### 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 Zhangi 

### 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 Requir ements and Guidance.................................................................. 2 AQMP Ozone Modeling History in the South Coast Air Basin ............................................... 2 AQMP PM Modeling History in th e South Coast Air Basin.................................................... 2 2007 AQMP Modeling An alysis Goals........................................................................................ 2 Ozone Design Value Determination......................................................................................... 2
Overview of the Mo deling Anal ysis............................................................................................. 2 2007 AQMP Schedule............................................................................................................. ..... 2 AQMP Modeling Techni cal Oversight......................................................................................... 2 MODEL SELECTION............................................................................................ 2

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

Background ..................................................................................................................... ......... 2 Previous AQMP Applications .................................................................................................. 2 2007 AQMP Meteorological Modeling A pproach ................................................................... 2 Air Quality Model .............................................................................................................. .......... 2 Background ..................................................................................................................... ......... 2 2007 AQMP Air Quality Modeling Approach ......................................................................... 2 MODELING DOMAIN.......................................................................................... 2

Meteorological Mo deling Do main ............................................................................................... 2
Ozone Modelin g Doma in .......................................................................................................... ... 2
PM Modelin g Domain............................................................................................................. ..... 2 HORIZONTAL AND VERTICAL GRID RESOLUTION ................................... 2

Horizontal Grid Resolu tion ..................................................................................................... ..... 2
Vertical Re solution............................................................................................................ ........... 2 Meteorological Modeling ........................................................................................................ . 2 Air Quality Modeling ........................................................................................................... .... 2 EPISODE SELECTION ......................................................................................... 2 Ozone Episodes................................................................................................................. ........... 2

August 3-7, 1997 (Sun day – Thur sday).................................................................................... 2 June 3-7, 2004 (Thu rsday – Mo nday) ...................................................................................... 2 August 4-8, 2004 (We dnesday – Sunday) ................................................................................ 2 May 17-24, 2005 (Tue sday – Tuesday).................................................................................... 2 July 14-19, 2005 (Thu rsday – Tu esday)................................................................................... 2 August 25-29, 2005 (T hursday – M onday) .............................................................................. 2
Possible Seasonal Ozone Episode: Su mmer 1997................................................................... 2 Ozone Episode Sta tistical Ranking .............................................................................................. 2 PM Epis odes.................................................................................................................... ............. 2

Annual PM: January 1 – December 31, 2005.......................................................................... 2 Episodic PM10/2.5: October 19-2 5, 2005 & Marc h 6-12, 2005 ............................................. 2 INITIAL AND BOUNDARY CONDITIONS ....................................................... 2

Initial Co nditions............................................................................................................. ............. 2 Boundary Co nditions............................................................................................................ ........ 2 METEOROLOGICAL INPUTS............................................................................. 2

Meteorological Input Evaluati on and Techni cal Review ............................................................. 2 Qualitative Analys es........................................................................................................... ...... 2ii Quantitative Analys es.......................................................................................................... ..... 2 EMISSION INVENTORY INPUTS ...................................................................... 2

Emissions Char acteriza tion ..................................................................................................... ..... 2

Point S ources.................................................................................................................. .......... 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 PERF ORMANCE EVALUATION .............................. 2

Statistical and Gr aphical An alyses ............................................................................................. .. 2

Subregional Performance ........................................................................................................ . 2 Multi-Species Evaluations...................................................................................................... .. 2 Aloft Comp arisons .............................................................................................................. ..... 2 Acceptable Model Performance ............................................................................................... 2 Sensitivity Analys es ........................................................................................................... .......... 2

Diagnostic Simulations......................................................................................................... .... 2 USE OF THE MODEL RESULTS......................................................................... 2

Attainment De monstration ....................................................................................................... .... 2
Relative Reducti on Factors..................................................................................................... .. 2 Carrying Capacity Estimation ................................................................................................... ... 2 REFERENCES ....................................................................................................... 2iii 

### LIST OF FIGURES

FIGURE 1 Nested MM5 Domains ................................................................................................. 2
FIGURE 2 The Fine-Scal e (5 km) MM5 Domain.......................................................................... 2 FIGURE 3 2007 AQMP Ozone Modelin g Domain ....................................................................... 2 FIGURE 4 2007 AQMP PM Modeling Doma in, inside the Ozon e Modeling Domain ................. 2



### LIST OF TABLES

TABLE 1 National Ambient Air Quality Standa rds Compliance Status in the South Coast Air
Basin.......................................................................................................................... ............... 2 TABLE 2 Ozone Design Value (ppb ) for Each Statio n, 2000-2004 .............................................. 2 TABLE 3 Summary of Proposed 2007 AQ MP Model Selection and Appli cation ........................ 2
TABLE 4 Tentative 2007 AQMP Schedule................................................................................... 2
TABLE 5 Vertical Structure fo r the MM5 Meteorol ogical Model................................................. 2
TABLE 6 Vertical Structures for the CAMx Ozone and PM Simulations..................................... 2 TABLE 7 Summary of Ozone Episodes to be Simulated fo r the 2007 AQMP.............................. 2 TABLE 8 Ozone Episod e Characteri zation.................................................................................... 2 TABLE 9 Highest 24-Hour Averaged SSI PM2.5 Concentration in 2005..................................... 2
TABLE 10 SAPRC-99 Chem ical Mechanis m Species .................................................................. 2 TABLE 11 Proposed Meteorologi cal Input Performa nce Benchmarks ......................................... 2
TABLE 12 Performance Go als for 1-Hour Ozone......................................................................... 2iv 

### LIST OF ACRONYMS

Several acronyms are used in the modeling protocol document. For convenience, the acrony ms used are listed below to aid the reader.

AAMA – A merican A utomobile M anufacturer's A ssociation

AGL – A bove G round L evel AQMD – A ir Q uality M anagement D istrict AQMP – A ir Q uality M anagement P lan AQMPAG A ir Q uality M anagement P lan A dvisory G roup AUSPEX – A tmospheric U tility S ignatures, P redictions, and

Ex periments

AVHRR – A dvanced V ery H igh R esolution R adiometer

BEIS – B iogenic E mission I nventory S ystem
CAA – C lean A ir A ct Amendment of 1990

CAMx – C omprehensive A ir-Quality M odel with Ex tensions

CARB – C alifornia A ir R esources B oard

CBM – C arbon B ond M echanism

CCAA – C alifornia C lean A ir A ct
CEFS – C alifornia E mission F orecasting S ystem CMAQ – C ommunity M ulti-scale A ir Q uality (model)

CO – C arbon M onoxide

COG – C ouncil o f G overnments
DARS – D ata A ttribute R ating S ystem
DWM – D iagnostic W ind M odel

DTIM – D irect T ravel I mpact M odel

EIWG – E mission I nventory W orking G roup EKMA – E mpirical K inetics M odeling A pproach

EMFAC – Em ission Fac tor (model)

FCM – F lexible C hemical M echanism

FDDA – F our-D imensional D ata A ssimilation

GAP – G eographical A pproach to P rotection of Biological Diversity

GCM – G lobal C limate M odel

ICAPCD – I mperial C ounty A ir P ollution C ontrol D istrict

IOP – I ntensive O peration P eriod

LIDAR – Li ght D etection a nd R anging MDAQMD – M ojave D esert A ir Q uality M anagement D istrict

MM5 – M esoscale Meteorological M odel (5 th generation)

MWG – M odeling W orking G roup NAAQS – N ational A mbient A ir Q uality S tandard

NDVI – N ormalized Difference V egetative I ndexv NOx – N itrogen Ox ides
NO2 – N itrogen D ioxide

PDT – P acific D aylight T ime

PM – P articulate M atter
PM10 – P articulate M atter less than 10 microns equivalent aerodynamic diameter

PM2.5 – P articulate M atter less than 2.5 microns equivalent aerodynamic diameter

PMcourse – PM10 – PM2.5 PMfine – PM2.5

RADM – R egional A cid D eposition M odel

RECLAIM – Re gional C lean A ir I ncentives M arket

ROG – R eactive O rganic G ases RRF – R elative R eduction F actor
SANDAG – San D iego A ssociation of G overnments
SAPRC – S tate A ir P ollution R esearch C enter SAQM – S ARMAP A ir Q uality M odel

SARMAP – S JVAQS/A USPEX R egional M odel A daptation P roject

SBCAG – S anta B arbara C ounty A ssociation of G overnments
SBCAPCD – S anta B arbara C ounty A ir P ollution C ontrol D istrict

