comparison of the moves2010a, mobile6.2, and emfac2007 ... · after initial review of the...

TECHNICAL PAPER

Comparison of the MOVES2010a, MOBILE6.2, and EMFAC2007mobile source emission models with on-road traffic tunnel and remotesensing measurementsEric M. Fujita,1,⁄ David E. Campbell,1 Barbara Zielinska,1 Judith C. Chow,1

Christian E. Lindhjem,2 Allison DenBleyker,2 Gary A. Bishop,3 Brent G. Schuchmann,3

Donald H. Stedman,3 and Douglas R. Lawson41Division of Atmospheric Sciences, Desert Research Institute, Nevada System of Higher Education, Reno, NV, USA2ENVIRON International Corporation, Novato, CA, USA3Department of Chemistry and Biochemistry, University of Denver, Denver, CO, USA4National Renewable Energy Laboratory, Golden, CO, USA⁄Please address correspondence to: Eric M. Fujita, Division of Atmospheric Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno, NV89512, USA; e-mail: [email protected]

The Desert Research Institute conducted an on-road mobile source emission study at a traffic tunnel in Van Nuys, California, inAugust 2010 to measure fleet-averaged, fuel-based emission factors. The study also included remote sensing device (RSD)measurements by the University of Denver of 13,000 vehicles near the tunnel. The tunnel and RSD fleet-averaged emissionfactors were compared in blind fashion with the corresponding modeled factors calculated by ENVIRON InternationalCorporation using U.S. Environmental Protection Agency’s (EPA’s) MOVES2010a (Motor Vehicle Emissions Simulator) andMOBILE6.2 mobile source emission models, and California Air Resources Board’s (CARB’s) EMFAC2007 (EMission FACtors)emission model. With some exceptions, the fleet-averaged tunnel, RSD, and modeled carbon monoxide (CO) and oxide of nitrogen(NOx) emission factors were in reasonable agreement (�25%). The nonmethane hydrocarbon (NMHC) emission factors(specifically the running evaporative emissions) predicted by MOVES were insensitive to ambient temperature as compared withthe tunnel measurements and the MOBILE- and EMFAC-predicted emission factors, resulting in underestimation of the measuredNMHC/NOx ratios at higher ambient temperatures. Although predicted NMHC/NOx ratios are in good agreement with the measuredratios during cooler sampling periods, the measured NMHC/NOx ratios are 3.1, 1.7, and 1.4 times higher than those predicted by theMOVES, MOBILE, and EMFAC models, respectively, during high-temperature periods. Although the MOVES NOx emission factorswere generally higher than the measured factors, most differences were not significant considering the variations in the modeledfactors using alternative vehicle operating cycles to represent the driving conditions in the tunnel. The three models predicted largedifferences in NOx and particle emissions and in the relative contributions of diesel and gasoline vehicles to total NOx andparticulate carbon (TC) emissions in the tunnel.

Implications: Although advances have been made to mobile source emission models over the past two decades, the evidencethat mobile source emissions of carbon monoxide and hydrocarbons in urban areas were underestimated by as much as a factor of 2–3in past inventories underscores the need for on-going verification of emission inventories. Results suggest that there is an overallincrease in motor vehicle NMHC emissions on hot days that is not fully accounted for by the emission models. Hot temperatures andconcomitant higher ratios of NMHC emissions relative to NOx both contribute to more rapid and efficient formation of ozone. Also,the ability of EPA’s MOVES model to simulate varying vehicle operating modes places increased importance on the choice ofoperating modes to evaluate project-level emissions.

Introduction

On March 2, 2010, the U.S. Environmental ProtectionAgency (EPA) announced the official release of the MotorVehicle Emissions Simulator (MOVES2010) model for use instate implementation air quality plan (SIP) submissions to EPAand regional emission analysis for transportation conformity

determination outside of California (Federal Register, 2010).MOVES2010 is the latest upgrade to EPA’s modeling tools forestimating emissions from cars, trucks, motorcycles, and buses,and it replaces the MOBILE6.2 model (EPA, 2010). It wasdeveloped by EPA, in part, as a response to a NationalResearch Council (NRC) review of the MOBILE model (NRC,2000). The NRC found that although MOBILE is suited for

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Journal of the Air & Waste Management Association, 62(10):1134–1149, 2012. Copyright © 2012 A&WMA. ISSN: 1096-2247 printDOI: 10.1080/10962247.2012.699016

aggregate regional and national analyses of emissions and airquality, it could not be used for assessment of mobile sourceemissions at temporal and spatial scales relevant to specifictransportation projects and control measures. Exhaust emissionfactors from the MOBILE model and the California AirResources Board’s (CARB’s) EMission FACtors (EMFAC)model are based upon cycle-average emissions that are correctedfor average speed. The MOVES model provides greater flexibil-ity to evaluate project-level emissions by allowing the user toinput any vehicle operation cycle and estimate running exhaustemissions as a function of vehicle-specific power, the instanta-neous power demand of the vehicle divided by its mass. TheFederal Register notice marked the beginning of a 2-yr transitionphase for MOVES, which was recently extended for transporta-tion conformity analysis to March 2, 2013 (EPA, 2012).

An important finding of the NRC review of the MOBILEmodel in the late 1990s was that EPA had not addressed modelvalidation and evaluation adequately during development of themodel and recommended evaluation studies involving fieldobservations (e.g., ambient air measurements, tunnel studies,and remote sensing), air quality modeling, and vehicle emissiondata (e.g., data from vehicle emission inspection and mainte-nance programs, roadside pullover inspections, and other directtailpipe emission measurements) to validate model outputs.These recommendations are also applicable to the MOVESmodel and the FACA (Federal Advisory Committee Act)MOVES Workgroup, which was formed to provide input onvarious issues regarding the development of the MOVESmodel, recommended that EPA give high priority to validationof the new model (Barth, 2009). Although advances have beenmade to mobile source emission models over the past two dec-ades, the evidence that mobile source emissions of carbon mon-oxide and hydrocarbons in urban areas were underestimated byas much as a factor of 2–3 in past inventories underscores theneed for on-going verification of emission inventories (Chicoet al., 1993; Fujita et al., 1992; Harley et al., 1993; Ingall et al.,1989; Pierson et al., 1990; Wagner and Wheeler, 1993;).

In this study, Desert Research Institute (DRI) conducted anon-road mobile source emission study at the traffic tunnel onSherman Way in Van Nuys, California (Van Nuys tunnel), inAugust 2010 to measure fleet-averaged fuel-based emissionfactors of regulated and unregulated pollutants. The measuredemission factors were compared with corresponding fleet-averaged emission factors estimated using MOVES2010a,MOBILE6.2, and EMFAC2007. ENVIRON InternationalCorporation (ENVIRON) provided the vehicle-specific emis-sion factors for ambient temperature and traffic conditionsobserved during the Tunnel Study.We also compared the averageof emission factors measured for about 13,000 vehicles by theUniversity of Denver with a remote sensing device (RSD) withthe average of the corresponding modeled emission factors. TheRSD measurements were made on August 12–16, 2010, a weekprior to the start of the Tunnel Study on eastbound ShermanWayabout 600 m west of the Van Nuys tunnel (Bishop et al., 2012).Data from each of the three study participants were submitted ina blind manner to one of the study’s coauthors (D. Lawson) at theNational Renewable Energy Laboratory, so that they could be

independently evaluated before they were shared among allstudy participants.

Experimental Methods

On-road measurements

The field study at the Van Nuys tunnelwas completed during a2-week period beginning August 17, 2010. Measurements andsamples were obtained during two 3-hr sampling periods each day(�09:00 a.m. to 12:00 noon and 12:15 p.m. to 15:15 p.m.) onAugust 20, 21, 22, 24, 25, 26, 28, and 29 (two Saturdays, twoSundays, and four weekdays). The tunnel is located on ShermanWay, a major east-west arterial street with three lanes of traffic ineach direction, where it passes under the runway of the Van NuysAirport. In-tunnel measurements were made in the eastboundbore. The eastbound roadway grades from the west to east portalsare 1.7%downgrade from0 to 29.5m, 0.4%downgrade from29.5to 111.5 m, 0.3% upgrade from 111.5 to 205.1 m, and 1.0% from205.1 to 239 m. The distance-weighted average grade is 0.088%downgrade. The traffic directions are separated by a concretewall,with eight door-size openings in the dividing wall, spaced evenlythrough the tunnel. The eight open doorways account for about0.3% of the interior surface area of one bore of the tunnel. Thetunnel is 239 m long, portal-to-portal, with a traffic turnout in theeastbound bore of the tunnel 147 m from the traffic entrance and75m from the exit of the tunnel. The tunnel is sufficiently far fromlocal neighborhood housing that vehicles passing through thetunnel are being driven under hot, stabilized operating conditions.

