Investigation of 

Techniques for 

High Evaporative 

Emissions Vehicle 

Detection: 

Denver Summer 2008 

Pilot Study 

at Lipan Street Station 

Report 

Version 5 

Prepared for: 

U.S. Environmental Protection 

Agency 

December 9, 2009 

Revised December 15, 2011 

through review process 
T:\Library\EPA-111215.docx 

ERG: 0218.02.002.001 

Investigation of Techniques for 

High Evaporative Emissions Vehicle Detection: 

Denver Summer 2008 Pilot Study 

at Lipan Street Station 

REPORT 

Version 5 

as part of 

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

Vehicle Passenger Car and Light Trucks Pilot Program (CRC E-77-3) 

Work Assignment 2-2 

Submitted to: 

Constance Hart 

U.S. Environmental Protection Agency 

2565 Plymouth Road 

Ann Arbor, MI 48105 

Submitted by: 

Timothy H. DeFries 

Jim Lindner 

Sandeep Kishan 

Cindy Palacios 

Eastern Research Group, Inc. 

3508 Far West Blvd., Suite 210 

Austin, TX 78731 

December 9, 2009 

Revised December 15, 2011 

through review process 
i 

Table of Contents 

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

1.1 Pilot Study Objectives....................................................................................... 1-2

1.2 Description of Pilot Study Tasks ...................................................................... 1-4

1.3 Target Population and Coverage ....................................................................... 1-5

1.4 Funding Partners and Cooperative Participants ................................................ 1-5

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

2.1 Findings from California RSD Pilot Project ..................................................... 2-2

2.2 Findings from Colorado DPHE ........................................................................ 2-5

2.3 RSD4000/4600 Data Acquisition and Concentration Calculations .................. 2-6

2.4 Influences of Evaporative Emissions on Method A and B Calculations ........ 2-12

3.0 Data Collection ............................................................................................................. 3-1

3.1 Sequence of Field Testing Procedures for the Pilot Study ............................... 3-2

3.2 Stratified Random Sample of Vehicles for Field Testing ................................. 3-7

3.3 New Test Procedures for the Pilot Study ........................................................ 3-10

 3.3.1 Modified California Method ............................................................... 3-11

 3.3.2 Infrared Video Camera ....................................................................... 3-12

 3.3.3 Evaluation of the Portable SHED ....................................................... 3-12

4.0 Analytical Methods ....................................................................................................... 4-1

4.1 RSD Emissions Instruments ............................................................................. 4-1

4.2 SEMTECH-G Analyzer for Portable SHED Measurements ............................ 4-4

4.3 HC Sniffer for Modified California Procedure ................................................. 4-7

4.4 Infrared Video Camera ..................................................................................... 4-8

4.5 Management of Field Data ................................................................................ 4-9

5.0 Analysis ............................................................................................................... 5-1

5.1 Evaluation of Using the Infrared Video Camera to Detect High Evaps ........... 5-2

5.2 Evaluation of the Modified California Method to Detect High Evaps ............. 5-3

5.3 Evaluation of Using RSD to Detect High Evaps .............................................. 5-5

 5.3.1 RSD Evap Index 1 Description ............................................................. 5-5

 5.3.2 Paired RSD and PSHED Observations ................................................. 5-8

 5.3.3 Prediction of PSHED Value Using RSD Evap Index 1 ...................... 5-10

 5.3.4 Prediction of PSHED “High Evap” Probability Using Evap Index 1 . 5-11

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

Appendix A Data Packet 

Appendix B Stratified Sampling 

Appendix C California Evaporative Visual Inspection Method 

Appendix D Participating Vehicle Data 

Appendix E Selection RSD Data 

Appendix F Conditioning Drive Data 

Appendix G Measurement RSD Data 

Appendix H PSHED Data 

Appendix I Modified California Method Data 

Appendix J Driver Interview Information 

Appendix K I/M Gas Cap Inspection Results 
ii 

List of Tables 

Table 3-1. Expected High Evap Fractions Using California RSD Data ................................... 3-8

Table 3-2. Random Number Thresholds for Soliciting at the Lipan Station ............................ 3-9

Table 3-3. Field Method Evaluation Stratified Sampling Design for RSD HC Bins ............ 3-10

Table 4-1. ESP RSD-4000 Instrument Specifications ............................................................. 4-3

Table 4-2. SEMTECH-G Measurement HC Specifications .................................................... 4-6

Table 4-3. SEMTECH-G Ambient Air Specifications ............................................................ 4-7

Table 5-1. Comparison of PSHED and MCM Overall Results ............................................... 5-4

Table 5-2. Counts of High Evap Designations ...................................................................... 5-12

List of Figures 

Figure 2-1. ASM CO vs. RSD CO .............................................................................................. 2-3

Figure 2-2. ASM NO vs. RSD NO ............................................................................................. 2-3

Figure 2-3. ASM HC vs. RSD HC ............................................................................................... 2-4

Figure 2-4. Reported RSD4000 HC on the Audit Truck with Induced Evaporative Emissions 2-6

Figure 2-5 Time Histories with Zero Evaporative Emissions and 1100 ppmC3 Tailpipe HC 

Emissions ......................................................................................................................... 2-8

Figure 2-6. Pollutant Attenuations with Zero Evaporative Emissions and 1100 ppmC3 Tailpipe 

HC Emissions Versus CO2 with Regression Lines Superimposed .................................. 2-9

Figure 2-7. Time Histories with 15 scfh Evaporative Emissions and 1100 ppmC3 Tailpipe HC 

Emissions ....................................................................................................................... 2-11

Figure 2-8. Pollutant Attenuations with 15 scfh Evaporative Emissions and 1100 ppmC3

Tailpipe HC Emissions with Regression Lines Superimposed ...................................... 2-12

Figure 2-9. Pollutant Attenuations with No Evaporative Emissions and 1100 ppmC3 Tailpipe 

HC Emissions with Methods A and B Lines Superimposed ......................................... 2-13

Figure 2-10. Pollutant Attenuations with 15 scfh Evaporative Emissions and 1100 ppmC3

Tailpipe HC Emissions with Methods A and B Lines Superimposed ........................... 2-14

Figure 3-1. Vehicle Testing Flow ................................................................................................ 3-4

Figure 3-2. Comparison of 15-Minute PSHED and 60-Minute LSHED Measurements ......... 3-15

Figure 4-1. Typical On-Road RSD-4600 Set-Up ........................................................................ 4-2

Figure 4-2. HC Sniffer ................................................................................................................ 4-7

Figure 4-3. FLIR GasFindIR Gas Detection Video Camera ....................................................... 4-9

Figure 5-1. RSD Evap Index 1 and PSHED Values (Speed is approximately 12 mph) ............ 5-9

Figure 5-2. Linear Correlation of PSHED and Transformed RSD Evap Index 1 ..................... 5-11

Figure 5-3. RSD Evap Index 1 and PSHED Values ................................................................. 5-12
1-1 

1.0 Introduction 

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

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

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

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

the evaporative emissions modeling needs of the MOVES model, EPA, with the support of the 

Coordinating Research Council (CRC), is conducting studies to quantify fleet evaporative 

emissions. Ultimately, EPA would like to know the distribution of the mass of evaporative 

emissions across all vehicles in the fleet. 

This project follows the CRC projects looking at aging enhanced evaporative emission 

vehicles. E-77 Pilot and E-77-2 were laboratory testing studies which looked at ethanol and RVP 

fuel effects as well as implanted leak effects on different laboratory test procedures to capture 

evaporative emissions. The implanted leak data indicates there may be significant effects from 

small leaks which can multiply in the inventory. This follow-on study can shed light on 

determining how often the leaks or “High Evap” are found in the fleet. 

The effort begins by concentrating on the fleet vehicles that have high evaporative 

emissions (High Evaps). Initially, the objective is to find the percentage of High Evaps in the 

average fleet of on-road motor vehicle passenger cars and light-duty trucks. Our initial estimate 

is that High Evaps are 1% of the gasoline-fueled vehicles in the fleet. The remaining 99% of 

gasoline-fueled vehicles in the fleet also have evaporative emissions, but their evaporative 

emissions are low. 

This work assignment was in support of the EPA’s National Portable Emissions 

Measurement System (PEMS) Deployment contract, and is intended to serve as a research 

project designed to develop methods to estimate evaporative emissions from light-duty vehicles. 

Innovative methods for measuring and testing real-world evaporative emissions were studied and 

procedures were developed, to allow the evaporative emission testing of a larger fleet at a later 

date. 

In response to recommendations from the scientific community and interested 

stakeholders, EPA and the CRC are continuing to collect data designed to improve the methods 

and tools used to estimate evaporative emissions from the light-duty vehicle fleet. This data 

collection effort includes this pilot test program, which was designed to develop a quick, 

inexpensive test procedure to quantify evaporative emissions from the light-duty fleet in an 

Inspection Maintenance (IM) style setting. The target population for the methodological pilot 
1-2 

was 50 to 100 vehicles in the Denver area. Vehicles were screened for high evaporative 

emissions using a newly developed technique, and then subjected to a battery of evaporative tests 

to determine both the effectiveness of each short test vis a vis the other proposed short tests, and 

the standard laboratory evaporative SHED test. 

An important focus of this study was to investigate a number of options for measuring 

evaporative emissions. The primary requirements in the work assignment specified that the 

screening method be non-intrusive, quick, inexpensive, not require owner cooperation, and 

correlate well with accepted evaporative measurement techniques, i.e. Sealed Housing for 

Evaporative Determination (SHED) tests. Given these criteria, the method which appears to 

hold the most promise is use of remote-sensing devices (RSD). 

1.1 Pilot Study Objectives 

The goal of the project’s initial effort is to estimate the percentage of high evaporative 

emissions vehicles in the on-road fleet of gasoline-powered passenger cars and light-duty trucks. 

Specifically, the primary question is: 

What fraction of the fleet is made up of high evaporative emissions vehicles? 

A large field effort to answer this primary question immediately encounters two 

problems. First, using the standard method to measure the evaporative emissions of vehicles is 

expensive, time-consuming, and requires special test facilities – specifically, a standard 

laboratory SHED. Second, there is not a clear, well-accepted definition of “high evaporative 

emitters.” To make it practical to answer the primary question in a field testing environment, the 

primary question can be re-stated in terms of two secondary questions: 

A. What field method can serve as a practical and substantially accurate method of 

identifying high-emitting evaporative emissions vehicles (High Evaps)? 

B. What fraction of the fleet is made up of High Evaps as defined by the above field 

method? 

The primary focus of this pilot study was to answer Question A. The primary focus of 

subsequent studies will be to answer Question B. In summary, the effort to determine the 

percentage of the fleet that is made up of High Evaps is made up of two studies: 

1) The Pilot Study – The pilot study is the subject of this report. The data from the 

pilot study will be used to develop the methods, procedures, and design to be used 

in the main study. The conclusion of this pilot study was the development of a 
1-3 

detailed work plan for a larger study to be conducted in a non-IM area that would 

use the testing methodology identified in this pilot. 

2) The Main Study – The main study is the large-scale field study and is the subject 

of a future work assignment to determine the fraction of High Evaps in the fleet. 

This kind of study could include the use of Remote Sensing Data (RSD) data from 

a large on-road program to estimate the fraction of high emitters in the fleet using 

the relationships developed in the pilot study. 

The pilot study was conducted for the purposes of refining procedures for the main study. 

Specifically, the pilot study was to determine whether the portable SHED should be used in the 

main study as a field version of the standard laboratory SHED to measure hot-soak and gross 

liquid leak emissions. The following questions were investigated in the pilot study: 

a) How well do the results of the portable SHED method agree with the results of 

the standard laboratory SHED method? What are the characteristics of portable 

SHED testing for measuring the evaporative emissions of vehicles in the field? 

This includes issues such as ease of implementation, cost, number of vehicles 

tested in the portable SHED per day, level of personnel necessary, and 

measurement precision and accuracy. 

b) Can portable SHED measurements be performed in a way such that the results 

can be used to estimate the distribution of evaporative emissions of the fleet? 

c) What is the approximate fraction of the fleet that is made up of high evaporative 

emissions vehicles? 

d) What are the characteristics of three methods (RSD, modified California method, 

infrared video camera) for screening vehicles as High Evaps? 

- What are the accuracy characteristics (four-quadrant, true-positive, and 

false-positive) of the screening methods for identifying high evaporative 

emitters? 

- What are the practical characteristics of the screening methods? This 

includes issues such as ease of implementation, cost, time to complete one 

test, level of personnel necessary (both quantity and skill). 

e) Based on the experience of the pilot study, what refinements or modifications 

would be considered for the design of a larger study? 

In order to answer the questions above, a test procedure and sampling plan were 

developed to characterize the high-emissions tail of the distribution of evaporative emitting 

vehicles in the relatively new fleet of aging enhanced evaporative emissions on-road light-duty 

motor vehicles. The current fleet consists of vehicles with newer enhanced evaporative emission 
1-4 

technologies, which are now aging and have a potentially different incidence of high evaporative 

vehicles in the fleet than previous technologies at similar ages. No frequency data currently 

exists on the range of higher evaporative emission vehicles for these newer technologies. 

1.2 Description of Pilot Study Tasks 

In this work assignment, ERG conducted a project to develop a sampling plan and test 

procedures for identifying vehicles with high evaporative emissions. In this pilot study, a 

number of technologies and ideas were explored, including non-intrusive RSD-style 

measurements, identification of evaporative leaks using a hydrocarbon sniffer device, and 

portable SHED (PSHED) measurements. In addition to those intrusive and non-intrusive 

methods, technologies used in other industries were investigated to determine if other tools could 

be of use in the measurement of mobile source evaporative emissions. 

Field work for this pilot study was conducted in July and August 2008 in Denver, 

Colorado, in collaboration with the Colorado Department of Public Health and Environment 

(CDPHE). CDPHE has recently done work measuring evaporative emissions using RSD, and 

they also have facilities that allowed official laboratory SHED tests to be performed during the 

pilot study on a subset of the vehicles which had PSHEDs. 

This study was preceded by the Pre-Testing study. The pre-testing investigation was 

performed on simulated high evaporative emissions vehicles to develop field methods for 

detecting High Evaps and measuring their evaporative emissions. This work has been reported 

separately [1]. 

This study was a field method evaluation. Testing on 85 vehicles that were recruited 

from the general public was used to develop and evaluate vehicle selection and emissions 

measurement. This work is the focus of this report. 

The key variables that were surveyed or measured include: 

 Vehicle identifiers: License plate and Vehicle Identification Number. 

 Vehicle description: Model year, make, model, and odometer reading. 

 Vehicle usage and maintenance history through a vehicle owner survey. 

 Fuel type was assumed from local area fuels. 

 Time trace of cumulative HC concentration of the air inside the portable SHED 

after a vehicle’s engine is turned off and the portable SHED doors are sealed [6] 
1-5 

 Measured values of evaporative emissions vehicle screening methods: 

 Remote Sensing Device HC measurement, and HC concentration relative to 

CO2 concentration 

 Modified California Method (Under-hood visual inspection and electronic HC 

vapor leak detector inspection) 

 Infrared video camera 

1.3 Target Population and Coverage 

The target population includes all gasoline-powered light-duty vehicles and trucks. Lightduty vehicles have gross vehicle weight ratings of less than 8500 lbs. Light-duty trucks are 

trucks with gross vehicle weight ratings of less than 8500 lbs. Passenger cars and light-duty 

trucks form the majority of the on-road motor vehicle fleet. Nationally, they account for 96.6% 

of the on-road vehicle fleet and 89.0% of the total on-road vehicle miles traveled. Heavy-duty 

vehicles account for the remainder of the on-road vehicle fleet and the on-road vehicle miles 

traveled. 

The geographical area used for pre-testing and this study was Denver, Colorado. Denver 

was chosen for several reasons. The Colorado Department of Public Health and Environment, 

which is located in Denver, operates a laboratory SHED, runs the state’s inspection/maintenance 

(I/M) program, and runs the state’s on-road RSD measurement program. Those CDPHE 

resources were used in the pilot study. The Lipan Street I/M inspection station was used as a 

convenient source of private vehicles to solicit for the pilot study. Finally, the team that 

originally developed the RSD technique is located at the University of Denver. 

1.4 Funding Partners and Cooperative Participants 

Funds were contributed by two government partners and one industry partner. The first 

government partner is the Assessment & Standards Division within the EPA Office of 

Transportation and Air Quality (OTAQ) and the second partner is the Colorado Department of 

Public Health and Environment (CDPHE). The industry advisor is the Coordinating Research 

Council (CRC), a nonprofit research organization whose members include the American 

Petroleum Institute (API), the Society of Automotive Engineers (SAE), General Motors, Ford 

Motor, Chrysler, Toyota, Mitsubishi, Nissan, Volkswagen, and Honda. The Colorado 

Department of Public Health and Environment supported EPA’s test program by providing RSD 

equipment and staff to operate the equipment to support the RSD portion of the test program. 
2-1 

2.0 Background 

Evaporative emissions from gasoline vehicles have been evaluated and regulated since 

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

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

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

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

emissions from individual vehicles has been quantified in previous studies [2, 3, 4], but the 

frequency of vehicles in such a condition in the general population has only been estimated 

based on limited data [2, 4, 5]. 

The Coordinating Research Council Real World Group has been conducting permeation 

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

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

were specifically recruited as aging enhanced vehicles, model years 1996-2000. Three preenhanced vehicles and one newer, near-zero emissions vehicle were also tested. A focus was on 

non-ethanol fuel looking at both 7 and 9 RVP. The program also looked at 24-hour diurnals at 

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

subset of the vehicles was tested for 72-hour diurnals. The higher temperature diurnal data 

indicated that the predictions that hydrocarbon emissions double every 10 C was correct and 

therefore, no further investigative testing was necessary. There were, however, unexpected 

findings for the influence of fuel volatility (RVP). 

One of the aging enhanced vehicles had an implanted leak. The series of tests were run 

both before and after implanting the leak. This was accomplished by making a small hole in the 

evaporative control system. The size of the hole, 0.02 inch equivalent diameter, was the 

minimum detectable size necessary for an on-board diagnostic (OBD) code to flag. The 

laboratory testing results show the resulting hydrocarbon emissions to be several orders of 

magnitude larger with the implanted leak. This indicates a potential impact for the emissions 

inventory, establishing the need to define the rate of occurrence of “leakers” in the in-use fleet. 

Such implanted leaks can be in liquid or vapor form. 

Vapor leaks can also have an impact on the inventory. Traditional evaporative emissions 

testing, as well as the current E-77 testing, has shown a range of evaporative emissions, 

particularly with aging vehicles. The higher emission levels (but lower than the gross liquid 

“leakers”) are associated with slow vapor leaks possibly occurring as the system ages. The 

enhanced evaporative emission technology is now starting to age, and we do not have 
2-2 

information on the number of higher emission vehicles in the fleet. The High Evap occurrence 

rate is anticipated to be much lower than in the past, but leaks do occur. 

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

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

leaking vehicles occur. A comprehensive program for finding the quantity 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 [2], or the California Bureau of Automotive Repair “Evaporative 

Emission Impact of Smog Check” in 2000 [4]. The current fleet consists of aging enhanced 

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

2.1 Findings from California RSD Pilot Project 

Recent data collected in the California RSD Pilot project [7] suggest that the tailpipe HC 

channel of the RSD instrument used in that study, the ESP Accuscan 4000, may be influenced by 

a vehicle’s evaporative emissions, which are HCs. 

The RSD instrument uses a light beam shining across the roadway to measure pollutants 

in a vehicle’s tailpipe plume. The instrument has HC, CO, NO and CO2 channels. In the 

California study, a few days to several months after vehicles were measured by the on-road RSD 

instrument, a subset of the vehicles received their regular state inspection program tailpipe 

emissions test, known as the Acceleration Simulation Mode (ASM) test. Analysis of bins of the 

76,982 paired RSD and ASM results showed a quite linear relationship for CO and NO when the 

logit1

 of the ASM failure rate was plotted against the natural log of the RSD concentration. 

Figures 2-1 and 2-2 show the relationships for CO and NO. Straight line fits of the trends and 

95% confidence limits on the individual points are included. The upward trend in both plots 

shows that, on the average, vehicles that have higher RSD tailpipe concentrations were more 

likely to fail their state tailpipe emissions inspection for the same pollutant. 

 

1

 The logit (x) = ln (x/(1-x)), where x is a fraction between 0 and 1. In this case, the logit represents the “log of the 

odds of failure”. 
2-3 

Figure 2-1. ASM CO vs. RSD CO 

-6

-5

-4

-3

-2

-1

0

-4 -3 -2 -1 0 1 2 3

ln(RSD CO) (%)

logit (ASM CO Fail Fraction)

Figure 2-2. ASM NO vs. RSD NO 

-5

-4

-3

-2

-1

0

1

3 4 5 6 7 8 910

ln(RSD NO) (ppm)

logit (ASM NO Fail Fraction)
2-4 

Figure 2-3. ASM HC vs. RSD HC 

-5

-4

-3

-2

-1

0

1

2345678 9

ln(RSD HC) (ppm)

logit (ASM HC Fail Fraction)

However, when the same type of plot is made for the analysis of bins of the paired data 

for HC, two different regions of behavior are observed. See Figure 2-3. In the low RSD HC 

region (ln RSD HC < 6), the ASM HC fail rate follows the trends seen for CO and NO. That is, 

vehicles with higher and higher on-road RSD HC concentrations are more and more likely to fail 

the inspection station ASM HC tailpipe test. The data fall on a relatively linear trend as 

approximated by the solid red line. However, on the right side of the plot (ln RSD HC > 6), a 

second, different trend is observed. Here, the data points reach a plateau (logit ASM HC fail 

fraction = -1), which means that about 27% of the vehicles fail the tailpipe ASM HC tailpipe test 

even though their on-road RSD HC concentrations range from 400 ppm up to 4000 ppm. That 

is, in the high RSD HC region, ever increasing RSD HC concentrations do not translate into an 

ever increasing probability of failing the inspection station ASM HC tailpipe test. 

One explanation, but perhaps not the only explanation, for the observed HC behavior is 

the presence of High Evaps in the fleet sample. High Evaps could pass the inspection station 

ASM HC test because the ASM test is a tailpipe test, and therefore it is not influenced by 

evaporative emissions, which are emitted only from the fuel and fuel vapor handling systems of 

vehicles – not from their tailpipes. However, when a vehicle drives on the road, evaporative 

emissions can be detected with tailpipe emissions in the plume behind the vehicle. Depending on 
2-5 

how the RSD instrument processes the data obtained from its light beam, evaporative emissions 

could increase the reported RSD HC readings over what one would expect on the basis of 

tailpipe emissions alone. If the evaporative emissions are very high, the increase could be large 

enough to cause points on the linear trend in Figure 2-3 to be moved to the right of the expected 

trend depicted by the red line. 

Since evaporative emissions were not measured in the California RSD Pilot study, this 

explanation of the trends in Figure 2-3 is unconfirmed. Nevertheless, the explanation makes 

sense. In addition, Don Stedman, the developer of the RSD technique, is familiar with the data 

processing algorithm of the Accuscan-4000 instrument and believes that its high RSD HC 

readings may be influenced by High Evaps. Since algorithms of other RSD instruments may be 

less sensitive to evaporative emissions, this finding, if confirmed, could lead to the development 

of new RSD processing algorithms that could specifically target the on-road measurement of 

evaporative emissions. 

2.2 Findings from Colorado DPHE 

CDPHE has empirical evidence that the RSD4000 instrument can detect evaporative 

emissions and liquid gasoline leaks. CDPHE has been involved with remote sensing devices 

since 1996. The Department’s RSD experience spans several generations of ESP’s RSD 

technology (RSD2000, 3000, 4000, and 4600). In recent years, CDPHE and the Regional Air 

Quality Council (RAQC) have participated in an RSD-based, high hydrocarbon emitter 

identification and repair pilot program funded by the Congestion Mitigation and Air Quality 

Improvement Program (CMAQ). The goal of the study was to use RSD to find vehicles emitting 

more than 550 ppm HC as hexane from the tailpipe. An unexpected result of the effort was the 

apparent ability of the RSD4000 instrument to find evaporative emissions and gasoline liquid 

leakers. Several RSD-identified high HC emitters brought to state IM Tech Centers for an IM240 

confirmatory exhaust emissions test easily passed EPA IM240 final standards but were found to 

have gasoline vapor and/or liquid leaks. 

A simple, semi-quantitative follow up experiment by CDPHE staff used an RSD4000 

instrument with metered amounts of propane, unmetered amounts of liquid gasoline, and known 

concentrations of simulated exhaust from an RSD audit truck. This experiment resulted in a 

small empirical dataset that seemed to corroborate the claim by state IM Technical Center 

personnel that the RSD was identifying evaporative and liquid leaks. The data shown in Figure 

2-4 were obtained using the audit truck. This information illustrates that RSD technology appears 

to detect evaporative emissions. Both sets of RSD data introducing an evaporative emissions 
2-6 

problem, gas cap removal2 and canister disconnection, are noticeably higher than the “no 

problem” RSD data. A second, very brief qualitative investigation using unknown amounts of 

liquid gasoline, an IM240-passing 2000 model year passenger vehicle, and an RSD3000 

instrument produced only invalid RSD readings, which indicated there might be something other 

than high exhaust emissions. 

This disparity between the RSD4000 and RSD3000 results prompted an inquiry to Don 

Stedman of the University of Denver. He clarified that the calculations used by the RSD3000 

instrument were specifically designed to see only tailpipe HC, but the RSD4000 instrument and 

its offspring, the RSD4600, could theoretically see HC from any source. 

Figure 2-4. Reported RSD4000 HC on the Audit Truck with Induced Evaporative 

Emissions 

2.3 RSD4000/4600 Data Acquisition and Concentration Calculations 

Some background on the operation and calculations of the RSD4000/4600 instrument is 

useful for understanding the conditions under which RSD might be able to detect vehicle 

evaporative emissions. While the detailed calculations that are used in RSD instrument software 

 

2

 The gas cap removal shows minimal impact on HC concentration levels compared to disconnecting the canister. 

This compares to the CRC E-77 lab data on implanted leaks. There was a negligible increase in HC emissions when 

a leak was implanted in a gas cap on an ORVR equipped vehicle compared to one which did not have ORVR or a 

leak implanted near the canister or the top of the tank. 
2-7 

are proprietary to ESP, ESP personnel have provided some general information about how their 

current instrument operates and calculates emissions concentrations. 

