## 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 prepari ng the Arizona Regional Haze SIP. In addition, this appendix provides background information on the Co mmunity Multi-Scale Air Quality (CMAQ) model described in Chapter 9. CMAQ wa s the primary regional modeling tool used to estimate 2018 visibility conditions in Oregon and all Western Class I areas, b ased 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 west ern states and tribes, a nd 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 su mmary 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 iden tify 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 W est. The TSS specifically summarizes results and consolidates information about air quality monito ring, 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/Monito ring/I MPROVE_Particulate_Monitoring_May_2007.doc

WRAP Data Substitution Methods: This document descr ibes 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/Monito ring/ WRAP_Data_Substitution_Methods_April_2007.doc

Algorithm: This document describes the development of natural visibility cond itions 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 am ong real-world visibility -related parameters and parameters defined by the IMPROVE program, th e 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.e du/tss/help/param eterkey.aspx or Excel at: http://vista.cira.colostate.edu/docs/ai rdata/m ethods/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 47VIEWS 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 regiona l 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 re port 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, pro cessing, 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- a nd off-road) emissions inventory was developed.
Emissions: Describes how the dust em issions inventory was developed.
Emissions: Describes how the off shor e emissions inventory was developed.
Emissions: Describes how the fire em issions inventory was developed. Mapping: This table summarizes the relationship am ong real-world visibility -related parameters and parameters defined by the IMPROVE program, th e CMAQ and CAMx models, and WRAP emissions inventories. Specific topical designations and direct co mparisons 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/ai rdata/m ethods/parametermappingguide.xls

Emissions Links and Projects

WRAP Emissions Data Mana gement System (EDMS) http://wrapedms.org/ : An emission inventory data warehouse and web-based application that provides a c onsistent 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 anal yses over the western U.S. by operating regional scale, three-dimensional air quality models that simula te 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 in terface 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, a nd 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 em issions 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 47Modeling M ethods

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 tr ajectory modeling was performed. This document is
available at http://vista.cira.colostate.edu/docs/wr ap/Modeling/BackTrajectory Modeling.doc

Mapping: This table summarizes the relationship am ong real-world visibility -related parameters and parameters defined by the IMPROVE program, th e CMAQ and CAMx models, and WRAP emissions inventories. Specific topical designations and direct co mparisons 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/ai rdata/m ethods/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 anal yses over the western U.S. by operating regional scale, three-dimensional air quality models that simula te 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 So urce 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 an d 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 poten tial 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 am ong real-world visibility -related parameters and parameters defined by the IMPROVE program, th e 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/ai rdata/m ethods/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 47Background on the CMAQ Modeling

Overview of Emissions Applied in Regional Analys is/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 simu lations summarized below. Boundary conditions reaching North America from the rest of the world we re jointed prepared by all 5 RPOs from the GEOSChem global model.

Appendix C – Arizona Regional Haze SIP Page 5 of 47Model
Scenario
& Da te 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

p lanning 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

p lanning 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 47TSS Resourc es Emissions
Emissions Methods

Emissions Overview ( http://vista.cira.colostate.edu/docs/wrap/emi ssions/EmissionsOverview.doc ) - These methods describe the collection, processing, a nd evaluation of monitoring data needed for evaluating regional haze in the WRAP region.
Point Emissions ( http://vista.cira.colostate.edu/docs/ wrap/em issions/PointEmissions.doc ) - Describes how the point emissions inventory was developed.
Area Emissions ( http://vista.cira.colostate.edu/do cs/wrap/em issions/AreaEmissions.doc ) - Describes how the area (including oil and gas) emissions inventory was developed.
Mobile Emissions ( http://vista.cira.colostate.edu/docs/wrap/e missions/MobileEmissions.doc ) - Describes how the mobile (on- and off-road) emissions inventory was developed
Dust Emissions ( http://vista.cira.colostate.edu/docs/ wrap/em issions/DustEmissions.doc ) - Describes how the dust emissions inventory was developed.
Offshore Emissions ( http://vista.cira.colostate.edu/docs/ wrap/emissions/OffshoreEm issions.doc ) -
Describes how the off shore emissi ons inventory was developed.
Fire Emissions ( http://vista.cira.colostate.edu/docs/ wrap/em issions/FireEmissions.doc ) - Describes how the fire emissions inventory was developed.