SCAB – S outh C oast A ir B asin

SCAG – S outhern C alifornia A ssociation of G overnments SCAQMD – S outh C oast A ir Q uality M anagement D istrict SCAQS – S outhern C alifornia A ir Q uality S tudy

SCE – S outhern C alifornia E dison

SCOS97 – S outhern C alifornia O zone S tudy (1997 )

SDCAPCD – S an D iego C ounty A ir P ollution C ontrol D istrict

SIP – S tate I mplementation P lan SJVAQS – S an J oaquin V alley A ir Q uality S tudy

SOx – S ulfur Ox ides

STMPRAG – S cientific, T echnical, M odeling and P eer R eview

A dvisory G roup TOG – T otal O rganic G ases UAM – U rban A irshed M odel

USEPA – U nited S tates E nvironmental P rotection A gency UTM – U niversal T ransverse M ercator
VCAPCD – V entura C ounty A ir P ollution C ontrol D istrict

VMT – V ehicle M iles T raveled

VOC – V olatile O rganic C ompound

WSPA – W estern S tates P etroleum A ssociationKRD: DRAFTModelingProtocol.doc 5/12/2006 1 

### INTRODUCTION

The federal Clean Air Act (CAA) requires areas with unhealthy levels of ozone , carbon monoxide, nitrogen dioxi de, sulfur dioxide, and inhalable particulate matter to develop plans, known as State Implementation Plans (SIPs), describing how they will a ttain national ambient air quality standards (NAAQS). The 2007 Air Quality Management Plan (AQMP or Plan) for the South Coast Air Basi n (Basin) will meet the SIP update requirements for this area, demonstra ting 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 e ffort between the staff of the South Coast Air Quality Management Dist rict (AQMD or District) and the California Air Resources Board (CARB ), with technical oversight from the Scientific, Technical, Modeling a nd 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 vertic al grid resolution;

• the annual PM period and ozone a nd 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 th e 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 dur ing the AQMP modeling process.

In order to devote the maximum resources practicable to the development of the District's 2007 AQMP, the Ex ecutive 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 te chnical staffs of both agencies are working closely together to plan and carry out th e necessary work for theKRD: 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 wo rk such that the product will be mutually acceptable modeling analys es 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 resp onse to new federal regulations, SIPs must also address ways to improv e 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 m odeling 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 attain ment 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 Attain ment of 1-hour Ozone NAAQS (USEPA, 1996) to assist states in preparing th e attainment demonstration required by the CAA. In addition, the CARB Technical Guidan ce Document: Photochemical Modeling (CARB, 1992) provides photochemical modeling guidance for use by the districts to en sure 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 m odeling protocol utilizes
the Draft Guidance for Demonstrating Atta inment of Air Quality Goals for PM2.5 and Regional Haze (USEPA, 2001b).KRD: DRAFTModelingProtocol.doc 5/12/2006 3 Under the federal Clean Air Act of 1990 (CAA), the South Coast Air Basin (Basin) was classified as an “ext reme” 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 de monstrate attainment of the NAAQS
for ambient 1-hour ozone of 0.12 5 parts per million (ppm) by December 2010. AQMD satisfied that CAA re quirement with the submittal of the 1994 AQMP in September 1994. A s ubsequent 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 Ozon e Study (SCOS97) and emissions inventory enhancements. It updated the attainment demonstration for the ozone and PM10 particulate matter NAA QS, 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, th e USEPA established new oz one NAAQS of 0.085 ppm based on an 8-hour average measurem ent. Due to legal challenges, the final form of the ozone NAAQS has be en implemented in two phases. The Phase 1 ozone implementation rule, fina lized on June 15, 2004, defined the
classification scheme for 8-hour ozone nonattainment areas and revoked the 1-hour ozone NAAQS, while requiring st ates to maintain control programs
which were included in their state implementation plans (SIP) for the 1-
hour standard. Fifteen areas in Califo rnia were designated that violate the
federal 8-hour ozone standard. Each nonattainment area's classification and
attainment deadline is based on the seve rity of its ozone problem. Southern
California’s nonattainment areas and atta inment deadlines are: South Coast Air Basin (2021); Coachella Valle y (2013); Ventura County (2010); Western Mojave Desert (2010); Ante lope Valley (2010); San Diego (2009- 2014); and Imperial County (2007).

The Phase 2 ozone rule, adopted Novemb er 9, 2005, described the actions that states must take to reduce gro und 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. Th is analysis effort was continued for the 2003 AQMP. The 2 007 AQMP modeling will e xpand the 8-hour ozone analysis and include an attainment de monstration for the current form of the NAAQS. The 2007 AQ MP will include an analys is of 1-hour ozone toKRD: DRAFTModelingProtocol.doc 5/12/2006 4 provide additional milestones for progre ss 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 AQM P would not be fully e ffective 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 revi sions, the city-specific Empirical Kinetics Modeling Approach (EKMA) wa s applied. The 1979 AQMP used the city-specific EKMA procedures th en in existence. The 1982 AQMP revision used a more sophisticated ve rsion of the EKMA procedures and also contained sensitivity analyses (Appendix VI-A of the 1982 AQMP revision). The UAM was used in conj unction with the EKMA analyses to evaluate the effect of applying a ll 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 gr eater 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 high lighted the need to use a threedimensional, photochemical model such as the UAM to better understand the complex interactions between pr ecursor emissions, meteorology, and
the formation of ozone in the Basin.

For the 1989 AQMP revi sion, the UAM was applie d to a single, multiday, ozone episode to demonstrate attain ment 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 woul d 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 a nd proven technologies from those technologies anticipated to be ava ilable within the next 20 years.KRD: DRAFTModelingProtocol.doc 5/12/2006 5 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 si ngle, 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 pe rformance, includi ng new graphical procedures and subregional performanc e statistics, were used to ensure adequate representation of the phys ical 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 f actors for population and vehicle miles traveled (VMT) were revised to reflect the 1 990 Census data and the economic climate of the early 1990s, and im proved transportation modeling was considered. The modeling analysis benefited from a number of AQMD, CARB, and SCAG studies that im proved the area source emission inventory (Appendi x 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 Southe rn California Air Quality Study (SCAQS) episodes (August 26-28, 1987 , and June 23-25, 1987) added for the 1991 AQMP analysis, two add itional episodes (July 13-15, 1987, a SCAQS episode, and September 7-9, 1987) were simulated for the 1994 AQMP. In this manner, control strate gy decisions were based on a range of meteorological conditions, thereby re ducing uncertainty in the control strategy’s effectiveness. It was dete rmined that hydrocarbon and oxides of nitrogen emission reductions on th e 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 sati sfactorily evaluated. Since the 1985 meteorological episode was based on routinely monitored data, it was believed that the 1987 SCAQS episode s provided improved performance. In October 1998, AQMD provided to the USEPA a “weight of evidence” analysis that indicated th at even without the June 1985 episode, a viable
ozone attainment demonstration could be made.

As a result of intense interest fo r aerometric databases to support regional ozone modeling, a large-scale field measurement program was carried outKRD: DRAFTModelingProtocol.doc 5/12/2006 6 in southern California during the Su mmer of 1997 to collect sufficient aerometric data to allow data analys ts 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 lo cal 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 capt ured 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 provide d a basis for a main tenance plan for CO for the future; and updated the ma intenance 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 AQ MP. 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 meteorol ogical episodes and new air quality modeling tools. This re vision pointed to the need for additional emission reductions (beyond those incorporat ed 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 fo cuses primarily on recent ozone episodes in 2004 and 20 05. These periods better reflect emissions conditions following the reformulation of gasoline in California. The August 1997 episod e from SCOS97 will be retained for continuity with the previous AQMP analyses. The 2007 AQMP will be consistent with and will build upon the modeli ng approaches take n in the previous SIP efforts for the South Coast Air Basin, utiliz ing the latest tools and technical guidance.

AQMP PM Modeling History in the South Coast Air Basin
PM is a multicomponent pollutant in cluding 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 av erage 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 theKRD: DRAFTModelingProtocol.doc 5/12/2006 7 multicomponent nature of the PM10. Therefore, a multi-pronged modeling methodology was employed to asse ss 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 orga nic carbon and the Particle-In-Cell (PIC) model for sulfate a nd 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 a pplied. 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 paramete rized linear chemistry (LC) module was incorporated into the UAM. UAM was adapted to address the formation of particulate nitrate, su lfate, 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 emissi ons change, and particulate nitrate concentrations. However, the UAM/LC model cannot handle secondary organic carbon because the current pa rameterized 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 carbo n and PM2.5 partition. The resulting UAMAEROLT model, for the first time, provided a more robust, stand-alone platform for primary and secondary annu al 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 NAAQ S attainment demonstration and reasonable further progress. The a pplicable NAAQS, along with the
current attainment status of the S outh Coast Air Basin and recent design values, are presented in Table 1. A lthough 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 Californi a ozone standards. In addition to the PM2.5 NAAQS attainment demonstr ation, 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 w ill address maintenance plans for Carbon Monoxide (CO) and Nitrogen Dioxide (NO2).

