EPA-AA-AMD-EIG-96-00
USER GUIDE
to
REMOTE SENSING PROGRAM I/M CREDIT UTILITY
April 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
1.0 BACKGROUND 3
2.0 DESCRIPTION OF REMOTE SENSING 4
3.0 REMOTE SENSING CREDITS 7
3.1 Basic Remote Sensing Methodology 7
3.2 Inspection Program Designs 9
3.3 Options for Fleet Coverage 10
3.4 Remote Sensing Effectiveness 13
3.5 Estimating Remote Sensing Benefits 16
4.0 REMOTE SENSING UTILITY 19
4.1 Remote Sensing Utility Input Structure 19
4.2 Using Remote Sensing I/M Credits with MOBILE5 .. 29
5.0 REFERENCES 30
LIST OF TABLES
TABLE TITLE PAGE
Table la: IM240 Excess Emission Thresholds
(California Standards) 13
Table lb: IM240 Excess Emission Thresholds
(Federal Standards) 14
Table 2: IM240 Excess Emissions Identified
Using Remote Sensing CO Cutpoints
(Combined El Monte/EPA Studies) 15
Table 3: Average IM240 Excess Emissions
Using Remote Sensing CO Cutpoints
(Combined El Monte/EPA Studies) 16
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1.0 BACKGROUND
EPA has agreed to develop a methodology and mathematical
formulas to generate remote sensing emission reduction credits for
use in State inventories. These algorithms will tie together the
important remote sensing variables such as inspection program design,
remote sensing coverage, and remote sensing effectiveness. This
process will not only enable a state to model the effect of remote
sensing in making inspections more frequent, but also allow for the
effect of making remote sensing cars subject to test-only inspections
instead of test-and-repair or hybrid inspections.
MOBILE5 is currently used to model fleet emission levels, and
the effect on these levels from most emission control strategies.
Inspection and Maintenance (I/M) is one of the emission control
programs which can be modeled by M0BILE5. Remote sensing with some
type of confirming I/M test and enforcement process is another
control program which can be modeled by the MOBILE5 model.
The remote sensing credits are determined generally by linear
interpolation between the MOBILE5 I/M benefit (denoted "B" for
purposes of discussion) that apply without remote sensing and the
MOBILE5 benefit (denoted "A") that apply if all cars in the fleet
received a remote sensing test and if remote sensing identified every
vehicle which fail the I/M test. The interpolation fraction reflect
the facts that remote sensing coverage will not be 100 percent, and
that remote sensing passes some cars which have failed an I/M
inspection. The interpolation occurs for each pollutant and each
model year cohort in the vehicle fleet for each vehicle age. The
interpolation allow remote sensing to be assigned the incremental
credit increases which are due to increased inspection frequency, and
(if desired) test-only confirmatory testing of remote sensing
failures.
The recent National Highway System Designation Act requires the
states to evaluate the effectiveness of their I/M programs within an
18 month period which ends September 1997. A number of methods can
be used in such evaluations and EPA is working in partnership with
the states and other organizations to develop criteria and a
framework to be used in these evaluations. While some organizations
have raised the issue of whether remote sensing technology can be
used in such evaluations, what role remote sensing has, perhaps in
combination with other elements, is yet to be defined.
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2.0 DESCRIPTION OF REMOTE SENSING
Remote sensing is a process by which the instantaneous
emissions, identity (i.e., license plate), speed, and acceleration of
in-use vehicles can be monitored while the vehicles are operating on
the road. The automated system is set up alongside the road and
works by measuring emission concentrations in vehicle tailpipe
exhaust plumes as they pass through the system. License plate
recording equipment captures the license plate of each vehicle as it
passes through the system for vehicle identification. Additional
equipment can also be used in conjunction with the system to monitor
vehicle speed and acceleration. Other parameters, such as
measurement of vehicle operating temperature, are in the experimental
stage.
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 car as a high emitter. A greater number of remote
sensing sites and days allow more of the local fleet to be tested,
which result in more opportunity for emission reductions. Tighter
cutpoints tend to identify more of the high emitters present on the
road, also generating more emission reductions. However, tighter
cutpoints also tend to fail some clean cars that are momentarily high
emitting because of driver behavior as the car passes the remote
sensing unit. Such false failures can be reduced by not failing any
car unless it has been measured to have high emissions in two (or
more) separate remote sensing encounters. This reduces the fraction
of the fleet which can be targetted, since some dirty cars have not
had two encounters, and repair benefit of these vehicles are
sacrificed.
