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5 Estimating Inspection and Maintenance Emissions Reductions Using the MOBILE Mode! The U.S. Environmental Protection Agency (EPA) mobile-source emissions factor (MOBILE) series of computer models historically have been used by state and local air-quality planning agencies to estimate emissions benefits of inspection and maintenance (~/M) programs. In this chapter, the regulatory context of EPA's MOBILE program is discussed. Comparisons of I/Mpro- gram evaluation data with MOBILE predictions are then provided, followed by a detailed explanation of proposedprocedures for estimating I/M program effects in MOBILES, EPA's latest version of the model. The chapter also includes abrief~iscussion of Californians model for estimating on-roadmobile- source emissions and I/M program effects, the EMFAC model. USE OF MOBILE IN REGULATORY APPLICATIONS The Clean Air Act and its amendments require that areas that have not met the National Ambient Air Quality Standards (NAAQS) develop state implementation plans (SIPs) that describe how they will attain compliance. The ~ 990 Clean Air Act Amendments (CAAA90) prescribe minimal control measures and attainment dates, depending on the severity of the NAAQS exceedance. Among other things, these SIPs must contain three main items: 118

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Estimating I/M Emissions Reductions Using the MOBILE Model ~19 (~) a detailed and comprehensive current-year emissions inventory; (2) a detailed and comprehensive future-year (for the prescribed attainment year) emissions inventory forecast using federal, state, and local emissions-control programs; and (3) an analysis offuture-year air quality showing attainment of the NAAQS by photochemical modeling. ~ To ensure that emissions reductions are occurnng, SIPs must also specify emissions targets for every third year toward the attainment year, and so-called rate-of-progress inventories must then be submitted to EPA. A second legislative requirement in the CAAA90, known as conformity, prohibits transportation projects if they impede progress toward meeting emis- sions targets and attaining the NAAQS. Forthe projects to proceed, metropol- itan planning organizations (MPOs) must evaluate the emissions effects of transportation plane, projects, end programs, andpass a conformity demonstra- tion with the U. S . Department of Transportation. Conformity is demonstrated if mobile-source emissions that are forecasted to result from transportation plans, programs, end projects do not exceed mobile-source emissions budgets established in the SIP . Conformity lapses if it cannot be demonstrated that the SIP mobile-source emissions budget will not be exceeded, or if 3 years have passed since the last conformity demonstration. During a conformity lapse, projects that are already under construction can proceed, but new projects requiring federal funding or approval cannot be advanced until the conformity lapse has been remedied. For both of these applications, states and regions outside California use EPA's MOBILE emissions factor mode} for estimating emissions and emis- sions reductions from mobile-source control programs such as I/M (California has its own emissions factor model, EMFAC).2 EPA introduced the first version ofthe model, MOBILE I, in ~ 978. Since then, there have been a series of mode} revisions with changes to modeling assumptions, methods, and the ways changes in the vehicle fleet are accounted for (e.g., with adoption of new emissions standards and other federal control programs). Many ofthe mode! revisions have incorporated data from testing programs that were designed to Carbon monoxide SIPs can use rollback modeling, which assumes that reductions in emissions produce a directly proportional reduction in pollutant concentrations (above background levels), to demonstrate future-year attainment. 2Although states are not mandated by any law or regulation to use MOBILE, SIPs developed with some other mobile-source emissions model would not be accepted by EPA (except for California, which must use the EMFAC model for their SIPs).

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120 Evaluating Vehicle Emissions I/M Programs assess characteristics of vehicle emissions that previously had been ill charac- terized or were underestimated. For the past several years, EPA has been working on the most significant mode] revision in its history. The new model, MOBlLE6, is expected to be released in 200 ~ for use in regulatory applica- tions. The recent National Research Council (2000) report and the Holmes and Russell (200~ review of MOBILE describe the history of the model's revi- sions and provide more details about the uses and implications of MOBILE as a regulatory emissions modeling tool. In the SIP process, MOBILE is used to estimate what are referred to as SIP credits. States use the model to estimate the emissions reduction in a future year with implementation of an I/M program (or changes to an existing I/M program). These SIP credits based on MOB TEE are only an estimate of the real emissions reductions. Actual emissions reductions from an I/M pro- gram can be measured only with real data from vehicles that have and have not been through the program. SIP credits are very important to states, be- cause if they do not accumulate enough credits to demonstrate future-year attainment, they can be penalized economically by withdrawal of federal trans- portation funds and limitations on new construction requiring environmental permits. On the other hand, if states claim too much credit for I/M and the emissions reductions are not fully realized, then progress toward attaining clean air standards is hindered. It should be noted that MOBILE estimates emissions factors in grams per mile by vehicle class (e.g., passenger cars, light-duty trucks, and heavy-duty diesels). To estimate or-road mobile-source emissions, these emission factors are then multiplied by estimates of vehicle miles traveled (VMT) by vehicle class. In most urban areas, VMT estimates are derived from transportation demand models. This chapter addresses issues in the MOBILE estimates of I/M program effects. There are just as many issues and problems in the esti- mation of VMT, but coverage of these issues is outside the scope of this re- port.3 MODEL PREDICTIONS COMPARED WITH PROGRAM EVALUATION DATA As discussed above, one of the more important uses of MOBILE is for states to generate SIP credits for an I/M program to be implemented in a Wee EPA (1992c) for guidance on development of VMT forecasts.

