EPA420-R-96-004
User Guide and Description
For
Interim Remote Sensing Program Credit Utility
September 1996
U.S. Environmental Protection Agency
Office of Air and Radiation
Office of Mobile Sources
Assessment and Modeling Division
Emission Inventory Group
National Vehicle and Fuel Emissions Laboratory
2565 Plymouth Road
Ann Arbor, Michigan 48105
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TABLE OF CONTENTS
Section Title Page Number
1.0 BACKGROUND AND SUMMARY 1
2.0 DESCRIPTION OF REMOTE SENSING 5
2.1 Remote Sensing Operation and Fleet Coverage 6
2.2 Follow-Up on High Emitters Identified by RSD 7
2.3 Other Aspects of a RSD Program 10
3.0 REMOTE SENSING CREDITS 10
3.1 Basic Remote Sensing Methodology 11
3.2 Inspection Program Designs 13
3.3 Options for Fleet Coverage 14
3.4 Remote Sensing Effectiveness 18
3.5 Estimating Remote Sensing Benefits 23
4.0 REMOTE SENSING UTILITY 25
4.1 Remote Sensing Utility Input Structure 25
4.2 Using Remote Sensing I/M Credits with MOBILES 35
5.0 REFERENCES 37
Appendix A
List of Records for RSD Utility 39
List of Tables
Table la: IM240 Excess Emission Thresholds (California Standards) 18
Table Ib: IM240 Excess Emission Thresholds 19
Table 2: Fraction of IM240 Excess Emissions Identified Using Remote Sensing
CO Cutpoints (Combined El Monte/EPA Studies) 20
Table 3: Ratio of Average IM240 Excess Identified Emissions Per
Vehicle to Average Excess Emissions Per Vehicle In Fleet
Using Remote Sensing CO Cutpoints (Combined CARS
El Monte/EPA Studies) 21
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1.0 BACKGROUND AND SUMMARY
A remote sensing device (RSD) can measure instantaneous hydrocarbon (HC) and carbon
monoxide (CO) exhaust emissions from a vehicle operating under actual in-use conditions.
Instrumentation is presently being evaluated to also measure nitrogen oxide (NOx) emissions.
RSD though does not measure evaporative HC emissions.
A number of states have expressed interest in using RSD as part of their vehicle inspection
and maintenance (I/M) programs. A number of RSD studies [1-5] using RSD and an
independent emission test such as the Federal Test Procedure (FTP) or IM240 (a transient driving
cycle developed by EPA for use in vehicle inspection programs) on a sample of vehicles
representative of the in-use fleet have been done by California and EPA. This includes, of
course, vehicles both above and below specific RSD cutpoints (which can be used to designate
RSD passes/failures). These studies demonstrate the effectiveness of RSD in identifying
vehicles with excess emissions that can be reduced in a vehicle I/M program.
On September 12,1995, EPA convened a meeting of,RSD technical experts (including those
from states, EPA Regions, academic institutions, RSD contractors) doing research and involved
in state I/M programs to outline the EPA approach to determining RSD credits and soliciting
their input; about 40 people participated in this meeting. The technical experts reviewed a
number of draft papers [6-11] prepared by EPA staff on which to provide input. Then, EPA
sought input from the Modeling Workgroup of the Federal Advisory Committee Act Technical
Advisory Subcommittee for mobile source issues. This Workgroup prepared a statement [12] on
the interim methodology for RSD credits. The Modeling Workgroup had also been asked to
review an initial version of this document to be sure its earlier recommendations were followed
and to have an opportunity for additional input. The Modeling Workgroup provided this review
and another statement [13] on the initial draft of this report. This additional statement was
approved by a majority vote of the standing members of the Modeling Workgroup. Also, the
draft report and the utility were sent to members of the I/M Subcommittee of STAPPA ALAPCO
(State and Territorial Air Pollution Program Administrators - Association of Local Air Pollution
Control Officials) for review of the utility itself to determine how easy it is to use [14]. The
STAPPA/ALAPCO members were able to provide only limited feedback in the time available.
As a result of the input from various technical experts, EPA has developed a methodology
and mathematical formulas to generate interim RSD emission reduction credits for use in
assigning credits in a state I/M program. These algorithms tie together the important remote
sensing variables such as I/M program design, remote sensing coverage, and remote sensing
effectiveness. This process enables a state to model the effect of more frequent inspections using
remote sensing and subjecting remote sensing failures to test-only inspections in a test-only I/M
program. (A test-only I/M program is one in which the I/M test is administered by a facility that
does only I/M testing and does not provide repair services for failing vehicles.) States can also
model benefits from use of RSD in conjunction with a test-and-repair I/M program with either
test-only or test-and-repair inspections of vehicles failing RSD. (A test-and-repair I/M program
is one in which the I/M test is administered by a facility that does I/M testing and provides
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vehicle repair services as well). States can also model the benefits from using RSD in a hybrid
program with test-only inspection of RSD failures. (A hybrid I/M program is one with test-only
inspection of some vehicles and test-and-repair inspection of other vehicles). States can also
model the benefits of using RSD to locate high emitters in areas that do not operate an I/M
program for the entire vehicle fleet. The high emitters located by RSD would receive a test-only
or test-and-repair I/M test and repairs if the vehicle failed the confirmatory I/M test. In such a
program, the only vehicles receiving the test-only or test-and-repair I/M test are those failing
RSD. As another option, RSD can also be used to exempt some clean vehicles ("clean
screening") from their next scheduled I/M test.
The EPA vehicle emissions model MOBILES is used to model vehicle fleet emission levels,
and how these levels are affected by most emission control strategies. Inspection and
Maintenance (I/M) is one of the emission control programs which can be modeled by MOBILES.
Remote sensing with some type of confirmatory I/M test and enforcement process is another
control program which now can also be modeled by the MOBILES model.
MOBILES is herein supplemented with a utility program to determine remote sensing
exhaust credits by linear interpolation between the MOBILES I/M benefif for a biennial program
(denoted as "B" for purposes of discussion later in this document) that applies without remote
sensing and the MOBILES benefit (denoted as "A" later in this document) that applies if all
vehicles in the fleet received a remote sensing test and if remote sensing identified every vehicle
which would fail the confirmatory I/M test on an annual basis. This interpolation reflects the
facts that remote sensing coverage will not be 100 percent, and that remote sensing misses some
vehicles which would have failed a confirmatory I/M test. The interpolation occurs for each
pollutant (HC, CO, and NOx) and each model year cohort in the vehicle fleet for the year of
interest. The interpolation allows remote sensing to be assigned the incremental credit increases
which result from increased inspection frequency, and test-only (or test-and-repair once the
effectiveness of test-and-repair programs are determined as explained below) confirmatory
testing of remote sensing failures.
In preparing this guidance, EPA faced a tradeoff between when its first formal guidance on
remote sensing credits could be released and how comprehensive and representative those credits
could be. The choice EPA has made is to provide credits sooner rather than later, realizing that
they may not be as large as they might have been with more investment of time, and may not
cover every case of interest to users. Thus, states can have some numerical guidance in a timely
fashion to estimate what RSD benefits might be.
One tradeoff factor has been the availability of relevant data; EPA has chosen to make use of
data collected up to and including the 1994-95 Sacramento study, and not wait to receive and
analyze data collected in Arizona or elsewhere in 1996 or 1997. The data from the Arizona RSD
program will indeed be valuable in helping determine what RSD credits could be; however, EPA
did not have the staffer contract resources to analyze the Arizona data available so far and still
release this document in a timely fashion. A consequence of this choice is that the guidance does
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not address HC or NOx outpoints. Also, it does not quantitatively address the advantages or
disadvantages of using multiple RSD readings to decide how to treat a vehicle. Possibly, the data
that were available for this guidance is not as favorable to remote sensing as will be data
collected with more modern versions of the instrumentation and with more modern practices on
how to site RSD units and validate the data they collect.
Another factor has been the obvious advantage of issuing a credit tool which would be
consistent with the current versions of MOBILES, instead of taking the considerable time and
staff hours needed to revise MOBILES itself, and rather than requiring users to go through
complex hand calculations. The choice to stay consistent with MOBILES means that this
guidance does not cover every program type in which a user might be interested. For example,
the guidance assumes that a state would not use remote sensing to subject a given vehicle to I/M
testing more often than once per year. Another limitation of this guidance is that it does not /'
consider residual benefits of remote sensing from year to year, particularly in test-and7fepair
programs. A vehicle which escapes proper testing and repair, but is caught by remote sensing
may obtain an effective benefit that extends into a later year in which by chance it is not seen by
remote sensing at all. The current structure of MOBILES is such that-it would have taken
considerable rethinking and reprogramming to capture this effect, so this guidance does not.
Recognizing that this guidance may not do full justice to the potential for remote sensing,
EPA intends to continue to analyze newly available data and refine its modeling approaches with
a view to revising or supplementing the guidance when practicable to do so. EPA currently plans
to have an updated RSD treatment in MOBILE6 if not before MOBILE6 is released. In addition,
EPA can meet with guidance users who are interested in collecting local data that would allow
reality based remote sensing credits to be developed for their specific area and program design.
The local data can be pilot studies on coverage; these studies, for example, as discussed later,
may show how RSD increases compliance with an I/M program by detecting high-emitting
vehicles that should have received I/M testing but do not. The local data can also include
updated information on the effectiveness of RSD.
Public acceptance of remote sensing is very important for its success. States should attempt
the educate the public about the uses and importance of remote sensing, before it is used. Also,
pilot testing might be done before the remote sensing program is implemented. One critical issue
discussed in more detail later is where to set cutpoints, so that there are not too many false
failures causing the public to lose confidence in RSD or too many false passes lowering the
effectiveness of RSD in reducing emissions.