Monitoring equipment and volatile organic compound (VOC)samplers were operated inside a minivan parked in the turnoutand particle samplers were located in a small pull trailer attachedto the van. Power for the samplers was supplied by a dieselgenerator located on the airport runway apron above the east-bound tunnel exit. Parallel measurements and samples were alsoobtained on airport property above the west edge of the tunnel tomeasure the urban background pollutant concentrations alreadypresent in the air entering the tunnel. A video camera at thislocation recorded the traffic entering the tunnel. An on-boarddiagnostics (OBDII)-based vehicle data logger was installed on acar that was driven through the tunnel up to 10 times during each3-hr sampling period. The recorded vehicle performance para-meters include second-by-second vehicle speed and 5-sec aver-age engine speed, engine load, and throttle position (mean of 41miles per hour [mph] for 69 measurements through the tunnel).

Table 1 shows the average temperature, relative humidity, andtraffic volume, speed, and average vehicle counts per hour byvehicle type during each of the sampling periods for which vehiclecounts and complete analytical datawere obtained. Video from thefirst three sampling periods were unusable due to poor resolutionand incomplete recordings. The video camerawas replaced with ahigher-resolution model during the second day of samplingbetween the morning (AM) and afternoon (PM) periods.Canister samples were invalid for the August 22 AM period dueto a sampling error. Sample collection during the August 28 PMperiod was interrupted due to failure of the generator that suppliedpower to the in-tunnel samplers. Time-integrated samples were

Fujita et al. / Journal of the Air & Waste Management Association 62 (2012) 1134–1149 1135

collected onAugust 29 for gas-phase pollutants only, using batterypower for the in-tunnel samplers.

After initial review of the analytical data, we eliminated threeadditional runs from further consideration. Measurements onAugust 26 were affected by road construction about 50 m westof the tunnel on the north side of Sherman Way. One lane wasblocked due to the construction and traffic frequently backed intothe tunnel on the westbound lanes. This resulted in higher-than-normal pollutant concentrations at the background site, whichwaslocated above the west end of the tunnel. Additionally, the poten-tial was greater for contributions of pollutants from westboundtraffic to the eastbound bore through the small door-size openingsat this end of the tunnel. The Sunday, August 29, PM period wasalso excluded because net CO2 was approximately equal to theanalytical uncertainty, resulting in unacceptably large uncertaintyin the calculated fuel-based emission factors.

The videotapes were processed by an experienced automotivemechanic, who logged the model year and EMFAC category foreach vehicle entering the tunnel for a subset of weekday andweekend runs. The resulting distributions were used to estimatefleet composition for other runs for which less detailed vehiclecounts were provided. For light-duty vehicles only, we usedmodel year distributions from the RSD data set obtained byUniversity of Denver from the California Department of MotorVehicles (DMV) on eastbound Sherman Way during the weekpreceding the tunnel experiment, which are more accuratebecause they are based on DMV license records. Because theobserved variations in fleet distributions were small from run to

run, it is not expected that any inaccuracies in the extrapolateddistributions would have a significant effect on model results.

Sampling and analysis methods

Measurements made during the Tunnel Study included thefollowing: nitric oxide (NO) and oxides of nitrogen (NOx) with aHoriba chemiluminescence analyzer (Irvine, CA) and 2BTechnology 400/401 analyzer (Boulder, CO); carbon dioxide(CO2), carbon monoxide (CO), methane (CH4), and C2–C11 spe-ciated hydrocarbons from 3-hr canister samples; PM2.5 (particulatematter with aerodynamic diameter �2.5 mm) mass and elementsfrom 3-hr filter samples on Teflo (2.0 mm pore size, 47 mmdiameter Teflon filters [RPJ047] sampling at 56.6 liters per minute[lpm]; Ann Arbor, MI); organic carbon/elemental carbon [OC/EC]from 3-hr Pallflex 47 mm diameter prefired quartz filters (2500QAT-UP) sampling at 56.6 lpm; C1–C7 carbonyl compoundscollected on Waters Sep-Pak 2,4-dinitrophenylhydrazine (DNPH)cartridges (Milford, MA) sampling at 1 lpm for 3 hr; and semi-volatile organic compounds (SVOCs) and PM organic speciationfrom Teflon-impregnated glass fiber filters (8 � 10 inch, T60A20Pallflex; Radnor, PA) backed up by 4 inch diameter XAD car-tridges collected with TE-PNY1123 Accuvol modified TIGF/XAD high-volume samplers (Tisch Environmental, Cleves, OH)operating at approximately 280 lpm for 6 hr (combined 3-hr AMand 3-hr PM sampling periods). Meteorological measurementswere obtained at the background site and supplemented with air-port data.

Table 1. Temperature, relative humidity, traffic volume, and vehicle speeds for sampling periods with complete valid data

Vehicles Per Hour

DateDay orType Time

Temperature(�F)

RH(%)

Speed(mph)

AllVehicles

AllDiesels

HDV (>10,000lbs GVW)

August 21 Sat PM 95 16 41.4 1486 37 25August 22 Sun PM 92 22 45.0 1290 8 7August 24 Tue AM 92 19 40.9 1335 59 25August 24 Tue PM 101 14 41.9 1654 64 27August 25 Wed AM 92 21 40.1 1419 57 10August 25 Wed PM 102 19 41.6 1335 59 13August 28 Sat AM 72 54 45.9 1294 26 8August 29 Sun AM 70 53 42.3 1048 13 9Average Weekday 97 18 41.1 1436 60 19Average Sat 83 35 43.7 1390 32 17Average Sun 81 37 43.6 1169 10 8Average High temp.

(101–102 �F)102 17 41.7 1495 62 20

Average Medium temp.(92–95 �F)

93 19 41.8 1383 40 17

Average Low temp.(70–72 �F)

71 53 44.1 1171 19 9

Average All 89 27 42.4 1358 40 16

Note: The average vehicle speeds shown for each sampling period are based on averages of up to 10 trips through the tunnel using an OBD data logger.

Fujita et al. / Journal of the Air & Waste Management Association 62 (2012) 1134–11491136

Methane, CO, and CO2 were measured from canister samplesusing a Shimadzu GC-17A gas chromatograph (GC; Pleasanton,CA) with flame ionization detector (FID) and a 20 feet� 1/8 inchinner diameter (i.d.) column, packed with a 60/80 mesh ofCarboxen 1000 (Supelco, St. Louis, MA). CO and CO2 werefirst converted to methane by a methanator (firebrick powderimpregnated with nickel catalyst) positioned between the GCcolumn and the FID. The FID response was calibrated with thegaseous standard mixtures (Scott Specialty Gases, NationalInstitutes of Standards and Technology [NIST] traceable;Plumsteadville, PA) containing CO, CO2, and CH4 in zero air.The minimum detection limit for CO, CH4, and CO2 were 0.06,0.2, and �3 ppmv, respectively, with precision better than 10%.

Speciated C2–C11 hydrocarbon compounds were measuredusing gas chromatography/mass spectrometry (GC/MS) techni-que according to EPAMethod TO-15 (EPA, 1999a). TheGC-FID/MS system includes a Lotus Consulting Ultra-Trace Toxics sam-ple preconcentration system (Lotus Consulting, Long Beach, CA)built into a Varian 3800GC (Walnut Creek, CA)with FID coupledto a Varian Saturn 2000 ion trapMS. Light hydrocarbons (C2–C4)are separated on a Chrompack Al2O3/KCl column (25 m � 0.53mm� 10 mm) leading to FID. The mid-range and heavier hydro-carbons (C4–C11) are deposited to a J&W DB-1 column (60 m �0.32 mm � 1 mm) connected to the ion trap MS. The GC initialtemperature is 5 �C, held for approximately 9.5 min, then ramps at3 �C/min to 200 �C for a total run time of 80 min. Calibration ofthe system is conducted with a mixture that contained the mostcommonly found hydrocarbons (75 compounds from ethane to n-undecane; purchased from Air Environmental) in the range of0.2–10 ppbv.

