User Guide and Description For EPA/ AA/ AMD/EIG/96-0l

Interim Remote Sensing Program Credit Utility

September 1996

U.S. Environmental Protection Agency Office ofAir and Radiation

Office ofMobile Sources Assessment and Modeling Division Emission Inventory Group National Vehicle and Fuel Emissions Laboratory 2565 Plymouth Road Ann Arbor, Michigan 48105 [Note: This electronicversion of this document mayhave minor formatting &page numberingdifferences from the originalhardcopy.]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 ofa RSD Program

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I0 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 MOBILES

. . . ...... . .. . 5.0 REFERENCES Appendix A List ofRecords for RSD Utility List ofTables Table la: IM240 Excess Emission Thresholds (California Standards)

.. .. . . Table Ib: IM240 Excess Emission Thresholds

. . . ........ .. . . ... .. . .. . Table 2: Fraction ofIM240 Excess Emissions Identified Using Remote Sensing CO Cutpoints (Combined El Monte/EPA Studies)

. . . . .. . .... . . Table 3: Ratio ofAverage 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

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### 1.0 BACKGROUND AND SUMMARY A remote sensing device (RSD) can measure instantaneous hydrocarbon (HC) and carbon monoxide (CO) exhaustemissions 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 ofstates have expressed interest in using RSD as part oftheir vehicle inspection and maintenance (I/M) programs. A number ofRSD studies [1-5] using RSD and an independent emission test such as the Federal Test Procedure (FTP) orIM240 (atransient driving cycle developed by EPA for use in vehicle inspection programs) on a sample ofvehicles representative ofthe in-use fleet have been done by California and EPA. This includes, ofcourse, vehicles both above and below specificRSD cutpoints (which can be used to designate RSD passes/failures). These studies demonstrate the effectiveness ofRSD in identifying vehicles with excess emissions that can be reduced in a vehicle I/M program. On September 12, 1995, EPA convened a meeting ofRSD 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 staffon which to provide input. Then, EPA sought input from the Modeling Workgroup ofthe Federal Advisory Committee ActTechnical 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 ofthis documentto 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 ofthis report. This additional statement was approved by a majority vote ofthe standing members ofthe Modeling Workgroup. Also, the draft report and the utility were sentto members ofthe I/M Subcommittee ofSTAPP A ALAPCO (State and Territorial AirPollution Program Administrators

- Association ofLocal AirPollution Control Officials) for review ofthe utility itselfto 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 ofthe 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 togetherthe 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 ofmore 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 ofRSD in conjunction with a test-and-repair I/M program with either test-only ortest-and-repair inspections ofvehicles 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 ofRSD failures. (A hybrid I/M program is one with test-only inspection-2- ofsome vehicles and test-and-repair inspection ofother vehicles). States can also model the benefits ofusing 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 ortest-and-repair l/M test and repairs ifthe vehicle failed the confirmatory I/M test. In such a program, the only vehicles receiving the test-only ortest-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 mostemission control strategies. Inspection and Maintenance (I/M) is one ofthe emission control programs which can be modeled by MOBILES

. Remote sensing with sometype ofconfirmatory 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 ofdiscussion later in this document) that applies without remote sensing and the MOBILES benefit (denoted as "A" later in this document) that applies ifall vehicles in the fleet received a remote sensing test and ifremote 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 oncethe effectiveness oftest-and-repair programs are determined as explained below) confirmatory testing ofremote sensing failures. In preparing this guidance, EPA faced a tradeoffbetween 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 oftime, and may not cover every case ofinterest to users. Thus, states can have some numerical guidance in a timely fashion to estimate what RSD benefits might be. Onetradeofffactor has been the availability ofrelevant data; EPA has chosen to make use of data collected up to and including the l994-9S Sacramento study, and not wait to receive and analyze data collected in Arizona orelsewhere in 1996 or 1997. The data from the Arizona RSD program will indeed be valuable in helping determine whatRSD credits could be; however, EPA did not have the stafforcontractresources to analyze the Arizona data available so far and still release this document in a timely fashion

