Appendix C

Photochemical Modeling Protocol for Developing Stra tegies to Attain the Federal 8-Hour Ozone Air Quality Standard in Centra l California(This page intentionally left blank.)May 30, 2007 Appendix C i

The Photochemical Modeling Protocol for Developing Strategies to Attain the Federal 8-Hour Ozone Air Quality Standard in Centra l California documentation includes the following appendices:

• Appendix A – Gridded Inventory Coordination Group Minutes

• Appendix B – Development of Stack Parameters and Vertical Distr ibutions for Modeling Large Wildfires in the CCOS Domain

• Appendix C – Proposed Method to Improve Temporal Distribution of
Gridded On-road Motor Vehicle Emissions

• Appendix D – Development of Version Two of the California Integr ated Transportation Network (ITN)

• Appendix E – Sample Letter from ARB to Transportation Planning Agencies (TPAs) Statewide Requesting Updated Activi ty Data for Base
Years and Forecasted Years

• Appendix F – Draft EMFAC Modeling Change Technical Memo

• Appendix G – Development of a Biogenic Hydrocarbon Emission Inventory for the Central California Ozone Study Do main

• Appendix H – Surrogate Cross-Reference Tables

These appendices are not included in this file, but are available for download at:

http://eos.arb.ca.gov/eos/SIP_Modeling/(This page intentionally left blank.)

## 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)(This page intentionally left blank.)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 (DV F ).................................................. ...... 93
9 REFERENCES.........................................

................................................... ........... 99(This page intentionally left blank.)3



### 1 INTRODUCTION

1.1 Purpose

This modeling protocol is intended to both guide an d describe the technical aspects of air quality modeling that is to be conducted in sup port of developing an 8-hour ozone State Implementation Plan (SIP) for central Califor nia. 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 ye ar air quality will be simulated. The approach taken follows U.S. EPA modeling guidan ce 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 mode ling system has demonstrated adequate performance, it will be used as a technica l resource to assist decision makers in selecting the most effective future-year emissio n 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 ni trogen?

• With regard to reducing 8-hour ozone concentration s, what are the carrying capacities for the on-attainment areas in the regio n?

• 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 poli cy groups within the region. In particular, the following three groups provided sub stantial input:

• The 2000 Central California Ozone Study (CCOS) – M ore information on CCOS, including the organizational structure, CCOS produc ts, and the Technical Committee (CCOS-TC) can be found via the following two links:4

http://www.arb.ca.gov/airways/crpaqs/organization.h tm

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 Managem ent 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 Calif ornia.

The regular participants for each of these three gr oups are listed in the following two tables. Table 1.1a lists non-government participan ts and Table 1.1b lists government agency participants. The suite of candidate episod es, models, model inputs to consider, and, ultimately, the selected episodes, m odeling 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 i ndividually or collectively.

Table 1.1a – Consultants and Public/Private Stakeholder Repre sentatives

Group Employer RepresentativeBA-MACCCOS-TCSIP-GICG

Acadamia/Consulting Alpine Geophysics James Wilkins on 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 Cl ark 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 X5

Table 1.1b . Government Agency Stakeholder Representatives

Group Employer* Representative BA-MAC CCOS-TC SIP-G ICG
Federal Government US-EPA (Region 9) Carol Bohnenka mp 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 Administra tion; ABAG – Assoc. of Bay Area Governments; BAAQMD – Bay Area Air Quality Management District; COG – Council of Gover nment; APCD – Air Pollution Control District; MTC – Metropolitan Transportation Commission; AQMD – Air Quality Manag ement 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 poli tical 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 th at develop over the Western United States, low wind speeds, and high temperatures. Th ese conditions occur frequently in the San Joaquin Valley between May and September, a nd may persist for several days. The complex features of airflow within the region c ontribute 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 precurso rs are distributed throughout the mixed layer by turbulent diffusion. When meteorolo gical conditions are favorable, daytime sea breezes are funneled through the Carqui nez Strait and nearby mountain passes, bringing ozone and precursors into the nort hern part of the San Joaquin Valley. Some inflow is also observed through the Pacheco Pa ss to the west side of the Valley.

Depending upon the nature of the airflow in the reg ion, ozone episodes in the San Joaquin Valley or Sacramento can be generated predo minantly 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 concentr ations 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 Sa n Joaquin Valley to the coast in the vicinity of San Luis Obispo area has also been obse rved.

Except on the warmest days, an inversion is almost always present within the Central Valley throughout the year. This inversion tends t o trap pollutants either emitted within the Valley or transported into the Valley from surr ounding regions. In this regime, mesoscale flow patterns such as sea breeze intrusio n, local eddies, bifurcation and convergence, and mountain/valley flows are especial ly important in determining the distribution of pollutants throughout the region. These mesoscale characteristics are described in more detail below, and provide a refer ence for features to consider during qualitatively assessing meteorological model perfor mance, 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, dens er air over the ocean and the warmer air over the land. The resulting pressure g radient draws marine air into the Valley during the day. Typically, with calm coasta l winds during mornings, rush hour pollutants can accumulate in the coastal source reg ion. As the sea breeze develops by7

mid-day, ozone and its precursors can enter the Val ley, encountering warmer temperatures and higher insolation.

Nocturnal jet and eddies: A low-level nocturnal wind maximum can develop du ring evening hours. As surface temperatures cool overni ght, a strong stable layer within the Central Valley can result. As this stable layer fo rms, 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 duri ng 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 t he 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 Sier ra foothills at the southeastern edge of the Valley. Smith et al. (1981) mapped the norther ly flow, sometimes called the Fresno eddy, with pibals and described an unusual case whe re it extended as far north as Modesto. During the Southern San Joaquin Ozone Stu dy, 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 o f this bifurcation zone may shift and can determine the relative proportion of Bay Area p ollutants transported to each downwind basin. The dynamics of this bifurcation z one are currently not well understood. However, this zone may also prevent tr ansport 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 provi ded by Blumenthal et al. (1985), where it is hypothesized that the increase in mixin g heights (~200 m higher than in the northern SJV) at the southern end of the San Joaqui n Valley was due to damming of the northerly flow against the Tehachapi mountains at t he 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 canyon s and valleys adjacent to the Central Valley causes s ignificant upslope flows during the afternoons in the San Joaquin and Sacramento Valley s. 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 nightt ime, 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 J oaquin Valley into Sequoia National Park. They found a net up-slope flow of most pollut ant species. The return flow can bring pollutants back down. Smith et al. (1981) us ed tracer data to estimate pollutant budgets due to slope flow fluxes (and other ventila tion 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 Joa quin Valley.8

Up-Valley and Down-Valley Flows : Up-valley and down-valley flows are similar to u pslope and down-slope flows, but take place along th e valley on a larger scale. During the summer, the Sacramento River Delta tends to hav e cooler air temperatures during the day and warmer temperatures at night than at th e extreme ends of the Central Valley due to higher humidity within the Delta. Du ring the daylight hours, up-valley flow draws air south into the San Joaquin Valley and nor th 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 S IP modeling for the region. From this collective body of work, the following four ep isodes were identified as having the greatest potential for SIP modeling in the region:

• July 7-13, 1999 (captured with routine State and L ocal 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 performa nce 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 t he most adequate for the initial round of 8-hour ozone SIP attainment planning. Bri ef summaries of the two episodes selected for SIP modeling are contained in the foll owing sections.

With regard to the two episodes being dropped from consideration, CCOS sponsored a project that was focused on developing the third (S eptember, 2000) episode while the Sacramento Metropolitan Air Quality Management Dist rict (SMAQMD) sponsored a project to develop the fourth (August, 2002) episod e. As indicated above, achieving adequate model performance for both of these period s was problematic. It should be noted, however, that a subsequent effort is now und erway (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 lin ks:

September 2000 (CCOS):

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

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

August 2002 (SMAQMD):

http://www.airquality.org/cleanairplan/modeling.sht ml12



### 2.1 July 7-13, 1999 (Routine Episode)

On July 8, 1999, high pressure began to build throu ghout the southern United States. Through July 10, the high pressure at 500mb increas ed 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 peri od were generally weak and variable.

During this episode period, the high-pressure ridge s that formed were generally shallow. The maximum 500mb pressure-heights were in excess o f 594 dm and are consistent with the high pressures and strong subsidence gener ally associated with the occurrence of high ozone concentrations within central Califor nia.

Ozone concentrations exceeded 125 ppb on July 8-13, 1999. On July 8, a concentration of 155 ppb was recorded at Parlier; h owever, this peak was isolated in time and space and was considered difficult to mode l. On July 10, high ozone concentrations were recorded throughout the Sacrame nto 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 there after and on July 13, the only concentration exceeding 125 ppb was the 132 ppb rec orded 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 th e San Joaquin Valley, ozone concentrations in excess of 85 ppb were recorded fo r at least 10 sites on each day of the episode period until July 13. The maximum 8-ho ur 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 o n July 11. In the San Francisco Bay Area, the maximum daily ozone concentrations di d not exceed 85 ppb until July 10. However, the daily maximums and number of sites rec ording concentrations greater than 85 ppb increased on July 11 and 12. The maxim um 8-hour ozone concentration in the San Francisco Bay Area during this episode peri od 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 concent rations

.

July 8 July 9 July 10 July 11 Total

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

July 12 July 13 Total

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 o f 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 durin g CCOS, while the coastal temperatures were not. For example the study perio d daily temperature maximum at Fresno, 91.4±0.7 

### ° F, was more than three standard deviations below th e climatological

value of 94.8 

### ° F. The study period daily temperature maximum at S an 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 si tes are not as influenced by the mitigating effects of the Pacific Ocean (after Lehr man 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 NAAQ S in the San Joaquin Valley. Lehrman (2003) reported that all days during the Ju ly-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 fi rst seven weeks of the study period, except for one brief incursion of an Eastern Pacifi c 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 study14

period, ridging during IOP#1, July 23-24, brought a Four Corners High. Unfortunately, this 500-mb high positioning can also foster monsoo nal flow. Too much positive vorticity (lifting) kept ozone concentrations low o ver much of the study area, in particular the southern San Joaquin Valley. This high persist ed the next week and moved over the Great Basin during IOP#2, July 30-Aug 2. By Au gust 6, the high had weakened and moved eastward leaving troughs or zonal flow over C alifornia 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 retreate d further east to Texas, Oklahoma, and Arkansas, a trough remained over the pacific Co ast as far south as Northern California, but cut-off lows and zonal flow over so uthern California kept ozone concentrations relatively low. Because of the lack of suitable episodes during the originally scheduled end of the field study on Sept ember 3, the CCOS was extended to late September.

On September 11, zonal flow over the Pacific Northw est and a weak cut-off low off the California coast were adjacent to a new high expand ing 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 deve loped offshore of the US-Canadian border and kept the high to the east. As the cut-o ff 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 fl ow through a trough axis from Hudson Bay to San Francisco Bay. As the trough gav e way to zonal flow over the next week, flights were conducted to monitor boundary co nditions during zonal flow conditions during September 30 through October 2.

