# FINAL 2007 AQMP

APPENDIX V

MODELING AND ATTAINMENT DEMONSTRATIONS

JUNE, 2007

# SOUTH COAST AIR QUALITY MANAGEMENT DISTRICT

GOVERNING BOARD

Chairman: WILLIAM A. BURKE, Ed.D. Speaker of the Assembly Appointee

Vice Chairman: S. ROY WILSON, Ed.D. Supervisor, Fourth District Riverside County Representative

MEMBERS:

MICHAEL D. ANTONOVICH Supervisor, Fifth District Los Angeles County Representative

BILL CAMPBELL Supervisor, Third District Orange County Representative

JANE W. CARNEY Senate Rules Committee Appointee

RONALD O. LOVERIDGE Mayor, City of Riverside Cities Representative, Riverside County

GARY OVITT Supervisor, Fourth District San Bernardino County Representative

JAN PERRY Councilmember, City of Los Angeles Cities Representative, Los Angeles County, Western Region

MIGUEL A. PULIDO Mayor, City of Santa Ana Cities Representative, Orange County

TONIA REYES URANGA Councilmember, City of Long Beach Cities Representative, Los Angeles County, Eastern Region

VACANT Governor’s Appointee

DENNIS YATES Mayor, City of Chino Cities Representative, San Bernardino County

EXECUTIVE OFFICER:

BARRY R. WALLERSTEIN, D.Env.

.

# i 

# CONTRIBUTORS

Elaine Chang, DrPH

Deputy Executive Officer

Planning, Rule Development and Area Sources

Laki Tisopulos, Ph.D., P.E.

Assistant Deputy Executive Officer

Planning, Rule Development and Area Sources

Joseph Cassmassi

Planning & Rules Manager

Planning, Rule Development and Area Sources

Kevin Durkee Chris Nelson

Mark Bassett Zorik Pirveysian

Kathy Hsiao Jill Whynot

Bong-Mann Kim Susan Yan

Satoru Mitsutomi Zhang, Xinqiu

Production

Faye Thomas

Donna Vernon

CALIFORNIA AIR RESOURCES BOARD

Linda Murchison

Division Chief

Technical Support Division

John DaMassa

Branch Chief Kurt Karperos

Branch Chief

Bruce Jackson Sylvia Oey

LucilleVan Ommering

# Table of Contents

i 

# Table of Contents

CHAPTER 1 Modeling Overview

Introduction ........................................................................................................... V-1-1

Attainment Designation and Control Strategy........................................... V-1-1

Model Selection ......................................................................................... V-1-2

Modeling Met hodology ........................................................................................ V-1-4

Design Values and Relative Re sponse Factors (RRF) .............................. V-1-4

PM2.5......................................................................................................... V-1-6

Annual PM2.5 Modeli ng Approach ............................................... V-1-7

Episodic 24-Hr Average PM2. 5 Modeling Approach.................... V-1-8

PM10.......................................................................................................... V-1-9

Ozone ......................................................................................................... V-1-10

Modeling Appr oach........................................................................ V-1-10

Application of RRF’s...................................................................... V-1-12

Uncertainties Associated with the Technical An alysis......................................... V-1-12

Demographic and Growth Projecti ons....................................................... V- 1-13

Input Elements to Air Quality Mode ls....................................................... V-1-13

Ambient Air Quality M onitoring Da ta ...................................................... V-1-13

Meteorological Measurements................................................................... V-1-13

Emissions Inve ntory .................................................................................. V-1-14

Air Quality M odels .................................................................................... V-1-14

Are There Any Safeguards Agai nst Uncertainties?................................... V-1-14

Document Organi zation ........................................................................................ V-1-15



# CHAPTER 2 The Federal PM2.5 Attainment Demonstration Plan

Introduction ........................................................................................................... V-2-1

PM2.5 Data: MATES -III and FRM.......................................................... V-2-2

MATES-III Monito ring .................................................................. V-2-2

MATES-III Speciate d Data ............................................................ V-2-3

FRM PM2.5 .................................................................................... V-2-10

Sandwich and Speciated Monitore d Attainment (SMAT).................................... V-2-12

Sandwich.................................................................................................... V-2-12

SMAT ........................................................................................................ V-2-15

CAMx and MM5 Ov erview.................................................................................. V-2-15

Modeling Doma in ...................................................................................... V-2-15

Boundary, Top C onditions ......................................................................... V-2-16

# Final 2003 AQMP Appendix V

ii MM5 Simulati ons ...................................................................................... V- 2-18

Emissions Inve ntory.............................................................................................. V-2-19

Paved Road Dust Emissions Uncertainties................................................ V-2-22

PM2.5 Split Profiles and Ammonia Inventory Adjustments..................... V-2-23

Base-Year Annual Si mulations ............................................................................. V-2-24

PM2.5 Component Species Performance Evaluation for the

MATES-III Site s................................................................................. V-2-25

Annual Average SSI Mass Perfo rmance Evaluation................................. V-2-47

Base Year Model Performance Stress Test Evaluation ............................. V-2-47

Future Air Quality................................................................................................. V-2-48

SMAT Annual PM2.5 Attainme nt Demonstration.................................... V-2-52

2015 CAMx Grid-Cell Evaluation............................................................. V-2-54

SMAT 24-Hour PM2.5 Attainme nt Demonstration .................................. V-2-54

Weight of Ev idence............................................................................................... V-2-59

Air Quality Tr ends ..................................................................................... V-2-59

Emissions Tr ends....................................................................................... V-2-60

Mobile Source Emissi ons and VM T.......................................................... V-2-62



# CHAPTER 3 The Federal 24-Hour PM10 Attainment

Demonstration Plan and Visibility Assessment

Introduction ........................................................................................................... V-3-1

Modeling Met hodology ........................................................................................ V-3-1

Future Year Air QUality ....................................................................................... V-3-2

PM10 24-hour Attainment Demonstration ................................................ V-3-2

PM10 Annual Anal ysis.............................................................................. V-3-2

Visibility................................................................................................................ V-3-4

Background ................................................................................................ V-3-4

Visibility Modeling.................................................................................... V-3-4

Prior Visibility Mode ling Results.............................................................. V-3-5

Predicted Future Air Quality...................................................................... V-3-6

Future Visibility Projections ...................................................................... V-3-6

Future Light Extinction Budge ts at Riverside ................................ V-3-8

# Table of Contents

iii 

# CHAPTER 4 Revision to the 2003 Ozone Attainment

Demonstration Plan

Introduction ........................................................................................................... V-4-1

Model Selection ......................................................................................... V-4-2

Modeling Appr oach ................................................................................... V-4-3

Federal 8-Hour Ozone Sta ndard Requireme nts......................................... V-4-4

California Requirements and P opulation Exposur e................................... V-4-6

Emissions Summary.............................................................................................. V-4-8

Introduction ................................................................................................ V-4-8

Historical Baseline Emissions ................................................................... V-4-9

Future Controlled Emissions ..................................................................... V-4-10

Episode Selec tion.................................................................................................. V-4-11

Conceptual Model of an 8- Hour Ozone Ep isode....................................... V-4-12

Statistical Episode Characte rization and Ra nking..................................... V-4-14

Model Input Prep aration ............................................................................ V-4-15

Boundary and Top Air Quality Concentra tions......................................... V-4-17

Future Boundary, Top and Initial Air Quality C onditions ........................ V-4-18

Meteorological Models .............................................................................. V-4-19

Base-Year Perfomance Evaluation ....................................................................... V-4-24

Statistical Eval uation ................................................................................. V-4-25

Graphical Eval uation ................................................................................. V-4-32

Effect of Emissions Uncertainties.............................................................. V-4-44

Ozone Air Quality Projections .............................................................................. V-4-45

Graphical Distri bution ............................................................................... V-4-47

Projection of 2018 Air Quality in the Coachella Valley ........................... V-4-51

Sensitivity Studies: Model Pe rformance and CEQA............................................ V-4-52

Weight of Ev idence............................................................................................... V-4-54



# CHAPTER 5 Summary and Conclusions

Comparison to State and Fe deral Standa rds ......................................................... V-5-1

Basin Emissions Carrying Capac ity (Emissions Budget)..................................... V-5-1

REFERENCES

# CHAPTER 1

MODELING OVERVIEW

Introduction

Modeling Methodology

Uncertainties Associated with the Technical Analysis

Document Organization

# Chapter 1 Modeling Overview



# INTRODUCTION

This appendix to the Final 2007 AQMP provides the details of the modeling attainment

demonstrations presented in Chapter V of the main document. The federal Clean Air

Act (CAA) sets forth specific criteria to use air quality simulation modeling techniques

to estimate future air quality in areas that do not meet the air quality standards. This

Final 2007 AQMP provides future year atta inment demonstrations for two new

pollutants: 8-hour average ozone and both annual and 24-hour average PM2.5.

The South Coast Air Basin (Basin) is curre ntly designated nonattainment for PM2.5,

ozone (8-hours), PM10 (24-hour s) and carbon monoxide. On February 24, 2006, CARB

forwarded the District’s request to U.S. EP A to redesignate the Basin attainment for

carbon monoxide. Air quality monitori ng data measured from 2001 through 2005

indicated that the standard had been achieved and currently continues to be met. Future

year projections of CO provided in the 2003 AQMP a nd projections from CARB’s

EMFAC2002 emissions model were used to support the redesignation request and

provide the basis for a CO maintenance plan for the Basin. EPA’s final approval of the

redesignation request is currently pending.

Similarly, on October 17, 2006, the Federal Register codified EPA’s decision revoking

the annual PM10 standard. The action left the 24-hour average PM10 standard in place.

The Basin has not experienced any violations of 24-hour av erage PM10 standard except

during a few special events. The District has yet to seek redesignation to attainment for

PM10 however it will pursue such a request in the near future. Regardless, the Final

2007 AQMP will provide an updated attainme nt demonstration for 24-hour average

PM10 to serve as the basis for a future maintenance plan.

The 2003 modeling attainment demonstrations served as an update of the 1997 AQMP

ozone, PM10 and carbon monoxide plans for the South Coast Air Basin and other

portions of the Southeast Desert Modified Nonattainment Area that are under the

District’s jurisdiction and we re submitted as part of the California SIP. The Final 2007

AQMP provides attainment de monstrations for 8-hour oz one and PM2.5 and provides

similar linkage to the 2003 1-hour ozone and PM10 attainment dem onstrations. This

plan reflects the updated emissions baseline and future year estimates, new technical

information and enhanced air quality modeling techniques and episodes.



# Attainment Designation and Control Strategy

The Basin is currently designated nona ttainment for PM2.5, and severe-17

nonattainment for ozone. The District will request that U.S. EPA accept a voluntary

reclassification for the Basin from “Severe- 17” to “Extreme” nonattainmnet through the

Governing Board’s adoption of this Final AQM P and resolution. This action will enable

V - 1- 1

# Final 2007 AQMP Appendix V: Modeling and Attainment Demonstrations

the use of long-term measures in the contro l strategy and extend the attainment date to

June 15, 2024. In addition, the District w ill request that U.S. EPA accept a voluntary

reclassification for the Coachella Valley portion of the Salton Sea Air Basin from

“Serious” to “Severe-15” nonattainmnet to exte nd the attainment date to June 15, 2018.

PM2.5 and ozone, are linked to common precur sor emissions. The District’s goal is to

develop an integrated control strategy whic h: 1) ensures that ambient air quality

standards for all criteria pollutants are met by the established deadlines in the federal

Clean Air Act (CAA); and 2) achieves an expe ditious rate of reduction towards the state

air quality standards. The overall control stra tegy is designed so that efforts to achieve

the standard for one criteria pollutant do not cause unnecessary deterioration of another.

A two-step modeling process has been conducte d for the 2007 AQMP. First, future year

annual and 24-hour average PM2.5 is simulate d to demonstrate atta inment by 2015. The

future year 8-hour average ozone emissions control strategy then builds upon the PM2.5

strategy to demonstrate attainment of the federal standard in the downwind Coachella

Valley by 2018 and the Basin by 2024. This two-step approach is described in Chapter

4 of the main document and the control measures are extensively discussed in

Appendices IVA, IVB and IVC. The two-step approach is also consistent with the

approach used in the 2003 AQMP to first de monstrate attainment in 2006 of the PM10

standard and subsequent attainment of th e 1-hour average ozone standard in 2010.



# Model Selection

During the development of the 2003 Plan, the District convene d a panel of seven experts

to independently review the regional ai r quality modeling conduc ted for ozone and

PM 10 

# . The focus of the panel’s review was to provide guidance in the selection of an

appropriate meteorological-air quality dispersi on platform for the attainment analysis.

At that time, District and CARB modeling st aff were evaluating three potential models

for application using SAPRC99 chemistry: California Photochemical Grid Model

(CALGRID) [Yamartino, et. Al, 1989], the Comprehensive Air Quality Model with

Extensions (CAMx) [Envir on, 2002], and the Urban Ai rshed Model (UAM) [EPA,

1990]. The performance of the three models varied with only UAM displaying the

capacity to closely recreate the peak 1-hour average ozone concentr ations observed for

the August 5, 1997 meteorological episode. The performance of the CAMx and

CALGRID simulations was similar a nd although they under-predicted peak

concentrations, model output provided a better characterization of the spatial distribution

of ozone in the Basin.

In general, the recommendations of the pa nel members supported the use of the UAM

modeling platform for the 2003 attainment demonstrations, primarily based upon the

District staff’s familiarity with the model and that goal of recrea ting the regional peak

ozone concentrations was critical. They also recommended that a relative reduction

V- 1 - 2

# Chapter 1 Modeling Overview

approach be applied to the performance of CAMx and CALGRID to see if future year

emissions reductions would be consistent with the UAM proj ected rates of reduction.

Most important, the consensus of the panel was for the District to move from UAM to

the more current state-of-the-art disper sion platforms and chemistry modules. Among

the recommended candidates were the Community Multiscale Air Quality Model

(CMAQ) [USEPA, 1999] and CAMx both coupl ed with SAPRC99 chemistry and the

prognostic Pennsylania State University / Na tional Center for Atmospheric Research

Mesoscale Model Version 5 (MM5) [Grell, et. al., 1994]. Both CAMx and CMAQ can

simulate ozone and PM2.5 concentrations together in a “one-atmosphere” approach and

in response to the expert panel recommendati ons, District and CARB staff has selected

CAMx as the primary regional dispersion modeling platform for the attainment

demonstrations.

Table V-1-1 provides a summary comparison of the modeling technology used in the

2003 and Final 2007 AQMP’s.

TABLE V-1-1

Comparison of Modeling Methodologies used in the 2003 and Final 2007 AQMP

Mechanism Ozone PM2.5

2003 AQMP 

# Final 2007

AQMP 2003 AQMP 

# Final 2007

AQMP

Dispersion

Platform UAM-IV CAMx UAM-IV CAMx

Chemistry SAPRC99 SAPRC99 AERO-LT/

CB-IV PMCAMx

“One

Atmosphere”

Meteorology CALMET/

Hybrid MM5/FDDA Diagnostic

Wind Model MM5

Mobile

Emissions EMFAC2002 EMFAC2007 EMFAC2002 EMFAC2007

Boundary EPA “Clean”/

SCOS97 WRAP-CAMxGEOCHEM Modified EPA

“Clean” WRAP-CAMxGEOCHEM



# The following sections provi de a brief overview of the PM2.5, PM10 and ozone

modeling methodologies. Wherev er possible, the Draft Mode ling Protocol will be used

V - 1- 3

# Final 2007 AQMP Appendix V: Modeling and Attainment Demonstrations

as a reference document to avoid duplicati ng presentation material. Draft Modeling

Protocol is included in this Appendix as Attachment 1.



# MODELING METHODOLOGY

Design Values and Relative Response Factors (RRF)

The Final 2007 AQMP modeling appr oach to demonstrate attainment of the air quality

standard relies heavily on the use of design values and relative response factors (RRF,

previously referred as relative reduction factors) to translate regional modeling

simulation output to the form of the air qua lity standard. Both ozone and PM2.5 have

standards that require three consecutive year s of monitored data, averaged by a designed

form, to assess compliance. In the case of ozone, compliance to the standard is

determined from a three year average of the 4 th 

# highest daily ozone 8-hour average

concentration. The PM2.5 annual design va lue is determined from quarterly average

PM2.5 concentrations, averaged by year, fo r a three year period. For the 24-hour

average PM2.5 design value, the 98 th 

# percentile daily concentr ation sampled from a year

is selected and then averaged for a three year period. The complexity of the design

values does not lend itself to a direct attain ment demonstration that relies on explicit air

quality model simulation predictions of fu ture air quality base d on one or several

meteorological episodes.

Design Value Selection

EPA guidance recommends the us e of multiple year averages of design values, where

appropriate, to dampen the effects of single ye ar anomalies to the air quality trend due to

factors such as adverse or extremely favorable meteorology or radical change in the local

emissions profile. For Basin 8-hour average ozone, the trend of the design values, each

calculated using 3-years of data (depite d in Figure V-1-1a.) is relative unchanged

between 2001 and 2005. Given this configuration, a three-year weighted average of the

design values is representative of the de sign value centered around 2002, the preferred

year for the baseline inventory developmen t and is used in the ozone attainment

demonstration.

The trend in the Basin PM2.5 design values (a lso calculated using 3-years of data) from

2001 through 2005 (Figure 5-1b) is significantly different from ozone, depicting a sharp

reduction in concentration over the peri od. The design value for 2001 is 30.1 μ g/m3

while the 2005 design value (based on data from 2003, 2004 and 2005) is 22.6 μ g/m3.

V- 1 - 4

# Chapter 1 Modeling Overview

110 115 120 125 130 135

2001 2002 2003 2004 2005Ozone Concentration (ppb)



# FIGURE V-1-1a

South Coast Air Basin 8-H our Ozone Design Values

(Each value represents the 3-year average of the 4 th 

# highest ozone concentration)

20 22 24 26 28 30 32

2001 2002 2003 2004 2005PM2.5 Concentration (ug/m3)



# FIGURE V-1-1b

South Coast Air Basin Annual PM2.5 Design Values

(Each value represents the 3-year average of the highest annual average PM2.5 concentration)



# The reduction of seven and one half micrograms per cubic meter occurred for the same

meteorology as the ozone desi gn trend. Similar reductions can be observed in the

component contributions of nitrate and su lfate in the PM10 FRM data over the same

period. Since the trend in PM2.5 is stea dily moving in the direction of air quality

improvement, it is more reasonable to use a re presentative design value that is not locked

in a multiple year average that overly reflects data that are not consistent with the current

air quality trend. The 2005 de sign value includes the speciated data (monitored in 2005)

that is used in the attainment demonstra tion. Furthermore, if the preliminary 2006

PM2.5 data are included in the analys is, the 2006 PM2.5 design would value 20.7 μ g/m 3



# . The revise weighted design value ce ntered around 2005 (including data from 2003

V - 1- 5

# Final 2007 AQMP Appendix V: Modeling and Attainment Demonstrations

through 2006) would be 22.7 μ g/m 3 

# , essentially the same value as the 2005 design of

22.6 μ g/m 3 

# . To reflect the ambient trend of PM2. 5 and preserve data consistency, the

PM2.5 attainment demonstration is based on the 2005 design value.

Relative Response Factors and Future Year Design Values

To bridge the gap between air quality model output evalua tion and applicability to the

health based air quality sta ndards, EPA guidance (EPA, 2006) has proposed the use of

relative response factors. The RRF is simply a ratio of future year predicted air quality

with the control strategy fully implemented to the simulated air quality in the base year.

The attainment demonstrati on consists of multiplying the non-dimensional RFF to the

base year design value to pr edict the future year design value. Thus, the simulated

improvement in air quality, based on one or mo re meteorological episodes, is translated

as a metric that directly de termines compliance in the form of the standard. EquationsV1 and V-2 summarize the calculation.

Eq. V-1.

RRF i 

# = Future-Year Model Prediction i 

# / Base-Year Model Prediction i



# where i is the pollutant or species

Eq V-2.

Attainment Demonstration

= 

# Σ RRF i 

# X Design Value i 

# ≤ Air Quality Standard

The modeling analyses descri bed above use the RRF and design value approach to

demonstrate future year attainment of the standards.



# PM2.5

The Final 2007 AQMP employs CAMx using the “one atmosphere” approach comprised

of the CB-IV gas phased chemistry and a static two-mode particle size aerosol module as

the particulate modeling platform. The an alysis follows EPA’s recommended speciated

modeling attainment test (SMAT), whereby m odel simulations for the base and futureyear controlled emissions are used to genera te RRFs at selected sites where monioting

data is available for individual species. The site and species specific RRFs are

calculated on a quarterly basis and then applie d to quarterly desing values to determine

attainment. The procedure is significant departure from the 2003 AQMP where a direct

V- 1 - 6

# Chapter 1 Modeling Overview

deterministic approach was used to direc tly calculate future year PM2.5 from model

output.

In the 2003 AQMP the UAMAERO-LT model wa s used to simulate annual average

Basin concentrations of PM2.5 and PM10. UAMAERO-LT model was a simplified

version of the UAM-AERO model. The detailed thermodynamic routine (ISOROPIA)

of the UAM-AERO model was replaced with the parameterized inorganic gas/aerosol

partitioning module. The sec ondary organic aerosol formation scheme was replaced

with a condensed version of the Carnegie Melon University (CMU) secondary organic

aerosol module. The CMU m odule treats organic products as semi-volatile species and

employs an equilibrium appro ach to the gas/aerosol partiti oning of these species. In

addition, the detailed partic le-sizing scheme used in the UAM-AERO model was also

replaced by an observation-based, two size (fi ne and coarse) particle-sizing scheme for

secondary aerosols. UAMAERO-LT utilized a full Carbon Bond IV gas-phase chemical

mechanism to simulate the formation of par ticulate nitrate, sulfate, ammonium, organic

carbon, elemental carbon and other primary par ticles. By implementing the fine and

coarse particle-sizing scheme for sec ondary aerosols, the 2003 AQMP was able to

provide a first look at future year PM2.5 and the initial required emissions reductions

that would be needed to attain the proposed federal standard.

The preliminary PM2.5 modeling approach crafted for the 2007 AQMP was to move the

empirical AERO-LT chemistry from the UAM to CAMx to take advantage of the

advanced dispersion platform. Parallel testing was conducted to evaluate the

CAMx/AERO-LT performance against CAMx using the “one atmosphere” approach

comprised of the the CB-IV ch emistry and a static two-mode particle size aerosol

module. The results of the analysis indicat ed that the two model/chemistry packages

were performing similarly and that the sp eed of simulating an annual average using

CAMx “one atmosphere” was approximate ly equal to that of the AERO-LT

combination. As a consequence, the PM2.5 modeling approach shifted to the use of the

CAMx “one atmosphere” as the primary tool.

Annual PM2.5 Modeling Approach

In the Final 2007 AQMP, CAMx annual aver age PM2.5 modeling simulations were

generated for 2005, 2014 a nd 2020 baseline emissions and 2014 and 2020 controlled

emsissions scenarios. The 2005 CAMx simulation was conduc ted using baseline

monthly temperature and humidity corrected emissions, for a weekday, Saturday and

Sunday activity profile. Seas onal boundary conditions were extracted from the Western

Regional Air Partnership (W RAP) regional modeling simulations (initialized from

global air quality model output) in support of the Regional Haze Rule demonstrations.

The simulations were driven by MM5 meteorol ogical fields; five day-simulations with a

one day “ramp-up” period using NC EP model inintialzation.

V - 1- 7

# Final 2007 AQMP Appendix V: Modeling and Attainment Demonstrations

CAMx simulations used the same region (5 km squared grid , 280 easting and 3650

northing, 65 by 40 grid cells) as that us ed for the 2003 UAMAERO-LT analyses. The

vertical structure was increased to 11 layers (compared with the 5-layer analysis of

UAMAERO-LT), but less than the 19 layers used for the MM5 simulati ons in an effort

to conserve computational resources. MM5 was used to generate the meteorological

profile for each day in 2005. The MM5 simulations were generated for the larger

SCOS97 modeling domain employing a 5 km squa re grid and fit to the smaller PM2.5

grid. The MM5 simulations were initialized from NCEP analyses and run for 5-day

increments without the four-dimensi onal-data-assimilation (FDDA) option.

Speciated PM2.5 data measured from the District’s Multiple Air Toxic Evaluation

Program (MATES-III) during 2005 provided the characterization for evaluation and

validation of the CAMx annual and episodic demonstrations. A brief summary of the

MATES-III field program and a detailed description of the data is provi ded in Chapter 2.

Model performance was evaluated against monitored particulate PM2.5 air quality data

for six species (ammonium, nitrates, sulfat es, organic carbon, elemental carbon, and

primary) and total particulate mass. Annual data from eight MATES-III monitoring

sites, including Los Angeles, Anaheim, W ilmington, Long Beach, Compton, Burbank,

Rubidoux, and Fontana, were used in the va lidation. The future year attainment

demonstration was analyzed for 2014 contro lled emissions, thus enabling an annual

demonstration based on a contro l strategy that would be fully implemented by January 1,

2015.

Future year PM2.5 air quality (2014 and 2020) was determined using site and species

specific RRF’s applied to 2005 PM2.5 design values per EP A guidance documents. The

quarterly RRF’s were calculated from the controlled 2014 simulation and the 2005

baseline simulation. The de sign values were determined from the federal reference

method Size Selective Inlet (SSI) High-Vol PM2. 5 data measured at the District’s air

monitoring network from 2003-2005. The SSI PM 2.5 design values were calculated by

quarter then apportioned by sp ecies based on the distribution observed in the MATES-III

data.

Episodic 24-Hr Average PM2.5 Modeling Approach

Per PM2.5 guidance, two options are provided to determine RRFs for the future year 24-

hour average PM2.5 attainment demonstration. The first option uses episodic modeling

with day-specific emissions for representativ e meteorological episodes to calculate RRFs

and apply the RRF to the design value. The second approach pr oposed by EPA relies on

an average response to implem entation of emissions control for the top 25 percentile of

days in each quarter of the annual model simulation.

The maximum 24-hour PM2.5 design value (based on 2003-2005 data) for the Basin

(64.8 μ g/m 3 

# ) meets the current federal standard. Of great interest is how will the 24-

V- 1 - 8

# Chapter 1 Modeling Overview

hour PM2.5 concentration fair comp ared to the new standard of 35 μ g/m 3 

# when that

standard become effective in 2010. On the basis of our initial simulations and analysis,

the District staff feels that the future design calculation based on the top 25 percentile

day, quarterly is more conservative than the episodic modeling. AQMD recommends

the use of the 25 percentile approach.

Episodic Simulations

The first approach to determine future year 24-hour maximum or 98 th 

# percentile PM2.5

impacts relied on the simulation of one or more representative peak PM2.5 epsiodes

where observed concentrations exceed 65 μ g/m 3 

# . The peak PM2.5 24-hour average

concentration observed in the Basin during the 2005 MATES-III monitoring program

(110 μ g/m3 at Rubidoux) occurred on October 22, 2005. Episode specific emissions for

the peak and preceding days were temper ature and humidity corrected and MM5/FDDA

simulations were generated to provide the meteorological imput.

Quarterly Top 25 Percentile

For this approach, the 2005 observational data are sorted by quarter of year and further

into the top 25 percent of days in each quarter. PM2.5 RRFs are calculated on a

quarterly basis from the future and base year annual simulations fo r only those days in

the top 25 percentile per quarter. The quart erly RRFs for the “top 25 percent days” are

then applied to the quarterly 24-hour averag e PM2.5 design values to develop quarterly

future year design values which are later a ggregated into an annua l 24-hour future year

design value to assess attainment. (The measured quarterly 24-hour average PM2.5

design values were comprised of the 98 th 

# percentile data in each quarter for the years

2003, 2004 and 2005). Several vari ations of the episodic and quarterly top 25 percentile

future year calculations were conducted and are presented and discussed in Chapter 2.



# PM10

As previously discussed, on September 21, 2006 the U.S. EPA administrator signed the

final documents that eliminat ed the existing annual PM10 standard. The action retained

24-hour PM10 standard at its existing concentration of 150 μ g/m 3 

# . The form of the 24-

hour PM10 standard allows fo r one violation of the sta ndard annually. The Basin

currently meets the 24-hour average federal standard however, no petition to EPA to redesignate the Basin as attainment status has been submitted. (The only days that exceed

the standard are associated with high wind natural events or exceptional events due to

wildfires).

For this analysis, the annual second maximum concentration is used for the attainment

demonstration (given the standard allows for one violation annually). RiversideRubidoux has been the PM10 24-hour design site in nine of the past ten years when high

V - 1- 9

# Final 2007 AQMP Appendix V: Modeling and Attainment Demonstrations

wind days have been excluded from the anal ysis. The 2005 design value at Rubidoux is

86 percent of the federal standard. The sta ndard attainment dem onstration is conducted

to assure that the Basin will continue to be in compliance in future years.

As a conservative analysis, only emissions re ductions associated with the PM2.5 portion

of the 24-hour PM10 concentrati on are assumed to be impacted by future year emission

controls. Future year predictions of maximum and second ma ximum 24-hour average

PM10 are calculated using the site specific annual average PM2.5 RRFs applied only to

the PM2.5 portion of the PM10 design conc entration. The average PM2.5 RRFs

calculated from the eight sites, for 2005 to 2014, are applied to the fine portion of the 24-

hour PM10 distribution for sites other th an the MATES-III, which do not have the

PM2.5 speciation. The coarse portion of the PM10 is assumed to be held constant in this

analysis. The predicted reductions to the fi ne portion are then adde d to the coarse to

estimate a 2015 second maximum PM10 24- hour average concentration.



# OZONE

The CAA requires that ozone nonattain ment areas designated as serious and above use a

photochemical grid model to demonstrate a ttainment. CAMx was selected as the

modeling tool used in the Final 2007 AQMP oz one modeling attainment demonstration.

CAMx is an urban scale, thre e-dimensional, grid-type, numeric al simulation model. For

the Final 2007 AQMP, CAMx has been coupled with SAPRC99 gaseous chemistry for

the ozone attainment demonstration. A lthough CAMx was not used as the primary

modeling tool, CAMx simulations provi ded supporting documen tation for the 2003

AQMP ozone attainment demonstr ation. In addition, as pr evouly discussed, CAMx is

one of the modeling platforms recommended by the peer review.

Modeling Approach

CAMx simulations were conduc ted using a Lambert Confroma l projection over the 5 km

squared grid SCOS97 modeling domain. Th e modeling analyses were run using 16

vertical layers up to 5000 m above ground level.

CAMx simulations were generated for six meteorological episode s including one period

in 2004, four periods in 2005 and one in 1997. Table V-1- 2 provides a comparison for

the meteorological episodes evaluated in the current and preceding attainment

demonstrations. The August 1997 SCOS97 meteor ological episode was retained for this

analysis to provide a bridge from the 2003 AQMP attainment demonstration. The five

episodes observed in 2004 a nd 2005 occurred during MATES-III, a period of enhanced

air quality monitoring in the Basin. S upporting MATES-III, the District operated three

radar wind profilers in the Basin, with radio acoustic sounding systems and also

enhanced its Photochemical Assessment M onitoring Stations (PAMS) programs.

V- 1 - 10

# Chapter 1 Modeling Overview

Additional profiler data was obtained from operating sites in Ve ntura and San Diego

Counties.

TABLE V-1-2

Comparison of Ozone Meteorological Ep siodes used in the 2003 and Final 2007

AQMPs

2003 AQMP Final 2007 AQMP

August 4-7, 1997 August 4-7, 1997

August 4-8 , 2004

May 17-24, 2005

July 14-19, 2005

August 2-9, 2005

August 25-29, 2005

Selection of episodes from 2004 and 2005 was also made to avoid the fuel commingling

associated with the Phase III California Fuel Reformulation where the primary

oxygenate was changed from MTBE to et hanol. Commingling of ethanol and nonethanol based fuels leads to enhanced evapor ative VOC emissions and thus more ozone.

Quantification of the amount of commingling taking place on a daily or episodic basis

was nearly impossible. Implementation of the fuel switch from MTBE to ethanol took

place in California during 2003 and was assu med to be completed by December 31,

2003. Selecting meteorological episodes pos t 2003 reduced the uncertainty associated

with the estimation of the VOC emi ssions inventory due to commingling.

The meteorological fields used for the CAMx ozone simulations we re generated using

MM5 with the FDDA option. The meteorol ogical fields were developed using a

Lambert Conformal grid adapted for th e the SCOS97 modeling domain. MM5 was

simulated using 34 vertical layers and si mulations were initialized using the NCEP

global weather forecast model analysis. The MM5 fields were post-processed to layeraveraged winds to the levels defined for the CAMx simulations and to adjust coordinates

to the UTM system.

Day-specific point, mobile and area emissi ons inventories were generated for each

meteorological episode. Mobile source emi ssions were temperature corrected by grid

V - 1- 11

# Final 2007 AQMP Appendix V: Modeling and Attainment Demonstrations

using a VMT weighted scheme. County-wide area source emissions were temperature

corrected and gridded using the spatial emi ssions surrogate profile s developed for the

2003 AQMP. A more detailed de scription of the meteorological episode selection,

meteorological modeling a nd validation and the epis odic emissions inventory

development is presented in Chapert 4.

Application of RRF’s

Unlike the regional ozone modeling conducted for the 2003 AQMP that based the

attainment demonstration on the direct results of a future year simulations, the procedure

for determining future year attainment of the 8-hour ozone standard for the Final 2007

AQMP relies on the use of site specific RRF’s determined from a series of simulations

for the 2002 and 2023 controlled emissions. Th e basic procedure is outlined earlier in

this chapter. The ozone attainment dem onstration is anchored by the 2002 base-year

emissions. The meteorological episodes are first validated based on model performance

using day-specific emissions for each base-c ase (e.g. 1997, 2004 or 2005). The suites of

validated episodes are then simulated us ing the 2023 controlled and 2002 emissions to

determine a site specific average set of RRFs. The site specific RRF is applied to the

2002 design value to determine whether attainment has been satisfied.

A minimum of 5-episode days are recommende d to determine the site specific RRF.

The evaluation requires that the model perfo rmance for the day is within specific

performance goals including observation with 25 percent of the station design value,

absolute prediction accuracy within 25 percent and that a minimum observed

concentration at each site 85 ppb or greater. If a site did not meet the 5-day threshold,

either the average of the RRFs for all Basin sites or the calculated RRF from the 19 days

simulated was applied to estimat e the future design value. In this situation, the lesser

reduction value (i.e., higher ratio of 2003 oz one divided by 2002 ozone ) was used as the

representative RRF for future year design calculation. Per EPA modeling guidance,

since the CAMx regional modeling is ba sed on a 5 km squared grid, the ozone

performance evaluation and peak RRF calcul ation is based on a comparison of the

observed concentration and the pr edicted concentration within a 15 km radius of the grid

hosting the observation. (Data are evaluated for a 7 X 7 grid area).



# UNCERTAINTIES ASSOCIATED WITH THE TECHNICAL ANALYSIS

As with any plan update there are uncertaintie s associated with the technical analysis.

The following paragraphs describe the primar y contributors to such uncertainties as well

as some of the safeguards bu ildt in to the air quality pla nning process to manage and

control such uncertainties.

V- 1 - 12

# Chapter 1 Modeling Overview



# Demographic and Growth Projections

Uncertainties exist in the demogr aphic and growth projections for the future base years.

As projections are made to longer periods (i .e., over ten or more years), the uncertainty

of the projections become gr eater. Examples of activities that may contribute to these

types of uncertainties include th e rate and the type of new sources locating in the Basin

and their geographic distribution, future year residential construction, military base reuse

and their air quality impact , and economic prosperity.

Input Elements to Air Quality Models

In addition to the above, there are also uncertainties in the technical information

gathered for the air quality analysis. There are three major input elements associated

with any air quality modeling analysis : ambient air quality monitoring data;

meteorological measurements; and emissions i nventory. All three input elements have

various levels of uncertainties im pacting the technical analysis.



# Ambient Air Quality Monitoring Data

Generally, ambient air quality m easurements are within plus or minus half of a unit of

measurement (e.g., for ozone usually reported in units of part-per-billion (ppb) would be

accurate to within ± 5 ppb). Due to this uncertainty, th e Basin’s 8-hour attainment status

based on ambient monitoring data would be achieved if all ozone monitors reported

ozone concentration levels less than or equa l to 84 ppb. Similar un certainty is observed

in particulate data measurements and la broratory analysis. For example, PM2.5 is

comprised of six primary constituents (NH4 + 

# , NO3, SO4

- 

# , OC, EC and crustal), as well

as bonded water and total mass. Each of the primary species has individual uncertainty

associated with the laboratory analysis proce dure used to analyze concentration, the type

of filter media to collect the sample and th e total mass can be affected by minor changes

in the volumetric flow that fall within the approved instru ment calibration range. As a

consequence, the sum of the total species may not add up to or may exceed the filter

measured mass.



# Meteorological Measurements

Air Quality models have to rely on reliabl e meteorological input data to accurately

simulate future ambient concentration levels . There are uncertain ties associated with

meteorological model input parameters, such as initializations from National Weather

Service global and hemispheric simulations , or satellite estimates of ground level

temperature and moisture. Direct measur ements of instantaneous wind speeds and

directions at varying levels above ground require averaging to hourly values before they

V - 1- 13

# Final 2007 AQMP Appendix V: Modeling and Attainment Demonstrations

can be assimilated into the numerical analys es. Layer averaging of model ouput reduces

the sensitivity of the model to changing patterns in the vertical structure.



# Emissions Inventory

As discussed in Chapter 3 of the main document, large uncer tainties in the mobile source

emissions inventory estimates have been observed as evident with the latest

EMFAC2007 release. On-road mobile source emission estimates have increased with

each new EMFAC release. On-road mobile s ource emissions have inherent uncertainties

also with the current methodologies used to estimate vehicle activity such as vehicle

miles traveled, the impacts of fuel additives such as ethanol and day-of-week diurnal

profiles of traffic volume. Stationary (o r point) source emission estimates have less

associated uncertainties compared to area source emission estimates. Major stationary

sources report emissions annually whereas ar ea source emissions are, in general,

estimated based on production or usage inform ation. Area source emissions including

paved road dust and fugitive dust have signi ficant uncertainties in the estimation of

particulate (PM 2.5 

# ) emissions due to the methodologies used for estimation, temporal

loading and weather impacts.



# Air Quality Models

The air quality models used fo r ozone and particulate air qua lity analysis are state-ofthe-art, complex 3-dimensional models that utilize 3-dimensional meteorological

models, complex chemical mechanisms that accurately simulate ambient reactions of

pollutants and sophisticated numerical me thods to solve complex mathematical

equations that lead to the prediction of ambient air quality concentrations. While air

quality models progressively became mo re sophisticated in employing improved

chemical reaction modules that more accurate ly simulate the complex ambient chemical

reaction mechanisms of the va rious pollutants, such improved modules are still based on

limited experimental data which carry associ ated uncertainties. In order to predict

ambient air quality concentrations, air qua lity models rely on the application of

sophisticated numerical methods to solve co mplex mathematical equations that govern

the highly complex physical and chemical processes that also have associated

uncertainties.



# Are There Any Safeguards Against Uncertainties?

Yes. While completely eliminating uncerta inties is an impossible task, there are a

number of features and practi ces build-into the air quality pl anning process that manage

and control such uncertainties and preserve the integrity of an air quality management

plan.

V- 1 - 14

# Chapter 1 Modeling Overview

The concerns regarding uncertainties in the te chnical analysis are reduced with future

AQMP revisions. Each AQMP revision em ploys the best available technical

information available. Under state la w, the AQMP revision process is a dynamic

process with revisions occurring every thr ee years. The AQMP revision represents a

“snapshot in time” providing the progress ac hieved since the previous AQMP revision

and efforts still needed in order to attain air quality standards.

Under the federal Clean Air Act, a state impl ementation plan (SIP) is prepared for each

criteria pollutant. The SIP is not updated on a routine basis under the federal Clean Air

Act. However, the federal Clean Air Act recognizes that uncertainties do exist and

provides a safeguard if a nonattainment area does not meet an applicable milestone or

attain federal air quality standards by their a pplicable dates. Contingency (or backstop)

measures are required in the AQMP and must be developed into regulations such that

they will take effect if a nonattainment area does not meet an app licable milestone or

attainment date. In addition, federal sa nctions may be imposed until an area meets

applicable milestone targets.

In September 2006, U.S. EPA released an updated guidance document on the use of

modeled results to demonstrat e attainment of the federal ozone, PM2.5 and regional haze

air quality standards. The gui dance document recognized that there will be uncertainties

with the modeling analysis and recommends supplemental analysis or weight of

evidence discussion that corrobor ates the modeling attainment analysis where attainment

is likely despite the modeled re sults which may be inconclusi ve. Table V-1-3, is taken

directly from the modeling guidance document to illustrate the value of supplemental

analyses. Where possible, the U.S. EPA recommends that at least one “mid-course”

review of air quality, emissi ons and modeled data be conducted. A second review,

shortly before the attainment date, should be conducted also. Statis tical trend analyses

can also provide support for assessing the like lihood for future year attainment. Such

actions will occur in the South Coas t Air Quality Management District.



# DOCUMENT ORGANIZATION

This document provides the federal attainme nt demonstrations for PM2.5, PM10 and

ozone. Chapter 2 provides the PM2.5 atta inment demonstration to meet the 2015

attainment date. The discussion include s future year (2015 and 2021) particulate

impacts for both PM2.5. Chapter 3 provi des an update to the 24-hour average PM10

attainment demonstration and a brief discussi on on the impacts of the control strategy to

regional visibility. Chapter 4 presents the ozone attainment demonstration based on the

CAMx modeling analyses. The ozone anal ysis includes a characterization of the

episodic, base-year modeling pe rformance, and future year attainment for the control

strategy. As with the particulate analyses, a series of alternative emissions simulations

are presented to test the sens itivity of the proposed control strategy. Weight of evidence

V - 1- 15

# Final 2007 AQMP Appendix V: Modeling and Attainment Demonstrations

discussions for ozone and PM2.5 will be inco rporated in Chapters 2 and 4 respectively

in the final document. Chapter 5 presents the summary compari ng predicted air quality

to the state and federal standards and the projected 201 4 5 PM2.5 and 2024 8-hour ozone

carrying capacities. Table V 1-4 lists the Attachments to this document.

TABLE V-1-3

Guidelines for Weight of Eviden ce Determinations (U.S. EPA, 2006)

Results of Modeled Attainment Test Supplemental Analyses

Ozone Annual PM2.5 24-Hour PM2.5

Future Design

Value < 82 ppb,

all monitoring sites Future Design

Value < 14.5 μ g/m 3



# , all monitoring sites Future Design

Value < 62 μ g/m 3



# , all monitoring sites Basic supplemental

analyses should be

completed to confirm the

outcome of the modeled

attainment test

Future Design

Value 82 - 87 ppb,

at one or more

sites/grid cells Future Design

Value 14.5 – 15.5

μ g/m 3 

# , at one or

more sites/grid cells Future Design

Value 62 – 67

μ g/m 3 

# , at one or

more sites/grid cells A weight of evidence

demonstration should be

conducted to determine if

aggregate supplemental

analyses support the

modeled attainment test

Future Design

Value > 87 ppb,

at one or more

sites/grid cells Future Design

Value > 15.5

μ g/m 3 

# , at one or

more sites/grid cells Future Design

Value > 67 μ g/m 3



# ,

at one or more

sites/grid cells More qualitative results

are less likely to support a

conclusion differing from

the outcome of the

modeled attainment test.

V- 1 - 16

# Chapter 1 Modeling Overview

TABLE V-1-4

Attachments

Number Description

References

Attachment-1 PM2.5 Annual and 24- Hour Attainment Calculations

Attachment-2 Model Performance Statistics

Attachment-3 Draft Modeling Protocol

Attachment-4 Critiques of the Expert Reviewers

Attachment-5 CEPA Source Level Emissions Reduction Summary for

2014: Annual Average Inventory

Attachment-6 CEPA Source Level Emissions Reduction Summary for

2017: Annual Average Inventory

Attachment-7 CEPA Source Level Emissions Reduction Summary for

2017: Planning Inventory

Attachment-8 CEPA Source Level Emissions Reduction Summary for

2023: Annual Average Inventory

Attachment-9 CEPA Source Level Emissions Reduction Summary for

2023: Planning Inventory

V - 1- 17

# CHAPTER 2

THE FEDERAL PM2.5 ATTAINMENT

DEMONSTRATION PLAN

Introduction

PM2.5 Data: MATES-III and FRM

Sandwich and Speciated Monitored Attainment (SMAT)

CAMx and MM5 Overview

Emissions Inventory

Base-Year Annual Simulations

Future Air Quality

Weight of Evidence

# Chapter 2 Federal PM2.5 Attainment

V - 2- 1 

# INTRODUCTION

As outlined in Chapter 1 of this document, the CAMx “one atmosphere” gas-aerosol

modeling system was used to develop the regional PM2.5 attainment demonstration

for the Final 2007 AQMP. The departure from the Urban Airshed Model with Linear

Chemistry (UAM/LC) [Kumar, et al, 1995] modeling system was made to take

advantage of CAMx’s better-more mass cons istent dispersion platform, integrated

gas phase (CB-IV) and aerosol chemistr y (two size partitioned) and readily

incorporated numerical prognostic meteorological model data.

EPA guidance on PM modeling for attainment demonstrations requires the use of a

regional dispersion model in combination with relative response factors. The

speciated modeling attainment test (SMAT) relies on the use of modeled

performance of individual particulate spec ies in the base year and future year

controlled scenarios to produce relative respons e factors to be applied to design year

data. The CAMx output provides compre hensive characterization of the six key

segments of the PM2.5 distribution (N H4+, NO3, SO4, organic carbon (OC),

elemental carbon (EC), crustal, as well as nitric acid and the standard chemical mix

associated with ozone production (O3, NO, NO2, CO, aldehydes, and VOC).

Particulate data measured in 2005 as pa rt of the Multiple Ai r Toxics III (MATES-III)

program provided the speciati on of the PM2.5 samples. The MATES-III monitoring

program began in April 2004 and continue d through March of 2006 . The data used

for the attainment demonstration was me asured from January 1, 2005 through

December 31, 2005, in the middle of the MATES-III program. Problems observed in

data typically associated with the star t-up of a field program and ensuing initial

laboratory analysis were minimized over the 8-months of lead sampling prior to

2005. All MATES-III measured data was subjected to extensive quality assurance

procedures following the protocol outlined by EPA criteria. A comprehensive

analysis of the MATES-III samplin g program is expected to be issued in the Spring

of 2007.

The speciated PM2.5 sampled by the MATE S-III program were a unique data set,

separate from the data acquired through the standard Federal Reference Method

(FRM) PM2.5 sampling network. Total mass sample d in parallel (MATES-III and

FRM) using side-by-side samplers are not expected to match directly. As such,

EPA’s “Sandwich” methodology was used in this demonstration to fit the MATESIII data by species to the annual FRM mass. The process calculates organic carbon

as the sum of the total filter mass minus the mass of the remaining species and

estimations of bonded water and filter contamination (“bl ank”). Bonded water and

filter contamination are inferred in the FR M PM2.5 data samples and their inclusion

in the analysis provides for a more direct comparis on to the FRM determined

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 2 regional design values. The Sandwich meth odology is discussed later in conjunction

with the SMAT.

The PM2.5 attainment demonstration is tw ofold to address the annual and 24-hour

portions of the standard. The initial secti ons of this chapter address the MATES- III

program and data, the District FRM PM 2.5 sampling network, the SMAT and

Sandwich data analyses, the CAMx mo deling setup and briefly the modeling

emissions inventory. The following secti ons of this chapter provide the annual

PM2.5 attainment demonstration and suppor ting weight of evidence analyses then

lastly, the episodic PM2. 5 24-hour standard atta inment demonstration.



# PM2.5 Data: MATES-III and FRM

MATES-III Monitoring

MATES-III is the second follow up to the or iginal MATES toxics analysis that took

place in the later 1980’s. MATES-II was comp rised of an extensive field monitoring

campaign and laboratory analysis, emissions inventory development and regional

toxics modeling. The MATES-II sampling generated speciated PM10 from the TEP2000 monitoring network using the PTEP samplers (described in the 2003 AQMP,

Appendix V). A comprehens ive discussion of the MATES -II program is provided in

the MATES-II final report and appendices.

MATES-III PM2.5 samples were collected upon a 47mm quartz and Teflon filters

simultaneously within the same particulat e sampler for a 24- hour duration using a

size selective sampler (SSI) in accordance to the method based on EPA’s Federal

Reference Method 40CFR50 (Draft MATE S-III Protocol, 2004). Samples were

taken every third day basis. Teflon filter s were used for the analysis of total

particulate mass, ions and metals. The PM2. 5 quartz filter was used for the analysis

of organic and elemental carbon usin g the IMPROVE or NIOSH method. The

District also operates co-l ocated speciated air sampling system (SASS) monitors for

the carbon measurement at two sites (Centr al Los Angeles and Riverside-Rubidoux)

as part of EPA’s STN sampling network. Only the IMPROVE carbon data are

incorporated in the atta inment demonstration.

The MATES-III sampling network was comp rised of eight monitoring sites at

locations used in the MATES-II st udy. At least one site is s ituated in each of the four

counties in the Basin w ith the bulk of the monitoring in Los Angeles. The locations

of the monitoring stations were chos en to bridge the MATES-II and MATES-III

exposure analysis but also to address envi ronmental justice issu es associated with

goods movement and exposure to mobile s ource emissions. The sites are listed in

Table V-2-1.

# Chapter 2 Federal PM2.5 Attainment

V - 2- 3 TABLE V-2-1

MATES-III Monitoring Network

Site Address County

Anaheim 1010 S. Harbor Blvd. Orange

Burbank 228 W. Palm Ave. Los Angeles

Compton 720 N. Bullis Ave. Los Angeles

Fontana 14360 Arrow Highway San Bernardino

Long Beach 3648 N. Long Beach Blvd. Los Angeles



# Los Angeles 1630 N. Main St. Los Angeles



# Rubidoux 5888 Mission Blvd Riverside

Wilmington 900 E. Lomita Blvd Los Angeles

MATES-III Speciated Data

Annual Data

Figure V-2-1 provides the PM2.5 mass di stribution for the 20 05 MATES-III data.

The data reflects the unadjusted direct me asurements of the key PM2.5 species at

each station. A category called “Others” whic h is included in the data is defined as

the net difference between the sum of the species mass and the total mass weighed on

the filter. Field measurements and lab analyses each have uncertainty and precisions

bounds. As identified by EP A, the greatest uncertainty in species mass lies with the

measurement and analysis of organic carbon . The uncertainty associated with the

organic carbon mass is treated through an adju stment that is discussed as part of the

“Sandwich Method”.

The highest PM2.5 mass is measured at Burbank, Fontana and Rubidoux and the

lowest at Anaheim. Figure V-2-1 prov ides the speciation of the adjusted 2005

MATES-III data including am monium, nitrates, sulfates, organic carbon (OC),

elemental carbon (EC), sodium, chloride , and metals including aluminum, iron,

silicon, titanium, nickel, and lead amon g others. Table V-2-2 provides the

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 4 concentrations of the PM2. 5 species observed in the MATES-III data while Table V2-3 provides the percentage of total ma ss for the major comp onent species .

2005 MATES-III A nnual PM2.5

-10

-5 0 5 10 15 20 25 30

Anah e im Burbank

C o

m

p ton

F on

t an

a

Lon g

B

e ach Los An g ele s

R ubidou x WilmingtonMass (ug/m3) 

### Others

Na & Cl

Metals

EC

OC

SO4 ion

NO3 ion

NH4 ion



# FIGURE V-2-1

MATES-III 2005 Annual Distribution of PM2.5 Species (μg/m3)

TABLE V-2-2

MATES-III 2005 Annual PM2.5 Species Concentrations (μg/m3)

Location NH4 NO3 SO4 OC EC Metals Na & Cl Others MASS

Anaheim 1.90 4.09 3.30 6.03 1.43 2.54 0.73 -3.04 16.97

Burbank 2.43 5.49 3.38 8.44 2.09 2.94 0.55 -3.47 21.83

Compton 2.07 4.18 3.74 6.99 1.84 2.80 0.72 -3.60 18.73

Fontana 2.52 6.43 3.15 8.01 2.14 2.88 0.55 -4.53 21.14

Long Beach 2.11 3.87 4.17 6.71 1.52 2.98 0.85 -4.59 17.61

Los Angeles 2.27 5.09 3.43 7. 58 1.95 2.55 0.59 -5.42 18.04

Rubidoux 2.73 7.31 3.05 6.88 1.69 2.52 0.61 -3.38 21.41

Wilmington 1.90 3.34 4.47 6. 66 2.12 3.26 0.88 -4.15 18.47

# Chapter 2 Federal PM2.5 Attainment

V - 2- 5 TABLE V-2-3

MATES-III Annual Percentage 2005 PM2.5 Species Contribution

Location NH4 NO3 SO4 OC EC Metals Na & Cl

Anaheim 9.5 20.4 16.5 30.1 7.1 12.7 3.6

Burbank 9.6 21.7 13.4 33.3 8.2 11.6 2.2

Compton 9.3 18.7 16.7 31.3 8.2 12.5 3.2

Fontana 9.8 25.0 12.3 31.2 8.3 11.2 2.1

Long Beach 9.5 17.4 18.8 30.2 6.8 13.4 3.8

Los Angeles 9.7 21.7 14.6 32.3 8.3 10.9 2.5

Rubidoux 11.0 29.5 12. 3 27.8 6.8 10.2 2.4

Wilmington 8.4 14.8 19.8 29.4 9.3 14.4 3.9



# In general, the organic carbon mass account s for approximately 30 percent of the

total mass (adjusted for th e negative contribution of th e other category) at each

station. Ammonium, sulfate and nitrates account for an approximate 45 percent of

the total mass at each location. Rubidoux and Fontana are the most heavily impacted

by nitrates. Sulfate is highest in the near coastal or ports of Los Angeles/Long Beach

areas, particularly Wilmington and Long Be ach. EC and metals measurements were

highest at Wimington accounting for almost 24 percent of the mass when combined.

All sites observed measurable concentrations of sodium and chloride ions reflecting

the influence of the marine air as it is transported inland.

Quarterly Data

Figures V-2-2a –V-3-2h depict the 2005 MATES-III PM2.5 data by component

species at each monitoring sites sorted by quarter. Table V-2-4 provides the

quarterly design values for each site. PM 2.5 concentrations are highest in either

Quarter-3 or Quarter-4 at each site. The lowest concentr ations are observed in the

second quarter (with the exceptions of Rubi doux and Fontana). The contribution of

the individual species varies by quarter as well. Sulfate is highest in Quarter-3 while

nitrate are highest in Quarter-4 and to some extent Quarter-1. The species

concentrations reflects the seasonal weathe r patterns where the higher values of

sulfate typically occur under strong-eleva ted inversions and sea breeze transport

inland, conditions that are prevalent in th e Basin in late spring and summer. Nitrate

chemistry is very dependen t on the availability of water vapor and as a result

Quarter-4, with the high humidity and frequen t nocturnal inversions enhance regional

formation. Organic carbon an d elemental carbon values ar e also highest in Quarter-4

due to the poor dispersion from weak wi nds and low level inversions. Quarter-2

tends to have the lowest concentrati ons due to spring storms and favorable

dispersion.

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 6 2005 Anaheim Quarterly MATES-III PM2.5

-5 0 5 10 15 20 25 30

Q1 Q2 Q3 Q4Mass (ug/m3) 

### Others

Na & Cl

Metals

EC

OC

SO4 ion

NO3 ion

NH4 ion



# FIGURE V-2-2a

2005 Quarterly Distribution of PM 2.5 Species at Anaheim (μg/m3)

2005 Burbank Quarterly MATES-III PM2.5

-5 0 5 10 15 20 25 30 35

Q1 Q2 Q3 Q4Mass (ug/m3) 

### Others

Na & Cl

Metals

EC

OC

SO4 ion

NO3 ion

NH4 ion



# FIGURE V-2-2b

2005 Quarterly Distribution of PM 2.5 Species at Burbank (μg/m3)

# Chapter 2 Federal PM2.5 Attainment

V - 2- 7 2005 Compton Quarterly MATES-III PM2.5

-5 0 5 10 15 20 25 30

Q1 Q2 Q3 Q4Mass (ug/m3) 

### Others

Na & Cl

Metals

EC

OC

SO4 ion

NO3 ion

NH4 ion



# FIGURE V-2-2c

2005 Quarterly Distribution of PM2.5 Species at Compton (μg/m3)

2005 Fontana Quarterly MATES- III PM2.5

-5 0 5 10 15 20 25 30 35

Q1 Q2 Q3 Q4Mass (ug/m3) 

### Others

Na & Cl

Metals

EC

OC

SO4 ion

NO3 ion

NH4 ion



# FIGURE V-2-2d

2005 Quarterly Distribution of PM 2.5 Species at Fontana (μg/m3)

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 8 2005 Long Beach Quarterly MATES- III PM2.5

-5 0 5 10 15 20 25 30

Q1 Q2 Q3 Q4Mass (ug/m3) 

### Others

Na & Cl

Metals

EC

OC

SO4 ion

NO3 ion

NH4 ion



# FIGURE V-2-2e

2005 Quarterly Distribution of PM2.5 Species at Long Beach (μg/m3)

2005 Los Angeles Quarterly MATES-III PM2.5

-5 0 5 10 15 20 25 30 35

Q1 Q2 Q3 Q4Mass (ug/m3) 

### Others

Na & Cl

Metals

EC

OC

SO4 ion

NO3 ion

NH4 ion



# FIGURE V-2-2f

2005 Quarterly Distribution of PM2.5 Species at Los Angeles (μg/m3)

# Chapter 2 Federal PM2.5 Attainment

V - 2- 9 2005 Rubidoux Quarterly MATES- III PM2.5

-5 0 5 10 15 20 25 30 35

Q1 Q2 Q3 Q4Mass (ug/m3) 

### Others

Na & Cl

Metals

EC

OC

SO4 ion

NO3 ion

NH4 ion



# FIGURE V-2-2g

2005 Quarterly Distribution of PM 2.5 Species at Rubidoux (μg/m3)

2005 Wilmington Quarterly MATES-III PM2.5

-5 0 5 10 15 20 25 30

Q1 Q2 Q3 Q4Mass (ug/m3) 

### Others

Na & Cl

Metals

EC

OC

SO4 ion

NO3 ion

NH4 ion



# FIGURE V-2-2h

2005 Quarterly Distribution of PM2.5 Species at Wilmington (μg/m3)

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 10 TABLE V-2-4

FRM Annual and Quarterly PM2.5 De sign Concentrations (2003-2005)

at MATES-III Monitoring Sites (μg/m3)

Location Quarter-1 Quarter-2 Quarter-3 Quarter-4 Annual

Anaheim 17.6 12.4 15.4 20.0 16.4

Burbank 18.7 15.2 20.7 20.3 18.7

Compton 16.7 13.3 18.2 21.8 17.5

Fontana 18.7 19.2 20.2 23.2 20.3

Los Angeles 19.7 16.3 20.2 22.2 19.6

Long Beach 18.0 12.7 15.7 22.9 17.3

Rubidoux 21.2 21.9 22.6 24.9 22.7

Wilmington 12.7 10.9 15.7 19.6 14.7



# On average, the annual MATES-III data are c onsistent with the annual design values.

The quarterly MATES-III data compares well with the quarterly FRM data with the

exceptions of Rubidoux and Fontana which exhibited higher Quarter-3 mass.

FRM PM2.5

The AQMD measures PM2.5 using the fede ral reference method Size Selective Inlet

(SSI) High-Vol method at 16 air monitoring sites in the Basin. The FRM PM2.5 data

are used in this analysis to expand the future year pred ictions to the entire Basin and

to corroborate the attainment demonstration at the grid level. Figure V-2-3 depicts

the isopleths of 2005 annual PM2.5 from the FRM sites in the Basin. Table V-2-5

provides the quarterly and an nual design values for the re maining FRM sites not used

as monitoring locations for MATES-III. (Not e: design values for the sites used for

the MATES-III networks are liste d in Table V-2-4 above).

The FRM data depicted in Figure V-2-3 cl early delineates the extent of the PM2.5

problem in the Basin. PM2.5 is essen tially a combustion gene rated pollutant and

with the volume of traffic flow, numbers of sources (both point and area) located in

the region, concentrations exceed the annu al federal standard (15 μg/m3) throughout

the Basin. The area with the highest annual concentra tion includes southwest San

Bernardino and Northwest Riverside Countie s. These areas have design values

exceeding 20 μg/m3 and encomp ass both the Fontana and Rubidoux air monitoring

stations. It is important to note that th e areas with the highest concentrations are

directly downwind of a major ammonia so urce area associated with dairies and

poultry farming. These industries are rapidly moving from the Basin and are

expected to contribute significantly less to pa rticulate formation in future years.

# Chapter 2 Federal PM2.5 Attainment

V - 2- 11 TABLE V-2-5

FRM Annual and Quarterly PM2.5 De sign Concentrations (2003-2005)

at the Remaining Basin PM2.5 Monitoring Sites (μg/m3)

Location Quarter-1 Quarter-2 Quarter-3 Quarter-4 Annual

Azusa 16.2 15.9 21.1 19.6 18.2

Big Bear 12.8 8.0 7.7 14.7 10.8

Lynwood 19.3 14.6 18.3 22.9 18.8

Mission Viejo 12.0 10.2 12.7 12.9 11.9

Ontario 21.0 17.9 20.5 25.3 21.2

Pasadena 15.5 14.6 18.6 18.5 16.8

Reseda 14.3 13.4 15.9 17.8 15.4

Riverside Magnolia 18.9 19.8 20.6 22.5 20.5

San Bernardino 18.2 20.3 21.6 21.8 20.5



# FIGURE V-2-3

2005 South Coast Air Basin Annual PM2.5 (μg/m3)

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 12 

# SANDWICH AND SPECIATED MONI TORED ATTAINMENT (SMAT)

Sandwich

The “sandwich” method (Frank, 2006) ca lculates the PM2.5 organic carbon mass

from the difference between th e total mass of the particulate sample and the other

component species. As previously described, there is uncertainty associated with the

monitoring and analytical methods used to develop the particulate profile. While

nitrate filter mass loss is expected, the analytical t echnique to determine the

concentrations of the remaining species is well established. Confidence is high in

determining the concentrations of the othe r ions (sulfates, ammonium, sodium and

chloride) and the measurements of di rectly emitted elemental and crustal

components. Primary and secondary organi c compounds express greater monitoring

and analytical variability and the sand wich method proposes to minimize this

uncertainty.

In the unadjusted 20 05 MATES-III data, the “others” is just the difference between

the measured component speci es mass and the total mass of the filter. In the 2003

AQMP, annual PM10 attainment demonstrati on the speciated particulate data used

the measurements of ammonium, sulfate, nitrate, organic carbon and elemental

carbon directly. The difference between the total filter mass and the sum of the five

components was categorized as the “others.” The others included the crustal [metals]

components, sea salts and a ccounted for any particle bonded water, filter blank

contamination and uncertaintie s in the data monitoring or laboratory analysis.

The sandwich method for PM2.5 (describ ed by Equation V-2-1) accounts for the

calculation of bonded water adds a filter blank contamination term and substitutes

organic carbon as the “others” compontent to be estimated from the mass difference.

Eq. V-2-1.

OC = PM2.5 – (NH4 + NO3 + SO4 + EC + bonded H2O + blank + crustal [metals]).

The sandwich method estimates ammonium (if not directly measured) and uses a

either a linear or polynomial empirical e quation to approximate the mass of bonded

water in the sample. The polynomial equation is an empirically derived

approximation of the thermodynamic Aeroso l Inorganic Model (AIM) (Clegg, 1998)

that uses the concentrations of NH4, NO3 and SO4 to estimate bonded water. The

alternate linear equation also approximat es bonded water assu ming that the water

content bonded to ammonium ni trate is equivalent to 12 percent of the mass and that

the water bonded to ammonium sulfate is approximately eq ual to 26 percent of that

mass. Comparisons of the calculated bond ed water using the two algorithms were

close and for the PM2.5 attainment demons tration, the primary method used to

calculate water was the polynomial approach.

# Chapter 2 Federal PM2.5 Attainment

V - 2- 13 The sandwich also incorporates a filter bl ank contamination estim ation of 0.5 μg/m3

into the calculation. AQMD procedures re quire the use of forceps to handle filter

media to avoid mass contamination. However, some mass inevitably does impact the

filter prior to exposure mo stly due to the conditioned air mass in the sequential

sampler as the filter is being queued for mo nitoring. The AQMD staff has discussed

the filter bank issue with EPA and af ter reviewing monito ring and laboratory

procedures as well as measurement of the bla nks has determined that the 0.5 μg/m 3



# value for the bank is an approp riate average for the Basin.

The sandwich methodology does not exclude the use of directly measured

ammonium or organic carbon. Estimates of ammonium calculate d using a empirical

relationships (0.29 X nitrat e and 0.375 X sulfate) clos ely matched the measured

ammonium. As a consequence, the directly measured ammonium is used in the Final

2007 PM2.5 attainment demonstration anal ysis. Second, measurements of PM2.5

OC were analyzed using the same techni que as for the previ ous 2003 AQMP PM10

analysis (although different filter media). The data were trend adjusted, based on

emissions reductions observed over recent ye ars and further adjusted to estimate the

carbon fraction. The carbon fraction factor can range in the Basin from 1.2 to 1.8

depending upon the location of the station relative to source areas. For the 2007

AQMP a carbon factor of 1.3 was applied to th e OC data measured at the eight sites.

The Sandwich methodology was applied to the MATES-III data for each station.

The MATES-III data were aggregated in to quarterly averag es, and the organic

carbon portion was calculated directly. If a quarterly value of organic carbon was

calculated to be a negative number, the aver age of the non-negative quarterly average

concentrations at that site were substituted for the negative value. To maintain mass

consistency, an adjustment to the total quarterly mass was made totaling the sum of

the substituted average organi c carbon concentration and the absolute value of the

negative calculation of organic carbon re sulting from the Sandwich methodology.

Bonded water was calculated usi ng the AIM model and a 0.5 μg/m 3 

# blank was added.

The Sandwich modified MATES-III quarterly data were then apportioned to the

quarterly design values at each (MATES-III) monitoring site (subtracting the blank

before scaling then adding the blank back to the total mass) pr ior to the SMAT.

Table V-2-6 provides the qu arterly design values for each of the eight MATES-III

sites with the Sandwich derived species distribution.

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 14 TABLE V-2-6

MATES-III/Sandwich Apportioned 2005 Quarterly Design Values

Quarter NH4 NO3 SO4 OC EC Water Crustal/Metals Blank Mass

Anaheim

1 1.49 4.95 2.20 3.25 1. 59 2.00 1.57 0.50 17.57

2 1.04 2.63 3.16 0.75 0. 65 1.95 1.72 0.50 12.40

3 1.60 2.71 4.47 1.74 0. 72 1.62 2.00 0.50 15.37

4 2.64 5.34 2.45 2.69 2. 39 1.80 2.19 0.50 20.00

Burbank

1 1.54 4.90 1.77 5.16 1. 74 1.62 1.48 0.50 18.70

2 1.48 3.63 2.94 1.97 1. 02 1.53 2.10 0.50 15.17

3 2.45 4.34 4.54 3.10 1. 25 2.20 2.27 0.50 20.67

4 3.06 6.90 2.42 3.80 3. 21 2.32 2.09 0.50 24.30

Compton

1 1.42 4.33 2.47 4.80 2. 13 1.86 1.81 0.50 19.30

2 1.30 3.11 3.78 0.68 0. 90 2.16 2.16 0.50 14.60

3 2.17 3.15 5.34 2.28 0. 82 2.07 1.94 0.50 18.27

4 2.76 5.63 2.69 3.60 3. 23 2.03 2.44 0.50 22.87

Fontana

1 1.89 6.22 1.82 2.95 1. 84 1.94 1.58 0.50 18.73

2 2.24 5.93 3.30 1.63 1. 46 1.94 2.16 0.50 19.17

3 2.10 4.83 4.27 1.37 1. 88 2.00 3.24 0.50 20.20

4 3.53 8.29 2.40 0.55 2. 51 2.72 2.67 0.50 23.17

Long Beach

1 1.37 4.19 2.55 3.74 1. 83 1.92 1.88 0.50 17.97

2 1.25 2.45 3.72 0.40 0. 70 1.67 1.98 0.50 12.67

3 1.95 2.53 4.75 1.26 0. 87 1.73 2.08 0.50 15.67

4 3.29 5.86 4.31 1.42 2. 50 2.53 2.49 0.50 22.90

Los Angeles

1 1.78 6.25 2.44 2.54 1. 98 2.55 1.69 0.50 19.73

2 1.58 4.07 3.74 0.73 1. 32 2.16 2.22 0.50 16.33

3 2.95 4.52 5.11 0.98 1. 45 2.47 2.22 0.50 20.20

4 3.17 7.06 2.92 0.52 3. 25 2.59 2.23 0.50 22.23

Rubidoux

1 1.79 6.51 1.93 4.18 2. 00 2.44 1.81 0.50 21.17

2 3.00 8.25 3.18 1.39 1. 18 2.23 2.15 0.50 21.87

3 2.94 7.29 4.30 1.62 1. 17 2.27 2.50 0.50 22.60

4 3.47 8.44 2.65 1.53 2. 61 3.04 2.64 0.50 24.87

Wilmington

1 1.27 3.49 2.91 3.43 2. 44 1.84 2.08 0.50 17.97

2 1.13 2.02 3.84 0.38 0. 92 1.74 2.14 0.50 12.67

3 1.73 1.95 5.12 1.34 1. 15 1.73 2.16 0.50 15.67

4 2.62 5.02 3.88 2.47 3. 37 2.27 2.79 0.50 22.90

# Chapter 2 Federal PM2.5 Attainment

V - 2- 15 

# SMAT

The federal guidance for developing a PM2.5 a ttainment test differs from past in that

the attainment demonstration does not dir ectly rely on explic it model output. The

attainment test in the new guidance requi res the use of the RRFs determined from the

modeling, applied to the current design va lues to create future design values. The

speciated modeling attainment test outlin ed in the guidance document further

requires the development of species depende nt RRFs from the base and future year

modeling simulations. The guidance tests th e model response for the major species

simulated. The analysis requires that th e Sandwich speciated design value data and

RRFs be assessed by the quarter of the year then recompile d into an annual future

year demonstration.

Note: in the SMAT, the blank is constant and the future year bonded water is

calculated as a function of the predic ted ammonium, nitrate, and sulfate

concentrations. The net amount of future y ear bonded water is expected to decrease

as a function of the control strategy implementation.



# CAMX AND MM5 OVERVIEW

As discussed in Chapter 1, CAMx and MM5 were selected as the dispersion platform

and meteorological model respectively for th e PM2.5 attainment demonstration. The

following sections briefly describe the m odeling domain, meteorological interface

and the boundary conditions applied in the analysis. The prescriptions for the MM5

domain initialization and coupling with th e modeling domain are addressed in the

Draft 2007 Modeling Protocol. Similar setu p procedures for th e CAMx simulations

can be found in the Protocol document.



# Modeling Domain

The CAMx modeling domain was defined by 2600 5 km squared grid cells a

Universal Transverse Mercator (UTM) pr ojection beginning at 275 easting through

3670 northing using a 65 by 40 grid cell structure. This is same grid specification

that was used for the 2 003 UAMAERO-LT analyses. Figure V-2-4 depicts the

modeling domain.

The PM2.5 domain extends approximately 80 km offshore to the west of the middle

Basin. The domain captures the international shipping routes that extend parallel to

the coast (northwest and southeast) and due west from the port areas. The northern

boundary of the domain extends to Santa Ba rbara County and Kern County while the

southern boundary resides primarily in No rthern San Diego County. The desert

portions of Riverside, San Bernardino a nd Imperial counties define the eastern

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 16 boundary of the modeling domain. The mo deling domain is smal ler than both the

ozone modeling and MM5 domain.

The vertical structure for the CAMx modeli ng was increased to 8 layers of varying

depth (compared with the 5-layer analysis of UAMAERO-LT) but less than the 19

layers used for the MM5 simulations in effo rt to conserve computational resources.

The top of the modeling domain was set at 5,000 m.

FIGURE V-2-4

PM2.5 Modeling Domain



# Boundary, Top Conditions

One of the more difficult ta sks of the modeling analysis was to determine a method

to define the boundary and top conditions for the PM2.5 simulations. Three options

were considered for the an alysis: (1) assume clean conditions, (2) use the ozone

modeling to generate concentration files at the PM2.5 grid boundary, or (3) use

hemispheric or global chemistry model outpu t to specify the boundaries. Option-3

with minor adjustments was selected for the attainment demonstration.

# Chapter 2 Federal PM2.5 Attainment

V - 2- 17 The Western Regional Air Partnership (W RAP) has been simulating hemispheric

particulates with a focus on the western U.S. as part of the Regional Haze Rule

demonstration using CAMx on a coarse grid extending into the Pacific Ocean.

Model output from the WRAP analysis for model year 2002 was extracted and

converted to develop hourly boundary cond itions for the PM2.5 modeling analyses.

For this analysis it is assu med that little uncertainty is introduced into the modeling

using the 2002 boundary da ta. The WRAP modeling used CB-IV gaseous chemistry

as does the Final 2007 AQMP PM2.5 CAMx modeling. The WRAP modeling was

conducted on a Lambert Conformal grid and therefore specificat ion of the boundary

conditions required remapping to the UTM coordinate syst em. Additional vertical

layer averaging and remapping to the PM2.5 grid assumed that the concentration is

uniform across each vertical layer.

The boundary and top concentr ation input files for the PM model were created on a

month by month basis. The files were derived by averaging the WRAP simulation

concentrations at each boundary point, vertical layer for ea ch hour of the day over the

course of a month. The values of the va rious boundary species were averaged over

the entire top of the modelin g domain for every hour in a month to create the top

concentration files. The CAMx top concen tration file only uses one concentration

value for the top of the mode l for the entire simulation. Table V-2-7 provides the

representative results for February and August.

Initial PM2.5 performance with the WRAP boundary cond itions suggested that SOx

concentrations along the western boundary in the shipping lanes were too low. A

minimum concentration of 5 ppb SO 2 

# was set for the sout hern boundary extending

westward from the San Diego coast to appr oximately 20 km offshore after which the

concentration was phased to a value less than 1 ppb at the extreme southwest corner

of the modeling domain. A similar adju stment was made along the north-south

boundary with SO 2 

# being set at 5 ppb from the co ast of Santa Barbara south to

approximately 15 km offshore, again be ing reduced to less than 1 ppb at the

southwest corner of the domain.

Future Boundary, Top and In itial Air Quality Conditions

For the future year scenar ios, the boundary, region top and ambient air quality

concentrations were adjusted to reflect pr ojected emissions reductions from the 2005

base-year.

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 18 MM5 Simulations

MM5 was used to generate th e meteorological profile for each day in 2005. The

MM5 simulations were generated fo r the larger SCOS97 modeling domain

employing a 5 km square grid and fit to the smaller PM2.5 grid. The MM5

simulations were initialized from NCEP analyses and run for 5-day increments

without the option for four dimensional da ta assimilation (FDDA ). For the annual

PM2.5 modeling, the ramp-up period for the MM5 simulations was approximately

one-half day. The total simulation time of 5 1⁄2 days allowed for an overlap from run

to run and provided consistenc y in the meteorological profile . The reader is directed

to the Draft 2007 Modeling Protocol where the developments of the MM5

meteorological simulations are discussed at length.

TABLE V-2-7

Top Concentration Files for the PM Runs Derive d from the WRAP simulation for February and

August (ppb gaseous species, ng/m 3 

# aerosol species).

Only species with non-zero values are shown.

Species February August Species February August

NO 0 0.01 SOA2F 0.53 13.32

NO2 0.02 0.03 SOA3F 0.53 13.32

O3 64.03 49.51 SOA4F 5.95 346.45

OLE 0 0.01 SOA5F 0.53 13.32

PAR 2.55 5.26 POMF 27.68 352.93

TOL 0 0.01 ECF 10.5 72.45

FORM 0.2 0.53 OTRF 4.58 175.45

ALD2 0.02 0.12 NH4C 0.03 0.17

ETH 0 0.03 NO3C 0.02 0.25

PAN 0.15 0.23 SO4C 0.12 0.25

CO 95.36 92.85 SOA1C 0 0.01

H2O2 0.92 3.07 SOA2C 0 0.01

HNO3 0.22 0.28 SOA3C 0 0.01

SO2 0.02 0.04 SOA4C 0.01 0.38

NH4F 31.77 161.54 SOA5C 0 0.01

NO3F 15.75 234.54 POMC 0.03 0.39

SO4F 109.71 237.17 ECC 0.01 0.08

SOA1F 0.53 13.32 OTRC 154.08 346.99



# MM5 produced wind speed and direct ion components (u,v,w), temperature,

humidity, insolation, and cloud cover data that were input to CAMx. Output from

the MM5 simulations were layer averaged to the CAMx vertical structure. Vertical

stability was estimated usin g the CMAQ-dispersion scheme option and the vertical

diffusivity minimum value was set at 1.0 m 2 

# /sec. Figures V- 2-5 through V-2-8

# Chapter 2 Federal PM2.5 Attainment

V - 2- 19 characterize the MM5 surface layer wind fields for morning (1000 PST) and

afternoon (1400 PST) for January 15, 2005 and July 15, 2005.



# EMISSIONS INVENTORY

Table V-2-8 provides the baseline 2005, 2014 and 2020 and controlled 2014 and

2020 modeling emissions inventories used in the attainment demonstration. CAMx

model is based on the annual average invent ory, with adjustments made for weekly

and monthly variations. A brief characterizat ion of the annual day emissions used for

the modeling analysis follows . An extensive discussi on of the overall emissions

inventory is summarized in the Final 2007 AQMP Appendix III.

TABLE V-2-8

Annual Average Day Emissions Inventory (tons/day)

Year VOC NO

x 

# SO

x 

# Diesel Geol PM2.5

(a) Baseline

2005 740 1029 62 22 25 106

2014 528 654 43 12 27 102

2020 499 525 50 7 28 103

(b) Controlled

2014 469 454 19 6 27 87

2020 398 293 20 3 28 87



# PM2.5 modeling emissions were develope d as monthly profiles corrected for

temperature and humidity. For each month, where applicable, point, area and offroad mobile sources were adjusted to a da y-of-week through-put profile consisting of

a Monday-Friday, Saturday an d Sunday schedule. On-road mobile sources were also

adjusted by the same day-of-week schedu le and overlaid with average diurnal

profiles that represent weekday and weeken d defined traffic patterns. The on-road

mobile source emission data incorporate month specific ambient temperature and

humidity input. Monthly bioge nic emissions inventories ( not listed in Table V-2-8)

were developed by the CARB.

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 20 FIGURE V-2-5

MM5 Surface Layer Winds: January 15, 2005, 1000 PST

FIGURE V-2-6

MM5 Surface Layer Winds: January 15, 2005, 1400 PST

# Chapter 2 Federal PM2.5 Attainment

V - 2- 21 FIGURE V-2-7

MM5 Surface Layer Winds: January 15, 2005, 1400 PST

FIGURE V-2-8

MM5 Surface Layer Winds: January 15, 2005, 1400 PST

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 22 Paved Road Dust Emissions Uncertainties

Uncertainties can be estimated for all sources of emission: point, mobile, and area.

With regard to PM2.5 and PM10 predicti on, quantification, sp atial allocation and

apportionment of dust sources is magnifi ed. Paved road dust accounted for the

largest percentage of the primary emissions category. The paved road dust emissions

are calculated based on the nu mber of rain days in the year, VMT and silt loading.

The 2005 paved road dust es timated emissions were imp acted by each of these

factors.

Rain Days

Precipitation summaries were reviewed to determine the dates on which measurable

rainfall (0.01 inches or more in the South Coast Drainage Division) fell in the Basin

during 2005. A total of 85 days met this criterion in the Basin for 2005. Table V-2-9

lists the dates meeting this criterion. This data was used adju st monthly entrained

paved road dust emissions by the rain-factor prescribed in EPA AP-42 (Fifth Edition,

Volume 1) 13.2.1--Paved Roads.

TABLE V-2-9

2005 Rain Days in the Basin:

Days Recording Measurable Precipitati on of at least 0.01 Inches of Rain

Month Dates

January 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 24, 26, 27, 28

February 6, 8, 10,11,12, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25

March 2, 3, 4, 6, 8, 10, 11, 12, 18, 19, 20, 21, 22, 23, 24, 28

April 4, 22, 23, 24, 28

May 5, 6, 9

June 2, 3

August 15

September 3, 5, 19, 20, 21

October 11, 15, 16, 17, 18, 19, 24, 25, 27, 28, 29 30

November 9, 10, 11, 25

December 2, 3, 9, 14, 24, 26, 28, 31



# VMT Capping

In addition, the paved road dust emissions are a function of VMT. In the 1997 and

2003 AQMP, paved road dust emissions were adjusted to reflect a cap on emissions

# Chapter 2 Federal PM2.5 Attainment

V - 2- 23 growth for high VMT road type s in future years. Base year emissions were not

capped at a given VMT level. The future year adjustment assu med that the silt

loading would be depleted by the entrainmen t from the traffic volume. Increasing the

traffic volume beyond a set point would no t increase dust entrai nment because the

silt would be essentia lly depleted. The Final 2007 AQMP continued this adjustment

of capping paved road dust on freeways in future years, allowing growth only

associated with the construction of ne w lanes or additional miles of freeway.

Differential Silt Loading

A third adjustment was made to the paved road dust emis sions to attempt to account

for the differential silt load ing content observed in the densely populated urban

portions of Los Angeles and Orange Countie s and the developing communities in the

east Basin. Analysis of the preliminary modeling indicated the paved road dust may

be overestimated by a factor of two in Los Angeles and Orange Counties where the

traffic volume is greatest and the majority of streets have curbs, gutters and are

regularly swept. A uniform silt loading fa ctor is used in the CARB model for the

entire Basin that doesn’t account for differe nces in land use. Corresponding field

studies conducted in Sacramento (2000) a nd Riverside (Fitz, 1998) indicated a wide

range of silt loading exists to arterials, collectors and local streets that departs from

the silt loading estimates provided in CARB’s emissions model.

In addition, examination of the MATES-III data indicates that the crustal-metals

portion of the PM2.5 distribution is essential constant across the basin. This infers

that although the paved road dust emissions contribution sh ould be uniform and that

west Basin VMT contributions are offset by higher silt loading in the east Basin. An

adjustment was made to the paved road dust emissions to normalize the total basin

loading by county; lowering Los Angeles by 55 percent, raising Orange County by

20 percent and doubling the emissions in Riverside and San Bernardino. No net

change in the Basin total paved road dust occurred. The adjustment was made for

base and future years by growing the county totals and redistributing the emissions

using the normalization.



# PM2.5 Split Profiles and Ammonia Inventory Adjustments

Revisions to the particulate emissions sp lit files were made to account for new

processes and AQMD rule development an d implementation. For the Final 2007

AQMP, a cooking PM2.5 split profile was added and the profiles for residual oil

burning and distillated o il burning were updated.

Revisions were made to the spatial distri bution and emissions categories defining the

ammonia inventory. In general, the tota l ammonia in the inventory did not change

significantly from the 2003 AQMP inventor y with emissions nominally exceeding

100 tons per day. The contributions of the soils, on-road m obile and livestock

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 24 categories however did change significantly placing a higher contribution to mobile

emissions at the expense of soils. Livestock emissions were halved as a result of the

review and estimation methodology modifi cations. Table V-2-10 summarizes the

changes made to the three main ammonia emissions categories.

Future year (2014) mobile source ammonia emissions are projected to be reduced by

45 percent from 2005 levels due to fleet turnover.

TABLE V-2-10

Comparison of Ammonia Soil, Mobile Source, and Lives Stock Emissions

Category 2003 AQMP (TPD) Final 2007 AQMP (TPD)

Soil 34.2 1.42

On-road Mobile 9.47 36.12

Live stock 60.37 25.67



# BASE-YEAR ANNUAL SIMULATIONS

CAMx was simulated for 2005 using the mont hly adjusted base-y ear annual average

day emissions and the meteorological and ai r quality data inpu ts outlined in the

preceding section. EPA guidance focuses m odel performance to th e ability to predict

the PM2.5 componen t species and the total mass. No specific criteria thresholds of

performance are recommended in EPA’s m odeling guidance document. This is

important since the model is used in a relative response fashi on compared to the

ozone and PM10 analyses in previous AQMPs.

Performance is evaluated by examining key statistics and graphical presentations of

differences between model pred icted concentrations and ob servations. The statistics

examine model bias and error while gr aphical presentations of error, model

prediction as a time series a nd concentration scatter plot s round out the prescribed

methods of model performance evaluation.

A nearest cell average of predicted concentr ations is typically used when comparing

individual grid concentrations to stati on measurements, because of possible spatial

misalignments of the predicted concentratio n fields. The CAMx modeling results are

presented based on a nearest nine-grid-cell av erage basis. Perfor mance evaluations at

each station are based on this average concentration.

Finally, model performance is assessed using ev ery third day predica tions that line up

with the observations. Statistics and gra phical presentations are not included where

observational data is missing.

# Chapter 2 Federal PM2.5 Attainment

V - 2- 25 

# PM2.5 Component Species Performance Evaluation for the MATES-III Sites

The CAMx 2005 base-year an nual average predicted PM2.5 and observations for the

six component species and total mass at th e MATES-III sites are presented in Table

V-2-11a through V-2-11g. Also presented in the tables are estimates of bias and

error for each component at each monitoring site.

Figure V-2-9 provides a “soccer goal” gr aphical presentation of error for model

performance. Figure V-2-10a through Figur e V-2-10h presents the time series of

model predicted vs. observations for each component at the MATES-III monitoring

sites. Figure V-2-11a throug h Figure V-2-11h presents th e scatter-plots of prediction

accuracy for each component at the MATES -III monitoring sites. Figure V-2-12

provides the CAMx predicted 2005 spatial distribution of the component species and

total mass.

