## Exhaust Emission Rates for Heavy - Duty On - road Vehicles in MOVES2014 DRAFT REPORT

USEPA Office of Transportation and Air Qualit y Assessment and Standards Division

2015

Disclaimer This technical report does not necessarily represent final EPA decisions or positions. It is intended to present technical analysis of issues using data that are currently available. The purpose of the release of such reports is to facilitate the exchange of technical information and to inform the public o

f technical developments which may form the basis for a final EPA decision, position, or regulatory action.

### Table	of	Contents

1 Principles of Modeling Heav y - dut y Emissions in MOVES ................................ .................... 5 1.1 Heav y - dut y Regulatory Classes ................................ ................................ ....................... 6 1.2 Emission Pollutants and Processes ................................ ................................ ................... 7 1.3 Operating Modes ................................ ................................ ................................ .............. 8 1.4 Vehicle Age ................................ ................................ ................................ .................... 12 2 Heav y Dut y Diesel Emissions ................................ ................................ ............................... 14 2.1 Running Exhaust Emissions ................................ ................................ ........................... 14 2.1.1 Nitrogen Oxides (NOx) ................................ ................................ ........................... 14 2.1.2 Particulate Matter (PM) ................................ ................................ .......................... 42 2.1.3 Hy drocarbons (HC) and Carbon Monoxide (CO) ................................ .................. 57 2.1.4 Energy ................................ ................................ ................................ ..................... 63 2.2 Start Exhaus t Emissions ................................ ................................ ................................ . 68 2.2.1 HC, CO, and NOx ................................ ................................ ................................ ... 69 2.2.2 Particulate Matter ................................ ................................ ................................ .... 72 2.2. 3 Adjusting Start Rates for Soak Time ................................ ................................ ...... 72 2.2.4 Start Energy

Rates ................................ ................................ ................................ ... 75 2.3 Extended I dling Exhaust Emissions ................................ ................................ ............... 77 2.3.1 Data Sources ................................ ................................ ................................ ........... 77 2.3.2 Anal ys is ................................ ................................ ................................ ................... 78 2.3.3 Results ................................ ................................ ................................ ..................... 80 2.3.4 MOVES Extended I dle Emission Rates ................................ ................................ . 80 2.3.5 Auxiliary

Power Unit Exhaust ................................ ................................ ................ 82 3 Heav y - Duty

Gasoline Vehicles ................................ ................................ ............................. 84 3.1 Running Exhaust Emissions ................................ ................................ ........................... 84 3.1.1 HC, CO, and NOx ................................ ................................ ................................ ... 84 3.1.2 Particulate Matter ................................ ................................ ................................ .. 109 3. 1.3 Energy

Consumption ................................ ................................ ............................ 113 3.2 Start Emissions ................................ ................................ ................................ ............. 117 3.2.1 Available Data ................................ ................................ ................................ ...... 117 3.2.2 Estimation of Mean Rates ................................ ................................ ..................... 1173.2.3 Estimation of Uncertainty ................................ ................................ ..................... 120

3.2.4 Projecting Rates bey ond the Available Data ................................ ........................ 122 3.2.5 Start Energy

Rates ................................ ................................ ................................ . 129 4 Heav y Dut y Compressed Natural Gas Transit Bus Emissions ................................ ............ 131 4.1 Transit Bus Driving C ycl es and Operating Mode Distributions ................................ .. 131 4.1.1 Heav y - Duty

Transit Bus Driving C ycles ................................ .............................. 131 4.1.2 Transit Bus Operating Mode Distributions ................................ ........................... 133 4.2 Comparison of Simulated Ra tes and Real - World Measurements ................................ 134 4.2.1 Simulating Cy cle Emission Aggregates from MOVES2010b Rates .................... 134 4.2.2 Published Chassis Dy namometer Measurements ................................ ................. 134 4.2.3 Plots of Simulated Aggregates and Published Measurements .............................. 136 4.3 Development of New Running Exhaust Emission Rates ................................ ............. 140 4.3.1 Determining Model Year Groups ................................ ................................ ......... 140 4.3.2 Scaling Model Years After 2007 ................................ ................................ .......... 141 4.3.3 Creating CNG Runni ng Rates for Future Model Years ................................ ........ 143 4.4 Start Exhaust Emission Rates for CNG Buses ................................ ............................. 144 4.5 Applications to Other Model Years and Age Groups ................................ .................. 145 4.6 PM and HC Speciation for CNG Buses ................................ ................................ ....... 145 4.7 Ammonia and Nitrous Oxide emissions ................................ ................................ ...... 147 5 Heav y - Duty

Crankcase Emissions ................................ ................................ ...................... 148 5.1 Background on Heav y - duty Diesel Crankcase Emissions ................................ ........... 148 5.2 Modeling Crankcase Emissions in MOVES ................................ ................................ 149 5.3 Conventional Heav y - Duty Diesel ................................ ................................ ................ 150 5.4 2007 + Heav y - Dut y Diesel ................................ ................................ ........................... 151 5.5 Heav y - dut y Gasoline and CNG Emissions ................................ ................................ .. 152 6 Nitrogen Oxide Composition ................................ ................................ ............................... 154 6.1 Heav y - dut y Diesel ................................ ................................ ................................ ........ 155 6.2 Heav y - dut y Gasoline ................................ ................................ ................................ .... 155 6.3 Compressed Natural Gas ................................ ................................ .............................. 156 Appendix A Calculation of Accessory Power Requirements ................................ ................ 158 Appendix B Tampering and Mal - maintenance ................................ ................................ ...... 160Appendix C Extended I dle Data Summary ................................ ................................ ........... 171

Appendix D Developing PM emission rates for missing operating modes ........................... 176 Appendix E Heav y - dut y Dies el EC/PM Fraction Calculation ................................ .............. 177 Appendix F Heav y - dut y Gasoline Start Emissions Analysis Figures ................................ ... 199 Appendix G Heav y - dut y CNG Bus Emissions Supplemental Figures ................................ .. 204 References ................................ ................................ ................................ ................................ ... 2085



## 1 **Principles of Modeling Heavy -**  **duty Em issions in MOVES**  This report describes the anal ys is conducted to generate emission rate s and energy rate s representing exhaust emissions and energy consumption for heav y - dut y vehicles in MOVES201 4 . Heav y - dut y vehicles in MOVES are defined by any vehicle with a Gross Vehicle Weight Rating (GVWR) above 8,500 lbs. This report discusses the development of e mission rate for total hy drocarbons (HC), carbon monoxide (CO), nitrogen oxides (NO x ), and particul ate matter (PM) . From HC emissions, MOVES produces other measures of organic gases, including volatile organic compounds (VOCs), and total organic gases ( TO G). From VOC emission rates and fuel propert y, MOVES estimates individual toxic compounds such as f ormaldehy de and benzene. The deri vation of the aggregate measures of organic gases and individual toxic emissions are available in the Speciation 41 and Toxics 1 MOVES Reports. MOVES estimate the PM emission rates according to 18 subspecies as outlined in the Speciation Report 41 . This report covers the derivation of EC/ PM fractions used to estimate elemental carbon (EC) emission rates and the remaining non - elemental carbon PM (nonEC) emission rates Energy consumption rates were developed based on measurements of carbon dioxide (CO 2 ) , CO and THC . We developed emission rates for heavy - duty vehicles powered by both diesel and gasoline fuels , as well as compressed natural gas (CNG) vehicles , al though emissions from the heav y - dut y sector predominantly come from diesel vehicles. As a result, the majority of the dat a anal yz ed were from diesel vehicles. This report first introduces the principles used to model heav y - dut y vehicles in MOVES. Then the emission rates for heav y - duty diesel, heavy - dut y gasoline, and CNG transit buses are documented. Chapter 5 documents th e crankcase emission rates used for each fuel ty pe of heavy - dut y vehicles. Chapter 6 documents the NO, NO 2 , and HONO rati os that are used to estimate NO, NO 2 , and HONO emissions from NO x emissions . Emission rates for criteria pollutants (HC, CO, NOx, and P M) are stored in the “ EmissionRateBy

Age” table in the MOVES database. The emission rates in the EmissionRateBy

Age table are stored according to 1. MOVES regulatory

class 2. Fuel T ype (Diesel, Gasoline, and CNG) 3. Model y ear group 4. Vehicle age 5. Emission process ( e.g. running exhaust, start exhaust, crankcase emissions) 6. Vehicle operating mode Energy emission rates are stored in the “EmissionRate” table, which is similar to the “E mission R ate B ya ge ” table, except emission rates are not differentiated by vehicle age . The MOVES framework and additional details regarding the “EmissionRateBy Age” and “EmissionRate table are discussed in the report documenting the rates for light - dut y vehicles 8

. In the next sections, the following parameters used to classify heavy - dut y emissions in MOVES are discussed in more detail: heav y - dut y regulatory classes, vehicle age, emission processes, an d vehicle operating modes. Although not discussed in detail, t he model y ear groupings are designed to represent major changes in EPA emission standards.6



### 1.1 **Heavy -**  **duty Regulatory Classes** The MOVES heav y - duty regulatory groups are largel y determined based on gross vehicle weight rating (GVWR) classifications . Each regulatory class group s vehicles together that have similar emission standards that may not be solel y distinguished by GVWR . For example, U rban B us engines are distinguished from other heav y heav y - duty vehicles (GVWR >33,000 lbs) because they have tighter EPA PM emission standards between 1994 and 2006 model years 2 . Urban bus is a regulatory

class that is also defined by its intended use , and not just the GVWR (“heav y heav y - duty diesel - powered pass enger - carry ing vehicles with a load capacit y of fifteen or more passengers and intended primaril y for intra - cit y operation 3 ” ). Regulatory

class 40 and 41 are also defined according to additional criteria than GVWR. LHD<=10K (RegClassID 40) are defined as trucks with GVWR between 8,500 and 10,000 lbs (Class 2b trucks) , and are trucks with only two axles and four tires. Class 2b trucks between 8,500 and 10,000 lbs with two axles and six tires are classified in regulatory class 41 (LHD < =14K), as well as all trucks between 10,000 and 14,000 lbs (Class 3 trucks). Unlike Urban B uses, the distinction between regClassID 40 and 41 in MOVES is not caused by differences in EPA exhaust emission standards. The reasons for the di stin ction between regulatory class 40 and 41 is due to (1) available activity information, and (2) the assignment of operating modes within MOVES source ty pes. (1) Available Activit y Information. As discussed in the Population and Activity Report 4 , the FHWA reports vehicle - mil es traveled ( VMT ) of Class 2b trucks with two axles and four tires into light - duty

vehicle categories, which correspond to MOVES sourceTy peI D 31 (Passenger Trucks) and sourceTy peI D 32 ( Li ght Commercial Trucks). FHWA reports VMT from Class 2b trucks with tw o axles and six tires, as single - unit vehicles , which corresponds to a different set of MOVES source t ype s: Refuse Trucks (51 ), Single Unit Short - haul (52), Single Unit L ong - Haul (53), and Motor Homes (54). (2) Assignment of Operating Modes with MOVES source t ype s. As discussed in the Population and Activity

Report 4 , MOVES a ssigns operating modes within source ty pe s. For light - dut y source t ypes (including passenger trucks and light - commercial) running operating modes as assigned according to Vehicle Specific Power (VSP). For single - unit source ty pes, operating modes are assigned according to Scaled Tractive Power (STP). As discussed in the operating mode subsection, the emission rates for regulatory class 40 use a different scaling factor when computing STP, such that the emission rates are consistent with VSP based operating modes. The emission rates for regulatory class 41 are based on the standard STP scaling facto r, to be consistent with the way MOVES assigns operating modes for heav y - dut y source t ypes. Regulatory

class 40 is a new regulatory class introduced in MOVES2014. Previous versions of MOVES classified all light - heav y dut y trucks with GVWR under 14,000 lbs as LHD2b3. The emission rates for LHD2b3 were compatible with VSP - based emission rates. As s uch, previous versions of MOVES were unable to model emission rates for LHD2b3 emission rates for single unit trucks. With the addition of regulatory class 40, and the new definition of regulatory class 41, MOVES is able to model light - heav y dut y trucks th at are classified in both the light - dut y and heav y - dut y source t ypes.7

The emission rates for all the heav y - dut y sources types are discussed in this report. As discussed later in the report, the data used to derive the emission rates for regulatory class 4 0 and 41 trucks are often the same, but analyzed with separate scaling factors to derive separate emission rates for regulatory

class 40, and r egulatory

class 41. Occasionally , the term LHD2b3 is used in this report, to refer to all light - heavy duty trucks with GVWR under 14,000 lbs.

**Table 1**  **- 1**  provides an overview of the regulatory class definitions in MOVES for Heav y - Dut y vehicles. **Table 1**  **- 1**  also provide a distinction if the emission rates are developed to be consistent with VSP or STP - based operating modes . **Table 1**  **- 1**  **. Regulatory Cla sses for Heavy**  **- Duty Vehicles**  **Regulatory Class Description**  **regClassName regClassID**  **Gross Vehicle Weight Rating**  **(GVWR) [lb] Source**  **Types (SourceTypeID)**  **Operating**

**Mode Basis**  **2** Light - heavy duty < 10,000 lbs . (Class 2b Trucks with 2 Axles and 4 Tires.) LHD<= 10 K 40 8,501

– 10 ,000 Passenger Trucks,(31) Light Commercial Trucks(32) VSP Light - heavy duty ≤ 14,000 lb . Class 2b (Trucks with 2 Axles and at least 6 Tires or Class 3 Trucks.) LHD<=14k 41 8,501

– 14,000 Buses (41, 43 ), and Single Unit Trucks (51,52,53,54) STP Light - heavy duty 4 - 5 LHD45 42 14,001

– 19,500 Buses (41, 42, 43) and Single Unit Trucks (5 1,52,53,54) STP

Medium - heavy duty MHD 46 19,501

– 33,000 Buses (41,42,43), Single Unit Trucks (51,52,53,54) , and Com bination Trucks (61,62) STP Heavy - heavy duty HHD 47 > 33,000 Buses (41,42,43), Single Unit Trucks (51,52,53,54), and Com bination Trucks (61,62) STP Urban Bus Urban Bus 1 48 > 33,000 Transit Bus (42) STP 1 see CFR § 86.091(2). 2 MOVES assigns operating modes based on VSP or STP, depending on source type8



### 1.2 **Emission Pollutants and**  **Processes** MOVES models vehicle emissions from fourteen different emission processes as listed in Table 1 - 2 . This report covers the emission rates for the exhaust emission processes (running exhaust, start exhaust, extended idle exhaust, auxillary power exhaust, crankcase running exhaust, crankcase start exhaust, and cr ankcase extended idle exhaust) for HC, CO, NOx and PM. The ‘running’ process occurs as the vehicle is operating on the road either under load or in idle mode. This process is further de lineated by

23 operating modes as discussed in the next subsection . T he ‘ext ended idle’ process occurs during an extended period of idling operation such as when a vehicle is parked for the night and left idling. Extended idle is generally a different mechanism (usually a higher RPM engine idle to power truck accessories f or operator comfort) than the regular ‘curb’ idle that a vehicle experiences while it is operating on the road. Estimation of energy

consumption rates for heav y - duty vehicles is also covered in this report. Energy consumption ( in units of kJ) is modeled f rom running exhaust, start exhaust, extended idle exhaust, and auxiliary

power exhaust. Emissions of greenhouse gases other than CO 2 are not covered. Estimation of the emissions of methane, nitrous oxide (N 2 O), and ammonia (NH 3 ) for gasoline and diesel heavy - dut y vehicles are described in separate report s . 5 , 6 The estimation of these emission rates from these pollutants for CNG transit bus vehicles are covered in this report. Evaporative and refueling emissions from heavy - duty vehicles are not covered i n this report. Estimation of evaporative hy drocarbon emissions from heavy - duty gasoline vehicles is described in the evaporative report . 7 The model does not estimate evaporative emissions for diesel - powered vehicles, but does estimate fuel spillage emissi ons which are part of the fueling emissions documented in the evaporative report . 7 Brake and Tire wear emission rates from heav y - duty vehicles are discussed in the Brake and Tire Wear Report. 10 **Table 1**  **- 2**  **. Emission Processes for On -**  **road Heavy -**  **Duty Vehicles**

processID processName Covered in this report? 1 Running Exhaust Y 2 Start Exhaust Y 9 Brakew ear N 10 Tirew ear N 11 Evap Permeation N 12 Evap Fuel Vapor Venting N 13 Evap Fuel Leaks N 15 Crankcase Running Exhaust Y 16 Crankcase Start Exhaust Y 17 Crankcase Extended Idle Exhaust Y 18 Refueling Displacement Vapor Loss N 19 Refueling Spillage Loss N 90 Extended Idle Exhaust Y 91 Auxiliary Pow er Exhaust Y9



### 1.3 **Operating Modes** O perating modes for heavy - dut y vehicles running exhaust are defined in terms of power output (with the exception of the idle and braking modes). For light - dut y vehicles, the parameter used is known as vehicle - specific power (VSP), which is calculated b y nor malizing the continuous power output for each vehicle to its own weight. Li ght - duty vehicles are tested on full chassis dy namometers, and emission standards are in units of grams per mile. Thus, the emission standards are largel y independent of the weight (and other phy sical characteristics) of the vehicle and dependent on distance (or miles). More in depth discussion of VSP is contained in the light - dut y emission rate report. 8 For heav y - dut y vehicles, we have continued to relate emissions to power output , but in a different way . Rather than normalize the tractive power for each vehicle to its own weight, we scale the power b y a fixed multiple designed to fit the resulting means into the existing operating mode framework. We refer to this parameter as “sca led - tractive power” (STP). Heavy - dut y vehicles are regulated using engine dynamometers, and emissions standards are in units of grams per brake horsepower - hour (g/bhp - hr). With these work - based emission standards, emission rates relate strongl y to power and are not independent of vehicle mass, so normalizing by mass is not appropriate. Thus, for heavy - duty modal modeling, the tractive power is used in its natural form but simply

scaled by a constant as described above to bring its numerical values into th e same range as the VSP values used for light - duty vehicles . The equation for STP is located here , with units in scaled kW or skW. :

scale axle 

### f P STP  **Equation 1**  **- 1**  Where: Paxle is the power demands at the axle for the heav y - dut y truck. As discussed later, Paxle can be estimated from an engine dy namometer or from an engine control unit (ECU) for on - road or chassis testing, b y measuring the engine power and estimating the a cce ssory loads and power - train efficiencies for the vehicle. For on - road tests, measuring power from the ECU is in general more accurate at than estimating power from road load coefficients . Unlike a generic road load equation where vehicle characteristics, such as aerody namic drag and rolling resistance are assumed, the ECU measures engine speed and torque directly during the test. Also, wind speed and wind direction , which can have a significant effect on aerod ynamic drag, are not ty picall y measured on the on - road tests. Additionally , the road load equations may not reflect the actual vehicle test weight, and the tests may not have accurate grade information for the entire route tested . For on - road tests we generally use power calculated from the ECU measur ements, because the vehicle and environmental characteristics determine the axle power (Section 2.1.1.2 ). In chassis d ynamometer tests, the road load equation works well because it directly determines the axle power during the test. For data collected on chassis dy namometer tests, with vehicles that do not have ECU measurements, we use road load equation to e stimate power (Section 2.1.2.2.1 ).10

The values of

f scale are located in Table 1 - 3 . As mentioned previousl y, the operating modes for regulatory

class 40 are VSP - based, because regulatory class 40 are modeled as passenger trucks and light commercial trucks, and MOVES assigns operating modes to these source t ypes using VSP. Thus, fo r regulatory class 40, f scale is equal to the mean source mass of light - commercial trucks 4 , to yield emission rates that are consistent with VSP - based operati ng modes. In contrast, all other heavy - dut y source t ypes use a cons tant 17.1 power scaling factor, which is approximately

the average running weight for all heav y - dut y vehicles, and yields STP ranges that are within the same range as the definitions for VSP, as shown in Table 1 - 4 . **Table 1**  **- 3**  **. Power scaling factor**  **f scale**  Regulatory Class (RegClassID) Pow er scaling factor (metric tonnes) LHD<=10K (40) 2.06 LHD<= 14K (41), LHD45 (42), MHD (46), HHD (47), Bus (48) 17.1 In cases where the power is not measured at the engine, it can be estimated from instantaneous speed, vehicle mass, and road load coefficients, using the following equation :

= + + + ∙ ( + ∙ ) **Equation 1**  **- 2**  where A = the rolling resistance coefficient [kW  sec/m], B = the rotational resistance coefficient [kW  sec 2 /m 2 ], C = the aerod yn amic drag coefficient [kW  sec 3 /m 3 ], m = mass of indiv idual test vehicle [ metric ton ], f scale = fixed mass factor ( see Table 1 - 3 ) , v t = instantaneous vehicle velocit y at time t [m/s], a t = instantaneous vehicle acceleration [m/s 2 ] is the acceleration due to gravity 9 . 8 m s ⁄ sin is the (fractional) road grade The derivation of the load road parameters is discussed in the Population and Activity Report 4 . This is the equation used b y MOVES to estimate the operating mode distribution from average speed and second - by - second driving cy cles as discussed in the Population and Activity Report. Howe ver, this equation is also used in this report to estimate the STP - based emission rates from emission tests where a more direct measure of Paxle is not available.11

**Table 1**  **- 4**  **. Definition of the Operating Mode**  **Attribute for Heavy -**  **Duty Vehicles (opModeID)** Operating Mode Operating Mode

Description Scaled Tractive Pow er

(STP t , skW) Vehicle Speed

( v t , mph) Vehicle Acceleration ( a, mph/sec) 0 Deceleration/Braking a t  - 2.0 OR ( a t < - 1.0 AND a t - 1 < - 1.0 AND a t - 2 < - 1.0) 1 Idle - v t < 1.0 11 Coast STP t< 0 0  v t < 25 12 Cruise/Acceleration 0  STP t< 3 0  v t < 25 13 Cruise/Acceleration 3  STP t< 6 0  v t < 25 14 Cruise/Acceleration 6  STP t< 9 0  v t < 25 15 Cruise/Acceleration 9  STP t< 12 0  v t < 25 16 Cruise/Acceleration 12  STP t 0  v t < 25 21 Coast STP t< 0 25  v t < 50 22 Cruise/Acceleration 0  STP t< 3 25  v t < 50 23 Cruise/Acceleration 3  STP t< 6 25  v t < 50 24 Cruise/Acceleration 6  STP t< 9 25  v t < 50 25 Cruise/Acceleration 9  STP t< 12 25  v t < 50 27 Cruise/Acceleration 12  STP t< 18 25  v t < 50 28 Cruise/Acceleration 18  STP t< 24 25  v t < 50 29 Cruise/Acceleration 24  STP t< 30 25  v t < 50 30 Cruise/Acceleration 30  STP t 25  v t < 50 33 Cruise/Acceleration STP t< 6 50  v t 35 Cruise/Acceleration 6  STP t< 12 50  v t 37 Cruise/Acceleration 12  STP t<18 50  v t 38 Cruise/Acceleration 18  STP t< 24 50  v t 39 Cruise/Acceleration 24  STP t< 30 50  v t 40 Cruise/Acceleration 30  STP t 50  v t Start emission rates are also distinguished according to operating modes in MOVES. MOVES uses eight operating modes to classify starts according to different soak times, vary ing from a hot start (opMode 101) where the vehicle has been soaking for less than 6 minutes, to a cold start (opMode 108) where the vehicle has been soaking for more than 12 hours.12 **Table 1**  **- 5**  **. Operating m odes for start em**  **issions (as a function of soak tim e)**  Operating Mode Description 101 Soak Time < 6 minutes 102 6 minutes <= Soak Time < 30 minutes 103 30 minutes <= Soak Time < 60 mi nutes 104 60 minutes <= Soak Time < 90 minutes 105 90 minutes <= Soak Time < 120 minutes 106 120 minutes <= Soak Time < 360 minutes 107 360 minutes <= Soak Time < 720 minutes 108 720 minutes <= Soak Time Extended idle exhaust and diesel APU exhaust are each modeled in MOVES with a single operating mode. 

### 1.4 **Vehicle Age** Emission rates for HC, CO, NOx and PM are differentiated b y vehicle age. Currently , start and running emission rates for HC, CO, NOx and PM are stored in the “emissionrateby age” ta ble by age group , meaning that different emission rates can be derived for different aged vehicles of the same model y ear, regulatory class, fuel t ype and operating mode. MOVES uses six different age classes to model the age effects, as shown in Table 1 - 6 . The effects of age on the emission rates are developed separately for gasoline and diesel vehicles. For diesel vehicles, we estimated the effects of tamperi ng and mal - maintenance on emission rates as a function of age. We adopted this approach due to the lack of adequate data to directly estimate the deterioration for heav y - duty vehicles. Based on survey s and studies, we developed estimates of frequencies an d emission impacts of specific emission control component malfunctions, and then aggregated them to estimate the overall emissions effects for each pollutant.For gasoline vehicles, the age effects are estimated directl y from the emissions data, or are adop ted from light - dut y deterioration as discussed in the text. **Table 1**  **- 6**  **. MOVES Age Group Definitions**  ageGroup ID Lower

bound (y ears) Upper

bound (y ears) 3 0 3 405 4 5 607 6 7 809 8 9 1014 10 14 1519 15 19 2099 20 ~13

Emission processes stored in the “EmissionRate” table do not distinguish emissions by age. Energy rates are stored in the “EmissionRate table.” This table also includes HC, CO, NOx, PM , and ammonia (NH 3 ) emissions from extended idle and auxiliary power units (APU), and nitrous oxide (N 2 O) from start and running emissions , tire and brake wear from running emissions. This report documents the HC, CO, NOx, and PM emissions from extended idle and APU usage, however the documentation of heav y - duty nitrous oxide and ammonia 9 and tire and brake wear 10 emission rates are documented in separate reports.14



## 2 **Heavy Duty**  **Diesel Em issions**  **2.1 Running Exhaust Emissions**  This section details our analy sis of data to develop emission rates for heavy - duty diesel vehicles . Four emission processes (running, extended idling, starts , and auxiliary power unit exhaust ) are discussed. Running Exhaust Emissions MOVES running - exhaust emis sions analy sis requires accurate second - by - second measurements of emission rates an d parameters that can be used to estimate the tractive power exerted b y a vehicle . T his sections describes how we analyzed continuous “ second - by - second ’ heavy - dut y diesel emission s data to develop emission rates applied within the predefined set of operati ng modes. Stratification of the data sample and generation of the final MOVES emission factors were done according to the combination of regulatory class (shown in **Table 1**  **- 1**  ) and the model y ear group. As mentioned, the emission rates were using scaled - tractive power (STP) , rather than VSP . 

### 2.1.1 **Nitrogen Oxides (NOx)** For NOx rates, we stratified heav y - dut y vehicles in to the model y ear groups listed in Table 1 - 6 These groups were defined based on changes in NOx emissions standards and the outcome of the Heav y Dut y Diesel Consent D ecree 11 , which required additional control of NOx emissions during highway driving for model y ears 1999 and later. This measure is referred to as the “ Not - to Exceed ” (NTE) limit.

**Table 2**  **- 1**  **. Model year groups**  **for NOx analysis based on emissions standards** Model year group FTP standard ( g/bhp - hr ) NTE limit ( g/bhp - hr ) Pre - 1988 None None 1988 - 1989 10.7 None 1990 6.0 None 1991 - 1997 5.0 None 1998 4.0 None 1999 - 2002 4.0 7.0 HHD; 5.0 other reg. classes 2003 - 2006 2.4 1.25 times the family emission level 2007 - 2009 1.2 2010+ 0.2 2.1.1.1 Data Sources In MOVES2010, we relied on two data sources f or NO X emissions from HHD, MHD, and urban buses : **ROVER .**  This dataset includes measurements collected during on - road operation using the ROVER sy stem, a p ortable e missions m easurement s ys tem (PEMS ) developed by the EPA. The measurements were cond ucted b y the U.S. Army

Aberdeen Test Center on behalf of U.S. EPA 12 : This ongoing program started in October 2000. Due to time constraints and data quality issues, we used only data collected from October 2003 through September15

2007. The d ata was compiled and reformatted for M OVES anal ys is by Sierra Research 13

. The proc ess of analy sis and rate development was performed by EPA . The data we used represents approximately 1,400 hours of operation by 12 4 trucks and buses in model y ear s 1999 thr o ugh 2007. The vehicles were driven mainly over two routes : • “Marathon” from Aberde en, MD to Colorado and back along Interstate 70

• Loop around Aberdeen Proving Grounds in Mary land **Consent Decree T esting**  **.** These data were conducted by West Virginia University using the Mobile Emissions Measurement Sy stem (MEMS) . 14 , 15 This program was initiated as a result of the consent decree between the several heavy - duty engine manufacturers and the US government, requiring the manufacturers to test in - use trucks over the road. D ata was collected from 2001 through 2006. T he data we used represente d approximately 1,100 hours of operation by 188 trucks in model y ear s 1994 through 2003. Trucks were heavil y loaded and tested over numerous routes involving urban, suburban, and rural driving. Several trucks were re - acquired and tested a second time aft er 2 - 3 y ears. Data were collected at 5 - H z frequency , which we averaged around each second to convert the data to a 1 .0 - H z basis . However, since the release of MOVES2010, two additional sources of data have become available . One source comprises data colle cted during compliance evaluations for the 2004 and 2007 Heav y Duty Diesel Motor Vehicle Engines Rule . This dataset includes results for HHD, MHD and LHD vehicles. The second source includes the results of a study of heavy - dut y trucks in dray age service in and around the port of Houston (Houston Dray age). Both programs are described in detail below . **He avy**  **- Duty Diesel In**  **- Use testing**  **(HDIU) .**  The in - use testing program for heav y - dut y diesel vehicles was promulgated in June 2005 to monitor the emissions performance of the engines operated under a wide range of real world driving conditions , within the engi ne’s useful life . 16 Itrequires each manufacturer of heavy - dut y highway diesel engines to assess the in - use exhaust emissions from their engines using onboard, portable emissions measurement s ys tems (PEMS) during t ypical operation while on the road. The P EMS unit must meet the requirements of 40 CFR 1065 subpart J. The in - use testing program began with a mandatory

two - ye ar pilot program for gaseous emissions in calendar y ears 2005 and 2006. The fully

enforceable program began in calendar y ear 2007 and is ongoing. The vehicles selected for participation in the program are within the engine’s useful life , and g enerall y, five unique vehicles are selected for a given e ngine fam ily . The data available for use in MOVES2014 were collected during calendar ye ar s 2005 through 2010 and represent trucks manufactured in model y ears 2003 to 2009 ( Table 2 - 2 ) . **Houston Drayage Data** . In coordination with the Texas Commission on Environmental Quality (TCEQ), the Houston - Galveston Area Council (H - GAC), and the Port of Houston Authority (PHA), EPA conducted a study collecting emissions data from trucks in dray age service using portable emi ssion measurement sy stems (PEMS) from December 2009 to March 2010. 17 The trucks studied were diesel - fueled, heavy - heavy - dut y trucks used to transport containers, bulk and break - bulk goods to and from ports and intermodal rail y ards to other locations. The se trucks conduct the majority of their travel on short - haul runs,16

repeatedly

moving containers across fixed urban routes. Note that only small fractions of trucks involved in dray age service are dedicated solely to this function, with most trucks spendin g large fractions of their time performing other ty pes of short - haul service. No s pecific drive cy cles were used and all PEMS testing was based on actual in - use loads and speeds. For MOVES2014, the HDIU and Houston Dray age data were anal yz ed to fulfill two objectives: (1) to evaluate the rates in MOVES2010 and (2) to be used as a new data source for updating the emission rates Updating MOVES emission rates currentl y in use was considere d when two conditions we re met : (1) when MOVES2010 rates for a specific regulatory - class and model - year - group combination were not based on actual data (i.e., due to gap s in the coverage of ROVER and Consent - Decree testing data i ) and (2) when the compariso ns between MOVES2010 and independent data show a clear indication of disagreement. From each data set, we used only tests we determined to be valid. For ROVER dataset , due to time constraints, we eliminated all tests that indicated any reported problems, including GPS malfunc tions, PEMS malfunctions, etc, whether or not they affected the actual emissions results. For HD IU and Houston Dray age , the time - alignment was visually confirmed by comparing relevant time - series plots , such as exhaust mass - flow rate vs. CO 2 concentration , and exhaust - mass flow rate vs. engine speed, as measured by the ECU. Data was generall y al igned within one second. When an issue with the time - alignment was found , efforts were made to realign the data as much as possible . A s our own high - level check on the qua lity of PEMS and ECU output, we, then, eliminated an y trip from ROVER, HDIU, and Houston Dray age where the Pearson correlation coefficient between CO 2 (from PEMS) and engine power (from ECU) was less than 0.6. In a ddition, d ata were excluded from the anal ys is when the vehicle speed was not available due to GPS and/or ECU malfunctions, when no exhaust flow was reported, and when a periodic zero correction was being performed on gas analyzers . For the WVU MEMS data, WVU itself reported on test validity

under the consent decree proce dure and n o additional detailed quality checks were performed b y EPA. Table 2 - 2 shows the total distribution of vehicles by model y ear group from the emissions test programs above , following evaluation of the validity of the data .

i Specific subsets of rates used in MOVES2010 rates w ere forecast ed by proportioning to emission standards as described in Section 2 .1.1.3.3 .17

**Table 2**  **- 2**  **. Number**  **s of vehicles by m**  **odel year group from t he ROVER**  **, WVU MEMS**  **, HDIU , and Houston**  **Drayage programs used for emission rate analysis**  **Regulatory Class Data Source**  **MYG HHD**  **MHD LHD**  **BUS ROVER and**  **Consent Decree Testing**  **1991 -**  **1997** 19 - - 2

**1998** 12 - - - **1999 -**  **2002** 78 30 - 25 **2003 -**  **2006** 91 32 - 19 **HDIU 2003**  **- 2006**  40 25 15 -

**2007 -**  **2009** 68 71 24 - **Houston Drayage 1991**  **- 1997**  8 - - -

**1998** 1 - - - **1999 -**  **2002** 10 - - - **2003 -**  **2006** 8 - - - 2.1.1.2 Calculate STP from 1 -H z data With on - road testing, using vehicle speed and acceleration to estimate tractive power is not accurate given the effect of road grade and wind speed. As a result, we needed to find an alternate approach. Therefore, w e decided to use tractive power from engine data collected during operation . We first identified the seconds in the data tha t the truck was either idling or braking based on acceleration and speed criteria shown in Table 1 - 4 . For all other operation, e ngine speed  eng and torque 

eng from the ECU were used to determine engine power P eng , as shown in Equation 2 - 1 . Only torque values greater than zero were used so as to only include operation where the engine was performing work. eng eng eng 

# P    **Equation 2**  **- 1**  We then determined the relationship between the power required at the wheels of the vehicle and the power required b y the engine. We first had to account for the losses due to accessory loads during operation. These power loads are not subtracted in the engine torque values that are output from the engine control unit . Heav y - d ut y trucks use accessories during operation. Some accessories are engine - based and are required for operation. These include the engine coolant pump, alternator, fuel pump, engine oil pump, and power steering. Other accessories are required for vehicle operation, such as cooling fans to keep the powertrain cool and air compressors to improve braking. The third ty pe of accessories is discretionary , such as air co nditioning, lights, and other electrical items used in the cab. The calculation of the accessory load requirements is derived below. We grouped the accessories into five categories: cooling fan, air conditioning, engine accessories, alternator ( to run el ectrical accessories ) , and air compressor. We identified where the accessories were predominatel y used on a vehicle speed versus vehicle load map to properly allocate the loads. For example, the cooling fan will be on at low vehicle speed where the force d vehicle cooling is low and at high vehicle loads where the engine requires additional cooling. The air compressor is used mostly

during braking operations; therefore it will have minimal load requirements at18

highway , or high, vehicle speeds. Table 2 - 3 identifies the predominant accessory use within each of the vehicle speed and load areas. At this point, we also translated the vehicle speed and engin e load map into engine power levels . The power levels were aggregated into low (green), medium (y ellow) and high (red) as identified in Table 2 - 3 . L ow power means the lowest third, medium is the middle third, and high is the highest third, of the engine’s rated power. For example, for an engine rated at 450 hp, the low power category would include operation between 0 and 150 hp, medium between 150 an d 300 hp, and high between 300 and 450 hp.

**Table 2**  **- 3**  **. Accessory use as a function of speed and load ranges**  **, co d**  **ed by power level**

We next estimated the power required when the accessory was “on” and percentage of time this occurred. The majority

of the load information and usage rates are based on information from " The Technology Roadmap for the 21st Century Truck ." 18 The total acc essory

l oad is equal to the power required to operate the accessory m ultiplied by

the percent of time the accessory is in operation. The total accessory load for a STP bin is equal to the sum of each accessory

load. The calculations are included in Appendix A . The total accessory

loads P loss,acc listed below in Table 2 - 4 are subtracted from the engine power determine d from Equation 2 - 1 to get net engine p ower available at the engine fly wheel. For LHD vehicles, w e assume d negligible accessory losses . **Table 2**  **- 4**  **. Estim**  **ates of accessory load in kW**  **by power range Engine power**  **HDT MHD**  **Urban Bus** Lo w 8.1 6.6 21.9 Mid 8.8 7.0 22.4 High 10.5 7.8 24.0 Speed Load Low Mid High

Cooling Fan Low Air cond. Air cond. Air cond.

Engine Access. Engine Access. Engine Access. Alternator Alternator Alternator Air Compress Air Compress Cooling Fan Cooling Fan Mid Air cond. Air cond. Air cond.

Engine Access. Engine Access. Engine Access. Alternator Alternator Alternator Air Compress Air Compress Cooling Fan Cooling Fan Cooling Fan High Air cond. Air cond. Air cond.

Engine Access. Engine Access. Engine Access. Alternator Alternator Alternator Air Compress Air Compress19

We then accounted for the driveline efficiency . The driveline efficiency accounts for losses in the wheel bearings, differential, driveshaft, and transmission. The efficiency values were determined through literature searches. Driveline efficiency driveline varies with engine speed, vehicle speed, and vehicle power requirements. Using sources available in the literature , we estimated an average value for driveline efficiency . 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 Table 2 - 5 summarizes our findings . **Table 2**  **- 5**  **. D**  **riveline efficiencies fo und through literature research** 

Based on this research, we used a driveline efficiency of 90% for all HD regulatory classes. Equation 2 - 2 shows the translation from engine power P eng to axle power Paxle . 

## ) ( , acc

loss eng driveline axle 

## P

P P

   **Equation 2**  **- 2**  Finally , we scaled the axle power b y a multiplicative factor f scale to fit light - duty operating - mode ranges . The LHD<= 14K, MHD, HHD, and Bus clas s es were scaled b y 17.1, which is approximately the average running weight for all heav y - dut y vehicles, and the LHD <= 10K trucks were scaled b y 2.06, which is equivalent to the fleet - average mass of light commercial trucks in MOVES as previousl y discussed in Section 1.3 to obtain emission rates for LHD<= 10K that are consistent with the VSP - based operating modes for passenger trucks and light - commercial trucks . shows the conversion of axle power to scaled tractive power using the method explained above.

scale axle 

### f P STP  **Equation 1**  **- 1**  We then constructed operating mode bins define d by STP and vehicle speed according to the methodology outlined earlier in MOVES development 28 and described in **Table 1**  **- 4**  . The General truck: Barth (2005) 80-85% Lucic (2001) 75-95% HDT: Rak ha 75-95% NREL (1998) 91% Goodyear Tire Comp. 86% Ramsay (2003) 91% 21st Century

Truck (2000) 94% SAE J2188 Revised OCT2003: Single Drive/direct 94% Single Drive/indirect 92% Single Drive/double indirect 91% Tandem Drive/direct 93% Tandem Drive/indiriect 91% Tandem Drive/double indirect 89% Bus: Pritchard (2004): Transmission Eff. 96% Hedrick (2004) 96% MIRA 80%20

implementation of STP in MOVES for heav y - duty emission rates is the same as that of VSP for light - duty emission rates. We will refer to the units of S TP as scaled kW or skW . 2.1.1.3 Calculat e emission rates 2.1.1.3.1 Means Emissions in the data set were reported in grams per second. First, w e averaged all the 1 - H z NOx emissions by

vehicle and operating mode. Then the emission rates were again averaged b y regulatory

class and model y ear group. Data sets were assumed to be representative and each vehicle received the same weighting. However, we averaged rates b y vehicles first because we did not believe the amount of driving done b y each truck was necessarily representative. Equation 2 - 3 summarize s how we calculated the mean emission rate for each strati fication group (i.e. model ye ar group, regulatory

class, and operating mode bin).

veh 1 1 ,

, veh

n n r

r n j j n i i j p

p j 

##  

  

            

 **Equation 2**  **- 3** 

where n

j = the number of 1 - H z data points for each vehicle j , n veh = the total number of vehicles , r p,j,i = the emission rate of pollutant p for vehicle j at second i ,

p r = the mean emission rate (meanBaseRate) for pollutant p . For NOx, we calculated a mean emission rate, denoted as the “ mean BaseR ate ” in the MOVES emissionRateBy

Age table , for each combination of regulatory class, model y ear group, and operating mode bin combination.

