Appendix C

Air Quality Modeling to Determine the Impacts of OGV Clean 

Fuel Regulations and Potential Shipping Routes on South Coast 

Air Basin Air Quality and Public Health
This page is intentionally blank.
Air Quality Modeling to Determine the Impacts of OGV Clean 

Fuel Regulations and Potential Shipping Routes on South Coast 

Air Basin Air Quality and Public Health

Modeling and Meteorology Branch

Planning and Technical Support Division

and

Health and Exposure Assessment Branch

Research Division

Air Resources Board

California Environmental Protection Agency

Sacramento, California 95814

April 2011
C - i

TABLE OF CONTENTS

Contents Page

I. Introduction .......................................................................................................C-1

II. Model Application..............................................................................................C-1

III. Simulation Results ............................................................................................C-8

References

Attachments

Attachment C-1: Ocean-going Vessel PM Speciation Profile Preparation ..............C-1-1

Attachment C-2: Ocean-going Vessel Gridded Emissions inventory ......................C-2-1

Attachment C-3: Health Benefits Associated with Reductions in PM Emissions 

from the OGV Regulation ............................................................C-3-1

Attachment C-4: Ocean-going Vessel Model-Adjusted 8 hour Ozone Design 

Values ..........................................................................................C-4-1
C - 1

I. Introduction

A regional air quality model was used to simulate ozone (O3) and fine particulate 

matter (PM2.5) concentrations within a Southern California modeling domain for

six scenarios of ocean-going vessel (OGV) emissions. The six emission 

scenarios varied based on:

• shipping activity levels within transit routes; 

• potential regulatory zone configurations around the transit routes; and

• fuel types used within the regulatory zones. 

Detailed information on the emission inventories used can be found in the 

Emissions Inventory section. Also, Attachment C-2 provides a detailed spatial 

summary of the OGV emission inputs used for modeling. 

The model-simulated impact of different OGV activity, transit routes, and 

corresponding fuel use on inland air quality and public health was estimated by 

analyzing the model-simulated concentration differences between each scenario

and baseline conditions. 

II. Model Application

Model Configuration

To simulate gaseous and PM2.5 concentrations, the Community Multi-scale Air 

Quality (CMAQ) model version 4.6 was exercised for the year 2005 

(http://www.cmaq-model.org/). The CMAQ model was developed by the 

U.S. EPA, and has been used by ARB in previous regional air quality modeling 

analyses. The year 2005 was selected because it was also used as the base 

year for the South Coast Air Quality Management District’s (SCAQMD) PM2.5

State Implementation Plan (SIP) development (SCAQMD, 2007).

For the analysis described herein, the Carbon Bond V (CB05) gas-phase 

chemical mechanism and the AERO4 aerosol modules are used. Within the 

CMAQ model, particulate matter is grouped into three log-normal modes that 

correspond to the ultrafine (aerodynamic diameter (Dp) < 0.1 μm), fine (0.1 μm < 

Dp < 2.5 μm), and coarse (Dp > 2.5 μm) particles sizes. Concentrations of PM2.5

are assumed to be the sum of all simulated particulate matter concentrations with 

Dp less than 2.5 μm. 

Domain Setup

The modeling system utilizes a domain comprised of a three-dimensional grid 

cell structure. The modeling domain covers the South Coast Air Basin (SCAB)
C - 2

with 116 by 80 horizontal grid cells with sides 5 km in length (Figure C-1). The 

vertical structure of the air quality modeling domain was determined by the layer 

structure of the meteorological model. In this analysis, there are nine vertical

layers extending to the top of the meteorological domain. The lowest eight layers 

extend to approximately 5 kilometers above the surface. 

The meteorological input fields required by the air quality model were generated 

using the MM5 prognostic meteorological model (Grell et al., 1994). The MM5 

model is recommended by the U.S. EPA (EPA, 2007) for air quality modeling 

applications and has been used for preparing ozone and PM SIP analyses in 

Central and Southern California. The MM5 model was used to generate hourly 

meteorological fields for the year 2005 (the utilized fields were produced by the 

South Coast AQMD and used for their air quality management plan). The 

Meteorology-Chemistry Interface Processor (MCIP) version 3.2, which is part of 

the CMAQ software package, was used to generate model-ready meteorological 

inputs for the CMAQ model from the MM5 output files 

(http://www.cmascenter.org). 

Figure C-1. The Southern California Ozone Study (SCOS) Modeling Domain 

Showing Terrain Contours
C - 3

Initial and Boundary Conditions

The boundary and initial gaseous and PM concentrations required for the air 

quality simulations were based on the U.S. EPA definition of "clean air" (EPA, 

1991). Since the area of concern, the Santa Barbara Shipping Channel and 

Point Mugu Sea Range, is near the center of the simulation domain, as shown in 

Figure 1, the impact of boundary conditions (BC) should be minimal. Each 

simulation included a 10-day spin-up period to minimize the influence of the initial 

conditions.

Emissions Inventory 

Emissions for all sources (e.g. stationary, area-wide, off-road, on-road, biogenic, 

and OGV) in the modeling domain are considered in the CMAQ air quality model 

simulations, since the model takes into account the chemical interactions of all 

pollutants in the airshed on the production of pollutants of interest (ozone, total 

PM2.5, PM2.5 nitrates, and PM2.5 sulfates). For non-OGV emissions, the year 

2005 emissions inventory that is used in this modeling analysis is based on the 

same California Emissions Inventory Forecast System (CEFS) version (1.06) of 

ARB’s Emissions Inventory as was used by the SCAQMD in the preparation of 

their PM2.5 SIP. 

A brief description of emissions for all 6 OGV simulation scenarios is provided 

below and in Table C-1. Figure C-2 provides a graphical depiction of the key 

vessel transit routes used in the modeling scenarios. 
C - 4

Figure C-2: Vessel Traffic Routes by the Channel Islands in Southern 

California

MS1: No OGV Clean Fuel Regulation, vessels in pre-regulation traffic 

patterns and use pre-rule fuels, primarily HFO

In this scenario, the vessel traffic pattern was based on actual pre-rule vessel 

routes in the SCOS domain. Under this scenario, the vast majority of all the nontanker vessels transiting north and southbound in the Santa Barbara Channel 

region used the existing traffic separation scheme inside the channel (Channel 

Route). It was assumed that all the vessels used dirty fuel (heavy fuel oil (HFO) 

at 2.5% S) at pre-regulation levels. 

MS2: OGV Clean Fuel Regulation implemented, vessels in pre-regulation 

traffic patterns

In this scenario, the vessel traffic pattern is the same as that in MS1. It was 

assumed that all the vessels within the channel (and all other traffic within the 

24 nm regulatory zone in the SCOS domain) used compliant clean distillate fuel 

(MGO at 0.1% S). It was assumed that all vessel activity outside the 24 nm 

regulatory zone in the SCOS domain used dirty fuel (HFO at 2.5% sulfur). 

Confidential Draft Do Not Cite or Quote

1

A

B

Outer 

Route

Channel 

Route Current 24 nm 

Regulatory 

Zone

Point Mugu 

Sea Range

Proposed Amended 

Regulatory Boundary
C - 5

MS6: OGV Clean Fuel Regulation implemented, vessels in Outer Route 

pattern using clean fuel. OGV Clean Fuel Regulation implemented, 100% of 

vessels transit outside the Santa Barbara Channel and use marine distillate 

fuels

This scenario was included to address the request by the Santa Barbara Air 

Quality Management District concerning the comparisons between using clean 

fuel in the Channel Route compared to using clean fuel in the Outer Route. In 

this scenario, 100% of the vessel traffic within the Santa Barbara Channel was 

relocated to the route outside the channel (Outer Route). It was assumed that all 

the relocated vessel traffic used compliant clean distillate fuel although the route 

was outside the regulatory zone. All other OGV traffic within the SCOS domain 

had the same routing and fuel type as Scenario MS2. 

MS1A: Vessels only subject to North American ECA Phase 1 requirements. 

ARB rule is forgone. Vessels in pre-regulation traffic patterns.

This scenario represents the condition where the ARB rule is forgone and air 

quality benefits depend solely on the North American Emission Control Area 

(ECA) 2012 Phase 1 requirements (assuming HFO fuel type at 1% sulfur). The 

vessel traffic pattern was based on actual pre-rule vessel routes. 

MS5: OGV Clean Fuel Regulation implemented, 50%1 of the vessels transit 

outside the Santa Barbara Channel and use HFO

This scenario reflects the current situation. 50% of the vessel traffic visiting the 

Ports of Los Angeles and Long Beach was relocated to the Outer Route. It was 

assumed that all the relocated vessel traffic used dirty fuel, as they were outside 

the regulatory zone. All other traffic within the 24 nm regulatory zone in the 

SCOS domain used clean fuel. All OGV traffic outside the 24 nm zone in the 

SCOS domain used dirty fuel. 

MS4ws: OGV Clean Fuel Regulation implemented with proposed 

regulatory boundary change. Vessels in pre-regulation traffic patterns and 

all vessels within the proposed amended regulatory zone, including those 

in the Outer Route, use clean fuel

This scenario reflects what we anticipate will happen if the proposed regulatory 

boundary change is implemented. Under this scenario, the vast majority of all 

the non-tanker vessels transiting north and southbound in the Santa Barbara 

Channel region used the existing traffic separation scheme inside Channel 

Route. All vessels within the amended clean fuel zone used compliant clean 

distillate fuel (MGO at 0.1% S). Since the “window” is primarily outside the 

modeling domain, the emissions that occurred outside the SCOS domain were 

 1 50 percent of the vessel traffic visiting the Ports of LA and LB corresponds to about 75% of the 

total vessel traffic that historically uses the Santa Barbara Channel. 
C - 6

placed in grid cells inside the “window” to capture any impact that the window 

may have on the on-shore concentrations.

The ‘baseline’ OGV emissions inventory for year 2005 (scenario MS1) is version 

v2-3f of ARB’s OGV inventory. This is a pre-regulatory 2005 inventory, where 

there is no OGV Clean Fuel Regulation in place. Emission scenario MS2

represents the anticipated emissions inventory from the original rule and was 

produced by making adjustments to the baseline inventory (MS1), where clean 

fuel is required for shipping activities within 24nm of the California coastline. .

It is assumed that ships transiting outside the regulatory boundary (i.e. requiring 

clean fuels) in the three scenarios emit pollutants at the same rate and at the 

same speed as they would travel inside of the regulatory zone without vessel 

speed restrictions or fuel sulfur restrictions. 

Emission inputs of specific chemical species are required for modeling. These 

inputs are produced by applying the latest ARB speciation profiles (i.e. species 

fractions) to the scenario-specific TOG and PM emission estimates. In 2010, 

OGV PM speciation profiles were updated to reflect the most recent information. 

These profiles (Attachment C-1) were developed based on a series of tests 

conducted on OGV main engines (ME) and auxiliary engines (AE) operating on 

HFO and MDO with various sulfur contents (0.1% to 2.5%).