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Estimating I/MEmissions Reductions Using the MOBILE Model 121 future year. In the ~ 992 enhanced I/M regulatory impact analysis, EPA esti- mated that enhanced I/M would reduce light-duty vehicle (LDV) exhaust hydrocarbon (HC) emissions by 28%, carbon monoxide (CO) emissions by 3 ~ /O, and nitrogen oxide (NOX) emissions by 9/O by the year 2000 from a non- I/M fleet (EPA ~ 992b). This prediction was made with version 4. ~ of the model. EPA's predicted emissions reductions for enhanced I/M using MOBlLE5, released shortly afterward, were likewise overly generous. Table 5-] shows MOBlLE5b predicted reductions in emissions estimates from the non-~/M case for light-duty gasoline vehicles (LDGV, passenger cars) for calendar years ~ 995 and 2000 under various I/M scenarios. The table shows the expected increased emissions reductions with more advanced testing, with the largest reductions occurring for the biennial IM240 with technician training. Predicted emissions reductions for calendar year 2000 are larger than for calendar year ~ 995, primarily because the base emissions (in the non-~/M case) are smaller in future years with fleet turnover. There have been only a few comparisons of emissions reductions esti- mated from program data or remote-sensing measurements to MOBILES predictions. These comparisons are shown in Table 5-2 for several I/M pro- grams across the counky; evaluations for most ofthe I/M programs listed in the table were discussed in Chapter 3. Ofthe studies referenced in Table 5-2, the analyses ofthe Arizona IM240 program are arguably the most detailed and rigorous; these analyses show slight overpredictionsbyMOBlLE5 of CO and HC emissions reductions and significant overprediction (by a factor of 2) of NOx reductions. Analyses of ColoradolM240 date also show signif~cantover- prediction of IM240 effects. Such overpredictions ofthe effectiveness of I/M programs hinder progress toward achieving air-quality goals, as states are granted too much SIP credit for planned I/M programs and therefore do not enact additional needed controls. MOBlLE6 was not available to the committee during most ofthe commit- tee's work. However, the draft MOBlLE6 model, just released, shows deteri- orationrates signif~cantlylower than thoseinMOBlI E5. Figure 5-l compares VOC and NOX emission rates in MOBILES and in draft MOBlLE6 with and without the effects ofthe Tier 2 and 2007 heavy-duty rulemakings. The figure shows that emission rates in draft MOBlLE6 are significantly higher in past and current years and significantly lower in future years (after about 2005~. If the emissions deterioration rates are closer to reality in MOBlLE6 than in MOBlLE5, this could be a major contributing factor to the MOBlI~E5 overesti- mation of I/M effects. EPA has been criticized in the past for overly pessimis- tic assumptions on deterioration rates for 198 ~ and later vehicles (see, e.g.,

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122 Evaluating Vehicle Emissions I/M Programs TABLE 5-l MOBlLE5b Predicted Exhaust Emissions Reductions for LDGVs in ~ 995 and 2000 for Various I/M Programsa Year 1995 Year 2000 CO HC NOx CO HC NOx (%) (%) (%) (%) (%) (%) Idle, annual 17.9 17.8 0.8 18.8 19.1 1.2 Idle, biennial 14.9 14.5 0.8 16.8 16.9 1.3 Idle/2500, annual Idle/2500, biennial Loaded idle, annual Loaded idle, biennial IM240 (1.2/20/3), biennial, without technician training IM240 (1.2/20/3), biennial, with technician training Acceleration simulation mode 2525/5015 (25,50,1), biennial 25.5 21.7 23.7 20.2 32.8 39.6 33.6 30.7 22.3 18.6 22.2 0.6 0.6 0.6 18.5 0.6 31.0 13.1 29.0 24.8 26.0 26.0 23.3 36.8 36.3 19.5 45.4 22.1 24.6 21.9 32.4 39.2 1.0 1.1 1.0 1.1 24.8 19.6 41.9 39.4 24.4 aFleet average grams-per-mile emission factors with I/M relative to non-I/M. Note: All MOBILESb runs used default fleet mix and registration distributions, 19.6 mph average speed, 75F temperature, 8.7 pounds per square inch RVP, no RFG or oxygenate, and default operating fractions. All I/M programs were assumed to start in 1992, 20% stringency, 0% waiver rates, 100% compliance, test only, centralized. Sierra Research ~ 994a). With these Tower emissions rates for future years in MOBlI-E6, as shown in Figure 5-l, the I/M credits are likely to be lower in MOBILES than in MOBILES. Early indications are that MOBlLE6 will indeed reduce the emissions-reduction benefits from I/M compared with MOBlLE5 (Clean Air Report ~ 999~. There are serious policy implications if MOBlLE6 SIP credits for I/M programs are significantly lower than MOBlLE5. One indication of I/M effectiveness in MOBlLE6 compared with evalua- tion of benefits using program data can be gleaned from the most recent audit of the Colorado I/M program. In this audit, EPA's Serious Area CO Mode! was used to estimate the benefits of the state's I/M program. The Serious Area CO Mode! is a version of MOBlLE5 that has some of the key features of MOBILES forCO emissions, includingiower deterioration rates. The 1999