As stated previously, one element of the benefit of remote sensing in a test-and-repair or
hybrid I/M program is to catch some vehicles which, for whatever reason, escaped a true repair
in the last scheduled inspection cycle. The incremental benefit of any given level and type of
remote sensing therefore depends on how many such vehicles there are, or more precisely, how
much of the fleet emissions reduction potential they represent. EPA regulations on I/M
previously specified a uniform estimate of the emission reduction loss or discount for test-and-
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repair I/M programs. The recent National Highway System Designation Act directs EPA to no
longer apply this uniform .estimate. Instead, each state will be given interim I/M program credit
for whatever estimate the state makes in good faith. Prospective remote sensing credits will also
depend on this good faith estimate of the I/M program credit since the discount included in this
estimate is an important factor in determining remote sensing credits. A test-and-repair state that
estimates a small or zero discount will model a smaller incremental remote sensing credit than a
state that estimates a larger discount. Unfortunately, the current versions of MOBILESa are hard
wired-with a significant percent discount (i.e., 50%). EPA plans to issue a new version
(MOBILESb) which will accept a user input for the discount factor. Meanwhile, users may need
to perform interpolations between the MOBILESa outputs for test-only (zero discount) and test-
and-repair (50 percent discount) to develop emission factors and RSD credits for discounts
between zero and 50 percent. Roughly speaking, using the guidance in this document, the upper
end estimate of incremental HC benefit from remote sensing is about 1 percent of fleet non-I/M
emissions if the test-and-repair discount is zero, but it is higher (about 4 to 5 percent) if the
discount is significant. The upper end NOx benefit ranges between 0.2 and 1.3 percent. These
upper end estimates assume complete fleet coverage and stringent remote sensing cutpoints that,
in fact, detect all (or almost all) excess emissions in the vehicle fleet.
The National Highway System Designation Act provides for an 18-month evaluation of each
I/M program to determine if the I/M program is as effective overall as the good faith estimate
made earlier. Beyond this 18 month evaluation, a longer term evaluation is being developed
under the auspices of ECOS (Environmental Council of States) which will be more focused at
program effectiveness. A substantial remote sensing program can help assure that an I/M
program will get a good result on the 18 month evaluation, since it can catch many of the cars
that would otherwise contribute to a discount. For example, if a state's 18-month evaluation
reveals that the scheduled inspection element of a test-and-repair program is in fact suffering a
significant percent discount, having an upper end remote sensing program would make the
overall credit for the program 4 to 5 percentage points greater than it otherwise would be. These
extra percentage points would be of substantial benefit in preparing subsequent state
implementation plans. A substantial remote sensing program can therefore be considered a
backstop to other state efforts to replace good faith, interim I/M credits with demonstrated, final
credits. There is a synergistic nature of interactions between the remote sensing and baseline I/M
programs (e.g., remote sensing can catch vehicles that were improperly inspected for whatever
reason in the I/M program or would not have gone through the I/M program). Thus, EPA
believes even those states whose I/M programs do not suffer a substantial discount could benefit
from deployment of substantial remote sensing programs. Accordingly, EPA believes that, if
done properly, such programs with remote sensing could potentially warrant additional SIP
credit, above and beyond whatever amount of SIP credit is awarded to their baseline I/M
programs. For that reason, EPA urges states to carefully consider the remote sensing option.
Of course, a lower level of remote sensing is also the most economical way to meet the
statutory requirement for on-road testing. In addition, on-road testing for emissions performance
is required of 0.5% or 20,000 vehicles (whichever is less) of vehicles subject to the I/M program.
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This on-road testing can also provide useful information on the fraction of fleet complying with
the I/M testing requirements and the overall effectiveness of the I/M program. Remote sensing
can, in conjunction with the confirmatory I/M test, help provide information on the overall
distribution of emissions in the fleet and how it changes with I/M.
Beyond using the remote sensing credit utility provided with this guidance, there are two
other opportunities that states can pursue to increase the credits for remote sensing. The first is
that EPA will consider state proposals for extra remote sensing credit on the basis that remote
sensing can help enforce the requirement that vehicle owners renew their vehicle registrations on
time, and that they register in the immediate I/M area. Remote sensing can provide information
on how many vehicles driving in a given area are registered elsewhere. Also, remote sensing can
get extra credit for helping to enforce against commuters who should get their vehicles inspected
but do not. It is not clear at this point what additional options states have for more enforcement
to increase compliance. Additional credit can be given to the extent that both of these"situations
contribute to non-compliance with the state's vehicle inspection program as accounted for in the
state implementation plan (SIP) emission inventory and will be reduced by remote sensing. I/M
programs already take measures to reduce such evasion and to monitor the degree to which this is
a problem. The determinations under the National Highway System Designation Act will help
define how effective a state's overall I/M program is and can provide information on what extent
RSD can make up shortfalls such as those from improved program compliance.
For example, many states assume a 96% compliance with the local I/M requirements. The
4% non-complying vehicles are assumed in MOBILE to have emissions twice that of the average
vehicle that fails I/M. There are substantial benefits for a state able to increase its compliance.
For example, a 1% increase in compliance with an I/M program for a state having a 20% I/M
failure rate could result in a 21% overall failure rate but the extra 1% failing vehicles have twice
as much emissions as the other failures. Individual states though are best able to ascertain what
their compliance rates truly are and how easy it is to increase the compliance rates through use of
RSD to obtain these additional benefits.
This draft document was prepared to discuss how RSD credits using the utility program in
conjunction with MOBILES. Beyond release of this document, work will be underway to
further update it as needed to reflect the additional experience states will have gained while
implementing RSD. For example, California, Arizona, Colorado, and possibly other states will
have experience using RSD and will be in a good position to provide data from their actual
experience for use in updating these credits. In particular, as recommended by the Modeling
Workgroup, EPA is reviewing the Arizona RSD data from its program.
2.0 DESCRIPTION OF REMOTE SENSING
The objective of RSD is to identify high emitters in the vehicle fleet. These include vehicles
that were clean on their last cycle (with or without need for repair to get clean), but have since
experienced an emissions problem. They also include vehicles that were not properly inspected
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and repaired "on-cycle," for example, at test-and-repair stations. This section discusses in detail
factors for states and others to consider in implementing RSD programs to accomplish this
objective.
2.1 Remote Sensing Operation and Fleet Coverage
Remote sensing is a process by which the instantaneous HC and/or CO (and NOx as NOx
instrumentation is now reasonably well developed) exhaust emissions and vehicle identity (i.e.,
license plate) of in-use vehicles can be monitored when vehicles pass through the RSD
measurement beam while operating on the road. The RSD system is set up alongside the road
to measure emissions and also incorporates video license plate recording equipment to record the
license plate number of each vehicle to enable the state or local government to trace vehicle ^
registration. Although remote sensing units are automated in that data collection does not require
operator action on a vehicle-by-vehicle basis, no researcher nor I/M program is now leaving
remote sensing units unattended while in operation. (One pilot involving unattended equipment
is in planning.) The units require daily set up and calibration, and on-site technicians can avoid
downtime that will only be discovered later if the unit were unattended. Also, security from
theft, damage, and even vandalism is a concern. When technicians are using the equipment at the
side of the road, attention must be paid to their physical needs and to their safety from oncoming
traffic, etc.
Additional equipment should also be used in conjunction with the system to monitor
instantaneous vehicle speed and acceleration. Vehicle acceleration is especially important since
high acceleration rates (and the resultant high load) can lead to enrichment events and high
instantaneous emissions which may not be representative of a vehicle's overall emissions.
Deceleration is also important since deceleration can result in low instantaneous emissions not
representative of a vehicle's overall emissions. Measuring other parameters, such as vehicle
engine or catalyst operating temperature (i.e., is the vehicle or catalyst warmed up?), with RSD is
in the initial stage of development and application. A vehicle under cold-start conditions (a cold
catalyst that has not yet become effective in reducing HC and CO) will also result in high
instantaneous emissions. Such technology should decrease the false passes and failures
associated with RSD and should, thus, improve the effectiveness of RSD. RSD data with such
systems on a large number of fully random vehicles (including vehicles passing and failing RSD
as they would occur representatively in the in-use fleet) on which a mass emissions test (e.g.,
g/mile HC, CO, and NOx from an IM240) is also obtained would be very valuable in revising
this guidance.
I/M program planners must decide where and how often to operate the remote sensing
devices and how to use the remote sensing results to designate a vehicle as a high emitter. A
greater number of remote sensing sites and measurement days allows more of the local fleet to be
tested, which results in more opportunity for emission reductions. However, optimal RSD sites
for vehicle speed and acceleration to assure that most vehicles are not under high loads (and thus
in enrichment operation leading to erroneously high RSD readings as mentioned above) may not
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always have high traffic flow. A state should evaluate its chosen RSD sites carefully to
minimize erroneously high RSD readings from such enrichments. For example, use of RSD after
a stop sign may not be appropriate due to high acceleration rates. Tighter RSD outpoints identify
more of the high emitters present on the road, thus generating more emission reductions.
However, tighter RSD cutpoints also fail more clean vehicles that are momentarily high emitting
because of driver behavior (e.g., heavy acceleration) or cold-start conditions as the vehicle passes
the remote sensing unit. In addition to using acceleration measurements and catalyst temperature
measurements, a state may wish to consider reducing such false failures by not failing any
vehicle unless it has been measured to have high emissions in two (or more) separate remote
sensing measurements. However, this reduces the fraction of the fleet which can be targeted,
since some "dirty" vehicles may not have two (or more) encounters, and repair benefit of these
vehicles from using RSD are sacrificed.
The number of vehicles that can be successfully tested per day with remote sensing varies
from site to site; weather can also be a factor. Each area has to experiment to see what can be
accomplished. As a rough guide, 500 unit-days of testing in Sacramento [1] at 337 sites
produced about 1,330,000 records containing an emission reading, 865,000 of which also
contained a manually decodable license plate image. Overall, a valid test was obtained on
376,000 unique, identifiable vehicles. Of the 810,000 vehicles registered in Sacramento County,
47 percent received at least one valid RSD reading.
2.2 Follow-Up On High Emitters Identified by RSD
After the vehicle's emissions and license plate number are recorded by the remote sensing
equipment, various strategies can be utilized to notify vehicle owners that their vehicles have
been identified as high emitting. These can range from an electronic sign along the roadway
which notifies the motorist of a potential emissions problem (similar to those used to alert a
driver of a vehicle's speed in an effort to reduce speeding) to a written summons in the mail to
bring the vehicle to an official emissions testing station for "off-cycle" I/M testing with
subsequent repair of a failing vehicle so that it passes the I/M test. Also, a state may issue fines
or suspend vehicle registrations for vehicle owners not complying with a RSD summons for a
confirmatory I/M test.
The failure rate in a remote sensing program depends on many factors, including, of course,
the cutpoints used but also the state of repair of the local fleet (affected by the specifics of the
periodic testing requirement), the roadway and traffic flow characteristics of the remote sensing
sites, and the age mix of the vehicles passing the remote sensing sites. Local pilot testing or
experimental testing as done by California Bureau of Automotive Repair (BAR) in Sacramento,
CARB in El Monte, and EPA under contract with Automotive Testing Laboratories in Mesa,
Arizona (and Hammond, Indiana) is the best approach to determining failure rates for the local
fleet. The State of Arizona is now performing large numbers of remote sensing tests in the
Phoenix area as part of its I/M program, and EPA will work to help communicate its experiences
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to other states. EPA has received some of the Arizona IM240 and RSD data; although these
data could not be analyzed in time for this document, they are now being reviewed.
