Document ID: EPA-HQ-OAR-2009-0548-0004
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2010-07-14T04:00Z

PART B OF THE SUPPORTING STATEMENT

Exhaust Emissions of Light-duty Vehicles in Metropolitan Detroit

OMB Control Number 2060-NEW

USEPA Agency Form Number 2363.01

May 27, 2010

  TOC \h \z \t
"icr_head_x,1,icr_head_xx,2,icr_head_xxx,3,icr_head_xxxx,4"    HYPERLINK
\l "_Toc262804354"  1.0	Survey Objectives, Key Variables and Other
Preliminaries	  PAGEREF _Toc262804354 \h  2  

  HYPERLINK \l "_Toc262804355"  1(a)	Survey Objectives	  PAGEREF
_Toc262804355 \h  2  

  HYPERLINK \l "_Toc262804356"  1(b) 	Key Variables	  PAGEREF
_Toc262804356 \h  5  

  HYPERLINK \l "_Toc262804357"  1(c) 	Statistical Approach	  PAGEREF
_Toc262804357 \h  5  

  HYPERLINK \l "_Toc262804358"  1(d) 	Feasibility	  PAGEREF
_Toc262804358 \h  6  

  HYPERLINK \l "_Toc262804359"  2.0	Survey Design	  PAGEREF
_Toc262804359 \h  6  

  HYPERLINK \l "_Toc262804360"  2(a) 	Target Population and Coverage	 
PAGEREF _Toc262804360 \h  6  

  HYPERLINK \l "_Toc262804361"  2(b) 	Sample Design	  PAGEREF
_Toc262804361 \h  7  

  HYPERLINK \l "_Toc262804362"  2(b)(i)	 Sample Frame	  PAGEREF
_Toc262804362 \h  7  

  HYPERLINK \l "_Toc262804363"  2(b)(ii)	Sample Size	  PAGEREF
_Toc262804363 \h  15  

  HYPERLINK \l "_Toc262804364"  2(b)(ii)(1)	 Capturing high-emitting
vehicles	  PAGEREF _Toc262804364 \h  15  

  HYPERLINK \l "_Toc262804365"  2(b)(ii)(2)	 Assessing age trends	 
PAGEREF _Toc262804365 \h  17  

  HYPERLINK \l "_Toc262804366"  2(b)(ii)(3)	Numbers of driving trips	 
PAGEREF _Toc262804366 \h  22  

  HYPERLINK \l "_Toc262804367"  2(b)(iii)	Stratification variables	 
PAGEREF _Toc262804367 \h  24  

  HYPERLINK \l "_Toc262804368"  2(b)(iv)	Sampling Method	  PAGEREF
_Toc262804368 \h  24  

  HYPERLINK \l "_Toc262804369"  2(b)(v)	Multi-Stage Sampling	  PAGEREF
_Toc262804369 \h  25  

  HYPERLINK \l "_Toc262804370"  2(c) 	Precision Requirements	  PAGEREF
_Toc262804370 \h  25  

  HYPERLINK \l "_Toc262804371"  2(c)(i) Precision Targets	  PAGEREF
_Toc262804371 \h  25  

  HYPERLINK \l "_Toc262804372"  2(c)(ii) Non-Sampling Error	  PAGEREF
_Toc262804372 \h  26  

  HYPERLINK \l "_Toc262804373"  2(c)(ii)(1) 	Frame-coverage error	 
PAGEREF _Toc262804373 \h  26  

  HYPERLINK \l "_Toc262804374"  2(c)(ii)(2) 	Non-response error	 
PAGEREF _Toc262804374 \h  26  

  HYPERLINK \l "_Toc262804375"  2(c)(ii)(3) 	Measurement error	  PAGEREF
_Toc262804375 \h  26  

  HYPERLINK \l "_Toc262804376"  2(c)(ii)(4) 	Equipment malfunction	 
PAGEREF _Toc262804376 \h  27  

  HYPERLINK \l "_Toc262804377"  2(c)(ii)(5) 	Respondent error	  PAGEREF
_Toc262804377 \h  27  

  HYPERLINK \l "_Toc262804378"  2(c)(ii)(6) 	Data entry error	  PAGEREF
_Toc262804378 \h  27  

  HYPERLINK \l "_Toc262804379"  2(d) 	Questionnaire	  PAGEREF
_Toc262804379 \h  27  

  HYPERLINK \l "_Toc262804380"  3.0 	Pretests and Pilot Tests	  PAGEREF
_Toc262804380 \h  28  

  HYPERLINK \l "_Toc262804381"  4.0 	Collection Methods and Follow-Up	 
PAGEREF _Toc262804381 \h  28  

  HYPERLINK \l "_Toc262804382"  4(a) 	Screening Methods	  PAGEREF
_Toc262804382 \h  28  

  HYPERLINK \l "_Toc262804383"  4(c) 	Measurement Methods	  PAGEREF
_Toc262804383 \h  29  

  HYPERLINK \l "_Toc262804384"  5.0 	Analyzing and Reporting Survey
Results	  PAGEREF _Toc262804384 \h  31  

  HYPERLINK \l "_Toc262804385"  5(a) 	Data Preparation	  PAGEREF
_Toc262804385 \h  31  

  HYPERLINK \l "_Toc262804386"  5(a)(i)	 Interview Information	  PAGEREF
_Toc262804386 \h  31  

  HYPERLINK \l "_Toc262804387"  5(a)(ii)	Emissions Data	  PAGEREF
_Toc262804387 \h  31  

  HYPERLINK \l "_Toc262804388"  5(b)	Data Analysis	  PAGEREF
_Toc262804388 \h  32  

  HYPERLINK \l "_Toc262804389"  5(b)(i)	 Sampling the population	 
PAGEREF _Toc262804389 \h  32  

  HYPERLINK \l "_Toc262804390"  5(b)(ii)	Simulating Response	  PAGEREF
_Toc262804390 \h  36  

  HYPERLINK \l "_Toc262804391"  5(b)(iii)	Analyzing for Age Trend	 
PAGEREF _Toc262804391 \h  40  

  HYPERLINK \l "_Toc262804392"  5(b)(iv)	Investigating Non-response Bias
  PAGEREF _Toc262804392 \h  41  

  HYPERLINK \l "_Toc262804393"  5(c)	Reporting Results	  PAGEREF
_Toc262804393 \h  45  

  HYPERLINK \l "_Toc262804394"  Appendix A:  Telephone Script	  PAGEREF
_Toc262804394 \h  46  

  HYPERLINK \l "_Toc262804395"  Appendix B: Questionnaire	  PAGEREF
_Toc262804395 \h  51  

  HYPERLINK \l "_Toc262804396"  Appendix C: Vehicle Information	 
PAGEREF _Toc262804396 \h  1  

  HYPERLINK \l "_Toc262804397"  REFERENCES	  PAGEREF _Toc262804397 \h  3
 

 1.0	Survey Objectives, Key Variables and Other Preliminaries

1(a)	Survey Objectives

One of the main issues in the study of vehicle emissions is the
difficulty in acquiring representative results. Major challenges include
the diversity of technology, the highly variable nature of emissions,
the complexity and expense of measurement, difficulty in acquiring and
retaining engines or vehicles, and the array of external variables that
influence emissions, ranging from temperature to driver behavior.  In
combination, these factors tend to limit the numbers of vehicles that
can be included in a given study.  Limited sample sizes in combination
with high variability make emissions data challenging to interpret.

Much research has shown that vehicle emissions tend to follow strongly
skewed distributions, such as lognormal, Weibull or gamma
distributions,. Most vehicles are “clean,” with a relatively small
number emitting at exceptionally “high” levels, and contributing
inordinately to the total emissions loading. To capture representative
results it is important that the entire distribution be sampled,
including the upper percentiles in the right tail.  However, the
high-emitting vehicles are relatively rare, enough so that it is
challenging for fully randomized sampling of the population as a whole
to capture them in sufficient numbers to deliver representative
averages. 

The variability and skewed nature of emissions distributions complicate
one of the most important analytic topics in emissions research,
“emissions deterioration,” or the tendency of the average emissions
levels to increase as vehicles age.  This behavior is well documented,
but remains difficult to study due to high variability. The sources of
deterioration are complex, including general wear as well as failures in
specific components. Analysis of available data shows that deterioration
is expressed through a relatively small shift in the central portion of
the distribution, accompanied by a larger and proportional spreading of
the tail. These factors imply that accurate assessment of emissions
deterioration requires represntative sampling across the entire age
range.

The problem is difficult, in that some common approaches to sampling
rare populations are not practical.  No separate frames are available. 
High-emitting vehicles cannot be reliably isolated geographically, nor
can they be exclusively associated with particular demographic groups.
As an approach to capturing high-emitting vehicles, some form of
screening would be valuable. 

A technology is available that has potential to serve as a screening
technique, known as “Infrared Long-path Photometry” or less formally
as “remote-sensing.” It has been in use for over 25 years, for a
variety of purposes, including general assessment of fleet emissions3,
and evaluation of Inspection and Maintenance programs.,  The instrument
directs infrared (IR) and ultraviolet (UV) beams across the roadway
between sources and detectors. When a vehicle travels through the beams,
its exhaust plume intersects the beams, allowing spectroscopic
measurements of exhaust constituents, including carbon monoxide (CO),
hydrocarbons (HC), both by IR, and nitrogen oxide (NO), by UV.  Some
researchers have developed instrumentation designed to measure
particulate-matter emissions. The technique does not capture absolute
measurements of emissions, but rather ratios of the constituents to
exhaust CO2 in the plume.  