The pollutants in the exhaust gas coming from the tailpipe of a vehicle are assumed to be 

well mixed and are assumed to be released from only the tailpipe outlet. It is also assumed that 

after emission from the tailpipe, the HC, CO, NO and CO2 all disperse into the ambient air at the 

same rate. 

When a vehicle drives past the RSD instrument, the RSD light beam passes through a 

portion of the dispersing tailpipe plume. The instrument measures the attenuation of IR or UV 

light caused by the presence of the chemical species in the plume 50 times at 10ms intervals. 

The size of the attenuation is the product of the concentration and the path length and therefore 

has units of ppm-cm for HC and NO and %-cm for CO and CO2. If the only source of the 

pollutants is the tailpipe exhaust, and if the ambient air has no pollutants, then the ratios of 

attenuations of any two pollutants will be constant for multiple readings taken in a vehicle’s 

exhaust plume even though the pollutant concentrations change as the plume disperses. Figure 

2-5 shows example time traces of the attenuations of HC, CO, NO, and CO2 as recorded by an 

RSD instrument for Test Set 18 and VDF 5 from the Summer 2008 Pre-Testing study [1]. The 

plot clearly shows that for this case, which does not have evaporative emissions, the attenuations 

move up and down together with time. That is, ignoring the different vertical scales, the time 

traces of all four pollutants have very similar shapes. 
2-8 

Figure 2-5 Time Histories with Zero Evaporative Emissions and 1100 ppmC3

Tailpipe HC Emissions 

0 50 100 150 200 250 300 350 400 450

0

5

10

15

20

25

30

35

40

45

Time (msec)

Pollutant Absorbance

HC (ppm-cm) / 100

CO (%-cm)

CO2 (%-cm)

NOx (ppm-cm) / 100

However, the purpose of the RSD instrument is not to determine the time dependence of 

the pollutant attenuations but instead is to determine the exhaust concentration of the pollutants 

at a given moment in time. To arrive at an estimate of the pollutant exhaust concentrations 

requires two steps. In the first step, the instrument uses the assumption that exhaust pollutant 

gases disperse together from the common exhaust point. If this is true, then plots of the 

attenuations of one pollutant against the attenuations of any other pollutant should produce a 

straight line passing through the origin. Figure 2-6 shows the HC, CO, and NO attenuations 

plotted against the CO2 absorbance for the data shown in Figure 2-5. The plot shows that the 

lines are quite straight with little scatter and pass near the origin. 
2-9 

Figure 2-6. Pollutant Attenuations with Zero Evaporative Emissions and 1100 

ppmC3 Tailpipe HC Emissions Versus CO2 with Regression Lines Superimposed 

5 10 15 20 25 30 35 40 45

0

5

10

15

20

25

30

35

40

CO2 (%-cm)

Pollutant Absorbance

HC (ppm-cm) / 100

CO (%-cm)

NOx (ppm-cm) / 100

If ambient pollutants are present before the vehicle passes by, then the instrument will 

also pick up contributions to the attenuations from the background. The RSD4000/4600 

instrument attempts to correct for the background by taking a measurement of all four pollutants 

just before the vehicle blocks the beam. These “front bumper” background attenuation values 

are subtracted from the raw tailpipe plume attenuation values to arrive at the backgroundcorrected attenuation values that are used to calculate the tailpipe emissions concentrations. The 

50 10ms attenuation observations provided with each RSD beam block and those plotted in 

Figures 2-5 and 2-6 have already had the background correction applied. Thus, if the 

background correction is accurate, plots of HC, CO, and NO attenuations versus CO2 attenuation 

should always pass through the origin. However, we have observed that even for vehicles with 

no evaporative emissions the regression lines through the data almost never pass through the 

origin and generally tend to have positive pollutant attenuation intercept values. 

Our understanding is that the RSD3000 calculation method (also known as Method B) 

uses the slope of the regression of the HC attenuation versus CO2 attenuation to calculate the 

concentration of HC relative to CO2. On the other hand, the RSD4000 calculation (also known 
2-10 

as Method A) uses the sum of the HC attenuations divided by the sum of the CO2 attenuations to 

determine the concentration of HC relative to CO2. 

The second step in the calculation uses combustion stoichiometry. The RSD instrument 

calculations assume a particular composition for gasoline that contains carbon, hydrogen, and 

oxygen which when combusted with air will produce a mixture of HC, CO, NO, and CO2. The 

balanced chemical equation for this reaction is then used to convert the relative pollutant 

concentrations as determined from the slopes of the 50 10ms attenuation measurements of the 

RSD instrument into estimates of the absolute pollutant concentrations by assuming that the 

gasoline in the vehicle has approximately the same composition as the average gasoline used for 

the calculations. This second step is used both for Method A and Method B calculations. 

The RSD4600 instrument uses the measured attenuations of HC, CO, NO, and CO2 plus 

the combustion stoichiometry of the combustion of typical gasoline to arrive at the reported RSD 

concentrations for the four pollutants. Based on 29,723 RSD measurements made in San Antonio 

in November 2008, the RSD4600’s reported concentrations always obey the following equation: 

[CO2] = 150537.66 - 0.7168 * [CO] - 0.3011 * [HC] - 0.3584 * [NO] 

where: [CO2] is the CO2 concentration in ppm, 

 [CO] is the CO concentration in ppm, 

 [HC] is the HC concentration in ppmC3, i.e., ppm Propane, and 

 [NO] is the NO concentration in ppm. 

Now we need to consider the situation when evaporative emissions are present. 

Evaporative emissions on a vehicle are predominantly HC (some oxygenates may be present if 

the fuel contains them) and can be emitted from the vehicle from one or multiple sources but the 

source of evaporative emissions will never be the tailpipe. Accordingly, the characteristics of 

dispersion of evaporative emissions will be different from the dispersion of the tailpipe 

emissions. Evaporative emissions can be emitted as vapor or as leaking liquid gasoline. Since 

the RSD instrument can detect only vapor, liquid leaks must at least partially evaporate for an 

RSD instrument to detect them. A vehicle’s evaporative emissions plume will not necessarily 

intermingle with its tailpipe plume. However, it may pass through the RSD instrument’s light 

beam at the same time that the tailpipe plume is passing through the light beam. When an 

evaporative emissions plume does this, it will cause the HC attenuation to be larger than it would 

be if the evaporative emissions were not present. 

Figure 2-7 shows the time series of the attenuations for the audit truck for Test Set 18 and 

VDF 11 when evaporative and tailpipe emissions were forced to occur. In this case, the shapes 
2-11 

of the time series for CO, NO, and CO2 are similar to each other, but the shape of the time series 

for HC is different. Comparison of the HC trend with the CO, NO, and CO2 trends indicates that 

the HC attenuation has a large increase beginning at about 100ms. This trend is much more 

obvious when the attenuations for the time series are plotted versus the CO2 attenuations as 

shown in Figure 2-8. While the CO versus CO2 and NO versus CO2 plots remain as straight 

lines, the HC versus CO2 curve shows an increase in HC attenuation relative to CO2 attenuation 

and also shows a non-linear behavior. The quantification of this non-linear behavior can be used 

to develop an RSD evaporative emissions index. 

Figure 2-7. Time Histories with 15 scfh Evaporative Emissions and 1100 ppmC3

Tailpipe HC Emissions 

0 50 100 150 200 250 300 350 400 450

-20

0

20

40

60

80

100

Time (msec)

Pollutant Absorbance

HC (ppm-cm) / 100

CO (%-cm)

CO2 (%-cm)

NOx (ppm-cm) / 100
2-12 

Figure 2-8. Pollutant Attenuations with 15 scfh Evaporative Emissions and 1100 

ppmC3 Tailpipe HC Emissions with Regression Lines Superimposed 

0 10 20 30 40 50 60

-20

0

20

40

60

80

100

CO2 (%-cm)

Pollutant Absorbance

HC (ppm-cm) / 100

CO (%-cm)

NOx (ppm-cm) / 100

2.4 Influences of Evaporative Emissions on Method A and B Calculations 

Methods A and B use two different calculations that are both based on the same 50 10ms 

absorbance pairs to arrive at different estimates of vehicle HC concentrations. Based on our 

understanding of the calculations, we believe that the difference between the reported values by 

the two methods provides an initial RSD evaporative index. This subsection describes why this 

might be the case. 

Figure 2-9 contains actual HC and CO2 attenuation data from an RSD beam block of the 

audit truck when it was producing simulated exhaust emissions but no simulated evaporative 

emissions. The slope of the line (blue dashed line) that connects the origin with the centroid of 

the HC attenuation versus CO2 attenuation cluster of points represents the Method A result. 

When the Method B regression line (red solid line) through the string of points passes through or 

near the origin, as in Figure 2-9, the slopes calculated by Methods A and B will be close to each 

other and will therefore produce similar RSD HC reported values. 
2-13 

Figure 2-9. Pollutant Attenuations with No Evaporative Emissions and 1100 

ppmC3 Tailpipe HC Emissions with Methods A and B Lines Superimposed 

0

5

10

15

20

25

30

35

40

45

0 10 20 30 40

CO2 (%-cm)

HC (ppm-cm) / 100

HC/100 vs. CO2

Method A line

Method B line

On the other hand, consider Figure 2-10 which contains actual RSD attenuation values 

for a case where the audit truck had simulated exhaust emissions and high artificial evaporative 

emissions. In this case, for Method A, the slope of the dashed blue line connecting the origin 

with the centroid of the string of points has a slope that is much larger than the slope of the red 

solid line through the points by regression. Thus, the reported value of RSD HC by Method A 

would be substantially larger than the value reported by Method B. The reason that the slope by 

Method A is larger than the corresponding line in Figure 2-9 is that the HC attenuation values 

have been shifted higher because of the presence of the evaporative emissions. When 

evaporative emissions are present but are not extremely high, we would expect that the HC 

attenuations would be higher than if evaporative emissions were not present but not as high as 

they are when evaporative emissions are higher. 
2-14 

Figure 2-10. Pollutant Attenuations with 15 scfh Evaporative Emissions and 1100 

ppmC3 Tailpipe HC Emissions with Methods A and B Lines Superimposed 

0

20

40

60

80

100

120

0 10 20 30 40 50 60

CO2 (%-cm)

HC (ppm-cm) / 100

HC/100 vs. CO2

Method A line

Method B line

Overall, therefore, the Method A calculation produces an RSD HC concentration that 

tends to be influenced both by the exhaust HC and the evaporative HC emissions. On the other 

hand, the Method B calculation tends to produce a reported HC concentration that is less 

influenced by evaporative HC emissions than the Method A calculation. As a consequence of 

these different sensitivities of Methods A and B to evaporative emissions, the difference between 

the Method A and Method B reported values could be a useful measure of the level of 

evaporative emissions that a vehicle produces. In addition, it is possible that this measure of 

evaporative emissions may be relatively independent of the level of exhaust hydrocarbon 

emissions from vehicles. We will call Method A minus Method B the RSD Evap Index 0. RSD 

Evap Index 0 will serve as the starting point for development of future RSD evaporative 

emissions indices. RSD Evap Index 0 is based on a calculation involving the backgroundcorrected 50 10ms absorbance values from each beam block. Many different RSD evaporative 

emissions indices could be developed from these 50 10ms observations. Since there is no 

particular reason to believe that RSD Evap Index 0 is the best index, a search for better 

performing indices can be made. 
3-1 

3.0 Data Collection 

The goal was to perform testing on about 100 vehicles for the purposes of evaluating 

screening methods that could identify vehicles with high evaporative emissions, to develop and 

qualify a field method that could distinguish between vehicles with low and high evaporative 

emissions, and to make an initial estimate of the fraction of high evaporative emissions vehicles 

in the fleet. Tests were ultimately performed on 85 vehicles by generating data in the pilot study 

which was performed in Denver, Colorado. 

To reduce the time associated with identifying high evaporative emissions vehicles, we 

tested vehicles in three steps, which are elaborated upon in Sections 3.1 through 3.3: 

In the first step, we screened the HC emissions of all vehicles entering the Lipan Street 

I/M station as they drove by a remote sensing device placed in the station’s entrance driveway. 

Based on their RSD HC emissions levels, these vehicles were categorized into 9 emitter groups 

that covered the entire range of RSD HC emissions (see Table 3-1). 

In the second step, we drew a stratified random sample of vehicles for participation in the 

field method evaluation (see Section 3.2). This set of vehicles was recruited and tested on site to 

evaluate three different evaporative emissions screening methods. Each vehicle also had its hotsoak and gross liquid leak emissions measured in the portable SHED (see Section 3.3.3). 

In the third step, a fraction of vehicles participating in the field method evaluation were 

requested to have evaporative emissions measured in CDPHE’s laboratory SHED near the 

recruitment site, and ultimately 23 were tested on both the PSHED and in a laboratory SHED. 

These data were compared with the portable SHED results to qualify the portable SHED 

technique for use in subsequent Work Assignments (this portion of the work has been previously 

reported [6]). 

The types of data that were surveyed or measured included: 

 Vehicle identifiers and description: license plate, vehicle identification number, 

model year, make, model, odometer reading, and other identifying information on 

vehicles that participated in the study. Photographs were taken of each vehicle to 

assist in data quality control. The vehicle pictures were taken from various angles 

and included a white board which included the test number assigned to the vehicle. 

Photos of the VECI label and VIN were also taken. 

 Vehicle usage and maintenance: A vehicle owner survey was used to acquire 

vehicle usage and maintenance history. 
3-2 

 Portable SHED measurements: These include the evaporative emissions measured 

in the portable SHED environment. These emissions are expressed in terms of the 

mass evaporative emissions released into the portable SHED, the maximum HC 

concentration within the SHED, or the time trace of HC concentration of the air 

inside the portable SHED after a vehicle’s engine is turned off and the portable 

SHED doors are sealed. They are the study’s measure of the estimated 

evaporative emissions from each vehicle. 

 RSD HC emissions measurements: These are the exhaust and evaporative 

emissions measured for the purposes of screening high evaporative emitters. 

 Qualitative observations of evaporative emissions: Vehicle observations via HC 

sniffer and visual inspection and infrared video camera were recorded by on-site 

technical staff. 

The experimental design and results for the Pre-Testing have already been described in a 

separate report [1], and the Portable SHED Evaluation has also been reported [6]. The 

procedures used for collecting data for the field evaluation are described in the following subsections. The different types of data that were collected are described in Section 3.1. The 

sampling plan that was used for recruitment of vehicles based on an RSD observation is 

described in Section 3.2, as well as a description of the test sequence that was performed on each 

of the sampled vehicles. 

3.1 Sequence of Field Testing Procedures for the Pilot Study 

For the pilot study, ERG conducted measurements on vehicles owned by the general 

public. Vehicles were screened for participation using RSD as they approached an I/M Station. 

Participating vehicles were subjected to a series of RSD measurements, evaporative emissions 

measurements, and visual inspection. 

The stratified sample system described in Section 3.2 was used to preferentially select 

higher evaporative emitting vehicles. The owners of vehicles that were identified as potential 

study participants were solicited by ERG personnel while they waited for their I/M test. They 

were asked if they would like to participate in the study, allowing their vehicle to undergo 

additional evaporative emissions testing. Those that agreed to participate were given a 

questionnaire that would help determine the most recent repairs, re-fuelings, usage patterns, and 

other information. The additional evaporative emissions tests were then performed on the 

vehicle. 

Solicitors and other testing personnel did not have knowledge of the observed RSD 

emissions of the candidate or selected vehicles. 
3-3 

The diagram in Figure 3-1 below illustrates how the procedures flowed from the initial 

RSD HC screening through the various evaporative emissions tests. A total of 85 vehicles were 

ultimately tested with this test matrix. 
3-4 

Figure 3-1. Vehicle Testing Flow 

I/M Testing

Collect Final Vehicle Info 

Portable SHED 

Non-Participants 

Participants 

1: Selection RSD 

3. Evaporative 

Emissions 

Data Collection 

Modified California Method 

Release Vehicle 

Conditioning Drive 

+ 2 Measurement RSDs 

2: Solicitation (+ Collect Participant Initial Vehicle Info) 

IR Camera 
3-5 

Flow Diagram Step 1: Screening Using Remote Sensing Device 

All vehicles entering the IM station driveway had the emissions plume scanned by an 

RSD-4600 instrument to measure emissions concentrations. RSD instruments perform these 

measurements by shining a light beam across the roadway. While the driver could see the 

equipment, measurements were performed without notifying the vehicle driver that they were 

being taken. For each vehicle the following quantities were automatically taken as the vehicle 

passed by the RSD instrument: 

 DateTime: The date, hour, minute, and second of the RSD measurement. 

 Speed and Acceleration: The speed and acceleration of the vehicle. 

 The calculated VSP of the vehicle. 

 RSD-4000 Emissions3

: The concentrations of HC, CO, and NO in the vehicle’s 

plume according to the Method A calculation. 

 RSD-3000 Emissions: The concentrations of HC, CO, and NO in the vehicle’s 

plume according to an approximation of the Method B calculation. 

 License Plate: A digital image of the rear of the vehicle was recorded so that the 

license plate could be determined. 

3.1.1.1 Flow Diagram Step 2: Solicitation 

Based on the screening Method A HC values, the VSP, and the number of vehicles 

desired for each RSD/age bin in the stratified random plan, a sample of passenger cars and lightduty trucks was approached by the solicitor. The solicitation was usually made as the owner’s 

vehicle was waiting in line for the inspection to begin. The solicitor performed the following 

activities: 

 Introduction: The solicitor explained that a U.S. EPA emissions study was being 

conducted and measurements would take about one hour. The solicitor explained 

that the measurements would involve driving the vehicle for about 20 minutes, 

driving past the RSD unit two times, performing under-hood visual inspections, 

and testing the air in the portable SHED after the vehicle had sat in it for 15 

minutes. 

 Ownership: The solicitor asked if the driver owned the vehicle. Only vehicles 

with their owners driving were eligible for participation. Company vehicles or 

dealer vehicles were not allowed as participants. 

 

3

 A single instrument was deployed, but the different calculation methods were used to estimate emissions: “RSD4000” and “RSD-3000”. 
3-6 

 Incentive: The solicitor offered a $20 cash incentive. The incentive was provided 

whether or not the vehicle owner participated. 

 Vehicle Information: The solicitor collected an initial vehicle description 

including license plate, model year, make, model, and color as described in 

Appendix A. 

 Owner Questionnaire: The solicitor administered the owner questionnaire to 

gather recent vehicle usage and maintenance history information. (Appendix A). 

Flow Diagram Step 3: Evaporative Emissions Data Collection 

Following completion of the IM inspection, participating vehicles would then undergo 

the following tests: 

 RSD Emissions: A technician conditioned the vehicle for 20 minutes on a set 8-

mile route4

, which simulated the Federal Test Procedure (FTP) typically used as 

preconditioning for a Hot Soak laboratory test. Then drove it past the RSD unit 

two times to obtain RSD measurements that were independent of the screening 

RSD used to select the vehicle for participation. The same type of data was 

recorded as for the screening drive. The technician driver verified with the RSD 

technician that all RSD results were valid and that the VSP was in range. 

 Portable SHED Emissions: With the engine still running, the vehicle was driven 

just outside the open PSHED door. Upon command from the PSHED technician, 

the engine was turned off, the vehicle was pushed into the PSHED, and the 

PSHED door was sealed. The HC emission concentration of the air inside the 

PSHED was measured continuously with the SEMTECH-G for 15 minutes. 

Following the measurement, the vehicle was driven out of the PSHED, and the air 

in the PSHED was flushed. 

 Modified California Method: A visual and olfactory inspection of evaporative 

emission control system and the fuel system was performed to look for missing, 

malfunctioning, damaged, or disconnected components. At the same time, a 

handheld electronic HC vapor detector was used to try to find sources of 

evaporative emissions by moving the small probe of the detector around 

components, fittings, and hoses. 

 Infrared Video Camera: The infrared video camera recorded video of those areas 

around the vehicle where evaporative emissions might be present. This included 

over the engine compartment with the hood opened and closed, and around the 

gasoline fill pipe. 

 

4

 Turn north on Lipan Street. Drive 200m. Turn east on W. Evans Avenue. Drive 675 m. Turn south on S. Santa Fe 

Drive. Drive 2875 m. Turn west on W. Hampden Avenue. Drive 2000 m. Turn north on S. Federal Blvd. Drive 

3000 m. Turn east on W. Evans Avenue. Drive 2000 m. Turn south on Lipan Street. Drive 200 m. 
3-7 

 Detailed Vehicle Information: Vehicle information was collected including VIN, 

engine family, evap family, odometer reading, transmission type, and photos as 

shown in the data packet in Appendix A. Digital photographs of serial numbers 

and equipment specification tags were taken as indicated, to help correct any 

inaccurate information recorded during on-site inventories. 

 Final Check: After verifying that all items were collected and were complete, the 

vehicle was released to its owner (unless the vehicle was a candidate for the 

portable SHED / laboratory SHED evaluation). 

Of the 85 vehicles that completed the steps described above, a subset of 23 vehicles 

participated in a portable SHED / laboratory SHED evaluation. A laboratory-grade 1-hour hotsoak5

 was conducted at CDPHE facilities for these 23 vehicles. Results of the portable SHED / 

laboratory SHED evaluation have previously been reported [6]. 

3.2 Stratified Random Sample of Vehicles for Field Testing 

During the planning phase of the study, we estimated that 99% of all vehicles are not 

High Evaps. Since most vehicles are not excessive emitters of evaporative emissions, stratified 

random sampling was used to seek a preferentially larger number of vehicles that were potential 

high evaporative emitters. In this pilot study, we used RSD HC to screen vehicles into nine HC 

emitting categories. We selected vehicles from each of the nine screening RSD HC bins to be 

solicited for participation in the pilot study. The sample was stratified with allocation among 

RSD HC emissions bins. 

As described in Section 2 (Background), we have circumstantial evidence that the HC 

channel of certain types of RSD instruments may be able to detect vehicles with high evaporative 

emissions levels. By preferentially sampling more vehicles from the higher RSD HC bins of 

screening RSD measurements, we attempted to capture a larger fraction of High Evaps in the 

pilot study than could be captured by completely randomized sampling from the fleet as a whole. 

Design of a stratified sampling strategy to achieve the desired precision required an 

estimate of the abundance of High Evaps in the fleet as a function of the stratifying variable. 

Unfortunately, no dataset existed that could clearly define High Evap levels. However, we 

assumed that the trends in Figure 2-3 were caused by High Evaps. Therefore, the data collected 

in the California study was used to develop a stratified sampling design for this study. 

 

5

 The lab SHED test was a standard one-hour hotsoak test. It was done with standard pre-conditioning, at standard 

SHED temperature for a hot-soak, and it was performed using the in-use fuel in the vehicle’s fuel tank. The singlevalue hot-soak result and the minute by minute SHED HC concentrations were both reported for each hot-soak test. 
3-8 

Table 3-1 summarizes the data from the California study that were used to develop a 

stratified sampling plan for this study. Columns B and C give the bin definitions in terms of the 

RSD HC as determined by Method A, which is influenced by evaporative emissions and exhaust 

emissions. Columns D and E give the distribution of vehicles that had an inspection station ASM 

test that followed the on-road RSD measurement. These columns indicate that the distribution of 

RSD HC emissions in the fleet is highly skewed with about 83% of the vehicles having RSD HC 

emissions below 148 ppm. Column F shows the number of High Evaps that we estimated from 

Figure 2-3. Specifically, we assumed that the gap between the data points and the red line in 

Figure 2-3 is caused by the presence of High Evaps in the fleet. Column G gives the estimated 

probability (=F/D) that vehicles in each bin are High Evaps. The fleet estimate from this data 

suggested 0.75% of the vehicles based on the California data are high evaporative emitters. 

Table 3-1. Expected High Evap Fractions Using California RSD Data 

A B C D E F G 

Bin Definitions: 

RSD HC (ppm) 

by Method A Vehicle Population 

High Evap Vehicles 

(estimated) 

Bin 

Greater 

Than 

Less 

Than 

or 

Equal 

To 

Number 

of 

Vehicles 

in Bin 

Population 

Fraction 

in Bin 

(Wh) 

Number 

of 

High 

Evaps 

in Bin 

Fraction of 

Vehicles in 

Bin that 

are High 

Evaps 

(ph) 

1 -Inf 0 24101 0.3131 0 0.000002 

2 0 90 32959 0.4281 1 0.00005 

3 90 148 7094 0.0922 3 0.0005 

4 148 245 5476 0.0711 15 0.0027 

5 245 403 3644 0.0473 49 0.013 

6 403 665 2138 0.0278 107 0.050 

7 665 1097 917 0.0119 143 0.156 

8 1097 1808 365 0.0047 112 0.308 

9 1808 Inf 288 0.0037 148 0.513 

 

All 76982 1.0000 578 0.75% 

From the information in Columns A through G, we used the technique for optimum 

stratified sampling, which is described in Appendix B, to develop a plan for achieving the 

precision target. The optimum plan was modified, however, to meet the objectives of this study 

and to address the vulnerabilities to sub-optimal sampling.[9] 

A random number generator was used to determine whether or not a vehicle observed by 

RSD entering the Lipan Street IM station would be solicited for the study. A threshold between 
3-9 

zero and one was assigned to each of the nine bins, with a higher threshold for the higher RSD 

HC bins. The random number generator assigned a random number between zero and one to 

each vehicle that was observed by RSD. The vehicle was then assigned to one of the nine bins, 

based on its RSD HC value. If the vehicle’s random number was below the threshold number for 

the vehicle’s bin, then the vehicle was solicited for the study. These thresholds represented 

selection probabilities. The random number thresholds for the nine bins were modified at times 

during the pilot study, as initial results were used to determine whether the actual sampled 

vehicles were falling into the desired distribution among the nine bins. The random number 

thresholds that were used and the dates that they were used are listed in Table 3-2. 

