DRAFT 

MODELING PROTOCOL 

FOR OZONE AND PARTICULATE MATTER MODELING 

IN SUPPORT OF THE SOUTH COAST AIR QUALITY 

MANAGEMENT DISTRICT 2007 AIR QUALITY 

MANAGEMENT PLAN UPDATE 

Draft Report 

May 9, 2006 

Jointly Prepared by:

California Air Resources Board 

Planning & Technical Support Division 

P.O. Box 2815 

Sacramento, CA 95812 

and 

South Coast Air Quality Management District 

Planning, Rule Development and Area Sources 

21865 Copley Drive 

Diamond Bar, CA 91765 
Send Comments to:

Kevin Durkee 

South Coast Air Quality Management District 

kdurkee@aqmd.gov

Contributors:

Mark Bassett 

Joseph Cassmassi 

John DaMassa 

Kevin Durkee 

Bruce Jackson 

Bong-Mann Kim 

Satoru Mitsutomi 

Xinqiu Zhang 
 i 

TABLE OF CONTENTS 

TABLE OF CONTENTS........................................................................................ ii 

LIST OF FIGURES ................................................................................................ ii 

LIST OF TABLES.................................................................................................. ii 

LIST OF ACRONYMS .......................................................................................... ii 

INTRODUCTION .................................................................................................. 2 

Background .................................................................................................................................. 2

Regulatory Modeling Requirements and Guidance.................................................................. 2

AQMP Ozone Modeling History in the South Coast Air Basin ............................................... 2

AQMP PM Modeling History in the South Coast Air Basin.................................................... 2

2007 AQMP Modeling Analysis Goals........................................................................................ 2

Ozone Design Value Determination......................................................................................... 2

Overview of the Modeling Analysis............................................................................................. 2

2007 AQMP Schedule.................................................................................................................. 2

AQMP Modeling Technical Oversight......................................................................................... 2

MODEL SELECTION............................................................................................ 2 

Meteorological Model .................................................................................................................. 2

Background .............................................................................................................................. 2

Previous AQMP Applications .................................................................................................. 2

2007 AQMP Meteorological Modeling Approach ................................................................... 2

Air Quality Model ........................................................................................................................ 2

Background .............................................................................................................................. 2

2007 AQMP Air Quality Modeling Approach ......................................................................... 2

MODELING DOMAIN.......................................................................................... 2 

Meteorological Modeling Domain ............................................................................................... 2

Ozone Modeling Domain ............................................................................................................. 2

PM Modeling Domain.................................................................................................................. 2

HORIZONTAL AND VERTICAL GRID RESOLUTION ................................... 2 

Horizontal Grid Resolution .......................................................................................................... 2

Vertical Resolution....................................................................................................................... 2

Meteorological Modeling ......................................................................................................... 2

Air Quality Modeling ............................................................................................................... 2

EPISODE SELECTION ......................................................................................... 2 

Ozone Episodes............................................................................................................................ 2

August 3-7, 1997 (Sunday – Thursday).................................................................................... 2

June 3-7, 2004 (Thursday – Monday) ...................................................................................... 2

August 4-8, 2004 (Wednesday – Sunday) ................................................................................ 2

May 17-24, 2005 (Tuesday – Tuesday).................................................................................... 2

July 14-19, 2005 (Thursday – Tuesday)................................................................................... 2

August 25-29, 2005 (Thursday – Monday) .............................................................................. 2

Possible Seasonal Ozone Episode: Summer 1997................................................................... 2

Ozone Episode Statistical Ranking .............................................................................................. 2

PM Episodes................................................................................................................................. 2

Annual PM: January 1 – December 31, 2005.......................................................................... 2

Episodic PM10/2.5: October 19-25, 2005 & March 6-12, 2005 ............................................. 2

INITIAL AND BOUNDARY CONDITIONS ....................................................... 2 

Initial Conditions.......................................................................................................................... 2

Boundary Conditions.................................................................................................................... 2

METEOROLOGICAL INPUTS............................................................................. 2 

Meteorological Input Evaluation and Technical Review ............................................................. 2

Qualitative Analyses................................................................................................................. 2
 ii 

Quantitative Analyses............................................................................................................... 2

EMISSION INVENTORY INPUTS ...................................................................... 2 

Emissions Characterization .......................................................................................................... 2

Point Sources............................................................................................................................ 2

Area Sources ............................................................................................................................ 2

On-Road Mobile Sources ......................................................................................................... 2

Other Mobile Sources............................................................................................................... 2

Biogenic Emissions .................................................................................................................. 2

Organic Gas Speciation ............................................................................................................ 2

Day-Specific Emissions ........................................................................................................... 2

Emissions Quality Assurance....................................................................................................... 2

Emission Projections .................................................................................................................... 2

AIR QUALITY MODEL PERFORMANCE EVALUATION .............................. 2 

Statistical and Graphical Analyses ............................................................................................... 2

Subregional Performance ......................................................................................................... 2

Multi-Species Evaluations........................................................................................................ 2

Aloft Comparisons ................................................................................................................... 2

Acceptable Model Performance ............................................................................................... 2

Sensitivity Analyses ..................................................................................................................... 2

Diagnostic Simulations............................................................................................................. 2

USE OF THE MODEL RESULTS......................................................................... 2 

Attainment Demonstration ........................................................................................................... 2

Relative Reduction Factors....................................................................................................... 2

Carrying Capacity Estimation ...................................................................................................... 2

REFERENCES ....................................................................................................... 2 
 iii 

LIST OF FIGURES 

FIGURE 1 Nested MM5 Domains ................................................................................................. 2

FIGURE 2 The Fine-Scale (5 km) MM5 Domain.......................................................................... 2

FIGURE 3 2007 AQMP Ozone Modeling Domain ....................................................................... 2

FIGURE 4 2007 AQMP PM Modeling Domain, inside the Ozone Modeling Domain ................. 2

LIST OF TABLES 

TABLE 1 National Ambient Air Quality Standards Compliance Status in the South Coast Air 

Basin......................................................................................................................................... 2

TABLE 2 Ozone Design Value (ppb) for Each Station, 2000-2004 .............................................. 2

TABLE 3 Summary of Proposed 2007 AQMP Model Selection and Application ........................ 2

TABLE 4 Tentative 2007 AQMP Schedule................................................................................... 2

TABLE 5 Vertical Structure for the MM5 Meteorological Model................................................. 2

TABLE 6 Vertical Structures for the CAMx Ozone and PM Simulations..................................... 2

TABLE 7 Summary of Ozone Episodes to be Simulated for the 2007 AQMP.............................. 2

TABLE 8 Ozone Episode Characterization.................................................................................... 2

TABLE 9 Highest 24-Hour Averaged SSI PM2.5 Concentration in 2005..................................... 2

TABLE 10 SAPRC-99 Chemical Mechanism Species .................................................................. 2

TABLE 11 Proposed Meteorological Input Performance Benchmarks ......................................... 2

TABLE 12 Performance Goals for 1-Hour Ozone......................................................................... 2
 iv 

LIST OF ACRONYMS 

Several acronyms are used in the modeling protocol document. For 

convenience, the acronyms used are listed below to aid the reader. 

AAMA – American Automobile Manufacturer's Association

AGL – Above Ground Level

AQMD – Air Quality Management District

AQMP – Air Quality Management Plan 

AQMPAG Air Quality Management Plan Advisory Group

AUSPEX – Atmospheric Utility Signatures, Predictions, and 

Experiments 

AVHRR – Advanced Very High Resolution Radiometer 

BEIS – Biogenic Emission Inventory System 

CAA – Clean Air Act Amendment of 1990 

CAMx – Comprehensive Air-Quality Model with Extensions 

CARB – California Air Resources Board 

CBM – Carbon Bond Mechanism 

CCAA – California Clean Air Act 

CEFS – California Emission Forecasting System 

CMAQ – Community Multi-scale Air Quality (model) 

CO – Carbon Monoxide 

COG – Council of Governments 

DARS – Data Attribute Rating System 

DWM – Diagnostic Wind Model 

DTIM – Direct Travel Impact Model 

EIWG – Emission Inventory Working Group 

EKMA – Empirical Kinetics Modeling Approach 

EMFAC – Emission Factor (model) 

FCM – Flexible Chemical Mechanism 

FDDA – Four-Dimensional Data Assimilation 

GAP – Geographical Approach to Protection of Biological 

Diversity 

GCM – Global Climate Model 

ICAPCD – Imperial County Air Pollution Control District 

IOP – Intensive Operation Period 

LIDAR – Light Detection and Ranging 

MDAQMD – Mojave Desert Air Quality Management District 

MM5 – Mesoscale Meteorological Model (5th generation) 

MWG – Modeling Working Group 

NAAQS – National Ambient Air Quality Standard 

NDVI – Normalized Difference Vegetative Index 
 v 

NOx – Nitrogen Oxides 

NO2 – Nitrogen Dioxide 

PDT – Pacific Daylight Time 

PM – Particulate Matter 

PM10 – Particulate Matter less than 10 microns equivalent 

aerodynamic diameter 

PM2.5 – Particulate Matter less than 2.5 microns equivalent 

aerodynamic diameter 

PMcourse – PM10 – PM2.5 

PMfine – PM2.5 

RADM – Regional Acid Deposition Model 

RECLAIM – Regional Clean Air Incentives Market 

ROG – Reactive Organic Gases 

RRF – Relative Reduction Factor 

SANDAG – San Diego Association of Governments

SAPRC – State Air Pollution Research Center 

SAQM – SARMAP Air Quality Model 

SARMAP – SJVAQS/AUSPEX Regional Model Adaptation Project 

SBCAG – Santa Barbara County Association of Governments 

SBCAPCD – Santa Barbara County Air Pollution Control District 

SCAB – South Coast Air Basin

SCAG – Southern California Association of Governments

SCAQMD – South Coast Air Quality Management District 

SCAQS – Southern California Air Quality Study 

SCE – Southern California Edison 

SCOS97 – Southern California Ozone Study (1997) 

SDCAPCD – San Diego County Air Pollution Control District 

SIP – State Implementation Plan 

SJVAQS – San Joaquin Valley Air Quality Study 

SOx – Sulfur Oxides 

STMPRAG – Scientific, Technical, Modeling and Peer Review 

Advisory Group 

TOG – Total Organic Gases 

UAM – Urban Airshed Model 

USEPA – United States Environmental Protection Agency 

UTM – Universal Transverse Mercator 

VCAPCD – Ventura County Air Pollution Control District 

VMT – Vehicle Miles Traveled 

VOC – Volatile Organic Compound 

WSPA – Western States Petroleum Association 

KRD: DRAFTModelingProtocol.doc 5/12/2006 1

INTRODUCTION 

The federal Clean Air Act (CAA) requires areas with unhealthy levels of 

ozone , carbon monoxide, nitrogen dioxide, sulfur dioxide, and inhalable 

particulate matter to develop plans, known as State Implementation Plans 

(SIPs), describing how they will attain national ambient air quality 

standards (NAAQS). The 2007 Air Quality Management Plan (AQMP or 

Plan) for the South Coast Air Basin (Basin) will meet the SIP update 

requirements for this area, demonstrating NAAQS attainment. The federal 

Clean Air Act (CAA) requires the use of photochemical grid models that 

are approved by the Unites States Environmental Protection Agency 

(USEPA) to perform the attainment demonstration. This document 

addresses the air quality modeling protocol for the 2007 AQMP, as 

developed through a joint cooperative effort between the staff of the South 

Coast Air Quality Management District (AQMD or District) and the 

California Air Resources Board (CARB), with technical oversight from the 

Scientific, Technical, Modeling and Peer Review Advisory Group 

(STMPRAG). 

The objective of this modeling protocol is to define the methodology to be 

used for simulating the formation and transport of ozone and particulate 

matter in the Basin, including: 

• the model(s) to be used; 

• the modeling domain; 

• the horizontal and vertical grid resolution; 

• the annual PM period and ozone and PM episodes to be simulated; 

• the model input data, including meteorology, emissions, initial 

conditions; and lateral and top boundary conditions; 

• the process for model performance evaluation. 

In addition, the protocol outlines the attainment demonstration process, 

including a review of the California Clean Air Act (CCAA) requirements. 

This protocol document is intended to be dynamic and will be updated in 

response to reviewer comments and to reflect the results of new 

information that will emerge during the AQMP modeling process. 

In order to devote the maximum resources practicable to the development 

of the District's 2007 AQMP, the Executive Officers of CARB and AQMD 

have agreed to jointly develop the emissions and air quality modeling 

needed to determine the carrying capacity and attainment demonstration for 

the ozone and PM standards. The technical staffs of both agencies are 

working closely together to plan and carry out the necessary work for the 
KRD: DRAFTModelingProtocol.doc 5/12/2006 2

AQMP and are committed to intensive and timely coordination to ensure 

that it is based on the soundest science possible. Both agencies agree that 

their staffs will collaborate on this work such that the product will be 

mutually acceptable modeling analyses for use in the 2007 Plan. 

Background 

Regulatory Modeling Requirements and Guidance

The 1990 amendments to the federal CAA set new deadlines for attainment 

based on the severity of the pollution problem and launched a 

comprehensive planning process for attaining the NAAQS. The 

promulgation of the new national eight-hour ozone standard and the fine 

particulate matter (PM2.5) NAAQS in 1997 required additional statewide 

air quality planning efforts. In response to new federal regulations, SIPs 

must also address ways to improve visibility in national parks and 

wilderness areas. SIPs demonstrating attainment of the federal ozone 

standard must be adopted by the local air districts and CARB, and 

submitted to the USEPA by June 15, 2007. 

USEPA's guidelines on air quality modeling previously recommended the 

use of the Urban Airshed Model (UAM) for attainment demonstrations 

involving entire urban areas. However, USEPA revised its 

recommendation (USEPA, 2001a) to no longer include a recommended air 

quality model for ozone. Instead, USEPA recommends that air quality 

models proposed for an ozone attainment demonstration be subjected to 

model performance evaluations to demonstrate that they are appropriate for 

attainment demonstration purposes. 

USEPA issued the Guideline for Regulatory Applications of the Urban 

Airshed Model (USEPA, 1991) and Guidance on the Use of Modeled 

Results to Demonstrate Attainment of 1-hour Ozone NAAQS (USEPA, 

1996) to assist states in preparing the attainment demonstration required by 

the CAA. In addition, the CARB Technical Guidance Document: 

Photochemical Modeling (CARB, 1992) provides photochemical modeling 

guidance for use by the districts to ensure the technical validity of the 

modeling results. Most recently, USEPA has finalized attainment 

demonstration guidance for the 8-hour ozone NAAQS (USEPA, 2005). 

The ozone modeling protocol in this document is based on these guideline 

documents. Guidance for the PM portion of the modeling protocol utilizes 

the Draft Guidance for Demonstrating Attainment of Air Quality Goals for 

PM2.5 and Regional Haze (USEPA, 2001b). 
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Under the federal Clean Air Act of 1990 (CAA), the South Coast Air Basin 

(Basin) was classified as an “extreme” nonattainment area for 1-hour 

ozone. Section 182(c)(2)(A) of the CAA set November 15, 1994 as the 

deadline for submission of a SIP to demonstrate attainment of the NAAQS 

for ambient 1-hour ozone of 0.125 parts per million (ppm) by December 

2010. AQMD satisfied that CAA requirement with the submittal of the 

1994 AQMP in September 1994. A subsequent revision was submitted to 

USEPA in February 1997. This was amended in 1999 to revise the Basin 

ozone portion of the 1997 AQMP due to its partial approval/disapproval by 

USEPA. The 2003 AQMP took advantage of information obtained from 

the 1997 Southern California Ozone Study (SCOS97) and emissions 

inventory enhancements. It updated the attainment demonstration for the 

ozone and PM10 particulate matter NAAQS, replaced the 1997 attainment 

demonstration for the CO NAAQS, and updated the maintenance plan for 

the NO2 NAAQS that the Basin has met since 1992. In 2005, AQMD 

submitted a CO attainment request and maintenance plan to CARB and 

USEPA; approval of this is pending. 

In July 1997, the USEPA established new ozone NAAQS of 0.085 ppm 

based on an 8-hour average measurement. Due to legal challenges, the 

final form of the ozone NAAQS has been implemented in two phases. The 

Phase 1 ozone implementation rule, finalized on June 15, 2004, defined the 

classification scheme for 8-hour ozone nonattainment areas and revoked the 

1-hour ozone NAAQS, while requiring states to maintain control programs 

which were included in their state implementation plans (SIP) for the 1-

hour standard. Fifteen areas in California were designated that violate the 

federal 8-hour ozone standard. Each nonattainment area's classification and 

attainment deadline is based on the severity of its ozone problem. Southern 

California’s nonattainment areas and attainment deadlines are: South Coast 

Air Basin (2021); Coachella Valley (2013); Ventura County (2010); 

Western Mojave Desert (2010); Antelope Valley (2010); San Diego (2009-

2014); and Imperial County (2007). 