In general, nitrate and ammonium tend to be over predicted by an average 2 μg/m 3 

# or

less at most sites. Ammonium model performance at Rubidoux and Fontana are

approximately within 35 percent of observ ations and within 20 percent or less for

nitrate. On average, sulfate is nominally under-predicted howe ver; OC and EC are

well simulated at all stations. Model pe rformance for the crustal-others category

indicates an average over-p rediction of about 1 μg/m 3 

# or 25 percent above

observations. Overall, the prediction of total mass refl ects the model performance for

ammonium, nitrate and the others with a tendency for over-prediction at about an

average level of 4 μg/m 3 

# or approximately 20 percent above observations.

TABLE V-2-11a

CAMx 2005 Base Year Ammonium Model Predictions (μg/m 3 

# )

Locations Mean

Observed Mean

Predicted Mean Bias Mean Error Normalized

Mean Bias Normalized

Mean Error

All Stations 2.60 4.19 1.59 2.32 0.61 0.90

Anaheim 2.23 3.71 1.48 2.00 0.66 0.90

Burbank 2.77 3.38 0.61 1.79 0.22 0.65

Compton 2.32 4.65 2.33 2.68 1.00 1.16

Fontana 2.95 3.97 1.02 2.29 0.34 0.78

N Long Beach 2.33 4. 15 1.82 2.42 0.78 1.04

Los Angeles 2.76 4.60 1.83 2.40 0.66 0.87

Rubidoux 3.20 4.74 1.54 2.34 0.48 0.73

Wilmington 2.13 3.89 1.77 2.41 0.83 1.13

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 26 TABLE V-2-11b

CAMx 2005 Base Year Nitrate Model Predictions (μg/m 3 

# )

Locations Mean

Observed Mean

Predicted Mean Bias Mean Error Normalized

Mean Bias Normalized

Mean Error

All Stations 5.35 7.67 2.31 3.98 0.43 0.74

Anaheim 4.55 7.10 2.55 3.50 0.56 0.77

Burbank 5.85 6.49 0.64 3.44 0.11 0.59

Compton 4.46 7.93 3.47 4.12 0.78 0.92

Fontana 6.76 7.65 0.90 4.38 0.13 0.65

N Long Beach 4.04 6. 52 2.48 3.38 0.61 0.84

Los Angeles 5.81 8.86 3.05 4.39 0.52 0.76

Rubidoux 7.67 9.68 2.01 4.81 0.26 0.63

Wilmington 3.37 5.51 2.14 2.87 0.63 0.85



# TABLE V-2-11c

CAMx 2005 Base Year Sulfate Model Predictions (μg/m 3 

# )

Locations Mean

Observed Mean

Predicted Mean Bias Mean Error Normalized

Mean Bias Normalized

Mean Error

All Stations 3.73 3.29 -0.44 2.03 -0.12 0.55

Anaheim 3.55 2.75 -0.80 1.77 -0.23 0.50

Burbank 3.63 2.21 -1.42 1.92 -0.39 0.53

Compton 3.96 4.09 0.13 2.53 0.03 0.64

Fontana 3.27 2.61 -0.66 1.63 -0.20 0.50

N Long Beach 4.36 4. 34 -0.02 2. 18 0.00 0.50

Los Angeles 3.78 3.17 -0.61 1.94 -0.16 0.51

Rubidoux 3.11 2.65 -0.46 1.59 -0.15 0.51

Wilmington 4.70 4.90 0.20 2.97 0.04 0.63

# Chapter 2 Federal PM2.5 Attainment

V - 2- 27 TABLE V-2-11d

CAMx 2005 Base Year Organic Carbon Model Predictions (μg/m 3 

# )

Locations Mean

Observed Mean

Predicted Mean Bias Mean Error Normalized

Mean Bias Normalized

Mean Error

All Stations 4.71 4.83 0.12 1.81 0.03 0.38

Anaheim 4.15 4.87 0.71 1.57 0.17 0.38

Burbank 4.73 4.10 -0.63 1.57 -0.13 0.33

Compton 4.20 5.65 1.44 1.80 0.34 0.43

Fontana 4.75 3.98 -0.77 1.71 -0.16 0.36

N Long Beach 4.19 4. 88 0.69 1.81 0.17 0.43

Los Angeles 4.75 6.03 1.28 1.81 0.27 0.38

Rubidoux 3.99 4.41 0.42 1.34 0.10 0.33

Wilmington 4.35 4.38 0.03 1.55 0.01 0.36



# TABLE V-2-11e

CAMx 2005 Base Year Elemental Carbon Model Predictions (μg/m 3 

# )

Locations Mean

Observed Mean

Predicted Mean Bias Mean Error Normalized

Mean Bias Normalized

Mean Error

All Stations 1.87 1.66 -0.21 0.82 -0.11 0.44

Anaheim 1.43 1.36 -0.08 0.66 -0.05 0.46

Burbank 2.08 1.25 -0.83 1.00 -0.40 0.48

Compton 1.79 2.04 0.24 0.78 0.13 0.44

Fontana 2.17 1.33 -0.84 1.02 -0.39 0.47

N Long Beach 1.44 2. 22 0.78 0.91 0.54 0.63

Los Angeles 1.97 1.94 -0.02 0.68 -0.01 0.34

Rubidoux 1.71 1.17 -0.54 0.76 -0.32 0.44

Wilmington 2.07 1.93 -0.14 0.80 -0.07 0.39

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 28 TABLE V-2-11f

CAMx 2005 Base Year Crustal- Others Model Predictions (μg/m 3 

# )

Locations Mean

Observed Mean

Predicted Mean Bias Mean Error Normalized

Mean Bias Normalized

Mean Error

All Stations 3.62 4.73 1.11 2.87 0.31 0.79

Anaheim 3.49 4.82 1.33 2.59 0.38 0.74

Burbank 4.79 3.12 -1.67 2.83 -0.35 0.59

Compton 3.59 5.23 1.65 3.07 0.46 0.86

Fontana 3.15 4.25 1.10 2.22 0.35 0.70

N Long Beach 3.40 5. 77 2.36 3.36 0.69 0.99

Los Angeles 3.47 4.82 1.35 2.87 0.39 0.83

Rubidoux 3.55 4.78 1.22 2.45 0.34 0.69

Wilmington 3.72 5.46 1.75 3.73 0.47 1.00



# TABLE V-2-11g

CAMx 2005 Base Year Total Mass Model Predictions (μg/m 3 

# )

Locations Mean

Observed Mean

Predicted Mean Bias Mean Error Normalized

Mean Bias Normalized

Mean Error

All Stations 19.62 26.14 6.52 10.49 0.33 0.53

Anaheim 17.63 24.45 6.81 8.84 0.39 0.50

Burbank 21.94 20.72 -1.22 8.76 -0.06 0.40

Compton 18.83 29.22 10.39 12.24 0.55 0.65

Fontana 21.44 23.42 1.98 9.29 0.09 0.43

N Long Beach 17.43 27.8 4 10.41 11.2 2 0.60 0.64

Los Angeles 19.15 29.38 10.23 12.47 0.53 0.65

Rubidoux 21.85 27.05 5.21 10.32 0.24 0.47

Wilmington 18.35 25.33 6.98 10.02 0.38 0.55

# Chapter 2 Federal PM2.5 Attainment

V - 2- 29 FIGURE V-2-9

CAMx 2005 Base Year Socce r Plots of Annual Average E rror at the MATES-III SitesNME

NMB 0 .2 .4 .6 .8 1.0 1.2 1.4 1.6 1.8 2.0

0 -1 -2 1 2NME

NMB 0 .2 .4 .6 .8 1.0 1.2 1.4 1.6 1.8 2.0

0 -1 -2 1 2NME

NMB 0 .2 .4 .6 .8 1.0 1.2 1.4 1.6 1.8 2.0

0 -1 -2 1 2NME

NMB 0 .2 .4 .6 .8 1.0 1.2 1.4 1.6 1.8 2.0

0 -1 -2 1 2NME

NMB 0 .2 .4 .6 .8 1.0 1.2 1.4 1.6 1.8 2.0

0 -1 -2 1 2NME

NMB 0 .2 .4 .6 .8 1.0 1.2 1.4 1.6 1.8 2.0

0 -1 -2 1 2NME

NMB 0 .2 .4 .6 .8 1.0 1.2 1.4 1.6 1.8 2.0

0 -1 -2 1 2

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 30 FIGURE V-2-10a

CAMx 2005 Base Year Time Series: Pr edicted vs. Observed at Anaheimug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 5 10 15 20 25 30 35 40

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 10 20 30 40 50 60 70 80

0 73 146 219 292 365

# Chapter 2 Federal PM2.5 Attainment

V - 2- 31ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 5 10 15 20 25 30 35 40

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 10 20 30 40 50 60 70 80

0 73 146 219 292 365



# FIGURE V-2-10b

CAMx 2005 Base Year Time Series: Pr edicted vs. Observed at Burbank

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 32 FIGURE V-2-10c

CAMx 2005 Base Year Time Series: Pr edicted vs. Observed at Comptonug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 5 10 15 20 25 30 35 40

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 5 10 15 20 25 30 35 40

0 73 146 219 292 365ug/m3

Julian Day 0 10 20 30 40 50 60 70 80

0 73 146 219 292 365

# Chapter 2 Federal PM2.5 Attainment

V - 2- 33 FIGURE V-2-10d

CAMx 2005 Base Year Time Series: Predicted vs. Observed at Fontanaug/m3

Julian Day 0 5 10 15 20 25 30 35 40

0 73 146 219 292 365ug/m3

Julian Day 0 5 10 15 20 25 30 35 40 45 50 55 60

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 10 20 30 40 50 60 70 80 90 100 110 120

0 73 146 219 292 365

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 34 FIGURE V-2-10e

CAMx 2005 Base Year Time Series: Pr edicted vs. Observed at Long Beachug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 5 10 15 20 25 30 35 40

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 10 20 30 40 50 60 70 80

0 73 146 219 292 365

# Chapter 2 Federal PM2.5 Attainment

V - 2- 35 FIGURE V-2-10f

CAMx 2005 Base Year Time Series: Pr edicted vs. Observed at Los Angelesug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 5 10 15 20 25 30 35 40

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 10 20 30 40 50 60 70 80

0 73 146 219 292 365

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 36 FIGURE V-2-10g

CAMx 2005 Base Year Ti me Series: Predicted vs. Observed at Rubidouxug/m3

Julian Day 0 5 10 15 20 25 30 35 40

0 73 146 219 292 365ug/m3

Julian Day 0 5 10 15 20 25 30 35 40 45 50 55 60

0 73 146 219 292 365ug/m3

Julian Day 0 5 10 15 20 25 30 35 40

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 5 10 15 20 25 30 35 40

0 73 146 219 292 365ug/m3

Julian Day 0 10 20 30 40 50 60 70 80 90 100 110 120

0 73 146 219 292 365

# Chapter 2 Federal PM2.5 Attainment

V - 2- 37 FIGURE V-2-10h

CAMx 2005 Base Year Time Series: Pr edicted vs. Observed at Wilmingtonug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 5 10 15 20 25 30 35 40

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 2 4 6 8 10 12 14 16 18 20

0 73 146 219 292 365ug/m3

Julian Day 0 5 10 15 20 25 30 35 40

0 73 146 219 292 365ug/m3

Julian Day 0 10 20 30 40 50 60 70 80

0 73 146 219 292 365

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 38 FIGURE V-2-11a

CAMx 2005 Base Year Bivariate Plots: Predicted vs. Observed at AnaheimPredicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 10 20 30 40 50 60 70 80

0 10 20 30 40 50 60 70 80

# Chapter 2 Federal PM2.5 Attainment

V - 2- 39 FIGURE V-2-11b

CAMx 2005 Base Year Bivariate Plots: Predicted vs. Observed at BurbankPredicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40Predicted

Observed 0 10 20 30 40 50 60 70 80

0 10 20 30 40 50 60 70 80

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 40 FIGURE V-2-11c

CAMx 2005 Base Year Bivariate Plots: Predicted vs. Observed at ComptonPredicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40Predicted

Observed 0 10 20 30 40 50 60 70 80

0 10 20 30 40 50 60 70 80

# Chapter 2 Federal PM2.5 Attainment

V - 2- 41 FIGURE V-2-11d

CAMx 2005 Base Year Bivariate Plots: Predicted vs. Observed at FontanaPredicted

Observed 0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40Predicted

Observed 0 5 10 15 20 25 30 35 40 45 50 55 60

0 5 10 15 20 25 30 35 40 45 50 55 60Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 10 20 30 40 50 60 70 80 90 100 110 120

0 10 20 30 40 50 60 70 80 90 100 110 120

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 42 FIGURE V-2-11e

CAMx 2005 Base Year Bivariate Plots: Predicted vs. Observed at Long BeachPredicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 10 20 30 40 50 60 70 80

0 10 20 30 40 50 60 70 80

# Chapter 2 Federal PM2.5 Attainment

V - 2- 43 FIGURE V-2-11f

CAMx 2005 Base Year Bivariate Plots: Predicted vs. Observed at Los AngelesPredicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 10 20 30 40 50 60 70 80

0 10 20 30 40 50 60 70 80

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 44 FIGURE V-2-11g

CAMx 2005 Base Year Bivariate Plots: Predicted vs. Observed at RubidouxPredicted

Observed 0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40Predicted

Observed 0 5 10 15 20 25 30 35 40 45 50 55 60

0 5 10 15 20 25 30 35 40 45 50 55 60Predicted

Observed 0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40Predicted

Observed 0 10 20 30 40 50 60 70 80 90 100 110 120

0 10 20 30 40 50 60 70 80 90 100 110 120

# Chapter 2 Federal PM2.5 Attainment

V - 2- 45 FIGURE V-2-11h

CAMx 2005 Base Year Bivariate Plots: Predicted vs. Observed at WilmingtonPredicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20Predicted

Observed 0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40Predicted

Observed 0 10 20 30 40 50 60 70 80

0 10 20 30 40 50 60 70 80

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 46 FIGURE V-2-12

CAMx 2005 Base Year Spa tial Distribution of the Pred icted PM2.5 Components and

Total Mass 1 1 1

1

1 2 2

2

2 3

3

3 4

4West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60

1 1 1

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South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

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South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 601 1

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South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60

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South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

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South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

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South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

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South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60

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South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

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0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60

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1 2 1 5 1 5

1 5 1 8 18

1 8 2 1

2 1

2 1 2 4

2 4West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60West

South 3675 3700 3725 3750 3775 3800 3825 3850

0 10 20 30 40275300325350375400425450475500525550575600 0 10 20 30 40 50 60

# Chapter 2 Federal PM2.5 Attainment

V - 2- 47 

# Annual Average SSI Mass Pe rformance Evaluation

Table V-2-12 presents the CAMx predic ted annual average PM2.5 and observed

annual average mass at the District's PM2.5 FRM monitoring network and at FRM

sites in neighboring air basins included in the modeling domain. The goal of this

analysis is to demonstrate th at the model is consistent in the simulation of PM2.5 at

the key sites and across the modeling domain.

In general, the 2005 base year si mulations over-predict observed PM2.5

measurements by the FRM methodology. The over prediction is greatest in the

western Basin, in particular metropolitan Los Angeles County. Over prediction in

the San Gabriel Valley and easte rn Basin is within 60 pe rcent of observations (with

the exception of Big Bear Lake which is significantly under-predicted. Southern

Orange County, Ventura County and the north ern desert stations are reasonably well

simulated. It is important to remember th at the attainment demonstration is based on

a relative response factor and not di rect future year simulations.

TABLE V-2-12

CAMx Predicted and FRM Observed 2 005 Base-Year Annual Average PM2.5

Location Predicted Annual Average

Concentration (μg/m 3 

### ) Observed Annual Average

Concentration (μg/m 3 

### ) Percentage

Prediction Error

Azusa 20.4 17.0 20.0

Big Bear 2.3 12.1 -81.7

Lynwood 30.4 17.5 73.7

Mission Viejo 16.8 10.7 57.0

Ontario 30.3 18.8 61.2

Pasadena 21.4 15.1 41.7

Reseda 17.2 13.9 23.7

Riverside Magnolia 26.7 18.0 48.3

San Bernardino 28.2 17.0 65.9

Lancaster-AV 6.8 8.9 -23.6

Victorville-MD 13.5 9.4 43.6

El Rio-SCCAB 12.0 10.6 13.2

Piru-SCCAB 7.1 9.3 -23.7

Simi Valley-SCCAB 8.9 11.2 -20.5

Thousand Oaks-SCCAB 11.2 10.5 6.7



# Base-Year Model Performan ce Stress Test Evaluation

EPA’s modeling guidance as well as the Draft Modeling Protocol outline a series of

basic stress tests that can be applied to th e base case simulation to determine the level

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 48 of sensitivity of model perform ance key parameters defining the simulations. These

stress tests include modifyin g the boundary conditions, in troducing gross changes in

the meteorological and emissions profiles. Th e goal for these analyses is to see if any

one factor is unduly biasing model perfor mance and in doing so jeopardizing the

validity of the analysis.

Table V-2-13 summarizes the suite of perfo rmance stress tests applied to the CAMx

PM2.5 simulations. Gross testing of the meteorological model performance was not

attempted for the PM2.5 analyses. Graphi cal and statistical evaluation of the MM5

simulations were reviewed for the particul ate analyses and are discussed in greater

detail in the Chapter 4 for th e episodic the ozone modeling evaluation. In general,

The PM2.5 model output re sponded in an expected manner to the changes in

simulation and emissions profiles outline in the stress tests.



# FUTURE AIR QUALITY

Under the federal Clean Air Act, the Basin must comply with the federal PM2.5 air

quality standards by April, 2010 [Section 172 (a)(2)(A)]. An extension of up-to five

years could be granted if attainment ca nnot be demonstrated and several other

conditions are satisfied. A simulation of 2010 annual average PM2.5 was conducted

to substantiate the severity of the PM2.5 problem in the Basin. The simulation used

the projected emissions for 2009 which included all existing and adopted control

measures that will be impl emented prior to 2010. Th e resulting 2010 future-year

design value (17.9 μ g/m 3 

# ) failed to meet the federal st andard. As a consequence and

as indicated in Chapter 1, the District is formally requesting U. S. EPA to grant the

five-year extension based upon the seve rity of the problem and the modeled

attainment demonstration that clearly indicates that significant reductions in daily

emissions of PM2.5, NOx, VOC and SOx ar e required to meet the 2015 attainment

date.

Figure V-2-13 depicts future annual average PM2.5 air quality projections based on

the SMAT at the eight PM2.5 monitoring sites having comprehensive particulate

species characterization compared to fe deral and state annual PM2.5 standards,

respectively. Shown in the figure are the estimated baseline conditions for 2005

along with projections for 2015, and 2021 with control meas ures in place. All sites

will attain the federal annual standard by th e year 2015. None of the sites will meet

the state annual PM2.5 standard (12 μg/m 3 

# ) by 2015. Implementation of the 8-hour

ozone control strategy will continue to lower annual PM2.5 concentrations.

The Basin currently meets the 24-hour PM2.5 federal standard (65 μg/m 3 

# ) although a

request for re-designation has not been fo rwarded to EPA. The SMAT applied to

episodic PM2.5 with emission controls s hows that the Basin will maintain its

attainment of the 24-hour average federal PM2.5 standard in 2015. However, as

# Chapter 2 Federal PM2.5 Attainment

V - 2- 49 shown in Figure V-2-15, the Final 2007 AQMP does not achieve the revised 24-hour

PM2.5 standard (35 μg/m 3 

# ) by 2015 or 2021. Additional controls are needed.

California does not have a separate 24-hour PM2.5 standard.

TABLE V-2-13

Annual PM2.5 Model Performance Stress Tests

Stress Test Methodology Simulated Impact

Boundary conditions only: no

biogenic or anthropogenic emissions Annual average concentration range 2-5 μg/m 3



# Boundary conditions and biogenic

emissions: no anthropogenic

emissions Annual average concentration range 2-5 μg/m 3



# Boundary conditions and

anthropogenic emissions: no

biogenic emissions Annual average concentr ation range 10-30 μg/m 3



# No NOx emissions in LA and

Orange Counties or offshore Annual average concentr ation range 10-22 μg/m 3



# . Westside lower 10-19 μg/m 3 

# but indicates sea

breeze dispersion and transport.

No NOx emissions in San

Bernardino and Riverside Counties Annual average concentr ation range 10-28 μg/m 3



# . East Basin 5 μg/m 3 

# lower but indicates sea breeze

dispersion and transport.

Gross VOC emissions reductions 40,

60 & 80 % Net 1-2 % reduction in PM 2.5 concentration for

20% VOC emissions reduction.

Gross NOx emissions reductions 40,

60 & 80 % Net 5-10 % reduction in PM2.5 concentration for

20% NOx emissions reduction.

Halving NO & NO2 concentrations

in boundaries Reduction of approximately about 1 μg/m 3



# EPA Clean SO2 Bound ary 10 ppb to

WRAP 1 ppb Reduction of approximately 5 μg/m 3

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 50 PM2.5 Annual Average Design

0.0 5.0 10.0 15.0 20.0 25.0

Los Angeles Burb an

k Fontana Ana he

i m

Co m pton

Ru bi

doux N. Long Beach

W i lmi

n gtonug/m3



### 2005 Design 2015 Controlled Design 2024 Controlled Design



# FIGURE V-2-13

Annual Average PM2.5 Design Concentrations:

2005, 2015 Controlled, and 2024 Controlled

PM2.5 24-Hour Average Design

0 10 20 30 40 50 60 70

Los A n g e le

s

B urban k Fon t a n a

An ah

e i m

C o mp

t o n Rubid o u x

N. Lon g Beach Wilmi n gtonug/m3



### 2005 Design 2015 Controlled Design 2024 Controlled Design



# FIGURE V-2-14

Maximum 24-Hour Average PM2.5 Design Concentrations:

2005 Baseline, 2015 Contro lled, and 2024 Controlled

# Chapter 2 Federal PM2.5 Attainment

V - 2- 51 Future-year PM2.5 air quality is projected using the procedur es and assumptions

previously described. Em issions for the 2005 and 20 14 baseline and controlled

scenarios are listed in Table V-2-8. Futu re year PM2.5 air quality was determined

using site and species specific relative respon se factors applied to 2005 PM2.5 design

values per EPA guidance documents.

The future year PM2.5 discussion follows th e order of the previous analysis on base

year model performance evaluation. Future y ear PM2.5 attainment is presented for:

(1) the MATES-III sites, (2) the annual averag e for total mass at the FRM PM2.5

sites, and (3) a weight of evidence th e 2015 gridded simulation "hot-spot" grid

analysis.

For the purpose of the Basin attainment de monstration, analyses of predicted PM2.5

outside the District jurisdiction are not presented in this draft analysis.

Control Strategy Choices

PM2.5 has five major precursors that cont ribute to the development of the aerosol

including ammonia, NOx, SOx, VOC, and directly emitted PM2.5

. 

# Various

combinations of reductions in these pollutants could all provide a path to clean air.

The attainment strategy pres ented in this Final 2007 AQMP relies on the maximum

extent possible reductions of SOx, direct PM2.5, followe d by VOC and NOx. As

discussed in Chapter 4 of the Final 2007 AQMP, the proposed strategy focuses on the

reductions of SOx and primar y PM2.5 through cleaner ma rine fuels and extensive

diesel trap retrofits respectively.

It is useful to weigh the value of the per ton precursor emissions to microgram

reductions of PM2.5. The fo rmation of PM2.5 is non-lin ear and as such individual

precursors contribute differen tly to the overall mass. The CAMx si mulations provide

a relative rate of reduction per ton of em issions reduced based on complex aerosol

chemistry. For PM2.5, the simulations determin e that VOC emissions reductions

have the lowest return in terms of micrograms reduced per ton reduction. NOx

reductions are approximately three times more effective in lowering PM2.5

concentrations but not as eff ective as sulfate and direct PM2.5 emissions reductions.

Table V-2-14 summarizes the relative importa nce of precursor emissions reductions

to the analysis.

The District’s proposed co ntrol strategy maximizes reductions of direct PM2.5 and

SOx to the extent possible due to their effectiveness as well as the likelihood

schedule of implementation with in the next seven years. Substantial additional VOC

and NOx emissions reductions are also required for attainment. However the

strategy, nonetheless attempts to maxi mize the potential PM2.5 concentration

reduction per identified ton precursor emi ssions reduction. The mix of the four

primary precursor’s emissions reductions ta rgeted for the PM2.5 focused approach

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 52 are listed in the Controlled Emissions Projection Algorithm (CEPA) output attached

at the end of this document.

SMAT Annual PM2.5 Atta inment Demonstration

As previously outlined, the SMAT is conducted on a quart erly basis using the CAMx

model output for the six species from the 2005 base-year and the 2014 controlled

emissions. Quarterly RRFs determined from the modeling ar e applied to the

MATES-III/Sandwich modified quarterly design value comp onent species. Bonded

water is calculated from the concentrations of ammonium, nitrate and sulfate using

the AIM polynomial regression equation. Bo nded water is not directly reduced by an

RRF but is recalculated af ter applying the RRFs to the ammonium, sulfate and

nitrate. Finally, the blank is added to the mass.

Tables V-2-15a throug h V-2-15c provide the 2014 baseline PM2. 5 air quality at the

eight key sites, the predicted PM2.5 ba sed on the CARB emissions reduction plan

and the estimation of the 2015 controlled an nual average PM2.5 adding the District’s

emissions reduction overlay to the CARB plan.

Without implementing any additional controls, PM2.5 concentrations at the key sites

will not meet the federal standard by 2015. Regional concentrations of PM2.5 will

be lower with implementation of the CA RB plan however the maximum predicted

PM2.5 concentration, will continue to exce ed the federal standa rd by approximately

four (4) percent. With implementation of the District overlay to the CARB plan, all

stations will meet the federal standard of 15.0 μ g/m 3 

# . The PM2.5 annual attainment

demonstration calculations using the SMAT and RRFs are provi ded in Tables V-A1a through V-A-1h in Attachment A.

TABLE V-2-14

Relative Contributions of Precursor Emissions Reductions to Simulated

Controlled Future-Year PM2.5 Concentrations

Precursor

(TPD) PM2.5 Component (μg/m 3 

# ) Standardized

Contribution to

Mass

VOC Organic Carbon Factor of 1

NOx Nitrate Factor of 3

PM2.5 Elemental Carbon & Others Factor of 5

SOx Sulfate Factor of 10

# Chapter 2 Federal PM2.5 Attainment

V - 2- 53 TABLE V-2-15a

2014 Predicted Baseline PM2.5

Location NH4 NO3 SO4 OC EC Metals/ Water Blank PM2.5

Others Mass

Anaheim 1.4 3.3 2.5 2.0 1.3 2.0 1.5 0.5 14.5

Burbank 1.8 4.1 2.4 3.2 1.7 2.1 1.6 0.5 17.4

Compton 1.6 3.6 2.9 2.5 1.6 2.0 1.7 0.5 16.5

Fontana 1.9 4.8 2.5 1.6 1.9 2.6 1.7 0.5 17.6

Los Angeles 2.0 4.8 2.9 1.1 1.9 2.1 2.1 0.5 17.3

Long Beach 1.6 3. 3 3.0 1.5 1.3 1. 9 1.6 0.5 14.6

Rubidoux 2.1 5.6 2.4 2.1 1.7 2.2 1.9 0.5 18.5

Wilmington 1.4 2.7 3.0 1.5 1.7 1.9 1.5 0.5 14.2



# TABLE V-2-15b

2014 Predicted PM2.5 with CARB Measures

Location NH4 NO3 SO4 OC EC Metals/ Water Blank PM2.5

Others Mass

Anaheim 1.2 2.9 2.0 1.7 1.2 1.9 1.3 0.5 12.6

Burbank 1.6 3.7 1.9 2.9 1.6 2.0 1.4 0.5 15.5

Compton 1.5 3.4 2.3 2.2 1.5 1.9 1.5 0.5 14.7

Fontana 1.6 4.0 2.0 1.4 1.8 2.5 1.4 0.5 15.2

Los Angeles 1.8 4.4 2.3 0.9 1.7 2.1 1.8 0.5 15.4

Long Beach 1.4 3. 1 2.3 1.3 1.2 1. 7 1.4 0.5 12.9

Rubidoux 1.7 4.5 1.9 1.9 1.6 2.1 1.5 0.5 15.7

Wilmington 1.2 2.5 2.2 1.4 1.4 1.7 1.3 0.5 12.1



# TABLE V-2-15c

2014 Predicted PM2.5 with CARB Measures and District Overlay

Location NH4 NO3 SO4 OC EC Metals/ Water Blank PM2.5

Others Mass

Anaheim 1.2 2.7 1.9 1.7 1.1 1.9 1.2 0.5 12.3

Burbank 1.5 3.5 1.9 2.8 1.5 2.0 1.3 0.5 14.9

Compton 1.4 3.4 2.2 2.2 1.4 1.9 1.5 0.5 14.5

Fontana 1.5 3.7 2.0 1.4 1.7 2.5 1.4 0.5 14.7

Los Angeles 1.7 4.2 2.2 0.9 1.6 2.0 1.7 0.5 14.9

Long Beach 1.4 3. 0 2.3 1.3 1.2 1. 7 1.4 0.5 12.7

Rubidoux 1.6 4.1 1.9 1.8 1.5 2.1 1.4 0.5 15.0

Wilmington 1.1 2.4 2.2 1.3 1.4 1.6 1.2 0.5 11.8

# Chapter 2 Federal PM2.5 Attainment

V - 2- 54 

# 2015 CAMx Grid-Cell Evaluation

Figure V-2-15 presents the grid cell extr apolated of 2005 PM2.5 annual design

values. Only grid cells over the federal standard are darkened. Extrapolation was

based on distance weighted interpolatio n scheme using desi gn values from sites

inside and outside the Basin to enhance the spatial representa tion. The pattern

depicted by the grid cell design display clos ely matches the pattern of annual average

PM2.5 presented in Figure V-2-3.

Using a similar interpolation scheme, the re lative percentage contributions of the six

component species at the eight MATES-III sites was extr apolated to each cell in the

basin. Predicted 2015 PM2.5 concentrati ons at the grid cell level (without using

multi-cell averaging) was estimated each gr id cell by multiplying the speciated RRFs

from the CAMx simulations by the relative percentage concentrations of the six

components contributing to th e grid cell mass and the inte rpolated grid cell design

value. Figure V-2-16 presents the 2015 ba seline distribution. PM2.5 levels will be

reduced regionally with implementing additi onal control measure however, almost 60

percent of the population of the Basin will continue to breathe air quality above the

federal standard. With the Final 2007 AQMP control strategy in place no cells in the

Basin will be above the federal standard.

SMAT 24-Hour PM2.5 Attainment Demonstration

As previously stated, the 2005 Basin maxi mum design value (64.8 μg/m3) meets the

federal 24-hour average PM2. 5 standard of 65 μg/m3. The SMAT for the 24-hour

standard is presented to assure that the PM 2.5 episodic levels continue to lower and

that the Basin continues to meet the standa rd in 2015 an d beyond. Six methods are

applied to the Sandwich modified MATES- III quarterly design values and episodic

PM2.5 data to quantify future year PM2.5 re ductions. All of the tests summarized in

Table V-2-15 demonstrate c ontinued attainment of th e 24-hour average PM2.5

standard in 2015.

The six versions of the 24-ho ur PM2.5 SMAT include:

• CAMx derived RRFs (2005-2 014) for the annual av erage PM2.5 attainment

demonstration are applied to the three- year average PM2.5 design values;

• the quarterly based SMAT prescribed in the EPA guidance document that uses

the CAMx quarterly RRF’s from the top 25 percentile applied to the quarterly

24-hour design values for each year in the three year period 2003-2005;

# Chapter 2 Federal PM2.5 Attainment

V - 2- 55 FIGURE V- 2-15

2005 Grid-Cell Extrapolated Design Values > 15.0 μg/m 3



# FIGURE V- 2-16

2015 Baseline Grid-Cell Extrapolat ed Predicted Design Values (μg/m 3 

# )

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 56 • a version of the quarterly based SM AT prescribed in the EPA guidance

document that uses the CAMx quarte rly RRF’s from the annual average

PM2.5 attainment demonstration applie d to the quarterl y 24-hour design

values for each year in the three year period 2003-2005;

• a modified version of the second optio n that applies the quarterly RRFs from

the top 25 percentile to the top th ree quarterly episodic 2005 PM2.5

measurements;

• the expected response of the peak ep isode PM2.5 [October 22, 2005, 110

μg/m3] to episodic specific RRFs is applied to the 3-year average Basin

maximum design value, and

• the expected response of the peak ep isode PM2.5 to the annual average RRFs

is applied to the 3-year averag e Basin maximum design value.

Table V-2-16 summarizes the different me thods for calculating the 2015 24-hour

PM2.5 design value.

The first test simply assumes that the RRFs calculated for the annual average attainment

demonstration can be directly applied to the 24-hour PM2.5 de sign values to estimate the

2015 reduction in PM2.5 due to implementation of the control strategy. The second test

is more conservative and follows the example specified in the EPA gu idance document.

The quarterly RRFs determined for the top 25 percent of th e quarterly distributions are

applied to the compon ent based design values for the period 2003 th rough 2015 to

recreate a design value. The third test recrea tes the second test but uses the quarterly

RRFs derived from the annual PM2.5 analysis.

The fourth analysis focuses on the top three episodic days in each quarter of 2005 to

establish both the percentage co ntributions of the components to the sample mass and an

average quarterly top-three distribution for an episodic period. The quarterly RRFs

determined for the top-25 pe rcentile analysis are applied to the top-three quarterly

measurements to determine a quarterly mass reduction pe rcentage. The quarterly

percentage reduction is then applied to the quarterly 2005 design to estimate a 2015

quarterly concentration.

The fifth and sixth analyses apply the day specific and annual av erage RRFs to the

component species observed on October 22, 2005, the day having the highest measured

PM2.5 at a majority of sites in the Basin. These two analyses, like the third, produce a

percentage reduction that is applied to th e 2005 design to estimate 2015 attainment

The PM2.5 24-hour attainment demonstration calculations using the SMAT and RRFs are

provided in Attachment A. Table V-A-2 pr ovides the annual average RRF applied to the

24-hour annual design value. Tables V- A-3a through V-A-3h present the EPA

# Chapter 2 Federal PM2.5 Attainment

V - 2- 57 recommended attainment test. Tables V-A- 4a through V-A-4h presents the modified

EPA test using the quarterly RRFs from the annual attainment demonstration as an

alternate reduction rate. Tables V-A-5a th rough V-A-5h present the application of the

quarterly top-25 percentile RR Fs to the 2005 quarterly top- three episodic measurements.

Tables V-A-6a through V-A-6h provide the applications of the episodic model derived

RRFs to the peak episodes and the applicati on of the annual RRFs to the peak episodesChapter 2 Federal PM2.5 AttainmentV - 2-58TABLE V-2-16Summary of Methodologies to Calculate 2015 24-Hour Average DesignValue (μg/m3)LocationAnnual RRFto DesignQuarterly Top25 PercentileRRF toQuarterlyDesignQuarterlyRRF fromAnnualto QuarterlyDesignQuarterly Top25 PercentileRRF toQuarterlyTop-3 Ratio toDesignPeak DayRRF to PeakDayAnnual RRFto Peak DayMaximum ofMethodsAnaheim35.7 42.841.838.5 34.6 34.1 42.8Burbank 40.5 47.746.744.1 43.8 39.4 47.7Compton 39.5 56.646.354.3 37.5 40.9 56.6Fontana 40.0 45.046.741.2 36.8 33.0 46.7Long Beach 32.4 41.939.137.3 31.2 33.3 41.9Los Angeles 46.1 55.150.747.8 50.3 46.4 55.1Rubidoux 42.8 46.949.347.1 38.6 37.4 49.3Wilmington 30.3 39.536.135.8 30.3 28.8 39.5

# Chapter 2 Federal PM2.5 Attainment

V - 2- 59 

# WEIGHT OF EVIDENCE

Table V-1-3 in Chapter 1 summarizes th e additional information requirements

needed to support th e modeling attainment demonstra tions based upon the projected

future year concentration. Specifically, “A weight of ev idence demonstration should

be conducted to determine if aggregate s upplemental analyses support the modeled

attainment test.” The “weight of eviden ce” supporting information for the current

PM2.5 attainment demonstration includes an analyses of the PM 2.5 air quality trend,

trends in population exposu re, emissions trends, and adjustments to the mobile

source emissions VMT distributions. While the weight of evidence discussion is

focused towards supporting the attainment demonstration, it is important to be

unbiased and present both sides of the ar gument “for” and “against” acceptance of

the analysis. In the case of the PM2.5, it is equally important to remember the

magnitude of the problem requires an ex traordinary commitmen t to implementation

of the control strategy. Positive trend da ta in either air quality or emissions

reductions should be viewed as encouraging but not taken solely as the remedy to the

air quality problem.

Air Quality Trends

Probably the most convincing argument that the Ba sin will attain the Federal

Standard for PM2.5 in 2015 is the ongoing trend of ambient PM2. 5 concentrations.

Figure V-2-17 depicts the trend of annua l average PM2.5 concentrations at key

monitoring stations in each of the four counties of the Basin. The trend clearly

depicts a steady rate of im proving PM2.5 air quality, rega rdless of locations in the

Basin. This trend line also supports the argument for selecting 2005 as the design

year to anchor the PM2.5 attainment dem onstration due to the rapid change in air

quality. The 4-station aver age PM2.5 has impr oved by more than 36 percent from

1999 through 2006 fo r an approximate 4.5 percent annual improvement rate.

It is dangerous, however, to directly extra polate the improvement rate. For example,

over the last 10 years, the first five ye ars of data would ha ve suggested vast

improvement for the following fi ve year period. But, the tr end flattened to little to no

improvement. Redistribution of population and industrial gr owth in the east Basin is

expected to continue to increase over the next 10 years. This shift in demographics is

expected to slow the east basin trend of improving air quality. Also, with the

expected tripling of goods movement thro ugh the Ports of Los Angeles and Long

Beach, emissions from heavy du ty truck and rail traffic is expected to increase

accordingly. Regardless, while the trend is encouraging, the rate of improvement

will slow and possibly turn ar ound if no further emissi ons reductions are made.

Implementation of the control strategy propo sals of the Ports, CARB through their

AQMP commitment and the District ’s total program is expect ed to ensure that PM2.5

levels will continue to improve regionally.

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 60 10 15 20 25 30 35

1999 2000 2001 2002 2003 2004 2005 2006Annual Average ug/m3



### Los Angeles Anaheim Rubidoux Fontana



# FIGURE V-2-17

Trend of Annual Average PM2.5 in th e South Coast Air Basin (1999-2006)



# Emissions Trends

Figures V-2-18 and V-2-19 depict the ba seline trends of the PM2.5 precursor

emissions from 2002 throu gh 2023. The VOC and NOx emissions a trend lines up

nicely against the PM2.5 air quality trend fo r the 2002 to 2005 period. Four and two

percent reductions of VOC and NOx emissi ons respectively are consistent with

improving air quality tre nd. However increases in dire ctly emitted particulate and

SOx suggests an offset to the other precurs or emissions benefits over the 2002 – 2005

period. Introduction of a 15 ppm sulfur fuel in California in 2006 is not depicted in

the 2005 bar but in reality, th e region may have experience d an early phase in of the

lower sulfur fuel. Clearly the baseline tre nd in sulfur emissions is lower over the

next six year before undergoing a moderate increase in 2014.

Figure V-2-20 show the impact of the emissions control strategy. Significant

additional reductions beyond baseline are demonstrated through 2020. With the

control strategy implemented in 2014, th e Basin will experience reductions in

emissions from 2005 of: 36.6 percent for VOC, 57.5 perc ent for NOx, 69.4 percent

for SOx, and 13.7 percent fo r directly emitted PM 2.5. Translated over the nine seven

years needed to implement an nual percent reductions are expected to be consistent

with those observed between 2002 and 2005. The confirms that implementations of

the control strategy on top of a decreasing baseline will move the air quality trend in

# Chapter 2 Federal PM2.5 Attainment

V - 2- 61 the direction of improving PM2.5 concentr ations and attainment of the federal

standard.

0 200 400 600 800 1000 1200

2002 2005 2008 2011 2014 2017 2020 2023Tons Per Day



### VOC NOX



# FIGURE V-2-18



# Trend of Baseline VOC and NOx Emissions (TPD) in the South Coast Air Basin

0 20 40 60 80 100 120

2002 2005 2008 2011 2014 2017 2020 2023Tons Per Day



### SOX PM2.5



# FIGURE V-2-19



# Trend of Baseline SOx and PM2.5 Emission s (TPD) in the South Coast Air Basin

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 62 0 100 200 300 400 500 600 700

2014 2014

Controlled 2020 2020

ControlledTons Per Day



### VOC NOX SOX PM2.5



# FIGURE V-2-20



# Comparison of Baseline and Controlled Emissi ons (TPD) in the South Coast Air Basin

Mobile Source Emissions and VMT

The emissions inventory used for the Octo ber 2006 / Draft 2007 AQMP relied on an

interim inventory and a work ing draft version of EMFA C 2007 that CARB provided

to AQMD staff to begin the analysis fo r the Draft 2007 AQMP. Several major

improvements to the EMFAC a nd off-road models were in progress at the time.

CARB staff released the official EMFA C2007 and OFFROAD emissions model on

November 1, 2006 and provide d subsequent additio nal external adjustment factors to

incorporate additional changes viewed necessary to the mob ile source inventories.

Significant changes occurred to VOC and NOx on-road mob ile source emissions. A

decrease in VOC emissions was made due to the modification in number of pending

vehicle registrations assumed in the EMFA C model. “Pending vehi cles” are those in

the DMV data base that are not fully proc essed for administrative reasons. In prior

EMFAC models, pending vehicl es were not included whic h might have contributed

to previous underestimation of VOC emissions in the inve ntory. In developing the

working draft of EMFAC2007 for the Draf t 2007 AQMP, CARB staff assumed all

pending registrations were on the road, and in corporated these vehicles in the vehicle

population analysis. Howeve r, with the official releas e of EMFAC2007, CARB staff

revised its assumption based on further ev aluation of the pending registration and

concluded that only 25 per cent of the total pending ve hicles were actually being

driven. The change in assu mption resulted in a 95 tons /day decrease in VOCs for

2005 with minor impact on the NOx emissi ons. This is due to the fact that the

pending vehicles have a gr eater impact on evaporative emissions (i.e., VOC) than

# Chapter 2 Federal PM2.5 Attainment

V - 2- 63 pending vehicles have a gr eater impact on evaporative emissions (i.e., VOC) than

tailpipe exhaust emissions sin ce total vehicle miles travel ed (VMT) were presumably

already captured by the EMFAC model.

The impact of the significant change in the VOC inventory is most apparent in the

performance of the CAMx base year oz one simulations. VOC emissions are a

critical component of the PM2.5 inventory ho wever as depicted in Table V-2-14 the

reduction of VOC emissions ha d a smaller impact compared with other precursors.

The net change in the inventory for 2005 NOx emissions was an increase of about 10

TPD based on EMFAC2007 de rived VMT profile.

Differing Estimates of VMT on Future Year PM2.5

Historically, VMT is calculated through se veral approaches. One approach, used by

metropolitan planning organiza tions such as SCAG, uses complex simulation models

to spatially estimate VMT ba sed on origin-destination surv eys. The approach used

by CARB is based on Department of Motor Vehicle (DMV) vehicle registrations and

Bureau of Automotive Repair (BAR) odometer readings co llected through the Smog

Check program.

Under state and federal law, transportatio n agencies develop VMT information for

transportation planning and conformity purposes. Under federal law, the Highway

Performance Monitoring System (HPMS) estimates of VMT ar e to be considered the

primary measure of VMT “within the por tion of the nonattainment area” [40 CFR

93.122(b)(3)]. In addition, “For areas with network-based travel models, a factor (or

factors) may be developed to reconcile a nd calibrate the network-based travel model

estimates of VMT in the base year of its validation to the HP MS estimates for the

same period. These factors may then be ap plied to model estimates of future VMT.

Locally developed count-based programs and other departures from these procedures

are permitted subject to the interagency cons ultation procedures of § 93.105(c)(1)(i).”

SCAG provided VMT estimates for calendar years 2000, 2002, 2005, 2010, 2015,

2020, and 2030 to CARB and AQMD fo r the development of the 2007 AQMP

revision. SCAG’s estimates are based on information developed for the 2004 RTP.

The South Coast Air Basin VMT totals ar e used for planning purposes and the

spatially disaggregated VMT is used for air quality modeling purposes. CARB

compares SCAG’s VMT estim ates with their VMT estim ates based on the DMV and

BAR registration data and atte mpts to reconcile with SC AG’s estimates using vehicle

population data and mileage accrual rate.

In the prior EMFAC models, CARB’s VMT estimates generally agreed with SCAG’s

estimates. For the latest EMFAC 2007 , CARB took DMV and BAR data from 2000

to 2005 to develop new VMT estimates for 2000, 2002, and 2005. When CARB

# Final 2007 AQMP Appendix V: Mode ling and Attainment Demonstrations



# V - 2- 64 VMT data are in close agreement with SCAG's except for 2005. AQMD staff

examination of the EMFAC VMT indicates that for 2005 the difference in CARB’s

VMT estimates and SCAG’s is on the order of 10 percent for light- and medium-duty

vehicles (or 30 million more VMT per day) and 20 percen t for heavy duty vehicles

(or about 5 million more VMT pe r day). CARB staff indicate d that they believe that

the differences are within the unc ertainties of estimating VMT.

Figure V-2-21 illustrates the difference be tween the current EM FAC on-road mobile

sources VMT assumption compared to SCAG’ s projections. The difference between

the two estimation methodologies is portr ayed as the 2005 “b lip” in the diagram.

FIGURE V-2-21

EMFAC2007 vs. SCAG’s Proj ect of Basin On-Road VMT

The AQMD retained two technical experts in the area of transportation analysis to

review the VMT estimates for 2005. The consultants reviewed CARB’s assumptions

and to the extent possible some of the DMV and BAR data used to produce the 2005

VMT estimates. They concluded that there is no independent evidence to support a

decline in VMT between 2005 and 2010, and recomme nded conducting sensitivity

analysis in the near-term (given the need to develop an AQMP Revision) to

determine the magnitude of the differences. 300 320 340 360 380 400 420 440 460 480

2000 2005 2010 2015 2020 2025 

# Adjusted EMFAC 2007

SCAG Projections EMFAC 2007

# Chapter 2 Federal PM2.5 Attainment

V - 2- 65 Impact to Projections of Future Year PM2.5

CAMx was simulated for PM2.5 using the SCAG estimate of VMT which resulted in

a reduction of 10 TPD fewer tons of 2005 base-year NOx emissions. A rough

estimate of this impact wa s between 0.08 and 0.10 μ g/m 3 

# less PM2.5 produced in

2005, the anchor year for the RRF calculation. (Le ss PM2.5 produced through the

2005 simulation affects the slope of the RRF and will require additional emissions

reductions in future years). Estimation of the impact of using SCAG’s VMT profile

to 2015 PM2.5 is roughly place the need for about 10 additional tons of NOx

reductions in 2014 or its equivalent reduc tion in either SOx, directly emitted PM2.5

or VOC. Using SCAG’s VMT profile as the basis for 2005 mobile source NOx

provided a conservative choice that confir med the direction of tonnage of emissions

reductions needed to meet the fe deral standard with confidence.

In light of uncertainty in VMT projections, the District staff recommended to retain

the mobile source emissions estimates made from EMFAC2007 for the planning

inventory. However, for modeling purposes, the gridded mobile source emissions

were based on the SCAG 2005 projection of VMT.

# CHAPTER 3

THE FEDERAL 24-HOUR PM10 ATTAINMENT

DEMONSTRATION PLAN AND VISIBILITY

ASSESSMENT

Introduction

Modeling Methodology

Future Year Air Quality

Visibility

# Final 2007 AQMP Appendix V: Modeli ng and Attainment Demonstrations



# V - 3- 1 

# INTRODUCTION

As discussed, in the main document, on September 21, 2006 the U.S. EPA

administrator signed the final documents th at eliminated the existing annual PM10

standard. The action retained 24-hour PM10 standard at its exis ting concentration of

150 μ g/m 3 

# . The form of the 24-hour PM10 standa rd allows for one violation of the

standard annually. The Basin currently m eets the 24-hour average federal standard.

(The only days that exceed th e standard are associated w ith high wind natural events

or exceptional events due to wildfires).

For this analysis, the annual second maximum concentratio n is used for the

attainment demonstration (given the sta ndard allows for one violation annually).

Riverside Rubidoux has been the PM10 24-hou r design site in nine of the past ten

years when high wind days have been ex cluded from the analysis. The 2005 design

value at Rubidoux is 86 percent of the fe deral standard. The standard attainment

demonstration is conducted to assure that the Basin will continue to be in compliance

in future years.



# MODELING METHODOLOGY

As a conservative analysis, only emissions reductions associated with the PM2.5

portion of the 24-hour PM10 concentration are assumed to be impacted by future

year emission controls. Co-located PM10 and PM2.5 measurements (monitored on

the same days) were used to determine the site specific average percentage

contribution of PM2.5 to the PM10 annual and second annual maximum

concentrations.

Future year predictions of maximum a nd second maximum 24-hour average PM2.5

were determined using the site specific annual PM2.5 RRFs calc ulated for 2005 and

2014. The annual PM2.5 RRF encumber s total mass rather than individual

component species. Th e site specific RRF was applied to the PM2.5 portion of the

PM10. The PM10-2.5 “coarse” portion remains constant in the analysis.

The RRFs for the six of the eight MATES-III sites, calculated from the annual PM2.5

attainment demonstration, for 2005 to 2014, are applied directly to the fine portion of

the 24-hour PM10 distribution measured at that location. The Compton RRF was

applied to the Lynwood PM10 (s ince the monitoring stations are closely located).

The average RRF determined from all eight MATES-III sites was used to estimated

the reductions of the PM2.5 por tion of the PM10 mass at th e remaining PM10 sites in

the Basin. The predicted reduc tions to the fine portion are then added to the coarse to

estimate a 2015 second maxi mum PM10 24-hour average design concentration.

# Chapter 3 Federal 24-Hou r PM10 Attainment Plan and Visibility Assessment

V -3- 2 

# FUTURE YEAR AIR QUALITY

PM10 24-hour attainment Demonstration

Table V-3-1 summarizes the PM10 24-hour atta inment demonstration. All sites meet

the federal PM10 standard of 150 μ g/m 3 

# in 2015. Th e predicted 2 nd 

# highest

maximum concentrations for 2015 is locate d at Rubidoux and va lues approximately

74 percent of the federal standard. Only five of the sixteen locations are expected to

meet the more restive state standard of 50 μ g/m 3 

# by 2015. Rubidoux is predicted to

exceed the state standard by 122 percent in 2015.



# PM10 Annual Analysis

The Final 2007 AQMP does not provide an upda ted regional attainment

demonstration to show compliance to the revoked annual PM10 standard (50 μ g/m 3 

# ).

At the writing of this document, it is expected that the 2006 design value for

Rubidoux will continue to no minally exceed the revoked federal standard but will

continue to exceed the Califor nia PM10 standard of 20 μ g/m 3 

# . Despite EPA’s

decision revoking the PM10 annual standard, the District will continue to work

towards meeting its former attainment target in the effort to protect public health,

demonstrate progress towards attaining the st ate PM10 annual standard and assist in

compliance of the federal 24-hour PM10 standard.

As part of the 2003 AQMP, the Distri ct proposed a comprehensive program to

examine the local emissions profile and poten tial for mitigation actions that could be

taken to bring PM10 concentra tions at Rubidoux within th e annual standard by 2006.

A survey of the local emi ssions was conducted and as a result two District rules

(1186 and 1174) targeting emissions from a ggregate operations and bag houses have

been strengthened in the efforts to redu ce impacts to the Rubi doux community. In

addition, the District has increased complia nce measures in the area and staff is

working with the Riverside C ounty Redevelopment agencies to expedite installation

of paved curbs and gutters to eliminate sources of fugitive dust emissions. The Final

2007 AQMP control measure BCM-02 PM Emissions Hot Spots continues this

concept of addressing localized PM impacts.Final 2007 AQMP Appendix V: Modeling and Attainment DemonstrationsV - 3- 3TABLE V-3-124-Hour Average Maximum and Average 2ndMaximum Basin PM10:2003-2005 Baseline Design and 2015 ControlledAverageMaximumAverage 2ndMaximumLocationMass(μg/m3)PM2.5/PM10RatioEst.2.5Mass(μg/m3)Est.CoarseMass(μg/m3)Mass(μg/m3)PM2.5/PM10RatioEst.2.5Mass(μg/m3)Est.CoarseMass(μg/m3)24-HourAveragePM2.5RRF2015EstimatedAverageMaximum(μg/m3)2015EstimatedAverage2ndMaximum(μg/m3)Azusa 93 0.51 47.6 45.4 79 0.54 42.3 36.7 0.75 81 68Burbank 82 0.51 42.0 40.0 73 0.69 50.2 22.8 0.79 73 62Long Beach 96 0.73 69.8 26.2 63 0.78 48.9 14.1 0.73 77 50Los Angeles 74 0.75 55.7 18.3 69 0.80 54.9 14.1 0.76 61 56Santa Clarita 60 0.56 33.6 26.4 54 0.54 29.2 24.8 0.75 52 47Hawthorne 53 0.56 29.7 23.3 61 0.54 32.9 28.1 0.75 46 53Anaheim 78 0.50 38.8 39.2 67 0.49 33.1 33.9 0.74 68 58Mission Viejo 51 0.69 35.4 15.6 44 0.33 14.7 29.3 0.74 42 40Rubidoux 141 0.60 84.4 56.6 129 0.42 54.3 74.7 0.66 112 111Perris 102 0.56 57.1 44.9 88 0.54 47.5 40.5 0.75 88 76Banning Airport 79 0.56 44.2 34.8 55 0.54 29.7 25.3 0.75 68 48Crestline 49 0.56 27.4 21.6 47 0.54 25.4 21.6 0.75 42 41Fontana 105 0.29 30.6 74.4 96 0.36 34.2 61.8 0.73 97 87San Bernardino 96 0.58 55.5 40.5 85 0.44 37.8 47.2 0.75 82 76Redlands 80 0.56 44.8 35.2 70 0.54 37.8 32.2 0.75 69 61Ontario 90 0.44 40.0 50.0 77 0.65 50.1 26.9 0.75 80 64

# Chapter 3 Federal 24-Hou r PM10 Attainment Plan and Visibility Assessment

V -3- 4 

# VISIBILITY

Background

In July 1999, U.S. EPA adopt ed the federal Regional Haze Regulations [40 CFR Part

51] to address Section 169A of the CAA which set forth a nati onal goal for future

visibility with specific focus to remedy an y visibility impairments to Class I areas

nationwide. States are required to provide to EPA emissions reduction strategies to

improve visibility in all mandatory Class I national pa rks and wilderness areas. In

response to the requirements of the re gulations, California joined the Western

Regional Air Partnership (WRAP), a multi-ag ency organization that is coordinating

implementation of the regional haze rules. States with PM2.5 non-attainment areas

are require to submit “haze plans” to EPA within 3-year s following PM2.5

designation and develop future year (2018) inventories of emissions that lead to

visibility reduction. The ARB has assume d the responsibility for the plan and

inventory development requirements for the state.

The emissions reductions need ed to attain the PM2.5 st andard in the Basin will

directly contribute to impr oved future year visibility. California continues to

maintain a state standard fo r visibility structured to re duce aerosol particles (8-hour

average) that contribute to an extinction co efficient value of 0.23 per kilometer (or 10

miles of visual range) when re lative humidity is less than 70 percent. The previous

form of the standard assess ed the number of days when visual range was less than 10

miles for the same humidity considerati on. Visibility is among the strongest

indicators to air quality and its value is para mount. As such, future year visibility is

used in the socioeconomic evaluation of the AQMP to estimate monetary benefits

that arise from improved visual range th rough the implementation of the plan.

Future-year visibility in the Basin is projected empirically using the results derived

from a regression analysis of visibility wi th air quality measurements. The regression

data set consisted of aeroso l composition data collected during a special monitoring

program conducted concurrently with visi bility data collection (prevailing visibility

observations from airports an d visibility measurements from District monitoring

stations). A full description of the visibility analysis is given in Technical Report V-C

of the 1994 AQMP.



# Visibility Modeling

To establish the most reasonabl e control strategy to meet the visibility standard in the

future, a relationship between visibility a nd concentrations of visibility reducing

particles must be established. This, in turn, requires visibility modeling techniques to

identify sources of visibility reducing par ticles and to quantify their impacts.

# Final 2007 AQMP Appendix V: Modeli ng and Attainment Demonstrations



# V - 3- 5 The total atmospheric light extinction can be broken down into four basic

components: scattering of light by partic les, absorption of light by particles,

absorption of light by gases, a nd scattering of light by gase s (Rayleigh scattering). In

general, total light extinction is dominated by scattering of light due to particles, with

light absorption by particles being second in importance. The components other than

scattering of light by par ticles have been well-charac terized by theory or from

previous studies. Therefore, light extinct ion by particle sca ttering is normally

estimated either by visi bility modeling or by direct measurement.

Multiple linear regression is a statistical tool commonly used for characterizing the

relationship between visibility and ambien t air quality of the visibility reducing

particles. When atmospheric light extinction due to particle scattering is regressed on

concentrations of visibility reducing particles, the regre ssion coefficients represent

the extinction efficiency due to particle scattering (ex tinction per unit concentration)

for each air pollutant species.

Multiple linear regression was employed in the 1991 AQMP to develop empirical

predictive equations. Empirical visibility model developed in the 1991 AQMP for

Riverside were utilized in the current AQMP analysis to estimate future visibilities

with new future-year (2015, and 2021) organic carbon concentrations, sulfate, and

nitrate concentrations which were obtained from the CAMx simulations. Details of

the statistical analysis used to develop the empirical predictive equations can be

found in Technical Report V- G of the 1991 AQMP.



# Prior Visibility Modeling Results

In the 1991 AQMP, the regression analysis resulted in several sets of extinction

efficiencies for light scattering by particles for Riverside (Rubido ux station) and four

additional measurement locations. (Since R ubidoux is the limiting PM2.5 station in

the Basin it is considered to be the repr esentative site for expected minimum Basin

visual range estimation.) Combining extinction efficien cies for light scattering by

particles with the empirical expressions for the other light extinction component

produces a series of empirical predictive equations. Empirical predictive equations

relate light extinction to co ncentrations of visibility re ducing air pollutants and have

the following form:

b

ext 

# = Summation ( b

i 

# . C

i 

# )+ b

RAY



# where b

i 

# = extinction efficien cy for ith species

(10 -4 

# m -1 

# /μg/m 3 

# or 10 -4 

# m -1 

# /pphm)

C

i 

# = mean concentration for ith species (μg/m 3 

# or pphm)

# Chapter 3 Federal 24-Hou r PM10 Attainment Plan and Visibility Assessment

V -3- 6 b

RAY 

# = extinction due to Rayleigh scattering in the Basin (10 -4



# m -1 

# )

Table V-3-2 is a summary of the 19 91 AQMP results, showing the extinction

efficiency, b

i 

# , for Riverside. (The extinction efficiency, b

i 

# , for the other locations

analyzed in the 1994 AQMP can be found in 1994 AQ MP, Technical Report V-C).

A baseline light extinction budget was de termined for each empirical predictive

equation using the mean meas ured values of the air quality components for the

baseline year 2005. The light extinction budget for Riverside during the baseline

emission year is summarized in Table V-3-3. These show the percent contribution to

total extinction from each com ponent for each equation. At Riverside light scattering

by particles accounts for up to 86 percent of the total light extinction with secondary

nitrate and carbon particles being dominant.



# Predicted Future Air Quality

Future air quality levels are needed to estimate future visual air quality. The

concentrations of sulfate, n itrate, organic carbon and elemental for future years 2015,

and 2021 are taken from the results of the CAMx modeling an alysis. Future

concentrations of NO

2 

# are estimated from the mean annual concentrations measured

using linear rollback of NO x emissions. Natural background concentrations for each

of these are assumed to be negligible for th is analysis. Estimated future baseline and

controlled levels for all pollutant species th at affect visibility are shown in Table V3-4.



# Future Visibility Projections

Tables V-3-5 and V-3- 6 compare the predicted future visibility with the current

levels based on measurements. The resu lts for the baseline em ission scenario (no

further emission controls) are shown in Table V-3-5 and the results for the controlled

emission scenarios are shown in Table V-3-6. Each table shows the predicted annual

average light extinction coeffi cients compared to the tota l light extinction coefficient

derived from 1986 measurements and the mean visual range estimated from the

measured and predicted ex tinction coefficients. Fi gure V-3-1 illustrates the

improvement in visibility in terms of th e annual visual rang e for both emission

control scenarios.

The results of the visibility analysis for R ubidoux illustrated in Figure V-3-1 indicate

that with future year reductions of PM 2.5 from implementation of all proposed

emission controls for 2015, the annual average visibilit y would improve from about

10 miles (calculated for 2005) to over 20 mile s at Rubidoux. Visu al range in 2021 is

estimated Visibility at all other Basin site s is expected to equal or exceed the

# Final 2007 AQMP Appendix V: Modeli ng and Attainment Demonstrations



# V - 3- 7 Rubidoux visual range. Visual range is expected to double from 2005 due to

reductions of secondary PM2.5 (by more than one third), direct PM2.5 emissions

including diesel soot and lo wer nitrogen dioxide concentr ations as a result of 2007

AQMP controls.

TABLE V-3-2

Riverside Extinction Efficiencies, b

i 

# , Defining Alternate Sets of Empirical Predictive

Equations for Light Extinction

Visibility-Reducing Alternate Equations 1



# Species Units 1 2 3 4

Riverside

SULF (10 -4 

# m -1 

# /μg/m 3 

# ) b
1



# NITR (10 -4 

# m -1 

# /μg/m 3 

# ) b
2 

# 0.070 0.075

IONS (10 -4 

# m -1 

# /μg/m 3 

# ) b 3 

# 0.055 0.058

OC (10 -4 

# m -1 

# /μg/m 3 

# ) b
4 

# 0.104 0.089

CRBN (10 -4 

# m -1 

# /μg/m 3 

# ) b 5 

# 0.062 0.053

EC (10 -4 

# m -1 

# /μg/m 3 

# ) b

6 

# 0.119 0.119 0.119 0.119

NO 2 

# (10 -4 

# m -1 

# /pphm) b

7 

# 0.033 0.033 0.033 0.033

molecules (10 -4 

# m -1 

# ) b
RAY 

# 0.114 0.114 0.114 0.114



# TABLE V-3-3

Current Light Extinction Budgets for Each Alternate Empirical

Predictive Equation at Each Measurement Location

(in percent of total light extinction)

Alt ______________ b sp


# ______________ Location Eq. SULF NITR IONS OC CRBN b

ap 

## b

ag 

## b

RAY



# Riverside 1 0 0 74 11 0 7 3 6

2 0 72 0 13 0 7 3 6

3 0 0 75 0 11 6 3 5

4 0 73 0 0 13 6 2 5

b

sp 

# – backscattering from particulates

b

ap 

# – absorption from particulates

b

ag 

# – absorption from gases

1 

## Alternate equations in the set of empirical predic tive equations defined for each measurement location.

# Chapter 3 Federal 24-Hou r PM10 Attainment Plan and Visibility Assessment

V -3- 8 TABLE V-3-4

Riverside Air Quality Levels for the Years 2005 and 2015 Fu ture Baseline and

Controlled

Component Units Baseline Controlled

2015

SULF 1 

# μg/m 3 

# 3.96 3.07

NITR 1 

# μg/m 3 

# 9.30 6.63

IONS μg/m 3 

# 13.27 9.71

OC 2 

# μg/m 3 

# 0.26 0.23

EC 2 

# μg/m 3 

# 1.73 1.52

CRBN μg/m 3 

# 2.07 1.82

NO 2 2 

# pphm 1.10 0.75

2020

SULF 1 

# μg/m 3 

# 4.24 3.22

NITR 1 

# μg/m 3 

# 7.77 4.74

IONS μg/m 3 

# 12.01 7.96

OC 2 

# μg/m 3 

# 0.23 0.25

EC 2 

# μg/m 3 

# 1.77 1.53

CRBN μg/m 3 

# 2.07 1.85

NO 2 2 

# pphm 0.88 0.49



# The predicted future visibilities are consis tent with the observed annual average

visual range in areas influenced by marine air (with the attendant marine haze).

Without significant air pollution sources, median mid-day visibilities along the

California coast are generally less th an 25 miles (Trijonis, 1980).

Future Light Extinction Budgets at Riverside

Table V-3-7 compares the baseline and fu ture projected light extinction budgets

determined from one of the alternate em pirical equations for each location to

illustrate changes in the importance of ea ch pollutant component to overall light

extinction. These changes re sult from alterations in the future pollutant mix and in

the spatial distribution of sources.

# Final 2007 AQMP Appendix V: Modeli ng and Attainment Demonstrations



# V - 3- 9 0 5 10 15 20 25 30

2005 2014 2020Visual Range (Miles)



## Baseline Controlled



# FIGURE V-3-1

Annual Average Daytime Visibility Projections at Rubidoux in Miles

TABLE V-3-5

Projected Future Visibility, Base line without Future Controls

Year Alt. Eq. 1 

# Total Light Extinction

Coefficient (10 -4 

# m -1 

# ) Calculated Visual

Range (miles)

Baseline 1.7 11.0

2014 1 1.109 16.8

2 1.035 18.0

3 1.235 15.1

4 1.182 15.8

2020 1 1.035 18.0

2 0.921 20.2

3 1.160 16.1

4 1.065 17.5

1 

# Alternate equations in the set of predictive em pirical equations defined for each measurement

location.

# Chapter 3 Federal 24-Hou r PM10 Attainment Plan and Visibility Assessment

V -3- 10 TABLE V-3-6

Projected Future Visibility, With Controls

Year Alt. Eq. Total Light Extinction

Coefficient (10 -4 

# m -1 

# ) Calculated Visual

Range (miles)

2014 1 0.874 21.3

2 0.808 23.1

3 0.979 19.0

4 0.930 20.0

2020 1 0.772 24.1

2 0.670 27.8

3 0.872 21.4

4 0.783 23.8



# TABLE V-3-7

Comparison of Baseline and Futu re Projected Light Extinction

Budgets for Riverside (% contribution)

Baseline Controlled Component

2005 2014 2020 2014 2020

NITR 66 63 59 57 50

OC 15 3 3 3 4

EC 9 20 23 22 27

NO 2 

# 3 4 2 3 2

RAY. 7 11 12 14 17



# The light extinction budget for Riverside changes nomina lly for the future baseline

emission cases except for the fo llowing: (1) nitrate remain s the major contributor but

its contribution decreases; and (2) elementa l carbon contributions increase from the

base year then remain constant through 2021.

The projected light extinc tion budgets for the years 2015 and 2021 with the

controlled emission scenarios continues to show reduced impacts of nitrates to

visibility but the relative contribution due to elemental ca rbon increases.

# CHAPTER 4

REVISION TO THE 2003 OZONE ATTAINMENT

DEMONSTRATION PLAN

Introduction

Modeling Approaches

Emissions Summary

Episode Selection

Base-Year Performance Evaluation

Ozone Air Quality Projections

Sensitivity Studies

Weight of Evidence

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 1 

# INTRODUCTION

The Final 2007 AQMP Ozone Attainment Demons tration Plan to meet the federal 8-

hour average standard (84 ppb) is presented in this chapte r. The Basin is currently

designated severe-17 nonattainment for ozone. As mentioned in Chapter 1 of the main

document, the submittal of the 2003 Californi a Ozone SIP served as the 1-hour ozone

attainment demonstration for the South Co ast Air Basin and t hose portions of the

Southeast Desert Modified Nonattainme nt Area which are under the District’s

jurisdiction. The attainment demonstrations provided in this Fi nal Plan address the

current 8-hour federal ozone standard a nd reflect the updated emissions baseline

estimates, new technical information, enhan ced air quality modeling techniques, and the

control strategy provided in Chapter 4 of the main document and Appendices IV-A

through IV-C.

The modeling Attainment Demonstration serves as a revisi on to the 1997 and 2003

ozone Attainment Demonstrati on Plans (Ozone Plan) submitte d to EPA as part of the

California State Implementation Plan (S IP). The ozone modeling attainment

demonstration relies on the CAMx modeli ng system with the SAPRC99 chemical

mechanism and six modeling episodes. The structure of the standard and the use of

RRFs differentiate this ozone modeling attainment demonstra tion from past endeavors.

The standard is based on the 4 th 

# highest annual 8-hour meas ured ozone concentration

averaged over a three year pe riod. The variability of meteorological episodes that can

generate ozone concentra tions equivalent to 4 th 

# highest in a three year period does not

lend to a direct deterministic simulated a ttainment demonstration. As such, EPA’s

modeling guidance recommends the use of RRFs determined from several simulated

ozone episodes to assess future year standa rd attainment. This analysis uses six

meteorological episodes to draw a representa tive sample of days when the 8-hour ozone

standard was exceeded at the set of Basi n stations with design values requiring

attainment demonstrations.

The meteorological episodes span thre e years: 2004 and 2005 when the MATES-III

monitoring program was in effect and pr imary modeling episode used in the 2003

AQMP, August 5-6, 1997, whic h occurred during the 1997 S outhern California Ozone

Study (SCOS97). The 2004 and 2005 ep isodes occurring during the MATES-III

sampling program integrate data from the network of radar wi nd and temperature

profiles distributed throughout S outhern California. In add ition, advances in satellite

data acquisition used in meteorological m odel initialization since SCOS97 and readily

available global model output have shifte d the focus of regional meteorological

modeling from diagnostic/objective analysis to wards 4-dimensional data assimilation in

prognostic modeling. Equally important, the 2004-2005 episodes occurred in the post

California Phase III reformul ation period and represent the current VOC emissions

profile. The 1997 episode is one of several meteorological episodes that were

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 2 intensively monitored through the SCOS97 field program and was included in the

analysis to provide continuity between the Final 2007 AQMP and 2003 Ozone Plans.

The base year for the ozone modeling demonstration and em issions inventory

characterization is 2002.

This chapter draws heavily from the Draft Modeling Protocol and provides the

background for the development of the co mponents that contribute to the ozone

modeling attainment demonstra tion. (Where necessary, the discussion will refer to the

Draft Modeling Protocol to a void duplication). Included are discussions of the modeling

tools selected for the demonstration, federa l and state air quality standard requirements,

and base and future year emissions. The selection and characterization of

meteorological episodes and prep aration of the ozone simula tion model input is provided

in detail. The analysis also provides th e base year model va lidation and supporting

statistical and graphical documentation.

Ozone air quality is projected using CAMx for the following futu re years: 2017 (for

impacts to the Coachella Valley portion of the Salton Sea Air Basin), and 2024 (to

demonstrate attainment of the federal ozone standard in the South Coast Air Basin).

Additional analyses provide characterization of future year air quality for alternative

emissions control strategies.



# Model Selection

The CAA requires that ozone nonattain ment areas designated as serious and above use a

photochemical grid model to demonstrate a ttainment. During the development of the

2003 Plan, the District convened a panel of se ven experts to independently review the

regional air quality modeling conducted for ozone and PM10. The consensus of the

panel was for the District to move to the more current state-of-the-art dispersion

platforms and chemistry modules. EPA ( CRF 51, Appendix W) does not recommend a

specific modeling dispersion platform or chem istry package to be used in an ozone

attainment demonstration but provides guidance in the selection process. The

comprehensive reviews of the peers are provided as attachments to the 2003 AQMP,

Appendix V and a summary of the panel reco mmendations is presented in Chapter 1 of

the 2003 AQMP.

The model selected for the Final 2007 AQM P attainment demonstrations is the

Comprehensive Air Quality M odel with Extensions (CAM x), version 4.4 [Environ,

2006], using SAPRC99 chemistry (Carter, 2000 ). Moreover, this model and chemistry

package is consistent with the previous advice of the outside peer re viewers. CAMx is a

state-of-the-art air quality model that can simulate ozone and PM 2.5 concentrations

together in a “one-atmosphere” approach for the attainment demonstrations. CAMx is

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 3 designed to integrate the output from bot h prognostic and diagnos tic meteorological

models.

The meteorological modeling platform se lected for the modeling attainment

demonstrations is the mesoscale meteor ological model MM5. MM5 is a hydrostatic

model system that can be run as a prognostic meteorological model or run in a historical

mode with the option for 4-dimensional data assimilation. MM5 is widely used through

the country by governmental agencies (the National Weather Service NWS), EPA, the

military, and numerous state and local air quality agencies) as well as most if not all

universities supporting a meteorol ogy program. The MM5 layer structure, portability for

including different mixing and cloud parameterization scheme s and grid specification

makes the model the ideal choice to couple w ith CAMx. One desirable aspect of the

CAMx-MM5 system is mass consistency. The Draft Modeling Pr otocol provides and

extended discussion on MM5 and the CAMx dispersion modeling platforms.



# Modeling Approach

The Final 2007 AQMP modeling approach for the 8-hour average federal standard

attainment demonstration involve s a series of steps which incorporate the simulations of

multiple air quality episodes fo r three emissions scenarios to develop a set of site

specific RRFs to be applied to the Basin de sign values. The sequence of the modeling

approach first relies on determining the ba se-year episode simulation performance using

day specific base-year emissions inventorie s in 2004 or 2005. Sub regional and site

specific performance statistics a for the Ba sin (and downwind rece ptor sites) are

evaluated to determine (1) if the simulati on is reasonably recrea ting the sub-regional

observed ozone patterns and (2) if the simula tion is able to produce concentrations of

ozone within an acceptable concentration range . Station and day specific simulations

that meet both criterions are used to devel op the RFFs. (A more detailed discussion of

the criterion is presented in the model performan ce evaluation section of this Appendix).

The second phase of the analysis involves simulating the meteorological episodes for

two additional day-specific emissions scen arios: 2002, the base year for the RRF

calculation and, 2023 with emissions control m easures fully implemented. (Note: For

the Coachella Valley portion of the Salton Sea Air Basin (SSAB), the future year

simulation is based on the controlled 2017 day-specific inventory.) Simulated

concentrations for the base year and futu re year controlled emissions scenario are

generated to establish site specific RRFs.

The final phase is the attainment demonstra tion where the site specific RRFs are applied

to the 2002 weighted station design values to determine the future year design

concentrations.

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 4 Table V-4-1 provides the weighted 2002 desi gn values for the Ba sin. Table V-4-2

provides the 2002 design values for the Coache lla Valley-SSAB air monitoring stations

and downwind transport stations in the SCCAB and MDAB. EPA guidance

recommends the use of a 5-year weighted desi gn values to minimize the impacts of year

to-year variations in weather and short term emissions trends. In Tables V-4-1 and V-4-

2, the sites exceeding the 8-hour federal sta ndard are delineated th rough bold lettering.

These stations are the focus of the analysis.



# Federal 8-Hour Ozone Standard Requirements

Air quality modeling is required by both th e federal Clean Air Act (CAA) and the

California Clean Air Act (CCAA). Section 182(b)(1)(A) of CAA requires that moderate

and above ozone nonattainment areas must reduce volatile orga nic compounds (VOC)

and oxides of nitrogen (NO

x 

# ) emissions sufficiently to a ttain the national ambient air

quality standard for ozone and an attainment demonstration must be performed using

photochemical grid modeling. According to Section 181(a)(1) of the CAA, ozone

nonattainment areas are classified and give n an attainment deadline based on their

design values. Within the jurisdiction of the District are the South Coast Air Basin

(Basin) and the Coachella Valle y of the Salton Sea Air Basin (see Figure V-4-1). The

District is seeking voluntary redesignations for the Basin and Coachella Valley. The

Basin is requesting to move from a “sever e-17” ozone nonattainment area to “extreme”

nonattainment area to make available long te rm control measures and to extend the

attainment date to June 15, 2024. The attainment demonstration for the Basin is the

primary subject of this chapter. The Coachella valley is also se eking to be reclassified as

“severe-15” nonattainment for oz one and therefore extend its attainment deadline to June

15, 2018.

The modeling domain used in the photochemi cal modeling analysis, also shown in

Figure V-4-1, encompasses the entire Ba sin, Ventura County, Antelope Valley

(AVAQMD), San Diego County, the Coachella Valley, and portions of the Mojave

Desert Air Quality Management District (MDAQMD) and Imperial County. Ventura

County, the Antelope Valley and Mojave Desert are classified as "moderate" (attainment

year: 2010). These areas experience pollutant transport from the Basin, and at times are

an upwind source of pollution. San Diego County is classified as “basic” with an

attainment year of 2009 and Im perial County is classified as “marginal” with an

attainment year of (2007).

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 5 TABLE V-4-1

8-Hour Average South Coast Air Basin Weighted Design Values

City 2002

Design 2003

Design 2004

Design Weighted

Design

Value

Azusa 102 101 98 100

Burbank 92 91 92 91

Long Beach 62 61 64 62

Reseda 94 107 110 103

Pomona 89 96 101 95

Lynwood 51 53 57 54

Pico Rivera 80 79 78 79

Los Angeles 79 78 79 79

Pasadena 96 95 96 96

Santa Clarita 113 127 125 124

West Los Angeles 69 73 77 73

Hawthorne 68 70 63 67

Glendora 111 114 109 111

Anaheim 70 72 79 72

La Habra 76 75 75 75

Costa Mesa 67 71 73 70

Mission Viejo 79 83 87 84

Rubidioux 108 113 113 111

Perris 113 115 106 111

Lake Elsinore 104 109 106 106

Banning Airport 110 119 117 115

Upland 111 110 107 111

Crestline 129 131 128 129

Fontana 112 123 119 118

San Bernardino 115 119 113 115

Redlands 120 128 124 124

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 6 TABLE V-4-2

8-Hour Average Weighted Design Values: Sa lton Sea Air Basin (SSAB), Mojave Desert

Air Basin (MDAB) and the South Central Coast Air Basin (SCCAB)

City 2002

Design 2003

Design 2004

Design Weighted

Design

Value

SSAB

Palm Springs 107 111 106 106

Indio 95 99 99 95

MDAB

Lancaster 71 82 100 84

Phelan 103 106 105 105

Twenty-nine

Palms 88 86 86 87

Hesperia 106 106 107 106

Joshua Tree 94 99 106 100

Barstow 87 88 87 87

Trona 80 83 86 83

Victorville 97 100 98 98

SCCAB

Ojai 95 95 94 95

El Rio 66 66 66 66

Piru 73 90 88 84

Simi 97 95 92 95

Thousand Oaks 81 83 84 83

Emma Woods 69 71 69 70



# California Requirements and Population Exposure

The CCAA requires the District to demons trate reasonable progress towards achieving

state ambient air quality standa rds in the Basin. To date, the Basin has not met the

California 1-hour ozone standa rd (90 ppb). Yet, ambient ozone air quality has greatly

improved. The CCAA requires per-capita e xposure reductions for the years 1994, 1997,

and 2000, as compared to a 1986-88 base period. Over all per-capita exposure to

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 7 ambient ozone must be reduced in accordan ce with the following schedule: 25 percent

by 1994, 40 percent by 1997, and 50 percent by 2000.

Reductions are to be calculated based on per-capita exposure and the severity of

exceedances. For the Basin, th is provision is applicable to ozone [H&S Code 40920(c)].

The definition of exposure is the number of persons exposed to a specific pollutant

concentration level above the state standa rd times the number of hours exposed. The

per-capita exposure is the population exposure (units of pphm-pers ons-hours) divided by

the total population. While th is requirement has already been met in previous AQMPs

(Appendix V, 2003 AQMP), the exposure dem onstration is exte nded through 2005 in

the Final 2007 AQMP for consistency.

The Regional Human Exposure (REHEX) model is used to estimate per-capita exposure

reduction. It considers population mobility; time spent indoors, outdoors and in transit;

exposure by age classification; and activity pattern by season and we ekday/weekend.

An analysis using the REHEX model indi cates that the CCAA Amendments exposure

reduction targets have been achieved for ozone with a margin of safety. Figure V-4-2

summarizes the results and compares exposure reductions to the targets.

. San Luis

Obispo Co.

Santa

Barbara Co. Kern Co.

Ventura

Co.

Orange

Co.

Imperial

Co. Los

Angeles Co.

Riverside Co.

San Diego

Co. San

Bernardino

Co.

Northern Mexico



# FIGURE V-4-1

Southern California Modeling Domain Used in the Ozone Attainment Demonstration

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 8 0% 20% 40% 60% 80% 100% 120%

1997 2000 2005

YearPercent Reduction

CCA Requirements REHEX



# FIGURE V-4-2

CAAA Population Exposure Assessment: Perc ent Reductions in Annual Average PerCapita Exposure to Ozone



# EMISSIONS SUMMARY

Introduction

There are specific emission inventories deve loped for the photochemical modeling. The

summer planning emission inventories devel oped for the historic al years (1997, 2004

and 2005) and future pl anning years (baseline and contro lled) are described in Appendix

III. Baseline modeling inventor ies for the historical years (1997, 2004 and 2005) and the

future years (2009, 2017, and 2023) are discu ssed next. Two emi ssion projections are

needed for each of the modeled future year s. The first is the projected emissions

assuming no further emission controls. Thes e projections are commonly referred to as

“baseline emissions” (e.g., 2023 baseline emi ssions), and reflect the emissions resulting

from increases in population and vehicle miles traveled (VMT ), as well as the

implementation of all adopted rules a nd regulations up thr ough 2005. The second

emission projections reflect the implem entation of the Final 2007 AQMP control Air Quality

Improvement

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 9 measures on the future baseline emissions. For a detailed description of the Final 2007

AQMP control measures, the reader is refe rred to the main volume and Appendix IV.

The July 2005 historical year emissions are summarized as representative ozone

episodes used for attainment demonstration. This is followed by a discussion of the

future-year (July 2005 episode) emission i nventory, assuming implementation of

proposed control measures, are presented. Appendix III contai ns emission summary

reports by source category for the historical base year, future baseline, and future

controlled scenarios used in this modeling analysis. Atta chments 5, 6 and 7 of this

appendix contain the Contro lled Emission Projection Algorithm (CEPA) emissions

summary report by source category for th e future (2014, 2017 and 2023) controlled

scenarios for the annual average inventory.

It should be noted that the i nventories reported here may be slightly different than those

reported in the Final 2007 AQMP and Appendi x III, since the inventories used for

modeling reflect day-specific conditions. Day specific point, mobile and area emissions

inventories were generated for each meteor ological episode. Mob ile source emissions

were temperature corrected by grid using a VMT weighted scheme. County-wide area

source emissions were temperature corrected and gridded using th e spatial emissions

surrogate profiles developed for the 2003 AQMP.



# Historical Baseline Emissions

Historical baseline emissions of oxides of nitrogen (NO

x 

# ) and volatile organic gases

(VOC) and carbon monoxide (CO) are summari zed in Table V-4-3 for the July 2005

meteorological episode. The day-specific July 2005 episode emissions inventory is

representative of the remaining meteorological episodes. Variations in the temperature

and humidity profiles among the episode da ys and between epis odes contribute to

variations in the weekday emi ssions totals of less than 50 tons/day or 5 percent. The

summaries of biogenic, on-road mobile a nd total antropogenic emi ssions for the July

2005 are reported for the Basin and the modeling region.

Emissions for the July episode span the weekend where signi ficant reductions in on-road

NOx and increases in VOC from off road activities occur. Based on CALTRANS data,

NOx emissions from heavy-duty vehicles are reduced by more than 60 percent on

Saturdays with further reductions occurring on Sundays. Increases in off-road mobile

source activities (e.g. pleasure craft and recr eational vehicles) account for the bulk of the

VOC increase on both Saturdays and Sundays.

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 10 

# Future Controlled Emissions

The control factors developed fro m CEPA were applied to the future base year emissions

to calculate the controlled emission invent ories. The future-year baseline emission

inventories estimation reflect the emissions resulting from increa ses in population and

vehicle miles traveled (VMT), as well as the implementation of all rules and regulations

adopted as of Decembe r 31, 2005. VOC and NO

x 

# baseline emissions decrease from the

historical base year through the year 2023. This decreasi ng trend in emissions reflects

the implementation of current state and lo cal air quality rules and regulations.

TABLE V-4-3

South Coast Air Basin July 2005 Histor ical Episode Emissions (tons/day)

Date Emission Category 2005

CO NOX VOC

Thursday 14-Jul-05 Biogenic 233

On-Road 2870 466 368

Total Anthropogenic 3911 895 825

Friday 15-Jul-05 Biogenic 200

On-Road 2823 451 350

Total Anthropogenic 3864 880 807

Saturday 16-Jul-05 Biogenic 209

On-Road 2286 314 314

Total Anthropogenic 4397 706 925

Sunday 17-Jul-05 Biogenic 224

On-Road 2177 280 309

Total Anthropogenic 4286 670 895

Monday 18-Jul-05 Biogenic 245

On-Road 2715 433 350

Total Anthropogenic 3756 862 806

Tuesday 19-Jul-05 Biogenic 245

On-Road 2905 445 372

Total Anthropogenic 3946 873 829



# Base year 2002, 2017, and 2023 and future-y ear controlled emissions, estimated from

the baseline emissions using the CEPA contro l factors for the simulations, are given in

Table V-4-4.

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 11 TABLE V-4-4

2002, 2017, 2023 Base Year and 2023 Future Year Controlled Emissions Scenarios (TPD)

Year Scenario VOC NOx CO

2002 Baseline 1030 1090 5525

2017 Baseline 509 581 2368

2017 Controlled 444 380 2283

2023 Baseline 496 515 2058

2023 Controlled without

Long- Term Measures 402 317 2058

2023 Controlled with LongTerm Measures 420 114 1966



# EPISODE SELECTION

The 2003 AQMP benefited from the intensive monitoring conducted under the Southern

California Ozone Study where th e August 4-7, 1997 episode was the cornerstone of the

modeling analysis. The requirements for multip le episode days at individual stations

pose a different challenge for the Final 2007 AQMP.

Five additional meteorological episodes with regionally observed higher ozone

concentrations were added to the 2003 AQM P modeling episode. The five episodes

observed in 2004 and 2005 occu rred during MATES-III, a period of enhanced air quality

monitoring in the Basin. Supporting MATES -III, the District operated three radar wind

profilers in the Basin, with radio acous tic sounders. Additional profiler data was

obtained from operating sites in Ventura and Sa n Diego Counties. Table V-4-5 lists the

complement of meteorological episodes used in the ozone attainment demonstration.

Selection of episodes from 2004 and 2005 was also made to avoid the commingling

associated with the Phase III California Fuel Reformulation where the primary

oxygenate was changed from MTBE to et hanol. Commingling of ethanol and nonethanol based fuels leads to enhanced evapor ative VOC emissions and thus more ozone.

Quantification of the amount of commingling taking place on a daily or episodic basis

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 12 was nearly impossible. Implementation of the fuel switch from MTBE to ethanol took

place in California during 2003 and was assu med to be completed by December 31,

2003. Selecting meteorological episodes for the post 2003 emissions reduced the

uncertainty associated with the estimation of the VOC emissions inventory due to

commingling.



# Conceptual Model of an 8-Hour Ozone Episode

Several field studies (SCAQS, [1987], and SCOS97, [1998]) and pr evious AQMPs have

described at length the development of an oz one episode in the Ba sin. The focus of

many of these analyses was to simulate th e observed 1-hour maximu m concentration in

the modeling domain. Cassmassi (1998) used Classification and Regression Tree

analysis (CART) to determine whether the conceptual model for a 1-hour ozone episode

differed from the meteorological profile char acterizing an 8-hour average ozone episode

in the Basin. The results of the analysis indicated that the peak 1-hour episodes were a

subset of the 8-hour episodes and the me teorological profiles contributing to both

scenarios were nearly identic al. As such, the developmen t of the 8-hour conceptual

model for the Basin and the methodology to select and characterize episodes relies on

the basic models constructed to descri be the Basin 1-hour ozone episode.

The Draft Modeling Protocol provides an extended discussi on of the meteorological and

air quality profile of four of the five 2004 and 2005 episodes, in addition to the August

1997 episode, selected for evaluation in the ozone attainment demonstration. The

August 2-9, 2005 meteorological episode was selected as a replacement for the June

2004 episode after the release of the Draft M odeling Protocol. In general, elevated

concentrations of ozone (bot h 1- and 8-hour average) occu r under a west coast or Four

Corners ridge of high pressure aloft. T ypically, the 500 mb pressure surface heights

above mean sea level (msl) exceed 5880 m a nd generate a strong low level subsidence

inversion (10 o 

# C in strength or higher). The surface pressure gradient (i.e. wind forcing)

typically is less than 5 mb between the co ast and the desert (approximately 200 km in

distance) and days often begin with a deck of morning coastal stratu s that extends into

the near valleys then burns o ff in the late morning hours. The more severe episodes tend

to have neutral to slightly off shore pre ssure gradient forcing and clear skies.

Each of the 2004 and 2005 meteorological epis odes selected for the ozone attainment

demonstration fit this mode l. Figure V-4-3 illustrates the 500 mb upper air structure

over the west coast during the July 2005 mete orological episode. Figure V-4-4 provides

the 1200 UTC (4:00 am PST) temperature profile for July 16, 2005.

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 13 FIGURE V-4-3

500 mb Upper Air Structure: Ju ly 2005 Meteorological Episode

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 14 FIGURE V-4-4

1200 UTC Upper Air Sounding at Miramar MCAS (San Diego, CA) July 16, 2005



# Statistical Episode Characterization and Ranking

CAMx simulations were generated for six meteorological episodes including two

periods in 2004, three periods in 2005 and one in 1997. Ta ble V-4-5 characterizes the

selected episodes two ways: first by an asse ssment of the meteorological profile using a

statistical model to rank the episodes base d on meteorological st agnation potential and

second by comparing observed maximum ozone concentrati ons to the annual design

values.

The meteorological classification is based on an empirical analysis presented in the 2003

AQMP which provides both a stagnation sever ity rank (1 being the highest) and the

percentile the meteorological episode had in a 22-year distribu tion. The observed

maximum 8-hour average concentrations on each episode day, and the average of the 8-

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 15 hour maximum concentrations observed for eac h multi-day episode are also provided for

comparison to the annual 4 th 

# highest 8-hour average ozone value observed in the year

that the episode takes place.

Briefly, the selected episode days mostly rank in the 95 th 

# percentile or higher for

meteorological stagnation potential. The episode average of the 8-hour maximum

concentrations is within 5 ppb of the annual 4 th 

# highest 8-hour obser ved concentration

for four of the six simulation periods.



# Model Input Preparation

The procedures for CAMx input file prepara tion are presented in this section. Unlike

previous AQMPs which relied on the use of UAM for the attainment demonstration,

CAMX is designed to marry seamlessl y with the MM5 model output. The

meteorological modeling domain, NCEP initializations and vertical dispersion schemes

are evaluated in the modeling are provide d in the Modeling Protocol Document.

Statistical meteorological model evaluati on was conducted using METSTAT software

package (Environ, Inc., 2005) and by Aerosp ace Corporation (McAtee, et al., 2006).

Data evaluation compared MM5 predictions vs. observational data at selected

meteorological monitoring sites from the SC AQMD, NWS, FAA, CIMIS and other air

quality agencies networks. A summary of the meteorological model performance was

presented at the 2006 National Air Quality C onference in San Ant onio, Texas. The

meteorological modeling was also presen ted to and critiqued by AQMP Scientific,

Technical, Modeling and Peer Review (S TMPR) Advisory group monthly meetings

from December 2005 through September, 2006.

As previously stated, the CAMx ozone simu lations were run on a 5 km squared grid

using a Lambert Conformal projection of the SCOS97 m odeling domain depicted in

Figure V-4-1. The modeling analyses were run using 16 vertical layers up to 5000 m

above ground level. The eastern extent of the domain is approximately 100 miles

offshore of the Basin. The large domain wa s chosen to minimize uncertainties in the

upwind boundary conditions.

The meteorological fields used for the CAMx ozone simulations we re generated using

MM5 with the FDDA option. The meteorol ogical fields were developed using a

Lambert Conformal grid that roughly ove rlaid the SCOS97 modeling domain. MM5

was simulated using 34 vertical layers a nd simulations were initialized using NCEP

global weather forecast model an alysis. The MM5 fields were post processed to layer

averaged winds to the levels defined for the CAMx simulations and to adjust coordinates

to the UTM system.

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 16 TABLE V-4-5

Ozone Meteorological Episodes Used for the Ozone Attainment Demonstration

Ranking Applied to Histori cal 22-Year Period (1981-2002)

Episode Stagnation

Severity

Rank Percentile 8-Hour

Maximum

Ozone

(ppb) Episode

Average

8-Hour

Maximum

Ozone

(ppb) Annual 4th Highest

Observed

8-Hour Maximum

Ozone /Station

(ppb)

8/4/1997 570 93 110

8/5/1997 198 98 124

8/6/1997 203 97 130

8/7/1997 515 95 130 124 127

San Bernardino

8/7/2004 331 96 127

8/8/2004 144 98 122 125 116

Crestline

5/21/2005 389 95 112

5/22/2005 50 99 145 129

7/15/2005 265 96 143

7/16/2005 22 99 141

7/17/2005 15 99 141

7/18/2005 73 99 127

7/19/2005 567 93 110 132

8/4/2005 270 97 108

8/5/2005 399 95 110

8/6/2005 288 96 119

8/7/2005 341 96 114 113

8/27/2005 160 98 130

8/28/2005 138 98 121 126 125

Crestline



# Selected objective-hybrid MM5 wind fields we re evaluated in the development of the

modeling episodes to test transport to the northern portion of Los Angeles Country and

Santa Clarita. The hybrid approach was not us ed in the ozone attainment demonstration.

Table V-4-6 summarizes some of the criti cal components of the air quality modeling

system. Of the components listed, treatment of the boundary conditions is the subject of

discussion in the following section.

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 17 TABLE V-4-6

Air Quality Modeling System Configuration

Component Source

Initial Conditions/Boundary Conditions Extr acted from WRAP Regional Haze Modeling

output

Meteorological Fields MM5/FDDA with NCEP initialization

Eta PBL – Mellor-Yamada scheme as used in

the Eta model, Janjic (1990, MWR) and Janjic (1994,

MWR). It predicts TKE and has local vertical mixing.

Horizontal Advection Solver Piecewise Parabolic Method (PPM) of Colella and

Woodward (1984), high order accuracy and little

numerical diffusion

Vertical Mixing/Diffusivity MM5 CAMx Option OB70 w/Kv Patch. Minimum

vertical diffusivity set at 1 m 2 

## /sec.

Chemistry (SAPRC99) CAMx Version 4.4 Beta. Version modified to treat

ETOH, MTBE and MBUT explicitly (Environ,

2006)

Chemistry Solver Chemical Mechanism Compiler (CMC), fast highly

efficient solver based on an “adaptive-hybrid”

approach compared to the standard chemistry solver

for the CB-IV mechanism

SAPRC99 Mechanism ID=5 The fixed parameter version of the SAPRC99

mechanism (Carter, 2000). 211 reactions and 74

species (56 state gasses and 18 radicals)

Dry Deposition of Gases Resistance model developed by Wesely (1989)



# Boundary and Top Air Quality Concentrations

The boundary concentrations can have a ma rked impact on RRFs for the South Coast

Air Basin. In the Draft 2007 AQMP (releas ed in October 2006) boundary conditions for

the ozone simulations were extracted fro m the annual WRAP Re gional Haze modeling

conducted for the model year 2002 (Tonnes on 2005). This modeling was conducted

using CB4 chemical mechanism. There ar e some uncertainties on conversion from CB4

to SAPRC species. In addition, the convert ed SAPRC99 species profile did not reflect

emissions inventory SAPRC99 species co mposition. The WRAP modeling results

included occasional high levels of VOC and NOX that may not represent existing

boundary concentrations.

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 18 For the Final 2007 AQMP, the ozone simulati ons were conducted us ing the EPA default

boundary concentration profile USEPA (1991). Uncertainties identified in the episodic

diurnal profiles for the ozone simulations were sufficiently large enough to warrant a

return to the boundary conditions used in the 2003 AQMP ozone attainment

demonstration. This profile is commonly referred to as the “EPA-Clean” boundary. The

boundary conditions were adjusted to match the ROG SAPRC profile.

Table V-4-7 summarizes the boundary cond itions used for all simulations. All

concentrations were constant horizontally and, except for NO2 , constant vertically. For

each profile, the sum of VOC concentrati ons is approximately 22 ppbC. For each

simulation, the domain top boundary concentr ations were the same as the lateral

boundaries, with the exception that th e top ozone concentration was 60 ppb.

The initial concentrations for the all episodes were set using the air quality data from the

District and neighboring district ’s air monitoring stations.

TABLE V-4-7

Boundary Profile Concentrations ( ppb) for the SCOS Modeling Domain.

SAPRC99 Species (ppb) SAPRC99 Species (ppb)

HCHO 0.930 ARO1 0.210

CCHO 0.530 ARO2 0.070

RCHO 0.250 OLE1 0.180

ISOP 0.020 OLE2 e-13

MEOH 0.100

ETOH 0.050 O3 40.0

ETHE 0.180 CO 200.

ALK1 2.500

ALK2 2.300 NO 0.100

ALK3 0.930 NO2 (surf) 2.000

ALK4 e-13 NO2 (aloft) 0.100

ALK5 e-13

Future Boundary, Top and Initia l Air Quality Conditions

For the future year scen arios, the boundary, region t op and ambient air quality

concentrations were rolled back based on the percentage reduction in emissions from

2002 base year to the projected emissions leve ls for future year of the simulation (2017,

or 2023).

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 19 The future year the top ozone concentrations were reduced to 45 ppb. The lateral

boundary concentrations were not rolled back since the bounda ry concentrations used

base year is considered to be clean.

