PHOTOCHEMICAL MODELING PROTOCOL 

FOR DEVELOPING STRATEGIES 

TO ATTAIN THE FEDERAL 8-HOUR OZONE AIR 

QUALITY STANDARD IN CENTRAL CALIFORNIA 

California Air Resources Board 

Planning and Technical Support Division 

Sacramento, California 95814 

Draft (5/22/07) 
2 

TABLE OF CONTENTS 

1 INTRODUCTION...................................................................................................... 3

1.1 Purpose............................................................................................................. 3

1.2 Approach........................................................................................................... 3

1.3 Background....................................................................................................... 6

2 EPISODE SELECTION.......................................................................................... 11

2.1 July 7-13, 1999 (Routine Episode).................................................................. 12

2.2 July 29-August 2, 2000 (CCOS Episode)........................................................ 13

2.3 Available Observational Data.......................................................................... 15

3 MODEL SELECTION............................................................................................. 19

3.1 Meteorological Model...................................................................................... 19

3.2 Photochemical Model...................................................................................... 20

4 MODELING DOMAIN AND GRID STRUCTURE ................................................... 22

4.1 Meteorological Modeling Domain.................................................................... 22

4.2 Photochemical Modeling Domain.................................................................... 26

5 MODEL INITIALIZATION AND BOUNDARY CONDITIONS.................................. 29

5.1 Initialization of the Meteorological Model ........................................................ 29

5.2 Air Quality Model Initial and Boundary Conditions .......................................... 31

6 EMISSION INVENTORY DEVELOPMENT............................................................ 40

6.1 Background..................................................................................................... 41

6.2 Point and Area Source Emissions................................................................... 42

6.3 Forecasted Emissions..................................................................................... 47

6.4 Day-Specific Emissions .................................................................................. 48

6.5 Temporally and Spatially Resolved Emissions................................................ 50

6.6 Surface Temperature and Relative Humidity Fields........................................ 51

6.7 On-Road Mobile Source Emissions ................................................................ 52

6.8 Biogenic Emissions......................................................................................... 69

6.9 Spatial Allocation ............................................................................................ 71

6.10 Speciation ....................................................................................................... 78

7 MODEL PERFORMANCE EVALUATION.............................................................. 83

7.1 Meteorological Model Performance ................................................................ 83

7.2 Air Quality Model Performance ....................................................................... 87

8 FUTURE-YEAR AIR QUALITY MODELING .......................................................... 92

8.1 Estimation of Future Design Value (DVF)........................................................ 93

9 REFERENCES....................................................................................................... 99
3 

1 INTRODUCTION 

1.1 Purpose 

This modeling protocol is intended to both guide and describe the technical aspects of 

air quality modeling that is to be conducted in support of developing an 8-hour ozone 

State Implementation Plan (SIP) for central California. It will describe the data, 

technical decisions, and the procedures associated with producing computer-based 

simulations of ozone concentrations. It will also describe how model results will be 

evaluated with field measurements and how future year air quality will be simulated. 

The approach taken follows U.S. EPA modeling guidance for 8-hour ozone SIPs (2005). 

1.2 Approach 

The intent of this protocol is to utilize the best available science, technical tools, and 

data to develop the modeling system. Once the modeling system has demonstrated 

adequate performance, it will be used as a technical resource to assist decision makers 

in selecting the most effective future-year emission control measures to include in the 

SIP. Some examples of the types of questions that will likely be considered are: 

• In terms of ozone formation, what are the regional and sub-regional effects of 

hydrocarbon emissions and emissions of oxides of nitrogen? 

• With regard to reducing 8-hour ozone concentrations, what are the carrying 

capacities for the on-attainment areas in the region? 

• What are the likely years that the non-attainment areas in the region will achieve 

attainment? 

The modeling approach draws heavily on the products of large-scale, scientific studies 

in the region, collaboration among technical staff of State and local regulatory agencies, 

as well as from participation in technical and policy groups within the region. In 

particular, the following three groups provided substantial input: 

• The 2000 Central California Ozone Study (CCOS) – More information on CCOS, 

including the organizational structure, CCOS products, and the Technical 

Committee (CCOS-TC) can be found via the following two links: 
4 

http://www.arb.ca.gov/airways/crpaqs/organization.htm

http://www.arb.ca.gov/airways/ccos/ccos.htm

• The Bay Area Modeling Advisory Committee (BA-MAC) – This technical group 

was established by the Bay Area Air Quality Management District (BAAQMD) to 

guide modeling in support of the 2004 Bay Area SIP revision. More information 

and results from this group can be found at: 

http://www.baaqmd.gov/pln/plans/ozone/2003_modeling/index.htm

• The SIP Gridded Inventory Coordination Group (SIP-GICG) – This group of 

regulatory agency staff (and, in some cases, their consultants) was established 

by ARB to coordinate the development and review of the emissions inventory 

inputs to SIP air quality modeling in central California. 

The regular participants for each of these three groups are listed in the following two 

tables. Table 1.1a lists non-government participants and Table 1.1b lists government 

agency participants. The suite of candidate episodes, models, model inputs to 

consider, and, ultimately, the selected episodes, modeling tools, and inputs for use in 

SIP modeling were discussed within and among these technical groups, including via 

projects or contracts that were sponsored by them individually or collectively. 

Table 1.1a – Consultants and Public/Private Stakeholder Representatives 

Group Employer Representative 

BA-MAC CCOS-TC SIP-GICG 

Acadamia/Consulting Alpine Geophysics James Wilkinson X X 

 Dowling and Associates Carrie Anderson X 

 Envair Steve Reynolds X 

 ENVIRON Chris Emery X X 

 Golden Gate University Ken Kloc X 

 UC Berkeley Rob Harley X 

Environmental West County Toxics Coalition Henry Clark X 

 Sierra Club John Holtzclaw X 

 Clean Air Partnership Jude Lamare X 

Industry Chevron-Texaco Steve Ziman X X 

 PG&E Sam Altshuler X 

 Shell Christopher Rabideau X X 
5 

Table 1.1b. Government Agency Stakeholder Representatives 

Group Employer* Representative BA-MAC CCOS-TC SIP-GICG 

Federal Government US-EPA (Region 9) Carol Bohnenkamp X X 

 NOAA James Wilczak X X 

Local Government ABAG Eugene Leong X 

 BAAQMD Amir Fanai X X 

 BAAQMD Gary Kendall X 

 BAAQMD Jean Roggenkamp X 

 BAAQMD Peter Hess X 

 BAAQMD Phil Martien X X X 

 BAAQMD Saffet Tanrikulu X X 

 BAAQMD Toch Mangat X 

 Fresno COG Mike Bitner X 

 Monterey Bay APCD Bob Nunes X 

 MTC Harold Brazil X X 

 Sacramento Area COG Gordon Garry X 

 San Joaquin Valley APCD David Nunes X X 

 San Joaquin Valley APCD Evan Shipp X X X 

 San Joaquin Valley APCD James Sweet X X 

 San Joaquin Valley APCD Gary Arcemont X 

 San Joaquin Valley APCD Leland Villalvazo X 

 San Joaquin Valley APCD Steven Shaw X 

 Sacramento Metro AQMD Brigette Tollstrup X 

 Sacramento Metro AQMD Bruce Katayama X X X 

 Sacramento Metro AQMD Charles Anderson X X 

 Sacramento Metro AQMD Hao Quinn X 

 Sacramento Metro AQMD Larry Greene X 

 TRANSDEF David Schonbrunn X 

 Yolo-Solano AQMD Rene Toledo X 

State Government CalTrans Leonard Seitz X 

 ARB Ajith Kaduwela X X 

 ARB Anne Lin X 

 ARB Bruce Tuter X 

 ARB Daniel Chau X 

 ARB Cheryl Taylor X X X 

 ARB Eugene Yang X X 

 ARB Jeff Lindberg X 

 ARB Jinyou Liang X 

 ARB John DaMassa X X 

 ARB Martin Johnson X 

 ARB Kemal Gurer X 

 ARB Mimi Sogutlugil X 

 ARB Paul Allen X 

 ARB Vernon Hughes X X X 

*NOAA – National Oceanic and Atmospheric Administration; ABAG – Assoc. of Bay Area Governments; BAAQMD – Bay Area Air 

Quality Management District; COG – Council of Government; APCD – Air Pollution Control District; MTC – Metropolitan 

Transportation Commission; AQMD – Air Quality Management District; CalTrans – California Department of Transportation. 
6 

1.3 Background 

The shaded relief maps provided at the end of this section illustrate the topography in 

California as well as the Air Basin and County political boundaries (Figure 1.1) and Air 

District and County boundaries (Figure 1.2). 

Generally, the weather conditions that lead to high ozone levels in the San Joaquin 

Valley include large-scale high-pressure systems that develop over the Western United 

States, low wind speeds, and high temperatures. These conditions occur frequently in 

the San Joaquin Valley between May and September, and may persist for several days. 

The complex features of airflow within the region contribute to various types of ozone 

episodes in the San Joaquin Valley, the Sacramento Valley, the Mountain Counties, and 

the San Francisco Bay Area. Ozone and its precursors are distributed throughout the 

mixed layer by turbulent diffusion. When meteorological conditions are favorable, 

daytime sea breezes are funneled through the Carquinez Strait and nearby mountain 

passes, bringing ozone and precursors into the northern part of the San Joaquin Valley. 

Some inflow is also observed through the Pacheco Pass to the west side of the Valley. 

Depending upon the nature of the airflow in the region, ozone episodes in the San 

Joaquin Valley or Sacramento can be generated predominantly from locally derived 

pollutants or by pollutants transported from upwind regions. In the San Francisco Bay 

Area (SFBA), ozone concentrations are elevated when airflow from the Bay Area to the 

Central Valley is limited. Elevated ozone concentrations are observed in the Mountain 

Counties due mostly to transported pollutants. The conditions that promote the 

formation of a nocturnal jet within the Valley may limit ventilation of the Valley. During 

the day, pollutants may be transported from the San Joaquin Valley to the Mojave 

Desert via the Tehachapi Pass. Outflow from the San Joaquin Valley to the coast in the 

vicinity of San Luis Obispo area has also been observed. 

Except on the warmest days, an inversion is almost always present within the Central 

Valley throughout the year. This inversion tends to trap pollutants either emitted within 

the Valley or transported into the Valley from surrounding regions. In this regime, 

mesoscale flow patterns such as sea breeze intrusion, local eddies, bifurcation and 

convergence, and mountain/valley flows are especially important in determining the 

distribution of pollutants throughout the region. These mesoscale characteristics are 

described in more detail below, and provide a reference for features to consider during 

qualitatively assessing meteorological model performance, which is discussed further in 

Chapter 7: 

Sea Breeze and Marine Air Intrusion: Differential heating between the land and ocean 

causes a pressure gradient between the cooler, denser air over the ocean and the 

warmer air over the land. The resulting pressure gradient draws marine air into the 

Valley during the day. Typically, with calm coastal winds during mornings, rush hour 

pollutants can accumulate in the coastal source region. As the sea breeze develops by 
7 

mid-day, ozone and its precursors can enter the Valley, encountering warmer 

temperatures and higher insolation. 

Nocturnal jet and eddies: A low-level nocturnal wind maximum can develop during 

evening hours. As surface temperatures cool overnight, a strong stable layer within the 

Central Valley can result. As this stable layer forms, the wind aloft may be decoupled 

from the surface and accelerate. The result is an overnight wind flow that may carry 

pollutants from one end of the Valley to the other. While this nocturnal jet may be 

present in other seasons, it has been observed during the ozone season (Smith et al. 

1981; Blumenthal 1985; Thuillier et al. 1994). It is believed to be a pollutant transport 

mechanism during the summer months. Depending on the temperature structure of the 

Valley, the jet may not be able to exit through the Tehachapi Pass (~1400 m); in which 

case the air is forced to turn north along the Sierra foothills at the southeastern edge of 

the Valley. Smith et al. (1981) mapped the northerly flow, sometimes called the Fresno 

eddy, with pibals and described an unusual case where it extended as far north as 

Modesto. During the Southern San Joaquin Ozone Study, Blumenthal et al. (1985) 

measured the Fresno eddy extending above 900 meters above ground level about 50% 

of the time. Neff et al. (1991) measured the eddy using radar wind profilers during the 

SJVAQS/AUSPEX study. 

Bifurcation and Convergence Zones: Marine air entering the Sacramento River Delta 

region from the west may diverge. It may flow into the San Joaquin Valley to the south 

and Sacramento Valley to the north. The position of this bifurcation zone may shift and 

can determine the relative proportion of Bay Area pollutants transported to each 

downwind basin. The dynamics of this bifurcation zone are currently not well 

understood. However, this zone may also prevent transport between air basins by 

functioning as a block to air moving north to south within the Delta. For example, the 

effect of convergence zones on air quality is provided by Blumenthal et al. (1985), 

where it is hypothesized that the increase in mixing heights (~200 m higher than in the 

northern SJV) at the southern end of the San Joaquin Valley was due to damming of the 

northerly flow against the Tehachapi mountains at the southern end. Without this 

damming effect, the mixing levels over Bakersfield, Arvin, and Edison would be lower, 

with correspondingly higher ozone concentrations. 

Up-slope and Down-slope Flows: The increased daytime heating in mountain canyons 

and valleys adjacent to the Central Valley causes significant upslope flows during the 

afternoons in the San Joaquin and Sacramento Valleys. This can act as a removal 

mechanism, and can lift mixing heights on the edges of the valleys, relative to the 

mixing heights at valley center. During the nighttime, mountain valleys and canyons 

may cool relative to the Valley floor, resulting in a reversal of the flow. Myrup et al. 

(1989) studied transport of aerosols from the San Joaquin Valley into Sequoia National 

Park. They found a net up-slope flow of most pollutant species. The return flow can 

bring pollutants back down. Smith et al. (1981) used tracer data to estimate pollutant 

budgets due to slope flow fluxes (and other ventilation mechanisms). Smith et al. 

suggested that polluted air at higher elevations is diluted, thus down-slope flows may 

result in lower pollution levels within the San Joaquin Valley. 
8 

Up-Valley and Down-Valley Flows: Up-valley and down-valley flows are similar to upslope and down-slope flows, but take place along the valley on a larger scale. During 

the summer, the Sacramento River Delta tends to have cooler air temperatures during 

the day and warmer temperatures at night than at the extreme ends of the Central 

Valley due to higher humidity within the Delta. During the daylight hours, up-valley flow 

draws air south into the San Joaquin Valley and north into the Sacramento Valley. At 

night, down-valley drainage winds tend to ventilate both valleys. Hayes et al. (1984) 

described both regimes for the Central Valley. 
9 

Figure 1.1. California Air Basins and Counties. 
10 

Figure 1.2. California Air Districts and Counties. 
11 

2 EPISODE SELECTION 

As indicated in the first chapter, a large body of work already exists that has been 

produced by stakeholder groups towards the goal of determining representative, 

candidate episode periods for use in 8-hour ozone SIP modeling for the region. From 

this collective body of work, the following four episodes were identified as having the 

greatest potential for SIP modeling in the region: 

• July 7-13, 1999 (captured with routine State and Local measurements) 

• July 29-August 2, 2000 (CCOS-studied episode) 

• September 17-21, 2000 (CCOS-studied episode) 

• August 8-15, 2002 (captured with routine State and Local measurements) 

Due to time constraints and based on model performance issues expressed by 

stakeholder efforts for the two later episodes, the first two episodes (July 7-13, 1999, 

and July 29-August 2, 2000) were determined to be the most adequate for the initial 

round of 8-hour ozone SIP attainment planning. Brief summaries of the two episodes 

selected for SIP modeling are contained in the following sections. 

With regard to the two episodes being dropped from consideration, CCOS sponsored a 

project that was focused on developing the third (September, 2000) episode while the 

Sacramento Metropolitan Air Quality Management District (SMAQMD) sponsored a 

project to develop the fourth (August, 2002) episode. As indicated above, achieving 

adequate model performance for both of these periods was problematic. It should be 

noted, however, that a subsequent effort is now underway (via a pending 2007 CCOS 

Request for Proposals) to improve model performance for the September, 2000 

episode. More information on efforts to initially develop or to improve these two 

dropped episodes can be found via the following links: 

September 2000 (CCOS): 

http://www.arb.ca.gov/airways/ccos/docs/03-01CCOS_Alpine_Final_Report_PDF.zip

http://www.arb.ca.gov/airways/ccos/RFPs/Sept_Modeling/RFP_septmodeling_FINAL.pdf

August 2002 (SMAQMD): 

http://www.airquality.org/cleanairplan/modeling.shtml
12 

2.1 July 7-13, 1999 (Routine Episode) 

On July 8, 1999, high pressure began to build throughout the southern United States. 

Through July 10, the high pressure at 500mb increased and a relative high formed over 

the 4-Corners area. After July 10, the 500mb high began to regress westward and 

weaken after July 12. Winds aloft during this period were generally weak and variable. 

During this episode period, the high-pressure ridges that formed were generally shallow. 

The maximum 500mb pressure-heights were in excess of 594 dm and are consistent 

with the high pressures and strong subsidence generally associated with the occurrence 

of high ozone concentrations within central California. 

Ozone concentrations exceeded 125 ppb on July 8-13, 1999. On July 8, a 

concentration of 155 ppb was recorded at Parlier; however, this peak was isolated in 

time and space and was considered difficult to model. On July 10, high ozone 

concentrations were recorded throughout the Sacramento area, with a peak of 147 ppb. 

On July 11, high ozone concentrations were recorded from the San Francisco Bay Area 

into Sacramento, and continued into July 13 with a high ozone concentration of 156 ppb 

recorded at Concord. Concentrations declined thereafter and on July 13, the only 

concentration exceeding 125 ppb was the 132 ppb recorded at Merced. 

The 8-hour ozone concentrations exceeding 85 ppb in this period were recorded on July 

8, and occurred through July 13 (Table 2.1). In the San Joaquin Valley, ozone 

concentrations in excess of 85 ppb were recorded for at least 10 sites on each day of 

the episode period until July 13. The maximum 8-hour ozone concentration was 

recorded at the Fresno – First Street site on July 11. In the Sacramento Area, the peak 

8-hour concentration was recorded at Folsom, also on July 11. In the San Francisco 

Bay Area, the maximum daily ozone concentrations did not exceed 85 ppb until July 10. 