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Estimating I/MEmissior~s Reductions Using the MOBILE Model 123 TABLE 5-2 Estimated Emissions Reductions Attributable to I/M As a Percent of MOBlLE5 Predictionsa HC (%) NOx CO (%) (%) Phoenix, AZ: Centralized IM240 Random sample of 1995 program data (EPA 1997a) All 1996-1997 program data with fast- pass/fast-fail converted to estimated full IM240 (Wenzel l999b) Random sample of 1996-1997 vehicles given full IM240 (Wenzel l999b) Colorado: Centralized biennial IM240 All 1997 program data (ENVIRON 1998) Idle IM240 Remote sensing in 1989 in I/M and non-I/M areas (Zhang et al. 1996b) Atlanta, GA: Decentralized idle (BAR97) Comparison of remote-sensing measurements in I/M vs. non-I/M areas (Corley and Rodgers 2000) Minneapolis, MN: Centralized annual idle Comparison of ambient CO concentrations (Scherrer and Kittelson 1994) with MOBILESb fleet reductions (O'Connor et al. 85 46 100 83 43 90 89 46 83 86-103 76-93 76-84 3-6 105-121 21 Cars, 209 Trucks, 72 14 aBoth program and MOBILES estimates are fleet average grams per mile emissions. Colorado audit estimated an 8% reduction in CO emissions for the IM240 program, compared with ~ 7/O for the Serious Area CO Mode! (Air Improve- ment Resource 19994. MOBILE AM INPUTS To obtain emissions factors from MOBILES, including credits for an I/M program, the user provides three types of input (~) program descriptive inputs,

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124 Evaluating Vehicle Emissions I/M Programs All Mighty Vehicles Viable ~ ~ ~C) Edit (Ir~udingEvapor~ive and Exhaust Enissions) 6- 5 4- _ '\\ - ~ 3 1 FF; MOEl1 Fly ~~ MOBI1 F`;wffl rer2&mrule . ., . \ it_ z . x ~~;a 1 o- 1990 1996 2~100 2005 21)10 2D15 ID ~ 2Do Yea FIGURE 5-1 VOC and NOX emission factors in MOBILES and draft MOBILE6. The line rule-makings. Source: Beardsley2001. (2) program effectiveness input, and (3) fleet characterization inputs. The model estimates emissions credits for the effects of up to five I/M programs specified by the user. For example, if an area has two-speed idle testing for older vehicles and IM240 testing for newer vehicles, then the user provides program specifications for each of these two types of I/M programs. The following I/M program descriptive inputs must be provided to MOBlLE6: Program start year (calendar year when program begins).

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Estimating I/MEmissions Reductions Using the MOBILE Model 125 45 4 &5- 3- All Hgh~yVehid" ~d" ~ nitrogen (~) him , _ M - LE6 ~ ~ ' ~ ! Fit; i.\, I An.` I 25 Q In 2 ~5 1 -a ~LE6~ffl ma.. Tier 2 & [D rule Fib 1 all\ An\ \ \ .. .. >. =~ ~ I. 1990 1995 2000 ~ ;Ino 2tn5 202D ~ 2D30 Ca er - Yea" marked "MOBILE6" is draft MOBILE6 without the effects ofthe Tier 2 and 2007 heavy-duty First (earliest) and last (latest) model years of vehicles subjectto the requirements of the program. Model years exempted from the program. Test type (idle, 2500/idle, acceleration simulation mode (ASM), IM240, and on-board diagnostics (ODD)). Program type (inspection only, inspection and repair (computerized or manual)). Frequency of inspection (annual, biennial, change of ownership). Vehicle classes covered (LDGV, light-duty gasoline truck (I DGT) weight classes, heavy-duty gasoline vehicle (HDGV) weight classes).