By July 25, the ridge had weakened slightly and dro pped southeastward into eastern New Mexico, and a trough developed along the West C oast 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. Howev er, on the 27th, the highpressure ridge once again regressed towards the wes t 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 Ju ly 30, and the period of the next IOP. During the IOP of July 30 through August 2, the rid ge remained strong and continued to slowly regress towards the west until it was center ed near Reno, Nevada by July 31. The OAK 850 mb temperature during the IOP reached a s high as 27 o C and the 500 mb height topped at 5,970 m.

During the July-August 2000 CCOS episode, the highe st ozone level was a recorded 151 ppb at Edison on August 2 in the southern porti on of Central Valley. Peak ozone values on July 30 occurred in the San Joaquin Valle y, where values near 130 ppb were recorded at Parlier and Edison; the Bay Area and Sa cramento region experienced no federal 1-hour exceedances that day. On July 30, o nly the San Joaquin Valley exceeded the 1-hour NAAQS for ozone. Concentration s at Parlier and Edison were 12915

and 128 ppb, respectively. The highest reading on July 31 occurred at Livermore in the Bay Area. The measured peak value was 126 ppb. Th is 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 on ly 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 co ncentration 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 concentra tions exceeded 85 ppb in the San Francisco Bay Area, the Sacramento Area, and the Sa n Joaquin Valley. The frequency of high 8-hour ozone concentrations within the Sacr amento Area and the San Joaquin Valley increased on August 1 and 2, with the maximu m 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 t hat 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 concent rations.

July 30 July 31 August 1 August 2 Total

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 t he 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 col lected regularly through different network systems, including (1) the State and Local Air Monitoring Stations (SLAMS) network, (2) the National Air Monitoring Station (N AMS) network, (3) the Photochemical Assessment Monitoring Station (PAMS) network and (4 ) Special Purpose Monitoring (SPM) that is performed at some sites. More detail ed information on routinely available
data can be obtained from the California Air Resour ces 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 comp rised of data collected during the summer of 2000 at a variety of special study statio ns, 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 su pplemental 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 duri ng the summer, 2000, field study can be accessed via interactive web queries at:

http://www.arb.ca.gov/airways/Datamaintenance/defau lt.asp17

Figure 2.1 Existing routine ozone and nitrogen oxi des monitoring sites.18

Figure 2.2 CCOS supplemental air quality and meteo rological monitoring sites, and Photochemical Assessment Monitoring Stations.19



### 3 MODEL SELECTION

This chapter describes the selection of the meteoro logical and air quality models to be
used.



### 3.1 Meteorological Model

Meteorological model selection is based on a need t o accurately simulate the synoptic and mesoscale meteorological features exhibited dur ing the selected episodic periods. The main difficulties in accomplishing this are Cal ifornia’s extremely complex terrain and its diverse climate. It is desirable that atmosphe ric modeling adequately represent essential meteorological fields, such as wind flows , ambient temperature variation, evolution of the boundary layer, etc. to properly c haracterize the meteorological component of photochemical modeling.

In the past, the ARB has applied prognostic, diagno stic, and objective models to prepare meteorological fields for photochemical mod eling. There are various numerical models that are used by the scientific community to study the meteorological characteristics of an air pollution episode. The m odels under consideration for SIP modeling are:

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

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

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

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

The NCEP ETA model is primarily used by the Nationa l Weather Service as a forecast model and has been used only in limited application s 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 (BAAQM D) 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 sim ulations for selected episodes. The recent air quality results of the BAAQMD indica ted some undesirable model performance characteristics of RAMS (Martien, BAAQM D, 2004, personal communication). Also, the intensive effort from BA QQMD on improving the RAMS model during the past years was curtailed due to li mited technical resources.

MM5 is a mesoscale, limited area, non-hydrostatic n umerical model developed by Penn State and the National Center for Atmospheric Resea rch (NCAR). It uses a terrainfollowing, Lambert Conformal, sigma coordinate syst em. MM5 allows users to study the atmospheric motions at small scales by explicitly t reating the effects of convective motions on atmospheric circulations. It has been i mproved on an ongoing basis over20

the last two decades by contributions from a broad scientific community and has been maintained by NCAR along with necessary meteorologi cal and geographical input data. Based on the complexity of terrain in northern and central California, the MM5 model represents an appropriate tool for resolving dynami cs 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 f or various meteorological regimes over the world and has been supported by the NCAR.

NCAR is currently developing the WRF model to event ually replace MM5. However, the model is still under development and has not been e xtensively tested or demonstrated for SIP use. In addition, the preliminary tests th at 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 mode ling.



### 3.2 Photochemical Model

ARB considered several photochemical air quality mo dels to simulate the two episodes
under consideration:

• SAQM (Chang et. al.,1997),

• CAMx (ENVIRON, 2004), and

• CMAQ (USEPA 1999).

The SAQM (S ARMAP A ir Q uality M odel) 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 facil itate updates (like alternative modules), the documentation is incomplete, and the model has not been successfully applied to ozone modeling problems outside of the S an Joaquin Valley since the SARMAP study. Embarking on updating and recoding t his 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 re garded photochemical model supported by the US EPA. It has been widely used t hroughout most of the United States for ozone, PM, and visibility analysis; howe ver, its successful application within
California has been limited. It is a flexible mode l and allows the selection of alternative modules, such as a different chemical mechanism, ad vection scheme, or chemical solver. The CMAQ model has been shown to run slowe r than alternative models; however it has been linked to the MPI (Multiple Pro cessor Integration) software library package to run in parallel through a distributed pr ocess 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 mod els. Periodic updates from the developers ensure that more recent technical develo pments are incorporated. The CAMx model has also been shown to be very flexible. Alternative chemical mechanisms (CB-IV and SAPRC99) and advection scheme s can be selected and meteorological inputs may be developed from objecti ve/diagnostic or prognostic meteorological models. It also has two built-in pr obing tools, DDM (Decoupled Direct Method) for formal sensitivity analysis and PA (Pro cess Analysis) for model dynamic
examination. A PM module for simultaneous simulati ons 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 config ured for the July-August, 2000 episode in approximately 1 day (excluding the devel opment of the required meteorological fields). The preparation of the SAQ M 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-si ze 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 meteorologi cal inputs, the CAMx model was selected as the primary air quality model for the C COS ozone modeling. This decision was reinforced by the selection of CAMx for SIP mod eling 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, a s resources allow, the CMAQ photochemical model may also be run for the episode s to compare the performance of
the two models.22



### 4 MODELING DOMAIN AND GRID STRUCTURE

As described in Chapter 1, stakeholders have alread y produced a significant body of work, including model simulations, for the two sele cted modeling episodes. Selection of the domain and grid structure described in this cha pter and to be used for SIP modeling are based on this prior experience.



### 4.1 Meteorological Modeling Domain

The MM5 meteorological modeling domain is consisten t for both episodes. It consists of three nested grids: 36 km, 12 km and 4 km unifor m, horizontal grid spacing (illustrated in Figure 4.1). The purpose of the co arse, 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 a nd D03, are placed within the coarser grids such that they are not too close to t he 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 stru cture is configured, each modeling domain was run independently using the output of it s 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 t his 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 model ing domain to resolve the small boundary layer atmospheric flow features such as la rge 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 epis ode 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 f or the numerical modeling for both SIP episodes using the MM5 model (D01 36km; D02 12km; and D03 4km). DO1 D02 D0324

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 0 025

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 0 026



### 4.2 Photochemical Modeling Domain

The objectives of the SIP require that the photoche mical 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 rectangula r to accept meteorological input fields from MM5. The resulting ozone modeling doma in 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 horizontal ly as 185x185, 4x4-km grid cells, shown in Figure 4.2. The domain lower, left-hand c orner is at -376 x -292 km from the defined origin. The MM5 output for the 4-km modeli ng 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, depen ding on the meteorological model configurations, will be adjusted accordingly to gen erate 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 S an Joaquin Valley is part of the larger Central Valley of California than runs rough ly north/south and is surrounded by mountains, except in the vicinity of the SFBA.

There are two scenarios being evaluated for the det ermination 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 si mulations) 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 wer e 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 veloc ities that may occur in the wind fields from some meteorological models (ENVIRON, 20 05a).27 Figure 4.2 Photochemical Modeling Domain with185x18 5
Grid Cells at 4x4-km Horizontal Resolution

The number of vertical layers used in the ozone sim ulations will be tied closely to the meteorological model ultimately used. Configuratio ns of 16-layer and 20-layer will be used for the July 1999 and July-August 2000 episode s, respectively, as shown in
Table 4.3.

All photochemical model simulations will be run usi ng a Pacific Daylight Time (PDT)
time base.28

Table 4.3 Vertical Layer Heights(m) of Photochemic al Modeling for July-August 2000 and July 1999 Episodes.

July-August episode July 1999 episode 50-Layer MM5 configuration 30-Layer MM5 configuration Layer No. 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 3229



### 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 sim ulation approximate ambient conditions. This chapter is divided into two sub-s ections 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 in itialization is based.



### 5.1 Initialization of the Meteorological Model

MM5 is a complex numerical model that requires sett ing a large number of input parameters and model options. Some of these requir ements include: the specification of initial and boundary conditions (IC/BCs); gather ing 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 p arameters, 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 o r options to be used in MM5. Rather, these decisions are determined based on optimizing model performance. Thus, during the preparation of preliminary meteorological field s for the July-August 2000 and July 1999 SIP episodes, vast amounts of data were p rocessed and many combinations of model options were tested. Based on the best mo del performance for these preliminary tests, the most successful MM5 model op tions and input datasets were determined. These are described in the following s ections.

5.1.1 

### MM5 Model Options

As indicated above, many sensitivity studies were c onducted to choose a set of model options that result in scientifically reasonable me teorological fields that are representative of the specific conditions during ea ch of the two selected ozone episodes.

For the July-August 2000 episode, the Kain and Frit sch (1993) cumulus parameterization scheme was selected for coarse gri ds, while no cumulus parameterization was used for the 4 km grid. In ad dition, the ETA model for the parameterization of boundary layer flow (Janjic, 19 94), Dudhia simple ice scheme for the treatment of cloud microphysics (Dudhia, 1989), the RRTM scheme for the calculation of radiation (Mlawer, 1997), and NOAH L and Surface Model for the calculation of surface energy balance (Chen and Dud hia, 2001) were used in all grids.30

For the July 1999 episode, no cumulus parameterizat ion scheme was used for any of the grids and a 5-layer slab model (Dudhia, 1996) w as used for the calculation of surface energy balance for all grids. The cloud ra diation scheme was used for the 4 km grid. All other model options were kept the same a s 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 Environ mental 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 synop tic 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 meteorologic al conditions observed during the July-August 2000 and July 1999 episodes by using th e analysis nudging option of the Four Dimensional Data Analysis (FDDA) for the 36 an d 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 en hance 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 dev eloping FDDA input datasets is different for the July-August 2000 and July 1999 ep isodes. The July-August 2000 episode benefited from the extensive CCOS field cam paign conducted during the summer of 2000. About 300 surface and 25 upper air meteorological stations were operated during CCOS field campaign (Section 2.3) a nd provided additional meteorological data in addition to the routine surf ace data that are available through the instruments operated by local air districts, ARB, a nd 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 out puts). Generally, all surface and upper air data are plotted in space and time to ide ntify extreme values that are suspected to be “outliers”. Data points are also c ompared to other, similar surrounding data points to determine whether there are any larg e 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. Mod el-simulated meteorological31

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 Conditio ns

Air quality model initial conditions define the con centration distributions of chemical species within the modeling domain at the beginning of the model simulation. Boundary conditions define the chemical species concentratio n distributions for air entering or leaving the modeling domain. To some extent the in itial and boundary conditions need to reflect the modeling domain dimensions, the epis ode, and the characteristics of the model being used.