Tool: http://vista.cira.colostate.e du/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/param eterkey.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 Em issions Reduction Techniques applied to 2000- 04 average Fire emissions

 New permits and state/EPA consent agreements sinc e 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 472002 actual data reported by states, locals, tribes, a nd 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_WRA P_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 includ ed in the inventories to ameliorate the bias effects of 2002 year specific me teorology 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_WRA P_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” inve ntory. 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 co ntrols 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 equipmen t 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 emis sions may have inadvertently exceeded an enforceable emission limit (i.e., emissions were adjust ed 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/doc uments/eictts/docs/WRAP_2018_EI-Version_1- Report_Jan2006.pdf

Final revisions were made to the 2002 actual point so urce 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 Inventor y (PRP18) conducted in May 2007;

 Update the current status of point sources rela ted to the Best Available Retrofit Technology (BART) requirements under the regional haze rule; and

 Correct erroneous or missing Standard Industria l 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 sour ce 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 projecti ons 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 sour ces, removed inactive sources, etc.).

 Corrected EGU Facility IDs for those EGUs re porting 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 lim its 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 upda tes to 2018 SO2 emission projections for NonUtility Point Sources.

See: http://wrapair.org/forums/ssjf /documents/eictts/Projections /PRP18_EI_tech%20mem o_061607.pdf

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

2002 actual area source emissions invent ory 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/Oil Gas/WRAP_O il&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” inve ntory. The projection methodology included the following steps:

Adjustments:

Appendix C – Arizona Regional Haze SIP Page 9 of 47Em issions 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 PM 10 and

PM 2.5 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 co ntrols do not include impacts from any future control scenarios that have yet to be determined.

Growth Factors : SCC-specific growth factors developed fro m 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 equipmen t 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 emis sions may have inadvertently exceeded an enforceable emission limit (i.e., emissions were adjust ed 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/doc uments/eictts/docs/WRAP_2018_EI-Version_1- Report_Jan2006.pdf

Final revisions were made to the 2002 actual area s ource 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 Inventor y (PRP18) conducted in May 2007;

 Update the current status of point sources rela ted to the Best Available Retrofit Technology (BART) requirements under the regional haze rule; and

 Correct erroneous or missing Standard Industria l 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/e ictts/Oil Gas/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 47m ost representative source-specific emissions projecti ons 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 s ources, removed inactive sources, etc.)

 Corrected EGU Facility IDs for those EGUs re porting 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 lim its 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 upda tes to 2018 SO2 emission projections for NonUtility Point Sources.

See: http://wrapair.org/forums/ssjf /documents/eictts/Projections /PRP18_EI_tech%20mem o_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, ra ilroads, 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/documen ts/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/doc uments/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 Pr ograms. 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 47Ammonia, Dust, & Biogenic Emissions – these projects were commissioned by the Dust Emissions Joint Forum and the Modeling Forum. RMC prepar ed 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/em issions/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/n h3_2002_12km/nh3_2002_W RAP12.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/Sc enario_Descriptions.doc )

 Domain Daily/Monthly Total Dust Emissions ( http://newpah.cert.ucr.edu/aqm/308/wb_dus t2002/w bdust_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_dus t2002/WRAP_PhaseII _WBDust_111505.ppt )

 2002 Dust Emissions (monthly and yearly plots) ( http://newpah.cert.ucr.edu/aq m/308/wb_dust2002/scen_b_042406 )

RMC prepared land-use data-based natural biogenic em ission inventories, driven by 2002 meteorological data from the CMAQ air quality model. The land use da ta 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 Databa se version 3 (BELD3). See: http://newpah.cert.ucr.edu/aqm/308/report s/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 modeli ng 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 201 8 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 fro m 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 Page 12 of 47Regional 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 Mexi co 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 Mexi co 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 Amer ica fro m 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 we re 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 Page 13 of 47(page intenti onally blank)