2.1.1.3.2 Statistics Estimates of uncertaint y were calculated for all the emission rates. Because the data represent subsets of p oints “clustered” b y vehicle, we calculated and combined two variance components, representing “within - vehicle” and “between - vehicle” variances. First, we calculated the overall within - vehicle variance 2 with 

## s .21 j tot n j j with 

### n n s n s j



### 

  

# 

 1 2 2 

### )

1 ( **Equation 2**  **- 4**  where 2

j 

### s = the variance within each vehicle , and n tot = the total number of data points for all the vehicles. Then we calculated the between - vehicle variance 2 betw 

## s (by source bin, age group, and operating mode) using the mean emission rates for individual vehicle s ( j p 

## r , ) as shown in Equation 2 - 5 . 

#   1 j 1 2

, 2 betw j



### 

  

# 

 

### n r r s n

j p j p **Equation 2**  **- 5**  Then, we estimated the total variance by combining the within - vehicle and between - vehicle variances to get the standard error p r 

### s ( Equation 2 - 6 ) and dividing the standard error by the mean emission rate to get the coefficient - of - variation of the mean p c ( Equation 2 - 7 ) .

tot 2 with j 2 betw n s n s r p 

### s   **Equation 2**  **- 6** 

p r p 

### r s c p 

###  **Equation 2**  **- 7** 

2.1.1.3.3 Heavy -Duty Trucks (HHD, MHD, Bus, and LHD not equipped with Lean NOx Traps) Since the data onl y covered model years 1994 through 200 9 , we needed to develop a method to forecast emissions for future model years and back - c ast emissions for past model y ears. For future model y ears ( 20 10 - and - later), we decreased the emission rates for all operating mode bins by a22

ratio proportional to the decrease in the applicable emissions standard s . Starting in MY 2010, the NOx standard for all heavy - dut y trucks is 0.2 g/bhp - hr. We projected that a lmost all of these trucks will be using SCR after - treatment technology , which we assum e to have a 90 percent NOx reduction efficiency

from levels for MY 2006 levels ( 2.4 g/bhp - hr ) , and thus, we estimated the rates for model year 2010 and later b y decreasing MY2003 - 2006 rates by 90 percent . For model ye ar groups 1988 - 1989 and 1990, we increased the 199 1 - 1997 mode l y ear group emission rates by a factor proportional to the increase of the certificat ion levels. The certification levels came from anal ys is conducted for MOBILE6 29

. We applied the 1988 - 1989 emission rates to model y ears 198 7 and earlier . For MHD and HHD trucks, the maximum operating mode (opModeID = 40) represents a tractive power greater than 513 kW (STP = 30 s kW × 17.1). This value exceeds the capacity of m ost HHD vehicles , and MHD vehicles and buses exert even lower levels. As a result, data are very limited in these modes. To e stimate rates in the modes bey ond the ranges of available data, we linearl y extrapolated the rates from the highest operating mode in each speed range where significant data were collected for each model ye ar group . In most cases, this mode was mode 16 fo r the lowest speed range, 27 or 28 for the middle speed range, and 37 or 38 for the highest speed range. For each of these operating modes, work - specific emissions factors (g/kW - hr) were calculated using the midpoint STP. Then, these emissions factors we re multiplied by the midpoint STP of the higher operating modes (e.g. modes 39 and 40 for speed>50mph) to i nput emission rates for the modes lacking data . For the highest bins in each speed range, a “midpoint” STP of 33 skW (564.3 kW) was used. For model ye ar 1998, data existed for HHD trucks but not buses. In these cases , the ratio of emission rates between the Bus regulatory class and HHD regulatory class from the 1999 - 2002 model y ear group was used to calculate rates for the Buses by multiply ing that ratio by the existing HHD emission rates for the corresponding model year group , as shown in Equation 2 - 8 .

= × **Equation 2**  **- 8** 

2.1.1.3.4 P ickup trucks equipped with Lean NOx Traps To meet NOx emissions standards for the 2010 model y ear , the use of after - treatment will probabl y be needed. For example, Cummins decided to use after - treatment starting in 2007 in engines designed to meet the 2010 standard and used in vehicles such as the Dodge Ram . The technology adopted for this purpose was the “ Lean NOx Trap ” (LNT) . This technology allows for the storage of NOx during fuel - le an operation and conversion of stored NOx into N 2 and H 2 O during brief periods of fuel - rich operation. Inaddition, to meet particulate standards in MY 2007 and later, heav y - dut y vehicles are equipped with diesel particulate filter s (DPF). At regular intervals, the DPF must be regenerated to remove and combust accumulated PM to relieve backpressure and ensure proper engine operation . This step require s high exhaust temperatures. However, the se conditions adversel y affect the LNT’s NOx storage ability, resulting in elevated NOx emissions.23

In 2007, EPA acquired a truck equipped with LNT and DPF and performed local on - road measurements using portable instrumentation and chassis dy namometer test s . We distinguished regimes of PM regeneration from normal operation based on operating characteristics, such as exhaust temperature , air - fuel ratio, and ECU signals . During the testing conducted on - road with onboard emission measurement and on the chassis dy namometer, we observed a PM regeneration frequency of approximately 10 percent of the operating time. We will look for opportunities to update this assumption based on an y additional information that becomes available . Emissions from this vehicle were not directly used to calculate emission rates. Rather, adjustments were made from the 2003 - 2006 model y ear group to develop emission rates for this model y ear group and regulatory

class. During PM regeneration, we assumed that the LNT did not reduce emissions from 2003 - 2006 levels. During all other times, we assumed that emissions were reduced by 90 percent from 2003 - 20 0 6 levels. Because we assume that LNT - equipped trucks account for about 25 percent of the LHDDT market, we again weighted the rates f or the two LHD regulatory classes for model y ears 2007 and later. For MY 2007 - 09, w e assume that the remaining 75 percent of LHD diesel trucks will not have after - treatment and will exhibit the 2007 - 2009 model y ear emission rates described earlier in th is s ection. Starting in MY2010, w e assume that the remaining 75 percent of LHD diesel trucks are equipped with SCR, and exhibit 90 percent NOx reductions from 2006 lev els, described in Section 2.1.1.3.3 . 2.1.1.3.5 Incorporation of Tier 3 Standards The Tier - 3 standards will affect “medium - dut y” (MD) diesel vehicles, i.e., vehicles in regulatory classes LHD<=10k and LHD<=14k (regClassID = 40, 41, respectivel y). However, reductions in emission rates attributable to the introduction of Tier 3 standards are applied only to rates for NO x . For HC and CO emissions, the emission rates currently in MOVES imply

levels on the FTP cy cle substantially

below the Tier 3 HC and CO stan dards. For example, when MOVES rates are combined to estimate a simulated FTP estimate for NMHC, the result is a rate of approximately 0.05 grams per mile, while the simulated FTP estimate for CO is less than 1.0 gram/mile. Consequently , we assumed that no additional reductions in HC and CO emissions would be realized through implementation of the Tier 3 standards on MD diesel vehicles. By contrast, we estimate that the Tier 3 NO x standard will results in a reduction emissions from diesel vehicles in Cla sses 2b and 3. Data on current NO x emissions are limited, as there is little in use data on MY 2010 and 2011 vehicles using selective catalytic reduction as a NO x control strategy , we used a proportional approach to estimate the reductions related to Tier 3, reducing NO X in proportion to the change in the emission standard. Because emission standards tend to impact start and running emissions differentl y, we applied a greater portion of the reduction to running emissions and a smaller reduction to start e missions. These reductions were phased - in over the same schedule as for gasoline vehicles, as detailed in Table 2 - 6 .24

**Table 2**  **- 6**  **. Phase -**  **in Assumptions for Tier -**  **3 Standards for m edium**  **- duty diesel vehicles.** 

Model Year Phase - in

fraction (%) Reduction in Running

Emission Rate (%) Reduction in St art Emission Rate (%) 2017 0 0 0 2018 38 23 9 2019 54 33 12 2020 69 42 16 2021 85 52 19 2022 100 6 1.5 23 In generating the reduced rates for running operation, the starting point was a subset of rates for MY2017, extracted from a previous database, and taken to represent the pre - Tier - 3 baseline. ii Then ending point, representing full Tier - 3 control, was model y ear 2022. These rates were calculated b y multiply ing the rates for MY2017 by a fraction of 0.3855. This fraction reflects application of the reduction fraction for running rates in MY2022 as shown in Table 2 - 6 . Rates in MY 2018 and later were calculated as weighted averages of the values for MY2017 and MY2022, using the same fractions applied to gasol ine vehicles, as shown in Table 3 - 16 (page 98 ) and Equation 3 - 2 (page 97 ). Note that these calculations were applied to running rates for the LHD< =10K regulatory

class (based on VSP) and to those for the LHD<=14k regulatory class (based on STP). Examples of rates for selected operating modes are shown in Figure 2 - 1 . Note that on the logarithmic scale used, the parallelism of the trends shows that the proportional reductions are identical for both regulatory classes. As with light - duty

vehicles, reductions in standards are assumed to be associated with an increa se in the regulatory

useful life. An increase in the useful life is interpreted as an improvement in durability , which is expressed through a delay in deterioration effects. To express this effect, rates estimated for the 0 - 3 y

r ageGroup are replicated to the 4 - 5 y ear ageGroup, i.e., the onset of deterioration is delay ed until the onset of the 6 - 7 year ageGroup. This effect is realized partially for model years 2018 - 2020 and fully in 2021.

ii The subset of rates representing MY2017 w as taken from MOVESDBTier320110331 .25

**Figure 2**  **- 1**  **. NO**  **x : Em**  **ission rates for running -**  **exhaust operation in selected operating m odes**  **vs. m odel year**  **, for**

**two light -**  **heavy -**  **duty regulatory classes (LOGARITHMIC SCALE).**

**Figure 2**  **- 2**  **. NO x**  **: : Emission rates for running**  **- exhaust operation in**  **a single operating m**  **ode (27) vs. age**  **, for two light**  **- heavy**  **- duty regulatory classes (L**  **INEAR SCALE).** 26 2.1.1.3.6 Summary Table 2 - 7 summarizes the method s used to estimate emission rates for each regulatory

– class / model - ye ar - group combination. The emission rates based on the analysis of ROVER and C onsent D ecree testing data were used to populate the rates in MOVES2010. For MOVES2014, we made a decision to update the emission rates, for MYG2007 - 2009 for HHD and MYG2003 - 2006 for LHD, based on the comparison of the emission rates in MOVES2010 to HDIU and Hous ton Dray age data, discussed in Section 2.1.1.5 . For all other combinations of regulatory classes and model ye ar groups, the rates from MOVES2010 were retained in M OVES2014.27

**Table 2**  **- 7**  **. Summary of m**  **ethods for heavy**  **- duty diesel**  **NOx emission rate development for each regulatory class and m**  **odel year group Model**  **year group**  **HHD MHD**  **Urban Bus**  **LHD 34 and**  **LHD <=14K**  **LHD <=10K** 

**1960 -**  **1989** HHD 1991 -

1997 rates p roportioned to ratio of certification levels Sam e rates as HHD Bus 1991 - 1997 rates p roportioned using ratio of HHD certification levels Sam e rates as

HHD LHD <=10K

1991 - 1993 rates p roportioned to LHD certification levels

**1990** HHD 1991 -

1997 rates p roportioned to certification levels Sam e rates as HHD Bus 1991 - 1997 rates p roportioned using ratio of HHD certification levels from 1991 - 1997 Sam e rates as

HHD LHD <=10K

1991 - 1993 rates p roportioned to LHD certification levels **1991 -**  **1997** Data

analysis 1,3 Sam e rates as HHD Data analysis 1 Sam e rates as HHD Proportioned to

1998 FTP standards per Table 2 - 1 **1998** Data

analysis 1,3 Sam e rates as HHD Bus 1999 - 2002 rates p roportioned using ratio of HHD 1998 rates to HHD 1999 - 2002 rates Sam e rates as

HHD Sam e rates as

1999 - 2002

**1999 -**  **2002** Data

analysis 1,3 Data analysis 1 Data analysis 1 Sam e rates as MHD MHD engine

data with 2.06 mass factor **2003 -**  **2006** Data

analysis 1,3 Data analysis 1,3 Data analysis 1 Data analysis 2 Data analysis with 2.06 mass factor 2 **2007 -**  **2009** Data analysis 2 Proportioned to 2003 - 2006 FTP standards per Table

2 - 1 3 Bus 2003 - 2006 rates proportioned to FTP standards per Table 2 - 1 Proportioned to

2003 - 2006 FTP standards per Table 2 - 1 3 Data (LNT),

and same rates as 2003 - 2006 (non - LNT) 3 **2010 -**  **2016** HHD 2003 -

2006 rates p roportioned to FTP standards per Table 2 - 1 MHD 2003 - 2006 rates

p roportioned to FT P standards per Ta

ble 2 - 1 Bus 2003 - 2006

rates p roportioned to FTP standards per Table 2 - 1 LHD34 2003 - 2006 p ropo rtioned to FTP standards per Table 2 - 1 LHD<10K

2003 - 2006 p roportioned to FTP standards per Table 2 - 1 **2017 -**  **2050** Sam e as HHD

2010 - 2016 Sam e as MHD 2010 - 2016 Sam e as Bus 2010 - 2016 Proportioned to

Tier 3 standards Proportioned to

Tier 3 standards 1Analysis based on ROVER and Consent Decree testing data; 2 Analysis based on HDIU data; 3 Confirmed by HDIU and Houston Drayage data28

An important point to note is that we did not project increases in NOx emissions with age for vehicles not equipped with NOx after - trea t ment technology (largel y 20 09 model y ear and earlier). This is because of a few reasons:

• The WVU MEMS data did not show an increase in NOx emissions with odometer (and consequentl y , age) during or following the regulatory usef ul life 30 . Since the trucks in this program were collected from in - use fleets, we do not believe that these trucks were necessaril y biased toward cleaner engines.

• Manufacturers often certify zero or low deterioration factors. We estimated tampering and mal - mainten ance effects on NOx emissions to be small compared to other pollutants

– around a 10 percent increase in NOx over the useful life of the engine. Our tampering and mal - maintenance estimation method s are discussed below and detailed in Appendix

2.1.1.3.7 Tampering and Mal - maintenance Table 2 - 8 shows the estimated aggregate NOx emissions increases due to T&M. It also shows the values that we actuall y us ed for MOVES emission rates. As previously mentioned, we assumed that in engines not equipped with aftertreatment, NOx does not increase due to T&M or deterioration. **Table 2**  **- 8**  **. Fleet**  **- average**  **NOx emissions inc**  **reases from zero**  **- mi**  **le levels over the**  **useful life due to**  **tam pering and**  **mal -**  **maintenance**

Model years NOx increase from T&M analysis [%] NOx increase in MOVES [%] 1994 - 1997 10 0 1999 - 2002 14 0 2003 - 2006 9 0 2007 - 2009 11 0 2010 - 2012 SCR 77 77 2010 - 2012 LNT 64 64 2013+ 58 58 As described in Ap pendix B , these emissions increases are combined with information in Table B - 2 to estimate the emissions increase for each age group prior to the end of the useful life for each regulatory

class. With the introduction of aftertreatment sy stems to meet regulatory requirements for MY 2010 and later, EPA expects tampering and mal - maintenance to s ubst antial ly increase emissions over time compared to the zero - mile level. Though 7 7 percent may appear to be a large increase in fleet - average emissions over time, i t should be noted that the 2010 model y ear standard (0.2 g/bhp - hr) is about 83 percent lower than the 200 9 model y ear effective standard (1.2 g/bhp - hr). This still yields a substantial reduction of about 71 percent from 2009 zero - mile levels to 2010 fully deteriorated levels. As more data becomes available for future model ye ars, we hope to update these tampering and mal - maintenance and overall aging effects.29 2.1.1.3.8 Defeat Device and Low - NO x Rebuilds The default emission rates in MOVES for model years 1991 through 1998 are intended to include the effects of defeat devices as well as the benefits of heav y - dut y low - NO x rebuilds (commonly called reflash) that occurred as the result of the heavy - duty diesel consent decree. Reflashes reduce NOx emissions on these engines b y reconfiguring certain engine calibrations, such as fuel injection timing. The MOVES database also includes a set of alternate emission rates for model y ears 1991 through 1998 assuming a hy pothetical fully

reflashed fleet. Since defeat devices were in effect mostly during highway

or steady cruising operation, we assume d that NO x e missions were elevated for only the top two speed ranges in the running exhaust operating modes (>25mph). To modify the relevant emission rates to re present reflash program s , we first calculated the ratios from the emission rates in modes 27 and 37 to tha t for opMode 16, for model y ear 1999 (the first model y ear with not - to - exceed emission limits). W e then multiplied the MY 1999 ratios by

the emission rates in mode 16 for model years 1991 through 1998 , to get estimated “reflashed” emission rates for operating modes 27 and 37. This step is described in Equation 2 - 9 and Equation 2 - 11 . To estimated “reflashed” rates in the remaining operating modes, we multiplied the reflashed rates b y ratios of the remaining operating modes to mode 27 for MY1991 - 98, as shown in Equation 2 **-** 10 and Equation 2 - 12 .

Operating modes (OM) 21 - 30 

###         

  16 , 1999 27 , 1999 16 , 98 91 27 , 98 91 , 

### r

r r r reflash 

###         

 

  27 , 98 91 OM , 98 91 27 , 98 91 , OM , 98 91 , 

### r

r r r x reflash x reflash **Equation 2**  **- 9**  **Equation 2**  **- 10** 

Operating modes (OM) 31 - 40 

###         

  16 , 1999 37 , 1999 16 , 98 91 37 , 98 91 , 

### r

r r r reflash 

###         

 

  37 , 98 91 OM , 98 91 37 , 98 91 , , 1998 1991 , 

### r r r r x reflash OMx MY reflash **Equation 2**  **- 11**  **Equation 2**  **- 12** 30

The default emission rates were also slightly adjusted for age for the consent decree model years. An EPA assessment shows that about 20 percent of all vehicles eligible for reflash had been reflashed b y the end of 2008. 31 W e assumed that vehicles were receiving the reflashes after the heav y - dut y diesel consent decree (post 1999/2000 calendar ye ar) steadily , such that in 2008, about 20 percent had been reflashed. We approximated a linear increase in reflash rate from age zero.

2.1.1.4 Sample r esults The charts in this sub - section show examples of the emission rates that resulted from the anal ys is of the data described in Section 2.1.1.1 . Not all rates are shown ; the intention is to illustrate the most common trends and hole - filling results. Figure 2 - 3 and Figure 2 - 4 show that NOx emission rates increase with STP for HHD trucks . Figure 2 - 5 adds the MH D and bus regulatory classes, with the error bars removed for clarity . As expected, the emissions increase with power, with the lowest emissions occurring in the idling/coasting/braking bins. **Figure 2**  **- 3**  **. T**  **rends in NOx**  **Emissions by operating m**  **ode fr om**  **HHD trucks for m**  **odel year 2002 . Error bars**  **represent the 95% confidence interval of the m ean.** 

**Figure 2**  **- 4**  **. Trends in NOx Emissions by operating m**  **ode from HHD**  **trucks for m odel year 2007**  **. Error bars represent the 95% confidence interval of the m**  **ean.** 0 500 1000 1500 2000 2500 3000 3500 4000

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e [** **g /** **h r** **]**

**Operating Mode**31

T he high est operating modes in each speed range will rarel y be attained due to the power limitations of heavy - dut y vehicles, but are included in the figures (and i n MOVES) for completeness. Nearl y all of the activity occurs in modes 0, 1, 11 - 16, 21 - 28, and 33 - 38, with activity for buses and MHD vehicles usuall y occurring over an even smaller range. In some model ye ar groups, the MHD and HHD classes use the same ra tes, based on lack of significant differences between those two classes’ emission rates.

0 500 1000 1500 2000 2500 3000 3500 4000

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e (** **g /** **h r** **)**

**Operating Mode**32

**Figure 2**  **- 5**  **. Trends in**  **NOx em**  **issions by operating m**  **ode fr om**  **LHD<= 14**  **K , LHD45**  **, MHD, HHD, and bus**  **regulat ory classes for**  **mo del year 2002**  **. LHD<= 14**  **K, LHD45, and MHD have the same NOx**  **zero -**  **mile NOx em**  **ission rates.**

The effects of model ye ar , representing a rough surrogate for technology or standards, can be seen in Figure 2 - 6 , which shows decreasing NOx rates by model y ear group for a sample operating mode (# 24) for HHD trucks . Other regulatory classes show similar trends. Th e rates in this chart were derived with a com bination of data anal ys is (model y ears 1991 through 200 9 ) and hole filling. The trends in the data are expected, since the model y ear groups were formed on the basis of NOx standards. Increasingl y s tringent emissions standards have caused NOx emissions t o decrease significantl y. **Figure 2**  **- 6**  **. Trends in NOx by m**  **odel year for HHD trucks in operating m ode 24**  **. Error bars represent the 95% confidence interval of the mean.**  0 1000 2000 3000 4000 5000 6000

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e [** **g /** **h r** **]**

**Operating Mode**

MHD Bus HHD33

Age effects were onl y implemented for after - treatment - equipped trucks (mostly model y ear 2010 and later) based on an analy sis of tampering and mal - maintenance effects . Due to faster mileage accumulation, the heav y - heav y dut y trucks reach their maximum emission at the y oungest ages , as shown in Figure 2 - 7 . Relative Standard Errors (based on c oefficients - of - variation for means) from previous model y ear groups were used to estimate uncertainties for MY 2010. **Figure 2**  **- 7**  **. M**  **odeled NOx trends by age for m**  **odel year 2010 for operating m**  **ode 24 for**  **MHD, HHD, and Urban**  **B us regulatory classes for model year 2002**  **. Error bars represent the 95% co**  **nfidence interval of the m ean.** 

0 500 1000 1500 2000 2500**M e** **a n** **N O** **x r** **a t** **e (** **g /** **h r** **)**

**Model year group**34

Figure 2 - 8 and Figure 2 - 9 shows the mean emission rates for LHD <= 10K trucks for model ye ars 2003 - 2006 and 2007 - 2009 , respectivel y . The estimated uncertainties are greater than for the other heav y - dut y regulatory

clas ses, since there were fewer vehicles in our test data. As described previously , model y ears 2007 - 2009 vehicles includes vehicles with LNTs (with NO x increases during PM regeneration) and vehicles without an y aftertreatment. **Figure 2**  **- 8**  **. M**  **ean NOx rates by operating m ode for m**  **odel years 2003 -**  **2006 LHD <=10K (RegClass 40)**  **trucks age**

**0 -**  **3 . Error bars represent the 95% confidence interval of the m**  **ean.** 0 20 40 60 80 100 120 140**M e** **a n** **N O** **x r** **a t** **e [** **g /** **h r** **]**

HHD MHD Bus 0 - 3 4 - 5 6 - 7 8 - 9 10 - 14 15 - 19 20+

Age group [years]

0 100 200 300 400 500 600

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e (** **g /** **h r** **)**

**Operating Mode**35

**Figure 2**  **- 9**  **. Mean NOx rates by operating m**  **ode for m odel years 2007**  **- 2009 LHD**  **<=10K trucks age 0**  **- 3**  **. Error bars represent the 95% confidence interval of the m**  **ean.**

2.1.1.5 Evaluation of NOx Emission Rates in MOVES2010 This section presents the results from the efforts to verif y the NOx emission rates in MOVES2010 by comparing the rates in the database to the emissions data from the Heavy Duty I n - Use and Houston Dray age data programs . The HDIU data includes results for HHD, MHD, and LHD trucks , whereas the Houston Dray age data onl y includes HHD trucks. As discussed in Section Data Sources , HDIU and Houston Dray age data have become available after the MOVES2010 release and have served two purposes

– to evaluate the rates in MOVES2010 and to be used as a new data source for updating existing emission rates. The emission rate s for a regulatory class and model y ear group combination were considered for an update if: 1) MOVES2 010 rates were not based on actual data , and 2) the comparison to independent data shows a clear indication of disagreement.

2.1.1.5.1 Heavy -Heavy Duty Trucks Figure 2 - 10 through Figure 2 - 12 show that MOVES2010 rates for pre - 2003 model y ears are generall y in good agreement with the Houston Drayage data and within the range of uncertainty of means calculated from these data . The error bars represent the 95% confidence intervals of the mean. MOVES rate is lower in the high speed operating modes (33 and above) compared to 1998 model ye ar trucks ( Figure 2 - 11 ) . However, o nly a single truck is represented in the Houston Dray age data. As expected, the dray age fleet ty pically did not reach the high - speed/high - power operating modes (operating modes 28 - 30 and 38 - 40) during normal operation.

0 100 200 300 400 500 600

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e [** **g /** **h r** **]**

**Operating mode**36

**Figure 2**  **- 10**  **. Com**  **parison of Means: MOVES em**  **ission rates vs. Houston Drayage Data**  **(n=8) for**  **mo del years** 

**1991 -**  **1997 HHD trucks**  **. Error bars represent the 95% confidence interval of the m ean.** 

**Figure 2**  **- 11**  **. Com**  **parison of Means: MOVES em**  **ission rates vs. Houston Drayage Data**  **(n=1) for m**  **odel year**

**1998 HHD trucks .**  **Error bars represent the 95% confidence interval of the m**  **ean.** 0 1000 2000 3000 4000 5000 6000

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e (** **g /** **h r** **)**

**Operating Mode**

MOVES Drayage

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e (** **g /** **h r** **)**

**Operating Mode**

MOVES Drayage37

**Figure 2**  **- 12**  **. Comparison of Means: MOVES**  **em ission**  **rates vs. Houston Drayage Data (n=10)**  **for m odel year** 

**1999 -**  **2002 HHD trucks . Error bars represent the 95%**  **confidence interval of the m ean.** 

In Figure 2 - 13 and Figure 2 - 14 , MOVES rates for model y ears 2003 - 2006 are compared to results from the Houston Dray ag e and HDI U datasets, respectively . Although MOVES ’ rates for middle and high speed operating modes are lo wer , it is within the 95% confidence intervals of the mean of Houston Dray age data in Figure 2 - 13 . When compared to HDIU data in Figure 2 - 14 , MOVES is generall y within the variability of the data except for the low speed operating modes. Although both comparisons showed that MOVES rates are slightly lower, since the rates in MOVES2 010 for model y ears 2003 - 2006 were also based on the analy sis of the testing data (ROVER and Consent Degree Testing) , no change was made to the rates in MOVES2014. **Figure 2**  **- 13**  **. Comparison of Means: MOVES**  **em ission rates vs. Houston Drayage Data (n=**  **8 ) for m**  **odel year**

**2003 -**  **2006 HHD trucks**  **. Error bars represent the 95% confidence interval of the m ean.**  0 500 1000 1500 2000 2500 3000 3500

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e (** **g /** **h r** **)**

**Operating Mode**

MOVES Drayage38

**Figure 2**  **- 14**  **. Com**  **parison of Me**  **ans: MOVES rates vs. HDIU (n=40**  **) for**  **mo del years 2003**  **- 2006**  **HHD trucks .** 

**Error bars represent the 95% confidence interval of the m ean.** 

In the MOVES2010 database, rates for model ye ars 2007 - 2009 were forecast ed from those for MY G 2003 - 2006 based on the ratio of emissions standards for these two model - ye ar groups , as described in Section 2.1.1.3.3 . This approach was adopted in view of the f act that neither of the two 0 500 1000 1500 2000 2500 3000

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e (** **g /** **h r** **)**

**Operating Mode**

MOVES Drayage

0 200 400 600 800 1000 1200 1400 1600 1800

0 1 1112 13 14 15 1621 22 23 24

25 27 2829 30

33 35 37 3839 40**M e** **a n** **N O** **x r** **a t** **e (** **g /** **h r** **)**

**Operating Mode** MOVES HDIUD39

datasets used at the time (ROVER and Consent - Decree) included data for trucks in this model - ye ar group. However, the availability o f the HDIU dataset makes it possible to compare the projected rates to a set of relevant measurem ents. Figure 2 - 15 shows that the MOVES rates are lower than corresponding means from the HDIU data and are generall y outside the uncertaint y of these means across oper ating modes. Because the rates for this model y ear group met the two conditions described above in Section 2.1.1.5 , th is subset of rates w as updated in MOVES2014 on the basis of HDIU data. **Figure 2**  **- 15**  **. Com**  **parison of Means: MOVES rates vs. HDIU (n=68**  **) for m**  **odel years 2007**  **- 2009 HHD trucks**  **.**

**Error bars represent the 95% confidence interval of the m ean.** 

2.1.1.5.2 Medium -Heavy Duty Trucks Figure 2 - 16 . and Figure 2 - 17 show that MOVES rates for MHD truck s compare well with the HDIU data for both model y ears groups 2003 - 2006 and 2007 - 2009. The data is generally scarce in high - power operation modes, and thus, no 95% confidence interval was calculated. The comparisons validated the MOVES2010 rates for MHD trucks, and no change was made in MOVES2014. **Figure 2**  **- 16**  **. Com**  **parison of Means: MOVES rates vs. HDIU (n=25**  **) for m**  **odel years 2003 -**  **2006 MHD trucks**  **.**

**Error bars represent the 95% confidence interval of the m ean.**  0 100 200 300 400 500 600 700 800 900

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e (** **g /** **h r** **)**

**Operating Mode**

MOVES HDIUD40

**Figure 2**  **- 17**  **. Com**  **parison of Means: MOVES rates vs. HDIU**  **(n=71 )**  **for mo**  **del years 2007**  **- 2009 MHD trucks**  **.**

**Error bars represent the 95% confidence interval of the m ean.**  0 200 400 600 800 1000 1200 1400 1600

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e (** **g /** **h r** **)**

**Operating Mode**

MOVES HDIUD

0 100 200 300 400 500 600 700 800

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e (** **g /** **h r** **)**

**Operating Mode**

MOVES HDIUD41 2.1.1.5.3 Light -Heavy Duty Trucks In MOVES2010, the LHD rates were not based on actual measurements

– they were scaled from MHD rates and forecast based on standards, for model y ears 2003 - 2006 and 2007 - 2009, respectivel y , as described in Section s 2.1.1.3.3 and 2.1.1.3.4 . The comparison to HDIU data for model y ears 2003 - 2006 ( Figure 2 - 18 ) shows that existing MOVES rates are generall y higher than the HDIU results . Thus, the L HD rates for model y ears 2003 - 2006 were updated based on HDIU data in MOVES201 4. In contrast, MOVES compares well with the HDIU data for model ye ars 2007 - 2009 ( Equation 2 - 18 ), and thus , MOVES2010 rates for this model - ye ar group were retained in MOVES2014. **Figure 2**  **- 18**  **. Com**  **parison of Means: MOVES rates vs. HDIU (n=15**  **) for**  **mo del years**  **2003 -**  **2006 LHD trucks .** 

**Error bars represent the 95% confidence interval of the m ean.** 

0 200 400 600 800 1000 1200 1400 1600

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e (** **g /** **h r** **)**

**Operating Mode**

MOVES HDIUD42

**Figure 2**  **- 19**  **. Com**  **parison of Means: MOVES rates vs. HDIU (n=24**  **) for**  **mo del years**  **2007 -**  **2009 LHD trucks .** 

**Error bars represent the 95% confidence interval of the m ean.** 



### 2.1.2 **Particulate Matter (PM)** In this section , particulate matter emissions refers to particles emitted from heav y - dut y engines which have a mean diameter less than 2.5 microns , known as PM 2.5 . Conventional diesel particulate matter are primarily carbonaceous, measured b y el emental carbon (EC) and or ganic carbon (OC). Particles also contain a complex mixture of metals, elements, and other ions, including sulfate. The total PM 2.5 emission rates are typically filter - based, which measure the mass of all the chemical components in the particle - phase. As d escribed above for NOx, t he heav y - dut y diesel PM emission rates in MOVES are a function of: (1) source bin, (2) operating mode, and (3) age group. We classified the data in to the following model ye ar groups for purposes of emission rate development . Th ese groups are generall y based on the introduction of emission s standards for heav y - dut y diesel engines. They also serve as a surrogate for continuall y advancing emission control technology

on heavy - duty

engines. Table 2 - 9 shows the model year group range and the applicable brake - specific emission s standards.

0 100 200 300 400 500 600 700 800

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **N O** **x r** **a t** **e (** **g /** **h r** **)**

**Operating Mode**

MOVES HDIUD43 **Table 2**  **- 9**  **. Model year groups used for anal**  **ysis based on the PM em issions standard** 

**Model Year Group Range PM Standard [**  **g/bhp -**  **hr ]**  1960 - 1987 No transient cycle standard 1988 - 1990 0.60 1991 - 1993 0.25 1994 - 1997 0.10 1998 - 2006 0.10 2007+ 0.01 2.1.2.1 Data Sources A ll of the data used to develop the MOVES PM2.5 emissio n rates was generated in the CRC E 55/ 59 research program 32 . The following description by Dr. Ying Hsu and Maureen Mullen of E. H. Pechan, in the “ Compilation of Diesel Emissions Speciation Data

– Final Report ” provides a good summary

of the program. It is reproduced in the following paragraphs immediately below : The objective of the CRC E55/59 test program was to improve the understanding of the California heav y - duty

vehicle emissions inventory by obtaining emissions from a represen tative vehicle fleet, and to include unregulated emissions measured for a subset of the tested fleet. The sponsors of this project include CARB, EPA, Engine Manufacturers Association, DOE/NREL, and SCAQMD. The project consisted of four segments, designat ed as Phases 1, 1.5, 2, and 3. Sevent y - five vehicles were recruited in total for the program, and recruitment covered the model year range of 1974 through 2004. The number and ty pes of vehicles tested in each phase are as follows: • Phase 1: 25 heav y he av y - dut y (HHD) diesel trucks • Phase 1.5: 13 HHD diesel trucks • Phase 2: 10 HHD diesel trucks , 7 medium heav y - dut y (MHD) diesel trucks ,

2 MHD gasoline trucks • Phase 3: 9 MHD diesel, 8 HHD diesel, and 2 MHD gasoline The vehicles tested in this study were procured in the L os Angeles area, based on model ye ars specified by

the sponsors and by

engine t yp es determined from a survey . WVU measured regulated emissions data from these vehicles and gathered emissions samples. Emission samples from a sub set of the vehicles were anal yz ed b y Desert Research Institute for chemical species detail. The California Trucking Association assisted in the selection of vehicles to be included in this study . Speciation data were obtained from a total of nine different vehicles. Emissions were measured using WVU’s Transportable Heavy - Duty Vehicle Emissions Testing Laboratory . The laboratory employ ed a chassis dy

namometer, with fly wheels and edd y - current power absorbers, a full - scale dilution tunnel, heated probes and sample lines and research grade gas anal yz ers. PM was measured gravimetrically .44

Additional sampling ports on the dilution tunnel supplied dilute exhaust for capturing unregulated species and PM size fractions. Background data for gaseous emissions were gath ered for each vehicle test and s eparate tests were performed to capture background samples of PM and unregulated species. In addition, a sample of the vehicles received Tapered Element Oscillating Microbalance (TEOM) measurement of real time particulate emissions. The HHDDTs were tested under unladen, 56,000 lb, and 30,000 lb truck load weights. The driving cy cles used for the HHDDT testing included: • AC50/80; • UDDS; • Five modes of an HHDDT test schedule proposed by C ARB: Id le, Creep, Transient, Cruis e, and HHDDT_S (a high speed cruise mode of shortened duration) • The U.S. EPA transient test The proposed CARB HHDDT test cy cle is based on California truck activity data, and was developed to improve the accuracy of emissions inventories. It should be no ted that the transient portion of this proposed CARB test schedule is similar but not the same as the EPA certification transient test. The tables below provide a greater detail of the data used in the anal ys is. Vehicles counts are provided by number of vehicles, number of tests , model y ear group and regulatory class (46 = MHD, 47=HHD) in Table 2 - 10 .

**Table 2**  **- 10**  **. Vehicle and Test Counts by Regulatory Class and Model Year Group**  Regulatory Class Model Year Group Number of tests Number of vehicles

MHD 1960 - 1987 82 7

1988 - 1990 39 5 1991 - 1993 22 2 1994 - 1997 39 4 1998 - 2006 43 5 2007 + 0 0

HHD 1960 - 1987 31 6

1988 - 1990 7 2 1991 - 1993 14 2 1994 - 1997 22 5 1998 - 2006 171 18 2007 + 0 0 C ounts of tests are provided by test cy cle in Table 2 - 11 .45 **Table 2**  **- 11**  **. Vehicle Test Counts by Test Cycle**  Test Cycle Number of tests CARB - T 71 CARB - R 66 CARB - I 42 UDDS_W 65 AC5080 42 CARB - C 24 CARBCL 34 MHDTCS 63 MHDTLO 23 MHDTHI 24 MHDTCR 29 2.1.2.2 Analysis 2.1.2.2.1 Calculate STP in 1 - hz data Within source bins, data was further sub - classified on the basis of operating mode . For motor vehicles, 23 operating modes are defined in terms of scaled tractive power (STP) , vehicle speed and vehicle acceleration. These modes are defined above in Table 1 - 4 . The first step in assigning operating mode is to cal culate scaled tractive power ( STP ) for each emissions measurement. At a given time t , the instantaneous STP t represents the vehicle’s tractive power scaled by

a constant factor . STP is calculated as a third - order pol yn omial in speed, with additional terms describing acceleration and road - grade effects. The coefficients for this expression, often called road load coefficients, factor in the tire rolling resistance, aerod ynamic drag, and friction lo sses in the drivetrain. We calculated STP using Equation 2 - 13 (which is the same as Equation 1 - 2 with grade = 0) :

scale t t t t t t f a

mv Cv Bv Av STP 

   3

2 **Equation 2**  **- 13**  where A = the rolling resistance coefficient [kW  sec/m], B = the rotational resistance coefficient [kW  sec 2 /m 2 ], C = the aerod yn amic drag coefficient [kW  sec 3 /m 3 ], m = mass of individual test vehicle [ metric ton ], f scale = fixed mass factor (see Table 1 - 3 ) ,46

v t = instantaneous vehicle velocit y at time t [m/s], a t = instantaneous vehicle acceleration [m/s 2 ] The values of coe fficients A , B , and C are the road load coefficients pertaining to the heavy - dut y vehicles 33 as determined through previous analy ses for EPA’s Phy sical Emission Rate Estimator (PERE). This method of calculating STP calculates tractive power using the same equation used to calculate vehicle - specific power ( VSP ) in the development of emission rates for light - dut y vehicle s except that the scaling factor is used in the denominator, instead of the actual test weight s of individual vehicle s 34

. Note that this approach differs fr om that the NO X emission rate s anal ys is described in Section 2.1.1.2 , since the particulate data was collected on a chassis dy namometer from vehicles lacking electronic control units (ECU) . We have not formally compared the results of the two methods of calculating STP. However, on average, we did find the operating - mode distributions to be similar between the two calculation methods for a given vehicle t ype. For example, we found that the maximum STP i n each speed range was approximately the same.

2.1.2.2.2 Compute Normalized TEOM Readings The TEOM readings were obtained f or a subset of tests in the E - 55/59 test program. Onl y 29 vehicles had a full complement of 1 - hz TEOM measurements . However, the continu ous particulate values were modeled for the remaining vehicles b y W est Virginia University , and results were provided to EPA. In the end, a total of 56 vehicles (out of a total of 75) and 470 tests were used in the anal ys is out of a possible 75 vehicles. V ehicles and tests were excluded if the total TEOM PM 2.5 reading was negative or zero , or if corresponding full - cy cle filter masses were not available. Table 2 - 12 provi des vehicle and test counts by vehicle class and model ye ar. The HDDV6 and HDDV7 groups were combined in the table because there were onl y s even HDDV6 vehicles in the study .47 **Table 2**  **- 12**  **. Vehicle and Test Coun**  **ts by Heavy -**  **Duty Class and Model Year** Model Year HDDV6/7 HDDV8

No. Vehicles No. Tests No. Vehicles No. Tests 1969 - - 1 6 1974 1 10 - - 1975 - - 2 10 1978 - - 1 5 1982 1 5 - - 1983 1 10 1 6 1985 1 28 1 10 1986 1 3 1 4 1989 2 11 1 4 1990 1 12 1 3 1992 1 11 1 11 1993 1 11 1 3 1994 1 9 3 15 1995 2 24 3 13 1998 2 20 3 28 1999 - - 3 43 2000 2 18 5 44 2001 1 5 2 21 2004 - - 4 29 2005 - - 1 6 Since the development of MOVES emission rates is cy cle independent, all available cy cles / tests which met the above requirement s were utilized. As a result, 488,881 seconds of TEOM data were u sed . The process required that each individual second by second TEOM rate be normalized to its corresponding full - cy cle filter mass, available for each combinat ion of vehicle and test . This step was necessary

because individual TEOM measurements are highl y uncertain and vary widely in terms of magnitude (extreme positive and negative absolute readings can occur). The e quation below shows the normalization process for a particular one second TEOM measurement.

i j j i

j j i 

### PM PM PM PM , , TEOM

, TEOM , filter

, , normalized  

###  **Equation 2**  **- 14**  Where i = an individual 1 - Hz measurement (g/sec), j = an individual test on an indivi dual vehicle, PM TEOM, j , i = an individual TEOM measurement on vehicle j at second i ,48

PM filter,j = the Total PM 2.5 filter mass on j , PM normalized,i,j = an estimated continuous emission result ( PM 2.5 ) emission result on vehicle j at second i . Kinsey e t al. (2006) 35 demonstrated that time - integrated TEOM measurements compare well with gravimetric filter measurements of diesel - generated particulate matter.

2.1.2.2.3 Compute Average Normalized TEOM measures by MOVES Bin After normalization, the data were classified b y r egulatory class, model - ye ar group and the 23 operating modes. Mean average results, sample sizes and standard deviation statistics for PM 2.5 emission values were computed in terms of g /hour for each mode . I n cases where the vehicle and TEOM samples were sufficient for a given mode , these mean values were adopted as the MOVES emission rates for total PM 2.5 . In cases of insufficient data for particular modes , a regression technique was utilized to impute missing values.

2.1.2.3 Hole filling and Forecasting 2.1.2.3.1 Missing operating modes Detailed i n Appendix D , a log - linear regression was performed on the existing PM data against STP to fill in emission rates for missing op erating mode bins. Similar to the NOx rates , emission rates were extrapolated for the highest STP operating modes .