The gridded OGV emissions inventory was developed for a large statewide 

domain comprised of 4 km-by-4 km grid cells. For use in air quality modeling, 

these statewide OGV emissions were mapped into the smaller, Southern 

California domain for which meteorological inputs were readily available

(described in the previous section). The air quality modeling domain has a 

different grid cell structure (5 km-by-5 km grid cells) and a different map 

projection than the domain on which the OGV emissions are produced (UTM 

versus Lambert Conformal, respectively). 

A comparison of speciated, daily-averaged emission rates between OGV 

emissions for each of the six scenarios and the total emissions for the South 

Coast Air Basin is shown in Table C-2. The emissions in Table C-2 are 

summarized from the Southern California modeling domain (i.e. these totals 

reflect the emissions used in modeling).

Attachment C-2 provides illustrations of the emission differences among 

scenarios. These tabulated emission estimates are summarized from the OGV 

emission inventory on the large statewide domain prior to converting the 

information to modeling domain. Because of the differences in grid cell size and 

map projection along the boundaries of the region summarized, the summaries in 

Table C-2 and Attachment C-2 differ slightly.

OGV emissions are treated as an area-wide emission source, thus all of the OGV 

emissions are limited to the surface layer. The impact of OGV emission height 
C - 7

on air quality model performance is considered to be negligible, as was 

discussed previously in appendix E-2 of the OGV Fuel Rule Initial Statement of 

Reasons (ARB, 2008).

Table C-1. Summary of the Modeling Scenarios.

Scenario

ID # Scenario3,4 SB Channel RouteStatus Rule Status2 ECA Status

MS1 Baseline

Channel Route-

(most Vessels 

using Channel 

Route)

No rule No ECA

MS2 With rule

Channel Route-

(most Vessels 

using Channel 

Route)

With rule No ECA

MS6

SB 100% ships 

moved outside 

channel using 

clean fuel

100 percent of 

vessels using Outer 

Route

using clean 

fuel for ships 

moved to 

outside 

channel

No ECA

MS1A Baseline

Channel Route-

(most Vessels 

using Channel 

Route)

No rule With ECA at 

1% Sulfur

MS4ws

Amended 

Zone with 

Window

Channel Route-

(most Vessels 

using Channel 

Route)

With rule No ECA

MS5

With rule and 

75% ships 

moved outside 

SB channel

Current Traffic 

Pattern 

(50% of Vessels 

using Outer Route)5

With rule No ECA

Table Notes:

1) In scenario MS1A, it is assumed under the ECA Phase 1, vessels use HFO fuel with 1% sulfur

2) Rule is assuming 0.1% distillate fuel

3) The 2005 emissions inventory used in the modeling analysis was generated using the ARB 

Emissions Inventory Forecast System and was consistent with that used by the SCAQMD in the 

preparation of their PM2.5 SIP 

4) Inventory version v2-3f

5) 50 percent of the vessel traffic visiting the Ports of LA and LB corresponds to about 75% of the 

total vessel traffic that historically uses the Santa Barbara Channel
C - 8

Table C-3. Comparison Between OGV Emissions for Each 

Scenario and Total Emissions from all Sources in 

the South Coast Air Basin. 

Emission 

species

OGV Emissions (Tons/Day)

MS1 MS2 MS6 MS1A MS4ws MS5

SCAB 

Total 

Emission*

(Tons/day)

NOx 118.2 114.1 114.4 118.8 109.8 110.5 1205.6

SOx 89 15.8 24 36.3 10.9 35.5 157.2

VOC 4.4 4.9 4.8 5 4.8 4.4 2463.6

PM2.5 SO4 3.6 0.7 1 0.6 0.5 1.6 20.7

PM2.5 EC 0.1 0.1 0.1 0.4 0.1 0.1 19.6

Other PM2.5 7.4 2.6 3.2 7.2 1.9 3.8 197.5

*Total emission in South Coast Air Basin was calculated based on scenario MS1, which includes 

shipping emissions in scenario MS1 and all non-shipping emissions

III. Simulation Results 

The CMAQ air quality model was run for calendar year 2005 for each scenario. 

Hourly gaseous and aerosol concentrations for each grid cell within the domain 

were calculated. The results from each simulation were used to calculate, by 

grid cell, the annual maximum 8-hour ozone (O3) concentration, and the annual 

average concentrations of PM2.5 total, PM2.5 sulfate (SO4), and PM2.5 nitrate 

(NO3). 

The difference in gaseous and particulate concentrations between the baseline 

scenario (MS1) and each of the other scenarios is used to illustrate the impact of 

each scenario on baseline air quality (i.e. where the baseline represents preclean-fuel-regulation conditions). Figures C-3 to C-7 provide a summary of the 

modeling results in the form of the percentage change in annual averaged PM2.5 

concentration and annual maximum 8-hour Ozone concentration from the 

baseline scenario (MS1).

Air quality model performance was discussed previously in appendix E-2 of the 

OGV Fuel Rule Initial Statement of Reasons. (ARB, 2008)
C - 9

Effects on PM2.5 Air Quality and Premature Cardiopulmonary Mortality

For annual average PM2.5 concentrations, all the scenarios show significant 

decreases in PM2.5 compared to the no-rule baseline MS1 (right panels in 

Figures C-3 through C-7). In addition, there are no on-shore areas of increased 

PM2.5 concentrations within the modeling domain for any of the scenarios. For 

all of the scenarios, the decreases in PM2.5 are the greatest around the Ports of 

Los Angeles and Long Beach and along the coastal regions. Although all 

scenarios show decreases in PM2.5, some of the scenarios, such as MS6 and 

MS4ws have large reductions over a wider geographic area. 

To evaluate the public health impacts of the changes in PM2.5 concentrations, 

the model-simulated PM2.5 results were used to estimate impacts on annual 

cardiopulmonary mortality avoided for each scenario. The differences between 

the non-cancer health impacts for the scenario compared to the Baseline (MS1) 

provide a relative quantification of the public health impacts of each scenario. 

The results are summarized below in Table C-4.

Premature deaths from cardiopulmonary causes associated with exposure to 

PM2.5 were estimated using an approach based on a peer-reviewed 

methodology developed by the U.S. Environmental Protection Agency (EPA, 

2010). Details of the approach and the key assumptions underlying it are 

described in Attachment C-3. Further details are supplied in a recent ARB staff 

report. (ARB, 2010)

To estimate premature deaths, staff developed population exposure estimates 

using the model-predicted concentrations of directly emitted diesel PM (primary 

diesel PM) and secondary PM within each modeling grid cell. The number of 

annual cases of death from cardiopulmonary causes associated with exposure to 

the PM2.5 was then estimated using a function relating PM2.5 exposure, the 

population affected, and the baseline incidence rates to cardiopulmonary

mortality. Following the U.S. EPA’s methodology, the PM2.5-mortality function

used was from a recent, comprehensive nationwide study on the health effects of 

PM2.5 (Krewski et al., 2009). The populations within each grid cell were 

determined from U.S. Census Bureau year 2000 census data, projected to 2005. 

Mortality incidence rates were computed from California individual death records 

for 2005. 
C - 10

TableC-4: Annual Cardiopulmonary Mortality Compared to No Rule 

Baseline Scenario

Scenario 

Comparison Description

Annual Cardiopulmonary 

Mortality

Low Mean High

MS2 vs MS1 Impacts of OGV Clean Fuel Regulation as originally 

anticipated when adopted-vessels continue to use the 

Channel Route and use clean fuels with in the 24 nm 

regulatory zone

540 700 850

MS6 vs MS1

Impacts if vessels use Outer Route and all use clean fuels

580 740 910

MS1A vs MS1 Impacts if only ECA Phase 1 ECA implemented, no ARB 

OGV Clean Fuel Regulation 280 360 440

MS4ws vs 

MS1

Impacts of proposed amendments – vessels return to preregulation traffic patterns and all vessels in the expanded 

regulatory zone use clean fuels 

560 710 870

MS5 vs MS1 Current Situation – 75% of vessels that historically used 

Channel Route use Outer Route and HFO 500 650 790

As can be seen in Table C-4, there is considerable uncertainty associated with 

the methodology to estimate annual cardiopulmonary mortality, on the order of 

+ 25 percent. In all cases, the uncertainties for the scenarios overlap with each 

other and this needs to be taken into consideration when interpreting the values. 

However, comparing the mean values is helpful in providing a qualitative or 

directional indication of the relative differences between the impacts of the 

various scenarios such as: 

• The cardiopulmonary premature deaths avoided are significant for all 

scenarios, greater than 350 premature deaths avoided annually for all 

scenarios. 

• When comparing the impacts of vessels in the Channel Route using clean 

fuel and vessels in the Outer Route using HFO there is a small difference 

in the mean values (700 vs. 650) with the Outer Route having a slightly 

lower mean value. (MS2 vs. MS1) and (MS5 vs. MS1)

• Having vessels that use the Outer Route use the cleaner marine distillate 

fuels results in a small increase in the cardiopulmonary premature deaths 

avoided mean values (740 vs. 650) relative to not having them use the 

cleaner fuel. Comparison between (MS6 vs. MS1) and (MS5 vs. MS1)

• The OGV Clean Fuel Regulation is providing significant public health 

benefits prior to 2015 that are above and beyond what would be provided 

if only the North American ECA was implemented (650, 700 or 710 vs. 

360) See comparison between (MS5, MS2 or MS4ws vs. MS1) and 

(MS1A vs. MS1)

• The proposed amendments to the OGV Clean Fuel Regulation will provide 

similar public health benefits to those anticipated when the regulation was 
C - 11

initially adopted (710 vs. 700). See comparison between (MS4ws vs. MS1)

and (MS2 vs. MS1)

Ozone Air Quality

For most of the scenarios, there is very little difference in on-shore ozone 

concentrations relative to the baseline. As illustrated in the left panel of Figures 2 

through 6, for the scenarios that do show ozone concentration changes in the 

figures, the differences are relatively small (+/-5%). 

Attachment C-4 provides a summary of model-simulated percent changes in 

ozone concentrations applied to site-specific, 2005 ozone design values. 

Percentage changes from the modeling are calculated two ways: from the grid 

cell containing a specific monitoring station as well as for the 9 grid cells 

immediately surrounding the monitoring station. This information provides a 

scenario- and site-specific estimate of percentage change to ozone and how the 

respective level of changes might impact 2005 health-based ozone attainment 

levels (ARB does not currently estimate mortality impacts based on ozone). As 

with the small percent changes in the modeling results, the impact on design 

value concentrations is very minor.
C - 12

References

(ARB, 2010) Estimate of Premature Deaths Associated with Fine Particle 

Pollution (PM2.5) in California Using a U.S. Environmental Protection Agency 

Methodology, ARB (2010). http://www.arb.ca.gov/research/health/pm-mort/pmreport_2010.pdf.

