Appendix C 

WRAP Technical Support Summary 
(page intentionally blank) 
Appendix C 

Appendix C references WRAP’s Technical Support System (TSS) that was the source for the majority of 

key technical information and data used for preparing the Arizona Regional Haze SIP. In addition, this 

appendix provides background information on the Community Multi-Scale Air Quality (CMAQ) model 

described in Chapter 9. CMAQ was the primary regional modeling tool used to estimate 2018 visibility 

conditions in Oregon and all Western Class I areas, based on application of the regional haze strategies 

included in this plan. 

Appendix C – Arizona Regional Haze SIP 

Page 1 of 47 
The WRAP Technical Support System

The Technical Support System (TSS) (http://vista.cira.colostate.edu/tss/) was developed by the Western 

Regional Air Partnership (WRAP) to provide a single portal to technical data and analytical results 

prepared by WRAP Forums and Workgroups. The data, results, and methods displayed on the TSS are 

intended to support the air quality planning needs of western states and tribes, and will be maintained and 

updated to support both the implementation of regional haze plans and other Western air quality analysis 

and management needs. The concept for the TSS is based on the final recommendations of the 

Attribution of Haze Phase I project (http://wrapair.org/forums/aoh/ars1/report.html). 

The primary purpose of the TSS is to provide key summary analytical results and methods documentation 

for the required technical elements of the Regional Haze Rule, to support the preparation, completion, 

evaluation, and implementation of the regional haze implementation plans to improve visibility in Class I 

areas. The TSS provides technical results prepared using a regional approach, including summaries and 

analyses of the comprehensive datasets used to identify the sources and regions contributing to regional 

haze in the Western Regional Air Partnership (WRAP) region. 

The secondary purpose of the TSS is to be the one-stop-shop for access, visualization, analysis, and 

retrieval of the technical data and regional analytical results prepared by WRAP Forums and Workgroups 

in support of regional haze planning in the West. The TSS specifically summarizes results and 

consolidates information about air quality monitoring, meteorological and receptor modeling data 

analyses, emissions inventories and models, and gridded air quality/visibility regional modeling 

simulations.

Monitoring Methods 

IMPROVE Particulate Monitoring: This document summarizes the IMPROVE network purpose, 

protocols, and associated WRAP-relevant issues. This document can be found at: 

http://vista.cira.colostate.edu/docs/wrap/Monitoring/IMPROVE_Particulate_Monitoring_May_2007.doc

WRAP Data Substitution Methods: This document describes the data substitution methods used at nine 

WRAP-region IMPROVE sites to achieve RHR data completeness or to fully populate the WRAP 

modeling year, 2002. This document can be found at: 

http://vista.cira.colostate.edu/docs/wrap/Monitoring/WRAP_Data_Substitution_Methods_April_2007.doc

Algorithm: This document describes the development of natural visibility conditions estimates for the 

revised IMPROVE algorithm, performed by the Natural Haze Levels II Committee of the RPO 

Monitoring/Data Analysis Workgroup. 

Mapping: This table summarizes the relationship among real-world visibility-related parameters and 

parameters defined by the IMPROVE program, the CMAQ and CAMx models, and WRAP emissions 

inventories. Specific topical designations and direct comparisons are presented. This document is 

available in HTML at: http://vista.cira.colostate.edu/tss/help/parameterkey.aspx or Excel at: 

http://vista.cira.colostate.edu/docs/airdata/methods/parametermappingguide.xls

Monitoring Links and Projects 

Visibility Information Exchange Web System (VIEWS): http://vista.cira.colostate.edu/views

Appendix C – Arizona Regional Haze SIP 

Page 2 of 47 
VIEWS is an online exchange of air quality data, research, and ideas designed to support the Regional 

Haze Rule enacted by the U.S. EPA to reduce regional haze and improve visibility in national parks and 

wilderness areas. 

Causes of Haze (COHA): http://www.coha.dri.edu/

The Causes of Haze Assessment web is an online report that answers questions about the chemical 

components that cause regional haze, relationships of haze to meteorology, the emissions that cause haze, 

and the effects of previous and future emissions reductions on the poorest and best visibility levels. 

Emissions Methods 

Overview: These methods describe the collection, processing, and evaluation of monitoring data needed 

for evaluating regional haze in the WRAP region (http://www.epa.gov/air/visibility/regional.html). 

Emissions: Describes how the point emissions inventory was developed. 

Emissions: Describes how the area (including oil and gas) emissions inventory was developed. 

Emissions: Describes how the mobile (on- and off-road) emissions inventory was developed. 

Emissions: Describes how the dust emissions inventory was developed. 

Emissions: Describes how the off shore emissions inventory was developed. 

Emissions: Describes how the fire emissions inventory was developed. 

Mapping: This table summarizes the relationship among real-world visibility-related parameters and 

parameters defined by the IMPROVE program, the CMAQ and CAMx models, and WRAP emissions 

inventories. Specific topical designations and direct comparisons are presented. This table is available in 

HTML at http://vista.cira.colostate.edu/tss/help/parameterkey.aspx or Excel at 

http://vista.cira.colostate.edu/docs/airdata/methods/parametermappingguide.xls 

Emissions Links and Projects 

WRAP Emissions Data Management System (EDMS) http://wrapedms.org/: An emission inventory data 

warehouse and web-based application that provides a consistent approach to regional emissions tracking 

to meet the requirements for State Implementation Plan (SIP) and Tribal Implementation Plan (TIP) 

development and periodic review and updates. 

WRAP Regional Modeling Center (RMC) http://pah.cert.ucr.edu/rmc/: The WRAP RMC assists State 

and Tribal agencies in conducting regional haze analyses over the western U.S. by operating regional 

scale, three-dimensional air quality models that simulate the emission, transformation, and transport of 

pollutants and the effects on visibility in WRAP Class I Areas. 

WRAP Fire Emissions Tracking System (FETS) http://www.wrapfets.org/: The WRAP’s Fire Emissions 

Tracking System (FETS) is a database with a web interface for planned and unplanned fire events. The 

design of the FETS enables efficient data acquisition, stable data storage and availability, real-time 

coordination among smoke managers, and cost effective development of fire emission inventories. 

Reliable and accepted technical methods to estimate fuel consumption and emissions from fire events are 

implemented in the FETS as are tools to estimate emissions averted due to the application Emission 

Reduction Techniques. Users can view fire data on-screen with a mapping tool and query the database 

for downloads of data into model-ready formats and CSV or DBF formats. 

Appendix C – Arizona Regional Haze SIP 

Page 3 of 47 
Modeling Methods 

Modeling: This comprehensive document summarizes the air quality and meteorological models used to 

evaluate the effects of domain-wide emissions in the WRAP region. This document is available at 

http://www.epa.gov/air/visibility/regional.html

Modeling: This document describes how the back trajectory modeling was performed. This document is 

available at http://vista.cira.colostate.edu/docs/wrap/Modeling/BackTrajectoryModeling.doc

Mapping: This table summarizes the relationship among real-world visibility-related parameters and 

parameters defined by the IMPROVE program, the CMAQ and CAMx models, and WRAP emissions 

inventories. Specific topical designations and direct comparisons are presented. This table is available in 

HTML at http://vista.cira.colostate.edu/tss/help/parameterkey.aspx or Excel at 

http://vista.cira.colostate.edu/docs/airdata/methods/parametermappingguide.xls 

Modeling Links and Projects 

WRAP Regional Modeling Center (RMC) (http://pah.cert.ucr.edu/rmc/): The WRAP RMC assists State 

and Tribal agencies in conducting regional haze analyses over the western U.S. by operating regional 

scale, three-dimensional air quality models that simulate the emission, transformation, and transport of 

pollutants and the effects on visibility in WRAP Class I Areas. 

Source Apportionment Methods 

Analysis: This document summarizes the PM Source Apportionment Technology (PSAT) analysis 

performed to trace SOx and NOx emissions leading to particulate species at WRAP region IMPROVE 

monitoring sites. This analysis was performed by the Regional Modeling Center. This document is 

available at http://pah.cert.ucr.edu/aqm/308/cmaq.shtml 

Tracer: This document summarizes the method used by the Regional Modeling Center to model the 

contribution of primary and secondary, natural and anthropogenic organic carbon at WRAP region 

IMPROVE monitoring sites. (Document under development) 

Analysis: This document summarizes the Weighted Emissions Potential (WEP) analysis performed by 

the TSS team. The WEP analysis identifies the potential for emissions source regions to impact haze 

constituents at WRAP region IMPROVE monitoring sites. This document is available at 

http://vista.cira.colostate.edu/docs/wrap/attribution/WEPMethods.doc 

Mapping: This table summarizes the relationship among real-world visibility-related parameters and 

parameters defined by the IMPROVE program, the CMAQ and CAMx models, and WRAP emissions 

inventories. Specific topical designations and direct comparisons are presented. This document is 

available in HTML at http://vista.cira.colostate.edu/tss/help/parameterkey.aspx or Excel at 

http://vista.cira.colostate.edu/docs/airdata/methods/parametermappingguide.xls 

Tom Moore 

Western Regional Air Partnership 

www.wrapair.org 

Air Quality Program Manager Western Governors' Association 

(970) 491-8837 

Appendix C – Arizona Regional Haze SIP 

Page 4 of 47 
Background on the CMAQ Modeling 

Overview of Emissions Applied in Regional Analysis/Air Quality Models in the WRAP Region for 

Regional Haze Planning 

The table and links below provide an overview of the emissions analyzed and applied in the CMAQ and 

CAMx regional air quality models for regional haze planning in the WRAP region. These emissions data 

are displayed on the TSS. Generally, emissions inputs were prepared by individual states and tribes for 

point, area, and most dust emissions categories. With input and review by states, tribes, and FLMs, 

WRAP Forums and Workgroups prepared consistent and comparable WRAP region emissions data for 

the mobile, fire, ammonia, area source oil and gas, eastern Pacific offshore shipping, some dust, and 

biogenic emissions categories. The WRAP Regional Modeling Center gathered the latest, best, and most 

representative emissions estimates at the time from the CENRAP, Eastern U.S., Canada, and Mexico 

regions in executing the sequence of modeling simulations summarized below. Boundary conditions 

reaching North America from the rest of the world were jointed prepared by all 5 RPOs from the GEOSChem global model. 

Appendix C – Arizona Regional Haze SIP 

Page 5 of 47 
Model 

Scenario 

& Date 

 Emissions 

Input 

Description 

 Point Area 

 Mobile 

(On- & 

Off-Road) 

 Fire 

 Ammonia, 

Dust, & 

Biogenics 

 Offshore 

shipping & 

remainder 

of North 

America 

 North 

American 

Domain 

Boundary 

Conditions 

 Base02b 

(Spring 

2006) 

 2002 best 

available 

actual data 

 SSJF/EF 

from 

state/tribal 

EIsP1 

 SSJF/EF 

from 

state/tribal 

EIA1, 

Phase I 

O&G A2 

 EF 

projectM1 

 FEJF - 

actual 

2002F1 

 RMC 2002 

data ADB1, 

ADB2, 

ADB3 

 EPA 

CMAQDefault 

 Plan02c 

(Summer 

2006) 

 2000-04 

planning data 

(2002 data in 

WRAP 

region except 

fire) 

 SSJF/EF 

from 

state/tribal 

EIsP1 

 SSJF/EF 

from 

state/tribal 

EIsA1, 

Phase I 

O&GA2 

 EF 

projectM1 

 FEJF - 

average 

2000-04F2 

 EF 

projectM1 

and other 

RPOsNA1 

 Plan02d 

(October 

2007) 

 2000-04 

planning data 

(2002 data in 

WRAP 

region except 

fire) 

 SSJF/EF 

detailed 

state 

updates P3 

 SSJF/EF 

detailed 

state 

updates A4, 

Phase II 

O&G A5 

 EF 

projectM1 

(EMFAC20

07 data for 

California) 

 FEJF -

average 

2000-04F2 

 

 Base18b 

(Summer 

2006) 

 2018 Base 

Case data 

(see 

footnotes by 

sector) 

 SSJF/EF 

from 

state/tribal 

EIsP2 

 SSJF/EF 

from 

state/tribal 

EIsA3, 

Phase I 

O&G A2 

 EF 

projectM1 

Preliminary 

Reasonable 

Progress - 

PRP18 

(June 2007) 

 2018 PRP 

data 

(Base18b 

except for 

footnotes 

noted by 

sector) 

 SSJF/EF, 

detailed 

state 

updates P4 

 SSJF/EF 

detailed 

state 

updates A6, 

Phase II 

O&GA5 

 EF 

projectM1 

 Final RP18 

(future, 

2009) 

 2018 FRP 

data – not 

completely 

specified at 

this time 

 BART 

sources‟

emissions 

rates as 

specified by 

permitting 

agency 

 Will use 

SSJF/EF 

detailed 

state 

updates A6, 

Phase II 

O&GA5 

 Will use 

EF 

projectM1 

& will 

apply 

EMFAC07 

data for 

California) 

 FEJF - 

average 

2000-04F3 

 RMC 2002 

data ADB1, 

ADB2, 

ADB3 

 Shipping 

will be 

updated to 

2018 

projections as 

a sensitivity 

simulation 

 2002 GEOSChemBC1 

Appendix C – Arizona Regional Haze SIP 

Page 6 of 47 
TSS Resources Emissions 

Emissions Methods 

Emissions Overview (http://vista.cira.colostate.edu/docs/wrap/emissions/EmissionsOverview.doc) - 

These methods describe the collection, processing, and evaluation of monitoring data needed for 

evaluating regional haze in the WRAP region. 

