## Final

-------------

Regional Technical Support Document

for the



## Requirements of §309 of the Regional Haze Rule (64 Federal Register 35714 – July 1, 1999)

December 15 , 2003 Revised May 7, 2008

May 7, 2008 revision – The Table of Contents for this December 15, 2003 document has

been annotated to note the sections still applicable to §309 regional haze plans. The annotations direct the reader to the WRAP TSS for the most current data and analysis methods.

Several sections of this document are no longer current. There has been no editing of the body of this document.



### WRAP Technical Oversight Committee www.wrapair.org

Final §309 Regional TSD Page 1 of 250 December 15, 2003 Revised May 7, 2008TSD Authors

Zac Adelman Lee Alter Patrick Cummins Mike George Bill Grantham Lee Gribovicz Marc Houyoux Pete Lahm Tom Moore Ralph Morris Alison Pollack David Randall James Scarborough Gail Tonnesen John Vimont Zion Wang Jim Wilson

Acknowledgements

The technical evaluation work of the Western Regional Air Partnership (WRAP) Forums and Technical Oversight Committee (TOC) described in this document addresses the requirements of §309 of the federal Regional Haze Rule, and comprises a major step in completing the technical work begun by the Grand Canyon Visibility Transport Commission (GCVTC). The technical analyses performe d and reviewed by staff from WRAP member organizations, interested stakeholders, and c ontractors are gratefully acknowledged. All of the contributing authors listed alphabetically above made important contributions to this document; doubtless there are other individuals not listed, also deserving particular recognition for their contributions to the §309 technical analysis process. The WRAP TOC thanks all individuals and organizations that have contributed their time and expertise to the preparation of this document.

TSD Point of Contact: Tom Moore, WRAP Technical Coordinator 970-491-8837 mooret@cira.colostate.edu

Final §309 Regional TSD Page 2 of 250 December 15, 2003 Revised May 7, 2008

## Preface: Regulatory Framework for Tribal Visibility Implementation Plans

The regional haze rule explicitly recognizes the authority of tribes to implement the provisions of the rule, in accordance with principles of federal Indian law, and as provided by the Clean Air Act §301(d) and the tribal authority rule (TAR) (40 CFR §§49.1– .11). Those provisions create the following framework:

1. Absent special circumstances, reservation lands are not subject to state jurisdiction.
1 2. Federally recognized tribes may apply for and receive delegation of federal authority to implement Clean Air Act (CAA) programs, including visibility regulation, or "reasonably severable" elements of such programs (40 CFR §§49.3, 49.7). The mechanism for this delegation is a tribal implementation plan (TIP). A reasonably severable element is one that is not integrally related to program elements that are not included in the plan submittal, and is consistent with applicable statutory and regulatory requirements. 3. The regional haze rule expressly provides that tribal visibility programs are “not dependent on the strategies selected by the state or states in which the tribe is located” (64. Fed. Reg. 35756), and that the authority to implement §309 TIPs extends to all tribes within the GCVTC region (40 CFR §51.309(d)(12)). 4. The Environmental Protection Agency (EPA) has indicated that under the TAR tribes are not required to submit §309 TIPs by the end of 2003. Rather, they may choose to opt-in to §309 programs at a later date (67 Fed. Reg. 30439). 5. Where a tribe does not seek delegation through a TIP, EPA, as necessary and appropriate, will promulgate a federal implementation plan (FIP) within reasonable timeframes to protect air quality in Indian country (40 CFR §49.11). EPA is committed to consulting with tribes on a government-to-government basis in developing tribe-specific or generally applicable TIPs where necessary (See, e.g., 63 Fed. Reg. 7263-64).

The amount of modification, if any, needed for this report to fulfill tribal needs may vary considerably from tribe to tribe. The authors have striven to ensure that all references to tribes in the document are consistent with principles of tribal sovereignty and autonomy as reflected in the above framework. Any inconsistency with this framework is strictly inadvertent and not an attempt to impose requirements on tribes that are not present under existing law.

1 Tribes also possess a more fundamental source of authority to regulate their environments, based on their inherent authority as sovereign nations, which predates the formation of the United States. However, in the context of air pollution regulation and visibility planning in particular, tribal authority will more likely be based on delegation of federal authority.

Final §309 Regional TSD Page 3 of 250 December 15, 2003 Revised May 7, 2008Tribal Participation in the WRAP

Tribes, along with states and federal agencies, are full partners in the WRAP, having equal representation on the WRAP Board as states. Whether Board members or not, it must be remembered that all tribes are governments, as distinguished from the “stakeholders” (private interest) which participate on Forums and Committees but are not eligible for the Board.

Despite this equality of representation on the Board, tribes are very differently situated than states. There are over four hundred federally-recognized tribes in the WRAP region, including Alaska. The sheer number of tribes makes full participation impossible. Moreover, many tribes are faced with pressing environmental, economic, and social issues, and do not have the resources to participate in an effort such as the WRAP, however important its goals may be. These factors necessarily limit the level of tribal input into and endorsement of WRAP products.

The tribal participants in the WRAP, including Board members Forum and Committee members and co-chairs, make their best effort to ensure that WRAP products are in the best interest of the tribes, the environment, and the public. One interest is to ensure that WRAP policies, as implemented by states and tribes, will not constrain the future options of tribes who are not involved in the WRAP. With these considerations and limitations in mind, the tribal participants have joined the state, federal, and private stakeholder interests in approving this report as a consensus document.