. A consequence ofthis choice is that the guidance does not address HC orNOxcutpoints. Also, it does not quantitatively address the advantages or disadvantages ofusing 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 ofthe instrumentation and with more modern practices on how to site-3- RSD units and validate the data they collect. Another factor has been the obvious advantage ofissuing a credittool which would be consistent with the current versions ofMOBILES, instead oftaking the considerabletime and staff hours needed to revise MOBILES itself, and rather than requiring users to go through complex hand calculations. The choiceto stay consistentwith MOBILES means that this guidance does not coverevery program type in which a user might be interested. Forexample, the guidance assumes that a state would not use remote sensingto subject a given vehicle to I/M testing more often than once per year. Another limitation ofthis guidance is that it does not considerresidual benefits of remote sensing from year to year, particularly in test-and-repair programs. A vehicle which escapes propertesting 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 ofMOBILES 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 fulljusticeto 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 ifnot 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 ofRSD. Public acceptance ofremote sensing is very important for its success. States should attempt the educatethe public aboutthe uses and importance ofremote sensing, before it is used. Also, pilottesting might be done before the remote sensing program is implemented. One critical issue discussed in more detail later is where to setcutpoints, so that there are not too many false failures causingthe public to lose confidence in RSD ortoo many false passes lowering the effectiveness ofRSD in reducing emissions. As stated previously, one elementofthe benefit ofremote 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 ofany given level and type ofremote sensingtherefore 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 ofthe emission reduction loss ordiscount for test-and-repair I/M programs. The recentNational Highway System Designation Act directs EPA to no longer apply this uniform estimate. Instead, each state will be given interim 1/M program creditfor whatever estimate the state makes in good faith. Prospective remote sensing credits will also depend on this good faith estimate ofthe 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 orzero discount will model a smaller incremental remote sensing creditthan a state that-4- estimates a larger discount. Unfortunately, the current versions ofMOBILE5aare 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 MOBILE5aoutputs 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 offleet non-1/M emissions ifthe test-and-repair discount is zero, but it is higher (about4 to 5 percent) ifthe 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. TheNational Highway System Designation Act provides for an 18-month evaluation ofeach I /M program to determine ifthe 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 ofECOS (Environmental Council ofStates) 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 ofthe cars that would otherwise contribute to a discount. Forexample, ifa state's 18-month evaluation reveals that the scheduled inspection element ofa 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 greaterthan 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 ofinteractions 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 ofsubstantial remote sensing programs. Accordingly, EPA believesthat, ifdone properly, such programs with remote sensing could potentially warrant additional SIP credit, above and beyond whatever amount ofSIP credit is awarded to their baseline I/M programs. Forthat reason, EPA urges states to carefully consider the remote sensing option. Ofcourse, a lower level ofremote sensing is also the mosteconomical way to meet the statutory requirement for on-road testing. In addition, on-road testing for emissions performance is required of0.5% or20,000 vehicles (whichever is less) ofvehicles subjectto the I /M program. This on-road testing can also provide useful information on the fraction offleet complying with the I /M testing requirements and the overall effectiveness ofthe I/M program. Remote sensing can, in conjunction with the confirmatory I/M test, help provide information on the overall distribution ofemissions 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 extraremote sensing credit on the basis that remote-5- sensing can help enforce the requirementthat 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 getextracredit for helping to enforce against commuters who should get theirvehicles inspected but do not. It is not clear atthis point what additional options states have for more enforcementto increase compliance. Additional creditcan be given to the extentthat both ofthese situations contributeto 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 measuresto reduce such evasion and to monitorthe degree to which this is a problem. The determinations under the National Highway System Designation Actwill 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. Forexample, many states assume a 96% compliance with the local I/M requirements. The 4% non-complying vehicles are assumed in MOBILE to have emissionstwicethat ofthe average vehicle that fails I/M. There are substantial benefits for a state able to increase its compliance. Forexample, 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 butthe 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 reflectthe additional experience states will have gained while implementing RSD. Forexample, 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 OFREMOTE SENSING The objective ofRSD is to identify high emitters in the vehicle fleet. These include vehicles that were clean on their last cycle (with orwithout need for repair to get clean), but have since experienced an emissions problem. They also include vehiclesthatwere 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) ofin-use vehicles can be monitored when vehicles passthrough the RSD measurement beam while operating on the road. The RSD system is set up alongside the road to-6-

measure emissions and also incorporates video license plate recording equipment to record the license plate number ofeach vehicle to enablethe state or local governmentto 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 ifthe unit were unattended

. Also, security from theft, damage, and even vandalism is a concern. When technicians are using the equipment at the side ofthe 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 enrichmentevents and high instantaneous emissions which may not be representative ofa vehicle's overall emissions. Deceleration is also important since deceleration can result in low instantaneous emissions not representative ofa vehicle's overall emissions. Measuring other parameters, such as vehicle engine orcatalyst operatingtemperature (i.e., is the vehicle or catalyst warmed up?), with RSD is in the initial stage ofdevelopment and application. A vehicle under cold-start conditions (acold catalystthat 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, improvethe effectiveness ofRSD. RSD data with such systems on a large number offully 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 operatethe 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 ofthe 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

. Forexample, use ofRSD after a stop sign may not be appropriate due to high acceleration rates. Tighter RSD cutpoints identify more ofthe 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 ofdriver behavior (e.g., heavy acceleration) or cold-start conditions as the vehicle passes the remote sensing unit. In addition to using acceleration measurements and catalysttemperature 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 ofthe fleet which can be targeted, since some "dirty" vehicles may not have two (or more) encounters, and repair benefit ofthese vehicles from using RSD are sacrificed.-7- The numberofvehicles that can be successfully tested per day with remote sensing varies from siteto site; weathercan also be a factor. Each area has to experimentto see what can be accomplished. As a rough guide, 500 unit-days oftesting in Sacramento [1] at 337 sites produced about 1,330,000 records containing an emission reading, 865,000 ofwhich also contained a manually decodable license plate image. Overall, a valid test was obtained on 376,000 unique, identifiable vehicles. Ofthe 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 Afterthe vehicle's emissions and license plate number are recorded by the remote sensing equipment, various strategies can be utilized to notify vehicle owners thattheir vehicles have been identified as high emitting. These can range from an electronic sign along the roadway which notifies the motorist ofa potential emissions problem (similarto those used to alert a driver ofa vehicle's speed in an effortto 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, ofcourse, the cutpoints used but also the state ofrepair ofthe local fleet (affected by the specifics ofthe periodic testing requirement), the roadway and traffic flow characteristics ofthe remote sensing sites, and the age mix ofthe vehicles passing the remote sensing sites. Local pilot testing or experimental testing as done by CaliforniaBureau ofAutomotive Repair (BAR) in Sacramento, CARB in El Monte, and EPA under contractwith AutomotiveTesting Laboratories in Mesa, Arizona (and Hammond, Indiana) is the best approach to determining failure rates for the local fleet. The State ofArizona is now performing large numbers ofremote sensing tests in the Phoenix area as part ofits I/M program, and EPA will workto help communicate its experiences-8- to other states. EPA has received some ofthe 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 ArizonaDepartmentofEnvironmental 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 oftypically emitting vehicles in the fleet; thus, these results cannot be compared with those from a random set ofvehicles 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 ofcommission rates (i.e., false RSD failures) of 15 percent results in RSD identification rate of80 percent for vehicles failing 

