User Guide and Description 

For 

EPA/ AA/ AMD/EIG/96-0 l 

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 

[Note : This electronic version of this document may have minor formatting 

& page numbering differences from the original hardcopy.] 
TABLE OF CONTENTS 

Section Page Number 

1.0 BACKGROUND AND SUMMARY .......... . ... .. .... . ... . 

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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I 0 

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 . . . ... .... . ...... .. . . . . 

4.2 Using Remote Sensing I/M Credits with MOB ILES . . . ...... . .. . 

5.0 REFERENCES 

Appendix A 

List of Records for RSD Utility 

List of Tables 

Table la: IM240 Excess Emission Thresholds (California Standards) .. .. . . 

Table I b: IM240 Excess Emission Thresholds . . . ........ .. . . ... .. . .. . 

Table 2: Fraction of IM240 Excess Emissions Identified Using Remote Sensing 

CO Cutpoints (Combined El Monte/EPA Studies) . . . . .. . .... . . 

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 CARB 

El Monte/EPA Studies) ... . ... . .. . .... . .. .. . ...... . .. . ... . 

25 

35 

37 

39 

18 

19 

20 

21 
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 l/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 STAPP A 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 l/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 l/M program is one in which the I/M test is administered by a facility that 

does only l/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 l/M program is 

one in which the I/M test is administered by a facility that does I/M testing and provides 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 
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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 

l/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 benefit 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 ofrelevant data; EPA has chosen to make use of 

data collected up to and including the l 994-9S 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 staff or 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 

not address HC or NOx cutpoints. 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 
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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-and-repair 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-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 1/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 1/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 
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estimates a larger discount. Unfortunately, the current versions of MOBILE5a are hard wired with 

a significant percent discount (i.e., 50%). EPA plans to issue a new version (MOBILE5b) which 

will accept a user input for the discount factor. Meanwhile, users may need to perform 

interpolations between the MOBILE5a 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 l percent of fleet non-1/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 l/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 l/M program or would not have gone 

through the I/M program). Thus, EPA believes even those states whose l/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. 

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 l/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 
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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 

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 
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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 l/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. 

l/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 ofremote 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 

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 cutpoints 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. 
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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, 4 7 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 l/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 l/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 l/M station in an I/M program for which 

the state has determined a large discount. Such an improper l/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 would make a greater 

effort to obtain a proper l/M test and repair. Also, l/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) 
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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 

unmeasurable. 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 l/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 1/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 [l] provides a good 

starting point for EPA to work with other states. California is presently developing such a system 

as part of its hybrid l/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 offcycle 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 l/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. 

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 
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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 l/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. 
-11-

3.0 REMOTE SENSING CREDITS 

The EPA MOBILES model stores the credits for all 1/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 1/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 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 l/M inspection. 

For biennial l/M programs, it is assumed that on average vehicles tested by remote sensing 

which fail the RSD cutpoints get one extra inspection at an l/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 1/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) ifthe 

RSD failures are subject to a test-only confirmatory I/M test. However, in the future, if warranted 

by 
-12-

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 testonly 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 l/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 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 vehicles. 

It is also possible for a state with a test-and-repair l/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 l/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. 
-13-

Mathematically, the process to generate the additional remote sensing program credits is as 

follows: 

RS Creditm,p = (Am,p - Bm,p) * Fm * Em,p 

Where: 

B = IIM credit for the on-cycle biennial program 

A = l/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 IIM program is represented by the variables 

Am,p and Bm,p which are chosen from the already-released IIM 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 Pro2ram Designs 

The remote sensing IIM credit utility allows the user to describe the remote sensing 

inspection used either in combination with a periodic JIM program or as a separate inspection 

program in a non-1/M area. There are five basic l/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 IIM inspection 

stations. This includes the case of test-only JIM programs with test-only confirmation and testand-repair JIM 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 ofllM as annual, the methods in this 

document do not calculate a supplemental benefit for RSD with annual inspection programs using 
-14-

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 Hvbrid 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 l/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 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 using coverage information derived from a 

Poisson distribution. The method used to make this estimate is described 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 is seen using remote 
-15-

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 l/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 ofremote 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 (l] 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) 

where k is the ratio of VMTs, 

k = VMT(current age)/VMT(age=l). 
-16-

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 l/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 Covera2e 

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 l/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. 

