### Appendix F

Emission Inventory Description(This page intentionally left blank.)May 7, 2007 Appendix F 1 

### TABLE OF CONTENTS

1. Introduction ............................................................................................................... 2 Purpose ...................................................................................................................... 2 Statutory Requirements ............................................................................................ 2 Emission Inventory Overview ................................................................................... 2 Basic Elements of the Emission Inventory .......................................................... 2 Agency Responsibilities ........................................................................................ 3 Pollutants and Precursors .................................................................................... 4 Geographic Considerations .................................................................................. 6 Base Year Inventory Selection for the SIP ........................................................... 7 Emission Estimation Concepts ............................................................................. 7 Emission Forecasting Overview ........................................................................... 7 Types of Inventories (Annual Average, Seasonal, Modeling) ............................ 8 2. Emission Inventory Improvements .......................................................................... 9 Overview ..................................................................................................................... 9 Improvement Process ............................................................................................... 9 Planning and Development - SIP .......................................................................... 9 Regulatory Support ................................................................................................ 9 Special Studies / Research ................................................................................... 9 Stakeholder Outreach .......................................................................................... 10 Quality Assurance/Qua lity Control (QA/QC) ...................................................... 10 Specific Category Improvements ........................................................................... 11 Stationary ............................................................................................................. 11 Mobile .................................................................................................................... 13 Biogenic ................................................................................................................ 18 3. Emissions Estimation Methods and Documentation ........................................... 19 Major NOx/ROG Categories .................................................................................... 19 Emissions Estimation Methods .............................................................................. 19 Stationary Emissions ........................................................................................... 19 Area-wide Emissions ........................................................................................... 21 CEFS Forecasting Algorithm (Used for Stationary, Area-wide, Aircraft, and Ships) .................................................................................................................... 22 Temporal Factors (Stationary and Area Sources Only) .................................... 23 Growth Factors (Stationary and Area Sources Only) ....................................... 23 Control Factors .................................................................................................... 24 Mobile Sources ..................................................................................................... 25 Biogenics (Overview of the BEIGIS Model) ....................................................... 34 Emissions Inventory Systems Data (CEIDARS, CEFS) ..................................... 35May 7, 2007 Appendix F 2 

### 1. Introduction

Purpose

This Emission Inventory Appendix describes the emission in ventory and the role it plays in the State Implementation Plan (SIP ) development process. An emission inventory is an estimate of the amount and type of pollutants emitted into the atmosphere over a defined period of time. Emission inventories se rve three principal roles in the developm ent of the SIP:

• They provide the emissions data used for developing control strategies;

• They are a primary input to photochem ical modeling used in attainment demonstrations; and

• They are used to track progress in m eeting the emission reduction commitments.

This document will discuss the requirements for SIP development as it pertains to emission inventories, descri be the elements (or sectors) that make up an emission inventory, provide an overview of the methodologies used to derive emission estimates, summarize the recent improvements to t he emission inventory, and explain how the emission inventory is document ed through Quality Assuranc e (QA) and Quality Control (QC).

Statutory Requirements
The federal Clean Air Act (CAA) contains the statut ory requirements for attaining clean air (air quality standards). The U.S. En vironmental Protection Agency (U.S. EPA) regulations list requirements pertaining to em issions information that must be included as part of the SIP submittal package. The regulations require t hat the SIP emission inventory contain the pollutants for whic h an area is nonattainment. For an 8-hour ozone SIP, the emission inventory must cont ain the two ozone precursors: oxides of nitrogen (NOx) and reactive organic gases (ROG) . (The terms, ROG and VOC (volatile organic compounds), are used in terchangeably in this documen t.) For the PM2.5 SIP, the emission inventory must contain directly emitted PM2.5 and PM precursors: NOx, ROG, sulfur oxides (SOx), and ammonia (NH3).

Emission Inventory Overview

Basic Elements of the Emission Inventory

An emission inventory is a critical tool in t he evaluation of air pollution. Very simply, an emission inventory is a system atic listing of the sources of air pollution along with the amount of pollution emitted from each source or category over a given time period. Emission inventories are an esti mate of the air pollution emissions that are actually released into the environment—they are not m easurements of ambient concentrations. The following are examples of pollu tion sources by key sectors:May 7, 2007 Appendix F 3 • Industrial sources—power plants and oil refineries;

• Area-wide sources—consumer products and residential fuel combustion for heating homes;

• On-road sources—passenger vehicles and heavy-duty trucks;

• Off-road mobile sources—aircraft, trains, ships, recreational boats, construction equipment and farm equipment; and

• Non-anthropogenic (natural) source s—biogenic (or vegetation), geogenic (petroleum seeps), and wildfires.

The first four sectors listed are examples of anthropogenic, or human caused, emission sources. California’s statewide emission inventory includes over 17,000 individual facilities (or point sources) where business a ddresses are specifically identified. These facilities comprise approximately 14,000 process/industry types.

The emission inventory also includes approx imately 450 area-wide source categories. The area-wide source categories include aggregated point sources, or facilities, that are not inventoried individually but are es timated as a group and reported as a single source category, for example, gas stat ions and dry cleaners. Area-wide sources typically are associated with human activi ty and take place over a wide geographic area. Consumer products and unpaved road dust are additional ex amples of area-wide sources. The complete methodologies fo r these and all area-wi de source categories can be found at: http://www.arb.ca.gov/e i/areasrc/areameth.htm

. The mobile source emission inventory is developed by ARB’s emission factor models - EMFAC and OFFROAD. EMFAC estimates on-road emissions from all on-road motor vehicles, from passenger cars through heavy duty diesel trucks, and groups them in 250 categories (or technology groups). Off- road categories include emissions from all vehicles and equipment that operate off-road wh ich includes aircraft, locomotives, lawn and garden, cargo handling, construction, and ships and harbor-craft. OFFROAD groups these equipment into 1300 categorie s. For each category in EMFAC and OFFROAD, emissions are given by emi ssion mode (e.g. running exhaust, starts, evaporative losses, and tire and brake wear).

Non-anthropogenic sources genera lly include source categories with naturally occurring emissions such as wildfires and geogenic sources (e.g., petroleum seeps).

Agency Responsibilities

ARB works jointly with the thirty-five local air pollution control and air quality management districts (districts) to devel op a comprehensive statewide emission inventory. Districts are responsible for devel oping emission estimate s for major facilities (point sources) in their jurisdictions by work ing closely with facility owners. Districts are also responsible for developing emission es timates for approximately one-third of the non-point (or area-wide) sources.May 7, 2007 Appendix F 4 ARB is responsible for developing the mob ile source inventory (both on-road and offroad sources) and the remaining two-thirds of the area-wide sources. ARB works with other state agencies including t he California Department of Transportation (CalTRANS), the Department of Motor Vehi cles (DMV), and local councils of government (COGs) to assemble motor vehicle activity information necessary for the mobile source emission estimation models. To estima te emissions for the remainin g two-thirds of the area-wide sources, ARB works jointly with State agencies, such as the Department of Pesticide Regulation (DPR) which develops the em ission estimates for pesticides and the California Energy Commission which mainta ins power production and energy demand data that are critical for estimating futu re emissions growth from power plants.

Pollutants and Precursors

California’s ozone attainment strategy is to reduce emissions of ozone precursors – NOx and VOC. The strategy for reduc ing PM2.5 focuses on reducing NOx.

Oxides of Nitrogen (NOx)

NO x from anthropogenic sources occurs primar ily when fossil fuels are combusted at high temperatures. The table below shows that in 2006, 85 percent of NO x emissions in California are generated from mobile sources su ch as cars, trucks, buses, aircraft, and trains, while area-wide and st ationary sources contribute the remaining 15 percent.

2006 Statewide Emission Inventory – Nitrogen Oxides

NOx Emissions (Annual Average) Source Tons/day Percent Stationary 419 12 percent Area-wide 96 3 percent On-road Mobile 1848 51 percent Gasoline vehicles 679 19 percent Diesel vehicles 1169 32 percent Other Mobile 1235 34 percent Gasoline vehicles 76 2 percent Diesel vehicles 825 23 percent Other 333 9 percent Total Statewide 3598 100 percentMay 7, 2007 Appendix F 5 Reactive Organic Gases (ROG)

Reactive Organic Gases 1 are any compound of carbon, excluding carbon monoxide, carbon dioxide, carbonic acid, metallic carbides or carbonates, and ammonium carbonate, which participate in atmospheric photochemical reactions to form ozone and particulate matter (PM10 and PM2.5) in the atmosphere. References ii and iii provide information on the differences between ROG and VOCs.