Point Emissions (http://vista.cira.colostate.edu/docs/wrap/emissions/PointEmissions.doc) - Describes how 

the point emissions inventory was developed. 

Area Emissions (http://vista.cira.colostate.edu/docs/wrap/emissions/AreaEmissions.doc) - Describes how 

the area (including oil and gas) emissions inventory was developed. 

Mobile Emissions (http://vista.cira.colostate.edu/docs/wrap/emissions/MobileEmissions.doc) - Describes 

how the mobile (on- and off-road) emissions inventory was developed 

Dust Emissions (http://vista.cira.colostate.edu/docs/wrap/emissions/DustEmissions.doc) - Describes how 

the dust emissions inventory was developed. 

Offshore Emissions (http://vista.cira.colostate.edu/docs/wrap/emissions/OffshoreEmissions.doc) - 

Describes how the off shore emissions inventory was developed. 

Fire Emissions (http://vista.cira.colostate.edu/docs/wrap/emissions/FireEmissions.doc) - Describes how 

the fire emissions inventory was developed. 

Tool: http://vista.cira.colostate.edu/TSS/Results/HazePlanning.aspx -> Emissions and Source 

Apportionment -> Emissions Review Tool 

Modeling resource page: http://vista.cira.colostate.edu/TSS/Results/Modeling.aspx 

Modeling description document: 

http://vista.cira.colostate.edu/docs/wrap/Modeling/AirQualityModeling.doc 

“Rosetta Stone” for interpreting monitoring/emissions/modeling results: 

http://vista.cira.colostate.edu/tss/help/parameterkey.aspx 

Regional Modeling Center website: http://pah.cert.ucr.edu/aqm/308/ 

Control Programs included in the WRAP region analysis of 2018 air quality modeling scenarios

 Smoke Management Programs accounted for using Emissions Reduction Techniques applied to 2000-

04 average Fire emissions 

 New permits and state/EPA consent agreements since 2002 reviewed with each state – through Spring 

2007 

 Ozone and PM10 SIPs in place in the WRAP region 

 State Oil and Gas Emissions control programs 

 Mobile sources 

o Heavy Duty Diesel (2007) Engine Standard 

o Tier 2 Tailpipe 

o Large Spark Ignition and Recreational Vehicle Rule 

o Nonroad Diesel Rule 

 Combustion Turbine and Industrial Boiler/Process Heater/RICE MACT 

 VOC 2-, 4-, 7-, and 10-year MACT Standards 

 Known BART (CO, UT, ND, OR [Boardman] – to date) 

 Presumptive SO2 BART for rest of WRAP region 

Point Sources – these projects were commissioned by the Stationary Sources Joint Forum and the 

Emissions Forum. 

Appendix C – Arizona Regional Haze SIP 

Page 7 of 47 
2002 actual data reported by states, locals, tribes, and EPA databases and used in the “Base02b” modeling 

scenario. See: http://www.wrapair.org/forums/ssjf/documents/Pivot_Tables/2006-

01_REPORT_QA_of_the_2002_WRAP_Stationary_Sources%20EI.pdf

2002 actual point source emissions inventory data inputs to the “Plan 02c” modeling scenario. The 

“Plan” designation comes from the fact that a five year average Fire total is included in the inventories to 

ameliorate the bias effects of 2002 year specific meteorology and fire events. The actual 2002 point 

source emissions data for were used in Plan02c. See: 

http://www.wrapair.org/forums/ssjf/documents/Pivot_Tables/2006-

01_REPORT_QA_of_the_2002_WRAP_Stationary_Sources%20EI.pdf

2018 Base Case point source emissions inventory projection data inputs to the “Base 18b” modeling 

scenario were projected from the 2002 “Plan02c” inventory. The projection methodology included the 

following steps: 

Adjustments: 

Emissions for new facilities that have come on-line since 2002 Corrections for facilities that retired in 

2003 or 2004 and will not return to operation in the future Other (i.e., ratios to correct certain PM10 and 

PM2.5 emission factors/SCCs for combustion of natural gas) 

Control Factors: Emission reductions due to known (i.e., on-the-books) controls, consent decrees 

reductions, SIP control measures, and other relevant regulations that have gone into effect since 2002, or 

will go into effect before the end of 2018. These controls do not include impacts from any future control 

scenarios that have yet to be determined. 

Growth Factors: SCC-specific growth factors developed from the EGAS projection factor model; special 

analysis of EGU growth relative to unit capacity threshold. 

Retirement & Replacement Rates: Effects of retirement estimated using annual retirement rates based on 

expected equipment lifetimes. Retired equipment replaced by lower-emitting new equipment. Unit 

lifetimes examined for natural gas-fired EGUs. No retirements assumed for coal-fired EGUs. 

Permit Limits: Used in the cases where the projected emissions may have inadvertently exceeded an 

enforceable emission limit (i.e., emissions were adjusted downward to the permit limit, as applicable). 

Section §309 Flags: Point sources in the Grand Canyon Visibility Transport Commission region (i.e., AZ, 

CA, CO, ID, NV, NM, OR, UT, and WY) whose 2002 facility-level SO2 emissions are at least 100 

tons/year.) 

See: http://wrapair.org/forums/ssjf/documents/eictts/docs/WRAP_2018_EI-Version_1-

Report_Jan2006.pdf 

Final revisions were made to the 2002 actual point source emissions inventory data as inputs to the “Plan 

02d” modeling scenario. The “Plan” designation comes from the fact that a five year average Fire total is 

included in the inventories to ameliorate the bias effects of 2002 year specific meteorology and fire 

events. The actual 2002 point source emissions data for were used in Plan02d. The focus was to address 

specific changes in these areas: 

Appendix C – Arizona Regional Haze SIP 

Page 8 of 47 
 Address any remaining 2002 issues that were deferred from the WRAP 2018 Preliminary 

Reasonable Progress Emissions Inventory (PRP18) conducted in May 2007; 

 Update the current status of point sources related to the Best Available Retrofit Technology 

(BART) requirements under the regional haze rule; and 

 Correct erroneous or missing Standard Industrial Classification (SIC) and/or source classification 

code (SCC) codes. 

See: http://www.wrapair.org/forums/ssjf/documents/Pivot_Tables/2008-01_Plan02d_Tech_Memo(1-

10).pdf 

2018 Preliminary Reasonable Progress (PRP) point source emissions inventory projection data inputs to 

the “PRP18a” modeling scenario were updated from the 2018 “Base18b” inventory (see above). The 

objective of this project was to provide the technical basis for making preliminary evaluations of 

reasonable progress toward individual Class I area visibility goals for Class I areas in Western Regional 

Air Partnership region, by conducting conference calls with individual state air agencies to establish the 

most representative source-specific emissions projections data as the basis for preparing regional haze 

plans. The PRP18a point source inventory revisions included the following elements: 

 Addressed State/Local Agency comments on the Base 18b inventory (i.e./fixed missing SCCs, 

changed facility names, added new sources, removed inactive sources, etc.). 

 Corrected EGU Facility IDs for those EGUs reporting to EPA’s Clean Air Markets Division 

(CAMD) acid rain database, and added missing EGU source records. 

 Revised EGU projections of future coal fired EGU construction (used EIA projections of 2018 

energy requirements - determined existing WRAP region capacity to identify total new 

requirements - added existing State/Local agency EGU permits - added 11 new “Model” 500MW 

EGU units operating at BACT levels). 

 Applied known or “presumptive” BART emission limits to coal fired EGUs determined to be 

“BART-eligible” or “Subject to BART” requirements. 

 Incorporated 2007 ENVIRON revisions to WRAP Region Oil & Gas point and area source 

emissions. 

 Incorporated 2006 Pechan and Associates updates to 2018 SO2 emission projections for NonUtility Point Sources. 

See: http://wrapair.org/forums/ssjf/documents/eictts/Projections/PRP18_EI_tech%20memo_061607.pdf

Area Sources – these projects were commissioned by the Stationary Sources Joint Forum and the 

Emissions Forum. 

2002 actual area source emissions inventory data reported by states, locals, tribes, and EPA databases and 

used in the “Base02b” modeling scenario. See: 

http://www.wrapair.org/forums/ssjf/documents/Pivot_Tables/200601_REPORT_QA_of_the_2002_WRA

P_Stationary_Sources%20EI.pdf

Phase I Oil and Gas emissions inventory project, see: 

http://wrapair.org/forums/ssjf/documents/eictts/OilGas/WRAP_Oil&Gas_Final_Report.122805.pdf

2018 Base Case area source emissions inventory projection data inputs to the “Base 18b” modeling 

scenario were projected from the 2002 “Plan02c” inventory. The projection methodology included the 

following steps: 

Adjustments: 

Appendix C – Arizona Regional Haze SIP 

Page 9 of 47 
Emissions for new facilities that have come on-line since 2002 Corrections for facilities that retired in 

2003 or 2004 and will not return to operation in the future Other (i.e., ratios to correct certain PM10 and 

PM2.5 emission factors/SCCs for combustion of natural gas) 

Control Factors: Emission reductions due to known (i.e., on-the-books) controls, consent decrees 

reductions, SIP control measures, and other relevant regulations that have gone into effect since 2002, or 

will go into effect before the end of 2018. These controls do not include impacts from any future control 

scenarios that have yet to be determined. 

Growth Factors: SCC-specific growth factors developed from the EGAS projection factor model; special 

analysis of EGU growth relative to unit capacity threshold. 

Retirement & Replacement Rates: Effects of retirement estimated using annual retirement rates based on 

expected equipment lifetimes. Retired equipment replaced by lower-emitting new equipment. Unit 

lifetimes examined for natural gas-fired EGUs. No retirements assumed for coal-fired EGUs. 

Permit Limits: Used in the cases where the projected emissions may have inadvertently exceeded an 

enforceable emission limit (i.e., emissions were adjusted downward to the permit limit, as applicable). 

Section §309 Flags: Point sources in the Grand Canyon Visibility Transport Commission region (i.e., AZ, 

CA, CO, ID, NV, NM, OR, UT, and WY) whose 2002 facility-level SO2 emissions are at least 100 

tons/year.) 

See: http://wrapair.org/forums/ssjf/documents/eictts/docs/WRAP_2018_EI-Version_1-

Report_Jan2006.pdf

Final revisions were made to the 2002 actual area source emissions inventory data as inputs to the “Plan 

02d” modeling scenario. The “Plan” designation comes from the fact that a five year average Fire total is 

included in the inventories to ameliorate the bias effects of 2002 year specific meteorology and fire 

events. The actual 2002 area source emissions data for were used in Plan02d. The focus was to address 

specific changes in these areas: 

 Address any remaining 2002 issues that were deferred from the WRAP 2018 Preliminary 

Reasonable Progress Emissions Inventory (PRP18) conducted in May 2007; 

 Update the current status of point sources related to the Best Available Retrofit Technology 

(BART) requirements under the regional haze rule; and 

 Correct erroneous or missing Standard Industrial Classification (SIC) and/or source classification 

code (SCC) codes. 