This section discusses the initial and boundary con ditions used by the Air Resources Board (ARB) in air quality modeling that will suppo rt developing the 8-hour ozone State Implementation Plan (SIP). The selected boundary c onditions 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 i nput at a March 10 th , 2005, meeting of regulatory agency modeling staff.

5.2.1 

### Photochemical Mechanism

Historically, over the last several decades, air qu ality modeling for ozone SIPs throughout California have predominately been condu cted using the Carbon Bond IV (CBIV) chemical mechanism. The CBIV mechanism uses 36 chemical species and 89 chemical reactions (may vary somewhat among differe nt 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 t he 1999 State Air Pollution Research Center chemical mechanism (SAPRC99; Carter , 2000), have been developed; however, the use of SAPRC99 has historic ally 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 mech anism available (74 chemical species and 211 chemical reactions) and has been th oroughly peer-reviewed, ARB’s Reactivity Scientific Advisory Committee recommende d unanimously in October of 1999
that ARB use SAPRC-99 instead of CBIV for SIP model ing. Minutes of the October 8, 1999, Reactivity Scientific Advisory Committee (RRA C) can be found at:

http://www.arb.ca.gov/research/reactivity/rsac/oct9 9-min.html

In central and northern California, SAPRC has been the mechanism of choice for over a decade. Consistent with this and with the expectat ion of better representation of32

atmospheric chemical behavior for ozone modeling, t he SAPRC99 chemical mechanism was selected for all 8-hour ozone air quality model ing in California.

5.2.2 

### Supporting Information

This section discusses ambient concentrations avail able from measurements, related studies, as well as USEPA guidance on initial condi tions, boundary conditions, and background concentrations of pollutants. Ideally, initial and boundary conditions used in modeling would be based upon measurements. Unfortu nately, for a domain as large as that defined for CCOS, the boundaries are located i n remote Pacific ocean areas (56km from northern coast line, 200km from S.F. coast lin e, and 360km from southern coast line) and there are few measurements that may be co nsidered uncontaminated by anthropogenic sources.

The USEPA (1991) recommends default initial/boundar y conditions based upon species
for the Carbon Bond IV chemical mechanism. Table 5 .1 shows USEPA’s
recommended initial concentrations for individual C BIV species, including 40 ppb ozone, 2 ppb NO
x , and approximately 22 ppbC ROG.

During the year 2000 CCOS field study, pollutant co ncentrations aloft were measured along several aircraft flight patterns and from ozo nesondes at two locations (Granite Bay and Parlier). However, comparison of measureme nts taken along the coast of
California versus historical data collected offshor e suggest that the CCOS concentrations may not represent concentrations occ urring over the Pacific Ocean. Thus, whether the CCOS samples are appropriate for the definition of top or lateral boundary concentrations is subject to interpretatio n.

Ozonesondes were launched four times per day from P arlier and Granite Bay during CCOS intensive measurement periods. However, these sites were located within the Sacramento and San Joaquin Valleys and are not nece ssarily well suited to represent
ozone concentrations on the lateral domain boundari es, several hundreds of kilometers from the launch locations. During the July-August, 2000 episode, ozone concentrations at 5000 meters above ground level (magl) ranged bet ween about 50 and 90 pbb. However, during other episode periods such as the S eptember 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 a nd flight-path, ozone
concentrations measured aloft from the aircraft ran ged from 15 ppb to more than 100 ppb. Ozone concentrations during CCOS in the 7 0-80 ppb range were measured from aircraft as far as 160 km offshore (to the wes t); however, these flights were few and the evaluation of wind flow patterns during the se 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. A mong them, the Trinidad Head site is
located at the north coast of California as shown i n Figure 5.2. Ozone measurements from two single day ozonesondes launched by the Nat ional Atmospheric and Oceanic Administration (NOAA) on July 21 and August 1, 200 0, at Trinidad Head and two CCOS ozonesondes during a CCOS intensive measuremen t period (IOP #2) are plotted in Figure 5.3. NOAA’s measurements at Trinidad Hea d show ozone concentrations at 15 km aloft of around 135 ppb. The CCOS measuremen ts at Granite Bay and Parlier have higher ozone levels below 5000 magl than the o zonesonde 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 a nalysis conducted by the Bay Area Air Quality Management District (BAAQMD) found a si milar difference: that measured concentrations along the California coastline consi stently 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 (pp b) 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 T rinidad Head (operated by the NOAA) as well as Granite Bay and P arlier (operated during CCOS).36

The USEPA recommends a boundary condition for ozone of 40 ppb. However, analyses conducted by the BAAQMD (2005) suggested t hat lower concentrations are
common at the surface near the Pacific coastline.

Reactive Organic Gases (ROGs) are the most difficul t pollutant category to provide a measurement-based assessment of boundary concentrat ions, since there are only a
limited number of measurements available. At the s urface, all of the ROG samples collected during CCOS were made at sites located wi thin 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 a n evaluation of the offshore flow patterns during these flights casts doubt on whethe r the measurements taken are representative of boundary concentrations.

For ROG boundary conditions, the U.S. EPA (1991) re commends default concentrations of 22 ppbC. However, ROG concentrat ions measured at the surface during CCOS were often higher than this. Aloft, co ncentrations 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-Aug ust, 2000 episode, ROG concentrations were between 20 and 40 ppbC. These data suggest that, while higher ROG concentrations occurred aloft, the concentratio ns aloft were not uniformly high. Analyses conducted by STI under a CCOS contract (20 05) reported no significant correlation between high ROG concentrations aloft a nd 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 Attac hment 1. The selected conditions were determined with BAAQMD, SJVAPCD, and SMAQMD st akeholder input at a
March 10 th , 2005, meeting of the SIP Modeling Working Group ( Attachment 2).

Because of their relatively clean values, the bound ary conditions for future years are kept the same as boundary conditions for the base y ears.37

Table 5.2. Recommended air quality modeling domain boundary conditions.

Region Top* Over-Water

Lateral Over-Land

Lateral
O 3 (ppb) 70 25-70 40-70 ROG (ppbC) 26 26 48
NO 2 (ppb) 1 1 1 CO (ppb) 200 200 200 * The July 1999 episode domain top is at approxima tely 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 Ca rbon Bond IV ROG (chemical species not listed were set to concentrations of 0.00001).38

Table 5.4. Summary of Episode-Specific Ozone & Precursor Bou ndary 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 0.05500 0

11 1,037 0.060000 0.062000 11 1,424 0.055000 0.058 000

12 1,275 0.062000 0.064000 12 1,781 0.058000 0.060 000

13 1,560 0.064000 0.065000 13 2,268 0.060000 0.060 000

14 2,306 0.065000 0.066000 14 2,935 0.062000 0.062 000

15 3,368 0.068000 0.068000 15 3,860 0.063000 0.063 000

16 4,873 0.070000 0.070000 16 5,289 0.064000 0.064 000

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 4839 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 allevi ate 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 initi al conditions based on emissions and boundary conditions. Utilizing a spin-up period al so 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 me teorology and chemical transformation processes to generate more represent ative 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 concent rations 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 estimat es. Emissions are broadly categorized into major stationary or point sources, area sources (which include off-road mobile sources), on-road mobile sources, and biogen ic sources.

To support the body of work conducted by stakeholde rs, modeling inventories have been developed by ARB staff on an on-going basis fo r the July 1999 and July-August 2000 episodes. The following sections describe how emissions estimates required by the selected air quality models (commonly and inter changeably referred to as ‘modeling inventories’ or ‘gridded inventories’) are estimate d and how they will be used to develop base case and future year emissions estimates for m odeling used to prepare the State Implementation Plan (SIP). As modifications to bas ic 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 summari zed.

To help coordinate the development of gridded inven tories for CCOS modeling, an Emission Inventory Coordination Group (CCOS EICG) w as established in February 1999. Participating in the group were many local a ir districts, regional transportation planning agencies (RTPAs), the California Departmen t of Transportation (Caltrans), the California Energy Commission, the U.S. Environmenta l Protection Agency, and the ARB. Local air districts that participated include d San Joaquin Valley APCD, Bay Area AQMD, Sacramento Metropolitan AQMD, Mendocino Count y APCD, Northern Sierra AQMD, Yolo/Solano AQMD, Placer County APCD, San Lui s 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 update s (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 develo p hourly distributions (Contract 00-04PM, UC Davis). Section 6.7.6 provides more de tail.

• Develop the Integrated Transportation Network (ITN ) and run the Direct Travel Impact Model (DTIM) (Contract 93-2PM, Alpine Geophy sics). 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 o ff-road sources (Contract 00- 24CCOS, Sonoma Technology, Inc.). Section 6.9 desc ribes this project in more
detail.

The CCOS EICG met on a regular basis to discuss CCO S 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 S IP Gridded Inventory Coordination Group (SIP-GICG) in February 2003. The GICG consis ts primarily of government agencies and their contractors that are responsible for the variety of data used to develop gridded emission inventories for SIP purpos es. 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. I n February 2005, the focus was changed to inventory development for the 8-hour ozo ne 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 emis sion inventory is developed. California’s emission inventory is an estimate of t he amounts and types of pollutants emitted from thousands of industrial facilities, mi llions of motor vehicles, and of hundreds of millions of applications of other produ cts such as paint and consumer products. The development and maintenance of the i nventory is a multi-agency effort involving the ARB, 35 local air pollution control a nd air quality management districts (districts), regional transportation planning agenc ies (RTPAs), and the California Department of Transportation (Caltrans). The ARB i s responsible for the compilation of the final, statewide emission inventory, and mainta ins this information in a complex electronic database. Each emission inventory refle cted the best information available at
the time.

To produce regulatory, countywide emissions estimat es, the basic principle for estimating emissions is to multiply an estimated, p er-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 yea r based on dynamometer tests of a small sample of that vehicle type and applied to al l applicable vehicles. The usage of
those vehicles is based on an estimate of such acti vities 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 th e state are driven under similar
conditions.

Developing emission estimates for stationary source s involves the use of per unit emission factors and activity levels. Under ideal conditions, facility-specific emission42

factors are determined from emission tests for a pa rticular 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 f acilities. This generic factor is then used to estimate emissions from similar types of pr ocesses 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 us ed, or fuel used.