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126 Evaluating Vehicle Emissions I/M Programs Cutpoints for HC, CO, and NOX for IM240 testing. Remote-sensing parameters. The user is required to provide three MOBILE inputs related to program ef- fectiveness: compliance rate, waiver rate, and stringency level. This section provides brief discussions of these parameters, including what they are and the common methods used by state agencies to determine their appropriate values. Compliance Rates Compliance rate, typically the most important ofthese parameters in terms of emissions reductions, is defined as the level of compliance with the inspec- tion program. However, compliance is a difficult concept, and it is not clear that EPA and the states have used a consistent definition or measurement of it in the past. Figure 5-2 shows a conceptual classification of vehicles in an area that has anI/M program. There are fourtypes of noncompliantvehicles: ~ ~ ~ those that are not registered, (2) those that avoid the program by registering outside the area,4 (3) those that are registered but never take the test, and (4) those that take the test and fait but never complete the test cycle with a pass- ing test.s EPA guidance for MOBILE 5b (EPA ~ 997b) stated that the compli- ance rate specified should include all registered vehicles that successfully complete an [/M cycle, including both passing and waived vehicles. This definition includes only one ofthe four types of noncomplying vehicles shown in Figure 5-2, and in the past, states have tended to estimate the compliance rate as the proportion of registered vehicles that actually take an I/M test and thus underestimate the true noncompliance rate and overstate the I/M benefits. In MOBlLE5, there is no default value for the compliance rate; it must be specified in the input file. However, EPA asks states to provide documenta- tion if the compliance rate is over 96%. As a result, this 96% value has be- 4An analysis of Dayton, Ohio, area registration statistics showed that when the enhanced I/M program was implemented, registrations decreased by 10% in the coun- ties in the I/M program and increased by a similar amount in the surrounding non-I/M counties (McClintock l999b). 5Two other categories of vehicles could be considered in noncompliance: those that receive inadequate or ineffective repairs; and those that pass the inspection be- cause of emissions variability, so they are never repaired. However, because both of these end up passing the test, they are not included in Figure 5-2.

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Estimating I/MEmissions Reductions Using the MOBILE Model 12 7 All vehicles driving in the area Registered Take initial test Pass ~ '1 rail Repair and pass Get waiver (possibly some | | Scrap repair) FIGURE 5-2 Classification of vehicles in an area subject to I/M. Shaded boxes indi- cate noncomplying vehicles. Source: Adapted from Hamngton et al. 1998. come a de facto default, because states could claim up to 96% compliance without any documentation. Similarly, MOBlLE6 defines noncomplying vehicles as "vehicles which show up for the initial test, but drop out of the process prior to a successful passing result or a waiver" (EPA 1 999e). However, EPA now recognizes a second type of noncomplying vehicle~ne that does not show up for its initial testand says that the input compliance rate should take these vehicles into account (EPA ~ 999e). For MOBTLE6, there is no default noncompliance rate; the rate must be specified in the input file. However, it is unclear what sort of documentation will be required by states in their SIPs to justify the rate that is used. Although improved over MOBILES, the MOBlLE6 definition of compli-

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128 Evaluating Vehicle Emissions I/M Programs ance rate remains an underestimate ofthe true noncompliance rate, as it does not include unregistered vehicles and vehicles that avoid the program by regis- tering out ofthe area. States will likely continue to estimate the compliance rate based on the number of registered vehicles, as there is no incentive to measure the proportion of vehicles that are unregistered or that are registered outside the region. Additionally, the registered fleet might not always accu- rately account for scrapped, moved, or change of ownership vehicles that are no longer in operation in the region. License-plate reading as part of remote- sensing measurement programs will help allow estimates of both of these components ofthe fleet being driven in the region. Once the total vehicle fleet is known, it is relatively easy to determine the complying vehicles from pro- gram data the number of vehicles that get tested and are either repaired or waived are complying. Some ofthese might have been incorrectly or fraudu- lently passed, and remote sensing could also help identify them. Waiver Rate Waiver rate refers to the fraction of vehicles that fail their initial tests but were never fully repairedbecause the repair cost limit (or some other criterion) has been met; these vehicles have complied with the program requirements but are still failing vehicles. This parameter is discussed in Chapter 3. in the model, separate waiver rates are used for pre-1981 and post-1980 LDVs. These rates must necessarily come from I/M program records. EPA recom- mends that, for historical inventory development, program-specific data be used to derive the waiver rates. For future inventories, the historical rates may be used. Stringency Rate Stringency rate (or failure rate) is the expected failure rate for pre-1981 model-year vehicles.6 Stringency rate is defined as the test failure rate ex- 6Stringency rate is input only because the older model years use a methodology (from the late 1970s) that calculates benefits based on failure rates rather than test procedures and cutpoints. For newer vehicles, MOBILE6 uses identification rates based on the proportion of total emissions from failing vehicles (not number of failing vehicles) identified.