See: http://www.wrapair.org/forums/ssjf/documents/Pivot_Tables/2008-01_Plan02d_Tech_Memo(1-

10).pdf

Phase II Oil and Gas emissions inventory project. See: 

http://wrapair.org/forums/ssjf/documents/eictts/OilGas/200710_Phase_II_O&G_Final_Report_v10-

07.pdf

2018 Preliminary Reasonable Progress (PRP) area source emissions inventory projection data inputs to 

the “PRP18a” modeling scenario were updated from the 2018 “Base18b” inventory (see above). The 

objective of this project was to provide the technical basis for making preliminary evaluations of 

reasonable progress toward individual Class I area visibility goals for Class I areas in Western Regional 

Air Partnership region, by conducting conference calls with individual state air agencies to establish the 

Appendix C – Arizona Regional Haze SIP 

Page 10 of 47 
most representative source-specific emissions projections data as the basis for preparing regional haze 

plans. The PRP18a area source inventory revisions included the following elements: 

 Addressed State/Local Agency comments on the Base 18b inventory (i.e./ fixed missing SCCs, 

changed facility names, added new sources, removed inactive sources, etc.) 

 Corrected EGU Facility IDs for those EGUs reporting to EPA’s Clean Air Markets Division 

(CAMD) acid rain database, and added missing EGU source records. 

 Revised EGU projections of future coal fired EGU construction (used EIA projections of 2018 

energy requirements - determined existing WRAP region capacity to identify total new 

requirements - added existing State/Local agency EGU permits - added 11 new “Model” 500MW 

EGU units operating at BACT levels). 

 Applied known or “presumptive” BART emission limits to coal fired EGUs determined to be 

“BART-eligible” or “Subject to BART” requirements. 

 Incorporated 2007 ENVIRON revisions to WRAP Region Oil & Gas point and area source 

emissions. 

 Incorporated 2006 Pechan and Associates updates to 2018 SO2 emission projections for NonUtility Point Sources. 

See: http://wrapair.org/forums/ssjf/documents/eictts/Projections/PRP18_EI_tech%20memo_061607.pdf 

Mobile Sources – this project was commissioned by the Emissions Forum. 

This project prepared EPA Mobile6 and NONROAD emissions modeling results for 2002 and 2018, 

taking into account federal and state mobile emissions rules. The project also included emissions from 

airplanes landings and takeoffs, railroads, and road dust for the WRAP region. The emissions were 

calculated from state reports of activity data and profiles. The project is documented at: 

http://wrapair.org/forums/ef/UMSI/index.html. State-reported port activity and river shipping emissions 

are in the states’ area source EIs. 

Fire Emissions – these projects were commissioned by the Fire Emissions Joint Forum. 

This 2-phase project (Phases 1 and 2) collected 2002 actual fire emissions data from federal, state, and 

tribal databases. See: 

http://www.wrapair.org/forums/fejf/documents/WRAP_2002_PhII_EI_Report_20050722.pdf

The Phase 3 project scaled 2002 actual data from their Phase 2 project by average fire activity data by 

state for the 2000-04 period, location and dates of fires in 2002 were held constant. See: 

http://www.wrapair.org/forums/fejf/documents/task7/Phase3-4EI/WRAP_Fire_Ph3-

4_EI_Report_20070515.pdf

The Phase 4 project built on Phase 3, to scale 2002 actual data by average fire activity data by state for the 

2000-04 period, location and dates of fires in 2002 were held constant, then applying Emission Reduction 

Techniques to agricultural and prescribed fire by season and region across the WRAP region to represent 

implementation of Enhanced Smoke Management Programs. This project also produced three 2018 

emissions scenarios, which have NOT been used in regional emissions analysis or regional modeling, as 

2000-04 data are thought to be the most representative estimates of 2018 emissions for haze planning 

purposes. See: http://www.wrapair.org/forums/fejf/documents/task7/Phase3-4EI/WRAP_Fire_Ph3-

4_EI_Report_20070515.pdf

Appendix C – Arizona Regional Haze SIP 

Page 11 of 47 
Ammonia, Dust, & Biogenic Emissions – these projects were commissioned by the Dust Emissions 

Joint Forum and the Modeling Forum. RMC prepared land-use data-based fugitive ammonia emission 

inventories, driven by 2002 meteorological data from the CMAQ air quality model. Separate ammonia 

emissions inventory data are also reported in point and area source emissions inventories and applied in 

the regional modeling scenarios. See: 

 NH3 EI Final Report: Volume I 

(http://newpah.cert.ucr.edu/aqm/308/ppt_files/emissions/nh3/Volume_I_FinalReport.3-07.pdf) 

 NH3 EI Final Report: Volume II, Appendices 

(http://newpah.cert.ucr.edu/aqm/308/ppt_files/emissions/nh3/Volume_II_Appendices_3-05.pdf) 

 NH3 2002 Inventory (12k) 

(http://newpah.cert.ucr.edu/aqm/308/nh3_2002_12km/nh3_2002_WRAP12.shtml) 

 NH3 2002 Inventory (36k) 

(http://newpah.cert.ucr.edu/aqm/308/nh3_2002_36km/nh3_2002_RPO36.shtml) 

RMC prepared land-use data-based windblown dust emission inventories, driven by 2002 meteorological 

data from the CMAQ air quality model. See: 

 Dust Emissions Scenario Descriptions 

(http://newpah.cert.ucr.edu/aqm/308/wb_dust2002/Scenario_Descriptions.doc) 

 Domain Daily/Monthly Total Dust Emissions 

(http://newpah.cert.ucr.edu/aqm/308/wb_dust2002/wbdust_domain_daily_totals.xls) 

 State Yearly Total Dust Emissions 

(http://newpah.cert.ucr.edu/aqm/308/wb_dust2002/state_yearly_totals.xls) 

 Presentation of the results 

(http://newpah.cert.ucr.edu/aqm/308/wb_dust2002/WRAP_PhaseII_WBDust_111505.ppt) 

 2002 Dust Emissions (monthly and yearly plots) 

(http://newpah.cert.ucr.edu/aqm/308/wb_dust2002/scen_b_042406) 

RMC prepared land-use data-based natural biogenic emission inventories, driven by 2002 meteorological 

data from the CMAQ air quality model. The land use data characterize the types of vegetation that exist 

in either county-total or grid cell values. The biogenic land use data in North America are from the 

Biogenic Emissions Landcover Database version 3 (BELD3). See: 

http://newpah.cert.ucr.edu/aqm/308/reports/final/2002_MPE_report_main_body_FINAL.pdf

Emissions from Pacific offshore shipping & remainder of North America – these projects were 

commissioned by the Emissions Forum and the Modeling Forum. 

M1.Eastern Pacific Offshore Shipping – this first-ever 2002 offshore shipping emissions inventory for the 

eastern Pacific Ocean region of the WRAP modeling domain was done as part of the Mobile Source 

project reference above at: http://wrapair.org/forums/ef/UMSI/index.html, discussed in Chapter 7 of the 

project report. This project also produced a 2018 emissions scenario, which has NOT been used in 

regional emissions analysis or regional modeling to date. Those data will be applied in the FRP18 

modeling scenario as a sensitivity simulation. 

NA1.The RMC collected emissions modeling data from the other RPOs, Canada, and Mexico and used 

them in the WRAP modeling scenarios as updates were completed by the RPOs, Canada, and Mexico. 

Data from the other 4 RPOs were obtained from the following contacts: 

Appendix C – Arizona Regional Haze SIP 

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Regional Planning Organization Contacts 

CENRAP 

Lee Warden, Oklahoma DEQ 

Oklahoma City, OK 

Phone: (405) 702-4201 

Email: lee.warden@deq.state.ok.us

VISTAS 

Greg Stella, Alpine Geophysics, LLC. 

Phone: (828) 675-9045 

Email: gms@alpinegeophysics.com

MANE-VU 

Megan Schuster, MARAMA 

Baltimore, MD 

Phone: (410) 467-0170 

Email: mschuster@marama.org

MRPO 

Marc Janssen, LADCO 

Des Plaines, IL 

Phone: (847) 296-2181 

Email: janssen@ladco.org 

Mexico – Emissions inventory data are for 1999 and were held constant in the 2018 WRAP modeling 

scenarios. Preparation of the Mexico inventory is documented at: 

http://www.wrapair.org/forums/ef/inventories/MNEI/index.html. The older WRAP modeling scenarios 

had emissions only for the 6 northern states of Mexico and the newer scenarios have emissions for the 

entire portion of Mexico within the modeling domain. 

Canada – Emissions inventory data are for 2000 and were held constant for 2018 until PRP18a. The 2020 

emissions inventory for Canada was processed by EPA and applied in the PRP18a modeling scenario. 

Boundary conditions reaching North America from the rest of the world – this project was 

commissioned by the VISTAS RPO on behalf of the 5 RPOs working on regional haze planning. 

BC1. Boundary condition (BC) data were generated in an annual simulation of the global-scale GEOSChem model that was completed by Jacob, et. al. (http://wwwas.harvard.edu/chemistry/trop/geos/) for calendar year 2002. 

Appendix C – Arizona Regional Haze SIP 

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Appendix C – Arizona Regional Haze SIP 

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Section 2: WRAP Air Quality Modeling Support Document 

Overview

Visibility impairment occurs when fine particulate matter (PM2.5) in the atmosphere scatters and absorbs 

light, thereby creating haze. PM2.5 can be emitted into the atmosphere directly as primary particulates, or 

it can be produced in the atmosphere from photochemical reactions of gas-phase precursors and 

subsequent condensation to form secondary particulates. Examples of primary PM2.5 include crustal 

materials and elemental carbon; examples of secondary PM include ammonium nitrate, ammonium 

sulfates, and secondary organic aerosols (SOA). Secondary PM2.5 is generally smaller than primary 

PM2.5, and because the ability of PM2.5 to scatter light depends on particle size, with light scattering for 

fine particles being greater than for coarse particles, secondary PM2.5 plays an especially important role in 

visibility impairment. Moreover, the smaller secondary PM2.5 can remain suspended in the atmosphere 

for longer periods and is transported long distances, thereby contributing to regional-scale impacts of 

pollutant emissions on visibility. 

The sources of PM2.5 are difficult to quantify because of the complex nature of their formation, transport, 

and removal from the atmosphere. This makes it difficult to simply use emissions data to determine 

which pollutants should be controlled to most effectively improve visibility. Photochemical air quality 

models offer opportunity to better understand the sources of PM2.5 by simulating the emissions of 

pollutants and the formation, transport, and deposition of PM2.5. If an air quality model performs well for 

a historical episode, the model may then be useful for identifying the sources of PM2.5 and helping to 

select the most effective emissions reduction strategies for attaining visibility goals. Although several 

types of air quality modeling systems are available, the gridded, three-dimensional, Eulerian models 

provide the most complete spatial representation and the most comprehensive representation of processes 

affecting PM2.5, especially for situations in which multiple pollutant sources interact to form PM2.5. For 

less complex situations in which a few large point sources of emissions are the dominant source of PM2.5, 

trajectory models (such as the California Puff Model [CALPUFF]) may also be useful for simulating 

PM2.5. 

Air Quality Models

The WRAP RMC utilized two regulatory air quality modeling systems to conduct all regional haze 

modeling. A brief discussion of each of these models is provided below. 

Community Multi-Scale Air Quality Model

EPA initially developed the Community Multi-Scale Air Quality (CMAQ) modeling system in the late 

1990s. The model source code and supporting data can be downloaded from the Community Modeling 

and Analysis System (CMAS) Center (http://www.cmascenter.org/), which is funded by EPA to distribute 

and provide limited support for CMAQ users. CMAQ was designed as a “one atmosphere” modeling 

system to encompass modeling of multiple pollutants and issues, including ozone, PM, visibility, and air 

toxics. This is in contrast to many earlier air quality models that focused on single-pollutant issues (e.g., 

ozone modeling by the Urban Airshed Model). CMAQ is an Eulerian model—that is, it is a grid-based 

model in which the frame of reference is a fixed, three-dimensional (3-D) grid with uniformly sized 

horizontal grid cells and variable vertical layer thicknesses. The number and size of grid cells and the 

number and thicknesses of layers are defined by the user, based in part on the size of the modeling 

domain to be used for each modeling project. The key science processes included in CMAQ are 

emissions, advection and dispersion, photochemical transformation, aerosol thermodynamics and phase 

Appendix C – Arizona Regional Haze SIP 

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transfer, aqueous chemistry, and wet and dry deposition of trace species. CMAQ offers a variety of 

choices in the numerical algorithms for treating many of these processes, and it is designed so that new 

algorithms can be included in the model. CMAQ offers a choice of three photochemical mechanisms for 

solving gas-phase chemistry: the Regional Acid Deposition Mechanism version 2 (RADM2), a fixed 

coefficient version of the SAPRC90 mechanism, and the Carbon Bond IV mechanism (CB-IV). 

