## Appendix G

Emissions Analysis Methodology and Results

Medium-Heavy Duty Diesel Trucks, Heavy-Heavy Duty D iesel Trucks,

and Regulated Bus Categories-iTable of Contents

List of Acronyms

................................................... ................................................... .....iii A. Introduction

................................................... ................................................... ..... 1 B. Methodology

................................................... ................................................... .... 2 C. Statewide Emissions Analysis Updates

................................................... ........... 2 1) Construction Trucks ................................

................................................... ....... 2 2) Medium Heavy-Duty Diesel Truck Population Split.... ....................................... 7 3) Impact of Recession and Growth by Category......... ......................................... 8 4) Motorcoach/Buses ...................................

................................................... ...... 8 5) Out-of-State Vehicle Miles Traveled ................ ............................................... 10 6) Odometer Capping...................................

................................................... .... 11 D. 2008 Truck Field Data Collection

................................................... .................... 12 1) Study Design and Data Collection Locations ......... ......................................... 12 2) Regional Allocation Based on OD Survey Data ........ ...................................... 18 3) OD and LP Survey Data Analysis ..................... .............................................. 22 4) Discussion of Field Study Results.................. ................................................. 29 E. Regional Allocation of Activity and Emissions ................................................ 30 1) Interstate Trucks ..................................

................................................... ........ 30 2) Heavy-Heavy Duty In-State Tractors ................. ............................................. 31 3) Heavy-Heavy Duty In-State Single-Unit Trucks ....... ....................................... 31 4) Medium-Heavy Duty In-State Trucks .................. ............................................ 33 5) Drayage Trucks.....................................

................................................... ....... 34 6) Construction Trucks ................................

................................................... ..... 35 7) Agricultural Trucks ................................

................................................... ....... 36 8) Utility Trucks .....................................

................................................... ........... 37 9) Regional Allocation Results ........................

................................................... . 38 F. Impact of the Recession on VMT and Vehicle Sales ........................................ 38 1) Drayage Trucks.....................................

................................................... ....... 39 2) Construction Trucks ................................

................................................... ..... 40 3) MHDDT..............................................

................................................... .......... 40 4) HHDDT ..............................................

................................................... .......... 42 G. Proposed Regulatory Scenarios and Compliance Assump tions .................... 43 1) MHDDT Rule Compliance (<26,000 lbs GVWR)........... .................................. 43 2) Small Fleet Rule Compliance (>26,000 lbs GVWR)..... ................................... 44 3) Large Fleet Rule Compliance (>26,000 lbs GVWR)..... ................................... 46 4) Agricultural Truck Compliance Assumptions.......... ......................................... 47 5) Utility Truck Compliance Assumptions............... ............................................. 47 6) Drayage Truck Compliance Assumptions ............... ........................................ 48 7) School Bus Compliance Assumptions .................. .......................................... 49 8) Regulatory Credit and Other Provisions - Not Modele d in the Inventory......... 49 H. Emissions Results

................................................... ........................................... 50 1) Evaluation of Baseline Statewide Fuel Usage Estimat es................................ 50 2) Estimating Statewide Emissions Benefits of the Prop osed Regulation........... 51-ii3) PM and NO x

Emissions Benefits from both the Truck and Bus and In-Use OffRoad Rules are Preserved...........................

................................................... ....... 55 4) Meeting SIP Targets ................................

................................................... .... 56 I. Additional Documentation

................................................... ............................... 58 J. References

................................................... ................................................... ..... 62 K. Attachment – CARB Origin-Destination Survey Questio nnaire ...................... 65-iiiList of Acronyms AADTT Annual Average Daily Truck Traffic ARB Air Resources Board BACT Best Available Control Technology BEA Bureau of Economic Analysis BLS Bureau of Labor Statistics BOE Board of Equalization BTS Bureau of Transportation Statistics Caltrans California Department of Transportation CBA California Bus Association CPUC California Public Utilities Commission CY Calendar Year DMV Department of Motor Vehicles DOF Department of Finance DPF Diesel Particulate Filter EDA Equipment Data Associates FHWA Federal Highway Administration GDP Gross Domestic Product GVWR Gross Vehicle Weight Rating HDT Heavy-Duty Truck HHDDT Heavy Heavy-Duty Diesel Trucks IFTA International Fuel Tax Agreement IRP International Registration Plan LP License plate MHDDT Medium Heavy-Duty Diesel Trucks MVSTAFF California Motor Vehicle Stock, Travel and Fuel Forecast MY Model Year NAAQS National Ambient Air Quality Standards NOOS Neighboring out-of-state NO x Nitrogen Oxides NNOOS Non-neighboring out-of-state OD Origin-Destination OOS Out-of-state PM Particulate Matter POAK Port of Oakland POLA Ports of Los Angeles and Long Beach PTO Power Take Off SCAG Southern California Association of Government s SIP State Implementation Plan TEU Twenty-foot equivalent unit UCC Uniform Commercial Code USDOE United States Department of Energy USEPA United States Environmental Protection Agenc y USGS United States Geological Survey VIUS Vehicle Inventory and Use Survey VMT Vehicle Miles Traveled-iv-G-1

A. Introduction

Commercial heavy-duty diesel trucks and buses (comm ercial diesel buses and trucks exceeding 14,000 lbs gross vehicle weight rating (G VWR)) are currently the single largest source of nitrogen oxide (NO x ) emissions in California, accounting for 30 percen t of statewide NO x

emissions (ARB, 2008). These same trucks and buse s are also the largest source of diesel particulate matter (diesel PM) in California, representing about 40 percent of statewide diesel PM emissions.

In December 2008, the Air Resources Board (ARB or B oard) adopted the Statewide Truck and Bus Rule (regulation). That regulation w as designed to achieve emissions reductions by requiring truck and bus fleet owners to modernize their fleets and install exhaust retrofits. As part of that regulatory deve lopment, staff developed a statewide emissions analysis that quantified emissions from d ifferent types of trucks and buses separated by registration type, body type and/or vo cation as well as mileage thresholds. The assumptions used in development of that emissio ns analysis were published in Appendix G of the technical support document (TSD) for the 2008 rulemaking, and the results of the inventory were published in an on-li ne database (ARB, 2008).

In December 2008, the National Bureau of Economic R esearch declared that the United States had entered an economic recession. That rec ession turned out to be the most severe in the United States since the Great Depress ion. In December 2009, staff reported its statewide estimate of the impact of th e economic recession on truck and bus emissions. Results suggested that emissions in 2009 across all trucks and buses operating in California were approximately 20 perce nt lower than estimates provided in the 2008 Rule staff report (ARB, 2009). As a resul t of the recession, the Board directed staff to develop regulatory amendments to the Rule that provide economic relief while ensuring air quality goals would still be attained.

The emissions inventory developed to support the pr oposed regulatory amendments builds upon the 2008 and 2009 work. We revised the inventory methodology to improve the quality and accuracy of emissions estimates. W e revised growth rates and forecasted vehicle age distributions to reflect the impact of the economic recession both today and in the future. We developed a new proced ure for allocating statewide emissions to each air basin in California so the im pact of the Rule could be evaluated regionally. Finally, we assessed the impact of the proposed regulatory amendments on emissions from Trucks and Buses on a statewide and regional basis. This appendix provides the fundamental analysis behind updates to the emissions analysis, regional emissions estimates, the impact of the economy on e missions, and the emissions impact of new regulatory amendments.G-2

B. Methodology

Emissions are calculated as the product of a popula tion of vehicles, the number of miles each vehicle travels, and emission rates per mile. Beneath this simple equation lies a series of data and assumptions about the population , miles traveled, and emission rates per vehicle model year in a given calendar year, gr owth and attrition estimates, deterioration, and other factors that affect emissi ons estimates, all of which were discussed in the 2008 Rulemaking. We applied the c oncept separately for each analysis category of trucks that shares similar tra vel, service, size, age or other characteristics. 

# ∑ MY, C 

# ( POP MY, C X AC MY, C X ER MY, C 

# ) = EMS CY , Equation 1

where: POP MY, C is the population of trucks for model year MY with in each analysis

category C for a given calendar year; AC MY, C is the accrual rate (miles traveled per year) per truck by model year MY and analysis category C in a given calendar year ; ER MY, C is the calculated emission rate, in grams pollutant per mile driven, assuming statewide speed travel distributions in EM FAC2007 and fleet specific cumulative mileage accrual over the life o f the truck, by model year MY and analysis category C; EMS CY is the emissions calculated in tons per day for a given calendar year.

The basic calculation methodology is unchanged from that described in Appendix G of the Rule Initial Statement of Reasons published in ARB (2008). Emissions analysis updates described in this document relate to change s in inputs to the methodology.

The emissions analysis methodology published in 200 8 involved applying annual average growth rates that are independent of the ec onomic cycle. In 2009 staff developed a scaling model that was designed to asse ss the impact of multiple potential economic forecast scenarios on emissions (ARB, 2009 ). For this rulemaking, staff are using an emissions forecast that reflects the avera ge of a fast recovery and slow recovery bounding scenario. This emissions forecas t scenario is input directly to the emissions model for the analysis described in this document.

C. Statewide Emissions Analysis Updates

1) Construction Trucks In the emissions analysis presented to the Board in December, 2008, ARB staff categorized the medium heavy-duty diesel trucks (MH DDT) and heavy heavy-duty diesel trucks (HHDDT) 1 into different fleet categories that differentiate d trucks and buses by registration type (in-state vs. interstate ), body type (tractor, single-unit, etc.),

1 Heavy-duty diesel trucks include MHDDT with a GVWR above 14,000 lbs and up to 33,000 lbs, and HHDDT with GVWR above 33,000 lbs.G-3

and vocation (agricultural, construction, drayage, etc.). Over the past year staff listened to stakeholder comments and evaluated the impact of the recession on the construction sector. Our analysis suggested that trucks involve d in construction have been impacted more severely by the recession than other trucking sectors. In order to reflect this finding, staff separated trucks owned by constructi on firms from other trucking categories in the inventory, and then developed an alternate economic forecast reflecting the economic downturn on construction tr ucking.

Like other vocational trucking categories, trucks o wned by construction firms cannot be easily identified through the California Department of Motor Vehicles (DMV) registration database. To identify a population of construction trucks, staff analyzed data in the Vehicle Inventory and Use Survey (VIUS), (VIUS, 200 2). Construction trucks were identified in the survey by selecting “construction ” as the industry in which the vehicle was most used. Based on the survey, staff estimate d the fractions of trucks in the construction industry relative to the population in the inventory fleet category and the results are shown in Table 1.

Table 1. Percentage of Construction Trucks by Inven tory Category

Percent of Construction Trucks in Each Category
MHDDT instate 21 HHDDT tractor 20 HHDDT single-unit 40 HHDDT CAIRP 2 6

Next, staff evaluated the age distribution of const ruction trucks in VIUS and compared that to all trucks in VIUS. Results suggested that construction trucks have roughly the same average age profile as other trucking categori es, and as a result staff decided to assume that construction trucks by body type have t he same age distribution as nonconstruction trucks of the same body type and regis tration status. Table 2 compares the average age of construction trucks against non- construction trucks by inventory category.

Table 2. Construction vs. Non-Construction Truck A verage Age (VIUS, 2002) 3

Construction General Tractor 8.0 7.4 Single Unit (non-tractor) 7.6 7.2

2 CAIRP: California registered International Registr ation Plan (IRP) trucks
3 VIUS grouped all age 16 and older into one categor y and only trucks age 15 or newer in VIUS were included in the analysis.G-4

Staff assumed that all in-state registered tractors and single-unit construction trucks followed the in-state single unit accrual schedule as documented (ARB, 2008).

The emission factors remain unchanged; as a result HHDDT single-unit emission rates are used for HHDDT construction single-unit trucks, HHDDT tractor emission rates for HHDDT construction tractors, HHDDT IRP truck emissi on rates for HHDDT construction IRP trucks, and MHDDT emission rates for MHDDT cons truction trucks.

To estimate the impact of the recession on construc tion activity in California, staff reviewed a variety of economic indicators. Surroga tes for construction emissions such as employment, gross domestic product, building per mits, value of equipment financed, and fuel consumption indicate that construction act ivity decreased between 30 percent and 80 percent in California between 2006 and 2009, as shown in Table 3.

Table 3. Impact of Recession on Economic Metrics

Data Type Change Relative to Peak Data Source
CA Construction Employment (2006-2009) -30% US Bureau of Labor Statistics (BLS, 2010) CA Construction GDP (2005-2008) -30% US Bureau of Economic Analysis; (BEA, 2009) CA Construction Taxable Fuel Refunds (2006-2008) -40% CA Board of Equalization; (BOE, 2008) CA Construction Valuation (2005-2009) -65% CA Dept of Finance; (DOF, 2010) CA New Building Permits (2004-2009) -80% CA Dept of Finance; (DOF, 2010) CA New Equipment Sales Financed (2005-2009) -80% Equipment Data Associates; (EDA, 2010)

Based on above reviews, staff estimated a 50 percen t reduction in activity from 2006 to 2009. Staff generated an estimation of average rec overy using bounding fast and slow recovery scenarios to represent both vehicle miles traveled (VMT) growth assumptions, as shown in Figure 1, and vehicle sales in the cons truction sector, as shown in Figure 2. Construction vehicle sales trends were estimated us ing the relationship between vehicle sales and economic trends documented in the staff r eport for the 2008 Truck and Bus rulemaking (ARB, 2008).G-5

Figure 1. Construction Vehicle Miles Travelled (VMT ) Growth Assumption

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

2006 2008 2010 2012 2014 2016 2018 2020 2022Growth Relative to 2006

Historical trend Slow Fast AverageG-6

Figure 2. Construction Truck Sales Trend and Estima ted Recovery

a) Heavy Heavy-Duty Construction Truck Sales

0 50 100 150 200 250 300

1985 1990 1995 2000 2005 2010 2015 2020 2025HHDDT Cosntruction Sales (thousands)

Historical Trend Fast Slow Average Historical

b) Medium Heavy-Duty Construction Truck Sales

0 50 100 150 200 250 300

1985 1990 1995 2000 2005 2010 2015 2020 2025MHDDT Construction Sales (thousands)

Historical Trend Fast Slow Average HistoricalG-7

2) Medium Heavy-Duty Diesel Truck Population Split

Diesel fueled trucks with a GVWR between 14,000 and 33,000 lbs GVWR are considered to be MHDDT. The proposed amended regu lation would exempt vehicles with a GVWR less than 26,000 lbs from meeting PM fi lter requirements. To estimate the population of vehicles that are above 26,000 lb s within the MHDDT category staff analyzed the GVWR code of MHDDT in the DMV registra tion data (DMV, 2005). Results suggest that about 30 percent of the MHDDTs statewide are above 26,000 lbs and they have different age distribution than the o verall medium heavy-duty diesel trucks as shown in Figure 3.