Appendix C – Arizona Regional Haze SIP Page 14 of 47

### Section 2: WRAP Air Qua lity Modeling Support Document

Overview

Visibility impairment occurs when fine particulate matter (PM 2.5 ) in the atmosphere scatters and absorbs light, thereby creating haze. PM 2.5 can be emitted into the atmosphere directly as primary particulates, or it can be produced in the atmosphere from photo chemical reactions of gas-phase precursors and subsequent condensation to form secondary particulates. Examples of primary PM 2.5 include crustal materials and elemental carbon; examples of seconda ry PM include ammonium nitrate, ammonium sulfates, and secondary organic aerosols (SOA). Secondary PM 2.5 is generally smaller than primary

PM 2.5 , and because the ability of PM 2.5 to scatter light depends on partic le size, with light scattering for fine particles being greater than for coarse particles, secondary PM 2.5 plays an especially important role in visibility impairment. Moreover, the smaller secondary PM 2.5 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 PM 2.5 are difficult to quantify because of the comple x 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 effect ively improve visibility. Photochemical air quality models offer opportunity to be tter understand the sources of PM 2.5 by simulating the emissions of pollutants and the formation, transport, and deposition of PM 2.5 . If an air quality model performs well for a historical episode, the model may then be useful for identifying the sources of PM 2.5 and helping to
select the most effective emissions reduction strate gies 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 PM 2.5 , especially for situations in which multip le pollutant sources interact to form PM 2.5 . For less complex situations in which a few large point sources of emissions are the dominant source of PM 2.5 , trajectory models (such as the California Puff Model [CALPUFF]) may also be useful for simulating

PM 2.5

.

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 Ai r Quality (CMAQ) modeling system in the late
1990s. The model source code a nd supporting data can be downloa ded 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 qua lity 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) gr id 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 dispersi on, photochemical transformation, aerosol thermodynamics and phase

Appendix C – Arizona Regional Haze SIP Page 15 of 47transfer, aqueous che mistry, 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 offe rs 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 extens ions (CAMx) model was initially developed by ENVIRON in the late 1990s as a nested-grid, gas-ph ase, Eulerian photochemical grid model. ENVIRON later revised CAMx to treat PM, visibility, and air t oxics. While there are many similarities between the CMAQ and CAMx systems, there are also some signifi cant differences in their treatment of advection, dispersion, aerosol formation, and dry and wet deposition.

Model Versions

Both EPA and ENVIRON periodically update a nd revise their models as new science or other improvements to the models are developed. For CM AQ, EPA typically provides a new release about once per year. The initial 2002 MPE for WRAP used CM AQ version 4.4, which was released in October 2004. In October 2005 EPA released CMAQ versi on 4.5, which includes the following updates and improvements to the modeling system:

 A new vertical advection algorith m 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 paramete rs 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 feat ures 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 elect ed 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 approxi mately every two years, or as new features
become available. The version used for the co mparison 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 a nd 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 m odels; however, the incorporation of the new mass conservation scheme in CMAQ v4.5 makes its vertical a dvection algorithm much more similar to that of

CAMx.

Major differences between the two models that still exis t 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 ve rsions of the model that include Tagged Species

Appendix C – Arizona Regional Haze SIP Page 16 of 47Source Apportionm ent (TSSA) for some PM species (e.g., sulfate and nitrat e). There are also research versions of CMAQ and CAMx that support the Dec oupled Direct Method (DDM) sensitivity tool for PM
and ozone.

The CAMx model is computationally more effici ent 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 emi ssions inventories for a 2002 actual emissions base case, a planning case to represent the 2000-04 regiona l 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 invent ories were developed using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system. Each of the se 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 em ission scenarios is documented under the emissions inventory sections of the TSS. In addition to va rious sensitivities scenarios, the WRAP performed air quality model simulations for each of the emissions scenarios as follows:

 The 2002 base case emissions scenario, referre d to as “2002 Base Case” or “Base02”. The purpose of the Base02 inventory is to represent th e 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 pr ogress 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, ta king into consideration growth and controls. Modeling results based on this emi ssion inventory are used to define the future year ambient air quality and visibility metrics.