Table 3-2. Random Number Thresholds for Soliciting at the Lipan Station 

A B C D E F 

Bin Definitions: 

RSD HC (ppm) 

By Method A Random Threshold

Bin 

Greater 

Than 

Less 

Than or 

Equal To 

7/28/2008 – 

8/7/2008 

8/7/2008 – 

8/12/2008 

8/12/2008 – 

8/30/2008 

1 -Inf 0 0.000883 0.0026 0.0280 

2 0 90 .00412 .0124 .0160 

3 90 148 .01299 .0361 .093 

4 148 245 .0318 .0579 .118 

5 245 403 .1535 .2257 .54 

6 403 665 .288 .2191 .42 

7 665 1097 .480 .2085 .52 

8 1097 1808 .622 .2477 1.00 

9 1808 Inf .657 .0627 .30 

There are at least two reasons that the rate of occurrence of High Evaps in each of the 

nine bins might not turn out to be the same as the expected values shown in Column G of Table 

3-1. First, our method to determine the rates was based on our interpretation of the trends seen in 

Figure 2-3 in terms of evaporative emissions. Second, while RSD can estimate the average 

emissions of a fleet of vehicles, RSD’s ability to properly classify individual vehicles in 

emissions bins is subject to considerable uncertainty caused by the variability of vehicle 

emissions and by variability in the RSD technique itself. 

Vehicles were screened for possible participation in the study based on their first RSD 

HC emissions measurement. However, RSD HC measurements are subject to large variability. 

Therefore, because of large expected regression-toward-the-mean effects, it was important to 

recognize that the RSD HC bin assignments and all data analyses must be based on a second 

RSD reading. Because the set of vehicles were selected by RSD HC, a measurement subject to 
3-10 

error, the second RSD HC measurements, on which the results of the study were based, were, in 

many cases, different from the first RSD HC measurement. 

The final sampling design for the Pilot Study is shown in Table 3-3. Of the 113 vehicles 

that were to be sampled, nine vehicles were expected to be High Evaps6

 as shown at the bottom 

of Column H. The primary High-Evap-occurrence results of the pilot study would be the nine 

High Evap fractions shown in Column I. With these nine values and population fractions for any 

application population (such as the nationwide fleet), the overall fraction of High Evaps in the 

application population could be calculated as described in Appendix B. 

Table 3-3. Field Method Evaluation Stratified Sampling Design for RSD HC Bins 

A B C E F G H I 

Bin Definitions: 

RSD-4600 HC (ppm) 

Planned Allocations 

in RSD Screening

Bins 

Expected Allocations 

in RSD Measurement

Bins 

Bin 

Greater 

Than 

Less 

Than 

or 

Equal 

To 

Population 

Fraction 

in Bin

Size of 

Screening 

Sample 

Needed to 

Fill Bin 

Number 

of 

Vehicles 

Number 

of 

Vehicles 

Number 

of High 

Evaps 

Fraction of 

Vehicles in 

Bin that 

are High 

Evaps 

1 -Inf 0 0.3131 0 0 7 0 0.000

2 0 90 0.4281 0 0 15 0 0.000

3 90 148 0.0922 0 0 8 0 0.000

4 148 245 0.0711 0 0 13 0 0.000

5 245 403 0.0473 681 32 18 0 0.000

6 403 665 0.0278 737 21 20 1 0.050

7 665 1097 0.0119 1647 20 15 2 0.133

8 1097 1808 0.0047 7298 34 8 2 0.250

9 1808 Inf 0.0037 1514 6 9 4 0.444

All 1.0000 113 113 9 

3.3 New Test Procedures for the Pilot Study 

Several of the testing procedures that were used in this pilot study were new to the field 

of mobile source evaporative emissions, and underwent preliminary testing and validation at the 

beginning of the study. This included the Modified California Method (see Section 3.3.1) and 

the IR Camera (see Section 3.3.2). Additionally, the use of the portable SHED (as opposed to 

the laboratory SHED) was new. The portable shed was tested and results compared to the 

laboratory SHED as part of separate task under this project, and those results have already been 

reported [6]. However, since the use of the portable SHED is integral to the pilot study 

 

6

 Note that for this project there was no agreed-upon definition of High Evap. For Table 3-3 a High Evap is defined 

as any vehicle that would cause the black dots to deviate from the red line in Figure 2-3, which was derived from the 

California RSD data. 
3-11 

described here, the results of the portable SHED to laboratory SHED comparisons will be 

summarized in Section 3.3.3. 

3.3.1 Modified California Method 

We used a modified version of the California IM Liquid Leak Test Procedure. See 

Appendix C for the standard operating procedure for the California Procedure, which is the 

visual inspection portion. The procedure was identical to the visual check implemented in 

January 2008 by the California Bureau of Automotive Repair, with the addition of a HC sniffer 

to aid in leak detection. California’s procedure is as follows: 

 The liquid fuel leak inspection shall be conducted with the engine running. Use 

extreme caution when working around moving parts and ensure the transmission 

is in “park” or “neutral” with the parking brake on. 

 Definition: For the purpose of conducting this inspection, a “Liquid fuel leak” is 

defined as follows: “Liquid fuel leak” means any fuel emanating from a vehicle’s 

fuel delivery, metering, or evaporation systems in liquid form that has created a 

visible drop or more of fuel on a component of a vehicle’s fuel delivery, metering 

or evaporation system or has created a fuel puddle on, around, or under a 

component of a vehicle’s fuel delivery, metering, or evaporation system. 

 Inspection: With the engine running, the smog check technician shall visually 

inspect the following components of the vehicle, if they are exposed and visually 

accessible, for liquid fuel leaks: 

Gasoline Fuel Tanks 

Carburetors 

Fuel Injectors 

Gasoline fill pipes and associated hoses, tanks, connections 

Gas Caps 

Fuel pressure regulators 

External fuel pumps 

Charcoal canisters 

Fuel delivery and return lines 

Any valves connected to any other fuel evaporative component 

Fuel vapor hoses 

Fuel filers 

The California procedure was modified by the addition of a HC sniffer. The “sniffing” 

test was conducted simultaneously with and at the same vehicle locations as the visual 

inspection. The HC sniffer (see Section 4.3) is a hand held combustible gas detector that has 

been found useful in locating small liquid and vapor leaks. These units are typically used by 
3-12 

home appliance installers to verify gas tight connections and in the automotive trade industry to 

pinpoint liquid and vapor gasoline leaks. 

When the unit detects hydrocarbons it signals the operator by flashing a small red light 

and creating an audible click. As the concentration of vapors increases, the frequency of the 

light and audible pulses increases. The sensitivity of the unit is adjusted to just stop pulsing at 

ambient HC levels. The flexible wand is then used to direct the detector tip to the vicinity of 

suspected leaks. This unit is extremely sensitive to HC vapor, and has been successfully used in 

a number of emission laboratory applications. Additional recommended tools are a small 

flashlight, an extendible inspection mirror, and a mechanic’s creeper to view under body fittings 

and components. 

3.3.2 Infrared Video Camera 

A technician used an infrared video camera (see Section 4.4) to “film” areas under the 

hood, around the fuel tank, and around fuel lines. The IR video camera recorded video of those 

areas around the vehicle where evaporative emissions might be present. This included areas near 

the engine compartment with the hood opened and closed, and around the gasoline fill pipe. 

The IR camera was used to capture evaporative emission images from a vehicle with the 

fuel cap removed. Again, this was done to establish confidence that the unit is capable of 

detecting gross level evaporative emissions. The possibility that the background heat from the 

engine could negatively impact the IR camera performance meant that extra care was taken 

during this portion of the experiment to develop detailed instructions for how the unit should be 

used to best capture evaporative images from a vehicle. 

3.3.3 Evaluation of the Portable SHED 

One of the field methods that was tested as part of this pilot was the use of a portable 

SHED (Sealed Housing for Evaporative Determination) or PSHED to perform a short hot-soak 

emission test in an I/M lane setting. The development of this technique included performing 

regular propane retention and recovery tests to establish the integrity of the PSHED. 

Additionally, paired hot-soak data on 23 vehicles were obtained to allow the comparison of 

PSHED measurements to the traditional laboratory SHED (LSHED) measurements. It should be 

noted that other evaporative emissions tests are commonly performed during EPA’s certification 

testing, such as running loss and diurnal testing; however, these are considerably more complex 

than a hot-soak test, and therefore these tests were not attempted using the PSHED equipment. 
3-13 

The PSHED is a portable 10’ x 20’ x 8’ sealed enclosure7. To determine the evaporative 

emissions produced by a vehicle, the vehicle is warmed-up by driving it for about 8 miles and is 

then immediately placed in the PSHED with the engine off, and the enclosure is sealed. During 

the next 15 minutes the HC concentration in the air inside the PSHED is measured as any 

evaporative emissions leave the vehicle. At the end of 15 minutes the HC concentration is used 

to calculate the mass of HC that has been emitted by the vehicle. This PSHED mass is the 

quantity that is used to determine whether or not the vehicle is a High Evap. 

The results of the PSHED evaluation are briefly summarized below; additional detail may 

be found in the PSHED evaluation report [6]. 

Recovery and Retention Data - A recovery test consists of injecting a known amount of 

propane into the PSHED and then measuring the amount of propane detected in the PSHED after 

a short period of time. This test verifies that the analytical equipment is working and establishes 

a baseline level of propane above the ordinary ambient background level. The retention test is 

the measurement of the propane level in the PSHED after 15 minutes, and it establishes the 

integrity of the PSHED ensuring that there are no major leaks during the time period that a 

vehicle would be tested. 

Both retention and recovery values are presented as a percent of the measured mass of 

propane injected initially. Values will likely be less than 100% because it is far more likely the 

propane will be lost from the PSHED (or any enclosure) than introduced into the enclosure by 

the ambient background or some other mechanism. For the purposes of this study, we believe 

the retention values reveal information about the accuracy of the PSHED since retention reflects 

directly on the unit’s leak integrity and will inherently capture recovery information. 

Additionally, the recovery data is superior to the paired vehicle data between the PSHED and the 

LSHED for evaluating PSHED performance simply due to the inescapable vehicle test-to-test 

variability. 

The average and standard deviation PSHED recovery and 15-minute retention 

measurements are presented below. This data was collected at the start of each day of vehicle 

testing and ensured that the analytical equipment was functioning properly and the PSHED had 

not developed any significant leaks. It can be seen from these values that the unit’s performance 

was good and in fact exceeded expectations for a PSHED unit costing less than $400 plus 

 

7

 Purchased through elitedeals.com. Manufactured by King Canopy, 1730 Five Points Lane, Fuquay-Varina, NC 

27526, (800)800-6296, Kingcanopy.com. 
3-14 

instrumentation and gas costs could provide such accurate and precise retention and recovery 

values. 

Average Recovery 97.6% 

Recovery Standard Deviation 3.3% 

Average Retention for 15 minutes 95.7% 

Retention Standard Deviation 2.3% 

PSHED vs. LSHED - The PSHED performed in the IM lane consisted of a 15-minute 

test that was performed after a test vehicle had been taken on a conditioning drive over a 

prescribed route. The conditioning drive was important because laboratory SHED hot soak 

evaporative emissions are measured following a standardized drive cycle on a dynamometer. 

Therefore, to mimic this procedure with the understanding that future PSHED measurements 

would likely be performed without any access to a dynamometer, the test vehicles were 

conditioned on the road over a set route. In addition to the standard dynamometer conditioning 

drive cycle, an LSHED is also a one hour test, with strict temperature and fuel level controls. 

Given the objective of developing a quick evaporative test, the PSHED was only 15 minutes in 

duration and there was no attempt made to control the temperature or the fuel level of the 

vehicle. 

The results of the paired tests are illustrated in Figure 3-2 below. The results are 

encouraging; however, closer agreement between the PSHED and LSHED measurements was 

hoped for given the PSHED retention and recovery results. At this time the major cause for this 

discrepancy is believed to be the non-repeatable nature of a vehicle’s evaporative emissions, 

which can be exacerbated by malfunctioning evaporative emission systems, the variability in the 

on-road preconditioning driving for the PSHED vs. the dynamometer FTP as well as the widely 

varying uncontrolled ambient temperatures for the PSHED measurements. 
3-15 

Figure 3-2. Comparison of 15-Minute PSHED and 60-Minute LSHED 

Measurements 

0

2

4

6

8

10

12

14

16

18

0 2 4 6 8 10 12 14 16 18

LSHED HC (g/60min)

PSHED HC (g/15min)
4-1 

4.0 Analytical Methods 

The following instrumentation was used to take the measurements for this study. Each is 

described in the following sub-sections. 

 ESP RSD-4600 remote sensing instrumentation 

 SEMTECH-G portable gas analyzer 

 SnapOn hand-held combustible gas detector 

 FLIR GasFindIR hydrocarbon vapor video camera 

4.1 RSD Emissions Instruments 

This pilot study used a single RSD-4600 unit for all RSD measurements. RSD systems 

are designed for non-intrusive measurement of vehicle emissions. They generate and monitor a 

light beam emitted and reflected approximately 10 to 18 inches above a single lane road. The 

RSD-4600 detects vehicle emissions when a car drives through an invisible light beam the 

system projects across a roadway. Figure 4-1 contains a diagram of a typical on-road set-up. The 

process of measuring emissions remotely begins when the RSD-4600 Source/Detector Module 

(SDM) sends an infrared (IR) and ultraviolet (UV) light beam across a single lane of road to a 

Corner Cube Mirror (CCM). The CCM reflects the beam back across the roadway (creating a 

dual beam path) into a series of detectors in the SDM. 

Fuel-specific concentrations of HC, CO, CO2, NO and smoke are measured in vehicle 

exhaust plumes based on their absorption of IR/UV light in the dual beam path. During this 

process, the Speed/Acceleration Module (S/A) measures the speed and acceleration of each 

vehicle while the Video System Camera captures an image of the rear of the vehicle. Emissions 

concentration values and other related data are stored in a computer processor monitored by an 

operator stationed in a mobile unit parked safely along the roadside. This entire process is 

accomplished in less than a second. 

The internal combustion equation assumes a non-oxygenated fuel with a hydrogen to 

carbon ratio of 1.85 to 1.0. Accuracy specifications apply when the distance between the SDM 

and the CCM is from 15 ft to 23 ft. 

The performance of the RSD-4600 Remote Sensing Device will meet or exceed the 

absolute and relative accuracy specifications shown in Table 4-1. These data are based on 

internal instrument specifications, not on-road controlled conditions. 
4-2 

Figure 4-1. Typical On-Road RSD-4600 Set-Up 
4-3 

Table 4-1. ESP RSD-4000 Instrument Specifications 

RSD-4600 General Specifications 

RSD-4600 specifications for CO2 plume greater than 20%-cm

RSD-4600 specifications for CO2 plume less than 20%-cm

The usual calculations used by the RSD-4600 instrument are provided by Method A. 

However, for this pilot study, a special supplemental query was written to also calculate reported 

emissions concentrations according to an approximation of Method B. Also, a special query was 

written to bin the Selection RSDs for every vehicle entering the IM station and to randomly 

select vehicles within each bin for solicitation. 

CO% / CO2 % 0.015 or ±15% of reading, whichever is greater 

HC ppm / CO2 % ±10 or ±15% of reading, whichever is greater 

(propane) 

NO ppm / CO2 % ±10 or ±15% of reading, whichever is greater 

CO% 0.15 or ±15% of reading, whichever is greater 

HC (propane) ppm 150 or ±15% of reading, whichever is greater 

NO ppm ±225 or ±15% of reading, whichever is greater 

Smoke number 0.1 or ±15% of reading, whichever is greater 

CO% / CO2 % 0.007 or ±10% of reading, whichever is greater 

HC ppm / CO2 % ±6.6 or ±10% of reading, whichever is greater 

(propane) 

NO ppm / CO2 % ±10 or ±10% of reading, whichever is greater 

CO% 0.1 or ±10% of reading, whichever is greater 

HC (propane) ppm ±100 or ±10% of reading, whichever is greater 

NO ppm ±150 or ±10% of reading, whichever is greater 

Smoke number ±0.05 or ±10% of reading, whichever is greater 

Ambient Temperature 

•Operating: -7°C to +49°C (20°F to 120°F) 

•Storage: -30°C to 60°C 

Ambient Humidity 0 to 95% (non-condensing) 

Altitude (operating) -1000 ft to 10,000 ft (-305 to 305 m) 

Speed Accuracy ±1 mph (5 – 70 mph) 

Acceleration Accuracy ±0.5 mph/second (5 – 70 mph)
4-4 

4.2 SEMTECH-G Analyzer for Portable SHED Measurements 

The SEMTECH-G analyzer is primarily intended for on-vehicle emission monitoring of 

gasoline (spark-ignition engine) powered vehicles. The analyzer can also be used for emission 

monitoring in other mobile applications, and also stationary applications such as engine test 

cells, where SEMTECH-G can be easily moved from one test cell to another. In this pilot study 

the SEMTECH-G was used to record the HC concentrations in the PSHED as vehicles produced 

evaporative emissions. 

The SEMTECH® (Sensors Emission Technology) product line is based on a number of 

modular, stand-alone measurement subsystems. The following is a list of measurement 

subsystems included in the SEMTECH-G emission analyzer. 

 Heated Flame Ionization Detector (FID) for total hydrocarbon (THC) 

measurement 

 Non-Dispersive Ultraviolet (NDUV) analyzer for nitric oxide (NO) measurement 

 Non-Dispersive Infrared (NDIR) analyzer for carbon monoxide (CO) and carbon 

dioxide (CO2) measurement 

 Electrochemical sensor for oxygen (O2) measurement 

All of the SEMTECH subsystems have been designed to match as closely as possible the 

analytical performance of laboratory grade instrumentation, and yet meet the special 

requirements of on-vehicle emission monitoring application. This requires a reduction in size, 

weight, and power consumption combined with reduced sensitivity to vibration and changes in 

ambient temperature, pressure, and humidity. 

In addition to above listed measurement subsystems SEMTECH-G includes following 

modules: 

 Data logger, I/O, and system control module 

 Wireless communication module for remote monitoring and control using a 

personal computer (PC) or a personal digital assistant (PDA) 

 Weather probe for ambient temperature and humidity measurement 

Total Hydrocarbon Heated FID Analyzer – To accurately measure total hydrocarbons 

(THC) over the range of 0 to 40,000 ppmC, a high-precision heated FID is used in the 

SEMTECH-G. A fraction of the sampled gas is routed through the stainless steel heated FID 

chamber for measurement. The FID chamber is heated to 191 °C. 
4-5 

All aspects of the THC FID are electronically controlled by the SEMTECH-G embedded 

control software. FID flame ignition and extinction is performed on demand from the user via the 

host computer software graphic user interface. All internal parameters, such as flow rates and 

pressures are monitored and controlled automatically by SEMTECH-G. 

The user can select a range of 100, 1,000, 10,000, or 40,000 ppmC. All enabled ranges 

are individually calibrated each time a zero calibration command is given, so the number of 

enabled ranges is minimized to reduce this process time. The user can also select a data rate of 

up to 4 Hz through the SENSOR Tech-PC application software. 

The THC FID fuel consists of a 40/60 blend of hydrogen/helium. The fuel cylinder is 

housed inside the SEMTECH-G chassis, and includes an electronic pressure sensor connected to 

the data acquisition system so the user can monitor the fuel capacity from the SENSOR Tech-PC 

application software. A warning will occur when the fuel pressure drops too low. The fuel 

pressure is regulated at the bottle with a two-stage, stainless steel regulator. The bottle holds 105 

liters (compressed), which will last approximately eight hours. The bottles are available both in 

the U.S. and Europe by Scott Gas, and are refillable. There is also an auxiliary FID fuel 

connection port for external cylinders for use in stationary environments such as test cells. 
4-6 

Table 4-2. SEMTECH-G Measurement HC Specifications 

User Selectable Ranges 

0 –100 ppmC 0 –1,000 ppmC 0 – 10,000 ppmC 0 – 40,000 ppmC 

Accuracy 

±2.0 % of reading 

or ±5 ppmC 

whichever is 

greater 

±2.0 % of reading 

or ±5 ppmC 

whichever is 

greater 

±2.0 % of reading 

or ±25 ppmC 

whichever is 

greater 

±2.0 % of reading 

or ±25 ppmC 

whichever is 

greater 

Resolution 0.1 ppmC 1.0 ppmC 1.0 ppmC 10.0 ppmC 

Linearity 

±1.0 % of reading 

or ±3 ppmC 

whichever is 

greater 

±1.0 % of reading 

or ±3 ppmC 

whichever is 

greater 

±1.0 % of reading 

or ±10 ppmC 

whichever is 

greater 

±1.0 % of reading 

or ±10 ppmC 

whichever is 

greater 

Repeatability 

±1.0 % of reading 

or ±2 ppmC 

whichever is 

greater 

±1.0 % of reading 

or ±2 ppmC 

whichever is 

greater 

±1.0 % of reading 

or ±10 ppmC 

whichever is 

greater 

±1.0 % of reading 

or ±10 ppmC 

whichever is 

greater 

Noise ±2 ppmC ±2 ppmC ±10 ppmC ±20 ppmC 

Span drift 

(over 8 hours) 

±1.0 % of reading 

or 3 ppmC 

whichever is 

greater 

±1.0 % of reading 

or 3 ppmC 

whichever is 

greater 

±1.0 % of reading 

or 15 ppmC 

whichever is 

greater 

±1.0 % of reading 

or 20 ppmC 

whichever is 

greater 

Zero drift 

(over 2 hours)

5 ppmC 5 ppmC 10 ppmC 20 ppmC 

Zero drift 

(over 8 hours)

10 ppmC 10 ppmC 20 ppmC 40 ppmC 

Warm up time 60 minutes 60 minutes 60 minutes 60 minutes 

Response time T90 < 2 seconds T90 < 2 seconds T90 < 2 seconds T90 < 2 seconds 

Flow rate 2 lpm 2 lpm 2 lpm 2 lpm 

Data rate Up to 4 Hz, 

configurable 

Up to 4 Hz, 

configurable 

Up to 4 Hz, 

configurable 

Up to 4 Hz, 

configurable 

Operating 

Temperature 

191 °C 191 °C 191 °C 191 °C 
4-7 

Ambient Air Sensors – The standard weather station for the SEMTECH product family 

consists of a remote mounted temperature and relative humidity sensing device. This package is 

connected to the SEMTECH-G front panel using the supplied cable. Cables of three meters in 

length are standard. The ambient pressure sensor is located in the SEMTECH-G unit and is 

vented through the rear panel. 

Table 4-3. SEMTECH-G Ambient Air Specifications 

Sensor Temperature Relative Humidity Pressure 

Range of measurement -39 °C to 60 °C 0.8% to 100% RH 15 to 115 kPa 

Accuracy ±0.2 °C ±2% RH at 0 to 90% RH 

±3% RH at 90 to 100% RH 

±1.5% 0 to 85 °C 

Response time T90 < 10 seconds at 20 °C T90 < 4 seconds 

4.3 HC Sniffer for Modified California Procedure 

Another evaporative emissions test was conducted using a handheld combustible gas 

detector that has been found useful in locating small liquid and vapor leaks. The unit used in this 

pilot study is the Combustible Gas Detector (Stock# ACT790) sold by SnapOn tools for $299.99, 

and shown in Figure 4-2. According to Snap-On, the unit can be used to find leaks in fuel and 

exhaust systems. Detectable compounds include acetylene, isobutane, methane, ethane, propane, 

hydrogen, acetone, methanol and gasoline. The battery-operated unit uses a solid electrolyte to 

detect hydrocarbons with a propane sensitivity of < 10 ppm. 

Figure 4-2. HC Sniffer 
4-8 

4.4 Infrared Video Camera 

Open-path gas detection units, or IR cameras, are used to detect leaks in industrial 

settings, and it is possible they may be useful in detecting evaporative leaks from vehicles as 

well. Although there is no standard test procedure for detecting HC leaks from vehicles, the oil 

and gas industry routinely monitors VOC leaks, and some of the methods used in that setting 

may prove promising for vapor and liquid leak detection in vehicles. 

In this pilot study, the FLIR (Forward Looking InfraRed) Systems8

 GasFindIR video 

camera was used for about two weeks to evaluate its efficacy for detecting the evaporative 

emissions of light-duty gasoline vehicles. A photo of the GasFindIR unit is shown in Figure 4-3. 

According to FLIR, the unit is a hand portable, battery-powered open-path gas monitor providing 

fast response and broad dynamic range. They are permanently calibrated, small and light (less 

than 5kg). Alignment is easy and stable. Monitor response does not depend on path length, so a 

series of paths of different lengths (between 1m and 1000m) can be measured in quick 

succession. These systems are typically used to provide exceptional detection capability of 

gas/vapor concentrations ranging from as low as ppm levels to Lower Explosive Limit levels in a 

wide range of hazardous conditions and ambient air monitoring applications. The device forms 

an image using infrared radiation, similar to a common video camera that forms an image using 

visible light. Instead of the 450–750 nanometer range of the visible light camera, infrared 

cameras operate in wavelengths as long as 14,000 nm (14 μm). 

According to the flir.com website, independent laboratory testing confirms that the GasFindIR 

cameras can see the following gases at the minimum detected leak rate (MDLR): 

1-Pentene - 5.6g/hr 

Benzene - 3.5g/hr 

Butane -0.4g/hr 

Ethane - 0.6g/hr 

Ethanol - 0.7g/hr 

Ethylbenzene - 1.5g/hr 

Ethylene - 4.4g/hr 

Heptane - 1.8g/hr 

Hexane - 1.7g/hr 

Octane - 1.2g/hr 

Pentane - 3.0g/hr 

Propane - 0.4g/hr 

Propylene - 2.9g/hr 

Toluene - 3.8g/hr 

Xylene - 1.9g/hr 

 

8

 25 Esquire Road, N. Billerica, MA, 01862, (978) 901-8000, www.flir.com. 
4-9 

Figure 4-3. FLIR GasFindIR Gas Detection Video Camera 

In infrared photography, the film or image sensor used is sensitive to infrared light. The 

part of the spectrum used is referred to as near-infrared to distinguish it from far-infrared, which 

is the domain of thermal imaging. Wavelengths used for photography range from about 700 nm 

to about 900 nm. Usually an “infrared filter” is used; this lets IR light pass through to the camera 

but blocks all or most of the visible light spectrum (and thus looks black or deep red). There are 

two different methods employed by IR cameras, one is known as Backscatter Absorption Gas 

Imaging (BAGI) and the other Image Multi-Spectral Sensing (IMSS). The former views the area 

illuminated by the IR source light and the camera images the reflected light. Volatile Organic 

Compound vapors absorb IR light, so the image produced is a negative. The cameras using the 

IMSS method capture an image using the full light spectrum and the optics separate and 

recombine selected wavelengths to create an image. 

