ATTACHMENT 1 

Modeling Protocol 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 2 

MODELING PROTOCOL FOR 1-HOUR OZONE MODELING 

IN SUPPORT OF THE SOUTH COAST AIR QUALITY 

MANAGEMENT DISTRICT'S YEAR 2001 AIR QUALITY 

MANAGEMENT PLAN UPDATE

(DRAFT #7) 

July 2, 2001 

Jointly Prepared by:

Planning & Technical Support Division 

California Air Resources Board 

P.O. Box 2815 

Sacramento, CA 95812 

and 

South Coast Air Quality Management District 

21865 E. Copley Drive 

Diamond Bar, CA 91765 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) i 

TABLE OF CONTENTS 

INTRODUCTION....................................................................................................1 

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

Overview of the Ozone Modeling Analysis......................................................4 

Schedule...........................................................................................................5

EPISODE SELECTION..........................................................................................6 

MODELING DOMAIN...........................................................................................8 

Non-SCAQS Episodes......................................................................................8 

AIR QUALITY MODEL SELECTION..............................................................10 

HORIZONTAL AND VERTICAL GRID RESOLUTION...............................13 

Horizontal Grid Resolution ...........................................................................13 

Vertical Resolution ........................................................................................14 

METEOROLOGICAL INPUTS ..........................................................................17 

Previous Applications....................................................................................18 

Proposed Approaches....................................................................................18 

Meteorological Input Validation and Technical Review...............................20 

EMISSION INVENTORY ....................................................................................24 

1997 (SCOS97) and 1998 Episodes ..............................................................24 

1987 (SCAQS) Episodes................................................................................31 

INITIAL AND BOUNDARY CONDITIONS .....................................................32 

Initial Conditions...........................................................................................32 

Boundary Concentrations..............................................................................32 

MODEL PERFORMANCE EVALUATION......................................................34 

Statistical and Graphical Analyses ...............................................................34 

Multi-Species Evaluations.............................................................................35 

Aloft Comparisons.........................................................................................35 

Diagnostic Simulations..................................................................................36 

USE OF THE MODEL RESULTS ......................................................................38 

Carrying Capacity Estimation.......................................................................39 

Attainment Demonstration.............................................................................39 

TECHNICAL OVERSIGHT................................................................................41 

REFERENCES.......................................................................................................43 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) ii 

ACRONYMS 

Several acronyms are used in the modeling protocol document. For convenience, 

the acronyms used are listed below to aid the reader. 

AAMA – American Automobile Manufacturer's Association

AGL – Above Ground Level

AQMP – Air Quality Management Plan

ARB – Air Resources Board 

AUSPEX – Atmospheric Utility Signatures, Predictions, and Experiments 

AVHRR – Advanced Very High Resolution Radiometer 

BEIS – Biogenic Emission Inventory System 

CAA – Clean Air Act of 1990 

CAMx – Comprehensive Air-Quality Model with Extensions 

CBM – Carbon Bond Mechanism 

CCAA – California Clean Air Act 

CEFS – California Emission Forecasting System 

CMAQ – Community Multi-scale Air Quality (model) 

CO – Carbon Monoxide 

COG – Council of Governments 

DARS – Data Attribute Rating System 

DWM – Diagnostic Wind Model 

DTIM – Direct Travel Impact Model 

EIWG – Emission Inventory Working Group 

EKMA – Empirical Kinetics Modeling Approach 

EMFAC – Emission Factor (model) 

FCM – Flexible Chemical Mechanism 

FDDA – Four-Dimensional Data Assimilation 

GAP – Geographical Approach to Protection of Biological Diversity 

GCM – Global Climate Model 

ICAPCD – Imperial County Air Pollution Control District 

IOP – Intensive Operation Period 

LIDAR – Light Detection and Ranging 

MDAQMD – Mojave Desert Air Quality Management District 

MM5 – Mesoscale Meteorological Model (5th generation) 

MWG – Modeling Working Group 

NAAQS – National Ambient Air Quality Standard 

NDVI – Normalized Difference Vegetative Index 

NOx – Nitrogen Oxides 

PDT – Pacific Daylight Time 

PM – Particulate Matter 

RADM – Regional Acid Deposition Model 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) iii 

RECLAIM – Regional Clean Air Incentives Market 

ROG – Reactive Organic Gases 

RRF – Relative Reduction Factor 

SANDAG – San Diego Association of Governments

SAPRC – State Air Pollution Research Center 

SAQM – SARMAP Air Quality Model 

SARMAP – SJVAQS/AUSPEX Regional Model Adaptation Project 

SBCAG – Santa Barbara County Association of Governments 

SBCAPCD – Santa Barbara County Air Pollution Control District 

SCAB – South Coast Air Basin

SCAG – Southern California Association of Governments

SCAQMD – South Coast Air Quality Management District 

SCAQS – Southern California Air Quality Study 

SCE – Southern California Edison 

SCOS97 – Southern California Ozone Study (1997) 

SDCAPCD – San Diego County Air Pollution Control District 

SIP – State Implementation Plan 

SJVAQS – San Joaquin Valley Air Quality Study 

SOx – Sulfur Oxides 

STMPRAG – Scientific, Technical, and Modeling Peer Review Advisory Group 

TOG – Total Organic Gases 

UAM – Urban Airshed Model 

USEPA – United States Environmental Protection Agency 

UTM – Universal Transverse Mercator 

VCAPCD – Ventura County Air Pollution Control District 

VMT – Vehicle Miles Traveled 

VOC – Volatile Organic Compound 

WSPA – Western States Petroleum Association 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 4 

INTRODUCTION 

Under the federal Clean Air Act of 1990 (CAA), the South Coast Air Basin 

(Basin) is classified an “extreme” nonattainment area for ozone. Section 

182(c)(2)(A) of the CAA sets November 15, 1994 as the deadline for 

submission of a State Implementation Plan (SIP) to demonstrate attainment 

of the federal ambient ozone air quality standard of 0.12 ppm by December 

2010. The South Coast Air Quality Management District (District) satisfied 

that Act requirement with the submittal of the 1994 Air Quality Management 

Plan (AQMP) in September 1994, and a subsequent revision was submitted 

to the U.S. Environmental Protection Agency (USEPA) in February 1997. 

In order to take advantage of more recent information such as the 1997 

Southern California Ozone Study (SCOS97) and enhancements to the 

emissions inventory, the District plans to revisit the recently submitted 

ozone attainment demonstration in a 2001 submittal. 

The USEPA promulgated a new ozone ambient air quality standard in July 

1997. While the SIP submittal date for the new federal ozone air quality 

standard is still being debated, the District committed to begin air quality 

analysis for the new standard. Subsequent to the release of the 

implementation guidance for the new ozone air quality standard, the U.S. 

Circuit Court ruled that USEPA could not enforce the new standard. 

USEPA is in the process of appealing that decision. However, the court 

decision may change the date for the SIP submittal for the new ozone air 

quality standard. A first look at the new ozone air quality standard was 

provided in the 1997 AQMP. However, the analysis was conducted before 

USEPA finalized the new ozone air quality standard. 

The CAA requires the use of an USEPA-approved, photochemical grid 

model to perform the attainment demonstration. USEPA's "Guideline on Air 

Quality Models (Revised)" recommends the use of the Urban Airshed Model 

(UAM) for attainment demonstrations involving entire urban areas (USEPA, 

1990). However, the USEPA recently revised its recommendation to no 

longer include a recommended air quality model for ozone. Instead, the 

USEPA recommends that air quality model(s) proposed for an ozone 

attainment demonstration, be subjected to model performance evaluations to 

demonstrate that they are appropriate for attainment demonstration purposes. 

The USEPA issued the "Guideline for Regulatory Applications of the Urban 

Airshed Model" to assist states in preparing the attainment demonstration 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 5 

required by the CAA (USEPA, 1991 and 1996). In addition, the ARB has 

issued photochemical modeling guidance for use by the districts to ensure 

the technical validity of the modeling results (ARB 1992). Finally, the 

USEPA is in the process of finalizing attainment demonstration guidance for 

the new, federal, 8-hour ozone air quality standards. This ozone modeling 

protocol is based on these guideline documents. 

In order to devote the maximum resources practicable to the development of 

the District's 2001 AQMP, the Executive Officers of the ARB and the 

SCAQMD have agreed to jointly develop the emissions and air quality 

modeling needed to determine the carrying capacity and attainment 

demonstration for the 1-hour ozone and PM10 standards. The technical staffs 

of both agencies are working closely together to plan and carry out the 

necessary work for the AQMP, and are committed to intensive and timely 

coordination to ensure that the AQMP is based on the soundest science 

possible. Both agencies agree that their staffs will collaborate on this work 

such that the product will be mutually acceptable modeling analyses for use 

in the 2001 plan. 

The objective of this protocol is to define the methodology to be used for 

simulating ozone formation in the basin, including: the episodes to be 

simulated; the model(s) to be used; the modeling domain; the model input 

data, including meteorology, emissions, and initial and boundary conditions; 

and the process for model performance evaluation. In addition, the protocol 

outlines the attainment demonstration process, including a review of the 

CCAA requirements. This protocol document is intended to be dynamic, 

and will be updated in response to reviewer comments and to reflect the 

results of new information that will emerge during the process. 

Background

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 ozone air quality standard 

of 0.12 ppm by 1982. Because the emission controls discussed in the 1979 

AQMP would not be fully effective until after 1982, the ARB and the 

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 District 

in 1982 which included a new series of modeling analyses to address 

concerns regarding the original 1979 modeling analysis. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 6 

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

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

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

revision used a more sophisticated version of the EKMA procedures and 

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

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

evaluate the effect of applying all feasible control measures by 1987 

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

modeling studies, it was determined that hydrocarbon reductions on the 

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

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

nitrogen. Forecasted emission data indicated that only a 33 percent 

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

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

the complex interactions between precursor emissions, meteorology, and the 

formation of ozone in the Basin. 

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

ozone episode to demonstrate attainment of the National Ambient Air 

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

analysis that hydrocarbon and oxides of nitrogen emission reductions of 

more than 80 percent would be needed in order to attain the NAAQS by the 

year 2007. The 1989 AQMP revision outlined three levels of controls 

(identified as Tiers I, II, and III) that separated the proposed control 

measures by known and proven technologies from those technologies 

anticipated to be available within the next 20 years. 

For the 1991 AQMP, the District used the UAM to further assess the 

effectiveness of the three tiers of control measures in reducing ambient 

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

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

investigate the effect of projected emission reductions on future ozone 

concentrations during a wider variety of meteorological conditions. 

Additional evaluations of model performance, including new graphical 

procedures and subregional performance statistics, were used to ensure 

adequate representation of the physical and chemical processes that 

influence ozone formation in the Basin. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 7 

A number of improvements were made to the modeling analysis for the 1994 

AQMP. Growth factors for population and vehicle miles traveled (VMT) 

were revised to reflect the 1990 Census data and the economic climate of the 

early 1990s, and improved transportation modeling was considered. The 

modeling analysis benefited from a number of District, ARB, and SCAG 

studies that improved the area source emission inventory (Appendix III-A). 

On-road, mobile emission estimates were improved with the use of the latest 

ARB emission factors program, EMFAC7F. Five ozone episodes were 

simulated to evaluate control strategy effectiveness. In addition to the June 

5-7, 1985, episode used in the 1989 AQMP, and the two Southern California 

Air Quality Study (SCAQS) episodes (August 26-28, 1987, and June 23-25, 

1987) added for the 1991 AQMP analysis, two additional episodes (July 13-

15, 1987, a SCAQS episode, and September 7-9, 1987) were simulated for 

the 1994 AQMP. In this manner, control strategy decisions were based on a 

range of meteorological conditions, thereby reducing uncertainty in the 

control strategy’s effectiveness. It was determined that hydrocarbon and 

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

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

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

preparing the 1994 AQMP, the District decided to drop the June 1985 

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

1987 meteorological episodes were satisfactorily evaluated. Since the 1985 

meteorological episode was based on routine monitored data, it was believed 

that the 1987 SCAQS episodes provided a greater certainty level relative to 

their performance evaluation. In October 1998, the District provided to the 

USEPA a “weight of evidence” analysis that indicated that even without the 

June 1985 episode, a viable ozone attainment demonstration could be made. 

Since the 1997 AQMP, other ozone episodes have been observed that could 

serve as complementary or replacement episodes: 

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

regional ozone modeling, a large-scale field measurement program was 

carried out in southern California during the Summer of 1997 to collect 

sufficient aerometric data to allow data analysts and modelers to 

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

agencies and others participated during the planning and operational 

phases of the field study, including the ARB, the USEPA, the local 

districts, the US Navy, the US Marines, and the marine industry. The 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 8 

1997 Southern California Ozone Study, or SCOS97, occurred over a four 

month period from June 15 through October 15, 1997, and captured 

several episodic ozone days. 

• A widespread and severe ozone episode occurred throughout California 

in July 1998, during which many districts in southern California 

experienced 1-hour and 8-hour ozone violations. Fortuitously, several 

radar wind profilers were operating in the region at the time, providing 

greater than normal upper air meteorological coverage. 

Overview of the Ozone Modeling Analysis

The proposed modeling analysis comprises the following tasks: 

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

episodes should represent the different meteorological conditions that are 

conducive to ozone formation in the Basin. The new ozone episodes 

would complement the 1987 episodes in the 1997 AQMP. 

• Evaluate at least two state-of-the-science ozone models for the attainment 

demonstration, for the new episodes. 

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

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

evaluated using graphical and other techniques. 

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

includes a separate performance evaluation for each episode and each 

model. Documentation of the simulation results and performance 

evaluations will be provided. 

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

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

demonstration. 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 the Mojave Desert Ozone 

Nonattainment Area. Ozone air quality projections to 2020 will be used 

to demonstrate that the control strategy maintains the federal ozone air 

quality standard and to establish emission budgets needed for conformity 

purposes. 

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

the ARB staffs, and they will fully share all analyses, model inputs and 

outputs, findings, and conclusions. Consensus on each component of the 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 9 

analysis shall be reached before proceeding with subsequent components. In 

the event of technical disagreement on any of the work elements, the staffs 

of the District and the ARB shall attempt to reach consensus on a mutually 

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

disagreement will be elevated to the Executive Officers for resolution. 

Schedule

Task Due Date 

1. Episode Selection Completed 

2. Air Quality and Meteorological Data Preparation Completed 

3. Emission Inventory Preparation July 2001 

4. Performance Evaluation Late-July 2001 

5. Attainment Demonstration August 2001 

6. Draft SIP Documents August 2001 

7. District Board Approval of Final SIP November 2001 

8. ARB Board Approval of Final SIP December 2001 

9. SIP Submittal to USEPA December 2001 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 10 

EPISODE SELECTION 

Four ozone episodes from 1987 were simulated for the 1994 SIP and the 

1997 AQMP: June 24-25, 1987; July 14-15, 1987; August 27-28, 1987; and 

September 8-9, 1987. To maintain continuity with recent plan submittals, 

the model performance for two of these episodes (June 24-25, 1987 and 

August 27-28, 1987) will be reevaluated using updated emission data. These 

two episodes have served as the controlling episodes for the 1994 and 1997 

plan submittals. No new meteorological work is planned, and the updated 

simulations will be conducted on the SCAQS modeling domain. Because of 

concerns with the age of these episodes and the limited aloft data associated 

with them, three additional episodes from SCOS97 and 1998, as briefly 

described below, are proposed to supplement the SCAQS episodes. 

During the four-month SCOS97 field study, the peak observed ozone 

concentration within the SCOS97 domain was 21 pphm. There were 13 

Intensive Operation Period (IOP) days during which additional 

measurements were taken, such as speciated hydrocarbons and air quality 

aloft. The peak ozone concentration observed over all of the IOP days was 

19 pphm. Because of the unique meteorological patterns during SCOS97 

associated with the El Nino phenomenon, there is a concern that the peak 

ozone concentrations measured during SCOS97 may not represent design 

values for southern California. In July 1998 a severe regional ozone episode 

occurred in southern California which included a peak, observed, one-hour 

ozone concentration in the SCOS97 domain of 24 pphm. Although intensive 

field study data are not available for this episode, there were approximately 

10 radar wind profilers operating during that period, thereby providing aloft 

meteorological data. These data are believed to be sufficient to support 

simulating an episode for this period. 

Therefore, in addition to the two 1987 episodes to be simulated, three multiday ozone episodes from SCOS97 and 1998, as discussed below, are 

proposed to be simulated. Synopses of the meteorology associated with 

each of the 1997 episodes can be found in the summary of SCOS97 field 

operations (ARB et. al, 1998). 

1) The SCOS97 episode period of August 3-7, 1997 (Sunday–Thursday). 

This episode was selected because the peak, 1-hour ozone concentration 

of 19 pphm and the peak, 8-hour concentration of 12.5 pphm measured in 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 11 

the South Coast during this period were the highest not associated with 

an exceptional event during SCOS97. High ozone concentrations were 

also observed within the Mojave Desert (1-hour peak of 14 pphm) and 

Ventura County (1-hour peak of 13 pphm, 8-hour peak of 11.5 pphm). 

2) The SCOS97 episode period of September 26-29 (Friday–Monday) was 

selected because it includes the second highest, 1-hour ozone 

concentration measured during an IOP, and because it represents a 

weekend episode. The peak 1-hour (17 pphm) and 8-hour (10.7 pphm) 

ozone concentrations were both observed at Upland. 

3) The July 13-18, 1998 (Tuesday–Saturday) episode was selected because 

it represents a severe, widespread high ozone event. The peak, observed, 

1-hour ozone concentration in the South Coast Air Basin was 24 pphm, 

and the peak, 8-hour, concentration observed was 20.6 pphm. Other 

areas also experienced elevated ozone peaks, including San Diego (1-

hour peak of 16 pphm), Ventura (1-hour peak of 17 pphm, 8-hour peak of 

15 pphm), and Mojave Desert (1-hour peak of 20 pphm, 8-hour peak of 

14 pphm). 

Summary of Episodes to be Simulated for the Current AQMP 

Episode Notes

• June 24-25, 1987 

• August 27-28, 1987 

Maintained for continuity with previous 

plans; updated emissions only; will 

utilize SCAQS modeling domain 

• August 3-7, 1997 Intensive SCOS97 episode. Peak 

1-hour O3 concentration of 19 pphm; 

peak 8-hour O3 concentration of 

12.5 pphm in the SCAB; will utilize 

regional modeling domain 

• September 26-29, 1997 Intensive SCOS97 weekend episode. 

Peak 1-hour O3 concentration of 

17 pphm; peak 8-hour O3 concentration 

of 10.7 pphm in the SCAB; will utilize 

regional modeling domain 

• July 13-18, 1998 Severe, widespread high ozone event in 

the region. Peak 1-hour O3

concentration of 24 pphm; peak 8-hour 

O3 concentration of 20.6 pphm in the 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 12 

SCAB; will utilize regional modeling 

domain 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 13 

MODELING DOMAIN 

As discussed previously, three new ozone episodes are planned for use in the 

current AQMP. These, which include two episodes captured during 

SCOS97 and an episode from 1998, will be simulated for a regional domain 

as described below. No new meteorological work is planned for the 1987 

episodes, therefore they will continue to utilize the SCAQS modeling 

domain, which is described in previous AQMPs. 

Non-SCAQS Episodes

Previous ozone modeling results in southern California, such as those in 

support of the 1994 State Implementation Plan (SIP), proved sensitive to 

boundary concentrations of air pollutants. This reflected the physical 

processes of recirculation of pollutants within southern California and the 

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

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

take field study measurements that are adequate to determine boundary 

conditions. Thus the boundary conditions used in previous studies were 

uncertain and the modeling domain has been extended in an attempt to 

minimize their influence. 

The photochemical modeling studies conducted for the 1994 SIP for the 

South Coast Air Basin, the San Diego Air Basin, and the South Central 

Coast Air Basin defined the upper domain boundary at a height of 2,000 m 

above ground level (AGL) or less. There were few air quality measurements 

above this height. However, terrain elevations in southern California often 

exceed 2,000 m above sea level and recirculation and transport above this 

height are possible. Ozonesonde measurements made during the 1997 

Southern California Ozone Study (SCOS97) have shown high 

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

The proposed regional modeling domain will minimize the influence of 

boundary conditions on simulation results and allow the effects of 

recirculation and interbasin transport to be better represented by 

meteorological and photochemical model simulations. The proposed 

modeling domain shown in Figure 1 will completely encompass the South 

Coast Air Basin and San Diego County, almost all of the South Central 

Coast Air Basin (excepting a small piece of San Luis Obispo County), the 

California-Mexico border regions, and includes most of the inland desert 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 14 

areas to eliminate the need to define boundary concentrations between them. 

The domain will extend far enough offshore to contain wind flow patterns 

conducive to overwater recirculation. Specifically, the UTM Zone 11 

coordinates of the domain are 150-700 km UTM East and 3580-3950 km 

UTM North. Vertically, the modeling domain will extend to a height of at 

least 5,000 m AGL for a more complete representation of atmospheric 

processes. This will contain observed high ozone concentrations aloft and 

allow three-dimensional wind flow patterns near elevated terrain features to 

be represented better than in previous simulations, providing more accurate 

representation of pollutant transport and recirculation. 

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
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 15 

Figure 1. The regional photochemical modeling domain for the SCOS97 

and the 1998 episodes. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 16 

AIR QUALITY MODEL SELECTION 

The Urban Airshed Model (UAM-IV) is the only Eulerian photochemical 

modeling tool that has been previously approved by the USEPA for ozone 

modeling studies. However, the UAM-IV (USEPA 1990) is widely 

acknowledged to have characteristics which limit its utility when applied to 

large modeling domains, or to domains that are not geographically uniform. 