Data Sources

The CMAQ model requires inputs of three-dime nsional gridded wind, temperature, humidity, cloud/precipitation, and boundary laye r 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 extensiv ely in past local, state, regional, and national modeling effo rts. MM5 has undergone extensiv e peer-review, with all of its components continually undergoing development a nd scrutiny by the modeling community. In-depth

Appendix C – Arizona Regional Haze SIP Page 17 of 47descriptions of MM5 can be f ound 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. Th e development of these data is documented in (Kemball-Cook, S. et al., 2005)

Emission inventories for all WRAP air quality simula tions 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 th e 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 sm all 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 incl uded 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 emi ssions data developed for CENRAP were used.

Boundary conditions (BCs) specify th e concentrations of gas and PM species at the four lateral
boundaries of the model domain. BCs determine the am ounts of gas and PM species that are transported
into the model domain when winds flow is into th e domain. Boundary conditions have a much larger
effect on model simulations than do ICs. For so me 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 simu lation 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-Che m data was required before using them in CMAQ and CAMx. The data first had to be mapped to th e 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_Mode l_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 u sed and detailed listing and description of the meteorological data and emission inventories for each scenario. These Spec Sh eets 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 impli cations, are also included. A link to each of the individual Specification Sheets for the model simu lations can be found on the RMC web site at: http://pah.cert.ucr.edu/aqm/308/cmaq.shtml

.

Appendix C – Arizona Regional Haze SIP Page 18 of 472002 Base Case Modeling

Base02 Sensitivity Simulations

The purpose of the 2002 Base Case modeling efforts w as 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, sens itivity, and emissions control strategy modeling. Model performance evaluation is performed by comp aring output from model simulations with ambient air quality data for the same time period. After crea ting 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 perf ormance evaluation, a model inter-comparison and discussion of various sensitivity simulations. This in formation 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 ar e 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 a nnual time period, and for a large number of sites.
The model must be evaluated for both the worst vi sibility conditions and for very clean conditions. Finally, final guidance on how to perform an MPE fo r 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 ambien t data overlaid on model predictions.

 Bar plots comparing the mean fractional bias (M FB) 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 extin ction for the average of the best-20% visibility days or the worst-20% visibility days at each si te; 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 re ferred 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 Page 19 of 47“ty pical”, 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 simulati ons 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 Car bon Bond Mechanism version 4 (CB4) with RADM aqueous chemistry, the SORGAM organic aerosol al gorithm, 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). Bounda ry 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 mi ght be more readily detected in the results from the CMAQ modeling.

In our initial analysis of the Plan02 scenario, plot s 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) sp atial plots showing the change in individual PM 2.5 species concentrations as daily, monthly, and annual averages. The final set of analysis products, available on the RMC web site, include PAVE diffe rence 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-y ear” 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 scenar io 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 ai r pollutants, taking into consideration growth and controls. Modeling results based on this emission inventor y are used to define the future year ambient air quality and visibility metrics.

Appendix C – Arizona Regional Haze SIP Page 20 of 47Base18 Simulation Input Data

Input data used for the 2018 Base Case model simu lations 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 Car bon Bond Mechanism version 4 (CB4) with RADM aqueous chemistry, the SORGAM organic aerosol al gorithm, and all other science algorithms detailed in Tonnesen et al., 2006 were used. Initial condition (IC) da ta 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 compar e 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 PM 2.5 species from 2002 to 2018. For planning purposes, on the other hand, states and tribes should focus on the visibility projecti ons and glide path calculations at individual Class I

Areas.

Figures 1 through 4 show the annual average concentrations for sulfate, nitrate, PM 2.5 and modelreconstructed visibility (in deciview s), respectively. In each figure, the bottom two plots show the
modeled concentration or deciviews for the Pla n02b 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 indicat e areas where modeled visibility became worse over that period. Figure 1 shows that reductions in su lfate were largest in the southwest corner of the WRAP region and in Texas and Oklahom a. This results from planned SO

x emissions reductions in the CENRAP region. There were smaller reductions in su lfate in the Los Angeles area, western Washington state, and southern Nevada. There were small increas es of sulfate, mostly in Wyoming, due to growth in

SO

x 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 bot h 2002 and 2018, the modeled nitrate was greatest in California, and there were reduction in nitrate in th at state in 2018 because of reductions in mobile-source

NO

x emissions. There were small reductions in the P hoenix area as well, also from reductions in mobilesource NO

x emissions.