The modeling effort may also include initial modeling strategy development toward demonstrating a ttainment 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: 98 th percentile 24-hour PM10-2.5 in a year, averaged over 3 years not to exceed 70.4 μg/m 3 ; no annual standard;

• PM2.5: 98 th percentile 24-hour PM2.5 in a year, averaged over 3 years not to exceed 35.4 μg/m 3 ; no change to annual standard.KRD: DRAFTModelingProtocol.doc 5/12/2006 9 TABLE 1 National Ambient Air Quality Standard s Compliance Status in the South
Coast Air Basin

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

Standard 3-year average of the

4 th 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/m 3 3-year average of 4 quarterly averages not to exceed 15.04 μg/m 3

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/m 3 (2002-2004) 24.8 μg/m 3 (2002-2004)

24-Hour PM10 Annual PM10

Standard 3-year average of the 99th percentile of 24-
hour concentrations not to exceed 154 μg/m 3 3-year average of 4 quarterly averages not to exceed 50.4 μg/m 3

Classification Serious
Non-Attainment Serious
Non-Attainment

Attainment

Date 2006 2006

Design Value 159 μg/m 3 (2002-2004) 57 μg/m 3 (2002-2004)KRD: DRAFTModelingProtocol.doc 5/12/2006 10 Ozone Design Value Determination
Since the base year emissions are fo r 2004, air quality da ta from the three overlapping 3-year periods from 2 000 through 2004 were used for
calculation of the 8-hour ozone de sign values for each AQMD air monitoring station. These are shown in Table 2, along with the Relative Reduction Factors (RRF) needed. Pe r 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 stati on concentration in ea ch 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 0.6241KRD: DRAFTModelingProtocol.doc 5/12/2006 11 

### Overview of the Modeling Analysis
The analysis techniques currently recommended for attainment demonstrations using ai r quality models have ch anged significantly from those used in past demonstrations. In Guidance on the Use of Models and Other Analyses in Attainment Demons tration for the 8-hour Ozone NAAQS (USEPA, 2005), USEPA recomm ends that the air quality models be used in
a relative sense in concert with obs erved 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 “ambie nt design value” to demonstrate attainment. The proposed ozone mode ling analysis is comprised of the following tasks:

• Identify potential, new ozone meteor ological episodes to be used. These episodes should represent the different meteorological conditions
that are conducive to ozone formation in the Basin.

• Among the widely accepte d state-of-the-science ozone models, CAMx
was selected for the attainment demonstration. CMAQ may be employed in the sensitivity analysis and weight-of-evide nce section as a supportive modeling tool.

• Develop model inputs. This task in cludes evaluation of the raw data and of the model input files devel oped from them. The input files will be evaluated using graphi cal and other techniques.

• Simulate each episode with the proposed ozone models. This task includes a separate performance eval uation 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, 2 014, and 2020. This task includes the required
attainment demonstrati on 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 Nonattain ment 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 th e foregoing tasks will be divi ded between the AQMD and CARB staffs and they will fully sh are all analyses, model inputs and outputs, findings, and c onclusions. Consensus on each component of the analysis shall be reached before proc eeding with subsequent components. In the event of technical disagreement on any of the work elements, theKRD: DRAFTModelingProtocol.doc 5/12/2006 12 AQMD and CARB staffs shall attempt to reach consensus on a mutually acceptable approach. In th e event that consensus cannot be reached, the disagreement will be elevated to the Executive Officers for resolution.
Table 3 summarizes the model selecti on and application elements for the 2007 AQMP and the changes from the 2003 AQMP modeling.KRD: DRAFTModelingProtocol.doc135/12/2006TABLE 3Summary of Proposed2007 AQMP Model Selection and Application2007 AQMP Element 2003 AQMP Element Selection Process/Issues/CommentsOzoneDispersion Platform: CAMxChemistry: SAPRC99OzoneDispersion Platform: UAMChemistry: SAPRC99•Peer Group Recommendation to move to state-of-art massconsistent model/chemistry•Integrates with numerical weather model output•CAMx used by several agencies for SIP development andsupported by Environ•Option for one atmosphere modeling•Alternates CMAQ: Emissions preprocessing more extensiveCALGRID: performance similar to CAMx with no oneatmosphere modelingPM10/PM2.5 Annual and EpisodicDispersion Platform: CAMxChemistry:•AERO-LT with CB-IV•Enhanced CFI scheme with CBIV•Optional One AtmosphereAerosol chemistryPM10/PM2.5Dispersion Platform: UAMChemistry: AERO-LT with CB-IV•CAMx PM dispersion consistent with ozone discussion above.•Installed SCAQMD version of AERO-LT into latest CAMxcode (V4.20).•Enhanced CAMx two section CFI aerosol scheme. It will becompared with AERO-LT.Meteorology•MM5/4DDA•Hybrid MM5/CALMET•MM5 initialized using NCEPdataMeteorology•CALMET Objective Analysis•HybridMM5/CALMET•EPA has expressed concerns about using the hybrid approach•MM5/4DDA is more mass consistent but doesn’t capturelocalize wind impacts (transport to San Fernando Valley)•Testing several land use assumptions with prognostic model tooptimize wind fields and vertical mixing/diffusivity fields.•Using Environ’s and Aerospace met-model performanceevaluation software.•Where possible take advantage of enhanced observation fielddata (e.g. 3D-Var)Domain/ CoordinatesSCOS97Meteorology: Lambert ConformalEmissions and Model application: UTMOzone: 16 layersPM10/2.5: 8 layersDomainSCOS97Meteorology: UTMEmissions and Model application: UTMOzone & PM10 5-layers•Maintained the SCOS97 domain however emissionsinventories require coordinate system offsets to adjust fromstatewide modeling domain. Impacts are to biogenic andCEDARS output.KRD: DRAFTModelingProtocol.doc145/12/2006TABLE 3 (Continued)2007 AQMP Element 2003 AQMP Element Selection Process/Issues/CommentsEmissions Inventories•2002 Base year•Enhanced aircraft/airport andshipping inventories•POLA/POLB updates•EMFAC2007ogross adjustmentso“focused” inventoriesoFinal public model•Adjustments to fugitivePM10/PM2.5 categoriesEmissions Inventories•1997 Ozone base year & 1995PM10 base year•Updated aircraft/airport andshipping inventories•EMFAC2002V2.01(major effort to developsurrogates for area sources)•2002 Inventory will be used to back-cast 1997, 2000 andproject inventories through 2030 for milestone years•Waiting on SCAGS’ growth estimates based on 2004 RTPwhich is expected to differ only slightly from the 2007 RTP.•Episodic temperature and humidity fields submitted to CARBfor biogenic emissions•CARB is adjusting temperature fields for planning inventorydevelopment•Gridded inventories awaiting focused on and off road modeloutput and supplemental inventories•No weekend trip model output available from SCAG•CARB will develop a “weekend” overlay to mimic VMTbased on Caltrans in-road counter dataAir Quality Model PerformanceOzone•Assess model performancebased on both 1-hour and 8-hourstatistics•60 ppb threshold (both indices)•Weight of Evidence Analysis•Mid-Course simulationsPM10/PM2.5 (annual and episodic)•Base statistics at speciation sites•Weight of evidence analysis•Mid-Course simulationsAir Quality Model PerformanceUse EPA recommendations for 1-hourozone and outline for PM10 and CO.Ozone•Mid-Course 2002 simulation•Comparative relative reductionfor UAM/CAMx/CMAQ perPeer Advisory GroupRecommendationPM10Analyzed “hot spot” grid cell emissions•Will review thresholds and geographical zones used for ozoneperformance evaluation.•Conduct sensitivity simulations to test emissions mass,VOC/NOx ratios, emissions timing (daily and weekend vs.weekday), ammonia massRelative Reduction FactorsRRF: sites specific applied to 3-yearaverage of the design value (PM2.5 andozone)Relative Reduction FactorsTested for ozone and PM10 but notappliedKRD: DRAFTModelingProtocol.doc155/12/2006TABLE 3 (Continued)2007 AQMP Element 2003 AQMP Element Selection Process/Issues/CommentsEpisode SelectionOzone•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 25-29PM10/2.5•Annual 2005 (January –December)•2005 October 19-25•2005 March 6-12Episode SelectionOzone•1997 August 4-7PM10/2.5•January – December 1995•Episodic: RollbackCO•1997 October 31-November 1•Meteorological episodes include SCOS97 and postCalifornia Fuel reformulation (2003)•MATES-III meteorological data base developmentconcurrent with AQMP data base development•Contract with Aerospaceto provide additionalobservations data and MM5 initialization fields using(satellite ingest and 3DVARInitial/Boundary ConditionsOzone•EPA recommended boundaryconditions•40 ppb ozone top profiled tolower layersPM10/2.5•Monthly varying emissionsgenerated boundary conditions(simulate model with zeroboundary conditions and letmodel generate boundary --using 3-5 grid cells from modeldomain boundary asrepresentative of boundary)Initial/Boundary ConditionsOzone•Use EPA recommendedboundary conditions•Per SCOS97 samplingtested 60 ppb ozone aloftPM10Monthly varying boundary conditionsbased on coastal monitoring site data•Will test varying top boundary concentration•Review alternate approachesfor quantifying boundaryconditionsKRD: DRAFTModelingProtocol.doc 16 5/12/2006 