(EPA, 1991) U.S. EPA, Guideline for regulatory application of the urban airshed 

model, U.S. EPA (1991). Research Triangle Park, North Carolina 

(EPA, 2007) U.S. EPA, Guidance on the use of models and other analyses for 

demonstrating attainment of air quality goals for ozone, PM2.5, and regional 

haze, U.S. EPA (2007). Research Triangle Park, North Carolina 

http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf

(EPA. 2010) U.S. EPA, Quantitative Health Risk Assessment for Particulate 

Matter, U.S. EPA (2010). 

http://www.epa.gov/ttn/naaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf

(Grell et al, 2007) Grell, A. G., J. Dubhia and D. R. Stauffer, A description of the 

fifth-generation Penn State/NCAR mesoscale model (mm5). NCAR Technical 

Note NCAR/TN 398+STR, National Center for Atmospheric Research, Boulder, 

CO (1994). 

(Krewski et al., 2009) Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi Y, 

Turner MC, Pope CA III, Thurston G, Calle EE, Thun M. Extended Follow-Up 

and Spatial Analysis of the American Cancer Society Study Linking Particulate 

Air Pollution and Mortality. HEI Research Report 140. Health Effects Institute, 

Boston, MA (2009).

(SCAQMD, 2007) South Coast Air Quality Management District, 2007 air quality 

management plan, South Coast Air Quality Management District (2007). 

http://www.aqmd.gov/aqmp/07aqmp/index.html

(ARB, 2008) State of California, Air Resources Board, Staff Report: Initial 

Statement of Reasons for Proposed Rulemaking: Proposed Regulation for Fuel 

Sulfur and Other Operational Requirements for Ocean-Going Vessels Within 

California Waters and 24 Nautical Miles of the California Baseline, June 2008.
C - 13

Figure C-3 (MS2 vs. MS1) The figures above illustrate model-simulated air quality benefits in the form of percentage 

decrease (i.e. a negative value is a decrease) in annual maximum 8-hour O3 concentrations (left) and annual average 

PM2.5 concentrations (right). Only changes >1% and <-1% are shown in the plots.
C - 14

Figure C-4 (MS6 vs. MS1) The figures above illustrate model-simulated air quality benefits in the form of percentage 

decrease (i.e. a negative value is a decrease) in annual maximum 8-hour O3 concentrations (left) and annual average 

PM2.5 concentrations (right). Only changes >1% and <-1% are shown in the plots.
C - 15

Figure C-5 (MS5 vs. MS1) The figures above illustrate model-simulated air quality benefits in the form of percentage 

decrease (i.e. a negative value is a decrease) in annual maximum 8-hour O3 concentrations (left) and annual average 

PM2.5 concentrations (right). Only changes >1% and <-1% are shown in the plots.
C - 16

Figure C-6 (MS4ws vs. MS1) The figures above illustrate model-simulated air quality benefits in the form of percentage 

decrease (i.e. a negative value is a decrease) in annual maximum 8-hour O3 concentrations (left) and annual average 

PM2.5 concentrations (right). Only changes >1% and <-1% are shown in the plots.
C - 17

Figure C-7 (MS1A vs. MS1) The figures above illustrate model-simulated air quality benefits in the form of percentage 

decrease (i.e. a negative value is a decrease) in annual maximum 8-hour O3 concentrations (left) and annual average 

PM2.5 concentrations (right). Only changes >1% and <-1% are shown in the plots.
C-1-1

Attachment C-1. Ocean-going Vessel (OGV) PM Speciation Profile Preparation

Background

PM speciation profiles 119 (Marine Vessel-Liquid Fuel) and 425 (Diesel Vehicle 

Exhaust)[1] were used for HFO (Heavy Fuel Oil) and MDO (Marine Diesel Oil) in the 

2008 air quality modeling analysis because no updated OGV exhaust source testing 

data were available for creating speciation profiles at the time. A summary of these two 

profiles is as follows: 

Table 1. PM Profiles 119 and 425 Used for 2008 Modeling Analysis

Size Fraction 

(by weight) PM2.5/TPM PM10/TPM

PM 119 0.937 0.96

PM 425 0.92 1.0

Weight

Fraction

(of PM 

mass)

PM2.5 PM10

EC OC SO4

2- others EC OC SO42- others

PM 119 0.04 0 0.15 0.81 0.04 0 0.15 0.81

PM 425 0.264 0.694 0.0186 0.0235 0.261 0.689 0.0174 0.0328

In 2010, four new OGV PM speciation profiles were developed based on a series of 

newly conducted OGV exhaust source tests [2-5]. These profiles were prepared for OGV 

main engine (ME) and auxiliary engine (AE) operating on HFO, MDO, and blended fuel, 

with various sulfur contents (0.1% to 2.5%), which were involved in the air quality 

modeling scenarios. The four profiles include: 

Profile Number Profile Name

PM 1191 Ocean-Going Vessel Exhaust--HFO (2.5% 

Sulfur)

PM 1192 Ocean-Going Vessel Exhaust--HFO (1.0% 

Sulfur)

PM 1193 Ocean-Going Vessel Exhaust-- Blend (1.0% 

Sulfur)

PM 4251 Ocean-Going Vessel Exhaust-- MDO (0.1% 

Sulfur)
C-1-2

Methodology

• PM 1191: Ocean-Going Vessel Exhaust--HFO (2.5% Sulfur)

This profile was obtained by averaging the weight fractions of EC, OC and SO4

2- in total 

PM mass from 18 source tests [2-5] of ME or AE running with HFO having sulfur contents 

ranging from 2.05% to 3.8%. Because the source tests were limited, it was assumed 

that the PM exhaust emitted from ME and AE have the same speciation composition for 

the same fuel. For each test, the weight fractions of EC, OC and SO4

2- were calculated 

by dividing the emission factors of these species by the emission factor of the total PM 

mass.

Table 2. PM Profile 1191

HFO

Emission Factor (g/kW-hr) Weight Fraction

(of PM mass)

PM Mass EC OC H2SO4·6.5H2

O EC OC SO4

2-

2.5% S 1.501

(±0.881)* 0.015

(±0.011)

0.244

(±0.112)

1.080

(±0.635)

0.013

(±0.012)

0.212

(±0.119)

0.335

(±0.076)

* Average value (±standard deviation)

• PM 1192: Ocean-Going Vessel Exhaust--HFO (1.0% Sulfur)

The emission factors of EC and OC for OGV running with 1.0% sulfur HFO were 

assumed to be the same as those for OGV running with 2.5% sulfur HFO, which were 

calculated based on the 18 source tests mentioned previously. The emission factor of 

SO4

2- was estimated by multiplying the fuel consumption rate (195 g/kW-hr)[6], fuel sulfur 

content (1.0%), conversion rate of fuel sulfur to SO4

2-

(3%)[6], and molecular weight ratio 

of SO4

2-

to sulfur. The weight fractions of EC, OC and SO4

2- were then calculated from 

the emission factors. 

Table 3. PM Profile 1192

HFO Emission Factor (g/kW-hr) Weight Fraction

(of PM mass)

PM Mass EC OC SO4

2- EC OC SO42-

1.0% S 1.10[6] 0.015 0.244 0.176 0.014 0.222 0.160

• PM 4251: Ocean-Going Vessel Exhaust-- MDO (0.1% Sulfur)

This profile was obtained by averaging the weight fractions of EC, OC and SO4

2- in total 

PM mass from 10 source tests [2, 3, 5] of ME or AE running with MDO with sulfur contents 

ranging from 0.05% to 0.2%. For each test, the weight fractions of EC, OC and SO4

2-
C-1-3

were calculated by dividing the emission factors of these species by the emission factor 

of the total PM mass.

Table 4. PM Profile 4251

MDO Emission Factor (g/kW-hr) Weight Fraction

(of PM mass)

PM Mass EC OC H2SO4·6.5H2O EC OC SO4

2-

0.1% S 0.338

(±0.177)

0.020

(±0.019)

0.111

(±0.438)

0.042

(±0.014)

0.052

(±0.037)

0.522

(±0.114)

0.080

(±0.068)

* Average value (±standard deviation)

• PM 1193: Ocean-Going Vessel Exhaust--Blend (1.0% Sulfur)

The emission factors of EC and OC for OGV running with 1.0% sulfur blend fuel were 

assumed to be the average values of those for HFO (2.5% S) and MDO (0.1% S). The 

emission factor of SO4

2- was estimated by multiplying the fuel consumption rate (190 

g/kW-hr)[6], fuel sulfur content (1.0%), conversion rate of fuel sulfur to SO4

2-

(3%) [6], and 

molecular weight ratio of SO4

2-

to sulfur. The weight fractions of EC, OC and SO4

2- were 

then calculated from the known emission factors.

Table 5. PM Profile 1193

Blend Emission Factor (g/kW-hr) Weight Fraction

(of PM mass)

PM Mass EC OC SO4

2- EC OC SO42-

1.0% S 0.80[6] 0.018 0.213 0.171 0.023 0.266 0.214

Summary

It should be noted that all of the source tests cited in this work were conducted for PM2.5 

only, and it was assumed that same speciation profiles can be used for PM10 and TPM 

for the same fuel. It was also assumed that PM 1191 and PM1192 have the same 

PM2.5 and PM10 size fractions as PM 119; PM 4251 and PM 1193 have the same PM2.5

and PM10 size fractions as PM 425. The size fractions, speciation profiles and factors 

used to convert profiles PM 119 and PM 425 to the updated profiles are summarized in 

the following tables.

Table 6. Size Fraction Summary of New OGV Profiles

Size Fraction 

(by weight) PM2.5/TPM PM10/TPM

PM 1191 0.937 0.96
C-1-4

PM 1192 0.937 0.96

PM 1193 0.92 1.0

PM 4251 0.92 1.0

Table 7. Speciation Summary of New OGV Profiles

Weigh 

Fraction

(of PM mass)

PM2.5 Fraction PM10 Fraction

EC OC SO4

2- others EC OC SO42- others

PM 1191 0.013 0.212 0.335 0.440 0.013 0.212 0.335 0.440

PM 1192 0.014 0.222 0.160 0.604 0.014 0.222 0.160 0.604

PM 1193 0.023 0.266 0.214 0.497 0.023 0.266 0.214 0.497

PM 4251 0.052 0.522 0.080 0.346 0.052 0.522 0.080 0.346

Table 8. Conversion Factors Used to Create New OGV Profiles from Old Profiles

Conversion Factor

PM2.5 Fraction PM10 Fraction

EC OC SO4

2- others EC OC SO42- others

PM 1191/ PM 119 0.33 2.23 0.54 0.33 2.23 0.54

PM 1192/ PM 119 0.35 1.07 0.75 0.35 1.07 0.75

PM 1193/ PM 119 0.58 1.43 0.61 0.58 1.43 0.61

PM 4251/PM 425 0.20 0.75 4.29 14.74 0.20 0.76 4.59 10.56

*Note: there is no OC in PM 119, so no conversion factor can apply. 

References for Appendix C Attachment C-1:

1. California Air Resources Board Main Speciation Profiles. California Air 

Resources Board: 2008; http://www.arb.ca.gov/ei/speciate/speciate.htm. Accessed July 

7, 2010.