After the vehicle's emissions and license plate are measured by
the remote sensing equipment, various strategies can be utilized to
address cars that have been identified as high emitting. These can
range from an electronic sign which notifies the motorist of a
potential emissions problem to a summons 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. Remote sensing has the effect of catching cars that were not
properly inspected and repaired "on-cycle" at test-and-repair
stations, and catching cars that were clean on their last cycle (with
or without need for repair to get clean) but have experienced an
emissions problem since then.
In addition to actually catching dirty cars and forcing them to
get repaired, remote sensing may have a motivational effect on
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vehicles' owners and others which could produce indirect but real
benefits. A vehicle owner who otherwise requests or acquiesces to an
improper on-cycle inspection at a test-and-repair station (or an
improper repair in any type of I/M program) might, when aware of the
new risk of failing a remote sensing test with its attendant expense
and inconvenience, instead be sure to obtain a proper I/M test and
repair. Also plausible is that vehicle owners who notice a
driveability problem or a check engine light between inspections
might seek prompter repair, lest their cars fail remote sensing. The
magnitude of such a motivational effect is unpredictable. However,
it 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 motivational effect might provide to inspection programs.
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 which commands certain cars to 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 cars are ensured a full I/M test that does not suffer
from conflict of interest or other testing problems. This document
does not cover this situation, but EPA will work with states
interested in this concept. Recent work by consultants to the
California Bureau.of Automotive Repair provides a good starting point
for EPA to work with other states.
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 cars passing the
remote sensing sites. Local pilot testing is the best approach to
determining failure rates. As a preliminary guide, Table 1 shows the
failure rates that were observed by EPA and California BAR in their
remote sensing projects in Mesa, Arizona and Sacramento, California
respectively. The state of Arizona is now performing large numbers
of remote sensing tests in the Phoenix area, and EPA will work to
help communicate its experiences to other states.
Although remote sensing units are automated in that data
collection does not require operator action on a car-by-car basis, no
researcher or I/M program is now leaving remote sensing units
unattended while in operation. (One pilot involving unattended
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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 and damage is a concern. When technicians are with the
equipment at the side of the road, attention must be paid to their
physical needs and to their safety from oncoming traffic, etc.
. The number of cars 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 (Reference
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.
Some cars that fail remote sensing, upon presentation for an
off-cycle inspection (or for an immediate roadside inspection) and
using the I/M program's normal tailpipe emissions test, pass even
though no repairs have been performed. Numerous studies listed in
the appendix have produced information on the frequency of this
occurrence. The reader is encouraged to consult them.
It is reasonable to suspect from the available evidence that
false failures on remote sensing are most frequent (as a percentage
of all remote sensing failures) among newer cars because newer cars
have the lowest incidence of actual emissions problems. Newer cars
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 some loss of benefits.
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. The periodic inspection program
benefit is reduced by these exemptions, since some of the vehicles
passing the remote sensing criteria will be I/M failures. However,
such clean screening may improve the cost effectiveness of a periodic
I/M program by eliminating unneccessary inspections and may increase
public acceptance of the I/M program.
One additional benefit that remote sensing may provide would be
additional emission reductions resulting from vehicles with
evaporative system problems that are identified as part of the off-
cycle inspection required by remote sensing. This document only
addresses the exhaust benefits of remote sensing options and does not
estimate the effect of remote sensing on evaporative emissions.
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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 targetted and required to have an off-cycle
inspection on a random basis. 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, both positive and negative, would be
linked to the additional failure rate associated with remote sensing
requirements.
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 (Reference 2). The
report from the recent Sacramento remote sensing study (Reference 1)
in particular contains many analyses and findings not summarized
here.
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3.0 REMOTE SENSING CREDITS
The MOBILE5 model stores the credits for all I/M programs in
separate data files that are read during MOBILE runs. These files
can be modified or supplemented to include new options that were not
included in the original release of the model, without the need for a
new version of the MOBILE model itself. These credits in the I/M
credit files can be adjusted to reflect the effects of a remote
sensing program.