This technique fulfills criteria for a screening technique.  It is
relatively quick and inexpensive, allowing the rapid acquisition of
large vehicle samples. It acquires a measurement of the vehicle’s
emissions prior to sampling or recruitment without burdening the owner
in any way. 

The collection is a survey, to be conducted by the Office of
Transportation and Air Quality (OTAQ) in the Office of Air and Radiation
(OAR). This study will be designed to develop and test novel screening,
sampling and measurement procedures. These approaches promise to
substantially reduce the cost of exhaust emissions measurement as well
as to improve the accuracy of resulting estimates. 

An innovative feature of this project will be the use of roadside
remote-sensing measurements to construct a pool of vehicles from which
vehicles can be sampled for purposes of recruitment and measurement
using portable emissions measurement systems (PEMS). The acquisition of
remote-sensing measurements for hydrocarbons, carbon-monoxide, and
oxides of nitrogen will provide an index of emissions for all vehicles
prior to sampling and recruitment for more intensive measurement. The
index is expected to facilitate recruitment of vehicles with an emphasis
on high-emitting vehicles, and provide a means to appropriately relate
measured vehicles to the target population.

Research questions for the project include: 

Can remote-sensing be used as a reliable index of emissions across the
range of emissions and vehicle age? 

Is it feasible to measure start emissions using portable instruments? 

When an engine is started, a large volume of fuel is injected into the
cylinder to initiate ignition while the engine is cold and only the
volatile gasoline components readily vaporize. This fuel results in an
excess increment of emissions over that expected when the engine has
come to operating temperature and the catalyst is  functioning
efficiently.  Despite advances in engine technology, fuel control and
emissions control, these “start” emissions remain a relatively high
fraction of total emissions, due to the technical difficulties in
controlling emissions while the engine is cold and the emissions control
systems have not warmed up. Start emissions have been measured in the
laboratory by repeating a pre-arranged test procedure under both cold
and hot engine conditions and estimating the start increment as the
difference of the two, as it is extremely difficult to distinguish
“start emissions” from “running emissions” without the reference
frame provided by replication.  However, with the increasing importance
of portable instruments, it is critical to develop or adapt techniques
for measuring start emissions.

Can the emissions index used for recruitment also serve as a means to
estimate potential non-response bias?

To the extent that the index serves as an efficient screening tool, it
may also serve as a valuable tool to investigate potential non-response
bias. The availability of an index of emissions for vehicles owned by
non-responders may allow assessment of the key factor in non-response
bias, namely, that response may be related to the parameter of interest,
i.e., vehicle emissions. 

How many “trips” do drivers make in a typical day, where a trip is
defined as “key-on” to “key-off?”  Does the number of trips/day
differ between weekdays and weekends? 

In this context, a trip begins when a driver turns the engine on and
proceeds to a destination, and ends when they park and turn the engine
off.  As defined above, “start” emissions occur at the outset of a
trip, and obviously, “running” emissions occur during the trip.   

This data collection is a survey designed to estimate the exhaust
emissions and usage of a sub-population of vehicles certified to
selected certification standards, and having similar vehicle technology.
 

1(b) 	Key Variables

Variables to be surveyed or measured include:

Vehicle identifiers: License plate and Vehicle Identification Number

Vehicle description : make, model, model year, engine displacement,
transmission type, engine family, and odometer reading

Vehicle usage and maintenance history

Exhaust gaseous emissions including CO2,  HC, CO and NOx, to be measured
on a continuous “second-by-second” basis over a specified drive
route

Exhaust particulate emissions to be measured on an aggregate basis over
a specified drive route

Vehicle and engine operating parameters, including on-board diagnostic
trouble codes, engine-coolant temperature, exhaust flow rate, engine
speed, engine load, vehicle speed, oxygen sensors, air:fuel ratio,  and
global-positioning system coordinates.

1(c) 	Statistical Approach

We have selected a statistical approach for this effort for two reasons:

(1) While a census or partial census would be ideal, the effort and
expense required is prohibitive.

(2)  To meet the objectives for use of these data, it is necessary to
draw valid and defensible inferences from sets of vehicles surveyed or
measured to the vehicle population sampled. This requirement in itself
rules out non-probabilistic approaches. 

1(d) 	Feasibility

Obstacles to Participation. We do not anticipate substantial obstacles
to participation.  Participation does impose some burden for some
potential participants, as they must modify their schedules to travel to
and from the facility to drop off and pick up their vehicles. Thus, we
anticipate that, in conjunction with incentives, solicitation will prove
no more difficult than than in previous studies.

Availability of Funds. At present we expect to have adequate funds
available to conduct the survey as designed.  However, if funding
shortfalls occur, we can take measures to reduce sampling costs. One
possibility would be to reduce the number of vehicles in the study. 

2.0	Survey Design

2(a) 	Target Population and Coverage

The target population is defined as the fleet of privately-owned
gasoline-powered passenger cars and light-duty trucks certified to
“Tier-2/Bin 5” or “LEV-II/LEV” standards.  Light-duty trucks are
trucks with gross vehicle weight ratings of less than 8,500 lbs.
Passenger cars and light-duty trucks form the majority of the on-road
motor vehicle fleet. 

Vehicles certified to Bin-5 standards were introduced in model year 2004
and comprised  approximately 70% of annual sales by 2007.  Tier-2
standards apply to vehicles sold in states following Federal emissions
standards, whereas the LEV-II program applies to vehicles certified for
sale in California and states having adopted “California” standards
since 2004.  Some vehicles are certifed for sale in either Federal or
“California” areas. Vehicles in both groups are certified to
comparable standards, as shown in Table 1.

Coverage for this study will comprise the fleet of vehicles operating in
the Detroit Metropolitan Statistical Area, Michigan.  The Detroit-Ann
Arbor-Flint Consolidated MSA  includes the Michigan counties of Lapeer ,
Macomb, Monroe, Oakland , St. Clair, Wayne, Lenawee, Livingston and
Washtenaw.

We selected this area in large part due to itx proximity to the EPA
laboratory in Ann Arbor, MI. As this study is primarily designed to
develop and test the effectiveness of sampling based on the screening
index, working in close proximity to our facility will bring several
logistical advantages.  First, it will not be necessary to transport the
instruments long distances for maintenance and repair which will reduce
both shipping costs and down time. Secondly, geographic proximity will
make it feasible for EPA technical personnel to regularly contribute to
field work without drawing on limited travel funds. Additionally,
supplementing the contributions of contractor personnel with Federal
personnel will also conserve project funds.

Table   SEQ Table \* ARABIC  1 .  Emissions Standards for the Federal
Test Procedure for Vehicles in the Target population (g/mile).

Standard	Type

	HC1	CO	NOx	PM

Tier-2/Bin 5 	certification	0.075	3.4	0.05	0.01

	Useful life

	0.090	4.2	0.07	0.01

LEV-II/LEV	certification	0.075	3.4	0.05	---

	Useful life	0.090	4.2	0.07	0.01

1  Hydrocarbons are defined as non-methane organic gases (NMOG).

2 Certification standards apply up the 50,000 of a vehicle’s useful
life.

3 Useful life standards apply from 50,000 miles up to the end of the
regulatory useful life, i.e., 120,000 miles or 10 years.

2(b) 	Sample Design

The goal of the design is to sample vehicles with probability
proportional to an emissions index constructed from remote-sensing
measurements for multiple pollutants.  We have constructed a draft set
of parameters for index construction using remote-sensing and exhaust
emissions data collected in an Inspection-and-Maintenance station in
Denver, Colorado, during summer, 2008. 

2(b)(i)		Sample Frame

The sample frame will be constructed from a pool of vehicles for which
we have acquired at least one valid remote-sensing measurement for all
emissions to be included in the index.  Remote-sensing measurements will
be acquired at sites around the MSA, selected to meet technical
requirements for the technique. Ideally, a site captures a single lane
of traffic in which vehicles are gradually accelerating, for which
reason, a highway or freeway on-ramp is a commonly selected site. 
Additional prerequisites include consideration of safety for both
drivers and technicians, and acquisition of permission from local
authorities.  At each site, technicians plan to collect measurements for
two or more days, to maximize acquisition of multiple measurements on as
many vehicles as possible.

The index is analogous to a tailpipe emissions test based on the IM240
procedure, a variable-speed 240-second driving trace commonly used for
tailpipe emissions testing in inspection and maintenance programs (  REF
_Ref262719278 \h  Figure 1 ). Rather than attempt to estimate absolute
IM240 emissions, we estimate the probability that a vehicle would
“fail, ” i.e., would exceed a specified threshold level on the
IM240, given its remote-sensing measurement(s). The threshold functions
like a “cutpoint” in an actual I/M test, although the values to be
used can be set specifically to meet the sampling objectives of this
study.

Figure   SEQ Figure \* ARABIC  1 .   Speed Trace for the IM240 test
cycle.

The ”failure” probabilities are estimated using logistic regression
on an applicable dataset, as shown in equations 1 and 2.  Equation 1
shows the logit for the probability pNOx that a vehicle’s NOx
measurement on the IM240 would exceed some specified threshold hNOx. 