2. Miller, J. W.; Nigam, A.; Welch, W. A.; Cocker, D. R. Measurement of Criteria 

and Greenhouse Gas Emissions from Auxiliary Engines on Ocean-Going Vessels 

Operating on Heavy Fuel Oil and Marine Diesel Oil; Califorina Air Resources Board: 

2009.

3. Miller, J. W.; Agrawal, H.; Welch, W. A. Criteria Emissions from the Main 

Propulsion Engine of a Post-Panamax Class Container Vessel Using Distillate and 

Residual Fuels; California Air Resources Board: 2009.

4. Miller, J. W.; Agrawal, H.; Welch, W. A. Measurement of Emissions from the 

Main Propulsion Engine (MAN B&W 11K90MC-C) on a Panamax Class Container Ship.; 

Califorina Air Resources Board: 2009.

5. Jayaram, V.; Miller, J. W.; Nigam, A.; Welch, W. A. Effect of Selective Catalytic 

Reduction Unit on Emissions from an Auxiliary Engine on an Ocean-Going Vessel; 

California Air Resources Board: 2009.
C-1-5

6. Soriano, B. L.; Milkey, P.; Alexis, A.; Di, P.; Du, S.; Lu, J.; Hand, R.; Houghton, 

M.; Komlenic, M.; Suer, C.; Williams, L.; Zuo, Y.-P. Initial Statement of Reasons for 

Proposed Rulemaking: Fuel Sulfur and Other Operational Requirements for OceanGoing Vessel within California Waters and 24 Nautical Miles of the California Baseline; 

California Air Resources Board: Sacramento, CA, June 2008.
C-2-1

Attachment C-2. Ocean-going Vessel (OGV) Gridded Emissions Inventory

The following plots show the spatial allocation of emissions in tons for NOx, PM25, and SOx by scenario. The accompanying tables 

give emission totals in tons for the entire domain (Total) and indicated sub-regions. SCOS refers to the SCOS domain only, 24nm

refers to the region within 24nm of the coastline, SCOS 24nm is the portion of the 24nm zone within the SCOS domain, and SB Box is 

an arbitrary region used to estimate emissions directly offshore of Santa Barbara County. Figure 1b visually identifies the sub-regions

Figure 1. NOx, PM25, and SOx emissions for Scenario MS1

Table 1. Scenario MS1 NOx, PM25, and SOx emission totals by region Figure 1b. 

MS1 NOx SOx PM2.5

Total 297.1 202.2 26.1

SCOS 118.8 88.7 10.9

24nm 169.4 124.9 15.4

SCOS 24nm 99.3 76.7 9.2

SB Box 63.2 38.5 5.3

SB Box

SCOS 

Boundary
C-2-2

Figure 2. NOx, PM25, and SOx emissions for Scenario MS2

Table 2. Scenario MS2 NOx, PM25, and SOx emission totals by domain and differences from Scenario MS1

MS2 NOx SOx PM2.5 MS2 - MS1 NOx SOx PM2.5

Total 289.0 83.0 12.8 Total -8.1 -119.2 -13.3

SCOS 114.1 15.7 3.1 SCOS -4.7 -73.0 -7.7

24nm 161.3 5.7 2.5 24nm -8.1 -119.2 -12.8

SCOS 24nm 94.6 3.7 1.5 SCOS 24nm -4.7 -73.0 -7.7

SB Box 60.2 3.0 1.0 SB Box -3.0 -35.5 -4.3
C-2-3

Figure 3. NOx, PM25, and SOx emissions for Scenario MS5

Table 3. Scenario MS5 NOx, PM25, and SOx emission totals by domain and differences from Scenario MS1

and Scenario MS2

MS5 NOx SOx PM2.5 MS5 - MS1 NOx SOx PM2.5 MS5 - MS2 NOx SOx PM2.5

Total 285.5 102.6 15.7 Total -11.6 -99.6 -10.4 Total -3.5 19.5 2.8

SCOS 110.6 35.2 5.5 SCOS -8.2 -53.5 -5.3 SCOS -3.5 19.5 2.4

24nm 124.4 4.9 2.1 24nm -45.0 -120.0 -13.3 24nm -36.9 -0.8 -0.4

SCOS 24nm 57.7 2.9 1.1 SCOS 24nm -41.6 -73.8 -8.2 SCOS 24nm -36.9 -0.8 -0.5

SB Box 27.8 3.1 0.7 SB Box -35.4 -35.4 0.7 SB Box -32.4 0.1 -0.3
C-2-4

Figure 4. NOx, PM25, and SOx emissions for Scenario MS6

Table 4. Scenario MS6 NOx, PM25, and SOx emission totals by domain and differences from Scenario MS1 and Scenario 

MS2

MS6 NOx SOx PM2.5 MS6 - MS1 NOx SOx PM2.5 MS6 - MS2 NOx SOx PM2.5

Total 280.2 85.6 13.6 Total -17.0 -116.6 -12.5 Total -8.8 2.6 0.8

SCOS 114.5 23.8 4.2 SCOS -4.3 -64.9 -6.6 SCOS 0.4 8.1 1.1

24nm 110.0 4.6 1.9 24nm -59.4 -120.3 -13.5 24nm -51.3 -1.1 -0.6

SCOS 24nm 46.9 4.7 1.1 SCOS 24nm -52.4 -72.0 -8.1 SCOS 24nm -47.7 1.1 -0.4

SB Box 14.8 2.0 0.4 SB Box -48.4 -36.5 0.4 SB Box -45.5 -1.0 -0.6
C-2-5

Figure 5. NOx, PM25, and SOx emissions for Scenario MS4ws

Table 5. Scenario 17ws NOx, PM25, and SOx emission totals by domain and differences from

Scenario MS1 and Scenario MS2

MS4ws NOx SOx PM2.5

MS4ws -

MS1 NOx SOx PM2.5

MS4ws -

MS2 NOx SOx PM2.5

Total 284.4 74.3 12.3 Total -12.7 -127.9 -13.8 Total -4.6 -8.7 -0.5

SCOS 109.8 10.9 2.6 SCOS -9.0 -77.8 -8.2 SCOS -4.3 -4.8 -0.5

24nm 163.0 9.1 3.1 24nm -6.4 -115.8 -12.3 24nm 1.7 3.4 0.6

SCOS 24nm 96.3 7.1 2.0 SCOS 24nm -3.0 -69.6 -7.2 SCOS 24nm 1.7 3.4 0.5

SB Box 59.1 4.3 1.2 SB Box -4.1 -34.2 0.4 SB Box -1.1 1.3 0.2
C-2-6

Figure 6. NOx, PM25, and SOx emissions for Scenario MS1A

Table 6. Scenario MS1A NOx, PM25, and SOx emission totals by domain and differences from Scenario MS1 and Scenario 

MS2

MS1A NOx SOx PM2.5 MS1A - MS1 NOx SOx PM2.5 MS1A - MS2 NOx SOx PM2.5

Total 297.1 81.7 19.1 Total 0.0 -120.5 -7.0 Total 8.1 -1.3 6.3

SCOS 118.8 36.0 8.0 SCOS 0.0 -52.7 -2.9 SCOS 4.7 20.3 4.9

24nm 169.4 50.7 11.3 24nm 0.0 -74.2 -4.1 24nm 8.1 45.0 8.7

SCOS 24nm 99.3 31.2 6.8 SCOS 24nm 0.0 -45.5 -2.4 SCOS 24nm 4.7 27.5 5.2

SB Box 63.2 15.5 3.9 SB Box 0.0 -23.1 3.5 SB Box 3.0 12.5 2.9
C-3-1

Attachment C-3: Health Benefits Associated with Reductions in PM Emissions from 

the OGV Regulation

Overview

The estimate of the number of PM2.5-associated premature deaths is based on a peer-reviewed 

methodology developed by the U.S. Environmental Protection Agency (U.S. EPA, 2010). Calculation 

of this estimate requires information on the concentration of PM2.5, the population exposed, the 

baseline incidence rates, and a concentration-response function relating changes in PM2.5 exposure 

to changes in mortality incidence. This information is available as part of the rulemaking package and 

can be found at the following link: http://www.arb.ca.gov/ports/marinevess/ogv/ogv1085.htm.

Estimating population exposure to PM2.5

PM2.5 concentrations were estimated for a domain covering southern California using the Community 

Multi-scale Air Quality model as described in this appendix. Primary and secondary PM2.5 

concentrations were modeled for five scenarios of OGV emission controls, and a baseline scenario 

representing no emission controls.

Population at the Census Tract Level

Age-resolved population data at the census tract level, for the 2000 Census, were obtained from the 

United States Census Bureau (Census Bureau). These were projected to 2005 using age-resolved 

county population projections from the California Department of Finance (CDOF).

Age-specific population growth factors for each county, for each year, were computed from the CDOF 

projections by dividing each county population for 2005 by the county population for the year 2000. 

Since each census tract lies entirely in a county, these growth factors were applied to each census 

tract in the county, each age group separately. Population was projected for ten-year age groups 25-

34 through 75-84, and for age 85 and older.

This method of projection reflects growth in overall county population, but does not model changes in 

population distribution within counties, such as expansion of urban areas into surrounding rural land.

Baseline Cardiopulmonary Mortality Incidence Rate

Baseline cardiopulmonary mortality incidence rates vary by age bracket. Incidence was estimated 

separately for ten-year age groups 25-34 through 75-84, and age 85 and older. Baseline incidence 

rates were estimated at the county level from individual death records for the year 2005, obtained 

from the California Department of Public Health (CDPH). Cardiopulmonary mortality was defined as 

ICD9 codes 161-187 and 192-214.

The county of residence of the decedent was generally not recorded. However, the Federal 

Information Processing Standards (FIPS) city code and the ZIP code were usually recorded. The 

FIPS city code unambiguously identifies the county, but was sometimes invalid, unrecorded, or 

recorded as “unknown”. When the FIPS code was not available it was sometimes possible to identify 

the county from the ZIP code, but ZIP codes can overlap multiple counties. In cases where 90% or 
C-3-2

more of the area of the decedent’s zip code lay entirely within a county, the death was assigned to 

that county. A handful of records included invalid dates. The breakdown of records was as follows:

County identified by FIPS code 231,181 96.6%

County identified by ZIP code 4,196 1.8%

Unidentified or invalid data 3,851 1.6%

Because the county could not be determined for 1.6% of the records, the incidence is slightly 

underestimated. No adjustment was made to compensate for excluded records.

Concentration-Response Function

The concentration-response (C-R) function used in this analysis is from U.S. EPA Quantitative Health 

Risk Assessment (EPA, 2010). In their assessment, the U.S. EPA used the C-R function from a 

recent comprehensive epidemiological study of the health effects of PM2.5 (Krewski et al., 2009).