Meteorological Models

The MM5 meteorological model using 4-dime nsional data assimilation (4DDA) was the

primary tool used to develop the meteorologi cal fields. The Mode ling Protocol provides

characterization of the nested MM5 m odeling domains, the layer structure and

initialization assumptions. Three-dimensi onal wind, temperatur e and mixing height

fields were extracted from the MM5 simula tions and post proce ssed using CALMET to

layer average variables to the CAMx structure. Vertical mi xing was calculated using the

ETA planetary boundary layer (PBL) scheme and a minimum value of vertical

diffusivity was set at 1.0 m 2 

# /sec.

The MM5 data fields were extensively anal yzed using the METSAT software. Figure

V-4-5 illustrates the extent of surface me teorological measurements in southern

California, and the data used in the mete orological model evaluation were derived from

a subset of the total archiv e. The summary performance statistics for the July 2005

episode are presented in Table V-4-8 a nd Figures V-5-6 thr ough V-4-8. (The

performance of MM5 for the July 2005 episode was characteristic of that of the

remaining five episodes).

As previously stated, an assessment of the meteorological model performance was

presented at EPA’s 2006 National Air Quality Conference and periodically during the

development of the ozone modeling episodes at the STMPR Advisory group. The data

has also been provided to th e independent Peer Reviewer s, and their evaluation is

pending.

150 250 350 450 550 650

UTM Easting 3600 3700 3800 3900UTM Northing



# FIGURE V-4-5

Locations of Surface Monitoring Used in Preparation of Meteorological Fields

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 20 TABLE V-4-8

METSTAT Statistical Evaluation of MM5-4DDA for the July 2005 Episode:

AQMD Air Monitoring Stations

Variable Statistric Units 7/14 7/15 7/16 7/17 7/18 7/19

Wind Speed Mean OBS (m/s) 1.62 1.7 1.78 1.93 1.72 1.76

Wind Speed Mean PRD (m/s) 1.85 1.78 1.76 1.89 1.84 1.91

Wind Speed Bias (m/s) 0.23 0.04 -0.01 -0.03 0.13 0.15

Wind Speed Gross Error (m/s) 0.96 0.88 0.89 0.91 0.83 1

Wind Speed RMSE (m/s) 1.27 1.18 1.12 1.17 1.08 1.37

Wind Speed Sys RMSE (m/s) 0.82 0.75 0.78 0.75 0.69 0.96

Wind Speed Unsys RMSE (m/s) 0.96 0.91 0.81 0.9 0.83 0.97

Wind Speed IOA 0.68 0.72 0.75 0.81 0.79 0.68

Wind Direction Mean OBS (deg) 227.83 220.61 235.33 252.18 237.75 209.55

Wind Direction Mean PRD (deg) 240.19 226.58 232.84 241.36 247.27 221.05

Wind Direction Bias (deg) 8.76 4.48 7.5 4.85 17.25 11.71

Wind Direction Gross Error (deg) 53.93 48.06 45.51 46.29 50.39 56.66

Temperature Mean OBS (K) 300.38 298.73 298.88 299.53 300.25 301.27

Temperature Mean PRD (K) 299.98 298.66 298.53 298.63 299.4 299.75

Temperature Bias (K) -0.06 0.23 0.06 -0.58 -0.46 -1.21

Temperature Gross Error (K) 2.59 2.37 2.22 2.59 2.62 2.6

Temperature RMSE (K) 3.37 3.29 3.25 3.76 3.93 3.55

Temperature Sys RMSE (K) 2.37 1.84 2.21 2.63 2.58 2.5

Temperature Unsys RMSE (K) 2.4 2.73 2.39 2.68 2.96 2.51

Temperature IOA 0.92 0.92 0.93 0.92 0.91 0.91

Humdity Mean OBS (g/kg) 11.5 12.2 12.31 12.19 12.92 13.91

Humdity Mean PRD (g/kg) 11.74 12.55 12.48 12.47 12.53 13.2

Humdity Bias (g/kg) -0.17 0.39 0.19 0.34 -0.42 -0.74

Humdity Gross Error (g/kg) 2.69 2.14 1.91 1.97 1.87 1.96

Humdity RMSE (g/kg) 3.57 3.04 2.72 2.85 2.64 2.83

Humdity Sys RMSE (g/kg) 2.05 2.42 1.99 2.57 1.63 1.98

Humdity Unsys RMSE (g/kg) 2.93 1.83 1.85 1.24 2.07 2.03

Humdity IOA 0.58 0.5 0.56 0.45 0.52 0.52

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 21 Observed/Predicted Windspeed

1.4 1.5 1.6 1.7 1.8 1.9

2

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19m/s Wind Spd Mean OBS Wind Spd Mean PRD



### Bias/Gross Error Windspeed

-0.2

0 0.2 0.4 0.6 0.8

1 1.2

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19m/s Wind Spd Bias Wind Spd Gross Error



### RMSE Windspeed

0 0.5

1 1.5

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19m/s Wind Spd RMSE Wind Spd Sys RMSE Wind Spd Unsys RMSE



### IOA Windspeed

0 0.2 0.4 0.6 0.8

1

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19 Wind Spd IOA



### Observed/Predicted Wind Direction

0 6 0 12 0 18 0 240 300 360

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19deg Wind Dir Mean OBS Wind Dir Mean PRD



### Bias/Gross Error Wind Direction

-90 -60 -30

0 3 0 6 0 9 0

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19deg Wind Dir Bias Wind Dir Gross Error



# Figure V-4-6

METSAT Evaluation of MM5 Winds vs. AQMD Station Data: July 2005 Episode

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 22 Observed/Predicted Temperature

297 297.5

298 298.5

299 299.5

300 300.5

301 3 0 1.5

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19K Temprt r Mean OBS Temprt r Mean PRD



### Bias/Gross Error Temperature

- 1.5

- 1 -0.5

0 0.5

1 1.5

2 2.5

3

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19K Temprt r Bias Temprt r Gross Error



### RMSE Temperature

0 0.5

1 1.5

2 2.5

3 3.5

4 4.5

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19K Temprt r RMSE Temprt r Sys RMSE Temprt r Unsys RMSE



### IOA Temperature

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19 Temprt r IOA



# Figure V-4-7

METSAT Evaluation of MM5 Temperature vs. AQMD Station Data: July 2005 Episode

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 23 Observed/Predicted Humidity

0 2 4 6 8 10 12 14 16

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19g/kg Humdit y Mean OBS Humdit y Mean PRD



### Bias/Gross Error Humidity

- 1 -0.5

0 0.5

1 1.5

2 2.5

3

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19g/kg Humdit y Bias Humdit y Gross Error



### RMSE Humidity

0 0.5

1 1.5

2 2.5

3 3.5

4

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19g/kg Humdit y RMSE Humdit y Sys RMSE Humdit y Unsys RMSE



### IOA Humidity

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

7/ 14 7/ 15 7/ 16 7/ 17 7/ 18 7/ 19 Humdit y IOA



# Figure V-4-8

METSAT Evaluation of MM5 Winds vs. AQMD Station Data: July 2005 Episode

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 24 

# BASE-YEAR PERFOMANCE EVALUATION

For the CAMx performance evaluation the mode ling domain is separated into nine subregions or zones. Figure V-4-9 depicts the sub-regional zones used for base-year

simulation performance. The different zone s present unique air quality profiles. In

previous ozone modeling atta inment demonstrations usi ng a smaller modeling domain,

the number and size of the zones was different . Seven zones repres ented the Basin and

portions of Ventura County, the Mojave Desert and the Coachella Valley.

For the current analysis the Basin is represen ted by three of the zones: Zone 3 – the San

Fernando Valley, Zone 4 – th e Eastern San Gabriel, Ri verside and San Bernardino

Valleys, and Zone 5 – the Los Angeles and Or ange County emissions source areas. Of

the four areas, Zone 4 represents the Ba sin maximum ozone concentrations and the

primary downwind impact zone. As such, th e priority in evaluating model performance

is focus on Zone 4.

FIGURE V-4-9

Performance Evaluation Zones

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 25 

# Statistical Evaluation

The statistics used to evaluate 1-hour aver age CAMx ozone performance do not change

from previous AQMPs and include the following:

Statistic for O 3 

# Criteria (%) Comparison Basis

Normalized Gross Bias ≤ ± 15 Paired in space and time

Normalized Gross Error ≤ 35 Paired in space (+2 grid

cells) and time

Peak Prediction Accuracy ≤ ± 20 Unpaired in space and time



# The same statistics are applied to the 8-hour average ozone.

The base-year 1- and 8-hour average regi onal model performance for the August 2004,

May 2005, July 2005, August 2005 (2-episodes) and August 1997 episodes for Zones 3,

4, and 5 are presented in Tabl es V-4-9 to V-4-14. Base-year performance statistics for

Zones 2, 8 and 9 for the downwind areas ar e provided in the Attachment B performance

summary evaluation tables. Performance statistics are presented for observed

concentrations of 60 ppb or gr eater. Data for 1 and 8-hour average ozone concentrations

for the sub regional peak concentrations are provide in the tables. Base-year station

statistics for all of the ozone episodes are presented in Attachment B.

Performance statistics for the ozone precursors, nitrogen dioxide, nitr ic oxide and carbon

monoxide will be provided separately.

The CAMx ozone simulations generally me t the 1-hour average unpaired peak model

performance goal in all three zones on most days . Nearly all stations in zone 4 met the

unpaired peak and normalized error goals w ith performance in zones 3 and 5 lagging,

particularly for the May 2005 episode. In general, the bias tends to be negative

indicating that model performance tended to under predict ozone concentrations.

Overall, the 8-hour average evaluation was slightly better.Final 2007AQMP Appendix V: Modeling and Attainment DemonstrationsV-4-26TABLE V-4-9CAMx Sub-Region-3 1-Hour Average Ozone Performance StatisticsOzone Threshold (60 PPB)August 2004May 2005July 2005Date8/7 8/8 5/21 5/22 7/15 7/16 7/17 7/18 7/19Julian Date220 221 141 142 196 197 198 199 200Ratio of Predicted Sub-RegionalPeak to Peak Observed1.15 1.09 0.97 0.68 1.40 0.85 0.95 0.70 1.08Ratio of Unpaired Station Peaks0.84 0.80 0.67 0.54 1.24 0.72 0.74 0.50 0.91Normalized Systematic Bias (%)-20 -12 -32 -46 9 -3 -16 -36 -11Normalized Gross Error (%)23 17 32 46 14 17 23 36 16Ozone Threshold (60 PPB)August 2005 August 2005 August 1997Date8/4 8/5 8/6 8/7 8/27 8/28 8/4 8/5 8/6 8/7Julian Date216 217 218 219 239 240 216 217 218 219Ratio of Predicted Sub-RegionalPeak to Peak Observed0.80 0.92 0.92 0.94 0.87 0.84 1.04 0.91 1.02 0.96Ratio of Unpaired Station Peaks0.72 0.89 0.91 0.91 0.74 0.67 0.86 0.78 0.77 0.71Normalized Systematic Bias (%)-5 -2 4 9 -26 -42 -11 -12 -12 -16Normalized Gross Error (%)16 17 21 23 32 42 17 30 25 21Chapter 4 Revision to the 2003 Ozone Attainment Demonstration PlanV-4-27TABLE V-4-10CAMx Sub-Region-4 1-Hour Average Ozone Performance StatisticsOzone Threshold (60 PPB)August 2004May 2005July 2005Date8/7 8/8 5/21 5/22 7/15 7/16 7/17 7/18 7/19Julian Date220 221 141 142 196 197 198 199 200Ratio of Predicted Sub-RegionalPeak to Peak Observed0.85 1.12 0.90 0.94 0.98 0.94 0.96 1.06 1.30Ratio of Unpaired Station Peaks0.82 1.07 0.80 0.92 0.88 0.85 0.92 0.97 1.18Normalized Systematic Bias (%)-13 3 -16 -25 7 -3 2 -8 15Normalized Gross Error (%)15 16 21 29 20 20 16 18 223Ozone Threshold (60 PPB)August 2005 August 2005 August 1997Date8/4 8/5 8/6 8/7 8/27 8/28 8/4 8/5 8/6 8/7Julian Date216 217 218 219 239 240 216217 218 219Ratio of Predicted Sub-RegionalPeak to Peak Observed1.02 1.01 1.04 1.04 0.56 0.74 1.04 0.79 0.98 0.88Ratio of Unpaired Station Peaks0.98 1.00 1.00 1.04 0.58 0.69 0.99 0.74 0.97 0.84Normalized Systematic Bias (%)5 3 -9 -4 -29 -28 -6 -4 13 -17Normalized Gross Error (%)17 24 20 20 34 33 19 17 23 23Final 2007AQMP Appendix V: Modeling and Attainment DemonstrationsV-4-28TABLE V-4-11CAMx Sub-Region-5 1-Hour Average Ozone Performance StatisticsOzone Threshold (60 PPB)August 2004May 2005July 2005Date8/7 8/8 5/21 5/22 7/15 7/16 7/17 7/18 7/19Julian Date220 221 141 142 196 197 198 199 200Ratio of Predicted Sub-RegionalPeak to Peak Observed1.14 1.43 0.94 0.96 1.68 2.01 1.41 ----- -----Ratio of Unpaired Station Peaks1.02 1.21 0.87 0.80 0.78 1.20 1.12 ----- -----Normalized Systematic Bias (%)-10 -8 -32 -33 -33 15 17 ----- -----Normalized Gross Error (%)15 16 36 34 33 15 21 ----- -----Ozone Threshold (60 PPB)August 2005 August 2005 August 1997Date8/4 8/5 8/6 8/7 8/27 8/28 8/4 8/5 8/6 8/7Julian Date216 217 218 219 239 240 216217 218 219Ratio of Predicted Sub-RegionalPeak to Peak Observed138 1.72 1.66 1.64 1.06 0.83 1.12 1.16 1.58 1.46Ratio of Unpaired Station Peaks1.55 1.48 1.59 1.28 0.83 0.71 0.81 0.84 1.19 0.84Normalized Systematic Bias (%)-2 48 56 35 -21 -24 -20 -20 -43 -16Normalized Gross Error (%)17 48 59 35 23 26 22 27 63 16Chapter 4 Revision to the 2003 Ozone Attainment Demonstration PlanV-4-29TABLE V-4-12CAMx Sub-Region-3 8-Hour Average Ozone Performance StatisticsOzone Threshold (60 PPB)August 2004May 2005July 2005Date8/7 8/8 5/21 5/22 7/15 7/16 7/17 7/18 7/19Julian Date220 221 141 142 196 197 198 199 200Ratio of Predicted Sub-RegionalPeak to Peak Observed1.11 1.04 0.95 0.76 1.35 0.88 0.87 0.82 0.97Ratio of Unpaired Station Peaks0.79 0.76 0.66 0.57 1.15 0.75 0.66 0.58 0.85Normalized Systematic Bias (%)-12 -9 -30 -43 11 0 -7 -27 -12Normalized Gross Error (%)13 11 30 43 11 12 18 27 12Ozone Threshold (60 PPB)August 2005 August 2005 August 1997Date8/4 8/5 8/6 8/7 8/27 8/28 8/4 8/5 8/6 8/7Julian Date216 217 218 219 239 240 216217 218 219Ratio of Predicted Sub-RegionalPeak to Peak Observed0.93 0.91 0.91 1.02 1.01 0.74 1.07 1.15 1.05 1.23Ratio of Unpaired Station Peaks0.77 0.83 0.86 0.96 0.92 0.65 0.96 1.07 0.85 0.87Normalized Systematic Bias (%)-9 -7 8 15 -17 -36 2 11 5 -9Normalized Gross Error (%)13 10 21 27 17 36 5 21 20 9Final 2007AQMP Appendix V: Modeling and Attainment DemonstrationsV-4-30TABLE V-4-13CAMx Sub-Region-4 8-Hour Average Ozone Performance StatisticsOzone Threshold (60 PPB)August 2004May 2005July 2005Date8/7 8/8 5/21 5/22 7/15 7/16 7/17 7/18 7/19Julian Date220 221 141 142 196 197 198 199 200Ratio of Predicted Sub-RegionalPeak to Peak Observed0.89 1.10 0.84 0.80 0.90 0.97 1.03 1.10 1.20Ratio of Unpaired Station Peaks0.84 1.06 0.77 0.77 0.79 0.88 0.99 0.97 1.14Normalized Systematic Bias (%)-10 7 -13 -18 13 -2 2 -4 21Normalized Gross Error (%)10 8 19 22 20 14 8 7 21Ozone Threshold (60 PPB)August 2005 August 2005 August 1997Date8/4 8/5 8/6 8/7 8/27 8/28 8/4 8/5 8/6 8/7Julian Date216 217 218 219 239 240 216217 218 219Ratio of Predicted Sub-RegionalPeak to Peak Observed0.99 1.02 0.95 0.96 0.70 0.77 1.10 1.09 1.09 0.84Ratio of Unpaired Station Peaks0.94 1.00 0.91 0.96 0.63 0.76 1.03 1.00 1.07 0.79Normalized Systematic Bias (%)10 8 -2 -2 -27 -22 4 2 26 -7Normalized Gross Error (%)16 17 13 13 31 29 11 6 28 22Chapter 4 Revision to the 2003 Ozone Attainment Demonstration PlanV-4-31TABLE V-4-14CAMx Sub-Region-5 8-Hour Average Ozone Performance StatisticsOzone Threshold (60 PPB)August 2004May 2005July 2005Date8/7 8/8 5/21 5/22 7/15 7/16 7/17 7/18 7/19Julian Date220 221 141 142 196 197 198 199 200Ratio of Predicted Sub-RegionalPeak to Peak Observed1.21 1.43 1.05 0.99 1.61 - - - 1.49 - - - - - -Ratio of Unpaired Station Peaks0.93 1.06 0.88 0.71 0.83 - - - 1.18 - - - - - -Normalized Systematic Bias (%)-10 -4 -22 -31 -17 - - - 22 - - - - - -Normalized Gross Error (%)13 11 24 31 17 - - - 22 - - - - - -Ozone Threshold (60 PPB)August 2005 August 2005 August 1997Date8/4 8/5 8/6 8/7 8/27 8/28 8/4 8/5 8/6 8/7Julian Date216 217 218 219 239 240 216217 218 219Ratio of Predicted Sub-RegionalPeak to Peak Observed1.45 - - - 1.61 1.56 1.30 1.01 1.46 1.29 1.62 - - -Ratio of Unpaired Station Peaks0.90 - - - 1.56 1.33 0.79 0.78 1.01 .86 1.01 - - -Normalized Systematic Bias (%)-15 - - - 44 29 -25 -29 -12 -2 1 - - -Normalized Gross Error (%)15 - - - 44 29 25 29 12 9 1 - - -

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 32 Graphical Evaluation

Figures V-4-10 through V-4-14 s how the tile plots of predicted maximum ozone for the

each day of the July 15-19, 2005 ozone si mulations. Figure V-4-15 provides the

cumulative scatter plot of CAMx predicte d vs. observed 1-hour average ozone for the

July 14-18 subset of the 2005 episode. Figures V-4-16a thr ough V-4-16h show the

station diurnal plots of pred icted and observed ozone. Sim ilar tile plots of predicted

maximum ozone, diurnal plots and scatter plots of performance for the remaining

episodes are provided as attachments to this appendix.

FIGURE V-4-10

CAMx Simulated Maximum 1-Hour Average Ozone, July 15, 2005

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 33 FIGURE V-4-11

CAMx Simulated Maximum 1-Hour Average Ozone, July 16, 2005

FIGURE V-4-12

CAMx Simulated Maximum 1-Hour Average Ozone, July 17, 2005

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 34 FIGURE V-4-13

CAMx Simulated Maximum 1-Hour Average Ozone, July 18, 2005

FIGURE V-4-14

CAMx Simulated Maximum 1-Hour Average Ozone, July 19, 2005

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 35 Scatter Plot of Predicted vs. Observed Concentrations

July 15-19

y = 0.8683x + 5.9521

R 2 = 0.7522

0 20 40 60 80 100 120 140

0 20 40 60 80 100 120 140

Observed [ppb]Predicted [ppb]



# FIGURE V-4-15

CAMx Predicted vs. Observed 8-Hour Averag e Ozone Concentrations: July 14-18, 2005

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 36 3176: Anaheim 7.422 421.646

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 60: Azusa 6.981 456.113

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 4164: Banning Airport 104.459 433.527

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 69: Burbank -27.848 461.308

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



# FIGURE V-4-16a

CAMx Simulated 8-Hour Average Ozone (Solid Line) Vs. Observed (Squares):

July, 2005 Ozone Meteorological Episode

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 37 87: Los Angeles -22.302 445.563

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 5181: Crestline 66.383 468.606

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 3195: Costa Mesa 6.780 405.599

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 4158: Elsinore 60.525 405.907

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



# FIGURE V-4-16b

CAMx Simulated 8-Hour Average Ozone (Solid Line) Vs. Observed (Squares):

July, 2005 Ozone Meteorological Episode

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 38 5197: Fontana 46.811 453.081

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 591: Glendora 13.487 457.010

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 4157: Indio Jackson 162.217 411.293

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 3177: La Habra 4.359 432.978

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



# FIGURE V-4-16c

CAMx Simulated 8-Hour Average Ozone (Solid Line) Vs. Observed (Squares):

July, 2005 Ozone Meteorological Episode

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 39 820: LAXH -36.352 433.685

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 72: Long Beach -17.171 421.904

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 84: Lynwood -19.237 432.753

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 5212: Mira Loma 42.938 438.915

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



# FIGURE V-4-16d

CAMx Simulated 8-Hour Average Ozone (Solid Line) Vs. Observed (Squares):

July, 2005 Ozone Meteorological Episode

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 40 3812: Mission Viejo 29.671 400.791

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 88: Pasadena -11.205 457.021

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 4149: Perris 69.051 417.376

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 85: Pico Rivera -5.273 442.860

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Predicted



# FIGURE V-4-16e

CAMx Simulated 8-Hour Average Ozone (Solid Line) Vs. Observed (Squares):

July, 2005 Ozone Meteorological Episode

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 41 4137: Palm Springs 132.826 423.133

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 75: Pomona 22.547 448.620

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 5204: Redlands 76.256 448.189

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 74: Reseda -48.000 463.105

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



# FIGURE V-4-16f

CAMx Simulated 8-Hour Average Ozone (Solid Line) Vs. Observed (Squares):

July, 2005 Ozone Meteorological Episode

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 42 4144: Rubidoux 52.093 442.557

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 90: Santa Clarita -48.140 483.357

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 5203: San Bernardino 65.874 453.300

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



### 5175: Upland 31.687 452.125

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



# FIGURE V-4-16g

CAMx Simulated 8-Hour Average Ozone (Solid Line) Vs. Observed (Squares):

July, 2005 Ozone Meteorological Episode

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 43 91: West Los Angeles -34.796 447.031

0 20 40 60 80 100 120 140

7/ 15/ 5 7/ 16/ 5 7/ 17/ 5 7/ 18/ 5 7/ 19/ 5



### DateO3 [ppb] M inimum to M aximum Range Observed Predicted



# FIGURE V-4-16h

CAMx Simulated 8-Hour Average Ozone (Solid Line) Vs. Observed (Squares):

July, 2005 Ozone Meteorological Episode

The diurnal plots illustrate a ra nge of model predictions base d on a 7 X 7 grid analysis.

In the diagram, and in the la ter attainment demonstration, the peak prediction in the 49

grid cell array is compared to the station observation.

The July episode spans a weekend (July 16 th 

# and 17 th 

# ) over the course of the 5 day

meteorological episode. Weekend inventorie s have become increasingly more reliable

but have not yet reached the level of certainty of the we ekday emissions profiles.

Overall, heavy duty truck traffic decreases by about 60 percent in the Basin on Saturday,

compared to Friday, and an additional 10- 15 percent on Sundays. NOx emissions are

greatly reduced along the primary transportati on corridors. Unfortunately, at this time,

there is no weekend trip model is available to accurately simulate the reduced usage of

trucks on weekends and the residual impact on the movement and speeds of passenger

cars and light duty vehicles. Hence, the simulation uncertainty is most pronounced

during the weekends. Weekday simulations pr ovide a more accurate characterization of

the observed ozone trends.

On July 16 th 

# and 17 th 

# , the peaks are nominally under predicted and tend to lag the

observed concentrations in the San Bernar dino Valley and mountain areas. Performance

in the Riverside area is split, where Rubi doux is generally under predicted but Lake

Elsinore and Perris are well simulated. The simulation tends to under predicted

observations in the eastern San Gabriel Valley but is reasonable in the coastalmetropolitan areas. The San Fernando Valley sites of Burbank and Reseda are well

simulated with a tendency for over prediction. Santa Clarita however is significantly

under predicted on these days.

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 44 

# Effect of Emissions Uncertainties

The Final 2007 AQMP emissions inventory built upon the effo rt undertaken in the 2003

AQMP to provide updates to the mobile and day specific point and biogenic inventories

used in the modeling attainme nt demonstrations. Aircraft and airport operations were

thoroughly reviewed and inventor ied. Shipping transits into the Ports of Los Angeles

and Long Beach were carefully logged and sh ipping lane transits up and down the coast

were logged for the major vesse ls. The episode specific biogenic emissions inventory

under went significant modification. The ar eas source emissions distribution continued

to rely on the emissions surrogates used in the 2003 AQMP to distribute emissions.

As previously discussed in Chapter 2 the revisions to ARB's on-road emissions program

EMFAC2007 and the update of the Off-Road co mpanion model had a significant impact

on the emissions inventory and resulting model simulations. The net impact of

EMFAC2007 was to raise the absolute tonnage of NOx and VOC in the mobile source

emissions inventory over the 2003 AQMP projected 2002 i nventory. The Basin 2002

base-year mobile source inventory totals for VOC and NOx increased from 559 and 968

TPD in the 2003 AQMP to 710 and 1001 TPD fo r the current effort. While VOC

emissions rose 27 percent, NOx emissions onl y rose by a 3 percent margin. Many of the

comments regarding the episode development in simulating previous episodes was that

there existed significantly more NOx relative to the amount of VOC in the domain. The

enhancement to the inventories have led to more accurate inventory estimates, however,

the ratio of VOC to NOx remains in favor of ozone titration in the coastal emissions

region.

Several additional factors resulting from the use of the EMFAC2007 and Direct

Transportation Impact Model (DTIM4) to gene rate grid level mobile source emissions

may have altered the VOC to NOx ratio in th e Basin. First, there exist differences

between the two models in the numbers of trips and lengths of trips inferred by the

regional transportation model output. More numerous starts and stops lead to greater

VOC emissions from vehicle use and standi ng evaporative loss. Similarly, speed

impacts NOx emissions, especially from heavy- duty vehicles. Differences exist between

the emissions models in how the heavy-dut y vehicle speed factors are assigned.

Significant movement was made to resolve diffe rences in the projections of truck travel,

most notably the redistribution of a percenta ge of the fleet to the eastern Basin and

second, out of the Basin to the northern and eastern air Ba sins. The redistribution of

truck travel is one of the c ontributing factor to the nominal increase in NOx as opposed

to previous inventory updates.

The impact of ethanol as an additive in the fuel has lead to increased VOC emissions due

to increase vapor permeation in the fuel and exhaust of passenger vehicles. While

progress has been made to capture the impa ct of the VOC evaporative emissions, there

continues to exist uncertainty to the total daily tonnage and in particular the response on

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 45 exceedingly hot days when evaporation can become an exponential function of

temperature. VOC emissions on hot days , which are synonymous with higher ozone

days may be under represented and the net impact to mode l performance would be for

under prediction of the total amount of ozone formed in the Basin.

Comments on the episode selec tion were directed at the exclusion of 2003 as a source

for potential simulations days. The su mmer of 2003 was exceptionally hot with

numerous days exceeding the 100 o 

# F temperature in the inland valleys. Given the

uncertainty associated with vapor permeati on, coupled with fuel commingling, it was

decided to avoid selecting days on which significant uncer tainty in the emissions

occurred.

Other areas of the invent ory uncertainty may have impacted model performance

including the assignment of surrogates used to distribute emissions through the Basin,

and the sub-county distri bution of vehicles by age. Seve ral sensitivity simulations were

conducted using emissions factors ge nerated by EMFAC2002 during the 2003 AQMP

and were regenerated for this analysis using a grid level ch aracterization of the passenger

vehicle age with each county. The analysis was designed to attempt to place older, high

emitting vehicles in the general areas where they operate. There are drawbacks to this

assumption in that the average trip distance in the Basi n exceeds one grid length and can

easily transverse a county line. The se nsitivity analyses were encouraging and

preliminary results improved the ozone simu lation model performance in some critical

areas (most notably, Santa Clarita).

Similar types of sensitivity analyses were c onducted to test the exte nt of reduced truck

travel (lower NOx) in the Basin on weekends and the movement, storage and usage of

pleasure craft on weekends and weekdays. The impacts of these prospective inventory

modifications varied by hour of da y and location in the Basin.

The biogenic inventory is also subject to uncertainties due to the critical roll daily

temperature and humidity has in the estimation of the emissions. This is clearly evident

in the day-to-day variation in total emissions over the five multi-day episodes, and in the

difference in the estimated emissions betw een spring and mid summer. Added to the

diurnal and seasonal variation ha s been the rapid die off of th e forests in the East Basin

due to an infestation of the Bark Beetle. Estimates of tree death by acre continue to

increase creating a moving target for emissions estimation. Finally, the several episodes

take place in August and it is difficult to a ssess cumulative stress on the biomass as the

season progresses and what impact did the stress have on daily emissions.



# OZONE AIR QUALITY PROJECTIONS

CAMx simulations were conducted for the year base emission scenarios (2017 and

2023), and future year controlled scenarios (2017, and 2023). As discussed earlier, the

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 46 ozone attainment demonstrati on relies on the use of site specific RRFs being applied to

the 2002 weighted design values. The RRFs are determined from the future year

controlled and the 2002 base year simulations.

Future year 8-hour ozone atta inment demonstrations are required for those sites with

design values that exceed 84 ppb. As such, the current demonstra tions are focused on 16

locations in the Basin. Th e initial screening for station days to be included in the

attainment demonstration included the fo llowing criteria: (1) having an observed

concentration equaling or ex ceeding 85 ppb and (2) a simula tion predicted base year

(1997, 2004 or 2005) concentrati on over 60 ppb. Additional cr iteria were added to the

selection process as the simulations were evaluated.

A minimum of 5-episode days are recommende d to determine the site specific RRF.

The evaluation requires that the model pe rformance for the day is within specific

performance goals. The final cr iteria used to select an episode station day for the RRF

calculation included: (1) the base-year observe d concentration lie with 25 percent of the

station design value, (2) the absolute prediction accuracy (p redicted minus observed in

the base- year) is within 25 percent a nd (3) that a minimum base-year observed

concentration at each site used in the an alysis 85 ppb or greater. A maximum of 19

episode days were evaluated fo r inclusion in the RRF calculation. If a site did not meet

the 5-episode day threshold, the smaller reduc tion determined from either the average of

the RRFs for all Basin sites or the 19 day average RRF from that site, regardless of

model performance, was applied to estimate th e future design value at that station.

Since the CAMx simulations are run on a 5 km grid, the maximum 8-hour average

concentration from the 49 grid cells representi ng the monitoring site are used to generate

the simulated concentration at the monitor.

The results of the attainment demonstrati on for 2023 are presented in Table V-4-15.

Table V-16 provides the CAMx 2023 future year day specific model simulation results.

The analysis indicates that the federal 8-hour ozone standard woul d be attained in 2024

at the key stations with the controlled em issions implemented to the 2023 inventory.

The controlled carrying capacity (420 TPD of VOC and 114 TPD of NOx) consists of

both short-term and long-te rm control measures. The CEPA output summarizing the

control strategy implementation and emissions reductions is provided as Attachment 7.

With controls in place, it is e xpected that all stations in the Basin will meet the federal 8-

hour ozone standard. The east Basin stations of Crestline a nd Fontana are projected to

have the highest 8-hour cont rolled design values. Both sites are downwind receptors

along the primary wind transport route that moves precursor emissions and developing

ozone eastward during by the daily sea breeze. Future year projections of ozone along

the northerly transport route through the Sa n Fernando Valley indicate that the ozone

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 47 design value in the Santa Cl arita Valley will be approximately 13 percent below the

standard.

It is important to reiterate that the form of the ozone standard allows for at least 3-days

to have 8-hour average concentrations that exceed 80 ppb in any year. So, although the

demonstration satisfies the criteria for atta inment, areas of the Basin are likely to

experience occasional highe r ozone days (greater than 80 ppb) under severe

meteorological conditions.

Equally important, is the rate of progress sp ecified by the timing of the new standard.

The 2003 AQMP 1-hour ozone dem onstration set a 2010 attainment carrying capacity

of 330 TPD of VOC and 540 TPD of NOx. Se nsitivity simulations were conducted to

assess progress towards attaining the revoke d 1-hour ozone standard for a current 2010

baseline emissions estimate. The results i ndicated that the currently predicted 1-hour

average ozone concentrations for 2010 are e xpected to be approximately 20 percent

above the revoked 1-hour federal standard a ssuming full implementation of all available

control measures.

Graphical Distribution

The spatial distribution of ozone design values for the 2002 base year is shown in Figure

V-4-17. The distribution was generated us ing GIS mapping of the station based ozone

design values overlaid onto the modeling grid while a pplying a distance weighted

interpolation scheme to expand the prediction. Future year ozone air quality projections

for 2024 with and without implementation of all control measures are presented in

Figures V-4-18 and V-4-19. The predicted ozone concentr ation will be significantly

reduced in the future years in all parts of the Basin with the implementation of proposed

control measures in the South Coast Air Basin.

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 48 TABLE V-4-15

2024 Projected Basin 8-Hour Ozone Design Values

Site 2002 2002 2023 No. 2024

Weighted Baseline Controlled Valid Controlled

Design

(PPB) Simulation

(PPB) Average

(PPB) Days RRF Design

(PPB)

(a) Criteria

Azusa 101.0 108.1 66.3 < 5 0.661 69

Banning

Airport 115.0 119.0 69.4 11 0.588 68

Burbank 92.0 103.8 77.0 < 5 0.661 63

Crestline 128.7 123.0 78.5 8 0.644 83

Elsinore 107.0 110.5 63.3 9 0.575 62

Fontana 117.7 108.1 73.6 11 0.685 81

Glendora 112.0 106.6 73.8 5 0.706 79

Pasadena 96.0 99.0 73.4 < 5 0.661 73

Perris 112.0 107.3 75.4 < 5 0.661 79*

Pomona 96.0 101.7 77.9 8 0.779 75

Redlands 125.0 116.5 75.2 13 0.649 81

Reseda 104.0 105.3 66.2 7 0.632 66

Rubidoux 111.0 111.4 76.0 12 0.688 76

Santa Clarita 122.0 109.8 66.4 10 0.610 74

San Bernardino 116.0 115.0 75.6 14 0.660 77

Upland 110.0 107.3 75.8 8 0.713 78

Avg. ≥ 5 Days 0.661

(b) All Days

Azusa 101.0 96.6 71.6 19 0.788 80*

Burbank 92.0 97.5 73.8 19 0.756 70*

Pasadena 96.0 97.8 75.7 19 0.774 74*

Perris 112.0 111.7 66.2 15 0.592 66

* The higher future year design value determined from either the average RRF from the criteria (0.661) or

from the RRF calculated from all simulation days (only 15 were evaluated for Perris).Chapter 4 Revision to the 2003 Ozone Attainment Demonstration PlanV-4-49TABLE V-4-16CAMx 2023 Controlled SimulationStation CAMx 2023 Controlled Simulation2004 2005 1997220 221 142 196 197 198 199 200 216 217 218 219 240 216 217 218 219Azusa66.3Banning78.6 78.4 70.8 69.8 73.5 76.8 75.8 58.2 52.8 70.7 58.2Burbank77Crestline88 82.4 79.9 84 82.9 82.6 58 70.2Elsinore70 71.3 70.1 67.9 66.8 56.2 59.7 53.4 54Fontana85.2 80.2 78.9 81.3 84 65.6 62.6 61.3 61.7 73 76.2Glendora87.6 80.3 68.9 65.6 66.8Indio59 51.5 52.9Pasadena77.9 68.9Perris75.4Palm Springs62.5 64.9 58.9 50.1 58.4 59.7 56.5 65.7 55.2Pomona83.7 77.8 82.5 87.4 83.9 67 66.5 74Redlands88 83.1 82.4 84 82.9 84.8 64.2 63.5 56.9 73 74.9 73.4 66.5Reseda63.7 72.3 64.3 65.1 59.4 74.2 64.5Rubidoux88 82.7 82.4 83.2 82.9 84.8 63.5 61.5 60.7 73 73.4 76.2Santa Clarita63.7 63.8 72.3 64.3 65.1 59.4 74.2 60.5 58.1 82.5San Bernardino88 83.1 82.4 79.9 84 82.9 84.8 64.2 63.5 61.3 58.7 73 74.9 75.3Upland83.5 77.6 78.5 82.7 80.9 61.9 64.5 76.2

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 50 FIGURE V-4-17

2002 Baseline 8-Hour Ozone De sign Concentrations (ppb)

FIGURE V-4-18

Model-Predicted 2024 Baseline 8-Hour Ozone Design Concentrations (ppb)

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 51 FIGURE V-4-19

Model-Predicted 2024 Controlled 8-Hour Ozone Design Concentrations (ppb)



# Projection of 2018 Air Quality in the Coachella Valley

One major component of the Final 2007 AQM P modeling attainment demonstration

addresses the issue of transport of ozone and precursor pollutant s into the Coachella

Valley. The Coachella Valley has a 2013 attainment date. After the initial simulations

using the 2012 controlled emissi ons inventory, it was determ ined that additional time

would be required to meet the standard, de spite implementation of the upwind control

strategy. (The projected 2013 ozone design value for the Coachella Valley using the

2012 controlled emissions invent ory was 88 ppb). As a consequence, the District is

seeking a voluntary redesignation of the Coachella valley’s oz one attainment status from

“serious” to “severe-15.” This action will pr ovide additional time, out to 2018, to attain

the federal 8-hour standard.

CAMx simulations were also generated fo r the suite of episode days using the 2017

baseline and controlled inventories. Emi ssion reductions through 2017 are expected to

take place through exiting established control measures and reductions in mobile source

emissions as projected by EMFAC2007 and th rough the CARB control strategy with the

District overlay.

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 52 Table V-4-17 provides the 2017 ozone attain ment demonstration for the Coachella

Valley. Again, RRFs are determined fro m CAMx simulations using the 2002 baseline

and 2017 controlled emissions. Each site used at least 5 simulation da ys to generate the

RRFs. The attainment demonstration shows that both Indio and Pa lm Springs will meet

the federal standard.

TABLE V-4-17

2017 Projected Coachella Valley 8-Hour Ozone Design Values

Site 2002 2002 2017 2024

Weighted Baseline Controlled Controlled

Design

(PPB) Simulation

(PPB) Average

(PPB) No.

Valid

Days RRF Design

(PPB)

Palm Springs 105.7 99.9 75.9 9 0.767 81.1

Indio Jackson 95.0 84.0 66.2 5 0.791 75.2



# TABLE V-4-18

CAMx 2017 Coachella Valley Controlled 8- Hour Average Ozone Simulation (PPB)

Station CAMx 2017 Simulation Days

2004 2005 1997

220 221 142 196 197 198 199 239 218

Palm Springs 80.2 82.6 76.5 73.6 84.6 82.3 79.7 67.6 56.2

Indio Jackson 73.2 66.3 71.7 63 56.8



# SENSITIVITY STUDIES: MODEL PERFORMANCE AND CEQA

A set of CAMx sensitivity simulations were generated to test, model performance and

evaluate alternate emissions scenarios fo r CEQA determination. The stress tests

conducted for the PM2.5 modeling analysis were also applied to the ozone simulations

where emissions motor vehicle emissions we re doubled, the model was run solely with

biogenic emissions and the t op ozone boundary condition was m odified. The results of

the simulations suggested a better fit of th e data when VOC emissions were increase

throughout the Basin bringing them in line w ith the ambient VOC/NOx ratio measured

through the PAMS network. Re ducing the top concentrati on (top boundary of the model

simulation) resulted in a reduction of su rface ozone ranging seve ral ppb, but with no

specific trend in time of day.

# Chapter 4 Revision to the 2003 Ozone Attainment Demonstration Plan

V-4- 

# 53 Table V-4-19 lists the emissions for the 2023 baseline, plan and CEQA alternative ozone

simulations conducted. Three CEQA ozone si mulations were conducte d: (Alt-1) Heavy

VOC and heavy NOx, (Alt-2) Extensive VOC reductions and little NOx, and the 2003

AQMP (No-Project). The results of the simulations indicated that only the first scenario

could result in attainment of the ozone sta ndard. (See Figure V-4-20). The Heavy VOC

scenario does not demonstrate attainment because of the influence of biogenic

emissions. The No-plan scenario did not me et the standard becau se of the lack of

emissions reductions.

TABLE V-4-19

CEQA Simulations Modeling Emissions

Ozone-2023

VOC NOX CO

Baseline 496 506 2142

Plan 420 117 2049

Alt-1 200 160 1965

Alt-2 100 250 1965

No-Project 283 373 2100



## 0 20 40 60 80 100 120

2007 AQMP No-Project Alternative 1Ozone Concentration (ppb)



# FIGURE V-4-20

CEQA 2023 Ozone Simulation Results

# Final 2007AQMP Appendix V: Modeling and Attainment Demonstrations

V-4- 

# 54 

# WEIGHT OF EVIDENCE

The 2007 AQMP will rely on the use of long term measures to ensure attainment of the

federal 8-hour ozone standard . Several sensitivity runs were conducted to address

questions pertaining the mobile sour ce emissions inventory, and VMT.

With the revisions to the mobile source inventory (from an earlier version of

EMFAC2007), the ozone model performance decreased slightly, although it was still

within the acceptable criteria. However, lower base year emissions (i.e., 2002

emissions) and a significant alteration in th e VOC-to-NOx ratio (due to pending vehicle

adjustments described earlier) lead to a lowe r carrying capacity for ozone attainment.

This means greater reductions are needed to meet the 8-hour ozone standard by 2023.

AQMD staff has retained outside consulta nts to perform peer review on AQMD's

modeling work. Their initial feedback s uggested that VOC emissions in 2002 might

have been underestimated. In light of the earlier disc ussion on VMT projections and

peer review comments on the VOC inventory, a series of sensitivity analyses were

performed to test potential policy implica tions. The VMT sensitivity modeling runs

included: 1) adjusting CARB VMT estimat es for 2005 to match with SCAG’s; 2)

readjusting CARB’s VMT estimates for 2002, 2014, and 2020 anchoring off its 2005

VMT in accordance with the grow th rates provided by SCAG. The results indicate that

the 2005 VMT “blip” helps reduc e the need for additional NOx reductions by 10 tpd by

2014. However, should CARB’s 2005 VMT be accurate 30 TPD of additional NOx

reductions would be required for 8-hour ozone attainment.

Several additional simulations were conduc ted by adjusting the VOC emissions in 2002

to attempt to equilibrate w ith the ambient VOC/NOx ratio. The net results of these

analyses were to generate more ozone in 2002 and as a consequence increase the slope

of the RRF’s. Regardless, all of the adjust ments to the 2002 inventor y did not negate the

need for long term measures a nd additional NOx controls.

In conclusion with the uncer tainties that have been previously discussed in the

inventory, the attainment demonstration fo r 2024 projects a maximum future year design

value of 83 ppb which provides an additional measure of confidence that the strategy, as

designed will meet the federal ozone standard.

# CHAPTER 5

SUMMARY AND CONCLUSIONS

Comparison to State and Federal Standards

Basin Emissions Carrying Capacity (Emissions Budget)

# Chapter 5 Summary and Conclusions

V-5-1 

# COMPARISON TO STATE AND FEDERAL STANDARDS

Figure V-5-1 shows the 2002 observed and model-predicted regional peak

concentrations for the three nonattainment crite ria pollutants, as percentages of the most

stringent federal standard, for the years 2010, 2015, and 2024 (with and without further

emission controls). Figure V-5-2 shows similar information related to the most stringent

California state standards. Note: the rev oked federal 1-hour standard comparison has

been included for reference. The 2010 baseline 1-hour aver age ozone concentrations are

projected to exceed the revoked standard.

Table V-5-1 summarizes the expected year for attainment of the various federal and state

standards for the four polluta nts analyzed. As shown, th e Basin will be in compliance

with federal standards by the year 2024. Th e Basin will require additional time beyond

2024 to meet the state ozone, PM2.5 and PM10 standards.



# BASIN EMISSIONS CARRYING CAPACITY (EMISSIONS BUDGET)

The District is required to separately iden tify the emission reductions and corresponding

type and degree of implementation measures required to meet federal and state ambient

air quality standards. Secti on 40463(b) of the California St ate Health and Safety Code

specifies that, with the active participation of the Southern California Association of

Governments, a South Coast Air Basin emi ssion carrying capacity for each state and

federal ambient air quality standard shall be established by the South Coast District

Board for each formal review of the Plan and shall be upda ted to reflect new data and

modeling results.

A carrying capacity is defined as the maxi mum level of emissions that enable the

attainment and maintenance of an ambient air quality standa rd for a pollutant. Emission

carrying capacity for state standards shall not be a part of the State Implementation Plan

requirements of the Clean Air Act for the South Coast Air Basin. Emission carrying

capacity as defined in the Hea lth and Safety Code is an ove rly simplistic measure of the

Basin-wide allowable emission le vels for specific ambient air quality standards. It is

highly dependent on the spatial and temporal pattern of the emissions. Because of the

multi-component nature of PM2.5, the carry ing capacity for the co ntributing emissions

can vary significantly and like ozone it is a non-linear function among their precursors.

The federal Clean Air Act require s that plans contain an emissi ons budget that represents

the remaining emissions levels that achieve the applicable attainment deadline. Based

on the modeling results, a set of carrying capacities can be defi ned corresponding to

federal and state ambient air quality standa rds for annual PM2.5 and 8-hour ozone.

VOC and oxides of nitrogen ar e used for ozone. PM2.5 additionally requires reductions

of sulfur oxides and directly emitted PM2.5. Table V-5-2 shows the emissions carrying

capacities for the Basin to meet federal air qu ality standards. These estimates are based

on emission patterns estimated for each of the federal attainment years: 2015 for PM2.5

and 2021 for ozone.

# Final 2007 AQMP Appendix V Mode ling Attainment Demonstrations

5 - 2 

# 0 20 40 60 80 100 120 140 160 180 200

1-Hour

Ozone 8-Hour

Ozone Annual

PM2.5 24-Hour

PM2.5 24-Hour

PM10Percent of Standard



# 2002 Baseline 2010 Controlled

2015 Controlled 2024 controlled

FIGURE V-5-1

Projection of Future Air Quality in the Basin in Comparison

with the Most Stringent Federal Standards.

0 50 100 150 200 250 300

1-Hour

Ozone 8-Hour

Ozone Annual

PM2.5 24-

Hour

PM2.5 Annual

PM10 24-

Hour

PM10Percent of Standard



## 2002 Baseline 2015 Controlled 2024 controlled



# FIGURE V-5-2

Projection of Future Air Quality in the Basin in Comparison with

Most Stringent California State Standards

# Chapter 5 Summary and Conclusions

V-5-3 TABLE V-5-1

Expected Year of Complian ce with State and Federal

Standards for the Four Criteria Pollutants

Concentration Expected

Pollutant Standard Level Compliance Year

Ozone NAAQS 8-hours 84 ppb 2024

CAAQS 1-hour 90 ppb beyond 2024

CAAQS 8-hours 70 ppb beyond 2024

PM 2.5 

# NAAQS Annual 15 ug/m 3 

# 2015

NAAQS 24-hours 65 ug/m 3 

# 2005

NAAQS 24-hours* 35 ug/m 3 

# 2005

CAAQS Annual 12 ug/m 3 

# beyond 2024

PM 10 

# NAAQS 24-hours 150 ug/m 3 

# 2000

CAAQS 24-hours 50 ug/m 3 

# beyond 2024

CAAQS Annual 20 ug/m 3 

# beyond 2024

CO** NAAQS 1-hour 35 ppm 1990

NAAQS 8-hours 9 ppm 2002

CAAQS 8-hours 9 ppm 2002

NO2 NAAQS Annual 0.0534 ppm 1995

CAAQS Annual 0.030 ppm Beyond 2005

CAAQS 24-hours 18 ppm 2003

* EPA adopted the new 24-Hour PM2.5 sta ndard in September 2006. The current SIP

requirements address the 65 ug/m 3 

### standard in place in 2005 when national area attainment

designations were adopted.

** On May 11, 2007, U.S. EPA re designated the South Coast Air Basin as attainment for

Carbon Monoxide

# Final 2007 AQMP Appendix V Mode ling Attainment Demonstrations

V-5-4 TABLE V-5-2

Emissions Carrying Capacity Estimations for the South Coast Air Basin (tons/day)

based on the Planning Inventory

a) PM2.5 Attainment Strategy to meet NAAQS (2015)

VOC NOx SOx PM2.5

469 454 19 87

b) Ozone Attainment Strategy to meet NAAQS (2024)

VOC NOx CO

420 114 2039

# References

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Assessment. Final Report to California Air Res ources Board Contract No. 92-329, and 95-308.

Cassmassi, J., 1998, Comparison of Meteorological Criteria Characterizing 1-Hour and 8-Hour

Average Ozone Episodes in the South Co ast Air Basin, A&WMA Annual Meeting ,

No. 98-814.

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Defined Properties for Organic Compounds.” U.S. Environmental Protection Agency,

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Publication No. EPA-450/4-91-013. U.S. Enviro nmental Protection Agency, Research Triangle

Park, NC.

U.S. EPA. 1995. User’s Guide for the CALMET Meteorological Model. EPA-454/B-95-002.

Emissions, Monitoring, and Analysis Division. USEPA. Research Triangle Park, NC. 27711.

March, 1995.

U.S. EPA. 1995. A User’s Guide for the CALMET Meteorological Model, EPA-454/B-95-002,

U.S. EPA, OAQPS, Research Triangle Park, NC.

U.S. EPA 1998b. Use of Models and Other Anal yses in Attainment Demonstrations for the 8-

Hour Ozone NAAQS (Draft). October 1998.

U.S. EPA, 1999, “EPA Third Generation Air Quality Modeling System, Models-3 Volume 9b

Users Manual

. EPA/600/R-98/069(b).

U.S. EPA, 2005,” Guidance on the Use of M odels and Other Analyses in Attainment

Demonstrations for the 8-hour Ozone NAAQS,” EPA-454/R-05-002, U.S. EPA OAQPS,

Research Triangle Park, N.C.

U.S. EPA, 2006,” Guidance on Use of Modeled and Other Analyses for Demonstrating

Attainment of Air Quality Goals for Ozone, PM 2.5 

## and Regional Haze NAAQS,” U.S. EPA,

Officeof Air Quality Planning and Standards, Emissi ons, Monitoring, and Analysis Divistion, Air

Quality Modeling Group, Research Triangl e Park, North Carolina, September,.

Yamartino, R. J.; J. Scier; S. R. Hana; G. R. Carmichael, and Y. S. Chang. 1989. CALGRID: A

Mesoscale Photochemical Grid Model. Volume I: Model Formulation Document Sigma Research

Report No. A6-215-74. PTSD, California Air Resources Board, Sacramento, CA 94814.

September 1989.

# Attachment-1

PM2.5 Annual and 24-Hour Attainment CalculationsTABLE V-A-1aPredicted 2015 PM2.5 at Anaheim Using the Speciated Modeling Attainment TestRRF (CAMx 2015/CAMx 2005)NH4 NO3 SO4 OC EC OTRQ1 0.70 0.70 0.71 0.80 0.86 1.03Q2 0.61 0.59 0.62 0.85 0.86 1.02Q3 0.63 0.65 0.58 0.82 0.85 1.00Q4 0.73 0.75 0.67 0.80 0.86 1.022005 Design Modified by MATES-III with SandwichNH4 NO3 SO4 OC EC OTR Water Blank FilterMassQ1 1.49 4.95 2.20 3.251.59 1.57 2.00 0.50 17.57Q2 1.04 2.63 3.16 0.750.65 1.72 1.95 0.50 12.40Q3 1.60 2.71 4.47 1.740.72 2.00 1.62 0.50 15.37Q4 2.64 5.34 2.45 2.692.39 2.19 1.80 0.50 20.00Annual 1.69 3.91 3.07 2.111.34 1.87 1.84 0.50 16.342015 Controlled PM2.5NH4 NO3 SO4 OC EC OTR Water Blank SubtotalQ1 1.04 3.47 1.56 2.601.37 1.62 1.43 0.50 13.58Q2 0.63 1.55 1.96 0.640.56 1.75 1.19 0.50 8.79Q3 1.01 1.76 2.59 1.430.61 2.00 0.95 0.50 10.85Q4 1.93 4.01 1.64 2.152.06 2.23 2.01 0.50 16.52Annual1.15 2.70 1.94 1.70 1.15 1.90 1.40 0.50 12.44TABLE V- A-1bPredicted 2015 PM2.5 at Burbank Using the Speciated Modeling Attainment TestRRF (CAMx 2015/CAMx 2005)NH4 NO3 SO4 OC EC OTRQ1 0.70 0.69 0.72 0.80 0.85 1.01Q2 0.63 0.62 0.62 0.83 0.83 1.00Q3 0.64 0.65 0.60 0.81 0.83 0.97Q4 0.76 0.77 0.71 0.81 0.84 1.012005 Design Modified by MATES-III with SandwichNH4 NO3 SO4 OC EC OTR Water Blank FilterMassQ1 1.54 4.90 1.77 5.161.74 1.48 1.62 0.50 18.70Q2 1.48 3.63 2.94 1.971.02 2.10 1.53 0.50 15.17Q3 2.45 4.34 4.54 3.101.25 2.27 2.20 0.50 20.67Q4 3.06 6.90 2.42 3.803.21 2.09 2.32 0.50 24.30Annual 2.13 4.94 2.92 3.511.81 1.99 1.92 0.50 19.712015 Controlled PM2.5NH4 NO3 SO4 OC EC OTR Water Blank SubtotalQ1 1.08 3.38 1.27 4.131.48 1.49 1.10 0.50 14.44Q2 0.93 2.25 1.82 1.640.85 2.10 0.92 0.50 11.01Q3 1.57 2.82 2.72 2.511.04 2.20 1.36 0.50 14.72Q4 2.33 5.31 1.72 3.082.70 2.11 1.73 0.50 19.47Annual1.48 3.44 1.88 2.84 1.51 1.98 1.28 0.50 14.91TABLE V- A-1cPredicted 2015 PM2.5 at Compton Using the Speciated Modeling Attainment TestRRF (CAMx 2015/CAMx 2005)NH4 NO3 SO4 OC EC OTRQ1 0.78 0.86 0.62 0.76 0.81 0.94Q2 0.70 0.75 0.63 0.80 0.81 0.91Q3 0.71 0.80 0.60 0.78 0.81 0.89Q4 0.78 0.86 0.63 0.76 0.80 0.932005 Design Modified by MATES-III with SandwichNH4 NO3 SO4 OC EC OTR Water Blank FilterMassQ1 1.42 4.33 2.47 4.802.13 1.81 1.86 0.50 19.30Q2 1.30 3.11 3.78 0.680.90 2.16 2.16 0.50 14.60Q3 2.17 3.15 5.34 2.280.82 1.94 2.07 0.50 18.27Q4 2.76 5.63 2.69 3.603.23 2.44 2.03 0.50 22.87Annual 1.91 4.06 3.57 2.841.77 2.09 2.03 0.50 18.762015 Controlled PM2.5NH4 NO3 SO4 OC EC OTR Water Blank SubtotalQ1 1.11 3.72 1.53 3.651.73 1.70 1.42 0.50 15.36Q2 0.91 2.33 2.38 0.540.73 1.97 1.41 0.50 10.77Q3 1.54 2.52 3.20 1.780.66 1.73 1.38 0.50 13.31Q4 2.15 4.84 1.69 2.742.58 2.27 1.62 0.50 18.40Annual1.43 3.35 2.20 2.18 1.43 1.92 1.46 0.50 14.46TABLE V- A-1dPredicted 2015 PM2.5 at Fontana Using the Speciated Modeling Attainment TestRRF (CAMx 2015/CAMx 2005)NH4 NO3 SO4 OC EC OTRQ1 0.65 0.63 0.77 0.87 0.91 1.06Q2 0.53 0.50 0.61 0.84 0.89 1.05Q3 0.53 0.49 0.64 0.83 0.88 1.03Q4 0.69 0.68 0.73 0.86 0.90 1.062005 Design Modified by MATES-III with SandwichNH4 NO3 SO4 OC EC OTR Water Blank FilterMassQ1 1.89 6.22 1.82 2.951.84 1.58 1.94 0.50 18.73Q2 2.24 5.93 3.30 1.631.46 2.16 1.94 0.50 19.17Q3 2.10 4.83 4.27 1.371.88 3.24 2.00 0.50 20.20Q4 3.53 8.29 2.40 0.552.51 2.67 2.72 0.50 23.17Annual 2.44 6.32 2.95 1.631.92 2.41 2.15 0.50 20.322015 Controlled PM2.5NH4 NO3 SO4 OC EC OTR Water Blank SubtotalQ1 1.23 3.92 1.40 2.571.67 1.67 1.28 0.50 14.24Q2 1.19 2.97 2.01 1.371.30 2.27 1.05 0.50 12.65Q3 1.11 2.37 2.73 1.141.65 3.34 1.23 0.50 14.07Q4 2.44 5.64 1.75 0.472.26 2.83 2.22 0.50 18.11Annual1.49 3.72 1.97 1.39 1.72 2.53 1.45 0.50 14.77TABLE V- A-1ePredicted 2015 PM2.5 at Long Beach Usingthe Speciated Modeling Attainment TestRRF (CAMx 2015/CAMx 2005)NH4 NO3 SO4 OC EC OTRQ1 0.75 0.81 0.62 0.73 0.79 0.84Q2 0.68 0.72 0.6 0.74 0.77 0.79Q3 0.68 0.77 0.57 0.72 0.76 0.76Q4 0.75 0.85 0.61 0.73 0.78 0.832005 Design Modified by MATES-III with SandwichNH4 NO3 SO4 OC EC OTR Water Blank FilterMassQ1 1.37 4.19 2.55 3.741.83 1.88 1.92 0.50 17.97Q2 1.25 2.45 3.72 0.400.70 1.98 1.67 0.50 12.67Q3 1.95 2.53 4.75 1.260.87 2.08 1.73 0.50 15.67Q4 3.29 5.86 4.31 1.422.50 2.49 2.53 0.50 22.90Annual 1.97 3.76 3.83 1.711.48 2.11 1.96 0.50 17.302015 Controlled PM2.5NH4 NO3 SO4 OC EC OTR Water Blank SubtotalQ1 1.03 3.39 1.58 2.731.45 1.44 1.49 0.50 13.61Q2 0.85 1.76 2.23 0.300.54 0.99 1.09 0.50 8.27Q3 1.33 1.95 2.71 0.910.66 1.14 1.19 0.50 10.38Q4 2.47 4.98 2.63 1.041.95 1.84 1.88 0.50 17.28Annual1.42 3.02 2.29 1.24 1.15 1.35 1.41 0.50 12.38TABLE V- A-1fPredicted 2015 PM2.5 at Los Angeles Usingthe Speciated Modeling Attainment TestRRF (CAMx 2015/CAMx 2005)NH4 NO3 SO4 OC EC OTRQ1 0.74 0.76 0.69 0.76 0.81 0.99Q2 0.67 0.69 0.62 0.79 0.81 0.98Q3 0.69 0.74 0.59 0.78 0.80 0.96Q4 0.78 0.82 0.67 0.77 0.81 0.992005 Design Modified by MATES-III with SandwichNH4 NO3 SO4 OC EC OTR Water Blank FilterMassQ1 1.78 6.25 2.44 2.541.98 1.69 2.55 0.50 19.73Q2 1.58 4.07 3.74 0.731.32 2.22 2.16 0.50 16.33Q3 2.95 4.52 5.11 0.981.45 2.22 2.47 0.50 20.20Q4 3.17 7.06 2.92 0.523.25 2.23 2.59 0.50 22.23Annual 2.37 5.48 3.55 1.192.00 2.09 2.44 0.50 19.622015 Controlled PM2.5NH4 NO3 SO4 OC EC OTR Water Blank SubtotalQ1 1.32 4.75 1.68 1.931.60 1.67 1.90 0.50 15.36Q2 1.06 2.81 2.32 0.581.07 2.18 1.38 0.50 11.89Q3 2.04 3.34 3.01 0.761.16 2.13 1.58 0.50 14.53Q4 2.47 5.79 1.96 0.402.63 2.21 2.03 0.50 17.99Annual1.72 4.17 2.24 0.92 1.62 2.05 1.72 0.50 14.94TABLE V- A-1gPredicted 2015 PM2.5 at Rubidoux Usingthe Speciated Modeling Attainment TestRRF (CAMx 2015/CAMx 2005)NH4 NO3 SO4 OC EC OTRQ1 0.63 0.61 0.72 0.86 0.89 0.94Q2 0.49 0.46 0.58 0.81 0.87 0.90Q3 0.48 0.45 0.59 0.80 0.85 0.90Q4 0.66 0.65 0.69 0.85 0.89 0.942005 Design Modified by MATES-III with SandwichNH4 NO3 SO4 OC EC OTR Water Blank FilterMassQ1 1.79 6.51 1.93 4.182.00 1.81 2.44 0.50 21.17Q2 3.00 8.25 3.18 1.391.18 2.15 2.23 0.50 21.87Q3 2.94 7.29 4.30 1.621.17 2.50 2.27 0.50 22.60Q4 3.47 8.44 2.65 1.532.61 2.64 3.04 0.50 24.87Annual 2.80 7.62 3.02 2.181.74 2.28 2.50 0.50 22.632015 Controlled PM2.5NH4 NO3 SO4 OC EC OTR Water Blank SubtotalQ1 1.13 3.97 1.39 3.591.78 1.70 1.52 0.50 15.58Q2 1.47 3.80 1.84 1.131.03 1.94 1.09 0.50 12.79Q3 1.41 3.28 2.54 1.300.99 2.25 1.19 0.50 13.46Q4 2.29 5.49 1.83 1.302.32 2.48 1.96 0.50 18.17Annual1.57 4.13 1.90 1.83 1.53 2.09 1.44 0.50 15.00TABLE V- A-1hPredicted 2015 PM2.5 at Wilmington Using the SpeciatedModeling Attainment TestRRF (CAMx 2015/CAMx 2005)NH4 NO3 SO4 OC EC OTRQ1 0.69 0.78 0.56 0.68 0.72 0.74Q2 0.66 0.72 0.56 0.69 0.69 0.70Q3 0.64 0.75 0.54 0.68 0.68 0.67Q4 0.71 0.81 0.57 0.68 0.71 0.742005 Design Modified by MATES-III with SandwichNH4 NO3 SO4 OC EC OTR Water Blank FilterMassQ1 1.27 3.49 2.91 3.432.44 2.08 1.84 0.50 17.97Q2 1.13 2.02 3.84 0.380.92 2.14 1.74 0.50 12.67Q3 1.73 1.95 5.12 1.341.15 2.16 1.73 0.50 15.67Q4 2.62 5.02 3.88 2.473.37 2.79 2.27 0.50 22.90Annual 1.69 3.12 3.94 1.911.97 2.29 1.90 0.50 17.302015 Controlled PM2.5NH4 NO3 SO4 OC EC OTR Water Blank SubtotalQ1 0.88 2.72 1.63 2.331.76 1.54 1.24 0.50 12.60Q2 0.75 1.45 2.15 0.260.63 1.50 0.93 0.50 8.18Q3 1.11 1.46 2.76 0.910.78 1.45 0.99 0.50 9.96Q4 1.86 4.07 2.21 1.682.39 2.06 1.63 0.50 16.40Annual1.15 2.43 2.19 1.30 1.39 1.64 1.20 0.50 11.79

# TABLE V - A- 2

Summary of Methodologies to Calculate 2015 24-Hour Average Design Value (μg/m3)

Location 24-Hour Average Design Annual RRF to Design

Anaheim 47.0 33.4

Burbank 53.3 37.8

Compton (Using Lynwood) 51.3 37.4

Fontana 54.8 40.6

Long Beach 44.6 30.8

Los Angeles 60.7 43.1

Rubidoux 64.8 42.8

Wilmington (Using Long Beach) 44.6 26.6

# TABLE V-A-3a

Anaheim 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.09 0.36 0.17 0.19 0.09 0.09

Q2 0.09 0.29 0.36 0.06 0.05 0.14

Q3 0.11 0.18 0.41 0.12 0.05 0.13

Q4 0.14 0.32 0.17 0.14 0.12 0.11

Design Q1 Q2 Q3 Q4

2003 51.8 46.3 27.6 47.3

2004 48.2 30.5 46.8 49.9

2005 41.8 27.6 42.9 43.8

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.78 0.78 0.80 0.81 0.78 1.00

Q2 0.67 0.68 0.83 0.72 0.80 0.90

Q3 0.73 0.78 0.57 0.80 0.83 1.00

Q4 0.72 0.74 0.67 0.84 0.89 1.03

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 3.6 12.3 5.6 11.3 4.1 9.7 5.6

2004 3.3 11.4 5.2 10.5 3.8 9.1 5.2

2005 2.9 9.9 4.5 9.1 3.3 7.8 4.5

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.6 14.5 7.0 8.0 3.6 4.7 4.4 0.5 46.4

2004 3.4 13.5 6.6 7.4 3.4 4.3 4.1 0.5 43.2

2005 2.9 11.7 5.7 6.4 2.9 3.8 3.5 0.5 37.5

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 2.7 8.2 9.2 11.5 2.3 5.0 6.4

2004 1.8 5.4 6.0 7.5 1.5 3.3 4.2

2005 1.6 4.9 5.4 6.8 1.4 3.0 3.8

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.8 9.1 13.8 2.0 1.9 5.8 5.8 0.5 41.7

2004 1.8 6.0 9.1 1.3 1.2 3.8 3.8 0.5 27.6

2005 1.7 5.4 8.2 1.2 1.1 3.5 3.4 0.5 25.1

## Q3 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 3.0 4.6 7.0 4.3 1.1 4.6 2.2

2004 5.1 7.9 12.0 7.4 1.9 7.9 3.7

2005 4.7 7.2 11.0 6.8 1.7 7.2 3.4

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.2 3.9 6.5 2.6 1.1 3.6 2.7 0.5 23.2

2004 3.8 6.6 10.9 4.5 1.9 6.1 4.7 0.5 38.9

2005 3.4 6.0 10.0 4.1 1.8 5.6 4.3 0.5 35.7

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 5.1 11.2 5.6 10.3 4.7 4.7 4.2

2004 5.4 11.9 5.9 10.9 4.9 4.9 4.4

2005 4.8 10.4 5.2 9.5 4.3 4.3 3.9

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 4.8 11.2 5.4 5.6 5.1 5.4 3.6 0.5 41.4

2004 5.0 11.8 5.7 5.9 5.3 5.7 3.8 0.5 43.7

2005 4.4 10.4 5.0 5.2 4.7 5.0 3.3 0.5 38.4

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 46.4 41.7 23.2 41.4 46.4

2004 43.2 27.6 38.9 43.7 43.7

2005 37.5 25.1 35.7 38.4 38.4 42.8

# TABLE V-A-3b

Burbank 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.08 0.32 0.13 0.28 0.10 0.08

Q2 0.10 0.28 0.27 0.13 0.07 0.14

Q3 0.12 0.24 0.31 0.15 0.06 0.11

Q4 0.13 0.35 0.14 0.16 0.13 0.09

Design Q1 Q2 Q3 Q4

2003 54.1 45.2 51.6 50.3

2004 37.8 41.6 51.5 60.1

2005 50.6 34.8 49.3 42.6

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.79 0.82 0.69 0.74 0.75 1.00

Q2 0.67 0.73 0.57 0.76 0.80 1.00

Q3 0.71 0.95 0.35 0.67 0.83 0.94

Q4 0.79 0.85 0.62 0.78 0.83 1.00

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 4.3 13.4 5.4 11.3 4.8 9.6 4.8

2004 3.0 9.3 3.7 7.8 3.4 6.7 3.4

2005 4.0 12.5 5.0 10.5 4.5 9.0 4.5

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.4 14.2 4.9 11.2 4.1 4.3 3.6 0.5 46.2

2004 2.4 9.9 3.4 7.8 2.8 3.0 2.5 0.5 32.4

2005 3.2 13.3 4.5 10.5 3.8 4.0 3.4 0.5 43.2

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 4.0 10.3 8.0 10.7 3.1 4.0 4.5

2004 3.7 9.5 7.4 9.9 2.9 3.7 4.1

2005 3.1 7.9 6.2 8.2 2.4 3.1 3.4

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.0 9.2 7.0 4.5 2.5 6.3 3.6 0.5 36.7

2004 2.8 8.5 6.4 4.1 2.3 5.8 3.4 0.5 33.8

2005 2.3 7.1 5.4 3.4 1.9 4.9 2.8 0.5 28.4

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 6.1 10.7 11.2 8.7 3.1 5.1 5.6

2004 6.1 10.7 11.2 8.7 3.1 5.1 5.6

2005 5.9 10.2 10.7 8.3 2.9 4.9 5.4

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 4.4 11.8 5.6 5.2 2.6 5.3 3.7 0.5 39.0

2004 4.4 11.7 5.6 5.2 2.6 5.3 3.7 0.5 39.0

2005 4.2 11.2 5.3 5.0 2.5 5.1 3.5 0.5 37.3

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 5.5 13.4 4.0 12.0 6.0 4.5 4.5

2004 6.6 16.1 4.8 14.3 7.2 5.4 5.4

2005 4.6 11.4 3.4 10.1 5.1 3.8 3.8

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 5.2 15.0 4.4 6.3 5.4 4.5 3.8 0.5 45.0

2004 6.2 17.9 5.2 7.5 6.5 5.4 4.5 0.5 53.7

2005 4.4 12.7 3.7 5.3 4.6 3.8 3.2 0.5 38.2

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 46.2 36.7 39.0 45.0 46.2

2004 32.4 33.8 39.0 53.7 53.7

2005 43.2 28.4 37.3 38.2 43.2 47.7

# TABLE V-A-3c

Compton/Lynwood 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.08 0.28 0.18 0.26 0.11 0.10

Q2 0.09 0.28 0.36 0.05 0.06 0.15

Q3 0.12 0.19 0.41 0.13 0.05 0.11

Q4 0.12 0.30 0.16 0.16 0.14 0.11

Design Q1 Q2 Q3 Q4

2003 45.3 44.7 45.5 52.5

2004 44.8 38.2 36.3 52.4

2005 41.0 31.8 51.7 53.0

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.77 0.97 0.27 0.82 0.91 1.17

Q2 0.85 0.48 2.22 0.60 0.57 0.68

Q3 0.81 0.96 0.62 0.74 0.75 0.90

Q4 0.76 0.87 0.54 0.80 0.82 0.93

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 2.7 9.4 5.4 10.3 4.5 8.5 4.5

2004 2.7 9.3 5.3 10.2 4.4 8.4 4.4

2005 2.4 8.5 4.9 9.3 4.1 7.7 4.1

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.6 12.4 2.2 9.5 4.7 5.1 2.5 0.5 39.5

2004 2.6 12.3 2.2 9.4 4.6 5.0 2.5 0.5 39.0

2005 2.4 11.2 2.0 8.6 4.2 4.6 2.3 0.5 35.8

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 3.5 8.4 9.7 9.7 2.7 4.9 4.9

2004 3.0 7.2 8.3 8.3 2.3 4.1 4.1

2005 2.5 5.9 6.9 6.9 1.9 3.4 3.4

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.5 6.0 36.1 1.3 1.6 4.7 12.4 0.5 66.1

2004 3.0 5.1 30.9 1.1 1.4 4.0 10.6 0.5 56.5

2005 2.5 4.2 25.7 0.9 1.2 3.3 8.8 0.5 47.2

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 5.0 7.7 12.2 6.3 1.8 6.8 5.0

2004 3.9 6.1 9.7 5.0 1.4 5.4 3.9

2005 5.6 8.7 13.8 7.2 2.0 7.7 5.6

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 4.5 8.1 11.5 4.3 1.6 4.5 5.1 0.5 40.1

2004 3.6 6.5 9.2 3.4 1.3 3.6 4.1 0.5 32.1

2005 5.1 9.2 13.1 4.9 1.8 5.1 5.8 0.5 45.5

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 5.2 13.0 4.7 11.4 6.2 5.7 4.7

2004 5.2 13.0 4.7 11.4 6.2 5.7 4.7

2005 5.3 13.1 4.7 11.6 6.3 5.8 4.7

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 4.9 13.7 4.6 6.8 6.2 5.3 3.7 0.5 45.7

2004 4.9 13.7 4.6 6.7 6.2 5.3 3.7 0.5 45.6

2005 5.0 13.8 4.7 6.8 6.3 5.4 3.7 0.5 46.1

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 39.5 66.1 40.1 45.7 66.1

2004 39.0 56.5 32.1 45.6 56.5

2005 35.8 47.2 45.5 46.1 47.2 56.6

# TABLE V-A-3d

Fontana 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.10 0.41 0.14 0.16 0.10 0.09

Q2 0.12 0.36 0.24 0.09 0.08 0.12

Q3 0.11 0.27 0.29 0.07 0.10 0.16

Q4 0.16 0.45 0.14 0.02 0.11 0.12

0.10 0.41 0.14 0.16 0.10 0.09

Design Q1 Q2 Q3 Q4

2003 53.6 48.8 46.6 55.7

2004 62.6 45.5 49.9 48.5

2005 48.2 43.7 38.4 43.0

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.66 0.65 0.67 0.80 1.00 1.00

Q2 0.56 0.67 0.37 1.25 1.33 1.25

Q3 0.56 0.58 0.58 0.79 1.00 1.06

Q4 0.63 0.66 0.56 0.84 0.86 1.00

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 4.2 14.9 4.2 10.6 4.8 9.6 4.2

2004 5.0 17.4 5.0 12.4 5.6 11.2 5.0

2005 3.8 13.4 3.8 9.5 4.3 8.6 3.8

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.7 14.4 4.9 6.9 5.4 4.6 3.7 0.5 44.1

2004 4.3 16.8 5.7 8.1 6.3 5.4 4.3 0.5 51.4

2005 3.3 12.9 4.4 6.2 4.9 4.2 3.3 0.5 39.7

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 4.8 12.6 7.2 10.1 3.9 5.8 4.3

2004 4.5 11.7 6.8 9.5 3.6 5.4 4.1

2005 4.3 11.2 6.5 9.1 3.5 5.2 3.9

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.3 11.7 4.3 5.3 5.1 7.1 3.2 0.5 40.4

2004 3.1 10.9 4.0 5.0 4.7 6.6 2.9 0.5 37.7

2005 2.9 10.5 3.9 4.8 4.5 6.3 2.8 0.5 36.3

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 4.1 9.7 8.3 8.3 3.7 8.3 3.7

2004 4.4 10.4 8.9 8.9 4.0 8.9 4.0

2005 3.4 8.0 6.8 6.8 3.0 6.8 3.0

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.8 7.3 8.0 2.6 4.4 8.1 3.7 0.5 37.3

2004 3.0 7.8 8.5 2.7 4.8 8.7 4.0 0.5 39.9

2005 2.3 6.0 6.6 2.1 3.7 6.7 3.0 0.5 30.9

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 7.7 17.7 4.4 9.4 5.5 5.5 5.0

2004 6.7 15.4 3.8 8.2 4.8 4.8 4.3

2005 6.0 13.6 3.4 7.2 4.3 4.3 3.8

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 5.5 16.5 4.5 1.1 5.3 6.6 4.0 0.5 43.9

2004 4.8 14.3 3.9 1.0 4.6 5.7 3.5 0.5 38.3

2005 4.2 12.7 3.5 0.9 4.1 5.1 3.1 0.5 34.0

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 44.1 40.4 37.3 43.9 44.1

2004 51.4 37.7 39.9 38.3 51.4

2005 39.7 36.3 30.9 34.0 39.7 45.0

# TABLE V-A-3e

Long Beach 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.08 0.30 0.20 0.21 0.10 0.11

Q2 0.10 0.23 0.41 0.03 0.06 0.16

Q3 0.13 0.17 0.43 0.08 0.06 0.14

Q4 0.15 0.31 0.26 0.06 0.11 0.11

Design Q1 Q2 Q3 Q4

2003 46.5 42.9 36.9 47.4

2004 45.8 32.9 34.6 45.9

2005 37.3 27.7 46.1 43.2

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR Water

Q1 0.81 0.82 0.75 0.79 0.87 0.89 0.81

Q2 0.71 0.93 0.62 0.67 0.75 0.75 0.71

Q3 0.70 0.95 0.58 0.68 0.75 0.77 0.70

Q4 0.77 0.75 0.83 0.89 0.83 0.84 0.77

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 2.8 10.1 5.1 10.1 3.7 9.2 5.1

2004 2.7 10.0 5.0 10.0 3.6 9.1 5.0

2005 2.2 8.1 4.0 8.1 2.9 7.4 4.0

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.0 11.3 6.9 7.9 4.2 4.5 3.9 0.5 42.1

2004 2.9 11.2 6.8 7.7 4.2 4.4 3.8 0.5 41.5

2005 2.4 9.1 5.5 6.3 3.4 3.6 3.1 0.5 33.9

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 3.0 6.8 9.8 9.8 2.5 5.5 5.5

2004 2.3 5.2 7.5 7.5 1.9 4.2 4.2

2005 1.9 4.4 6.3 6.3 1.6 3.5 3.5

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 1.7 3.9 5.2 7.6 1.9 4.4 2.8 0.5 28.1

2004 1.3 3.0 4.0 5.8 1.5 3.4 2.1 0.5 21.6

2005 1.1 2.5 3.4 4.9 1.2 2.8 1.8 0.5 18.2

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 4.4 5.8 9.8 5.8 1.8 5.5 3.6

2004 4.1 5.5 9.2 5.5 1.7 5.1 3.4

2005 5.5 7.3 12.3 7.3 2.3 6.8 4.6

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.3 5.9 9.1 2.1 1.6 3.9 4.0 0.5 30.4

2004 3.1 5.6 8.5 2.0 1.5 3.7 3.7 0.5 28.5

2005 4.1 7.4 11.4 2.6 2.0 4.9 4.9 0.5 37.8

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 6.1 10.8 6.1 9.8 4.7 5.6 3.8

2004 5.9 10.4 5.9 9.5 4.5 5.4 3.6

2005 5.6 9.8 5.6 9.0 4.3 5.1 3.4

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 5.4 10.9 10.3 2.7 4.4 4.4 5.2 0.5 43.7

2004 5.2 10.5 10.0 2.6 4.3 4.3 5.0 0.5 42.3

2005 4.9 9.9 9.4 2.4 4.0 4.0 4.7 0.5 39.8

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 42.1 36.8 30.4 43.7 43.7

2004 41.5 28.3 28.5 42.3 42.3

2005 33.9 23.9 37.8 39.8 39.8 41.9

# TABLE V-A-3f

Los Angeles 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.09 0.41 0.17 0.13 0.10 0.09

Q2 0.10 0.31 0.32 0.05 0.08 0.14

Q3 0.15 0.26 0.35 0.05 0.07 0.11

Q4 0.15 0.40 0.18 0.02 0.15 0.10

Design Q1 Q2 Q3 Q4

2003 53.6 55.1 51.0 55.3

2004 49.7 44.0 55.9 61.3

2005 53.5 38.2 36.8 52.0

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.69 0.57 1.22 1.05 1.29 1.28

Q2 0.76 0.83 0.55 0.78 0.83 0.94

Q3 0.77 0.89 0.6 0.76 0.78 0.96

Q4 0.84 0.87 0.7 0.79 0.82 1

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 3.7 13.3 5.3 11.2 4.8 9.0 5.8

2004 3.4 12.3 4.9 10.3 4.4 8.4 5.4

2005 3.7 13.3 5.3 11.1 4.8 9.0 5.8

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.4 12.6 11.3 7.4 7.1 6.0 5.4 0.5 53.8

2004 3.2 11.7 10.4 6.9 6.6 5.6 5.0 0.5 49.9

2005 3.4 12.6 11.2 7.4 7.1 6.0 5.4 0.5 53.7

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 4.4 10.9 10.4 14.2 3.8 4.9 6.0

2004 3.5 8.7 8.3 11.3 3.0 3.9 4.8

2005 3.0 7.5 7.2 9.8 2.6 3.4 4.1

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 4.2 14.1 9.7 2.0 3.8 7.3 5.2 0.5 46.8

2004 3.3 11.3 7.8 1.6 3.0 5.8 4.2 0.5 37.5

2005 2.9 9.8 6.7 1.4 2.6 5.0 3.6 0.5 32.6

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 6.6 9.6 10.6 8.6 3.0 7.6 5.1

2004 7.2 10.5 11.6 9.4 3.3 8.3 5.5

2005 4.7 6.9 7.6 6.2 2.2 5.4 3.6

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 5.9 11.9 10.8 1.9 2.9 5.5 5.5 0.5 44.9

2004 6.4 13.0 11.8 2.1 3.2 6.0 6.0 0.5 49.2

2005 4.2 8.6 7.8 1.4 2.1 4.0 4.0 0.5 32.6

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 6.0 14.2 4.9 13.2 6.6 4.9 4.9

2004 6.7 15.8 5.5 14.6 7.3 5.5 5.5

2005 5.7 13.4 4.6 12.4 6.2 4.6 4.6

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 6.8 19.1 7.1 1.0 6.8 5.7 5.3 0.5 52.2

2004 7.5 21.1 7.8 1.2 7.5 6.3 5.9 0.5 57.8

2005 6.4 17.9 6.6 1.0 6.4 5.3 5.0 0.5 49.1

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 53.8 46.8 44.9 52.2 53.8

2004 49.9 37.5 49.2 57.8 57.8

2005 53.7 32.6 32.6 49.1 53.7 55.1

# TABLE V-A-3g

Rubidoux 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.09 0.32 0.09 0.20 0.10 0.09

Q2 0.14 0.39 0.15 0.07 0.06 0.10

Q3 0.13 0.33 0.20 0.07 0.05 0.11

Q4 0.14 0.35 0.11 0.06 0.11 0.11

Design Q1 Q2 Q3 Q4

2003 72.9 61.6 60.5 66.0

2004 59.5 60.5 55.3 76.6

2005 56.6 55.8 47.0 49.5

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.69 0.66 0.70 0.80 0.86 0.91

Q2 0.56 0.58 0.42 0.81 1.00 1.06

Q3 0.52 0.52 0.50 0.79 0.80 0.89

Q4 0.54 0.56 0.50 0.79 0.80 0.89

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 5.1 18.8 5.8 15.2 6.5 14.5 7.2

2004 4.1 15.3 4.7 12.4 5.3 11.8 5.9

2005 3.9 14.6 4.5 11.8 5.0 11.2 5.6

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 4.5 15.2 4.7 11.7 6.3 6.0 3.8 0.5 52.7

2004 3.7 12.4 3.9 9.5 5.1 4.9 3.1 0.5 43.1

2005 3.5 11.8 3.7 9.1 4.9 4.6 3.0 0.5 41.0

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 7.3 19.6 7.9 11.0 3.1 4.9 7.3

2004 7.2 19.2 7.8 10.8 3.0 4.8 7.2

2005 6.6 17.7 7.2 10.0 2.8 4.4 6.6

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 4.8 13.8 3.9 3.5 3.7 6.5 3.4 0.5 40.1

2004 4.7 13.5 3.8 3.4 3.6 6.4 3.4 0.5 39.4

2005 4.4 12.5 3.5 3.2 3.3 5.9 3.1 0.5 36.4

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 8.4 16.8 9.0 8.4 2.4 8.4 6.6

2004 7.7 15.3 8.2 7.7 2.2 7.7 6.0

2005 6.5 13.0 7.0 6.5 1.9 6.5 5.1

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 4.1 10.4 5.9 3.3 2.4 5.9 3.6 0.5 36.1

2004 3.7 9.5 5.4 3.1 2.2 5.4 3.3 0.5 33.1

2005 3.2 8.1 4.6 2.6 1.9 4.6 2.8 0.5 28.2

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 9.2 18.3 4.6 14.4 7.2 7.2 4.6

2004 10.7 21.3 5.3 16.7 8.4 8.4 5.3

2005 6.9 13.7 3.4 10.8 5.4 5.4 3.4

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 5.0 12.8 3.6 3.1 5.8 6.5 3.3 0.5 40.5

2004 5.8 14.8 4.2 3.6 6.7 7.5 3.8 0.5 47.0

2005 3.7 9.6 2.7 2.3 4.4 4.8 2.4 0.5 30.5

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 52.7 40.1 36.1 40.5 52.7

2004 43.1 39.4 33.1 47.0 47.0

2005 41.0 36.4 28.2 30.5 41.0 46.9

# TABLE V-A-3h

Wilmington 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.07 0.25 0.23 0.20 0.14 0.12

Q2 0.09 0.20 0.43 0.03 0.08 0.18

Q3 0.11 0.12 0.46 0.09 0.08 0.14

Q4 0.12 0.26 0.24 0.11 0.15 0.12

Design Q1 Q2 Q3 Q4

2003 46.5 42.9 36.9 47.4

2004 45.8 32.9 34.6 45.9

2005 37.3 27.7 46.1 43.2

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.74 0.78 0.67 0.77 0.71 0.78

Q2 0.50 1.46 0.24 0.70 0.67 0.76

Q3 0.60 1.00 0.47 0.65 0.57 0.68

Q4 0.74 1.02 0.56 0.69 0.75 0.78

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 3.4 11.4 10.5 9.1 6.5 5.5 5.1

2004 3.3 11.2 10.4 9.0 6.4 5.5 5.0

2005 2.7 9.2 8.4 7.3 5.2 4.4 4.1

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.5 8.9 7.0 7.0 4.6 4.3 3.6 0.5 38.5

2004 2.5 8.8 6.9 6.9 4.5 4.3 3.5 0.5 37.9

2005 2.0 7.1 5.7 5.6 3.7 3.5 2.9 0.5 31.0

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 4.0 8.4 18.4 1.3 3.2 7.5 7.2

2004 3.1 6.5 14.1 1.0 2.5 5.8 5.5

2005 2.6 5.4 11.9 0.9 2.1 4.9 4.7

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.0 12.3 4.4 0.9 2.2 5.7 3.1 0.5 31.1

2004 1.5 9.4 3.4 0.7 1.7 4.4 2.4 0.5 24.0

2005 1.3 7.9 2.8 0.6 1.4 3.7 2.0 0.5 20.3

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 4.2 4.5 16.9 3.3 2.8 5.3 6.3

2004 3.9 4.2 15.9 3.1 2.6 4.9 5.9

2005 5.3 5.6 21.1 4.1 3.5 6.6 7.9

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.5 4.5 7.9 2.1 1.6 3.6 3.3 0.5 26.1

2004 2.4 4.2 7.5 2.0 1.5 3.4 3.1 0.5 24.5

2005 3.2 5.6 9.9 2.6 2.0 4.5 4.2 0.5 32.5

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 5.5 12.5 11.1 5.2 7.1 5.9 5.6

2004 5.4 12.1 10.8 5.1 6.9 5.7 5.5

2005 5.1 11.4 10.2 4.8 6.5 5.4 5.1

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 4.1 12.7 6.2 3.6 5.3 4.6 4.0 0.5 41.1

2004 4.0 12.3 6.0 3.5 5.2 4.5 3.8 0.5 39.8

2005 3.7 11.6 5.7 3.3 4.9 4.2 3.6 0.5 37.5

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 38.5 31.1 26.1 41.1 41.1

2004 37.9 24.0 24.5 39.8 39.8

2005 31.0 20.3 32.5 37.5 37.5 39.5

# TABLE V-A-4a

Alternate Anaheim 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.09 0.36 0.17 0.19 0.09 0.09

Q2 0.09 0.29 0.36 0.06 0.05 0.14

Q3 0.11 0.18 0.41 0.12 0.05 0.13

Q4 0.14 0.32 0.17 0.14 0.12 0.11

Design Q1 Q2 Q3 Q4

2003 51.8 46.3 27.6 47.3

2004 48.2 30.5 46.8 49.9

2005 41.8 27.6 42.9 43.8

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.70 0.70 0.71 0.80 0.86 1.03

Q2 0.61 0.59 0.62 0.85 0.86 1.02

Q3 0.63 0.65 0.58 0.82 0.85 1.00

Q4 0.73 0.75 0.67 0.80 0.86 1.02

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 3.6 12.3 5.6 11.3 4.1 9.7 5.6

2004 3.3 11.4 5.2 10.5 3.8 9.1 5.2

2005 2.9 9.9 4.5 9.1 3.3 7.8 4.5

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.3 13.1 6.3 7.9 4.0 4.8 3.9 0.5 43.7

2004 3.0 12.1 5.8 7.3 3.7 4.5 3.6 0.5 40.7

2005 2.6 10.5 5.0 6.4 3.2 3.9 3.2 0.5 35.3

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 2.7 8.2 9.2 11.5 2.3 5.0 6.4

2004 1.8 5.4 6.0 7.5 1.5 3.3 4.2

2005 1.6 4.9 5.4 6.8 1.4 3.0 3.8

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.5 7.9 10.3 2.4 2.0 6.6 4.5 0.5 36.7

2004 1.7 5.2 6.8 1.6 1.3 4.4 3.0 0.5 24.4

2005 1.5 4.7 6.2 1.4 1.2 3.9 2.7 0.5 22.1

## Q3 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 3.0 4.6 7.0 4.3 1.1 4.6 2.2

2004 5.1 7.9 12.0 7.4 1.9 7.9 3.7

2005 4.7 7.2 11.0 6.8 1.7 7.2 3.4

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 1.9 3.2 6.6 2.7 1.2 3.6 2.7 0.5 22.4

2004 3.2 5.5 11.1 4.6 2.0 6.1 4.5 0.5 37.6

2005 3.0 5.0 10.2 4.2 1.8 5.6 4.1 0.5 34.5

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 5.1 11.2 5.6 10.3 4.7 4.7 4.2

2004 5.4 11.9 5.9 10.9 4.9 4.9 4.4

2005 4.8 10.4 5.2 9.5 4.3 4.3 3.9

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 4.8 11.4 5.4 5.3 4.9 5.3 3.6 0.5 41.2

2004 5.1 12.0 5.7 5.6 5.1 5.6 3.8 0.5 43.4

2005 4.5 10.5 5.0 4.9 4.5 4.9 3.4 0.5 38.2

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 43.7 36.7 22.4 41.2 43.7

2004 40.7 24.4 37.6 43.4 43.4

2005 35.3 22.1 34.5 38.2 38.2 41.8

# TABLE V-A-4b

Alternate Burbank 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.08 0.32 0.13 0.28 0.10 0.08

Q2 0.10 0.28 0.27 0.13 0.07 0.14

Q3 0.12 0.24 0.31 0.15 0.06 0.11

Q4 0.13 0.35 0.14 0.16 0.13 0.09

Design Q1 Q2 Q3 Q4

2003 54.1 45.2 51.6 50.3

2004 37.8 41.6 51.5 60.1

2005 50.6 34.8 49.3 42.6

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.70 0.69 0.72 0.80 0.85 1.01

Q2 0.63 0.62 0.62 0.83 0.83 1.00

Q3 0.64 0.65 0.60 0.81 0.83 0.97

Q4 0.76 0.77 0.71 0.81 0.84 1.01

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 4.3 13.4 5.4 11.3 4.8 9.6 4.8

2004 3.0 9.3 3.7 7.8 3.4 6.7 3.4

2005 4.0 12.5 5.0 10.5 4.5 9.0 4.5

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.0 11.9 5.1 12.1 4.6 4.4 3.4 0.5 45.0

2004 2.1 8.3 3.5 8.5 3.2 3.1 2.4 0.5 31.6

2005 2.8 11.2 4.7 11.3 4.3 4.1 3.2 0.5 42.1

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 4.0 10.3 8.0 10.7 3.1 4.0 4.5

2004 3.7 9.5 7.4 9.9 2.9 3.7 4.1

2005 3.1 7.9 6.2 8.2 2.4 3.1 3.4

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.8 7.8 7.6 4.9 2.6 6.3 3.6 0.5 36.2

2004 2.6 7.2 7.0 4.5 2.4 5.8 3.4 0.5 33.4

2005 2.2 6.0 5.8 3.8 2.0 4.9 2.8 0.5 28.0

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 6.1 10.7 11.2 8.7 3.1 5.1 5.6