Figure 3 shows the comparison of PM 2.5 for 2002 and 2018. In most areas of the WRAP region, changes

in PM 2.5 were less than 1  g/m 3 . Locations with increases in PM 2.5 correspond to areas of increased

Appendix C – Arizona Regional Haze SIP Page 21 of 47sulfate (se e Figure 3-1). Areas with the largest reductions in PM 2.5 were the areas in California that had large reductions in modeled nitrate in 2018 (see Figur e 3-2). Results for other species that contribute to

PM 2.5 are available on the RMC web site at http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#base18bvsplan02b

.

Figure 4 compares model-reconstructed visibility fo r 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 PM 2.5 mass (Figure 3) because the visibility in deciviews is a relative metric, so small mass changes in PM 2.5 in good visibility areas can resu lt in large relative improvements in visibility.

Appendix C – Arizona Regional Haze SIP Page 22 of 47Figure 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 Page 23 of 47Figure 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 Page 24 of 47Figure 3 – Annual average PM 2.5 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 Page 25 of 47Figure 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 Page 26 of 47Visibility Projections

The Regional Haze Rule (RHR) goals include achievi ng 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 wh at 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 visi bility 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 ar e defined as the ratio of the future-year modeling results to the current-year modeling results. The cal culated RRFs are applied to the baseline observed visibility conditions to project future-year observed vi sibility. These projections can then be used to
assess the effectiveness of the simulated emission contro l 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 PM 10 are divided into six major components; the first five are
assumed to be PM 2.5 and the sixth is PM 2.5-10

.

o SO 4 (sulfate)

o NO 3 (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 deve lop RRFs between future and current predicted concentrations of each component.

 Component-specific RRFs are multiplied by curre nt 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 su lfate is in the form of ammonium sulfate [(NH 4 ) 2 SO 4 ] and all particulate nitrate is in the form of ammonium nitrate [NH 4 NO 3 ].

To facilitate tracking the progress toward visibility goal s, two important visibility parameters are required
for each Class I area:

 Baseline Conditions: “Baseline Conditions” represent visibilit y 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 dur ing the 2000-2004 five-year period and are the

Appendix C – Arizona Regional Haze SIP Page 27 of 47 Natural Conditions: “Natural Conditi ons,” the RHR goal for 2064 for the Federally mandated Class I areas, represent estimates of natural visib ility conditions for the B20% and W20% days at a given Class I area.

Baseline Conditions

Baseline Conditions for Class I areas are calculated usi ng fine and coarse PM concentrations measured at Interagency Monitoring of Protected Visual Envi ronments (IMPROVE) monitors (Malm et al., 2000).
Each Class I area in the WRAP domain has an asso ciated IMPROVE PM monitor. The IMPROVE monitors do not measure visibility directly, but instead measure speciated fine particulate (PM 2.5 ) and
total PM 2.5 and PM 10 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 [(NH 4 ) 2 SO 4 ]

 Particulate nitrate [(NH 4 NO 3 ]

 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 neutrali zed by ammonium and only the fine mode (PM 2.5 ) is speciated to obtain sulfate and nitr ate measurements (that is, any coarse-mode sulfate and nitrate in the real atmosphere ma y be present in the IMPROVE CM measurement). Concentrations for the above six components of li ght 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 NO 3 -

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 47Where

 S is elem ental sulfur as determined from proton-in duced x-ray emissions (PIXE) analysis of the IMPROVE Module A. To estimate the mass of the sulfate ion (SO 4 = ), S is multiplied by 3 to account for the presence of oxygen. If S is missing then the sulfate (SO 4 ) 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

SO 4 ).