### l/M based on this sample. However, the sample does not include vehicles passing RSD (some ofwhich 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 ofRSD with summonsto 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 ofthis occurrence offalsely failing RSD. (These references also discuss how frequently vehicles can falsely pass RSD tests.) As technology to utilize speed, acceleration, and engine/catalysttemperature readings along with emission readings is perfected, false passes/failures should greatly decrease. It is reasonable to expectfrom the available evidence that false failures on remote sensing are most frequent (as a percentage ofall remote sensing failures) among newer vehicles because they have the lowest incidence ofactual emissions problems. Newervehicles 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 ofbenefits 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 improperl/M test can also be associated with an improper oreven no repair test that would lower emissions. With the vehicle owner aware of the new risk offailing a remote sensingtest 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 ora "check engine" (malfunction indicator)-9- light coming on between inspections might seek more prompt repair lesttheirvehicles fail remote sensing. (Also, vehicle owners ignoring the "checkengine" 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 ofadditional testing. The magnitude ofsuch a deterrent effect is currently unmeasurable. However, much depends on the level and hence public visibility ofremote sensing, the public's perceptions ofthe possibility ofavoidance, 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 ofany 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 ownersto inconvenient off-cycle inspection is to use remote sensing readings as one input ofan algorithm with which to request that certain vehicles obtain their "on-cycle" I/M test at a certain type ofinspection station, particularly at a test-only inspection station. In this way, some high-emitting vehicles are ensured a full l/M testthat does not suffer from conflict of interest (i

.e., the facility doing the test also does the vehicle repairs) or othertesting 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 documentdoes 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 ofAutomotive Repair [l] provides a good starting point for EPA to work with other states. California is presently developing such a system as part ofits 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 ofthe 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 exhaustemissions and required to have an off-cycle inspection on a random basis which could also catch evaporative emission problems. In the case ofclean 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 ofremote sensing options and does not estimate this indirect effect ofremote sensing on evaporative emissions, since there are no data yet to quantify credits for repair ofevaporative emissions on RSD failures. More information about the remote sensing process and references to a considerable literature ofremote 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-10- recentEPA review paper (17] summarizes available RSD studies. 

### 2.3 Other Aspects ofa RSD Program One innovative use ofremote 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 ofa single RSD reading leading to a large numberoffalse passes. EPA is still analyzing the use ofRSD in clean screening and welcomes input. Clean screening should improve the costeffectiveness ofa periodic l/M program by eliminating unnecessary inspections and should increase public awareness and acceptance ofthe I/M program. EPA and CARB El Monte studies showthat using a single RSD reading allows a large number of RSD passing vehicles to be exempted that in fact would fail an IM240 orequivalent I /M test. Requiring multiple (e.g., possibly three oreven four) passing RSD readings on a vehicle will greatly lessen the chances offalse passing. Also, a state could implementclean 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 sincethey do not have multiple RSD readings on vehicles, the initial ArizonaRSD results on actual in-use vehicles can be used

. EPA is analyzing the ArizonaRSD 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 ofclean screening (e.g., cutpoints, fraction ofthe 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 itselfincrease credits, it can improve the costeffectiveness ofa periodic I/M program by eliminating unnecessary inspections and increase public acceptance ofthe 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 ofRSD in this fashion greatly limits the test-only ortest-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 supplementthese files to add new data and/or options that were not included in the original release ofthe model, withoutthe need for a new version ofthe 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 datafiles 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 offailures)

. 

### It is critical to emphasizethat 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 ofRSD from those in the reference documents. Also, more information will be available on fleet coverage. The MOBILES RSD credit programs will be revised atthat point (ifnot sooner) to use the updated information

. Ofcourse, 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 BasicRemote Sensing Methodology 

### It is assumed that vehicles targeted by remote sensing are required to submitto 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 ofthe 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 thatthis inspection occurs halfway between the on-cycle biennial inspections. Some vehicles though may fail RSD and receive the extra inspection beforethe half-way point; other vehicles may have an RSD failure with the extra inspection afterthe half-way point. Increased emission benefits result from these increased numbers ofinspections 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. Foran annual test-and-repair program with a discounted effectiveness, RSD benefits can be determined (at least up to the point ofan annual test-only program) ifthe RSD failures are subject to a test-only confirmatory I/M test. However, in the future, ifwarranted by-12-

user interest, EPA could develop credits that reflect the possibility offailing 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 ofmaking 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 ofthe difference in benefits ofthe test-only and test-and-repair programs. Conceptually, this type ofprogram would produce the largest benefit attributable to remote sensing because ofwhatever 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. Sincetest-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. 