The primary anthropogenic sources of ROG in California, as shown in the table below, are on-road mobile which includes exhaus t, fuel evaporation, and incomplete fuel combustion, and area-wide sources which includes solvents from dry-cleaning, degreasing, and coating operations.
2006 Statewide Emission Inventory – Reactive Organic Gases

ROG Emissions (Annual Average) Source Tons/day Percent Stationary 389 17 percent Area-wide 654 28 percent On-road Mobile 743 32 percent
Gasoline vehicles 670 29 percent Diesel vehicles 73 3 percent Other Mobile 541 23 percent
Gasoline vehicles 389 17 percent Diesel vehicles 111 5 percent Other 41 2 percent Total Statewide 2327 100 percent

Particulate Matter (PM2.5)
Particulate matter (PM) is a complex mixt ure of varying sized particles and liquid droplets including components such as acids, organic chemicals, metal, and soil or dust. There are two types of PM: primary and secondary. Prim ary PM is emitted directly from a source, such as unpaved ro ads, fires, and demolition or construction sites. Secondary PM is formed by complex reactions in the atmo sphere with chemicals such as sulfur and nitrogen dioxides. The majo rity of “fine particl es” (PM2.5) is formed by these complex reactions. A subset of PM 2.5 of particular concern are those that are emitted by diesel engines.May 7, 2007 Appendix F 6 2006 Statewide Emission Inventory – PM2.5

PM Emissions (Annual Average) Source tons/day Percent Stationary 103 13 percent Area-wide 568 70 percent On-road Mobile 66 8 percent Gasoline vehicles 15 2 percent Diesel vehicles 43 5 percent Break & Tire wear 8 1 percent Other Mobile 81 10 percent Gasoline vehicles 10 1 percent Diesel vehicles 41 5 percent Other 29 4 percent Total Statewide 817 100 percent

Geographic Considerations

In California, the emission inve ntory is constructed at the district, air basin, and county levels. This provides the flexibility needed to meet the jurisdictional requirements of local and State governments wh ile addressing the spatial aspec ts of air quality issues. In cases where nonattainment areas do not line up with these geographic boundaries, ARB further resolves the inventory to estima te the portion of emissions that occur within nonattainment area boundary lines. This is d one by selecting only the point sources that fall within the nonatta inment area boundaries, and estimating the emissions contribution of each area-wide and mobile source category to the nonattainment area.

California recently approved the state wide Emission Reduction Plan for Ports and Goods Movement that ext ends the off-shore boundaries fo r estimating the emission impact from shipping activities, thus lead ing to a modification in the geographic accounting of the off-shore emi ssions in the inventory. Forme rly, only marine emissions occurring within a 3-mile range were included as part of California’s emission inventory. The SIP emission inventory now includes emissi ons from shipping activities out to 100 miles. In the new definition, emissions occurring within thr ee miles of the shore line are assigned to the nearest air basin (e.g., emissi ons occurring within three miles of the Los Angeles County shore line are assigned to Los Angeles County in the South Coast Air Basin). Emissions occurring outside the 3-mile boundary are assigned to the Outer Continental Shelf Air Basin, but are still associated with the responsible county and district (e.g. the emissions occurring out side of the 3-mile boundary of Los Angeles County would be assigned to the Outer Cont inental Shelf Air Basin and would be the responsibility of the South Coast Air District).May 7, 2007 Appendix F 7 Base Year Inventory Selection for the SIP

The SIP is based on emission projections that are linked to a base year inventory. U.S. EPA identified 2002 as t he emission inventory base y ear for the 8-hour ozone and PM2.5 SIPs. This selection was announc ed by memoranda to the States in November 18, 2002 2

.

Emission Estimation Concepts

The key to the emission inventory is found in what is termed the “emission process”. The emission process defines the fundamental level at whic h emissions are calculated, stored, and maintained in the inventory. The total emissions for each emission process are calculated by applying a process rate (i.e., the number of emission activity units) to the emission factor (i.e., the amount of emissions per activity unit).

In principle, this simple concept holds for all emission sectors (i.e., point, area-wide, on-road mobile, and off-road mobile). Differe nces lie in the method or approach used to derive the process rate and emission factors. For example, the emissions from a power plant could be calculated by applying an emis sion factor (pounds of emissions per megawatt-hour) times the number of megawatt- hours of electrical energy generated. The emissions from the light -duty passenger car fleet could be calculated by applying an emission factor (grams of emissions per mile traveled) times the total vehicle miles traveled (VMT) for that mobile category. It should be noted that the examples above are simplified—particularly for on-road and off -road emissions. These emission factors are specific to model year, technology and f uel type, and are adjust ed for temperature, humidity, and operat ing speed.

Emission Forecasting Overview

Emissions forecasting depends on two independent variables: growth and control. For stationary sources, socioeconomic growth profiles are developed and linked to the inventory via the inventory pr ocess and/or industry types. For example, power plant growth is estimated based on the combined e ffects of economic growth in the sector along with the projected energy utilization by the California Energy Commission (CEC). Growth for consumer products is estimated by population growth at the county level. These growth profiles are constructed for t he entire timeframe addressed in the SIP.

The California Emission Forecasting System (CEFS) includes growth data for the period 1990–2030 for SIP forecasting exercises. Sim ilarly, control profiles are constructed to reflect adopted emission control measures. A c ontrol profile is developed that describes the implementation of the rule and the profile is included as part of the baseline CEFS forecasts. CEFS projections are run for the entire Stat e by applying the growth and control profiles to the 2002 base year inventor y to obtain the projections needed for SIP analysis.

EMFAC and OFFROAD do not use a base y ear projection approach. Rather, the emission models employ sophistic ated routines that predict vehicle fleet turnover by vehicle model year. As with stationary sources, EMFAC and OFFROAD include controlMay 7, 2007 Appendix F 8 algorithms that account for all adopted regulatory actions (e.g., an amended emission standard to be implemented by a certain y ear, or increased stringency on a roadside inspection program to be implemented in a fu ture year) which, when combined with the fleet turnover algorithms, pr ovide baseline projections.

CEFS integrates the projected inventorie s for stationary and mob ile sources into a single database to provide a comprehensiv e statewide forecast inventory. Nonattainment area inventories are extracted fr om this comprehensive inventory. This establishes the baseline projections from whic h the SIP inventory is constructed. The data are accessible to ARB and district planners via user-friendly web reporting tools.

Types of Inventories (Annual Average, Seasonal, Modeling)

Annual Average

Annual average emissions data by major source category in tons per day are derived from a simple average by ta king the annual emissions reported (or estimated) in tons per year and dividing the emissions by 365 days.

Seasonal Inventories
Summer and winter planning emissions take in to account the temporal variation (e.g. activity variation) of emissions at a facilit y or a source category throughout the year. This is accomplished by collecting throughput or activity data from point source facilities where available, and developing temporal prof iles for area sources to estimate monthly variation of the emissions. CEFS calcul ates summer and winter planning emissions based on these temporal data. The summe r planning season for California is MayOctober, and the winter planning season is No vember-April. (Note: U.S. EPA assumes a shorter summer planning period (June-August) in its temporal data collection schema.) California requires planning ev aluations to be based on a longer summer because it experiences longer periods in which temperatures are warm enough to contribute to ozone formation than most othe r states). The summer season planning emission inventory is used in ozone SIP planning.
Modeling Inventories

CEFS also generates emission inputs for ph otochemical modeling exercises. The categories handled by CEFS are stationary, area-wide and off -road mobile sources. (Note: Modeling emissions for the on-r oad sector are generated with EMFAC/DTIM, and these modeling inputs are handled outside of CEFS.) Modeling inventories are month/day-specific estimates that are tempor ally and spatially resolved at a finer level than planning inventories—CEFS calculates emi ssions for a typical weekday or a typical weekend day based on weekly oper ation profiles identified for t he emissions processes. Point source locations are identified in CE FS by the Universal Transverse Mercator (UTM--North American Datum (NAD83) ve rsion) geographic coordinate system. Modeling inventory projections include facility level emissions at the facility, device, and process level with stack locations and associ ated flow characteristic as applicable. Area source emissions are maintained at the county level and are further resolvedMay 7, 2007 Appendix F 9 based on spatial surrogates as a post-proce ssing step prior to modeling. The day specific emissions are generated for ty pical weekday and weekend periods.

Planning and Modeling Inventory Differences

The temporal information used to determine s easonal planning inventories is also used to develop month/day-specific modeling inventories—the data are just further resolved for modeling inventories. It should be not ed that modeling inventories generated by CEFS are not actual day specific emissi ons based on field studies—they are estimates of emissions for “typical” oper ating conditions. In some cases, “actual” day-specific information is available and this can be used to overwrite the results of the CEFS emission modeling inventory output s. It is important to note that because modeling inventories are month/day-s pecific, the emission numbers will not match the planning inventory emissions.



### 2. Emission Inventory Improvements

Overview

ARB devotes substantial effort to continually im prove the emission in ventory. Updates to the inventory occur through three proce sses: planning and development, regulatory support, and special studies. The new information acquired and methodologies developed are folded back into the models as well as the methodologies for sources not previously included. This is a dynamic process and ARB will continually work to improve its methodologies through research and stakeholder feedback.

Improvement Process

Planning and Development - SIP

The planning process is undertaken to develop st rategies for meeting air quality goals. The SIP inventory is the cent erpiece data element for determining future attainment in photochemical modeling exercises, and the basis for defining control strategies.
Regulatory Support
The regulatory process identifie s potential emission reductions. This typically involves new surveys to gather the most up-to- date data (e.g. equipment activity and populations). These improved data allow for more accurate emission estimates and identify key areas where reductions can be achieved. The planning and regulatory processes are also used to solicit peer review and stakeholder input.

Special Studies / Research

Another mechanism through which improvement s are made is special studies. These studies may or may not be associated with t he SIP or regulatory support processes but are used to further the underst anding of emission modeling an d verification. Special studies include funding or participating in cutting edge research projects, developingMay 7, 2007 Appendix F 10 innovative emission inventory methodologies, and collaborating with industry, academia and the public to better understand emissions proc esses. Examples of special studies include, but are not limited to, Southern California Ozone Study and Central California Ozone Study related research. The results from these projects have provided data that allow validation checks between obs erved air quality and emission trends 3 , and emissions associated wit h air quality exceedances 4

.

Stakeholder Outreach

ARB works jointly with other State agencies, districts, and stakeholders to develop a comprehensive emission inventory on an annual basis. Comprehensive inventories are submitted triennially and partial inventorie s are submitted annually to U.S. EPA for the National Emission Inventory 5 (NEI). ARB also publishes the most current emission inventory in the annual Almanac of Emissions and Air Quality 6

.