In addition, much of the science in the model is outdated, and both the 

USEPA and the ARB are removing that model’s recommended status. A 

number of photochemical models have been developed over the last decade 

to improve upon the UAM-IV; these include: 

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

developed for the ARB in the late 1980's. The model has been applied by 

various air pollution agencies around the world. It is modular to allow 

the user to substitute various types of wind fields and chemical 

mechanisms. CALGRID incorporates refined treatments of numerical 

advection, vertical transport and dispersion, and dry deposition. The 

model can be exercised with either the Carbon Bond IV (CB-IV) or 

SAPRC chemical mechanisms, and contains highly efficient chemical 

integration routines. The vertical structure of the atmosphere can be 

optionally defined relative to a mixing height field, similar to the UAM, 

or can be based on fixed layer heights and a derived mixing height. 

• The Comprehensive Air-Quality Model with Extensions (Environ, 1997). 

CAMx contains a number of advanced features, including grid nesting, 

sub-grid-scale plume-in grid simulation, alternative numerical advection 

solvers, and the ability to use alternative chemical mechanisms. In 

addition it has the ability to tag emissions so that at the end of the 

simulation one can determine the sources of emissions impacting a 

particular receptor. Since CAMx is a relatively new model, there is a 

relatively short history of experience applying the model. 

• Models-3 (USEPA, 1998a). Models-3 is a flexible software system 

designed for applications ranging from regulatory and policy analysis to 

understanding the complex interactions of atmospheric chemistry and 

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

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

At the heart of the current version of Models-3 is the Community Multi-
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 17 

scale Air Quality (CMAQ) model. Its capabilities include urban to 

regional scale air quality simulation of ozone, acid deposition, visibility, 

and fine particles. CMAQ is a modular system capable of using output 

from the MM5 prognostic meteorological model, along with the CBIV, 

RADM-2, or SAPRC-99 chemical mechanism. 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 cloudmodule to simulate precipitating and non-precipitating clouds. A 

forthcoming aerosol module will allow simulation of PM2.5 and PM10. 

Since the Models-3 system is new, some implementation and application 

problems are likely. 

• SARMAP Air Quality Model (J.S. Chang et. al, 1997). SAQM is a threedimensional non-hydrostatic model based upon the Regional Acid 

Deposition Model (Chang et. al 1987, 1990). However, SAQM includes 

a number of improvements over RADM, including: a fixed vertical 

coordinate system that is compatible with MM5; a horizontal coordinate 

system defined in a Lambert-Conformal projection that accounts for 

curvature of the Earth; a mass conservation module for compatibility 

with non-hydrostatic meteorological inputs; the Bott advection scheme 

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

numerical accuracy; two-way nesting, and the capability to use either the 

CB-IV or SAPRC chemical mechanisms. A version of SAQM with 

plume-in-grid treatment is also available. 

• Urban Airshed Model-Flexible Chemical Mechanism (Kumar et. al, 

1995). The UAM-FCM is an alternate version of the UAM-IV that has 

been enhanced to allow the flexibility to incorporate any Carbon Bondor SAPRC-type chemical mechanism. The FCM allows incorporation of 

reaction-specific photolysis rates. In addition, the UAM-FCM has a 

generalized methodology to solve the set of differential equations that is 

mechanism independent. However, the meteorological dispersion 

algorithms are the same as in UAM-IV. 

• Urban Airshed Model-Variable (SAI, 1996). The UAM-V is an updated 

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

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

models, and representation of emissions. Perhaps the most significant 

additions are: an updated CB-IV mechanism to include aqueous phase 

chemistry; plume-in-grid capabilities; an improved dry deposition 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 18 

algorithm; and an improved plume rise algorithm. Other enhancements 

over UAM-IV include allowing the user a fixed vertical structure as 

opposed to one that is relative to the diffusion break, the ability to use 

three dimensional inputs from prognostic models, and two-way grid 

nesting. However, the present non-public domain status of UAM-V may 

preclude regulatory usage. The model developers have indicated that the 

model could be made available for any party to review if the party agrees 

that the use of the model would be solely for the review of the AQMP. 

The ozone meteorological episodes to be simulated for the current AQMP 

include two from 1987 for continuity with previous AQMPs. It is planned to 

simulate those episodes using the UAM-IV or the UAM-FCM, with updated 

emissions only. For the SCOS97 and later episodes, the staffs of the ARB 

and the SCAQMD have agreed to test at least two of the available models: 

CAMx and CMAQ. CAMx will be run using both diagnostic and prognostic 

windfields. 

The models will be run using the Carbon Bond IV (Gery et. al, 1989) and 

SAPRC (Carter1990; Carter et. al, 1993; Carter 1995; Carter et. al, 1996; 

Carter et. al, 1997) chemical mechanisms. The SAPRC chemical 

mechanism is the basis for chemical reactivity scales. 

At its meeting on October 8, 1999, the ARB’s Reactivity Scientific Advisory 

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

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

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

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

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

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

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

the most up-to-date mechanism available, be used for SIP modeling. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 19 

HORIZONTAL AND VERTICAL GRID RESOLUTION 

The horizontal and vertical grid resolution for the 1987 SCAQS episodes 

will be unchanged; the details are provided in previous AQMPs. The 

remainder of this section describes the resolution for the SCOS97 and later 

episodes. 

Horizontal Grid Resolution

Over the past decade, photochemical models have been applied in California 

with horizontal grid resolutions in the range from 2 x 2 km to 8 x 8 km. The 

specific grid resolution chosen was primarily dependent on the size of the 

modeling domain, computer resources available, and the time and money 

available to carry out the simulations. In effect the final resolution was a 

compromise between the accuracy desired and the cost. However, the 

current generation of high-speed computers have minimized cost and 

resource constraints. 

The horizontal grid resolution plays an important role in the modeling 

process. Large grid resolution tends to smooth emission gradients and 

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

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

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

terrain features which may affect the air flow. 

• Air Quality Modeling 

For the year 2001 AQMP revision, 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 (e.g., 

SCAQMD, 1994) and for San Diego. In addition, this will reduce resources 

needed to create gridded emissions for the SCAQS episodes which are based 

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

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

and 74 cells in the north-south direction. The CMAQ model will be based 

on a Lambert-Conformal map projection system, as will CAMx when used 

with MM5-based meteorological fields; these models will use a slightly 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 20 

different domain. All other air quality models will use a UTM-based 

horizontal coordinate system. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 21 

• Meteorological Modeling 

Meteorological inputs to the air quality model will be provided for the same 

horizontal grid resolution and coordinate system (i.e., UTM or Lambert 

Conformal). More details of the meteorological modeling can be found in 

the section “METEOROLOGICAL INPUTS.” 

• Emission Inventory 

The emission inventory is based on a UTM coordinate system, with a 

horizontal resolution of 2 km. For CMAQ, the emissions will be mapped 

from UTM into a Lambert Conformal coordinate system. More information 

on the inventory can be found in the section entitled “EMISSION 

INVENTORY.” 

Vertical Resolution

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

defined for photochemical modeling domains has been limited by 

computational resources. In addition, available aloft meteorological and air 

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

result, simulation results have been limited by the relatively few number of 

vertical layers within the surface mixed layer, resulting in poor 

representation of the stratification of the atmosphere. 

As enhanced aerometric databases have become available—such as the 1990 

San Joaquin Valley Air Quality Study and 1997 SCOS97—more has been 

learned about the vertical structure of the atmosphere. The ability to better 

simulate the vertical structure of the atmosphere is emerging due to the 

availability of measurements aloft, the emergence of higher-speed 

computers, and our increased experience with diagnostic and prognostic 

meteorological models. 

• Air Quality Modeling 

To improve the vertical representation of the atmosphere, the number of 

vertical layers below 500 m (the nominal height of the afternoon mixing 

height within the Los Angeles coastal plain) will be increased from previous 

studies, and the modeling domain top will be set to a height of at least 5,000 

m. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 22 

CAMx/MM5 – The vertical structure is shown in Table 1. 

CAMx/CALMET - The fixed height vertical layers are the same as those 

from CALMET, and are shown in Table 2. 

UAM/FCM – As for the UAM, the vertical atmosphere is defined in two 

zones: that above the mixing height and that below. A total of 5-8 vertical 

layers are proposed for the SCOS97 simulations. 

CMAQ – The vertical structure is shown in Table 1. 

Table 1

Proposed vertical layer heights for CMAQ and CAMx/MM5 

 Layer # Height (m)*

 1 ...................58 

 2 ..................146 

 3 ..................250 

 4 ..................369 

 5 ..................490 

 6 ..................613 

 Layer # Height (m)*

 7...................737 

 8...................879 

 9.................1,022 

 10................ 1,234 

 11................ 1,450 

 Layer # Height (m)*

 12................1,767 

 13................2,094 

 14................2,942 

 15................3,962 

 16................4,986 

* These height estimates are based on sigma-level calculations at sea level using 

standard conditions. Height increments will decrease as terrain elevation 

increases.

• Meteorological Modeling 

For the terrain-following CALMET model, the proposed vertical layer 

definition is shown below. 

Table 2

Proposed vertical layer heights for CALMET/CALGRID 

 Layer # Height (m)* Layer # Height (m)* Layer # Height (m)*
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 23 

 1.................20.0 

 2.................60.0 

 3................100.0 

 4................300.0 

 5................400.0 

 6................500.0 

 7 ............... 600.0 

 8 ............... 800.0 

 9 ............ 1,000.0

 10 ........... 1,500.0

 11 ........... 2,000.0 

 12 .............. 2,500.0 

 13 .............. 3,000.0 

 14 .............. 3,500.0 

 15 .............. 4,000.0 

 16 .............. 5,000.0 

* Heights are for a constant-height, terrain-following coordinate system
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 24 

For the MM5 prognostic model, the following vertical structure is proposed. 

Table 3

Proposed vertical structure for MM5 

 Level Height (m)*

 30................. 61 

 29................ 154 

 28................ 263 

 27................ 389 

 26................ 516 

 25................ 646 

 24................ 776 

 23................ 926 

 22............... 1077 

 21............... 1300 

 Level Height (m)*

 20 ..............1528 

 19 ..............1862 

 18 ..............2207 

 17 ..............2714 

 16 ..............3228 

 15 ..............3837 

 14 ..............4452 

 13 ..............5083 

 12 ..............5816 

 11 ..............6551 

 Level Height (m)*

 10 ............. 7270 

 9 .............. 7981 

 8 .............. 8773 

 7 .............. 9624 

 6 ............. 10499 

 5 ............. 11371 

 4 ............. 12230 

 3 ............. 13227 

 2 ............. 14334 

 1 ............. 15635 

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

scale. The above layer heights were calculated from sea level using standard 

conditions. Layer heights are lower relative to ground level as terrain height 

increases.
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 25 

METEOROLOGICAL INPUTS 

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

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

windfields describe the transport and dispersion of pollutants. Mixing 

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

Temperature and insolation fields influence emission rates and the rates of 

chemical transformation. Because meteorological measurements can be 

made only at discrete locations, meteorological models are required to 

develop the 3-dimensional fields required by models. 

The meteorological models used to generate these three-dimensional fields 

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

Objective models are the least sophisticated meteorological models. These 

models rely on interpolation of observations. Obtaining a reasonable field 

requires sufficient observations to accurately represent the atmosphere. This 

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

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

complex, and a successful objective analysis would require an extremely 

large number of observations. 

Diagnostic models rely both on observations and constraints based on 

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

model can simulate thermally induced circulations and the effects of surface 

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

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

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

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

effects of terrain. Available observations are then used to develop 

meteorological fields using objective analysis. The initial guess and the 

objective analysis are then combined using a weighting function based on 

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

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

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

relatively independent, the model is criticized for not being 

thermodynamically consistent between the meteorological parameter fields. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 26 

Prognostic models are the most sophisticated of the meteorological models. 

They are based on principles of atmospheric physics, i.e., conservation of 

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

computationally intensive. The use of four dimensional data assimilation 

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

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

meteorology to improve the accuracy of the outputs. Another approach is 

objective combination, in which observations are introduced after the model 

has estimated a value. Prognostic models are capable of explicitly 

incorporating many of the physical flow processes important in the domain. 

However, prognostic models have historically had problems estimating finescale flow features due to the limited resolution of datasets used for 

describing geographic features 

As indicated previously, no new meteorological work is planned for the 

1987 episodes, which will be re-simulated with new emissions only for 

continuity with prior plans. The remainder of this section focuses on the 

preparation and review of meteorological inputs for the SCOS97 and later 

episodes. 

Previous Applications

In the past, the ARB and the SCAQMD have utilized prognostic, diagnostic, 

and objective models to generate meteorological inputs for modeling. The 

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

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

planning in the San Joaquin Valley. The SCAQMD also has experience with 

the SAIMM prognostic model. Diagnostic models (WIND2D, WIND3D, 

DWM) have been applied in the Sacramento area and in southern California 

to prepare meteorological input fields for the application of photochemical 

models in those areas. The ARB and the SCAQMD have also begun a 

review of CALMET, which may be viewed as an improved version of the 

DWM and which is being distributed through the USEPA for air quality 

modeling applications. The CALMET model has an added feature that 

allows a hybrid meteorological field to be developed by merging the results 

from a prognostic model, such as MM5, with an objective or diagnostic 

analysis characteristic of the CALMET model. This hybrid approach has the 

potential to take advantage of the prognostic capabilities of MM5 in areas of 

the domain where meteorological measurements are few, and utilizing 

measurements in an objective analysis where there are many. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 27 

Proposed Approaches

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

density of meteorological observations. While this dataset suggests that an 

objective/diagnostic model could be adequate to develop the meteorological 

parameter fields required for air quality modeling, there are large portions of 

the modeling domain—such as over the ocean or the inland desert—where 

there are few observations. Therefore, three approaches are proposed to 

develop the necessary meteorological fields. After the fields have been 

developed using each approach, they will be evaluated to determine which is 

the most suitable for air quality modeling. This evaluation is described 

below. 

• Diagnostic Modeling 

The first approach will be to use the diagnostic meteorological model 

CALMET. As described previously, CALMET uses a fixed-height, terrainfollowing coordinate system. For the AQMP modeling, 16 vertical layers 

will be used to a height of 5,000 m above the ground (see Table 2). 

• Prognostic Modeling 

The second approach will be to use the MM5 prognostic model. The 

meteorological boundary conditions for MM5 are generated using the output 

from a Global Climate Model (GCM) with a relatively coarse grid scale of 

45 km. Nested domains of 15 km and 5 km are then defined within MM5 to 

simulate meteorological fields for the fine grid scale of the modeling 

domain. The modeling domain for MM5 is defined in a Lambert-Conformal 

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

modeling domain over such a large region. Figure 2 shows the nested MM5 

domains. Figure 3 shows the finest scale (interior) MM5 domain. 

The vertical structure of MM5 is defined in a terrain-following, “sigma” 

coordinate system based upon a normalized pressure index. The 30 vertical 

layers defined for MM5 (see Table 3) can be transformed to fit the 

requirements of any air quality model. 

• Hybrid Approach 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 28 

The third approach for developing meteorological parameter fields will be to 

combine the results of the CALMET and MM5 models into a hybrid 

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

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

observations. The resultant fields benefit from the capabilities of the 

prognostic model in those areas of the modeling domain with few 

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

and benefit from the objective analysis component of the diagnostic model 

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

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

common horizontal and vertical coordinate domains. The CALMET model 

code is structured to facilitate this mapping. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 29 

Meteorological Input Validation and Technical Review

The meteorological inputs have a profound influence on the spatial and 

temporal resolution of ozone and other pollutant concentrations estimated by 

the air quality model. It is therefore essential that the products of 

meteorological models undergo a rigorous evaluation. By evaluating both 

offshore and onshore flow characteristics of the windfield and other key 

meteorological parameters the uncertainty in the air quality simulations can 

be minimized. 

This process includes an evaluation of the gross circulation features in the 

modeling region to determine if the model is replicating those essential 

features (Mulberg, 1995, Lolk and Douglas, 1996). These features include 

areas of convergence and divergence, eddy circulations, land/sea breezes, 

slope flows, and transport corridors. Since the modeling domain includes 

large overwater areas it is also necessary to evaluate offshore flows as well. 

Key features of the windfield are areas of convergence and divergence. 

These features result in vertical velocities which can transport pollutants 

upward (in the case of convergence) or bring pollutants from aloft down to 

the surface (with divergence). The evaluation should include a review of the 

convergence and divergence zones in the simulated windfield to determine if 

they agree with measurements or conceptual models in terms of location, 

timing, and extent. The impact on vertical velocities should also be 

evaluated. 

Converging flows can sometimes result in an eddy circulation. In the 

SCOS97 domain two key eddy features are prevalent: the Catalina eddy 

(centered near Catalina Island), and the Gaviota Eddy in the Santa Barbara 

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

transport mechanisms; they are capable of transporting precursors and aged 

ozone concentrations onshore. Exceedances of the ozone standards are often 

observed with the presence of an eddy circulation. The timing of the onset 

of eddy circulation, its persistence, and spatial extent should be considered 

as part of the windfield validation. 

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

breeze is one method whereby pollutants generated in the Los Angeles Basin 

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

determine how far precursors and ozone generated near the coast will be 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 30 

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

precursor emissions to be transported to the wrong locations instead of 

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

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

observations for reasonableness. 

The onshore portion of the SCOS97 domain includes areas of complex 

terrain. Slope flows are important as a recirculation mechanism that may 

influence ozone concentrations. Slope flows are probably the most 

challenging feature for prognostic meteorological models, due to the sparse 

observational data in complex terrain. A proposed qualitative approach is to 

determine if wind speeds estimated by the model appear to be reasonable in 

areas of complex terrain. 

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

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

throughout the SCOS97 domain (Seaman et. al., 1995, Bigler-Engler et. al., 

1996). Temporal plots for key sites will be examined to determine 

agreement with observations. Quantitative techniques will make use of 

statistical measures such as the mean gross error and mean bias to compare 

modeled and measured wind speeds (Mulberg, 1995). 

The proposed approaches for generating meteorological inputs incorporate 

observations, thus it should be expected that good agreement near those 

observation sites where data was used as input to the model. In order to 

diagnose the impact that incorporation of the observations has on the 

meteorological models, it may be useful to consider withholding some 

observations when executing the models to have an independent set of 

observations for comparison. The sites withheld should have some relation 

to the sites used to provide some assurance in the results from the 

comparison. This diagnostic evaluation is proposed to be conducted once 

acceptable meteorological fields have been prepared. 

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

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

estimated and observed temperatures. The interface at the coastline will also 

be examined. Mean bias and mean gross error statistics will also be 

calculated to provide quantitative measures of performance. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 31 

In addition, the vertical temperature profiles generated by the models will be 

compared to those observed at rawinsonde sites and wind profiler locations. 

The vertical temperature profile influences the stability characteristics of the 

modeling domain. One of the most notable affects is on mixing heights. 

The evaluation will include temporal and spatial evaluations of simulated 

mixing heights as compared to those estimated from observed soundings and 

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

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

concentrations. 

The staffs of the District and the ARB will consider the above procedures in 

judging the meteorological fields and in reaching consensus on the 

appropriateness of those fields for use in the AQMP. 

Figure 2

Nested MM5 Domains 

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

middle domain is 15 km, and for the fine scale domain is 5 km. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 32 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 33 

Figure 3

The Fine-Scale (5 km) MM5 Domain. 

-10

-10

1000

1000

1000

1000

1000

1000

1000

1000

1000

1000

1000

1000

1000 1000

1000
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 34 

EMISSION INVENTORY 

Ozone episodes occurring in 1987, 1997, and 1998 will be simulated for the 

AQMP. Base year inventories for those years for CO, NOx, SOx, and TOG 

are needed for photochemical ozone modeling. PM is also included in the 

inventory in order to support inputs needed for aerosol modeling. 

Photochemical air quality models require gridded, hourly emissions. The 

information needed to complete the emission inventory for the modeling 

region is obtained from local air pollution control districts, transportation 

planning agencies, and the California Air Resources Board. In addition, 

contracts have been established with universities and the private sector to 

provide important inventory components. 

1997 (SCOS97) and 1998 Episodes

The 1997 (SCOS97) and 1998 episodes will be simulated using the larger, 

regional modeling domain. To develop a regional emission inventory for 

these episodes, a SCOS97 Emission Inventory Working Group (EIWG) was 

formed. The EIWG is comprised of members from the ARB, SCAQMD, 

SDCAPCD, VCAPCD, ICAPCD, MDAQMD, SBCAPCD, and US Navy 

(ARB 1997a). The products of that effort, which is described below, will 

directly benefit the AQMP process. 

Anthropogenic Emissions 

• Point Sources 

Point sources are the responsibility of the districts. Emission inventories for 

point sources (including RECLAIM facilities) are compiled by local districts 

and reported to the ARB. If annual emissions for a facility fall below 10 

tons/year (this cutoff varies with district) the source is included in the area 

source inventory. Point sources are allocated to grid cells using the location 

that is stored as part of the point source emission database. Temporal codes 

which describe hours of operation are also included in the emission 

database. Factors are also stored to convert annual average emissions to a 

specific month and day of week. 

Point sources have been inventoried for 1997. The SCAQMD’s point source 

inventory for 1997 includes an update to locations (UTM coordinates) and 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 35 

stack parameters. Point source emissions for 1998 will be estimated using 

the ARB’s California Emission Forecast System (Johnson, 1997). 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 36 

• Area Sources 

Area sources are comprised of emission source types that are difficult to 

inventory individually. Examples are architectural coatings, residential 

water heating, gasoline stations, and off-road mobile sources not included in 

the ARB OFFROAD model. Districts and the ARB share responsibility for 

estimating area source emissions according to a long-standing division of 

categories. Methodologies used to estimate emissions from area sources are 

described in several documents (ARB 1997b). Factors are also included that 

allow estimates of specific month and day of week emissions from annual 

average emissions. Temporal codes which describe hours of operation are 

also included in the area source emission database. 

Area source categories have been inventoried for 1997. Emissions for 1998 

will be grown using ARB’s emission forecasting system. 