### 2007 AQMP Schedule
The schedule of 2007 AQMP modeling ef forts 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 establis hed several advisory groups to assist with technical oversight a nd scientific community and business involvement in air quality programs. The mission of the Air Quality Management Plan Advi sory 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 re commendations concerning emission inventories, modeling, control meas ures, 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) soci oeconomic data and evaluations.KRD: DRAFTModelingProtocol.doc 17 5/12/2006 • Foster coordinated approaches towa rd overall attainment strategies.

• Assist in resolving key technical issues.

In addition, the AQMD Governing Boar d has established a more focused technical oversight committee to revi ew 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 M odeling Working Group (MWG) during the adoption of the 1989 AQMP revi sion. 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 Scientif ic, Technical, and Modeling Peer Review Advisory Group (STMPRAG). The STMPRAG role expands upon that of the MWG and includes expert s in socioeconomic assessment and human health, providing review of AQMD modeling, monitoring and
related scientific issues.

The STMPRAG assists AQMD in resolvin g 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 pro cess meteorological, aerometric and emission data with a specific focus on air quality modeling.

• Provide technical guidance to the ai r quality modeling efforts, with an emphasis on ozone and particulate matter. Some specific areas of technical guidance include: (1) Fo rmulation of modeling approaches; (2) Selection and development of a ppropriate 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 m odeling 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 impor tance of future growth and economic factors on future air qua lity attainment demonstrations.

The STMPRAG consists of approxim ately 20 members appointed by the Governing Board, with representa tives from USEPA, CARB, Southern California Association of Governments (SCAG), the California Small Business Alliance (CSBA), Southern California Edison (SCE), WesternKRD: DRAFTModelingProtocol.doc 18 5/12/2006 States Petroleum Association (WSPA), and technical experts from
universities and cons ultant firms.

Finally, as progress is made and produc ts are available, interim results will
be shared with th e interested public at appropriate times and locations.KRD: DRAFTModelingProtocol.doc 19 5/12/2006 

### MODEL SELECTION

Meteorological Model

Background
Air quality models require three-dimens ional, meteorological inputs. The key parameters are winds, mixing heights, temperatur e, and insolation. The windfields describe the transport a nd dispersion of po llutants. Mixing heights define the vertical extent of pollutant mixing near the surface. Temperature and insolation fields infl uence emission rates and the rates of
chemical transformation. Because meteorological measurements can be made only at discrete locations, mete orological models are required to develop the 3-dimensional fields required by air quality models.

The meteorological models used to ge nerate these three-dimensional fields are generally of three types: objective, diagnostic or prognostic. Objective
models are the least sophisticated meteor ological models. These models rely on interpolation of observations. Obtaining a reasonable field requires sufficient observations to accurately re present the atmosphere. This is especially true for windfields. In ar eas with complex terrain and bodies of water, such as the proposed modeling domain, the meteorology can be quite complex, and a successful objective an alysis would require an extremely large number of observations.

Diagnostic models rely both on observations and constraints based on physical concepts such as the cons ervation 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 obser vations are then used to develop meteorological fields using objectiv e analysis. The initial guess and the objective analysis are th en combined using a we ighting 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, temper ature, and mixing height fields are relatively independent, these models are also criticiz ed 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 atmos pheric physics, i.e. , conservation ofKRD: DRAFTModelingProtocol.doc 20 5/12/2006 mass, momentum, energy and moistu re. 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 p hysical flow processes important in the domain. However, prognostic mode ls have historically had problems estimating fine-scale flow features due to the limited reso lution of datasets used for describing geographic features.

Previous AQMP Applications
In the past, CARB and AQMD have utilized prog nostic, diagnostic, and objective models to generate meteorological inputs for modeling. The National Center for Atmospheric Res earch’s prognostic, non-hydrostatic Mesoscale Model (MM5) was applie d for modeling in support of attainment planning in the San Jo aquin Valley. The SCAQMD also has experience with the SAI MM 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 bei ng distributed through the USEPA for air quality modeling applications. The CALMET model has an added feature that allows a hybrid meteor ological field to be developed by merging the results from a prognostic model, such as the National Center for Atmospheric Research Mesoscal e Model Version 5 (MM5), with an objective or diagnostic analysis characte ristic of the CALMET model. This hybrid approach has the potential to take advantage of the prognostic capabilities of MM5 in areas of th e domain where meteorological measurements are few, a nd utilizing measurements in an objective analysis where there are many.

2007 AQMP Meteorological Modeling Approach
The SCOS97 field study gene rated a dataset with a relatively high spatial density of meteorological observations. While this dataset suggests that an objective/diagnostic model could be adeq uate to develop the meteorological parameter fields required for air qu ality modeling of the August SCOS97 episode, there are large portions of th e modeling domain—such as over the
ocean or the inland desert—where there are few observations. TheKRD: DRAFTModelingProtocol.doc 21 5/12/2006 approach for the 2007 AQMP modeling w ill be to use the MM5 prognostic model with a 5 km grid resolution. The meteorological boundary conditions for MM5 are generated usi ng 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 designe d to use inputs from the prognostic models, such as MM5, and the use of su ch a model is stro ngly 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 extr eme meteorological conditions and complex distribution of precursor em issions. However, the prediction of ozone with MM5 meteorological fields on most days is comparable to the results using other models. Since th e air quality model will be employed in
more of a relative sense for the 20 07 AQMP, with the use off relative reduction factors instead of peak conc entration comparisons, the MM5 is an appropriate choice for the AQMP mode ling. The premise is that the magnitude of RRF will reflect the ozone concentration resulting from the various meteorological episode classifi cations. With the use of the MM5 meteorological model, th e AQMP modeling effort will move closer to the “one atmosphere” air quality modeling perspective (i.e., ozone and fine particles simulated with the same m odel). The successful application of this prognostic model is critical fo r the development of multipollutant control strategies.

Several MM5 initialization fields and da ta ingest options are also being explored for the 2007 AQMP modeling effort:

• MM5 model initialized with the Na tional Centers for Environmental Prediction (NCEP) 12 km ETA/No rth American Model (NAM);

• MM5 model with Aerospace Corp 3DVAR forecast fields;

• Weather Research and Forecasti ng (WRF) community model using Aerospace Corporation 3DVAR;

• MM5 model with NCEP database of upper air and surface observations and the 1degree by 1 degree Gl obal Tropospheric Analysis

• Above method of MM5 with NCEP da tabase and Global Tropospheric Analysis and four-dimensional da ta assimilation (4DDA) of AQMD station meteorological data (this me thod 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 meteorol ogical modeling, the CALMET/MM5 hybrid meteorological model will be used to bolster the sensitivity analyses and weight-of-evidence disc ussions. 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 ba sed 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 comp lex terrain, and in the desert areas), and benefit from the objective analysis component of the diagnostic model to force the fields to ag ree with observations. To develop the hybrid fields, the fields developed using CALMET an d MM5 will need to be mapped into
common horizontal and ve rtical coordinate domai ns. 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 utilit y when applied to large modeling domains or to domains that are not ge ographically uniform. In addition,
much of the science in the model is outdated, and both the USEPA and CARB are no longer recommending that m odel for most analyses. Several photochemical models have been developed to impr ove 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 mode l has been applied by various air pollution agencies around th e world. It is modula r to allow the user to substitute various types of wind fi elds and chemical mechanisms. CALGRID incorporates refined trea tments of numerical advection, vertical transport and dispersion, a nd dry deposition. The model can be
exercised with either the Carbon B ond IV (CB-IV) or SAPRC chemical mechanisms, and contains highly effici ent chemical integration routines.
The vertical structure of the atmo sphere can be optionally definedKRD: DRAFTModelingProtocol.doc 23 5/12/2006 relative to a mixing height field, si milar to the UAM, or can be based on fixed layer heights and a derived mixing height.