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Estimating I/MEmissior~s Reductions Using the MOBILE Model 135 ~ 999a). Although only a small number of vehicles (22~7 in the remote-sensing data set were matched to the IM240 program records and there are issues that complicate the analysis (e.g., comparing emissions reductions from IM240 tests with remote-sensing measurements) these results indicate that the MOBlLE6 repair-effectiveness rates might be overstated. Chapter 7 contains a discussion of emissions-repair studies. The waiver vehicles are those that still fail the emissions test after a set minimum amount was spent on only partially successful repairs. Although not fillly repaired, they are assumed on average to have some repairs. The pro- posed default for MOB TLE6 is that the waived vehicle emissions rate is 20% less than the failed vehicle emissions rate. This default is an assumption, as there was no available analysis of emissions from waived vehicles from oper- ating IM240 programs at the time EPA prepared the draft MOBILES I/M credits (EPA ~ 999e). Figure 5-5 shows an example ofthe rates estimated for repaired and waived vehicles, for the same ~ 990- ~ 993 PET LDGV example as in Figure 5-4. Note that the change in emissions as a function of mileage for the repaired vehicles is not the deterioration rate for the repaired vehicles; rather, these emissions rates are used to determine the I/M credit at a given age. Note also for this example that the EPA methodology results in the re- paired vehicle emissions multiple ofthe normal emissions rate increasing from 0 to about ~ 00,000 miles and then decreasing until the repaired and normal- emitter rates are the same at about 170,000 miles. After 170,000 miles, the calculation actually results in repaired vehicle rates being less than normal- emitter rates, but they are set to the normal-emitter rates. T4entif~cation Rate The high-emitter identification rate (IDR) is the proportion of emissions from high emitters in the fleet that are correctlyidentified. If the cutpoints are set so that all high emitters are properly identified (i.e., fait the test), then the IDR is ~ 00%. The TDR depends on the test method used (IM240, ASM, idle testing) and the test cutpoints (but not model-year group or vehicle technol- ogy). The lower the cutpoints, the higher the IDR; however, lower cutpoints also increase the chances that normal emitters will fail the test. To estimate 7In general, a large sample of vehicles should be measured repeatably using re- mote sensing to help establish emissions trends and repair effectiveness.

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136 Evaluating Vehicle Emissions I/M Programs IDRs for MOBILES, EPA used a database of 9 ~ 0 model-year ~ 98 ~ and later cars and trucks that had both an IM240 test and an FTP test from EPA emis- sions factor testing in Ann Arbor, Michigan, and Hammond, Indiana, and also Arizona data on randomly recruite~vehicles. These identification rates, used to calculate the average emissions ofthe fleet after a cycle of I/M testing and repair (described below), are estimated from a regression analysis ofthe Toga- rithms of the test cutpoints (cut) as follows (EPA 1999e): HO IDR = ~ .145 ~ - 0. ~ 365 x In(HCcut) - 0.1069 x In(COcut) CO IDR = I . ~ ~80 - 0.1073 x In(HCcut) - 0.1298 x In(COcut) (5-2) NOX IDR= 0.5453 + 0.7568 x NOcut - 0.3687 x NOcut2 + 0.0406 x NOcut3 Another method that states commonly use to increase identification of failing vehicles is to require a passing inspection for change of vehicle owner- ship. This method can increase the fraction ofthe fleet that is inspected and increases the likelihood of failure identification and repair. In the draft MOBlLE6 documentation, change of ownership is assumed to be a fixed fraction of the fleet based on an analysis of Wisconsin data, but there have been comments suggesting that this be a user input to reflect the actual change of ownership rates in an individual area. Noncomplying Vehicles As described above, the compliance rate input to MOBlLE6 is assumed to represent vehicles that fail the initial test and do not complete the testing process, obtaining either a passing test or a waiver, and also those vehicles that do not show up for the required I/M testing. Although some of the no-show vehicles could be normal emitters, the draft MOBlLE6 documentation indi- cates that EPA considers all noncomplying vehicles as high-emitting vehicles that are unaffected by the I/M program, and the input compliance rate should be set with this understanding. The high emitters, therefore, consist ofthree types of vehicles: (~) the identified high emitters that are repaired (but with emissions rates higher than normal emitters), (2) the identified high emitters that are partially repaired and receive waivers, and (3) noncomplying high emitters.

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EstimatingI/MEmissions Reductions Using the MOBILE Model 137 This treatment of noncomplying vehicles is different from the MOBILES assumptions in severalways. InMOBlLE5, the noncompliance rate is defined as a share ofthe fleet as a whole; noncomplying vehicles are assumed to have higher emissions than normal vehicles. In MOBlLE6, noncompliance is part of the high-emitter fraction only, and although MOB TLE5 assumes that the failure rate ofthe noncomplying vehicles is higher than that ofthe complying vehicles, it did not assume that all of them are high emitters as is the case for MOBILE6. Average Emissions after I/M Average emissions after I/M are defined for each vehicle class, model- year group, and technology type from a combination of normal emitters, re- pairedvehicles, waived vehicles, end high emitters not repaired (either because I/M failed to identify them or because they are noncomplying). Table 5-3 shows the five subsets of vehicles that contribute to the average and the weighting factor for each subset. Once the average emissions rate after I/M has been calculated for each vehicle class/model-year group/technology-type combination, then sales weights are used to calculate the fleet average emis- sions after I/M across all LDVs in the fleet. A significant problem with the draft EPA methodology is that the IDR has been defined es the fraction of emissions from the identified high emitters, yet this same IDR is used as FID in Table 5-3, which is supposed to represent the fraction of high-emitting vehicles identified. Because the distnbution of emis- sions from high-emitting vehicles is so skewed, the infraction of emissions from high emitters identified is substantially greater than the fraction of high-emitting vehicles identified. Using the estimated IDR for Fain the estimation of aver- age emissions after I/M results in an overestimate for the I/M credit (i.e., estimated average emissions after I/M are too low). Application of the I/M Credit The I/M credit in MOBlI E6 is the difference in estimated emissions before and after I/M. Emissions before I/M are the basic emissions rates described previously, and average emissions after I/M are calculated as the weighted average across subsets of vehicles as shown in Table 5-3.