Quality Assurance/Qua lity Control (QA/QC)

QA/QC is a formal process focused specific ally on ensuring the in tegrity and accuracy of the inventories for the California 2007 8- hour ozone SIP and PM2.5 SI P in California. ARB formally initiated this comprehensiv e effort in September 2004 through its Emission Inventory Technical Advisory Committ ee (EITAC). This st arted an iterative process of continual updates to the 2002 base year emission inventory for stationary point and area-wide sources (including growth and control data), leading up to the finalization of the SIP inventor y. In addition, ARB emission in ventory staff worked jointly with the ARB’s modeling staf f through the Gridded Emission Coordination Group that includes district modeling st aff and industry representatives . A principle aim of this group is to provide a critical feedback loop to emission inv entory staff to ensure that inventory inputs have been reliably prepared for use in photochemical modeling.

The statewide emissions inventories are assembled and maintained by ARB in the California Emission Inventory Devel opment and Reporting System (CEIDARS) and California Emission Forecasting and Planni ng Inventory System (CEFS) databases. Before an emission inventory is approved, a series of checks are performed to assure that the correct statewide emissions estima tes are reported. Upon completion of the models, various scenarios are run to provide emissions by county, ai r basin, district, EIC code, pollutant, calendar year, and season. Queries are then run on these scenarios to provide the statewide emission totals by pol lutant and year. Finally, as a check to ensure that what CEFS estimates as st ate totals match what EMFAC/OFFROAD estimates, the CEFS queries are matc hed against EMFAC/OFFROAD model output.

Inventory verification 7 is another important step to ens ure the accuracy of the emission inventory. Since 1987 t here have been a number of studie s that have contributed to model development. In these tests, emissi ons from vehicle fleets are compared to output for similar fleets calc ulated from the EM FAC model. Comparisons have been made between EMFAC fuel consumption estimate s and actual fuel sales for that fleet reported by the State Board of Equalization, and emissions derived from fuel sales and remote sensing data.May 7, 2007 Appendix F 11 Specific Category Improvements

Stationary

Methods for estimating emi ssions from industrial point sources have continually improved as district staff work with facility owners to ensure that reported emissions are real as opposed to permitted (or potential) values. The California Clean Air Act Fee Regulation imposes fees on large permitted point sources thus encouraging facility owners to report the most accurate emissi ons possible. ARB audits the district emission inventory programs on a planned cy cle to further ensure that emission inventory program requirements are met.

Area-wide
New survey data for consumer products and architectural coat ings improve the aggregated inventory estimates as well as a be tter characterization of emissions at the product type. Currently there are 152 consum er product categories and 56 architectural coatings categories - the 1994 SIP inventory only had three categories each. The same is true for degreasing categor ies - the 1994 SIP had only five categories and the current inventory now has 32 categories. Im provements have also been made to the agricultural industry categories as a resu lt of a partnership between ARB and the agricultural community. ARB participates in an ad-hoc agricultural advisory committee to approve methods used for estimating base year emissions and future year growth and control assumptions for forecasting. T he methodologies for the following categories have also been updated: auto refinishing, in dustrial coatings and solvents, livestock husbandry, pesticides, waste burning, wildland fire use, ammonia, and wineries. The most recent improvements to the area-wide emission inv entory sectors are described below:

 Ammonia A comprehensive 2002 ammonia inventory wa s developed for all area-wide sources (in addition to on-road, off-road, and natural sources). The emissions were calculated using data provided in repor ts by ENVIRON and the NASA Ames Research Center.

 Architectural Coatings In response to the California Clean Air Act F ee Regulation, several architectural coating manufacturers submitted revi sions to their 2001 survey dat a (calendar year 2000 sales). The changes in sales volume and emissions were applied uniformly across all categories.

 Auto Refinishing This category was updated using data from a 2001 ARB survey and the emissions were grown to 2002.

 Consumer Products Two sets of changes have been incorporated in to the consumer products inventory. First, manufacturers submitt ed revisions to their 1997 survey data in response to the California Clean Air Act Fee Regulation and resulted in updates for 13 categories. Next,May 7, 2007 Appendix F 12 results of the 2001 Consumer Products Survey have been incorporated. Some categories have been expande d and several new categories have been added.

 Industrial Coatings and Solvents The emissions for several of thes e categories have been updated using the U.S. Census Bureau’s 2002 Industrial Repor t for Paint and Allied Products. The nationwide consumptio n data were apportioned to Calif ornia using manufacturing employment data for the industry sectors likel y to use the specific types of industrial coatings.

 Livestock Husbandry ARB published a new methodology in 2004 for estimating ROG emissions from livestock facilities including dairies, feedl ots, poultry operations, and other confined animal facilities. The met hodology also includes PM estima tes for dairies and feedlots. The emission estimates are based on animal popu lation data from the year 2000. ARB uses district-specific data where avai lable and employs a generalized methodology where data are lacking. For permitting purposes and to more fully reflect the processspecific livestock emissions, the San Joaquin Valley Air District has adopted livestock emission factors that differ fr om the ARB default values.

 Pesticides The pesticide inventory includes the emissi ons from agricultural and structural applications. Updated emissions for 2003 are calculated using information from the annual Pesticide Use Report (PUR) database and pesticide product emission potential (EP) data maintained by the Ca lifornia Department of Pesticide Regulat ion (DPR). The EPs that are assigned to the products ar e either measured by thermogravimetric analysis (TGA), calculated by DPR chemists, or assigned a default value equal to the median TGA-based EP in each formulation ca tegory. Prior to 2002, DPR assigned EP values to products with no measured values as the maximum TGA in that category. The use of median rather than maximum TGA values has resulted in a significant lowering of ROG emissions for pesticides with default em ission values. The average 2003 TOG and ROG emissions from all agricul tural and structural pesticides were 55 percent lower in 2003 than in 1995. The most significant reducti on was in the structural methyl bromide category, likely due the Montr eal Protocol, which mandates the phase-out of methyl bromide.

 Waste Burning Emissions result from t he waste burning of agricultural residues, weed abatement, range improvement, and other materials. Al though this is a district-reported category, ARB has developed default 2002 emission esti mates for several dist ricts that have not provided updates in recent years. Updated emissions data have been developed for the following areas: South Coast Air Basin, Mojave Desert Air Basin, San Diego Air Basin, Salton Sea Air Basin, Amador C ounty, and Western Nevada County. There have been no changes to the under lying methodology for calculating emissions. The emissions are based on distri ct-reported burning activity. Emission factors and fuel loading values have been expanded to in clude more specific crop types.May 7, 2007 Appendix F 13  Wildland Fire Use (WFU) A WFU is a naturally ignit ed lightning fire that is m anaged for resources benefit. The WFU emission inventory category was created in 2004, and inventory back-populated to 1994 where data were available. WFU emissi ons are calculated using the Geographic Information System (GIS) bas ed Emission Estimating System model developed for ARB by UC Berkeley’s Center for the A ssessment and Monitoring of Forest and Environmental Resources laboratory.

 Wineries This is a newly updated methodology for wi ne fermentation emissions. The emission factors for wine fermentati on have not been updated from the older methodology, but the source of wine fermentation activity has been updated. The Tax and Trade Bureau (TTB) is now the source of estimates for amounts of wine ferment ed in the state. Temporal data gained from the TTB records allow us to give detailed monthly breakouts of wine fermentation in the state.

Mobile

ARB is continually improving EMFAC and O FFROAD, and emission inventory elements as represented in each model. The following discussion includes evolution of EMFAC and OFFROAD, inventory verification, and improvements made for EMFAC2007 and the SIP.

On-road
The following are modifications to the model and the inventory since the last SP. For a more thorough technical explanation please refer to the tech memos at http://www.arb.ca.gov/msei/msei.htm, and http://www.arb.ca.gov/msei/ onroad/previous_version.htm . The changes below are those changes reflected in EMFAC2007 t hat have occurred since EMFAC2002.

 Revisions to the Methodology used to C haracterize the On-road Vehicle Fleet Vehicle population data by class, age and geographic area used in EMFAC are based on the Vehicle Registration Database maintai ned by the California Department of Motor Vehicles (DMV). EMFAC back-casts histor ic changes in vehicle population using the oldest DMV data available, and forecasts fr om the most recent . As new data are received, projections are replaced with act ual data. EMFAC2007 population estimates were updated based on 2000-2005 data.

 Modification of Mileage Accrual Rates Previous mileage accrual rates were based on Smog Check data acquired from 1991- 1995. To more accurately model vehicle mi les traveled, the new model incorporates data from Bureau of Automotive Repair (BAR) 2001-2003 for the following gasoline powered vehicle classes: passenger cars, li ght-duty trucks (Tiers 1 and 2), mediumduty trucks, light heavy-duty trucks (Tier 4 and 5), and motor homes.May 7, 2007 Appendix F 14  Updating Vehicle M iles Traveled and Speed Distributions Vehicle miles traveled and speed distributio ns were submitted by transportation planning agencies for the South Coast, t he San Francisco Bay Area, and San Diego and Santa Barbara Counties and used to update EMFAC.

 Modifications to the Inspection Maintenance Programs Modifications were made to reflect the c hanges in inspection and maintenance (I&M) programs for areas that upgraded to the enhanced Accelerated Simulation Mode program and change-of-ownership exemptions.