• Models-3 Models-3 (USEPA, 1998a) is a flexib le software system designed for applications ranging from regul atory and policy analysis to understanding the complex interacti ons of atmospheric chemistry and physics. The Models-3 system is a fra mework that allows the user to go from developing model inputs to visualiz ing 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 si mulation of ozone, acid deposition, visibility and fine particles. CM AQ is a modular system capable of using output from the MM5 prognos tic 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). Howe ver, SAQM includes a number of improvements over RADM, including: a fixed vertical coordinate system that is compatib le with MM5; a horizontal coordinate system defined in a Lambert-Conformal proj ection that accounts for curvature of the Earth; a mass conservation module for compa tibility with nonhydrostatic meteorological inputs; the Bott advection scheme (Bott 1989a, 1989b) to reduce numerical diffusion and increase numerical accuracy; two-way nesting, and the cap ability to use either the CB-IV or
SAPRC chemical mechanis ms. 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-specif ic photolysis rates. In addition, the UAM-FCM has a generalized methodology to solve the set of differential equations that is mech anism independent. However, the meteorological dispersion algorith ms 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 In ternational, 1996) is an updated
version of the Urban Airshed M odel (UAM-IV) which incorporates many state-of-the-art enhancemen ts in chemical mechanisms, meteorological models and the repres entation of emissions. Perhaps the most significant additions are: an updated CB-IV mechanism to include aqueous phase chemistry; plume-in-grid capab ilities; an improved dry deposition algorithm; and an improve d 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 input s from prognostic models and twoway grid nesting. However, the pres ent 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 Mode l 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 solver s 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 rece ptor. Since CAMx is a relatively new model, thus there is a relativ ely 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 plat form integrates we ll with numerical meteorological model out put and it will be run us ing 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 br ing AQMD closer to the “one atmosphere” modeling concept, where oz one 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 AdvisoryKRD: DRAFTModelingProtocol.doc 25 5/12/2006 Committee (chaired by Dr. John Sein feld, with participation by other members Dr. Roger Atkinson, Dr. Jack Calvert, Dr. Harvey Jeffries, Dr. Jana Milford, and Dr. Armistead Russe ll) discussed a peer review of the SAPRC-99 mechanism conducted by D r. William Stockwell. Members of the committee agreed that the peer re view was excellent, that SAPRC-99
was a state-of-the-art chemical mech anism, and they approved the peer
review. The Committee then unanim ously recommended that SAPRC-99, as the most up-to-date m echanism available, be used for SIP modeling.

The particulate matter air quality m odel will use CAMx with the AEROLT/CB-IV chemical mechan ism and the enhanced two-section CFI aerosol scheme with CV-IV. The AQMD vers ion of the AERO-LT chemistry and
the enhanced version of the CAMx CFI scheme have been installed in the latest CAMx code and comparative anal yses will be presented. Advisory group recommendations have been to mo ve toward a state-of-the-art, massconsistent model and chem istry. 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 gr id scale of the modeling domain. The modeling domain for MM5 is defined in a Lambert-Conformal projection with two parallels to acco unt 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 (int erior) MM5 domain, covering most of
southern California. The vertical stru cture of MM5 is defined in a terrainfollowing, “sigma” coordinate system based upon a normalized pressure index. The 30 vertical layers defi ned for MM5 to approximately 15,000 m above ground level (AGL) can be transf ormed to fit the requirements of any air quality model. The MM5 meteorol ogical 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 out ermost 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 - 1 0

- 1 0 1 0

0 0 1 0 0 0

1 0 0 0

1 0 0 0 1

0 0 0

1 0 0 0 1 0

0 0 1 0 0 0 1 0

0 0 1 0 0 0

1 0 0 0 1

0 0 0

1 0 0 0 1 0 0 0

1 0 0 0

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



### Ozone Modeling Domain
The proposed ozone regional modeling do main 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 domai n are 150-700 km UTM East and 3580-3950 km UTM North. This corresp onds to 100 by 74 grid cells at 5 km grid spacing. The vertical mode ling domain will extend to a height of approximately 5,000 m AGL for a mo re 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 Or ange County and the non-desert portions of Los Ange les, Riverside and San Bernardino Counties), the Coachella Valley and San Diego County; the CaliforniaMexico border regions; most of Impe rial County; most of the inland deserts; and almost all of the Sout h 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 photochemic al model simulations. It also
eliminates the need to define boundary concentrations between the air basins and it extends far enough of fshore to contain wind flow patterns
conducive to over-water recirculation.



### PM Modeling Domain
The modeling domain for the particul ate 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 computationalKRD: 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 n eeded in the southe rnmost counties of California and adequate ammonia emis sions inventories are not available from many areas surrounding th e South Coast Air Basin.

FIGURE 4 2007 AQMP PM Mode ling Domain, inside the Ozone Modeling
DomainKRD: 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, th e resolution should be sufficiently small to pick up emission gradients in urba n areas and be consistent with the major terrain features which may a ffect 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 fina l resolution was a compromise between the accuracy desired and the cost. Ho wever, the current generation of highspeed computers has minimized co st and resource constraints.

For the year 2007 AQMP ozone, partic ulate 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 a nd for San Diego. In addition, this
will reduce resources needed to creat e gridded emissions, which are based on 5 km grid cells. For the propose d ozone modeling domain, use of a 5 km resolution results in a modeling gr id with 110 cells in the east-west
direction and 74 cells in the nort h-south direction. The Universal Transverse Mercator (UTM) coordinate system is adopted as the primary coordinate system for the air quality m odeling. There are variations in Lambert-Conformal map projection systems, such as the Normal Sphere (6471 km radius) used in MM5, th e North American 1927 Clerk 1866 used in CARB’s emissions development system, and the Arakawa-C or Arakawa-B variable configuration wh ich 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 horizon tal grid resolution, the vertical resolution defined for air quality m odeling domains has been limited by computational resources. In addition, available aloft meteorological and airKRD: 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 atmosphe ric boundary layer, resulting in poor representation of the stratification of the atmosphere. The ability to better
simulate the vertical structure of th e atmosphere has improved significantly due to the increased availability of measurements aloft (including radar wind profilers and aircraft measurem ents), the emergence of higher-speed computers, and our increased experi ence 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 15,0 00 meters AGL, as shown in Table 5. For input into the air quality model, th e 34 layers are reduced to match the
vertical resolution of th e 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 P 0 (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 co nditions. 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 modeli ng, 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 coastalKRD: DRAFTModelingProtocol.doc 34 5/12/2006 plain). The computational resource s 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 approximat ely 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 CA Mx 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 0KRD: 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 continu ity with the last plan submittal, the model performance for the August 1997 episode will be reevaluated using updated emission data and modeling protocols. Fi ve new recent episode periods from 2004 and 2005 will eval uated to better represent current conditions, including those associated w ith the reformulation of gasoline in the past several years. The six epis odes 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 effo rt. This episode was the primary modeling episode for the 20 03 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 peri od did not occur on a weekend. Model input data supporting the August 1997 simulations were derived from intensive field monitoring that o ccurred 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 particulat e and oxides of nitrogen sampling technology.

The August 1997 ep isode included the peak ozone concentrations measured in the South Coast Air Basin during SCOS 97 that were not associated with an exceptional event. A peak 1-hour ozone concentra tion of 0.187 ppm was measured at the AQMD Metropo litan Riverside Count y (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 al so 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 p eak of 0.115 ppm).

The August 1997 meteorological epis ode began on Sunday, August 3 under a ridge of high pressure aloft with 50 0 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 5 th , illustrate a classic “south route” transport regime that has been identified as characteristic of past severe Basi n 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. P eak inland aftern oon temperatures crested over 100 degrees Fahrenheit on each day during the episode and downtown Los Angeles consistently r eached the mid to upper 90’s. The excessive regional surface temperatures and stagnant flow also contributed
to a massive wildfire in the mountai nous portions of eas tern Ventura and
southeastern Santa Barbara counties dur ing the later part of the episode.