 NO 3 - is the particulate nitrate measured by ion ch romatography analysis of Module B. For the IMPROVE aerosol extinction calculation, it is assumed to be completely neutralized by ammonium (1.29 x NO 3 ).

 The IMPROVE organic carbon (OC) measurements ar e 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 t hose 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 sh own explicitly.

 MT and MF are total PM 10 and PM 2.5 mass, respectively.

Associated with each PM species is an extinction efficiency that converts concentrations (in  g/m 3 ) 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 particl es’ extinction efficiency with increasing RH; this accounts for the particles’ ta king on water and having greater light scattering. Note that some organic matter (OMC) compounds may also have hygroscopic properties, but the IMPROVE aerosol extinction equatio n assumes OMC is nonhygroscopic.

β Sulfate = 3 x f(RH) x [sulfate]
β Nitrate = 3 x f(RH) x [nitrate]
β OM = 4 x [OMC]
β EC = 10 x [EC]
β Soil = 1 x [soil]
β CM = 0.6 x [CM]

The total light extinction ( β ext ) is assumed to be the sum of the light extinctions due to the six PM species listed above plus Rayleigh (blu e sky) background extinction ( β Ray ), which is assumed to be 10 Mm -1 . This is reflected in the IMPROVE extinction equation:

β ext = β Ray + b Sulfate + β Nitrate + β EC + β OMC + β Soil + β CM

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

VR = 3912 / β ext

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( β ext /10)

Appendix C – Arizona Regional Haze SIP Page 29 of 47The equations above, with measur ements from the asso ciated IMPROVE monitor, are used to estimate the daily average visibility at each Class I area for each IMPROVE mon itored day. For each year from the 2000-2004 baseline period, these daily average vi sibility 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 th e 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 re presents the fit between measured PM species concentrations and visibility impairment. Although c onceptually similar to the Old IMPROVE equation, the New IMPROVE equation includes updates to many of the parameters and the addition of extinctions
due to NO 2 absorption and sea salt. 2018 visibility projecti ons and comparisons with the URP glide path goals were performed using both the New and Ol d 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 mo deling 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 ma ss 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 neutra lized; 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 47RRFs cal culated from modeling results can be used to project future-year visibility. For the current modeling efforts, RRFs are the ratio of the 2018 m odeling 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 discussi on below is for W20% days but also applies to B20% days):

1. For each Class I area and each monitored day, dail y 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 obta in 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 av erage deciview visibility for the W20% days.

6. Average the five years of average deciview visi bility 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” th e 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, resu lts 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 RR F for the W20% days from 2002 (Modeled Worst Days) is obtained for the Plan02c and the Ba se18b 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 SO4 i,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 47Glide 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 Cond itions 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 Cond itions value for the W20% days (U.S. EPA, 2003b), shown as the green line, is the 2064 visibility goal at G RCA 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 va riation in visibility. The Baseline Conditions are the average of the W20% visibility from 2000-2004, whic h is the starting point for the glide path in 2004 (12.04 dv for GRCA). A linear URP from the Baselin e 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

YearHaziness 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 47Preliminary Visibility Projection Results

For all of the WRAP Class I areas, the RMC perform ed preliminary 2018 vi sibility projections and compared them to the 2018 URP goals using the Plan02c and Base18b CMAQ m odeling results and the Old and New IMPROVE equations. As an exampl e, 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 visib ility 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 IMPROV E 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 em issions from sources that are not controllable, such as wildfires, dust, and/or international transport. Additional analysis of th ese results and alternative projection techniques are currently under study.

Appendix C – Arizona Regional Haze SIP Page 33 of 47Method 1 predictions for Colorado Plateau and Desert Southwest sites

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%BRCA1CANY1GRCA2MEVE1SAPE1WEMI1ZION1BAND1BOAP1CHIR1GICL1GRSA1IKBA1SACR1SAGU1SIAN1WHIT1WHPE1Percent 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%BADL1CABI1GAMO1LOST1MELA1MONT1SULA1THRO1ULBE1WICA1CRMO1JARB1SAWT1BRID1NOAB1ROMO1WHRI1YELL2Percent 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%HECA1KALM1MOHO1MORA1NOCA1OLYM1PASA1SNPA1STAR1THSI1WHPA1AGTI1BLIS1DOME1HOOV1JOSH1LAVO1PINN1REDW1SAGA1SAGO1YOSE1Percent 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 47PM Source Apportionment

I mpairment of visibility in Class I areas is caused by a combination of local air pollutants and regional pollutants that are transported long distances. To de velop 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 particul ar 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 th at in the base case. This approach is known as a “brute force” sensitivity because a separate m odel run is required for each sensitivity.