### Ifthe on-cycle program is biennial, remote sensing could create incremental benefits based on both more frequent inspections and test-only inspections for more vehicles. Itis also possible for a state with a test-and-repair l/M program to allow vehicles failed by remote sensingto 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 ofremote sensing is the increase in testing frequency for part ofthe fleet. This avoids the need for setting up any test-only stations for purposes ofconfirmatory 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 consideringthe interaction from whatever discount is determined for test-and-repair programs. The remote sensing credits are thus a function offour design choices. These choices are: 1) the structure ofthe periodic l/M program; 2) whetherthe test which is used to confirm the remote sensing failures is performed at test-only ortest-and-repair programs (ifthe periodic I/M program is test-only, the confirmatory test must be test-only); 3) the fraction ofthe fleet, by model year, measured by remote sensing; and 4) the effectiveness ofthe remote sensor at identifying high emitters including the influence ofthe remote sensing cutpoints or emission standards.-13- Mathematically, the process to generatethe 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 ortest & repair) F = Adjusted fraction ofthe inspected fleet scanned at remote sensing sites

E = Effectiveness ofremote sensing identification and repair ofhigh emitters.

m Quantity is a function of vehicle model year; and p Quantity is a function ofpollutant (i.e., HC, CO orNOx).

In the equation, the influence ofthe underlying IIM program is represented by the variables Am, pand Bm, pwhich 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 userto 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: BasicRemoteSensingProgramDesign High-emitting vehicles identified by remote sensing are sentto the periodic IIM inspection stations. This includes the case oftest-only JIM programs with test-only confirmation and testand-repairJIM programs with test-and-repair confirmation ofRSD 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-RepairRemoteSensingProgram In a test-and-repair I/M area, high-emitting vehicles identified by remote sensing are sentto 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: RetestHvbridRemoteSensingProgram 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: RemoteSensing OnlyProgram 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 RemoteSensingProgram 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 singleRSD readings on any given vehicles. Use ofa 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 ofthe 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: Commitmentto aLevelof Effort The user specifies the number ofvalid 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: Commitmentto aSpecificFleet Coverage The user specifies the fraction ofthe fleet in each model yearthat is seen using remote-15- sensing. This fraction should be onlythe fraction ofthe fleet which has had sufficient valid remote sensing measurements to be identified as remote sensing failures for purposes offurther I/M inspection. Forexample, ifa 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 ofthe 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: Commitmentto aNumber of Failures The user specifies the fraction ofadditional 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 counttowards this additional fraction offailures. Vehicles failing RSD butthat are repaired before the confirmatory I/M test are discussed below. 

### 3.3.1 Option 1: Commitmentto a Level ofEffort This is the simplest ofthe three options in which a state looks at its resources and commits to obtaining a specific number ofRSD readings annually. In this option, a modified Poisson algorithm is used to estimate the number ofvehicles seen by remote sensing in orderto calculate the fraction ofthe fleet tested by remote sensing (factorF). This is necessary, sincethe fraction of all vehicles in the fleet which are measured by remote sensing is a function ofthe total number of remote sensing measurements, but is less than obtained by looking at the number ofRSD 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 overthe course ofthe study. This fraction is a function ofthe annual average VMT ofa vehicle model year at a given age compared to its VMTwhen new. In addition, the fraction ofexcess emissions identified by remote sensing in the vehicles seen must be estimated (factorE) which the utility does based on excess emissions found in the California and EPA studies as a function ofRSD 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 ofthe VMTofthe youngest model year (age) to the VMT ofthe model year (age) being estimated. This adjustment uses national average VMT information, butthe VMT information can be modified by the user to reflect local, ratherthan national default, information. The form ofthe equation is as follows: P = 1.0

- exp( k 

## *-Lambda) where k is the ratio ofVMTs,

k = VMT(current age)/VMT(age=l).-16- This algorithm is calculated for each model year and gives the probability ofmeasuring emissions from vehicles ofa 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 lowerthe fraction ofthe fleet seen. However, the fraction ofexcess emissions discussed in the next section is a function ofwhat was found in EPA and CARB studies which utilized a single RSD reading; thus, while the fraction ofthe fleet seen will change with multiple RSD readings, the fraction ofexcess emission found for a given fraction ofthe fleet inspected will not change with multiple RSD readings due to the databases used. Multiple RSD readings would alterthis fraction ofexcess 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: Commitmentto a Specific FleetCovera2e

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 ofthe 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 sufficientRSD readings for each vehicle age category (i.e., by model year) separatelyto meet the commitmentofvehicles for RSD targeting. This requires the user to supply the number of vehicles currently ofeach age and the number ofthose vehicles which are committed to be seen by remote sensing in the next year. Otheruser inputs related to the remote sensing cutpoints remain and are used to calculate the fraction ofexcess emissions identified by remote sensing in the vehicles seen (factor E). The fraction ofvehicles sentto an l/M station is calculated directly from the user input. Itis assumed thatthis fraction ofvehicles replaces the calculation ofvehicle 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 ofexcess 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 ofits fleet measured multiple times by RSD. EPA will address the effect of multiple readings in the future. 