Ozone air quality reached the Califor nia Ozone Health Advisory level (0.150 ppm or higher) on two day dur ing the episode at Redlands, San Bernardino, Rubidoux and Mira Loma. The peak observed value of 0.187KRD: DRAFTModelingProtocol.doc 38 5/12/2006 ppm occurred on the August 5 at Rubido ux. Eleven locati ons exceeded the
federal 1-hour ozone standard. Areas such as Azusa, Pasadena, Glendora and Santa Clarita that routinely expe rience higher values of ozone during episodic conditions were spared the br unt 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 Cl arita 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 (T uesday – 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 lo w, strong temperature inversion layer in the Basin, which continued for se veral days, and hot weather except at
the immediate coast. Skies were mo stly clear, except for low clouds and fog offshore an at the co astline for most of the day. Ozone levels were starting to increase in the inland va lley areas. The inland valley areas remained hot on July 14 while the coas t remained much c ooler 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 in creased onshore flow, bringing night and morning low clouds and fog into the coastal valleys and transporting ozone and ozone precursors towards the Inland Empi re with a 8-hour ozone peaking at Crestline (0.143 ppm). Sk ies 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 coas tal 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 ex cessive heat advisory and record temperature possible in some areas on Monday, July 18. A lower temperature inversion confined morni ng low clouds and f og 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 demonstra tion a statistical model was developed to characterize the oz one meteorological episodes selected for regional modeling evaluation. The statistical model related degree of ozone meteorological episode severity re lative to the long term trend (1981- 2002). Multi-variate regression was conducted using th e Basin 1-hour average maximum ozone concentra tion and surface and upper air meteorological data for 1996 to gene rate 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 th e 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 meteorologi cal conditions that lead to high 1- hour average concentrations were th e same as those for peak 8-hourKRD: DRAFTModelingProtocol.doc 40 5/12/2006 concentrations. In addition, station specific correlations between maximum 1- and 8-hour average ozone concentra tions 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 assume d 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 th e 2004 and 2005 candidate episodes and their predicte d 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 th e 95th percentile in episode severity with only August 6, 2004 failing to rank in th e 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 brac keted 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 4 th 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 entir e year of 2005, taking advantage of additional specia ted particulate measurements and meteorological data archived in asso ciation with the Multiple Air Toxics Exposure Study III (MATES-III) in the Sout h 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 mu ltiple stations. Only Ju ly 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 th e 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/m 3 ) BAM PM2.5 ( μg/m 3 ) SSI PM10 ( μg/m 3 )

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/m 3 at Rubidoux

• Peak Annual Averag e PM10: 52.2 μg/m 3 at Rubidoux

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

• Peak 24-Hour PM2.5 was 132.7 μg/m 3 at Azusa on July 5, 2005 (due to Independence Day fireworks)

• Second Peak 24-Hour Average PM2.5: 106.3 μg/m 3 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/m 3 at South Long Beach on May 4, 2005

• Second Peak 24-Hour Average PM10: 123 μg/m 3 at Rubidoux on October 22, 2005

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

### INITIAL AND BOUNDARY CONDITIONS

Previous ozone modeling results in so uthern 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 measuremen ts that are adequate to determine boundary conditions. Ozonesonde measurements made during SCOS97 have shown high concentrations of oz one at heights above 3,000 m AGL. The modeling domain developed for th e SCOS97 episodes, which will be used for the 2007 AQMP , has been expanded both horizontally and vertically from that of earlier studi es 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 se nsitivity analyses. Chemical species concentration measurements, where av ailable from the SCOS97 field study
archive and the PAMS meas urements, 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 “backgr ound” values based on the minimum
concentrations measured from monito ring sites where measurements are available. The use of larger-domai n air quality models to provide the initial, top and lateral boundary conc entrations will also be explored. Speciated gridded pollutant and precu rsor profiles from the 36 km grid CMAQ model used for the WRAP visi bility modeling is currently being evaluated to provide the initial a nd boundary conditions. The boundary profiles will vary with time and height level, as well as location, while the top boundary concentr ation will vary by time and grid location.



### Initial Conditions
Initial conditions in the air quality models define the spatial distribution of chemical species concentrations th roughout the 3-dimensional modeling
domain at the time at whic h 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 tim e periods. In particular, observed VOC data is sparse although some PAMS monitoring stations data areKRD: DRAFTModelingProtocol.doc 44 5/12/2006 available. Therefore, observed concen trations must be extrapolated to
estimate concentrations throughout the modeling domain. The second
limitation is that observed chemical species concentrations may not represent chemical equilibrium, especia lly since not all important chemical species are measured explicitly.

To minimize the importance of initia l conditions on air quality model simulation results, the simulation is freque ntly started at some time interval before the period of interest. Histor ically, this “spin-up” time interval has ranged between 8 and 72 hours. Fo r the 2007 AQMP episodes, the modeling period starts early in the mo rning (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 re ach chemical equilibrium. Since most of the modeling episodes encompass se veral 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 th e air mass moving into the modeling
domain. Unlike initial cond itions 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 simulati on on the 2-dimensional, vertical planes on each of the horizontal bound aries 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 do main, the influence of boundary conditions on the simulation results may be problema tic. Beyond the northern boundary,
emissions from central California could have an impact on the domain. To the south, emissions from Mexico coul d 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 rang ing 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 su rface due to advection or vertical diffusion. Ideally boundary conditions would be determined fromKRD: DRAFTModelingProtocol.doc 45 5/12/2006 measured chemical species concentrati ons, 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 rela tively clean initial and boundary conditions, based on the resu lts 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 unc ertainty in future-year model predictions. The calculated RRF shou ld only reflect the impact of anthropogenic emissions reductions. Al so, as the future year air quality becomes close to backgr ound concentrations, the treatment of boundary conditions may be problematic, particul arly in 8-hour ozone attainment demonstration. A part of the ai r quality model evaluation process, sensitivity analysis and weight of evid ence analysis will be to assess the influence of boundary and initial conc entrations 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

ISOPKRD: DRAFTModelingProtocol.doc 47 5/12/2006 

### METEOROLOGICAL INPUTS

Meteorological Input Evaluation and Technical Review
The quality of the meteorological inputs can have a profo und influence on the accuracy of the simulations conc entrations of oz one, 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 qu ality 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 fo r use in the 2007 AQMP. Graphical and statistical analysis software is av ailable to facilitate the meteorological input field evaluation.

The use of routine and special study monitoring data and model analysis archives provides a robust data se t for comparing and analyzing the simulated meteorological fi elds. 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 (MAT ES-III) project during part of 2004 and all of 2005;

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

• Routine National Weather Service a nd military radiosonde observation (RAOB) data, including the sta tions at Miramar MCAS, Point Mugu NAS, San Nicolas Island NAS, Vandenberg AFB, Edwards AFB, China Lake NAS, Oakland, Mercur y/Desert Rock, and Tucson;

• Southern California radar wind an d temperature profiling network, including stations operated by:

 South Coast AQMD (Los Angeles In ternational Airport, Ontario International Airpor t and Moreno Valley),KRD: DRAFTModelingProtocol.doc 48 5/12/2006  Ventura County APCD (Simi Valley)

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

 NOAA project and SCOS97 profil ers, when available (e.g., Goleta, San Clemente Island, Santa Catalina Island during SCOS97).

• National Center for Environmen tal Prediction (NCEP) gridded
observational databases and mode l analysis fields, including:

 NCEP ds353.4 ADP Global Upper Air Observations database,

 NCEP ds464.0 ADP Global Surfa ce Observations database,

 NCEP ds083.2 Global Troposphe ric Analyses, 1 degree x 1 degree gridded database,

 NCEP ETA-12 km mode l forecast fields,

 NCEP ETA-40 km M odel Forecast Fields,

 NCEP EDAS-40 km Gridded Data;

• Aerospace Corporation MM5/3-Dimensional Va riational Analysis System (3DVAR) archives (incorpor ating surface, upper-air, ships, buoys, aircraft and satellite observations)

Qualitative Analyses
The qualitative analysis of modeled wind fields incl udes 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/s ea breezes, slope flows, and transport corridors. Since the modeling domain includes large overwater areas it is also necessary to evaluate offshore fl ows 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 includ e a review of the convergence and divergence zones in the simulated wind field, 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 SCOS 97 domain two key eddy features are prevalent: the Catalina Eddy (named si nce its center is often near Santa Catalina Island), and the Gaviota E ddy in the Santa Barbara Channel (Smith, et. al., 1984). Both eddy ci rculations are important transport mechanisms; they are capable of tran sporting precursors and aged ozone
concentrations onshore and northwar d to Santa Clarita and sometimes
Ventura and Santa Barbara Counties. Exceedances of the ozone standardsKRD: DRAFTModelingProtocol.doc 49 5/12/2006 are often observed with th e presence of an eddy circulation and the deep of the marine layer that accompanies a ma ture coastal eddy can end an ozone episode. The timing of the onset, persis tence, 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 pollutant s generated in the Los Angeles Basin are transported eastward. That is, the strength of the sea breeze will determine how far precursors and ozon e generated near the coast will be transported inland. Errors in the timing of the sea breeze can cause precursor emissions to be transporte d to the wrong locations instead of inland where peak concentrations are obser ved. It is essential that the onset
of the sea and land breezes simula ted 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 concentrati ons. Slope flows are probably the most challenging feature for prognostic meteorological models, due to the sparse observational data in comple x terrain and these models have a tendency to overdo the speed of the slope flows. A proposed qualitative approach is to determine if wind sp eeds estimated by the model appear to be reasonable in areas of complex terrain.

