118 3 156 73 0.14 322 14.94420 21AUG09:14:13:00 86 24.49 2.260 21AUG09:14:10:54 14 96 235 103 3 112 5 0.12 501 14.95424 21AUG09:16:36:00 88 24.45 1.935 21AUG09:16:32:40 13 107 300 90 2 1249 638 3.41 1775 12.51424 21AUG09:16:36:00 88 24.45 1.935 21AUG09:16:33:43 11 107 301 176 4 1010 202 2.50 2007 13.16425 24AUG09:10:23:00 84 24.38 0.008 24AUG09:10:19:24 14 80 54 65 2 3 12 0.03 240 15.03425 24AUG09:10:23:00 84 24.38 0.008 24AUG09:10:20:20 14 80 57 73 2 7 6 0.03 26 15.03427 24AUG09:13:54:00 85 24.38 26.900 24AUG09:13:49:48 12 87 180 871 6 2422 2687 1.32 1405 13.98427 24AUG09:13:54:00 85 24.38 26.900 24AUG09:13:50:55 14 87 181 3625 7 2857 4244 0.44 2054 14.58428 24AUG09:15:24:00 81 24.41 7.751 24AUG09:15:20:03 13 80 227 1305 7 4576 3939 3.80 701 12.174-18PSHED, Before-Repair Measurement RSD, Before-RepairCombinedPacketID(unique tovehicle)Seal DateTimeSealTemperature(F)SealBarometricPressure(in. Hg)MeasuredPSHEDHC at 15MinuteSoak(grams) DateTimeSpeed(mph)Temperature(F) VDF EI23EI23BinHCMethodA(ppmC3)HCMethodB(ppmC3)CO(%)NO(ppm)CO2(%)428 24AUG09:15:24:00 81 24.41 7.751 24AUG09:15:21:22 13 80 230 895 6 4490 3923 4.42 640 11.73430 24AUG09:12:09:00 85 24.38 0.073 24AUG09:12:05:54 13 85 116 94 3 21 -5 0.03 22 15.03430 24AUG09:12:09:00 85 24.38 0.073 24AUG09:12:07:00 12 85 119 82 2 -2 -21 0.03 30 15.03431 24AUG09:16:59:00 80 24.40 0.054 24AUG09:16:55:41 15 76 273 66 2 180 130 0.17 469 14.91431 24AUG09:16:59:00 80 24.40 0.054 24AUG09:16:57:09 13 76 275 107 3 143 127 0.21 194 14.89437 25AUG09:14:37:00 76 24.50 0.133 25AUG09:14:34:19 13 71 209 104 3 47 75 0.06 1223 14.97437 25AUG09:14:37:00 76 24.50 0.133 25AUG09:14:35:21 14 69 211 73 2 19 17 0.05 1607 14.96438 25AUG09:16:49:00 77 24.47 6.933 25AUG09:16:46:01 12 75 280 531 5 3412 4307 0.81 2439 14.28438 25AUG09:16:49:00 77 24.47 6.933 25AUG09:16:47:07 13 75 281 1391 7 2717 1985 0.74 2196 14.36439 25AUG09:16:03:00 78 24.48 0.018 25AUG09:15:57:23 13 79 263 42 1 11 13 0.04 253 15.02439 25AUG09:16:03:00 78 24.48 0.018 25AUG09:15:58:31 14 79 264 112 3 16 -2 0.05 93 15.02454 25AUG09:15:32:00 74 24.48 0.455 25AUG09:15:27:03 13 73 236 140 3 20 6 0.05 25 15.02454 25AUG09:15:32:00 74 24.48 0.455 25AUG09:15:30:09 10 73 240 202 4 21 13 0.01 16 15.04460 26AUG09:11:16:00 81 24.52 0.032 26AUG09:11:12:52 11 80 89 159 4 87 -23 0.02 334 15.03460 26AUG09:11:16:00 81 24.52 0.032 26AUG09:11:13:57 11 81 90 133 3 76 -47 0.05 23 15.01466 27AUG09:13:53:00 88 24.56 0.018 27AUG09:13:46:22 14 91 150 100 3 13 19 0.03 6 15.03466 27AUG09:13:53:00 88 24.56 0.018 27AUG09:13:47:28 15 91 152 78 2 -1 -4 0.05 10 15.01468 27AUG09:15:39:00 86 24.55 0.022 27AUG09:15:35:40 14 99 207 71 2 15 36 0.04 3 15.02468 27AUG09:15:39:00 86 24.55 0.022 27AUG09:15:36:47 12 99 208 77 2 -22 -45 0.02 6 15.04469 27AUG09:14:24:00 88 24.56 0.052 27AUG09:14:20:47 12 95 172 92 3 36 -66 0.06 231 15.00469 27AUG09:14:24:00 88 24.56 0.052 27AUG09:14:21:53 12 95 173 90 2 30 75 0.02 12 15.04475 27AUG09:17:00:00 85 24.54 2.454 27AUG09:16:55:13 13 102 243 3560 7 1444 1537 0.29 67 14.80475 27AUG09:17:00:00 85 24.54 2.454 27AUG09:16:57:54 13 102 246 457 5 731 558 0.44 59 14.72481 28AUG09:09:55:00 77 24.65 0.036 28AUG09:09:51:27 13 70 49 115 3 35 -30 0.01 4 15.05481 28AUG09:09:55:00 77 24.65 0.036 28AUG09:09:52:26 13 70 51 86 2 20 -12 0.03 239 15.02482 28AUG09:11:14:00 80 24.64 0.195 28AUG09:11:09:53 15 74 101 144 3 82 69 0.35 282 14.79482 28AUG09:11:14:00 80 24.64 0.195 28AUG09:11:11:22 13 74 103 85 2 19 10 0.16 217 14.93486 28AUG09:12:53:00 82 24.62 0.277 28AUG09:12:49:15 15 81 157 129 3 192 130 2.58 340 13.18486 28AUG09:12:53:00 82 24.62 0.277 28AUG09:12:50:34 3 81 160 110 3 249 158 2.94 276 12.93489 28AUG09:14:46:00 87 24.57 28.800 28AUG09:14:43:57 11 98 238 8618 7 4132 3047 0.58 486 14.49489 28AUG09:14:46:00 87 24.57 28.800 28AUG09:14:45:12 11 98 241 5404 7 8811 9569 0.71 129 