### 3.3.3 Option 3: Commitmentto a NumberofFailures In this option, the user provides an estimate ofthe expected number ofl/M failures provided by remote sensing, again by model year/age. Ifthere 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- ofadditional I/M failures is found by RSD would be reasonable. Beyond the first year ofan RSD program, ofcourse, actual numbers ofadditional 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 ofadditional 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 ofI/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 sensingto identify vehicles for out-of-sequence testing

. The state would then be committing to adequate vehicle coverage and effort to supply sufficient number ofvehicles for out-of-sequence testing to result in the additional 500 failures. Although this option provides the clearestconnection between remote sensing activity and confirmed emission repairs, it, like the other options, may underestimatethe 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 submittingtheir vehicle for testing. Ifsuch vehicles pass the confirmatory test, they could mistakenly be thought to not count toward the commitment offailures by the program, even though the repairs were done. States can use special studies to correcttheir vehicle count. Analysis ofoperating remote sensing programs may provide new information on the impact ofthis behavior in the future

. In this option, the ratio ofthe additional remote sensing failures to the expected failures represents the fraction ofthe fleet tested by remote sensing (factorF) used to calculate remote sensing benefits. F

### = (Additional remote sensing failures/expected failures) Although the number ofremote sensing failures available to be found will be decreased by the use ofhigher cutpoints (and the ease with which these failures are found will be decreased since more vehicles will haveto be screened to find a given number offailures using higher RSD cutpoints), higher remote sensing cutpoints increase the benefits per failure, since marginal failures are not targeted. Highercutpoints 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 ofremote 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 ofremote sensing cutpoint. That is, RSD failures with higher cutpoints are assumed to have higher excess emissions found by the confirmatory I/M testthat are lowered on vehicle repair. Users are allowed to change this array in the external data file ifa 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. Beforethe 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 ofthe RSD failures; such levels could come from available RSD studies which also measure IM240 orFTP emission levels. All othercalculations 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 ofRSD 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 SensingEffectiveness Remote sensing effectiveness refers to the ability ofremote sensing to correctly identify vehicles which fail an I/M inspection. Assumingthat 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 ofremote sensing is 100 percent. In practice, even ifvehicle coverage were complete, not all I/M failures are identified by remote sensing. The shortfall in identification depends primarily on the remote sensing CO measurementcutpoint chosen by the program. For purposes ofdetermination ofremote sensing effectiveness, the emissions ofindividual 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 laand 1 b for California standards and Federal standards data. Therefore, by definition, vehicles with emissions lower than these IM240 levels have no excess emissionsthat can be identified and reduced by vehicle repairs. The excess emissions are assumed to be the only potential benefit ofidentification ofa vehicle by remote sensing for repairs, since the vehicle must fail an IM240 type inspection in orderto 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 ofall excess emissions for each pollutant (HC, CO and NOx) from vehicles identified by remote sensing. The identification rate is determined for three technology groupings ofvehicles 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 ofthe CO reading is also a reasonably accurate surrogate for exhaustHC). An identification rate was determined for each case, from 0.5 through 7.5 percent CO, in increments of0.5 percent CO. Forthe default values, the data from the El MonteParking Lot Study by the CaliforniaAir 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 ofIM240 

### 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 0.387 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 0.308 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 committo a number offailures. The average excess emissions as a ratio ofthe 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 of0 percent for CO, all (100 percent) ofthe excess emissions are obviously identified. The results are summarized in Table 3 below

.-21-

Table 3 Ratio ofAverage IM240 Excess Identified Emissions per Vehicle to Average Excess Emissions perVehicle in Fleet Using Remote Sensing CO Cutpoints (Combined CARB El Monte/EPA Studies) (0% CO CutpointGives Ratio of1) 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 1.0I 1.87 3.54 0.93 2.0% 0.85 2.67 I.I0 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 similarto 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] notbeing included. Excluding the California Sacramento study for the effectiveness calculations increases the RSD benefits somewhat. 

### 3.5 EstimatingRemote Sensing Benefits

### -22- As described in Section 3.1 , the effect ofremote sensing on I/M credits are determined by one ofthe following methods: Interpolating between existing annual and biennial I /M credits;. Interpolating between test-and-repair orretest-based hybrid l/M credits and test-only I/M credits; or Interpolating between zero credit and the applicable annual I/M credits 

### Ifthe existing periodic JIM program (eithertest-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 ofthe sametype. 

### Ifthe existing periodic JIM program is either test-and-repair orretest-based hybrid, and vehicles are directed only to test-only stations, then not only is the frequency ofinspection increased, but the effectiveness ofthe 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. 

### Ifthere 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 ofdetermination ofIIM credits, effectiveness is defined as the ability ofremote 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 contributeto additional I /M benefits. Therefore, it is the fraction ofemissions represented by vehicles identified by remote sensing which fail the I/M test which can contribute to additional I /M benefits. The calculation ofthe 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 representthe effect ofthe 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-repairJIM credit-23- B(RH): Biennial retest-based hybrid I/M credit F: Fraction ofthe fleet tested by remote sensing: Remote sensing program effectiveness (identification and repair ofhigh 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 ofSO 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 ofthe 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. Ifa test-and-repair discount ofotherthan 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, separatetest-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 ofthe I/M program. For example, anti-tampering benefits are assigned for the program as a whole on top ofwhich a per-vehicle benefit is assigned for I/M failures

. Thus, increasing the number ofl/M failures by, for example, a factor oftwo will not increase the l/M program benefits by a factor of2. 