Studies indicate that the BAGI style IR camera can detect VOC leaks from 6 to 17 feet 

away with leak rates ranging from 3-60 grams/hr. Work in the area of refinery leak detection has 

progressed to the point that emission factors have been developed for various piping components 

and Monte Carlo simulation methods have been employed to aid in the estimation of VOC 

fugitive emission releases. 

4.5 Management of Field Data 

Vehicle information was collected on hardcopy forms similar to the “Vehicle Data 

Collection Forms” included in Appendix A. VINs, make, model, model year numbers and other 
4-10 

information was also collected, as available. Photographs were taken of unique identifiers such 

as VINs to serve as conformation information and to correct or clarify transcription errors and 

discrepancies. Vehicle information was transmitted to an ERG office for entry into a spreadsheet 

or database. 
5-1 

5.0 Analysis 

This section of the report will present an initial analysis of the data collected in this pilot 

study. The main goal here is to describe the general trends in the data. Data observations are 

shown in Appendices D through K for participating vehicle information, selection RSDs, 

conditioning drives, measurement RSDs, PSHED results, modified California method results, 

driver interviews, and I/M gas cap inspection results. 

In this study the reference method for determining the evaporative emissions of vehicles 

was the measurement of hot-soak emissions produced by a vehicle while it soaked in the PSHED 

for 15 minutes. Three methods were evaluated as candidate methods for detecting vehicles that 

have high PSHED values: 

IR video camera – This qualitative method uses a special IR detector coupled to a video 

recorder that can see the motion of HC vapors as they are emitted. The results of these tests are 

reported in Section 5.1. 

Modified California Method – This qualitative method supplements a visual and 

olfactory inspection of the gasoline liquid and vapor handling systems of a vehicle (the 

California Method) with a hand-held electronic HC vapor detector. The results of this testing is 

reported in Section 5.2. 

RSD – This quantitative method uses the detailed absorbance information from an RSD 

beam block to calculate an “index” that is related to evaporative emissions.9

 Section 5.3 

describes the relationship between RSD Evap Index 110 and the PSHED measurements in this 

pilot study. Note that since better RSD evaporative emissions indices will probably be developed 

in the future, this analysis is just an early look at the potential for RSD to detect evaporative 

emissions. 

Section 5.4 demonstrates how to estimate the fraction of High Evap vehicles in a sample 

of a fleet. In this calculation, fleet on-road RSD measurements are used to calculate RSD Evap 

Index 1, which is in turn used to estimate each vehicle’s probability of being a High Evap. 

 

9

 Since RSD instruments were developed with the goal of detecting vehicles with elevated exhaust emissions, 

finding an index that is sensitive to evaporative emissions without being influenced by exhaust HC emissions is a 

challenge. We expect that no theory can be used to determine the perfect index. Instead, evap index development is 

a signal analysis problem. Consequently, ever improved indices can be developed as index developers get more 

inventive. 

10 Developed by H.J. Williamson [11] using the Summer 2008 PreTesting data. 
5-2 

5.1 Evaluation of Using the Infrared Video Camera to Detect High Evaps 

FLIR Systems, Inc. has developed an infrared video camera for the detection of a variety 

of hydrocarbon gases. The camera used in the pilot study was the FLIR ThermaCAM 

GasFindIR Infrared Camera. This camera features a high resolution video image based on an 

indium antimonite (InSb) focal plane array. The visual detection of hydrocarbon gases is 

provided by a narrow band pass cold filter specifically centered in the spectrum to detect various 

hydrocarbon gases. The camera is a hand held package weighing less than five pounds and uses 

a separate video recorder with an LCD screen. The camera was designed to find fugitive 

emissions such as those that might be present as small hydrocarbon vapor leaks in the piping of a 

refinery or chemical plant. 

The IR video camera was used in this pilot study from August 1 through 15, 2008. To 

initially test the ability of the IR camera to see evaporative emissions, we removed the gas cap of 

a vehicle after it had been driven on the 8-mile conditioning drive. Since the ambient 

temperature was hot, we expected that the fuel tank temperature at the end of the drive would be 

especially elevated, and therefore a substantial amount of gasoline vapor should be emitted from 

the open fuel fill pipe. When the gas cap was removed the vehicle, the video camera showed 

quite clearly the hydrocarbon emissions coming from the opening. Thus, this first test was 

encouraging. The features that made the hydrocarbon emissions visible in the video were the 

relative motion of the darkly colored image of hydrocarbon vapors compared with the stationary 

background of the fender of the vehicle. If the vehicle fender was light colored, then it was easy 

to see the hydrocarbon emissions. However, if the fender of the vehicle was darkly colored, then 

it was difficult or impossible to see the motion of the hydrocarbon emissions. 

During the routine testing of private vehicles during the two weeks that we had rented the 

IR camera, we used the camera to search for evaporative emissions from the fuel cap, under the 

hood, and under the vehicle near the fuel tank. We found that when the background was 

complex (e.g., under the hood) or was dark (e.g., under the vehicle, in the fender well, or under 

the hood) the video camera operator would probably not be able to see the wisps of hydrocarbons 

that might be being emitted as evaporative emissions. It was also rather difficult to aim the 

camera at the fuel system or evaporative emission system components under the vehicle and 

under the hood and at the same time watch the video screen for wisps of hydrocarbon with the 

complex background and poor lighting conditions. Those difficulties hampered the ability of the 

operator to interpret the video. We found that the use of the hand-held hydrocarbon sniffer with 

its audio clicking was a far better tool for detecting and locating evaporative emissions leaks. 

Therefore, after about one week of attempting to use the IR video camera, we abandoned it. 
5-3 

5.2 Evaluation of the Modified California Method to Detect High Evaps 

In this subsection, the modified California method (MCM), which includes visual, 

olfactory, and hand-held electronic HC vapor sniffer inspection components, was evaluated 

against the PSHED results (g HC / 15 minutes) for the 85 vehicles participating in the 

evaporative emissions testing at the Lipan Street station. 

The sequence of the PSHED and MCM tests is important to the interpretation of the 

results. Participating vehicles were given an 8-mile on-road conditioning drive and two IM 

station driveway RSDs. Then, the vehicle was driven to the PSHED door, the engine was shut 

down, and the vehicle was pushed into the PSHED for the 15-minute test. During this 15 minutes 

the vehicle cooled somewhat. At the end of the PSHED test the vehicle was started in the 

PSHED and backed out of the enclosure approximately 50 feet, but the vehicle was still inside 

the IM station. The MCM inspection was then performed. For the first part of the MCM the 

engine was left running. For the last part the engine was off. Because of the 8-mile conditioning 

drive immediately before the PSHED test but not immediately before the MCM test and the 15-

minute cooling during the PSHED test, it is likely that the MCM test was conducted under less 

severe evaporative emissions conditions than the PSHED test. This may mean that in some cases 

the MCM test may not have detected evaporative emissions that might have occurred if the 

vehicle had been just as severely conditioned as for the PSHED test. 

In addition to the different conditioning used for the PSHED test and for the MCM test, 

there was another factor that could have affected the MCM results. Personnel conducting the 

MCM test always knew the PSHED test result. Therefore, if the PSHED result was high, the 

MCM inspectors worked hard to try to find the source of the HC emissions. In fact, we 

instructed the MCM inspectors to try to find the exact source of the emissions on high-PSHED 

vehicles. On the other hand, it would only be human nature that an inspector would not look so 

hard to find a leak on a vehicle that he knew to have a low PSHED result. 

In this study the PSHED result was treated as the true evaporative emissions of each 

vehicle. In reality, the PSHED result is closest to a hot-soak evaporative emissions result and 

may be only a crude estimate of diurnal, running losses, resting losses, or fugitive evaporative 

emissions. The PSHED results, which are given in the last column of the table in Appendix H, 

are repeated in Table 5-1. 
5-4 

Table 5-1. Comparison of PSHED and MCM Overall Results 

PacketID 

PSHED 

(g / 15 minutes) 

MCM 

(1=Detected, 

0=NotDetected) PacketID

PSHED 

(g / 15 minutes) 

MCM 

(1=Detected, 

0=NotDetected)

10 0.130 0 191 0.087 0 

11 14.022 1 193 11.219 0 

16 0.538 1 194 24.073 0 

19 14.809 1 200 0.171 0 

22 1.301 0 203 0.022 0 

24 11.194 1 213 0.890 1 

29 0.088 1 217 0.066 0 

35 15.472 1 218 0.354 0 

46 11.998 . 220 1.094 0 

50 0.903 1 221 0.133 0 

60 0.109 1 222 2.795 0 

61 0.079 0 223 2.022 1 

62 0.082 0 225 0.335 0 

63 0.186 0 226 0.174 0 

65 0.177 0 229 3.438 1 

70 0.040 0 236 1.047 1 

72 0.186 0 240 0.109 0 

78 0.312 1 242 9.021 1 

79 0.021 0 245 0.134 0 

84 0.153 0 247 2.203 1 

89 0.385 1 248 1.115 0 

98 0.032 0 250 0.709 1 

108 0.705 0 251 0.027 0 

110 3.121 1 254 0.333 0 

116 6.753 1 256 2.181 0 

117 0.256 0 257 0.076 0 

118 9.364 1 258 0.028 0 

123 0.209 0 266 0.133 0 

124 0.152 0 270 1.062 1 

128 3.461 0 271 1.292 1 

130 0.156 0 272 1.365 0 

131 1.940 1 274 0.022 0 

132 1.516 0 281 0.262 0 

137 0.285 0 283 0.068 0 

138 0.046 0 284 0.067 0 

146 1.589 1 286 13.180 1 

151 0.354 0 287 2.988 0 

157 0.178 1 290 1.227 0 

161 3.366 1 294 0.064 0 

165 0.262 1 298 0.045 0 

169 2.998 0 299 0.323 0 

174 0.048 0 300 0.266 0 

177 0.068 0 304 0.167 1 

184 0.044 0 
5-5 

The detailed results of the MCM inspection as reported on the data packets are shown in 

Appendix I. When the MCM inspection found evidence of evaporative emissions, various types 

of comments were recorded. 

In many cases, especially for the newer vehicles, it was difficult to access much of the 

fuel system with a sniffer. Vehicle designs are much more tightly packed and without invasive 

diagnostics (i.e. hoisting the vehicle and pulling down the fuel tank and fuel lines), the 

technicians were unable to determine the source of leaks in even high PSHED readings as can be 

seen in Table 5-1. 

5.3 Evaluation of Using RSD to Detect High Evaps 

5.3.1 RSD Evap Index 1 Description 

The RSD instrument uses a light beam shining across the roadway to measure pollutants 

in a vehicle’s tailpipe plume. The instrument has HC, CO, NO, and CO2 channels. When a 

vehicle drives past the instrument, the light beam shines through the emissions plume behind the 

vehicle and takes 50 10-millisecond-spaced measurements for each of these channels. 

For vehicles with zero evaporative emissions, the 50 data points in the HC-versus-CO2

plot tend to fall on a straight line. As described in Section 2.3, the exhaust HC emissions 

concentration is closely related to the slope of the line. This method used to calculate exhaust HC 

concentration from the 50 10ms RSD data is a standard method and has been known for many 

years. However, the Denver pilot study also found that for vehicles with high evaporative 

emissions, the 50 data points in the HC-versus-CO2 plot tend to not fall on a straight line. We 

believe this is a consequence of the HC evaporative emissions plume, which is produced by nontailpipe sources on the vehicle, wafting into the light beam at the same time as the tailpipe plume 

is in the light beam. Thus, the degree to which the 50 data points deviate from a straight line in 

the HC-vs-CO2 plot is a measure of the amount of evaporative emissions produced by the vehicle 

at the time that it passes through the RSD light beam. The evaporative emissions that may be 

observed by RSD represent the “running losses” of the vehicle, that is, the evaporative emissions 

that take place while the vehicle is being driven. 

We investigated several measures of the characteristics of the deviations from the straight 

line as they relate to evaporative emissions. The statistical measures that were investigated 

included the average deviation from the straight line, correlation coefficient, principal 

component analysis, and spectral analysis. Each approach has advantages and disadvantages, but 
5-6 

using the average deviation from the straight line outperforms the other measures in most cases. 

Therefore, this measure was used in this study, and is referred to as the RSD Evap Index 1. 

For the analysis of the data in this pilot study, we used the RSD Evap Index 1, which is 

the best second generation evap index that we have developed. It is an improvement over the 

RSD Evap Index 0, which was simply the difference between the Method A and Method B RSD 

HC values. The following text, which was taken from H.J. Williamson’s report [11] on the 

development of RSD evaporative emissions indices, provides a description of the RSD Evap 

Index 1: 

 Each RSD beam block typically produces 50 attenuations (concentration 

times path length) with 10 ms spacing for each pollutant. The attenuations 

generally decrease with time after the vehicle has passed the RSD unit because of 

the dispersion of the plume in ambient air. The light beam for the RSD unit is 

placed so that it is at tailpipe elevation. The above-mentioned gasses [HC, CO, 

NOx, CO2] emitted from the tailpipe are well mixed and disperse together. 

Therefore, the [RSD optical] path length for each tailpipe pollutant is the same, 

and the RSD attenuations reflect only the ratios of the tailpipe pollutant 

concentrations. 

 On the other hand, evaporative gasses (only HC) are emitted from nontailpipe sources – even multiple sources. Therefore, evaporative emissions do not 

disperse in the same way the tailpipe emissions do. The difference in dispersion 

between evaporative and tailpipe emissions is the key quality that can be used to 

detect evaporative emissions. The evaporative emissions can waft into and out of 

the RSD’s light beam. 

 If hydrocarbon fuel is being burned, the CO2 emissions will be above the 

detection limit. Thus, CO2 is an effective tracer for the tailpipe dispersion effect 

in any case of interest. This is not guaranteed to be the case with CO, NOx, or HC. 

 Thus, consider the following. Suppose we perform a regression analysis 

of the 50 attenuations for HC on CO2. If the HC measurements are above the 

detection limit, this regression analysis will account for the tailpipe-related part of 

the trend in the HC measurements. The residuals, that is, the observed minus 

predicted values, contain what is left after the trend associated with the tailpipe 

effect has been mathematically removed. 

If there are no evaporative emissions, the residuals should be at the noise 

level. If there are significant evaporative emissions, the residuals will typically 

contain a remaining trend that is larger than the noise level. The evaporative 

trend may not be strictly independent of the tailpipe trend, but the evaporative 

trend is not expected to be the same as the tailpipe trend. Differences between the 
5-7 

time histories for tailpipe emissions and evaporative emissions produce the 

remaining trend that allows us to detect evaporative emissions. 

If the HC measurements are below the detection limit, they will be 

essentially noise. If the HC measurements are this low, both the tailpipe and 

evaporative emissions are probably quite low. There will be no tailpipe trend to 

remove, and the residuals will be at the noise level. 

Now suppose the car has very low tailpipe emissions but has significant 

evaporative emissions. For reasons discussed above, we do not expect the time 

history for the HC evaporative emissions to have the same characteristics as the 

tailpipe CO2 emissions. Thus, as in the case with measurable tailpipe emissions 

and significant evaporative emissions, we expect the residuals to have a remaining 

trend that is larger than the noise level. 

 Therefore, using the residuals appears to satisfy our need to remove the 

tailpipe HC trend if it is present. Whether the tailpipe emissions are measurable 

or not, the residuals are characteristic of noise in the absence of evaporative 

emissions. Further, the residuals typically have a remaining trend above the noise 

level if there are significant evaporative emissions. Therefore, the residuals 

appear to provide an avenue for calculating measures that have a common 

meaning in different scenarios. 

 The residuals have other characteristics that we can exploit. If the 

residuals are characteristic of noise alone, they are likely to oscillate back and 

forth about zero fairly rapidly. But, if present, the evaporative plume probably 

does not waft into and out of the RSD’s light beam with high frequency. The 

influence of the evaporative plume is likely to produce sustained periods when the 

residuals are positive and sustained periods when the residuals are negative. 

Again, the residual is the HC value after the trend associated with tailpipe 

emissions has been mathematically removed. 

 We have experimented with various measures that are based on the properties discussed 

above. The measure, which we call RSD Evap Index 1,that is related to the presence or absence 

of evaporative emissions can be described as follows: 

 1) First, discard the first four HC and CO2 attenuations. These typically contain noise that 

can degrade the sensitivity of the index to evaporative emissions. 

 2) Second, regress the remaining HC attenuations on the CO2 attenuations using ordinary 

least squares regression, and calculate the 46 regression residuals. 

3) Third, exclude the smallest residual and the largest residual (some would say the most 

negative and most positive residuals). This step eliminates isolated outliers, if present. 
5-8 

4) Finally, average the absolute values of the remaining residuals. This is the value of the 

RSD Evap Index 1. 

5.3.2 Paired RSD and PSHED Observations 

While RSD observes the running losses of a vehicle, the PSHED test measures the “hotsoak” evaporative emissions of a vehicle. However, we expect that a vehicle with a 

malfunctioning evaporative emissions control system could be expected to have elevated PSHED 

hot-soak emissions and elevated RSD running loss emissions. 

Accordingly, we examined the correlation between the RSD Evap Index 1 and the 

PSHED test using the 175 observations of the Denver pilot study. Each of 85 PSHED tests was 

paired with two (and in several cases, more than two) RSD observations, for a total of 175 paired 

observations. Each RSD measurement produced the 50-sample-point data, which in turn was 

used to calculate the RSD Evap Index 1 value for the RSD beam block. In this study, all RSD 

measurements were made in the driveway entrance to the I/M station. Speeds were about 12 

mph. 

The 85 vehicles tested in this pilot study were selected using a stratified random sampling 

plan. Therefore, the measured PSHED and RSD results do not directly represent the Colorado 

fleet; the results would need to be de-stratified. However, the relationship between the RSD and 

the PSHED results is an estimate of the relationship that the Colorado fleet follows. 

Figure 5-1 shows a plot of the 175 observations. The vertical axis is the PSHED result, 

and the horizontal axis is the RSD Evap Index 1. The plot shows widely scattered data with 

positively skewed values on both axes. The skewed nature of the PSHED values is derived from 

the fact that most vehicles (even those in this stratified sample) have low evaporative emissions; 

only a few vehicles are “High Evaps.” In spite of the scatter of the data points, the plot shows 

that at low values of the RSD Evap Index, most vehicles have low PSHED emissions. In 

addition, the group of data points with the highest PSHED values increase with increasing RSD 

Evap Index 1. Thus, it is reasonable to conclude that vehicles with higher RSD Evap Index 1 

tend to have higher PSHED values. The relationship between RSD Evap Index 1 and PSHED 

value needed to be quantified so that it can be used as a predictive tool. 
5-9 

Figure 5-1. RSD Evap Index 1 and PSHED Values 

(Speed is approximately 12 mph) 

Two different approaches may be used to quantify the relationship between the PSHED 

and RSD values. Because the RSD observations of evaporative emissions represent running 

losses, while the PSHED test measures hot soak evaporative emissions, the two tests do not 

actually measure the same type of evaporative emissions. Therefore, a logical correlation 

between the two tests would seek to determine whether or not there is a gross problem with the 

evaporative emissions system of a vehicle, rather than attempting to use evaporative emissions 

observed by RSD to predict a specific level of the different type of evaporative emissions that are 

measured in a PSHED test. Nevertheless, we want a quantitative relationship to predict if a 

vehicle is likely to be a High Evap. 

In one approach, the PSHED HC mass is regressed against the RSD Evap Index 1 value, 

to identify an equation to estimate the PSHED HC mass based on a measured RSD Evap Index 1 

value. This approach is explored below in Section 5.3.3. Alternatively, each PSHED value can 
5-10 

be compared to a high-evap PSHED benchmark value to classify each PSHED measurement as a 

low-evap or a high-evap value. Then, the classifications are compared to the RSD Evap Index 1 

using logistic regression to arrive at a model that calculates the probability that a vehicle is a 

High Evap based on its measured RSD Evap Index 1. This approach is described in Section 

5.3.4. 

5.3.3 Prediction of PSHED Value Using RSD Evap Index 1 

To quantify the relationship between the PSHED value and the RSD Evap Index 1, we 

performed a linear regression. First, we found that the natural log of the RSD Evap Index 1 was 

an advantageous transformation. It caused the variances of the RSD Evap Index 1 to be more 

nearly homogeneous. Using the log transformation, the PSHED value was predicted from the 

RSD Evap Index 1 as: 

PSHED_g = -12.1209 + 3.24789 * ln(RSD Evap Index 1) 

The r2 for the regression was 0.52, indicating that the data include a substantial amount of 

scatter. The 175 paired data points and the linear correlation are shown in Figure 5-2. 
5-11 

Figure 5-2. Linear Correlation of PSHED and Transformed RSD Evap Index 1 

5.3.4 Prediction of PSHED “High Evap” Probability Using Evap Index 1 

For the purposes of this analysis, we used a PSHED value of 3 grams was used as the 

standard for “High Evaps” when measured in the PSHED. Accordingly, the 175 paired RSD and 

PSHED data points are shown again in Figure 5-3 with a dashed horizontal reference line at 3 

grams. Points above this line represent “High Evaps.” High-evap PSHED HC mass values other 

than 3 grams could also be used as the definition of a High Evap. 

Rather than attempt to predict the PSHED value based on the RSD Evap Index 1, here we 

choose to calculate the probability that a vehicle with a given RSD Evap Index 1 is likely to be a 

High Evap. To help visualize the trend in probability, vertical dashed lines at 80, 270, and 900 in 

Figure 5-3 are used to divide the plot into four RSD Evap Index 1 bins. The count of the number 

of observations within each of these bins and the number of “High Evaps” within each of the 

bins is given in Table 5-2. The table shows a monotonically increasing High Evap fraction as the 

RSD Evap Index 1 increases. 
5-12 

Figure 5-3. RSD Evap Index 1 and PSHED Values 

Table 5-2. Counts of High Evap Designations 

Range of RSD 

Evap Index 1 Count Measured 

High Evap 

(fraction) 

Modeled 

High Evap 

Low High PSHED > 3g Total (fraction) 

0 80 7 112 0.06 0.054 

80 270 8 41 0.20 0.225 

270 900 10 16 0.63 0.568 

900 Inf 5 6 0.83 0.856 
5-13 

To further quantify the relationship, we performed a logistic regression to predict the 

probability that the PSHED value would be larger than 3 grams using RSD Evap Index 1 as a 

predictor. First, we found that the natural log of the RSD Evap Index 1 was an advantageous 

transformation. It caused the values of the RSD Evap Index 1 to be more nearly homogeneous. 

The log transformation also was able to predict the PSHED failure probability from a simple 

expression with no lack of fit, which indicates that the model is an adequate fit to these data: 

Pfail = exp (arg) / (1 + exp(arg)) 

Where 

 Pfail = Probability that the PSHED test has a value above 3 grams, 

 

 arg = -7.5262 + 1.2582 * ln(RSD Evap Index 1) 

During model development, we found that the model had only a small dependence on 

exhaust emissions level. This means that the RSD Evap Index 1 is largely independent of exhaust 

emissions influence and therefore a given RSD Evap Index 1 value has the same meaning 

whether the vehicle has high exhaust emissions or low exhaust emissions. Thus, even though 

older model year vehicles tend to have higher exhaust emissions, the RSD Evap Index 1 is 

independent of model year. 

The model development also revealed that alternative High Evap definitions (other than a 

PSHED value of 3 grams in 15 minutes) could also be used to develop a logistic regression 

models with the same favorable properties. The concordance11 for the logistic regression was 

79.6%. 

The PSHED failure probabilities predicted by the model at the center of the ln(RSD Evap 

Index 1) bins are given in the last column of Table 5-2. The values calculated by the model are 

reasonably close to the values derived from the counts in Table 5-2. 

 

11 Concordance is a statistic that evaluates the agreement between the predicted probabilities of a logistic regression 

model and the pass and fail values of the individual observations in the training set. Concordance can have a value 

from 0% to 100%. If the predicted probabilities completely agree with the pass and fail values of the response 

variable, then the concordance is 100%. 
6-1 

6.0 References 

1. T.H. DeFries, J.H. Lindner, C.F. Palacios, S. Kishan, “Investigation of RSD for High 

Evaporative Emissions Vehicle Detection: Denver Summer 2008 Pre-Testing Study 

(Version 1),” EPA-090306, prepared for U.S. Environmental Protection Agency, 

prepared by Eastern Research Group, Austin, Texas, March 6, 2009. 

2. 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. 

3. 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. 

4. D. Amlin, R. Carlisle, S. Kishan, R.F. Klausmeier, H. Haskew, “Evaporative Emissions 

Impact of Smog Check,” prepared for California Bureau of Automotive Repair, prepared 

by California Bureau of Automotive Repair, Eastern Research Group, de la Torre 

Klausmeier Consulting, Harold Haskew & Associates, September 15, 2000. 

5. L.C. Landman, “Evaporative Emissions of Gross Liquid Leakers in MOBILE6,” 

M6.EVP.009, U.S. Environmental Protection Agency, EPA420-R-01-024, April 2001. 

6. J. Lindner, T.H. DeFries, “Evaluation of Portable SHED Characteristics,” Draft 

Technical Note, EPA-090209, prepared for US Environmental Protection Agency, 

prepared by Eastern Research Group, February 9, 2009. 

7. A.D. Burnette, S. Kishan, T.H. DeFries, “Evaluation of Remote Sensing for Improving 

California’s Smog Check Program (Version 15 final),” Final Report, ARB-080303, 

prepared for California Air Resources Board and California Bureau of Automotive 

Repair, prepared by Eastern Research Group, Austin, Texas, March 3, 2008. 

8. R.O. Gilbert, Statistical Methods for Environmental Pollution Monitoring, Van Nostrand 

Reinhold, New York, 1987. 

9. T.H. DeFries, S. Kishan, “ICR Part B,” submitted to U.S. Environmental Protection 

Agency, prepared by Eastern Research Group, EPA-080508b, May 8, 2008. 