U.S. EPA chose Krewski et al. (2009) for quantifying PM2.5-related mortality from long-term PM2.5 

exposure for several reasons. First, the cohort includes both men and women, and enrollment was 

not dependent on underlying health status. It includes data from cities from across the U.S. PM2.5

exposure was based on monitored data collected over two time periods (1979-1983 and 1999-2000); 

the effect estimates were presented both for each time period and as an average. The study was 

validated through extensive reanalysis that demonstrated the results to be robust. Extensive 

exploratory analysis of potential individual and ecologic covariates was conducted, and the results 

were adjusted for all covariates that influenced the model fit. Finally, spatial autocorrelation was 

evaluated and adjusted for in the ecologic covariates.

The C-R function employed in this analysis was the one developed for the 1999-2000 time period.

Aggregating results to county, air basin and state

To aggregate results from census tracts to larger geographical subdivisions such as counties or air 

basins, we used a GIS technique called areal interpolation. Areal interpolation is a procedure for 

translating spatial data from one set of geographical subdivisions to another when the boundaries do 

not exactly overlap. Numerous variants of the technique exist, but for the purpose of this analysis the 

simplest form, which uses area of polygon intersection, was employed (Goodchild and Lam, 1980; 

Flowerdew and Green, 1994).

The precision of areal interpolation based on area of intersection depends on the relative size of the 

geographical subdivisions and the homogeneity of the spatial distribution of the quantity being 

apportioned. In urban areas, where census tracts are small and population is distributed more 

evenly, areal interpolation to larger subdivisions such as air basins yields relatively precise estimates. 

In rural areas where the population is distributed unevenly over large census tracts, estimates are 

less precise.
C-3-3

Based on our analysis, we estimated the average numbers of cases per year for each of the six 

scenarios. The results of this analysis are provided in Table C-3-1 below.

Table C-3-1: Annual Cardiopulmonary Mortality Compared to No-rule Baseline Scenario

Scenario Annual Cardiopulmonary Mortality

Low Mean High

MS2 vs MS1 540 700 850

MS6 vs MS1 580 740 910

MS1A vs MS1 280 360 440

MS4ws vs MS1 560 710 870

MS5 vs MS1 500 650 790
C-4-1

Attachment C-4. Ocean-going Vessel (OGV) Model-Adjusted 8 Hour Ozone Design Values

Background

The following tables show estimates of the potential effects of OGV scenarios on 2005 8-hr ozone 

design values for ozone montoring stations located in the domain. The tables include the original,

observation-based design values (DVcurrent) and the model- adjusted design values (DVadjusted). The 

model-adjusted DVs are calculated by applying the model-simulated percent difference calculated in 

the grid cell(s) containing or surrounding the monitoring station to the current DVs (where the percent 

difference is calculated between the specified modeling scenario and the baseline case, 

Scenario MS1). For example:

Current DV * (Modeled OzoneMS2 - Modeled OzoneMS1) / Modeled OzoneMS1 = Adjusted DV

Model-adjusted DVs are provided for two cases: 1) for the single grid cell containing the monitoring 

site; and 2) the min, max and average for the 9-cells surrounding the monitoring site, including the 

cell within which the site is located.

Design site

 1-cell

9-cells
C-4-2

Table C-4-1. Current DVs (“Obs”) and Adjusted DVs based on Modeled Percent Differences MS2 vs. 

MS1

Ozone (ppm) MS2 vs MS1 Model Adjusted

1-Cell 9-Cell

County Basin SiteID Site Name Obs Adj Min Ave Max

Imperial SS 2551 El Centro-9th Street 0.084 0.084 0.084 0.084 0.084

SS 2997 Calexico-Grant Street 0.069 0.069 0.069 0.069 0.069

SS 3135 Calexico-Ethel Street 0.071 0.071 0.071 0.071 0.071

SS 3143 Westmorland-W 1st Street 0.079 0.079 0.079 0.079 0.079

SS 3173 Calexico-East 0.075 0.075 0.075 0.075 0.075

SS 3186 Niland-English Road 0.072 0.072 0.072 0.072 0.072

Kern SJV 2312 Edison 0.097 0.097 0.097 0.097 0.097

SJV 2772 Oildale-3311 Manor Street 0.096 0.096 0.096 0.096 0.096

SJV 2919 Maricopa-Stanislaus Street 0.091 0.091 0.091 0.091 0.091

SJV 2941 Arvin-Bear Mountain Blvd 0.113 0.113 0.113 0.113 0.113

SJV 2981 Shafter-Walker Street 0.090 0.090 0.090 0.090 0.090

MD 3121 Mojave-923 Poole Street 0.090 0.090 0.090 0.090 0.090

SJV 3145 Bakersfield-Golden State Highway 0.090 0.090 0.090 0.090 0.090

SJV 3146 Bakersfield-5558 California Avenue 0.097 0.097 0.097 0.097 0.097

Los Angeles SC 2160 Pasadena-S Wilson Avenue 0.093 0.093 0.093 0.093 0.093

SC 2166 Pico Rivera 0.070 0.070 0.070 0.070 0.070

SC 2420 Reseda 0.106 0.106 0.106 0.106 0.106

SC 2429 North Long Beach 0.064 0.064 0.064 0.064 0.064

SC 2484 Azusa 0.094 0.094 0.094 0.094 0.094

SC 2492 Burbank-W Palm Avenue 0.089 0.089 0.089 0.089 0.089

SC 2494 West Los Angeles-VA Hospital 0.078 0.078 0.078 0.078 0.078

SC 2583 Lynwood 0.061 0.061 0.061 0.061 0.061

SC 2849 Glendora-Laurel 0.105 0.105 0.105 0.105 0.105

SC 2898 Pomona 0.100 0.100 0.100 0.100 0.100

SC 2899 Los Angeles-North Main Street 0.076 0.076 0.076 0.076 0.076

SC 3502 Santa Clarita 0.120 0.120 0.120 0.120 0.120

MD 3658 Lancaster-43301 Division Street 0.098 0.098 0.098 0.098 0.098

Orange SC 2249 La Habra 0.073 0.073 0.073 0.073 0.073

SC 2937 Costa Mesa-Mesa Verde Drive 0.073 0.073 0.073 0.073 0.073

SC 3265 Mission Viejo-26081 Via Pera 0.086 0.086 0.086 0.086 0.086

SC 3674 Anaheim-Pampas Lane 0.081 0.081 0.081 0.081 0.081

Riverside SS 2199 Palm Springs-Fire Station 0.104 0.104 0.104 0.104 0.104

SC 2525 Perris 0.098 0.098 0.098 0.098 0.098

SC 2596 Riverside-Rubidoux 0.112 0.112 0.112 0.112 0.112

SS 2878 Indio-Jackson Street 0.095 0.095 0.095 0.095 0.095

SC 2943 Lake Elsinore-W Flint Street 0.104 0.104 0.104 0.104 0.104

SC 3168 Banning Airport 0.119 0.119 0.119 0.119 0.119
C-4-3

San

Bernardino

SC 2077 Redlands-Dearborn 0.123 0.123 0.123 0.123 0.123

SC 2221 San Bernardino-4th Street 0.116 0.116 0.116 0.116 0.116

SC 2266 Fontana-Arrow Highway 0.118 0.118 0.118 0.118 0.118

SC 2485 Upland 0.106 0.106 0.106 0.106 0.106

SC 2499 Crestline 0.127 0.127 0.127 0.127 0.127

MD 2650 Hesperia-Olive Street 0.104 0.104 0.104 0.104 0.104

MD 2830 Phelan-Beekley Road and Phelan Road 0.100 0.100 0.100 0.100 0.100

MD 2923 Barstow 0.085 0.085 0.085 0.085 0.085

MD 3124 Twentynine Palms-Adobe Road #2 0.085 0.085 0.085 0.085 0.085

MD 3152 Joshua Tree-National Monument 0.105 0.105 0.105 0.105 0.105

MD 3500 Victorville-14306 Park Avenue 0.094 0.094 0.094 0.094 0.094

San Diego SD 2040 San Diego-Overland Avenue 0.074 0.074 0.074 0.074 0.074

SD 2263 Escondido-E Valley Parkway 0.073 0.073 0.073 0.073 0.073

SD 2327 El Cajon-Redwood Avenue 0.070 0.070 0.070 0.070 0.070

SD 2368 Del Mar-Mira Costa College 0.070 0.070 0.070 0.070 0.070

SD 2460 Alpine-Victoria Drive 0.086 0.086 0.086 0.086 0.086

SD 2589 Chula Vista 0.064 0.064 0.064 0.064 0.064

SD 2933 Otay Mesa-Paseo International 0.065 0.065 0.065 0.065 0.065

SD 2964 San Diego-12th Avenue 0.060 0.060 0.060 0.060 0.060

SD 3198 Camp Pendleton 0.076 0.076 0.076 0.076 0.076

San Luis 

Obispo

SCC 2321 Morro Bay 0.056 0.056 0.056 0.056 0.056

SCC 2671 Grover City-Lesage Drive 0.058 0.058 0.058 0.058 0.058

SCC 2709 San Luis Obispo-Marsh Street 0.057 0.057 0.057 0.057 0.057

SCC 2955 Paso Robles-Santa Fe Avenue 0.071 0.071 0.071 0.071 0.071

SCC 2965 Atascadero-Lewis Avenue 0.068 0.068 0.068 0.068 0.068

SCC 3251 Nipomo-Regional Park 0.064 0.064 0.064 0.064 0.064

Santa Barbara SCC 2008 El Capitan Beach 0.067 0.067 0.067 0.067 0.067

SCC 2360 Lompoc-S H Street 0.056 0.056 0.056 0.056 0.056

SCC 2593 Santa Ynez-Airport Road 0.068 0.068 0.068 0.068 0.068

SCC 2954 Gaviota-GTC Site B 0.060 0.060 0.060 0.060 0.060

SCC 2957 Paradise Road-Los Padres National Forest 0.078 0.078 0.078 0.078 0.078

SCC 2992 Lompoc-HSandP 0.070 0.070 0.070 0.070 0.070

SCC 3003 Carpinteria-Gobernador Road 0.064 0.064 0.064 0.064 0.064

SCC 3023 Vandenberg Air Force Base-STS Power 0.069 0.069 0.069 0.069 0.069

SCC 3101 Las Flores Canyon #1 0.074 0.074 0.074 0.074 0.074

SCC 3153 Goleta-Fairview 0.064 0.064 0.064 0.064 0.064

SCC 3486 Santa Maria-906 S Broadway 0.053 0.053 0.053 0.053 0.053

SCC 3665 Santa Barbara-700 East Canon Perdido 0.064 0.064 0.064 0.064 0.064

Ventura SCC 2088 Ventura-Emma Wood State Beach 0.068 0.068 0.068 0.068 0.068

SCC 2880 Simi Valley-Cochran Street 0.091 0.091 0.091 0.091 0.091

SCC 2984 Thousand Oaks-Moorpark Road 0.083 0.083 0.083 0.083 0.083

SCC 2991 El Rio-Rio Mesa School #2 0.066 0.066 0.066 0.066 0.066

SCC 3172 Ojai-Ojai Avenue 0.090 0.090 0.090 0.090 0.090

SCC 3505 Piru-3301 Pacific Avenue 0.087 0.087 0.087 0.087 0.087
C-4-4

Table C-4-2. Current DVs (“Obs”) and Adjusted DVs based on Modeled Percent Differences MS5 vs. 