2004 6.1 10.7 11.2 8.7 3.1 5.1 5.6

2005 5.9 10.2 10.7 8.3 2.9 4.9 5.4

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 4.0 8.0 9.6 6.3 2.6 5.5 4.4 0.5 40.9

2004 4.0 8.0 9.6 6.3 2.6 5.5 4.4 0.5 40.8

2005 3.8 7.7 9.2 6.0 2.5 5.3 4.2 0.5 39.1

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 5.5 13.4 4.0 12.0 6.0 4.5 4.5

2004 6.6 16.1 4.8 14.3 7.2 5.4 5.4

2005 4.6 11.4 3.4 10.1 5.1 3.8 3.8

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 5.0 13.6 5.0 6.5 5.5 4.6 3.8 0.5 44.4

2004 5.9 16.2 6.0 7.8 6.6 5.5 4.5 0.5 52.9

2005 4.2 11.5 4.2 5.5 4.7 3.9 3.2 0.5 37.7

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 45.0 36.2 40.9 44.4 45.0

2004 31.6 33.4 40.8 52.9 52.9

2005 42.1 28.0 39.1 37.7 42.1 46.7

# TABLE V-A-4c

Alternate Compton/Lynwood 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.08 0.28 0.18 0.26 0.11 0.10

Q2 0.09 0.28 0.36 0.05 0.06 0.15

Q3 0.12 0.19 0.41 0.13 0.05 0.11

Q4 0.12 0.30 0.16 0.16 0.14 0.11

Design Q1 Q2 Q3 Q4

2003 45.3 44.7 45.5 52.5

2004 44.8 38.2 36.3 52.4

2005 41.0 31.8 51.7 53.0

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.78 0.86 0.62 0.76 0.81 0.94

Q2 0.70 0.75 0.63 0.80 0.81 0.91

Q3 0.71 0.80 0.60 0.78 0.81 0.89

Q4 0.78 0.86 0.63 0.76 0.80 0.93

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 2.7 9.4 5.4 10.3 4.5 8.5 4.5

2004 2.7 9.3 5.3 10.2 4.4 8.4 4.4

2005 2.4 8.5 4.9 9.3 4.1 7.7 4.1

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.7 11.0 5.0 8.8 4.2 4.1 3.2 0.5 39.4

2004 2.6 10.9 5.0 8.7 4.1 4.1 3.2 0.5 39.0

2005 2.4 10.0 4.5 8.0 3.8 3.7 2.9 0.5 35.7

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 3.5 8.4 9.7 9.7 2.7 4.9 4.9

2004 3.0 7.2 8.3 8.3 2.3 4.1 4.1

2005 2.5 5.9 6.9 6.9 1.9 3.4 3.4

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.9 9.3 10.2 1.7 2.3 6.2 4.7 0.5 37.9

2004 2.5 8.0 8.8 1.5 2.0 5.3 4.0 0.5 32.4

2005 2.1 6.6 7.3 1.2 1.6 4.4 3.3 0.5 27.1

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 5.0 7.7 12.2 6.3 1.8 6.8 5.0

2004 3.9 6.1 9.7 5.0 1.4 5.4 3.9

2005 5.6 8.7 13.8 7.2 2.0 7.7 5.6

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.9 6.8 11.1 4.6 1.7 4.4 4.8 0.5 37.8

2004 3.1 5.4 8.9 3.6 1.4 3.5 3.8 0.5 30.3

2005 4.5 7.7 12.7 5.2 1.9 5.0 5.4 0.5 42.9

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 5.2 13.0 4.7 11.4 6.2 5.7 4.7

2004 5.2 13.0 4.7 11.4 6.2 5.7 4.7

2005 5.3 13.1 4.7 11.6 6.3 5.8 4.7

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 5.1 13.5 5.4 6.4 6.1 5.3 3.9 0.5 46.2

2004 5.0 13.5 5.4 6.4 6.1 5.3 3.9 0.5 46.1

2005 5.1 13.6 5.5 6.5 6.1 5.4 4.0 0.5 46.6

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 39.4 37.9 37.8 46.2 46.2

2004 39.0 32.4 30.3 46.1 46.1

2005 35.7 27.1 42.9 46.6 46.6 46.3

# TABLE V-A-4d

Alternate Fontana 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.10 0.41 0.14 0.16 0.10 0.09

Q2 0.12 0.36 0.24 0.09 0.08 0.12

Q3 0.11 0.27 0.29 0.07 0.10 0.16

Q4 0.16 0.45 0.14 0.02 0.11 0.12

0.10 0.41 0.14 0.16 0.10 0.09

Design Q1 Q2 Q3 Q4

2003 53.6 48.8 46.6 55.7

2004 62.6 45.5 49.9 48.5

2005 48.2 43.7 38.4 43.0

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.65 0.63 0.77 0.87 0.91 1.06

Q2 0.53 0.50 0.61 0.84 0.89 1.05

Q3 0.53 0.49 0.64 0.83 0.88 1.03

Q4 0.69 0.68 0.73 0.86 0.90 1.06

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 4.2 14.9 4.2 10.6 4.8 9.6 4.2

2004 5.0 17.4 5.0 12.4 5.6 11.2 5.0

2005 3.8 13.4 3.8 9.5 4.3 8.6 3.8

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.6 13.9 5.6 7.5 4.9 4.9 3.9 0.5 44.9

2004 4.2 16.2 6.5 8.8 5.7 5.8 4.5 0.5 52.3

2005 3.2 12.5 5.0 6.8 4.4 4.4 3.5 0.5 40.4

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 4.8 12.6 7.2 10.1 3.9 5.8 4.3

2004 4.5 11.7 6.8 9.5 3.6 5.4 4.1

2005 4.3 11.2 6.5 9.1 3.5 5.2 3.9

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.1 8.7 7.1 3.6 3.4 5.9 3.7 0.5 36.0

2004 2.9 8.2 6.7 3.3 3.2 5.5 3.4 0.5 33.6

2005 2.8 7.8 6.4 3.2 3.0 5.3 3.3 0.5 32.3

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 4.1 9.7 8.3 8.3 3.7 8.3 3.7

2004 4.4 10.4 8.9 8.9 4.0 8.9 4.0

2005 3.4 8.0 6.8 6.8 3.0 6.8 3.0

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.6 6.1 8.8 2.7 3.9 7.9 3.8 0.5 36.3

2004 2.8 6.6 9.4 2.9 4.2 8.5 4.1 0.5 38.9

2005 2.2 5.1 7.2 2.2 3.2 6.5 3.1 0.5 30.0

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 7.7 17.7 4.4 9.4 5.5 5.5 5.0

2004 6.7 15.4 3.8 8.2 4.8 4.8 4.3

2005 6.0 13.6 3.4 7.2 4.3 4.3 3.8

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 6.0 17.0 5.8 1.2 5.6 7.0 4.6 0.5 47.5

2004 5.2 14.8 5.1 1.0 4.8 6.1 4.0 0.5 41.5

2005 4.6 13.1 4.5 0.9 4.3 5.4 3.5 0.5 36.8

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 44.9 36.0 36.3 47.5 47.5

2004 52.3 33.6 38.9 41.5 52.3

2005 40.4 32.3 30.0 36.8 40.4 46.7

# TABLE V-A-4e

Alternate Long Beach 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.08 0.30 0.20 0.21 0.10 0.11

Q2 0.10 0.23 0.41 0.03 0.06 0.16

Q3 0.13 0.17 0.43 0.08 0.06 0.14

Q4 0.15 0.31 0.26 0.06 0.11 0.11

Design Q1 Q2 Q3 Q4

2003 46.5 42.9 36.9 47.4

2004 45.8 32.9 34.6 45.9

2005 37.3 27.7 46.1 43.2

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR Water

Q1 0.75 0.81 0.62 0.73 0.79 0.84 0.75

Q2 0.68 0.72 0.6 0.74 0.77 0.79 0.68

Q3 0.68 0.77 0.57 0.72 0.76 0.76 0.68

Q4 0.75 0.85 0.61 0.73 0.78 0.83 0.75

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 2.8 10.1 5.1 10.1 3.7 9.2 5.1

2004 2.7 10.0 5.0 10.0 3.6 9.1 5.0

2005 2.2 8.1 4.0 8.1 2.9 7.4 4.0

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.7 11.2 5.7 7.3 3.8 4.2 3.5 0.5 38.9

2004 2.7 11.0 5.6 7.2 3.8 4.1 3.4 0.5 38.4

2005 2.2 9.0 4.6 5.8 3.1 3.4 2.8 0.5 31.3

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 3.0 6.8 9.8 9.8 2.5 5.5 5.5

2004 2.3 5.2 7.5 7.5 1.9 4.2 4.2

2005 1.9 4.4 6.3 6.3 1.6 3.5 3.5

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.0 7.1 10.7 1.0 1.9 5.5 4.5 0.5 34.3

2004 2.3 5.4 8.2 0.8 1.5 4.2 3.5 0.5 26.4

2005 1.9 4.6 6.9 0.7 1.2 3.6 2.9 0.5 22.3

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 4.4 5.8 9.8 5.8 1.8 5.5 3.6

2004 4.1 5.5 9.2 5.5 1.7 5.1 3.4

2005 5.5 7.3 12.3 7.3 2.3 6.8 4.6

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.2 4.8 8.9 2.2 1.6 3.8 3.8 0.5 28.9

2004 3.0 4.5 8.4 2.1 1.5 3.6 3.5 0.5 27.1

2005 4.0 6.0 11.2 2.8 2.0 4.8 4.7 0.5 36.0

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 6.1 10.8 6.1 9.8 4.7 5.6 3.8

2004 5.9 10.4 5.9 9.5 4.5 5.4 3.6

2005 5.6 9.8 5.6 9.0 4.3 5.1 3.4

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 5.2 12.3 7.6 2.2 4.1 4.4 4.5 0.5 40.8

2004 5.1 11.9 7.3 2.1 4.0 4.2 4.3 0.5 39.5

2005 4.8 11.2 6.9 2.0 3.8 4.0 4.1 0.5 37.2

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 38.9 34.3 28.9 40.8 40.8

2004 38.4 26.4 27.1 39.5 39.5

2005 31.3 22.3 36.0 37.2 37.2 39.1

# TABLE V-A-4f

Alternate Los Angeles 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.09 0.41 0.17 0.13 0.10 0.09

Q2 0.10 0.31 0.32 0.05 0.08 0.14

Q3 0.15 0.26 0.35 0.05 0.07 0.11

Q4 0.15 0.40 0.18 0.02 0.15 0.10

Design Q1 Q2 Q3 Q4

2003 53.6 55.1 51.0 55.3

2004 49.7 44.0 55.9 61.3

2005 53.5 38.2 36.8 52.0

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.74 0.76 0.69 0.76 0.81 0.99

Q2 0.67 0.69 0.62 0.79 0.81 0.98

Q3 0.69 0.74 0.59 0.78 0.80 0.96

Q4 0.78 0.82 0.67 0.77 0.81 0.99

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 3.7 13.3 5.3 11.2 4.8 9.0 5.8

2004 3.4 12.3 4.9 10.3 4.4 8.4 5.4

2005 3.7 13.3 5.3 11.1 4.8 9.0 5.8

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.7 16.8 6.4 5.4 4.5 4.7 4.4 0.5 46.3

2004 3.4 15.6 5.9 5.0 4.1 4.3 4.1 0.5 43.0

2005 3.7 16.8 6.4 5.4 4.5 4.7 4.4 0.5 46.2

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 4.4 10.9 10.4 14.2 3.8 4.9 6.0

2004 3.5 8.7 8.3 11.3 3.0 3.9 4.8

2005 3.0 7.5 7.2 9.8 2.6 3.4 4.1

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.7 11.8 11.0 2.0 3.7 7.6 5.3 0.5 45.5

2004 2.9 9.4 8.7 1.6 3.0 6.0 4.2 0.5 36.4

2005 2.6 8.1 7.6 1.4 2.6 5.3 3.7 0.5 31.7

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 6.6 9.6 10.6 8.6 3.0 7.6 5.1

2004 7.2 10.5 11.6 9.4 3.3 8.3 5.5

2005 4.7 6.9 7.6 6.2 2.2 5.4 3.6

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 5.3 9.9 10.6 2.0 3.0 5.5 5.1 0.5 41.9

2004 5.8 10.8 11.6 2.2 3.3 6.0 5.6 0.5 45.9

2005 3.8 7.1 7.6 1.4 2.2 4.0 3.7 0.5 30.4

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 6.0 14.2 4.9 13.2 6.6 4.9 4.9

2004 6.7 15.8 5.5 14.6 7.3 5.5 5.5

2005 5.7 13.4 4.6 12.4 6.2 4.6 4.6

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 6.3 18.0 6.8 1.0 6.7 5.6 5.0 0.5 49.9

2004 7.0 19.9 7.5 1.1 7.4 6.2 5.6 0.5 55.2

2005 5.9 16.9 6.4 1.0 6.3 5.3 4.7 0.5 46.9

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 46.3 45.5 41.9 49.9 49.9

2004 43.0 36.4 45.9 55.2 55.2

2005 46.2 31.7 30.4 46.9 46.9 50.7

# TABLE V-A-4g

Alternate Rubidoux 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.09 0.32 0.09 0.20 0.10 0.09

Q2 0.14 0.39 0.15 0.07 0.06 0.10

Q3 0.13 0.33 0.20 0.07 0.05 0.11

Q4 0.14 0.35 0.11 0.06 0.11 0.11

Design Q1 Q2 Q3 Q4

2003 72.9 61.6 60.5 66.0

2004 59.5 60.5 55.3 76.6

2005 56.6 55.8 47.0 49.5

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.63 0.61 0.72 0.86 0.89 0.94

Q2 0.49 0.46 0.58 0.81 0.87 0.90

Q3 0.48 0.45 0.59 0.80 0.85 0.90

Q4 0.66 0.65 0.69 0.85 0.89 0.94

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 5.1 18.8 5.8 15.2 6.5 14.5 7.2

2004 4.1 15.3 4.7 12.4 5.3 11.8 5.9

2005 3.9 14.6 4.5 11.8 5.0 11.2 5.6

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 4.1 14.0 4.9 12.5 6.5 6.2 3.7 0.5 52.4

2004 3.4 11.4 4.0 10.2 5.3 5.0 3.0 0.5 42.9

2005 3.2 10.9 3.8 9.7 5.0 4.8 2.9 0.5 40.8

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 7.3 19.6 7.9 11.0 3.1 4.9 7.3

2004 7.2 19.2 7.8 10.8 3.0 4.8 7.2

2005 6.6 17.7 7.2 10.0 2.8 4.4 6.6

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 4.2 10.9 5.3 3.5 3.2 5.5 3.5 0.5 36.7

2004 4.2 10.7 5.2 3.4 3.2 5.4 3.4 0.5 36.1

2005 3.8 9.9 4.8 3.2 2.9 5.0 3.2 0.5 33.3

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 8.4 16.8 9.0 8.4 2.4 8.4 6.6

2004 7.7 15.3 8.2 7.7 2.2 7.7 6.0

2005 6.5 13.0 7.0 6.5 1.9 6.5 5.1

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.8 9.0 7.0 3.4 2.6 6.0 3.7 0.5 35.9

2004 3.5 8.2 6.4 3.1 2.4 5.5 3.4 0.5 32.8

2005 2.9 7.0 5.4 2.6 2.0 4.7 2.9 0.5 28.0

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 9.2 18.3 4.6 14.4 7.2 7.2 4.6

2004 10.7 21.3 5.3 16.7 8.4 8.4 5.3

2005 6.9 13.7 3.4 10.8 5.4 5.4 3.4

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 6.1 14.8 5.0 3.4 6.5 6.8 4.1 0.5 47.1

2004 7.1 17.2 5.8 3.9 7.5 7.9 4.7 0.5 54.6

2005 4.6 11.1 3.7 2.5 4.8 5.1 3.0 0.5 35.5

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 52.4 36.7 35.9 47.1 52.4

2004 42.9 36.1 32.8 54.6 54.6

2005 40.8 33.3 28.0 35.5 40.8 49.3

# TABLE V-A-4h

Alternate Wilmington 24-Hour 2015 Design Value Estimation

Split NH4 NO3 SO4 OC EC OTR

Q1 0.07 0.25 0.23 0.20 0.14 0.12

Q2 0.09 0.20 0.43 0.03 0.08 0.18

Q3 0.11 0.12 0.46 0.09 0.08 0.14

Q4 0.12 0.26 0.24 0.11 0.15 0.12

Design Q1 Q2 Q3 Q4

2003 46.5 42.9 36.9 47.4

2004 45.8 32.9 34.6 45.9

2005 37.3 27.7 46.1 43.2

Top-25

Percentile

RRF NH4 NO3 SO4 OC EC OTR

Q1 0.69 0.78 0.56 0.68 0.72 0.74

Q2 0.66 0.72 0.56 0.69 0.69 0.70

Q3 0.64 0.75 0.54 0.68 0.68 0.67

Q4 0.71 0.81 0.57 0.68 0.71 0.74

Q1 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 3.4 11.4 10.5 9.1 6.5 5.5 5.1

2004 3.3 11.2 10.4 9.0 6.4 5.5 5.0

2005 2.7 9.2 8.4 7.3 5.2 4.4 4.1

Q1 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.3 8.9 5.9 6.2 4.7 4.1 3.2 0.5 35.8

2004 2.3 8.8 5.8 6.1 4.6 4.0 3.1 0.5 35.3

2005 1.9 7.1 4.7 5.0 3.8 3.3 2.6 0.5 28.8

Q2 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 4.0 8.4 18.4 1.3 3.2 7.5 7.2

2004 3.1 6.5 14.1 1.0 2.5 5.8 5.5

2005 2.6 5.4 11.9 0.9 2.1 4.9 4.7

Q2 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.6 6.1 10.3 0.9 2.2 5.3 4.3 0.5 32.2

2004 2.0 4.6 7.9 0.7 1.7 4.0 3.3 0.5 24.8

2005 1.7 3.9 6.6 0.6 1.4 3.4 2.7 0.5 21.0

Q3 Components

## Year NH4 NO3 SO4 OC EC OTR Water

2003 4.2 4.5 16.9 3.3 2.8 5.3 6.3

2004 3.9 4.2 15.9 3.1 2.6 4.9 5.9

2005 5.3 5.6 21.1 4.1 3.5 6.6 7.9

Q3 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 2.7 3.4 9.1 2.2 1.9 3.5 3.6 0.5 26.9