10. J. Kemper, J. Sidebottom, J.H. Lindner, S. Kishan, T.H. DeFries, C. Hart, D. Brzezinski, 

J. Warila, C. Fulper, H.J. Williamson, “Investigation of the Ability of RSD to Detect 

Evaporative Emissions,” presented by T.H. DeFries, presented at the Nineteenth 

Coordinating Research Council On-Road Vehicle Emissions Workshop, San Diego, 

California, March 23, 2009. 

11. H.J. Williamson, “Measures Useful for Identifying Vehicles with High Evaporative 

Emissions,” CACI, 11211 Taylor Draper Lane, Austin, Texas 78759, CACI-081222, 

December 22, 2008. 
Appendix A 

Data Packet 
1 

A-1 

Packet ID______(give to RSD van)

Date __________ 

Time __________ am/pm 

Vdf # ____ /____/____(get from RSD van)

Make__________ Model_________ 

MY____________ Color_________ 

State_____Plate __________ Metal Paper

Participant: Yes No 

LSHED: Yes No 

Participant First Name _________________________ 

Pilot Testing 

Lipan Street Station 

Denver, Colorado 
2 

A-2 

First Contact

Are you the owner of this vehicle? Y N 

Is this a Fleet Vehicle? Y N 

What is the MY of this vehicle? ______ 

Is this your normal, every-day car? Y N 

 (no cream puffs, collector’s cars, mechanics specials) 

Your vehicle is eligible for this study. Are you 

interested in hearing about the project? Y N 

I, _____________________, would like to participate in this study. 

 (printed name) 

____________________________ 

Signature 

____________________________ 

Date 
3 

A-3 

I have received $20.00. 

____________________________ 

Signature 

____________________________ 

Date 
4 

A-4 

Driver Questionnaire

I am a contractor for the Environmental Protection Agency. We are 

conducting a study to understand the evaporative emissions of the vehicle 

fleet. We would like to do some testing of your vehicle. The testing of your 

vehicle would take about 1 hour. Your vehicle was randomly selected for this 

study. Your participation is voluntary. 

1. How many miles do you drive in a given year (12,000 is average)? 

 (a)< 8,000 (b) 8,000-12,000 (c)12,000-24,000 (d) > 24,000 (e) Don’t know 

2. How long have you owned your car? ________ months / years Don’t know

3. At night, do you park this vehicle: 

 (a) Inside a garage (b) Outside (c) Both 

4. When was the last time you fueled your vehicle? 

 (a) Last 24 hours (b) 2 days ago (c) 5 days ago or greater (d) Don’t know 

5. Does this vehicle get regular, routine maintenance from you or someone 

else? 

 (a) Yes (b) No (c) Don’t know 

6. Have you had any of the listed maintenance performed on the vehicle, 

and if so, how long ago? 

 a b c d e 

 Oil Change: Y N ? /// 0-3 months 4-6 months 7 -12 months Don’t Know 

 Tune Up: Y N ? /// 0-3 months 4-6 months 7-12 months Don’t Know 

 NewGasCap: Y N ? /// 0-3 months 4-6 months 7-12 months Don’t Know 

 Fuel System: Y N ? /// 0-3 months 4-6 months 7-12 months Don’t Know 

 MajEngWrk: Y N ? /// 0-3 months 4-6 months 7-12 months Don’t Know 

7. Have you ever had a gasoline smell around your vehicle? Yes No

If yes, could you describe the circumstance. 

If yes, have you done anything to fix it? 

8. Has the car ever been in an accident to your knowledge? 
5 

A-5 

Copy of I/M Inspection Report

(staple here) 
6 

A-6 

 RSD and P-SHED Testing

Start Pre-Condition 7-mile Drive. Time: ____________ am / pm 

 (Lipan, Evans, 85, 285, Federal, Evans, Lipan) 

Drive past RSD #2. Time: ____________ am / pm 

Drive past RSD #3. Time: ____________ am / pm 

Label Semtech file as: ___ ___ ___ . xml using Data Packet ID 

 Time HC (ppmC) Pbaro (mb) T (C) 

Initial P-SHED Put vehicle in SHED and seal the door. 

Door Sealed Final P-SHED 

 Remove vehicle from SHED. 
7 

A-7 

Vehicle Information Sheet 2

Photos: 

Front Quarter View (with white board and Packet ID#) 

All four sides of vehicle 

License plate close-up (rear) 

VECI close-up photo 

VIN close-up photo (windshield or door frame) 

VECI label (under hood): 

 Certification Year: ___________ 

 Engine Family: _________________________________ 

 Evap Family: __________________________________ 

VIN: 

 

Interior: 

Transmission Type: Manual Automatic 

Fuel Level (circle one) F 3/4 1/2 1/4 <1/4 

Odometer Reading ________________________ 

Odometer Digit Resolution (circle one) 5 6 
8 

A-8 

Modified California Method

Locate and identify the source of strong fuel odors. 

Fuel Metering Type: Carburetted Fuel-Injected 

Engine warmed up and running 

(Check one descriptor for each Location and each of the 3 methods.) 

Location Visual Sniffer IR Camera

Underbody fuel lines 0 m S G NP Y N NP Y N NP

Bottom of fuel pump 0 m S G NP Y N NP Y N NP

Fuel pump to carb 0 m S G NP Y N NP Y N NP

In-line fuel filter 0 m S G NP Y N NP Y N NP

Fuel rail + connectors 0 m S G NP Y N NP Y N NP

All fuel-injectors 0 m S G NP Y N NP Y N NP

Ground under vehicle 0 m S G NP Y N NP Y N NP

Engine off 

(Check one descriptor for each Location and each of the 3 methods.) 

Location Visual Sniffer IR Camera

Fuel fill pipe to tank joint 0 m S G NP Y N NP Y N NP 

Tank: rust,straps,damage 0 m S G NP Y N NP Y N NP 

Non-OEM installations 0 m S G NP Y N NP Y N NP 

IR Camera avi file: ____________ 

General comments: 

Descriptors: 

0 =No visual evidence of liquid fuel leaks 

m =Minor signs of fuel (staining, damp spots), wicking<1" 

S =Significant leaks with single drops of fuel from vehicle to the ground, wicking>1" 

G =Gross leaks, regular flow of drops to the ground, or a large pool of fuel, wicking>1" 

NP =Not Performed 

Y =Positive Instrument Response (Sniffer & IR Camera only) 

N =Negative Instrument Response (Sniffer & IR Camera only) 
9 

A-9 

 Permission for Additional Testing

We would like to perform additional testing on your vehicle. This will 

require overnight laboratory testing at: 

Colorado Department of Public Health and Environment 

15608 East 18th Avenue 

Aurora, Colorado 80011 

Your vehicle will be returned to this inspection facility on 

_________________ by _ : am / pm 

I give my permission for this testing. 

Signature: __________________________ 

Name: _____________________________ 

Street: _____________________________ 

City/State: __________________________ 

ZIP: _______________________________ 

Circle Primary Phone Number to Contact 

Home (_____) _______--___________ 

Work (_____) _______--___________ 

Cell (_____) _______--___________ 
10 

A-10 

Rental Car Checkout

Staple here: 

 

 Photocopy of Driver’s License (front and back) 

Visual Pre-Inspection of Owner’s Vehicle

(Use this only when we keep owner’s vehicle over night.) 
11 

A-11 

Colorado Lab SHED Test

Perform a 1-hour hotsoak test with LA-4 pre-conditioning and in-use fuel 

Date _________________ Time_______________ am / pm 

License Plate _____________________ 

State ___________________________ 

Test Number _____________________ 

Average SHED Temperature (F) __________________________ 

Average SHED Pressure (mm Hg)__________________________ 

Hotsoak Emissions ___________ g during first 15 minutes 

 ___________ g over 1 hour 

The name of the file with minute-by-minute hotsoak data is 

___________________________ 
Appendix B 

Stratified Sampling 
B-1 

The equations pertaining to stratified sampling discussed in this section are presented by Gilbert 

[8]. The goal is to determine the number of observations to be taken from each of several strata 

so that the variability of the mean is minimized. The equation for the optimal sample size for a 

given stratum is: 





 h h

L

h=1

h h

h

W

n W

n =

where 

nh = the sample size in stratum number h, 

n = the total sample size for all strata, 

Wh = the fraction of the actual population that falls in stratum h, 

L = the number of strata, and 

σh = the standard deviation of the distribution from which the individual data 

values in stratum h are sampled. 

This equation follows conceptual guidelines. The number of points taken from a stratum is 

directly proportional to the fraction of the population comprised of that stratum (the fraction is 

Wh). Also, the number of points from a stratum is directly proportional to σh, which is a measure 

of the variability in the stratum. 

The estimate of the population mean, xpop , is the weighted mean of the stratum means, xh : 

x = Wh xh

L

h=1

pop 

The point here is that the strata are not sampled proportionately to their actual representation in 

the population. If a simple arithmetic average of the complete stratified sample were calculated, 

the different strata would be weighted disproportionately to their representation in the population, 

and a biased average would result. The weighting scheme in the calculation of xpop accounts for 

the nature of the sample and produces an unbiased estimate of the population mean. The 

formulation here produces the unbiased estimate of the population mean with the minimum error 

variance, given the total sample size, n. The standard error of the mean is the square root of its 

error variance. The standard error of this weighted mean estimate is as follows: 

x = Wh xh

L

h=1

pop 

where fh is the number of data points in stratum h divided by the population size of this stratum. 

(1-f )

n = W h

h

2

2 h

h

L

h=1

xpop



 
B-2 

The factor (1-fh) accounts for the finitude of the population in stratum h. If the sample sizes are 

small compared to the sizes of the strata in the population, this factor can be ignored. The result 

is somewhat conservative (larger) estimates of the standard errors for the stratified results. The 

factor (1-fh) has been ignored (set to 1) in the calculations presented below, so the standard errors 

for the stratified analysis are somewhat conservative. 

In practice the true standard deviations, σh, are not known and are estimated on the basis of 

historical data that exist before the planned stratified sampling effort. The sample standard 

deviation, sh, based on a sample, xh,i, i=1 to m, is: 

m-1

(x - x )

s =

2

h,i h

m

i=1

h



where x h is the arithmetic mean. 

When the individual data values are dichotomous, for example, 1 for a vehicle with high evap 

and 0 for a vehicle with low evap, then the standard deviation can also be expressed using the 

probability ph that the vehicle has high evap: 

h h h s = p q

where: 

 ph is the probability that a vehicle in stratum h has high evap, and 

 qh is the probability that a vehicle in stratum h has low evap (qh = 1- ph). 
Appendix C 

California Evaporative Visual Inspection Method 
C-1 

In three CRC studies, signs of some fuel staining were noted on many vehicles. Evidence 

of liquid fuel including, for example, areas where oil and grease on the engine block had been 

rinsed away, were noted. Gross liquid leaks were reported on other vehicles. Identification of 

leaks in this study differentiated between minor signs of fuel (staining, damp spots) and those 

leaks with certain potential for reduction in evaporative emissions. The latter type of leak results 

in appreciable pools of liquid fuel or regular drops of fuel from the vehicle to the ground. These 

leaks are further classified as significant (single drops of fuel) and gross (a regular flow of drops 

or a large pool of fuel). 

A mechanic inspected vehicles using the attached procedure and tools. Vehicles were not 

to be repaired. Owners were notified if liquid fuel leaks were detected. They were told that the 

liquid fuel inspection was not a part of the state I/M procedure, but that the liquid fuel presents 

an extreme hazard that warrants immediate attention. 

Inspection results were tabulated to provide statistics regarding major and minor leaks 

detected by model year inspected. Vehicles were additionally be categorized with respect to 

carburetion/fuel injection and accumulated odometer at time of inspection. Both cars and trucks 

were included in the sample. 

We estimated that an inspector could inspect up to six vehicles per hour when vehicles 

were available for inspection. 

Inspection Procedure

Tools

Inspection mirror - 2 X 4" typical, extension handle, ball joint 

Small flashlight - 6" - to maneuver in and around underhood components 

Forceps - 12" 

Folded paper towels, blotter paper, or filter paper. 

Infrared “snoop” HC analyzer 

The corner of a folded paper towel or other blotter will "wick" up fuel from a significant 

pool, but not a minor drop or a surface stain. Forceps can be used to hold the paper and to 

approach a suspect area otherwise blocked by vehicle hardware. To differentiate between a 

"damp" area and a "liquid" area, the corner of the paper was touched to a suspect area. Visual 

inspection indicated if a significant amount of liquid was absorbed by the paper (more than 1 

inch wet spot). A strong gasoline fuel odor would be detected from fuel leaks. Water, 
C-2 

antifreeze, oil, windshield washer solvent, or other leaks would result in the strong gasoline odor. 

The “snoop” analyzer was used to pinpoint the source of HC vapors. 

Using the inspection mirror and flashlight, the procedure was to inspect as applicable: 

With engine warmed up and running:

Inspect bottom of fuel pump and around pressure line from fuel pump to carb. 

Inspect in-line fuel filter 

Inspect fuel inlet to carb 

Check underbody fuel delivery and return lines. 

Check fuel rail, connectors and individual fuel injectors. 

Check floor under vehicle for any sign of fuel accumulation. 

When strong fuel odors are noted, locate and identify the source.

Additional checks which can be performed with engine off:

Check fuel fill pipe, particularly around joint to tank. 

Check bottom of tank, particularly around rust spots, mounting straps, and any spots 

showing road damage. 

Non OEM installations (particularly second fuel tank add-ons) merit close inspection. 

The carburetor base plate, fuel pump and other areas will typically show signs of slow 

fuel seepage (fuel stains or oil deposits washed off). These are not counted as gross leakers 

unless a measurable pool of fuel or droplets of fuel are detected. 

Record model year, make, model, vehicle type (car, pickup, van), fuel 

injection/carbureted, odometer, and results of inspection. 
Appendix D 

Participating Vehicle Data 
D-1 

packetID 

pi_plate 

pi_metalorpaper 

pi_state 

pi_ModelYear 

pi_Make 

pi_Model 

pi_style 

pi_VECICertYear 

pi_EngineFamil

y

pi_EvapFamily

pi_VIN 

pi_Transmission 

pi_FuelLevel 

Packet 

Number Plate 

Paper 

or 

Metal 

Plate? State 

Model 

Year Make Model Style 

VECI 

Cert 

Year Engine Family Evap Family VIN 

Transmission Fuel Level 

PIL_010 371416G 1994 Subaru Legacy Sedan 1993 RFJ2.2VJGAEK RFJ1030BYM03 A . PIL_011 137SJM P CO 1977 Chevrolet Pickup Pickup 1977 A . PIL_016 LUV4LK 1995 Chevrolet Monte Carlo Sedan 1995 SIG3.1V8GFEA SIG1058AYMOA A . PIL_019 030284F P CO 1965 Ford Mustang Sedan 1965 M . PIL_022 721IGF 2002 Subaru Impreza Sedan 2002 2FJXV02.5JEJ 2FJXR01251BB M . PIL_024 AC00919 CO 1991 Toyota Corolla Sedan 1991 MTY1.6V5FFD5 EV-E A . PIL_029 921887F P CO 1995 Chevrolet Corsica Sedan 1995 SIG3.1V8GFEA SIG1058AYMOA A . PIL_035 1992 Ford Explorer Sedan 1992 NFM4.0T5FYH8 4.0L-0HM 0.25 PIL_046 158EVO M CO 1988 Dodge Pickup Pickup . A 0 PIL_050 266BZR M CO 1994 Subaru Legacy Sedan 1994 RFJ22VJGAEK RFJ1030BYM03 A 0.5 PIL_060 620722E P CO 1984 Lincoln Towncar Sedan 1984 A 0.25 PIL_061 3957796 P CO 1991 Plymouth Acclaim Sedan . A 0 PIL_062 914EXZ M CO 1991 Toyota Camry Wagon 1991 MTY2.0V5FFF1 EV-E A 0 PIL_063 PBT3734 M CO 1991 Chevrolet Cavalier Sedan 1991 M 0.5 PIL_065 124IOG M CO 1996 Mitsubishi Eclipse Convertibl 1996 A 0.25 PIL_070 398718G P CO 2000 Chevrolet Suburban SUV 2000 YGMXT05.3182 YGMXE0111920 A 0.5 PIL_072 ADH889 M CO 1989 Mercedes 190E Sedan 1989 KMB2.6V6FA12 KMBV62 A 0 PIL_078 316KWT M CO 1991 GMC Jimmy SUV 1990 M3G4.3T5XEB2 MBO-3E A 0.5 PIL_079 5978395 M LA 1997 Ford F150 Pickup 1997 VFM5.458GFEK VFM1160AYMFD A 0.25 PIL_084 185371G P CO 1995 Nissan Maxima Sedan 1995 SNS3.0VGBEK SNS1057BYM0A A 0 PIL_089 401250G P CO 1995 Ford Contour Sedan 1995 SFM2.5VJGFEA SFM1045AYP0A A 0.5 PIL_098 524JJX M CO 1997 Dodge Dakota Pickup 1997 VCR5.928GFGK VCR1090AYPBB A 0.5 PIL_108 549NPA M CO 1995 Jeep Wrangler SUV 1995 SCR2.578GAEA SCR1058AYMON M 0.5 PIL_110 410451G P CO 1987 Chevrolet Blazer SUV 1985 FIG2.8T2TRA3 M 0.5 PIL_116 821HBR M CO 1994 GMC Jimmy SUV 1994 R3G4.329GFEA R361053AYMON A 0.5 PIL_117 35061AA M CO 1992 Ford F150 Truck 1992 5.0L-OHM NFM5.8T5HZL1-0L A 0.5 PIL_118 803KWW M CO 2001 Nissan Xterra SUV 2001 1NSXT03.3C5A 1N5XR0120RCA A 0 PIL_123 275NFE M CO 1992 Dodge Stealth Sedan 1992 NMT3.0V5FFD6 IB M 0.75 PIL_124 184129G P CO 1989 Ford Bronco SUV . KFM5.875HZZ4 A 0.75 PIL_128 921NPB M CO 1994 Chevrolet Sierra 1500 Pickup 1994 REG5.785GAEB R3G1085AYM0A A 0.5 PIL_130 404917G P CO 1994 Dodge Ram Pickup 1990 LCR5.9T5H6F8 LCRTE A 0.5 PIL_131 278ITC M CO 1994 Jeep Cherokee SUV 1994 RCR4.078GAEA RCR1058AYMON A 0.25 PIL_132 401442G P CO 1995 Pontiac Grand Prix SE Sedan 1995 S1G3.1V8GFEA SIG1058AYMOA A 0.25 PIL_137 938268F P CO 1989 Ford Aerostar Van 1989 KKFM3.0T5FYK2 A 1 PIL_138 039302F P CO 1999 Infinity C20 Sedan 1999 XNSXV02.0AZA XNSXE011OMBA M 1 PIL_146 939783F P CO 1994 Jeep Cherokee SUV 1994 RCR4.078GAEA RCR1058AYMON A 0.5 PIL_151 318NAA M CO 1986 Chevrolet Sprint Plus Sedan 1986 OSK1.0V2FFL5 EV1 M 0.25 PIL_157 461NOU M CO 1989 Chevrolet Blazer SUV 1989 K3G5.7T5TYA3 KDO-3C M 0.25 PIL_161 679NAZ M CO 1991 Subaru Legacy Wagon 1991 MFJ2.2V5FFE1 Y-HU A . PIL_165 801774E P CO 1992 Saturn SL Sedan 1992 N4G1.9V5JPH5 NAO-4B A 0.5 PIL_169 475MPG M CO 1987 Porsche 944 Sedan 1987 19HPR151V5FE39? ..K? M 0.5 PIL_174 051LIT M CO 2000 Toyota Corolla Sedan 2000 YTYXV01.8FFA YTYXR0115AK1 A 0.25 PIL_177 29262 P CO 2002 Toyota Camry Sedan 2002 2TYXV02.4JJASFI 2TYXR0135AK1 M 0.5 PIL_184 BLM831 M CO 1991 Honda Civic Sedan . M 0.75 PIL_191 499NXU M CO 1994 Toyota Tercel Sedan 1994 RTY1.5BHGAFA RTY1047DYM00 M 0.25 PIL_193 YLL142 M CO 1998 Ford Mustang Sedan 1998 WFMXE0045AAA A . PIL_194 833GTT M CO 1990 Ford Taurus Sedan 1990 LFM3.0V5FXG5 TWC-H02J1MPI A 1 
D-2 

packetID 

pi_plate 

pi_metalorpaper 

pi_state 

pi_ModelYear 

pi_Make 

pi_Model 

pi_style 

pi_VECICertYear 

pi_EngineFamil

y

pi_EvapFamily

pi_VIN 

pi_Transmission 

pi_FuelLevel 

Packet 

Number Plate 

Paper 

or 

Metal 

Plate? State 

Model 

Year Make Model Style 

VECI 

Cert 

Year Engine Family Evap Family VIN 

Transmission Fuel Level 

PIL_200 51966ML M CO 1988 GMC Sierra Pickup 1988 1364.3T5TAA0 3F0-3B A 0.75 PIL_203 643KCU M CO 2000 Ford Taurus Sedan . A 0.5 PIL_213 VTTBOTS M CO 1987 Toyota Camry Sedan 1987 HTY2.0V5FBB4 EV-E M 1 PIL_217 028RJT M CO 2001 Mercury Sable Sedan 2001 1FMXV03.0VF3 3.0L-FMXR011SBA A 0.5 PIL_218 850FTE M CO 1984 Porsche 944 Sedan 1984 151V5FE03 ?H M 0.75 PIL_220 348AOP M CO 1996 Ford Ranger Pickup 1996 TFM4.028GKEK TEM1045AYPBA M 0 PIL_221 NO PLATE 1993 Buick LeSabre Sedan 1993 PIG3.8V8JGB4 PBO-1R A 0.5 PIL_222 034KAT M CO 1989 Dodge Caravan Van 1989 KCR3.0T5FBL6 KCRTC A 0.5 PIL_223 340FUB M CO 1994 Mazda B2300 Pickup 1994 RTK2.318GFEA 2-3L-RTK1065AYP M 0 PIL_225 939231F P CO 1985 Jeep CJ7 SUV 1994 P5249610 M 0.5 PIL_226 NO PLATE 1991 Ford F250 Pickup 1990 LFM07.585WX 7.5C9HW M 1 PIL_229 292AKJ M CO 1985 Chrysler New Yorker Sedan . A 0.25 PIL_236 401454G P CO 1995 Ford Contour Sedan 1995 SFM2.5VJGFEA SFM1045AYP0A A 0.25 PIL_240 034366F P CO 1997 Isuzu Rodeo SUV 1997 VSZ3.22JGKFK VSZ1095AYMEO M 0.25 PIL_242 PAX259 M CO 1993 Jeep Wrangler SUV 1994 RCR4.078GAEA RCR1058AYMON M 0 PIL_245 358NPT M CO 1997 Lincoln Towncar Sedan 1997 VFM4.6V8GKFL VFM1090AYMEP A 0.5 PIL_247 937581F P CO 1992 Cadillac Deville Sedan 1992 N2G4.9V8XGA8 NG0-2B A 0.5 PIL_248 581LNL M CO 1987 Ford F150 Pickup 1987 HFM5.0Y5HAGX 5.0L-7HM M 1 PIL_250 399022G P CO 1990 Volvo 740GLE Wagon 1990 LVV2.3V5F897 E3 A 0.25 PIL_251 PBN6774 M CO 1990 Mercury Topaz Sedan . A 0.25 PIL_254 371MVY M CO 1996 Jeep Grand Cherokee SUV 1996 TCRA4.028GKEK TCR1073AYPBP A 0.25 PIL_256 NO PLATE 1999 Oldsmobile Alero Sedan 1999 XGMXV03.4042 XGMXR0124912 A 0 PIL_257 754KCN M CO 1990 Chevrolet S10 Pickup . M 0.5 PIL_258 761JBG M CO 2002 Chevrolet Trailblazer SUV 2002 2GMXT04.2185 2GMXR0175922 A 0.5 PIL_266 UFF9944 M CO 1989 Volkswagen Golf Wagon . M 0.25 PIL_270 406626G P CO 1992 Jeep Wrangler SUV . M 1 PIL_271 842NNA M CO 1991 Dodge Dakota Pickup 1993 PCR3.9T5FFL7 PTAPF A 0.5 PIL_272 665ELF M CO 1990 Jeep Wrangler SUV 1990 LAM4.2T2HEA4 LAM4.2F-1P A 0.25 PIL_274 405028G P CO 1999 Dodge Intrepid Sedan 1999 XCRV02.7VB0 XCRXR0101GBF A 0 PIL_281 PN7271 M CO 1986 Honda Accord Sedan . M 0.25 PIL_283 938NNI M CO 1986 Ford Mustang Sedan 1986 GFM5.0V5HBF9 5-0L6HM M 0.75 PIL_284 403NEV M CO 1991 Toyota Tercel Sedan 1991 MTY1.5V5FFF1 EV-E M 0.5 PIL_286 ADV872 M CO 1983 Toyota Landcruiser SUV 1983 DTY4.2T2AFF3 EV-F M 0.75 PIL_287 99051AA M CO 1996 Ford F150 Pickup 1996 TFM4.958GFJK 4.9L-TFM1045AYM M 0.25 PIL_290 360GNT M CO 1998 Nissan Altima Sedan 1998 WNSXV02.433B WNSXR0110RCA A 0 PIL_294 916BPC M CO 2001 Subaru Legacy Wagon 2002 2FJXV02.5JHM 2FJXR01251CC A 0.25 PIL_298 579XBG M CO 2002 Mazda Protege Sedan 2002 2TKXV02.0GJA 2TKXR0125PMA A 0.5 PIL_299 405129G P CO 1986 Chevrolet Chevette Sedan 1986 G1G1.6V2NEA3 6B5-1A A 0.5 PIL_300 551PAU M CO 2003 Ford Focus Sedan 2003 3FMXV02.0VH1 3FMXR0080BBE A 0.25 PIL_304 781NPM M CO 1993 Jeep Cherokee SUV . A 0.25 
Appendix E 

Selection RSD Data 
E-1 

packetID 

HCBinFlagAll 

RndThreshAll 

sR_1stcontactdate 

sR_1stcontacttime 

sR_date 

sR_vdf 

bs_samSpeed 

bs_samAccel 

VSP 

bn_HC3000_ppmC3 

bs_HC4000_ppmC3 

bs_CO4000_pct 

bs_NX4000_ppm 

Packet 

Number 

RSD4000 

HC Bin 

Fraction 

of Sample 

Selected 

First 

Contact 

Date 

First 

Contact

Time 

Selection 

RSD Date 

Selection 

RSD 

VDF 

Speed 

(mph) 