MS1

Ozone (ppm) MS5 vs MS1 Model Adjusted

1-Cell 9-Cell

County Basin SiteID Site Name Obs Adj Min Ave Max

Imperial SS 2551 El Centro-9th Street 0.084 0.084 0.084 0.084 0.084

SS 2997 Calexico-Grant Street 0.069 0.069 0.069 0.069 0.069

SS 3135 Calexico-Ethel Street 0.071 0.071 0.071 0.071 0.071

SS 3143 Westmorland-W 1st Street 0.079 0.079 0.079 0.079 0.079

SS 3173 Calexico-East 0.075 0.075 0.075 0.075 0.075

SS 3186 Niland-English Road 0.072 0.072 0.072 0.072 0.072

Kern SJV 2312 Edison 0.097 0.097 0.097 0.097 0.097

SJV 2772 Oildale-3311 Manor Street 0.096 0.096 0.096 0.096 0.096

SJV 2919 Maricopa-Stanislaus Street 0.091 0.091 0.091 0.091 0.091

SJV 2941 Arvin-Bear Mountain Blvd 0.113 0.113 0.113 0.113 0.113

SJV 2981 Shafter-Walker Street 0.090 0.090 0.090 0.090 0.090

MD 3121 Mojave-923 Poole Street 0.090 0.090 0.090 0.090 0.090

SJV 3145 Bakersfield-Golden State Highway 0.090 0.090 0.090 0.090 0.090

SJV 3146 Bakersfield-5558 California Avenue 0.097 0.097 0.097 0.097 0.097

Los Angeles SC 2160 Pasadena-S Wilson Avenue 0.093 0.093 0.093 0.093 0.093

SC 2166 Pico Rivera 0.070 0.071 0.070 0.071 0.071

SC 2420 Reseda 0.106 0.105 0.104 0.105 0.106

SC 2429 North Long Beach 0.064 0.065 0.064 0.065 0.065

SC 2484 Azusa 0.094 0.095 0.094 0.095 0.095

SC 2492 Burbank-W Palm Avenue 0.089 0.088 0.088 0.088 0.089

SC 2494 West Los Angeles-VA Hospital 0.078 0.077 0.076 0.077 0.078

SC 2583 Lynwood 0.061 0.062 0.061 0.062 0.062

SC 2849 Glendora-Laurel 0.105 0.106 0.105 0.106 0.106

SC 2898 Pomona 0.100 0.101 0.101 0.101 0.101

SC 2899 Los Angeles-North Main Street 0.076 0.076 0.075 0.076 0.076

SC 3502 Santa Clarita 0.120 0.120 0.120 0.120 0.120

MD 3658 Lancaster-43301 Division Street 0.098 0.098 0.098 0.098 0.098

Orange SC 2249 La Habra 0.073 0.074 0.073 0.074 0.074

SC 2937 Costa Mesa-Mesa Verde Drive 0.073 0.074 0.073 0.074 0.074

SC 3265 Mission Viejo-26081 Via Pera 0.086 0.086 0.085 0.086 0.087

SC 3674 Anaheim-Pampas Lane 0.081 0.081 0.080 0.081 0.081

Riverside SS 2199 Palm Springs-Fire Station 0.104 0.104 0.104 0.104 0.104

SC 2525 Perris 0.098 0.098 0.098 0.098 0.098

SC 2596 Riverside-Rubidoux 0.112 0.112 0.112 0.112 0.113

SS 2878 Indio-Jackson Street 0.095 0.095 0.095 0.095 0.095

SC 2943 Lake Elsinore-W Flint Street 0.104 0.104 0.104 0.104 0.104

SC 3168 Banning Airport 0.119 0.119 0.118 0.119 0.120
C-4-5

San 

Bernardino

SC 2077 Redlands-Dearborn 0.123 0.123 0.123 0.123 0.123

SC 2221 San Bernardino-4th Street 0.116 0.116 0.116 0.116 0.117

SC 2266 Fontana-Arrow Highway 0.118 0.119 0.119 0.119 0.119

SC 2485 Upland 0.106 0.107 0.107 0.107 0.107

SC 2499 Crestline 0.127 0.127 0.127 0.127 0.127

MD 2650 Hesperia-Olive Street 0.104 0.104 0.104 0.104 0.104

MD 2830 Phelan-Beekley Road and Phelan Road 0.100 0.100 0.100 0.100 0.100

MD 2923 Barstow 0.085 0.085 0.085 0.085 0.085

MD 3124 Twentynine Palms-Adobe Road #2 0.085 0.085 0.085 0.085 0.085

MD 3152 Joshua Tree-National Monument 0.105 0.105 0.105 0.105 0.105

MD 3500 Victorville-14306 Park Avenue 0.094 0.094 0.094 0.094 0.094

San Diego SD 2040 San Diego-Overland Avenue 0.074 0.074 0.074 0.074 0.074

SD 2263 Escondido-E Valley Parkway 0.073 0.073 0.073 0.073 0.073

SD 2327 El Cajon-Redwood Avenue 0.070 0.070 0.070 0.070 0.070

SD 2368 Del Mar-Mira Costa College 0.070 0.071 0.070 0.070 0.071

SD 2460 Alpine-Victoria Drive 0.086 0.086 0.086 0.086 0.086

SD 2589 Chula Vista 0.064 0.064 0.063 0.064 0.064

SD 2933 Otay Mesa-Paseo International 0.065 0.065 0.065 0.065 0.065

SD 2964 San Diego-12th Avenue 0.060 0.060 0.060 0.060 0.060

SD 3198 Camp Pendleton 0.076 0.076 0.076 0.076 0.077

San Luis 

Obispo

SCC 2321 Morro Bay 0.056 0.056 0.055 0.056 0.057

SCC 2671 Grover City-Lesage Drive 0.058 0.059 0.057 0.059 0.061

SCC 2709 San Luis Obispo-Marsh Street 0.057 0.056 0.055 0.056 0.056

SCC 2955 Paso Robles-Santa Fe Avenue 0.071 0.070 0.069 0.070 0.071

SCC 2965 Atascadero-Lewis Avenue 0.068 0.067 0.067 0.067 0.067

SCC 3251 Nipomo-Regional Park 0.064 0.064 0.063 0.063 0.064

Santa Barbara SCC 2008 El Capitan Beach 0.067 0.066 0.066 0.067 0.068

SCC 2360 Lompoc-S H Street 0.056 0.056 0.054 0.055 0.056

SCC 2593 Santa Ynez-Airport Road 0.068 0.068 0.067 0.068 0.068

SCC 2954 Gaviota-GTC Site B 0.060 0.060 0.060 0.060 0.060

SCC 2957 Paradise Road-Los Padres National Forest 0.078 0.077 0.077 0.078 0.078

SCC 2992 Lompoc-HSandP 0.070 0.069 0.068 0.069 0.070

SCC 3003 Carpinteria-Gobernador Road 0.064 0.064 0.064 0.065 0.066

SCC 3023 Vandenberg Air Force Base-STS Power 0.069 0.071 0.067 0.070 0.072

SCC 3101 Las Flores Canyon #1 0.074 0.073 0.073 0.073 0.074

SCC 3153 Goleta-Fairview 0.064 0.064 0.064 0.064 0.064

SCC 3486 Santa Maria-906 S Broadway 0.053 0.052 0.052 0.052 0.053

SCC 3665 Santa Barbara-700 East Canon Perdido 0.064 0.064 0.064 0.064 0.065

Ventura SCC 2088 Ventura-Emma Wood State Beach 0.068 0.069 0.067 0.070 0.074

SCC 2880 Simi Valley-Cochran Street 0.091 0.091 0.091 0.091 0.092

SCC 2984 Thousand Oaks-Moorpark Road 0.083 0.083 0.083 0.083 0.083

SCC 2991 El Rio-Rio Mesa School #2 0.066 0.066 0.065 0.066 0.068

SCC 3172 Ojai-Ojai Avenue 0.090 0.090 0.088 0.090 0.090

SCC 3505 Piru-3301 Pacific Avenue 0.087 0.087 0.087 0.087 0.087
C-4-6

Table C-4-3 Current DVs (“Obs”) and Adjusted DVs based on Modeled Percent Differences MS6 vs. 