### 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 ofscenariosthat 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 ofthe information

### -25- required by the utility and supplied by the user. The user is prompted for the name (including path, ifnot in the local directory) ofthe input file. Enterthe name ofthe remote sensing input file: (default RSD.D) Note that all inputs have default values. Here, ifthe 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 ofthe remaining information needed to calculate the remote sensing effects, including the location ofthe original l/M credit data files and the names and location ofthe 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 thatrecord 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 of000 are considered commentrecords and are notprocessed. In addition, text may be added to records beyond the last formatted data entry on any card to clarify the contents ofthat record. This additional text is not read or processed by the utility. 

### ControlSection of InputFile Records 00l and 002 are mandatory records. Record 001 contains the user selection ofthe fleet coverage option. The format ofthis record is (13, 11X,I l), meaning the first three characters contain the record number, followed by 11 blank characters, followed by the user selection ofOption. Any characters following the user selection are ignored by the utility, but can be used to annotatethe input file. The available option levels as described in Section 3.3 on fleet coverage are as follows. 1: Commitmentto a Level ofEffort 2: Commitmentto a Specific Fleet Coverage 3: Commitmentto a Number ofFailures Record 002 contains the user selection ofl/M program design. The format ofthis 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-RepairRemote Sensing Program-26-

3: Retest Hybrid Remote Sensing Program 4: Remote Sensing Only Program 5: Clean Screening Remote Sensing Program A complete description ofthese user options is located in the previous section regarding the basic utility description. The user must always enter both Record 001 and 002 in orderto use the remote sensing utility. The following is an example input ofthe control section, including some added comments to add clarity. 000 Control Section 000

--------------- 001 2 Option (may be 1, 2, or 3) 002 1Program Type (may be 1, 2, 3, 4, or 5)



### Filenames Section In this section, Records 005 through 008 indicate the name and location ofthe standard 

### l/M credit data files and Records 015 through 018 determine the location and name ofthe 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 ofthe I/M credits to reflectthe effects ofthe user specified remote sensing program. The format ofeach 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. 

### Ifno 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. 

### InputFiles 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 

### OutputFiles Record Description-27- 015: Adjusted 1981 and newer model year credits 016: Adjusted 1981 and newermodel yearRetest-Hybrid 017: Adjusted Pre-1981 model year credits 018: Adjusted Pre-1981 model yearRetest-Hybrid credits Since there are default filenames for the standard l/M credit files and the outputfiles, the user may skip all ofthese input records and the default names are used. All ofthe files, however, then must be in the local directory. The following is an example input ofthe filename section, using files otherthan the default filenames, including some added comments to add clarity. 000 Input and Output Filenames 000

---------------------------------------- Def auIt 005 C:\DATA\IMI.D IMDATA.D 006 C:\DATA\IMH.D HYBRID.IMC 007 TCI.D TECH12.D 008 TCH.D TECH12.D 015RSDDATl1.D RSDDATA.D 016 RSODATA.H RSDDATA.H 017 TECDATl l.D TECDATA.D 018 TECDATA.H TECDATA.H Formaximum 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. Forthis reason, the default input for that case is identical to the standard input file. 

### One-TimeData 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 oldermodel year vehicles 032: The CO cutpoints to be applied to remote sensing measurements for 1975 through 1980 model yearvehicles 033: The CO cutpoints to be applied to remote sensing measurements for 1981 and newer model year vehicles 041: Thetest and repair effectiveness for HC emissions 042: Thetest and repair effectiveness for CO emissions 043: Thetest 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. Newervehicles tend to produce fewer benefits-28- and more false failures than oldervehicles. 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 cutpointto be applied to remote sensing measurements for targeting must be provided for each ofthree model year groupings ofvehicles. 1974 and oldermodel 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 selectthe effectiveness ofthe remote sensing measurement in determining whether vehicles pass or fail an l/M inspection. It is assumed that all vehicles which are subjectto the inspection program, are measured the specified number oftimes, and exceed the CO cutpointfor their model year, are required to undergo an additional, out-of-sequence l/M inspection. Ifthe 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. Ifa 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 oldervehicles), Record 032 (for 1975 through 1980 vehicles) and Record 033 (for 1981 and newer vehicles). The format for this record is (13,lX,Fl1

.3). The current version ofthe model, MOBILE5a, adjusts the l/M credits for test and repair I/M program designs to be 50 percent ofthe benefits oftest-only program designs. Future versions of the model will allow for user input oftest-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 creditutility, therefore, allows the user to specify the value for the effectiveness oftest and repair l/M program designs to be used in determining the effects ofremote 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. Onethen interpolates to estimate what a credit would be for a particulartest-and-repairprogram with a discount between 50-100 percent. The provisions oftheNational Highway System Designation Act set up criteria by which to determine the propertest-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 ofthe 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 ifnot input is provided. The calculations ofbenefits require the use ofan estimate ofthe effectiveness ofremote sensing in identification ofexcess emissions for the remote sensing CO cutpointchosen 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 NOxemissions. 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 newermodel years The format for the record is (13,lX,Fl 1.3,Fl 1.3,Fl 1.3). This means thatthe 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 ofvalid vehicle measurements using remote sensing. This information includes: 