3.3.3 Option 3: Commitment to a Number of Failures 

In this option, the user provides an estimate of the expected number ofl/M failures 

provided by remote sensing, again by model year/age. If there is an existing periodic l/M 

program, the actual failure rate for the overall l/M can be used as input in determining what level 
-17-

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 l/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 l/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-yearold vehicles might specify that an additional 500, 5-year-old vehicles would be failed by using 

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. 

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 
-18-

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 ofl/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 ofIM240 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 1 b 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. 

Table la 

IM240 Excess Emission Thresholds 

(for California Emission 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 
-19-

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 

Table lb 

IM240 Excess Emission Thresholds 

(for Federal Emission 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 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) 

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. 
-20-

Table 2 

Fraction of IM240 Excess Emissions Identified 

Using Remote Sensing CO Cutpoints 

(Combined CARB El Monte/EPA Studies) 

Remote 1980 and Older 1981 and Newer 

Sensing Model Years Model Years 

co 

Cutpoint HC co NOx HC co NOx 

0.5% 0.543 0.945 0.436 0.570 0.596 0.283 

1.0% 0.487 0.899 0.423 0.433 0.499 0.178 

1.5% 0.487 0.758 0.335 0.3 87 0.442 0.122 

2.0% 0.487 0.751 0.295 0.348 0.396 0.091 

2.5% 0.272 0.676 0.232 0.319 0.352 0.059 

3.0% 0.272 0.662 0.146 0.262 0.278 0.054 

3.5% 0.262 0.584 0.118 0.217 0.213 0.042 

4.0% 0.184 0.489 0.067 0.182 0.178 0.018 

4.5% 0.110 0.467 0.067 0.150 0.133 0.015 

5.0% 0.110 0.420 0.063 0.109 0.107 0.009 

5.5% 0.095 0.398 0.052 0.071 0.072 0.006 

6.0% 0.095 0.398 0.000 0.060 0.053 0.003 

6.5% 0.095 0.398 0.000 0.046 0.044 0.003 

7.0% 0.088 0.3 08 0.000 0.039 0.034 0.003 

7.5% 0.022 0.205 0.000 0.028 0.017 0.003 

In addition, the average excess emissions ofremote 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. 
-21-

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 CARB El Monte/EPA Studies) 

(0% CO Cutpoint Gives Ratio of 1) 

Remote 1980 and Older 1981 and Newer 

Sensing Model Years Model Years 

co 

Cutpoint HC co NOx HC co NOx 

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 1.0 I 1.87 3.54 0.93 

2.0% 0.85 2.67 I. I 0 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 I.QI 

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 

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 CARB 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 
-22-

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 l/M credits and test-only I/M 

credits; or 

Interpolating between zero credit and the applicable annual I/M credits 

If the existing periodic JIM 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 JIM 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 IIM 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 ofIIM credits, effectiveness is defined as the ability of remote 

sensing to properly identify l/M failures. Vehicles which fail JIM 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 l/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. 

The calculation of the effects depends on the inspection program type. The benefit is 

calculated from the existing IIM credits for the inspection program type in combination with 

similar IIM 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 l/M credit 

B(T&R): Biennial test-and-repair JIM credit 
-23-

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: 

I. 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 SO 

percent 

applied within MOBILES; for different test-and-repair discounts, one uses the MOBILES case with 

SO 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 

SO% is used, then an interpolation is necessary. 
-24-

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 l/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-1/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 l/M failures by, for example, a factor of two will not 

increase the l/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 
-25-

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 Jacking 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 l/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 00 l and 002 are mandatory records. 

Record 001 contains the user selection of the fleet coverage option. The format of this record is 

(13, 11 X,I l ), 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 ofl/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. 