ARB maintains an electronic database of emissions a nd other useful information. Annual average emissions are stored for each county , air basin, and district. The
database is called the California Emission Inventor y Development and Reporting System (CEIDARS). Emissions are stored in CEIDARS for criteria and toxic pollutants. The criteria pollutants are total organic gases (TO G), carbon monoxide (CO), oxides of nitrogen (NOx), oxides of sulfur (SOx), and total p articulate matter (PM). Reactive organic gases (ROG) and particulate matter 10 micro ns in diameter and smaller (PM 10 ) are calculated from TOG and PM, respectively. Foll owing are more details on how emissions are estimated for point and area sources, on-road motor vehicles, and biogenic sources. Additional information on emissi on 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 inv entory is comprised of more than 17,000 individual facilities, called “point sources ”, and about 160 categories of “aggregated point sources”. Aggregated point sourc es 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 p oint 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 cuto ff smaller than 10 tons per year for reporting facilities as point sources. Any remaini ng sources not captured in the point source inventory are reported as aggregated point s ources.

The area-wide source component includes several hun dred source categories and is made up of sources of pollution mainly linked to th e activity of people. Examples of these categories are emissions from consumer produc ts, architectural coatings, pesticide applications, and wind-blown dust from ag ricultural lands. The emissions for these categories are located mostly within major po pulation centers. Some of the emissions in these categories come from agricultura l centers and construction sites.

The off-road mobile source inventory is based on th e population, activity, and emissions estimates of the varied types of off-road equipment . The major categories of engines43

and vehicles include agricultural, construction, la wn and garden, and off-road recreation, and include equipment from hedge trimme rs to cranes. ARB’s OFFROAD model estimates the relative contribution of gasoli ne, diesel, compressed natural gas, and liquefied petroleum gas powered vehicles to the overall emissions inventory of the state. In previous versions of the inventory, emis sions from the OFFROAD model were aggregated into about 100 broad categories. Since April 2006, the inventory reports emissions in about 1800 detailed categories that ma tch what is produced by the OFFROAD model. Carrying this level of detail allow s for more accurate application of control measures as well as more specific assignmen ts 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 s ources. The districts provide point source information to ARB to update the annual aver age CEIDARS database. Estimating emissions from area sources is a coopera tive effort between ARB and air district staffs. Updating the emission inventory i s a continual process, as new
information becomes available.

6.2.1.1 

### Terminology

There can be confusion regarding the terms “point s ources” and “area sources”. Traditionally, these terms have had two different m eanings to the developers of emissions inventories and the developers of modelin g 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. “Are a sources” refers collectively to areawide sources, stationary-aggregated sources, and ot her 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 poss ible elevated sources. Processing of the inventory for the photochemical model (e.g. CAM x) will determine which vertical layer the emissions from a process will be placed i nto. 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 condu ct quality assurance (QA) of the inventory. Staffs from many local districts, inclu ding the Bay Area AQMD, Monterey Bay Unified APCD, Sacramento Metro AQMD and San Joa quin Valley APCD conducted extensive quality assurance to provide an accurate and complete inventory. Districts in the southern part of California had recently comple ted a similar exercise to improve their inventories as part of the Southern California Ozon e Study (SCOS).

In particular, facility location, stack data, and t emporal data were closely checked. This information is critical whenever photochemical mode ling is conducted, such as during SIP preparation or special studies such as CCOS. H owever these data are not always of sufficient quality in the inventory database sin ce this information is not needed in the
actual calculation of emissions and resources are l imited. 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 C EIDARS database that was used initially for CCOS and 1-hour ozone SIP inventory p reparation. 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 SI P.

• Stack data – The report checks for missing or inco rrect stack data. The report lists missing stack data and also checks the data f or reasonable stack height, diameter, temperature, and stack velocity. Additio nally, the report compares the reported stack flow rate with the computed theoreti cal flow rate (calculated using the diameter and stack velocity).

• Location data – The report checks for missing or w rong Universal Transverse Mercator) UTM coordinates. The report lists missin g UTM coordinates for both
facilities and stacks. UTM coordinates are also ch ecked to ensure that they are45

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 w hich 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. Addit ionally, 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 i nvalid temporal information. Temporal codes used to describe the hours per day, days per week, and weeks per year are checked for completeness, accuracy, an d 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 (SC C) and Standard Industrial Classification Codes (SIC) were reviewed for accura cy. The SCC is used to determine the speciation profile assigned (speciati on 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) a long 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 ab ove, the CCOS Emission Inventory Coordination Group agreed to assist the small distr icts in the CCOS domain. Many small districts in the CCOS region have limited sta ff 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 seco nd on area sources.

District staff said that they did have emission est imates 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 stude nts 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 Count y AQMD, Colusa County APCD, El Dorado County APCD, Feather River AQMD, Glenn Co unty APCD, Northern Sierra AQMD, Placer County APCD, Shasta County AQMD, Teham a County APCD, Tuolumne County APCD, and Yolo/Solano AQMD. The re sults of this project have been incorporated into the 1999, 2000, and 2002 CEI DARS inventories.

For area sources, district staff said that the best way to provide assistance would be to have a contractor develop emission estimates for th e area source categories for which the districts were responsible. The CCOS study con tracted with Sonoma Technology, Inc. (STI) (Coe, 2003) to prepare revised emissions estimates . STI would format the emissions and related data for input into the CEIDA RS database. District staffs have46

included these updates in the 2002 database. STI d eveloped protocol memoranda that contained the following elements:

• Description of emission source

• Emission factors

• Activity data

• Emissions calculations, including a sample calcula tion

• Temporal allocation

• References and contacts

The protocols were pulled together from a variety o f resources, including local air districts’ past methods documents, U.S. Environment al Protection Agency documents, ARB documents, and original ideas based on the disc overy of new information sources through library research, Internet research, and te lephone contacts. Generally, STI attempted to incorporate data and information resou rces into the protocols that are readily available to the general public at no or lo w 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 th e costs of these efforts are
warranted.

Emissions were estimated for the following counties : Amador, Butte, Calaveras, Colusa, E. Solano, El Dorado, Glenn, Mariposa, Mendocino, N evada, Placer, Plumas, Sacramento, Shasta, Sierra, Sutter, Tehama, Tuolumn e, Yolo, and Yuba. Area source methodologies were developed for the following broa d 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 indust rial processes)

• Petroleum marketing

The protocol memoranda can be found on a password-p rotected 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 pred ictive 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, popu lation growth, and emission
controls.

ARB’s model to forecast or backcast emissions is kn own as the California Emission Forecasting System (CEFS). The CEFS model is desig ned to generate year-specific emissions estimates for each county/air basin/distr ict combination taking into account two factors: 1) the effects of growth and 2) the ef fects of adopted emission control rules. It does this by linking these growth and control fa ctors directly to CEIDARS emission categories for a particular base year (2002 for thi s project). A key component of the model is the Rule Tracking Subsystem (RTS). The RT S was developed to link yearspecific implementation of emission control rules t o 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 C lassification (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 e mission categories statewide.

Reports of year-specific emissions are available to district staff on-line. District staffs should contact their emission inventory liaisons fo r URL and password information. The reports can be generated for a variety of years, po llutants, source types, seasons, and geographical areas.

6.3.1 

### Growth Factors
Growth factors are derived from county-specific eco nomic activity profiles, population forecasts, and other socio/demographic activity. T hese data are obtained from a number of sources, such as:

• districts and local regional transportation planni ng agencies (RTPAs) when they
are available

• economic activity studies contracted by the ARB

• demographic data such as population survey data fr om the California Department of Finance (DOF) and Vehicle Miles Trave led (VMT) data from the California Department of Transportation (Caltrans)

Growth profiles are typically associated with the t ype of industry and secondarily to the type of emission process. For point sources, econo mic 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 technolo gical change on a particular48

emission process. These data are provided by the a gencies responsible for overseeing the regulatory action for the particular emission c ategories 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 d evelop control factors for locally enforceable stationary source regulations that affe ct emissions from such equipment as internal combustion engines or power plant boilers. The Department of Pesticide Regulation (DPR) supplies control data for pesticid es. 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 var iations and upsets

• Rule Penetration which estimates the degree a control strategy will penetrate a certain regulated sector taking into account such t hings as equipment exemptions.

Control factors are closely linked to the type of e mission process and secondarily to the type of industry. Control levels are assigned to e mission 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 Coordinatio n Group (EICG), made up of ARB and district staff to guide inventory development f or CCOS, requested that districts within the CCOS domain collect day-specific data fr om facilities and other sources within their jurisdiction. The EICG gathered hourl y/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 emiss ion estimates for 67 facilities. The districts which provided data were Amador County AP CD, Bay Area AQMD, Colusa County APCD, Monterey Bay Unified APCD, Placer Coun ty APCD, Sacramento Metro AQMD, San Joaquin Valley APCD, San Luis Obispo Coun ty APCD, Shasta County AQMD, Tehama County APCD, and Yolo/Solano AQMD. Da y-specific emissions replaced emissions estimated from CEFS. Additional ly, the Bay Area AQMD provided emission estimates from unusual events, such as equ ipment 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 agri cultural 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 av erage emission estimates for ships in the ocean and San Francisco Bay as well as for tugb oats. Professor Bornstein developed factors for July 4 through July 14,1999, covering all the days needed for modeling (July 8 through 13, 1999). Professor Born stein developed factors for July 29 through August 3, 2000. Since July 27 through Augu st 2, 2000 is being modeled, August 3 was selected to approximate July 27 and 28 , 2000.

Emissions from ships are estimated for two air basi ns: San Francisco Bay Area (SF) and the Outer Continental Shelf (OCS). Emissions f rom 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 shi pping 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 P rofessor 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 adjus ted.

6.4.4 

### Wildfires

Emissions were estimated for known wildfires that o ccurred during the CCOS episodes. There were about 30 fires that occurred during epis odes 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 Sequo ia 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 throu gh July 31, 2000

. Due to these fires’ duration, scale, and coincidence with the Ce ntral California Ozone Study, modeling staff requested that an estimate of fire e missions be developed in order to assess these fires’ potential impacts on regional e missions and photochemistry.

To develop emission estimates, the ARB emission inv entory staff turned to an on-going contract with UC Berkeley’s Center for the Assessme nt 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) w ithin a Geographic Information System (GIS). FOFEM is a standard fire effects mod el 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 the50

current contract, CAMFER was tasked to extend the E ES to enable the model to estimate temporally-resolved emissions for individu al fires, for an expanded suite of pollutants (CARB,2000).

The CAMFER EES runs within ArcView software and uti lizes emission algorithms, emission factors, combustion efficiencies, fuel loa dings, and other parameters from FOFEM. In the EES, GIS-based spatial data layers ( polygon shapefiles), representing burned areas, are overlaid onto a GIS vegetation da ta layer in which vegetation community types are coupled with corresponding FOFE M biomass fuel profiles. For each fuel component (there are 10 fuel components r epresenting 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 elap sed since ignition. The FOFEM and CAMFER EES models generate daily emissions from bot h phases. Emissions generated by the EES from flaming and smoldering ph ases are combined in the final outputs.