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138 Evaluating Vehicle Emissions I/M Programs TABLE 5-3 Calculation of Average Emissions after I/M Vehicle Subset Weighting Factor Emissions Rate Normal emitters, no change in emissions after I/M High emitters not identified by I/M, no . . . c range in emissions Noncomplying high emitters, no change in . . emlsslons High emitters identified and given cost waivers, some repair below high-emitter level High emitters identified and successfully repaired 1 - - H FH * (1 FID) FH X FID X FNC FH X FID X FW FH X FID X FR .N EH EH * 0.80 ER Note: The average for each vehicle-class, model-year, technology group is the weighted average emissions rate across five subsets of vehicles. FH = fraction of high emitters before I/M FID = fraction of high emitters identified by I/M FNC = fraction of identified high emitters . , . In noncompliance FW = fraction of identified high emitters . . given a waiver FR = fraction of identified high emitters fixed FNc+Fw+FR= 1 EN = emissions rate for normal emitters EH = emissions rate for high emitters EW = emissions rate for waiver vehicles ER = emissions rate for repaired vehicles ER 2 EN by constraint MOBILES models the effect of I/M as a reduction in emissions at the time of inspection; this is referred to as the I/M credit. Emissions are modeled to increase between inspections at the same deterioration rate as vehicles not subject to an I/M program. This results in the so-called "sawtooth" pattern (also the basis for I/M credits in MOBILES) shown schematically for a biennial program in Figure 5-6. For an annual program, the I/M credit is calculated and applied once per year, and there is half the time for vehicle deterioration before the next test cycle. MOBILES, the current regulatory model, has a very small increase in the emissions reduction in I/M benefits for an annual enhanced ]/M program instead of a biennial programonly a 2-6% increase in emissions reductions,

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Estimating I/MEmissions Reductions Using the MOBILE Model 139 c, - co o ._ u, u' .e _ Before repair After repair _ ,._ - _ ~: ~spechon effect | ~ , 1 ~ I Vehicle Age FIGURE 5-6 Schematic of I/M credit algorithm (sawtooth) for a cohort of vehicles in a biennial program. depending on the pollutant. If the I/M credit for an annual instead of biennial program in MOBILES is equally small, then states have no incentive to test vehicles more frequently and repair high emitters quickly. Although no analy- ses have been published that indicate significantly greater emissions reductions for annual programs, if repair durability is less than 2 years, annual programs are likely to reduce emissions more than only a few percent from biennial programs. When MOBlLE6 is released, the additional credit modeled for annual programs should be compared with real-worId data. This sawtooth pattern for modeling I/M (the same approach used in MOBlLE5) suffers from a number of problems and is inappropriate for a number of reasons, including the following: Vehicles with end withoutI/M are assumed to deteriorate et the same rate, but it is very likely that the repaired fleet will deteriorate at a rate differ- ent from that of the fleet that has not been repaired. Some repairs will be effective and lasting and others will be ineffective and cause emissions to increase beck to the unrepaired level. A comparison of Arizona IM240 data with remote-sensing data shows that repair effectiveness diminishes over time (Wenzel 1999b).

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140 Evaluating Vehicle Emissions I/M Programs There is no explicit allowance in the model for repaired vehicles to revert beck to high-emitter status. Analysis of Arizona IM240 data shows that 42% of cars that initially failed their IM240 and then resumed and passed, failed again in their initial test in the next cycle 2 years later (Wenze} ~ 999b). The I/M credit algorithm does not allow for vehicles being scrapped or sold outside the area (and still used in the I/M area) rather than repaired. MOBILES includes vehicle scrappage, but it is not modeled as a function of high-emitter status.8 There is no estimate of the effect of vehicles being repaired just be- fore I/M testing so that they will pass the test the first time. Some of these repairs will not be done or not be long-lasting, and the vehicle will revert back to high-emitter status just after the test. OBD Effects MOBlLE6 includes emissions reductions for vehicles equipped with OBDIT systems ( 1996 model years and later). OBDTI is discussed further in Chapters 2 and 4. Estimates for these emissions reductions depend on three parameters, which have assumed levels because in-use data are not yet avail- able (EPA 1999i): The ability ofthe OBD system to identify high emitters is assumedto be a fixed fraction of high emitters at 85/O. The remaining 1 5/O of vehicles that are high emitters but are not detected by the OBD system are assumed to remain as high emitters. The response rate is the fraction of owners who will respond to a malfunction indicator light (MTL) and have the vehicle repaired. MOBILES assumes that owners are much more likely to respond to a MIL in an OBD- based I/M area, where repairs are required. In OBD-based I/M areas, MOBlLE6 assumes that the response rate is 90/O over the lifetime of the vehicle. Without such an I/M program encouraging repair, the response rate is assumed to be 90/O up to 36,000 miles (the standard full vehicle warranty period), ~ 0/O from 36,000 to 80,000 miles (the age limit for federally mandated emissions-control system warranty), and zero after 80,000 miles. MOBILES includes vehicle scrappage for vehicles destroyed in an accident or retired from the fleet. Emissions credits for scrappage programs are estimated outside the model.