14.28494 28AUG09:16:13:00 86 24.54 0.190 28AUG09:16:09:57 17 105 285 72 2 478 492 4.47 315 11.82494 28AUG09:16:13:00 86 24.54 0.190 28AUG09:16:11:10 14 105 287 116 3 572 384 3.80 121 12.31497 31AUG09:10:38:00 73 24.39 0.126 31AUG09:10:33:10 12 70 120 109 3 266 335 0.31 1321 14.78497 31AUG09:10:38:00 73 24.39 0.126 31AUG09:10:34:10 13 70 121 90 2 178 102 0.12 578 14.94498 31AUG09:11:40:00 79 24.37 0.027 31AUG09:11:38:39 15 77 168 71 2 1 -11 0.21 89 14.90498 31AUG09:11:40:00 79 24.37 0.027 31AUG09:11:39:30 15 77 170 72 2 29 111 0.34 119 14.81504 31AUG09:14:28:00 84 24.33 0.132 31AUG09:14:24:37 12 85 292 89 2 167 100 0.08 672 14.97504 31AUG09:14:28:00 84 24.33 0.132 31AUG09:14:28:18 13 85 298 127 3 103 60 0.04 659 15.00507 31AUG09:13:08:00 82 24.36 0.064 31AUG09:13:04:45 14 85 227 78 2 -23 -24 -0.02 35 15.06507 31AUG09:13:08:00 82 24.36 0.064 31AUG09:13:05:49 15 85 230 72 2 -3 -31 0.06 32 15.014-19PSHED, Before-Repair Measurement RSD, Before-RepairCombinedPacketID(unique tovehicle)Seal DateTimeSealTemperature(F)SealBarometricPressure(in. Hg)MeasuredPSHEDHC at 15MinuteSoak(grams) DateTimeSpeed(mph)Temperature(F) VDF EI23EI23BinHCMethodA(ppmC3)HCMethodB(ppmC3)CO(%)NO(ppm)CO2(%)512 31AUG09:16:52:00 84 24.31 0.020 31AUG09:16:49:10 15 81 371 94 3 -5 -53 0.02 -8 15.04512 31AUG09:16:52:00 84 24.31 0.020 31AUG09:16:50:07 15 81 373 87 2 4 -51 0.02 3 15.04517 01SEP09:10:23:00 82 24.45 0.033 01SEP09:10:20:48 14 77 68 105 3 7 -4 0.03 566 15.01517 01SEP09:10:23:00 82 24.45 0.033 01SEP09:10:21:50 14 77 69 103 3 35 20 0.06 556 14.99521 31AUG09:15:58:00 85 24.30 0.248 31AUG09:15:56:22 13 86 345 87 2 328 481 2.62 266 13.15521 31AUG09:15:58:00 85 24.30 0.248 31AUG09:15:57:23 12 86 348 84 2 316 297 1.34 140 14.08527 01SEP09:11:55:00 87 24.43 2.202 01SEP09:11:51:45 13 86 112 267 4 1103 831 0.08 1258 14.92527 01SEP09:11:55:00 87 24.43 2.202 01SEP09:11:53:14 13 86 116 566 6 770 672 0.08 1260 14.93540 01SEP09:14:58:00 86 24.40 2.098 01SEP09:14:55:43 14 100 196 334 5 933 933 0.03 -35 15.00540 01SEP09:14:58:00 86 24.40 2.098 01SEP09:14:56:44 15 100 199 1050 7 694 237 0.03 38 15.01542 01SEP09:17:28:00 85 24.39 0.130 01SEP09:17:25:26 13 90 254 51 1 -8 -14 0.53 133 14.67542 01SEP09:17:28:00 85 24.39 0.130 01SEP09:17:26:22 15 90 255 87 2 -10 -25 0.05 197 15.01546 02SEP09:12:10:00 81 24.52 0.622 02SEP09:12:07:05 14 84 121 460 5 1745 -135 0.54 647 14.59546 02SEP09:12:10:00 81 24.52 0.622 02SEP09:12:08:06 14 84 122 526 6 1219 -1155 0.52 434 14.63547 02SEP09:10:47:00 78 24.52 6.837 02SEP09:10:45:53 14 76 81 82 2 1161 624 4.39 714 11.84547 02SEP09:10:47:00 78 24.52 6.837 02SEP09:10:46:59 12 76 83 85 2 1300 554 4.37 792 11.85550 02SEP09:15:41:00 88 24.46 14.696 02SEP09:15:38:37 12 102 207 2959 7 3192 -395 0.16 415 14.83550 02SEP09:15:41:00 88 24.46 14.696 02SEP09:15:39:44 10 102 209 2839 7 3000 -166 0.08 681 14.88559 03SEP09:13:37:00 82 24.46 4.127 03SEP09:13:34:26 14 92 132 351 5 704 -832 0.08 1045 14.94559 03SEP09:13:37:00 82 24.46 4.127 03SEP09:13:35:24 15 92 133 361 5 299 -306 0.06 520 14.98563 03SEP09:16:21:00 82 24.42 0.050 03SEP09:16:17:32 13 89 207 86 2 -8 5 -0.01 13 15.06563 03SEP09:16:21:00 82 24.42 0.050 03SEP09:16:18:56 13 89 209 105 3 -9 17 0.01 105 15.04568 04SEP09:11:53:00 83 24.43 0.387 04SEP09:11:50:44 12 83 117 4456 7 2848 -980 0.10 15 14.90568 04SEP09:11:53:00 83 24.43 0.387 04SEP09:11:51:10 14 83 118 439 5 . 778 . . .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 show s 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 em issions 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 pr ovide an accurate pict ure of the relationship between PSHED and that portion of EI23 that dete cts evaporative emissions. To determine that relationship, the EI23 Bin values, which have b een created to attempt to minimize the exhaust HC artifact, need to be considered.