These emissions were then utilized to develop a plu me profile, using the techniques outlined in a recent report of the Fire Emissions J oint Forum (FEJF) of the Western Regional Air Partnership (WRAP) (Air Sciences, 2004 ). Appendix B describes the vertical distribution of emissions in greater detai l.

For all other fires in the summer of 2000, emission s were calculated based on the number of acres of three vegetation types: chaparra l, grass, and timber. The U.S. Forest Service provided fuel loading and emission f actors. The number of acres, vegetation type, fire duration, and location inform ation were taken from California Department of Forestry (CDF) fire incident reports and newspaper articles. The vertical distributions of the plumes were calculated using t he FEJF methodology referenced
above.

There were also about 15 fires, totaling approximat ely 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 crea te temporally resolved inventories for modeling purposes, for the base year and future years. The annual average emissions are adjusted to account for monthly and w eekly 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 ne eded 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 d aily emissions are distributed to each hour of the day is provided for later incorporation

.51

The emission inventories for CCOS were developed fr om the 2002 annual average CEIDARS inventory for TOG, NOx, SO X , CO, PM, and ammonia. Since the episodes to be modeled (1999 and 2000) were earlier than the in ventory 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 proc essed into the formats needed for input to air quality models.

The backcasting of emissions for point and area sou rces usesg the best available data. Backcasting is handled differently for point and ar ea sources. Point sources use historical data as stored in that year’s CEIDARS in ventory. In other words, the 1999 point source emissions come from the 1999 CEIDARS d atabase and the 2000 point
source emissions come from the 2000 CEIDARS databas e. 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 up dated emission calculation methodologies.



### 6.6 Surface Temperature and Relative Humidity Fields

The calculation of gridded emissions for some categ ories of the emissions inventory is dependent on gridded air temperature (T), relative humidity (RH), and solar radiation fields. Biogenic emissions are sensitive to air te mperatures and solar radiation, and
emissions from on-road mobile sources are sensitive to air temperature and relative humidity. Gridded temperature, humidity, and radia tion fields are readily available from prognostic meteorological models such as MM5, used to prepare meteorological inputs for the air quality model. However, analysis of th e 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 usin g the prognostic meteorological model, air temperature and humidity fields for calc ulation of the emission inventory were prepared by objective analysis. In the objective a nalysis, hourly temperatures for each grid cell within the study domain were calculated u sing a distance-weighted average of the nearest three temperature measurements. Becaus e few temperature measurements were available at higher terrain eleva tions, 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 a ssumed 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 difference s in relative humidity of 40% among sites within a 25-kilometer radius. To reduce larg e horizontal variations in the relative humidity fields developed for the emission inventor y calculations, relative humidity fields52

were calculated assuming a daily constant absolute humidity for each grid cell. The absolute humidity was calculated from the minimum d aily temperature and assuming a maximum daily relative humidity of 80%.

The solar radiation fields needed for biogenic emis sion inventory calculations were
taken from the MM5 simulation.



### 6.7 On-Road Mobile Source Emissions

EMFAC is the ARB approved on-road motor vehicle emi ssion 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 heav y-duty diesel vehicles

• Diesel fuel correction factors

Gasoline Vehicles

• The impact of ethanol in gasoline on evaporative e missions

• Addition of areas into the Enhanced Smog Check pro gram

The EMFAC model provides emission estimates for 13 classes of vehicles for exhaust, evaporation, and PM emissions from tire wear and br ake wear. EMFAC also produces estimates of fuel consumption, vehicle miles travel ed (VMT), and the number of vehicles in use. EMFAC does not output a gridded emission f ile. 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 gridd ing 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 late st 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 (l inks) and traffic analysis zone activity centroids to allocate emissions for travel and trip ends. DTIM4 gridded emission files have fewer categories than EMFAC outputs. Ea ch DTIM4 output category will be used to spatially allocate emissions for several EM FAC emission categories. There are53

also several categories of emissions that EMFAC pro duces that are not estimated by
DTIM4.

DTIM4 is used to estimate both the spatial and temp oral distribution of all on-road motor vehicle emissions. It is important to recognize th at 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 wit h EMFAC2007 and DTIM4 to produce day-specific gridded on-road motor vehicle emission estimates. The procedures described here are carried out separatel y for each county in the CCOS modeling domain.

6.7.1 

### EMFAC Emissions Categories

EMFAC2007 produces emission estimates for the follo wing 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 w ithin each vehicle type:

1. Catalyst 2. Non-catalyst
3. Diesel

For each of the combinations of vehicle type and te chnology there can be many emission categories:

1. Start Exhaust 2. Running Exhaust54

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 emiss ion factors for each EMFAC technology type for each emission category. The ve hicle 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 de pend on vehicle type, the technology, and whether the vehicle is catalyst, no n-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 Dust56 6.7.3 

### Creating the Emission Rate File

EMFAC will create an emission rate file for any des ired 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, 6 0, 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 d ay of each episode, by county. County average hourly temperatures, weighted by gri dded 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 cat egory to the EMFAC emissions estimates for each county/air basin area. EMFAC bu s 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 hum idity (RH) fields for each episode day are prepared for input to DTIM4. The T and RH fields are derived either from meteorological model prediction s, observations, or some hybrid combination of model predictions and observa tions.

Step 2 . EMFAC is run to prepare on-road mobile source em ission factors by speed, temperatures, and relative humidity for each county.

Step 3 . DTIM4 is run using data from the Integrated Trans portation Network version 2 (ITNv2) and EMFAC to estimate gridded, ho urly 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 in puts for EMFAC are generated57

via averaging (VMT-weighted) the gridded, hourly me teorology from Step1 by county and hour.

Step 5 . Two sub-steps are taken:

Temporal adjustments

5a Sum the hourly volumes by vehicle type and coun ty on the ITNv2 network.

5b For heavy-duty vehicles on core days (Tuesday t hrough Thursday) redistribute the hourly emissions but make no daily VMT adjustment. Light duty vehicle emissions from EMFAC will not be adjusted a t all for core days.

5c For Friday, Saturday, Sunday, and Monday, use C altrans count data to develop a set of ratios of Caltrans daily VMT to co re 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 h eavy-duty trucks.

5d Apply Caltrans daily factors by county, and sec ondly, apply Caltrans’ new hourly distributions by county to ITNv2 link activi ty.

5e Run DTIM with revised ITNv2 activity.

5f Run EMFAC with day-specific temperatures.

5g Adjust DTIM output emissions to EMFAC weekday b y 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 cate gory 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:

cnty cat daily P cat

hr ij P cat P

cat hr ij P 

# DTIM

DTIM EF 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 emissio ns are our best estimates at the present time; however additional work in three area s 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 transportati on modeling done to explicitly model
trucks is for Southern California counties covered by the Southern California Association of Governments (SCAG). For the remaini ng counties, heavy-duty trucks are assigned as a ratio of light-duty vehicles.

A second area of improvement is in developing emiss ions 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 vehic le emissions on weekend days should be considered an approximation since there a re no transportation models to describe weekend traffic. In other words, people a re still traveling to work; the emissions are just scaled down.

A third area of improvement is determining the hour ly emissions from on-road motor vehicles. Local regional transportation agencies ( RTPAs) and Caltrans supply traffic estimates for several time periods in a day. In th e development of previous modeling inventories for CCOS, traffic within the time perio d was allocated to each hour using the hourly profiles that were developed by UC Davis. (L am 2002). UC Davis developed two hourly profiles, one for weekdays and one for w eekend days, which differed by county. However, there was no distinction by vehic le class. The same hourly profile59

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 for med in 2004 to investigate a way to improve day-of-week adjustments, for vehicle typ es as needed, but particularly for heavy-duty trucks. Participants in the subcommitte e are members of the GICG with particular knowledge and/or interest in improving t he 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 Classifie r (AVC) count data from about 139 sites in the state for calendar year 2004 (see Figu re 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 c ount data are separated using the Federal Highway Administration (FHWA) vehicle class ification scheme (see Table 6.3). Passenger cars are defined as FHWA classes 1 throug h 3. Light and medium heavyduty trucks are defined as FHWA classes 7 and 8. H eavy-heavy duty trucks are defined as FHWA classes 9 through 14. Separate factors we re prepared for each Caltrans District. One or more counties may fall into a sin gle 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 duri ng the summer of 2004 were used, specifically the months of June, July and August ex cluding data from July 2-5 to remove unusual traffic patterns around the July 4 th holiday.

Temporal on-road activity adjustments by county wer e made for:

1. Heavy duty vehicles – all days 2. Light-duty vehicles – Friday, Saturday, Sunday, Monday

Daily total activity (daily VMT) adjustments were m ade for all vehicle types for Friday, Saturday, Sunday, and Monday. Tuesday, Wednesday, and Thursday are considered as one day. Adjustments applied to heavy-duty vehi cles on Tuesdays, Wednesdays, and Thursdays were the same for each of the three d ays.

Since it is EMFAC emission estimates that are being adjusted to derive the final on-road inventory, the relation between EMFAC vehicle class es 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 t hrough 3

LM based on count data for FhwA classes 7 and 8

HHDT based on count data for FhwA classes 9 thr ough 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 F riday through Monday, EMFAC weekday emissions will be scaled to reflect Caltran s day of week factors. Appendix C provides more detail on the methodology developed b y the Weekend Truck
Subcommittee.

Although significant improvements have been made to improve the temporal distribution of on-road motor vehicles, some assumptions were ma de that may cause uncertainty in the adjustments. For example, one assumption is th at the count data represent the temporal distribution of all road types, including local roads. The count data are gathered only on state highways. Another assumptio n is the link between EMFAC and FHWA classes. EMFAC classes are based on gross veh icle 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 vehi cle falls into based on the number of axles, particularly for trucks. Additional work may provide improvements to estimating hourly emissions by vehicle type, especi ally on weekend days.61

Figure 6.1 Caltrans Weigh-In-Motion Data Sites62

Figure 6.2 Caltrans District and County Boundaries63

Table 6.3 Federal Highway Administration (FHwA) Ve hicle 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 SingleUnits (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 Trailer64 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 wei ghting for emission rate. The vehicle type VMT for each county/air basin output from EMFA C is used, which is then reformatted by the CONVIRS4 computer program and co mposited by vehicle type distribution from BURDEN in the IRS4 computer progr am. For the counties in CCOS that are covered by the ITN network, we process lig ht/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 nee ds 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. Speci fically, 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, Mo nday

Link and trip end activity adjustments were made fo r all vehicle types for Friday, Saturday, Sunday, and Monday. Tuesday, Wednesday, and Thursday are considered as one day. Adjustments applied to heavy-duty vehi cles on Tuesdays, Wednesdays, and Thursdays were the same for each of the three d ays.