Final §309 Regional TSD Page 4 of 250 December 15, 2003 Revised May 7, 2008

### Table of Contents

Preface: Regulatory Framework for Tribal Visibility Implementation Plans .................. 3 Tribal Participation in the WRAP ................................................................................................... 4

Technical Support Document Executive Summary ............................................................. 9

Grand Canyon Visibility Transport Commission Technical Analysis History .............. 11

Chapter 1 – WRAP Technical Analysis Approach ............................................................ 14 May 7, 2008 revision - the emissions inventory data and modeling data/methods for regional haze planning across the WRAP region were modified and improved during 2004 through 2006, to support preparation, review, and implementation of haze plans for all Class I areas in the WRAP region. The monitoring, emissions, and modeling data and technical methods for haze planning and implementation in the WRAP region are now provided though the WRAP TSS.

1.1. Overview of Analytical Approach – Emissions ................................................................. 14 May 7, 2008 revision - base year emissions data were updated to 2002 and 2018 emissions projections were completely revised - these results are available through Data presented in Section 1.1 of Chapter 1 are no longer current. WRAP TSS. 1.1.1. 1996 Base Year and 2018 Projection Year Emission Inventories ............ 15 1.1.2. Stationary Source Emissions ........................................................................ 25 1.1.3. Area Source Emissions ................................................................................. 30 1.1.4. Mobile Source Emissions .............................................................................. 34 1.1.5. Dust emissions from paved and unpaved roads ......................................... 40 1.1.6. Pollution Prevention Emissions Inventory ................................................. 44 1.1.7. Fire Emissions ............................................................................................... 46 1.1.8. Biogenics ........................................................................................................ 56 1.1.9. Windblown Dust ............................................................................................ 61

1.2. Overview of Analytical Approach – Modeling .................................................................. 61 May 7, 2008 revision - modeling was updated to a 2002 base year and 2018 emissions projections modeling analyses were completely revised - these results are available through WRAP TSS

. Data presented in Section 1.2 of Chapter 1 are no longer current. 1.2.1. Processing of emissions inventories in SMOKE ......................................... 62 1.2.2. MM5/MCIP meteorological processing ...................................................... 88 1.2.3. Use of CMAQ for §309 Visibility Modeling ............................................... 96 1.2.4. Model Performance Results ....................................................................... 114 1.2.5. Model Uncertainties and Quality Assurance ............................................ 118 1.2.6. 1996 base cases and 2018 projection cases ................................................ 120 1.2.7. Projection of Future-Year Visibility Using Relative Reduction Factors 122

Chapter 2 - Projection of Improvement ........................................................................... 125 May 7, 2008 revision - The baseline year was updated to 2002, 2018 emissions projections were revised, and additional monitoring sites began providing data for the

Final §309 Regional TSD Page 5 of 250 December 15, 2003 Revised May 7, 20082000-04 baseline period for regional haze planning. The methodology and current data showing projected visibility improvement in 2018 are available through WRAP TSS

. Data presented in Chapter 2 are no longer current.

2.1 2018 visibility improvement scenarios using §309 control strategies ............124

2.2. Projected visibility improvement in 2018 ........................................................................ 125 2.2.1. Absolute Modeling Results ......................................................................... 125 2.2.2. Visibility Modeling Results Using Relative Reduction Factors .............. 129

Chapter 3 – Assessment of Clean Air Corridors ............................................................. 134

3.1. Clean Air Corridor (CAC) Requirements ....................................................................... 134

3.2. CAC Definition ................................................................................................................... 134

3.3. Emission Changes .............................................................................................................. 136

3.4. CAC emissions tracking using the WRAP Emissions Data Management System ...... 136

3.5. Process to analyze emissions growth in, and surrounding, the CAC ............................ 137

3.6. Other Clean Air Corridors ............................................................................................... 137

Chapter 4 – Assessment of Stationary Sources ................................................................ 138 May 7, 2008 revision - data for the baseline year was updated to 2002 and 2018 emissions projections were revised by WRAP, and specific revisions to the Regional Haze Rule addressing BART and Alternatives to BART were promulgated by EPA during 2005. The current emissions and modeling data and results for stationary sources in the WRAP region are available through WRAP TSS . The methodology and data for the revised SO Milestone Program are available at: 2 http://www.wrap air.org/forums/309/docs.html

. Data presented in Sections 4.1 and 4.2 of Chapter 4 are no longer current. Chapter 4 – Assessment of Stationary Sources ............................................... 140 4.1.1. Stationary Source SO 2 Control Scenario Modeling Results at the 16 Class I Areas on the Colorado Plateau ............................................................................... 141 4.1.2. Stationary Source SO 2 Control Scenario Modeling Results for Western Class I Areas ................................................................................................................ 146

4.2. Stationary Source NO x and PM sensitivity modeling .................................................... 154
4.2.1. Emission Scenarios ..................................................................................... 154 4.2.2. Model Performance and Future Improvements ...................................... 155 4.2.3. Modeling Results ......................................................................................... 155

4.3. Tracking pre-trigger stationary source SO emissions using the WRAP EDMS ........ 165 2

Chapter 5 – Assessment of Mobile Sources ...................................................................... 167 May 7, 2008 revision - data for the baseline year was updated to 2002 and 2018 emissions projections were revised by WRAP, and comprehensive national rules addressing mobile engines and fuels were promulgated by EPA since 2003. The current

Final §309 Regional TSD Page 6 of 250 December 15, 2003 Revised May 7, 2008emissions and modeling data and results for mobile sources in the WRAP region are available through WRAP TSS

. Data presented in Chapter 5 are no longer current

. 5.1. Mobile source emissions inventory requirements ........................................................... 167

5.2. Mobile source emissions inventory trends ....................................................................... 169 5.2.1. On-road emissions trends ........................................................................... 169 5.2.2. Non-road emissions trends ......................................................................... 175 5.2.3. Total mobile source emissions trends ........................................................ 176

Chapter 6 – Assessment of Fire Programs ....................................................................... 178 May 7, 2008 revision - data for the baseline year was updated to 2002, 2000-04 average fire emissions inventories were added to th e analysis, and 2018 emissions projections were revised between 2004 and 2006 by WRAP. The current emissions and modeling data and results for fire sources in the WRAP region are available through WRAP TSS

. Data presented in Section 6.1 of Chapter 6 are no longer current.