 	Equation   SEQ Equation \* ARABIC  1 

In this expression, fNOx is the “fractile” of the vehicle’s NOx
remote-sensing measurement, rather than the absolute measurement, taken
to the 0.4th power. The “fractile” is the “reverse-normalized
rank” of the measurement, given by 

 	Equation   SEQ Equation \* ARABIC  2 

where rNOx is the rank of each measurement, assigned in reverse order,
meaning that the highest measurement receives a rank of 1, and the
smallest a rank of n, giving corresponding values of fNOX of 1/n and
1.0, respectively. This non-parametric handling of the remote-sensing
measurements is helpful in coping with two common aspects of
remote-sensing data, namely, high variability and the presence of
negative values.  Because the remote sensing measurement represents
pollutant concentration relative to CO2 concentration within the exhaust
plume, relative to background outside the plume, a high background value
for whatever reason gives a negative remote-sensing value, i.e., the
vehicle appears to be “cleaner than background.”  Note also that the
term β0-β1lnh acts as a constant intercept term for a given value of
h. 

Throughout we will illustrate the approach as developed for a sample of
the general fleet in Denver, as described above.  A set of cutpoints for
each pollutant is shown in   REF _Ref255822472 \h  Table 2 , and example
coefficients for the logistic model in   REF _Ref262719504 \h  Table 3 .

The fleet in the examples is broader than the target population for the
study; it will be necessary to reparameterize the relationships for the
target vehicle population before the onset of sampling. 

Table   SEQ Table \* ARABIC  2 .  Example IM240 Cutpoints for Creation
Replicate Modeling Datasets

Replicate

	IM240 Cutpoint (g/mile)

	HC	CO	NOx

1	0.07	1.1	0.23

2	0.14	1.9	0.38

3		0.26		3.1	0.61

4	0.49	5.3	1.00

5	0.93	8.8	1.63

6	1.76	14.9	2.66



Table   SEQ Table \* ARABIC  3 .   Example Parameters for the Logistic
Screening Model.

β0	2.8999	6.1709	4.0208

β1	-6.6913	-4.9337	-6.1990

β2	-1.6421	-2.0835	-2.1894

Figure   SEQ Figure \* ARABIC  2 .    Fitted Values for NOx
Remote-sensing and IM240 values.

  REF _Ref255822965 \h  Figure 2  shows fitted values for NOx, for the
six example thresholds shown in   REF _Ref255822472 \h  Table 2 , with
each threshold coded by color, as in Table 2.   To illustrate an
application of the logistic models we used a remote-sensing dataset
collected over two days in the on-going Colorado fleet remote-sensing
program.   REF _Ref255823061 \h  Figure 3  shows the model year
distribution of the vehicles in the dataset, after filtering for valid
remote-sensing and license plate values.  As the target population
includes most vehicles certified since 2004, the figure shows that the
target population should currently include about 35% of vehicles in the
light-duty fleet.

 The models described above were applied to this dataset using IM240
cutpoints of 0.8, 15, and 2.0 g/mile for HC, CO, and NOx, respectively. 
 In the discussion that follows we will focus on results for model years
2000-2010, as these more closely resemble the target population than
vehicles in previous model years. The simulated distributions of IM240
failure probabilities for NOx are shown in   REF _Ref255823106 \h 
Figure 4 , which range from 0 to 63%.  

Figure   SEQ Figure \* ARABIC  3 .   Model-Year Distribution of the
Application Dataset

Figure   SEQ Figure \* ARABIC  4 .   IM240 NOx Failure Probability
Distribution for a 2.0 g/mile cutpoint (Model Years 2000-2010).

At this point, it is convenient to classify or “bin” the failure
probabilities of the observations in the dataset.  We created bins for
each of the three pollutants and based on the logit of the failure
probabilities predicted by the models.    REF _Ref255823156 \h  Table 4 
shows the number of observations in each of the failure probability
bins.  

   SEQ Table \* ARABIC  4 .  Classification of Observations in the
Application Dataset for Selected Cutpoints.

Bin Label	Range of IM240 Failure Probabilities	No. Observations 

HC

(0.8 g/mile)	CO

(15.0 g/mile)	NOx

(2.0 g/mile)

2	>82%	83	0	111

1	62 to 82%	145	5	515

0	38 to 62%	574	250	2,428

-1	18 to 38%	4,177	1,339	5,530

-2	8 to 18%	12,888	6,583	9,981

-3	3 to 8%	21,273	13,349	14,600

-4	<3%	0	17,614	5,975

	Total	39,140	39,140	39,140

At the conclusion of the preceding analysis, projected failure
probabilities are available for all three pollutants.  However, for
sampling purposes it is highly desirable to resolve these results into a
single variable. At the same time, sampling will be targeted to acquire
roughly similar numbers of vehicles in each of the failure probability
bins.

Because a vehicle with high emissions on one pollutant need not have
high emissions on either of the other two (although such results are
possible), sampling with respect to only one pollutant would give
unsatisfactory results, with potentially distorted sampling
distributions for the other two.

However, the failure probabilities for HC, CO, and NOx are somewhat
correlated since there is a tendency for clean vehicles to have low
emissions for all three, and the reverse for dirty vehicles. 
Consequently, principal components analysis can be used to generate a
rotation of axes to create a new variable to serve as a single index
incorporating all three pollutants. As with the fractiles, the index can
then be classified as a convenient way to guide vehicle sampling.  We
have performed an example of this rotation of axes in the three
dimensional logit space for the three pollutants.  Using the
observations in the application dataset, the first principal component
PCA1 is a linear combination of the HC, CO, and NOx logits:

 	Equation   SEQ Equation \* ARABIC  3 

  REF _Ref255829995 \h  Figure 5  shows the distribution of the first
principal component values.  As with the logits, it is convenient to
classify the values of PCA1 for sampling purposes, as shown in   REF
_Ref255830081 \h  Table 5 . Note that for high values of PCA1, the IM240
failure probabilities of HC, CO, and NOx all tend to be high, and for
low values of PCA1, the IM240 failure probabilities of HC, CO, and NOx
may tend to be correspondingly low.  

A comparison of the counts of observations in the PCA1 bins with respect
to the IM240 failure probability bins shows for each pollutant a ridge
of large counts that moves from high positive values to low (or negative
values)  This behaviour demonstrates that PCA1 carries information about
the IM240 failure probabilities for each of the three pollutants.

Figure   SEQ Figure \* ARABIC  5 .    Distribution of First Principal
Component Values (PCA1) based on IM240 cutpoints for HC, CO and NOx
(0.8, 15, 2.0 g/mile).

 

Table   SEQ Table \* ARABIC  5 . Cross-tabulation of PCA1 and Pollutant
Failure Probability Bins for the Observation Dataset, by Pollutant.

PCA1 Bin

6	5	4	3	2	1	-0	-1	-2	Total

Fprob HC Bin	2	13	22	19	17	12	0	0	0	0	83

	1	8	44	37	25	22	9	0	0	0	145

	0	0	63	180	135	115	73	8	0	0	574

	-1	0	10	211	785	1300	1170	659	42	0	4,177

	-2	0	0	5	157	1,480	3,899	4,925	2,422	0	12,888

	-3	0	0	0	3	131	1,753	5,996	10,182	3,208	21,273

	Fprob CO Bin	1	1	2	2	0	0	60	0	0	0	5

	0	6	31	89	79	39	6	0	0	0	250

	-1	11	79	203	329	392	270	55	0	0	1,339

	-2	3	22	138	535	1,562	2,515	1,633	175	0	6,583

	-3	0	5	17	147	851	3,160	5,964	3,204	1	13,349

	-4	0	0	3	32	216	953	3,936	9,267	3,207	17,614

	Fprob NOx 

Bin	2	5	16	29	23	24	14	0	0	0	111

	1	3	41	82	122	146	102	19	0	0	515

	0	10	34	142	384	743	817	298	0	0	2,428

	-1	3	37	148	360	1,081	2,007	1,612	282	0	5,530

	-2	0	11	42	179	779	2,376	4,127	2,466	1	9,981

	-3	0	0	8	44	231	1,237	4,318	7,114	1,648	14,600

	-4	0	0	1	10	56	351	1,214	2,784	1,559	5,975

	Total	21	139	452	1,122	3,060	6,904	11,588	12,646	3,208	39,140

The pool of vehicles screened using remote sensing and having been
assigned values of PCA1, will serve as the sampling frame for the
project.

2(b)(ii)	Sample Size

For this collection we plan to recruit and measure emissions for
approximately 250 vehicles. Assuming a response rate of about 30%, we
plan to solicit approximately 830 vehicles. 

The sample size will be based on two considerations: (1) the likelihood
of capturing the “upper tail” or the high percentiles of the skewed
emissions distribution, and (2), the power for a simple regression
relating the the natural logarithm of the emission rate to vehicle age
at time of measurement.

2(b)(ii)(1)		Capturing high-emitting vehicles

With respect to this criterion, we have estimated sample size
conservatively, to improve our ability to evaluate the screening index
and to increase confidence that the sample would include high-emitting
vehicles under the assumption that the screening index may not work
efficiently.

Numbers of vehicles to be measured are given by an equation that relates
the probability of capturing a specified population percentile (P) at a
desired level of probability (K). More specifically, the equation gives
the probability K that at least one vehicle in the sample would be at or
above the percentile level P (Equation 4). 