C1–C7 carbonyl compounds were measured as their hydra-zone derivative according to EPAMethod TO-11A (EPA, 1999b)using a high-performance liquid chromatograph (HPLC; Waters2690 Alliance HPLC System with 996 Photodiode ArrayDetector). After sampling, the cartridges were eluted with acet-onitrile. An aliquot of the eluent was transferred into a 2-mLseptum vial and injected with an autosampler into a Polaris C18-A 3 mm 100 � 2.0-mm HPLC column.

The Teflon filters were weighed on a Mettler Toledo MT5electro microbalance (Columbus, OH) and analyzed for elementsby energy-dispersive X-ray fluorescence (EDXRF) analysis on aPANalytical Epsilon 5 EDXRFanalyzer (Westborough, MA). PMsamples were also analyzed by inductively coupled plasma massspectrometry (ICP-MS) for totalMg, Al, Ca, V, Cr,Mn Fe, Ni, Cu,Zn, Mo, Ba, Ce, Hg, and Pb. The quartz filters were analyzed forEC and OC by thermal optical reflectance (TOR) method (Chowet al., 2001) using the IMPROVE_A (Interagency Monitoring ofProtected Visual Environments) temperature/oxygen cycle proto-col (Chow et al., 2007).

Semivolatile and condensed-phase organic species that wereidentified and quantified from the TIGF filters and XAD-4 resinsamples included 55 polycyclic aromatic hydrocarbons (PAHs),23 hopanes and steranes, and 50 alkanes and cycloalkanes in theC12–C40 range using a modified EPA Method TO-13A (EPA,1999c; Wang et al., 1994a, 1994b). The higher-molecular-weight�C20–C35 alkanes and cycloalkanes in lubricating oils appear as asingle hump on the gas chromatograms. These compounds werequantified together based on the ions with mass-to-charge ratios

(m/z) of 57 and 55, which are characteristic aliphatic hydrocarbons,and reported as total unresolved complex mixture (UCM) ofalkanes. The TIGF filters and XAD-4 resins were extracted sepa-rately using the Dionex ASE (Sunnyvale, CA) with dichloro-methane followed by hexane extraction under 1500 psi at 70�C. Prior to extraction, the following deuterated internal standardswere added to each filter and XAD-4 sorbent: naphthalene-d8,biphenyl-d10, acenaphthene-d10, phenanthrene-d10, anthracene-d10, pyrene-d10, benz[a]anthracene-d12, chrysene-d12, benz[k]fluoranthene-d12, benzo[e]pyrene-d12, benzo[a]pyrene-d12, perylene-d12, benzo[g,h,i]perylene-d12, coronene-d12, cholestane-d6,hexadecane-d34, eicosane-d42, tetracosane-d50, octacosane-d58,and triacontane-d62. All extracts were concentrated by rotaryevaporation at 35 �C under gentle vacuum to �1 mL and filteredthrough 0.2-mm polytetrafluoroethylene (PTFE) disposal filter(Whatman Pura disc 25TF; Florham Park, NJ), rinsing the flaskthree times with 1 mL dichloromethane and hexane (50/50 v/v)each time. The solvent was exchanged to toluene under ultra-high-purity nitrogen.

The TIGF filters and XAD-4 extracts were analyzed separatelyby GC/MS, using a Varian CP-3800 GC equipped with a CP8400autosampler and interfaced to aVarian 4000 ion trap for analysis ofall semivolatile and condensed-phase organic compounds excepthopanes and steranes, as described before (Fujita et al., 2007).Hopanes and steranes were analyzed using the Varian 1200 triplequadrupole gas chromatograph/mass spectrometer (GC/MS/MS)system with CP-8400 autosampler due to the higher sensitivity ofthis system. Quantification of the individual compounds wasobtained by the selective ion mode (SIM) technique, monitoringthemolecular (or themost characteristic) ion of each compound ofinterest and the corresponding deuterated internal standard.

Calculation of emission rates

Composite fleet-averaged fuel-based emission rates weredetermined using the carbon balance method shown in eq 1(Ban-Weiss et al., 2008; Fraser et al., 1998; Kirchstetter et al.,1999). In this method the increase of CO2, CO, and organic gasesplus organic PM within the tunnel is proportional to the amountof carbon that was present in the fuel consumed, and emissionfactors for each pollutant species per unit fuel consumed can becomputed based on a carbon balance in the tunnel. Most of thecarbon in gasoline and diesel fuel is emitted as CO2, with smalleramounts emitted as CO. Even smaller amounts of fuel carbonemitted as PM and unburned hydrocarbons are typically left outof the denominator in eq 1. The emission factor EP (g of pollutantP per kg fuel burned) can be calculated as

Ep ¼ � P½ �� CO2½ � þ� CO½ �

� �wc (1)

where �[P] is the background-subtracted (tunnel minus back-ground) mass concentration of pollutant P (mg/m3). The fuelcarbon components are similarly background-subtracted con-centrations in mg C per m3. The fuel carbon mass fraction, wc,is 0.85 g C per g fuel. This method is insensitive to uncertaintiesin air flow rates through the tunnel and is insensitive to the effectsof any small air exchange between adjacent bores of the tunnel.


Modeled emission rates

Three motor vehicle emission models were used to comparemodeled emission rates with fleet emission rates measured in theVan Nuys tunnel: (1) California EMFAC2007, (2) EPAMOBILE6.2, and (3) EPA MOVES2010a. Each model was runby ENVIRON to provide emission rates (in gram per mile units)by model year within each vehicle category for total hydrocarbons(THC), CO, NOx, PM2.5, and CO2. EC and total carbon (TC)estimates were also evaluated, although only MOVES calculatedthese parameters directly. For MOBILE, which only provides ECand OC for diesel vehicles, GASPM (gasoline vehicle PM emis-sions) minus SO4 (gasoline vehicle sulfate particle emissions) wasused as substitute for TC from gasoline vehicles. For EMFAC, weestimated diesel TC as PM2.5 exhaust � k � SO2, where k ¼0.0694 is the slope of regression of SO2 and sulfate for all dieselvehicles (1985–2010) in MOBILE6.2 (r2 ¼ 0.985). For gas vehi-cles, we used TC ¼ PM2.5 exhaust. Because measuredbackground-subtracted THC in the tunnel had a high degree ofuncertainty due to the relatively large background methane con-centrations, the measured background-subtracted nonmethanehydrocarbon (NMHC) was used for the comparison. ModeledTHC emission rates were adjusted to approximate NMHC bysubtracting an average fraction of methane derived from gasolineand diesel exhaust speciation profiles (CARB, 2000).

In the EMFAC and MOBILE models, vehicle emission ratesrepresent averages over a driving schedule with a defined aver-age speed. The MOVES2010a model estimates emissions usingrelative time and emission rates in vehicle speed and specificpower bins. MOVES2010a provides default test cycles to simu-late the approach used for EMFAC and MOBILE6.2, or can usecustomized drive cycles, as was done in this study. Speed tracesamples in increments of �1 mph lasting about 9–10 sec eachwere collected for 69 drive-through events during the actualtunnel sampling periods with an average speed of 40.9 � 0.8mph. Average start and end speeds relative to the average speedwere �0.2 and �0.7 mph, respectively, and the highest andlowest speeds relative to the average speed were þ3.6 and�4.6 mph. Average acceleration was �0.03 m/sec2 and themaximum acceleration and deceleration was þ0.9 and �1.3m/sec2. The closest MOVES default cycle with comparableaverage speed simulates typical urban traffic conditions with awider range of speeds and accelerations than any modeled here.Drive-through data from the tunnel indicated that, for mostdrive-through cycles, vehicle speed varied little from the aver-age; speeding up slightly until about halfway through and thenslowing down slightly when exiting the tunnel. A 9-sec drivecycle (40, 40, 41, 42, 43, 42, 41, 40, 40 mph) that mimicked theaverage speed and pattern in 1-mph speed increments was cre-ated and used in calculating the MOVES emission factors. Thisdrive cycle has an average speed of 41 mph, with beginning andend speeds below the average, absolute average deviation fromthe mean of 0.9 mph, and maximum and minimum speedsrelative to the average of þ2 and �1 mph, respectively.