6.1. Fire emissions inventories for 2018 Smoke Management Scenarios ............................. 178 6.1.1. Wildfire scenario comparison .................................................................... 178 6.1.2. Prescribed burning scenario comparison ................................................. 179 6.1.3. Findings of Initial Investigation of FEP Results ...................................... 183 6.1.4. Agricultural burning scenario comparison .............................................. 183 May 7, 2008 revision - The WRAP Fire Emissions Tracking System (FETS) was implemented in 2007 to address the ongoing fire tracking requirement for §309 regional haze plans. Section 6.2 of Chapter 6 is no longer current

. 6.2. Tracking fire emissions using the WRAP EDMS ........................................................... 186

Chapter 7 – Assessment of Road Dust .............................................................................. 188

7.1. Emissions inventories for re-entrained road dust from paved and unpaved roads .... 188 May 7, 2008 revision - data for the baseline year was updated to 2002 and 2018 emissions projections were revised between 2004 and 2006 by WRAP. The current emissions data for road dust sources in the WRAP region are available through WRAP TSS

. Data presented in Section 7.1 of Chapter 7 are no longer current.

7.2. Road Dust significance analysis for the Colorado Plateau 16 Class I areas ................. 189

7.3. Tracking road dust emissions using the WRAP EDMS ................................................. 190

Chapter 8 – Assessment of Pollution Prevention ............................................................. 191

Appendix A: Methods Used to Incorporate State and Local control programs in WRAP Emission Inventories .......................................................................................................... 194 May 7, 2008 revision - the baseline year was updated to 2002 and 2018 emissions projections revised between 2004 and 2006 by WRAP. The methods for incorporating federal and state emissions control programs are detailed in a series of reports specific to point, area, mobile, fire, and dust sources. The references for these reports are available through WRAP TSS

. Appendix A is no longer current

.

Final §309 Regional TSD Page 7 of 250 December 15, 2003 Revised May 7, 2008A.1. Area Sources ....................................................................................................................... 194

A.2. Prescribed Forest/Range and Agricultur al Fire Smoke Management Programs ....... 199

A.3. On-Road Mobile Sources .................................................................................................. 201 A.3.1. 1996 Control Programs .............................................................................. 201 A.3.2. Inspection and Maintenance (I/M) Programs .......................................... 201 A.3.3. Oxygenated Fuel ......................................................................................... 202 A.3.4. Stage II Refueling Controls ........................................................................ 203 A.3.5. Processing of California Data .................................................................... 203 A.3.6. Future Control Programs for 2003, 2008, 2013, and 2018 ...................... 203 A.3.7. Processing of Future California Data ....................................................... 205

A.4. Non-Road Mobile Sources ................................................................................................. 205

A.5. Stationary Sources - Existing Source State Regulation Analyses .................................. 206 A.5.1. NO ............................................................................................................... 206 x A.5.2. SO ................................................................................................................ 217 2 A.6. Stationary Sources – Retirement Factors, Unit Lifetime Analysis ............................... 225 A.6.1. Industrial Sources ....................................................................................... 225 A.6.2. Industrial Boilers ........................................................................................ 226

Appendix B: Use of EPA Guidance and best practices ................................................... 231 May 7, 2008 revision - with the baseline year updated to 2002 and 2018 emissions projections revised between 2004 and 2006 by WRAP, the emissions data presented in Appendices C through K have been replaced with the emissions and modeling scenarios available through WRAP TSS

. Appendices C through K are no longer current. Appendix C: 1996 Base Case Emissions Used in Air Quality Modeling ....................... 232 Appendix D: 2018 Base Case Emissions Used in Air Quality Modeling ....................... 233 Appendix E: 2018 Scenario 1 Emissions Used in Air Quality Modeling ....................... 234 Appendix F: 2018 Scenario 2 Emissions Used in Air Quality Modeling ....................... 235 Appendix G: 2018 BART with Uncertainty Scen ario Emissions Used in Air Quality Modeling .............................................................................................................................. 236 Appendix H: 2018 SO 2 Annex Milestones Scenario Emissions Used in Air Quality Modeling .............................................................................................................................. 237

Appendix I: 2018 Stationary Source 50% NO x Reduction Scenario Emissions Used in Air Quality Modeling ......................................................................................................... 238

Appendix J: 2018 Stationary Source 50% PM 10 Reduction Scenario Emissions Used in Air Quality Modeling ......................................................................................................... 239

Appendix K: 2018 Stationary Source Simultaneous 25% NO and 25% PM x 10 Increase Scenario Emissions Used in Air Quality Modeling .......................................................... 240 Appendix L: Clean Air Corridor Change in Emissions 1996 to 2018 ............................ 241 Appendix M: References .................................................................................................... 246

Final §309 Regional TSD Page 8 of 250 December 15, 2003 Revised May 7, 2008

## Technical Support Document Executive Summary

The purpose of this Technical Support Document (TSD) is to summarize and synthesize key information resulting from the regional technical analyses of regional programs developed (or under development) for states and tribes to use in preparing State Implementation Plans (SIPs) and Tribal Implementation Plans (TIPs) under §309 of the federal Regional Haze Rule (RHR). Underlying the key information presented in the chapters of the TSD are the contractor reports prepared for WRAP Forums and WRAP technical memoranda; complete versions (as web links) of these documents will be included in Appendix D of the TSD. The data files underlying these documents will also be available electronically on the WRAP website www.wrapair.org , or by request. The intent of WRAP is to have a web-based TSD, with CD-ROMs available to states and tribes as requested.