2.1.2.3.2 Other Regulatory Classes The TEOM data was only available in quantity

for MHD and HHD classes . There were no data available for the LHD or bu s classes . The Urban Bus ( regulatory class 48) emission rates were proportioned to HHD rates according to differences in the PM standards . Because the certification standards in terms of brake horsepower - hour (bhp - hr) are the same for all of the heavy - dut y engines , the emission rate of LHD<=14K and LHD45 is assumed to be equivalent to the MHD emission rate. T he emission rate s of LHD <= 10K (regulatory c lass 40) need to be compatible with VSP - based operating modes as discussed in Section 1.1 . In D raft MOVES2009, heavy - duty e mission rates were VSP - based. 28 The PM emission rates for LHD<=10K in MOVES2014 are equivalent to the PM emission rates for LHD2b3 from MOVES2009. The LHD2b3 emission rates in MOVES2009 were derived by

a ppl yi ng a factor to the VSP - based MHD PM emission factors derived from the E55/59 TEOM data. A factor of 0.46 was obtained from the MOBILE6.2 heav y - dut y conversion factors 36 , which accounts for the lower power requirements per mile (bhp - hr/mile) of light - heav y duty trucks versus MHD trucks. The equation used to derive the PM emission rates for reg ulatory class 40 is shown below: ≤ 10 = 0 . 46 × (

_ ) **Equation 2**  **- 15**  Where the MHD VSP - based emission rate is obtained from MOVES2009. Urban Bus ( Regulatory

c lass48) emission rates are assumed to be either the same as the HHD emission rates, or for some selected model ye ar groups, to be a ratio of the EPA certification49

standards . Table 2 - 13 display s the model ye ars for which the Urban Bus regulatory c lass has different PM emission standards from other heavy - dut y compression - ig nition engines. For the these model ye ars (1991 - 2006), the urban bus PM emission standards are equal to the HHD emission rates multiplied by

the ratio in emission standards. In addition, the urban bus emissions have different emission deterioration effects as discussed in Appendix B.6 . **Table 2**  **- 13**  **. Urban Bus PM Standards in Comparison to Heavy -**  **duty Highway Compression Engines.** Heavy - duty

Highway Com pression - Ignition Engines Urban Buses Ratio in standards 1991 - 1993 a 0.25 0.1 0.4 1994 - 1995 0.1 0.07 0.7 1996 - 2006 0.1 0.05 0.5 a The 0.1 g/bhp - hr US EPA Urban Bus standard began with model year 1993. In California, the 0.1 g/bhp - hr Urban Bus standard began in 1991. MOVES assumes all Urban Buses met the stricter CA standard beginning in 1991. 2.1.2.3.3 Model year 2 007 and later trucks (with diesel particulate filters) EPA heav y - dut y diesel emission regulations were made considerabl y more stringent for total PM 2.5 emissions starting in model y ear 2007. Ignoring phase - ins and banking and trading issues, the basic emission standard fell fr om 0.1 g/bhp - hr to 0.01 g/bhp - hr. This increase b y a factor of ten in the level of regulatory

stringency

required the use of particulate trap s ys tems on heavy - dut y diesels. As a result, we expect the emission performance of diesel vehicles has changed dram atically . Unfortunatel y, no continuous TEOM data were available for anal ys is on the 2007 and later model ye ar vehicles. However, heav y and medium heavy - dut y diesel PM 2.5 data are available from the EPA engine certification program on model ye ars 2003 through 2007. These data provide a snapshot of new engine emission performance before and after the introduction of particulate trap technology

in 2007. The existence of th ese data makes it possible to determine the relative improvement in PM emissions from model y ears 2003 through 2006 to model y ear 2007. This same relative improvement can then be applied to the existing, TEOM based, 1998 - 2006 model ye ar PM emission factor s to estimate in - use factors for 2007 and later vehicles. An anal ys is of the available certification data is shown in Table 2 - 14 below. I t suggests that the actu al ratio of improvement due to the particulate trap is reduction of a factor of 27.7. This factor is considerabl y higher than the relative change in the certification standards , i.e., a factor of 10. The reason for the change is that the new trap equippe d vehicles certify at emission levels which are much lower than the standard and thus create a much larger ‘margin of safet y’ than previous technologies could achieve. As an additional check on the effectiveness of the trap technology EPA conducted some li mited in house testing of a Dodge Ram truck, and carefully reviewed the test results from the CRC Advanced Collaborative Emission Study (ACES) phase - one program , designed to characterize emissions from diesel engines meeting 2007 standards. The limited re sults from these studies50

demonstrate that the effectiveness of working particulate traps is very high. The interested reader can review the ACES report . 37 **Table 2**  **- 14**  **. The**  **average certification results for m odel years 2003**  **- 2007. Average ratio from** **M Ys 2003**  **- 2006 to**  **MY 2007 is 27.7** Certification Model Year Mean (g/bhp - hr) St. Dev. n 2003 0.08369 0.01385 91 2004 0.08783 0.01301 59 2005 0.08543 0.01440 60 2006 0.08530 0.01374 60 2007 0.00308 0.00228 21 2.1.2.3.4 Tampering and Mal - maintenance The MOVES model contains assumptions for the frequency and emissions effect of tampering and mal - maintenance on heavy - dut y diesel trucks and buses. The assumption of tampering and mal maintenance (T&M) of heav y - dut y diesel vehicles is a departure from the MOBILE6.2 model which assumed such vehicles operated from build to final scrappage at a design emission level which was lower than the prevailing EPA emission standards. Both long term anecdotal data sources and more comprehensive studies now suggest that the assumption of no natural deterioration and/or no deliberate tampering of emission control components in the heavy - dut y diesel fleet was likely

an unrealistic assumption , partic ularl y with the transition to emission aftertreatment devices with the 2007/2010 standards The primary

data set was collected during a limited calendar y ear period, yet MOVES requires data from a complete range of model y ear/age combinations. As a result , the T&M factors shown below in Table 2 - 15 were used to forecast or back - cast the basic PM emission rates to predict model y ear group and age group co mbinations not covered b y the primary data set. For example, for the 1981 through 1983 model ye ar group, the primary dataset contained data which was in either the 15 to 19 or the 20+ age groups. However, for completeness, MOVES must have emission rates f or these model y ears for age Groups 0 - 3, 4 - 5, 6 - 7, etc. As a result, unless we assum e that the higher emission rates which are were measured on the older model y ear vehicles have alway s prevailed

– even when they

were young, a modeling approach such as T&M must be employ ed. Likewise, more recent model y ears could only be tested at young er ages . The T&M methodology used in the MOVES anal ys is allows for the filling of age

– model y ear group combinations for which no data is available. One criticism of the T&M approach is that it may double count the effect of T&M on the fleet because the primary

emission measurements, and base emission rates, were made on in - use vehicles that may

have had some maintenance issues during the testing period. This issue would be mo st acute for the 2007 and later model year vehicles where all of the deterioration is subject to projection. However, for this model y ear group of vehicles, the base emission rates start at low levels, and represent vehicles that are virtually free from T& M.51

We followed the same tampering and mal - maintenance methodology and anal ys is for PM as we did for NOx, as described i n Appendix B.8 . The overall MOVES tampering and m al - m aintenance e ffects on PM emissions over the fleet’s useful life are shown in Table 2 - 15 . The value of 89 percent for 2010 - 2012 model ye ars re flects the projected e ffect of heavy - duty o n - board diagnostic deterrence/earl y repair of Tampering and Mal - maintenance effects. It i s an eleven percent improvement from model ye ars which do not have OBD (i.e., 2007 - 2009). The 67% value for 2013+ is driven by the assumed full - implementation of the OBD in 2013 and later trucks, which assumes a 33% decrease in tampering and mal - maintenance emission effects. **Table 2**  **- 15**  **. Estim**  **ated increases in PM**  **em issions attributed to**  **T am**  **pering and mal -**  **maintenance over the useful life of**  **Heavy -**  **Duty Vehicles Model Year Group**  **Percent increase in PM due to T&M**  Pre - 1998 85 1998 - 2002 74 2003

– 2006 48 2007

– 2009 100 2010

– 2012 89 2013+ 67 2.1.2.3.5 Computation of E lemental Carbon and Non - Elemental Carbon Emission Factors Particulate matter from conventional diesel engines is dominantly composed of elemental carbon emissions. Elemental carbon emissions are often uses sy nony mousl y with soot and black carbon emissions. Black carbon is important because of its negative - health effects and to its environmental impacts as a climate forcer 38

. Elemental carbon from vehicle exhaust is routinely measured with filter - based measurements using thermal optical methods. Continuous surrogate measures of elemental carbon can also be made with available photoacoustic instruments. MOVES models EC emissions explicitly at the operating mode level , because of the availability of EC emission measurements at the operating mode level, and it s importance in quantify ing the composition of PM emissions. MOVES models Total PM 2.5 emissions by vehicle operating mode using elemental carbon (EC) and non - elemental particulate matter carbon (NonECPM) , as shown in Equation 2 - 16 . NonECPM EC PM 5

. 2   **Equation 2**  **- 16**  No nECPM is a species used to represent the fraction of PM that is not elemental carbon, computed using Equation 2 - 17 .

5 . 2 5 . 2 

### PM

EC 0 . 1 PM NonECPM



 **Equation 2**  **- 17** 52

The EC f ractions used in MOVES for pre - 2007 model y ear trucks (i.e. before diesel particulate filters (DPFs) were standard) are shown in Figure 2 - 20 . These vary according to regulatory

class and MOVES operating mode. They ty picall y range from 25 percent at low loads (low STP ) to over 90 percent at highl y loaded modes. All of the EC fractions were developed in a separate analy sis and are documented in Appendix E . The primary dataset used in the anal ys is came from Kweon et al. (2004) where particulate composition and mass rate data were collected on a Cummins N14 series test engine over the CARB eight - mode engine test cy cle. The EPA PERE model and a Monte Carlo approach were used to simulate and translate the primary PM emission results into M OVES parameters (i.e., operating modes).

**Figure 2**  **- 20**  **. Elem**  **ental Carbon fraction by operating m ode for pre**  **- DPF**  **- equipped trucks** 

For 2007 and later model y ear DPF - equipped diesel engines, we used the elemen tal carbon fraction of 9.98% measured in Phase 1 of the Advanced Collaborative Emissions Study (ACES) Report 39

. D iesel particulate filters preferentiall y reduce elemental c arbon emissions, resulting in the low percentage of elemental carbon emissions. The average EC/PM fraction is based on the 16 - hour cy cle which composes several different operating cy cles. Because the fraction is based upon a range of driving conditions, we applied the constant 9.98% EC/PM fraction across all operating modes for the 2 007+ diesel emissions rates. The nonECPM fraction of emissions contains organic carbon (OC), sulfate, and other trace elements and ions. MOVES uses the fuel sulfur content to adjust the sulfate emission contribution to NonECPM as discussed in the MOVES2014 Fuel Adjustment Report 40 . MOVES uses speciation profiles to estimate the composition of organic carbon, ions, and elements in NonECPM as discussed in the MOVES2014 TOG and PM Speciation Report 41

. 0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9 1

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40 **Op erating mode**  **bi n** **E C** **f r** **a c** **t i** **o n** 

HHD MH D53 2.1.2.4 Sample r esults Figure 2 - 21 and Figure 2 - 22 show the trend of increasing PM rates with STP . As with the NO x plots, t he hi gh est operating modes in each speed range will rarel y be attained due to the power limitations of heavy - dut y vehicles, but are included in the figures for completeness . At high speeds (greater than 50 mph; operating modes  30), the overall PM rate s are lower than the other speed ranges. For pre - 2007 model y ears the PM rates are dominated b y EC. With the introduction of DPFs in model y ear 2007, we model the large reductions in overall PM rates and the smaller relative EC contribution to PM emissions. **Figure 2**  **- 21**  **. P**  **articulate M**  **atter rates by operating m**  **ode representing**  **Heavy heavy -**  **duty vehicles ( mo**  **del year 2002**  **at age 0**  **- 3**  **years)**54

**Figure 2**  **- 22**  **. Particulate**  **Matter rates by operating m ode for Heavy heavy**  **- duty vehicles (m**  **odel year 2007 at age 0**  **- 3 years)** 

Figure 2 - 23 shows an example of how tampering and mal - maintenance est imates increase PM with age. The EC / PM proportion does not change b y age, but the overall rate increases and levels off after the end of useful life. This figure shows the age effect for MHD. The rate at which emissions increase toward their maximum dep ends on regulatory class.55

**Figure 2**  **- 23**  **. Particulate Matter rates by age group for Medium heavy**  **- duty vehicles (model year 2002, operating**  **mode 24)**

Figure 2 - 24 . shows the effect of model year on emission rates. Emissions generall y decrease with new PM standards. The EC fraction stays constant until model y ear 2007, when it is reduced to less than ~ 10% due the implementation of diesel particle filters . The overall PM level is substantially

lower starting in model year 2007. The emission rates shown here for earlier model ye ars are an extrapolation of the T&M anal ys is since y oung - age engines from ear ly model y ears could not be tested in the E - 55 program.56

**Figure 2**  **- 24**  **. P**  **articulate M**  **atter rates**  **for Heavy heavy -**  **duty vehicles by m**  **odel year group (**  **age 0 -**  **3 years,**  **operating m ode 24**  **)**57



### 2.1.3 **Hydrocarbons (**

**HC ) and**  **Carbon Monoxide (**  **CO )**  Diesel engines account for a substantial portion of the mobile source HC or CO emission inventories. Recent regulations on non - methane hy drocarbons (NM HC ) (sometimes in conjunction with NOx) combined with the common use of diesel oxid ation cataly sts will yield reductions in both HC and CO emissions from heav y - dut y diesel engines. As a result, data collection efforts do not focus on HC or CO from heav y - dut y engines . In this report, hy drocarbons are sometimes referred to as total hy dro carbons (THC). We used certification levels combined with emissions standards to develop appropriate model y ear groups. Since standards did not change frequently in the past for either HC or CO, we created fewer model year groups than we did from NOx and PM . The HC/CO model year groups are : • 1960 - 1989

• 1990 - 2006

• 2007+ 2.1.3.1 Data Sources The heav y - duty

diesel HC and CO emission rate development followed a methodology that resembles the light - dut y methodology , where emission rates were calculated from 1 - hz data produced from chassis dy namometer testing . Data sources were all heav y - duty chassis test programs: 1. **CRC E -**  **55 /59**  **32 :**  Mentioned earlier, this program represents the largest volume of heav y duty emissions data collected from chassis dy namometer tests. All tests were used, not just those using the TEOM. Overall, 75 trucks were tested on a variet y of drive cycles. Model y ears ranged from 1969 to 2005, with testing conducted b y West Virginia University from 2001 to 2005. 2. **Northern Front Range Air Quality Study (NFRAQ S**  **) 42**  **:** This study was performed by the Colorado Institute for Fuels and High - Altitude Engine Research in 19 97. Twenty - one HD diesel vehicles from model y ears 1981 to 1995 selected to be representative of the in use fleet in the Northern Front Range of Colorado were tested over three different transient drive cy cles. 3. **New York Department of Environmental Conserv ation (**  **NYSDEC )**  **43 :**  NYSDEC sponsored this study

to investigate the nature and extent of heavy - dut y diesel vehicle emissions in the New York Metropolitan Area. West Virginia Universit y tested 25 heav y heav y and 12 medium - heavy dut y diesel trucks under transi ent and steady - state drive cy cles. 4. **West Virginia University:** Additional historical data collected on chassis dy namometers by WVU is available in the EPA Mobile Source Observation Database. The on - road data used for the NOx anal ys is was not used since HC and CO were not collected in the MEMS program, and the ROVER program used the less accurate non - dispersive infrared (NDIR) technology

instead of flame - ionization detection (FID) to measure HC . To keep HC and CO data sources consistent, we used chassis test programs exclusively for the anal ys is of these two58

pollutants. Time - series alignment was performed using a method similar to that used for light - duty chassis test data. The number s of veh icles in the data sets are shown in Table 2 - 16 .

**Table 2**  **- 16**  **. N**  **um ber**  **s of vehicles by m**  **odel year group, r egulatory class, and age group**  Model year group Regulatory class Age group 0 - 3 4 - 5 6 - 7 8 - 9 10 - 14 15 - 19 20+

1960 - 2002 HHD 58 19 16 9 16 6 7

MHD 9 6 5 4 12 15 6 Bus 26 1 3 LHD45 2 1 LHD2b3 6 2003 - 2006 HHD 6 2.1.3.2 Analysis As for PM, STP was calculated using an equation similar to the light - duty VSP equation, but normalized with averag e regulatory class weight instead of test weight, as described b y Equation 2 - 18 .

scale t t t t t t 

### f a

mv Cv Bv Av     3

2 

### STP **Equation 2**  **- 18**  The track road - load c oefficients A , B , and C pertaining to heav y - duty vehicles 33 were estimated through previous anal ys es for EPA’s Phy sical Emission Rate Estimator (PERE) . 21 Using a method similar to that used in the NOx analy sis, we averaged emissions by vehicle and operating mode. We then averaged across all vehicles by model y ear group, age group, and operating mode. Estimates of uncertainty for each mean rate were calculated using the same equations and method s u sed in development of the NOx rates . Instead of using our results to directly populating all the emission rates, we directly populated onl y the age group that was most prevalent in each regulatory class and model ye ar group combination . These age groups are shown in Table 2 - 17 .59

**Table 2**  **- 17**  **. A**  **ge groups used directly in MOVES em**  **ission rate inputs for each regulatory class and m**  **odel year group present in the data**  **Regulatory class Model year group**  **Age group** HHD 1960 - 2002 0 - 3 HHD 2003 - 2006 0 - 3 MHD 1960 - 2002 15 - 19 BUS 1960 - 2002 0 - 3 LHD41 1960 - 2002 0 - 3 We then applied tampering and mal - maintenance effects through that age point, either lowering emissions for y ounger ages or raising them for older ages, using the methodology described in Appenidx B.9 . We applied the same tampering and mal - maintenance effects for CO as HC , which are shown in Table 2 - 18 . **Table 2**  **- 18**  **. T**  **am pering and**  **mal -**  **maintenance effects**  **for HC and CO over the useful life of trucks**  **Model year s**  **I ncrease in HC**  **and CO**  **Emissions (%)** Pre - 2003 300 2003

– 200 6 150 200 7

– 200 9 15 0 2010 - 2012 29 2013+ 22 We multiplied these increases b y the T&M adjustment factors in Table B - 2 in Appendix B.6 to get the emissions by

age group. With the increased use of diesel oxidation cataly sts (DOCs) in con junction with DPFs, we assume an 80 percent reduction in zero - mile emission rates for both HC and CO starting with model y ear 2007. 2.1.3.3 Sample r esults The charts in this sub - section show examples of the emission rates that derived from the anal ys is described above . Not all rates are shown ; the intent is to illustrate the most common trends and hol e - filling results. For simplicity , the light - heavy duty regulatory

classes are not shown, but since the medium - heav y data were used for much of the light - heavy dut y emission rate development , the light - heav y dut y rates follow similar trends. Uncertaintie s were calculated as for NOx. In Figure 2 - 25 . and Figure 2 - 26 , we see that HC and CO mean emission rates increase with STP , though there is much higher uncertaint y than for the NOx rates. This pattern could be due to the smaller data set or may

truly r eflect a less direct correlation between HC , CO and STP . Inthese figures, the data for HHD and bus classes were combined to generate one set of rates for HHD and buses. **Figure 2**  **- 25**  **. T**  **HC em ission rates [g/**  **hr] by operating m ode for m**  **odel year 2002 and age group 0**  **- 3**  **. Error bars represent the 95% confidence interval of the m**  **ean.**60

**Figure 2**  **- 26**  **. CO em**  **ission rates [g/hr] by operating m ode for m**  **odel year 2002 and age group 0**  **- 3**  **. Error bars represent the 95% confidence interval of the m**  **ean.** 0 10 20 30 40 50 60 70 80 90 100

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **H C** **r a** **t e** **[ g** **/ h** **r ]** 

**Operating mode**

MHD HHD/Bus

0 100 200 300 400 500 600

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **C O** **r a** **t e** **[ g** **/ h** **r ]** 

**Operating mode**

MHD HHD/Bus61

Figure 2 - 27 and Figure 2 - 28 show HC and CO emission rates by age group. Due to our projections of T&M effects, there are large increases as a function of age. Additional data collection would be valuable to determin e if real - world deterioration effects are consistent with those in the model, especiall y in model years where diesel oxidation catalysts are most prevalent (2007 and later). **Figure 2**  **- 27**  **. T**  **HC em ission rates**  **[g/hr] by age group for model year 2002 and op**  **erating m ode 24**  **. Error bars represent the 95% confidence interval of the m**  **ean.**

**Figure 2**  **- 28**  **. CO em**  **ission rates [g/hr] by age group for model year 2002 and operat**  **ing m ode**  **24 . Error bars**  **represent the 95% confidence interval of the m ean.**  0 10 20 30 40 50 60 70 80 90 100**M e** **a n** **H C** **r a** **t e** **[ g** **/ h** **r ]** 

HHD MHD Bus 0 - 3 4 - 5 6 - 7 8 - 9 10 - 14 15 - 19 20+ Age group [years]62

Figure 2 - 29 and Figure 2 - 30 show sample HC and CO emission rates by model y ear group. The two earlier model y ear groups are relativel y similar. The rates in the model y ear group reflect the use of diesel oxidation cataly sts. Due to the sparseness of the data and the fact that HC and CO emission do not correlate as well with STP (or power) as NOx an d PM do, uncertainties are much greater. Rates from HHD regulatory class were used for buses. All regulatory classes have the same rates for model ye ars 2003 and later. **Figure 2**  **- 29**  **. T**  **HC em ission rates by m**  **odel year group for operating m ode 24 and age**  **group 0**  **- 3**  **. Error bars represent the 95% confidence interval of the m**  **ean.** 0 100 200 300 400 500 600**M e** **a n** **C O** **r a** **t e** **[ g** **/ h** **r ]** 

HHD MHD Bus 0 - 3 4 - 5 6 - 7 8 - 9 10 - 14 15 - 19 20+ Age group [years]63

**Figure 2**  **- 30**  **. CO em**  **ission rates by m odel year group for operating m**  **ode 24 and age group**  **0 -**  **3 . Error bars**  **represent the 95% confidence interval of the m ean.** 



### 2.1.4 **Energy**

2.1.4.1 LHD<=10k Energy Rates for Model Years 1960 - 2013 0 5 10 15 20 25 30

1960-2002 2003-2006 2007-2050**M e** **a n** **H C** **r a** **t e** **[ g** **/ h** **r ]** 

**Model year group** MHD HHD/Bus

0 20 40 60 80 100 120 140 160 180 200

1960-2002 2003-2006 2007-2050**M e** **a n** **C O** **r a** **t e** **[ g** **/ h** **r ]** 

**Model year group** MHD HHD/Bus64

. The energy

rates for LHD<=10k for pre - 2007 diesel energy rates are unchanged from the LHD2b3 regulatory

class from MOVES2010a. In MOVES2010a, the energy rates for this regulatory

class, along with the light - duty regulatory

classes, were consolidated across weight classes and engine technologies, as discussed in the energy updates report . 5 2.1.4.2 LHD<=10k Energy Rates for Model Years 2014 - 2050 Lower energy

consumption rates for LHD<=10k vehicles are expected due to the Phase 1 Medium and Heav y Duty

Greenhouse Gas Rule , as discussed in more detail in Section 2.1.4.4 . The CO 2 emission reductions for diesel 2b - 3 trucks in Table 2 - 20 were applied equally to the 2013 model ye ar energy

consumption rates in each running operating mode bins to derive 2014 and later energy consumption rates. Figure 2 - 31 display s the average energy consumption (across all running operating modes) for model y ears 1970 through 2030. The rates are constant between 1960 to 1983, and from 2018 to 2050. **Figure 2**  **- 31**  **. Average Energy Consumption Rates for LHD<=10K diesel vehicles across all running operating modes** 

0. 0e+ 00 3. 0e+ 06 6. 0e+ 06 9. 0e+ 06 1. 2e+ 07

1970 1980 1990 2000 2010 2020 2030 mo del Ye ar IDAverageenergyemissionrate,kJ/hour

**re gC**  **la ssN**  **am e**  LHD <= 1 0k65 2.1.4.3 LHD<=14k

, LHD45, MHD, Urban Bus, and HHD Energy Rates for Model Years 1960 - 2013 The data used to develop N O x rates was also used to develop running - exhaust energy rates for most of the heav y - duty

source ty pes. The energy

rates were based on the same data, STP structure and calculation steps as in the NOx analy sis; however, unlike N O x , we did not classify the en ergy

rates by model y ear or b y age, because neither variable had a significant impact on energy rates or CO 2 . As for previous versions of MOVES, CO 2 emissions were used as the basis for calculating energy rates. To calculate energy rates [kJ/hour] from CO 2 emissions, we used a heating value (HV) of 138,451 kJ/gallon and CO 2 fuel - specific emission factor ( f CO2 ) of 10,084 g/gallon for diesel fuel, using Equation 2 - 19 .

2 2 CO CO energy f

HV r r  **Equation 2**  **- 19**  This analy sis updates the running - exhaust energy rates estimated for MOVES2004 for diesel LHD<=14k , LHD45, MHD, urban bus, and HHD regulatory classes. 28 The energy rates for these heav y - dut y diesel vehicle classes are shown in Figure 2 - 32 .

. **Figure 2**  **- 32**  **. Diesel running exhaust energy rates for LHD<=14K, LHD45 ,**  **MHD, HHD, and Urban B**  **uses for** 

**1960 -**  **2013 m odel years**  **. Error bars represent the 95% c onfidence interval of the m**  **ean.**

0 1 2 3 4 5 6

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **E n** **e r** **g y** **r a** **t e** **[ k** **J /** **h r** **] M** **i l** **l i** **o n** **s**

**Operating mode**66

Compared to other emissions, the uncertainties in the energy rates are smaller in part because there is no classification by

age, model y ear, or regulatory class. Thus, the number of vehicles used to determine each rate i s larger, providing for a greater certaint y of the mean energy rate.

2.1.4.4 LHD<=14k

, LHD45, MHD, Urban Bus, and HHD Energy Rates for Model Years 2014 - 2050 The energy

rates are revised for 2014 and later model y ears, to reflect the impact of the 2014 Medium and Heav y Duty Greenhouse Gas Rule . 44 The medium and heav y duty greenhouse gas program begins with 2014 model y ear and increases in stringency through 2018. T he standards continue indefinitely

after 2018. The program breaks the diverse truck sector into 3 distinct categories, including  Line haul tractors (largest heav y - dut y tractors used to pull trailers, combination trucks in MOVES)  Heav y - dut y pickups and vans (3/4 and 1 ton trucks and vans)  Vocational trucks (buses, refuse trucks, motorhomes, single - unit trucks) The program set separate standards for engines and vehicles and ensures improvements in both. I t also sets separate standards for fuel consumptio n, CO 2 , N 2 O, CH 4 and HFCs. iii In MOVES, the improved fuel consumption from the HD GHG Rule is implemented in two way s. First, the running emission rates for total energy are reduced. Second, the truck weights and road load coefficients are updated to refle ct the lower vehicle weights, lower resistance tires, and improved aerod yn amics of the vehicle chassis. The discussion of the vehicle weights and road load coefficients is included in the Population and Activity Report. 4 The revised running emission rates for total energy are drawn from the HDGHG rulemaking modeling. 44 The estimated reductions for heav y - duty diesel vehicles, including all rates are for include new running, start and extended idle rates, are shown in Table 2 - 19 . These rates are for the 2014 and later model years, and reflect the improvements expected from improved energy efficiency in the powertrain. The reductions from the baseline were applied to the appropriate regulatory

classes and model y ears in the MOVES emissionrate table. **Table 2**  **- 19**  **Estim ated Reductions in**  **Diesel and Gasoline Engine CO**  **2 Emission Rates from**  **the HD GHG Pr**  **ogram Phase 1**  G VWR Class Fuel Model Years C O 2 Reduction From Baseline HHD (8a - 8b) Diesel 2014 - 2016 3% 2017+ 6%

iii HFCs are not modeled in MOVES, and the N2O and CH4 standards are not considered forcing on emissions.67

LHD(4 - 5) and MHD (6 - 7) Diesel 2014 - 2016 5% 2017+ 9% Gasolin e 2016+ 5% Unlike the HHD standards, the HD pickup truck/van standards are evaluated in terms of grams of CO 2 per mile or gallons of fuel per 100 miles. Table 2 - 22 describes the estimated expected changes in CO 2 emissions due to improved engine and vehicle technologies. Since nearl y all HD pickup trucks and vans will be certified on a chassis dy namomete r, the CO 2 reductions for these vehicles are not represented as engine and road load reduction components , but total vehicle CO 2 reductions . MOVES2014 models the HD pickup truck/van standards by lowering the energy

rates stored in the emissionrate table. N o change is made to the road - load coefficients or weights of passenger or light - dut y truck source t yp es. The energy consumption rates for LHD<=10 and LHD<=14K were lowered by the percentages shown in Table 2 - 22 for the corresponding model ye ars. **Table 2**  **- 20**  **Estim ated Total Vehicle CO**  **2 Reductions for HD Diesel and Gasoline Pickup Trucks and Vans** 

GVWR CL ASS FUEL MODEL YEARS CO 2 REDUCTI ON

FROM BASELINE LHD 2b - 3 Gasoline 2014 1.5% 2015 2% 2016 4% 2017 6% 2018+ 10% Diesel 2014 2.3%

2015 3% 2016 6% 2017 9% 2018+ 15% Figure 2 - 33 display s the average energy consumption rates for the heavy - duty diesel source t ypes that are modeled using Scaled Tractive Power (STP) with a fixed mass factor of 17.1. The energy rates for all these source ty pes are equivalent for model y ears 1960 - 2013. The reduction in the average energy

consumption rates is display ed in Figure 2 - 33 , with separate redu ctions for the class 2b and 3 trucks (LHD<=14k), class 4 - 7 trucks (LHD45, MHD), and class 8 trucks (HHD). The urban bus regulatory is by definition a heavy

heavy - dut y vehicle, and is treated the same as the other heav y - heav y dut y vehicles (HHD). For LHD<= 14k the energy rates are constant 2018 going forward, for the other categories (LHD45, MHD, Urban Bus, HHD) the energy rates are constant going forward starting in model y ear 2017.68

**Figure 2**  **- 33**  **. Average Ener gy Consumption Rates for LHD<=14k (41), LHD45 (42), MHD (46), Urban Bus (48),** 

**and HHD (47) diesel vehicles across all running operating m odes.** 



### 2.2 **Start Exhaust Emissions** The ‘start’ process occurs when the vehicle is started and is operating in some mode in which the engine is not fully

warmed up. For modeling purposes , we define start emissions as the increase in emissions due to an engine start. That is , we use the dif ference in emissions between a test cy cle with a cold start and the same test cy cle with a hot start. There are also eight intermediate stages which are differentiated by soak time length (time duration between engine key off and engine key on), between a cold start (> 720 minutes of soak time) and a hot start FTP (< 6 minutes of soak time). More details , on how start emission rates are calculated as a function o f soak time , can be69

found later in this section and in the MOVES light - duty emission rate counterpart document 8

. The impact of ambient temperature on cold starts is discussed in the Emission Adjustments MOVES report 45

. 

### 2.2.1 **HC, CO, and NOx** For light - duty diesel vehicles, start emissions are estimated by subtracting FTP bag 3 emissions from F TP bag 1 emissions. Bag 3 and Bag 1 are the same dy namometer cycle, except that Bag 1 starts with a cold start, and Bag 3 starts with a hot start. A similar approach was performed for LHD vehicles tested on the FTP and ST01 cy cles , which also have separate bags containing cold and hot start emissions over identical drive cy cles . Data from 21 LHD diesel vehicles, ranging from model y ears 1988 to 2000, were anal yz ed . No classifications were made for model ye ar or age due to the limited number of vehicles. The results of this analy sis for HC, CO, and NOx are shown in Table 2 - 21 . **Table 2**  **- 21**  **. The**  **average start em issions increases for light**  **- heavy duty**  **diesel vehicles**  **(g) for regulatory class 40,**  **41, and 42. No differentiation by m odel year or age.** 

**HC CO**  **NOx** 0.13 1.38 1.68 For HHD and MHD trucks, data were unavailable . To provide at least a minimal amount of information, w e measured emissions from a 2007 Cummins I SB on an engine dy namometer at the EPA National Vehicle and Fuel Emissions L aboratory in Ann Arbor, Michigan. Among other idle tests, we performed a cold start idle test at 1 , 100 RPM lasting four hours, long enough for the engine to warm up. Essentially , the “drive cy cle” we used to compare cold start and warm emissions was the idle cycle, analogous to the FTP and ST01 cy cles used for LHD vehicles. Emissions and temperature stabilized about 25 minutes into the test. The emission rates throug h time are shown in Figure 2 - 34 . The biggest drop in emission rate through the test is with CO, whereas there is a slight increase in NOx (cold start NOx is lower than hot start NOx), and an insignificant change in HC.70

**Figure 2**  **- 34**  **. Trends in the**  **stabilization of idle**  **em issions from**  **a diesel engine following a cold start**  **. Data were**

**collected from**

**a 2007 Cu mmins ISB m**  **e asured on an engine dynam**  **om eter** 

We calculated the area under each trend for the first 25 minutes and divided by 25 minutes to get the average emission rate during the cold start idle portion. Then, we averaged the data for the remaining portion of the test, or the warm idle portion. The difference between cold start and war m start is in Table 2 - 22 . The NOx increment is negative since cold start emissions are lower than warm start emissions.

**Table 2**  **- 22**  **. Cold**  **- start em**  **issions increases in grams**  **on the 2007 Cummins ISB HC**  **CO NOx**  0.0 16.0 - 2.3 We also considered data from Universit y of Tennessee 46 , which tested 24 trucks with PEMS at different load levels during idling. Each truck was tested with a cold start going into low - RPM idle with air - conditioning on. We integrated the emissions over the warm - up period to get the total cold start idling em issions. We calculated the hot - start idling emissions by multiply ing the reported warm idling rate b y the stabilization time. We used the stabilization period from our engine dy namometer tests (25 minutes) . Then we subtracted the cold - idle emissions fro m the warm idle emissions to estimate the cold start increment. We found that several trucks produced lower NOx emissions during cold start ( similar to our own work ), and several trucks produces higher NOx emissions during cold start. Due to these confli cting results, and the recognition that many factors affect NOx emission during start (e.g. air - fuel ratio, injection timing, etc), we set the cold - start increment to zero. Table 2 - 23 shows our final MOVES inputs for HHD and MHD diesel start emissions increases from our 2007 MY in - house testing . Due to the limited data, the emission rate is constant for all model y ears and ages. 0 5 10 15 20 25 30 35 40 45 50

0. 00 1.00 2.00 3.00 4. 00 5. 00 **time [h**  **rs] m** **g /** **s**

NOx HC CO71

**Table 2**  **- 23**  **. MOVES inputs for HHD and MHD**  **diesel start em**  **issions (**  **gram s**  **/start) for regulatory class 46, 47,**  **and 48. No differentiation by m odel year or age.** 

**HC CO**  **NOx** 0.0 16.0 0.0 As discussed in the Emission Adjustments Report 45 , MOVES2014 applies an additive adjustment to HC cold - start emissions to the diesel start emissions for ambient temperatures below 72F. No adjustments are applied to CO or NOx. Therefore, MOVE S2014 produces start emissions from heav y - dut y diesel vehicles for HC and CO at ambient temperatures below 72 F.

2.2.1.1 Incorporation of Tier - 3 Standa rds for Light -Heavy -Duty Diesel The Tier - 3 exhaust emission standards affect medium - duty diesel vehicles in the L HD<=10k and LHD<=14k (regClassID = 40, 41, respectivel y). Reductions are applied to rates for NO x only starting in MY2018 and culminating in MY2021. No reductions are applied to HC and CO rates. For NO x , reductions for start emissions are applied as pre viousl y described for running emissions in 2.1.1.3.5 (page 23 ). Examp les of rates during the phase - in period are shown in Figure 2 - 35 . Note that start rates are identical for the two regulatory classes. **Figure 2**  **- 35**  **. NO x**  **: Start em ission rates in selected operating m**  **odes vs. model year for two regulatory classes**  **(LOGARITHMIC SCALE).**72



### 2.2.2 **Particulate Matter** Data for particulate start emissions from h eav y - duty vehicles are rare. Ty pically , heavy - dut y vehicle emission measurements are performed on fully warmed up vehicles. Th ese procedure s by pass the engine crank and earl y operating periods when the vehicle is not fully warmed up. Data from engine dy

namometer testing performe d on one heav y - heav y - duty engine , using the FTP cy cle with particulate mass collected on filters . The engine was manufactured in MY 2004 . The cy cle was repeated six times, under both hot and cold start conditions (two tests for cold start and four replicat e tests for hot start). The average difference in PM 2.5 emissions (filter measurement - FTP cy cle) was 0.10985 grams. The data are shown here: Cold start FTP average = 1.931 4 g PM 2.5 Warm start FTP average = 1.821 5 g PM 2.5 Cold start

– warm start = 0.1099 g PM 2.5 We applied this value to 1960 through 2006 model ye ar vehicle s . A c orresponding value of 0.0109 9 g was used for 2007 and later model ye ar vehicles (90 percent reduction due to DPFs ) . We plan to update this value when more data becomes available . The value is the same for all heav y - dut y diesel regulatory class vehicles. 2.2.3 

### Adjusting Start Rates for Soak Tim **e** The discussion to this point has concerned the development o f rates for cold - start emissions . In addition, it was necessary to derive rates for additional operating modes that account for vary ing (shorter) soak times . As with light - duty vehicles, we accomplished this step by appl yi ng soak fractions. As no data are available for heav y - duty vehicl es, we applied the same fractions used for light - duty emissions. Table 2 - 24 describes the different start - related operating mode s in MOVES as a functio n of soak time. The value at 720 min (1 2 hours) represents cold start. These modes are not related to the operating modes defined in Table 1 - 4 which are for running exhaust emissions.

**Table 2**  **- 24**  **. O**  **perating m ode**  **s for start emissions (as a function of soak time)**  Operating Mode Description 101 Soak Time < 6 minutes 102 6 minutes <= Soak Time < 30 minutes 103 30 minutes <= Soak Time < 60 minutes 104 60 minutes <= Soak Time < 90 minutes 105 90 minutes <= Soak Time < 120 minutes 106 120 minutes <= Soak Time < 360 minutes 107 360 minutes <= Soak Time < 720 minutes 108 720 minutes <= Soak Time73

The soak fractions we used for HC, CO, and NOx are illustrated in Figure 2 - 36 below. Due to limited data, we applied the same soak fractions that we applied to 1996+ MY light - dut y gasoline vehicle as documented in the light - dut y emission rate report 8

. For light - heavy dut y vehicles (regulatory

classes 40, 41, and 42), the soak distributions apply to the cold starts for HC, CO and NOx. For heav y - heavy

duty vehicles (regulatory classes 46, 47, and 48) only the CO soak fractions in Figure 2 - 36 is applied to the cold - start emissions, because the base cold start HC and NOx emission rates for h eav y - heavy dut y emission rates are zero. **Figure 2**  **- 36**  **. Soak Fractions Applied to Cold**  **- St**  **art Em issions (opModeID = 108)**  **to Estim ate Emissions for**  **shorter Soak Periods (operating m**  **odes 101 -**  **107) . This Figure is**  **reproduced from Figure 43 in the Light -**  **duty em**  **issions Report 8** 

The start emission rates used for heav y - dut y vehicles, derived from appl yi ng the soak fractions are display ed in Table 2 - 25 for HC , CO, and NOx.

**0.00 0.10**  **0.20 0.30**  **0.40 0.50**  **0.60 0.70**  **0.80 0.90**  **1.00 1.10**  **1.20**

**0 120**  **240 360**  **480 600**  **720 Soak Time (minutes)** **R a** **t i** **o o** **f S** **t a** **r t** **t o** **C o** **l d** **- S** **t a** **r t** 

**NOx CO**  **HC**74

**Table 2**  **- 25**  **. Heavy -**  **duty diesel HC, CO, and NOx Start emissions (g/start)**  **by operating m ode for all m**  **odel year and all ages i**  **n MOVES.** HC CO NOx opModeID LHD 1 Other HD 2 LHD Other HD LHD Other HD 101 0.0052 0 0.055 0.64 0.275 0 102 0.0273 0 0.276 3.2 0.760 0 103 0.0572 0 0.607 7.04 1.350 0 104 0.0780 0 0.869 10.08 1.481 0 105 0.0832 0 1.007 11.68 1.481 0 106 0.0949 0 1.090 12.64 1.468 0 107 0.1183 0 1.256 14.56 1.376 0 108 0.1300 0 1.380 16 1.298 0 1 LHD refers to regClassIDs 40, 41, and 42 2 Other HD refers to the Medium - heavy duty, heavy - heavy duty, and Urban Bus Regulatory classes (46, 47, and 48) The PM start rates b y operating mode are given in Table 2 - 26 below. They are estimated assuming a linear decrease in emissions between a full cold start (>720 minutes) and zero emissions at a short soak time (< 6 minutes) . **Table 2**  **- 26**  **. P**  **articulate M**  **atter Start**  **Emission Rate s**  **by Operating Mode (soak fraction) for all HD**  **vehicles (regClass ID 40 through 4 8)**  Operating Mode PM 2.5 (grams per start)

1960 - 2006 MY PM 2.5 (grams per start) 2007+ MY 101 0.0000 0.00000 102 0.0009 0.00009 103 0.0046 0.00046 104 0.0092 0.00092 105 0.0138 0.00138 106 0.0183 0.00183 107 0.0549 0.00549 108 0.1099 0.01099

2.2.3.1 Adjusting Start Rates for Ambient Temperature The emission adjustments report discusses the impact of ambient temperature on cold start emission rates (opModeID 108) 45 . The ambient temperature effects in MOVES model the ambient temperature has on cooling the engine and aftertreatment sy stem on vehicle emissions. The temperature effect is greatest for a vehicle that has been soaking for a long period of time, suc h that75

the vehicle is the same temperature of the ambient conditions. Accordingl y, the impact of ambient temperature should be less for vehicles that are still warm from driving. However, for HC the temperature effects applied in MOVES are modeled as addi tive adjustments . Accordingl y, the additive temperature adjustments need to be reduced for the hot and warm starts. Due to lack of data, we applied soak fractions, to obtain cold start temperature adjustments for opModeID 101 through 107. The additive cold start adjustment for HC emission factors are display ed in Table X, along with the soak fractions applied. The soak fractions are taken from the non - catal ys t soak fractions derived in a CARB report 47 and reproduced in a Mobile 6 report 48 . We used the non - catal yt ic converter soak fractions to adjust the diesel start temperature adjustments for HC temperature starts . These additive HC starts are applied to all diesel sources in MOVES , including light - dut y diesel (regul atory class 20 and 30). As stated in the emissions adjustments report, there are currently no diesel temperature effects in MOVES for PM, CO, and NOx.