Additionally, EMFAC has different fleet mixes by co unty 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 E MFAC. During the preprocessing of the EMFAC output, which occurs prior to a comple te DTIM4 run that is performed by the IRS/CONVIRS programs, there is generally an adj ustment applied to the EMFAC
emissions factors based on vehicle counts. In most cases, the regional transportation planning agencies (RTPAs) who supplied the transpor tation 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 EMF AC.66 6.7.8 

### Differences Between DTIM4 and EMFAC

6.7.8.1 

### Evaporative Emissions

DTIM4 and EMFAC use different methods to estimate e vaporative 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 diu rnal and resting emissions) and put all EMFAC resting emissions in DTIM4 category 210/3 10, 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 mod els do not estimate VMT for buses or excess idling categories, these are added to DTI M4 emissions. The exhaust CO, NOx, SOx, and PM emissions that DTIM4 allocates to category 1 are reassigned to catalyst starts, non-catalyst starts, catalyst stab ilized, 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 20 03) is a seamless on-road transportation network that covers all of Californi a. The ITN was developed from many regional transportation planning agencies (RTPAs) a s well as the California Department of Transportation (Caltrans) Statewide Model. The San Joaquin Valleywide Air Pollution Study Agency and Air Resources Board contracted wit h Alpine Geophysics to develop the ITN. After the ITN was developed, additional l ocal transportation networks became
available that were not included in the first versi on. 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 m entioned earlier, the ITNv2.0 is used to spatially distribute the on-road mobile sou rce emissions generated by EMFAC. Figure 6.3 shows the link-based ITNv2.0 for Califor nia.

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 Bernardi no, 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 world67

coordinates were not available. The Caltrans state wide 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 Integ rated Transportation Network (ITN)”.

It is important to recognize that EMFAC (and the as sociated 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 emi ssion ratios for each grid-cell in a county. These ratios will be used to distribute co unty-specific, daily EMFAC emissions to each hour and grid-cell. This intended use nega tes the need to update countywide VMT on the ITN. That is, if up-to-date VMT in a sp ecific 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 li nk VMT (in the same county) are factored by the same amount, the ratio of link-to-c ounty 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 mo re 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 emis sions into the proper four by four kilometers grid cell. Given that the intended purp ose of the ITN is for use in estimating on-road mobile source emissions for photochemical m odeling efforts, this accuracy is
sufficient.68

Note: The county boundaries are in red. The Caltra ns 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 Ne twork (ITN) version 2.069 6.7.10 

### Motor Vehicle Activity

Motor vehicle activity data are an important part o f EMFAC for estimating emissions. As part of an on-going effort to use the best data ava ilable, ARB periodically updates the vehicle miles traveled (VMT) and speed distribution s by VMT used in the model. In November 2004, ARB sent letters to transportation p lanning 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 coordina tion 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 refer s to supporting documents that provide additional details as well as discussions o f issues. ARB included additional updates as time permitted before finalizing EMFAC20 07.

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 t aken from EMFAC for the desired future year. The method used to calculate the futu re 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 i n California requires accurate emission inventories, including biogenic volatile o rganic compounds (BVOCs) such as isoprene and monoterpenes. Due to the heterogeneit y of vegetation land cover, species composition, and leaf mass distribution in California, quantifying BVOC emissions in this domain requires an emission inven tory 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 (km 2 ) and an hourly temporal resolution.

The BEIGIS isoprene emission algorithm (Guenther et al. 1991, 1993) is of the form

I = I S × C L × C T

where I is the isoprene emission rate (grams per gr am dry leaf mass per hour) at temperature T and photosynthetically active radiati on flux PAR. I S is a base emission rate (grams per gram dry leaf mass per hour) at a s tandard temperature of 30 ° C and70

PAR flux of 1000 μ mol m -2 s -1 . C L and C T are environmental adjustment functions for PAR and temperature, respectively. The monoterpene emission algorithm adjusts a base monoterpene emission rate by a temperature fun ction (Guenther et al. 1993). Methylbutenol (MBO) emissions are modeled with an a lgorithm 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 emiss ions for a given spatial domain therefore represent the contribution by various pla nt species (through their leaf mass and emission rates) to the total BVOC emissions.

The main inputs to BEIGIS are land use and vegetati on land cover maps, gridded leaf area indices (LAI) derived from AVHRR satellite dat a (Nikolov 1999), leaf area/dry leaf mass factors, base emission rates, and gridded hour ly ambient temperature and light intensity data (from a meteorological model). For urban areas, land use/vegetation land cover databases were developed from regional planni ng 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 re presented using the GAP vegetation database (also satellite-derived and air photo inte rpreted) developed by the U.S.G.S. Gap Analysis Program (Davis et al. 1995). Agricult ural areas are represented using crop land cover databases developed by the Californ ia 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 da tabases 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 emi ssions 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 b iogenic emissions were calculated, the emissions from the 1-km cells were aggregated f or each 4-km grid cell. Two additions are then made to the biogenic emissions e stimates for input to air quality
models.

First, biogenic OVOCs (other VOCs) are added. Biog enic OVOCs comprise around twenty percent of some biogenic inventories and are known to affect air quality modeling predictions (e.g. Hanna et al., 2002). Gu enther 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 r esult 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 th at estimates of OVOC were reported by some sources to be as great as the inventoried s pecies (isoprene, m-butenol, and monoterpenes). Since OVOCs are very uncertain in b oth mass and species characterization, ARB had not been including them i n the modeling programs. Dr. Carter suggested this omission was inappropriate. He recommended that OVOC71

emissions be included with the best estimates we co uld make. During this discussion it was decided that doubling the known species would b e like an upper limit which was felt to be too high. Collectively it was decided that a n OVOC amount equal to 30% of the total known species would be a reasonable estimate.

ARB reviewed the literature to find a reasonable sp eciation to assign to OVOCs. Arthur Weiner sent a list of the compounds that were inten ded at that time to be included in BEIS-3. No amounts were fixed to species and ARB c ould not derive an OVOC profile. Allen Goldstein had published an article “In Situ M easurements of C2-C10 Volatile Organic Compounds Above a Sierra Nevada Ponderosa P ine 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 ace taldehyde. 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 o f information from BEIS-3 or other models may allow ARB to create BVOC inventories tha t 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 emiss ions. Biogenic NO emissions were estimated using a soil NO algorithm found in BEIS-3

.

For a more detailed description of the estimation o f biogenic emissions, see Appendix G.

Biogenic emissions are not estimated for future yea rs 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 bi ogenic emissions developed for the base year.



### 6.9 Spatial Allocation

Once the base year or future year inventories are d eveloped, as described in the previous sections, the next step of modeling invent ory development is to spatially allocate the emissions. Air quality modeling attem pts to replicate the physical and chemical processes that occur in an inventory domai n. 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 exa ctly. In reality, however, the spatial allocation of emissions in a modeling inventory onl y 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 a rea that is sufficient in size to contain72

all emission sources that could affect modeling res ults. The definition of the CCOS modeling domain is described below in Section 6.9.1

.

Once a grid is defined, the spatial allocation of e missions can be performed. Each area source category is assigned a spatial surrogate tha t is used to allocate emissions to a grid cell. Examples of surrogates include populati on, land use, and other data with known geographic distributions for allocating emiss ions to grid cells. Section 6.9.2 discusses in detail the spatial surrogates develope d 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 cen troid is used.

Emissions are also distributed vertically into thei r proper layer in the air quality model. The vertical layer is determined from the calculati on of buoyancy for those emissions that are released from an elevated height with a si gnificant upward velocity and/or buoyancy. Most vertical allocation is from signifi cant point sources with stacks. In most modeling exercises, low-level point sources are scr eened out at this point and placed with the area sources. However, in this modeling e xercise, 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 m odel. 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 ac tivity on the Integrated Transportation Network version 2 (ITNv2.0) as descr ibed in Section 6.7.9. For biogenic emissions, the spatial allocation is built “from th e ground up” since ARB’s biogenic model, BEIGIS, estimates emissions using a Geograph ic Information System (GIS) at a 1 square kilometer resolution. Section 6.8 describ es how biogenic emissions are
estimated.

6.9.1 

### Grid Definition

The CCOS emissions inventory domain was defined bas ed on the MM5 model used to generate the meteorological parameter fields used f or air quality modeling. However, the MM5 model uses only an approximation to the sha pe of the Earth. Therefore, there
was a small offset error between the MM5-defined do main 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 l atitude, with a central meridian at 120.5 W longitude. The coordinate system origin wa s offset to 37 N latitude. The emissions inventory was gridded with a resolution o f 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 specifica tions 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 geographical ly distribute countywide area source emissions to individual grid cells. These spatial a llocation factors were developed from spatial surrogate data. Spatial surrogates are eco nomic, 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 a re allocated to grid cells using the
location of the emission source. On-road motor veh icle emissions are allocated by DTIM4 (see Section 6.7). Biogenic emissions are al located by BEIGIS (see Section 6.8).

In support of CRPAQS and CCOS, Sonoma Technology, I nc. (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 Cali fornia 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 sur rogate. This assignment provides a cross-reference between the spatial allocation fact ors and the emission inventory categories. A total of 65 unique surrogates were d eveloped as part of this project. A summary of the spatial surrogates, for which spatia l 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 lis t. Designating the surrogate-toemission inventory assignments was an iterative pro cess among STI staff, ARB staff, and local air district staff. Note that the spatia l allocation factors and emissions category assignments vary by county depending on th e data available for each county.

Three basic types of surrogate data were used to de velop the spatial allocation factors:

• land use and land cover

• facility location

• demographic and socioeconomic data

Land use and land cover data are associated with sp ecific land uses, such as agricultural tilling, feedlots, or recreational boa ts. 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 residen tial, 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 inform ation used to develop spatial allocation factors. Table 6.6 shows the sources of demographi c and socioeconomic data used to develop spatial allocation factors. Table 6.7 provi des a list of the counties covered by each data set. To develop spatial allocation facto rs of high quality and resolution, local socioeconomic and demographic data were used when a vailable; for rural regions for which local data were not available, the Caltrans S tatewide 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/refinishin g shops Locations of hospitals, institutions, population, a nd commercial employment Total housing, service, commercial, golf courses Industrial employment and locations of auto body/re finishing 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 (residen tial & 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 plants76

Table 6.5. Sources of land use/land cover and faci lity 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 demo graphic and socioeconomic surrogate data77

Data Source Parameter (Years) Resolution and Coverage

Caltrans Statewide Transportation Model (Caltrans STM) Population, housing, employment (base and future) TAZ a – 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) TAZ a – Sacramento Urban Region Tahoe Regional Planning Agency (TRPA) Population, housing, employment (base and future) b TAZ a – Lake Tahoe Region

Association of Monterey Bay Area Governments (AMBAG)
and 1990 U.S. Census Population (base and future) Census Tract – Montere y Bay
Area

South Coast Association of Governments (SCAG) Population, housing, employment (base and future) TAZ a – 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) TAZ a – Fresno County

San Diego Association of Governments (SANDAG) Population, housing, employment (base and future) TAZ a – San Diego County

San Joaquin Council of Governments (SJCOG) Population, housing, employment (base and future) TAZ a – 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) TAZ a – Kern County78

Table 6.7. Counties covered by each of the demogra phic 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, Made ra, Merced, Mendocino, Modoc, Mono, Nevada, Plumas, east Rivers ide, east San Bernardino, San Luis Obispo, Santa Barbara , Shasta, Sierra, Siskiyou, Tehama, Trinity, Tuolumne ABAG Alameda, Contra Costa Marin, Napa, San Francis co, 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). Ph otochemical models simulate the processes leading to ozone formation and fate i n the lower atmosphere, and include all emissions of the important compounds involved i n ozone photochemistry. Organic gases are one of the most important classes of chem icals involved in the formation of surface ozone. Organic gases emitted to the atmosp here are referred to as total organic gases (TOG). ARB's chemical speciation pro files (CARB 2006) are applied to characterize the chemical composition of the TOG em itted 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 a tmosphere, 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 o rganic compounds that can become airborne (through evaporation, sublimation, as aero sols, etc.), excluding carbon monoxide, carbon dioxide, carbonic acid, metallic c arbides 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) e mission 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. F or all practical purposes, use of the terms ROG and VOC are interchangeable. Also, vario us regulatory uses of the term "VOC", such as that for consumer products exclude s pecific, additional compounds from particular control requirements.