• On-Road Mobile Sources 

On-road mobile source inventories are prepared using vehicle activity data 

from transportation planning agencies. The majority of travel is reflected in 

transportation plans developed by: 

3⁄4 Southern California Association of Governments (SCAG); 

3⁄4 San Diego Association of Governments (SANDAG); 

3⁄4 Santa Barbara County Association of Governments (SBCAG); and 

3⁄4 Kern Council of Governments (Kern COG). 

Travel for areas not covered by the transportation planning agencies is 

extracted from the California Statewide Planning Model maintained by the 

California Department of Transportation. 

Emission factors for on-road mobile sources will be estimated using the 

ARB’s EMFAC2000 (EMFAC2K) emission factor model. DTIM4 will use 

both the emission factors and travel activity data to produce hourly gridded 

emission estimates for the SCOS97 region. 

The ARB has an extramural contract to acquire all travel data needed for this 

modeling study. The contractor will ensure that the digitized highways are 

consistent at the boundaries of the various planning areas. The network and 

travel activity data provided by transportation planning agencies is 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 37 

developed for peak and off-peak time periods, which will be processed into 

24 hourly data sets. As discussed below, day-specific traffic count data will 

be used to calibrate DTIM4 inputs for development of day-specific on-road 

mobile source emissions. 

ARB staff will use the network and travel activity data to produce gridded 

DTIM4 inventories for episode days not run by the contractor. One of the 

contractor’s tasks is to provide training in processing inputs and executing 

the DTIM4 code. 

• Other Mobile Sources 

Area source emissions from most categories of off-road mobile sources will 

be estimated using ARB’s off-road mobile source emission model 

(OFFROAD). OFFROAD covers more than 12 off-road categories, 

including lawn and garden equipment, small utility and construction 

equipment, as well as farm equipment. Categories not estimated by 

OFFROAD will be covered under “area sources”. However, specific 

emissions for aircraft, marine vessels, and locomotives will be provided 

through separate special studies. OFFROAD will produce countywide 

emission inventories for each calendar year desired. The OFFROAD model 

will have the capability to estimate exhaust, starting, and evaporative 

emissions for differing spatial and temporal conditions. 

• Day-Specific Emissions 

Emissions from many sources vary from day to day. Evaporative emissions 

from vehicles and vegetation increase with ambient temperature. Exhaust 

emissions are also a function of ambient temperature. Increased air 

conditioning demands on hot days also lead to increased emissions from 

electrical generation. Hourly surface temperatures for episode days are 

interpolated to each grid cell and are used in estimating emissions from 

vegetation and on-road mobile sources. 

Criteria pollutant emissions from approximately 80 major point sources will 

also be estimated hourly for each specific episode day. Each district has 

acquired data from major point sources for 1997 episode days and has 

already finished or is developing day-specific point source inventories for 

that year. Day-specific data for the July 1998 episode have been solicited 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 38 

from the districts. Districts also collected information on variances, 

temporary breakdowns, and shutdowns. 

The results from a University of California, Davis project (that is nearing 

completion) to incorporate traffic count data from episode days will be used 

to calibrate DTIM4 inputs. The contractor will run the DTIM4 program to 

develop mobile source inventories for several episode days, including 

weekend days. 

Wildfire emissions occurring on 1997 and 1998 episode days have been 

compiled by ARB staff, and hourly emissions have been estimated for each 

wildfire. The type of information collected will allow development of 

temporally and spatially resolved emission estimates. 

A computer model to estimate emissions from large ships for the SCOS97 

episodes is being developed by ARB staff. Ship activity data (for 

commercial vessels) from shipping ports, ship-specific engine characteristics 

data, and the latest emission factors will be used to estimate emissions for 

transit in the shipping lanes and at the ports. 

The ARB has purchased aircraft activity data for the SCOS97 and July 1998 

episode days. This data has one minute radar-derived locations for all 

aircraft in the SCOS97 region. Hourly landing, takeoff, approach, climbout, 

and cruise emissions can be determined from these data for each episode 

day. This database will be used to construct a three dimensional commercial 

jet emissions array that can be input to photochemical models. 

In addition to the ARB efforts, the District is sponsoring special studies to 

collect aircraft and marine vessel activity data that will complement the 

ARB database. 

An analysis of air quality-related special events (such as air shows, sporting 

events, and unusual traffic problems) has turned up an absence of such 

events. 

Natural Emissions 

• Biogenics 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 39 

The derivation of a gridded biogenic emission inventory requires data sets 

describing the spatial distributions of plant species, biomass, and emission 

factors that define rates of hydrocarbon emissions for each plant species. 

The Biogenic Emission Inventory System (BEIS 2.3) (USEPA, 1995) 

model, distributed by the USEPA for this purpose, is one source of these 

data sets for areas throughout the United States. However, the BEIS model 

has been shown to have limited use in California because of poor spatial 

resolution within the referenced data sets and a simplified scheme for 

assigning emission factors (e.g., Jackson, et al., 1996). The development of 

a gridded biogenic emission inventory for the SCOS97 domain will benefit 

from research conducted within California that describes the needed data 

sets in more detail than is defined within the BEIS model (Benjamin et. al., 

1998). 

ARB staff in consultation with researchers at UCLA has developed a 

methodology to complete a gridded biogenics inventory for the SCOS97 

region. The following paragraphs describe this approach. 

Gridded Plant Species Maps 

The distribution of plant species within the SCOS97 modeling domain will 

be determined using a composite of a number of data sets. Plant species as a 

function of urban land-use classifications were described by SCAQMD 

(1990) for the Los Angeles basin and updated by Benjamin et. al. (1996) and 

Arey et. al. (1995). These land-use classifications were extrapolated to other 

urban areas within the modeling domain. For the SCOS97 modeling 

domain, plant species distributions were taken from the GAP data base 

(Davis et. al., 1995), an inventory of biomass diversity for the United States. 

Biomass Distribution 

Plant biomass is difficult to measure and there are few descriptive data sets 

of biomass distribution for areas within the SCOS97 modeling domain. 

Therefore, estimates of biomass distribution were determined using 

published correlations between biomass and Normalized Difference 

Vegetative Index (NDVI). The NDVI is an index of relative "greeness" 

calculated from Advanced Very High Resolution Radiometer (AVHRR) 

data. Spatial distribution of NDVI from satellite remote sensing data sets 

were acquired from the United States Geological Survey EROS Data Center. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 40 

Emission Factors 

The chemical species and rates of hydrocarbon emissions vary by plant 

species. Emission factors have been measured for only a relatively small 

subset of the more than 6,000 plant species known in California, and for 

only a few general categories of chemical species. However, research has 

shown that emission factors for various plant species can be correlated using 

taxonomic relationships between the plant species (Benjamin et. al., 1996). 

Using this “taxonomic model”, emission factors were assigned for all plant 

species known to exist within the SCOS97 modeling domain. However, 

because of the limited research results available, biogenic emission factors 

are available for only three classes of hydrocarbons: isoprene, monoterpenes, 

and methyl butenol. 

Gridded Biogenic Inventory 

The gridded plant species, biomass distribution, and emission factor 

databases will be combined with episode-specific ambient temperature and 

photosynthetically active radiation data using a Geographic Information 

System to produce gridded hourly emissions of isoprene, monoterpenes, and 

methylbutenol. 

• Soil NOx Emissions 

Soil emissions of NOx to the air have been associated with the use of 

nitrogen containing fertilizers. Emissions have been estimated from 

fertilizer usage in the San Joaquin Valley. The soil NOx emissions were 

seen to have insignificant impacts on ozone concentrations. As a result, a 

soil NOx inventory has not been planned for this study. 

• Oil and Gas Seeps 

There are substantial emissions from oil and gas seeps near the coast in the 

area of Santa Barbara county. Estimates for these sources are provided to 

the ARB by the SBCAPCD. 

Organic Gas Speciation 

Organic gas speciation profiles are applied to all categories of TOG 

emissions to obtain estimates for each organic gas species emitted in the 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 41 

modeling region. ARB maintains a database of current profiles that are 

routinely updated to reflect recent information. The most recent updates 

were for gasoline exhaust and evaporation, diesel exhaust and jet engine 

exhaust. These recent updates were presented by the ARB at the September 

10, 1998, VOC Speciation Workshop held in Sacramento. The ARB 

publication “Identification of VOC Species Profiles” (ARB 1991) 

documents the organic gas profiles; an update is planned by summer 1999 to 

reflect the recent workshop. 

Gridding Surrogates 

Area and OFFROAD emissions are estimated and stored in the emission 

inventory database at the county level. There are many types of data (with 

highly resolved spatial resolution) that can be used as surrogates for one or 

more emission categories. For example, census tract population can be used 

to allocate consumer product emissions to grid cells. Housing units, also 

available for census tracts, can be used to spatially allocate residential lawn 

and garden equipment emissions. 

Maps are used to digitize and spatially allocate emissions for several 

categories of watercraft, locomotives, and airports. 

The ARB contractor working on the DTIM4 runs will also provide gridded 

surrogates for all area and off-road mobile source emission categories. The 

contractor will seek inputs from the districts and ARB on the appropriate 

gridding surrogates to use for each emission category for the SCOS97 

region. 

SCOS97 surrogates are being resolved to a 2 kilometer grid scale to allow 

high spatial resolution if needed. The 2 kilometer inventory can easily be 

aggregated to larger grid cells. 

Northern Mexico Inventory 

A portion of northern Mexico is included in the SCOS97 modeling region. 

A gridded inventory for this region was developed as part of a study to 

develop a 1990 gridded inventory for the region (SAI, 1997). The ARB 

contractor will also review and recommend updates to the gridded inventory 

for Northern Mexico. Organic gas emissions will be speciated using the 

most appropriate species profiles used for California emission categories. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 42 

Quality Assurance Procedures 

ARB provided specific guidelines to assist state and local agencies in 

implementing uniform and systematic approaches for collecting, compiling, 

and reporting emission inventory data. A comprehensive quality control and 

quality assurance plan was prepared to ensure good quality practices during 

development of the 1997, and future year emission inventories. These 

procedures include: quality control checks for collecting non-emission data, 

updating activity data, and using appropriate emission factors for calculating 

emissions; emission calculation methodology; quality assurance evaluation 

using the Data Attribute Rating System (DARS); and quality review of the 

entire inventory. The DARS program, originally developed by the USEPA, 

will be used as an additional quality assurance tool to quantify the relative 

accuracy of the annual emission inventories. ARB has also provided the 

districts with a variety of quality assurance reports to aid in the review of 

inventory data important for modeling. These reports were intended to 

provide checks on the accuracy of the emission calculations, stack data, 

facility location data, temporal data, devices data, process data, etc. 

Emission Projections 

Future year emissions form the basis for an air quality emission reduction 

target. Future year emissions for area and point sources are projected by 

accounting for growth and control, generally using growth and control 

factors applied to the base year (1997) 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 ARB-sponsored research factors elsewhere. Area source and 

offroad emissions are gridded using the appropriate surrogates as used for 

1997. The ARB contractor is also preparing gridded future year surrogates 

for the entire SCOS97 region for 2005, 2010, and 2020. Surrogates for other 

years can be interpolated as needed. 

Future year traffic activity and network data are also prepared by local 

planning agencies. EMFAC2K will give estimates of future year emission 

factors. DTIM4 uses future year emission factors and network travel data to 

obtain gridded future year on-road mobile emissions. DTIM4 inputs for 

future years are being compiled and prepared (for DTIM4 input) under 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 43 

contract to the ARB. Ambient temperatures that occurred during 1997 are 

also used in calculating future year emissions for each episode day. 

Biogenic emissions will not change for future years. Even though there may 

be a shift in farm or landscaping plans and species, the capability does not 

exist to incorporate any potential changes into the inventory. Seep 

emissions will also remain constant in future year inventories. 

1987 (SCAQS) episodes

The 1987 (SCAQS) episodes will be simulated for continuity with the 1994 

SIP and the 1997 AQMP, using the SCAQS modeling domain. Emissions 

for these episodes will be updated to reflect the following: 

• a 1987 inventory re-created from a 1997 base year; 

• improved VOC speciation profiles; 

• ARB's EMFAC2K on-road mobile source emission factor model; 

• the latest version of the Direct Travel Impact Model (DTIM4); and 

• the ARB's OFFROAD (off-road) mobile source emission factor model. 

In addition, work is currently underway to review stack parameter data 

within the SCAQMD, and the results of that effort will be incorporated when 

available. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 44 

INITIAL AND BOUNDARY CONDITIONS 

Initial and boundary conditions for the 1987 episodes have been developed 

previously; no new work is planned for those episodes. 

Initial Conditions

Initial conditions define the spatial distribution of chemical species 

concentrations throughout the 3-dimensional modeling domain at the time at 

which the air quality model simulation begins. There are two limitations 

inherent in defining initial conditions. The first is that chemical species 

concentrations are only measured at discrete locations and, for some species, 

for discrete time periods. Therefore, observed concentrations must be 

extrapolated to estimate concentrations throughout the modeling domain. 

The second limitation is that observed chemical species concentrations may 

not represent chemical equilibrium, especially since not all important 

chemical species are measured explicitly. 

To minimize the importance of initial conditions on air quality model 

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

before the period of interest. Historically, this “spinup” time interval has 

ranged between 8 and 72 hours. For the 1997 and 1998 episodes, the spinup 

period will begin at between 0000 PDT and 0500 PDT of the day before the 

first day of interest (the difference in the begin time reflects the difference in 

time-base – GMT vs. local time – for some meteorological models). This 

spinup period will allow a full diurnal cycle of sunlight, prior to the period 

of interest, to enable the air quality model to reach chemical equilibrium. 

Initial conditions will be determined by interpolation/extrapolation of the 

chemical species concentration measurements available from the SCOS97 

field study archive or other episode-specific data. For the large areas of the 

study domain in which there are few such measurements, initial-conditions 

will be assigned “background” values based on the minimum concentrations 

measured from monitoring sites where measurements are available. 

Boundary Concentrations

Boundary concentrations are chemical species concentrations on the study 

domain boundaries and represent the concentrations for the air mass moving 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 45 

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

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

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

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

the modeling domain. 

Ideally, modeling domain boundaries are placed so remotely that simulation 

results are insensitive to boundary conditions. For the SCOS97 study 

domain, the influence of boundary conditions on the simulation results from 

an air quality model is problematic. Beyond the northern SCOS97 

boundary, emissions from central California could have an impact on the 

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

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

issue. Boundary conditions will be determined from measured chemical 

species concentrations where they are available from the SCOS97 field 

study. Where measurements are not available, background chemical species 

concentrations will be assigned based on the lowest concentrations from 

sites where concentrations were measured. A part of the air quality model 

evaluation process will be to assess the influence of boundary and initial 

concentrations on simulation results. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 46 

MODEL PERFORMANCE EVALUATION 

It is a well established tenet of the modeling community that for an air 

quality modeling simulation to give reliable results, it must be capable of 

giving the right answers for the right reasons. That is, not only must the 

model be capable of reproducing observed ozone measurements with a 

reasonable level of accuracy, but it must also pass a series of prescribed tests 

designed to ensure that the apparently accurate results are not produced by a 

combination of compensating errors. As discussed below, several tests on 

the modeling simulations, both at the surface and aloft–for precursor and 

secondary species in addition to ozone–as part of the model performance 

process are proposed to be conducted. The statistical tests will be performed 

for the domain, by district, and for several subregions to ensure that the 

domainwide statistics do not mask subregional problems. This information 

should allow a determination that the model is working properly. Much of 

the following information is taken from the ARB’s photochemical modeling 

guidance (ARB 1992). 

Statistical and Graphical Analyses

The evaluation will include both graphical and statistical analyses. 

Graphical analyses compare simulated pollutant concentrations with 

measured values. These will include time series plots showing temporal 

variations, contour plots showing spatial variations, scatter plots showing 

tendencies for over- or underestimation, and residual plots showing the 

distribution of the differences between observed and predicted 

concentrations. The statistical analyses will examine the accuracy of peak 

estimates (both paired and unpaired in time and space), mean normalized 

bias, mean absolute gross error, and mean absolute normalized gross error. 

The statistical performance criteria outlined in the ARB’s guidance 

document for Class B or better ozone performance will be used to guide the 

determination of acceptable model performance. These statistical criteria 

will be used as a criterion for acceptable model performance. However, 

other analyses (graphical, multi-species, aloft comparisons, and the 

diagnostic simulations) will also be used to determine acceptable model 

performance, and ultimately a conclusion that the model is working properly 

must be made considering all of the analyses. 

• SUBREGIONAL PERFORMANCE 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 47 

Since the South Coast Air Basin is very large, six subregions are proposed to 

be evaluated for model performance: San Fernando Valley, west (or coastal) 

basin, mid-basin, San Gabriel Valley, east basin, and the Coachella Valley 

area. 

The same statistical acceptance criteria for the subregions as for the entire 

domain will be used. 

Multi-Species Evaluations

To be useful for planning or other purposes, an air quality model must be 

able to replicate measured ozone concentrations with reasonable accuracy. 

However, it is also important to compare estimated and measured 

concentrations of ozone precursors and secondary species, to establish 

confidence that the chemistry processes are being simulated properly. The 

important precursors are NO, NOx, HONO, and organic gas species; 

important secondary species are HNO3 and PAN. Organic gas 

concentrations will be lumped according to the scheme employed by each 

model’s chemical mechanism. Comparisons will be made for each of the 

estimated precursor species and lumped organic gas species, for each 

monitoring location. In addition, comparisons will also be made for NOx, 

and total ROG. 

These comparisons may reveal problems not associated with those for ozone 

concentrations. Many of the precursor species have a secondary component 

as well. Concentrations of primary pollutants tend to have higher gradients 

than do secondary species. This makes it more difficult to assume that a 

measured concentration of a primary pollutant represents a grid cell average. 

For these reasons it is probably unreasonable to expect the same accuracy in 

replicating precursor concentrations as for ozone concentrations. Thus use 

of a specific statistical error or bias criterion is not recommended. These 

comparisons should be viewed as qualitative, to uncover potential problems 

in precursor and secondary performance. 

Aloft Comparisons

During the SCOS97 field study, concentrations of selected air pollutants 

were measured above the ground using aircraft, balloons, and LIDAR. The 

primary component of these measurements was oxidant concentrations 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 48 

measured with ozonesondes to a height of 5,000 m AGL. Ozonesondes 

were flown at seven sites, at 6-hour intervals, for selected episode days. 

Also, four aircraft were flown up to three times per day and an ozone 

LIDAR was operated continuously on selected episode days. 

The performance of air quality model simulations above the ground will be 

determined by comparing simulated oxidant and ozone concentrations with 

measurements, for the same times and locations. Measured concentration 

profiles will be averaged for the vertical layer increments corresponding to 

those of the air quality model. Because of the vertical spacing required for 

the air quality models, the vertical resolution of this comparison will be 

relatively poor. Therefore, initially this comparison will be of a qualitative 

nature only. 

In addition to measuring ozone, three of the aircraft measured oxides of 

nitrogen and collected samples for later hydrocarbon analysis. Comparisons 

between these precursor data and concentrations simulated using the air 

quality models will also be made. However, there are relatively few samples 

and because an aircraft is not in one grid cell for an hour, comparisons may 

not be consistent with modeled concentrations. Comparisons to see if any 

large discrepancies exist between modeled and measured concentrations 

aloft will be made. 

Diagnostic Simulations

Several diagnostic, or investigative, simulations are proposed to further 

determine the fidelity of the model results. These are anticipated to include 

the following: 

• Zero emissions – all anthropogenic and biogenic emissions will be set to 

zero to test the model’s sensitivity to emissions and to ensure that the 

base case results are influenced appropriately by the emission inputs. 

• Double anthropogenic emissions – all anthropogenic emissions will be 

doubled to test the model's sensitivity to increased man-made 

emissions. In addition, as separate tests of anthropogenic emissions 

affects, only mobile source emissions will be doubled and only 

stationary source emissions will be doubled. 

• Zero biogenics – biogenic emissions will be set to zero to test the 

model’s sensitivity to biogenic emissions. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 49 

• Zero and clean air boundary and initial conditions – the initial (interior) 

and boundary (at the top and sides of the modeling domain) 

conditions will be set to zero and USEPA recommended clean air 

values to determine model sensitivity to these inputs. 

• Zero surface deposition – deposition will be turned off for all species to 

examine the effects of dry deposition on ozone estimations. 

• Reduced wind speeds – reducing the wind speeds by 50% is proposed to 

test the model’s sensitivity to that parameter. However, the feasibility 

of doing so in the event that a dynamically consistent, prognostic 

model is used, has not yet been investigated. 

Acceptable Model Performance

While it is expected that acceptable model performance can be achieved for 

the new (SCOS97) and updated (SCAQS) ozone meteorological episodes, it 

is not always possible given the regulatory deadlines for plan submittals. 

Should the model performance of any of the new episodes not meet 

acceptable performance goals, that episode will not be used for carrying 

capacity and attainment demonstration purposes. The episode(s) will 

continue to be analyzed to improve on model performance for possible use 

in future plan revisions and SIP submittals. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 50 

USE OF THE MODELING RESULTS 

We anticipate that the model results will be used for carrying capacity 

estimation and attainment demonstration. Use of the model results for these 

goals is contingent upon acceptable base case model performance for the 

episodes simulated. That is, only episodes for which the model is judged to 

be operating properly and which meet the model performance acceptance 

criteria will be used. 

Historically, the District developed the carrying capacity and attainment 

demonstration for ozone based on a set of specific control measures that was 

projected to achieve the national 1-hour ozone air quality standard for all 

modeled episodes. For the 2001 AQMP revision, the ozone carrying 

capacity and attainment demonstration will again be based on a specific set 

of control measures such that the ozone concentrations predicted by the air 

quality model will be at or below 0.124 ppm (in order to be a viable 

attainment demonstration). However, we also propose to consider the use of 

adjustment factors (described below) to account for model bias and to use 

the model to address the ozone design value. 