Accel 

(mph/s) 

VSP 

(kW/Mg) 

RSD3000 

HC 

(ppmC3) 

RSD4000 

HC 

(ppmC3) 

RSD4000 

CO 

(%) 

RSD4000 

NO 

(ppm) 

R

PIL_010 1 0.000883 07/30/08 11:19 07/30/08 47 14.4 0.2 1.7 -54 -4 0.04 -8 PIL_011 9 0.657 07/30/08 13:44 07/30/08 104 5.5 0.1 0.5 12425 11579 3.50 778 PIL_016 7 0.48 07/30/08 16:26 07/30/08 206 12.2 0.6 2.5 489 874 1.91 709 PIL_019 8 0.622 07/31/08 12:51 07/31/08 112 11.0 1.9 5.2 927 1133 3.94 324 PIL_022 6 0.288 07/31/08 14:40 07/31/08 163 6.6 2.1 3.4 483 617 4.74 75 PIL_024 8 0.622 08/01/08 10:39 08/01/08 67 11.9 2.0 6.1 495 1221 0.06 475 PIL_029 8 0.622 08/01/08 13:55 08/01/08 175 19.9 0.5 3.7 780 1261 0.74 103 PIL_035 9 0.657 08/01/08 16:35 08/01/08 266 14.5 0.5 2.7 6331 4174 0.82 110 PIL_046 8 0.622 08/04/08 14:50 08/04/08 258 16.0 3.5 13.4 -1943 1757 0.08 1791 PIL_050 6 0.288 08/04/08 15:17 08/04/08 272 20.1 5.3 24.9 26 453 3.95 602 PIL_060 7 0.48 08/05/08 12:52 08/05/08 147 13.7 3.0 9.9 730 789 7.62 57 PIL_061 7 0.48 08/05/08 13:19 08/05/08 163 15.0 3.9 14.0 504 700 4.19 1000 PIL_062 6 0.288 08/05/08 13:40 08/05/08 173 11.4 2.9 8.0 491 632 0.30 1274 PIL_063 5 0.1535 08/05/08 14:14 08/05/08 186 13.5 1.0 4.1 271 298 0.49 168 PIL_065 8 0.622 08/05/08 15:16 08/05/08 256 11.3 1.9 5.6 1046 1670 1.36 37 PIL_070 8 0.622 08/06/08 13:00 08/06/08 140 13.9 1.8 6.6 1468 1619 1.62 285 PIL_072 7 0.48 08/07/08 10:45 08/07/08 69 16.5 6.3 23.9 742 933 8.60 321 PIL_078 6 0.2191 08/08/08 9:50 08/08/08 50 14.6 3.2 11.2 381 489 1.08 1451 PIL_079 3 0.0361 08/08/08 10:35 08/08/08 71 11.3 1.6 4.7 182 118 0.02 289 PIL_084 2 0.0124 08/08/08 13:39 08/08/08 182 17.4 6.2 25.3 41 69 0.15 666 PIL_089 9 0.0627 08/09/08 10:30 08/09/08 64 11.7 2.3 6.8 1786 2052 4.28 -6 PIL_098 2 0.0124 08/11/08 13:58 08/11/08 201 12.6 2.0 6.5 -51 31 0.14 323 PIL_108 4 0.118 08/12/08 16:19 08/12/08 252 13.1 2.2 7.3 -49 186 0.22 202 PIL_110 9 0.3 08/12/08 16:51 08/12/08 266 15.1 3.3 11.9 5820 5599 5.71 332 PIL_116 7 0.52 08/13/08 10:56 08/13/08 72 12.0 1.4 4.6 -1291 725 0.06 69 PIL_117 9 0.3 08/13/08 11:15 08/13/08 81 13.3 1.8 6.3 1871 2137 0.65 1222 PIL_118 5 0.54 08/13/08 11:45 08/13/08 92 15.8 3.3 12.7 -69 356 0.24 16 PIL_123 1 0.028 08/13/08 15:08 08/13/08 189 14.2 1.2 4.9 -64 -44 0.13 -68 PIL_124 3 0.093 08/13/08 15:52 08/13/08 218 10.6 1.2 3.5 34 132 0.05 340 PIL_128 7 0.52 08/14/08 9:40 08/14/08 49 9.9 1.5 4.0 1113 983 0.53 1951 PIL_130 6 0.42 08/14/08 11:20 08/14/08 100 15.7 4.2 15.8 467 529 6.05 419 PIL_131 8 1 08/14/08 12:30 08/14/08 142 13.2 1.8 6.3 154 1270 0.63 1549 PIL_132 6 0.42 08/14/08 12:45 08/14/08 153 12.8 1.8 6.0 126 495 0.05 133 PIL_137 6 0.42 08/14/08 16:45 08/14/08 272 16.6 3.5 13.9 408 494 2.25 1453 PIL_138 3 0.093 08/18/08 9:00 08/18/08 16 15.0 4.2 15.0 33 92 0.06 440 PIL_146 5 0.54 08/18/08 13:00 08/18/08 151 7.2 1.6 3.1 136 357 0.01 -42 PIL_151 5 0.54 08/18/08 15:40 08/18/08 246 10.2 3.0 7.4 234 261 4.16 19 PIL_157 7 0.52 08/19/08 11:15 08/19/08 112 14.6 1.4 5.6 997 827 1.80 691 PIL_161 5 0.54 08/19/08 14:34 08/19/08 222 13.6 0.9 3.6 121 324 0.28 110 PIL_165 7 0.52 08/19/08 16:48 08/19/08 276 11.6 1.7 5.1 373 757 1.40 39 PIL_169 5 0.54 08/20/08 10:34 08/20/08 74 12.4 1.4 4.6 332 358 0.64 1909 PIL_174 1 0.028 08/20/08 12:17 08/20/08 129 17.2 4.0 16.5 -31 -12 0.03 342 PIL_177 1 0.028 08/20/08 14:41 08/20/08 202 15.7 1.4 6.1 -30 -8 0.01 32 PIL_184 5 0.54 08/21/08 9:42 08/21/08 45 13.7 3.7 12.2 -18 261 1.16 464 
E-2 

packetID 

HCBinFlagAll 

RndThreshAll 

sR_1stcontactdate 

sR_1stcontacttime 

sR_date 

sR_vdf 

bs_samSpeed 

bs_samAccel 

VSP 

bn_HC3000_ppmC3 

bs_HC4000_ppmC3 

bs_CO4000_pct 

bs_NX4000_ppm 

Packet 

Number 

RSD4000 

HC Bin 

Fraction 

of Sample 

Selected 

First 

Contact 

Date 

First 

Contact

Time 

Selection 

RSD Date 

Selection 

RSD 

VDF 

Speed 

(mph) 

Accel 

(mph/s) 

VSP 

(kW/Mg) 

RSD3000 

HC 

(ppmC3) 

RSD4000 

HC 

(ppmC3) 

RSD4000 

CO 

(%) 

RSD4000 

NO 

(ppm) 

R

PIL_191 8 1 08/21/08 13:37 08/21/08 162 17.6 1.1 5.6 1120 1139 3.88 1235 PIL_193 3 0.093 08/21/08 14:12 08/21/08 174 16.1 2.4 9.8 63 115 0.18 53 PIL_194 8 1 08/21/08 14:25 08/21/08 178 8.4 2.7 5.7 1083 1426 2.32 298 PIL_200 6 0.42 08/22/08 9:30 08/22/08 34 15.7 2.5 9.8 413 435 0.41 836 PIL_203 4 0.118 08/22/08 11:51 08/22/08 124 19.0 5.2 23.4 189 208 4.92 39 PIL_213 8 1 08/23/08 8:23 08/23/08 29 13.1 1.8 6.1 969 1119 0.29 2021 PIL_217 5 0.54 08/23/08 10:50 08/23/08 107 8.3 2.7 5.5 198 267 -0.09 -157 PIL_218 3 0.093 08/23/08 11:30 08/23/08 120 9.2 4.7 10.0 109 137 0.57 1289 PIL_220 4 0.118 08/23/08 12:19 08/23/08 154 16.9 2.1 8.9 -17 170 0.52 42 PIL_221 5 0.54 08/25/08 9:10 08/25/08 31 10.5 0.9 2.8 19 373 0.17 377 PIL_222 8 1 08/25/08 9:45 08/25/08 50 14.7 1.1 4.6 897 1364 0.35 1159 PIL_223 6 0.42 08/25/08 10:31 08/25/08 85 8.2 2.9 5.8 133 575 0.02 44 PIL_225 9 0.3 08/25/08 12:05 08/25/08 144 12.8 2.2 7.1 7149 7301 12.24 1268 PIL_226 6 0.42 08/25/08 12:15 08/25/08 147 14.5 2.9 10.1 -43 441 1.47 2266 PIL_229 4 0.118 08/25/08 15:12 08/25/08 251 12.2 3.5 10.3 172 179 0.46 1241 PIL_236 8 1 08/26/08 9:11 08/26/08 37 15.1 0.9 4.2 2140 1684 0.33 818 PIL_240 4 0.118 08/26/08 10:20 08/26/08 76 18.7 5.6 24.5 136 178 2.86 94 PIL_242 8 1 08/26/08 12:12 08/26/08 138 15.0 0.5 2.7 993 1106 0.03 32 PIL_245 4 0.118 08/26/08 13:40 08/26/08 182 17.9 0.7 4.2 -162 187 0.05 31 PIL_247 9 0.3 08/26/08 16:43 08/26/08 273 13.6 3.3 10.9 1830 5098 0.27 454 PIL_248 6 0.42 08/27/08 9:20 08/27/08 31 16.7 4.9 19.1 824 560 1.99 1475 PIL_250 7 0.52 08/27/08 11:20 08/27/08 91 16.8 4.2 16.7 301 705 0.15 436 PIL_251 5 0.54 08/27/08 11:55 08/27/08 106 16.3 4.0 15.4 241 379 0.23 3169 PIL_254 3 0.093 08/27/08 12:40 08/27/08 135 16.0 3.1 12.1 111 136 0.41 2508 PIL_256 4 0.118 08/27/08 13:50 08/27/08 175 13.9 4.5 14.7 210 210 0.03 338 PIL_257 1 0.028 08/27/08 14:15 08/27/08 184 14.2 0.9 3.7 -39 -20 -0.05 -12 PIL_258 1 0.028 08/27/08 14:30 08/27/08 187 11.6 2.4 7.0 -72 -6 0.01 58 PIL_266 8 1 08/28/08 9:55 08/28/08 41 16.7 6.4 24.7 1165 1115 0.81 3724 PIL_271 6 0.42 08/28/08 12:05 08/28/08 105 18.8 5.1 22.6 608 584 5.95 395 PIL_272 7 0.52 08/28/08 12:21 08/28/08 108 12.1 2.6 7.9 724 977 4.50 443 PIL_274 6 0.42 08/28/08 12:40 08/28/08 134 11.1 1.4 4.1 377 405 0.18 439 PIL_281 5 0.54 08/28/08 16:45 08/28/08 275 13.5 2.7 8.9 242 334 0.94 589 PIL_283 5 0.54 08/29/08 9:31 08/29/08 57 13.9 3.6 12.2 207 248 0.24 455 PIL_284 5 0.54 08/29/08 9:55 08/29/08 70 11.8 3.0 8.6 322 345 0.41 233 PIL_286 7 0.52 08/29/08 11:55 08/29/08 150 12.6 0.8 3.1 467 850 0.96 2114 PIL_287 7 0.52 08/29/08 13:00 08/29/08 196 16.5 6.0 23.1 533 900 0.72 245 PIL_290 6 0.42 08/29/08 14:05 08/29/08 227 10.1 1.9 5.0 610 561 0.02 31 PIL_294 1 0.028 08/29/08 16:25 08/29/08 346 10.6 1.4 4.0 -53 -20 0.83 -27 PIL_298 6 0.42 08/30/08 9:03 08/30/08 29 14.8 2.2 8.2 35 507 0.02 -4 PIL_299 5 0.54 08/30/08 9:25 08/30/08 44 12.8 2.9 9.0 98 343 0.09 3722 PIL_300 5 0.54 08/30/08 9:45 08/30/08 51 15.7 4.4 16.2 78 298 0.19 67 PIL_304 7 0.52 08/30/08 11:42 08/30/08 115 18.9 1.7 8.4 685 708 10.09 300 
Appendix F 

Conditioning Drive Data 
F-1 

packetID dr_date dr_time 

Packet Number Drive Date Drive Time 

PIL_010 07/30/08 11:46 

PIL_011 07/30/08 14:01 

PIL_016 07/30/08 17:05 

PIL_019 07/31/08 13:17 

PIL_022 07/31/08 15:22 

PIL_022 07/31/08 16:45 

PIL_024 08/01/08 11:18 

PIL_029 08/02/08 8:17 

PIL_029 08/04/08 7:53 

PIL_035 08/01/08 17:12 

PIL_046 08/04/08 15:35 

PIL_050 08/04/08 15:50 

PIL_050 08/04/08 16:20 

PIL_060 08/05/08 13:22 

PIL_061 08/05/08 13:43 

PIL_062 08/05/08 14:38 

PIL_063 08/05/08 15:19 

PIL_065 08/05/08 16:31 

PIL_070 08/06/08 13:30 

PIL_072 08/07/08 12:25 

PIL_078 08/08/08 10:22 

PIL_079 08/08/08 11:11 

PIL_084 08/08/08 14:12 

PIL_089 08/09/08 11:18 

PIL_098 08/11/08 14:16 

PIL_108 08/12/08 17:10 

PIL_110 08/12/08 17:50 

PIL_116 08/13/08 13:15 

PIL_117 08/13/08 11:42 

PIL_118 08/13/08 12:16 

PIL_123 08/13/08 15:19 

PIL_124 08/13/08 16:14 

PIL_128 08/14/08 10:20 

PIL_130 08/14/08 11:40 

PIL_131 08/14/08 12:55 

PIL_132 08/14/08 13:35 

PIL_137 08/14/08 17:02 

PIL_138 08/18/08 9:37 

PIL_146 08/18/08 13:30 

PIL_151 08/18/08 16:10 

PIL_157 08/19/08 11:52 

PIL_161 08/19/08 15:07 

PIL_165 08/20/08 8:16 

PIL_169 08/20/08 11:27 

PIL_174 08/20/08 12:51 

PIL_177 08/20/08 15:13 

PIL_184 08/21/08 10:14 

PIL_191 08/21/08 14:07 

PIL_193 08/21/08 14:36 

PIL_194 08/21/08 15:12 

PIL_200 08/22/08 10:10 
F-2 

packetID dr_date dr_time 

Packet Number Drive Date Drive Time 

PIL_203 08/22/08 12:36 

PIL_213 08/23/08 8:58 

PIL_217 08/23/08 11:23 

PIL_218 08/23/08 11:59 

PIL_220 08/23/08 12:54 

PIL_221 08/25/08 9:44 

PIL_222 08/25/08 10:21 

PIL_223 08/25/08 11:08 

PIL_225 08/25/08 13:30 

PIL_226 08/25/08 12:45 

PIL_229 08/25/08 15:53 

PIL_236 08/26/08 9:56 

PIL_240 08/26/08 11:20 

PIL_242 08/26/08 12:58 

PIL_245 08/26/08 14:29 

PIL_247 08/26/08 17:39 

PIL_248 08/27/08 9:36 

PIL_250 08/27/08 11:41 

PIL_251 08/27/08 15:09 

PIL_254 08/27/08 13:23 

PIL_256 08/27/08 14:29 

PIL_257 08/28/08 8:50 

PIL_258 08/27/08 16:07 

PIL_266 08/28/08 10:34 

PIL_270 08/28/08 12:20 

PIL_271 08/28/08 13:43 

PIL_272 08/28/08 13:01 

PIL_274 08/28/08 14:26 

PIL_281 08/28/08 17:15 

PIL_283 08/29/08 10:05 

PIL_284 08/29/08 10:48 

PIL_286 08/29/08 13:17 

PIL_287 08/29/08 14:10 

PIL_290 08/29/08 14:50 

PIL_294 08/29/08 16:51 

PIL_298 08/30/08 9:37 

PIL_299 08/30/08 10:13 

PIL_300 08/30/08 10:51 

PIL_304 08/30/08 12:15 
Appendix G 

Measurement RSD Data 
G-1 

packetID 

mR_date 

mR_time 

mR_vdf 

bs_samSpeed 

bs_samAccel 

VSP 

bn_HC3000_ppmC3 

bs_HC4000_ppmC3 

bs_CO4000_pct 

bs_NX4000_ppm 

Packet 

Number 

Measurement 

RSD 

Date 

Measurement 

RSD 

Time 

Measurement 

RSD 

VDF 

Speed 

(mph) 

Accel 

(mph/s) 

VSP 

(kW/Mg) 

RSD3000 

HC 

(ppmC3) 

RSD4000 

HC 

(ppmC3) 

RSD4000 

CO 

(%) 

RSD4000 

NO 

(ppm) 

PIL_010 07/30/08 12:02 68 15.8 2.8 10.9 26 21 0.03 901 PIL_010 07/30/08 12:04 70 17.3 3.3 13.9 27 36 0.14 1204 PIL_011 07/30/08 15:50 195 16.1 2.7 10.7 11662 10456 11.71 478 PIL_011 07/30/08 16:10 205 16.4 2.7 10.9 11635 10705 12.33 607 PIL_019 07/31/08 13:35 135 15.4 2.8 10.7 834 1252 0.80 1321 PIL_019 07/31/08 13:38 138 15.7 3.8 14.2 1151 1384 0.80 2054 PIL_022 07/31/08 15:38 197 19.8 4.8 22.5 24 165 0.19 60 PIL_022 07/31/08 17:11 247 20.5 5.0 24.3 126 99 0.01 23 PIL_022 07/31/08 17:13 248 19.6 4.9 22.5 25 12 0.03 167 PIL_024 08/01/08 11:32 98 17.3 3.1 13.1 992 1845 0.09 1052 PIL_024 08/01/08 11:34 99 16.7 2.6 10.7 927 2268 0.06 1232 PIL_029 08/02/08 8:33 17 19.6 3.7 17.4 -13 42 0.15 49 PIL_029 08/02/08 8:35 18 18.5 3.7 16.5 3 37 0.05 69 PIL_029 08/04/08 8:09 14 19.3 3.5 16.6 -3 80 0.12 760 PIL_029 08/04/08 8:10 15 0.0 0.0 0.0 72 40 0.04 4284 PIL_035 08/01/08 18:12 282 17.5 3.2 13.5 8050 4951 1.57 97 PIL_035 08/01/08 18:13 283 18.0 3.8 16.2 4109 3975 6.00 29 PIL_046 08/04/08 15:58 301 15.7 0.6 3.2 -1565 2127 0.06 750 PIL_046 08/04/08 16:00 302 19.9 4.3 20.3 -198 1268 2.74 473 PIL_050 08/04/08 16:05 304 15.3 4.0 14.7 43 296 0.09 361 PIL_050 08/04/08 16:08 306 19.6 4.0 18.8 299 473 4.00 298 PIL_050 08/04/08 16:40 326 20.5 4.8 23.0 312 428 4.48 286 PIL_050 08/04/08 16:42 327 20.2 3.7 18.0 255 445 2.68 430 PIL_060 08/05/08 13:35 176 15.6 3.3 12.3 353 399 1.85 105 PIL_060 08/05/08 13:42 180 16.3 4.0 15.5 286 323 1.59 334 PIL_061 08/05/08 13:57 191 15.4 3.7 13.7 538 557 0.55 2733 PIL_061 08/05/08 14:07 196 16.8 3.8 15.4 746 728 2.53 1606 PIL_062 08/05/08 14:53 220 15.9 3.7 14.2 269 285 0.19 2031 PIL_062 08/05/08 15:01 232 11.0 42.0 102.6 286 278 0.30 1797 PIL_063 08/05/08 15:36 246 20.7 8.0 37.8 7 222 0.27 1366 
G-2 

packetID 

mR_date 

mR_time 

mR_vdf 

bs_samSpeed 

bs_samAccel 

VSP 

bn_HC3000_ppmC3 

bs_HC4000_ppmC3 

bs_CO4000_pct 

bs_NX4000_ppm 

Packet 

Number 

Measurement 

RSD 

Date 

Measurement 

RSD 

Time 

Measurement 

RSD 

VDF 

Speed 

(mph) 

Accel 

(mph/s) 

VSP 

(kW/Mg) 

RSD3000 

HC 

(ppmC3) 

RSD4000 

HC 

(ppmC3) 

RSD4000 

CO 

(%) 

RSD4000 

NO 

(ppm) 

PIL_063 08/05/08 15:39 248 20.5 7.6 35.8 132 220 0.27 1791 PIL_065 08/05/08 16:52 284 18.9 5.7 24.9 520 579 8.38 38 PIL_065 08/05/08 16:57 286 19.2 5.6 25.2 294 315 7.28 94 PIL_070 08/06/08 13:46 168 13.9 3.0 10.1 913 988 1.37 37 PIL_070 08/06/08 13:50 171 14.7 3.2 11.4 1347 1564 5.66 330 PIL_072 08/07/08 12:42 127 17.7 6.6 26.9 973 962 6.59 255 PIL_072 08/07/08 12:45 132 15.5 4.1 15.2 484 498 3.25 261 PIL_078 08/08/08 10:35 72 15.9 4.2 15.7 -19 105 0.13 2523 PIL_078 08/08/08 10:41 81 15.2 3.6 13.1 202 275 0.33 1117 PIL_079 08/08/08 11:27 108 19.1 2.8 13.0 60 90 0.62 243 PIL_079 08/08/08 11:29 119 16.7 2.9 11.8 42 22 0.16 71 PIL_084 08/08/08 14:28 214 15.9 3.5 13.4 59 76 0.55 115 PIL_084 08/08/08 14:34 220 15.8 4.1 15.3 85 68 0.38 340 PIL_089 08/09/08 11:32 92 14.7 2.9 10.5 1364 1674 2.18 380 PIL_089 08/09/08 11:40 105 15.2 4.2 15.2 170 192 3.28 413 PIL_098 08/11/08 14:32 225 18.8 5.1 22.4 -171 48 0.16 2126 PIL_098 08/11/08 14:35 231 18.9 4.8 21.2 32 -19 0.01 331 PIL_108 08/12/08 17:22 277 16.1 4.0 15.5 271 721 1.76 547 PIL_108 08/12/08 17:25 278 15.5 3.4 12.8 180 301 0.45 419 PIL_110 08/12/08 18:05 279 19.7 2.0 10.1 2120 2431 6.44 -34 PIL_110 08/12/08 18:07 280 17.7 3.3 14.2 1270 1958 9.69 171 PIL_116 08/13/08 13:30 141 25.0 1.6 10.9 75 472 0.11 124 PIL_116 08/13/08 13:32 144 18.0 5.1 21.4 -129 527 0.06 3313 PIL_117 08/13/08 11:55 98 18.5 5.2 22.4 888 984 3.61 589 PIL_117 08/13/08 11:58 99 18.6 5.1 22.2 1078 1650 3.76 527 PIL_118 08/13/08 12:26 122 23.4 2.8 16.1 -2 61 0.58 0 PIL_118 08/13/08 12:30 123 18.8 4.1 18.5 303 276 0.03 124 PIL_123 08/13/08 15:33 208 17.6 3.2 13.8 -81 -47 0.36 219 PIL_123 08/13/08 15:35 209 18.5 6.4 27.5 288 351 2.59 754 PIL_124 08/13/08 16:31 234 23.2 1.1 7.3 785 920 0.48 1183 
G-3 

packetID 

mR_date 

mR_time 

mR_vdf 

bs_samSpeed 

bs_samAccel 

VSP 

bn_HC3000_ppmC3 

bs_HC4000_ppmC3 

bs_CO4000_pct 

bs_NX4000_ppm 

Packet 

Number 

Measurement 

RSD 

Date 

Measurement 

RSD 

Time 

Measurement 

RSD 

VDF 

Speed 

(mph) 

Accel 

(mph/s) 

VSP 

(kW/Mg) 

RSD3000 

HC 

(ppmC3) 

RSD4000 

HC 

(ppmC3) 

RSD4000 

CO 

(%) 

RSD4000 

NO 

(ppm) 

PIL_124 08/13/08 16:32 235 24.2 0.7 5.8 3668 2510 1.89 464 PIL_128 08/14/08 10:37 76 23.4 2.6 15.2 296 626 0.67 2855 PIL_128 08/14/08 10:39 79 22.5 2.3 13.3 330 412 0.67 2777 PIL_130 08/14/08 11:56 125 20.2 2.8 13.8 382 365 3.77 846 PIL_130 08/14/08 11:58 126 20.8 2.7 14.0 360 431 3.39 1004 PIL_131 08/14/08 13:15 170 26.6 0.9 7.6 733 1217 2.76 1400 PIL_131 08/14/08 13:16 172 21.1 2.7 14.1 215 966 0.26 2503 PIL_132 08/14/08 13:52 187 24.7 4.8 28.3 -49 207 0.03 1602 PIL_132 08/14/08 13:55 188 21.8 4.0 20.9 -346 300 0.04 348 PIL_137 08/14/08 17:18 282 18.1 4.3 18.4 435 522 1.68 2268 PIL_137 08/14/08 17:22 283 17.6 4.0 16.9 507 615 1.59 2542 PIL_138 08/18/08 9:52 52 18.5 4.7 20.5 -7 83 0.11 805 PIL_138 08/18/08 9:55 55 16.4 4.1 15.9 56 165 0.11 310 PIL_146 08/18/08 13:49 190 14.3 3.3 11.4 -504 633 0.08 802 PIL_146 08/18/08 13:51 194 14.6 3.5 12.2 -114 476 0.07 1004 PIL_151 08/18/08 16:25 280 14.2 3.3 11.2 30 35 0.20 152 PIL_157 08/19/08 12:07 143 12.8 2.5 7.8 274 443 0.20 1641 PIL_157 08/19/08 12:08 145 12.7 2.0 6.4 110 209 0.48 1305 PIL_161 08/19/08 15:22 243 15.6 4.0 14.8 63 455 0.23 973 PIL_161 08/19/08 15:24 244 14.2 3.3 11.4 97 622 0.27 542 PIL_165 08/20/08 8:32 11 15.6 3.2 12.2 137 221 0.98 29 PIL_165 08/20/08 8:37 18 15.8 2.6 10.1 200 343 1.61 149 PIL_169 08/20/08 11:43 115 15.3 3.2 11.8 311 377 0.55 2737 PIL_169 08/20/08 11:44 117 15.1 3.2 11.7 341 418 0.63 2915 PIL_174 08/20/08 13:05 159 16.1 3.8 14.7 5 0 0.04 68 PIL_174 08/20/08 13:06 160 15.3 4.3 15.6 35 44 0.05 55 PIL_184 08/21/08 10:29 66 16.4 2.9 11.7 23 42 0.19 3087 PIL_184 08/21/08 10:30 67 15.5 2.9 10.9 169 202 1.45 449 PIL_191 08/21/08 14:22 186 5.7 0.4 0.8 437 516 0.17 1210 PIL_191 08/21/08 14:23 187 0.0 0.0 0.0 799 860 1.33 1828 
G-4 

packetID 

mR_date 

mR_time 

mR_vdf 

bs_samSpeed 

bs_samAccel 

VSP 

bn_HC3000_ppmC3 

bs_HC4000_ppmC3 

bs_CO4000_pct 

bs_NX4000_ppm 

Packet 

Number 

Measurement 

RSD 

Date 

Measurement 

RSD 

Time 

Measurement 

RSD 

VDF 

Speed 

(mph) 