MS1

Ozone (ppm) MS6 vs MS1 Model Adjusted

1-Cell 9-Cell

County Basin SiteID Site Name Obs Adj Min Ave Max

Imperial SS 2551 El Centro-9th Street 0.084 0.084 0.084 0.084 0.084

SS 2997 Calexico-Grant Street 0.069 0.069 0.069 0.069 0.069

SS 3135 Calexico-Ethel Street 0.071 0.071 0.071 0.071 0.071

SS 3143 Westmorland-W 1st Street 0.079 0.079 0.079 0.079 0.079

SS 3173 Calexico-East 0.075 0.075 0.075 0.075 0.075

SS 3186 Niland-English Road 0.072 0.072 0.072 0.072 0.072

Kern SJV 2312 Edison 0.097 0.097 0.097 0.097 0.097

SJV 2772 Oildale-3311 Manor Street 0.096 0.096 0.096 0.096 0.096

SJV 2919 Maricopa-Stanislaus Street 0.091 0.091 0.091 0.091 0.091

SJV 2941 Arvin-Bear Mountain Blvd 0.113 0.113 0.113 0.113 0.113

SJV 2981 Shafter-Walker Street 0.090 0.090 0.090 0.090 0.090

MD 3121 Mojave-923 Poole Street 0.090 0.090 0.090 0.090 0.090

SJV 3145 Bakersfield-Golden State Highway 0.090 0.090 0.090 0.090 0.090

SJV 3146 Bakersfield-5558 California Avenue 0.097 0.097 0.097 0.097 0.097

Los Angeles SC 2160 Pasadena-S Wilson Avenue 0.093 0.092 0.092 0.092 0.092

SC 2166 Pico Rivera 0.070 0.070 0.070 0.071 0.071

SC 2420 Reseda 0.106 0.104 0.103 0.105 0.106

SC 2429 North Long Beach 0.064 0.064 0.064 0.065 0.065

SC 2484 Azusa 0.094 0.094 0.094 0.094 0.095

SC 2492 Burbank-W Palm Avenue 0.089 0.088 0.087 0.088 0.089

SC 2494 West Los Angeles-VA Hospital 0.078 0.076 0.075 0.076 0.077

SC 2583 Lynwood 0.061 0.062 0.061 0.062 0.062

SC 2849 Glendora-Laurel 0.105 0.105 0.105 0.105 0.106

SC 2898 Pomona 0.100 0.101 0.101 0.101 0.101

SC 2899 Los Angeles-North Main Street 0.076 0.075 0.074 0.075 0.076

SC 3502 Santa Clarita 0.120 0.120 0.120 0.120 0.120

MD 3658 Lancaster-43301 Division Street 0.098 0.098 0.097 0.098 0.098

Orange SC 2249 La Habra 0.073 0.074 0.073 0.073 0.074

SC 2937 Costa Mesa-Mesa Verde Drive 0.073 0.074 0.072 0.073 0.074

SC 3265 Mission Viejo-26081 Via Pera 0.086 0.085 0.085 0.086 0.087

SC 3674 Anaheim-Pampas Lane 0.081 0.080 0.080 0.080 0.081

Riverside SS 2199 Palm Springs-Fire Station 0.104 0.104 0.104 0.104 0.104

SC 2525 Perris 0.098 0.098 0.098 0.098 0.098

SC 2596 Riverside-Rubidoux 0.112 0.112 0.112 0.112 0.113

SS 2878 Indio-Jackson Street 0.095 0.095 0.095 0.095 0.095

SC 2943 Lake Elsinore-W Flint Street 0.104 0.104 0.104 0.104 0.104

SC 3168 Banning Airport 0.119 0.119 0.118 0.119 0.120
C-4-7

San 

Bernardino

SC 2077 Redlands-Dearborn 0.123 0.123 0.123 0.123 0.123

SC 2221 San Bernardino-4th Street 0.116 0.116 0.116 0.116 0.117

SC 2266 Fontana-Arrow Highway 0.118 0.119 0.119 0.119 0.119

SC 2485 Upland 0.106 0.106 0.106 0.107 0.107

SC 2499 Crestline 0.127 0.127 0.127 0.127 0.127

MD 2650 Hesperia-Olive Street 0.104 0.104 0.104 0.104 0.104

MD 2830 Phelan-Beekley Road and Phelan Road 0.100 0.100 0.100 0.100 0.100

MD 2923 Barstow 0.085 0.085 0.085 0.085 0.085

MD 3124 Twentynine Palms-Adobe Road #2 0.085 0.085 0.085 0.085 0.085

MD 3152 Joshua Tree-National Monument 0.105 0.104 0.104 0.104 0.104

MD 3500 Victorville-14306 Park Avenue 0.094 0.094 0.094 0.094 0.094

San Diego SD 2040 San Diego-Overland Avenue 0.074 0.074 0.074 0.074 0.074

SD 2263 Escondido-E Valley Parkway 0.073 0.073 0.073 0.073 0.073

SD 2327 El Cajon-Redwood Avenue 0.070 0.070 0.069 0.070 0.070

SD 2368 Del Mar-Mira Costa College 0.070 0.071 0.069 0.070 0.071

SD 2460 Alpine-Victoria Drive 0.086 0.086 0.086 0.086 0.086

SD 2589 Chula Vista 0.064 0.064 0.063 0.064 0.064

SD 2933 Otay Mesa-Paseo International 0.065 0.065 0.065 0.065 0.065

SD 2964 San Diego-12th Avenue 0.060 0.060 0.060 0.060 0.060

SD 3198 Camp Pendleton 0.076 0.076 0.076 0.076 0.077

San Luis 

Obispo

SCC 2321 Morro Bay 0.056 0.056 0.055 0.056 0.057

SCC 2671 Grover City-Lesage Drive 0.058 0.059 0.057 0.059 0.062

SCC 2709 San Luis Obispo-Marsh Street 0.057 0.055 0.055 0.055 0.056

SCC 2955 Paso Robles-Santa Fe Avenue 0.071 0.070 0.069 0.070 0.071

SCC 2965 Atascadero-Lewis Avenue 0.068 0.066 0.066 0.066 0.067

SCC 3251 Nipomo-Regional Park 0.064 0.063 0.063 0.063 0.064

Santa Barbara SCC 2008 El Capitan Beach 0.067 0.066 0.066 0.066 0.067

SCC 2360 Lompoc-S H Street 0.056 0.055 0.054 0.055 0.056

SCC 2593 Santa Ynez-Airport Road 0.068 0.068 0.067 0.068 0.068

SCC 2954 Gaviota-GTC Site B 0.060 0.060 0.059 0.059 0.060

SCC 2957 Paradise Road-Los Padres National Forest 0.078 0.077 0.076 0.077 0.078

SCC 2992 Lompoc-HSandP 0.070 0.068 0.068 0.068 0.069

SCC 3003 Carpinteria-Gobernador Road 0.064 0.064 0.064 0.064 0.066

SCC 3023 Vandenberg Air Force Base-STS Power 0.069 0.073 0.066 0.071 0.074

SCC 3101 Las Flores Canyon #1 0.074 0.073 0.073 0.073 0.074

SCC 3153 Goleta-Fairview 0.064 0.064 0.063 0.064 0.065

SCC 3486 Santa Maria-906 S Broadway 0.053 0.052 0.051 0.052 0.053

SCC 3665 Santa Barbara-700 East Canon Perdido 0.064 0.064 0.064 0.064 0.064

Ventura SCC 2088 Ventura-Emma Wood State Beach 0.068 0.069 0.067 0.070 0.074

SCC 2880 Simi Valley-Cochran Street 0.091 0.091 0.091 0.091 0.092

SCC 2984 Thousand Oaks-Moorpark Road 0.083 0.082 0.082 0.083 0.083

SCC 2991 El Rio-Rio Mesa School #2 0.066 0.066 0.064 0.065 0.068

SCC 3172 Ojai-Ojai Avenue 0.090 0.090 0.088 0.089 0.090

SCC 3505 Piru-3301 Pacific Avenue 0.087 0.087 0.087 0.087 0.087
C-4-8

Table C-4-4. Current DVs (“Obs”) and Adjusted DVs based on Modeled Percent Differences MS4ws vs. 