2004 2.5 3.2 8.6 2.1 1.8 3.3 3.4 0.5 25.3

2005 3.4 4.2 11.4 2.8 2.4 4.4 4.5 0.5 33.5

Q4 Components

Year NH4 NO3 SO4 OC EC OTR Water

2003 5.5 12.5 11.1 5.2 7.1 5.9 5.6

2004 5.4 12.1 10.8 5.1 6.9 5.7 5.5

2005 5.1 11.4 10.2 4.8 6.5 5.4 5.1

Q4 2015 Estimates

Year NH4 NO3 SO4 OC EC OTR Water Blank Mass

2003 3.9 10.1 6.4 3.6 5.1 4.4 3.7 0.5 37.6

2004 3.8 9.8 6.2 3.4 4.9 4.2 3.6 0.5 36.4

2005 3.6 9.2 5.8 3.2 4.6 4.0 3.3 0.5 34.3

Weighted 2015 Design Value

Year Q1 Q2 Q3 Q4 Max FDV

2003 35.8 32.2 26.9 37.6 37.6

2004 35.3 24.8 25.3 36.4 36.4

2005 28.8 21.0 33.5 34.3 34.3 36.1TABLE V-A-5a2015 Estimated Reduction Ratios to be Applied Anaheim 24-Hour PM2.5 DesignRRFNH4NO3SO4OCECOTRQ1 0.78 0.78 0.80 0.81 0.78 1.00Q2 0.67 0.68 0.83 0.72 0.80 0.90Q3 0.73 0.78 0.57 0.80 0.83 1.00Q4 0.72 0.74 0.67 0.84 0.89 1.030.78 0.78 0.80 0.81 0.78 1.00Q1 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio22-Jan-05 8.5 21.8 7.1 12 3.1 3.9 5.9 0.5 62.311-Mar-05 7.3 17.2 6 6.2 1.3 3.3 4.8 0.5 46.125-Jan-05 4.5 11.8 4.1 6.8 2.2 3 3.3 0.5 35.7Average48.0Q1 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass22-Jan-05 6.6 17.0 5.7 9.7 2.4 3.9 4.6 0.5 50.511-Mar-05 5.7 13.4 4.8 5.0 1.0 3.3 3.8 0.5 37.525-Jan-05 3.5 9.2 3.3 5.5 1.7 3.0 2.6 0.5 29.3Average 39.1 0.81Q2 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio25-May-05 4.5 5.0 9.4 5.3 0.9 1.7 4.1 0.5 30.830-Jun-05 3.7 5.2 9.2 5.2 0.8 1.3 4.1 0.5 29.422-May-05 2.2 4.8 5.8 6.5 0.9 1.9 2.8 0.5 24.9Average24.8Q2 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass25-May-05 3.0 3.4 7.8 3.8 0.7 1.5 3.2 0.5 24.030-Jun-05 2.5 3.5 7.6 3.7 0.6 1.2 3.1 0.5 22.822-May-05 1.5 3.3 4.8 4.7 0.7 1.7 2.1 0.5 19.2Average 22.0 0.78Q3 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio4-Sep-05 6.5 6.0 2.9 8.5 1.0 2.5 2.0 0.5 29.322-Sep-05 4.6 5.7 5.6 6.9 2.2 2.3 2.9 0.5 30.11-Sep-05 3.8 4.8 6.5 5.8 0.9 3.4 3.1 0.5 28.3Average29.2Q3 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass4-Sep-05 4.7 4.7 1.7 6.8 0.8 2.5 1.6 0.5 23.422-Sep-05 3.4 4.4 3.2 5.5 1.8 2.3 1.9 0.5 23.11-Sep-05 2.8 3.7 3.7 4.6 0.7 3.4 1.9 0.5 21.4Average 22.6 0.77Q4 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio4-Sep-05 9.8 17.4 8.9 8.1 1.7 2.1 5.9 0.5 53.922-Sep-05 8.6 19.9 4.6 10.8 2 2.5 4.7 0.5 53.11-Sep-05 6 12.6 3.8 9.9 4.2 2.7 3.3 0.5 42.6Average49.9Q4 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass4-Sep-05 7.1 12.9 6.0 6.8 1.5 2.2 4.3 0.5 41.122-Sep-05 6.2 14.7 3.1 9.1 1.8 2.6 3.5 0.5 41.41-Sep-05 4.3 9.3 2.5 8.3 3.7 2.8 2.4 0.5 34.0Average 38.8 0.78DesignQ1Q2Q3Q42003 51.8 46.3 27.6 47.32004 48.2 30.5 46.8 49.92005 41.8 27.6 42.9 43.8Top-3Ratio0.81 0.78 0.77 0.78Predicted 20142003 42.2 35.9 21.4 36.8 42.22004 39.2 23.7 36.2 38.9 39.22005 34.0 21.4 33.2 34.1 34.1 38.5TABLE V-A-5b2015 Estimated Reduction Ratios to be Applied Burbank 24-Hour PM2.5 DesignRRFNH4NO3SO4OCECOTRWaterQ1 0.79 0.82 0.69 0.74 0.75 1.00 0.79Q2 0.67 0.73 0.57 0.76 0.80 1.00 0.67Q3 0.71 0.95 0.35 0.67 0.83 0.94 0.71Q4 0.79 0.85 0.62 0.78 0.83 1.00 0.79Q1 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio11-Mar-05 11.4 28.9 9.0 12.2 3.7 3.8 7.7 0.5 76.822-Jan-05 6.9 19.7 3.8 14.0 3.9 3.8 4.4 0.5 56.48-Mar-05 8.4 20.7 7.8 7.7 2.2 4.1 6.0 0.5 57.0Average 63.4Q1 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass11-Mar-05 9.0 23.7 6.2 9.0 2.8 3.8 5.9 0.5 60.922-Jan-05 5.5 16.2 2.6 10.4 2.9 3.8 3.4 0.5 45.28-Mar-05 6.6 17.0 5.4 5.7 1.7 4.1 4.5 0.5 45.5Average 50.5 0.80Q2 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio25-May-05 6.5 10.8 10.2 7.9 1.7 1.7 5.3 0.5 44.24-May-05 5.7 9.8 7.7 7.0 1.5 1.7 4.3 0.5 37.716-Apr-05 1.2 5.3 3.0 11.3 1.9 1.6 1.9 0.5 26.3Average 36.1Q2 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass25-May-05 4.4 7.9 5.8 6.0 1.4 1.7 3.3 0.5 30.94-May-05 3.8 7.2 4.4 5.3 1.2 1.7 2.7 0.5 26.816-Apr-05 0.8 3.9 1.7 8.6 1.5 1.6 1.1 0.5 19.7Average 25.80.71Q3 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio3-Jul-05 5.2 7.5 9.7 10.6 0.8 3.3 4.6 0.5 41.79-Jul-05 3.9 6.1 8.5 7.1 1.0 2.1 4.0 0.5 32.719-Sep-05 7.6 9.6 8.4 9.3 1.9 2.2 4.5 0.5 43.5Average 39.3Q3 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass3-Jul-05 3.7 7.1 3.4 7.1 0.7 3.1 2.4 0.5 27.99-Jul-05 2.8 5.8 3.0 4.8 0.8 2.0 2.0 0.5 21.619-Sep-05 5.4 9.1 2.9 6.2 1.6 2.1 2.7 0.5 30.5Average 26.7 0.68Q4 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio6-Nov-05 10.0 25.2 3.9 15.4 3.1 2.1 5.3 0.5 65.022-Oct-05 11.0 27.2 8.7 6.7 1.9 2.1 7.3 0.5 65.012-Dec-05 4.9 13.2 1.9 15.2 5.8 2.4 2.7 0.5 46.0Average 58.7Q4 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass6-Nov-05 7.9 21.4 2.4 12.0 2.6 2.1 4.3 0.5 53.222-Oct-05 8.7 23.1 5.4 5.2 1.6 2.1 5.5 0.5 52.112-Dec-05 3.9 11.2 1.2 11.9 4.8 2.4 2.2 0.5 38.0Average 47.8 0.81DesignQ1Q2Q3Q42003 54.1 45.2 51.6 50.32004 37.8 41.6 51.5 60.12005 50.6 34.8 49.3 42.6Top-3Ratio 0.80 0.71 0.68 0.81Predicted 20142003 43.1 32.3 35.0 41.0 43.12004 30.1 29.7 34.9 48.9 48.92005 40.3 24.9 33.4 34.7 40.3 44.1TABLE V-A-5c2015 Estimated Reduction Ratios to be Applied to Compton 24-Hour PM2.5 DesignRRFNH4NO3SO4OCECOTRWaterQ1 0.77 0.97 0.27 0.82 0.91 1.17 0.77Q2 0.85 0.48 2.22 0.60 0.57 0.68 0.85Q3 0.81 0.96 0.62 0.74 0.75 0.90 0.81Q4 0.76 0.87 0.54 0.80 0.82 0.93 0.76Q1 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio22-Jan-05 8.5 21.8 7.1 12.0 3.1 3.9 5.9 0.5 62.311-Mar-05 7.3 17.2 6.0 6.2 1.3 3.3 4.8 0.5 46.125-Jan-05 4.5 11.8 4.1 6.8 2.2 3.0 3.3 0.5 35.7Average48.0Q1 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass22-Jan-05 6.5 21.1 1.9 9.8 2.8 4.6 3.9 0.5 51.311-Mar-05 5.6 16.7 1.6 5.1 1.2 3.9 3.2 0.5 37.725-Jan-05 3.5 11.4 1.1 5.6 2.0 3.5 2.1 0.5 29.7Average 39.6 0.82Q2 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio25-May-05 4.5 5.0 9.4 5.3 0.9 1.7 4.1 0.5 30.830-Jun-05 3.7 5.2 9.2 5.2 0.8 1.3 4.1 0.5 29.422-May-05 2.2 4.8 5.8 6.5 0.9 1.9 2.8 0.5 24.9Average24.8Q2 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass25-May-05 3.8 2.4 20.9 3.2 0.5 1.2 7.3 0.5 39.730-Jun-05 3.1 2.5 20.4 3.1 0.5 0.9 7.1 0.5 38.122-May-05 1.9 2.3 12.9 3.9 0.5 1.3 4.5 0.5 27.8Average 35.2 1.42Q3 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio4-Sep-05 6.5 6.0 2.9 8.5 1.0 2.5 2.0 0.5 29.322-Sep-05 4.6 5.7 5.6 6.9 2.2 2.3 2.9 0.5 30.11-Sep-05 3.8 4.8 6.5 5.8 0.9 3.4 3.1 0.5 28.3Average29.2Q3 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass4-Sep-05 5.3 5.8 1.8 6.3 0.8 2.3 1.9 0.5 24.522-Sep-05 3.7 5.5 3.5 5.1 1.7 2.1 2.2 0.5 24.21-Sep-05 3.1 4.6 4.0 4.3 0.7 3.1 2.2 0.5 22.4Average 23.7 0.81Q4 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio22-Oct-05 9.8 17.4 8.9 8.1 1.7 2.1 5.9 0.5 53.96-Nov-05 8.6 19.9 4.6 10.8 2.0 2.5 4.7 0.5 53.115-Dec-05 6.0 12.6 3.8 9.9 4.2 2.7 3.3 0.5 42.6Average49.9Q4 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass22-Oct-05 7.4 15.1 4.8 6.5 1.4 2.0 4.2 0.5 41.96-Nov-05 6.5 17.3 2.5 8.6 1.6 2.3 3.6 0.5 43.115-Dec-05 4.6 11.0 2.1 7.9 3.4 2.5 2.5 0.5 34.5Average 39.8 0.80DesignQ1Q2Q3Q42003 45.3 44.7 45.5 52.52004 44.8 38.2 36.3 52.42005 41 31.8 51.7 53Top-3Ratio 0.82 1.42 0.81 0.80Predicted 20142003 37.4 63.4 36.9 41.9 63.42004 36.9 54.2 29.5 41.8 54.22005 33.8 45.1 42.0 42.3 45.1 54.3TABLE V-A-5d2015 Estimated Reduction Ratios to be Applied Fontana 24-Hour PM2.5 DesignRRFNH4NO3SO4OCECOTRWaterQ1 0.66 0.65 0.67 0.80 1.00 1.00 0.66Q2 0.56 0.67 0.37 1.25 1.33 1.25 0.56Q3 0.56 0.58 0.58 0.79 1.00 1.06 0.56Q4 0.63 0.66 0.56 0.84 0.86 1.00 0.63Q1 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio11-Mar-05 7.2 20.0 5.3 14.3 7.1 3.3 5.0 0.5 62.28-Mar-05 6.5 16.1 4.6 14.2 4.3 3.6 4.1 0.5 53.622-Jan-05 5.9 18.8 1.8 15.8 2.9 3.3 3.5 0.5 51.9Average 55.9Q1 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass11-Mar-05 4.8 13.0 3.6 11.4 7.1 3.3 3.2 0.5 46.98-Mar-05 4.3 10.5 3.1 11.4 4.3 3.6 2.7 0.5 40.322-Jan-05 3.9 12.2 1.2 12.6 2.9 3.3 2.3 0.5 39.0Average 42.1 0.75Q2 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio25-May-05 5.2 10.7 8.3 10.1 2.6 2.4 4.6 0.5 44.015-Jun-05 7.4 17.9 6.1 7.7 1.6 2.5 4.9 0.5 48.128-May-05 7.7 13.9 6.3 7.2 1.4 2.6 4.4 0.5 43.5Average 45.2Q2 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass25-May-05 2.9 7.2 3.1 12.6 3.5 3.0 2.2 0.5 34.915-Jun-05 4.1 12.0 2.3 9.6 2.1 3.1 2.6 0.5 36.428-May-05 4.3 9.3 2.3 9.0 1.9 3.3 2.4 0.5 32.9Average 34.7 0.77Q3 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio18-Jul-05 3.7 3.7 8.9 12.5 3.7 4.0 3.7 0.5 40.21-Sep-05 3.6 5.5 5.5 9.7 3.0 5.3 2.8 0.5 35.329-Aug-05 3.4 3.7 5.3 12.1 3.7 5.1 2.5 0.5 35.7Average 37.1Q3 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass18-Jul-05 2.1 2.1 5.2 9.9 3.7 4.2 2.1 0.5 29.81-Sep-05 2.0 3.2 3.2 7.7 3.0 5.6 1.6 0.5 26.829-Aug-05 1.9 2.1 3.1 9.6 3.7 5.4 1.4 0.5 27.7Average 28.1 0.76Q4 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio22-Oct-05 22.0 52.8 11.0 10.5 3.5 3.3 12.1 0.5 115.26-Nov-05 9.9 26.4 3.4 11.8 3.2 2.4 5.3 0.5 62.427-Dec-05 7.1 18.0 0.3 10.6 3.9 2.9 2.9 0.5 45.7Average 74.4Q4 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass22-Oct-05 13.9 34.8 6.2 8.8 3.0 3.3 7.8 0.5 78.36-Nov-05 6.2 17.4 1.9 9.9 2.8 2.4 3.4 0.5 44.627-Dec-05 4.5 11.9 0.2 8.9 3.4 2.9 2.0 0.5 34.2Average 52.3 0.70DesignQ1Q2Q3Q42003 53.6 48.8 46.6 55.72004 62.6 45.5 49.9 48.52005 48.2 43.7 38.4 43Top-3Ratio 0.75 0.77 0.76 0.70Predicted 20142003 40.3 37.5 35.3 39.2 40.32004 47.1 35.0 37.8 34.1 47.12005 36.3 33.6 29.1 30.3 36.3 41.2TABLE V-A-5e2015 Estimated Reduction Ratios to be Applied to Long Beach 24-Hour PM2.5 DesignRRFNH4NO3SO4OCECOTRWaterQ1 0.81 0.82 0.75 0.79 0.87 0.89 0.81Q2 0.71 0.93 0.62 0.67 0.75 0.75 0.71Q3 0.70 0.95 0.58 0.68 0.75 0.77 0.70Q4 0.77 0.75 0.83 0.89 0.83 0.84 0.77Q1 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio22-Jan-05 8.2 18.6 7.9 8.7 2.2 4.3 5.7 0.5 55.51-Jan-05 0.3 1.9 2.0 18.8 6.0 4.5 1.0 0.5 34.511-Mar-05 5.8 11.6 6.9 11.2 1.2 3.8 4.3 0.5 44.8Average44.9Q1 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass22-Jan-05 6.6 15.3 5.9 6.9 1.9 3.8 4.5 0.5 45.41-Jan-05 0.2 1.6 1.5 14.9 5.2 4.0 0.7 0.5 28.611-Mar-05 4.7 9.5 5.2 8.8 1.0 3.4 3.3 0.5 36.5Average 36.8 0.82Q2 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio25-May-05 4.6 5.4 11.1 5.4 1.1 1.8 4.8 0.5 34.230-Jun-05 5.0 4.4 11.0 4.4 1.0 1.5 4.6 0.5 32.022-May-05 2.5 3.6 7.7 12.6 0.9 1.8 3.3 0.5 32.4Average32.9Q2 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass25-May-05 3.3 5.0 6.9 3.6 0.8 1.4 3.1 0.5 24.630-Jun-05 3.6 4.1 6.8 2.9 0.8 1.1 3.0 0.5 22.822-May-05 1.8 3.3 4.8 8.4 0.7 1.4 2.1 0.5 23.0Average 23.5 0.71Q3 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio22-Sep-05 6.2 8.5 0.3 8.7 2.9 6.0 1.4 0.5 34.011-Aug-05 3.4 6.5 14.3 4.4 1.4 7.1 6.1 0.5 43.125-Sep-05 5.6 9.1 0.3 6.2 1.3 6.0 1.5 0.5 30.0Average 35.7Q3 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass22-Sep-05 4.3 8.1 0.2 5.9 2.2 4.6 1.5 0.5 27.311-Aug-05 2.4 6.2 8.3 3.0 1.1 5.5 3.6 0.5 30.525-Sep-05 3.9 8.6 0.2 4.2 1.0 4.6 1.6 0.5 24.6Average 27.5 0.77Q4 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio6-Nov-05 8.5 18.1 5.5 11.1 3.9 2.7 4.8 0.5 54.522-Oct-05 9.7 15.6 10.0 6.1 1.7 2.3 6.0 0.5 51.324-Dec-05 4.1 8.7 4.4 9.3 2.4 2.5 2.9 0.5 34.4Average 46.7Q4 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass6-Nov-05 6.5 13.6 4.6 9.9 3.2 2.3 3.8 0.5 44.422-Oct-05 7.5 11.7 8.3 5.4 1.4 1.9 4.9 0.5 41.624-Dec-05 3.2 6.5 3.7 8.3 2.0 2.1 2.3 0.5 28.5Average 38.2 0.82DesignQ1Q2Q3Q42003 46.5 42.9 36.9 47.42004 45.8 32.9 34.6 45.92005 37.3 27.7 46.1 43.2Top-3Ratio0.82 0.71 0.77 0.82Predicted 20142003 38.1 30.6 28.4 38.8 38.82004 37.6 23.5 26.6 37.5 37.62005 30.6 19.8 35.5 35.3 35.5 37.3TABLE V-A-5f2015 Estimated Reduction Ratios to be Applied to Los Angeles 24-Hour PM2.5 DesignRRFNH4NO3SO4OCECOTRWaterQ1 0.69 0.57 1.22 1.05 1.29 1.28 0.69Q2 0.76 0.83 0.55 0.78 0.83 0.94 0.76Q3 0.77 0.89 0.60 0.76 0.78 0.96 0.77Q4 0.84 0.87 0.70 0.79 0.82 1.00 0.84Q1 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio11-Mar-05 11.8 30.3 10.5 11.5 3.4 3.7 8.4 0.5 79.622-Jan-05 7.2 20.6 4.5 13.3 4.0 3.5 4.8 0.5 57.98-Mar-05 2.4 19.6 4.3 8.0 2.1 2.8 4.6 0.5 43.9Average 60.5Q1 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass11-Mar-05 8.1 17.3 12.8 12.1 4.4 4.7 7.1 0.5 67.022-Jan-05 5.0 11.7 5.5 14.0 5.2 4.5 3.7 0.5 50.08-Mar-05 1.7 11.2 5.2 8.4 2.7 3.6 3.2 0.5 36.4Average 51.1 0.85Q2 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio25-May-05 5.8 8.8 11.6 8.1 1.5 2.5 5.5 0.5 43.930-Jun-05 5.0 8.8 8.2 7.1 1.7 2.4 4.3 0.5 37.422-May-05 2.4 5.9 6.3 12.5 1.6 2.9 3.2 0.5 34.9Average 38.7Q2 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass25-May-05 4.4 7.3 6.4 6.3 1.2 2.4 3.4 0.5 31.930-Jun-05 3.8 7.3 4.5 5.5 1.4 2.3 2.7 0.5 28.122-May-05 1.8 4.9 3.5 9.8 1.3 2.7 1.9 0.5 26.4Average 28.8 0.74Q3 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio5-Aug-05 7.9 15.8 18.3 11.2 4.2 5.7 9.0 0.5 72.23-Jul-05 5.9 6.7 10.6 7.2 1.0 4.3 4.8 0.5 40.619-Sep-05 8.0 10.5 9.0 13.6 1.8 4.2 4.9 0.5 51.9Average 54.9Q3 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass5-Aug-05 6.1 14.1 11.0 8.5 3.3 5.5 5.8 0.5 54.73-Jul-05 4.5 6.0 6.4 5.5 0.8 4.1 3.2 0.5 31.019-Sep-05 6.2 9.3 5.4 10.3 1.4 4.0 3.5 0.5 40.7Average 42.2 0.77Q4 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio22-Oct-05 12.5 26.3 9.2 7.6 1.9 2.6 7.4 0.5 67.56-Nov-05 9.8 25.8 4.4 12.2 2.9 2.5 5.6 0.5 63.124-Nov-05 4.7 13.7 3.0 12.0 5.2 2.6 3.2 0.5 44.3Average 58.3Q4 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass22-Oct-05 10.5 22.9 6.4 6.0 1.6 2.6 6.0 0.5 56.56-Nov-05 8.2 22.4 3.1 9.6 2.4 2.5 4.6 0.5 53.424-Nov-05 3.9 11.9 2.1 9.5 4.3 2.6 2.6 0.5 37.4Average 49.1 0.84DesignQ1Q2Q3Q42003 53.6 55.1 51 55.32004 49.7 44 55.9 61.32005 53.5 38.2 36.8 52Top-3Ratio0.85 0.74 0.77 0.84Predicted 20142003 45.3 41.0 39.2 46.6 46.62004 42.0 32.7 42.9 51.6 51.62005 45.2 28.4 28.3 43.8 45.2 47.8TABLE V-A-5g2015 Estimated Reduction Ratios to be Applied to Rubidoux 24-Hour PM2.5 DesignRRFNH4NO3SO4OCECOTRWaterQ1 0.69 0.66 0.70 0.80 0.86 0.91 0.69Q2 0.56 0.58 0.42 0.81 1.00 1.06 0.56Q3 0.52 0.52 0.50 0.79 0.80 0.89 0.52Q4 0.54 0.56 0.50 0.79 0.80 0.89 0.54Q1 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio11-Mar-05 0.0 16.7 3.2 13.9 3.4 3.1 3.7 0.5 44.122-Jan-05 5.4 17.3 2.0 10.6 3.9 2.9 3.4 0.5 45.58-Mar-05 5.8 16.2 4.4 8.2 3.2 3.7 4.1 0.5 45.5Average45.0Q1 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass11-Mar-05 3.8 11.0 2.2 11.1 2.9 2.8 2.5 0.5 36.922-Jan-05 3.7 11.4 1.4 8.5 3.4 2.6 2.3 0.5 33.88-Mar-05 4.0 10.7 3.1 6.6 2.8 3.4 2.7 0.5 33.7Average 34.8 0.74Q2 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio15-Jun-05 9.4 17.1 5.4 5.9 1.1 1.5 4.6 0.5 44.825-May-05 7.1 16.5 7.5 8.1 1.6 1.8 5.2 0.5 47.816-Apr-05 5.4 17.6 3.3 7.1 1.3 1.7 3.9 0.5 40.1Average44.2Q2 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass15-Jun-05 5.3 9.9 2.3 4.8 1.1 1.6 2.5 0.5 27.925-May-05 4.0 9.6 3.2 6.6 1.6 1.9 2.6 0.5 29.916-Apr-05 3.0 10.2 1.4 5.8 1.3 1.8 2.0 0.5 26.0Average 27.9 0.62Q3 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio6-Jul-05 4.1 10.6 5.4 14.1 2.1 3.3 3.6 0.5 43.214-Aug-05 4.0 13.0 6.7 5.3 0.9 3.6 4.4 0.5 37.93-Jul-05 5.4 11.6 6.7 7.2 1.0 3.3 4.2 0.5 39.4Average 40.2Q3 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass6-Jul-05 2.1 5.5 2.7 11.1 1.7 2.9 1.8 0.5 28.414-Aug-05 2.1 6.8 3.4 4.2 0.7 3.2 2.1 0.5 22.93-Jul-05 2.8 6.0 3.4 5.7 0.8 2.9 2.1 0.5 24.2Average 25.2 0.62Q4 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio22-Oct-05 20.6 55.6 21.1 10.9 3.5 2.7 16.1 0.5 130.56-Nov-05 10.2 26.1 3.5 15.8 3.1 2.3 5.3 0.5 66.412-Nov-05 7.6 20.5 2.7 9.9 2.8 2.4 4.1 0.5 50.1Average82.3Q4 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass22-Oct-05 11.1 31.1 10.6 8.6 2.8 2.4 8.4 0.5 75.56-Nov-05 5.5 14.6 1.8 12.5 2.5 2.0 3.0 0.5 42.312-Nov-05 4.1 11.5 1.4 7.8 2.2 2.1 2.3 0.5 31.949.9 0.60DesignQ1Q2Q3Q42003 72.9 61.6 60.5 662004 59.5 60.5 55.3 76.62005 56.6 55.8 47 49.5Top-3Ratio 0.74 0.62 0.62 0.60Predicted 20142003 53.6 38.3 37.6 39.8 53.62004 43.7 37.6 34.4 46.2 46.22005 41.6 34.7 29.2 29.9 41.6 47.1TABLE V-2A-5h2015 Estimated Reduction Ratios to be Applied to Wilmington 24-Hour PM2.5 DesignRRFNH4NO3SO4OCECOTRWaterQ1 0.74 0.78 0.67 0.77 0.71 0.78 0.74Q2 0.50 1.46 0.24 0.70 0.67 0.76 0.50Q3 0.60 1.00 0.47 0.65 0.57 0.68 0.60Q4 0.74 1.02 0.56 0.69 0.75 0.78 0.74Q1 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio22-Jan-05 8.7 18.7 9.6 9.5 3.4 4.5 6.3 0.5 60.81-Jan-05 0.2 1.9 2 15.8 4.9 4.6 1 0.5 30.413-Jan-05 1.6 6.3 2.6 12.5 5.7 5.2 1.9 0.5 35.7Average42.3Q1 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass22-Jan-05 6.4 14.6 6.4 7.3 2.4 3.5 4.5 0.5 45.71-Jan-05 0.1 1.5 1.3 12.2 3.5 3.6 0.6 0.5 23.313-Jan-05 1.2 4.9 1.7 9.6 4.0 4.1 1.3 0.5 27.3Average 32.1 0.76Q2 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio30-Jun-05 4.6 2.6 11.1 4.3 1.2 1.5 4.4 0.5 29.625-May-05 4.6 4 11.4 6 1.1 1.7 4.7 0.5 33.522-May-05 2.5 2 10.4 4.3 1 1.8 4 0.5 26.1Average29.7Q2 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass30-Jun-05 2.3 3.8 2.7 3.0 0.8 1.1 1.6 0.5 15.825-May-05 2.3 5.8 2.7 4.2 0.7 1.3 1.8 0.5 19.422-May-05 1.3 2.9 2.5 3.0 0.7 1.4 1.3 0.5 13.5Average 16.2 0.55Q3 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio21-Jul-05 1.6 3.2 9 4.6 1.9 2.4 3.7 0.5 26.312-Jul-05 7 3.4 16.4 5.3 1.8 2.2 6.4 0.5 42.522-Sep-05 5.4 9.3 0.3 7.4 3.8 5.3 1.5 0.5 33Average33.9Q3 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass21-Jul-05 1.0 3.2 4.2 3.0 1.1 1.6 1.8 0.5 16.412-Jul-05 4.2 3.4 7.7 3.4 1.0 1.5 3.3 0.5 25.122-Sep-05 3.2 9.3 0.1 4.8 2.2 3.6 1.5 0.5 25.3Average 22.3 0.66Q4 Observed ComponentsNH4+NO3-SO4=OCECOTRWaterBlankMassRatio24-Nov-05 3.4 11.9 5.3 12.7 3.1 3 3.7 0.5 43.122-Oct-05 7.3 14 14.7 5.5 1.7 2.3 7.4 0.5 52.86-Nov-05 7.3 14.2 5.1 15.1 2.9 2.8 4 0.5 51.5Average49.1Q4 2015 EstimatesNH4+NO3-SO4=OCECOTRWaterBlankMass24-Nov-05 2.5 12.1 3.0 8.8 2.3 2.3 2.7 0.5 34.222-Oct-05 5.4 14.3 8.2 3.8 1.3 1.8 4.9 0.5 40.26-Nov-05 5.4 14.5 2.9 10.4 2.2 2.2 3.3 0.5 41.3Average 38.6 0.79DesignQ1Q2Q3Q42003 46.5 42.9 36.9 47.4 Design lgbh2004 45.8 32.9 34.6 45.92005 37.3 27.7 46.1 43.2Top-3 Ratio 0.76 0.55 0.66 0.79Predicted 20142003 35.3 23.4 24.2 37.2 37.22004 34.8 18.0 22.7 36.1 36.12005 28.3 15.1 30.3 33.9 33.9 35.8TABLE V-A-6a2015 Episode Day Estimated Reduction Ratios tobe Applied Anaheim 24-Hour PM2.5 DesignEpisode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioRRF 22-Oct-050.736 0.842 0.549 0.838 0.866 0.985Observed22-Oct-05 9.8 17.4 8.9 8.1 1.7 2.1 5.9 0.5 53.92015 Predicted22-Oct-05 7.2 14.7 4.9 6.8 1.5 2.1 4.2 0.5 41.7 0.74Episode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioAverage 4-Q RRF0.73 0.75 0.72 0.79 0.83 0.98Observed22-Oct-05 9.8 17.4 8.9 8.1 1.7 2.1 5.9 0.5 53.92015 Predicted22-Oct-05 7.1 13.0 6.4 6.4 1.4 2.1 4.4 0.5 41.2 0.73Episode Day UsingRatio Design Future DesignRRF 22-Oct-050.74 47 34.6Episode Day UsingRatio Design Future DesignAverage 4-Q RRF0.73 47 34.1TABLE V-A-6b2015 Episode Day Estimated Reduction Ratios tobe Applied Burbank 24-Hour PM2.5 DesignEpisode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioRRF 22-Oct-050.822 0.888 0.588 0.856 0.859 0.972Observed22-Oct-05 11 27.2 8.7 6.7 1.9 2.1 7.3 0.5 652015 Predicted22-Oct-05 9.0 24.2 5.1 5.7 1.6 2.0 5.6 0.5 53.8 0.82Episode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioAverage 4-Q RRF0.74 0.84 0.56 0.74 0.80 0.99Observed22-Oct-05 11 27.2 8.7 6.7 1.9 2.1 7.3 0.5 652015 Predicted22-Oct-05 8.1 22.8 4.9 4.9 1.5 2.1 5.2 0.5 50.0 0.74Episode Day UsingRatio Design Future DesignRRF 22-Oct-050.82 53.3 43.8Episode Day UsingRatio Design Future DesignAverage 4-Q RRF0.74 53.3 39.4TABLE V-A-6c2015 Episode Day Estimated Reduction Ratios tobe Applied to Compton 24-Hour PM2.5 DesignEpisode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioRRF 22-Oct-050.731 0.822 0.601 0.809 0.823 0.866Observed22-Oct-05 9.8 17.4 8.9 8.1 1.7 2.1 5.9 0.5 53.92015 Predicted22-Oct-05 7.2 14.3 5.3 6.6 1.4 1.8 4.2 0.5 41.3 0.73Episode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioAverage 4-Q RRF0.80 0.82 0.91 0.74 0.76 0.92Observed22-Oct-05 9.8 17.4 8.9 8.1 1.7 2.1 5.9 0.5 53.92015 Predicted22-Oct-05 7.8 14.3 8.1 6.0 1.3 1.9 5.2 0.5 45.1 0.80Episode Day UsingRatio Design Future DesignRRF 22-Oct-050.73 51.3 37.5Episode Day UsingRatio Design Future DesignAverage 4-Q RRF0.80 51.3 40.9TABLE V-A-6d2015 Episode Day Estimated Reduction Ratios tobe Applied Fontana 24-Hour PM2.5 DesignEpisode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioRRF 22-Oct-050.672 0.703 0.59 0.844 0.886 1.003Observed22-Oct-05 22 52.8 11 10.5 3.5 3.3 12.1 0.5 115.22015 Predicted22-Oct-05 14.8 37.1 6.5 8.9 3.1 3.3 8.3 0.5 82.4 0.67Episode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioAverage 4-Q RRF0.60 0.64 0.55 0.92 1.05 1.08Observed22-Oct-05 22 52.8 11 10.5 3.5 3.3 12.1 0.5 115.22015 Predicted22-Oct-05 13.3 33.8 6.0 9.7 3.7 3.6 7.5 0.5 77.9 0.60Episode Day UsingRatio Design Future DesignRRF 22-Oct-050.67 54.8 36.8Episode Day UsingRatio Design Future DesignAverage 4-Q RRF0.60 54.8 33.0TABLE V-A-6e2015 Episode Day Estimated Reduction Ratios to beApplied to Long Beach 24-Hour PM2.5 DesignEpisode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioRRF 22-Oct-050.76 0.82 0.67 0.78 0.90 0.7Observed22-Oct-05 9.7 15.6 10.0 6.1 1.7 2.3 6.0 0.5 51.32015 Predicted22-Oct-05 7.3 12.8 6.7 4.7 1.5 1.6 4.4 0.5 39.5 0.77Episode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioAverage 4-Q RRF0.562 0.571 0.565 0.816 0.829 1.011 0.573Observed22-Oct-05 9.7 15.6 10.0 6.1 1.7 2.3 6.0 0.5 51.32015 Predicted22-Oct-05 5.4 8.9 5.6 4.9 1.4 2.3 3.4 0.5 32.6 0.64Episode Day UsingRatio Design Future DesignRRF 22-Oct-050.70 44.6 31.2Episode Day UsingRatio Design Future DesignAverage 4-Q RRF0.75 44.6 33.3TABLE V-A-6f2015 Episode Day Estimated Reduction Ratios to beApplied to Los Angeles 24-Hour PM2.5 DesignEpisode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioRRF 22-Oct-050.82 0.87 0.67 0.84 0.93 0.93Observed22-Oct-05 12.5 26.3 9.2 7.6 1.9 2.6 7.4 0.5 67.52015 Predicted22-Oct-05 10.3 22.9 6.2 6.4 1.7 2.4 5.7 0.5 56.1 0.83Episode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioAverage 4-Q RRF0.562 0.571 0.565 0.816 0.829 1.011 0.573Observed22-Oct-05 12.5 26.3 9.2 7.6 1.9 2.6 7.4 0.5 67.52015 Predicted22-Oct-05 7.0 15.0 5.2 6.2 1.5 2.7 4.2 0.5 42.3 0.63Episode Day UsingRatio Design Future DesignRRF 22-Oct-050.83 60.7 50.3Episode Day UsingRatio Design Future DesignAverage 4-Q RRF0.77 60.7 46.4TABLE V-A-6g2015 Episode Day Estimated Reduction Ratios tobe Applied to Rubidoux 24-Hour PM2.5 DesignEpisode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioRRF 22-Oct-050.61 0.60 0.61 0.82 0.93 0.83Observed22-Oct-05 20.6 55.6 21.1 10.9 3.5 2.7 16.1 0.5 130.52015 Predicted22-Oct-05 12.6 33.3 12.8 9.0 3.3 2.2 9.8 0.5 83.0 0.64Episode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioAverage 4-Q RRF0.562 0.571 0.565 0.816 0.829 1.011 0.573Observed22-Oct-05 20.6 55.6 21.1 10.9 3.5 2.7 16.1 0.5 130.52015 Predicted22-Oct-05 11.6 31.7 11.9 8.9 2.9 2.7 9.2 0.5 79.0 0.61Episode Day UsingRatio Design Future DesignRRF 22-Oct-050.60 64.8 38.6Episode Day UsingRatio Design Future DesignAverage 4-Q RRF0.58 64.8 37.4TABLE V-A-6h2015 Episode Day Estimated Reduction Ratios to beApplied to Wilmington 24-Hour PM2.5 DesignEpisode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioRRF 22-Oct-050.7 0.8 0.7 0.8 0.8 0.6Observed22-Oct-05 7.3 14 14.7 5.5 1.7 2.3 7.4 0.5 52.82015 Predicted22-Oct-05 5.4 11.2 9.5 4.1 1.4 1.4 5.1 0.5 38.2 0.72Episode Day UsingNH4NO3SO4OCECOTRWaterBlankMassRatioAverage 4-Q RRF0.6 0.6 0.6 0.8 0.8 1 0.6Observed22-Oct-05 7.3 14 14.7 5.5 1.7 2.3 7.4 0.5 52.82015 Predicted22-Oct-05 4.1 8 8.3 4.5 1.4 2.4 4.2 0.5 32.8 0.62Episode Day UsingRatio Design Future DesignRRF 22-Oct-050.68 44.6 30.3DesignlgbhEpisode Day UsingRatio Design Future DesignAverage 4-Q RRF0.65 44.6 28.8DesignlgbhATTACHMENT-2Model Performance Statistics* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubRegional DescriptionsSubRegion 003 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0069 Burbank 53 48 -27.848 461.3080088 Pasadena 56 47 -11.205 457.0210074 Reseda 49 48 -48.000 463.1050090 Santa Clarita 49 52 -48.140 483.357SubRegion 004 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0060 Azusa 60 47 6.981 456.1134164 Banning Airport 79 42 104.459 433.5275181 Crestline 72 49 66.383 468.6064158 Elsinore 71 37 60.525 405.9075197 Fontana 68 46 46.811 453.0810591 Glendora 61 47 13.487 457.0105212 Mira Loma 67 43 42.938 438.9154149 Perris 72 39 69.051 417.3760075 Pomona 63 45 22.598 448.6105204 Redlands 74 45 76.256 448.1894144 Rubidoux 69 44 52.093 442.5575203 San Bernardino 72 46 65.874 453.2995175 Upland 65 46 31.687 452.125SubRegion 005 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------3176 Anaheim 60 40 7.422 421.6450087 Los Angeles 54 45 -22.302 445.5633195 Costa Mesa 60 37 6.793 405.6263177 La Habra 59 42 4.359 432.9780820 LAXH 51 42 -36.352 433.6850072 Long Beach 55 40 -17.171 421.9030084 Lynwood 55 42 -19.237 432.7533812 Mission Viejo 64 36 29.671 400.7910085 Pico Rivera 57 44 -5.273 442.8600091 West Los Angeles 52 45 -34.796 447.031SubRegion 009 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------4157 Indio Jackson 91 38 162.217 411.2934137 Palm Springs 85 40 132.826 423.133* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 214 (08/02) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 23 12.3 15 9.1 15 0 0.74 -0.9 1.3 -0.10 0.17 0.61Subregional Peak: 9.4 15 0 0.77 (at cell 50 x 53)0069 Burbank 2 6.9 15 7.6 15 0 1.11 0.1 0.1 -1.11 1.93 -99.000088 Pasadena 5 6.6 15 7.0 13 -2 1.06 0.1 0.1 -0.44 0.77 0.060074 Reseda 5 9.1 15 8.6 15 0 0.94 0.0 0.1 -0.44 0.77 0.650090 Santa Clarita 11 12.3 15 9.1 15 0 0.74 -0.3 0.3 -0.20 0.35 0.88* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 214 (08/02) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 81 11.4 15 9.9 16 1 0.87 -0.2 1.3 -0.01 0.16 0.20Subregional Peak: 10.2 16 1 0.89 (at cell 74 x 44)0060 Azusa 1 6.1 15 7.5 15 0 1.23 0.2 0.2 -0.86 13.06 -99.004164 Banning Airport 10 11.4 15 7.6 17 2 0.66 -0.2 0.2 -0.09 1.31 0.255181 Crestline 15 9.3 17 8.7 17 0 0.94 -0.2 0.2 -0.06 0.87 0.824158 Elsinore 7 9.4 14 6.8 16 2 0.73 -0.3 0.3 -0.12 1.87 0.275197 Fontana 5 8.2 15 9.3 15 0 1.14 0.1 0.1 -0.17 2.61 0.940591 Glendora 2 7.0 15 8.7 15 0 1.24 0.2 0.2 -0.43 6.53 -99.005212 Mira Loma 7 8.4 14 9.4 14 0 1.11 0.1 0.1 -0.12 1.87 0.934149 Perris 7 7.9 13 8.8 16 3 1.11 0.1 0.2 -0.12 1.87 -0.380075 Pomona 2 7.4 14 9.2 14 0 1.24 0.2 0.2 -0.43 6.53 -99.005204 Redlands 8 8.5 15 9.9 16 1 1.17 0.1 0.1 -0.11 1.63 0.954144 Rubidoux 7 8.4 15 9.6 15 0 1.14 0.1 0.1 -0.12 1.87 0.975203 San Bernardino 6 8.4 16 9.5 16 0 1.13 0.1 0.1 -0.14 2.18 0.865175 Upland 4 8.4 15 9.5 15 0 1.13 0.2 0.2 -0.22 3.26 0.82* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 214 (08/02) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 4 7.4 20 4.0 20 0 0.54 -3.1 3.1 -0.47 0.47 0.91Subregional Peak: 8.0 17 -3 1.09 (at cell 81 x 44)4157 Indio Jackson 2 6.3 21 3.3 22 1 0.52 -0.5 0.5 -0.94 0.94 -99.004137 Palm Springs 2 7.4 20 4.0 20 0 0.54 -0.4 0.4 -0.94 0.94 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 215 (08/03) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 20 9.9 15 9.2 14 -1 0.93 0.2 0.9 0.04 0.13 0.44Subregional Peak: 9.6 15 0 0.97 (at cell 52 x 53)0069 Burbank 2 6.7 13 8.5 14 1 1.27 0.2 0.2 0.44 1.26 -99.000088 Pasadena 4 7.2 14 8.1 15 1 1.12 0.1 0.1 0.22 0.63 0.840074 Reseda 6 7.6 13 8.5 16 3 1.12 0.1 0.1 0.15 0.42 -0.200090 Santa Clarita 8 9.9 15 9.2 14 -1 0.93 -0.1 0.1 0.11 0.31 0.88* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 215 (08/03) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 84 9.9 16 10.4 15 -1 1.05 0.8 1.2 0.12 0.17 0.35Subregional Peak: 10.5 15 -1 1.06 (at cell 73 x 44)0060 Azusa 3 7.4 15 8.6 15 0 1.16 0.2 0.2 3.27 4.76 1.004164 Banning Airport 9 9.9 16 8.8 17 1 0.89 -0.2 0.2 1.09 1.59 0.565181 Crestline 12 9.6 15 10.0 16 1 1.04 0.0 0.1 0.82 1.19 0.564158 Elsinore 8 8.0 14 8.5 17 3 1.06 0.0 0.1 1.23 1.78 -0.385197 Fontana 6 8.0 14 9.2 15 1 1.15 0.2 0.2 1.63 2.38 0.890591 Glendora 4 8.0 15 9.2 15 0 1.14 0.2 0.2 2.45 3.57 0.915212 Mira Loma 7 8.4 14 9.7 15 1 1.15 0.1 0.1 1.40 2.04 0.844149 Perris 6 8.5 14 9.3 16 2 1.10 0.1 0.2 1.63 2.38 -0.210075 Pomona 4 7.2 14 9.0 15 1 1.25 0.3 0.3 2.45 3.57 0.705204 Redlands 6 8.7 14 10.3 16 2 1.18 0.2 0.2 1.63 2.38 0.564144 Rubidoux 7 8.5 14 10.0 15 1 1.18 0.2 0.2 1.40 2.04 0.775203 San Bernardino 7 9.3 14 10.4 15 1 1.12 0.2 0.2 1.40 2.04 0.895175 Upland 5 7.1 13 9.2 15 2 1.29 0.2 0.2 1.96 2.85 0.71* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 215 (08/03) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 5 6.6 19 6.4 13 -6 0.97 -1.7 1.8 -0.26 0.28 -0.95Subregional Peak: 9.8 14 -5 1.48 (at cell 67 x 35)3176 Anaheim 1 6.1 13 6.4 13 0 1.05 0.0 0.0 -1.31 1.41 -99.000820 LAXH 2 6.6 19 4.5 18 -1 0.68 -0.4 0.4 -0.66 0.71 -99.000091 West Los Angeles 2 6.6 18 5.1 17 -1 0.78 -0.3 0.3 -0.66 0.71 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 215 (08/03) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 5 8.7 19 5.7 18 -1 0.66 -2.2 2.2 -0.31 0.31 0.84Subregional Peak: 8.9 19 0 1.02 (at cell 82 x 41)4157 Indio Jackson 1 6.1 21 3.9 21 0 0.63 -0.4 0.4 -1.55 1.55 -99.004137 Palm Springs 4 8.7 19 5.7 18 -1 0.66 -0.3 0.3 -0.39 0.39 0.72* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 216 (08/04) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 27 14.1 14 10.1 14 0 0.72 -0.7 1.5 -0.05 0.16 0.50Subregional Peak: 11.2 16 2 0.80 (at cell 53 x 51)0069 Burbank 3 7.7 13 10.1 14 1 1.31 0.3 0.3 -0.46 1.48 -0.320088 Pasadena 7 8.4 14 9.5 15 1 1.13 0.0 0.1 -0.20 0.64 0.750074 Reseda 8 10.3 14 9.7 15 1 0.95 -0.1 0.1 -0.17 0.56 0.930090 Santa Clarita 9 14.1 14 9.5 16 2 0.68 -0.2 0.2 -0.15 0.49 0.84* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 216 (08/04) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 99 11.8 15 11.6 16 1 0.98 0.3 1.3 0.05 0.17 0.35Subregional Peak: 12.0 15 0 1.02 (at cell 70 x 42)0060 Azusa 5 9.1 14 9.4 15 1 1.03 0.2 0.2 0.92 3.29 0.594164 Banning Airport 13 10.8 16 9.1 17 1 0.84 -0.2 0.2 0.35 1.27 0.555181 Crestline 12 11.8 15 10.2 16 1 0.87 -0.2 0.2 0.38 1.37 0.864158 Elsinore 9 10.5 16 10.1 17 1 0.96 0.1 0.1 0.51 1.83 0.315197 Fontana 7 9.4 14 10.9 15 1 1.16 0.1 0.1 0.66 2.35 0.810591 Glendora 6 10.2 15 10.0 14 -1 0.98 0.1 0.1 0.77 2.74 0.725212 Mira Loma 8 9.4 13 11.4 14 1 1.22 0.1 0.2 0.58 2.06 0.724149 Perris 6 9.1 14 11.6 16 2 1.28 0.2 0.2 0.77 2.74 -0.540075 Pomona 5 9.0 14 10.2 13 -1 1.13 0.2 0.2 0.92 3.29 0.365204 Redlands 7 9.9 15 11.1 16 1 1.12 0.2 0.2 0.66 2.35 0.704144 Rubidoux 7 9.7 13 11.5 15 2 1.19 0.2 0.2 0.66 2.35 0.655203 San Bernardino 8 10.3 14 10.8 15 1 1.05 0.1 0.1 0.58 2.06 0.915175 Upland 6 9.1 16 10.9 14 -2 1.20 0.1 0.2 0.77 2.74 0.48* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 216 (08/04) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 24 7.2 16 10.0 13 -3 1.38 -0.2 1.1 -0.02 0.17 -0.14Subregional Peak: 11.1 14 -2 1.55 (at cell 65 x 39)3176 Anaheim 4 7.0 12 7.2 12 0 1.03 0.0 0.1 -0.15 1.02 0.730087 Los Angeles 4 6.7 15 7.3 14 -1 1.08 0.0 0.1 -0.15 1.02 0.533177 La Habra 1 6.5 13 6.5 13 0 1.00 0.0 0.0 -0.60 4.09 -99.000820 LAXH 7 7.2 16 6.2 12 -4 0.86 -0.2 0.2 -0.09 0.58 -0.563812 Mission Viejo 2 6.4 13 10.0 13 0 1.56 0.6 0.6 -0.30 2.05 -99.000091 West Los Angeles 6 7.1 16 7.9 13 -3 1.12 0.0 0.1 -0.10 0.68 0.43* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 216 (08/04) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 9 8.7 11 7.0 12 1 0.80 -1.1 1.4 -0.14 0.19 -0.45Subregional Peak: 9.8 18 7 1.13 (at cell 82 x 41)4157 Indio Jackson 2 6.3 12 7.0 12 0 1.11 0.1 0.1 -0.62 0.87 -99.004137 Palm Springs 7 8.7 11 5.7 9 -2 0.66 -0.2 0.2 -0.18 0.25 0.30* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 217 (08/05) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 23 12.3 13 10.9 15 2 0.89 -0.5 1.5 -0.02 0.17 0.51Subregional Peak: 11.4 16 3 0.92 (at cell 50 x 53)0069 Burbank 3 7.3 14 8.6 15 1 1.18 0.2 0.2 -0.17 1.33 0.740088 Pasadena 3 6.6 14 8.4 14 0 1.28 0.2 0.2 -0.17 1.33 0.290074 Reseda 7 11.1 13 10.6 14 1 0.95 0.0 0.1 -0.07 0.57 0.650090 Santa Clarita 10 12.3 13 10.9 15 2 0.89 -0.2 0.2 -0.05 0.40 0.70* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 217 (08/05) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 91 11.9 15 11.9 16 1 1.00 0.0 1.9 0.03 0.24 0.20Subregional Peak: 12.0 16 1 1.01 (at cell 69 x 43)0060 Azusa 1 7.4 13 9.8 13 0 1.32 0.3 0.3 2.39 21.87 -99.004164 Banning Airport 12 11.8 16 6.0 17 1 0.51 -0.4 0.4 0.20 1.82 0.575181 Crestline 9 11.9 15 9.6 17 2 0.81 -0.1 0.2 0.27 2.43 0.714158 Elsinore 9 8.8 11 9.3 17 6 1.06 0.0 0.2 0.27 2.43 -0.445197 Fontana 7 10.8 15 11.3 16 1 1.05 0.1 0.2 0.34 3.12 0.490591 Glendora 5 7.9 13 11.6 15 2 1.47 0.5 0.5 0.48 4.37 0.315212 Mira Loma 7 10.2 14 11.6 15 1 1.14 0.2 0.2 0.34 3.12 0.264149 Perris 7 10.1 14 10.9 17 3 1.07 0.1 0.3 0.34 3.12 -0.440075 Pomona 4 8.8 14 11.8 15 1 1.34 0.4 0.4 0.60 5.47 0.075204 Redlands 9 11.2 14 10.3 17 3 0.92 0.0 0.2 0.27 2.43 0.514144 Rubidoux 7 10.5 14 11.9 16 2 1.13 0.2 0.2 0.34 3.12 0.195203 San Bernardino 9 11.2 14 10.5 16 2 0.94 0.0 0.2 0.27 2.43 0.605175 Upland 5 10.1 15 11.7 15 0 1.15 0.1 0.2 0.48 4.37 0.61* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 217 (08/05) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 1 6.3 12 9.3 12 0 1.48 3.0 3.0 0.48 0.48 -99.00Subregional Peak: 10.9 14 2 1.72 (at cell 63 x 41)3812 Mission Viejo 1 6.3 12 9.3 12 0 1.48 0.5 0.5 0.48 0.48 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 217 (08/05) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 18 11.2 19 5.4 19 0 0.48 -3.1 3.1 -0.39 0.39 0.21Subregional Peak: 7.6 19 0 0.68 (at cell 82 x 41)4157 Indio Jackson 5 7.4 21 5.1 11 -10 0.69 -0.3 0.3 -1.39 1.39 -0.714137 Palm Springs 13 11.2 19 5.4 19 0 0.48 -0.4 0.4 -0.54 0.54 0.67* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 218 (08/06) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 37 14.3 13 13.1 15 2 0.91 0.1 1.9 0.04 0.21 0.24Subregional Peak: 13.2 15 2 0.92 (at cell 53 x 48)0069 Burbank 7 8.8 14 13.1 15 1 1.49 0.3 0.3 0.19 1.10 0.770088 Pasadena 8 9.9 15 12.6 16 1 1.27 0.2 0.2 0.17 0.96 0.900074 Reseda 9 11.0 12 10.2 15 3 0.93 0.1 0.1 0.15 0.85 0.730090 Santa Clarita 13 14.3 13 9.8 15 2 0.68 -0.2 0.2 0.10 0.59 0.76* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 218 (08/06) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 111 12.7 14 12.7 16 2 1.00 -0.9 1.8 -0.09 0.20 0.49Subregional Peak: 13.2 16 2 1.04 (at cell 61 x 45)0060 Azusa 7 9.7 15 12.0 16 1 1.24 0.2 0.2 -1.47 3.13 0.454164 Banning Airport 6 8.2 12 4.7 11 -1 0.58 -0.4 0.4 -1.71 3.66 0.465181 Crestline 14 11.9 17 7.9 19 2 0.67 -0.3 0.3 -0.73 1.57 0.384158 Elsinore 11 9.6 17 8.6 18 1 0.89 -0.1 0.2 -0.93 1.99 0.355197 Fontana 9 12.7 14 10.4 17 3 0.82 -0.2 0.2 -1.14 2.44 0.680591 Glendora 8 11.3 13 11.7 17 4 1.04 0.0 0.2 -1.28 2.74 0.475212 Mira Loma 9 11.8 16 12.4 16 0 1.05 0.0 0.1 -1.14 2.44 0.584149 Perris 4 9.1 12 5.6 13 1 0.62 -0.3 0.3 -2.57 5.48 -0.060075 Pomona 8 11.0 16 12.7 16 0 1.15 0.1 0.1 -1.28 2.74 0.675204 Redlands 9 8.3 13 9.5 18 5 1.15 0.0 0.2 -1.14 2.44 0.134144 Rubidoux 8 11.5 16 12.0 17 1 1.04 0.0 0.2 -1.28 2.74 -0.105203 San Bernardino 9 11.5 13 9.0 18 5 0.78 -0.1 0.1 -1.14 2.44 0.065175 Upland 9 12.2 15 11.7 17 2 0.96 -0.1 0.2 -1.14 2.44 0.61* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 218 (08/06) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 23 7.9 15 12.5 15 0 1.59 3.7 3.9 0.56 0.59 0.29Subregional Peak: 13.1 15 0 1.66 (at cell 59 x 44)3176 Anaheim 6 6.9 13 11.7 13 0 1.70 0.7 0.7 2.14 2.25 0.340087 Los Angeles 7 7.2 14 11.5 14 0 1.60 0.3 0.4 1.84 1.93 0.573177 La Habra 3 6.6 14 12.5 15 1 1.90 0.9 0.9 4.29 4.50 0.800084 Lynwood 1 6.1 13 11.0 13 0 1.80 0.8 0.8 12.86 13.49 -99.003812 Mission Viejo 6 7.9 15 12.0 15 0 1.52 0.6 0.6 2.14 2.25 0.54* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 218 (08/06) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 10 7.0 11 5.1 11 0 0.73 -1.9 1.9 -0.29 0.29 0.42Subregional Peak: 7.8 19 8 1.11 (at cell 82 x 41)4157 Indio Jackson 5 7.0 11 5.1 11 0 0.73 -0.3 0.3 -0.58 0.58 0.364137 Palm Springs 5 7.0 10 4.6 9 -1 0.66 -0.3 0.3 -0.58 0.58 0.61* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 219 (08/07) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 29 13.9 13 12.6 14 1 0.91 0.5 2.1 0.09 0.23 0.11Subregional Peak: 13.1 14 1 0.94 (at cell 53 x 49)0069 Burbank 5 10.0 12 12.6 13 1 1.26 0.3 0.3 0.52 1.33 0.900088 Pasadena 7 10.3 12 12.6 14 2 1.23 0.3 0.3 0.37 0.95 0.560074 Reseda 7 9.6 12 9.7 14 2 1.01 0.1 0.2 0.37 0.95 0.480090 Santa Clarita 10 13.9 13 9.3 15 2 0.67 -0.2 0.2 0.26 0.66 0.77* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 219 (08/07) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 125 13.4 13 13.9 15 2 1.04 -0.6 1.9 -0.04 0.20 0.42Subregional Peak: 14.0 15 2 1.04 (at cell 60 x 47)0060 Azusa 6 11.5 13 13.9 15 2 1.21 0.2 0.3 -0.91 4.16 0.014164 Banning Airport 15 11.0 15 8.7 18 3 0.79 -0.2 0.2 -0.37 1.66 0.135181 Crestline 14 12.9 13 10.3 18 5 0.80 -0.2 0.3 -0.39 1.78 0.024158 Elsinore 10 10.6 11 7.7 16 5 0.73 -0.2 0.3 -0.55 2.49 0.145197 Fontana 8 12.1 15 12.2 16 1 1.01 -0.1 0.2 -0.68 3.12 0.220591 Glendora 8 13.4 13 13.9 15 2 1.04 0.2 0.3 -0.68 3.12 0.325212 Mira Loma 11 11.7 14 12.1 15 1 1.04 0.0 0.1 -0.50 2.27 0.764149 Perris 6 7.9 14 8.3 15 1 1.05 0.0 0.1 -0.91 4.16 -0.900075 Pomona 8 12.5 13 12.9 16 3 1.03 0.1 0.2 -0.68 3.12 0.625204 Redlands 11 11.6 13 11.6 17 4 1.00 -0.1 0.1 -0.50 2.27 0.484144 Rubidoux 10 11.4 14 12.0 16 2 1.05 0.0 0.1 -0.55 2.49 0.575203 San Bernardino 10 13.3 15 11.7 17 2 0.88 -0.1 0.2 -0.55 2.49 0.455175 Upland 8 12.3 14 13.2 16 2 1.07 0.1 0.2 -0.68 3.12 0.39* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 219 (08/07) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 20 8.1 15 10.4 15 0 1.28 2.4 2.4 0.35 0.35 0.18Subregional Peak: 13.3 14 -1 1.64 (at cell 58 x 46)3176 Anaheim 6 7.3 12 9.5 13 1 1.30 0.3 0.3 1.18 1.18 0.750087 Los Angeles 4 6.6 12 9.9 12 0 1.50 0.4 0.4 1.76 1.76 0.373177 La Habra 4 6.4 12 10.4 15 3 1.63 0.5 0.5 1.76 1.76 -0.400072 Long Beach 1 6.1 12 7.5 12 0 1.23 0.2 0.2 7.05 7.05 -99.003812 Mission Viejo 5 8.1 15 10.2 12 -3 1.26 0.3 0.3 1.41 1.41 -0.28* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 219 (08/07) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 18 10.0 18 5.6 10 -8 0.56 -2.6 2.6 -0.35 0.35 -0.48Subregional Peak: 9.1 20 2 0.91 (at cell 82 x 41)4157 Indio Jackson 3 7.7 21 4.9 16 -5 0.63 -0.4 0.4 -2.10 2.10 -1.004137 Palm Springs 15 10.0 18 5.6 10 -8 0.56 -0.3 0.3 -0.42 0.42 -0.48* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 220 (08/08) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 22 11.2 14 11.4 14 0 1.01 1.5 1.9 0.21 0.26 0.12Subregional Peak: 13.5 15 1 1.20 (at cell 52 x 51)0069 Burbank 5 7.9 13 11.2 14 1 1.41 0.4 0.4 0.94 1.14 0.420088 Pasadena 4 8.3 13 11.4 14 1 1.37 0.5 0.5 1.17 1.42 -0.150074 Reseda 5 10.5 13 9.2 12 -1 0.87 0.0 0.1 0.94 1.14 0.880090 Santa Clarita 8 11.2 14 10.4 13 -1 0.93 0.1 0.1 0.59 0.71 0.61* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 220 (08/08) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 88 10.2 14 13.0 15 1 1.28 2.1 2.3 0.27 0.31 0.52Subregional Peak: 13.5 16 2 1.32 (at cell 59 x 49)0060 Azusa 4 8.4 13 12.7 15 2 1.51 0.6 0.6 5.97 6.76 0.064164 Banning Airport 7 7.7 11 6.3 10 -1 0.82 -0.1 0.1 3.41 3.87 0.565181 Crestline 17 9.4 17 10.7 18 1 1.14 0.1 0.2 1.40 1.59 0.794158 Elsinore 8 8.2 15 8.8 17 2 1.07 0.0 0.1 2.98 3.38 -0.305197 Fontana 5 9.4 15 12.7 16 1 1.35 0.5 0.5 4.77 5.41 0.210591 Glendora 5 9.9 14 12.8 15 1 1.29 0.5 0.5 4.77 5.41 0.045212 Mira Loma 9 10.2 14 12.6 15 1 1.24 0.2 0.2 2.65 3.01 0.784149 Perris 5 8.1 17 10.5 17 0 1.30 0.3 0.3 4.77 5.41 0.520075 Pomona 6 9.9 14 12.9 14 0 1.30 0.5 0.5 3.98 4.51 0.885204 Redlands 5 7.7 16 11.5 17 1 1.50 0.4 0.4 4.77 5.41 0.844144 Rubidoux 6 10.1 14 12.6 16 2 1.25 0.2 0.3 3.98 4.51 0.665203 San Bernardino 6 9.6 15 11.7 16 1 1.22 0.5 0.5 3.98 4.51 0.165175 Upland 5 9.5 14 13.0 15 1 1.37 0.4 0.4 4.77 5.41 0.75* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 220 (08/08) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 15 8.0 16 10.5 14 -2 1.31 1.2 1.9 0.20 0.29 -0.21Subregional Peak: 11.8 14 -2 1.48 (at cell 58 x 46)3176 Anaheim 5 8.0 16 8.3 14 -2 1.04 0.0 0.2 0.60 0.88 -0.220087 Los Angeles 2 6.4 15 7.8 14 -1 1.22 0.1 0.1 1.50 2.19 -99.003177 La Habra 2 6.5 12 9.0 12 0 1.38 0.4 0.4 1.50 2.19 -99.003812 Mission Viejo 3 7.5 13 10.5 14 1 1.40 0.6 0.6 1.00 1.46 0.420091 West Los Angeles 3 6.7 14 8.1 13 -1 1.20 0.1 0.1 1.00 1.46 -0.01* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 220 (08/08) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 5 6.8 13 6.3 13 0 0.92 -0.7 0.7 -0.11 0.11 0.83Subregional Peak: 8.0 11 -2 1.17 (at cell 105 x 13)4137 Palm Springs 5 6.8 13 6.3 13 0 0.92 -0.1 0.1 -0.11 0.11 0.83* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 221 (08/09) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 7 7.2 13 9.8 13 0 1.36 2.4 2.4 0.37 0.37 0.41Subregional Peak: 12.2 15 2 1.69 (at cell 51 x 51)0069 Burbank 3 7.2 13 9.8 14 1 1.36 0.3 0.3 0.86 0.86 -0.160088 Pasadena 3 6.3 13 9.0 14 1 1.42 0.4 0.4 0.86 0.86 -0.030074 Reseda 1 6.3 13 9.8 13 0 1.55 0.6 0.6 2.59 2.59 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 221 (08/09) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 81 11.8 13 13.0 15 2 1.10 2.0 2.5 0.28 0.33 0.20Subregional Peak: 13.2 15 2 1.12 (at cell 74 x 46)0060 Azusa 2 6.5 14 10.4 13 -1 1.60 0.6 0.6 11.46 13.28 -99.004164 Banning Airport 9 9.0 12 10.8 16 4 1.20 0.0 0.1 2.55 2.95 0.415181 Crestline 13 11.8 13 12.6 16 3 1.07 0.0 0.2 1.76 2.04 0.334158 Elsinore 6 7.4 13 9.2 16 3 1.25 0.3 0.3 3.82 4.43 -0.735197 Fontana 6 8.5 12 12.0 15 3 1.42 0.4 0.4 3.82 4.43 0.550591 Glendora 3 7.7 14 11.0 14 0 1.43 0.4 0.4 7.64 8.85 0.385212 Mira Loma 8 9.0 12 11.9 14 2 1.33 0.4 0.4 2.86 3.32 0.384149 Perris 4 9.3 14 11.6 16 2 1.24 0.3 0.3 5.73 6.64 -0.280075 Pomona 5 7.4 13 11.3 14 1 1.53 0.5 0.5 4.58 5.31 0.795204 Redlands 6 10.8 13 13.0 15 2 1.20 0.4 0.4 3.82 4.43 0.384144 Rubidoux 7 9.5 12 12.4 15 3 1.30 0.4 0.4 3.27 3.79 0.185203 San Bernardino 6 10.5 13 12.8 15 2 1.22 0.4 0.4 3.82 4.43 0.175175 Upland 6 8.1 14 11.9 14 0 1.47 0.5 0.5 3.82 4.43 0.68* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 221 (08/09) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 4 7.1 13 10.6 13 0 1.50 2.8 2.8 0.42 0.42 0.91Subregional Peak: 11.9 15 2 1.68 (at cell 67 x 36)3176 Anaheim 2 6.1 13 7.9 15 2 1.30 0.3 0.3 0.85 0.85 -99.003812 Mission Viejo 2 7.1 13 10.6 13 0 1.50 0.6 0.6 0.85 0.85 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 221 (08/09) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 5 7.3 12 7.2 11 -1 0.99 -0.5 0.9 -0.06 0.13 -0.43Subregional Peak: 11.1 17 5 1.51 (at cell 81 x 43)4137 Palm Springs 5 7.3 12 7.2 11 -1 0.99 -0.1 0.1 -0.06 0.13 -0.43* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubRegional DescriptionsSubRegion 003 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0069 Burbank 53 48 -27.848 461.3080088 Pasadena 56 47 -11.205 457.0210074 Reseda 49 48 -48.000 463.1050090 Santa Clarita 49 52 -48.140 483.357SubRegion 004 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0060 Azusa 60 47 6.981 456.1134164 Banning Airport 79 42 104.459 433.5275181 Crestline 72 49 66.383 468.6064158 Elsinore 71 37 60.525 405.9075197 Fontana 68 46 46.811 453.0810591 Glendora 61 47 13.487 457.0105212 Mira Loma 67 43 42.938 438.9154149 Perris 72 39 69.051 417.3760075 Pomona 63 45 22.598 448.6105204 Redlands 74 45 76.256 448.1894144 Rubidoux 69 44 52.093 442.5575203 San Bernardino 72 46 65.874 453.2995175 Upland 65 46 31.687 452.125SubRegion 005 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------3176 Anaheim 60 40 7.422 421.6450087 Los Angeles 54 45 -22.302 445.5633195 Costa Mesa 60 37 6.793 405.6263177 La Habra 59 42 4.359 432.9780820 LAXH 51 42 -36.352 433.6850072 Long Beach 55 40 -17.171 421.9030084 Lynwood 55 42 -19.237 432.7533812 Mission Viejo 64 36 29.671 400.7910085 Pico Rivera 57 44 -5.273 442.8600091 West Los Angeles 52 45 -34.796 447.031SubRegion 009 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------4157 Indio Jackson 91 38 162.217 411.2934137 Palm Springs 85 40 132.826 423.133* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 214 (08/02) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 2 9.4 10 7.5 11 1 0.79 -0.9 1.1 -0.09 0.12 -99.00Subregional Peak: 7.9 11 1 0.84 (at 51 x 54) NSte: 0090; NSPk: 7.5* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 214 (08/02) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 10 8.9 13 8.1 11 -2 0.91 0.0 1.0 0.02 0.13 -100.09Subregional Peak: 8.3 11 -2 0.93 (at 74 x 44) NSte: 5204; NSPk: 8.2* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 215 (08/03) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 2 8.8 10 7.9 10 0 0.90 0.1 1.0 0.03 0.13 -99.00Subregional Peak: 8.3 11 1 0.94 (at 51 x 53) NSte: 0090; NSPk: 7.9* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 215 (08/03) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 11 8.5 13 9.0 14 1 1.05 0.9 1.1 0.13 0.16 -130.65Subregional Peak: 9.0 11 -2 1.06 (at 72 x 44) NSte: 5204; NSPk: 8.8* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 215 (08/03) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 1 6.3 15 5.9 9 -6 0.93 -0.4 0.4 -0.07 0.07 -99.00Subregional Peak: 7.8 17 2 1.23 (at 82 x 41) NSte: 4137; NSPk: 5.8* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 216 (08/04) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 3 10.8 10 8.3 10 0 0.77 -1.0 1.2 -0.09 0.13 -101.96Subregional Peak: 10.0 14 4 0.93 (at 53 x 51) NSte: 0069; NSPk: 7.7* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 216 (08/04) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 13 10.1 12 9.6 10 -2 0.94 0.7 1.3 0.10 0.16 -123.56Subregional Peak: 10.0 10 -2 0.99 (at 70 x 41) NSte: 4149; NSPk: 9.7* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 216 (08/04) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 2 6.7 10 6.1 10 0 0.90 -1.0 1.0 -0.15 0.15 -99.00Subregional Peak: 9.7 10 0 1.45 (at 67 x 36) NSte: 3812; NSPk: 8.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 216 (08/04) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 1 7.0 9 6.0 12 3 0.85 -1.0 1.0 -0.15 0.15 -99.00Subregional Peak: 7.8 15 6 1.12 (at 82 x 41) NSte: 4137; NSPk: 5.9* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 217 (08/05) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 2 11.0 10 9.1 11 1 0.83 -0.8 1.0 -0.07 0.10 -99.00Subregional Peak: 9.9 15 5 0.91 (at 53 x 51) NSte: 0069; NSPk: 7.1* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 217 (08/05) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 12 9.9 12 9.9 11 -1 1.00 0.5 1.3 0.08 0.17 -60.13Subregional Peak: 10.1 11 -1 1.02 (at 70 x 43) NSte: 4144; NSPk: 9.9* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 217 (08/05) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 7.9 13 5.0 9 -4 0.63 -2.1 2.1 -0.29 0.29 -99.00Subregional Peak: 6.0 16 3 0.76 (at 82 x 41) NSte: 4137; NSPk: 4.8* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 218 (08/06) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 11.8 10 10.2 11 1 0.86 0.4 1.9 0.08 0.21 -111.46Subregional Peak: 10.7 13 3 0.91 (at 53 x 49) NSte: 0069; NSPk: 10.2* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 218 (08/06) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 13 11.0 11 10.0 11 0 0.91 -0.3 1.1 -0.02 0.13 -46.49Subregional Peak: 10.5 11 0 0.95 (at 62 x 43) NSte: 0075; NSPk: 10.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 218 (08/06) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 2 6.6 11 10.3 10 -1 1.56 2.9 2.9 0.44 0.44 -99.00Subregional Peak: 10.7 10 -1 1.61 (at 62 x 41) NSte: 3176; NSPk: 10.1* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 218 (08/06) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 6.3 8 5.0 9 1 0.79 -1.4 1.4 -0.22 0.22 -99.00Subregional Peak: 6.6 18 10 1.04 (at 82 x 41) NSte: 4137; NSPk: 4.8* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 219 (08/07) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 11.0 10 10.6 10 0 0.96 1.0 2.3 0.15 0.27 -66.80Subregional Peak: 11.2 11 1 1.02 (at 54 x 49) NSte: 0069; NSPk: 10.6* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 219 (08/07) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 13 11.4 10 11.0 10 0 0.96 -0.3 1.3 -0.02 0.13 -67.69Subregional Peak: 11.0 10 0 0.96 (at 61 x 47) NSte: 0591; NSPk: 11.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 219 (08/07) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 2 6.7 10 8.9 9 -1 1.33 1.9 1.9 0.29 0.29 -99.00Subregional Peak: 10.5 10 0 1.56 (at 58 x 46) NSte: 3177; NSPk: 8.7* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 219 (08/07) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 7.7 14 5.3 7 -7 0.69 -1.8 1.8 -0.25 0.25 -99.00Subregional Peak: 8.1 17 3 1.05 (at 82 x 41) NSte: 4137; NSPk: 5.2* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 220 (08/08) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 8.5 9 9.5 10 1 1.12 1.8 1.8 0.26 0.26 -179.28Subregional Peak: 12.0 13 4 1.41 (at 58 x 49) NSte: 0088; NSPk: 9.4* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 220 (08/08) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 13 8.5 10 11.1 10 0 1.30 2.7 2.8 0.39 0.39 -85.14Subregional Peak: 12.1 13 3 1.42 (at 59 x 49) NSte: 0060; NSPk: 11.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 220 (08/08) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 1 6.5 11 6.8 9 -2 1.05 0.3 0.3 0.05 0.05 -99.00Subregional Peak: 9.9 11 0 1.53 (at 65 x 39) NSte: 3812; NSPk: 8.6* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 220 (08/08) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 1 6.0 10 6.3 8 -2 1.04 0.2 0.2 0.04 0.04 -99.00Subregional Peak: 7.7 21 11 1.27 (at 82 x 41) NSte: 4137; NSPk: 6.2* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 221 (08/09) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 11 10.8 9 11.3 10 1 1.05 2.5 2.6 0.35 0.36 -86.25Subregional Peak: 11.4 10 1 1.06 (at 74 x 45) NSte: 5204; NSPk: 11.4* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0000 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 214 through 221Unpaired Subregional Maximum of 9.3 at Cell 82 x 10 -- Nearest Site: 4158- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 214 215 216 217 218 Avg. 214 215 216 217 218 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0001 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 214 through 221Unpaired Subregional Maximum of 6.5 at Cell 27 x 54 -- Nearest Site: 0090- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 214 215 216 217 218 Avg. 214 215 216 217 218 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0002 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 214 through 221Unpaired Subregional Maximum of 8.6 at Cell 49 x 52 -- Nearest Site: 0090- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 214 215 216 217 218 Avg. 214 215 216 217 218 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0003 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 214 through 221Unpaired Subregional Maximum of 9.6 at Cell 54 x 50 -- Nearest Site: 0069- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 214 215 216 217 218 Avg. 214 215 216 217 218 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----0069 Burbank 7.3 5.1 5.4 5.9 5.4 7.7 8.1 6.1 6.9 7.6 7.0 10.0 1.35 0.35 0.350088 Pasadena 7.5 5.6 5.8 7.0 5.3 8.6 8.1 6.1 6.4 7.3 7.1 10.2 1.23 0.25 0.250074 Reseda 7.9 6.7 6.6 9.1 8.8 8.6 8.4 6.9 7.7 8.3 9.0 9.4 1.03 0.07 0.100090 Santa Clarita 10.2 9.4 8.8 10.8 11.0 11.8 8.5 7.5 7.9 8.2 9.1 8.9 0.78 -0.16 0.18* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0004 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 214 through 221Unpaired Subregional Maximum of 9.6 at Cell 69 x 43 -- Nearest Site: 4144- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 214 215 216 217 218 Avg. 214 215 216 217 218 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----0060 Azusa 7.5 4.9 5.6 6.7 5.2 8.4 8.8 6.6 7.0 8.0 8.6 9.8 1.27 0.34 0.344164 Banning Airport 8.3 8.9 8.3 9.3 9.0 6.7 7.0 6.5 7.0 7.3 5.6 6.5 1.00 -0.14 0.185181 Crestline 9.8 8.8 8.5 10.1 9.9 10.5 8.8 8.0 9.0 8.4 8.8 7.4 0.90 -0.09 0.154158 Elsinore 7.7 7.6 7.2 8.1 7.6 7.9 7.6 6.1 7.6 9.0 7.5 7.3 0.97 0.01 0.135197 Fontana 8.1 6.6 6.7 7.9 8.2 11.0 9.0 7.4 8.1 8.6 8.7 8.5 0.99 0.14 0.210591 Glendora 7.7 5.7 6.1 7.7 6.3 9.6 9.1 7.0 7.4 8.5 9.0 9.7 1.14 0.24 0.245212 Mira Loma 8.4 7.2 7.3 8.0 8.4 10.2 9.4 7.8 8.2 9.1 9.7 9.9 1.01 0.13 0.144149 Perris 6.8 6.7 6.9 7.6 7.7 6.1 8.5 7.2 7.9 9.6 8.3 8.3 1.29 0.25 0.250075 Pomona 7.5 5.5 5.7 6.7 6.4 9.1 9.0 6.9 7.2 8.0 8.8 10.0 1.12 0.30 0.305204 Redlands 8.0 7.3 7.3 8.0 9.1 7.3 9.2 8.1 8.8 9.2 8.8 7.9 1.09 0.17 0.204144 Rubidoux 8.3 7.3 7.4 7.8 8.4 9.8 9.5 7.9 8.6 9.4 9.9 9.4 1.03 0.17 0.185203 San Bernardino 8.5 7.1 7.4 8.4 9.4 8.9 9.2 7.8 8.8 8.9 9.1 8.0 0.95 0.11 0.195175 Upland 7.6 6.1 6.1 7.2 7.3 10.3 9.1 7.3 7.6 8.5 8.8 9.1 1.08 0.22 0.25* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0005 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 214 through 221Unpaired Subregional Maximum of 9.3 at Cell 66 x 42 -- Nearest Site: 5212- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 214 215 216 217 218 Avg. 214 215 216 217 218 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----3176 Anaheim 6.5 4.9 5.1 5.8 5.2 5.9 7.2 5.9 5.8 5.9 7.6 10.0 1.54 0.15 0.150087 Los Angeles 6.3 4.5 4.7 5.6 3.7 6.3 6.2 4.9 5.3 5.5 5.5 8.4 1.33 0.33 0.330820 LAXH 6.5 4.8 5.5 6.5 4.1 4.7 5.7 4.8 5.3 5.1 5.4 7.6 1.17 -0.21 0.213812 Mission Viejo 6.7 4.5 4.5 5.0 4.8 6.6 8.5 6.7 7.1 8.3 8.5 10.3 1.54 0.44 0.440091 West Los Angeles 6.7 4.9 5.9 6.7 4.3 5.3 6.6 5.3 5.8 6.1 6.2 8.8 1.32 -0.10 0.10* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0006 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 214 through 221Unpaired Subregional Maximum of 9.4 at Cell 80 x 18 -- Nearest Site: 4158- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 214 215 216 217 218 Avg. 214 215 216 217 218 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0007 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 214 through 221Unpaired Subregional Maximum of 6.5 at Cell 40 x 66 -- Nearest Site: 0090- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 214 215 216 217 218 Avg. 214 215 216 217 218 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0008 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 214 through 221Unpaired Subregional Maximum of 7.4 at Cell 70 x 58 -- Nearest Site: 5181- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 214 215 216 217 218 Avg. 214 215 216 217 218 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0009 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 214 through 221Unpaired Subregional Maximum of 7.7 at Cell 91 x 41 -- Nearest Site: 4157- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 214 215 216 217 218 Avg. 214 215 216 217 218 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----4157 Indio Jackson 6.2 4.4 4.6 5.8 6.1 6.3 5.5 4.6 5.3 6.6 5.0 5.0 1.06 -0.19 0.194137 Palm Springs 6.9 6.0 6.3 7.0 7.9 6.2 5.5 4.5 5.9 6.0 4.8 4.7 0.85 -0.19 0.20* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aSubRegional DescriptionsSubRegion 003 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0069 Burbank 53 48 -27.848 461.3080088 Pasadena 56 47 -11.205 457.0210074 Reseda 49 48 -48.000 463.1050090 Santa Clarita 49 52 -48.140 483.357SubRegion 004 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0060 Azusa 60 47 6.981 456.1134164 Banning Airport 79 42 104.459 433.5275181 Crestline 72 49 66.383 468.6064158 Elsinore 71 37 60.525 405.9075197 Fontana 68 46 46.811 453.0810591 Glendora 61 47 13.487 457.0105212 Mira Loma 67 43 42.938 438.9154149 Perris 72 39 69.051 417.3760075 Pomona 63 45 22.598 448.6105204 Redlands 74 45 76.256 448.1894144 Rubidoux 69 44 52.093 442.5575203 San Bernardino 72 46 65.874 453.2995175 Upland 65 46 31.687 452.125SubRegion 005 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------3176 Anaheim 60 40 7.422 421.6450087 Los Angeles 54 45 -22.302 445.5633195 Costa Mesa 60 37 6.793 405.6263177 La Habra 59 42 4.359 432.9780820 LAXH 51 42 -36.352 433.6850072 Long Beach 55 40 -17.171 421.9030084 Lynwood 55 42 -19.237 432.7533812 Mission Viejo 64 36 29.671 400.7910085 Pico Rivera 57 44 -5.273 442.8600091 West Los Angeles 52 45 -34.796 447.031SubRegion 009 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------4157 Indio Jackson 91 38 162.217 411.2934137 Palm Springs 85 40 132.826 423.133* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 217 (08/04) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 22 9.9 15 6.7 13 -2 0.68 -2.0 2.0 -0.26 0.26 0.41Subregional Peak: 8.5 15 0 0.86 (at cell 53 x 55)0069 Burbank 4 7.2 14 5.9 13 -1 0.82 -0.2 0.2 -1.44 1.44 0.770088 Pasadena 4 7.1 14 5.2 14 0 0.73 -0.3 0.3 -1.44 1.44 0.850074 Reseda 7 8.0 16 6.4 12 -4 0.80 -0.3 0.3 -0.82 0.82 0.430090 Santa Clarita 7 9.9 15 6.7 13 -2 0.68 -0.3 0.3 -0.82 0.82 0.21* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 217 (08/04) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 86 12.0 14 7.9 15 1 0.66 -3.2 3.2 -0.38 0.38 0.56Subregional Peak: 8.8 15 1 0.73 (at cell 79 x 40)0060 Azusa 3 7.4 15 5.3 14 -1 0.72 -0.3 0.3 -10.89 10.89 0.764164 Banning Airport 13 12.0 14 7.9 15 1 0.66 -0.5 0.5 -2.51 2.51 0.755181 Crestline 15 10.9 15 6.2 13 -2 0.57 -0.4 0.4 -2.18 2.18 0.454158 Elsinore 7 11.1 15 7.9 14 -1 0.71 -0.3 0.3 -4.67 4.67 0.735197 Fontana 5 8.1 13 5.0 13 0 0.62 -0.4 0.4 -6.53 6.53 0.680591 Glendora 4 8.3 15 5.5 14 -1 0.66 -0.3 0.3 -8.17 8.17 0.164149 Perris 9 10.2 15 7.2 13 -2 0.71 -0.3 0.3 -3.63 3.63 0.480075 Pomona 4 6.5 15 5.2 15 0 0.80 -0.3 0.3 -8.17 8.17 0.555204 Redlands 7 10.0 16 7.0 13 -3 0.70 -0.4 0.4 -4.67 4.67 0.154144 Rubidoux 9 9.1 14 6.1 11 -3 0.67 -0.4 0.4 -3.63 3.63 0.275203 San Bernardino 5 9.1 15 6.6 13 -2 0.72 -0.4 0.4 -6.53 6.53 0.145175 Upland 5 7.5 16 5.1 15 -1 0.68 -0.3 0.3 -6.53 6.53 0.08* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 217 (08/04) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 14 8.0 13 6.5 13 0 0.82 -2.2 2.3 -0.30 0.32 -0.59Subregional Peak: 8.0 14 1 1.00 (at cell 68 x 37)3176 Anaheim 6 8.0 13 4.7 14 1 0.58 -0.5 0.5 -0.71 0.74 0.130820 LAXH 2 7.0 12 4.9 12 0 0.70 -0.3 0.3 -2.12 2.21 -99.003812 Mission Viejo 4 6.6 15 6.5 13 -2 0.99 0.0 0.1 -1.06 1.10 -0.780085 Pico Rivera 2 6.7 14 5.0 13 -1 0.74 -0.2 0.2 -2.12 2.21 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 217 (08/04) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 12 10.9 18 6.3 17 -1 0.58 -2.2 2.2 -0.26 0.26 -0.05Subregional Peak: 12.6 13 -5 1.16 (at cell 106 x 13)4157 Indio Jackson 2 6.7 11 5.8 11 0 0.87 -0.2 0.2 -1.56 1.56 -99.004137 Palm Springs 10 10.9 18 6.3 17 -1 0.58 -0.3 0.3 -0.31 0.31 -0.17* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 218 (08/05) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 29 11.0 15 8.9 13 -2 0.81 -2.8 2.8 -0.36 0.36 0.74Subregional Peak: 10.8 15 0 0.98 (at cell 52 x 54)0069 Burbank 5 8.6 14 6.6 14 0 0.77 -0.4 0.4 -2.08 2.10 0.960088 Pasadena 6 8.3 14 6.3 13 -1 0.76 -0.4 0.4 -1.73 1.75 0.780074 Reseda 10 9.6 14 7.9 14 0 0.82 -0.4 0.4 -1.04 1.05 0.720090 Santa Clarita 8 11.0 15 8.9 13 -2 0.81 -0.2 0.2 -1.30 1.31 0.64* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 218 (08/05) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 98 11.2 14 11.0 14 0 0.98 -2.1 2.6 -0.24 0.31 0.24Subregional Peak: 11.7 14 0 1.04 (at cell 77 x 41)0060 Azusa 5 7.9 13 6.8 14 1 0.86 -0.2 0.2 -4.78 5.99 0.364164 Banning Airport 10 9.7 13 11.0 14 1 1.14 -0.1 0.2 -2.39 2.99 0.405181 Crestline 14 10.6 15 7.7 13 -2 0.73 -0.4 0.4 -1.71 2.14 0.414158 Elsinore 7 8.7 12 10.1 15 3 1.16 0.2 0.2 -3.42 4.28 -0.035197 Fontana 6 9.3 15 5.6 12 -3 0.60 -0.4 0.4 -3.99 4.99 0.080591 Glendora 6 8.5 14 7.3 15 1 0.86 -0.2 0.2 -3.99 4.99 0.895212 Mira Loma 7 10.0 11 6.1 13 2 0.61 -0.4 0.4 -3.42 4.28 0.754149 Perris 9 10.7 15 9.2 13 -2 0.86 0.0 0.1 -2.66 3.33 0.640075 Pomona 6 8.7 14 6.1 16 2 0.70 -0.3 0.3 -3.99 4.99 0.345204 Redlands 9 11.2 14 9.4 13 -1 0.84 -0.4 0.4 -2.66 3.33 0.574144 Rubidoux 7 9.9 12 7.9 11 -1 0.80 -0.3 0.4 -3.42 4.28 0.235203 San Bernardino 6 9.6 15 8.2 13 -2 0.86 -0.4 0.4 -3.99 4.99 0.835175 Upland 6 8.7 15 6.1 16 1 0.70 -0.3 0.3 -3.99 4.99 0.71* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 218 (08/05) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 52 8.7 16 7.6 13 -3 0.87 -3.0 3.1 -0.43 0.45 0.30Subregional Peak: 10.0 14 -2 1.15 (at cell 68 x 37)3176 Anaheim 7 7.6 11 5.2 15 4 0.68 -0.5 0.5 -3.23 3.35 0.790087 Los Angeles 6 7.0 15 5.4 12 -3 0.77 -0.5 0.5 -3.77 3.91 0.443195 Costa Mesa 2 6.3 3 1.4 3 0 0.23 -0.8 0.8 -11.30 11.72 -99.003177 La Habra 5 6.9 12 5.2 15 3 0.76 -0.4 0.4 -4.52 4.69 -0.340820 LAXH 15 8.7 16 5.8 12 -4 0.67 -0.6 0.6 -1.51 1.56 0.700084 Lynwood 2 6.2 13 5.8 13 0 0.94 -0.2 0.2 -11.30 11.72 -99.003812 Mission Viejo 3 6.7 12 7.6 13 1 1.13 0.1 0.1 -7.53 7.81 0.440085 Pico Rivera 4 7.3 13 6.5 14 1 0.90 -0.2 0.2 -5.65 5.86 -0.340091 West Los Angeles 8 8.6 16 5.8 13 -3 0.68 -0.4 0.4 -2.82 2.93 0.04* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 218 (08/05) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 18 8.1 18 8.8 15 -3 1.08 -0.8 1.4 -0.11 0.21 0.50Subregional Peak: 12.6 13 -5 1.56 (at cell 107 x 12)4157 Indio Jackson 8 7.3 10 7.3 11 1 1.00 -0.2 0.3 -0.25 0.47 0.514137 Palm Springs 10 8.1 18 8.8 15 -3 1.08 0.0 0.2 -0.20 0.38 0.15* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 219 (08/06) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 37 10.0 14 9.8 13 -1 0.98 -1.9 2.0 -0.25 0.27 0.78Subregional Peak: 12.3 15 1 1.23 (at cell 55 x 54)0069 Burbank 7 8.7 13 8.6 13 0 0.99 -0.2 0.2 -1.32 1.41 0.390088 Pasadena 8 9.8 14 7.8 14 0 0.79 -0.3 0.3 -1.16 1.23 0.870074 Reseda 11 8.6 15 9.0 12 -3 1.05 -0.2 0.3 -0.84 0.90 0.870090 Santa Clarita 11 10.0 14 9.8 13 -1 0.98 -0.3 0.3 -0.84 0.90 0.89* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 219 (08/06) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 97 10.7 16 11.9 14 -2 1.11 0.2 1.6 0.03 0.21 0.36Subregional Peak: 12.8 14 -2 1.20 (at cell 71 x 37)0060 Azusa 2 8.8 14 8.3 14 0 0.94 0.0 0.1 1.41 10.24 -99.004164 Banning Airport 12 8.1 16 11.5 15 -1 1.42 0.0 0.2 0.24 1.71 0.725181 Crestline 18 10.7 16 9.0 15 -1 0.84 -0.2 0.2 0.16 1.14 0.644158 Elsinore 7 8.5 14 11.9 14 0 1.40 0.3 0.4 0.40 2.93 0.445197 Fontana 7 8.7 14 10.4 13 -1 1.20 0.1 0.3 0.40 2.93 0.320591 Glendora 5 9.6 15 8.6 14 -1 0.89 -0.1 0.1 0.56 4.10 0.435212 Mira Loma 8 9.2 13 10.5 12 -1 1.15 0.0 0.2 0.35 2.56 0.314149 Perris 3 8.3 16 10.1 15 -1 1.21 0.2 0.2 0.94 6.83 -0.250075 Pomona 5 7.5 16 8.7 14 -2 1.16 0.1 0.2 0.56 4.10 -0.435204 Redlands 8 10.7 15 11.0 15 0 1.03 0.1 0.2 0.35 2.56 0.424144 Rubidoux 10 10.1 14 11.0 13 -1 1.09 0.0 0.2 0.28 2.05 0.655203 San Bernardino 6 9.8 15 10.5 14 -1 1.07 0.2 0.2 0.47 3.41 0.805175 Upland 6 7.9 14 9.3 14 0 1.17 0.1 0.2 0.47 3.41 0.49* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 219 (08/06) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 49 9.0 13 8.9 14 1 0.99 -1.7 1.9 -0.23 0.25 0.22Subregional Peak: 9.5 15 2 1.06 (at cell 68 x 36)3176 Anaheim 8 8.4 14 5.7 13 -1 0.68 -0.4 0.4 -1.40 1.56 0.760087 Los Angeles 3 8.0 16 5.3 11 -5 0.66 -0.4 0.4 -3.74 4.15 0.133195 Costa Mesa 4 6.9 11 7.9 13 2 1.14 0.0 0.2 -2.80 3.11 0.213177 La Habra 6 6.9 14 7.2 14 0 1.04 -0.1 0.1 -1.87 2.08 0.170820 LAXH 8 8.2 14 5.4 14 0 0.66 -0.4 0.4 -1.40 1.56 0.690084 Lynwood 3 6.9 12 6.1 12 0 0.88 -0.1 0.1 -3.74 4.15 1.003812 Mission Viejo 5 8.7 13 8.9 14 1 1.02 0.0 0.1 -2.24 2.49 0.050085 Pico Rivera 5 9.0 13 7.4 13 0 0.82 -0.2 0.2 -2.24 2.49 0.920091 West Los Angeles 7 8.6 15 6.4 12 -3 0.75 -0.2 0.2 -1.60 1.78 -0.03* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 219 (08/06) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 27 8.1 12 8.3 15 3 1.03 -0.5 0.9 -0.07 0.13 0.57Subregional Peak: 11.6 15 3 1.43 (at cell 81 x 44)4157 Indio Jackson 14 8.1 12 8.3 15 3 1.03 -0.1 0.1 -0.14 0.25 0.864137 Palm Springs 13 8.1 19 7.8 14 -5 0.96 0.0 0.1 -0.15 0.26 -0.33* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 220 (08/07) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 36 12.8 14 10.8 12 -2 0.84 -1.6 1.9 -0.20 0.23 0.62Subregional Peak: 14.7 15 1 1.15 (at cell 55 x 53)0069 Burbank 7 9.1 13 10.7 13 0 1.18 0.0 0.1 -1.00 1.18 0.940088 Pasadena 8 9.2 14 9.8 13 -1 1.06 -0.2 0.2 -0.88 1.04 0.800074 Reseda 11 10.8 13 10.8 12 -1 1.00 -0.2 0.3 -0.64 0.75 0.900090 Santa Clarita 10 12.8 14 8.6 13 -1 0.67 -0.3 0.3 -0.70 0.83 0.89* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 220 (08/07) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 134 15.6 16 12.7 15 -1 0.82 -1.3 1.5 -0.13 0.15 0.72Subregional Peak: 13.3 16 0 0.85 (at cell 61 x 52)0060 Azusa 7 10.2 13 10.2 14 1 1.00 -0.1 0.2 -2.43 2.93 0.784164 Banning Airport 15 15.6 16 11.5 16 0 0.74 -0.1 0.2 -1.13 1.37 0.805181 Crestline 18 13.8 16 11.2 15 -1 0.81 -0.2 0.2 -0.94 1.14 0.634158 Elsinore 11 10.6 15 12.4 14 -1 1.17 -0.1 0.2 -1.54 1.86 0.835197 Fontana 9 12.3 14 10.8 14 0 0.88 -0.1 0.1 -1.89 2.28 0.870591 Glendora 7 11.5 13 10.6 15 2 0.92 -0.1 0.2 -2.43 2.93 0.685212 Mira Loma 10 11.8 13 10.5 12 -1 0.89 -0.1 0.1 -1.70 2.05 0.794149 Perris 11 12.1 14 10.8 14 0 0.90 -0.1 0.1 -1.54 1.86 0.740075 Pomona 7 11.6 14 11.0 15 1 0.95 -0.1 0.1 -2.43 2.93 0.925204 Redlands 12 14.8 15 12.7 15 0 0.86 -0.2 0.2 -1.42 1.71 0.784144 Rubidoux 10 12.7 13 11.3 13 0 0.89 -0.1 0.1 -1.70 2.05 0.905203 San Bernardino 9 13.7 15 12.4 14 -1 0.90 -0.1 0.2 -1.89 2.28 0.785175 Upland 8 11.8 14 10.8 15 1 0.92 -0.1 0.1 -2.12 2.56 0.92* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 220 (08/07) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 27 9.3 14 9.5 14 0 1.02 -0.8 1.2 -0.10 0.15 0.52Subregional Peak: 10.6 15 1 1.14 (at cell 68 x 36)3176 Anaheim 6 8.4 13 8.1 13 0 0.97 -0.2 0.2 -0.46 0.69 0.920087 Los Angeles 4 9.3 14 7.0 12 -2 0.75 -0.2 0.2 -0.68 1.04 0.133177 La Habra 4 7.9 14 8.8 14 0 1.11 0.1 0.1 -0.68 1.04 0.980084 Lynwood 1 6.1 12 6.8 12 0 1.12 0.1 0.1 -2.73 4.14 -99.003812 Mission Viejo 7 9.2 16 9.5 14 -2 1.04 -0.1 0.1 -0.39 0.59 0.760085 Pico Rivera 5 8.6 13 8.8 13 0 1.02 -0.1 0.1 -0.55 0.83 0.80* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 220 (08/07) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 23 11.3 18 9.4 13 -5 0.84 -1.6 2.5 -0.15 0.29 -0.51Subregional Peak: 10.7 15 -3 0.95 (at cell 82 x 42)4157 Indio Jackson 11 10.8 20 9.4 13 -7 0.87 -0.1 0.3 -0.31 0.60 -0.614137 Palm Springs 12 11.3 18 8.7 16 -2 0.77 -0.2 0.2 -0.29 0.55 -0.43* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 221 (08/08) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 32 13.6 14 10.8 12 -2 0.80 -1.3 1.7 -0.12 0.17 0.34Subregional Peak: 14.9 14 0 1.09 (at cell 53 x 52)0069 Burbank 7 10.0 13 10.2 12 -1 1.02 0.1 0.1 -0.56 0.79 0.970088 Pasadena 7 9.8 14 9.7 13 -1 0.99 0.0 0.1 -0.56 0.79 0.850074 Reseda 8 10.4 12 10.8 12 0 1.04 -0.1 0.1 -0.49 0.69 0.940090 Santa Clarita 10 13.6 14 8.2 12 -2 0.60 -0.4 0.4 -0.39 0.55 0.80* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 221 (08/08) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 132 13.6 15 14.6 14 -1 1.07 0.3 1.4 0.03 0.16 0.75Subregional Peak: 15.2 14 -1 1.12 (at cell 71 x 36)0060 Azusa 6 9.8 13 10.0 13 0 1.02 0.0 0.0 0.62 3.54 0.944164 Banning Airport 15 12.2 14 13.1 16 2 1.07 -0.1 0.2 0.25 1.42 0.875181 Crestline 15 11.0 15 12.9 14 -1 1.17 0.1 0.3 0.25 1.42 0.444158 Elsinore 13 12.2 14 14.4 14 0 1.18 0.0 0.2 0.29 1.64 0.905197 Fontana 8 10.8 14 12.8 14 0 1.19 0.2 0.2 0.47 2.66 0.790591 Glendora 6 10.9 13 10.8 14 1 0.99 0.0 0.1 0.62 3.54 0.915212 Mira Loma 9 11.4 14 11.8 12 -2 1.03 0.0 0.1 0.42 2.36 0.774149 Perris 13 10.0 12 13.2 14 2 1.32 0.1 0.2 0.29 1.64 0.720075 Pomona 7 11.9 13 11.2 14 1 0.94 0.0 0.1 0.53 3.04 0.965204 Redlands 13 13.6 15 14.6 14 -1 1.07 -0.1 0.2 0.29 1.64 0.844144 Rubidoux 10 12.0 14 13.1 13 -1 1.09 0.1 0.2 0.37 2.13 0.725203 San Bernardino 9 13.6 15 14.3 14 -1 1.05 0.1 0.2 0.42 2.36 0.695175 Upland 8 11.2 14 12.0 14 0 1.07 0.1 0.1 0.47 2.66 0.99* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 221 (08/08) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 25 8.4 14 10.1 13 -1 1.21 -0.6 1.2 -0.08 0.16 0.52Subregional Peak: 12.0 12 -2 1.43 (at cell 68 x 37)3176 Anaheim 7 8.4 14 6.9 13 -1 0.82 -0.3 0.3 -0.30 0.58 0.830087 Los Angeles 3 7.4 13 6.0 12 -1 0.81 -0.2 0.2 -0.69 1.36 0.133177 La Habra 5 8.2 14 8.2 13 -1 1.00 0.0 0.1 -0.41 0.82 0.443812 Mission Viejo 6 8.2 14 10.1 13 -1 1.23 0.1 0.1 -0.35 0.68 0.660085 Pico Rivera 4 7.7 12 7.6 12 0 0.99 0.0 0.0 -0.52 1.02 0.86* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 221 (08/08) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 43 11.0 16 8.9 16 0 0.81 -1.9 2.2 -0.24 0.27 0.43Subregional Peak: 12.2 16 0 1.11 (at cell 82 x 42)4157 Indio Jackson 22 9.4 19 8.0 16 -3 0.85 -0.3 0.3 -0.47 0.54 0.084137 Palm Springs 21 11.0 16 8.9 16 0 0.81 -0.2 0.2 -0.49 0.56 0.79* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aSubRegional DescriptionsSubRegion 003 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0069 Burbank 53 48 -27.848 461.3080088 Pasadena 56 47 -11.205 457.0210074 Reseda 49 48 -48.000 463.1050090 Santa Clarita 49 52 -48.140 483.357SubRegion 004 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0060 Azusa 60 47 6.981 456.1134164 Banning Airport 79 42 104.459 433.5275181 Crestline 72 49 66.383 468.6064158 Elsinore 71 37 60.525 405.9075197 Fontana 68 46 46.811 453.0810591 Glendora 61 47 13.487 457.0105212 Mira Loma 67 43 42.938 438.9154149 Perris 72 39 69.051 417.3760075 Pomona 63 45 22.598 448.6105204 Redlands 74 45 76.256 448.1894144 Rubidoux 69 44 52.093 442.5575203 San Bernardino 72 46 65.874 453.2995175 Upland 65 46 31.687 452.125SubRegion 005 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------3176 Anaheim 60 40 7.422 421.6450087 Los Angeles 54 45 -22.302 445.5633195 Costa Mesa 60 37 6.793 405.6263177 La Habra 59 42 4.359 432.9780820 LAXH 51 42 -36.352 433.6850072 Long Beach 55 40 -17.171 421.9030084 Lynwood 55 42 -19.237 432.7533812 Mission Viejo 64 36 29.671 400.7910085 Pico Rivera 57 44 -5.273 442.8600091 West Los Angeles 52 45 -34.796 447.031SubRegion 009 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------4157 Indio Jackson 91 38 162.217 411.2934137 Palm Springs 85 40 132.826 423.133* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 217 (08/04) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 2 7.8 11 5.7 10 -1 0.72 -2.0 2.0 -0.26 0.26 -99.00Subregional Peak: 6.6 11 0 0.85 (at 52 x 55) NSte: 0090; NSPk: 5.6* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 217 (08/04) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 10 11.1 11 6.7 10 -1 0.60 -2.9 2.9 -0.34 0.34 -33.58Subregional Peak: 7.1 11 0 0.64 (at 72 x 36) NSte: 4158; NSPk: 6.5* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 217 (08/04) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 1 6.9 9 3.5 10 1 0.51 -3.4 3.4 -0.49 0.49 -99.00Subregional Peak: 6.8 10 1 0.98 (at 68 x 37) NSte: 3812; NSPk: 5.5* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 217 (08/04) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 1 7.4 12 6.3 10 -2 0.85 -1.1 1.1 -0.15 0.15 -99.00Subregional Peak: 10.4 10 -2 1.41 (at 106 x 12) NSte: 4157; NSPk: 6.1* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 218 (08/05) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 9.0 11 7.0 10 -1 0.78 -2.1 2.1 -0.27 0.27 -68.38Subregional Peak: 8.3 11 0 0.92 (at 53 x 55) NSte: 0090; NSPk: 7.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 218 (08/05) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 13 9.9 11 9.0 10 -1 0.91 -1.8 2.0 -0.21 0.25 -38.55Subregional Peak: 9.2 10 -1 0.92 (at 71 x 37) NSte: 4158; NSPk: 9.2* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 218 (08/05) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 6 8.0 11 5.1 10 -1 0.64 -2.4 2.4 -0.35 0.35 -80.39Subregional Peak: 8.5 10 -1 1.07 (at 68 x 37) NSte: 3812; NSPk: 6.5* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 218 (08/05) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 7.4 12 7.8 10 -2 1.05 0.8 0.8 0.13 0.13 -99.00Subregional Peak: 9.2 9 -3 1.23 (at 107 x 12) NSte: 4157; NSPk: 7.5* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 219 (08/06) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 8.9 10 7.4 9 -1 0.83 -1.4 1.4 -0.17 0.17 -186.06Subregional Peak: 9.2 11 1 1.04 (at 55 x 53) NSte: 0069; NSPk: 6.7* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 219 (08/06) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 11 9.4 14 9.9 10 -4 1.06 0.7 1.1 0.10 0.15 -100.48Subregional Peak: 10.6 10 -4 1.13 (at 76 x 44) NSte: 5204; NSPk: 9.4* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 219 (08/06) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 6 7.7 9 6.8 10 1 0.88 -1.6 1.6 -0.21 0.21 -117.69Subregional Peak: 8.6 10 1 1.12 (at 68 x 37) NSte: 3812; NSPk: 6.7* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 219 (08/06) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 7.7 10 7.7 10 0 1.01 0.2 0.2 0.02 0.02 -99.00Subregional Peak: 8.9 11 1 1.16 (at 81 x 44) NSte: 4137; NSPk: 7.4* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 220 (08/07) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 10.3 10 8.1 10 0 0.79 -1.1 1.2 -0.12 0.13 -131.28Subregional Peak: 11.5 13 3 1.11 (at 59 x 50) NSte: 0088; NSPk: 7.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 220 (08/07) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 13 12.4 15 10.4 10 -5 0.84 -1.1 1.1 -0.10 0.10 -86.36Subregional Peak: 11.1 10 -5 0.89 (at 76 x 44) NSte: 5204; NSPk: 10.5* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 220 (08/07) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 5 7.6 11 7.1 10 -1 0.93 -0.7 0.9 -0.10 0.13 -146.64Subregional Peak: 9.1 10 -1 1.21 (at 68 x 37) NSte: 3812; NSPk: 6.8* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 220 (08/07) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 9.1 17 8.4 10 -7 0.92 -1.1 1.1 -0.12 0.12 -99.00Subregional Peak: 9.7 11 -6 1.06 (at 81 x 43) NSte: 4137; NSPk: 7.4* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 221 (08/08) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 11.3 10 8.6 9 -1 0.76 -1.1 1.2 -0.09 0.11 -83.40Subregional Peak: 11.9 11 1 1.04 (at 59 x 50) NSte: 0088; NSPk: 7.6* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 221 (08/08) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 13 12.2 11 12.9 10 -1 1.06 0.7 0.8 0.07 0.08 -57.58Subregional Peak: 13.4 10 -1 1.10 (at 74 x 46) NSte: 5204; NSPk: 12.9* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 221 (08/08) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 3 7.1 10 7.6 10 0 1.06 -0.3 0.8 -0.04 0.11 -205.62Subregional Peak: 10.2 9 -1 1.43 (at 68 x 37) NSte: 3812; NSPk: 7.2* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aStatistics were calculated for the 24-hour period of DOY 221 (08/08) 2004Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 9.9 13 7.8 11 -2 0.79 -1.3 1.3 -0.13 0.13 -99.00Subregional Peak: 10.7 11 -2 1.08 (at 81 x 43) NSte: 4137; NSPk: 7.3* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aSubregion 0000 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 217 through 221Unpaired Subregional Maximum of 9.4 at Cell 83 x 8 -- Nearest Site: 4157- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 217 218 219 220 221 Avg. 217 218 219 220 221 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aSubregion 0001 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 217 through 221Unpaired Subregional Maximum of -99.0 at Cell -9 x -9 -- Nearest Site: 0820- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 217 218 219 220 221 Avg. 217 218 219 220 221 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aSubregion 0002 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 217 through 221Unpaired Subregional Maximum of 7.3 at Cell 49 x 52 -- Nearest Site: 0090- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 217 218 219 220 221 Avg. 217 218 219 220 221 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aSubregion 0003 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 217 through 221Unpaired Subregional Maximum of 9.5 at Cell 53 x 52 -- Nearest Site: 0090- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 217 218 219 220 221 Avg. 217 218 219 220 221 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----0069 Burbank 7.7 6.0 7.3 7.8 7.7 7.9 6.5 4.6 5.4 6.5 8.0 8.2 1.04 -0.09 0.120088 Pasadena 7.7 5.9 7.3 8.0 8.0 7.7 5.8 3.8 4.7 5.8 7.0 7.6 0.95 -0.19 0.190074 Reseda 8.3 7.2 8.5 8.0 8.7 8.9 7.2 5.4 6.6 7.4 8.1 8.6 0.97 -0.13 0.130090 Santa Clarita 9.5 7.8 9.0 8.9 10.3 11.3 6.9 5.7 7.0 7.4 7.2 7.1 0.65 -0.27 0.27* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aSubregion 0004 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 217 through 221Unpaired Subregional Maximum of 10.7 at Cell 76 x 44 -- Nearest Site: 5204- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 217 218 219 220 221 Avg. 217 218 219 220 221 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----0060 Azusa 7.5 5.5 6.7 5.5 8.2 7.6 6.1 4.1 5.2 6.4 7.1 7.8 0.95 -0.11 0.124164 Banning Airport 10.0 11.1 9.1 7.3 11.2 11.4 9.1 6.5 8.7 8.7 10.2 11.5 1.01 -0.07 0.155181 Crestline 10.2 9.6 9.9 9.4 12.4 9.7 8.0 5.3 6.2 7.6 9.4 11.3 0.92 -0.22 0.284158 Elsinore 9.1 10.3 7.3 7.3 10.1 10.9 9.5 6.7 9.0 9.9 10.0 11.8 1.08 0.07 0.215197 Fontana 8.2 6.8 7.9 7.3 10.0 9.3 7.7 4.6 5.3 8.2 9.4 10.7 1.08 -0.08 0.200591 Glendora 7.5 6.1 7.1 7.3 8.8 8.3 6.7 4.4 5.5 7.0 7.9 8.6 0.98 -0.13 0.145212 Mira Loma 9.2 0.5 8.5 7.8 10.4 10.0 7.5 4.6 5.7 7.9 9.1 10.3 0.99 -0.10 0.124149 Perris 9.4 9.1 8.7 5.9 10.4 9.4 8.9 6.1 8.5 9.2 9.6 11.2 1.07 -0.06 0.160075 Pomona 8.0 5.6 7.2 6.3 9.0 9.3 6.8 4.2 4.7 7.4 8.5 9.1 0.98 -0.06 0.155204 Redlands 9.9 8.4 9.0 8.1 12.0 12.2 8.9 5.5 6.7 9.2 10.4 12.9 1.06 -0.11 0.184144 Rubidoux 9.3 8.3 8.4 8.2 11.2 10.7 8.1 5.0 6.2 8.4 9.7 11.4 1.02 -0.14 0.175203 San Bernardino 9.1 7.1 8.6 7.3 11.0 11.4 8.5 5.2 6.0 8.6 9.9 12.6 1.11 -0.08 0.195175 Upland 7.7 6.1 7.1 6.8 9.5 9.2 7.1 4.4 4.9 7.9 8.8 9.7 1.02 -0.09 0.17* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aSubregion 0005 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 217 through 221Unpaired Subregional Maximum of 9.7 at Cell 69 x 38 -- Nearest Site: 4158- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 217 218 219 220 221 Avg. 217 218 219 220 221 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----3176 Anaheim 7.0 6.9 6.6 7.6 6.8 7.1 4.7 3.5 4.2 4.6 5.6 5.5 0.74 -0.33 0.330087 Los Angeles 6.7 4.8 6.6 4.2 6.7 5.7 4.6 3.3 4.2 5.0 5.4 5.3 0.81 -0.28 0.283177 La Habra 6.2 4.8 6.1 6.3 6.1 6.5 5.2 3.4 3.7 5.8 6.5 6.6 1.02 -0.10 0.140820 LAXH 7.8 5.6 8.0 7.7 5.2 5.3 4.7 4.5 5.1 4.8 4.8 4.4 0.64 -0.37 0.373812 Mission Viejo 7.2 5.9 5.6 7.0 7.6 7.0 6.7 5.6 6.4 6.8 7.1 7.6 1.00 -0.01 0.060085 Pico Rivera 6.6 4.8 6.1 6.7 7.0 5.9 5.2 3.6 4.5 5.7 6.0 6.0 0.86 -0.18 0.180091 West Los Angeles 7.5 4.9 7.7 7.2 5.2 5.1 5.3 4.4 5.0 5.3 6.0 5.6 0.77 -0.30 0.30* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aSubregion 0006 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 217 through 221Unpaired Subregional Maximum of 9.1 at Cell 80 x 24 -- Nearest Site: 4158- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 217 218 219 220 221 Avg. 217 218 219 220 221 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aSubregion 0007 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 217 through 221Unpaired Subregional Maximum of 6.2 at Cell 50 x 70 -- Nearest Site: 0090- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 217 218 219 220 221 Avg. 217 218 219 220 221 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aSubregion 0008 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 217 through 221Unpaired Subregional Maximum of 7.5 at Cell 78 x 56 -- Nearest Site: 5181- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 217 218 219 220 221 Avg. 217 218 219 220 221 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df04aSubregion 0009 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 217 through 221Unpaired Subregional Maximum of 9.0 at Cell 84 x 43 -- Nearest Site: 4137- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 217 218 219 220 221 Avg. 217 218 219 220 221 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----4157 Indio Jackson 7.6 5.7 6.2 7.7 8.9 7.8 7.5 6.2 7.6 7.7 8.4 7.8 0.94 0.04 0.074137 Palm Springs 8.2 7.4 7.4 7.1 9.1 9.9 7.2 6.3 7.8 7.4 7.4 7.3 0.79 -0.10 0.14* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubRegional DescriptionsSubRegion 003 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0069 Burbank 53 48 -27.848 461.3080088 Pasadena 56 47 -11.205 457.0210074 Reseda 49 48 -48.000 463.1050090 Santa Clarita 49 52 -48.140 483.357SubRegion 004 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0060 Azusa 60 47 6.981 456.1134164 Banning Airport 79 42 104.459 433.5275181 Crestline 72 49 66.383 468.6064158 Elsinore 71 37 60.525 405.9075197 Fontana 68 46 46.811 453.0810591 Glendora 61 47 13.487 457.0105212 Mira Loma 67 43 42.938 438.9154149 Perris 72 39 69.051 417.3760075 Pomona 63 45 22.598 448.6105204 Redlands 74 45 76.256 448.1894144 Rubidoux 69 44 52.093 442.5575203 San Bernardino 72 46 65.874 453.2995175 Upland 65 46 31.687 452.125SubRegion 005 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------3176 Anaheim 60 40 7.422 421.6450087 Los Angeles 54 45 -22.302 445.5633195 Costa Mesa 60 37 6.793 405.6263177 La Habra 59 42 4.359 432.9780820 LAXH 51 42 -36.352 433.6850072 Long Beach 55 40 -17.171 421.9030084 Lynwood 55 42 -19.237 432.7533812 Mission Viejo 64 36 29.671 400.7910085 Pico Rivera 57 44 -5.273 442.8600091 West Los Angeles 52 45 -34.796 447.031SubRegion 009 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------4157 Indio Jackson 91 38 162.217 411.2934137 Palm Springs 85 40 132.826 423.133* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 237 (08/25) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 5 8.1 15 7.4 14 -1 0.91 -0.7 1.0 -0.08 0.14 -0.38Subregional Peak: 9.8 14 -1 1.21 (at cell 49 x 47)0088 Pasadena 1 7.5 13 6.4 13 0 0.85 -0.2 0.2 -0.42 0.70 -99.000074 Reseda 1 6.7 16 7.0 16 0 1.04 0.0 0.0 -0.42 0.70 -99.000090 Santa Clarita 3 8.1 15 7.4 14 -1 0.91 -0.1 0.2 -0.14 0.23 -0.25* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 237 (08/25) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 35 8.6 15 8.7 15 0 1.01 -1.2 1.6 -0.17 0.22 0.02Subregional Peak: 9.3 14 -1 1.08 (at cell 72 x 32)0060 Azusa 2 6.9 14 6.5 14 0 0.94 -0.1 0.1 -2.89 3.83 -99.004164 Banning Airport 5 8.0 16 6.2 15 -1 0.78 -0.4 0.4 -1.16 1.53 -0.205181 Crestline 3 7.7 17 7.2 16 -1 0.94 -0.1 0.1 -1.93 2.55 0.074158 Elsinore 2 6.8 14 6.2 14 0 0.91 -0.1 0.1 -2.89 3.83 -99.005197 Fontana 2 7.8 15 6.3 15 0 0.81 -0.3 0.3 -2.89 3.83 -99.000591 Glendora 1 6.9 14 6.2 14 0 0.90 -0.1 0.1 -5.78 7.66 -99.005212 Mira Loma 5 8.6 15 8.2 14 -1 0.96 -0.1 0.3 -1.16 1.53 0.000075 Pomona 2 7.3 14 5.3 15 1 0.73 -0.3 0.3 -2.89 3.83 -99.005204 Redlands 3 7.1 17 6.4 16 -1 0.91 -0.1 0.1 -1.93 2.55 -0.674144 Rubidoux 4 8.0 16 8.7 15 -1 1.08 -0.1 0.2 -1.44 1.91 0.315203 San Bernardino 4 7.4 16 6.6 15 -1 0.89 -0.2 0.2 -1.44 1.91 0.405175 Upland 2 8.0 15 5.7 14 -1 0.72 -0.2 0.2 -2.89 3.83 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 237 (08/25) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 29 10.9 14 6.5 14 0 0.60 -2.7 2.7 -0.36 0.36 0.55Subregional Peak: 8.2 13 -1 0.75 (at cell 68 x 36)3176 Anaheim 3 6.6 16 4.0 14 -2 0.61 -0.4 0.4 -3.44 3.44 -0.790087 Los Angeles 3 7.2 15 4.8 12 -3 0.67 -0.4 0.4 -3.44 3.44 -0.063195 Costa Mesa 5 8.0 13 6.5 14 1 0.81 -0.3 0.3 -2.06 2.06 0.820820 LAXH 7 8.0 12 5.6 12 0 0.70 -0.3 0.3 -1.47 1.47 0.740072 Long Beach 4 7.2 15 5.7 13 -2 0.80 -0.3 0.3 -2.58 2.58 -0.490091 West Los Angeles 7 10.9 14 5.9 13 -1 0.55 -0.4 0.4 -1.47 1.47 0.88* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 237 (08/25) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 4 7.2 17 4.3 17 0 0.60 -2.9 2.9 -0.42 0.42 0.41Subregional Peak: 7.9 13 -4 1.10 (at cell 108 x 11)4137 Palm Springs 4 7.2 17 4.3 17 0 0.60 -0.4 0.4 -0.42 0.42 0.41* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 238 (08/26) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 16 8.8 13 7.3 14 1 0.83 -1.6 1.6 -0.21 0.22 0.47Subregional Peak: 8.9 15 2 1.01 (at cell 55 x 49)0069 Burbank 5 8.0 15 7.3 14 -1 0.92 -0.2 0.2 -0.69 0.69 0.620088 Pasadena 6 8.2 14 6.6 14 0 0.80 -0.3 0.3 -0.57 0.58 0.880074 Reseda 3 8.8 13 6.2 11 -2 0.71 -0.2 0.2 -1.15 1.15 -0.980090 Santa Clarita 2 7.0 12 6.1 13 1 0.88 -0.1 0.1 -1.72 1.73 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 238 (08/26) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 77 10.4 15 11.0 15 0 1.06 -1.8 2.3 -0.23 0.30 0.21Subregional Peak: 11.9 15 0 1.15 (at cell 78 x 39)0060 Azusa 5 7.9 14 7.1 15 1 0.89 -0.2 0.2 -3.50 4.55 0.094164 Banning Airport 9 9.4 16 11.0 15 -1 1.17 0.0 0.3 -1.94 2.53 0.235181 Crestline 10 8.1 16 6.7 13 -3 0.82 -0.3 0.3 -1.75 2.27 0.014158 Elsinore 7 10.0 14 7.8 12 -2 0.78 -0.1 0.2 -2.50 3.25 -0.045197 Fontana 5 9.1 16 5.9 16 0 0.65 -0.3 0.3 -3.50 4.55 0.340591 Glendora 7 9.1 14 7.5 15 1 0.82 -0.3 0.3 -2.50 3.25 0.595212 Mira Loma 6 10.3 14 5.9 16 2 0.58 -0.4 0.4 -2.91 3.79 0.164149 Perris 1 7.1 14 8.3 14 0 1.17 0.2 0.2 -17.49 22.73 -99.000075 Pomona 5 9.6 15 7.6 15 0 0.79 -0.2 0.2 -3.50 4.55 0.835204 Redlands 5 9.0 16 9.3 14 -2 1.03 -0.3 0.3 -3.50 4.55 0.784144 Rubidoux 6 10.4 15 6.4 13 -2 0.62 -0.2 0.2 -2.91 3.79 -0.235203 San Bernardino 6 8.9 15 7.3 13 -2 0.82 -0.3 0.4 -2.91 3.79 0.145175 Upland 5 9.6 16 7.0 16 0 0.73 -0.3 0.3 -3.50 4.55 0.97* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 238 (08/26) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 36 9.4 15 7.0 14 -1 0.74 -2.8 2.8 -0.39 0.39 0.35Subregional Peak: 8.5 16 1 0.90 (at cell 68 x 37)3176 Anaheim 7 8.2 15 5.0 14 -1 0.61 -0.5 0.5 -2.03 2.03 0.630087 Los Angeles 4 8.0 16 4.9 14 -2 0.62 -0.6 0.6 -3.55 3.55 0.563195 Costa Mesa 3 7.6 16 6.0 14 -2 0.79 -0.3 0.3 -4.74 4.74 -0.983177 La Habra 2 6.8 15 6.0 15 0 0.88 -0.3 0.3 -7.11 7.11 -99.000820 LAXH 2 6.9 15 4.7 15 0 0.68 -0.3 0.3 -7.11 7.11 -99.000072 Long Beach 3 6.8 14 5.0 13 -1 0.74 -0.3 0.3 -4.74 4.74 0.320084 Lynwood 2 6.9 14 5.4 13 -1 0.78 -0.2 0.2 -7.11 7.11 -99.003812 Mission Viejo 7 8.3 16 7.0 14 -2 0.84 -0.3 0.3 -2.03 2.03 0.720091 West Los Angeles 6 9.4 15 5.5 13 -2 0.58 -0.4 0.4 -2.37 2.37 0.25* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 238 (08/26) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 12 10.2 17 6.5 16 -1 0.63 -2.4 2.4 -0.30 0.30 0.30Subregional Peak: 10.8 15 -2 1.05 (at cell 81 x 43)4157 Indio Jackson 2 7.1 19 4.0 19 0 0.57 -0.5 0.5 -1.80 1.80 -99.004137 Palm Springs 10 10.2 17 6.5 16 -1 0.63 -0.3 0.3 -0.36 0.36 0.12* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 239 (08/27) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 21 12.2 13 9.1 14 1 0.74 -2.3 2.7 -0.26 0.32 0.40Subregional Peak: 10.6 14 1 0.87 (at cell 52 x 49)0069 Burbank 6 10.3 12 9.1 14 2 0.88 -0.1 0.2 -0.93 1.12 0.660088 Pasadena 6 10.4 12 8.2 14 2 0.79 -0.2 0.3 -0.93 1.12 0.070074 Reseda 4 12.2 13 7.4 13 0 0.61 -0.4 0.4 -1.39 1.68 0.890090 Santa Clarita 5 9.3 11 4.9 11 0 0.52 -0.4 0.4 -1.11 1.34 0.82* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 239 (08/27) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 99 18.2 16 10.3 15 -1 0.56 -3.2 3.5 -0.29 0.34 0.22Subregional Peak: 10.6 14 -2 0.58 (at cell 71 x 35)0060 Azusa 6 12.6 13 8.9 14 1 0.70 -0.2 0.3 -4.82 5.66 0.104164 Banning Airport 10 13.5 18 8.8 14 -4 0.66 -0.2 0.3 -2.89 3.40 -0.365181 Crestline 14 18.2 16 5.2 13 -3 0.29 -0.5 0.5 -2.06 2.43 0.614158 Elsinore 3 10.2 13 8.9 14 1 0.87 0.0 0.1 -9.63 11.32 -0.305197 Fontana 7 14.4 14 9.5 16 2 0.66 -0.3 0.3 -4.13 4.85 0.340591 Glendora 7 13.4 13 9.5 14 1 0.71 -0.1 0.3 -4.13 4.85 0.135212 Mira Loma 10 11.6 13 9.2 15 2 0.79 -0.3 0.3 -2.89 3.40 0.540075 Pomona 7 12.5 13 9.9 15 2 0.79 -0.3 0.4 -4.13 4.85 0.155204 Redlands 9 12.5 15 8.8 16 1 0.71 -0.3 0.3 -3.21 3.77 0.964144 Rubidoux 9 11.0 13 9.5 16 3 0.87 -0.3 0.3 -3.21 3.77 0.555203 San Bernardino 9 14.5 15 9.1 16 1 0.63 -0.4 0.4 -3.21 3.77 0.765175 Upland 8 14.5 14 10.3 15 1 0.71 -0.3 0.3 -3.61 4.25 0.48* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 239 (08/27) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 30 9.5 13 7.9 12 -1 0.83 -1.6 1.7 -0.21 0.23 0.29Subregional Peak: 10.1 14 1 1.06 (at cell 68 x 36)3176 Anaheim 4 8.4 14 6.8 13 -1 0.81 -0.3 0.3 -1.59 1.75 0.930087 Los Angeles 4 8.8 13 5.8 13 0 0.66 -0.3 0.3 -1.59 1.75 0.403195 Costa Mesa 5 6.9 13 6.2 13 0 0.89 -0.2 0.2 -1.27 1.40 0.443177 La Habra 3 7.8 14 7.4 14 0 0.94 -0.1 0.1 -2.12 2.33 1.000820 LAXH 1 6.1 12 5.3 12 0 0.88 -0.1 0.1 -6.35 6.98 -99.000072 Long Beach 4 7.7 14 5.6 13 -1 0.73 -0.2 0.2 -1.59 1.75 0.270084 Lynwood 3 7.6 13 6.2 13 0 0.82 -0.2 0.2 -2.12 2.33 0.323812 Mission Viejo 3 6.4 18 7.9 12 -6 1.23 -0.1 0.3 -2.12 2.33 -0.970091 West Los Angeles 3 9.5 13 6.1 12 -1 0.65 -0.3 0.3 -2.12 2.33 0.02* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 239 (08/27) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 29 11.4 21 6.5 15 -6 0.57 -3.1 3.1 -0.35 0.35 -0.59Subregional Peak: 9.2 15 -6 0.80 (at cell 84 x 45)4157 Indio Jackson 15 11.4 21 6.2 14 -7 0.55 -0.4 0.4 -0.67 0.67 -0.844137 Palm Springs 14 10.9 19 6.5 15 -4 0.60 -0.3 0.3 -0.72 0.72 -0.22* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 240 (08/28) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 34 14.2 13 9.6 13 0 0.67 -4.1 4.1 -0.42 0.42 0.87Subregional Peak: 11.9 14 1 0.84 (at cell 54 x 49)0069 Burbank 8 14.2 13 9.6 13 0 0.67 -0.3 0.3 -1.79 1.79 0.960088 Pasadena 10 13.6 14 9.2 14 0 0.67 -0.4 0.4 -1.43 1.43 0.950074 Reseda 10 13.8 13 7.1 12 -1 0.52 -0.5 0.5 -1.43 1.43 0.830090 Santa Clarita 6 12.0 12 5.5 12 0 0.46 -0.4 0.4 -2.38 2.38 0.67* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 240 (08/28) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 129 15.6 15 10.8 14 -1 0.69 -2.8 3.1 -0.28 0.33 0.57Subregional Peak: 11.5 14 -1 0.74 (at cell 75 x 46)0060 Azusa 9 13.8 14 9.2 14 0 0.67 -0.4 0.4 -3.99 4.71 0.974164 Banning Airport 12 10.0 19 10.2 16 -3 1.02 0.0 0.3 -2.99 3.53 -0.385181 Crestline 10 13.3 17 5.3 15 -2 0.40 -0.5 0.5 -3.59 4.24 0.694158 Elsinore 10 7.7 12 9.1 15 3 1.18 0.0 0.2 -3.59 4.24 0.105197 Fontana 10 14.1 14 10.2 16 2 0.73 -0.3 0.3 -3.59 4.24 0.860591 Glendora 9 15.6 15 9.8 15 0 0.63 -0.4 0.4 -3.99 4.71 0.975212 Mira Loma 11 12.2 12 9.4 15 3 0.77 -0.3 0.3 -3.26 3.85 0.644149 Perris 4 7.8 17 8.9 17 0 1.14 -0.2 0.2 -8.97 10.60 0.610075 Pomona 9 12.4 12 10.5 15 3 0.85 -0.3 0.3 -3.99 4.71 0.835204 Redlands 12 11.0 16 10.8 14 -2 0.98 -0.1 0.2 -2.99 3.53 0.854144 Rubidoux 11 12.0 13 10.2 16 3 0.85 -0.3 0.3 -3.26 3.85 0.845203 San Bernardino 12 13.3 15 9.9 13 -2 0.74 -0.3 0.3 -2.99 3.53 0.955175 Upland 10 14.9 14 10.6 15 1 0.71 -0.5 0.5 -3.59 4.24 0.95* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 240 (08/28) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 36 11.4 13 8.1 14 1 0.71 -2.0 2.1 -0.24 0.26 0.12Subregional Peak: 9.7 15 2 0.85 (at cell 68 x 36)3176 Anaheim 6 7.6 15 6.6 13 -2 0.87 -0.2 0.2 -1.44 1.58 0.210087 Los Angeles 7 11.4 13 5.7 13 0 0.50 -0.4 0.4 -1.23 1.35 0.803195 Costa Mesa 2 6.9 13 6.3 13 0 0.91 -0.1 0.1 -4.32 4.73 -99.003177 La Habra 3 7.7 15 8.0 14 -1 1.04 -0.1 0.3 -2.88 3.16 -0.060820 LAXH 2 6.7 12 5.2 12 0 0.77 -0.2 0.2 -4.32 4.73 -99.000072 Long Beach 1 6.6 13 5.5 13 0 0.83 -0.2 0.2 -8.64 9.47 -99.000084 Lynwood 2 7.2 13 6.1 12 -1 0.84 -0.2 0.2 -4.32 4.73 -99.003812 Mission Viejo 8 8.8 12 8.1 14 2 0.92 -0.1 0.1 -1.08 1.18 0.740091 West Los Angeles 5 10.9 14 6.1 12 -2 0.56 -0.4 0.4 -1.73 1.89 0.04* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 240 (08/28) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 19 9.1 10 6.6 12 2 0.73 -2.4 2.4 -0.32 0.32 0.32Subregional Peak: 10.4 15 5 1.14 (at cell 81 x 43)4157 Indio Jackson 11 8.3 10 6.6 12 2 0.80 -0.3 0.3 -0.56 0.56 0.354137 Palm Springs 8 9.1 10 5.9 14 4 0.65 -0.4 0.4 -0.77 0.77 0.50* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 241 (08/29) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 30 15.2 13 7.1 14 1 0.47 -3.8 3.8 -0.42 0.42 0.65Subregional Peak: 8.8 14 1 0.58 (at cell 52 x 51)0069 Burbank 6 8.5 14 6.1 13 -1 0.72 -0.3 0.3 -2.12 2.12 0.960088 Pasadena 6 7.5 14 5.2 14 0 0.69 -0.4 0.4 -2.12 2.12 0.690074 Reseda 9 11.6 13 6.8 13 0 0.59 -0.5 0.5 -1.41 1.41 0.770090 Santa Clarita 9 15.2 13 7.1 14 1 0.47 -0.5 0.5 -1.41 1.41 0.65* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 241 (08/29) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 89 10.8 16 10.4 14 -2 0.96 -2.2 2.8 -0.25 0.34 -0.10Subregional Peak: 11.3 15 -1 1.05 (at cell 76 x 47)0060 Azusa 5 8.2 14 5.7 15 1 0.69 -0.4 0.4 -4.44 6.00 0.184164 Banning Airport 8 8.0 15 10.4 14 -1 1.30 0.0 0.4 -2.77 3.75 0.435181 Crestline 12 10.8 16 8.4 13 -3 0.78 -0.3 0.3 -1.85 2.50 -0.154158 Elsinore 9 7.9 14 9.7 13 -1 1.23 0.1 0.2 -2.47 3.33 0.715197 Fontana 6 10.6 14 5.0 13 -1 0.47 -0.4 0.4 -3.70 5.00 -0.480591 Glendora 6 9.5 14 6.1 15 1 0.65 -0.4 0.4 -3.70 5.00 0.335212 Mira Loma 9 10.6 11 6.3 14 3 0.60 -0.4 0.4 -2.47 3.33 0.434149 Perris 1 6.2 13 8.8 13 0 1.42 0.4 0.4 -22.19 30.01 -99.000075 Pomona 5 9.7 13 5.8 15 2 0.60 -0.4 0.4 -4.44 6.00 -0.275204 Redlands 7 8.5 13 9.6 13 0 1.13 -0.1 0.3 -3.17 4.29 0.354144 Rubidoux 8 9.8 12 6.5 15 3 0.66 -0.3 0.3 -2.77 3.75 0.615203 San Bernardino 7 9.5 12 8.3 13 1 0.87 -0.2 0.2 -3.17 4.29 -0.045175 Upland 6 10.5 14 5.5 16 2 0.52 -0.4 0.4 -3.70 5.00 0.46* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 241 (08/29) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 13 7.2 15 7.3 12 -3 1.01 -1.5 1.7 -0.22 0.25 0.29Subregional Peak: 8.8 14 -1 1.22 (at cell 68 x 36)3176 Anaheim 2 6.7 16 5.1 14 -2 0.76 -0.3 0.3 -1.45 1.62 -99.000087 Los Angeles 3 6.7 16 3.9 15 -1 0.59 -0.5 0.5 -0.96 1.08 0.003812 Mission Viejo 5 6.9 11 7.3 12 1 1.06 0.0 0.0 -0.58 0.65 0.570091 West Los Angeles 3 7.2 15 5.2 14 -1 0.72 -0.3 0.3 -0.96 1.08 -0.78* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 241 (08/29) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 14 8.6 16 7.7 13 -3 0.90 -0.9 1.4 -0.13 0.21 0.20Subregional Peak: 11.2 11 -5 1.30 (at cell 113 x 12)4157 Indio Jackson 10 6.9 16 7.7 13 -3 1.12 -0.1 0.2 -0.18 0.30 0.124137 Palm Springs 4 8.6 16 6.6 14 -2 0.76 -0.1 0.1 -0.45 0.75 0.54* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubRegional DescriptionsSubRegion 003 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0069 Burbank 53 48 -27.848 461.3080088 Pasadena 56 47 -11.205 457.0210074 Reseda 49 48 -48.000 463.1050090 Santa Clarita 49 52 -48.140 483.357SubRegion 004 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0060 Azusa 60 47 6.981 456.1134164 Banning Airport 79 42 104.459 433.5275181 Crestline 72 49 66.383 468.6064158 Elsinore 71 37 60.525 405.9075197 Fontana 68 46 46.811 453.0810591 Glendora 61 47 13.487 457.0105212 Mira Loma 67 43 42.938 438.9154149 Perris 72 39 69.051 417.3760075 Pomona 63 45 22.598 448.6105204 Redlands 74 45 76.256 448.1894144 Rubidoux 69 44 52.093 442.5575203 San Bernardino 72 46 65.874 453.2995175 Upland 65 46 31.687 452.125SubRegion 005 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------3176 Anaheim 60 40 7.422 421.6450087 Los Angeles 54 45 -22.302 445.5633195 Costa Mesa 60 37 6.793 405.6263177 La Habra 59 42 4.359 432.9780820 LAXH 51 42 -36.352 433.6850072 Long Beach 55 40 -17.171 421.9030084 Lynwood 55 42 -19.237 432.7533812 Mission Viejo 64 36 29.671 400.7910085 Pico Rivera 57 44 -5.273 442.8600091 West Los Angeles 52 45 -34.796 447.031SubRegion 009 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------4157 Indio Jackson 91 38 162.217 411.2934137 Palm Springs 85 40 132.826 423.133* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 237 (08/25) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 1 6.1 10 6.3 9 -1 1.03 0.2 0.2 0.03 0.03 -99.00Subregional Peak: 7.3 10 0 1.18 (at 48 x 47) NSte: 0074; NSPk: 7.1* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 237 (08/25) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 2 6.3 13 6.2 9 -4 0.98 -0.2 0.2 -0.03 0.03 -99.00Subregional Peak: 7.2 10 -3 1.14 (at 72 x 32) NSte: 4158; NSPk: 5.7* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 237 (08/25) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 4 8.3 10 5.1 10 0 0.61 -2.4 2.4 -0.34 0.34 -74.00Subregional Peak: 6.8 9 -1 0.82 (at 68 x 36) NSte: 3812; NSPk: 4.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 238 (08/26) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 2 6.9 11 4.8 10 -1 0.70 -1.9 1.9 -0.29 0.29 -99.00Subregional Peak: 6.8 10 -1 0.99 (at 54 x 50) NSte: 0069; NSPk: 5.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 238 (08/26) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 12 8.0 11 8.8 10 -1 1.10 -1.4 1.6 -0.19 0.22 -77.67Subregional Peak: 10.1 10 -1 1.26 (at 78 x 38) NSte: 4164; NSPk: 8.9* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 238 (08/26) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 5 7.4 12 5.7 10 -2 0.77 -2.3 2.3 -0.34 0.34 -66.08Subregional Peak: 8.0 11 -1 1.08 (at 68 x 37) NSte: 3812; NSPk: 5.5* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 238 (08/26) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 1 8.0 12 5.8 10 -2 0.72 -2.3 2.3 -0.28 0.28 -99.00Subregional Peak: 8.8 10 -2 1.10 (at 81 x 43) NSte: 4137; NSPk: 5.8* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 239 (08/27) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 7.7 10 7.0 10 0 0.92 -1.2 1.2 -0.17 0.17 -120.12Subregional Peak: 7.8 10 0 1.01 (at 54 x 49) NSte: 0069; NSPk: 7.3* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 239 (08/27) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 12 13.0 11 8.2 11 0 0.63 -2.9 3.1 -0.27 0.31 -52.11Subregional Peak: 9.1 11 0 0.70 (at 70 x 35) NSte: 4158; NSPk: 7.8* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 239 (08/27) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 3 6.6 10 5.2 10 0 0.79 -1.6 1.6 -0.25 0.25-4764.13Subregional Peak: 8.6 11 1 1.30 (at 68 x 37) NSte: 3812; NSPk: 6.6* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 239 (08/27) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 9.4 15 5.9 10 -5 0.63 -3.5 3.5 -0.38 0.38 -99.00Subregional Peak: 7.7 10 -5 0.82 (at 82 x 44) NSte: 4137; NSPk: 5.8* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 240 (08/28) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 11.3 10 7.3 10 0 0.65 -3.7 3.7 -0.36 0.36 -69.61Subregional Peak: 8.3 10 0 0.74 (at 54 x 49) NSte: 0069; NSPk: 7.6* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 240 (08/28) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 12 12.1 11 9.2 11 0 0.76 -2.6 3.1 -0.22 0.29 -42.70Subregional Peak: 9.4 10 -1 0.77 (at 76 x 44) NSte: 5204; NSPk: 9.3* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 240 (08/28) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 4 8.5 10 6.6 10 0 0.78 -2.2 2.2 -0.29 0.29 -61.42Subregional Peak: 8.6 10 0 1.01 (at 68 x 36) NSte: 3812; NSPk: 6.3* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 240 (08/28) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 7.7 8 5.8 10 2 0.76 -1.7 1.7 -0.22 0.22 -99.00Subregional Peak: 8.4 11 3 1.10 (at 81 x 43) NSte: 4137; NSPk: 5.7* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 241 (08/29) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 10.9 10 6.1 10 0 0.56 -3.5 3.5 -0.40 0.40 -27.48Subregional Peak: 6.4 10 0 0.59 (at 49 x 51) NSte: 0090; NSPk: 6.1* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 241 (08/29) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 12 9.2 22 8.8 10 -12 0.95 -1.9 2.3 -0.23 0.30 -36.93Subregional Peak: 9.3 11 -11 1.01 (at 79 x 38) NSte: 4164; NSPk: 8.8* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 241 (08/29) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 1 6.2 10 6.2 9 -1 0.99 0.0 0.0 -0.01 0.01 -99.00Subregional Peak: 7.5 10 0 1.21 (at 68 x 37) NSte: 3812; NSPk: 6.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 241 (08/29) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 6.4 11 7.0 11 0 1.09 0.2 0.4 0.04 0.06 -99.00Subregional Peak: 9.0 11 0 1.41 (at 82 x 44) NSte: 4137; NSPk: 6.3* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0000 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 237 through 241Unpaired Subregional Maximum of 9.5 at Cell 83 x 10 -- Nearest Site: 4157- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 237 238 239 240 241 Avg. 237 238 239 240 241 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0001 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 237 through 241Unpaired Subregional Maximum of -99.0 at Cell -9 x -9 -- Nearest Site: 0820- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 237 238 239 240 241 Avg. 237 238 239 240 241 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0002 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 237 through 241Unpaired Subregional Maximum of 6.4 at Cell 42 x 53 -- Nearest Site: 0090- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 237 238 239 240 241 Avg. 237 238 239 240 241 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0003 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 237 through 241Unpaired Subregional Maximum of 7.8 at Cell 54 x 48 -- Nearest Site: 0069- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 237 238 239 240 241 Avg. 237 238 239 240 241 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----0069 Burbank 7.9 4.3 6.4 7.3 10.8 7.1 6.0 5.8 4.8 7.0 7.3 4.8 0.68 -0.23 0.230088 Pasadena 7.7 5.6 6.9 7.3 10.2 6.5 5.2 4.8 4.6 6.2 6.5 3.9 0.63 -0.31 0.310074 Reseda 9.7 4.7 5.3 7.7 11.3 10.1 6.2 7.0 5.8 6.1 6.3 5.7 0.62 -0.36 0.360090 Santa Clarita 7.8 6.1 4.9 6.6 7.8 10.9 5.6 6.3 5.8 4.6 5.4 6.1 0.58 -0.25 0.27* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0004 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 237 through 241Unpaired Subregional Maximum of 9.2 at Cell 77 x 48 -- Nearest Site: 5204- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 237 238 239 240 241 Avg. 237 238 239 240 241 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----0060 Azusa 7.8 4.8 6.5 7.7 10.6 6.4 5.2 4.8 4.6 6.1 6.4 4.2 0.61 -0.31 0.314164 Banning Airport 7.7 6.3 7.6 10.6 7.0 7.0 8.0 6.0 8.8 7.8 8.6 8.8 0.84 0.07 0.205181 Crestline 10.2 5.3 7.4 13.0 11.0 9.2 5.7 5.9 5.8 5.0 4.9 6.7 0.51 -0.42 0.424158 Elsinore 7.1 5.2 7.4 6.7 6.9 7.4 7.5 5.6 7.0 8.2 8.1 8.4 1.13 0.12 0.155197 Fontana 9.1 4.8 6.8 9.9 11.9 7.8 5.8 5.1 4.9 6.5 8.2 4.5 0.69 -0.34 0.340591 Glendora 8.8 4.9 7.3 8.4 11.8 7.6 5.8 5.2 5.0 6.7 7.2 4.7 0.62 -0.32 0.325212 Mira Loma 8.7 6.3 8.0 10.0 10.6 8.7 6.2 6.2 5.2 6.8 7.4 5.3 0.70 -0.28 0.280075 Pomona 8.3 4.9 7.0 8.8 10.6 7.0 5.6 4.7 5.1 6.4 7.4 4.3 0.70 -0.31 0.315204 Redlands 8.8 5.3 6.8 10.7 10.0 7.6 7.4 5.5 7.1 7.5 9.2 7.6 0.86 -0.08 0.104144 Rubidoux 9.2 5.9 8.0 9.6 10.6 8.7 6.8 6.4 5.9 7.3 8.5 6.0 0.80 -0.25 0.255203 San Bernardino 9.5 5.3 6.9 11.2 11.4 8.3 6.8 5.5 5.9 7.0 9.0 6.4 0.79 -0.24 0.245175 Upland 9.0 4.8 7.0 9.3 12.1 7.7 5.7 5.0 5.0 6.5 7.7 4.3 0.64 -0.35 0.35* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0005 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 237 through 241Unpaired Subregional Maximum of 8.2 at Cell 69 x 37 -- Nearest Site: 4158- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 237 238 239 240 241 Avg. 237 238 239 240 241 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----3176 Anaheim 6.6 5.9 6.9 6.5 6.3 5.8 4.1 3.4 3.4 4.9 4.6 4.0 0.71 -0.34 0.340087 Los Angeles 7.1 5.8 6.2 6.6 8.5 5.4 4.0 3.5 3.6 4.7 4.6 3.6 0.55 -0.39 0.393195 Costa Mesa 6.3 6.5 6.1 5.9 5.1 4.5 5.2 4.8 5.3 5.4 5.5 5.3 0.83 -0.20 0.200820 LAXH 7.1 7.1 5.6 5.0 5.2 4.3 4.8 5.1 4.7 5.0 4.7 4.4 0.71 -0.29 0.290072 Long Beach 6.0 6.0 5.2 5.4 4.7 4.3 4.1 3.7 3.9 4.5 4.5 3.6 0.75 -0.38 0.383812 Mission Viejo 7.1 5.2 7.4 5.5 7.6 6.2 5.9 4.2 5.7 6.8 6.6 6.2 0.90 -0.12 0.120091 West Los Angeles 7.3 8.3 7.1 6.5 7.5 5.4 4.8 4.7 4.3 5.2 5.2 4.4 0.63 -0.33 0.33* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0006 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 237 through 241Unpaired Subregional Maximum of 9.2 at Cell 82 x 11 -- Nearest Site: 4158- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 237 238 239 240 241 Avg. 237 238 239 240 241 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0007 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 237 through 241Unpaired Subregional Maximum of 6.0 at Cell 44 x 66 -- Nearest Site: 0090- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 237 238 239 240 241 Avg. 237 238 239 240 241 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0008 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 237 through 241Unpaired Subregional Maximum of -99.0 at Cell -9 x -9 -- Nearest Site: 0820- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 237 238 239 240 241 Avg. 237 238 239 240 241 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0009 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 237 through 241Unpaired Subregional Maximum of 8.4 at Cell 87 x 45 -- Nearest Site: 4137- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 237 238 239 240 241 Avg. 237 238 239 240 241 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----4157 Indio Jackson 7.7 3.6 5.7 9.4 7.2 6.4 6.1 5.9 6.1 5.9 5.8 7.0 0.74 -0.15 0.224137 Palm Springs 7.9 5.9 8.0 9.4 7.7 6.4 5.7 4.9 5.8 5.9 5.7 6.3 0.67 -0.23 0.23* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubRegional DescriptionsSubRegion 003 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0069 Burbank 53 48 -27.848 461.3080088 Pasadena 56 47 -11.205 457.0210074 Reseda 49 48 -48.000 463.1050090 Santa Clarita 49 52 -48.140 483.357SubRegion 004 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0060 Azusa 60 47 6.981 456.1134164 Banning Airport 79 42 104.459 433.5275181 Crestline 72 49 66.383 468.6064158 Elsinore 71 37 60.525 405.9075197 Fontana 68 46 46.811 453.0810591 Glendora 61 47 13.487 457.0105212 Mira Loma 67 43 42.938 438.9154149 Perris 72 39 69.051 417.3760075 Pomona 63 45 22.598 448.6105204 Redlands 74 45 76.256 448.1894144 Rubidoux 69 44 52.093 442.5575203 San Bernardino 72 46 65.874 453.2995175 Upland 65 46 31.687 452.125SubRegion 005 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------3176 Anaheim 60 40 7.422 421.6450087 Los Angeles 54 45 -22.302 445.5633195 Costa Mesa 60 37 6.793 405.6263177 La Habra 59 42 4.359 432.9780820 LAXH 51 42 -36.352 433.6850072 Long Beach 55 40 -17.171 421.9030084 Lynwood 55 42 -19.237 432.7533812 Mission Viejo 64 36 29.671 400.7910085 Pico Rivera 57 44 -5.273 442.8600091 West Los Angeles 52 45 -34.796 447.031SubRegion 009 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------4157 Indio Jackson 91 38 162.217 411.2934137 Palm Springs 85 40 132.826 423.133* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 195 (07/14) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 15 8.6 14 9.3 14 0 1.09 -0.3 1.3 -0.04 0.18 0.30Subregional Peak: 11.4 15 1 1.33 (at cell 51 x 52)0069 Burbank 1 6.1 15 6.0 15 0 0.98 0.0 0.0 -0.64 2.70 -99.000088 Pasadena 2 7.4 17 4.4 16 -1 0.60 -0.4 0.4 -0.32 1.35 -99.000074 Reseda 4 8.6 14 9.0 14 0 1.05 0.0 0.1 -0.16 0.68 0.400090 Santa Clarita 8 8.6 13 9.3 14 1 1.09 0.0 0.2 -0.08 0.34 0.28* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 195 (07/14) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 64 10.0 14 10.8 15 1 1.08 -0.6 1.5 -0.08 0.21 0.25Subregional Peak: 11.6 15 1 1.16 (at cell 70 x 36)4164 Banning Airport 10 8.5 14 7.9 14 0 0.93 -0.2 0.2 -0.52 1.37 0.635181 Crestline 6 10.0 14 8.3 15 1 0.83 -0.1 0.1 -0.87 2.28 0.594158 Elsinore 8 9.0 12 10.8 15 3 1.20 0.1 0.2 -0.65 1.71 0.105197 Fontana 4 7.7 14 6.1 14 0 0.79 -0.2 0.2 -1.31 3.42 0.720591 Glendora 1 6.6 14 5.2 14 0 0.79 -0.2 0.2 -5.24 13.67 -99.005212 Mira Loma 6 8.4 14 7.1 13 -1 0.85 -0.1 0.2 -0.87 2.28 0.304149 Perris 4 6.6 17 9.4 13 -4 1.42 0.3 0.3 -1.31 3.42 -0.640075 Pomona 2 7.0 14 4.5 13 -1 0.64 -0.4 0.4 -2.62 6.83 -99.005204 Redlands 6 8.7 13 9.3 15 2 1.07 0.0 0.3 -0.87 2.28 -0.354144 Rubidoux 8 8.4 13 8.7 13 0 1.04 -0.1 0.1 -0.65 1.71 0.945203 San Bernardino 6 8.5 12 8.9 15 3 1.05 -0.1 0.2 -0.87 2.28 -0.165175 Upland 3 8.4 14 5.0 13 -1 0.59 -0.4 0.4 -1.75 4.56 0.21* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 195 (07/14) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 2 7.7 16 4.4 16 0 0.57 -2.7 2.7 -0.37 0.37 -99.00Subregional Peak: 11.0 14 -2 1.43 (at cell 68 x 37)3176 Anaheim 1 6.2 16 4.4 16 0 0.71 -0.3 0.3 -0.75 0.75 -99.000087 Los Angeles 1 7.7 16 4.2 16 0 0.54 -0.5 0.5 -0.75 0.75 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 195 (07/14) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 5 8.9 14 6.8 13 -1 0.76 -1.7 1.7 -0.21 0.21 0.92Subregional Peak: 18.2 13 -1 2.04 (at cell 106 x 13)4137 Palm Springs 5 8.9 14 6.8 13 -1 0.76 -0.2 0.2 -0.21 0.21 0.92* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 196 (07/15) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 14 9.1 16 11.3 15 -1 1.24 0.8 1.1 0.09 0.14 0.75Subregional Peak: 12.7 16 0 1.40 (at cell 51 x 55)0088 Pasadena 1 6.6 15 5.6 15 0 0.85 -0.1 0.1 1.29 1.91 -99.000074 Reseda 6 8.2 15 9.0 14 -1 1.09 0.1 0.1 0.21 0.32 0.750090 Santa Clarita 7 9.1 16 11.3 15 -1 1.24 0.1 0.2 0.18 0.27 0.45* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 196 (07/15) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 88 15.8 14 13.9 14 0 0.88 0.3 1.8 0.07 0.20 0.44Subregional Peak: 15.4 15 1 0.98 (at cell 77 x 43)4164 Banning Airport 12 11.6 16 13.0 16 0 1.12 -0.1 0.1 0.53 1.48 0.865181 Crestline 13 15.8 14 11.5 14 0 0.73 -0.3 0.3 0.49 1.37 0.794158 Elsinore 9 9.1 15 10.8 16 1 1.18 0.2 0.2 0.71 1.97 0.465197 Fontana 8 9.2 12 10.8 12 0 1.17 0.0 0.2 0.79 2.22 0.840591 Glendora 1 6.4 16 5.4 16 0 0.85 -0.2 0.2 6.35 17.75 -99.005212 Mira Loma 7 8.1 16 9.7 11 -5 1.20 0.2 0.2 0.91 2.54 0.044149 Perris 5 8.4 16 11.4 15 -1 1.35 0.4 0.4 1.27 3.55 0.310075 Pomona 2 6.6 16 7.7 15 -1 1.17 0.1 0.1 3.17 8.88 -99.005204 Redlands 8 11.3 13 13.9 14 1 1.23 0.2 0.2 0.79 2.22 0.884144 Rubidoux 8 8.3 16 11.8 12 -4 1.42 0.3 0.3 0.79 2.22 0.645203 San Bernardino 8 11.9 13 12.7 14 1 1.07 0.0 0.1 0.79 2.22 0.935175 Upland 7 7.5 13 8.5 11 -2 1.13 0.0 0.1 0.91 2.54 0.79* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 196 (07/15) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 6 6.8 18 5.3 15 -3 0.78 -2.1 2.1 -0.33 0.33 -0.37Subregional Peak: 11.5 14 -4 1.68 (at cell 68 x 37)0820 LAXH 6 6.8 18 5.3 15 -3 0.78 -0.3 0.3 -0.33 0.33 -0.37* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 196 (07/15) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 25 11.2 16 9.7 16 0 0.86 -0.8 1.9 -0.08 0.23 0.14Subregional Peak: 16.0 13 -3 1.43 (at cell 105 x 14)4157 Indio Jackson 12 8.9 19 9.7 16 -3 1.08 0.1 0.2 -0.17 0.48 0.354137 Palm Springs 13 11.2 16 8.6 12 -4 0.77 -0.2 0.2 -0.16 0.45 0.71* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 197 (07/16) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 32 17.3 14 12.4 14 0 0.72 -0.7 1.8 -0.03 0.17 0.72Subregional Peak: 14.7 15 1 0.85 (at cell 51 x 54)0069 Burbank 7 9.0 12 10.7 14 2 1.19 0.1 0.1 -0.13 0.76 0.740088 Pasadena 6 8.7 13 10.5 15 2 1.21 0.1 0.2 -0.15 0.89 0.420074 Reseda 9 10.7 15 10.1 15 0 0.94 0.0 0.1 -0.10 0.59 0.890090 Santa Clarita 10 17.3 14 12.4 14 0 0.72 -0.2 0.2 -0.09 0.53 0.91* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 197 (07/16) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 119 16.6 15 14.1 15 0 0.85 -0.6 1.9 -0.03 0.20 0.55Subregional Peak: 15.6 14 -1 0.94 (at cell 76 x 41)0060 Azusa 5 9.9 14 9.1 16 2 0.92 0.0 0.2 -0.70 4.75 -0.534164 Banning Airport 15 13.4 17 14.1 15 -2 1.05 -0.1 0.1 -0.23 1.58 0.895181 Crestline 14 16.6 15 10.7 15 0 0.65 -0.3 0.3 -0.25 1.70 0.864158 Elsinore 11 10.4 17 13.8 15 -2 1.33 0.4 0.4 -0.32 2.16 0.255197 Fontana 8 13.5 16 10.4 16 0 0.77 -0.2 0.2 -0.44 2.97 0.970591 Glendora 6 11.2 14 8.9 16 2 0.79 -0.1 0.1 -0.59 3.96 0.545212 Mira Loma 9 12.2 14 11.1 15 1 0.91 0.0 0.2 -0.39 2.64 0.624149 Perris 10 12.6 14 13.2 15 1 1.05 0.2 0.2 -0.35 2.37 0.900075 Pomona 7 10.8 14 9.9 15 1 0.91 0.0 0.1 -0.50 3.39 0.595204 Redlands 9 14.6 15 13.2 15 0 0.91 0.0 0.1 -0.39 2.64 0.974144 Rubidoux 8 13.0 14 11.6 15 1 0.89 0.0 0.2 -0.44 2.97 0.585203 San Bernardino 9 15.6 15 12.4 14 -1 0.80 -0.2 0.2 -0.39 2.64 0.935175 Upland 8 12.7 15 9.8 15 0 0.77 -0.2 0.2 -0.44 2.97 0.92* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 197 (07/16) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 4 6.6 14 7.9 13 -1 1.20 0.9 0.9 0.15 0.15 -0.13Subregional Peak: 13.3 15 1 2.01 (at cell 68 x 37)0087 Los Angeles 1 6.4 12 7.5 12 0 1.18 0.2 0.2 0.59 0.59 -99.000091 West Los Angeles 3 6.6 14 7.9 13 -1 1.20 0.1 0.1 0.20 0.20 -0.10* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 197 (07/16) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 27 13.9 18 8.8 14 -4 0.64 -3.0 3.2 -0.28 0.31 -0.05Subregional Peak: 17.8 14 -4 1.28 (at cell 107 x 13)4157 Indio Jackson 11 10.6 20 8.8 14 -6 0.83 -0.2 0.2 -0.70 0.77 -0.694137 Palm Springs 16 13.9 18 8.3 12 -6 0.60 -0.4 0.4 -0.48 0.53 0.41* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 198 (07/17) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 33 16.5 14 12.2 13 -1 0.74 -1.9 2.5 -0.16 0.23 0.54Subregional Peak: 15.7 14 0 0.95 (at cell 52 x 52)0069 Burbank 5 11.0 13 12.2 13 0 1.11 0.1 0.1 -1.07 1.54 0.930088 Pasadena 6 11.0 13 11.2 14 1 1.02 0.0 0.1 -0.89 1.29 0.850074 Reseda 9 12.9 12 11.1 13 1 0.86 -0.2 0.2 -0.59 0.86 0.820090 Santa Clarita 13 16.5 14 11.6 13 -1 0.70 -0.4 0.4 -0.41 0.59 0.89* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 198 (07/17) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 137 16.3 15 15.0 15 0 0.92 0.0 1.5 0.02 0.16 0.71Subregional Peak: 15.6 15 0 0.96 (at cell 72 x 47)0060 Azusa 7 12.3 13 12.1 14 1 0.98 0.1 0.2 0.38 3.10 0.864164 Banning Airport 17 14.2 13 13.2 15 2 0.93 -0.2 0.3 0.16 1.28 0.785181 Crestline 13 14.5 15 11.6 16 1 0.80 -0.2 0.2 0.20 1.67 0.934158 Elsinore 12 13.9 15 12.9 14 -1 0.93 0.0 0.1 0.22 1.81 0.875197 Fontana 10 14.5 15 13.1 14 -1 0.90 0.1 0.2 0.26 2.17 0.860591 Glendora 8 13.7 14 13.0 15 1 0.95 0.1 0.1 0.33 2.72 0.925212 Mira Loma 9 13.5 14 11.4 16 2 0.84 0.0 0.2 0.29 2.41 0.544149 Perris 14 10.1 16 12.1 14 -2 1.20 0.2 0.2 0.19 1.55 0.900075 Pomona 7 13.2 13 12.5 16 3 0.95 0.1 0.1 0.38 3.10 0.825204 Redlands 11 13.9 15 15.0 15 0 1.08 0.1 0.1 0.24 1.97 0.954144 Rubidoux 9 13.4 15 12.2 13 -2 0.91 0.1 0.2 0.29 2.41 0.625203 San Bernardino 11 16.3 15 14.5 14 -1 0.89 0.0 0.1 0.24 1.97 0.905175 Upland 9 13.9 15 13.8 14 -1 0.99 0.1 0.1 0.29 2.41 0.97* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 198 (07/17) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 19 8.9 13 9.9 15 2 1.12 1.1 1.4 0.17 0.21 0.17Subregional Peak: 12.5 14 1 1.41 (at cell 68 x 37)3176 Anaheim 5 6.7 11 9.9 13 2 1.47 0.2 0.3 0.63 0.81 -0.560087 Los Angeles 2 7.8 13 8.4 12 -1 1.07 0.0 0.0 1.58 2.02 -99.003177 La Habra 4 8.2 12 9.9 15 3 1.21 0.2 0.2 0.79 1.01 -0.690084 Lynwood 1 6.1 12 8.5 12 0 1.40 0.4 0.4 3.16 4.04 -99.003812 Mission Viejo 7 8.9 13 9.7 12 -1 1.08 0.2 0.2 0.45 0.58 0.77* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 198 (07/17) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 42 10.9 10 7.9 17 7 0.73 -3.0 3.0 -0.36 0.36 0.38Subregional Peak: 12.8 15 5 1.17 (at cell 81 x 43)4157 Indio Jackson 20 10.9 10 7.9 17 7 0.73 -0.3 0.3 -0.76 0.76 0.394137 Palm Springs 22 10.7 9 6.1 17 8 0.57 -0.4 0.4 -0.69 0.69 0.48* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 199 (07/18) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 32 17.1 14 8.9 13 -1 0.52 -3.4 3.4 -0.36 0.36 0.57Subregional Peak: 11.9 13 -1 0.70 (at cell 59 x 50)0069 Burbank 5 7.6 16 6.6 13 -3 0.87 -0.1 0.1 -2.30 2.30 -0.640088 Pasadena 5 7.7 12 6.5 14 2 0.84 -0.2 0.2 -2.30 2.30 -0.970074 Reseda 12 11.5 14 8.1 13 -1 0.71 -0.5 0.5 -0.96 0.96 0.820090 Santa Clarita 10 17.1 14 8.9 13 -1 0.52 -0.4 0.4 -1.15 1.15 0.76* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 199 (07/18) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 118 14.4 16 14.0 15 -1 0.97 -0.8 1.6 -0.08 0.18 0.68Subregional Peak: 15.2 15 -1 1.06 (at cell 76 x 46)0060 Azusa 5 7.5 13 7.0 14 1 0.93 0.0 0.1 -1.95 4.15 -0.054164 Banning Airport 18 14.4 16 13.2 16 0 0.91 -0.2 0.2 -0.54 1.15 0.885181 Crestline 10 12.3 13 12.8 15 2 1.04 0.1 0.1 -0.97 2.08 0.934158 Elsinore 11 12.9 15 12.6 14 -1 0.97 -0.1 0.1 -0.89 1.89 0.795197 Fontana 7 11.9 15 10.0 13 -2 0.84 -0.1 0.2 -1.39 2.97 0.330591 Glendora 6 8.4 13 7.8 14 1 0.93 0.0 0.1 -1.62 3.46 0.765212 Mira Loma 9 11.3 15 9.6 13 -2 0.85 -0.2 0.3 -1.08 2.31 0.154149 Perris 12 10.4 15 11.6 14 -1 1.11 0.0 0.2 -0.81 1.73 0.500075 Pomona 5 9.1 14 8.4 14 0 0.93 -0.1 0.1 -1.95 4.15 0.985204 Redlands 10 12.7 14 14.0 15 1 1.10 -0.1 0.2 -0.97 2.08 0.844144 Rubidoux 9 11.9 15 11.9 13 -2 1.00 -0.1 0.3 -1.08 2.31 0.165203 San Bernardino 10 11.9 16 13.4 14 -2 1.12 -0.1 0.2 -0.97 2.08 0.715175 Upland 6 11.3 14 9.0 13 -1 0.80 -0.1 0.1 -1.62 3.46 0.83* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 199 (07/18) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 35 11.7 16 9.4 13 -3 0.80 -2.2 2.8 -0.25 0.34 0.22Subregional Peak: 15.5 13 -3 1.32 (at cell 105 x 13)4157 Indio Jackson 17 8.6 9 9.4 13 4 1.09 -0.1 0.3 -0.51 0.69 -0.014137 Palm Springs 18 11.7 16 7.3 12 -4 0.62 -0.4 0.4 -0.48 0.65 0.90* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 200 (07/19) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 23 12.4 14 11.3 14 0 0.91 -1.1 1.4 -0.11 0.16 0.66Subregional Peak: 13.4 15 1 1.08 (at cell 52 x 56)0069 Burbank 2 6.4 15 7.3 14 -1 1.14 0.1 0.1 -1.31 1.85 -99.000088 Pasadena 3 7.6 15 6.7 15 0 0.89 -0.1 0.1 -0.87 1.23 1.000074 Reseda 7 10.1 16 8.4 13 -3 0.84 -0.1 0.2 -0.37 0.53 -0.150090 Santa Clarita 11 12.4 14 11.3 14 0 0.91 -0.2 0.2 -0.24 0.34 0.74* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 200 (07/19) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 71 13.3 15 15.8 14 -1 1.18 1.2 1.9 0.15 0.23 0.71Subregional Peak: 17.3 15 0 1.30 (at cell 75 x 47)0060 Azusa 3 6.9 14 6.5 16 2 0.95 -0.1 0.1 3.57 5.52 -0.894164 Banning Airport 9 7.4 11 10.7 12 1 1.45 0.3 0.4 1.19 1.84 0.325181 Crestline 14 13.3 15 13.9 16 1 1.05 0.0 0.1 0.76 1.18 0.954158 Elsinore 3 6.8 9 9.0 10 1 1.32 0.3 0.3 3.57 5.52 0.325197 Fontana 5 9.4 15 8.7 15 0 0.93 -0.1 0.1 2.14 3.31 0.410591 Glendora 5 8.4 15 8.2 17 2 0.98 -0.1 0.2 2.14 3.31 -0.165212 Mira Loma 5 9.5 14 11.1 14 0 1.17 0.1 0.2 2.14 3.31 0.670075 Pomona 5 8.3 15 9.3 15 0 1.12 0.1 0.2 2.14 3.31 -0.015204 Redlands 8 11.4 15 15.8 14 -1 1.38 0.4 0.4 1.34 2.07 0.934144 Rubidoux 5 9.8 14 13.0 15 1 1.33 0.3 0.3 2.14 3.31 0.865203 San Bernardino 6 11.3 15 13.2 15 0 1.16 0.4 0.4 1.78 2.76 0.965175 Upland 3 9.0 15 8.1 15 0 0.90 -0.2 0.2 3.57 5.52 0.94* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 200 (07/19) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 6.4 16 8.3 17 1 1.30 1.7 1.7 0.27 0.27 -99.00Subregional Peak: 11.6 14 -2 1.81 (at cell 81 x 44)4137 Palm Springs 2 6.4 16 8.3 17 1 1.30 0.3 0.3 0.27 0.27 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubRegional DescriptionsSubRegion 003 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0069 Burbank 53 48 -27.848 461.3080088 Pasadena 56 47 -11.205 457.0210074 Reseda 49 48 -48.000 463.1050090 Santa Clarita 49 52 -48.140 483.357SubRegion 004 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0060 Azusa 60 47 6.981 456.1134164 Banning Airport 79 42 104.459 433.5275181 Crestline 72 49 66.383 468.6064158 Elsinore 71 37 60.525 405.9075197 Fontana 68 46 46.811 453.0810591 Glendora 61 47 13.487 457.0105212 Mira Loma 67 43 42.938 438.9154149 Perris 72 39 69.051 417.3760075 Pomona 63 45 22.598 448.6105204 Redlands 74 45 76.256 448.1894144 Rubidoux 69 44 52.093 442.5575203 San Bernardino 72 46 65.874 453.2995175 Upland 65 46 31.687 452.125SubRegion 005 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------3176 Anaheim 60 40 7.422 421.6450087 Los Angeles 54 45 -22.302 445.5633195 Costa Mesa 60 37 6.793 405.6263177 La Habra 59 42 4.359 432.9780820 LAXH 51 42 -36.352 433.6850072 Long Beach 55 40 -17.171 421.9030084 Lynwood 55 42 -19.237 432.7533812 Mission Viejo 64 36 29.671 400.7910085 Pico Rivera 57 44 -5.273 442.8600091 West Los Angeles 52 45 -34.796 447.031SubRegion 009 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------4157 Indio Jackson 91 38 162.217 411.2934137 Palm Springs 85 40 132.826 423.133* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 195 (07/14) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 2 7.0 10 7.0 10 0 1.00 0.4 0.4 0.06 0.06 -99.00Subregional Peak: 8.0 11 1 1.15 (at 54 x 53) NSte: 0090; NSPk: 7.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 195 (07/14) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 7 7.7 10 8.5 10 0 1.10 -0.5 0.7 -0.07 0.10 -215.59Subregional Peak: 9.1 10 0 1.18 (at 71 x 35) NSte: 4158; NSPk: 8.3* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 195 (07/14) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 1 6.9 10 6.0 10 0 0.87 -0.9 0.9 -0.13 0.13 -99.00Subregional Peak: 10.8 9 -1 1.57 (at 104 x 13) NSte: 4157; NSPk: 6.5* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 196 (07/15) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 2 7.9 11 9.1 11 0 1.15 0.8 0.8 0.11 0.11 -99.00Subregional Peak: 10.7 11 0 1.35 (at 51 x 55) NSte: 0090; NSPk: 9.1* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 196 (07/15) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 10 14.3 12 11.3 11 -1 0.79 0.7 1.7 0.13 0.20 -35.40Subregional Peak: 12.8 11 -1 0.90 (at 77 x 45) NSte: 5204; NSPk: 11.5* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 196 (07/15) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 1 6.3 14 5.2 11 -3 0.83 -1.1 1.1 -0.17 0.17 -99.00Subregional Peak: 10.1 10 -4 1.61 (at 68 x 37) NSte: 3812; NSPk: 6.4* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 196 (07/15) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 9.4 10 8.7 10 0 0.93 -0.3 1.7 -0.01 0.20 -99.00Subregional Peak: 11.8 10 0 1.25 (at 105 x 13) NSte: 4157; NSPk: 8.8* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 197 (07/16) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 14.1 11 10.5 11 0 0.75 -0.5 1.3 0.00 0.12 -30.92Subregional Peak: 12.4 12 1 0.88 (at 52 x 55) NSte: 0090; NSPk: 10.7* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 197 (07/16) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 13 13.8 12 12.2 11 -1 0.88 -0.4 1.4 -0.02 0.14 -31.16Subregional Peak: 13.4 11 -1 0.97 (at 75 x 41) NSte: 4149; NSPk: 12.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 197 (07/16) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 11.6 13 8.3 10 -3 0.72 -2.6 2.6 -0.24 0.24 -99.00Subregional Peak: 11.9 11 -2 1.02 (at 81 x 43) NSte: 4137; NSPk: 7.6* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 198 (07/17) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 14.2 10 9.4 9 -1 0.66 -1.3 2.0 -0.07 0.18 -95.50Subregional Peak: 12.4 11 1 0.87 (at 53 x 53) NSte: 0090; NSPk: 9.2* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 198 (07/17) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 13 13.2 11 13.1 11 0 0.99 0.2 0.9 0.02 0.08 -101.58Subregional Peak: 13.6 11 0 1.03 (at 76 x 45) NSte: 5204; NSPk: 13.3* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 198 (07/17) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 3 7.1 10 8.4 10 0 1.18 1.4 1.4 0.22 0.22 -238.21Subregional Peak: 10.6 10 0 1.49 (at 68 x 37) NSte: 3812; NSPk: 8.3* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 198 (07/17) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 9.4 7 7.5 11 4 0.79 -2.5 2.5 -0.27 0.27 -99.00Subregional Peak: 11.3 11 4 1.20 (at 81 x 43) NSte: 4137; NSPk: 5.9* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 199 (07/18) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 12.7 11 7.4 10 -1 0.58 -2.7 2.7 -0.27 0.27 -22.06Subregional Peak: 10.4 11 0 0.82 (at 59 x 50) NSte: 0088; NSPk: 5.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 199 (07/18) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 13 12.0 11 11.7 11 0 0.97 -0.4 0.7 -0.04 0.07 -22.57Subregional Peak: 13.2 12 1 1.10 (at 76 x 48) NSte: 5204; NSPk: 11.7* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 199 (07/18) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 10.6 10 8.4 11 1 0.79 -1.4 2.3 -0.12 0.24 -99.00Subregional Peak: 12.5 10 0 1.17 (at 105 x 13) NSte: 4157; NSPk: 8.5* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 200 (07/19) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 2 10.6 10 9.0 12 2 0.85 -1.2 1.2 -0.12 0.12 -99.00Subregional Peak: 10.3 12 2 0.97 (at 52 x 56) NSte: 0090; NSPk: 9.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 200 (07/19) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 11 11.0 12 12.6 11 -1 1.14 1.6 1.6 0.21 0.21 -24.58Subregional Peak: 13.2 11 -1 1.20 (at 74 x 47) NSte: 5204; NSPk: 12.6* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0000 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 195 through 200Unpaired Subregional Maximum of 9.6 at Cell 87 x 6 -- Nearest Site: 4157- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 195 196 197 198 199 Avg. 195 196 197 198 199 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0001 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 195 through 200Unpaired Subregional Maximum of 6.3 at Cell 32 x 69 -- Nearest Site: 0090- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 195 196 197 198 199 Avg. 195 196 197 198 199 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0002 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 195 through 200Unpaired Subregional Maximum of 8.9 at Cell 49 x 53 -- Nearest Site: 0090- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 195 196 197 198 199 Avg. 195 196 197 198 199 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0003 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 195 through 200Unpaired Subregional Maximum of 9.9 at Cell 52 x 55 -- Nearest Site: 0090- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 195 196 197 198 199 Avg. 195 196 197 198 199 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----0069 Burbank 7.2 4.4 4.5 7.6 7.5 6.4 6.7 5.2 6.0 8.3 8.8 5.6 1.16 0.04 0.130088 Pasadena 7.1 5.3 4.5 7.1 8.1 6.2 5.9 4.0 4.6 8.0 8.4 5.0 1.03 -0.01 0.120074 Reseda 8.5 6.1 6.8 8.9 10.9 9.9 7.8 6.9 7.2 9.2 9.4 6.5 0.86 -0.06 0.130090 Santa Clarita 11.1 7.0 7.9 14.1 14.2 12.7 8.7 7.0 9.1 10.5 9.1 7.4 0.74 -0.17 0.22* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0004 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 195 through 200Unpaired Subregional Maximum of 11.5 at Cell 76 x 46 -- Nearest Site: 5204- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 195 196 197 198 199 Avg. 195 196 197 198 199 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----0060 Azusa 7.2 4.9 4.6 7.0 8.6 6.0 6.3 4.1 4.8 7.6 9.8 5.8 1.14 0.06 0.094164 Banning Airport 10.2 7.2 10.3 12.0 13.2 12.0 10.4 7.1 10.2 12.1 11.7 11.7 0.91 0.05 0.105181 Crestline 11.5 7.3 14.3 13.8 12.5 10.2 9.6 6.6 9.3 8.9 10.0 10.9 0.81 -0.14 0.194158 Elsinore 8.9 7.7 7.7 8.4 11.9 11.7 10.1 8.5 9.6 12.2 11.5 10.4 1.02 0.17 0.225197 Fontana 9.2 5.8 8.0 10.8 11.4 8.7 8.2 4.8 8.5 8.8 11.6 8.1 1.01 -0.03 0.070591 Glendora 7.8 4.7 4.8 7.8 9.8 6.9 7.0 4.4 5.5 8.1 10.9 6.6 1.11 0.02 0.055212 Mira Loma 8.5 7.1 7.0 9.6 10.6 9.5 8.3 6.1 8.5 9.3 10.3 7.6 0.97 -0.01 0.134149 Perris 9.0 6.0 6.8 10.4 9.6 9.3 10.1 8.1 9.9 12.0 11.4 10.5 1.16 0.24 0.240075 Pomona 7.9 5.2 5.5 8.2 9.9 6.9 7.2 3.9 6.8 7.8 10.9 7.0 1.10 0.04 0.065204 Redlands 10.2 6.8 9.4 12.0 12.3 11.9 11.0 6.3 11.3 11.3 13.1 11.6 1.06 0.09 0.144144 Rubidoux 8.9 7.5 7.6 10.0 10.8 10.2 9.6 6.7 9.7 10.2 11.4 9.3 1.05 0.08 0.155203 San Bernardino 10.1 7.0 9.9 12.4 12.9 10.5 10.3 6.0 10.5 10.5 12.6 10.5 0.98 0.03 0.145175 Upland 8.4 5.8 6.8 9.6 11.2 8.1 7.5 4.2 7.4 7.9 11.6 7.2 1.04 -0.03 0.09* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0005 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 195 through 200Unpaired Subregional Maximum of 10.5 at Cell 69 x 37 -- Nearest Site: 4158- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 195 196 197 198 199 Avg. 195 196 197 198 199 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----3176 Anaheim 6.1 4.8 4.5 5.0 6.1 4.9 5.6 4.2 5.4 6.6 7.3 4.2 1.21 0.21 0.213177 La Habra 6.2 3.8 3.3 4.6 6.2 4.0 5.7 3.2 5.6 7.0 7.8 4.8 1.26 0.26 0.260820 LAXH 6.3 3.9 6.3 5.3 5.0 4.4 5.0 4.2 5.2 6.1 5.6 4.4 0.97 -0.17 0.173812 Mission Viejo 7.1 3.8 4.5 4.6 7.1 4.9 7.0 5.9 6.3 7.2 8.4 6.8 1.18 0.18 0.18* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0006 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 195 through 200Unpaired Subregional Maximum of 10.3 at Cell 70 x 36 -- Nearest Site: 4158- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 195 196 197 198 199 Avg. 195 196 197 198 199 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0007 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 195 through 200Unpaired Subregional Maximum of 6.7 at Cell 41 x 68 -- Nearest Site: 0090- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 195 196 197 198 199 Avg. 195 196 197 198 199 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0008 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 195 through 200Unpaired Subregional Maximum of 8.6 at Cell 73 x 58 -- Nearest Site: 5181- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 195 196 197 198 199 Avg. 195 196 197 198 199 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0009 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 195 through 200Unpaired Subregional Maximum of 10.6 at Cell 105 x 13 -- Nearest Site: 4157- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 195 196 197 198 199 Avg. 195 196 197 198 199 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----4157 Indio Jackson 8.3 5.2 7.4 9.5 8.9 7.5 7.9 6.6 8.7 8.3 7.5 8.4 0.91 0.00 0.154137 Palm Springs 9.6 6.9 9.4 11.6 9.4 10.6 7.0 6.0 7.4 7.6 5.9 6.8 0.70 -0.28 0.28* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubRegional DescriptionsSubRegion 003 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0069 Burbank 53 48 -27.848 461.3080088 Pasadena 56 47 -11.205 457.0210074 Reseda 49 48 -48.000 463.1050090 Santa Clarita 49 52 -48.140 483.357SubRegion 004 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0060 Azusa 60 47 6.981 456.1134164 Banning Airport 79 42 104.459 433.5275181 Crestline 72 49 66.383 468.6064158 Elsinore 71 37 60.525 405.9075197 Fontana 68 46 46.811 453.0810591 Glendora 61 47 13.487 457.0105212 Mira Loma 67 43 42.938 438.9154149 Perris 72 39 69.051 417.3760075 Pomona 63 45 22.598 448.6105204 Redlands 74 45 76.256 448.1894144 Rubidoux 69 44 52.093 442.5575203 San Bernardino 72 46 65.874 453.2995175 Upland 65 46 31.687 452.125SubRegion 005 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------3176 Anaheim 60 40 7.422 421.6450087 Los Angeles 54 45 -22.302 445.5633195 Costa Mesa 60 37 6.793 405.6263177 La Habra 59 42 4.359 432.9780820 LAXH 51 42 -36.352 433.6850072 Long Beach 55 40 -17.171 421.9030084 Lynwood 55 42 -19.237 432.7533812 Mission Viejo 64 36 29.671 400.7910085 Pico Rivera 57 44 -5.273 442.8600091 West Los Angeles 52 45 -34.796 447.031SubRegion 009 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------4157 Indio Jackson 91 38 162.217 411.2934137 Palm Springs 85 40 132.826 423.133* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 137 (05/17) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 2 6.2 15 5.0 15 0 0.81 -1.3 1.3 -0.22 0.22 -99.00Subregional Peak: 6.8 14 -1 1.10 (at cell 53 x 52)0090 Santa Clarita 2 6.2 15 5.0 15 0 0.81 -0.2 0.2 -0.22 0.22 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 137 (05/17) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 10 7.3 16 6.9 14 -2 0.95 -1.3 1.4 -0.20 0.22 0.36Subregional Peak: 7.7 15 -1 1.06 (at cell 79 x 38)4164 Banning Airport 4 7.3 16 6.9 14 -2 0.95 -0.1 0.2 -0.51 0.55 0.225212 Mira Loma 2 6.5 15 4.8 15 0 0.74 -0.3 0.3 -1.01 1.09 -99.004144 Rubidoux 4 7.0 15 5.4 13 -2 0.77 -0.3 0.3 -0.51 0.55 -0.63* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 137 (05/17) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 12 7.2 16 6.4 15 -1 0.88 -0.9 0.9 -0.14 0.14 0.44Subregional Peak: 7.0 15 -1 0.97 (at cell 82 x 42)4157 Indio Jackson 7 6.8 17 6.1 16 -1 0.90 -0.1 0.1 -0.23 0.23 0.434137 Palm Springs 5 7.2 16 6.4 15 -1 0.88 -0.1 0.1 -0.33 0.33 0.48* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 138 (05/18) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 15 7.6 14 5.6 12 -2 0.74 -1.9 1.9 -0.28 0.28 0.45Subregional Peak: 6.6 14 0 0.87 (at cell 54 x 55)0069 Burbank 1 6.3 15 4.7 15 0 0.75 -0.3 0.3 -4.25 4.25 -99.000074 Reseda 7 7.0 14 4.9 14 0 0.70 -0.3 0.3 -0.61 0.61 0.560090 Santa Clarita 7 7.6 14 5.6 12 -2 0.74 -0.3 0.3 -0.61 0.61 -0.02* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 138 (05/18) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 78 9.3 16 7.8 14 -2 0.84 -2.0 2.1 -0.28 0.28 0.20Subregional Peak: 9.2 15 -1 0.99 (at cell 79 x 38)4164 Banning Airport 11 8.8 16 7.8 14 -2 0.89 -0.2 0.2 -1.97 2.01 0.135181 Crestline 6 9.3 16 6.1 14 -2 0.66 -0.3 0.3 -3.62 3.69 -0.384158 Elsinore 9 8.1 15 6.1 12 -3 0.75 -0.2 0.2 -2.41 2.46 0.095197 Fontana 7 8.2 14 5.1 13 -1 0.62 -0.3 0.3 -3.10 3.16 0.250591 Glendora 5 6.5 15 5.0 15 0 0.76 -0.3 0.3 -4.34 4.42 0.165212 Mira Loma 8 8.0 14 5.0 12 -2 0.63 -0.4 0.4 -2.71 2.76 0.394149 Perris 4 6.7 15 6.2 14 -1 0.93 -0.2 0.2 -5.43 5.53 -0.520075 Pomona 3 6.7 13 5.0 15 2 0.75 -0.3 0.3 -7.24 7.37 -0.995204 Redlands 5 8.1 16 7.0 14 -2 0.86 -0.2 0.2 -4.34 4.42 0.214144 Rubidoux 9 8.9 14 5.7 13 -1 0.64 -0.4 0.4 -2.41 2.46 0.625203 San Bernardino 7 8.4 15 6.2 13 -2 0.74 -0.3 0.3 -3.10 3.16 0.295175 Upland 4 7.1 14 4.5 13 -1 0.64 -0.3 0.3 -5.43 5.53 0.54* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 138 (05/18) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 8 6.6 14 5.0 16 2 0.76 -1.8 1.8 -0.29 0.29 -0.02Subregional Peak: 6.4 13 -1 0.97 (at cell 66 x 33)3176 Anaheim 4 6.5 13 4.7 14 1 0.72 -0.3 0.3 -0.58 0.58 -0.183812 Mission Viejo 1 6.2 16 5.0 16 0 0.81 -0.2 0.2 -2.30 2.30 -99.000091 West Los Angeles 3 6.6 14 4.8 14 0 0.73 -0.3 0.3 -0.77 0.77 0.70* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 138 (05/18) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 22 8.9 13 8.5 15 2 0.96 -0.4 0.7 -0.06 0.10 0.65Subregional Peak: 10.8 13 0 1.22 (at cell 107 x 12)4157 Indio Jackson 10 8.9 13 8.5 15 2 0.96 -0.1 0.1 -0.13 0.21 0.794137 Palm Springs 12 7.6 12 7.4 15 3 0.98 0.0 0.1 -0.11 0.18 0.38* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 139 (05/19) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 14 9.6 15 6.0 12 -3 0.62 -2.7 2.7 -0.34 0.34 0.16Subregional Peak: 7.5 14 -1 0.78 (at cell 55 x 52)0069 Burbank 4 9.6 15 5.5 13 -2 0.58 -0.4 0.4 -1.19 1.19 0.290088 Pasadena 4 8.6 15 5.4 14 -1 0.63 -0.4 0.4 -1.19 1.19 -0.090074 Reseda 3 9.6 12 5.3 13 1 0.55 -0.3 0.3 -1.58 1.58 0.060090 Santa Clarita 3 7.6 12 6.0 12 0 0.79 -0.2 0.2 -1.58 1.58 0.84* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 139 (05/19) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 100 10.7 16 8.6 14 -2 0.80 -3.3 3.3 -0.39 0.39 0.23Subregional Peak: 8.9 15 -1 0.84 (at cell 79 x 39)0060 Azusa 5 8.2 15 5.2 14 -1 0.63 -0.4 0.4 -7.85 7.85 0.514164 Banning Airport 10 9.5 17 8.6 14 -3 0.90 -0.2 0.2 -3.92 3.92 -0.075181 Crestline 6 10.7 16 6.2 14 -2 0.58 -0.3 0.3 -6.54 6.54 -0.584158 Elsinore 9 9.6 18 6.2 11 -7 0.65 -0.3 0.3 -4.36 4.36 -0.485197 Fontana 8 10.2 14 5.3 16 2 0.52 -0.5 0.5 -4.91 4.91 0.460591 Glendora 7 8.9 16 5.4 15 -1 0.61 -0.4 0.4 -5.61 5.61 0.815212 Mira Loma 10 9.3 11 5.4 15 4 0.58 -0.4 0.4 -3.92 3.92 0.554149 Perris 4 10.7 17 5.6 16 -1 0.53 -0.5 0.5 -9.81 9.81 0.020075 Pomona 6 9.3 16 5.7 15 -1 0.61 -0.4 0.4 -6.54 6.54 0.255204 Redlands 9 8.5 16 7.3 13 -3 0.86 -0.3 0.3 -4.36 4.36 0.274144 Rubidoux 9 10.3 14 6.0 11 -3 0.58 -0.4 0.4 -4.36 4.36 0.265203 San Bernardino 10 10.1 15 6.2 13 -2 0.62 -0.4 0.4 -3.92 3.92 0.365175 Upland 7 8.8 16 5.2 15 -1 0.59 -0.5 0.5 -5.61 5.61 0.66* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 139 (05/19) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 20 8.4 13 5.8 14 1 0.68 -2.8 2.8 -0.38 0.38 0.36Subregional Peak: 6.1 13 0 0.73 (at cell 66 x 33)3176 Anaheim 3 7.4 15 4.0 14 -1 0.54 -0.5 0.5 -2.55 2.55 0.820087 Los Angeles 3 7.9 14 4.5 13 -1 0.56 -0.5 0.5 -2.55 2.55 0.183177 La Habra 1 7.1 15 4.3 15 0 0.60 -0.4 0.4 -7.66 7.66 -99.000820 LAXH 1 6.5 13 4.7 13 0 0.72 -0.3 0.3 -7.66 7.66 -99.000072 Long Beach 2 6.4 14 4.2 13 -1 0.66 -0.4 0.4 -3.83 3.83 -99.000084 Lynwood 2 6.3 13 4.5 13 0 0.71 -0.3 0.3 -3.83 3.83 -99.003812 Mission Viejo 5 8.3 14 5.8 14 0 0.69 -0.3 0.3 -1.53 1.53 -0.240091 West Los Angeles 3 8.4 13 5.2 13 0 0.62 -0.3 0.3 -2.55 2.55 0.77* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 139 (05/19) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 23 9.4 17 9.3 14 -3 0.99 -0.7 1.4 -0.08 0.19 0.15Subregional Peak: 13.2 12 -5 1.40 (at cell 109 x 13)4157 Indio Jackson 9 9.2 11 9.3 14 3 1.01 0.1 0.2 -0.20 0.48 0.164137 Palm Springs 14 9.4 17 7.8 12 -5 0.83 -0.2 0.2 -0.13 0.31 0.36* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 140 (05/20) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 8 8.5 13 5.7 12 -1 0.67 -2.0 2.0 -0.26 0.26 0.23Subregional Peak: 7.8 14 1 0.91 (at cell 55 x 53)0069 Burbank 3 8.1 14 5.6 13 -1 0.69 -0.3 0.3 -0.71 0.71 -0.190088 Pasadena 3 7.3 14 5.1 14 0 0.71 -0.3 0.3 -0.71 0.71 0.490074 Reseda 2 8.5 13 5.7 12 -1 0.67 -0.2 0.2 -1.06 1.06 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 140 (05/20) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 95 10.4 13 8.0 14 1 0.77 -2.3 2.4 -0.30 0.31 0.37Subregional Peak: 9.1 15 2 0.87 (at cell 78 x 32)0060 Azusa 3 7.0 14 4.7 14 0 0.68 -0.3 0.3 -9.58 9.87 1.004164 Banning Airport 12 8.9 12 8.0 14 2 0.90 -0.1 0.2 -2.40 2.47 0.395181 Crestline 11 10.4 13 6.6 13 0 0.64 -0.4 0.4 -2.61 2.69 0.714158 Elsinore 6 7.8 10 6.4 12 2 0.81 -0.2 0.2 -4.79 4.93 -0.515197 Fontana 8 8.1 14 5.5 15 1 0.68 -0.4 0.4 -3.59 3.70 0.730591 Glendora 4 8.6 15 5.0 15 0 0.58 -0.4 0.4 -7.19 7.40 0.895212 Mira Loma 9 8.1 13 5.0 15 2 0.61 -0.4 0.4 -3.19 3.29 0.800075 Pomona 6 6.9 16 5.3 14 -2 0.77 -0.3 0.3 -4.79 4.93 0.415204 Redlands 10 8.2 15 7.6 13 -2 0.93 -0.2 0.2 -2.87 2.96 0.344144 Rubidoux 10 10.1 10 5.9 11 1 0.59 -0.4 0.4 -2.87 2.96 0.845203 San Bernardino 10 9.3 15 6.8 12 -3 0.73 -0.3 0.3 -2.87 2.96 0.685175 Upland 6 7.9 16 5.2 15 -1 0.65 -0.3 0.3 -4.79 4.93 0.43* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 140 (05/20) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 5 7.4 16 5.5 15 -1 0.74 -1.8 1.8 -0.26 0.26 -0.05Subregional Peak: 6.5 13 -3 0.88 (at cell 66 x 34)0087 Los Angeles 1 6.5 13 4.3 13 0 0.67 -0.3 0.3 -1.32 1.32 -99.003812 Mission Viejo 2 7.4 16 5.5 15 -1 0.74 -0.3 0.3 -0.66 0.66 -99.000091 West Los Angeles 2 7.0 13 5.4 14 1 0.77 -0.2 0.2 -0.66 0.66 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 140 (05/20) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 16 9.2 11 7.4 12 1 0.81 -1.8 1.8 -0.22 0.22 0.73Subregional Peak: 11.1 12 1 1.21 (at cell 110 x 11)4157 Indio Jackson 9 9.2 11 7.4 12 1 0.81 -0.2 0.2 -0.39 0.39 0.694137 Palm Springs 7 8.8 12 6.6 12 0 0.75 -0.2 0.2 -0.50 0.50 0.73* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 141 (05/21) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 31 11.0 14 7.4 13 -1 0.67 -2.6 2.6 -0.32 0.32 0.33Subregional Peak: 10.7 15 1 0.97 (at cell 55 x 51)0069 Burbank 7 8.1 16 7.4 13 -3 0.91 -0.3 0.3 -1.42 1.42 -0.080088 Pasadena 8 8.9 14 6.8 14 0 0.77 -0.4 0.4 -1.24 1.24 0.490074 Reseda 8 8.3 12 7.2 13 1 0.87 -0.2 0.2 -1.24 1.24 0.860090 Santa Clarita 8 11.0 14 6.4 13 -1 0.58 -0.4 0.4 -1.24 1.24 0.77* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 141 (05/21) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 93 12.7 16 10.1 14 -2 0.80 -1.4 1.8 -0.16 0.21 0.40Subregional Peak: 11.4 15 -1 0.90 (at cell 72 x 35)0060 Azusa 7 9.0 14 6.7 15 1 0.75 -0.3 0.3 -2.09 2.78 0.764164 Banning Airport 7 8.3 16 9.2 15 -1 1.10 0.0 0.1 -2.09 2.78 0.855181 Crestline 12 12.7 16 8.0 15 -1 0.63 -0.3 0.3 -1.22 1.62 0.524158 Elsinore 8 8.2 14 10.1 14 0 1.23 0.2 0.2 -1.82 2.43 0.735197 Fontana 7 11.1 15 7.3 14 -1 0.66 -0.2 0.2 -2.09 2.78 0.910591 Glendora 5 9.7 14 7.0 15 1 0.72 -0.2 0.2 -2.92 3.89 0.575212 Mira Loma 9 9.9 13 7.2 13 0 0.73 -0.2 0.2 -1.62 2.16 0.804149 Perris 5 9.1 15 9.0 14 -1 0.99 0.0 0.1 -2.92 3.89 0.840075 Pomona 4 9.2 13 7.0 15 2 0.77 -0.2 0.2 -3.65 4.86 -0.145204 Redlands 7 10.4 16 8.9 14 -2 0.85 0.0 0.1 -2.09 2.78 0.684144 Rubidoux 10 11.1 14 8.1 14 0 0.73 -0.2 0.2 -1.46 1.95 0.865203 San Bernardino 6 12.3 15 8.3 15 0 0.67 -0.2 0.2 -2.43 3.24 0.875175 Upland 6 10.3 14 7.0 15 1 0.68 -0.2 0.2 -2.43 3.24 0.78* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 141 (05/21) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 30 10.0 15 8.7 14 -1 0.87 -2.4 2.6 -0.32 0.36 -0.04Subregional Peak: 9.4 15 0 0.94 (at cell 67 x 35)3176 Anaheim 2 6.3 14 5.7 14 0 0.91 -0.3 0.3 -4.82 5.38 -99.000087 Los Angeles 7 8.4 16 5.3 13 -3 0.63 -0.5 0.5 -1.38 1.54 -0.233195 Costa Mesa 4 6.9 14 6.4 14 0 0.92 -0.3 0.3 -2.41 2.69 0.890072 Long Beach 3 6.4 16 4.4 16 0 0.69 -0.5 0.5 -3.21 3.58 0.710084 Lynwood 3 7.0 15 5.1 14 -1 0.73 -0.3 0.3 -3.21 3.58 0.733812 Mission Viejo 5 7.4 13 8.7 14 1 1.17 0.1 0.1 -1.93 2.15 0.580091 West Los Angeles 6 10.0 15 5.5 12 -3 0.55 -0.5 0.5 -1.61 1.79 -0.09* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 141 (05/21) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 22 9.6 18 7.1 17 -1 0.74 -1.1 1.3 -0.14 0.18 -0.07Subregional Peak: 9.7 12 -6 1.01 (at cell 110 x 10)4157 Indio Jackson 11 7.9 20 7.1 13 -7 0.90 -0.1 0.2 -0.29 0.35 -0.434137 Palm Springs 11 9.6 18 7.1 17 -1 0.74 -0.2 0.2 -0.29 0.35 0.17* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 142 (05/22) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 39 16.4 14 8.9 13 -1 0.54 -4.5 4.5 -0.46 0.46 0.73Subregional Peak: 11.2 15 1 0.68 (at cell 55 x 50)0069 Burbank 9 13.7 13 8.9 13 0 0.65 -0.4 0.4 -1.97 1.97 0.950088 Pasadena 10 14.5 13 8.1 14 1 0.56 -0.5 0.5 -1.78 1.78 0.930074 Reseda 11 11.2 12 7.5 12 0 0.67 -0.5 0.5 -1.61 1.61 0.900090 Santa Clarita 9 16.4 14 6.1 14 0 0.37 -0.4 0.4 -1.97 1.97 0.84* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 142 (05/22) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 151 16.0 15 14.7 15 0 0.92 -2.6 2.9 -0.25 0.29 0.57Subregional Peak: 15.0 15 0 0.94 (at cell 74 x 46)0060 Azusa 9 14.5 14 8.0 15 1 0.55 -0.5 0.5 -4.13 4.80 0.904164 Banning Airport 16 12.1 15 11.7 17 2 0.96 -0.1 0.2 -2.32 2.70 0.875181 Crestline 13 15.7 15 13.6 15 0 0.87 -0.2 0.2 -2.86 3.32 0.684158 Elsinore 10 10.1 14 12.4 14 0 1.23 0.1 0.1 -3.71 4.32 0.895197 Fontana 12 15.0 15 12.3 13 -2 0.82 -0.4 0.4 -3.10 3.60 0.820591 Glendora 10 16.0 15 8.4 15 0 0.53 -0.4 0.4 -3.71 4.32 0.845212 Mira Loma 12 13.2 13 11.7 13 0 0.89 -0.3 0.3 -3.10 3.60 0.764149 Perris 11 9.7 15 10.7 15 0 1.11 0.0 0.2 -3.38 3.93 0.820075 Pomona 10 14.0 14 9.2 12 -2 0.66 -0.4 0.4 -3.71 4.32 0.745204 Redlands 14 13.0 15 14.7 15 0 1.13 -0.2 0.3 -2.65 3.09 0.714144 Rubidoux 12 14.4 13 13.2 14 1 0.92 -0.3 0.3 -3.10 3.60 0.745203 San Bernardino 13 14.9 15 14.2 14 -1 0.96 -0.3 0.3 -2.86 3.32 0.785175 Upland 9 13.5 14 9.9 13 -1 0.74 -0.3 0.3 -4.13 4.80 0.63* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 142 (05/22) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 55 12.5 14 10.0 14 0 0.80 -2.8 2.9 -0.33 0.34 0.40Subregional Peak: 11.9 12 -2 0.96 (at cell 68 x 37)3176 Anaheim 9 8.0 13 6.1 14 1 0.77 -0.4 0.4 -2.04 2.08 0.830087 Los Angeles 8 12.1 13 6.2 13 0 0.52 -0.5 0.5 -2.29 2.34 0.893195 Costa Mesa 7 6.9 17 6.8 12 -5 0.99 -0.2 0.2 -2.62 2.67 -0.063177 La Habra 8 8.0 12 7.0 11 -1 0.87 -0.2 0.2 -2.29 2.34 0.570084 Lynwood 6 11.1 13 5.9 13 0 0.53 -0.4 0.4 -3.06 3.11 0.823812 Mission Viejo 9 12.5 14 10.0 14 0 0.80 -0.2 0.2 -2.04 2.08 0.800091 West Los Angeles 8 11.4 12 6.8 13 1 0.59 -0.4 0.4 -2.29 2.34 0.87* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 142 (05/22) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 34 12.7 18 8.7 13 -5 0.69 -2.6 3.0 -0.28 0.33 -0.03Subregional Peak: 11.0 16 -2 0.87 (at cell 92 x 10)4157 Indio Jackson 14 10.2 20 8.7 13 -7 0.86 -0.2 0.3 -0.67 0.81 -0.354137 Palm Springs 20 12.7 18 7.7 15 -3 0.60 -0.3 0.3 -0.47 0.56 0.32* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 143 (05/23) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 28 11.4 15 8.3 13 -2 0.73 -2.4 2.4 -0.30 0.30 0.59Subregional Peak: 9.8 15 0 0.86 (at cell 53 x 55)0069 Burbank 6 7.0 12 6.5 12 0 0.93 -0.2 0.2 -1.38 1.38 0.970088 Pasadena 4 7.1 13 5.9 13 0 0.84 -0.2 0.2 -2.08 2.08 0.290074 Reseda 9 9.4 16 7.5 13 -3 0.80 -0.4 0.4 -0.92 0.92 0.680090 Santa Clarita 9 11.4 15 8.3 13 -2 0.73 -0.3 0.3 -0.92 0.92 0.85* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 143 (05/23) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 113 13.0 15 11.2 13 -2 0.86 -1.5 2.1 -0.17 0.25 0.46Subregional Peak: 11.9 13 -2 0.91 (at cell 77 x 40)0060 Azusa 4 8.6 14 5.9 15 1 0.69 -0.3 0.3 -4.91 7.02 0.034164 Banning Airport 15 13.0 15 11.2 13 -2 0.86 -0.2 0.2 -1.31 1.87 0.855181 Crestline 18 11.6 15 8.6 13 -2 0.74 -0.4 0.4 -1.09 1.56 0.914158 Elsinore 9 8.8 11 11.0 13 2 1.25 0.2 0.3 -2.18 3.12 0.125197 Fontana 7 10.6 13 8.1 12 -1 0.76 -0.3 0.3 -2.81 4.01 0.680591 Glendora 5 9.6 14 6.2 15 1 0.65 -0.3 0.3 -3.93 5.61 0.065212 Mira Loma 8 9.2 14 8.2 12 -2 0.89 -0.2 0.2 -2.46 3.51 0.784149 Perris 9 9.8 12 10.4 13 1 1.06 0.1 0.2 -2.18 3.12 0.800075 Pomona 6 8.2 13 6.6 12 -1 0.80 -0.3 0.3 -3.27 4.68 0.215204 Redlands 9 10.1 12 10.9 13 1 1.08 0.0 0.1 -2.18 3.12 0.924144 Rubidoux 8 10.4 14 9.1 12 -2 0.87 -0.2 0.2 -2.46 3.51 0.665203 San Bernardino 9 10.0 13 10.2 13 0 1.02 -0.1 0.1 -2.18 3.12 0.895175 Upland 6 9.5 14 7.0 12 -2 0.74 -0.3 0.3 -3.27 4.68 -0.19* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 143 (05/23) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 8 8.0 15 5.2 12 -3 0.65 -3.3 3.3 -0.48 0.48 0.69Subregional Peak: 9.1 12 -3 1.14 (at cell 68 x 37)0091 West Los Angeles 8 8.0 15 5.2 12 -3 0.65 -0.5 0.5 -0.48 0.48 0.69* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 143 (05/23) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 40 11.4 16 9.0 14 -2 0.79 -2.4 2.6 -0.28 0.30 0.51Subregional Peak: 11.3 14 -2 0.99 (at cell 106 x 12)4157 Indio Jackson 19 10.1 18 9.0 14 -4 0.89 -0.3 0.3 -0.59 0.63 0.384137 Palm Springs 21 11.4 16 7.8 16 0 0.68 -0.3 0.3 -0.54 0.57 0.90* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 144 (05/24) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 10 8.8 15 7.8 13 -2 0.88 -1.4 1.4 -0.18 0.18 0.79Subregional Peak: 9.2 14 -1 1.04 (at cell 52 x 55)0074 Reseda 5 7.7 13 6.4 12 -1 0.83 -0.2 0.2 -0.37 0.37 0.530090 Santa Clarita 5 8.8 15 7.8 13 -2 0.88 -0.2 0.2 -0.37 0.37 0.80* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 144 (05/24) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 90 10.6 15 9.6 15 0 0.91 -1.7 2.1 -0.22 0.27 0.34Subregional Peak: 9.9 14 -1 0.94 (at cell 77 x 41)0060 Azusa 3 6.6 14 5.8 15 1 0.88 -0.2 0.2 -6.51 8.21 0.854164 Banning Airport 13 10.6 15 9.6 15 0 0.91 -0.2 0.2 -1.50 1.89 0.895181 Crestline 13 9.5 16 7.0 13 -3 0.74 -0.4 0.4 -1.50 1.89 0.164158 Elsinore 9 7.7 11 9.1 14 3 1.18 0.1 0.2 -2.17 2.74 -0.145197 Fontana 6 8.1 15 5.8 12 -3 0.71 -0.4 0.4 -3.26 4.10 0.060591 Glendora 5 7.4 15 5.7 15 0 0.78 -0.2 0.2 -3.91 4.93 0.695212 Mira Loma 7 8.4 12 6.2 15 3 0.73 -0.2 0.2 -2.79 3.52 0.904149 Perris 6 7.7 14 8.8 13 -1 1.15 0.1 0.2 -3.26 4.10 0.260075 Pomona 5 7.6 14 4.1 12 -2 0.54 -0.4 0.4 -3.91 4.93 -0.735204 Redlands 6 8.8 14 8.5 13 -1 0.96 -0.1 0.2 -3.26 4.10 0.244144 Rubidoux 7 9.2 12 7.0 12 0 0.76 -0.3 0.3 -2.79 3.52 0.815203 San Bernardino 6 8.6 15 7.5 13 -2 0.87 -0.2 0.2 -3.26 4.10 0.265175 Upland 4 7.2 14 4.3 13 -1 0.60 -0.4 0.4 -4.89 6.16 -0.97* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 144 (05/24) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 1 6.1 18 2.4 18 0 0.39 -3.7 3.7 -0.61 0.61 -99.00Subregional Peak: 7.8 14 -4 1.28 (at cell 68 x 37)0091 West Los Angeles 1 6.1 18 2.4 18 0 0.39 -0.6 0.6 -0.61 0.61 -99.00* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 144 (05/24) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm)------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 27 9.5 13 8.2 14 1 0.86 -1.8 2.0 -0.23 0.26 0.24Subregional Peak: 12.6 14 1 1.33 (at cell 106 x 13)4157 Indio Jackson 12 8.1 13 8.2 14 1 1.01 -0.1 0.2 -0.51 0.59 0.434137 Palm Springs 15 9.5 13 6.4 11 -2 0.67 -0.3 0.3 -0.41 0.47 0.50* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubRegional DescriptionsSubRegion 003 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0069 Burbank 53 48 -27.848 461.3080088 Pasadena 56 47 -11.205 457.0210074 Reseda 49 48 -48.000 463.1050090 Santa Clarita 49 52 -48.140 483.357SubRegion 004 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------0060 Azusa 60 47 6.981 456.1134164 Banning Airport 79 42 104.459 433.5275181 Crestline 72 49 66.383 468.6064158 Elsinore 71 37 60.525 405.9075197 Fontana 68 46 46.811 453.0810591 Glendora 61 47 13.487 457.0105212 Mira Loma 67 43 42.938 438.9154149 Perris 72 39 69.051 417.3760075 Pomona 63 45 22.598 448.6105204 Redlands 74 45 76.256 448.1894144 Rubidoux 69 44 52.093 442.5575203 San Bernardino 72 46 65.874 453.2995175 Upland 65 46 31.687 452.125SubRegion 005 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------3176 Anaheim 60 40 7.422 421.6450087 Los Angeles 54 45 -22.302 445.5633195 Costa Mesa 60 37 6.793 405.6263177 La Habra 59 42 4.359 432.9780820 LAXH 51 42 -36.352 433.6850072 Long Beach 55 40 -17.171 421.9030084 Lynwood 55 42 -19.237 432.7533812 Mission Viejo 64 36 29.671 400.7910085 Pico Rivera 57 44 -5.273 442.8600091 West Los Angeles 52 45 -34.796 447.031SubRegion 009 Contains the Following Sites:Site Site Description Xcell Ycell XPos(km) YPos(km)---- -------------------------------- ------ ------ --------- ---------4157 Indio Jackson 91 38 162.217 411.2934137 Palm Springs 85 40 132.826 423.133* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 137 (05/17) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 2 6.1 11 5.9 9 -2 0.97 -0.7 0.7 -0.11 0.11 -99.00Subregional Peak: 6.8 11 0 1.11 (at 79 x 38) NSte: 4164; NSPk: 6.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 137 (05/17) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 6.4 11 5.8 10 -1 0.91 -0.6 0.6 -0.09 0.09 -99.00Subregional Peak: 6.2 14 3 0.97 (at 84 x 35) NSte: 4137; NSPk: 5.9* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 138 (05/18) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 2 6.8 11 5.0 10 -1 0.73 -1.8 1.8 -0.28 0.28 -99.00Subregional Peak: 5.7 10 -1 0.83 (at 61 x 58) NSte: 0088; NSPk: 3.9* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 138 (05/18) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 9 8.0 10 6.9 10 0 0.87 -1.7 1.7 -0.23 0.23 -103.56Subregional Peak: 8.0 11 1 1.00 (at 79 x 38) NSte: 4164; NSPk: 7.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 138 (05/18) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 8.2 10 7.6 10 0 0.93 -0.2 0.3 -0.03 0.04 -99.00Subregional Peak: 8.2 9 -1 1.00 (at 106 x 12) NSte: 4157; NSPk: 7.6* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 139 (05/19) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 3 6.7 10 4.8 10 0 0.72 -2.2 2.2 -0.33 0.33 -664.68Subregional Peak: 5.9 11 1 0.89 (at 58 x 50) NSte: 0088; NSPk: 3.9* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 139 (05/19) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 13 9.5 11 7.1 10 -1 0.75 -2.7 2.7 -0.34 0.34 -53.45Subregional Peak: 8.0 10 -1 0.84 (at 79 x 38) NSte: 4164; NSPk: 7.1* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 139 (05/19) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 1 6.7 11 4.5 10 -1 0.67 -2.2 2.2 -0.33 0.33 -99.00Subregional Peak: 5.3 10 -1 0.78 (at 68 x 37) NSte: 3812; NSPk: 4.4* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 139 (05/19) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 8.5 12 8.4 10 -2 0.99 -0.2 0.9 -0.02 0.11 -99.00Subregional Peak: 9.5 9 -3 1.12 (at 108 x 13) NSte: 4157; NSPk: 8.4* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 140 (05/20) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 10 9.6 11 7.0 9 -2 0.74 -2.1 2.1 -0.27 0.27 -42.25Subregional Peak: 7.8 10 -1 0.82 (at 78 x 33) NSte: 4149; NSPk: 5.9* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 140 (05/20) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 8.7 10 6.7 9 -1 0.77 -1.6 1.6 -0.19 0.19 -99.00Subregional Peak: 9.2 9 -1 1.06 (at 109 x 12) NSte: 4157; NSPk: 6.6* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 141 (05/21) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 8.7 11 5.8 10 -1 0.66 -2.3 2.3 -0.30 0.30 -228.96Subregional Peak: 8.3 12 1 0.95 (at 59 x 51) NSte: 0088; NSPk: 5.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 141 (05/21) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 13 11.2 13 8.6 10 -3 0.77 -1.1 1.6 -0.13 0.19 -45.03Subregional Peak: 9.4 11 -2 0.84 (at 72 x 35) NSte: 4158; NSPk: 8.3* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 141 (05/21) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 4 7.4 11 6.6 11 0 0.88 -1.6 1.7 -0.22 0.24 -65.46Subregional Peak: 7.8 11 0 1.05 (at 68 x 37) NSte: 3812; NSPk: 6.4* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 141 (05/21) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 7.7 14 6.5 10 -4 0.85 -0.7 0.7 -0.10 0.10 -99.00Subregional Peak: 8.3 9 -5 1.09 (at 110 x 10) NSte: 4157; NSPk: 6.6* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 142 (05/22) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 4 11.4 10 6.5 10 0 0.57 -4.5 4.5 -0.43 0.43 -231.13Subregional Peak: 8.6 11 1 0.76 (at 58 x 49) NSte: 0088; NSPk: 6.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 142 (05/22) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 13 14.5 12 11.1 10 -2 0.77 -2.4 2.7 -0.18 0.22 -48.40Subregional Peak: 11.6 10 -2 0.80 (at 73 x 47) NSte: 5203; NSPk: 11.1* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 142 (05/22) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 7 9.8 10 7.0 10 0 0.71 -2.7 2.7 -0.31 0.31 -54.11Subregional Peak: 9.7 9 -1 0.99 (at 68 x 37) NSte: 3812; NSPk: 6.7* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 142 (05/22) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 10.9 15 7.8 10 -5 0.72 -2.5 2.5 -0.24 0.24 -99.00Subregional Peak: 9.5 11 -4 0.87 (at 81 x 43) NSte: 4137; NSPk: 6.9* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 143 (05/23) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 3 9.5 11 6.4 10 -1 0.68 -2.3 2.3 -0.27 0.27 -72.40Subregional Peak: 7.4 11 0 0.77 (at 53 x 55) NSte: 0090; NSPk: 6.5* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 143 (05/23) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 12 10.9 10 9.6 9 -1 0.88 -1.0 1.6 -0.12 0.19 -32.97Subregional Peak: 10.3 9 -1 0.95 (at 76 x 42) NSte: 4164; NSPk: 9.7* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 143 (05/23) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0005 SubRegion 1 7.1 12 4.7 10 -2 0.66 -2.4 2.4 -0.34 0.34 -99.00Subregional Peak: 8.0 8 -4 1.13 (at 68 x 37) NSte: 3812; NSPk: 6.1* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 143 (05/23) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 10.8 11 8.5 10 -1 0.78 -2.1 2.1 -0.20 0.20 -99.00Subregional Peak: 9.5 11 0 0.88 (at 106 x 12) NSte: 4157; NSPk: 8.4* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 144 (05/24) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0003 SubRegion 2 7.6 11 6.4 10 -1 0.85 -1.3 1.3 -0.18 0.18 -99.00Subregional Peak: 7.4 11 0 0.98 (at 52 x 55) NSte: 0090; NSPk: 6.4* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 144 (05/24) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0004 SubRegion 10 9.2 11 8.1 10 -1 0.88 -1.0 1.4 -0.14 0.20 -40.82Subregional Peak: 8.5 10 -1 0.92 (at 76 x 41) NSte: 4164; NSPk: 8.0* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aStatistics were calculated for the 24-hour period of DOY 144 (05/24) 2005Included were data-pairs with observed concentrations above a threshold of 6.0 (pphm); Averaged over 8 hours------- Peak Concentrations -------- --- Comparisons with Observations ---Observed Predicted Time Peak Mean Mean NormalizedSite Description No Value Time Value Time Lag Ratio Bias Error Bias Error (r)---- ------------------------ ---- ------ ---- ------ ---- ---- ----- ----- ----- ----- ----- --------0009 SubRegion 2 8.9 12 7.4 10 -2 0.84 -1.3 1.3 -0.15 0.15 -99.00Subregional Peak: 8.8 9 -3 1.00 (at 105 x 13) NSte: 4157; NSPk: 7.4* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0000 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 137 through 144Unpaired Subregional Maximum of 7.7 at Cell 108 x 11 -- Nearest Site: 4157- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 137 138 139 140 141 Avg. 137 138 139 140 141 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0001 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 137 through 144Unpaired Subregional Maximum of -99.0 at Cell -9 x -9 -- Nearest Site: 0820- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 137 138 139 140 141 Avg. 137 138 139 140 141 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0002 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 137 through 144Unpaired Subregional Maximum of 6.6 at Cell 49 x 54 -- Nearest Site: 0090- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 137 138 139 140 141 Avg. 137 138 139 140 141 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0003 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 137 through 144Unpaired Subregional Maximum of 8.2 at Cell 59 x 50 -- Nearest Site: 0060- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 137 138 139 140 141 Avg. 137 138 139 140 141 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----0069 Burbank 7.6 4.3 5.2 6.4 5.4 7.1 5.0 4.6 4.4 4.4 4.7 5.5 0.62 -0.28 0.280088 Pasadena 8.6 4.4 5.3 6.5 5.3 8.0 4.5 4.0 3.9 3.9 4.1 5.0 0.52 -0.42 0.420074 Reseda 7.6 5.2 6.3 6.7 5.1 7.3 5.4 5.0 4.5 4.8 5.0 5.8 0.66 -0.27 0.270090 Santa Clarita 8.7 5.5 6.8 5.7 4.0 8.7 5.5 5.0 5.0 5.3 5.1 5.5 0.60 -0.32 0.32* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0004 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 137 through 144Unpaired Subregional Maximum of 9.2 at Cell 68 x 48 -- Nearest Site: 5197- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 137 138 139 140 141 Avg. 137 138 139 140 141 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----0060 Azusa 8.6 4.2 5.4 6.3 5.0 7.2 4.6 4.1 4.1 3.9 3.9 5.1 0.51 -0.39 0.394164 Banning Airport 8.5 6.1 7.5 9.0 7.3 7.2 7.8 5.9 6.9 7.1 7.0 7.5 0.92 -0.08 0.095181 Crestline 9.9 5.2 7.1 7.9 9.6 11.2 6.6 4.9 5.4 5.6 5.9 7.0 0.74 -0.31 0.314158 Elsinore 7.4 5.3 7.2 7.1 6.2 7.3 7.4 5.7 5.6 5.8 5.7 8.6 1.11 0.03 0.165197 Fontana 8.4 5.1 6.7 8.5 7.3 8.1 5.7 4.3 4.6 4.7 4.9 6.1 0.76 -0.30 0.300591 Glendora 8.1 4.7 5.9 7.4 6.2 7.3 4.9 4.4 4.3 4.2 4.2 5.5 0.55 -0.34 0.345212 Mira Loma 8.3 5.6 7.3 8.4 7.6 7.9 5.9 4.7 4.4 4.8 4.6 6.4 0.81 -0.28 0.284149 Perris 7.1 4.6 6.1 7.1 4.1 6.6 7.3 5.6 5.7 6.0 6.0 7.9 1.14 0.08 0.150075 Pomona 7.7 4.5 5.9 7.4 5.9 6.5 5.0 4.2 4.3 4.3 4.4 5.8 0.72 -0.29 0.295204 Redlands 8.1 4.6 6.8 7.5 7.6 7.9 7.2 5.1 5.8 5.8 6.4 7.6 0.98 -0.08 0.104144 Rubidoux 8.9 6.1 8.0 9.5 8.8 8.9 6.5 4.9 4.9 5.3 5.3 7.0 0.79 -0.28 0.285203 San Bernardino 8.7 4.8 6.9 8.3 8.5 8.5 6.7 4.8 5.3 5.4 5.9 7.1 0.85 -0.20 0.205175 Upland 8.1 4.3 5.9 7.5 6.3 7.5 5.1 4.1 4.2 4.2 4.3 5.8 0.74 -0.31 0.31* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0005 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 137 through 144Unpaired Subregional Maximum of 8.8 at Cell 69 x 37 -- Nearest Site: 4158- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 137 138 139 140 141 Avg. 137 138 139 140 141 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----3176 Anaheim 7.5 5.2 5.9 5.7 4.7 5.8 4.1 3.9 3.5 3.2 3.2 4.2 0.65 -0.35 0.350087 Los Angeles 8.4 4.6 4.9 4.8 4.1 6.9 3.9 3.8 3.6 3.5 3.6 4.1 0.47 -0.47 0.473195 Costa Mesa 6.2 4.5 5.0 4.7 3.8 6.0 5.2 5.2 5.3 4.7 4.9 5.3 0.87 -0.12 0.123177 La Habra 7.4 3.7 4.6 3.7 3.1 4.7 4.2 3.8 3.8 3.8 3.8 4.8 0.83 -0.17 0.170084 Lynwood 8.2 4.2 4.4 4.4 3.6 5.6 4.2 4.0 4.0 3.7 3.9 4.4 0.60 -0.40 0.403812 Mission Viejo 7.2 4.8 5.4 6.7 5.5 6.4 5.5 4.8 4.9 4.5 4.5 6.6 0.82 -0.16 0.180091 West Los Angeles 7.8 4.8 5.4 4.9 4.8 7.4 4.6 4.6 4.3 4.3 4.6 4.4 0.56 -0.40 0.40* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0006 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 137 through 144Unpaired Subregional Maximum of 9.0 at Cell 70 x 36 -- Nearest Site: 4158- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 137 138 139 140 141 Avg. 137 138 139 140 141 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0007 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 137 through 144Unpaired Subregional Maximum of -99.0 at Cell -9 x -9 -- Nearest Site: 0820- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 137 138 139 140 141 Avg. 137 138 139 140 141 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0008 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 137 through 144Unpaired Subregional Maximum of 7.4 at Cell 80 x 60 -- Nearest Site: 5181- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 137 138 139 140 141 Avg. 137 138 139 140 141 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----* * * Model Performance Evaluation * * *Pollutant: O3 (pphm) Project: CAMx v4.4 Simulation ID: df05aSubregion 0009 Spatially Paired Average 8-Hour Concentrations above 6.0 pphm for DOY 137 through 144Unpaired Subregional Maximum of 8.1 at Cell 107 x 12 -- Nearest Site: 4157- - - - - Observed - - - - - - - - - - Simulated - - - - -Site Site Site DOY DOY DOY DOY DOY Site DOY DOY DOY DOY DOY Max. Max. Max.ID Description Avg. 137 138 139 140 141 Avg. 137 138 139 140 141 Ratio Bias Error---- ------------------------ --- --- --- --- --- --- --- --- --- --- --- --- ---- ---- ----4157 Indio Jackson 7.9 6.4 8.2 7.7 8.7 6.7 7.3 5.7 7.6 8.4 6.7 6.5 0.93 -0.07 0.094137 Palm Springs 8.5 6.4 7.0 8.5 7.5 7.7 6.7 5.8 7.1 7.3 6.3 6.4 0.68 -0.19 0.19

