San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix F: Modeling Protocol

2012 PM2.5 Plan 

2012 PM2.5 Plan

SJVUAPCD

Appendix F

Modeling Protocol 
San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix F: Modeling Protocol

2012 PM2.5 Plan 

This page intentionally blank.
1

Modeling Protocol

Photochemical Modeling for the 24-Hour 

PM2.5State Implementation Plan in the San 

Joaquin Valley

Prepared by 

California Air Resources Board

San Joaquin Valley Air Pollution Control District

Prepared for

United States Environmental Protection Agency Region IX

August 2, 2012
2

Table of Contents

Acronyms: ....................................................................................................................... 9

1. Introduction ............................................................................................................ 12

Recent History of SIPs in SJV and the Need for a 24-hour PM2.5 SIP...................... 12

1.1. Modeling Roles for the Current SIP.................................................................. 13

1.2. Stakeholder Participation in the SIP Modeling Process ................................... 13

1.3. Involvement of External Scientific/Technical Experts and Their Input on the 

Photochemical Modeling.......................................................................................... 14

1.4. Schedule for Completion of the Plan................................................................ 15

2. Description of the Conceptual Model for the Nonattainment Area.......................... 17

2.1. History of Field Studies in the Region .............................................................. 17

2.2. CRPAQS Relevance to SIP ............................................................................. 21

2.3. Description of PM2.5 Monitoring in the SJV....................................................... 22

2.4. PM2.5 Air Quality Trends ................................................................................... 24

2.5. Major PM2.5 Components ................................................................................. 27

2.6. Conditions Leading to PM2.5 Exceedances ...................................................... 28

2.7. Past Modeling Efforts and Results ................................................................... 31

3. Selection of the Modeling Periods.......................................................................... 39

3.1. Reference Year Selection and Justification...................................................... 39

3.2. Future years and justifications.......................................................................... 39

3.3. Justification for Quarterly Modeling instead of Several Episodes..................... 40
3

3.4. Identification of Exceptional Events to be excluded from Base/Reference/Future 

Year Modeling.......................................................................................................... 40

4. Development of Emissions Inventories .................................................................. 42

4.1. PM2.5 Emissions Inventory Development.......................................................... 42

4.1.1. Background.............................................................................................. 43

4.1.2. Terminology ............................................................................................. 45

4.2. Point and Area Source Emissions.................................................................... 48

4.2.1. Development of Base-Year Emission Inventory....................................... 48

4.2.2. Quality Assurance of Base Year Emissions............................................. 49

4.3. Future Year (Forecasted) Emissions................................................................ 51

4.3.1. Growth Factors ........................................................................................ 51

4.3.2. Control Factors ........................................................................................ 52

4.4. Day-Specific Emissions.................................................................................... 53

4.4.1. Wildfires and Prescribed Burns................................................................ 53

4.4.2. Paved Road Dust..................................................................................... 53

4.4.3. Unpaved Road Dust ................................................................................ 55

4.4.4. Agricultural Burn Data for San Joaquin Valley ......................................... 56

4.4.5. Ocean-Going Vessels.............................................................................. 57

4.5. Temporally and Spatially Resolved Emissions................................................. 57

4.6. Surface Temperature and Relative Humidity Fields ......................................... 58

4.7. On-Road Mobile Source Emissions ................................................................. 59
4

4.7.1. General Methodology............................................................................... 60

4.8. Biogenic Emissions .......................................................................................... 65

4.9. Spatial Allocation.............................................................................................. 67

4.9.1. Grid Definition .......................................................................................... 68

4.9.2. Spatial Surrogates ................................................................................... 69

4.10. Speciation.................................................................................................... 74

4.10.1. Speciation Profiles ................................................................................... 75

4.10.2. Chemical Mechanisms............................................................................. 77

4.11. Quality Assurance ....................................................................................... 80

4.11.1. Examples of Standard Tabular Summaries ............................................. 81

4.11.2. Spatial Plots............................................................................................. 84

4.11.3. Time Series Plots..................................................................................... 85

5. Models and Inputs.................................................................................................. 88

5.1. Rationale for the Selection of Models............................................................... 88

5.1.1. Meteorology Model .................................................................................. 88

5.1.2. Air Quality Model ..................................................................................... 89

5.2. Model Setup and Inputs ................................................................................... 91

5.2.1. Meteorology Model (modeling domains, horizontal and vertical resolution, 

physics options, regional reanalysis data, etc.)...................................................... 91

5.2.2. Air Quality Model (modeling domains, horizontal and vertical resolution, 

chemical mechanisms, PM routines, initial and boundary conditions, etc.)............ 94
5

5.2.3. Construction of the Simulated PM2.5 Mass............................................. 104

5.2.4. Quality Assurance of Model Inputs ........................................................ 104

6. Meteorological Model Performance Evaluation .................................................... 105

6.1. Known Performance Issues of Meteorological Models in the Complex Terrain of 

California and Current Attempts to Improve Performance ..................................... 105

6.2. Ambient Data Base and Quality of Data......................................................... 107

6.3. Model Performance Evaluation Procedures and Metrics ............................... 108

6.3.1. Statistical Evaluation.............................................................................. 108

6.3.2. Phenomenological Evaluation................................................................ 111

7. Air Quality Model Performance Evaluation........................................................... 113

7.1. Ambient Data Base and Quality of Data......................................................... 113

7.2. Model Performance Evaluation Procedures and Metrics ............................... 115

7.3. Diagnostic Testing.......................................................................................... 119

8. Attainment Plan.................................................................................................... 120

8.1. Calculation of Relative Response Factors ..................................................... 120

8.2. Sulfate, Adjusted Nitrate, DerivedWater, Inferred Carbonaceous Material 

Balance Approach (SANDWICH) and Potential Modifications ............................... 120

8.3. Estimation of Species Concentrations at Federal Reference Method (FRM) 

Monitors that Lack Speciation Data ....................................................................... 124

8.4. Speciated Modeled Attainment Test (SMAT) ................................................. 125

8.5. Sensitivity Analyses ....................................................................................... 127

8.6. Unmonitored Area Analysis............................................................................ 127
6

9. Supplemental Analyses........................................................................................ 129

9.1. Additional Analyses to be completed to corroborate the Modeling................. 129

9.2. Base Case Air-Quality Modeling with Meteorological Fields Generated with the 

Weather and Research Forecast (WRF) Model..................................................... 130

10. Procedural Requirements................................................................................. 132

10.1. How Modeling and other Analyses will be Archived, Documented, and 

Disseminated ......................................................................................................... 132

10.2. Specific Deliverables to U.S. EPA............................................................. 133

References:................................................................................................................. 134
7

List of Tables

Table 1-1: The Timeline for Completion of the Plan. .................................................... 16

Table 2-1: Major Field Studies in Central California and surrounding areas. ............... 20

Table 2-2: PM2.5 monitoring sites in the San Joaquin Valley nonattainment area......... 23

Table 2-3: 98th Percentiles and 24-hour Design Value................................................. 26

Table 4-1: Inventory Terms for Emission Source Types............................................... 45

Table 4-2: DTIM Emission Categories.......................................................................... 63

Table 4-3: Summary of spatial surrogates.................................................................... 72

Table 4-4: ARB’s SAPRC-99 Emitted Organic Model Species..................................... 77

Table 4-5: Model Species for NOx ............................................................................... 80

Table 5-1: MM5 30 Vertical Layer Configuration. ......................................................... 93

Table 5-2: MM5 Physics Options.................................................................................. 94

Table 5-3: CMAQv4.7.1 Schemes used for Current Simulations. ................................ 96

Table 7-1: Observations for evaluating model performance....................................... 113

Table 8-1: The FRM sites in SJV and their companion speciation sites..................... 125
8

List of Figures

Figure 2-1: PM2.5 composition on an average exceedance day.................................. 28

Figure 5-1: The three nested grids for the MM5 model (D01 36km; D02 12km; and D03 

4km). ............................................................................................................................. 92

Figure 5-2: Modeling domains used by ARB................................................................ 95

Figure 5-3: 1-hour O3 mixing ratio at 3 am of Jan 10, 2007 (UTC) predicted by 

CMAQv4.7.1 with the Yamartino advection scheme. .................................................... 99

Figure 5-4: Difference in monthly average PM2.5 predictions for January 2007 simulated 

using CMAQv4.7.1 with the PPM and Yamartino schemes......................................... 100

Figure 5-5: Comparison of MOZART (red) simulated CO (left), ozone (center), and PAN 

(right) to observations (black) along the DC-8 flight track. Shown are mean (filled 

symbol), median (open symbols), 10th and 90th percentiles (bars) and extremes (lines). 

The number of data points per 1-km wide altitude bin is shown next to the graphs. 

Adapted from Figure 2 in Pfister et al. (2011).............................................................. 101

Figure 5-6: Difference in average PM2.5 prediction for January 2007 between simulation 

with the nested domain and simulation using 4km grid for the entire coarse domain.. 103

Figure 6-1: Terrain height changes along with counties and major rivers and lakes in 

California (http://geology.com/state-map/california.shtml)........................................... 107

Figure 7-1: Example of “bugle plots” showing PM2.5 actual model performance as 

compared to model performance criteria and goals (from Boylan and Russell, 2006).117

Figure 7-2: Example of a “soccer plot” showing PM2.5 fractional bias and error (from 

Tesche et al., 2006)..................................................................................................... 117

Figure 7-3: An example of a Taylor diagram. ............................................................. 118
9

Acronyms:

ARB – Air Resources Board

ARCTAS-CARB – California portion of the Arctic Research of the Composition of the 

Troposphere from Aircraft and Satellites conducted in 2008

CalNex – Research at the Nexus of Air Quality and Climate Change conducted in 2010

CCOS - Central California Ozone Study

CMAQ Model – Community Multi-scale Air Quality Model

CIT – California Institute of Technology

CRPAQS – California Regional PM10/PM2.5 Air Quality Study

FDDA – Four-Dimensional Data Assimilation

FEM – Federal Equivalence Monitors

FRM – Federal Reference Monitors

HNO3 – Nitric Acid

IMPROVE – Interagency Monitoring of Protected Visual Environments

IMS-95 – Integrated Monitoring Study of 1995

LIDAR – Light Detection And Ranging

MADRID – Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution

MM5 – Mesoscale Meteorological Model Version 5

MOZART – Model for Ozone and Related chemical Tracers 

NASA – National Aeronautics and Space Administration

NCAR – National Center for Atmospheric Research
10

NH3 – Ammonia

NIOSH – National Institute for Occupational Safety and Health

NOAA - National Oceanic and Atmospheric Administration

NOx – Oxides of nitrogen

OC – Organic Carbon

PAN – Peroxy Acetyl Nitrate

PM2.5 – Particulate Matter with aerodynamic diameter less than 2.5 micrometers

PM10 – Particulate Matter with aerodynamic diameter less than 10 micrometers

RRF – Relative Response Factor

RSAC – Reactivity Scientific Advisory Committee

SANDWICH – Application of the Sulfate, Adjusted Nitrate, Derived Water, Inferred 

Carbonaceous Material Balance Approach 

SAPRC – Statewide Air Pollution Research Center

SARMAP – SJVAQS/AUSPEX Regional Modeling Adaptation Project

SIP – State Implementation Plan

SJV – San Joaquin Valley

SJVAPCD – San Joaquin Valley Air Pollution Control District 

SJVAQS/AUSPEX – San Joaquin Valley Air Quality Study/Atmospheric Utilities 

Signatures Predictions and Experiments

SLAMS – State and Local Air Monitoring Stations

SMAT – Application of the Speciated Modeled Attainment Test 
11

SOA – Secondary Organic Aerosol 

SOx – Oxides of Sulfur

STN – Speciated Trend Network

UCD – University of California at Davis

U.S. EPA – United States Environmental Protection Agency

VOC – Volatile Organic Compounds

WRF Model – Weather and Research Forecast Model
12

1. Introduction

The purpose of this modeling protocol is to detail and formalize the procedures for 

conducting the 24-hour PM2.5 State Implementation Plan photochemical modeling for 

the San Joaquin Valley. The protocol is intended to communicate up front how the 

modeling attainment test will be performed. In addition this protocol discusses

additional analyses that are intended to help corroborate the modeled attainment test. 

Recent History of SIPs in SJV and the Need for a 24-hour PM2.5 SIP

Over the past decade, the San Joaquin Valley Air Pollution Control District (SJVAPCD

or District) has adopted State Implementation Plans (SIPs or Plans) that set forth State 

and local emission reduction strategies to bring the San Joaquin Valley (SJV) into 

attainment for federal ozone (O3) and particulate matter (PM) air quality standards 

(standards) by specified dates. In 2004, SJVAPCD adopted the 1-hour O3 SIP. In 

addition, SJVAPCD adopted the 2007 Ozone Plan to address the 8-hour standard of 

0.08 parts per million (ppm) set by U.S. EPA in 1997. On March 1, 2012, U.S. EPA 

finalized its approval this SIP (76 FR 57846). 

Implementation of State and local control measures mapped out in the SJV 2003 PM10

Plan, resulted in the Valley reaching attainment of the PM10 standard ahead of 

schedule. In November 2008, the San Joaquin Valley was officially re-designated to 

attainment for PM10 (73 FR 66759). To ensure continued maintenance of PM10

attainment, SJVAPCD adopted and U.S. EPA approved the SJV 2007 PM10

Maintenance Plan. In 1997, U.S. EPA adopted their first PM2.5 standard, which set two 

levels, an annual standard of 15 μg/m3and a 24-hour standard of 65 μg/m

3

. The SJV 

2008 PM2.5 Plan adopted by SJVAPCD sets the course for the Valley to attain the 1997 

annual standard in 2014. The plan focused on the annual standard, as in 2008, the 

Valley already met the 24-hour PM2.5 standard of 65 μg/m3. On September 30, 2011,

U.S. EPA officially approved the SJV 2008 PM2.5 Plan and the approval will be effective 

on January 9th 2012 (76 FR 69896). 
13

In 2006, U.S. EPA tightened the 24-hour PM2.5 standard to 35 μg/m3, but left the annual 

standard unchanged. Based on 2006-2008 air quality data, U. S. EPA designated the 

SJV as nonattainment for the 2006 24-hour PM2.5 standard effective December 14, 

2009. Per the federal Clean Air Act (Act), the corresponding SIP is due to U.S. EPA 

three years after designation. Thus, the SJV SIP addressing the 2006 24-hour PM2.5

standard is to be submitted to U.S. EPA by December 14, 2012.

1.1. Modeling Roles for the Current SIP

The Act establishes the planning requirements for those areas that routinely exceed the 

health-based air quality standards. As discussed above, these nonattainment areas 

must adopt and implement a SIP that demonstrates how they will attain the standards 

by specified dates. Air quality modeling is an important technical component of the SIP; 

it is used in combination with other technical information to project the attainment status 

of an area and to develop appropriate emission control strategies to achieve attainment.

For the current SIP, the SJVAPCD and ARB will jointly develop the emission inventories 

which are an integral part of the modeling. Working closely with the district, the ARB 

will perform the meteorological and air quality modeling. The SJVAPCD will then 

develop and adopt their local air quality plan. Upon approval by the ARB, the SIP will 

be submitted to U.S.EPA for approval.

1.2. Stakeholder Participation in the SIP Modeling Process

Public participation constitutes an integral part of the SIP development. It is equally 

important in all technical aspects of SIP development, including the modeling. As the 

SIP is developed, SJVAPCD and ARB will hold public workshops on the modeling and 

other SIP elements. Representatives from the private sector, environmental interest 

groups, academia, and the federal, state, and local public sectors are invited to attend 

and provide comments. In addition, Draft Plan documents will be available for public 

review and comment at various stages of plan development and at least 30 days before 

Plan consideration by the SJVAPCD’s Governing Board and subsequently by the ARB 

Board. These documents will include descriptions of the technical aspects of the SIP. 
14

Stakeholders have the choice to provide written and in-person comments at any of the 

Plan workshops and public Board hearings. The agencies take the comments into 

consideration when finalizing the Plan.

1.3. Involvement of External Scientific/Technical Experts and Their 

Input on the Photochemical Modeling

The California Air Resource Board (ARB) and the San Joaquin Valley Air Pollution 

Control District (SJVAPCD) plan to engage a group of experts on prognostic 

meteorological modeling and photochemical particulate matter modeling to help prepare 

the modeling protocol document (protocol) , which is the blueprint for the air quality 

modeling portion of the SIP. ARB and district staff will then carry out the work described 

in the protocol as part of the SIP development.

The structure of the proposed group of technical experts is:

Conveners: John DaMassa – ARB

Samir Sheikh – SJVAPCD 

Members: Scott Bohning – U.S. EPA Region 9

Ajith Kaduwela – ARB

James Kelly – U.S. EPA Office of Air Quality Planning and Standards

Michael Kleeman – University of California at Davis

Jonathan Pleim – U.S. EPA Office of Research and Development

Anthony Wexler – University of California at Davis

This technical consultant group will provide technical consultations/guidance to the 

staffs of the ARB and SJVAPCD during the development of the protocol. This group is 

expected to provide technical expertise on the following components of the protocol:

 Selection of the physics and chemistry options for the prognostic meteorological 

and photochemical air quality models

 Selection of methods to prepare initial and boundary conditions for the air quality 

model
15

 Performance evaluations of both prognostic meteorological and photochemical 

air quality models. This includes statistical, diagnostic, and phenomenological 

evaluations of simulated results.

 Selection of emissions profiles (size and speciation) for particulate-matter 

emissions.

 Methods to determine of the limiting precursors for PM2.5 formation.

 Application of the Sulfate, Adjusted Nitrate, Derived Water, Inferred 

Carbonaceous Material Balance Approach (SANDWICH) with potential 

modifications.

 Application of the Speciated Modeled Attainment Test (SMAT).

 Selection of methodologies for the determination of PM2.5 precursor equivalency 

ratios.

 Preparation of Technical Support Documents.

The specific tasks for this group include:

 Attending regular meetings with ARB and SJVAPCD staff (in person or via 

teleconference) as needed during protocol development. These meeting are 

expected to take place monthly starting approximately in November 2011.

1.4. Schedule for Completion of the Plan

Final area designations kick-off the three year SIP development process. For the first 

two years, efforts center on updates and improvements to the Plan’s technical and 

scientific underpinnings. These include the development of emission inventories,

selection of modeling periods, model selection, model input preparation, model 

performance evaluation and supplemental analyses. During the last year, modeling, 

further supplemental analyses and control strategy development proceed in an iterative 

manner and the public participation process gets under way. After thorough review the 

District Board and subsequently the ARB Board consider the Plan. The Plan is then 

submitted to U.S. EPA. The table below summarizes the overall anticipated schedule 

for Plan completion:
16

Table 1-1: The Timeline for Completion of the Plan.

Timeline Action

November 2011 Emission Inventory Completed

Summer 2012 Modeling Completed

Spring/Summer 2012 Public Workshop(s) on the Draft Plan 

October 2012

San Joaquin Valley Governing Board 

Hearing to consider the Draft Plan

November 2012 

ARB Board Hearing to consider the SJV 

Adopted Plan

December 14, 2012 Plan is due to U.S. EPA
17

2. Description of the Conceptual Model for the Nonattainment Area

2.1. History of Field Studies in the Region

The San Joaquin Valley (SJV) airshed is perhaps the second most studied airshed in 

the world, in terms of the number of publications in peer-reviewed international 

scientific/technical journals and other major reports. The Los Angeles airshed is the 

first. Major field studies that have taken place in the SJV and surrounding areas are 

listed in Table 2-1. A comprehensive listing of publications (reports and peer-reviewed 

journal articles) up to 2005, compiled by Professor John Watson of the Desert Research 

Institute, can be found at http://www.arb.ca.gov/airways/crpaqs/publications.htm.

The first major air quality study in the SJV, dubbed Project Lo-Jet, took place in 1970 

and resulted in the identification of the Fresno Eddy (Lin and Jao, 1995 and references 

therein). The first Valley-wide study that formed the foundation for a SIP was the San 

Joaquin Valley Air Quality Study/Atmospheric Utilities Signatures Predictions and 

Experiments (SJVAQS/AUSPEX) study, also known as SARMAP (SJVAQS/AUSPEX 

Regional Modeling Adaptation Project). A 1-hour Extreme Ozone Attainment 

Demonstration Plan based on the SARMAP Study was submitted to the U.S. EPA in 

2004 and was approved in 2009 (74 FR 33933; 75 FR 10420). The next major study 

was the Integrated Monitoring Study in 1995 (IMS-95), which was the pilot study for the 

subsequent California Regional PM10/PM2.5 Air Quality Study (CRPAQS) in 2000 

(Solomon and Magliano, 1998). IMS-95 formed the technical basis for the 2003 PM10

SIP which was approved by the U.S. EPA in 2006 (71 FR 63642). The area was redesignated as attainment in 2008 (73 FR 66759). The first annual field campaign in the 

SJV was CRPAQS, and embedded in it was the Central California Ozone Study 

(CCOS) that took place during the summer of 2000 (Fujita et al., 2001). CRPAQS was 

a component of the technical foundation for the 2008 annual PM2.5 SIP which was 

approved by the U.S. EPA in 2011 (76 FR 41338; 76 FR 69896), and CCOS was part of 

the technical basis for the 2007 8-hour O3 SIP (76 FR 57846). 
18

While CRPAQS is still very relevant to the current 24-hour PM2.5 SIP, there are four 

subsequent studies that are noteworthy for several different reasons. Any of these 

studies would not form the technical basis for a future SIP itself, but they contributed 

significantly to our understanding of various atmospheric processes. The first involved 

NASA making airborne LIDAR measurements in the SJV in June 2003 (Rosen et al., 

2006; De Yong, et al., 2006; Lewis et al., 2010). While the PM2.5 concentrations are not 

high in the SJV during non-winter periods, this study demonstrated the utility of airborne 

LIDARs in studying PM2.5 loadings in the SJV. 

The second was the U.S. EPA Advanced Monitoring Initiative, which involved flying an 

aircraft fitted with a high-resolution aerosol LIDAR in the San Joaquin Valley in January 

2005 (Lewis et al. 2010). NASA’s B200 King Air aircraft equipped with an airborne highspectral- resolution LIDAR (HSRL) was flown in the SJV for several days. The 

downward-looking HSRL measured the aerosol optical depth. These vertically-resolved 

measurements were very useful in determining the horizontal and vertical structure of 

the PM2.5 loadings along the flight paths. This study confirmed that high aerosol 

loadings occur in urban areas near the surface. This study also provided a sound data 

set to evaluate the performance of air quality models (Ying, Jackson, and Kaduwela, 

2011). Together, this study and the previous NASA study, provide the first example of 

the applicability of airborne LIDARs in the SJV to study PM2.5 loadings.

The third was the California portion of the Arctic Research of the Composition of the 

Troposphere from Aircraft and Satellites (ARCTAS-CARB) which took place during 

May-July 2010 (Jacob, et al., 2010).This involved two instrumented aircraft. As Jacob 

et al. (2010) describe, the planning for the ARCTAS-CARB flights were based on the 

following questions:

 How good is our current understanding of the HOx-NOx-O3-aerosol photochemical 

system over the Los Angeles Basin as represented in air quality models?

 How should upwind boundary conditions for simulating air quality in California be 

specified?
19

 How do ship emissions and long-range transport affect the sulfur budget in southern 

California?

 What are the state’s emissions of VOCs and greenhouse gases from urban and 

industrial activities, agricultural operations, and wildfires?

The analyses of ARCTAS-CARB data are still in progress, but some of the findings 

could be applicable to the current 24-hour PM2.5 SIP (Kaduwela and Cai, 2009, Huang 

et al., 2010; Singh et al, 2010; Pfister et al., 2011a,b; Huang et al., 2011; D’Allura et al., 

2011). Note, however, that the ARCTAS-CARB field work was conducted during JuneJuly, 2008 but the high PM2.5 loadings in SJV occur during winter months. 

The ARCTAS-CARB campaign was considered to be the pilot phase for a more 

comprehensive multi-platform study known as CalNex 2010 (Research at the Nexus of 

Air Quality and Climate Change conducted in 2010)(www.esrl.noaa.gov/csd/calnex/). 

This campaign was coordinated by NOAA and CARB together with researchers from 

several universities and national laboratories. It involved several instrumented aircraft, 

an instrumented ship, two surface supersites (one in Bakersfield and another in 

Pasadena), and networks of meteorological and ozonesonde measurements. It was 

designed to answer a much broader set of questions than ARCTAS-CARB did, however 

the data analysis phase is still in progress and only very preliminary air quality modeling 

has been conducted to date (Cai and Kaduwela, 2011; Kelly et al., 2011).
20

Table 2-1: Major Field Studies in Central California and surrounding areas.