The USEPA's draft 8-hour ozone guidance (USEPA, 1998b) proposes the 

use of relative reduction factors (RRFs) as part of the attainment 

demonstration process for the 8-hour ozone standard, assuming that 

satisfactory base year model performance is established. The RRF approach 

incorporates design period monitoring data directly into the attainment test 

along with the ratio of future to current year model predictions. This allows 

the model to be used in a relative, rather than absolute, sense to reduce 

uncertainty in the predictions. The use of RRFs also potentially address two 

problems in model applications that tend to result in underestimation of 

emission reductions needed to attain standards. The first problem is that 

modeled episodes usually have ozone concentrations lower than the design 

value. The second problem is that simulation results have historically 

exhibited a tendency towards underestimation of observed concentrations. 

By utilizing monitored data along with model estimations, RRFs address 

both problems. 

However, there may be some limitations in using RRFs for the one-hour 

standard. Examples of such situations include: 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 51 

• 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 is unreasonable to include the 

non-representative sites in the RRF analysis. 

• Model performance typically varies considerably between sites and 

episodes in a domain. The reported ozone performance measures (such 

as peak prediction accuracy, bias, and gross error) do not capture this 

variation. It may be unreasonable to include sites which have poor model 

performance for a given episode. 

The potential use of RRFs, and details of application, will be considered 

after model performance evaluations are completed and analyzed. If modelestimated peaks are near design values for some episodes then RRF-type 

adjustments will probably not be necessary. 

Carrying Capacity Estimation

A traditional use of models for planning has been the estimation of carrying 

capacities for ozone precursors. This is typically achieved by exercising the 

model with a series of across-the-board precursor emission reductions from 

the future year baseline, from which an ozone isopleth (“EKMA”) diagram 

is constructed. The metric used for the isopleth diagram can be one of 

several, such as peak 1-hour or 8-hour ozone concentrations within the 

modeling domain (or subregion), number of grid cells above the standard, or 

one of many population exposure metrics. Since the carrying capacity for 

each precursor is based on across-the-board emission changes, rather than 

source- and location-specific controls as would be specified in a plan, it 

should be viewed as an initial estimate only.

For the AQMP process, ozone isopleth diagrams by episode for the 

following air quality metrics will be constructed: 

• Peak 1-hour ozone concentration for the domain. 

• Population exposure for 1-hour ozone concentrations. 

• Peak 8-hour ozone concentration for the domain. This information will 

serve as an indicator of the need for potential additional precursor 

emission reductions to meet the 8-hour ozone NAAQS. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 52 

The resulting isopleth diagrams will be used as an initial guide for 

determining the emission reductions necessary for attainment. 

Attainment Demonstration

The attainment strategy will be developed as in the past. All proposed 

control measures (the control strategy) will be modeled with the future year 

(2010) emission inventory to predict if the control strategy achieves the 

ozone standard for the episode(s) simulated. When predicted ozone 

concentrations in all grid cells are 124 ppb or less for each episode this step 

is completed. 

Depending on the results from model performance evaluations, the use of 

RRFs will be considered, to account for differences between the episodes 

simulated and the ozone design values. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 53 

TECHNICAL OVERSIGHT 

The District has established a technical oversight committee to review the 

technical aspects of the ongoing modeling analyses. During the adoption of 

the 1989 AQMP revision, the District's Governing Board passed a resolution 

to form such a group to provide oversight and to enhance technical 

consensus on AQMP modeling issues. The District has budgeted for the 

formation of a Modeling Working Group (MWG) comprised of individuals 

with photochemical and aerosol modeling expertise. 

In 1997, the District Governing Board reconstituted the MWG and formed 

the Scientific, Technical, and Modeling Peer Review Advisory Group 

(STMPRAG). The STMPRAG role expands upon that of the MWG and 

includes experts in socioeconomic assessment and human health. 

The STMPRAG assists the District in resolving technical issues related to air 

quality modeling by providing ongoing technical review of procedures and 

analyses. The objectives of the STMPRAG are as follows: 

1. Suggest methods to gather and process meteorological, aerometric, and 

emission data with a specific focus on air quality modeling. 

2. Provide technical guidance to the District's air quality modeling efforts, 

with an emphasis on ozone and PM10. Areas of technical guidance 

include: 

• Formulation of modeling approaches; 

• Selection and development of appropriate modeling techniques; 

and 

• Identification of model performance evaluation methods. 

3. Review and provide comments on the District's modeling procedures and 

analysis. 

4. Make recommendations on future resource requirements (i.e., staffing 

and computational needs). 

5. Recommend methods for interpretation of modeling results. 

The MWG consists of representatives from the District, ARB, USEPA, 

American Automobile Manufacturers Association (AAMA), Southern 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 54 

California Association of Governments (SCAG), Southern California Edison 

(SCE), Western States Petroleum Association (WSPA), and special technical 

consultants. 

In addition to the STMPRAG, many of the technical work elements are 

being separately reviewed by a series of technical working groups 

established as part of the SCOS97 regional modeling effort. An Emission 

Inventory Working Group has already been established, and has met several 

times to discuss inventory-related issues and coordinate inventory 

preparation activities. A Meteorological Modeling Working Group has been 

convened with the goal of reviewing the preparation of regional 

meteorological inputs. Other working groups are planned, such as for air 

quality modeling. The participation of these groups will provide a valuable 

additional source of technical review to that of the STMPRAG. 

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

be shared with the interested public at appropriate times and locations. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 55 

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Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 58 

Conference on Applications of Air Pollution Meteorology with 

A&WMA, Atlanta. 

Mulberg, E. 1995. “A Comparison of Three Wind Models for the Broader 

Sacramento Area.” Proceedings Regional Photochemical Measurement 

and Modeling Studies, A&WMA, San Diego. 

SCAQMD. 1990. “Inventory of Leaf Biomass and Emission Factors for 

Vegetation in the South Coast Bain Basin.” Technical Report III-C. 

Draft Air Quality Managenent Plan. South Coast Air Quality 

Management District. Diamond Bar, CA. 91765. 

SCAQMD. 1994. “Ozone Modeling - Performance Evaluation.” Technical 

Report V-B. Air Quality Managenent Plan. South Coast Air Quality 

Management District. Diamond Bar, CA. 91765. 

Seaman, N.L., D.R. Stauffer, and A.M. Lario. 1995. “A Multi-Scale Four 

Dimensional Data Assimilation System Applied in the San Joaquin 

Valley During SARMAP. Part 1: Modeling Design and Basic 

Performance Characteristics.” Journal of Applied Meteorology, 34:1739-

1776. 

Sonoma Technology, Inc. 1996. “Development of a Gridded Leaf Biomass 

Inventory for use in Estimating Biogenic Emissions for Urban Airshed 

Modeling”. Final Report STI-996086-1599-R. Ventura County APCD. 

Ventura, CA. 93003-5401. August 28, 1996. 

Smith, T.B., W.D. Saunders, and D.M. Takeuchi. 1984. Application of 

Climatological Analysis to Minimize Air Pollution Impacts in California. 

Prepared for the California Air Resources Board, Sacramento. Report 

A2-119-32. 

Systems Applications, Intl. 1996. User’s Guide to the Variable-Grid Urban 

Airshed Model (UAM-V). October 1996. SYSAPP-96-95/27r. 

Systems Applications, Intl. 1997. Preparation of a Draft 1990 Gridded 

Emission Inventory for Southern California. March 1997. Prepared for 

the California Air Resources Board. SYSAPP-97/08. 
Year 2001 AQMP 1-Hour Ozone Modeling Protocol Document (DRAFT #7, 7/2/01) 59 

United States Environmental Protection Agency. 1990. User’s Guide for 

the Urban Airshed Model, Volume I: User’s Manual for UAM (CB-IV). 

Office of Air Quality Planning and Standards. EPA-450/4-90-007A. 

United States Environmental Protection Agency. 1991. Guideline For 

Regulatory Application of the Urban Airshed Model. EPA Publication 

No. EPA-450/4-91-013. U.S. Environmental Protection Agency, 

Research Triangle Park, NC. 

United States Environmental Protection Agency. 1995. Urban Airshed 

Model (UAM) Biogenic Emission Inventory System Version 2. (BEIS2) 

User's Guide. Final Report. EPA Contract No. 68-D3-0034. Work 

Assignment No. 1-9. EC/R Project No. AQM-108. United States 

Enviromental Protection Agency. Air Quality Modeling Group. RTP, NC 

27711. 

United States Environmental Protection Agency. 1998a. EPA ThirdGeneration Air Quality Modeling System: Models-3 Volume 9b User 

Manual. June 1998. Office of Research and Development. EPA-600/R98/069(b). 

United States Environmental Protection Agency. 1998b. Use of Models and 

Other Analyses in Attainment Demonstrations for the 8-Hour Ozone 

NAAQS (Draft). October, 1998. 

Yamartino, R.J., J.S. Scire, S.R. Hanna, G.R. Carmichael, and Y.S. Chang. 

1989. CALGRID: A Mesoscale Photochemical Grid Model, Volume I: 

Model Formulation Document. June, 1989. Prepared for the California 

Air Resources Board by Sigma Research Corp. 

ATTACHMENT 2 

Expert Panel Modeling Critiques 
Review of Operational Evaluation for South Coast Air Quality 

Management District 2003 Air Quality Management Plan 

Judith C. Chow 

January 18, 2003 

Introduction 

The South Coast Air Quality Management District (SCAQMD) is 

conducting air quality modeling for ozone (O3) and PM10 in support of its 

2003 Air Quality Management Plan. The UAM (Ver 6.21), CALGRID, and 

CAMx air quality models were applied to 1997 base-year emissions and one 

field study episode (August 4-6, 1997) to estimate O3 in southern California 

for an operational evaluation. The models were configured to be as similar 

as possible in terms of grid size, boundary conditions, initial conditions, and 

deposition. The CB-IV chemical mechanism was used in UAM and both the 

CB-IV and SAPRC99f mechanisms were used in CALGRID and CAMx. 

The MM5 meteorological model generated prognostic wind fields that drove 

each of the models. The number and depths of vertical layers differed 

among the different models. 

Operational evaluations compare model outputs to measured concentrations 

of O3 and intermediate chemical components. The objective of this 

operational evaluation is to justify the selection of one of these modeling 

systems for evaluating effects of planned emission reductions in the South 

Coast Air Basin (SoCAB). Several comparison statistics and plots were 

created for O3, NOx, and CO model estimates and observations as part of this 

evaluation. 

The review evaluates these performance measures and comments on how 

these might be used to justify the model selection. 

Operational Evaluation Statistics for Ozone 

An initial examination of the plots and statistics for the UAM, CALGRID 

and CAMx models using CB-IV and/or SAPRC99 shows that the SAPRC 

mechanism yields much better agreement between model-estimated and 

measured O3. Given that all other inputs to CALGRID and CAMx are the 

same, and that the improvement it performance measures is consistent across 

the two models, it appears that the SAPRC99 mechanism better represents 

the ozone-forming chemistry in the modeled region, at least on an 

operational basis. Normalized bias, normalized error, and correlation 

statistics were tabulated for each sub-region and each of the five model 

applications. It was clear from this tabulation (not shown here) that biases 
and errors were smaller and correlations were higher (especially for r>0.6) 

for most of the sub-regions when the SAPRC99 mechanism was used. 

Table 1 summarizes the ranges of performance measures for O3 classified by 

the ten sub-regions. Entries in Table 1 are those that were closest to the U.S. 

EPA (1991) performance objectives of 0.8 t 1.2 for the unpaired peak ratio, 

±15% for average normalized bias, and <35% for average normalized gross 

error. For the models with both mechanisms, the SAPRC99 mechanism 

better attained these objectives than the CB-IV. However, the UAM with 

CB-IV was equivalent to or slightly better than the other models for subregions 1. N. Central Coast, 3. San Fernando Valley, and 5. LA. It would be 

useful to determine the extent to which performance measures would 

improve if the UAM was capable of using both chemical mechanisms as are 

the CALGRID and CAMx. 

All of the models tend to underestimate peak hourly O3 more often than they 

overestimate these concentrations. CALGRID-S appears to have the 

smallest bias most of the time. It usually underestimates O3 by ~10% to 

20% for all sub-regions except at the boundaries, where O3 is 

underestimated by ~30%. UAM-C appears to have performed best in the 

Los Angeles sub-region 5, and comparably with CALGRID-S in the N. 

Central Coast (2) and San Fernando Valley (3) sub-regions. The 

CALGRID-S August 6 statistics for the Los Angeles sub-region show a very 

large bias that contrast with good agreement between calculated and 

measured values on the previous two days. Valid O3 measurements were 

available at only the Pasadena and Palos Verdes monitors in this region, and 

this may be a partial cause of this and other biases. 

The ranges in Table 1 must be qualified by the fact that different numbers of 

modeled/measured comparisons are included in the comparison statistics. 

CALGRID-S and CAMx-S comparisons include three more sites (Tijuana, 

Trona, & Pt. Mugu) than were included for the CALGRID-C and CAMx-C 

comparison for sub-region 0. It is unclear why the Tijuana site is included in 

the background category. Pt. Mugu was included in sub-region 2 for the 

CB-IV comparisons. The Phelan-Beekley site was in sub-region 4 for the 

CB-IV comparisons and in sub-region 8 for the SAPRC99 comparisons. 

The sub-region 7 LA N. Main and SJVUCD sites used for SAPRC99 

comparisons were omitted for CB-IV comparisons. Documentation of the 

reasons for including different monitors in each sub-region for CB-IV and 

SAPRC99 runs is needed. It’s not clear why the sub-regions are defined as 

they are. 
Plots of residuals were available only for the CB-IV results. These show 

that all models tend to overestimate NO at low concentrations and especially 

at night. They tend to underestimate NO at high concentrations. Residuals 

appear to be largest for the UAM-C comparisons. NO2 concentrations 

biases are smaller than those for NO and shift from overestimation to 

underestimation at 70 to 80 ppb. 

Time series comparisons of calculated and measured O3 for CALGRID-S 

and CAMx-S show a tendency to underestimate the peak concentrations. 

Overestimation is more prevalent at outlying sites (sub-regions 1, 2, 7, 8, 

and 9). At low concentrations both CALGRID-S and CAMx-S show peaks 

and valleys that are not similar to those observed in the measurements. 

Model Selection for OZONE Control Strategy Simulations 

Operational evaluation is only one part of a comprehensive model evaluation 

effort. Seigneur et al. (2000) recommend: 1) operational testing that 

demonstrates an ability to estimate O3 and its intermediate chemical 

components; 2) diagnostic testing that examines the degree to which 

precursor and intermediate concentrations are reproduced; 3) mechanistic 

testing that determines the effects of emission and meteorological changes 

on estimated concentrations; and 4) probabilistic testing that quantifies 

uncertainties in model results. The operational comparison statistics indicate 

that the SAPRC99 mechanism probably performs better than the CB-IV. It 

is not clear that other differences between the models improve or degrade 

performance, although the CALGRID-S seems to meet the EPA criteria for 

most of the sub-regions. In many cases, CAMx-S produced comparable 

results. The use of more than one model over several episodes would 

facilitate understanding of the uncertainties related to proposed emission 

reduction strategies. 

 PM10 Modeling 

PM10 modeling is not as well documented or evaluated as O3 modeling. 

Information given consisted of: 1) tables of a linear rollback calculation, 2) 

tables of annual average concentrations estimated by UAM-AEROLT and 

compared with sulfate, nitrate, organic carbon, elemental carbon, 

ammonium, and “other” concentrations at five sites, and 3) time series plots 

of calculated and measured concentrations. Ambient measurements are 

from the 1995 PM10 Technical Enhancement Program (PTEP) that partially 

represent sub-regions 4 and 5 from Table 1. 
Comparison statistics show 7% to 34% differences in annual averages 

between calculated and measured concentrations for the different chemical 

components. For quarterly averages, the model overestimates measured 

PM10 by 40% to 50% for the first quarter and underestimates PM10 by 20% 

to 50% during the fourth quarter. Part of this might be due to the use of 

calendar quarters than seasonal quarters (e.g., Dec, Jan, and Feb for winter). 

Nitrate at Rubidoux is underestimated by 13%, but this includes a 35% 

overestimate during the first quarter and a 39% underestimate during the 

fourth quarter. The annual average differences masks more serious 

differences over shorter time periods. Comparisons of intermediate species 

concentrations such as ammonia and nitric acid is needed to evaluate the 

extent to which the model gives the right answers for the right reasons. 

There needs to be a greater unity between O3 and PM10 modeling, as NOx

and VOC strategies for O3 may also affect the ammonium nitrate in PM10. 

This is another argument in favor of using the SAPRC99 mechanism for 

ozone, as the CB-IV mechanism cannot be adapted to secondary organic 

aerosol formation (Stockwell, 2002). It appears that the UAM-AEROLT 

underestimates secondary organic carbon by factors of 2 to 3 compared to 

CMB (STMPR presentation, Aug 3, 2001). Several recent studies have used 

CAMx and CMAQS models for air quality forecasting (e.g., Cai et al., 2002, 

Stockwell et al., 2002) and for regional modeling in New England and 

Southern Nevada. Unifying assessment and forecasting capabilities for both 

O3 and PM10 would be a worthwhile goal that could provide continuous 

performance evaluation and continued improvement in the 

modeling/measurement system within the SoCAB. 

References 

Cai.,C., Hogrefe,C. Biswas, J. Rao,S.T., Seaman, N. Gibbs,A. Kallos, G., Katsfados,P. Walcek, C., Chang 

J., and Demerjian,K.L. (2002). An experimental air quality forecast modeling system (AWFMS) for the 

northeast United States: A demonstration study. Presented at the Fourth Conference on Atmospheric 

Chemisty, Orlando FL, American Meteorological Society. 

Seigneur,C., Pun,B.K., Pai,P., Louis,J., Solomon,P., Emery,C., Morris,R., Zahniser,M., Worsnop,D., 

Koutrakis,P., White,W.H., and Tombach,I.H. (2000). Guidance for the performance evaluation of threedimensional air quality modeling systems for particulate matter and visibility. JAWMA 50, 588-599. 

Stockwell, W.R., Artz, RS., Meagher, J.F., Petersen, R.A., Shere,K.L., Grell, G.A., Peckham,S.E., Stein 

A.F., Piercde, R.V., O’Sullivan, J.M., Whung, P.Y. (2002). The scientific basis of NOAA’s air quality 

forecasting program. EM 8, 20-27. 

Stockwell, W.R. (2002). Brief overview of gas-phase atmospheric chemistry mechanisms for air quality 

models. Internal NOAA report, National Oceanic and Atmospheric Administration, Silver Springs, MD. 

U.S. EPA (1991). Guideline for regulatory application of the Urban Airshed Model. EPA-450/4-91-013, 

U.S. Environmental Protection Agency, Research Triangle Park, NC. 
Table 1. Ranges of the best performance statistics for ozone modeling systems by modeling sub-region for 

the August 4-6, 1997 episode. (More than one modeling system is tabulated when statistics are 

comparable. Based on reports from Simulation ID arb97b for UAM-C, CALGRID-C, and CAMx-C, p85 

for CALGRID-S and p800s for CAMx-S. CALGRID-C and CAMx-C comparisons were examined but 

they consistently yielded poorer performance statistics than other models.) 

Sub-Regiona Modelb

Unpaired 

Ratio 

(range)c

Percent 

Normalized 

Bias 

(range)d

Percent 

Normalized 

Gross 

Error 

(range)e

0. Boundary CALGRIDS 0.67 to 

0.75 

-42 to -38 38 to 43 

1. N. Central Coast CALGRIDS 0.89 to 

0.95 

-14 to 0 8 to 20 

 UAM-C 0.8 to 1.02 -19 to -12 16 to 24 

2. Ventura County CALGRIDS 0.82 to 

1.24 

-4 to 20 11 to 37 

 CAMx-S 0.75 to 

1.01 

-28 to -4 13 to 29 

3. San Fernando 

Valley 

CALGRIDS 0.91 to 1.1 -3 to 14 15 to 31 

 UAM-C 0.8 to 1.1 -20 to -6 25 to 32 

4. Eastern SoCAB CALGRIDS 0.82 to 

0.99 

-5 to 16 24 to 34 

5. Los Angeles UAM-C 0.72 to 

1.09 

3 to 8 22 to 40 

 Aug 4&5 CALGRIDS 0.81 to 1.0 -10 to -5 12 to 25 

 Aug 6 CALGRIDS 1.52 -10 60 

6. SanDiego/Baja CAMx-S 0.94 to 

0.97 

-29 to-26 34 to 38 

 CALGRIDS 1.09 to 

1.27 

-16 to 2 33 to 42 

7. S. San Joaquin CALGRID- 0.82 to -28 to -14 20 to 42 
Valley S 0.86 

8. Antelope 

Valley 

CAMx-S 0.73 to 

1.07 

-27 to -8 20 to 28 

 CALGRIDS 1.02 to 

1.36 

3 to 8 22 to 40 

9. Imperial Valley CAMx-S 0.75 to 

1.02 

-40 to -33 41 to 44 

 CALGRIDS 1.13 to 

1.60 

-3 to 3 26 to 52 

a

Groups of monitoring sites used for model/measurement comparisons. 

b

S designates the SAPRC99 mechanism and C designates the CB-IV mechanism. c

Ratio of highest modeled to highest measured O3 within the sub-region for each modeled day, regardless 

of hour or location. Range is for the 3-day episodes. Measured O3 concentrations below 60 ppb are 

excluded. 

d

Average of differences between modeled and measured O3 divided by measured O3 for each day. 