The technical analyses conducted by WRAP we re designed to address §309 requirements for control strategy evaluations and related administrative programs. This TSD strives to maintain a regional focus and a consistent terminology, addressing the analysis of visibility impacts at the 16 Class I Areas on the Colorado Plateau, as well as at the additional Class I Areas in the 9-state Grand Canyon Visibility Transport Commission (GCVTC) transport region. This TSD will describe the history of the technical analysis process used by the GCVTC, with a detailed overview of the technical analysis approach used by the WRAP Forums, and succinct individual chapters summarizing and describing the various technical analyses needed to support development of SIPs and TIPs for §309. The chapters are listed
next:

Executive Summary GCVTC Technical Analysis History 1. WRAP Technical Analysis Approach 2. Projection of Improvement 3. Clean Air Corridors 4. Stationary Sources 5. Mobile Sources 6. Fire Programs 7. Road Dust 8. Pollution Prevention

The following appendices summarize technical and regulatory information:

A. State and local control programs included in WRAP emissions inventories B. Use of EPA guidance and best practices C. 1996 base case emissions used in air quality modeling D. 2018 base case emissions used in air quality modeling E. 2018 Scenario 1 emissions used in air quality modeling F. 2018 Scenario 2 emissions used in air quality modeling G. 2018 BART with Uncertainty Scenario emissions used in air quality modeling H. 2018 SO Annex Milestones Scenario emissions used in air quality modeling 2

Final §309 Regional TSD Page 9 of 250 December 15, 2003 Revised May 7, 2008I. 2018 Stationary Source 50% NO x Reduction Scenario emissions used in air quality modeling J. 2018 Stationary Source 50% PM 10 Reduction Scenario emissions used in air quality modeling K. 2018 Stationary Source Simultaneous 25% NO and 25% PM x 10 Increase Scenario emissions used in air quality modeling L. Clean Air Corridor Change in Emissions 1996 to 2018 (County-level by pollutant) M. References

Final §309 Regional TSD Page 10 of 250 December 15, 2003 Revised May 7, 2008

## Grand Canyon Visibility Transport Commission Technical Analysis History

The federal Clean Air Act (CAA) Amendments of 1990 required the creation of the Grand Canyon Visibility Transport Commission (GCVTC). Once chartered in 1991, the GCVTC was composed of the governors of eight west ern states (Arizona, Utah, California, New Mexico, Colorado, Nevada, Oregon, and Wyoming), four tribes (Acoma, Hopi, Hualapai, Navajo) four federal land managers (Bureau of Land Management, U.S. Fish and Wildlife Service, U.S. Forest Service, and the National Park Service), the Columbia River Inter-Tribal Fish Commission, and the Environmental Protection Agency (EPA). The GCVTC was charged with advising EPA as to strategies for protecting visual air quality in the 16 mandatory federal Class I areas on the Colorado Plateau. In particular, the GCVTC was to address clean air corridors, the effect of new or modified stationary sources, and the development of long-range strategies.

The technical analyses for the GCVTC were based on two primary technical tools. The VARED air quality model, a grid-based air quality model with simplified chemistry, provided estimates of visibility impairment for four Class I areas on the Colorado Plateau. This information was then used to derive transf er coefficients. Transfer coefficients quantify the relative transport relationship between a source and a receptor. They represent the composite effect of winds, chemical changes in the atmosphere, deposition, and other factors on emissions. The transfer coefficients were then integrated into a comprehensive control strategy analysis tool called the Integrated Assessment System (IAS). The IAS also includes a base year emission inventory of all visibility impairing pollutants. The base year is 1990, and the pollutants are sulfur dioxide, oxides of nitrogen, organic compounds, speciated fine particulate, and coarse particulate. Also included in the IAS are emissions growth and control factors that allow one to forecast fu ture emissions out to the 2040 projection year, and in combination with transfer coefficients, predict visibility conditions. Cost information for controls is also included. In summary, the purpose of the IAS is to assess the effect of future control strategies on visibility and the cost of those controls.

The GCVTC identified several scenarios that were to be analyzed to assist in the evaluation of a range of control options. These included five scenarios:

1. Baseline Forecast Scenario; 2. Regional Emissions Cap; 3. Maximum Management Alternative; 4. Visibility Standard for the Colorado Plateau; and 5. Standardized Control Technology and Process Requirements.

The Baseline Forecast Scenario was intended to represent the effect of current laws, and based on several assumptions:

1. Emissions growth or decline was based on economic demand in the region; 2. Sources retire at a specific age on average;

Final §309 Regional TSD Page 11 of 250 December 15, 2003 Revised May 7, 20083. New sources were a mixture of new technology and assumed control levels; State Implementation Plans (SIPs) under the CAA were accounted for through 2010 after which emissions growth is allowed based on economic demand; 4. There was no growth in paved road dust; and 5. Emissions from Mexico were held constant.

The range of visibility improvements associated with the emissions cap, the maximum management alternative, the visibility standard, and technology solution was estimated and compared to the Baseline Forecast Scenario.