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 car
must be seen by the remote sensing units and must fail the remote
sensing cutpoint.
For biennial I/M programs, it is assumed that on average cars
tested by remote sensing get one extra inspection due to remote
sensing, and that this inspection occurs half-way between the
on-cycle biennial inspections. Increased emission benefits result
from these increased numbers of inspections between "on-cycle"
inspections.
Currently, the MOBILE5 model does not calculate inspection
frequencies that are greater than annual frequencies, i.e., there are
no semiannual I/M credits to use for the "A" case. Thus, with the
current MOBILE5 structure, remote sensing benefits attributable to
more frequent inspections for programs with annual inspections cannot
be generated. However, if warranted by user interest, EPA in the
future 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 car to get a test-only confirmatory test. Remote sensing in
this case also has the effect of making some cars -- 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, in addition to an increase in inspection
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frequency. Conceptually, this type of program should produce the
largest benefit attributable to remote sensing because of the sizable
differences between test-only I/M benefits and test-and-repair
benefits. Test-and-repair programs may also have confirmatory
testing done at the standard, periodic inspection stations. However,
the additional benefits from remote sensing would then be subject to
any emission benefit discounts resulting from the program design of
the periodic inspection program.
Similarly, a hybrid I/M program requires only some cars to get a
test-only on-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
cars.
It is also possible for a state to allow cars 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.
The remote sensing credits are 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.
Mathematically, the process to generate the additional remote
sensing program credits is:
RS Creditmp = (A^ - Bm,p) * Fm *
Where: B = I/M credit for the on-cycle program
A = I/M credit for an annual inspection
(test-only or test & repair)
F = Adjusted fraction of the inspected fleet scanned at
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remote sensing sites
E = Effectiveness of remote sensing identification and
repair of high emitters,
m Subscript denotes that the quantity is a function of
vehicle model year,
p Subscript denotes that the 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 A^p and Bm>p which are chosen from the
already-released I/M credits used with the MOBILE5 model.
The variables Fm and E^p in the equation represent the remote
sensing fleet coverage and the remote sensing effectiveness.
This additional RSD benefit can be added directly to the base
program I/M credit (Bmp) 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 1: 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.
Since this scenario is modeled by only increasing the
inspection frequency, the methods in this document do
not calculate a benefit for 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-
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only inspection stations.
Program 3: Retest Hybrid Remote Sensing Program
In a retest hybrid I/M area, high emitting vehicles
identified by remote sensing are sent to the test-only
inspection stations only.
Program 4: Remote Sensing Only Program
In a non-I/M area, high emitting vehicles identified
by remote sensing are sent to special inspection
stations.
Program 5: Clean Screen Remote Sensing Program
In any I/M area, remote sensing is used to identify
low emitting vehicles which are exempted from the
periodic I/M inspection.
Clean screening refers to a program where remote sensing is used
to identify vehicles which then are exempted from the periodic I/M
program inspection. An adjustment of the periodic inspection program
benefit is needed, since some vehicles passing the remote sensing
criteria will be I/M failures. In this case, all of the cutpoint and
coverage information is used to determine the portion of the I/M
credits which are lost when vehicles passing remote sensing are
exempted from having their periodic I/M inspection. However, such
clean screening can improve the cost effectiveness of a periodic I/M
program by eliminating unneccessary inspections and increase public
acceptance of the I/M program.
For example, a certain set of program cutpoints might identify
60% of the excess emissions, with a coverage of 50%. This means that
40% of the excess emissions are from vehicles which pass remote
sensing. Only one-half of these latter vehicles might actually be
tested by remote sensing. Therefore, the periodic I/M program would
lose 20% of the excess emissions it would otherwise be able to
identify.
In combination with these five program designs, the user must
specify the design of the remote sensing program element. There are
two critical parts to the remote sensing program design, vehicle
coverage and remote sensing effectiveness.
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3.3 Options for Fleet Coverage
There are three user options for indicating remote sensing program
vehicle coverage:
Option 1: 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. The method used to make this
estimate is descibed in a later section.
Option 2: Commitment to a Specific Fleet Coverage
The user specifies the fraction of the fleet in each model
year that are seen using remote sensing. 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 counts as being
seen.