MS1

Ozone (ppm) MS4ws vs MS1 Model Adjusted

1-Cell 9-Cell

County Basin SiteID Site Name Obs Adj Min Ave Max

Imperial SS 2551 El Centro-9th Street 0.084 0.084 0.084 0.084 0.084

SS 2997 Calexico-Grant Street 0.069 0.069 0.069 0.069 0.069

SS 3135 Calexico-Ethel Street 0.071 0.071 0.071 0.071 0.071

SS 3143 Westmorland-W 1st Street 0.079 0.079 0.079 0.079 0.079

SS 3173 Calexico-East 0.075 0.075 0.075 0.075 0.075

SS 3186 Niland-English Road 0.072 0.072 0.072 0.072 0.072

Kern SJV 2312 Edison 0.097 0.097 0.097 0.097 0.097

SJV 2772 Oildale-3311 Manor Street 0.096 0.096 0.096 0.096 0.096

SJV 2919 Maricopa-Stanislaus Street 0.091 0.091 0.091 0.091 0.091

SJV 2941 Arvin-Bear Mountain Blvd 0.113 0.113 0.113 0.113 0.113

SJV 2981 Shafter-Walker Street 0.090 0.090 0.090 0.090 0.090

MD 3121 Mojave-923 Poole Street 0.090 0.090 0.090 0.090 0.090

SJV 3145 Bakersfield-Golden State Highway 0.090 0.090 0.090 0.090 0.090

SJV 3146 Bakersfield-5558 California Avenue 0.097 0.097 0.097 0.097 0.097

Los Angeles SC 2160 Pasadena-S Wilson Avenue 0.093 0.093 0.093 0.093 0.093

SC 2166 Pico Rivera 0.070 0.070 0.070 0.070 0.070

SC 2420 Reseda 0.106 0.106 0.106 0.106 0.106

SC 2429 North Long Beach 0.064 0.064 0.064 0.064 0.064

SC 2484 Azusa 0.094 0.094 0.094 0.094 0.094

SC 2492 Burbank-W Palm Avenue 0.089 0.089 0.089 0.089 0.089

SC 2494 West Los Angeles-VA Hospital 0.078 0.078 0.078 0.078 0.078

SC 2583 Lynwood 0.061 0.061 0.061 0.061 0.061

SC 2849 Glendora-Laurel 0.105 0.105 0.105 0.105 0.105

SC 2898 Pomona 0.100 0.100 0.100 0.100 0.100

SC 2899 Los Angeles-North Main Street 0.076 0.076 0.076 0.076 0.076

SC 3502 Santa Clarita 0.120 0.120 0.120 0.120 0.120

MD 3658 Lancaster-43301 Division Street 0.098 0.098 0.098 0.098 0.098

Orange SC 2249 La Habra 0.073 0.073 0.073 0.073 0.073

SC 2937 Costa Mesa-Mesa Verde Drive 0.073 0.073 0.073 0.073 0.073

SC 3265 Mission Viejo-26081 Via Pera 0.086 0.086 0.086 0.086 0.086

SC 3674 Anaheim-Pampas Lane 0.081 0.081 0.081 0.081 0.081

Riverside SS 2199 Palm Springs-Fire Station 0.104 0.104 0.104 0.104 0.104

SC 2525 Perris 0.098 0.098 0.098 0.098 0.098

SC 2596 Riverside-Rubidoux 0.112 0.112 0.112 0.112 0.112

SS 2878 Indio-Jackson Street 0.095 0.095 0.095 0.095 0.095

SC 2943 Lake Elsinore-W Flint Street 0.104 0.104 0.104 0.104 0.104

SC 3168 Banning Airport 0.119 0.119 0.119 0.119 0.119
C-4-9

San 

Bernardino

SC 2077 Redlands-Dearborn 0.123 0.123 0.123 0.123 0.123

SC 2221 San Bernardino-4th Street 0.116 0.116 0.116 0.116 0.116

SC 2266 Fontana-Arrow Highway 0.118 0.118 0.118 0.118 0.118

SC 2485 Upland 0.106 0.106 0.106 0.106 0.106

SC 2499 Crestline 0.127 0.127 0.127 0.127 0.127

MD 2650 Hesperia-Olive Street 0.104 0.104 0.104 0.104 0.104

MD 2830 Phelan-Beekley Road and Phelan Road 0.100 0.100 0.100 0.100 0.100

MD 2923 Barstow 0.085 0.085 0.085 0.085 0.085

MD 3124 Twentynine Palms-Adobe Road #2 0.085 0.085 0.085 0.085 0.085

MD 3152 Joshua Tree-National Monument 0.105 0.105 0.105 0.105 0.105

MD 3500 Victorville-14306 Park Avenue 0.094 0.094 0.094 0.094 0.094

San Diego SD 2040 San Diego-Overland Avenue 0.074 0.074 0.074 0.074 0.074

SD 2263 Escondido-E Valley Parkway 0.073 0.073 0.073 0.073 0.073

SD 2327 El Cajon-Redwood Avenue 0.070 0.070 0.070 0.070 0.070

SD 2368 Del Mar-Mira Costa College 0.070 0.070 0.070 0.070 0.070

SD 2460 Alpine-Victoria Drive 0.086 0.086 0.086 0.086 0.086

SD 2589 Chula Vista 0.064 0.064 0.064 0.064 0.064

SD 2933 Otay Mesa-Paseo International 0.065 0.065 0.065 0.065 0.065

SD 2964 San Diego-12th Avenue 0.060 0.060 0.060 0.060 0.060

SD 3198 Camp Pendleton 0.076 0.076 0.076 0.076 0.076

San Luis 

Obispo

SCC 2321 Morro Bay 0.056 0.056 0.056 0.056 0.056

SCC 2671 Grover City-Lesage Drive 0.058 0.058 0.058 0.058 0.058

SCC 2709 San Luis Obispo-Marsh Street 0.057 0.057 0.057 0.057 0.057

SCC 2955 Paso Robles-Santa Fe Avenue 0.071 0.071 0.071 0.071 0.071

SCC 2965 Atascadero-Lewis Avenue 0.068 0.068 0.068 0.068 0.068

SCC 3251 Nipomo-Regional Park 0.064 0.064 0.064 0.064 0.064

Santa Barbara SCC 2008 El Capitan Beach 0.067 0.067 0.067 0.067 0.067

SCC 2360 Lompoc-S H Street 0.056 0.056 0.056 0.056 0.056

SCC 2593 Santa Ynez-Airport Road 0.068 0.068 0.068 0.068 0.068

SCC 2954 Gaviota-GTC Site B 0.060 0.060 0.060 0.060 0.060

SCC 2957 Paradise Road-Los Padres National Forest 0.078 0.078 0.078 0.078 0.078

SCC 2992 Lompoc-HSandP 0.070 0.070 0.070 0.070 0.070

SCC 3003 Carpinteria-Gobernador Road 0.064 0.064 0.064 0.064 0.064

SCC 3023 Vandenberg Air Force Base-STS Power 0.069 0.068 0.068 0.068 0.069

SCC 3101 Las Flores Canyon #1 0.074 0.074 0.074 0.074 0.074

SCC 3153 Goleta-Fairview 0.064 0.064 0.064 0.064 0.064

SCC 3486 Santa Maria-906 S Broadway 0.053 0.053 0.053 0.053 0.053

SCC 3665 Santa Barbara-700 East Canon Perdido 0.064 0.064 0.064 0.064 0.064

Ventura SCC 2088 Ventura-Emma Wood State Beach 0.068 0.068 0.068 0.068 0.068

SCC 2880 Simi Valley-Cochran Street 0.091 0.091 0.091 0.091 0.091

SCC 2984 Thousand Oaks-Moorpark Road 0.083 0.083 0.083 0.083 0.083

SCC 2991 El Rio-Rio Mesa School #2 0.066 0.066 0.066 0.066 0.066

SCC 3172 Ojai-Ojai Avenue 0.090 0.090 0.090 0.090 0.090

SCC 3505 Piru-3301 Pacific Avenue 0.087 0.087 0.087 0.087 0.087
C-4-10

Table C-4-5. Current DVs (“Obs”) and Adjusted DVs based on Modeled Percent Differences MS1A vs. 

MS1

Ozone (ppm) MS1A vs MS1 Model Adjusted

1-Cell 9-Cell

County Basin SiteID Site Name Obs Adj Min Ave Max

Imperial SS 2551 El Centro-9th Street 0.084 0.084 0.084 0.084 0.084

SS 2997 Calexico-Grant Street 0.069 0.069 0.069 0.069 0.069

SS 3135 Calexico-Ethel Street 0.071 0.071 0.071 0.071 0.071

SS 3143 Westmorland-W 1st Street 0.079 0.079 0.079 0.079 0.079

SS 3173 Calexico-East 0.075 0.075 0.075 0.075 0.075

SS 3186 Niland-English Road 0.072 0.072 0.072 0.072 0.072

Kern SJV 2312 Edison 0.097 0.097 0.097 0.097 0.097

SJV 2772 Oildale-3311 Manor Street 0.096 0.096 0.096 0.096 0.096

SJV 2919 Maricopa-Stanislaus Street 0.091 0.091 0.091 0.091 0.091

SJV 2941 Arvin-Bear Mountain Blvd 0.113 0.113 0.113 0.113 0.113

SJV 2981 Shafter-Walker Street 0.090 0.090 0.090 0.090 0.090

MD 3121 Mojave-923 Poole Street 0.090 0.090 0.090 0.090 0.090

SJV 3145 Bakersfield-Golden State Highway 0.090 0.090 0.090 0.090 0.090

SJV 3146 Bakersfield-5558 California Avenue 0.097 0.097 0.097 0.097 0.097

Los Angeles SC 2160 Pasadena-S Wilson Avenue 0.093 0.093 0.093 0.093 0.093

SC 2166 Pico Rivera 0.070 0.070 0.070 0.070 0.070

SC 2420 Reseda 0.106 0.106 0.106 0.106 0.106

SC 2429 North Long Beach 0.064 0.064 0.064 0.064 0.064

SC 2484 Azusa 0.094 0.094 0.094 0.094 0.094

SC 2492 Burbank-W Palm Avenue 0.089 0.089 0.089 0.089 0.089

SC 2494 West Los Angeles-VA Hospital 0.078 0.078 0.078 0.078 0.078

SC 2583 Lynwood 0.061 0.061 0.061 0.061 0.061

SC 2849 Glendora-Laurel 0.105 0.105 0.105 0.105 0.105

SC 2898 Pomona 0.100 0.100 0.100 0.100 0.100

SC 2899 Los Angeles-North Main Street 0.076 0.076 0.076 0.076 0.076

SC 3502 Santa Clarita 0.120 0.120 0.120 0.120 0.120

MD 3658 Lancaster-43301 Division Street 0.098 0.098 0.098 0.098 0.098

Orange SC 2249 La Habra 0.073 0.073 0.073 0.073 0.073

SC 2937 Costa Mesa-Mesa Verde Drive 0.073 0.073 0.073 0.073 0.073

SC 3265 Mission Viejo-26081 Via Pera 0.086 0.086 0.086 0.086 0.086

SC 3674 Anaheim-Pampas Lane 0.081 0.081 0.081 0.081 0.081

Riverside SS 2199 Palm Springs-Fire Station 0.104 0.104 0.104 0.104 0.104

SC 2525 Perris 0.098 0.098 0.098 0.098 0.098

SC 2596 Riverside-Rubidoux 0.112 0.112 0.112 0.112 0.112

SS 2878 Indio-Jackson Street 0.095 0.095 0.095 0.095 0.095

SC 2943 Lake Elsinore-W Flint Street 0.104 0.104 0.104 0.104 0.104

SC 3168 Banning Airport 0.119 0.119 0.119 0.119 0.119
C-4-11

San 

Bernardino

SC 2077 Redlands-Dearborn 0.123 0.123 0.123 0.123 0.123

SC 2221 San Bernardino-4th Street 0.116 0.116 0.116 0.116 0.116

SC 2266 Fontana-Arrow Highway 0.118 0.118 0.118 0.118 0.118

SC 2485 Upland 0.106 0.106 0.106 0.106 0.106

SC 2499 Crestline 0.127 0.127 0.127 0.127 0.127

MD 2650 Hesperia-Olive Street 0.104 0.104 0.104 0.104 0.104

MD 2830 Phelan-Beekley Road and Phelan Road 0.100 0.100 0.100 0.100 0.100

MD 2923 Barstow 0.085 0.085 0.085 0.085 0.085

MD 3124 Twentynine Palms-Adobe Road #2 0.085 0.085 0.085 0.085 0.085

MD 3152 Joshua Tree-National Monument 0.105 0.105 0.105 0.105 0.105

MD 3500 Victorville-14306 Park Avenue 0.094 0.094 0.094 0.094 0.094

San Diego SD 2040 San Diego-Overland Avenue 0.074 0.074 0.074 0.074 0.074

SD 2263 Escondido-E Valley Parkway 0.073 0.073 0.073 0.073 0.073

SD 2327 El Cajon-Redwood Avenue 0.070 0.070 0.070 0.070 0.070

SD 2368 Del Mar-Mira Costa College 0.070 0.070 0.070 0.070 0.070

SD 2460 Alpine-Victoria Drive 0.086 0.086 0.086 0.086 0.086

SD 2589 Chula Vista 0.064 0.064 0.064 0.064 0.064

SD 2933 Otay Mesa-Paseo International 0.065 0.065 0.065 0.065 0.065

SD 2964 San Diego-12th Avenue 0.060 0.060 0.060 0.060 0.060

SD 3198 Camp Pendleton 0.076 0.076 0.076 0.076 0.076

San Luis 

Obispo

SCC 2321 Morro Bay 0.056 0.056 0.056 0.056 0.056

SCC 2671 Grover City-Lesage Drive 0.058 0.058 0.058 0.058 0.058

SCC 2709 San Luis Obispo-Marsh Street 0.057 0.057 0.057 0.057 0.057

SCC 2955 Paso Robles-Santa Fe Avenue 0.071 0.071 0.071 0.071 0.071

SCC 2965 Atascadero-Lewis Avenue 0.068 0.068 0.068 0.068 0.068

SCC 3251 Nipomo-Regional Park 0.064 0.064 0.064 0.064 0.064

Santa Barbara SCC 2008 El Capitan Beach 0.067 0.067 0.067 0.067 0.067

SCC 2360 Lompoc-S H Street 0.056 0.056 0.056 0.056 0.056

SCC 2593 Santa Ynez-Airport Road 0.068 0.068 0.068 0.068 0.068

SCC 2954 Gaviota-GTC Site B 0.060 0.060 0.060 0.060 0.060

SCC 2957 Paradise Road-Los Padres National Forest 0.078 0.078 0.078 0.078 0.078

SCC 2992 Lompoc-HSandP 0.070 0.070 0.070 0.070 0.070

SCC 3003 Carpinteria-Gobernador Road 0.064 0.064 0.064 0.064 0.064

SCC 3023 Vandenberg Air Force Base-STS Power 0.069 0.069 0.069 0.069 0.069

SCC 3101 Las Flores Canyon #1 0.074 0.074 0.074 0.074 0.074

SCC 3153 Goleta-Fairview 0.064 0.064 0.064 0.064 0.064

SCC 3486 Santa Maria-906 S Broadway 0.053 0.053 0.053 0.053 0.053

SCC 3665 Santa Barbara-700 East Canon Perdido 0.064 0.064 0.064 0.064 0.064

Ventura SCC 2088 Ventura-Emma Wood State Beach 0.068 0.068 0.068 0.068 0.068

SCC 2880 Simi Valley-Cochran Street 0.091 0.091 0.091 0.091 0.091

SCC 2984 Thousand Oaks-Moorpark Road 0.083 0.083 0.083 0.083 0.083

SCC 2991 El Rio-Rio Mesa School #2 0.066 0.066 0.066 0.066 0.066

SCC 3172 Ojai-Ojai Avenue 0.090 0.090 0.090 0.090 0.090

SCC 3505 Piru-3301 Pacific Avenue 0.087 0.087 0.087 0.087 0.087