Measured O3 concentrations below 60 ppb are excluded. Range is for the 3-day episode. eAverage of absolute differences between modeled and measured O3 divided by measured O3 for each 

day. Measured O3 concentrations below 60 ppb are excluded. Range is for the 3-day episode. 
Review, Critique and Recommendations Concerning Modeling of Ozone 

and Particulate Matter in the South Coast Air Quality Management District 

By 

Edwin L. Meyer, Jr., PhD, PE 

Prepared in partial fulfillment of AQMD Contract Number 03223 

January 27, 2003 
Acknowledgment of Support and Disclaimer 

This report was prepared as a result of work sponsored, paid for, in whole or in part, by the South 

Coast Air Quality Management District (AQMD). The opinions, findings, conclusions, and 

recommendations are those of the author and do not necessarily represent the views of AQMD. AQMD, its 

officers, employees, contractors, and subcontractors make no warranty, expressed or implied, and assume 

no legal liability for the information in this report. AQMD has not approved or disapproved this report, nor 

has AQMD passed upon the accuracy or adequacy of the information contained herein.
Table of Contents 

Acknowledgment of Support and Disclaimer 2 

1.0 Introduction 4 

2.0 Performance Evaluation for Ozone and Precursors 4 

3.0 Questions Posed by the South Coast Air Quality Management District 15 

4.0 Conclusions About PM10 and PM2.5 19 

5.0 Recommendations for Additional Work 23 

6.0 Summary 26 

7.0 Cited References 28 
1.0 Introduction 

 This review is based on materials provided by the South Coast Air Quality Management District 

(AQMD) as well as discussions with the AQMD and other reviewers in a meeting on January 10, 2003. 

Prior to the January 10 meeting, I was provided with the following information: 

 1. an initial modeling protocol; 

 2. peer review comments on the initial protocol; 

3. a series of presentations by the technical staff of AQMD and/or California Air Resources Board 

(CARB) to stakeholders; 

4. a draft appendix describing current and future inventories, as well as methods used to make 

these estimates; 

5. a brief discussion addressing model selection; 

6. results of performance tests for ozone and precursors using CALGRID, CAMx and UAM6.21, 

all with the carbon bond 4 (CB4) chemical mechanism; 

7. results of performance tests performed for 6 components of particles less than or equal to 10 

micrometers aerodynamic diameter (PM10) and particles less than or equal to 2.5 micrometers 

aerodynamic diameter (PM2.5) using the UAM-LT air quality model, and 

8. projected estimates in 2006 and 2010 for PM10 and PM2.5 and their components. 

Subsequent to the January 10 meeting, I was also provided with the results of performance tests for ozone, 

N0x and CO obtained using the CALGRID and CAMx models with the SAPRC99f chemical mechanism 

(rather than the CB4 mechanism). 

 Much of the AQMD’s concern is focused on whether the modeling approaches examined are 

adequate to support the 2003 State implementation plan (SIP) revision. In particular, they wish to know 

which, if any, of the 5 modeling approaches considered for ozone (UAM/CB4, CALGRID/CB4, 

CAMx/CB4, CALGRID/SAPRC99f, CAMx/SAPRC99f) is likely to be the most reliable approach. The 

AQMD has asked reviewers to respond to 8 questions regarding each of the 5 ozone modeling approaches 

under consideration. AQMD also poses three additional, general modeling questions for us to respond to. 

In Section 2.0, I describe results of my review of ozone model performance and make a recommendation 

regarding choice of a modeling approach for the 2003 SIP revision. Section 3.0 addresses questions posed 

by the AQMD. Section 4.0 discusses information presented about PM10 and PM2.5. Section 5.0 makes 

recommendations concerning future analyses and model performance evaluations. Section 6.0 summarizes 

material presented in the preceding sections. 

2.0 Performance Evaluation for Ozone and Precursors 

Model performance information I review covers a 3-day period (August 4-6, 1997). This period 

corresponds to an intensive monitoring study (SCOS97) and is one of the episodes modeled for the 2003 

SIP revision. The performance evaluations for ozone include several aggregate statistical measures: ratios 

of unpaired global predicted to observed peak concentrations, ratios of spatially paired peaks, normalized 

bias, gross error and time series correlations between observations and predictions. All such comparisons 

are compartmentalized into 9 zones within a large, regional modeling domain. Demarcation of the zones is 

somewhat subjective. It is based on location of primary sources of precursors, as well as locations of high 

observed ozone. Other considerations, like assurance of a sufficient number of air quality monitors in each 

zone and some correspondence with neighboring air quality basins, which in the past have been modeled 

separately, also appear to apply. 

 Graphical displays constitute the other major means for evaluating model performance. The 

displays include diurnal comparisons of predicted and observed ozone concentrations at monitoring sites. 

Diurnal curves are available for ozone for all five modeling approaches. For the 3 approaches featuring the 

CB4 mechanism, diurnal curves are also available for NO, NO2 and CO. Stratified curves, examining 

dependencies between model performance and level of observations are also available. 

For the two approaches featuring the SAPRC99f mechanism, all that is available are diurnal curves for 

N0x, CO and ozone. 
 My review proceeds by first considering how modeling results have usually been interpreted by 

the U.S. EPA to determine whether attainment of the national ambient air quality standard (NAAQS) for 

ozone will be achieved by a required date. Based on this, I emphasize several performance measures for 

ozone, which, in my opinion, appear closely related to the predictions likely to receive greatest attention in 

the attainment demonstration. I then combine these performance measures in such a way to develop a 

numerical rating for each of the 5 modeling approaches. This procedure produces an initial choice. Next, I 

compare the initially chosen model’s ability to replicate observed precursor concentrations against that for 

alternative approaches. Markedly worse performance would be cause for reexamining the initial choice. 

2.1 Model Performance Predicting Ozone 

I used six criteria to evaluate performance and weighted each as shown. 

 1. Normalized bias (all sites in each zone) < +15%. (BIAS) (1 point) 

 2. Gross error (all sites in each zone) < 30%. (ERROR) (1 point) 

3. Most sites, including the site with the highest observed peak have predicted and observed peaks 

within + 20%. (SITE PEAK) (1 point) 

4. Within a zone, the predicted global peak is at least as high as the observed global peak, but not 

more than 20% higher than the observed global peak. (GLOBAL PEAK) (2 points) 

5. Within a zone, the time of the predicted global peak is within 2 hours of the observed global 

peak and the same is true for most site-specific peaks. (TIMING) (1 point) 

6. Within a zone, predicted exceedances of 124 ppb coincide with observed exceedances 

(EXCEEDANCES) 

 (a) at all sites with an observed or predicted exceedance (2 points) 

 or 

 (b) at most sites with an observed or predicted exceedance. (1 point) 

 For any given zone, a model could have a score as high as 8 points on each of the modeled days, 

or 24 points for the 3-day episode. In a zone where there are no observed or predicted exceedances at any 

site, the maximum daily score is 6 points, or 18 points for the episode. 

Criteria 1 and 2 are similar to ones suggested by the U.S. EPA (U.S.EPA, 1991, 1996). They 

address, all predicted/observed pairs, not just cases where predictions and/or observations are high. This 

provides some assurance that the model is working well under variety of conditions. The second criterion 

puts a limit on the size of residual error between predictions and observations, while the first criterion 

ensures that there is a balance between over- and underpredictions. 

Criterion 3 emphasizes performance in predicting peak hourly ozone concentration at each site 

with observations. Peak predicted ozone usually receives great emphasis in attainment demonstrations. 

The fourth criterion also emphasizes ability to predict peak hourly ozone concentrations 

accurately. Predicted global maximum is often the key factor in an attainment demonstration. For 

attainment to be shown, it needs to be < 124 ppb or to be explained and discounted through a weight of 

evidence determination. Because there are many more surface grid cells than monitoring sites, the criterion 

requires predicted global maxima to be at least as high as observed global maxima. Since the South Coast 

Air Basin has a dense array of monitors, an upper bound has been added for the predicted global maximum 

ozone concentration to suggest adequate performance. Because the predicted global maximum is such an 

important factor in an attainment demonstration, satisfactory performance according to this criterion is 

awarded 2 points. 
Criterion 5 addresses the timing of predicted vs. observed peak ozone concentrations. Poor timing 

could be due either to inadequate meteorological characterization or to some flaw in the modeled 

chemistry. If a chemical problem exists, large delays in predicted peaks could overstate the benefits of 

VOC control. In contrast, if predicted peaks occur well before the observed ones, benefits of N0x control 

might be overstated. If the poor timing is attributable to meteorological problems, incorrect combinations 

of sources could be mixing at inappropriate times, leading to a potential error in the predicted effectiveness 

of controls. 

Criterion 6 addresses how well each of the models performs in predicting concentrations that 

approach the level of the NAAQS. This is likely to be a critical issue in the “post-control” state, with 

relatively small differences resulting in potentially large differences in the cost of control efforts. Thus, 

consistently accurate performance in meeting this criterion is given 2 points. If the model gets this measure 

right most of the time, 1 point is awarded. Note that a very small difference (e.g., 125 ppb predicted vs. 

124 ppb observed) precludes a model from getting the full 2 points. 

Tables 2.1 – 2.5 show performance results for each of the five modeling approaches. I summarize 

the results from Tables 2.1 – 2.5 in two ways. The first considers results from all 9 zones. However, in 

several of the zones monitoring sites are spread out over large areas. Further, these zones may experience 

lower ozone concentrations and are probably not as critical for determining whether the South Coast Air 

Quality Management District will attain the NAAQS. Thus, model performance is also summarized for 

zones 2-5. The summary is shown in Table 2-6. Numbers in the body of the table depict the average daily 

score per zone for each group of zones. 

Table 2.6. Summary of Model Performance Predicting Ozone 

Model Zones 1 – 9 Zones 2 – 5 

UAM/CB4 2.36 2.73 

CALGRID/CB4 2.40 3.45 

CAMx/CB4 2.44 3.09 

CALGRID/SAPRC99f 3.24 4.45 

CAMx/SAPRC99f 2.76 3.82 

It is apparent from Table 2.6 that there is little to choose among the 

modeling approaches when the CB4 mechanism is used and all 9 zones 

are considered. Focusing only on zones 2-5 with CB4, CALGRID 

begins to emerge as having the best performance. Substituting 

SAPRC99f for CB4, substantially improves the performance of 

CALGRID. Performance of CAMx is also considerably improved. 

Although a version of UAM exists which permits substituting a different 

chemical mechanism, this version was not used by AQMD. Thus, it is 

not possible to note the effects using SAPRC99f has on the UAM’s 

predictions. Table 2.6 indicates that CALGRID/SAPRC99f is the best 

performing model for predicting observed ozone on the three days tested. 
Table 2.1. Performance of UAM/CB4 Predicting Observed Ozone 

Zone August 4, 1997 August 5, 1997 August 6, 1997 

1 Sample too small Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

2 Error-1 pt. 

Site peak-1 pt. 

Global peak-2 pts. 

Error-1 pt. 

Global peak-2 pts. 

Bias-1 pt. 

Site peak-1 pt. 

Exceedances-1 pt. 

3 Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

0 pts. Bias-1 pt. 

Site peak-1 pt. 

4 Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Timing-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

Bias-1 pt. 

Global peak-2 pts. 

Exceedances-1 pt. 

5 Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

Site peak-1 pt. Sample too small 

6 Site peak-1 pt. 

Timing-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Bias-1 pt. 

Timing-1 pt. 

7 Error-1 pt. 

Global peak-2 pts. 

Site peak-1 pt. 0 pts. 

8 Error-1 pt. 

Timing-1 pt. 

Error-1 pt. 

Global peak-2 pts. 

Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

Exceedances-2 pts. 

9 Bias-1 pt. Bias-1 pt. 

Site peak-1 pt. 

0 pts. 

Total = 59 points. 
Table 2.2. Performance of CALGRID/CB4 Predicting Ozone 

Zone August 4, 1997 August 5, 1997 August 6, 1997 

1 Sample too small Error-1 pt. 

Timing-1 pt. 

0 pts. 

2 Error-1 pt. 

Site peak-1 pt. 

Global peak-2 pts. 

Timing-1 pt. 

Timing-1 pt. Bias-1 pt. 

3 Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Global peak-2 pts. 

Timing-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Timing-1 pt. 

4 Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt 

Global peak-2 pts. 

Timing-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

Bias-1 pt. 

Timing-1 pt. 

5 Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

Global peak-2 pts. 

Site peak-1 pt. 

Timing-1 pt. 

Global peak-2 pts. 

Sample too small 

6 Bias-1 pt. 

Timing-1 pt. 

Timing-1 pt. 

Site peak-1 pt. 

Site peak-1 pt. 

Global peak-2 pts. 

7 Error-1 pt. 

Timing-1 pt. 

Timing-1 pt. Timing-1 pt. 

8 Bias-1 pt. 

Timing-1 pt. 

Global peak-2 pts. Timing-1 pt. 

Exceedances-1 pt. 

9 0 pts. 0 pts. Site peak-1 pt. 

 

Total = 60 pts. 
Table 2.3. Performance of CAMx/CB4 Predicting Ozone 

Zone August 4, 1997 August 5, 1997 August 6, 1997 

1 Sample too small Timing-1 pt. 0 pts. 

2 Error-1 pt. 

Site peak-1 pt. 

Global peak-2 pts. 

Timing-1 pt. 

0 pts. Bias-1 pt. 

Site peak-1 pt. 

3 Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Exceedances-2 pts. 

4 Timing-1 pt. Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

Exceedances-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Timing-1 pt. 

5 Bias-1 pt. 

Timing-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

Global peak-2 pts. 

Sample too small 

6 Global peak-2 pts. Error-1 pt. 

Global peak-2 pts. 

Error-1 pt. 

Site peak-1 pt. 

Timing-1 pt. 

7 Timing-1 pt. Timing-1 pt. Timing- 1 pt. 

8 Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

Global peak-2 pts. Bias-1 pt. 

Error-1 pt. 

Global peak-1 pt. 

Timing-1 pt. 

9 0 pts. Global peak-2 pts. Global peak-2 pts. 

Timing-1 pt. 

Total = 61 pts. 
Table 2.4. Performance of CALGRID/SAPRAC99f Predicting Ozone 

Zone August 4, 1997 August 5, 1997 August 6, 1997 

1 Sample too small Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Timing-1 pt. 

2 Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Global peak-2 pts. 

Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Global peak-2 pts. 

Timing-1 pt. 

Timing-1 pt. 

Exceedances-1 pt. 

3 Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Global peak- 2 pts. 

Timing-1 pt. 

Bias-1 pt. 

Site peak-1 pt. 

Global peak-2 pts. 

Exceedances-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Timing-1 pt. 

Exceedances-1 pt. 

4 Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Global peak-2 pts. 

Timing-1 pt. 

Exceedances-1 pt. 

Site peak-1 pt. 

Exceedances-1 pt. 

Bias-1 pt. 

Site peak-1 pt. 

Timing-1 pt. 

Exceedances-1 pt. 

5 Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Timing-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Sample too small 

6 Bias-1 pt. 

Site peak-1 pt. 

Timing-1 pt. 

Bias-1 pt. Bias-1 pt. 

Site peak-1 pt. 

7 Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Timing-1 pt. 

Error-1 pt. 

Timing-1 pt. 

8 Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

Bias-1 pt. Bias-1 pt. 

Error-1 pt. 

Timing-1 pt. 

9 Bias-1 pt. Bias-1 pt. 

Error-2 pts. 

Bias-1 pt. 

Total = 81 pts. 
Table 2.5. Performance of CAMx/SAPRC99f Predicting Ozone 

Zone August 4, 1997 August 5, 1997 August 6, 1997 

1 Sample too small Timing-1 pt. Error-1 pt. 

2 Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Timing-1 pt. 

Error-1 pt. 

Timing-1-pt. 

Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Exceedances-1 pt. 

3 Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Global peak-2 pts. 

Timing-1 pt. 

0 pts. Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Timing-1 pt. 

Exceedances-1 pt. 

4 Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Global peak-2 pts. 

Timing-1 pt. 

Exceedances-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Exceedances-1 pt. 

Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

5 Error-1 pt. 

Global peak-2 pts. 

Timing-1 pt. 

Error-1 pt. 

Global peak-2 pts. 

Timing-1 pt. 

Sample too small 

6 Site peak-1 pt. 

Global peak-2 pts. 

Global peak-2 pts. 

Timing-1 pt. 

Site peak-1 pt. 

Global peak-2 pts. 

Timing-1 pt. 

7 Timing-1 pt. Timing-1 pt. Timing-1 pt. 

8 Bias-1 pt. 

Error-1 pt. 

Site peak-1 pt. 

Timing-1 pt. 

Error-1 pt. Error-1 pt. 

Global peak-2 pts. 

Timing-1 pt. 

9 0 pts. Global peak-2 pts. Site peak-1 pt. 

Total = 69 pts.
2.2 Model Performance Predicting Precursors and Other Considerations 

 Agreement between predicted and observed primary precursors (NO and CO) is poor for all three 

models which use the CB4 mechanism. Only N0x prediction/observation comparisons are provided for 

CALGRID/SAPRC99f and CAMx/SAPRC99f. However, observed data, as well as data with the 3 models 

using CB4, indicate NO peaks occur in the early morning, usually about 6 – 9 am local time. NO2 peaks, 

when they occur, usually happen about 11 am – noon local time. Thus, it is possible to infer whether it is 

NO or NO2 which influences predicted N0x concentrations at various times of day. Generally, like the 

other 3 models, those which use SAPRC99f do not reproduce observed concentrations of NO very well. 

Based on composite sets of predicted/observed NO comparisons presented for each of the three 

models using CB4, the CALGRID and CAMx models tend to underpredict highest observed NO and 

overpredict the lowest observed concentrations. This tendency is a little more pronounced for the 

CALGRID model than it is for CAMx. There is much scatter between observations and predictions 

within the range of observations. In my opinion, it is likely that the poor performance of the CALGRID 

and CAMx models predicting NO (and CO) is attributable to the incommensurability problem. That is, 

sources of NO and CO are not uniformly distributed, and monitored data are likely to vary more than 

predictions, because they are subject to small scale meteorological and emissions variations which are not 

reflected by the uniform model inputs and outputs spread over 5 km grid cells of various depths. 

In contrast to the CALGRID/CB4 and CAMx/CB4 models, UAM/CB4 almost uniformly 

overpredicts observed NO, sometimes by considerable amounts. Since NO peaks tend to be observed in 

the early morning (before much chemistry has happened), I believe the contrast among the three models is 

most likely a reflection of the first few vertical layers assumed in the three approaches. Perhaps in the early 

morning, the first layer is deeper in the UAM approach, allowing for more uniform mixing of NO, which is 

seldom reflected by the monitored data. 

Performance improves for all three models when paired comparisons of predicted and observed 

concentrations of NO2 are considered. There is a tendency for each of the models to underestimate highest 

observed concentrations of NO2 (say above 5 pphm). This tendency is a little more pronounced for 

CALGRID/CB4 than it is with the other two models. Performance of CAMx/CB4 and UAM/CB4 in 

predicting NO2 appears about the same. 

All we are able to consider with CAMx/SAPRC99f and CALGRID/SAPRC99f are N0x, rather 

than NO and NO2 data. I examine zone 5 (a source-intensive area) and zone 4 (an area where highest peak 

ozone concentrations are often observed). Numerical performance statistics for the ability of these two 

models to predict observed N0x in the two zones are not particularly meaningful, since the predictions and 

observations reflect a mixture of NO and NO2. Timing of predicted peaks (probably largely attributable to 

NO) is better for CAMx/SAPRC99f than for CALGRID/SAPRC99f. Although the time series correlation 

coefficients between observations and predictions are not especially good, they are systematically higher 

for CAMx/SAPRC99f than for CALGRID/SAPRC99f. 

Because the numerical comparisons between observed and predicted N0x are misleading (due to 

N0x being a mixture of NO and NO2), I rely primarily on graphical data to assess performance of the 

models in replicating observed concentrations of NO and NO2. Thus, my assessment of performance is 

more subjective than it is for ozone. 

I compare performance of CALGRID/SAPRC99f with that of CAMx/SAPRC99f and UAM/CB4 

at 8 sites in zone 5: Anaheim, Hawthorne, Los Angeles, La Habra, Lynwood, Long Beach, Pasadena and 

Pico Rivera. These sites are in source intensive areas, and monitored observations may be higher (and 

perhaps more reliable) than elsewhere. I consider factors such as ability of predicted peaks to track 

observed patterns at each of these sites, as well as the extent to which levels of observed peak N0x 

concentrations are captured. In the case of the UAM/CB4, I have to “eyeball” a sum of NO and NO2 

observations. Often, the differences between models are either small, or there are compensating features 

(e.g., one model predicts diurnal patterns better, while another captures an observed peak concentration 

more accurately). 
Table 2.7. Selected Comparisons of N0x Performance 

Location CALGRID/SAPRC99f vs. 

UAM/CB4 

CALGRID/SAPRC99f vs. 

CAMx/SAPRC99f 

Anaheim CALGRID CAMx 

Hawthorne CALGRID Even 

Los Angeles CALGRID CAMx 

La Habra Even CALGRID 

Lynwood UAM Even 

Long Beach Even CAMx 

Pasadena CALGRID Even 

Pico Rivera Even CAMx 

One might infer from Table 2.7 that adding the SAPRC99f mechanism to 

CALGRID improved its performance vis a vis UAM. However, the 

conclusions in the table may simply reflect the finding that UAM appears 

to do a much worse job predicting NO and a slightly better job predicting 

NO2. Results of the comparison between CALGRID/SAPRC99f and 

CAMx/SAPRC99f are consistent with the earlier finding that 

CAMx/CB4 does slightly better than CALGRID/CB4 replicating 

observed concentrations of NO and NO2. It appears as though predicted 

N0x disappears more slowly with CAMx/SAPRC99f than it does with 

CALGRID/SAPRC99f. 

2.3 Recommendations 

CALGRID/SAPRC99f does the best job predicting observed ozone. On the 

other hand, CAMx/SAPRC99f does a somewhat better job predicting 

observed NO and NO2. Both models that use SAPRC99f do a better job 

predicting ozone than any of the three models using CB4. 