The Phase 2 ozone rule, adopted November 9, 2005, described the actions 

that states must take to reduce ground level ozone and set the deadline for 

ozone SIP submittal of June 2007. The AQMD began air quality modeling 

analyses related to the 8-hour ozone standard during the 1997 AQMP, prior 

to the final NAAQS implementation. This analysis effort was continued for 

the 2003 AQMP. The 2007 AQMP modeling will expand the 8-hour ozone 

analysis and include an attainment demonstration for the current form of 

the NAAQS. The 2007 AQMP will include an analysis of 1-hour ozone to 
KRD: DRAFTModelingProtocol.doc 5/12/2006 4

provide additional milestones for progress of ongoing control programs and 

for continuity with previous efforts. 

AQMP Ozone Modeling History in the South Coast Air Basin

The first Air Quality Management Plan (AQMP) for the Basin was 

produced in 1979 as part of a revision to California's SIP. The 1979 AQMP 

indicated that it would not be possible to achieve the federal 1-hour ozone 

air quality standard of 0.12 ppm by 1982. Because the emission controls 

discussed in the 1979 AQMP would not be fully effective until after 1982, 

CARB and USEPA granted an extension to 1987 for achievement of the 

standard. As part of that extension, a revision to the AQMP was performed 

by the AQMD in 1982 which included a new series of modeling analyses to 

address concerns regarding the original 1979 modeling analysis. 

For both the 1979 and 1982 AQMP revisions, the city-specific Empirical 

Kinetics Modeling Approach (EKMA) was applied. The 1979 AQMP used 

the city-specific EKMA procedures then in existence. The 1982 AQMP 

revision used a more sophisticated version of the EKMA procedures and 

also contained sensitivity analyses (Appendix VI-A of the 1982 AQMP 

revision). The UAM was used in conjunction with the EKMA analyses to 

evaluate the effect of applying all feasible control measures by 1987 

(Appendix VI-E of the 1982 AQMP revision). On the basis of those 

modeling studies, it was determined that hydrocarbon reductions on the 

order of 75 percent or greater would be required to attain the federal 

standard by 1987, given a forecasted 23 percent reduction in oxides of 

nitrogen. Forecasted emission data indicated that only a 33 percent 

hydrocarbon reduction could be expected by 1987. Issues raised during the 

1979 and 1982 AQMP revisions highlighted the need to use a threedimensional, photochemical model such as the UAM to better understand 

the complex interactions between precursor emissions, meteorology, and 

the formation of ozone in the Basin. 

For the 1989 AQMP revision, the UAM was applied to a single, multiday, 

ozone episode to demonstrate attainment of the National Ambient Air 

Quality Standard (NAAQS) for ozone. It was determined from the 

modeling analysis that hydrocarbon and oxides of nitrogen emission 

reductions of more than 80 percent would be needed in order to attain the 1-

hour ozone NAAQS by the year 2007. The 1989 AQMP revision outlined 

three levels of controls (identified as Tiers I, II, and III) that separated the 

proposed control measures by known and proven technologies from those 

technologies anticipated to be available within the next 20 years. 
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For the 1991 AQMP, the AQMD used the UAM to further assess the 

effectiveness of the three tiers of control measures in reducing ambient 

ozone levels. To complement the single, multiday ozone episode used for 

the 1989 AQMP revision, two additional ozone episodes were modeled to 

investigate the effect of projected emission reductions on future ozone 

concentrations during a wider variety of meteorological conditions. 

Additional evaluations of model performance, including new graphical 

procedures and subregional performance statistics, were used to ensure 

adequate representation of the physical and chemical processes that 

influence ozone formation in the Basin. 

A number of improvements were made to the modeling analysis for the 

1994 AQMP. Growth factors for population and vehicle miles traveled 

(VMT) were revised to reflect the 1990 Census data and the economic 

climate of the early 1990s, and improved transportation modeling was 

considered. The modeling analysis benefited from a number of AQMD, 

CARB, and SCAG studies that improved the area source emission 

inventory (Appendix III-A). On-road, mobile emission estimates were 

improved with the use of the latest CARB emission factors program, 

EMFAC7F. Five ozone episodes were simulated to evaluate control 

strategy effectiveness. In addition to the June 5-7, 1985, episode used in 

the 1989 AQMP, and the two Southern California Air Quality Study 

(SCAQS) episodes (August 26-28, 1987, and June 23-25, 1987) added for 

the 1991 AQMP analysis, two additional episodes (July 13-15, 1987, a 

SCAQS episode, and September 7-9, 1987) were simulated for the 1994 

AQMP. In this manner, control strategy decisions were based on a range of 

meteorological conditions, thereby reducing uncertainty in the control 

strategy’s effectiveness. It was determined that hydrocarbon and oxides of 

nitrogen emission reductions on the order of 80 and 60 percent, 

respectively, would be needed in order to attain the NAAQS. 

Based on the AQMD’s experience with the five ozone episodes used in 

preparing the 1994 AQMP, it was decided to drop the June 1985 

meteorological episode for the 1997 AQMP. The AQMD believed that the 

1987 meteorological episodes were satisfactorily evaluated. Since the 1985 

meteorological episode was based on routinely monitored data, it was 

believed that the 1987 SCAQS episodes provided improved performance. 

In October 1998, AQMD provided to the USEPA a “weight of evidence” 

analysis that indicated that even without the June 1985 episode, a viable 

ozone attainment demonstration could be made. 

As a result of intense interest for aerometric databases to support regional

ozone modeling, a large-scale field measurement program was carried out 
KRD: DRAFTModelingProtocol.doc 5/12/2006 6

in southern California during the Summer of 1997 to collect sufficient 

aerometric data to allow data analysts and modelers to characterize and 

simulate ozone formation and fate in the region. Several agencies and 

others participated during the planning and operational phases of the field 

study, including CARB, USEPA, the local air districts, the US Navy, the 

US Marines, and the marine industry. The 1997 Southern California Ozone 

Study, or SCOS97, occurred over a four month period from June 15 

through October 15, 1997 and captured several episodic ozone days. 

The 2003 AQMP updated the attainment demonstration for the federal 

standards for ozone and PM10; replaced the 1997 attainment demonstration 

for the federal CO standard and provided a basis for a maintenance plan for 

CO for the future; and updated the maintenance plan for the federal NO2 

standard that the Basin has met since 1992. New ozone episodes, including 

these from SCOS97, were included as complementary or replacement 

episodes in the 2003 AQMP. This revision to the AQMP also addressed 

several state and federal planning requirements and incorporated significant 

new scientific data, primarily in the form of updated emissions inventories, 

ambient measurements, new meteorological episodes and new air quality 

modeling tools. This revision pointed to the need for additional emission 

reductions (beyond those incorporated in the 1997/99 Plan) from all 

sources, specifically those under the jurisdiction of CARB and the USEPA 

which account for approximately 80 percent of the ozone precursor 

emissions in the Basin. 

The 2007 AQMP modeling effort focuses primarily on recent ozone 

episodes in 2004 and 2005. These periods better reflect emissions 

conditions following the reformulation of gasoline in California. The 

August 1997 episode from SCOS97 will be retained for continuity with the 

previous AQMP analyses. The 2007 AQMP will be consistent with and 

will build upon the modeling approaches taken in the previous SIP efforts 

for the South Coast Air Basin, utilizing the latest tools and technical 

guidance. 

AQMP PM Modeling History in the South Coast Air Basin

PM is a multicomponent pollutant including inorganic species such as 

sulfate, nitrate, ammonium, sodium, chloride, and organic compounds, 

elemental carbon, and a variety of trace metals. The PM10 modeling 

analysis shows that the annual average PM10 concentration is the 

controlling factor for attainment of the federal PM10 standards in the 

future. Although there were several PM10 modeling tools, there had been 

no single reliable annual PM10 model available to address the 
KRD: DRAFTModelingProtocol.doc 5/12/2006 7

multicomponent nature of the PM10. Therefore, a multi-pronged modeling 

methodology was employed to assess regional PM10 and demonstrate 

future compliance with the federal PM standards. 

For the 1989, l991, and 1994 AQMP, the Chemical Mass Balance (CMB) 

model for primary and secondary organic carbon and the Particle-In-Cell 

(PIC) model for sulfate and nitrate were used for annual PM10 analysis. 

And speciated linear rollback (SLR) was used for maximum 24-hour PM10 

analysis. 

For 1997 AQMP, a new annual PM10 modeling methodology, the 

UAM/LC model, was developed and applied. The Urban Airshed Model 

(UAM) (Ames, et al., 1985; and Morris, et al., 1990a, 1990b) was used as a 

host air quality model and the parameterized linear chemistry (LC) module 

was incorporated into the UAM. UAM was adapted to address the 

formation of particulate nitrate, sulfate, and ammonium and handling of 

primary particles by replacing the UAM standard chemical mechanism with 

the parameterized linear chemistry module. UAM/LC, unlike the PIC 

model, addresses the 3-dimensional aspects of transport and diffusion, 

varying mixing height, ammonia emissions change, and particulate nitrate 

concentrations. However, the UAM/LC model cannot handle secondary 

organic carbon because the current parameterized linear chemistry does not 

include organic chemistry. Secondary organic carbon is treated separately 

by the CMB model. 

For the 2003 AQMP, UAM/LC model was further enhanced to include 

secondary organic carbon and PM2.5 partition. The resulting UAMAEROLT model, for the first time, provided a more robust, stand-alone platform 

for primary and secondary annual PM2.5 and PM10 simulations. 

2007 AQMP Modeling Analysis Goals 

The 2007 AQMP modeling will focus primarily on the 8-hour ozone and 

the annual and 24-hour PM2.5 NAAQS attainment demonstration and 

reasonable further progress. The applicable NAAQS, along with the 

current attainment status of the South Coast Air Basin and recent design 

values, are presented in Table 1. Although the 1-hour federal standard was 

revoked in 2005, the analysis of 1-hour ozone will be retained as a 

benchmark of progress toward meeting the former 1-hour ozone NAAQS, 

as well as toward the State of California ozone standards. In addition to the 

PM2.5 NAAQS attainment demonstration, the particulate modeling 

analysis will include annual and 24-hour PM10 attainment demonstrations. 
KRD: DRAFTModelingProtocol.doc 5/12/2006 8

Further, the 2007 AQMP modeling will address maintenance plans for 

Carbon Monoxide (CO) and Nitrogen Dioxide (NO2). 

The modeling effort may also include initial modeling strategy 

development toward demonstrating attainment of the inhalable coarse 

particle (PM10-2.5) NAAQS and a stricter PM2.5 NAAQS recently 

proposed by USEPA. The proposed standards are: 

• PM10-2.5: 98th percentile 24-hour PM10-2.5 in a year, averaged over 3 

years not to exceed 70.4 μg/m3

; no annual standard; 

• PM2.5: 98th percentile 24-hour PM2.5 in a year, averaged over 3 years 

not to exceed 35.4 μg/m3

; no change to annual standard. 
KRD: DRAFTModelingProtocol.doc 5/12/2006 9

TABLE 1 

National Ambient Air Quality Standards Compliance Status in the South 

Coast Air Basin

8-Hour Ozone 24-Hour PM2.5 Annual PM2.5 

Standard 

3-year average of the 

4th highest 

concentration not to 

exceed 0.084 ppm 

3-year average of the 

98th percentile of 24-

hour concentrations not 

to exceed 65.4 μg/m3

3-year average of 4 

quarterly averages not to 

exceed 15.04 μg/m3

Classification 

Severe-17 

[may petition for 

Extreme] 

Non-Attainment Non-Attainment 

Attainment 

Date 

2021 

[2024, if Extreme] 

2015 2015 

Design Value 0.127 ppm 

(2002-2004) 

67 μg/m3

(2002-2004) 

24.8 μg/m3

(2002-2004) 

24-Hour PM10 Annual PM10 

Standard 

3-year average of the 

99th percentile of 24-

hour concentrations not 

to exceed 154 μg/m3

3-year average of 4 

quarterly averages not 

to exceed 50.4 μg/m3

Classification Serious 

Non-Attainment 

Serious 

Non-Attainment 

Attainment 

Date 2006 2006 

Design Value 159 μg/m3

(2002-2004) 

57 μg/m3

(2002-2004)
KRD: DRAFTModelingProtocol.doc 5/12/2006 10

Ozone Design Value Determination

Since the base year emissions are for 2004, air quality data from the three 

overlapping 3-year periods from 2000 through 2004 were used for 

calculation of the 8-hour ozone design values for each AQMD air 

monitoring station. These are shown in Table 2, along with the Relative 

Reduction Factors (RRF) needed. Per USEPA guidance, the design value 

averages are truncated (not rounded). 

TABLE 2 

Ozone Design Value (ppb) for Each Station, 2000-2004 

(Average of the 4th highest 8-hour station concentration in each 3-year period) 

Station 

2000-2002 

Design 

Value 

2001-2003

Design 

Value 

2002-2004

Design 

Value 

Current 

Design Value 

(DVC) 

RRF 

Required 

AZUS 102.3 101.0 101.0 101.43 0.8284 

BURK 91.7 91.3 91.3 91.43 0.9190 

LGBH 61.7 60.7 60.7 61.03 

RESE 93.3 106.3 106.3 101.97 0.8235 

POMA 89.7 96.7 96.7 94.37 0.8898 

LYNN 51.0 53.3 53.3 52.53 

PICO 80.3 79.0 79.0 79.43 

CELA 79.3 78.3 78.3 78.63 

PASA 96.3 95.3 95.3 95.63 0.8787 

SCLR 113.3 126.7 126.7 122.23 0.6874 

WSLA 69.3 73.3 73.3 71.97 

HAWT 69.3 71.0 71.0 70.43 

GLEN 110.7 114.3 114.3 113.10 0.7427 

ANAH 69.7 71.7 71.7 71.03 

LAHB 75.7 74.7 74.7 75.03 

CSTA 67.3 71.3 71.3 69.97 

MSVJ 80.0 82.7 82.7 81.80 

PLSP 105.3 108.3 108.3 107.30 0.7829 

RIVR 108.0 112.7 112.7 111.13 0.7561 

PERI 114.0 115.7 115.7 115.13 0.7298 

INDI 92.3 96.7 96.7 95.23 0.8824 

ELSI 104.3 109.0 109.0 107.43 0.7821 

UCRI 113.3 117.3 117.3 115.97 0.7241 

BNAP 110.3 118.7 118.7 115.90 0.7248 

UPLA 114.0 113.0 113.0 113.33 0.7414 

CRES 129.0 131.7 131.7 130.80 0.6422 

FONT 112.3 123.0 123.0 119.43 0.7035 

SNBO 114.7 118.7 118.7 117.37 0.7155 

RDLD 120.0 128.3 128.3 125.53 0.6693 

MLOM 103.0 106.0 106.0 105.00 0.8000 

RHIS 130.3 136.7 136.7 134.57 0.6241 
KRD: DRAFTModelingProtocol.doc 5/12/2006 11

Overview of the Modeling Analysis 

The analysis techniques currently recommended for attainment 

demonstrations using air quality models have changed significantly from 

those used in past demonstrations. In Guidance on the Use of Models and 

Other Analyses in Attainment Demonstration for the 8-hour Ozone NAAQS

(USEPA, 2005), USEPA recommends that the air quality models be used in 

a relative sense in concert with observed air quality data rather than 

applying the air quality model in a deterministic sense. The Relative 

Reduction Factor (RRF) which takes the ratio of future to present predicted 

air quality is multiplied to an “ambient design value” to demonstrate 

attainment. The proposed ozone modeling analysis is comprised of the 

following tasks: 

• Identify potential, new ozone meteorological episodes to be used. 

These episodes should represent the different meteorological conditions 

that are conducive to ozone formation in the Basin. 

• Among the widely accepted state-of-the-science ozone models, CAMx 

was selected for the attainment demonstration. CMAQ may be 

employed in the sensitivity analysis and weight-of-evidence section as a 

supportive modeling tool. 

• Develop model inputs. This task includes evaluation of the raw data 

and of the model input files developed from them. The input files will 

be evaluated using graphical and other techniques. 

• Simulate each episode with the proposed ozone models. This task 

includes a separate performance evaluation for each episode and each 

model. Documentation of the simulation results and performance 

evaluations will be provided. 

• Project ozone air quality with proposed control measures in effect for 

the years 2007, 2010, 2014, and 2020. This task includes the required 

attainment demonstration using RRF. Model projections for the year 

2007 are necessary since that is the year that the CAA requires 

attainment for severe-17 areas, such as the Coachella Valley and 

Mojave Desert Ozone Nonattainment Areas. Ozone air quality 

projections to 2020 will be used to demonstrate that the control strategy 

maintains the ozone NAAQS and to establish emission budgets needed 

for conformity purposes. 

The work to do the foregoing tasks will be divided between the AQMD and 

CARB staffs and they will fully share all analyses, model inputs and 

outputs, findings, and conclusions. Consensus on each component of the 

analysis shall be reached before proceeding with subsequent components. 

In the event of technical disagreement on any of the work elements, the 
KRD: DRAFTModelingProtocol.doc 5/12/2006 12

AQMD and CARB staffs shall attempt to reach consensus on a mutually 

acceptable approach. In the event that consensus cannot be reached, the 

disagreement will be elevated to the Executive Officers for resolution. 