In assessing these scenarios, the GCVTC advanced visibility science and improved understanding of visibility impairment significantly during its five-year existence. However, much remained to be done, in particular with respect to visibility modeling. The size and complex terrain characteristic of the transport region, along with time and resource constraints resulted in outputs that were sometimes limited in their applicability. Of particular concern were situations where a source and Class I area were near each other.

The GCVTC still had enough confidence in the insights provided by the work to make a number of recommendations for improving visibility and visibility science. The GCVTC recommended control strategies for selected stationary, mobile, and area sources. In addition, approaches were proposed with respect to air pollution prevention, clean air corridors, near-field emissions, emissions transport from Mexico, and visibility issues in Indian Country.

The stationary source recommendations call for the establishment of a series of emissions targets for sulfur dioxide that will be compared to actual emissions. If those targets were exceeded, then a backstop cap and trade program would be triggered. Hence, the technical need would be for an emissions tracking system, as well as estimating the visibility improvement associated with the emissions reductions.

The mobile source recommendations recommend support for national engine and fuel standards, as well as assessment of several potential regional and local approaches. One of the key local strategies calls for the assessment of the contribution of mobile sources in major urban areas on visibility on the Colorado Plateau, and the development of emissions budgets if deemed significant. A technical demonstration of this significance test would be needed.

As for area sources, two categories were targeted, road dust and fire. During the course of the GCVTC technical analyses, substantial uncertainty arose as to the accuracy of the road dust emissions, as well as how the air quality model transported those emissions. So, even though the analyses demonstrated that road dust was a significant contributor to visibility impairment on the Colorado Plateau, the GCVTC recommended further study before controls were undertaken. Therefore, the technical need would be to improve the science with respect to characterizing road dust, and then reevaluate its significance. With respect to fire, the GCVTC recommended the development of enhanced smoke management programs along with the establishment of a tracking system and the setting of emissions goals. Therefore, the

Final §309 Regional TSD Page 12 of 250 December 15, 2003 Revised May 7, 2008tracking system and assessment of the effect of the goals on visibility are the technical products that would result.

The air pollution prevention recommendations call for additional utilization of renewable energy, energy efficiency, and pollution preventi on, and to model the effect of reduced or avoided emissions associated with those programs. The GCVTC also suggested the exploration of specific approaches like incentives and environmental labeling. The technical product would be the modeling of the composite benefit and cost of the strategies undertaken.

As previously discussed, the evaluation of clean air corridors is specifically required by the CAA Amendments of 1990. The GCVTC assessed the existence of Clean Air Corridors, identified one to the northwest of the Colorado Plateau, and determined that establishing emissions control programs in the area was not needed at that time. The GCVTC did recommend that emissions and their effect on visibility be tracked, and that triggers be established. This is to ensure that clean visibility days stay that way. The technical need would again be for a tracking system.

The recommendations for emissions in and near Class I areas call for improved planning both within Class I areas and in adjacent communities. The recommendations with regard to Mexican emissions call for bi-national mechanisms like local initiatives and financial incentives, as well as the development of a comprehensive inventory. The technical need would then be to document the development of emissions estimates for Mexico. The tribal recommendations call for additional ambient monitoring and improved estimates of emissions.

There are also a number of recommendations related to improving future visibility assessment work related to emission inventories, ambient monitoring, air quality modeling, and assessment tools. With regard to emission inventories, these include:

1. Development of a regional inventory, with regular updates; 2. Standardization of data collection; and 3. The need for micro-inventories.

As for visibility monitoring, the continuation and expansion of the network is called for. With respect to modeling, both an improved regional model and development of a “reduced form model” are identified as critical needs. Better meteorology data is also suggested.

An additional recommendation of the GCVTC was the creation of a successor organization to implement its recommendations. Therefore, the Western Regional Air Partnership was created in 1997.

Final §309 Regional TSD Page 13 of 250 December 15, 2003 Revised May 7, 2008

## Chapter 1 – WRAP Technical Analysis Approach

This chapter describes the technical analysis approach used by the WRAP. These technical analyses were selected to satisfy the requirements of §309, and focus on the implementation requirements of control strategies recommended to EPA by the GCVTC. EPA had adopted the 1996 GCVTC recommendations in §309 of the Regional Haze Rule, and as such, the technical methodologies and analyses described in this TSD may differ from more traditional approaches, generally used by states to prepare a TSD. The purpose of WRAP technicalwork described in this TSD is to analyze and document the visibility benefits of GCVTCrecommended control strategies, and to present emissions inventory data for use in planning future emissions management programs, as required by §309. Summarized in the next two sections are the emissions estimates and modeling simulations performed by the WRAP.

1.1. Overview of Analytical Approach – Emissions

In order to develop technical information needed by states and tribes to prepare implementation plans, the WRAP needed to prepare base year and projection year emissions inventories. Calendar year 1996 was selected as the base year for two important reasons. EPA had prepared 1996 meteorological simulations, called MM5 data, which WRAP could use for modeling. The periodic national emissions inventory, called NEI, was available for 1996, following the every-third-year cycle of the NEI. WRAP used NEI data as the basis of some of the base year inventory, refining it as necessary. WRAP also built new emissions inventory sections as needed, particularly for fire emissions. Calendar year 2018 was selected as the projection year for emissions because EPA had identified it as the end of the planning period for §309. WRAP also prepared in termediate year inventories, particularly for point and mobile emissions sources.

WRAP needed to perform several critical emissions-related tasks to analyze §309 control strategies, and provide emissions estimates for future emissions management programs.