Figure 3. Medium Heavy-Duty Diesel Truck Age Distr ibutions

0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 AgePercent in Category

Between 26,001 and 33,000 lbs GVWR All Medium-Heavy

The average age of diesel trucks with GVWR between 26,000 and 33,000 lbs is about 11 years old while the overall medium heavy-duty di esel is about 8 years old. To reflect the differences in age distribution and population fraction, staff assumed that 30 percent of MHDDTs between age 9 and 13, 58 percent of the o lder than 13 years and 20 percent of vehicles newer than nine years old are M HDDT above 26,000 lbs. This assumption is applied to MHDDT instate and intersta te trucks.G-8

3) Impact of Recession and Growth by Category

As described in ARB (2009), the recession has had a major impact on the trucking industry. In order to develop the updated emission s analysis, staff revisited fuel and economic truck activity indicators to estimate the impact of the recession on truck and bus operations in California. Using the same metho dology as described in ARB (2009) staff developed an estimate for an average recovery forecast based on fast and slow bounding recovery scenarios.

Staff’s previous analysis identified a relationship between overall economic trends and truck sales. The impact of this relationship is th at as the economy has deteriorated as a result of the recession, national new truck sales h ave decreased and fewer new vehicles are registered in California (ARB, 2009). Because of this relationship, staff has developed both forecasts for the impact of the redu ced total vehicle activity caused by the recession on emissions by truck category, and f orecasts for the impact of reduced vehicle sales on emissions by truck category. Deta iled assumptions by category are presented in Section F.

4) Motorcoach/Buses

In the December 2008 Board Hearing, the Board adopt ed specific regulatory provisions for motorcoaches. In order to reflect these regula tory provisions, staff consulted with the California Bus Association (CBA) to obtain info rmation to assist in estimating the population, mileage accrual rates, and other assump tions for the motorcoach inventory category. The CBA provided population estimates fr om the California Public Utility Commission (CPUC, 2009), and results of a survey (C BA, 2010) that could be used to assess mileage accrual. The age distribution was a ssessed by matching registered motorcoach companies against the buses those firms registered in the DMV database. Staff subtracted the motorcoach population by age f rom the other bus category, and assumed a 1.75 percent growth rate per year in the absence of the recession. Staff used HHDDT emission rates because motorcoaches are rated in excess of 33,000 lbs GVWR, and emission rates for the other bus category from which motorcoaches were extracted are based on a weighted average of heavie r and lighter buses.

To estimate the impact of the recession, staff used CBA provided population estimates in 2000 (3376), 2008 (3589), and 2009 (3125). Age distributions were forecasted by assuming heavy heavy-duty vehicle sales forecasts d eveloped using the fast and slow recovery bounding scenarios. Table 4 provides assu med motorcoach populations and VMT, while Figure 4 shows the assumed accrual sched ule.G-9 Table 4. Motorcoach Population and VMT

Motor Coach Population VMT (million miles per year)

Year No Recession With Recession No Recession With Recession

2000 3320 3320 147 147
2005 3670 3670 160 160
2007 3760 3451 166 153
2008 3914 3294 169 143
2009 4042 3125 172 133
2010 4113 3240 175 137
2014 4374 3775 187 158
2020 4834 4375 208 183

Figure 4. Motorcoach Mileage Accrual

0 10,000 20,000 30,000 40,000 50,000 60,000-1135791113151719212325272931333537394143 AgeMiles per year

Other Bus MotorcoachG-10

5) Out-of-State Vehicle Miles Traveled

The 2008 inventory analysis assumed a set amount of VMT by out-of-state (OOS) trucks in California in 2005, based on information from the International Fuel Tax Agreement (IFTA) program provided by email from the California State Board of Equalization (BOE, 2010a). After the 2008 inventor y was developed BOE provided 2007 calendar year estimated out-of-state truck VMT and fuel usage in California. This number was substantially lower than what had been p rovided in 2005. In 2010, staff received 2008 and 2009 estimated out-of-state truck VMT and fuel usage in California. Overall, data representing California registered tr ucks in the IFTA program appears plausible, with a 20 percent increase between 2005 and 2007, and then a 20 percent decrease due to the recession from 2007 to 2009. O ut-of-state VMT data shown in Table 6 are not consistent between 2005 and 2007-20 09. For example, the 2005 data provided by BOE are much higher than any of the 200 7-2009 data, and the 2008 data are higher than 2007 data. These trends are not co nsistent with either economic surrogates or overall on-road fuel trends. Because of these anomalies in the IFTA data, staff decided to adjust the IFTA numbers for out-of -state trucks to reflect our best estimate of what out-of-state truck VMT in Californ ia should be given the totality of available data. To adjust the data, staff calculat ed the average annual truck VMT between 2007 and 2009 for OOS and for CA registered IFTA respectively, and then back-casted 2005 out-of-state IFTA miles based on t he ratio of the two averages and 2005 CA registered IFTA truck miles. Staff then ap plied the impact of the recession as measured by the BOE data for on-road fuel trends. The BOE IFTA data, as well as staff assumptions based on the data are shown in Table 5.

Table 5. Reported and Estimate Interstate VMT (mil lion miles/year) in California: 2005-2009

Year CA Registered

IFTA Truck Miles in CA Reported

OOS Truck Miles in CA Modeled OOS

Truck Miles in CA no recession Modeled OOS

Truck Miles in CA with recession

2005 2,189 4,398 3,144 3,144

2007 2,660 2,943 3,419 3,397

2008 2,213 3,761 3,601 3,115

2009 2,134 3,358 3,654 2,843G-11 0 10 20 30 40 50 Truck AgeOdometer

Current Method Revised Method 6) Odometer Capping

Truck emission rates are a function of cumulative m ileage on the vehicle through an emissions process called deterioration. The cumula tive lifetime mileage on a vehicle can be measured using the vehicle’s odometer. Prev ious analyses, as well as the EMFAC2007 model, assumed that odometer is a functio n of the year by year mileage accrual. To evaluate this assumption, staff collec ted truck “for-sale” records from truckpaper.com that contained odometer readings, as well as odometer records from the Goods Movement Bond Program and data in VIUS (2 002). Results demonstrated that odometer is not simply a function of the year by year mileage accrual, and instead odometer readings tend to level off depending on en gine design life. This concept is shown in Figure 5.

Figure 5. Schematic Representation of Difference b etween Current and Revised Method for Estimating Vehicle Odometer

The reason for the difference is that when a truck or vehicle is young, the average odometer is a function of some high accruing and so me low accruing vehicles. As vehicles age higher mileage vehicles are scrapped m ore quickly than lower mileage vehicles. So, surviving older vehicles are more li kely to have accrued fewer miles than average when those vehicles were younger. The net result is that odometer is a function of both accrual rates and vehicle survival rates, and observed odometer readings in practice tend to level out to a constan t level for older vehicles. Staff decided to cap odometer readings at a specific value for ea ch truck category and used cappedG-12

odometer-age relation to generate emission rates by age. The choice of the cap depends on the data sources evaluated; in this case staff decided to cap odometer levels at 800,000 for HHDDTs, and at 400,000 for MH DDTs, as shown in Table 6. Overall, the change in odometer led to less than a 5 percent change in the inventory for any calendar year.

Table 6. Odometer Cap by Inventory Category

Inventory Category Survival Rate Based Odometer Reading Cap Value
MHDDT Ag 400,000 MHDDT CAIRP 400,000 MHDDT instate 400,000 MHDDT OOS 400,000 HHDDT Ag 800,000 HHDDT CAIRP 800,000 HHDDT NNOOS 800,000 HHDDT NOOS 800,000 HHDDT other port 800,000 HHDDT POAK 800,000 HHDDT POLA 800,000 HHDDT single No change from ARB (2008) HHDDT tractor 800,000 MHDDT utility No change from ARB (2008) HHDDT utility No change from ARB (2008) MHDDT instate construction 400,000 HHDDT single construction No change from ARB (2008) HHDDT tractor construction 800,000 HHDDT CAIRP construction 800,000

D. 2008 Truck Field Data Collection

1) Study Design and Data Collection Locations
In 2008 ARB staff implemented a large-scale field d ata collection effort. The primary objective of this effort was to obtain information to improve regional allocation estimates of heavy-duty truck (HDT) activity. The data colle ction effort consisted of two components: license plate (LP) surveys and origin- destination (OD) surveys. License plate surveys consisted of time-stamped digital pho tographs at each location, and provided the mixture of trucks by registration type at 15 weigh stations and 15 roadside locations 4 . OD surveys were collected by intercepting and ta lking with truck drivers to obtain information on the truck and on the current trip. OD surveys provided trip information which helped to allocate the trip VMT i nto geographic areas, and were

4 The license plate data collection effort obtained data from 16 weigh stations and 30 roadside locatio ns. However, given the time required to process data, s taff prioritized the list of locations to be proces sed so that most weigh stations and major representative r oadside locations in the South Coast, the San Joaqu in Valley and the San Fransisco Bay Area are included in the analysis.G-13

collected at 18 weigh stations and 3 roadside locat ions. Attachment A provides the OD survey instrument. A total of 54,570 license plate s and 6,215 OD surveys were collected and processed for the regional allocation analysis. Most of the data were collected during the daytime and in the middle of t he week.

The survey was designed to capture both inter-regio nal and local trips. Two types of survey sites were considered: locations on major f reeways at the border of an air basin and locations inside a particular region or air bas in. Locations near state borders were omitted from this study to focus on intra-state tru ck traffic. Interstate truck traffic data collected by UC Davis (Lutsey, 2008) were used to i nform and supplement data collected through the ARB field data collection; th at collection effort was focused on roadways near California’s border with Mexico, Neva da, Arizona, and Oregon.

Data collection covered all major portions of the C alifornia state highway system, but a higher concentration of data was collected in the S outh Coast Air Basin and San Joaquin Valley Air Basin. OD surveys were conducte d primarily at weigh stations for safety purposes, and tended to be biased towards in ter-regional trips because weigh stations are typically located between rather than within regions. License plate collection sites were focused on assessing the mix of different types of vehicles operating within a region, and selected locations w ere located across a region of interest. Table 7 and Figure 6 show survey locations.G-14 Table 7. ARB 2008 Statewide HDT LP and OD Survey Pl an

Site ID# Route County Name/Description Direction*

LP OD

1 I-80 Solano Cordelia EB/WB 24 2 3 2 I-10 Riverside Desert Hills EB/W B 24 3 3 3 CA-91 Orange Peralta EB/W B 9 2 3 4 I-5 Los Angeles Castaic NB 9 3 5 I-15 San Bernardino Cajon NB/SB 9 2 3 6 US-101 Ventura Conejo NB/SB 9 2 3 8 I-5 San Diego San Onofre NB/SB 9 2 3
9 I-680 Contra Costa Walnut Creek NB/SB 9 3
10 I-680 Alameda Mission Grade NB 9 2 3
11 I-880 Alameda Nimitz NB/SB 9 2 3
12 I-580 Alameda Livermore EB/W B 9 2 3 13 US-101 Santa Clara Gilroy NB/SB 9 2 3 14 I-15 San Diego Rainbow NB/SB 9 3 15 5 Kern Grapevine SB 9 2 3
16 5 Merced Santa Nella NB/SB 9 2 3 17 99 Merced Chowchilla River NB 9 2 3
18 58 Kern Keene EB 9 2 3
19 58 Kern Cache Creek W B 9 2 3

21 I-710 Los Angeles Near Jct 105 NB/SB 9 2 23 I-605 Los Angeles Near Jct. 5 NB/SB 9 2 25 I-105 Los Angeles Near Jct 19 EB/WB 9 2 26 CA-60 Los Angeles Near Jct 710 EB/W B 9 2
30 I-15 San Bernardino South of Jct CA 60 NB/SB 9 2
34 CA-91 Riverside Near Jct 215 EB/W B 9 1 35 CA-60 Riverside Near Jct 91 EB/W B 9 1 38 CA-237 Santa Clara Near Great America Parkway EB/W B 9 1
39 I-880 Alameda Near Oakland/Jct Rte 77 NB/SB 9 1 40 I-280 San Mateo Near Daly City/Ocean Ave NB/SB 9 1
41 US-101 Alameda Near Jct 84 NB/SB 9 1
42 I-5 Sacramento at Arena Blvd NB/SB 9 1
47 CA-99 Fresno Fowler NB/SB 9 1
49 CA-198 Kings Near Jct 41 EB/WB 9 1
50 CA-46 Kern Near Jct 43 EB/WB 9 1

51 9 1
52 9 1
53 9 1 Number of days per
direction

Weigh Stations

Roadside/overpass Survey Sites Hours of work per day

Etiwanda and the 60 Riverside and Agua Mansa (colton) 91 Fwy and the 15 Fwy (Corona)

* Direction: NB-northbound; SB-southbound; EB-east bound; WB-westboundG-15 Figure 6. ARB 2008 HDT Field Study Survey Location s

Information from OD surveys and collected license p lates were annotated and integrated into a database. License plate data wer e evaluated and cross referenced to the DMV database to obtain state of registration fr om all trucks, model year and GVWR from California-registered trucks.