However, in a recent draft report [15] from the Arizona Department of Environmental
Quality (ADEQ) from a program done in late 1995, 97 in-use vehicles were identified as
possibly being high emitters by a single RSD measurement. Since these vehicles were
potentially high emitters, they do not represent a cross-section of typically emitting vehicles in
the fleet; thus, these results cannot be compared with those from a random set of vehicles such as
in the California and EPA studies. The data from these vehicles suggested to the ADEQ that a
single RSD measurement did not give a satisfactory pass/fail result. The report also suggests
RSD error of commission rates (i.e., false RSD failures) of 15 percent results in RSD
identification rate of 80 percent for vehicles failing I/M based on this sample. However, the
sample does not include vehicles passing RSD (some of which will be I/M failures) to the extent
these vehicles occur in the fleet.
Other state programs implemented or planned will provide valuable information on RSD
failure rates. These include a Colorado pilot-type program in Greely and-California's
implementation of RSD with summons to failing vehicles. Also, an experimental program under
rigidly controlled conditions was recently completed in Toronto.
Some vehicles that fail remote sensing, which later obtain an off-cycle inspection (or even an
immediate roadside inspection) using the I/M program's normal tailpipe emissions test, pass the
off-cycle inspection even though no repairs have been performed. Numerous studies listed in the
references have produced information on the frequency of this occurrence of falsely failing RSD.
(These references also discuss how frequently vehicles can falsely pass RSD tests.) As
technology to utilize speed, acceleration, and engine/catalyst temperature readings along with
emission readings is perfected, false passes/failures should greatly decrease.
It is reasonable to expect from the available evidence that false failures on remote sensing are
most frequent (as a percentage of all remote sensing failures) among newer vehicles because they
have the lowest incidence of actual emissions problems. Newer vehicles can be exempted from
remote sensing by discarding their data once model year is determined via the license plate.
Doing this may greatly reduce false failures with only minor loss of benefits from missing the
relatively few high emitters in newer vehicles.
In addition to actually identifying "dirty" vehicles and forcing them to get repaired, remote
sensing may have a motivational effect on vehicles' owners and others which could produce
indirect but real benefits. A vehicle owner may request, acquiesce to, or otherwise receive an
improper on-cycle inspection possibly at a test-and-repair I/M station in an I/M program for
which the state has determined a large discount. Such an improper I/M test can also be
associated with an improper or even no repair test that would lower emissions. With the vehicle
owner aware of the new risk of failing a remote sensing test with its attendant expense and
inconvenience to have the vehicle reinspected and repaired correctly, the vehicle owner instead
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would make a greater effort to obtain a proper I/M test and repair. Also, I/M facilities would
have a greater incentive to correctly administer I/M tests with RSD monitoring in-use emission
levels. Also plausible is that vehicle owners who notice a driveability problem or a "check
engine" (malfunction indicator) light coming on between inspections might seek more prompt
repair lest their vehicles fail remote sensing. (Also, vehicle owners ignoring the "check engine"
light coming on can have serious damage, such as to the catalyst, which would be expensive to
repair.) In general, a remote sensing program may induce drivers to keep vehicle emissions
equipment in good repair, to avoid the inconvenience of additional testing. The magnitude of
such a deterrent effect is currently immeasurable. However, much depends on the level and
hence public visibility of remote sensing, the public's perceptions of the possibility of avoidance,
whether fines apply to remote sensing failures or only a requirement to pass a confirmatory test,
and other factors. This document does not address the potential magnitude of any additional
benefits that this deterrence effect might provide to inspection programs. Quantifying deterrent
effects is very complex and involves much social science. EPA though welcomes advice from
technical experts on what work would be needed to provide data to accurately quantify such
effects. Another strategy that does not subject vehicle owners to inconvenient off-cycle
inspection is to use remote sensing readings as one input of an algorithm with which to request
that certain vehicles obtain their "on-cycle" I/M test at a certain type of inspection station,
particularly at a test-only inspection station. In this way, some high-emitting vehicles are ensured
a full I/M test that does not suffer from conflict of interest (i.e., the facility doing the test also
does the vehicle repairs) or other testing problems (e.g., test-and-repair I/M facilities generally
test fewer vehicles than test-only facilities and thus cannot afford more complex and accurate test
equipment). This document does not cover developing and using such an algorithm, but EPA
will work with states interested in this concept. Recent work by consultants to the California
Bureau of Automotive Repair [1] provides a good starting point for EPA to work with other
states. California is presently developing such a system as part of its hybrid I/M program.
One additional benefit that remote sensing may provide is additional emission reductions
resulting from vehicles with evaporative system problems that are identified as part of the off-
cycle inspection required by remote sensing. Since remote sensing measures exhaust emissions,
such testing would not be expected to target vehicles with evaporative system problems.
However, it could be assumed that some vehicles with evaporative system problems would be
targeted by RSD for failing exhaust emissions and required to have an off-cycle inspection on a
random basis which could also catch evaporative emission problems. In the case of clean
screening, it would be assumed that some vehicles exempted from inspection would also have
evaporative system problems and their benefit would be lost to the program. This effect of
exhaust failures flagged by remote sensing also having evaporative problems, both positive (with
remote sensing exhaust failures getting evaporative emissions repairs) and negative (with clean
screening passing what would be exhaust I/M failures), would be linked to the additional failure
rate associated with remote sensing requirements. This document only addresses the exhaust
benefits of remote sensing options and does not estimate this indirect effect of remote sensing on
evaporative emissions, since there are no data yet to quantify credits for repair of evaporative
emissions on RSD failures.
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More information about the remote sensing process and references to a considerable literature of
remote sensing studies are contained in EPA's latest fact sheet on remote sensing [16] which also
lists numerous other RSD studies. The report from the recent Sacramento remote sensing study [1]
in particular contains many analyses and findings not summarized here. A recent EPA review paper
[17] summarizes available RSD studies.
2.3 Other Aspects of a RSD Program
One innovative use of remote sensing is to identify vehicles with the lowest emission levels
which are then exempted from the periodic I/M program inspection. A state should use multiple
RSD readings in arriving at a clean-screening exemption decision due to the tendency of a single
RSD reading leading to a large number of false passes. EPA is still analyzing the use of RSD in
clean screening and welcomes input. Clean screening should improve the cost effectiveness of a
periodic I/M program by eliminating unnecessary inspections and should increase public awareness
and acceptance of the I/M program.
EPA and CARB El Monte studies show that using a single RSD reading allows a large number
of RSD passing vehicles to be exempted that in fact would fail an IM240 or equivalent I/M test.
Requiring multiple (e.g., possibly three or even four) passing RSD readings on a vehicle will greatly
lessen the chances of false passing. Also, a state could implement clean screening (at least in its
initial phases) on certain specific model years (e.g., newer model years) not expected to have many
I/M failures. Even though EPA and CARB El Monte studies cannot be used to assess how useful a
clean-screening program might be since they do not have multiple RSD readings on vehicles, the
initial Arizona RSD results on actual in-use vehicles can be used. EPA is analyzing the Arizona
RSD database comparing RSD readings to the IM240 results to determine specifics on how many
RSD tests are useful for optimal clean-screening results minimizing false RSD passes. Results from
other programs (e.g., California, Colorado, and the Toronto study) will also be useful.
The specifics of clean screening (e.g., cutpoints, fraction of the fleet measured, how close to a
scheduled I/M test the vehicles must be measured) would be determined by the state; though EPA
will give advice as needed. While clean screening does not in itself increase credits, it can improve
the cost effectiveness of a periodic I/M program by eliminating unnecessary inspections and
increase public acceptance of the I/M program.
In addition, RSD can be used in areas not otherwise needing a full I/M program for the entire
vehicle fleet. RSD can flag high emitters and only those high emitters would then be sent for a
regular I/M test. Use of RSD in this fashion greatly limits the test-only or test-and-repair I/M
facilities needed. However, high emitting vehicles not flagged by RSD in such areas would have no
emission reductions.
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3.0 REMOTE SENSING CREDITS
The EPA MOBILES model stores the credits for all I/M programs in separate data files that are
read during MOBILE runs. EPA can modify or supplement these files to add new data and/or
options that were not included in the original release of the model, without the need for a new
version of the MOBILE model itself. The credits in the I/M credit files can thus be adjusted, as
needed, in the coming year to reflect the experience from an in-use remote sensing program. The
numbers in the separate data files for determining RSD credits are based on presently available
information (discussed in the references) for emission benefits (using IM240/FTP data coupled with
RSD readings) and fleet coverage. However, as discussed below, EPA RSD credits allow local
areas to use their own specific programs to project fleet coverage (commitments to percent fleet f
coverage or number of failures). It is critical to emphasize that after RSD programs have been in
place for a certain time period (e.g., a year or so), information will be available to update the
emission benefits of RSD from those in the reference documents. Also, more information will be
available on fleet coverage. The MOBILES RSD credit programs-will be^revised at that point (if
not sooner) to use the updated information. Of course, EPA and the states will have much more
experience with RSD programs in general, including strong and weak points, not fully realized now,
and perhaps some information will be available on deterrence effects.
3.1 Basic Remote Sensing Methodology
It is assumed that vehicles targeted by remote sensing are required to submit to an "off-cycle"
I/M inspection in addition to the mandatory periodic inspection. These off-cycle inspections in
effect increase the inspection frequency for portions of the fleet. To experience the increase in
testing frequency, a high-emitting vehicle must be seen by the remote sensing units, fail the remote
sensing cutpoint, and be called in for an "off-cycle" confirmatory I/M inspection.
For biennial I/M programs, it is assumed that on average vehicles tested by remote sensing
which fail the RSD cutpoints get one extra inspection at an I/M station due to remote sensing, and
that this inspection occurs halfway between the on-cycle biennial inspections. Some vehicles
though may fail RSD and receive the extra inspection before the half-way point; other vehicles may
have an RSD failure with the extra inspection after the half-way point. Increased emission benefits
result from these increased numbers of inspections between "on-cycle" inspections.
Currently, the MOBILES model does not calculate inspection frequencies that are greater than
annual frequencies, i.e., there are no semi-annual I/M credits to use for the "A" case mentioned
below. Thus, with the current MOBILES structure, remote sensing benefits attributable to more
frequent inspections for programs with annual inspections cannot be generated at least for a test-
only I/M program. For an annual test-and-repair program with a discounted effectiveness, RSD
benefits can be determined (at least up to the point of an annual test-only program) if the RSD
failures are subject to a test-only confirmatory I/M test. However, in the future, if warranted by
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user interest, EPA could develop credits that reflect the possibility of failing vehicles using remote
sensing more frequently than annually.