**Table 2**  **- 27**  **. HC Diesel Start Temperature Adjustment by opM odeID.**  opModeID Start Temp Adjustment Soak fraction 101 - 0.0153 × (Temp

– 75) 0.38 102 - 0.0152 × (Temp

– 75) 0.37 103 - 0.0180 × (Temp

– 75) 0.44 104 - 0.0201 × (Temp

– 75) 0.50 105 - 0.0211 × (Temp

– 75) 0.52 106 - 0.0254 × (Temp

– 75) 0.62 107 - 0.0349 × (Temp

– 75) 0.86 108 - 0.0406 × (Temp

– 75) 1.00 

### 2.2.4 **Start Energy Rates** The MOVES start energy rates for the heavy - duty

diesel regulatory

classes are shown in Figure 2 - 37 . The energy

start rates were developed for MOVES2004 28 , and updated in MOVES2010 as documented in the updates report 5 . As shown, there is more granularit y in the pre - 2000 emission rates. The spike in fuel economy at 1984 - 1985 reflects variability in the data used to derive starts, which was consistent with the ‘data - driven’ approach used to derive the energy rates in MOVES2004. T he only

updates to the energy

rates post - 2000 is the impact of the Phase 1 Heavy duty GHG standards, which begin phase - in in 2014 and have the same reductions as the running energy rates as presented in Table 2 - 19 .76

**Figure 2**  **- 37**  **. Heayv -**  **duty e**  **nergy c**  **old start**  **energy r**  **ates (opMode 108) by m**  **odel year and regulatory class.**

The start energy

rates are adjusted in MOVES for increased fuel consumption required to start a vehicle at cold ambient temperature s. The temperature effects are documented in the 2004 Energy Report 28 . Additionally , the energy consumption is reduced for starts that occur when the vehicles is soaking for a short period of time. The soak fractions used to reduced by the energy

consumption emission rates at cold start are provided in Table X. These fractions are used for all model ye ars and regulatory

classes of diesel vehicles. **Table 2**  **- 28**  **. Fraction of energy consum ed at start of varying soak lengths compared to the energy consum**  **ed at a full cold start (operating m**  **ode 108).** Operating Mode Description F raction of energy consumption compared to cold start 101 Soak Time < 6 minutes 0.013 102 6 minutes <= Soak Time < 30 minutes 0.0773 103 30 minutes <= Soak Time < 60 minutes 0.1903 104 60 minutes <= Soak Time < 90 minutes 0.3118 105 90 minutes <= Soak Time < 120 minutes 0.4078 106 120 minutes <= Soak Time < 360 minutes 0.5786 107 360 minutes <= Soak Time < 720 minutes 0.8751 108 720 minutes <= Soak Time 177

Table 2 - 29 display s the relative contribution of total energy consumption estimated from a National MOVES calendar ye ar 2011, using MOVES2014. As shown, the estimated energy consumed due to starts, is m inor in comparison to the energy use of running activity. One of the reasons that energy rates for heav y - duty

starts has not been updated, is the relativel y s mall contribution the starts have on the energy

inventory . **Table 2**  **- 29**  **. Relative contribution of total energy consumption from each pollutant process by regulatory class**  **for heavy**  **- duty**  **diesel vehicles in calendar year**  **2011.** processID processName 40 41 42 46 47 48 1 Running Exhaust 97.4% 99.2% 99.3% 98.1% 95.1% 99.7% 2 Start Exhaust 2.6% 0.8% 0.7% 0.6% 0.1% 0.3% 90 Extended Idle Exhaust 1.3% 4.7% 91 Auxiliary Pow er Exhaust 0.01% 0.04% 

### 2.3 **Extended Idling Exhaust**  **Emissions** In the MOVES model, extended idling is "discretionary " idle operation characterized by idle periods more than an hour in duration , ty pically overnight , including higher engine speed settings and extensive use of accessories by the vehicle operator. Extende d idling most often occurs during long lay overs between trips by long - haul trucking operators where the truck is used as a residence, and is sometimes referred to as "hotel l ing." The use of accessories such as air conditioning s ys tems or heating s ys tems wi ll affect emissions emitted by the engine during idling. Extended idling by vehicles will also allow cool - down of the vehicle’s catal yt ic converter s ys tem or other exhaust emission after - treatments, when these controls are present. Extended idle is treated as a separate emission process in MOVES. Extended idling does not include vehicle idle operation which occurs during normal road operation, such as the idle operation which a vehicle experiences while waiting at a traffic signal or during a relativel y s hort stop, such as idle operation during a delivery . Although frequent stops and idling can contribute to overall emissions, these modes are alread y included in the normal vehicle hours of operation. Extended idling is characterized by idling periods that last hours, rather than minutes. In the MOVES model, diesel long - haul combination trucks are the only sourceTy pe assumed to have an y significant extended idling activity . As a result, an estimate for the extended idling emission rate has not been made for an y of the other source use t ypes modeled in MOVES. 

### 2.3.1 **Data Sources** The data used in the analy sis of extended idling emission rates includes idle emission results from several test programs conducted b y a variet y of researchers at different times. Not all o f the studies included all the pollutants of interest. The references contain more detailed descriptions of the data and how the data was obtained.78

Testing was conducted on twelve heav y - duty diesel trucks and twelve transit buses in Colorado (McCormick) 49

. Ten of the trucks were Class 8 heavy - dut y axle semi - tractors, one was a Class 7 truck, and one of the vehicles was a school bus. The model y ear range d from 1990 through 1998. A ty pical Denver area wintertime diesel fuel (NFRAQS) was used in all tests. Id le measurements were collected during a 20 minute time period. All testing was done at 1,609 meters above sea level (high altitude). Testing was conducted by EPA on five trucks in May 2002 (Lim) 50 . The model y ears ranged from 1985 through 2001. The veh icles were put through a battery of tests including a variet y of discretionary

and non - discretionary

idling conditions. Testing was conducted on 42 diesel trucks in parallel with roadside smoke opacity testing in California (Lambert) 51 . All tests were condu cted b y the California Air Resources Board (CARB) at a rest area near Tulare, California in April 2002. Data collected during this study were included in the data provided b y IdleAire Technologies (below) that was used in the analy sis. A total of 63 truck s (nine in Tennessee, 12 in New York and 42 in California) were tested over a battery of idle test conditions including with and without air conditioning (Irick) 52 . Not all trucks were tested under all conditions. Only results from the testing in Tennessee and New York are described in the IdleAire report. The Tulare, California , data are described in the Clean Air Study cited above. All anal yt ical equipment for all testing at all locations was operated by Clean Air Technologies. Fourteen trucks were tested as part of a large Coordinating Research Council (CRC) study of heav y duty diesel trucks with idling times either 900 or 1,800 seconds long (Gautam) 53

. The National Cooperative Highway Research Program (NCHRP) 54 obtained the idling portion of con tinuous sampling during transient testing was used to determine idling emission rates on two trucks. A total of 33 heav y - dut y diesel trucks were tested in an internal study by the City of New York (Tang) 55 . The model years ranged from 1984 through 1999. One hundred seconds of idling were added at the end of the WVU five - mile transient test driving cy cle. A Class 8 Freightliner Century with a 1999 engine was test ed using EPA's on - road emissions testing trailer based in Research Triangle Park, North Carolin a (Broderick) 56 . Both short (10 minute) and longer (five hour) measurements were made during idling. Some testing was also done on three older trucks. Five heav y - dut y trucks were tested for particulate and NOx emissions under a variety of conditions at Oak Ridge Laboratories (Story ) 57 . These are the same trucks used in the EPA study (Lim). The Universit y of Tennessee tested 24 1992 through 2006 model ye ar heavy duty diesel trucks using a variet y of idling conditions including variations of engine idle spee d and load (air conditioning) 46

. 

### 2.3.2 **Analysis** EPA estimated mean emission rates during extended idling operation for particulate matter (PM), oxides of nitrogen (NOx), hy drocarbons (HC) , and carbon monoxide (CO) . This analy sis used all of the data sources referenced above. The MOVES2010b update reflects new data available since79

the initial development of extended idle emissions for the MOVES model. Th e additions include the testing at Research Triangle Park (Broderick), the Universit y of Tennessee study (Calcagno), and the completed E - 55/59 study conducted by

WVU and CRC. I n addition, the data was separated b y truck and bus and by idle speed and acces sory usage to develop an emission rate more representative of extended idle rates. The important conclusion from the 2003 anal ys is was that factors affect ing engine load, such as accessory use, and engine idle speed are the important parameters in estimati ng the emission rates of extended idling. The impacts of most other factors, such as engine size, altitude, model y ear within MOVES groups, and test cy cle are negligible. This makes the behavior of truck operators very important in estimating the emissio n rates to assign to periods of extended idling. The use of accessories ( air conditioners , heaters, televisions, etc.) provides recreation and comfort to the operator and increases load on the engine. There is also a tendency to increase idle speed durin g long idle periods for engine durability . The emission rates estimated for the extended idle pollutant process assume both accessory use and engine idle speeds set higher than used for "curb" (non - discretionary ) idling. The studies focused on three t yp es of idle conditions. The first is considered a curb idle, with low engine speed (<1 , 000 rpm) and no air conditioning. The second is representative of an extended idle condition with higher engine speed (>1 , 000 rpm) and no air conditioning. The third represents an extended idle condition with higher engine speed (>1 , 000 rpm) and air conditioning. The idle emission rates for heav y dut y diesel trucks prior to the 1990 model year are based on the anal ys is of the 18 trucks from 1975 - 1990 model years used i n the CRC E - 55/59 study and one 1985 truck from the Lim study . The onl y data available represents a curb idle condition. No data was available to develop the elevated NOx emission rates characteristic of higher engine speed and accessory loading, therefo re, the percent increase developed from the 1991 - 2006 trucks was used. Extended idle emission rates for 1991 - 2006 model y ear heavy duty diesel trucks are based on several studies and 184 tests detailed in Appendix C . The increase in NOx emissions due to higher idle speed and air conditioning was estimated based on three studies that included 26 tests. The average emissions from these trucks using the high idle eng ine speed and with accessory loading was used for the emission rates for extended idling. The expected effects of the 2007 heav y dut y diesel vehicle emission standards on extended idling emission rates are taken from the EPA guidance analy sis (EPA 2003). The 2007 heavy dut y diesel emission standards are expected to result in the widespread use of PM filters and exhaust gas recirculation (EGR) and 2010 standards will result in after - treatment technologies. However, since there is no requirement to address extended idling emissions in the emission certification procedure, EPA expects that there will be little effect on HC, CO , and NOx emissions after hours of idling due to cool - down effects on EGR and most aftertreatment sy stems. However, we do not expect DPFs to lose much effectiveness during extended idling. As a result, we project that idle NOx emissions will be reduced 12 percent and HC and CO emissions will be reduced 9 percent from the extended idle emission rates used for 1988 - 2006 model y ear trucks . The reduction estimates are based on a ratio of the 2007 standard to the previous standard and assuming that the emission control of the new standard will only last for the first hour of an eight hour idle. For PM, we assum e an extended idling emission rate equal to the curb idling rate (operating mode 1 from the running exhaust analysis). Detailed equations are included in the a ppendix.80



### 2.3.3 **Results** Table 2 - 30 shows the resulting NOx, HC, and CO emission rates estimated for heav y - dut y diesel trucks from the data analy sis . Extended idling measurements have large variability due to low engine loads, which is reflected in the variation of the mean statistic.

**Table 2**  **- 30**  **. Me**  **an E**  **xtended idle emission rate s**  **from data analysis**  **( g/hour**  **)**

Model years NOx HC CO PM Pre - 1990 112 108 84 8.4 1990 - 2006 227 56 91 4.0 2007 and later 201 53 91 0. 2 

### 2.3.4 **MOV**

**ES Extended**  **Idle E**

**mi ssion Rates**  Table 2 - 31 shows the emission rates used in MOVES for extended idle for diesel MHD67 and HHD trucks. These are the only regulatory

classes in MOVES within diesel combination trucks, which are the only

t yp es of trucks with extended idle vehicle activity in MOVES . The emission rates for regulatory

class 47 (HHD) are equal to the mean extended emission rates from Table 2 - 30 for HC, CO, and NOx. Due to limited data we used the calculated the MHD67 (regClassID 46) extended idle emission rates as 1⁄2 of the extended idle emission rates of the HHD emission rates for HC, CO and NOx. There are no age effects mode led for extended idle emissions in MOVES. **Table 2**  **- 31**  **. Extended idle emission rates in MOVES by pollutant and regulatory class (g/hour**  **)** HC CO NOx MHD67 HHD MHD67 HHD MHD67 HHD 1960 - 1990 54 108 42 84 56 112 1991 - 2006 28 56 45.5 91 113.5 227 2007+ 26.5 53 45.5 91 100.5 201 Table 2 - 32 shows the extended idle PM emission rates in MOVES. MOVES stores PM emission rates according to EC and NonECPM , but the total PM, and EC/PM fraction are reported in Table 2 - 32 as well . The PM emission rates in MOVES are similar in magnitude to the PM emission rates measured to the mean rates reported in Table 2 - 30 .81 **Table 2**  **- 32**  **. Particulate Matter Emission rates for Extended Idle Emission Rates** Regulatory Class MHD67 EC NonECPM PM EC/PM 1960 - 1993 1.77 2.44 4.21 42.1% 1994 - 1997 3.07 4.21 7.28 42.1% 1998 - 200 2 2.91 4.00 6.91 42.1% 2003 - 2006 2.63 3.60 6.23 42.1% 2007+ 0.032 0.288 0.32 9.98%

Regulatory Class HHD EC NonECPM PM EC/PM 1960 - 1993 1.08 3.13 4.21 25.7% 1994 - 1997 1.66 4.78 6.44 25.7% 1998 - 200 2 1.58 4.57 6.16 25.7% 2003 - 2006 1.43 4.13 5.56 25.7% 2007+ 0.03 0.31 0.35 9.98% The extended idle energy emission rates were originally developed in MOVES2004 28 , and are display ed in Figure 2 - 38 . The extended idle energy consumption rates are the same for both regulatory

class MHD67 and HHD diesel vehicles. As shown in Table 2 - 29 , extended idle is estimated to contribute 1.3% and 4.7% of the energy consumption from regulatory class MHD67 and HHD8 diesel vehicles in the United States in calendar ye ar 2011.82

**Figure 2**  **- 38**  **. Extended idle energy em ission rates for regulatory class HHD8 and MHD67 diesel trucks.** 



### 2.3.5 **Auxiliary Power Unit Exhaust** In MOVES2014, we added a n ew emission process for auxiliary power unit ( APU ) exhaus t. APU usage onl y applies to the vehicles with extended idling activit y, which are the heav y - dut y regulatory

classes (MHD and HHD) within the combination truck source ty pe s. The MOVES default activit y assumes APU’s are used for 30% hotelling activity for model y ear 2010 and later trucks iv , with extended idling occurring for the remaining 70% of hotelling activity . Users can update hotelling activity

among extended idling, APU usage, and engine off activit y as discussed in the MOVES2014 User Guide 58 The APUs in MOVES are assumed to be Tier 4 - compliant, small (< 8 kW ) nonr oad compression ignition engines. We use the THC, CO, NOx, and PM 2.5 emission rates from the NONROAD2008 model for this category

of nonroad engine to develop the APU emissions rates, as was don e in the 2014 Medium and Heav y Duty Greenhouse Gas Rule 44 . The PM 2.5 emissions were divided into EC

iv The 2014 Medium and Heavy - duty Greenhouse Gas rulemaking assumed a 30% APU penetration in model years 2010 - 2014, and 100% APU penetration for 2015 and later. The assumption for APU penetration for 2015 and later w as revised in MOVE S2014 to continue at 30%.83

(25%) and 75% (nonEC) using fractions similar to the EC/PM split for conventional extended idling exhaust from HHD trucks ( Table 2 - 32 ). The APU emission rates are display ed in Table 2 - 33 . The APU energy

usage (per hour) is 22% of the MOVES extended idle emission rate for 2002 and later trucks, demonstrating the potential energy savings from using an auxiliary power unit. **Table 2**  **- 33**  **– APU em**  **ission rates** Pollutant Em ission Rate Units THC 6.72 g/hr CO 36 g/hr NOx 26.88 g/hr EC 0.45 g/hr NonEC 1.35 g/hr EC/PM 2.5 25% % Total Energy 27171.336 KJ/hr84



## 3 **Heavy -**  **Duty Gasoline**  **Ve hicles**  **3.1 Running Exhaust Emissions**  **3.1.1 HC, CO, and NOx**  3.1.1.1 Data and Analysis for 1960 - 2007 Model Year Trucks As gasoline - fueled vehicles are a small percentage of the heav y - duty vehicle fleet, the amount of data available for anal ys is was small. We relied on four medium - heav y duty gasoline trucks from the CRC E - 55 program and historical data from EPA’s Mobile Source Observation Database (MSOD), which has re sults from chassis tests performed by EPA , contractors and outside parties . The heav y - duty

gasoline data in the MSOD is mostly from pickup trucks which fall mainly in the LHD2b3 regulatory

class. Table 3 - 1 shows the number of vehicles in cumulative data sets. In the real world, most heav y - duty gasoline vehicles fall in either the LHD2b3 or LHD45 class, with a smaller percentage in the MHD class. There are very few, if an y, HHD gasoline trucks remaining in use . **Table 3**  **- 1**  **. D**  **istribution of vehicles in the data sets by m odel**  **- year group**  **, regulatory class and age group** 

Model year group Regulatory class Age group 0 - 5 6 - 9 1960 - 1989 MHD 2

LHD2b3 10 1990 - 1997 MHD 1

LHD2b3 33 19 1998 - 2002 MHD 1

LHD2b3 1 Similar to the HD diesel PM, HC , and CO analy sis, the chassis vehicle speed and acceleration, coupled with the average weight for each regulatory class, were used to calculate STP ( Equation 1 - 2 ) . To s upplement the meager data available, we examine d certification data as a guide to developing model y ear groups for analy sis. Figure 3 - 1 shows averages of certification results b y model y ear.85

**Figure 3**  **- 1**  **. B**  **rake -**  **specific certification**  **em ission rates by model year**  **for heavy -**  **duty g asoline engines** 

Based on these certification results, we decided to classify the data into the coarse model y ear groups listed below.  1960 - 1989  1990 - 1997  1998 - 2007 Unlike the anal ys is for HD diesel vehicles , we used the age effects present in the data itself. We did not incorporate external tampering and mal - maintenance assumptions into the HD gasoline rates. Due to sparseness of data we used onl y the two age groups listed in Table 3 - 1 . We also did not classify by

regulatory

class since there was not sufficient data to estimate emission rates by separate regulatory

classes. The derivation of the model y ear 2008 and later emission rates are discussed in Sections 3.1.1.3 and 3.1. 1.6 .

3.1.1.2 Emission Rates for Regulatory Class LHD <=10K The emission rates are initially anal yz ed b y binning the emission rates using the STP with a fixed mass factor of 2.06, to bring the emission rates into VSP - equivalent sp ace, used for modeling emissions for regulatory

class 40 (LHD<=10K). Figure 3 - 2 shows all three pollutants vs. operating mode for the LHD<=10K. In general, emissions follow the expected trend with STP , though the trend is most pronounced for NO x . As expected, NOx emissions for light - heavy - duty gasoline vehicles are much lower than for light - heavy - duty diesel vehicles. 0 5 10 15 20 25 30

1980 1985 1990 1995 2000 2005 2010 **Mo del**  **y ear** **C O** **, N** **O x** **[ g** **/ b** **h p** **- h** **r ]** 

-0 .2 0 0.2 0.4 0.6 0.8 1 1.2 1.4**H C** **[ g** **/ b** **h p** **- h** **r ]**  CO Nox HC86

**Figure 3**  **- 2**  **. E**  **mi ssion**  **Rates by operating m**  **ode for MY groups 1960**  **- 1989, 1990**  **- 1997, and 1998**  **- 2007**  **at age 0**  **- 3**  **years for Regulatory Class LHD <= 10K** 

Figure 3 - 3 shows the emissions trends by age group. Since we did not use the tampering and mal maintenance methodology as we did for diesels, the age trends reflect our coarse binning with age. For ea ch pollutant, only

two distinct rates exist

– one for ages 0 - 5 and another for age 6 and older . 0 1000 2000 3000 4000

0 50 100 150

0 100 200 300 400 500COHCNOx

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

opM odeI DEmissionrate,g/hour **Mode l.Y**  **ear s**  1960-1989 1990-1997 1998-200787

**Figure 3**  **- 3**  **. E**  **mi ssion**  **rates by**  **operating m ode**  **and age group for MY**  **1998 -**  **2007 vehicles in Regulatory Class**  **LHD < =10K** 

Table 3 - 2 display s the multiplicative age effect s by operating mode for Regulatory Class LHD<=10K vehicles. The relative age effects are de rived from the sample of vehicle tests summarized in Table 3 - 1 . The multiplicat ive age effects are used to estimate the aged emission rates (ages 6+) years from the base emission rates (ages 0 - 5) for HC, CO, and NOx. These multiplicative age effects apply to all model y ear groups between 1960 - 2007. 0 500 1000 1500 2000 2500

0 25 50 75 100

0 50 100 150 200COHCNOx

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40 opM odeI DEmissionrate,g/hour **ag e**  6+ 0-588

**Table 3**  **- 2**  **. Relative age effect on em ission rates between age 6+ and age 0**  **- 5 for LHD<=10K gasoline vehicles in**  **model years 1960**  **- 2007.**  OpModeID HC CO NOx

0 2.85 1.45 1.67 1 2.43 1.79 1.85 11 3.12 1.66 1.88 12 2.85 2.05 1.69 13 3.55 2.68 1.48 14 3.43 2.84 1.46 15 3.37 3.03 1.26 16 3.76 3.88 1.06 21 2.78 1.67 1.42 22 2.64 1.64 1.36 23 2.96 1.67 1.32 24 2.83 1.62 1.21 25 3.23 2.79 1.43 27 3.21 3.20 1.21 28 3.20 4.04 1.11 29 3.00 3.90 1.05 30 2.55 2.56 1.05 33 1.95 2.00 1.77 35 2.67 2.20 1.59 37 2.80 2.24 1.42 38 2.46 2.06 1.34 39 2.46 2.30 1.27 40 2.47 2.59 1.17 3.1.1.3 E

mission Rates for RegClass 40 for 2008 through 2017 model years In MOVES2014, we introduced a new regulatory class (40) that classifies LHD2b trucks that are classified as passenger or light - commercial trucks. Regulatory class 41 also contains LHD2b trucks, but only

v ehicles that are classified as single - unit trucks. The distinction was made in MO VES2014 because passenger and light - commercial trucks assign operating modes using VSP, and MOVES assigns STP - based operating modes to single - unit trucks. In previous versions of MOVES (2010b and earlier), regulatory class 41 was used to model all Class 2b and 3 trucks. Most of the anal ys is conducted in this section was conducted assuming that there would be a single regulatory

class to represent Class 2b and 3 trucks (LHD2b3). We thus used the term LHD2b3 trucks to refer to trucks in both regulatory class 40 and 41. Howe ver, we only used the data in this section to update the emission rates for regulatory class 40. Emission rates for regulatory class 41 for 2008+ vehicles are discussed in the following section.89 3.1.1.3.1 Comparison of LHD2b3 emission rates in MOVES2010 with releva nt emission standards Gasoline vehicles in MOVES reg ulatory clas s LHD2b3 (RegClassID 40 and 41) are a mixture of engine certified HD vehicles, chassis certified HD vehicles, and medium duty passenger vehicles (MDPV s ) . Each group has a separate set of regu lations governing their emissions. These emission standards are summarized below ( Table 3 - 3 ). v **Table 3**  **- 3**  **. Useful Lif** 

**e FTP Standards vi**  MDPV (Tier 2 Bin 5) 59 8.5k

– 10k

(Class 2B) 10k - 14k

(Class 3) Engine

Certified Units g/mile g/mile g/mile g/bhp - hr Fully Phased in MY 2009 2009 2009 2010 HC 0.09 NMOG 0.195NMHC 0.230 NMHC 0.14 NMHC CO 4.2 7.3 8.1 14.4 NOx 0.07 0.2 0.4 0.2 The relative proportions of the vehicles within MOVES L HD2b3 regulatory class vary

each y ear depending on demand. Consequently , we estimated proportions based on recent y ear data and engineering judgment. MOBILE 6 documentation from 2003 indicates that MDPVs are approximately

16% of the gasoline 8,500 to 10,000 truck class. 60 InMOVES2014, w e project that MDPVs are 15% of total MOVES L HD2b3 reg ulatory class in MYs 2008 and later . The MOBILE 6 document also states that more than 95% of cl ass 2B trucks are chassis certified. 60 Extrapolating, w e estimate that 5% of all vehicles in the L HD2b3 regulatory class are engine certified. Based on anal ys is from the recent medium and heav y dut y greenhouse gas rulemaking, we assume that sales of 2B class trucks vehicles were triple that of 3 class trucks. 61 This is roughl y consistent with recent model y ear sales totals . 62 Combining these assumptions, we get the sales fractions shown below ( Table 3 - 4 ). **Table 3**  **- 4**  **. Population Percentage**  **of LHD2b3 Trucks** % of Reg Class MDPV 15% Class 2B 60% Class 3 20% Engine Certified 5%

v This mixture of vehicles was not explicitly considered during the development of MOVES2010. vi The FTP differs between engine and chassis certified vehicles. We used adjustment factors described in the MOBILE 6 documentation to convert from g/bhp - hr to g/mile (1.2x), but these adjustment factors may vary in their utility. The small proportion of engine certified vehicles in this sample dilutes the impact.90

To generate an aggregate FTP standard for LHD2b3 regulatory class, we weighted the individual certification standards shown in Table 3 - 3 using the proportions shown in Table 3 - 4 . vii While the model produce s estimates of on - road emissions rather than certification emissions , the weighted certification standard is a useful benchmark for the modeled rates ( Table 3 - 5 ). viii **Table 3**  **- 5**  **. Aggregate**  **Useful Life FTP**  **for LHD2b3 Trucks** g/mile NMOG 0.18 CO 7.49 NOX 0.22 As a benchmark, we compared the calculated aggregate FTP standard to an FTP calculated using the emission rates in the MOVES2010a database. The Phy sical Emission Rate Estimator ( PERE ), 33 modified to produce Scaled Tractive Power (STP) distributions , was used to generate the operating mode mix of a LHD2b3 regulatory class vehicle on the Federal Test Procedure drive cy cle . For the STP modification , we changed the vehicle weight in PERE to match sourceT yp eID 32 (L ig ht Commercial Truck) in MOVES (2.06 Tons). We incorporated emission rates from the MOVES DB for the age 0 - 3 group, and added in a cold start (operating mode 108) and a hot start (operating mode 102) from the MOVES databa se . The modified version of PERE produced the operating mode distribution shown in Table 3 - 6 . **Table 3**  **- 6**  **. Operating Mode**  **Bin Distribution**  **for a Light**  **- Commercial Truck on the Federal Test Procedure**  **(FTP)** OpModeID N % OpModeID N %

**0** 160 12% **25** 41 3% **1** 258 19% **27** 49 4% **11** 94 7% **28** 17 1% **12** 68 5% **29** 13 1% **13** 70 5% **30** 15 1% **14** 36 3% **33** 13 1% **15** 48 3% **35** 12 1% **16** 141 10% **37** 13 1% **21** 68 5% **38** 17 1% **22** 44 3% **39** 15 1% **23** 97 7% **40** 6 0% **24** 77 6% Total 1372 100%

vii The engine standard was converted to a g/mile standard using a factor of 1.2 as described in the MOBILE6 report viii Several simplifications were made in calculating this aggregate useful life FTP . The distinction between NMHC and NMOG was ignored in calculating the aggregate FTP, and w ould have yielded only minor variation in the aggregate certification standard. The engine standard w as also converted to a chassis equivalent as discussed above.91

Using this operating mode distribution, we constructed a simulated FTP out of four components (bag 1/3 running, ix cold start, hot start, and bag 2 running). We constructed bag 1 (cold start + bag 1 running) and bag 3 (hot start + bag 3 running) and weighted the resulting components together according to the FTP formula, x and compared the 2008 and later rates in MOV ES to the aggregate standard calculated above ( Table 3 - 7 ). MOVES 2010a estimates at age 0 - 3 are two to ten times larger than the standard, which indicates that the average vehicle HD gas vehicle in MOVES2010a is significantl y out of compliance with the relevant standard in this timeframe.

**Table 3**  **- 7**  **. Com**  **parison between MOVES DB FTP and Aggregate FTP for LHD2b3 Trucks**  MOVES 2010 FTP for LHD2b3 Trucks (g/mile) LHD2b3

Aggregate FTP Standard (g/mile) Ratio

– MOVES

to Aggregate Standard NMOG 0.36 0.18 1.93 CO 14.54 7.49 1.94 NOx 2.04 0.22 9.28 3.1.1.3.2 Validation against In - Use Verification Program Data We reviewed In Use Verification Program (IUVP) data for MYs 2004 - 2008 vehicles (estimated test weights of 7,500 pounds to 10,000 pounds) to determine the appropriateness of the MOVES2010 emission rates. xi We evaluated whether vehicles during these MYS were achieving the standard, or if alternate methods were being used for compliance. While the IUVP data is not fully representative of the in - use fleet, it provides a reasonable snap - shot. Without weighting for sales, and accounting for the standards applicable to each vehicle, we calculate average ratios of test value to standard of 0 .42 (NMOG) and 0 .23 (NOx) ( Table 3 - 8 & Figure 3 - 4 ) . These ratios indicate that vehicles t yp ically comply with the standard, with a significant amount of headroom. **Table 3**  **- 8**  **. Average Com pliance Margin and Headroom** **for LHD2b3 Trucks** Average Average Ratio Certification FTP/ Standard Headroom NMOG 0.42 0.5 8 NO x 0.2 3 0.77

ix Bag 1 and Bag 3 are considered to have the same emission rate. x FTP =( (Bag 1 + Bag 2)*0.43+ (Bag 3+ Bag 2)*0.57)/ 7.45 xi While this population of vehicles is not identical, these test weights significantly overlap with these GVWR classes.92 **Figure 3**  **- 4**  **. Distribution of IUVP FTP Tests**  **for LHD2b3 Trucks**

The emission rates in MOVES include all vehicles, and consequentl y represent a broader sample than the IUVP data. As a result, we expect that the onroad vehicles would have higher emission rates than vehicles in the IUVP program. xii However, the emission rates represented b y MOVES2010 are higher than those that would be expected from vehicle s compliant with the standards in place in MY 2008 and later.

3.1.1.3.3 Emis sion Rates Given that (a) the MOVES2010 L HD2b3 emission rates are significantl y above the calculated aggregate standard, and (b) the IUVP data shows that most light - heav y 2b trucks achieve the standard, we calculated new MOVES2014 HC/CO/NOx emission rates for Regulatory Class ID 40 vehicles in 2008 and later MYs. In conducting this anal ys is, we lacked an y modal data on regulatory class 40 vehicles. As such, we conducted the anal ys is using a method that we have used repeatedl y on the light duty side, which is ratioing the modal emission profile b y the difference in standards. 34 By MY 2008, the medium duty vehicles are nearing the emission levels of Tier 2 Bin 8 vehicles. Consequently , we relied on the anal ys is of in - use Tier 2 Bin 8 vehicles conducted for the light dut y vehicle emission rates . 34

xii Even in the absence of emission equipment deterioration, tampering and malmaintenance will increase the emissions from a on - road vehicle.93

Because we are basing the emission rates from light - duty emission rates (which are also VSP based), the emission rate update is limited to regulatory class 40 vehicles. We scaled the modal data from Tier 2 Bin 8 vehicles by the ratio of FTP standards xiii so that the rates would be consistent with the higher emission rates of regulatory class 40 vehicles. **Table 3**  **- 9**  **. Aggregate LHD2b3 Standard Ratios against**  **Bin 8 Modal Rates** 

Aggregate LHD2b3 FTP Standard Bin 8

FTP Standard Aggregate/Bin 8 NMOG 0.18 0.1 1.8 CO 7.49 3.4 2.2 NOx 0.22 0.14 1.6 We converted this ratio into a “split” ratio, where the running rates increased twice as much as the start rates, but the same overall emissions were simulated on the FTP. This split ratio is consistent with ty pical emission reduction trends, where running emissions are reduced about twice as much as start emissions 34

. **Error! Bookmark not defined.** The “split” ratios for running and star t, which were applied to the light - duty Tier 2 Bin 8 vehicle emission rates are shown in Table 3 - 10 . **Table 3**  **- 10**  **. Ratio Applied to Light**  **- Duty Tier 2 Bin 8 em**  **ission rates to estim ate Regulatory Class 40 em**  **ission rates for 2008**  **- 2017 MY.**  HC CO NOx Running 2.73 2.73 1.95 Start 1.37 1.37 1.00 We also adopted the light - duty deterioration effects and applied them to the 2009 and later regulatory

class emission rates. The light - dut y emission rates have age effects that change with each of the 6 age groups in MOVES, as shown in Table 3 - 11 .

xiii The aggregate FTP standards used include both Class 2b and 3 trucks. How ever, the ratio is only applied to develop updated regu latory class 40 emission rates (which only contain 2b trucks). The LHD2b3 aggregate emission factors are 2%, 28%, and 25% higher than aggregate emission factors based on 2b trucks only for NMOG, CO, and NOx How ever, as discussed later, the final emission rates are still below the aggregate standard. So, w e believe using the LHD2b3 aggregate standard is appropriate.94

**Table 3**  **- 11**  **. Multiplicative Age Effect Used for Running Emissions for RegClass 40 2008+ m odel years.**  ageGroupID HC CO NOx

3 1 1 1 405 1.95 2.31 1.73 607 2.80 3.08 2.21 809 3.71 3.62 2.76 1014 4.94 4.63 3.20 1519 5.97 5.62 3.63 2099 7.20 6.81 4.11 After appl yi ng the above mentioned steps (scaling the emission factors by ratio of FTP standards, and appl yi ng light - dut y deterioration trends), w e restricted the scaled data so that the individual emission rates by

operating mode were never scaled to be higher than MY 2006 regulatory class 40 rates. This essentiall y capped the emission rates, such that none of the operating mode, or age

– specific emission rates for 2009 an d later model year vehicles are higher than the 2007 and earlier model y ear emission rates . This final step capped emission rates in the highest operating modes. For HC, emission rates in operating modes 28 - 30 and 38 - 40 were capped for some or all age grou ps by the pre - 2007 emission rates. For CO, emission rates in 12 of the 23 running operating modes (1, 16, 23 - 24, 27 - 30, 35 - 40) were capped b y the pre - 2007 rates. None of the NOx emission rates were impacted by this step. Figure 3 - 5 shows the regclass 40 model y ear 2008 - 2017 emission rates for CO, HC, and NOx. Emission rates that exhibit the start - step deterioration trend are the emission rates that were capped with the pre - 2007 emission rates. Even with the capped emission rates, the regulatory 40 emission rates are higher than the regulatory 30 emission rates with a few exceptions. The few exceptions are some of the age - dependent HC and or CO emission rates in operating modes 1, 30, 38, 39, and 40. However, the majorit y of emission rates are significantly higher in regulatory class 40 than regulatory

class 30, and when used in MOVES, the simulated FTP emission rates are significantl y higher for regulatory

class 40 vehicl es.95

**Figure 3**  **- 5**  **. Age Effects for CO, HC, and NOx em ission rates for RegClass 40 vehicles in Running Operating**  **Modes for MY 2008**  **- 2017**  **.**

After calculating new regulatory class 40 emission rates, we used the emission rates to simulate an FTP cy cle, as shown in Table 3 - 12 . We compared these emission rate s to the the calculated aggregate standard . The calculated headroom for NOx is less than that shown in the IUVP data, and the calculated headroom for NMOG is greater than that shown in the IUVP data ( Table 3 - 8 ) . For NOx, this difference is more significant. However, as stated above, the IUVP data is not fully representative of in - use vehicles. By contrast, the Bin 8 rates are based on extensive I/M testing, and are cons idered more representative of the entire fleet.

0 500 1000 1500 2000 2500

0 20 40 60

0 50 100 150COHCNOx

5 10 15 20

ageAverageemisionrate,g/hour **opMode ID**  0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 4096 **Table 3**  **- 12**  **. Ratio of Final rates against standards** Simulated regulatory class class 4 0 20 08 + FTP (g/mile) Aggregate 2010+ LHD2b3 FTP Standard (g/mile) Ac hieved /

Aggregate FTP NMOG 0.06 0.18 33% CO 3.08 7.49 41% NOx 0.18 0.22 84% In terms of the phase - in, we assumed that the regulatory class 40 rates phase in at a rate of 50% in MY2008 and considered fully phased in MY2009. The MY 2008 running emission rates are interpolated values between the 2007 and 2009 emission rates by operating mode and age group. 3.1.1.4 Running Emission Rates for RegClass 40 Vehicles for 2018 and later The Tier 3 program will affect not only light - dut y vehicles (below 8,500 pounds GVWR), but also chassis - certified vehicles between 8,500 and 14,000 pounds GVWR. This class of vehicles is referred to “light - heavy - duty ” or “medium - dut y” vehicles. This regulatory

class comprises several classes of vehicles, includi ng Class 2b and Class 3 trucks, medium - duty

passenger vehicles (MDPV) and engine - certified trucks . However, the latter two groups of vehicles are not regulated under the medium - duty standards described here . However, for completeness, however, they are re flected in the emission rates. During the phase - in period, we assumed that Class 2b and 3 vehicles would be certified to four standard levels. Composite FTP values for these standard levels are shown in Table 3 - 13 . Phase - in fractions for each standard level are also shown in Table 3 - 14 . The phase - in fractions were applied to the FTP values to calculate weighted average FTP values for these two truck classes for each model y ear during the phase - in, as shown in Table 3 - 15 . In addition to 2b and 3 vehicles, “medium - dut y” vehicles also include MDPV and engine - certified vehicles. Composite FTP values were estimated for these classes as well . The levels for MDPV were assumed to be equivalent to Bin 8 vehicles in 2017 and to light - dut y vehicles in 2022 (30 mg/mi). Interim values were calculated for each model y ear during the phase - in by assuming a linear decrease over each y ear between the initial and final values. The FTP values for the engine certified vehicles were assumed to be unaffected by the new standards and to therefore remain constant throughout. The projected averaged FTP values for these two vehicle classes are also shown in Table 3 - 15 . Finally , weighted average values for all four vehicle classes were calculated as shown in Equation 3 - 1 . Note that the weights assigned to each vehicle class are equivalent to those previousl y shown in Table 3 - 4 (page 89 ) . Values of the weighted means by model y ear are shown in Table 3 - 15 . 

#   MDPV certified - Engine 3 2b weighted 

### FTP 15 . 0 FTP 05 . 0 FTP 25 . 0 FTP 75 . 0 8 . 0 

   FTP **Equation 3**  **- 1** 97

**Table 3**  **- 13**  **. Com**  **posite FTP NMOG+NOx Standards for Class 2b and 3 Vehicles (m g/mi).**  Vehicle Class LEV ULEV34 ULEV25 SULEV17 2b 395 340 250 170 3 630 570 400 230

**Table 3**  **- 14**  **. Phase -**  **in fractions by standard level for Class 2b and 3 Vehicles.** Model Year LEV ULEV34 ULEV25 SULEV17 2017 0.10 0.50 0.40 0.0 2018 0.0 0.40 0.50 0.10 2019 0.0 0.30 0.40 0.30 2020 0.0 0.20 0.30 0.50 2021 0.0 0.10 0.20 0.70 2022 0.0 0.0 0.10 0.90 **Table 3**  **- 15**  **. Projected FTP com posite values for four vehicle classes (mg/mi), plus weighted m**  **eans, by m odel**  **year during the Tier -**  **3 phase -**  **in.**

Model Year Vehicle Class Weighted Mean

2b 3 MDPV Engine - Certified 2017 400 2018 278 451 102 408 293 2019 253 400 84 408 265 2020 228 349 66 408 237 2021 203 298 48 408 209 2022 178 247 30 408 181 If we take the initial value before onset of the phase - in (400 mg/mi) and the final value when the phase - in is complete (181 mg/mi), and treat these two values as references, we can calculate the phase - in fractions that correspond to the weighted means in eac h intervening model y ear from 2018 to 2021 inclusive, as shown in Equation 3 - 2 . Resulting phase - in fractions so calculated are shown in Table 3 - 16 . ) 1 ( 400 181 FTP 3 3 weighted T T f f

   **Equation 3**  **- 2** 98

**Table 3**  **- 16**  **. Phase**  **- in fractions applied to rat**  **es in m odel years 2018 and later to represent partial and full Tier**  **- 3**  **control.** Model Year f T3 1 − f

T3 2017 0.00 1.00 2018 0.49 0.51 2019 0.62 0.38 2020 0.75 0.25 2021 0.87 0.13 2022 1 1.00 0.00 1 Also applicable to model years 2022 and later. To calculate modal emission rates in MY2018 and later, we applied the fractions s hown in Table 3 - 16 above to sets of modal rates representing MY 2017 and MY2022. The r ates for MY201 7 were extracting from a previous version of the MOVES database used in anal ys es supporting the Tier - 3 Rulemaking , and represented existing rates prior to the adoption of Tier - 3 standards. xiv T he rates for MY2022 were estimated as equivalent to light - dut y rates, assuming a fleet composition of 10% Bin 8 and 90% Bin 5 standards. These rates were designed to represent full Tier - 3 control. Thus, starting with these subsets of rates for MY2017 and MY2022, the calculation shown in Equation 3 - 2 was performed for all rates across all operating modes and ageGroups. Resulting rates for HC, CO and NOx are shown in <F to <F, respectivel y.

xiv The database version used was MOVESt3DB20110331.99

**Figure 3**  **- 6**  **. THC: Running -**  **exhaust emission rates for vehicles in the LHD<=10k regulatory class (regClassID=40), during the Tier**  **- 3 phase**  **- in.** 100 **Figure 3**  **- 7**  **. CO: Running -**  **exhaust emission ra tes for vehicles in the LHD<=10k regulatory class**  **(regClassID=40), during the Tier -**  **3 phase -**  **in.**101 **Figure 3**  **- 8**  **. NO x**  **: Running -**  **exhaust emission rates for vehicles in the LHD<=10k regulatory class (regClassID=40)**  **, during the Tier -**  **3 phase -**  **in.**

Figure 3 - 9 display s the average emission rates (across all operating modes) for CO, HC, and NOx across 1980 to 2007 model y ears for LHD<=10k vehicles. Although not shown, the emission rates within the model y ears 1960 - 1980 are the same, as well as the emission rates within model y ears 2030 - 2050. Figure 3 - 9 summarizes the decreasing trend in emissions from the anal ys is documented in this chapter.102

**Figure 3**  **- 9**  **. Average em**  **ission rate (across all operating modes) for regulatory class 40 trucks for CO, HC, and**  **NOx. The1960**  **- 2007 em**  **ission rates only differ according to two broad age groups (0 -**  **5) and (6 +**  **). For 2008 and**

**later em ission rates, the emissions differ according to the age groups shown in the legend.** 

3.1.1.5 Running Emission Rates for Regulatory Class LHD <1 4 K, LHD45, and MHD, and HHD for 1960 - 2007 model years The emission rates are equivalent across all the heavy - duty gasoline running emission rates for these regulatory

classes (41, 42, 46, and 47). The same emissions data is used to derive these regulatory

class emission rates as was used to derive the regulatory class 40 emission rates. As discussed earlier, the emissions data comes from a mix of L HD2b/3 and MHD vehicles outlined in Table xx. The o nly difference in the anal ys is with regulatory class 40 emission rates is that these emission rates were anal yz ed using STP operating modes with a fixed mass factor of 17.1. The same model ye ar groups are used to classify the emission rates: 1960 - 1989, 1 990 - 1997, and 1998 - 2007 . Also, the same relative increase in emission rates for the age effect a s derived for LHD <= 10K is used .