Table 4-3 shows the distribution of the EI 23 Bin values for the stratified set.4-21 Table 4-3. Distribution of 12-mph Measurement RSDs 2 in the Stratified Sample 3 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 PSHED s within each EI23 Bin for di fferent 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/Qh r is determined by dividing the high-PSHED count for a particular EI23 Bin by th e corresponding total count from Table 4-3. For example, according to Table 4-4, 16 PSHEDs we re 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 we re 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 othe r 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 Bi n 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 ag reed 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 7High-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 part icipation 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 sa me RSD instrument as those that were used for the 12-mph Measurement RSDs of the participan ts. Thus, these Selection RSDs can be used to de-stratify the stratified set and provide an es timate of the high-PSHED fraction of the fleet of vehicles that entered the Ken Caryl IM station during the study. As me ntioned at the beginning of this section, the estim ate of the high-PSHED fraction of th e Ken Caryl fleet is not an unbiased measure of the high-PSHED fraction of the Denve r-wide fleet because the vehicles entering the Ken Caryl IM station are not randoml y selected from the Denver fleet.

The random set includes the strati fied set of study pa rticipants, as well as vehicles that were solicited but refused to pa rticipate, and the set of vehicles that we re not identified for
solicitation. To create a clean, random set of vehicles and th eir Selection RSD values, a number4-23 of filters were applied to the se t of all RSDs on all vehicles entering the station. We examined all RSDs taken by the IM station driveway RSD un it 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 no t included in the dataset. Because EI23 andEI23 Bin are calculated from each RSD beam block’s 50 10ms data points and the RSDcalculated HC Method B concentration, an RSD i nvalid 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 give n 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 c hose the first RSD EI23 Bin value.