In a test-and-repair I/M program, a remote sensing failure can force a vehicle to get a test-only
confirmatory test. Remote sensing in this case also has the effect of making some vehicles, those
seen and failed by remote sensing units, behave as though they were in a test-only program. To
model this scenario, the "A" credit is the credit for a test-only program, and the "B" program is the
test-and-repair program. The benefit attributed to remote sensing is a portion of the difference in
benefits of the test-only and test-and-repair programs. Conceptually, this type of program would
produce the largest benefit attributable to remote sensing because of whatever differences exist
between test-only I/M benefits on as frequent as an annual basis and test-and-repair benefits from a
biennial program affecting the result.
Similarly, a hybrid I/M program requires only some vehicles to obtain a test-only ^n-cycle
inspection, based on age and/or retest status. Since test-only stations exist, remote-sensing failures
can be sent to them for confirmatory testing. In this case, the "A" program is test-only and the "B"
program is hybrid. If the on-cycle program is biennial; remote sensing could create incremental
benefits based on both more frequent inspections and test-only inspections for more vehicles.
It is also possible for a state with a test-and-repair I/M program to allow vehicles failed by
remote sensing to be confirmatory tested at a test-and-repair station, in which case both "A" and "B"
credits are test-and-repair and the only effect of remote sensing is the increase in testing frequency
for part of the fleet. This avoids the need for setting up any test-only stations for purposes of
confirmatory testing. However, in a test-and-repair program with the RSD failures going to a test-
and-repair I/M station, the additional benefits from remote sensing would have to adjusted
considering the interaction from whatever discount is determined for test-and-repair programs.
The remote sensing credits are thus a function of four design choices. These choices
are: 1) the structure of the periodic I/M program; 2) whether the test which is used to confirm the
remote sensing failures is performed at test-only or test-and-repair programs (if the periodic I/M
program is test-only, the confirmatory test must be test-only); 3) the fraction of the fleet, by model
year, measured by remote sensing; and 4) the effectiveness of the remote sensor at identifying high
emitters including the influence of the remote sensing cutpoints or emission standards.
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Mathematically, the process to generate the additional remote sensing program credits is as
follows:
RS Credit^ = (Am>p - Bm,p) * Fm * Em,p
Where:
B = I/M credit for the on-cycle biennial program
A = I/M credit for an annual inspection (test-only or test & repair)
F = Adjusted fraction of the inspected fleet scanned at remote sensing sites
E = Effectiveness of remote sensing identification and repair of high emitters.
m Quantity is a function of vehicle model year; and
p Quantity is a function of pollutant (i.e., HC, CO or NOx).
In the equation, the influence of the underlying I/M program is represented by the variables
Am p and Bm p which are chosen from the already-released I/M credits used with the MOBILES
model. The variables Fm and Em p in the equation represent the remote sensing fleet coverage and the
remote sensing effectiveness. The fleet coverage is determined as explained below. The
effectiveness is also discussed below and is based on the EPA and California studies. This
additional RSD benefit can be added directly to the base program I/M credit (Bm p) to give the
overall inspection program benefit.
3.2 Inspection Program Designs
The remote sensing I/M credit utility allows the user to describe the remote sensing inspection
used either in combination with a periodic I/M program or as a separate inspection program in a
non-I/M area. There are five basic I/M program designs that can be selected.
Program I: Basic Remote Sensing Program Design
High-emitting vehicles identified by remote sensing are sent to the periodic I/M inspection
stations. This includes the case of test-only I/M programs with test-only confirmation and test-and-
repair I/M programs with test-and-repair confirmation of RSD failures.
Since this (and any other) scenario is modeled by increasing the inspection frequency and
presently MOBILES allows a maximum frequency of I/M as annual, the methods in this document
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' do not calculate a supplemental benefit for RSD with annual inspection programs using this
approach.
Program 2: Test-and-Repair Remote Sensing Program
In a test-and-repair I/M area, high-emitting vehicles identified by remote sensing are sent to
special test-only inspection stations. As mentioned earlier, credits can be calculated for an annual
test-and-repair program with a discounted effectiveness using RSD with the RSD failures being sent
to a test-only I/M station.
Program 3: Retest Hybrid Remote Sensing Program
In a retest hybrid I/M area, high-emitting vehicles identified by remote sensing are sent only to
the test-only inspection stations.
Program 4: Remote Sensing Only Program
In a "non-I/M" area, high-emitting vehicles identified by remote sensing are sent only to the
test-only inspection stations.
Program 5: Clean Screen Remote Sensing Program
In any I/M area, remote sensing is used to identify some low-emitting vehicles which are
exempted from the periodic I/M inspection. The databases available for EPA analysis for these
credits included only single RSD readings on any given vehicles. Use of a single RSD reading for
clean screening, as mentioned previously, should be avoided and instead a state should use multiple
RSD readings. Clean screening does not increase the RSD credits, but can help improve the
cost/effectiveness and public acceptance of the I/M program. More specific guidance on clean
screening with multiple tests will be provided later based largely on the AZ RSD program results
which will be analyzed.
3.3 Options for Fleet Coverage
There are three user options for indicating remote sensing program vehicle coverage listed
briefly below and discussed in more detail further in Sections 3.3.1, 3.3.2, and 3.3.3. Fleet coverage
is the fraction "F" in the equation given earlier.
Option I: Commitment to a Level of Effort
The user specifies the number of valid remote sensing measurements done. The utility estimates
vehicle coverage from this information using coverage information derived from a Poisson
distribution. The method used to make this estimate is described in a later section.
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Option 2: Commitment to a Specific Fleet Coverage
The user specifies the fraction of the fleet in each model year that is seen using remote sensing.
This fraction should be only the fraction of the fleet which has had sufficient valid remote sensing
measurements to be identified as remote sensing failures for purposes of further I/M inspection. For
example, if a state decides (to minimize false confirmatory I/M failures for vehicles failing RSD)
that three RSD failures are needed to have a vehicle sent for off-cycle I/M, this fraction should
represent the portion of the fleet that has received three RSD readings. The decision on how many
RSD failures are needed to send a vehicle for confirmatory I/M testing is left up to the state.
Option 3: Commitment to a Number of Failures
The user specifies the fraction of additional confirmatory I/M failures (beyond those failing the
regular periodic I/M test) that are presented for and fail inspection as a result of remote sensing
identification. Only vehicles identified for inspection by remote sensing and which, fail the I/M
inspection count towards this additional fraction of failures. Vehicles failing RSD but that are
repaired before the confirmatory I/M test are discussed below.
3.3.1 Option 1; Commitment to a Level of Effort
This is the simplest of the three options in which a state looks at its resources and commits to
obtaining a specific number of RSD readings annually. In this option, a modified Poisson algorithm
is used to estimate the number of vehicles seen by remote sensing in order to calculate the fraction
of the fleet tested by remote sensing (factor F). This is necessary, since the fraction of all vehicles
in the fleet which are measured by remote sensing is a function of the total number of remote
sensing measurements, but is less than obtained by looking at the number of RSD readings since
some vehicles are seen multiple times by RSD. A Poisson algorithm is a standard method to model
such a situation. This phenomenon was demonstrated in the Sacramento Study [1] where some
individual vehicles were measured several times over the course of the study. This fraction is a
function of the annual average VMT of a vehicle model year at a given age compared to its VMT
when new. In addition, the fraction of excess emissions identified by remote sensing in the vehicles
seen must be estimated (factor E) which the utility does based on excess emissions found in the
California and EPA studies as a function of RSD cutpoint. [2, 3, 4, 5, 6] This modified Poisson
distribution was discussed as the second option for fleet coverage at the Technical Experts
Workshop held on September 12,1995.[8]
The algorithm used to calculate remote sensing coverage involves a modification to Lambda
in the Poisson series using the ratio of the VMT of the youngest model year (age) to the VMT of the
model year (age) being estimated. This adjustment uses national average VMT information, but the
VMT information can be modified by the user to reflect local, rather than national default,
information. The form of the equation is as follows:
P = 1.0-exp(k* -Lambda)
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where k is the ratio of VMTs,
k = VMT(current age)/VMT(age=l).
This algorithm is calculated for each model year and gives the probability of measuring
emissions from vehicles of a given model year. One sums the probabilities for each model year to
obtain an overall probability for all vehicles. Also, as mentioned later, this option permits the user
to specify only vehicles with more than one RSD reading as being flagged for RSD failures. Such a
specification will lower the fraction of the fleet seen. However, the fraction of excess emissions
discussed in the next section is a function of what was found in EPA and CARB studies which
utilized a single RSD reading; thus, while the fraction of the fleet seen will change with multiple
RSD readings, the fraction of excess emission found for a given fraction of the fleet inspected will/
not change with multiple RSD readings due to the databases used. Multiple RSD readings would
alter this fraction of excess emissions found. Multiple readings mean fewer vehicles fail RSD
which may increase the vehicles falsely passing RSD thus decreasing RSD benefits;/on the other
hand, those vehicles failing RSD would more likely be true I/M^failures increasing the per vehicle
benefit for RSD. Unfortunately, there is inadequate.data,in these studies to get a good estimate of
the RSD excess emission benefits with multiple RSD readings; more data are needed and should be
available as states (such as Arizona) conduct RSD programs.
3.3.2 Option 2; Commitment to a Specific Fleet Coverage
In this option, the user inputs related to remote sensing effort are replaced by a commitment to
obtain valid remote sensing readings on a fraction of the fleet as determined by the state. These
readings are used to direct remote sensing failures to I/M stations for inspection. This commitment
is for sufficient RSD readings for each vehicle age category (i.e., by model year) separately to meet
the commitment of vehicles for RSD targeting. This requires the user to supply the number of
vehicles currently of each age and the number of those vehicles which are committed to be seen by
remote sensing in the next year.
Other user inputs related to the remote sensing cutpoints remain and are used to calculate the
fraction of excess emissions identified by remote sensing in the vehicles seen (factor E). The
fraction of vehicles sent to an I/M station is calculated directly from the user input. It is assumed
that this fraction of vehicles replaces the calculation of vehicle coverage that is done in Option 1
using the Poisson distribution (factor F). All other calculations for remote sensing credits remain
the same.
As previously noted, the fraction of excess emissions found is not altered in this present credit
utility with multiple RSD readings. Data from RSD programs with multiple readings such as the
AZ program must be analyzed to provide additional credits when a state decides to have a certain
fraction of its fleet measured multiple times by RSD. EPA will address the effect of multiple
readings in the future.
