## 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

a nd

Health and Exposure Assessment Branch

Research Division

Air Resources Board

California Environmental Protection Agency

Sacramento, California 95814



## April 2011

### C

- i 

# T ABLE OF 

# C ONTENTS

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 (O 3 

### ) and fine particulate

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

six scenarios of ocean

- going vessel (OGV) emission s . The six emission

scenarios varied based on :

• s hipping a ctivity levels within transit route s ;

• p otential r egulatory zone configuration s around the transit routes; and

• f uel types used within the regulatory zones

.

D etai led information o n the emission inventor ies used can be found in the

Emission s 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 difference s between each scenario

and baseline conditions

.



## II. Model Application

Model C onfiguration

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

yea r for the South Coast Air Quality Management District’s (SCAQMD) PM 2.5

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 (D

p 

### ) < 0.1 μm), f ine (0.1 μm <

D p 

### < 2.5 μm), and coarse (D

p 

### > 2.5 μm) particles sizes. Concentrations of PM 2.5

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

D p 

### less than 2.5 μm.



### Domain S etup

The modeling system utilizes a domain comp rised of a three

- dimensional grid

cell structure. The modeling domain covers the South Coast Air Basi n (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

- Che mistry 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 Californ ia Ozone Study (SCOS) M odeling D omain

S howing T errain C ontours

### C

- 3 

### Initial and B oundary C onditions

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 s hown in

Figure 1, the impact of boundary condition s (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

PM 2.5 , PM 2.5 nitrates, and PM 2.5 sulfates). For non

- OGV emissions, the year

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

same California Emissions Inv entory Forecast System (CEFS) version (1.06) of

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

their PM 2.5 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 non

tanker 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 Regu lation 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 Ame nded

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 Man agement 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 A merican E mission 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 dir ty 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 implem ented with proposed

regulatory boundary change. Vessels in pre

- regulation traffic patterns and

all vessels within the proposed amended regulatory zone, including those

in the Out er 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 outs ide 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 . T his is a pre

- regulatory 2005 inventory

, where

there is no OGV C lean F uel R egulation in place

. E mission scenario MS2

represents the anticipated emissions inventory from the original rule and was

produced by making adjustments to the b aseline 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 an d 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 speci es 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 estimate s. 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 engine s (ME) and auxiliary engine s (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 do main 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. t hese 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 a n 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 Rul e Initial Statement of

Reasons (ARB, 2008).

Table C

- 1 . Summary of the M odeling S cenarios.

Scenario

ID # Scenario 3,4 SB Channel Route

Status Rule Status 2 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) W ith 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 Note s :

1) In scenario MS1A

, it is assumed under the ECA Phase 1, vessels use HFO fuel with 1% s ulfur

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 us es 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 M S 6 MS1A MS4ws MS5 SCAB

Total

Emission *

(Tons/day)

NO

x 118.2 114.1 114.4 118.8 109.8 110.5 1205.6

SO

x 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 SO 4 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 MS 1, which includes

shipping emissions in scenario MS 1 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 (O 3 ) concentration, and the annual

average concentrations of PM 2.5 total, PM 2.5 sulfate (SO 4 ), and PM 2.5 nitrate

(NO 3 

### ).

The difference in gaseous and particulate concentrations between the baseline

scenario ( MS 1 ) 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 pre

clean

- fuel

- regulation conditions )

. Figures C

- 3 to C

- 7 provide a summary of the

modeling results in the for m 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 Rul e 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 Be ach 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 impa cts 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.

P remature deaths from cardiopulmonary causes associated with exposure to

P M2.5 were estimated using an approach based on a peer

- reviewed

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

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

described in Attachment C

- 3. Further details a re supplied in a rec ent 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 mode ling grid cell. T he 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., 20 09 ). 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 pre

regulation 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 35 0 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 t here 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 No rth 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

F or most of the scenarios, there is very little difference in on

- shore ozone

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

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

figures , the differences are relatively small ( +/

- 5%).