As a qualitative and quantitative evaluatio n of the windfields, wind speeds are proposed to be statistically summa rized 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 ex amined to determin e agreement with observations. Quantitative techniques w ill 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 reas onably 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 m odels, it may be useful to consider withholding some observations when executing the models to have an independent set of observations for co mparison. The sites withheld should
have some relation to the sites used to provide some assurance in the results from the comparison. Th is diagnostic evaluation is proposed to be conducted once acceptable me teorological 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 temp eratures, as well as c onsistency of the gridded data within regions.. Th e 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 calc ulated to provide quantitative measures of performance. In addition, the vertical temperature profiles generated by the models will be compared to thos e observed at rawinsonde sites and boundary layer wind and temperatur e profiler locations. The vertical temperature profile influences the st ability characteristics of the modeling domain which significantly affects ver tical mixing. The evaluation will include temporal and spatial evaluations of simulated vertical temperatures and mixing as compared to those es timated 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 develope d a statistical analysis software package, called METSTAT, to statis tically and graphically analyze the meteorological fields. METSTAT is publicly available and widely used by the modeling community. It can re ad the MM5 output files and the observational data, and then calcula te the following statistics: mean observation, mean prediction, bias erro r, gross error, root mean square error (RMSE), systematic root mean squa re error (RMSEs), unsystematic root mean square error (RMSEu), and index of agreement (IOA). It should be
noted that the statistical evaluatio ns 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 pr ovides assurance that the model performance is comparab le with performance achi eved in the past. METSTAT can be used as a screening to ol to identify the periods when the performance is poor that require furthe r analysis. These statistics can also be used to identify stations where pe rformance 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 mode led 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 reevaluate d 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 y ear 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 inpu ts 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 do main. The 2002 emissions will be backcasted to the 1997 ep isode year and grown to the 2004 and 2005 episode years. Specific month an d 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 change s from the 2003 AQMP inventory:

• Overall emissions invent ory 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 w ith EMFAC Gross Adjustments (to be provided by Spri ng 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 inventor y 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 onKRD: DRAFTModelingProtocol.doc 53 5/12/2006 technical-adjustments to the SCAG 2004 Regional Transportation Plan. No weekend trip model will be availabl e from SCAG, so CARB will develop a “weekend” overlay to mimic VMT ba sed 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 sour ce emission is the responsibility of
the local air districts. Emission inve ntories for point sources (including RECLAIM facilities) are compiled by lo cal districts and reported to CARB. If annual emissions for a facility fall be low 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. Te mporal codes which describe hours of operation are also incl uded in the emission database. Factors are also stored to convert annual average emis sions 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 ba se years and future years.

Area Sources
Area sources are comprised of emission source types that are difficult to inventory individually. Examples ar e architectural coatings, residential water heating, gasoline stations and o ff-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 responsib ility 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 annu al average emissions. Temporal codes which describe hours of operation are also included in the area sourceKRD: DRAFTModelingProtocol.doc 54 5/12/2006 emission database. Area source categor ies 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 ag encies. The majority of travel is reflected in transportation plan s developed by:

• Southern California Association of Governments (SCAG);

• San Diego Association of Governments (SANDAG);

• Santa Barbara County Associatio n 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 Statew ide Planning Model maintained by the California Department of Transpor tation. 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 m odeling will proceed using the 2002 base year emissions invent ory from the 2003 AQMP with gross EMFAC adjustments based on CARB t echnical documentation. DTIM4
will use both the emission factors and trav el 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 tr avel 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 sour ce 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 categor ies 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 gard en equipment, small utility and construction equipment, as well as farm equipment. Categories not estimated by
OFFROAD will be covered under “are a sources”. However, specificKRD: 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 calenda r year desired. The OFFROAD model will have the capability to estimat e exhaust, starting, and evaporative emissions for differing spa tial and temporal conditions.

Biogenic Emissions
The derivation of a gri dded natural biogenic emis sion inventory requires data sets describing the spatial distri butions 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 area s throughout the United Stat es. 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 co nducted 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 res earchers at UCLA, developed a methodology to complete a gridded biogenics inventory for the SCOS97 modeling domain. The methodology involv es 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 St ates; (2) biomass distribution, determined using published correlati ons between biomass and Normalized Difference Vegetative Index (NDVI), an index of relative “greenness” from Advanced Very High Resolution Radi ometer (AVHRR) satellite remote sensing data sets; (3) emission factor s of isoprene, monoterpenes, methyl butenol, and other VOCs fo r various plant species k nown to exist within the modeling domain using taxonomic rela tionships between the plant species (Benjamin et. al., 1996). The gridde d biogenic inventory, including the gridded plant species, biomass distribu tion and emission factor databases,
are combined with ambi ent 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 theKRD: DRAFTModelingProtocol.doc 56 5/12/2006 modeling region. CARB maintains a database of current profiles that are routinely updated to reflect recent in formation. The most recent updates were for gasoline exhaust and evapora tion, 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 da y to day. Evaporative emissions from vehicles and vegetation increase with ambient temperature. Exhaust
emissions are also a function of am bient temperature. Increased air conditioning demands on hot days also lead to increased emissions from electrical generation. Hourly surfac e temperatures for episode days are interpolated to each grid cell and ar e used in estimating emissions from vegetation and on-road mobile sources.

Criteria pollutant emissions from a pproximately 80 major point sources
will also be estimated hour ly for each specific episode day. Each district has acquired data from major point s ources for the episode days and is developing day-specific point source i nventories for those years. The
districts also collect information on variances, temporary breakdowns and
shutdowns. DTIM4 will be run to develop mob ile source inventories for several episode days, in cluding weekend days.

Where feasible, wildfire emissions will be estimated. Emissions from large ships in the shipping lanes are also es timated, using ship activity data (for commercial vessels) from shipping ports, ship-specific engine characteristics data, and the latest emis sion factors. Emissions from aircraft will be estimated using aircraft ac tivity data, including hourly landing, takeoff, approach, climbout and cruise em ission. 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 a pproaches for collecting, compiling, and reporting emission in ventory data. A comprehensive quality control and quality assurance plan was prepared to ensure good quality practices during development of the 2002 and fu ture year emission inventories. These procedures incl ude: quality control checks for collecting nonemission data, updating activity data, and using appropriate emission factors for calculating emissions; emission calculation methodology;KRD: DRAFTModelingProtocol.doc 57 5/12/2006 quality assurance evaluation using th e Data Attribute Rating System (DARS); and quality review of the entire inventory. The DARS program, originally developed by the USEPA, w ill be used as an additional quality assurance tool to quantify the rela tive accuracy of the annual emission inventories. CARB has also provided th e districts with a variety of quality assurance reports to aid in the revi ew of inventory data important for modeling. These reports were intende d 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 basi s for an air quality emission reduction target. Future year emissions for area and point sources are projected by accounting for growth and control, ge nerally 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 appropri ate surrogates as used for 2002. Gridded future year surr ogates for the entire modeling domain region and also being prepared for m ilestone and attainment demonstration years. Surrogates for other years can be interpolated as needed.

Future year traffic acti vity and network data are also prepared by local planning agencies. EMFAC will give estimates of future year emission factors. DTIM4 uses future year em ission factors and network travel data to obtain gridded future year on-road mobile emissions. DTIM4 inputs for future years are being compiled and pr epared. Ambient temperatures that occurred during 2002 are al so used in calculating fu ture year emissions for each episode day.