Table 3 summarizes the model selection and application elements for the 

2007 AQMP and the changes from the 2003 AQMP modeling. 
KRD: DRAFTModelingProtocol.doc 13 

TABLE 3 

Summary of Proposed 2007 AQMP Model Selection and Application

2007 AQMP Element 2003 AQMP Element Selection Process/Issues/Co

Ozone Dispersion Platform: CAMx Chemistry: SAPRC99 

Ozone Dispersion Platform: UAM Chemistry: SAPRC99 

• Peer Group Recommendation to mov

consistent model/chemistry 

• Integrates with numerical weather m• CAMx used by several agencies for S

supported by Environ 

• Option for one atmosphere modeling • Alternates CMAQ: Emissions prepr

CALGRID: performance similar to Catmosphere modeling 

PM10/PM2.5 Annual and Episodic Dispersion Platform: CAMx Chemistry: 

• AERO-LT with CB-IV 

• Enhanced CFI scheme with CBIV • Optional One Atmosphere 

Aerosol chemistry 

PM10/PM2.5 Dispersion Platform: UAM Chemistry: AERO-LT with CB-IV 

• CAMx PM dispersion consistent with • Installed SCAQMD version of AERO

code (V4.20). 

• Enhanced CAMx two section CFI ae

compared with AERO-LT. 

Meteorology 

• MM5/4DDA • Hybrid MM5/CALMET • MM5 initialized using NCEP 

data 

Meteorology 

• CALMET Objective Analysis • Hybrid MM5/CALMET 

• EPA has expressed concerns about u• MM5/4DDA is more mass consisten

localize wind impacts (transport to S

• Testing several land use assumptions 

optimize wind fields and vertical mix

• Using Environ’s and Aerospace met-

evaluation software. 

• Where possible take advantage of en

data (e.g. 3D-Var) 

Domain/ CoordinatesSCOS97 Meteorology: Lambert Conformal Emissions and Model application: UTM Ozone: 16 layers PM10/2.5: 8 layers 

Domain SCOS97 Meteorology: UTM Emissions and Model application: UTM Ozone & PM10 5-layers 

• Maintained the SCOS97 domain how

inventories require coordinate system statewide modeling domain. Impacts CEDARS output. 
KRD: DRAFTModelingProtocol.doc 14 

TABLE 3 (Continued) 

2007 AQMP Element 2003 AQMP Element Selection Process/Issues/Co

Emissions Inventories 

• 2002 Base year • Enhanced aircraft/airport and 

shipping inventories 

• POLA/POLB updates • EMFAC2007 

o gross adjustments o “focused” inventories o Final public model 

• Adjustments to fugitive 

PM10/PM2.5 categories 

Emissions Inventories 

• 1997 Ozone base year & 1995 

PM10 base year 

• Updated aircraft/airport and 

shipping inventories 

• EMFAC2002V2.01  (major effort to develop 

surrogates for area sources) 

• 2002 Inventory will be used to back

project inventories through 2030 for 

• Waiting on SCAGS’ growth estimat

which is expected to differ only sligh

• Episodic temperature and humidity f

for biogenic emissions 

• CARB is adjusting temperature field

development 

• Gridded inventories awaiting focuse

output and supplemental inventories 

• No weekend trip model output avail• CARB will develop a “weekend” ov

based on Caltrans in-road counter d

Air Quality Model Performance Ozone

• Assess model performance 

based on both 1-hour and 8-hour statistics 

• 60 ppb threshold (both indices) • Weight of Evidence Analysis • Mid-Course simulations 

PM10/PM2.5 (annual and episodic) 

• Base statistics at speciation sites • Weight of evidence analysis • Mid-Course simulations 

Air Quality Model Performance Use EPA recommendations for 1-hour ozone and outline for PM10 and CO. Ozone

• Mid-Course 2002 simulation • Comparative relative reduction 

for UAM/CAMx/CMAQ per Peer Advisory Group Recommendation 

PM10

Analyzed “hot spot” grid cell emissions 

• Will review thresholds and geograph

performance evaluation. 

• Conduct sensitivity simulations to te

VOC/NOx ratios, emissions timing (weekday), ammonia mass 

Relative Reduction Factors RRF: sites specific applied to 3-year average of the design value (PM2.5 and ozone)

Relative Reduction Factors Tested for ozone and PM10 but not applied 
KRD: DRAFTModelingProtocol.doc 15 

TABLE 3 (Continued) 

2007 AQMP Element 2003 AQMP Element Selection Process/Issues/Co

Episode Selection Ozone

• 1997 August 3-7 • 1997 Seasonal: August • 2004 June 3-7 • 2004 August 4-8 • 2005 May 17-24 • 2005 July 14-19 • 2005 August 25-29 

PM10/2.5

• Annual 2005 (January – 

December) 

• 2005 October 19-25 • 2005 March 6-12 

Episode Selection Ozone

• 1997 August 4-7 

PM10/2.5

• January – December 1995 • Episodic: Rollback 

CO

• 1997 October 31- 

November 1 

• Meteorological episodes include 

California Fuel reformulation (2

• MATES-III meteorological data 

concurrent with AQMP data bas

• Contract with Aerospace to prov

observations data and MM5 init(satellite ingest and 3DVAR 

Initial/Boundary Conditions Ozone

• EPA recommended boundary 

conditions 

• 40 ppb ozone top profiled to 

lower layers 

PM10/2.5

• Monthly varying emissions 

generated boundary conditions (simulate model with zero boundary conditions and let model generate boundary -- using 3-5 grid cells from model domain boundary as representative of boundary) 

Initial/Boundary Conditions Ozone 

• Use EPA recommended 

boundary conditions 

• Per SCOS97 sampling 

tested 60 ppb ozone aloft 

PM10

Monthly varying boundary conditions based on coastal monitoring site data

• Will test varying top boundary conc• Review alternate approaches for qua

conditions 
KRD: DRAFTModelingProtocol.doc 16 5/12/2006 

2007 AQMP Schedule 

The schedule of 2007 AQMP modeling efforts is driven by the regulatory 

deadlines for SIP submittal to USEPA, which is June 15, 2007 for 8-Hour 

Ozone. Table 4 outlines the tentative schedule of events leading up to the 

SIP submittal. 

TABLE 4 

Tentative 2007 AQMP Schedule 

Task Due Date 

Episode Selection January 2006 

Air Quality and Meteorological Data Preparation January 2006 

Emission Inventory Preparation April 2006 

Performance Evaluation May 2006 

Control Strategy Development May 2006 

Attainment Demonstration June 2006 

Draft SIP Documents September 2006 

District Board Approval of Final SIP November 2006 

CARB Board Approval of Final SIP February 2007 

SIP Submittal to USEPA June 15, 2007 

[Add EIR Schedule, 

Alternative Modeling, 

Public Workshops] 

AQMP Modeling Technical Oversight 

The AQMD Governing Board has established several advisory groups to 

assist with technical oversight and scientific community and business 

involvement in air quality programs. The mission of the Air Quality 

Management Plan Advisory Group (AQMPAG), whose membership is 

appointed by the Board, is to review the overall aspects of a draft air quality 

management plan and to make recommendations concerning emission 

inventories, modeling, control measures, and socioeconomic impacts. 

Tasks of the AQMPAG, include: 

• Provide review and comments on (1) studies relevant to advancing 

scientific and technical knowledge in support of AQMP preparation; (2) 

emissions inventory development and modeling approaches; (3) the 

development of new and revised control measures, including on-and 

off-road mobile sources; (4) socioeconomic data and evaluations. 
KRD: DRAFTModelingProtocol.doc 17 5/12/2006 

• Foster coordinated approaches toward overall attainment strategies. 

• Assist in resolving key technical issues. 

In addition, the AQMD Governing Board has established a more focused 

technical oversight committee to review the technical aspects of the 

ongoing modeling analyses. Since the late 1980’s the AQMD has had 

socioeconomic and modeling working groups, when the Governing Board 

passed a resolution to form the Modeling Working Group (MWG) during 

the adoption of the 1989 AQMP revision. The MWG, comprised of 

individuals with photochemical and aerosol modeling expertise, provided 

oversight and technical consensus on AQMP modeling issues. In 1997, the 

MWG was reconstituted as the Scientific, Technical, and Modeling Peer 

Review Advisory Group (STMPRAG). The STMPRAG role expands upon 

that of the MWG and includes experts in socioeconomic assessment and 

human health, providing review of AQMD modeling, monitoring and 

related scientific issues. 

The STMPRAG assists AQMD in resolving technical issues related to air 

quality and socio-economic modeling by providing ongoing technical 

review and consensus of procedures and analyses. The objectives of the 

STMPRAG are as follows: 

• Suggest methods to gather and process meteorological, aerometric and 

emission data with a specific focus on air quality modeling. 

• Provide technical guidance to the air quality modeling efforts, with an 

emphasis on ozone and particulate matter. Some specific areas of 

technical guidance include: (1) Formulation of modeling approaches; 

(2) Selection and development of appropriate modeling techniques; and 

(3) Identification of model performance evaluation methods. 

• Review and provide comments on the AQMP modeling procedures and 

analyses. 

• Make recommendations on future modeling resource requirements (i.e., 

staffing and computational needs). 

• Recommend methods for interpretation of modeling results. 

• Provide a linkage between the air quality and socio-economic modeling 

communities, emphasizing the importance of future growth and 

economic factors on future air quality attainment demonstrations. 

The STMPRAG consists of approximately 20 members appointed by the 

Governing Board, with representatives from USEPA, CARB, Southern 

California Association of Governments (SCAG), the California Small 

Business Alliance (CSBA), Southern California Edison (SCE), Western 
KRD: DRAFTModelingProtocol.doc 18 5/12/2006 

States Petroleum Association (WSPA), and technical experts from 

universities and consultant firms. 

Finally, as progress is made and products are available, interim results will 

be shared with the interested public at appropriate times and locations. 
KRD: DRAFTModelingProtocol.doc 19 5/12/2006 

 MODEL SELECTION 

Meteorological Model 

Background

Air quality models require three-dimensional, meteorological inputs. The 

key parameters are winds, mixing heights, temperature, and insolation. The 

windfields describe the transport and dispersion of pollutants. Mixing 

heights define the vertical extent of pollutant mixing near the surface. 

Temperature and insolation fields influence emission rates and the rates of 

chemical transformation. Because meteorological measurements can be 

made only at discrete locations, meteorological models are required to 

develop the 3-dimensional fields required by air quality models. 

The meteorological models used to generate these three-dimensional fields 

are generally of three types: objective, diagnostic or prognostic. Objective 

models are the least sophisticated meteorological models. These models 

rely on interpolation of observations. Obtaining a reasonable field requires 

sufficient observations to accurately represent the atmosphere. This is 

especially true for windfields. In areas with complex terrain and bodies of 

water, such as the proposed modeling domain, the meteorology can be quite 

complex, and a successful objective analysis would require an extremely 

large number of observations. 

Diagnostic models rely both on observations and constraints based on 

physical concepts such as the conservation of mass. A diagnostic wind 

model can simulate thermally induced circulations and the effects of 

surface friction. One example of this type of model is the Diagnostic Wind 

Model (DWM) which is distributed by the USEPA. For the DWM, the user 

first defines an initial-guess mean wind field that can be representative of 

synoptic scale patterns. The domain mean wind is then adjusted for the 

effects of terrain. Available observations are then used to develop 

meteorological fields using objective analysis. The initial guess and the 

objective analysis are then combined using a weighting function based on 

distance from observations. A criticism of diagnostic models is that the 

fields produced are not consistent from one hour to the next. Since the 

processes which create the wind, temperature, and mixing height fields are 

relatively independent, these models are also criticized for not being 

thermodynamically consistent between the meteorological parameter fields. 

Prognostic models are the most sophisticated of the meteorological models. 

They are based on principles of atmospheric physics, i.e., conservation of 
KRD: DRAFTModelingProtocol.doc 20 5/12/2006 

mass, momentum, energy and moisture. As a result, they are 

computationally intensive. The use of four dimensional data assimilation 

(FDDA) or observational nudging – where observations are introduced to 

the model as an additional forcing term – is typically used in areas of 

complex meteorology to improve the accuracy of the outputs. Another 

approach is objective combination, in which observations are introduced 

after the model has estimated a value. Prognostic models are capable of 

explicitly incorporating many of the physical flow processes important in 

the domain. However, prognostic models have historically had problems 

estimating fine-scale flow features due to the limited resolution of datasets 

used for describing geographic features. 

Previous AQMP Applications

In the past, CARB and AQMD have utilized prognostic, diagnostic, and 

objective models to generate meteorological inputs for modeling. The 

National Center for Atmospheric Research’s prognostic, non-hydrostatic 

Mesoscale Model (MM5) was applied for modeling in support of 

attainment planning in the San Joaquin Valley. The SCAQMD also has 

experience with the SAIMM prognostic model. Diagnostic models 

(WIND2D, WIND3D, DWM) have been applied in the Sacramento area 

and in southern California to prepare meteorological input fields for the 

application of photochemical models in those areas. CARB and AQMD 

conducted a review of CALMET, which may be viewed as an improved 

version of the DWM and which is being distributed through the USEPA for 

air quality modeling applications. The CALMET model has an added 

feature that allows a hybrid meteorological field to be developed by 

merging the results from a prognostic model, such as the National Center 

for Atmospheric Research Mesoscale Model Version 5 (MM5), with an 

objective or diagnostic analysis characteristic of the CALMET model. This 

hybrid approach has the potential to take advantage of the prognostic 

capabilities of MM5 in areas of the domain where meteorological 

measurements are few, and utilizing measurements in an objective analysis 

where there are many. 

2007 AQMP Meteorological Modeling Approach

The SCOS97 field study generated a dataset with a relatively high spatial 

density of meteorological observations. While this dataset suggests that an 

objective/diagnostic model could be adequate to develop the meteorological 

parameter fields required for air quality modeling of the August SCOS97 

episode, there are large portions of the modeling domain—such as over the 

ocean or the inland desert—where there are few observations. The 
KRD: DRAFTModelingProtocol.doc 21 5/12/2006 

approach for the 2007 AQMP modeling will be to use the MM5 prognostic 

model with a 5 km grid resolution. The meteorological boundary 

conditions for MM5 are generated using the output from a Global Climate 

Model (GCM) with a relatively coarse grid of 45 km. The MM5 prognostic 

model uses more accurate and complete physics than the diagnostic models 

used previously. The MM5 has relatively good replication of 

meteorological features of the Basin, such as the coastal eddies, Santa Ana 

winds, recirculation, & strong inversions. 

The recent air quality models are designed to use inputs from the prognostic 

models, such as MM5, and the use of such a model is strongly encouraged 

by USEPA. In the past, the use of MM5 meteorological fields in air quality 

models has brought limited success in the prediction of peak ozone 

concentrations that result from extreme meteorological conditions and 

complex distribution of precursor emissions. However, the prediction of 

ozone with MM5 meteorological fields on most days is comparable to the 

results using other models. Since the air quality model will be employed in 

more of a relative sense for the 2007 AQMP, with the use off relative 

reduction factors instead of peak concentration comparisons, the MM5 is an 

appropriate choice for the AQMP modeling. The premise is that the 

magnitude of RRF will reflect the ozone concentration resulting from the 

various meteorological episode classifications. With the use of the MM5 

meteorological model, the AQMP modeling effort will move closer to the 

“one atmosphere” air quality modeling perspective (i.e., ozone and fine 

particles simulated with the same model). The successful application of 

this prognostic model is critical for the development of multipollutant 

control strategies. 

Several MM5 initialization fields and data ingest options are also being 

explored for the 2007 AQMP modeling effort: 

• MM5 model initialized with the National Centers for Environmental 

Prediction (NCEP) 12 km ETA/North American Model (NAM); 

• MM5 model with Aerospace Corp 3DVAR forecast fields; 

• Weather Research and Forecasting (WRF) community model using 

Aerospace Corporation 3DVAR; 

• MM5 model with NCEP database of upper air and surface observations 

and the 1degree by 1 degree Global Tropospheric Analysis 

• Above method of MM5 with NCEP database and Global Tropospheric 

Analysis and four-dimensional data assimilation (4DDA) of AQMD 

station meteorological data (this method is more mass consistent, but 

may be difficult to capture localized wind impacts (e.g., transport to San 

Fernando Valley); 
KRD: DRAFTModelingProtocol.doc 22 5/12/2006 

• Hybrid CALMET with MM5 as background field 

To supplement the MM5 meteorological modeling, the CALMET/MM5 

hybrid meteorological model will be used to bolster the sensitivity analyses 

and weight-of-evidence discussions. The RRF can be adjusted or supported 

by the air quality modeling results using this alternative hybrid 

meteorological field. In this approach, the parameter fields will be overlaid 

using a weighting scheme that is based on the proximity to meteorological 

observations. The resultant fields benefit from the capabilities of the 

prognostic model in those areas of the modeling domain with few 

observations (such as offshore, in complex terrain, and in the desert areas), 

and benefit from the objective analysis component of the diagnostic model 

to force the fields to agree with observations. To develop the hybrid fields, 

the fields developed using CALMET and MM5 will need to be mapped into 

common horizontal and vertical coordinate domains. The CALMET model 

code is structured to facilitate this mapping. 

Air Quality Model 

Background

The air quality model employed for previous AQMP efforts, the Urban 

Airshed Model (UAM-IV (USEPA 1990), is widely acknowledged to have 

characteristics which limit its utility when applied to large modeling 

domains or to domains that are not geographically uniform. In addition, 

much of the science in the model is outdated, and both the USEPA and 

CARB are no longer recommending that model for most analyses. Several 

photochemical models have been developed to improve upon the UAM-IV. 