Accel 

(mph/s) 

VSP 

(kW/Mg) 

RSD3000 

HC 

(ppmC3) 

RSD4000 

HC 

(ppmC3) 

RSD4000 

CO 

(%) 

RSD4000 

NO 

(ppm) 

PIL_193 08/21/08 15:02 206 17.4 4.4 18.2 225 312 6.08 63 PIL_193 08/21/08 15:03 211 18.3 3.7 16.4 558 1170 6.49 12 PIL_194 08/21/08 15:27 228 20.3 1.6 8.7 2224 5476 2.50 63 PIL_194 08/21/08 15:29 229 18.7 1.4 7.3 1515 10592 2.05 -61 PIL_200 08/22/08 10:26 68 16.9 3.6 14.6 267 347 0.37 2345 PIL_200 08/22/08 10:27 70 16.2 2.1 8.5 443 571 0.34 2145 PIL_203 08/22/08 12:53 148 16.7 4.2 16.8 -149 12 0.02 69 PIL_203 08/22/08 12:54 149 17.4 4.3 17.7 13 24 0.07 65 PIL_213 08/23/08 9:16 67 16.5 4.8 18.7 473 559 0.40 1416 PIL_213 08/23/08 9:17 69 15.6 4.5 16.5 588 765 1.46 1782 PIL_217 08/23/08 11:39 140 15.9 4.2 15.9 34 65 0.51 72 PIL_217 08/23/08 11:40 141 15.6 4.3 15.8 -7 27 0.08 77 PIL_218 08/23/08 12:15 155 14.1 3.6 12.2 140 192 0.29 1055 PIL_218 08/23/08 12:16 156 14.6 4.3 14.9 103 118 0.23 1165 PIL_220 08/23/08 13:09 171 20.8 4.6 22.5 -59 857 0.09 1455 PIL_220 08/23/08 13:10 172 20.9 4.7 23.1 117 401 0.42 -51 PIL_221 08/25/08 10:02 64 18.5 4.8 20.8 -106 29 0.09 1430 PIL_221 08/25/08 10:04 66 18.6 4.7 20.4 26 82 0.06 1221 PIL_222 08/25/08 10:37 88 20.2 4.8 23.0 -7967 3758 0.38 3493 PIL_222 08/25/08 10:38 90 18.7 4.6 20.2 1108 1474 1.47 1795 PIL_223 08/25/08 11:24 126 18.2 4.0 17.6 53 291 0.11 43 PIL_223 08/25/08 11:26 129 17.5 4.0 16.6 219 279 0.09 112 PIL_225 08/25/08 13:45 201 0.0 0.0 0.0 961 1075 10.10 230 PIL_225 08/25/08 13:48 204 0.0 0.0 0.0 961 963 7.35 502 PIL_226 08/25/08 13:05 177 17.6 5.4 22.4 189 177 0.77 3106 PIL_226 08/25/08 13:07 181 18.8 6.1 26.6 -28 170 0.51 3112 PIL_229 08/25/08 16:09 284 16.1 4.7 17.9 82 125 0.73 1568 PIL_229 08/25/08 16:11 285 16.0 4.5 16.9 137 190 0.58 1411 PIL_236 08/26/08 10:12 77 16.0 4.5 17.0 1139 1460 0.74 987 PIL_236 08/26/08 10:14 80 15.0 4.3 15.2 3073 3288 3.59 489 
G-5 

packetID 

mR_date 

mR_time 

mR_vdf 

bs_samSpeed 

bs_samAccel 

VSP 

bn_HC3000_ppmC3 

bs_HC4000_ppmC3 

bs_CO4000_pct 

bs_NX4000_ppm 

Packet 

Number 

Measurement 

RSD 

Date 

Measurement 

RSD 

Time 

Measurement 

RSD 

VDF 

Speed 

(mph) 

Accel 

(mph/s) 

VSP 

(kW/Mg) 

RSD3000 

HC 

(ppmC3) 

RSD4000 

HC 

(ppmC3) 

RSD4000 

CO 

(%) 

RSD4000 

NO 

(ppm) 

PIL_240 08/26/08 11:36 120 19.6 4.2 19.4 29 63 0.15 1148 PIL_240 08/26/08 11:38 123 15.7 4.0 15.0 12 58 0.11 1110 PIL_242 08/26/08 13:15 163 14.8 5.2 17.9 -1038 413 0.11 855 PIL_242 08/26/08 13:17 166 14.3 4.6 15.5 -1425 1000 0.11 601 PIL_245 08/26/08 14:43 218 19.9 5.5 25.7 49 113 3.30 -9 PIL_245 08/26/08 14:45 219 18.6 5.5 24.1 69 85 4.05 31 PIL_247 08/26/08 17:55 292 17.8 5.8 24.0 -849 1486 0.16 1886 PIL_247 08/26/08 17:57 293 17.3 5.7 23.0 -2535 2745 0.14 1866 PIL_248 08/27/08 9:57 44 14.8 3.2 11.3 532 549 0.88 2048 PIL_248 08/27/08 9:59 49 15.9 2.5 9.9 409 386 0.14 1922 PIL_250 08/27/08 11:56 109 17.9 5.0 20.9 442 468 0.17 732 PIL_250 08/27/08 11:59 111 17.8 5.0 21.0 311 511 0.35 637 PIL_251 08/27/08 15:27 232 17.6 4.2 17.4 245 316 1.02 2427 PIL_251 08/27/08 15:28 235 16.0 4.1 15.6 199 305 0.95 2459 PIL_254 08/27/08 13:39 174 18.3 4.2 18.2 79 173 0.44 3261 PIL_254 08/27/08 13:41 176 18.2 3.8 16.6 97 139 0.34 2975 PIL_256 08/27/08 14:45 211 19.4 4.8 21.9 776 1015 0.02 623 PIL_256 08/27/08 14:47 213 19.9 5.0 23.5 1168 1656 0.03 539 PIL_257 08/28/08 9:04 19 16.5 4.3 16.8 79 48 0.14 550 PIL_257 08/28/08 9:06 23 12.1 3.0 8.8 103 44 0.04 2470 PIL_258 08/27/08 16:24 270 19.1 5.2 23.1 -13 9 0.03 12 PIL_258 08/27/08 16:26 271 16.6 4.8 18.9 20 47 0.01 3 PIL_266 08/28/08 10:48 63 20.1 3.9 18.9 481 567 0.56 2893 PIL_266 08/28/08 10:50 64 21.4 2.9 15.2 345 440 0.40 2816 PIL_270 08/28/08 12:37 131 14.1 4.1 13.7 -24 60 0.01 2032 PIL_270 08/28/08 12:41 136 17.3 3.4 14.0 -1 72 0.03 599 PIL_271 08/28/08 13:57 182 18.6 3.8 16.7 567 568 4.47 337 PIL_271 08/28/08 13:59 185 18.2 4.9 20.8 390 580 3.52 539 PIL_272 08/28/08 13:17 156 18.4 3.4 15.3 511 657 3.15 963 PIL_272 08/28/08 13:19 160 13.5 2.2 7.4 833 937 5.99 383 
G-6 

packetID 

mR_date 

mR_time 

mR_vdf 

bs_samSpeed 

bs_samAccel 

VSP 

bn_HC3000_ppmC3 

bs_HC4000_ppmC3 

bs_CO4000_pct 

bs_NX4000_ppm 

Packet 

Number 

Measurement 

RSD 

Date 

Measurement 

RSD 

Time 

Measurement 

RSD 

VDF 

Speed 

(mph) 

Accel 

(mph/s) 

VSP 

(kW/Mg) 

RSD3000 

HC 

(ppmC3) 

RSD4000 

HC 

(ppmC3) 

RSD4000 

CO 

(%) 

RSD4000 

NO 

(ppm) 

PIL_274 08/28/08 14:43 207 18.2 4.7 20.4 540 549 0.16 1906 PIL_274 08/28/08 14:45 210 18.7 4.4 19.5 421 422 0.17 1525 PIL_281 08/28/08 17:33 290 19.1 3.4 15.7 83 137 0.27 814 PIL_281 08/28/08 17:35 291 22.5 3.4 18.6 292 317 2.08 1220 PIL_283 08/29/08 10:23 87 16.3 4.6 17.9 285 317 0.25 1240 PIL_283 08/29/08 10:25 88 21.6 4.5 23.0 286 285 0.37 1276 PIL_284 08/29/08 11:06 114 21.6 3.6 18.8 619 703 0.49 1557 PIL_284 08/29/08 11:08 116 18.3 3.5 15.6 561 727 0.44 1082 PIL_286 08/29/08 13:35 211 21.7 2.3 12.9 133 916 0.17 1867 PIL_286 08/29/08 13:37 214 18.6 2.7 12.3 267 340 0.09 2610 PIL_287 08/29/08 14:25 252 19.7 3.5 16.7 454 403 0.01 815 PIL_287 08/29/08 14:27 254 20.1 2.9 14.4 417 1153 0.03 231 PIL_290 08/29/08 15:07 302 17.0 5.3 20.9 188 263 3.79 52 PIL_290 08/29/08 15:10 306 17.7 5.1 21.1 354 492 0.18 49 PIL_294 08/29/08 17:11 362 14.6 5.3 18.1 -48 -11 0.02 10 PIL_294 08/29/08 17:12 363 16.1 5.3 19.9 60 64 0.07 8 PIL_298 08/30/08 9:50 59 18.6 4.9 21.3 -3 8 0.02 -32 PIL_298 08/30/08 9:52 60 17.7 4.2 17.8 -13 18 0.02 0 PIL_299 08/30/08 10:28 78 14.5 2.1 7.8 263 313 0.05 2467 PIL_299 08/30/08 10:30 80 15.8 2.1 8.4 239 299 0.04 2545 PIL_300 08/30/08 11:05 98 15.6 3.4 12.8 65 339 0.05 -32 PIL_300 08/30/08 11:07 99 15.5 3.0 11.5 15 104 0.03 32 PIL_304 08/30/08 12:31 143 17.4 3.6 15.2 737 744 8.96 367 PIL_304 08/30/08 12:32 144 18.1 3.9 16.7 799 874 12.00 275 
Appendix H 

PSHED Data 
H-1

packetID 

PS_date 

PS_SealTime 

PS_DoorSealHC_ppmC 

PS_DoorSealP_mb 

PS_DoorSealTemp_C 

PS_FinalTime 

PS_FinalHC_ppmC 

PS_FinalP_mb 

PS_FinalTemp_C 

PS_Min0_g 

PS_Min1_g 

PS_Min2_g 

PS_Min3_g 

PS_Min4_g 

PS_Min5_g 

PS_Min6_g 

PS_Min7_g 

PS_Min8_g 

PS_Min9_g 

Packet 

Number 

PSHED 

Date 

PSHED 

Seal 

Time 

PSHED 

Seal 

HC 

(ppmC) 

PSHED 

Seal 

Pbaro 

(mb) 

PSHED 

Seal 

Temperature 

(C) 

PSHED 

Final Time 

PSHED 

Final 

HC 

(ppmC) 

PSHED 

Final 

Pbaro 

(mb) 

PSHED 

Final 

Temperature 

(C) 

Cumulative PSHED HC Mass at Minutes Since D

0 1 2 3 4 5 6 7 8 9 

PIL_010 07/30/08 12:05 2 836 34 12:22 8 836 40 0.00 0.02 0.02 0.04 0.04 0.05 0.07 0.07 0.08 0.09 PIL_011 07/30/08 16:13 25 834 38 16:28 850 834 42 0.00 0.27 0.98 1.91 2.16 4.73 4.89 6.20 7.32 7.69 PIL_016 07/30/08 17:43 24 834 36 18:00 28 833 42 0.00 0.42 0.49 0.49 0.50 0.51 0.52 0.52 0.52 0.52 PIL_019 07/31/08 13:44 36 837 35 13:59 729 837 40 0.00 0.46 1.11 1.83 2.54 3.24 3.96 4.81 5.70 6.91 PIL_022 07/31/08 17:14 3 835 36 17:30 . . . 0.00 0.03 0.07 0.14 0.24 0.36 0.46 0.57 0.69 0.81 PIL_024 08/01/08 11:38 170 837 36 11:53 529 837 40 0.00 0.32 2.61 4.22 6.09 6.62 7.70 8.60 8.99 9.49 PIL_029 08/02/08 8:37 2 836 31 8:52 6 836 34 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.02 0.04 0.04 PIL_029 08/04/08 8:11 2 837 26 8:28 6 . . 0.00 0.00 0.01 0.02 0.02 0.02 0.04 0.05 0.05 0.07 PIL_035 08/01/08 17:37 . 834 40 17:53 . 834 41 0.00 1.21 4.43 6.07 6.70 7.71 9.01 9.99 10.40 11.63 PIL_046 08/04/08 16:02 41 838 34 . . . . 0.00 0.41 1.01 1.51 2.01 2.69 3.55 4.49 6.10 7.98 PIL_050 08/04/08 16:42 3 838 33 16:59 46 838 37 0.00 0.36 0.44 0.50 0.55 0.61 0.69 0.74 0.78 0.79 PIL_060 08/05/08 13:45 2 842 31 14:01 7 842 37 0.00 0.00 0.01 0.02 0.02 0.04 0.04 0.04 0.06 0.07 PIL_061 08/05/08 14:07 3 840 33 14:23 7 841 34 0.00 0.02 0.02 0.02 0.03 0.04 0.04 0.04 0.04 0.04 PIL_062 08/05/08 15:04 2 841 30 15:19 . 841 . 0.00 0.00 0.00 0.02 0.03 0.02 0.02 0.04 0.04 0.04 PIL_063 08/05/08 15:44 2 841 31 16:00 . . . 0.00 0.02 0.03 0.05 0.05 0.08 0.08 0.10 0.10 0.12 PIL_065 08/05/08 16:56 7 842 31 17:12 10 842 35 0.00 0.11 0.11 0.12 0.13 0.13 0.13 0.13 0.15 0.16 PIL_070 08/06/08 13:52 2 841 31 14:09 4 841 40 0.00 0.00 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 PIL_072 08/07/08 12:45 2 843 27 13:00 10 843 33 0.00 0.02 0.03 0.05 0.07 0.06 0.09 0.10 0.11 0.12 PIL_078 08/08/08 10:42 1 839 26 10:57 15.4 838 31 0.00 0.01 0.04 0.06 0.10 0.12 0.15 0.17 0.19 0.21 PIL_079 08/08/08 11:40 2 838 27 11:55 2.6 838 34 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 PIL_084 08/08/08 14:35 3 836 29 14:50 9.3 836 33 0.00 0.02 0.02 0.04 0.05 0.05 0.06 0.07 0.08 0.10 PIL_089 08/09/08 11:42 3 836 29 11:57 20 836 36 0.00 0.07 0.09 0.11 0.14 0.17 0.19 0.22 0.24 0.27 PIL_098 08/11/08 14:36 1 836 32 14:51 2 836 40 0.00 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 PIL_108 08/12/08 17:28 14 839 32 17:45 . . . 0.00 0.13 0.09 0.25 0.27 0.34 0.44 0.52 0.57 0.62 PIL_110 08/12/08 18:11 5 839 32 18:28 171 839 37 0.00 0.12 0.44 0.57 0.80 0.98 1.25 1.48 1.63 1.75 PIL_116 08/13/08 13:34 41 841 32 13:57 314 841 37 0.00 1.03 1.73 2.34 3.18 3.71 4.25 4.74 5.14 5.53 PIL_117 08/13/08 11:59 1 842 31 12:14 13 841 38 0.00 0.02 0.03 0.04 0.05 0.07 0.08 0.10 0.11 0.13 PIL_118 08/13/08 12:38 13 841 32 12:56 465 841 38 0.00 0.28 1.08 2.02 2.67 3.48 4.17 4.87 5.57 6.28 PIL_123 08/13/08 15:38 2 841 32 15:55 13 841 37 0.00 0.02 0.02 0.04 0.04 0.07 0.07 0.09 0.10 0.12 PIL_124 08/13/08 16:34 2 840 33 16:51 10 840 40 0.00 0.02 0.04 0.04 0.06 0.07 0.07 0.09 0.09 0.11 PIL_128 08/14/08 10:42 29 845 25 10:59 160.1 845 31 0.00 0.39 0.69 1.22 1.56 1.83 2.11 2.38 2.55 2.77 PIL_130 08/14/08 12:01 3 844 27 12:18 9 844 32 0.00 0.03 0.05 0.07 0.09 0.09 0.09 0.11 0.11 0.11 PIL_131 08/14/08 13:18 32 844 27 13:36 89 844 . 0.00 0.17 0.71 1.07 1.38 1.67 1.87 1.96 1.97 1.96 PIL_132 08/14/08 13:57 4 844 27 14:14 87.1 844 31 0.00 0.09 0.16 0.27 0.36 0.45 0.55 0.70 0.80 0.89 PIL_137 08/14/08 17:22 3 843 28 17:39 15 843 33 0.00 0.04 0.05 0.07 0.09 0.11 0.12 0.14 0.16 0.18 PIL_138 08/18/08 9:57 0 845 22 10:12 1 845 28 0.00 0.00 0.00 0.02 0.02 0.02 0.02 0.02 0.02 0.02 PIL_146 08/18/08 13:53 5 845 26 14:08 72 . . 0.00 0.15 0.44 0.58 0.71 0.81 0.94 1.04 1.13 1.22 PIL_151 08/18/08 16:29 2 844 27 16:44 17 844 31 0.00 0.02 0.05 0.05 0.07 0.08 0.11 0.14 0.16 0.18 PIL_157 08/19/08 12:10 2 842 28 12:25 9 842 34 0.00 0.02 0.02 0.04 0.06 0.07 0.09 0.10 0.11 0.13 PIL_161 08/19/08 15:27 13 841 30 15:42 171.2 841 35 0.00 0.86 1.46 1.97 2.48 2.70 2.92 3.05 3.17 3.21 PIL_165 08/20/08 8:39 2 840 22 8:56 15 840 26 0.00 0.02 0.04 0.05 0.07 0.09 0.10 0.13 0.14 0.16 PIL_169 08/20/08 11:47 5 839 29 12:02 153.2 839 33 0.00 0.07 0.17 0.40 0.58 0.79 1.00 1.25 1.47 1.69 PIL_174 08/20/08 13:11 1 838 31 13:26 3 838 35 0.00 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.04 0.04 PIL_177 08/20/08 15:34 3 839 29 15:51 5 839 30 0.00 0.01 0.02 0.02 0.02 0.02 0.03 0.05 0.05 0.05 PIL_184 08/21/08 10:33 2 839 29 10:54 3 838 32 0.00 0.00 0.00 0.00 0.02 0.02 0.02 0.02 0.02 0.02 PIL_191 08/21/08 14:28 2 836 34 14:43 6 836 37 0.00 0.00 0.02 0.02 0.02 0.02 0.04 0.04 0.04 0.05 PIL_193 08/21/08 15:05 200 836 35 15:20 515.4 836 41 0.00 2.34 5.38 7.46 9.13 9.72 10.15 10.60 10.94 11.15 
H-2

packetID 

PS_date 

PS_SealTime 

PS_DoorSealHC_ppmC 

PS_DoorSealP_mb 

PS_DoorSealTemp_C 

PS_FinalTime 

PS_FinalHC_ppmC 

PS_FinalP_mb 

PS_FinalTemp_C 

PS_Min0_g 

PS_Min1_g 

PS_Min2_g 

PS_Min3_g 

PS_Min4_g 

PS_Min5_g 

PS_Min6_g 

PS_Min7_g 

PS_Min8_g 

PS_Min9_g 

Packet 

Number 

PSHED 

Date 

PSHED 

Seal 

Time 

PSHED 

Seal 

HC 

(ppmC) 

PSHED 

Seal 

Pbaro 

(mb) 

PSHED 

Seal 

Temperature 

(C) 

PSHED 

Final Time 

PSHED 

Final 

HC 

(ppmC) 

PSHED 

Final 

Pbaro 

(mb) 

PSHED 

Final 

Temperature 

(C) 

Cumulative PSHED HC Mass at Minutes Since D

0 1 2 3 4 5 6 7 8 9 

PIL_194 08/21/08 15:37 33 835 36 15:52 1151.2 835 42 0.00 8.03 14.41 20.62 24.32 24.31 24.26 24.23 24.22 24.15 PIL_200 08/22/08 10:29 4 842 29 10:45 12 842 34 0.00 0.02 0.04 0.05 0.07 0.09 0.09 0.11 0.11 0.11 PIL_203 08/22/08 12:52 2 842 29 13:07 2 844 35 0.00 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 PIL_213 08/23/08 9:19 10 849 23 9:35 43.1 849 28 0.00 0.26 0.27 0.35 0.41 0.47 0.53 0.58 0.63 0.68 PIL_217 08/23/08 11:41 4 847 26 11:56 7 847 32 0.00 0.02 0.02 0.02 0.02 0.02 0.03 0.04 0.04 0.04 PIL_218 08/23/08 12:19 4 847 27 12:33 20 847 32 0.00 0.03 0.05 0.07 0.09 0.12 0.14 0.17 0.19 0.21 PIL_220 08/23/08 13:11 17 846 28 13:28 54.1 846 32 0.00 0.11 0.20 0.31 0.65 0.75 0.80 0.84 0.88 0.91 PIL_221 08/25/08 10:08 . . . 10:23 8 843 33 0.00 0.02 0.02 0.02 0.04 0.05 0.05 0.07 0.07 0.08 PIL_222 08/25/08 10:43 4 843 29 10:58 127 843 33 0.00 . . . . . . . . . PIL_223 08/25/08 11:29 10 843 28 11:44 94 843 34 0.00 0.22 0.28 0.46 0.65 0.81 0.97 1.10 1.23 1.34 PIL_225 08/25/08 13:52 0 841 33 14:11 19 841 38 0.00 0.03 0.05 0.07 0.10 0.12 0.14 0.16 0.19 0.22 PIL_226 08/25/08 13:11 2 842 33 13:26 10 841 39 0.00 . . . . . . . . . PIL_229 08/25/08 16:15 30 840 32 16:30 158 840 36 0.00 0.02 0.13 1.01 1.66 2.43 2.95 3.26 3.42 3.46 PIL_236 08/26/08 10:18 4 838 30 10:33 48 838 35 0.00 0.07 0.10 0.13 0.17 0.25 0.34 0.43 0.60 0.70 PIL_240 08/26/08 11:41 1 837 32 11:56 6 837 37 0.00 0.02 0.02 0.03 0.04 0.04 0.05 0.07 0.07 0.07 PIL_242 08/26/08 13:21 50 837 33 13:36 425 837 38 0.00 0.68 1.81 2.72 3.53 4.25 4.94 5.56 6.19 6.77 PIL_245 08/26/08 14:49 3 837 35 15:04 8 836 40 0.00 0.04 0.05 0.07 0.07 0.08 0.09 0.09 0.10 0.11 PIL_247 08/26/08 18:02 30 836 33 18:17 109 837 38 0.00 0.24 0.77 0.96 1.11 1.32 1.48 1.60 1.68 1.78 PIL_248 08/27/08 10:03 5 844 22 10:18 53 844 28 0.00 0.12 0.24 0.35 0.44 0.53 0.60 0.67 0.73 0.79 PIL_250 08/27/08 12:03 6 842 25 12:18 34 841 31 0.00 0.09 0.22 0.31 0.38 0.44 0.48 0.53 0.56 0.59 PIL_251 08/27/08 15:37 0 837 31 15:52 1 837 35 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 PIL_254 08/27/08 13:45 5 840 28 14:00 16 839 34 0.00 0.07 0.13 0.17 0.20 0.23 0.26 0.28 0.29 0.31 PIL_256 08/27/08 14:51 10 838 30 15:06 100 838 36 0.00 0.22 0.23 0.61 1.07 1.50 1.75 1.94 2.07 2.15 PIL_257 08/28/08 9:11 1 847 22 9:26 4 847 27 0.00 0.02 0.02 0.02 0.03 0.05 0.05 0.05 0.05 0.06 PIL_258 08/27/08 16:30 2 837 33 16:45 3 837 41 0.00 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 PIL_266 08/28/08 10:53 2 847 23 11:08 8 847 28 0.00 0.02 0.02 0.04 0.04 0.04 0.06 0.07 0.07 0.09 PIL_270 08/28/08 12:44 2 846 26 12:59 48 846 29 0.00 0.04 0.12 0.22 0.34 0.45 0.53 0.60 0.68 0.75 PIL_271 08/28/08 14:03 4 845 27 14:18 62 845 33 0.00 0.03 0.06 0.09 0.14 0.26 0.40 0.56 0.69 0.84 PIL_272 08/28/08 13:24 3 846 27 13:39 57 846 31 0.00 0.01 0.23 0.39 0.52 0.64 0.75 0.88 0.98 1.02 PIL_274 08/28/08 14:48 2 845 27 15:03 3 845 33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 PIL_281 08/28/08 17:38 0 844 27 17:53 12 843 32 0.00 0.02 0.02 0.03 0.05 0.06 0.08 0.09 0.11 0.13 PIL_283 08/29/08 10:29 2 846 25 10:44 4 846 30 0.00 0.02 0.02 0.02 0.02 0.03 0.05 0.05 0.05 0.05 PIL_284 08/29/08 11:11 3 846 26 11:26 5.5 846 30 0.00 0.01 0.02 0.02 0.02 0.02 0.03 0.05 0.05 0.05 PIL_286 08/29/08 13:41 4 844 29 13:56 620 844 36 0.00 0.05 0.19 0.66 1.35 2.38 3.76 4.99 6.48 7.79 PIL_287 08/29/08 14:30 13 844 30 14:45 136 844 38 0.00 0.25 0.82 1.18 1.46 1.69 1.95 2.18 2.34 2.51 PIL_290 08/29/08 15:13 20 843 31 15:28 58 843 35 0.00 0.34 0.58 0.73 0.94 1.06 1.14 1.18 1.19 1.20 PIL_294 08/29/08 17:15 4 842 31 17:30 6 842 35 0.00 0.02 0.04 0.04 0.04 0.03 0.04 0.04 0.04 0.04 PIL_298 08/30/08 9:56 1 842 26 10:11 3 842 30 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.02 0.02 PIL_299 08/30/08 10:33 1 842 27 10:48 19 842 31 0.00 0.02 0.04 0.05 0.06 0.07 0.09 0.09 0.12 0.13 PIL_300 08/30/08 11:11 3 841 28 11:26 13 841 33 0.00 0.04 0.06 0.09 0.13 0.15 0.16 0.18 0.18 0.20 PIL_304 08/30/08 12:36 0 840 30 12:51 8 840 37 0.00 0.01 0.02 0.05 0.05 0.07 0.07 0.09 0.09 0.11 
Appendix I 