# ATTACHMENT-3

Draft Modeling Protocol

# DRAFT

MODELING PROTOCOL

FOR OZONE AND PARTICULATE MATTER MODELING

IN SUPPORT OF THE SOUTH COAST AIR QUALITY

MANAGEMENT DISTRICT 2007 AIR QUALITY

MANAGEMENT PLAN UPDATE

Draft Report

May 9, 2006

Jointly Prepared by:

California Air Resources Board

Planning & Technical Support Division

P.O. Box 2815

Sacramento, CA 95812

and

South Coast Air Quality Management District

Planning, Rule Development and Area Sources

21865 Copley Drive

Diamond Bar, CA 91765

# Send Comments to:

Kevin Durkee

South Coast Air Quality Management District

kdurkee@aqmd.gov

Contributors:

Mark Bassett

Joseph Cassmassi

John DaMassa

Kevin Durkee

Bruce Jackson

Bong-Mann Kim

Satoru Mitsutomi

Xinqiu Zhang

# TABLE OF CONTENTS

TABLE OF CONTENTS......................................................................................liii

LIST OF FIGURES ..............................................................................................liii

LIST OF TABLES................................................................................................liii

LIST OF ACRONYMS ........................................................................................liii

INTRODUCTION ................................................................................................ 53

Background ..................................................................................................................... ........... 53

Regulatory Modeling Requir ements and Guidance................................................................ 53

AQMP Ozone Modeling History in the South Coast Air Basin ............................................. 53

AQMP PM Modeling History in th e South Coast Air Basin.................................................. 53

2007 AQMP Modeling An alysis Goals...................................................................................... 53

Ozone Design Value Determination....................................................................................... 53

Overview of the Mo deling Anal ysis........................................................................................... 53

2007 AQMP Schedule............................................................................................................. ... 53

AQMP Modeling Techni cal Oversight....................................................................................... 53



# MODEL SELECTION.......................................................................................... 53

Meteorological Model ........................................................................................................... ..... 53

Background ..................................................................................................................... ....... 53

Previous AQMP Applications ................................................................................................ 53

2007 AQMP Meteorological Modeling A pproach ................................................................. 53

Air Quality Model .............................................................................................................. ........ 53

Background ..................................................................................................................... ....... 53

2007 AQMP Air Quality Modeling Approach ....................................................................... 53



# MODELING DOMAIN........................................................................................ 53

Meteorological Mo deling Do main ............................................................................................. 53

Ozone Modelin g Doma in .......................................................................................................... . 53

PM Modelin g Domain............................................................................................................. ... 53



# HORIZONTAL AND VERTICAL GRID RESOLUTION ................................. 53

Horizontal Grid Resolu tion ..................................................................................................... ... 53

Vertical Re solution............................................................................................................ ......... 53

Meteorological Modeling ....................................................................................................... 5 3

Air Quality Modeling ........................................................................................................... .. 53



# EPISODE SELECTION ....................................................................................... 53

Ozone Episodes................................................................................................................. ......... 53

August 3-7, 1997 (Sun day – Thur sday).................................................................................. 53

June 3-7, 2004 (Thu rsday – Mo nday) .................................................................................... 53

August 4-8, 2004 (We dnesday – Sunday) .............................................................................. 53

May 17-24, 2005 (Tue sday – Tuesday).................................................................................. 53

July 14-19, 2005 (Thu rsday – Tu esday)................................................................................. 53

August 25-29, 2005 (T hursday – Monday) ............................................................................ 53

Possible Seasonal Ozone Episode: Summer 1997................................................................. 53

Ozone Episode Sta tistical Ra nking ............................................................................................ 53

PM Epis odes.................................................................................................................... ........... 53

Annual PM: January 1 – December 31, 2005........................................................................ 53

Episodic PM10/2.5: October 19-2 5, 2005 & Marc h 6-12, 2005 ........................................... 53



# INITIAL AND BOUNDARY CONDITIONS ..................................................... 53

Initial Co nditions............................................................................................................. ........... 53

Boundary Co nditions............................................................................................................ ...... 53



# METEOROLOGICAL INPUTS........................................................................... 53

Meteorological Input Evaluati on and Techni cal Review ........................................................... 53

Qualitative Analys es........................................................................................................... .... 53

## Quantitative Analys es.......................................................................................................... ... 53



# EMISSION INVENTORY INPUTS .................................................................... 53

Emissions Char acteriza tion ..................................................................................................... ... 53

Point S ources.................................................................................................................. ........ 53

Area Sources ................................................................................................................... ....... 53

On-Road Mobile Sources ....................................................................................................... 53

Other Mobile Sources........................................................................................................... .. 53

Biogenic Emissions ............................................................................................................. ... 53

Organic Gas Speciation ......................................................................................................... . 53

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

Emissions Quality Assurance.................................................................................................... . 53

Emission Projections ........................................................................................................... ....... 53



# AIR QUALITY MODEL PERF ORMANCE EVALUATION ............................ 53

Statistical and Gr aphical An alyses ............................................................................................. 53

Subregional Pe rformance ....................................................................................................... 5 3

Multi-Species Evaluations...................................................................................................... 53

Aloft Comp arisons .............................................................................................................. ... 53

Acceptable Model Performance ............................................................................................. 53

Sensitivity Analys es ........................................................................................................... ........ 53

Diagnostic Simulations......................................................................................................... .. 53



# USE OF THE MODEL RESULTS....................................................................... 53

Attainment De monstration ....................................................................................................... .. 53

Relative Reducti on Factors..................................................................................................... 53

Carrying Capacity Estimation ................................................................................................... . 53



# REFERENCES ..................................................................................................... 53

# LIST OF FIGURES

FIGURE 1 Nested MM5 Domains ............................................................................................... 53

FIGURE 2 The Fine-Scale (5 km) MM5 Domain........................................................................ 53

FIGURE 3 2007 AQMP Ozone Modeling Domain ..................................................................... 53

FIGURE 4 2007 AQMP PM Modeling Doma in, inside the Ozone Modeling Domain ............... 53



# LIST OF TABLES

TABLE 1 National Ambient Air Quality Standa rds Compliance Status in the South Coast Air

Basin.......................................................................................................................... ............. 53

TABLE 2 Ozone Design Value (ppb ) for Each Statio n, 2000-2004 ............................................ 53

TABLE 3 Summary of Proposed 2007 AQ MP Model Selection and Appli cation ...................... 53

TABLE 4 Tentative 2007 AQMP Schedule................................................................................. 53

TABLE 5 Vertical Structure fo r the MM5 Meteorol ogical Model............................................... 53

TABLE 6 Vertical Structures for the CAMx Ozone and PM Simulations................................... 53

TABLE 7 Summary of Ozone Episodes to be Simulated for the 2007 AQMP............................ 53

TABLE 8 Ozone Episod e Characteri zation.................................................................................. 53

TABLE 9 Highest 24-Hour Averaged SSI PM2.5 Concentration in 2005................................... 53

TABLE 10 SAPRC-99 Chem ical Mechanis m Species ................................................................ 53

TABLE 11 Proposed Meteorologi cal Input Performance Benchmarks ....................................... 53

TABLE 12 Performance Goals for 1-Hour Ozone....................................................................... 53

# LIST OF ACRONYMS

Several acronyms are used in the modeling protocol document. For

convenience, the acrony ms used are listed below to aid the reader.

AAMA – A merican A utomobile M anufacturer's A ssociation

AGL – A bove G round L evel

AQMD – A ir Q uality M anagement D istrict

AQMP – A ir Q uality M anagement P lan

AQMPAG A ir Q uality M anagement P lan A dvisory G roup

AUSPEX – A tmospheric U tility S ignatures, P redictions, and

Ex periments

AVHRR – A dvanced V ery H igh R esolution R adiometer

BEIS – B iogenic E mission I nventory S ystem

CAA – C lean A ir A ct Amendment of 1990

CAMx – C omprehensive A ir-Quality M odel with Ex tensions

CARB – C alifornia A ir R esources B oard

CBM – C arbon B ond M echanism

CCAA – C alifornia C lean A ir A ct

CEFS – C alifornia E mission F orecasting S ystem

CMAQ – C ommunity M ulti-scale A ir Q uality (model)

CO – C arbon M onoxide

COG – C ouncil o f G overnments

DARS – D ata A ttribute R ating S ystem

DWM – D iagnostic W ind M odel

DTIM – D irect T ravel I mpact M odel

EIWG – E mission I nventory W orking G roup

EKMA – E mpirical K inetics M odeling A pproach

EMFAC – Em ission Fac tor (model)

FCM – F lexible C hemical M echanism

FDDA – F our-D imensional D ata A ssimilation

GAP – G eographical A pproach to P rotection of Biological

Diversity

GCM – G lobal C limate M odel

ICAPCD – I mperial C ounty A ir P ollution C ontrol D istrict

IOP – I ntensive O peration P eriod

LIDAR – Li ght D etection a nd R anging

MDAQMD – M ojave D esert A ir Q uality M anagement D istrict

MM5 – M esoscale Meteorological M odel (5 th generation)

MWG – M odeling W orking G roup

NAAQS – N ational A mbient A ir Q uality S tandard

NDVI – N ormalized Difference V egetative I ndex

# NOx – N itrogen Ox ides

NO2 – N itrogen D ioxide

PDT – P acific D aylight T ime

PM – P articulate M atter

PM10 – P articulate M atter less than 10 microns equivalent

aerodynamic diameter

PM2.5 – P articulate M atter less than 2.5 microns equivalent

aerodynamic diameter

PMcourse – PM10 – PM2.5

PMfine – PM2.5

RADM – R egional A cid D eposition M odel

RECLAIM – Re gional C lean A ir I ncentives M arket

ROG – R eactive O rganic G ases

RRF – R elative R eduction F actor

SANDAG – San D iego A ssociation of G overnments

SAPRC – S tate A ir P ollution R esearch C enter

SAQM – S ARMAP A ir Q uality M odel

SARMAP – S JVAQS/A USPEX R egional M odel A daptation P roject

SBCAG – S anta B arbara C ounty A ssociation of G overnments

SBCAPCD – S anta B arbara C ounty A ir P ollution C ontrol D istrict

SCAB – S outh C oast A ir B asin

SCAG – S outhern C alifornia A ssociation of G overnments

SCAQMD – S outh C oast A ir Q uality M anagement D istrict

SCAQS – S outhern C alifornia A ir Q uality S tudy

SCE – S outhern C alifornia E dison

SCOS97 – S outhern C alifornia O zone S tudy (1997 )

SDCAPCD – S an D iego C ounty A ir P ollution C ontrol D istrict

SIP – S tate I mplementation P lan

SJVAQS – S an J oaquin V alley A ir Q uality S tudy

SOx – S ulfur Ox ides

STMPRAG – S cientific, T echnical, M odeling and P eer R eview

A dvisory G roup

TOG – T otal O rganic G ases

UAM – U rban A irshed M odel

USEPA – U nited S tates E nvironmental P rotection A gency

UTM – U niversal T ransverse M ercator

VCAPCD – V entura C ounty A ir P ollution C ontrol D istrict

VMT – V ehicle M iles T raveled

VOC – V olatile O rganic C ompound

WSPA – W estern S tates P etroleum A ssociationKRD: DRAFTModelingProtocol.doc 3/2/2007 

# 1 

# INTRODUCTION

The federal Clean Air Act (CAA) requires areas with unhealthy levels of

ozone , carbon monoxide, nitrogen dioxi de, sulfur dioxide, and inhalable

particulate matter to develop plans, known as State Implementation Plans

(SIPs), describing how they will a ttain national ambient air quality

standards (NAAQS). The 2007 Air Quality Management Plan (AQMP or

Plan) for the South Coast Air Basi n (Basin) will meet the SIP update

requirements for this area, demonstra ting NAAQS attainment. The federal

Clean Air Act (CAA) requires the use of photochemical grid models that

are approved by the Unites States Environmental Protection Agency

(USEPA) to perform the attainment demonstration. This document

addresses the air quality modeling protocol for the 2007 AQMP, as

developed through a joint cooperative e ffort between the staff of the South

Coast Air Quality Management Dist rict (AQMD or District) and the

California Air Resources Board (CARB ), with technical oversight from the

Scientific, Technical, Modeling a nd Peer Review Advisory Group

(STMPRAG).

The objective of this modeling protocol is to define the methodology to be

used for simulating the formation and transport of ozone and particulate

matter in the Basin, including:

• the model(s) to be used;

• the modeling domain;

• the horizontal and vertic al grid resolution;

• the annual PM period and ozone a nd PM episodes to be simulated;

• the model input data, including meteorology, emissions, initial

conditions; and lateral and top boundary conditions;

• the process for model performance evaluation.

In addition, the protocol outlines th e attainment demonstration process,

including a review of the California Clean Air Act (CCAA) requirements.

This protocol document is intended to be dynamic and will be updated in

response to reviewer comments and to reflect the results of new

information that will emerge dur ing the AQMP modeling process.

In order to devote the maximum resources practicable to the development

of the District's 2007 AQMP, the Ex ecutive Officers of CARB and AQMD

have agreed to jointly develop the emissions and air quality modeling

needed to determine the carrying capacity and attainment demonstration for

the ozone and PM standards. The te chnical staffs of both agencies are

working closely together to plan and carry out th e necessary work for theKRD: DRAFTModelingProtocol.doc 3/2/2007 

# 2 

# AQMP and are committed to intensive and timely coordination to ensure

that it is based on the soundest science possible. Both agencies agree that

their staffs will collaborate on this wo rk such that the product will be

mutually acceptable modeling analys es for use in the 2007 Plan.



# Background

Regulatory Modeling Requirements and Guidance

The 1990 amendments to the federal CAA set new deadlines for attainment

based on the severity of the pollution problem and launched a

comprehensive planning process for attaining the NAAQS. The

promulgation of the new national eight -hour ozone standard and the fine

particulate matter (PM2.5) NAAQS in 1997 required additional statewide

air quality planning efforts. In resp onse to new federal regulations, SIPs

must also address ways to improv e visibility in national parks and

wilderness areas. SIPs demonstrating attainment of the federal ozone

standard must be adopted by the local air districts and CARB, and

submitted to the USEPA by June 15, 2007.

USEPA's guidelines on air quality m odeling previously recommended the

use of the Urban Airshed Model (UAM ) for attainment demonstrations

involving entire urban areas. However, USEPA revised its

recommendation (USEPA, 2001a) to no longer include a recommended air

quality model for ozone. Instead, USEPA recommends that air quality

models proposed for an ozone attain ment demonstration be subjected to

model performance evaluations to demonstrate that they are appropriate for

attainment demonstration purposes.

USEPA issued the Guideline for Regulatory Applications of the Urban

Airshed Model (USEPA, 1991) and Guidance on the Use of Modeled

Results to Demonstrate Attain ment of 1-hour Ozone NAAQS (USEPA,

1996) to assist states in preparing th e attainment demonstration required by

the CAA. In addition, the CARB Technical Guidan ce Document:

Photochemical Modeling (CARB, 1992) provides photochemical modeling

guidance for use by the districts to en sure the technical validity of the

modeling results. Most recently, USEPA has finalized attainment

demonstration guidance for the 8-hour ozone NAAQS (USEPA, 2005).

The ozone modeling protocol in this document is based on these guideline

documents. Guidance for the PM portion of the m odeling protocol utilizes

the Draft Guidance for Demonstrating Atta inment of Air Quality Goals for

PM2.5 and Regional Haze (USEPA, 2001b).KRD: DRAFTModelingProtocol.doc 3/2/2007 

# 3 

# Under the federal Clean Air Act of 1990 (CAA), the South Coast Air Basin

(Basin) was classified as an “ext reme” nonattainment area for 1-hour

ozone. Section 182(c)(2)(A) of the CAA set November 15, 1994 as the

deadline for submission of a SIP to de monstrate attainment of the NAAQS

for ambient 1-hour ozone of 0.125 parts per million (ppm) by December

2010. AQMD satisfied that CAA re quirement with the submittal of the

1994 AQMP in September 1994. A s ubsequent revision was submitted to

USEPA in February 1997. This was amended in 1999 to revise the Basin

ozone portion of the 1997 AQMP due to its partial approval/disapproval by

USEPA. The 2003 AQMP took advantage of information obtained from

the 1997 Southern California Ozon e Study (SCOS97) and emissions

inventory enhancements. It updated the attainment demonstration for the

ozone and PM10 particulate matter NAA QS, replaced the 1997 attainment

demonstration for the CO NAAQS, and updated the maintenance plan for

the NO2 NAAQS that the Basin has met since 1992. In 2005, AQMD

submitted a CO attainment request and maintenance plan to CARB and

USEPA; approval of this is pending.

In July 1997, th e USEPA established new oz one NAAQS of 0.085 ppm

based on an 8-hour average measurem ent. Due to legal challenges, the

final form of the ozone NAAQS has be en implemented in two phases. The

Phase 1 ozone implementation rule, fina lized on June 15, 2004, defined the

classification scheme for 8-hour ozone nonattainment areas and revoked the

1-hour ozone NAAQS, while requiring st ates to maintain control programs

which were included in their state implementation plans (SIP) for the 1-

hour standard. Fifteen areas in Califo rnia were designated that violate the

federal 8-hour ozone standard. Each nonattainment area's classification and

attainment deadline is based on the seve rity of its ozone problem. Southern

California’s nonattainment areas and atta inment deadlines are: South Coast

Air Basin (2021); Coachella Valle y (2013); Ventura County (2010);

Western Mojave Desert (2010); Ante lope Valley (2010); San Diego (2009-

2014); and Imperial County (2007).

The Phase 2 ozone rule, adopted Novemb er 9, 2005, described the actions

that states must take to reduce gro und level ozone and set the deadline for

ozone SIP submittal of June 2007. The AQMD began air quality modeling

analyses related to the 8-hour ozone standard during the 1997 AQMP, prior

to the final NAAQS implementation. Th is analysis effort was continued for

the 2003 AQMP. The 2 007 AQMP modeling will e xpand the 8-hour ozone

analysis and include an attainment de monstration for the current form of

the NAAQS. The 2007 AQ MP will include an analys is of 1-hour ozone toKRD: DRAFTModelingProtocol.doc 3/2/2007 

# 4 

# provide additional milestones for progre ss of ongoing control programs and

for continuity with previous efforts.

AQMP Ozone Modeling History in the South Coast Air Basin

The first Air Quality Management Plan (AQMP) for the Basin was

produced in 1979 as part of a revision to California's SIP. The 1979 AQMP

indicated that it would not be possible to achieve the federal 1-hour ozone

air quality standard of 0. 12 ppm by 1982. Because the emission controls

discussed in the 1979 AQM P would not be fully e ffective until after 1982,

CARB and USEPA granted an extension to 1987 for achievement of the

standard. As part of that extension, a revision to the AQMP was performed

by the AQMD in 1982 which included a new series of modeling analyses to

address concerns regarding the original 1979 modeling analysis.

For both the 1979 and 1982 AQMP revi sions, the city-specific Empirical

Kinetics Modeling Approach (EKMA) wa s applied. The 1979 AQMP used

the city-specific EKMA procedures th en in existence. The 1982 AQMP

revision used a more sophisticated ve rsion of the EKMA procedures and

also contained sensitivity analyses (Appendix VI-A of the 1982 AQMP

revision). The UAM was used in conj unction with the EKMA analyses to

evaluate the effect of applying a ll feasible control measures by 1987

(Appendix VI-E of the 1982 AQMP revision). On the basis of those

modeling studies, it was determined that hydrocarbon reductions on the

order of 75 percent or gr eater would be required to attain the federal

standard by 1987, given a forecasted 23 percent reduction in oxides of

nitrogen. Forecasted emission data indicated that only a 33 percent

hydrocarbon reduction could be expected by 1987. Issues raised during the

1979 and 1982 AQMP revisions high lighted the need to use a threedimensional, photochemical model such as the UAM to better understand

the complex interactions between pr ecursor emissions, meteorology, and

the formation of ozone in the Basin.

For the 1989 AQMP revi sion, the UAM was applie d to a single, multiday,

ozone episode to demonstrate attain ment of the National Ambient Air

Quality Standard (NAAQS) for ozone . It was determined from the

modeling analysis that hydrocarbon and oxides of nitrogen emission

reductions of more than 80 percent woul d be needed in order to attain the 1-

hour ozoneNAAQS by the year 2007. The 1989 AQMP revision outlined

three levels of controls (identified as Tiers I, II, and III) that separated the

proposed control measures by known a nd proven technologies from those

technologies anticipated to be ava ilable within the next 20 years.KRD: DRAFTModelingProtocol.doc 3/2/2007 

# 5 

# For the 1991 AQMP , the AQMD used the UAM to further assess the

effectiveness of the three tiers of control measures in reducing ambient

ozone levels. To complement the si ngle, multiday ozone episode used for

the 1989 AQMP revision, two additional ozone episodes were modeled to

investigate the effect of projected emission reductions on future ozone

concentrations during a wider variety of meteorological conditions.

Additional evaluations of model pe rformance, includi ng new graphical

procedures and subregional performanc e statistics, were used to ensure

adequate representation of the phys ical and chemical processes that

influence ozone formation in the Basin.

A number of improvements were made to the modeling analysis for the

1994 AQMP. Growth f actors for population and vehicle miles traveled

(VMT) were revised to reflect the 1 990 Census data and the economic

climate of the early 1990s, and im proved transportation modeling was

considered. The modeling analysis benefited from a number of AQMD,

CARB, and SCAG studies that im proved the area source emission

inventory (Appendi x III-A). On-road, mobile emission estimates were

improved with the use of the latest CARB emission factors program,

EMFAC7F. Five ozone episodes were simulated to evaluate control

strategy effectiveness. In addition to the June 5-7, 1985, episode used in

the 1989 AQMP, and the two Southe rn California Air Quality Study

(SCAQS) episodes (August 26-28, 1987 , and June 23-25, 1987) added for

the 1991 AQMP analysis, two add itional episodes (July 13-15, 1987, a

SCAQS episode, and September 7-9, 1987) were simulated for the 1994

AQMP. In this manner, control strate gy decisions were based on a range of

meteorological conditions, thereby re ducing uncertainty in the control

strategy’s effectiveness. It was dete rmined that hydrocarbon and oxides of

nitrogen emission reductions on th e order of 80 and 60 percent,

respectively, would be needed in order to attain the NAAQS.

Based on the AQMD’s experience with the five ozone episodes used in

preparing the 1994 AQMP, it was decided to drop the June 1985

meteorological episode for the 1997 AQMP. The AQMD believed that the

1987 meteorological episodes were sati sfactorily evaluated. Since the 1985

meteorological episode was based on routinely monitored data, it was

believed that the 1987 SCAQS episode s provided improved performance.

In October 1998, AQMD provided to the USEPA a “weight of evidence”

analysis that indicated th at even without the June 1985 episode, a viable

ozone attainment demonstration could be made.

As a result of intense interest fo r aerometric databases to support regional

ozone modeling, a large-scale field measurement program was carried outKRD: DRAFTModelingProtocol.doc 3/2/2007 

# 6 

# in southern California during the Su mmer of 1997 to collect sufficient

aerometric data to allow data analys ts and modelers to characterize and

simulate ozone formation and fate in the region. Several agencies and

others participated during the planning and operational phases of the field

study, including CARB, USEPA, the lo cal air districts, the US Navy, the

US Marines, and the marine industry. The 1997 Southern California Ozone

Study, or SCOS97, occurred over a four month period from June 15

through October 15, 1997 and capt ured several episodic ozone days.

The 2003 AQMP updated the attainment demonstration for the federal

standards for ozone and PM10; replaced the 1997 attainment demonstration

for the federal CO standard and provide d a basis for a main tenance plan for

CO for the future; and updated the ma intenance plan for the federal NO2

standard that the Basin has met since 1992. New ozone episodes, including

these from SCOS97, were included as complementary or replacement

episodes in the 2003 AQ MP. This revision to the AQMP also addressed

several state and federal planning requirements and incorporated significant

new scientific data, primarily in the form of updated emissions inventories,

ambient measurements, new meteorol ogical episodes and new air quality

modeling tools. This re vision pointed to the need for additional emission

reductions (beyond those incorporat ed in the 1997/99 Plan) from all

sources, specifically those under the jurisdiction of CARB and the USEPA

which account for approximately 80 percent of the ozone precursor

emissions in the Basin.

The 2007 AQMP modeling effort fo cuses primarily on recent ozone

episodes in 2004 and 20 05. These periods better reflect emissions

conditions following the reformulation of gasoline in California. The

August 1997 episod e from SCOS97 will be retained for continuity with the

previous AQMP analyses. The 2007 AQMP will be consistent with and

will build upon the modeli ng approaches take n in the previous SIP efforts

for the South Coast Air Basin, utiliz ing the latest tools and technical

guidance.

AQMP PM Modeling History in the South Coast Air Basin

PM is a multicomponent pollutant in cluding inorganic species such as

sulfate, nitrate, ammonium, sodium , chloride, and organic compounds,

elemental carbon, and a variety of trace metals. The PM10 modeling

analysis shows that the annual av erage PM10 concentration is the

controlling factor for attainment of the federal PM10 standards in the

future. Although there were several PM10 modeling tools, there had been

no single reliable annual PM10 model available to address theKRD: DRAFTModelingProtocol.doc 3/2/2007 

# 7 

# multicomponent nature of the PM10. Therefore, a multi-pronged modeling

methodology was employed to asse ss regional PM10 and demonstrate

future compliance with the federal PM standards.

For the 1989, l991 , and 1994 AQMP, the Chemical Mass Balance (CMB)

model for primary and secondary orga nic carbon and the Particle-In-Cell

(PIC) model for sulfate a nd nitrate were used for annual PM10 analysis.

And speciated linear rollback (SLR) was used for maximum 24-hour PM10

analysis.

For 1997 AQMP, a new annual PM10 modeling methodology, the

UAM/LC model, was developed and a pplied. The Urban Airshed Model

(UAM) (Ames, et al., 1985; and Morris, et al., 1990a, 1990b) was used as a

host air quality model and the paramete rized linear chemistry (LC) module

was incorporated into the UAM. UAM was adapted to address the

formation of particulate nitrate, su lfate, and ammonium and handling of

primary particles by replacing the UAM standard chemical mechanism with

the parameterized linear chemistry module. UAM/LC, unlike the PIC

model, addresses the 3-dimensional aspects of transport and diffusion,

varying mixing height, ammonia emissi ons change, and particulate nitrate

concentrations. However, the UAM/LC model cannot handle secondary

organic carbon because the current pa rameterized linear chemistry does not

include organic chemistry. Secondary organic carbon is treated separately

by the CMB model.

For the 2003 AQMP, UAM /LC model was further enhanced to include

secondary organic carbo n and PM2.5 partition. The resulting UAMAEROLT model, for the first time, provided a more robust, stand-alone platform

for primary and secondary annu al PM2.5 and PM10 simulations.



# 2007 AQMP Modeling Analysis Goals

The 2007 AQMP modeling will focus primarily on the 8-hour ozone and

the annual and 24-hour PM2.5 NAAQ S attainment demonstration and

reasonable further progress. The a pplicable NAAQS, along with the

current attainment status of the S outh Coast Air Basin and recent design

values, are presented in Table 1. A lthough the 1-hour federal standard was

revoked in 2005, the analysis of 1- hour ozone will be retained as a

benchmark of progress toward meeting the former 1-hour ozone NAAQS,

as well as toward the State of Californi a ozone standards. In addition to the

PM2.5 NAAQS attainment demonstr ation, the particulate modeling

analysis will include annual and 24- hour PM10 attainment demonstrations.KRD: DRAFTModelingProtocol.doc 3/2/2007 

# 8 

# Further, the 2007 AQMP modeling w ill address mainte nance plans for

Carbon Monoxide (CO) and Nitrogen Dioxide (NO2).

The modeling effort may also include initial modeling strategy

development toward demonstrating a ttainment of the inhalable coarse

particle (PM10-2.5) NAAQS and a stricter PM2.5 NAAQS recently

proposed by USEPA. The proposed standards are:

• PM10-2.5: 98 th 

# percentile 24-hour PM10-2.5 in a year, averaged over 3

years not to exceed 70.4 μg/m 3 

# ; no annual standard;

• PM2.5: 98 th 

# percentile 24-hour PM2.5 in a year, averaged over 3 years

not to exceed 35.4 μg/m 3 

# ; no change to annual standard.KRD: DRAFTModelingProtocol.doc 3/2/2007 

# 9 

# TABLE 1

National Ambient Air Quality Standard s Compliance Status in the South

Coast Air Basin

8-Hour Ozone 24-Hour PM2.5 Annual PM2.5

Standard 3-year average of

the 4 th 

# highest

concentration not

to exceed 0.084

ppm 3-year average of

the 98th percentile

of 24-hour

concentrations not

to exceed 65.4

μg/m 3 

# 3-year average of 4

quarterly averages

not to exceed 15.04

μg/m 3



# Classification Severe-17

[may petition for

Extreme] Non-Attainment Non-Attainment

Attainment

Date 2021

[2024, if Extreme] 2015 2015

Design Value 0.127 ppm

(2002-2004) 67 μg/m 3



# (2002-2004) 24.8 μg/m 3



# (2002-2004)

24-Hour PM10 Annual PM10

Standard 3-year average of

the 99th percentile

of 24-hour

concentrations not

to exceed 154

μg/m 3 

# 3-year average of 4

quarterly averages

not to exceed 50.4

μg/m 3



# Classification Serious

Non-Attainment Serious

Non-Attainment

Attainment

Date 2006 2006

Design Value 159 μg/m 3



# (2002-2004) 57 μg/m 3



# (2002-2004)KRD: DRAFTModelingProtocol.doc 3/2/2007 

# 10 

# Ozone Design Value Determination

Since the base year emissions are fo r 2004, air quality da ta from the three

overlapping 3-year periods from 2 000 through 2004 were used for

calculation of the 8-hour ozone de sign values for each AQMD air

monitoring station. These are shown in Table 2, along with the Relative

Reduction Factors (RRF) needed. Pe r USEPA guidance, the design value

averages are truncated (not rounded).

TABLE 2

Ozone Design Value (ppb) for Each Station, 2000-2004

(Average of the 4th highest 8-hour stati on concentration in ea ch 3-year period)

Station 2000-2002

Design

Value 2001-2003

Design

Value 2002-2004

Design

Value Current

Design Value

(DVC) RRF

Required



# AZUS 102.3 101.0 101.0 101.43 0.8284

BURK 91.7 91.3 91.3 91.43 0.9190

LGBH 61.7 60.7 60.7 61.03

RESE 93.3 106.3 106.3 101.97 0.8235

POMA 89.7 96.7 96.7 94.37 0.8898

LYNN 51.0 53.3 53.3 52.53

PICO 80.3 79.0 79.0 79.43

CELA 79.3 78.3 78.3 78.63

PASA 96.3 95.3 95.3 95.63 0.8787

SCLR 113.3 126.7 126.7 122.23 0.6874

WSLA 69.3 73.3 73.3 71.97

HAWT 69.3 71.0 71.0 70.43

GLEN 110.7 114.3 114.3 113.10 0.7427

ANAH 69.7 71.7 71.7 71.03

LAHB 75.7 74.7 74.7 75.03

CSTA 67.3 71.3 71.3 69.97

MSVJ 80.0 82.7 82.7 81.80

PLSP 105.3 108.3 108.3 107.30 0.7829

RIVR 108.0 112.7 112.7 111.13 0.7561

PERI 114.0 115.7 115.7 115.13 0.7298

INDI 92.3 96.7 96.7 95.23 0.8824

ELSI 104.3 109.0 109.0 107.43 0.7821

UCRI 113.3 117.3 117.3 115.97 0.7241

BNAP 110.3 118.7 118.7 115.90 0.7248

UPLA 114.0 113.0 113.0 113.33 0.7414

CRES 129.0 131.7 131.7 130.80 0.6422

FONT 112.3 123.0 123.0 119.43 0.7035

SNBO 114.7 118.7 118.7 117.37 0.7155KRD: DRAFTModelingProtocol.doc 3/2/2007 

# 11 RDLD 120.0 128.3 128.3 125.53 0.6693

MLOM 103.0 106.0 106.0 105.00 0.8000

RHIS 130.3 136.7 136.7 134.57 0.6241



# Overview of the Modeling Analysis

The analysis techniques currently recommended for attainment

demonstrations using ai r quality models have ch anged significantly from

those used in past demonstrations. In Guidance on the Use of Models and

Other Analyses in Attainment Demons tration for the 8-hour Ozone NAAQS

(USEPA, 2005), USEPA recomm ends that the air quality models be used in

a relative sense in concert with obs erved air quality data rather than

applying the air quality model in a deterministic sense. The Relative

Reduction Factor (RRF) which takes the ratio of future to present predicted

air quality is multiplied to an “ambie nt design value” to demonstrate

attainment. The proposed ozone mode ling analysis is comprised of the

following tasks:

• Identify potential, new ozone meteor ological episodes to be used.

These episodes should represent the different meteorological conditions

that are conducive to ozone formation in the Basin.

• Among the widely accepte d state-of-the-science ozone models, CAMx

was selected for the attainment demonstration. CMAQ may be

employed in the sensitivity analysis and weight-of-evide nce section as a

supportive modeling tool.

• Develop model inputs. This task in cludes evaluation of the raw data

and of the model input files devel oped from them. The input files will

be evaluated using graphi cal and other techniques.

• Simulate each episode with the proposed ozone models. This task

includes a separate performance eval uation for each episode and each

model. Documentation of the simulation results and performance

evaluations will be provided.

• Project ozone air quality with proposed control measures in effect for

the years 2007, 2010, 2 014, and 2020. This task includes the required

attainment demonstrati on using RRF. Model projections for the year

2007 are necessary since that is the year that the CAA requires

attainment for severe-17 areas, such as the Coachella Valley and

Mojave Desert Ozone Nonattain ment Areas. Ozone air quality

projections to 2020 will be used to demonstrate that the control strategy

maintains the ozone NAAQS and to establish emission budgets needed

for conformity purposes.KRD: DRAFTModelingProtocol.doc 3/2/2007 

# 12 

# The work to do th e foregoing tasks will be divi ded between the AQMD and

CARB staffs and they will fully sh are all analyses, model inputs and

outputs, findings, and conclusions. Consensus on each component of the

analysis shall be reached before proc eeding with subsequent components.

In the event of technical disagreement on any of the work elements, the

AQMD and CARB staffs shall attempt to reach consensus on a mutually

acceptable approach. In th e event that consensus cannot be reached, the

disagreement will be elevated to the Executive Officers for resolution.