I recommend that AQMD use CALGRID/SAPRC99f as its primary model 

to support the 2003 SIP revision. This recommendation is based on the 

following: 

- the difference in reproducing key ozone observations is more 

pronounced than the differences in reproducing observed NO and 

NO2; 

- I have more confidence in the representativeness and validity of the 

observed ozone data than I do in the NO and NO2 data, and the 

method I use to compare performance vis a vis NO, NO2 and N0x is 
more subjective than that used to assess performance replicating 

observed ozone; 

- past peer reviewers have strongly recommended using the 

SAPRC99f mechanism over CB4 on the basis of scientific merit, and 

it appears feasible for the AQMD to do so; 

- staff of the California Air Resources Board has had considerably 

more experience using CALGRID than CAMx; 

- I think it will be essential to use a diagnostic wind model 

(CALMET) to augment coarse scale MM5 meteorological inputs. I 

have concerns about the feasibility of using fine scale MM5 

predictions alone, or even with FDDA in a complex area like the 

AQMD. It is probably easier to interface CALMET with CALGRID 

than it is with CAMx. 

3.0 Questions Posed by the South Coast Air Quality Management 

District 

The AQMD has asked each reviewer to address 8 questions to each 

of the five models that they are considering for use. 

1. Is the modeling protocol adequate for the proposed attainment 

demonstration? Does it require revisions to satisfy EPA modeling 

guidance? 

Most current (draft) EPA guidance (U.S. EPA, 1999) for ozone modeling 

identifies 15 topics that should be addressed in the protocol. Many of 

these, are in fact, addressed. Those which are not include (1) identifying 

the stakeholders involved with reviewing results and the procedure 

followed in revising the initial protocol as the analysis proceeds; (2) 

types of analyses used in weight of evidence (WOE) determinations, if 

WOE is used; (3) procedures used to archive, document and report 

results; (4) identification of specific deliverables and schedule for 

delivery to U.S. EPA Region IX. 

The preceding omissions notwithstanding, I think the AQMD developed 

a good initial protocol, which provided an adequate basis to proceed with 

the analysis. Further, evidence is presented to show that there were 
numerous briefings in which subsequent results were discussed and 

proposed changes to initial ideas were pursued. The main concern I have 

about the protocol is that it is no longer clear what was actually done to 

produce the results whose performance we reviewed. The procedures 

that were finally followed need to be documented. It would also be 

helpful to mention how these differ from what was originally proposed. 

For example, I was uncertain about what was finally assumed regarding 

vertical structure of the atmosphere in CALGRID vs. CAMx vs. UAM. 

This makes it difficult to come up with explanations about why predicted 

and observed precursor concentrations sometimes disagree. 

 

2. Is the science embodied in the model and mechanism adequate for use 

in the AQMP? If not, why not? 

The science in the UAM is older than that in CALGRID and, especially, 

CAMx. However, if UAM/CB4 had outperformed the other 4 

approaches or it had not been feasible to implement any of the newer 

approaches, I would not have hesitated to recommend its use. After all, it 

has been used in many other applications. In the same vein, the CB4 

mechanism is older than SAPRC99f, and I believe a consensus of 

prominent scientists has stated that SAPRC99f is the most current 

chemical mechanism available. CB4 has advantages in that it is less 

resource intensive to use and is consistent with many existing emissions 

databases. I would not have hesitated to recommend it if practical 

considerations had precluded use of SAPRC99f in concert with a grid 

model. All of the models being considered have to make a series of 

assumptions. It is the combination of these assumptions which produces 

predicted air quality values. Although some features of a particular 

model may reflect a more current scientific understanding, it does not 

necessarily follow that all of its assumptions are correct. Indeed, it is 

possible for performance to deteriorate if a less appropriate combination

of assumptions results. 

3. Are the meteorological models, pre- and post processors adequate for 

using this model/mechanism in the AQMP? 

At the January 10 meeting, we were assured by CARB and AQMD 

technical staffs that it was feasible to use any of the 5 models within the 

timeframe required for preparing the 2003 SIP revision. Although I am 

not sure what post-processors the question is alluding to, I assume that 
since any of the 5 approaches can be used to produce results within the 

required time frame, they are adequate. One reservation I have is with 

the time and resources to run MM5 on a fine (<~8-10 km) scale with 

many vertical layers, as well as how realistic such predictions are likely 

to be in a complex terrain area like the south coast. This is one of the 

factors I considered in making the recommendation I did in Section 2.3. 

4. Was the model applied (e.g., initial and boundary conditions, modeling 

domain, number layers, etc.) in a manner that would make it adequate 

for use in the AQMP? 

I think the initial protocol and subsequent technical presentations gave 

evidence that these issues were addressed thoughtfully. The initial 

assumptions, as described in the protocol, seem plausible to me. I am 

reasonably confident that these issues were considered in a technically 

competent manner and that changes which were made were warranted 

technically. As I noted in response to question 1 however, it is not clear 

how the models were finally applied, given the various changes that 

became adviseable in the evolving analyses. 

5. Is the sub-regional zone definition used to group model performance 

adequate to characterize model performance for ozone and precursors? 

Dividing the domain into zones is a very good idea and is, incidentally, 

consistent with a recommendation in the most current U.S. EPA ozone 

modeling guidance (U.S. EPA, 1999). It has not been done too often, 

because most locations do not have a sufficient number of monitors to 

permit robust comparisons. With its large network, complex topography 

and geographic distribution of precursor emissions and observed high 

ozone, the AQMD is well advised to divide the area into zones. The 

zones are appropriate in that they appear to be based on past ozone 

observations, distribution of emissions, topography and distinctive air 

basins which heretofore have been considered separately. 

6. Are the graphical ozone model performance presentations consistent 

with the statistical evaluation? 

I don’t believe I could have done a meaningful evaluation without the 

statistical and the graphical presentations. The graphical presentations 

are helpful for trying to understand what is happening to cause the 
numbers generated by the numerical procedures. I used both to evaluate 

model performance predicting ozone. I relied primarily on graphical data 

assess model performance predicting observed precursor concentrations. 

The several procedures utilized appear to lead to consistent conclusions. 

7. Does the model/mechanism meet U.S. EPA and ARB model 

performance acceptance criteria? 

With respect to ozone, all of the models meet these criteria (gross error, 

normalized bias, peak ratios) in most of the zones most of the time. I 

found however, that these criteria were among the easiest to meet of 

those that I used to assess performance. Based on a more complete 

analysis, I believe the CALGRID/SAPRC99f model performs the best in 

predicting ozone. There are no criteria for assessing performance 

predicting NO or NO2. Based primarily on a review of graphical 

material, CAMx/SAPRC99f appears to perform a little better than 

CALGRID/SAPRC99f predicting these precursors. 

8. Considering all of the factors above, is this model/mechanism 

significantly better than the others? 

As a result of my performance review, past scientific critiques and 

practical considerations, I believe the CALGRID/SAPRC99f model 

should be the preferred approach to support the 2003 SIP revision. 

Three additional general questions have been asked by the AQMD. 

9. Using the existing tools and information, is there anything that could 

be done to provide greater confidence in the modeling used in the 

AQMP? 

Yes. I believe a data base exists or can be created relatively easily which 

would enable the AQMD to compare the response of predicted and 

observed ozone to changes in emissions and/or changes in emissions + 

meteorology. Such an analysis should be done. Ideally, performance of 

model response could be evaluated by reconstructing 1987 emissions 

using updated estimation procedures. Observations and predictions 

could then be noted for the 1987 episodes. Next, 1997 emissions and 

episodes could be run. Changes in predictions vs. changes in 

observations could then be compared. These changes would be 
attributable to differences both in emissions and meteorology. Another, 

more subjective, approach might be used to assess the accuracy of a 

model’s response to primarily to changes in emissions. If 1997 episodes 

“similar” to the 1987 episodes could be selected, it would be possible to 

assess accuracy of a model’s response that is due, primarily, to changes 

in emissions. Evaluations of this sort would help circumvent the concern 

expressed by some about “getting the right answers for the wrong 

reasons”. The reason some feel this concern is valid, is that when model 

performance is evaluated solely on the ability to replicate one or more 

past episodes, one is not asking the question which is of paramount 

concern: how accurately does a model predict changes in air quality? If 

one asks this key question directly in the performance evaluation, getting 

the right answer provides greater confidence than asking a related, but 

less relevant question for SIP purposes. I would like to see this test 

applied for both the CALGRID/SAPRC99f and CAMx/SAPRC99f 

models. 

10. Many of the model runs exhibit underestimation of observed peaks to 

some degree. Is there sufficient understanding of the reason(s) for the 

underestimation to warrant making an adjustment? 

I do not believe there is, at least for this SIP revision. The process 

analysis technique, which I believe can be utilized with the CAMx 

model, might provide some useful subsequent insights about appropriate 

changes. I also think it would be useful to apply the 

CALGRID/SAPRC99f, CAMx/SAPRC99f and UAM/CB4 approaches 

and then use the relative reduction approach with the appropriate ozone 

design values for several of the zones. This would likely provide some 

insight into the potential significance of using absolute predictions of 

ozone in an attainment demonstration with a model that systematically 

underpredicts observed peak hourly concentrations of ozone. 

11. Is model performance acceptance biased by the underestimation of 

the peak concentrations? 

Ability to predict peak observed ozone concentrations is a very important 

consideration. Ratio of peak predicted to peak observed concentrations is 

one of three U.S. EPA ozone performance measures for which a criterion 

exists. Predicted future peak concentrations are generally compared to 

the level specified in the ozone NAAQS to judge whether a SIP is 
adequate. As I noted in Section 2.0, I try to consider how model results 

are typically used in a demonstration in order to judge how to evaluate 

performance of the 5 models under consideration. Thus, several 

measures related to the ability to predict the magnitude of peaks are 

included in my assessment. There are other measures, such as timing, 

ability to predict exceedances, normalized bias and gross error that are 

also considered. 

One should remember that no matter how scientifically current certain 

components of a model are, what is also critical is how these components 

interact to produce an air quality prediction. Regardless of how 

sophisticated a model is, assumptions have to be made about how to 

consider emissions, parameterize meteorological and chemical 

phenomena, etc., etc. The best combination of assumptions is not 

intuitively obvious. This is why the ability to produce an answer that 

appears to coincide with observations is a very important consideration in 

choosing a model for use in a SIP. 

In short, I believe it is appropriate to give heavy consideration to a 

model’s ability to predict a measure that is a key one in accepting an 

attainment demonstration. I would not characterize this as “bias”. 

4.0 Conclusions About PM10 and PM2.5 

 4.1 UAM-LT Predictions and Observations of PM10 

 Annual and quarterly mean predictions and observations are provided for the following 

components of PM: ammonium (NH4), nitrate (NO3), sulfate (SO4), organic carbon (OC), elemental 

carbon (EC) and “Other”. Comparisons are an aggregate of days when 24-hour mean observations are 

taken, typically once in 6 or once in 3 days. AQMD has defined acceptable model performance for each 

component as agreement between prediction/observation spatially averaged pairs to within + 30%. The 

basis for this criterion is unclear, but I have nothing better to offer. 

 Looking at comparisons between spatially averaged observations and predictions for annual means 

of each component, the performance criterion is satisfied for NH4, NO3 and OC components, or about 86% 

of the reported mass (i.e., the sum of the 6 components). Measurements are made at 5 sites. Acceptable 

performance for spatially averaged annual concentrations, as well as agreement within + 30% at individual 

sites, is as follows: 

Anaheim---NH4, NO3, OC, EC 

Diamond Bar---NH4, NO3, OC 

Fontana---NH4, NO3, SO4, Other 

LA---NH4, NO3, OC 

Rubidoux---NH4, NO3, SO4, OC, EC, Other 

Spatial Average---NH4, NO3, OC 
 However, due to seasonal variations in emissions and meteorology, it is good practice to evaluate 

performance (as well as perform strategy simulations) separately for each quarter of the year. Upon doing 

this, one finds generally poor performance for UAM-LT during the first quarter. The model typically 

overpredicts observations during this quarter. Indeed, agreement within + 30% is limited to the following 

during quarter 1: 

Anaheim---EC 

Diamond Bar---SO4, OC, EC 

Fontana---NH4, NO3, OC, Other 

LA---SO4, EC 

Rubidoux---none. 

Spatial Average---EC (~7% of the observed spatially averaged mass of PM10 is predicted “correctly”) 

Of the 4 quarters, observed mean PM10 concentrations are generally lowest during the first quarter. 

Performance improves for the second quarter. Agreement within + 30% is noted below. 

Anaheim---NH4, NO3, OC, EC, Other 

Diamond Bar---OC, EC, Other 

Fontana---NH4, NO3, OC, Other 

LA---NH4, NO3, OC, EC, Other 

Rubidoux---OC, EC, Other 

Spatial Average---NH4, NO3, OC, EC, Other (~90% of the observed spatially averaged 

mass of PM10 is predicted “correctly”) 

Observed quarterly means for the second quarter are generally not as high as those observed in the third 

and fourth quarters. 

Agreement within + 30% during the third quarter is as follows: 

 

Anaheim: NH4, NO3, OC, EC 

Diamond Bar---NH4, NO3 

Fontana---SO4, Other 

LA---NH4, NO3, OC 

Rubidoux---NH4, NO3, Other 

Spatial Average---NH4, NO3, Other (~64% of the observed spatially averaged mass of PM10 is predicted 

“correctly”) 

 Observations during the fourth quarter are generally 50-60% higher than those seen during the 

next highest (i.e., the third) quarter. Agreement within + 30% during the 4th quarter occurs as follows: 

Anaheim---NH4, NO3, OC 

Diamond Bar---NO3, OC 

Fontana---SO4, Other 

LA---NO3, OC 

Rubidoux---SO4, OC, Other 

Spatial Average---NO3, OC (~49% of the observed spatially averaged mass of PM10 is predicted 

“correctly”) 

 Looking at projections to 2006 and 2010, it appears the annual NAAQS will not be met at only 3 

of 57 sites. Further, the projected violations are within about 10% of the concentration level specified in 

the NAAQS. As noted below, it is projected to be much more difficult to meet the annual NAAQS for 

PM2.5. Thus, once a SIP is developed for PM2.5, attainment of the PM10 NAAQS should “follow along” 

from efforts to meet the PM2.5 NAAQS. Further, if efforts to meet the PM10 NAAQS focus on reducing 

the “Other” (primary?) component, they will have little impact on reducing PM2.5. 
4.2 UAM-LT Predictions and Observations of Annual/Quarterly PM2.5 

 Although AQMD is not yet required to submit a SIP revision addressing PM2.5, a modeling 

analysis has been performed in anticipation of a future requirement to do so. The same performance 

criteria are applied to the same components noted in the discussion of PM10. The extent to which mean 

quarterly or annual predictions agree with corresponding observations within + 30 % is noted below. 

Annual Concentrations 

Anaheim---NH4, NO3, OC 

Diamond Bar---NH4, NO3 

Fontana---NH4, NO3 

LA---NH4, NO3, OC 

Rubidoux---SO4, OC, EC 

Spatial Average---NH4, NO3, OC (~73% of the observed PM2.5 is predicted “correctly”) 

Note that for the spatial average, it is 73% of the measured mass has associated acceptable model 

performance. 

Quarter 1 

Anaheim---SO4, EC 

Diamond Bar---NH4, NO3, SO4, EC 

Fontana---NH4, NO3, EC 

LA---NH4, SO4, OC 

Rubidoux---None 

Spatial Average---EC (~11% of the observed PM2.5 is predicted “correctly”) 

Quarter 2 

Anaheim---NH4, OC, EC 

Diamond Bar---NH4, OC 

Fontana---NH4, NO3 

LA---NH4, OC 

Rubidoux---OC, EC 

Spatial Average---NH4, NO3, OC, EC (~72% of the observed PM2.5 is predicted “correctly”) 

Quarter 3 

Anaheim---NH4 

Diamond Bar---NH4 

Fontana---NH4 

LA---NH4 

Rubidoux---NH4, NO3 

Spatial Average---NH4 (~14% of the observed PM2.5 is predicted “correctly”) 

Quarter 4 

Anaheim---NO3, OC 

Diamond Bar---NO3, OC 

Fontana---SO4, OC 

LA---OC 

Rubidoux---SO4, OC 

Spatial Average---OC (~18% of the observed PM2.5 is predicted “correctly”) 
The seasonal pattern for observed PM2.5 is similar to that for PM10, only more pronounced. Quarterly 

average values during the 4th quarter are about double that of the next highest quarter. This is mostly 

attributable to large increases in nitrate and ammonium. 

 One major difference between model performance for PM2.5 and PM10 is the very poor 

performance predicting the “Other” component for PM2.5. The model drastically overpredicts this 

component (by factors ranging from “3” to “61”). Although the “Other” component is the least important 

component according to the observations, it is the second (to nitrates) most important component according 

to the model’s predictions. Thus, the potential exists for misleading results when simulating strategies. 

Generally poor performance in predicting PM2.5 as well as the relative importance of components of 

PM2.5 argues for using a “relative reduction factor” (RRF) type of approach for PM2.5 strategy 

simulations. Note also that the comparisons emphasize the need to consider each quarter separately. 

Looking simply at the annual averages implies better performance than is actually the case. 

As with PM10, it appears that sulfates and all, or nearly all, of the nitrates are likely to be ammonium salts. 

 Simulations projected to 2006 and 2010 suggest that meeting the annual NAAQS for PM2.5 will 

be a challenge in the AQMD. 36 of 57 sites have projections exceeding the annual NAAQS. Assuming 

PM2.5 is the sum of the 6 components which are reported, levels exceed that of the annual NAAQS by 

anywhere from 0.1 to 12.1 micrograms/cubic meter. 

4.3 Some Concerns with the Analysis 

 Details of the modeling analysis for particulate matter (and for that matter about the model which 

was used) are sketchy. Thus, it is not possible to comment at length about what was done. However, based 

on the material reviewed, I have at least two concerns. 

First, it appears that an assumption has been made that the spatially averaged composition 

observed at 5 monitoring sites is representative of the entire modeling domain. Predicted differences in 

each of the components were then most likely weighted according to their importance to derive future 

concentrations of PM10 and PM2.5 at 50-60 monitoring sites. If this was what was done, a great deal more 

effort is needed to explain the procedure and to justify the use of just 5 locations to draw inferences about 

the entire domain. 

The second concern is related to the first. It has to do with using spatially averaged predicted and 

observed concentrations to evaluate model performance. Due to the incommensurability problem, I think 

use of spatial averages may be a legitimate way to assess a grid model’s performance predicting 

concentrations of primary pollutants. However, it seems to me that the evaluation should be preceded by 

some effort to document why it is appropriate to group information from certain sites, be it proximity, 

presence of nearby, similar sources, or whatever. 

Since the PM10 problem in the AQMD appears to be a relatively minor one, a great deal more in 

the way of a modeling effort may not be warranted. However, PM2.5 looks to be a major future problem. 

Draft U.S. EPA guidance for modeling PM2.5 (U.S. EPA, 2001) closely parallels the Agency’s draft 

guidance for applying models to address the 8-hour NAAQS for ozone. Both sets of guidance require 

developing a modeling protocol and addressing many of the same problems which the AQMD faced in 

applying models to address the 1-hour NAAQS for ozone. The guidance recommends using models in a 

relative sense to develop relative reduction factors that are applied to observed air quality. 

5.0 Recommendations for Additional Work 

1. AQMD should document what modeling assumptions, modeling preprocessors and/or components were 

actually used to produce the estimates that underlie the strategy reflected in the 2003 SIP revision. It is 

clear that the approach originally outlined in the modeling protocol has been changed in response to 

subsequent findings and discussions. 
2. As another peer reviewer has suggested, AQMD needs to put a great deal of effort into producing a 

relatively brief (e.g., 15-20 page) narrative summarizing what analyses were done and how they lead to the 

conclusion that the SIP revision will meet its intended goals. This narrative should be aimed at managers 

and the lay public. More detailed descriptions (e.g., for CARB and U.S. EPA technical staffs) should be in 

appendices. 

3. I strongly recommend that model performance in predicting secondary pollutants like ozone, NO2 and 

particulate mass associated with nitrates, sulfates and ammonium ion focus on how accurately the model is 

able to replicate the observed response to changes in emissions, meteorology and both. In the near future, 

this can likely only be done for ozone and its precursors. I recommend that AQMD revise 1987 emissions 

(using the current, state of the art methodologies), rerun the 1987 episodes with the revised emissions and 

compare changes in predicted ozone and NO2 between the 1987 and 1997 episodes with the observed 

changes between the 1987 and 1997 episodes. Adding this approach to the others for model performance 

evaluation, addresses a key concern about model performance: how well does the model replicate the 

response of secondary pollutants to changes in precursor emissions and/or changes in meteorology. 

Contrasting changes in predictions vs. observations for weekends and weekdays is another potential way to 

evaluate a model’s response. However, I am less sure whether the differences in emissions on weekends 

vs. weekdays are sufficient to send a signal strong enough to be discerned over “noise” attributable to 

uncertainties in weekend vs. weekday inventories. 

4. AQMD should retain and archive all files that are needed to perform simulations in the future that 

contrast predicted vs. observed changes in ozone over time. These files should also be made available to 

the research community to test whether incremental changes (reflecting more current scientific findings) 

improve the performance of grid models. 

5. Some additional suggestions about model performance evaluation for predicting ozone and its precursors 

follow. 

(a) Site by site comparisons in which predictions within a surface grid cell are matched with 

observations occurring within the cell at the same time may present a test that is needlessly 

restrictive. For example, the EPA Guidance for the 1-hr ozone NAAQS (pp.10-11) (U.S. EPA, 

1996) suggests looking at a 3x3 array of 5-km cells centered on the monitor and using the highest 

prediction within this array in what that guidance defines as “the statistical attainment test”. 

U.S. EPA monitoring regulations suggest that ozone monitors should represent an "urban scale”. 