Comprehensive Air Quality Model with Extensions 

The Comprehensive Air Quality Model with extensions (CAMx) model was initially developed by 

ENVIRON in the late 1990s as a nested-grid, gas-phase, Eulerian photochemical grid model. ENVIRON 

later revised CAMx to treat PM, visibility, and air toxics. While there are many similarities between the 

CMAQ and CAMx systems, there are also some significant differences in their treatment of advection, 

dispersion, aerosol formation, and dry and wet deposition. 

Model Versions 

Both EPA and ENVIRON periodically update and revise their models as new science or other 

improvements to the models are developed. For CMAQ, EPA typically provides a new release about once 

per year. The initial 2002 MPE for WRAP used CMAQ version 4.4, which was released in October 

2004. In October 2005 EPA released CMAQ version 4.5, which includes the following updates and 

improvements to the modeling system: 

 A new vertical advection algorithm with improved mass conservation 

 Changes in deposition velocities for some PM species 

 A new sea-salt emissions model and inclusion of sea salt in the aerosol thermodynamics 

 An option to make vertical mixing parameters vary as a function of land use type 

The RMC completed the initial CMAQ MPE using CMAQ v.4.4. When version 4.5 was released in 

October, the modeling was revised and a comparison of the model performance using the two versions 

was compared. Note that some of the new features in CMAQ v4.5 (e.g., sea salt in the AE4 aerosol 

dynamics module, and percent urban minimum vertical diffusivity) require the reprocessing of the MM5 

data using the new version of MCIP (MCIP v3.0). However, because such reprocessing could potentially 

jeopardize the WRAP modeling schedule, WRAP elected to operate CMAQ v4.5 using the MM5 data 

processed using a previous MCIP version, MCIP v2.3, and the AE3 aerosol module that does not include 

active sea salt chemistry. 

ENVIRON releases updated versions of CAMx approximately every two years, or as new features 

become available. The version used for the comparison of CMAQ and CAMx was CAMx v4.3. There 

are many similarities between CMAQ and CAMx regarding the science algorithms and chemical 

mechanisms used, including the CB-IV gas-phase and RADM aqueous-phase chemistries, ISORROPIA 

aerosol thermodynamics, and PPM horizontal advection scheme. In the past, the treatment of vertical 

advection was a major difference between the two models; however, the incorporation of the new mass 

conservation scheme in CMAQ v4.5 makes its vertical advection algorithm much more similar to that of 

CAMx. 

Major differences between the two models that still exist are in the basic model code, in the treatment of 

horizontal diffusion SOA formation mechanisms, and in grid nesting (CAMx supports one-way and twoway nesting, whereas CMAQ supports just one-way grid nesting). Both models include process analysis 

for the gas-phase portions of the model. The publicly released version of CAMx supports ozone and PM 

source apportionment through its Ozone and PM Source Apportionment Technology (OSAT/PSAT) 

probing tools, while for CMAQ there are research versions of the model that include Tagged Species 

Appendix C – Arizona Regional Haze SIP 

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Source Apportionment (TSSA) for some PM species (e.g., sulfate and nitrate). There are also research 

versions of CMAQ and CAMx that support the Decoupled Direct Method (DDM) sensitivity tool for PM 

and ozone. 

The CAMx model is computationally more efficient than CMAQ. However, CAMx is currently 

supported for use on only a single central processing unit (CPU) and can perform multiprocessing using 

Open Multi-Processing (OMP) parallelization (i.e., shared memory multiprocessors). CMAQ 

parallelization, on the other hand, is implemented using Message Passing Interface (MPI) multiprocessing 

and therefore can be run using any number of CPUs. Depending on the number of model simulations to 

be performed and the manner in which they are set up, there can be a slight advantage either to CAMx or 

to CMAQ in regard to computational efficiency. 

Model Simulations

In support of the WRAP Regional Haze air quality modeling efforts, the RMC developed air quality 

modeling inputs including annual meteorology and emissions inventories for a 2002 actual emissions base 

case, a planning case to represent the 2000-04 regional haze baseline period using averages for key 

emissions categories, and a 2018 base case of projected emissions determined using factors known at the 

end of 2005. All emission inventories were developed using the Sparse Matrix Operator Kernel 

Emissions (SMOKE) modeling system. Each of these inventories has undergone a number of revisions 

throughout the development process to arrive at the final versions used in CMAQ and CAMx air quality 

modeling. The development of each of these emission scenarios is documented under the emissions 

inventory sections of the TSS. In addition to various sensitivities scenarios, the WRAP performed air 

quality model simulations for each of the emissions scenarios as follows: 

 The 2002 base case emissions scenario, referred to as “2002 Base Case” or “Base02”. The 

purpose of the Base02 inventory is to represent the actual conditions in calendar year 2002 with 

respect to ambient air quality and the associated sources of criteria and particulate matter air 

pollutants. The Base02 emissions inventories are used to validate the air quality model and 

associated databases and to demonstrate acceptable model performance with respect to replicating 

observed particulate matter air quality. 

 The 2000-04 baseline period planning case emissions scenario is referred to as “Plan02”. The 

purpose of the Plan02 inventory is to represent baseline emission patterns based on average, or 

“typical”, conditions. This inventory provides a basis for comparison with the future year 2018 

projected emissions, as well as to gauge reasonable progress with respect to future year visibility. 

 The 2018 future-year base case emissions scenario, referred to as “2018 Base Case” or “Base18”. 

These emissions are used to represent conditions in future year 2018 with respect to sources of 

criteria and particulate matter air pollutants, taking into consideration growth and controls. 

Modeling results based on this emission inventory are used to define the future year ambient air 

quality and visibility metrics. 

Data Sources

The CMAQ model requires inputs of three-dimensional gridded wind, temperature, humidity, 

cloud/precipitation, and boundary layer parameters. The current version of CMAQ can only utilize output 

fields from the PSU/NCAR MM5 meteorological model. MM5 is a state-of-the-science atmosphere 

model that has proven useful for air quality applications and has been used extensively in past local, state, 

regional, and national modeling efforts. MM5 has undergone extensive peer-review, with all of its 

components continually undergoing development and scrutiny by the modeling community. In-depth 

Appendix C – Arizona Regional Haze SIP 

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descriptions of MM5 can be found in Dudhia (1993) and Grell et al. (1994), and at 

http://www.mmm.ucar.edu/mm5. All meteorological data used for the WRAP air quality modeling 

efforts are derived from MM5 model simulations. The development of these data is documented in 

(Kemball-Cook, S. et al., 2005) 

Emission inventories for all WRAP air quality simulations were developed using the Matrix Operator 

Kernel Emissions (SMOKE) modeling system. The development of these data has been discussed and 

documented elsewhere (Tonnesen, G. et al., 2006) 

Initial conditions (ICs) are specified by the user for the first day of a model simulation. For continentalscale modeling using the RPO Unified 36-km domain, the ICs can affect model results for as many as 15 

days, although the effect typically becomes very small after about 7 days. A model spin-up period is 

included in each simulation to eliminate any effects from the ICs. For the WRAP modeling, the annual 

simulation is divided into four quarters, and included a 15-day spin-up period for the quarters beginning 

in April, July, and October. For the quarter beginning in January 2002, a spin-up period covering 

December 16-31, 2001, using meteorology and emissions data developed for CENRAP were used. 

Boundary conditions (BCs) specify the concentrations of gas and PM species at the four lateral 

boundaries of the model domain. BCs determine the amounts of gas and PM species that are transported 

into the model domain when winds flow is into the domain. Boundary conditions have a much larger 

effect on model simulations than do ICs. For some areas in the WRAP region and for clean conditions, 

the BCs can be a substantial contributor to visibility impairment. 

For this study BC data generated in an annual simulation of the global-scale GEOS-Chem model that was 

completed by Jacob et al. (http://www-as.harvard.edu/chemistry/trop/geos/) for calendar year 2002 were 

applied. Additional data processing of the GEOS-Chem data was required before using them in CMAQ 

and CAMx. The data first had to be mapped to the boundaries of the WRAP domain, and the gas and PM 

species had to be remapped to a set of species used in the CMAQ and CAMx models. This work was 

completed by Byun and coworkers (http://wwwas.harvard.edu/chemistry/trop/geos/meetings/2005/ppt/Expanding_Model_Capabilities/GEOSCMAQ_april_4_Byun.ppt

The CMAQ model options and configuration used for the WRAP 36-km model simulations are described 

in Tonnesen, G. et al., 2006. 

Model Run Specification Sheets

In order to provide documentation for each of the CMAQ and CAMx air quality model simulations 

conducted by the WRAP RMC during Calendar year 2006, a series of Model Run Specification Sheets 

were developed. These “Spec Sheets” provide a description of each simulation; the various air quality 

model options and configurations used and detailed listing and description of the meteorological data and 

emission inventories for each scenario. These Spec Sheets also provide a means for the RMC to track the 

development of each of the input data sets and defined the modeling schedule. The purpose of each 

simulation, and expected results, including their implications, are also included. A link to each of the 

individual Specification Sheets for the model simulations can be found on the RMC web site at: 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml. 

Appendix C – Arizona Regional Haze SIP 

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2002 Base Case Modeling 

Base02 Sensitivity Simulations

The purpose of the 2002 Base Case modeling efforts was to evaluate air quality/visibility modeling 

systems for a historical episode—in this case, for calendar year 2002—to demonstrate the suitability of 

the modeling systems for subsequent planning, sensitivity, and emissions control strategy modeling. 

Model performance evaluation is performed by comparing output from model simulations with ambient 

air quality data for the same time period. After creating emissions and meteorology inputs for the two air 

quality models, CMAQ and CAMx, the next step was to perform the visibility modeling and the model 

performance evaluations, which are described below. A detailed discussion of the results of the CMAQ 

and CAMx model simulations can be found in Tonnesen, G. et al., 2006. Also documented in Tonnesen, 

G. et al., 2006 are the results of the model performance evaluation, a model inter-comparison and 

discussion of various sensitivity simulations. This information was used as the basis for recommending 

the selection of CMAQ and/or CAMx to complete the remaining modeling efforts in RMC’s support of 

WRAP. 

Model Performance Evaluation

The objective of a model performance evaluation (MPE) is to compare model-simulated concentrations 

with observed data to determine whether the model’s performance is sufficiently accurate to justify using 

the model for simulating future conditions. There are a number of challenges in completing an annual 

MPE for regional haze. The model must be compared to ambient data from several different monitoring 

networks for both PM and gaseous species, for an annual time period, and for a large number of sites. 

The model must be evaluated for both the worst visibility conditions and for very clean conditions. 

Finally, final guidance on how to perform an MPE for fine-particulate models is not yet available from 

EPA. Therefore, the RMC experimented with many different approaches for showing model performance 

results. The plot types that were found to be the most useful are the following: 

 Time-series plots comparing the measured and model-predicted species concentrations. 

 Scatter plots showing model predictions on the y-axis and ambient data on the x-axis. 

 Spatial analysis plots with ambient data overlaid on model predictions. 

 Bar plots comparing the mean fractional bias (MFB) or mean fractional error (MFE) performance 

metrics 

 “Bugle plots” showing how model performance varies as a function of the PM species 

concentration. 

 Stacked-bar plots of contributions to light extinction for the average of the best-20% visibility 

days or the worst-20% visibility days at each site; the higher the light extinction, the lower the 

visibility. 

Examples of each of these MPE metrics and analysis products can be found in Tonnesen, G. et al., 2006. 

The results of the MPE are available from the WRAP RMC website 

(http://pah.cert.ucr.edu/aqm/308/eval.shtml) 

2002 Planning Scenario 

The 2000-04 baseline period planning case scenario is referred to as “Plan02”. The purpose of the Plan02 

scenario is to simulation the air quality representative of baseline emission patterns based on average, or 

Appendix C – Arizona Regional Haze SIP 

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“typical”, conditions. This scenario provides a basis for comparison with the future year 2018 scenario 

based on projected emissions, as well as to gauge reasonable progress with respect to future year 

visibility. 

Plan02 Simulations Input Data 

Input data used for the 2002 Planning model simulations consisted of the same meteorology as for the 

2002 Base Case and the Plan02 emission inventories described under the Emissions Modeling section of 

the TSS. 