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3.3.3 Option 3; Commitment to a Number of Failures
In this option, the user provides an estimate of the expected number of I/M failures provided
by remote sensing, again by model year/age. If there is an existing periodic I/M program, the actual
failure rate for the overall I/M can be used as input in determining what level of additional I/M
failures is found by RSD would be reasonable. Beyond the first year of an RSD program, of course,
actual numbers of additional I/M failures found by RSD can be used. New I/M areas need to
estimate the expected failures found by RSD, perhaps from other operating periodic I/M programs.
The user enters the number of additional I/M program failures, by model year/age, that are provided
by remote sensing targeting in the next year. The user also needs to know the number of I/M
failures from its base I/M program which is also entered. Other user inputs related to the remote
sensing cutpoints remain.
For example, a state with 10,000 regular periodic I/M program failures last year for 5-year-old
vehicles might specify that an additional 500, 5-year-old vehicles would be failed byusing remote
sensing to identify vehicles for out-of-sequence testing. The state would then be committing to
adequate vehicle coverage and effort to supply sufficient number of vehicles for out-of-sequence
testing to result in the additional 500 failures. ''
Although this option provides the clearest connection between remote sensing activity and
confirmed emission repairs, it, like the other options, may underestimate the remote sensing
benefits. Some or possibly most vehicle owners, confronted with a requirement to appear for a
confirmatory emission test, will have their vehicle repaired before submitting their vehicle for
testing. If such vehicles pass the confirmatory test, they could mistakenly be thought to not count
toward the commitment of failures by the program, even though the repairs were done. States can
use special studies to correct their vehicle count. Analysis of operating remote sensing programs
may provide new information on the impact of this behavior in the future.
In this option, the ratio of the additional remote sensing failures to the expected failures
represents the fraction of the fleet tested by remote sensing (factor F) used to calculate remote
sensing benefits.
F = (Additional remote sensing failures/expected failures)
Although the number of remote sensing failures available to be found will be decreased by the
use of higher cutpoints (and the ease with which these failures are found will be decreased since
more vehicles will have to be screened to find a given number of failures using higher RSD
cutpoints), higher remote sensing cutpoints increase the benefits per failure, since marginal failures
are not targeted. Higher cutpoints also help minimize vehicles failing RSD that will pass the
confirmatory I/M test (false RSD failures). Therefore, the remote sensing effectiveness (factor E)
determined from the user input of remote sensing cutpoints must be adjusted in this option so that it
increases as the cutpoints are loosened.
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E= function (cutpoint)
This function is determined by examining the emission identification and failure rate of remote
sensing samples. A functional relationship array is added into the remote sensing utility which
depends on the user input of remote sensing cutpoint. That is, RSD failures with higher cutpoints
are assumed to have higher excess emissions found by the confirmatory I/M test that are lowered on
vehicle repair. Users are allowed to change this array in the external data file if a user has actual
information on the excess emissions found in these failures; such information would be available
after an RSD program is in operation. Before the RSD program is in operation (e.g., when making
projections for the first year), a state should document its rationale for changing the emission level
of the RSD failures; such levels could come from available RSD studies which also measure IM240
or FTP emission levels. All other calculations for remote sensing credits remain the same.
Note in this option that a state commits to a certain number of I/M failures found by RSD. The
number of RSD readings per vehicle, single or multiple, does not affect the credit assigned in this
option. All that matters is that the vehicles are found to be I/M failures on the confirmatory I/M
test after failing RSD.
3.4 Remote Sensing Effectiveness
Remote sensing effectiveness refers to the ability of remote sensing to correctly identify vehicles
which fail an I/M inspection. Assuming that all vehicles in a fleet were tested using remote sensing,
and if, using remote sensing, it were possible to identify every vehicle in that fleet that fails an I/M
inspection, then the effectiveness of remote sensing is 100 percent. In practice, even if vehicle
coverage were complete, not all I/M failures are identified by remote sensing. The shortfall in
identification depends primarily on the remote sensing CO measurement cutpoint chosen by the
program.
For purposes of determination of remote sensing effectiveness, the emissions of individual
vehicles were defined as their IM240 scores measured in grams per mile. Excess emissions were
defined as any IM240 emissions in excess of IM240 emission levels selected to identify emission
that can be reduced by repairs. This approach is used by California and was discussed at the EPA
Technical Experts Workshop held September 15,1995.[1] These IM240 emission levels are shown
in Tables la and Ib for California standards and Federal standards data. Therefore, by definition,
vehicles with emissions lower than these IM240 levels have no excess emissions that can be
identified and reduced by vehicle repairs. The excess emissions are assumed to be the only
potential benefit of identification of a vehicle by remote sensing for repairs, since the vehicle must
fail an IM240 type inspection in order to be required to have repairs performed. Repaired vehicles
are assumed to pass the IM240-type test procedure after repairs.
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Table la
IM240 Excess Emission Thresholds
(for California Emission Standards)
Model Year
1975-76
1977-79
1980
1981-86
1987-88
1989+
HC (g/mi)
2.70
1.23
1.23
0.59
0.39
0.39
CO (g/mi)
18.0
18.0
18.0
10.5
7.0
7.0
NOx (g/mi)
2.00
1.50
1.00
1.05
0.70
0.40
Table Ib
IM240 Excess Emission Thresholds
(for Federal Emission Standards)
Model Year
1975-76
1977-79
1980
1981-86
1987-88
1989+
HC(g/mi)
4.50
4.50
1.23
0.62
0.41
0.41
CO (g/mi)
30.0
30.0
14.0
5.1
3.4
3.4
NOx (g/mi)
3.1
2.0
2.0
1.5
1.0
1.0
The identification rate is the fraction of all excess emissions for each pollutant (HC, CO and
NOx) from vehicles identified by remote sensing. The identification rate is determined for three
technology groupings of vehicles as below (generally the first two categories are combined).
• Pre-1975 model years (non-catalyst)
• 1975 through 1980 model year (oxidation catalyst)
• 1981 and newer model years (3-way catalyst)
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The identification rate was based solely on the CO emission measurement from remote sensing
(use of the CO reading is also a reasonably accurate surrogate for exhaust HC). An identification
rate was determined for each case, from 0.5 through 7.5 percent CO, in increments of 0.5 percent
CO. For the default values, the data from the El Monte Parking Lot Study by the California Air
Resources Board was combined with EPA testing in Arizona. [2, 3,4, 5] The results are
summarized in Table 2 below.
Table 2
Fraction of IM240 Excess Emissions Identified
Using Remote Sensing CO Cutpoints
(Combined CARS El Monte/EPA Studies)
Remote
Sensing
CO
Cutpoint
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
5.0%
5.5%
6.0%
6.5%
7.0%
7.5%
1980 and Older
Model Years
HC
0.543
0.487
0.487
0.487
0.272
0.272
0.262
0.184
0.110
0.110
0.095
0.095
0.095
0.088
0.022
CO
0.945
0.899
0.758
0.751
0.676
0.662
0.584
0.489
0.467
0.420
0.398
0.398
0.398
0.308
0.205
NOx
0.436
0.423
0.335
0.295
0.232
0.146
0.118
0.067
0.067
0.063
0.052
0.000
0.000
0.000
0.000
1981 and Newer
Model Years
HC
0.570
0.433
0.387
0.348
0.319
0.262
0.217
0.182
0.150
0.109
0.071
0.060
0.046
0.039
0.028
CO
0.596
0.499
0.442
0.396
0.352
0.278
0.213
0.178
0.133
0.107
0.072
0.053
0.044
0.034
0.017
NOx
0.283
0.178
0.122
0.091
0.059
0.054
0.042
0.018
0.015
0.009
0.006
0.003
0.003
0.003
0.003
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In addition, the average excess emissions of remote sensing failures for each cutpoint was
needed for each technology grouping for programs that commit to a number of failures. The
average excess emissions as a ratio of the average IM240 excess emissions identified per vehicle at
various RSD cutpoints compared to the average excess emissions per vehicle in the fleet as a whole
were determined using the same combined data sets and cutpoints used for determination
ofidentification rates. For an RSD cutpoint of 0 percent for CO, all (100 percent) of the excess
emissions are obviously identified. The results are summarized in Table 3 below.
Table3
Ratio of Average IM240 Excess Identified Emissions per
Vehicle to Average Excess Emissions per Vehicle in Fleet
Using Remote Sensing CO Cutpoints
(Combined CARB El Monte/EPA Studies)
(0% CO Cutpoint Gives Ratio of 1)
Remote
Sensing
CO
Cutpoint
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
5.0%
5.5%
6.0%
6.5%
7.0%
7.5%
1980 and Older
Model Years
HC
0.84
0.85
0.85
0.85
0.54
0.54
0.61
0.52
0.38
0.38
0.44
0.44
0.44
0.62
0.30
CO
1.86
2.13
2.34
2.67
3.20
3.61
3.77
4.34
5.53
5.96
7.06
7.06
7.06
7.28
7.29
NOx
0.98
1.06
1.01
1.10
1.05
0.82
0.76
0.76
1.00
1 .42
2.34
0.00
0.00
0.00
0.00
1981 and Newer
Model Years
HC
1.51
1.57
1.87
2.05
2.28
2.70
2.88
3.07
3.08
2.88
3.28
4.43
4.27
4.82
5.25
CO
2.47
3.11
3.54
4.05
4.73
5.42
6.30
7.08
6.54
6.49
8.31
9.13
11.38
11.63
8.71
NOx
0.95
0.97
0.93
0.91
0.88
0.92
1.01
0.69
0.72
0.49
1.00
0.90
0.90
0.90
0.90
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These results are similar to those presented at the EPA Technical Experts Workshop. [6] The
major differences is that only the data from the EPA and GARB El Monte studies [2,3,4,5] are used
with the data from the California Sacramento pilot study [1] not being included. Excluding the
California Sacramento study for the effectiveness calculations increases the RSD benefits
somewhat.
3.5 Estimating Remote Sensing Benefits
As described in Section 3.1, the effect of remote sensing on I/M credits are determined by one of
the following methods:
• Interpolating between existing annual and biennial I/M credits;.
• Interpolating between test-and-repair or retest-based hybrid I/M credits and test-only I/M credits;
or
• Interpolating between zero credit and the applicable annual I/M credits
If the existing periodic I/M program (either test-and-repair, retest-based hybrid or test-only) is
biennial and vehicles are directed to standard I/M stations used in that program, the addition of
remote sensing is modeled as an increase in the inspection frequency. This is done solely by
interpolating between the annual and biennial program credits of the same type.