Year Study Significance

1970 Project Lo-Jet Identified summertime low-level jet and Fresno

eddy

1972 Aerosol Characterization Experiment 

(ACHEX)

First TSP chemical composition and size 

distributions

1979-1980 Inhalable Particulate Network First long-term PM2.5 and PM10 mass and 

elemental measurements in Bay Area, Five 

Points

1978 Central California Aerosol and 

Meteorological Study 

Seasonal TSP elemental composition, seasonal 

transport patterns

1979-1982 Westside Operators First TSP sulfate and nitrate compositions in 

western Kern County

1984 Southern SJV Ozone Study First major characterization of O3 and 

meteorology in Kern County

1986-1988 California Source Characterization 

Study

Quantified chemical composition of source 

emissions

1988-1989 Valley Air Quality Study First spatially diverse, chemical characterized, 

annual and 24-hour PM2.5 and PM10

Summer 

1990

San Joaquin Valley Air Quality 

Study/Atmospheric Utilities 

Signatures Predictions and 

Experiments (SJVAQS/AUSPEX) –

Also known as SARMAP 

(SJVAQS/AUSPEX Regional 

Modeling Adaptation Project)

First central California regional study of O3 and 

PM2.5

July and 

August 1991

California Ozone Deposition 

Experiment

Measurements of dry deposition velocities of O3

using the eddy correlation technique made over 

a cotton field and senescent grass near Fresno

Winter 1995 Integrated Monitoring Study (IMS-95, 

the CRPAQS Pilot Study)

First sub-regional winter study

December California Regional PM10/PM2.5 Air First year-long, regional-scale effort to measure 
21

1999-

February 

2001

Quality Study (CRPAQS) and Central 

California Ozone Study 

both O3 and PM2.5

December 

1999 to 

present: 

Fresno Supersite First multi-year experiment with advanced 

monitoring technology

July 2003 NASA high-resolution lidar flights First high-resolution airborne lidar application in 

SJV in the summer

February

2007

U.S. EPA Advanced Monitoring 

Initiative

First high-resolution airborne lidar application in 

SJV in the winter

June 2008 ARCTAS - CARB First measurement of high-time resolution (1-

10s) measurements of organics and free radicals 

in SJV.

May-July 

2010

CalNex 2010 (Research at the Nexus 

of Air Quality and Climate Change)

Expansion of ARCTAS-CARB type researchgrade measurements to multi-platform and 

expanded geographical area including the ocean.

2.2. CRPAQS Relevance to SIP

As discussed in the previous section, CRPAQS has provided key technical information 

to support SIP development in the San Joaquin Valley. CRPAQS was a public/private 

partnership designed to advance our understanding of the nature of PM2.5 in the Valley 

and guide development of effective control strategies. The study included monitoring at 

over 100 sites as well as data analysis and modeling, results of which have been 

published in over 60 papers and presented at national and international conferences. 

The field campaign was carried out between December 1999 and February 2001. The 

key findings remain relevant to the development of the current 24-hr PM2.5 SIP. The 

Study improved our understanding of the spatial and temporal distribution of PM2.5 in the 

Valley, its chemical composition, transport and transformation, and contributing sources. 

More details on CRPAQS can be found at the following link: 

http://www.arb.ca.gov/airways/ccaqs.htm
22

Key findings include the interplay between local and regional components and the 

resulting concentrations at urban versus rural sites, the sources of carbonaceous 

material, and identification of limiting precursors for ammonium nitrate formation. A 

brief description of these findings is provided in sections 2.6 and 2.7. More detailed

results will be discussed in the SIP documentation.

2.3. Description of PM2.5 Monitoring in the SJV

The San Joaquin Valley nonattainment area (the Valley) is an agricultural region 

encompassing approximately 64,000 km2and with a total population approaching four 

million. The majority of the population is centered in the large urban areas of 

Bakersfield, Fresno, Modesto, and Stockton. The nonattainment area includes seven 

full counties and one partial county. The full counties are San Joaquin, Stanislaus, 

Merced, Madera, Fresno, Kings, and Tulare. Kern is the partial county with only 

western Kern County included in the PM2.5 nonattainment area. The Valley is bordered 

on the west by the coastal mountain ranges and on the east by the Sierra Nevada 

range. These ranges converge at the southern end of the basin at the Tehachapi 

Mountains. 

There are 21 monitoring sites collecting PM2.5 data in the San Joaquin Valley (see Table 

2-1). These include seven Federal Reference Monitors (FRMs), four PM2.5 speciation 

monitors, and 19 continuous monitors (eleven Federal Equivalence Monitors (FEMs) 

and eight non-FEMs). Several sites include multiple monitoring instruments running in 

parallel. 

The FRM sampling frequency varies from daily to one in six days. Two monitoring sites, 

Bakersfield-California and Fresno-1

st, collect daily FRM samples. All other FRM 

monitors operate on a one in three days schedule, with the exception of Corcoran and 

Fresno-Hamilton which operate on a one in three days schedule during the high 

season, but reduce frequency to one in six days during the low season.
23

Table 2-2: PM2.5 monitoring sites in the San Joaquin Valley nonattainment area.

AQS SiteID Site Name FRM FEM non-FEM Speciation

Fresno County

060195001 Clovis-N Villa Avenue 1

060190008 Fresno-1st Street 1 1 1

060195025 Fresno-Hamilton and Winery 1

060192008 Huron-16875 4th Street 1

060192009 Tranquillity 1

Kern County

060290016 Bakersfield-410 E Planz Road 1

060290014 Bakersfield-5558 California Avenue 1 1 1

060292009 Lebec-Beartrap Road 1

Kings County

060310004 Corcoran-Patterson Avenue 1 1

060311004 Hanford 1

Madera County

060392010 Madera-28261 Avenue 14 1

Merced County

060470003 Merced-Coffee 1

060472510 Merced-2334 M Street 1

Stockton County

060772010 Manteca-530 Fishback Rd 1

060771002 Stockton-Hazelton Street 1

060773005 Tracy-Airport 1

Stanislaus County
24

060990005 Modesto-14th Street 1 1

060990006 Turlock-S Minaret Street 1

Tulare County

061072010 Porterville-1839 Newcomb Street 1 1

061070009 Sequoia-Ash Mountain 1

061072002 Visalia-N Church Street 1 1 1

Total 7 11 8 4

2.4. PM2.5 Air Quality Trends

Table 2-3 lists 98th percentiles and design values for FRM and FEM sites with at least 

one year of data. All sites in the PM2.5 nonattainment area exceed the 24-hour 

standard, with 2010 design values ranging from 41 μg/m3at Stockton-Hazelton to 

65 μg/m3 at Bakersfield-Planz. However, over the last ten years, the San Joaquin 

Valley has experienced an improvement in PM2.5 air quality, although different rates of 

progress have occurred across the Valley. The biggest decreases, on the order of 

25 percent to 45 percent, occurred in the northern and central Valley. The southern 

San Joaquin Valley, which includes the Bakersfield area, showed lesser improvement in 

PM2.5 concentrations of 17 percent to 35 percent. These design values reflect a three 

year average of the individual year 98th percentiles. The 98th percentile values for 2010, 

the most recent year of data, provide a further indication of continuing progress, with 

values ranging from 34.5 μg/m3to 56.2 μg/m3. 

It is important to note that the aforementioned air-quality improvements in the SJV are 

not entirely due to changes in meteorology. In order to understand the effectiveness of 

emission control strategies and regulations on ambient air pollution levels, it is important 

to investigate air quality trends and link them to the impacts from meteorology versus

changes in emissions. The strong linkage between meteorological conditions and air 

pollutant levels can obscure the effects of the change of emission levels over time. 

Therefore, the meteorological effects need to be removed so that the emissions-related
25

trends may be studied. The Classification and Regression Trees (CART) method was 

used to define the relationship between PM2.5 concentrations and meteorological 

conditions in both the Bakersfield and Fresno areas of the San Joaquin Valley. Three 

years (2004-2006) were selected as base years to define these relationships. The 

CART model was able to explain approximately 75-80 percent of the variation in daily 

PM2.5 concentrations during these years based on the local meteorological conditions. 

Based on the CART-defined relationships, daily PM2.5 concentrations were predicted for 

all the other years using the observed meteorological data and assuming the emissions 

stayed constant (i.e. the predicted concentrations only represent the PM2.5 

conduciveness of meteorology). The measured PM2.5 concentrations were then 

corrected for the influences of meteorology to estimate the meteorologically adjusted 

trends. For example, in a year with meteorology conditions that were more conducive 

to PM2.5 formation, PM2.5 concentrations were adjusted downward. Conversely, PM2.5 

concentrations were adjusted upward in years with meteorological conditions that were 

less conducive.

The meteorology-adjusted trend at Bakersfield indicates a greater decline than the 

unadjusted trend, while the two trends are generally similar at Fresno. Overall, the 

meteorology-adjusted trends indicate that the PM2.5 annual averages decreased about 

40-50 percent in both the Bakersfield and Fresno areas from 1999 to 2010, with an 

average rate of decrease of approximately 0.8 ug/m3per year. These meteorologyadjusted trends provide a more robust indicator of the impacts of emission reductions 

from on-going control programs
26

Table 2-3: 98th Percentiles and 24-hour Design Value.

Site Name 98th Percentiles (μg/m3) 24-hr Design Values (μg/m3)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Bakersfield-410 E Planz Road 76.5 90.6 66.8 47.5 47.6 66.4 64.7 72.2 72.3 65.5 56.2 43.2 78 68 54 54 60 68 70 70 65 55

Bakersfield-5558 California Avenue 98 92.7 94.9 73 48.3 61.5 63.2 60.5 73 64.5 66.7 53.3 65.5 95 87 72 61 58 62 66 66 68 62 62

Clovis-N Villa Avenue 59.2 72.5 71.5 53.2 48.1 52.4 63 51.3 60.9 49 49 44.3 68.5 68 66 58 51 55 56 58 54 53 47 54

Corcoran-Patterson Avenue 53 55.1 89.5 65.1 42.2 49.4 74.5 50.1 57.9 47.9 53.4 46.8 66 70 66 52 55 58 61 52 53 49

Fresno-1st Street 120 90 75 75 56 52 71 51 67 57.4 55.8 48.8 69.5 95 80 69 61 60 58 63 58 60 54 58

Fresno-Hamilton and Winery 64.8 61.5 71.9 49.7 49.4 71.2 55 57.4 44.5 48.2 37 59.6 66 61 57 57 59 61 52 50 43 48

Merced-2334 M Street 91.9 60 49.3 55.1 44.2 43 48.3 43.8 52.7 54 45.2 35.5 35.4 67 55 50 47 45 45 48 50 51 45 39

Modesto-14th Street 100 71 69 69 47 45 55 52 57.4 53.9 54.5 38.9 54.7 80 70 62 54 49 51 55 54 55 49 49

Stockton-Hazelton Street 79 55 58 50 41 36 44 42 48 61.6 40.4 34.5 44.8 64 54 50 42 40 41 45 51 50 46 40

Turlock-S Minaret Street 53.1 39 57.4 51

Visalia-N Church Street 114 103 96 70 47 54 65 50 59.7 62.1 53.9 36.3 50.7 104 90 71 57 55 56 58 57 59 51 47
27

2.5. Major PM2.5 Components

Four monitoring sites collect PM2.5 chemical composition data in the San Joaquin Valley: 

Bakersfield-California, Fresno-1

st, Modesto, and Visalia. The Bakersfield and Fresno 

speciation monitors are part of the national Speciation Trends Network (STN) while 

Modesto and Visalia are part of the State and Local Air Monitoring Stations (SLAMS) 

network. All four sites use SASS samplers (Spiral Aerosol Speciation Sampler, Met 

One, Grants Pass, OR.) for data collection. The STN data are analyzed by the 

Research Triangle Institute and the SLAMS data are analyzed by ARB. In recent years, 

changes were made to the carbon sampling and analysis method. The collection 

method changed from the MetOne SASS to the URG3000N sampler, which is very 

similar to the IMPROVE module C sampler. The analytical method was changed from 

the NIOSH-like thermal optical transmittance method to IMPROVE_A thermal optical 

reflectance. At Bakersfield, Modesto, and Visalia these changes were implemented in 

May of 2007. Consequently, these sites have over three years of data collected using 

the new sampling and analysis method. The Fresno site switched to the new carbon 

system in April of 2009, so there is less than two years of new data.

Figure 2-1 illustrates the average chemical composition on exceedance days at each of 

the four speciation sites. Widespread ammonium nitrate is the major contributor to 

wintertime PM2.5 episodes, accounting for 50 percent to 67 percent of PM2.5 mass on a 

typical exceedance day. Carbonaceous aerosol contributions range from 16 percent at 

Bakersfield to 33 percent at Fresno. Ammonium sulfate, geological material, and 

elements are smaller components of PM2.5.
28

Figure 2-1: PM2.5 composition on an average exceedance day

2.6. Conditions Leading to PM2.5 Exceedances

PM2.5 concentrations in the San Joaquin Valley exhibit a strong seasonal variability, with 

highest concentrations during the months of November through February. The highest 
29

PM2.5 concentrations occur almost exclusively during multiday pollution episodes under 

stagnant winter weather when a high pressure system (the Great Basin High) reduces 

the ventilation in the Valley (Ferreria et al., 2005). These stagnation events, 

sandwiched between two weather systems, are characterized by low wind speeds, 

moderate temperatures, vertical atmospheric stability, and high relative humidity. This 

stable atmosphere prevents precursor gases and primary (or directly emitted) PM2.5 

released at the surface in the Valley from rapidly dispersing. The moderate 

temperatures and high relative humidity also enhance the formation of secondary 

particulate matter, especially ammonium nitrate and sulfate. 

PM2.5 episodes can last for many days, resulting in multiple exceedances of the 24-hour 

PM2.5 standard. At the beginning of an episode, concentrations are low but increase 

daily because of both the accumulation of primary pollutants and formation of secondary 

pollutants (Watson et al, 2002). Concentrations continue to build until there is a change 

in the weather significant enough to wash out particles through rainfall or increased 

ventilation of the Valley. The two main episodes captured during CRPAQS had up to 18

days with PM2.5 concentrations exceeding 65 μg/m3(Turkiewicz et al., 2006). During 

episodes, urban sites recorded elevated concentrations earlier than rural sites, and as a 

consequence, had a greater number of days with high concentrations. However, due to 

the buildup of PM2.5 concentrations, rural sites can achieve concentrations of similar 

magnitude as urban sites by the end of an episode.

PM2.5 particles can be either directly emitted (known as primary particulate matter) or 

formed via atmospheric reactions (known as secondary particulate matter). Ammonium 

nitrate, the dominant PM2.5 component throughout the Valley, is formed in the 

atmosphere as a result of chemical reactions between precursor pollutants such as 

NOx, VOC, and ammonia. Carbonaceous aerosol, the second most abundant 

component, is mostly directly emitted, and is the result of contributions from wood 

combustion, mobile sources, and cooking. 

As shown earlier in Figure 2-1, carbonaceous aerosols and ammonium nitrate together 

comprise approximately 85 to 90 percent of the PM2.5 mass during an episode. Each 
30

episode has a local component (primarily carbonaceous aerosols) and regional 

component (primarily ammonium nitrate). The relative proportions between ammonium 

nitrate and carbonaceous material differ among urban and rural sites. Since most of the 

carbonaceous aerosol is emitted into the atmosphere as directly emitted particles, its 

transport is much more limited compared to gaseous precursors of ammonium nitrate. 

Concentrations of carbonaceous material are two to three times higher at urban sites 

than at rural, corresponding to the higher emission density of primary carbon sources in 

urban areas (Turkiewicz et al., 2006). Ammonium nitrate can be formed both at the 

surface and aloft. Concentrations of ammonium nitrate, which result from more 

regional-scale secondary formation and mixing of emissions, can be fairly uniform 

across urban and rural sites. The spatial homogeneity of ammonium nitrate is 

influenced by higher wind speeds aloft (which allow more efficient transport), and the 

diurnal variation in mixing heights (which allow entrainment of ammonium nitrate down 

to the surface). 

Ammonium nitrate is also formed via both daytime and nighttime chemistry. The 

amount of ammonium nitrate produced will be limited by the relative abundance of its 

precursors in the atmosphere. In the San Joaquin Valley, the nighttime formation is 

considered to be the most important pathway (Lurmann et al., 2006). The nighttime 

pathway involves gas-phase oxidation of NO2, followed by reaction with ammonia to 

form ammonium nitrate. Since ammonia is abundant in the Valley in the winter, NOx is 

considered to be the limiting precursor. In contrast, the daytime pathway also involves 

VOCs. Modeling studies that investigated winter episodes in the Valley estimated that 

reductions in VOC emissions have a small impact on nitrate concentrations only at very 

high PM2.5 concentrations (Pun, Balmori, and Seigneur, 2009). However, at current 

PM2.5 levels the impact was very limited, and in some cases VOC reductions lead to an 

increase in PM2.5 concentrations (Kleeman, et al., 2005). The results of these studies 

are discussed in greater detail in the following section. 
31

2.7. Past Modeling Efforts and Results

The first peer-reviewed journal article on photochemical simulation in the SJV was 

conducted using a photochemical box model to study conversion of NOx to nitrate 

(Stockwell et al., 2000). That study found that about 33% of emitted moles of NOx were 

converted to nitrate. The study also found that about 80% of the nitric acid (HNO3) 

produced was in the particle phase, suggesting an ammonia rich environment. These 

observations were found to be in reasonable agreement with observations. Stockwell et 

al., (2000) also reported that while increasing NOx emissions led to increased 

production of particle nitrate, the reduction of VOC had no appreciable effect on nitrate 

production. However, increases in VOC emissions led to reduction in nitrates. Taken 

together, these three observations suggest that the PM nitrate in the SJV is NOx limited. 

Pun and Seigneur (2001) also employed a photochemical box model that covered urban 

Fresno, and determined that VOC controls would be more effective than NOx controls in 

reducing PM2.5 nitrate. This finding is in conflict with that of Stockwell et al., (2000). 

However, box modeling approaches have a number of limitations, including lack of 

transport in/out of the box, robust vertical transport, and use of older chemical 

mechanisms. In addition, in the work done by Pun and Seigneur (2001), the VOC 

emissions were increased by a factor of two to improve model performance. As such 

the box modeling does not fully represent the complete scope of atmospheric variations 

and has limited usefulness in assessing the responsiveness to VOC controls.

The first published application of a full-scale photochemical grid model with diagnostic 

meteorological data to simulate PM concentrations in the SJV, which was also the first 

study outside of the Los Angeles area to include complete PM model performance 

statistics, was conducted by Held et al. (2004). In this study, the source-oriented 

external mixture CIT-UCD model was applied during the January 4-6, 1996 episode of 

the IMS-95 (Solomon and Magliano, 1998). As Held et al., (2004) reported, the CITUCD model was able to capture many key air quality features of the January 4–6, 1996 

episode including (1) regional ozone, (2) regional PM mass, (3) chemically speciated 

mass at core stations, and (4) the size distribution of major PM species. Given the 

robust model agreement with both gas and condensed phase measurements, it 
32

appeared that the CIT-UCD model adequately captured the fundamental chemistry and 

transport in the IMS-95 domain, suggesting that this model could be used to explore

various control scenarios designed to improve the air quality in the SJV. The results 

reported in this publication also confirmed previous unpublished findings based on the 

application of UAM-Aero to the same IMS-95 episode (Kaduwela, 2003). These 

findings were a part of the SJV’s 2003 PM10 SIP. In a follow-up analysis, Held et al. 

(2005) compared the source apportionment PM2.5 obtained using the CIT-UCD model 

with that obtained using the Chemical Mass Balance method and concluded that the 

model was able to predict source contributions to airborne particulate matter at all 

locations and times throughout the study domain. 

Investigation of precursor limitations for the January 4-6, 2006 IMS-95 episode using 

the CIT-UCD model revealed that NOx controls were the most effective control strategy 

to reduce PM2.5 concentrations (Kleeman, Ying, and Kaduwela, 2005). A 50 percent

reduction in NOx in the SJV resulted in a 25 percent reduction in total nitrate, while a 

50 percent reduction in VOCs resulted in a 17.5 percent reduction. A 50 percent

reduction in ammonia resulted in a 10 percent reduction in total nitrate. However, to 

evaluate the significance and effectiveness of VOC controls in the context of control 

strategy design, the study’s isopleths of PM2.5 nitrate response to combined NOx/VOC 

emission reductions provide more in-depth information. Modeled isopleths show that, 

based on the shapes of the graphs, NOx controls are the most effective approach to 

reduce PM2.5 nitrate concentrations at Fresno and the location of the highest modeled 

PM2.5 nitrate concentration. Once NOx controls are taken into consideration, VOC 

emission reductions produce essentially no benefit, and in some instances, may actually 

lead to an increase in PM2.5 nitrate formation. Nitrogen-containing molecules such as 

PAN can act as temporary sinks for NO2. When VOCs are controlled, the reduced 

availability of certain radicals which are generated from VOCs reduces the amount of 

NO2 that is sequestered, thereby increasing the availability of NO2 and enhancing 

ammonium nitrate formation (Meng et al., 1997). This may be generally true not only for 

PAN, but also for organic nitrates which can lead to increases in NOx and ozone 
33

concentrations if emissions of specific VOC compounds are controlled (Farmer et al., 

2011). 

It was also revealed in a subsequent analysis of the same episode that approximately 

45-57 percent of the PM2.5 nitrate and 34-40 percent of the PM2.5 ammonium ion in the 

SJV were formed from precursor gaseous species released from sources upwind of the 

modeling domain (Ying and Kleeman, 2006). However, it is important to note that the 

modeling domain did not include the entire Valley, with the domain starting just north of 

Fresno.

Longer periods were simulated as a part of CRPAQS (Solomon and Magliano, 1998) 

using the Community Multi-scale Air Quality (CMAQ) model (Liang and Kaduwela, 

2005; Liang et al., 2006; Fahey et al., 2006; Livingstone et al., 2009), CMAQ-UCD

model (Zhang et al., 2005), UCD/CIT model (Ying et al., 2008a,b; Ying et al., 2009a,b; 

Ying, 2011), and the CMAQ-MADRID model (Pun, Balmori, and Seigneur, 2009; Zhang 

et al., 2010). The UCD/CIT model is an improved version of the CIT-UCD model with 

significant modifications made at the University of California at Davis.

The first group of simulations for the December 2000-January 2001 episode of 

CRPAQS were focused on revisions to the CMAQ model (Liang and Kaduwela, 2005), 

comparison of the standard and UC Davis versions of the CMAQ model (Zhang et al., 

2005), and a detailed CMAQ model performance analysis (Liang et al., 2006). This was 

followed by an annual simulation of the CRPAQS period using the CMAQ model (Fahey 

et al., 2006). These simulations established the ability of the CMAQ model to 

satisfactorily simulate the PM2.5 concentrations in the SJV, and the results were used in 

the 2008 annual PM2.5 SIP (76 FR 41338; 76 FR 69896).

The first published PM2.5 modeling in the SJV using the UCD/CIT model at 4 km 

horizontal grid spacing (Ying et al., 2008a) found general agreement between simulated 

and observed concentrations for both gaseous and PM species. The UCD/CIT model 

was able to simulate the major observed features of this 22 day severe particulate

pollution event. The over-prediction of PM2.5 at the rural site of Angiola was due to 

excessive emissions of fugitive dust. The model was able to reproduce the observed 
34

PM nitrate during the first half of the episode, but the performance degraded during the 

second half due to issues with the meteorological fields.

The source apportionment of primary PM2.5 in the SJV (Ying et al., 2008b) found 

elemental and organic carbon (EC and OC) to be the two major components. Higher 

concentrations of these two pollutants occur in urban areas and the concentrations were 

lower in rural areas. Wood burning and emissions from diesel engines were the two 

major sources of EC and OC. The source apportionment of secondary PM2.5 revealed 

that diesel engines are the largest contributor to PM nitrate, while catalyst equipped 

gasoline engines also contributed significantly. The major ammonia source that 

contributed to the ammonium ion concentrations was agriculture. Sharp gradients of 

PM2.5 concentrations were observed in urban areas.

The apportionment of PM OC to primary and secondary components is a very active 

area of current research. The oxidation of emitted organic compounds make gaseous, 

semi-volatile, and non-volatile products depending on the vapor pressure of the 

products. Using the UCD/CIT model, the apportionment of PM OC was investigated for 

the same 22 day period that was discussed above (Chen et al., 2010). It was found 

that, of the total predicted PM OC at Fresno, Angiola, and Bakersfield, 6 percent, 

37 percent, and 4 percent were secondary in nature, respectively. On a SJV-wide basis 

~20 percent of the total PM OC was secondary. The major precursors of secondary 

organic aerosol (SOA) were long-chain alkanes followed by aromatic compounds. The 

sources of these precursors were solvent use, catalyst gasoline engines, wood smoke, 

non-catalyst gasoline engines, and other anthropogenic sources, in that order. 

In contrast, air quality modeling exercises conducted as part of the San Joaquin Valley 

2008 PM2.5 SIP using the CMAQ model showed that primary PM2.5 emissions are the 

main contributor to organic aerosols and SOA contribute to only a small extent. 

Furthermore, SOA are primarily formed during the summertime, when total PM2.5 

concentrations are low, and are mainly derived from biogenic emission sources. 