The setup of the CMAQ model (including science options, run scripts, simulation periods, and ancillary 

data) for the Plan02 cases was identical to that used in the Base02 modeling, as described in the 2002 

MPE report (Tonnesen et al., 2006). In summary, CMAQ v4.5 (released by EPA in October 2005) was 

used on the RPO Unified 36-km domain. The Carbon Bond Mechanism version 4 (CB4) with RADM 

aqueous chemistry, the SORGAM organic aerosol algorithm, and all other science algorithms detailed in 

Tonnesen et al., 2006 were used. Initial condition (IC) data for January 1, 2002, were developed using a 

15-day spin-up period (December 16-31, 2001). Boundary condition (BC) data were generated in an 

annual simulation of the global-scale GEOS-Chem model that was completed by Jacob et al. (http://wwwas.harvard.edu/chemistry/trop/geos/) for calendar year 2002. 

Comparison With Base02 Simulations

For each of the three Plan02 emissions datasets, annual visibility modeling was performed using the 

CMAQ model. This was a key aspect of the QA procedure, since errors in the emissions inventories that 

might not be apparent during the emissions QA steps might be more readily detected in the results from 

the CMAQ modeling. 

In our initial analysis of the Plan02 scenario, plots were prepared for QA purposes that compared the 

Plan02a CMAQ results with the Base02a CMAQ results for daily and monthly averages. After revising 

Plan02a to create Plan02b and Plan02c, additional QA plots were prepared to compare the CMAQ results 

of each revised Plan02 case to the previous iteration. These were prepared as Program for the Analysis 

and Visualization of Environmental data (PAVE) spatial plots showing the change in individual PM2.5

species concentrations as daily, monthly, and annual averages. The final set of analysis products, 

available on the RMC web site, include PAVE difference plots comparing the CMAQ-predicted annual 

average species concentrations from the Plan02c case with those from the Base02b case. Note that these 

plots are not useful for visibility planning purposes, but are being provided to show the magnitudes of 

changes when moving from the 2002 Base Case to the 2002 Planning Case—in other words, from the 

actual emissions for the year 2002 to the “typical-year” emissions created for the final Plan02 scenario. 

The primary analysis “product” from the Plan02 CMAQ modeling is the use of its output in combination 

with the CMAQ output from the 2018 modeling to develop the visibility progress calculations and glide 

path plots, described below. 

2018 Model Simulations 

The 2018 future-year base case scenario is referred to as “2018 Base Case” or “Base18”. The purpose of 

the Base18 scenario is to simulation the air quality representative of conditions in future year 2018 with 

respect to sources of criteria and particulate matter air pollutants, taking into consideration growth and 

controls. Modeling results based on this emission inventory are used to define the future year ambient air 

quality and visibility metrics. 

Appendix C – Arizona Regional Haze SIP 

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Base18 Simulation Input Data 

Input data used for the 2018 Base Case model simulations consisted of the same meteorology as for the 

2002 Base Case and the Base18 emission inventories described under the Emissions Modeling section of 

the TSS. 

The setup of the CMAQ model (including science options, run scripts, simulation periods, and ancillary 

data) for the Base18 cases was identical to that used in the Base02 modeling, as described in the 2002 

MPE report (Tonnesen et al., 2006). In summary, CMAQ v4.5 (released by EPA in October 2005) was 

used on the RPO Unified 36-km domain. The Carbon Bond Mechanism version 4 (CB4) with RADM 

aqueous chemistry, the SORGAM organic aerosol algorithm, and all other science algorithms detailed in 

Tonnesen et al., 2006 were used. Initial condition (IC) data for January 1, 2002, were developed using a 

15-day spin-up period (December 16-31, 2001). Boundary condition (BC) data were generated in an 

annual simulation of the global-scale GEOS-Chem model that was completed by Jacob et al. (http://wwwas.harvard.edu/chemistry/trop/geos/) for calendar year 2002. 

Base18 Simulation Results

The purpose of modeling 2018 visibility is to compare the 2018 visibility predictions to the 2002 typicalyear visibility modeling results, as discussed below. Some improvements in visibility by 2018 are 

expected because of reductions in emissions due to currently planned regulations and technology 

improvements. A brief summary is provided here of the comparison between the 2018 and 2002 results 

using annual average PAVE spatial plots. The goal of this summary is to convey the scale and spatial 

extent of changes in key PM2.5 species from 2002 to 2018. For planning purposes, on the other hand, 

states and tribes should focus on the visibility projections and glide path calculations at individual Class I 

Areas. 

Figures 1 through 4 show the annual average concentrations for sulfate, nitrate, PM2.5 and modelreconstructed visibility (in deciviews), respectively. In each figure, the bottom two plots show the 

modeled concentration or deciviews for the Plan02b and Base18b cases, while the top plot shows the 

change in visibility calculated as Base18b minus Plan02b. The Plan02b results are presented here instead 

of Plan02c results because these plots had previously been prepared with version B. As the differences 

between Plan02b and Plan02c are extremely small, new plots prepared using Plan02c would be 

essentially identical to the results in Figure 1 through 4. 

In each of the top plots in the four figures, cool colors indicate areas in which model-predicted visibility 

improved from 2002 to 2018, while warm colors indicate areas where modeled visibility became worse 

over that period. Figure 1 shows that reductions in sulfate were largest in the southwest corner of the 

WRAP region and in Texas and Oklahoma. This results from planned SOx emissions reductions in the 

CENRAP region. There were smaller reductions in sulfate in the Los Angeles area, western Washington 

state, and southern Nevada. There were small increases of sulfate, mostly in Wyoming, due to growth in 

SOx emissions. Most regions of the WRAP domain had low concentrations of sulfate in 2002 and little 

change in sulfate by 2018. 

Figure 2 shows the results for nitrate. In the both 2002 and 2018, the modeled nitrate was greatest in 

California, and there were reduction in nitrate in that state in 2018 because of reductions in mobile-source 

NOx emissions. There were small reductions in the Phoenix area as well, also from reductions in mobilesource NOx emissions. 

Figure 3 shows the comparison of PM2.5 for 2002 and 2018. In most areas of the WRAP region, changes 

in PM2.5 were less than 1 g/m3

. Locations with increases in PM2.5 correspond to areas of increased 

Appendix C – Arizona Regional Haze SIP 

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sulfate (see Figure 3-1). Areas with the largest reductions in PM2.5 were the areas in California that had 

large reductions in modeled nitrate in 2018 (see Figure 3-2). Results for other species that contribute to 

PM2.5 are available on the RMC web site at 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#base18bvsplan02b. 

Figure 4 compares model-reconstructed visibility for 2002 and 2018. Note that these results are 

calculated using the modeled relative humidity (RH), so they differ from the results that use site-specific 

monthly average RH. Nonetheless, the results in Figure 4 are indicative of the direction and magnitude of 

visibility changes in from 2002 to 2018. Although the largest improvements are in California and the 

Pacific Northwest, there were improvements throughout the WRAP region. The change in deciviews is 

more dramatic than the change in PM2.5 mass (Figure 3) because the visibility in deciviews is a relative 

metric, so small mass changes in PM2.5 in good visibility areas can result in large relative improvements 

in visibility. 

Appendix C – Arizona Regional Haze SIP 

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Figure 1 – Annual average aerosol sulfate (ASO4) concentration comparisons between Base18b and 

Plan02b. Top plot: difference between the two (Base18b – Plan02b); 

bottom left plot: Plan02b results; bottom right plot: Base18b results. 

Appendix C – Arizona Regional Haze SIP 

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Figure 2 – Annual average aerosol nitrate (ANO3) concentration comparisons between Base18b 

and Plan02b. Top plot: difference between the two (Base18b – Plan02b); 

bottom left plot: Plan02b results; bottom right plot: Base18b results. 

Appendix C – Arizona Regional Haze SIP 

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Figure 3 – Annual average PM2.5 concentration comparisons between Base18b 

and Plan02b. Top plot: difference between the two (Base18b – Plan02b); 

bottom left plot: Plan02b results; bottom right plot: Base18b results. 

Appendix C – Arizona Regional Haze SIP 

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Figure 4 – Annual average deciview comparisons between Base18b and Plan02b. 

Top plot: difference between the two (Base18b – Plan02b); bottom left 

plot: Plan02b results; bottom right plot: Base18b results. 

Appendix C – Arizona Regional Haze SIP 

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Visibility Projections 

The Regional Haze Rule (RHR) goals include achieving natural visibility conditions at 156 Federally 

mandated Class I areas by 2064. In more specific terms, that RHR goal is defined as (1) visibility 

improvement toward natural conditions for the 20% of days that have the worst visibility (termed “20% 

worst,” or W20%, visibility days) and (2) no worsening in visibility for the 20% of days that have the best 

visibility (“20% best,” or B20%, visibility days). One component of the states’ demonstration to EPA 

that they are making reasonable progress toward this 2064 goal is the comparison of modeled visibility 

projections for the first milestone year of 2018 with what is termed a uniform rate of progress (URP) goal. 

As explained in detail below, the 2018 URP goal is obtained by constructing a “linear glide path” (in 

deciviews) that has at one end the observed visibility conditions during the mandated five-year (2000-

2004) baseline period and at the other end natural visibility conditions in 2064; the visibility value that 

occurs on the glide path at year 2018 is the URP goal. 

Preliminary WRAP 2018 visibility projections have been made using the Plan02c and Base18b CMAQ 

36-km modeling results, following EPA guidance that recommends applying the modeling results in a 

relative sense to project future-year visibility conditions (U.S. EPA, 2001, 2003a, 2006). Projections are 

made using relative response factors (RRFs), which are defined as the ratio of the future-year modeling 

results to the current-year modeling results. The calculated RRFs are applied to the baseline observed 

visibility conditions to project future-year observed visibility. These projections can then be used to 

assess the effectiveness of the simulated emission control strategies that were included in the future-year 

modeling. The major features of EPA’s recommended visibility projections are as follows (U.S. EPA, 

2003a,b, 2006): 

 Monitoring data should be used to define current air quality. 

 Monitored concentrations of PM10 are divided into six major components; the first five are 

assumed to be PM2.5 and the sixth is PM2.5-10. 

o SO4 (sulfate) 

o NO3 (particulate nitrate) 

o OC (organic carbon) 

o EC (elemental carbon) 

o OF (other fine particulate or soil) 

o CM (coarse matter). 

 Models are used in a relative sense to develop RRFs between future and current predicted 

concentrations of each component. 

 Component-specific RRFs are multiplied by current monitored values to estimate future 

component concentrations. 

 Estimates of future component concentrations are consolidated to provide an estimate of future 

air quality. 

 Future estimated air quality is compared with the goal for regional haze to see whether the 

simulated control strategy would result in the goal being met. 

 It is acceptable to assume that all measured sulfate is in the form of ammonium sulfate 

[(NH4)2SO4] and all particulate nitrate is in the form of ammonium nitrate [NH4NO3]. 

To facilitate tracking the progress toward visibility goals, two important visibility parameters are required 

for each Class I area: 

 Baseline Conditions: “Baseline Conditions” represent visibility for the B20% and W20% days for 

the initial five-year baseline period of the regional haze program. Baseline Conditions are 

calculated using monitoring data collected during the 2000-2004 five-year period and are the 

Appendix C – Arizona Regional Haze SIP 

Page 27 of 47 
 Natural Conditions: “Natural Conditions,” the RHR goal for 2064 for the Federally mandated 

Class I areas, represent estimates of natural visibility conditions for the B20% and W20% days at 

a given Class I area. 

Baseline Conditions

Baseline Conditions for Class I areas are calculated using fine and coarse PM concentrations measured at 

Interagency Monitoring of Protected Visual Environments (IMPROVE) monitors (Malm et al., 2000). 

Each Class I area in the WRAP domain has an associated IMPROVE PM monitor. The IMPROVE 

monitors do not measure visibility directly, but instead measure speciated fine particulate (PM2.5) and 

total PM2.5 and PM10 mass concentrations from which visibility is calculated using the IMPROVE aerosol 

extinction equation, discussed later. 

Visibility conditions are estimated starting with the IMPROVE 24-h average PM mass measurements 

related to six PM components of light extinction: 

 Sulfate [(NH4)2SO4] 

 Particulate nitrate [(NH4NO3] 

 Organic matter [OMC] 

 Light-absorbing carbon [LAC] or elemental carbon [EC] 

 Soil 

 Coarse matter [CM] 

The IMPROVE monitors do not directly measure some of these species, so assumptions are made as to 

how the IMPROVE measurements can be adjusted and combined to obtain these six components. For 

example, sulfate and particulate nitrate are assumed to be completely neutralized by ammonium and only 

the fine mode (PM2.5) is speciated to obtain sulfate and nitrate measurements (that is, any coarse-mode 

sulfate and nitrate in the real atmosphere may be present in the IMPROVE CM measurement). 