 	Equation   SEQ Equation \* ARABIC  4 

  REF _Ref255831043 \h  Figure 6  shows the coverage probability trends
related to population percentile for selected sample sizes ranging from
50 to 300 vehicles.  Based on these projections, the planned sample size
(n = 250) should give virtual assurance that the 95th-percentile vehicle
would be captured, and approximately 90% probability that the
99th-percentile vehicle would be captured.

Note that these theoretical projections do not account for the possible
effects of non-response bias which could reduce the prospects for
capturing the dirtiest vehicles.  Nor do they account for the
effectiveness of screening, which should increase the likelihood of
capturing high emitting vehicles, depending on its level of efficiency.

Figure   SEQ Figure \* ARABIC  6 .  Estimated coverage probabilities for
selected population percentiles, for a selection of sample sizes
(Equation 4).

2(b)(ii)(2)		Assessing age trends

 An important analytic for this project is to estimate the trend (if
any) in emissions versus vehicle age.  This research question can be
tested using a simple regression model of the natural log of NOx
emissions vs. age.

 	Equation   SEQ Equation \* ARABIC  5 

A simulation of this relationship for a population of N ~ 10,500
vehicles was performed using the parameters shown in   REF _Ref255831338
\h  Table 6 .  The emissions results represent the “hot-running”
phase of the Federal Test Procedure, which is of interest because
“hot-running” emissions are considerably more variable than start
emissions (in relative terms), and thus represent the limiting factor in
power estimation. The simulated population is shown in   REF
_Ref255831513 \h  Figure 7  as lnNOx with respect to age, which shows a
wide scatter around a definite age trend.   REF _Ref256695931 \h  Figure
8  shows NOx with respect to age, and illustrates the highly skewed
nature of the distribution, and how the increase in the mean emissions
level is driven by the “spreading” of the tail as vehicles age.   
REF _Ref255831726 \h  Figure 9  shows a histogram of the log-normal
population. Note that the population has been trimmed at the 97.5th
percentile level at each age to prevent simulation of unrealistically
high values in the heavy log-normal upper tails. These figures highlight
the relatively small numbers of vehicles in the tail that to be targeted
by screening, as described above.

Table   SEQ Table \* ARABIC  6 .  Parameters describing a Simulated
Population of lnNOx for Bin-5 Vehicles Between 0 and 6 Years of Age (N =
10,185 vehicles). 

Source	DF	Sum-of-Squares	Mean Square	F-value	Pr > F

Model	1	457.76	457.76	267.63	<0.0001

Error	10,498	17,956.01	1.71

Total	10,499	18,413.77

R2

Root MSE	Mean

0.0249

1.307	-5.049

Parameter	Estimate	Standard Error	t-value	Pr > | t|

	Intercept (b0)	-5.411	0.02554	-2.1186	<0.0001

	Age (m)	0.1211	0.007400	16.36	<0.0001

Figure   SEQ Figure \* ARABIC  7 .   Simulated lnNOx Emissions for a
population of Bin-5 vehicles, represented by the Hot- Running Phase of
the Federal Test Procedure.  The red line represents the simulated age
trend, reflecting an assumed slope of 0.12.

Figure   SEQ Figure \* ARABIC  8 .  Simulated NOx Emissions for a
population of Bin-5 vehicles, represented by the Hot- Running Phase of
the Federal Test Procedure.  The green and red lines represent the
geometric and arithmetic means, respectively.    

Figure   SEQ Figure \* ARABIC  9 .  Histogram of  Simulated NOx 
(reverse-transformed lnNOx) for a population of Bin-5 vehicles,
represented by the Hot Running Phase of the Federal Test Procedure.  

Needless to say, given the large “sample size,” the analysis of the
simulated data in   REF _Ref255831338 \h  Table 6  above shows a highly
significant effect for vehicle age.  The overall F-test for
goodness-of-fit as well as the parameter t-tests show very small
p-values.  It is important to note, however, the small R2 of 0.022,
which reflects the very high variability of measurements around the
trend, which is characteristic of emissions data.  The R2 is the key
parameter for purposes of power analysis for the prospective vehicle
sample.

  REF _Ref255833363 \h  Table 7  shows estimated power levels for a
Type-III F test for two confidence levels over a range of sample sizes. 
This analysis suggests that we might expect a power level for
goodness-of-fit of approximately 60% at the 95% confidence level and 75%
at the 90% confidence level.

Table   SEQ Table \* ARABIC  7 .  Projected Sample Sizes for a Range of
Power Levels for an Analysis of lnNOx Emissions. 

Table ??   Projected Sample Sizes for a Range of Power Levels

For an analysis of lnNOx Emissions vs. Age.  (Results for Type-III 

F test, R2 = 0.02).

Power

	Sample size (n) 

α = 0.05	Sample Size (n)

α = 0.10

0.2	  58	  29

0.3	 94	  57

0.4	132	  88

0.5	173	122

0.6	220	162

0.7	277	211

0.8	351	277

0.9	470	383

Table   SEQ Table \* ARABIC  8 .  Cross-tabulation of PCA1 and Failure
Probability Bins for the Expected Vehicle Sample.

PCA1 Bin

6	5	4	3	2	1	0	-1	-2	Total

Fprob HC Bin	2	4	7	3

	14

	1	2	13	3	1

19

	0

19	27	5	2

	53

	-1

3	28	21	16	5	1

	74

	-2

6	19	10	9	4

48

	-3

	1	5	14	13	9	42

	Fprob CO Bin	1

1

1

	0	2	9	17	2

30

	-1	3	24	26	11	5

	69

	-2	1	7	16	16	17	6	2

	65

	-3

2	2	4	12	10	13	3

46

	-4

	0	1	2	4	10	13	9	39

	Fprob NOx

 Bin	2	2	5	5	1

13

	1	1	12	8	5	1

1

	28

	0	3	10	19	12	9	2	1

	56

	-1

11	21	11	14	7	6	1

72

	-2

3	7	4	10	6	7	2

39

	-3

	1	1	3	2	8	7	5	27

	-4

1	1	1	3	5	4	15

	Total	6	42	61	33	38	20	24	17	9	250

Note that through the use of screening index, the number of observations
in each of the failure probability bins for the three pollutants is
roughly similar.  This can be put in perspective by comparing the counts
in the Total column in the sample in   REF _Ref255834701 \h  Table 8 
above with the corresponding counts in the Total column in the original
dataset in   REF _Ref255830081 \h  Table 5 .  Please note that even
though we have selected all vehicles in PCA1 bins 5 and 6, the total
number of vehicles in Bin 6 is not very large.

2(b)(ii)(3)	Numbers of driving trips

As mentioned above, a “trip” is defined as a period of driving or
operation  that begins when a driver turns the engine on, and ends when
the driver turns the engine off.   As defined, the number of trips
driven defines the number of engine starts, as each trip begins with a
start. 

For purposes of estimating emissions, we assume that the number of daily
trips differs slightly between weekdays and weekends.  For example, for
passenger cars, we assume that average values are 5.89 and 5.30 trips
per day for weekdays and weekends, respectively. To assess the expected
behavior of the proposed sample, we have estimated the minimum
detectable difference between weekdays and weekends with 95% power at
the 5% confidence level.

The analysis is driven by the assumption that starts can be represented
as a Poisson process, as described by the parameters in   REF
_Ref260407472 \h  Table 9 .  

Table   SEQ Table \* ARABIC  9 .  Parameters for Simulated Poisson
Counts of Trips over 8-week Measurement Period.

Parameter	Units	Weekday (1)	Weekend (2)	Difference (1) – (2)

Mean	Trips/day	5.89	5.71	0.18

Measurement period	Days	40	16

	Trips per vehicle

235.60	91.36

	Sample size	vehicles	250	250

	Total trips	count	58,900	22,840

	Test statistic (t)

3.98

Critical t

1.96

Confidence level

0.95

Power

0.95

The weekday/weekend difference will be assessed by counting starts
across all vehicles and days measured. Based on the counts, the test
statistic t is estimated as

 	Equation   SEQ Equation \* ARABIC  6 

where n1 and n2 represent trip counts for weekdays and weekends, and m1
and m2 represent measurement periods for weekdays and weekends,
respectively.  For the proposed assumptions and sample size, a mean
daily difference as small as  0.18 starts/vehicle/day would be
detectable with 95% power and 95% confidence. 

2(b)(iii)	Stratification variables

In addition to screening, we plan to stratify the vehicles by age
classes, as defined below in   REF _Ref257290269 \h  Table 10 .  This
approach was adopted to avoid the potential for bias for selection of
older vehicles over younger ones, were we to rank measurements and
calculate fractiles across the entire six-year range.   

Table   SEQ Table \* ARABIC  10 .   Age strata definitions for purposes
of screening and sampling.

Age Range (years)	Class Midpoint (years)

0.0 <= age < 1.0	0.5

1.0 <= age < 2.0	1.5

2.0 <= age < 3.0	2.5

3.0 <= age < 4.0	3.5

4.0 <= age < 5.0	4.5

5.0 <= age <= 6.0	5.5

2(b)(iv)	Sampling Method

The sampling method to be employed will be a form of “probability
proportional to emissions”, as expressed through the PCA1 screening
index described above (see 2(b)(i) ).  The sampling will be implemented
through a process analogous to stratified sampling in which vehicles
will be classified or “binned” by their PCA1 values, and different
sampling frequencies applied to each bin.  The frequencies will range
from sampling with certainty for the “dirtiest” vehicles to sampling
fewer than 10 out of a 1,000 for the cleanest vehicles (  REF
_Ref255834186 \h  Table 11 ).