An alternative approach is to implement a mass-tra cking algorithm in the air quality model to explicitly track for a given emissions source the chemical transf ormations, 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 complete d by the RMC during 2004 used the CMAQ Tagged Species Source Apportionment (TSSA) method. Un fortunately, there were problems with mass conservation in the version of CMAQ used in that st udy, 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 m ass 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 ev aluate 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 geograp hic 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 s ource apportionment based on user-defined source groups. A source group is the combination of a ge ographic source region and an emissions source category. Examples of source regions include stat es, 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 s ource category as separate, gridded low-level emissions and/or elevated-point-source emissions. The model th en determines each source group by overlaying the source categories on the source region map. For furt her 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_fi nal_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 (SO 4 ) and aerosol nitrate (NO 3 ) and their related species (e.g., SO 2 , NO, NO 2 , HNO 3 , NH 3 , and NH 4 ). 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 chem ical conversions of precursors to the secondary PM species SO 4 , NO 3 , NH 4 , and secondary organic aerosols (SOA). Table 3 summarizes the computer run time required for each species. The practical implicati on of this table for WRAP is that it is much more expensive to perform PSAT simulations for NO 3 and especially for SOA than it is to perform simulations for other species.

Table 3. Benchmarks for PSAT comput ational 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

SO 4 2 1.6 GB 1.1 GB 4.7 h/day

NO 3 7 1.7 GB 2.6 GB 13.2 h/day

SO 4 and NO 3 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 im pairment 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 an alyses of these results are available on the TSS under http://vista.cira.colostate.ed u/tss/Tools/ResultsSA.asp x

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 simula tions. 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 (SO 4 ) and nitrate (NO 3 ) is approximately 14.5 CPU hours with an AMD Opteron CPU. The source regions used in
the PSAT simulations are shown in Fi gure 7 and Table 4. The six em issions 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 ce lls 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 47co mputation 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 (i ncluding 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 fo r 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 concentra tions 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 ou tput only the model’s surface layer concentrations

Appendix C – Arizona Regional Haze SIP Page 38 of 47because of t he vast disk storag e 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 groupi ngs 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 av erage source contributions for the 20% worst visibility days.

If the 3-D tracer output files are saved, it is also possi ble to prepare animations of PM species plumes from each of the source groups. However, these plot s 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 PM 2.5 , both direct emissions of primary PM 2.5 (such as windblown dust) and emissions of gaseous species that undergo photochemical transformation or condensation to form secondary PM 2.5 . Natural sources of PM 2.5 are of concern because they represent
sources that cannot be controlled. Estimates of na tural 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 leve ls 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 ca lculations were based on ambient data analysis, not on visibility modeling. This question thus arises: W ould modeled levels of na tural haze be consistent with the values estimated by EPA for visibility planni ng? 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 natura l haze levels even if all anthropogenic sources of

PM 2.5 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 modificati ons of the environment. For example, wildfire emissions are a large source of natu ral emissions in our modeling, but how much larger might that source

Appendix C – Arizona Regional Haze SIP Page 39 of 47be in the absence of fire suppression efforts? For al l of these reasons, it was decided to describe this sensitivity simulation as a “clean conditions” scenario rather than a “natural conditio ns” scenario. In this simulation, all anthropogenic emissions were remove d from the inventory and only those emissions that were defined as biogenic in the 2002 base case (Base02) were included. Thus, th is 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 categor y modeled included three anthropogenic sources (domestic animals, livestock, and fertilizer a pplication) and two natural sources (soils and wildlife). Only the two natural s ources 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 we re 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 de tails on this dust inventory are available at http://www.cert.ucr.edu/aqm/308/w b_dust2002/wb_dust_ii_36k.sht ml

.