OD surveys were generally conducted during daytime hours, with the exception of the Desert Hills weigh stations where additional survey s were collected through the night. Survey teams invited truck drivers to complete a su rvey while the trucks were weighedG-16

and inspected. The surveyor inspected the truck an d engine to obtain basic truck information and asked truck drivers questions verba lly to help fill out the OD survey questionnaire. The questionnaire was prepared in b oth English and Spanish. Staff manually input OD survey data from each paper surve y into a database. Using Arc GIS tools, each OD trip, including its origin, destinat ion and route, was mapped onto the roadway system. Trips were parsed into segment by county boundary. Therefore, total trip distance and miles traveled in each air basin were generated for each trip. VMT were summarized by each truck category, survey loca tion, trip type, and air basin.

License plate images were collected from trucks pul ling through a weigh station or traveling on the roadway network at normal speeds u sing high performance digital cameras between February and May 2008. At each dir ection-specific survey location, license plate images were collected during daylight hours on weekdays for one to two days, except for the Desert Hills weigh station. A t the Desert Hills weigh station, additional license plate images were also collected during night hours and on Saturday in both directions. LP pictures were manually inpu t from each digital photograph into a database, and cross-referenced against the DMV regi stration database to obtain model year, body type, and other characteristics. Table 8 shows the data collection period, the number of OD records and the number of LP records c ollected and analyzed at each site.G-17 Table 8. ARB 2008 Statewide HDT LP and OD Data Summ ary

Date Start End Weigh Stations

1 I-80 Cordelia WB 12/17/2007 14 23 519

WB 12/18/2007 0 9 746

4/15/2008 6 15 132 4/16/2008 6 16 137 4/17/2008 x x 133
2 I-10 Desert Hills EB/WB 3/4/2008 2 16 71 691 3/5/2008 1 23 82 62
3/6/2008 0 12 85 513
3/8/2008 6 13 51 3/19/2008 8 15 128 562
3/20/2008 7 12 130 361
3 CA-91 Peralta EB/WB 4/21/2008 6 14 59 4/22/2008 6 14 64 4/23/2008 6 14 56 4/24/2008 6 13 69 4/28/2008 6 15 62 4/29/2008 6 14 76 5/12/2008 8 11 284 5/13/2008 7 15 1570
4 I-5 Castaic NB 4/2/2008 8 12 46 4/3/2008 7 12 70 4/7/2008 7 17 70 4/9/2008 7 12 42 4/10/2008 8 13 70 5 I-15 Cajon NB/SB 3/17/2008 7 14 28 163 3/18/2008 7 14 26 207 4/2/2008 7 14 33 249 4/3/2008 8 12 14 4/7/2008 9 13 60 4/28/2008 8 15 127 4/29/2008 7 15 124 4/30/2008 7 10 34 6 US-101 Conejo NB x 7 17 212 4/15/2008 8 15 83 4/16/2008 6 15 20 333 4/17/2008 7 14 52 4/30/2008 6 10 54
8 I-5 San Onofre NB/SB 4/21/2008 7 15 141 1206 4/22/2008 7 15 141 4/23/2008 7 15 143 1004
9 I-680 W alnut Creek NB/SB 4/28/2008 7 13 80 4/30/2008 7 9 36 5/15/2008 6 15 53
10 I-680 Mission Grade NB 4/28/2008 6 13 64 576 4/29/2008 7 14 112 557 4/30/2008 x x 25
11 I-880 Nimitz NB 5/19/2008 6 16 70 903 5/20/2008 6 16 136 431 5/21/2008 6 15 55
12 I-580 Livermore EB/WB 5/12/2008 6 16 132 584 5/13/2008 6 16 134 134 5/14/2008 6 16 135 5/22/2008 7 7 1 13 US-101 Gilroy SB 5/7/2008 7 15 201 544 5/8/2008 9 13 200 411
14 I-15 Rainbow NB/SB 4/28/2008 7 16 144 4/29/2008 7 15 138 4/30/2008 7 15 133 15 5 Grapevine SB 4/8/2008 7 15 50 695 4/9/2008 7 15 71 384 4/10/2008 7 14 75 1014
16 5 Santa Nella NB / SB 5/4/2008 7 9 168 5/5/2008 7 7 201 199 5/6/2008 6 7 161 199 Number of LP Survey Hour Number

of OD Survey Site ID # Route Name/Description DirectionG-18

(continued)

Date Start End

17 99 Chowchilla River NB 4/15/2008 7 14 34 444 4/16/2008 7 14 43 521 4/17/2008 7 14 46 454
18 58 Keene EB 4/21/2008 12 13 80 4/22/2008 9 9 18 4/23/2008 10 12 46 5/19/2008 9 16 71 263 5/20/2008 6 15 85 524 5/21/2008 7 12 74
19 58 Cache Creek WB 4/21/2008 9 16 67 349 4/22/2008 7 15 90 482 4/23/2008 7 14 70 Roadside/overpass Survey Sites

21 I-710 Near Jct 105 NB/SB 2/26/2008 8 12 1543 2/27/2008 6 11 3163 2/28/2008 5 10 2044
23 I-605 Near Jct. 5 NB/SB 3/4/2008 6 13 1972 3/5/2008 5 13 4823 3/6/2008 8 14 3848
25 I-105 Near Jct 19 EB/W B 3/12/2008 8 13 1269 3/13/2008 8 13 1854

26 CA-60 Near Jct 710 EB/WB 3/17/2008 8 13 1153
30 I-15 South of Jct CA 60 NB/SB 4/1/2008 9 15 863
34 CA-91 Near Jct 215 EB/WB 4/7/2008 9 9 87 4/9/2008 8 13 459
35 CA-60 Near Jct 91 EB/WB 4/8/2008 8 14 1293 4/9/2008 14 14 1
38 CA-237 Near Great America Parkway EB/WB 4/13/2008 9 15 241 4/14/2008 9 15 133

39 I-880 Near Oakland/Jct Rte 77 NB/SB 4/15/2008 8 14 2892 40 I-280 Near Daly City/Ocean Ave NB/SB 4/16/2008 8 15 679
41 US-101 Near Jct 84 NB/SB 4/17/2008 8 15 1521 42 I-5 At Arena Bl (95834) NB/SB 4/21/2008 8 16 2644
47 CA-99 Fowler NB/SB 4/29/2008 8 23 2814
49 CA-198 Near Jct 41 EB/WB 4/30/2008 13 24 419
50 CA-46 Near Jct 43 EB/WB 5/8/2008 8 14 405

51 4/15/2008 6 13 162

52 4/16/2008 6 12 106

53 4/17/2008 6 9 35

Total 6215 54570

Etiwanda and the 60 Riverside and Agua Mansa (colton) 91 Fwy and the 15 Fwy (Corona) Number of LP Survey Hour Number

of OD Survey Site ID # Route Name/Description Direction

2) Regional Allocation Based on OD Survey Data

To analyze OD data staff used the NORTAD (BTS, 1998 ) roadway network, which is comprised of the North American Transportation Atla s, as well as highway networks representing Mexico and Canada. This roadway netwo rk included an estimate of the congested speed and length of each roadway link. T he United States Geological Survey Geographic Names Information System (USGS, 2 009) and other internet sources were used to assign GIS coordinates for eac h origin and destination in the OD database. Using ArcGIS Network Analyst and congest ed speeds for each link staff estimated the route traveled for each origin-destin ation pair in the OD database using both a fastest path and a shortest path algorithm. Results were assessed visually using GIS, and quantitatively by calculating VMT by truck category and air basin. Because the number of samples collected at each survey loca tion is not proportional to the number of trips passing the specific location, staf f scaled up the trip-VMT based on the Average Annual Daily Truck Traffic (AADTT) at the l ocations where the trips wereG-19

observed. Staff used Caltrans reported AADTT for l ocation on state highways 5 (Caltrans, 2009) and FAF2 AADTT for off-highway loc ations (FHWA, 2008).

Results showed that many trucks traveling in Califo rnia are also participating in trucking activities across the United States. More importan tly, results suggested interstate trucks have a different travel pattern from trucks that do not travel outside of California, and that California registered single-unit trucks h ave a different travel pattern from California-registered tractors. While this finding is relatively intuitive, the field study data represent the first attempt at a quantitative study to assess differences in travel patterns across different types of trucking. Figure 7 compa res the OD results visually across four inventory categories.

5 Caltrans Truck Traffic report only provides AADTT on California State Highways.G-20 Figure 7. Estimated Travel Pattern by Inventory Ca tegory

H H D D T T r a c t o r M H D D T H H D D T O u t

- o f

- S t a t e H H D D T C A I R PG-21

Figure 8 compares results from all trucking categor ies in the OD data to results generated through the US Freight Analysis Framework (FAF2), (FHWA, 2008). Results are generally similar, and reflect differences betw een the various approaches and mix of vehicles contained in each study. Table 9 presents the percentage of travel by air basin estimated using the OD data by inventory category.

Figure 8. Heavy Duty Truck Travel Patterns: ARB O D Survey Results vs. FAF2

F A F 2 T r u c k F l o w A R B O D S u r v

e yG-22

Table 9. Truck VMT Regional Allocation Factors by F leet Based on OD Analysis

Air Basin HHDDT OOS HHDDT

CA IRP HHDDT

Tractor HHDDT

Single HHDDT

All MHDDT

All Great Basins Valley 0.3% 0.6% 0.2% 0.2% 0.3% 0.4% Lake County 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Lake Tahoe 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Mountain Counties 2.6% 0.9% 0.6% 0.6% 1.6% 0.9% Mojave Desert 27.7% 19.3% 5.8% 10.4% 19.1% 11.7% North Coast 0.4% 0.3% 0.4% 0.7% 0.4% 0.8% North Central Coast 0.4% 0.5% 0.8% 0.9% 0.6% 0.9%
Northeast Plateau 3.2% 2.6% 0.4% 0.0% 2.2% 0.1% South Coast 14.3% 21.1% 28.6% 24.9% 20.2% 26.2% South Central Coast 0.5% 0.6% 1.0% 2.2% 0.7% 1.8% San Diego County 1.3% 2.9% 6.7% 3.9% 3.2% 5.0% San Francisco Bay 4.5% 4.0% 10.8% 15.1% 6.4% 12.3% San Joaquain Valley 29.2% 33.1% 37.9% 36.9% 32.8% 30.7% Salton Sea 2.8% 3.8% 3.1% 2.6% 3.1% 4.8% Sacramento Valley 12.8% 10.4% 3.7% 1.6% 9.3% 4.4% Total 100% 100% 100% 100% 100% 100%

3) OD and LP Survey Data Analysis

In addition to evaluating the OD analysis results, staff developed an alternative analysis using both the LP and OD data. The general approac h was to estimate the number of inter air basin trips and intra air basin trips by inventory category and air basin, and to multiply those trip estimates by an assumed trip le ngth within each air basin to estimate VMT by air basin for each inventory truck category. These results could then be used to develop a percent VMT allocation by inventory truck category.

The first step in this process was to separate LP d ata collection sites into two types: inter-regional sites and intra-regional sites. Int er-regional sites are located near the border of one air basin and were likely to capture inter-regional truck trips, while intraregional sites were located in the middle of air ba sins and were likely to capture trips within a single air basin. Interregional site are marked with circles and intra-regional sites are marked with stars as shown in Figure 9.G-23 Figure 9. License Plate Survey Locations by Trip Ty pe

License plates surveyed were then cross-referenced to the California DMV registration database to obtain vehicle registration information . Based on the location of vehicle registration, staff separated data at each site int o trucks traveling inside or outside of the air basin in which they were registered. Since the survey was conducted in selected urban areas (South Coast and Bay area) and rural ar eas (Mojave Desert and San Joaquin Valley) in California, we also grouped the LPs into urban and rural groups. The average fractions of trips by trucks registered ins ide vs. outside of an air basin observed at inter-air basin locations and intra-air basin lo cations, respectively, are shown in Table 10, where “inAB” refers to trucks traveling within their air basin of registration and “outAB” refers to trucks traveling outside their ai r basin of registration.G-24

Table 10. Average Percentages of Truck License Pla tes by Registered Air Basin

for Intra and Inter Air Basin Survey Location Group s

Air Basin Survey T7CAIRP T7Tractor T7Single T6 Group Location inAB outAB inAB outAB inAB outAB inAB outAB

SC Inter 0.34 0.66 0.37 0.63 0.43 0.57 0.39 0.61 SC Intra 0.76 0.24 0.89 0.11 0.92 0.08 0.90 0.10 SF Inter 0.20 0.80 0.40 0.60 0.48 0.52 0.46 0.54 SF Intra 0.27 0.73 0.56 0.44 0.70 0.30 0.76 0.24 MD Inter 0.15 0.85 0.09 0.91 0.06 0.94 0.10 0.90 SJV Inter 0.25 0.75 0.32 0.68 0.40 0.70 0.22 0.81 SJV Intra 0.52 0.48 0.71 0.29 0.82 0.18 0.66 0.34
URBAN Inter 0.28 0.72 0.38 0.62 0.45 0.55 0.41 0.59 URBAN Intra 0.63 0.37 0.80 0.20 0.86 0.14 0.86 0.14 RURAL Inter 0.22 0.78 0.25 0.75 0.26 0.74 0.17 0.83 RURAL Intra 0.52 0.48 0.71 0.29 0.82 0.18 0.66 0.34

Staff categorized three types of trips for each are a basin and inventory category: trips within an air basin by local trucks (trucks registe red in that air basin), inter-air basin trips by local trucks, and inter-air basin trips by non-l ocal trucks (trucks registered outside of the air basin of travel). Staff assumed that the d istribution of the number of trips is consistent with the distribution of the overall tru ck population, and that at intra-regional sites the observed ratio of trips between local tru cks and non-local trucks represented the real ratio of population of trucks registered i n vs. out of the specific air basin. The population of trucks registered in each air basin w as obtained from analyzing DMV truck registration data.