0 500 1000 1500

0 25 50 75 100

0 50 100 150 200COHCNOx

1980 1990 2000 2010 2020 2030

mo del Ye arI DAverageemisionrate,g/hour **ag eG**  **roupN am**  **e** 20 or mo re ye ars o ld 15 to 1 9 ye ars o ld 10 to 1 4 ye ars o ld 8 or 9 ye ars o ld 6 or 7 ye ars o ld 4 or 5 ye ars o ld 0 to 3 ye ars o ld103

Sample emission rates for HC, CO, and NOx for the 1994 MY Group are presented in Figure X for these source t yp es. **Figure 3**  **- 10**  **. Em**  **ission Rates by STP**  **operating m ode for MY 1994 at age 0**  **- 3 years**  **for Regulatory Class LHD < 14K, LHD45, MHD, and HHD** 

Figure 3 - 11 and Table 3 - 17 display the multiplicative age effect s by operating mode for LHD< 14 K , LHD45, MHD, and HHD gasoline vehicles. Once again, the age effects were derived from the same data as the L HD<=10K vehicles. The age effects are slightl y different for these vehicles, because the operating modes are defined with the STP scaling factor of 17.1. For operating modes that do not depend on the scaling factor (opModeID 0, 1, 11, and 21) the age effects are the same as the LHD<10K age effects. Also, because the vehicles tested were LHD2b/3 and MHD vehicles, no data was binned in the high STP pow er modes (that ty picall y onl y a HHD 0 10000 20000 30000

0 200 400 600

0 500 1000 1500 2000COHCNOx

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

opM odeI DEmissionrate,g/hour **Mode l.Y**  **ear s**  1960-1989 1990-1997 1998-2007104

truck would reach). As such, the higher operating modes use the same values as the closest bin with data (opModeID 13 - 16, 24 - 30, and 35 - 40). **Figure 3**  **- 11**  **. Em**  **ission rates by operating m**  **ode and age group for MY**  **1998 -**  **2007 vehicles in Regulatory Class**  **LHD < =**  **14K, LHD45, MHD, and HHD gasoline vehicles.**

0 2500 5000 7500

0 100 200 300 400 500

0 250 500 750 1000COHCNOx

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40 opM odeI DEmissionrate,g/hour **ag e**  6+ 0-5105

**Table 3**  **- 17**  **Relative age effect on em ission rates between age 6+ and age 0**  **- 5 for L**  **HD< 14**  **K , LHD45, MHD, and**  **HHD gasoline vehicles in**  **all model years 1960**  **- 20**  **50 .**  OpModeID HC CO NOx

0 2.85 1.45 1.67 1 2.43 1.79 1.85 11 3.12 1.66 1.88 12 3.36 3.12 1.13 13 3.53 3.16 1.11 14 3.53 3.16 1.11 15 3.53 3.16 1.11 16 3.53 3.16 1.11 21 2.78 1.67 1.42 22 3.08 2.59 1.23 23 2.97 3.31 1.05 24 1.80 1.54 1.03 25 1.80 1.54 1.03 27 1.80 1.54 1.03 28 1.80 1.54 1.03 29 1.80 1.54 1.03 30 1.80 1.54 1.03 33 2.45 2.41 1.33 35 2.16 2.41 1.19 37 2.16 2.41 1.19 38 2.16 2.41 1.19 39 2.16 2.41 1.19 40 2.16 2.41 1.19 3.1.1.6 Running Emission Rates for Regulatory Class LHD <14 K, LHD45, and MHD, and HHD for 2008 and later model years Of the on - road heavy

duty vehicles GVW class 4 and above, about 15% are gasoline, as opposed to 85% diesel. xv The gasoline percentage decreases as the GVW class increases. Consequently, there is relatively

little data on these vehicles, and we did not updat e the 2008 and later model year emission rates from MOVES2010 63 The 2008 and later model ye ars are modeled w ith a 70% reduction in the running rates starting in MY 2008 , which is consistent with the emission standard reduction with the “H eavy - dut y 2007 R ule ” . 64 The 2008 and later model ye ar emission rates have two age groups (0 - 5 , and 6+ ) and the same relative multiplicative age effects as the pre - 2007

xv Negligible portions are run on other fuels. The figures are aggregated from data supplied by Polk.106

emission rates, as shown in Figure 3 - 14 . The anal ysis of regulatory class 40 emission rates for 2008 and later model ye ars is based on light - dut y truck VSP - based emission rates. We did not have load based data on class 2b and 3 trucks to derive STP - based emission rates specific for regulatory class 41 trucks. As such, we estimate regulatory class 41 trucks fo r 2008 - 2017, using the relativel y simple 70% reduction from the 1998 - 2007 baseline

3.1.1.7 Running Emission Rates for Regulatory Class LHD<14K for 201 8 and later model years As discussed earlier, regulatory class 41 includes Class 2b and 3 trucks , as such the Tie r 3 Vehicle Emission standards apply to 2b trucks. Rates for vehicles in this regulatory class were developed in the same way

as those for the LHD<=10k regulatory class, as described in 3.1.1.4 ( page 96 ) . However, for these two classes, the rates for running operation differ in that those for regClass 40 are based on vehicl e - specific power (as are rates for light - dut y vehicles), whereas those for regulatory

class 41 are based on “scaled - tractive power” (STP), as are rates for heav y - dut y vehicles in regClasses 42, 46 and 47. For these two sets of rates, the absolute values of the running rates differ but the relative reductions representing Tier - 3 control in each model y ear are applied in the same proportions. These patterns are shown in Figure 3 - 12 and Figure 3 - 13 , which show rates for regClasses 40 and 41 in selected operating modes for running emissions. Note that the results are shown on logarithmic scales, and that the parallelism in the trends ind icates that the proportional reductions are identical for both the VSP - based (regClass 40) and the STP - based (regClass 41) rates. Note also that start rates for the two regulatory

classes are identical, as they are not defined in terms of either VSP or ST P.107

**Figure 3**  **- 12**  **. THC em ission rates vs. m**  **odel year for regulatory classes 40 and 41, showing selected operating**

**modes for the running -**  **exhaust process (Note the logarithmic scale).**108

**Figure 3**  **- 13**  **. NO**  **x em**  **ission rates vs. m odel year for regulatory classes 40 and 41, showing selected operating**  **modes for the running exhaust process (Note the logarithmic scale).**109

**Figure 3**  **- 14**  **. Average em ission rate (across all operating modes) for regulatory class 41, 42, 46, 47, and 48 for** 

**CO, HC, and NOx. Emission rates for 1960**  **- 1989, and 2022**  **– 2050 are constant.** 



### 3.1.2 **P articulate**  **M**

**atter** Unfortunatel y, the PM 2.5 emission data from heavy - dut y gasoline trucks are too sparse to develop the detailed emission rates for which the MOVES model is designed . As a result, only a very limited analy sis could be done . EPA will likely revisit and update these emission rates whe n sufficient additional data on PM 2.5 emissions from heav y - dut y gasoline vehicles become available. In MOVES2010 and MOVES2014 , the heav y - duty gas PM 2.5 emission rates are calculated b y multiplying the light - duty gasoline truck PM 2.5 emission rates by a fa ctor of 1.40, as explained below. Since the MOVES light - duty gasoline PM 2.5 emission rates comprise a complete set of factors - classified by

particulate sub - ty

pe ( EC and nonECPM ), operating mode, model year and regulatory

class, the heavy - dut y PM 2.5 emiss ion factors are also a complete set. No change to the

41 42, 46, 47

0 5000 10000 15000

0 100 200 300 400

0 250 500 750COHCNOx

1980 1990 2000 2010 2020 1980 1990 2000 2010 2020 mo del Ye ar IDAverageemisionrate,g/hour **pol lu**  **ta nt**  CO HC NOx **ag e**  0-5 6+110

PM emission rates are made, because the HD 2007 Rule PM standards are not assumed to be technology - forcing standards for spark - ig nition medium and heav y - dut y gasoline vehicles. xvi

.

3.1.2.1 Data Sources T his anal ys is is based on the PM 2.5 emission test results from the four gas oline trucks tested in the CRC E55 - E59 test program. The specific data used were collected on the UDDS test cy cle. Each of the four vehicles in the sample received two UDDS tests, conducted at different test weights. Other emission tests using different cy cles were also available on the same vehicles, but were not used in the calculation. The use of the UDDS data enabled the anal ys is to have a consistent driving cy cle. The trucks and tests are described in Table 3 - 18 .

**Table 3**  **- 18**  **. Summ**  **ary of data used in HD gasoline PM emission rate analysis Vehicle**  **MY Age**  **Test cycle GVWR**  **[lb] PM**  **2.5 mg**  **/m i**  1 2001 3 UDDS 12,975 1.81

2001 3 UDDS 19,463 3.61 2 1983 21 UDDS 9,850 43.3

1983 21 UDDS 14,775 54.3 3 1993 12 UDDS 13,000 67.1

1993 12 UDDS 19,500 108.3 4 1987 18 UDDS 10,600 96.7

1987 18 UDDS 15,900 21.5 The table shows onl y four vehicles, two of which are quite old and certified to fairl y lenient standards. A third truck is also fairly old at 12 ye ars and certified to an intermediate standard. The fourth is a relatively

new truck at age three and certif ied to a more stringent standard. No trucks in the sample are certified to the Tier2 or equivalent standards. Examination of the heavy - dut y data shows two distinct levels: vehicle #1 (MY 2001) and the other three vehicles. Because of its lower age ( 3 ye ars old ) and newer model ye ar status, this vehicle has substantially lower PM emission levels than the others, and was separated in the analy sis. The emissions of the other three vehicles were averaged togethe r to produce these mean results : Mean for Vehi cles 2 through 4: 65 . 22 m g/mi Older Group Mean for Vehicle 1: 2 . 71 m g/mi Newer Group

xvi As presented in Section XX, the MOVES2014 PM rates for 2008+ vehicles is based on UDDS results of 2 mg/mile, while the standard for 2008+ spark - ignition vehicles is 20 mg/mile 64111 3.1.2.2 Emission Rates for Regulatory Class LHD <=10K To compare these rates with rates from light - dut y gasoline vehicles, we simulated UDDS cy cle emission rates based on MOVES light - dut y gas PM 2.5 emission rates ( with normal deterioration assumptions) for light - duty gasoline trucks (regulatory class 30) . The UDDS cy cle represents standardized operation for the heav y - duty vehicles. To make the comparisons appropriate , th e simulated light - dut y UDDS results were matched t o the results from the four heavy - dut y gas trucks in the sample. This comparison meant that the emission rates from the following MOVES model y ear groups and age groups for light - dut y trucks were used:

 MY group 1983 - 1984, age 20+  MY group 1986 - 19 87 , age 15 - 19  MY group 1991 - 19 93 , age 10 - 14  MY group 2001 , age 0 - 3 T he simulated PM 2.5 UDDS emission factors for the older light - duty gas truck group using MOVES2010b are 38.84 m g/mi 2.5 ( Ignoring sulfate emissions which are o n the order of 1  10 - 4 mg /mile for low sulfur fuels) , This valu e leads to the computation of the ratio : 

### 679 . 1 84 . 38 22 . 65 mile mg mile mg 

###  . The simulated PM 2.5 UDDS emission rates for the newer light - dut y gas truck group are 4 . 687 m g/mi using MOVES2010b ( Ignoring sulfate emissions(which are in the order of 1  10 - 5 m g/mile for low sulfur fuels) , This value leads to the computation of the ratio : 

### 578 . 0 687 . 4 71 . 2 mile mg mile mg 

###  . The newer model ye ar group produces a ratio which is less than one and implies that large trucks produce less PM 2.5 emissions than smaller trucks. This result is intuitively i nconsistent, and is the likely result of a very

small sample and a large natural variabilit y in emission results. All four data points were retained and averaged together by g iving the older model ye ar group a 75 percent weighting and the newer model year group (MY 2001) a 25 percent weighting. This is consistent with the underly ing data sample. It produces a final ratio of: 

## ) 1 ( Ratio Ratio Ratio newer older final 

## WtFrac

WtFrac

   = 1.679  0.75 + 0.578  0.25 = **1.4 0** 112

We then multiplied this final ratio of 1.4 0 by the light - dut y gasoline truck PM rates to calculate the input emission rates for heavy - dut y gasoline PM rates. This approach works for regulatory c lass 40 (LHD <= 40) because th e emission rates for both regulatory c lass 30 and 40 are normalized to vehicle mass (or VSP - based emission rates). As documented in the light - duty report (cite), the PM emission rates for light - duty vehicles were revised in MOVES2014. This analy sis used t he light - duty truck PM emission rates from MOVES2010b PM emission rates to derive the 1.4 ratio, and the subsequent heav y - dut y gasoline PM emission rates. Hence, a comparison of PM emission rates in MOVES2014 between light - dut y, and LHD < 10K, will y ield a different ratio than the 1.4 derived for MOVES2010b.

3.1.2.3 Emission Rates for Regulatory Class LHD <14 K, LHD45, MHD, and HHD For the larger heav y - dut y gasoline emission rates, the emission rates are STP - based with a fixed mass factor of 17.1 . Unlike for the ga seous emission rates, we do not have sec/sec emission rates associated with power output that would enable us to calculate a 17.1 metric ton STP - based PM emission rates directl y. We used an indirect approach to derive STP - based PM emission rates from the emission rates derived for the LHD <= 10K regulatory class. We assume that the relationship of HC between STP and VSP based emission rates is a reasonable surrogate to map PM emission rates to STP - based emission rates. To do so, we first calculated the emi ssion rate ratio for HC emissions for each operating mode between regulatory class 41 and 40. We then multiplied this ratio to the PM emission rates in regulatory class 40 to obtain STP - based PM emission rates in regulatory class 41, 42, 46 and 47. The reg ulatory class 40 PM emission rates, STP/VSP HC ratios, and the calculated STP - based PM 2.5 emission rates are display ed in Table 3 - 19 .113

**Table 3**  **- 19**  **. A dem onstration of how STP**  **- based PM em**  **ission rates were derived from VSP -**  **based rates using the ratio of HC VSP to STP em**  **ission rates as a surrogate, using m**  **odel year 2001 as an exam ple.** 

opModeID RegClass 40

(g /hr ) HC STP to

VSP Ratio RegClass 41, 42, 46, 47 (g/hr ) 0 0.59 1.000 0.59 1 0.54 1.000 0.54 11 0.60 1.000 0.60 12 0.79 2.263 1.78 13 1.38 3.677 5.08 14 2.62 5.095 13.37 15 5.55 5.443 30.22 16 64.52 5.427 350.13 21 8.38 1.000 8.38 22 2.92 1.154 3.37 23 2.08 2.173 4.52 24 2.92 2.825 8.24 25 10.94 4.842 52.95 27 20.50 7.906 162.10 28 126.42 8.796 1 , 112.05 29 523.16 6.471 3 , 385.32 30 2 , 366.75 7.102 16 , 809.50 33 26.59 2.121 56.40 35 10.76 4.780 51.42 37 13.29 4.010 53.28 38 43.61 8.979 391.56 39 75.73 9.522 721.06 40 74.96 5.300 397.26 

### 3.1.3 **Energy Consu**  **mp tion**  3.1.3.1 LHD<=10k Energy Rates for Model Years 1960 - 2013 The energy

rates for LHD<=10k for pre - 2007 gasoline energy rates are unchanged from the LHD2b3 regulatory

class from MOVES2010a. In MOVES2010a, the energy rates for this regulatory

class, along with the light - duty regulatory

classes, were consolidated across weight classes and engine technologies, as discus sed in the energy updates report . 5114 3.1.3.2 LHD<=10k Energy Rates for Model Years 2014 - 2050 Lower energy

consumption rates for LHD< =10k vehicles are expected due to the Phase 1 Medium and Heav y Duty

Greenhouse Gas Rule , as discussed in more detail in Section 2.1.4.4 . The CO 2 emission reduction s for gasoline 2b trucks in Table 2 - 20 were applied equally to the 2013 model ye ar energy

consumption rates in each running operating mode bins to derive 2014 and lat er energy consumption rates. Figure 2 - 31 display s the average energy consumption (across all running operating modes) for model y ears 1970 through 2030. The rates are constant between 1960 to 1973, and from 2018 to 2050. **Figure 3**  **- 15**  **. Average Energy Consumption Rates for LHD<=10K gasoline vehicles across all running operating modes** 

0. 0e+ 00 5. 0e+ 06 1. 0e+ 07 1. 5e+ 07

1970 1980 1990 2000 2010 2020 2030 mo del Ye ar IDAverageenergyemissionrate,kJ/hour

**re gC**  **la ssN**  **am e**  LHD <= 1 0k115 3.1.3.3 LHD<=14k

, LHD45, MHD, and HHD Ene rgy Rates for Model Years 1960 - 2013 The data used to develop heav y - dut y running exhaust gasoline rates were the same as those used for HC, CO, and NO x . However, new energy rates were onl y developed for L HD<14K, LHD45, MHD, HHD, and bus classes. Similar to the diesel running exhaust energy rates, classifications were not made based on model y ear group, age, or regulatory class. To calculate energy rates (kJ/hour) from CO 2 emissions, we used a heating value (HV) of 122,893 kJ/gallon and CO 2 fuel specific emis sion factor (

f CO2 ) of 8,788 g/gallon for gasoline (see Equation 2 - 19 ). STP was calculated using Equation 1 - 2 . Figure 3 - 16 summarizes the gasoline running exhaust energy rates stored in MOVES for the STP - based re gulatory classes (LHD= <14K, MHD, and HHD) . **Figure 3**  **- 16**  **. Gasoline running exhaust energy rates for**  **LHD <14K (1960**  **- 2013)**  **,, LHD45 (1960**  **- 2015)**  **, MHD**  **(1960 -**  **2015) , and HHD**  **(1960 -**  **2015)**

A linear extrapolation to determine rates at the highest operating modes in each speed range was performed analogousl y t o diesel energy and NO x rates (see Section 2.1.1.3.3 ). 3.1.3.1 LHD<=14k

, LHD 45, MHD, Urban Bus, and HHD Energy Rates for Model Years 2014 - 2050 Updates to the rates display ed in Figure 3 - 16 were made to the heav y - dut y gasolin e energy rates for model years 2014+ based on the 2014 Medium and Heav y - duty Greenhouse Gas Rule 44 as discussed in Section 2.1.4.4 . Figure 3 - 17 display s the average energy consumption rates for the heav y - dut y gasoline source t ypes that are modeled using Scaled Tractive Power (STP) with a fixed 0 1 2 3 4 5 6

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**M e** **a n** **E n** **e r** **g y** **r a** **t e** **[ k** **J /** **h r** **] M** **i l** **l i** **o n** **s**

**Operating mode**116

mass factor of 17.1. The energy rates for all these source t ypes are e quivalent for model y ears 1960 - 2013. The reduction in the average energy consumption rates is display ed in Figure 3 - 17 , with separate reductions for the class 2b and 3 trucks (LHD<=14k), class 4 - 7 trucks (LHD45, MHD), and class 8 trucks (HHD). For LHD<=14k the energy rates are constant 2018 going forward, for the other categories (LHD45, MHD, HHD) the energy rates are constant going forward starting in model y ear 201 7. **Figure 3**  **- 17**  **. Average Energy Consumption Rates for LHD<=14k (41), LHD45 (42), MHD (46) and HHD (47) gasoline vehicles across all running operating m**  **odes.**

0e+ 00 1e+ 07 2e+ 07 3e+ 07 4e+ 07

2012 2014 2016 2018 2020

mo del Ye arI DAverageenergyemissionrate,kJ/hour

**Re gul**  **at ory**  **Cl as**  **s** 41 42, 46, 47117



### 3.2 **Start Emissions 3.2.1**  **Available Data** To develop start emission rates for heav y - duty gasoline - fueled vehicles, we extracted data available in the USEPA Mobile - Source Observation Database (MSOD). These data represent aggregate test results for heav y - dut y spark - ig nition (gasoline powered) engin es measured on the Federal Test Procedure (FTP) cy cle. The GVWR for all trucks was between 8,500 and 14,000 lb, placing all trucks in the LHD2b3 regulatory class . Ta ble 3 - 20 shows the model - ye ar by age classification for the data. The model y ear groups in the table were assigned based on the progression in NOx standards between MY 1990 and 2004. Standards for CO and HC are stable over this period, until MY 2004, when a combined NMHC+ NOx standard was introduced. However, no measurements for trucks were available for MY2004 or later. Start emissions are not dependent on power, and the emission rates do not need to be calculated differentl y to account for different scaling factors used to calculate S TP - based operating modes, as was done for running exhaust rates. As discussed later, start emission rates are modeled differentl y among regulatory

classes to account for differences in the emission standards/and or the data support ed different start emission rates. N o difference s in start emissions are modeled between regul atory

class 40 and 41 vehicles, because no differentiation by STP mass scaling factor are needed. **Ta ble**  **3 -**  **20 .**  **Model -**  **year Group by AgeGroup Structure for the Sam ple of Heavy**  **- Duty Gasoline Engines** 

Model - year Group Standards (g/hp - hr) Age Group (Years) Total

CO HC NOx 0 - 3 4 - 5 6- 7 8 - 9 10 - 14 1960 - 1989 19 22 41 1990 14.4 1.1 6.0 1 29 30 1991 - 1997 14.4 1.1 5.0 73 59 32 4 168 1998 - 2004 14.4 1.1 4.0 8 8 Total 81 59 33 52 22 247 

### 3.2.2 **Estim ation of Mean Rates**  As with light - duty

vehicles, we estimated the “cold - start” as the mass from the cold - start phase of the FTP (bag 1) less the “hot - start” phase (Bag 3). As a preliminary exploration of the data, we averaged b y model y ear group and a ge g roup and produced the graphs shown in Appendix F118

Heavy - duty Gasoline Start Emissions Analysis Figures Sample sizes are small overall and very small in some cases (e.g. 1990, age 6 - 7) and the behavior of the averages is somewhat erratic. In contrast to light - dut y vehicle emissions, strong model - ye ar effects are not apparent. This may not be surprising for CO or HC, given the uniformity of standards throughout. This result is more surprising for NOx but model y ear trends are no more evident for NOx than for the other two. Broadl y speaking, it appears that an age trend may be evident. If we assume that the underly ing population distributio ns are approximately log - normal, we can visualize the data in ways that illustrate underly ing relationships. As a first step, we calculated geometric mean emissions, for purposes of comparison to the arithmetic means calculated b y simply averaging the data . Based on the assumption of log - normality , the geometric mean ( x g ) was calculated in terms of the logarithmic mean ( x l ) as

l

x g 

### x ln 

### e  **Equation 3**  **- 3**  This measure is not appropriate for use as an emission rate, but is useful in that it represents the “center” of the skewed parent distribution. As such, it is less strongly influenced b y unusually high or outly ing measurements than the arithmetic means in Appendix F . In general, the small differences between geometric means and arithmetic means suggest that the distributions represented by the data do not show strong skew in most cases. Assuming that emis sions distributions should be strongl y skewed suggests that these data are not representative of “real - world” emissions for these vehicles. This conclusion appears to be reinforced b y the values in Figure F - 3 which represent the “logarithmic standard deviation” calculated by model - ye ar and age groups. This measure ( s l ), is the standard deviation of natural logarithm of emissions ( x l ) . The values of s l are highl y variable, and generally less than 0.8, showing that the degree of skew in the data is also highl y variable as well as generall y low for emissions data; e.g., corresponding values for light - dut y running emissions are generally 1.0 or greater. Ov erall, review of the geometric means confirms the impression of age trends in the CO and HC results, and the general lack of an age trend in the NOx results. Given the conclusion that the data as such are probably unrepresentative, assuming the log - normal parent distributions allows us to re - estimate the arithmetic mean after assuming reasonable values for s l . For this calculation we assumed values of 0.9 for CO and HC and 1.2 for NOx. These values approximate the maxima seen in these data and are broadly c omparable to rates observed for light duty vehicles. The re - estimated arithmetic means are calculated from the geometric means, by a dding a term that represents the influence of the “dirtier” or “higher - emitting” vehicles, or the “upper tail of the distrib ution,” as shown in Figure F - 4 .

2 2 e l s g a x x  **Equation 3**  **- 4** 119

For purposes of rate development using these data, we concluded that a model - ye ar group effect was not evident and re - averaged all data by a ge g roup alone. Results of the coarser averaging are presented in Figure 3 - 1 8 with the arithmetic mean (directl y calculated and re - estimated) and geometric means shown separately . We then addressed the question of the projection of age trends. As a general principle , we did not allow emissions to decline with age. We implemented this assumption by s tabilizing emissions at the maximum level reached between the 6 - 7 and 10 - 14 age groups.120

**Figure 3**  **- 1**  **8 .**  **Cold -**  **start FTP Emissions for Heavy -**  **Duty Gasoline Trucks, averaged by Age Group only (g =**

**geom etric mean, a= arithmetic m**  **ean recalculated from x**  **l and**  **s l**  **)**



### 3.2.3 **Estim ation of Uncertainty**  We calculated standard errors for each mean in a manner consistent with the re - calculation of the arithmetic means. Because the (arithmetic) means were recalculated with assumed values of s l , it was necessary

t o re - estimate corresponding standard deviations for the parent distribution s , as shown in Equation 3 - 5 . 0 20 40 60 80 10 0 12 0 14 0 16 0 18 0 20 0

0 2 4 6 8 10 12 14 **Ag e**  **(y ea**  **rs )** **F T** **P C** **o l** **d S** **t a** **r t** **( g** **)**

co g_ co a_c o

0 2 4 6 8 10 12 14 16

0 2 4 6 8 10 12 14 **Ag e**  **(ye ar**  **s) F** **T P** **C o** **l d** **S t** **a r** **t (** **g )** 

th c g_t hc a_th c

0 1 2 3 4 5 6 7 8

0 2 4 6 8 10 12 14 **Ag e**  **(yea rs**  **) F** **T P** **C o** **l d** **S t** **a r** **t (** **g )**  no x g_ no x a_ no x 

### (a **) CO** 

**(b )**  **TH C** 

**(c )**  **NO x** 121 

### ) 1 e ( e 2 2 s 2



###   s g 

### x s **Equation 3**  **- 5**  After recalculating the standard deviations, the calculation of corresponding standard errors was simple. Because each vehicle is represented by o nly o ne data point, there was no within - vehicle variability

t o consider, and the standard error could be calcu lated as 

## n s /

. We divided the standard errors by

their respective means to obtain CV - of - the - mean or “relative standard error.” Means, standard deviations and uncertainties are presented in Table 3 - 21 and in Figure 3 - 19 . Note that these results represent only “cold - start” rates (opMod eID 108). **Table 3**  **- 21**  **. Cold**  **- Start Emission Rates (g) for Heavy**  **- Duty Gasoline Trucks, by Age Group (italicized values**  **replicated from previous age Groups)**  Age Group n Pollutant CO THC NOx Means 0 - 3 81 101.2 6.39 4.23 4 - 5 59 133.0 7.40 5.18 6 - 7 33 155.9 11.21 6.12 8 - 9 52 190.3 11.21 7.08 10 - 14 22 189.1 11.21 7.08 Standard Deviations 0 - 3 108.1 6.82 8.55 4 - 5 142.0 7.90 6 - 7 166.5 11.98 12.39 8 - 9 203.2 11.98 14.32 10 - 14 202.0 11.98 14.32 Standard Errors 0 - 3 12.01 0.758 0.951 4 - 5 18.49 1.03 1.18 6 - 7 28.98 2.08 2.16 8 - 9 28.18 2.08 1.99 10 - 14 43.06 2.08 1.99122

**Figure 3**  **- 19**  **. Cold**  **- start Em**  **ission Rates for Heavy -**  **Duty Gasoline Trucks, with 95% Confidence Intervals**



### 3.2.4 **Projecting Rates beyond**  **the Available Data** The steps described so far involved reduction and analy sis of the available emissions data. Inthe next step, we describe approaches used to impute rates for model ye ars not represented in these data. For purposes of analy sis we delineated four model y ear groups: 1960 - 2004, 2005 - 2007 , 2008 - 2017 and 201 8 and later. We describe the derivation of rates in each group below. 0 50 10 0 15 0 20 0 25 0 30 0

0 5 1 0 15 20 25 **Ag e (**  **yea rs**  **) F** **T P** **C o** **l d** **S t** **a r** **t (** **g )** 

0 2 4 6 8 10 12 14 16 18

0 5 10 15 20 25 **Ag e**  **(y ears**  **) F** **T P** **C o** **l d** **S t** **a r** **t (** **g )** 

0 2 4 6 8 10 12

0 5 10 15 20 25 **Ag e**  **(years**

**) F** **T P** **C o** **l d** **S t** **a r** **t (** **g )**  

### (a **) CO** 

**(b )**  **TH C** 

**(c )**  **NO x** 123 3.2.4.1 Regulatory class LHD2b3 (R egClassID 40 and 41) For CO the approach was simple. We applied the values in Table 3 - 21 to all model - year groups. The rationa le for this approach is that the CO standards do not change over the full range of model ye ars considered. For HC and NOx we imputed values for the 2005 - 07 and 2008 - 2017 model - ye ar groups by multiplying the values in Table 3 - 21 by r atios expressed in terms of the applicable standards. Starting in 2005, a combined HC+NOx standard was introduced. Itw as necessary f or modeling purpose s to partition the stan dard into HC and NOx components. We assumed that the proportions of NMHC and NOx would be similar to those in the 2008 standards, which separate NMHC and NOx while reducing both. We calculated the HC value by multiply ing the 1960 - 2004 value b y the fraction f HC , where 

###   37

. 0 hr - g/hp 1 . 1 hr

- g/hp 0 . 1 hr - g/hp 0.20) (0.14 hr

- g/hp 14 . 0 HC            f **Equation 3**  **- 6**  This ratio represents the component of the 2005 combined standard attributed to NMHC. We calculated the corresponding value for NO x as

147 . 0 hr - g/hp 0 . 4 hr

- g/hp 0 . 1 hr g/hp 0.20) (0.14 hr

g/hp 20 . 0 NOx         

  

 f **Equation 3**  **- 7**  For these rates we neglected the THC/NMHC conversions, to which we gave attention for light duty . For the 2008 - 2017 model y ears , the approach to projecting rates was modified so as to adopt two refinements developed for light - dut y rates. First, start emission rates from LHD2b3 gasoline vehicles were estimated by appl yi ng the “start split - ratio” shown in Table 3 - 10 to a set of rates representing light - dut y vehicles in Tier - 2/ Bin 8 . A ccordingl y , start emission rates adopted the same age effects as the light - duty start emission rates. The multipli cative age effect s for start emission rates for 2008 - 2017 vehicles are shown in Table 3 - 22 .

3.2.4.2 Incorporating Tier - 3 Standards: Model years 2018 and later. Emission rates for the start - exhaust process were developed emplo yi ng the techniques described for running - exhaust emissions, as described above in 3.1.1.4 (page 96 ). Start rates for HC, CO and NO x during the Tier - 3 phase - in (2018 - 2022) are shown below in Figure 3 - 19 to Figure 3 - 22 . Note that start rates are identical for both the LHD<=10k and LHD<=14k regulatory classes (regClassID = 40, 41, respectivel y).124

**Figure 3**  **- 20**  **. THC: Emission rates for the start -**  **exhaust process, for the LHD<=10k (40) and the LHD<=14k (41)**

**regulatory classes, by operating m ode and age group, during the Tier**  **- 3 phase**  **- in.** 125

**Figure 3**  **- 21**  **. CO: Em ission rates for the start**  **- exhaust process, for the LHD<=10k (40) and the LHD<=14k (41)** 

**regulatory classes, by operating m**  **ode and age group, during the Tier**  **- 3 phase**  **- in.** 126

**Figure 3**  **- 22**  **. NO x**  **: Em ission rates for the start**  **- exhaust process, for the LHD<=10k (40) and the LHD<=14k** 

**(41) regulatory classes, by operating m ode and age group, during the Tier**  **- 3 phase**  **- in.** 

**Table 3**  **- 22**  **. Multiplicative Age Effect Used for Start Em issions for RegClass 40 and 41 vehicles for 2008**  **- 2017**  **model years. Adopted from the deterioration effects for RegClass 30 vehicles from**  **the Light -**  **Duty Report.** ageGroupID HC CO NOx

3 1 1 1 405 1.65 1.93 1.73 607 2.20 2.36 2.21 809 2.68 2.54 2.76 1014 3.30 3.00 3.20 1519 3.66 3.35 3.63 2099 4.42 4.06 4.11127 3.2.4.3 Regulatory class es LHD45 , MHD, and HHD For LHD45 and MHD , we estimated values relative to the LHD2b3 start emission rates estimated in MOVES2010 . F or CO and HC, we estimated values for the heavier vehicles by multiplying them by

ratios of standards for the heavier class to those for the lighter class. The value for CO based on 1990 - 200 4 model y ear vehicles is 58 . 2 hr - g/hp 4 . 14 hr

- g/hp 1 . 37 CO   f **Equation 3**  **- 8**  and the corresponding value for HC based on 1990 - 2004 model y ear vehicles is 1.73. 73 . 1 hr - g/hp 1 . 1 hr - g/hp 9 . 1 HC   f **Equation 3**  **- 9**  The ratios derived in Equation 3 - 8 and Equation 3 - 9 (2.58 and 1.73) are applied to estimate the start emission rates for all three model y ear groups for the LHD45, MHD, and HHD gasoline vehicles ( Table 3 - 23 ) . For NOx, all MOVES2014 start emissions for medium and heav y - dut y vehicles are equal to for the MOVES2010 LHD2b3 start emission rates , because the same standards appl y to both classes throughout. The approaches for all three regulatory classes in all three model ye ars are shown in Table 3 - 23 .128

**Table 3**  **- 23**  **. Me**  **tho ds used to Calculate and**  **Start Em ission Rates for Heavy**  **- Duty Spark** **- Ignition Engines**  Regulatory Class Model - year Group Method

CO THC NOx LHD2b3 (LHD<= 10K and LHD < 14K) 1960 - 2004 Values from Table 3 - 21 Values from Table 3 - 21 Values from Table 3 - 21 2005 - 2007 Values from

Table 3 - 21 Reduce in proportion t o standards from 1960 - 200 4 Reduce in proportion t o standards from 1960 - 2004 2008 - 2 017 Values from

Table 3 - 21 Section 3.2.4.1 Section 3.2.4.1 201 8 + Section 3.2.4.2 Section 3.2.4.2 Section 3.2.4.2

LHD45, MHD , HHD 1960 - 2004 Increase in proportion t o standards from LHD2b3 Increase in proportion t o standards from LHD2b3 Sam e values as LHD2b3 2005 - 2007 Increase in proportion

t o standards from LHD2b3 Increase in proportion t o standards from LHD2b3 Sam e values as LHD2b3 2008 + Increase in proportion

t o standards from MOVES2010b LHD2b3 Increase in proportion t o standards from MOVES2010b LHD2b3 Sam e values as MOVES2010b LHD2b3 As for heav y - dut y diesel and light - dut y vehicles we applied the curve in Figure 2 - 36 to adjust the start emission rates for varying soak times. The rates described in this section were for cold starts (soak time > 720 minutes).

3.2.4.4 Particulate Matter D ata on PM start emissions from heav y - dut y gasoline vehicles were unavailable . As a result, we used the multiplication factor from the running exhaust emissions analy sis of 1.4 0 to scale up start emission rates for light - duty trucks (regClassID 30) for model ye ars 1960 - 2017. For 2018 and later model y ears, the start PM emissions for heavy - duty g asoline are estimated to be the same as the rates in 2017. xvii As such, the start PM emission rates for heav y - dut y gasoline vehicles exhibit the same relative effects of soak time, and deterioration as the light - dut y PM start emission rates .

xvii The light - duty PM start rates are projected to decrease in model year 2018 w ith the implementation of the Tier 3Vehicle Emissions and Fuel Standards Program. MOVES2014 does not model a reduction in HD PM start emissions fro m the Tier 3 program, so the 1.4 scaled ratio is not applicable for 2018 and later model years.129



## 3.2.5 **Start Energy Ra tes**  The heav y - duty

gasoline start energy

rates were originall y derived in MOVES2004, and updated in MOVES2010 as described in the corresponding reports 28 , 5 . Post - 2000, the only changes to the start energy rates, is the projected impact of the Phase 1 Heavy - dut y GHG standards, which begin phase - in in 2014 and have the same reductions as the running energy rates as presented in Table 2 - 19 . **Figure 3**  **- 23**  **. Heayv -**  **dut y gasoline cold start energy rates (opMode 108) by m**  **odel year and regulatory class.**

**The start energy rates are reduced for shorter soak times using the same factors for diesel vehicles, as presented**  **in Table**  **2 -**  **28 .**  Table 3 - 24 display s the relative contribution of running and start operation to total energy consumption from the he avy - duty gasoline regulatory

classes from a National MOVES run for calendar year 2011. As for diesel vehicles, starts are estimated to be a relatively small contributor to the total energy

demand of vehicle operation. Due to the small contribution to the t otal energy inventory , the start emissions have not been prioritized to be updated with more recent data. **Table 3**  **- 24**  **. Relative contribution of total energy consumption from each pollutant process by**  **regulatory class for heavy**  **- duty gasoline vehicles in calendar year 2011.**  processID processName 40 41 42 46 47 1 Running Exhaust 96.3% 98.9% 99.0% 98.1% 98.1%130

2 Start Exhaust 3.7% 1.1% 1.0% 1.9% 1.9%131



## 4 **Heavy Duty Compr essed Natural Gas**  **Tr ansit Bus**  **Em issions**  While natural gas lacks the ubiquitous fueling infrastructure of gasoline , compressed natural gas (CNG) has grown as a transportation fuel for public transit, government, and corporate fleets . Such fleets ty picall y utilize centralized, privately - owned refueling stations. Within this segment, some of the most rapid growth in CNG vehicles over the last 15 y ears has occurred among city transit bus fleets , as seen in Figure 4 - 1 . 65

**Figure 4**  **- 1**  **. US natural gas bus population**  **by year and fuel type from** **1996 -**  **2011 (**  **APTA )**  **66**

MOVES2010b and earlier versions can model emissions from CNG bus fleets. However, in absence of better data, MOVES2010b used the emission rates originall y developed for m edium heav y - dut y gasoline trucks (regulatory class 46). These rates were used for hy drocarbon (HC), nitro gen oxide s (NOx), carbon monoxide (C O), and particulate matter (PM) emission rates. 67 Medium HD gasoline trucks are reasonable proxies in terms of vehicle weight and engine size, but as this report shows, there are substantial differences in the MOVES2010b emissions rates and real - world measurements of CNG transit buses. This section updates the CNG bus emission rates in MOVES based on measurements from CN G buses and future projections. 

### 4.1 **Transit Bus Driving Cycles**  **and Operating Mode Distributions 4.1.1**  **Heavy -** 

**Duty Transit Bus Driving Cycles**  To evaluate whether the existing MOVES2010b rates for gasoline vehicles w ere appropriate surrogate s for buses powered with CNG, we generated test cy cle simulations using MOVES and compared the simulat ed results against chassis d yn amometer measurements from published test programs . This process involved using MOVES to determine the distribution of operating mode s for each drive cy cle, and then multiplying the time spent in each mode by the corresponding 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

DIESEL ELECTRICITY & HYBRIDS GASOLINE NATURAL GAS OTHER132

emission rates in the E mission RateBy Age table . As in a t ransient emissions test, the sum of the emissions at each second over the duration of the test yi elds the total mass of emissions over the test cy cle. Dividing the total by distance y

ields the emission rate over the test. These test programs included onl y running emissions and were based on a variet y of heavy - duty

and transit bus driving cy cles. We configured MOVES to simulate the drive cy cles b y importing each drive cycle into MOVES using the Link Driving Schedules template in the Project Data Manager t ool. As these were d yn amometer measurements, we set the grade to “0” over the duration of each cy cle. We imported two driving cy cles: 1) the Central Business District (CBD) , and 2) Washington Metropolitan Area Transit Authority (WMATA). The CBD cy cle is defined as a driving pattern with constant acceleration from rest to 20 mph, a short cruise period at 20 mph, constant deceleration back to rest, and then repeated for 600 seconds ( see Figure 4 - 2 ). 68 The WMATA cy cle was developed using GPS data from city buses in Washington, DC, and has higher speeds and greater per iods of acceleration than the CBD cy cle (see Figure 4 - 3 ). 70

**Figure 4**  **- 2**  **. Driving schedule trace**  **of the Central Business District (CBD) cycle (DieselNet) 69** 

**Figure 4**  **- 3**  **Driving schedule trace of the Washington Metropolitan Area Transit Authority (WMATA) cycle Melendez et al. 2005)**  **70**133



### 4.1.2 **Transit Bus Operatin** **g Mode Distributions** The MOVES project level importer was used to input the second - by - second drive cy cle. A single link was created, with the test cy cle entered as a drive trace . Running MOVES generated the operating mode distribution , which is created b y allocating the time spent in each operating mode according to the cy cle speed and acceleration, as shown in Figure 4 - 4 and Figure 4 - 5 . The derivation of scaled tractive power ( STP ) and op erating mode attribution for heav y - dut y vehicles are discussed earlier in this report, in Section 1.3 . Since STP is dependent on mass (among other factors), the average vehicle inertial test mass for each cy cle was inserted into the MOVES2010b sourceUseTy pe table in place of the default transit bus mass to ensure a more accurate simulation. Using the measured vehicle masses across all the test programs reviewed, t he CBD cy cle ha d an average test ma ss of 14.957 metric to ns and the WMATA cy cle had an average mass of 16.308 metric tons, compared to the default of 16.556 metric tons. We used the road load coefficients from MOVES201 0b for transit buses, and any changes in the coefficients ( A , B , and C

) with the tested buses were assumed to be negligible . **Figure 4**  **- 4**  **. O**  **perating m ode**  **distribution for the CBD cycle**

**Figure 4**  **- 5**  **. O**  **perating m ode distribution for the**  **WMATA cycle** 0 50 100 150 200 250 300 350

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**T i** **m e** **( s** **e c** **)**

**Operating Mode**

0 100 200 300 400 500 600 700 800 900

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40**T i** **m e** **( s** **e c** **)**

**Operating Mode**134



### 4.2 **Comparison of Simulated Rates**  **and Real -**  **World Measurements**  **4.2.1 Sim**  **ulating Cycle Emission Aggregates from MOVES2010b Rates**  Having determined the total amount time spent in each operating mode over the course of each drive cy cle, using the emission rates in the MOVES database (DB), we were able to simulate emissions over each cy cle. Using this method , the simulat ed cy cle emission aggregates were calculated as a function of the following parameters :  fuel type,  driving cycle,  age group,  regulato ry class,  mo del year, and  pollutant and process . We simulated a distance - specific emission factor (EF sim , g/mile) for each pollutant for each cy cle based on the operating mode distributions, existing MOVES emission rates, and the distance of the drive cy cle, using the equation b elow:

cycle OM OM

p cycle OM cycle p sim d r

t EF 

## 

 ,

,

, , **Equation 4**  **- 1**  where t OM

,cycle = cy cle’s total time spend in operating mode OM , d cycle = distance of the cycle , r p,OM = time - specific emission rate of pollutant p in operating mode OM . We compare d the published test measurements to simulations using the MOVES2010b CNG transit bus rates from Equation 28 . W e also specified the age group and model year to match individual vehicles in the testing programs from the literature on CNG transit buses . 