After reviewing the initial comparisons of the modeled andmeasured emission factors, staff at the EPA OfficeTransportation and Air Quality (OTAQ) noted that the operatingmode distribution of the 9-sec cycle (“speed-constructed” cycle)

has a greater fraction of higher-VSP (vehicle-specific power)bins, and thus higher NOx emission factors, than using theoperating mode distribution of the combined 69 drive-throughsamples (“69-sample” cycle). Comparisons of the measured andmodeled emission factors presented are based on calculationsmade by ENVIRON using the speed-constructed tunnel cycle inaccordance with the study protocol requirements for a blindcomparison. MOVES NOx emission factors calculated by EPAare also presented for a cycle utilizing combined 69 drive-through samples and a flat 41-mph cycle to illustrate the model’ssensitivity to the operating modes.

Ambient conditions of temperature and humidity can alsoaffect modeled emissions, so a sample of the conditions usedduring the field testing was averaged to provide a typical condi-tion. These conditions were estimated to be 88 �F and 27%relative humidity, and were used in all model calculations. As asensitivity analysis, the exhaust and evaporative THC emissionrates were also estimated using the minimum (64.7 �F) andmaximum (104.4 �F) ambient temperatures during the studyperiod. The modeled NMHC emission rates most appropriatefor the conditions shown in Table 1 were used in the comparisonfor each measurement period (i.e., 104.4 �F for runs with ambi-ent temperature of 101 and 102 �F; 88 �F for runs in the range of92–95 �F; and 64.7 �F for ambient temperature of 71 and 72 �F).

The models were run with and without inspection and main-tenance (I/M) program benefits included in the estimates.EMFAC used the benefits from the Smog Check program,whereas MOVES and MOBILE were run using the IM240program credits. The IM240 program was the default I/M pro-gram for Los Angeles County in the MOVES2010a model andrepresents the maximum emission reduction for I/M programs.All model results shown in this paper are with I/M, unlessotherwise noted. The magnitudes of the credits were determinedfor comparison with differences between measured and modelemission factors.

Numerous other differences in modeling methods existbetween the three models used, including the way in whichvehicles are categorized. The EMFAC and MOBILE6.2 modelssegregate vehicles by body type (car, truck, or bus) and grossvehicle weight rating (GVWR). MOVES2010a identifies thevehicle types differently in order to take advantage of the classi-fication data collected by U.S. Department of Transportation,which distinguishes vehicles only by body type and purpose(passenger cars and trucks, commercial trucks, etc.) rather thanweight. Even within a particular model, the distinction betweenlight-duty cars and light-duty trucks can be unclear becausemany light-duty trucks may appear to be cars, such as minivans,“crossover” sport utility vehicles, off-road station wagons, andother vehicles with sufficient ground clearance, cargo space,weight capacity, and other distinguishing characteristics frompassenger cars to be defined as trucks. Classification of thevehicles by the manufacturers is also somewhat arbitrary makingDepartment of Motor Vehicles (DMV) records, when available,unreliable for matching some types of vehicles to the modelcategories. In classifying the vehicles observed on the videorecords during tunnel measurements, each was assigned to 1 ofthe 13 EMFAC categories, and the cross-platform mappingsystem shown in Table 2 was used to translate these assignments


Table 2. Vehicle categories used to assign emission factors in the three models

EMFAC MOBILE6 MOVES2010

Fractionof Fleet Category

NOx(g/mi) Category

NOx(g/mi) Category

NOx(g/mi)

Gasoline vehicles77.3% Passenger car 0.07 LD vehicles (passenger cars) 0.15 Passenger car 0.099.9% LD truck <3750

GVW0.07 LD trucks 1 (0–6000 lbs. GVWR 0–3750

lbs. LVW)0.18 Passenger truck 0.16

7.7% LD truck <5750GVW

0.09 LD trucks 2 (0–6001 lbs. GVWR37515750 lbs. LVW)

0.23

0.5% Medium-duty truck<8500 GVW

0.16 LD trucks 3 (6001–8500 lbs. GVWR05750 lbs. ALVW)

0.49 Light commercialtruck

0.24

LD trucks 4 (6001–8500 lbs. GVWR5751 lbs. and greater A LVW)

0.49

0.2% Light-HD truck<10,000 GVW

0.22 HD vehicles (8501–10000 lbs. GVWR) 0.78 Single unit short-haul truck

1.95


0.22 HD vehicles (10001–14000 lbs. GVWR) 0.80

0.0% Medium-HD truck<33,000 GVW

0.19 HD vehicles (14001–16000 lbs. GVWR) 0.79HD vehicles (16001–19500 lbs. GVWR) 0.92HD vehicles (19501–26000 lbs. GVWR) 0.91HD vehicles (26001–33000 lbs. GVWR) 1.00

0.0% Heavy-HD truck>33,000 GVW

4.37 HD vehicles (33001–60000 lbs. GVWR) 1.07 Combinationshort-haul truck

NDHD vehicles (>60000 lbs. GVWR) ND

0.1% Motorcycle 1.27 Motorcycles (gasoline) 0.81 Motorcycle 0.370.0% School bus ND School buses 1.19 School bus 1.95

Diesel vehicles0.0% Passenger car ND LD vehicles (passenger cars) 0.07 Passenger car 2.200.0% LD truck <3750

GVWND LD trucks 1and 2 (0–6000 lbs. GVWR) ND Passenger truck 4.08

0.0% LD truck <5750GVW

ND

0.0% Medium-duty truck<8500 GVW

ND LD trucks 3 and 4 (6001–8500 lbs.gVWR)

0.22 Light commercialtruck

4.08


4.23 HD vehicles (8501–10000 lbs. GVWR) 2.10 Single unitshort-haul truck

5.04


4.22 HD vehicles (10001–14000 lbs. GVWR) 2.41

0.8% Medium-HD truck<33,000 GVW

5.10 HD vehicles (14001–16000 lbs. GVWR) 2.81HD vehicles (16001–19500 lbs. GVWR) 3.04HD vehicles (19501–26000 lbs. GVWR) 3.74HD vehicles (26001–33000 lbs. GVWR) 4.63

0.5% Heavy-HD truck>33,000 GVW

10.02 HD vehicles (33001–60000 lbs. GVWR) 5.25 Combinationshort-haul truck

9.86HD vehicles (>60000 lbs. GVWR) 5.90

0.0% Urban bus 1.36 Transit and urban buses 8.08 Transit bus 8.320.1% School bus 4.98 School buses 5.72 School bus 5.88

CNG vehicles0.3% Urban bus ND Transit and urban buses ND Transit bus 2.98

Note: NOx emission rates shown are for 2005 model year, which was the peak year of the tunnel fleet distribution.


for usewithMOBILE andMOVES. As the example values in thetable show, reclassification of vehicles between categories gen-erally would not result in a significant change in the overallemission rate relative to the differences that exist between thethree models.

Additionally, alternative emission rates for vehicles meetingCalifornia Low-Emission Vehicle (LEV) emission standards wereused for this study. MOBILE6 and MOVES light-duty vehicleemission factors were adjusted to account for the California LEVprogram, which began with the 1994 model year. No adjustmentsfor California regulations were made for vehicles older than 1994because reliable deterioration rates do not exist for these oldervehicles. Federal and California standards for heavy-duty dieselvehicles were the same for model years beginning in 1990. Noadjustments were made for differences in the standards prior to1990, as the fleet through the tunnel included very few pre-1990diesel trucks. No other adjustments were made for California-specific motor vehicle control programs such as the Carl MoyerProgram (CARB, 2012). This program was established to providefinancial incentives to replace or retrofit high-polluting engines.However, this program focused on reductions of PM emissionsprimarily in economically disadvantaged residential areas locatedclose to industrial areas. Effect of the program on emissions in thetunnel are likely minimal.

The modeled emission rates for each vehicle type and modelyear were multiplied by the number of such vehicles observedduring each 3-hr measurement period. Then the resulting com-bined fleet emissions in grams per mile were converted to fuel-based emission rates by dividing by the fleet emissions of CO2

plus CO and multiplying the resulting ratio by the fuel carbonmass fraction of 0.85 g per gram of fuel.