This is a rather wide range, but the modeling guidance has selected a value (15 km) near the lower 

end of the range. The recommendation to consider an array of cells was also made in recognition 

that, in many cases, it may be nearly impossible to get the exact time and location correctly and 

that, in any event, small discrepancies may not be important. Likewise, it may often be legitimate 

to compare the highest nearby prediction within + 1-2 hours to each site’s peak observation when 

evaluating model performance, to see whether a seemingly poor result is simply a reflection of a 

bunch of “near misses”. 

(b) The time series correlations for predicted vs. observed ozone presented by AQMD are almost 

uniformly bad. However, this may reflect the relatively narrow range of observations available at 

most sites on the days selected for modeling. Excluding all hours with ozone observations <.06 

ppm from the analysis exacerbates this problem. Often, the range of observations is only .06 – 

about .13 ppm. If these low correlations are a concern, AQMD should repeat the analysis 

including all observations and predictions to increase the range. 

(c) Comparisons may yield worse than expected results due to the apparently different rounding 

conventions used for the monitoring data (to the nearest whole pphm for everything but CO) and 

modeled data, reported to the nearest 0.1 pphm (for everything but CO). Differing conventions for 

reported monitoring and modeled data may be even more of a problem for CO, as it appears 

measured values of this pollutant are rounded to the nearest 0.5 ppm. Much of the diurnal data 

presented for CO show little hour-to-hour variability, in part perhaps, due to this convention. If it 
is possible to access more precise observed data from the archives, AQMD should see whether 

using identical levels of precision in the predicted and observed data improves performance. 

(d) I urge AQMD to make greater use of special air quality measurements that may have been 

taken during the SCOS97 field program to help evaluate model performance. More specifically, 

observed and predicted “indicator ratios” of pollutants should be looked at when the 

measurements permit. Accurate prediction of observed indicator ratios may suggest that a model’s 

ozone predictions will respond correctly to changes in precursor emissions. Identity and use of 

indicator ratios are described in Sillman (1995), Sillman (1998) and Blanchard, et al. (2000), as 

well as elsewhere. 

6. Not surprisingly, agreement between predicted and observed primary pollutants (NO and CO) is poor. In 

my opinion, this likely reflects an incompatibility between assuming a uniform concentration of these 

pollutants within a 5-km grid cell and effects of smaller scale phenomena on the monitored observations. It 

may be preferable to average modeled and monitored data of these pollutants over several sites when 

making comparisons. Perhaps this might help random, small-scale fluctuations in the observations to 

balance. Another potentially useful approach may be to compare observed and predicted ratios of primary 

pollutants. 

If spatial averaging is used, there should be an accompanying rationale for combining the data from several 

locations. This was lacking in the UAM-LT performance evaluation comparing observed and predicted 

components of PM10 and PM2.5. 

7. I believe its underlying science is sufficient and it performed sufficiently well for the recommended 

primary model for ozone (CALGRID/SAPRC99f) to be used to support the 2003 SIP revision for ozone. 

Nevertheless, performance of CALGRID/SAPRC99f is not outstanding. Further, my assessment is based 

on only 3 modeled days. It is also troubling that CAMx/SAPRC99f and UAM/CB4 appear to better 

replicate observed concentrations of NO2. Therefore, I believe AQMD should make plans to perform a 

“mid-course review” in accordance with U.S. EPA guidance (U.S. EPA, 2002), as well as perform a weight 

of evidence (WOE) analysis. More specifically, AQMD should archive modeling files used to create 

predictions for the 1997 episodes. Project emssions to a future “mid-course” date (e.g., 2006) and to the 

attainment date (e.g., 2010) and apply the model to obtain corresponding estimates. When 2006 comes, 

derive ambient trend information and normalize this for meteorological differences. Compare the apparent 

relative progress toward the NAAQS from the normalized trend data against the relative progress projected 

for 2006 by the model. By “relative progress”, I mean the percentage of the way between 1997 predicted 

(or observed) air quality and the air quality goal (e.g., 124 ppb) that is predicted (or observed) in 2006. If 

the relative change in the air quality observations is less than that predicted, a SIP adjustment or 

reexamination of the model may be needed. 

The weight of evidence analysis could include looking at changes predicted between 1997, 2006 and 2010 

with the most current model in 2006 that exhibits adequate performance. Whether a new SIP revision is 

deemed necessary may depend on whether the normalized trend in observed ozone leads to a relative 

changes in high ozone which is less than that predicted with the updated model. 

8. Because it appears likely that the PM10 NAAQS will be close to being attained and subsequent efforts to 

meet the NAAQS for PM2.5 will likely lead to improvement in PM10 as well, I do not believe that a major 

additional effort to model PM10 is necessary. AQMD should instead focus its efforts on performing 

modeling to support a SIP to meet the NAAQS for PM2.5. As outlined in draft U.S. EPA modeling 

guidance related to the PM2.5 NAAQS (U.S. EPA, 2001), this will require a major effort, comparable to 

the ones needed to support the 1-hour and 8-hour NAAQS for ozone. 

6.0 Summary 

 In this review, I first examine the ability of 5 modeling approaches to replicate observed ozone 

concentrations during a 3-day episode in 1997. In Section 2.1, I use six measures to evaluate the models’ 
performance in each of 9 geographical zones identified by the AQMD. Choice of measures is influenced 

by my familiarity with how modeling results are used by regulatory agencies to determine whether a SIP is 

likely to lead to attainment of the NAAQS for ozone. Using these measures, CALGRID/SAPRC99f 

performs substantially better than the other models. CAMx/SAPRC99f performs second best. UAM/CB4, 

CALGRID/CB4 and CAMx/CB4 perform comparably. 

 In Section 2.2, I review performance of CALGRID/SAPRC99f, CAMx/SAPRC99f and 

UAM/CB4 in predicting observed concentrations of NO, CO and NO2. Evaluation of this performance is 

based on graphical presentations and is more subjective than the evaluation for ozone. For understandable 

reasons, no model does very well predicting NO or CO. CAMx/SAPRC99f and UAM/CB4 do better than 

CALGRID/SAPRC99f in replicating observed concentrations of NO2. The latter model’s predictions of 

NO2 are lower than those of the former two on some days at some sites. 

 Despite the outcome of the NO2 comparisons, in Section 2.3 I recommend that AQMD use 

CALGRID/SAPRC99f as the primary modeling approach to support the 2003 SIP revisions for ozone. 

This recommendation reflects greater confidence in the observed ozone data, larger differences among the 

models than was true for NO2, as well as some practical considerations. 

 In Section 3.0, I address a series of questions posed by the AQMD. In general, these questions 

concern (a) whether the five ozone models considered for use by the AQMD are sufficiently well grounded 

scientifically; (b) whether the methodologies used by the AQMD in applying the models are adequate, and 

(c) whether the procedures followed are consistent with regulatory guidance issued by the CARB and the 

U.S. EPA. My sense is that all of the models are adequate provided those applying them prepare a detailed 

protocol which is discussed in the local scientific community, model performance is assessed and found 

adequate and diagnostic tests are applied to see how sensitive conclusions might be to assumptions which 

need to be made in the modeling. 

In general, the assumptions that were made by the AQMD and CARB in applying the models seem 

plausible. Finally, I did not detect major inconsistencies with U.S. EPA guidance nor problems relative to 

the guidance that cannot be easily addressed. 

 In Section 4.0, I summarize some of the findings regarding application of the UAM-LT model to 

estimate annual and quarterly mean concentrations of PM10, PM2.5 and 6 components of these mixtures. 

In general, model performance is pretty good for PM10. Typically, components which comprise well over 

50% of the measured mass of PM10 are predicted accurately according to the criterion of + 30% agreement 

established by the AQMD. The exception occurs in the winter, when performance is poor, but observed 

concentrations are low. Usually, the relative amounts that each component comprises of predicted spatially 

averaged PM10 agree reasonably well with spatially averaged observations. 

 Performance predicting PM2.5 is not good. Usually, less than 50% of the measured mass is 

accurately predicted. As with PM10, performance is worst during the winter months. More significantly, 

performance is not good during the 3rd and 4th quarters, when observations are the highest. More 

significantly still, the model substantially overestimates the relative importance of primary particulate 

matter. This latter shortcoming could lead to selection of ineffective control strategies, unless models are 

applied in a relative sense using relative reduction factors. 

 The documentation for the PM10 modeling is poor and should be improved. However, I do not 

believe substantial additional resources need to be spent on PM10 modeling. I recommend modeling of 

particulate matter focus on PM2.5 and that results should be used in a relative sense using relative 

reduction factors. 

 

Section 5.0 contains a series of recommendations concerning 

documentation, performance evaluation, data archiving, mid-course 

reviews and use of weight of evidence in subsequent analyses. Key 

recommendations follow. 
-A major effort should be made to summarize for managers and the lay 

public the modeling used to justify the strategy reflected in the SIP 

revision. 

-More emphasis should be placed on efforts to evaluate how well the model 

responds to changes in emissions and meteorological conditions. 

-Although the available models provide an adequate basis for selecting SIP 

control strategies, there are uncertainties associated with the predictions. 

There should be ongoing efforts to perform a mid-course review which 

includes comparing the response of the chosen modeling approach to 

changes in emissions and meteorology to that obtained with the most 

current, viable model (e.g., in 2006) and with observed air quality trends 

normalized for meteorological fluctuations. 

-Care should be taken to archive modeling files to facilitate future midcourse reviews, performance evaluations and model improvements. 

7.0 Cited References 

Blanchard, C.L., P.M. Roth, S.J. Tanenbaum, S.D. Ziman and J.H. Seinfeld, (2000), “The Use of Ambient 

Measurements to Identify Which Precursor Species Limit Aerosol Nitrate Formation”, Accepted for 

publication in J.Air & Waste Management Association. 

Sillman, S., (1995), “The Use of N0y, H2O2 and HNO3 as Indicators for O3-N0x-ROG Sensitivity in 

Urban Locations”, J.Geophys. Res. 100, pp.14175-14188. 

Sillman, S., (1998), Evaluating the Relation Between Ozone, N0x and Hydrocarbons: The Method of 

Photochemical Indicators, EPA/600R-98/022. 

U.S. EPA, (1991), Guideline for Regulatory Application of the Urban Airshed Model, EPA-450/4-91-013. 

U.S. EPA, (1996), Guidance on Use of Modeled Results to Demonstrate Attainment of the Ozone NAAQS, 

EPA-454/B-95-007. 

U.S. EPA, (1999), Draft Guidance on the Use of Models and Other Analyses in Attainment Demonstrations 

for the 8-Hour Ozone NAAQS, EPA-454/R-99-004. 

U.S. EPA, (2001), Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and Regional 

Haze (draft 2.1), January 2, 2001, available on www.epa.gov/ttn/scram. 

U.S. EPA, (2002), Recommended Approach for Performing Mid-course Review of SIP’s to Meet the 1-hour 

NAAQS for Ozone, January 2001, available on www.epa.gov/ttn/scram. 

 
 

This page is left blank intentionally 
TO: Barry Wallerstein 

 Joe Cassmassi 

FROM: John Seinfeld 

DATE: January 21, 2003 

SUBJECT: Initial Critique of AQMP Models and Results 

 Five models have been used to simulate the August 4-6, 1997 episode, for which 

the peak 1-hour ozone level is 187 ppb, which occurred on August 5 at Riverside. The 

∆VOC is that needed to reduce the 1-hour peak ozone from 187 to 120 ppb, a ∆O3 of 67 

ppb. If a model exactly predicts the 187 ppb peak concentration for August 5, 1997, then 

one needs only to vary VOC emissions until the peak is reduced to 120 ppb. The level of 

VOC emissions August 4-6, 1997 for the meteorological conditions of required to lower 

the peak to 120 ppb then becomes the so-called “VOC carrying capacity” of the Basin. 

The actual ∆VOC that would be required in 2010 is the different between the estimated 

base VOC emissions in 2010 (about 600 tons/day) and the calculated VOC carrying 

capacity. 

 

 If a model does not reproduce the peak 187 ppb on August 5, the ∆O3 required to 

reach 120 ppb is different from 67 ppb. Those models that do not predict 187 ppb all 

happen to underpredict the peak value. A typical peak prediction might be 160 ppb. 

Then the ∆O3 required from that predicted peak is only 40 ppb, rather than the actual 67 

ppb. Consequently, the VOC carrying capacity that results from that model will reflect 

an ozone reduction of only 40 ppb and cannot be expected to accurately reflect the ∆VOC 

needed if starting from a 187 ppb peak O3. So, if a model is used that underpredicts the 

peak O3 concentration on the design day, one issue that must be dealt with is how to 

correct the VOC carrying capacity to reflect the effect of that underprediction. 

 

 The elements of a model simulation include: 

 emissions 

 meteorology (velocity and temperature field, mixing depth) 

 chemistry 

 boundary conditions 

 dry deposition 

 vertical and horizontal diffusion 

 numerical analysis of advection, diffusion, and chemical reaction 

 grid system 

Each of these elements is complicated in its own right. Ideally, when comparing 

performance of several models, one would like to have identical emissions 

meteorological fields, and boundary conditions, so that differences in prediction reflect 
differences in chemical mechanisms, dry deposition treatments, diffusion treatments, and 

numerical methods used to solve the governing equations. Even so, interactions among 

these submodels are nonlinear so that behavior of one submodel can compensate for 

inaccuracies in another submodel to produce a simulation that matches actual data. The 

difficulty with this situation is that when emissions are reduced these factors that 

compensate each other in the base case may not do so in the reduced emission case. 

 In principal, one should employ the model that most accurately reflects the 

underlying physics and chemistry. Of the five models, UAM, and especially CB4, is 

known to be the most out of date. UAM also has a notoriously inaccurate advection 

solver (Smolarkewicz) that strongly smears out concentration distributions. Yet, the 

UAM CB4 simulation of the August 4-6, 1997 episode produces the closest match to the 

observed peak O3 of the five models. Enhanced photochemical production of O3 is, most 

likely, compensating for the artificial spreading induced by the Smolarkewicz advection 

algorithm. SAPRC-99 is the preferred chemical mechanism, and indeed would be 

considered as state-of-the-science. Both CALGRID and CAMx represent improvements 

over UAM in scientific treatments; however, both of these models underpredict peak O3

on August 5, 1997 with both SAPRC 99 and CB4. If CB4 is “hot” chemically, as 

suggested above, one might expect O3 predictions to be higher with both CALGRID and 

CAMx using CB4; this is not the case. 

 I have not been associated with this AQMP modeling process long enough to be 

able to diagnose why UAM is matching the design day peak O3 and why the other models 

are underpredicting. The AQMD staff should continue to attempt to determine the 

underlying explanations for the behavior of the models through diagnostic simulations, 

and I will assist with designing these as much as possible. In summary, however, it is not 

apparent why each of the models is performing as they are. As discussed at the meeting 

at the AQMD, based on overprediction of NOx levels in the western portion of the Basin, 

it appears that there may be some problem with CAMx. 

 While the AQMD staff should continue to identify the reasons for underprediction 

of the peak O3 by each of the four models, the AQMD must decide on a strategy for 

determining the VOC carrying capacity of the Basin in 2010 based on the meteorology of 

the August 4-6, 1997 episode in the event that it is not possible to improve the 

simulations before the AQMP must be submitted. The following potential strategies 

exist: 

1. Because it is close to the actual observed peak O3, use the UAM CB4 simulation as the 

basis for determining the VOC carrying capacity. 

2. Use either the CALGRID or CAMx simulations as the basis for determining the VOC 

carrying capacity with no adjustment for underpredicted peak O3. 

3. Artificially increase the CALGRID or CAMx predicted peak O3, perhaps by enhancing 

photolysis rates, to match the measured O3 and use that simulation as the base case. 

4. Determine the percentage underprediction of peak O3 in the base case simulation and 

reduce the target concentration of 120 ppb by the same percentage. 

5. Starting from the simulated peak O3, determine the VOC reduction required for a 

reduction of ∆O3 = 67 ppb. 
 

 None of these is ideal. In the absence of more information, I would recommend 

option #4. By reducing the target concentration (120 ppb) by the percentage by which 

the peak O3 is underpredicted, one obtains a more realistic ∆O3 from which to determine 

the VOC carrying capacity. Hopefully, it will be possible to continue to evaluate the 

model simulations over the next couple of months. 
MEMORANDUM 

DATE: January 13, 2003 

TO: Joe Cassmassi 

FROM: Mel Zeldin 

SUBJECT: Ozone Modeling Evaluation and Suggestions 

 This memo is in response to your request for written input regarding the ozone modeling 

evaluation as an outcome of the meeting held last Friday. As I stated in the meeting, it is my 

belief that the UAM model has been the historical workhorse for ozone attainment modeling 

purposes for the last decade, and in order for another model to be used, either of two conditions 

would need to occur: (1) the UAM model would have to be shown to be deficient in the validation 

process; or (2) either the CALGRID or CAMx models would have to be shown to be definitively 

superior to the UAM. 

In my opinion, neither of these conditions is evident in the validation statistics. While 

there are advantages and disadvantages shown for each model, there is clearly nothing to show 

that any one model is superior to the others. Therefore, I would suggest that the UAM continue to 

be the model used for the ozone attainment process. I believe that other members of the Review 

Panel shared that opinion. I further believe that changing to another model, without clear 

superiority of that model, could be considered “gaming” or “model shopping” to a desired result. 

 In my advisory capacity to the Staff, however, I do have some suggestions: 

1) The AQMP (Plan) should be based on the UAM results at this time. 

2) It should be noted in the Plan, however, that the science experts believe that, in theory, at 

least, the CALGRID and CAMx models utilize newer techniques that should be better 

than the older UAM. It should be further noted that the ARB believes the CALGRID 

model to be the preferred model. 

3) It should further be noted that due to the number of model runs involved, and the short 

time frame in which to produce the attainment demonstration to avoid conformity lapse, 

one episode is deemed to be insufficient to determine if either CALGRID or CAMx are 

superior to the UAM. 
4) The District should commit to perform additional model evaluations of other episodes to 

determine if indeed CALGRID or CAMx show superior performance to the UAM, and 

these evaluations would be completed within a two-year time frame. 

5) Since the UAM has the lowest VOC carrying capacity, and it is presumed that the 

additional tonnages to be reduced would be placed in the “black box,” the District would 

commit not only to the larger “black box,” but also to proceed with studies, in parallel 

during the same two-year period, to explore and evaluate advanced technologies for 

additional VOC reductions. Any rulemaking activity to gain the extra VOC tonnages 

would be deferred until 2005 – when the model evaluation would be completed. 

6) As part of the current Plan and SIP submittal, the District should state that, at the end of 

the two-year period, the results of the model evaluations will be presented. If the results 

do not show any superiority from either CALGRID or CAMx, then the District would 

proceed to move forward with identifying measures in the “black box” to get to the UAM 

VOC carrying capacity. If, on the other hand, the additional model evaluations show that 

either the CALGRID or CAMx models are superior to the UAM, then the extra tonnages 

assigned to the “black box” would be removed. This would likely involve a SIP 

amendment. 

7) In taking this course of action, the District should state that there is too much uncertainty 

in the model evaluations at this time, and is therefore erring on the side of public health 

interests until a more complete evaluation is completed. In stating this, the District should 

make clear at this time, that any changes to the carrying capacity, at the end of the twoyear period, are reflecting better science in evaluating future year attainment, and 

removing “black box” tonnages are not to be considered “backsliding,” since attainment 

would be demonstrated with a (presumably) higher VOC carrying capacity. If either 

CALGRID or CAMx were shown to be the superior model, the only thing “lost” by the 

District would be the parallel effort for identifying new VOC control technologies, and I 

don’t think such an effort could ever be deemed “wasted” in the sense that such methods 

may ultimately be needed for 8-hr attainment in the future. 

8) In making additional model evaluations, I would suggest using the other episodes 

mentioned in the model protocol: September 26-29, 1997 and July 13-18, 1998. While 

only the August 1997 episode is used for attainment demonstration purposes, I see no 

reason why the other episodes can’t be used for model evaluation purposes only. Since 

some effort has been made to identify these episodes already, presumably these events 

will be easiest to develop the model inputs. Also, I would recommend that the August 

1997 episode be “backcasted” to 1987 for an additional evaluation when emissions were 

considerably different than the 1997 period. An evaluation of an episode in 1987, which 

is meteorologically similar to the 1997 episode, could be used for approximated validation 

data, or, as you suggested, a mean value for that meteorological classification could be 

used. This will at least demonstrate if any of the new models “blow up” with a 1987 

inventory, or are able to reasonably simulate those conditions. 

9) I like the idea suggested by another panel member for some kind of scoring system for 

evaluating model performance, other than just the performance statistics. This could be 

something that evaluates: the pattern representativeness (i.e, the modeled ozone spatial 

pattern replicates the observed pattern); the peak ozone representativeness; the 

temporal representativeness (i.e., the timing of the peaks at sites across the Basin match 

the observed time of the peaks); and similar evaluations for other pollutants, such as NO 

and NOx. It would be advisable to establish a scoring system prior to undertaking the 

additional model evaluations. 
I hope these comments are helpful. Please call me if you have any questions 

or would like to discuss further. 