The resulting dataset containe d 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 mode l 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 valu es from the stratified da taset as will be shown
in the next subsection.4-24 Table 4-5. Distribut ion of Selection RSDs 4 in the Random Sample 5

EI23 Bin
1 2 3 4 5 6 7 TotalModel YearGroup 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 Strati fied Sample According to EI23 Bin

The distribution of vehicles in the Ken Ca ryl 5830-vehicle random set described in SubSection 4.3 can be used to de-str atify 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-stra tified with a variable that contains no error. However, since EI23 Bin has a large uncertainty, th e standard de-stratifica tion calculations using it may produce errors in the calcu lated high-PSHED fraction of the Ken Caryl fleet. The size of
these errors as well as an estima tion of uncertainty and biases in the high-PSHED fraction will be
discussed in Section 4.5. Nevert heless, in this section, we wi ll get started by performing the
standard de-stratification calculations with the as sumption 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 upda ted 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 calcula tion 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 ra ndom 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 th e 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 s how the fraction of elevated PSHEDs and their
standard errors which are calcula ted from Columns 2 and 3. Colu mn 6 shows the distribution of
EI23 Bin values in the 5830-vehicle random sa mple, 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 cal culated, 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 randomsample (Column 7). The resultindicates 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-Stratifi cation Calculation for High- PSHED Definition = 2 g/Qhr