Results

This comparison is applicable to on-road vehicles in hot,stabilized mode at near-constant speeds of approximately 40mph and excludes start emissions, and diurnal and hot-soakevaporative hydrocarbon emissions. In addition to uncertaintiesassociated with the tunnel measurements, there are severaluncertainties inherent in the emission factor modeling wheremodeled conditions may not match tunnel conditions. Theseareas of uncertainty include the vehicle drive cycle, vehiclecondition, and fleet characterization. Except in the case of theMOVES model, the vehicle drive cycle used in the modelingdoes not represent in-use activity in a tunnel where vehiclesoperate at a relatively stable vehicle speed. In the case of theMOVES2010a model, the NOx emission factors were sensitiveto differences in the drive cycles or modes used to represent thedriving pattern through the tunnel. Neither MOBILE norMOVES was developed to represent the California vehicle emis-sion control program. For the MOBILE and MOVES modelresults, no adjustments were made to account for the Californiafleet for the pre-LEV (model years prior to 1994) light-dutyvehicles or other state and local emission reduction programsthat might affect gasoline or diesel vehicles. Gasoline vehicles ofmodel years prior to 1994 were estimated to account for about25–40% of the NMHC, CO, and NOx fleet total emissions.Although California light-duty gasoline vehicle NOx standards

were about 25% lower during most of the decade prior to 1994,the contemporary contributions of this portion of the fleet arelikely related to emissions of high emitters rather than the origi-nal emission standards. University of Denver estimated fromRSD measurements that more than a third of the currentSherman Way total emissions of CO and HC are contributedby less than 1% of the fleet and half of the total measured CO,HC, and NO was produced by 2.0%, 2.1%, and 5.0%, respec-tively, of the 13,000 measurements (Bishop et al., 2012). Forthese reasons, the potential differences in the contemporary in-use vehicle fleet that might be attributable to differences in theFederal and California programs are difficult to quantify.

Comparisons of measured and modeled emissionfactors

The measured fleet-average fuel-based emission factors(grams of pollutant per kilogram of fuel) for CO, NOx, NMHC,EC, and TC for each of eight runs with valid data are comparedin Table 3 with the corresponding model estimates usingMOVES2010a, MOBILE6.2, and EMFAC2007 (estimates with-out I/M credits in parentheses). Uncertainty estimates of about�30% for the measured CO, NOx, and NMHC fuel-based emis-sion factors are based on the propagated 1-sigma analyticalerrors for the background-subtracted pollutant and CO2 concen-trations. Medians of the model to measured emission factorratios are also shown in Table 3 for weekday and weekendsamples. The median, rather than the mean, values are presentedin order to exclude the effect of the Sunday AM sample, whichgave significantly lower emission factors for all pollutants.These low fuel-based emission rates were due to a relativelyhigh background-subtracted CO2 concentration on that morning,but because the individual CO2 measurements are within therange of other samples they are still deemed to be valid.

Themodeled CO emission factors fromMOVES and EMFACwere both in reasonable agreement, within the measurementerror, with the measured fleet-averaged emission factors. TheMOBILE CO emission factors were significantly higher than themeasured emission factors, with median model/tunnel ratios of2.0 and 1.6 on weekdays and weekends, respectively. TheMOVES NOx emission factors using the speed-constructed tun-nel cycle were higher than the measured emission factors for allsampling periods, with median model/tunnel ratios of 1.5 and1.4 on weekdays and weekends, respectively. However, half ofthe MOVES factors were within the uncertainty of the measuredfactors. The sensitivity of the MOVES NOx emission factors tovehicle operation is described in the following section. TheMOBILE NOx emission factors were also higher than the mea-sured factors for all sampling periods with median model/tunnelratios of 1.3 for both weekdays and weekends. EMFAC NOx

emission factors were generally lower, with median model/tun-nel ratios of 0.8 for the weekday samples and 0.7 for the week-ends when traffic volumes of diesel vehicles were lower. Themeasured NMHC emission factors for the eight sampling peri-ods increased with ambient temperature. Dependence of mea-sured and modeled NMHC emission factors and NMHC/NOx

ratios on ambient temperature is described in a followingsection.


Table 3.Comparisons of measured fleet-averaged fuel-based CO, NMHC,NOx, EC, and TC emission factors (g/kg of fuel) and CO/NOx and NHMC/NOxmolar ratioswith corresponding model estimates using MOVES2010a, MOBILE6.2, and EMFAC2007 for each run with I/M credit (without I/M credit)

CO NOx NMHC EC TC CO/NOx NMHC/NOx

Aug 21, Sat PM, 95 �FTunnel 23.0 � 5.2 3.2 � 0.8 1.59 � 0.35 0.01 � 0.01 0.03 � 0.02 11.7 � 1.6 1.48 � 0.19MOVES 26.9 (33.8) 4.9 (5.9) 0.98 (1.26) 0.03 (0.03) 0.08 (0.08) 9.0 (9.4) 0.6 (0.6)MOBILE 37.9 (49.7) 4.5 (5.5) 1.48 (1.86) 0.01 (0.01) 0.04 (0.04) 13.8 (14.9) 0.9 (1.0)EMFAC 24.0 (31.2) 2.5 (3.6) 0.91 (1.32) 0.09 (0.09) 15.6 (14.3) 1.0 (1.0)

Aug 22, Sun PM, 92 �FTunnel 25.4 � 8.2 3.6 � 1.2 1.98 � 0.62 0.02 � 0.01 0.07 � 0.03 11.7 � 1.7 1.64 � 0.22MOVES 27.2 (34.6) 4.4 (5.4) 1.15 (1.48) 0.01 (0.01) 0.04 (0.04) 10.2 (10.4) 0.7 (0.8)MOBILE 39.9 (52.5) 4.2 (5.3) 1.73 (2.17) 0.00 (0.00) 0.03 (0.03) 15.5 (16.4) 1.1 (1.2)EMFAC 24.7 (32.3) 2.0 (3.1) 1.05 (1.53) 0.08 (0.08) 19.9 (17.0) 1.4 (1.4)

Aug 24, Tue AM, 92 �FTunnel 16.7 � 5.1 2.8 � 1.1 1.40 � 0.41 0.03 � 0.02 0.03 � 0.02 9.6 � 2.8 1.35 � 0.40MOVES 26.2 (32.8) 5.7 (6.7) 1.22 (1.52) 0.06 (0.06) 0.13 (0.13) 7.5 (8.1) 0.6 (0.6)MOBILE 36.5 (47.9) 4.7 (5.7) 1.63 (2.04) 0.01 (0.01) 0.05 (0.05) 12.6 (13.8) 1.0 (1.0)EMFAC 23.0 (30.0) 2.4 (3.0) 1.02 (1.46) 0.10 (0.10) 16.0 (16.1) 1.2 (1.3)

Aug 24, Tue PM, 101 �FTunnel 19.1 � 5.4 4.0 � 1.2 2.51 � 0.68 0.04 � 0.02 0.06 � 0.03 7.8 � 1.4 1.84 � 0.30MOVES 30.8 (38.5) 5.1 (6.0) 1.19 (1.49) 0.06 (0.06) 0.12 (0.12) 9.9 (10.6) 0.7 (0.7)MOBILE 36.8 (48.2) 4.8 (5.8) 1.89 (2.40) 0.01 (0.01) 0.05 (0.05) 12.5 (13.7) 1.1 (1.2)EMFAC 23.7 (30.8) 3.4 (4.4) 1.76 (1.89) 0.10 (0.10) 11.5 (11.4) 1.5 (1.2)

Aug 25, Wed AM, 92 �FTunnel 18.9 � 6.1 3.4 � 1.2 1.35 � 0.43 0.06 � 0.03 0.12 � 0.05 9.0 � 1.9 1.05 � 0.24MOVES 23.6 (29.3) 5.6 (6.5) 1.17 (1.37) 0.08 (0.08) 0.16 (0.16) 7.0 (7.4) 0.6 (0.6)MOBILE 39.0 (50.9) 5.0 (5.9) 1.69 (2.07) 0.01 (0.01) 0.05 (0.05) 12.9 (14.2) 1.0 (1.0)EMFAC 22.5 (29.2) 3.5 (4.3) 0.91 (1.30) 0.10 (0.10) 10.6 (11.1) 0.7 (0.8)