### 4.2.2 **Published Chassis Dynam om**  **eter Measurem ents**  These programs were conducted at several research locations around the country on different heav y - dut y chassis dynamome ter equipment. In our anal ys is, we collected 35 unique dy namometer measurements

—

which consisted of running emissions rates in mass per unit distance for each of the pollutants and total energy below: 1. total hydrocarbons (THC) , 2. methane (CH 4 ) , 3. carbon monoxide (CO) , 4. oxides of nitrogen (NOx ) , 5. particulate matter ( EC + non - E C ) , and 6. total energy consumption .135

Note that methane emissions are not estimated using emission rates, as are the other pollutants listed above. Rather, methane is estimated in relation to THC, using ratios stored in the “ methaneTHCratio ” table. The ratios are categorized by fuel t ype, pollutant process, source t ype , model - ye ar group , and age group. We then multiplied the THC rate by the corresponding ratio f rom the “ methanethcratio ” table to c alculate the CH 4 rate. All criteria emission rates are dependent on vehicle age, and thus are stored in the emissionRateBy

Age table and, and total energy consumption is age independent, and therefore stored in the “ emissionRate ” table. Some of the publish ed studies did not report total energy consumption directly , so it was necessary to compute energy

from a stoichiometric equation based on the carbon content in the emitted pollutants or from reported values of miles per gallon equivalent of diesel fuel. In the former case, w e used 0.8037 as the carbon fraction coefficient for nonmethane h ydrocarbons (NMHC) when the bus was equipped with an oxidation catal ys t and 0.835 without due to high ethene levels , using speciation profiles from Ay ala et al. (2003) 71 d iscussed later in this section . All other conversion factors to energy were taken from Melendez et al. (2005). 70 On a similar note, MOVES does not report partic ulate matter (PM) as a single rate; it reports one rate for PM from elemental carbon (EC) of 2.5 microns or less , and another rate for non - elemental carbon of 2.5 microns or less . These separate rates for PM ( EC ) and PM (Non EC ) from the emission R ate B y

Ag e t able are added together for a total PM rate used for comparison to the measurements. The number of unique measurements is approximately equal to the number of vehicles tested, although not exactl y. Some vehicles were tested more than once because a variable in the experiment changed, such as the after - treatment technology . A ll of the vehicles were in service with a transit agency

at the time of testing. In addition, these measurements were t ypically reported as cy cle averages based on multiple runs with the same vehicle and configuration over a specific driving cy cle. Many

of the testing programs also included diesel transit buses, but these vehicles were excluded from this anal ys is. Table 4 - 1 shows a summary of the number of unique CNG bus measurements b y driving cycle for each study. Navistar published a similar study of CNG and diesel buses in 2008, and this analy sis shares many of the same sources. 72136

**Table 4**  **- 1**  **. S**  **ummary of external emissions testing programs by driving cycle and number of unique**  **measurements Paper/Article**  **Lead Research Unit Driving**  **Cycle(s) Number of Unique** 

**Me asurem**  **ents** Melendez 2005 70 National Renewable Energy Laboratory (NREL) WMATA 7 Ayala 2002 73 California Air Resources Board (CARB) CBD 2 Ayala 2003 71 CARB CBD 6 Lanni 2003 74 New York Department of Environmental Conservation CBD 3 McCormick 1999 75 Colorado School of Mines CBD 3 LaTavec 2002 76 ARCO (a BP Company) CBD 2 McKain 2000 77 WVU CBD 3 Clark 1997 78 WVU CBD 10 **TOTAL 35**  As see n above , the CBD driving cy cle was applied in each study except for one. Since it (a) had the largest sample size and (b) appeared to be representative of the data from other cy cles, we focus ed our anal ys is on the CBD cy cle results . W e approximated the vehicle’s age by s ubtracting the y ear the study was conducted from the model y ear of the vehicle. Most vehicles tested were less than three y ears old (ageGr oupID “3”), whereas 9 vehicles fell into the four to five ye ar - old age group (ageGroupID “405”). In the CBD cy cle, 5 out of 2 8 vehicles were in ageGroupID “405”, and their performance was generally similar to the 0 - 3 age vehicle results. Consequently , we combine d the vehicles from age group 405 with the vehicles from group 3. xviii Vehicle model ye ars ranged fro m MY 2001 to MY 2004 for the WMATA cy cle and from MY 1994 to MY 2001 for the CBD cy cle. 

### 4.2.3 **Plots of Sim ulated Aggregates and Published Measurem**  **ents** Below a re graphs of the CBD measurements b y model y ear for each pollutant compared to simulated MOVES 2010b CNG (MHD gasoline) rates.

xviii Note that for MY 1994 in Figure 4 - 6 through **Error! Reference source not found.** , CNG (MHD gasoline) MOVES predictions are based off age group 405 instead of group 3.137

**Figure 4**  **- 6**  **. NOx em**  **ission comparisons of**  **CNG transit bus dynamometer measurements and MOVES2010b**  **simulated aggregates on the CBD cycle**  **.**

**Figure 4**  **- 7**  **. CO em**  **ission comparisons of CNG transit bus dynamometer measurements and MOVES2010b simulated aggregates on the CBD cycle.**  0 10 20 30 40 50 60

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002**N O** **x (** **g r** **a m** **s /** **m i** **l e** **)**

**Model Year** Measurements CNG (0-3 Age Group) Measurements CNG (4-5 Age Group) MOVES 2010b CNG

0 10 20 30 40 50 60 70

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002**C O** **( g** **r a** **m s** **/ m** **i l** **e )** 

**Model Year** Measurements CNG (0-3 Age Group) Measurements CNG (4-5 Age Group) MOVES 2010b CNG138

**Figure 4**  **- 8**  **. PM emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b**  **simulated aggregates on the CBD cycle.**

**Figure 4**  **- 9**  **. THC em**  **ission comparisons of CNG transit bus dynamometer measurements and MOVES2010b**  **simulated aggregates on the CBD cycle.** 0 0.05 0.1 0.15 0.2 0.25 0.3

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002**P M** **( g** **r a** **m s** **/ m** **i l** **e )** 

**Model Year** Measurements CNG (0-3 Age Group) Measurements CNG (4-5 Age Group) MOVES 2010b CNG

0 5 10 15 20 25

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002**T H** **C (** **g r** **a m** **s /** **m i** **l e** **)**

**Model Year** Measurements CNG (0-3 Age Group) Measurements CNG (4-5 Age Group) MOVES 2010b CNG139

**Figure 4**  **- 10**  **. C**  **H 4**  **em ission com**  **parisons of CNG transit bus dynamo meter m**  **easurements and MOVES2010b simulated aggregates on the CBD cycle.** 

**Figure 4**  **- 11**  **. Total energy consumption**  **comparisons of CNG transit bus dynamometer m easur**  **em ents and**  **MOVES2010b simulated aggregates on the CBD cycle.**

In Figure 4 - 6 , the MOVES2010b CNG rates slightly under - predict the bus NOx measurements . As shown in Figure 4 - 7 , MOVES2010b predictions for CO emissions are similar to the CNG measurements , particularly after 1999 . Figure 4 - 8 shows that the MOVES2010b CNG predictions 0 5 10 15 20 25

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002**C H** **4 (** **g r** **a m** **s /** **m i** **l e** **)**

**Model Year** Measurements CNG (0-3 Age Group) Measurements CNG (4-5 Age Group) MOVES 2010b CNG

0 10000 20000 30000 40000 50000 60000

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002**E n** **e r** **g y** **( B** **T U** **/ m** **i l** **e )** 

**Model Year** Measurements CNG (0-3 Age Group) Measurements CNG (4-5 Age Group) MOVES 2010b CNG140

are low er for PM. As seen in Figure 4 - 9 , MOVES2010b CNG predictions for THC emi ssions are lower than the measurements by an order of magnitude. As seen in Figure 4 - 10 , this underestimate of THC is largel y attributable to a significant underestimate of CNG related CH 4 in MOVES2010b. These relatively

high CH 4 emissions from CNG buses compared to gasoline or diesel buses are likely from the exhaust of uncombusted natural gas , but further stud y is warranted. Figure 4 - 11 shows that MOVES2010b under - predicts the total energy consumption seen in the literature. We address these discrepancies by using the published measurements to develop new CNG exhaust emission rates MOVES2014 rates across all the pollutants and total energy discussed above. These comparisons show that updating the MOVES2010b with these additional data from CNG buses have significant impact on the emissions inventories from CNG buses. As shown in this anal ys is, the existing CNG rates based on MHD gasoline trucks are not adequate surrogates. As discussed in the next section, we develop ed new rates based on cy cle averages from the dy namometer measurements. The remainder of this report is devoted to a discussion on the development of new time - based emission rates (g/hr) for CNG buses. 

### 4.3 **Development of New Running Exhaust**  **Emission Rates 4.3.1**  **Determ ining Model Year Groups**  Id eall y, new MOVES emission rates are developed through anal ys is of second by second data of vehicles of the appropriate regulatory class, model ye ar, and age. Unfortunately , such modal data is not readily

available in this case. H owever, we s ubstantially improve d the CNG bus emission rates in MOVES201 4 relative to MOVES2010b by raising or lowering the MY emission rates wholesale (as opposed to individual adjustments by operating mode). Fundamentall y, the first necessary step is ass ig ning a set of appropriate model y ear groups for the data. Using too few model y ear groups may miss important differences in the emission rates, while using too man y model ye ar groups can give false prominence to outliers and introduce artificial “jumps” in the emission rates. We chose to group the model y ear groups according to similar emission rates in the criteria pollutants (THC, CO, NOx and PM). We separated CNG buses

– equipped with oxidation catal ys ts and those not equipped, to determine if this was a reasonable distinction, and to see if these vehicles’ criteria emission rates varied b y model y ear and b y age. For some model y ears, there are both after - treatment equipped and non equipped vehicles. In these years, there was not a visible difference in the criteria emission levels between the vehicles in the testing programs with after - treatment (AT) equipment and those with no after - treatment ( see the figures in Section Heavy - dut y CNG Bus Emissions Supplemental Figures in the appendix ). xix Given the small sample size, the lack of clear trends among the individual vehicle models, and our lack of data on the relative distribution of after - treatment equip ped vehicles versus unequipped vehicles in each of these model years, we chose to group all the CBD measurements from the literature into one model y ear group, spanning from MY 1994 to

xix The CNG studies have a large observed impact on several of the unregu lated pollutants (e.g. formaldehyde) between after - treatment and non - equipped vehicles. This impact is discussed under the PM and HC speciation subsection.141

MY 2001. No data on CNG buses equipped with three - way catal ys ts (TWC) w as readily available at the time of this analy sis; we will look to incorporate data from buses that have TWCs and spark ignited, stoichiometric - burn engine technology as it becomes available. Of the survey ed data, only one study

had any vehicles newer than MY 2001. **xx** This paper, a joint study between NREL and WMATA , had a small sample of vehicles from MY 2004. These vehicles have a visibly

different emissions profile than the other vehicles. 70 While these buses were only tested on the WMATA driving cy cle, they were all equipped with oxidation catal ys ts and had substantially

lower emissions from the 199 4 - 2001 buses , particularl y for PM emissions . As a result, a second model ye ar group runs from MY 2002 to MY 2006 based on a group of MY 2004 WMATA buses. This MY group ends before MY 2007 when a new series of stringent emission standards went into effect. **79** We did, however, apply a sales - weighted adjust ment factor to the unusually low CO rate for these John Deere CNG buse s a ccording to certification data **xxi** , as described below. 

### 4.3.2 **Scaling Model Years After 2007**  Without published data on in - use vehicles past MY 2004, we use emission certification levels as a proxi es to estimate emission rate change s since then. Certification levels are reported in grams per brake horsepower - hour and are not directly used in formulating MOVES emission rates because they do not include real - world effects such as vehicle mileage or de terioration. 80 , xxii These effects were present in the testing programs, so we created scaling factor s that we could apply to the measured data from the testing programs to estimate rates after MY 2004. Natural gas transit bus emission certification data by model y ear is publicly available on the EPA’s Office of Transportation and Air Qualit y website. 81 Analy sis of these data showed that from MY 2002 to MY 2012 there have been changes in certification levels for all the pollutants consider ed in this report. In particular, NOx and PM levels have dropped dramaticall y over the past decade . This effect is largel y attributable to increasingl y strict emission standards, which have affected both diesel and CNG buses. Table 4 - 2 below indicates the number of CNG transit bus models certified for each model ye ar.

xx A number of papers have discussed more recent vehicles. Examples include “ Clark, N., Wayne, W., K han, A., Lyo ns, D. et al., "Effects of Average Driving Cycle Speed on Lean - Burn Natural Gas Bus Emissions and Fuel Economy," SAE Technical Paper 2007 - 01 - 0054, 2007 . Data from these ne wer studies w ould provide further validation and refinement to the rates discussed in this report, how ever it w as not available in time. xxi This rate w as a full order of magnitude lower than the other 2004 CNG models, and was not supported by additional test results, and therefore, was not incorporated into the model. xxii As w ith other MOVES emission rates, the certification rates w ere not used as a direct estimate of on - road em issions. How ever, in absence of contradictory evidence, for projecting CNG emissions, we assumed that changes in on - road em ission rates correlate w ith c hanges in certification levels.142

**Table 4**  **- 2**  **. A summary of the number of certified CNG transit**  **buses by model year (USEPA OTAQ).** Model Year Number of

Vehicle Models 2002 4 2003 4 2004 4 2005 6 2006 4 2007 3 2008 1 2009 1 2010 2 2011 2 2012 2 TO TA L **33** To improve the accuracy

of the scaling factor we weighted the emission levels with projected US sales figures for the certified CNG buses. These figures are confidential business information and cannot be shared publicly but have been incorporated as ratio s to calculate the MY group 2007 - 2012 emission rates . The aggregated certification levels with these annual sales weighted averages for MY group 2002 - 2006 and MY group 2007 - 2012 are shown in Table 4 - 3 below . **Table 4**  **- 3**  **. Model year group 2002**  **- 2006 and 2007**  **- 2012 certification levels for CNG buses used for scaling of**  **measured emission rate data** Model Year Group NOx CO PM NMHC 1 Certification (g/bhp - hr) 2002 - 2006 1.208 1.355 0.0078 0.147 Certification (g/bhp - hr) 2007 - 2012 0.2902 3.032 0.0033 0.057 1. Certification data has measurements of organic material non - methane hydrocarbon e quivalent (OMNMHCE). For this analysis they were treated as NMHC values. Methane levels are not reported in the certification data, so we estimate CH 4 rates for MY group 2007 - 2012 through an analy sis of the CH 4 to THC ratio by model y ear from the dy

namometer measurements from the WMATA study . T he CH 4 /THC ratio for every model y ear fell within one standard deviation of the average ratio across all model y ears . The CH 4 /THC ratios are calculated from averaged CH 4 and THC measurements on the respective CBD and WMATA cy cles, as di splay ed in Table 4 - 4 . We left the CH 4 /THC ratio constant from MY group 2002 - 2006 to MY group 2007 - 2012 and estimated the new CH 4 rate (given in Table 4 - 5 ) off that previous ratio.143 **Table 4**  **- 4**  **Summary of CH4/THC ratios for MOVES2014** Model Year Age Group CH4/THC Ratio 1994 - 2001 0 - 3 0.917 2002 - 2006 0 - 3 0.950 2007 - 2012 0 - 3 0.95 0 We scale d the newer model y ear rates based off the measurements in the MY group 2002 - 2006 in proportion to the ratio of certification levels from MY group 200 7 - 2012 to MY group 2002 - 2006. In this case,

, = , ∙ ,

, . **Equation 4**  **- 2**  As mentioned before, the measured CO rate for MY group 2002 - 2006 was not used . We adjusted the original rate b y the ratio between the sales - weighted average for the MY 2004 certification level of all models and the certification level for that particular MY 2004 John Deere bus with the anomalous CO rate . T he estimated CO rate for MY group 2007 - 2012 is notably greater than the previous MY group , but this change was reflected in our certification level proxie s, and has been observed in more recent testing with three - way cataly st, s toichiometric CNG buses 82

. N ote that there was limited data on older vehicles in the literature, so the ratios that were develop ed using vehicles in the 0 - 3 age group have been applied to all other age groups. Therefore, we are assuming that CNG buses exhibit deterioration rates in c ontrol equipment proportional to medium heav y - dut y gasoline trucks. S ince there is no certification data on carbon dioxide (CO 2 ) or other greenhouse gases , until 2011, we maintain ed the same total energy consumption rate from MY group 2002 - 2006 to MY 2007 - 2012. 

### 4.3.3 **Creating CNG**  **Running Rates for Future Model Years**  Table 4 - 5 shows C NG transit bus emissions on each drive cy cle calculated using MOVES2010b rates for each MY group . These calculations are shown using a single model y ear within the group. The table also shows the emission rates estimated from our meta - anal ys is of the lite rature. T he ratios between these rates were applied to the 199 7, 2004 and 2009 MOVES2010b CNG bus rates in order to calculate the MOVES2014 rates .144

**Table 4**  **- 5**  **Summary of MOVES2010b distance**  **- dependent emission rates for**  **CNG transit buses and**  **the ratios to be applied to the MOVES2010b**  **STP -**  **based rates to compute the**  **MOVES2014 rates**  **.**

1. This CO rate was uncharacteristically low (0.14 g/mi) , determined to be an outlier, and has been adjusted using sales - we ighted certification data, as described in more detail in the text abo ve. 2. Note that the increase in the CO rate from MY group 2002 - 2006 to MY 2007 and later seems to be an outcome of transitioning from lean - burn CNG engines to stoichiometric - burn engines. This upward CO trend is supported by certification data as w ell as testing results from some recent studies comparing these two technologies. 83 T he model y ear basis for scaling was selected based on temporal similarity . For instance, our choice to use MY 1997 for MY group 1994 - 2001 was due to it being a median y ear in the group . For MY group 2002 - 2006, we selected MY 2004 because that was the y ear al l the vehicles in that group were manufactured. As for MY group 2007 - 2012, MY 2009 was also chosen simply for being one of th e two model y ears near the median for the group . For 2014 and later model y ear, the CNG energy consumption rates are reduced by th e same reduction as diesel Urban Bus vehicles, in response to the 2014 Medium and Heav y Dut y Greenhouse Gas R

ule as docu mented in Table 2 - 19 . 

### 4.4 **Start Ex haust**  **Emission Rates for CNG Buses** In the absence of an y measured start exhaust emissions from CNG transit buses, their start rates are copies of heav y - dut y diesel start rates , for all pollutants including energy rates . We believe this is an environmentall y conservative approach , rather than assuming zero CNG start emissions . MOVES still estimates that the majority of emissions from CNG buses are from running emissions, MOVES2010b CNG Rates (g/mile) MY Age

Group Cy cle NOx CO PM_NonE C PM_EC TO TA L

ENERGY (BTU/mi) THC CH4 1997 0 - 3 CBD 9.63 62. 4 0.002 4 0. 0002 31137 1.84 0. 049 2004 and 2009 0 - 3 WMATA 5.45 18.9 0.003 5 0. 0003 35489 1.43 0.032 MOVES201 4 CNG Rates (g/mile - measured/estimated from analysis) MY Age

Group Cy cle NOx CO PM_NonE C PM_EC TO TA L

ENERGY (BTU/mi) THC CH4 1994 - 2001 0 - 3 CBD 20.8 9.97 0.037 0. 0038 42782 13.2 12.1 2002 - 2006 0 - 3 WMATA 9.08 2.17 1 0. 0039 0. 0005 40900 11.2 10.6 2007 and later 0 - 3 WMATA 2.18 5.93 2 0.0016 0.0002 40900 4.33 4.12 Ratios Applied to the STP - Based MOVES201 4 Rates MY Age

Group Cy cle

ratioed NOx CO PM_NonE C PM_EC TO TA L

ENERGY THC CH4 1994 - 2001 all CBD 2.16 0. 16 15. 5 21 . 6 1.37 7. 17 250 2002 - 2006 all WMATA 1. 67 0.11 1. 09 1. 87 1.15 7.79 330 2007 and later all WMATA 0.40 0.31 0. 46 0. 78 1.15 3.02 128145

which are based on CNG test programs. We readily acknowledge that the diesel start rates may not accuratel y represent CNG start rates. This assumption will be revisited for future releases of MOVES as new data on CNG start rates becomes available. 

### 4.5 **Applications to Other Model Years and Age Groups** We applied the ratios in Table 4 - 5 t o all ages of CNG bus emission running rates in MOVES2010b. In this way , the deterioration assumptions for criteria pollutants in the MOVES 2010b running rates are preserved in the MOVES2014 CNG bus rates. For completeness, CNG buses prior to MY 1994 use the same rates as MY group 1994 - 2001 . R ates for buses built after MY 2012 maintain the same rate as MY group 2007 - 2012. As new certification data arrives and more testing programs are run, these rates will be revisited in future MOVES releases. 

### 4.6 **PM and HC S** 

**peciatio n**  **for CNG Buses** MOVES estimate methane and nonmethane hy drocarbons (NMHC) through the use of CH 4 /THC ratios, as shown in Table 4 - 4 . The MOVES2014 CH 4 /THC ratios are constant across all age groups. We set the start CH 4 /THC ratios equal to the running ratios for all model y ears and ages. MOVES calculates emissions of tota l organic gases (TOG), nonmethane organic gases (NMOG) and volatile organic carbons (VOC) using information regarding the h yd rocarbon speciation of emissions. S tudies have shown that the speciation of hy drocarbon can be drastically different between uncontrolled CNG buses and CNG buses with oxidation cataly sts. For example , formaldeh yde emissions can be quite large from uncontrolled CNG buses 72 , 74 , 84 , , but are significantl y redu ced with oxidation cataly sts 71

. Large f ormaldehy de emissions have a large impact on the NMOG and VOC emissions estimated from THC emissions from CNG buses because THC FID measurements have a small response to formaldehy de concentrations 41 , 85

. We used hy drocarbon speciation measurements from the Ay ala et al. (2003) using measurements from the 2000 MY Transit Bus, with a Detroit Die sel Series 50G engine with and without an oxidation cataly st collected on the CBD cy cle. We used the speciated measurements made on the single transit bus to isolate the impact of the oxidation catal ys t. We used the CBD test cy cle to be consistent with our anal ysis of the criteria emission rates . The NMOG and VOC conversion factors are located in Table 4 - 6 . The NMOG values are calculated following EPA’s regulation requirements using Equation 9 from the MOVES2014 S peciation R eport 41

. The VOC emissions are calculated from subtracting the ethane from the NMOG values . MOVES definition of VOC emissions from mobile - sources is NMOG minus ethane and acetone xxiii

. The emissions of hazardous air pollutants, inclu ding formaldeh yde and acetaldehyde, are also estimated from this study

as documented in the MOVES2014 Toxics Emissions Report 86

.

xxiii In the original analysis of the CNG emissions, acetone was not considered in the VOC calculation. Upon realization of the oversight, the emission values were not recalculated, due to the small fraction of acetone measured in the exhaust. The VOC results are impacted by less than 2% whether they are included or not1 46

**Table 4**  **- 6**  **NM OG and VOC Conversion values for CNG transit emissions with no control and with oxidation**  **catalyst from Ayala et al. (2003)**  **71 Me**  **asured value s (m**  **g/m ile)**  No Control Oxidation Catalyst THC 8660 6150 Methane 7670 5900 Ethane 217 72.2 Acetone 4.67 5.51 Form aldehyde 860 38.4 Acetaldehyde 50.7 32.6 **Calculated values (m g/m**  **ile)** NMHC 990 250 NMOG 1881.0 309.0 VOC 1664.0 236.8 **Ratios** NMOG/NMHC 1.90 1.24 VOC/NMHC 1.68 0.95 In MOVES2014, we appl ied the uncontrolled NMOG and VOC factors to the pre - 2002 model y ear vehicles. This anal ys is demonstrated that majority of the 200 1 and earlier model y ear CNG transit buses were not equipped with exhaust aftertreament . The study conducted on MY 2004 buses 70 , suggested that 2002 - 2006 model y ear vehicles are equipped with oxidation catal ys ts. Therefor e we apply the oxidization cataly st profile to CNG emissions for the 2002 to 2006 MY group. At the time of the anal ys is, we did not have information on 2007 and later CNG buses , and also applied the oxidation cataly st from the lean - burn engine results to 2 007 and later groups. The composition of PM 2.5 emissions are estimated from CARB’s measurement s 87 on the 2000 MY Detroit Diesel Series 50G with and without the oxidation cataly st. The EC/PM 2.5 fraction s are reported in Table 4 - 7 and are used to estimate the base PM components in MOVES: elemental carbon (EC) and non - elemental carbon (nonECPM) rates. By using the single bus, we again isolate the impact of the control, without confounding differences in different engine technologies. Similar for the HC speciation, we appl y the uncontrolled EC/PM fraction to the pre - 2002 MY CNG buses, and the oxidation cataly st equipped EC/PM profile for the 2002 and later bus es. **Table 4**  **- 7**  **MOVES2014 EC/PM Fraction fo**  **r CNG transit bus em**  **issions by m**  **odel year groups** Pre - 2002 2003+ 9.25% 11.12% The CARB measurements are also used to estimate the individual PM 2.5 composition, including organic carbon, elements, and sulfate as discussed in the TOG and PM 2.5 s peciation report . s toichiometric - burn spark ignition CNG engines with three - way cataly sts have been introduced in 2007 and later buses. Future work should be d one to improve the emission rates and speciation profiles used in MOVES to represent emissions from recent technology CNG buses.147



### 4.7 **Ammonia and Nitrous Oxide**  **emissions** No data were available on ammonia emissions rates . As such, we used the ammonia emissions for heav y - dut y ga soline vehicles , which are documented in a separate report 6

. We did not update the nitrous oxide emission rates for CNG in MOVES2014. They remain unchanged from MOVES2009 and later versions as documented in a separate MOVES report 5148



## 5 **Heavy -**  **Duty Crankcase Emissions** Crankcase emissions, also referred to as crankcase blowby , are combustion gases that pass the piston rings into the crankcase, and are subsequentl y vented to the atmosphere. Crankcase blowb y includes oil - enriched air from the turbochar g er shaft, air compressors, and valve stems that enters the crankcase . The crankcase blowby contains combustion generated pollutants, as well as oil droplets from the engine components and engine crankcase . 88 

### 5.1 **Background on Heavy -**  **duty Diesel Crankcase Emissions** Federal regulations permit 2006 and earlier heav y - duty diesel - fueled engines equipped with “turbochargers, pumps, blowers, or superchargers” to vent crankcase emi ssions to the atmosphere . 89 Crankcase emissions from pre - 2007 diesel engines were t ypically vented to the atmosphere, using an open unfiltered crankcase s ystem, referred to as a ‘road draft tube’. 88 Researchers have found that crankcase emissions vented to the atmosphere can be the dominant source of diesel particulate matter concentrations measured within the vehicle cabin 90 91 92

. Beginning with 2007 model y ear heavy - dut y diesel vehicles, federal regulations no longer permit crankcase emissions to be vented to the atmosphere, unless they are included in the certification exhaust me asurements . 93 Most manufacturers have adopted open crankcase filtration sy stems. 88 These s ys tems vent the exhaust gases to the atmosphere after the gases have passed a coalescing filter which removes oil and a substantial fraction of the partic les in the crankcase blowb y . 88 Inthe ACES Phase 1 program, four MY2007 diesel engines from major diesel engine manufactures (Caterpillar, Cummins, Detroit Dies el, and Volvo) all employ ed filtered crankcase ventilation sy stems . 94 A summary

of published estimates of diesel crankcase emissions as percentages of the total emissions (exhaust + crankcase) are provided in Table 5 - 1 . For the conventional diesel technologies, hy drocarbon and particulate matter emissions have the largest contributions from crankcase emissions. There is a substantial decrease in PM emissions beginning with the 2007 model y ear diesel engines. The 2007 diesel technology reduces the tailpipe emissions more than the crankcase emissions, resulting in an increase in the relative crankcase contribution for HC, CO, and PM emissions. NOx emissions for the 2007 and later are reported as a negative number. In reality , the crankcase emission contribution cannot be negative, and the negative number is attributed to sampling variability

from the tests with and without the crankcase emissions.149 **Table 5**  **- 1**  **Literature r**  **eview on the contribution of c**  **rankcase em issions to diesel exhaust.**  Study Model Year Ty

pe # Engines/ Vehicles HC CO NOx PM Hare and Baines, 1977 97 1966, 1973 Conv.

Diesel 2 0.2% - 3.9% 0.01 -

0.4% 0.01% -

0.1% 0.9% -

2.8% Zielinska et al. 2008 90 , Ireson et al. 2011 91 2000, 2003 Conv.

Diesel 2 13.5%

- 41.4% Clark et al. 2006 96 , Clark et al. 2006 95 2006 Conv. Diesel 1 3.6% 1.3% 0.1% 5.9% Khalek et al. 2009 2007 DPF equipped 4 95.6% 27.2% - 0.2% 38.2% 

### 5.2 **Modeling Crankcase Emissions in MOVES**  MOVES 2014 calculates THC, CO, NO x, and PM 2.5 using a gaseous and a particulate crankcase emission calculator. Within the calculator, c rankcase emissions are calculated as a fraction of tailpipe exhaust emissions , including start, running, and extende d - idle . As discussed in the background, the 2007 heavy - duty diesel emission regulations impacted the technologies used to control exhaust and crankcase emissions. The regulations also expanded the ty pe s of emissions data included in certification tests , by including crankcase emissions in the regulatory standards , which previously

included only

tailpipe emissions . Because heav y - dut y diesel engine manufacturers are using open - filtration crankcase sy stems , the crankcase emissions are included in the emission certification results. In MOVES2014, the base exhaust rates for 2007 and later diesel engines are based on certification levels. In response to the changes in certification testing, we changed the data and the methodology with which crankcase emissions are modeled in MOVES . For 2007 and later diesel engines, the crankcase emissions are included in the base exhaust emission rates. A new crankcase calculator in MOVES2014 divides the base exhaust emission rates in to components representing the contributions fr om exhaust and crankcase emissions . The exhaust emission ratio is equal to 1 .0 for all pre - 2007 diesel engines , and less than 1 .0 for all 2007 and later diesel engines, to account for the inclusion of crankcase emissions in the base rates . Unfortunatel y, d ue to budget and time constraints, only

the PM 2.5 species are incorporated using the new crankcase calculator in MOVES2014. An overview of the crankcase calculator is provided in the MOVES2014 Speciation Report . 41 MOVES2014 continues to use the same calculator as MOVES2010 for the gaseous crankcase pollutants, THC, CO, and NOx. The gaseous crankcase calculator chains the crankcase emission rates to th e base exhaust emissions, but it does not allow the ability to reduce the exhaust emission contribution, which is desired for the 2007+ diesel technologies. The 2007+ diesel subsection discusses how MOVES2014 handles THC, CO, and NOx to avoid double - count ing crankcase emissions. W e anticipate that future versions of MOVES will include the updated crankcase calculator for all crankcase emission pollutants, including THC, CO, and NOx.150



### 5.3 **Conventional Heavy -**  **Duty Diesel** Table 5 - 2 includes the crankcase/ t ail - pipe emission ratios used for conventional diesel exhaust. For HC, CO, and NOx, we selected the values reported on the MY 2006 diesel engine reported b y Clark et al. 2006 96

. These values compare well with the previous HC, CO, NOx values reported much earlier b y Hare and Baines (19 7 7) , 97 which represent much older diesel technology . The similarit y of the crankcase emission ratios across several decades of diesel engines, sugge sts that for conventional diesel engines, crankcase emissions can be well represented as a fraction of the exhaust emissions. For PM 2.5 emissions, we selected a crankcase/tail - pipe ratio of 20%. Zielinska et al. 200 8 90 and Ireson et al. 2011 91 reported crankcase contributions to total PM 2.5 emissions as high as 40%. Jääskeläinen (2012) 88 reported that crankcase can contribute as much as 20% of the total emissions from a review of six diesel crankcase studies. Similarly , an industry report estimated that crankcase emissions contributed 20% of total particulate emissions from 1994 - 2006 diesel engines 98

. **Table 5**  **- 2**  **MOVES2014 Conventional Diesel Crankcase/Tail -**  **pipe Ratios for HC, CO, NO**  **x and PM**  **2.5**

Pollutant Crankcase/Tailpipe ratio Crankcase/(Crankcase + Tailpipe) ratio HC 0.037 0.036 CO 0.013 0.013 NOx 0.001 0.001 PM 2.5 0.200 0.167 As outlined in the MOVES 2014 TOG and PM Speciation Report, MOVES does not apply the crankcase/tailpipe emission ratio in Table 5 - 4 to the total exhaust PM 2.5 emissions. MOVES applies the crankcase/tailpipe emission ratios to P M 2.5 sub species: elemental carbon PM 2.5 , sulfate PM 2.5 , aerosol water PM 2.5 , and the remaining PM (nonECnonSO4PM ). This allows MOVES to account for important differences in the PM speciation between tailpipe and crankcase emissions. The pre - 2007 diesel ratios are derived such that the crankcase PM 2.5 / exhaust PM 2.5 ratio is 20% , and the crankcase emissions EC/PM fraction reflects measurements f rom in - use crankcase emissions. Zielinska et al. 2008 90 reported that the EC/PM fraction of crankcase emissions from two conventional diesel buses is 1.57%. Tailpipe exhaust from conventional diesel engines is dominated by

elemental carbon emissio ns from combustion of the diesel fuel, while crankcase emissions are dominated by organic carbon emissions largel y contributed from the lubricating oil. 90 , 91

. The crankcase emission factors shown in Table 5 - 3 are derived such th at the crankcase PM 2.5 emissions are 20% of the PM 2.5 ex haust measurements, and have an EC/PM split of 1.57%. The PM 10 emission rates are subsequently estimated from the PM 2.5 ex haust and crankcase emission rates using PM 10 /PM 2.5 emission ratios as docum ented in the MOVES2014 TOG and PM Speciation Report.151

**Table 5**  **- 3**  **. MOVES2014**  **Exhaust and Crankcase Emission Factors for pre**  **- 2007 Diesel by**  **Pollutant , Process,**  **and Model Year Group for PM**  **2.5 species**  **.** Pollutant Process Start Running Extended Idle EC Exhaust 1 1 1 nonECnonSO4PM 1 1 1 SO4 1 1 1 H2O 1 1 1 EC Crank case 0.009 0.004 0.012 nonECnonSO4PM 0.295 0.954 0.268 SO4 0.295 0.954 0.268 H2O 0.295 0.954 0.268 

### 5.4 **2007 + Heavy -** 

**Duty Diesel** The 2007+ heav y - dut y diesel THC, CO, and NOx crankcase emissions are included in the exhaust emissions. However, with the current gaseous crankcaseemission calculator, the crankcase contribution of THC, CO, and NOx to the base exhaust emission rates cannot be properl y accounted. For MOVES2014, the crankcase to tailpipe emission ratios for THC, CO, and NOx are set to 0 as shown in Table 5 - 4 , and MOVES2014 produces no cr ankcase emissions for each of the pollutants. Table 5 - 4 also lists the crankcase to tailpipe emission ratios based on ACES Phase 1 tests. Based on the ACES Phase 1 program, the MOVES2014 estimate of no crankcase emissions is reasonable for NOx, bu t not for THC and CO emissions. MOVES2014 does not report crankcase emissions for THC and CO because they are included in the exhaust emission rates for 2007 and later model years from heav y - dut y diesel vehicles l . Users can use the ratios listed in Table 5 - 4 to post - process the exhaust emission rates if the crankcase contributions to THC and CO emissions are desired. **Table 5**  **- 4**  **MOVES2014 2007 and Later Diesel Crankcase/Tailpipe ratio for HC, CO, and NO**  **x .**  Pollutant MOVES2014

Crankcase/Tailpipe ratio ACES Phase 1 Crankcase/Tail - pipe ratio ACES Phase 1 Crankcase/ (Crankcase + Tail - pipe) l ratio HC 0 21.95 95.6% CO 0 0.37 27.2% NOx 0 0.00 0.0% For PM 2.5 emissions, we used data from the ACES Phase 1 test program to inform the crankcase and exhaust ratios for the updated PM 2.5 crankcase emissions calculator. The crankcase emissions measured in the ACES Phase 1 test program contributed 38% of the total PM 2.5 emissions on the hot - FTP driving cy cle. Emission results reported from industry , have reported that the crankcase emissions can contribute to over 50% of the particulate matter emissions from 2007 and later diesel technol ogies 98

.152

For PM 2.5 emissions, MOVES applies crankcase ratios to each of the intermediate PM 2.5 species (EC, nonECnonSO4PM, SO4, and H2O). For 2007+ heav y - duty diesel engines, the same crankcase ratio is applied to each of the intermediate species. The MOVES PM 2.5 speciation profile developed from the ACES Phase 1 study combined the crankcase and tailpipe emissions. As such, MOVES2014 uses the same speciation pr ofile for both crankcase and tailpipe emissions. The resulting exhaust and crankcase emission ratios for 2007 and later heav y - duty diesel are provided in Table 5 - 5 . As shown, the exhaust crankcase emission factor is less than one for 2007+ diesel vehicles, to account for the contribution of crankcase emissions in the base exhaust emission rates. **Table 5**  **- 5**  **MOVES2014 Exhaust and Crankcase Emission Factors**  **for 2007 + Heavy -**  **duty Diesel by Pollutant**  **, Process, and Model Year Group**  **for PM 2.5**  **species.** Pollutant Process All processes EC Exhaust 0.62 nonECnonSO4PM 0.62 SO4 0.62 H2O 0.62 EC Crank case 0.38 nonECnonSO4PM 0.38 SO4 0.38 H2O 0.38 

### 5.5 **Heavy -**  **duty Gasoline and CNG Emissions** The data on heav y - dut y gasoline and CNG crankcase emissions are limited. All 1969 and later otto cy cle (spark ignition) heavy - dut y engines are required to control crankcase emissions. All gasoline engines are assumed to use positive crankcase ventilation ( PCV) sy stems, which route the crankcase gases into the intake manifold. For h eavy - dut y gasoline engines we use the same values of crankcase emission ratios as light - duty gasoline , which are documented in the MOVES2014 l ight - d uty e mission rates report . 8 We assume 4% of PCV sy stems fail , resulting in the small crankcase to exhaust emission ratios shown in Table 5 - 6 for 1969 and later gasoline engines. Due to limited information, we use d the gasoline heav y - duty crankcase emission factors for heav y - dut y CNG engines because they have low blow - by particle e missions. **Table 5**  **- 6**  **Crankcase to Tailpipe Exhaust Emission Ratio for Heavy -**  **duty Gasoline and CNG Vehicles**  **for HC, CO, NO**  **x and PM**  **2.5** Pollutant pre - 1969) 1969 and later HC 0.33 0.013 CO 0.013 0.00052 NO x 0.001 0.00004 PM (all species) 0.20 0.008153

The crankcase and exhaust ratios used by the crankcase calculator for PM 2.5 emissions from heavy duty gasoline and compressed natural gas vehicles are provided in Table 5 - 7 . No information is available to estimate separate speciation between exhaust and crankcase, so the factors are the same between the PM subspecies . **Table 5**  **- 7**  **MOVES2014 Exhaust and Crankcase Emission Factors by Pollutant, Process, Model Year Group, and Fuel Type, and Source Type**  **for PM 2.5**  **Species** 1960 - 1968 Gasoline Vehicles 1969 - 2050

Gasoline/ CNG Pollutant Process All processes All

processes EC Exhaust 1 1 nonECnonSO4PM 1 1 SO4 1 1 H2O 1 1 EC Crankcase 0.2 0.008 nonECnonSO4PM 0.2 0.008 SO4 0.2 0.008 H2O 0.2 0.008154



## 6 **Nitrogen Oxide Composition** Nitrogen oxides (NO x ) are defined as NO + NO 2 . 99 , 100 NO x is considered a subset of reactive nitrogen species (NO y ) with an nitrogen oxidation state of +2 or greater which contain oth er nitrogen containing species (NOz), thus NO y = NO x + NO z . 99 NO z compounds are formed in the atmosphere as oxidation products of NO x 100

. Chemiluminescent analyzers used for exhaust NO x measur ements, directl y measure NO, as NO is oxidized by

ozone to form NO 2 and produces florescence light . Chemiluminescent analyzers measure NO x (NO + NO 2 ) by using a catal ys t that reduces the NO 2 to NO in the sample air stream before measurement . NO 2 is calc ulated as the difference between NO x and NO measurements . The NO x converter within chemiluminescent anal yz ers can also reduce other reactive nitrogen species (NO z ), including HONO to NO. If the concentrations of NOz interfering species in the sample stream are significant relative to NO 2 concentrations, than they can bias the NO 2 measurements high. 101 MOVES produces estimates of NO and NO 2 by apply ing an NO/NO x or NO 2 /NO x fraction to the NO x emission rates. The NO/NO 2 and NO 2 /NO x fractions are stored in a MOVES table called nono2ratio. The nono2ratio enables the nitrogen oxide composition to vary according to source ty pe, fuel ty

pe, model y ear, and pollutant process. For the heavy - dut y vehicle source types, the NO x fractions onl y var y according to fuel t yp e, model y ear, and emission process. The NO x fractions in MOVES were developed from a literature review reported b y Sierra Research to the EPA , from emission test programs conducted in the laboratory with constant volume sampling dilution tunnels . 6 MOVES also produces estimates of one important NOz species, nitrous acid (HONO) , from the NO x values . HONO emissions are estimated as a fraction (0.8%) of NO x emissio ns from all vehicle ty pes in MOVES, based on HONO and NO x measurements made at road tunnel in Europe. 102 In MOVES, we assume HONO contributes to the NO x values , because either (1) the chemiluminescent anal yz ers are biased slightly high by HONO in the exhaust stream , or (2) HONO is formed almost immediately upon dilution into th e roadway environment from NO 2 emissions . To avoid overcounting reactive nitrogen formation, we include H ONO in the sum of NO x in MOVES. HONO emissions are also estimated using the non2ratio MOVES table. For each source ty pe, fuel ty

pe, and emission process, the NO, NO2, and HONO values in the non2ratio sum to unity . MOVES users should be aware that the definition of NO x in MOVES (NO+NO 2 +HONO) is different than the actual NO x def inition of NO x (NO + NO 2 ). This is because we are correcting the exhaust NO x emission in MOVES for potential interference with HONO measurements . MOVES users should consider which measure they would like to use depending on their use - case. For example, for comparing NO x results with a vehicle emission test program, MOVES may want to simply use NO x (pollutantI D 3), whereas a MOVES users developing air quality inputs of NO, NO 2 , and HONO, should estimate NO x as the sum of NO + NO 2 (pollutantIDs 32 and 33) , ra ther than using the direct NO x output in MOVES (polluantID 3). Future work is needed to (1) update the NO x and HONO fractions in MOVES based on more recent measurements, (2) reconcile the definition of NO x in MOVES, while also correctl y accounting for155

th e emissions of NOz species that may impact NO x measurements and (3) reconcile measurement differences that may

occur between NO y species measured at the tailpipe, with NO y species measured on road side measurements . 103 

### 6.1 **Heav**

**y -**  **duty Diesel** The conventional diesel (1960 - 2006 model y ear) NO x fractions were estimated as the average reported fraction from three studies of heav y - dut y vehicles not equipped with diesel particulate filters. 6 The 2010+ NO 2 fractions are based on the average of three diesel programs of diesel vehicles measured with diesel particulate filters. The 2007 - 2009 values are an average of the 1960 - 2006 and 2010 - 2050 values, which assumes that the NO x fractions change incrementall y, as trucks equipped with catal yz ed diesel particulate filters were phased - into the fleet. The NO x fractions are the same across all diesel source t ypes (including light - dut y) and across all emission processes (running, s tart, extended idle), except for auxiliary power units, which use the conventional NO x fractions (1960 - 2006) for all model y ears because it is assumed that they are not fitted with diesel particulate filters. The NO 2 fractions originall y developed from th e Sierra report 6 , were reduced b y 0.008, to account for the HONO emissions.