The non-local truck population was estimated using the local truck population and the ratio of non-local to local trips observed at intra -regional sites (Equation 2). Next, staff assumed that at inter-regional sites, the observed ratio of trips by local trucks vs. nonlocal trucks represented the ratio of population be tween local trucks making interregional trips and non-local trucks making inter-re gional trips. Thus the population of local trucks making inter-regional trips was derive d using the population of non-local trucks and the ratio of trips observed at inter-reg ional sites (Equation 3). Finally, the population of local-trucks that make inter-regional trips is estimated as the difference between total registered population in the air basi n and the population of local trucks making inter-regional trips (Equation 4). Staff al so assumed that the existence of nonlocal trucks traveling completely within an air bas in is insignificant and distributed evenly statewide, and therefore will not affect the analys is.G-25 ) (*

,

,

, , fl ab fl ab fl ab fl ab RAI

RAO P PIO = Equation 2

) (*

,

.

, , fl ab fl ab fl ab fl ab RIO

RII PIO PII = Equation 3

fl ab fl ab fl ab PII P PIR

,

, , − = Equation 4

where, PIO = the population of trucks traveling in and out o f an air basin other than the air basin in which they are registered. ab = air basin fl = inventory category P = truck population registered in an air basin by i nventory category RAO = the average fraction of trucks traveling in an a ir basin other than their registration air basin, seen at an intra-air basin survey location RAI = the average fraction of trucks traveling in their registration air basin, seen at an intra-air basin survey location. PII = the population of trucks traveling in and out of their registration air basin, seen at an inter-air basin survey location RII = the average fraction of trucks traveling in an a ir basin where they are registered, seen at an inter-air basin survey locat ion RIO = the average fraction of trucks traveling in an a ir basin other than their registration air basin, seen at an inter-air basin survey location PIR = the population of trucks traveling completely in their registration air basin

The relative ratio between the above populations re flected the relative ratio of trips by corresponding population. It should be noted that it is the relative ratio, not the absolute value that impacts the regional allocation. Table 11, Table 12, and Table 13 display estimated truck population (surrogate for trips) by trip type and inventory category.G-26

Table 11. Intra Air Basin Truck Population by Truck s Registered in the Air Basin

Air Basin HHDDT CA IRP HHDDT InState Tractor HHDDT InState Single MHDDT

All
South Coast 168,447 240,514 112,619 2,471,067
San Francisco 6,829 34,336 30,360 828,013 Mojave Desert 16,845 10,110 8,409 106,131 San Joaquin Valley 51,421 102,215 45,940 667,300 Great Basins Valley 255 316 462 3,894 Lake County 44 462 673 6,368
Lake Tahoe 444 21 131 3,468
Mountain Counties 2,585 4,281 7,811 69,687
North Coast 1,548 4,057 6,914 38,309
North Central Coast 6,047 7,450 7,051 90,369
Northeast Plateau 1,535 1,069 1,357 9,917 Sacramento Valley 25,513 33,221 28,210 354,499
South Central Coast 3,963 13,572 14,200 243,396 San Diego County 12,124 34,952 21,444 419,807
Salton Sea 4,613 10,455 5,494 76,899

Table 12. Inter Air Basin Truck Population by Truck s Registered in the Air Basin

Air Basin HHDDT CA IRP HHDDT InState Tractor HHDDT InState Single MHDDT All
South Coast 32,887 20,005 8,320 193,157
San Francisco 15,872 37,841 20,264 307,155
Mojave Desert 3,406 411 124 6,541 San Joaquin Valley 22,772 24,695 6,798 110,492 Great Basins Valley 151 111 105 2,250 Lake County 26 161 153 3,680
Lake Tahoe 263 7 30 2,004
Mountain Counties 439 598 1,002 7,753
North Coast 916 1,417 1,577 22,139
North Central Coast 3,578 2,602 1,608 52,223
Northeast Plateau 908 373 309 5,731 Sacramento Valley 15,094 11,600 6,433 204,861
South Central Coast 1,371 2,185 1,214 30,172 San Diego County 2,282 3,107 1,271 15,297
Salton Sea 1,596 1,683 470 9,533G-27 Table 13. Inter Air Basin Truck Population by Truc ks Registered out of the Air Basin

Air Basin HHDDT OOS HHDDT

CA IRP HHDDT

Tractor HHDDT

Single MHDDT All
South Coast 1,319,639 64,380 33,720 10,997 299,380
San Francisco 257,693 62,613 56,087 21,777 367,524 Mojave Desert 153,728 19,005 4,358 1,927 58,710 San Joaquin Valley 728,206 69,627 52,563 11,909 405 ,282 Great Basins Valley 3,341 381 177 128 3,201 Lake County 3,625 66 258 187 5,236
Lake Tahoe 2,139 663 12 36 2,851
Mountain Counties 39,897 1,108 956 1,218 11,031
North Coast 39,212 2,312 2,267 1,917 31,498
North Central Coast 67,631 9,033 4,163 1,955 74,300
Northeast Plateau 13,249 2,293 597 376 8,154 Sacramento Valley 286,578 38,108 18,564 7,823 291,4 64
South Central Coast 87,128 5,006 6,526 3,481 142,54 7 San Diego County 167,315 8,329 9,280 3,643 72,270
Salton Sea 58,024 5,827 5,027 1,347 45,037

Next staff converted the estimated population of tr ucks by trip type, air basin and fleet category to VMT by trip type, air basin and fleet c ategory. This required estimation of average travel distance within each air basin for e ach trip type. Average travel distances per trip were estimated using the OD data . Each OD record was crossreferenced to the DMV database to identify the air basin in which each truck was registered. Using these data, staff calculated the average travel miles by travel air basin, trip type and inventory category. In evalua ting the OD data, staff found that intraair basin trips were under-represented. This was a consequence of conducting surveys at weigh stations which tend to be located in betwe en air basins and capture interregional travel. As a result, staff estimated an a verage intra-air basin trip distance based on travel demand model output developed by SC AG and the San Joaquin Valley transportation agencies in their heavy-duty truck m odels. Average travel miles of intraair basin trips by HHDDT and MHDDT were estimated t o be 25 and 20 miles, respectively, in San Joaquin Valley air basin (Camb ridge Systematics, 2008), and 15 and 13 miles, respectively, in South Coast air basi n (SCAG, 1999). For inter-air basin trips, that is, trips traversing multiple air basin s, staff divided trips into segments in each air basin and compiled the average travel miles in each air basin by trip type. For interstate trips, only travel miles accrued within California were used. Table 14 and Table 15 show average travel miles within a specifi c air basin for inter air basin truck trips by local trucks and those by non-local trucks , respectively.G-28

Table 14. Average Travel Miles for Inter Air Basin Truck Trips by Trucks Registered in the Air Basin

Air Basin HHDDT CA IRP HHDDT InState Tractor HHDDT InState Single MHDDT All South Coast 97 85 59 67 San Francisco 62 53 61 61 Mojave Desert 104 56 28 40 San Joaquin Valley 182 138 70 77 Great Basins Valley 115 74 39 54 Lake County 99 74 39 54 Lake Tahoe 99 74 39 54 Mountain Counties 99 74 39 33 North Coast 99 74 39 54 North Central Coast 55 52 28 39 Northeast Plateau 82 74 39 54 Sacramento Valley 100 58 44 46 South Central Coast 48 40 46 33 San Diego County 75 77 65 47 Salton Sea 107 102 17 107

Table 15. Average Travel Miles for Inter Air Basin Truck Trips by Trucks Not Registered in the Air Basin

Air Basin HHDDT OOS HHDDT

CA IRP HHDDT InState Tractor HHDDT InState Single MHDDT

All South Coast 88 108 97 70 75 San Francisco 66 56 56 56 56 Mojave Desert 169 135 91 68 80 San Joaquin Valley 207 190 149 189 163 Great Basins Valley 160 127 68 131 118 Lake County 97 100 66 76 77 Lake Tahoe 13 100 2 76 77 Mountain Counties 65 47 36 61 38 North Coast 72 114 65 65 82 North Central Coast 53 57 45 33 38 Northeast Plateau 83 81 83 76 82 Sacramento Valley 136 129 75 52 66 South Central Coast 45 48 40 48 41 San Diego County 55 64 61 59 53 Salton Sea 66 73 69 40 61G-29

To calculate regional allocation factors by invento ry category using the LP and OD study data, staff multiplied the estimated trips pe r air basin by inventory category by the assumed average travel miles per trip by air basin by inventory category. Results are shown in Table 16.

Table 16. Truck VMT Regional Allocation Factors by Fleet Based on OD/LP Analysis

Air Basin HHDDT

OOS HHDDT

CAIRP HHDDT InState Tractor HHDDT InState Single HHDDT

All MHDDT

All
South Coast 30.4% 24.9% 24.1% 21.1% 26.9% 23.2%
San Francisco 4.3% 10.5% 15.9% 20.7% 9.9% 17.2% Mojave Desert 7.2% 6.6% 1.9% 2.5% 5.3% 2.4% San Joaquin Valley 40.4% 36.6% 38.7% 27.8% 38.0% 30 .1% Great Basins Valley 0.1% 0.1% 0.1% 0.2% 0.1% 0.2% Lake County 0.1% 0.0% 0.1% 0.3% 0.1% 0.2%
Lake Tahoe 0.0% 0.2% 0.0% 0.1% 0.0% 0.1%
Mountain Counties 0.7% 0.3% 0.5% 2.2% 0.7% 0.7%
North Coast 0.7% 0.8% 1.0% 2.6% 1.0% 1.6%
North Central Coast 1.0% 1.7% 1.4% 2.1% 1.3% 2.3%
Northeast Plateau 0.3% 0.6% 0.3% 0.5% 0.4% 0.4% Sacramento Valley 10.3% 13.9% 8.1% 10.0% 10.5% 12.3 %
South Central Coast 1.0% 0.8% 1.9% 4.1% 1.5% 4.0% San Diego County 2.4% 1.7% 3.7% 4.4% 2.8% 3.4%
Salton Sea 1.0% 1.4% 2.2% 1.4% 1.4% 1.8%
Total 100% 100% 100% 100% 100% 100%

4) Discussion of Field Study Results
The ARB field study is the most comprehensive study of its kind that has been conducted to date on truck travel in California. R esults show significant differences in truck travel between broad types of trucking operat ions in California that are important to consider when developing an emissions inventory representing heavy duty trucks. Despite the large amount of data collected there ar e still some limitations to consider. For example, it was necessary to obtain OD data pri marily at weigh stations. This was done for practical purposes as conducting OD survey s at most roadside locations is disruptive to traffic and places survey staff unacc eptably close to traffic. However most weigh stations are located on roadways on the outsk irts of urban areas and as a result will see more inter-regional than intra-regional tr ips. As was observed in the LP data, most trips at roadside locations were intra-air bas in trips traveled by trucks that are also registered in the air basin – local trips for which OD data were limited. In another example, while many sites were sampled, some region s, especially rural areas such as the northern portion of the state were not well cov ered by the field study due to staffing limitations on data collection. Regional allocatio n factors developed to represent these areas may be more uncertain as a result.G-30

In light of these limitations, staff evaluated fiel d study results along with other corroborative data on an inventory category specifi c basis to decide on regional allocation factors for each category.

E. Regional Allocation of Activity and Emissions

Staff derived a regional allocation for each truck category by analyzing relevant field study results and corroborative data sources specif ic to the trucking type or vocation.

1) Interstate Trucks Table 17 compares the regional distribution factors developed using the OD and OD/LP approaches to regional distribution assumptions in EMFAC2002 and EMFAC2007.

Table 17. Comparison of CA Interstate Truck VMT Re gional Allocations

Air Basin OD LP/OD EMFAC2002 EMFAC2007 Assumed Great Basins Valley 0.6% 0.1% 0.2% 0.6% 0.6% Lake County 0.0% 0.0% 0.1% 0.0% 0.0% Lake Tahoe 0.0% 0.2% 0.1% 0.1% 0.1% Mountain Counties 0.9% 0.3% 1.1% 1.8% 1.8% Mojave Desert 19.3% 6.7% 1.5% 15.1% 15.1% North Coast 0.3% 0.8% 1.3% 1.4% 1.4% North Central Coast 0.5% 1.7% 2.5% 1.8% 1.8%
Northeast Plateau 2.6% 0.6% 0.3% 1.5% 1.5% South Coast 21.1% 25.2% 36.9% 20.0% 20.0% South Central Coast 0.6% 0.8% 2.6% 1.3% 1.3% San Diego County 2.9% 1.8% 7.5% 3.8% 3.8% San Francisco Bay 4.0% 9.1% 17.3% 7.8% 7.8% San Joaquin Valley 33.1% 37.2% 17.6% 28.9% 28.9% Salton Sea 3.8% 1.4% 1.8% 6.5% 6.5% Sacramento Valley 10.4% 14.1% 9.3% 9.6% 9.6%
Total 100% 100% 100% 100% 100%

ARB staff revised the VMT distribution for HHDDT in EMFAC2007, as documented in ARB (2006a). The document described the redistribu tion of HHDDT VMT from a registration to a travel basis. The travel based V MT distribution was estimated using a combination of data from a survey conducted by Cali fornia Department of Transportation (Caltrans) in 1999, and modeled info rmation from MVSTAFF, an annual report developed by Caltrans. Detailed description s of the methodologies in developing the VMT redistribution can be found in the Technica l Memo. Although the distribution was applied to the entire HHDDT fleet in EMFAC2007, the underlying Caltrans survey was geared toward long-haul trucks since the survey data were collected at weigh stations, agricultural inspection stations and rest areas. The ARB 2008 field study was designed to focus on i ntra-California trips. Compared to the Caltrans 1999 study, the ARB field study captur ed more intra-state trips and local trips in both origin-destination and license plate data. In particular, two thirds of the license plate data were collected at roadside locat ions where intra-state trips were moreG-31

likely to be captured. As a result, regional alloc ations based on the OD and LP analysis is not appropriate for distributing inter-state tri p VMT. Staff chose to use the regional allocation developed for HHDDT VMT redistribution i n EMFAC2007 for all interstate trucks, including out-of-state registered trucks an d California registered inter-state trucks. The “assumed” allocations in the table wer e used for interstate fleets.