1. Prepare base case emissions inventories for both 1996 and 2018, to characterize, as fully and completely as possible, the change in emissions expected to occur without implementation of GCVTC-recommended control strategies. 2. Compare the visibility improvement of a declining SO 2 emissions cap program for major stationary sources, combined with a backstop SO 2 emissions trading program, against a more traditional “command and control” scenario of Best Available Retrofit Technology (BART) on the same group of major stationary SO 2 emissions sources, in 2018. The SO 2 emissions from the resulting from application of BART were characterized in two different scenarios, discussed later in this TSD. 3. Prepare emissions estimates for mobile sources and road dust emissions, to allow assessment of the significance of those emissions at the 16 mandatory federal Class I areas on the Colorado Plateau. 4. Analyze and estimate the emissions changes resulting from implementation of pollution prevention programs in the 9-state GCVTC transport region.

Final §309 Regional TSD Page 14 of 250 December 15, 2003 Revised May 7, 20085. Characterize fire emissions for development of enhanced smoke management programs and annual emissions goals, across the WRAP region. 6. Following the preparation of the §309 implementation plans, provide an emissions tracking system, in support of stationary and fire emissions management programs.

1.1.1. 1996 Base Year and 2018 Projecti on Year Emission Inventories

The availability of point, area, mobile, and biogenic emissions inventory data from 1996 was the primary reason for its choice as the base year for §309 modeling and other technical analyses. The choice of 1996 emissions as the base year is complemented by the availability of 1996 MM5 meteorological data used in air quality modeling, described later in this chapter. 2018 was chosen as the projection year for emissions data for air quality modeling and other technical analyses, as required in §309.

Emission Inventory Sectors

Emission inventories traditionally consider 4 sectors, those being:

• Stationary Point Sources

• Area Sources

• Mobile Sources

• Biogenic Sources

Within the WRAP region, fire has been determined to be a unique, hard to quantify, and potentially a very significant emission source. Therefore, within the WRAP emission inventories, fire has been treated as its own separate emission sector.

Forums involved in producing the WRAP emission inventories include:

• The Emission Forum (EF), which produced the Stationary Point Sources’, and Area Sources’ inventories;

• The Mobile Sources Forum (MSF), which produced the On-Road (tailpipe, evaporative, tire, and brake wear emissions), the Non-Road (tailpipe and evaporative emissions only), and the Paved & Unpaved Road Dust (PM only) inventories;

• The Fire Emissions Joint Forum (FEJF), which produced Wildfire, Prescribed Wildland, and Agricultural (Ag) Fire inventories.

The Biogenics inventory represents natural emissions coming from vegetation, and was produced by the WRAP Regional Modeling Center (RMC), under the oversight of the Modeling Forum (MF). Emission values were calculated by the Sparse Matrix Operator Kernel Emissions (SMOKE) emissions preprocessor model, using the Biogenic Emissions Inventory System (BEIS2) module. There are no inventory values directly available for biogenic emissions; therefore these emissions were approximated as follows:

• SMOKE calculates a tons/day value for biogenic emissions from each state

Final §309 Regional TSD Page 15 of 250 December 15, 2003 Revised May 7, 2008• The RMC ran one day per month to obtain 12 representative values for each state

• The resultant average daily totals were summed for each month, for each state

• The resultant 12 monthly totals were summed to approximate biogenic emissions for the
year

Pollutants Inventoried

The WRAP inventoried seven visibility-related pollutants for all emission sectors, except Biogenics. The seven pollutants in the WRAP emission inventory include:

• Volatile Organic Compounds (VOCs);

• Nitrogen Oxides (NO ); x

• Carbon Monoxide (CO);

• Sulfur Dioxide (SO ); 2

• Particulate under 10 microns (PM 10 );

• Particulate under 2.5 microns (PM 2.5 ); and ). • Ammonia (NH 3
The only two pollutants emitted by surface vegetation are VOCs and NO x ; therefore the WRAP inventories contain only those two pollu tants from the Biogenics sector. BEIS2 reports pollutants in the form of nitrogen oxide (NO), aldehydes (ALD2), isoprene (ISOP), non-reactive volatile compounds (NR), olefins (OLE), paraffins (PAR) and terpenes (TERPB). The VOC values were taken from the sum of ALD2, ISOP, OLE and PAR. It was determined that TERPB was already accounted for in the paraffins, and would cause double counting to add it in separately. The total mass of NO was added directly into the inventory as NO

. x
Emission Inventory Geographic Domain

The WRAP Emission Inventory was compiled for two separate years; those being 1996, the Base Year and 2018, the Projection Year (represents the end of the first "Planning Period" under the Regional Haze Rule). Comparisons must be made between 1996 and 2018 to project the improvement in visibility resulting from implementing the requirements of §309, and to make a demonstration that the SO Market Trading Program is "Better than BART". 2
The geographic domain for the WRAP Emission Inventories includes:

• 13 WRAP States (AZ, CA, CO, ID, MT, ND, NM, NV, OR, SD, UT, WA, and WY) {excludes Alaska, which was not in the §309 modeling domain}

• 9 CENRAP States (NB, KA, OK, TX, MN, IA, MO, AK, and LA)

Figure 1, below shows the states inventoried. The nine states in brown were part of the Grand Canyon Visibility Transport Commission, and are eligible for §309. The four additional states shown in yellow are also WRAP members. These 13 states are listed above. The four states shown in blue, in a tier south of North and South Dakota (Nebraska, Kansas,

Final §309 Regional TSD Page 16 of 250 December 15, 2003 Revised May 7, 2008Oklahoma, and Texas) are referred to in some WRAP emissions inventories and visibility modeling work as “Tier 1” states. The remaining states in blue east of Tier 1, are referred to in some WRAP emissions inventories as “Tier 2” states.