 Ethanol Permeation In the prior EMFAC, evaporative emissions were correlated to oxygen content. Evaporative emissions from me thyl-tert-butyl ether (MT BE) and ethanol (EtOH) were assumed to be similar since both contained two percent oxygen. Because recently completed studies indicate that EtOH has much higher permeation rates through fuel tank walls, hoses and fittings, EMFAC was m odified to reflect these findings.

 Revision of Heavy Heavy-Duty Die sel Truck Emission Factors and Speed Correction Factors New chassis dynamometer te sting data developed throug h the CRC E55-59 program were analyzed and integrated into EMFAC. Emi ssion factors for this category reflect all available data from CRC as well as other data sets. These data were used to develop new emission factors and speed correction fact ors. New tampering, mal-maintenance and malfunction survey data were used to update deterioration rates, demonstrating that heavy heavy-duty diesel tr uck emissions are greater t han previously estimated. Trucks were for the first time tested over an ARB- developed heavy-heavy-duty test cycle, generating idle emissions data covering a wide span of model years as well as emission data at several different speeds and idle. These new data have been adopted by ARB and used to generate new emission factors and new speed correction factors.

 Redistribution of Heavy Heavy-Duty Die sel Truck Vehicle Miles Traveled in California Vehicle miles traveled estimates are provided by the transportation planning agencies, but ARB is responsible for t he distribution of that VMT by vehicle class and region. Heavy duty diesel truck VMT in EMFAC2002 was allocated based on truck registration. This method is appropriate for light duty v ehicles and heavy duty gasoline trucks, but heavy heavy-duty diesel trucks travel extensivel y outside their registration counties. VMT was redistributed in EMFAC2007 using survey information from the California Department of Transportation (CalTrans) and modeled from MVSTAFF, an annual report by CalTrans. The new distribution more accurately reflects actual truck travel patterns in California.

 Revised Break Wear PM Emission Factors EMFAC has been modified to account for tr ue brake counts. Previously a single emission factor for brake wear, based on a 1983 study, 8 was used to calculate the amount of airborne dust attributable to break wear (12.8 mg/mile). This emission factorMay 7, 2007 Appendix F 15 assumed that all vehicles were equipped with four brakes, even if the vehicle had more than four brakes.

 On-road Fuel Correction Factors Fuel correction factors are used to reflect the impact on emissions of commercial fuels compared to the fuel used duri ng the certification process. Modifications to the fuel correction factors (FCFs) were incor porated into EMFAC2007 for both diesel and gasoline. For example, the FCFs for P hase II reformulated gasoline (RFG) were modified for the 1996-2003 calendar years to be cumulative to the Phase I (1992-1995 calendar years) values. Additionally, the impacts of ethanol on evaporative processes, changes in sulfur and aromatics composition in fuel, exhaus t hydrocarbon benefits for clean diesel, and an out-of-state di esel fueling rate of 10 per cent were incorporated.

 Revised Planning Humidity Profiles Meteorological conditions impact the emissions process in vehicles, therefore, EMFAC2007 incorporates ambient rela tive humidity profiles to determine NO x conversion in exhaust as well as air condi tioner usage by automobile drivers. Previously, EMFAC used annual average daily rela tive humidity profiles for each county or geographical area in the State. EM FAC2007 now incorpor ates hour-by-hour temperature, relative humidity, and ozone conc entration data from 700 stations for the years 1996-2004.

 Revised Planning Tem perature Profiles EMFAC2007 contains daily ambient tem perature profiles for use in evaluating evaporative emissions and other heating/cooling related proce sses. In the previous EMFAC, hourly temperature observations co llected on high ozone days were spatially distributed by zip code and weighted by popu lation. The resulting weighted profiles seemed rather cool. Weighting in EMFAC2 007 is done by vehicle miles traveled (VMT) and provides more representat ive temperature profiles.

 Corrections to Heavy-duty Gas Cap Benefits In reviewing the gas cap benefits staff not iced reductions on hydrocarbon emissions were negligible for heavy duty gas oline vehicles. Further review revealed the gas cap benefits were not fully implem ented in EMFAC. In EMFAC 2007, the gas cap algorithm has been implemented for heavy duty vehicles.

 Coding Corrections Several coding errors have been corrected which results in a minor change to emissions estimates.

Other Mobile

The following measures are reflected in OFFR OAD2007 or the external modules. For a more thorough discussion on improvements to the other mobile inventory, please refer to the tech memos at http://www.arb.ca.gov/msei/msei.htm

.May 7, 2007 Appendix F 16  Addition of Tier 4 Emission Factor s to Off-Road Large Compression Ignition Engines (>25 hp)

The previous OFFROAD model only provided emission factor s based on Tier 0, 1, 2 and 3 emission standards for compression-ig nited engines greater than 25 horsepower (hp). In 2003, Tier 4 emission standar ds were adopted by ARB and the OFFROAD model has been updated to incorporate these new standards.

 Off-road exhaust emissions invent ory fuel correction factors Fuel correction factors have been modified to be consistent with EMFAC and to take into account current reformulated gasoline im pacts. Carbon monoxide fuel correction factors for summertime and win tertime fuel which vary geographically, and the benefits of ultra low sulfur diesel (requirement commencing in 2007) were applied.

 Addendum to Evaporative Emissions The current version of t he OFFROAD model includes an updated estimate of the evaporative emissions from large spark-ignited engines, including those used in recreational marine applications.

 Revisions to the Diesel Transport Refri geration Units (TRU) Off-road Inventory TRU emissions are calculated in the OFFROA D model as a function of emission rate, population, activity, average horsepower, and l oad factor. With th e exception of the emission rates, all other factors were based on the 1997 Power Systems Research (PSR) report. Since late 2002, ARB has obtained more up-to -date population and activity estimates from surveys of TRU manuf acturers. These results have revised the input factors of population, activity, load factor, average horsepower , growth factors, survival rates, and useful lifetime estimates to the OFFROAD model.

 Estimation of the Impact of Ethanol on Off-Road Evaporative Emissions Since the phase out of methyl-tert-butyl ether (MTBE) as a gasoline additive in 2004 ethanol (EtOH) has been used as an additive to meet the required two percent oxygen content in commercially dispensed gasoli ne. Because MTBE and EtOH both contained two percent oxygen, their evaporative emissions were assumed to be similar. However, recently completed studies indicate this is not a correct assumption. These tests indicate that EtOH has much higher permeation rates through fuel tank walls, hoses and fittings. OFFROAD2007 refl ects these findings.

 Recreational Marine Growth factors and engine-to-boat ratios were updated with more recent DMV and National Marine Manufacturers Association (NMMA) data. Additionally, evaporative emissions are separated into ope rating and storage emissions.

 Agricultural Based on findings from the Agricultural Advi sory Committee, grow th factors for the San Joaquin Valley were updated.May 7, 2007 Appendix F 17  Recreational Vehicles The OFFROAD model has been modified to a ccount for updated population for MC, All Terrain Vehicles (ATVs) and snowmobiles us ing Department of Mo tor Vehicles (DMV) calendar year 1991-2005 totals. Human popul ation data from Census 2000, personal income, and median household income data from Department of Finance were also applied to develop the growth rate for recrea tional vehicles. DMV 2005 registration data has been used to update county allocation factors. In addition, DMV registration also shows the inactive population for MC, AT V and snowmobile. Thus, the diurnal and resting emissions for inactive population fo r these three equipments are included in the OFFROAD2007. Also, the activity and load fa ctor for snowmobile were updated to be the same as U.S. EPA’s NONROAD model. Activity results from U.S. EPA’s NONROAD model for specialty vehicles, benefits of golf ca rts and specialty vehicles subject to small off-road engine and large spark ignition (LSI) controls, benefits of snowmobiles subject to the U.S. EPA’s capping standard, and the addition of OFMC and ATV evaporative emission factors as grams per mile were calculated in OFFROAD2007.

Although the overall statewide emissions fr om recreational vehicl e are the same, the distribution of emissions to the specific county/air bas in/air district has been changed relative the location where the equipment is stored or operated. The storage allocation is relevant to the diurnal and resting emi ssions from active and inactive recreational vehicles and is based on the county of DMV registration. The operational allocation applies to exhaust, hot soak and running lo ss emissions and represents where the off-road trails are located.

 Construction and Ground Service Equipment (D> 25 horsepower) The OFFROAD model was updated with population, useful life, age distribution, annual activity, growth data and a deterioration rate cap introduced based on the 2003 McKay & Co. Study, ARB’s 2005 Off-Road Diesel In -Use Equipment Survey, ARB’s 2006 OffRoad Diesel In-Use Mini Survey, 2003 TIAX Su rvey of Public Fleets, and input from industry and stakeholders in public workgroup meetings and workshops.

 Small Off-Road Equipment (SORE) The OFFROAD model was updated with: new exhaust and evaporative standards; new population and activity estimates for resi dential lawn and garden equipment obtained from the Lawn and Garden Survey; new populat ion estimates for commercial lawn and garden equipment from an ERG su rvey; and modified small off-road engine survival rate curves using information from the Ou tdoor Power Equipment Institute.

The following changes were ma de to external modules:

 Changes to the Locomotive Inventory Locomotive growth factors were revised in the OFFROAD model based on new data that better reflected locomotive operations , including projected growth in intermodal freight traffic, both domestic and internatio nal, U.S. industrial production, and various railroad statistics available from the Associat ion of American Railroads (AAR). OtherMay 7, 2007 Appendix F 18 measures such as the 1998 South Coast and 2005 State memorandum of understanding were also reflected.