Selection RSD EI23 Bin Stratified Sample Random Sample

Number of Selection

RSDs

(N h ) Number of Elevated PSHEDs Fraction of Elevated PSHEDs (p h ) Std Error of Fraction of Elevated PSHEDs (s h /sqrt(N h )) Number of Vehicles Fraction of Vehicles (W h ) 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 differe nt 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 estimate d high-PSHED fraction of the Ken Caryl fleet is low. When the high-PSHED definition is low, th en 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 Cary l EI23 Bin De-Stratifications for Various High-PSHED Definitions

HighPSHED Definition

(g/Qhr) Estimated Ken Caryl FleetHighPSHED 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 Fleetwith High PSHED4-27 Calculation of the Ken Caryl high-PSH ED fraction by model year group – The estimate of the high-PSHED fraction of differe nt model year groups within the Ken Caryl
random dataset can be estimated in a similar ma nner. 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 dist ributions 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 mode l 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-PSH ED Fraction of the Ken Caryl Fleet for Various High-PSHED Definitions by Model Year Group

Model Year GroupHighPSHED 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-s tratification 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 in correct, then the relationship between PSHED and EI23 Bin values may differ among model y ear 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 Fleetwith 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 Group 6

EI23 Bin 1 2 3 4 5 6 7 TotalModel YearGroup 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 Quantificati on of Uncertainties and Biases in the High PSHED Fraction of the Ken Caryl Fl eet for EI23 Bin De-Stratification

The de-stratification results reported in S ubsection 4.4 were calculated and presented without any regard for the quali ty 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 frac tion, 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 uncerta inties 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 fr action of the Ken Caryl
fleet will also be biased.

The first source of error (shown in Column 1 of Table 4-10) is th e uncertainty of the high-PSHED fraction within each EI23 Bin in the stra tified 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-str atification in Table 4-6. For EI23 Bin = 1, 30 PSHED measurements were used. Of these, two had PSHEDs greater th an 2 g/Qhr for a highPSHED fraction for the Bin of 0.067. The two el evated PSHEDs could easily have been 1 or 3 elevated PSHEDs by chance alone. Accordingly, th ere is uncertainty in the value of 0.067. If
300 PSHEDs had been obtained for EI23 Bin = 1 inst ead, 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 a nd contributes to the un certainty of the high-PSHED fraction that is calculated during de-stratifi cation. This source of error is a part of the standard destratification technique and is pr esented in Appendix B. Table 4-11 shows the size of this source of error for the Ken Caryl fleet overall high-PSH ED 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 theHighPSHED 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 thefleet 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 th e Size of the Stratified Sa mple to the Uncertainty in the High-PSHED Fraction

High PSHED Definition

(g/Qhr) Estimated Ken Caryl FleetHighPSHED Fraction Size of Error due toStratifiedSample 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 0.0284-31 The second source of error (shown in Column 2 of Table 4-10) is th e uncertainty in the
measured PSHEDs for the 175 vehicles in the stratif ied dataset. This uncertainty is made up of two parts. The first part is the measurement e rror of the PSHED technique. We know that the measurement error is quite small as indicated by the propane retenti on and recovery tests (PSHED HC recovery was 97.6% a nd 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 quantifie s the sum of these two contributions to PSHED uncertainty. Those results indicated that the plus and minus one standard deviation
limits of PSHED uncertainties we re 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 discu ssed later in this section.

The PSHED values are also affected by noise in the second-by-second HC concentrations during each stratified data set PSHED test. This uncertainty is presented in the Column 3 of Table 4-10. During initial testing at the Lipan Stre et Station, our personnel who operated the PSHED indicated that they expected th e detection limit of th e PSHED method was about 0.1 g/Qhr. This estimate was based on the variability of the PSH ED 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 th at 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 pr ecisely by examination of the PSHED HC
concentration time series for th e PSHED measurements of the 175 participants in the Ken Caryl study. In the period just before the door to the PSHED was sealed, any drif t or variability in
baseline PSHED HC values will define the PSH ED detection limit. Examination of the HC time
trace for the vehicle w ith the lowest PSHED value (0.008 g/ Qhr) clearly shows increasing HC concentrations that noisily rise above a noisy baseline af ter the vehicle was sealed in the PSHED. Thus, the actual detec tion limit of the PSHED technique is much lower than 0.1 g/Qhr. The
standard deviations of the PSHED emission ra te 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 devi ation of 0.0040 g/Qhr (=4-32 SQRT(2) * 0.0028 g/Qhr) since the noi se in the baseline is un correlated with the noise 900
seconds later.