Among those models, CAMx and CMAQ were widely accepted models as 

the state of the science models that include the most up-to-date chemical 

mechanisms, physics and the efficient numerical algorithms. The following 

summarizes the current models. 

• CALGRID 

The CALGRID model (Yamartino et. al, 1989) was developed for 

CARB in the late 1980's. The model has been applied by various air 

pollution agencies around the world. It is modular to allow the user to 

substitute various types of wind fields and chemical mechanisms. 

CALGRID incorporates refined treatments of numerical advection, 

vertical transport and dispersion, and dry deposition. The model can be 

exercised with either the Carbon Bond IV (CB-IV) or SAPRC chemical 

mechanisms, and contains highly efficient chemical integration routines. 

The vertical structure of the atmosphere can be optionally defined 
KRD: DRAFTModelingProtocol.doc 23 5/12/2006 

relative to a mixing height field, similar to the UAM, or can be based on 

fixed layer heights and a derived mixing height. 

• Models-3 

Models-3 (USEPA, 1998a) is a flexible software system designed for 

applications ranging from regulatory and policy analysis to 

understanding the complex interactions of atmospheric chemistry and 

physics. The Models-3 system is a framework that allows the user to go 

from developing model inputs to visualizing results all in one package. 

At the heart of the current version of Models-3 is the Community Multiscale Air Quality (CMAQ) Model. The capabilities of CMAQ include 

urban to regional scale air quality simulation of ozone, acid deposition, 

visibility and fine particles. CMAQ is a modular system capable of 

using output from the MM5 prognostic meteorological model, along 

with the CB-IV, RADM-2, or SAPRC-99 chemical mechanisms. The 

CMAQ model also includes a plume-in-grid module, vertical and 

horizontal growth due to turbulence and shear, a choice of advection 

schemes and a cloud- module to simulate precipitating and nonprecipitating clouds. Since the Models-3 system is relatively new, some 

implementation and application problems are likely. 

• SARMAP Air Quality Model (SAQM) 

SAQM (Chang, et. al, 1997) is a three-dimensional non-hydrostatic 

model based upon the Regional Acid Deposition Model (RADM) 

(Chang et. al 1987, 1990). However, SAQM includes a number of 

improvements over RADM, including: a fixed vertical coordinate 

system that is compatible with MM5; a horizontal coordinate system 

defined in a Lambert-Conformal projection that accounts for curvature 

of the Earth; a mass conservation module for compatibility with nonhydrostatic meteorological inputs; the Bott advection scheme (Bott 

1989a, 1989b) to reduce numerical diffusion and increase numerical 

accuracy; two-way nesting, and the capability to use either the CB-IV or 

SAPRC chemical mechanisms. A version of SAQM with plume-in-grid 

treatment is also available. 

• Urban Airshed Model-Flexible Chemical Mechanism (UAM-FCM) 

The UAM-FCM (Kumar et. al, 1995) is an alternate version of the 

UAM-IV that has been enhanced to allow the flexibility to incorporate 

any Carbon Bond- or SAPRC-type chemical mechanism. The FCM 

allows incorporation of reaction-specific photolysis rates. In addition, 

the UAM-FCM has a generalized methodology to solve the set of 

differential equations that is mechanism independent. However, the 

meteorological dispersion algorithms are the same as in UAM-IV. 
KRD: DRAFTModelingProtocol.doc 24 5/12/2006 

• Urban Airshed Model-Variable (UAM-V) 

The UAM-V (Systems Applications International, 1996) is an updated 

version of the Urban Airshed Model (UAM-IV) which incorporates 

many state-of-the-art enhancements in chemical mechanisms, 

meteorological models and the representation of emissions. Perhaps the 

most significant additions are: an updated CB-IV mechanism to include 

aqueous phase chemistry; plume-in-grid capabilities; an improved dry 

deposition algorithm; and an improved plume rise algorithm. Other 

enhancements over UAM-IV include allowing the user a fixed vertical 

structure as opposed to one that is relative to the diffusion break, the 

ability to use three dimensional inputs from prognostic models and twoway grid nesting. However, the present non-public domain status of 

UAM-V may preclude regulatory usage. The model developers have 

indicated that the model could be made available for any party to review 

if the party agrees that the use of the model would be solely for the 

review of the AQMP. 

• Comprehensive Air-Quality Model with Extensions (CAMx) 

CAMx (Environ, 1997) contains a number of advanced features, 

including grid nesting, sub-grid scale plume-in-grid simulation, 

alternative numerical advection solvers and the ability to use alternative 

chemical mechanisms. In addition it has the ability to tag emissions so 

that at the end of the simulation one can determine the sources of 

emissions impacting a particular receptor. Since CAMx is a relatively 

new model, thus there is a relatively short history of experience 

applying the model. 

2007 AQMP Air Quality Modeling Approach

CAMx will be the primary air quality model for the attainment 

demonstration. This dispersion platform integrates well with numerical 

meteorological model output and it will be run using both the prognostic 

(MM5) and hybrid (CALMET/MM5) meteorological fields. The 

application of the MM5 and CAMx modeling system for both ozone and 

particulate matter simulation will bring AQMD closer to the “one 

atmosphere” modeling concept, where ozone and particulates are simulated 

in the same model. CMAQ model may also be run as a supporting model 

in the sensitivity analysis discussion. 

The ozone air quality models will be run using the SAPRC (Carter 1999, 

2001) chemical mechanism, based on chemical reactivity scales. At its 

meeting on October 8, 1999, CARB’s Reactivity Scientific Advisory 
KRD: DRAFTModelingProtocol.doc 25 5/12/2006 

Committee (chaired by Dr. John Seinfeld, with participation by other 

members Dr. Roger Atkinson, Dr. Jack Calvert, Dr. Harvey Jeffries, Dr. 

Jana Milford, and Dr. Armistead Russell) discussed a peer review of the 

SAPRC-99 mechanism conducted by Dr. William Stockwell. Members of 

the committee agreed that the peer review was excellent, that SAPRC-99 

was a state-of-the-art chemical mechanism, and they approved the peer 

review. The Committee then unanimously recommended that SAPRC-99, 

as the most up-to-date mechanism available, be used for SIP modeling. 

The particulate matter air quality model will use CAMx with the AEROLT/CB-IV chemical mechanism and the enhanced two-section CFI aerosol 

scheme with CV-IV. The AQMD version of the AERO-LT chemistry and 

the enhanced version of the CAMx CFI scheme have been installed in the 

latest CAMx code and comparative analyses will be presented. Advisory 

group recommendations have been to move toward a state-of-the-art, massconsistent model and chemistry. This system will integrate well with 

numerical weather model output and with also use the MM5 model for 

meteorological fields. 
KRD: DRAFTModelingProtocol.doc 26 5/12/2006 

MODELING DOMAIN 

Meteorological Modeling Domain 

Nested domains of 15 km and 5 km are defined within MM5 to simulate 

meteorological fields for the fine grid scale of the modeling domain. The 

modeling domain for MM5 is defined in a Lambert-Conformal projection 

with two parallels to account for curvature of the Earth within the modeling 

domain over such a large region. Figure 1 shows the nested MM5 domains. 

Figure 2 shows the finest scale (interior) MM5 domain, covering most of 

southern California. The vertical structure of MM5 is defined in a terrainfollowing, “sigma” coordinate system based upon a normalized pressure 

index. The 30 vertical layers defined for MM5 to approximately 15,000 m 

above ground level (AGL) can be transformed to fit the requirements of any 

air quality model. The MM5 meteorological fields are converted from 

Lambert-Conformal projection to UTM coordinates for input into the air 

quality models. 
KRD: DRAFTModelingProtocol.doc 27 5/12/2006 

FIGURE 1 

Nested MM5 Domains 

The horizontal grid resolution of the outermost domain is 45 km, for the middle 

domain is 15 km, and for the fine scale domain is 5 km. 
KRD: DRAFTModelingProtocol.doc 28 5/12/2006 

-10

-10

1000

1000

1000

1000

1000

1000

1000

1000

1000

1000

1000

1000

1000 1000

1000

FIGURE 2 

The Fine-Scale (5 km) MM5 Domain. 

Ozone Modeling Domain 

The proposed ozone regional modeling domain is that previously developed 

for the modeling of the SCOS97 field study episodes, encompassing a 600 

km wide by 160 km area, as shown in Figure 3. Specifically, the UTM 

Zone 11 coordinates of the domain are 150-700 km UTM East and 

3580-3950 km UTM North. This corresponds to 100 by 74 grid cells at 5 

km grid spacing. The vertical modeling domain will extend to a height of 

approximately 5,000 m AGL for a more complete representation of 

atmospheric processes. This will contain observed high ozone 

concentrations aloft and allow three-dimensional wind flow patterns near 

elevated terrain features to be represented, providing accurate 

representation of pollutant transport and recirculation. This same domain 

will be used for all of the ozone episodes. 
KRD: DRAFTModelingProtocol.doc 29 5/12/2006 

San Luis

Obispo Co.

Santa

Barbara Co.

Kern Co.

Ventura

Co.

Orange

 Co.

Imperial

Co.

Los

Angeles Co.

Riverside Co.

San Diego

Co.

San

Bernardino

Co.

Northern Mexico

FIGURE 3 

2007 AQMP Ozone Modeling Domain 

The ozone modeling domain encompasses much of southern California, as 

follows: all of the South Coast Air Basin (including Orange County and 

the non-desert portions of Los Angeles, Riverside and San Bernardino 

Counties), the Coachella Valley and San Diego County; the CaliforniaMexico border regions; most of Imperial County; most of the inland 

deserts; and almost all of the South Central Coast Air Basin (excepting a 

small piece of San Luis Obispo County). This large domain minimizes the 

influence of boundary conditions on simulation results and allows the 

effects of recirculation and interbasin transport to be better represented by 

the meteorological and photochemical model simulations. It also 

eliminates the need to define boundary concentrations between the air 

basins and it extends far enough offshore to contain wind flow patterns 

conducive to over-water recirculation. 

PM Modeling Domain 

The modeling domain for the particulate matter modeling will be smaller 

than the ozone domain, encompassing a 325 km wide by 200 km area, as 

shown in Figure 4. This corresponds to 65 by 40 grid cells at 5 km grid 

resolution. The reduced domain is due in part to the computational 
KRD: DRAFTModelingProtocol.doc 30 5/12/2006 

resource and time constraints of modeling the full 2005 year for annual PM. 

In addition, PM SIP modeling is not needed in the southernmost counties of 

California and adequate ammonia emissions inventories are not available 

from many areas surrounding the South Coast Air Basin. 

FIGURE 4 

2007 AQMP PM Modeling Domain, inside the Ozone Modeling 

Domain 
KRD: DRAFTModelingProtocol.doc 31 5/12/2006 

 HORIZONTAL AND VERTICAL GRID RESOLUTION

Horizontal Grid Resolution 

The horizontal grid resolution plays an important role in the modeling 

process. Large grid resolution tends to smooth emission gradients and 

meteorological inputs, which in turn leads to a smoothing of the resulting 

concentration fields. In general, the resolution should be sufficiently small 

to pick up emission gradients in urban areas and be consistent with the 

major terrain features which may affect the air flow. In the past, 

photochemical models have been applied in California with horizontal grid 

resolutions ranging from 2 x 2 km to 8 x 8 km. The specific grid resolution 

chosen was primarily dependent on the size of the modeling domain, 

computer resources available and the time and money available to carry out 

the simulations. In effect the final resolution was a compromise between 

the accuracy desired and the cost. However, the current generation of highspeed computers has minimized cost and resource constraints. 

For the year 2007 AQMP ozone, particulate and meteorological modeling, 

a horizontal grid resolution of 5 km is proposed to be used for the air 

quality modeling. No grid nesting is anticipated. This resolution is 

consistent with the grid resolution used in earlier photochemical modeling 

studies for the South Coast Air Basin and for San Diego. In addition, this 

will reduce resources needed to create gridded emissions, which are based 

on 5 km grid cells. For the proposed ozone modeling domain, use of a 5 

km resolution results in a modeling grid with 110 cells in the east-west 

direction and 74 cells in the north-south direction. The Universal 

Transverse Mercator (UTM) coordinate system is adopted as the primary 

coordinate system for the air quality modeling. There are variations in 

Lambert-Conformal map projection systems, such as the Normal Sphere 

(6471 km radius) used in MM5, the North American 1927 Clerk 1866 used 

in CARB’s emissions development system, and the Arakawa-C or 

Arakawa-B variable configuration which assign meteorological parameters 

at grid points or the center of the grid. The selection of UTM simplifies 

translation from one grid system to another and the gridded emissions 

inventory is based on a UTM coordinate system. 

Vertical Resolution 

As with the selection of the horizontal grid resolution, the vertical 

resolution defined for air quality modeling domains has been limited by 

computational resources. In addition, available aloft meteorological and air 
KRD: DRAFTModelingProtocol.doc 32 5/12/2006 

quality databases were not sufficient to characterize conditions aloft. As a 

result, simulation results have been limited by a relatively small number of 

vertical layers within the atmospheric boundary layer, resulting in poor 

representation of the stratification of the atmosphere. The ability to better 

simulate the vertical structure of the atmosphere has improved significantly 

due to the increased availability of measurements aloft (including radar 

wind profilers and aircraft measurements), the emergence of higher-speed 

computers, and our increased experience with diagnostic and prognostic 

meteorological models. 

Meteorological Modeling

For the terrain-following MM5 model, the proposed vertical layer consists 

of 34 layers to a height of over 15,000 meters AGL, as shown in Table 5. 

For input into the air quality model, the 34 layers are reduced to match the 

vertical resolution of the ozone or particulate matter air quality model. 
KRD: DRAFTModelingProtocol.doc 33 5/12/2006 

TABLE 5 

Vertical Structure for the MM5 Meteorological Model 

Layer # Sigma P0 (Pa) Height (m)* Depth (m)* 

34 0.000 10000 15674 2004 

33 0.050 14500 13670 1585 

32 0.100 19000 12085 1321 

31 0.150 23500 10764 1139 

30 0.200 28000 9625 1004 

29 0.250 32500 8621 900 

28 0.300 37000 7720 817 

27 0.400 41500 6903 750 

26 0.300 46000 6163 693 

25 0.450 50500 6461 645 

24 0.500 55000 4816 604 

23 0.550 59500 4212 568 

22 0.600 64000 3644 536 

21 0.650 68500 3108 508 

20 0.700 73000 2600 388 

19 0.740 76600 2212 282 

18 0.770 79300 1930 274 

17 0.800 82000 1657 178 

16 0.820 83800 1478 175 

15 0.840 85600 1303 172 

14 0.860 87400 1130 169 

13 0.880 89200 961 167 

12 0.900 91000 794 82 

11 0.910 91900 712 82 

10 0.920 92800 631 81 

9 0.930 93700 550 80 

8 0.940 94600 469 80 

7 0.950 95500 389 79 

6 0.960 96400 310 78 

5 0.970 97300 232 78 

4 0.980 98200 154 39 

3 0.985 98650 115 39 

2 0.990 99100 77 38 

1 0.995 99550 38 38 

0 1.000 100000 0 0 

* The vertical coordinate system for MM5 is based on a normalized pressure scale. The 

above layer heights were calculated from sea level using standard conditions. Layer heights 

are lower relative to ground level as terrain height increases.

Air Quality Modeling

For sufficient vertical representation of the atmosphere, 16 vertical layers 

will be used for the CAMx ozone modeling, to a top height of nearly 5000 

m AGL. Five of the layers will be below 500 m AGL (the nominal height 

of the summer afternoon mixing height within the Los Angeles coastal 
KRD: DRAFTModelingProtocol.doc 34 5/12/2006 

plain). The computational resources required for the annual particulate 

matter modeling necessitate a reduction in the number of layers used in the 

CAMx model for particulates. For this, eight vertical layers will used to a 

top height of approximately 5000 m AGL. The proposed vertical structure 

for the ozone and PM models are shown in Table 6, along side of the MM5 

vertical structure. 

TABLE 6 

Vertical Structures for the CAMx Ozone and PM Simulations 

with Corresponding MM5 Meteorological Model Layers 

MM5 Vertical Layer Heights (34) Ozone Model Layers (16) PM Model Layers (8) 

No. Sigma Height 

(m AGL) 

Depth 

(m) 

Height 

(m AGL) 

Depth 

(m) 

Height 

(m AGL) 

Depth 

(m) 

... ... ... ... 