If the existing periodic I/M program is either test-and-repair or retest-based hybrid, and vehicles
are directed only to test-only stations, then not only is the frequency of inspection increased, but the
effectiveness of the inspection is enhanced. This is done by interpolating between the base program
credits (either test-and-repair or retest-based hybrid, either annual or biennial) and test-only annual
credits.
If there is no existing I/M program and RSD is used to flag high emitters for inspection and
repair, the benefits are calculated directly from the applicable annual I/M credits interpolating as
necessary accounting for the vehicle coverage and remote sensing effectiveness.
For purposes of determination of I/M credits, effectiveness is defined as the ability of remote
sensing to properly identify I/M failures. Vehicles which fail I/M but do not fail remote sensing
(false RSD passes) cannot contribute to additional I/M benefits from remote sensing. Vehicles
which fail remote sensing but do not fail I/M are not required to be repaired and are assumed not to
contribute to additional I/M benefits. Therefore, it is the fraction of emissions represented by
vehicles identified by remote sensing which fail the I/M test which can contribute to additional I/M
benefits.
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The calculation of the effects depends on the inspection program type. The benefit is calculated
from the existing I/M credits for the inspection program type in combination with similar I/M
credits that represent the effect of the additional failures targeted by remote sensing. The following
definitions are used in the formulas giving RSD credits:
A(TO): Annual test-only I/M credit
A(T&R): Annual test-and-repair I/M credit
A(RH): Annual retest-based hybrid I/M credit
B(TO): Biennial test-only I/M credit
B(T&R): Biennial test-and-repair I/M credit
B(RH): Biennial retest-based hybrid I/M credit
F: Fraction of the fleet tested by remote sensing:
Remote sensing program effectiveness
(identification and repair of high emitters)
C: Overall I/M credit with remote sensing added
The IM credit for the following inspection program types are included in the remote sensing
utility:
1. Remote sensing failures are inspected, at standard periodic I/M program stations (either test-
only or test-and-repair).
C = B(TO) + (A(TO)-B(TO)) * F * E
or
C = B(T&R) + (A(T&R)-B(T&R)) * F * E
The test-and-repair case is simply the test-only case with a test-and-repair discount of 50 percent
applied within MOBILES; for different test-and-repair discounts, one uses the MOBILES case with 50
percent and the corresponding test-only no discount case and interpolates. Future versions of the
MOBILE model will apply the user supplied test-and-repair discount. Therefore, for MOBILES,
separate test-and-repair I/M credit files are not needed. If a test-and-repair discount of other than 50%
is used, then an interpolation is necessary.
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2. Periodic test-and-repair I/M program with remote sensing failures inspected at special test-
only stations.
C = A(T&R) + (A(TO)-A(T&R)) * F * E
C = B(T&R) + (A(TO)-B(T&R)) * F * E
Test-and-repair situations are handled as in case 1. above.
3. .Periodic retest-based hybrid I/M program with remote sensing failures inspected only at
test-only stations.
C = A(RH) + (A(TO)-A(RH)) * F * E
C = B(RH) + (A(TO)-B(RH)) * F * E
4. Non-I/M area with remote sensing failures inspected at special stations.
C = A(TO) * F * E
or
C = A(T&R) * F * E
The test-and-repair case is, again, simply the test-only case with the test-and-repair discount
applied. Therefore, for MOBILES, separate test-and-repair I/M credit files are not needed.
When using the RSD utility and for MOBILE modeling in general, one should note that I/M
credits are based on different aspects of the I/M program. For example, anti-tampering benefits are
assigned for the program as a whole on top of which a per-vehicle benefit is assigned for I/M failures.
Thus, increasing the number of I/M failures by, for example, a factor of two will not increase the I/M
program benefits by a factor of 2.
4.0 REMOTE SENSING UTILITY
4.1 Remote Sensing Utility Input Structure
The remote sensing utility is designed to be used once for a scenario selected by the user. It is
assumed that the number of scenarios that a user might consider is small enough so that batch run
options are not necessary. Each run requires a single input file which contains all of the information
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required by the utility and supplied by the user. The user is prompted for the name (including path, if
not in the local directory) of the input file.
Enter the name of the remote sensing input file: (default RSD.D)
Note that all inputs have default values. Here, if the user does not enter a name instead using
the EPA files (which the user is generally expected to use lacking any specific local data), the utility
will look for the "RSD.D" file in the local directory.
The input file contains all of the remaining information needed to calculate the remote sensing
effects, including the location of the original I/M credit data files and the names and location of the
output remote sensing credit files.
The input file is structured so that each line (record) begins with an identification number. This
number indicates what information is contained on that record and allows the records to be,entered in
any order. Although some records are mandatory, any records missing from the input file revert to
default values stored in the utility. In this way, only the information that the user wishes to supply
need be included in the input file. Any records"with a record number of 000 are considered comment
records and are not processed. In addition, text may be added to records beyond the last formatted data
entry on any card to clarify the contents of that record. This additional text is not read or processed by
the utility.
Control Section of Input File
Records 001 and 002 are mandatory records.
Record 001 contains the user selection of the fleet coverage option. The format of this record is
(13,11X,I1), meaning the first three characters contain the record number, followed by 11 blank
characters, followed by the user selection of Option. Any characters following the user selection are
ignored by the utility, but can be used to annotate the input file. The available option levels as
described in Section 3.3 on fleet coverage are as follows.
1: Commitment to a Level of Effort
2: Commitment to a Specific Fleet Coverage
3: Commitment to a Number of Failures
Record 002 contains the user selection of I/M program design. The format of this record is identical
to Record 001. The available program design levels (described in Section 3.2) are as follows.
1: Basic Remote Sensing Program Design
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2: Test-and-Repair Remote Sensing Program
3: Retest Hybrid Remote Sensing Program
4: Remote Sensing Only Program
5: Clean Screening Remote Sensing Program
A complete description of these user options is located in the previous section regarding the basic
utility description. The user must always enter both Record 001 and 002 in order to use the remote
sensing utility. The following is an example input of the control section, including some added
comments to add clarity.
000 Control Section
000
001 2 Option (may be 1,2, or 3>
002 1 Program Type (may be 1,2, 3,4, or 5)
Filenames Section
In this section, Records 005 through 008 indicate the name and location of the standard I/M
credit data files and Records 015 through 018 determine the location and name of the resulting remote
sensing adjusted I/M credit data files. Normally, the resulting I/M credit data will be written to the
filenames in Records 015-018. The indicated I/M credit input files are not altered by the utility.
Instead, new replacement credit files are created with the appropriate adjustment of the I/M credits to
reflect the effects of the user specified remote sensing program.
The format of each record is (I3,1X,A40), meaning the first three characters contain the record
number, the next character is blank, followed by up to 40 characters which indicate the file name,
including any necessary path information. If no path is specified, the data files must reside in the same
(local) directory from which the utility is run. The record number for each file are as follows.
Input Files
Record Description
005: 1981 and newer model year credits
006: 1981 and newer model year Retest-Hybrid credits
007: Pre-1981 model year credits
008: Pre-1981 model year Retest-Hybrid credits
Output Files
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Record Description
015: Adjusted 1981 and newer model year credits
016: Adjusted 1981 and newer model year Retest-Hybrid
017: Adjusted Pre-1981 model year credits
018: Adjusted Pre-1981 model year Retest-Hybrid credits
Since there are default filenames for the standard I/M credit files and the output files, the user
may skip all of these input records and the default names are used. All of the files, however, then must
be in the local directory. The following is an example input of the filename section, using files other
than the default filenames, including some added comments to add clarity.
000 Input and Output Filenames
000 Default
005 C:\DATA\IM1.D IMDATA.D
006 C:\DATA\IMH.D HYBRID.IMC
007 TCI.D TECH12.D
008 TCH.D TECH12.D
015 RSDDAT11 .D RSDDATA.D
016 RSDD AT A.H RSDDATA.H
017 TECDAT11 .D TECDATA.D
018 TECD AT A.H TECDATA.H
For maximum flexibility, separate input has been allowed for Retest-Hybrid I/M credits for the
pre-1981 model year vehicles, even though EPA has not calculated separate credits for that case. For
this reason, the default input for that case is identical to the standard input file.
One-Time Data Section
Some remote sensing program information applies to all options. This information includes the
following which are described in more detail afterwards.
Record Description
024: The age at which vehicles first become eligible for targeting by remote sensing
031: The CO cutpoints to be applied to remote sensing measurements for 1974 and older model
year vehicles
032: The CO cutpoints to be applied to remote sensing measurements for 1975 through 1980 model
year vehicles
033: The CO cutpoints to be applied to remote sensing measurements for 1981 and newer model
year vehicles
041: The test and repair effectiveness for HC emissions
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042: The test and repair effectiveness for CO emissions
043: The test and repair effectiveness for NOx emissions
The age at which vehicles first become eligible for targeting by remote sensing allows the user to
exempt newer vehicles in the fleet from targeting. Newer vehicles tend to produce fewer benefits and
more false failures than older vehicles. Exempting vehicles from targeting does not reduce their I/M
benefits, but does not provide additional benefits from remote sensing. The age that vehicles are first
eligible for RSD targeting is entered on Record 024. The format for this record is (I3,1X,I11).
The CO cutpoint to be applied to remote sensing measurements for targeting must be provided for
each of three model year groupings of vehicles.
• 1974 and older model year vehicles (Record 031)
1975 through 1980 model year vehicles (Record 032)
• 1981 and newer model year vehicles (Record 033)
This cutpoint is used to select the effectiveness of the remote sensing measurement in determining
whether vehicles pass or fail an I/M inspection. It is assumed that all vehicles which are subject to the
inspection program, are measured the specified number of times, and exceed the CO cutpoint for their
model year, are required to undergo an additional, out-of-sequence I/M inspection.
If the user enters 99.9 for a CO cutpoint, vehicles in those model years are assumed to be exempted
from the remote sensing program and the I/M credits for those model years are not adjusted. If a more
complicated scheme for use remote sensing measurements to identify vehicles for I/M testing is
proposed, the user should consult with EPA. The cutpoints (or 99.9 for exempted) are entered on
Record 031 (for 1974 and older vehicles), Record 032 (for 1975 through 1980 vehicles) and Record
033 (for 1981 and newer vehicles). The format for this record is (I3,1X,F11.3).