However, the daily maximums and number of sites recording concentrations greater 

than 85 ppb increased on July 11 and 12. The maximum 8-hour ozone concentration in 

the San Francisco Bay Area during this episode period was 123 ppb recorded at 

Concord on July 12. 
13 

Table 2.1 The number of monitoring sites with measured 8-hour ozone concentrations 

exceeding 85 ppb and daily maximum measured concentrations.

Total July 8 July 9 July 10 July 11 

Sites Sites Max Sites Max Sites Max Sites Max 

Bay Area 19 0 72 0 73 2 95 11 116 

Sacramento 

Valley 17 4 96 8 116 13 129 14 124 

San Joaquin 

Valley 20 13 110 13 102 15 108 19 123 

Total July 12 July 13 

Sites Sites Max Sites Max 

Bay Area 19 7 123 0 74 

Sacramento 

Valley 17 13 107 4 91 

San Joaquin 

Valley 20 11 116 8 117 

2.2 July 29-August 2, 2000 (CCOS Episode) 

During the 2000 CCOS field campaign, the duration of high pressure ridging, which 

fosters ozone production, was somewhat shorter than those recorded in previous 

summers. When compared to the 30-year climatology for June to September for 

Fresno and San Francisco, (Table 2.5-1, CCOS Field Study Plan, Fujita et al. 1999), the 

inland temperatures were statistically colder during CCOS, while the coastal 

temperatures were not. For example the study period daily temperature maximum at 

Fresno, 91.4±0.7 °F, was more than three standard deviations below the climatological 

value of 94.8 °F. The study period daily temperature maximum at San Francisco, 

71.5±0.7 °F, was below the climatological value of 72.0 °F. This can be explained by 

the occurrence of fewer high pressure ridges and/or ridging of shorter duration passing 

over the western United States, where the inland sites are not as influenced by the 

mitigating effects of the Pacific Ocean (after Lehrman et. al., 2003). Statistical analyses 

indicate that this episode is in the upper range of poor air pollution dispersion 

meteorology that results in exceedances of the NAAQS in the San Joaquin Valley. 

Lehrman (2003) reported that all days during the July-August 2000 ozone episode fall 

into meteorological categories within one standard deviation of the mean for days 

greater than the NAAQS. 

Inspection of 500-mb and surface daily weather maps shows that low-pressure troughs, 

cut-off lows, and zonal flow occurred during the first seven weeks of the study period, 

except for one brief incursion of an Eastern Pacific High, which brought some ridging 

over the West Coast. That occurred on June 14-15, which became the Practice 

Intensive Operating Period (IOP) on June 14 and 16. After this slow start to the study 
14 

period, ridging during IOP#1, July 23-24, brought a Four Corners High. Unfortunately, 

this 500-mb high positioning can also foster monsoonal flow. Too much positive 

vorticity (lifting) kept ozone concentrations low over much of the study area, in particular 

the southern San Joaquin Valley. This high persisted the next week and moved over 

the Great Basin during IOP#2, July 30-Aug 2. By August 6, the high had weakened and 

moved eastward leaving troughs or zonal flow over California for almost another week. 

IOP#3 was conducted on August 14 when the high had broadened to include southern 

California. But IOP#3 lasted only one day, as the high retreated from a trough moving 

down from the Gulf of Alaska. As the high retreated further east to Texas, Oklahoma, 

and Arkansas, a trough remained over the pacific Coast as far south as Northern 

California, but cut-off lows and zonal flow over southern California kept ozone 

concentrations relatively low. Because of the lack of suitable episodes during the 

originally scheduled end of the field study on September 3, the CCOS was extended to 

late September. 

On September 11, zonal flow over the Pacific Northwest and a weak cut-off low off the 

California coast were adjacent to a new high expanding up from the south over Northern 

Mexico. Due to the slow start in the study period, IOP#4 was called for September 14. 

Unfortunately, a relatively strong cut-off low developed offshore of the US-Canadian 

border and kept the high to the east. As the cut-off low moved east over Idaho, a 

relatively strong high built in behind it over the eastern Pacific. IOP#5 was initiated on 

September 17, a ramp-up day, and continued through the 21st when the high had 

regressed back westward leaving strong northerly flow through a trough axis from 

Hudson Bay to San Francisco Bay. As the trough gave way to zonal flow over the next 

week, flights were conducted to monitor boundary conditions during zonal flow 

conditions during September 30 through October 2. 

By July 25, the ridge had weakened slightly and dropped southeastward into eastern 

New Mexico, and a trough developed along the West Coast from Point Conception to 

British Columbia. This resulted in the lowering of 500 mb heights and 850 mb 

temperatures somewhat during July 25 and 26. However, on the 27th, the highpressure ridge once again regressed towards the west and strengthened somewhat to 

become centered once again in the Four Corners area. With this regression of the 

ridge, the 850 mb temperature and 500 mb heights at Oakland (OAK) once again rose 

during that period and continued to rise through July 30, and the period of the next IOP. 

During the IOP of July 30 through August 2, the ridge remained strong and continued to 

slowly regress towards the west until it was centered near Reno, Nevada by July 31. 

The OAK 850 mb temperature during the IOP reached as high as 27oC and the 500 mb 

height topped at 5,970 m. 

During the July-August 2000 CCOS episode, the highest ozone level was a recorded 

151 ppb at Edison on August 2 in the southern portion of Central Valley. Peak ozone 

values on July 30 occurred in the San Joaquin Valley, where values near 130 ppb were 

recorded at Parlier and Edison; the Bay Area and Sacramento region experienced no 

federal 1-hour exceedances that day. On July 30, only the San Joaquin Valley 

exceeded the 1-hour NAAQS for ozone. Concentrations at Parlier and Edison were 129 
15 

and 128 ppb, respectively. The highest reading on July 31 occurred at Livermore in the 

Bay Area. The measured peak value was 126 ppb. This was the only exceedance of 

the federal standard on that day in the CCOS domain. The only federal 1-hour 

exceedances on August 1 occurred in the Sacramento region, which experienced its 

highest ozone readings of the episode. On that day a peak value of 133 ppb ozone was 

observed at the Sloughouse site. Similarly, the only exceedances on August 2 occurred 

in the San Joaquin Valley, which had peak readings of 131 ppb in the northern part of 

the valley (Turlock and Modesto) and the maximum concentration for the episode at 

Edison of 151 ppb. 

Maximum daily 8-hour ozone concentrations exceeded 85 ppb beginning on July 30 

(see Table 2.2). Most of these were located in the San Joaquin Valley with a maximum 

of 106 ppb at Parlier. On July 31, ozone concentrations exceeded 85 ppb in the San 

Francisco Bay Area, the Sacramento Area, and the San Joaquin Valley. The frequency 

of high 8-hour ozone concentrations within the Sacramento Area and the San Joaquin 

Valley increased on August 1 and 2, with the maximum of 113 ppb recorded at Edison 

on August 2. 

During this episode period, there were a number of large wildfires in the southern part of 

the study domain. Model-based analyses suggested that these fires impacted ozone 

concentrations measured in Kern County. 

Table 2.2 The number of monitoring sites with measured 8-hour ozone concentrations 

exceeding 85 ppb and daily maximum measured concentrations. 

Total July 30 July 31 August 1 August 2 

Sites Sites Max Sites Max Sites Max Sites Max 

Bay Area 11 0 66 2 90 1 91 2 94 

Sacramento 

Valley 16 4 93 2 89 11 109 9 107 

San Joaquin 

Valley 25 14 106 11 104 18 110 19 113 

2.3 Available Observational Data 

Model performance for the computer simulations of the two episodes characterized 

above will be based on comparing model predictions with observational data collected 

from both routine field measurement efforts as well as from the Central California Ozone 

Study. The data networks for both of these sources are described below. 
16 

2.3.1 Routinely Collected Data (1999) 

Routine meteorological and air quality data are collected regularly through different 

network systems, including (1) the State and Local Air Monitoring Stations (SLAMS) 

network, (2) the National Air Monitoring Station (NAMS) network, (3) the Photochemical 

Assessment Monitoring Station (PAMS) network and (4) Special Purpose Monitoring 

(SPM) that is performed at some sites. More detailed information on routinely available 

data can be obtained from the California Air Resources Board web site at: 

http://www.arb.ca.gov/aqd/namslams/namslams.htm

2.3.2 Data Collected During CCOS (2000) 

The Central California Ozone Study database is comprised of data collected during the 

summer of 2000 at a variety of special study stations, routine field stations, and 

supplemental sources. The CCOS monitoring network is illustrated in Figures 2.1 and 

2.2. More specific information regarding the CCOS field study design and CCOS data 

collection efforts, including information on the supplemental data sources, can be found 

in the documents located under the following link: 

http://www.arb.ca.gov/airways/CCOS/CCOS.htm

In addition, CCOS observational data collected during the summer, 2000, field study 

can be accessed via interactive web queries at: 

http://www.arb.ca.gov/airways/Datamaintenance/default.asp
17 

Figure 2.1 Existing routine ozone and nitrogen oxides monitoring sites. 
18 

Figure 2.2 CCOS supplemental air quality and meteorological monitoring sites, 

and Photochemical Assessment Monitoring Stations. 
19 

3 MODEL SELECTION 

This chapter describes the selection of the meteorological and air quality models to be 

used. 

3.1 Meteorological 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 fields, 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. The models under consideration for SIP 

modeling are: 

• NCEP ETA model (Mesinger et al, 1988), 

• Regional Atmospheric Modeling System (RAMS) (Pielke et al, 1992), 

• Mesoscale Meteorological Model Version 5 (MM5) (Grell et al, 1994), and 

• Weather and Research Forecasting Model (WRF) (Skaramock et al, 2005). 

The NCEP ETA model is primarily used by the National Weather Service as a forecast 

model and has been used only in limited applications as a research tool. The RAMS 

model has been used extensively both as a research tool as well as a forecast model by 

various scientific communities. 

The Bay Area Air Quality Management District (BAAQMD) has been using RAMS along 

with MM5 over the last decade. The application of RAMS conducted by BAAQMD 

showed consistent results or no better than MM5 simulations for selected episodes. 

The recent air quality results of the BAAQMD indicated some undesirable model 

performance characteristics of RAMS (Martien, BAAQMD, 2004, personal 

communication). Also, the intensive effort from BAQQMD on improving the RAMS 

model during the past years was curtailed due to limited technical resources. 

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 
20 

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 

decades, since it has been widely used and tested for various meteorological regimes 

over the world and has been supported by the NCAR. 

NCAR is currently developing the WRF model to eventually replace MM5. However, the 

model is still under development and has not been extensively tested or demonstrated 

for SIP use. In addition, the preliminary tests that have been conducted by NCAR to 

date have not reported any significant improvement over MM5. 

Based on preliminary work by the stakeholder groups mentioned in Chapter 1 as well as 

the long history of utilizing MM5 for SIP modeling, the MM5 numerical model will be 

used to generate meteorological fields for SIP modeling. 

3.2 Photochemical Model 

ARB considered several photochemical air quality models to simulate the two episodes 

under consideration: 

• SAQM (Chang et. al.,1997), 

• CAMx (ENVIRON, 2004), and 

• CMAQ (USEPA 1999). 

The SAQM (SARMAP Air Quality Model) model was used for the 1994 ozone SIP 

modeling for the San Joaquin Valley (SJVUAPCD, 1994). This model was built upon 

the basis of the RADM (Regional Acid and Deposition Model) in 1993 for the SARMAP 

air quality study domain with non-optimal numerical calculation and coding. The 

program was not coded in a modular fashion to facilitate updates (like alternative 

modules), the documentation is incomplete, and the model has not been successfully 

applied to ozone modeling problems outside of the San Joaquin Valley since the 

SARMAP study. Embarking on updating and recoding this model for the present day 

would require an unwarranted level of resources to adopt the most recent transport 

numerical schemes and photochemical mechanisms (or any other major modification). 

The CMAQ model is a widely recognized and highly regarded photochemical model 

supported by the US EPA. It has been widely used throughout most of the United 

States for ozone, PM, and visibility analysis; however, its successful application within 

California has been limited. It is a flexible model and allows the selection of alternative 

modules, such as a different chemical mechanism, advection scheme, or chemical 

solver. The CMAQ model has been shown to run slower than alternative models; 

however it has been linked to the MPI (Multiple Processor Integration) software library 

package to run in parallel through a distributed process mode, significantly reducing 

episodic run times. 
21 

The CAMx model is used throughout the United States. It is widely viewed as one of 

the better documented and supported air quality models. Periodic updates from the 

developers ensure that more recent technical developments are incorporated. The 

CAMx model has also been shown to be very flexible. Alternative chemical 

mechanisms (CB-IV and SAPRC99) and advection schemes can be selected and 

meteorological inputs may be developed from objective/diagnostic or prognostic 

meteorological models. It also has two built-in probing tools, DDM (Decoupled Direct 

Method) for formal sensitivity analysis and PA (Process Analysis) for model dynamic 

examination. A PM module for simultaneous simulations of ozone and aerosols has 

recently been updated. 

Tonneson (2003a) prepared test case simulations for the July-August, 2000 episode 

using the CAMx, SAQM, and CMAQ models. Within the allotted time, the simulations 

using CAMx were the only ones completed. However, the study strongly suggested 

better model performance by CAMx over the other two (Tonneson, 2003b). At CARB 

(CARB, 2003), the CAMx air quality model was configured for the July-August, 2000 

episode in approximately 1 day (excluding the development of the required 

meteorological fields). The preparation of the SAQM took approximately 2 weeks, and 

the CMAQ model took approximately 2 months (much of this time was spent 

investigating the installation of the CMAQ code and the code for the IOAPI and MPI 

libraries on an LINUX system and addressing file-size limitations). Given this relative 

ease of use, its acknowledge stature as a state-of-the-art photochemical air quality 

model, and its flexibility in accepting meteorological inputs, the CAMx model was 

selected as the primary air quality model for the CCOS ozone modeling. This decision 

was reinforced by the selection of CAMx for SIP modeling by the South Coast Air 

Quality Management District (SCAQMD, 2005) and the hope that one model can be 

used throughout the State of California. 

While selecting CAMx as the primary model of choice for CCOS modeling, it was also 

acknowledged that CMAQ is an alternative model that is being widely used across the 

United States for ozone SIP modeling. Therefore, as resources allow, the CMAQ 

photochemical model may also be run for the episodes to compare the performance of 

the two models. 
22 

4 MODELING DOMAIN AND GRID STRUCTURE 

As described in Chapter 1, stakeholders have already produced a significant body of 

work, including model simulations, for the two selected modeling episodes. Selection of 

the domain and grid structure described in this chapter and to be used for SIP modeling 

are based on this prior experience. 

4.1 Meteorological Modeling Domain 

The MM5 meteorological modeling domain is consistent for both episodes. It consists 

of three nested grids: 36 km, 12 km and 4 km uniform, horizontal grid spacing 

(illustrated in Figure 4.1). 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 subsequent two inner grids, D02 and D03, are placed within the 

coarser grids such that they are not too close to the lateral boundaries. The innermost 

grid D03 consists of 189 x 189 grid cells having an origin at -384 km x -300 km (Lambert 

Conformal projection). Although a nested grid structure is configured, each modeling 

domain was run independently using the output of its coarser, parent grid as input. The 

D03 grid is intended to resolve the fine details of atmospheric motion and is used to 

feed the air quality model simulations. 

4.1.1 July-August, 2000 Episode Application 

The vertical structure of the modeling domain for this episode was developed under 

CCOS (Chapter 1) and consists of 50 vertical layers, for which the top layer extends to 

a height of approximately 15,000 magl (Table 4.1). 20 vertical layers were placed within 

the first 1000 magl of the whole depth of the modeling domain to resolve the small 

boundary layer atmospheric flow features such as large eddies and vertical advection of 

the fluxes of all meteorological quantities. Model integration was executed between 

July 29, 2000 at 12Z and August 3, 2000 at 12Z. 

 

4.1.2 July, 1999 Episode Application 

The vertical layer structure for the July 1999 episode was developed in collaboration 

with the Bay Area MAC (Chapter 1) and is configured in 30 layers, as shown in Table 

4.2. 
23 

Figure 4.1: The structure of the three nested grids adopted for the numerical 

modeling for both SIP episodes using the MM5 model (D01 36km; D02 12km; 

and D03 4km). 

DO1 

D02 

D03 
24 

Table 4.1 MM5 50 Vertical Layer Configuration for July-August 2000 Episode 

Layer No. Height (m) Layer Thickness (m) 

50 15674 1503 

49 14171 1502 

48 12669 1503 

47 11166 1503 

46 9663 1152 

45 8511 993 

44 7518 856 

43 6662 738 

42 5924 636 

41 5288 547 

40 4741 473 

39 4268 408 

38 3860 351 

37 3509 303 

36 3206 272 

35 2934 246 

34 2688 221 

33 2467 199 

32 2268 180 

31 2088 162 

30 1926 146 

29 1780 131 

28 1649 119 

27 1530 106 

26 1424 96 

25 1328 87 

24 1241 82 

23 1159 81 

22 1078 77 

21 1001 74 

20 927 71 

19 856 69 

18 787 65 

17 722 63 

16 659 61 

15 598 59 

14 539 56 

13 483 54 

12 429 50 

11 379 48 

10 331 44 

9 287 41 

8 246 39 

7 207 36 

6 171 34 

5 137 31 

4 106 29 

3 77 27 

2 50 26 

1 24 24 

0 0 0 
25 

Table 4.2 MM5 30 Vertical Layer Configuration for July 1999 Episode 

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 

5 241 67 

4 174 56 

3 118 47 

2 71 39 

1 32 32 

0 0 0 
26 

4.2 Photochemical Modeling Domain 

The objectives of the SIP require that the photochemical modeling domain include all of 

the Central Valley of California and upwind areas. The constraints of the CAMx air 

quality model require that the domain be rectangular to accept meteorological input 

fields from MM5. The resulting ozone modeling domain is mapped in a Lambert 

Conformal, Conic Projection with parallels at 30°N and 60°N latitude, with a central 

meridian at 120.5°W longitude. The domain origin is defined at 37°N x 120.5°W. The 

photochemical modeling domain is defined horizontally as 185x185, 4x4-km grid cells, 

shown in Figure 4.2. The domain lower, left-hand corner is at -376 x -292 km from the 

defined origin. The MM5 output for the 4-km modeling domain consists of a grid of 189 

x 189 grid cells having an origin at -384 km x -300 km, and has been processed to 

match the air quality model domain. The emissions inventory domain that has 190x190 

grid cells is also processed in order to match the air quality model domain. The vertical 

structure of the air quality modeling domain, depending on the meteorological model 

configurations, will be adjusted accordingly to generate the required inputs for two 

episodes.