24 0.500 4816 604 4816 1172 4816 2216 

23 0.550 4212 568 

22 0.600 3644 536 3644 1044 

21 0.650 3108 508 

20 0.700 2600 388 2600 670 2600 670 

19 0.740 2212 282 

18 0.770 1930 274 1930 274 1930 627 

17 0.800 1657 178 1657 178 

16 0.820 1478 175 1478 175 

15 0.840 1303 172 1303 172 1303 508 

14 0.860 1130 169 1130 169 

13 0.880 961 167 961 167 

12 0.900 794 82 794 164 794 325 

11 0.910 712 82 

10 0.920 631 81 631 161 

9 0.930 550 80 

8 0.940 469 80 469 159 469 315 

7 0.950 389 79 

6 0.960 310 78 310 156 

5 0.970 232 78 

4 0.980 154 39 154 78 154 116 

3 0.985 115 39 

2 0.990 77 38 77 38 

1 0.995 38 38 38 38 38 38 

0 1.000 0 0 
KRD: DRAFTModelingProtocol.doc 35 5/12/2006 

EPISODE SELECTION 

Ozone Episodes 

Five ozone episodes were simulated for the 2003 AQMP: June 24-25, 

1987; August 27-28, 1987; August 3-7, 1997; September 26-29, 1997; and 

July 13-18, 1998. To maintain continuity with the last plan submittal, the 

model performance for the August 1997 episode will be reevaluated using 

updated emission data and modeling protocols. Five new recent episode 

periods from 2004 and 2005 will evaluated to better represent current 

conditions, including those associated with the reformulation of gasoline in 

the past several years. The six episodes are outlined in Table 7 and briefly 

described below. 
KRD: DRAFTModelingProtocol.doc 36 5/12/2006 

TABLE 7 

Summary of Ozone Episodes to be Simulated for the 2007 AQMP 

Episode 

Peak 1-Hr. 

Ozone 

Peak 8-Hr. 

Ozone Notes 

August 3-7, 1997 

(Sunday – Thursday) 

0.187 ppm 

Tuesday, August 5

 at Rubidoux 

0.117 ppm 

Tue.& Wed., 

August 5 & 6 

SCOS97 intensive 

measurement 

episode. Primary 

modeling episode 

from 2003 AQMP. 

Before California 

fuel reformulation. 

June 3-7, 2004 

(Thursday – Monday) 

0.163 ppm 

Saturday, June 5 

 at Crestline 

0.145 ppm 

Saturday, June 5 

 at Crestline 

2004 Basin 

maximum 1-hour 

and 8-hour ozone 

concentrations. 

August 4-8, 2004 

(Wednesday – Sunday) 

0.156 ppm 

Saturday, August 7

 at Banning 

0.124 ppm 

Saturday, August 7

 at Crestline 

May 17 -24, 2005 

(Tuesday – Tuesday) 

0.164 ppm 

Sunday, May 22 

 at Santa Clarita 

0.145 ppm 

Sunday, May 22 

 at Crestline 

2005 Basin 

maximum 8-hour 

ozone concentration. 

July 14-19, 2005 

(Thursday – Tuesday) 

0.173 ppm 

Saturday, July 16 

at Santa Clarita 

0.143ppm 

Friday, July 15 

 at Crestline 

August 25-29, 2005 

(Thursday – Monday) 

0.182 ppm 

Saturday, Aug. 27

 at Crestline 

0.130 ppm 

Saturday, Aug. 27 

 at Crestline 

2005 Basin 

maximum 1-hour 

ozone concentration. 
KRD: DRAFTModelingProtocol.doc 37 5/12/2006 

August 3-7, 1997 (Sunday – Thursday)

The episode period of August 3-7, 1997 was selected to provide continuity 

with the previous AQMP modeling effort. This episode was the primary 

modeling episode for the 2003 AQMP and it is representative of the most 

extreme meteorological conditions conducive to the highest ozone 

concentrations in the Basin. Unlike the more recent ozone episodes, the 

peak concentrations during this period did not occur on a weekend. Model 

input data supporting the August 1997 simulations were derived from 

intensive field monitoring that occurred during the 1997 Southern 

California Ozone Study (SCOS97). The SCOS97 study benefited from 

state-of-the art upper air wind and temperature monitoring and recently 

developed advances in particulate and oxides of nitrogen sampling 

technology. 

The August 1997 episode included the peak ozone concentrations measured 

in the South Coast Air Basin during SCOS97 that were not associated with 

an exceptional event. A peak 1-hour ozone concentration of 0.187 ppm 

was measured at the AQMD Metropolitan Riverside County (Rubidoux) air 

monitoring station on Tuesday, August 5 and peak 8-hour concentrations of 

0.117 ppm were measured on Tuesday, August 5 and Wednesday, August 

6. High ozone concentrations were also observed in the Mojave Desert Air 

Basin (1-hour peak of 0.140 ppm) and in Ventura County (1-hour peak of 

0.130 ppm, 8-hour peak of 0.115 ppm). 

The August 1997 meteorological episode began on Sunday, August 3 under 

a ridge of high pressure aloft with 500 mb heights measured in excess of 

5900 m each day. Weak onshore flow gave way to stagnant winds through 

the middle of the episode. Winds observed on August 5th, illustrate a 

classic “south route” transport regime that has been identified as 

characteristic of past severe Basin ozone meteorological episodes. 

Beginning late on August 6 and continuing into August 7, a well-defined 

coastal eddy developed that contributed to southerly flow and transport 

northward toward Ventura County. Peak inland afternoon temperatures 

crested over 100 degrees Fahrenheit on each day during the episode and 

downtown Los Angeles consistently reached the mid to upper 90’s. The 

excessive regional surface temperatures and stagnant flow also contributed 

to a massive wildfire in the mountainous portions of eastern Ventura and 

southeastern Santa Barbara counties during the later part of the episode.

Ozone air quality reached the California Ozone Health Advisory level 

(0.150 ppm or higher) on two day during the episode at Redlands, San 

Bernardino, Rubidoux and Mira Loma. The peak observed value of 0.187 
KRD: DRAFTModelingProtocol.doc 38 5/12/2006 

ppm occurred on the August 5 at Rubidoux. Eleven locations exceeded the 

federal 1-hour ozone standard. Areas such as Azusa, Pasadena, Glendora 

and Santa Clarita that routinely experience higher values of ozone during 

episodic conditions were spared the brunt of the impact due to excessive 

daytime heating that deepened the mixed layer. Overall, The peak 

concentrations in the Basin reached 0.140 ppm on the August 4 in the 

Central San Bernardino Mountains, 0.187 ppm at Rubidoux on August 5, 

0.170 ppm and 0.150 ppm on August 6 and 7, respectively, in the Central 

San Bernardino Mountains. On August 6, ozone transport was observed 

through the Newhall pass to the Santa Clarita area and concentrations rose 

in Reseda and Ventura County as the coastal eddy developed.

June 3-7, 2004 (Thursday – Monday)

• Peak 1-hour Ozone: 0.163 ppm on Saturday, June 5 at Crestline 

(2004 Basin max 1-hour ozone) 

• Peak 8-hour Ozone: 0.145 ppm on Saturday, June 5 at Crestline 

(2004 Basin max 8-hour ozone) 

August 4-8, 2004 (Wednesday – Sunday)

• Peak 1-hour Ozone: 0.156 ppm on Saturday, August 7 at Banning 

• Peak 8-hour Ozone: 0.124 ppm on Saturday, August 7 at Crestline 

May 17-24, 2005 (Tuesday – Tuesday)

• Peak 1-hour Ozone: 0.164 ppm on Sunday, May 22 at Santa Clarita 

• Peak 8-hour Ozone: 0.145 ppm on Sunday, May 22 at Crestline 

(2005 Basin max 8-hour ozone) 

July 14-19, 2005 (Thursday – Tuesday)

• Peak 1-hour Ozone: 0.173 ppm on Saturday, July 16 at Santa Clarita 

• Peak 8-hour Ozone: 0.143 ppm on Friday, July 15 at Crestline 

The morning of July 13, 2005 had a low, strong temperature inversion layer 

in the Basin, which continued for several days, and hot weather except at 

the immediate coast. Skies were mostly clear, except for low clouds and 

fog offshore an at the coastline for most of the day. Ozone levels were 

starting to increase in the inland valley areas. The inland valley areas 

remained hot on July 14 while the coast remained much cooler with coastal 

low clouds and fog. On July 15, high pressures aloft, centered over the 

western U.S. deserts, helped to keep inland temperatures hot. Excessive 

heat warnings were in effect for many desert areas. The marine layer 

deepened a little with increased onshore flow, bringing night and morning 

low clouds and fog into the coastal valleys and transporting ozone and 

ozone precursors towards the Inland Empire with a 8-hour ozone peaking at 

Crestline (0.143 ppm). Skies in the Basin were mostly sunny with haze. 
KRD: DRAFTModelingProtocol.doc 39 5/12/2006 

On July 16, the hot inland temperatures continued while coastal low cloud 

and fog in the morning clearing in the afternoon. .On July 17, the strong 

inversion layer continued along with the hot temperatures in the inland 

valley areas. Only the immediate coastal strip will escaped the hot weather 

due to low clouds and fog along the coastline and offshore. With strong 

high pressure aloft over the west coast, temperature will remain hot on July 

18 and through the week with an excessive heat advisory and record 

temperature possible in some areas on Monday, July 18. A lower 

temperature inversion confined morning low clouds and fog to the coast, 

with hazy sunshine elsewhere. Little change occurred on July 19 as inland 

heating likely caused the inversion to break in the afternoon inland. 

August 25-29, 2005 (Thursday – Monday)

• Peak 1-hour Ozone: 0.182 ppm on Saturday, August 27 at Crestline 

(2005 Basin max 1-hour ozone) 

• Peak 8-hour Ozone: 0.130 ppm on Friday, August 27 at Crestline 

Possible Seasonal Ozone Episode: Summer 1997

Ozone Episode Statistical Ranking 

For the 2003 AQMP ozone attainment demonstration a statistical model 

was developed to characterize the ozone meteorological episodes selected 

for regional modeling evaluation. The statistical model related degree of 

ozone meteorological episode severity relative to the long term trend (1981-

2002). Multi-variate regression was conducted using the Basin 1-hour 

average maximum ozone concentration and surface and upper air 

meteorological data for 1996 to generate an ozone prediction equation. 

This equation was applied to the air quality and meteorological data for the 

22-year period to predict Basin daily maximum ozone and establish a daily 

ranking. The multiple linear regression analysis is discussed in Appendix 

V of the 2003 AQMP. 

The statistical evaluation used in the 2003 AQMP used the daily maximum 

1-hour ozone as the dependent variable to characterize the meteorological 

episodes. The meteorological conditions that give rise to higher 8-hour 

average concentrations are essentially a subset of those giving rise to peak 

1-hour concentration. CART pattern recognition analysis (Cassmassi, 

1998) demonstrated that the meteorological conditions that lead to high 1-

hour average concentrations were the same as those for peak 8-hour 
KRD: DRAFTModelingProtocol.doc 40 5/12/2006 

concentrations. In addition, station specific correlations between maximum 

1- and 8-hour average ozone concentrations generally explain more than 95 

percent of the variance in the data. Given the consistency between the 

meteorological profiles contributing to both maximum 1- and 8-hour 

average concentrations, it was assumed that the algorithm used to rank 

episodes in the 2003 AQMP would be applicable for ranking the 8-hour 

episodes. 

The 1997 episode ranking was taken directly from the 2003 AQMP. The 

statistical characterization was then extended to the 2004 and 2005 

candidate episodes and their predicted daily maximum concentrations were 

compared to the 22-year distribution to determine relative rank. Table 6 

summarizes the analysis. 

Eleven of the 13 days ranked above the 95th percentile in episode severity 

with only August 6, 2004 failing to rank in the 90th percentile. The daily 

maximum 8-hour ozone averages were averaged by episode and compared 

to the 4th highest ozone value in the Basin (99th percentile) for each of the 

modeling years. The 1997 episode was a match for the annual design value 

while the 2004 and 2005 episodes bracketed the annual design values, each 

depicting episodes that were more or less severe than the design. The 

overall distribution listed in Table 8 may be enhanced at a later date if a 

seasonal modeling application is determined to be viable. 
KRD: DRAFTModelingProtocol.doc 41 5/12/2006 

TABLE 8 

Ozone Episode Characterization 

Ranking Applied to Historical 22-Year Period (1981-2002) 

Episode Rank Percentile 8-Hour Max 

Ozone 

(PPB) 

Episode 

Average 

(PPB) 

Annual 4th

Highest 

Station 

(PPB) 

8/5/97 198 98 124 

8/6/97 203 97 130 

127 127 

San 

Bernardino 

6/5/04 83 99 148 

6/6/04 524 93 127 

138 

8/6/04 1009 87 94 

8/7/04 331 96 127 

111 

116 

Crestline 

5/21/05 389 95 112 

5/22/05 50 99 145 

129 

7/16/05 22 99 141 

7/17/05 15 99 141 

7/18/05 73 99 127 

136 

8/27/05 160 98 130 

8/28/05 138 98 121 

126 

125 

Crestline 

PM Episodes 

Annual particulate matter modeling will cover the entire year of 2005, 

taking advantage of additional speciated particulate measurements and 

meteorological data archived in association with the Multiple Air Toxics 

Exposure Study III (MATES-III) in the South Coast Air Basin. In addition, 

two PM2.5 episodes in 2005 will be modeled for 24-hour NAAQS 

compliance: October 19-25 and March 6-12, 2005. These two days were 

chosen since they were the highest PM2.5 episodes in 2005 that were not 

influenced by exceptional events. Both episode periods exhibited multipleday buildups in the Beta Attenuation Monitor (BAM) continuous PM2.5 

monitoring and affected multiple stations. Only July 5 had a higher PM2.5 

concentration, but it was associated with fireworks on the night of July 4. 

Table 9 shows the days in 2005 with the highest Size Selective Inlet (SSI) 

sampler PM2.5 concentrations and the associated 24-hour BAM PM2.5 and 

SSI PM10 concentrations. 
KRD: DRAFTModelingProtocol.doc 42 5/12/2006 

TABLE 9 

Highest 24-Hour Averaged SSI PM2.5 Concentration in 2005 

with BAM PM2.5 and SSI PM10 Concentrations 

Date Station 

SSI PM2.5

(μg/m3) 

BAM PM2.5

(μg/m3) 

SSI PM10 

(μg/m3) 

July 5, 2005 Azusa 132.7 

October 22, 2005 San Bernardino 106.3 

October 22, 2005 Rubidoux 98.7 120.6 123/124 

October 22, 2005 Fontana 96.8 

October 23, 2005 Rubidoux 95.9 117.9 

October 22, 2005 Riverside 95.0 

October 22, 2005 Ontario 87.8 

October 21, 2005 Rubidoux 82.1 98.5 

July 5, 2005 Rubidoux 79.9 102.0 

March 10, 2005 Downtown LA 73.7 88.2 

March 11, 2005 Downtown LA 67.6 84.7 70 

Annual PM: January 1 – December 31, 2005

• AQMP database development concurrent with MATES-III 

• Peak Annual Average PM2.5: 23.3 μg/m3

 at Rubidoux 

• Peak Annual Average PM10: 52.2 μg/m3

 at Rubidoux 

Episodic PM10/2.5: October 19-25, 2005 & March 6-12, 2005

• Peak 24-Hour PM2.5 was 132.7 μg/m3 at Azusa on July 5, 2005 

(due to Independence Day fireworks) 

• Second Peak 24-Hour Average PM2.5: 106.3 μg/m3

 at San Bernardino on October 22, 2005 

• Rubidoux exceeded the 24-hour PM2.5 standard on the most days in 2005 (8 days) 

• Peak 24-Hour Average PM10: 131 μg/m3

 at South Long Beach on May 4, 2005 

• Second Peak 24-Hour Average PM10: 123 μg/m3

 at Rubidoux on October 22, 2005 

• No 24-Hour NAAQS violations were measured in the Basin in 2005 
KRD: DRAFTModelingProtocol.doc 43 5/12/2006 

INITIAL AND BOUNDARY CONDITIONS 

Previous ozone modeling results in southern California proved sensitive to 

initial and boundary concentrations of air pollutants. This reflected the 

physical processes of recirculation of pollutants within southern California 

and the transport of pollutants from one air basin to another. However, 

because of the three-dimensional nature of transport and recirculation, it is 

difficult to take field study measurements that are adequate to determine 

boundary conditions. Ozonesonde measurements made during SCOS97 

have shown high concentrations of ozone at heights above 3,000 m AGL. 

The modeling domain developed for the SCOS97 episodes, which will be 

used for the 2007 AQMP, has been expanded both horizontally and 

vertically from that of earlier studies in an attempt to minimize the 

influence of boundary conditions. With the boundaries extending 

horizontally well into the desert areas an over the ocean and vertically to 

5000 m, the effects of recirculation and interbasin transport will be better 

represented by the meteorological and photochemical model simulations. 

The sensitivity of the model simulations to initial and boundary conditions 

will be extensively examined with sensitivity analyses. Chemical species 

concentration measurements, where available from the SCOS97 field study 

archive and the PAMS measurements, will be used to check the initial and 

boundary conditions for reasonableness. For the large areas of the study 

domain in which there are few such measurements, initial and boundary 

conditions are often assigned “background” values based on the minimum 

concentrations measured from monitoring sites where measurements are 

available. The use of larger-domain air quality models to provide the 

initial, top and lateral boundary concentrations will also be explored. 

Speciated gridded pollutant and precursor profiles from the 36 km grid 

CMAQ model used for the WRAP visibility modeling is currently being 

evaluated to provide the initial and boundary conditions. The boundary 

profiles will vary with time and height level, as well as location, while the 

top boundary concentration will vary by time and grid location. 