The current version of the model, MOBILESa, adjusts the I/M credits for test and repair I/M
program designs to be 50 percent of the benefits of test-only program designs. Future versions of the
model will allow for user input of test-and-repair effectiveness values as established by data from the
states in accord with the National Highway System Designation Act. The remote sensing I/M credit
utility, therefore, allows the user to specify the value for the effectiveness of test and repair I/M
program designs to be used in determining the effects of remote sensing. One option is, for now, to
interpolate between the 50 percent discount provided by MOBILESa for test-and-repair and the benefit
for a test-only program with no discount. One then interpolates to estimate what a credit would be for
a particular test-and-repair program with a discount between 50-100 percent. The provisions of the
National Highway System Designation Act set up criteria by which to determine the proper test-and-
repair discount. There are three records (Records 041, 042 and 043) for HC, CO and NOx
effectiveness values. The format for these records are (I3,1X,F11.2).
The following is an example input of the one-time data section, using the default age, cutpoint and
effectiveness values, including some added comments to add clarity:
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OQQ One-Time Data Section
000
024 1 Age when first eligible (1-24), default 1
031 3.0 CO cutpoint, 74 & older model years, default 3.0
032 3.0 CO cutpoint, 75 - '80 model years, default 3.0
033 3.0 CO cutpoint, '81 & newer model years, default 3.0
041 0.50 Test and repair effectiveness for HC, default .50
042 0.50 Test and repair effectiveness for CO, default .50
043 0.50 Test and repair effectiveness for NOx, default .50
The default values above are what is assumed if not input is provided.
The calculations of benefits require the use of an estimate of the effectiveness of remote sensing in
identification of excess emissions for the remote sensing CO cutpoint chosen by the user. The default
values for this ratio may be overridden by the user by entering Records 401 through 415 for HC,
Records 501 through 515 for CO and Records 601 through 615 for NOx emissions. Each of the fifteen
records contain three ratios for each CO cutpoint from 0.5% through 7.5%~for three model year
groupings.
• 1974 and older model years
• 1975 through 1980 model years
• 1981 and newer model years
The format for the record is (I3,1X,F11.3,F11.3,F11.3). This means that the first 3 characters
contain the record number, the next character is a blank followed by a number (including a decimal)
within the next 11 spaces, indicating the effectiveness that are used for 1974 and older model year
vehicles, followed by another number (including a decimal) within the next 11 spaces, indicating the
effectiveness that are used for 1975 through 1980 model year vehicles, followed by another number
(including a decimal) within the next 11 spaces, indicating the effectiveness that are used for 1981 and
newer model year vehicles.
Option 1: Data Section
The first vehicle coverage input option requires that the user supply information on the level of
effort which is applied to make a given number of valid vehicle measurements using remote sensing.
This information includes:
Record Description
021: The number of vehicles in the fleet
022: The number of valid measurements per month that are made using remote sensing devices
023: The number of times that a vehicle must be failed by RSD before it can be targeted for I/M
inspection.
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In addition, the user may supply the average vehicle miles traveled per year by vehicle age to
override the MOBILES default values normally used in the calculations.
The number of vehicles in the fleet represents the population of vehicles which are subject to the
inspection program in the area. This number excludes out-of-area vehicles and vehicles exempted
from the inspection. The number of vehicles in the fleet is entered on Record 021. The format for this
record is (I3,1X,I11).
The number of valid measurements per month that are made using remote sensing devices is the
primary measure of the level of effort related to vehicle coverage. The number of valid measurements
that can be made with remote sensing devices depends on a great variety of factors including the
number of practical remote sensing locations (as mentioned before, optimal RSD sites for vehicle
speed and acceleration to assure most vehicles are not under high loads and thus in enrichment
operation may not always have high-traffic flow), the number of devices provided, the amount of staff
required and available to operate the-remote sensing devices, the density of vehicles subject to the
inspection program at the remote sensing'sites, the number of hours and days that the remote sensing
devices are operated, the staff allocated to remote sensing data processing and the quality of the remote
sensing readings. The number entered by the user represents the commitment by the program to
expend sufficient effort to make that number of valid measurements in each month. This number is
entered on Record 022. The format for this record is (I3,1X,I11).
The input of the number of times that a state determines a vehicle must be failed using remote
sensing before it can be targeted for I/M inspection allows for the use of multiple measurements to
reduce the number of false failures. The utility allows the user to specify up to 11 measurements;
although, again, the present EPA credit data are based on a single measurement and give no additional
credit for multiple measurements. This means that vehicles which are measured less than the user
specified number of times cannot be used for targeting. Increasing the number of times a vehicle must
be measured noticeably reduces the vehicle coverage, since for a fixed number of measurements, the
same vehicles must be measured multiple times. This number is entered on Record 023. The format
for this record is (I3,1X,I11).
The following is an example input of the Option 1 data section, using values other than the default
values, including some added comments to add clarity.
OOP Option 1 Data Section
000 -v,
021 810498 No. of veh. in inspection area default I'OOOOOO
022 110808 Valid veh. measurements per month default 50000
023 3 No. times a veh. must be measured default 1 (1-11)
In addition, the user may supply the average vehicle miles traveled per year by vehicle age to
override the MOBILES default values normally used in the calculations. This requires the entry of 25
separate records (Records 101 through 125). Record 101 contains the mileage accumulation of
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vehicles from 0 to 1 year of age. Record 102 contains the mileage accumulation of vehicles from 1 to
2 years of age, and so forth. Since vehicles are more likely to be measured if they drive more, a higher
mileage accumulation in proportion to other vehicles increases the expected number of vehicles of that
age that are^measured. The default values assume that all vehicles travel the roadways which are
monitored using remote sensing. If remote sensing is to be restricted to only some roadways (such as
limited access freeways), the distribution of mileages should be adjusted to reflect the actual
distribution of ages expected on those roadways. The format for these records is (I3,1X,I11).
Option 2: Data Section
The second vehicle coverage input option requires that the user supply information on the fraction
of the fleet in each model year which have sufficient valid vehicle measurements using remote sensing
to be targeted each year. For example, if it takes three RSD failures to have a vehicle be sent for an
I/M test, this is the fraction of the fleet which will be measured three times by RSD. Again, our
present data base allows no extra credit in excess emission found for such multiple measurements
simply because this type of information is not available in the studies used. This is a commitment on
the part of the program to apply sufficient resources to find and measure a fraction of each model year
using remote sensing. The fraction of the fleet measured can vary from model year to model year,
reflecting the difficulty in finding and measuring older model years, which drive less and tend to avoid
some roadway types, such as limited access freeways.
A separate record must be entered for each of 25 vehicle ages (Records 201 through 225). There are
no default values for these inputs. Each record contains the record number, the number of vehicles
which are eligible for targeting each year (i.e., the total number of vehicles on which remote sensing
readings are taken), and the total number of vehicles of that age in the vehicle fleet subject to
inspection (i.e., the total number of vehicles in the fleet on which a state commits to obtain remote
sensing readings). The entry of both the number of vehicles subject to inspection and the number
expected to be eligible, instead of a single fractional estimate, for each vehicle age allows for an
explicit count of the number of vehicles which must be measured and eligible for targeting.
The format for the record is (I3,1X,2I11). This means that the first 3 characters contain the record
number, the next character is a blank followed by an integer number in the next 11 spaces, indicating
the total number of eligible vehicles of that age, followed by another integer number in the next 11
spaces, indicating the number of vehicles in that age which have sufficient valid vehicle measurements
using remote sensing to be targeted each year.
The following is an example input of the Option.2?data section. The example assumes a
MOBILES default distribution of 1 million vehicles and assuming, using remote sensing, that 1% of
each model year are eligible for targeting. The records include some added comments to add clarity.
OOP Option 2 Data Section
000 Total Eligible
000
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201 49000 490 Option 2: Age 0- 1
202 79000 790 Option 2 : Age 1-2
203 83000 830 Option 2 : Age 2 - 3
204 82000 820 Option 2 : Age 3 - 4
205 84000 840 Option 2 : Age 4 - 5
206 81000 810 Option 2 : Age 5 - 6
207 77000 770 Option 2 : Age 6 - 7
208 56000 560 Option 2 : Age 7 - 8
209 50000 500 Option 2 : Age 8 - 9
210 51000 510 Option 2: Age 9-10
211 50000 500 Option 2 : Age 10 -11
212 54000 540 Option 2 : Age 11 -12
213 47000 470 Option 2 : Age 12 -13
214 37000 370 Option 2 : Age 13 -14
215 24000 240 ,0ption 2 : Age 14-15
216 19000 190 Option 2 : Age 15 -16
217 14000 140 Option 2 : Age 16 -17
218 15000 150 Option 2 : Age 17 -18
219 11000 110 Option 2: Age 18-19
220 8000 80 Option 2 : Age 19 - 20
221 6000 60 Option 2 : Age 20 - 21
222 5000 50 Option 2 : Age 21 - 22
223 4000 40 Option 2 : Age 22 - 23
224 3000 30 Option 2 : Age 23 - 24
225 10000 100 Option 2 : Age 24 - 25
Option 3: Data Section
The third vehicle coverage input option allows the user to specify an estimate of the expected
number of I/M failures provided by remote sensing, by age, in the current year. In this way, the
number of vehicles in the fleet which are measured or how many times each vehicle is seen are not
needed. The user only need indicate the CO cutpoints used for remote sensing and a commitment to
the number of failures in each age that are provided.
A separate record must be entered for each of 25 vehicle ages (Records 301 through 325). There are
no default values for these inputs. Each record contains the record number, the number of vehicles of
that age which normally fail this year in the periodic inspection program, and the number of additional
vehicles of that age which are referred to the I/M inspection by remote sensing targeting and fail the
inspection this year (i.e., remote sensing failures which also fail the confirmatory I/M test). The format
for the record is (I3,1X,2I11). This means that the first 3 characters contain the record number, the
next character is a blank followed by an integer number in the next 11 spaces, indicating the number of
vehicles of that age which normally fail this year in the periodic inspection program, followed by
another integer number in the next 11 spaces, indicating the number of additional vehicles of that age
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which are referred to the I/M inspection by remote sensing targeting and fail the inspection this year.
The following1 is an example input of the Option 3 data section. The example assumes an equal
number of failures in each age and assuming that, using remote sensing, that an additional 1% are
failed by the I/M inspection. The records include some added comments to provide clarity.