Attachment C

- 4 provides a summary of m odel

- simulated percent changes in

ozone concentrations applied to site

- specific, 2005 ozone design values.

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

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

immediately surrounding the monitoring station. T his 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 concen trations 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/pm

report_2010.pdf

.

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

model, U.S. EPA (1991). Resear ch 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 Distri ct, 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 R eport: 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

PM 2.5 

### 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

PM 2.5 

### 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

PM 2.5 

### 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

PM 2.5 

### 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

PM 2.5 

### concentrations (right). Only changes >1% and <

- 1% are shown in the plots.

### C

- 1

- 1 Attachment C

- 1. Ocean

- g oing 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

profil es is as follows:

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

Size Fraction

(by weight) PM 2.5 /TPM PM 10 /TPM

PM 119 0.937 0.96

PM 425 0.92 1.0

Weigh t

Fraction

(of PM

mass) PM 2.5 PM 10

EC OC SO 4 2

- others EC OC SO 4 2

- 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] 

### . T hese profiles were prepared for OGV

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

, and blend ed fuel,

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

modeling scenarios. The four profiles include:

Pro file 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 SO 4 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 weigh t fractions of EC, OC and SO 4 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 H 2 SO 4 ·6.5H2

O EC OC SO 4 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

SO 4 2

- 

### was estimat ed by multiplying the fuel consumption rate (195 g/kW

- hr) [6] 

### , fuel sulfur

content (1.0%), conversion rate of fuel sulfur to SO 4 2

- 

### (3%) [6] 

### , and molecular weight ratio

of SO 4 2

- 

### to sulfur. The weight fractions of EC, OC and SO 4 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 SO 4 2

- EC OC SO 4 2

-

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 SO 4 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 SO 4 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 H 2 SO 4 ·6.5H2O EC OC SO 4 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 SO 4 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 SO 4 2

- 

### (3%) [6]



### , and

molecular weight ratio of SO 4 2

- 

### to sulfur. The weight fractions of EC, OC and SO 4 2

- 

### were

then calc ulated 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 SO 4 2

- EC OC SO 4 2

-

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 PM 2.5 

### only, and it was assumed that same speciation profiles can be used for PM 10 

### and TPM

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

PM 2.5 

### and PM 10 

### size fractions as PM 119; PM 4251 and PM 1193 have the same PM 2.5



### and PM 10 

### 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) PM 2.5 /TPM PM 10 /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) PM 2.5 Fraction PM 10 Fraction

EC OC SO 4 2

- others EC OC SO 4 2

- 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 PM 2.5 Fraction PM 10 Fraction

EC OC SO 4 2

- others EC OC SO 4 2

- 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 Resource s 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 Ocean

Going 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

- g oing Vessel (OGV) Gridded Emission s I nventory

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 w ithin 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 MS 1

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

MS 1 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 MS 2

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

MS2 NOx SOx PM2.5 MS 2

- 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 MS 2

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 MS 6

Table 4. Scenario MS 6 NOx, PM25, and SOx emission totals by domain and differences from Scenario MS 1 and Scenario

MS 2

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 emission s for Scenario MS4ws

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

Scenario MS1 and Scenario MS 2

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 fol lowing link: http://www.arb.ca.gov/p orts/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 censu s 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 populatio n 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 a pplied 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 mod el 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 reco rded. 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 over lap 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:

Co unty 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 fu nction 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 coh ort includes both men and women, and enrollment was

not dependent on underlying health status. It includes data from cities from across the U.S. PM 2.5

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 w as 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 interpol ation 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 s patial 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 ar eas 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 C ompared 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

- g oing 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

, observ ation

- based design value s (DV current 

### ) and the model

- adjusted design values (DV adjusted 

### ) . 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 MS 1). For example:

Current DV * (Modeled Ozone MS2 

### - Modeled Ozone MS1 

### ) / Modeled Ozone MS1 

### = 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