 Cargo Handling Equipment A separate methodology for cargo handling equipm ent at the ports and inter-modal rail yards per the Cargo Handli ng Equipment Air Toxic Cont rol Measure (December 2005) was incorporated.

 Ocean Going Vessels The OGV inventory reflects updated populat ion and activity data for ocean-going vessels statewide. The emission estima tes were developed for main and auxiliary engines in eight vessel types (auto carrier , bulk cargo, container, general cargo, passenger, reefer, roll-on roll-off and tankers) . In addition, methodology was developed to allocate statewide emissions to indivi dual ports and districts. The ARB regulation requiring vessels with auxiliary engines to swit ch from heavy fuel oil to marine distillate within 24 nautical miles of California was incorporated into the inventory.

 Commercial Harbor Craft The commercial harbor craft inventory was re vised with a single statewide approach. OFFROAD emission factors were used to ca lculate emissions and adjusted to better reflect the “E3” test cycle which is more representative of marine engines.

 Portable Fuel Containers/Gas Cans Emission estimates were developed from data such as percentage of household/businesses with cans, average cans per household/business, average volume of cans, and refill frequencies collected during two surveys in 1998 and 2004. Allocation factors and growth rates fo r residential gas cans are based on the Department of Finance's Census 2000 dat a and landscaping employment growth projected by EDD (Calif ornia Employment Devel opment Department).

Biogenic

After its original application to the S outhern California Ozone Study domain, the biogenic emission inventor y GIS (BEIGIS) model was expanded to the Central California Ozone Study/Californi a Regional Particulate Air Quality Study domain. In order to represent central and northern California conditions during summer and winter seasons, the BEIGIS model has incorporated fu rther inputs, such as crop Geographic Information System (G IS) data for central and northern California developed by the California Department of Water Resources (DWR). Because it is a statewide land use/land cover database, the Gap Analysis Pr ogram (GAP) is still used to represent natural areas in the model. Urban vegetati on survey data for Fresno, Oakland, and Sacramento were compiled from several studies and used to develop urban land use biogenic volatile organic co mpounds (BVOC) inputs for other urban areas in the central and northern California portion of the model. Crop BVOC and leaf mass factors for additional crops listed in DWR crop GIS databas es were compiled from literature. Crop calendars developed by the Univ ersity of California-Davis Department of AgriculturalMay 7, 2007 Appendix F 19 and Resource Economics were consulted in order to develop growing season and dormant season crop BVOC inpu ts. In-leaf and off-leaf season BVOC profiles were also developed for natural and urban areas, in order to account for the presence of deciduous and evergreen specie s in landscapes. A 1 km 2 resolution winter Leaf Area Index (LAI) raster derived from Adv anced Very High Resolution Radiometer ( AVHRR) satellite data 9 was used in order to account for winter dormancy in natural areas and croplands.



### 3. Emissions Estimation Methods and Documentation

Major NOx/ROG Categories

Mobile sources account for about 75 percent of ozone precursor emissions (ROG and NOx) in the State in 2006 and will continue to dominate the inventory in 2020, however total emissions from all sources will drop 28 percent from 5,924 tons/day to 4,237 tons/day.

Stationary 14%

Area-Wide 13%

Mobile

73% 

### 2006 Stationary
21%

Area-Wide 19% Mobile

60% 

### 2020

Emissions Inventory for ROG and NOx by Category Statewide 2005 and 2020

Emissions Estimation Methods

Stationary Emissions

Stationary emission sources identified on an individual basis or a single source are called point sources. Emissions from point sources are estimated from facilities that emit more than ten tons per y ear of any criteria pollutant. They are identified by name and location. A facility may have many indivi dual, identifiable sources of emissions or (points) of emissions. For example, a coffee processing facility may have a boiler, roaster, grinder, and packing station as emissi on points. In addition , a point source may have one or more processes or operation. For example, a boiler burning natural gas part-time and oil part-time is considered to have two processes because emission rates are different for the two fuels.May 7, 2007 Appendix F 20 The emissions for each point are computed as t he product of use factor (an indicator of the extent of the activity per unit time) and an emission factor (the quantity of pollutant emitted per unit of the use factor). Point s ource use factors usually relate to a process rate, such as the amount of fuel oil consumed in a boi ler or the amount of asphalt concrete produced at a batch plant. T he general equation used to calculate the emissions is shown below:

EMS =PR x EF x CONV

Where: EMS = Emissions (tons/year) PR = Process Rate or activity data in (SCC unit/year) EF = Emissi on factor (mass/SCC unit) CONV = Unit conversion factor

Emission factors are derived from tests that relate emissions to the process causing the emissions. When adequate source test data are available for a source, these data are used to derive emission factors for the em ission calculations. When adequate source test data for a source are not available, an alternative is to use average emission factors for similar emitting sources. These factors are usually derived by averaging the results of many tests of similar sources. Such factors are published by federal U.S. EPA in Compilation of Air Poll utant Emission Factors . The published emission factors in AP-42 are usually calculated for devices without control equipment. Because controls may very among different facilities, it is therefor e necessary to adjust th e emissions factors to account for the specific control equipment being used.

In the base year emission inventory, cont rolled emissions are estimated by applying control efficiency factors to the “uncontro lled” emissions. These base year control factors take into account operational upset s and equipment malfunctions that lead to excess emissions. The resulting “controlled” inventory represents the actual emissions that have occurred over the 365 day calendar year. Due to the wide variation in how districts account for these operational fluctu ations, rule effectiveness is not accounted for as an explicit reporting variable in CEIDAR S, rather, rule effectiveness is treated as an implied variable maintained by the districts as part of the actual “controlled” emission estimates that are submitted to ARB.

In some cases, it is possible to determi ne emissions using a materials balance which reconciles the amounts of materials that ent er and leave a process. This method is used for many sources, such as painting proc esses, where it is assumed that all the solvent used in the coating eventually evaporates.

The source information along with activity data and emission factors used to calculate emissions for all point sources are kept in district’s files and are reported to ARB on a regular basis (generally once a year).May 7, 2007 Appendix F 21 Data Acquisition

Information on emissions from stationary source s is obtained primarily from the districts which have primary responsib ility under state law (Health and Safety Code section 40000) for regulation of stationary sources of air pollution.

In developing the emission inv entory, there is considerab le interaction between ARB and the districts. The planning process fo r an inventory development starts with the assessment of needs and require ments. Following the init ial inventory planning, ARB collaborates with districts to prepare inventory developme nt schedules and updates to the inventory.

Area-wide Emissions

Area source methodologies are used to calc ulate emissions from aggregated point sources and area-wide sources. Aggregated point source s are many small point sources, or facilities, that are not inventoried individually but are estimated as a group and reported as a single source category. Examples include gas stations and dry cleaners. Because many of these sources ar e already accounted for as facilities or point sources (see the previous section on Stationary Emissions), the area source emissions are reconciled with point emi ssions before being added to the emission inventory. For example, the area source methodology for dry cleaners is used to calculate the total emissions for a particula r county. These area source emissions are then reconciled by subtracting the emission s for dry cleaners already accounted for in the point source inventory.

Area-wide sources include source categor ies associated with human activity and emissions take place over a wide geographic area. Consumer products and unpaved road dust are examples of area-wide sources.

Both ARB and districts share responsibility in updating t he various area-wide source categories. Some categories are the responsib ility of the district while other categories are ARB’s responsibility. ARB methodologie s and many of the dist ricts methodologies can be accessed at http://www.ar b.ca.gov/ei/areasrc/index0.htm.

Data Acquisition

When ARB develops an area-wide source me thodology, many possible sources of activity data and emission factors are cons idered. Often data are obtained during the development of regulations. For example, in developing consumer product regulations, ARB routinely conducts statewide surveys. Research studies, some of which are sponsored by ARB, are another source of emissions data. Finally, various state and federal agencies may have relevant data needed to estimate emissions.

If activity data are not available at the county level, then statewide emissions are apportioned to counties using a su rrogate. In the case of consumer products, statewide emissions are apportioned to counties using population.May 7, 2007 Appendix F 22 Once ARB has developed a draft methodology, it is submitted to Emission Inventory Technical Advisory Committee (EITAC) for review. EITAC is composed of emission inventory staff from the districts, ARB, and U.S. EPA. Following EITAC review and incorporation of recommended changes, the methodology becomes final and, where appropriate, emissions are reconciled with the point source inventory. Finally, process rate and emissions transaction format file s are prepared and the CEIDARS database is updated.
The districts also provide updated emissions data to ARB. Process rate and emissions transaction format files provided by the di stricts are used to update the CEIDARS database. When they are provided, the me thodologies developed by districts are reviewed by ARB.

CEFS Forecasting Algorithm (U sed for Stationary, Area-wide, Aircraft, and Ships)

The general forecasting equation for stationar y sources is shown below. The CEFS processor calculates seasonal projecti ons for planning and month/day specific projections for photochemical modeling inputs. CEFS employs a region and emission category selection hierarchy when selecting and applying growth and control factors. Consider, for example, a rule which applies to the entire universe of a particular process type (identified by source classification code (SCC )). If that rule is more stringent for a particular industry (identified by SIC), then a control profile specific to the SCC/SIC combination can be assigned. CEFS will cycle through the control data records applying the highest level of the selection hierar chy to that category (i.e. the growth and control data can be “layered” to target the sour ce categories exactly as the rule calls for without double-counting (or overla ps). The same type of logic is used for the growth data.