000 Option 3 Data Section
000 Failures Additional
nnn
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
9000
90 Option 3 : Age 0 - 1
90 Option 3 : Age 1 - 2
90 Option 3 : Age 2 - 3
90 Option 3 : Age 3 - 4
90 Option 3 : Age 4 - 5
90 Option 3 : Age 5 - 6
90 Option 3 : Age 6 - 7
90 Option 3 : Age 7 - 8
90 Option 3 : Age 8 - 9
90 Option 3 : Age 9-10
90 Option 3 : Age 10- 11
90 Option3 : Age 11-12
90 Option 3 : Age 12- 13
90 Option3 : Age 13- 14
90 Option3 : Age 14- 15
90 Options : Age 15- 16
90 Options : Age 16- 17
90 Options : Age 17- 18
90 Options : Age 18- 19
90 Options : Age 19-20
90 Option 3 : Age 20-21
90 Options : Age 21 -22
90 Option 3 : Age 22 - 23
90 Option 3 : Age 23 - 24
90 Option 3 : Age 24 - 25
The calculation of benefits used in this option uses a measure of the average excess emission levels
of vehicles failing the remote sensing cutpoint chosen by the user. This measure is the ratio of the
average excess emissions of vehicles identified by the remote sensing cutpoint divided by the average
emissions of all vehicles with excess emissions. In this way, the fact that remote sensing may be used
to target only the highest emitting vehicles can be used in determination of the benefits of identification
of these vehicles.
The default values for this ratio may be overridden by the user by entering Records 701 through
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715 for HC, Records 801 through 815 for CO and Records 901 through 915 for NOx emissions.
Overriding the default values which come from the California/EPA studies would require data from an
actual in-use RSD program showing the actual emissions of the remote sensing failures. Each of the
fifteen records contain three ratios corresponding to a cutpoint from 0.5% through 7.5% for three
model year groupings:
• 1974 and older model years
• 1975 through 1980 model years
• 1981 and newer model years
The format for the record is (I3,1X,F11.3,F11.3,F11.3). This means that the first 3 characters
contain the recordnumber, the next character is a blank followed by a number (including a decimal)
within the next 11 spaces, indicating the ratio that are used for 1974 and older model year vehicles,
followed by another number (including a decimal) within the next 11 spaces, indicating the ratios that
are used for 1975 through 1980 model year vehicles, followed by another number (including a
decimal) within the next 11 spaces, indicating the ratios that are used for 1981 and newer model year
vehicles.
Appendix A contains a more concise narrative listing of the records for the RSD utility.
4.2 Using Remote Sensing I/M Credits with MOBILES
MOBILES uses two external data files which contain the I/M credits whenever an I/M program
is specified in the user input. The benefit of I/M program options can be adjusted by altering the
numbers contained in those data files. The remote sensing I/M credit utility takes advantage of that
fact by adjusting the default I/M credit files to reflect the user supplied information about the use of
remote sensing in the inspection programs. In this way, the current version of MOBILES (MOBILES,
March 26,1993 or MOB5a_H, February 1995) carrbe used to evaluate remote sensing options.
The first step is to describe the remote sensing program to be modeled in sufficient detail to
create an input file for the remote sensing I/M credit utility. For some proposed programs, it may be
necessary to estimate or assume some of the necessary input data. However, the inputs should reflect,
as near as possible, the actual expected performance of the remote sensing program element.
Once the remote sensing program design has been determined, the necessary input data must be
collected together in the input data file. An example input data file is provided with the remote sensing
I/M credit utility which shows the format for all of unnecessary input parameters. The user should
carefully read the User Guide to identify the necessary data and to properly locate the data in the input
file. The input data file is a simple ASCII text file that can be changed using any standard editor or
word processor. However, the user must save any changes in a text format. The Remote Sensing I/M
Credit Utility cannot read input files which are saved in a word processing format.
The next step is to create an alternative set of I/M credit data files using the remote sensing I/M
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credit utility. The input file designates the names of the default I/M credit files to be used and the
names of the altered I/M credit files output by the remote sensing utility. These filenames can include
"path" information if the I/M credit files are not located in the local directory. If a path is not specified,
the default I/M credit files must be in the local directory when the remote sensing utility is run.
The remote sensing utility is run by simply invoking its name (RSDUTIL.EXE) at the DOS prompt.
There are no interactive features to the remote sensing utility, and so no further user input is required.
The processing is quite lengthy, and some time will pass. There will be some diagnostic information
on the screen during processing. When completed without errors, the remote sensing utility will
display a completion message on the screen.
Once the processing has been completed, the new I/M credits, adjusted for remote sensing, will be
in the filenames indicated by the user in the input file. Although these files can be renamed to the
MOBILES default I/M credit filenames, there will be no output in MOBILES which indicates that
alternate I/M credits were used. It may be less confusing to require that these alternate I/M credits be
accessed using the alternate credit option in MOBILES described in the MOBILES^User Guide Section
2.2.5.4. In this case, the input file for MOBILES would indicate which set of alternate credits were
used.
Since the effect of remote sensing is contained in the alternate I/M credit files, there should be no
need to change any of the normal MOBILES input parameters (other than those to access the use of
alternate I/M credits) to reflect the use of remote sensing. It is very important, therefore, to carefully
choose the right combination of factors in the remote sensing I/M credit utility that properly reflect the
features of the remote sensing program elements.
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5.0 REFERENCES
1. "Evaluation of the California Pilot Inspection/Maintenance (I/M) Program," Draft Final
Report, Prepared for California Bureau of Automotive Repair by de la Torre Klausmeier Consulting
Inc. and Radian Corporation, March 31,1995.
2. "Identifying Excess Emitters with a Remote Sensing Device: A Preliminary Analysis,"
Glover, Edward L. and William B. Clemmens, SAE Paper 911672,1991.
3. "Evaluation of a Remote Sensing Device at a Centralized I/M Lane," Whitney, Kevin A. and
Edward L. Glover, SAE Paper 922315,1992.
4. "Evaluation of the Ability of Multiple Remote Sensing Devices to Identify High Emitters,"
Glover, Edward L., Draft EPA Report, March 1995.
5. "Technologies to Improve the Detection of High-Emitting Vehicles in a Vehicl^ Inspection
Program," California Air Resources Board, El Monte, California, December 1992.
6. "Remote Sensing Effectiveness," Draft EPA Paper, August 31, 1995.
7. "Approaches to Integrating Remote Sensing Benefits into the MOBILES Structure," Draft
EPA Paper, August 1995.
8. "Methods to Assess Fleet Coverage," Draft EPA Paper, August 31,1995.
9. "Remote Sensing Program Scenarios," Draft EPA paper, August 31,1995.
10. "Description of Remote Sensing and Regulatory Requirements," Draft EPA Paper, August
31,1995.
11. "Potential Remote Sensing Device Specifications," Draft EPA paper, August 31,1995.
12. "Final Statement on Remote Sensing Policy," Memo from Steve Gerritson to Candy
Garrett, Chris Kite, Celia Shih, Doug Surpitski, Jon Heuss, Bob Stevens, Lois Platte, Joe Somers, and
Dave Brzezinski, February 23,1996.
13. "Review of Draft EPA RSD Paper," Merrtp from Randy Guensler and Lois A. Platte to
Members of the Mobile Source Technical Advisory Subcommittee, June 12, 1996.
14. Letter from Lois A. Platte to Gerry Gallagher, Colorado Department of Health, June 11,
1996.
15. "Laboratory Assessment of the Remote Sensing Device Program," Arizona Department of
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Environmental Quality Draft Report, Cox, Frank W., John R. Walls, Mark W. Carrel, Andy G. Clifton,
Jimmy D. Lewis, and Warren C. Mason, 1996.
16. "Remote Sensing: A Supplemental Tool for Vehicle Emission Control," EPA Office of
Mobile Sources Fact Sheet. August 15,1993.
17. "The Potential Use of Remote Sensing Devices in Inspection and Maintenance Programs,"
Morgan, Julie K., EPA - Region 9, August 1995.
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APPENDIXA
List of Records for RSD Utility
000 control section
001 fleet coverage
1 commitment to level of effort RSD program
2 commitment to specific fleet coverage RSD program
3 commitment to a number of failures RSD program
0021/M program design
1 basic remote sensing program
2 test-and-repair remote sensing program
3 retest hybrid remote sensing
4 remote sensing only program
5 clean screening remote sensing program
005-008 - no user input; indicates name & location of standard I/m credit data input files
015-018 - no user input; indicates location and name of the resulting remote sensing adjusted I/M
credit data output files
Input Files
005 1981 & newer model year credits
006 1981 & newer model year retest-hybrid credits
007 pre-1981 model year credits
008 pre-1981 model year retest-hybrid credits
Output Files
015 adjusted 1981 & newer model year credits
016 adjusted 1981 and newer model year only for retest-hybrid credits
017 adj usted pre-19 81 model year credits
018 adjusted pre-1981 model year only for retest-hybrid credits
024 the age at which vehicles first become eligible for targeting
by remote sensing, 1 to 24 (default = 1 year)
031 the CO cutpoints to be applied to remote sensing measurements
for 1974 and older model year vehicles, default = 3%
032 the CO cutpoints to be applied to remote sensing measurements
for 1975 through 1980 model year vehicles, default = 3%
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033 the CO cutpoints to be applied to remote sensing measurements
for 1981 and newer model year vehicles, default = 3%
041 the test and repair effectiveness for HC emissions, default = 0.5 042
the test and repair effectiveness for CO emissions, default = 0.5
043 the test and repair effectiveness for NOx emissions, default = 0.5
Option 1: Data Section (level of effort)
021 the # of vehicles in the fleet (in inspection area)
022 the # of valid measurements per month that are made using RSD
023 the # of times a vehicle must be failed by RSD before it can be targeted for I/M
inspection; default 1, range 1-11
Option 2: Data Section (specific fleet coverage)
201-225
the # of vehicles for each of last 25 model years that are eligible for RSD targeting and the #
of vehicles of that age that actually receive RSD; the ratio of the two #s is the fleet coverage by model
year
Option 3: Data Section (# of failures)
301-325 The # of vehicles which normally fail I/M annually for each of the past 25 model years and
the # of additional vehicles which are targeted by RSD and also fail the confirmatory I/M test
The program contains default benefits for RSD. However, the user also has the option of specifying
benefits for option 3 based on a measure of the average excess emission levels of vehicles failing the
remote sensing cutpoint chosen. Such values would presumably come from an operating I/M program
using RSD. This measure is the ratio of the average excess emissions of vehicles.found at that remote
sensing cutpoint divided by the average emissions of all vehicles with excess emissions. There are 15
records for each of the three pollutants (HC, CO, and NOx) corresponding to RSD cutpoints from 0.5%
to 7.5%. These records are the ratio of the average IM240 excess emissions identified per vehicle to
the average excess emissions per vehicle in the fleet as a whole for a particular CO RSD cutpoint.
Each of the 15 records has three model year groupings (1974 and older, 1975-80, and 1981 and newer).
701-715 values for HC
801-815 values for CO
901-915 values for NOx
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