2) Heavy-Heavy Duty In-State Tractors Table 18 compares regional distribution factors dev eloped using the OD and OD/LP approaches to assumptions in EMFAC2002 and EMFAC200 7.

Table 18. Comparison of CA In-State Tractor HHDT V MT Regional Allocations

Air Basin OD LP/OD EMFAC2002 EMFAC2007 Assumed Great Basins Valley 0.2% 0.1% 0.2% 0.6% 0.16% Lake County 0.0% 0.1% 0.1% 0.0% 0.06% Lake Tahoe 0.0% 0.0% 0.1% 0.1% 0.00% Mountain Counties 0.6% 0.5% 1.1% 1.8% 0.56% Mojave Desert 5.8% 1.9% 1.5% 15.1% 3.83% North Coast 0.4% 1.0% 1.3% 1.4% 0.71% North Central Coast 0.8% 1.4% 2.5% 1.8% 1.13%
Northeast Plateau 0.4% 0.3% 0.3% 1.5% 0.32% South Coast 28.6% 24.1% 36.9% 20.0% 26.34% South Central Coast 1.0% 1.9% 2.6% 1.3% 1.49% San Diego County 6.7% 3.7% 7.5% 3.8% 5.20% San Francisco Bay 10.8% 15.9% 17.3% 7.8% 13.34% San Joaquin Valley 37.9% 38.7% 17.6% 28.9% 38.32% Salton Sea 3.1% 2.2% 1.8% 6.5% 2.61% Sacramento Valley 3.7% 8.1% 9.3% 9.6% 5.93%
Total 100% 100% 100% 100% 100%

The HHDDT in-state tractor fleet category is a mix of vocational trucks that are dedicated to specific industries/services and trave l mostly in California close to their base of operations, and trucking companies that reg ularly travel across California. Therefore neither a travel based nor a registration based allocation would fully describe the HHDDT tractor travel. ARB field study results are particularly relevant to this category.

Although the scale of OD survey conducted by ARB in 2008 was smaller than Caltrans survey in 1999, it was designed to capture both int er-regional and local trips by different inventory categories, and was supplemented by the l arge-scale LP survey. Staff used the average of the results from OD survey, and OD/L P survey for the regional allocation as shown in Table 18.

3) Heavy-Heavy Duty In-State Single-Unit Trucks

Staff believes the vast majority of single-unit tru cks make short haul trips because the trucks are generally used for local delivery or voc ational applications such as streetG-32

sweeping, liquids transport, drilling and other use s. These types of trips were underrepresented in both the Caltrans 1999 study and the ARB field study. The degree to which local trips were under-estimated is highly un certain and depends on the selection of survey locations. Therefore, regional allocatio ns developed based on these survey data are not appropriate for allocating single-unit truck VMT. Staff assumed that singleunit trucks travel mostly in their registered air b asin and applied a registration-based regional allocation method. This assumption was su pported by our observations in the ARB field study. Staff developed registration popu lations by air basin from DMV data after excluding specific fleets including agricultu ral trucks and construction trucks, and multiplied populations with age-specific accrual ra tes to derive VMT by registration area basin. Table 19 presents regional distribution fac tors developed using the OD and OD/LP approaches, regional distribution assumptions in EMFAC2002 and EMFAC2007 and the one developed in this analysis.

Table 19. Comparison of CA In-state Single-Unit HH DT VMT Regional Allocations

Air Basin OD LP/OD EMFAC2002 EMFAC2007 Assumed Great Basins Valley 0.2% 0.2% 0.2% 0.6% 0.1% Lake County 0.0% 0.3% 0.1% 0.0% 0.1% Lake Tahoe 0.0% 0.1% 0.1% 0.1% 0.1% Mountain Counties 0.6% 2.2% 1.1% 1.8% 2.5% Mojave Desert 10.4% 2.5% 1.5% 15.1% 1.6% North Coast 0.7% 2.6% 1.3% 1.4% 2.4% North Central Coast 0.9% 2.1% 2.5% 1.8% 2.3%
Northeast Plateau 0.0% 0.5% 0.3% 1.5% 0.0% South Coast 24.9% 21.1% 36.9% 20.0% 40.7% South Central Coast 2.2% 4.1% 2.6% 1.3% 4.3% San Diego County 3.9% 4.4% 7.5% 3.8% 7.7% San Francisco Bay 15.1% 20.7% 17.3% 7.8% 18.0% San Joaquin Valley 36.9% 27.8% 17.6% 28.9% 10.1% Salton Sea 2.6% 1.4% 1.8% 6.5% 1.1% Sacramento Valley 1.6% 10.0% 9.3% 9.6% 9.0%
Total 100% 100% 100% 100% 100%G-33

4) Medium-Heavy Duty In-State Trucks Like HHDDT single-unit trucks, MHDDT instate trucks mostly make local delivery or vocational trips that were under-represented in bot h the Caltrans 1999 and ARB 2008 OD surveys. Staff applied the registration-based r egional allocation method to mediumheavy duty in-state trucks, including both GVWR abo ve and below 26,000 lbs. Table 20 compares regional distribution factors developed us ing the OD and OD/LP approaches, regional distribution assumptions in EMFAC2002 and EMFAC2007 and the one developed in this analysis.

Table 20. Comparison of CA In-State MHDDT VMT Regi onal Allocations

Air Basin OD LP/OD EMFAC2002 EMFAC2007 Assumed Great Basins Valley 0.4% 0.2% 0.2% 0.1% 0.1% Lake County 0.0% 0.2% 0.1% 0.1% 0.1% Lake Tahoe 0.0% 0.1% 0.1% 0.1% 0.1% Mountain Counties 0.9% 0.7% 1.1% 0.9% 1.1% Mojave Desert 11.7% 2.4% 1.5% 2.2% 1.4% North Coast 0.8% 1.6% 1.3% 0.7% 0.8% North Central Coast 0.9% 2.3% 2.5% 2.6% 2.0%
Northeast Plateau 0.1% 0.4% 0.3% 0.2% 0.0% South Coast 26.2% 23.2% 36.9% 42.5% 44.5% South Central Coast 1.8% 4.0% 2.6% 3.5% 4.1% San Diego County 5.0% 3.4% 7.5% 7.9% 7.3% San Francisco Bay 12.3% 17.2% 17.3% 17.1% 19.0% San Joaquin Valley 30.7% 30.1% 17.6% 12.1% 9.9% Salton Sea 4.8% 1.8% 1.8% 1.3% 1.0% Sacramento Valley 4.4% 12.3% 9.3% 8.7% 8.6%
Total 100% 100% 100% 100% 100%G-34

5) Drayage Trucks Drayage trucks were not identified during the 2008 field study and as a result were not specifically analyzed as part of that study. The r egional allocation of drayage truck emissions is based upon the emissions analysis deve loped for ARB’s Drayage Truck Regulation (ARB, 2007a)

. As described in Appendix B of the Technical Suppor t Document for the Drayage Truck Regulation, the regi onal allocations were based on OD surveys conducted by Ports of Los Angles and Long B each and a study done by the Metropolitan Transportation Commission in the Bay A rea. Using the study results, staff estimated VMT in each air basin by the drayage truc ks serving the ports and developed the regional allocations. These are shown in Table 21.

Table 21. Comparison of CA Drayage Truck Regional Allocations



### Air Basin Los Angeles Ports/Railyards Bay Are

Ports/Railyards Other Drayage

Trucks Great Basins Valley 0.0% 0.0% 0.0% Lake County 0.0% 0.0% 0.0%
Lake Tahoe 0.0% 0.0% 0.0% Mountain Counties 0.0% 2.0% 0.0% Mojave Desert 4.3% 0.0% 0.0% North Coast 0.0% 0.0% 3.5% North Central Coast 0.0% 1.7% 0.0%
Northeast Plateau 0.0% 0.0% 0.0% South Coast 81.7% 0.0% 0.0% South Central Coast 0.1% 0.0% 11.6% San Diego County 1.4% 0.0% 34.0% San Francisco Bay 0.0% 63.6% 28.8% San Joaquin Valley 8.8% 28.2% 18.3% Salton Sea 3.7% 0.0% 0.0% Sacramento Valley 0.0% 4.5% 3.9%
Total 100% 100% 100%G-35

6) Construction Trucks

For construction fleets, staff assumed that the reg ional allocation is proportional to the regional rate of population growth. This surrogate is designed to reflect where populations are growing and therefore where constru ction is more likely to be focused. The Demographic Research Unit of California Departm ent of Finance (DOF, 2009) provides population estimates and projections by co unty. The populations within counties were then distributed into air basins base d on census data. Staff calculated the year to year increase in population within each air basin and used the fraction of the population growth in air basin versus statewide gro wth as a surrogate for construction fleets regional allocation. The resulting allocati ons are shown in Table 22 for calendar year 2008, and projected for 2020 and 2030 where DO F provided a population forecast. Staff interpolated regional allocation factors line arly between forecast years.

Table 22. Regional Allocation for Construction Fle ets

Calendar Year Air Basin 2008 2020 2030 Great Basin Valleys 0.0% 0.1% 0.1% Lake County 0.1% 0.2% 0.2%
Lake Tahoe 0.2% 0.2% 0.2% Mountain Counties 0.3% 1.5% 1.2%
Mohave Desert 2.4% 3.4% 3.2% North Coast 0.3% 0.7% 0.6% North Central Coast 1.7% 1.6% 1.7%
Northeast Plateau 0.0% 0.3% 0.2% South Coast 35.4% 37.7% 31.9% South Central Coast 3.0% 3.0% 2.8% San Diego 10.7% 6.5% 7.8% San Francisco Bay 22.0% 10.1% 13.7% San Joaquin Valley 13.1% 21.7% 23.3% Salton Sea 3.0% 3.8% 3.3% Sacramento Valley 7.7% 9.3% 9.7%
Total 100% 100% 100%G-36

7) Agricultural Trucks

ARB staff, in conjunction with agricultural trade a ssociations, administered a survey to farmers, ranchers, and other agricultural businesse s designed to identify and characterize trucks serving the agricultural sector . The results were used to develop population, age distribution and accrual rates as d escribed in the Appendix G of the Technical Support Document (ARB, 2008). In additio n to the characteristics in Appendix G, the survey also collected information r egarding where the trucks had traveled. The results indicated that although most of the agricultural trucks traveled within one air basin boundary, about 30 percent of them did travel across air basins. The results, shown in Table 23 are generally consis tent with the distribution of irrigated acreage given the 30 percent of agricultural trucks that travel across multiple regions. In this analysis, staff used the agricultural surve y-based regional allocation factors.

Table 23. Regional Allocation for Agricultural Act ivities

Air Basin Irrigated Acreage Agricultural

Truck Survey Assumed Great Basins Valley 0.3% 0.3% 0.3% Lake County 0.8% 1.6% 1.6%
Lake Tahoe 0.0% 0.0% 0.0% Mountain Counties 1.1% 2.1% 2.1% Mojave Desert 5.3% 0.7% 0.7% North Coast 0.6% 5.3% 5.3% North Central Coast 4.2% 3.9% 3.9%
Northeast Plateau 5.0% 2.3% 2.3% South Coast 0.5% 2.4% 2.4% South Central Coast 4.1% 4.6% 4.6% San Diego County 1.1% 2.1% 2.1% San Francisco Bay 2.1% 6.5% 6.5% San Joaquin Valley 49.3% 45.1% 45.1% Salton Sea 5.5% 3.7% 3.7% Sacramento Valley 20.0% 19.3% 19.3%
Total 100% 100% 100%G-37

8) Utility Trucks

A utility company is defined in the fleet rule for public agencies and utilities as a privately-owned company that provides water, natura l gas, and electricity (ARB, 2006b). Some of the large utility companies that operate th roughout California register all their trucks at their corporate office making it infeasib le to use the actual registration data to allocate utility fleet VMT. However, staff believe s most utility trucks are used to service the general population and would have similar alloc ation as MHDDTs. As a result, staff assumed the same MHDDT spatial allocation in EMFAC 2007 for utility trucks, as shown in Table 24.

Table 24. Regional Allocation for Utility Trucks

Air Basin MHDDT Utility HHDDT

Utility MHDDT

EMFAC 2007 HHDDT

EMFAC 2007 Great Basins Valley 0.1% 0.1% 0.1% 0.6% Lake County 0.1% 0.1% 0.1% 0.0%
Lake Tahoe 0.1% 0.1% 0.1% 0.1% Mountain Counties 0.9% 0.9% 0.9% 1.8% Mojave Desert 2.2% 2.2% 2.2% 15.1% North Coast 0.7% 0.7% 0.7% 1.4% North Central Coast 2.6% 2.6% 2.6% 1.8%
Northeast Plateau 0.2% 0.2% 0.2% 1.5% South Coast 42.5% 42.5% 42.5% 20.0% South Central Coast 3.5% 3.5% 3.5% 1.3% San Diego County 7.9% 7.9% 7.9% 3.8% San Francisco Bay 17.1% 17.1% 17.1% 7.8% San Joaquin Valley 12.1% 12.1% 12.1% 28.9% Salton Sea 1.3% 1.3% 1.3% 6.5% Sacramento Valley 8.7% 8.7% 8.7% 9.6%
Total 100% 100% 100% 100%G-38

9) Regional Allocation Results

Using the regional allocation described in the prev ious section, staff estimated the MHDDT and HHDDT VMT for each air basin and calendar year. Table 25 shows the VMT results for selected air basin by fleet categor y in 2008.