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Estimating I/MEmissio7?s Reductions Using the MOBILE Model 141 The emissions level after a repair in response to a MTE is assumed to be ~ .5 times the appropriate 50,000-mile emissions standard; this is the thresh- old level for illuminating the MIL. Because of the Tow emissions for new vehicles and the low response rate at higher mileage, as currently modeled, the emissions reduction associated with OBD is low in the absence of an I/M program. The MOBlLE6 approach does not take into account the ability of the system to identify a failed component and take corrective action to minimize the effect ofthe emissions. For example, when an oxygen sensor fails, some OBD systems can revert to a known open-Ioop calibration that has good, but not optimal, emissions. Thus, some OBD identified failures might have little emissions increase, even if the owner ignores the MIL. Figure 5-7 (EPA 1999f) shows MOBlLE6 projected nonmethane HC basic emissions rates for light-duty Tier ~ vehicles with OBD systems from EPA's draft MoBlLE6 documentation (EPA ~ 999i). The figure shows that MOBlLE6 will generate a small emissions reduction for OBD systems in areas without OBD-based I/M and much larger emissions reductions in areas with OBD-based I/M. Such emissions reductions might tee overly optimistic because they might tee based on optimistic assumptions about owner response to the MIL in the I/M areas and pessimistic assumptions about response in non-~/M areas. Antitampering Programs Antitampering benefits in MOBlLE6 are intended to be as similar to MOBlLE5 as possible with the same fractional reduction in high emitters associated with antitampering programs for vehicles before the ~ 996 mode] year. After ~ 996, OBD is assumed to catch all tampered vehicles. This de- scription ofthe approach, obtained from conversations with EPA staff, was not available to the committee in written form. Evaporative Emissions and I/M Evaporative emissions are modeled with three distinct groupings: normal (functioning), purge-failure, end pressure-faiTure vehicles. Purge failure refers to failure ofthe system that allows regeneration ofthe carbon canisters used

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142 Evaluating Vehicle Emissions I/M Programs ~8 0.7 Me ~5 0.4 Em 0.3 ~ i ~ 2 A $ 5. Q . 7.5 Mileage x 11]- 10.0 12.5 No OBO J No IM OBO / No IM - - - OBO I IM ]~.0 17,5 20.0 FIGURE 5-7 MOBILE6 non-methane HC basic emissions rates for light-duty Tier 1 vehicles with OBD systems. Source: EPA l999f. to capture evaporative emissions through vapor purge into the combustion system. A purge failure can be a result of a failed valve or disconnected hose that leads to the intake manifold on the engine. A pressure failure refers to the loss of integrity ofthe system end caninclude a missing, split, or disconnected hose; a missing or failed gas cap, or a leak in the tank. Purge and pressure failures have been measured in I/M programs through the use of flow rate and pressure tests on the vehicle during the exhaust emis- sions test. The test procedures can introduce their own problems, as not all vehicles have accessible components. Perhaps the greatest problem, however, is that the purge test is very invasive, with many hoses and components actu- ally being damaged when these tests were first tried. Although MOB ILE6 will mode! emissions reductions associated with pressure and purge tests, few areas are actually perfo~ing these tests. The future use of purge and pres- sure checks appears doubtful. A gas-cap check and a targeted physical in- spection offer the most likely benefit of I/M programs on evaporative emis- s~ons.

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Estimating I/MEmissions Reductions Using the MOBILE Model 143 CALIFORNIA'S EMFAC MODEL FOR ESTIMATING I/1\] EMISSIONS REDUCTIONS Historically, California has had more restrictive air-quality and automobile emissions standards than the rest of the United States. The Clean Air Act allows California to regulate automobiles in the state and use its own computer models to predict emissions inventories. The California Air Resources Board (CARB) has developed its own emissions inventory model, called EMFAC. Similar to MOBILE, EMFAC was developed well over a decade ago and has continually been improved over the years. The current version of EMFAC is EMFAC2000 (available at ht~p://arbis.arb.ca.gov/msei/msei.htm). EMFAC has a number of differences with MOBILE, as outlined in NRC (2000~. For estimating emissions reductions from California's Smog Check I/M program, EMFAC uses a similar modeling methodology as MOBILE, with a sawtooth representation of inspection and repair over the life of a vehicle (section 4 in the online EMFAC2000 documentation at ht~p://arbis.arb.ca.gov/ msei/doctabletest/doctable_test.htmI). EMFAC has four categories for high emitters, compared with three in MOBlLE5 and one in MOBlLE6. The per- centage of each technology group inthese high-emitter groups (or regimes, in EMFAC terminology) is determined from vehicle surveillance program data and I/M recapture fleet data. These percentages are then multiplied by reg~me- specific identification rates (i.e., the percentage of vehicles that will fait a given I/M program), as described in section ~ in the online documentation (http:// arbis.arb.ca.gov/msei/doctabletest/doctable_test.htmI) . The identification rates in EMFAC2000 are based on failing fractions of vehicles in the various Smog Check programs. For estimating the repair effectiveness, EMFAC uses a two-step process. During CARB's surveillance programs, high-emitting vehicles were exten- sivelly repaired to determine the maximum gain achievable through a "perfect" repair. These perfect repair values are then modified by "correction eff~cien- cies," which are a function ofthe I/M program being simulated.9 These effi- ciencies vary on the basis of such factors as I/M repair cost limits and esti- mates oftechnician training. After the identification and repair percentages have been determined, the percentages of vehicles in the different high-emitter 9Although it is questionable whether these "correction efficiencies" have been measured in a realistic repair setting.