Table   SEQ Table \* ARABIC  11 .  Sampling Fractions by Screening
Index Bin (PCA1)

PCA1 Bin

	Sampling Fraction

6	1.0

5	1.0

4	0.50

3	0.10

2	0.040

1	0.010

0	0.007

-1	0.005

-2	0.008

2(b)(v)	Multi-Stage Sampling

This collection will not employ multi-stage sampling.

2(c) 	Precision Requirements

2(c)(i) Precision Targets

Precision targets are geared to the power analyses performed for
estimation of sample size, as described above. 

For purposes of capturing emissions from vehicles in the high
percentiles of the distribution, our targets are to achieve 99% and 95%
confidence levels that the 95th and 99th percentiles have been captured,
respectively.

For purposes of estimating the age trend of lnNOx, the precision target
is a 90% confidence level for the type III F test of fit at the 75%
power level.

For purposes of estimating numbers of starts for weekdays and weekends,
the precision target is 95% power at 95% confidence for a difference
between Poisson counts of driving trips.  

2(c)(ii) Non-Sampling Error

2(c)(ii)(1) 	Frame-coverage error

This error is defined as potential bias in key variables resulting from
imperfections in the sample frame.  The central issue is incomplete
coverage, in which members of the target population are simply absent
from the frame. The bias that may result from incomplete coverage may
reduce the representativeness of the sample in a way analogous to that
from whole-survey non-response.  In this project, because the sample
frame will be constructed from the pool of vehicles measured by remote
sensing for screening purposes, actual frame coverage may be limited by
factors that reduce representative coverage of the entire light-duty
fleet. One such factor may be the the inability to obtain valid license
plate readings on some fraction of measured vehicles.  To minimize this
factor, we plan to have license plates read manually, which gives a
higher yield rate than the use of software.  In any case, given that the
frame will be based on vehicles themselves, rather than households, it
will be possible to address frame coverage issues by post-stratification
against the state vehicle registration database.

2(c)(ii)(2) 	Non-response error

As in any survey, non-response is one of the most important potential
sources of error in final results. Survey non-response occurs when no
response at all is obtained from a potential participant in the study,
whereas item-nonresponse occurs when a respondent provides responses to
some but not all items. Survey non-response occurs if a respondent
refuses to participate. Item-nonresponse may occur in a number of ways.
A respondent may answer some items but refuse others, or may break off
an interview for unrelated reasons. A form of item-nonresponse
detrimental to emissions measurement but unrelated to the respondent
could occur in cases where equipment malfunction or measurement errors
make emissions datasets unsuitable for subsequent analysis.

2(c)(ii)(3) 	Measurement error

The measurement of exhaust emissions involves the use of complex
instrumentation in a non-laboratory environment. The potential for
measurement error in the RSD equipment and PEMS equipment is well
understood.  During field work, all instruments, including the remote
sensor and PEMS equipment will be calibrated regularly following
standard operating procedures.  

2(c)(ii)(4) 	Equipment malfunction

Following the measurements based on the various instruments,
quality-assurance measures will be undertaken to verify that the
instruments operated correctly and that the results are reliable for
further analysis.  The QA process will involve the use of computer
programs that automatically scan the time-series for patterns that may
suggest instrument error, combined with graphic presentation of the data
to allow case-by-case visual inspection.

2(c)(ii)(5) 	Respondent error

The emphasis on collection of key information for the survey through
direct inspection and instrumentation involves a conscious decision to
reduce reliance on human memory to the maximum extent possible. A
primary example is the use of electronic dataloggers to measure vehicle
emissions and activity. As much as possible, we have restricted
interview items to general questions that can be easily answered without
involved or detailed estimation and without heavy reliance on human
memory. 

2(c)(ii)(6) 	Data entry error

Emissions results and other data collected electronically will not be
input manually. Data files will be downloaded directly from the
measurement instrument and transferred to the database, following
quality-assurance procedures.  

2(d) 	Questionnaire

During the initial phone contact, we will conduct a very brief interview
to verify that the respondent owns the vehicle selected, to solicit
participation, and to schedule an appointment to bring their vehicle to
the test facility. This script is presented as Appendix A.

Vehicle owners that participate in the study will receive another brief
interview when they arrive at the testing facility.  We will collect
recent vehicle usage and maintenance history information using the
questionnaire in Appendix B.  The questionnaire contains ten items
designed to assess general maintenance history of the vehicle, including
major repairs or accidents. 

Before instrumentation, an additional twelve items of information will
be collected from the vehicle itself by inspection, as shown in Appendix
C. These items are redundant with information in the sample frame, but
redundant collection at the time of measurement will allow confirmation
of the vehicle’s identity as well as verification of the sample frame.
 Additionally, technicians will take several photographs to document key
information on the vehicle.

3.0 	Pretests and Pilot Tests

The sampling and recruitment methods to be used in this collection will
be similar to those used in other studies that we have undertaken within
the past two years (control number 2060-0615). The recruitment approach
and logistics will be similar enough to those used for the previous
collection that the current collection will employ approaches that have
been tried in the field.  

4.0 	Collection Methods and Follow-Up

Implementation of the program will include the following elements:

4(a) 	Screening Methods

Remote sensing vans will be deployed throughout the Detroit Metropolitan
area to collect screening measurements on the vehicle population.  Sites
will be selected to be representative of the population and appropriate
for the remote-sensing technique.  Criteria for a good site include
moderate vehicle speed, moderate acceleration, mild positive slope, a
single lane of traffic, a safe area to park the van and deploy
instruments, and a moderate level of vehicle traffic.  

All vehicles will have the emissions plume scanned by an RSD instrument
to measure emissions concentrations. The instruments perform these
measurements by shining a light beam across the roadway to a receptor.
Associated equipment will also simultaneously determine other
quantities. These measurements are collected without without imposing
any burden on vehicle owners. For each vehicle the following quantities
will automatically be taken as the vehicle drive past the RSD
instrument:

Item 1: DateTime: The date, hour, minute, and second of the RSD
measurement.

Item 2: Speed and Acceleration: The speed and acceleration of the
vehicle.

Item 3: Emissions Absorbances: The absorbances (concentration ×
pathlength) of HC, CO, NO, and CO2 in the vehicle’s plume. 

Item 4: License Plate: A digital image of the rear of the vehicle so
that the license plate may be read.

4(b) 	Solicitation Methods

Based on the screening level and associated sampling frequency, a sample
of passenger cars and light-duty trucks will be selected for
solicitation. The sample will be released in a series of replicates,
each of which will be thoroughly processed before subsequent replicates
are released.

Step 1: Selected vehicles will be transmitted to the call center for
solicitation.

Step 2: Vehicle owners will be sent an initial notification of the study
by mail. 

Step 3: Potential respondents having received the mailing will be
contacted by phone. Interviewers will make multiple attempts at contact
with each respondent. During the phone contact the interviewer will
notify potential respondents that they will receive a $10 incentive
whether or not they participate. 

Step 4: During initial or successive phone contacts interviewers will
attempt to schedule an appointment for the respondent to bring their
vehicle to the project facility for measurement.

Step 5: Upon arrival for an appointment at the test facility, the
respondent will participate in the vehicle survey (Appendix B). The
brief interview will obtain information regarding recent maintenance,
malfunctions, service needed, etc.  An overall assessment will be made
of the vehicle’s interior and exterior condition, which will be
reviewed with and signed by the vehicle’s driver. After these steps,
technical personnel will install instrumentation and perform measurement
procedures on the vehicle. At the respondent’s option, rental vehicles
will be available for their use while their own vehicle is being
measured.  Following completion of measurement, technicians will remove
the instrumentation and return the vehicle to the respondent. The
respondent will be provided an additional incentive at the completion of
their participation.

4(c) 	Measurement Methods

After receiving final consent to instrument the vehicle, technical
personnel will perform the following steps:

Step 6: Collect Vehicle information – A technician will visually
examine the vehicle to collect the vehicle identity information listed
in Appendix C. Some items will be documented with a camera to reduce the
chance of transcription errors.

Step 7: Each vehicle will receive a unique identification code for
documentation and tracking purposes, and will then be inspected for
measurement feasibility.   This preliminary inspection will involve a
short road test to check the vehicle’s brakes and steering.  A brief
check will also be made of the vehicle’s exhaust system to ensure that
no major leaks are present (which could affect emissions measurements). 
A fluid check will be performed to reduce the possibility of
overheating, low oil operation, or other problems during instrumentation
or measurement.  

Step 8: If the vehicle is not considered safe for instrumentation, it
will be returned to the respondent, whose participation would then be
complete.  If the vehicle is considered safe for instrumentation
technicians will install a portable emissions measurement system. They
will then perform warm-up and check procedures to verify correct
functioning of the various systems and analyzers.  As necessary, systems
will be audited and recalibrated to ensure accuracy.  

A test driver will then start the vehicle and take it over a
pre-arranged route designed to include selected driving patterns
including stop-and-go traffic, acceleration at high speed, freeway
driving and idling.  Following completion of the first route, the
vehicle will be parked for a specified period with the engine off, after
which the engine will be re-started and the driving route repeated. 
Repetition of the route allows for replication as well as capture of the
start emissions increment.  At the completion of the drive routes, the
vehicle will be parked overnight with the instrumentation installed.