Just as the measured PSHED values have uncer tainty, the measured EI23 Bin values also have uncertainty. The uncertainty in EI23 Bi n value affects the EI23 Bin assignments of
individual RSD measurements for both the stratif ied sample (shown in Column 4 of Table 4-10) and for the 5830-vehicle random sample (shown in Co lumn 5 of Table 4-10). The uncertainty in the bin assignments affects the uncertainty in th e high-PSHED fraction calc ulated during this destratification. We expect that the uncertainty in the stratified sample will have a much larger influence on the calculated high-PSHED fraction th an the uncertainty in the random sample will. The characteristic uncertainty in an EI23 Bin va lue can be determined from the duplicate 12-mph
measurement RSDs taken on the 175-vehicle stratif ied dataset. We expect that one standard
deviation of the error in EI23 Bi ns is approximately equal to one EI23 Bin unit (see Appendix A). To determine the value more pr ecisely 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 de tected by the RSD EI23 evaporative emissions
index. The bias would affect both the EI23 Bin values in the stratif ied 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 conditio ns, artificial evapora tive emissions can be detected with about 90% reliability for PSH ED-equivalent running loss levels of about 185 g/Qhr. However, the reliability dropped to about 50% for runni ng loss emission levels of about 19 g/Qhr. For running loss levels of about 2 g/Qh r, 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 data sets. 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 da ta 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 th e RSD EI23 Bin detection limit should be evaluated
on vehicles with natural exhaust because variabil ity in the exhaust HC concentrations degrades
the detection limit of RSD EI23.

The influences of uncertainties and bias es on the high-PSHED fraction using a
Monte Carlo simulation – The above discussions explain th e reasons for sources of error in PSHED measurements and EI23 Bin assignments. However, their influence on the calculated4-33 Ken Caryl high-PSHED fraction also has to be de termined. These influences are critical to defining the quality of the calcula ted 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 hi gh-PSHED fraction at all levels of high-PSHED definitions. The relationship between these so urces 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 si mulation 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 Bi n 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 detect ion limit biases for the EI23 Bin variables. While this clearly can only be done in an approximate manner, the re sulting approximate true EI23 Bin values give us the opportunity to perturb th e uncertainties in PSHED and EI 23 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 defin ition (for example, see Figure 4-2) “jumps around” during perturbation will be an indication of the amount of va riability that the sources of
error introduce into the de-stratif ication. In the Monte Carlo simu lation, additional sets of trials can be calculated by varying the sizes of the unc ertainties and biases in PSHED and EI23 Bin.
The results would be an indication of the influen ces of the sources of e rror on the de-stratified value for high-PSHED fracti on of the Ken Caryl fleet.

Overall, we expect that seve ral features of the Ken Caryl stratified data (EI23/PSHED pairs) and random data (EI23s) cause uncertain ties and biases in the estimated high-PSHED
fraction of the fleet. The sources of these unc ertainties and biases are discussed above and
include the small number of ve hicles (175) in the Ken Caryl study, PSHED measurement error, variability in the hot-soak emissions of i ndividual vehicles, noi se 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 th e influences of these uncertainties on the estimated high-PSHED fraction of th e 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 th is fraction for different high-PSHED definitions.
We also want to determine th e high-PSHED fraction for the ove rall 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 EP A 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 mi ght be present in the calculated high-PSH ED 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 ve hicle speed, exhaust HC, and use multiple RSDs to arrive at a single comprehensive index for each ve hicle. 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 th e 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 relati onship) between any RSD evap index and a PSHED
hot-soak emission rate. It does not matter that the ve hicles in the stratified dataset were selected using EI23 Bin values. Once the stratified datase t is created, any relationship between any
variables on the 175 vehicles will represent the relationship betw een 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 PSH ED and any evap index and be confident that the same relationship between PSHED and th e evap index will hold for Denver fleet.