Option 3: Commitment to a Number of Failures
The user specifies the fraction of additional failures that
are presented for 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 the additional fraction of failures.
Option 1: Commitment to a Level of Effort
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 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. This was demonstrated in
the Sacramento Study (Reference 1) where individual vehicles were
measured several times over the course of the study. In addition,
the fraction of excess emissions identified by remote sensing in the
vehicles seen must be estimated (factor E).
The algorithm used to calculate remote sensing coverage involves
a modification to Lambda in the Poission series using the ratio of
the VMT of the youngest model year (age) to the VMT of the model year
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(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:
P = 1.0 - exp( k * -Lambda)
where k is the ratio of VMTs:
k = VMT(current age)/VMT(age=l)
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. These readings are used to direct remote
sensing failures to I/M stations for inspection. This commitment is
for each age separately. This requires the user to supply the number
of vehicles currently of each age and the number of those vehicles
which are 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 are 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.
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, by age. If there
is an existing periodic I/M program, the actual failure rate can be
used. New I/M areas need to estimate the expected failures, perhaps
from other operating periodic I/M programs. The user also enters a
commitment for the number of additional I/M program failures, by age,
that are provided by remote sensing targetting in the.next year.
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 by using remote
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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 may
underestimate the remote sensing benefits. Some 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 the vehicle passes the confirmatory test, it
will not count towards the committment of failures by the program,
even though the repairs were done. The magnitude of this problem is
purely speculative at this time, and for purposes of the remote
sensing benefits calculated in this document, the impact of this
behavior is assumed to be insignificant. Analysis of operating
remote sensing programs may provide new information of the impact of
this behavior in the future.
In this option, the ratio of the additional remote sensing
failures to the expected failures represent 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, higher remote
sensing cutpoints increase the benefits per I/M failure, since
marginal failures are not targetted. Therefore, the remote sensing
effectiveness (factor E) determined from the user input of remote
sensing cutpoints must be adjusted so that it increases as the
cutpoints are loosened.
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.
Users are allowed to change this array in the external data file.
All other calculations for remote sensing credits remain the same.
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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 fail an I/M inspection, then the
effectiveness of remote sensing is 100%. 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. These IM240
emission levels are shown in Table la and lb 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. 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 inspection in order
to be required to have repairs performed. Repaired vehicles are
assumed to pass the IM240 test procedure after repairs.
Table la
IM240 Excess Emission Thresholds
(California Standards)
Model Year
HC (g/mi)
CO (g/mi)
NOx (g/mi)
1975-76
2.70
18.0
2.00
1977-79
1.23
18.0
1.50
1980
1.23
18.0
1.00
1981-86
0.59
10.5
1.05
1987-88
0.39
7.0
0.70
1989 +
0.39
7.0
0.40
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Table lb
IM240 Excess Emission Thresholds
(Federal Standards)
Model Year
HC (g/mi)
CO (g/mi)
NOx (g/mi)
1975-76
4.50
30.0
3.1
1977-79
4.50
30.0
2.0
1980
1.23
14.0
2.0
1981-86
0.62
5.1
1.5
1987-88
0.41
3.4
1.0
1989+
0.41
3.4
1.0
The identification rate is the percent 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:
o Pre-1975 model years (non-catalyst)
o 1975 through 1980 model year (oxidation catalyst)
o 1981 and newer model years (3-way catalyst)
The identification rate was based solely on the CO emission
measurement from remote sensing. An identification rate was
determined for each case, from 0.5% through 7.5% CO, in increments of
0.5% 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. The results are summarized in Table 2.