The 4-km resolution domain included areas of ocean and land, and terrain elevations 

(cell-averaged) from sea level to 3712 magl. The San Joaquin Valley is part of the 

larger Central Valley of California than runs roughly north/south and is surrounded by 

mountains, except in the vicinity of the SFBA. 

There are two scenarios being evaluated for the determination of the height of the 

ozone modeling domain. In the first view, referred to herein as the ‘MM5’ view 

(although not used for all MM5-based air quality simulations) the vertical reach of the 

ozone modeling domain extends to the height of the top of the prognostic modeling 

domain at 100 millibars (~15 km). To reflect this view, the vertical structure of the ozone 

modeling domain was defined as 20 layers using the sigma coordinate system. In the 

sigma coordinate system, vertical layer heights were defined in terms of normalized 

pressure levels, therefore, the exact thickness of each layer varies somewhat as air 

temperature and density change across the domain. An additional justification for a 

vertical domain to 15,000 magl is that the presence of deep vertical layers aloft would 

dampen adverse effects from spurious vertical velocities that may occur in the wind 

fields from some meteorological models (ENVIRON, 2005a). 
27 

Figure 4.2 Photochemical Modeling Domain with185x185 

Grid Cells at 4x4-km Horizontal Resolution 

The number of vertical layers used in the ozone simulations will be tied closely to the 

meteorological model ultimately used. Configurations of 16-layer and 20-layer will be 

used for the July 1999 and July-August 2000 episodes, respectively, as shown in 

Table 4.3. 

All photochemical model simulations will be run using a Pacific Daylight Time (PDT) 

time base. 
28 

Table 4.3 Vertical Layer Heights(m) of Photochemical Modeling for 

July-August 2000 and July 1999 Episodes.

July-August episode July 1999 episode 

Layer No. 50-Layer MM5 configuration 30-Layer MM5 configuration

Height(m) Height(m) 

20 15673 

19 12669 

18 9663 

17 7518 

16 5289 4873 

15 3860 3368 

14 2935 2306 

13 2268 1560 

12 1781 1275 

11 1424 1037 

10 1159 839 

9 927 673 

8 722 534 

7 540 418 

6 329 322 

5 246 241 

4 172 174 

3 107 118 

2 50 71 

1 24 32 
29 

5 MODEL INITIALIZATION AND BOUNDARY CONDITIONS 

Regional meteorological and air quality models must be initialized so that the chemical 

and physical conditions at the start of a model simulation approximate ambient 

conditions. This chapter is divided into two sub-sections that cover the initialization of 

the meteorological model (MM5) and the air quality model (CAMx) separately. Each 

section briefly covers the data upon which model initialization is based. 

5.1 Initialization of the Meteorological Model 

MM5 is a complex numerical model that requires setting a large number of input 

parameters and model options. Some of these requirements include: the specification 

of initial and boundary conditions (IC/BCs); gathering and processing representative 

data to be used for initial/boundary conditions as well as FDDA; and the selection of a 

variety of algorithms to calculate meteorological parameters, such as winds, 

temperature, humidity, pressure, soil temperature, the depth of the planetary boundary 

layer, cloud microphysics, and radiative transfer. 

There is no apriori guidance on the specific data or options to be used in MM5. Rather, 

these decisions are determined based on optimizing model performance. Thus, during 

the preparation of preliminary meteorological fields for the July-August 2000 and 

July 1999 SIP episodes, vast amounts of data were processed and many combinations 

of model options were tested. Based on the best model performance for these 

preliminary tests, the most successful MM5 model options and input datasets were 

determined. These are described in the following sections. 

5.1.1 MM5 Model Options 

As indicated above, many sensitivity studies were conducted to choose a set of model 

options that result in scientifically reasonable meteorological fields that are 

representative of the specific conditions during each of the two selected ozone 

episodes. 

For the July-August 2000 episode, the Kain and Fritsch (1993) cumulus 

parameterization scheme was selected for coarse grids, while no cumulus 

parameterization was used for the 4 km grid. In addition, the ETA model for the 

parameterization of boundary layer flow (Janjic, 1994), Dudhia simple ice scheme for 

the treatment of cloud microphysics (Dudhia, 1989), the RRTM scheme for the 

calculation of radiation (Mlawer, 1997), and NOAH Land Surface Model for the 

calculation of surface energy balance (Chen and Dudhia, 2001) were used in all grids. 
30 

For the July 1999 episode, no cumulus parameterization scheme was used for any of 

the grids and a 5-layer slab model (Dudhia, 1996) was used for the calculation of 

surface energy balance for all grids. The cloud radiation scheme was used for the 4 km 

grid. All other model options were kept the same as those used for July-August 2000 

episode. 

5.1.2 MM5 Initial and Boundary Conditions (IC/BC) 

The MM5 IC/BCs were prepared based on 3-D analyses of ETA model output that is 

archived at NCAR by the National Center for Environmental Prediction (NCEP). These 

data are archived for the continental United States and have a 40 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 by NCEP 

are also used to further refine the IC/BCs. 

5.1.3 MM5 Four Dimensional Data Analysis (FDDA) 

The MM5 model was forced to follow the meteorological conditions observed during the 

July-August 2000 and July 1999 episodes 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 

observational nudging option of FDDA was used to enhance these input conditions for 

the two episodes. Only wind measurements were used for observational FDDA due to 

some inconsistent temperature measurements. 

The extent of meteorological data available for developing FDDA input datasets is 

different for the July-August 2000 and July 1999 episodes. The July-August 2000 

episode benefited from the extensive CCOS field campaign conducted during the 

summer of 2000. About 300 surface and 25 upper air meteorological stations were 

operated during CCOS field campaign (Section 2.3) and provided additional 

meteorological data in addition to the routine surface data that are available through the 

instruments operated by local air districts, ARB, and National Weather Service. Since a 

separate field campaign was not in place during the July 1999 episode, this episode had 

only six upper air meteorological stations plus the hourly surface data available from 

routine monitoring networks. 

5.1.4 Meteorological Data Quality Assurance 

In developing the IC/BCs and FDDA datasets, quality control is performed on all 

associated meteorological data (both inputs and outputs). 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 (e.g. 

after discussion with peers and stakeholders), the outlier data points are flagged as 

invalid and not used in the modeling analyses. Model-simulated meteorological 
31 

parameters such as 3-D winds, temperature, pressure, and humidity values are 

compared against surface and upper air observations to study the temporal and 3-D 

spatial structure of atmospheric motions as well as to evaluate the model performance. 

More details on the evaluation of model performance are provided in Chapter 7. 

5.2 Air Quality Model Initial and Boundary Conditions 

Air quality model initial conditions define the concentration distributions of chemical 

species within the modeling domain at the beginning of the model simulation. Boundary 

conditions define the chemical species concentration distributions for air entering or 

leaving the modeling domain. To some extent the initial and boundary conditions need 

to reflect the modeling domain dimensions, the episode, and the characteristics of the 

model being used. 

This section discusses the initial and boundary conditions used by the Air Resources 

Board (ARB) in air quality modeling that will support developing the 8-hour ozone State 

Implementation Plan (SIP). The selected boundary conditions are summarized in 

Tables 5.2 and 5.3, while episode-specific initial conditions are provided in Table 5.4. 

These conditions were determined with stakeholder input at a March 10th, 2005, 

meeting of regulatory agency modeling staff. 

5.2.1 Photochemical Mechanism 

Historically, over the last several decades, air quality modeling for ozone SIPs 

throughout California have predominately been conducted using the Carbon Bond IV 

(CBIV) chemical mechanism. The CBIV mechanism uses 36 chemical species and 89 

chemical reactions (may vary somewhat among different air quality models) to describe 

the relationship between ozone and ozone precursors in the atmosphere. Over the last 

decade, more complex chemical mechanisms, such as the 1999 State Air Pollution 

Research Center chemical mechanism (SAPRC99; Carter, 2000), have been 

developed; however, the use of SAPRC99 has historically been restricted by limited 

implementation in newer air quality models and the relatively large computational 

requirements. 

Since SAPRC-99 is the most up-to-date chemical mechanism available (74 chemical 

species and 211 chemical reactions) and has been thoroughly peer-reviewed, ARB’s 

Reactivity Scientific Advisory Committee recommended unanimously in October of 1999 

that ARB use SAPRC-99 instead of CBIV for SIP modeling. Minutes of the October 8, 

1999, Reactivity Scientific Advisory Committee (RRAC) can be found at: 

http://www.arb.ca.gov/research/reactivity/rsac/oct99-min.html

In central and northern California, SAPRC has been the mechanism of choice for over a 

decade. Consistent with this and with the expectation of better representation of 
32 

atmospheric chemical behavior for ozone modeling, the SAPRC99 chemical mechanism 

was selected for all 8-hour ozone air quality modeling in California. 

5.2.2 Supporting Information 

This section discusses ambient concentrations available from measurements, related 

studies, as well as USEPA guidance on initial conditions, boundary conditions, and 

background concentrations of pollutants. Ideally, initial and boundary conditions used in 

modeling would be based upon measurements. Unfortunately, for a domain as large as 

that defined for CCOS, the boundaries are located in remote Pacific ocean areas (56km 

from northern coast line, 200km from S.F. coast line, and 360km from southern coast 

line) and there are few measurements that may be considered uncontaminated by 

anthropogenic sources. 

The USEPA (1991) recommends default initial/boundary conditions based upon species 

for the Carbon Bond IV chemical mechanism. Table 5.1 shows USEPA’s 

recommended initial concentrations for individual CBIV species, including 40 ppb 

ozone, 2 ppb NOx, and approximately 22 ppbC ROG. 

During the year 2000 CCOS field study, pollutant concentrations aloft were measured 

along several aircraft flight patterns and from ozonesondes at two locations (Granite 

Bay and Parlier). However, comparison of measurements taken along the coast of 

California versus historical data collected offshore suggest that the CCOS 

concentrations may not represent concentrations occurring over the Pacific Ocean. 

Thus, whether the CCOS samples are appropriate for the definition of top or lateral 

boundary concentrations is subject to interpretation. 

Ozonesondes were launched four times per day from Parlier and Granite Bay during 

CCOS intensive measurement periods. However, these sites were located within the 

Sacramento and San Joaquin Valleys and are not necessarily well suited to represent 

ozone concentrations on the lateral domain boundaries, several hundreds of kilometers 

from the launch locations. During the July-August, 2000 episode, ozone concentrations 

at 5000 meters above ground level (magl) ranged between about 50 and 90 pbb. 

However, during other episode periods such as the September 18-20, 2000 episode, 

ozonesonde measurements showed ozone concentrations at 5000 magl of 40 ppb or 

less. 

Unfortunately, the CCOS aircraft measurements were conducted at altitudes that rarely 

exceeded 1500 magl. Depending on the time of day and flight-path, ozone 

concentrations measured aloft from the aircraft ranged from 15 ppb to more than 

100 ppb. Ozone concentrations during CCOS in the 70-80 ppb range were measured 

from aircraft as far as 160 km offshore (to the west); however, these flights were few 

and the evaluation of wind flow patterns during these flights casts doubt on the 

representativeness of these measurements for model boundary concentrations. 
33 

A study by Newchurch et. al. (2003) reported annual-average ozone concentrations 

aloft from ozonesondes at four sites in the U.S. Among them, the Trinidad Head site is 

located at the north coast of California as shown in Figure 5.2. Ozone measurements 

from two single day ozonesondes launched by the National Atmospheric and Oceanic 

Administration (NOAA) on July 21 and August 1, 2000, at Trinidad Head and two 

CCOS ozonesondes during a CCOS intensive measurement period (IOP #2) are plotted 

in Figure 5.3. NOAA’s measurements at Trinidad Head show ozone concentrations at 

15 km aloft of around 135 ppb. The CCOS measurements at Granite Bay and Parlier 

have higher ozone levels below 5000 magl than the ozonesonde at Trinidad Head. This 

is likely due to location – higher ozone levels in the Sacramento and San Joaquin 

Valleys are expected versus on the coastline. An analysis conducted by the Bay Area 

Air Quality Management District (BAAQMD) found a similar difference: that measured 

concentrations along the California coastline consistently have lower ozone levels in 

comparison with inland measurements. 
34 

Table 5.1 USEPA Default Background Concentrations for Carbon 

Bond-IV Species (1991).

_____________________________________________________________

Species Species Names Concentration (ppbC)

OLE Olefins 0.60 

PAR Paraffins 14.94 

TOL Toluene 1.26 

XYL Xylene 0.78 

FORM Formaldehyde 2.10 

ALD2 Higher Aldehydes 1.11 

ETH Ethene 1.02 

CRES Cresol, Higher Phenols 0.01 

MGLY Methyl Glyoxal 0.01 

OPEN Aromatic ring fragment Acid 0.01 

PNA Peroxynitric Acid 0.01 

NXOY Total Nitrogen Compunds 0.01 

PAN Peroxyacyl Nitrate 0.01 

HONO Nitrous Acid 0.01 

H2O2 Hydrogen Peroxide 0.01 

HNO3 Nitric Acid 0.01 

MEOH Methanol 0.10 

ETOH Ethanol 0.10 

O3 Ozone 40.00 (ppb) 

NO2 Nitrogen Dioxide 2.00 (ppb) 

CO Carbon Monoxide 350.00 (ppb) 

ISOP Isoprene 0.10 (ppb) 

_____________________________________________________________ 
35 

Figure 5.2. Trinidad Head ozonesonde site operated by NOAA. 

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26 0.28 0.3 0.32 0.34 0.36 0.38 0.4

Ozone (ppm)

Altitude (km)

21-Jul-00

1-Aug-00

PLR(073000-080200)

GNBY(073000-080200)

Figure 5.3. Ozone vertical profiles measured by ozonesonde at Trinidad Head 

(operated by the NOAA) as well as Granite Bay and Parlier (operated during CCOS). 
36 

The USEPA recommends a boundary condition for ozone of 40 ppb. However, 

analyses conducted by the BAAQMD (2005) suggested that lower concentrations are 

common at the surface near the Pacific coastline. 

Reactive Organic Gases (ROGs) are the most difficult pollutant category to provide a 

measurement-based assessment of boundary concentrations, since there are only a 

limited number of measurements available. At the surface, all of the ROG samples 

collected during CCOS were made at sites located within the San Joaquin Valley, which 

are unlikely to represent boundary concentrations. A few dozen aircraft-based ROG 

samples were collected during the July-August, 2000, episode. However, the aircraft 

samples collected were of only short duration and an evaluation of the offshore flow 

patterns during these flights casts doubt on whether the measurements taken are 

representative of boundary concentrations. 

For ROG boundary conditions, the U.S. EPA (1991) recommends default 

concentrations of 22 ppbC. However, ROG concentrations measured at the surface 

during CCOS were often higher than this. Aloft, concentrations of ROG measured 

during CCOS aircraft flights ranged from less than 10 to 100 ppbC. For most of the 

CCOS aircraft samples collected during the July-August, 2000 episode, ROG 

concentrations were between 20 and 40 ppbC. These data suggest that, while higher 

ROG concentrations occurred aloft, the concentrations aloft were not uniformly high. 

Analyses conducted by STI under a CCOS contract (2005) reported no significant 

correlation between high ROG concentrations aloft and high ozone concentrations 

observed throughout the episode. The data further suggested that the ROG 

concentration of 22 ppbC suggested by the USEPA was a reasonable estimate of clean 

air concentrations. 

5.2.3 Boundary Concentrations 

The recommended initial and boundary conditions are tabulated in Tables 5.2 and 5.3. 

More episode-specific details are provided in Attachment 1. The selected conditions 

were determined with BAAQMD, SJVAPCD, and SMAQMD stakeholder input at a 

March 10th, 2005, meeting of the SIP Modeling Working Group (Attachment 2). 

Because of their relatively clean values, the boundary conditions for future years are 

kept the same as boundary conditions for the base years. 
37 

Table 5.2. Recommended air quality modeling domain boundary conditions. 

Region Top* Over-Water 

Lateral 

Over-Land 

Lateral 

O3 (ppb) 70 25-70 40-70 

ROG (ppbC) 26 26 48 

NO2 (ppb) 1 1 1 

CO (ppb) 200 200 200 

* The July 1999 episode domain top is at approximately 5 km and the CCOS 2000 domain top 

is at approximately 15 km. 

Table 5.3. Recommended SAPRC99 boundary conditions (ROG). 

Over-Land Over-Ocean 

Specie(s) ppb* Specie(s) ppb* 

HCHO 2.0 HCHO 2.0 

RCHO 0.5 RCHO 0.5 

ALK1 10.0 ALK1 6.0 

ALK2 2.50 ALK2 1.0 

OLE1 0.50 OLE1 0 

OLE2 0.20 OLE2 0 

ARO1 0.35 ARO1 0 

ARO2 0.25 ARO2 0 

ISOP 0.10 ISOP 0 

ACET 1.0 ACET 1.0 

PAN 0.005 PAN 0.005 

* Based on USEPA (1991) and approximate 22 ppbC Carbon Bond IV ROG (chemical species not 

listed were set to concentrations of 0.00001). 
38 

Table 5.4. Summary of Episode-Specific Ozone & Precursor Boundary Conditions. 

July 1999 

(ppm) 

July-Aug. 