Table 3 summarizes the model selecti on and application elements for the

2007 AQMP and the changes from the 2003 AQMP modeling.KRD: DRAFTModelingProtocol.doc133/2/2007TABLE 3Summary of Proposed2007 AQMP Model Selection and Application2007 AQMP Element 2003 AQMP Element Selection Process/Issues/CommentsOzoneDispersion Platform: CAMxChemistry: SAPRC99OzoneDispersion Platform: UAMChemistry: SAPRC99•Peer Group Recommendation tomove to state-of-artmass-consistent model/chemistry•Integrates with numerical weather model output•CAMx used by several agencies for SIPdevelopment and supported by Environ•Option for one atmosphere modeling•Alternates CMAQ: Emissions preprocessing moreextensive CALGRID: performance similar to CAMxwith no one-atmosphere modelingPM10/PM2.5 Annual andEpisodicDispersion Platform: CAMxChemistry:•AERO-LT with CB-IV•Enhanced CFI schemewith CB-IV•Optional One AtmosphereAerosol chemistryPM10/PM2.5Dispersion Platform: UAMChemistry: AERO-LT with CBIV•CAMx PM dispersion consistent with ozonediscussion above.•Installed SCAQMD version of AERO-LT into latestCAMx code (V4.20).•Enhanced CAMx two section CFI aerosol scheme.It will be compared with AERO-LT.Meteorology•MM5/4DDA•Hybrid MM5/CALMET•MM5 initialized usingNCEP dataMeteorology•CALMET ObjectiveAnalysis•HybridMM5/CALMET•EPA has expressed concerns about using the hybridapproach•MM5/4DDA is more mass consistent but doesn’tcapture localize wind impacts (transport to SanFernando Valley)•Testing several land use assumptions withprognostic model to optimize wind fields andvertical mixing/diffusivity fields.•Using Environ’s and Aerospace met-modelperformance evaluation software.KRD: DRAFTModelingProtocol.doc143/2/2007•Where possible take advantage of enhancedobservation field data (e.g. 3D-Var)Domain/ CoordinatesSCOS97Meteorology: Lambert ConformalEmissions and Model application:UTMOzone: 16 layersPM10/2.5: 8 layersDomainSCOS97Meteorology: UTMEmissions and Model application:UTMOzone & PM10 5-layers•Maintained the SCOS97 domain however emissionsinventories require coordinate system offsets toadjust from statewide modeling domain. Impactsare to biogenic and CEDARS output.KRD: DRAFTModelingProtocol.doc153/2/2007TABLE 3 (Continued)2007 AQMP Element 2003 AQMP Element Selection Process/Issues/CommentsEmissions Inventories•2002 Base year•Enhanced aircraft/airportand shipping inventories•POLA/POLB updates•EMFAC2007ogross adjustmentso“focused”inventoriesoFinal public model•Adjustments to fugitivePM10/PM2.5 categoriesEmissions Inventories•1997 Ozone base year &1995 PM10 base year•Updated aircraft/airportand shipping inventories•EMFAC2002V2.01(major effort to developsurrogates for areasources)•2002 Inventory will be used to back-cast 1997,2000 and project inventories through 2030 formilestone years•Waiting on SCAGS’ growth estimates based on2004 RTP which is expected to differ only slightlyfrom the 2007 RTP.•Episodic temperature and humidity fields submittedto CARB for biogenic emissions•CARB is adjusting temperature fields for planninginventory development•Gridded inventories awaiting focused on and offroad model output and supplemental inventories•No weekend trip model output available fromSCAG•CARB will develop a “weekend” overlay to mimicVMT based on Caltransin-road counter dataAir Quality Model PerformanceOzone•Assess model performancebased on both 1-hour and8-hour statistics•60 ppb threshold (bothindices)•Weight of EvidenceAnalysis•Mid-Course simulationsPM10/PM2.5 (annual andepisodic)•Base statistics at speciationAir Quality Model PerformanceUse EPA recommendations for 1-hour ozone and outline for PM10and CO.Ozone•Mid-Course 2002simulation•Comparative relativereduction forUAM/CAMx/CMAQ perPeer Advisory GroupRecommendationPM10•Will review thresholds and geographical zonesused for ozone performance evaluation.•Conduct sensitivity simulations to test emissionsmass, VOC/NOx ratios, emissions timing (dailyand weekend vs. weekday), ammonia massKRD: DRAFTModelingProtocol.doc163/2/2007sites•Weight of evidenceanalysis•Mid-Course simulationsAnalyzed “hot spot” grid cellemissionsRelative Reduction FactorsRRF: sites specific applied to 3-year average of the design value(PM2.5 and ozone)Relative Reduction FactorsTested for ozone and PM10 butnot appliedKRD: DRAFTModelingProtocol.doc173/2/2007TABLE 3 (Continued)2007 AQMP Element 2003 AQMP Element Selection Process/Issues/CommentsEpisode SelectionOzone•1997 August 3-7•1997 Seasonal:August•2004 June 3-7•2004 August 4-8•2005 May 17-24•2005 July 14-19•2005 August 25-29PM10/2.5•Annual 2005 (January– December)•2005 October 19-25•2005 March 6-12Episode SelectionOzone•1997 August 4-7PM10/2.5•January – December1995•Episodic: RollbackCO•1997 October 31-November 1•Meteorological episodes include SCOS97 andpost California Fuel reformulation (2003)•MATES-III meteorological data basedevelopment concurrent with AQMP data basedevelopment•Contract with Aerospace to provide additionalobservations data and MM5 initialization fieldsusing (satelliteingest and 3DVARInitial/Boundary ConditionsOzone•EPA recommendedboundary conditions•40 ppb ozone top profiledto lower layersPM10/2.5•Monthly varyingemissions generatedInitial/Boundary ConditionsOzone•Use EPArecommendedboundary conditions•Per SCOS97 samplingtested 60 ppb ozonealoftPM10•Will test varying top boundary concentration•Review alternate approaches for quantifyingboundary conditionsKRD: DRAFTModelingProtocol.doc183/2/2007boundary conditions(simulate model with zeroboundary conditions andlet model generateboundary -- using 3-5 gridcells from model domainboundary as representativeof boundary)Monthly varying boundaryconditions based on coastalmonitoring site data1/4/2006KRD: DRAFTModelingProtocol.doc 

# 19

3/2/2007 

# 2007 AQMP Schedule

The schedule of 2007 AQMP modeling ef forts is driven by the regulatory

deadlines for SIP submittal to USEPA, which is June 15 , 2007 for 8-Hour Ozone.

Table 4 outlines the tentativ e schedule of events leading up to the SIP submittal.

TABLE 4

Tentative 2007 AQMP Schedule

Task Due Date

Episode Selection January 2006

Air Quality and Meteorological Data Preparation January 2006

Emission Inventory Preparation April 2006

Performance Evaluation May 2006

Control Strategy Development May 2006

Attainment Demonstration June 2006

Draft SIP Documents September 2006

District Board Approval of Final SIP November 2006

CARB Board Approval of Final SIP February 2007

SIP Submittal to USEPA June 15, 2007

[Add EIR Schedule,

Alternative Modeling,

Public Workshops]



# AQMP Modeling Technical Oversight

The AQMD Governing Board ha s established several advi sory groups to assist

with technical oversight and scientific community and business involvement in air

quality programs. The mission of the Ai r Quality Management Plan Advisory

Group (AQMPAG), whose membership is appoi nted by the Board, is to review the

overall aspects of a draft air quality management plan and to make

recommendations concerning emission inve ntories, modeling, control measures,

and socioeconomic impacts. Task s of the AQMPAG, include:

• Provide review and comments on (1) studies relevant to adva ncing scientific

and technical knowledge in support of AQMP preparation; (2) emissions

inventory development and modeling appr oaches; (3) the development of new

and revised control measur es, including on-and off-road mobile sources; (4)

socioeconomic data and evaluations.

• Foster coordinated approaches towa rd overall attainment strategies.

• Assist in resolving key technical issues.KRD: DRAFTModelingProtocol.doc 

# 20

3/2/2007 

# In addition, the AQMD Governing Board ha s established a more focused technical

oversight committee to review the tec hnical aspects of the ongoing modeling

analyses. Since the late 1980’s the AQMD has had socioeconomic and modeling

working groups, when the Governing Bo ard passed a resolution to form the

Modeling Working Group (MWG) during the adoption of the 1989 AQMP

revision. The MWG, comprised of indi viduals with photochemical and aerosol

modeling expertise, provi ded oversight and techni cal consensus on AQMP

modeling issues. In 1997, the MWG was reconstituted as the Scientific,

Technical, and Modeling Peer Review Advisory Group (STMPRAG). The

STMPRAG role expands upon that of the MWG and includes experts in

socioeconomic assessment and human health, providing review of AQMD

modeling, monitoring and re lated scientific issues.

The STMPRAG assists AQMD in resolving te chnical issues related to air quality

and socio-economic modeling by provid ing ongoing technical review and

consensus of procedures and analyses. The objectives of the STMPRAG are as

follows:

• Suggest methods to gather and pro cess meteorological, aerometric and

emission data with a specific fo cus on air quality modeling.

• Provide technical guidance to the ai r quality modeling efforts, with an

emphasis on ozone and particulate matter. Some specific areas of technical

guidance include: (1) Formulation of modeling approaches; (2) Selection and

development of appropriate modeling t echniques; and (3) Identification of

model performance evaluation methods.

• Review and provide comments on the AQMP modeling procedures and

analyses.

• Make recommendations on future m odeling resource requirements (i.e.,

staffing and computational needs).

• Recommend methods for interpretation of modeling results.

• Provide a linkage between the air quality and socio-economic modeling

communities, emphasizing the importance of future growth and economic

factors on future air quality attainment demonstrations.

The STMPRAG consists of approxim ately 20 members appointed by the

Governing Board, with representatives from USEPA, CARB, Southern California

Association of Governments (SCAG), the California Small Business Aliance

(CSBA), Southern California Edison (SCE), Western States Petroleum

Association (WSPA), and technical expert s from universities and consultant firms.KRD: DRAFTModelingProtocol.doc 

# 21

3/2/2007 

# Finally, as progress is made and products are available, interim results will be

shared with the interest ed public at appropriate times and locations.KRD: DRAFTModelingProtocol.doc 

# 22

3/2/2007 

# MODEL SELECTION

Meteorological Model

Background

Air quality models require three-dimensi onal, meteorological inputs. The key

parameters are winds, mixing he ights, temperature, and in solation. The windfields

describe the transport and dispersion of pollutants. Mixing heights define the

vertical extent of pollutant mixing near the surface. Temperature and insolation

fields influence emission rates and the rate s of chemical transformation. Because

meteorological measurements can be made only at discrete locations,

meteorological models are required to de velop the 3-dimensional fields required

by air quality models.

The meteorological models used to gene rate these three-dimensional fields are

generally of three types: objective, diagnostic or prognostic. Objective models are the least sophisticated meteorological mode ls. These models rely on interpolation

of observations. Obtaining a reasonable fi eld requires sufficient observations to

accurately represent the atmosphere. This is especially true for windfields. In

areas with complex terrain and bodies of water, such as the proposed modeling

domain, the meteorology can be quite comp lex, and a successful objective analysis

would require an extremely la rge number of observations.

Diagnostic models rely both on observations and constraints based on physical

concepts such as the conservation of mass. A diagnostic wind model can simulate

thermally induced circulations and the eff ects of surface friction. One example of

this type of model is the Diagnostic Wind Model (DWM) which is distributed by

the USEPA. For the DWM, the user firs t defines an initial-guess mean wind field

that can be representative of synoptic scale patterns. The domain mean wind is

then adjusted for the effects of terrain. Available observations are then used to

develop meteorological fields using obje ctive analysis. The initial guess and the

objective analysis are then combined using a weighting function based on distance

from observations. A criticism of diagnostic models is that the fields produced are

not consistent from one hour to the next . Since the processes which create the

wind, temperature, and mixing height fiel ds are relatively independent, these

models are also criticized for not being thermodynamically consistent between the

meteorological parameter fields.

Prognostic models are the most sophisticated of the meteorological models. They

are based on principles of atmospheri c physics, i.e., conservation of mass,

momentum, energy and moisture. As a result , they are computationally intensive.

The use of four dimensiona l data assimilation (FDDA) or observational nudging –KRD: DRAFTModelingProtocol.doc 

# 23

3/2/2007 

# where observations are introduced to the model as an additional forcing term – is

typically used in areas of complex mete orology to improve the accuracy of the

outputs. Another approach is objective combination, in which observations are

introduced after the model ha s estimated a value. Prognostic models are capable

of explicitly incorporating many of the phy sical flow processes important in the

domain. However, prognostic models have historically had problems estimating

fine-scale flow features due to the lim ited resolution of datasets used for

describing geographic features.

Previous AQMP Applications

In the past, CARB and AQM D have utilized prognostic , diagnostic, and objective

models to generate meteorological inputs for modeling. The National Center for

Atmospheric Research’s prognostic, no n-hydrostatic Mesoscale Model (MM5)

was applied for modeling in support of attainment planning in the San Joaquin

Valley. The SCAQMD also has experien ce with the SAIMM pr ognostic model.

Diagnostic models (WIND2D, WIND3D, DWM) have been applied in the

Sacramento area and in southern California to prepare meteorological input fields

for the application of photochemical m odels in those area s. CARB and AQMD

conducted a review of CALM ET, which may be viewed as an improved version of

the DWM and which is be ing distributed through the USEPA for air quality

modeling applications. The CALMET mode l has an added feature that allows a

hybrid meteorological field to be de veloped by merging the results from a

prognostic model, such as the Nationa l Center for Atmospheric Research

Mesoscale Model Version 5 (MM5), with an objective or diagnostic analysis

characteristic of the CALMET model. Th is hybrid approach has the potential to

take advantage of the prognostic capa bilities of MM5 in areas of the domain

where meteorological measurements are fe w, and utilizing measurements in an

objective analysis where there are many.

2007 AQMP Meteorological Modeling Approach

The SCOS97 field study genera ted a dataset with a relati vely high spatial density

of meteorological observations. Wh ile this dataset suggests that an

objective/diagnostic model could be adeq uate to develop the meteorological

parameter fields required for air quality modeling of the August SCOS97 episode,

there are large portions of the modeli ng domain—such as over the ocean or the

inland desert—where there are few observ ations. The approach for the 2007

AQMP modeling will be to us e the MM5 prognostic m odel with a 5 km grid

resolution. The meteorolog ical boundary conditions for MM5 are generated using

the output from a Global Climate Model (GCM ) with a relatively coarse grid of 45

km. The MM5 prognostic model uses more accurate and complete physics than

the diagnostic models used previously. The MM5 has relatively good replicationKRD: DRAFTModelingProtocol.doc 

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3/2/2007 

# of meteorological features of the Basin, such as the coastal eddies, Santa Ana

winds, recirculation, & strong inversions.

The recent air quality models are designe d to use inputs from the prognostic

models, such as MM5, and the use of such a model is strong ly encouraged by

USEPA. In the past, the use of MM5 me teorological fields in air quality models

has brought limited success in the prediction of peak ozone concentrations that

result from extreme meteorological c onditions and complex distribution of

precursor emissions. However, the predic tion of ozone with MM5 meteorological

fields on most days is comparable to the results using other models. Since the air

quality model will be employed in more of a relative sense fo r the 2007 AQMP,

with the use off relative reduction fact ors instead of peak concentration

comparisons, the MM5 is an appropriat e choice for the AQMP modeling. The

premise is that the magnitude of RRF w ill reflect the ozone concentration resulting

from the various meteorological episode cl assifications. With the use of the MM5

meteorological model, the AQMP modeling effort will m ove closer to the “one

atmosphere” air quality modeling perspec tive (i.e., ozone and fine particles

simulated with the same model). The succ essful application of this prognostic

model is critical for the development of multipollutant control strategies.

Several MM5 initialization fields and data ingest options are also being explored

for the 2007 AQMP modeling effort:

• MM5 model initialized with the National Centers for Environmental Prediction

(NCEP) 12 km ETA/North American Model (NAM);

• MM5 model with Aerospace Co rp 3DVAR forecast fields;

• Weather Research and Forecasting (W RF) community model using Aerospace

Corporation 3DVAR;

• MM5 model with NCEP database of up per air and surface observations and the

1degree by 1 degree Global Tropospheric Analysis

• Above method of MM5 with NCEP da tabase and Global Tropospheric

Analysis and four-dimensional data assimilation (4DDA) of AQMD station

meteorological data (this method is more mass consistent, but may be difficult

to capture localized wind impacts (e.g ., transport to San Fernando Valley);

• Hybrid CALMET with MM 5 as background field

To supplement the MM5 meteorologic al modeling, the CALMET/MM5 hybrid

meteorological model will be used to bols ter the sensitivity analyses and weightof-evidence discussions. The RRF can be adjusted or supported by the air quality

modeling results using this alternative hy brid meteorological field. In this

approach, the parameter fields will be ove rlaid using a weighti ng scheme that is

based on the proximity to meteorologica l observations. The resultant fieldsKRD: DRAFTModelingProtocol.doc 

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# benefit from the capabilities of the progno stic model in those areas of the

modeling domain with few observations (suc h as offshore, in complex terrain, and

in the desert areas), and benefit from the objective analysis component of the

diagnostic model to force the fields to ag ree with observations. To develop the

hybrid fields, the fields developed us ing CALMET and MM5 will need to be

mapped into common horizontal and vertic al coordinate domains. The CALMET

model code is structured to facilitate this mapping.



# Air Quality Model

Background

The air quality model empl oyed for previous AQMP effo rts, the Urban Airshed

Model (UAM-IV (USEPA 1990), is widely acknowledged to have characteristics

which limit its utility when applied to large modeling domains or to domains that

are not geographically uniform. In additi on, much of the science in the model is

outdated, and both the USEPA and CA RB are no longer recommending that

model for most analyses. Several photoc hemical models have been developed to

improve upon the UAM-IV. Among th ose models, CAMx and CMAQ were

widely accepted models as the state of the science models that include the most

up-to-date chemical mechanisms, physics and the efficient numerical algorithms.

The following summarizes the current models.

• CALGRID

The CALGRID model (Yamartino et. al, 1989) was developed for CARB in

the late 1980's. The model has been applied by various air pollution agencies

around the world. It is modular to allo w the user to substitute various types of

wind fields and chemical mechanis ms. CALGRID incorporates refined

treatments of numerical advection, ver tical transport and dispersion, and dry

deposition. The model can be exercise d with either the Carbon Bond IV (CBIV) or SAPRC chemical mechanisms, and contains highly efficient chemical

integration routines. The vertical struct ure of the atmosphere can be optionally

defined relative to a mixing height field, similar to the UAM, or can be based

on fixed layer heights and a derived mixing height.

• Models-3

Models-3 (USEPA, 1998a) is a flexib le software system designed for

applications ranging from regulatory a nd policy analysis to understanding the

complex interactions of atmospheric ch emistry and physics. The Models-3

system is a framework that allows the user to go from developing model inputs

to visualizing results all in one package. At the heart of th e current version of

Models-3 is the Community Multi-scale Air Quality (CMAQ) Model . The

capabilities of CMAQ include urban to regi onal scale air quality simulation ofKRD: DRAFTModelingProtocol.doc 

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# ozone, acid deposition, visibility and fine particles. CMAQ is a modular

system capable of using output fro m the MM5 prognostic meteorological

model, along with the CB-IV, RADM-2, or SAPRC-99 chemical mechanisms.

The CMAQ model also includes a plume-in-grid module, vertical and

horizontal growth due to turbulence a nd shear, a choice of advection schemes

and a cloud- module to simulate prec ipitating and non-prec ipitating clouds.

Since the Models-3 system is rela tively new, some implementation and

application problems are likely.

• SARMAP Air Quality Model (SAQM)

SAQM (Chang, et. al, 1997) is a thre e-dimensional non- hydrostatic model

based upon the Regional Acid Depositio n Model (RADM) (C hang et. al 1987,

1990). However, SAQM includes a number of improvements over RADM,

including: a fixed vertical coordinate sy stem that is compatible with MM5; a

horizontal coordinate system defined in a Lambert-Conformal projection that

accounts for curvature of the Eart h; a mass conservation module for

compatibility with n on-hydrostatic meteorological inputs; the Bott advection

scheme (Bott 1989a, 1989b) to re duce numerical diffusion and increase

numerical accuracy; two-way nesting, a nd the capability to use either the CBIV or SAPRC chemical mechanisms. A version of SAQM with plume-in-grid

treatment is also available.

• Urban Airshed Model-Flexible Chemical Mechanism (UAM-FCM)

The UAM-FCM (Kumar et. al, 1995) is an alternate version of the UAM-IV

that has been enhanced to allow the flexibility to incor porate any Carbon

Bond- or SAPRC-type chemical mechan ism. The FCM allows incorporation

of reaction-specific photolysis rates. In addition, the UAM-FCM has a

generalized methodology to solve the se t of differential equations that is

mechanism independent. However, th e meteorological dispersion algorithms

are the same as in UAM-IV.

• Urban Airshed Model-Variable (UAM-V)

The UAM-V (Systems Applications Intern ational, 1996) is an updated version

of the Urban Airshed Model (U AM-IV) which incorporates many

state-of-the-art enhancements in chemic al mechanisms, meteorological models

and the representation of emissions. Perh aps the most significant additions are:

an updated CB-IV mechanism to in clude aqueous phase chemistry;

plume-in-grid capabilities; an improve d dry deposition algorithm; and an

improved plume rise algorithm. Othe r enhancements over UAM-IV include

allowing the user a fixed vertical structur e as opposed to one that is relative to

the diffusion break, the ab ility to use three dimensional inputs from prognostic

models and two-way grid nesting. However, the present non-public domain

status of UAM-V may preclude regulatory usage. The model developers haveKRD: DRAFTModelingProtocol.doc 

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# indicated that the model could be made av ailable for any party to review if the

party agrees that the use of the model would be solely for the review of the

AQMP.

• Comprehensive Air-Quality Mode l with Extensions (CAMx)

CAMx (Environ, 1997) contains a number of advanced features, including grid

nesting, sub-grid scale plume-in-gri d simulation, alternative numerical

advection solvers and the ability to use alternative chemical mechanisms. In

addition it has the ability to tag emissions so that at the end of the simulation

one can determine the sources of emissi ons impacting a particular receptor.

Since CAMx is a relatively new model, th us there is a relatively short history

of experience applying the model.

2007 AQMP Air Quality Modeling Approach

CAMx will be the primary air quality mode l for the attainment demonstration.

This dispersion platform integrates we ll with numerical meteorological model

output and it will be run using both the prognos tic (MM5) and hybrid

(CALMET/MM5) meteorological fields. Th e application of the MM5 and CAMx

modeling system for both ozone and particulate matter simulation will bring

AQMD closer to the “one atmospher e” modeling concept, where ozone and

particulates are simulated in the same m odel. CMAQ model may also be run as a

supporting model in the sens itivity analysis discussion.

The ozone air quality models will be r un using the SAPRC (Carter 1999, 2001)

chemical mechanism, based on chemical reactivity scal es. At its meeting on

October 8, 1999, CARB’s Reactivity Scie ntific Advisory Co mmittee (chaired by

Dr. John Seinfeld, with pa rticipation by other members Dr. Roger Atkinson, Dr.

Jack Calvert, Dr. Harvey Jeffries, Dr. Ja na Milford, and Dr. Armistead Russell)

discussed a peer review of the SAP RC-99 mechanism con ducted by Dr. William

Stockwell. Members of the committee agre ed that the peer review was excellent,

that SAPRC-99 was a state-of-the-art chem ical mechanism, and they approved the

peer review. The Committee then unani mously recommended th at SAPRC-99, as

the most up-to-date mechanism available, be used for SIP modeling.

The particulate matter air quality model will use CAMx with the AERO-LT/CBIV chemical mechanism and the enhanced two-section CFI aerosol scheme with

CV-IV. The AQMD version of the AE RO-LT chemistry and the enhanced

version of the CAMx CFI sche me have been installed in the latest CAMx code and

comparative analyses will be presented. Advisory group recommendations have

been to move toward a state-of-the-art , mass-consistent model and chemistry.

This system will integrate well with numerical weather model output and with also

use the MM5 model for meteorological fields.KRD: DRAFTModelingProtocol.doc 

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# MODELING DOMAIN

Meteorological Modeling Domain

Nested domains of 15 km and 5 km are defined within MM5 to simulate

meteorological fields for the fine gr id scale of the modeling domain. The

modeling domain for MM5 is defined in a Lambert-Conformal projection with

two parallels to account for curvature of the Earth within the modeling domain

over such a large region. Figure 1 s hows the nested MM5 domains. Figure 2

shows the finest scale (interior) MM5 domain, covering most of southern

California. The vertical structure of MM5 is defined in a terrain-following,

“sigma” coordinate system based upon a normalized pr essure index. The 30

vertical layers defined for MM5 to a pproximately 15,000 m above ground level

(AGL) can be transformed to fit the requi rements of any air quality model. The

MM5 meteorological fields are converted from Lambert-Conformal projection to

UTM coordinates for input into the air quality models.KRD: DRAFTModelingProtocol.doc 

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# FIGURE 1

Nested MM5 Domains

The horizontal grid resolution of the outermo st domain is 45 km, for the middle domain

is 15 km, and for the fine scale domain is 5 km.KRD: DRAFTModelingProtocol.doc 

# 31

3/2/2007 - 1 0

- 1 0 1 0

0 0 1 0 0 0

1 0 0 0

1 0 0 0 1

0 0 0

1 0 0 0 1 0

0 0 1 0 0 0 1 0

0 0 1 0 0 0

1 0 0 0 1

0 0 0

1 0 0 0 1 0 0 0

1 0 0 0



# FIGURE 2

The Fine-Scale (5 km) MM5 Domain.



# Ozone Modeling Domain

The proposed ozone regional modeling doma in is that previously developed for

the modeling of the SCOS97 field study episodes, encompassing a 600 km wide

by 160 km area, as shown in Figure 3. Specifically, the UTM Zone 11 coordinates

of the domain are 150-700 km UTM East and 3580-3950 km UTM North. This

corresponds to 100 by 74 grid cells at 5 km grid spaci ng. The vertical modeling

domain will extend to a height of a pproximately 5,000 m AGL for a more

complete representation of atmospheric pr ocesses. This will contain observed

high ozone concentrations aloft and allo w three-dimensional wind flow patterns

near elevated terrain features to be re presented, providing accurate representation

of pollutant transport and r ecirculation. This same domain will be used for all of

the ozone episodes.KRD: DRAFTModelingProtocol.doc 

# 32

3/2/2007 San Luis

Obispo Co.

Santa

Barbara Co. Kern Co.

Ventura

Co.

Orange

Co.

Imperial

Co. Los

Angeles Co.

Riverside Co.

San Diego

Co. San

Bernardino

Co.

Northern Mexico



# FIGURE 3

2007 AQMP Ozone Modeling Domain

The ozone modeling domain encompasses much of southern California, as

follows: all of the South Coast Air Basi n (including Orange County and the nondesert portions of Los Angeles, Rivers ide and San Bernardi no Counties), the

Coachella Valley and San Diego County; the California-Mexico border regions;

most of Imperial County; most of the inland deserts; and almost all of the South

Central Coast Air Basin (excepting a sma ll piece of San Luis Obispo County).

This large domain minimizes the influenc e of boundary conditions on simulation

results and allows the effects of recircula tion and interbasin transport to be better

represented by the meteorologi cal and photochemical model simulations. It also

eliminates the need to define boundary concentrations between the air basins and it

extends far enough offshore to contain wind flow patterns conducive to over-water

recirculation.



# PM Modeling Domain

The modeling domain for the particulate ma tter modeling will be smaller than the

ozone domain, encompassing a 325 km wide by 200 km area, as shown in Figure

4. This corresponds to 65 by 40 grid cells at 5 km gr id resolution. The reduced

domain is due in part to the computa tional resource and time constraints of

modeling the full 2005 year for annual PM . In addition, PM SIP modeling is notKRD: DRAFTModelingProtocol.doc 

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# needed in the southernmost counties of California and adequate ammonia

emissions inventories are not availabl e from many areas surrounding the South

Coast Air Basin.

FIGURE 4

2007 AQMP PM Modelin g Domain, inside the Ozone Modeling DomainKRD: DRAFTModelingProtocol.doc 

# 34

3/2/2007 

# HORIZONTAL AND VERTICAL GRID RESOLUTION

Horizontal Grid Resolution

The horizontal grid resolutio n plays an important role in the modeling process.

Large grid resolution tends to smooth emission gradients and meteorological

inputs, which in turn leads to a smoothing of the resulting concentration fields. In

general, the resolution should be sufficiently small to pick up emission gradients

in urban areas and be consistent with th e major terrain features which may affect

the air flow. In the past, photochemical models have been applied in California

with horizontal grid resolutions ranging fr om 2 x 2 km to 8 x 8 km. The specific

grid resolution chosen was primarily de pendent on the size of the modeling

domain, computer resources available a nd the time and money available to carry

out the simulations. In effect the fina l resolution was a comp romise between the

accuracy desired and the cost. However, the current generati on of high-speed

computers has minimized cost and resource constraints.

For the year 2007 AQMP ozone, partic ulate and meteorological modeling, a

horizontal grid resolution of 5 km is proposed to be used for the air quality

modeling. No grid nesting is anticipated. This resolu tion is consistent with the

grid resolution used in earlier photochemi cal modeling studies for the South Coast

Air Basin and for San Diego. In additio n, this will reduce resources needed to

create gridded emissions, which are based on 5 km grid cells. For the proposed

ozone modeling domain, use of a 5 km reso lution results in a modeling grid with

110 cells in the east-west direction and 74 cells in the north-south direction. The

Universal Transverse Mercator (UTM) c oordinate system is adopted as the

primary coordinate system fo r the air quality modeling. There are variations in

Lambert-Conformal map projection systems, such as the Normal Sphere (6471 km

radius) used in MM5, the North Americ an 1927 Clerk 1866 used in CARB’s

emissions development system, and th e Arakawa-C or Arakawa-B variable

configuration which assign mete orological parameters at gr id points or the center

of the grid. The selection of UTM simplif ies translation from one grid system to

another and the gridded emissions inve ntory is based on a UTM coordinate

system.



# Vertical Resolution

As with the selection of the horizontal grid resolution, the vertical resolution

defined for air quality modeling domain s has been limited by computational

resources. In addition, available aloft meteorological and air quality databases

were not sufficient to characterize conditions aloft. As a result, simulation results

have been limited by a relatively small number of vertical layers within theKRD: DRAFTModelingProtocol.doc 

# 35

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# atmospheric boundary layer, re sulting in poor representati on of the stratification of

the atmosphere. The ability to better si mulate the vertical structure of the

atmosphere has improved significantly due to the increased availability of

measurements aloft (including radar wind profilers and aircraft measurements), the

emergence of higher-speed computers, and our increased experience with

diagnostic and prognostic meteorological models.

Meteorological Modeling

For the terrain-following MM5 m odel, the proposed vertical layer consists of 34

layers to a height of over 15,000 meters AGL, as shown in Table 5. For input into

the air quality model, the 34 layers are re duced to match the ve rtical resolution of

the ozone or particulate matter air quality model.KRD: DRAFTModelingProtocol.doc 

# 36

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# TABLE 5

Vertical Structure for the MM5 Meteorological Model

Layer # Sigma P 0 

# (Pa) Height (m)* Depth (m)*

34 0.000 10000 15674 2004

33 0.050 14500 13670 1585

32 0.100 19000 12085 1321

31 0.150 23500 10764 1139

30 0.200 28000 9625 1004

29 0.250 32500 8621 900

28 0.300 37000 7720 817

27 0.400 41500 6903 750

26 0.300 46000 6163 693

25 0.450 50500 6461 645

24 0.500 55000 4816 604

23 0.550 59500 4212 568

22 0.600 64000 3644 536

21 0.650 68500 3108 508

20 0.700 73000 2600 388

19 0.740 76600 2212 282

18 0.770 79300 1930 274

17 0.800 82000 1657 178

16 0.820 83800 1478 175

15 0.840 85600 1303 172

14 0.860 87400 1130 169

13 0.880 89200 961 167

12 0.900 91000 794 82

11 0.910 91900 712 82

10 0.920 92800 631 81

9 0.930 93700 550 80

8 0.940 94600 469 80

7 0.950 95500 389 79

6 0.960 96400 310 78

5 0.970 97300 232 78

4 0.980 98200 154 39

3 0.985 98650 115 39

2 0.990 99100 77 38

1 0.995 99550 38 38

0 1.000 100000 0 0

* The vertical coordinate system for MM5 is based on a normalized pressure

scale. The above layer heights were ca lculated from sea level using standard

conditions. Layer heights are lower rela tive to ground level as terrain height

increases.KRD: DRAFTModelingProtocol.doc 

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# Air Quality Modeling

For sufficient vertical representation of th e atmosphere, 16 vertic al layers will be

used for the CAMx ozone modeling, to a t op height of nearly 5000 m AGL. Five

of the layers will be below 500 m AG L (the nominal height of the summer

afternoon mixing height with in the Los Angeles coastal plain). The computational

resources required for the annual par ticulate matter modeling necessitate a

reduction in the number of layers used in the CAMx model for particulates. For

this, eight vertical layers will used to a top height of approxima tely 5000 m AGL.

The proposed vertical structure for the oz one and PM models are shown in Table

6, along side of the MM5 vertical structure.KRD: DRAFTModelingProtocol.doc 

# 38

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# TABLE 6

Vertical Structures for the CA Mx Ozone and PM Simulations

with Corresponding MM5 Meteorological Model Layers

MM5 Vertical Layer Heights

(34) Ozone Model Layers

(16) PM Model Layers

(8)

No. 

# Sigma Height

(m

AGL) Depth

(m) Height

(m AGL) Depth

(m) Height

(m

AGL) Depth

(m)

... ... ... ... 24 0.500 4816 604 4816 1172 4816 2216

23 0.550 4212 568

22 0.600 3644 536 3644 1044

21 0.650 3108 508

20 0.700 2600 388 2600 670 2600 670

19 0.740 2212 282

18 0.770 1930 274 1930 274 1930 627

17 0.800 1657 178 1657 178

16 0.820 1478 175 1478 175

15 0.840 1303 172 1303 172 1303 508

14 0.860 1130 169 1130 169

13 0.880 961 167 961 167

12 0.900 794 82 794 164 794 325

11 0.910 712 82

10 0.920 631 81 631 161

9 0.930 550 80

8 0.940 469 80 469 159 469 315

7 0.950 389 79

6 0.960 310 78 310 156

5 0.970 232 78

4 0.980 154 39 154 78 154 116

3 0.985 115 39

2 0.990 77 38 77 38

1 0.995 38 38 38 38 38 38

0 1.000 0 0KRD: DRAFTModelingProtocol.doc 

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# EPISODE SELECTION

Ozone Episodes

Five ozone episodes were simulated for the 2003 AQMP: June 24-25, 1987;

August 27-28, 1987; August 3-7, 1997; September 26-29, 1997; and July 13-18,

1998. To maintain continu ity with the last plan s ubmittal, the model performance

for the August 1997 episode will be reev aluated using updated emission data and

modeling protocols. Five new recent episode periods from 2004 and 2005 will

evaluated to better represent current cond itions, including those associated with

the reformulation of gasoline in the past several years. The six episodes are

outlined in Table 7 and briefly described below.KRD: DRAFTModelingProtocol.doc 

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# TABLE 7

Summary of Ozone Episodes to be Simulated for the 2007 AQMP

Episode Peak 1-Hr.

Ozone Peak 8-Hr.

Ozone Notes

August 3-7, 1997

(Sunday – Thursday) 0.187 ppm

Tuesday, August 5

at Rubidoux 0.117 ppm

Tue.& Wed.,

August 5 & 6 SCOS97 intensive

measurement

episode. Primary

modeling episode

from 2003 AQMP.

Before California

fuel reformulation.

June 3-7, 2004

(Thursday – Monday) 0.163 ppm

Saturday, June 5

at Crestline 0.145 ppm

Saturday, June 5

at Crestline 2004 Basin

maximum 1-hour

and 8-hour ozone

concentrations.

August 4-8, 2004

(Wednesday – Sunday) 0.156 ppm

Saturday, August 7

at Banning 0.124 ppm

Saturday, August 7

at Crestline

May 17 -24, 2005

(Tuesday – Tuesday) 0.164 ppm

Sunday, May 22

at Santa Clarita 0.145 ppm

Sunday, May 22

at Crestline 2005 Basin

maximum 8-hour

ozone concentration.

July 14-19, 2005

(Thursday – Tuesday) 0.173 ppm

Saturday, July 16

at Santa Clarita 0.143ppm

Friday, July 15

at Crestline

August 25-29, 2005

(Thursday – Monday) 0.182 ppm

Saturday, Aug. 27

at Crestline 0.130 ppm

Saturday, Aug. 27

at Crestline 2005 Basin

maximum 1-hour

ozone concentration.KRD: DRAFTModelingProtocol.doc 

# 41

3/2/2007 

# August 3-7, 1997 (Sunday – Thursday)

The episode period of August 3-7, 1997 wa s selected to provide continuity with

the previous AQMP modeling effort. This episode was the primary modeling

episode for the 2003 AQMP and it is representative of the most extreme

meteorological conditions conducive to th e highest ozone concentrations in the

Basin. Unlike the more recent ozone epis odes, the peak concen trations during this

period did not occur on a weekend. Mode l input data supporti ng the August 1997

simulations were derived from intensive fi eld monitoring that oc curred during the

1997 Southern California Ozone Study (S COS97). The SCOS97 study benefited

from state-of-the art upper air wind and temperature monitoring and recently

developed advances in par ticulate and oxides of nitr ogen sampling technology.

The August 1997 epis ode included the peak ozone concentrations measured in the

South Coast Air Basin during SCOS97 th at were not associated with an

exceptional event. A peak 1-hour oz one concentration of 0.187 ppm was

measured at the AQMD Metropolitan Rivers ide County (Rubido ux) air monitoring

station on Tuesday, August 5 and peak 8- hour concentrations of 0.117 ppm were

measured on Tuesday, August 5 and Wednesday, August 6. High ozone

concentrations were also observed in the Mojave Desert Air Basin (1-hour peak of

0.140 ppm) and in Ventura County (1-hour peak of 0. 130 ppm, 8-hour peak of

0.115 ppm).

The August 1997 meteorological epis ode began on Sunday, August 3 under a

ridge of high pressure aloft with 500 mb heights measured in excess of 5900 m

each day. Weak onshore flow gave way to stagnant winds through the middle of

the episode. Winds observed on August 5 th 

# , illustrate a classic “south route”

transport regime that has been identified as characteristic of past severe Basin

ozone meteorological episodes. Beginning late on August 6 and continuing into

August 7, a well-defined coastal eddy deve loped that contributed to southerly flow

and transport northward toward Ventur a County. Peak inland afternoon

temperatures crested over 100 degrees Fa hrenheit on each day during the episode

and downtown Los Angeles consistently reached the mid to upper 90’s. The

excessive regional surface temperatures a nd stagnant flow also contributed to a

massive wildfire in the m ountainous portions of easter n Ventura and southeastern

Santa Barbara counties during the later part of the episode.

Ozone air quality reached the California Oz one Health Advisory level (0.150 ppm

or higher) on two day during the episod e at Redlands, San Bernardino, Rubidoux

and Mira Loma. The peak observed valu e of 0.187 ppm occurred on the August 5

at Rubidoux. Eleven locati ons exceeded the federal 1-h our ozone standard. Areas

such as Azusa, Pasadena, Glendora and Sa nta Clarita that routinely experience

higher values of ozone during episodic c onditions were spared the brunt of theKRD: DRAFTModelingProtocol.doc 

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# impact due to excessive daytime heating th at deepened the mixed layer. Overall,

The peak concentrations in the Basin reached 0.140 ppm on the August 4 in the

Central San Bernardino Mountains, 0.18 7 ppm at Rubidoux on August 5, 0.170

ppm and 0.150 ppm on August 6 and 7, respectively, in the Central San

Bernardino Mountains. On August 6, oz one transport was observed through the

Newhall pass to the Santa Clarita area a nd concentrations rose in Reseda and

Ventura County as the coastal eddy developed.

June 3-7, 2004 (Thursday – Monday)

• Peak 1-hour Ozone: 0.163 ppm on Sa turday, June 5 at Crestline

(2004 Basin max 1-hour ozone)

• Peak 8-hour Ozone: 0.145 ppm on Sa turday, June 5 at Crestline

(2004 Basin max 8-hour ozone)



# August 4-8, 2004 (Wednesday – Sunday)

• Peak 1-hour Ozone: 0.156 ppm on Saturday, August 7 at Banning

• Peak 8-hour Ozone: 0.124 ppm on Sa turday, August 7 at Crestline



# May 17-24, 2005 (T uesday – Tuesday)

• Peak 1-hour Ozone: 0.164 ppm on S unday, May 22 at Santa Clarita

• Peak 8-hour Ozone: 0.145 ppm on Sunday, May 22 at Crestline

(2005 Basin max 8-hour ozone)



# July 14-19, 2005 (Thursday – Tuesday)

• Peak 1-hour Ozone: 0.173 ppm on Satu rday, July 16 at Santa Clarita

• Peak 8-hour Ozone: 0.143 ppm on Friday, July 15 at Crestline



# The morning of July 13, 2005 had a low, strong temperature inversion layer in the

Basin, which continued for several days, and hot weather except at the immediate

coast. Skies were mostly clear, except for low clouds and fog offshore an at the

coastline for most of the day. Ozone levels were starting to increase in the inland

valley areas. The inland valley areas rema ined hot on July 14 while the coast

remained much cooler with coastal lo w clouds and fog. On July 15, high

pressures aloft, centered over the western U.S. deserts, helped to keep inland

temperatures hot. Excessive heat warnings were in effect for many desert areas.

The marine layer deepened a little with increased onshore flow, bringing night and

morning low clouds and fog into the co astal valleys and transporting ozone and

ozone precursors towards the Inland Empi re with a 8-hour ozone peaking at

Crestline (0.143 ppm). Skies in the Basin were mostly sunny with haze. On July

16, the hot inland temperatures continued while coastal low cloud and fog in the

morning clearing in the afternoon. .O n July 17, the strong inversion layer

continued along with the hot temperatures in the inland valley areas. Only theKRD: DRAFTModelingProtocol.doc 

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# immediate coastal strip will escaped the hot weather due to low clouds and fog

along the coastline and offshore. With st rong high pressure aloft over the west

coast, temperature will remain hot on Ju ly 18 and through the week with an

excessive heat advisory and record temperature possible in some areas on

Monday, July 18. A lower temperature inversion confined morning low clouds

and fog to the coast, with hazy sunshine elsewhere. Little cha nge occurred on July

19 as inland heating likely caused the inve rsion to break in the afternoon inland.

August 25-29, 2005 (Thursday – Monday)

• Peak 1-hour Ozone: 0.182 ppm on Sa turday, August 27 at Crestline

(2005 Basin max 1-hour ozone)

• Peak 8-hour Ozone: 0.130 ppm on Friday, August 27 at Crestline



# Possible Seasonal Ozone Episode: Summer 1997



# Ozone Episode Statistical Ranking

For the 2003 AQMP ozone attainment demonstration a statistical model was

developed to characterize the ozone meteor ological episodes selected for regional

modeling evaluation. The statistical mode l related degree of ozone meteorological

episode severity relative to the long term trend (1981-2002). Multi-variate

regression was conducted using the Ba sin 1-hour average maximum ozone

concentration and surface and upper air me teorological data fo r 1996 to generate

an ozone prediction equation. This equation was app lied to the air quality and

meteorological data for the 22-year peri od to predict Basin daily maximum ozone

and establish a daily ranking. The multiple linear regression analysis is discussed

in Appendix V of the 2003 AQMP.

The statistical evaluation us ed in the 2003 AQ MP used the daily maximum 1-hour

ozone as the dependent variable to char acterize the meteorological episodes. The

meteorological conditions that give rise to higher 8-hour aver age concentrations

are essentially a subset of those giving ri se to peak 1-hour concentration. CART

pattern recognition analysis (Cassmas si, 1998) demonstrated that the

meteorological conditions that lead to high 1-hour average concentrations were the

same as those for peak 8-hour concentr ations. In addition, station specific

correlations between maximum 1- and 8-hour average ozone concentrations

generally explain more than 95 percent of the variance in the data. Given the

consistency between the meteorological pr ofiles contributing to both maximum 1-KRD: DRAFTModelingProtocol.doc 

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# and 8-hour average concentra tions, it was assumed that the algorithm used to rank

episodes in the 2003 AQMP would be app licable for ranking the 8-hour episodes.

The 1997 episode ranking was taken directly from the 2003 AQMP. The

statistical characterization was then ex tended to the 2004 and 2005 candidate

episodes and their predicted daily maximum concentrations were compared to the

22-year distribution to dete rmine relative rank. Table 6 summarizes the analysis.

Eleven of the 13 days ranke d above the 95th percentile in episode severity with

only August 6, 2004 failing to rank in the 90th percentile. The daily maximum 8-

hour ozone averages were averaged by episode and co mpared to the 4th highest

ozone value in the Basin (99th percentile) for each of the modeling years. The

1997 episode was a match for the annual design valu e while the 2004 and 2005

episodes bracketed the annual design values, each depicting episodes that were

more or less severe than the design. The overall distribution listed in Table 8 may

be enhanced at a later date if a seasonal modeling application is determined to be

viable.KRD: DRAFTModelingProtocol.doc 

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# TABLE 8

Ozone Episode Characterization

Ranking Applied to Historical 22-Year Period (1981-2002)



# Episode Rank Percentile 8-Hour Max

Ozone

(PPB) Episode

Average

(PPB) Annual 4 th



# Highest

Station

(PPB)

8/5/97 198 98 124

8/6/97 203 97 130 127 127

San

Bernardino

6/5/04 83 99 148

6/6/04 524 93 127 138

8/6/04 1009 87 94

8/7/04 331 96 127 111 116

Crestline

5/21/05 389 95 112

5/22/05 50 99 145 129

7/16/05 22 99 141

7/17/05 15 99 141

7/18/05 73 99 127 136

8/27/05 160 98 130

8/28/05 138 98 121 126 125

Crestline



# PM Episodes

Annual particulate matter modeling will cover the entire year of 2005, taking

advantage of additional speciated partic ulate measurements and meteorological

data archived in association with th e Multiple Air Toxics Exposure Study III

(MATES-III) in the South Coast Air Basin. In addition, two PM2.5 episodes in

2005 will be modeled for 24-hour NAAQS compliance: Octobe r 19-25 and March

6-12, 2005. These two days were chos en since they were the highest PM2.5

episodes in 2005 that were not influenced by exceptional even ts. Both episode

periods exhibited multiple-d ay buildups in th e Beta Attenuation Monitor (BAM)

continuous PM2.5 monitoring and affected multiple stati ons. Only July 5 had a

higher PM2.5 concentration, but it was asso ciated with firework s on the night of

July 4. Table 9 shows the da ys in 2005 with the highest Size Selective Inlet (SSI)

sampler PM2.5 concentrations and the associated 24-hour BAM PM2.5 and SSI

PM10 concentrations.KRD: DRAFTModelingProtocol.doc 

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# TABLE 9

Highest 24-Hour Averaged SSI PM2.5 Concentration in 2005

with BAM PM2.5 and SSI PM10 Concentrations

Date Station SSI PM2.5

( μg/m 3 

# ) BAM PM2.5

( μg/m 3 

# ) SSI PM10

( μg/m 3 

# )

July 5, 2005 Azusa 132.7

October 22, 2005 San Bernardino 106.3

October 22, 2005 Rubidoux 98.7 120.6 123/124

October 22, 2005 Fontana 96.8

October 23, 2005 Rubidoux 95.9 117.9

October 22, 2005 Riverside 95.0

October 22, 2005 Ontario 87.8

October 21, 2005 Rubidoux 82.1 98.5

July 5, 2005 Rubidoux 79.9 102.0

March 10, 2005 Downtown LA 73.7 88.2

March 11, 2005 Downtown LA 67.6 84.7 70



# Annual PM: January 1 – December 31, 2005

• AQMP database development concurrent with MATES-III

• Peak Annual Average PM2.5: 23.3 μg/m 3 

# at Rubidoux

• Peak Annual Average PM10: 52.2 μg/m 3 

# at Rubidoux



# Episodic PM10/2.5: October 19-25, 2005 & March 6-12, 2005

• Peak 24-Hour PM2.5 was 132.7 μg/m 3 

# at Azusa on July 5, 2005

(due to Independence Day fireworks)

• Second Peak 24-Hour Average PM2.5: 106.3 μg/m 3 

# at San Bernardino on October

22, 2005

• Rubidoux exceeded the 24-hour PM2.5 standard on the most days in 2005 (8 days)

• Peak 24-Hour Average PM10: 131 μg/m 3 

# at South Long Beach on May 4, 2005

• Second Peak 24-Hour Average PM10: 123 μg/m 3 

# at Rubidoux on October 22, 2005

• No 24-Hour NAAQS violations were measured in the Basin in 2005KRD: DRAFTModelingProtocol.doc 

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# INITIAL AND BOUNDARY CONDITIONS

Previous ozone modeling resu lts in southern California proved sensitive to initial

and boundary concentrations of air pollu tants. This reflected the physical

processes of recirculation of pollutant s within southern California and the

transport of pollutants from one air basi n to another. However, because of the

three-dimensional nature of transport and re circulation, it is difficult to take field

study measurements that are adequate to determine boundary conditions.

Ozonesonde measurements made during SC OS97 have shown high concentrations

of ozone at heights above 3,000 m AGL. The modeling domain developed for the

SCOS97 episodes, which will be used fo r the 2007 AQMP, has been expanded

both horizontally and vertically from that of earlier studies in an attempt to

minimize the influence of boundary conditions. With the boundaries extending

horizontally well into the desert areas an over the ocean and vertically to 5000 m,

the effects of recirculation and interbasin transport will be better represented by

the meteorological and photoch emical model simulations.

The sensitivity of the model simulations to initial and boundary conditions will be

extensively examined with sensitivity analyses. Chemic al species concentration

measurements, where available from th e SCOS97 field study archive and the

PAMS measurements, will be used to check the initial and boundary conditions

for reasonableness. For the large areas of the study domain in which there are few

such measurements, initial and bou ndary conditions are often assigned

“background” values based on the mini mum concentrations measured from

monitoring sites where measur ements are available. The use of larger-domain air

quality models to provide the initial, top and lateral boundary concentrations will

also be explored. Speciated gridded pol lutant and precursor profiles from the 36

km grid CMAQ model used for the WRAP visibility modeling is currently being

evaluated to provide the initial and bou ndary conditions. Th e boundary profiles

will vary with time and height level, as well as location, while the top boundary

concentration will vary by time and grid location.



# Initial Conditions

Initial conditions in the air quality models define the spatial distribution of

chemical species concentrations throughou t the 3-dimensional modeling domain at

the time at which the air quality mode l simulation begins. There are two

limitations inherent in defi ning initial conditions. Th e first is that chemical

species concentrations are only measured at discrete locations and, for some

species, for discrete time periods. In particular, observed VOC data is sparse

although some PAMS monitori ng stations data are availa ble. Therefore, observed

concentrations must be extrapolated to estimate concentrations throughout theKRD: DRAFTModelingProtocol.doc 

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# modeling domain. The second limitation is that observed chemical species

concentrations may not represent chemical equilibrium, especially since not all

important chemical species are measured explicitly.

To minimize the importance of initial c onditions on air quality model simulation

results, the simulation is frequently started at some time interval before the period

of interest. Historically, this “spin-up ” time interval has ranged between 8 and 72

hours. For the 2007 AQMP episodes, the modeling period starts early in the

morning (typically 0000 PDT) of the day before the first day of interest for spinup. This allows a full diurnal cycle of sunlight for air quality model to reach

chemical equilibrium. Since most of the modeling episodes encompass several

days, the day with the worst air quality is typically well into the simulation.



# Boundary Conditions

The top and lateral boundary conditions in the air quality models are the chemical

species concentrations on the study domain boundaries and represent the

concentrations for the air mass moving in to the modeling doma in. Unlike initial

conditions which need to be defined on ly for the beginning of the simulation,

boundary conditions must be defined for each hour of an air quality model

simulation on the 2-dimensional, vertical planes on each of the horizontal

boundaries of the domain and at the top of the modeling domain.

Ideally, the modeling domain boundaries are placed so remotely that simulation

results are insensitive to boundary conditions. Ev en for the large SCOS97

modeling domain, the influence of bounda ry conditions on the simulation results

may be problematic. Beyond the north ern boundary, emissions from central

California could have an impact on the domain. To the south, emissions from

Mexico could have an impact. The west ern boundary is over the Pacific Ocean,

where recirculation may be an issue.

Also, the determination of vertical profile s of chemical species is problematic.

During SCOS97, ozone concentrations aloft were measured by launching balloonborne ozonesondes. The measurements i ndicated that there are layers of high

ozone ranging 60 to 80 ppb at near 3000 m. Prescribing a 60 ppb ozone

concentration aloft in the model would cont ribute to high ozone concentrations at

the surface due to advection or vertical diffusion. Ideally boundary conditions

would be determined from measured chem ical species concentrations, but these

are rarely available for the most of the episode days or in all locations needed.

For the 2007 AQMP, AQMD pr oposes to use relatively clean initial and boundary

conditions, based on the results of a larger domain model, the WRAP CMAQ

visibility simulations. The SAPRC specie s for the initial and boundary conditionsKRD: DRAFTModelingProtocol.doc 

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# are shown in Table 10 for the ozone m odeling. The use of relatively clean

boundary conditions could significantly impact th e predicted peak ozone

concentration which results in poor mode l performance for ozone peak prediction.

However, the use of clean boundary conditio n minimizes the uncertainty in futureyear model predictions. The calculated RRF should only refl ect the impact of

anthropogenic emissions reductions. Also, as the future year air quality becomes

close to background concen trations, the treatment of boundary conditions may be

problematic, particularly in 8-hour ozone attainment demonstration. A part of the

air quality model evaluation pr ocess, sensitivity analysis and weight of evidence

analysis will be to assess the influence of boundary and initial concentrations on

simulation results and RRF.KRD: DRAFTModelingProtocol.doc 

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# TABLE 10

SAPRC-99 Chemical Mechanism Species

Species Species

ACET ISPD

ALK1 MEK

ALK2 MEOH

ALK3 METH

ALK4 MGLY

ALK5 MPAN

ARO1 MVK

ARO2 NO

BACL NO2

BALD NOXY

CCHO NPHE

CO O3

CO2H OLE1

CO3H OLE2

COOH PAN

CRES PAN2

DCB1 PBZN

DCB2 PHEN

DCB3 PROD

ETHE RC2H

GLY RC3H

HC2H RCHO

HC2H RNO3

HCHO ROOH

HNO3 SO2

HNO4 SULF

HO2H TERP

HONO XN

ISOPKRD: DRAFTModelingProtocol.doc 

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# METEOROLOGICAL INPUTS

Meteorological Input Evaluation and Technical Review

The quality of the meteorological inputs can have a profound influence on the

accuracy of the simulations concentrations of ozone, PM and other pollutants by

the air quality models. It is therefor e essential that the products of the

meteorological models undergo a rigorous evaluation. By evaluating the flow

characteristics of the wind fields, as well as the representativeness of the

temperature, relative humidity and mixing height fields, the uncertainty in the air

quality simulations can be minimized. AQMD and CARB staff will consider both

qualitative and quantitative analyses in j udging the meteorological fields and in

reaching consensus on the appropriateness of those fields for use in the 2007

AQMP. Graphical and statistical analysis software is available to facilitate the

meteorological input field evaluation.

The use of routine and special study mon itoring data and model analysis archives

provides a robust data set for co mparing and analyzing the simulated

meteorological fields. Some of the available data sets include:

• Routine surface meteorological network data, including:

 South Coast AQMD (~32 stations),

 Ventura County APCD,

 San Diego County APCD,

 Mojave Desert/Antelope Valley APCD,

 NOAA/FAA Stations (METAR obs),

 California Remote Access Weather Stations (RAWS),

 California Irrigation Management Information System (CIMIS)

Stations;

• Special study meteorological station da ta, such that from the Multiple Air

Toxics Exposure Study III (MATES-III) proj ect during part of 2004 and all of

2005;

• Marine buoy data from National Data Buoy Center (NDBC);

• Routine National Weather Service a nd military radiosonde observation

(RAOB) data, including the stations at Miramar MCAS, Point Mugu NAS,

San Nicolas Island NAS, Vandenberg AFB, Edwards AFB, China Lake NAS,

Oakland, Mercury/Dese rt Rock, and Tucson;

• Southern California radar wind and te mperature profiling ne twork, including

stations operated by:

 South Coast AQMD (Los Angeles In ternational Airport, Ontario

International Airpor t and Moreno Valley),

 Ventura County APCD (Simi Valley)KRD: DRAFTModelingProtocol.doc 

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#  San Diego County APCD (Pt. Lo ma, Valley Center or Miramar)

 NOAA project and SCOS97 profilers, when available (e.g., Goleta, San

Clemente Island, Santa Catalina Island during SCOS97).

• National Center for Environmental Pred iction (NCEP) gridded observational

databases and model analysis fields, including:

 NCEP ds353.4 ADP Global Upper Air Observations database,

 NCEP ds464.0 ADP Global Surfa ce Observations database,

 NCEP ds083.2 Global Tropospheric Analyses, 1 degree x 1 degree

gridded database,

 NCEP ETA-12 km mode l forecast fields,

 NCEP ETA-40 km Model Forecast Fields,

 NCEP EDAS-40 km Gridded Data;

• Aerospace Corporation MM5/ 3-Dimensional Variati onal Analysis System

(3DVAR) archives (incorporating surface, upper-air, ships, buoys, aircraft and

satellite observations)

Qualitative Analyses

The qualitative analysis of m odeled wind fields includes an evaluation of the gross

circulation features in the modeling region to determine if the model is replicating

those essential features (Mulberg, 1995, Lo lk and Douglas, 1996). Such features

include areas of convergence and divergen ce, eddy circulations, land/sea breezes,

slope flows, and transport corridors. Since the modeling domain includes large

overwater areas it is also necessary to evaluate offshore flows as well. Key

features of the windfield are areas of co nvergence and divergence. These features

result in vertical velocities which can tr ansport pollutants upward (in the case of

convergence) or bring pollutants from alof t down to the surface (with divergence).

The evaluation will include a review of the convergence and divergence zones in

the simulated windfield, and their impact on realistic vertical velocities, to

determine agreement with measurements or conceptual models in terms of

location, timing, and extent.

Synoptic forcing and mesoscale flow char acteristics can sometimes result in eddy

circulations. In the SCOS97 domain two key eddy features are prevalent: the

Catalina Eddy (named since its center is of ten near Santa Catalina Island), and the

Gaviota Eddy in the Santa Barbara Channel (Smith, et. al., 1984). Both eddy

circulations are important transport mech anisms; they are capab le of transporting

precursors and aged ozone concentrations onshore and northward to Santa Clarita

and sometimes Ventura and Santa Barbara Counties. Exceedan ces of the ozone

standards are often observed with the presen ce of an eddy circulation and the deep

of the marine layer that accompanies a mature coastal ed dy can end an ozone

episode. The timing of the onset, persis tence, and spatial extent of eddy

circulations, are a critical part of the windfield validation.KRD: DRAFTModelingProtocol.doc 

# 53

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# Land/sea breeze circulations are another impor tant flow feature. The sea breeze is

one method whereby pollutants genera ted in the Los Angeles Basin are

transported eastward. That is, the stre ngth of the sea breeze will determine how

far precursors and ozone gene rated near the coast will be transported inland.

Errors in the timing of the sea breeze can cause precursor emissions to be

transported to the wrong locations instead of inland where peak concentrations are

observed. It is essential that the onset of the sea and land breezes simulated by the

model be compared to observations for reasonableness.

The onshore portion of the 2007 AQMP modeling domains includes areas of

complex terrain. Slope flows are important as a recirculation mechanism that may

influence ozone concentrations. Slope flows are probably the most challenging

feature for prognostic meteorological mode ls, due to the sparse observational data

in complex terrain and thes e models have a tendency to overdo the speed of the

slope flows. A proposed qualitative ap proach is to determine if wind speeds

estimated by the model appear to be reasonable in areas of complex terrain.

As a qualitative and quantitative evaluation of the windfields, wind speeds are

proposed to be statistically summarize d and plotted by site and globally

throughout the domain (Seaman et. al ., 1995, Bigler-Engler et. al., 1996).

Temporal plots for key sites will be ex amined to determin e agreement with

observations. Quantitative techniques will make use of statistical measures such

as the mean gross error and mean bias to compare modeled and measured wind

speeds (Mulberg, 1995).

Some of the methods being explored for the meteorological modeling incorporate

observations, thus reasonably good agre ement should be expected near those

observation sites where data was used as i nput to the model. In order to diagnose

the impact that incorporation of the observations has on the meteorological

models, it may be useful to consider withholding some observations when

executing the models to have an independ ent set of observations for comparison.

The sites withheld should have some rela tion to the sites used to provide some

assurance in the results from the comparison. This diagnostic evaluation is

proposed to be conducted once acceptab le meteorological fields have been

prepared.

Temperature fields will also be examined . At the surface, qualitative analyses will

include an examination of the diurnal and spatial variation of estimated and

observed temperatures, as well as consistency of the gridde d data within regions..

The interface at the coastline will be examined for the ex pected gradients between

the ocean and the land. Mean bias and mean gross error sta tistics will also be

calculated to provide quantitative measures of performance. In addition, theKRD: DRAFTModelingProtocol.doc 

# 54

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# vertical temperature profiles generated by the models will be compared to those

observed at rawinsonde sites and bounda ry layer wind and temperature profiler

locations. The vertical temperature profile influences the stability characteristics

of the modeling domain which significan tly affects vertical mixing. The

evaluation will include temporal and spa tial evaluations of simulated vertical

temperatures and mixing as compared to those estimated fro m observed soundings

and profiler data. The timing of the onset and breakup of the inversion will also

be evaluated, as this phenomenon has a profound effect on estimated ozone

concentrations.

Quantitative Analyses

ENVIRON Corporation International (Emery , et al., 2001) proposed performance

benchmarks and developed a statistical analysis software package, called

METSTAT, to statistically and graphically analyze the meteorological fields.

METSTAT is publicly available and widely used by the modeling community. It

can read the MM5 output files and the obse rvational data, and then calculate the

following statistics: mean observation, mean prediction, bias error, gross error,

root mean square error (RMSE), system atic root mean s quare error (RMSEs),

unsystematic root mean square error (RMS Eu), and index of agreement (IOA). It

should be noted that the st atistical evaluations are in fluenced by the number of

stations and the duration of sampling period. The benc hmark statistics will be

applied to all observational stations ava ilable and to specific geographic groupings

(e.g., coastal, mid-Basin, inland areas). Both daily and hourly statistics will be

compiled for each modeled period.

Meeting the METSTAT benchmarks provides assurance that the model

performance is comparable with performance achieved in the past. METSTAT

can be used as a screening tool to iden tify the periods when the performance is

poor that require further an alysis. These statistics can also be used to identify

stations where performance is consistently poor. Table 11 shows the proposed

performance benchmarks for the meteor ological inputs for the 2007 AQMP air

quality modeling. In addition, temporal plots will provide direct comparison of

modeled meteorological parameters at gr id points corresponding to observational

stations.

TABLE 11

Proposed Meteorological Input Performance Benchmarks

Parameter Benchmark

Wind Speed Total RMSE ≤ 2.0 m/s

Wind Speed Bias ≤ ±0.5 m/sKRD: DRAFTModelingProtocol.doc 

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# Wind Speed IOA ≥ 0.6

Wind Direction Gross Error ≤ 30 degrees

Wind Direction Bias ≤ ±10 deg

Temperature Gross Error ≤ 2.0 K

Temperature Bias ≤ ±0.5 K

Temperature IOA ≥ 0.8

Humidity Gross Error ≤ 2 g/Kg

Humidity Bias ≤ ±1.0 g/Kg

Humidity IOA ≥ 0.6

[These benchmarks may be too stringent for MM5, especially Temperature. These

may need to be reevaluated after seeing more results.]KRD: DRAFTModelingProtocol.doc 

# 56

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# EMISSION INVENTORY INPUTS

Ozone episodes occurring in 1997, 2004, a nd 2005 will be simu lated for the 2007

AQMP. Gridded, hourly base year em issions inventories, including CO, NOx,

SOx, and TOG emsissions, for those year s are needed for photochemical ozone

modeling. The 2005 base year particulat e matter emissions will also be needed to

support inputs needed for aerosol modeling. The information ne eded to complete

the emission inventory for the modeling region is obtained from the local air

pollution control districts, transportation planning agencies and CARB. For the

2007 AQMP, the 2002 base year emissions will be used. The statewide emissions

inventory will be gr idded to the modeling domain. The 2002 emissions will be

backcasted to the 19 97 episode year and grown to the 2004 and 2005 episode

years. Specific month and day-of-wee k emissions will be estimated from the

annual average emissions, based on temp erature corrections derived from ambient

measurements. The emissions will also be grown to the attain ment milestone and

demonstration years of 2005, 2010, 2020 and, possibly 2015 and 2030.

Adjustments to the 2002 base year inventory for the 2007 AQMP will likely

reflect the following changes from the 2003 AQMP inventory:

• Overall emissions inventory changes w ill likely include higher VOCs, lower

NOx and lower CO emissions. New temp erature and relative humidity profiles

will be used for annual inventory adjustments.

• The stationary source inve ntory will reflect that the actual 2002 emissions were

mostly lower than 2003 AQMP-projected emissions.

• The mobile source inventory will be projected w ith EMFAC Gross

Adjustments (to be provided by Spring 2006). It will reflect increased VOC

and NOx emissions from the 2003 AQMP inventory. Key areas of mobile

source inventory adjustment include:

 Truck Distribution/VMT/deterioration rate;

 Ethanol & evaporatives and permeation issues;

 Modified temperature distribution.

• For the particulate matter emissions categories, the new USEPA fugitive

PM10/PM2.5 ratio will be evaluated and applied.

• Temperature and humidity corrections will be applied to the biogenic

inventory.

Other potential emissions inventory changes will po ssibly result from improved

inventories for ports, the Alameda Corrido r, shipping, aircraft and airports. The

2007 AQMP on-road emissions will be based on technical-adjustments to the

SCAG 2004 Regional Transportation Plan . No weekend trip model will be

available from SCAG, so CARB will deve lop a “weekend” overlay to mimicKRD: DRAFTModelingProtocol.doc 

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# VMT based on California Department of Tr ansportation (Caltran s) in-road counter

data. Air quality modeling analyses will stress emissions sensitivity runs, since

the spatial distribution of emissions will be critical to model performance due to

the use of Relative Reduction Factors ( RRFs) instead of peak concentration

performance metrics.



# Emissions Characterization

Point Sources

Characterizing anthorpogenic point source emission is the responsibility of the

local air districts. Emission inventorie s for point sources (including RECLAIM

facilities) are compiled by local distri cts and reported to CARB. If annual

emissions for a facility fall below 10 tons/yea r (this cutoff varies with district) the

source is included in the area source inventor y. Point sources ar e allocated to grid

cells using the location that is stored as pa rt of the point source emission database.

Temporal codes which describe hours of operation are also included in the

emission database. Factors are also stored to convert annual average emissions to

a specific month and day of week. Point so urces have been inve ntoried for 2002.

SCAQMD’s point source inventory for 2002 includes an update to locations

(UTM coordinates) and stack paramete rs. Point source emissions will be

estimated using the CARB California Em ission Forecast System (Johnson, 1997)

for the modeling episode base years and future years.

Area Sources

Area sources are comprised of emission sour ce types that are difficult to inventory

individually. Examples are architectural coatings, residential water heating,

gasoline stations and off-road mobile sources not incl uded in the CARB

OFFROAD model. The area sources include point sources smaller than 10 tons

per year and area surrogates are used fo r sources such as consumer products.

Districts and CARB share responsibility for estimating area source emissions

according to a long-standing division of categories. CARB, 1997b describes

methodologies used to estimate emissions from area sources. Factors are also

included that allow estimates of specific month and day of week emissions from

annual average emissions. Temporal code s which describe hours of operation are

also included in the area source emission database. Area source categories have

been inventoried for 2002. Emissions for the modeling episode base years and

future years will be grown usi ng CARB emission forecasting system.KRD: DRAFTModelingProtocol.doc 

# 58

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# On-Road Mobile Sources

On-road mobile source inventories are pr epared using vehicle activity data from

transportation planning agencies. The majority of travel is reflected in

transportation plan s developed by:

• Southern California Association of Governments (SCAG);

• San Diego Association of Governments (SANDAG);

• Santa Barbara County Associatio n of Governments (SBCAG); and

• Kern Council of Governments (Kern COG).

Travel data for areas not covered by th e transportation planning agencies are

extracted from the California Statewid e Planning Model maintained by the

California Department of Transportation. Emission factors for on-road mobile

sources will ultimately be estimated using the CARB EMFAC2007 emission

factor model. However, the release of EMFAC2007 will likely be concurrent with

the 2007 SIP submittal, so the modeling will proceed using the 2002 base year

emissions inventory from the 2003 AQMP with gross EMFAC adjustments based

on CARB technical documenta tion. DTIM4 will use both the emission factors and

travel activity data to pr oduce hourly gridded emission estimates for the SCOS97

region.

CARB is leading the effort to acquire a ll travel data needed for this modeling

study. The network and travel activity data provided by tran sportation planning

agencies is developed for peak and off-peak time peri ods, which will be processed

into 24 hourly data sets. Day-specific traffic count data will be used to calibrate

DTIM4 inputs for development of day-speci fic on-road mobile so urce emissions.

CARB will use the network and travel ac tivity data to produce gridded DTIM4

inventories for episode days.

Other Mobile Sources

Area source emissions from most categorie s of off-road mobile sources will be

estimated using the CARB off-road mob ile source emission model (OFFROAD).

OFFROAD covers more than 12 off-road categories, including lawn and garden

equipment, small utility and construction equipment, as well as farm equipment.

Categories not estimated by OFFROAD will be covered under “area sources”.

However, specific emissions for aircraft, marine vessels, and locomotives will be

provided through separate special studies. OFFR OAD will pro duce gridded

emission inventories for each calendar y ear desired. The OFFROAD model will

have the capability to estimate exhaust, starting, and evaporative emissions for

differing spatial and temporal conditions.KRD: DRAFTModelingProtocol.doc 

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# Biogenic Emissions

The derivation of a gridded natural biogenic emission inve ntory requires data sets

describing the spatial distributions of pl ant species, biomass, and emission factors

that define rates of hydrocarbon emissions for each plant species. The Biogenic

Emission Inventory System (BEIS 2.3) (U SEPA, 1995) model, distributed by the

USEPA for this purpose, is one source of these data sets for areas throughout the

United States. However, the BEIS model has been shown to have limited use in

California because of poor spatial resoluti on within the referenced data sets and a

simplified scheme for assigning emission f actors (e.g., Jackson, et al., 1996). The

development of a gridded biogenic emi ssion inventory for the SCOS97 domain

will benefit from research co nducted within California th at describes the needed

data sets in more detail than is define d within the BEIS mode l (Benjamin et. al.,

1998).

CARB, in consultation with researchers at UCLA, developed a methodology to

complete a gridded biogenics inventory for the SCOS97 modeling domain. The

methodology involves the use of: (1) gr idded plant species maps using the GAP

data base (Davis et. al., 1995), an inve ntory of biomass diversity for the United

States; (2) biomass distribution, determin ed using published correlations between

biomass and Normalized Difference Ve getative Index (NDVI), an index of

relative “greenness” from Advanced Very High Resolution Radiometer (AVHRR)

satellite remote sensing data sets; (3) emission factors of isoprene, monoterpenes,

methyl butenol, and other VOCs for variou s plant species known to exist within

the modeling domain using taxonomic re lationships between the plant species

(Benjamin et. al., 1996). The gridded bi ogenic inventory, including the gridded

plant species, biomass distribution and em ission factor databases, are combined

with ambient temperature and radiation da ta to produce gridded hourly emissions

of isoprene, monoterpenes, me thyl butenol, and other VOCs.

Organic Gas Speciation

Organic gas speciation profiles are applied to all categories of TOG emissions to

obtain estimates for each organic gas spec ies emitted in the modeling region.

CARB maintains a database of current profil es that are routinely updated to reflect

recent information. The most recent updates were for gasoline exhaust and

evaporation, diesel exhaust and jet e ngine exhaust. The CARB publication

Identification of VOC Species Profiles (CARB, 1991) documents the organic gas

profiles.

Day-Specific Emissions

Emissions from many sources vary from day to day. Evaporative emissions from

vehicles and vegetation increase with ambi ent temperature. Exhaust emissions areKRD: DRAFTModelingProtocol.doc 

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# also a function of ambient temperature. Increased air c onditioning demands on

hot days also lead to increased emissi ons from electrical generation. Hourly

surface temperatures for episode days are interpolated to each grid cell and are

used in estimating emissions from vege tation and on-road mobile sources.

Criteria pollutant emissions from approxima tely 80 major point sources will also

be estimated hourly for each specific episode day. Each distri ct has acquired data

from major point sources for the episode days and is developing day-specific point

source inventories for those years. The districts also colle ct information on

variances, temporary breakdowns and shutdo wns. DTIM4 will be run to develop

mobile source inventories for several episode days, incl uding weekend days.

Where feasible, wildfire emissions will be estimated. Emissions from large ships

in the shipping lanes are also estimated, using ship activity data (for commercial

vessels) from shipping ports, ship-specific engine characteristics data, and the

latest emission factors. Emissions from ai rcraft will be estimated using aircraft

activity data, including hourly landing, takeoff, approach, climbout and cruise

emission. This type of information will allow devel opment of temporally and

spatially resolved emission estimates.



# Emissions Quality Assurance

CARB has provided specific guidelines to assist state and local agencies in

implementing uniform and systematic appr oaches for collecting, compiling, and

reporting emission inventory data. A comp rehensive quality control and quality

assurance plan was prepared to ensure good quality practices during development

of the 2002 and future year emission i nventories. These procedures include:

quality control checks for co llecting non-emission data, updating activity data, and

using appropriate emission factors for calculating emissions; emission calculation

methodology; quality assurance evaluation using the Data Attribute Rating System

(DARS); and quality review of the entire inventory. The DARS program,

originally developed by the USEPA, w ill be used as an additional quality

assurance tool to quantify the relative accuracy of the annual emission inventories.

CARB has also provided the districts with a variety of quality assurance reports to

aid in the review of inventory data impor tant for modeling. These reports were

intended to provide checks on the accurac y of the emission calculations, stack

data, facility location data, temporal da ta, devices data, process data, etc.



# Emission Projections

Future year emissions form the basis for an air quality emission reduction target.

Future year emissions for area and poin t sources are projected by accounting for

growth and control, generally using growth and control factors applied to the baseKRD: DRAFTModelingProtocol.doc 

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# year (2002) emissions. Control factors are derived based on adopted measures.

Growth factors are derived from socioec onomic and demographic data provided

by districts and local agencies, and CARB-s ponsored research factors elsewhere.

Area source and offroad emissions are grid ded using the appropriate surrogates as

used for 2002. Gridded future year surrogates for the entire modeling domain

region and also being prepared for milest one and attainment demonstration years.

Surrogates for other years can be interpolated as needed.

Future year traffic activity and network data are also prepared by local planning

agencies. EMFAC will give estimates of future year emission factors. DTIM4

uses future year emission factors and networ k travel data to ob tain gridded future

year on-road mobile emissions. DTIM4 i nputs for future years are being compiled

and prepared. Ambient temperatures that occurred during 2002 are also used in

calculating future year emissi ons for each episode day.

Biogenic emissions will not change for futu re years. Even though there may be a

shift in farm or landscaping plans and species, the capability does not exist to

incorporate any potential cha nges into the inventory. Seep emissions will also

remain constant in future year inventories.KRD: DRAFTModelingProtocol.doc 

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# AIR QUALITY MODEL PERFORMANCE EVALUATION

It is a well established tenet of the m odeling community that for an air quality

modeling simulation to give reliable results, it must be capable of giving the right

answers for the right reasons. That is, not only must the model be capable of

reproducing observed air pollution meas urements with a reasonable level of

accuracy, but it must also pass a series of prescribed tests designed to ensure that

the apparently accurate resu lts are not produced by a co mbination of compensating

errors. Several tests on the modeling simu lations, both at the surface and aloft are

proposed to be conducted as part of th e model performance evaluation. Both

precursor and secondary species will be ev aluated, in addition to 1-hour and 8-

hour ozone, PM10 and PM2. 5 for each episode and model variation. Statistical

and graphical analyses will compare simulated concentration to measured values,

throughout the domain and by geographic region. Diagnostic simulations will be

used to analyze the sens itivity of the model to the input parameters and

assumptions. This performance evaluation should allow a determination that the

model is working properly. The following evaluation tools are based on previous

modeling practices, the CARB photochem ical modeling guidance (CARB 1992),

and the USEPA attainment demonstration guidance for ozone (USEPA, 2005) and

particulate matter (USEPA, 2001b).



# Statistical and Graphical Analyses

The model performance evaluation effort w ill include both graphical and statistical

analyses. These will compare simulated po llutant concentrations with measured

values from the routine air monitoring st ations and special study sites, including

the PAMS stations. The st atistical evaluations for th e particulate matter modeling

will focus primarily on comparisons to th e speciated particulate data from the

MATES-III study. The graphi cal analyses will include time series plots showing

temporal variations, contour plots showing spatial variations, scatter plots showing

tendencies for over- or under- estimation, and residual plots showing the

distribution of the differences between observed and predicted concentrations.

The statistical analyses will examine the accuracy of pe ak estimates (both paired

and unpaired in time and space), mean nor malized bias, mean absolute gross error,

and mean absolute normalized gross erro r. The statistical performance criteria

outlined in the CARB guidan ce document for Class B or better ozone performance

will be used to guide the determination of acceptable model performance. These

statistical criteria will be used as a cr iterion for acceptable model performance.

However, other analyses (graphical, mu lti-species, aloft comparisons and the

diagnostic simulations) will also be us ed to determine acceptable model

performance, and ultimately a conclusion that the model is working properly mustKRD: DRAFTModelingProtocol.doc 

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# be made considering the evidence from a ll of the analyses. Table 12 shows some

of the statistical performance goals for the ozone simulations.

TABLE 12

Performance Goals for 1-Hour Ozone

Statistic for 1-Hour

Ozone Criteria

(%) Comparison Basis

Normalized Gross Bias ≤ ±15 Paired in space and time

Normalized Gross Error ≤ 35 Paired in space (+2 grid cells) and

time

Peak Prediction

Accuracy ≤ ±20 Unpaired in space and time



# Subregional Performance

The performance tests will be evaluated fo r the entire domain, by district or air

basin, and for several geographic subreg ional zones to ensure that the domainwide statistics do not mask subregional issues with the simulation. Since the

modeling domains are very large, six ge ographic zones are proposed to be

evaluated for model performance: San Fern ando Valley, west (or coastal) Basin,

mid-Basin, San Gabriel Valley, east Ba sin, and Coachella Valley. The same

statistical acceptance criteri a will be used for the s ubregions as for the entire

domain.

Multi-Species Evaluations

To be useful for planning orother purposes, an air quality model must be able to

replicate measured concentrations with reasonable accuracy. However, it is also

important to compare estimated and measur ed concentrations of precursors and

secondary species, to establish confidence that the chemistry is being simulated

properly. The important ozone precursors are NO, NOx, HONO, and organic gas

species; important secondary specie s are HNO3 and PAN. Organic gas

concentrations will be lumped according to the scheme employed by each model’s

chemical mechanism. Co mparisons will be made fo r each of the estimated

precursor species and lumped organic gas species, for each monitoring location.

In addition, comparisons will also be made for NOx, and total ROG.

The multi-species comparisons may reveal modeling issues that were not obvious

from the direct ozone comparison. Ma ny of the precursor species have a

secondary component as well. Concentra tions of primary pollutants tend to have

higher gradients than do secondary species. This ma kes it more difficult toKRD: DRAFTModelingProtocol.doc 

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# assume that a measured concentration of a primary pollutant represents a grid cell

average. For these reasons it is proba bly unreasonable to expect the same

accuracy in replicating precursor concentr ations as for ozone concentrations.

Thus, use of a specific statistical error or bias criterion is not recommended.

These comparisons should be viewed as more qualitative, to uncover potential

problems in precursor and secondary performance.

Aloft Comparisons

Aloft air quality measurement data for th e 2004 and 2005 episodes is minimal.

The vertical profile of the chemical sp ecies will be evaluated qualitatively and a

more quantitative analysis will be conduc ted whenever observational data are

available. For the SCOS97 August 1997 episode, more extensive the upper air

measurements are available. The concentr ations of selected air pollutants were

measured above the ground using aircraft, balloons and LIDAR. The primary

component of these measurem ents is the oxidant concentrations measured with

ozonesondes to a height of 5,000 m AGL. Ozonesondes were flown at seven sites,

at 6-hour intervals, for selected episode da ys. Also, four aircra ft were flown up to

three times per day and an ozone LIDAR was operated continuously on selected

episode days.

When air quality data aloft is availabl e, the performance of air quality model

simulations above the ground will be determined by quantitatively comparing

simulated oxidant and ozone concentrati ons with measurements, at reasonable

close times and locations. Measured concentration profiles will be averaged for

the vertical layer increments corresponding to those of the air quality model. Due

to the vertical resolution of the air quality models, the vertical resolution of the

aloft comparisons is likely to be somewhat inconclusive and the evaluation will be

of a more qualitative nature.

In addition to measuring ozone, three of th e SCOS97 aircraft measured oxides of

nitrogen and collected samples for late r hydrocarbon analysis. Comparisons

between these precursor data and concen trations simulated using the air quality

models will also be made. However, ther e are relatively few samples and because

an aircraft is not in one grid cell for an hour, comparisons may not be consistent

with modeled concentrations. Comparisons to see if any large discrepancies exist

between modeled and measured conc entrations aloft will be made.

Acceptable Model Performance

While it is expected that acceptable mode l performance can be achieved for the

ozone and particulate episodes, this is not always feasible given the regulatory

deadlines for plan submittals. While th e modeling results of some episodes may

not meet all the performance goals, the episode can still be used for carryingKRD: DRAFTModelingProtocol.doc 

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# capacity and attainment demonstration pu rposes assuming the relative reduction

factors reflect the change in emission re duction. The RRF will be extensively

evaluated with sensitivity anal yses and such issues will be described in the weightof-evidence discussions.



# Sensitivity Analyses

Diagnostic Simulations

Several diagnostic or investigative si mulations will be employed to further

determine the fidelity of the model results . These sensitivity analyses will help

evaluate potential concerns regarding su ch factors as emissions mass, VOC/NOx

ratios, ammonia mass, and emissions tim ing, including daily and weekend vs.

weekday emissions. The diagno stic simulations that are anticipated help evaluate

model sensitivity and performance will include the following:

• Zero emissions

– all anthropogenic and biogenic emissions will be set to zero

to test the model’s sensitiv ity to emissions and to en sure that the base case

results are influenced appropriately by the emission inputs.

• Double anthropogenic emissions

– all anthropogenic emissions will be

doubled to test the model' s sensitivity to increased man-made emissions. In

addition, as separate tests of anthro pogenic emissions affects, only mobile

source emissions will be do ubled and only stationary source emissions will be

doubled.

• Emissions adjusted based on uncertainty analysis results

– The

anthropogenic emissions estimate include various inherent uncertainties

because of the nature of human activity, such as the possibility that some VOC

sources could not be accounted and uncertain ty in the spatial distribution of the

emission sources. The adjustment fact ors will be developed based on the

ambient VOC species adjust ed within the bounds of the uncertainty. Various

emissions estimate scenarios will be test ed to diagnose model sensitivity and

performance.

• Zero biogenic emissions

– biogenic emissions will be set to zero to test the

model’s sensitivity to biogenic emissions.

• Zero surface deposition

– deposition will be turned off for all species to

examine the effects of dry de position on ozone estimations.

• Reduced wind speeds

– reducing the wind speeds by 50% is proposed to test

the model’s sensitivity to that paramete r. However, it is possible that the

resulting wind fields will not be dynamica lly consistent, so these results will

need to be approach ed with caution.KRD: DRAFTModelingProtocol.doc 

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# • Zero and estimated or measured boundary and in itial conditions – A range of

boundary and initial conditions will be an alyzed to test the sensitivity of the

models to these inputs. The modeling results using the following initial and

boundary conditions will be analyzed: (1) the bounda ry conditions at the top

and sides of the modeling domain and the three-dimensional initial conditions

will be set to zero; (2) the observed ai r quality data is interpolated for the

initializations hours, using data fro m PAMS and other measurements as

available to prepare estimated specia ted initial and boundary profiles; (3) a

range of boundary and initia l conditions will be evaluate d, based on the larger

scale WRAP modeling results.

• Grid cell averaging sensitivity – For the attainment de monstration, relative

reduction factors (RRF) will be calcul ated using 9-cell (15 km by 15 km)

averages. As a sensitivity run, 1-cell (5 km by 5 km), 4-cell (10 km by 10 km)

and 16-cell (20 km by 20 km) averages will be examined.KRD: DRAFTModelingProtocol.doc 

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# USE OF THE MODEL RESULTS

Attainment Demonstration

The modeling results are anticipated to be used for estimating carrying capacities

and demonstrating future attainment of the NAAQS. For the attainment

demonstration, the years 2007, 2010, 2014 and 2021 will be simulated with the

proposed control measures (the contro l strategy) for 8-hour ozone NAAQS

attainment. Attainment of the rev oked 1-hour ozone NA AQS will also be

demonstrated for the future year 2010 as a milestone and to show reasonable

further progress. The years 2006, 2010 , 2015 and 2020 will be simulated to

demonstrate the particulate matter NAAQS a ttainment. In the past the use of the

model results for these goals has been cont ingent upon acceptable base case model

performance for the episodes simulated. That is, only episodes for which the

model is judged to be ope rating properly and which meet the model performance

acceptance criteria will be used.

Weight-of-evidence discussions will also factor into the attainment demonstration

by providing supportive analyses to confirm or compliment the modeling

assessment. Examples of the weight-o f-evidence considerations may include:

trend analyses, sensitivity modeling analys es (e.g., altered emissions scenarios),

hot spot grid evaluations, and statistical analyses. Special analyses may also be

targeted to problem locati ons, for example, incorporating the Rubidoux study

results.

Relative Reduction Factors

Historically, AQMD developed the ca rrying capacity and attainment

demonstration for ozone based on a set of specific control measures that was

projected to achieve the 1-hour ozone NAAQS for all modeled episodes. The

USEPA 8-hour ozone guidance (USEPA, 2005) and draft particulate matter

guidance (USEPA, 2001b) recommend the use of relative reduction factors

(RRFs) as part of the attainment demons tration process, assu ming that satisfactory

base year model performance is establishe d. The RRF is a non-dimensional factor

that incorporates design peri od monitoring data, using the 3-year average of the

design value, directly into the attainment test along with the ratio of future to

current year model predictions. The RRF is defined as the ratio of the future daily

maximum concentration predicted near a monitor (averaged ove r multiple days) to

the baseline daily maximum concentration predicted near the monitor (averaged

over the same days).

The RRF are site specific and will be ba sed on the 9-cell av erage (15 km by 15

km) for multiple episodes. Areas with severe or higher nonattainment statusKRD: DRAFTModelingProtocol.doc 

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# require a minimum of 15 simulated days. It allows the model to be used in a

relative, rather than absolu te, sense to reduce uncertainty in the predictions. The

use of RRFs also potentially address two problems in model applications that tend

to result in underestimation of emission redu ctions needed to attain standards. The

first problem is that modeled episodes us ually have ozone concentrations lower

than the design value. The second pr oblem is that simulation results have

historically exhibited a tendency to wards underestimation of observed

concentrations. By utilizing monitored data along with model estimations, RRFs

address both problems.

However, there may be some limitations in using RRFs, especially for 1-hour

ozone. Examples of su ch situations include:

• Measured ozone concentrations at some sites and for some episodes may differ

substantially from design values for those sites. That is, each available ozone

episode will not be representative of desi gn value conditions at all sites. In

such instances it may not be reasonable to include the non-representative sites

in the RRF analysis.

• Model performance typically varies consid erably between sites and episodes in

a domain. The reported ozone performan ce measures (such as peak prediction

accuracy, bias, and gross error) may not capt ure this variation. Thus it may not

be reasonable to include sites which have poor model performance for a given

episode.

Some characteristics of RRF s include the following:

• More robust analysis due to multiple episodes;

• Less reliant on peak concentra tion performance statistics;

• Allows for episodic, seasonal or annual composite application;

• Can be site specific;

• Directly applied to design values so unusually adverse years weigh heavily;

• Weekend/weekday differences may no t be adequately characterized;

• More applicable to 8-h our than 1-hour ozone;

• Not applied for previous AQMPs



# Carrying Capacity Estimation

A traditional use of models for planni ng has been the estimation of carrying

capacities for ozone precursors. This is typically achieved by exercising the

model with a series of across-the-board precursor emission reductions from the

future year baseline, fro m which an ozone isopleth (“EKMA”) diagram is

constructed. The metric used for the isopl eth diagram can be one of several, suchKRD: DRAFTModelingProtocol.doc 

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# as peak 1-hour or 8-hour ozone concentrations with in the modeling domain or

subregion, number of grid cells above the standard, or one of many population

exposure metrics. Since the carrying capacity for each precursor is based on

across-the-board emission ch anges, rather than source- and location-specific

controls as would be specified in a plan , it should only be viewed as an initial

estimate for determining the emissions reductions necessary for attainment.

For the 2007 AQMP, ozone is opleth diagrams for the fo llowing air quality metrics

will be constructed by episode:

• Peak 1-hour ozone concen tration for the domain.

• Population exposure for 1-h our ozone concentrations.

• Peak 8-hour ozone concentration for th e domain. This in formation will serve

as an indicator of the need for po tential additional precursor emission

reductions to meet the 8-hour ozone NAAQS.KRD: DRAFTModelingProtocol.doc 

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. EPA Publication No. EPA-450/4-91-013. U. S. Environmental Protection Agency,

Research Triangle Park, NC.

USEPA. 1995. Urban Airshed Model ( UAM) Biogenic Emission Inventory System

Version 2. (BEIS2) User's Guide . Final Report. USEP A Contract No. 68-D3-0034.

Work Assignment No. 1-9. EC/R Project No. AQM-108. United States Enviromental

Protection Agency. Air Quality Modeling Group. RTP, NC 27711.

USEPA. 1996. Guidance on the Use of Modele d Results to Demonstrate Attainment of

1-hour Ozone NAAQS. June 1996. Office of Air Qual ity Planning and Standards.

EPA-454/B-95-007.

USEPA. 1998a. EPA Third-Generation Ai r Quality Modeling System: Models-3

Volume 9b User Manual . June 1998. Office of Research and Development. EPA600/R-98/069(b).

USEPA. 1998b. Use of Models and Other An alyses in Attainment Demonstrations for

the 8-Hour Ozone NAAQS (Draft) . October, 1998.

USEPA. 2001a. Guideline on Air Quality Models . July 2001. Appendix W to Part 51,

Federal Register, 40 CFR Ch. I.

USEPA. 2001b. Draft Guidance for Demonstra ting Attainment of Air Quality Goals for

PM2.5 and Regional Haze . January 2001.

http://www.epa.gov/scram001/guidance/guide/draft_pm.pdf

USEPA. 2005. Guideline on the Use of M odels and Other Analyses in Attainment

Demonstrations for the 8-Hour Ozone NAAQS . October 2005. Office of Air Quality

Planning and Standards. EPA-454/R-05-002.

http://www.epa.gov/scram001/guidance/gui de/8-hour-o3-guidance-final-version.pdfKRD: DRAFTModelingProtocol.doc 

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# Yamartino, R.J., J.S. Scire, S.R. Hanna, G.R. Carmichael, and Y.S. Chang. 1989.

CALGRID: A Mesoscale Photochemical Grid Model, Volume I: Model Formulation

Document . June, 1989. Prepared for the Calif ornia Air Resources Board by Sigma

Research Corp.KRD: DRAFTModelingProtocol.doc 

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# ATTACHMENT-4

Summary of Preliminary Critiques of Peer ReviewersKRD: DRAFTModelingProtocol.doc 

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# Draft AQMP Appendix V

(December 16, 2006 Version) 

# Peer Review Summary of Comments

Scientific, Technical & Modeling Peer

Review Advisory Group Meeting

February 2007



# General Appendix V Comments

• Ozone modeling methodology is state-of-the-art.

• Ozone modeling performance is quite good.

• PM modeling methodology is solid but a more complete description

of the CAMx PM model, including a description of each sub-model is

desirable.

• Some concerns of PM model predictions of ammonium, sulfate and

nitrate. These depend on the ammonia emissions inventory, the

generation of nitric acid in CB4 and the CAMx thermodynamic

module.

• How were boundary conditions treated between the large scale and

the nested model (Page V-1-6)? For example, were nested grid

values relaxed to large-scale values using a few rows? Also, did the

vertical layers match between the large scale and the nested grids?KRD: DRAFTModelingProtocol.doc 

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# General Appendix V Comments (cont.)

• Define “Severe-17 nonattainment” (Page V-1-1)

• It seems inconsistent to use different meteorology and chemistry

algorithms to study ozone versus PM2.5 (Table V-1-1). It would be

useful to have a short discussion explaining why this was done.

• Clarify whether future-year and base-year model predictions in the

RRF equation are an average over the model domain versus over a

specified region versus over a specific location and whether they are

time-averaged values versus values at a single time. (Page V-1-4)

• What is the temperature-correction algorithm used to adjust mobile

emissions (either a reference or an equation)? (Page V-1-10)

• The time series CO figure curves should fit into the entire figure

rather than be scrunched near the bottom (e.g., instead of a max

vertical axis of 6000 ppb, use 2000 ppb for most figures). Same with

other chemicals. (Page 319 ff ?)



# General Appendix V Comments (cont.)

• “...vertical structure was increased to 11 layers (compared with the

5-layer analysis of UAMAERO-LT), but less than the 19 layers used

for the MM5 simulations (Page V-1-6)” My concern is that mapping

the MM5 vertical gridding onto the CAMx gridding will introduce

substantial error in the results.KRD: DRAFTModelingProtocol.doc 

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# General Appendix V Comments (cont.)

Emissions Control Suggestions

• Consider a rule to restrict heavy-duty truck access to the ports of Los Angeles and Long Beach and nearby distribution terminals to allow only new and/or retrofitted clean diesel engines. The ports are a magnet for diesel trucks, and the environmental burden of rapidly growing freight movement on nearby neighborhoods is disproportionate and unfair.

• Consider a rule to require use of new/retrofitted diesel equipment at construction sites in the South Coast Air Basin, once more stringent standards for off-road diesel engine emissions take effect in 2015.