Table 25. VMT by Fleet Category in Selected Air Ba sins, 2008

2008 Air Basin VMT, Thousand Miles/Day Fleet Category SCAB SFBAB SJVAB SVAB MHDDT Ag 7 20 139 59 MHDDT CAIRP 13 5 19 6 MHDDT instate 3,577 1,878 959 832 MHDDT instate construction 530 329 196 115 MHDDT OOS 8 3 11 4 MHDDT utility 19 9 7 5 HHDDT Ag 18 49 343 146 HHDDT CAIRP 1,341 524 1,936 641 HHDDT CAIRP construction 102 63 38 22 HHDDT NNOOS 1,508 590 2,178 721 HHDDT NOOS 488 191 705 233 HHDDT other port - 59 37 8 HHDDT POAK - 205 91 14 HHDDT POLA 1,514 - 163 - HHDDT single 721 318 178 160 HHDDT single construction 264 164 98 57 HHDDT tractor 1,891 958 2,752 426 HHDDT tractor construction 197 122 73 43 HHDDT utility 11 5 4 3

F. Impact of the Recession on VMT and Vehicle Sales

The recession impacts the emissions inventory both in terms of reduced activity and in terms of aging fleets due to reduced new truck sale s. Staff developed assumptions for the impact of the recession and future year forecas ts for both VMT and vehicle sales by inventory category. These assumptions were fundame ntally based on the approach described in previous documentation (ARB, 2008 and ARB, 2009), where staff defined a fast recovery scenario and a slow recovery scenario . The chosen forecast is the average of the two bounding scenarios. This sectio n provides assumptions on activity and new truck sales for each inventory category in graphical format.

To forecast VMT by model year staff followed a mult i-step process. To forecast future year VMT, staff multiplied base year VMT by the rev ised growth factor reflecting the recession for each calendar year for each inventory category. Staff used the methodology described (ARB, 2008 and ARB, 2009) to forecast a model year distribution in each future year based on the assum ed sales fraction for each category.G-39

Next, staff multiplied the age-accrual rate schedul e by the assumed age distribution in each calendar year for each category to estimate th e population-weighted mileage accrual rates for each calendar year and category. To estimate population by model year, staff divided total calendar year VMT by weig hted accrual rates in each calendar year.

This approach is a simplification because it assume s the total population in each category will fluctuate with the recession. In rea lity, to respond to the recession some fleets will downsize or park vehicles, and some fle ets will continue to operate most if not all of their vehicles with less mileage accrual per year.

1) Drayage Trucks Drayage truck activity is developed based on Twenty -foot equivalent unit (TEU) throughput at ports of Los Angeles, Long Beach, and Oakland. Staff estimates that drayage truck VMT in southern California decreased 25 percent between 2006 and 2009 as shown in Figure 10; and in northern Califor nia decreased by 14 percent based on container throughput, as shown in Figure 11. Th e Drayage Truck category was not adjusted for sales during the recession because of the impacts of the Drayage Truck Rule.

Figure 10. Drayage Truck VMT Projection for Ports of Los Angeles and Long Beach and Intermodal Railyards

0.00 0.50 1.00 1.50 2.00 2.50

2006 2008 2010 2012 2014 2016 2018 2020 2022Ratio to 2006 VMT

No Recession Fast Recovery Slow Recovery AverageG-40

Figure 11. Drayage Truck VMT Projection for the Por t of Oakland and Intermodal Railyards

0.00 0.50 1.00 1.50 2.00 2.50 3.00

2006 2008 2010 2012 2014 2016 2018 2020 2022Ratio to 2006 VMT

No Recession Fast Recovery Slow Recovery Average

2) Construction Trucks

Assumptions for this category are described in Sect ion C-1.

3) MHDDT

To estimate the impact of recession on MHDDTs, staf f reviewed BOE diesel fuel sales data (BOE, 2010b) and estimated VMT growth using bo unding scenarios as shown in Figure 12. To forecast MHDDT truck sales, staff us ed the same method documented (ARB, 2008 and ARB, 2009).G-41 Figure 12. MHDDT VMT Growth Assumption

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

2006 2008 2010 2012 2014 2016 2018 2020 2022Ratio to 2006 VMT

No Recession Fast Recovery Slow Recovery Average

Figure 13. MHDDT Sales Forecast

0 50 100 150 200 250 300

1985 1990 1995 2000 2005 2010 2015 2020 2025Thousands of Units Sold Nationally

Fast average Slow Historical Trend HistoricalG-42

4) HHDDT

To estimate the impact of recession on HHDDTs, staf f reviewed BOE diesel fuel sales data (BOE, 2010b) and estimated VMT growth using bo unding scenarios as shown in Figure 14. To forecast HHDDT truck sales, staff us ed the same method documented (ARB, 2008; ARB, 2009). VMT and sales assumptions apply to all HHDDT categories unless otherwise noted including interstate trucks.

Figure 14. HHDDT VMT Growth Assumption

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

2006 2008 2010 2012 2014 2016 2018 2020 2022Ratio to 2006 VMT

No Recession Fast Recovery Slow Recovery Average

Figure 15. HHDDT Sales Forecast

0 50 100 150 200 250 300

1985 1990 1995 2000 2005 2010 2015 2020 2025Thousands of Units Sold Nationally

Fast average Slow Historical Trend HistoricalG-43

G. Proposed Regulatory Scenarios and Compliance Ass umptions
The proposed regulatory amendments provide substant ial economic relief through the focusing of early PM requirements on vehicles that are most cost-effective to control, and on slowing the penetration of NO x targets over time. To reflect these amendments, staff has made assumptions about how trucks in each category will respond to proposed regulatory requirements. Because of the i mpact of the recession and the increased life of retrofits before turnover is requ ired in the Rule amendments, staff assumes that all vehicles subject to early PM requi rements (2014 and prior) will install a Diesel Particulate Filter (DPF), and that turnover to comply with the regulation will generally occur only when explicitly required.

This section provides the assumptions used to calcu late the emissions benefits of the proposed amended regulation for each inventory cate gory for which regulatory requirements changed between the 2008 regulation an d the proposed regulation. If the requirements did not change, the assumptions docume nted in ARB (2008) are still valid. The proposed rule provides specific compliance requ irements for MHDDT fleets of truck GVWR of 26,000 lbs or less, regardless of their fle et size. Medium-heavy duty trucks with GVWR above 26,000 lbs are assumed to use the s ame compliance schedules as heavy-heavy duty trucks depending on their fleet si ze.

1) MHDDT Rule Compliance (<26,000 lbs GVWR)
This compliance schedule applies to all MHDDT with a GVWR less than 26,000 lbs. It also applies to MHDDT non-specialty agricultural tr ucks with a GVWR of 26,000 lbs or less and above first mileage threshold as described in ARB (2008).

Table 26. Turnover Assumptions for Medium Heavy Du ty Truck Fleets GVWR<26,000 lbs

By Jan. 1 Model Year Turn over to
2015 pre-1996 2010 2016 1996 2011 2017 1997 2012 2018 1998 2013 2019 1999 2014 2020 2000-2002 2015 2021 2003-2006 2016 2023 2007-2009 2017G-44

2) Small Fleet Rule Compliance (>26,000 lbs GVWR)

The proposed amendments to the Regulation continue to provide flexibility for small fleets subject to early PM requirements. A small f leet is defined as a fleet that has three trucks or fewer. Staff assumes that small fleets w ill take advantage of these provisions. Amendments require the first truck in a small fleet to be DPF equipped by 2014, the second truck to be DPF equipped by 2015, and the th ird truck to by DPF equipped by 2016. Small fleets must then meet Best Available C ontrol Technology (BACT) requirements starting in 2020.

Table 27 presents the revised assumptions for inter state trucks in small fleets, Table 28 presents the revised assumptions for single-unit tr ucks in small fleets, and Table 29 presents the revised assumptions for tract ors in small fleets.

Table 27. Retrofit/Turnover Assumptions for >26,00 0 lbs GVWR Interstate Trucks in Small Fleets

Due Date January 1st of Calendar Year Model Year

Group 1st truck 2nd truck 3rd truck

2014 2003-2006 DPF
2014 1998-2002 DPF -- --
2015 2003-2006 DPF DPF 2015 1998-2002 DPF 1/3 DPF, 1/3 2008, 1/3 2010 --
2015 Pre-1994 2010 2010 --
2016 2003-2006 DPF DPF DPF 2016 1998-2002 DPF 1/3 DPF, 1/3 2008, 1/3 2010 2011
2016 Pre-1996 2010 2010 2010
2017 1996-1997 2010 2010 2010
2020 1998-2000 2013 2013 --
2021 2001-2004 2014 2014 2014
2022 2005-2006 2015 2015 2015 2023 all remaining 2015 2015 2015G-45

Table 28. Retrofit/Turnover Assumptions for >26,00 0 lbs GVWR Single-Unit Trucks in Small Fleets

Due Date January 1st of Calendar Year Model Year

Group 1st truck 2nd truck 3rd truck

2014 1998-2006 DPF -- --
2015 1998-2006 DPF DPF --
2015 Pre-1994 2010 2010 --
2016 1998-2006 DPF DPF DPF
2016 Pre-1996 2010 2010 2010
2017 1996-1997 2010 2010 2010
2020 1998-2000 2013 2013 2013
2021 2001-2004 2014 2014 2014
2022 2005-2006 2015 2015 2015 2023 all remaining 2015 2015 2015

Table 29. Retrofit/Turnover Assumptions for >26,00 0 lbs GVWR Tractor Trucks in Small Fleets

Due Date January 1st of Calendar Year Model Year

Group 1st truck 2nd truck 3rd truck

2014 2003-2006 DPF 2014 1998-2002 DPF -- -- 2015 2003-2006 DPF DPF 2015 1998-2002 DPF 20% DPF, 60% 2008, 20% 2010 -- 2015 Pre-1994 2010 2010 -- 2016 2003-2006 DPF DPF DPF 2016 1998-2002 DPF 20% DPF, 60% 2008, 20% 2010 2011 2016 Pre-1996 2010 2010 2010
2017 1996-1997 2010 2010 2010
2020 1998-2000 2013 2013 --
2021 2001-2004 2014 2014 2014 2022 2005-2006 2015 2015 2015 2023 all remaining 2015 2015 2015G-46

3) Large Fleet Rule Compliance (>26,000 lbs GVWR)
The proposed regulatory amendments provide relief t o large fleet HHDDTs, because pre-1998 trucks would no longer be subject to early PM requirements. To reflect this change, staff updated compliance assumptions for la rge fleets. These assumptions are specific to each vehicle category because of differ ences in average miles traveled and vehicle dollar values between different categories. Table 30 presents compliance assumptions for >26,000 lbs GVWR tractors in large fleets; Table 31 presents compliance assumptions for >26,000 lbs GVWR single- unit trucks in large fleets. The proposed regulatory amendments contain a large flee t provision that allows fleets to install DPFs on 90% instead of 100% of their fleet. To reflect this provision, emissions benefits calculated using Tables 30 and 31 are mult iplied by 0.9 to estimate the final benefit from large fleets. This assumption is also discussed in G8 below.

Table 30. Retrofit/Turnover Assumptions for >26,00 0 lbs GVWR Tractor Trucks in Large Fleets

Due Date January 1st of Calendar Year Model Year Group Assumed Compliance

2012 1998-2000 DPF 2013 2001-2002 DPF 2013 2003-2004 DPF 2014 2005-2006 DPF 2015 Pre-1994 2010
2016 1994-1995 2011
2017 1996-1997 2012
2020 1998-2000 2015
2021 2001-2002 2016
2021 2003-2004 2016
2022 2005-2006 2017 2023 all remaining 2017

Table 31. Retrofit/Turnover Assumptions for >26,00 0 lbs GVWR Single-Unit Trucks in Large Fleets

Due Date January 1st of Calendar Year Model Year Group Assumed Compliance

2012 1998-2000 DPF
2013 2001-2004 DPF
2014 2005-2006 DPF
2015 Pre-1994 2010
2016 1994-1995 2010
2017 1996-1997 2010
2020 1998-2000 2013
2021 2001-2004 2014
2022 2005-2006 2015 2023 all remaining 2015G-47

4) Agricultural Truck Compliance Assumptions

The proposed regulatory amendments do not alter the fundamental compliance structure for agricultural trucks. However, becaus e of changes to BACT requirements, compliance assumptions for specialty vehicles and s ome model year groups have been altered slightly, as shown in Table 32.

Table 32. Retrofit/Turnover Assumptions for Agricu lture Trucks

Below 10,000 miles/yr and specialty agricultural t rucks Due Date January 1st of Calendar Year Model Year Turnover to

2023 pre-2010 2012

Above 10,000 miles/yr but below first mileage thre shold

Due Date January 1st of Calendar Year Model Year Retrofit /Turnover to

2017 pre-1998 2010
2017 1998-2006 50% 2010 and 50% 2008
2023 2007-2009 2012

5) Utility Truck Compliance Assumptions

As a result of proposed regulatory amendments, the following assumptions in Table 33 are made for compliance for trucks owned by Utiliti es.

Table 33. Retrofit/Turnover Assumptions for Utility Trucks

T6 Utility T7 Utility Due Date January 1st of Calendar Year Model Year Retrofit /Turnover to Model Year Retrofit /Turnover to 2014 Pre-1998 2010 Pre-1998 2010
2014 1998-2006 DPF 1998-2006 DPF
2021 1998-2006 2013 1998-2004 2013
2022 2005-2006 2014
2023 2007-2009 2015 2007-2009 2015G-48

6) Drayage Truck Compliance Assumptions Proposed regulatory amendments would harmonize dray age truck and truck and bus regulatory requirements by removing the Phase II dr ayage truck requirement to turnover to a 2007 truck by 2014, while keeping Phase I retr ofit requirements. Due to requirements imposed by the Ports of Los Angeles an d Long Beach, all drayage trucks will meet 2007 standards in 2014; as a result regul atory amendments will have little effect in southern California. Drayage trucks in t he Bay Area are not subject to this local requirement, and so benefits of the Drayage Truck R ule will be reduced by these amendments in that area. By 2017, drayage trucks w ill be required to follow truck and bus requirements, as shown in Table 34.