2000 (ppm) 

 

Layer 

Elevation 

(m) Over water Over land Layer 

Elevation 

(m) Over water Over land 

1 32 0.025000 0.040000 1 24 0.025000 0.040000 

2 71 0.025000 0.040000 2 50 0.025000 0.040000 

3 118 0.026000 0.042000 3 107 0.026000 0.042000 

4 174 0.028000 0.043000 4 172 0.027000 0.043000 

5 241 0.030000 0.045000 5 246 0.028000 0.045000 

6 322 0.040000 0.050000 6 379 0.030000 0.045000 

7 418 0.045000 0.052000 7 540 0.030000 0.050000 

8 534 0.050000 0.055000 8 722 0.040000 0.052000 

9 673 0.055000 0.058000 9 927 0.045000 0.055000 

10 839 0.058000 0.060000 10 1,159 0.050000 0.055000 

11 1,037 0.060000 0.062000 11 1,424 0.055000 0.058000 

12 1,275 0.062000 0.064000 12 1,781 0.058000 0.060000 

13 1,560 0.064000 0.065000 13 2,268 0.060000 0.060000 

14 2,306 0.065000 0.066000 14 2,935 0.062000 0.062000 

15 3,368 0.068000 0.068000 15 3,860 0.063000 0.063000 

16 4,873 0.070000 0.070000 16 5,289 0.064000 0.064000 

 17 7,518 0.065000 0.065000 

 18 9,663 0.066000 0.066000 

 19 12,669 0.068000 0.068000 

 20 15,673 0.070000 0.070000 

 

Precursors (ppm) 

 Over water Over land 

NO 0.000050 0.000050 

NO2 0.001000 0.001000 

CO 0.200000 0.200000 

HCHO 0.002000 0.002000 

RCHO 0.000500 0.000500 

PAN 0.000005 0.000005 

ALK1 0.006000 0.010000 

ALK2 0.001000 0.002500 

OLE1 0.000000 0.000500 

OLE2 0.000000 0.000200 

ARO1 0.000000 0.000350 

ARO2 0.000000 0.000250 

ISOP 0.000000 0.000100 

ACET 0.001000 0.001000 

ROG 

(ppbC) 26 48 
39 

5.2.4 Initial Conditions and Spin-Up Period 

User-defined initial concentrations are often based on limited observational data and 

associated with a degree of uncertainty. To alleviate these uncertainties, the air quality 

model is started prior to the period of interest (i.e. a spin-up period) in an effort to allow 

the air quality model to generate appropriate initial conditions based on emissions and 

boundary conditions. Utilizing a spin-up period also reduces the affects of not 

specifying secondary reaction products or chemical radicals at start-up. That is, the 

spin-up period allows the model to use simulated meteorology and chemical 

transformation processes to generate more representative secondary and radical 

concentrations prior to beginning the simulation of air quality during the episode days of 

interest. 

For both modeling episodes, a 2-day (48 hours) spin-up period will be utilized to 

minimize the impacts of the defined initial concentrations on the model predictions. 
40 

6 EMISSION INVENTORY DEVELOPMENT 

One of the necessary inputs to air quality modeling is an emission inventory with 

temporally and spatially resolved emissions estimates. Emissions are broadly 

categorized into major stationary or point sources, area sources (which include off-road 

mobile sources), on-road mobile sources, and biogenic sources. 

To support the body of work conducted by stakeholders, modeling inventories have 

been developed by ARB staff on an on-going basis for the July 1999 and July-August 

2000 episodes. The following sections describe how emissions estimates required by 

the selected air quality models (commonly and interchangeably referred to as ‘modeling 

inventories’ or ‘gridded inventories’) are estimated and how they will be used to develop 

base case and future year emissions estimates for modeling used to prepare the State 

Implementation Plan (SIP). As modifications to basic inventory inputs are approved by 

the responsible regulatory agencies, including ARB, they will be incorporated into final 

SIP modeling. Once final SIP modeling is complete, the specific versions of the 

emission inputs used will be documented and summarized. 

To help coordinate the development of gridded inventories for CCOS modeling, an 

Emission Inventory Coordination Group (CCOS EICG) was established in February 

1999. Participating in the group were many local air districts, regional transportation 

planning agencies (RTPAs), the California Department of Transportation (Caltrans), the 

California Energy Commission, the U.S. Environmental Protection Agency, and the 

ARB. Local air districts that participated included San Joaquin Valley APCD, Bay Area 

AQMD, Sacramento Metropolitan AQMD, Mendocino County APCD, Northern Sierra 

AQMD, Yolo/Solano AQMD, Placer County APCD, San Luis Obispo County APCD, and 

Monterey Bay Unified APCD. All local air districts in the CCOS region were invited to 

participate. The CCOS-EICG coordinated six studies through CCOS to improve the 

emission inventory: 

• Small district assistance with point source updates (Contract 00-22CCOS, UC 

Davis). Section 6.2.1.3 describes this project in more detail. 

• Small district assistance with area source updates (Contract 00-24CCOS, 

Sonoma Technology, Inc). Section 6.2.1.3 describes this project in more detail. 

• Collect day-specific traffic count data and develop hourly distributions (Contract 

00-04PM, UC Davis). Section 6.7.6 provides more detail. 

• Develop the Integrated Transportation Network (ITN) and run the Direct Travel 

Impact Model (DTIM) (Contract 93-2PM, Alpine Geophysics). Section 6.7.9 

describes this project in more detail. 

• Validate databases for modeling biogenic emissions (Contract 00-16CCOS, UC 

Cooperative Extension). Section 6.8 provides more detail. 
41 

• Develop spatial surrogates for gridding area and off-road sources (Contract 00-

24CCOS, Sonoma Technology, Inc.). Section 6.9 describes this project in more 

detail. 

The CCOS EICG met on a regular basis to discuss CCOS emission inventory 

development issues into 2002. 

As indicated in Chapter 1, as modeling inventories became available for the 1-hour 

ozone SIPs, the Air Resources Board established a SIP Gridded Inventory Coordination 

Group (SIP-GICG) in February 2003. The GICG consists primarily of government 

agencies and their contractors that are responsible for the variety of data used to 

develop gridded emission inventories for SIP purposes. Many of the same participants 

in the CCOS-EICG participate in the SIP-GICG. The purpose of the SIP-GICG is to 

conduct quality assurance of the emissions, and to distribute and coordinate the 

development of emission inputs for SIP modeling. In February 2005, the focus was 

changed to inventory development for the 8-hour ozone SIPs. Minutes from the SIPGICG meetings are provided in Appendix A. 

6.1 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 reflected the best information available at 

the time. 

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 
42 

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 called the California Emission Inventory Development and Reporting 

System (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). Reactive 

organic gases (ROG) and particulate matter 10 microns in diameter and smaller (PM10) 

are calculated from TOG and PM, respectively. Following are more details on how 

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

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

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

6.2 Point and Area Source Emissions 

6.2.1 Development of Base-Year Emission Inventory 

The stationary source component of the emission inventory is comprised of more than 

17,000 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. Some 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 consumer products, architectural coatings, 

pesticide applications, and wind-blown dust from agricultural lands. 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 off-road mobile source inventory is based on the population, activity, and emissions 

estimates of the varied types of off-road equipment. The major categories of engines 
43 

and vehicles include agricultural, construction, lawn and garden, and off-road 

recreation, and include equipment from hedge trimmers to cranes. ARB’s OFFROAD 

model estimates the relative contribution of gasoline, diesel, compressed natural gas, 

and liquefied petroleum gas powered vehicles to the overall emissions inventory of the 

state. In previous versions of the inventory, emissions from the OFFROAD model were 

aggregated into about 100 broad categories. Since April 2006, the inventory reports 

emissions in about 1800 detailed categories that match what is produced by the 

OFFROAD model. 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. 

6.2.1.1 Terminology 

There can be confusion regarding the terms “point sources” and “area sources”. 

Traditionally, these terms have had two different meanings to the developers of 

emissions inventories and the developers of modeling inventories. Table 6.2 

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 areawide 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 CCOS 

inventories, all point sources were treated as possible elevated sources. Processing of 

the inventory for the photochemical model (e.g. CAMx) will determine which vertical 

layer the emissions from a process will be placed into. So, for the CCOS modeling 

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

or emission inventory definition. 
44 

Table 6.2 Inventory Terms 

Modeling Term Emission Inventory Term Examples 

Point Stationary – Point Facilities Stacks at Individual 

Facilities 

Area Off-Road Mobile 

Farm Equipment, 

Construction Equipment, 

Aircraft, Trains 

Area Area-wide 

Consumer Products, 

Architectural Coatings, 

Pesticides 

Area Stationary - Aggregated Industrial Fuel Use 

On-Road Motor Vehicles On-Road Mobile Automobiles 

Biogenic Biogenic Trees 

6.2.1.2 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 many local districts, including the Bay Area AQMD, Monterey 

Bay Unified APCD, Sacramento Metro AQMD and San Joaquin Valley APCD conducted 

extensive quality assurance to provide an accurate and complete inventory. Districts in 

the southern part of California had recently completed a similar exercise to improve their 

inventories as part of the Southern California Ozone Study (SCOS). 

In particular, facility location, stack data, and temporal data were closely checked. This 

information is critical whenever photochemical modeling is conducted, such as during 

SIP preparation or special studies such as CCOS. 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 that was used 

initially for CCOS and 1-hour ozone SIP inventory preparation. This QA process has 

continued with the 2002 CEIDARS database, which is the basis for the modeling 

inventories being developed for the 8-hour ozone SIP. 

• 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 
45 

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 Section 

6.10). The SIC and SCC combined determine emission control rules that may 

apply for forecasting emissions (see Section 6.3) along with the categorization of 

emissions for reporting purposes. 

6.2.1.3 Improvements to Base Year Emissions for 

CCOS 

In addition to the extensive QA checks described above, the CCOS Emission Inventory 

Coordination Group agreed to assist the small districts in the CCOS domain. Many 

small districts in the CCOS region have limited staff and resources to provide updated 

emission inventories to the ARB. After discussion with staff from districts in the 

Sacramento Valley and Mountain Counties Air Basins, two studies were decided upon. 

One study would focus on point sources and the second on area sources. 

District staff said that they did have emission estimates for their point source facilities, 

but that they did not have the resources to provide the data to ARB. The first study sent 

engineering students from UC Davis (Kleeman, 2000) to visit several districts to gather 

the emissions and related data for 1999. The students then put the information into 

ARB’s CEIDARS database. Two teams containing three students and one ARB staff 

person each visited Amador County APCD, Butte County AQMD, Colusa County APCD, 

El Dorado County APCD, Feather River AQMD, Glenn County APCD, Northern Sierra 

AQMD, Placer County APCD, Shasta County AQMD, Tehama County APCD, 

Tuolumne County APCD, and Yolo/Solano AQMD. The results of this project have 

been incorporated into the 1999, 2000, and 2002 CEIDARS inventories. 

For area sources, district staff said that the best way to provide assistance would be to 

have a contractor develop emission estimates for the area source categories for which 

the districts were responsible. The CCOS study contracted with Sonoma Technology, 

Inc. (STI) (Coe, 2003) to prepare revised emissions estimates. STI would format the 

emissions and related data for input into the CEIDARS database. District staffs have 
46 

included these updates in the 2002 database. STI developed protocol memoranda that 

contained the following elements: 

• Description of emission source 

• Emission factors 

• Activity data 

• Emissions calculations, including a sample calculation 

• Temporal allocation 

• References and contacts 

The protocols were pulled together from a variety of resources, including local air 

districts’ past methods documents, U.S. Environmental Protection Agency documents, 

ARB documents, and original ideas based on the discovery of new information sources 

through library research, Internet research, and telephone contacts. Generally, STI 

attempted to incorporate data and information resources into the protocols that are 

readily available to the general public at no or low cost. And, while these methods and 

information resources are useful, it is recognized that it is more ideal to use highly 

customized or bottom-up emissions estimates when the costs of these efforts are 

warranted. 

Emissions were estimated for the following counties: Amador, Butte, Calaveras, Colusa, 

E. Solano, El Dorado, Glenn, Mariposa, Mendocino, Nevada, Placer, Plumas, 

Sacramento, Shasta, Sierra, Sutter, Tehama, Tuolumne, Yolo, and Yuba. Area source 

methodologies were developed for the following broad categories: 

• Asphalt paving/roofing 

• Chemical and related products manufacturing 

• Cleaning and surface coatings and related process solvents 

• Fuel combustion: 

- Commercial natural gas 

- Commercial liquid fuels 

- Industrial natural gas 

- Industrial liquid fuels 

- Unspecified 

- Resource recovery 

- Petroleum production 

• Cooking 

• Wastes (e.g. livestock waste and landfills) 

• Food and agriculture 

• Mineral and metal processes 

• Miscellaneous processes (e.g. miscellaneous industrial processes) 

• Petroleum marketing 

The protocol memoranda can be found on a password-protected project web site 

(URL: www.sonomatech.com/ccosii/; user name: “ccosii”; password: “emissions”). 
47 

6.3 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 

Forecasting System (CEFS). The CEFS model 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 CEIDARS emission 

categories for a particular base year (2002 for this project). A key component of the 

model is the Rule Tracking Subsystem (RTS). The RTS was developed to link yearspecific 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. 

Reports of year-specific emissions are available to district staff on-line. District staffs 

should contact their emission inventory liaisons for URL and password information. The 

reports can be generated for a variety of years, pollutants, source types, seasons, and 

geographical areas. 

6.3.1 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 

• 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 SIC. For area-wide and aggregated point 

sources, other growth parameters such as population, dwelling units, and fuel usage 

may be used. 

6.3.2 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 
48 

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. 

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. 

6.4 Day-Specific Emissions 

As part of CCOS, the Emission Inventory Coordination Group (EICG), made up of ARB 

and district staff to guide inventory development for CCOS, requested that districts 

within the CCOS domain collect day-specific data from facilities and other sources 

within their jurisdiction. The EICG gathered hourly/daily emission information for: 

1) large point sources (> 100 tons per year of NOx or ROG) 

2) sources with large variability in emissions (e.g. power plants) 

3) unusual events (e.g. source shut down, variances, breakdowns) 

4) agricultural or prescribed burning 

5) shipping emissions for the Bay Area 

6) wildfires 

6.4.1 Point Sources 

Eleven air districts provided daily or hourly emission estimates for 67 facilities. The 

districts which provided data were Amador County APCD, Bay Area AQMD, Colusa 

County APCD, Monterey Bay Unified APCD, Placer County APCD, Sacramento Metro 

AQMD, San Joaquin Valley APCD, San Luis Obispo County APCD, Shasta County 

AQMD, Tehama County APCD, and Yolo/Solano AQMD. Day-specific emissions 

replaced emissions estimated from CEFS. Additionally, the Bay Area AQMD provided 

emission estimates from unusual events, such as equipment breakdowns. These 

emissions were added to the modeling inventories on the day when the unusual event 

occurred. 
49 

6.4.2 Area Sources 

Three districts provided day-specific data for agricultural burning. In most districts, no 

agricultural burning occurred because no-burn days were declared during the episode. 

6.4.3 Shipping in the Bay Area 

Professor Bob Bornstein, San Jose State University, developed day-specific shipping 

adjustments for the Bay Area Air Quality Management District. Professor Bornstein 

provided factors that were applied to the annual average emission estimates for ships in 

the ocean and San Francisco Bay as well as for tugboats. Professor Bornstein 

developed factors for July 4 through July 14,1999, covering all the days needed for 

modeling (July 8 through 13, 1999). Professor Bornstein developed factors for July 29 

through August 3, 2000. Since July 27 through August 2, 2000 is being modeled, 

August 3 was selected to approximate July 27 and 28, 2000. 

Emissions from ships are estimated for two air basins: San Francisco Bay Area (SF) 

and the Outer Continental Shelf (OCS). Emissions from ships within 3 miles of the 

coast are considered in the SF air basin; emissions from ships beyond the 3-mile limit 

are in the OCS air basin. However, the current shipping surrogates used by the ARB to 

distribute emissions into grid cells differentiate by county, but not air basin. Emissions 

from ocean-going vessels in both the OCS and SF air basins are evenly distributed 

among the San Francisco Bay, the 3-mile coast, and the coast beyond the 3-mile limit. 

For ocean-going vessels, the factors developed by Professor Bornstein were applied 

only to grid cells that are within the 3-mile limit of the San Francisco coast. Grid cells 

beyond the 3-mile limit of the coast were not adjusted. 

6.4.4 Wildfires 

Emissions were estimated for known wildfires that occurred during the CCOS episodes. 

There were about 30 fires that occurred during episodes in the summer of 2000. All of 

the fires were less than 1,000 acres except for two. Two large wildfires occurred during 

the July-August 2000 episode. The Manter fire was a large-scale wildfire (over 73,000 

acres) which occurred in Tulare County in the Sequoia National Forest and adjoining 

Bureau of Land Management areas on July 22 through August 9, 2000. The Plaskett2 

fire was a large-scale wildfire (over 58,000 acres) which occurred in the Los Padres 

National Forest in Monterey County on July 23 through July 31, 2000. Due to these 

fires’ duration, scale, and coincidence with the Central California Ozone Study, 

modeling staff requested that an estimate of fire emissions be developed in order to 

assess these fires’ potential impacts on regional emissions and photochemistry. 

To develop emission estimates, the ARB emission inventory staff turned to an on-going 

contract with UC Berkeley’s Center for the Assessment and Monitoring of Forest and 

Environmental Resources (CAMFER) laboratory. In a prior ARB contract, CAMFER 

staff implemented the fire emissions module of the USDA Forest Service First Order 

Fire Effects Model (FOFEM, Reinhardt et al. 1997) within a Geographic Information 

System (GIS). FOFEM is a standard fire effects model used by federal and state land 

management agencies. The CAMFER model, called the Emissions Estimation System 

(EES), was initially devised to develop annual ARB fire emission inventories. In the 
50 

current contract, CAMFER was tasked to extend the EES to enable the model to 

estimate temporally-resolved emissions for individual fires, for an expanded suite of 

pollutants (CARB,2000). 

The CAMFER EES runs within ArcView software and utilizes emission algorithms, 

emission factors, combustion efficiencies, fuel loadings, and other parameters from 

FOFEM. In the EES, GIS-based spatial data layers (polygon shapefiles), representing 

burned areas, are overlaid onto a GIS vegetation data layer in which vegetation 

community types are coupled with corresponding FOFEM biomass fuel profiles. For 

each fuel component (there are 10 fuel components representing foliage, litter, and 

stem diameter classes) in each vegetation type, the EES determines pre-burn fuel 

loadings (tons per acre), fuel mass consumed by the fire, combustion efficiency, and 

emissions released. Burning occurs in two distinct phases: flaming and smoldering. 