The biogenic and windblown dust emissions from the Base02a SMOKE outputs that are stored at the RMC were used directly. For the fire (including bot h point and area fires), natural area, and ammonia emissions, these data were reprocess ed 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 extinc tion in the clean emissions model simulation.
Because the natural fire emissions in the WRAP st ates were a major component of the clean emissions, the largest visibility impairment is in the regions w ith 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

.

Appendix C – Arizona Regional Haze SIP Page 40 of 47Although there are large uncertainties in the natural emissions, and it is known that there are missing types of natural emissions, the components of the natu ral inventory used in this sensitivity simulation did contribute to relatively large visibility impairment in regions where there were la rge 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 th e large Biscuit fire in Oregon, plus contributions from smaller fires and other natural emissions. These visibility impairment leve ls exceed the natural visibility levels specified in the EPA regional haze natural visib ility 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 fi re 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, accu rate natural emissions inventory.

Figure 8 – Annual average model-reconstructed “c lean conditions” visibility as extinction
coefficient.

These results are all very tentative because of the la rge uncertainties in natural emissions. Considerable effort would be needed to more fully investigate na tural conditions in future modeling studies. It will always be difficult to determine and quantify “cl ean conditions” based on observations because of the pervasive influence of anthropogenic emissions.

Also as part of this sensitivity analysis, the contri butors to organic carbon aerosols (OC) for the clean conditions scenario were evaluated. The CMAQ m odel represents explicitly three classes of organic
carbon aerosols:

 AORGPA: Primary anthropogenic OC resulting from di rect organic mass emissions, such as primary organic aerosol (POA).

 AORGA: Secondary anthropogenic OC resulting from ar omatic VOCs, such as xylene, toluene,
and cresols.

Appendix C – Arizona Regional Haze SIP Page 41 of 47 AORGB: Secondar y 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 an alyzed, and then compared those results to the Base02b case in an attempt to infer the relative contributions of biogeni c 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 b ased simply on the species name. Thus, some biogenic OC might be included with AORGA, and so me anthropogenic OC might be included in

AORB.

 Some fire emissions are classified as anthropoge nic, but these emissions might include species such as terpenes that are typically considered bioge nic. 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 appr oach of analyzing OC in terms of AORGPA,
AORGA and AORGB.

In spite of these difficulties, however, the results shoul d classify the majority of the emissions correctly as either biogenic or anthropogenic.

For each of the above three components of OC, plot s of the annual average mass in the Base02b case were prepared, and then the controllable mass was estimated as the differenc e between the Base02b case the Base02nt clean emissions scenario. Figure 9 show s 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 fir es in southern Oregon and north of Tucson, AZ.

Figure 10 shows the annual average mass of second ary 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 c ontributed 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 si gnificant amount of mass is controllable. It is likely that the controllable AORGB mass results fro m 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 O 3 precursor emissions is effective at reducing a small but significant fraction of the biogenic OC.

Appendix C – Arizona Regional Haze SIP Page 42 of 47Figure 9 – Annual average modeled primary anthro pogenic OC (AORGPA) in Base02b (top) and the portion that is “controllable” primary anthropogenic OC (bottom).

Appendix C – Arizona Regional Haze SIP Page 43 of 47Figure 10 – Annual average modeled secondary anth ropogenic OC (AORGA) in Base02b (top) and the portion that is “controllable” secondary anthropogenic OC (bottom).

Appendix C – Arizona Regional Haze SIP Page 44 of 47Figure 11 – Annual average modeled primary biogenic OC (AORGB) in Base02b (top) and the portion that is “controllable” primary biogenic OC (bottom).

Appendix C – Arizona Regional Haze SIP Page 45 of 47It m ight be difficult for the WRAP states and trib es 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 biogeni c VOCs. It is not possible to evaluate the model performance individually for bioge nic 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 mode l 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 compone nt of OC increases the un certainty of the results described here and illustrated in Figures 9 throu gh 11, so caution should be used when drawing
conclusions about the sources of OC based on these results.

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