### Record Description 021 :The number ofvehicles in the fleet 022: The number ofvalid measurements per month that are made using remote sensing devices 023: The number oftimes 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 peryear by vehicle age to override the MOBILES default values normally used in the calculations. The number ofvehicles in the fleet represents the population ofvehicles which are subjectto the inspection program in the area. This number excludes out-of-areavehicles and vehicles exempted-30- from the inspection. The number ofvehicles in the fleet is entered on Record 021. The format for this record is (13,lX,Il 1). The number ofvalid measurements per month that are made using remote sensing devices is the primary measure ofthe level ofeffort related to vehicle coverage. The number ofvalid measurements that can be made with remote sensing devices depends on a great variety offactors including the number ofpractical 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 ofdevices provided, the amount ofstaffrequired and available to operate the remote sensing devices, the density ofvehicles subject to the inspection program at the remote sensing sites, the number ofhours and days that the remote sensing devices are operated, the staffallocated to remote sensing data processing and the quality ofthe remote sensing readings. The number entered by the user represents the commitment by the program to expend sufficient effort to make that number ofvalid measurements in each month. This number is entered on Record 022. The format for this record is (13,1X,Il1). The input ofthe number oftimes 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 ofmultiple measurements to reduce the number offalse failures. The utility allows the user to specify up to 11 measurements; although, again, the presentEPA creditdata 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 oftimes cannot be used for targeting. Increasing the number oftimes 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 ofthe Option 1data section, using values otherthan 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 1year ofage. Record 102 contains the mileage accumulation ofvehicles from 1 to 2 years ofage, and so forth. Since vehicles are more likely to be measured ifthey drive more, a higher mileage accumulation in proportion to othervehicles increases the expected number of vehicles ofthat age that are measured. The default values assume that all vehicles travel the roadways which are monitored using remote sensing. Ifremote sensing is to be restricted to only some roadways (such as limited access freeways), the distribution ofmileages should be adjusted to reflectthe actual distribution ofages 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 ofthe fleet in each model yearwhich have sufficient valid vehicle measurements using remote sensing to be targeted each year. Forexample, ifit takes three RSD failures to have a vehicle be sent for an I/M test, this is the fraction ofthe fleet which will be measured threetimes by RSD

. Again, our present data base allows no extra credit in excess emission found for such multiple measurements simply becausethis type ofinformation is not available in the studies used. This is a commitment on the part ofthe program to apply sufficient resources to find and measure a fraction ofeach model year using remote sensing. The fraction ofthe fleet measured can vary from model yearto 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 of25 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 ofvehicles on which remote sensing readings are taken), and the total number ofvehicles ofthat age in the vehicle fleet subjectto inspection (i.e., the total number ofvehicles in the fleet on which a state commits to obtain remote sensing readings). The entry ofboth the number ofvehicles subjectto inspection and the numberexpected to be eligible, instead ofa single fractional estimate, for each vehicle age allows for an explicitcount ofthe number ofvehicles which must be measured and eligible for targeting. The format for the record is (13,lX,2111). This means that the first 3 characters containthe record number, the next character is a blank followed by an integer number in the next 11 spaces, indicatingthetotal number ofeligible vehicles ofthat age, followed by another integer number in the next 11 spaces, indicating the number ofvehicles in that age which have sufficient valid vehicle measurements using remote sensingto be targeted each year. The following is an example input ofthe Option 2 data section. The example assumes a MOBILES default distribution of 1million vehicles and assuming, using remote sensing, that 1% of each model year are eligible for targeting. The records include some added commentsto 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 470 Option 2 : Age 12

- 13 214 37000 370 Option 2: Age 13

- 14 215 24000 240 Option 2: Age 14

- 15 216 19000 190 Option 2 : Age 15

- 16 217 14000 140 Option 2 : Age 16

- 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 ofthe expected number ofl/M failures provided by remote sensing, by age, in the current year. In this way, the number ofvehicles 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 offailures in each age that are provided

. A separate record must be entered for each of25 vehicle ages (Records 301 through 325). There are no default values for these inputs. Each record containsthe record number, the number of vehicles ofthat age which normally fail this year in the periodic inspection program, and the number ofadditional vehicles ofthat 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/Mtest). 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 ofvehicles ofthat age which normally fail this year in the periodic inspection program, followed by another integer number in the next 11 spaces, indicating the number ofadditional vehicles ofthat 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 ofthe Option 3 data section

. The example assumes an equal number offailures 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 ofbenefits used in this option uses a measure ofthe average excess emission levels of vehicles failing the remote sensing cutpointchosen by the user. This measure is the ratio ofthe average excess emissions ofvehicles identified by the remote sensing cutpoint divided by the average emissions ofall vehicles with excess emissions. In this way, the fact thatremote sensing may be used to target only the highest emitting vehicles can be used in determination ofthe benefits ofidentification ofthese 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 NOxemissions. 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 ofthe 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,F11.3,F11.3). This means that the first 3 characters contain the record number, the next character is a blank followed by a number (including a decimal) within the next 11 spaces, indicating the 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. AppendixA contains a more concise narrative listing ofthe records for the RSD utility. 