Simulations of the CRPAQS wintertime episode conducted using CMAQ-MADRID (Pun 

et al., 2009) a model with an enhanced secondary organic aerosol formation 
35

mechanism, also found that organic aerosol concentrations were dominated by directly 

emitted PM2.5. Because of the dominance of directly emitted PM2.5 organic matter, 

overall a 50 percent reduction in anthropogenic VOC emissions had limited effects on 

the modeled PM2.5 organic matter concentrations.

The transport of gaseous precursors and PM2.5 from upwind areas has to be taken into 

account when developing effective control strategies for a given region. The UCD/CIT 

model was employed to investigate such transport in the SJV during the 22-day 

CRPAQS modeling period (Ying and Kleeman, 2009b). It was found that transport 

distances for PM2.5 diminish as the air mass moves from north to south in the SJV due 

to diminishing wind speeds. The gaseous precursors transport longer distances 

compared to directly emitted PM2.5, but the rate of active nitrogen partitioning into the 

particle phase increases as the air masses move towards the central and southern 

portions of the SJV. This is mainly due to the increased availability of ammonia in the 

central and southern SJV. Thus, the transported PM2.5 impacts are the least in the 

southern SJV where the design values are the highest. For example, nearly 70 percent

of the ammonium nitrate in the most polluted areas of the SJV is of local origin. 

The CRPAQS winter period was also simulated using the CMAQ-MADRID model (Pun, 

Balmori, and Seigneur, 2009). Their main finding was that NOx controls were the most 

effective strategy, followed by VOC and ammonia. This finding was consistent with that 

for the 1995 IMS-95 episode (Kleeman, Ying, and Kaduwela, 2005). They also found 

that VOC controls tend to reduce the oxidant concentrations, but had a relatively small 

effect on PM nitrate concentrations, indicating that background oxidant concentrations 

were sufficient to sustain the PM nitrate production. They observed that NOx reductions 

can, in some cases, increase the night-time PM nitrate concentrations. This was due to 

the fact that reduced NOx would lead to increased O3 at the end of the day enhancing 

the N2O5 formation leading to increased PM nitrate formation. But, in general a 50

percent NOx reduction resulted in ~30-50 percent reduction in PM nitrate. 

Pun, Balmori, and Seigneur (2009) also found VOC and ammonia controls to be 

beneficial at some locations at certain times. However, the response of PM2.5 nitrate to 
36

a 50 percent reduction in VOC emissions increased as PM2.5 levels rose during the 

episode. The difference in the VOC response on the days with the higher PM2.5 

concentrations as compared to those days with lower concentrations may be due to a 

difference in the chemical formation regime for nitrate. In general, there is sufficient 

background ozone to generate enough free radicals to initiate and propagate the 

chemistry of nitrate formation (Ying et al., 2009). However, on days with high PM2.5 

concentrations, the daytime photochemistry may have contributed to a rapid increase in 

nitrate, resulting in higher VOC and NOx sensitivity. It does not appear that VOCs 

contributed significantly to the free radical budget on the simulated days, mainly 

because rapid increases in ozone were not observed. The effect of VOC levels on 

nitrate formation may also have a diurnal pattern since the hydroxyl and hydroperoxyl 

radical levels are high during the daytime and negligible at night. In addition, more 

reactive VOCs react quickly during the day and there is a minimal carryover to the next 

day. Therefore it is reasonable to assume that the higher response to VOC and NOx at 

higher concentrations may be due to the nitrate formation mechanisms rather than the 

PM2.5 accumulation due to the length of the episode. Overall, nitrate was only 

responsive to a 50 percent reduction in VOCs at PM2.5 concentration levels that are no 

longer reached in the San Joaquin Valley. 

Pun at al., 2009 also shared Ying et al.’s (2008a) concern regarding the need for 

improved meteorological fields by stating that “... misprediction in the timing and 

coverage of the meteorological phenomenon can put a stop to PM accumulation in key 

areas of the SJV. Therefore, weaknesses in the meteorological models for simulating 

calm wintertime conditions would necessarily translate into performance issues 

pertaining to the air quality simulation.”

The issues related to meteorological fields were further investigated by developing three 

alternative meteorological fields (Hu et al, 2010). In this study the Weather and 

Research Forecast (WRF) model was used to generate two meteorological fields, with 

and without four dimensional data assimilation. The third field was generated using a 

diagnostic wind model. After using all three models to simulate air quality with the 

UCD/CIT model, it was concluded that the “diagnostic wind fields based on a dense 
37

measurement network are the preferred choice for air quality model studies during 

stagnant periods in locations with complex topography.” This finding is also consistent 

with that of a previous investigation of O3 production in the SJV (Jackson et al., 2006). 

However, at this time, there is no preprocessor to process diagnostic wind fields for the 

CMAQ model and the diagnostic wind fields do not have all the quantities required by 

the CMAQ model. Therefore, we will continue to use the prognostic meteorological 

fields developed using both the MM5 and WRF models for this SIP.

For a shorter CRPAQS period (December 25-31, 2000) PM2.5 mass, number, and size 

distributions were simulated using CMAQ-MADRID model (Zhang et al., 2010). While 

the model was able to reproduce the observed 24-hour PM2.5 mass well, the prediction 

of component mass and time evolutions needed improvements. This study also 

highlighted the difficulties in simulating particle numbers and size distributions due to 

inaccuracies in model inputs and uncertainties in model formulations.

Recently, the UCD/CIT model was updated to include a process analyses scheme 

(Ying, 2011). Application of this updated UCD/CIT model to the same 22-day CRPAQS 

period indicated that, during the day, PM nitrate is photochemically formed within a few 

hundred meters above ground. This formation is more pronounced in urban areas 

where NOx concentrations are higher relative to rural areas. During the early afternoon, 

the temperatures may be high enough to evaporate some of the PM nitrate. During the 

night, PM nitrate is formed via the N2O5 pathway within a few hundred meters above the 

surface. This formation is enhanced in the rural areas due to relatively higher O3. 

During stagnant days, in which PM nitrate concentrations are generally higher, the PM 

nitrate concentrations build up aloft and lead to rapid increases in surface PM nitrate 

concentrations due to vertical diffusion.

The post-2000 applications of photochemical models in the SJV include the 

February 10-18, 2007 U.S. EPA Advanced Monitoring Initiative to measure the aerosol 

optical depth in the SJV (Rosen et al., 2006; Lewis et al., 2010; Ying, Jackson, and 

Kaduwela, 2011), the California portion of the Arctic Research of the Composition of the 

Troposphere from Aircraft and Satellites (ARCTAS-CARB) which took place during 
38

May-July 2008 (Kaduwela and Cai, 2009; Jacob, et al., 2010; Huang et al., 2010; Singh 

et al, 2010; Pfister et al., 2011a,b; Huang et al., 2011; D’Allura et al., 2011), and CalNex 

2010 (Research at the Nexus of Air Quality and Climate Change conducted in 

2010,http://www.arb.ca.gov/research/calnex2010/calnex2010.htm, 

www.esrl.noaa.gov/csd/calnex/) for which only very preliminary air quality modeling has 

been conducted to date (Cai and Kaduwela, 2011; Kelly et al., 2011).
39

3. Selection of the Modeling Periods

3.1. Reference Year Selection and Justification

From an air quality perspective, ARB and the District have selected 2007 baseline 

design values for the modeled attainment test. These baseline concentrations values 

will serve as the anchor point for estimating future year projected concentrations. The 

selection of 2007 is based on the following considerations:

 The extensive wild fires that occurred in the San Joaquin Valley and throughout 

Northern California during 2008, adversely impacted air quality and the resulting 

PM2.5 design values from 2008 through 2010. Therefore, this period is not 

suitable for air quality modeling purposes due to the atypical conditions;

 The design values recorded in 2007 were some of the highest in recent years. 

Analysis of the impacts of meteorology on PM2.5 levels in the Valley over the last 

ten years indicate that the 2007 meteorology was one of the most conducive to 

PM2.5 formation. Thus, the selection of 2007 represents a conservative approach 

to the attainment demonstration modeling.

Thus, 2007 will be used as both the base case and baseline year. A 2007 base case 

inventory will be used, together with 2007 meteorology fields, for model performance 

evaluation. The same emissions, without day-specific information, will then be 

projected to the future year for the calculation of Relative Response Factors (RRF). 

3.2. Future years and justifications

As specific U.S. EPA guidance for the implementation of the 2006 PM2.5 standard is not 

yet available, we have used the framework set forth in the original PM2.5 Implementation 

Rule to determine appropriate deadlines for attainment. The PM2.5 Implementation Rule 

requires areas to achieve attainment within five years from the date of designation, with 

the potential for up to a five year extension. The Valley was designated nonattainment 

on December 14, 2009. For the 2006 PM2.5 standard, these dates would be December 
40

14, 2014 and December 14, 2019. Given the severity of the San Joaquin Valley’s PM2.5 

problem, initial modeling will be focused on evaluating attainment in the ten year 

timeframe. 

December 14th is for all practical purposes the end of the year; we will conduct the 

attainment year modeling for the calendar year January 1, 2019 through December 31, 

2019. This is consistent with U.S. EPA guidance published on March 2, 2012.

3.3. Justification for Quarterly Modeling instead of Several Episodes

One of the key observations made in the revised modeled attainment test for 24-hour 

PM2.5 is that the temporal distribution of high days in the base and future periods will not 

remain the same (Fox, 2011). This requires that we simulate at least eight high PM2.5

days per quarter for each year we simulate. It is possible that these eight high days will 

not come from a single episode and, thus, more than one, and perhaps several, 

episodes would need to be simulated. If that is the case, it is simpler to simulate the 

whole quarter in one attempt than to keep track of simulations for several episodes. It is 

possible, however, that there will be no high PM2.5 days in quarters other than winter 

months for the San Joaquin Valley. In that case, modeling the 4

th and 1st quarters (that 

include winter months) would suffice. In fact, preliminary modeling has verified that this 

is the case. Therefore, we propose to simulate only the first and fourth quarters instead 

of all four quarters, since this will not affect the attainment modeling outcome.

3.4. Identification of Exceptional Events to be excluded from 

Base/Reference/Future Year Modeling

Exceptional events are unusual or natural events that can overwhelm existing control 

strategies for man-made pollution. If such an event occurs, U.S. EPA allows states to 

exclude these values from use in SIP air quality modeling since these events cannot be 

controlled. Projecting these events into the future is not representative of an area’s 

ability to attain the PM2.5 standard. ARB and the district will review the data proposed 

for use in the modeling and identify exceptional events. Examples of exceptional events 
41

that will be evaluated include (but are not limited to): wildfires, high winds and dust, and 

fireworks. For each event identified, documentation will be included justifying exclusion. 
42

4. Development of Emissions Inventories

In support of the various SIPs across California to meet the federal 24-hour PM2.5

standard, emission inputs for modeling (commonly and interchangeably referred to as 

‘modeling inventories’ or ‘gridded inventories’) have been developed by ARB and district 

staff. The following sections of this document describe how base case and future year 

emissions estimates for modeling were prepared.

A document that provides a more detailed description of the emission inventory will be 

prepared separately and submitted to U.S. EPA as a part of the SIP documentation.

4.1. PM2.5 Emissions Inventory Development

In support of emissions inventory development, the Air Resources Board convened two

inventory coordination groups:

 The PM2.5 SIP Emission Inventory Working Group. This group was focused on 

annual average emission estimates for each county, air basin, and district. ARB 

maintains an electronic database of emissions and other useful information for 

these aggregate emission estimates, which provide a foundation for the 

development of a more refined (hourly, grid-cell specific) set of emission inputs 

that are required by air quality models. ARB’s database is called the California 

Emission Inventory Development and Reporting System (CEIDARS). This group 

was focused on improving ARB-District emission estimates in ARB’s CEIDARS 

database. Participants included district staff from the Bay Area, Imperial, 

Sacramento, San Joaquin, Butte, South Coast, El Dorado, Yolo-Solano, Shasta, 

Northern Sierra, Feather River and Placer regions. The purpose of this group 

was to update the 2005 CEIDARS inventory (emissions and other needed data) 

in preparation for the SIPs. 

 The SIP Gridded Inventory Coordination Group (SIP-GICG). This group was 

focused on more refined emissions estimates to be used in air quality modeling 
43

(e.g. for a specific grid cell and hour). The purpose of the SIP-GICG is to 

conduct quality assurance of the associated data, and to distribute and 

coordinate the development of emission inputs for SIP modeling. Local air 

districts that participated included San Joaquin Valley Unified APCD, Bay Area 

AQMD, Sacramento Metropolitan AQMD, South Coast AQMD, Ventura County 

APCD, San Diego County APCD, Imperial County APCD, Mojave Desert AQMD, 

Northern Sierra AQMD, Yolo/Solano AQMD, Placer County APCD, El Dorado 

County APCD, San Luis Obispo County APCD, and Santa Barbara County 

APCD. 

In addition to the two coordination groups described above, a great deal of work 

preceded this modeling effort through the Central California Air Quality Studies 

(CCAQS). CCAQS consists of two studies: 1) the Central California Ozone Study 

(CCOS); and 2) the California Regional PM10/PM2.5 Air Quality Study (CRPAQS). 

The sections below provide details as to how the emissions inputs required by air 

quality modeling are created.

4.1.1. Background

In order to understand how the modeling inventories are developed, it is necessary to 

understand the basics of how an annual average emission inventory is developed. 

California’s emission inventory is an estimate of the amounts and types of pollutants 

emitted from thousands of industrial facilities, millions of motor vehicles, and of 

hundreds of millions of applications of other products such as paint and consumer 

products. The development and maintenance of the inventory is a multi-agency effort 

involving the ARB, 35 local air pollution control and air quality management districts 

(Districts), regional transportation planning agencies (RTPAs), and the California 

Department of Transportation (Caltrans). The ARB is responsible for the compilation of 

the final, statewide emission inventory, and maintains this information in a complex 

electronic database. Each emission inventory reflects the best information available at 

the time.
44

To produce regulatory, countywide emissions estimates, the basic principle for 

estimating emissions is to multiply an estimated, per-unit emission factor by an estimate 

of typical usage or activity. For example, on-road motor vehicle emission factors are 

estimated for a specific vehicle type and model year based on dynamometer tests of a 

small sample of that vehicle type and applied to all applicable vehicles. The usage of 

those vehicles is based on an estimate of such activities as a typical driving pattern, 

number of vehicle starts, typical miles driven, and ambient temperature. It is assumed 

that all vehicles of this type in each region of the state are driven under similar 

conditions.

Developing emission estimates for stationary sources involves the use of per unit 

emission factors and activity levels. Under ideal conditions, facility-specific emission 

factors are determined from emission tests for a particular process at a facility. More 

commonly, a generic emission factor is developed by averaging the results of emission 

tests from similar processes at several different facilities. This generic factor is then 

used to estimate emissions from similar types of processes when a facility-specific 

emission factor is not available. Activity levels from point sources are measured in such 

terms as the amount of product produced, solvent used, or fuel used.

ARB maintains an electronic database of emissions and other useful information. 

Annual average emissions are stored for each county, air basin, and district. The 

database is CEIDARS. Emissions are stored in CEIDARS for criteria and toxic 

pollutants. The criteria pollutants are total organic gases (TOG), carbon monoxide 

(CO), oxides of nitrogen (NOx), oxides of sulfur (SOx), and total particulate matter (PM). 

Emissions may also be reported for reactive organic gases (ROG), particulate matter 10 

microns in diameter and smaller (PM10) and particulate matter 2.5 microns in diameter 

and smaller (PM2.5) in CEIDARS. However, for modeling inventories, ROG, PM10 and 

PM2.5 are calculated from TOG and PM, respectively. Modeling inventories require 

speciated emissions that are calculated from total organics or total particulate matter. 

Ammonia emissions are also estimated for some sources. Following are more details 

on how emissions are estimated for point and area sources, on-road motor vehicles, 
45

and biogenic sources. Additional information on emission inventories can be found at: 

http://www.arb.ca.gov/ei/ei.htm

4.1.2. Terminology

Emission Source Type Terminology: The terms “point sources” and “area sources” are 

often confused. Traditionally, these terms have had different meanings to the 

developers of emissions inventories and the developers of modeling inventories. Table 

4-1 summarizes the difference in the terms. Both sets of terms are used in this 

document. In modeling terminology, “point sources” refers to elevated emission 

sources that exit from a stack and have a potential plume rise. “Area sources” refers 

collectively to area-wide sources, stationary-aggregated sources, and other mobile 

sources (including aircraft, trains, ships, and all off-road vehicles and equipment). That 

is, “area sources” are low-level sources from a modeling perspective. In the 

development of the PM2.5 inventories, all point sources were treated as possible 

elevated sources. Processing of the inventory for the air quality model will determine 

which vertical layer the emissions from a process will be placed into. So, for the 

modeling inventories, the use of the term “point sources” is the same whether using the 

modeling or emission inventory definition.

Table 4-1: Inventory Terms for Emission Source Types

Modeling Term Emission Inventory Term Examples

Point Stationary – Point Facilities Stacks at Individual Facilities

Area Off-Road Mobile Industrial Equipment, 

Construction Equipment, 

Vessels, Trains

Area Area-wide Fugitive Dust,, Wood 

Stoves/Fireplaces, Farming 

Operations, Consumer Products

Area Stationary - Aggregated Industrial Fuel Use

On-Road Motor Vehicles On-Road Mobile Cars and Trucks

Biogenic Biogenic Trees
46

Emissions Scenarios and the Modeled Attainment Test: Since emission changes have 

a significant influence on the calculation of the relative response factor (RRF), it’s 

important that the emission inventory scenarios used in modeling are consistent with 

modeling guidance and that the terms used for the emission inventory scenarios are 

clearly defined.

 Base Case Modeling Inventory (2007): Base case modeling is only intended to 

demonstrate confidence in the capability of the modeling system that is used for 

the modeled attainment test; however, it is not used as part of the modeled 

attainment test itself. Since model performance is assessed relative to how well 

model-simulated concentrations match actual measured concentrations, the 

modeling inputs are developed to represent (as best as possible) actual, dayspecific conditions. Thus, for use in assessing model performance, a dayspecific base case modeling inventory for 2007 will be developed. This will 

include, for instance, actual SJVAPCD-reported point source emissions 

information for 2007 as well as other available day-specific activities and 

emission adjustments. The year 2007 was selected to coincide with the year 

selected for baseline design values (described below). The U.S. EPA modeling 

guidance states that once the model has been shown to perform adequately, the 

use of day-specific emissions is no longer needed. In preparation for SIP 

development, both ARB and the SJVAPCD began a comprehensive review and 

update of the emission inventory several years ago. At that time, the 2005

emissions inventory was the most recent inventory required to be submitted to 

the U.S. EPA. Therefore, 2005 was selected as the emission inventory base 

year for the SJVAPCD’s 24-hour PM2.5 SIP. As a result, where day-specific 2007 

emissions information is not available for certain categories, the 2005 base year 

emission inventory will be projected to 2007.

 Reference Year (or Baseline) Modeling Inventory (2007): Unlike the base 

case modeling inventory for 2007described previously, the reference year 

inventory is not developed to capture day-specific emission characteristics. 
47

Rather, per U.S. EPA guidance, the reference year inventory is intended to be a 

representation of emission patterns occurring through the baseline design value 

period (described above) and the emission patterns expected in the future year. 

U.S. EPA modeling guidance describes the reference year modeling inventory as 

“a common starting point” that represents average or “typical” conditions that are 

consistent with the baseline design value period. U.S. EPA guidance also states 

“using a ‘typical’ or average year reference year inventory provides an 

appropriate platform for comparisons between the base year and future years.” 

The 2007 reference year inventory represents typical, average conditions and 

emission patterns through the 2007 design value period; and it will exclude dayspecific information other than temperature, humidity, and solar insolation effects

and District-reported point source emissions information for 2007.

 Future Year Modeling Inventory (2019): As described previously, future year 

modeling inventories along with the reference year modeling inventory are used 

in the model-derived RRF calculation. These inventories maintain the “typical”, 

average patterns of the 2007 Reference Year modeling inventory. The 2019 

inventory will include temperature, humidity, and solar insolation effects from 

reference year (2007) meteorology. Future year point source emissions will be 

projected from the 2007 District-reported point source emissions used in the 

2007 Reference Year Modeling Inventory.

In summary and based on the terminology above, the following modeling emission 

inventories will be developed:

 2007 Base Case Modeling Inventory: This day-specific inventory will be used 

for the model performance evaluation.

 2007 Reference Year (Baseline) Modeling Inventory: This 2007 reference 

year inventory will be used to determine site-specific RRFs in the modeled 

attainment test. It is not a day-specific inventory. Rather, the 2007 reference 

year modeling inventory represents typical, average conditions and emission 

patterns over the baseline design value period, excluding day-specific 
48

information other than 2007 meteorological effects and District-reported point 

source emissions information for 2007. 

 Future Year Modeling Inventories for 2019: These typical, average-day 

inventories will be used to determine year and site-specific RRFs in the modeled 

attainment test. Consistent with the 2007 Reference Year Modeling Inventory, 

the 2019 inventory includes 2007 meteorological effects.

4.2. Point and Area Source Emissions

4.2.1. Development of Base-Year Emission Inventory

The stationary source component of the emission inventory is comprised of nearly 

20,000 individual facilities, called “point sources”, and about 160 categories of 

“aggregated point sources”. Aggregated point sources are groupings of many small 

point sources that are reported as a single source category (gas stations, dry cleaners, 

and print shops are some examples). These emission estimates are based mostly on 

area source methodologies or emission models. Thus, the aggregated point sources 

include emissions data for the entire category of point sources, not each specific facility. 

All districts report as point sources any facility with criteria pollutant emissions of 10 tons 

per year and greater. Most districts choose a cutoff smaller than 10 tons per year for 

reporting facilities as point sources. Any remaining sources not captured in the point 

source inventory are reported as aggregated point sources.

The area-wide source component includes several hundred source categories and is 

made up of sources of pollution mainly linked to the activity of people. Examples of 

these categories are emissions from paved and unpaved road dust; wood stoves and 

fireplaces; farming operations, and consumer products. The emissions for these 

categories are located mostly within major population centers. Some of the emissions 

in these categories come from agricultural centers and construction sites.

The other mobile source inventory is based on the population, activity, and emissions 

estimates of the varied types of off-road equipment. Major categories includes engines 

and vehicles used in industrial, agricultural, construction, airport ground support, and 
49

lawn and garden activities, from hedge trimmers to cranes. Other sources include 

ocean-going vessels, locomotives, aircraft and recreational boats and vehicles. 

Emissions are estimated by fuel type, such as gasoline, diesel, compressed natural gas, 

and liquefied petroleum gas. Emissions are estimated for about 2,000 separate 

categories. Carrying this level of detail allows for more accurate application of control 

measures as well as more specific assignments of speciation and spatial distribution. 

For more information, see: http://www.arb.ca.gov/msei/offroad/offroad.htm.

Local air districts estimate emissions from point sources. The districts provide point 

source information to ARB to update the annual average CEIDARS database. 

Estimating emissions from area sources is a cooperative effort between ARB and air 

district staffs. Updating the emission inventory is a continual process, as new

information becomes available.

4.2.2. Quality Assurance of Base Year Emissions

In order to prepare the best inventory possible for use in modeling, ARB and district 

staff devoted considerable time and effort to conduct quality assurance (QA) of the 

inventory. Staffs from local air districts conducted extensive quality assurance to 

provide an accurate and complete inventory. 

In particular, facility location, stack data, and temporal data were closely checked. This 

information is critical whenever air quality modeling is conducted, such as during SIP 

preparation or special studies such as CCAQS. However, these data are not always of 

sufficient quality in the inventory database since this information is not needed in the 

actual calculation of emissions and resources are limited. ARB ran several types of QA 

reports on the inventory to assist the districts in locating errors or incomplete 

information. This QA process began with the 1999 CEIDARS database, and continued 

with the 2002 CEIDARS database that was used for previous PM2.5 and ozone 

inventory preparation. The QA process has continued with the 2005 and subsequent 

CEIDARS databases. The 2005 CEIDARS database is the basis for the modeling 

inventories developed for the 24-hour PM2.5 SIPs in northern California. Staff of the 
50

South Coast AQMD is using the 2008 CEIDARS database for their modeling effort 

covering southern California (approximately the Tehachapi Mountains southward).

 Stack data – The report checks for missing or incorrect stack data. The report 

lists missing stack data and also checks the data for reasonable stack height,

diameter, temperature, and stack velocity. Additionally, the report compares the 

reported stack flow rate with the computed theoretical flow rate (calculated using 

the diameter and stack velocity).

 Location data – The report checks for missing or wrong Universal Transverse 

Mercator) UTM coordinates. The report lists missing UTM coordinates for both 

facilities and stacks. UTM coordinates are also checked to ensure that they are 

in the range for a given county. Another report is also run that shows the UTM 

coordinates for a facility grouped by the city in which the facility is located. This 

allows staff to look for outliers that may indicate facilities whose locations are in 

the county, but not in the correct location. Additionally, ARB staff reviewed 

location coordinates for accuracy and completeness. Comparisons were made 

using address or zip code mapping.

 Temporal data – The report checks for missing or invalid temporal information. 