The temporal evolution of emissions from a burning area is therefore a function of the 

phase in which a fire is burning, and the time elapsed since ignition. The FOFEM and 

CAMFER EES models generate daily emissions from both phases. Emissions 

generated by the EES from flaming and smoldering phases are combined in the final 

outputs. 

These emissions were then utilized to develop a plume profile, using the techniques 

outlined in a recent report of the Fire Emissions Joint Forum (FEJF) of the Western 

Regional Air Partnership (WRAP) (Air Sciences, 2004). Appendix B describes the 

vertical distribution of emissions in greater detail. 

For all other fires in the summer of 2000, emissions were calculated based on the 

number of acres of three vegetation types: chaparral, grass, and timber. The U.S. 

Forest Service provided fuel loading and emission factors. The number of acres, 

vegetation type, fire duration, and location information were taken from California 

Department of Forestry (CDF) fire incident reports and newspaper articles. The vertical 

distributions of the plumes were calculated using the FEJF methodology referenced 

above. 

There were also about 15 fires, totaling approximately 6,000 acres, which occurred 

during the July 1999 episode. Emissions from these fires have not been calculated. 

6.5 Temporally and Spatially Resolved Emissions 

In addition to forecasting emissions, CEFS can create temporally resolved inventories 

for modeling purposes, for the base year and future years. The annual average 

emissions are adjusted to account for monthly and weekly variations. CEFS generates 

an inventory for point and area sources (including off-road mobile sources) for a 

weekday and a weekend day in the year and months needed for an episode (e.g. July 

1999 or August 2000). Emissions are estimated for each county, air basin, and district 

combination. In addition, information on how the daily emissions are distributed to each 

hour of the day is provided for later incorporation. 
51 

The emission inventories for CCOS were developed from the 2002 annual average 

CEIDARS inventory for TOG, NOx, SOX, CO, PM, and ammonia. Since the episodes to 

be modeled (1999 and 2000) were earlier than the inventory base year (2002), 

emissions were backcasted from 2002 (see Section 6.3 for more information on 

forecasting emissions). Inventories for point and area sources were developed for a 

weekday and a weekend day for each of the 12 months for all years from 1990 to 2030. 

Note that all of these years may not have been processed into the formats needed for 

input to air quality models. 

The backcasting of emissions for point and area sources usesg the best available data. 

Backcasting is handled differently for point and area sources. Point sources use 

historical data as stored in that year’s CEIDARS inventory. In other words, the 1999 

point source emissions come from the 1999 CEIDARS database and the 2000 point 

source emissions come from the 2000 CEIDARS database. Area source emissions are 

backcast from 2002 using growth and control factors. This procedure allows emissions 

to reflect changes that may have occurred due to updated emission calculation 

methodologies. 

6.6 Surface Temperature and Relative Humidity Fields 

The calculation of gridded emissions for some categories of the emissions inventory is 

dependent on gridded air temperature (T), relative humidity (RH), and solar radiation 

fields. Biogenic emissions are sensitive to air temperatures and solar radiation, and 

emissions from on-road mobile sources are sensitive to air temperature and relative 

humidity. Gridded temperature, humidity, and radiation fields are readily available from 

prognostic meteorological models such as MM5, used to prepare meteorological inputs 

for the air quality model. However, analysis of the MM5 outputs prepared for the JulyAugust 2000 episode revealed poor agreement between simulated humidity and 

temperature fields and the available measurements. 

As an alternative to the data fields generated using the prognostic meteorological 

model, air temperature and humidity fields for calculation of the emission inventory were 

prepared by objective analysis. In the objective analysis, hourly temperatures for each 

grid cell within the study domain were calculated using a distance-weighted average of 

the nearest three temperature measurements. Because few temperature 

measurements were available at higher terrain elevations, temperatures were adjusted 

using a vertical lapse rate (-0.0098 C/m to -0.0065 C/m) multiplied by elevation 

differences prior to averaging. Since this is an assumed constant, there may be 

uncertainty in temperatures at higher elevations. 

Relative humidity measurements show a wide range of variability. Within the CCOS 

study domain, it was not unusual to find differences in relative humidity of 40% among 

sites within a 25-kilometer radius. To reduce large horizontal variations in the relative 

humidity fields developed for the emission inventory calculations, relative humidity fields 
52 

were calculated assuming a daily constant absolute humidity for each grid cell. The 

absolute humidity was calculated from the minimum daily temperature and assuming a 

maximum daily relative humidity of 80%. 

The solar radiation fields needed for biogenic emission inventory calculations were 

taken from the MM5 simulation. 

6.7 On-Road Mobile Source Emissions 

EMFAC is the ARB approved on-road motor vehicle emission inventory model. The 

current version is EMFAC2007 v2.3 (November 2006) (CARB, 2006). ARB staff sought 

public input on this new version of EMFAC (see http://www.arb.ca.gov/msei/msei.htm

for workshop notices and technical documentation). The improved inventories have 

undergone public review as part of the SIP outreach process. 

Here are the main areas of change between the last version of EMFAC, EMFAC2002, 

and EMFAC2007: 

Diesel Vehicles: 

• Redistribution of heavy-duty diesel vehicle miles traveled (VMT) 

• Adjustment to heavy-duty diesel emission factors 

• Modifications to the speed correction factors for heavy-duty diesel vehicles 

• The inclusion of high idle emission rates for heavy-duty diesel vehicles 

• Diesel fuel correction factors 

Gasoline Vehicles 

• The impact of ethanol in gasoline on evaporative emissions 

• Addition of areas into the Enhanced Smog Check program 

The EMFAC model provides emission estimates for 13 classes of vehicles for exhaust, 

evaporation, and PM emissions from tire wear and brake wear. EMFAC also produces 

estimates of fuel consumption, vehicle miles traveled (VMT), and the number of vehicles 

in use. EMFAC does not output a gridded emission file. However, EMFAC will produce 

a file of emission rates that can be used with the Direct Travel Impact Model (DTIM) or 

other external on-road motor vehicle emission gridding program. These same emission 

rates are part of the information used by EMFAC to produce emission estimates for 

California counties or air basins. 

DTIM4 (Systems Applications, Inc. 2001) is the latest version of DTIM, and is used to 

estimate gridded on-road motor vehicle emissions. In addition to the EMFAC emission 

rate file, DTIM4 uses digitized roadway segments (links) and traffic analysis zone 

activity centroids to allocate emissions for travel and trip ends. DTIM4 gridded emission 

files have fewer categories than EMFAC outputs. Each DTIM4 output category will be 

used to spatially allocate emissions for several EMFAC emission categories. There are 
53 

also several categories of emissions that EMFAC produces that are not estimated by 

DTIM4. 

DTIM4 is used to estimate both the spatial and temporal distribution of all on-road motor 

vehicle emissions. It is important to recognize that EMFAC (and its associated activity), 

and not DTIM, is used to calculate county-specific emissions. DTIM output, using the 

Integrated Transportation Network (ITN) activity as inputs, was used to create hourly 

emission ratios for each grid cell in a county. These ratios were used to distribute 

county-specific, daily EMFAC emissions to each hour and grid cell. A horizontal grid 

resolution of 4 x 4 km is used. 

Below we describe the procedures that were used with EMFAC2007 and DTIM4 to 

produce day-specific gridded on-road motor vehicle emission estimates. The 

procedures described here are carried out separately for each county in the CCOS 

modeling domain. 

6.7.1 EMFAC Emissions Categories 

EMFAC2007 produces emission estimates for the following 13 vehicle classes: 

1. LDA Light Duty Autos 

2. LDT1 Light Duty Trucks < 3,750 pounds GVW 

3. LDT2 Light Duty Trucks > 3,750 - 5,750 

4. MDV Medium Duty Vehicles > 5,750 – 8,500 

5. LHD1 Light Heavy Duty Vehicles > 8,500 – 10,000 

6. LHD2 Light Heavy Duty Vehicles > 10,000 – 14,000

7. MHD Medium Heavy Duty Vehicles > 14,000 – 33,000

8. HHD Heavy Heavy Duty Vehicles > 33,000 

9. OB Other Buses 

10. SBUS School Buses 

11. UBUS Urban Buses 

12. MH Motorhomes 

13. MCY Motorcycles 

Additionally, there are up to 3 technology groups within each vehicle type: 

1. Catalyst 

2. Non-catalyst 

3. Diesel 

For each of the combinations of vehicle type and technology there can be many 

emission categories: 

1. Start Exhaust 

2. Running Exhaust 
54 

3. Idle Exhaust 

4. Hot Soak 

5. Running Evaporatives 

6. Resting Evaporatives 

7. Partial Day Resting Evaporatives 

8. Multi-Day Resting Evaporatives 

9. Diurnal Evaporatives 

10. Partial Day Diurnal Evaporatives 

11. Multi-Day Diurnal Evaporatives 

12. Break Wear PM 

13. Tire Wear PM 

A DTIM4 preprocessor calculates fleet average emission factors for each EMFAC 

technology type for each emission category. The vehicle type distribution used to 

calculate fleet emission factors is an input, so it can be varied as needed. 

6.7.2 DTIM4 Emissions Categories 

During DTIM4 operation, all emissions are collapsed into a total of 40 emission 

categories, represented by the SCCs below, which depend on vehicle type, the 

technology, and 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. 
55 

SCC for Light-duty 

and Medium-duty 

Vehicles 

SCC for Heavy-Duty 

Vehicles Description 

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 
56 

6.7.3 Creating the Emission Rate File 

EMFAC will create an emission rate file for any desired combination of vehicle speeds, 

ambient temperatures, and relative humidities (RH). However, DTIM4 places 

restrictions on the total array size. The sets of values we use to build the array are: 

Speed: 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65 

Temp: 30, 45, 60, 70, 75, 80, 85, 90, 100, 110 

RH: 0, 30, 50, 70, 80, 90, 100 

6.7.4 Day-Specific EMFAC Inventories 

Emission estimates are produced by EMFAC for each day of each episode, by county. 

County average hourly temperatures, weighted by gridded VMT, are input to EMFAC to 

produce a ‘BURDEN’ inventory in a comma separated (.bcd) format. Both DTIM4 

exhaust and evaporative emissions are scaled by category to the EMFAC emissions 

estimates for each county/air basin area. EMFAC bus and idle emission categories are 

not estimated by DTIM4. These categories are added to the gridded emission files. 

6.7.5 CCOS Emissions Gridding 

The method to estimate on-road mobile emissions at the grid cell level is described 

briefly in the following five steps: 

Step 1. Gridded, hourly temperature (T) and relative humidity (RH) fields for 

each episode day are prepared for input to DTIM4. The T and RH fields are 

derived either from meteorological model predictions, observations, or some 

hybrid combination of model predictions and observations. 

Step 2. EMFAC is run to prepare on-road mobile source emission factors by 

speed, temperatures, and relative humidity for each county. 

Step 3. DTIM4 is run using data from the Integrated Transportation Network 

version 2 (ITNv2) and EMFAC to estimate gridded, hourly on-road mobile source 

emission estimates by day for DTIM4 categories. 

Step 4. EMFAC is run again using episode-specific T and RH data to provide 

countywide on-road mobile source emission estimates by day for EMFAC 

categories. The episode-specific meteorological inputs for EMFAC are generated 
57 

via averaging (VMT-weighted) the gridded, hourly meteorology from Step1 by 

county and hour. 

Step 5. Two sub-steps are taken: 

Temporal adjustments 

5a Sum the hourly volumes by vehicle type and county on the ITNv2 network. 

5b For heavy-duty vehicles on core days (Tuesday through Thursday) 

redistribute the hourly emissions but make no daily VMT adjustment. Light duty 

vehicle emissions from EMFAC will not be adjusted at all for core days. 

5c For Friday, Saturday, Sunday, and Monday, use Caltrans count data to 

develop a set of ratios of Caltrans daily VMT to core days. For example, develop 

ratios for Saturday to Tues-Thurs. Develop ratios for each Caltrans district for 

passenger cars, light and medium duty trucks, and heavy-duty trucks. 

5d Apply Caltrans daily factors by county, and secondly, apply Caltrans’ new 

hourly distributions by county to ITNv2 link activity. 

5e Run DTIM with revised ITNv2 activity. 

5f Run EMFAC with day-specific temperatures. 

5g Adjust DTIM output emissions to EMFAC weekday by county. 

5h For Friday, Saturday, Sunday, and Monday, apply daily ratios from step 5c to 

hourly DTIM emissions by county. 

See Section 6.7.6 for more information. 

Spatial/Temporal Distribution 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 DTIM4 output (Step 3) as a spatial and temporal 

surrogate. 
58 

The disaggregation follows the equation: 

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 

6.7.6 Suggested Improvements for On-road Motor Vehicle 

Gridding 

The five step process described above in section 6.7.5 is used to generate sets of dayspecific, gridded on-road emissions. These emissions are our best estimates at the 

present time; however additional work in three areas would improve the estimates. One 

area of improvement, and likely the most important, is in the allocation of heavy-duty 

truck emissions. At present, the only transportation modeling done to explicitly model 

trucks is for Southern California counties covered by the Southern California 

Association of Governments (SCAG). For the remaining counties, heavy-duty trucks 

are assigned as a ratio of light-duty vehicles. 

A second area of improvement is in developing emissions for weekend days. Both the 

spatial and temporal distribution of on-road motor vehicle emissions is different on 

weekend days than on weekdays. On-road motor vehicle emissions on weekend days 

should be considered an approximation since there are no transportation models to 

describe weekend traffic. In other words, people are still traveling to work; the 

emissions are just scaled down. 

A third area of improvement is determining the hourly emissions from on-road motor 

vehicles. Local regional transportation agencies (RTPAs) and Caltrans supply traffic 

estimates for several time periods in a day. In the development of previous modeling 

inventories for CCOS, traffic within the time period was allocated to each hour using the 

hourly profiles that were developed by UC Davis. (Lam 2002). UC Davis developed 

two hourly profiles, one for weekdays and one for weekend days, which differed by 

county. However, there was no distinction by vehicle class. The same hourly profile 
59 

was used for heavy-duty vehicles as for light-duty vehicles within a county. This is of 

concern because trucks are known to have different diurnal distributions than cars and 

they have high NOx emissions. 

Due to this concern, the Weekend Truck Subcommittee of the northern California SIP 

Gridded Inventory Coordination Group (GICG) was formed in 2004 to investigate a way 

to improve day-of-week adjustments, for vehicle types as needed, but particularly for 

heavy-duty trucks. Participants in the subcommittee are members of the GICG with 

particular knowledge and/or interest in improving the adjustment factors and include 

representatives from Caltrans, ARB, Bay Area AQMD, San Joaquin Valley APCD, and 

Alpine Geophysics (the developer of the ITN). 

Caltrans staff acquired Automatic Vehicle Classifier (AVC) count data from about 139 

sites in the state for calendar year 2004 (see Figure 6.1). Caltrans staff prepared hourly 

day of week factors for (1) passenger cars (LD), (2) light and medium duty trucks (LM), 

and (3) heavy-heavy duty trucks (HHDT). Caltrans count data are separated using the 

Federal Highway Administration (FHWA) vehicle classification scheme (see Table 6.3). 

Passenger cars are defined as FHWA classes 1 through 3. Light and medium heavyduty trucks are defined as FHWA classes 7 and 8. Heavy-heavy duty trucks are defined 

as FHWA classes 9 through 14. Separate factors were prepared for each Caltrans 

District. One or more counties may fall into a single District. All counties within each 

Caltrans district will receive the same adjustment. Figure 6.2 shows a map of county 

and Caltrans district boundaries. Only counts during the summer of 2004 were used, 

specifically the months of June, July and August excluding data from July 2-5 to remove 

unusual traffic patterns around the July 4th holiday. 

Temporal on-road activity adjustments by county were made for: 

1. Heavy duty vehicles – all days 

2. Light-duty vehicles – Friday, Saturday, Sunday, Monday 

Daily total activity (daily VMT) adjustments were made for all vehicle types for Friday, 

Saturday, Sunday, and Monday. Tuesday, Wednesday, and Thursday are considered 

as one day. Adjustments applied to heavy-duty vehicles on Tuesdays, Wednesdays, 

and Thursdays were the same for each of the three days. 

Since it is EMFAC emission estimates that are being adjusted to derive the final on-road 

inventory, the relation between EMFAC vehicle classes and Caltrans’ adjustment 

factors is shown below. 
60 

EMFAC Class Description Caltrans’ Factor 

 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 No data in ITNv2 

 10 School Bus Unadjusted on weekdays, 

 zero on weekend days 

 11 Urban Bus LD 

 12 Motorhomes LD 

 13 Motorcycles LD 

where LD based on count data for FhwA classes 1 through 3 

 LM based on count data for FhwA classes 7 and 8

 HHDT based on count data for FhwA classes 9 through 14 

To summarize, for core days light- and medium-duty vehicle emissions will equal 

EMFAC emissions by county and hour. For core days, heavy-duty emissions will equal 

EMFAC but have Caltrans hourly distribution. For Friday through Monday, EMFAC 

weekday emissions will be scaled to reflect Caltrans day of week factors. Appendix C 

provides more detail on the methodology developed by the Weekend Truck 

Subcommittee. 