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Table 2
IM240 Excess Emissions Identified
Using Remote Sensing CO Cutpoints
(Combined El Monte/EPA Studies)
Remote
Sensing
1980 and Older
Model Years
1981 and Newer
Model Years
CO
Cutpoint
HC
CO
NOx
HC
CO
NOx
0.5%
.543
.945
.436
.570
.596
.283
1.0%
.487
.899
.423
.433
.499
.178
1.5%
.487
.758
.335
.387
.442
.122
2.0%
.487
.751
.295
.348
.396
.091
2.5%
.272
.676
.232
.319
.352
.059
3.0%
.272
.662
.146
.262
.278
.054
3.5%
.262
.584
.118
.217
.213
.042
4.0%
.184
.489
.067
.182
.178
.018
4.5%
.110
.467
.067
.150
.133
.015
5.0%
.110
.420
.063
.109
.107
.009
5.5%
.095
.398
.052
.071
.072
.006
6.0%
.095
.398
.000
.060
.053
.003
6.5%
.095
.398
.000
.046
.044
.003
7.0%
.088
.308
.000
.039
.034
.003
7.5%
.022
.205
.000
.028
.017
.003
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 were determined using the same combined datasets and
cutpoints used for determination of identification rates. The
results are summarized in Table 3.
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Table 3
Average IM240 Excess Emissions
Using Remote Sensing CO Cutpoints
(Combined El Monte/EPA Studies)
Remote
Sensing
1980 and Older
Model Years
1981 and Newer
Model Years
CO
Cutpoint
Up
(g/mi)
CO
(g/mi)
NOx
(g/mi)
(g/mi)
CO
(g/mi)
NOx
(g/mi)
0.5%
0.84
1.86
0.98
1.51
2.47
0.95
1.0%
0.85
2.13
1.06
1.57
3 .11
0.97
1.5%
0.85
2.34
1.01
1.87
3 .54
0.93
2.0%
0.85
2.67
1.10
2.05
4.05
0.91
2.5%
0.54
3 .20
1.05
2.28
4.73
0.88
3.0%
0.54
3 .61
0.82
2.70
5.42
0.92
3.5%
0.61
3 .77
0.76
2.88
6.30
1.01
4.0%
0.52
4.34
0.76
3.07
7.08
0.69
4.5%
0.38
5.53
1.00
3.08
6.54
0.72
5.0%
0.38
5.96
1.42
2.88
6.49
0.49
5.5%
0.44
7.06
2.34
3 .28
8.31
1.00
6.0%
0.44
7.06
0.00
4.43
9.13
0.90
6.5%
0.44
7.06
0.00
4.27
11.38
0.90
7.0%
0.62
7.28
0.00
4.82
11.63
0.90
7.5%
0.30
7.29
0.00
5.25
8.71
0.90
March 28, 1996
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3.5 wat-.imating Remote Sensing Benefits
As described in Section 3.1, the effect of remote sensing on I/M
credits are determined either by:
o Interpolating between existing annual and biennial I/M
credits.
o Interpolating between test-and-repair or retest-based
hybrid I/M credits and test-only I/M credits.
o Adjusting the I/M credits proportionally.
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, the addition of remote sensing is
modeled as and increase in the inspection frequency. This is done 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, the benefits are calculated
directly from the annual I/M credits proportionally to the vehicle
coverage and remote sensing effectiveness. Clean screening effects
are also calculated proportionally, reducing the I/M program benefits
by the number of I/M failures estimated to be exempted from
inspection.
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
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 and are assumed not contribute to
additional I/M benefits. Therefore, it is the fraction of emissions
represented by vehicles failing the I/M test and identified by the
remote sensing program which can contribute to additional I/M
benefits.
March 28, 1996
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The calculation of the effects depend 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 targetted by
remote sensing. If:
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
E: Remote Sensing program effectiveness (identification
and repair of high emitters)
C: Overall I/M credit with remote sensing added
The following inspection program types are included:
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
the test-and-repair discount applied within MOBILE5.
Therefore, for MOBILE5, separate test-and-repair I/M credit
files are not needed.
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
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
March 28, 1996
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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 simply the test-only case with
the test-and-repair discount applied. Therefore, for
MOBILE5, separate test-and-repair I/M credit files are not
needed.
5. Remote Sensing is used to exempt vehicles from the periodic
I/M program ("clean screening") This option can be applied
to any I/M program. The method for determination of the
effects of this option simply and directly reduces the I/M
credit by the fraction of emissions represented by vehicles
exempted from inspection.
C = A (TO) - (A (TO) * F * (1-E) )
or
C = B (TO) - (B (TO) * F * (1-E))
or
C = A(T&R) - (A(T&R) * F * (1-E))
or
C = B (T&R) - (B (T&R) * F * (1-E))
or
C = A(RH) - (A(RH) * F * (1-E))
or
C = B (RH) - (B (RH) * F * (1-E))
The factors related to I/M credits and remote sensing
effectiveness depends both on vehicle age and pollutant. The fleet
coverage depends only on vehicle age.