PreTesting RSD data from Sum mer and Fall 2008 and Summer 2009 – CDPHE collected a large amount of RSD data on multiple r uns of test vehicles with simulated and real tailpipe exhaust and simulated evaporative emissi ons. The advantage of this data is that the evaporative emissions rate is known since we pr oduced it artificially by metering propane or5-2 liquid gasoline. Also, up to 20 replicate RSD runs were made on each test condition. These data can be used to develop a wi de 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 vehi cle’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 fu el volatility. The RSD dataset contains the license plates of the vehicles. Th e model years of many vehicles can therefore be determined via the registration database. The data set 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 Summ er 2010 study toward estimating th e 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 seve ral new RSD evap indices that were intended to work over a wide range of vehicle speeds – not just near 12 mph. Th e various evap indices were used to attempt to identify vehicles wi th the highest chance of being high evaporative emitting vehicles. Vehicle owners were then requ ested to participate in the study. Participants
received hot-soak emissions test s by either PSHED at the West Tech Center or by LSHED at Aurora. The problem with this set of recruited ve hicles (as far as de-stratification to determine fleet high-PSHED fraction is concer ned) is that the vehicles were not selected us ing a stratified random design. Because the dataset does not repr esent the Denver fleet and is not a stratified sample, it cannot be de-stratified to determine th e 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 calculat e 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. Kl ausmeier, H. Haskew, “Evaporative Emissions Impact of Smog Check,” prepared for Califor nia Bureau of Automotive Repair, prepared by California Bureau of Automotive Repair , Eastern Research Group, de la Torre Klausmeier Consulting, Harold Hask ew & Associates, September 15, 2000.

4. L.C. Landman, “Evaporative Emissions of Gross Liquid Leakers in MOBILE6,” M6.EVP.009, U.S. Environmental Protec tion 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, “Investiga tion of Techniques for High Evaporative Emissions Vehicle Det ection: 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 Sa n Antonio Pilot Study,” report, Version 1, submitted to U.S. Environmental Prot ection 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, Denve r, 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 Fracti on of the Fleet with High Evaporative Emissions,” EPA-091001, October 1, 2009.

### Appendix A

Definition of EI23 and EI23 BinA-1 Calculation of RSD Evap Index 23 (EI23)

a) The RSD Evap Index 23 is calcul ated from the 50 10ms HC and CO 2 concentration-pathlength values obtained by the ESP 4000 or 4600 series instrument.

b) Collect the 50 10ms HC and CO 2 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 inform ation is available for that plume. This can occur, for example, when a tailgater truncates da ta acquisition of the plum e 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 tempor arily 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 sq uares regression of HC (ppm-cm) on CO 2 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 regr ession. The residual is defined as the observed (or measured) HC minus the predicted (or fitted) HC.

i) Divide N (the number of remaining observati ons) 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 th e value of EI23.

EI23 Dependences

Generally, EI23 seems to be linear with the evap orative HC mass emissions flow rate. However, EI23 also has a small, but important, dependen ce on exhaust HC in two ways and a dependence on vehicle speed in at least one way. First, EI 23 seems to linearly increas e 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 “thi nking” that moderately high evaporative emissions are pres ent. Second, exhaust HC causes the uncertainty of EI23 to beA-2 larger than when no exhaust HC is present. Th is means that we are ev en less certain of the amount of evaporative emissions.