Biogenic emissions will not change fo r 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 th e modeling community that for an air quality modeling simulation to give relia ble 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 obser ved 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 compen sating errors. Several tests on the modeling simulations, both at the surf ace and aloft are proposed to be conducted as part of the model perform ance evaluation. Both precursor and secondary species will be evaluated, in addition to 1-hour and 8-hour ozone, PM10 and PM2.5 for each episod e and model variation. Statistical and graphical analyses will compare si mulated 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 prev ious modeling practices, the CARB photochemical modeling guidance (CARB 1992), and the USEPA attainment demonstration guidan ce for ozone (USEPA, 2005) and particulate matter (USEPA, 2001b).



### Statistical and Graphical Analyses
The model performance evaluation effo rt will include both graphical and statistical analyses. These will compar e simulated pollutant concentrations
with measured values fro m the routine air monitori ng stations and special study sites, including the PAMS stations . The statistical evaluations for the particulate matter modeling will focu s primarily on comparisons to the speciated particulate data from the MATES-III study. The graphical analyses will include time series plots showing temporal va riations, contour plots showing spatial variations, sca tter plots showing tendencies for overor under- estimation, and residual pl ots showing the distribution of the
differences between observed and predicted concentrations.

The statistical analyses will examine th e accuracy of peak estimates (both paired and unpaired in time and sp ace), mean normalized bias, mean absolute gross error, a nd mean absolute norma lized gross error. The statistical performance criteria outlined in the CARB gu idance document for Class B or better ozone perform ance will be used to guide theKRD: DRAFTModelingProtocol.doc 59 5/12/2006 determination of acceptable model perfo rmance. These statistical criteria will be used as a criterion for acceptable model performance. However, other analyses (graphical, multi-sp ecies, aloft comparisons and the diagnostic simulations) will also be used to determine acceptable model performance, and ultimately a conclu sion that the model is working properly must be made considering th e evidence from all of the analyses.
Table 12 shows some of the statisti cal 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 s ubregional zones to ensure that the
domain-wide statistics do not mask subr egional 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 planni ng or other purposes, an air quality model must be able to replicate measur ed concentrations with reasonable accuracy. However, it is also important to compare estimated and measured concentrations of precurs ors and secondary species, to establish confidence that the chemistry is being simula ted 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 em ployed by each model’s chemical mechanism. Comparisons will be made for each of the estimated precursorKRD: 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 rev eal 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 me asured concentration of a primary pollutant represents a grid cell averag e. For these reasons it is probably unreasonable to expect the same ac curacy in replicating precursor
concentrations as for ozone concentr ations. Thus, use of a specific
statistical error or bias criterion is not recommended. These comparisons
should be viewed as mo re 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 th e chemical species will be evaluated qualitatively and a more quantitative anal ysis will be conducted whenever
observational data are available. For the SCOS97 August 1997 episode, more extensive the upper air meas urements are available. The concentrations of selected air polluta nts were measured above the ground using aircraft, balloons and LIDAR. The primar y component of these
measurements is the oxidant concentra tions measured with ozonesondes to a height of 5,000 m AGL. Ozonesondes were flown at seven sites, at 6-hour intervals, for select ed episode days. Also, fo ur 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 avai lable, 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 laye r increments corresp onding to those of the air quality model. Due to the ve rtical resolution of the air quality models, the vertical resolution of th e aloft comparisons is likely to be
somewhat inconclusive and the eval uation will be of a more qualitative

nature.

In addition to measuring ozone, three of the SCOS97 aircraft measured oxides of nitrogen and collected samp les for later hydrocarbon analysis. Comparisons between thes e precursor data and concentrations simulated using the air quality models will al so be made. However, there areKRD: DRAFTModelingProtocol.doc 61 5/12/2006 relatively few samples and because an airc raft is not in one grid cell for an hour, comparisons may not be consiste nt with modeled concentrations. Comparisons to see if any large disc repancies exist between modeled and
measured concentrations aloft will be made.

Acceptable Model Performance
While it is expected that acceptable m odel performance can be achieved for the ozone and particulate episodes, this is not always feasible given the regulatory deadlines for plan submitta ls. 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 factor s reflect the change in emission
reduction. The RRF will be extensivel y evaluated with sensitivity analyses
and such issues will be described in the weight-of-evidence discussions.



### Sensitivity Analyses

Diagnostic Simulations
Several diagnostic or investigative si mulations will be employed to further determine the fidelity of the model re sults. These sensitivity analyses will help evaluate potential concerns regard ing such factors as emissions mass, VOC/NOx ratios, ammonia mass, and emissions timing, including daily and weekend vs. weekday emissions. Th e diagnostic simulations that are anticipated help evaluate model se nsitivity and performance will include the following:

• Zero emissions

– all anthropogenic and biog enic emissions will be set to zero to test the model’s sensitivity to emissions and to ensure that the
base case results are influenced a ppropriately by the emission inputs.

• Double anthropogenic emissions

– all anthropogenic emissions will be
doubled to test the mode l's sensitivity to increased man-made emissions. In addition, as separate tests of anthropogenic emissions affects, only
mobile source emissions will be do ubled and only st ationary 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 ac tivity, such as th e possibility that
some VOC sources could not be account ed and uncertainty in the spatial distribution of the emission sources. The adjustment factors will be developed based on the ambient VO C species adjust ed within theKRD: 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 de position 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 in itial conditions – A range of boundary and initial conditions will be analyzed to test the sensitivity of the models to these inputs. The m odeling results using the following initial and boundary conditio ns will be analyzed: (1) the boundary conditions at the top a nd sides of the modeling domain and
the three-dimensional initial conditi ons will be set to zero; (2) the observed air quality data is interpolated for the initializations hours, using data from PAMS and other meas urements as available to prepare estimated speciated initial and bou ndary 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 a ttainment of the NAAQS. For the attainment demonstration, the years 2007, 2010, 2014 and 2021 will be simulated with the propos ed control measures (the control strategy) for 8- hour ozone NAAQS attainment. Attain ment of the revoked 1-hour ozone NAAQS will also be demonstrated for th e future year 2010 as a milestone and to show reasonable further progre ss. The years 2006, 2010, 2015 and
2020 will be simulated to demons trate the particulate matter NAAQS attainment. In the past the use of th e model results for these goals has been contingent upon acceptable base case model performance for the episodes simulated. That is, only episodes fo r 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 al so 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 an alyses, 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 th e Rubidoux study results.

Relative Reduction Factors
Historically, AQMD developed the ca rrying capacity and attainment
demonstration for ozone based on a set of specific control measures that was projected to achieve the 1-ho ur ozone NAAQS for all modeled episodes. The USEPA 8-hour ozone guidance (USEPA, 2005) and draft particulate matter guidance (USEPA, 200 1b) recommend the use of relative reduction factors (RRFs) as part of th e attainment demonstration process, assuming that satisfactory base year m odel performance is established. The
RRF is a non-dimensional factor that incorporates design period monitoring data, using the 3-year average of th e design value, directly into the attainment test along with the ratio of future to current year model predictions. The RRF is defined as th e ratio of the futu re daily maximum concentration predicted near a monito r (averaged over multiple days) to the baseline daily maximum concentra tion predicted near the monitor (averaged over the same days).KRD: DRAFTModelingProtocol.doc 64 5/12/2006 The RRF are site specific and will be based on the 9-cell average (15 km by 15 km) for multiple episodes. Areas w ith severe or higher nonattainment status require a minimum of 15 simulate d 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 re sult in underestimation of emission reductions needed to attain standards. The first problem is that modeled episodes usually have ozone concentrati ons lower than the design value. The second problem is that simulation results have historically exhibited a tendency towards unde restimation of observed co ncentrations. By utilizing monitored data along with model es timations, 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 ma y 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, a nd gross error) may not capture this variation. Thus it may not be reas onable to include sites which have poor model performance for a given episode.

Some characteristics of RRF s include the following:

• More robust analysis due to multiple episodes;

• Less reliant on peak concentra tion 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 no t be adequately characterized;

• More applicable to 8-h our than 1-hour ozone;

• Not applied for previous AQMPs



### Carrying Capacity Estimation
A traditional use of models for planni ng has been the estimation of carrying capacities for ozone precursors. This is typically achieved by exercisingKRD: DRAFTModelingProtocol.doc 65 5/12/2006 the model with a series of across-th e-board precursor emission reductions from the future year ba seline, 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 ozon e concentrations within the modeling domain or subregion, numb er of grid cells above the standard, or one of many population exposure me trics. Since the carrying capacity for each precursor is based on across- the-board emission changes, rather than source- and location-specific contro ls as would be specified in a plan, it should only be viewed as an initial es timate 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 concen tration for the domain.

• Population exposure for 1-h our 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.KRD: DRAFTModelingProtocol.doc 66 5/12/2006 

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