] CF

... CF CF [ GF FRAC TF by

_ E fy

_ E ) p
, s
, m
, r ( ) p
, s
, m
, r ( ) p
, s
, m
, r ( ) s
, r ( ) y
, p
, s
, r ( ) s
, r ( ) o
, p
, s
, r ( ) y
, p
, s
, r ( i ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ =
2 1 where Primary Variables: E_fy = Emissions in the future year (tons/day; annual average or seasonal) E_by = Emissions in the base year (tons/year) TF = Temporal Factor (or “Seasonal Adjustment Factor”) FRAC = ROG, VOC, PM 10 , or PM 2.5 fraction (if applicable) GF = Growth Factor (i s the ratio of two activity le vels at the end-point years) CF = Control Factor (is the ratio of two control le vels at the end-point years)

Subscript Variables: r = region (distr ict, air basin, county) s = the source category (SCC/SIC or EIC for the TREND algorithm; The GIS algorithm performs the projecti ons at the facility level.) p = the pollutant m = the cont rol measure (i = the i th control measure) y = the y ear to be projected o = the base yearMay 7, 2007 Appendix F 23 Temporal Factors (Stationary and Area Sources Only)

To better characterize emissions affected by seasons, seasonal adjustment factors (or “temporal factors”) are used to apporti on emissions into the periods under consideration. The temporal algorithm used for calculating seasonal emission inventories was rewritten to better approximat e the seasonal variation of emissions. In the prior algorithm used in the 1994 SIP, emission estimates for point sources represented an “average annual oper ating day”. The assessm ent of this algorithm showed that it did not accurately estima te seasonal emissions because the algorithm was driven by the number of operating days a point source process operates. For processes that operated intermittently, this resulted in exaggerated seasonal emission estimates because the algorithm assumed t hat all intermittent processes operated simultaneously. For area sources , the prior algorithm estima ted emissions based on an “average seasonal op erating day”.

ROG, VOC, PM 10 and PM 2.5 Fractions
The reactive portion of the total organic gas (TOG) emissions (expressed as ROG) are calculated by applying reactive fractions wh ich are maintained in CEIDARS. Districts may supply fractions at the fa cility/device/process level for point source processes. If these data are not provided at this level, default fractions maintained by ARB at the SCC level are invoked. In like manner, t he portion of PM falling within the 10 micron and 2.5 micron size ranges (i.e. PM 10 and PM 2.5 respectively) are estimated from district-supplied fractions or by applying size fractions maintained by ARB in CEIDARS. As with the temporal factor, an emissi on-weighted fraction is developed for each SCC/SIC pair.

Growth Factors (Stationary and Area Sources Only)

Growth factors are derived from county-specif ic economic activity profiles, population forecasts, and other socio/demographic acti vity. Growth profiles are typically associated with the type of industry and secondar ily to the type of emission process. For point sources, economic output profiles by industrial sector are typically linked to the emission sources via SIC. For area sour ces, other growth parameters such as population, dwelling units, fuel usage etc. may be used. The growth factor is the ratio of the growth level in the future year to the growth level in the base year. These growth levels are also region and source category dependent.
Data Acquisition

Growth factor data for use in CEFS are acqui red from the district s, and where no data are available, growth activity projecti ons are constructed from ARB contracts 10 with experts in the economic and demographic gr owth. ARB may also develop growth estimates in consultation with stakeholders. For example, ARB led an effort to revise growth assumptions in agricultural categorie s, under guidance of the State’s Agricultural Advisory Committee. Geographically speaki ng, much of the State relies on these constructed growth projections while other high emission regions, such as the South Coast and Bay Area, submit their own gr owth estimates based on data provided byMay 7, 2007 Appendix F 24 local councils of government (COGs). The varied sources of growth data are assembled into a comprehensive statewide growth data set that can be used for statewide emission projections.

Control Factors

Control factors are derived from “adopted” ARB regulations, district rules, and “proposed” measures which impose emission reductions or a technological change on a particular emission process. Control fact ors comprise three components: Control Efficiency, Rule Effectiveness, and Rule Penetr ation. Control factors are closely linked to the type of emission proce ss and secondarily to the type of industry. Control levels are assigned to emission categories which are targeted by the rules via emission inventory codes (SCC/SIC, EIC etc.) used in CEID ARS. The control factor is the ratio of the control level in the future year to the control level in the base year. These control levels are also region, control measure, source category, and pollutant dependent.
Data Acquisition

The baseline emission projections require a fu ll complement of control factor data that account for the “adopted” rules that are on the books by a predetermined cutoff date. A major component of the CEFS program is it s ability to link rules to targeted emission categories and quantify the emission reduc tions associated with the rules. Implementation of CEFS requires districts to submit local c ontrol rule profiles (for all rules leading to emission reductions) that link the rules to the appropriate emission categories. The profiles must describe the behavior of the rule (i.e. whether the rule is phased in a linear fashion or abr upt implementation in the form of a step function). The rule profiles must be desig ned to be pollutant specif ic and must carry enough data points to adequately characterize the rule ov er the implementation period. The CEFS program interpolates the profile for intermedi ate years in either a step or linear mode depending on what the district has flagged fo r the rule. The construction of a comprehensive control rule data set for a lar ge district is a sizeable undertaking. Some districts develop the control profiles in house, while others have opted to contract the work out. In either case, ARB staff work clos ely with the district st aff and/or contractors to educate staff on the proper construction of t he control profiles. In gearing up for the 8-Hour Ozone SIP, in Fall 2004, ARB conduc ted a special forecasting workshop to provide guidance to districts on all aspects of the emission forecasting program including presentations on forecasting logic and input data requir ements. In most cases, districts responded by submitting rule-s pecific control profiles for their adopted rules as required.

ARB also develops control profiles for state rules and International Maritime Organizations (IMOs) such as for consumer products, pesticides, architectural coatings, and ships. For state rules, ARB deploys inte rdivisional teams to develop, refine, and finalize the control profiles to be used in SIP projections. Similar data requirements exist for the development of these control profiles as descr ibed above for district rules.May 7, 2007 Appendix F 25 Mobile Sources

Mobile sources include a ll non-stationary sources of air pollution such as cars , trucks, motorcycles, buses, airplanes, and locomotives. In general, emissions are calculated as the product of the number of sources population/volume), activi ty and emission factor.

E = Pop * A * EF

where, E = pollutant specific emissions [mass emitted per unit time] Pop = population of on-road mobile sources [-] A = activity (travel data) [e.g. miles traveled per day, or hours operational] EF = source specific emission factor [mass per unit activity]

In California, most mobile source invent ories are estimated by two mathematical modeling tools: EMFAC for on-road source s and OFFROAD for off-road sources. Because many different types of data are nece ssary to develop an inventory, ARB must rely on other organizations to provide that dat a. The inventory has involved the efforts of the California Air Pollution Control Offi cers Association, CalTrans, the Southern California Council of Governments (SCAG), and the DMV. CalTrans provides SCAG with information regarding highway projects so that they can estimate and project vehicle miles traveled (VMT) from their Travel Demand Model (TDM). This activity data are then coupled with the emission factor s generated from ARB’s Emission Factor Model (EMFAC2007) and popula tion data provided by the DMV to calculate the emission inventory. Finally , all statewide emission inve ntories are assembled and maintained by ARB in the CEIDARS and CEFS databases.

On-Road Emission Inventory

EMFAC contains several different modules t hat account for different portions of the onroad inventory calculation process. EMFA C covers on-road mobile sources including gas and diesel cars, trucks, buses, and motor cycles. The subsequent sections illustrate the methods by which the on-road emission in ventory is developed. For more detailed information please refer to the ARB website at http://www.arb.ca .gov/msei/onroad/onroad.htm . A guide to online documentation is also provided at the end of this section.

Source Categories

On-road mobile sources are categorized into thirteen vehicle classes, two fuel types (gas and diesel), and three technology categories (catalyst, noncatalyst and diesel) as indicated in the table below.May 7, 2007 Appendix F 26 EMFAC Vehicle Categories

Class Description Weight (GVW) Abbreviation Technology Group

PC Light Duty Autos (Passenger Cars) All LDA NCAT, CAT, DSL

T1 Light-Duty Trucks (LDT1) 0-3,750 LDT1 NCAT, CAT, DSL

T2 Light-Duty Trucks (LDT2) 3,751-5,750 LDT2 NCAT, CAT, DSL

T3 Medium-Duty Trucks 5,751-8,500 MDV NCAT, CAT, DSL

T4 Light-Heavy Duty Trucks (LHDV1) 8,501-10,000 LHDT1 NCAT, CAT, DSL

T5 Light

- Heavy Duty Trucks (LHDV2) 10,001-14,000 LHDT2 NCAT, CAT, DSL

T6 Medium-Heavy Duty Trucks (MHDV) 14,001-33,000 MHDT NCAT, CAT, DSL

T7 Heavy-Heavy Duty Trucks (HHDV) 33,001+ HHDT NCAT, CAT, DSL

UB Urban Bus (UB) All UB CAT, DSL

OB Other Bus OBUS CAT, DSL

SB School Buses All SBUS CAT, DSL

MH Motor Homes All MH CAT, DSL

MC Motorcycles All MCY NCAT, CAT

Population and Activity
In general, population data are obtained from vehicle registration data compiled by DMV and classified according to fuel, class, technology group, age (model year) and geographic area. Further calculations r equire data on population growth rates by calendar year, vehicle class, fuel ty pe and geographic area. These estimates are coupled with activity data and emission factors, as indicated previously, to estimate total emissions.

On-road activity refers most commonly to vehicle miles traveled, speed, and number of trips for each vehicle type and model year.