Temporal codes used to describe the hours per day, days per week, and weeks 

per year are checked for completeness, accuracy, and validity. The relative 

monthly throughput, which assigns a relative amount of activity to each month of 

the year, is checked to ensure the sum is 100%.

 Code Assignments – Source Classification Codes (SCC) and Standard Industrial 

Classification Codes (SIC) were reviewed for accuracy. The SCC is used to 

determine the speciation profile assigned (speciation is discussed in another 

section of this document). The SIC and SCC combined determine which 

emission control rules may apply for forecasting emissions (see Section 4.3) 

along with the categorization of emissions for reporting purposes.
51

4.3. Future Year (Forecasted) Emissions

Air pollution programs have always depended on predictive models for gaining a better 

understanding of what the emissions will be in the future—these predictions are based 

on expectations of future economic conditions, population growth, and emission 

controls.

ARB’s model to forecast or backcast emissions is known as the California Emission 

Projection Analysis Model (CEPAM). One module of CEPAM is the California Emission 

Forecasting System (CEFS) that has been used for many years to project emissions. 

CEPAM is designed to generate year-specific emissions estimates for each county/air 

basin/district combination taking into account two factors: 1) the effects of growth and 2) 

the effects of adopted emission control rules. It does this by linking these growth and 

control factors directly to emission categories for a particular base year. A key 

component of the model is the Rule Tracking Subsystem (RTS). The RTS was 

developed to link year-specific implementation of emission control rules to the emission 

process level. The emission process level is identified in one of two ways. For facilities, 

the Source Classification Code (SCC) and Standard Industrial Classification (SIC) are 

used. For all other sources, the Emission Inventory Code (EIC) is used. In total, the 

emission process level comprises more than 30,000 possible emission categories 

statewide.

4.3.1. Growth Factors 

Growth factors are derived from county-specific economic activity profiles, population 

forecasts, and other socio/demographic activity. These data are obtained from a 

number of sources, such as:

 Districts and local regional transportation planning agencies (RTPAs) when they 

are available;

 Economic activity studies contracted by the ARB; and
52

 Demographic data, such as population survey data from the California 

Department of Finance (DOF) and Vehicle Miles Traveled (VMT) data from the 

California Department of Transportation (Caltrans).

Growth profiles are typically associated with the type of industry and secondarily to the 

type of emission process. For point sources, economic output profiles by industrial 

sector are linked to the emission sources via industrial sector classification, such as SIC 

or NAICS codes. For area-wide and aggregated point sources, other growth 

parameters such as population, dwelling units and fuel usage may be used. Growth 

factors are developed from the latest and best available data sources with input from 

stakeholders.

4.3.2. Control Factors

Control factors are derived from adopted State and Federal regulations and local district 

rules that impose emission reductions or a technological change on a particular 

emission process. These data are provided by the agencies responsible for overseeing 

the regulatory action for the particular emission categories affected. For example, the 

ARB staff develops the control factors for sectors regulated by the ARB, such as 

consumer products and clean fuels. The districts develop control factors for locally 

enforceable stationary source regulations that affect emissions from such equipment as 

internal combustion engines or power plant boilers. The Department of Pesticide 

Regulation (DPR) supplies control data for pesticides. In general, control factors 

account for three variables:

 Control Efficiency which estimates the technological efficiency of the abatement 

strategy

 Rule Effectiveness which estimates the “real-world” application of the strategy 

taking into account factors such as operational variations and upsets

 Rule Penetration which estimates the degree a control strategy will penetrate a 

certain regulated sector taking into account such things as equipment 

exemptions.
53

Control factors are closely linked to the type of emission process and secondarily to the 

type of industry. Control levels are assigned to emission categories, which are targeted 

by the rules via emission inventory codes (SCC/SIC, EIC etc.) that are used in 

CEIDARS.

4.4. Day-Specific Emissions 

Day-specific data were used for preparing base case inventories when data were 

available. In previous studies, day-specific data were gathered for large point sources, 

unusual events (e.g. breakdowns), shipping, prescribed burns, and wildfires. Those 

previous studies focused on an episode lasting a few days. In this current work, 

inventories have been created for multiple years. The gathering of day- or hourspecific data from certain kinds of sources, such as large facilities or ship activity, 

becomes very resource intensive. However, ARB and district staffs were able to gather 

hourly/daily emission information for 1) wildfires and prescribed burns 2) paved and 

unpaved road dust and 3) agricultural burns in the San Joaquin Valley and Sacramento 

County. Additionally, a special model developed for ocean-going vessels was used.

4.4.1. Wildfires and Prescribed Burns

Day-specific, base case estimates of emissions from wildfires and prescribed fires were 

developed in a two part process. The first part consists of estimating micro-scale, firespecific emissions (i.e. at the fire polygon scale, which can be at a smaller spatial scale 

than the grid cells used in air quality modeling). The second part consists of several 

steps of post-processing fire polygon emission estimates into gridded, hourly emission 

estimates that are formatted for use in air quality modeling. 

4.4.2. Paved Road Dust

Statewide emissions from paved road dust were adjusted for each day of the year 2007. 

The adjustment reduced emissions by 25% from paved road dust on days when 

precipitation occurred. 
54

Paved road dust emissions are calculated using the method described in AP-42, Fifth 

Edition, Volume I, Chapter 13: Miscellaneous Sources, Section 13.2.1 Paved Road, 

January 2011, (http://www.epa.gov/ttn/chief/ap42/ch13/final/c13s0201.pdf). This 

methodology includes equations that adjust emissions based on average precipitation in 

a month; these precipitation-adjusted emissions were placed in the CEIDARS 

databases. Since daily precipitation totals are readily available, ARB and district staff 

agreed that paved road dust emissions should be estimated for each day rather than by 

month. The emissions from CEIDARS were replaced with day-specific data for the 

appropriate years. A description of the steps used to calculate day-specific emissions is 

as follows:

1) Daily uncontrolled emissions for each county/air basin are estimated from the 

AP-42 methodology [Equation (1) on page 13.2.1-4]. No monthly precipitation 

adjustments are incorporated into the equation to estimate emissions.

2) To adjust for precipitation, daily precipitation data for 2005 and 2007 from ARB’s 

meteorological database (http://www.arb.ca.gov/aqmis2/metselect.php) are used. 

The specific data sources for these data include: Remote Automated Weather 

Stations (RAWS), AIRS, and California Irrigation Management Information 

System (CIMIS) networks. Precipitation data are not available from ARB’s 

meteorological database for San Francisco County and the Lake Tahoe Air Basin 

portion of Placer County (Placer/LT). Precipitation at the San Francisco 

International Airport in San Mateo County is used to determine precipitation in 

San Francisco County. Likewise, precipitation measured at stations in the Lake 

Tahoe Air Basin portion of El Dorado County is used to determine precipitation in 

Placer/LT.

3) The emissions from item 1 are adjusted using the precipitation data from item 2. 

If the precipitation is greater than or equal to 0.01 inches (measured anywhere in 

a county or county/air basin piece on a particular day), then the uncontrolled 

emissions are reduced by 25% for that day only. This reduction of emissions 

follows the recommendation in AP-42 as referenced above.
55

4) Replace the annual average emissions with day-specific emissions for every day 

in the corresponding emission inventory dataset. 

4.4.3. Unpaved Road Dust

Statewide emissions from unpaved road dust were adjusted for rainfall suppression for 

each day of the year. The adjustment reduced countywide emissions by 100% (total 

suppression) from unpaved road dust on days when precipitation greater than 0.01” 

occurred in a county. 

Dust emissions from unpaved roads were calculated using an emission factor (EF) 

derived from tests conducted by the University of California, Davis, (UCD) and the 

Desert Research Institute (DRI). Unpaved road vehicle miles traveled (VMT) were 

based on county-specific road mileage estimates. Emissions were assumed to be 

suppressed for each day with rainfall of 0.01 inch or greater using the method described 

in AP-42, Fifth Edition, Volume I, Chapter 13: Miscellaneous Sources, Section 13.2.2 

Unpaved Road, November 2006, 

(http://www.epa.gov/ttn/chief/ap42/ch13/final/c13s0202.pdf). Equation (2) adjusts 

emissions based on annual precipitation; these precipitation-adjusted emissions were 

placed in the CEIDARS database. Similar to paved road dust, ARB and district staff 

agreed that unpaved road dust emissions should be estimated for each day. The 

emissions from CEIDARS were replaced with day-specific data for the appropriate 

years. Following is a description of the steps that were taken to calculate day-specific 

emissions.

1) Start with the daily uncontrolled emissions for each county/air basin as estimated 

from ARB’s methodology. In other words, no precipitation adjustments have been 

incorporated in the emission estimates.

2) Use daily precipitation data from ARB’s meteorological database 

(http://www.arb.ca.gov/aqmis2/metselect.php). Data sources come from outside 

sources, including Remote Automated Weather Stations (RAWS), AIRS, and 

California Irrigation Management Information System (CIMIS) networks. Convert 

from millimeters to inches.
56

3) If the precipitation is greater than or equal to 0.01 inches measured anywhere in a 

county or county/air basin portion on a particular day, then the emissions are 

removed for that day only.

4) Replace the annual average emissions with day-specific emissions for every day. 

5) Precipitation data are not available from ARB’s meteorological database for San 

Francisco County and the Lake Tahoe Air Basin portion of Placer County 

(Placer/LT). Precipitation at the San Francisco International Airport in San Mateo 

County is used to determine precipitation in San Francisco County. Likewise, 

precipitation measured at stations in the Lake Tahoe Air Basin portion of El Dorado 

County is used to determine precipitation in Placer/Lake Tahoe.

4.4.4. Agricultural Burn Data for San Joaquin Valley

The San Joaquin Valley Air Pollution Control District estimated emissions for each day 

during 2005 through 2010 when agricultural burning occurred. Emissions were 

estimated for the burning of prunings, field crops, weed abatement and other solid fuels. 

Information needed to estimate emissions came from the district’s Smoke Management 

System, which stores information on burn permits issued by the district. In order to 

obtain a daily burn authorization, the person requesting the burn provides information to 

the district, including the acres and type of material to be burned, the specific location of 

the burn and the date of the burn. Acres are converted to tons of fuel burned using a 

fuel loading factor based on the specific crop to be burned. Emissions are calculated by 

multiplying the tons of fuel burned by a crop-specific emission factor. More information 

is available at: http://www.arb.ca.gov/ei/areasrc/distmiscprocwstburndis.htm

To determine the location of the burn, district staff created spatial allocation factors for 

each 4 kilometer grid cell used in modeling. These factors were developed for “burn 

zones” in the San Joaquin Valley based on the agricultural land coverage. Daily 

emissions in each “agricultural burn zone” were then distributed across the zone/grid 

cell combinations using the spatial allocation factors. Emissions were summarized by 

grid cell and day.
57

Burning was assumed to occur over three hours from 10:00 a.m. to 1:00 p.m., except 

for two categories. Orchard removals were assumed to burn over eight hours from 

10:00 a.m. to 6:00 p.m. Vineyard removals were assumed to burn over five hours from 

10:00 a.m. to 3:00 p.m.

4.4.5. Ocean-Going Vessels

The emissions for ocean-going vessels were generated with version 2-3H of the ARB 

Marine Model. The model uses a power-based methodology to estimate emissions. 

Inputs to the model include vessel call data obtained from the California Lands 

Commission; vessel specifications and power ratings from Lloyds-Fairplay, vessel 

berthing statistics from port authorities, and vessel routing based upon the Ship 

Transportation Energy and Economic Model (STEEM) developed by the University of 

Delaware under contract with the Air Resources Board. Emissions were calculated by 

estimating ship emissions on a ship by ship and a port call by port call basis, using 

actual ship engine power estimates, speeds, and actual ship hoteling times where 

possible. 

Emission control measures included in the inventory include the South Coast 20/40

nautical-mile voluntary vessel speed reduction program, the 2007 Shore Power 

regulation, the 2005 auxiliary engine regulation (while in effect) and the subsequent 

2008 low sulfur fuel regulation, IMO tier 1 NOx engine standards, and the IMO North 

American Environmental Control Area which includes the IMO tier 3 NOx engine 

standards.

4.5. Temporally and Spatially Resolved Emissions

Emission inventories that are temporally and spatially resolved are needed for modeling 

purposes, for both the base year and future years. Annual average emissions for point 

and area sources were used as input to version 2.6 of SMOKE (Sparse Matrix Object 

Kernel Emission). The SMOKE processor was developed by the MCNC-North Carolina 

Supercomputing Center, Environmental Sciences Division, with U.S. EPA cooperation 

and support. Temporal information is input into SMOKE. Adjustments are made for 
58

variations in months, day of week and hour of day. Emissions are estimated for each 

county, air basin, and district combination for each day of the year. The SMOKE 

processor also distributes emissions to each grid cell. The spatial allocation of 

emissions is discussed in Section 4.9.

The emission inventories for PM2.5 modeling in northern California were developed from 

the 2005 annual average CEIDARS database for TOG, NOx, SOx, CO, PM, and 

ammonia. Inventories for point and area sources were developed for each day for a 

variety of years between 2005 and 2020 as needed for input to air quality models.

4.6. Surface Temperature and Relative Humidity Fields

The calculation of gridded emissions for some categories of the emissions inventory is 

dependent on meteorological variables. More specifically, biogenic emissions are 

sensitive to air temperatures and solar radiation while emissions from on-road mobile 

sources are sensitive to air temperature and relative humidity. As a result, estimates of 

air temperature (T), relative humidity (RH), and solar radiation are needed for each grid 

cell in the modeling domain in order to take into account the effects of these 

meteorological variables on mobile source and biogenic emissions in each grid cell.

Gridded temperature, humidity, and radiation fields are readily available from prognostic 

meteorological models such as MM5, which is used to prepare meteorological inputs for 

the air quality model. However, it is widely recognized that diagnostic (i.e. observationbased) models provide more accurate local-scale estimates of ground surface 

temperature and humidity. As a result, the CALMET diagnostic meteorological model is 

used to generate a gridded temperature field and an objective analysis scheme is used 

to generate a gridded humidity field. The solar radiation fields needed for biogenic 

emission inventory calculations were taken from the MM5 prognostic model, which is 

also used to generate meteorology for the air quality model.

The principal steps involved in generating a gridded, surface-level temperature field 

using CALMET include the following:
59

1) Compute the relative weights of each surface observation station to each grid cell 

(the weight is inversely proportional to the distance between the surface observation 

station and grid cell center). 

2) Adjust all surface temperatures to sea level. In this step, a lapse rate of -0.0049 

oC/m is used (this lapse rate is based on private communication with Gary Moore of 

Earth Tech, Inc., Concord, MA). This lapse rate (=2.7 F/1000 feet) is based on 

observational data.

3) Use the weights to compute a spatially-averaged sea-level temperature in each grid 

cell.

4) Correct all sea-level temperatures back to 10 m height above ground level (i.e. the 

standard height of surface temperature measurement) using the lapse rate of -

0.0049 oC/m again.

The current version of CALMET does not generate estimates of relative humidity. As a 

result, a post-processing program was used to produce gridded, hourly relative humidity 

estimates from observed relative humidity data. The major steps needed to generate 

gridded, surface-level relative humidity are described as follows: 

1) Calculate actual vapor pressure from observed relative humidity and temperature at 

all meteorological stations. The McRae (1980) method is used to calculate the 

saturated vapor pressure from temperature;

2) Compute the relative weights of each surface observation station to each grid in 

question, exactly as done by CALMET to compute the temperature field; 

3) Use the weights from step 2 to compute a spatially-averaged estimate of actual 

vapor pressure in each grid cell;

4) For each grid cell, calculate relative humidity from values for actual vapor pressure 

and temperature for the same grid cell.

4.7. On-Road Mobile Source Emissions

As described in the prior sections, air quality models require gridded, hourly emission 

inputs. However, California’s official on-road motor vehicle emission inventory model, 

EMFAC, is designed to produce county-level, average-day estimates. As a result, 
60

emission estimates from EMFAC must be disaggregated spatially and temporally from 

county-level, average-day estimates into gridded, hourly estimates. The general 

methodology that ARB has used to disaggregate EMFAC emission estimates in the past 

is described below and will be used again. Basically, it involves using the Direct Travel 

Impact Model (DTIM) (Systems Applications, Inc. 2001) to produce gridded, hourly 

emission estimates, and then uses these estimates as a gridded hourly spatial 

surrogate to distribute EMFAC emissions. The methodology has been peer reviewed 

by the Institute of Transportation Studies department at the University of California, 

Irvine, under a CCOS contract.

The most recent version of EMFAC, EMFAC2011, is comprised of two separate 

emission model components: EMFAC2011-LD and EMFAC2011-HD. The LD model 

generates emissions for light- and medium- duty gasoline vehicles, heavy-duty gasoline 

vehicles, and light- and medium-duty diesel vehicles. The HD model generates 

emissions for heavy-duty diesel vehicles. The general methodology described below 

will be performed three times: the first time for light duty gasoline vehicle emissions from 

EMFAC2011-LD; a second time for heavy duty gasoline vehicle emission estimates 

from EMFAC2011-LD; and a third time for heavy duty diesel vehicle emissions from 

EMFAC2011-HD. Methodological details are currently being updated where necessary 

to work with the new version of EMFAC.

4.7.1. General Methodology

Mobile source emissions are sensitive to ambient temperature and humidity. Both 

EMFAC and DTIM account for meteorological effects using day-specific inputs (the 

gridded, hourly meteorological data used are described under the prior section titled 

“Surface Temperature and Relative Humidity Fields”). For EMFAC, hourly gridded 

temperature and humidity fields are averaged by county using a gridded VMT weighted 

average (i.e. weighted proportional to the VMT per grid cell in a county). DTIM accepts 

gridded, hourly data directly.

EMFAC provides vehicle-class-specific emissions estimates for exhaust emissions, 

evaporative emissions, tire wear emissions and brake wear emissions. EMFAC also 
61

produces estimates of fuel consumption, vehicle miles traveled (VMT), and the number 

of vehicles in use. More information on EMFAC is available at the following link. 

http://www.arb.ca.gov/msei/modeling.htm

Temporal Adjustment (Day-of-Week adjustments to EMFAC daily totals): Day-ofWeek (DOW) adjustments are made to the total daily emissions estimated by EMFAC 

for Friday, Saturday, Sunday, and Monday days of the week. The logic behind this is 

that EMFAC produces emission estimates for an average day of the week. It is 

assumed that EMFAC’s average day of week emissions generally represents Tuesday, 

Wednesday, and Thursday. Day of week adjustment factors were developed using 

Automatic Vehicle Classifier (AVC) count data from the California Department of 

Transportation (Caltrans). These data were collected at 139 sites in the state during the 

summer of 2004 (specifically, data for the months of June, July and August were used, 

excluding data from July 2-5 to remove unusual traffic patterns around the July 4th

holiday). Three Caltrans factors were developed: (1) passenger cars (LD), (2) light and 

medium duty trucks (LM), and (3) heavy-heavy duty trucks (HHDT). An example of the 

prior assignment of these factors to EMFAC2007 classifications is summarized below. 

Caltrans’ Factor for 

EMFAC2007 Class*

Description Day-of-Week (DOW)

1

LDA LD

2

LDT1 LD

3

LDT2 LD

4

MDV LD

5

LHDT1 LM

6

LHDT2 LM

7

MHDT LM

8

HHDT HHDT

9

Other Bus LM

10 School Bus Unadjusted on 

weekdays, zeroed on 
62

weekend days

11 Urban Bus LD

12 Motorhomes LD

13 Motorcycles LD

*

Vehicle classes are being updated for use with EMFAC2011

Separate factors were developed for each Caltrans District. All counties within each 

Caltrans district use the same adjustment. So, the day of week adjustment process 

consists of applying four Caltrans day of week (DOW) factors to EMFAC daily total 

emission estimates (i.e. which represent Tuesday, Wednesday, and Thursday): one 

each for Friday, Saturday, Sunday, and Monday. 

Temporal Adjustment (Hour-of-Day re-distribution of hourly travel network 

volumes): The travel networks provided by local government agencies and used for 

DTIM represent an average day hourly distribution. Like for EMFAC, it is assumed that 

these average day of week hourly distributions represent hourly mid-week activities (i.e. 

for Tuesday, Wednesday, and Thursday). As such, they lack the day-of-week temporal 

variations that are known to occur on other days of the week. To rectify this, hour-ofday profiles for Friday through Monday were developed using Caltrans data. These are 

used to re-allocate the hourly travel network distributions used in DTIM to Friday 

through Monday.

Spatial Adjustment: The spatial allocation of countywide EMFAC emissions is 

accomplished using gridded, hourly emission estimates from DTIM normalized by 

county. DTIM uses emission rates from EMFAC along with activity data, digitized 

roadway segments (links) and traffic analysis zone centroids to calculate gridded, hourly 

emissions for travel and trip ends. DTIM considers fewer vehicle categories than 

EMFAC outputs, so a mapping between EMFAC and DTIM vehicle categories is 

necessary (this is being updated to work with EMFAC2011). DTIM’s 40 emission 

categories are presented in the table below. The categories are represented by the 

listed source classification codes (SCC) and depend on vehicle type, technology, and 
63

whether the vehicle is catalyst, non-catalyst, or diesel. Light- and medium-duty vehicles 

are separated from heavy-duty vehicles to allow for separate reporting and control 

strategy applications.

Table 4-2: DTIM Emission Categories

SCC for Light-duty 

and Medium-duty 

Vehicles

SCC for HeavyDuty VehiclesDescription

202 302 Catalyst Start Exhaust

203 303 Catalyst Running Exhaust

204 304 Non-catalyst Start Exhaust

205 305 Non-catalyst Running Exhaust

206 306 Hot Soak

207 307 Diurnal Evaporatives

208 308 Diesel Exhaust

209 309 Running Evaporatives

210 310 Resting Evaporatives

211 311 Multi-Day Resting

212 312 Multi-Day Diurnal

213 313 PM Tire Wear

214 314 PM Brake Wear

215 315 Catalyst Buses

216 316 Non-catalyst Buses

217 317 Diesel Bus

218 318 Catalyst Idle

219 319 Non-catalyst Idle

220 320 Diesel Idle

221 321 PM Road Dust
64

Summary of On-road Emissions Processing Steps: Five general steps are used to 

spatially and temporally allocate EMFAC emissions by hour and grid cell:

Step 1 (DTIM T & RH inputs). Gridded, hourly temperature (T) and relative humidity 

(RH) fields for each day are prepared as inputs to DTIM.

Step 2 (DTIM emission factor inputs). EMFAC is run in default mode (i.e. without 

day-specific temperature and relative humidity) to generate a look-up table of onroad mobile source emission factors by speed, temperature, and relative humidity for 

each county.

Step 3 (Day-specific EMFAC runs to yield daily and hourly estimates). EMFAC is 

run using episode-specific T and RH data to provide countywide on-road mobile 

source emission estimates by day and hour for EMFAC categories. The episodespecific meteorological inputs for EMFAC are generated via averaging (VMTweighted) the gridded, hourly meteorology from Step1 by county and hour.

Step 4 (DTIM inputs – redistribute roadway network hourly volumes using Caltrans 

DOW factors)

 4a. Sum the hourly volumes by vehicle type and county on the roadway network 

into daily totals.

 4b. Tuesday through Thursday. No DOW adjustment. For hour of day, 

redistribute heavy-duty volumes by county using Caltrans hourly profiles. No 

change to light duty hourly volumes.

 4c. Friday, Saturday, Sunday, and Monday. Adjust total daily volumes by county 

using Caltrans DOW factor. Use Caltrans hourly profiles by county to redistribute 

DOW-adjusted total volumes using Caltrans hourly profiles for all vehicles. 

Step 5 (Run DTIM and spatially/temporally distribute EMFAC emissions)

 5a. Run DTIM with revised roadway network activity from Step 4.

 5b. Sum DTIM emissions by county and SCC.

 5c. Distribute EMFAC emissions. EMFAC daily, countywide emissions (adjusted 

for weekend days, if needed), are disaggregated by category into grid-cells for 

each hour of the day using the DTIM output as a spatial and temporal surrogate. 

The disaggregation follows the equation:
65

P,daily,cat,cnty

P,cat P,ij,hr,cat

P,ij,hr,cat DTIM

EF DTIM

E





where:

E = grid cell emissions

EF = EMFAC emissions

DTIM = DTIM emissions

P = pollutant 

ij = grid cell

hr = hourly emissions

cat = Emission Category

daily = daily emissions

cnty = county

Future Year On-road Emissions: Forecasted on-road modeling inventories are 

developed using the same methodology, where future year emissions are based on 

running EMFAC for the associated future year.

4.8. Biogenic Emissions

Development of a comprehensive emissions inventory requires estimation of both manmade and biogenic emissions. These biogenic volatile organic compounds (BVOCs) 

include compounds such as isoprene and monoterpenes. Due to the heterogeneity of 

vegetation land cover, species composition, and leaf mass distribution in California, 

quantifying BVOC emissions in this domain requires an emission inventory model with 

region-specific input databases and a high degree of spatial and temporal resolution. In 

response to this need, the California Air Resources Board (CARB) developed a 

Geographic Information System (GIS)-based model for estimating BVOC emissions, 

called BEIGIS (Scott and Benjamin, 2003), which uses California-specific input 

databases with a minimum spatial resolution of 1 km2and an hourly temporal resolution. 