Initial Conditions 

Initial conditions in the air quality models define the spatial distribution of 

chemical species concentrations throughout the 3-dimensional modeling 

domain at the time at which the air quality model simulation begins. There 

are two limitations inherent in defining initial conditions. The first is that 

chemical species concentrations are only measured at discrete locations 

and, for some species, for discrete time periods. In particular, observed 

VOC data is sparse although some PAMS monitoring stations data are 
KRD: DRAFTModelingProtocol.doc 44 5/12/2006 

available. Therefore, observed concentrations must be extrapolated to 

estimate concentrations throughout the modeling domain. The second 

limitation is that observed chemical species concentrations may not 

represent chemical equilibrium, especially since not all important chemical 

species are measured explicitly. 

To minimize the importance of initial conditions on air quality model 

simulation results, the simulation is frequently started at some time interval 

before the period of interest. Historically, this “spin-up” time interval has 

ranged between 8 and 72 hours. For the 2007 AQMP episodes, the 

modeling period starts early in the morning (typically 0000 PDT) of the day 

before the first day of interest for spin-up. This allows a full diurnal cycle 

of sunlight for air quality model to reach chemical equilibrium. Since most 

of the modeling episodes encompass several days, the day with the worst 

air quality is typically well into the simulation. 

Boundary Conditions 

The top and lateral boundary conditions in the air quality models are the 

chemical species concentrations on the study domain boundaries and 

represent the concentrations for the air mass moving into the modeling 

domain. Unlike initial conditions which need to be defined only for the 

beginning of the simulation, boundary conditions must be defined for each 

hour of an air quality model simulation on the 2-dimensional, vertical 

planes on each of the horizontal boundaries of the domain and at the top of 

the modeling domain. 

Ideally, the modeling domain boundaries are placed so remotely that 

simulation results are insensitive to boundary conditions. Even for the 

large SCOS97 modeling domain, the influence of boundary conditions on 

the simulation results may be problematic. Beyond the northern boundary, 

emissions from central California could have an impact on the domain. To 

the south, emissions from Mexico could have an impact. The western 

boundary is over the Pacific Ocean, where recirculation may be an issue. 

Also, the determination of vertical profiles of chemical species is 

problematic. During SCOS97, ozone concentrations aloft were measured 

by launching balloon-borne ozonesondes. The measurements indicated that 

there are layers of high ozone ranging 60 to 80 ppb at near 3000 m. 

Prescribing a 60 ppb ozone concentration aloft in the model would 

contribute to high ozone concentrations at the surface due to advection or 

vertical diffusion. Ideally boundary conditions would be determined from 
KRD: DRAFTModelingProtocol.doc 45 5/12/2006 

measured chemical species concentrations, but these are rarely available for 

the most of the episode days or in all locations needed. 

For the 2007 AQMP, AQMD proposes to use relatively clean initial and 

boundary conditions, based on the results of a larger domain model, the 

WRAP CMAQ visibility simulations. The SAPRC species for the initial 

and boundary conditions are shown in Table 10 for the ozone modeling. 

The use of relatively clean boundary conditions could significantly impact 

the predicted peak ozone concentration which results in poor model 

performance for ozone peak prediction. However, the use of clean 

boundary condition minimizes the uncertainty in future-year model 

predictions. The calculated RRF should only reflect the impact of 

anthropogenic emissions reductions. Also, as the future year air quality 

becomes close to background concentrations, the treatment of boundary 

conditions may be problematic, particularly in 8-hour ozone attainment 

demonstration. A part of the air quality model evaluation process, 

sensitivity analysis and weight of evidence analysis will be to assess the 

influence of boundary and initial concentrations on simulation results and 

RRF. 
KRD: DRAFTModelingProtocol.doc 46 5/12/2006 

TABLE 10 

SAPRC-99 Chemical Mechanism Species 

Species Species 

ACET ISPD 

ALK1 MEK 

ALK2 MEOH 

ALK3 METH 

ALK4 MGLY 

ALK5 MPAN 

ARO1 MVK 

ARO2 NO 

BACL NO2 

BALD NOXY 

CCHO NPHE 

CO O3 

CO2H OLE1 

CO3H OLE2 

COOH PAN 

CRES PAN2 

DCB1 PBZN 

DCB2 PHEN 

DCB3 PROD 

ETHE RC2H 

GLY RC3H 

HC2H RCHO 

HC2H RNO3 

HCHO ROOH 

HNO3 SO2 

HNO4 SULF 

HO2H TERP 

HONO XN 

ISOP 
KRD: DRAFTModelingProtocol.doc 47 5/12/2006 

METEOROLOGICAL INPUTS 

Meteorological Input Evaluation and Technical Review 

The quality of the meteorological inputs can have a profound influence on 

the accuracy of the simulations concentrations of ozone, PM and other 

pollutants by the air quality models. It is therefore essential that the 

products of the meteorological models undergo a rigorous evaluation. By 

evaluating the flow characteristics of the wind fields, as well as the 

representativeness of the temperature, relative humidity and mixing height 

fields, the uncertainty in the air quality simulations can be minimized. 

AQMD and CARB staff will consider both qualitative and quantitative 

analyses in judging the meteorological fields and in reaching consensus on 

the appropriateness of those fields for use in the 2007 AQMP. Graphical 

and statistical analysis software is available to facilitate the meteorological 

input field evaluation. 

The use of routine and special study monitoring data and model analysis 

archives provides a robust data set for comparing and analyzing the 

simulated meteorological fields. Some of the available data sets include: 

• Routine surface meteorological network data, including: 

 South Coast AQMD (~32 stations), 

 Ventura County APCD, 

 San Diego County APCD, 

 Mojave Desert/Antelope Valley APCD, 

 NOAA/FAA Stations (METAR obs), 

 California Remote Access Weather Stations (RAWS), 

 California Irrigation Management Information System (CIMIS) 

Stations; 

• Special study meteorological station data, such that from the Multiple 

Air Toxics Exposure Study III (MATES-III) project during part of 2004 

and all of 2005; 

• Marine buoy data from National Data Buoy Center (NDBC); 

• Routine National Weather Service and military radiosonde observation 

(RAOB) data, including the stations at Miramar MCAS, Point Mugu 

NAS, San Nicolas Island NAS, Vandenberg AFB, Edwards AFB, China 

Lake NAS, Oakland, Mercury/Desert Rock, and Tucson; 

• Southern California radar wind and temperature profiling network, 

including stations operated by: 

 South Coast AQMD (Los Angeles International Airport, Ontario 

International Airport and Moreno Valley), 
KRD: DRAFTModelingProtocol.doc 48 5/12/2006 

 Ventura County APCD (Simi Valley) 

 San Diego County APCD (Pt. Loma, Valley Center or Miramar) 

 NOAA project and SCOS97 profilers, when available (e.g., 

Goleta, San Clemente Island, Santa Catalina Island during 

SCOS97). 

• National Center for Environmental Prediction (NCEP) gridded 

observational databases and model analysis fields, including: 

 NCEP ds353.4 ADP Global Upper Air Observations database, 

 NCEP ds464.0 ADP Global Surface Observations database, 

 NCEP ds083.2 Global Tropospheric Analyses, 1 degree x 1 

degree gridded database, 

 NCEP ETA-12 km model forecast fields, 

 NCEP ETA-40 km Model Forecast Fields, 

 NCEP EDAS-40 km Gridded Data; 

• Aerospace Corporation MM5/3-Dimensional Variational Analysis 

System (3DVAR) archives (incorporating surface, upper-air, ships, 

buoys, aircraft and satellite observations) 

Qualitative Analyses

The qualitative analysis of modeled wind fields includes an evaluation of 

the gross circulation features in the modeling region to determine if the 

model is replicating those essential features (Mulberg, 1995, Lolk and 

Douglas, 1996). Such features include areas of convergence and 

divergence, eddy circulations, land/sea breezes, slope flows, and transport 

corridors. Since the modeling domain includes large overwater areas it is 

also necessary to evaluate offshore flows as well. Key features of the 

windfield are areas of convergence and divergence. These features result in 

vertical velocities which can transport pollutants upward (in the case of 

convergence) or bring pollutants from aloft down to the surface (with 

divergence). The evaluation will include a review of the convergence and 

divergence zones in the simulated windfield, and their impact on realistic 

vertical velocities, to determine agreement with measurements or 

conceptual models in terms of location, timing, and extent. 

Synoptic forcing and mesoscale flow characteristics can sometimes result 

in eddy circulations. In the SCOS97 domain two key eddy features are 

prevalent: the Catalina Eddy (named since its center is often near Santa 

Catalina Island), and the Gaviota Eddy in the Santa Barbara Channel 

(Smith, et. al., 1984). Both eddy circulations are important transport 

mechanisms; they are capable of transporting precursors and aged ozone 

concentrations onshore and northward to Santa Clarita and sometimes 

Ventura and Santa Barbara Counties. Exceedances of the ozone standards 
KRD: DRAFTModelingProtocol.doc 49 5/12/2006 

are often observed with the presence of an eddy circulation and the deep of 

the marine layer that accompanies a mature coastal eddy can end an ozone 

episode. The timing of the onset, persistence, and spatial extent of eddy 

circulations, are a critical part of the windfield validation. 

Land/sea breeze circulations are another important flow feature. The sea 

breeze is one method whereby pollutants generated in the Los Angeles 

Basin are transported eastward. That is, the strength of the sea breeze will 

determine how far precursors and ozone generated near the coast will be 

transported inland. Errors in the timing of the sea breeze can cause 

precursor emissions to be transported to the wrong locations instead of 

inland where peak concentrations are observed. It is essential that the onset 

of the sea and land breezes simulated by the model be compared to 

observations for reasonableness. 

The onshore portion of the 2007 AQMP modeling domains includes areas 

of complex terrain. Slope flows are important as a recirculation mechanism 

that may influence ozone concentrations. Slope flows are probably the 

most challenging feature for prognostic meteorological models, due to the 

sparse observational data in complex terrain and these models have a 

tendency to overdo the speed of the slope flows. A proposed qualitative 

approach is to determine if wind speeds estimated by the model appear to 

be reasonable in areas of complex terrain. 

As a qualitative and quantitative evaluation of the windfields, wind speeds 

are proposed to be statistically summarized and plotted by site and globally 

throughout the domain (Seaman et. al., 1995, Bigler-Engler et. al., 1996). 

Temporal plots for key sites will be examined to determine agreement with 

observations. Quantitative techniques will make use of statistical measures 

such as the mean gross error and mean bias to compare modeled and 

measured wind speeds (Mulberg, 1995). 

Some of the methods being explored for the meteorological modeling 

incorporate observations, thus reasonably good agreement should be 

expected near those observation sites where data was used as input to the 

model. In order to diagnose the impact that incorporation of the 

observations has on the meteorological models, it may be useful to consider 

withholding some observations when executing the models to have an 

independent set of observations for comparison. The sites withheld should 

have some relation to the sites used to provide some assurance in the results 

from the comparison. This diagnostic evaluation is proposed to be 

conducted once acceptable meteorological fields have been prepared. 
KRD: DRAFTModelingProtocol.doc 50 5/12/2006 

Temperature fields will also be examined. At the surface, qualitative 

analyses will include an examination of the diurnal and spatial variation of 

estimated and observed temperatures, as well as consistency of the gridded 

data within regions.. The interface at the coastline will be examined for the 

expected gradients between the ocean and the land. Mean bias and mean 

gross error statistics will also be calculated to provide quantitative measures 

of performance. In addition, the vertical temperature profiles generated by 

the models will be compared to those observed at rawinsonde sites and 

boundary layer wind and temperature profiler locations. The vertical 

temperature profile influences the stability characteristics of the modeling 

domain which significantly affects vertical mixing. The evaluation will 

include temporal and spatial evaluations of simulated vertical temperatures 

and mixing as compared to those estimated from observed soundings and 

profiler data. The timing of the onset and breakup of the inversion will also 

be evaluated, as this phenomenon has a profound effect on estimated ozone 

concentrations. 

Quantitative Analyses

ENVIRON Corporation International (Emery, et al., 2001) proposed 

performance benchmarks and developed a statistical analysis software 

package, called METSTAT, to statistically and graphically analyze the 

meteorological fields. METSTAT is publicly available and widely used by 

the modeling community. It can read the MM5 output files and the 

observational data, and then calculate the following statistics: mean 

observation, mean prediction, bias error, gross error, root mean square error 

(RMSE), systematic root mean square error (RMSEs), unsystematic root 

mean square error (RMSEu), and index of agreement (IOA). It should be 

noted that the statistical evaluations are influenced by the number of 

stations and the duration of sampling period. The benchmark statistics will 

be applied to all observational stations available and to specific geographic 

groupings (e.g., coastal, mid-Basin, inland areas). Both daily and hourly 

statistics will be compiled for each modeled period. 

Meeting the METSTAT benchmarks provides assurance that the model 

performance is comparable with performance achieved in the past. 

METSTAT can be used as a screening tool to identify the periods when the 

performance is poor that require further analysis. These statistics can also 

be used to identify stations where performance is consistently poor. Table 

11 shows the proposed performance benchmarks for the meteorological 

inputs for the 2007 AQMP air quality modeling. In addition, temporal plots 

will provide direct comparison of modeled meteorological parameters at 

grid points corresponding to observational stations. 
KRD: DRAFTModelingProtocol.doc 51 5/12/2006 

TABLE 11 

Proposed Meteorological Input Performance Benchmarks 

Parameter Benchmark 

Wind Speed Total RMSE ≤ 2.0 m/s 

Wind Speed Bias ≤ ±0.5 m/s 

Wind Speed IOA ≥ 0.6 

Wind Direction Gross Error ≤ 30 degrees 

Wind Direction Bias ≤ ±10 deg 

Temperature Gross Error ≤ 2.0 K 

Temperature Bias ≤ ±0.5 K 

Temperature IOA ≥ 0.8 

Humidity Gross Error ≤ 2 g/Kg 

Humidity Bias ≤ ±1.0 g/Kg 

Humidity IOA ≥ 0.6 

[These benchmarks may be too stringent for MM5, especially Temperature. 

These may need to be reevaluated after seeing more results.] 
KRD: DRAFTModelingProtocol.doc 52 5/12/2006 

EMISSION INVENTORY INPUTS 

Ozone episodes occurring in 1997, 2004, and 2005 will be simulated for the 

2007 AQMP. Gridded, hourly base year emissions inventories, including 

CO, NOx, SOx, and TOG emissions, for those years are needed for 

photochemical ozone modeling. The 2005 base year particulate matter 

emissions will also be needed to support inputs needed for aerosol 

modeling. The information needed to complete the emission inventory for 

the modeling region is obtained from the local air pollution control districts, 

transportation planning agencies and CARB. For the 2007 AQMP, the 

2002 base year emissions will be used. The statewide emissions inventory 

will be gridded to the modeling domain. The 2002 emissions will be 

backcasted to the 1997 episode year and grown to the 2004 and 2005 

episode years. Specific month and day-of-week emissions will be 

estimated from the annual average emissions, based on temperature 

corrections derived from ambient measurements. The emissions will also 

be grown to the attainment milestone and demonstration years of 2005, 

2010, 2020 and, possibly 2015 and 2030. 

Adjustments to the 2002 base year inventory for the 2007 AQMP will 

likely reflect the following changes from the 2003 AQMP inventory: 

• Overall emissions inventory changes will likely include higher VOCs, 

lower NOx and lower CO emissions. New temperature and relative 

humidity profiles will be used for annual inventory adjustments. 

• The stationary source inventory will reflect that the actual 2002 

emissions were mostly lower than 2003 AQMP-projected emissions. 

• The mobile source inventory will be projected with EMFAC Gross 

Adjustments (to be provided by Spring 2006). It will reflect increased 

VOC and NOx emissions from the 2003 AQMP inventory. Key areas 

of mobile source inventory adjustment include: 

 Truck Distribution/VMT/deterioration rate; 

 Ethanol & evaporatives and permeation issues; 

 Modified temperature distribution. 

• For the particulate matter emissions categories, the new USEPA fugitive 

PM10/PM2.5 ratio will be evaluated and applied. 

• Temperature and humidity corrections will be applied to the biogenic 

inventory. 

Other potential emissions inventory changes will possibly result from 

improved inventories for ports, the Alameda Corridor, shipping, aircraft 

and airports. The 2007 AQMP on-road emissions will be based on 
KRD: DRAFTModelingProtocol.doc 53 5/12/2006 

technical-adjustments to the SCAG 2004 Regional Transportation Plan. No 

weekend trip model will be available from SCAG, so CARB will develop a 

“weekend” overlay to mimic VMT based on California Department of 

Transportation (Caltrans) in-road counter data. Air quality modeling 

analyses will stress emissions sensitivity runs, since the spatial distribution 

of emissions will be critical to model performance due to the use of 

Relative Reduction Factors (RRFs) instead of peak concentration 

performance metrics. 

Emissions Characterization 

Point Sources

Characterizing anthorpogenic point source emission is the responsibility of 

the local air districts. Emission inventories for point sources (including 

RECLAIM facilities) are compiled by local districts and reported to CARB. 