**Table 6**  **- 1**  **. NO x**  **and HONO fractions f or Heavy**  **- Duty Diesel Vehicles**  Model Year NO NO 2 HONO 1960 - 2006* 0.935 0.057 0.008 2007 - 2009 0.764 0.228 0.008 2010 - 2050 0.594 0.398 0.008 * All Model Year of Auxiliary Pow er Units (APUs) use the 1960 - 2006 NO x and HONO fractions. 

### 6.2 **Heavy -**  **duty Gasoline** The NO x fractions for heavy - duty gasoline are based on the MOVES values used for light - duty gasoline measurements . Separate values are used for running and start emission processes. A s stated in the Sierra Report , 6 the values are shifted to later model year groups to be consistent with emission standards and emission control technologies. These values are shown in Table 6 - 2 for both light - duty and heavy - duty

gasoline vehicles. The NO 2 fractions originally developed from the Sierra report 6 , were reduced b y 0.008, to account for the HONO emissions. **Table 6**  **- 2**  **. NO x**  **and HONO Fractions for Light**  **- duty (**  **sourceTypeID 21, 31, 32) and**  **Heavy -**  **duty Gasoline**

**Vehicles ( sourceTypeID**  **41, 42, 43, 51, 52, 53, 54, 61, and 62)**  Light - Duty Gasoline Model Year Groups Heavy - Duty Gasoline

Model Year Groups Running Start NO NO 2 HONO NO NO 2 HONO 1960 - 1980 1960 - 1987 0.975 0.017 0.008 0.975 0.017 0.008 1981 - 1990 1988 - 2004 0.932 0.06 0.008 0.932 0.031 0.008 1991 - 1995 2005 - 2007 0.954 0.038 0.008 0.987 0.005 0.008 1996 - 2050 2008 - 2050 0.836 0.156 0.008 0.951 0.041 0.008156



### 6.3 **Compressed Natural Gas** We used the average of three NO 2 /NO x fraction reported on three CNG transit buses with DDC Series 50 G engines b y Lanni et al. (2003 ) 74 , along with the 0.008 HONO fraction assumed for other sourc e t yp es, to estimate the NO x fractions of NO, NO2, and the HONO fraction. These assumptions yield the NO x and HONO fractions in Table 6 - 3 , which are used for all model ye ar CNG transit buses. **Table 6**  **- 3**  **NO x**  **and HONO fractions CNG transit buses**  Model Year NO NO 2 HONO 1960 - 2050 0.86 5 0.127 0.008157158



## Appendix A **Calculation of Accessory Power Requirements Table**  **A -**  **1 .**  **Accessory load estim ates for HHD trucks** 

**Table A**  **- 2**  **. Accessory load**  **estim ates for MHD trucks**  VSP Cooling Fan Air cond Air comp Alternator Engine Accessories Total Accessory Load (kW ) Low Off = 0.5 kW Power (kw) 19.0 2.3 3.0 1.5 1.5 % time on 10% 50% 60% 100% 100% Total (kW ) 1.9 1.2 2.0 1.5 1.5 8.1 Mid Off = 0.5 kW Power (kw) 19.0 2.3 2.3 1.5 1.5 % time on 20% 50% 20% 100% 100% Total (kW ) 3.8 1.2 0.9 1.5 1.5 8.8 High Off = 0.5 kW Power (kw) 19.0 2.3 2.3 1.5 1.5 % time on 30% 50% 10% 100% 100% Total (kW ) 5.7 1.2 0.7 1.5 1.5 10.5 **Ac cessory Load (kW): MDT**  VSP Cooling Fan Air cond Air comp Alternator Engine Accessories Total Accessory Load (kW ) Low Off = 0.5 kW Power (kw) 10.0 2.3 2.0 1.5 1.5 % time on 10% 50% 60% 100% 100% Total (kW ) 1.0 1.2 1.4 1.5 1.5 6.6 Mid Off = 0.5 kW Power (kw) 10.0 2.3 2.0 1.5 1.5 % time on 20% 50% 20% 100% 100% Total (kW ) 2.0 1.2 0.8 1.5 1.5 7.0 High Off = 0.5 kW Power (kw) 10.0 2.3 2.0 1.5 1.5 % time on 30% 50% 10% 100% 100% Total (kW ) 3.0 1.2 0.7 1.5 1.5 7.8159 **Table A**  **- 3**  **. Accessory load estim**  **ates for buses** VSP Cooling Fan Air cond Air comp Alternator Engine Accessories Total Accessory Load (kW ) Low Off = 0.5 kW Power (kw) 19.0 18.0 4.0 1.5 1.5 % time on 10% 80% 60% 100% 100% Total (kW ) 1.9 14.4 2.6 1.5 1.5 21.9 Mid Off = 0.5 kW Power (kw) 19.0 18.0 4.0 1.5 1.5 % time on 20% 80% 20% 100% 100% Total (kW ) 3.8 14.4 1.2 1.5 1.5 22.4 High Off = 0.5 kW Power (kw) 19.0 18.0 4.0 1.5 1.5 % time on 30% 80% 10% 100% 100% Total (kW ) 5.7 14.4 0.9 1.5 1.5 24.0160



## Appendix B **Tam pering and Mal**  **- ma**  **intenance** Tampering and mal - maintenance (T&M) effects represent the fleet - wide average increase in emissio ns over the useful life of the engines. In laboratory testing, properl y maintained engines often y ield very

small rates of emissions deterioration through time. However, we assume that in real - world use, tampering and mal - maintenance yield higher rates of emissions deterioration over time. As a result, we feel it is important to model the amount of deterioration we expect from this tampering and mal - maintenance. We estimated these flee t - wide emissions effects by multiply ing the frequencies of engine component failures by the emissions impacts related to those failures for each pollutant . Details of this analy sis appear later in this section . 

### B.1 **Modeling Tampering and Mal -**  **maintenance** As T &M affect s emissions through age, we developed a simple function of emission deterioration with age. We applied the zero - age rates through the emissions warranty period (5 y ears/100,000 miles), then increased the rates linearl y up to the useful life. The n we assumed that all the rates level off bey ond the useful life age. Figure B - 1 shows this relationship.

**Figure B**  **- 1**  **. Q**  **ualitative Depiction of**  **the im plementation of age effects.** 

The useful life refers to the length of time that engines are required to meet emissions standards. We incorporated this age relationship by averaging emissions rates across the ages in each age group. Mileage was converted to age with VIUS 104 (Vehicle Inv entory and Use Survey ) data, which contains data on how quickly trucks of different regulatory classes accumulate mileage. Table B - 1 shows the emissions warrant y peri od and approximate useful life requirement period for each of the regulatory

classes. Final emission rate due to T&M

Zero - mile emission rate

End of warranty

period End of useful life **Age Emission rate** 161 **Table B**  **- 1**  **. Warranty and useful life requirements by regulatory class**  Regulatory class Warranty age (Requirement: 100,000 miles or 5 years) Useful life mileage/age requirement Useful

life age HHD 1 435,000/10 4 MHD 2 185,000/10 5 LHD45 4 110,000/10 4 LHD2b3 4 110,000/10 4 BUS 2 435,000/10 10 While both age mileage metric s are given for these periods, whichever comes first determines the applicability

of the warranty . As a result, since MOVES deals with age and not mileage, we need to convert all the mileage values to age equivalents , as the mileage limit is usually reache d before the age limit. The data show that on average, heavy - heavy - dut y trucks accumulate mileage much more quickly

than other regulatory

classes. Therefore, an y deterioration in heav y - heavy - dut y truck emissions will presumably happen at younger ages than for other regulatory classes. Buses, on average, do not accumulate mileage quickl y. Therefore, their useful life period is governed b y the age requirement, not the mileage requirement. Since MOVES deals with age groups and not individual ages, the incr ease in emissions by age must be calculated b y age group. We assumed that there is an even age distribution within each age group (e.g. ages 0, 1, 2, and 3 are equall y represented in the 0 - 3 age group). This is important since, for example, HHD trucks re ach useful life at four y ears, which means they will increase emissions through the 0 - 3 age group. As a result, the 0 - 3 age group emission rate will be higher than the zero - mile emission rate for HHD trucks. Table B - 2 shows the multiplicative T&M adjustment factor b y age. We determined this factor using the mileage - age data from Table B - 1 and the emissions - age relationship that we described in Figure B - 1 . We multiplied this factor by the emissions increase of each pollutant over the useful life of the engine, which we determined from the anal ys is in this section.162 **Table B**  **- 2**  **. T&M**  **multiplicative adjustm**  **ent factor by age**  **( f**  **TM,age group**  **).** Age Group LHD MHD HHD Bus 0 - 3 0 0.083 0.25 0.03125 4 - 5 1 0.833 1 0.3125 6 - 7 1 1 1 0.5625 8 - 9 1 1 1 0.8125 10 - 14 1 1 1 1 15 - 19 1 1 1 1 20+ 1 1 1 1 In this table , a value of 0 indicates no deterioration , or zero - mile emissions level (ZML) , and a value of 1 indicates a fully d eteriorated engine , or maximum emissions level, at or bey ond useful life (UL) . The calculation of emission rate by a ge group is described in the equation below. TM pol represents the estimated emissions rate increase through the useful life for a given pollutant. 

## ) 1 ( , , , pol agegroup TM ZML pol agegrp pol 

## TM

f r r   **Equation B**  **- 1**  

### B.2 **Data Sources** EPA used the following information to develop the tamper and mal - maintenance occurrence rates used to develop emission rates used in MOVES:  California’s ARB EMFAC2007 Modeling Change Technical Memo 105 (2006). T he basic EMFAC occurrence rates for tampering and mal - maintenance were developed from the Radian and EFEE reports and internal CARB engineering judgment.  Radian Study

( 1988). T he report estimated the mal function rates based on survey and observation. T he data may b e questionable for current heavy - dut y trucks due to advancements such as electronic controls, injection sy stems, and exhaust aftertreatment.  EFEE report (1998) on PM emission deterioration rate s for in - use vehicles. T heir work included heav y - dut y diesel vehicle chassis dy namometer testing at Southwest Research Institute.  EMFAC2000 (2000) Tampering and Mal - maintenance Rates  EMA’s comments on ARB’s Tampering, Malfunction, and Mal - maintenance Assu mptions for EMFAC 2007  University

o f California

– Riverside (UCR) “Incidence of Malfunctions and Tampering in Heav y - Dut y Vehicles”163

 Air Improvement Resources, Inc.’s Comments on Heav y - Dut y Tampering and Mal maintenance Sy

mposium  EPA internal engineering judg ment 

### B.3 **T &M Categories** EPA generall y adopted the categories developed by CARB, with a few exceptions. The high fuel pressure category

was removed. We added a category for misfueling to represent the use of nonroad diesel, not ULSD onroad diesel. We comb ined the injector categories into a single group. We reorganized the EGR categories into “ Stuck Open ” and “ Disabled/Low Flow . ” We included the PM regeneration sy stem, including the igniter, injector, and combustion air sy stem in the PM filter leak catego ry . EPA will group the LHDD, MHDD, HHDD, and Diesel bus groups together, except for 2010 and bey ond. We assumed that the LHDD group will primarily use Lean NOx Traps (LNT) for the NOx control in 2010 and bey ond. On the other hand, we also assumed that Selective Cataly st Reduction (SCR) sy stems will be the primary N Ox aftertreatment sy stem for HHDD. T herefore, the occurrence rates and emission impacts will vary i n 2010 and bey ond depending on the regulatory class of the vehicles. 

### B.4 **T&M Model Year Groups** E PA developed the model y ear groups based on regulation and technology changes.  Pre - 1994 represents non - electronic fuel control.  1998 - 2002 represents the time period with consent decree issues.  2003 represents early use of EGR.  2007 and 2010 contain significant PM and NOx regulation changes.  EPA issued a rule to require OBD f or heavy d uty trucks , beginning in MY 2010 with complete phase - in by MY 2013. 

### B.5 **T & M Occurrence Rates and Differences from EMFAC2007**  EPA a dopted the CARB EMFAC2007 occurrence rates, except as noted below. **Clogged Air Filter** : EPA reduced the frequency rate from EMFAC ’s 15 percent to 8 percent . EPA reduced this value based on the UCR results, the Radian study , and EMA’s comments that air filters are a maintenance item. Many trucks contain indicators to notify the driver of dirt y air filters and the drivers have incentive to replace the filters for other performance reasons. **Other Air Problems** : EPA reduced the frequency rate from EMFAC ’s 8 perce nt to 6 percent based on the UCR results. **Electronics Failed** : EPA will continue to use the 3 percent frequency rate for all model y ears bey ond 2010. CARB increased the rate to 30 percent in 2010 due to sy stem complexity . EPA does not agree with CARB’s a ssertion that the complexity of electronic s ys tems will increase enough to justify a ten - fold increase in malfunction occurrence rates. We believe that the hardware will evolve through 2010, rather than be replaced with completely new s ys tems that would j ustify a164

higher rate of failure. EPA asserts that man y of the 2010 changes will occur with the aftertreatment sy stems which are accounted for separatel y. **EGR Stuck Open:** EPA believes the failure frequency of this item is rare and therefore set the leve l at 0.2 percent . This failure will lead to drivability issues that will be noticeable to the driver and serve as an incentive to repair. **EGR Disabled/Low Flow:** EPA believes the EMFAC 20 percent EGR failure rate is too high and reduced the rate to 10 per cent . All but one major engine manufacturer had EGR previous to the 2007 model y ear and all have it after 2007. Therefore, EMFAC ’s frequency rate increase in 2010 due to the increase truck population using EGR does not seem valid. However, the Illinois EPA stated that “ EGR flow insufficient ” is the top OBD issue found in their LDV I/M program 106 so it cannot be ignored. **NOX Aftertreatment malfunction:** EPA developed a NOx aftertreatment malfunction rate that is dependent on the t ype of sy stem used. We assumed that HHDD will use primarily SCR sy stems and LHDD will primarily use LNT sy stems. We estimated the failure rates of the various components within each sy stem to develop a composite malfunction rate. The individual failure rates were developed cons idering the experience in agriculture and stationary industries of NOx aftertreatment sy stems and similar component applications. Details are included in the chart below. We assumed that tank heaters had a 5 percent failure rate, but were onl y required i n one third of the country and one fifth of the y ear. The injector failure rate is lower than fuel injectors, even though they have similar technology , because there is only one required in each sy stem and it is operating in less severe environment of pre ssure and temperature. We believe the compressed air delivery

sy stem is very

mature based on a similar use in air brakes. We also believe that manufacturers will initiate engine power de - rate as incentive to keep the urea suppl y sufficient.165 **Table B**  **- 3**  **. NOx Aftertreatment Failure Rates**

**NOx aftertreatment sensor:** EPA believes the 53 percent occurrence rate in EMFAC2007 is too high and will use 10 percent . CARB assumed a mix of SCR, which uses one se nsor per vehicle, and NOx adsorbers, which use two sensors per vehicle. They justified the failure rate based on the increased number of sensors in the field beginning in 2010. We developed the occurrence rate based on the following assumptions:  Populat ion: HHDD: vast majority of heav y - duty

applications will use SCR technology with a maximum of one NOx sensor. NOx sensors are not required for SCR

– manufacturers can use models or run open loop. S everal engine manufacturers representing 30 percent of the market plan to delay

the use of NOx aftertreatment devices through the use of improved engine - out emissions and emission credits.  Durability

e xpectations: S wRIc ompleted 6000 hours of ESC cy cling with NOx sensor. Internal testing supports longer life durability . D iscussions with OEMs in 2007 indicate longer life expected b y 2010.  Forward looking assumptions: M anufacturers have a strong incentive to improve the reliability and durabilit y of the sensors because of the high cost associated with fre quent replacements. **PM Filter**  **Leak:** EPA will use 5 percent PM filter leak and sy stem failure rate. C ARB used 14 percent failure rate. They discounted high failure rates currentl y seen in the field. **PM Filter Disable:** EPA agrees with CARB’s 2 percent tamper rate of the PM filter. The filter causes a fuel economy

penalty so the drivers have an incentive to remove it. **Oxidation Catalyst Malfunction/Remove:** EPA believes most manufacturers will install oxidation cataly sts initially in the 2007 model y ear and agrees with CARB’s assessment of 5 percent failure rate. This rate consists of an approximate 2 percent tampering rate and 3 percent Occurrence Rate **SCR** Urea tank 0.5% Tank heaters 1% In-exhaust injectors 2% Compressed air delivery to injector 1% Urea supply

pum p 1% Control system 5% Exhaust temperature sensor 1%

Urea supply 1% **Overall 13%**  **LNT** Adsorber 7%

In-exhaust injectors 2% Control system 5% Exhaust temperature sensor 1%

**Overall 16%** 166

malfunction rate. The cataly sts are more robust than PM filters, but have the potential to experience degradation when exposed to high temperatures. **Misfuel:** EPA estimated that operators will use the wrong t ype of fuel, such as agricultural diesel fuel with higher sulfur levels, approximately 0.1 percent of the time. 

### B.6 **Tampering & Mal -**  **maintenance Occurrence Rate Summary**

**B.7 NOx**  **Emission Effects** EPA developed the emission effect from each tampering and mal - maintenance incident from CARB’s EMFAC, Radian’s dy namometer testing with and without the malfunction present, EFEE results, and internal testing experience. EPA estimated that the lean NOx traps (LNT) in LHDD are 80 percent efficient and the selective catal ys t reduction (SCR) sy stems in HHDD are 90 percent efficient at reducing NOx. EPA developed the NOx emission factors of the NOx sensors based on SCR sy stems’ ability to run in open - loop mode and still achieve NOx reduct i ons. The Manufacturers of Emission Controls Association ( MECA ) has stated that 75 - 90 percent NOX reduction with open loop control and >95 percent reduction with closed loop control. 107 Visteon reports 60 - 8 0 percent NOX reduction with open loop control. 108 The failure of the NOx aftertreatment s ys tem had a different impact on the NOx emissions depending on the t ype of aftertreatment. The HHDD vehicles with SCR systems would experience a 1000 percent increas e in NOx during a complete failure, therefore we estimated a 500 percent increase as a midpoint between normal operation and a complete failure. The LHDD vehicles with LNT s ys tems would experience a 500 percent increase in NOx during a complete failure. We **Tamper & Malmaintenance Frequency of Occurrence: Average rate over life of vehicle**  **Frequency Rates** 1994-97 1998-2002 2003-2006 2007-2009 2010+ HHDT 2010+ LHDT Timing Advanced 5% 2% 2% 2% 2% 2% Timing Retarded 3% 2% 2% 2% 2% 2% Injector Problem (all) 28% 28% 13% 13% 13% 13% Puff Limiter Mis-set 4% 0% 0% 0% 0% 0% Puff Limiter Disabled 4% 0% 0% 0% 0% 0% Max Fuel High 3% 0% 0% 0% 0% 0% Clogged Air Filter - EPA 8% 8% 8% 8% 8% 8% Wr ong/W orn Turbo 5% 5% 5% 5% 5% 5% Intercooler Clogged 5% 5% 5% 5% 5% 5% Other Air Problem - EPA 6% 6% 6% 6% 6% 6% Engine Mechanical Failure 2% 2% 2% 2% 2% 2% Excessive Oil Consumption 5% 3% 3% 3% 3% 3% Electronics Failed - EPA 3% 3% 3% 3% 3% 3% Electronics Tampered 10% 15% 5% 5% 5% 5% EGR Stuck Open 0% 0% 0.2% 0.2% 0.2% 0.2% EGR Disabled/Low Flow - EPA 0% 0% 10% 10% 10% 10% Nox Aftertreatment Sensor 0% 0% 0% 0% 10% 10% Replacement Nox Aftertreatment Sensor 0% 0% 0% 0% 1% 1% Nox Aftertreatment Malfunction - EPA 0% 0% 0% 0% 13% 16% PM Filter Leak 0% 0% 0% 5% 5% 5% PM Filter Disabled 0% 0% 0% 2% 2% 2% Oxidation Cataly

st Malfunction/Remove - EPA 0% 0% 0% 5% 5% 5% Mis-fuel - EPA 0.1% 0.1% 0.1% 0.1% 0.1% 0.1%167

estimated a 300 percent increase as a value between a complete failure and normal sy stem operation. The values with 0 percent effect in shaded cells represent areas which have no occurrence rate.



### B.8 **PM Emission Effects** EPA developed the PM emission effects from each tampering and mal - maintenance incident from CARB’s EMFAC, Radian’s dy namometer testing with and without the malfunction present, EFEE results, and internal testing experience. EPA estimates that the PM filter has 95 percent effectiveness . Many of the tampering and mal maintenance items that impact PM also have a fuel efficiency and drivability impact. Therefore, operators will have an incentive to fix the se issue s . E PA estimated that excessive oil consumption will have the same level of impact on PM as engine mechanical failure. The failure of the oxidation cataly st is expected to cause a PM increase of 30 percent ; however , this value is reduced b y 95 percent due to the PM filter effectiv eness . We also considered a DOC failure will cau se a secondary failure of PM filter regeneration. We accounted for this PM increase within the PM filter disabled and leak categories. The values with 0 percent effect in shaded cells represent areas which have no occurrence rate. **Tamper & Malmaintenance NOX Emission Effect**  1994-97 1998-2002 2003-2006 2007-2009 2010+ HHDT 2010 LHDT Federal Emission Standard 5.0 5.0 4.0 2.0 0.2 0.2 Timing Advanced 60% 60% 60% 60% 6% 12% Timing Retarded -20% -20% -20% -20% -20% -20% Injector Problem (all) -5% -1% -1% -1% -1% -1% Puff Limiter Mis-set 0% 0% 0% 0% 0% 0% Puff Limiter Disabled 0% 0% 0% 0% 0% 0% Max Fuel High 10% 0% 0% 0% 0% 0% Clogged Air Filter 0% 0% 0% 0% 0% 0% Wrong/W orn Turbo 0% 0% 0% 0% 0% 0% Intercooler Clogged 25% 25% 25% 25% 3% 5% Other Air Problem 0% 0% 0% 0% 0% 0% Engine Mechanical Failure -10% -10% -10% -10% -10% -10% Excessive Oil Consumption 0% 0% 0% 0% 0% 0% Electronics Failed 0% 0% 0% 0% 0% 0% Electronics Tampered 80% 80% 80% 80% 8% 16% EGR Stuck Open 0% 0% -20% -20% -20% -20% EGR Disabled / Low Flow 0% 0% 30% 50% 5% 10% Nox Aftertreatment Sensor 0% 0% 0% 0% 200% 200% Replacement Nox Aftertreatment Sensor 0% 0% 0% 0% 200% 200% Nox Aftertreatment Malfunction 0% 0% 0% 0% 500% 300% PM Filter Leak 0% 0% 0% 0% 0% 0% PM Filter Disabled 0% 0% 0% 0% 0% 0% Oxidation Catalyst Malfunction/Remove 0% 0% 0% 0% 0% 0% Mis-fuel168

**Tamper & Ma**  **lmaintenance PM**  **Emission Effect** 1994 - 1997 1998 - 2002 2003 - 2006 2007 - 2009 2010 Federal Emission Standard 0.1 0.1 0.1 0.01 0.01 Timing Advanced - 10% - 10% - 10% 0% 0% Timing Retarded 25% 25% 25% 1% 1% Injector Problem 100% 100% 100% 5% 5% Puff Limiter Mis - set 20% 0% 0% 0% 0% Puff Limiter Dsabled 50% 0% 0% 0% 0% Max Fuel High 20% 0% 0% 0% 0% Clogged Air Filter 50% 50% 30% 2% 2% Wr ong/W orn Turbo 50% 50% 50% 3% 3% Intercooler Clogged 50% 50% 30% 2% 2% Other Air Problem 40% 40% 30% 2% 2% Engine Mechanical Failure 500% 500% 500% 25% 25% Excessive Oil Consumption 500% 500% 500% 25% 25% Electronics Failed 60% 60% 60% 3% 3% Electronics Tampered 50% 50% 50% 3% 3% EGR Stuck Open 0% 0% 100% 5% 5% EGR Disabled/Low Flow 0% 0% - 30% - 30% - 30% Nox Aftertreatment Sensor 0% 0% 0% 0% 0% Replacement Nox Aftertreatment Sensor 0% 0% 0% 0% 0% Nox Aftertreatment Malfunction 0% 0% 0% 0% 0% PM Filter Leak 0% 0% 0% 935% 935% PM Filter Disabled 0% 0% 0% 2670% 2670% Oxidation Catalyst Malfunction/Remove 0% 0% 0% 0% 0% Mis - fuel - EPA 30% 30% 30% 100% 100% 

### B.9 **HC Emission Effects** EPA estimated oxidation catal ys ts are 80 percent effective at reducing h yd rocarbons. All manufacturers will utilize oxidation cataly sts in 2007, but only a negligible number were installed prior to the PM regulation reduction in 2007. We reduced CARB’s HC emission effect for timing advanced because ear lier timing should reduce HC, not increase them. The effect of injector problems was reduced to 1000 percent based on internal experience. We increased the HC emission effect of high fuel pressure to 10 percent because the higher pressure will lead to ex tra fuel in earl y model years and therefore increased HC. Lastl y, we used the HC emission effect of advanced timing for the electronics tampering since this was the most significant type of tampering that occurred. The values with 0 percent effect in shad ed cells represent areas which have no occurrence rate.169

A separate tampering analy sis was not performed for CO ; rather, the HC effects were assumed to apply for CO. Combining all of the emissions effects and failure frequencies discussed in this section, we summarized the aggregate emissions impacts over the useful life of the fleet due to in the main body of the document in Table 2 - 8 (NOx), Table 2 - 15 (PM), and Table 2 - 18 (HC and CO). 

### B.10 **HD OBD impacts** With the finalization of the heav y - dut y onboard diagnostics (HD OBD) rule, we made adjustments to our draft 2010 and later model y ear to reflect the rule’s impl ementation. Specificall y, we reduced our emissions increases for all pollutants due to tampering and mal maintenance b y 33 percent . As data are not y et available for heavy - dut y trucks equipped with OBD, this number is probably a conservative estimate. St ill, PM and NO x reductions from 2010 and later model y ear vehicles will be substantial compared to prior model years regardless of the additional incremental benefit from OBD. We assumed, since the rule phases in OBD implementation, that 33 percent of all engines will have OBD in 2010, 2011, and 2012 model ye ars, and 100 percent will have OBD b y 2013 model year and later . Equation B - 2 describes the calculation of TM pol , the increase in emission rate through useful life, where f OBD represents the fraction of the fleet equipped with OBD ( 0 percent for model years 2009 and earlier, 33 percent for model y ears 2010 - 2012, and 100 percent for model y ears 2013 and later). T he result from this equation can be plugged into Equation B - 1 to determine the emission rate for an y age group. 

#   OBD nonOBD pol OBD nonOBD pol pol 

## f TM f TM TM ,

, 

## 67

. 0 1     **Equation B**  **- 2**  **Tamper & Malmaintenance HC Emission Effect**  1994-97 1998-2002 2003-2006 2007-2009 2010+ HHDT 2010 LHDT Federal Emission Standard 1.3 1.3 1.3 0.2 0.14 0.14 Timing Advanced 0% 0% 0% 0% 0% 0% Timing Retarded 50% 50% 50% 50% 10% 10% Injector Problem (all) 1000% 1000% 1000% 1000% 200% 200% Puff Limiter Mis-set 0% 0% 0% 0% 0% 0% Puff Limiter Disabled 0% 0% 0% 0% 0% 0% Max Fuel High 10% 0% 0% 0% 0% 0% Clogged Air Filter 0% 0% 0% 0% 0% 0% Wr ong/W orn Turbo 0% 0% 0% 0% 0% 0% Intercooler Clogged 0% 0% 0% 0% 0% 0% Other Air Problem 0% 0% 0% 0% 0% 0% Engine Mechanical Failure 500% 500% 500% 500% 100% 100% Excessive Oil Consumption 300% 300% 300% 300% 60% 60% Electronics Failed 50% 50% 50% 50% 10% 10% Electronics Tampered 0% 0% 0% 0% 0% 0% EGR Stuck Open 0% 0% 100% 100% 20% 20% EGR Disabled / Low Flow 0% 0% 0% 0% 0% 0% Nox Aftertreatment Sensor 0% 0% 0% 0% 0% 0% Replacement Nox Aftertreatment Sensor 0% 0% 0% 0% 0% 0% Nox Aftertreatment Malfunction 0% 0% 0% 0% 0% 0% PM Filter Leak 0% 0% 0% 0% 0% 0% PM Filter Disabled 0% 0% 0% 0% 0% 0% Oxidation Catalyst Malfunction/Remove 0% 0% 0% 50% 50% 50% Mis-fuel170

These OBD impacts apply to an y truck in Class 4 and above. An y lighter trucks are assumed to follow light - dut y OBD impacts and will be full y phased in starting in model y ear 2010. As data for current and future model ye ars become available , we may consider refin ing these estimate s and methodology .171



## Appendix C **Extended Idle Data Summary Idle HC Rates (gram/hour) Summary**  **Program Condition**  **# Samples Me**  **an HC Emiss Rate 1991-2006 Low Speed Idle, A**  **/C Off - HDT** McCormick, High Altitude, HDT Low Idle, AC Off 12 10.2 WV U - 1991-2004 Low Idle, AC Off 48 9.5 Storey Low Idle, AC Off 4 28 **Overall 64**  **10.8 1991-2006 High Speed Idle, A**  **/C On - HDT** Broderick UC Davis High Idle, AC On 1 86 Storey High Idle, AC On 4 48 **Overall 5**  **55.6 1975-1990 M**  **Y Low Speed Idle, A /C Off - HDT**  **Program Condition**  **Samples Me**  **an** WV U - 1975-1990 Low Idle, AC Off 18 21 **Overall 18**  **21.0 1991-2006 M**  **Y Low Speed Idle, A /C Off - Bus**  **Program Condition**  **Samples Me**  **an** McCormick, High Altitude, Bus Low Idle, AC Off 12 8.2 **Overall 12**  **8.2**172

**Idle CO Rates (gram/hour) Summary Program**  **Condition # Samples**  **Me an CO Emiss Rate**  **1991-2006 Low Speed Idle, A /C Off - HDT**  McCormick, High Altitude, HDT Low Idle, AC Off 12 71 Calcagno Low Idle, AC Off 27 37 WV U - 1991-2004 Low Idle, AC Off 48 23 Storey Low Idle, AC Off 4 25 **Overall 91**  **33.6 1991-2006 High Speed Idle, A**  **/C On - HDT** Calcagno High Idle, AC On 21 99 Broderick UC Davis High Idle, AC On 1 190 Storey High Idle, AC On 4 73 **Overall 26**  **91.2 1975-1990 M**  **Y Low Speed Idle, A /C Off - HDT**  **Program Condition**  **Samples Me**  **an** WV U - 1975-1990 Low Idle, AC Off 18 31 **Overall 18**  **31.0 1991-2006 M**  **Y Low Speed Idle, A /C Off - Bus**  **Program Condition**  **Samples Me**  **an** McCormick, High Altitude, Bus Low Idle, AC Off 12 79.6 **Overall 12**  **79.6**173

**Idle PM Rates (gram/hour) Summary Program**  **Condition # Samples**  **Me an PM Emiss Rate**  **1991-2006 Low Speed Idle, A /C Off - HDT**  McCormick, High Altitude, HDT Low Idle, AC Off 12 1.8 Calcagno Low Idle, AC Off 27 2.55 WV U - 1991-2004 Low Idle, AC Off 48 1.4 Storey Low Idle, AC Off 4 1.3 **Overall 91**  **1.8 1991-2006 High Speed Idle, A**  **/C On - HDT** Calcagno High Idle, AC On 21 4.11 Storey High Idle, AC On 4 3.2 **Overall 25**  **4.0 1975-1990 M**  **Y Low Speed Idle, A /C Off - HDT**  **Program Condition**  **Samples Me**  **an** WV U - 1975-1990 Low Idle, AC Off 18 3.8 **Overall 18**  **3.8 1991-2006 M**  **Y Low Speed Idle, A /C Off - Bus**  **Program Condition**  **Samples Me**  **an** McCormick, High Altitude, Bus Low Idle, AC Off 12 2.88 **Overall 12**  **2.9**174

**2007 Extended Idle Emissions calculation:**  Assumed 8 hour idle period where the emissions controls, such as EGR, oxidation catal ys t, an d NOx aftertreatment, are still active for the first hour.  HC emissions standards: o Pre - 2007: 0.50 g/bhp - hr o 2007: 0.14 g/bhp - hr  NOx emissions standards: o Pre - 2010: 5.0 g/bhp - hr **Idle Nox Rates (gram/hour) Summary Program**  **Condition # Samples**  **Me an NOX Emiss Rate**  **1991-2006 Low Speed Idle, A /C Off**  McCormick, High Altitude, HDT Low RPM, AC Off 12 85 Lim , EPA Low RPM, No access 12 109 Irick, Clean Air Tech & IdleAire 49 87 WV U - 1991-2004 Low RPM, AC Off 48 83 WV U, NCHRP 2 47 Tang, Metro NY, 1984-1999 33 81 Calcagno Low RPM, AC Off 27 120 Broderick UC Davis Low RPM, AC Off 1 104 Storey Low RPM, AC Off 4 126 **Overall 188**  **94 1991-2006 High Speed Idle, A**  **/C Off** Lim , EPA CCD High RPM, No access 5 169 Calcagno High RPM, AC Off 21 164 **Overall 26**  **165 1991-2006 High Speed Idle, A**  **/C On** Lim , EPA CCD High RPM, AC On 5 212 Broderick UC Davis High RPM, AC On 1 240 Calcagno High RPM, AC On 21 223 Storey High RPM, AC On 4 262 **Overall 31**  **227 1975-1990 M**  **Y Low Speed Idle, A /C Off**  **Program Condition**  **Samples Me**  **an** WV U - 1975-1990 Low RPM, AC Off 18 48 Lim , EPA CCD, 1985 MY Low RPM, AC Off 1 20 **Overall 19**  **47 1991-2006 M**  **Y Low Speed Idle, A /C Off - Bus**  **Program Condition**  **Samples Me**  **an** McCormick, High Altitude, Bus Low Idle, AC Off 12 121 **Overall 12**  **121.0**175

o 2010: 0.2 g/bhp - hr Idle HC Rate Reduction = 1 - [(1/8 * 0.14 g/bhp - hr + 7/8 * 0.5 g/bhp - hr) / 0.5 g/bhp - hr] = 9% Idle NOx Rate Reduction = 1 - [(1/8 * 0.2 g/bhp - hr + 7/8 * 5.0 g/bhp - hr) / 5.0 g/bhp - hr] = 12%176



## Appendix D **D evelop**  **ing PM emission rates for missing operating**  **mode s**  In cases where an estimated rate could not be directly calculated from data, we imputed the missing value using a log - linear least - squares regression procedure. Regulatory class, model y ear group and speed class (0

– 25 mph, 25 - 50 mph and 50+ mph ) were represented by dummy variables in the regression. The n atural log ari thm of emissions was regressed versus scaled tractive power ( STP ) to represent the operating mode bins. The regression assumed a constant slope versus STP for each regulatory

class. L og arithmic transformation factors (mean square error of the regression squared / 2) were used to transform the regression results from a log based form to a linear form. Due to the huge number of individual second - by - second data points, all of the regression relationships were statistically

significant at a high level (99% c onfident level). The table below shows the regression statistics, and the equation shows the form of the resulting regression equation. **Table D**  **- 1**  **. Regression Coefficients for PM Em**  **ission Factor Model Model**  **- year**  **group Speed Class (mph)**  **Type Medium**  **Heavy -**  **Duty Heavy Heavy**  ****

**Duty** 1960 - 87 1 - 25 Intercept ( β 0 ) - 5.419 - 5.143 25 - 50 - 4.942 - 4.564 50+ - 4.765 - 4.678 1988 - 90 1 - 25 - 5.366 - 5.847

25 - 50 - 4.929 - 5.287 50+ - 4.785 - 5.480 1991 - 93 1 - 25 - 5.936 - 5.494

25 - 50 - 5.504 - 5.269 50+ - 5.574 - 5.133 1994 - 97 1 - 25 - 5.927 - 6.242

25 - 50 - 5.708 - 5.923 50+ - 5.933 - 6.368 1998 - 2006 1 - 25 - 6.608 - 6.067

25 - 50 - 6.369 - 5.754 50+ - 6.305 - 6.154 STP Slope ( β 1 ) 0.02821 0.0968 Transformation Coefficient (0.5σ 2 ) 0.5864 0.84035

2 1 0 

## 5

. 0 STP ) PM ln( 

     Where : β 0 = an intercept term for a speed class within a model year group, as shown in the table above, β 1 = a slope term for STP , and σ 2 = the mean - square error or r esidual error for the model fit, STP = the midpoint value for each operating mode ( kW/metric ton, see Table 1 - 4 ).177



## Appendix E **Heavy -**  **duty Diesel EC/ PM**  **Fraction Calculation E.1**  **Introduction** This memo describes the development and application of a “rough cut” emission model for estimating elemental and organic carbonaceous material (EC and OM) emission rates (or EC/OM ratios) from MOVES. The memo describes the following steps involved in pre dicting EC/OM ratios. The memo also briefly describes comparisons with independent emission data collected using the “Mobile Emission L aboratory , ” Operated by the University

of California Riverside. The subsequent sections of the memo describe the follow ing topics :  the extension of Phy sical Emission Rate Simulator ( PERE ) to estimate heavy - dut y fleet - average emission factors for an y specified driving cy cle;  the acquisition of data used in estimating EC/OC rates as a function of engine operating mode and the fitting of simple empirical models to them;  the application of PERE to estimate EC and OC emission rates for different test cy cles; and,  the comparison of PERE - based EC and OC emission rates to those measured by independent researchers in HD trucks. 

### E.2 **P**

**ERE for Heavy -**  **duty Vehicles (PERE -**  **HD) and Its Extensions** The Phy sical Emission Rate Estimator (PERE ) is a model employ ed by EPA in earl y development of MOVES. 33 Inparticular, the MOVES team employ ed it in development of MOVES2004 to impute greenhouse gas emission rates for combinations of S ourceBin and Op erating Mode for which data was unavailable or of insufficient quality . The underl yi ng theory

behind PERE an d its comparison with measured fuel consumption data is described b y Nam and Giannelli (2005 ). 33 B riefl y, PERE estimates fuel consumption and emission rates on the basis of fundamental phy sical and mathematical relationships describing the road load that a vehicle meets when driving a particular speed trace. Accessory loads are handled by

addition of an accessory

power term. In the heavy - dut y version of PERE (h ereafter, “PERE - HD” ) , accessory loads were described b y a single value. For the current project, PERE was modified to incorporate several “extensions” that allowed it to estimate fleet - average emission rates, simulate a variet y of accessory load condition s, and predict EC rates for an y given driving cy cle. 

### E.2.1 **PERE**

**- HD Fleet**  **- wide Average Em**  **ission Rate Estim ator**  PERE - HD requires a number of user - specified inputs, including:  vehicle - level descriptors (model y ear, running weight, track road - load coefficients ( A , B , C ) , transmission ty

pe, class [MDT/HDT/bus]);  engine parameters (fuel ty pe, displacement); and  driving cy cle (expressed through a speed trace) .178

The specification of these inputs allows PERE to model the engine operation, fuel consumption, and GHG emissions for a HDV on a specified driving cy cle. However, the baseline PERE - HD provides output for only one combination of these parameters at once. To estimate fleet - wide average a large number of PERE - HD runs would be required. Furthermore, the specification o f only fleet - wide average coefficients is likely to substantiall y underestimate variability

in fuel consumption and emissions. Emissions data from a large number of laboratory

and field studies suggest that a very large fraction of total emissions from al l vehicles derives from a small fraction of the study fleet. Therefore, it is desirable to develop an approach that comes closer to spanning the range of likel y combinations of inputs than using a small selection of “ average ” or “ty pical” values. For the current application, PERE - HD ( built within Microsoft Excel ) was expanded to allow for a representative sample of [running weight] × [engine displacement] × [model y ear] combinations. A third - part y add - on package to Excel, @Risk 4.5 (Palisade Corporation, 2004) , allows users to supplement deterministic inputs within spreadsheet models with selected continuous probability distributions, sample input value s from each input distribution, and re - run the spreadsheet model with sets of selected inputs over a spec ified number of iterations . This ty pe of procedure is commonly

referred to as “Monte Carlo” simulation. 