Modified California Method Data 

Legend: 

0 No visual evidence of fuel leaks 

m Minor visual signs of fuel (staining, damp spots), paper towel wicking < 1 inch 

S Significant visual leaks with single drops of fuel from vehicle to the ground, paper towel 

wicking > 1 inch 

G Gross visual leaks, regular flow of drops to the ground, or a large pool of fuel, paper 

towel wicking > 1 inch 

NP Not Performed (usually because component was not present, could not be found, or was 

hidden) 

Y Positive HC sniffer response 

N No HC sniffer response 

# Data entry was missing from data packet 
I-1 

packetID 

MCM_Date 

MCM_Time 

MCM_UnderbodyV 

MCM_FuelPumpV 

MCM_PumpToCarbV 

MCM_FuelFilterV 

MCM_FuelRailV 

MCM_FuelInjectorsV 

MCM_UnderVehicleV 

MCM_FillPipeToTankV 

MCM_TankV 

MCM_NonOEMV 

MCM_UnderbodyS 

MCM_FuelPumpS 

MCM_PumpToCarbS 

MCM_FuelFilterS 

wMCM_FuelRailS 

MCM_FuelInjectorsS 

MCM_UnderVehicleS 

MCM_FillPipeToTankS 

MCM_TankS 

MCM_NonOEMS 

pi_VideoFileNum 

Packet 

Number 

MCM 

Date 

MCM 

Time 

Visual Sniffer 

Video 

File 

Underbod Number y

Fuel Pump 

Pump to Carb 

Fuel Filter 

Fuel Rail 

Fuel Injectors 

Under Vehicle 

Fill Pipe to Tank 

Tank 

Non-OEM 

Underbody

Fuel Pump 

Pump to Carb 

Fuel Filter 

Fuel Rail 

Fuel Injectors 

Under Vehicle 

Fill Pipe to Tank 

Tank 

Non-OEM 

PIL_010 07/30/08 12:22 0 0 0 0 0 0 0 0 0 NP # # # # # # # # # NP None No problems PIL_011 07/30/08 16:28 0 0 0 0 0 0 0 0 0 0 # # Y # # # # Y # # None 

The end of thwhere the galocking gas cthe vehicle. Npipe is homeconnections. crossover val

PIL_016 07/30/08 18:00 0 NP NP 0 0 0 0 0 0 NP # # # # # # # # # # None 

Bad vapor lesaturated. 

PIL_019 07/31/08 13:59 0 0 0 NP NP NP 0 0 0 0 # # # # # # # # # # None 

New fuel tancap is vented

PIL_022 07/31/08 17:30 0 0 NP NP 0 0 0 0 0 NP # # # # # # # # # # None Clean. No snPIL_024 08/01/08 11:53 0 NP NP NP 0 0 0 0 0 0 # # # # # # # Y # # 96 Sniffer: trace PIL_029 08/02/08 8:52 0 NP NP 0 0 0 0 m 0 0 # # # # # # # # # # None No problems PIL_035 08/01/08 17:53 0 NP NP NP 0 0 # # # # NP NP NP NP Y Y NP Y Y # None 

No visual siggas cap, bottoof tank. 

PIL_046 08/04/08 16:19 # # # # # # # # # # # # # # # # # # # # None Pass I/M and PIL_050 08/04/08 16:59 0 NP NP 0 0 0 0 0 0 NP N N N N N N N Y # # 

103, 104, 

105 

Vapor return fuel inlet retu

PIL_060 08/05/08 14:01 0 NP NP 0 0 NP 0 M M 0 # # # # # # # Y N N 106 Trace fill pipPIL_061 08/05/08 14:23 0 NP NP 0 0 NP 0 0 0 0 N N N N N N N N N N 107 Pass I/M and PIL_062 08/05/08 15:19 0 NP NP 0 0 0 0 0 0 0 N NP NP N N N N N N NP 108 PIL_063 08/05/08 16:00 0 NP NP 0 0 NP 0 0 0 0 N NP NP N N NP N N N NP 109 PIL_065 08/05/08 17:12 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None Bad miss, faiPIL_070 08/06/08 14:09 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP 110 

Very clean - problems fou

PIL_072 08/07/08 13:00 0 NP NP NP 0 0 0 0 0 NP N NP NP NP N N N N N NP 115 File corruptePIL_078 08/08/08 10:57 0 NP 0 NP 0 0 0 0 0 NP Y NP N NP N N N N N # 113 

Passed IM, pfound. 

PIL_079 08/08/08 11:55 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N # 114 No problems PIL_084 08/08/08 14:50 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP 119 Pass IM cap, PIL_089 08/09/08 11:57 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N Y N # 120 

No gas cap, soverheating, 

PIL_098 08/11/08 14:51 0 0 NP # 0 0 0 0 0 NP N N NP N N N N N N NP None PIL_108 08/12/08 17:45 0 0 0 0 0 0 0 0 0 NP N N NP # N NP N N N NP None Rejected from PIL_110 08/12/08 18:28 S 0 NP 0 S S S # m # Y N NP N Y Y Y # Y # None This is definiPIL_116 08/13/08 13:57 0 NP NP 0 0 0 0 0 0 0 # # # # # # # Y NP NP None Possible leak PIL_117 08/13/08 12:14 0 0 NP 0 0 0 0 0 0 NP N N NP N N N N N N NP N/A Dual fuel tanPIL_118 08/13/08 12:56 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP N/A Strong fuel smPIL_123 08/13/08 15:55 0 NP NP NP 0 0 0 0 0 NP N NP NP NP N N N N N NP None PIL_124 08/13/08 16:51 0 0 0 0 0 0 0 0 0 0 N N N N N N N N N N None PIL_128 08/14/08 10:59 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None PIL_130 08/14/08 12:18 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N # None No dyno, uns
I-2 

packetID 

MCM_Date 

MCM_Time 

MCM_UnderbodyV 

MCM_FuelPumpV 

MCM_PumpToCarbV 

MCM_FuelFilterV 

MCM_FuelRailV 

MCM_FuelInjectorsV 

MCM_UnderVehicleV 

MCM_FillPipeToTankV 

MCM_TankV 

MCM_NonOEMV 

MCM_UnderbodyS 

MCM_FuelPumpS 

MCM_PumpToCarbS 

MCM_FuelFilterS 

wMCM_FuelRailS 

MCM_FuelInjectorsS 

MCM_UnderVehicleS 

MCM_FillPipeToTankS 

MCM_TankS 

MCM_NonOEMS 

pi_VideoFileNum 

Packet 

Number 

MCM 

Date 

MCM 

Time 

Visual Sniffer 

Video 

File 

Underbod Number y

Fuel Pump 

Pump to Carb 

Fuel Filter 

Fuel Rail 

Fuel Injectors 

Under Vehicle 

Fill Pipe to Tank 

Tank 

Non-OEM 

Underbody

Fuel Pump 

Pump to Carb 

Fuel Filter 

Fuel Rail 

Fuel Injectors 

Under Vehicle 

Fill Pipe to Tank 

Tank 

Non-OEM 

PIL_131 08/14/08 13:36 0 NP NP 0 0 0 0 0 0 NP N NP NP N Y Y N N N NP None #2 and #4 injPIL_132 08/14/08 14:14 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None PIL_137 08/14/08 17:39 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None 

Owner says mpurchased us

PIL_138 08/18/08 10:12 0 0 NP NP 0 0 0 0 0 NP N N NP NP N N N N N NP None PIL_146 08/18/08 14:08 0 NP NP NP 0 0 0 m m NP N NP NP NP N N N Y Y NP None 

Tank very rufill pipe. Can

PIL_151 08/18/08 16:44 0 NP 0 NP NP NP 0 0 0 NP N NP N NP NP NP N N N NP None PIL_157 08/19/08 12:25 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N N None Leak at PCVPIL_161 08/19/08 15:42 0 NP NP 0 0 0 0 0 0 NP N NP NP N N Y N N N NP None Leaking at inPIL_165 08/20/08 8:56 0 NP NP 0 0 0 0 0 0 NP N NP NP N Y N N N N NP N/A Fuel leak aloPIL_169 08/20/08 12:02 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None Could not finPIL_174 08/20/08 13:26 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None No leaks, 3ppPIL_177 08/20/08 15:51 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None Rained durinPIL_184 08/21/08 10:54 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None Appears to bPIL_191 08/21/08 14:43 0 NP NP NP 0 0 0 NP 0 NP N NP NP NP N N N N N NP None Smokes from PIL_193 08/21/08 15:20 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None Could not finPIL_194 08/21/08 15:52 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N # None 

Unable to finppm, no visib

PIL_200 08/22/08 10:45 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None PIL_203 08/22/08 13:07 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None No emissions PIL_213 08/23/08 9:35 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N Y N NP None Filler neck lePIL_217 08/23/08 11:56 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None Clean. PIL_218 08/23/08 12:33 0 NP 0 NP 0 0 0 NP NP NP N NP N NP N N N NP NP NP None 

Questionnairthere is a leak 

PIL_220 08/23/08 13:28 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None No leak founPIL_221 08/25/08 10:23 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None 

Computer bostill open wh

PIL_222 08/25/08 10:58 0 NP 0 0 0 0 0 0 0 NP NP NP N N N N N N NP NP None 

Could not fintest: N; N; N

PIL_223 08/25/08 11:44 0 NP NP 0 0 0 0 m 0 NP N NP NP N N N N Y N NP None Leak at filler PIL_225 08/25/08 14:11 0 0 NP 0 0 0 0 0 0 0 N N NP N N N N N N N None Aftermarket PIL_226 08/25/08 13:26 NP NP 0 0 0 0 0 0 0 NP NP NP NP NP NP NP NP NP NP NP None No leak. PIL_229 08/25/08 16:30 0 NP 0 NP m 0 0 0 0 NP N NP N NP N N N N N NP None 

Could not finfuel lines. 

PIL_236 08/26/08 10:33 0 NP 0 0 0 0 0 0 0 NP N NP N Y N N NP N N NP None 

No I/M due tfuel filter (pe

PIL_240 08/26/08 11:56 0 0 0 NP 0 0 0 0 0 NP N N N NP N N NP N N NP None PIL_242 08/26/08 13:36 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N NP Y N NP None 

Leaks at gas area (top of t

PIL_245 08/26/08 15:04 0 NP NP NP 0 0 0 0 0 NP NP NP NP NP N N NP N N NP None Very clean. PIL_247 08/26/08 18:17 0 NP NP 0 0 0 0 0 0 NP N NP NP N N Y NP N N NP None Small leak in PIL_248 08/27/08 10:18 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N NP N N NP None Could not finPIL_250 08/27/08 12:18 NP NP NP NP 0 0 0 0 0 NP NP NP NP NP Y Y NP N N NP None 

2 Leaks: 3rd line and fuel 
I-3 

packetID 

MCM_Date 

MCM_Time 

MCM_UnderbodyV 

MCM_FuelPumpV 

MCM_PumpToCarbV 

MCM_FuelFilterV 

MCM_FuelRailV 

MCM_FuelInjectorsV 

MCM_UnderVehicleV 

MCM_FillPipeToTankV 

MCM_TankV 

MCM_NonOEMV 

MCM_UnderbodyS 

MCM_FuelPumpS 

MCM_PumpToCarbS 

MCM_FuelFilterS 

wMCM_FuelRailS 

MCM_FuelInjectorsS 

MCM_UnderVehicleS 

MCM_FillPipeToTankS 

MCM_TankS 

MCM_NonOEMS 

pi_VideoFileNum 

Packet 

Number 

MCM 

Date 

MCM 

Time 

Visual Sniffer 

Video 

File 

Underbod Number y

Fuel Pump 

Pump to Carb 

Fuel Filter 

Fuel Rail 

Fuel Injectors 

Under Vehicle 

Fill Pipe to Tank 

Tank 

Non-OEM 

Underbody

Fuel Pump 

Pump to Carb 

Fuel Filter 

Fuel Rail 

Fuel Injectors 

Under Vehicle 

Fill Pipe to Tank 

Tank 

Non-OEM 

PIL_251 08/27/08 15:52 NP NP NP NP 0 0 0 0 0 NP NP NP NP NP NP NP NP NP NP NP None Ratty, but vaPIL_254 08/27/08 14:00 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N N N N NP None No leak detecPIL_256 08/27/08 15:06 NP NP NP NP 0 0 0 0 0 NP NP NP NP NP N N NP N N NP None No leak founPIL_257 08/28/08 9:26 0 NP NP NP 0 0 0 0 0 NP N NP NP NP N N NP N N NP None PIL_258 08/27/08 16:45 NP NP NP NP 0 0 0 0 NP NP NP NP NP NP NP NP NP NP NP NP None Clean. DifficPIL_266 08/28/08 11:08 0 NP NP NP 0 0 0 0 0 NP N NP NP NP N N NP N N NP None Ratty, but no PIL_270 08/28/08 12:59 0 NP NP NP 0 0 0 m 0 NP N NP NP NP N N NP Y N NP None 

Gas cap was test. 

PIL_271 08/28/08 14:18 0 NP NP NP 0 NP 0 0 0 NP N NP NP NP N NP NP N N NP None 

Leak at carboclutch out, bo

PIL_272 08/28/08 13:39 0 NP NP 0 0 0 0 0 0 NP N NP NP NP N N NP N N NP None No leak founPIL_274 08/28/08 15:03 0 NP NP NP 0 0 0 0 0 NP N NP NP NP N N NP N N NP None PIL_281 08/28/08 17:53 0 NP NP NP 0 0 0 0 0 NP N NP NP NP N N NP N N NP None Rusty body, bPIL_283 08/29/08 10:44 0 NP NP NP 0 0 0 0 0 NP N NP NP NP N N NP N N NP None PIL_284 08/29/08 11:26 0 0 NP 0 0 0 0 0 0 NP N N NP N N N NP N N NP None PIL_286 08/29/08 13:56 0 S 0 0 0 NP 0 0 0 NP N Y N N N NP NP Y N NP None Leaks at fuel PIL_287 08/29/08 14:45 0 0 0 NP 0 0 0 0 0 NP N N N NP N N NP N N NP None 

Rapid screeninjectors but 

PIL_290 08/29/08 15:28 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N NP N N NP None No leak founPIL_294 08/29/08 17:30 0 0 0 0 0 0 0 0 0 NP N N N N N N NP N N NP None PIL_298 08/30/08 10:11 0 NP NP NP 0 0 0 0 0 NP N NP NP NP N N NP N N NP None PIL_299 08/30/08 10:48 0 0 0 0 0 NP 0 0 0 NP N N N N N NP NP N N NP None Rebuilt enginPIL_300 08/30/08 11:26 0 NP NP 0 0 0 0 0 0 NP N NP NP N N N NP N N NP None PIL_304 08/30/08 12:51 0 0 NP 0 m m 0 0 0 NP N N NP N Y Y NP N N NP None 

Leaks at sevefalse positiveset off the sn
Appendix J 

Driver Interview Information 
J-1

packetID 

pi_FleetVehicle 

pi_ModelYear_gt1980 

pi_EveryDayCar 

pi_Eligible 

pi_MilesYear 

pi_MonthsOwned 

pi_ParkingLocation 

pi_LastTimeFueled 

pi_RegularMaintenance 

pi_OilChange 

pi_TuneUp 

pi_NewGasCap 

pi_FuelSystemRepairs 

Packet 

Number 

Fleet 

Vehicle? 

Model 

Year 

> 1980? 

EveryDay 

Car? Eligible? 

Miles 

per 

year? 

Months owned? 

Parking 

location? 

Last 

Time 

Fueled? 

Regular 

Maintenance? 

Oil 

Change? 

Tuneup? 

New 

Gas 

Cap? 

Fuel 

System 

Repairs? 

PIL_010 N Y Y Y A 1 B A Y B A A A PIL_011 N N Y Y B 1 B C N A A A A PIL_016 N Y Y Y C 48 A C Y C A B B PIL_019 N N N Y A 14 B C Y C C A A PIL_022 N Y Y Y B 72 B C N A A A A PIL_024 N Y Y Y C 120 B B Y D D A A PIL_029 N Y Y Y E 1 B A C E E E E PIL_035 N Y Y Y A 12 B C N A A A A PIL_046 N Y Y Y A 108 B C Y D B C D PIL_050 N Y Y Y C 156 B C B B B A A PIL_060 N Y Y Y B 2 B C A B B E A PIL_061 N Y Y Y B 1 B C A B B A A PIL_062 N Y Y Y B 1 B B A B B A A PIL_063 N Y Y Y B 204 B A A B B A A PIL_065 N Y Y Y B 0 B A A C A A A PIL_070 N Y Y Y B 0 B B A C C A A PIL_072 N Y N Y A 180 A C A B D A A PIL_078 N Y Y Y A 96 B B A B D B A PIL_079 N Y Y Y B 1 B A A C E A B PIL_084 N Y Y Y B 1 A A A B B A A PIL_089 N Y Y Y C 2 B A A B B B E PIL_098 N Y Y Y B 60 A A A C D A A PIL_108 N Y Y Y A 48 B C Y B B A A PIL_110 N Y Y Y C 2 B A A B B B A PIL_116 N Y Y Y B 96 A C A C C C A PIL_117 N Y Y Y B 156 B C A B C A A PIL_118 N Y Y Y D 36 A B A B E A A PIL_123 N Y Y Y A 144 A C A B D A A PIL_124 N Y Y Y B 0 B B C E E E E PIL_128 N Y Y Y B 144 B A A B B A B PIL_130 N Y Y Y A 3 B B A B B B A PIL_131 N Y Y Y B 36 B A A B A A A PIL_132 N Y Y Y B 0 B D C B E E E PIL_137 N Y Y Y C 1 A A C B B A A PIL_138 N Y Y Y B 0 B A C E E E E 
J-2

packetID 

pi_FleetVehicle 

pi_ModelYear_gt1980 

pi_EveryDayCar 

pi_Eligible 

pi_MilesYear 

pi_MonthsOwned 

pi_ParkingLocation 

pi_LastTimeFueled 

pi_RegularMaintenance 

pi_OilChange 

pi_TuneUp 

pi_NewGasCap 

pi_FuelSystemRepairs 

Packet 

Number 

Fleet 

Vehicle? 

Model 

Year 

> 1980? 

EveryDay 

Car? Eligible? 

Miles 

per 

year? 

Months owned? 

Parking 

location? 

Last 

Time 

Fueled? 

Regular 

Maintenance? 

Oil 

Change? 

Tuneup? 

New 

Gas 

Cap? 

Fuel 

System 

Repairs? 

PIL_146 N Y Y Y A 12 B B C B B A B PIL_151 N Y Y Y A 36 B C A B C A A PIL_157 N Y Y Y B 36 A A A B D C A PIL_161 N Y Y Y B 24 B B Y D B A A PIL_165 N Y Y Y E 0 A C A B B B A PIL_169 N Y Y Y A 24 B A A C D A A PIL_174 N Y Y Y B 36 A C A C E A A PIL_177 N Y Y Y C 1 B C C A A A A PIL_184 N Y Y Y A 72 A D A D A B A PIL_191 N Y Y Y B 48 B C A B B A A PIL_193 N Y Y Y C 120 A C A C D A A PIL_194 N Y Y Y A 36 B A B D D A B PIL_200 N Y Y Y A 1 B C C B B A E PIL_203 N Y Y Y B 72 A C A B D B A PIL_213 N Y Y Y A 168 B A A C E A A PIL_217 N Y Y Y E 0 B B A B B A A PIL_218 N Y Y Y A 48 B A A B D A A PIL_220 N Y Y Y D 96 B B A B D A A PIL_221 N Y Y Y A 3 B A A A B A A PIL_222 N Y Y Y A 60 B A A E A E A PIL_223 N Y Y Y E 1 B A A A A A A PIL_225 N Y Y Y A 144 A A A B B A A PIL_226 N Y Y Y A 1 C A A B A A B PIL_229 N Y Y Y A 240 B C A D D A A PIL_236 N Y Y Y B 4 B A A B B B A PIL_240 N Y Y Y A 3 B C A B E A A PIL_242 N Y Y Y A 180 B C A B B A A PIL_245 N Y Y Y A 36 A C A C D A A PIL_247 N Y Y Y B 0 B B A A A A A PIL_248 N Y Y Y D 132 B C B B B A A PIL_250 N Y Y Y C 0 B B A A A A A PIL_251 N Y Y Y A 204 B D B B B A A PIL_254 N Y Y Y A 72 B A A C C A D PIL_256 N Y Y Y B 2 B B B A A A B PIL_257 N Y Y Y A 72 B A A B A A A PIL_258 N Y Y Y B 72 A C A C A A A 
J-3

packetID 

pi_FleetVehicle 

pi_ModelYear_gt1980 

pi_EveryDayCar 

pi_Eligible 

pi_MilesYear 

pi_MonthsOwned 

pi_ParkingLocation 

pi_LastTimeFueled 

pi_RegularMaintenance 

pi_OilChange 

pi_TuneUp 

pi_NewGasCap 

pi_FuelSystemRepairs 

Packet 

Number 

Fleet 

Vehicle? 

Model 

Year 

> 1980? 

EveryDay 

Car? Eligible? 

Miles 

per 

year? 

Months owned? 

Parking 

location? 

Last 

Time 

Fueled? 

Regular 

Maintenance? 

Oil 

Change? 

Tuneup? 

New 

Gas 

Cap? 

Fuel 

System 

Repairs? 

PIL_266 N Y Y Y A 144 B C A B B E A PIL_270 N Y Y Y A 1 B A A B B E E PIL_271 N Y Y Y A 204 B B A B B A A PIL_272 N Y Y Y A 104 B D A B B D A PIL_274 N Y Y Y A 1 B C A E E E E PIL_281 N Y Y Y A 264 B C A B E E E PIL_283 N Y Y Y A 24 A A A B E E A PIL_284 N Y Y Y A 72 B A A B B D C PIL_286 N Y Y Y B 300 B C A C B E E PIL_287 N Y Y Y C 96 C A A B D B A PIL_290 N Y Y Y B 12 B A A B B B B PIL_294 N Y Y Y B 36 B B A B B E E PIL_298 N Y Y Y B 48 B C B D A A A PIL_299 N Y Y Y A 264 B C A B B A B PIL_300 N Y Y Y A 18 A D A E E E E PIL_304 N Y Y Y C 48 B A A C C E A 
Appendix K 

I/M Gas Cap Inspection Results 
K-1 

packetID pi_CapResult 

Packet Number IM Gas Cap Inspection Result

PIL_010 P 

PIL_011 

PIL_016 F 

PIL_019 

PIL_022 P 

PIL_024 P 

PIL_029 P 

PIL_035 P 

PIL_046 P 

PIL_050 P 

PIL_060 P 

PIL_061 P 

PIL_062 P 

PIL_063 F 

PIL_065 P 

PIL_070 P 

PIL_072 P 

PIL_078 P 

PIL_079 P 

PIL_084 P 

PIL_089 F 

PIL_098 P 

PIL_108 

PIL_110 P 

PIL_116 P 

PIL_117 P 

PIL_118 P 

PIL_123 P 

PIL_124 P 

PIL_128 F 

PIL_130 

PIL_131 P 

PIL_132 P 

PIL_137 P 

PIL_138 P 

PIL_146 P 

PIL_151 P 

PIL_157 P 

PIL_161 P 

PIL_165 P 

PIL_169 P 

PIL_174 P 

PIL_177 P 

PIL_184 P 

PIL_191 P 

PIL_193 P 
K-2 

packetID pi_CapResult 

Packet Number IM Gas Cap Inspection Result

PIL_194 P 

PIL_200 P 

PIL_203 P 

PIL_213 P 

PIL_217 P 

PIL_218 P 

PIL_220 P 

PIL_221 P 

PIL_222 P 

PIL_223 

PIL_225 P 

PIL_226 

PIL_229 P 

PIL_236 

PIL_240 P 

PIL_242 F 

PIL_245 F 

PIL_247 P 

PIL_248 P 

PIL_250 P 

PIL_251 P 

PIL_254 F 

PIL_256 P 

PIL_257 P 

PIL_258 P 

PIL_266 P 

PIL_270 P 

PIL_271 P 

PIL_272 P 

PIL_274 P 

PIL_281 P 

PIL_283 P 

PIL_284 P 

PIL_286 P 

PIL_287 

PIL_290 P 

PIL_294 P 

PIL_298 P 

PIL_299 P 

PIL_300 P 

PIL_304 P