Figure 1.1.1. Area of the contiguous WRAP and CENRAP regions’ states covered in emission inventories prepared for §309, as well as the Class I areas.

International Emissions

The WRAP also attempted to compile international emission inventories for the two countries bordering the WRAP region, Canada and Mexico.

Regarding Canada, an incomplete emission inventory provided by the emissions modeling contractor (MCNC) was used. That invent ory contained 1995 data for area sources, and onroad and non-road mobile sources only. There were no point source data included. In "growing" this Canadian inventory to 2018, the effort was only partially completed for those sources with Source Classification Codes (SCCs) available in the Economic Growth Analysis System (EGAS) model. Also, no new emission controls were applied in this projection.

Final §309 Regional TSD Page 17 of 250 December 15, 2003 Revised May 7, 2008The EPA is working with Canada to improve the inventory, and future information will include some point source data. However, becau se of confidentiality provisions of Canadian laws, this point source data will be presented as gridded values, showing emission totals from a four-kilometer grid square. This data will be chemically speciated and temporally allocated, but because of the reporting format there will be no individual facility locations or stack parameters.

A crude emissions inventory was produced by a c ontractor (ERG) for states in northwestern Mexico. These included Baja California Nort e, Sonora and Chihuahua. The Mexico point source information was incomplete, with data available for all seven visibility-related pollutants, only from three large sources previously considered by the GCVTC. Those included two copper smelters; Nacozari and Cananea. Cananea operated in 1996, but was shut down in 1999; consequently there were no emissions for the 2018 projection year for this smelter. The third large source that was included was the two units (I & II) of the Carbon coal fired power plant, located over the Chihuahua boarder in neighboring Coahuila. Point source data was available for only 15 other individual Mexico plants and factories, and the data were only for SO emissions. 2
Regarding the area source data, there were urban area emissions for three border towns; Ciudad Juarez, Mexicali, and Tijuana. This data was spatially allocated throughout the three Mexican States, using population as the surrogate. Unfortunately that means that the data assumes an "urban profile", which skews the results. For example, the inventory data probably contains more automobile emissions than in reality because there are more cars in the towns than in rural area; while agricultural dust is underreported, because urban inventories have less unpaved surface than in rural areas. Because this Mexico inventory has so much uncertainty, it was decided that it would be impractical to attempt to "grow" the data to the 2018 projection year. Consequently the 2018 values were "flat-lined", and represent the same emission totals as reported for 1996.

1996 Emission Inventory Results

For the 1996 inventory summaries shown at the end of this section, it should be noted that the area source category contains no "Wind Erosion Fugitive Dust". The EF determined that data for this wind-blown dust component was so unreliable that it would be detrimental to the modeling effort to try to include it. The Forum will develop a fugitive dust module to fill this hole in future WRAP inventories.

Regarding the fire sector, the FEJF was able to collect information on wildfire (> 100 acres in size), and prepare a partial wildland prescribed fire inventory. The FEJF collected agricultural burning activity data for 1996, however the temporal and spatial resolution of the information was not adequate for calculating emissions to be used in regional air quality modeling.

Only the EF attempted to compile data directly from the nine Central Regional Air Planning Association (CENRAP) states, in addition to the 13 WRAP states. The MSF and FEJF completed their work solely for the 13 WRAP states. However, all CENRAP data was based

Final §309 Regional TSD Page 18 of 250 December 15, 2003 Revised May 7, 2008on the EPA’s National Emission Trends (NET) Emission Inventory for 1996, regardless of whether compiled by the forums (for point and area sources), or not (mobile and fire sources). Regarding paved/unpaved road dust emissions, the MSF compiled this information separately for the 13 WRAP states, while the paved/unpaved road dust data for the nine CENRAP states was already included in their NET area source files.

2018 Emission Inventory Results

For the area, mobile and road dust emission sectors, only one 2018 emission projection was made. However, a variety of different scenar ios were developed for the stationary point source sector, and for the fire emissions sector.

Because biogenic emissions are affected solely by the annual meteorology (primarily temperature), and because the WRAP has no way of predicting future year meteorology, the 2018 biogenic emission inventory was the same as used for 1996 when actual meteorological conditions are known.

Regarding the stationary point source sector, th ere were five different simulations projected for 2018, two of which are described in this document. These different point source scenarios affected nine WRAP region states onl y, leaving the point source projections in the remaining four WRAP states (WA, MT, ND, and SD), and CENRAP constant in all five alternatives. The alternative 2018 scenarios only affected SO 2 emission totals in those nine states, except for Pollution Prevention (P2), that also affected NO x . The five scenarios were compiled to address the Backstop SO 2 Market Trading Program, and the Renewable Energy and Energy Efficiency (RE/EE) mandates of the RHR. These five scenarios include:

• 2018 Base Case (642,000 tons per year (tpy) of SO ) 2

• Annex Milestones Case (510,000 tpy of SO ) 2

• Command & Control BART Implementation Case (486,000 tpy of SO ) 2 ) • Command & Control BART Implementation w/ Uncertainty Case (550,000 tpy of SO 2

• Pollution Prevention Case (510,000 tpy of SO2)

The SO 2 emission totals did not change between the Annex and the P2 case because the Market Trading milestones put an absolute cap on sulfur emissions, and any savings due to RE/EE implementation simply allows that tonnage to remain uncontrolled under the Point Source limit. Because NO x is not subject to this cap and trade program, any energy saving due to RE/EE implementation reduces the demand for fossil fuel-fired electricity, and the associated NO x emissions go down. The table below hi ghlights the state-by-state differences in EGU emissions when the Annex case is compared with the P2 case. Negative emission values indicate where P2 case emissions are lower than Annex case emissions.