6.10.1 

### Speciation Profiles

Speciation profiles are used to estimate the amount s 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 emi ssions from a particular source type. Each process or product category is keyed to one of several hundred currently available speciation profiles. The speciation prof iles are applied to TOG to develop both the photochemical model inputs and the emission inv entory for ROG.

To the extent possible given available data, ARB's organic gas speciation profiles contain all emitted organic species that can be ide ntified (ideally, detected to very low levels). This includes reactive compounds, unreact ive and exempt compounds, and to the extent the data are available, low vapor pressu re compounds. Research studies are conducted regularly to improve ARB's species profil es. These profiles support ozone modeling studies but are also designed to be used f or aerosol and regional toxics modeling. The profiles are also used to support ot her health or welfare related modeling studies where the compounds of interest ca nnot always be anticipated. Therefore, organic gas emission profiles should be as complete and accurate as possible.

The speciation profiles used in the emission invent ory 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 wei ght fraction data (expressed as percent for ease of display) of each chemical in ea ch 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 a ddition to the chemical name for each chemical constituent, the file also shows the chemi cal code (a 5-digit internal identifier) and the Chemical Abstracts Service (CAS) number, wh ich is a unique identifying code (up to 9 digits) assigned to chemicals by the CAS R egistry 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 sourc e 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 Inv entory Code (EIC) for area and80

mobile sources. This file also contains the fracti on of reactive organic gas (FROG) values for organic profiles. Some of the Organic G as 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 co mposition over time.

ARB has an ongoing effort to update speciation prof iles as data become available, such as through testing of emission sources or surveys o f product formulation. New speciation data generally undergo technical and pee r review, and updating of the profiles is coordinated with users of the data. Se veral 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 gasoli ne-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 p rovide the only available scientific basis for making quantitative estimates of changes in air quality resulting from changes in emissions. The chemical mechanism is the portio n of the model that represents the processes by which emitted primary pollutants, such as TOG, carbon monoxide (CO), and oxides of nitrogen (NO
x ), react in the gas phase to form secondary polluta nts such as ozone (O 3 ) and other oxidants.

For State Implementation Plan (SIP) attainment demo nstrations 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 scient ific literature (Carter 2000). Table 6.8 shows modeled ROG species (or species categories) f or the SAPRC-99 chemical mechanism. Table 6.9 shows modeled species for NOx

.81

Table 6.8 ARB’s SAPRC-99 Emitted Organic Model Spe cies

Model Species
Name Description

HCHO Formaldehyde CCHO Acetaldehyde RCHO Lumped C3+ Aldehydes
ACET Acetone MEK Ketones and other non-aldehyde oxygenated produ cts
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 10 2 ppm-1 min-1. (Primarily ethane) ALK2 Alkanes and other non-aromatic compounds that react only with OH, and have kOH

between 5 x 10 2 and 2.5 x 10 3 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 10 3 and 5 x 10 3 ppm-1 min-1. ALK4 Alkanes and other non-aromatic compounds that react only with OH, and have kOH

between 5 x 10 3 and 1 x 10 4 ppm-1 min-1. ALK5 Alkanes and other non-aromatic compounds that react only with OH, and have kOH

greater than 1 x 10 4 ppm-1 min-1.
ARO1 Aromatics with kOH < 2x10 4 ppm-1 min-1.
ARO2 Aromatics with kOH > 2x10 4 ppm-1 min-1. OLE1 Alkenes (other than ethene) with kOH < 7x10 4 ppm-1 min-1.
OLE2 Alkenes with kOH > 7x10 4 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 res ults for ozone non-attainment areas have demonstrated that even compounds with lo w photochemical reactivity or low vapor pressure contribute to photochemical ozone fo rmation. For example, even an "exempt VOC" like ethane has been shown to have a c ontribution to ozone formation. If all exempt compounds and low vapor pressure compoun ds were omitted from photochemical model simulations, the ozone attainme nt demonstration would be compromised. The model takes into account that, in dividually, compounds with low reactivity or that are present in small amounts hav e a small impact on ozone formation. However, the cumulative effect of several low react ive compounds or many low emission compounds can be a significant contributor to photochemical ozone formation.

The implementation of the chemical mechanism is uni que in each air quality model. In the case of the CAMx model, the chemical species ET OH (ethanol), MTBE (methyl tertbutyl ether) and MBUT (methyl butenol) are not trea ted explicitly. These species are considered important to ozone chemistry in Californ ia because ETOH and MTBE are motor-vehicle fuel components and MBUT is emitted b y 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 OLE183



## 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 q uantitatively using statistical metrics as well as qualitatively against known conc eptual meteorological flows and observed episodic meteorological features.

7.1.1 

### Quantitative Performance Evaluation

There are a number of statistical and graphical app roaches for evaluating meteorological model outputs. However, none of the m are independently conclusive. Most of these approaches involve comparisons betwee n observed and simulated meteorological parameter values. These analyses po se a difficult challenge, since most of the available meteorological monitoring stations are located in urbanized areas. Thus, the majority of observations tend to represen t those areas versus the full complexity of meteorology throughout the CCOS domai n. Furthermore, since the use of objective analysis and observational nudging forces the meteorological modeling results towards the observations, model performance problem s 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 mode l. This preprocessing may inadvertently perturb the meteorological fields. T herefore, meteorological model performance should be based on the air quality mode l input files, rather than the meteorological model outputs.

The SIP modeling domain is geographically very comp lex and the observational data on which meteorological model outputs were evaluated a re not distributed uniformly. Therefore, it is unreasonable to evaluate model per formance for the domain as a whole. For purposes of meteorological model performance an alysis, the CCOS domain is divided into sub-regions, representing areas of sim ilar meteorological features. The graphical and statistical model evaluations will be done for each of these sub-regions.

A number of standard statistical and graphical tech niques are used for meteorological model performance analysis. The most widely used a pplication is the METSTAT program (Tesche, 1994, Tesche et al, 2001). Two gr aphical representations of the METSTAT statistics were used in meteorological mode l performance analysis conducted here: a) “Root Mean Square Error (RMSE) o f 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 fro m the user documentation of the METSTAT program and are given below:

Bias Error (B) : calculated as the mean difference in prediction-o bservation pairings with valid data within a given analysis re gion and for a given time period (hourly or daily):

Here, P and O indicate model predictions and observations, respe ctively. Similarly, I and J are the indices of grid points i n x and y directions, respectively.

Gross Error (E) : calculated as the mean absolutedifference 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 ca lculated from the predictedobserved residuals in speed and direction (not from vector components u and v ). The direction error for a given prediction-observat ion 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 pairin gs 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 (d ue 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 j O P IJ B

1 1 1



# ∑∑

= = − = J

j I

i i

j i j O P IJ E

1 1 1



# ( ) 2/1

1 1 2 1 

    

− = 

# ∑∑

= = J

j I

i i

j i j O P IJ RMSE85

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 evaluat ion would be done independent of the air quality model results. However, in practice, m eteorological field evaluation is limited by the relative paucity of observational data, espe cially aloft. Therefore, base year air quality model performance was also considered in th e selection of meteorological fields used for air quality simulations.

Table 7-2 Graphical analysis of meteorological mode l 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 temperatures86 7.1.2 

### Qualitative Performance Analyses

Given episode-specific information on the meteorolo gical features that were observed with field measurements, additional subjective anal yses of observed versus predicted mesoscale features can be conducted. Examples of s uch qualitative analyses that will
be considered are described below.

1. Determine and compare modeled and observed horiz ontal flow patterns throughout the modeling domain. Features to consid er include flow splitting, the structure of the sea breeze, urban circulations, lo cal flows such as Fresno and Schultz eddy circulations, slope and drainage flows , up/down valley flows, and the existence of cloud formations (Described in Cha pter 1).

2. Study the 3-D spatial characteristics of the flo w field by using time-height cross sections of wind profiler observations and the simu lated wind field at the wind profiler location.

3. Determine the spatial and temporal characteristi cs 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. an alysis 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 strat egies for attaining the federal 8-hour ozone standard. The development of these strategie s relies on the use of relative reduction factors (RRFs). A more detailed discussi on of RRFs is provided in other documents. However, the use of RRFs requires an ev aluation 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 uncertaint y in the use of the modeling results,
and casts doubt on conclusions based on the modelin g. Although it is desirable to include as many days as possible in the RRF calcula tions, our experience has demonstrated that not all modeled days meet the min imum performance standards, and are thus not suitable for use. Therefore only thos e days that satisfy the following model performance criteria will be utilized in RRF calcul ations.

The USEPA (1991) and ARB (1990) outline a number of procedures for analysis of base year, air quality model performance. These in clude spatial and time-series plots, statistical analyses, comparing simulated and obser ved 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 per forming 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 unpa ired peak ozone concentration.
Three statistical measures were recommended to eval uate model performance: unpaired peak ratio (UPR), paired mean normalized b ias (NB), and paired gross error (GE). These statistical measures were calculated f or the modeling domain as a whole, and the NB and GE were calculated from all hourly c oncentrations in excess of 60 ppb (to avoid biasing the statistical measures with low concentrations). To meet performance guidelines, recommendations were that t he UPR should be within ± 20%,

NB should be within ± 15%, and the GE less than 35%. However, Californi a’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 ca lculated independently for each subregion. The configuration of these subregions i s somewhat arbitrary; however, they should be configured to isolate "common" regions of higher ozone. Figure 7-1 illustrates the proposed subregions for the CCOS do main.

The USEPA (2005) recommends that model performance be evaluated for 8-hour
concentrations as well. The recommended statistica l measures to assess simulated
versus observed maximum 8-hour ozone concentrations include paired (in space, but88

not time) peak prediction accuracy (PPPA), paired m ean normalized bias (NB), and paired gross error (GE). Although limited performa nce analysis has been completed for 8-hour ozone modeling in California, it seems prude nt at this point to carry forward the 1-hour statistical goals and apply them for the 8-h our standard (UPR within ± 20%, NB

within ± 15%, and the GE less than 35%). However, these li mits may need to be revised as 8-hour SIP modeling progresses and rigor ous model performance evaluations are completed.