March 28, 1996
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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 contain all of the information 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)
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 reverts 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
Records 001 and 002 are mandatory records. Record 001 contains
the user selection of the fleet coverage option. The format of this
record is (I3,11X,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 are:
1: Commitment to a Level of Effort
2: Commitment to a Specific Fleet Coverage
March 28, 1996
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3: Commitment to a Number of Failures
A complete description of these options is located in the
previous section on remote sensing program vehicle coverage. 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 are:
1: Basic Remote Sensing Program Design
2: Test-and-Repair Remote Sensing Program
3: Retest Hybrid Remote Sensing Program
4: Remote Sensing Only Program
5: Clean Screening Remote Sensing Program
Use of the Clean Screening Remote Sensing Program (5) design in
conjunction with the Committment to a Number of Failures (Option 3)
cannot be modeled and will not be accepted as an input. 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. 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
March 28, 1996
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- 24 -
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 directory from which the utility is
invoked. The record number for each file are:
Input Files
Record Description
005
006
007
008
1981 and newer model year credits
1981 and newer model year Retest-Hybrid credits
Pre-1981 model year credits
Pre-1981 model year Retest-Hybrid credits
Output Files
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 these input records and
the default names are used. All of the files, however, 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:
Input and Output Filenames
000
000
005 C:\DATA\IMl.D
006 C:\DATA\IMH.D
007 TC1.D
008 TCH.D
015 RSDDATll.D
016 RSDDATA.H
017 TECDAT11.D
018 TECDATA.H
Default
IMDATA.D
HYBRID.IMC
TECH12.D
TECH12.D
RSDDATA.D
RSDDATA.H
TECDATA.D
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
March 28, 1996
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- 25 -
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:
Record Description
024: The age at which vehicles first become eligible for
targetting by remote sensing
031: The CO outpoints 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
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 targetting
by remote sensing allows the user to exempt newer vehicles in the
fleet from targetting. Newer vehicles tend to produce fewer benefits
and more false failures than older vehicles. Exempting vehicles from
targetting does not reduce their I/M benefits, but does not provide
additional benefits from remote sensing. This number 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
targetting must be provided for each of three model year groupings of
vehicles:
o 1974 and older model year vehicles (Record 031)
o 1975 through 1980 model year vehicles (Record 032)
o 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.
March 28, 1996
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- 26 -
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. These numbers 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 (13,1X,F11.3).
The current version of the model, MOBILE5a, adjusts the I/M
credits for test and repair I/M program designs to be 50% of the
benefits of test-only program designs. Future versions of the model
allow for user input of test and repair effectiveness values. 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. There are three records (Records 041, 042 and 043) for HC,
CO and NOx effectiveness values. The format for these records are
(13,1X,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:
default
1
default
3.0
default
3.0
default
3.0
default
.50
default
.50
default
.50
000 One-Time Data Section
ooo
024 1 Age when first eligible (1-24),
031 3.0 CO cutpoint, '74 & older model years,
032 3.0 CO cutpoint, '75 - '80 model years,
033 3.0 CO cutpoint, '81 & newer model years,
041 .50 Test and repair effectiveness for HC,
042 .50 Test and repair effectiveness for CO,
043 .50 Test and repair effectiveness for NOx, default .50
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 cutpoint from 0.5%
through 7.5% for three model year groupings:
o 1974 and older model years
o 1975 through 1980 model years
March 28, 1996
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- 27 -
o 1981 and newer model years
The format for the record is (13,IX,Fll.3,Fll.3, Fll.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 measured before
it can be targetted for I/M inspection
In addition, the user may supply the average vehicle miles
traveled per year by vehicle age to override the MOBILE5 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. This number is entered on Record 021. The format
for this record is (13,IX,111).
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 depend on a great variety of
factors including the number of practical remote sensing locations,
the number of devices provided, the amount of staff required and
March 28, 1996
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- 28 -
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 is the committment 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
(13,IX,111).
The input of the number of times that a vehicle must be measured
using remote sensing before it can be targetted 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. This means that vehicles which are measured less than
the user specified number of times cannot be used for targetting.