ATTACHMENT 3 

Mid-Course Modeling Reviews 
ATTACHMENT 4 

CEPA Source Level Emissions Reduction Summary for 2006: 

 Annual Average Inventory 

 Year 2006 Emission Reductions Excluding Natural Sources by Control Measure 

 in the South Coast Air Basin (Annual Average Inventory - Tons/Day) 

 (A) Reductions Without Overlapping/Double-Counting With Other Control Measures (1) 

 (Reductions - Tons/Day) 

Measure Name VOC NOx CO SOx PM10 PM2.5 

BA-2202 Baseline adjustment for R2202 2.67 2.79 29.47 0.02 0.08 0.05 

BA-POWER Power Plants Adjustments 0.00 0.29 0.00 0.00 0.00 0.00 

BA-FVR ARB-Petroleum Marketing - Bas. Adj. -1.20 0.00 0.00 0.00 0.00 0.00 

BA-GSE ARB-Airport Ground Support Equip - Bas. Adj. 0.03 0.78 0.00 0.00 0.00 0.00 

BA-SMOG ARB-Smog Check II - Bas. Adj. 2.50 3.98 0.00 0.00 0.00 0.00 

CTS-07 FER Architectural Ctg (R1113 Phase 3) (VOC) 3.27 0.00 0.00 0.00 0.00 0.00 

CTS-10 Misc. Industrial Coating&Solvent Opr. (VOC) 0.86 0.00 0.00 0.00 0.00 0.00 

FUG-05 ER from fugitive Emission Sources (VOC) 1.68 0.00 0.00 0.00 0.00 0.00 

CMB-09 Petroleum FCCU (PM10,PM2.5,NH3) 0.00 0.00 0.00 0.00 0.10 0.08 

BCM-7 FER from Fugitive Dust Sources (PM10) 0.00 0.00 0.00 0.00 0.00 0.00 

BCM-08 Aggregate Operation (PM10) 0.00 0.00 0.00 0.00 0.62 0.33 

PRC-03 COE fr Restaurant Operations (PM10) 0.00 0.00 0.00 0.00 0.33 0.33 

PRC-07 Industrial Process Operations (VOC) 0.81 0.00 0.00 0.00 0.00 0.00 

WST-01 Ems Reduction fr Livestock Waste (VOC,NH3) 4.81 0.00 0.00 0.00 0.00 0.00 

WST-02 COE fr Composting (PR1133) (VOC,NH3,PM10) 0.40 0.00 0.00 0.00 0.00 0.00 

CMB-07 Refinery Flares (VOC,NOX,SOX,CO,PM) 0.17 0.17 0.99 2.16 0.03 0.03 

MSC-05 Truckstop Electrification (ALL) 0.00 0.00 0.00 0.00 0.00 0.00 

Grand Total (Net) 16.00 8.01 30.47 2.18 1.16 0.81 
EMISSION SUMMARY FOR 

(POINT, AREA, MOBILE SOURCE, AND OFF-ROAD MV) 

Baseline Emissions VOC NOx CO SOx PM10 PM2.5 

 Point source 67.62 14.50 45.27 6.11 12.97 11.58 

 Area source 224.53 46.24 165.36 0.95 239.62 69.90 

 RECLAIM 0.00 34.20 0.00 12.03 0.00 0.00 

 Total Stationary 292.15 94.94 210.63 19.09 252.59 81.48 

 On-road 257.65 555.89 2513.55 4.84 19.27 13.09 

 Off-road 134.06 264.82 1109.53 34.62 20.21 17.69 

 Aircraft 5.33 27.01 50.16 0.95 0.62 0.62 

 TOTAL 689.20 942.66 3883.87 59.50 292.69 112.88 

EMISSION REDUCTIONS 

 Point source 2.15 0.45 0.99 2.16 0.41 0.27 

 Area source 8.64 0.00 0.00 0.00 0.67 0.49 

 Total Stationary 10.79 0.45 0.99 2.16 1.08 0.76 

 On-road 5.17 6.77 29.47 0.02 0.08 0.05 

 Off-road 0.03 0.78 0.00 0.00 0.00 0.00 

 Aircraft 0.00 0.00 0.00 0.00 0.00 0.00 

 TOTAL 16.00 8.01 30.47 2.18 1.16 0.81 

REMAINING EMISSIONS 

 Point source 65.47 14.04 44.28 3.95 12.56 11.31 

 Area source 215.89 46.24 165.36 0.95 238.95 69.41 

 RECLAIM 0.00 34.20 0.00 12.03 0.00 0.00 

 Total Stationary 281.36 94.48 209.64 16.92 251.52 80.72 

 On-road 252.48 549.12 2484.08 4.82 19.19 13.04 

 Off-road 134.03 264.04 1109.53 34.62 20.21 17.69 

 Aircraft 5.33 27.01 50.16 0.95 0.62 0.62 

 TOTAL 673.20 934.66 3853.41 57.31 291.53 112.07 

 NSR/Set-Aside 0.00 0.00 0.00 0.00 0.00 0.00 

 GRAND TOTAL (T/D)(2) 673.20 934.66 3853.41 57.31 291.53 112.07 

 SCAG’s RTP/TCMs (3) 8.49 6.98 88.80 0.08 0.64 0.47 

(1) Emission reductions for individual measures were estimated based on the sequence of listing 

 contained here. When the sequence changes, reductions from each measure could be affected, 

 but the net total remain the same. The purpose of this table is to estimate 

 total emission reductions without overlapping or double-counting between measures. 

(2) Total remaining emissions are slightly different from figures in 2003 AQMP main document due to rounding. 

(3) Reflects SCAG’s 2001 Regional Transportation Plan (including transportation control measures). 
ATTACHMENT 5 

CEPA Source Level Emissions Reduction Summary for 2010: 

 Annual Average Inventory 
 Year 2010 Emission Reductions Excluding Natural Sources by Control Measure 

 in the South Coast Air Basin (Annual Average Inventory - Tons/Day) 

 (A) Reductions Without Overlapping/Double-Counting With Other Control Measures (1) 

 (Reductions - Tons/Day) 

Measure Name VOC NOx CO SOx PM10 PM2.5 

BA-2202 Baseline adjustment for R2202 1.81 1.88 20.34 0.02 0.09 0.05 

BA-POWER Power Plants Adjustments 0.00 0.33 0.00 0.00 0.00 0.00 

BA-FVR ARB-Petroleum Marketing - Bas. Adj. -1.20 0.00 0.00 0.00 0.00 0.00 

BA-GSE ARB-Airport Ground Support Equip - Bas. Adj. 0.16 0.81 0.00 0.00 0.00 0.00 

BA-SMOG ARB-Smog Check II - Bas. Adj. 1.85 3.66 0.00 0.00 0.00 0.00 

CTS-07 FER Architectural Ctg (R1113 Phase 3) (VOC) 7.22 0.00 0.00 0.00 0.00 0.00 

CTS-10 Misc. Industrial Coating&Solvent Opr. (VOC) 2.79 0.00 0.00 0.00 0.00 0.00 

FUG-05 ER from fugitive Emission Sources (VOC) 1.99 0.00 0.00 0.00 0.00 0.00 

CMB-09 Petroleum FCCU (PM10,PM2.5,NH3) 0.00 0.00 0.00 0.00 0.30 0.24 

BCM-7 FER from Fugitive Dust Sources (PM10) 0.00 0.00 0.00 0.00 0.00 0.00 

BCM-08 Aggregate Operation (PM10) 0.00 0.00 0.00 0.00 0.67 0.36 

PRC-03 COE fr Restaurant Operations (PM10) 0.00 0.00 0.00 0.00 0.99 0.98 

PRC-07 Industrial Process Operations (VOC) 1.74 0.00 0.00 0.00 0.00 0.00 

WST-01 Ems Reduction fr Livestock Waste (VOC,NH3) 4.81 0.00 0.00 0.00 0.00 0.00 

WST-02 COE fr Composting (PR1133) (VOC,NH3,PM10) 1.20 0.00 0.00 0.00 0.00 0.00 

CMB-07 Refinery Flares (VOC,NOX,SOX,CO,PM) 0.16 0.17 0.99 2.16 0.03 0.03 

CMB-10 Further RECLAIM Reductions 0.00 2.85 0.00 0.00 0.00 0.00 

MSC-05 Truckstop Electrification (ALL) 0.09 1.69 0.55 0.00 0.02 0.02 

CONS-1 ARB-Consumer Products Limits for 2006 (2) 2.27 0.00 0.00 0.00 0.00 0.00 

CONS-2 ARB-Consumer Products Limits to 2010 (2) 14.69 0.00 0.00 0.00 0.00 0.00 

FVR-1 ARB-Vapor from Aboveground Storage Tanks (2) 0.08 0.00 0.00 0.00 0.00 0.00 

FVR-2 ARB-Vapor from Gasoline Dispensing at (2) 0.08 0.00 0.00 0.00 0.00 0.00 

FVR-3 ARB-Gasoline Dispenser Hoses (2) 0.60 0.00 0.00 0.00 0.00 0.00 

LMD-1 ARB-Passenger Cars + Light Duty Trucks (2) 19.21 19.17 160.39 0.00 0.00 0.00 

LMD-2 ARB-Smog Check Improvements (2) 5.80 9.02 58.73 0.00 0.00 0.00 

ONHD-1 ARB-Truck and Bus Highway Inspections (2) 0.08 0.00 0.00 0.00 0.08 0.07 

ONHD-2 ARB-Vapor from Gasoline Cargo Tanks (2) 4.94 0.00 0.00 0.00 0.00 0.00 

ONHD-3 ARB-Clean-up Existing Truck/Bus Fleet (2) 4.52 11.61 19.97 0.00 1.51 1.39 

OFCI-1 ARB-Clean-up Existing IC Engines (Diesel) (2) 7.89 0.00 0.00 0.00 2.05 1.89 

OFCI-2 ARB-Off Road Equipment Inspection Program (2) 0.00 0.10 0.00 0.00 0.00 0.00 

OFLSI-1 ARB-Off-Road New Standards (Gasoline + Nat. (2) 0.00 0.76 8.01 0.00 0.00 0.00 

OFLSI-2 ARB-Clean-up Existing Off-Road (2) 1.29 3.33 0.00 0.00 0.00 0.00 

OFLSI-3 ARB-Electrified New Forklifts (Gasoline + Nat. (2) 0.54 2.67 24.07 0.00 0.05 0.04 

SMOF-1 ARB-Handheld Lawn & Garden Equipment (2) 1.81 0.13 0.00 0.00 0.00 0.00 

SMOF-2 ARB-Non-Handheld Lawn & Garden Equipment (2) 7.28 1.71 0.00 0.00 0.00 0.00 

MARINE-1 ARB-Clean-up Existing Harbor Craft (2) 0.10 2.70 0.00 0.00 0.05 0.05 

EPA-01 EPA-Clean-up Existing Truck/Bus Fleet (3) 1.11 2.75 4.62 0.00 0.33 0.30 

EPA-02 EPA-Harbor Craft and Ocean-Going Ship Stds. (3) 0.39 2.92 0.00 0.00 0.30 0.28 

EPA-03 EPA-Clean-up Existing Ocean-Going Ships (3) 1.47 16.62 0.00 0.00 1.21 1.12 

EPA-04 EPA-Reductions from Jet Aircraft (3) 0.56 1.80 0.00 0.00 0.00 0.00 

LT1-DIST Mid-Term District Measures (VOC) 10.23 0.00 0.00 0.00 0.00 0.00 

LT1 Long Term Measure 1 42.45 147.43 0.00 0.00 0.00 0.00 

LT2 Long Term Measure 2 174.89 0.00 0.00 0.00 0.00 0.00 

Grand Total (Net) 324.88 234.08 297.67 2.18 7.68 6.81 
EMISSION SUMMARY FOR 

(POINT, AREA, MOBILE SOURCE, AND OFF-ROAD MV) 

Baseline Emissions VOC NOx CO SOx PM10 PM2.5 

 Point source 71.32 12.68 48.41 6.22 13.79 12.29 

 Area source 224.17 42.09 168.47 0.99 247.22 71.85 

 RECLAIM 0.00 34.20 0.00 12.03 0.00 0.00 

 Total Stationary 295.50 88.97 216.87 19.24 261.01 84.14 

 On-road 196.68 425.76 1882.82 1.99 19.07 12.65 

 Off-road 117.48 225.14 1041.66 37.79 18.96 16.56 

 Aircraft 5.01 31.08 51.77 1.05 0.51 0.51 

 TOTAL 614.67 770.95 3193.11 60.07 299.55 113.86 

EMISSION REDUCTIONS 

 Point source 18.70 0.49 0.99 2.16 0.64 0.45 

 Area source 110.41 0.00 0.00 0.00 1.36 1.16 

 RECLAIM 0.00 2.85 0.00 0.00 0.00 0.00 

 Total Stationary 129.11 3.34 0.99 2.16 1.99 1.61 

 On-road 130.55 157.46 264.05 0.02 2.01 1.82 

 Off-road 64.65 65.13 32.63 0.00 3.68 3.39 

 Aircraft 0.56 8.15 0.00 0.00 0.00 0.00 

 TOTAL 324.88 234.08 297.67 2.18 7.68 6.81 

REMAINING EMISSIONS 

 Point source 52.62 12.18 47.42 4.06 13.15 11.85 

 Area source 113.77 42.09 168.47 0.99 245.86 70.68 

 RECLAIM 0.00 31.35 0.00 12.03 0.00 0.00 

 Total Stationary 166.39 85.62 215.88 17.08 259.02 82.53 

 On-road 66.13 268.30 1618.76 1.97 17.06 10.83 

 Off-road 52.83 160.01 1009.03 37.79 15.28 13.17 

 Aircraft 4.45 22.93 51.77 1.05 0.51 0.51 

 TOTAL 289.79 536.87 2895.44 57.89 291.87 107.04 

 NSR/Set-Aside 5.00 3.00 0.50 1.00 0.85 0.85 

 GRAND TOTAL (T/D) (4) 294.79 539.87 2895.94 58.89 292.72 107.89 

 SCAG’s RTP/TCMs (5) 15.65 8.73 165.24 0.17 1.69 1.24 

(1) Emission reductions for individual measures were estimated based on the sequence of listing 

 contained here. When the sequence changes, reductions from each measure could be affected, 

 but the net total remain the same. The purpose of this table is to estimate 

 total emission reductions without overlapping or double-counting between measures. 

(2) The higher end of emission reduction range is included for these CARB’s short-term control measure. However 

 CARB is only committing to the mid point of the reduction range for these measures with the balance to be 

 achieved under the long-term strategy. 

(3) Emission reductions for these control measures affecting federal sources are considered under long-term 

 strategy. These measures which were originally contained in CARB’s draft State and Federal Element (Jan 2003) 

 are re-numbered to avoid confusion with CARB’s revised control measure numbers. 

(4) Total remaining emissions are slightly different from figures in 2003 AQMP main document due to rounding. 

(5) Reflects SCAG’s 2001 Regional Transportation Plan (including transportation control measures).
ATTACHMENT 6 

CEPA Source Level Emissions Reduction Summary for 2006: 

 Planning Inventory 
 Year 2010 Emission Reductions Excluding Natural Sources by Control Measure in the 

 South Coast Air Basin (Planning Inventory - Tons/Day) 

 (A) Reductions Without Overlapping/Double-Counting With Other Control Measures (1) 

 (Reductions - Tons/Day) 

Measure Name VOC NOx CO NO2 

BA-2202 Baseline adjustment for R2202 1.71 1.74 20.00 2.05 

BA-POWER Power Plants Adjustments 0.00 0.33 0.00 0.33 

BA-FVR ARB-Petroleum Marketing - Bas. Adj. -1.20 0.00 0.00 0.00 

BA-GSE ARB-Airport Ground Support Equip - Bas. Adj. 0.16 0.81 0.00 0.82 

BA-SMOG ARB-Smog Check II - Bas. Adj. 1.84 3.38 0.00 3.99 

CTS-07 FER Architectural Ctg (R1113 Phase 3) (VOC) 8.52 0.00 0.00 0.00 

CTS-10 Misc. Industrial Coating&Solvent Opr. (VOC) 3.00 0.00 0.00 0.00 

FUG-05 ER from fugitive Emission Sources (VOC) 2.01 0.00 0.00 0.00 

CMB-09 Petroleum FCCU (PM10,PM2.5,NH3) 0.00 0.00 0.00 0.00 

BCM-7 FER from Fugitive Dust Sources (PM10) 0.00 0.00 0.00 0.00 

BCM-08 Aggregate Operation (PM10) 0.00 0.00 0.00 0.00 

PRC-03 COE fr Restaurant Operations (PM10) 0.00 0.00 0.00 0.00 

PRC-07 Industrial Process Operations (VOC) 1.95 0.00 0.00 0.00 

WST-01 Ems Reduction fr Livestock Waste (VOC,NH3) 4.81 0.00 0.00 0.00 

WST-02 COE fr Composting (PR1133) (VOC,NH3,PM10) 1.20 0.00 0.00 0.00 

CMB-07 Refinery Flares (VOC,NOX,SOX,CO,PM) 0.16 0.17 0.99 0.17 

CMB-10 Further RECLAIM Reductions 0.00 3.00 0.00 3.00 

MSC-05 Truckstop Electrification (ALL) 0.11 2.07 0.43 1.31 

CONS-1 ARB-Consumer Products Limits for 2006 (2) 2.27 0.00 0.00 0.00 

CONS-2 ARB-Consumer Products Limits to 2010 (2) 14.69 0.00 0.00 0.00 

FVR-1 ARB-Vapor from Aboveground Storage Tanks (2) 0.08 0.00 0.00 0.00 

FVR-2 ARB-Vapor from Gasoline Dispensing at Marinas (2) 0.08 0.00 0.00 0.00 

FVR-3 ARB-Gasoline Dispenser Hoses (2) 0.60 0.00 0.00 0.00 

LMD-1 ARB-Passenger Cars + Light Duty Trucks (2) 19.10 17.71 157.71 20.91 

LMD-2 ARB-Smog Check Improvements (2) 5.73 8.35 57.80 9.81 

ONHD-1 ARB-Truck and Bus Highway Inspections (2) 0.08 0.00 0.00 0.00 

ONHD-2 ARB-Vapor from Gasoline Cargo Tanks (2) 4.94 0.00 0.00 0.00 

ONHD-3 ARB-Clean-up Existing Truck/Bus Fleet (2) 4.52 11.32 19.97 12.32 

OFCI-1 ARB-Clean-up Existing IC Engines (Diesel) (2) 7.92 0.00 0.00 0.00 

OFCI-2 ARB-Off Road Equipment Inspection Program (2) 0.00 0.10 0.00 0.10 

OFLSI-1 ARB-Off-Road New Standards (Gasoline + Nat.(2) 0.00 0.80 7.36 0.72 

OFLSI-2 ARB-Clean-up Existing Off-Road (2) 1.40 3.51 0.00 3.16 

OFLSI-3 ARB-Electrified New Forklifts (Gasoline + Nat.(2) 0.59 2.82 21.97 2.53 

SMOF-1 ARB-Handheld Lawn & Garden Equipment (2) 1.92 0.15 0.00 0.12 

SMOF-2 ARB-Non-Handheld Lawn & Garden Equipment (2) 7.72 1.90 0.00 1.52 

MARINE-1 ARB-Clean-up Existing Harbor Craft (2) 0.10 2.70 0.00 2.70 

EPA-01 EPA-Clean-up Existing Truck/Bus Fleet (3) 1.11 2.68 4.62 2.91 

EPA-02 EPA-Harbor Craft and Ocean-Going Ship Stds. (3) 0.39 2.92 0.00 2.92 

EPA-03 EPA-Clean-up Existing Ocean-Going Ships (3) 1.47 16.62 0.00 16.62 

EPA-04 EPA-Reductions from Jet Aircraft (3) 0.57 1.84 0.00 1.76 

LT1-DIST Mid-Term District Measures (VOC) 10.99 0.00 0.00 0.00 

LT1 Long Term Measure 1 44.66 144.78 0.00 153.07 

LT2 Long Term Measure 2 183.44 0.00 0.00 0.00 

Grand Total (Net) 338.62 229.67 290.85 242.82 
EMISSION SUMMARY FOR 

(POINT, AREA, MOBILE SOURCE, AND OFF-ROAD MV) 

BASELINE EMISSIONS 

 VOC NOx CO NO2 

 Point source 84.31 14.86 51.93 14.86 

 Area source 226.06 33.10 332.05 55.00 

 RECLAIM 0.00 35.67 0.00 35.67 

 Total Stationary 310.36 83.63 383.97 105.53 

 On-road 193.95 406.03 1856.71 456.70 

 Off-road 134.25 234.40 861.54 216.06 

 Aircraft 5.09 31.91 50.20 30.47 

 TOTAL 643.65 755.97 3152.42 808.76 

EMISSION REDUCTIONS 

 Point source 22.22 0.49 0.99 0.49 

 Area source 112.85 0.00 0.00 0.00 

 RECLAIM 0.00 3.00 0.00 3.00 

 Total Stationary 135.07 3.49 0.99 3.49 

 On-road 128.81 149.61 260.11 169.26 

 Off-road 74.17 68.20 29.76 62.08 

 Aircraft 0.57 8.37 0.00 7.99 

 TOTAL 338.62 229.67 290.85 242.82 

REMAINING EMISSIONS 

 Point source 62.09 14.37 50.93 14.37 

 Area source 113.20 33.10 332.05 55.00 

 RECLAIM 0.00 32.67 0.00 32.67 

 Total Stationary 175.29 80.14 382.98 102.04 

 On-road 65.14 256.42 1596.60 287.44 

 Off-road 60.08 166.20 831.78 153.98 

 Aircraft 4.52 23.54 50.20 22.48 

 TOTAL 305.03 526.30 2861.57 565.94 

 NSR/Set-Aside 5.00 3.00 0.50 3.00 

 GRAND TOTAL (T/D)(4) 310.03 529.30 2862.07 568.94 

 SCAG’s RTP/TCMs (5) 15.68 7.86 161.76 9.62 

(1) Emission reductions for individual measures were estimated based on the sequence of listing 

 contained here. When the sequence changes, reductions from each measure could be affected, 

 but the net total remain the same. The purpose of this table is to estimate 

 total emission reductions without overlapping or double-counting between measures. 

(2) The higher end of emission reduction range is included for these CARB’s short-term control measure. However 

 CARB is only committing to the mid point of the reduction range for these measures with the balance to be 

 achieved under the long-term strategy. 

(3) Emission reductions for these control measures affecting federal sources are considered under long-term 

 strategy. These measures which were originally contained in CARB’s draft State and Federal Element (Jan 2003) 

 are re-numbered to avoid confusion with CARB’s revised control measure numbers. 

(4) Total remaining emissions are slightly different from figures in 2003 AQMP main document due to rounding. 

(5) Reflects SCAG’s 2001 Regional Transportation Plan (including transportation control measures).