The value of EI23 decreases with vehicle spee d. 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 ve hicle is like the wind speed dependence of the dispersion of large plumes from stationary s ources, the dependence of the RSD evaporative emissions signal might be proportional to the inve rse 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 stati on. 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 corre ct the dependences of EI23 on exhaust HC concentration, but we believe we do need to account for these eff ects so that the assignment of vehicles to EI23 Bins is not systematically bias ed. So, instead of correc ting 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 Lipa n 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 depende nce 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 a nd 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 ba nds. The size of the increment in lnlnei23 space (assuming 8 increments between the lower a nd 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 EI 23 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 highe r numbered bins tend to have hi gher 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 em issions of vehicles in Bin 3.

The width of each EI23 Bin represents approximatel y one standard deviation of the variability of a single EI23 measurement after accounting for the effects of th e 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 signi ficant difference. Thus, a beam block that produces a Bin 5 is significantly higher than a Bi n 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 stati on that had speeds between 8.7 and 19.5 mph, with known model years, and with measured Method B HC concen trations, 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 la rge variability in EI23, some low evaporative and some high evaporative em itters will also be in Bins 4 and 5. Vehicles that fall into Bins 6 and 7 have the largest pr obability of being high ev aporative emitters. For example, of the Lipan vehicles that received PSH ED measurements, 5 of 8 (63%) Bin 6 vehicles and 9 of 11 (82%) Bin 7 vehicles had PSHED ev aporative 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 emi ssions 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)” be tween 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 pr ogram assigned a random number from the uniform (0,1) distribution to the beam bl ock. 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 SamplingB-1 The equations pertaining to stratified sampli ng discussed in this se ction are presented by
Gilbert 7 . The goal is to determine the number of obs ervations 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

1 = h h h h W W n

= n

where

n h = the sample size in stratum number h, n = the total sample size for all strata, W h = the fraction of the actual populat ion that falls in stratum h, L = the number of strata, and

σ h = 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 fr action of the population comprised of that stratum (the fraction is
W h ). Also, the number of points from a stratum is directly proportional to σ h , which is a measure of the variability in the stratum.

The estimate of the population mean, pop 

### x , is the weighted mean of the stratum means,

: x h

The point here is that the strata are not sampled propor tionately to their actual representation in the popul ation. If a simple arithmetic averag e of the complete stratified sample were calculated, the different strata would be we ighted disproportionately to their representation in the population, and a biased aver age would result. The weighting scheme in the calculation of

pop 

### x accounts for the nature of the sample and pr oduces an unbiased estimate of the population mean. The formulation here pr oduces the unbiased estimate of the population mean with the

7 Gilbert, R.O., Statistical Methods for Environmental Pollution Monitoring, Van Nost rand Reinhold, New York, 1987. 

### x W = x h h L

1 = h pop 

# B-2 minimum error variance, given the total sample si ze, n. The standard error of the mean is the square root of its error variance. The standard e rror of this weighted mean estimate is as follows:

where f h is the number of data points in stratu m h divided by the population size of this

stratum.

The factor (1-f h ) accounts for the finitude of the popul ation 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 conservati ve (larger) estimates of the sta ndard errors for the stratified results. The factor (1-f h ) has been ignored (set to 1) in th e calculations presented below, so the
standard errors for the stratified analysis are somewhat conservative.

In practice the true standard deviations, σ h , are not known and are estimated on the basis
of historical data that exist before the planned stratified sa mpling effort. The sample standard deviation, s h , based on a sample, x h,i , i=1 to m, is:

where h x is the arithmetic mean.

When the individual data values are dichotom ous, 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 p h that the vehicle has high evap:

h h h q p = s

where:

p h is the probability that a vehicl e in stratum h has high evap, and
q h is the probability that a vehicle in stratum h has low evap (q h = 1- p h ). ) f

- (1

n W = h h 2 h 2 h L

1 = h x pop   

## 

1 m- ) x - x (

= s 2 h i h, m

1 = i h 

# 