Although significant improvements have been made to improve the temporal distribution 

of on-road motor vehicles, some assumptions were made that may cause uncertainty in 

the adjustments. For example, one assumption is that the count data represent the 

temporal distribution of all road types, including local roads. The count data are 

gathered only on state highways. Another assumption is the link between EMFAC and 

FHWA classes. EMFAC classes are based on gross vehicle weight, whereas FHWA 

classes are based on type of vehicle and number of axles. It is not an easy process to 

determine which EMFAC class a specific type of vehicle falls into based on the number 

of axles, particularly for trucks. Additional work may provide improvements to 

estimating hourly emissions by vehicle type, especially on weekend days. 
61 

 Figure 6.1 Caltrans Weigh-In-Motion Data Sites 
62 

 

Figure 6.2 Caltrans District and County Boundaries 
63 

Table 6.3 Federal Highway Administration (FHwA) Vehicle Classification 

Graphic Depiction FHWA 

Class 

Description 

1 Motorcycles 

2 Passenger Cars (With 1- or 2-

Axle Trailers) 

3 2 Axles, 4-Tire Single Units, 

Pickup or Van (With 1- or 2-

Axle Trailers) 

4 Buses 

5 2D - 2 Axles, 6-Tire Single 

Units (Includes HandicappeEquipped Bus and Mini 

School Bus) 

6 3 Axles, Single Unit 

7 4 or More Axles, Single Unit 

8 3 to 4 Axles, Single Trailer 
64 

Graphic Depiction FHWA 

Class 

Description 

9 5 Axles, Single Trailer 

10 6 or More Axles, Single 

Trailer 

11 5 or Less Axles, Multi-Trailers

12 6 Axles, Multi-Trailers 

13 7 Axles, Multi-Trailers 

No graphic available 14 5 Axles: 3 axle tractor pulling 

a 2 axle trailer (FHWA 

considers this type of truck a 

class 9;Caltrans counts these 

trucks separately for 

operational purposes.) 
65 

6.7.7 Fleet Emission Factors 

An important input to DTIM4 is the vehicle type weighting for emission rate. The vehicle 

type VMT for each county/air basin output from EMFAC is used, which is then 

reformatted by the CONVIRS4 computer program and composited by vehicle type 

distribution from BURDEN in the IRS4 computer program. For the counties in CCOS 

that are covered by the ITN network, we process light/medium duty (LM) and heavyduty vehicles (HDV) separately. The VMT for LM is the sum of EMFAC categories LDA, 

LDT1, LDT2, MDV, SBUS, UB, MCY and MH. The HDV VMT is the sum of LHD1, 

LHD2, MHD and HHD. 

Besides the composite emission rate file, DTIM4 needs link and trip end activity files. 

All activity has been resolved to one-hour periods for each county using the method 

described in Sections 6.7.5 and 6.7.6 above. Specifically, temporal on-road activity (link 

and trip end) adjustments by county were made for: 

• Heavy duty vehicles – all days 

• Light-duty vehicles – Friday, Saturday, Sunday, Monday 

Link and trip end activity adjustments were made for all vehicle types for Friday, 

Saturday, Sunday, and Monday. Tuesday, Wednesday, and Thursday are considered 

as one day. Adjustments applied to heavy-duty vehicles on Tuesdays, Wednesdays, 

and Thursdays were the same for each of the three days. 

Additionally, EMFAC has different fleet mixes by county based on vehicle registrations. 

It is the fleet mixes in EMFAC that ultimately are the basis for the on-road mobile source 

emissions processing that has been done in support of CCOS. The fleet mixes in the 

DTIM4 runs are based on the fleet mixes in EMFAC. The DTIM4 runs are based on the 

composite emissions factors that are generated by EMFAC. During the preprocessing 

of the EMFAC output, which occurs prior to a complete DTIM4 run that is performed by 

the IRS/CONVIRS programs, there is generally an adjustment applied to the EMFAC 

emissions factors based on vehicle counts. In most cases, the regional transportation 

planning agencies (RTPAs) who supplied the transportation data provided the vehicle 

counts that were used to adjust the EMFAC emissions factors. In the remaining cases, 

the vehicle count data were taken directly from EMFAC. 
66 

6.7.8 Differences Between DTIM4 and EMFAC 

6.7.8.1 Evaporative Emissions 

DTIM4 and EMFAC use different methods to estimate evaporative emissions. 

However, as mentioned previously, we use the DTIM4 evaporative emissions as spatial 

and temporal “surrogates” to resolve EMFAC emission estimates. During processing, 

we drop the DTIM4 evaporative categories 211, 212, 311, and 312 (because those 

emissions are included in EMFAC’s estimates for diurnal and resting emissions) and put 

all EMFAC resting emissions in DTIM4 category 210/310, and all diurnal emissions in 

DTIM4 category 207/307. 

6.7.8.2 Exhaust Emissions 

The exhaust emissions from EMFAC are also resolved spatially and temporally by 

DTIM4 emission estimates. Since transportation models do not estimate VMT for buses 

or excess idling categories, these are added to DTIM4 emissions. The exhaust CO, 

NOx, SOx, and PM emissions that DTIM4 allocates to category 1 are reassigned to 

catalyst starts, non-catalyst starts, catalyst stabilized, non-catalyst stabilized, and diesel 

exhaust categories according to the appropriate day-specific EMFAC inventory. 

6.7.9 Integrated Transportation Network (ITN) 

The Integrated Transportation Network (Wilkinson 2003) is a seamless on-road 

transportation network that covers all of California. The ITN was developed from many 

regional transportation planning agencies (RTPAs) as well as the California Department 

of Transportation (Caltrans) Statewide Model. The San Joaquin Valleywide Air Pollution 

Study Agency and Air Resources Board contracted with Alpine Geophysics to develop 

the ITN. After the ITN was developed, additional local transportation networks became 

available that were not included in the first version. Some RTPAs had also updated 

their networks since the original development. For these reasons, version two of the 

ITN (ITNv2.0) was developed (Wilkinson 2005). As mentioned earlier, the ITNv2.0 is 

used to spatially distribute the on-road mobile source emissions generated by EMFAC. 

Figure 6.3 shows the link-based ITNv2.0 for California. 

Local networks were used for all or portions of the following counties: Alameda, Contra 

Costa, El Dorado, Fresno, Kern, Kings, Los Angeles, Madera, Marin, Merced, Napa, 

Orange, Placer, Riverside, Sacramento, San Bernardino, San Diego, San Francisco, 

San Joaquin, San Mateo, Santa Barbara, Santa Clara, Solano, Sonoma, Stanislaus, 

Sutter, Tulare, Ventura, and Yolo. Data that were provided for Imperial and San Luis 

Obispo could not be used because the parameters to conflate the networks to real world 
67 

coordinates were not available. The Caltrans statewide model was used to supplement 

the local data. More details on the ITNv2.0 can be found in Appendix D, the final report 

“Development of Version Two of the California Integrated Transportation Network 

(ITN)”. 

It is important to recognize that EMFAC (and the associated activity), and not DTIM4, 

will be used to calculate county-specific emissions. DTIM4 output, using the ITN activity 

as inputs, will simply be used to create hourly emission ratios for each grid-cell in a 

county. These ratios will be used to distribute county-specific, daily EMFAC emissions 

to each hour and grid-cell. This intended use negates the need to update countywide 

VMT on the ITN. That is, if up-to-date VMT in a specific county were 10% higher than is 

currently reflected in the ITN, all the VMT on ITN links for that county would be 

increased by 10%. Since both the county VMT and link VMT (in the same county) are 

factored by the same amount, the ratio of link-to-county VMT for every link in that 

county does not change. Similarly, DTIM4 grid-cell-to-county emissions ratios do not 

change. Thus, for the intended use and assuming no changes to ITN activity 

distribution, adjusting the ITN county totals to more accurate countywide VMT will not 

affect the outcome. 

With regard to the spatial accuracy of the ITN, it is important to recognize that current 

modeling efforts in the region utilize square grid cells that are four kilometers on each 

side. Thus, the spatial accuracy of the statewide or local components of the ITN only 

requires enough resolution to distribute EMFAC emissions into the proper four by four 

kilometers grid cell. Given that the intended purpose of the ITN is for use in estimating 

on-road mobile source emissions for photochemical modeling efforts, this accuracy is 

sufficient. 
68 

Note: The county boundaries are in red. The Caltrans statewide network is in black. The various 

individual networks are in colors other than black or red. The 190 x 190 4 kilometer CCOS emissions 

modeling domain is shown as the green box. 

Figure 6.3. Link-based Integrated Transportation Network (ITN) version 2.0 
69 

6.7.10 Motor Vehicle Activity 

Motor vehicle activity data are an important part of EMFAC for estimating emissions. As 

part of an on-going effort to use the best data available, ARB periodically updates the 

vehicle miles traveled (VMT) and speed distributions by VMT used in the model. In 

November 2004, ARB sent letters to transportation planning agencies (TPAs) statewide 

requesting updated activity data for base years and forecasted years. A sample letter 

can be found in Appendix E. All major urban areas in the state responded. The data 

was reviewed and processed by ARB staff in coordination with the TPAs. ARB’s 

Technical Memorandum on the activity data update is provided in Appendix F. The 

memorandum provides summaries of the data and refers to supporting documents that 

provide additional details as well as discussions of issues. ARB included additional 

updates as time permitted before finalizing EMFAC2007. 

6.7.11 Forecasted Emissions for On-Road Motor Vehicles 

Forecasted modeling inventories were developed for on-road motor vehicles as needed 

to complete the inventory inputs to episodes being modeled. For future year 

inventories, emissions and other needed data were taken from EMFAC for the desired 

future year. The method used to calculate the future year emissions was the same as 

the base year for each episode, including the same gridded, hourly temperature and 

relative humidity information. 

6.8 Biogenic Emissions 

Development of effective ozone control strategies in California requires accurate 

emission inventories, including biogenic volatile organic compounds (BVOCs) 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) has developed a Geographic 

Information System (GIS)-based model for estimating BVOC emissions, called BEIGIS, 

which uses California-specific input databases with a minimum spatial resolution of 1 

square kilometer (km2) and an hourly temporal resolution. 

The BEIGIS isoprene emission algorithm (Guenther et al. 1991, 1993) is of the form 

I = IS × CL × CT

where I is the isoprene emission rate (grams per gram dry leaf mass per hour) at 

temperature T and photosynthetically active radiation flux PAR. IS is a base emission 

rate (grams per gram dry leaf mass per hour) at a standard temperature of 30 °C and 
70 

PAR flux of 1000 μmol m-2s

-1. CL and CT are environmental adjustment functions for 

PAR and temperature, respectively. The monoterpene emission algorithm adjusts a 

base monoterpene emission rate by a temperature function (Guenther et al. 1993). 

Methylbutenol (MBO) emissions are modeled with an algorithm developed by Harley et 

al. (1998) similar to that for isoprene. Dry leaf mass/leaf area ratios, and base emission 

rates for isoprene, monoterpenes, and MBO are plant species-specific and assembled 

from the scientific literature. Modeled BVOC emissions for a given spatial domain 

therefore represent the contribution by various plant species (through their leaf mass 

and emission rates) to the total BVOC emissions. 

The main inputs to BEIGIS are land use and vegetation land cover maps, gridded leaf 

area indices (LAI) derived from AVHRR satellite data (Nikolov 1999), leaf area/dry leaf 

mass factors, base emission rates, and gridded hourly ambient temperature and light 

intensity data (from a meteorological model). 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). Ground surveys have been funded by CCOS to 

validate the vegetation land cover and LAI input databases used in BEIGIS (Winer et al. 

1998; Karlik and McKay 1999; Winer and Karlik 2001, Karlik 2002). Validation using 

flux measurements in the field is on going. 

Using BEIGIS, the ARB developed hourly-resolved emissions of isoprene, 

monoterpenes, and methyl butanol (MBO), gridded at a 1-km resolution. Each 4-

kilometer (km) grid cell, using the statewide 4-km grid cell domain defined by the ARB, 

was divided into 16 1-km grid squares. After the biogenic emissions were calculated, 

the emissions from the 1-km cells were aggregated for each 4-km grid cell. Two 

additions are then made to the biogenic emissions estimates for input to air quality 

models. 

First, biogenic OVOCs (other VOCs) are added. Biogenic OVOCs comprise around 

twenty percent of some biogenic inventories and are known to affect air quality 

modeling predictions (e.g. Hanna et al., 2002). Guenther et al. (1994) estimates that 

the OVOCs comprise 8-73% of total BVOCs. OVOCs are estimated by ARB as an 

added fraction of 30%, scaled to the total isoprene, monoterpene, and MBO emissions. 

The estimate of OVOC emissions used by ARB is the result of an August 2001 peer 

review of modeling procedures by Dr. William P. L. Carter (Carter 2001). During the 

discussion with ARB modeling staff, it was noted that estimates of OVOC were reported 

by some sources to be as great as the inventoried species (isoprene, m-butenol, and 

monoterpenes). Since OVOCs are very uncertain in both mass and species 

characterization, ARB had not been including them in the modeling programs. Dr. 

Carter suggested this omission was inappropriate. He recommended that OVOC 
71 

emissions be included with the best estimates we could make. During this discussion it 

was decided that doubling the known species would be like an upper limit which was felt 

to be too high. Collectively it was decided that an OVOC amount equal to 30% of the 

total known species would be a reasonable estimate.

ARB reviewed the literature to find a reasonable speciation to assign to OVOCs. Arthur 

Weiner sent a list of the compounds that were intended at that time to be included in 

BEIS-3. No amounts were fixed to species and ARB could not derive an OVOC profile. 

Allen Goldstein had published an article “In Situ Measurements of C2-C10 Volatile 

Organic Compounds Above a Sierra Nevada Ponderosa Pine Plantation” in the Journal 

of Geophysical Research (9-20-1999) which did allow us to create a profile to use until 

better information could be obtained. This profile is dominated by methanol and acetone 

and also contains ethene, propene, hexanal, and acetaldehyde. These compounds 

were all part of the species proposed for BEIS3. 

ARB’s intention is to use this profile for all OVOC from all vegetation types until better 

information becomes available. In the future, use of information from BEIS-3 or other 

models may allow ARB to create BVOC inventories that contain enough compounds so 

that the additional step of adding a chosen amount of 'OVOCs' can be eliminated. 

The second addition is to include biogenic NO emissions. Biogenic NO emissions were 

estimated using a soil NO algorithm found in BEIS-3. 

For a more detailed description of the estimation of biogenic emissions, see 

Appendix G. 

Biogenic emissions are not estimated for future years because future inputs to BEIGIS, 

such as changes in climate and land use/land cover, are highly uncertain. 

Photochemical modeling for future years uses the biogenic emissions developed for the 

base year. 

6.9 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 determined as accurately as possible. Ideally, the 

actual location of all emissions would be known exactly. In reality, however, 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 
72 

all emission sources that could affect modeling results. The definition of the CCOS 

modeling domain is described below in Section 6.9.1. 

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. Section 6.9.2 

discusses in detail the spatial surrogates developed for CCOS. 

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 

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. Please refer to 

section 6.4.4 and Appendix C for more information. 

The spatial treatment of area and point sources has been described above. The spatial 

allocation of on-road motor vehicles is based on activity on the Integrated 

Transportation Network version 2 (ITNv2.0) as described in Section 6.7.9. For biogenic 

emissions, the spatial allocation is built “from the ground up” since ARB’s biogenic 

model, BEIGIS, estimates emissions using a Geographic Information System (GIS) at a 

1 square kilometer resolution. Section 6.8 describes how biogenic emissions are 

estimated. 

6.9.1 Grid Definition 

The CCOS emissions inventory domain was defined based on the MM5 model used to 

generate the meteorological parameter fields used for air quality modeling. However, 

the MM5 model uses only an approximation to the shape of the Earth. Therefore, there 

was a small offset error between the MM5-defined domain and the emissions domain 

defined using GIS software, which uses a more exact Earth shape. 

The emissions inventory domain was defined using a Lambert Conical Projection with 

two parallels. The Parallels were at 30 and 60 N latitude, with a central meridian at 

120.5 W longitude. The coordinate system origin was offset to 37 N latitude. The 

emissions inventory was gridded with a resolution of 4 km. However, because of 

differences between the MM5-defined domain and the GIS defined domain, the lower, 
73 

left-hand corner of the emissions inventory domain was not a integer multiple of 4-km 

(cell size) from the domain origin. The specifications of the emissions inventory domain 

grid were: 

DEFINITION OF GRID 

190 x 190 cells ( 4 km x 4 km ) 

Lambert Origin @ ( -385131.6m , -302910.3m ) 

Geographic Origin @ -124.7423 deg. Latitude and 34.1210 deg. Longitude 

MAP PROJECTION 

LAMBERT 

Units: Meters 

Datum: NONE (Clarke 1866 spheroid) 

PARAMETERS

1st Standard Parallel: 30 0 0.000 

2nd Standard Parallel: 60 0 0.000 

Central Meridian: -120 30 0.00 

Latitude of Projection Origin: 37 0 0.000 

X-Shift (meters): 0.0000 

Y-Shift (meters): 0.0000 

6.9.2 Spatial Surrogates 

Spatial allocation factors are used to geographically distribute countywide area source 

emissions to individual grid cells. These spatial allocation factors were developed from 

spatial surrogate data. Spatial surrogates are economic, demographic, and land cover 

patterns that vary geographically. 

In this context, “area source emissions” refers to all source categories that are not point 

sources, biogenics, or on-road motor vehicles (see Table 6.2 for description). 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 allocated by 

DTIM4 (see Section 6.7). Biogenic emissions are allocated by BEIGIS (see Section 

6.8). 

In support of CRPAQS and CCOS, Sonoma Technology, Inc. (Funk et al. 2001) was 

contracted to develop spatial allocation factors. Using a GIS-based approach, STI 

developed gridded spatial allocation factors for a 2000 base-year and three future years 

(2005, 2010, and 2020) for the entire state of California 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. 
74 

Each area source category is assigned a spatial surrogate. This assignment provides a 

cross-reference between the spatial allocation factors and the emission inventory 

categories. A total of 65 unique surrogates were developed as part of this project. A 

summary of the spatial surrogates, for which spatial allocation factors were developed, 

is listed in Table 6.4. 

A listing of all surrogates and spatial allocation factors, and their corresponding spatial 

surrogate codes (SSC), are contained in Appendix H. Appendix H also includes the 

surrogate-to-emission inventory cross-reference list. Designating the surrogate-toemission inventory assignments was an iterative process among STI staff, ARB staff, 

and local air district staff. Note that the spatial allocation factors and emissions 

category assignments vary by county depending on the data available for each county. 