Concentrations for the above six components of light extinction in the IMPROVE aerosol extinction 

equation are obtained from the IMPROVE measured species using the formulas shown in Table 1. 

Table 1. Definition of IMPROVE components from measured species. 

IMPROVE 

Component Calculation of Component from IMPROVE Measured Species 

Sulfate 1.375 x (3 x S) 

Nitrate 1.29 x NO3

-

OMC 1.4 x OC 

LAC EC 

Soil (2.2 x Al) + (2.49 x Si) + (1.63 x Ca) + (2.42 x Fe) + (1.94 x Ti) 

CM MT – MF 

Appendix C – Arizona Regional Haze SIP 

Page 28 of 47 
Where 

 S is elemental sulfur as determined from proton-induced x-ray emissions (PIXE) analysis of the 

IMPROVE Module A. To estimate the mass of the sulfate ion (SO4

=

), S is multiplied by 3 to 

account for the presence of oxygen. If S is missing then the sulfate (SO4) measured by ion 

chromatography analysis of Module B is used to replace (3 x S). For the IMPROVE aerosol 

extinction calculation, sulfate is assumed to be completely neutralized by ammonium (1.375 x 

SO4). 

 NO3

-

 is the particulate nitrate measured by ion chromatography analysis of Module B. For the 

IMPROVE aerosol extinction calculation, it is assumed to be completely neutralized by 

ammonium (1.29 x NO3). 

 The IMPROVE organic carbon (OC) measurements are multiplied by 1.4 to obtain organic matter 

(OMC), which adjusts the OC mass for other elements assumed to be associated with OC. 

 Elemental carbon (EC) is also referred to as light-absorbing carbon (LAC). 

 Soil is determined as a sum of the masses of those elements (measured by PIXE) predominantly 

associated with soil (Al, Si, Ca, Fe, K, and Ti), adjusted to account for oxygen associated with the 

common oxide forms. Because K is also a product of the combustion of vegetation, it is 

represented in the formula by 0.6 x Fe and is not shown explicitly. 

 MT and MF are total PM10 and PM2.5 mass, respectively. 

Associated with each PM species is an extinction efficiency that converts concentrations (in g/m3

) to 

light extinction (in inverse megameters, Mm-1), as listed below. Sulfate and nitrate are hygroscopic, so 

relative humidity (RH) adjustment factors, f(RH), are used to increase the particles’ extinction efficiency 

with increasing RH; this accounts for the particles’ taking on water and having greater light scattering. 

Note that some organic matter (OMC) compounds may also have hygroscopic properties, but the 

IMPROVE aerosol extinction equation assumes OMC is nonhygroscopic. 

βSulfate = 3 x f(RH) x [sulfate] 

βNitrate = 3 x f(RH) x [nitrate] 

βOM = 4 x [OMC] 

βEC = 10 x [EC] 

βSoil = 1 x [soil] 

βCM = 0.6 x [CM] 

The total light extinction (βext) is assumed to be the sum of the light extinctions due to the six PM species 

listed above plus Rayleigh (blue sky) background extinction (βRay), which is assumed to be 10 Mm-1. This 

is reflected in the IMPROVE extinction equation: 

βext = βRay + bSulfate + βNitrate + βEC +βOMC + βSoil + βCM

The total light extinction (βext) in Mm-1 is related to visual range (VR) in kilometers using the following 

relationship: 

VR = 3912 / βext

The RHR requires that visibility be expressed in terms of a haze index (HI) in units of deciview (dv), 

which is calculated as follows: 

HI = 10 ln(βext/10) 

Appendix C – Arizona Regional Haze SIP 

Page 29 of 47 
The equations above, with measurements from the associated IMPROVE monitor, are used to estimate 

the daily average visibility at each Class I area for each IMPROVE monitored day. For each year from 

the 2000-2004 baseline period, these daily average visibility values are then ranked from highest to 

lowest. The “worst days” visibility for each of the five years in the baseline period is defined as the 

average visibility across the 20% worst-visibility days (highest deciview values); similarly, the “best 

days” visibility is defined as the average visibility across the 20% best-visibility days (lowest deciview 

values) for each year. The Baseline Conditions for the best and worst days are defined as the five-year 

average of the B20% visibility days and of the W20% visibility days, respectively, across the five-year 

baseline period. 

The set of equations given above for relating measured PM species to visibility (light extinction) are 

referred to as the “Old IMPROVE” equation. The IMPROVE Steering Committee has developed a “New 

IMPROVE” equation that they believe better represents the fit between measured PM species 

concentrations and visibility impairment. Although conceptually similar to the Old IMPROVE equation, 

the New IMPROVE equation includes updates to many of the parameters and the addition of extinctions 

due to NO2 absorption and sea salt. 2018 visibility projections and comparisons with the URP glide path 

goals were performed using both the New and Old IMPROVE equations. The reader is referred 

elsewhere for details on the New IMPROVE extinction equation (e.g., EPA, 2006a,b). 

Mapping Model Results to IMPROVE Measurements

As noted above, future-year visibility at Class I areas is projected by using modeling results in a relative 

sense to scale current observed visibility for the B20% and W20% visibility days. This scaling is done 

using RRFs, the ratios of future-year modeling results to current-year results. Each of the six components 

of light extinction in the IMPROVE reconstructed mass extinction equation is scaled separately. Because 

the modeled species do not exactly match up with the IMPROVE measured PM species, assumptions 

must be made to map the modeled PM species to the IMPROVE measured species for the purpose of 

projecting visibility improvements. For example, in the model’s chemistry (which explicitly simulates 

ammonium), sulfate may or may not be fully neutralized; the IMPROVE extinction equation, on the other 

hand, assumes that observed sulfate is fully neutralized by ammonium. For the CMAQ v4.5 model 

(September 2005 release) used in the WRAP RMC modeling, the mapping of modeled species to 

IMPROVE measured PM species is listed in Table 2.

Table 2 – Mapping of CMAQ v4.5 modeled species concentrations 

to IMPROVE measured components. 

IMPROVE 

Component CMAQ V4.3 Species 

Sulfate 1.375 x (ASO4J + ASO4I) 

Nitrate 1.29 x (ANO3J + ANO3I) 

OMC AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI 

LAC AECJ + AECI 

Soil A25J + A25I 

CM ACORS + ASEAS + ASOIL 

Projecting Visibility Changes Using Modeling Results

Appendix C – Arizona Regional Haze SIP 

Page 30 of 47 
RRFs calculated from modeling results can be used to project future-year visibility. For the current 

modeling efforts, RRFs are the ratio of the 2018 modeling results to the 2002 modeling results, and are 

specific to each Class I area and each PM species. RRFs are applied to the Baseline Condition observed 

PM species levels to project future-year PM levels, which are then used with the IMPROVE extinction 

equation listed above to assess visibility. The following six steps are used to project future-year visibility 

for the B20% and W20% visibility days (the discussion below is for W20% days but also applies to 

B20% days): 

1. For each Class I area and each monitored day, daily visibility is ranked using IMPROVE data and 

IMPROVE extinction equation for each year from the five-year baseline period (2000-2004) to 

identify the W20% visibility days for each year. 

2. Use an air quality model to simulate a base-year period (ideally 2000-2004, but in reality just 

2002) and a future year (e.g., 2018), then apply the resulting information to develop Class-I-areaspecific RRFs for each of the six components of light extinction in the IMPROVE aerosol 

extinction equation. 

3. Multiply the RRFs by the measured 24-h PM data for each day from the W20% days for each 

year from the five-year baseline period to obtain projected future-year (2018) 24-h PM 

concentrations for the W20% days. 

4. Compute the future-year daily extinction using the IMPROVE aerosol extinction equation and the 

projected PM concentrations for each of the W20% days in the five-year baseline from Step 3. 

5. For each of the W20% days within each year of the five-year baseline, convert the future-year 

daily extinction to units of deciview and average the daily deciview values within each of the five 

years separately to obtain five years of average deciview visibility for the W20% days. 

6. Average the five years of average deciview visibility to obtain the future-year visibility Haze 

Index estimate that is compared with the 2018 progress goal. 

In calculating the RRFs, EPA draft guidance (U.S. EPA, 2001, 2006a) recommends selecting modeled 

PM species concentrations “near” the monitor by taking a spatial average of PM concentrations across a 

grid-cell-resolution–dependent NX by NY array of cells centered on the grid containing the monitor. For 

the WRAP 36-km CMAQ modeling, the model estimates for just the grid cell containing the monitor are 

used (i.e., NX=NY=1). 

For the preliminary 2018 visibility projections, results are presented only for “Method 1,” which is the 

recommended approach in EPA’s draft modeling guidance documents (U.S. EPA, 2001, 2006a). In the 

Method 1 Average RRF Approach, an average RRF for the W20% days from 2002 (Modeled Worst 

Days) is obtained for the Plan02c and the Base18b CMAQ simulations by averaging the PM 

concentration components across the Modeled Worst Days and then calculating the (future year):(base 

year) ratio of the average PM concentrations. For example, if SO4i,j is the measured sulfate 

concentrations at Class I area j for the i=1,...,N 20% worst visibility days in 2002, then the RRF for 

sulfate on the W20% days would be obtained as: 















   N

i

ij

N

i

ij

N

i

ij

N

i

ij

j

SO

SO

SO

N

SO

N SORRF

1

1

1

1

)2002(4

)2018(4

)2002(4 1

)2018(4 1

)4(

For each Class I area and each of the W20% days, the average RRF for each PM component would be 

applied to concentrations for the W20% days from the 2000-2004 baseline period to estimate future-year 

PM concentrations for each of the W20% days. Extinction and HI would then be calculated to obtain the 

projected future-year visibility conditions using the procedures given previously. 

Appendix C – Arizona Regional Haze SIP 

Page 31 of 47 
Glide Path to Natural Conditions

The presumptive visibility target for 2018 is the URP goal that is obtained by constructing a linear glide 

path from the current Baseline Conditions to Natural Conditions in 2064 (both expressed in deciviews). 

For instance, Figure 5 displays an example visibility glide path for the Grand Canyon National Park 

(GRCA) Class I area. EPA’s default Natural Conditions value for the W20% days (U.S. EPA, 2003b), 

shown as the green line, is the 2064 visibility goal at GRCA of 6.95 dv. The blue diamonds at the left of 

the plot are the annual average current conditions, based on IMPROVE observations for the W20% days 

as obtained from the Visibility Information Exchange Web System (VIEWS) web site 

(http://vista.cira.colostate.edu/views/). These annual average visibility values for the 20% worst days 

allow an assessment of trends and the year-to-year variation in visibility. The Baseline Conditions are the 

average of the W20% visibility from 2000-2004, which is the starting point for the glide path in 2004 

(12.04 dv for GRCA). A linear URP from the Baseline Conditions in 2004 to Natural Conditions in 2064 

(sloping pink line with triangles) is assumed, and the value on the glide path at 2018 is the presumptive 

URP visibility target that the modeled 2018 projections are compared against to judge progress. In this 

example, the visibility progress goal in 2018 would be 10.85 dv. Meeting this would require a 1.19 dv 

reduction in visibility by 2018 to meet that milestone year’s visibility progress target at the Grand Canyon 

National Park. 

Uniform Rate of Reasonable Progress Glide Path

Grand Canyon NP - 20% Worst Days

12.04 11.70 10.85 10.01 9.16

8.31 7.46 6.95

11.71

0

5

10

15

20

25

30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Figure 5 – Example of URP glide path using IMPROVE data from the Grand Canyon 

National Park for the W20% days and comparison with Base18b visibility projections. 