Vehicle Miles Traveled Vehicle miles traveled (VMT) are the number of miles traveled by a given vehicle in a specified time period and is estimated from the inspection and maintenance program (I &M) where the model year and odometer reading is taken. The COG’s provide VMT as estimated by their transportation demand models. EMFAC then matches VMT estimated in the model to those values provided by the COGs.May 7, 2007 Appendix F 27 This analysis is completed for each county and helps to elucidate regional differences in travel. VMT is calculated based on the v ehicle population and accrual rates by age, vehicle class, fuel type, by geographic and inspection and maintenance option. The model also contains hourly dist ributions of VMT by class.

Vehicle Starts or Trips Vehicle starts are obtained for gasoline-power ed vehicles except heavy-duty trucks and included as an input to the inventory. Star ts are used to calculate trip emissions and calculated as the product of a per-vehicle star t rate (starts per v ehicle per day) and the fleet population. The estimates of trips per day are based on travel surveys and vehicle instrumented data for passenger cars, and light and medium duty trucks.

Ambient Temperatures Because motor vehicle emissions are dependent on temperature and humidity profiles, historical meteorological data are compiled. The emission inventory process includes data on averaged monthly, summer and winter epi sodic diurnal temperatures for each geographic area as well as averaged mont hly relative humidity for each geographic area. The following table indi cates the primary data sources for activity and population:

Emission Factors

Emission factor used in EMFAC are based on emission tests with dynamometers coupled with certification data yield base em ission rates (BERs) that are adjusted with correction factors, and vehicle inspection and maintenance (I&M) programs (e.g. smog check scenarios). Correction factors are applied for non-standard operating conditions such as what mode the vehicle is in (e .g. hot/cold/start/running), trip speed, environmental conditions, and inspection an d maintenance progr ams within that geographic area. These correction factors include, but are not limited to, speed correction factors, temperatur e correction factors, fuel co rrection factors and driving cycle correction factors. Emission factors are provided for different temperatures, operating speeds, emission modes, model types, model years, technology types, fuel, and relative humidity. These em ission factors can be expressed as grams per vehicle, grams per mile, grams/hour and grams per start. Activity data are matched to the corresponding emission factor to estima te total emissions.

EMFAC includes tailpipe exhaust and evaporative emissions as part of the on-road inventory. Tailpipe exhaust emissions include running, idle and start exhausts. Running exhaust represents emissions released from the tailpipe under hot st abilized conditions, while idle and start exhaust, the other ta ilpipe emissions, include any emissions associated with idling and starti ng the vehicle. Evaporative emissions consist of hotMay 7, 2007 Appendix F 28 soaks, diurnals, resting losses and running losses all of which are generated by the evaporation of fuel from an engine. The inv entory accounts for particulate matter (PM) associated with tire and break wear.

Guide to Online Documentation

“Understanding the On-Road Emissions Inventory Program” - http://www.arb.ca.gov/msei/onroad/on-road.htm

Mobile Source Emission Estimates - http://www.arb.ca.gov/msei/onroad/emfac2002_output_table.htm

Previous Model Versions and Revisions - http://www.arb.ca.gov/msei/onroad/previous_version.htm

, http://www.arb.ca.gov/msei/onroad/latest_revisions.htm

EMFAC Technical Support Documentation - http://www.arb.ca.gov/msei/onroad/doctable_test.htm

Tech Memos - http://www.arb.ca.gov/msei/msei.htm

Other Mobile Emission Inventory

Off-road mobile sources include off-road vehi cles such as boats, outdoor recreational vehicles (ORV’s), industrial and constr uction equipment, farm equipment, lawn and garden equipment, ships, aircraft, and trains. OFFROAD is used to estimate emissions for most of these categories. Other sources such as ocean going vessels and commercial harbor craft are based on calculati ons in modules external to OFFROAD. Off-road emissions are calculated much in the same way as on-road emissions – the product of emission factor, activity, and popul ation. The model also incorporates elements such as technology types, population, activity, horsepower , load factors, and control factors. For more detailed info rmation please refer to the ARB website at http://www.arb.ca.gov/mse i/offroad/off-road.htm . In addition, a guide to online documentation is provided at t he end of this section.

Source Categories

Off-road sources are divided up in to eight major categories: aircraft, trains, ships and commercial boats, recreational boats, off-road recreational vehicles, off-road equipment, farm equipment, and fuel storage and handling. The categories are then subdivided by fuel type, engine type, horsepower group and preempted or non-preempted status to better characterize emissions, adopted and proposed control strategies, and use. Outlined below are a few categorical descriptions.May 7, 2007 Appendix F 29 Commercial Marine Vessels
Commercial marine vessels include ocean-go ing ships and harbor craft, but exclude recreational vessels. Ocean-going ships incl ude international trade vessels such as container ships, bulk carriers, general ca rgo ships, tankers, and auto carriers. Passenger cruise ships, and some military and Coast Guard vessels, are also included in this category.
The diesel engines powering the majority of oceangoing ships are re ferred to by U.S. EPA as “Category 3” engines, m eaning they have a displacem ent greater than 30 liters per cylinder. These engines ar e available in configurations with 4 to 14 cylinders, and power outputs ranging from roughly 5 to 100 megawatts.

Ocean-going ships generally run boilers and dies el generators in addition to propulsion engines, particularly while “hotelling” in por t. Diesel generators provide electrical power for lights and equipment, and boilers provide st eam for hot water and fuel heating.

Most ocean-going ships run their main propul sion engines (and many newer ships also run their auxiliary engines) on intermediate fuel oil (IFO 180 or IFO 380). This fuel is also referred to as “bunker f uel,” and requires heating to redu ce its viscosity to a point where it can be properly atomiz ed and combusted. Bunker fuel typically contains much higher levels of sulfur, nitrogen, ash, and other compounds which increase exhaust emissions. Diesel-powered gas turbine eng ines and auxiliary engines on many oceangoing ships use lighter “distillate” diesel fuel (also referred to as marine gas oil), which is much lower in sulfur and other contaminants.

Harbor craft (or the “captive fleet”) incl ude tugboats, commercial fishing vessels, commercial passenger fishing vessels, work boat s, crew boats, ferries, and some Coast Guard and military vessels. These vessels generally stay within California coastal waters and often leave and return to the sa me port. Most harbor craft use dieselpowered propulsion and auxiliary engines that generally run on distillate diesel fuel.

Locomotives and Rail-yards

Railroads operate national locomotive fleets that travel between states daily, currently moving more than 40 percent of the total inte rcity revenue ton-miles of freight in the United States.

Compression-Ignition (diesel) Engines

Off-road compression-ignition (CI) engines are diesel engines primarily used in farm, construction, and industrial equi pment. Examples include tr actors, excavators, dozers, scrapers, portable generat ors, transport refri geration units (TRUs), irrigation pumps, welders, compressors, scrubbers, and sweeper s. Locomotives, commercial marine vessels, marine engines over 37 kilowatts (k W), or recreational vehicles are excluded from this category.May 7, 2007 Appendix F 30 Large Spark-Ignition Engines
The large spark-ignition engine (LSI) cat egory consists of off-road spark-ignition engines greater than 25 hors epower and typically fueled by gasoline or liquefied petroleum gas (LPG). A sm all number are fueled by comp ressed natural gas (CNG), and some have dual fuel capability. LSI engi nes are most commonly found in forklifts, specialty vehicles, portabl e generators, pumps, compre ssors, farm equipment, and construction equipment. This category ex cludes marine propulsion engines, engines used in equipment that operat e on rails, recreational vehi cles, snowmobiles, and gas turbines.

Recreational Vehicles
This category consists of off-road motorcycl es and all terrain vehicles (ATVs), both of which are designed for operation over rough terrain. U.S. EPA standards for exhaust and evaporative for off-road motorcycles and ATVs were established in 2006. Although the federal exhaust standards ar e not as stringent as Califo rnia’s, California will benefit from the evaporative st andards which control fuel tank and fuel line permeability.
In 1994, ARB approved off-highway recreational vehicle regulations (including off-road motorcycles). These regulations estab lished exhaust emission standards and test procedures that included off-road motorcycles and ATVs. The regulations also provided specific coding requirements of the vehicle i dentification number to distinguish an emission-compliant vehicle. In 1998, t he regulations were amended to link vehicle registration and usage to compliance with Ca lifornia’s exhaust emission standards. Those in compliance are eligible for off-hi ghway vehicle (OHV) green sticker registration that allows year-round operation in designated off-road areas. Those not in compliance are eligible for OHV red sticke r registration that allows oper ation only during designated months when ozone levels are low. These re visions affect engines built in 1997 greater than 90 cc. The same standards also apply to engines built in 1999 of 90 cc or less. Engines built pre-1997 and pre- 1999, respectively, are not subject to this regulation.
Small Off-Road Engine (SORE)
The small off-road engine (SORE or "small engi ne") category consists of off-road sparkignition engines fueled typically by gasolin e, liquid petroleum gas (LPG) or other alternative fuels and below 25 horsepower. The SORE categor y includes lawn, garden and other maintenance utility equipment. Within this category, engines are typically grouped by engine displacement measured in cubic centimeters (c c). Engines under 65 cc displacement are traditionally associ ated with handheld equipm ent such as weed trimmers, leaf blowers and chain saws. E ngines greater than 65 cc displacement are collectively referred to as non-handhel d small off-road engines. Non-handheld equipment is primarily lawn mowers, but al so includes other equipment such as riding mowers and generator sets.
The sources of evaporative emissions from this category are from gasoline vapors vented from the carburetor and fuel cap and occur from diurnal (emissions due to daily temperature changes), hot soak (occur afte r shutdown of equipm ent), and running loss (occur during equipment operation) processes. Permeation (liquid gasoline migrating through the walls of plastic fuel tanks) is also another source of emissions.May 7, 2007 Appendix F 31 Activity and Population

As with the on-road inventory, emission fa ctors must be coupl ed with population and activity data to estimate total emissions . The statewide equipment population of offroad mobile sources was collected from a variety of sources including industry and government agencies. Then growth factors based on socioeconomic indicators and scrappage factors were applied to determine future populations. Scrappag e, or attrition, functions describe the relationship between equipment age and the proportion of equipment that has been removed from service. It is expressed as a fraction of the average lifetime of the equipment and is a f unction of the expected life of the engine, the annual hours of operation, and the average load factor.