Table 34. Retrofit/Turnover Assumptions for Drayag e Trucks

Assumptions for Southern California Drayage Trucks Calendar year Population distribution 2010 2011 2012 2013 2014 2014-2016 MY 1994-2003, DPF 4% 4% 4% 4% 4% MY 2004 10% 10% 0% 0% 0% MY 2004 DPF 0% 0% 6% 6% 6%
MY 2005-2006 21% 21% 11% 0% 0% MY 2005-2006, DPF 0% 0% 0% 9% 9%
MY 2007-2009 58% 58% 70% 72% 72% MY 2010 and newer 7% 7% 9% 9% 9% Trucks new to the drayage service would meet 2007 engine standard

Approach for Drayage Trucks Outside of Southern Cal ifornia Due Date January 1st of Calendar Year Model Year Retrofit / Turnover to

2010 Pre -1994 1994

2010 1994 - 2003 DPF

2012 2004 DPF

2013 2005-2006 DPF

Truck and Bus Regulation Assumptions starting in 20 17 Due Date January 1st of Calendar Year Model Year Turnover to

2020 pre-1998 2010
2020 1998-2000 2012
2021 2001-2004 2012
2022 2005-2006 2012
2023 2007-2009 2012G-49

7) School Bus Compliance Assumptions Under the proposed regulation, school bus complianc e requirements would be deferred for two years, as shown in Table 35.

Table 35. Retrofit/Turnover Assumptions for School Buses

Due Date January 1st of Calendar Year Model Year Turnover to Percent with DPF
2012 1987-2006 33%
2013 1987-2006 66%
2014 1987-2006 100%
2014 pre-1987 2007

8) Regulatory Credit and Other Provisions - Not Mo deled in the Inventory

The proposed regulatory amendments contain several provisions that are not modeled within the inventory, including reduced activity cr edits, early retrofit credits, alternative fuel credits, and large fleet phase-in. These prov isions cannot be explicitly modeled because they are not mandatory, and the choice to e xercise these provisions depends on many factors that are difficult to predict. The ir impact on the margin is estimated, but other inventory charts shown in Section H do not in clude the impact of these provisions.

The reduced activity credits and alternative large fleet phase-in relief provisions in the regulation will likely reduce the number of filter equipped trucks, especially in the 2014 timeframe, from what is modeled directly in the inv entory. Whether a fleet will choose to exercise reduced activity credits depends on how it has been impacted, and how it has responded to the recession. Some fleets will choos e to take advantage of alternative fuel vehicle credits or early retrofits and others will not.

The reduced activity credits are designed to be sel f-correcting in that if the economy recovers more slowly more credits would be generate d, and if the economy recovers more quickly fewer credits would be generated. For example, if the economy recovers more slowly than anticipated, more fleets would use reduced activity credits. This would reduce the benefits the Rule would achieve, but tho se disbenefits would be offset by reduced activity and emissions caused by the recess ion.G-50

H. Emissions Results

1) Evaluation of Baseline Statewide Fuel Usage Esti mates

The Truck and Bus Rule is not expected to have a si gnificant impact on reducing statewide emissions of CO 2 . However, comparing modeled fuel-use in the emiss ions analysis to the total amount of diesel fuel taxed i n California annually is a valuable tool to ensure the emissions analysis is reasonably accu rate. To develop this analysis, staff added annual CO 2 emissions estimates for diesel trucks and buses in this inventory to estimates of CO 2 emissions representing light-duty and light heavy- duty diesel vehicles. The combined total represents all on-road use of ta xable diesel in California. Staff assumed 10,159 grams of CO 2 emissions per gallon diesel fuel to convert CO 2 emissions to gallons of diesel fuel. Figure 16 com pares estimated fuel usage in the inventory analysis (including lighter duty vehicles from EMFAC2007) with on-road taxable diesel fuel provided by the BOE in their 10 year report (BOE, 2010b). Results show very good agreement, within 5 percent or less, between the inventory analysis and reported fuel data.

Figure 16. ARB Modeled Estimates of On-Road Diesel Fuel Use Based on the

Emissions Analysis vs. BOE On-Road Taxable Fuel by Calendar Year

0 0.5 1 1.5 2 2.5 3 3.5

2005 2006 2007 2008 2009Billion Gallons Diesel Fuel per Year

BOE On-Road Taxable Fuel ARB Estimates - All On-Road DieselG-51

2) Estimating Statewide Emissions Benefits of the P roposed Regulation

When the Board adopted the regulation in 2008, the Rule was designed to achieve maximum reduction of diesel particulate matter emis sions by 2014, and to ensure maximum NO x

emissions reductions by 2023. The rule was also d esigned to achieve sufficient NO x

reductions to achieve reductions in nitrate partic ulate to meet the particulate matter ambient air quality standard in both the South Coast Air Basin and the San Joaquin Valley.

The proposed amendments to the regulation are desig ned to ensure these overall benefits are achieved at a much lower cost. This i s possible because the recession is projected to continue to reduce anticipated emissio ns levels well into the future. Overall, staff anticipates the amended regulation w ill reduce diesel PM emissions by 50 percent from currently anticipated levels in 2014, and that the regulation will ensure that by 2020 practically all trucks operating in Califor nia will be equipped with a diesel particulate filter. Figure 17 and Figure 18 compar e anticipated baseline emissions (without the Rule) to emissions with the amended Ru le if it is adopted. Staff anticipates the amended Rule will achieve a 15 percent reductio n in NO x emissions and a 50 percent reduction in PM 2.5 emissions from anticipated levels without the Rule in place between 2015 and 2023.

The emissions benefits shown in this section cover only BACT requirements and other major portions of the regulation that are modeled i n the inventory. Credit provisions discussed in Section G8 of this document are covere d quantitatively below in Section H4.G-52

Figure 17. Statewide NO X Emissions Estimates Baseline and with Regulation

0 100 200 300 400 500 600

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 202 2 2023NOx(tons per day) Baseline

With Regulation

Figure 18. Statewide PM 2.5 Emissions Estimates Baseline and with Regulation

0 5 10 15 20 25

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 202 2 2023PM2.5(tons per day) Baseline

With RegulationG-53

Figure 19 and Figure 20 compare anticipated emissio ns without the Rule to emissions with the amended Rule in the South Coast.

Figure 19. South Coast NO X Emissions Estimates Baseline and with Regulation

0 20 40 60 80 100 120 140 160 180 200

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 202 2 2023NOx(tons per day) Baseline

With Regulation

Figure 20. South Coast PM 2.5 Emissions Estimates Baseline and with Regulation

0 1 2 3 4 5 6

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 202 2 2023PM2.5(tons per day) Baseline

With RegulationG-54

Figure 21 and Figure 22 compare anticipated emissio ns without the Rule to emissions with the amended Rule in the San Joaquin Valley.

Figure 21. San Joaquin Valley NO X Emission Estimates Baseline and with Regulation

0 20 40 60 80 100 120 140

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 202 2 2023NOx(tons per day) Baseline

With Regulation

Figure 22. San Joaquin Valley PM 2.5 Emissions Estimates Baseline and with Regulation

0 1 2 3 4 5 6

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 202 2 2023PM2.5(tons per day) Baseline

With RegulationG-55

3) PM and NO x

Emissions Benefits from both the Truck and Bus and In-Use Off-Road Rules are Preserved

Staff designed proposed regulatory amendments acros s both the Statewide Truck and Bus and In-Use Off-Road Rules to provide the maximu m amount of economic relief possible while still preserving as much of the orig inally envisioned benefits as possible. Our estimates suggest that statewide the combined i mpact of the recession with amended regulations on both on-road and off-road in ventories will provide essentially the same cumulative remaining emissions levels betw een 2011 and 2023 to what the adopted Rules would have achieved without the reces sion. This concept is shown in Figure 23 for PM 2.5 and Figure 24 for NO x below.

Figure 23. Year by Year Comparison of PM 2.5 Emissions after On-Road and OffRoad Regulations are Applied: Adopted Rules withou t Recession vs.

Amended Rules with Recession

0 5 10 15 20 25 30

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 202 3Statewide PM2.5Emissions (tons/day)

Adopted Without Recession With Proposed AmendmentsG-56

Figure 24. Year by Year Comparison of NO X Emissions after On-Road and OffRoad Regulations are Applied: Adopted Rules withou t Recession vs.

Amended Rules with Recession

0 100 200 300 400 500 600 700 800

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 202 3Statewide NOxEmissions (tons/day)

Adopted Without Recession With Proposed Amendments

4) Meeting SIP Targets

In September 2007, ARB adopted a State Implementati on Plan (SIP) committing the State to develop measures to achieve emission reduc tions from sources under State regulatory authority. The reductions are needed to attain the Clean Air Act National Ambient Air Quality Standards (NAAQS) for PM 2.5 in 2014 and for ozone longer term. While multiple areas throughout the State exceed fe deral air quality standards, the air quality in the South Coast and the San Joaquin Vall ey poses the greatest challenge and defines the amount of reductions needed. Reduction s are needed by 2014 to meet the PM 2.5 attainment deadline and by 2023 to meet the ozone attainment deadline for compliance with the NAAQS.

Staff decided to consider the impact of the recessi on and inventory changes on fleets affected by the truck and off-road regulations toge ther in deciding how to provide regulatory relief so that emissions reductions coul d be targeted more cost effectively, and staff could ensure the combined emissions benef its achieved by the two rules would meet SIP legal requirements for the combined SIP categories.G-57

To develop this comparison, remaining emissions ant icipated after the application of the heavy-duty truck and in-use off-road regulatory con cepts in the 2007 SIP were compared to anticipated remaining emissions from he avy-duty trucks and in-use offroad equipment in the updated rule inventories. T he difference between the remaining emissions in the SIP and in the revised inventory i s referred to as the margin. The margin defines how much relief can be provided unde r the truck and off-road rules while still meeting SIP legal requirements. The margin i s compared in NO x equivalent emissions which is a weighted combination of NO x and PM 2.5 . The PM 2.5 to NO x ratio is 10:1 in South Coast and 9:1 in the San Joaquin Vall ey; these numbers were based on photochemical modeling in the 2007 SIP (ARB, 2007b)

. Reduced activity credit provisions of the proposed regulatory amendments could not be accurately modeled within the inventory, but are ex pected to have a significant impact in reducing emissions benefits from strictly what BACT compliance would provide in 2014. This is because under the proposed credit provision s fleets that have downsized as a result of the recession can treat a retired vehicle as compliant, which reduced early PM compliance requirements for other vehicles in the f leet. This effect will be most significant in 2014 when SIP compliance is measured . The large-fleet phase-in requirements could lead to many large fleets meetin g early PM requirements for 90 percent instead of 100 percent for vehicles subject to regulatory requirements.

Table 36 compares the 2014 margin in the San Joaqui n Valley air basin for several inventory scenarios. The rules as currently adopte d would provide a margin of 40 tons NO x equivalent emissions. After modeling the major po rtions of the proposed Rule amendments without credit provisions, the remaining margin is estimated at 2.4 tons.

Table 36. Comparison of 2014 Remaining Emissions i n the San Joaquin Valley

Air Basin After Truck and Off-Road Rules Are Applie d: SIP, Adopted Rule, With Rule Inventory (Not Including Credit Pro visions)

San Joaquin Valley 2014 NO X Eq. Emissions (tons/day) SIP Adopted* Proposed Off-Road NO X 29.1 12.1 13.4 PM 2.5 0.7 0.4 0.6 Trucks NO X 88.6 75.2 101.8 PM 2.5 1.9 1.1 2.0 NO X Eq (pm*9) 141.1 101 138.7 Margin without Credit Provisions 40 2.4

Margin with Credit Provisions n/a 0

* From August 2010 Workshop

Staff estimates that a maximum of 15 percent of the total NO x equivalent benefits could be lost as a result of the reduced activity credit and large-fleet phase-in requirements of the proposed amended regulation. Staff believes th at if the reduced activity credit and large-fleet phase-in requirements of the proposed r egulation are considered, the remaining margin would be eliminated. This would r epresent a reduction in overall benefits of about 14 percent.G-58

Table 37 compares the 2014 margin in the South Coas t air basin for several inventory scenarios. The rules as currently adopted would pr ovide a margin of 62 tons NO x equivalent emissions. After modeling the major por tions of the proposed Rule amendments, the remaining margin is estimated to be 7.4 tons. Staff estimates a maximum of 15 percent of the total NO x equivalent benefits could be lost as a result of the reduced activity credit and large-fleet phase-i n requirements of the proposed amended regulation. The total NOx equivalent benef it in the South Coast in 2014 is 18 tons/day. A 15% benefit loss would be equal to 2.7 tons; a 2.7 ton reduction from the 7.4 margin would leave a margin of 4.6 tons NOx equ ivalent emissions after all provisions of the Rule are considered.

Table 37. Comparison of 2014 Remaining Emissions i n the South Coast Basin

After Truck and Off-Road Rules Are Applied: SIP, A dopted Rule, Provisions Modeled in the Inventory, and All Regula tory Provisions

SIP Adopted* Proposed Off-Road NO X 85.6 24.9 27.6 PM 2.5 2.3 0.9 1.3 Trucks NO X 55.1 81.5 117.6 PM 2.5 2.3 0.9 2.1 NO X Eq (pm*10) 186.7 124.4 179.3 Margin without Credit Provisions 62 7.4

Margin with Credit Provisions n/a 4.6

* From August 2010 Workshop

With the proposed amendments the truck rules will c ontinue to provide major diesel PM emissions reductions to protect public health and t o ensure all trucks operating in 2023 are equipped with the cleanest engines available. The combined margin for trucks/buses and off-road equipment is minimized, p roviding maximum relief while still meeting SIP legal obligations.

I. Additional Documentation

As part of the Rulemaking package staff is providin g the emissions inventory model and associated documentation. The following is a guide to the model files and documentation.