### E.2.2 **Monte Carlo Sim**  **ulation in PERE -**  **HD** To illustrate how @Risk performs this process, we illustrate the application of a simple model, employ ing both determ inistic calculations and stochastic Monte Carlo simulation :

2 L M BMI  This equation defines the body mass index for humans, a simple surrogate indicating overweight and underweight conditions . According to the Centers for Disease Control and Prevention (CDC), the average U.S. woman weighed 164.3 lb (74.5 kg) in 2002 and was 5’4” (1.6 m) tall. This result corresponds to a BMI of 28, suggesting that the average U.S. woman is overweight. While this is useful information from a public health pe rspective, it does not provide any indication as to which individuals are likely

to experience the adverse effects of being overweight and obese. However, if we were to assume (arbitraril y) that the range of weight and height within the U.S. population wa s +/ - 50% of the mean, distributed uniformly , and perform a Monte Carlo simulation (5 , 000 iterations) using @Risk, we would predict a probability distribution of BM I in the population as follows:179

In contrast, here is the BMI distribution in the entire U.S. population, according to the CDC’s National Health and Nutrition Examination Survey (NHANES):

These graphs illustrate how Monte Carlo simulation can be used to provide meaningful information about the variability

in a population. Although the model example is very simple, it illustrates the point that a model with “ ty pical ” inputs provides much less information than Monte Carlo simulat ion does with variable inputs. For emission modeling purposes using PERE - HD, several key inputs were modeled as probability distributions. 

### E.2.3 **Model Year** Model y ear is an important factor in PERE, as the frictional losses in the model , expressed as “friction m ean effective pressure” (FMEP) , vary by model y ear, improving with later model y ears. As such, model y ear was simulated as a probability distribution, based on data from the Census Bureau’s 1997 Vehicle Inventory and Use Survey (VIUS) , which reports “vehi cle miles traveled”

Dis trib ut io n of

BM I inSi mu lated Population%ofSimulatedPopulation

BM

I 0. 000 0. 010 0. 020 0. 030 0. 040 0. 050 0. 060

10 20 30 40 50180

( VMT ) by

model y ear . Accordin g ly these data were normalized to total VMT to develop a probability

distribution. Model y ear distributions in 1997 were normalized to the current calendar ye ar (2008). xxiv For instance, the fraction of 1996 vehicles reported in the 1997 VIUS is treated as the fraction of 2002 vehicles in the 2003 calendar y ear. Although a 2002 VIUS is available, previous anal ys es (unpublished) have shown the “relative” model y ear distribution of trucks to have changed little between 1997 and 2002, though this assumption is one limitation of this anal ys is. The model y ear distribution for PERE - HD was represented as a discrete probability distribution, as shown below:



### E.2.4 **Vehicle Weight and Engine Displacem ent**  Vehicle running wei ghts and engine displacements were modeled as a two - way probability distribution with engine displacement depending on running weight. These data were derived from VI US micro data obtained from the Census Bureau. 109 A two - way table was constructed to estim ate VMT classified by combinations of [weight class] × [displacement class]. Analy ses were restricted to diesel - powered trucks onl y. As a first step , @Risk selects a running weight from a probability distribution representing the fraction of truck VMT occ urring at a given running weight:

xxiv VIUS reports model years 11 years old and greater as a single number. For the current analysis, the fraction of vehicles within each model year older than 10 years of age through 25 years was estimated using an exponential decay of the form p(x) = A *exp[ - B *( x - 10)]. Coefficients representing the A and B param eters were estimated by minimizing least squares of the residuals. The sum of probabilities for model y ears older than 10 years was constrained the fraction of VMT driven by trucks older than 10 years in VIUS. Probabilit y a nd Cum ulativ e Probabilit y D istr ibutions o f Model Ye ars in PER E- HD

0 0. 02 0. 04 0. 06 0. 08 0. 1 0. 12

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Rel at iv e Ag e (Calendar Ye ar - Model Year)Probability

0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9 1Cum.Probability Probabilit y b y Ag e

Cumulat iv

e Pr obabil ity b y Ag e181

Because V IUS reports class es defined as ranges in running weight, an y value of weight within each V IUS - specified class was considered equally likely and modeled as a uniform probability distribution within the class . Fo r the upper and lower bounds of the distribution the minimum and maximum running weights were assumed to be 7,000 and 240,000 lb, respectivel y. After @Risk selects a running weight, it selects an engine displacement based on a discrete distribution assigne d to every weight class in VIUS, represented below:

Again, because VIUS describes ranges of values for displacement, all values within each range were given uniform weight and assigned a uniform distribution. For the extreme classes , the minimum and maximum engine displacements were assumed to be 100 in 3 and 915 in 3 , respectivel y. Probability D istr ibutio n of Ve hic le Running We ight b as ed on VI US

0 0. 05 0. 1 0. 15 0. 2 0. 25 0. 3 0. 35 0. 4[0,15000)[15000,30000)[30000,45000)[45000,60000)[60000,75000)[75000,90000)[90000,105000)[105000,120000)[120000,135000)[135000,150000)[150000,165000)[165000,180000)[180000,195000)[195000,210000)[210000,225000)[225000,240000)[240000,)

We ight R angeProbability

Di str ibut ion of D isp lac em ent ( cu . in .) b y R unni ng We ig ht ( lb) in TI US

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%150000andunder15001to3000030001to4500045001to6000060001to7500075001to9000090001to105000105001to120000120001to135000135001to150000150001to165000165001to180000180001to195000195001to210000210001to225000225001to240000240000andover

Runni ng We ig htCumulativeProbability 850+ 800-849 750-799 700-749 650-699 600-649 550-599 500-549 470-499 350-469 430-449 400-429 370-399 350-369 300-349 250-299 1-249182

This procedure reflects the range in running weights present among HDV in operation, and constrains the combinations of weight and displacement to plausible pairs of values based o n survey ed truck operator responses. Th ese steps allow for plausible variability in weight - engine pairings, which translates into differences in engine parameters influencing EC and OC emissions. For use in PERE - HD, all units were converted to SI units (kg and L ). 

### E.2.5 **Accessory Load** The original PERE - HD treats accessory load as a fixed value, which may be varied b y the user. It is set at 0.75, and used in calculating fuel rate and total power demand at each second of driving. Following the developmen t of PERE - HD, a more detailed set of accessory load estimates was developed based on several accessories’ power demand while in use and the fraction of time each accessory is in use (see Table 2 - 4 ) . 110 H igh, medium, and low accessory use categories were estimated for three vehicle classes: HDT, MDT, and buses. For the current version of the model, only the HDT accessory

load estimates were employ ed, though a sensitivi ty analy sis indicated that mean EC/OM ratios were most sensitive to accessory load during idle and creep driving cy cles. I n the “base case,” a mean ratio of 0.54 was predicted, while in the sensitivity case, a mean ratio of 0.50 was predicted. This issue may be revisited at some point, al though the limited sensitivity of total results limits the importance of the accessory terms within the current exercise. Within @Risk, the variable in PERE - HD, Pacc for accessory use was substituted with a variable repre senting the distribution (in time) of accessory loads as estimated as the sum of a number of discrete probability

distributions. Depending on the assumption of high, medium or low use, the power demand for these accessories is distributed in time as foll ows:



### E.2.6 **Driving Cycle** For purposes of this exercise, the four phases of the California Air Resources Board’s Heav y Heav y - Duty

Diesel Truck (HHDDT) chassis dy namometer testing cy cle were used to reflect variability

in vehicle operations for PERE - HD.

Co mp ar is on of

H igh / M edium / L ow

A cce sso ry Load Cas es

0. 000 0. 200 0. 400 0. 600 0. 800 1. 000P8P13P18

0 10 20 30183



### E.2.7 **Other Factors**  Some elements of variability were not examined as part of this study . Hy brid - electric transmissions and fuel cell power plants were excluded from the anal ys is, due to their low prevalence within the current truck fleet. One important source of variability that was not examined in this analy sis is the variation in resistive forces among vehicles with identical running weights. This exclusion is important, given the potential role for aerody namic improvements, low rolling resistance tires, and othe r technologies in saving fuel for long - distance trucking firms and drivers. Such considerations could be incorporated into PERE - HD in the future as a means of estimating the emission benefits of fuel saving technologies. 

### E.3 **Prediction of Elemental Carbon and Organic Mass based on**  **PERE -**  **HD E.3.1**  **Definition of Elem ental and Organic Carbon and Organic Mass**  In motor vehicle exhaust, the terms “EC,” “elemental carbon,” and “black carbon” refer to the fraction of total carbonaceous mass within a particle sample that co nsists of light - absorbing carbon. Alternativel y, they refer to the portion of carbonaceous mass that has a graphitic crystalline structure. Further, one can define EC as the portion of carbonaceous mass that has been altered b y py rol ys is, that is, the ch emical transformation that occurs in high temperature in the absence of oxy gen. EC forms in diesel engines as a result of the stratified combustion process within a cy linder. Fuel injectors spray

aerosolized fuel into the cylinder during the compression s troke. The high - pressure and high temperature during the cy linder cause spontaneous ignition of the fuel vaporizing from the injected droplets. Because temperature can rise more quickl y than oxy gen can diffuse to the fuel at the center of each droplets, py rol ys is can occur as hy drogen and other atoms are removed from the carbonaceous fuel, resulting in extensive C - C bond interlinking. As a result, py rol yz ed carbon is produced in a cry stalline form similar to graphite. “Organic carbon” or “organic mass” ( OC or OM) is used to denote the portion of carbonaceous material in exhaust that is not graphitic. Chemical analy sis of this non - graphitic carbon mass indicates that it is composed of an extensive mixture of different organic molecules, including C15 to C 44 alkanes, poly cy clic aromatic hy drocarbons, lub ricating oil constituents (hopanes, steranes, and carpanes), and a sizeable fraction of uncharacterized material. This component of exhaust can derive from numerous processes inside the engine involving both fuel and oil. Because of the complex chemical mixture that comprises this mass, its measurement is highly dependent on sampling conditions. The wide range of organics that compose it undergo evaporation and condensation at different temperatures, and the phase - partitioning behavior of each molecule is dependent on other factors, such as the sorption of vapor - phase organics to available surface area in a dilution tunnel or background aerosol.184



### E.3.2 **EPA Carbon Analysis**  **Techniques in Am bient Air**  The definitions of EC and OM are critical, as different groups use different techniques for quantify ing their concentrations within a given medium. For purposes of this document , it is assumed that EC, OC, and OM are operationally defined quantities, meaning that they are defined by the measurement technique used to quantify their concentrations on a filter or in air. The different t ypes of commonly used approaches for carbon include:  Therm al/ optical techniques, where the evaporation and oxidation of carbon are used in c onjunction with a laser to measure optical properties of a particle sample. The major methods used for this ty

pe of anal ys is include: o Thermal/optical reflectance (TOR) . EPA is adopting this technique for the PM 2.5 speciation monitoring network nationwide . I t is also employ ed b y the IMPROVE program (Interagency Monitoring of Protected Visual Environments) in national parks. This technique heats a punch from a quartz fiber filter according to a certain schedule. A Helium gas atmosphere is first emplo yed within the oven, and the evolved carbon is measured with a FID as temperatures are increased in steps up to 580°C. All carbon evolved in this way is assumed to be volatilized organic material. Next, 2% oxy gen gas is added to the atmosphere, and temperatures are stepped up a number of times to a maximum of 840°C. All carbon evolved after the introduction of ox ygen is assumed to be elemental carbon. The reflection of light from a laser b y the filter is employed to account for the p yrol ysis of organic carb on that occurs during the warm - up process. o Thermal/optical transmission (TOT) . The National Institute of Occupational Safety and Health (NIOSH) uses this technique for measuring EC concentrations in occupational environments. I t is based on similar princi ples to TOR, but employ s a different heating schedule and transmission of light as opposed to reflectance.  Radiation absorption techniques o Aethalometer ®

– This instrument reports “black carbon” (BC) concentrations based the extent of light absorption by a “filter tape,” that allows for a time series of BC concentrations to be estimated. I t has a time resolution of several minutes. o Photoacoustic Spectrometer (PAS)

– This instrument irradiates an air sample with a laser. The resulting heat that occurs from the absorption of the laser light by light - absorbing carbon in the air sample produces a pressure wave that is measured b y the device. The signal from this pressure wave is proportional to the light - absorbing carbon content in exhaust.  Thermogravimetric t echniques, where the “volatile organic fraction” (VOF) is separated by heat from the non - volatile refractory component of a particle sample.  Chemical extraction, where solvents are used to separate the soluble and insoluble components of exhaust.185

A number of additional techniques are also described in the published literature, but the above techniques have been most commonly applied in emissions and routine ambient PM measurement . Among the available techniques, it has been a point of controvers y among acad emics as to wh ich method provides the “correct” carbon signal. Rather than addressing these arguments in detail, this anal ys is adopts the technique employ ed by the EPA ambient speciation monitoring network, TOR. Needless to say , different researchers employ different sampling, measurement and anal ys is techniques. Desert Research Institute (DRI) employ ed TOR in anal yz ing the Kansas Cit y gasoline PM emission study

samples 111 , while other prominent academics employ TOT, notably

the U niversity

of California Riverside College of Engineering Center for Environmental Research and Technology

(CE - CERT) and the Universit y of Wisconsin - Madison (UWM) State Hy gi ene Laboratory . As r esearch results from these groups is employ ed throughout this analy sis , an inter comparison of the methods of TOT/TOR is necessary to “recalibrate” various datasets with respect to each other . EPA defines measurement techniques for d ynamometer - based sampling and anal ys is of particulate matter, in addition to technique s for sampling and anal yz ing particles in ambient air. Inventories estimated for EC and OM can be considered to reflect both broad categories of measurement techniques, depending on context. The user communit y for MOVES is predominantly concerned with emissions that occur into ambient air. EPA regulations for demonstration of attainment of state implementation plans (SI Ps) are based on monitored ambient particulate matter using Federal Reference Methods (FRM) for ambient air. FRM monitors for particle speciation in ambient air undergo analy sis for EC and OC according to a defined standard operating procedure. 112 That standard operating procedure defines therm al/ optical reflectance (TOR) as the desired method for anal ys is of ambient carbon PM. 

### E.3.3 **TOR –**  **TO**

**R Calibration Curve** In the course of the Gasoline/Diesel PM Split Study funded by

the Department of Energy (DOE), researchers from DRI analyzed filter samples using both TOR and TOT methods [cite] . These data were obtained and anal yz ed in the SPSS 9.0 stat istical package. Briefl y , the DOE study

included emissions characterizations of 57 light - duty gasoline vehicles (LDGV) and 34 HD diesel vehicles (HDDV). The vehicles were operated on a number of different test cy cles including cold - start and warm - start cy cles. The data set employed in this study was generated b y DR I and obtained from the DOE study web site. 113 Both EC and OC were anal yz ed using the same approach. All data from all vehicles were compiled. First, EC and OC measured by TOR (denoted EC - TOR and OC - TOR) were regressed on EC - TOT and OC - TOT. Studentized residuals from these regressions were noted, and those with Studentized residuals >3 were excluded from further analy sis. Second, each test in the reduced data set was assigned a random number (RAN D) o n the range [0,1 ] . Those cases with RAND ≥ 0.95 were set aside as a cross - validation data set, and excluded from additional regression anal ys es . Third, those cases with RAND < 0.95 were regressed again, this time using an inverse uncertaint y weighting procedure for each data point. When DRI anal yz es a filter sample, it reports an anal yt ical uncertaint y associated with the primary estimate of EC and OC. Accordingl y, the qualit y186

of each datum depends on the level of analy tical uncertaint y reported. Th e inverse of the DRI reported uncertaint y (1/  ) associated with the TOR - based measurement was used to weight each point in the weighted regression. It should be noted that for each regression, the intercept term was set to zero. Models including intercept s did not have intercept terms that reached statistical significance. As such, R2 values are not considered valid. C oefficients from the weighted regression for EC and OC are reported below: Slope Beta Std. Error t - value Sig. EC - TOR 1.047 0.011 91.331 < 0.00 0 1 OC - TOR 1.014 0.007 153.923 <0.00 0 1 To evaluate the quality

of predictions resulting from these statistically - based adjustment factors, they were used to predict EC - TOR and OC - TOR values for the subset of data with RAND ≥ 0.95. Scatter plots of the statistical fits are illustrated below (note logarithmic scaling).

EC-TOR Pre dic ted 10 00 50 0 40 0 30 0 20 0 10 0 50 40 30 20 10 5 4 3 2 1 .5 .4 .3 .2 .1EC-TORMeasured 1000 500 400 300 200 100 50 40 30 20 10 5 4 3 2 1 .5 .4 .3 .2 .1187

When measured values are regressed against predicted values, the following statistical estimates of fit are obtained: Prediction Slope Std. Error Intercept Std. Error EC 1.080 0.009 3.737 3.173 OC 1.092 0.069 - 4.417 16.188 As shown, the prediction vs. observed comparison y ields a slope near unit y for both EC - TOR and OC - TOR, with n onsignificant intercepts. On this basis, the “calibration” factors for converting EC TOT and OC - T OT into their respective TOR - based metrics appear reasonable. It remains an unverified assumption that the “calibration” factors derived from the emission s data derived from DRI as part of the DOE Gasoline / Diesel PM Split Study are general enough to appl y to EC - TOT measurements obtained by other research groups. 

### E.3.4 **EC and OC Em ission Rates**  **Selection of Engine Parameter s**  **for Predictive Modeling** PERE - HD produce s estimates of engine operating conditions and fuel consumption for a given driving cy cle. Prediction of EC and OM emissions requires information on the composition of particulate matter as a function of some factor that may be related back to MOVES’ acti vity basis, the time spent in a particular operating mode ( o pMode ID ).

OC TO R-Pre dic ted 10 00 50 0 40 0 30 0 20 0 10 0 50 40 30 20 10 5 4 3 2 1 .5 .4 .3 .2 .1OC-TORMeasured 1000 500 400 300 200 100 50 40 30 20 10 5 4 3 2 1 .5 .4 .3 .2 .1188

It should be noted that continuous (“second - by - second, or “real time” ) measurement of EC and OM is an exceptionally

complicated endeavor. While measurement techniques for EC have been developed that produce apparentl y good correlation with traditional filter - based methods, While numerous publications report the EC and OM (or OC) exhaust emission rates across an entire driving cy cle, it is not clear which parameter of a particular dri ving cy cle, such as average speed (or power) , might be applicable to the extrapolation of the observed rates to other vehicles or driving conditions. As a result, identify ing one or more engine parameter s that explain the observed variation in driving cy c le - based emission rates for EC and OM is desirable. Such parameter (s) will assist in estimating emission associated with short - term variations in driving. One good candidate for establishing an engine - based emission model is mean effective pressure (MEP) . MEP is defined as: 

### N V Pn MEP d R 

###  Here, P is the power (in kW or hp), n R is the number of crank revolutions per power stroke per cy linder (2 for four - stroke engines, 1 for two - strokes), V d is the engine displacement, and N is the engine speed. In other words, MEP is the engine torque normalized b y volume. MEP can be broken into various components. “Indicated MEP” or IMEP refers to the sum of BMEP (brake MEP) and FMEP (friction MEP). Hey wood (1988) writes that maximum BME P is an indicator of good engine design and “essentially constant over a wide range of engine sizes. [cite] ” Nam and Giannelli (2004) note that it can be related to fuel MEP multiplied by the indicated or thermal efficiency of an engine, and have developed trend lines in FMEP by model ye ar. As such, since maximum BMEP is comparable across well - designed engines and FMEP can be well - predicted by

Nam and Gian n elli’s trends within PERE, I MEP should be an appropriate metric for building an engine emission model that can be applied across vehicles with different loads and engine displacements. **Emission Data** Kweon et al. (2004) measured particle composition and mass emission rates from a single - cy linder research engine based on an in - line 2.333 liter turbo - charged direct - injection six cylinder Cummins N14 - series engine, with a quiescent, shallow dish piston chamber and a quiescent combustion chamber. Emission data were obtained from all eight modes of the CARB 8 - mode engine test cy cle: Mode 1 Mode 2 Mode 3 Mode 4 Mode 5 Mode 6 Mode 7 Mode 8 Speed 1800 1800 1800 1200 1200 1200 1200 700 Load% 100 75 50 25 100 75 50 10 (idle) Equiv. Ratio (φ) 0.69 0.50 0.34 0.21 0.82 0.69 0.41 0.09 IMEP (MPa) 1.083 0.922 0.671 0.524 1.491 1.225 0.878 0.150189

The study reports exhaust mass composition, including PM 2.5 , EC, and organic mass (OM, estimated as 1.2 x OC) measured with TOT (denoted here as EC - TOT and OC - TOT). In the main study , the authors report that EC and OC are highly sensitive to the equivalence ratio. However , IMEP is highl y correlated with the measured equivalence ratio (R2 = 0.96). As such, it is reasonable to report the data as a function of IMEP, expecting it to have approximately equal explanatory

power as has the equivalence ratio variable. The figure below plots the emission data from Kweon et al. (2002) as a function of IMEP.

As shown in the figure, the EC - TOT work - specific emission rate is relatively insensitive to IMEP except between IMEP of approximately 0.85 and 1.1, where it undergoes a rapid increase. Overall, the EC - TOR/I MEP curve is S - shaped, similar to a logistic curve or growth curve. OC - TOT work specific emissions are highest at low IMEP (i.e. idle) and are monotonically lower with higher IMEP. Total work - specific PM 2.5 is not monotoni c, but appears to be described by a single global minimum around I MEP ~ 0.9 and two local maxima around IMEP of 0.2 and 1.2, respectivel y. The oppositely

signed slopes of the emission - IM EP curves for EC - TOT and OC - TOT suggest that there are different under ly ing phy sical processes. It is not the intent of this document to explicitly describe the particle - formation mechanisms in a diesel engine. However, the use of two separate functions to predict EC - TOT and OC - TOT separately is warranted. This implies th at the EC/OC ratio will vary

by engine operating mode. The following figure depicts the EC/OC ratio as a function of IMEP. **Pa rtic**  **le Ma**  **ss, EC-**  **TO T,**  **a nd**  **OC -TOT vs. IMEP** 

0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7

0 0. 2 0. 4 0. 6 0. 8 1 1. 2 1. 4 1. 6 **IME P** **S p** **e c** **i f** **i c** **e m** **i s** **s i** **o n** **( g** **/ I** **H P** **- H** **R )**  OC-T OT EC -T OT PM2.5190

**Estimation of IMEP**

**- based Emissions of EC and OC**  To produce a relationship that generalizes the implied relationship between EC - TO T and OC - TOT work - specific emissions and IMEP in the data presented by Kweon et al. (2004), it is necessary to specify some functional form of a relationship between the two. A priori , on the basis of visual inspection of the data, a flexible logistic - ty pe curve was fit to the data b y a least - squares minimization procedure using the Microsoft Excel “Solver” tool, which employ s the GRG2 optimization approach. The functional form of the logistic - ty pe curves fit to both the EC - TOT and OC - TOT data from Kweon et al. (2004) is as follows: C e A Y Bx  

 A least - squared error approach was implemented within Microsoft Excel to derive the coefficients for the logistic curves for EC - TOT and OC - TOT. The solutions to the fits are as follows:

Y A B C EC - TOT 2.12 × 10 - 5

- 9.79 4.67× 10 - 5 OC - TOT 0.155 - 2.275 - 0.859 Graphicall y, in comparison to observed values of EC - TOT and OC - TOT, the fitted curves result in predictions reasonabl y cl ose to the observed values. Furthermore, when compared to the observed PM 2.5 values, the sum of predicted EC - TOT and OC - TOT values predict the lack of monotonicit y and patterns of maxima and minimum seen in the PM 2.5 data. However, as a result of the values predicted b y these sigmoid - ty pe curves at high and low IMEP values, extrem e patterns in the EC - TOT/OC - TOT ratios predicted occur. These extreme values are **EC/ OC**  **R atio vs. IME**  **P fro**  **m K we**  **on et al. (2004)** 

**Not e**  **Logar ith**  **mic S cale** 

0.1 1 10 100

0 0.2 0.4 0. 6 0. 8 1 1.2 1.4 1. 6 IM EPEC/OC191

artifacts that result solely from the behavior of simplistic logistic curves at the bounds of IMEP in the observed data sets. As a result, for predictive purposes, the maxim um and minimum observed EC - TOT and OC - TOT values observed in the data set were set as the artificial limits of predicted EC - TOT and OC - TOT, respectivel y. While this approach is arbitrary, it does ensure that extreme predictions resulting from the selectio n of the logistic functional form do not occur. The following graph (log - scale) depicts the behavior of the TOT - based EC/OC ratio as a function of IMEP. As demonstrated on the graph, without the max/min constraints on predicted EC - TOT and OC - TOT, the pred icted ratio assumes values with a much broader range than found in the data.

The approach of constraining predictions to the maximum and minimum values observed in the measured data set is not grounded in an y theoretical basis, but is a “brute force” app roach. Future revisions to this analy sis may consider alternative approaches more grounded in accepted theoretical or statistical methodology . The logistic curves described above receive IMEP predictions from PERE to predict EC - TOT and OC - TOT emission rat es (g/bhp - hr) for every second of a driving cy cle. Combined with real - time work estimates from PERE, emissions are expressed in g/s, the same units required for MOVES. EC - TOT and OC - TOT emission rates are converted to TOR - equivalent rates for use in MOVES , using the TOT - TOR “calibration” relationships described above. Alternatively , TOT - equivalent rates can be used to compare with data from studies employ ing TOT for carbon anal ys is. It should be noted that these emission estimates are based on a single en gine. Therefore, predictions of EC and OC emission rates based on these relationships are insensitive to model y ear, although PERE - HD does vary frictional MEP as a function of model y ear. **Organic Carbon to Organic Mass Conversion** Carbon is only

one component of the organic material found in PM emission samples. Hy drogen, oxy gen, and nitrogen are also components of organic molecules found in exhaust PM. For this study , a simple set of OC/OM conversion ratios were emplo yed. Compari so n of EC

/O C Rat io (T

OT ) by I MEP With a nd With out M ax /M in C ons tr aints

0. 01 0. 1 1 10 100

0 0. 2 0. 4 0. 6 0. 8 1 1. 2 1. 4 1. 6

IM EPEC/OCRatio(TOT) EC /O C(T OT -P redic te d) EC /O C(T OT -P redic te d) wi th Cons tr aints EC /O C(T OT -Meas ured)192

Hey wood (1988) presents data on the chemical composition of diesel exhaust PM, presenting characterization of both the “extractable composition” and “dry soot” components of PM measured at idle and at 48 km/h. 114 The composition data is as follows: **Idle 48 km/h**  **Atomic formula** C 23 H 29 O 4.7 N 0.21 C 24 H 30 O 2.6 N 0.18 **OM/OC Ratio** 1.39 1.26 The data for the “extractable composition” is assumed to represent the organic mass of particles. The total molar weight to carbon molar weight ratio was used to convert OC to OM. The idle data from Hey wood were used when engine I MEP was 0.15 or under, corresponding to the idle mode of the cy cle employ ed by

Kweon et al. (2004). All other engine conditions employ ed the ratio based on the 48 km/h sample in Hey wood. 

### E.4 **Comparison of Predicted Emissions with Independent**  **Measurements** To ensure that predicted EC and OC emission rates from this approach are reasonable prior to an y application for MOVES, PERE - HD based EC and OC emission factors were compared with measured emission factors from an independent study . Shah et al. (2004) report EC and OC emission factor and rates for a series of heav y heavy - dut y diesel trucks (HHDT) in California. 115 Shah et al. report the results of emission testing using the CE - CERT Mobile Emissions Laborator y (MEL), a 53 - foot combination truck trailer containing a full - scale dilution tunnel designed to meet Code of Federal Register (CFR) requirements. The primary dilution tunnel is a full - flow constant volume sampler, with a double - wall insulated stainless s teel snorkel that connects the MEL directly to the exhaust sy stem of a diesel truck. PM collection sy stems were designed to meet 2007 CFR specification, including a secondary dilution sy stem (SDS). The 11 trucks sampled in this study were all large HHDDTs with engine model y ears 1996 - 2000, odometers between approximately 9,000 and 547,000 miles, and rated powers from 360 - 475 hp. I t should be noted that these trucks, on average, have larger engines and higher rated power than “ty pical” trucks on the road. Furthermore, they were loaded with only

the MEL , which weighs 20,400 kg. As a result, the emissions from these trucks do not reflect the expected variability in truck running weight described above and used in the PERE - HD runs for this study . Shah et al. (2004) report emission data for each of the four modes of the CARB HHDDT cy cle, including cold start/idle, creep, transient, and cruise. The test cy cle represents a wide range of driving patterns, as suggested in the table below. Note that th ese test cy cles are trip - based, so each begins and ends with the vehicle at stop.193

**Cycle Distance (mi)**  **Duration (s) Average**  **Speed (mph) Maximum** 

**Speed (mph) Maximum** 

**Acceleration (m**  **ph/s)** Cold start/idle 0 600 0 0 0 Creep 0.124 253 1.77 8.24 2.3 Transient 2.85 668 15.4 47.5 3.0 Cruise 23.1 2083 39.9 59.3 2.3 The following table presents the EC - TOT and OC - TOT emission rates reported in Table 6 of the study : **Rate Idle**  **Creep Transient**  **Cruise** EC (mg/mi) 340±140 446±115 175±172 OC (mg/mi) 607±329 182.9±51.2 74.7±56.3 EC (mg/min) 4.10±2.38 10.4±4.8 110.7±27.0 93.0±68.3 OC (mg/min) 20.9±11.6 17.0±6.4 45.5±13.2 42.3±26.8 The following graph illustrates the comparison between predicted EC - TOT and OC - TOT emission factors predicted b y PERE - HD and those reported by Shah et al. (2004). The letters “H,” “M,” and “L” refer to high, medium, and low accessory loads employ ed in the PERE - HD runs with IMEP based emission rates. As shown in the graph, it appears that for transient and cruise conditions, PERE - HD predicts the general between - cy cle trends in EC - TOT and OC - TOT emission factors. It appears that for the low - speed “creep cy cle,” PERE - HD or the IMEP - based emission rates underpredict total carbon (EC+OC) emission factors, but that the general trend in the EC/OC ratio is directionally

correct.194



### E.5 **Variability in Predicted EC and OC Emission Rates** Through the modeling approach us ed here the influence of variab i l ity in vehicle weight and engine displacement on heav y - duty EC and OC emission rates can be assessed. It should be noted that these relationships are contingent on the particular algorithms emplo yed in PERE - HD for estimati ng power and IMEP, as well as on the functional form of the IMEP - based emission relationship described above. As such, the anal ys is of variability in EC and OC emission rates is constrained within the functional forms of all models employ ed. The graph bel ow depicts the TOR - specific ratios of the total amount of EC and OM emitted across the transient driving cy cle. As is apparent, increasing running weight per unit of engine displacement is associated with an increased EC/OC ratio. The highest EC/OM ratio s, located in the upper right - hand - quadrant of the graph, correspond to vehicles loaded with extreme weight relative to the total available engine displacement.

Predicted EC and OC Emission Factors(g/mi) vs. Measured Values in Shah et al. (2004)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

H L M Shah avg. H Shah

avg. H L M Shah avg. Creep Cruise TransientEmissionFactor(g/mi) Average of OC g/mi Average of EC g/mi195

In general, these results reflect the role that running weight has on IMEP in a truck. Since IMEP correlates highl y with the air/fuel ratio (or equivalence ratio φ), the data suggest that EC/OC partitioning is driven b y t he py roly sis that occurs in engine s under load. Very few weight/displacement pairings are greater than 3,300 kg/ L . The following graph depicts the cumulative frequency distribution (CFD) of simulated weight/displacement ratios in PERE - HD.

For a 12 L engine, 3,000 kg/ L would correspon d to a running weight of 39600 kg (87,302 lb). Such vehicle loadings are infrequent, as they exceed Federal and state limits for vehicle weights on highway s. The graph below presents the cumulative distribution of simulated weights, based on EC /O M Rat io (T

OR-S pec if

ic) v ers us We ight /D isp laceme nt R at io f or Indi vi dual Tr uc k Sa mples Tr ans ient Dri vi ng Cy cle , H igh Accessor y L oad

0 5 10 15 20 25

0 1000 2000 3000 4000 5000 6000 7000 8000 **Running W eight** 

**/ D is**  **pl ac**  **emen t**  **(kg/l )** **E C** **/ O** **M R** **a t** **i o** 

**Di st**  **ribut io**  **n of k**  **g/l R at**  **io s**  **in Tra ns** **ie nt,**  **H ig**  **h Acce**  **sso ry**  **Loa d**  **Simulat io**  **n**

0 1000 2000 3000 4000 5000 6000 7000 8000

0 0. 05 0.1 0. 15 0. 2 0. 25 0. 3 0. 35 0. 4 0. 45 0.5 0. 55 0. 6 0. 65 0. 7 0. 75 0. 8 0. 85 0.9 0.95 1 Pe rc ent ilekg/l kg /l196

the VIUS mic rodata. Furthermore, the graph presents cumulative frequency distributions for several broad weight categories reported b y Ahanotu (1999) for trucks in the Atlanta metropolitan area. 116 Note that in the graph, the highest weight category reported by

Ahanotu (1999) is represented as 100%, although the actual maxima of observed trucks are unknown.

In general, the sensitivity of EC/OM ratios to the weight/displacement ratio suggest that properl y capturing the variability

in both inputs is key

to developing representative inputs for MOVES. 

### E.6 **Calculating EC/OC fraction by O**  **perating M** 

**ode** The modeling described in the previous sections has been employ ed to create second - by - second estimates of EC - TOR and OC - TOR emission factors for use in the MOVES emissionRateBy Age table . The next step of consists of appropriatel y binning the outputs to fit the MOVES operating mode structure . EC and nonECPM emission rates, , are the inputs to the MOVES model for PM inventory calculations. To convert the total PM rates calculated from heavy - dut y emissions anal ys is into EC and nonECPM rates, we must cal culate EC and nonECPM fractions b y operating mode s . Then, the total PM rate can be multiplied by the EC and nonECPM fractions to obtain EC and NonECPM input emission rates. PM emissions contain additional inorganic species. However, the total carbon (TC =EC + OC) composes almost all the PM 2.5 emissions from con ventional diesel emissions. As such, we use the EC/TC as a surrogate for the EC/PM emissions in MOVES. One of PERE’s outputs for heav y - dut y vehicles is the track road - load coefficients. For each individual weight in the distribution, PERE output s a set of A / B / C coefficients similar to the ones used to calculate VSP in the HC, CO, and PM emission rate anal ys is. We used these coefficients and weights to calculate VSP for each second using the equation below.

**Com par**  **ison of Si**  **mul ated Weights w**  **ith Atlan**  **ta Are**  **a Truck Measurements**

0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9 1

0 20000 40000 60000 80000 100000 120000 **Weig ht (lb)** **C u** **m u** **l a** **t i** **v e** **D i** **s t** **r i** **b u** **t i** **o n**  Simu la te d We ight GA T ec h Cl as s 9 Monday Midday GA T ec h Cl as s 9 Monday 3-7 PM GA T ec h Cl as s 8 Truc ks M- F Midday GA T ec h Cl as s 1 0-13 Truc ks D ay time GA T ec h Cl as s 1 0-13 Truc ks Ni ght time197 m a mv Cv Bv Av VSP t t t t t t 

   3

2 This equation is impleme nted slightly different ly than the one used for analy sis of the chassis dy namometer testing for PM, HC, and CO since the road load coefficients ( A , B , and C

) and weight (or mass) m were specific to each individual vehicle, not general to the regulatory class. In the PM, HC, and CO equation, the road load coefficients and denominator mass were not specific to the vehicle and the numerator mass was specific to the vehicle. We felt c onfident in using vehicle specific numbers because we performed the anal ys is using a full representative distribution of weights and displacements. Also, since we are interested in the EC and nonECPM fractions rather than the actual rates themselves, norm alizing b y the actual weight provides a more accurate picture. For example, a large engine operating at 90% of rated power (high VSP) would have a similar EC fraction as a small er engine operating at 90% of rated power, even though the large engine would likely be hauling a proportionally greater amount of weight. This is also supported by the previous research and anal ys is that relat es EC fraction to IMEP and not power itself. The large engine would, however , emit a larger EC rate than the smaller engine , but this difference in rates is captured b y our PM emission rate anal ys is. We separated vehicles into two different regulatory classes based on running weight (we did not have GVWR information). The weight distribution used in the anal ys is is shown belo w .

**R epresentative distribution of weights used in the EC/OC analysis.** 

Based on this weight distribution, we considered all vehicles weighing more than 40,000 lb to be HHD vehicles and all vehicles less than 40,000 to be MHD vehicles. This was a very si mple approach to stratifying by regulatory

class. As EC and nonECPM rates were also computed for each second during each cycle, we were able to average the EC and nonECPM rates by operating mode. Then, we calculated the fractions of EC and nonECPM for e ach operating mode. For the LHD classes, we used the MHD fractions, and for buses, we used the HHD fractions. 5000 15000 25000 35000 45000 55000 65000 75000 85000 95000 105000 115000 125000 135000 145000 0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5Percent

Weight_lb198 

##      OC

EC EC EC r r r f , EC NonEC f f



 1 The resulting EC fractions by operating mode are shown in Figure 2 - 20 in the main body of this report.199



## Appendix F **Heavy -**  **duty Gasoline Start Emissions Analysis Figures**  **Figure F**  **- 1**  **. Cold -**  **Start Em issions (**  **FTP, g) for Heavy -**  **Duty Gasoline Vehicles, averaged by Model -**  **year and Age Groups**  **(a )**  **CO**

**(b )**  **TH C** 

**(c )**  **NO x** 200

**Figure F**  **- 2**  **. Cold -**  **Start FTP Emissions for Heavy -**  **Duty Gasoline Vehicles, GEOMETRIC MEANS by Model**   ** year and Age Groups** 

### (a **) CO** 

**(b) TH**  **C**

**(c )**  **NO x** 201

**Figure F**  **- 3**  **. Cold -**  **start FTP Emissions for Heavy -**  **Duty Gasoline Trucks: LOGARITHMIC STANDARD DEVIATION by Model**  **- year and Age Groups**  **.** 

### (a **) CO** 

**(b) TH**  **C**

**(c )**  **NO x** 202

**Figure F**  **- 4**  **. Co ld**  **- Start Em**  **issions for Heavy -**  **Duty Gasoline Trucks: RECALCULATED ARITHMETIC ME**  **ANS by Model -**  **year and Age Groups.** 

### (a **) CO** 

**(b )**  **TH C** 

**(c ) N**  **Ox**203 **Table F**  **- 1**  **. Emission Standards for Heavy**  **- Duty Spark** **- Ignition On**  **- road Engines**  Regulatory Class Model Year Emissions Standards (g/hp - hr) CO THC NMHC NOx NMHC + NOx LHD2b3 1990 14.4 1.1 6.0

1991 - 1997 14.4 1.1 5.0 1998 - 2004 14.4 1.1 4.0 2005 - 2007 14.4 1.0 2008+ 14.4 0.14 0.20 LHD45 , MHD 1990 37.1 1.9 6.0

1991 - 1997 37.1 1.9 5.0 1998 - 2004 37.1 1.9 4.0 2005 - 2007 37.1 1.0 2008+ 14.4 0.14 0.20204



## Appendix G **Heavy -**  **duty CNG Bus**  **Em issions**  **Supplemental Figures**  In our anal ys is, we found for model y ear group 1994 - 2001 that whether or not the bus was equipped with after - treatment technology did not greatl y affect its emissions, so we decided to group all the buses together regardless, as shown in the figures below. **Figure G**  **- 1**  **. NOx emissions of vehicles with after**  **- treatm**  **ent versus no after -**  **treatm ent tested on the CBD cycle.** 

0 10 20 30 40 50 60

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002**G r** **a m** **s /** **M i** **l e** 

**Model Year** No AT (0-3 Age Group) No AT (4-5 Age Group) AT (0-3 Age Group)205

**Figure G**  **- 2**  **. CO em issions of vehicles with after**  **- treatm**  **ent versus no after -**  **treatm ent tested on the CBD cycle.** 

**Figure G**  **- 3**  **. PM em issions of vehicles with after**  **- treatm**  **ent versus no after -**  **treatm ent tested on the CBD cycle.**  0 5 10 15 20 25

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002**G r** **a m** **s /** **M i** **l e** 

**Model Year** No AT (0-3 Age Group) No AT (4-5 Age Group) AT (0-3 Age Group)

0 0.05 0.1 0.15 0.2 0.25 0.3

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002**G r** **a m** **s /** **M i** **l e** 

**Model Year** No AT (0-3 Age Group) No AT (4-5 Age Group) AT (0-3 Age Group)206

**Figure G**  **- 4**  **. THC emissions of vehicles with after**  **- treatment versus no after**  **- treatm**  **ent tested on the CBD cycle.**

**Figure G**  **- 5**  **. CH 4**  **em issions of vehicles with after**  **- treatm**  **ent versus no after -**  **treatm ent tested on the CBD cycle.**  0 5 10 15 20 25

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002**G r** **a m** **s /** **M i** **l e** 

**Model Year** No AT (0-3 Age Group) No AT (4-5 Age Group) AT (0-3 Age Group)

0 5 10 15 20 25

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002**G r** **a m** **s /** **M i** **l e** 

**Model Year** No AT (0-3 Age Group) No AT (4-5 Age Group) AT (0-3 Age Group)207

**Figure G**  **- 6**  **. Total energy consumption of vehicles with after -**  **treatm ent versus no after**  **- treatm**  **ent tested on the CBD cycle.** 

0 10000 20000 30000 40000 50000 60000

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002**B T** **U /** **M i** **l e** 

**Model Year** No AT (0-3 Age Group) No AT (4-5 Age Group) AT (0-3 Age Group)208



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