To take advantage of recent scientific advances in biogenic emissions modeling, CARB 

has recently transitioned from the BEIGIS model to the Model of Emissions of Gases 
66

and Aerosols from Nature (MEGAN) version 2.04 (Guenther et al., 2006). MEGAN is a 

state-of-the-science biogenic emissions model, which represents an evolution of the 

Biogenic Emissions Inventory System (BEIS), and is being integrated into the 

Community Multi-scale Air Quality (CMAQ) modeling system by U.S. EPA scientists.

MEGAN estimates biogenic emissions as a function of normalized emission rates (i.e., 

emission rates at standard conditions), which are adjusted to reflect variations in 

temperature, light, leaf area index (LAI), and leaf age (estimated from changes in LAI). 

MEGAN requires input datasets of Emission Factors (EF; at standard conditions: 

temperature = 303  ̊K, LAI = 5, photosynthetically active radiation ~ 1500 μmol m-2s

-1

), 

Plant Functional Type (PFT), and hourly surface temperature and insolation. The 

default MEGAN input databases for EFs, PFTs, and LAI are not used in the application 

of MEGAN in California. Instead, California-specific emission factor and PFT databases 

were translated from those used in BEIGIS to improve emission estimates and to 

maintain consistency with previous California biogenic emission inventories. LAI data is 

derived from the MODIS 8-day LAI satellite product. Hourly surface temperatures are 

from observations gridded with the CALMET meteorological model and insolation (light 

reaching the surface) data is provided by the MM5 meteorological model. Emissions of 

isoprene, monoterpenes, and methylbutenol are estimated from California-specific 

gridded emission factor data, while emissions of sesquiterpenes, methanol, and other 

volatile organic compounds are estimated from California-specific PFT data and PFT 

derived emission rates. For urban areas, land use/vegetation land cover databases 

were developed from regional planning agency data and botanical surveys (Horie et al. 

1990; Nowak 1991; Sidawi and Horie 1992; Benjamin et al. 1996, 1997; McPherson et 

al. 1998). Natural areas are represented using the GAP vegetation database (also 

satellite-derived and air photo interpreted) developed by the U.S.G.S. Gap Analysis 

Program (Davis et al. 1995). Agricultural areas are represented using crop land cover 

databases developed by the California Department of Water Resources 

(http://www.waterplan.water.ca.gov).

Future-year specific biogenic emissions are not estimated because future inputs to 

BEIGIS, such as changes in climate and land use/land cover, are highly uncertain. 
67

Photochemical modeling for future years uses the biogenic emissions developed for the 

base year.

4.9. Spatial Allocation

Once the base year or future year inventories are developed, as described in the 

previous sections, the next step of modeling inventory development is to spatially 

allocate the emissions. Air quality modeling attempts to replicate the physical and 

chemical processes that occur in an inventory domain. Therefore, it is important that 

the physical location of emissions be specified as accurately as possible. Ideally, the 

actual location of all emissions would be known exactly. In reality, however, some 

categories of emissions would be virtually impossible to determine – for example, the 

actual amount and location of consumer products used every day. Therefore, the 

spatial allocation of emissions in a modeling inventory only approximates the actual 

location of emissions.

Before any spatial allocation can be performed, the modeling grid domain must be 

defined. A modeling grid domain is a rectangular area that is sufficient in size to contain 

all emission sources that could affect modeling results. The definition of the modeling 

domain for this SIP is described below.

Once a grid is defined, the spatial allocation of emissions can be performed. Each area 

source category is assigned a spatial surrogate that is used to allocate emissions to a

grid cell. Examples of surrogates include population, land use, and other data with 

known geographic distributions for allocating emissions to grid cells. The sections 

below discuss in detail the spatial surrogates developed for the SJV PM2.5 SIP 

modeling.

Point sources are allocated to grid cells using the UTM coordinates reported for each 

stack. If there are no stack UTM coordinates, the facility UTM coordinates are used. 

When location data are not reported, the county centroid is used.

Emissions are also distributed vertically into their proper layer in the air quality model. 

The vertical layer is determined from the calculation of buoyancy for those emissions 
68

that are released from an elevated height with a significant upward velocity and/or 

buoyancy. Most vertical allocation is from significant point sources with stacks. In most 

modeling exercises, low-level point sources are screened out at this point and placed 

with the area sources. However, in this modeling exercise, all point sources from the 

inventory were kept as possible elevated sources. The air quality model will then place 

the point sources in the appropriate layer of the model. Additionally in this modeling 

exercise, day-specific wildfire emissions were also distributed vertically.

The spatial treatment of area and point sources has been described above. The spatial 

allocation of on-road motor vehicles is based on DTIM as described previously. For 

biogenic emissions, the spatial allocation is built “from the ground up” since MEGAN 

estimates emissions using a Geographic Information System (GIS) at a minimum 

resolution of one square kilometer.

4.9.1. Grid Definition

The ARB emissions inventory domain is defined to match the MM5 model domain, 

which is used to generate the meteorological parameter fields used for air quality 

modeling. MM5 uses a Lambert projection and assumes a spherical Earth. The 

emission grid is defined in a similar way to match as closely as possible.

The emission inventory grid uses a Lambert Conical Projection with two parallels. The 

Parallels are at 30° and 60° N latitude, with a central meridian at 120.5° W longitude. 

The coordinate system origin is offset to 37° N latitude. The emissions inventory uses a 

grid with a spatial resolution of 4 km x 4 km. 

The domain extends entirely over California and 100 nautical miles west over the Pacific 

Ocean. A smaller subdomain is often used when modeling is being done for the San 

Joaquin Valley. It has the same grid definitions and resolution as the main domain, but 

has a smaller area offset to cover central and northern California.

The specifications of the emissions inventory domain and CCOS subdomain are:

MAP PROJECTION 
69

Lambert Conformal Conic

Datum: NONE (Clarke 1866 spheroid) 

1st Standard Parallel: 30.0° N

2nd Standard Parallel: 60.0° N

Central Meridian: -120.5° W

Latitude of Projection Origin: 37.0° N

COORDINATE SYSTEM 

Units: Meters 

Semi-major Axis: 6370 km

Semi-minor Axis: 6370 km

DEFINITION OF GRID 

321 x 291 cells (4 km x 4 km) 

Lambert Origin @ (-684,000 m, -564,000 m) 

Geographic Origin @ -120.5°Latitude and 37.0°Longitude 

DEFINITION OF SUBGRID (CCOS)

192 x 192 cells (4 km x 4 km) 

Lambert Origin @ (-384,000 m, -300,000 m) 

Geographic Origin @ -120.5°Latitude and 37.0°Longitude 

4.9.2. Spatial Surrogates

Spatial surrogates are processed into spatial allocation factors for use in geographically 

distributing countywide area source emissions to individual grid cells. Spatial 

surrogates are developed based on economic, demographic, and land cover data which 

exhibit patterns that vary geographically. As has previously been discussed, point 

source emissions are allocated to grid cells using the location of the emission source. 

On-road motor vehicle emissions are spatially allocated by DTIM. Biogenic emissions 

are allocated by the MEGAN emissions model.

In support of CRPAQS and CCOS, Sonoma Technology, Inc. (Funk et al. 2001) 

developed gridded spatial allocation factors for a 2000 base-year and three future years 
70

(2005, 2010, and 2020) for the entire state of California. STI’s work was based on the 

statewide 4-kilometer (km) grid cell domain defined by the ARB. The definition and 

extent of the 4-km grid were used to create a 2-km nested grid for which spatial 

allocation factors were developed. In 2007, STI was contracted by CCOS again to 

update the spatial allocation factors. STI updated the underlying spatial data and 

updated the spatial surrogate cross-reference file to account for new emission source 

categories (Reid et al., 2006). STI then updated spatial allocation factors for ARB’s 

statewide modeling domain for a base year of 2000 and future years of 2010, 2015, and 

2020. This task was completed in March 2008.
71

In preparation for modeling for the PM2.5 SIPs, ARB staff reviewed the STI spatial

surrogates associated with the highest emissions to see which surrogates were 

candidates for update. ARB staff searched for more recent or improved sources of 

data, since the underlying data used by STI were pre-recession, then updated 15 of the 

surrogates using more recent data. A total of 61 unique surrogates are available for 

use. A summary of the spatial surrogates for which spatial allocation factors were 

developed is listed in the table below.

Three basic types of surrogate data were used to develop the spatial allocation factors: 

land use and land cover; facility location; and demographic and socioeconomic data. 

Land use and land cover data are associated with specific land uses, such as 

agricultural tilling or recreational boats. Facility locations are used for sources such as 

gas stations and dry cleaners. Demographic and socioeconomic data, such as 

population and housing, are associated with residential, industrial, and commercial 

activity (e.g. residential fuel combustion). To develop spatial allocation factors of high 

quality and resolution, local socioeconomic and demographic data were used where 

available; for rural regions, for which local data were not available, data from the 

Caltrans Statewide Transportation Model were used.
72

Table 4-3: Summary of spatial surrogates

Spatial Surrogate Description

Airports Spatial locations of all airports

All_PavedRds Spatial distribution of road network (all paved roads)

AutobodyShops Locations of autobody repair and refinishing shops

Cemeteries Spatial locations of cemeteries

Comm_Airports Spatial locations of commercial airports

Devplnd_HiDensity Spatial distribution of high-density developed land

Devplnd_LoDensity Spatial distribution of low-density developed land

Drycleaners Locations of drycleaning facilities

DryLakeBeds Locations of Dry lake beds

Elev5000ft

Elevation over 5000 feet developed from topological 

contours

Employ_Roads

Spatial distribution of total employment and road density 

(all paved roads)

Forestland Spatial distribution of forest land

Fugitive_Dust Spatial distribution of undeveloped, open land

GasStations Locations of gasoline service stations

GasWells Locations of gas wells

GolfCourses Spatial locations of golf courses

HE_Sqft

Computed surrogate based on housing and employment 

(est. ft2 / person)

Hospitals Spatial locations of hospitals

Housing Spatial distribution of total housing

Housing_Autobody Spatial distribution of housing and autobody refinishing 

Housing_Com_Emp

Spatial distribution of total housing and commercial 

employment

Housing_Restaurants Spatial distribution of total housing and 

IndusEmploy_Autobody

Spatial distribution of industrial employment and 

autobody/refinishing shops

Industrial_Emp Spatial distribution of industrial employment

InlandShippingLanes

Spatial distribution of major shipping lanes within bays and 

inland areas

Irr_Cropland Spatial location of agricultural cropland

Lakes_Coastline Locations of lakes, reservoirs, and coastline

Landfills Locations of landfills

LiveStock

Spatial distribution of cattle ranches, feedlots, dairies, and 

poultry farms

Metrolink_Lines Spatial distribution of metrolink network

MiltaryAirBases Location of military air bases

MiltaryBases Locations of military bases

NonIrr_Pastureland Spatial location of non-irrigated pasture land

NonRes_Chg

Computed surrogate based on the change in spatial 

distribution of non-residential areas
73

Table 4-3. Summary of spatial surrogates (continued)

Spatial Surrogate Description

OffShore_OilWells Locations of off-shore oil wells

OilWells Locations of oil wells

Pop_ComEmp_Hos

Spatial distribution of hospitals, population and commercial 

employment

Population Spatial distribution of population

Ports Locations of shipping ports

POTWs Coordinate locations of Publically Owned Treatment 

PrimaryRoads Spatial distribution of road network (primary roads)

Raillines Spatial distribution of railroad network

RailYards Locations of rail yards

Rds_HE

Calculated surrogate based on road densities and 

housing/employment (est. ft2 / person)

RefinieriesTankFarms Coordinate locations of refineries and tank farms

Res_NonRes_Chg

Computed surrogate based on the change in spatial 

distribution of residential and non-residential areas

ResGasHeating Spatial distribution of gas heating population

Residential_Chg

Computed surrogate based on the change in spatial 

distribution of residential areas

ResNonResChg_IndEmp

Spatial distribution of industrial employment and 

residential/ non-residential change

Restaurants Locations of bakeries and restaurants

ResWoodHeating Spatial distribution of wood heating population

SandandGravelMines Locations of sand/gravel excavation and mining

Schools Spatial locations of schools

SecondaryPavedRds Spatial distribution of road network (secondary roads)

Ser_ComEmp_Sch_GolfC

_Cem

Spatial distribution of service and commercial 

employment, schools, cemeteries, and golf courses

Service_Com_Emp Spatial distribution of service and commercial employment

Service_Emp Spatial distribution of service employment

Shiplanes Spatial distribution of major shipping lanes

SingleHousingUnits Spatial distribution of single dwelling units

UnpavedRds Spatial distribution of road network (unpaved roads)

Wineries Locations of wineries
74

4.10. Speciation

The ARB's emission inventory and photochemical air quality models both quantify 

organic compounds as Total Organic Gases (TOG). Photochemical models simulate 

the physical and chemical processes in the lower atmosphere, and include all emissions 

of the important compounds involved in photochemistry. Organic gases are one of the 

most important classes of chemicals involved in photochemistry. Organic gases emitted 

to the atmosphere are referred to as total organic gases (TOG). ARB's chemical 

speciation profiles (CARB 2006) are applied to characterize the chemical composition of 

the TOG emitted from each source type.

TOG includes compounds of carbon, excluding carbon monoxide, carbon dioxide, 

carbonic acid, metallic carbides or carbonates, and ammonium carbonate. TOG 

includes all organic gas compounds emitted to the atmosphere, including the low 

reactivity, or exempt, VOC compounds (e.g., methane, ethane, various chlorinated 

fluorocarbons, acetone, perchloroethylene, volatile methyl siloxanes, etc.). TOG also 

includes low volatility or low vapor pressure (LVP) organic compounds (e.g., some 

petroleum distillate mixtures). TOG includes all organic compounds that can become 

airborne (through evaporation, sublimation, as aerosols, etc.), excluding carbon 

monoxide, carbon dioxide, carbonic acid, metallic carbides or carbonates, and 

ammonium carbonate.

Total Organic Gas (TOG) emissions are reported in the ARB's emission inventory and 

are the basis for deriving the Reactive Organic Gas (ROG) emission components, which 

are also reported in the inventory. ROG is defined as TOG minus ARB's "exempt" 

compounds (e.g., methane, ethane, CFCs, etc.). ROG is nearly identical to U.S. EPA's 

term "VOC", which is based on U.S. EPA's exempt list. For all practical purposes, use 

of the terms ROG and VOC are interchangeable. Also, various regulatory uses of the 

term "VOC", such as that for consumer products exclude specific, additional compounds 

from particular control requirements.
75

4.10.1. Speciation Profiles

Speciation profiles are used to estimate the amounts of various organic compounds that 

make up TOG. A speciation profile contains a list of organic compounds and the weight 

fraction that each compound comprises of the TOG emissions from a particular source 

type. Each process or product category is keyed to one of several hundred currently 

available speciation profiles. The speciation profiles are applied to TOG to develop both 

the photochemical model inputs and the emission inventory for ROG. 

It should be noted that districts are allowed to report their own reactive fraction of TOG 

that is used to calculate ROG rather than use the information from the assigned organic 

profiles. These district-reported fractions are not used in developing modeling 

inventories because the information needed to calculate the amount of each organic 

compound is not available.

To the extent possible (i.e. given available data), ARB's organic gas speciation profiles 

contain all emitted organic species that can be identified (ideally, detected to very low 

levels). This includes reactive compounds, unreactive and exempt compounds, and to 

the extent the data are available, low vapor pressure compounds. Research studies are 

conducted regularly to improve ARB's species profiles. These profiles support ozone 

modeling studies but are also designed to be used for aerosol and regional toxics 

modeling. The profiles are also used to support other health or welfare related 

modeling studies where the compounds of interest cannot always be anticipated. 

Therefore, organic gas emission profiles should be as complete and accurate as 

possible.

The speciation profiles used in the emission inventory are available for download from 

the ARB's web site at: http://www.arb.ca.gov/ei/speciate/speciate.htm. 

The Organic Speciation Profiles (ORGPROF) file contains the weight fraction data 

(expressed as percent for ease of display) of each chemical in each profile. Each 

chemical fraction is multiplied by the Total Organic Gas (TOG) emissions for a source 

category to get the amount of each specific constituent chemical. In addition to the 
76

chemical name for each chemical constituent, the file also shows the chemical code (a 

5-digit internal identifier) and the Chemical Abstracts Service (CAS) number, which is a 

unique identifying code (up to 9 digits) assigned to chemicals by the CAS Registry 

Service.

Also available for download from ARB’s web site is a cross-reference file that indicates 

which Organic Gas profile is assigned to each source category in the inventory. The 

inventory source categories are represented by an 8-digit Source Classification Code 

(SCC) for point sources, or a 14-digit Emission Inventory Code (EIC) for area and 

mobile sources. This file also contains the fraction of reactive organic gas (FROG) 

values for organic profiles. Some of the Organic Gas Speciation Profiles related to 

motor vehicles and fuel evaporative sources vary by the inventory year of interest, due 

to changes in fuel composition and vehicle fleet composition over time.

ARB has an ongoing effort to update speciation profiles as data become available, such 

as through testing of emission sources or surveys of product formulations. New 

speciation data generally undergo technical and peer review, and updating of the 

profiles is coordinated with users of the data. Several recent changes to ARB's 

speciation profiles were for: 1) consumer products, 2) aerosol coatings, 3) architectural 

coatings, 4) pesticides and 5) hot soak from gasoline-powered vehicles.

The particulate matter emissions are size fractionated by using PM size profiles, which 

contain the total weight fraction for PM2.5 and PM10 out of total PM. The fine and coarse 

PM chemical compositions are characterized by applying the PM chemical speciation 

profiles for each source type, which contain the weight fractions of each chemical 

species for PM2.5, PM10 and total PM. PM size profiles and speciation profiles are 

typically generated based on source testing data. In most previous source testing 

studies aimed at determining PM chemical composition, filter-based sampling 

techniques are used to collect PM samples for chemical analyses. Recently, the MicroOrifice Uniform Deposit Impactor (MOUDI) has been used to collect PM samples for 

size resolved chemical composition analysis. 
77

4.10.2. Chemical Mechanisms

Airshed models are essential for the development of effective control strategies for 

reducing photochemical air pollution because they provide the only available scientific 

basis for making quantitative estimates of changes in air quality resulting from changes 

in emissions. The chemical mechanism is the portion of the model that represents the 

processes by which emitted primary pollutants, such as TOG, carbon monoxide (CO), 

and oxides of nitrogen (NOx), react in the gas phase to form secondary pollutants such 

as ozone (O3) and other oxidants.

For State Implementation Plan (SIP) attainment demonstrations and evaluations, the 

U.S. EPA has approved the California Air Resources Board’s photochemical air quality 

models. The air quality models used by the ARB for SIP attainment demonstrations use 

the SAPRC photochemical mechanism. This mechanism is based on extensive 

scientific research and is documented in the scientific literature (Carter 2000). Table 4-

4 shows modeled ROG species (or species categories) for the SAPRC-99 chemical 

mechanism. Table 4-5 shows modeled species for NOx. 

Table 4-4: ARB’s SAPRC-99 Emitted Organic Model Species

Model 

Species 

Name

Description

HCHO Formaldehyde

CCHO Acetaldehyde

RCHO Lumped C3+ Aldehydes

ACET Acetone

MEK Ketones and other non-aldehyde oxygenated products

PROD

RNO3 Lumped Organic Nitrates

PAN Peroxy Acetyl Nitrate
78

PAN2 PPN and other higher alkyl PAN analogues

BALD Aromatic aldehydes (e.g., benzaldehyde)

PBZN PAN analogues formed from Aromatic Aldehydes

PHEN Phenol

CRES Cresols

NPHE Nitrophenols

GLY Glyoxal

MGLY Methyl Glyoxal

MVK Methyl Vinyl Ketone

MEOH Methanol

HC2H Formic Acid

CH4 Methane

ETHE Ethene

ISOP Isoprene

TERP Terpenes

MTBE Methyl Tertiary Butyl Ether

ETOH Ethanol

NROG Non-reactive

LOST Lost carbon

ALK1 Alkanes and other non-aromatic compounds that react only with 

OH, and have kOH < 5 x 102ppm-1 min-1. (Primarily ethane)

ALK2 Alkanes and other non-aromatic compounds that react only with 

OH, and have kOH between 5 x 102and 2.5 x 103ppm-1 min-1. 

(Primarily propane and acetylene)

ALK3 Alkanes and other non-aromatic compounds that react only with 

OH, and have kOH between 2.5 x 103and 5 x 103ppm-1 min-1.

ALK4 Alkanes and other non-aromatic compounds that react only with 

OH, and have kOH between 5 x 103and 1 x 104ppm-1 min-1.

ALK5 Alkanes and other non-aromatic compounds that react only with 
79

OH, and have kOH greater than 1 x 104ppm-1 min-1.

ARO1 Aromatics with kOH < 2x104ppm-1 min-1.

ARO2 Aromatics with kOH > 2x104ppm-1 min-1.

OLE1 Alkenes (other than ethene) with kOH < 7x104ppm-1 min-1.

OLE2 Alkenes with kOH > 7x104ppm-1 min-1.
80

Table 4-5: Model Species for NOx

Model Species Name

Description

HONO

Nitrous Acid

NO

Nitric Oxide

NO2

Nitrogen Dioxide

Both U.S. EPA's and ARB's models require estimates of total organic gases, which 

include the "exempt VOCs", and, to the extent data are available, any low vapor 

pressure compounds that become airborne. Model results for ozone non-attainment 

areas have demonstrated that even compounds with low photochemical reactivity or low 

vapor pressure can contribute to photochemical ozone formation. For example, even 

an "exempt VOC" like ethane has been shown to have a contribution to ozone 

formation. If all exempt compounds and low vapor pressure compounds were omitted 

from photochemical model simulations, the ozone attainment demonstration would be 

compromised. The model takes into account that, individually, compounds with low 

reactivity or that are present in small amounts have a small impact on ozone formation. 

However, the cumulative effect of several low reactive compounds or many low 

emission compounds can be a significant contributor to photochemical ozone formation.

4.11. Quality Assurance

To facilitate thorough quality assurance (QA), a variety of standardized emission 

summary reports for the periods simulated will be produced. Some examples of the 

standardized reports are contained in the sections below.

As indicated in the prior section, day-specific and external baseline adjustments were 

applied to baseline emission estimates. For the purpose of checking adjustment levels 

for accuracy, “baseline” and “adjusted” emission summary reports will be generated.
81

Inventory corrections will be prioritized based on emissions magnitude, schedule, and 

potential impact on air quality modeling results. As gridded emissions are processed 

and quality assured, suspect or unresolvable issues that may impact air quality model 

performance will be summarized and reported.

4.11.1. Examples of Standard Tabular Summaries

This section contains examples of tabular summaries that will be provided for review.