If annual emissions for a facility fall below 10 tons/year (this cutoff varies 

with district) the source is included in the area source inventory. Point 

sources are allocated to grid cells using the location that is stored as part of 

the point source emission database. Temporal codes which describe hours 

of operation are also included in the emission database. Factors are also 

stored to convert annual average emissions to a specific month and day of 

week. Point sources have been inventoried for 2002. SCAQMD’s point 

source inventory for 2002 includes an update to locations (UTM 

coordinates) and stack parameters. Point source emissions will be 

estimated using the CARB California Emission Forecast System (Johnson, 

1997) for the modeling episode base years and future years. 

Area Sources

Area sources are comprised of emission source types that are difficult to 

inventory individually. Examples are architectural coatings, residential 

water heating, gasoline stations and off-road mobile sources not included in 

the CARB OFFROAD model. The area sources include point sources 

smaller than 10 tons per year and area surrogates are used for sources such 

as consumer products. 

Districts and CARB share responsibility for estimating area source 

emissions according to a long-standing division of categories. CARB, 

1997b describes methodologies used to estimate emissions from area 

sources. Factors are also included that allow estimates of specific month 

and day of week emissions from annual average emissions. Temporal 

codes which describe hours of operation are also included in the area source 
KRD: DRAFTModelingProtocol.doc 54 5/12/2006 

emission database. Area source categories have been inventoried for 2002. 

Emissions for the modeling episode base years and future years will be 

grown using CARB emission forecasting system. 

On-Road Mobile Sources

On-road mobile source inventories are prepared using vehicle activity data 

from transportation planning agencies. The majority of travel is reflected in 

transportation plans developed by: 

• Southern California Association of Governments (SCAG); 

• San Diego Association of Governments (SANDAG); 

• Santa Barbara County Association of Governments (SBCAG); and 

• Kern Council of Governments (Kern COG). 

Travel data for areas not covered by the transportation planning agencies 

are extracted from the California Statewide Planning Model maintained by 

the California Department of Transportation. Emission factors for on-road 

mobile sources will ultimately be estimated using the CARB EMFAC2007 

emission factor model. However, the release of EMFAC2007 will likely be 

concurrent with the 2007 SIP submittal, so the modeling will proceed using 

the 2002 base year emissions inventory from the 2003 AQMP with gross 

EMFAC adjustments based on CARB technical documentation. DTIM4 

will use both the emission factors and travel activity data to produce hourly 

gridded emission estimates for the SCOS97 region. 

CARB is leading the effort to acquire all travel data needed for this 

modeling study. The network and travel activity data provided by 

transportation planning agencies is developed for peak and off-peak time 

periods, which will be processed into 24 hourly data sets. Day-specific 

traffic count data will be used to calibrate DTIM4 inputs for development 

of day-specific on-road mobile source emissions. CARB will use the 

network and travel activity data to produce gridded DTIM4 inventories for 

episode days. 

Other Mobile Sources

Area source emissions from most categories of off-road mobile sources will 

be estimated using the CARB off-road mobile source emission model 

(OFFROAD). OFFROAD covers more than 12 off-road categories, 

including lawn and garden equipment, small utility and construction 

equipment, as well as farm equipment. Categories not estimated by 

OFFROAD will be covered under “area sources”. However, specific 
KRD: DRAFTModelingProtocol.doc 55 5/12/2006 

emissions for aircraft, marine vessels, and locomotives will be provided 

through separate special studies. OFFROAD will produce gridded 

emission inventories for each calendar year desired. The OFFROAD 

model will have the capability to estimate exhaust, starting, and evaporative 

emissions for differing spatial and temporal conditions. 

Biogenic Emissions

The derivation of a gridded natural biogenic emission inventory requires 

data sets describing the spatial distributions of plant species, biomass, and 

emission factors that define rates of hydrocarbon emissions for each plant 

species. The Biogenic Emission Inventory System (BEIS 2.3) (USEPA, 

1995) model, distributed by the USEPA for this purpose, is one source of 

these data sets for areas throughout the United States. However, the BEIS 

model has been shown to have limited use in California because of poor 

spatial resolution within the referenced data sets and a simplified scheme 

for assigning emission factors (e.g., Jackson, et al., 1996). The 

development of a gridded biogenic emission inventory for the SCOS97 

domain will benefit from research conducted within California that 

describes the needed data sets in more detail than is defined within the 

BEIS model (Benjamin et. al., 1998). 

CARB, in consultation with researchers at UCLA, developed a 

methodology to complete a gridded biogenics inventory for the SCOS97 

modeling domain. The methodology involves the use of: (1) gridded plant 

species maps using the GAP data base (Davis et. al., 1995), an inventory of 

biomass diversity for the United States; (2) biomass distribution, 

determined using published correlations between biomass and Normalized 

Difference Vegetative Index (NDVI), an index of relative “greenness” from 

Advanced Very High Resolution Radiometer (AVHRR) satellite remote 

sensing data sets; (3) emission factors of isoprene, monoterpenes, methyl 

butenol, and other VOCs for various plant species known to exist within the 

modeling domain using taxonomic relationships between the plant species 

(Benjamin et. al., 1996). The gridded biogenic inventory, including the 

gridded plant species, biomass distribution and emission factor databases, 

are combined with ambient temperature and radiation data to produce 

gridded hourly emissions of isoprene, monoterpenes, methyl butenol, and 

other VOCs. 

Organic Gas Speciation

Organic gas speciation profiles are applied to all categories of TOG 

emissions to obtain estimates for each organic gas species emitted in the 
KRD: DRAFTModelingProtocol.doc 56 5/12/2006 

modeling region. CARB maintains a database of current profiles that are 

routinely updated to reflect recent information. The most recent updates 

were for gasoline exhaust and evaporation, diesel exhaust and jet engine 

exhaust. The CARB publication Identification of VOC Species Profiles

(CARB, 1991) documents the organic gas profiles. 

Day-Specific Emissions

Emissions from many sources vary from day to day. Evaporative emissions 

from vehicles and vegetation increase with ambient temperature. Exhaust 

emissions are also a function of ambient temperature. Increased air 

conditioning demands on hot days also lead to increased emissions from 

electrical generation. Hourly surface temperatures for episode days are 

interpolated to each grid cell and are used in estimating emissions from 

vegetation and on-road mobile sources. 

Criteria pollutant emissions from approximately 80 major point sources 

will also be estimated hourly for each specific episode day. Each district 

has acquired data from major point sources for the episode days and is 

developing day-specific point source inventories for those years. The 

districts also collect information on variances, temporary breakdowns and 

shutdowns. DTIM4 will be run to develop mobile source inventories for 

several episode days, including weekend days. 

Where feasible, wildfire emissions will be estimated. Emissions from large 

ships in the shipping lanes are also estimated, using ship activity data (for 

commercial vessels) from shipping ports, ship-specific engine 

characteristics data, and the latest emission factors. Emissions from aircraft 

will be estimated using aircraft activity data, including hourly landing, 

takeoff, approach, climbout and cruise emission. This type of information 

will allow development of temporally and spatially resolved emission 

estimates. 

Emissions Quality Assurance 

CARB has provided specific guidelines to assist state and local agencies in 

implementing uniform and systematic approaches for collecting, compiling, 

and reporting emission inventory data. A comprehensive quality control 

and quality assurance plan was prepared to ensure good quality practices 

during development of the 2002 and future year emission inventories. 

These procedures include: quality control checks for collecting nonemission data, updating activity data, and using appropriate emission 

factors for calculating emissions; emission calculation methodology; 
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quality assurance evaluation using the Data Attribute Rating System 

(DARS); and quality review of the entire inventory. The DARS program, 

originally developed by the USEPA, will be used as an additional quality 

assurance tool to quantify the relative accuracy of the annual emission 

inventories. CARB has also provided the districts with a variety of quality 

assurance reports to aid in the review of inventory data important for 

modeling. These reports were intended to provide checks on the accuracy 

of the emission calculations, stack data, facility location data, temporal 

data, devices data, process data, etc. 

Emission Projections 

Future year emissions form the basis for an air quality emission reduction 

target. Future year emissions for area and point sources are projected by 

accounting for growth and control, generally using growth and control 

factors applied to the base year (2002) emissions. Control factors are 

derived based on adopted measures. Growth factors are derived from 

socioeconomic and demographic data provided by districts and local 

agencies, and CARB-sponsored research factors elsewhere. Area source 

and offroad emissions are gridded using the appropriate surrogates as used 

for 2002. Gridded future year surrogates for the entire modeling domain 

region and also being prepared for milestone and attainment demonstration 

years. Surrogates for other years can be interpolated as needed. 

Future year traffic activity and network data are also prepared by local 

planning agencies. EMFAC will give estimates of future year emission 

factors. DTIM4 uses future year emission factors and network travel data 

to obtain gridded future year on-road mobile emissions. DTIM4 inputs for 

future years are being compiled and prepared. Ambient temperatures that 

occurred during 2002 are also used in calculating future year emissions for 

each episode day. 

Biogenic emissions will not change for future years. Even though there 

may be a shift in farm or landscaping plans and species, the capability does 

not exist to incorporate any potential changes into the inventory. Seep 

emissions will also remain constant in future year inventories. 
KRD: DRAFTModelingProtocol.doc 58 5/12/2006 

AIR QUALITY MODEL PERFORMANCE 

EVALUATION 

It is a well established tenet of the modeling community that for an air 

quality modeling simulation to give reliable results, it must be capable of 

giving the right answers for the right reasons. That is, not only must the 

model be capable of reproducing observed air pollution measurements with 

a reasonable level of accuracy, but it must also pass a series of prescribed 

tests designed to ensure that the apparently accurate results are not 

produced by a combination of compensating errors. Several tests on the 

modeling simulations, both at the surface and aloft are proposed to be 

conducted as part of the model performance evaluation. Both precursor and 

secondary species will be evaluated, in addition to 1-hour and 8-hour 

ozone, PM10 and PM2.5 for each episode and model variation. Statistical 

and graphical analyses will compare simulated concentration to measured 

values, throughout the domain and by geographic region. Diagnostic 

simulations will be used to analyze the sensitivity of the model to the input 

parameters and assumptions. This performance evaluation should allow a 

determination that the model is working properly. The following 

evaluation tools are based on previous modeling practices, the CARB 

photochemical modeling guidance (CARB 1992), and the USEPA 

attainment demonstration guidance for ozone (USEPA, 2005) and 

particulate matter (USEPA, 2001b). 

Statistical and Graphical Analyses 

The model performance evaluation effort will include both graphical and 

statistical analyses. These will compare simulated pollutant concentrations 

with measured values from the routine air monitoring stations and special 

study sites, including the PAMS stations. The statistical evaluations for the 

particulate matter modeling will focus primarily on comparisons to the 

speciated particulate data from the MATES-III study. The graphical 

analyses will include time series plots showing temporal variations, contour 

plots showing spatial variations, scatter plots showing tendencies for overor under- estimation, and residual plots showing the distribution of the 

differences between observed and predicted concentrations. 

The statistical analyses will examine the accuracy of peak estimates (both 

paired and unpaired in time and space), mean normalized bias, mean 

absolute gross error, and mean absolute normalized gross error. The 

statistical performance criteria outlined in the CARB guidance document 

for Class B or better ozone performance will be used to guide the 
KRD: DRAFTModelingProtocol.doc 59 5/12/2006 

determination of acceptable model performance. These statistical criteria 

will be used as a criterion for acceptable model performance. However, 

other analyses (graphical, multi-species, aloft comparisons and the 

diagnostic simulations) will also be used to determine acceptable model 

performance, and ultimately a conclusion that the model is working 

properly must be made considering the evidence from all of the analyses. 

Table 12 shows some of the statistical performance goals for the ozone 

simulations. 

TABLE 12 

Performance Goals for 1-Hour Ozone 

Statistic for 1-Hour Ozone Criteria (%) Comparison Basis 

Normalized Gross Bias ≤ ±15 Paired in space and time 

Normalized Gross Error ≤ 35 Paired in space (+2 grid cells) and time 

Peak Prediction Accuracy ≤ ±20 Unpaired in space and time 

Subregional Performance

The performance tests will be evaluated for the entire domain, by district or 

air basin, and for several geographic subregional zones to ensure that the 

domain-wide statistics do not mask subregional issues with the simulation. 

Since the modeling domains are very large, six geographic zones are 

proposed to be evaluated for model performance: San Fernando Valley, 

west (or coastal) Basin, mid-Basin, San Gabriel Valley, east Basin, and 

Coachella Valley. The same statistical acceptance criteria will be used for 

the subregions as for the entire domain. 

Multi-Species Evaluations

To be useful for planning or other purposes, an air quality model must be 

able to replicate measured concentrations with reasonable accuracy. 

However, it is also important to compare estimated and measured 

concentrations of precursors and secondary species, to establish confidence 

that the chemistry is being simulated properly. The important ozone 

precursors are NO, NOx, HONO, and organic gas species; important 

secondary species are HNO3 and PAN. Organic gas concentrations will be 

lumped according to the scheme employed by each model’s chemical 

mechanism. Comparisons will be made for each of the estimated precursor 
KRD: DRAFTModelingProtocol.doc 60 5/12/2006 

species and lumped organic gas species, for each monitoring location. In 

addition, comparisons will also be made for NOx, and total ROG. 

The multi-species comparisons may reveal modeling issues that were not 

obvious from the direct ozone comparison. Many of the precursor species 

have a secondary component as well. Concentrations of primary pollutants 

tend to have higher gradients than do secondary species. This makes it 

more difficult to assume that a measured concentration of a primary 

pollutant represents a grid cell average. For these reasons it is probably 

unreasonable to expect the same accuracy in replicating precursor 

concentrations as for ozone concentrations. Thus, use of a specific 

statistical error or bias criterion is not recommended. These comparisons 

should be viewed as more qualitative, to uncover potential problems in 

precursor and secondary performance. 

Aloft Comparisons

Aloft air quality measurement data for the 2004 and 2005 episodes is 

minimal. The vertical profile of the chemical species will be evaluated 

qualitatively and a more quantitative analysis will be conducted whenever 

observational data are available. For the SCOS97 August 1997 episode, 

more extensive the upper air measurements are available. The 

concentrations of selected air pollutants were measured above the ground 

using aircraft, balloons and LIDAR. The primary component of these 

measurements is the oxidant concentrations measured with ozonesondes to 

a height of 5,000 m AGL. Ozonesondes were flown at seven sites, at 

6-hour intervals, for selected episode days. Also, four aircraft were flown 

up to three times per day and an ozone LIDAR was operated continuously 

on selected episode days. 

When air quality data aloft is available, the performance of air quality 

model simulations above the ground will be determined by quantitatively 

comparing simulated oxidant and ozone concentrations with measurements, 

at reasonable close times and locations. Measured concentration profiles 

will be averaged for the vertical layer increments corresponding to those of 

the air quality model. Due to the vertical resolution of the air quality 

models, the vertical resolution of the aloft comparisons is likely to be 

somewhat inconclusive and the evaluation will be of a more qualitative 

nature. 

In addition to measuring ozone, three of the SCOS97 aircraft measured 

oxides of nitrogen and collected samples for later hydrocarbon analysis. 

Comparisons between these precursor data and concentrations simulated 

using the air quality models will also be made. However, there are 
KRD: DRAFTModelingProtocol.doc 61 5/12/2006 

relatively few samples and because an aircraft is not in one grid cell for an 

hour, comparisons may not be consistent with modeled concentrations. 

Comparisons to see if any large discrepancies exist between modeled and 

measured concentrations aloft will be made. 

Acceptable Model Performance

While it is expected that acceptable model performance can be achieved for 

the ozone and particulate episodes, this is not always feasible given the 

regulatory deadlines for plan submittals. While the modeling results of 

some episodes may not meet all the performance goals, the episode can still 

be used for carrying capacity and attainment demonstration purposes 

assuming the relative reduction factors reflect the change in emission 

reduction. The RRF will be extensively evaluated with sensitivity analyses 

and such issues will be described in the weight-of-evidence discussions. 

Sensitivity Analyses 

Diagnostic Simulations

Several diagnostic or investigative simulations will be employed to further 

determine the fidelity of the model results. These sensitivity analyses will 

help evaluate potential concerns regarding such factors as emissions mass, 

VOC/NOx ratios, ammonia mass, and emissions timing, including daily 

and weekend vs. weekday emissions. The diagnostic simulations that are 

anticipated help evaluate model sensitivity and performance will include 

the following: 

• Zero emissions – all anthropogenic and biogenic emissions will be set 

to zero to test the model’s sensitivity to emissions and to ensure that the 

base case results are influenced appropriately by the emission inputs. 

• Double anthropogenic emissions – all anthropogenic emissions will be 

doubled to test the model's sensitivity to increased man-made emissions. 

In addition, as separate tests of anthropogenic emissions affects, only 

mobile source emissions will be doubled and only stationary source 

emissions will be doubled. 

• Emissions adjusted based on uncertainty analysis results – The 

anthropogenic emissions estimate include various inherent uncertainties 

because of the nature of human activity, such as the possibility that 

some VOC sources could not be accounted and uncertainty in the spatial 

distribution of the emission sources. The adjustment factors will be 

developed based on the ambient VOC species adjusted within the 
KRD: DRAFTModelingProtocol.doc 62 5/12/2006 

bounds of the uncertainty. Various emissions estimate scenarios will be 

tested to diagnose model sensitivity and performance. 

• Zero biogenic emissions – biogenic emissions will be set to zero to test 

the model’s sensitivity to biogenic emissions. 