### 4.2 UsingRemote Sensing l/M Creditswith MOBILES MOBILES uses two external datafiles which contain the l/M credits whenever an I/M program is specified in the user input. The benefit ofl/M program options can be adjusted by altering the numbers contained in those data files. The remote sensing I/M credit utility takes advantage ofthat fact by adjusting the default l/M credit files to reflect the user supplied information aboutthe use ofremote sensing in the inspection programs. In this way, the current version ofMOBILES (MOBILES, March 26, 1993 orMOBSa_H, February 199S) can be used to evaluateremote 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 ofthe necessary input data. However, the inputs should reflect, as near as possible, the actual expected performance ofthe 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 showsthe format for all ofthe 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 SensingI/M CreditUtility 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 ofthe default I/M credit files to be used and the names ofthe altered JIM credit files output by the remote sensing utility. These filenames can include "path" information ifthe l/M credit files are not located in the local directory. Ifa 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 sometime 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. Itmay 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 ofremote sensing is contained in the alternate l/M credit files, there should be no need to change any ofthe normal MOBILES input parameters (otherthan those to access the use ofalternate I/M credits) to reflectthe use ofremote sensing. It is very important, therefore, to carefully choose the right combination offactors in the remote sensing l/M credit utility that properly reflect the features ofthe remote sensing program elements.-36- 

### 5.0 REFERENCES 1. "Evaluation ofthe California Pilot Inspection/Maintenance (I/M) Program," Draft Final Report, Prepared for California Bureau ofAutomotive 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 ofa Remote Sensing Device at a Centralized I/M Lane," Whitney, Kevin A. and Edward L. Glover, SAE Paper 922315, 1992

. 4

. "Evaluation ofthe Ability ofMultiple Remote Sensing Devices to Identify High Emitters," Glover, Edward L., DraftEPA Report, March 1995. 5. "Technologies to Improve the Detection ofHigh-Emitting Vehicles in a Vehicle Inspection Program," CaliforniaAirResources Board, El Monte, California, December 1992. 6. "Remote Sensing Effectiveness," Draft EPA Paper, August 31 , 1995. 7. "Approachesto Integrating Remote Sensing Benefits into the MOBILES Structure," Draft EPA Paper, August 1995. 8

. "Methods to Assess Fleet Coverage," DraftEPA Paper, August 31 , 1995. 9. "Remote Sensing Program Scenarios," Draft EPA paper, August 31 , 1995. 10. "Description ofRemote Sensing and Regulatory Requirements," Draft EPA Paper, August 31 , 1995. 11. "Potential Remote Sensing Device Specifications," DraftEPA 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 ofDraftEPA RSD Paper," Memo from Randy Guensler and Lois A

.Platte to Members ofthe Mobile Source Technical Advisory Subcommittee, June 12, 1996

. 14

. Letter from Lois A. Platte to Gerry Gallagher, Colorado DepartmentofHealth,June 11 , 1996. 15. "Laboratory Assessment ofthe Remote Sensing Device Program," Arizona Department ofEnvironmental 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 VehicleEmission Control," EPA Office of Mobile Sources Fact Sheet, August 15, 1993. 17. "The Potential Use ofRemote Sensing Devices in Inspection and Maintenance Programs," Morgan, Julie K., EPA

- Region 9, August 1995.-38-



### APPENDIXA List ofRecords for RSD Utility 000 control section 001 fleet coverage 1 commitmentto level ofeffortRSD program 2 commitmentto specific fleet coverage RSD program 3 commitmentto a number offailures RSD program 002 l/M program design 1 basic remote sensing program 2 test-and-repairremote 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 ofstandard I/m credit data input files 015-018

- no user input; indicates location and name ofthe resulting remote sensing adjusted I/M credit data output files 

### InputFiles 005 1981 & newer model year credits 006 1981 & newer model yearretest-hybrid credits 007 pre-1981 model year credits 008 pre-1981 model yearretest-hybrid credits 

### OutputFiles 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, 1to 24 (default= 1year)

·031 the CO cutpoints to be applied to remote sensing measurements for 1974 and older model year vehicles, default= 3% 032 the CO cutpointsto 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%-39- 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: DataSection Oevel of e{fort) 021 the# ofvehicles in the fleet (in inspection area) 022 the# ofvalid measurements per month that are made using RSD 023 the# oftimes a vehicle must be failed by RSD before it can be targeted for 

### l/M inspection; default 1, range 1-11 

### Option 2: DataSection (specificfleet coverage) 201- 225 the# ofvehicles for each oflast 25 model years that are eligible for RSD targeting and the #ofvehicles ofthat age that actually receive RSD; the ratio ofthe two #sisthe fleet coverage by model year 

### Option 3: DataSection (#of failures) 301-325 The# ofvehicles which normally fail I/M annually for each ofthe past 25 model years and the# ofadditional 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 ofthe average excess emission levels of vehicles failing the remote sensing cutpointchosen. Such values would presumably come from an operating 

### I/M program using RSD. This measure is the ratio ofthe average excess emissions of vehicles found at thatremote sensing cutpoint divided by the average emissions ofall vehicles with excess emissions. There are 15 records for each ofthe three pollutants (HC, CO, and NOx) corresponding to RSD cutpoints from 0.5% to 7.5%. These records are the ratio ofthe 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 ofthe 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