Three basic types of surrogate data were used to develop the spatial allocation factors: 

• land use and land cover 

• facility location 

• demographic and socioeconomic data 

Land use and land cover data are associated with specific land uses, such as 

agricultural tilling, feedlots, 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). Table 6.5 shows the sources of land use and 

land cover data as well as facility location information used to develop spatial allocation 

factors. Table 6.6 shows the sources of demographic and socioeconomic data used to 

develop spatial allocation factors. Table 6.7 provides a list of the counties covered by 

each data set. To develop spatial allocation factors of high quality and resolution, local 

socioeconomic and demographic data were used when available; for rural regions for 

which local data were not available, the Caltrans Statewide Transportation Model data 

were used. 
75 

Table 6.4 Summary of spatial surrogates developed as part of the CCOS gridded 

surrogate project 

Surrogate Description 

Agricultural cropland 

Agricultural land 

Feedlots 

Feedlots, dairies, and poultry farms 

Non-pasture agricultural land 

All airports 

Commercial airport locations 

Total employment & road density 

Total housing and locations of auto body/refinishing shops 

Locations of hospitals, institutions, population, and commercial employment 

Total housing, service, commercial, golf courses 

Industrial employment and locations of auto body/refinishing shops 

Road density & housing/employment (ft2/person) 

Population, institutions, and commercial employment

Total housing and locations of restaurants/bakeries

Single dwelling units and non-urban land 

Housing/employment (ft2/person) 

Computed surrogate - residential 

Computed surrogate - non-residential 

Computed surrogate - residential & non-residential 

Industrial employment + computed surrogate (residential & non-residential) 

Population 

Residential, service, commercial, golf courses 

Industrial employment and population 

Total housing and commercial employment 

Total employment 

Total housing 

Total housing and total employment 

Single dwelling units 

Single and multiple dwelling units 

Non-retail employment 

Industrial employment 

Service and commercial employment 

Elevation > 5000 ft 

Forest land 

Locations of bulk plants 
76 

Table 6.5. Sources of land use/land cover and facility locations

Data Source Parameter Resolution Vintage Coverage 

United States 

Electronic Yellow 

Pages (ProCD 

Select Phone) 

Autobody shops, 

dry cleaners, 

restaurants, gas 

stations, and 

wineries 

Address locations 1997 Statewide 

Environmental 

Systems Research 

Institute 

Airports, parks, 

golf courses, 

hospitals, 

institutions 

Coordinate 

locations and 

polygon 

coverages 

1997 Statewide 

U.S. Census 

Bureau (ESRI 

ADOL version) 

Water bodies Polygon 

coverages 

2000 Statewide 

United States 

Geological Survey 

Land use and land 

cover for 38 

counties 

Gridded data 1993 Statewide 

ARB CEIDARS 

Database 

Bulk plant 

locations 

Coordinate 

locations 

1999 Statewide 

National Atlas Mine locations Coordinate 

locations 

1998 Statewide 

Bureau of 

Transportation 

Statistics 

Ports and shipping 

lanes 

Coordinate 

locations and line 

coverages 

Publication 

date is 2000; 

source date 

varies 

Statewide 

State Water 

Resources Control 

Board 

Publicly owned 

water treatment 

works locations 

Coordinate 

locations 

2001 Statewide 

Integrated Waste 

Management Board 

Landfill locations Coordinate 

locations 

Downloaded 

from the 

Internet, no 

dates 

Statewide 

StreetWorks Military bases Polygon 

coverages 

1995 Statewide 

Digital Chart of the 

World 

Elevation data Polygon 

coverages 

1993 Statewide 

California 

Department of Oil 

and Gas 

Oil and gas well 

and field locations 

Coordinate 

locations and 

polygon 

coverages 

1998 Statewide 

California Teale 

Data Center (from 

ARB) 

Urban and rural 

roads and 

railroads 

Line and polygon 

coverages 

RR, updated 

1991; RDS, 

updated 1993 

Statewide 

Department of 

Water Resources 

(from ARB) 

Agricultural land 

cover 

Polygon 

coverages 

1995 San Joaquin 

Valley 

Table 6.6. Sources of statewide and local TPA demographic and socioeconomic 

surrogate data
77 

Data Source Parameter (Years) Resolution and Coverage 

Caltrans Statewide 

Transportation Model 

(Caltrans STM) 

Population, housing, 

employment (base and future) 

TAZa – data for rural counties 

only

Association of Bay Area 

Governments (ABAG) and 

1990 U.S. Census 

Population, housing, 

employment (base and future) 

Census Tract – San Francisco 

Bay Area 

Sacramento Area Council of 

Governments (SACOG) 

Population, housing, 

employment (base and future) 

TAZa – Sacramento Urban 

Region 

Tahoe Regional Planning 

Agency (TRPA) 

Population, housing, 

employment (base and future)b

TAZa – Lake Tahoe Region 

Association of Monterey Bay 

Area Governments (AMBAG) 

and 1990 U.S. Census 

Population (base and future) Census Tract – Monterey Bay 

Area 

South Coast Association of 

Governments (SCAG) 

Population, housing, 

employment (base and future) 

TAZa – South Coast Region 

Amador County 

Transportation Commission 

(ACTC) 

Population, housing, 

employment (base and future)b

Growth Allocation Districts 

(unincorporated areas) and 

incorporated areas – Amador 

County 

Council of Fresno County 

Governments (FresnoCOG) 

Population, housing, 

employment (base and future) 

TAZa – Fresno County 

San Diego Association of 

Governments (SANDAG) 

Population, housing, 

employment (base and future) 

TAZa – San Diego County 

San Joaquin Council of 

Governments (SJCOG) 

Population, housing, 

employment (base and future) 

TAZa – San Joaquin County 

Tulare County Association of 

Governments (TCAG) 

Population, housing, 

employment (base and future) 

Incorporated and unincorporated 

areas – Tulare County 

Stanislaus Council of 

Governments (StanCOG) 

Population, housing, 

employment (base and future) 

Incorporated and unincorporated 

areas – Stanislaus County 

Kern Council of 

Governments (KernCOG) 

Population, housing, 

employment (base and future) 

TAZa – Kern County 
78 

Table 6.7. Counties covered by each of the demographic and socioeconomic 

data sets listed in Table 6.6

Data Source County Coverage 

Caltrans STM Alpine, Butte, Calaveras, Colusa, Del Norte, Glenn, Humboldt, 

Imperial, Inyo, Kings, Lake, Lassen, Mariposa, Madera, Merced, 

Mendocino, Modoc, Mono, Nevada, Plumas, east Riverside, 

east San Bernardino, San Luis Obispo, Santa Barbara, Shasta, 

Sierra, Siskiyou, Tehama, Trinity, Tuolumne 

ABAG Alameda, Contra Costa Marin, Napa, San Francisco, San 

Mateo, Santa Clara, Solano, Sonoma 

SACOG/TRPA El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba 

AMBAG Monterey, San Benito, Santa Cruz 

SCAG Los Angeles, Orange, west Riverside, west San Bernardino, 

Ventura 

ACTC Amador 

FresnoCOG Fresno 

SANDAG San Diego 

SJCOG San Joaquin 

TCAG Tulare 

StanCOG Stanislaus 

KernCOG Kern 

6.10 Speciation 

The ARB's emission inventory and photochemical air quality models both quantify 

organic compounds as Total Organic Gases (TOG). Photochemical models simulate 

the processes leading to ozone formation and fate in the lower atmosphere, and include 

all emissions of the important compounds involved in ozone photochemistry. Organic 

gases are one of the most important classes of chemicals involved in the formation of 

surface ozone. 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. 
79 

Total Organic Gas 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 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. 

6.10.1 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 composes 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. 

To the extent possible 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 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 
80 

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 formulation. 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. 

6.10.2 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 6.8 

shows modeled ROG species (or species categories) for the SAPRC-99 chemical 

mechanism. Table 6.9 shows modeled species for NOx. 
81 

Table 6.8 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 

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 102 ppm-1 min-1. (Primarily ethane) 

ALK2 Alkanes and other non-aromatic compounds that react only with OH, and have kOH 

between 5 x 102 and 2.5 x 103 ppm-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 103

 and 5 x 103 ppm-1 min-1. 

ALK4 Alkanes and other non-aromatic compounds that react only with OH, and have kOH 

between 5 x 103 and 1 x 104 ppm-1 min-1. 

ALK5 Alkanes and other non-aromatic compounds that react only with OH, and have kOH 

greater than 1 x 104

 ppm-1 min-1. 

ARO1 Aromatics with kOH < 2x104 ppm-1 min-1. 

ARO2 Aromatics with kOH > 2x104 ppm-1 min-1. 

OLE1 Alkenes (other than ethene) with kOH < 7x104 ppm-1 min-1. 

OLE2 Alkenes with kOH > 7x104 ppm-1 min-1. 
82 

Table 6.9 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 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. 

The implementation of the chemical mechanism is unique in each air quality model. In 

the case of the CAMx model, the chemical species ETOH (ethanol), MTBE (methyl tertbutyl ether) and MBUT (methyl butenol) are not treated explicitly. These species are 

considered important to ozone chemistry in California because ETOH and MTBE are 

motor-vehicle fuel components and MBUT is emitted by vegetation. Therefore, to 

include emissions of these species in the emissions inventory for CAMx, they were 

mapped as follows: 

(moles of ETOH)*1.3 = moles converted to ALK3 

(moles of MTBE)*1.2 = moles converted to ALK3 

(moles of MBUT)*1.8 = moles converted to OLE1 
83 

7 MODEL PERFORMANCE EVALUATION 

The following subsections summarize the recommended model performance evaluation 

procedures (Emery & Tai, 2001; Tesche et al., 2002; USEPA, 1991 & 2005) for 

meteorological and photochemical models. 

7.1 Meteorological Model Performance 

Meteorological model performance is assessed both quantitatively using statistical 

metrics as well as qualitatively against known conceptual meteorological flows and 

observed episodic meteorological features. 

7.1.1 Quantitative Performance Evaluation 

There are a number of statistical and graphical approaches for evaluating 

meteorological model outputs. However, none of them are independently conclusive. 

Most of these approaches involve comparisons between observed and simulated 

meteorological parameter values. These analyses pose a difficult challenge, since most 

of the available meteorological monitoring stations are located in urbanized areas. 

Thus, the majority of observations tend to represent those areas versus the full 

complexity of meteorology throughout the CCOS domain. Furthermore, since the use of 

objective analysis and observational nudging forces the meteorological modeling results 

towards the observations, model performance problems can increase in areas away 

from observation locations. 

It also needs to be recognized that output from the various meteorological models must 

be preprocessed for input into the air quality model. This preprocessing may 

inadvertently perturb the meteorological fields. Therefore, meteorological model 

performance should be based on the air quality model input files, rather than the 

meteorological model outputs. 

The SIP modeling domain is geographically very complex and the observational data on 

which meteorological model outputs were evaluated are not distributed uniformly. 

Therefore, it is unreasonable to evaluate model performance for the domain as a whole. 

For purposes of meteorological model performance analysis, the CCOS domain is 

divided into sub-regions, representing areas of similar meteorological features. The 

graphical and statistical model evaluations will be done for each of these sub-regions. 

A number of standard statistical and graphical techniques are used for meteorological 

model performance analysis. The most widely used application is the METSTAT 

program (Tesche, 1994, Tesche et al, 2001). Two graphical representations of the 

METSTAT statistics were used in meteorological model performance analysis 

conducted here: a) “Root Mean Square Error (RMSE) of Wind Speed” vs. “Gross Error 

(E) of Wind Direction”, and b) “Bias Error (B)” vs. “Gross Error (E)” for temperature. 
84 

Equations used for these comparisons were taken from the user documentation of the 

METSTAT program and are given below: 

Bias Error (B): calculated as the mean difference in prediction-observation 

pairings with valid data within a given analysis region and for a given time period 

(hourly or daily): 

Here, P and O indicate model predictions and observations, respectively. 

Similarly, I and J are the indices of grid points in x and y directions, respectively. 

Gross Error (E): calculated as the mean absolute difference in predictionobservation pairings with valid data within a given analysis region and for a given 

time period (hourly or daily): 

Note that the bias and gross error for winds are calculated from the predictedobserved residuals in speed and direction (not from vector components u and v). 

The direction error for a given prediction-observation pairing is limited to the 

range from 0 to ±180°. 

Root Mean Square Error (RMSE): calculated as the square root of the mean 

squared difference in prediction-observation pairings with valid data within a 

given analysis region and for a given time period (hourly or daily): 

The RMSE, as is the gross error, is a good overall measure of model performance. 

However, since large errors are weighted heavily (due to squaring), large errors in small 

subregions may produce a large RMSE even though the errors may be small and quite 

acceptable elsewhere. 

Table 7-1 shows the criteria used to decide if the results of a given model fall within 

acceptable performance limits. 

∑∑( )

= =

= −

J

j

I

i

i

j

i Pj O

IJ

B

1 1

1

∑∑= =

= −

J

j

I

i

i

j

i Pj O

IJ

E

1 1

1

( )

2/1

1 1

1 2













= ∑∑ −

= =

J

j

I

i

i

j

i Pj O

IJ

RMSE
85 

Table 7-1 Statistical comparisons between observed 

and simulated meteorological parameter values. 

Statistical comparisons are made by model 

performance sub-regions. 

Parameter Abbreviation Benchmark 

Wind Speed RMSE: 

Bias: 

IOA: 

< 2 m/s 

< ±0.5 m/s 

≥ 0.6 

Wind Direction Gross Error: 

Bias: 

< 30 deg 

< ±10 deg 

Temperature Gross Error: 

Bias: 

IOA: 

< 2 oK 

< ±0.5 oK 

≥ 0.8 

In an ideal situation, meteorological field evaluation would be done independent of the 

air quality model results. However, in practice, meteorological field evaluation is limited 

by the relative paucity of observational data, especially aloft. Therefore, base year air 

quality model performance was also considered in the selection of meteorological fields 

used for air quality simulations. 

Table 7-2 Graphical analysis of meteorological model fields. Time 

series plots are made for each station and spatial plots are made over 

the whole modeling domain. 

 Time-series plots of hourly mean air temperature 

 Time-series plots of hourly mean wind speeds. 

 Spatial plots of hourly wind vectors 

 Spatial plots of hourly air temperatures 
86 

7.1.2 Qualitative Performance Analyses 

Given episode-specific information on the meteorological features that were observed 

with field measurements, additional subjective analyses of observed versus predicted 

mesoscale features can be conducted. Examples of such qualitative analyses that will 

be considered are described below. 

1. Determine and compare modeled and observed horizontal flow patterns 

throughout the modeling domain. Features to consider include flow splitting, the 

structure of the sea breeze, urban circulations, local flows such as Fresno and 

Schultz eddy circulations, slope and drainage flows, up/down valley flows, and 

the existence of cloud formations (Described in Chapter 1). 

2. Study the 3-D spatial characteristics of the flow field by using time-height cross 

sections of wind profiler observations and the simulated wind field at the wind 

profiler location. 

3. Determine the spatial and temporal characteristics of the mixing layer height 

using available upper air observations, and compare it with the simulated 

behavior of mixing layer heights. 

4. Perform sensitivity tests to see the effects of certain model parameters on the 

model results, such as observational nudging vs. analysis nudging, the choice of 

soil physics, and boundary layer parameterizations.
87 

7.2 Air Quality Model Performance 

Air quality model results are used to develop strategies for attaining the federal 8-hour 

ozone standard. The development of these strategies relies on the use of relative 

reduction factors (RRFs). A more detailed discussion of RRFs is provided in other 

documents. However, the use of RRFs requires an evaluation of relative air quality 

model response at specific monitoring sites in the base year(s), a reference year, and a 

future year. 

Adequate model performance is a requirement for use of modeled results. The lack of 

acceptable performance greatly increases uncertainty in the use of the modeling results, 

and casts doubt on conclusions based on the modeling. Although it is desirable to 

include as many days as possible in the RRF calculations, our experience has 

demonstrated that not all modeled days meet the minimum performance standards, and 

are thus not suitable for use. Therefore only those days that satisfy the following model 

performance criteria will be utilized in RRF calculations. 

The USEPA (1991) and ARB (1990) outline a number of procedures for analysis of 

base year, air quality model performance. These include spatial and time-series plots, 

statistical analyses, comparing simulated and observed pollutant concentrations, as well 

as sensitivity analysis of selected input fields. The purpose of the performance analysis 

is to provide some confidence that the air quality simulations – which are the basis of 

future-year ozone concentration estimates – are performing properly and for the right 

reasons. 

The application of air quality modeling results to demonstrate attainment of the federal 

1-hour ozone standard emphasized the simulated unpaired peak ozone concentration. 

Three statistical measures were recommended to evaluate model performance: 

unpaired peak ratio (UPR), paired mean normalized bias (NB), and paired gross error 

(GE). These statistical measures were calculated for the modeling domain as a whole, 

and the NB and GE were calculated from all hourly concentrations in excess of 60 ppb 

(to avoid biasing the statistical measures with low concentrations). To meet 

performance guidelines, recommendations were that the UPR should be within ± 20%, 

NB should be within ± 15%, and the GE less than 35%. However, California’s 

geography is very complex and modeling domains have evolved to cover large 

geographic areas. Thus it is recommended that the domains be divided into 

subregions, and that the performance measures be calculated independently for each 

subregion. The configuration of these subregions is somewhat arbitrary; however, they 

should be configured to isolate "common" regions of higher ozone. Figure 7-1 

illustrates the proposed subregions for the CCOS domain. 

The USEPA (2005) recommends that model performance be evaluated for 8-hour 

concentrations as well. The recommended statistical measures to assess simulated 

versus observed maximum 8-hour ozone concentrations include paired (in space, but 
88 

not time) peak prediction accuracy (PPPA), paired mean normalized bias (NB), and 

paired gross error (GE). Although limited performance analysis has been completed for 

8-hour ozone modeling in California, it seems prudent at this point to carry forward the 

1-hour statistical goals and apply them for the 8-hour standard (UPR within ± 20%, NB 

within ± 15%, and the GE less than 35%). However, these limits may need to be 

revised as 8-hour SIP modeling progresses and rigorous model performance 

evaluations are completed. 

While statistical measures for 1-hour model performance were typically calculated 

independently for each modeled day available, the USEPA also suggests that PPPA, 

NB, and GE be calculated for each site over all modeled days. However, because the 

number of episode days available may be very limited, the statistical uncertainties in 

these latter calculations would be large and they are not recommended or used herein. 

In order to have confidence in future year estimates from air quality models, there must 

be confidence in the air quality modeling for the base year. That is, days not meeting 

model acceptance criteria provide high uncertainty, and should not be used for the 

modeled attainment test. 