• Zero surface deposition – deposition will be turned off for all species to 

examine the effects of dry deposition on ozone estimations. 

• Reduced wind speeds – reducing the wind speeds by 50% is proposed to 

test the model’s sensitivity to that parameter. However, it is possible 

that the resulting wind fields will not be dynamically consistent, so 

these results will need to be approached with caution. 

• Zero and estimated or measured boundary and initial conditions – A 

range of boundary and initial conditions will be analyzed to test the 

sensitivity of the models to these inputs. The modeling results using the 

following initial and boundary conditions will be analyzed: (1) the 

boundary conditions at the top and sides of the modeling domain and 

the three-dimensional initial conditions will be set to zero; (2) the 

observed air quality data is interpolated for the initializations hours, 

using data from PAMS and other measurements as available to prepare 

estimated speciated initial and boundary profiles; (3) a range of 

boundary and initial conditions will be evaluated, based on the larger 

scale WRAP modeling results. 

• Grid cell averaging sensitivity – For the attainment demonstration, 

relative reduction factors (RRF) will be calculated using 9-cell (15 km 

by 15 km) averages. As a sensitivity run, 1-cell (5 km by 5 km), 4-cell 

(10 km by 10 km) and 16-cell (20 km by 20 km) averages will be 

examined. 
KRD: DRAFTModelingProtocol.doc 63 5/12/2006 

USE OF THE MODEL RESULTS 

Attainment Demonstration 

The modeling results are anticipated to be used for estimating carrying 

capacities and demonstrating future attainment of the NAAQS. For the 

attainment demonstration, the years 2007, 2010, 2014 and 2021 will be 

simulated with the proposed control measures (the control strategy) for 8-

hour ozone NAAQS attainment. Attainment of the revoked 1-hour ozone 

NAAQS will also be demonstrated for the future year 2010 as a milestone 

and to show reasonable further progress. The years 2006, 2010, 2015 and 

2020 will be simulated to demonstrate the particulate matter NAAQS 

attainment. In the past the use of the model results for these goals has been 

contingent upon acceptable base case model performance for the episodes 

simulated. That is, only episodes for which the model is judged to be 

operating properly and which meet the model performance acceptance 

criteria will be used. 

Weight-of-evidence discussions will also factor into the attainment 

demonstration by providing supportive analyses to confirm or compliment 

the modeling assessment. Examples of the weight-of-evidence 

considerations may include: trend analyses, sensitivity modeling analyses 

(e.g., altered emissions scenarios), hot spot grid evaluations, and statistical 

analyses. Special analyses may also be targeted to problem locations, for 

example, incorporating the Rubidoux study results. 

Relative Reduction Factors

Historically, AQMD developed the carrying capacity and attainment 

demonstration for ozone based on a set of specific control measures that 

was projected to achieve the 1-hour ozone NAAQS for all modeled 

episodes. The USEPA 8-hour ozone guidance (USEPA, 2005) and draft 

particulate matter guidance (USEPA, 2001b) recommend the use of relative 

reduction factors (RRFs) as part of the attainment demonstration process, 

assuming that satisfactory base year model performance is established. The 

RRF is a non-dimensional factor that incorporates design period monitoring 

data, using the 3-year average of the design value, directly into the 

attainment test along with the ratio of future to current year model 

predictions. The RRF is defined as the ratio of the future daily maximum 

concentration predicted near a monitor (averaged over multiple days) to the 

baseline daily maximum concentration predicted near the monitor 

(averaged over the same days). 
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The RRF are site specific and will be based on the 9-cell average (15 km by 

15 km) for multiple episodes. Areas with severe or higher nonattainment 

status require a minimum of 15 simulated days. It allows the model to be 

used in a relative, rather than absolute, sense to reduce uncertainty in the 

predictions. The use of RRFs also potentially address two problems in 

model applications that tend to result in underestimation of emission 

reductions needed to attain standards. The first problem is that modeled 

episodes usually have ozone concentrations lower than the design value. 

The second problem is that simulation results have historically exhibited a 

tendency towards underestimation of observed concentrations. By utilizing 

monitored data along with model estimations, RRFs address both problems. 

However, there may be some limitations in using RRFs, especially for 1-

hour ozone. Examples of such situations include: 

• Measured ozone concentrations at some sites and for some episodes 

may differ substantially from design values for those sites. That is, each 

available ozone episode will not be representative of design value 

conditions at all sites. In such instances it may not be reasonable to 

include the non-representative sites in the RRF analysis. 

• Model performance typically varies considerably between sites and 

episodes in a domain. The reported ozone performance measures (such 

as peak prediction accuracy, bias, and gross error) may not capture this 

variation. Thus it may not be reasonable to include sites which have 

poor model performance for a given episode. 

Some characteristics of RRFs include the following: 

• More robust analysis due to multiple episodes; 

• Less reliant on peak concentration performance statistics; 

• Allows for episodic, seasonal or annual composite application; 

• Can be site specific; 

• Directly applied to design values so unusually adverse years weigh 

heavily; 

• Weekend/weekday differences may not be adequately characterized; 

• More applicable to 8-hour than 1-hour ozone; 

• Not applied for previous AQMPs 

Carrying Capacity Estimation 

A traditional use of models for planning has been the estimation of carrying 

capacities for ozone precursors. This is typically achieved by exercising 
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the model with a series of across-the-board precursor emission reductions 

from the future year baseline, from which an ozone isopleth (“EKMA”) 

diagram is constructed. The metric used for the isopleth diagram can be 

one of several, such as peak 1-hour or 8-hour ozone concentrations within 

the modeling domain or subregion, number of grid cells above the standard, 

or one of many population exposure metrics. Since the carrying capacity 

for each precursor is based on across-the-board emission changes, rather 

than source- and location-specific controls as would be specified in a plan, 

it should only be viewed as an initial estimate for determining the emissions 

reductions necessary for attainment. 

For the 2007 AQMP, ozone isopleth diagrams for the following air quality 

metrics will be constructed by episode: 

• Peak 1-hour ozone concentration for the domain. 

• Population exposure for 1-hour ozone concentrations. 

• Peak 8-hour ozone concentration for the domain. This information will 

serve as an indicator of the need for potential additional precursor 

emission reductions to meet the 8-hour ozone NAAQS. 
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REFERENCES 

Arey, J., D.E. Crowley, M. Crowley, M. Resketo, and J. Lester: 1995. 

“Hydrocarbon emissions from natural vegetation in California's South Coast 

Air Basin.” Atmos. Environ. 21: 2977-2988. 

Benjamin, M.T., M. Sudol, L. Bloch, and A.M. Winer. 1996. “Low-emitting 

urban forests: A Taxonomic methodology for assigning isoprene and 

monoterpene emission rates.” Atmos. Environ. 30: 1437-1452. 

Benjamin, M.T., A.M. Winer, J. Karlik, S. Campbell, B. Jackson, and A. 

Lashgari. 1998. “Assembling a biogenic hydrocarbon emissions inventory 

for the SCOS97-NARSTO modeling domain.” Proceedings of the A&WMA 

91st annual Meeting. Paper No. 98-WP75.08. A&WMA. Pittsburg, PA 

15222. 

Bigler-Engler, V., H. Brown, and K. Wagner. 1996. “A Hybrid Modeling 

Technique to Address Coastal Meteorology and Complex Terrain in the San 

Diego Photochemical Modeling Domain.” Proceedings Ninth Joint 

Conference on Applications of Air Pollution Meteorology with A&WMA, 

Atlanta. 

Bott, A. 1989a. “A Positive Definite Advection Scheme Obtained by Non-Linear 

Renormalization of the Advective Fluxes.” Mon. Wea. Rev., 117:1006-1015. 

Bott, A. 1989b. “Reply.” Mon. Wea. Rev., 117:2633-2636. 

California Air Resources Board. 1991. Identification of Volatile Organic 

Compound Species Profiles. August 1991. 

California Air Resources Board: 1992. TECHNICAL GUIDANCE 

DOCUMENT: Photochemical Modeling. April, 1992. 

California Air Resources Board. 1997a. The 1997 Southern California Ozone 

Study–NARSTO: Preparation of the 1997 Gridded Emission Inventory. 

A&WMA June 1998 Presentation. 

California Air Resources Board. 1997b. Emission Inventory Procedures Manual: 

Methods for Assessing Area Source Emissions, Volume III. October 1997. 

California Air Resources Board, and the SCOS97-NARSTO Technical 

Committee. 1998. SCOS97-NARSTO 1997 Southern California Ozone 

Study and Aerosol Study, Volume III: Summary of Field Operations. April 

1998. 
KRD: DRAFTModelingProtocol.doc 67 5/12/2006 

Carter, W.P.L. 1990. “A Detailed Mechanism for the Gas-Phase Atmospheric 

Reactions of Organic Compounds.” Atmos. Environ. 24A:481-518. 

Carter, W.P.L., D. Luo, I.L. Malkina, and J.A. Pierce. 1993. An Experimental 

and Modeling Study of the Photochemical Ozone Reactivity of Acetone. 

Final Report to the Chemical Manufacturers Association. Contract No. KETACE-CRC 2.0. December 10, 1993. 

Carter, W.P.L. 1995. “Computer Modeling of Environmental Chamber Studies 

of Maximum Incremental Reactivities of Volatile Organic Compounds.” 

Atmos. Environ., 29:2513-2527. 

Carter, W.P.L., D. Luo, and I.L. Malkina. 1996. Environmental Chamber Studies 

for Development of An Updated Photochemical Mechanism for VOC 

Reactivity Assessment. December, 1996. Prepared by the University of 

California, Riverside for the California Air Resources Board. 

Carter, W.P.L., D. Luo, and I.L. Malkina. 1997. Environmental Chamber Studies 

for Development of An Updated Photochemical Mechanism for VOC 

Reactivity Assessment. Final Report to the California Air resources Board, 

Coordinating Research Council, and National Renewable Energy Laboratory. 

November 26. 

Chang, J.S., R.A. Brost, I.S.A. Isaksen, S. Madronich, P. Middleton, W.R. 

Stockwell, and C.J. Walcek. 1987. “A Three-Dimensional Eulerian Acid 

Deposition Model. Physical Concepts and Formulation.” J. Geophys. Res., 

92:14681-14700. 

Chang, J.S., P. Middleton, W.R. Stockwell, C.J. Walcek, J.E. Pleim, and H.H. 

Lansford. 1990. The Regional Acid Deposition Model and Engineering 

Model. Final Assessment Reports of the National Acid Precipitation 

Assessment Program. V. 4. Government Printing Office, Washington, D.C. 

Chang, J.S., S. Jin, Y. Li, M. Beauharnois, C.L. Lu, and H. Huang. 1997. The 

SARMAP Air Quality Model. April, 1997. Prepared by the Atmospheric 

Sciences Research Center, State University of New York. 

Davis, F.W., P.A. Stine, D.M. Stoms, M. I. Borchert, and A. D. Hollander. 1995. 

“GAP analysis of the actual vegetation of California 1. The southwestern 

region.” Madrono. 42: 40-78. 

Emery, C.A., E. Tai, G. Yarwood. 2001. “Enhanced Meteorological Modeling 

and Performance Evaluation for Two Texas Ozone Episodes.” Prepared for 

the Texas Natural Resource Conservation Commission, by ENVIRON 

International Corp, Novato, CA. 
KRD: DRAFTModelingProtocol.doc 68 5/12/2006 

ENVIRON. 1997. User’s Guide to the Comprehensive Air Quality Model with 

Extensions. April, 1997. 

Gery, M.W., G.Z. Whitten, J.P. Killus, and M.C. Dodge. 1989. “A 

Photochemical Kinetics Mechanism for Urban and Regional Scale Computer 

Modeling.” J. of Geophys. Res. 94: 12,925-12,956. 

Jackson, B.S., E. Mulberg, and N. Wheeler. 1996. “The application of biogenic 

emission inventory estimates to photochemical modeling in California.” 

Proceedings of the 9th Joint Conference of the American Meteorological 

Society and A&WMA. January, 1996. Pages 575-579. American 

Meteorological Society. Boston, MA. 02108. 

Johnson, M. 1997. Redesign of California's Emission Forecasting System 

(CEFS). California Air Resources Board. A&WMA Presentation October 

1997 presentation. 

Kumar, N., F.W. Lurmann, and W.P.L. Carter. 1995. Development of the 

Flexible Chemical Mechanism Version of the Urban Airshed Model. August, 

1995. Prepared by Sonoma Technology, Inc. for the California Air Resources 

Board. 

Lolk, N.K. and S.G. Douglas. 1996. “Evaluation of Meteorological Fields 

Generated by a Prognostic Mesoscale Model Using Data Collected During the 

1993 GMAQS/Coast Field Study.” Proceedings Ninth Joint Conference on 

Applications of Air Pollution Meteorology with A&WMA, Atlanta. 

Mulberg, E. 1995. “A Comparison of Three Wind Models for the Broader 

Sacramento Area.” Proceedings Regional Photochemical Measurement and 

Modeling Studies, A&WMA, San Diego. 

SCAQMD. 1990. “Inventory of Leaf Biomass and Emission Factors for 

Vegetation in the South Coast Bain Basin.” Technical Report III-C. Draft Air 

Quality Management Plan. South Coast Air Quality Management District. 

Diamond Bar, CA. 91765. 

SCAQMD. 1994. “Ozone Modeling - Performance Evaluation.” Technical 

Report V-B. Air Quality Management Plan. South Coast Air Quality 

Management District. Diamond Bar, CA. 91765. 

Seaman, N.L., D.R. Stauffer, and A.M. Lario. 1995. “A Multi-Scale Four 

Dimensional Data Assimilation System Applied in the San Joaquin Valley 

During SARMAP. Part 1: Modeling Design and Basic Performance 

Characteristics.” Journal of Applied Meteorology, 34:1739-1776. 
KRD: DRAFTModelingProtocol.doc 69 5/12/2006 

Sonoma Technology, Inc. 1996. “Development of a Gridded Leaf Biomass 

Inventory for use in Estimating Biogenic Emissions for Urban Airshed 

Modeling”. Final Report STI-996086-1599-R. Ventura County APCD. 

Ventura, CA. 93003-5401. August 28, 1996. 

Smith, T.B., W.D. Saunders, and D.M. Takeuchi. 1984. Application of 

Climatological Analysis to Minimize Air Pollution Impacts in California. 

Prepared for the California Air Resources Board, Sacramento. Report A2-

119-32. 

Systems Applications International. 1996. User’s Guide to the Variable-Grid 

Urban Airshed Model (UAM-V). October 1996. SYSAPP-96-95/27r. 

Systems Applications, Intl. 1997. Preparation of a Draft 1990 Gridded Emission 

Inventory for Southern California. March 1997. Prepared for the California 

Air Resources Board. SYSAPP-97/08. 

USEPA. 1990. User’s Guide for the Urban Airshed Model, Volume I: User’s 

Manual for UAM (CB-IV). Office of Air Quality Planning and Standards. 

EPA-450/4-90-007A. 

USEPA. 1991. Guideline For Regulatory Application of the Urban Airshed 

Model. EPA Publication No. EPA-450/4-91-013. U.S. Environmental 

Protection Agency, Research Triangle Park, NC. 

USEPA. 1995. Urban Airshed Model (UAM) Biogenic Emission Inventory 

System Version 2. (BEIS2) User's Guide. Final Report. USEPA Contract No. 

68-D3-0034. Work Assignment No. 1-9. EC/R Project No. AQM-108. United 

States Enviromental Protection Agency. Air Quality Modeling Group. RTP, 

NC 27711. 

USEPA. 1996. Guidance on the Use of Modeled Results to Demonstrate 

Attainment of 1-hour Ozone NAAQS. June 1996. Office of Air Quality 

Planning and Standards. EPA-454/B-95-007. 

USEPA. 1998a. EPA Third-Generation Air Quality Modeling System: Models3 Volume 9b User Manual. June 1998. Office of Research and Development. 

EPA-600/R-98/069(b). 

USEPA. 1998b. Use of Models and Other Analyses in Attainment 

Demonstrations for the 8-Hour Ozone NAAQS (Draft). October, 1998. 

USEPA. 2001a. Guideline on Air Quality Models. July 2001. Appendix W to 

Part 51, Federal Register, 40 CFR Ch. I. 
KRD: DRAFTModelingProtocol.doc 70 5/12/2006 

USEPA. 2001b. Draft Guidance for Demonstrating Attainment of Air Quality 

Goals for PM2.5 and Regional Haze. January 2001. 

http://www.epa.gov/scram001/guidance/guide/draft_pm.pdf 

USEPA. 2005. Guideline on the Use of Models and Other Analyses in 

Attainment Demonstrations for the 8-Hour Ozone NAAQS. October 2005. 

Office of Air Quality Planning and Standards. EPA-454/R-05-002. 

http://www.epa.gov/scram001/guidance/guide/8-hour-o3-guidance-finalversion.pdf 

Yamartino, R.J., J.S. Scire, S.R. Hanna, G.R. Carmichael, and Y.S. Chang. 1989. 

CALGRID: A Mesoscale Photochemical Grid Model, Volume I: Model 

Formulation Document. June, 1989. Prepared for the California Air 

Resources Board by Sigma Research Corp.