While statistical measures for 1-hour model perform ance were typically calculated independently for each modeled day available, the U SEPA also suggests that PPPA, NB, and GE be calculated for each site over all mod eled days. However, because the number of episode days available may be very limite d, the statistical uncertainties in these latter calculations would be large and they a re not recommended or used herein.

In order to have confidence in future year estimate s from air quality models, there must be confidence in the air quality modeling for the b ase 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, anal yses 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 preliminar y modeling in support of the 8-hour ozone standard conducted by the ARB and the distric ts. These results imply that RRF calculations should be restricted to days with pred icted high ozone concentrations. It is
thus reasonable to establish a minimum threshold fo r predicted peak 8-hour ozone concentrations in the reference year. Days for whi ch the predicted daily peak 8-hour
ozone concentrations at a site are less than the th reshold, would not be used for calculating RRFs at that site. Consistent with USE PA’s recommendation, we propose to use a value of 85 ppb for the reference year thr eshold. 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 follo wing 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 crite ria will be used to calculate sitespecific RRFs:

1) The modeled daily 8-hour peak ozone concentratio n within 15 km of the site for the base year of the modeling (the model perfor mance year) must be

within ± 20% of the observed value at the site. 2) The modeled daily 8-hour peak ozone concentratio n 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 me asures of NB and GE must

fall within the thresholds of ± 15% and 35%, respectively.

Of these three criteria, only the third is consider ed 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 inherentl y subjective, they are designed to provide confidence that the air quality model is no t only performing well, but is also properly responding to changes in inputs.90

Figure 7-1 Sub-regions of air quality model perfor mance 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

1591

Table 7-3. Statistics for evaluating base year ai r 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 c oncentrations ( 

### ≥ 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 concentr ations ( 

### ≥ 60 ppb), unpaired in time for all sites

• Mean normalized gross error for all 8-hour ozone c oncentrations ( 

### ≥ 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 ye ar air quality model performance.

• Time-series plots comparing 1-hour measured and si mulated concentrations of ozone, NO, NO2, and CO for each site.

• Hourly spatial plots of 1-hour measured and simula ted concentrations of ozone, NO, NO2, and CO for the CCOS modeling domain

.

• Scatter plot of 1-hour ozone concentrations for ea ch day, and for each subregion of the modeling domain.92

Table 7-5. Sensitivity tests for evaluation of Bas e 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 mod eling results in attainment demonstrations is to utilize relative model respons e 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 estim ate 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 R RFs and the estimation of future year design values at non-attainment monitoring sit es; however, the USEPA (2005) also requires analysis to demonstrate that high ozone co ncentrations occurring away from monitors (e.g., unpaired in space) will also be con trolled in future years to meet air quality standards. This latter analysis is not add ressed 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 (r un with all emissions)

8 Set initial conditions to 0.1 ppb NO2 and 0.0 NO (r un with biogenic emissions only)

9 Double biogenic emissions

10 Remove wildfires

11 Zero mobile emissions

12 Set top ozone boundary conditions to 135ppb at 15km93

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 pr ocedures for analysis of base-year air quality model performance. These include spati al and time-series plots comparing simulated and observed pollutant concentrations, st atistical analyses comparing simulated and observed pollutant concentrations, an d 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 provi de some confidence that the air quality simulations on which the estimates of futur e-year ozone concentrations will be based, will have some semblance to reality. The se cond is the issue of representative ozone concentrations for base- and future-year conc entrations at each site from which
the RRFs will be calculated.



### 8.1 Estimation of Future Design Value (DV F 

### )

The application of photochemical ozone models has a long history in California, for uses ranging from the preparation of State Implementatio n 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 evol ved is that photochemical models
are best used to estimate the relative difference b etween scenarios, rather than for absolute concentration estimates. That is, their s trength is in estimating the relative change in concentration levels from a reference con dition (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 scenari o.

The USEPA’s guidance on the use of models for attai nment demonstrations in support of 8-hour ozone planning (USEPA, 2005) is consisten t with the fundamental strength of
models described above. USEPA’s recommended modele d 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 v alues. 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 d esign values, and a future year for the
attainment test.

DV F = (RRF) (DV R )

where DV R = a reference year (2002) concentration (design va lue) measured at a monitoring site
DV

F = the estimated future year design value at the sa me site
RRF = the relative reduction factor at the same si te

The RRF is calculated as the ratio of future year t o reference year modeled ozone
concentrations at a site:94 hr 8 hr 8 RY FY RRF

−

− =

where RRF = the relative reduction factor for a mon itor
FY

8-hr = the modeled future year 8-hour daily maximum concentration predicted near the same monitor
RY

8-hr = the modeled reference year 8-hour daily maximum concentration predicted near the same monitor

In principle, this concept is simple. Unfortunatel y, it can be confounded by a number of factors, including the limited number of modeled da ys 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 ba ckground 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 ke y consideration in the modeled attainment test, since this is the value that is pr ojected forward and used to test for attainment at each site. Since the reference desig n value is presumably reflective of conditions in the reference year, it should be repr esentative of the emissions used for that year. However, many areas experience fluctuat ions in their year-to-year meteorology, as well as emissions levels. In recog nition of this year-to-year variability, the reference design value should in some fashion a lso reflect this variability. A standard methodology for minimizing the influence o f year-to-year variations is to calculate an average value over multiple years. Th erefore, the following methodology is recommended for specification of the reference desi gn value at each monitoring site:

The reference design value (DV R ) will be calculated as the average of the three design values for the three years commenc ing with the reference year of the modeling. The reference year for modeling in support of the 8-hour ozone SIPs is 2002. Therefor e, the reference design value will be calculated at each monitoring site as the average of the design values for 2002, 2003, and 20 04.

California design values are calculated as the thre e-year average of the 4 th highest 8-hour ozone peak values, and are assigned to the l ast year. Thus, a design value for
2002 would be based on data for 2000-2002. The rec ommendation 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 200 2, since that year is included in the calculation of the design value for all three years

.95

The following table summarizes the recommended proc ess 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 Model ed Attainment Test

2002-2004 Average 9 Year ) 2)(Year ( ) 3)(Year ( ) (2)(Year Year DV 2004 2003 2002 2001 2000 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 conc entrations simulated in a future year, divided by daily peak concentrations simulate d in a reference year. To be consistent with the principle that the modeled atta inment test and design values should be robust and stable over a number of different typ es 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, div ided by the average daily peak 8- hour modeled ozone concentration in the reference y ear. Only those days satisfying the model performance and threshold criteria described below shall be included in the RRF
calculation.


# ( )

( )
AVG hr 8 AVG hr 8 AVG RY FY RRF

−

− =

where RRF

AVG = the average relative reduction factor for a moni tor (FY

8-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 (RY

8-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 criteria96 8.1.3 

### Criteria for Use of Modeled Days in RRF Calculation s

Adequate model performance is a requirement for use of modeled results. The lack of acceptable performance greatly increases uncertaint y in the use of the modeling results,
and casts doubt on conclusions based on the modelin g. Although it is desirable to include as many days as possible in the RRF calcula tions, our experience has demonstrated that not all modeled days meet the min imum performance standards, and are thus not suitable for use. Therefore only thos e 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 in clude spatial and time-series plots, statistical analyses, comparing simulated and obser ved 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 per forming properly.

The application of air quality modeling results to demonstrate attainment of the federal 1-hour ozone standard emphasized the simulated unpa ired peak ozone concentration.
Three statistical measures were recommended to eval uate model performance: unpaired peak ratio (UPR), paired mean normalized b ias (NB), and paired gross error (GE). These statistical measures were calculated f or the modeling domain as a whole, and the NB and GE were calculated from all hourly c oncentrations in excess of 60 ppb (to avoid biasing the statistical measures with low concentrations). To meet performance guidelines, recommendations were that t he UPR should be within ± 20%,

NB should be within ± 15%, and the GE less than 35%. However, Californi a’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 calcu lated 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 vic inity of, individual monitoring sites. Specifically, modeled concentrations occurring with in 15 km of a site are considered to be in the vicinity of the site. The recommended st atistical measures to assess
simulated versus observed maximum 8-hour ozone conc entrations include paired (in space, but not time) peak prediction accuracy (PPPA ), paired mean normalized bias (NB), and paired gross error (GE). Although limite d 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 appl y them for the 8-hour standard (UPR

within ± 20%, NB within ± 15%, and the GE less than 35%). However, these li mits may need to be revised as 8-hour SIP modeling progresse s and rigorous model performance evaluations are completed.97

While statistical measures for 1-hour model perform ance were typically calculated independently for each modeled day available, the U SEPA also recommends that PPPA, NB, and GE be calculated for each site over a ll 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 estimate s from air quality models, there must be confidence in the air quality modeling for the b ase 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, anal yses 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 preliminar y modeling in support of the 8-hour ozone standard conducted by the ARB and the distric ts. These results imply that RRF calculations should be restricted to days with pred icted high ozone concentrations. It is
thus reasonable to establish a minimum threshold fo r predicted peak 8-hour ozone concentrations in the reference year. Days for whi ch the predicted daily peak 8-hour
ozone concentration at a site is less than the thre shold, would not be used for calculating RRFs at that site. Consistent with USE PA’s recommendation, we propose to use a value of 85 ppb for the reference year thr eshold.

Based on the above discussion, we propose the follo wing methodology for determining sites and modeled days to be used in the RRF calcul ations:

1) The modeled daily 8-hour peak ozone concentratio n 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 concentratio n within 15 km of the site in the reference year must be 85 ppb or greate r.
3) The sub-regional 1-hour and 8-hour statistical m easures 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 gui dance recommends utilizing relative model response on a site-by-site basis, in the form of an average relative reduction factor (RRF AVG ), 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 lev els 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 coul d 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 backgro und ozone value, and it is quite likely that continental background is some other, s omewhat higher, value. One possibility for estimating background ozone values in a given modeling domain would be to exercise the model without anthropogenic emis sions, 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 lar ge influence, and appropriate temporally- and spatially-resolved data to specify boundary conditions rarely exist. Thus boundary conditions can be subjective and unce rtain. 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 (DV F ) be calculated at each site and compared to the standard to determine if the site is predict ed to be in attainment. To calculate the future year Design Value, the Design Value for the reference year (DV R ) is multiplied by
RRF AVG . Although EPA’s guidance says nothing about backg round ozone, we propose to calculate the future year Design Value with cons ideration of background. The Table
below illustrates calculation of the DV F 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 Facto r and Future Year Design Values* with and without Consideration of Ba ckground Ozone Without consideration of background With consideration of background



## ( ) 

## ( )
AVG AVG

AVG RY FY RRF =

DV F = (RRF AVG ) x (DV R ) 

## ( ) 

## ( )
AVG

AVG

AVG BG

- RY BG

- FY RRF =

DV F = [(RRF AVG ) x (DV R – BG)] + BG

Definitions
DV R = 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 va lues are truncated rather than rounded.99



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