Aug 25, Wed PM, 102 �FTunnel 30.4 � 9.2 4.5 � 1.5 3.05 � 0.90 0.05 � 0.03 0.11 � 0.04 11.1 � 1.8 2.03 � 0.32MOVES 27.7 (34.3) 5.3 (6.1) 1.14 (1.34) 0.08 (0.08) 0.16 (0.16) 8.6 (9.2) 0.6 (0.6)MOBILE 38.8 (50.6) 5.3 (6.2) 2.18 (2.72) 0.01 (0.01) 0.06 (0.06) 12.1 (13.3) 1.2 (1.2)EMFAC 22.4 (29.1) 3.7 (4.6) 1.81 (1.59) 0.10 (0.10) 9.9 (10.4) 1.4 (1.0)

Aug 28, Sat AM, 72 �FTunnel 25.9 � 11.0 3.9 � 1.7 1.09 � 0.46 0.02 � 0.02 0.03 � 0.03 10.9 � 1.9 0.71 � 0.16MOVES 18.1 (22.5) 4.8 (5.8) 1.18 (1.40) 0.04 (0.04) 0.10 (0.10) 6.2 (6.4) 0.7 (0.7)MOBILE 41.7 (54.5) 4.5 (5.5) 1.63 (2.12) 0.00 (0.00) 0.04 (0.04) 15.2 (16.3) 1.0 (1.1)EMFAC 24.0 (31.3) 2.4 (3.3) 0.81 (1.22) 0.08 (0.08) 16.5 (15.5) 0.9 (1.0)

Aug 29, Sun AM, 70 �FTunnel 10.7 � 2.2 1.3 � 0.3 0.51 � 0.10 13.9 � 2.4 1.11 � 0.20MOVES 19.7 (25.0) 4.7 (5.8) 1.21 (1.53) 0.02 (0.02) 0.07 (0.07) 6.9 (7.1) 0.7 (0.7)MOBILE 39.8 (52.3) 4.3 (5.4) 1.48 (2.03) 0.00 (0.00) 0.03 (0.03) 15.1 (16.0) 1.0 (1.1)EMFAC 25.0 (32.5) 2.2 (3.3) 0.92 (1.36) 0.08 (0.08) 18.6 (16.3) 1.2 (1.2)

Median ratios—WeekdayMOVES/Meas 1.41 1.46 0.67 1.56 1.77 0.78 0.40MOBILE/Meas 1.99 1.33 0.96 0.27 0.65 1.37 0.65EMFAC/Meas 1.22 0.84 0.69 1.29 1.33 0.74

Median ratios—WeekendMOVES/Meas 1.12 1.38 0.85 1.89 2.52 0.67 0.55MOBILE/Meas 1.63 1.29 1.22 0.23 1.31 1.25 0.78EMFAC/Meas 1.01 0.70 0.66 2.60 1.42 0.97


The modeled emission factors for total particulate carbon(TC) varied among the models by about a factor of 3. Relativeto MOBILE, EMFAC was twice as high and MOVES was about3 times higher. MOVES has the greatest sensitivity to the largerfractions of diesel vehicles on weekdays (4.2%) compared withSundays (0.9%), with emission factors that were about 3 timeshigher on weekdays relative to Sundays. In comparison, theweekday/Sunday ratios in TC emission factors were 1.7 forMOBILE, 1.3 for EMFAC, and about 1.1 for the measuredfactors. As a result, MOVES slightly underestimated measure-ments on Sunday and overestimated measurements by nearly afactor of 2 on weekdays. The same relative differences wereobtained for comparison of MOVES EC emission factors withmeasurements. MOBILE TC and EC emission factors under-estimated measurements during both weekdays and Sundays. TCemission factors from EMFAC were in generally in good agree-ment with measurements during all sampling periods.

Sensitivity of MOVES emission factors to operatingcycle

Initial comparisons of modeled and measured emission fac-tors were presented to EPA OTAQ for comment. According toEPA staff, the operating mode distribution of the speed-constructed cycle has a greater fraction of higher-VSP binsthan the distribution of the 69-sample cycle, as shown inFigure 1. Figure 2 shows that by EPA’s calculations, the fleetNOx emission factors are, on average, 14% and 36% higher forthe speed-constructed cycle than using operating mode distribu-tion of all 69 drive-through samples or a flat 41-mph cycle,respectively. The median model/tunnel ratios of the NOx emis-sion factors are 1.5, 1.3, and 1.1 for speed-constructed, 69-sample, and flat cycles, respectively. The median ratios arewithin the margin of the measurement uncertainty for the 69-sample and flat cycles. All models predict some increase inevaporative hydrocarbon emissions with increasing ambienttemperature; however, MOVES also includes increases in CO2

and CO emissions related to increased use of air conditioning

resulting in a decrease in fuel-based evaporative emissions withincreasing temperature (see Figure 3). Because this contradictsthe other models and observations of higher hydrocarbon emis-sions with increasing temperature, we excluded the temperatureeffect on CO2 and CO in calculating the MOVES fuel-basedNMHC emissions so that these factors would be directly com-parable with the corresponding MOBILE and EMFAC factors.

Sensitivity of NMHC emission factors to ambienttemperature

Both MOVES and EMFAC NMHC emission factors were inbetter agreement with measurements at lower temperatures.Underprediction at higher temperatures suggests that the modelsare not sufficiently sensitive to ambient temperature. Figure 4shows that the measured NMHC emission factors were 2.0 and3.5 times higher at 85–95 and 95–105 �F, respectively, comparedwith 65–75 �F. Figure 4 also shows the corresponding modeledemission factors for the exhaust and evaporative contributions toNMHC emissions separately. Whereas EMFAC and, to a lesserextent, MOBILE predict higher emissions with increasing

Figure 1. Operating mode distribution by vehicle-specific power based on thespeed-constructed cycle and the 69-sample cycle. Adapted from calculationsprovided by EPA OTAQ.

Figure 2. Comparisons of measured NOx fuel-based emission factors by sampling period with correspondingMOVES emission factors using the “speed-contructed,”“69-sample,” and flat cycles. Adapted from calculations provided by EPA OTAQ, February 2012.


temperature, the MOVES evaporative NMHC emission factorsare relatively insensitive to temperature. The ratios of modeledevaporative to exhaust emission factors at 104 �F relative to65 �F are 4.0, 1.7, and 1.1 for EMFAC, MOBILE, andMOVES, respectively. The corresponding ratio of the estimatedevaporative emission factor from the tunnel measurements atambient temperatures of 101–102 �F relative to 70–72 �F wasabout 6. The contributions of evaporative emissions to the mea-sured NMHC emission factors were estimated by subtracting anaverage exhaust NMHC contribution of 0.4 g/kg of fuel (basedon 0.5 times measured NMHC at 65 �F) from the total.

The MOVES model predicts running evaporative emissionsthat are in reasonable agreement with the estimates from

measurements at lower ambient temperatures, but underesti-mates the emissions at higher temperature. MOBILE predictsthe highest evaporative emission factors, but has less tempera-ture sensitivity than indicated by the measurements. The appar-ent temperature sensitivity observed in the tunnel measurementsis best replicated by EMFAC, though it too underestimatedNMHC emissions at high ambient temperatures. The measuredfuel-based emission factors for species that are produced only byfuel combustion (e.g., acetylene) were insensitive to ambienttemperature, whereas species that are typically enriched in gaso-line vapor (e.g., n-butane) showed greater temperature depen-dence (Table 4). The ratios of the measured emission factors athigh and low ambient temperatures were 0.9 for acetylene and

Figure 3. Temperature dependence of MOVES CO2, CO, NOx, and THC (exhaust) and THC (evaporative) emission factors (both distance and fuel-based) normalizedto 88 �F.

Figure 4. Sensitivity of measured NMHC emission factors to ambient temperature compared with corresponding modeled exhaust and evaporative NMHC emissionfactors.


8.1 for n-butane. These results suggest that the models, espe-cially MOVES, may not fully account for running evaporativeemissions at higher ambient temperature possibly due to anunderprediction, at these higher temperatures, of the vapor vent-ing, fuel permeation, or in combination with fuel system mal-functions, such as leaks, missing gas caps, or faulty canisterpurge valves.