Step 9: The following morning, the instrumentation will be turned on and
warmed up as before.  When the system is ready the test driver will
restart the car, with the engine cold, and repeat the drive route. 
After completion of the first route, the car will be again parked with
the engine off and the route repeated.

Step 10: Following completion of the final driving route, technicians
will download and backup the emissons datafile. They will perform
designated post-check procedures and remove the emissions
instrumentation from the vehicle.

Step 11:  Technicians will install a “portable activity measurement
system” (PAMS) on the vehicle. The PAMS will record selected operating
parameters while the vehicle is operating that will allow
characterization of trips and other driving patterns.

Step 12: The vehicle will be returned to the respondent.   The
respondent will drive the vehicle for a specified measurement period of
three months with the PAMS installed.

Step 13:  At the completion of the measurement period, technicians will
retrieve the PAMS unit. At the respondents’ convenience, a field team
will travel to the respondents’ locations to spare the respondent the
additional burden of returning to the facility for instrument removal.

5.0 	Analyzing and Reporting Survey Results

5(a) 	Data Preparation

5(a)(i)		Interview Information

During field work interviewers will record respondent’s information on
paper questionnaires and interview sheets. At the end of each day,
personnel will scan the field copies into computer files to prevent loss
of data should the originals be lost or damaged. Computer files will be
backed up and stored in different locations.

Personnel trained in data entry will enter the survey information into
electronic data files. In this process, we plan to use
“double-entry” techniques, meaning that all information will be
keyed in independently by two individuals. Following an item-by-item
comparison of the two versions, all instances where the files differ
will be manually checked to ensure accuracy. 

5(a)(ii)	Emissions Data

Technicians will download emissions and activity data directly from the
instruments prior to removal. Following calibration and quality
assurance, these data will be compiled into relational-database format,
linking emissions data to relevant vehicle and engine information. 
During the loading process, additional quality-assurance measures will
be taken to ensure that the instrumentation was operating correctly and
that the data are reliable for further analysis.

5(b)	Data Analysis

Using the simulated population of NOx emissions developed for purposes
of sample-size analysis, we have simulated a process of sampling and
analysis, including the following steps:

1)   Sampling the population

2)   Simulating non-response

3)   Analyzing the data for age trend

4)   Using the screening measure to investigate non-response bias

5(b)(i)		Sampling the population

“Sampling the population” involved simulating the steps of
remote-sensing, development of the screening index, and assignment of
sampling weights.

The simulated “remote-sensing measurements” were designed as
non-negative values showing broad correlation to the “actual”
emission values.  This translation was achieved simply by adding an
additional normally-distributed disturbance in ln-space and applying a
positive offset such that most of the simulated values would be
positive.  A given remote-sensing measurement RSDi was generated by
exponentiating the sum of a randomly generated lnNOx measurement u, a
random normal variate distributed with mean 0.0 and standard deviation
σRSDZ, and a positive offset of 9.0.  The value of σRSD was set at
1.2; Z represents a standard normal variate. 

 	Equation   SEQ Equation \* ARABIC  7 

The resulting estimates pictured in   REF _Ref256701362 \h  Figure 10 
show a realistic degree of spread and scatter.  While the parametric or
Pearson correlation for these variates is low (~0.45), the corresponding
non-parametric Spearman correlation is reasonably high (~0.77). 

Figure   SEQ Figure \* ARABIC  10 .  Simulated “RSD measurements” in
relation to corresponding “emissions measurements” for the simulated
Bin-5 vehicle population.

Having “performed remote-sensing,” the next step was to simulate the
development of the screening index.  For this exercise, we simulated
screening in terms of a single pollutant, NOx, but did not attempt to
model the correlation among pollutants or the principal components
analysis previously described.

The logit for exceedance of the target threshold was calculated using  
REF _Ref256701789 \h  Equation 1 , having calculated “fractiles” as
in   REF _Ref256702023 \h  Equation 2 , and using the parameters listed
in   REF _Ref256702553 \h  Table 12 .  These parameters are similar to
those in   REF _Ref255823009 \h  Table 1 , but modified to apply to the
target population.  The assigned cutpoint (h=0.05 g/mile) was assigned
based on actual Bin-5 results and correponds to approximately the 95th
percentile at age 1.   Ranking and calculation of ‘fractiles’ (f)
was performed separately within six age strata, as defined above in
2(b)(iii).  

Table   SEQ Table \* ARABIC  12 .  Parameters to estimate simulated
logits for the probability that simulated NOx would exceed the cutpoint
of 0.05 g/mile.

parameter	Estimate

β0	-3.0

β1	-6.2

β2	-2.1

h	0.05

As mentioned, for this exercise the logit for NOx was used as the
screening index.  Accordingly, we binned it for sampling purposes, as
shown in   REF _Ref257031498 \h  Table 13  (Note that the Bins in
Table13, based on the simulation, are not the same as those in Table 11,
which as based on actual data).   The structure of the simulated vehicle
population, by screening Bin and age strata, is shown in Table 10. The
uniformity of the population profile across screening bins within age
classes reflects the calculation of fractiles based on ranks, rather
than on absolute remote sensing values.

Table   SEQ Table \* ARABIC  13 .  Simulated Classes (“Bins”) for
the NOx-based Screening Index

Lower bound	Upper bound	Midpoint	Bin Label

	-3.00	-3.300		  0

-3.00	-2.15	-2.575	1

-2.15	-1.30	-1.725	2

-1.30	-0.45	-0.875	3

-0.45	0.40	-0.025	4

0.40	1.25	0.825	5

1.25	2.10	1.675	6

2.10	3.00	2.525	7

3.00

3.375	8

Table   SEQ Table \* ARABIC  14 .  Structure of simulated Population,
by Screening Bin and Age class.

Bin	Age Stratum (Midpoint, years)

	0.5	1.5	2.5	3.5	4.5	5.5	Total

1	451	471	477	478	475	487	2,839

2	404	422	427	428	426	436	2,543

3	306	319	324	324	322	330	1,925

4	217	227	229	231	229	235	1,368

5	140	145	148	148	147	151	879

6	74	78	79	79	79	80	469

7	26	27	27	27	27	28	162

Total	1,618	1,689	1,711	1,715	1,705	1,747	10,185

To simulate analysis, we drew “samples” from the simulated
population. The overall sample was targeted to the same size as that
described previously (ntotal~830).  For purposes of illustration, the
characteristics of one replicate sample are shown in   REF _Ref257292851
\h  Table 15 .  Within each of the 42 screening-bin × age-Class cells,
a sampling rate was assigned so as to achieve a “sub-sample” of
approximately 20 vehicles, accordingly, the sampling frequency in each
bin was calculated as 20/n.   As expected, the sampling process achieved
broadly uniform sub-samples in each cell, in contrast to the pronounced
pyramidal shape of the population.

   Table   SEQ Table \* ARABIC  15 .  Structure of a Sample of the
Simulated Population, by Screening Bin and Age Class

Bin	Age Stratum (Midpoint, years)

	0.5	1.5	2.5	3.5	4.5	5.5	Total

1	17	23	22	18	18	19	117

2	21	27	19	15	20	22	124

3	20	23	13	25	16	19	116

4	17	20	17	21	23	17	115

5	12	19	32	17	19	17	116

6	20	22	17	23	26	20	128

7	20	16	13	17	16	15	97

Total	127	150	133	136	138	129	813

5(b)(ii)	Simulating Response

A first step in analysis of response will be to assess response patterns
in relation to characteristics of respondents or their vehicles.
Detailed demographic data on vehicle owners, such as household size,
age, educational level or income will not be available. However, vehicle
characteristics such as number of vehicles owned, model year, vehicle
type, and vehicle manufacturer will be readily available.  Additional
items may be constructed to incorporate information such as maintenance
level and occurrence of accidents. As an index of burden, it will also
be possible to construct additional measures such as driving distances
from the respondents’ home addresses to the study site. Based on these
characteristics, response patterns can be assessed through response
cells or logistic regression. This level of analysis may identify
patterns of interest, may suggest potential for non-response bias, and
serve as the basis for non-response weighting, but is not sufficient to
confirm or estimate bias. An advantage of having screened all vehicles
in the sample frame is that it may be possible to assess response
patterns using the screening measure as an index of exhaust emissions,
the response variable for the study.  The success of this analysis will
depend on the degree of correlation between the screening measure (RSD
emissions) and the “truth” measurement (PEMS emissions).  If this
correlation is fairly high to high it should be possible to relate
participation directly to emissions.

For the sample, we simulated participant response as an additional
“sampling process.” We projected a worst case in which response was
inversely related to vehicle emissions.  However, in contrast to the
sampling, the response process was related to the “actual” emissions
measurement, rather than the remote-sensing screening measurement.  This
approach reflects a worst-case assumption that vehicle owners are to
some degree aware of their emissions and that owners of “dirty”
vehicles respond at lower rates than those of “clean” vehicles.

To implement this approach, we ranked all measurements in the sample by
their “actual” values and assigned them to quintile groups.  We
assigned each quintile a response rate, designed to give an overall
response rate of about 30%, which we believe to be achievable, and which
would give measurement samples of about 250 vehicles.