In addition to the issue of model performance, analyses conducted by the USEPA 

(2005) suggest that air quality models respond more to emission reductions at higher 

predicted ozone values. Correspondingly, the model predicts less benefit at lower 

concentrations. This is consistent with preliminary modeling in support of the 8-hour 

ozone standard conducted by the ARB and the districts. These results imply that RRF 

calculations should be restricted to days with predicted high ozone concentrations. It is 

thus reasonable to establish a minimum threshold for predicted peak 8-hour ozone 

concentrations in the reference year. Days for which the predicted daily peak 8-hour 

ozone concentrations at a site are less than the threshold, would not be used for 

calculating RRFs at that site. Consistent with USEPA’s recommendation, we propose 

to use a value of 85 ppb for the reference year threshold. However, USEPA guidelines 

allow the use of the maximum 8-hour concentrations within 15km of the site for this 

purpose. 

Based on the above discussion, we propose the following model performance based 

methodology for determining sites and modeled days to be used in the RRF 

calculations: 
89 

Only those modeled days meeting the following criteria will be used to calculate sitespecific RRFs: 

1) The modeled daily 8-hour peak ozone concentration within 15 km of the site 

for the base year of the modeling (the model performance year) must be 

within ±20% of the observed value at the site. 

2) The modeled daily 8-hour peak ozone concentration within 15 km of the site in 

the reference year must be 85 ppb or greater. 

3) The subregional 1-hour and 8-hour statistical measures of NB and GE must 

fall within the thresholds of ± 15% and 35%, respectively. 

Of these three criteria, only the third is considered in this document. 

Along with the statistical measures discussed above, the graphical and statistical tests 

recommended by the USEPA (1991 and 2005) and shown in Tables 7-3 and 7-4 will be 

used to assess overall model performance. Several sensitivity tests recommended by 

the USEPA (1991) will also be used (Table 7-5) for qualitative evaluation. While the 

results of these sensitivity analyses are inherently subjective, they are designed to 

provide confidence that the air quality model is not only performing well, but is also 

properly responding to changes in inputs. 
90 

Figure 7-1 Sub-regions of air quality model performance evaluation (3: Bay Area 

region, 6: Sacramento Metro region, 7: Central San Joaquin Valley region , 8 

Southern San Joaquin Valley region, 9: Northern San Joaquin Valley region). 

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15
91 

Table 7-3. Statistics for evaluating base year air quality model performance for 

all sub-regions. 

• Mean normalized bias for all 1-hour ozone concentrations (60 ppb), unpaired 

in time and space for all sites 

• Mean normalized gross error for all 1-hour ozone concentrations (≥60 ppb), 

unpaired in time and space for all sites 

• Peak 1-hour ozone concentration ratio, unpaired in time and space 

• Mean normalized bias for all 8-hour ozone concentrations (≥60 ppb), 

unpaired in time for all sites 

• Mean normalized gross error for all 8-hour ozone concentrations (≥60 ppb), 

unpaired in time for all sites 

• Peak 8-hour ozone concentration ratio, unpaired in time and space 

Table 7-4. Graphical tools for evaluating base year air quality model 

performance. 

• Time-series plots comparing 1-hour measured and simulated concentrations 

of ozone, NO, NO2, and CO for each site. 

• Hourly spatial plots of 1-hour measured and simulated concentrations of 

ozone, NO, NO2, and CO for the CCOS modeling domain. 

• Scatter plot of 1-hour ozone concentrations for each day, and for each 

subregion of the modeling domain. 
92 

Table 7-5. Sensitivity tests for evaluation of Base Year air quality simulations. 

The results of these analyses will be tabulated by subregion. 

8 FUTURE-YEAR AIR QUALITY MODELING 

The current thinking for the use of air quality modeling results in attainment 

demonstrations is to utilize relative model response to predict future-year 8-hour ozone 

concentrations. The Relative Reduction Factor (RRF) is calculated as the ratio of 

future-year and reference year ozone concentrations at a site. The RRF is then 

multiplied by a site-specific design value to estimate the future-year design value. In 

principle, this concept is simple. In practice, it is confounded by the limited record of 

available observed ozone concentrations during the available episodes and the 

uncertainties inherent in air quality modeling. 

The emphasis of this document is on site-specific RRFs and the estimation of future 

year design values at non-attainment monitoring sites; however, the USEPA (2005) also 

requires analysis to demonstrate that high ozone concentrations occurring away from 

monitors (e.g., unpaired in space) will also be controlled in future years to meet air 

quality standards. This latter analysis is not addressed in this document. 

1 Minimize vertical diffusivity based on land cover 

2 Zero anthropogenic emissions 

3 Zero biogenic emissions 

4 Set lateral ozone boundary conditions to 50 ppb 

5 Set lateral ozone boundary conditions to 90 ppb 

6 Set initial ozone conditions to 40 ppb everywhere 

7 Set initial conditions to 0.1 ppb NO2 and 0.0 NO (run with all emissions) 

8 Set initial conditions to 0.1 ppb NO2 and 0.0 NO (run with biogenic emissions only)

9 Double biogenic emissions 

10 Remove wildfires 

11 Zero mobile emissions 

12 Set top ozone boundary conditions to 135ppb at 15km
93 

There are two primary components to the application of air quality modeling results for 

the estimation of future-year ozone concentrations. The first is model performance 

analysis. The USEPA (1991) outlines a number of procedures for analysis of base-year 

air quality model performance. These include spatial and time-series plots comparing 

simulated and observed pollutant concentrations, statistical analyses comparing 

simulated and observed pollutant concentrations, and sensitivity analysis of selected 

input fields. This document will only address the more basic statistical analysis tests. 

The purpose of the performance analyses is to provide some confidence that the air 

quality simulations on which the estimates of future-year ozone concentrations will be 

based, will have some semblance to reality. The second is the issue of representative 

ozone concentrations for base- and future-year concentrations at each site from which 

the RRFs will be calculated. 

8.1 Estimation of Future Design Value (DVF) 

The application of photochemical ozone models has a long history in California, for uses 

ranging from the preparation of State Implementation Plans to research activities to 

regulatory development. The modeling community has applied these tools in the State 

for over 30 years, and much has been learned about their proper uses and limitations. 

One of the fundamental understandings that has evolved is that photochemical models 

are best used to estimate the relative difference between scenarios, rather than for 

absolute concentration estimates. That is, their strength is in estimating the relative 

change in concentration levels from a reference condition (e.g., a current year) to an 

alternative scenario (e.g., a future year), rather than predicting the exact concentration 

level that will result from the alternative scenario. 

The USEPA’s guidance on the use of models for attainment demonstrations in support 

of 8-hour ozone planning (USEPA, 2005) is consistent with the fundamental strength of 

models described above. USEPA’s recommended modeled attainment test is to utilize 

relative model response on a site-by-site basis, in the form of a relative reduction factor 

(RRF), to predict future-year 8-hour ozone design values. This methodology relies on 

the base year for the modeling for conducting model performance analyses, a reference 

year of 2002 for projecting forward site-specific design values, and a future year for the 

attainment test. 

DVF = (RRF) (DVR) 

where DVR = a reference year (2002) concentration (design value) 

measured at a monitoring site 

 DVF = the estimated future year design value at the same site 

 RRF = the relative reduction factor at the same site 

The RRF is calculated as the ratio of future year to reference year modeled ozone 

concentrations at a site: 
94 

8 hr

8 hr

RY

FY

RRF

−

− =

where RRF = the relative reduction factor for a monitor 

 FY8-hr = the modeled future year 8-hour daily maximum 

concentration predicted near the same monitor 

 RY8-hr = the modeled reference year 8-hour daily maximum 

concentration predicted near the same monitor 

In principle, this concept is simple. Unfortunately, it can be confounded by a number of 

factors, including the limited number of modeled days available, the choice of year(s) to 

use for specification of the reference design value, the uncertainties inherent in air 

quality modeling, and the presence of a non-zero background level of ozone. As a 

result of this, EPA technical staff have indicated that there is flexibility in the application 

of RRFs, as long as the methodology is technically sound and is properly documented. 

8.1.1 Estimating Reference Year (2002) Design Values 

Specification of the reference design value is a key consideration in the modeled 

attainment test, since this is the value that is projected forward and used to test for 

attainment at each site. Since the reference design value is presumably reflective of 

conditions in the reference year, it should be representative of the emissions used for 

that year. However, many areas experience fluctuations in their year-to-year 

meteorology, as well as emissions levels. In recognition of this year-to-year variability, 

the reference design value should in some fashion also reflect this variability. A 

standard methodology for minimizing the influence of year-to-year variations is to 

calculate an average value over multiple years. Therefore, the following methodology is 

recommended for specification of the reference design value at each monitoring site: 

The reference design value (DVR) will be calculated as the average of 

the three design values for the three years commencing with the 

reference year of the modeling. The reference year for modeling in 

support of the 8-hour ozone SIPs is 2002. Therefore, the reference 

design value will be calculated at each monitoring site as the 

average of the design values for 2002, 2003, and 2004. 

California design values are calculated as the three-year average of the 4th highest 

8-hour ozone peak values, and are assigned to the last year. Thus, a design value for 

2002 would be based on data for 2000-2002. The recommendation above implies that 

the reference design value at each monitoring site will be calculated as the average of 

nine design values over five years: the three years which make up the 2002 design 

value (2000-2002), the 2003 design value (2001-2003), and the 2004 design value 

(2002-2004). This gives the greatest weight to 2002, since that year is included in the 

calculation of the design value for all three years. 
95 

The following table summarizes the recommended process for calculating the reference 

design value at each monitoring site. 

Year Years Averaged for Design Value 

2002 2000 2001 2002 

2003 2001 2002 2003 

2004 2002 2003 2004 

Yearly Weighting for Average Design Value for Modeled Attainment Test

2002-2004 

Average 9

Year (2)(Year ) (3)(Year ) (2)(Year ) Year

DV 2000 2001 2002 2003 2004

R

+ + + +

=

8.1.2 Relative Reduction Factors

As discussed above, the relative reduction factor (RRF) is a monitor-specific value that 

is calculated based on daily peak 8-hour ozone concentrations simulated in a future 

year, divided by daily peak concentrations simulated in a reference year. To be 

consistent with the principle that the modeled attainment test and design values should 

be robust and stable over a number of different types of meteorology, the RRF should 

be based on multiple simulated days. The following methodology will be used to 

calculate site-specific RRFs: 

Site-specific RRFs will be calculated as the ratio of the average daily peak 8-hour 

modeled ozone concentration in the future year, divided by the average daily peak 8-

hour modeled ozone concentration in the reference year. Only those days satisfying the 

model performance and threshold criteria described below shall be included in the RRF 

calculation. 

( )

( ) 8 hr AVG

8 hr AVG

AVG RY

FY

RRF

−

−

=

where RRFAVG = the average relative reduction factor for a monitor 

 (FY8-hr)AVG = the average future year 8-hour daily maximum 

concentration predicted near the same monitor, 

averaged over those days which satisfy model 

performance and threshold criteria 

 (RY8-hr)AVG = the modeled reference year 8-hour daily maximum 

concentration predicted near the same monitor, 

averaged over those days which satisfy model 

performance and threshold criteria 
96 

8.1.3 Criteria for Use of Modeled Days in RRF Calculations 

Adequate model performance is a requirement for use of modeled results. The lack of 

acceptable performance greatly increases uncertainty in the use of the modeling results, 

and casts doubt on conclusions based on the modeling. Although it is desirable to 

include as many days as possible in the RRF calculations, our experience has 

demonstrated that not all modeled days meet the minimum performance standards, and 

are thus not suitable for use. Therefore only those days which satisfy the following 

model performance criteria will be utilized in RRF calculations. 

The USEPA (1991) and ARB (1990) outline a number of procedures for analysis of 

base year, air quality model performance. These include spatial and time-series plots, 

statistical analyses, comparing simulated and observed pollutant concentrations, as well 

as sensitivity analysis of selected input fields. The purpose of the performance analysis 

is to provide some confidence that the air quality simulations – which are the basis of 

future-year ozone concentration estimates – are performing properly. 

The application of air quality modeling results to demonstrate attainment of the federal 

1-hour ozone standard emphasized the simulated unpaired peak ozone concentration. 

Three statistical measures were recommended to evaluate model performance: 

unpaired peak ratio (UPR), paired mean normalized bias (NB), and paired gross error 

(GE). These statistical measures were calculated for the modeling domain as a whole, 

and the NB and GE were calculated from all hourly concentrations in excess of 60 ppb 

(to avoid biasing the statistical measures with low concentrations). To meet 

performance guidelines, recommendations were that the UPR should be within ± 20%, 

NB should be within ± 15%, and the GE less than 35%. However, California’s 

geography is very complex and modeling domains have evolved to cover large 

geographic areas. Thus it is recommended that the domains be divided into subregions, and that the performance measures be calculated independently for each subregion. The configuration of these sub-regions is somewhat arbitrary; however, they 

should be configured to isolate "common" regions of higher ozone. 

The USEPA (2005) recommends that the emphasis for 8-hour model performance be 

based on concentrations occurring at, or in the vicinity of, individual monitoring sites. 

Specifically, modeled concentrations occurring within 15 km of a site are considered to 

be in the vicinity of the site. The recommended statistical measures to assess 

simulated versus observed maximum 8-hour ozone concentrations include paired (in 

space, but not time) peak prediction accuracy (PPPA), paired mean normalized bias 

(NB), and paired gross error (GE). Although limited performance analysis has been 

completed for 8-hour ozone modeling in California, it seems prudent at this point to 

carry forward the 1-hour statistical goals and apply them for the 8-hour standard (UPR 

within ± 20%, NB within ± 15%, and the GE less than 35%). However, these limits may 

need to be revised as 8-hour SIP modeling progresses and rigorous model performance 

evaluations are completed. 
97 

While statistical measures for 1-hour model performance were typically calculated 

independently for each modeled day available, the USEPA also recommends that 

PPPA, NB, and GE be calculated for each site over all modeled days. However, 

because the number of episode days available may be very limited, the statistical 

uncertainties in these latter calculations would be large and they are not recommended 

herein. 

In order to have confidence in future year estimates from air quality models, there must 

be confidence in the air quality modeling for the base year. That is, days not meeting 

model acceptance criteria provide high uncertainty, and should not be used for the 

modeled attainment test. 

In addition to the issue of model performance, analyses conducted by the USEPA 

(2005) suggest that air quality models respond more to emission reductions at higher 

predicted ozone values. Correspondingly, the model predicts less benefit at lower 

concentrations. This is consistent with preliminary modeling in support of the 8-hour 

ozone standard conducted by the ARB and the districts. These results imply that RRF 

calculations should be restricted to days with predicted high ozone concentrations. It is 

thus reasonable to establish a minimum threshold for predicted peak 8-hour ozone 

concentrations in the reference year. Days for which the predicted daily peak 8-hour 

ozone concentration at a site is less than the threshold, would not be used for 

calculating RRFs at that site. Consistent with USEPA’s recommendation, we propose 

to use a value of 85 ppb for the reference year threshold. 

Based on the above discussion, we propose the following methodology for determining 

sites and modeled days to be used in the RRF calculations: 

1) The modeled daily 8-hour peak ozone concentration within 15 km of the 

site for the base year (model performance year) of the modeling must be 

within ± 20% of the observed value at the site. 

2) The modeled daily 8-hour peak ozone concentration within 15 km of the 

site in the reference year must be 85 ppb or greater. 

3) The sub-regional 1-hour and 8-hour statistical measures of NB and GE 

must fall within the thresholds of ± 15% and 35%, respectively. 

8.1.4 Estimating Future-Year Design Values 

As discussed above, the USEPA’s 8-hour modeling guidance recommends utilizing 

relative model response on a site-by-site basis, in the form of an average relative 

reduction factor (RRFAVG), to predict future-year 8-hour design values for attainment 

planning. The average RRF is then multiplied by a site-specific design value to 

estimate the future-year design value. One of the confounding factors in this approach 

is consideration of the effects that background levels have on the effectiveness of 

emission control programs. 
98 

There is a large body of information that suggests that ambient concentrations consist 

of some (perhaps nonlinear) background value and a contribution due to anthropogenic 

emissions. That is, if all man-made emissions could be zeroed out, ozone 

concentrations would not go to zero but rather some finite value. The literature 

suggests that 40 ppb is a reasonable global background ozone value, and it is quite 

likely that continental background is some other, somewhat higher, value. One 

possibility for estimating background ozone values in a given modeling domain would 

be to exercise the model without anthropogenic emissions, and to thus develop a 

gridded “background” ozone field. One concern with this approach is that at such low 

levels, the model’s boundary conditions exert a large influence, and appropriate 

temporally- and spatially-resolved data to specify boundary conditions rarely exist. 

Thus boundary conditions can be subjective and uncertain. Whether the background 

value is established at some finite value (e.g., 40 ppb) or is model-derived, it represents 

that portion of a site’s ozone problem that cannot be mitigated by anthropogenic 

emission controls. 

According to EPA’s 8-hour ozone modeling guidance, the modeled attainment test 

requires that a future year Design Value (DVF) be calculated at each site and compared 

to the standard to determine if the site is predicted to be in attainment. To calculate the 

future year Design Value, the Design Value for the reference year (DVR) is multiplied by 

RRFAVG. Although EPA’s guidance says nothing about background ozone, we propose 

to calculate the future year Design Value with consideration of background. The Table 

below illustrates calculation of the DVF with and without background. Because the 

model’s boundary conditions exert a large influence on modeled background ozone 

levels, 40 ppb will be used to represent background ozone concentrations. 

Calculation of the Average Relative Reduction Factor and Future Year 

Design Values* with and without Consideration of Background Ozone 

Without consideration of background With consideration of background 

( )

( )AVG

AVG

AVG RY

FY

RRF =

DVF = (RRFAVG) x (DVR) 

( )

( )AVG

AVG

AVG RY - BG

FY - BG

RRF =

DVF = [(RRFAVG) x (DVR – BG)] + BG 

Definitions 

DVR = Design Value for the reference year 

RY = Reference year model prediction 

FY = Future year model prediction 

BG = Background ozone 

* Note: As per EPA guidance, future year design values are truncated rather than rounded. 
99 

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