I: Basic Remote Sensing Program Design 

2: Test-and-Repair Remote Sensing Program 
-26-

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 l/M 

credit data files and Records 015 through 018 determine the location and name of the resulting 

remote sensing adjusted l/M credit data files. Normally, the resulting l/M credit data will be written 

to the filenames in Records 015-018. The indicated l/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 (13, 1 X,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 

Record Description 
-27-

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 l/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 ---------------------------------------- Def au It 

005 C:\DATA\IMI.D IMDATA.D 

006 C:\DA TA \IMH.D HYBRID.IMC 

007 TCI.D TECH12.D 

008 TCH.D TECH12.D 

015RSDDATl1.D RSDDATA.D 

016 RS ODA T A.H RSDDA T A.H 

017 TECDATl l.D TECDATA.D 

018 TECDATA.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 

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 
-28-

and more false failures than older vehicles. Exempting vehicles from targeting does not reduce 

their l/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 

(13,lX,111). 

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 l/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 l/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 l/M credits for those model years are not 

adjusted. If a more complicated scheme for use remote sensing measurements to identify vehicles 

for l/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 

(13 ,lX,Fl 1.3). 

The current version of the model, MOBILE5a, adjusts the l/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 

l/M credit utility, therefore, allows the user to specify the value for the effectiveness of test and 

repair l/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 MOBILE5a 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 (13 , lX,Fl 1.2). 

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: 

000 One-Time Data Section 

000-------------------------------

024 l Age when first eligible (l-24), default 1 
-29-

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 401through415 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 (13 ,lX,Fl 1.3,Fl 1.3,Fl 1.3). 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 JIM 

inspection. 

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 
-30-

from the inspection. The number of vehicles in the fleet is entered on Record 021. The format for 

this record is (13 ,lX,Il 1). 

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 (13, 1X,Il1 ). 

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 l/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 (13,lX,Il 1). 

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 

000 ---------------------

021 810498 No. ofveh. 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 (l-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 101through125). 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 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 
-31-

(13, lX,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 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 (13, lX,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 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. 

000 Option 2 Data Section 

000 Total Eligible 

000 ----------------------

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 
-32-

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 4 70 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 - 1 7 

218 15000 150 Option 2 : Age 1 7 - 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 

Oe.tion 3: Data Section 

The third vehicle coverage input option allows the user to specify an estimate of the expected 

number ofl/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,2Il 1). 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 targeting 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 l/M inspection. The records include some added comments to provide clarity. 

000 Option 3 Data Section 
-33-

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 

2.13 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 - 1 7 

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 

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. 

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 
-34-

The format for the record is (13, 1X,F11.3,F 11.3,F 11.3). 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 ratios 

that are used for 197S 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 l/M Credits with MOBILES 

MOBILES uses two external data files which contain the l/M credits whenever an I/M 

program is specified in the user input. The benefit of l/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 l/M credit files to reflect the user supplied 

information about the use ofremote sensing in the inspection programs. In this way, the current 

version of MOBILES (MOBILES, March 26, 1993 or MOBSa_H, February 199S) 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 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 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 ofl/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 JIM credit files output by the remote sensing utility. These filenames can 

include "path" information if the l/M credit files are not located in the local directory. If a path is 

not specified, the default l/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 
-3Sdiagnostic 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 l/M credits were used. It may be Jess confusing to require that these alternate l/M 

credits be accessed using the alternate credit option in MOBILES described in the MOBILES User 

Guide Section 2.2.S.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 l/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 l/M credit utility that 

properly reflect the features of the remote sensing program elements. 
-36-

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 Vehicle 

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," Memo 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 Environmental Quality Draft Report, Cox, Frank W., John R. Walls, Mark W. Carrel, Andy G. 

Clifton, Jimmy D. Lewis, and Warren C. Mason, 1996. 
-37-

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. 
-38-

APPENDIX A 

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 

002 l/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 adjusted pre-1981 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% 

033 the CO cutpoints to be applied to remote sensing measurements 

for 1981 and newer model year vehicles, default= 3% 
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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 Oevel of e{fort) 

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 l/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 #sis 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 l/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