• Strengthen rules to control other sources of primary PM2.5 including wood smoke, meat charbroiling at restaurants, etc.

• After major successes in control of light-duty motor vehicle VOC emissions during the 1990s, attention seems to have turned to other issues. To further advance the control of air toxics, VOC mass emissions and reactivity, and secondary organic aerosol
contributions to PM2.5, consider further reductions in the aromatic content of California gasoline. This could help reduce the air quality impact of both on-road and off-road gasoline engine emissions.



# General Appendix V Comments (cont.)

Episode Selection

• Ozone planning should be matched to meteor ological conditions that lead to the “design value” (i.e., the 4th highest ozone day in each of 3 consecutive years). As noted in the modeling protocol document, the 4th highest daily maximum value out of 365 days corresponds to the 99th percentile of the annual distribution. There is no discussion of the high O3 summers of 1998 and 2003; why were they excluded from the analysis? The modeling protocol document (page 40) states that 11 out of 13 of the days considered in the AQMP are at the 95th percentile or higher; this undershoots the requirement to consider conditions corresponding to the 99th percentile from 3 consecutive years. Even if 2004 and 2005 “bracket the annual design value [shouldn’t the design value be based on 2003-05?]”, it is possible that inclusion of the 4th highest day from 2003 would increase emission control requirements.

• I don’t understand why the phase-out of MTBE and switch to ethanol in California gasoline is relevant to episode selection for ozone modeling. This fuel switch occurred 2003-04, but I expect small effects on emissions compared to the much larger Phase 2 reformulated gasoline changes that took effect 1995-96.

• A major field measurement program (MATES III) focusing on the composition of particulate matter in the South Coast Air Basin was conducted during 2005. Staff efforts to develop needed emissions, meteorological, and other data for this timeframe will be helpful in the analysis of MATES data. Likewise, MATES will provide useful aerometric data to support air quality modeling. However, since the applicable air quality standard for PM2.5 requires consi deration of a 3-year average, was 2005 cleaner, dirtier, or typical relative to 2003-05?KRD: DRAFTModelingProtocol.doc 

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# General Appendix V Comments (cont.)

Emission Inventory

• Maps showing spatial distributions of VOC, NOx, SO2, NH3, and PM2.5 emissions should be developed and analyzed. Have anthropogenic emissions been inventoried for the entire modeling domain? Also I wonder if in-transit emissions from ships have been included; traditionally only the in-port emissions have been inventoried. Emissions from shipping lanes are likely a significant source of NOx,

SO2, and PM.

• An emission inventory question needing more work is the description of heavy-duty diesel truck activity and emissions. How were the spatial and temporal patterns of diesel truck activity estimated on weekdays? Setting the truck fraction to a constant proportion of total vehicle travel in each grid cell and hour is not accurate. Truck traffic follows different spatial and temporal patterns; for example, there should be hot-spots of truck travel near the ports, and long-haul truck traffic takes place almost entirely on highways. This is important as one of the major emission control programs that will affect air quality in future years is more stringent standards on heavy-duty truck emissions.



# General Appendix V Comments (cont.)

Emission Inventory (cont.)

• For schedule reasons, the 2007 AQMP modeling was conducted using a motor vehicle emission inventory developed prior to the latest release of California’s on-road vehicle emission inventory model (EMFAC2007). Heavy-duty truck activity and NOx emission rates were significantly

revised in EMFAC2007. Are these revisions to diesel emissions captured at least approximately in the AQMP?

• An important uncertainty in the emission inventory is the amount of offroad mobile source activity, specifically heavy-duty diesel engines used in construction and agriculture. Kean et al. ( J. A&WMA 50 , 1929-1939, 2000) found that some major categories of off-road engine activity may be overstated in the U.S. by a factor of about 2. Curently on-road and off-road engine emissions are estimated separately. While the dividing line between on-road and off-road fuel use may be difficult to draw clearly, the sum should be easier to estimate and compare to known total diesel fuel production/supply to the California market.

• The modeling protocol document mentions briefly CARB’s library of organic gas emission speciation profiles. Similar information for particulate matter and NOx emissions is missing. I am concerned that a 10% NO2 fraction and a 2% nitrous acid (HONO) fraction in all direct sources of NOx emissions may be too high.KRD: DRAFTModelingProtocol.doc 

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# General Appendix V Comments (cont.)

Boundary Conditions

• It is not clear from the materials received if/how a vertical concentration profile of pollutants was specified along the lateral boundaries of the modeling domain. From ozone soundings at Trinidad Head (northern California coastal site), we know that there are vertical gradients in ozone from ~20 ppb at the surface to 40-50 ppb above the planetary boundary layer. Over the ocean, stable stratification is expected in the marine boundary layer. This could allow large vertical concentration gradients to develop for pollutants

that are soluble/reactive at the ocean surface. Surface observations at coastal or offshore sites may therefore not represent the true inflow concentrations along the western boundary of the modeling domain, especially aloft.



# PM Modeling Comments

• Predicted ammonium at Compton, LA & Pico Rivera exceed

Rubidoux. Ammonium not well correlated with sulfate, except at

Compton (Table V-2-10a).

• Nitrate not correlated with amonium, except at Pico Rivera (Table V2-10b).

• Why is predicted annual average PM2.5 largest at LA, Compton and

Pico Rivera (Table V-2-10g)?

• OC is substantially underpredicted at Fontana (Figure V-2-11d).

• EC is underpredicted everywhere, except Long Beach (Figures V-2-

12a through -12e). This suggests that the emissions inventory is

underestimating EC. Cross-check emissions from diesel fleet with

the NOx inventory from the fleet.

• Predicted annual average PM2.5 at Big Bear is curiously low in

relation to the measured value (Table V-2-11). Make sure this is not

an indication of a larger problem. Also, Ontario, San Bernardino and

Riverside-Magnolia are higher than observed.KRD: DRAFTModelingProtocol.doc 

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# PM Modeling Comments (cont)

• It is not clear whether a two-mode particle size aerosol module is

accurate enough to predict aerosol physical processes (Page V-1-

5). Why was this method chosen instead of the size-resolved

method?

• “The peak PM2.5 24-hour average concentration...occurred on

October 22, 2005” (Page V-1-7). This date is a typical date for a

Santa Ana wind. If a Santa Ana wind was occurring, this might be

mentioned. Does the model treat natural soildust emissions and/or

would it be able to account for high PM during Santa Ana conditions

or does it rely solely on anthropogenic sources?

• “The coarse portion of the PM10 is assumed to be held constant in

this analysis” (Page V-1-8). Do you mean PM10-PM2.5 is held

constant? How was the constant value determined?

• Were aerosol and cloud effects on UV radiation accounted for in the

simulation of ozone with CAMx? (Page V-1-8)



# PM Modeling Comments (cont)

• How are sea-spray chemicals treated in the model (Na+, Ca2+, K+,

Mg2+, Cl-, etc.). Since these components can form a significant

portion of PM10 and a portion of PM2.5 in Los Angeles, they would

appear relevant. Are they added as “crustal?” (Page V-2-1)

• Figure V-2-2 shows the components of PM2.5 mass (Page V-2-4).

Does the “blank” capture mass that is not speciated? Please clarify.

• Please provide the dates of each quarter. (Page V-2-6)

• Please provide the relative humidity at which the aerosol water

content was determined for. (Page V-2-6, Figure V-2-3a and related

figures)

• Based on Eq. V-2-1, it seems that OC (organic carbon) should be

called (OM) organic matter, since OM contains noncarbon elements

in addition to pure OC. (Page V-2-13)

• The calculation of bonded water and, thus, of OC, depends

significantly on the relative humidity. Also, bonded water is a

function of Na+ and Cl- as well as the other components. This

should be mentioned. (Page V-2-13)KRD: DRAFTModelingProtocol.doc 

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# PM Modeling Comments (cont)

• “The boundary and top concentration input files were created on a month by month basis. ” How were top-boundary values incorporated into the model? Were they outside-model valuestreated during downward inflow or were they really fixed top-modellayer values. If the latter, mass must not have been conserved during vertical transport. Please discuss. (Page V-2-17)

• Similarly, for the lateral boundaries, were specified mixing ratios values just outside the model domain used for inflow or were they values fixed at inside of the boundary? (Page V-2-17)

• Top and lateral boundary values should change diurnally due to chemistry. Keeping the values constant may provide a source of error. This should be discussed here. (Page V-2-17)

• The use of 4-5 significant digits in the boundary condition values gives the appearance of too much confidence in those values. (Page V-2-18, Table V-2-6)

• Why was a lower limit placed on the vertical diffusivity (1 m 2 

### /s)? Does the code fail at a lower diffusivity? This is quite a high minimum. (Page V-2-19)



# PM Modeling Comments (cont)

• Define “controlled” emissions. What controls, specifically? (Page V2-19)

• There is little discussion about how the future emission inventories were developed. This is discussed more in the protocol but could be expanded on briefly here. (Page V-2-19)

• “A nearest cell average of predicted concentrations is typically used when comparing...to station measurements.” Using bilinear interpolation to the exact location is a more physically correct

method and has been used in several studies. A comment about this option would be beneficial. (Page V-2-24)

• Are the statistics based on hourly comparisons? Is there a lower cutoff value for comparisons? The table should also include a column (or state in the caption) the number of comparisons that were made for each city. (Page V-2-25, Tables V-2-10a-...)

• Are the normalized bias and error fractions or mass concentrations? I would suggest using normalize gross error as a percent = 100% * (1/N) * sum(abs(Pi-Oi)/Oi), where N=number of observations, Oi = ith observation, Pi=ith paired-in-time-and-space prediction, and the sum is over all comparisons. (Page V-2-25, Tables V-2-10a-...)KRD: DRAFTModelingProtocol.doc 

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# PM Modeling Comments (cont)

• For the yearly simulations, how frequently was the model restarted from initial conditions (or was the model run for a year straight)? (Page P. V-2-31)

• Table V-2-11 is not such a useful table since a comparison of two annual concentrations is a bias rather than an error comparison (Thus the column, “Percentage prediction error” is mislabeled and should be “Percentage prediction bias”). The normalized bias will be less than the normalized gross error, so including a normalized gross error, where the comparisons are paired in time (as well as space), would be beneficial. (Page V-2-47)

• “NOx reductions are approximately three time more effective...” Is

this true at low NH4+ levels as well as at current NH4+ levels? (Page V-2-50)

• “Figure V-2-14 presents the grid cell extrapolated of 2005 PM2.5 annual design values.” I do not understand what this sentence and figure are showing. Similarly, the description for Table V-2-14 is unclear as are the descriptions for Tables V-2-15a.... More description in the text or table captions would be helpful. (Page V-2- 61)



# PM Modeling Comments (cont)

• It is difficult to distill what information is important from these tables. Can the key information be summarized? (Tables V-2-16a...)

• “Future year predictions of maximum and second maximum 24-hour average PM10 are calculated using the site specific ratio between annual PM2.5 calculated for 2005 and 2015.” Why not just use information from 2015 simulations directly? Why is it necessary to apply a ratio? 2015 simulations could be initialized with similar meteorology as 2005. (Page V-3-1)

• “Visual range in 2021 is estimated... (Page V-3-6).” How is visual range being calculated? The standard Koschmieder equation for visual range (3.912/bext) differs significantly from a practical prevailing visibility equation based on a fit to data (1.9/bext) (e.g., Griffing, 1980, Atmos. Environ, 14, 577).

• Light extinction depends significantly on liquid water content of aerosol particles. Page V-3-7, Tables V-3-2 and V-3-3 do not appear to include liquid water (or if they do, they must be at a constant RH). More discussion is needed.

• Why is the extinction due to sulfate 0 (Page V-3-7, Table V-3-3)?KRD: DRAFTModelingProtocol.doc 

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# PM Modeling Comments (cont)

• Page V-3-8, Table V-3-4 estimates significant reductions in PM concentrations but Table V-2-7 indicates only an 18% reduction in

PM2.5 emissions in 2020 under controlled conditions relative to 2005. Is something missing from Table V-3-4?

• Why is PM10 being discussed instead of PM-coarse (PM10-2.5)? (Page V-1-8)

• Page V-2-3 to V-2-5: Three points:

– (1) the total mass differs from the sum of the component masses.

This is common and should be addressed in the discussion especially since uncertainty was just discussed a few pages

earlier.

– (2) The metals content seems unusually high. I’m more used to ng/m3 concentrations of metals. Any explanation for this?

– (3) The sulfate seems very high, considering the current fuel

sulfur levels. Are there data from San Nicholas or a coastal

location – I would like to know how much of this sulfate is background or long range transport and how much is associated with marine shipping?



# PM Modeling Comments (cont)

• Page V-2-13 to V-2-14 (Sandwich): Of course, all the error in mass measurement then goes into the estimate of OC using this method. A context would help: What will this Sandwich estimated OC value

be used for?

• Page V.2.15: “The vertical structure for the CAMx modeling was increased to 8 layers of height dependent varying depth (compared with the 5-layer analysis of UAMAERO-LT) but less than the 19 layers used for the MM5 simulations in effort to conserve computational resources.” This statement disagrees with the one above (Page V-1-6) but agrees with the main document regarding vertical layers. Are the 11 layers for ozone whereas the 8 layers are

for PM?

• Page V-2-22 ( VMT Capping ): Capping depends on whether the

source of the silt is related to the vehicle traffic. Some of the silt is from airborne or waterborne dust, which may make sense to cap,

but much is from brake and tire wear which will increase with VMT (although brake wear may not increase as much as hybrids continue to increase their market penetration).KRD: DRAFTModelingProtocol.doc 

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# PM Modeling Comments (cont)

• Page V-2-25 (PM2.5 Component Species Performance Evaluation for the MATES-III Sites): The agreements are good but possibly the average agreement is good but the agreement during peak episodes is not as good or better. How do these look?

• Page V-2-50: “the proposed strategy focuses on the reductions of SOx and primary PM2.5 through cleaner marine fuels and extensive diesel trap retrofits respectively.” Is marine fuel sulfur content within the SCAQMD jurisdiction? The state’s? Even if the local fuel sold has the lower sulfur content, there is no guarantee that ships will buy this more expensive fuel and they will unlikely be using it as they come into port.

• Page V-2-51, Table V-2-12: How can SOx reductions have twice the PM2.5 impact of primary PM2.5 emissions? Most of SOx is SO2,

much of which will not be converted to sulfate within the basin. The

sulfate will have water and ammonium associated with it, but is that enough to overcome the fraction of sulfate produced?



# Ozone Modeling Comments

• Ozone is underpredicted for weekend episode on:

– 7/15 – 7/17 at Crestline (Figure V-4-17b),

– 7/16 – 7/18 at Fontana (Figure V-4-17c),

– 7/16 – 7/19 at Santa Clarita and San Bernardino (Figure V-4-

17g).

• Sensitivity analyses should address the possibility that an inaccurate

weekend emissions inventory caused this underprediction.

• What is the effect of this underprediciton on the RRFs derived for

these sites?KRD: DRAFTModelingProtocol.doc 

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# Ozone Modeling Comments (cont.)

• “...a minimum observed concentration...exceeds 70 ppb...” Is this averaged over 8 hours, 1 hour, or is it instantaneous? (Page V-1-10)

• It would be useful to see how accurate the model is in predicting the peak at the exact location of the peak (using bilinear interpolation from four surrounding grid cells in the model to estimate the value at the exact location of the peak) in addition to seeing whether the model predicted the peak within 15 km. (Page V-1-11)

• “Overall per-capita exposure to ambient ozone...” Does the exposure model account for the 3-D modeled ozone concentration in a grid cell multiplied by the population in the grid cell? A discussion of how exposure is calculated would be useful. Also, ozone health effects begin to occur above 35 ppbv, so shouldn’t an exposure

model account for such concentrations as well as those above the state standard? (Page V-4-6)

• “Minimum vertical diffusivity set at 1 m2/sec. (Page V-4-17, Table V4-6).” Again, this limit appears to be arbitrary and may result in

over-diffusion.



# Ozone Modeling Comments (cont.)

• How is photolysis treated (Page V-4-17, Table V-4-6)? How above cloud effects on photolysis and on gas/particle processing?

• For future years, the boundary values could increase due to longrange transport of pollutants (e.g., growth of emissions from Asia) as well as decrease due to lower local emissions (Page V-4-21). The effects of long-range transport of pollution from Asia is not discussed in the AQMD. This is increasingly thought to be important, particularly in April for PM but also year around.

• The Gross errors for MM5 with data assimilation appear quite large (particularly for temperature and wind direction) (Page V-4-22, Table V-4-8). Is data assimilation providing any benefit relative to no data assimilation? If so this might be stated.

• Normalized gross errors should be paired exactly in space and time (e.g., using bilinear interpolation) rather than within 2 grid cells and time (Page V-4-27). This would make comparisons of error statistics

with evaluations from other models consistent. It would also make the statistic consistent with the normalized gross bias (which are stated to be paired in time and space although my guess is that bilinear interpolation was not used for that either).KRD: DRAFTModelingProtocol.doc 

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# Ozone Modeling Comments (cont.)

• Using a cutoff of 60 ppb for ozone statistics is also inconsistent with several studies that use 40 ppbv as the cutoff. (Page V-4-27)

• Page V-4-12: “Cassmassi (1998) used Classification and Regression Tree analysis (CART) to determine whether the conceptual model for a 1-hour ozone episode differed from the meteorological profile characterizing an 8-hour average ozone episode in the Basin.” For analyzing single particle mass spec data, we found that ART2a was easier to understand and employ than

CART.

• Page V-4-21: “For the future year scenarios, the boundary, region top and ambient air quality concentrations were rolled back based on the percentage reduction in emissions from 2002 base year to the projected emissions levels for future year of the simulation (2009, 2012, or 2020).” How sensitive are the peak basin predictions to the boundary conditions, especially the top ones? Rolling them back in proportion to the emissions reductions ignores the inevitable increase in background from Asia over the coming years.

• Page V-4-27: Again, I do not know what unpaired statistical comparisons really mean. If there is peak agreement but no correspondence in space or time, does that really tell us anything other than that coincidences happen sometimes?



# Meteorological Model Comments

• Was the NCEP reanalysis used at 2.5 degrees or 1 degree

resolution? (Page V-1-6)

• How frequently were MM5 fields fed into CAMx? (Page V-1-10)

# ATTACHMENT-5

2014 Control Measures Basin PM2.5 AttainmentRun Date: 8/16/2007 1:53:51 PM

(PC-CEPA V. 4.2/ May 2001)

C:\AQMP2007\CF0207\CM3-0607\cf2014-060607.txt

C:\AQMP2007\CF0207\CM3-0607\master-060607.txt

C:\AQMP2007\dump0906\xz011607\ems14ocs.txt

C:\AQMP2007\CF0207\CM3-06 07\scen4-060607-noltm.txt

C:\AQMP2007\CF0207\CM3-0507\impact-051107.txt

Year 2014 Emission Reductions Excluding Natural Sources by Control Measure

in the South Coast Air Basin (Annual Average Inventory - Tons/Day)

(A) Reductions Without Overlapping/Double-C ounting With Other Control Measures (1)

(Reductions - Tons/Day)

Measure Name VOC NOx CO SOx PM10 PM2.5

BA-LSI ARB Baseline Adjustment for large spark-ignited engines [NOX] 0.00 2.40 0.00 0.00 0.00 0.00

BA-CONS ARB Baseline Adjustment for Consumer Products [VOC] 4.50 0.00 0.00 0.00 0.00 0.00

BCM-03 Emission Red. from Wood Burn ing Fireplaces and Wood Stoves 0.00 0.00 0.00 0.00 1.05 1.01

BCM-05 Reductions from Under-fired Charbroilers [PM] 0.00 0.00 0.00 0.00 1.08 1.07

CMB-01 Nox Reduction from Non-Reclaim Ovens, Dryers and Furnaces 0.00 3.48 0.00 0.00 0.00 0.00

CMB-02 Reduction SOx Reductions 0.00 0.00 0.00 2.89 0.00 0.00

CMB-03 NOx Reductions from Residential Space Heaters [NOx] 0.00 0.79 0.00 0.00 0.00 0.00

CTS-01 Industrial Lubricants [VOC] 1.87 0.00 0.00 0.00 0.00 0.00

FUG-02 Emission Red. from Gasoline Tran sfer and Dispensing Facilities [V 3.71 0.00 0.00 0.00 0.00 0.00

MCS-01N Facility Modernization [NOx] 0.00 1.59 0.00 0.00 0.00 0.00

MCS-01P Facility Modernization [PM] 0.00 0.00 0.00 0.00 0.43 0.38

MCS-01V Facility Modernization [VOC] 2.02 0.00 0.00 0.00 0.00 0.00

MCS-05 Non-Dairy Livestock Waste [VOC,NH3] 0.75 0.00 0.00 0.00 0.00 0.00

EGM-01N Emission Reductions from New & Re-Development Projects 0.00 0.00 0.00 0.00 0.00 0.00

EGM-01P Emission Reductions from New & Re-Development Projects [PM] 0.00 0.00 0.00 0.00 0.00 0.00

EGM-01V Emission Reductions from New & Re-Development Projects 0.00 0.00 0.00 0.00 0.00 0.00

FLX-02P Petroleum Refinery Pilot Program [PM] 0.00 0.00 0.00 0.00 0.48 0.44

FLX-02V Petroleum Refinery Pilot Program [VOC] 0.74 0.00 0.00 0.00 0.00 0.00

ARB-ON1 Smog Check Enhancements [VOC,NOX,PM] 10.09 12.55 0.00 0.00 0.21 0.19

ARB-ON2 Expand Vehicle Retirement [VOC,NOX,PM] 2.84 2.49 0.00 0.00 0.06 0.05

ARB-ON3 Modifications to Reformulat ed Gasoline Program [VOC] 3.72 0.00 0.00 0.00 0.00 0.00

ARB-ON4 Cleaner In-Use Heavy-Duty Trucks [VOC,NOX,PM] 5.13 47.14 0.00 0.00 3.26 3.00

ARB-ON5 Port Truck Modernization [NOX,PM] 0.00 1.98 0.00 0.00 0.54 0.50

ARB-OFF1 Marine Vessels - Fuel, Aux. & Main Eng. [VOC,NOX,PM] 0.00 38.53 0.00 19.98 2.77 2.70

ARB-OFF2 Accel. Intro. Of Cleaner Line-Haul Loco. [VOC,NOX,PM] 0.70 4.30 0.00 0.00 0.22 0.20

ARB-OFF3 Clean Up Existing Harbor Craft [VOC,NOX,PM] 0.00 4.60 0.00 0.00 0.21 0.20

ARB-OFF4 Cleaner In-Use Off-Road Equipment [VOC,NOX,PM] 2.72 10.52 0.00 0.00 2.72 2.50

ARB-OFF5 New Emis Stds for Recreational Boats [VOC,NOX] 2.85 0.25 0.00 0.00 0.00 0.00

ARB-OFF6 Expanded Off-Road Rec. Veh Ems Stds [VOC] 1.85 0.00 0.00 0.00 0.00 0.00

ARB-CONS Consumer Products [VOC] 12.93 0.00 0.00 0.00 0.00 0.00

MOB-05 AB 923 Light-Duty High-Emitter Id. [VOC,NOX,CO,PM] 0.77 0.36 11.16 0.00 0.00 0.00

MOB-06 AB 923 Med-Duty High-Emitter Id. [VOC,NOX,CO,PM] 0.46 0.48 5.55 0.00 0.00 0.00

SCONRD-1* Accelerated Penetration of ATPEVs and ZEVs [VOC,NOX,CO,PM] 0.40 0.92 11.78 0.00 0.05 0.04

SCONRD-3* Further Emis. Red. from Heavy-Duty Vehicles [VOC,NOX,CO,PM] 0.00 24.89 0.00 0.00 1.35 1.24

SCONRD-4* Further Emis. Red. from Port Trucks [NOX,PM] 0.00 5.99 0.00 0.00 0.02 0.02

SCOFRD-1* Construction/Industrial Fleet Modernization [VOC,NOX] 2.88 14.14 0.00 0.00 0.00 0.00

SCOFRD-2* Cargo Handling Equipment [NOX,PM] 0.00 1.08 0.00 0.00 0.02 0.02

SCOFRD-3* Further Ems. Red. From Locomotives [NOX,PM] 0.00 10.95 0.00 0.00 0.45 0.41

SCOFRD-6* Accelerated Turnover Pleasure Craft [VOC,NOX,PM] 2.89 1.06 0.00 0.00 0.84 0.63

SCFUEL-1* California Phase III Reformulation Gasoline [NOX,SOX] 0.00 0.00 0.00 1.35 0.00 0.00

SCFUEL-2* Greater Use of Diesel Fuel Alt. & Reformulation [NOX,SOX,PM] 0.00 3.98 0.00 0.05 0.25 0.23

Grand Total (Net) 63.80 194.49 28.49 24.27 16.00 14.84Year 2014 Emission Reductions Excluding Natural Sources by Control Measure in the South Coast Air Basin (Annual

Average Inventory - Tons/Day)

(B) Reductions With Overlapping/Double-Counting With Other Control Measures (2)

(Reductions - Tons/Day)

Measure Name VOC NOx CO SOx PM10 PM2.5

BA-LSI ARB Baseline Adjustment for large spark-ignited engines 0.00 2.40 0.00 0.00 0.00 0.00

BA-CONS ARB Baseline Adjustment for Consumer Products [VOC] 4.50 0.00 0.00 0.00 0.00 0.00

BCM-03 Emission Red. from Wood Bu rning Fireplaces and Wood 0.00 0.00 0.00 0.00 1.05 1.01

BCM-05 Reductions from Under-fired Charbroilers [PM] 0.00 0.00 0.00 0.00 1.08 1.07

CMB-01 Nox Reduction from Non-Recl aim Ovens, Dryers and 0.00 3.48 0.00 0.00 0.00 0.00

CMB-02 Reduction SOx Reductions 0.00 0.00 0.00 2.89 0.00 0.00

CMB-03 NOx Reductions from Residential Space Heaters [NOx] 0.00 0.79 0.00 0.00 0.00 0.00

CTS-01 Industrial Lubricants [VOC] 1.87 0.00 0.00 0.00 0.00 0.00

FUG-02 Emission Red. from Gasolin e Transfer and Dispensing 3.71 0.00 0.00 0.00 0.00 0.00

MCS-01N Facility Modernization [NOx] 0.00 1.59 0.00 0.00 0.00 0.00

MCS-01P Facility Modernization [PM] 0.00 0.00 0.00 0.00 0.43 0.38

MCS-01V Facility Modernization [VOC] 2.02 0.00 0.00 0.00 0.00 0.00

MCS-05 Non-Dairy Livestock Waste [VOC,NH3] 0.75 0.00 0.00 0.00 0.00 0.00

EGM-01N Emission Reductions from New & Re-Development 0.00 0.00 0.00 0.00 0.00 0.00

EGM-01P Emission Reductions from New & Re-Development 0.00 0.00 0.00 0.00 0.00 0.00

EGM-01V Emission Reductions from New & Re-Development 0.00 0.00 0.00 0.00 0.00 0.00

FLX-02P Petroleum Refinery Pilot Program [PM] 0.00 0.00 0.00 0.00 0.48 0.44

FLX-02V Petroleum Refinery Pilot Program [VOC] 0.74 0.00 0.00 0.00 0.00 0.00

ARB-ON1 Smog Check Enhancements [VOC,NOX,PM] 10.09 12.55 0.00 0.00 0.21 0.19

ARB-ON2 Expand Vehicle Retirement [VOC,NOX,PM] 3.03 2.74 0.00 0.00 0.06 0.05

ARB-ON3 Modifications to Reformulat ed Gasoline Program [VOC] 3.96 0.00 0.00 0.00 0.00 0.00

ARB-ON4 Cleaner In-Use Heavy-Duty Trucks [VOC,NOX,PM] 5.13 47.14 0.00 0.00 3.26 3.00

ARB-ON5 Port Truck Modernization [NOX,PM] 0.00 2.75 0.00 0.00 1.19 1.09

ARB-OFF1 Marine Vessels - Fuel, Aux. & Main Eng. [VOC,NOX,PM] 0.00 38.53 0.00 19.98 2.77 2.70

ARB-OFF2 Accel. Intro. Of Cleaner Line-Haul Loco. [VOC,NOX,PM] 0.70 4.30 0.00 0.00 0.22 0.20

ARB-OFF3 Clean Up Existing Harbor Craft [VOC,NOX,PM] 0.00 4.60 0.00 0.00 0.21 0.20

ARB-OFF4 Cleaner In-Use Off-Road Equipment [VOC,NOX,PM] 2.72 10.52 0.00 0.00 2.72 2.50

ARB-OFF5 New Emis Stds for Recreational Boats [VOC,NOX] 2.90 0.25 0.00 0.00 0.00 0.00

ARB-OFF6 Expanded Off-Road Rec. Veh Ems Stds [VOC] 1.88 0.00 0.00 0.00 0.00 0.00

ARB-CONS Consumer Products [VOC] 12.93 0.00 0.00 0.00 0.00 0.00

MOB-05 AB 923 Light-Duty High-Emitter Id. 0.87 0.41 11.16 0.00 0.00 0.00

MOB-06 AB 923 Med-Duty High-Emitter Id. [VOC,NOX,CO,PM] 0.51 0.53 5.55 0.00 0.00 0.00

SCONRD-1* Accelerated Penetration of ATPEVs and ZEVs 0.45 1.05 11.89 0.00 0.05 0.04

SCONRD-3* Further Emis. Red. from Heavy-Duty Vehicles 0.00 42.42 0.00 0.00 4.05 3.72

SCONRD-4* Further Emis. Red. from Port Trucks [NOX,PM] 0.00 11.42 0.00 0.00 0.20 0.18

SCOFRD-1* Construction/Industrial Fleet Modernization [VOC,NOX] 3.62 15.85 0.00 0.00 0.00 0.00

SCOFRD-2* Cargo Handling Equipment [NOX,PM] 0.00 1.08 0.00 0.00 0.02 0.02

SCOFRD-3* Further Ems. Red. From Locomotives [NOX,PM] 0.00 14.32 0.00 0.00 0.63 0.58

SCOFRD-6* Accelerated Turnover Pleasure Craft [VOC,NOX,PM] 3.13 1.09 0.00 0.00 0.84 0.63

SCFUEL-1* California Phase III Reformulation Gasoline [NOX,SOX] 0.00 0.00 0.00 1.35 0.00 0.00

SCFUEL-2* Greater Use of Diesel Fuel Alt. & Reformulation 0.00 6.35 0.00 0.05 0.64 0.59

Grand Total (with potential overlapping) 65.48 226.18 28.61 24.27 20.10 18.62EMISSION SUMMARY FOR

(POINT, AREA, MOBILE SOURCE, AND OFF-ROAD MV)

Baseline Emissions VOC NOx CO SOx PM10 PM2.5

Point source 37.71 8.69 54.15 1.99 10.09 8.85

Area source 218.83 41.16 123.68 2.47 236.24 59.69

RECLAIM 0.00 26.51 0.00 11.76 0.00 0.00

Total Stationary 256.54 76.36 177.83 16.23 246.33 68.54

On-road 144.06 292.24 1392.93 2.22 24.01 16.83

Off-road 117.11 263.06 936.79 22.30 17.33 15.25

Aircraft 9.99 21.95 69.16 2.08 1.03 1.01

TOTAL 527.69 653.62 2576.71 42.83 288.70 101.62

EMISSION REDUCTIONS

Point source 2.76 1.34 0.00 0.00 0.91 0.82

Area source 23.74 4.17 0.00 0.00 2.13 2.08

RECLAIM 0.00 0.00 0.00 2.89 0.00 0.00

Total Stationary 26.51 5.51 0.00 2.89 3.05 2.91

On-road 22.06 98.58 28.49 1.31 5.54 5.10

Off-road 15.24 90.40 0.00 20.06 7.41 6.84

Aircraft 0.00 0.00 0.00 0.00 0.00 0.00

TOTAL 63.80 194.49 28.49 24.27 16.00 14.84

REMAINING EMISSIONS

Point source 34.94 7.35 54.15 1.99 9.18 8.03

Area source 195.09 36.99 123.68 2.47 234.10 57.60

RECLAIM 0.00 26.51 0.00 8.87 0.00 0.00

Total Stationary 230.03 70.85 177.83 13.34 243.28 65.63

On-road 122.00 193.66 1364.44 0.91 18.48 11.73

Off-road 101.87 172.66 936.79 2.24 9.91 8.41

Aircraft 9.99 21.95 69.16 2.08 1.03 1.01

TOTAL 463.89 459.13 2548.22 18.56 272.70 86.78

NSR/Set-Aside 5.00 -1.20 0.00 0.00 0.00 0.00

Public Funding 0.00 -4.20 0.00 0.00 0.00 -0.10

GRAND TOTAL (T/D) 468.89 453.73 2548.22 18.56 272.70 86.68

Mobility Adjustments (3) 0.00 0.00 0.00 0.00 0.00 0.00(1) Emission reductions for individual measures were estimated based on the sequence of listing

contained here. When the sequence changes, reductions from each measure could be affected,

but the net total remain the same. The purpose of this table is to estimate

total emission reductions without ov erlapping or double-counting between measures.

(2) Emission reductions for individual measures were estimated in the absence of other measures.

Therefore, the sequence of listing does not affect the reduction estimates. The purpose of

this table is to provide emission reduc tion estimates for Appendix IV control measure

summary tables as well as cost effectiveness analysis.

(3) Mobility Adjustment includes TCM-01, ATT-01, ATT-02, ATT-05 and adjustments are reflected

in the CEPA baseline beyond year 2000.

*For the purpose of attainment demonstration, these mobile source measures were selected to achieve the additional

63 tons per day of NOx reductions needed by 2014 for PM 2.5 attainment. However, CARB can implement any

combination of strategies to achieve the needed reductions.

# ATTACHMENT-6

2017 Control Measures For Coachella Valley Ozone AttainmentRun Date: 8/22/2007 4:29:10 PM

(PC-CEPA V. 4.2/ May 2001)

C:\AQMP2007\CF0207\CM3-0607\cf2017-060607.txt

C:\AQMP2007\CF0207\CM3-0607\master-060607.txt

C:\AQMP2007\dump0906\xz011607cv\ems17cv.txt

C:\AQMP2007\CF0207\CM3-06 07\scen4-060607-noltm.txt

C:\AQMP2007\CF0207\CM3-0507\impact-051107.txt

Year 2017 Emission Reductions Excluding Natural Sources by Control Measure

in Coachella Valley (Planni ng Inventory - Tons/Day)

(A) Reductions Without Overlapping/Double-Counting With Other Control Measures (1)

( eductions Tons/Day R - )

Measure Name VOC NOx CO NO2

BA-LSI ARB Baseline Adjustment for large spark-ignited engines NOX] 0.00 0.02 0.00 0.02 [

BA-CONS ARB Baseline Adjustment for Consumer Products [VOC] 0.14 0.00 0.00 0.00

BCM-03 Emission Red. from Wood Burn ing Fireplaces and Wood Stoves 0.00 0.00 0.00 0.00

BCM-05 Reductions from Under-fired Charbroilers [PM] 0.00 0.00 0.00 0.00

CMB-01 Nox Reduction from Non-Reclaim Ovens, Dryers and F rnaces 0.00 0.10 0.00 0.10 u

CMB-0 NOx Reductions from Residential Space Heaters [NOx] 0.00 0.01 0.00 0.07 3

CTS-01 Industrial Lubricants [VOC] 0.07 0.00 0.00 0.00

FUG-02 Emission Red. from Gasoline Tran sfer and Dispensing Facilities [V 0.09 0.00 0.00 0.00

MCS-01N Facility Modernization [NOx 0.00 0.05 0.00 0.04 ]

MCS-01P Facility Modernization [PM] 0.00 0.00 0.00 0.00

MCS-01V Facility Modernization [VOC] 0.00 0.00 0.00 0.00

MCS-05 Non-Dairy Livestock Waste [VOC,NH3] 0.00 0.00 0.00 0.00

EGM-01N Emission Reductions from New & Re-Development Projects [NOx 0.00 0.00 0.00 0.00 ]

EGM-01P Emission Reductions from New & Re-Development Projects [PM] 0.00 0.00 0.00 0.00

EGM-01V Emission Reductions from New & Re-Development Projects [VOC] 0.00 0.00 0.00 0.00

FLX-02P Petroleum Refinery Pilot Program [PM] 0.00 0.00 0.00 0.00

FLX-02V Petroleum Refinery Pilot Program [VOC] 0.00 0.00 0.00 0.00

ARB-ON1 Smog Check Enhancements [VOC,NOX,PM] 0.33 0.29 0.00 0.30

ARB-ON2 Expand Vehicle Retirement [VOC,NOX,PM] 0.06 0.05 0.00 0.05

ARB-ON3 Modifications to Reformulat ed Gasoline Program [VOC] 0.16 0.00 0.00 0.00

ARB-ON4 Cleaner In-Use Heavy-Duty Trucks [VOC,NOX,PM] 0.52 3.98 0.00 4.07

ARB-ON5 Port Truck Modernization [NOX,PM] 0.00 0.25 0.00 0.26

ARB-OFF1 Marine Vessels - Fuel, Aux. & Main Eng. [VOC,NOX,PM] 0.00 0.00 0.00 0.00

ARB-OFF2 Accel. Intro. Of Cleaner Line-Haul Loco. [VOC,NOX,PM] 0.10 0.63 0.00 0.60

ARB-OFF3 Clean Up Existing Harbor Craft [VOC,NOX,PM] 0.00 0.00 0.00 0.00

ARB-OFF4 Cleaner In-Use Off-Road Equipment [VOC,NOX,PM] 0.05 0.19 0.00 0.19

ARB-OFF5 New Emis Stds for Recreational Boats [VOC,NOX] 0.10 0.01 0.00 0.00

ARB-OFF6 Expanded Off-Road Rec. Veh Ems Stds [VOC] 0.14 0.00 0.00 0.00

ARB-CONS Consumer Products [VOC] 0.40 0.00 0.00 0.00

MOB-05 AB 923 Light-Duty High-Emitter Id. [VOC,NOX,CO,PM 0.03 0.01 0.35 0.01 ]

MOB-06 AB 923 Med-Duty High-Emitter Id. [VOC,NOX,CO,PM] 0.01 0.01 0.11 0.01

SCONRD-1* Accelerated Penetration of ATPEVs and ZEVs [VOC,NOX,CO,PM] 0.05 0.11 1.11 0.11

SCONRD-3* Further Emis. Red. from Heavy-Duty Vehicles [VOC,NOX,CO,PM] 0.00 1.94 0.00 1.99

SCONRD-4* Further Emis. Red. from Port Trucks [NOX,PM] 0.00 0.41 0.00 0.41

SCOFRD-1* Construction/Industrial Fleet Modernization [VOC,NOX] 0.05 0.53 0.00 0.54

SCOFRD-2* Cargo Handling Equipment [NOX,PM] 0.00 0.00 0.00 0.00

SCOFRD-3* Further Ems. Red. From Locomotives [NOX,PM] 0.00 1.11 0.00 1.06

SCOFRD-6* Accelerated Turnover Pleasure Craft [VOC,NOX,PM] 0.20 0.11 0.00 0.03

SCFUEL-1* California Phase III Reformulation Gasoline [NOX,SOX] 0.00 0.00 0.00 0.00

SCFUEL-2* Greater Use of Diesel Fuel Alt. & Reformulation [NOX,SOX,PM] 0.00 0.21 0.00 0.21

Grand Total (Net) 2.52 10.02 1.57 10.08Year 2017 Emission Reductions Excluding Natural Sources by Control Measure

in Coachella Valley (Planni ng Inventory - Tons/Day)

(B) Reductions With Overlapping/Double-Counting With Other Control Measures (2)

( eductions Tons/Day R - )

Measure Name VOC NOx CO NO2

BA-LSI ARB Baseline Adjustment for large spark-ignited engines NOX] 0.00 0.02 0.00 0.02 [

BA-CONS ARB Baseline Adjustment for Consumer Products [VOC] 0.14 0.00 0.00 0.00

BCM-03 Emission Red. from Wood Burn ing Fireplaces and Wood Stoves 0.00 0.00 0.00 0.00

BCM-05 Reductions from Under-fired Charbroilers [PM] 0.00 0.00 0.00 0.00

CMB-01 Nox Reduction from Non-Reclaim Ovens, Dryers and F rnaces 0.00 0.10 0.00 0.10 u

CMB-0 NOx Reductions from Residential Space Heaters [NOx] 0.00 0.01 0.00 0.07 3

CTS-01 Industrial Lubricants [VOC] 0.07 0.00 0.00 0.00

FUG-02 Emission Red. from Gasoline Tran sfer and Dispensing Facilities [V 0.09 0.00 0.00 0.00

MCS-01N Facility Modernization [NOx 0.00 0.05 0.00 0.04 ]

MCS-01P Facility Modernization [PM] 0.00 0.00 0.00 0.00

MCS-01V Facility Modernization [VOC] 0.00 0.00 0.00 0.00

MCS-05 Non-Dairy Livestock Waste [VOC,NH3] 0.00 0.00 0.00 0.00

EGM-01N Emission Reductions from New & Re-Development Projects [NOx 0.00 0.00 0.00 0.00 ]

EGM-01P Emission Reductions from New & Re-Development Projects [PM] 0.00 0.00 0.00 0.00

EGM-01V Emission Reductions from New & Re-Development Projects [VOC] 0.00 0.00 0.00 0.00

FLX-02P Petroleum Refinery Pilot Program [PM] 0.00 0.00 0.00 0.00

FLX-02V Petroleum Refinery Pilot Program [VOC] 0.00 0.00 0.00 0.00

ARB-ON1 Smog Check Enhancements [VOC,NOX,PM] 0.33 0.29 0.00 0.30

ARB-ON2 Expand Vehicle Retirement [VOC,NOX,PM] 0.07 0.06 0.00 0.06

ARB-ON3 Modifications to Reformulat ed Gasoline Program [VOC] 0.17 0.00 0.00 0.00

ARB-ON4 Cleaner In-Use Heavy-Duty Trucks [VOC,NOX,PM] 0.52 3.98 0.00 4.07

ARB-ON5 Port Truck Modernization [NOX,PM] 0.00 0.35 0.00 0.36

ARB-OFF1 Marine Vessels - Fuel, Aux. & Main Eng. [VOC,NOX,PM] 0.00 0.00 0.00 0.00

ARB-OFF2 Accel. Intro. Of Cleaner Line-Haul Loco. [VOC,NOX,PM] 0.10 0.63 0.00 0.60

ARB-OFF3 Clean Up Existing Harbor Craft [VOC,NOX,PM] 0.00 0.00 0.00 0.00

ARB-OFF4 Cleaner In-Use Off-Road Equipment [VOC,NOX,PM] 0.05 0.19 0.00 0.19

ARB-OFF5 New Emis Stds for Recreational Boats [VOC,NOX] 0.10 0.01 0.00 0.00

ARB-OFF6 Expanded Off-Road Rec. Veh Ems Stds [VOC] 0.15 0.00 0.00 0.00

ARB-CONS Consumer Products [VOC] 0.40 0.00 0.00 0.00

MOB-05 AB 923 Light-Duty High-Emitter Id. [VOC,NOX,CO,PM 0.03 0.02 0.35 0.02 ]

MOB-06 AB 923 Med-Duty High-Emitter Id. [VOC,NOX,CO,PM] 0.01 0.02 0.11 0.02

SCONRD-1* Accelerated Penetration of ATPEVs and ZEVs [VOC,NOX,CO,PM] 0.06 0.12 1.13 0.13

SCONRD-3* Further Emis. Red. from Heavy-Duty Vehicles [VOC,NOX,CO,PM] 0.00 2.88 0.00 2.95

SCONRD-4* Further Emis. Red. from Port Trucks [NOX,PM] 0.00 0.72 0.00 0.74

SCOFRD-1* Construction/Industrial Fleet Modernization [VOC,NOX] 0.07 0.60 0.00 0.61

SCOFRD-2* Cargo Handling Equipment [NOX,PM] 0.00 0.00 0.00 0.00

SCOFRD-3* Further Ems. Red. From Locomotives [NOX,PM] 0.00 1.53 0.00 1.46

SCOFRD-6* Accelerated Turnover Pleasure Craft [VOC,NOX,PM] 0.22 0.11 0.00 0.03

SCFUEL-1* California Phase III Reformulation Gasoline [NOX,SOX] 0.00 0.00 0.00 0.00

SCFUEL-2* Greater Use of Diesel Fuel Alt. & Reformulation [NOX,SOX,PM] 0.00 0.37 0.00 0.37

Grand Total (with potential overlapping) 2.58 12.05 1.59 12.13EMISSION SUMMARY FOR

(POINT, AREA, MOBILE SOURCE, AND OFF-ROAD MV)

BASELINE EMISSIONS

VOC NOx CO NO2

Point source 0.63 0.40 0.16 0.40

Area sourc 7.70 0.90 2.43 1.24 e

RECLAIM 0.00 0.00 0.00 0.00

Total Stationary 8.33 1.30 2.58 1.64

On-road 4.99 17.15 35.23 17.58

Off-road 4.33 5.53 19.97 5.00

Aircraft 0.43 2.53 6.17 2.53

TOTAL 18.08 26.52 63.96 26.75

EMISSION REDUCTIONS

Point source 0.00 0.02 0.00 0.02

Area sourc 0.71 0.13 0.00 0.18 e

RECLAIM 0.00 0.00 0.00 0.00

Total Stationary 0.72 0.16 0.00 0.21

On-road 1.09 7.22 1.57 7.39

Off-road 0.71 2.64 0.00 2.48

Aircraft 0.00 0.00 0.00 0.00

TOTAL 2.52 10.02 1.57 10.08

REMAINING EMISSIONS

Point source 0.63 0.38 0.16 0.38

Area sourc 6.98 0.77 2.43 1.05 e

RECLAIM 0.00 0.00 0.00 0.00

Total Stationary 7.61 1.15 2.58 1.43

On-road 3.90 9.93 33.67 10.19

Off-road 3.62 2.89 19.97 2.52

Aircraft 0.43 2.53 6.17 2.53

TOTAL 15.56 16.50 62.39 16.67

NSR/Set-Aside 0.00 0.00 0.00 0.00

Public Funding 0.00 0.00 0.00 0.00

GRAND TOTAL (T/D) 15.56 16.50 62.39 16.67

Mobility Adjustments (3) 0.00 0.00 0.00 0.00(1) Emission reductions for individual measures were estimated based on the sequence of listing

contained here. When the sequence changes, reductions from each measure could be affected,

but the net total remain the same. The purpose of this table is to estimate

total emission reductions without ov erlapping or double-counting between measures.

(2) Emission reductions for individual measures were estimated in the absence of other measures.

Therefore, the sequence of listing does not affect the reduction estimates. The purpose of

this table is to provide emission reduc tion estimates for Appendix IV control measure

summary tables as well as cost effectiveness analysis.

(3) Mobility Adjustment includes TCM-01, ATT-01, ATT-02, ATT-05 and adjustments are reflected

in the CEPA baseline beyond year 2000.

*For the purpose of attainment demonstration in the South Coast Ai r Basin, these mobile source measures were selected to achiev e the additional

63 tons per day of NOx reductions needed by 2014 for PM2.5 attainment. The corresponding Co achella Valley emission reduction s for these measures in 2017 are provided here. However, CA RB can implement any combination of strategies to achieve

the needed reductions.

# ATTACHMENT-7

2017 Control Measures For Basin Ozone AttainmentRun Date: 8/16/2007 11:42:24 AM

(PC-CEPA V. 4.2/ May 2001)

C:\AQMP2007\CF0207\CM3-0607\cf2017-060607.txt

C:\AQMP2007\CF0207\CM3-0607\master-060607.txt

C:\AQMP2007\dump0906\xz011607\ems17ocs.txt

C:\AQMP2007\CF0207\CM3-06 07\scen4-060607-noltm.txt

C:\AQMP2007\CF0207\CM3-0507\impact-051107.txt

Year 2017 Emission Reductions Excluding Natural Sources by Control Measure in the South Coast Air Basin (Planning

Inventory - Tons/Day)

(A) Reductions Without Overlapping/Double-Counting With Other Control Measures (1)

( eductions Tons/Day R - )

Measure Name VOC NOx CO NO2

BA-LSI ARB Baseline Adjustment for large spark-ignited engines NOX] 0.00 2.09 0.00 2.22 [

BA-CONS ARB Baseline Adjustment for Consumer Products [VOC] 4.60 0.00 0.00 0.00

BCM-03 Emission Red. from Wood Burn ing Fireplaces and Wood Stoves 0.00 0.00 0.00 0.00

BCM-05 Reductions from Under-fired Charbroilers [PM] 0.00 0.00 0.00 0.00

CMB-01 Nox Reduction from Non-Reclaim Ovens, Dryers and F rnaces 0.00 3.84 0.00 3.84 u

CMB-0 NOx Reductions from Residential Space Heaters [NOx] 0.00 0.56 0.00 2.68 3

CTS-01 Industrial Lubricants [VOC] 1.91 0.00 0.00 0.00

FUG-02 Emission Red. from Gasoline Tran sfer and Dispensing Facilities [V 3.82 0.00 0.00 0.00

MCS-01N Facility Modernization [NOx 0.00 1.93 0.00 1.70 ]

MCS-01P Facility Modernization [PM] 0.00 0.00 0.00 0.00

MCS-01V Facility Modernization [VOC] 4.63 0.00 0.00 0.00

MCS-05 Non-Dairy Livestock Waste [VOC,NH3] 0.69 0.00 0.00 0.00

EGM-01N Emission Reductions from New & Re-Development Projects [NOx 0.00 0.00 0.00 0.00 ]

EGM-01P Emission Reductions from New & Re-Development Projects [PM] 0.00 0.00 0.00 0.00

EGM-01V Emission Reductions from New & Re-Development Projects [VOC] 0.00 0.00 0.00 0.00

FLX-02P Petroleum Refinery Pilot Program [PM] 0.00 0.00 0.00 0.00

FLX-02V Petroleum Refinery Pilot Program [VOC] 1.19 0.00 0.00 0.00

ARB-ON1 Smog Check Enhancements [VOC,NOX,PM] 9.47 9.96 0.00 11.22

ARB-ON2 Expand Vehicle Retirement [VOC,NOX,PM] 2.26 1.92 0.00 2.17

ARB-ON3 Modifications to Reformulat ed Gasoline Program [VOC] 4.19 0.00 0.00 0.00

ARB-ON4 Cleaner In-Use Heavy-Duty Trucks [VOC,NOX,PM] 4.14 36.83 0.00 38.79

ARB-ON5 Port Truck Modernization [NOX,PM] 0.00 1.56 0.00 1.63

ARB-OFF1 Marine Vessels - Fuel, Aux. & Main Eng. [VOC,NOX,PM] 0.00 43.38 0.00 43.38

ARB-OFF2 Accel. Intro. Of Cleaner Line-Haul Loco. [VOC,NOX,PM] 0.72 4.65 0.00 4.65

ARB-OFF3 Clean Up Existing Harbor Craft [VOC,NOX,PM] 0.00 4.08 0.00 4.08

ARB-OFF4 Cleaner In-Use Off-Road Equipment [VOC,NOX,PM] 2.25 8.19 0.00 8.33

ARB-OFF5 New Emis Stds for Recreational Boats [VOC,NOX] 4.08 0.42 0.00 0.11

ARB-OFF6 Expanded Off-Road Rec. Veh Ems Stds [VOC] 2.62 0.00 0.00 0.00

ARB-CONS Consumer Products [VOC] 13.23 0.00 0.00 0.00

MOB-05 AB 923 Light-Duty High-Emitter Id. [VOC,NOX,CO,PM 0.78 0.48 13.46 0.54 ]

MOB-06 AB 923 Med-Duty High-Emitter Id. [VOC,NOX,CO,PM] 0.54 0.60 6.76 0.64

SCONRD-1* Accelerated Penetration of ATPEVs and ZEVs [VOC,NOX,CO,PM] 1.56 3.93 44.48 4.47

SCONRD-3* Further Emis. Red. from Heavy-Duty Vehicles [VOC,NOX,CO,PM] 0.00 15.81 0.00 16.62

SCONRD-4* Further Emis. Red. from Port Trucks [NOX,PM] 0.00 2.51 0.00 2.63

SCOFRD-1* Construction/Industrial Fleet Modernization [VOC,NOX] 2.39 23.60 0.00 24.02

SCOFRD-2* Cargo Handling Equipment [NOX,PM] 0.00 0.84 0.00 0.84

SCOFRD-3* Further Ems. Red. From Locomotives [NOX,PM] 0.00 9.88 0.00 9.88

SCOFRD-6* Accelerated Turnover Pleasure Craft [VOC,NOX,PM] 12.44 5.81 0.00 1.49

SCFUEL-1* California Phase III Reformulation Gasoline [NOX,SOX] 0.00 0.00 0.00 0.00

SCFUEL-2* Greater Use of Diesel Fuel Alt. & Reformulation [NOX,SOX,PM] 0.00 3.14 0.00 3.15

Grand Total (Net) 77.53 186.01 64.69 189.08Year 2017 Emission Reductions Excluding Natural Sources by Control Measure in the South Coast Air Basin (Planning

Inventory - Tons/Day)

(B) Reductions With Overlapping/Double-Counting With Other Control Measures (2)

( eductions Tons/Day R - )

Measure Name VOC NOx CO NO2

BA-LSI ARB Baseline Adjustment for large spark-ignited engines NOX] 0.00 2.09 0.00 2.22 [

BA-CONS ARB Baseline Adjustment for Consumer Products [VOC] 4.60 0.00 0.00 0.00

BCM-03 Emission Red. from Wood Burn ing Fireplaces and Wood Stoves 0.00 0.00 0.00 0.00

BCM-05 Reductions from Under-fired Charbroilers [PM] 0.00 0.00 0.00 0.00

CMB-01 Nox Reduction from Non-Reclaim Ovens, Dryers and F rnaces 0.00 3.84 0.00 3.84 u

CMB-0 NOx Reductions from Residential Space Heaters [NOx] 0.00 0.56 0.00 2.68 3

CTS-01 Industrial Lubricants [VOC] 1.91 0.00 0.00 0.00

FUG-02 Emission Red. from Gasoline Tran sfer and Dispensing Facilities [V 3.82 0.00 0.00 0.00

MCS-01N Facility Modernization [NOx 0.00 1.93 0.00 1.70 ]

MCS-01P Facility Modernization [PM] 0.00 0.00 0.00 0.00

MCS-01V Facility Modernization [VOC] 4.63 0.00 0.00 0.00

MCS-05 Non-Dairy Livestock Waste [VOC,NH3] 0.69 0.00 0.00 0.00

EGM-01N Emission Reductions from New & Re-Development Projects [NOx 0.00 0.00 0.00 0.00 ]

EGM-01P Emission Reductions from New & Re-Development Projects [PM] 0.00 0.00 0.00 0.00

EGM-01V Emission Reductions from New & Re-Development Projects [VOC] 0.00 0.00 0.00 0.00

FLX-02P Petroleum Refinery Pilot Program [PM] 0.00 0.00 0.00 0.00

FLX-02V Petroleum Refinery Pilot Program [VOC] 1.19 0.00 0.00 0.00

ARB-ON1 Smog Check Enhancements [VOC,NOX,PM] 9.47 9.96 0.00 11.22

ARB-ON2 Expand Vehicle Retirement [VOC,NOX,PM] 2.42 2.11 0.00 2.39

ARB-ON3 Modifications to Reformulat ed Gasoline Program [VOC] 4.44 0.00 0.00 0.00

ARB-ON4 Cleaner In-Use Heavy-Duty Trucks [VOC,NOX,PM] 4.14 36.83 0.00 38.79

ARB-ON5 Port Truck Modernization [NOX,PM] 0.00 2.17 0.00 2.26

ARB-OFF1 Marine Vessels - Fuel, Aux. & Main Eng. [VOC,NOX,PM] 0.00 43.38 0.00 43.38

ARB-OFF2 Accel. Intro. Of Cleaner Line-Haul Loco. [VOC,NOX,PM] 0.72 4.65 0.00 4.65

ARB-OFF3 Clean Up Existing Harbor Craft [VOC,NOX,PM] 0.00 4.08 0.00 4.08

ARB-OFF4 Cleaner In-Use Off-Road Equipment [VOC,NOX,PM] 2.25 8.19 0.00 8.33

ARB-OFF5 New Emis Stds for Recreational Boats [VOC,NOX] 4.15 0.42 0.00 0.11

ARB-OFF6 Expanded Off-Road Rec. Veh Ems Stds [VOC] 2.68 0.00 0.00 0.00

ARB-CONS Consumer Products [VOC] 13.23 0.00 0.00 0.00

MOB-05 AB 923 Light-Duty High-Emitter Id. [VOC,NOX,CO,PM 0.88 0.54 13.46 0.61 ]

MOB-06 AB 923 Med-Duty High-Emitter Id. [VOC,NOX,CO,PM] 0.60 0.66 6.76 0.71

SCONRD-1* Accelerated Penetration of ATPEVs and ZEVs [VOC,NOX,CO,PM] 1.78 4.46 45.16 5.08

SCONRD-3* Further Emis. Red. from Heavy-Duty Vehicles [VOC,NOX,CO,PM] 0.00 27.03 0.00 28.48

SCONRD-4* Further Emis. Red. from Port Trucks [NOX,PM] 0.00 4.50 0.00 4.70

SCOFRD-1* Construction/Industrial Fleet Modernization [VOC,NOX] 2.99 26.46 0.00 26.92

SCOFRD-2* Cargo Handling Equipment [NOX,PM] 0.00 0.84 0.00 0.84

SCOFRD-3* Further Ems. Red. From Locomotives [NOX,PM] 0.00 12.96 0.00 12.96

SCOFRD-6* Accelerated Turnover Pleasure Craft [VOC,NOX,PM] 13.44 5.97 0.00 1.53

SCFUEL-1* California Phase III Reformulation Gasoline [NOX,SOX] 0.00 0.00 0.00 0.00

SCFUEL-2* Greater Use of Diesel Fuel Alt. & Reformulation [NOX,SOX,PM] 0.00 5.15 0.00 5.23

Grand Total (with potential overlapping) 80.05 208.76 65.37 212.71EMISSION SUMMARY FOR

(POINT, AREA, MOBILE SOURCE, AND OFF-ROAD MV)

BASELINE EMISSIONS

VOC NOx CO NO2

Point source 45.23 10.05 57.66 10.08

Area sourc 225.49 31.53 231.28 46.85 e

RECLAIM 0.00 27.23 0.00 27.23

Total Stationary 270.72 68.82 288.94 84.16

On-road 129.18 231.52 1136.72 251.59

Off-road 138.59 253.82 788.67 239.38

Aircraft 10.96 24.29 74.29 24.29

TOTAL 549.45 578.45 2288.63 599.42

EMISSION REDUCTIONS

Point source 5.82 1.78 0.00 1.78

Area sourc 24.25 4.16 0.00 6.14 e

RECLAIM 0.00 0.00 0.00 0.00

Total Stationary 30.08 5.94 0.00 7.92

On-road 21.26 75.20 64.69 80.40

Off-road 26.20 104.87 0.00 100.76

Aircraft 0.00 0.00 0.00 0.00

TOTAL 77.53 186.01 64.69 189.08

REMAINING EMISSIONS

Point source 39.41 8.27 57.66 8.30

Area sourc 201.24 27.37 231.28 40.71 e

RECLAIM 0.00 27.23 0.00 27.23

Total Stationary 240.65 62.87 288.94 76.24

On-road 107.93 156.32 1072.03 171.19

Off-road 112.39 148.96 788.67 138.62

Aircraft 10.96 24.29 74.29 24.29

TOTAL 471.92 392.44 2223.93 410.34

NSR/Set-Aside 5.00 -1.20 0.00 -1.20

Public Funding 0.00 -4.20 0.00 -4.20

GRAND TOTAL (T/D) 476.92 387.04 2223.93 404.94

Mobility Adjustments (3) 0.00 0.00 0.00 0.00(1) Emission reductions for individual measures were estimated based on the sequence of listing

contained here. When the sequence changes, reductions from each measure could be affected,

but the net total remain the same. The purpose of this table is to estimate

total emission reductions without ov erlapping or double-counting between measures.

(2) Emission reductions for individual measures were estimated in the absence of other measures.

Therefore, the sequence of listing does not affect the reduction estimates. The purpose of

this table is to provide emission reduc tion estimates for Appendix IV control measure

summary tables as well as cost effectiveness analysis.

(3) Mobility Adjustment includes TCM-01, ATT-01, ATT-02, ATT-05 and adjustments are reflected

in the CEPA baseline beyond year 2000.

*For the purpose of attainment demonstration, these mobile source measures were selected to achieve the additional

63 tons per day of NOx reductions needed by 2014 for PM2.5 attainment. The corresponding emission reductions

for these measures in 2017 are provided here. However, CA RB can implement any combination of strategies to achieve

the needed reductions.

# ATTACHMENT-8

2023 Control Measures For Basin Ozone Attainment (Annual)Run Date: 8/21/2007 12:28:06 PM

(PC-CEPA V. 4.2/ May 2001)

C:\AQMP2007\CF0207\CM3-0607\cf2023-060607.txt

C:\AQMP2007\CF0207\CM3-0607\master-060607.txt

C:\AQMP2007\dump0906\xz011607\ems23ocs.txt

C:\AQMP2007\CF0207\CM3-0607\scen4-060607-wbx.txt

C:\AQMP2007\CF0207\CM3-0507\impact-051107.txt

Year 2023 Emission Reductions Excluding Natural Sources by Control Measure

in the South Coast Air Basin (Annual Average Inventory - Tons/Day)

(A) Reductions Without Overlapping/Double-Counting With Other Control Measures (1)

eductions - Tons/Day) (R

Measure Name VOC NOx CO SOx PM10 PM2.5

BA-LSI ARB Baseline Adjustment for large spark-ignited engines NOX] 0.00 1.96 0.00 0.00 0.00 0.00 [

BA-CONS ARB Baseline Adjustment for Consumer Products [VOC] 4.80 0.00 0.00 0.00 0.00 0.00

BCM-03 Emission Red. from Wood Burn ing Fireplaces and Wood Stoves 0.00 0.00 0.00 0.00 1.64 1.58

BCM-05 Reductions from Under-fired Charbroilers [PM] 0.00 0.00 0.00 0.00 1.17 1.16

CMB-01 Nox Reduction from Non-Reclaim Ovens, Dryers and Furnaces 0.00 3.84 0.00 0.00 0.00 0.00

CMB-02 Reduction SOx Reductions 0.00 0.00 0.00 2.89 0.00 0.00

CMB-0 NOx Reductions from Residential Space Heaters [NOx] 0.00 3.34 0.00 0.00 0.00 0.00 3

CTS-01 Industrial Lubricants [VOC] 2.00 0.00 0.00 0.00 0.00 0.00

FUG-02 Emission Red. from Gasoline Tran sfer and Dispensing Facilities [V 4.06 0.00 0.00 0.00 0.00 0.00

MCS-01N Facility Modernization [NOx 0.00 1.98 0.00 0.00 0.00 0.00 ]

MCS-01P Facility Modernization [PM] 0.00 0.00 0.00 0.00 1.91 1.70

MCS-01V Facility Modernization [VOC] 7.99 0.00 0.00 0.00 0.00 0.00

MCS-05 Non-Dairy Livestock Waste [VOC,NH3] 0.61 0.00 0.00 0.00 0.00 0.00

EGM-01N Emission Reductions from New & Re-Development Projects 0.00 0.79 0.00 0.00 0.00 0.00

EGM-01P Emission Reductions from New & Re-Development Projects PM] 0.00 0.00 0.00 0.00 5.23 0.52 [

EGM-01V Emission Reductions from New & Re-Development Projects 0.53 0.00 0.00 0.00 0.00 0.00

FLX-02P Petroleum Refinery Pilot Program [PM] 0.00 0.00 0.00 0.00 0.48 0.44

FLX-02V Petroleum Refinery Pilot Program [VOC] 1.64 0.00 0.00 0.00 0.00 0.00

ARB-ON1 Smog Check Enhancements [VOC,NOX,PM] 7.28 7.24 0.00 0.00 0.21 0.19

ARB-ON2 Expand Vehicle Retirement [VOC,NOX,PM] 0.54 0.23 0.00 0.00 0.07 0.06

ARB-ON3 Modifications to Reformulat ed Gasoline Program [VOC] 2.09 0.00 0.00 0.00 0.00 0.00

ARB-ON4 Cleaner In-Use Heavy-Duty Trucks [VOC,NOX,PM] 1.70 18.24 0.00 0.00 1.08 0.99

ARB-ON5 Port Truck Modernization [NOX,PM] 0.00 6.99 0.00 0.00 0.33 0.30

ARB-OFF1 Marine Vessels - Fuel, Aux. & Main Eng. [VOC,NOX,PM] 0.00 70.67 0.00 29.50 4.20 4.10

ARB-OFF2 Accel. Intro. Of Cleaner Line-Haul Loco. [VOC,NOX,PM] 1.90 15.60 0.00 0.00 0.61 0.56

ARB-OFF3 Clean Up Existing Harbor Craft [VOC,NOX,PM] 0.00 5.91 0.00 0.00 0.32 0.29

ARB-OFF4 Cleaner In-Use Off-Road Equipment [VOC,NOX,PM] 1.92 14.01 0.00 0.00 1.42 1.30

ARB-OFF5 New Emis Stds for Recreational Boats [VOC,NOX] 12.17 1.51 0.00 0.00 0.00 0.00

ARB-OFF6 Expanded Off-Road Rec. Veh Ems Stds [VOC] 4.89 0.00 0.00 0.00 0.00 0.00

ARB-CONS Consumer Products [VOC] 13.69 0.00 0.00 0.00 0.00 0.00

MOB-05 AB 923 Light-Duty High-Emitter Id. [VOC,NOX,CO,PM 0.67 0.42 13.00 0.00 0.01 0.01 ]

MOB-06 AB 923 Med-Duty High-Emitter Id. [VOC,NOX,CO,PM] 0.59 0.66 6.20 0.00 0.00 0.00

SCONRD-1* Accelerated Penetration of ATPEVs and ZEVs [VOC,NOX,CO,PM] 1.93 4.66 53.83 0.00 0.39 0.36

SCONRD-3* Further Emis. Red. from Heavy-Duty Vehicles [VOC,NOX,CO,PM] 0.00 5.84 0.00 0.00 0.21 0.19

SCONRD-4* Further Emis. Red. from Port Trucks [NOX,PM] 0.00 0.00 0.00 0.00 0.00 0.00

SCOFRD-1* Construction/Industrial Fleet Modernization [VOC,NOX] 1.32 15.24 0.00 0.00 0.00 0.00

SCOFRD-2* Cargo Handling Equipment [NOX,PM] 0.00 0.55 0.00 0.00 0.01 0.01

SCOFRD-3* Further Ems. Red. From Locomotives [NOX,PM] 0.00 3.26 0.00 0.00 0.11 0.10

SCOFRD-6* Accelerated Turnover Pleasure Craft [VOC,NOX,PM] 7.78 5.75 0.00 0.00 5.31 4.01

SCFUEL-1* California Phase III Reformulation Gasoline [NOX,SOX] 0.00 0.00 0.00 1.47 0.00 0.00

SCFUEL-2 Greater Use of Diesel Fuel Alt. & Reformulation [NOX,SOX,PM] 0.00 4.32 0.00 0.11 0.28 0.26 *

SCLTM-03 Further Reduction from Consumer Products [VOC] 20.03 0.00 0.00 0.00 0.00 0.00

LTM-04 Phase II Gasoline Fuels [VOC,NOX] 6.28 6.66 0.00 0.00 0.00 0.00

LTM-05 Phase II Diesel Fuel A lternatives [NOX,SOX,PM] 0.00 10.32 0.00 0.20 0.54 0.49

SCLTM1-2 Fu ther Reductions from On-Roa d and Off-Road Mobile Sources 0.00 171.76 0.00 0.00 0.00 0.00 r
Grand Total (Net) 106.41 381.75 73.03 34.16 25.53 18.65Year 2023 Emission Reductions Excluding Natural Sources by Control Measure in the South Coast Air Basin (Annual

Average Inventory - Tons/Day)

(B) Reductions With Overlapping/Double-Counting With Other Control Measures (2)

eductions - Tons/Day) (R

Measure Name VOC NOx CO SOx PM10 PM2.5

BA-LSI ARB Baseline Adjustment for large spark-ignited engines 0.00 1.96 0.00 0.00 0.00 0.00

BA-CONS ARB Baseline Adjustment for Consumer Products [VOC] 4.80 0.00 0.00 0.00 0.00 0.00

BCM-03 Emission Red. from Wood Bu rning Fireplaces and Wood 0.00 0.00 0.00 0.00 1.64 1.58

BCM-05 Reductions from Under-fired Charbroilers [PM] 0.00 0.00 0.00 0.00 1.17 1.16

CMB-01 Nox Reduction from Non-Recl aim Ovens, Dryers and 0.00 3.84 0.00 0.00 0.00 0.00

CMB-02 Reduction SOx Reductions 0.00 0.00 0.00 2.89 0.00 0.00

CMB-0 NOx Reductions from Residential Space Heaters [NOx] 0.00 3.34 0.00 0.00 0.00 0.00 3

CTS-01 Industrial Lubricants [VOC] 2.00 0.00 0.00 0.00 0.00 0.00

FUG-02 Emission Red. from Gasolin e Transfer and Dispensing 4.06 0.00 0.00 0.00 0.00 0.00

MCS-01N Facility Modernization [NOx 0.00 1.98 0.00 0.00 0.00 0.00 ]

MCS-01P Facility Modernization [PM] 0.00 0.00 0.00 0.00 1.91 1.70

MCS-01V Facility Modernization [VOC] 7.99 0.00 0.00 0.00 0.00 0.00

MCS-05 Non-Dairy Livestock Waste [VOC,NH3] 0.61 0.00 0.00 0.00 0.00 0.00

EGM-01N Emission Reductions from New & Re-Development 0.00 0.79 0.00 0.00 0.00 0.00

EGM-01P Emission Reductions from New & Re-Development 0.00 0.00 0.00 0.00 5.23 0.52

EGM-01V Emission Reductions from New & Re-D velopment 0.53 0.00 0.00 0.00 0.00 0.00 e

FLX-02P Petroleum Refinery Pilot Program [PM] 0.00 0.00 0.00 0.00 0.48 0.44

FLX-02V Petroleum Refinery Pilot Program [VOC] 1.64 0.00 0.00 0.00 0.00 0.00

ARB-ON1 Smog Check Enhancements [VOC,NOX,PM] 7.28 7.24 0.00 0.00 0.21 0.19

ARB-ON2 Expand Vehicle Retirement [VOC,NOX,PM] 0.57 0.25 0.00 0.00 0.07 0.06

ARB-ON3 Modifications to Reformulat ed Gasoline Program [VOC] 2.19 0.00 0.00 0.00 0.00 0.00

ARB-ON4 Cleaner In-Use Heavy-Duty Trucks [VOC,NOX,PM] 1.70 18.24 0.00 0.00 1.08 0.99

ARB-ON5 Port Truck Modernization [NOX,PM] 0.00 8.67 0.00 0.00 0.50 0.46

ARB-OFF1 Marine Vessels - Fuel, Aux. & Main Eng. [VOC,NOX,PM] 0.00 70.67 0.00 29.50 4.20 4.10

ARB-OFF2 Accel. Intro. Of Cleaner Line-Haul Loco. [VOC,NOX,PM] 1.90 15.60 0.00 0.00 0.61 0.56

ARB-OFF3 Clean Up Existing Harbor Craft [VOC,NOX,PM] 0.00 5.91 0.00 0.00 0.32 0.29

ARB-OFF4 Cleaner In-Use Off-Road Equipment [VOC,NOX,PM] 1.92 14.25 0.00 0.00 1.42 1.30

ARB-OFF5 New Emis Stds for Recreational Boats [VOC,NOX] 12.32 1.51 0.00 0.00 0.00 0.00

ARB-OFF6 Expanded Off-Road Rec. Veh Ems Stds [VOC] 4.94 0.00 0.00 0.00 0.00 0.00

ARB-CONS Consumer Products [VOC] 13.69 0.00 0.00 0.00 0.00 0.00

MOB-05 AB 923 Light-Duty High-Emitter Id. 0.73 0.47 13.00 0.00 0.01 0.01

MOB-06 AB 923 Med-Duty High-Emitter Id. [VOC,NO ,CO,PM] 0.63 0.70 6.20 0.00 0.00 0.00 X

SCONRD-1* Accelerated Penetration of ATPEVs and ZEVs 2.14 5.24 54.92 0.00 0.40 0.37

SCONRD-3* Further Emis. Red. from Heavy-Duty Vehicles 0.00 9.70 0.00 0.00 0.39 0.36

SCONRD-4* Further Emis. Red. from Port Trucks [NOX,PM] 0.00 0.00 0.00 0.00 0.00 0.00

SCOFRD-1* Construction/Industrial Fleet Modernization [VOC,NOX] 1.72 22.19 0.00 0.00 0.00 0.00

SCOFRD-2* Cargo Handling Equipment [NOX,PM] 0.00 0.55 0.00 0.00 0.01 0.01

SCOFRD-3* Further Ems. Red. From Locomotives [NOX,PM] 0.00 10.55 0.00 0.00 0.41 0.38

SCOFRD-6* Accelerated Turnover Pleasure Craft [VOC,NOX,PM] 11.85 6.76 0.00 0.00 5.31 4.01

SCFUEL-1* California Phase III Reformulation Gasoline [NOX,SOX] 0.00 0.00 0.00 1.47 0.00 0.00

SCFUEL-2 Greater Use of Diesel Fuel Alt. & Reformulation 0.00 7.05 0.00 0.11 0.59 0.54 *

SCLTM-03 Further Reduction from Consumer Products [VOC] 22.89 0.00 0.00 0.00 0.00 0.00

LTM-04 Phase II Gasoline Fuels [VOC,NOX] 7.65 8.52 0.00 0.00 0.00 0.00

LTM-05 Phase II Diesel Fuel A lternatives [NOX,SOX,PM] 0.00 17.63 0.00 0.25 1.23 1.13

SCLTM1-2 Further Reductions from n-Roa d and Off-Road Mobile Sources 0.00 319.50 0.00 0.00 0.00 0.00 O

Grand Total (with potential overlapping) 115.75 563.10 74.13 34.21 27.20 20.19EMISSION SUMMARY FOR

(POINT, AREA, MOBILE SOURCE, AND OFF-ROAD MV)

Baseline Emissions VOC NOx CO SOx PM10 PM2.5

Point source 41.72 9.47 57.87 2.10 10.91 9.56

Area sourc 234.49 38.39 128.19 2.67 255.07 63.84 e

RECLAIM 0.00 26.51 0.00 11.76 0.00 0.00

Total Stationary 276.21 74.37 186.06 16.53 265.98 73.40

On-road 99.13 164.07 837.54 2.42 23.63 16.04

Off-road 107.25 238.56 1034.26 33.41 16.94 14.60

Aircraft 13.08 29.34 85.03 2.69 1.19 1.17

TOTAL 495.67 506.34 2142.90 55.05 307.74 105.21

EMISSION REDUCTIONS

Point source 9.63 1.71 0.00 0.00 2.40 2.14

Area sourc 45.72 7.48 0.00 0.00 8.07 3.29 e

RECLAIM 0.00 0.00 0.00 2.89 0.00 0.00

Total Stationary 55.35 9.18 0.00 2.89 10.47 5.43

On-road 18.18 136.99 73.03 1.58 2.83 2.60

Off-road 32.88 213.77 0.00 29.69 12.23 10.62

Aircraft 0.00 21.80 0.00 0.00 0.00 0.00

TOTAL 106.41 381.75 73.03 34.16 25.53 18.65

REMAINING EMISSIONS

Point source 32.10 7.76 57.87 2.10 8.52 7.41

Area sourc 188.77 30.91 128.19 2.67 247.00 60.55 e

RECLAIM 0.00 26.51 0.00 8.87 0.00 0.00

Total Stationary 220.86 65.18 186.06 13.64 255.51 67.97

On-road 80.95 27.08 764.51 0.84 20.80 13.43

Off-road 74.37 24.79 1034.26 3.72 4.70 3.98

Aircraft 13.08 7.54 85.03 2.69 1.19 1.17

TOTAL 389.26 124.59 2069.87 20.89 282.21 86.55

NSR/Set-Aside 5.00 -1.20 0.00 0.00 0.00 0.00

Public Funding 0.00 -6.20 0.00 0.00 0.00 -0.20

GRAND TOTAL (T/D) 394.26 117.19 2069.87 20.89 282.21 86.35

Mobility Adjustments (3) 0.00 0.00 0.00 0.00 0.00 0.00(1) Emission reductions for individual measures were estimated based on the sequence of listing

contained here. When the sequence changes, reductions from each measure could be affected,

but the net total remain the same. The purpose of this table is to estimate

total emission reductions without ov erlapping or double-counting between measures.

(2) Emission reductions for individual measures were estimated in the absence of other measures.

Therefore, the sequence of listing does not affect the reduction estimates. The purpose of

this table is to provide emission reduc tion estimates for Appendix IV control measure

summary tables as well as cost effectiveness analysis.

(3) Mobility Adjustment includes TCM-01, ATT-01, ATT-02, ATT-05 and adjustments are reflected

in the CEPA baseline beyond year 2000.

*For the purpose of attainment demonstration, these mobile source measures were selected to achieve the additional

63 tons per day of NOx reductions needed by 2014 for PM2.5 attainment. The corresponding emission reductions

for these measures in 2023 are provided here. However, CA RB can implement any combination of strategies to achieve

the needed reductions.

# ATTACHMENT-9

2023 Control Measures For Basin Oz one Attainment (Planning)Run Date: 8/21/2007 12:28:06 PM

(PC-CEPA V. 4.2/ May 2001)

C:\AQMP2007\CF0207\CM3-0607\cf2023-060607.txt

C:\AQMP2007\CF0207\CM3-0607\master-060607.txt

C:\AQMP2007\dump0906\xz011607\ems23ocs.txt

C:\AQMP2007\CF0207\CM3-0607\scen4-060607-wbx.txt

C:\AQMP2007\CF0207\CM3-0507\impact-051107.txt

Year 2023 Emission Reductions Excluding Natural Sources by Control Measure in the South Coast Air Basin (Planning

Inventory - Tons/Day)

(A) Reductions Without Overlapping/Double-Counting With Other Control Measures (1)

( eductions Tons/Day R - )

Measure Name VOC NOx CO NO2

BA-LSI ARB Baseline Adjustment for large spark-ignited engines NOX] 0.00 1.91 0.00 2.03 [

BA-CONS ARB Baseline Adjustment for Consumer Products [VOC] 4.80 0.00 0.00 0.00

BCM-03 Emission Red. from Wood Burn ing Fireplaces and Wood Stoves 0.00 0.00 0.00 0.00

BCM-05 Reductions from Under-fired Charbroilers [PM] 0.00 0.00 0.00 0.00

CMB-01 Nox Reduction from Non-Reclaim Ovens, Dryers and F rnaces 0.00 4.11 0.00 4.11 u

CMB-0 NOx Reductions from Residential Space Heaters [NOx] 0.00 1.15 0.00 5.54 3

CTS-01 Industrial Lubricants [VOC] 2.00 0.00 0.00 0.00

FUG-02 Emission Red. from Gasoline Tran sfer and Dispensing Facilities [V 4.06 0.00 0.00 0.00

MCS-01N Facility Modernization [NOx 0.00 2.18 0.00 1.95 ]

MCS-01P Facility Modernization [PM] 0.00 0.00 0.00 0.00

MCS-01V Facility Modernization [VOC] 9.16 0.00 0.00 0.00

MCS-05 Non-Dairy Livestock Waste [VOC,NH3] 0.61 0.00 0.00 0.00

EGM-01N Emission Reductions from New & Re-Development Projects [NOx 0.00 0.78 0.00 0.80 ]

EGM-01P Emission Reductions from New & Re-Development Projects [PM] 0.00 0.00 0.00 0.00

EGM-01V Emission Reductions from New & Re-Development Projects [VOC] 0.63 0.00 0.00 0.00

FLX-02P Petroleum Refinery Pilot Program [PM] 0.00 0.00 0.00 0.00

FLX-02V Petroleum Refinery Pilot Program [VOC] 1.64 0.00 0.00 0.00

ARB-ON1 Smog Check Enhancements [VOC,NOX,PM] 7.49 6.92 0.00 7.83

ARB-ON2 Expand Vehicle Retirement [VOC,NOX,PM] 0.54 0.22 0.00 0.24

ARB-ON3 Modifications to Reformulat ed Gasoline Program [VOC] 2.50 0.00 0.00 0.00

ARB-ON4 Cleaner In-Use Heavy-Duty Trucks [VOC,NOX,PM] 1.69 18.32 0.00 19.16

ARB-ON5 Port Truck Modernization [NOX,PM] 0.00 7.04 0.00 7.27

ARB-OFF1 Marine Vessels - Fuel, Aux. & Main Eng. [VOC,NOX,PM] 0.00 70.67 0.00 70.67

ARB-OFF2 Accel. Intro. Of Cleaner Line-Haul Loco. [VOC,NOX,PM] 1.90 15.60 0.00 15.60

ARB-OFF3 Clean Up Existing Harbor Craft [VOC,NOX,PM] 0.00 5.91 0.00 5.91

ARB-OFF4 Cleaner In-Use Off-Road Equipment [VOC,NOX,PM] 1.90 13.89 0.00 14.14

ARB-OFF5 New Emis Stds for Recreational Boats [VOC,NOX] 17.74 2.40 0.00 0.61

ARB-OFF6 Expanded Off-Road Rec. Veh Ems Stds [VOC] 6.40 0.00 0.00 0.00

ARB-CONS Consumer Products [VOC] 13.69 0.00 0.00 0.00

MOB-05 AB 923 Light-Duty High-Emitter Id. [VOC,NOX,CO,PM 0.71 0.40 12.70 0.46 ]

MOB-06 AB 923 Med-Duty High-Emitter Id. [VOC,NOX,CO,PM] 0.56 0.64 6.26 0.68

SCONRD-1* Accelerated Penetration of ATPEVs and ZEVs [VOC,NOX,CO,PM] 2.07 4.46 52.58 5.07

SCONRD-3* Further Emis. Red. from Heavy-Duty Vehicles [VOC,NOX,CO,PM] 0.00 5.87 0.00 6.12

SCONRD-4* Further Emis. Red. from Port Trucks [NOX,PM] 0.00 0.00 0.00 0.00

SCOFRD-1* Construction/Industrial Fleet Modernization [VOC,NOX] 1.31 15.11 0.00 15.37

SCOFRD-2* Cargo Handling Equipment [NOX,PM] 0.00 0.55 0.00 0.55

SCOFRD-3* Further Ems. Red. From Locomotives [NOX,PM] 0.00 3.26 0.00 3.26

SCOFRD-6* Accelerated Turnover Pleasure Craft [VOC,NOX,PM] 12.57 9.08 0.00 2.32

SCFUEL-1* California Phase III Reformulation Gasoline [NOX,SOX] 0.00 0.00 0.00 0.00

SCFUEL-2 Greater Use of Diesel Fuel Alt. & Reformulation [NOX,SOX,PM] 0.00 4.40 0.00 4.38 *

SCLTM-03 Further Reduction from Consumer Products [VOC] 20.03 0.00 0.00 0.00

LTM-04 Phase II Gasoline Fuels [VOC,NOX] 7.39 6.59 0.00 6.94

LTM-05 Phase II Diesel Fuel A lternatives [NOX,SOX,PM] 0.00 10.52 0.00 10.46

SCLTM1-2 Fu ther Reductions from On-Roa d and Off-Road Mobile Sources 0.00 172.48 0.00 174.62 r
Grand Total (Net) 121.37 384.48 71.54 386.10Year 2023 Emission Reductions Excluding Natural Sources by Control Measure in the South Coast Air Basin (Planning

Inventory - Tons/Day)

(B) Reductions With Overlapping/Double-Counting With Other Control Measures (2)

( eductions Tons/Day R - )

Measure Name VOC NOx CO NO2

BA-LSI ARB Baseline Adjustment for large spark-ignited engines NOX] 0.00 1.91 0.00 2.03 [

BA-CONS ARB Baseline Adjustment for Consumer Products [VOC] 4.80 0.00 0.00 0.00

BCM-03 Emission Red. from Wood Burn ing Fireplaces and Wood Stoves 0.00 0.00 0.00 0.00

BCM-05 Reductions from Under-fired Charbroilers [PM] 0.00 0.00 0.00 0.00

CMB-01 Nox Reduction from Non-Reclaim Ovens, Dryers and F rnaces 0.00 4.11 0.00 4.11 u

CMB-0 NOx Reductions from Residential Space Heaters [NOx] 0.00 1.15 0.00 5.54 3

CTS-01 Industrial Lubricants [VOC] 2.00 0.00 0.00 0.00

FUG-02 Emission Red. from Gasoline Tran sfer and Dispensing Facilities [V 4.06 0.00 0.00 0.00

MCS-01N Facility Modernization [NOx 0.00 2.18 0.00 1.95 ]

MCS-01P Facility Modernization [PM] 0.00 0.00 0.00 0.00

MCS-01V Facility Modernization [VOC] 9.16 0.00 0.00 0.00

MCS-05 Non-Dairy Livestock Waste [VOC,NH3] 0.61 0.00 0.00 0.00

EGM-01N Emission Reductions from New & Re-Development Projects [NOx 0.00 0.78 0.00 0.80 ]

EGM-01P Emission Reductions from New & Re-Development Projects [PM] 0.00 0.00 0.00 0.00

EGM-01V Emission Reductions from New & Re-Development Projects [VOC] 0.63 0.00 0.00 0.00

FLX-02P Petroleum Refinery Pilot Program [PM] 0.00 0.00 0.00 0.00

FLX-02V Petroleum Refinery Pilot Program [VOC] 1.64 0.00 0.00 0.00

ARB-ON1 Smog Check Enhancements [VOC,NOX,PM] 7.49 6.92 0.00 7.83

ARB-ON2 Expand Vehicle Retirement [VOC,NOX,PM] 0.58 0.24 0.00 0.27

ARB-ON3 Modifications to Reformulat ed Gasoline Program [VOC] 2.60 0.00 0.00 0.00

ARB-ON4 Cleaner In-Use Heavy-Duty Trucks [VOC,NOX,PM] 1.69 18.32 0.00 19.16

ARB-ON5 Port Truck Modernization [NOX,PM] 0.00 8.74 0.00 9.02

ARB-OFF1 Marine Vessels - Fuel, Aux. & Main Eng. [VOC,NOX,PM] 0.00 70.67 0.00 70.67

ARB-OFF2 Accel. Intro. Of Cleaner Line-Haul Loco. [VOC,NOX,PM] 1.90 15.60 0.00 15.60

ARB-OFF3 Clean Up Existing Harbor Craft [VOC,NOX,PM] 0.00 5.91 0.00 5.91

ARB-OFF4 Cleaner In-Use Off-Road Equipment [VOC,NOX,PM] 1.90 14.13 0.00 14.37

ARB-OFF5 New Emis Stds for Recreational Boats [VOC,NOX] 17.94 2.40 0.00 0.61

ARB-OFF6 Expanded Off-Road Rec. Veh Ems Stds [VOC] 6.48 0.00 0.00 0.00

ARB-CONS Consumer Products [VOC] 13.69 0.00 0.00 0.00

MOB-05 AB 923 Light-Duty High-Emitter Id. [VOC,NOX,CO,PM 0.77 0.45 12.70 0.51 ]

MOB-06 AB 923 Med-Duty High-Emitter Id. [VOC,NOX,CO,PM] 0.60 0.68 6.26 0.72

SCONRD-1* Accelerated Penetration of ATPEVs and ZEVs [VOC,NOX,CO,PM] 2.28 5.01 53.66 5.70

SCONRD-3* Further Emis. Red. from Heavy-Duty Vehicles [VOC,NOX,CO,PM] 0.00 9.74 0.00 10.18

SCONRD-4* Further Emis. Red. from Port Trucks [NOX,PM] 0.00 0.00 0.00 0.00

SCOFRD-1* Construction/Industrial Fleet Modernization [VOC,NOX] 1.71 21.99 0.00 22.38

SCOFRD-2* Cargo Handling Equipment [NOX,PM] 0.00 0.55 0.00 0.55

SCOFRD-3* Further Ems. Red. From Locomotives [NOX,PM] 0.00 10.55 0.00 10.55

SCOFRD-6* Accelerated Turnover Pleasure Craft [VOC,NOX,PM] 19.18 10.69 0.00 2.73

SCFUEL-1* California Phase III Reformulation Gasoline [NOX,SOX] 0.00 0.00 0.00 0.00

SCFUEL-2 Greater Use of Diesel Fuel Alt. & Reformulation [NOX,SOX,PM] 0.00 7.14 0.00 7.18 *

SCLTM-03 Further Reduction from Consumer Products [VOC] 22.89 0.00 0.00 0.00

LTM-04 Phase II Gasoline Fuels [VOC,NOX] 9.10 8.78 0.00 8.50

LTM-05 Phase II Diesel Fuel A lternatives [NOX,SOX,PM] 0.00 17.84 0.00 17.95

SCLTM1-2 Further Reductions from n-Roa d and Off-Road Mobile Sources 0.00 323.03 0.00 321.61 O

Grand Total (with potential overlapping) 133.70 569.51 72.61 566.44EMISSION SUMMARY FOR

(POINT, AREA, MOBILE SOURCE, AND OFF-ROAD MV)

BASELINE EMISSIONS

VOC NOx CO NO2

Point source 48.47 10.65 60.13 10.68

Area sourc 236.06 31.02 236.91 46.92 e

RECLAIM 0.00 27.23 0.00 27.23

Total Stationary 284.53 68.89 297.03 84.83

On-road 103.19 161.32 824.43 174.19

Off-road 135.17 246.02 850.98 231.35

Aircraft 13.08 29.34 85.03 29.34

TOTAL 535.98 505.57 2057.47 519.71

EMISSION REDUCTIONS

Point source 10.80 2.07 0.00 2.07

Area sourc 45.82 5.39 0.00 9.56 e

RECLAIM 0.00 0.00 0.00 0.00

Total Stationary 56.62 7.46 0.00 11.62

On-road 19.08 134.79 71.54 145.39

Off-road 45.68 220.43 0.00 207.29

Aircraft 0.00 21.80 0.00 21.80

TOTAL 121.37 384.48 71.54 386.10

REMAINING EMISSIONS

Point source 37.67 8.58 60.13 8.61

Area sourc 190.25 25.62 236.91 37.37 e

RECLAIM 0.00 27.23 0.00 27.23

Total Stationary 227.92 61.43 297.03 73.21

On-road 84.11 26.53 752.89 28.80

Off-road 89.50 25.59 850.98 24.06

Aircraft 13.08 7.54 85.03 7.54

TOTAL 414.60 121.10 1985.93 133.61

NSR/Set-Aside 5.00 -1.20 0.00 -1.20

Public Funding 0.00 -6.20 0.00 -6.20

GRAND TOTAL (T/D) 419.60 113.70 1985.93 126.21

Mobility Adjustments (3) 0.00 0.00 0.00 0.00(1) Emission reductions for individual measures were estimated based on the sequence of listing

contained here. When the sequence changes, reductions from each measure could be affected,

but the net total remain the same. The purpose of this table is to estimate

total emission reductions without ov erlapping or double-counting between measures.

(2) Emission reductions for individual measures were estimated in the absence of other measures.

Therefore, the sequence of listing does not affect the reduction estimates. The purpose of

this table is to provide emission reduc tion estimates for Appendix IV control measure

summary tables as well as cost effectiveness analysis.

(3) Mobility Adjustment includes TCM-01, ATT-01, ATT-02, ATT-05 and adjustments are reflected

in the CEPA baseline beyond year 2000.

*For the purpose of attainment demonstration, these mobile source measures were selected to achieve the additional

63 tons per day of NOx reductions needed by 2014 for PM2.5 attainment. The corresponding emission reductions

for these measures in 2023 are provided here. However, CA RB can implement any combination of strategies to achieve

the needed reductions.