NMHC and NOx are the key precursors of ozone, and theratios of NMHC to NOx affect the rate and efficiency of ozoneformation in the atmosphere. The comparisons of the measuredNMHC/NOx ratios with corresponding modeled ratios inFigure 5 illustrate the greater underestimation with increasingambient temperature. Whereas the predicted NMHC/NOx ratiosare in good agreement with the measured ratios during coolersampling periods, the measured NMHC/NOx ratios are 3.1, 1.7,

and 1.4 times higher during high-temperature periods than pre-dicted by MOVES, MOBILE, and EMFAC, respectively.

Model estimates of relative contributions of gasolineand diesel vehicles to fleet emissions

Separate emission factors can be estimated for gasoline anddiesel vehicles from measurements in tunnels with mixed trafficby extrapolating the linear regressions of fleet-averaged emis-sion factors with fractions of diesel vehicles to each end of theabscissa. This was not done in this study due to the consistentlylow fractions of diesel vehicles (from 0.9% on Sundays to 4.2%on weekdays) and the resulting large uncertainties in the extra-polated emission factors. Sensitivity of the fleet-averaged emis-sion factors from the tunnel measurements to variations in

Table 4. Fuel-based emission rates (grams per kg of fuel) as a function of ambient temperature for combustion products and major components of whole gasoline

Sampling Period Temp �F High 101–102 Med 92–95 Low 71–72 Med/Low T Ratio Hi/Low T Ratios

Combustion Productsacetylene 0.043 � 0.013 0.048 � 0.016 0.051 � 0.023 0.94 0.86ethylene 0.021 � 0.006 0.014 � 0.005 0.018 � 0.007 0.80 1.171,3-butadiene 0.015 � 0.004 0.010 � 0.003 0.013 � 0.005 0.80 1.19

Major Componentsof Gasolinen-butane 0.084 � 0.024 0.036 � 0.011 0.010 � 0.005 3.42 8.12i-butane 0.016 � 0.005 0.009 � 0.004 0.002 � 0.001 5.45 9.71i-pentane 0.726 � 0.208 0.278 � 0.082 0.130 � 0.057 2.14 5.59n-pentane 0.197 � 0.056 0.086 � 0.025 0.037 � 0.016 2.34 5.352,2-dimethylbutane 0.052 � 0.015 0.024 � 0.007 0.016 � 0.007 1.47 6.472,3-dimethylbutane 0.044 � 0.013 0.018 � 0.005 0.007 � 0.002 2.58 3.182-methylpentane 0.078 � 0.022 0.039 � 0.012 0.028 � 0.012 1.42 3.093-methylpentane 0.134 � 0.041 0.067 � 0.022 0.043 � 0.019 1.55 2.84n-hexane 0.054 � 0.016 0.029 � 0.009 0.019 � 0.008 1.57 2.872,3-dimethylpentane 0.058 � 0.017 0.034 � 0.010 0.023 � 0.009 1.50 2.563-methylhexane 0.040 � 0.012 0.024 � 0.007 0.018 � 0.008 1.31 2.172,2,4-tri methyl pentane 0.010 � 0.003 0.006 � 0.002 0.005 � 0.002 1.22 1.98n-heptane 0.021 � 0.006 0.014 � 0.004 0.006 � 0.002 2.46 3.572,3,4-tri methyl pentane 0.023 � 0.007 0.017 � 0.005 0.014 � 0.006 1.24 1.67toluene 0.114 � 0.033 0.094 � 0.028 0.071 � 0.031 1.33 1.60ethylbenzene 0.018 � 0.005 0.015 � 0.005 0.012 � 0.005 1.30 1.58m&p-xylenes 0.066 � 0.019 0.056 � 0.016 0.043 � 0.019 1.28 1.52o-xylene 0.024 � 0.007 0.020 � 0.006 0.015 � 0.007 1.33 1.58

NMHCMeasured - Tunnel 2.78 � 0.80 1.58 � 0.46 0.80 � 0.33 1.97 3.45MOVES 1.17 1.13 1.20 0.94 0.98MCBILE6 2.03 1.63 1.56 1.05 1.30EMFAC 1.78 0.97 0.86 1.13 2.06

NHMC/NOxMeasured - Tunnel 1.93 � 0.31 1.38� 0.28 0.91 � 0.18 1.52 2.13MOVES 0.63 0.72 0.71 1.01 0.89MCBILE6 1.13 1.00 0.99 1.01 1.14EMFAC 1.41 1.10 1.06 1.04 1.34

Note: Measured and model total NMHC are also shown.


fractions of diesel and gasoline vehicles was examined by calcu-lating modeled fuel-based emission factors separately for thegasoline and diesel vehicles that passed through the tunnel dur-ing each sampling period. The resulting average modeledNMHC, NOx, and total carbon emission factors for all vehicles,only gasoline vehicles, and only diesel vehicles are compared inFigure 6 with the measured mean weekday and Sunday fleet-averaged emission factors. Variations in the fleet fractions ofdiesel vehicles have negligible effect on fleet-averaged NMHCemission factors. Most of the differences are due to the higheraverage ambient temperatures on weekdays relative to Sundaysduring the study period.

Unlike NMHC and CO, the measured fleet-averaged emis-sion factors for NOx and TC are expected to be higher on week-days due to higher fractions of diesel vehicles, which have muchhigher NOx and TC emission rates than gasoline vehicles.Therefore, the modeled NOx and TC emission factors for gaso-line vehicles should be less than or equal to the measured fleet-averaged emission factors. This is the case, within the uncertain-ties, for EMFAC. This is not the case for MOVES and MOBILEmodels, which predict higher gasoline-only emission factorsthan the fleet-averaged tunnel measurements and therefore resultin higher modeled versus measured fleet-averaged NOx and TCemission factors. Figure 7 shows how the three models distributeemissions by model year within the two fuel categories for atypical weekday fleet observed at the Van Nuys tunnel. Althoughthe overall patterns are similar for all models, there are substan-tial differences in the distribution for NMHC from gasolinevehicles in MOBILE and for NOx from gas vehicles inEMFAC. Also, MOVES attributes much more NMHC to dieselvehicles in the 2000–2006 model years than the other models.

The modeled emission factors and observed traffic volumesby vehicle typewere used to estimate the relative contributions ofgasoline and diesel vehicles to total fleet CO, NOx, and TCemissions, as shown in Figure 8. Although gasoline vehiclesare the dominant source of CO with all three models, largevariations exist among the models for relative contributions ofdiesel and gasoline vehicles to total NOx and TC emissions. With

an average fleet fraction of 4.2% on weekdays, the diesel vehiclecontributions to total NOx emissions were 50% using EMFAC,30% for MOVES, and 20% for MOBILE and contributions tototal carbon were 75% using MOVES and about 50% for bothEMFAC andMOBILE. These comparisons illustrate the varyingcontributions of diesel and gasoline vehicles to total NOx andPM emissions that would be estimated using EMFAC orMOVES, and the potential changes from a transition fromMOBILE to MOVES.

Comparisons of RSD and model emission factors

RSD measurements were included in the study to examine thedistribution of emissions in the local fleet of light-duty vehicles(Bishop et al., 2012). As configured for this study, the RSD couldnot detect elevated exhaust plumes from trucks and buses.Therefore, comparisons of RSD and tunnel measurements areapplicable to only light-duty vehicles and tailpipe emissions,which would exclude evaporative emissions. Considering theselimitations, the most appropriate comparison of RSD and tunnelmeasurements is for CO, and possibly for NOx on weekends whenthere were minimal heavy-duty vehicles. Table 5 shows the aver-age remote sensing measurements compared with the mean (�standard error) of the weekend tunnel measurements with andwithout the Sunday AM sample. Despite differences in locationand time period of the measurements, fleet-averaged fuel-basedCO and NOx emission factors from the tunnel during weekendsand RSD measurements agreed reasonably well for both CO andNOx.