Appendix C – Arizona Regional Haze SIP 

Page 32 of 47 
Preliminary Visibility Projection Results

For all of the WRAP Class I areas, the RMC performed preliminary 2018 visibility projections and 

compared them to the 2018 URP goals using the Plan02c and Base18b CMAQ modeling results and the 

Old and New IMPROVE equations. As an example, Figure 5 above compares the Base18b visibility 

projections with the URP goal based on the glide path for GRCA and the Old IMPROVE equation. To 

achieve the 2018 URP goal, the modeled 2018 visibility projection would have to show a 1.19 dv 

(=12.04-10.85) reduction. However, the modeled 2018 visibility projection shows only a 0.33 dv 

(=12.04-11.71) reduction by 2018, which indicates that the emission controls simulated in case Base18b 

would not achieve the modeled URP goal; the 2018 visibility projection achieves only 28% of the goal 

(28% = 100 x 0.33/1.19). Figure 6 displays the 2018 visibility projections for all WRAP Class I areas, 

using both the Old and New IMPROVE equations, expressed as a percentage of achieving the URP goal, 

with values of 100% or greater achieving the goal. Using the procedures outlined above, none of the 

WRAP Class I areas are projected to achieve their URP goals. There are various reasons for this, such as 

the presence of W20% days that are dominated by emissions from sources that are not controllable, such 

as wildfires, dust, and/or international transport. Additional analysis of these results and alternative 

projection techniques are currently under study. 

Appendix C – Arizona Regional Haze SIP 

Page 33 of 47 
Method 1 predictions for Colorado Plateau and Desert Southwest sites

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

BRCA1

CANY1

GRCA2

MEVE1

SAPE1

WEMI1

ZION1

BAND1BOAP1

CHIR1

GICL1

GRSA1

IKBA1

SACR1

SAGU1

SIAN1

WHIT1

WHPE1

Percent of target reduction achieved

base18b old algorithm

base18b new algorithm

Colorado Plateau Desert Southwest

Method 1 predictions for North, Great Basin and Rockies sites

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

BADL1

CABI1

GAMO1

LOST1

MELA1

MONT1

SULA1

THRO1

ULBE1

WICA1

CRMO1

JARB1

SAWT1

BRID1

NOAB1

ROMO1

WHRI1

YELL2

Percent of target reduction achieved

base18b old algorithm

base18b new algorithm

North Great Basin Rockies

Method 1 predictions for Pacific Northwest and California sites

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

HECA1

KALM1

MOHO1

MORA1

NOCA1

OLYM1

PASA1

SNPA1

STAR1

THSI1

WHPA1

AGTI1

BLIS1

DOME1

HOOV1

JOSH1LAVO1

PINN1

REDW1

SAGA1

SAGO1

YOSE1

Percent of target reduction achieved

base18b old algorithm

base18b new algorithm

Pacific Northwest California

Figure 6 – 2018 visibility projections at WRAP Class I areas expressed as a 

percent of achieving the 2018 URP goal using the Old and New IMPROVE 

equation and the WRAP Base18c CMAQ 36-km modeling results. 

Appendix C – Arizona Regional Haze SIP 

Page 34 of 47 
PM Source Apportionment 

Impairment of visibility in Class I areas is caused by a combination of local air pollutants and regional 

pollutants that are transported long distances. To develop effective visibility improvement strategies, the 

WRAP member states and tribes need to know the relative contributions of local and transported 

pollutants, and which emissions sources are significant contributors to visibility impairment at a given 

Class I area. 

A variety of modeling and data analysis methods can be used to perform source apportionment of the PM 

observed at a given receptor site. Model sensitivity simulations have been used in which a “base case” 

model simulation is performed and then a particular source is “zeroed out” of the emissions. The 

importance of that source is assessed by evaluating the change in pollutants at the receptor site, calculated 

as pollutant concentration in the sensitivity case minus that in the base case. This approach is known as a 

“brute force” sensitivity because a separate model run is required for each sensitivity. 

An alternative approach is to implement a mass-tracking algorithm in the air quality model to explicitly 

track for a given emissions source the chemical transformations, transport, and removal of the PM that 

was formed from that source. Mass tracking methods have been implemented in both the CMAQ and 

CAMx air quality models. Initial work completed by the RMC during 2004 used the CMAQ Tagged 

Species Source Apportionment (TSSA) method. Unfortunately, there were problems with mass 

conservation in the version of CMAQ used in that study, and these affected the TSSA results. A similar 

algorithm has been implemented in CAMx, the PM Source Apportionment Technology (PSAT). 

Comparisons of TSSA and PSAT showed that the results were qualitatively similar, that is, the relative 

ranking of the most significant source contributors were similar for the two methods. However, the total 

mass contributions differed. With separate funding from EPA, UCR has implemented a version of TSSA 

in the new CMAQ release (v4.5) that corrects the mass conservation error, but given the uncertainty of 

the availability of this update, the CAMx/PSAT source apportionment method was used for the WRAP 

modeling analysis. 

The main objective of applying CAMx/PSAT is to evaluate the regional haze air quality for typical 2002 

(Plan02c) and future-year 2018 (Base18b) conditions. These results are used: 

 to assess the contributions of different geographic source regions (e.g., states) and source 

categories to current (2002) and future (2018) visibility impairment at Class I areas, to obtain 

improved understanding of (1) the causes of the impairment and (2) which states are included in 

the area of influence (AOI) of a given Class I area; and 

 to identify the source regions and emissions categories that, if controlled, would produce the 

greatest visibility improvements at a Class I area. 

 

CAMx/PSAT

The PM Source Apportionment Technology performs source apportionment based on user-defined source 

groups. A source group is the combination of a geographic source region and an emissions source 

category. Examples of source regions include states, nonattainment areas, and counties. Examples of 

source categories include mobile sources, biogenic sources, and elevated point sources; PSAT can even 

focus on individual sources. The user defines a geographic source region map to specify the source 

regions of interest. He or she then inputs each source category as separate, gridded low-level emissions 

and/or elevated-point-source emissions. The model then determines each source group by overlaying the 

source categories on the source region map. For further information, please refer to the white paper on 

the features and capabilities of PSAT 

Appendix C – Arizona Regional Haze SIP 

Page 35 of 47 
(http://pah.cert.ucr.edu/aqm/308/reports/PSAT_White_Paper_111405_final_draft1.pdf), with additional 

details available in the CAMx user’s guide (ENVIRON, 2005; http://www.camx.com). 

PM source apportionment modeling was performed for aerosol sulfate (SO4) and aerosol nitrate (NO3) 

and their related species (e.g., SO2, NO, NO2, HNO3, NH3, and NH4). The PSAT simulations include 9 

tracers, 18 source regions, and 6 source groups. The computational cost for each of these species differs 

because additional tracers must be used to track chemical conversions of precursors to the secondary PM 

species SO4, NO3, NH4, and secondary organic aerosols (SOA). Table 3 summarizes the computer run 

time required for each species. The practical implication of this table for WRAP is that it is much more 

expensive to perform PSAT simulations for NO3 and especially for SOA than it is to perform simulations 

for other species. 

Table 3. Benchmarks for PSAT computational costs for each PM species. 

Run time is for one day (01/02/2002) on the WRAP 36-km domain. 

Species No. of Species 

Tracers 

RAM 

Memory 

Disk Storage 

per Day 

Run Time with

1 CPU 

SO4 2 1.6 GB 1.1 GB 4.7 h/day 

NO3 7 1.7 GB 2.6 GB 13.2 h/day 

SO4 and NO3

combined 

9 1.9 GB 3.3 GB 16.8 h/day 

SOA 14 6.8 GB Not tested Not tested 

Primary PM 

species 

6 1.5 GB 3.0 GB 10.8 h/day 

Two annual 36-km CAMx/PSAT model simulations were performed: one with the Plan02c typical-year 

baseline case and the other with the Base18b future-year case. It is expected that the states and tribes will 

use these results to assess the sources that contribute to visibility impairment at each Class I Area, and to 

guide the choice of emission control strategies. The RMC web site includes a full set of source 

apportionment spatial plots and receptor bar plots for both Plan02b and Base18b. These graphical 

displays of the PSAT results, as well as additional analyses of these results are available on the TSS under 

http://vista.cira.colostate.edu/tss/Tools/ResultsSA.aspx

CAMx/PSAT 2002 and 2018 Setup

PSAT source apportionment simulations for 2002 and 2018 were performed using CAMx v4.30. Table 4 

lists overall specifications for the 2002 PSAT simulations. The domain setup was identical to the 

standard WRAP CMAQ modeling domain. The CAMx/PSAT run-time options are shown in Table 5. 

The CAMx/PSAT computational cost for one simulation day with source tracking for sulfate (SO4) and 

nitrate (NO3) is approximately 14.5 CPU hours with an AMD Opteron CPU. The source regions used in 

the PSAT simulations are shown in Figure 7 and Table 4. The six emissions source groups are described 

in Table 6. The development of these emissions data are described in more detail below. 

The annual PSAT run was divided into four seasons for modeling. The initial conditions for the first 

season (January 1 to March 31, 2002) came from a CENRAP annual simulation. For the other three 

seasons, we allowed 15 model spin-up days prior to the beginning of each season. Based on the chosen 

set of source regions and groups, with nine tracers, and with a minimum requirement of 87,000 point 

sources and a horizontal domain of 148 by 112 grid cells with 19 vertical layers, the run-time memory 

requirement is 1.9 GB. Total disk storage per day is approximately 3.3 GB. Although the RMC’s 

Appendix C – Arizona Regional Haze SIP 

Page 36 of 47 
computation nodes are equipped with dual Opteron CPUs with 2 GB of RAM and 1 GB of swap space, 

the high run-time memory requirements prevented running PSAT simulations using the OpenMP shared 

memory multiprocessing capability implemented in CAMx. 

Table 4 – WRAP 2002 CAMx/PSAT specifications. 

WRAP PSAT Specs Description 

Model CAMx v4.30 

OS/compiler Linux, pgf90 v.6.0-5 

CPU type AMD Opteron with 2 GB of RAM 

Source region 18 source regions; see Figure 4.1 and Table 4.4 

Emissions source groups Plan02b, 6 source groups; see Table 4.5 

Initial conditions From CENRAP (camx.v4.30.cenrap36.omp.2001365.inst.2) 

Boundary conditions 3-h BC from GEOS-Chem v2 

Table 5. WRAP CAMx/PSAT run-time options. 

WRAP PSAT specs Description 

Advection solver PPM 

Chemistry parameters CAMx4.3.chemparam.4_CF 

Chemistry solver CMC 

Plume-in-grid Not used 

Probing tool PSAT 

Dry/wet deposition TRUE (turned on) 

Staggered winds TRUE (turned on) 

Table 6 – WRAP CAMx/PSAT source regions cross-reference table. 

Source 

Region ID 

Source Region 

Description 1

Source 

Region ID 

Source Region 

Description 1

1 Arizona (AZ) 10 South Dakota (SD) 

2 California (CA) 11 Utah (UT) 

3 Colorado (CO) 12 Washington (WA) 

4 Idaho (ID) 13 Wyoming (WY) 

5 Montana (MT) 14 Pacific off-shore & Sea of Cortez (OF) 

6 Nevada (NV) 15 CENRAP states (CE) 

7 New Mexico (NM) 16 Eastern U.S., Gulf of Mexico, & 

Atlantic Ocean (EA) 

8 North Dakota (ND) 17 Mexico (MX) 

9 Oregon (OR) 18 Canada (CN) 

1 The abbreviations in parentheses are used to identify source regions in PSAT receptor bar plots. 

Appendix C – Arizona Regional Haze SIP 

Page 37 of 47 
-2736 -2412 -2088 -1764 -1440 -1116 -792 -468 -144 180 504 828 1152 1476 1800 2124 2448

-2088

-1872

-1656

-1440

-1224

-1008

-792

-576

-360

-144

72

288

504

720

936

1152

1368

1584

1800

2 6

9

12

4

5 8

13

11 3

1 7

15

15

16

16

16

18 18

14

17

14

10

Figure 7 – WRAP CAMx/PSAT source region map. Table 6 defines the source region IDs. 

Table 7 – WRAP CAMx/PSAT emissions source groups. 

Emissions 

Source 

Groups 

Low-level Sources Elevated Sources 

1 Low-level point sources (including stationary offshore) Elevated point sources (including 

stationary off-shore) 

2 Anthropogenic wildfires (WRAP only) Anthropogenic wild fires (WRAP only) 

3 Total mobile (on-road, off-road, including planes, 

trains, ships in/near port, off-shore shipping) 

4 Natural emissions (natural fire, WRAP only, 

biogenics) 

Natural emissions (natural fire, WRAP 

only, biogenics) 

5 Non-WRAP wildfires (elevated fire sources in 

other RPOs) 

Non-WRAP wild fires (elevated fire 

sources in other RPOs) 

6 Everything else (area sources, all dust, fugitive 

ammonia, non-elevated fire sources in other 

RPOs) 

PSAT Results 

The source apportionment algorithms implemented in CAMx generate output files in the same format as 

the standard modeled species concentrations files. This typically consists of a two-dimensional, gridded 

dataset of hourly-average surface concentrations for each source group tracer that gives the contribution 

of the tracer to all the surface grid cells in the model domain for each hour of the simulation. Threedimensional instantaneous concentrations are also output for the last two hours of the simulation, which 

are used to restart the model. Although there are options to output hourly 3-D average tracer 

concentrations, the model is usually configures to output only the model’s surface layer concentrations 

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because of the vast disk storage space needed for the 3-D file output for all the source group 

contributions. 