Table   SEQ Table \* ARABIC  16 .  Assigned Response Rates for the
Replicate Samples

Quintile	Percentile Range	Response Rate

5	80 - 100	0.10

4	60 - 80	0.18

3	40 - 60	0.36

2	20 - 40	0.40

1	0 - 20	0.43

Using these response rates, a sub-sample of respondents was drawn from
the initial sample.  We re-classified respondents and non-respondents by
the seven screening bins and re-calculated effective response rates for
each bin. The secondary respondent sample and corresponding response
rates are portrayed in   REF _Ref257294306 \h  Table 17  and   REF
_Ref257307245 \h  Table 18 , respectively.

Table   SEQ Table \* ARABIC  17 .   Structure of a Secondary
“Respondent” Sample, by Screening Bin and Age Class.

Bin	Age Stratum (Midpoint, years)

	0.5	1.5	2.5	3.5	4.5	5.5	Total

1	3	12	11	3	7	7	43

2	5	9	9	8	8	11	50

3	3	12	6	6	4	10	41

4	7	6	6	6	9	5	39

5	3	4	12	3	6	6	34

6	5	3	3

4	5	20

7	2	3	5	1	2	1	14

Total	28	49	52	27	40	45	241

Table   SEQ Table \* ARABIC  18 .  Simulated Response Rates for a
Vehicle Sample, by Screening Bin and Age Class.

Bin	Age Stratum (midpoint, years)

	0.5	1.5	2.5	3.5	4.5	5.5

1	0.17	0.52	0.50	0.17	0.39	0.37

2	0.24	0.33	0.47	0.53	0.40	0.50

3	0.15	0.52	0.46	0.24	0.25	0.53

4	0.41	0.30	0.35	0.29	0.39	0.29

5	0.25	0.21	0.38	0.18	0.32	0.35

6	0.25	0.14	0.18	0.00	0.13	0.07

7	0.10	0.19	0.38	0.06	0.13	0.07

As shown in   REF _Ref257292851 \h  Table 15 , the sample is designed to
be “top heavy” with respect to the screening index, to ensure
representation of the high-emitting vehicles. Having employed this
sample design, however, the using of sample weighting in analysis is
critical. Thus, using sampling frequencies (fsample) and response rates
(fresp), we calculated final weights as the product of their
reciprocals. 

 	Equation   SEQ Equation \* ARABIC  8 

  REF _Ref257635521 \h  Table 19  shows sums of final weights for the
respondent sample.  The weights recreate the pyramidal shape of the
vehicle population, as expected. However, the sum of the sampling
weights falls slightly short of the population total, reflecting
nonresponse in cells where no respondents “participated”, which
cannot be compensated by non-response weighting.

Table   SEQ Table \* ARABIC  19 .  Sums of Final Weights for the
Respondent Sample, by Screening Bin and Age Class.

Bin	Age Stratum (Midpoint, years)

	0.5	1.5	2.5	3.5	4.5	5.5	Total

1	451	471	477	478	475	487	2,839

2	404	422	427	428	426	436	2,543

3	306	319	324	324	322	330	1,925

4	217	227	229	231	229	235	1,368

5	140	145	148	148	147	151	879

6	74	78	79

79	80	390

7	26	27	27	27	27	28	162

Total	1,618	1,689	1,711	1,636	1,705	1,747	10,106

5(b)(iii)	Analyzing for Age Trend

Having calculated final weights based on sampling and non-response
weights, it is possible to analyze the simulated vehicle samples for age
trend.   This step estimates the “actual” population values (  REF
_Ref255831338 \h  Table 6 ), based on the samples, using a simple model
for age (  REF _Ref257108352 \h  Equation 5 ). 

Table   SEQ Table \* ARABIC  20 .   Simulated Goodness-of-Fit and Model
Parameters for Models of lnNOx v. Age for two Replicate Samples.

Sample

	Parameter	Estimate	Std. Error	t	P>t

1  (R2= 0.024)	Intercept	-5.358	0.113	-47.29	<0.000001

(repl 7)	Slope

	0.1671	0.0348	 4.79	  0.000002

2  (R2= 0.0032)	Intercept	-5.076	0.157	-32.41	<0.000001

(repl 2)	Slope

	0.01739	0.0428	0.406	  0.68

  REF _Ref257703252 \h  Figure 11  represents the mean trend predicted
from the sample, as well as the upper and lower 95% confidence bounds.
The bounds were calculated using the upper and lower confidence limits
of intercepts and slopes, respectively.   They assume no correlation
between intercepts and slopes for specific samples and may thus be wider
than expected in real data.  In both cases, the confidence bounds
capture the actual values.   

In the second sample the slope term is not significant, whereas in the
first it is highly significant.  This result is consistent with the
results of the power analysis, which suggested that the chances of
Type-II error would be in the range of 30-40%. At first glance it is
difficult to tell whether the error is due to non-response, or to
sampling error as such. 

Figure   SEQ Figure \* ARABIC  11 .   Simulated Age Trends for the
Population (“Actual”) and two Samples.

5(b)(iv)	Investigating Non-response Bias

Due in large measure to the difficulty and expense of measurement, which
increases the challenge of obtaining high levels of response, the
possibility that non-response may reduce the accuracy of emissions
estimates is a long-standing concern. Anecdotal evidence suggests that
some motorists with high emissions are aware of the fact, and presumably
avoid participation in studies.  Presumably, this generalization is more
applicable in areas with Inspection-and-Maintenance programs than in
those without.

In previous efforts, we have attempted to address the question by
comparing the demographics or geographic dispersion of respondents and
non-respondents, and by comparing results for respondents and converted
refusals.  While failure to see marked or significant differences
between respondents and non-respondents with respect to these
characteristics may suggest that non-response does not substantially
affect the results, these analyses cannot definitively rule out the
possibility of non-response bias.

An advantage with the current project is that the construction of the
sampling pool using the remote-sensing screening measure gives at least
a rough index of emissions for all vehicles in the sampling frame,
including respondents and non-respondents.  Our goal is to investigate
the prospects for making use of the screening index to identify and
characterize non-response bias. As with the sampling process, the
efficiency of the index for this analysis will depend on the degree of
correlation between screening and actual measurements.

A simple initial approach would be to average the remote-sensing
measurements for the population and for the sample (using final
weights), by screening bin and age class.  Again, we have calculated
these values using the simulated “remote-sensing” values, which have
an overall Spearman rank correlation of 0.77 with the “actual”
values. 

Figure   SEQ Figure \* ARABIC  13 .  Mean Simulated Remote-Sensing
Values for the Population and for the Sample (Replicate 2), by Screening
Bin and Age Class.  (a)  All values.  (b) Close-up view of values <
1,000.

These results appear consistent with the occurrence of non-response
bias, in that agreement falls off as the response rate declines (  REF
_Ref257307245 \h  Table 18 ).   However, if this were the case, it is
important to note that the effects differ in the two replicates, with
replicates 1 and 2 showing positive and negative biases for the slope
terms, respectively (  REF _Ref257703205 \h  Table 20 ,   REF
_Ref257703252 \h  Figure 11 ). 

A tentative conclusion from this exercise is that, in practice, the
effects of non-response bias may be difficult to distinguish from
sampling error.  Stated differently, it appears that the presence of
non-response bias may affect inferences drawn from samples by
compounding sampling error, with results that are difficult to predict.
It is important to further test these conclusions, drawn from
simulations, with actual screening and emissions data.

5(c)	Reporting Results

Results of the study will be made available to the public and within the
agency through the following means:

Results of the study will be uploaded into the Mobile Source Observation
Database (MSOD). This database contains emissions measurements and
supporting data, develop dand maintained by the USEPA National Vehicle &
Fuel Emissions Laboratory.  Results for key variables pluse necessary
metadata will be entered. However, the identities of respondents will be
protected. Specific identifying information will not be entered.  This
database is available to the public, upon request.



Appendix A:  Telephone Script

INTRO:

Hello, my name is __________________, and I’m calling on behalf of the
U.S. Environmental Protection Agency. May I speak with
___________________?

I’m following up on the letter we mailed you regarding the study of
passenger vehicle emissions. Did you receive and read our letter?

YES

NO

Great.  As a reminder, the EPA is conducting a study of passenger
vehicle emissions and is looking for qualified participants. In order
for you to qualify for this study I need to collect some basic
information about your vehicle. Within the next few weeks you will
receive $10 for talking to me today. 

We are inviting people who live in the [greater] Detroit area like
yourself to take part in a study of vehicle emissions. The main goal of
the research is to study tailpipe emissions from some vehicles
manufactured since 2004. As I mentioned, we will be sending you $10 with
our thanks just for talking to us. 

You may be asked to set up and appointment with a team of specially
trained professional engineers. You would bring your vehicle to our test
facility, where the engineers will install instruments on your vehicle
to measure its emissions.  Then a trained driver will drive the vehicle
over a route like the driving you might do each day.  We’ll keep your
vehicle overnight, and repeat the emissions measurements the next
morning.

After that, technicians will remove the emissions instruments and
install a small instrument that will measure certain operating
parameters of your car while you drive normally over the next
[MEASUREMENT PERIOD] weeks.  The instrument is very small and will not
interfere with your driving or use of the vehicle.  We will then release
the vehicle to you and provide you with an inventive of [INCENTIVE]  to
reimburse you for your inconvenience and expense.