The modeled emissions for approximately 13,000 light-dutygasoline vehicles included in the remote sensing tests wereaveraged and were compared with the mean RSD fuel-basedCO, THC, and NOx emission factors in Table 5. MOVES andEMFAC gave average CO emission factors that were 1.4 and 1.3times higher than the averages of the 13,000 RSD measure-ments, respectively, whereas the average MOBILE CO emissionfactor was more than double the RSD average. The averageMOVES and MOBILE NOx emission factors were only slightlyhigher than RSD measurements. However, the EMFAC NOx

emission factors were about half the average RSD factors,which were consistent with underprediction by EMFAC forweekend periods relative to the measured NOx emission factorsfrom the tunnel experiment. The modeled THC emission factorswere about a third of the average RSD emission factor for bothMOVES and EMFAC and about half for MOBILE.

Discussion and Conclusions

As motor vehicles are major sources of both VOC and NOx

emissions in urban area, changes in ozone photochemistry, his-toric trends in ambient ozone levels, and the magnitude andspatial extent of the weekend ozone effect have been closelylinked to changes in vehicle emissions (Chinkin et al., 2003;Fujita et al., 2003a, 2003b). It is well established that mobilesource emissions in the South Coast Air Basin (SoCAB) forcarbon monoxide and hydrocarbons were underestimated inpast emission inventories by as much as a factor of 2–3 relativeto NOx (Fujita et al., 1992; Harley et al., 1993; Ingall et al.,

Figure 5. Comparison of measured and modeled NMHC/NOx molar ratios forthree temperature ranges.


1989; Pierson et al., 1990). During the 1987 Southern CaliforniaAir Quality Study (SCAQS), the on-road emission rates mea-sured in the Van Nuys tunnel were more than 2 times larger thanthose calculated by EMFAC (version 7E at the time) for hydro-carbons and CO (Ingalls et al., 1989). Additionally, the ambientVOC/NOx ratios measured during SCAQS (�8–10 in ppb C toppb NOx) were about 2–2.5 times higher than the correspondingemission inventory ratios (�4) (Fujita et al., 1992). The SCAQSdatabase was used by the California Air Resources Board, theSouth Coast Air Quality Management District, and Carnegie

Melon University/California Institute of Technology for air qual-ity model evaluations and all obtained predicted ozone valuesthat were much lower than observed (Chico et al., 1993; Harleyet al., 1993; Wagner and Wheeler, 1993). Because ozone forma-tion is most efficient at VOC/NOx ratios near 10, the actual rateof ozone formation was faster than expected from the base modelresults. Model performance was greatly improved in modelsensitivity tests by increasing the total on-road motor vehicleNMHC emissions by a factor of 2.5 over the official inventory. Inthis study, we derived fleet-averaged fuel-based emission factors

Figure 6. Comparisons of measured fleet-averaged NMHC, NOx, and TC emission factors with modeled emission factors for all vehicles, only gasoline vehicles, andonly diesel vehicles.


Figure 7. Gasoline and diesel fleet-averaged NMHC and NOx emission rates (g/mi) by model year for MOVES2010a, MOBILE6.2, and EMFAC2007. Fleetdistribution is from the Tuesday AM tunnel run.

Figure 8. Relative contributions of gasoline and diesel vehicles to modeled fleet-averaged CO, NOx, and total particulate carbon emissions for average weekday orSunday runs.


from on-road emission measurements at the same tunnel used inthe 1987 SCAQS. The 2010 Van Nuys tunnel study is part of anevaluation of the emission inventory and update of the 1987“top-down” ambient versus inventory reconciliation analysisfor the SoCAB.

A significant finding of the 2010 Van Nuys tunnel study wasthat measured NMHC fuel-based emission factors were about3.5 times higher during high-temperature periods (101–102 �F)than cool periods (71–72 �F). The increased emissions duringhot periods were attributed to light hydrocarbons that are asso-ciated with headspace evaporative emissions. These results aregenerally consistent with an ambient source apportionmentstudy that estimated a 6.5%� 2.5% increase in the contributionsof evaporative emissions from motor vehicles per degree Celsiusincrease in maximum temperature (Rubin et al., 2006). Althoughthe NMHC emission rates predicted by all three models were ingood agreement with measurements during cool periods, therunning evaporative emissions for all models exhibited insuffi-cient sensitivity to temperature during hot periods, especiallyMOVES. The measured NMHC/NOx ratios were 3.1, 1.7, and1.4 times higher than predicted by MOVES, MOBILE, andEMFAC, respectively, during hot periods. These results suggestthat there is an overall increase in motor vehicle NMHC emis-sions on hot days that is not fully accounted for by the emissionmodels. Hot temperatures and concomitant higher ratios ofNMHC emissions relative to NOx both contribute to morerapid and efficient formation of ozone.

Another significant finding relates to the sensitivity of theMOVES to vehicle operating modes, which is an essential designelement of the model. The speed-constructed 9-sec drive cycleused by ENVIRON to estimate the MOVES emission factors forour blind comparisons with the measured emission factors yieldsabout 14% and 36% higher NOx emission factors than thevehicle operating mode distributions of the combined 69 drive-through samples and 41-mph flat cycle, respectively. Althoughthe MOVES NOx emission factors reported here were generallyhigher than the measured factors, most differences were notsignificant considering this factor. The variations related tosensitivity of MOVES to operating mode increases the uncer-tainty of the comparisons with the measured emission factorsand illustrate the importance of selecting the appropriate operat-ing modes for project-level analysis.

Irrespective of the model with measurement comparisons, weobserved large variations among the three models in the pre-dicted emission factors and relative contributions of diesel andgasoline vehicles to total NOx and particulate carbon (TC) emis-sions in the tunnel. During weekday, diesel trucks accounted for33% of the total NOx emissions in the tunnel according toMOVES and 50% by EMFAC. Contributions of diesel trucksto total carbon were 75% by MOVES and 30% byEMFAC. Although the MOVES NOx emission factors werehigher than EMFAC for gasoline vehicles (4.0 vs. 1.8 g/kg offuel), they were lower for diesel vehicles (15.6 vs. 18.0 g/kg offuel). The MOVES TC emission factors were 0.03 and 0.95 g/kgfor gasoline and diesel, respectively, whereas the correspondingEMFAC TC emission factors were 0.08 and 0.33 g/kg. Thesedifferences may be partly related to the aforementioned differ-ences in the federal and California emission standards and emis-sion control programs. Another possible explanation may berelated to the cycle-average approach used in EMFAC versusthe MOVES modal modeling approach. Vehicle emission testshave shown that a large fraction of the PM emissions fromnormal emitters are associated with hard acceleration events inthe vehicle test cycle (Fujita et al., 2007). Measurements duringthe Kansas City Vehicle Emissions Characterization Studyshowed that the hard acceleration event that occurs between840 and 880 sec of the unified driving cycle accounted forabout 40% of the total hot running PM emission (Lindhjemet al., 2009) and that this fraction was similar for all modelyears newer than 1980. The modal approach should predictlower PM emissions than the cycle-average approach for therelatively stable driving pattern in the tunnel.

Acknowledgments

This project was funded by the U.S. Department of EnergyOffice of Vehicle Technologies (Dr. James Eberhardt, ChiefScientist) through the National Renewable Energy Laboratory.We thank the staff of the Air Quality and Modeling Center of theU.S. EPA Office of Transportation and Air Quality for theirreview of the draft manuscript and for providing additionalmodel calculations. The authors thank the following DRI per-sonnel for their assistance with sample analysis: AnnaCunningham and Mark McDaniel for organic speciation analy-sis, and Steven Kohl, Ed Hackett, and Brenda Cristani for ana-lysis of inorganic species. We also acknowledge the assistance ofAndy Chew of Andy’s Automotive for processing the videorecordings of traffic through the Van Nuys tunnel.

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About the AuthorsEric M. Fujita, Barbara Zielinska, and Judith C. Chow are research profes-sors and David E. Campbell is an associate research scientist in the Division ofAtmospheric Sciences at the Desert Research Institute (Nevada System of HigherEducation).

Christian E. Lindhjem is a senior manager and Allison DenBleyker is anassociate at ENVIRON International Corporation.

Gary A. Bishop is a research engineer, Brent G. Schuchmann is a researchassistant, and Donald H. Stedman is Professor Emeritus in the Department ofChemistry and Biochemistry at the University of Denver.

Douglas R. Lawson (now retired) was a principal scientist at the NationalRenewable Energy Laboratory.


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