Increasing the number of times a vehicle must be measured noticibly
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
(13,IX,111).
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:
000 Option 1 Data Section
021 810498 No. of veh. in inspection area default 1000000
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 M0BILE5 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 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
March 28, 1996
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- 29 -
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 (13,IX,111).
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 targetted each year. This is a committment 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 targetting each year and the total
number of vehicles of that age in the vehicle fleet subject to
inspection. 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 targetting.
The format for the record is (13,IX,2111). 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 targetted each year.
The following is an example input of the Option 2 data section.
The example assumes a MOBILE5 default distribution of 1 million
vehicles and assuming that, using remote sensing, that 1% of each
model year are eligible for targetting. The records include some
added comments to add clarity:
March 28, 1996
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- 30 -
000 Option 2 Data Section
000 Total Eligible
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
Option
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 committment 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
March 28, 1996
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- 31 -
which are referred to the I/M inspection by remote sensing
targettting and fail the inspection this year. The format for the
record is (13,IX,2111). 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
which are referred to the I/M inspection by remote sensing
targettting and fail the inspection this year.
The following 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
add clarity:
000 Option 3 Data Section
000
Failures
Additional
000 -
201
9000
90
Option
3
Age
0
- 1
202
9000
90
Option
3
Age
1
- 2
203
9000
90
Option
3
Age
2
- 3
204
9000
90
Option
3
Age
3
- 4
205
9000
90
Option
3
Age
4
- 5
206
9000
90
Option
3
Age
5
- 6
207
9000
90
Option
3
Age
6 -
- 7
208
9000
90
Option
3
Age
7
- 8
209
9000
90
Option
3
Age
8
- 9
210
9000
90
Option
3
Age
9
- 10
211
9000
90
Option
3
Age
10
- 11
212
9000
90
Option
3
Age
11
- 12
213
9000
90
Option
3
Age
12
- 13
214
9000
90
Option
3
Age
13 -
- 14
215
9000
90
Option
3
Age
14 -
- 15
216
9000
90
Option
3
Age
15
- 16
217
9000
90
Option
3
Age
16 -
- 17
218
9000
90
Option
3
Age
17 -
- 18
219
9000
90
Option
3
Age
18 -
- 19
220
9000
90
Option
3
Age
19 -
- 20
221
9000
90
Option
3
Age
20 -
- 21
222
9000
90
Option
3
Age
21 -
- 22
223
9000
90
Option
3
Age
22
- 23
224
9000
90
Option
3
Age
23 -
- 24
225
9000
90
Option
3
Age
24 -
- 25
March 28, 1996
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- 32 -
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 715 for HC, Records 801 through 815
for CO and Records 901 through 915 for NOx emissions. Each of the
fifteen records contain three ratios for each cutpoint from 0.5%
through 7.5% for three model year groupings:
o 1974 and older model years
o 1975 through 1980 model years
o 1981 arid newer model years
The format for the record is (13,IX,Fll.3,Fll.3,Fll.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 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 ratio
that are used for 1975 through 1980 model year vehicles, followed by
another number (including a decimal) within the next 11 spaces,
indicating the ratio that are used for 1981 and newer model year
vehicles.
4.2 Using Remote Sensing I/M Credits with MOBILES
MOBILE5 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 MOBILE5 (MOBILE5a, March 26, 1993 or MOB5a_H,
February 1995) can be 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
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necessary to estimate or assume some of the necessary input data.
However, the inputs should reflect, as near as possible, the actual
expected preformance 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 the necessary
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 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 it's 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 while 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 MOBILE5 default I/M credit filenames, there will be no output in
M0BILE5 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 MOBILE5 descibed in the
MOBILE5 User Guide Section 2.2.5.4. In this case, the input file for
MOBILE5 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
M0BILE5 input parmaters (other than those to access the use of
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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.
5.0 REFERENCES
1. "Evaluation of the California Pilot Inspection/Maintenance (I/M)
Program," Draft Final Report, 31 March 1995. Prepared for
California Bureau of Automotive Repair by de la Torre Klausmeier
Consulting Inc. and Radian Corporation.
2. EPA's latest fact sheet on remote sensing.
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