Domain Totals by Pollutant and Time Period for Baseline and Adjusted Emissions

CO NOx SOx TOG PM NH3 ROG PM10 PM2.5

17,939.63 4,308.18 285.01 7,334.56 4,109.78 762.98 3,620.07 2,472.03 810.70

Totals by Major Category, Pollutant, and Time Period for Baseline and Adjusted 

Emissions

EIC1 DESCRIPTION CO NOx SOx TOG PM NH3 ROG PM10 PM2.5

0 FUEL COMBUSTION 384.18 406.63 48.20 148.62 45.55 5.49 34.17 40.08 37.24

1 WASTE DISPOSAL 2.18 3.02 0.67 1,245.77 1.62 42.56 14.86 0.83 0.73

EIC1 DESCRIPTION CO NOx SOx TOG PM NH3 ROG PM10 PM2.5

2 CLEANING AND SURFACE COATINGS 0.15 0.40 0.04 381.17 0.39 2.13 279.20 0.38 0.36

3 PETROLEUM PROD AND MARKETING 10.08 13.97 58.60 536.56 4.90 1.85 219.60 3.05 2.26

4 INDUSTRIAL PROCESSES 53.52 96.16 31.57 95.55 174.20 9.22 79.44 100.22 51.50

5 SOLVENT EVAPORATION 0.00 0.00 0.00 475.95 0.03 37.45 419.42 0.03 0.03

6 MISCELLANEOUS PROCESSES 2,545.81 156.27 9.64 1,811.66 3,726.68 538.27 300.23 2,173.18 586.03

7 ON-ROAD MOTOR VEHICLES 12,726.85 2,315.33 11.27 1,343.71 74.73 75.25 1,233.16 74.09 57.91

8 OTHER MOBILE SOURCES 2,216.86 1,316.41 125.03 484.40 81.69 0.00 431.80 80.18 74.65

9 NATURAL SOURCES 0.00 0.00 0.00 811.17 0.00 50.76 608.19 0.00 0.00
82

Totals by Summary Category, Pollutant, and Time Period for Baseline and Adjusted 

Emissions

EIC3 DESCRIPTION CO NOX SOX TOG PM NH3 ROG PM10 PM2.5

010 ELECTRIC UTILITIES 56.74 51.52 4.76 30.97 6.82 2.35 4.97 6.35 5.89

020 COGENERATION 49.01 30.87 1.87 17.27 4.43 0.18 4.04 4.03 3.72

030

OIL AND GAS PRODUCTION 

(COMBUSTION)

22.66 45.18 7.44 26.59 2.09 0.10 4.15 2.08 2.08

040

PETROLEUM REFINING 

(COMBUSTION) 10.22 46.03 12.75 3.52 4.26 0.61 1.79 4.06 3.98

050 MANUFACTURING AND INDUSTRIAL 52.77 86.07 14.52 20.28 5.92 1.63 3.96 5.71 5.45

052

FOOD AND AGRICULTURAL 

PROCESSING

111.24 22.60 2.69 7.72 3.02 0.10 6.06 2.94 2.89

060 SERVICE AND COMMERCIAL 71.00 104.86 3.66 35.62 8.31 0.40 6.90 8.24 8.19

099 OTHER (FUEL COMBUSTION) 10.55 19.50 0.50 6.65 10.70 0.11 2.31 6.68 5.05

110 SEWAGE TREATMENT 0.25 0.39 0.28 1.29 0.03 0.25 0.70 0.02 0.02

120 LANDFILLS 0.85 0.67 0.21 1,182.55 0.89 9.78 7.92 0.40 0.35

130 INCINERATORS 1.01 1.77 0.14 0.94 0.23 0.09 0.16 0.11 0.10

140 SOIL REMEDIATION 0.06 0.09 0.03 0.49 0.11 0.00 0.34 0.04 0.03

199 OTHER (WASTE DISPOSAL) 0.01 0.10 0.00 60.49 0.36 32.42 5.74 0.25 0.25

210 LAUNDERING 0.00 0.00 0.00 8.60 0.00 0.00 0.84 0.00 0.00

EIC3 DESCRIPTION CO NOX SOX TOG PM NH3 ROG PM10 PM2.5

220 DEGREASING 0.00 0.00 0.00 178.79 0.00 0.00 99.87 0.00 0.00

230

COATINGS AND RELATED PROCESS 

SOLVENTS 0.11 0.16 0.04 122.45 0.32 0.03 114.08 0.30 0.29

240 PRINTING 0.01 0.05 0.00 25.31 0.05 0.04 25.31 0.05 0.04

250 ADHESIVES AND SEALANTS 0.00 0.00 0.00 35.84 0.01 0.00 31.80 0.01 0.01

299

OTHER (CLEANING AND SURFACE 

COATINGS)

0.03 0.19 0.00 10.17 0.02 2.06 7.30 0.02 0.02

310 OIL AND GAS PRODUCTION 1.91 3.32 0.53 104.11 0.10 0.00 53.90 0.08 0.08

320 PETROLEUM REFINING 6.03 9.85 58.06 49.04 3.99 1.85 38.43 2.54 2.08

330 PETROLEUM MARKETING 2.14 0.80 0.00 382.93 0.81 0.00 126.85 0.43 0.10

399

OTHER (PETROLEUM PROD AND 

MARKETING) 0.00 0.00 0.00 0.47 0.00 0.00 0.42 0.00 0.00

410 CHEMICAL 0.44 1.82 2.69 34.07 5.99 0.25 27.38 5.09 4.71
83

420

FOOD AND AGRICULTURE (Note: 

Skipping some categories from here to 

fit on page...)

2.71 9.60 2.52 23.33 29.67 0.07 21.15 12.05 2.79

499 OTHER (INDUSTRIAL PROCESSES) 10.37 9.31 0.85 22.72 18.20 8.82 18.42 11.70 7.86

510 CONSUMER PRODUCTS 0.00 0.00 0.00 305.34 0.00 0.00 259.30 0.00 0.00

520

ARCHITECTURAL COATINGS AND 

SOLVENTS 0.00 0.00 0.00 111.39 0.00 0.00 108.74 0.00 0.00

530 PESTICIDES/FERTILIZERS 0.00 0.00 0.00 39.41 0.00 37.45 32.38 0.00 0.00

540 ASPHALT PAVING / ROOFING 0.00 0.00 0.00 19.82 0.03 0.00 19.01 0.03 0.03

610 RESIDENTIAL FUEL COMBUSTION 1,741.05 129.11 8.59 274.46 270.85 12.36 120.38 253.79 244.63

620 FARMING OPERATIONS 0.00 0.00 0.00 1,419.61 147.04 467.32 113.57 72.64 17.07

630 CONSTRUCTION AND DEMOLITION 0.00 0.00 0.00 0.00 415.08 0.00 0.00 203.10 20.30

640 PAVED ROAD DUST 0.00 0.00 0.00 0.00 810.83 0.00 0.00 370.71 55.62

645 UNPAVED ROAD DUST 0.00 0.00 0.00 0.00 235.99 0.00 0.00 140.25 14.02

650 FUGITIVE WINDBLOWN DUST 0.00 0.00 0.00 0.00 1,718.35 0.00 0.00 1,016.94 135.06

660 FIRES 10.14 0.24 0.00 1.01 1.17 0.00 0.71 1.15 1.08

670 WASTE BURNING AND DISPOSAL 793.31 26.85 1.05 107.70 92.67 4.64 59.38 90.31 83.67

690 COOKING 0.16 0.00 0.00 8.77 33.40 0.00 6.13 23.38 14.03

699

OTHER (MISCELLANEOUS 

PROCESSES)

1.15 0.07 0.00 0.10 1.31 53.95 0.07 0.92 0.55

EIC3 DESCRIPTION CO NOX SOX TOG PM NH3 ROG PM10 PM2.5

700 On-Road Motor Vehicles 12,726.85 2,315.33 11.27 1,343.71 74.73 0.00 1,233.16 74.09 57.91

710 LIGHT DUTY PASSENGER (LDA) 0.00 0.00 0.00 0.00 0.00 41.86 0.00 0.00 0.00

722 LIGHT DUTY TRUCKS - 1 (LDT1) 0.00 0.00 0.00 0.00 0.00 9.32 0.00 0.00 0.00

723 LIGHT DUTY TRUCKS - 2 (LDT2) 0.00 0.00 0.00 0.00 0.00 15.73 0.00 0.00 0.00

724 MEDIUM DUTY TRUCKS (MDV) 0.00 0.00 0.00 0.00 0.00 5.82 0.00 0.00 0.00

732

LIGHT HEAVY DUTY GAS TRUCKS - 1 

(LHDV1)

0.00 0.00 0.00 0.00 0.00 1.20 0.00 0.00 0.00

733

LIGHT HEAVY DUTY GAS TRUCKS - 2 

(LHDV2) 0.00 0.00 0.00 0.00 0.00 0.38 0.00 0.00 0.00

734

MEDIUM HEAVY DUTY GAS TRUCKS 

(MHDV)

0.00 0.00 0.00 0.00 0.00 0.26 0.00 0.00 0.00

736

HEAVY HEAVY DUTY GAS TRUCKS 

(HHDV) 0.00 0.00 0.00 0.00 0.00 0.14 0.00 0.00 0.00
84

742

LT HEAVY DUTY DIESEL TRUCKS - 1 

(LHDV1)

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

743

LT HEAVY DUTY DIESEL TRUCKS - 2 

(LHDV2) 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00

744

MED HEAVY DUTY DIESEL TRUCKS 

(MHDV)

0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00

746

HEAVY HEAVY DUTY DIESEL 

TRUCKS (HHDV) 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00

750 MOTORCYCLES (MCY) 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00

760

HEAVY DUTY DIESEL URBAN BUSES 

(UB) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

762

HEAVY DUTY GAS URBAN BUSES 

(UB)

0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00

770 SCHOOL BUSES (SB) 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00

776 OTHER DIESEL BUSES 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00

780 MOTOR HOMES (MH) 0.00 0.00 0.00 0.00 0.00 0.26 0.00 0.00 0.00

810 AIRCRAFT 249.71 54.02 2.81 40.28 9.03 0.00 35.91 8.81 8.72

820 TRAINS 28.90 194.16 8.05 13.29 4.40 0.00 11.12 4.40 4.05

830 SHIPS AND COMMERCIAL BOATS 38.84 276.79 109.70 17.62 20.28 0.00 14.77 19.62 18.94

840 RECREATIONAL BOATS 126.38 3.82 0.01 36.92 1.39 0.00 34.86 1.25 0.95

EIC3 DESCRIPTION CO NOX SOX TOG PM NH3 ROG PM10 PM2.5

850 OFF-ROAD RECREATIONAL 

VEHICLES

135.10 1.08 0.25 41.00 0.80 0.00 38.28 0.72 0.54

860 OFF-ROAD EQUIPMENT 1,536.69 680.34 3.49 259.95 39.32 0.00 225.28 38.92 35.52

870 FARM EQUIPMENT 101.24 106.20 0.72 24.87 6.47 0.00 21.29 6.46 5.93

890 FUEL STORAGE AND HANDLING 0.00 0.00 0.00 50.46 0.00 0.00 50.28 0.00 0.00

910 BIOGENIC SOURCES 0.00 0.00 0.00 709.42 0.00 14.54 578.69 0.00 0.00

920 GEOGENIC SOURCES 0.00 0.00 0.00 101.75 0.00 36.22 29.50 0.00 0.00

4.11.2. Spatial Plots

Spatial plots are useful to ensure that emissions are distributed correctly into each grid 

cell. 
85

Plots by Pollutant and Time Period for Baseline and Adjusted Emissions

4.11.3. Time Series Plots

Time series plots are useful to ensure that emissions are distributed correctly in time 

across the modeling period. 
86

Weekly Time-Series Plots of Emissions by Year
87

Hourly Time-Series Plots of Emissions by Week
88

5. Models and Inputs

5.1. Rationale for the Selection of Models

5.1.1. Meteorology Model

Meteorological model selection is based on a need to accurately simulate the synoptic 

and mesoscale meteorological features exhibited during the selected episodic periods. 

The main difficulties in accomplishing this are California’s extremely complex terrain and 

its diverse climate. It is desirable that atmospheric modeling adequately represent 

essential meteorological features, such as wind flows, ambient temperature variation, 

evolution of the boundary layer, etc., to properly characterize the meteorological 

component of photochemical modeling.

In the past, the ARB has applied prognostic, diagnostic, and objective models to 

prepare meteorological fields for photochemical modeling. There are various numerical 

models that are used by the scientific community to study the meteorological 

characteristics of an air pollution episode. For this SIP, the models under consideration 

for meteorological modeling are: 

 Mesoscale Meteorological Model Version 5 (MM5) (Grell et al, 1994), and

 Weather and Research Forecasting Model (WRF) (Skamarock et al, 2005).

MM5 is a mesoscale, limited area, non-hydrostatic numerical model developed by Penn 

State and the National Center for Atmospheric Research (NCAR). It uses a terrainfollowing, Lambert Conformal, sigma coordinate system. MM5 allows users to study the 

atmospheric motions at small scales by explicitly treating the effects of convective 

motions on atmospheric circulations. It has been improved on an ongoing basis over 

the last two decades by contributions from a broad scientific community and has been 

maintained by NCAR along with necessary meteorological and geographical input data. 

Based on the complexity of terrain in northern and central California, the MM5 model 

represents an appropriate tool for resolving dynamics and thermodynamics using 

nesting capabilities. The ARB has also been using the MM5 model over the last two 
89

decades, since it has been widely used and tested for various meteorological regimes 

over the world and has been supported by NCAR. NCAR terminated model 

development for MM5 in October 2006 and the code was frozen at the minor version of 

V3-7-4.

Since then NCAR has devoted its resources to the development of the WRF model, 

which was designed to be the replacement for MM5. The WRF model is being 

continually updated, but ARB’s experience with the model is limited compared to that 

with MM5. The preliminary WRF fields produced by ARB have not shown any 

significant improvement over those from MM5.

Based on the long history of using MM5 by ARB and stakeholder groups in California in 

regulatory modeling, the MM5 numerical model was chosen to generate meteorological 

fields for SIP modeling. A more detailed description of prognostic meteorology models 

and their known limitations in the complex terrain of California, see Section 6.1.

ARB will continue to evaluate the WRF model for future SIP modeling and potentially as 

a corroborative tool to MM5 for this SIP. More details on this effort are provided in 

Section 9.3.

5.1.2. Air Quality Model

U.S. EPA guidance requires several factors to be considered as criteria for choosing a 

qualifying air quality model to support the attainment demonstration. These criteria 

include: (1) documentation and past track record of candidate models in similar 

applications; (2) advanced science and technical features available in the model and/or 

modeling system; (3) experience of staff and available contractors; (4) required time and 

resources versus available time and resources; and (5) in the case of regional 

applications, consistency with regional models applied in adjacent regions (U.S. EPA, 

2007). For the PM2.5 modeled attainment test, a grid-based photochemical model is 

necessary to offer the best available representation of important atmospheric processes 

and the ability to analyze the impacts of proposed emission controls on PM2.5 

concentrations.
90

The Community Multiscale Air Quality (CMAQ) Modeling System has been selected for 

modeling PM2.5 in the SJV. The CMAQ model, a state-of-the-science “one-atmosphere” 

modeling system developed by U.S. EPA, was designed for applications ranging from 

regulatory and policy analysis to understanding of the atmospheric chemistry and 

physics. It is a three-dimensional Eulerian modeling system that simulates ozone, 

particulate matter, toxic air pollutants, visibility, and acidic pollutant species throughout 

the troposphere (UNC, 2010). The CMAQ model has undergone peer review every few 

years and was found to be state of the science (Aiyyer et al., 2007). The CMAQ model 

is regularly updated to incorporate new mechanisms, algorithms, and data as they 

become available in the scientific literature (e.g., Foley, et al., 2010). In addition, the 

CMAQ model is well documented in terms of its underlying scientific algorithms as well 

as guidance on operational uses (e.g., Binkowski and Roselle, 2003; Byun and Ching, 

1999; Byun and Schere, 2006; Carlton et al., 2010; Foley et al., 2010; Kelly, et al., 

2010a; UNC, 2010). 

The CMAQ model was the regional air quality model used for the 2008 SJV annual 

PM2.5 SIP. A number of previous studies have also used the CMAQ model to study 

ozone and PM2.5 in the SJV (e.g., Jin et al., 2008, 2010; Kelly et al., 2010b; Liang and 

Kaduwela, 2005; Livingstone, et al., 2009; Pun et al, 2009; Tonse et al., 2008; 

Vijayaraghavan et al., 2006; Zhang et al., 2010). The CMAQ model has also been used 

for regulatory analysis for many of U.S. EPA’s rules, such as the Clean Air Interstate 

Rule (U.S. EPA, 2005) and Light-duty and Heavy-duty Greenhouse Gas Emissions 

Standards (U.S. EPA, 2010, 2011a). There are numerous applications of the CMAQ 

model in the U.S. and in the world (e.g., Appel, et al., 2007, 2008; Civerolo et al., 2010; 

Eder and Yu, 2006; Hogrefe et al., 2004; Lin et al., 2008, 2009; Marmur et al., 2006; 

O’Neill, et al., 2006; Philips and Finkelstein, 2006; Sokhi et al., 2006; Smyth et al., 2006; 

Tong et al., 2006; Wilczak et al., 2009; Zhang et al., 2004, 2006). Staff at CARB has

developed expertise in applying the CMAQ model, since it has been used at CARB for 

over a decade. In addition, technical support for the CMAQ model is available from the 
91

Community Modeling and Analysis System (CMAS) Center 

(http://www.cmascenter.org/) established by the U.S. EPA.

The most recent version, CMAQv4.7.1 (Foley et al., 2010) will be used. While U.S. EPA 

released the CMAQ version 5.0 in October 2011, that release came too late for current 

modeling efforts.

5.2. Model Setup and Inputs

5.2.1. Meteorology Model (modeling domains, horizontal and vertical 

resolution, physics options, regional reanalysis data, etc.) 

The MM5 meteorological modeling domain consists of three nested grids, of 36 km, 

12 km and 4 km uniform, horizontal grid spacing (illustrated in Figure 5.1). The purpose 

of the coarse, 36 km grid (D01) is to provide synoptic-scale conditions to all three grids, 

while the purpose of the 12 km grid (D02) is to provide input data to the 4 km grid (D03). 

The D01 grid is centered at 37 N x 120.5 W while the two inner grids, D02 and D03, are 

placed within the coarser grid such that they are not too close to the lateral boundaries. 

The D01 grid consists of 70 x 70 grid cells. The D02 grid consists of 132 x 132 grid 

cells and the D03 grid consists of 327 x 297 grid cells having an origin at -696 km x -576 

km (Lambert Conformal projection). The first two coarse grids were run simultaneously, 

and the D03 grid was run independently using the output of its coarser, parent D02 grid 

as input. The D03 grid is intended to resolve the fine details of atmospheric motion and 

is used to feed the air quality modeling simulations. The vertical layer structure has 30 

layers, as shown in Table 5.1. The physics options are shown in Table 5.2.

The initial and boundary conditions (IC/BC) for MM5 were prepared based on 3-D 

analyses of the NCEP/NCAR Reanalysis Project (NNRP) that is archived at NCAR. 

These data are archived from global simulations and have a 209 km horizontal 

resolution. Initial conditions to MM5 were updated at 6-hour intervals for the 36 and 

12 km grids. In addition, surface and upper air synoptic observations obtained from 

NCEP are also used to further refine the IC/BCs.
92

The MM5 model was nudged toward observed meteorological conditions by using the 

analysis nudging option of the Four Dimensional Data Analysis (FDDA) for the 36 and 

12 km grids only. Input conditions for the 4 km grid were obtained from the output of the 

12 km grid, and the observational nudging option of FDDA was used to enhance these 

input conditions. Only wind measurements were used for observational nudging.

Figure 5-1: The three nested grids for the MM5 model (D01 36km; D02 12km; and D03 

4km).
93

Table 5-1: MM5 30 Vertical Layer Configuration.

Layer No. Height (m) Layer Thickness (m)

30 15674 998

29 14676 982

28 13694 976

27 12718 970

26 11748 972

25 10776 973

24 9803 979

23 8824 983

22 7841 994

21 6847 1002

20 5845 972

19 4873 818

18 4055 687

17 3368 577

16 2791 484

15 2307 407

14 1900 339

13 1561 285

12 1276 238

11 1038 199

10 839 166

9 673 139

8 534 115

7 419 97

6 322 81
94

5 241 67

4 174 56

3 118 47

2 71 39

1 32 32

0 0 0

Table 5-2: MM5 Physics Options.

Physics Option D01 D02 D03

Cumulus Parameterization Grell Grell None

Planetary Boundary Layer Scheme Gayno-Seaman Gayno-Seaman Gayno-Seaman

Explicit Moisture Scheme Dudhia Simple Ice Dudhia Simple Ice Dudhia Simple Ice

Radiation Scheme RRTM RRTM RRTM

Soil Temperature Model 5-layer soil slab 5-layer soil slab 5-layer soil slab

5.2.2. Air Quality Model (modeling domains, horizontal and vertical 

resolution, chemical mechanisms, PM routines, initial and boundary 

conditions, etc.) 

The principle determinants of the extent of the modeling domain are the nature of the 

PM2.5 problem and the scale of the emissions that impact the nonattainment area. 

Isolated nonattainment areas that are not impacted by regional transport and its 

precursors may be able to use a relatively small domain (U.S. EPA, 2007). Figure 5.2 

shows modeling domains used by ARB. The two modeling domains that are proposed 

for this work are shown in blue (12 km coarse domain) and magenta (4 km nested 

domain). The coarse domain (blue) includes 107x97 lateral 12 km grid cells for each 
95

vertical layer. This domain extends from the Pacific Ocean in the west to the Eastern 

Nevada in the east and runs from the U.S.-Mexico border in the south to the CaliforniaOregon border in the north. The nested domain (magenta) covers Central California 

with 192x192 lateral 4 km grid cells. The domain is based on the Lambert Conformal 

Conic projection with reference longitude at -120.5°W, reference longitude at 37°N, and 

two standard parallels at 30°N and 60°N, respectively. 

Figure 5-2: Modeling domains used by ARB

For the coarse portions of nested regional grids, U.S. EPA guidance suggests a grid cell 

size of 12 km if feasible but not larger than 36 km. For the fine scale portions of nested 

regional grids, it is desirable to use grid cells about 4 km (U.S. EPA, 2007). Our 

selection of modeling domains is consistent with the guidance. U.S. EPA guidance 

does not require a minimum number of vertical layers for an attainment demonstration, 

although typical applications of “one- atmosphere” models (with the model top at 
96

100 mb) employ 12 to 21 vertical layers. For the present SIP, 15 vertical layers will be 

used in the CMAQ model, extending from the surface to 100 mb, consistent with the 

number of vertical layers used for the 2008 SJV PM2.5 SIP. The vertical structure is 

based on the sigma-pressure coordinate, with the layers separated at 1.0, 0.9958, 

0.9907, 0.9846, 0.9774, 0.9688, 0.9585, 0.9463, 0.9319, 0.9148, 0.8946, 0.7733, 

0.6254, 0.293, 0.0788, and 0.0. This ensures that the majority of the layers are in the 

planetary boundary layer.

The small black domain in the center of Figure 5-2 is the air quality modeling domain 

used for the previous annual PM2.5 SIP which is now approved by the U.S. EPA (76 FR 

41338; 76 FR 69896). The originally proposed 4 km domain is ~5 times larger than the 

previously used 4 km domain. However, preliminary modeling for the current SIP, in 

combination with the anticipated number of modeling runs that will be necessary to 

complete the SIP modeling, have demonstrated the infeasiblity of using the larger 4 km 

(magenta) domain with available resources. Therefore, we will use the small 4 km 

(black) domain which has already been approved by the U.S. EPA (76 FR 41338; 76 FR 

69896). 

Table 5.3 shows the CMAQv4.7.1 configuration that will be used to model PM2.5 in the 

SJV. The same configuration will be used for all simulations for the base, reference, 

and future years. CMAQv4.7.1 will be compiled using the Portland Group FORTRAN 

Compiler version 10.9. 

Table 5-3: CMAQv4.7.1 Schemes used for Current Simulations.

Processes Scheme

Horizontal advection PPM (piecewise parabolic method)

Vertical advection PPM (piecewise parabolic method)

Horizontal diffusion Multi-scale

Vertical diffusion Eddy

Gas-phase chemical 

mechanism SAPRC99

Chemical solver EBI
97

Aerosol module Aero5

Cloud module ACM_AE5

Photolysis rate Table Generated by the JPROC program

In order to simulate the complex mixture of PM2.5 species in the SJV, the SAPRC99 

mechanism coupled with the CMAQ model aerosol code version 5 and aqueous phase 

chemistry (AE5-AQ) has been chosen for this application. SAPRC99, developed by Dr. 

William Carter at the University of California, Riverside, is a detailed mechanism 

describing the gas-phase reactions of volatile organic compounds (VOCs) and oxides of 

nitrogen (NOx) (Carter, 2000). It is a well-known chemical mechanism and has been 

used widely in California and the U.S. (e.g., Hakami, et al., 2004a, 2004b; Liang and 

Kaduwela, 2005; Lin et al., 2005; Jackson, et al., 2006; Napelenok, 2006; Dennis et al., 

2008; Jin et al., 2008, 2010; Lane et al., 2008; Tonse et al., 2008; Ying et al., 2008; 

Livingstone et al., 2009; Pun et al., 2009; Kelly, et al., 2010b; Zhang et al., 2010; Zhang 

and Ying, 2011). 

CARB established the Reactivity Scientific Advisory Committee (RSAC) in April 1996. 

RSAC is a group of independent scientists who make non-binding recommendations on 

the science related to the reactivity of VOCs. RSAC consists of the following members: 

Drs. John Seinfeld (Chair, California Institute of Technology), Roger Atkinson 

(University of California at Riverside), Jack Calvert (National Center for Atmospheric 

Research), Harvey Jeffries (University of North Carolina at Chapel Hill), Jana Milford 

(University of Colorado at Boulder), and Armistead Russell (Georgia Institute of 

Technology). In 1998, RSAC recommended that the SAPRC99 mechanism undergo a 

scientific review. Following RSAC’s recommendation, CARB contracted Dr. William R. 

Stockwell in 1999 to conduct a review of the SAPRC99 mechanism, its documentation, 

and the Maximum Incremental Reactivity scale derived from SAPRC99. Stockwell 

(1999) compared the chemical kinetic data used in the SAPRC99 mechanism with 

values from standard kinetic databases (e.g., Atkinson et al., 1994, 1997; DeMore et al., 

1997) and the most recent literature available at the time. The kinetic parameters 
98

checked included the reactions, rate constants, product yields, and lumping methods. 

Stockwell’s (1999) comments led to the revision of the mechanism and identification of 

outstanding issues to be resolved with further experimental studies. Stockwell (1999) 

concluded that SAPRC99 reflected the best available science at its completion date, 

and RSAC approved both the SAPRC99 peer review and the mechanism in October 

1999. They also recommended that the SAPRC family of mechanisms be used for 

regulatory photochemical modeling activities in California.