The source apportionment model results are typically presented in two ways: 

 Spatial plots showing the area of influence of a source group’s PM species contributions 

throughout the model domain, either at a given hourly-average point in time or averaged over 

some time interval (e.g., monthly average). 

 Receptor bar plots showing the rank order of source groupings that contribute to PM species at 

any given receptor site. These plots also can be at a particular point in time or averaged over 

selected time intervals—for example, the average source contributions for the 20% worst 

visibility days. 

If the 3-D tracer output files are saved, it is also possible to prepare animations of PM species plumes 

from each of the source groups. However, these plots are less useful than the others for quantitative 

analysis, are expensive to produce, and require saving 3-D hourly output, which is disk-space intensive. 

The primary products of the WRAP PSAT modeling were receptor bar plots showing the emission source 

groups that contribute the most to the model grid cells containing each IMPROVE monitoring site and 

other receptor sites identified by WRAP. 

Model Sensitivity Simulations 

A variety of sensitivity simulations were conducted by the RMC as part of their modeling efforts to 

support the WRAP in addressing the Regional Haze Rule requirements. These sensitivity simulations are 

described below. 

2002 Clean Case

There are many natural sources of ambient PM2.5, both direct emissions of primary PM2.5 (such as 

windblown dust) and emissions of gaseous species that undergo photochemical transformation or 

condensation to form secondary PM2.5. Natural sources of PM2.5 are of concern because they represent 

sources that cannot be controlled. Estimates of natural haze levels have been developed by EPA for 

visibility planning purposes and are described in Guidance for Estimating Natural Visibility Conditions 

Under the Regional Haze Rule (U.S. EPA, 2003a). These are the natural haze levels to be used in glide 

path calculations, such as those we performed as part of the visibility projections for 2018. However, the 

natural haze levels developed by EPA for glide path calculations were based on ambient data analysis, not 

on visibility modeling. This question thus arises: Would modeled levels of natural haze be consistent 

with the values estimated by EPA for visibility planning? If the natural haze levels calculated by the 

model were substantially higher than the levels used for planning purposes, this would make it more 

difficult for modeling studies to demonstrate progress in attaining visibility goals, because the model 

would predict haze levels that exceeded EPA’s natural haze levels even if all anthropogenic sources of 

PM2.5 were removed from the modeling. The RMC explored this issue by conducting a CMAQ 

sensitivity “clean conditions” simulation 

There are many uncertainties and unknowns regarding natural emissions. There have been only limited 

studies of natural emissions conditions. It is known that there are very large uncertainties in the 

categories of natural emissions included in the WRAP emissions inventories, and that some categories of 

natural emissions are not included at all. Also, it is difficult to know what truly natural emissions would 

have been like in the absence of human modifications of the environment. For example, wildfire 

emissions are a large source of natural emissions in our modeling, but how much larger might that source 

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be in the absence of fire suppression efforts? For all of these reasons, it was decided to describe this 

sensitivity simulation as a “clean conditions” scenario rather than a “natural conditions” scenario. In this 

simulation, all anthropogenic emissions were removed from the inventory and only those emissions that 

were defined as biogenic in the 2002 base case (Base02) were included. Thus, this model simulation does 

not represent true natural conditions. It indicates instead the lowest haze levels that could be achieved in 

the model if all anthropogenic emissions were zeroed out. 

Emission Inventories 

The emissions for the clean 2002 sensitivity case were derived from case Base02a. Because it was a 

sensitivity analysis to test the impacts of natural emissions sources on visibility, it is referred to it as 

scenario Base02nt, where “nt” refers to natural. The following emissions categories in Base02nt were 

included: 

 Biogenics: Generated in case Base02a by BEIS3.12 using SMOKE. 

 WRAP Ammonia: The Base02a ammonia emissions for the WRAP region were developed with a 

GIS by ENVIRON. The five emissions category modeled included three anthropogenic sources 

(domestic animals, livestock, and fertilizer application) and two natural sources (soils and 

wildlife). Only the two natural sources in scenario Base02nt were used. 

 CENRAP and MRPO Ammonia: To create ammonia inventory files for only natural sources, we 

used a list of SCCs representing natural sources to extract the emissions records of these sources 

from the monthly inventory files that were used in Base02a. It was found that there were no 

natural ammonia sources in the MRPO monthly inventory files. 

 Natural Area Sources: The Base02a area-source inventory files included natural sources, such as 

wildfires and wild animals. These records were extracted from the stationary-area-source 

inventories. Note that the WRAP area-source files did not include any natural sources. 

 Natural Fires: Of the five fire categories modeled in Base02a (wildfires, wildland fire use, nonFederal rangeland prescribed fires, prescribed fires [which were split into natural and 

anthropogenic prescribed for this purpose of this sensitivity], and agricultural fires), only the 

categories that represent natural fires (wildfires, wildland fire use, and natural prescribed fires) 

were included. 

 Windblown Dust: We used the windblown dust inventory that ENVIRON and the RMC 

developed for use in case Base02a. Additional details on this dust inventory are available at 

http://www.cert.ucr.edu/aqm/308/wb_dust2002/wb_dust_ii_36k.shtml. 

The biogenic and windblown dust emissions from the Base02a SMOKE outputs that are stored at the 

RMC were used directly. For the fire (including both point and area fires), natural area, and ammonia 

emissions, these data were reprocessed specifically for scenario Base02nt using the same ancillary data 

(temporal, chemical, and spatial allocation data) used in case Base02a. QA plots and documentation for 

scenario Base02nt are posted on the RMC web site at 

http://pah.cert.ucr.edu/aqm/308/qa_Base02nt36.shtml. 

Modeling Results 

Figure 8 shows the model-reconstructed light extinction in the clean emissions model simulation. 

Because the natural fire emissions in the WRAP states were a major component of the clean emissions, 

the largest visibility impairment is in the regions with natural fire emissions. Contributions to light 

extinction from natural sources were small in regions without large fire emissions, as evidenced in the 

eastern U.S., where the extinction was only slightly larger (about 2 Mm-1) than perfectly clean Rayleigh 

conditions of 10 Mm-1. 

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Although there are large uncertainties in the natural emissions, and it is known that there are missing 

types of natural emissions, the components of the natural inventory used in this sensitivity simulation did 

contribute to relatively large visibility impairment in regions where there were large wildfires. Extinction 

coefficients as large as 90 Mm-1 were simulated in the southern Oregon and northern California regions; 

this was most likely a result of the large Biscuit fire in Oregon, plus contributions from smaller fires and 

other natural emissions. These visibility impairment levels exceed the natural visibility levels specified in 

the EPA regional haze natural visibility guidance document. It will thus be more difficult for the 

modeling to demonstrate attainment of progress goals in areas of the country subject to wildfires because 

of their large contribution to visibility impairment that is not controllable. In other regions of the country 

for which the inventories lacked large natural fire emissions, the modeled clean visibility was only 

slightly greater than clean Rayleigh conditions. Note the model results may be overly optimistic in these 

regions because we lack a complete, accurate natural emissions inventory. 

Figure 8 – Annual average model-reconstructed “clean conditions” visibility as extinction 

coefficient. 

These results are all very tentative because of the large uncertainties in natural emissions. Considerable 

effort would be needed to more fully investigate natural conditions in future modeling studies. It will 

always be difficult to determine and quantify “clean conditions” based on observations because of the 

pervasive influence of anthropogenic emissions. 

Also as part of this sensitivity analysis, the contributors to organic carbon aerosols (OC) for the clean 

conditions scenario were evaluated. The CMAQ model represents explicitly three classes of organic 

carbon aerosols: 

 AORGPA: Primary anthropogenic OC resulting from direct organic mass emissions, such as 

primary organic aerosol (POA). 

 AORGA: Secondary anthropogenic OC resulting from aromatic VOCs, such as xylene, toluene, 

and cresols. 

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 AORGB: Secondary biogenic OC resulting from biogenic VOCs, such as terpenes. 

Because it was not cost effective to carry out CAMx/PSAT simulations with OC, the explicit OC results 

for the clean conditions case were analyzed, and then compared those results to the Base02b case in an 

attempt to infer the relative contributions of biogenic and anthropogenic VOCs to OC. These results are 

difficult to interpret for at least two reasons: 

 Because of the simplified approach used by CMAQ and the Carbon Bond Mechanism version 4 

(CB4) to represent these species, it is not possible to accurately classify all emissions into the 

CMAQ model as either biogenic or anthropogenic based simply on the species name. Thus, some 

biogenic OC might be included with AORGA, and some anthropogenic OC might be included in 

AORB. 

 Some fire emissions are classified as anthropogenic, but these emissions might include species 

such as terpenes that are typically considered biogenic. Using the analysis approach in which all 

terpenes are assumed biogenic then incorrectly causes some anthropogenic emissions to be 

labeled biogenic when we use the simplified approach of analyzing OC in terms of AORGPA, 

AORGA and AORGB. 

In spite of these difficulties, however, the results should classify the majority of the emissions correctly as 

either biogenic or anthropogenic. 

For each of the above three components of OC, plots of the annual average mass in the Base02b case 

were prepared, and then the controllable mass was estimated as the difference between the Base02b case 

the Base02nt clean emissions scenario. Figure 9 shows the annual average mass of OC contributed from 

AORGPA in case Base02b (top) and the portion of that mass attributed to controllable emissions 

(bottom). Comparing these two plots indicates that in the western U.S. there is considerable AORGPA 

mass that is not controllable. It is likely that much of this mass is from fires, since uncontrollable 

AORGPA mass is present at the site of large fires in southern Oregon and north of Tucson, AZ. 

Figure 10 shows the annual average mass of secondary OC contributed from AORGA in the Base02b 

case (top) and the portion of that mass attributed to controllable emissions (bottom). These plots indicate 

that virtually all of the AORGA mass is controllable, since the bottom plot is almost identical to the top 

plot. 

Figure 11 shows the annual average mass of OC contributed from AORGPA in the Base02b case (top) 

and the portion of that mass attributed to controllable emissions (bottom). These plots indicate that 

although most of the AORGB mass is not controllable, a significant amount of mass is controllable. It is 

likely that the controllable AORGB mass results from VOC oxidation chemistry and the larger amount of 

biogenic mass that is oxidized and subsequently condenses to form OC in the Base02b case. These 

results indicate that controlling O3 precursor emissions is effective at reducing a small but significant 

fraction of the biogenic OC. 

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Figure 9 – Annual average modeled primary anthropogenic OC (AORGPA) in Base02b (top) and 

the portion that is “controllable” primary anthropogenic OC (bottom). 

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Figure 10 – Annual average modeled secondary anthropogenic OC (AORGA) in Base02b (top) and 

the portion that is “controllable” secondary anthropogenic OC (bottom). 

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Figure 11 – Annual average modeled primary biogenic OC (AORGB) in Base02b (top) 

and the portion that is “controllable” primary biogenic OC (bottom). 

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It might be difficult for the WRAP states and tribes to use these results quantitatively in developing 

emissions control strategies for visibility SIPs and TIPs. However, the results do provide some insight 

into the relative contributions of biogenic and anthropogenic OC as well as the amount of each that is 

controllable in the model simulations. 

Finally, it is noted that there are uncertainties in the modeled emissions of anthropogenic VOCs, and 

larger uncertainties in the modeled emissions of biogenic VOCs. It is not possible to evaluate the model 

performance individually for biogenic and anthropogenic OC because the OC measurements do not 

distinguish between those two forms. Instead, only comparisons of total modeled OC to total measured 

OC can be made. Therefore, even when the model achieves good performance for total OC, it is possible 

that the model may be overpredicting one component of total OC and underpredicting the other. The 

inability to evaluate model performance for each component of OC increases the uncertainty of the results 

described here and illustrated in Figures 9 through 11, so caution should be used when drawing 

conclusions about the sources of OC based on these results. 

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