There are three folders in the inventory documentat ion package:

• EI models recession proposed – this folder contain s the actual calculations of the emissions inventory for the proposed regulation and with the average of faster and slower growth scenarios

o Baseline by fleet 10210.mdb – is the starting poin t of the inventory calculation. Population and VMT by fleet category, model year and air basin are calculated and forecasted. The impacts o f Drayage TruckG-59 regulation and Public and Utility Fleets regulation on drayage trucks and utility fleets are also estimated.

o Base by sub cat 100810.mdb – is where the fleet ca tegories in “Baseline by fleet 100810.mdb” were further divided into sub categories based on fleet size, annual mileage, etc.

o Modeling assumptions.xls – documents the assumptio ns for fleet turnover and retrofit under the regulation.

o XXXAB 102010.mdb – based on the population and VMT in “Base by sub cat 100810.mdb” and the assumptions in “modeling as sumptions.xls”, estimates the population and VMT results of fleet t urnover and retrofit for each air basin.

o Combined scenario 102010.mdb – combines the air ba sin results into one table and calculates the emissions under the propos ed rule

• EI models no recession adopted rule – this folder contains the emissions inventory calculations for the adopted regulation w ithout the recession.

o Baseline by fleet no recession.mdb – is the starti ng point of the inventory calculation. Population and VMT by fleet category, model year and air basin are calculated and forecasted. The impacts o f Drayage Truck regulation and Public and Utility Fleets regulation on drayage trucks and utility fleets are also estimated.

o Base by sub cat no recession.mdb – is where the fl eet categories in “Baseline by fleet no recession.mdb” were further d ivided into sub categories based on fleet size, annual mileage, etc

. o XXXAB no recession adopted.mdb – based on the popu lation and VMT in “Base by sub cat no recession.mdb” and the assumpti ons for the adopted rule, estimates the population and VMT results of f leet turnover and retrofit for each air basin.

o Combined no recession adopted.mdb– combines the ai r basin results into one table and calculates emissions estimates with a dopted rule and without recession.

• EI models with recession adopted rule – this folde r contains the emissions inventory calculations for the adopted regulation w ith recession.

o Baseline by fleet with recession.mdb – is the star ting point of the inventory calculation. Population and VMT by fleet category, model year and air basin are calculated and forecasted. The impacts o f Drayage Truck regulation and Public and Utility Fleets regulation on drayage trucks and utility fleets are also estimated.

o Base by sub cat with recession.mdb – is where the fleet categories in “Baseline by fleet with recession.mdb” were further divided into sub categories based on fleet size, annual mileage, etc

. o XXXAB with recession adopted.mdb – based on the po pulation and VMT in “Base by sub cat with recession.mdb” and the ass umptions for the adopted rule, estimates the population and VMT resu lts of fleet turnover and retrofit for each air basin.G-60 o Combined with recession adopted.mdb– combines the air basin results into one table and calculates emissions estimates w ith adopted rule and without recession.

• Supporting files – this folder contains supporting data/analysis for the inventory

o Buses – Buses and motorcoach related files

 Buses population accrual.xls – analysis of DMV dat a and data provided by CBA to estimate the population and accr ual rates for all other buses and motorcoaches.

 California Public Utilities Commissions.xls - pro vides the motorcoach population.

 CBA survey.xls – provide mileage accrual by model year for motorcoaches.

 EF for bus_PTO.xls – emission factors for buses an d PTO.

o Construction – construction fleets related files

 BOE Construction Taxable Fuel Refunds.xls

 Construction allocation pop growth by AB.xls – spa tial allocation for construction fleet VMT based on DOF population fore cast.

 Construction Avg age&accrual in VIUS.xls – analysi s of age and accrual rate of construction vehicles in VIUS 2002.

 EDA UCC data analysis.xls – new construction equip ment sales financed.

 Percentage of Construction Trucks.xls – VIUS based analysis on construction population as percentages of HHDDT and MHDDT populations.

o Emission Factor – emission factor related files

 NOx adjustment details.xls – calculation to reflec t engine manufacturer’s compliance approaches for NOx standa rd.

 HHDDT EF NOx adj cap 400_800K.xls – emission facto rs that reflect both the NO x

adjustment and odometer capping for HHDDT.

 MHDDT EF NOx adj cap 400_800K.xls – emission facto rs that reflect both the NO x

adjustment and odometer capping for MHDDT.

 EF cap 400_800K w NOx rev.xls – combines HH and MH DDT EF files for input to the Access models.

o Field Study – field study related files

 Cambridge 2008 trip length.pdf – for estimating th e trip length in San Joaquin Valley Air Basin

 SCAG 1999 trip length.pdf – for estimating the tri p length in SCAG

 Field study folder – documents released in 2010 re garding field study as part of California Public Records Act. Fo lder contains “Readme.xls” describing the content.

o Growth & Sales – growth and sales forecasts

 EIA sales VMT forecast.xls – the 2009 EIA forecast of VMT and new truck sales used for sales forecast

 Growth w recession.xls – VMT growth trends that re flect recession recovery assumptionsG-61  Sales w recession.xls – Sales forecast reflecting the VMT growth trend with recession

o Misc

 2005 T6 GVW split.xls – analysis based on 2005 DMV to estimate the percent of MHDDTs that are above 26,000 lbs GVW R

 Fuel comparion.xls – based on estimated CO 2 emissions, compare the estimated on-road fuel consumptions to the repo rted diesel taxable fuel from BOE

 IFTA 2005~2009.xls - IFTA fuel/VMT from correspon ding with BOE

 Truck NOx_PM contribution to 2010_2020 STWD.xls – contains the analysis of the truck contribution to 2010 statewid e mobile source emissions.

o Jan2010 folder – contains files released in 2010 a s part of California Public Records Act. README.doc describes the conte nt in the folder.G-62

J. References

ARB, 2006a. California Air Resources Board. EMFAC Modeling Change Technical Memo: Redistribution of Heavy-Heavy Duty Diesel Tru ck Vehicle Miles Traveled in California . September 2006. Available at: http://www.arb.ca.gov/msei/onroad/techmemo/on-2006- 05.pdf

ARB, 2006b. California Air Resources Board. Final Statement of Reasons – Proposed Diesel Particulate Matter Control Measure for On-Ro ad Heavy-Duty Diesel Fueled Vehicles Owned or Operated by Public Agencies and U tilities . October 2006. Available at: http://www.arb.ca.gov/regact/dpmcm05/revfsor.pdf

ARB, 2007a. California Air Resources Board, Drayag e Truck Regulation Available at: http://arb.ca.gov/regact/2007/drayage07/drayage07.h tm and http://www.arb.ca.gov/msprog/onroad/porttruck/draya getruckbenefit100507.xls

ARB, 2007b. California Air Resources Board, Proposed State Strategy for California's State Implementation Plan (SIP) for the New Federal PM 2.5 and 8-Hour Ozone Standards. Available at: http://www.arb.ca.gov/planning/sip/2007sip/2007sip. htm

ARB, 2008. California Air Resources Board. Staff Report: Initial Statement of Reasons (ISOR), Technical Support Document . October 2008. Available at: http://www.arb.ca.gov/regact/2008/truckbus08/truckb us08.htm

ARB, 2009. California Air Resources Board, ARB Staff Assessment of the Impact of the Economy on California Trucking Activity and Emissio ns 2006-2014. Available at: http://www.arb.ca.gov/msprog/onrdiesel/documents/tr uck_economic_documentation_v0 15.pdf

BEA, 2009. Industry and State Level GDP, 1997-2008 . Last updated: Tuesday, June 02, 2009. Available at: http://bea.gov/regional/gsp/

BLS, 2010. U.S. 1939-current, CA 1990-current. St atewide and National Employment (Variety Sectors). Last assessed June 2010. Availa ble at: http://www.bls.gov/data/#employment

BOE, 2008. BOE Construction Taxable Fuel Refunds.x ls. Available at Air Resources Board, Diesel Activities - AB1085 Compliance. Available at: http://www.arb.ca.gov/dieseltruck

BOE, 2010a. IFTA 2005~2009.xls. Available at Air R esources Board, Diesel Activities - AB1085 Compliance. Available at: http://www.arb.ca.gov/dieseltruck

BOE, 2010b. Taxable Diesel Gallons 10 Year Report. Available at: http://www.boe.ca.gov/sptaxprog/reports/Diesel_10_Y ear_Report.pdf . Last assessed June 2010.G-63

BTS, 1998. Research and Innovative Technology Admi nistration, NORTAD Roadway Network . Available at: http://www.bts.gov/publications/north_american_tran sportation_atlas_data/

Caltrans, 2009. Truck Traffic (Annual Average Dail y Truck Traffic) on California State Highways. 2007Truck.xls. Available at: http://traffic-counts.dot.ca.gov/2007truck.xls

. Last accessed February 2009.

Cambridge Systematics, Inc., 2008. Cambridge 2008 trip length.pdf. Available at Air Resources Board, Diesel Activities - AB1085 Complia nce. http://www.arb.ca.gov/dieseltruck

CBA, 2010. CBA survey.xls. Available at Air Resou rces Board, Diesel Activities - AB1085 Compliance. http://www.arb.ca.gov/dieseltruck

CPUC, 2009. California Public Utilities Commission .xls

. Available at Air Resources Board, Diesel Activities - AB1085 Compliance. http://www.arb.ca.gov/dieseltruck

DMV, 2005. California Vehicles Registration Data. Confidential.

DOF, 2009. California County Population Estimates and Components of Change 2000 2008, December 2008 and Population Projections by R ace/Ethnicity for California and Its Counties 2000–2050, July 2007. Last accessed F ebruary 2009. Available at: http://www.dof.ca.gov/research/demographic/reports/ estimates/e-6/2000-08/ and http://www.dof.ca.gov/HTML/DEMOGRAP/ReportsPapers/P rojections/P1/P1.asp

DOF, 2010. Residential Unit and Valuation, 1975-20 09 (annual), 1995-current (monthly). Last assessed February 2010. Available at: http://www.dof.ca.gov/html/fs_data/latestecondata/f s_construction.htm

EDA, 2010. California Equipment Financed, 2005-cur rent. Available at: www.EDAData.com

FHWA, 2008. Federal Highway Administration (FHWA), Freight Management and Operations, FAF2, 2002-2035. Available at: http://ops.fhwa.dot.gov/freight/freight_analysis/fa f/faf2_high.htm (last accessed Dec 2, 2008).

Lutsey, Nicholas, 2008. Assessment of Out-of-State Heavy-Duty Truck Activit y Trends in California – Final Report . Institute of Transportation Studies, University of California at Davis, March 14, 2008. Available at: http://www.arb.ca.gov/research/apr/past/04- 328.pdf

SCAG, 1999. SCAG 1999 trip length.pdf. Heavy Duty Truck Model and VMT Estimation, Figures 3B &3C. Available at Air Resources Board, Diesel Activities - AB1085 Compliance. http://www.arb.ca.gov/dieseltruckG-64

USGS, 2009. Geographic Names Information System. Available at: http://geonames.usgs.gov/index.html (last accessed March 30, 2009).

VIUS, 2002. US Census Bureau, Service Sector Stati stics Division, Transportation Characteristics Branch. Vehicle Inventory and Use Survey – Discontinued Products. Available at: http://www.census.gov/svsd/www/vius/products.htmlG-65

K. Attachment – CARB Origin-Destination Survey Ques tionnaire

CARB HDT Origin-Destination Survey Questionnaire

Survey Location:

_____________________ Direction

_______ Date:

_________ Time:

________ Surveyor

___________________ Selection Reason: X_ Random ___ Excess Smoke ( ____ %) ___ Other: ___________

Truck Information 1. Vehicle Type (circle one): Single Unit w/ trailer Tractor-Trailer (Trailer is 53’ or lo nger? Y / N) Multiple-Trailer Tractor Dump truck Concrete mixer If a tractor-trailer, does the owner of the tractor also own the trailer? Yes___ No__ Don't Know_ 2. Number of axles: Tractor _____, Trailer ______, Total: _____ 3. Where is the home base of this vehicle? City ____________________ State ________ 6. License Plates: US State _________ N umber _______________ Mexico _____________ Canada ____________

__ 7. Make and Model Year of truck: __________ _

_________ 8. Make and Model Year of engine: __________

__________ 10. Has the engine been rebuilt or replaced?(circl e one) Yes No Unknown 13. Odometer: _________________________ 14. What is the current loaded weight of this truc k? _______________ lb 15. What is the Gross Vehicle Weight Rating (GVWR or GCWR) of this truck? _______________ lb Fueling Information 16. Total Fuel capacity of this truck: __________ gallons 17. Where did you last fuel up this truck? City ____________________ State ________ 18. Where will you next fuel up this truck? City ____________________ State ________ 19. Where do you usually buy fuel? CA Mexico Canada Other US State (specify): __________ Activity Information 20a. Where did your current trip begin? City______ ________________ State_______ Country_______ Address___________________________________________

______________ 21. What is the destination of your current trip? City______________________________________________ ___State________ Country______ Address___________________________________________

______________ 21a. How many tractors/trailers are there in your company? (OK if estimate) Tractors _____/ Trailers__

___ Maintenance Information 34. In the past 12 months, has your truck had any downtime for repairs due to problems with the truc k engine? Yes__ No__ Un known__ 35. During the past 12 months, which of the follo wing malfunctions have you had on your truck? (Check all that apply): Fuel Injection Problems: Mild _____ Medium ___ __ Severe _____ Engine Failure _____ Electronic Failure _____ Turbocharger Problems _____ Intercooler Problems _____ EGR Problems _____ Air/Fuel Ratio Control _____ Clogged Air Filter _____ Other Air Induction Problems _____ Excessive Engine Oil Consumption _____ PM Filter (DPF) Problems (if equipped) _____ Other ____________________________________________

____________ PrePass Program (skip this if survey conducted at weigh stations) 36. Is this truck currently enrolled in the Caltra ns PrePass TM program? Yes___ No___ Unknown__ Comments : ________________________________________________

______________________________ Contact : Zhen Dai, CARB PTSD Mobile Source Analysis Branch (916) 322-7455 or zdai@arb.ca.gov