The 2008 SJV PM2.5 SIP also used the SAPRC99 mechanism. While a newer version, 

SAPRC07 (Azzi et al., 2010; Carter, 2010a,b; Derwent et al., 2010;Mollner et al., 2010; 

Cai et al., 2011a,b), will be incorporated into CMAQv5.0, the timeline of the official 

release of SAPRC07 and CMAQv5.0 is not consistent with the current modeling effort. 

AE5-AQ, the newest aerosol and aqueous-phase chemistry code in CMAQv4.7.1, when 

coupled with a gas phase mechanism, simulates the formation and evaporation of 

aerosol and the evolution of the aerosol size distribution (Foley et al., 2010). AE5 

includes a comprehensive yet computationally efficient inorganic thermodynamic model 

ISORROPIA to simulate the physical state and chemical composition of inorganic 

atmospheric aerosols (Nenes, et al., 1998). ISORROPIA has been proven to be the 

model of choice for many three-dimensional air quality models (Yu et al., 2005). AE5 

also features an improved secondary organic aerosol (SOA) module with up-to-date 

scientific information (Carlton et al., 2010). In addition to SOA formation from more 

traditional aromatic compounds and biogenic monoterpene species, the SOA module in 

AE5 incorporates SOA formation from benzene, isoprene, and sequiterpenes, in-cloud 

SOA production from glyoxal and methylglyoxal, particle-phase oligomerization, acid 

enhancement of isoprene SOA, and NOx dependent aromatic SOA yields (Carlton et 

al., 2010).

CMAQv4.7.1 offers two advection schemes: the piecewise parabolic method (PPM) and 

the Yamartino scheme. PPM is based on the finite-volume subgrid definition of the 

advected scalar. It is implemented in a global mass-conserving scheme in the CMAQ 

model (UNC, 2010). We chose the PPM scheme because the Yamartino scheme leads 
99

to unrealistic O3 mixing ratio predictions in the mountainous areas of Central California 

during winter. For example, Figure 5-3 shows an example of 1-hour O3 mixing ratio 

predictions during January 2007 simulated by CMAQv4.7.1 with the Yamartino 

advection scheme. 1-hour O3 mixing ratios greater than 100 ppb were predicted for 

some mountainous areas. This is not supported by observed O3 mixing ratios, which 

only range up to 50 ppb in Yosemite and Sequoia National Parks in January 2007. The 

excessive O3 prediction is due to the advection scheme that brings the upper 

troposphere O3 down to the surface levels. PM2.5 predictions from the PPM and the 

Yamartino schemes are comparable. For example, Figure 5-4 shows that the difference 

in monthly average PM2.5 predictions for January 2007 using CMAQv4.7.1 with the PPM 

and Yamartino schemes is between -0.4 to 1.0 μg/m3.

Figure 5-3: 1-hour O3 mixing ratio at 3 am of Jan 10, 2007 (UTC) predicted by 

CMAQv4.7.1 with the Yamartino advection scheme.
100

Figure 5-4: Difference in monthly average PM2.5 predictions for January 2007 

simulated using CMAQv4.7.1 with the PPM and Yamartino schemes.

U.S. EPA guidance recommends using a “ramp-up” period by beginning a simulation 5-

10 days prior to the period of interest for modeling PM2.5 (U.S. EPA, 2007). Instead of 

running the CMAQ model sequentially from the beginning to the end of the simulation 

year, we simulate each month in parallel. For each month, we run seven spin-up days 

prior to the beginning of each month to generate the initial conditions for the coarse 

domain. We then use the output from the coarse modeling domain to specify the initial 

conditions for the nested domain because the nested domain simulation starts after the 

beginning of the simulation for the outer grid, consistent with U.S. EPA guidance.

In recent years, the use of global chemical transport model (CTM) outputs as boundary 

conditions (BCs) in regional CTM applications has become increasingly common 

(Hogrefe et al., 2011; Chen et al., 2008; Lin et al., 2010; Lam and Fu, 2009; Lee et al., 

2011), and has been shown to improve model performance in many cases (Tong and 
101

Mauzerall, 2006; Tang et al., 2007; Tang et al., 2009; Borge et al., 2010; Appel et al., 

2007). The advantage of using global CTM model outputs as opposed to fixed 

climatological-average BCs is that the global CTM derived BCs capture spatial, diurnal, 

and seasonal variability, as well as provide a set of chemically consistent pollutant 

concentrations. The Model for Ozone And Related chemical Tracers (MOZART; 

Emmons et al., 2010a) is a global CTM that has been widely used for such applications. 

MOZART has been extensively peer-reviewed and applied in a range of studies 

including global change impacts on air quality (e.g., Wiedinmyer et al., 2006; Brasseur 

et al., 2006; Huang et al., 2008; Avise et al., 2009), long-range transport of pollution 

(e.g., Liu et al., 2005; Liu an Mauzerall, 2007; Pfister et al., 2010), and atmospheric 

chemistry/air quality studies (e.g., Emmons et al., 2010b; Pfister et al., 2008; Apel et al., 

2010; Fiore et al., 2005).

The MOZART model is a comprehensive global model for simulating atmospheric 

composition including both gases and bulk aerosols (Emmons et al., 2010a). It was 

developed by the National Center for Atmospheric Research, the Max-Planck-Institute 

for Meteorology (in Germany), and the Geophysical Fluid Dynamics Laboratory of the 

National Oceanic and Atmospheric Administration, and is widely used in the scientific 

community. In addition to inorganic gases and VOCs, boundary conditions were 

extracted for aerosol species including elemental carbon, organic matter, sulfate, soil 

and nitrate. 

Figure 5-5: Comparison of MOZART (red) simulated CO (left), ozone (center), and 

PAN (right) to observations (black) along the DC-8 flight track. Shown are mean (filled 
102

symbol), median (open symbols), 10th and 90th percentiles (bars) and extremes (lines). 

The number of data points per 1-km wide altitude bin is shown next to the graphs. 

Adapted from Figure 2 in Pfister et al. (2011).

In particular, MOZART version 4 (MOZART-4) was recently used in a study 

characterizing summertime air masses entering California from the Pacific Ocean 

(Pfister et al., 2011). In their work, Pfister et al. (2011) compared MOZART-4 simulation 

results to measurements of CO, ozone, and PAN made off the California coast during 

the ARCTAS-CARB airborne field campaign (Jacob et al., 2010) and showed good 

agreement between the observations and model results (see Figure 5-5).

Boundary conditions for the outer 12-km modeling domain were derived from 

MOZART4-GEOS5 simulations by Louisa Emmons (NCAR) for the year 2007; available 

for download at http://www.acd.ucar.edu/wrf-chem/mozart.shtml. These simulations are 

similar to those of Emmons et al. (2010), but with updated meteorological fields. 

Boundary condition data was extracted from the MOZART-4 output and processed to 

CMAQ model ready format using computer code developed by ARB staff, which has 

been used to generate BCs for previous air quality studies (Chen et al., 2008; Avise et 

al., 2009; Chen et al., 2009a,b; Cai et al., 2011; Kelly et al., 2011). The final BCs 

represent day-specific concentrations, which vary in both space (horizontal and vertical) 

and time (every six hours).

The boundary conditions for the nested 4 km domain were extracted from the output for 

the coarse 12 km domain simulation using the BCON program in the CMAQ modeling 

system. The boundary conditions for the coarse domain for the reference year will be 

used for future years as well, consistent with U.S. EPA guidance.

Overall, using a 4 km nested domain within the 12 km coarse domain will reduce the 

computational burden without compromising the accuracy of the modeling results when 

compared to a simulation using a 4 km grid for the entire outer domain. Figure 5-6

shows the difference in average PM2.5 prediction for January 2007 between a simulation 
103

with the nested domain and a simulation using a 4km grid for the entire outer domain. 

The discrepancy in monthly average PM2.5 predictions is extremely small (± 0.1 μg/m3) 

for the areas of interest.

Figure 5-6: Difference in average PM2.5 prediction for January 2007 between 

simulation with the nested domain and simulation using 4km grid for the entire coarse 

domain.

The dry and wet deposition (also known as lost processes) of pollutants (both gaseous 

and particulate) is explicitly included in the continuity equation solved by the CMAQ 

model. The time-varying species-dependent dry deposition velocities are calculated in 

the Meteorology-Chemistry Interface Processor (MCIP) and are passed along to the 

CAMQ model for the calculation of dry deposition fluxes. The wet deposition fluxes due 

to rainfall are calculated in the cloud module of the CMAQ model. Dry and wet 

deposition estimates are then saved in separate output files.
104

5.2.3. Construction of the Simulated PM2.5 Mass

The CMAQ model does not output PM2.5 total mass concentrations. Instead, it outputs 

concentrations for individual aerosol components in each aerosol mode (e.g., sulfate in 

the accumulation mode, nitrate in the coarse mode, etc.). These outputs require 

additional processing to generate predictions for PM2.5 mass. For this effort, we choose 

to use the hourly average model species concentrations saved in the ACONC file as 

modeled concentrations (UNC, 2010). We will use the “combine” program in the CMAQ 

modeling system to generate the predictions for total PM2.5 mass as well as PM2.5

components that can be compared with observations. 

5.2.4. Quality Assurance of Model Inputs

In developing the IC/BCs and FDDA datasets, quality control is performed on all

associated meteorological data. Generally, all surface and upper air data are plotted in 

space and time to identify extreme values that are suspected to be “outliers”. Data 

points are also compared to other, similar surrounding data points to determine whether 

there are any large relative discrepancies. If a scientifically plausible reason for the 

occurrence of suspected outliers is not known, the outlier data points are flagged as 

invalid and not used in the modeling analyses.
105

6. Meteorological Model Performance Evaluation

6.1. Known Performance Issues of Meteorological Models in the 

Complex Terrain of California and Current Attempts to Improve 

Performance

The San Joaquin Valley is bordered on the west by the Coastal Mountain Range and on 

the east by the Sierra Nevada range. These ranges converge at the southern end of 

the basin at the Tehachapi Mountains. West of the Coastal Mountain Range is the 

Pacific Ocean. The SJV is considered to be the most fertile desert in the world. The 

ocean-land interface, mountain-valley topography, and the drastic temperature changes 

make the SJV one of the most challenging areas in the country to simulate using 

meteorological models. 

One can generate meteorological fields using two different methods. The first is known 

as the diagnostic method where observed fields are interpolated. These fields 

represent the actual meteorological state of the atmosphere where the measurements 

were made. However, such measurements are sparse and often made at the surface 

level. Some monitors may have limited spatial representation due to their locations 

(e.g., in canyons). These diagnostic meteorological fields do not have dynamic 

consistency among variables (Seaman, 2000) and may not have all the variables 

required by modern air quality models. However, they have been shown to provide 

better air-quality model performance during the summer (Jackson et al., 2006) and 

winter (Hu et al., 2010) in SJV. This may be due to their ability to better represent the 

wind speeds and temperatures.

When a dense network of representative meteorological measurements are not 

available, one can use a set of non-linear partial differential equations, known as 

governing equations, which describe the time evolution of the atmospheric system 

through space and time. The governing equations are comprised of the equations of 

conservation of mass, motion, heat, and water (Pielke, 1984). Meteorological models 

that integrate the set of governing equations through space-time are known as 
106

prognostic models. There is a long history of prognostic meteorological model 

applications in the SJV (Seaman, Stauffer, and Lario-Gibbs, 1995; Stauffer et al., 2000; 

Tanrikulu et al., 2000; Jackson et al., 2006; Bao et al., 2008; Livingstone et al., 2009; 

Michelson et al., 2010; Jin et al., 2010; Hu at al., 2010).

The integration of the governing equations requires simplifying assumptions that lend 

them to numerical integrations methods. These simplifying assumptions can lead to two 

undesirable consequences. First, they may cause the simulated solution to stray from 

the ideal solution. To minimize this, four-dimensional data assimilation (FDDA) 

techniques were developed. While FDDA is known to steer the simulated solution 

towards the measured fields, the momentum redistribution within the model causes 

spurious features where no measurements are available. While FDDA is not 

considered to be a panacea, it is an operational necessity to develop meteorological 

fields that are accurate enough for the operation of air quality models. 

The second undesirable consequence is due to the complex terrain of California itself. 

The centered finite difference scheme used in prognostic models works well when the 

terrain features are smooth and continuous. However, the SJV is bounded by three 

steep and rugged mountain ranges. The elevation can change by tens to hundreds of 

meters in one 4 km grid cell. The Coastal Range on the west is near the ocean-land 

interface which is also difficult to simulate. This makes the terrain in California complex 

compared to other parts of the country where the application of prognostic models have 

been more successful. To overcome this difficulty, the grid sizes were reduced from 

4 km to 1.33 km as a test. The minor improvements in the fine-scale meteorological 

fields did not justify the nine fold increase in the computational time. Another option is 

to investigate the effect of using different model options, especially those related to subgrid-scale processes. This is being done now in collaboration with Professor Robert 

Fovell of the University of California at Los Angeles with funding from the San Joaquin 

Valley Study Agency.
107

Figure 6-1: Terrain height changes along with counties and major rivers and lakes in 

California (http://geology.com/state-map/california.shtml).

6.2. Ambient Data Base and Quality of Data

The Air Quality and Meteorological Information System (AQMIS) is a web-based source 

for real-time and official air quality and meteorological data 
108

(www.arb.ca.gov/airqualitytoday/). This database contains 1969-2011 meteorological 

data (partial months for 2011). The data until the end of 2010 are quality assured and 

deemed official. The air quality data from 1980 to 2009 are also available on a DVD 

and at http://www.arb.ca.gov/aqd/aqdcd/aqdcd.htm. In addition ARB also has qualityassured upper-air meteorological data obtained using balloons, aircraft, and profilers.

6.3. Model Performance Evaluation Procedures and Metrics

While there are several U.S. EPA approved meteorological models that can be used for 

SIP applications, the MM5 and WRF models have been used most frequently. For the 

reasons provided in Section 5.1.1, the MM5 model will be used here to demonstrate 

model performance for the year 2007. A comparison between MM5 and WRF will be

provided for the months of July and December to demonstrate the model performance 

differences between the two models.

6.3.1. Statistical Evaluation

Statistical analyses will be performed to evaluate how well the MM5 model captured the 

overall structure of the observed atmosphere during the 12-month simulation period, 

using wind speed, wind direction, and temperature. Since observed moisture data are 

very scarce, relative humidity or mixing ratio will not be used in these comparisons. It is 

quite common to see, especially in such a long numerical simulation period, that 

observed statistical characteristics of atmospheric flow may be captured well by the 

model during a certain time period and/or within some sub-domain while the agreement 

between the model and observations may not be reasonably good at other times and/or 

locations. As a result, the very first sign that we look for in model results is whether the 

model can capture the overall characteristics of the atmosphere in a statistical sense 

during the entire simulated period and within the entire domain. Then, the same 

statistical calculations will be repeated within each subregion to find out in which 

subregions model predictions are good or acceptable and which subregions predictions 

are not acceptable, so that the reason for weak model performance issues in a

subregion can be investigated.
109

For this purpose, the performance of the MM5 model against observations will be 

evaluated using the METSTAT analysis tool (Emery et al, 2001). The model output and 

observations for all 12 months in 2007 will be read, and data points at each 

observational site for wind speed, wind direction, temperature, and moisture data will be 

extracted. Then, the following values will be calculated: Mean values of observations 

and model estimates, bias error (BE), gross error (GE), normalized mean bias (NMB),

root mean square error (RMSE), and the index of agreement (IOA) when applicable.

The mathematical expressions for these quantities are:

,

N

(Model Obs)

BE

N

1

 



,

N

| Model Obs |

GE

N

1

 



100%,

Obs

(Model Obs)

NMB N

1

N

1 









,

N

(Model Obs)

RMSE

1/2 N

1

2































 

,





  



  N

1

2

N

1

2

(Model Obs) (Model Obs)

(Model Obs)

IOA 1

where, “Model” is the simulated concentrations, “Obs” is the observed value, and N is 

the number of observations. The model performance expectations are:
110

Wind Speed RMSE ≤ 2 m/s

Bias < ±0.5 m/s

IOA ±0.6

Wind Direction Gross Error: ≤30 deg

Bias ≤ ±10 deg 

Temperature Gross Error ≤ 2 K

Bias < ±0.5 K

IOA ±0.8

Humidity Gross Error ≤ 2 g/kg

Bias < ±1 g/kg

IOA ±0.6

These values will be tabulated and plotted for the entire domain as well as eight 

subregions (the Mountain Counties; North Central Coast; South Central Coast; San 

Francisco Bay Area; north, central, and southern San Joaquin Valley; and the 

Sacramento Valley) to obtain an overall understanding of model performance within 

each subregion. Then, model results of the u and v-components of the wind and 

temperature will be plotted against observations at each station to see the degree of 

agreement visually, as well.

Another way to quantify the agreement between the simulated and observed quantities 

is to examine their frequency distributions. Model results and observations of u and vcomponents of the wind and temperature will be accumulated into several bins and a 

frequency distribution of each variable will be plotted. The observed and predicted 

frequency distribution indicates the dominant bins or categories of a particular variable 

and how the model prediction compares to the observed frequency distribution.

Time-history plots reveal information that is not readily apparent from the 

aforementioned analyses. Thus, a direct comparison of model results using temporal 

variation of wind speed, wind direction, and temperature at each station, hour-by-hour, 
111

for each week in every month will be conducted to study the model performance much 

more closely than can be done using statistical analyses. Due to the limited availability 

of continuous hourly relative humidity measurements compared to other meteorological 

variables, hourly comparison of relative humidity will not be performed. Based on our 

previous experience with meteorological simulations in California, we expect the 

analysis to show that wind speed is overestimated at some stations while the difference 

is small at others. The diurnal variations of temperature and wind direction at most 

stations would be captured reasonably well. However, we expect the model to 

underestimate the larger magnitudes of temperature during the day and smaller 

magnitudes at night.

6.3.2. Phenomenological Evaluation

One possible performance evaluation technique is to examine the meteorological 

observations in relation to ambient air quality values, to determine the relationships

between air quality and key meteorological variables. As indicated above, we will 

examine the simulated results to see if these relationships are also evident in simulated 

meteorological variables and air quality. This analysis will be conducted at the 

station/region level.

Another possibility is to generate the geopotential height charts at 500 and 850 mb 

using the simulated results and to compare them to the standard charts. This will reveal 

if the large-scale weather systems at those pressure levels were adequately simulated 

by the regional prognostic meteorology model.

Another similar approach is to identify the larger-scale meteorological conditions 

associated with air quality events using the NCEP Reanalysis dataset. We plan to

examine the simulated meteorological fields to see if those large-scale meteorological 

conditions were accurately simulated. We will then examine if the relationships 

observed in the NCEP reanalysis were present in the simulated data sets.

Trajectory analyses can be used estimate the area of influence of a monitor using both 

simulated and observed wind data. In the SJV, the high PM2.5 values are observed 
112

during winter months when the air is stagnant. Under such conditions, back trajectories 

constructed using observed winds would be concentrated in the area of observations. 

We expect to see very similar back trajectory patterns with simulated winds as well. 

Spectral analysis may also be used to separate various time-scales (e.g., seasonal, 

synoptic, inter- and intra-day) in the PM2.5 time-series to determine which time-scales 

contribute most to peak 24-hr PM2.5 concentrations. For example, the synoptic scale 

PM2.5 concentration leading up to an exceedance may contribute more to the 

exceedance than the intra-day contribution. This would likely mean that synoptic scale 

meteorological model performance is more important than performance based on hourly 

statistics. We will explore the possibility of using spectral analysis to separate various 

time scales in the SJV.

CMAQ also has process analyses capabilities. Depending on available time and 

resources, we may explore the possibility of using process analyses as a part of our 

phenomenological evaluation. 
113

7. Air Quality Model Performance Evaluation

7.1. Ambient Data Base and Quality of Data

Air quality observations are routinely made at state and local monitoring stations. Gas 

species and PM species are measured on various time scales (e.g., hourly, daily, 

weekly). Quality controlled air quality observations for 2007 will be used for model 

evaluation. The U.S. EPA guidance recommends model performance evaluations for 

the following gaseous pollutants: ozone (O3), nitric acid (HNO3), nitric oxide (NO), 

nitrogen dioxide (NO2), peroxyacetyl nitrate (PAN), volatile organic compounds (VOCs), 

ammonia (NH3), NOy (sum of NOx and other oxidized compounds), sulfur dioxide (SO2), 

carbon monoxide (CO), and hydrogen peroxide (H2O2). The U.S. EPA recognizes that 

not all of these species are routinely measured (U.S. EPA, 2007) and therefore may not 

be available for evaluating every model application. Recognizing that PM2.5 is a mixture, 

U.S. EPA recommends model performance evaluation for the following individual PM2.5 

species: sulfate (

2 SO4

), nitrate (

 NO3

), ammonium (

 NH4

), elemental carbon (EC), 

organic carbon (OC) or organic mass (OM), crustal, and other primary PM2.5 (U.S. EPA, 

2007).

Table 7-1: Observations for evaluating model performance.

Species Sampling frequency # of sites-2007

O3 1 hour

NO 1 hour

NO2 1 hour

NOx 1 hour

CO 1 hour

SO2 1 hour

Selected VOCs from 

the PAMS 

3 hours (not every day)
114

measurement

PM2.5 measured using 

FRM1

24 hours (daily to one in 

six days)

PM2.5 Speciation sites 24 hours (not every day)

Sulfate ion 24 hours (not every day)

Nitrate ion 24 hours (not every day)

Ammonium ion 24 hours (not every day)

Organic carbon 24 hours (not every day)

Elemental carbon 24 hours (not every day)

Other primary 

particulate matter

24 hours (not every day)

1

Direct comparison between modeled and FRM PM2.5 may not be appropriate because of various 

positive and negative biases associated with FRM measurement procedures.

Table 7-1 lists the species for which observations are available in the SJV for 2007. 

They will be used for the model performance evaluation. All observational data will be 

obtained from the official California ambient air quality database 

(http://www.arb.ca.gov/aqd/aqdcd/aqdcd.htm). The PM2.5 speciation data was originally 

obtained from the U.S. EPA’s Air Quality System (http://www.epa.gov/ttn/airs/airsaqs/). 

Quality assurance information on ambient air quality monitoring data in California can 

be found in http://www.arb.ca.gov/aaqm/qa/qa.htm. 

These species cover the majority of pollutants of interest for model performance 

evaluations as recommended by the U.S. EPA. Other species such as H2O2, HNO3, 

NH3, and PAN are not routinely measured. Observations of these species are not 

available in the SJV for 2007 and are therefore not available for model evaluations. 

However, Zhang et al. (2010) have evaluated the CMAQ model (with the SAPRC99 

mechanism) performance for NH3 and PAN in the SJV during the winter episode of 

CRPAQS. In addition, the CMAQ model performance for species such as H2O2 and 

HNO3 has been carried out in other studies and was found to be favorable (e.g., Yu et 

al., 2007; 2010). 
115

7.2. Model Performance Evaluation Procedures and Metrics

As recommended by U.S. EPA, we will use a number of metrics to evaluate the model 

performance for PM2.5 mass as well as PM2.5 components. These metrics include mean 

fractional bias (MFB), mean fractional error (MFE), normalized mean bias (NMB), and 

normalized mean error (NME). The formulae for estimating these metrics are given 

below (U.S. EPA, 2007).

100%,

Model Obs

Model Obs

N

2 MFB

N

1

















 

100%,

Model Obs

Model Obs

N

2 MFE

N

1























 

100%,

Obs

(Model Obs)

NMB N

1

N

1 









100%,

Obs

| Model Obs |

NME N

1

N

1 









where, “Model” is the simulated concentration, “Obs” is the observed value, and N is the 

number of observations. 

For evaluating O3, we will also use mean normalized bias (MNB) and mean normalized 

gross error (MNGE). Their definitions are given below.

100%,

Obs

Model Obs

N

1 MNB

N

1











 

 

100%.

Obs

Model Obs

N

1 MNGE

N

1

















 

 
116

In addition, we will also calculate other statistics such as mean bias, mean error, and 

the correlation coefficient whenever they provide meaningful information.

In terms of averaging time, both daily and seasonally averaged simulated and observed

values will be compared for PM2.5 mass and PM2.5 components, consistent with 

U.S. EPA’s Guidance. The FRM and STN measurements are averaged daily, so a 

detailed comparison of daily pairs is helpful for assessing model performance. 

Typically, gaseous pollutants are measured on an hourly basis, so hourly comparisons

between simulated and observed values will be made.

In addition, various forms of graphics will be created to visually examine comparison of 

the model predictions to observations. As recommended by U.S. EPA, time series plots

are useful in the examination of temporal comparisons of predictions and observations. 

Tile plots are useful in examining spatial comparisons. Scatter plots, on the other hand, 

are useful in understanding the comparisons of magnitudes. However, the frequency 

distributions of observed and simulated variables are not readily visible on scatter plots. 