This information will allow us to relate the emissions that we measure
to how people actually drive. For example, it will enable us to
understand how often, and how far people drive, as well as how they
speed up, slow down and brake.  These aspects of driver behavior are
important to understanding emissions, because the way vehicles emit
depends heavily on how they are driven. 

After the completion of the [MEASUREMENT PERIOD], our technicians will
be happy to come to you to disinstall the instrument.  At that time, we
will provide you with a final incentive of [INCENTIVE].

We are happy to accommodate your preference for dates to come in and can
work around your schedule.  This study is for scientific research
purposes so that all information about you will be kept confidential.

To see if you qualify I would like to obtain some information about your
household and vehicles.

Q1:   

My records show that you live in [AREA].  Is that correct?

YES

NO

OTHER/SPECIFY

DK

REFUSED

Q2:   

Our records show that you drive a [COLOR]-colored [MAKE] [MODEL].  Is
this correct?

YES

NO

OTHER/SPECIFY

DK

REFUSED

EXPL:   

Based on the information you’ve provided us, you are eligible to
participate.  We would like to schedule you for an appointment.

Q3:  

When would you like to schedule an appointment?

NOW

LATER

OTHER/SPECIFY

DK

REFUSED

ASSN:   

We have an opening on [DATE].    Would you be able to bring your vehicle
to our facility at

[ADDRESS], [CITY]?

IF NO:   what day/date would work best for you?

[LIST DATES]:

REFUSED

TSLOT: 

We have the following time slots available.  Which would you prefer?

[LIST TIMES]:

DK

REFUSED

CPHON:   

Is [PHONE] the best number to reach you?

YES

NO

IF NO:   Is there another number where we can reach you?

TEL02:  

 ___ ___ ___ - ___ ___ ___ - ___ ___ ___ ___.  

THANK:  

Great.  So to confirm, your appointment is scheduled for [DATE], at
[TIME]. We will shortly mail you a letter with $10, a reminder of your
day and time, and contact information in case you have any questions.
Thank you so much for your help and participation in this very important
study!

 

Appendix B: Questionnaire

Measurement Questionnaire 

Respondent ID	__ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __
__ __ __ __           

Vehicle ID	__ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __
__ __ __

Interviewer ID	__ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __
__ __ __ __

Date (mm/dd/yyyy)	__ __ / __ __ / __ __ __ __.

GREETING:

Hello, I am <NAME>, with  <CONTRACTOR>.  You completed a phone
conversation with us on <DATE>.  At that time, you consented to allow us
to place an instrument on your vehicle to measure its emissions. At that
time, we made an appointment for this visit.

We want to emphasize that the instrument is noninstrusive. It will not
interfere with your vehicle or with your driving in any way. Technicians
will install the equipment, after which one of our drivers will drive
the vehicle over a carefully designed drive route to measure its
emissions during different kinds of driving, such as residential
streets, thoroughfares, and the freeway. 

We’ll keep your vehicle overnight, and repeat the emissions
measurements tomorrow morning.

After that, technicians will remove the emissions instruments and
install a small instrument that will measure certain operating
parameters of your car while you drive normally over the next
[MEASUREMENT PERIOD] weeks.  The instrument is very small and will not
interfere with your driving or use of the vehicle.  

We will then release the vehicle to you and provide you with an
inventive of [INCENTIVE]  to reimburse you for your inconvenience and
expense.

This information will allow us to relate the emissions that we measure
to how people actually drive. For example, it will enable us to
understand how often, and how far people drive, as well as how they
speed up, slow down and brake.  These aspects of driver behavior are
important to understanding emissions, because the way vehicles emit
depends heavily on how they are driven. 

After the completion of the [MEASUREMENT PERIOD], our technicians will
be happy to come to you to disinstall the instrument.  At that time, we
will provide you with a final incentive of [INCENTIVE], and your
participation in the study will be complete.

Your participation is entirely voluntary, and your name will not be
connected with the data in any way.

We estimate that this process will take approximately [duration] hours. 

Do you have any questions?  May we proceed?

ANSWER QUESTIONS, PROCEED IF R CONSENTS.

ENTER TIME NOW:  __ __ : __ __.

Q1.	How long have you owned the vehicle:   		__ __  years.

		REFUSED					999.

Q2.	Do you park the car in a garage at night?

	YES							1

	NO							0

	DON’T KNOW					998

	REFUSED						999

Q3	As far as you know, has the vehicle been in any accidents?

	YES							1

NO							0

DON’T KNOW					998

REFUSED						999

IF YES,  DESCRIBE ____________________________________________________

________________________________________________________________________
___.

Q4.   	Can you remember when the oil was last changed in this vehicle?

	YES							1

NO							0

DON’T KNOW					998

REFUSED						999

IF YES, ENTER DATE:   month: __ __  year:  __ __ __ __, AND GO TO Q3,

IF NO, GO TO Q2

Q5	Can you tell me which answer is closest to when you think the oil was
last changed?

DURING THE LAST MONTH			1

DURING THE LAST 2 TO 3 MONTHS		2

DURING THE LAST 3 TO 6 MONTHS		3

DURING THE LAST 6 TO 12 MONTHS		4

MORE THAN 12 MONTHS				5

DON’T KNOW					998

REFUSED						999

Q6     Have you had the muffler replaced?

	YES							1

NO							0

DON’T KNOW					998

REFUSED						999

IF YES, GO TO Q4,

IF NO,  GO TO Q5.

Q7     Can you tell me which answer is closet to when the muffler was
replaced?

DURING THE LAST MONTH			1

DURING THE LAST 2 TO 3 MONTHS		2

DURING THE LAST 3 TO 6 MONTHS		3

DURING THE LAST 6 TO 12 MONTHS		4

MORE THAN 12 MONTHS				5

DON’T KNOW					998

REFUSED						999

Q8     Have you had the catalytic converter replaced?

	(explain, if necessary, that in some vehicles the exhaust system,
including the catalyst, manifold, muffler and tailpipe may be a single
unit, and must be replaced as such, and in others, a modular system may
allow these parts to be replaced individually).

	YES							1

NO							0

DON’T KNOW					998

REFUSED						999

IF YES, GO TO Q4,

IF NO,  GO TO Q5.

Q9     Can you tell me which answer is closet to when the catalyst was
replaced?

DURING THE LAST MONTH			1

DURING THE LAST 2 TO 3 MONTHS		2

DURING THE LAST 3 TO 6 MONTHS		3

DURING THE LAST 6 TO 12 MONTHS		4

MORE THAN 12 MONTHS				5

DON’T KNOW					998

REFUSED						999

Q10	Have you had other major repair or maintenance performed on the
vehicle within the last 12 months?

	YES							1

NO							0

DON’T KNOW					998

REFUSED						999

IF YES, DESCRIBE ____________________________________________________

_____________________________________________________________________.

EXIT:  Thank you. Those are all the questions I have.  We appreciate
your taking time to help with the research project. 



Appendix C: Vehicle Information



Respondent ID	__ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __
__ __ __ __           

Vehicle ID	__ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __
__ __ __

Technician ID	__ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __
__ __ __ __

Date (mm/dd/yyyy)	___ ___ / ___ ___ / ___ ___ ___ ___.

Time (hh:mm)	___ ___ :  ___ ___

USE ALL CAPITAL LETTERS.

plete □

10	Photograph of license plate	Complete □

11	Photograph of VIN	Complete □

12	Photograph of VECI label	Complete □

REFERENCES

 PAGE   

  PAGE  i 

 PAGE   12 

 PAGE   22 

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 with numbers for 2000-2010 MY?

 Zhang, Yi, Gary A. Bishop and Donald H. Stedman.  1994.  Automobile
emissions are statistically γ-distributed.  Environmental Science and
Technology. 28:1370-74.

  Frey, H. C., A. Unal, J. Chen, S. Li and C. Xuan.  Methodology for
Developing Modal Emission rates for EPA’s Multi-scale Motor Vehicle
and Equipment Emission System.  Dept. Civil Engineering, North Carolina
State University, Raleigh. EPA420-R-02-007. October, 2002.

 Bishop, Gary, and Donald H. Stedman.  2008. A decade of on-road
emissions measurements.  Environmental Science and Technology.
42:1651-1656.

  Bishop, Gary, John R. Starkey, Anne Ihlenfeldt, Walter J. Williams and
Donald Stedman. 1989. IR long-path photometry:  a remote sensing tool
for automobile emissions.  Analytical Chemistry. 61(10): 671A-677A.

 Wenzel, T.  2001. Reducing emissions from in-use vehicles: an
evaluation of the Phoenix inspection and maintenance program using test
results and independent emissions measurements.  Environmental Science
and Policy.  4:359-376.

 DeHart-Davis, Leisha, Elizabeth Corley and Michael O. Rodgers.  2002.
Evaluating vehicle inspection/maintenance programs using on-road
emissions data: the Atlanta reference method. Evaluation Review. 26(2):
111-146.  

azzoleni, Claudio, Hampden D. Kuhns, Hans Moosmműller, Rober E.
Keislar, Peter W. Barber, Norman F. robinson, and John G. Watson. 2004.
On-road vehicle particulate matter and gaseous emission distributions in
Las Vegas, Nevada, compared with other areas.  J. Air & Waste Management
Association.  54:711-726.

 Callahan, Michael A., Robert P. Clickner, Roy W. Whitmore, Graham
Kalton and Ken Sexton.  1995. Overview of important design issues for a
national human exposure assessment survey. J. Exposure Analysis and
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