Activity includes information such as annual average usage hours, load factor, brake-specific fuel consumption, and starts per year for each equipment type by fuel, engine type, and horsepower
group.

Data Acquisition

The statewide equipment populations were obtained through various industry and government agency sources. Power Systems Re search (PSR) is the source for many of the state population estimates for off -road categories. PS R’s Power Systems Research/Compass International’s 1996 Dat abase contained engine data compiled over the last 7 years, and included engine sales records for every engine installed in North America, as well as imported engine-dr iven products. E ngine data includes horsepower, model number and engine descripti on (number of cylinders, turbocharged versus naturally-aspirated, displacement, etc) . Some of the principal data sources, by category, are listed in the table below:

The Portable Equipment Database is maintai ned by ARB. Data are obtained through a voluntary registration program with the State as an alter nate method of obtaining an operational permit for off-r oad non-propulsion engines. Equipment populations for dredging, oil drilling, lawn and garden, m ilitary tactical support and other portable equipment categories were obtai ned directly from the Portable Equipment Database. In order to avoid double count ing, the population of generat ors, pumps and compressors found in the drilling, dredging, tactical support and other portable equipment, were subtracted from the i ndustrial and light-commercial categories.May 7, 2007 Appendix F 32 Category Data Source Agricultural United States Department of Agriculture(USDA), Power Systems Research (PSR) Construction and Mining MacKay & Company, PSR, Portable Equipment Database, Energy & Environmental Analysis (EEA) Light Commercial PSR, Portable Equipment Database Industrial PSR, Booz Allen-Hamilton (BAH) Logging Power Systems Research Lawn and Garden Portable Equipment Database, BAH Airport Ground Support Air Transport Association (ATA), EEA Transport Refrigeration Units PSR, Department of Motor Vehicles (DMV) Dredging Portable Equipment Database Oil Drilling Portable Equipment Database Military Tactical Support Equipment Portable Equipment Database Recreational Equipment Department of Motor Vehicles (DMV) Pleasure Craft U.S. Coast Guard, DMV Commercial and Government Vessels BAH, US Coast Guard, CA Department of Fish and Game, ARB Survey Locomotive and Rail Operations Booz Allen-Hamilton Aircraft (Commercial, Military and General) Booz Allen-Hamilton Agricultural Aircraft Booz Allen-Hamilton Ocean-Going Vessels CA State Lands Commission, Port of Los Angeles EI, Survey, US Army Corp of Engineers, Entec Agricultural Engines District Permit Data, Carl Moyer Program, Sonoma Technology, CA Department of Water Resources Farmland Monitoring and Mapping Program

MacKay & Company conducted surveys in 1991, 1995, and 1999 to estimate nationwide construction equipmen t population and usage. This work was sponsored by Construction Equipment Magazine. Ma cKay & Company’s 1999 Construction Equipment population analysis was used to obt ain populations for those construction equipment contained in the analysis. Since dat a in MacKay’s analysis are nationwide, staff used a factor of 10 percent in order to estimate Ca lifornia’s statewide population. U.S. EPA staff estimated that 10 percent of the total nationw ide dollar amount spent on construction-related activities occurs in Calif ornia. For specific pieces of construction equipment not found in MacKay’s analysis, 10 per cent of PSR’s nationwide data were used.

Finally, all statewide emissi on inventories are assembled and maintained by ARB in CEIDARS and CEFS databases.May 7, 2007 Appendix F 33 Emission Factors

ARB estimates all off-road mobile emissi ons with OFFROAD. OFFROAD integrates emission factors by model year for hydroc arbons (HC), carbon monoxide (CO), carbon dioxide (CO 2 ), oxides of nitrogen (NO x ), particulate matter (PM) , and oxides of sulfur (SO x ). Hydrocarbon emissions are modeled fo r three types of engine processes: exhaust, evaporative and start emissions.

The emission factors for each engine are a function of new engine emissions expressed in grams per brake horsepower hour and deterioration rates. Deterioration is due primarily to tampering and mal-maintenance and wear on engine parts with use, and occurs at different ra tes for each pollutant. The OFFROAD methodology for estimating deterioration is based on the zero-hour emission rate (g/bhp-hr), the deterioration rate (increase in emissions per unit of activity, g/bhp-hr 2 ), and the number of hoursaccumulated on the equipment. Equipmentspecific emission factors are obtained by adjusting the appropriate engine emission rate by how the equipment is used (duty cycle). Finally, control factors can be applied to emissions or population to account for regulatory control scenarios.

Guide to Online Documentation

“Understanding the Off-Road Emissions Inventory Program” - http://www.arb.ca.gov/msei/offroad/off-road.htm

Mobile Source Emission Estimates - http://www.arb.ca.gov/msei/onroad/emfac2002_output_table.htm

Model Revisions - http://www.arb.ca.gov/msei/offroad/updates.htm

OFFROAD Technical Support Documentation - http://www.arb.ca.gov/msei/offroad/pubs.htm http://www.arb.ca.gov/research/resnotes/notes/97-8.htm

Tech Memos - http://www.arb.ca.gov/msei/msei.htmMay 7, 2007 Appendix F 34 Biogenics (Overview of the BEIGIS Model)

Development of effective ozone and PM2.5 cont rol strategies in California requires accurate emission inventories of their precur sor emissions, including biogenic volatile organic compounds (BVOCs) such as is oprene and monoterpenes. Due to the heterogeneity of vegetation land cover, species composition and leaf mass distribution in California, quantifying BVOC emissions in this domain requires an emission inventory model with region-specific input databases and a high degree of spatial and temporal resolution. In response to this need, AR B developed a GIS-based model for estimating BVOC emissions, called BEIGIS, which uses Ca lifornia-specific in put databases with a minimum spatial resolution of 1 km 2 and an hourly temporal resolution.

The BEIGIS isoprene emission algorithm 11 is of the form I = IS × CL × CT , where I is the isoprene emission rate (grams isopr ene per gram dry leaf mass per hour) at temperature T and photosynthetically active r adiation flux PAR. IS is a reference emission factor (grams isoprene per gram dry leaf mass per hour) at a standard temperature of 30 °C and PAR flux of 1000 μmol m -2 s -1 . CL and CT are environmental adjustment functions for PAR and temperat ure, respectively. The monoterpene emission algorithm adjusts a reference mono terpene emission factor by a temperature function 11 . Methylbutenol (MBO) emissions are modeled with an algorithm 12 similar to that for isoprene. Dry leaf ma ss/ leaf area ratios, and reference emission factors for isoprene, monoterpenes and MBO, are plant spec ies-specific and assembled from the scientific literature. Model ed BVOC emissions for a gi ven spatial domain therefore represent the contribution by various pl ant species (through their leaf mass and emission rates) to t he total BVOC emissions.

The main inputs to BEIGIS are land use la nd cover (LULC) GIS databases, gridded leaf area index (LAI) derived fr om AVHRR satellite data 13 , leaf area/dry leaf mass factors, reference emission factors, and gridded hourly ambient temperatur e and sunlight data (generated by the CALMET and/ or MM5 models). For urban areas, LULC databases were developed from regional pla nning agency data and botanical surveys 14 . Natural areas are represented using GAP, a st atewide LULC database developed by the University of California-Sant a Barbara for the U.S.G.S. Bi ological Resources Division and the California Department of Fish and Game 15 . Agricultural areas are represented using crop GIS databases developed by the Ca lifornia Department of Water Resources (http://www.landwateruse.water.ca.gov ). Ground surveys have been funded by ARB to validate vegetation land cover and LAI input databases used in BEIGIS 16 . Model validation using micrometeorological fl ux measurements is on-going. BEIGIS assimilates spatial and temporal inputs and executes BVOC emission algorithms within a GIS. Outputs from BEIGIS are gridded 1 km 2 hourly emissions of isoprene, monoterpenes and MBO, aggregated to 16 km 2 or 25 km 2 resolution, depending on photochemical modeling requirem ents. A fourth BVOC categor y called “other” biogenic VOC (OVOC) is comprised of compounds such as acetone, methanol, and other oxygenated BVOCs. OVOC is not modeled directly. Gr idded hourly OVOC emissions are estimated as an added fraction, scaled to 30 percent of the sum of isoprene, monoterpene, and MBO within each grid cell. A speciation profile is applied to theMay 7, 2007 Appendix F 35 OVOC fraction based on OVOCs measur ed at Blodgett Experimental Forest 17 . The BEIGIS model was first applied in the Southern California Ozone Study 18 (SCOS97).

Emissions Inventory Systems Data (CEIDARS, CEFS)

ARB redesigned its base year emission inve ntory system (known as the California Emissions Inventory Development and R eporting System (CEIDARS)) in 1995 and emission forecasting system (known as the California Emission Forecasting System (CEFS)). Technical papers describi ng these systems are available on request.May 7, 2007 Appendix F 36 

### References

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