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144 Evaluating Vehicle Emissions I/M Programs regimes are modified. Last, the same standard deterioration rate is applied across all emissions regimes. Total reductions across entire fleets are then calculated by appropriately weighting the vehicle technology groups by VMT for a specific mode! year. Like MOBILE, EMFAC has overpredicted emissions reductions from the state's I/M programs, thus generating SIP credits that were too large and contributing to problems in meeting air-quality standards. With EMFAC7G (the version prior to EMFAC2000), substantial emissions reductions were modeled for California's 1984 biennial two-speed idle VM program12% reduction in HC, ~ ~ % reduction in CO, and 5/O reduction in NOX. With a draft version of EMFAC2000 and additional analyses of VM evaluation program data from tests of over ~ ,000 vehicles, CARB now suggests a ~ 5/O reduction for HC exhaust, 9/O for CO, and 7/O for NOX attributed to the 1 984 program (CARB 2000b). Early independent evaluations of this program showed no emissions benefit (Lawson 1993; Lawson et al. 1995, 1996a). As discussed in Chapter 3, CARB (2000c) recently evaluated the CaTifor- nia enhanced VM program. A draft version of EMFAC2000 predicts emis- sions reductions in ~ 999 from the program at ~ 9/O for HC,6% for NOX, and ~ 8/O for CO, but CARB's analyses of roadside tests showed emissions reduc- tions of only 14/O for HC, 6% for NOX, and ~ 3% for CO. However, these percentage reductions should be treated with caution due to a potential location bias in the roadside sampling that took place. SUMMARY The MOBILE model will continue to be used to determine future emis- sions-reduction credits that states will receive from implementing VM or from modifying their current I/M programs. MOBILE is a static, not a dynamic, mode} and is therefore a simplified representation of emissions changes from VM. Historically, MOBILE has overestimated emissions reductions from VM programs. It remains to be seen whether MOBILES, whichis a major revision from MOBILES, will also overestimate VM benefits or whether it will be a more accurate representation of VM benefits. Indications are thatMOBTLE6 will estimate lower emissions reductions from VM programs than are estimated by MOBII~E5. Estimates of model inputs, such as compliance rates, have a large effect on the VM credits estimated by MOBILE. To date, states have been allowed

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Estimating I/MEmissions Reductions Using the MOBILE Model 145 lo use optimistic estimates ofthese inputs, instead of justifying them with anal- ysis of program data or other assessments. The model inputs should be set by default to pessimistic values, thus providing an incentive to states to document evidence for inputting more optimistic values. Such evidence should include program evaluations in the state or reliance on program evaluations from other states with similar I/M programs. Model-based forecasts should be closely linked to I/M program performance measurements. There are also a large number of assumptions internal to the model that significantly affectMOBlLE's emissions projections and I/M credits. These include the absolute number and average emissions of high emitters, average emissions of initially failing end passing vehicles, average repair effects, identi- bcation rates under different cutpoints, and OBD effects. Actual data from state programs and special studies could be used to improve model parameters and assumptions. In the long term, the overall I/M estimation methodology in MOBILE should be improved based on I/M evaluation data. For example, empirical data already show that the sawtooth modeling approach is not reaTis- tic. Human behavior, an important factor missing in the model, should be incorporated into future models. Further, embedded assumptions in the mode} should be given parameters as much as possible so that users can improve their I/M benefit forecasts with the latest available data. In the short term, sensitivity analyses should be done to demonstrate the effects of changes in model inputs and in assumptions built into the model. The results should be incorporated into the MOBILES guidance documentation and related documents. A mode} such as MOBILE will continue to be needed for forecasting future-year emissions end the effects of mobile-source contro]programs such as I/M. The mode! should not, however, be used to evaluate actual perfor- mance. Instead, program evaluation studies should be done to estimate current program effects, and results from actual I/M performance should be used to calibrate the MOBILE estimates. As stated elsewhere in this report, guidance from EPA is needed to accomplish these goals.