Thus, we will either present scatter plots together with their frequency distribution plots

or combine them so that scatter and frequency would be visible on the same plot. All 

these plots will be created for the pairs of observations and predictions over time scales 

dictated by the averaging frequencies of observations (i.e., hourly, daily, monthly, 

seasonally) for the species of interest. They will provide a comprehensive view of 

model performance during different time periods, in different sub-regions, and over 

different concentrations levels. 

Model performance goals will be based on U.S. EPA guidance as well as performance 

recommendations proposed in peer-reviewed journal articles (e.g., Boylan and Russell, 

2006; U.S. EPA, 2007). For example, for PM2.5 and its components, we will create the 

so-called “bugle plots” that were recommended by Boyland and Russell (2006), which 

show the model performance criteria as goal lines together with actual model 

performance. An example of a “bugle plot” from Boylan and Russell (2006) is shown in 

Figure 7-1. We will also create the so-called “soccer plots.” The soccer plot visualizes 

model performance by showing both the model bias and error (e.g., MFB and MFE, or 
117

NMB and NME) on a single plot with various performance “goals” shown as dashed 

lines. An example of a “soccer plot” from Tesche (2006) is also given in Figure 7-2. 

Figure 7-1: Example of “bugle plots” showing PM2.5 actual model performance as 

compared to model performance criteria and goals (from Boylan and Russell, 2006).

Figure 7-2: Example of a “soccer plot” showing PM2.5 fractional bias and error (from 

Tesche et al., 2006).
118

Another convenient way to summarize the comparison between a simulated and an 

observed field is to use a Taylor diagram (Taylor, 2001). These diagrams can provide a 

concise statistical summary of how well two patterns match each other in terms of their 

correlation, root-mean-square difference, and ratio of their variances. These three 

quantities are interrelated with only two independent quantities and, thus, we are able to 

plot all three on a two dimensional diagram. Figure 7-3 shows an example of a Taylor 

diagram. Here, the radial distances from the origin to the points are proportional to the 

standard deviations for the test patterns (e.g., simulations) with that for the reference 

field (e.g., observations) indicated as REF on the x-axis. The azimuthal positions give 

the correlation coefficient between the reference and test fields. The dotted lines, 

representing circular arches centered on the standard deviation of the reference field 

(REF on the x-axis) indicate the root-mean-square error.

Figure 7-3: An example of a Taylor diagram.
119

When the metrics are normalized to the variance of the observed field (as shown in 

Figure 7-3), the comparison of several simulated and observed fields can be plotted on 

the same diagram. For example, these diagrams may be useful in displaying the 

model’s skill at simulating PM2.5 and its components on one diagram. We will explore 

the applicability and the feasibility of using Taylor diagrams in the model performance 

evaluation.

7.3. Diagnostic Testing

Possible Diagnostic Testing that could be used to Improve Model Performance is 

discussed in this section. Throughout the modeling process, many sensitivity analysis 

runs will be performed to improve the model performance and to find out the best set of 

model combinations and configurations. Examples of these analyses include different 

meteorological models (i.e., WRF or MM5), different meteorological model inputs and 

physics options, different algorithms/schemes in the CMAQ model, different setup of 

modeling domain and resolutions, etc. The best combinations of

configurations/schemes will be used along with the consideration of computational 

burden.

Receptor models such as Positive Matrix Factorization can also be performed to 

complement the grid-based photochemical models. These models do not use 

emissions and meteorological data. Instead, they only rely on the chemical 

compositions to identify and quantify the contributions to the ambient PM2.5 from various 

source types. 

Furthermore, other techniques such as decoupled direct method (DDM), dynamic, or 

probabilistic model evaluations (Dennis et al., 2010) could also be explored as part of a 

broader ongoing model performance evaluation and improvement project. 
120

8. Attainment Plan

8.1. Calculation of Relative Response Factors

According to new U.S. EPA guidance (U.S. EPA, 2011b), for the 24-hour PM2.5 

attainment test, for each quarter, the relative response factor (RRF) is calculated as the 

ratio of future year to reference year modeled predictions for sulfate, nitrate, elemental 

carbon, organic carbon, salt, and other primary PM2.5 for the top 10% of modeled PM2.5 

days based on predicted concentrations of 24-hour average PM2.5 for each quarter. 

Since we are modeling each day of the year, the top 10% of simulated PM2.5 days 

would be equal to nine days per quarter. The RRF for component j at a site i is given 

by:

,

C

C

RRF

i

j,reference

j,future

ij 

















where Cj,reference is the reference year mean species concentrations (for the nine high 

modeled PM2.5 days for each quarter) predicted at the grid cell containing the monitoring 

site i; and Cj,future is the future year mean species concentrations (for the high nine 

modeled PM2.5 days for each quarter) predicted at the grid cell containing the monitoring 

site i. 

8.2. Sulfate, Adjusted Nitrate, DerivedWater, Inferred Carbonaceous 

Material Balance Approach (SANDWICH) and Potential 

Modifications

Federal Reference Method (FRM) PM2.5 mass measurements provide the basis for the 

attainment/nonattainment designations. For this reason it is recommended that the 

FRM data be used to project future air quality and progress towards attainment. 

However, given the complex physicochemical nature of PM2.5, it is necessary to 

consider individual PM2.5 species as well. While the FRM measurements give the mass 

of the bulk sample, a method for apportioning this bulk mass to individual PM2.5 
121

components is the first step towards determining the best emissions controls strategies 

to reach NAAQS levels in a timely manner.

The FRM measurement protocol finds its roots in the past epidemiological studies of 

health effects associated with PM2.5 exposure. It is upon these studies that the NAAQS 

is based. The protocol is sufficiently detailed so that results might be easily 

reproducible and involves the measurement of filter mass before and after sampling 

together with equilibrating at narrowly defined conditions. Filters are equilibrated for 

more than 24 hours at a standard relative humidity between 30 and 40% and 

temperature between 20 and 23 oC. Due to the sampler construction and a lengthy filter 

equilibration period, FRM measurements are subjected to a number of known positive 

and negative artifacts. FRM measurements do not necessarily capture the PM2.5 

concentrations in the atmosphere and can differ substantially from what is measured by

speciation monitors including the Speciation Trends Network (STN) monitors (see 

http://www.epa.gov/ttnamti1/specgen.html for more details). Nitrate and semi-volatile 

organic mass can be lost from the filter during the equilibration process, and particle 

bound water associated with hygroscopic species like sulfate provides a positive 

artifact. These differences present an area for careful consideration when one attempts 

to utilize speciated measurements to apportion the bulk FRM mass to individual 

species. Given that (1) attainment status is currently dependent upon FRM 

measurements and (2) concentrations of individual PM2.5 species need to be considered 

in order to understand the nature of and efficient ways to ameliorate the PM2.5 problem 

in a given region, a method has been developed to speciate bulk FRM PM2.5 mass with 

known FRM limitations in mind. This method is referred to as the measured Sulfate, 

Adjusted Nitrate, Derived Water, Inferred Carbonaceous material balance approach or 

“SANDWICH” (Frank, 2006). SANDWICH is based on speciated measurements from 

other (often co-located) samplers, such as those from STN, and the known sampling

artifacts of the FRM. The approach strives to provide mass closure, reconciliation 

between speciated and bulk mass concentration measurements, and the basis for a 

connection between observations, modeled PM2.5 concentrations, and the air quality 

standard.
122

The main steps in estimating the PM2.5 composition are as follows:

(1) Calculate the nitrate retained on the FRM filter using hourly relative humidity and 

temperature together with the STN nitrate measurements,

(2) Calculate quarterly averages for retained nitrate, sulfate, elemental carbon, sea 

salt, and ammonium1,

(3) Calculate particle bound water using the concentrations of ammonium, sulfate, 

and nitrate, using an equilibrium model like the Aerosol Inorganic Model (AIM) or a 

polynomial equation derived from model output2,

(4) Add 0.5 μg/m3as blank mass, and

(5) Calculate organic carbon mass (OCMmb) by difference, subtracting all inorganic 

species (including blank mass) from the PM2.5 mass.

The FRM does not retain all of the semi-volatile PM2.5 mass, and at warmer 

temperatures, loss of particulate nitrate from filters has been commonly observed 

(Chow et al., 2005). In order to estimate how much nitrate is retained on the FRM filter, 

simple thermodynamic equilibrium relations may be used. Necessary inputs include 

24-hour average nitrate measurements and hourly temperature and relative humidity 

data. Frank (2006) suggests the following methodology for estimating retained nitrate. 

For each hour i of the day, calculate the dissociation constant,

Ki

, from ambient 

temperature and relative humidity (RH).

For RH < 61%: 

ln(K ) 118.87 (24084/T) 6.025 ln(T) i   i   i

,

 

1

Ammonium mass will be calculated assuming complete neutralization of retained nitrate and sulfate. As we 

described in the supporting documentation for the 2008 Annual PM2.5 SIP approval, the abundance of ammonia in the 

San Joaquin Valley makes both nitrate and sulfate fully neutralized. This will also make the calculation of ammonium 

mass consistent for both reference and future years.

2 We will use the polynomial regression equation used during the preparation of the 2008 Annual PM2.5 SIP.
123

where,Tiis the hourly temperature in Kelvins and 

Ki

is in nanobars.

For RH ≥ 61%, 

Ki

is replaced by: 

i

1.75

i

2

1 2 i 3 i

' Ki  [P P (1 a ) P (1 a ) ](1 a ) K ,

where, aiis “fractional” relative humidity and

ln(P) 135.94 8763/T 19.12 ln(T) 1    i   i

,

ln(P ) 122.65 9969/T 16.22 ln(T) 2    i   i

,

ln(P ) 182.61 13875/T 24.46 ln(T)

3    i   i

.

Using this information, calculate the nitrate retained on the filter as:

Retained Nitrate = STN nitrate – 

  

24

i 1

R Ki

24

1

745.7/T (κ γ) ,

where,

TR

is the daily average temperature for the sampled air volume in Kelvin, Kiis 

the dissociation constant for NH4NO3 at ambient temperature for hour i, and 

(κ  γ)

relates to the temperature rise of the filter and vapor depletion from the inlet surface and 

is assumed to have a value equal to one (Hering and Cass, 1999). 

Under the FRM filter equilibration conditions, hygroscopic aerosol will retain its particle 

bound water (PBW) and be included in the observed FRM PM2.5 mass. PBW can be 

calculated using an equilibrium model like the Aerosol Inorganics Model (AIM). AIM 

requires the concentrations of ammonium, nitrate, sulfate, and estimated H+as inputs. 

In addition to inorganic concentrations, the equilibration conditions are also necessary 

model inputs. In this case, a temperature of 294.15 K and 35% RH is recommended. 

For simplification, a polynomial regression equation may be constructed by fitting the 

calculated water concentration from an equilibrium model and the concentrations of 

nitrate, ammonium, and sulfate. Here, we will use the polynomial regression equation 

used during the preparation of the 2008 Annual PM2.5 SIP.
124

Other components that may be represented on the FRM filter include elemental carbon, 

crustal material, sea salt, and passively collected mass. Depending on location certain 

species may be neglected (e.g., sea salt for inland areas).

While carbonaceous aerosol may make up a large portion of airborne aerosol, 

speciated measurements of carbonaceous PM are considered highly uncertain. This is 

due to the large number of carbon compounds in the atmosphere and the measurement 

uncertainties associated with samplers of different configurations. In the SANDWICH 

approach, organic carbonaceous mass is calculated by difference. The sum of all 

nonorganic carbon components will be subtracted from the FRM PM2.5 mass to estimate 

the mass of organic carbon.

After having calculated the species concentrations as outlined above, we will calculate 

the percentage contribution of each species to the measured FRM mass (minus the 

blank concentration of 0.5 μg/m3) for each quarter of the years represented by the 

speciated data. Note that blank mass is kept constant at 0.5 μg/m3between the base 

and future years, and future year particle bound water needs to be calculated for the 

future year values of nitrate, ammonium, and sulfate.

8.3. Estimation of Species Concentrations at Federal Reference Method 

(FRM) Monitors that Lack Speciation Data

Speciation data for four STN (speciation) sites was used to speciate the FRM mass for 

all FRM sites. For those sites not collocated with STN monitors, surrogate speciation 

sites were determined based on analysis of CRPAQS data to determine which sites had 

similar speciation profiles. The composition was assumed to be the same at both

Bakersfield sites (BAC and BEP). Similarly, the percent composition at the three

Fresno sites (CLO, FSF and FSH) was assumed to be the same. Stockton (SOH), 

Corcoran (COP), and Modesto (MRM) were assumed to have the same speciation as 

one of the four speciation sites based on CRPAQS data analysis. For a list of all FRM 

sites and their associated speciation site, see Table 8.1.
125

Table 8-1: The FRM sites in SJV and their companion speciation sites.

Site Name Code Speciation

Bakersfield-410 E Planz Road BEP BAC

Bakersfield-5558 California Avenue BAC BAC

Clovis-N Villa Avenue CLO FSF

Corcoran-Patterson Avenue COP VCS

Fresno-1st Street FSF FSF

Fresno-Hamilton and Winery FSH FSF

Merced-2334 M Street MRM M14

Modesto-14th Street M14 M14

Stockton-Hazelton Street SOH M14

Visalia-N Church Street VCS VCS

8.4. Speciated Modeled Attainment Test (SMAT)

Following U.S. EPA’s latest guidance (U.S. EPA, 2011b), the modeled attainment test 

will be performed with the following steps.

Step 1: Determine the eight highest observed 24-hour PM2.5 concentrations days 

in each quarter for each year at each FRM site (32 days per year), and determine 

the day rank of the observed 98th percentile value for each year based on the 

number of collected ambient samples. 

Step 2: Calculate quarterly ambient species fractions on “high” PM2.5 days for 

each of the major component species of PM2.5 (i.e., sulfate, nitrate, ammonium, 
126

elemental carbon, organic carbon, particle bound water, salt, and blank mass). 

The “high” days are the top 10% of days in each quarter. Depending on the 

sampling frequency, the top 10% of days would range from three to nine. The 

species fractions of PM2.5 are calculated using the “SANDWICH” approach which 

was described previously. These quarter-specific fractions along with the FRM 

PM2.5 concentrations are then used to calculate species concentrations for each 

of the 32 days per year determined in step 1.

Step 3: Apply the component and quarter specific RRF, described in Section 8.1,

to observed daily species concentrations from step 2 to obtain future year 

concentrations for sulfate, nitrate, elemental carbon, organic carbon, salt, and 

other primary PM2.5.

Step 4: Calculate the future year concentrations for the remaining PM2.5 

components (i.e., ammonium, particle bound water, and blank mass). The future 

year ammonium is calculated based on the calculated future year sulfate and 

nitrate, using a constant value for the degree of neutralization of sulfate from the 

ambient data. The future year particle bound water is calculated from an 

empirical formula derived from the AIM model.

Step 5: Add the concentrations of species components to produce total PM2.5 

concentrations for each of the 32 days per year at each site. Then the 32 days

for each site for each year are sorted by total PM2.5 concentrations. For each site 

and year, the monitored 98th percentile rank is used to determine the 98th

percentile rank for each year.

Step 6: Average the future-year 98th percentile values to obtain the future-year 

design value. Compare the future-year 24-hour design values to the NAAQS. 

The 24-hour PM2.5 design values are truncated after the first decimal place. Any 

value that is less than 35.5 μg/m3is compliant with the NAAQS.
127

8.5. Sensitivity Analyses

The effectiveness of reducing PM2.5 precursors, such as NOx, SOx, VOCs, and NH3, as 

compared to reducing direct PM2.5 emissions is quantified using inter-pollutant 

equivalency ratios. Sensitivity analysis will be performed for five cases involving 10% 

reductions of primary PM2.5, NOx, SOx, VOCs, and NH3 emissions separately. The 

changes in simulated PM2.5 concentrations compared to the base case without the 10% 

emission reductions will be quantified at a given FRM monitor. The effectiveness of 

reducing emissions, or the change in concentrations per unit emissions change, will 

then be determined by dividing the change in the 24-hour PM2.5 design value by the 

amount of emission reductions corresponding to the 10% reduction. The equivalency 

ratios between PM2.5 precursors (i.e., NOx, SOx, VOCs, and NH3) and primary PM2.5 will 

be determined by dividing primary PM2.5 effectiveness by the precursors’ effectiveness 

(i.e., the effectiveness of NOx, SOx, VOCs, and NH3).

This analysis will be conducted for the FRM sites in the Bakersfield and Fresno areas in 

the future year using only anthropogenic emissions changes. The emissions changes 

will be implemented only in and around these two urban areas. We will determine the

area of influence for these monitors depending on the season in which the 98th

percentile value occurs. Past experience dictates that, in the San Joaquin Valley, the 

98th percentile value will occur in the winter and it has been shown previously that, 

during the winter, monitors in these areas are impacted predominantly by local 

emissions (Ying et al., 2009b). 

In addition, carrying capacity diagrams for pairs of precursors will also be developed. 

These pairs will include NOx vs. primary PM2.5, NOx vs. VOC, NOx vs. NH3, and NOx vs. 

SOx. These diagrams will be used to assess significant precursors.

8.6. Unmonitored Area Analysis

The unmonitored area analysis ensures that a control strategy leads to reductions in 

PM2.5 at other locations which could have baseline or future design values exceeding 

the NAAQS if a monitor was located there (U.S. EPA, 2007). U.S. EPA recommends 
128

combining interpolated spatial fields and modeled gradients to generate the gradient 

adjusted spatial fields for PM2.5. Future year estimates for unmonitored grids are 

created by applying the grid specific RRFs to the gradient adjusted spatial fields. The 

general procedures are as given below:

Step 1: Interpolate base year ambient PM2.5 to create a set of spatial fields. For 

24-hour PM2.5, U.S. EPA recommends interpolating the PM2.5 concentrations in 

each quarter which is equal to or less than the 98th percentile value of the year. 

For the PM2.5 component species, U.S. EPA recommends interpolating the high 

PM2.5 days in each quarter.

Step 2: Adjust the spatial fields using gridded model output gradients for the 

base year. For 24-hour PM2.5, the gradient adjusted fields can be created from 

the high end of the distribution of daily averages in each quarter.

Step 3: Apply the gridded model RRFs to the gradient adjusted spatial fields 

created in step 2 to obtain the future year concentrations. For PM2.5, the RRFs 

for each of the species in each quarter are multiplied by the gradient adjusted 

spatial fields for each species and each quarter.

We do not know at this time if we will use the U.S. EPA software (MAPS), since 

complete source code is not available, or will develop in-house software for this task.
129

9. Supplemental Analyses

9.1. Additional Analyses to be completed to corroborate the Modeling

The Weight of Evidence (WOE) analysis provides a corroborative set of analyses 

supplementing the SIP required modeling that provides confidence that the correct 

pollutants are being controlled and the attainment demonstration is appropriate. These 

analyses can include consideration of measured air quality, emissions, and 

meteorological data, evaluation of other air quality indicators, and additional air quality 

modeling. Each analysis is weighted based on its ability to quantitatively assess the 

ability of the proposed control measures to yield attainment. 

For the San Joaquin Valley, the PM2.5 WOE analysis will include an evaluation of air 

quality trends, emission trends, observational models, indicator species, meteorological 

trends, and air quality modeling results to demonstrate attainment of the 35 μg/m3 PM2.5

standard. The air quality trends will include data from the official Federal Reference 

Monitor and chemical composition networks and other types of PM2.5 monitoring data. 

ARB will analyze the data to examine the yearly, seasonal, and spatial trends. In 

addition, ARB will discuss the yearly meteorological conditions and the impact of these 

conditions on the measured PM2.5 air quality. The met-adjusted trends can also be 

used in conjunction with emissions trends to review the impacts of emission reductions. 

Two complementary observational models, chemical mass balance (CMB) and positive 

matrix factorization (PMF) will be used to identify the sources contributing to the San 

Joaquin Valley PM2.5 problem. ARB will use the latest version of CMB and PMF and 

appropriate input data for California. In addition, ARB will compare these PMF and 

CMB results with other published results on the sources of PM2.5 in the San Joaquin 

Valley. 

The speciated rollback technique (NRC, 1993) can also be applied to study the changes 

in ambient concentrations due to changes in emissions. A fundamental assumption 

here is that the changes in ambient concentration of a pollutant is linear with changes in 

its precursors. While this assumption may be sound for directly-emitted pollutants (such 
130

as primary PM2.5), the response of secondary pollutants to changes in their precursors 

is known to be non-linear. Fully allowing for such non-linearity is not possible within the 

speciated rollback technique.

ARB will explore the possibility of using an indicator species approach to investigate the 

effectiveness of precursor controls on secondary species such as ammonium nitrate. 

This approach would incorporate air quality data, a review of San Joaquin Valley 

specific published papers, and air quality modeling sensitivity runs to identify the limiting 

precursors for ammonium nitrate formation in the context of the 24-hour PM2.5 standard. 

Finally, ARB will evaluate the grid-based modeling results in the context of the 

corroborative analyses to provide confidence that PM2.5 attainment is reached as 

expeditiously as practicable in the San Joaquin Valley.

9.2. Base Case Air-Quality Modeling with Meteorological Fields 

Generated with the Weather and Research Forecast (WRF) Model

The prognostic meteorological model that will be used to generate meteorology will be 

MM5 for the reasons outlined in Section 5.1.1.

A base year CMAQ model simulation will also be performed using meteorological fields 

generated with the WRF model (Skamarock et al., 2008). The primary objective is to 

study how PM2.5 predictions in the San Joaquin Valley differ when the meteorological 

fields from WRF instead of MM5 are used to drive the CMAQ model. Traditionally, MM5 

has been used to provide meteorological data for air quality simulations (Appel et al., 

2009). With the emergence of WRF, many air quality model simulations started to use 

WRF to provide the meteorological fields for air quality models (e.g., Appel et al., 2009; 

de Meji, 2009; Eder et al., 2009; Hu, et al., 2010; Lin et al., 2010; Shimadera, 2011). 

The WRF model version 3.3 will be used. Detailed configuration of the WRF model can 

be found in the meteorological modeling sections. The WRF model output will be 

processed by the Meteorology-Chemistry Interface Processor (MCIP, Otte and Pleim, 

2010) of the CMAQ modeling system. The MCIP version 3.6 in the CMAQv4.7 

modeling system will be used. Compared to the base year simulation using MM5 
131

meteorological fields (MM5-CMAQ), all modeling inputs and configurations for the 

CMAQ model will be the same except that the meteorological fields are generated by 

the WRF model (WRF-CMAQ). This will ensure that the difference in the CMAQ model 

output is only attributed to the different meteorological fields. 

WRF-CMAQ model output in terms of PM2.5 total mass and individual PM2.5 components 

(i.e., nitrate, sulfate, ammonium, elemental and organic carbon, other primary PM2.5 

components) will be compared to the MM5-CMAQ model outputs. WRF-CMAQ model 

outputs will also be evaluated against the ambient air quality data using the same 

modeling performance procedures and metrics used for the MM5-CMAQ model outputs. 

Model performance metrics such as mean fractional bias (MFB), mean fractional error 

(MFE), normalized mean bias (NMB), and normalized mean error (NME) will be 

calculated and will be compared to the performance metrics of the MM5-CMAQ model 

outputs. 
132

10. Procedural Requirements

10.1. How Modeling and other Analyses will be Archived, Documented, 

and Disseminated

The air quality modeling system covers the central portion of California with 4x4 km2

grids. In total there are approximately half a million grid cells in each simulation (192 x 

192 cells in the lateral direction and 15 levels in the vertical). The meteorological 

modeling system has roughly double the number of grid cells since it has 30 vertical 

layers. Archiving of all the inputs and outputs takes several terabytes (TB) of computer 

disk space (for comparison, one single-layer DVD can hold roughly 5 gigabytes (GB) of 

data and it would take ~200 DVDs to hold one TB). Please note that this estimate is for 

simulated surface-level pollutant concentrations only. If three-dimensional pollutant 

concentrations are needed, it would add a few more TB. Therefore, transferring the 

modeling inputs/outputs over the internet using file transfer protocol (FTP) is not 

practical. Interested parties may send a request for model inputs/outputs to Mr. John 

DaMassa, Chief of the Modeling and Meteorology Branch at the following address. 

John DaMassa, Chief

Modeling and Meteorology Branch

Planning and Technical Support Division

Air Resources Board

California Environmental Protection Agency

P.O. Box 2815

Sacramento, CA 95814, USA

The requesting party will need to send an external disk drive(s) to facilitate the data 

transfer. The requesting party should also specify what input/output files are requested 

so that ARB can determine the capacity of the external disk drive(s) that the requester 

should send. 
133

10.2. Specific Deliverables to U.S. EPA

The following is a list of modeling-related documents that will be provided to the U.S. 

EPA.

 The modeling protocol

 Emissions preparation and results

 Meteorology 

o Preparation of model inputs

o Model performance evaluation 

 Air Quality 

o Preparation of model inputs

o Model performance evaluation 

 Documentation of corroborative and weight-of-evidence analyses

 Predicted Future 24-hour PM2.5 Design Value Calculations using SANDWICH, 

RRF, and SMAT 

 Unmonitored area analysis

 Access to input data and simulated results
134

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