Final §309 Regional TSD Page 19 of 250 December 15, 2003 Revised May 7, 2008State/Pollutant NO X SO 2 Arizona -3,267 5,558 Colorado -1,370 -1,119 Nevada - 430 - 307 New Mexico -7,053 -5,135 Utah - 780 -595 Wyoming -1,374 1,598 Regional change -14,274 0

Regarding the fire emissions sector, it was determined that actual 1996 wildfire activity was abnormally high, compared with the historical trend. Therefore for 2018, the FEJF determined what an average wildfire year would look like and used that "typical wildfire" scenario as their 2018 projection.

While wildfire is largely uncontrollable, the FEJF determined that there are measures that can be taken to reduce emissions from Prescribed Wildland and Ag burning. Thus for both of these fire categories, they developed two different scenarios for 2018. These are referred to as the Base Smoke Management (BSM) and the Optimal Smoke Management (OSM) scenarios.

Inventory Summaries

The options selected for the 2018 Emission Inventory Summary Results presented here represents a "maximum control scenario", which includes:

• SO Annex Milestones for the Stationary Source Inventory; 2

• Optimal Smoke Management for both the Prescribed Wildland and Ag Burning Inventories; and

• "Typical" Year for the Wildfire Inventory.

Table 1.1.1. Changes in annual emissions by pollutant, within the 13-state WRAP region, between 1996 and 2018.
Changes in Pollutant Totals in the 13 State WRAP Region: 1996 to 2018

VOC down 30%

NO down 28% x
CO down 31%

SO down 23% 2

PM 10 up 4%

PM up 4% 2.5

NH up 13% 3

Final §309 Regional TSD Page 20 of 250 December 15, 2003 Revised May 7, 2008The following eight pairs of pie charts ( Figures 1.1.2 through 1.1.9) show the emission totals for the 13-state inventory region, and the changes that are predicted to occur, from 1996 through 2018. The data and additional plots are shown at: http://wrapair.org/forums/ef/inventories/combined/index.html

. 

# VOC Em issions
1996 2018 4,217,014 tons 2,934,752 tons Decrease = 1,282,260 tons (-30%) from 1996

On-R oad M obile 37% Non-R oad M obile 15% Point 5%

Area 33% R x Fire 1% W ildfire

9%

O n-R oad Mobile 16% Non-R oa d Mobile 12% Point 10%

Are a 52% Ag Fire

0% Rx Fire

6 % W ildfire

4%



# VO C Em issions w / Biogenics
1996 2018 11,215,433 tons 9,933,173 tons

O n-Road

Mobile 14%

Non-Road

M ob ile

6% Point 2% Area 13%

B iog en ic E m ission s

62% Rx F ire 0 % W ild fire

3 % On-R oad M obile

4%

Biogenic Emissions

71% Non-Road M obile

4% Point 3 % Area 15%

Ag Fire 0 % R x Fire 2% W ildfire

1%

Final §309 Regional TSD Page 21 of 250 December 15, 2003 Revised May 7, 2008

# NOx Emissions
1996 2018 4,745,231 tons 3,420,265 tons Decrease = 1,324,967 tons (-28%) from 1996

Wildfire

4% Rx Fire 0%

On-Road Mobile 37% Area 7% Point 23% Non-Road

Mobile 29% Wildfire 2% Rx Fire 10% Ag Fire 0%

On-Road Mobile 14% Area 13% Point 33%

Non-Road Mobile 28%



# NOx Emissions w/ Biogenics
1996 2018 5,294,966 tons 3,969,999 tons

Non-Road

Mobile 26% Point 20%

Area 7%

On-Road Mobile 34% Bioge nic Emissions 10% Rx Fire

0%

W ildfire

3 % Biogenic Em issions 14%

Non-R oad Mobile 24% Point 28%

A rea 11%

O n-R oad Mobile 12% A g Fire

0%

R x Fire 9 %

W ildfire

2 %

Final §309 Regional TSD Page 22 of 250 December 15, 2003 Revised May 7, 2008

# SO2 Emissions
1996 2018 1,630,218 tons 1,263,134 tons Decrease = 367,083 tons (-23%) from 1996

W ildfire

3% Rx Fire 0% Non-Road Mobile 13%

On-Road

Mobile

2%

Area 9%

Point 73% Wildfire 1% Rx Fire 2% Ag Fire 0%

Non-Ro ad M o bile 22%

On-Road M o bile

1%

Area 13%

Point 61%



# NH3 Emissions
1996 2018 1,020,181 tons 1,157,569 tons Increase = 137,389 tons (+13%) from 1996

Area 91%

On-Road Mobile

4% Non-Road Mobile

0% Point 2%

Rx Fire 0%

W ildfire

3% Area 84%

On-R oad M obile

6% Non-Road M obile

0% Point 3%

Ag Fire

0%

Rx Fire

6%

Wildfire

1%

Final §309 Regional TSD Page 23 of 250 December 15, 2003 Revised May 7, 2008

# PM-2.5 Emissions
1996 2018 1,557,098 tons 1,620,072 tons Increase = 62,975 tons (+4 %) from 1996

Unpaved Road Dust 4%

On-Road Mobile

3% Area 34% Point 8%

Non-Road Mobile

6% Paved Road Dust 1%

Rx Fire

3%

W ildfire 41% Paved Road Dust 3% Unpaved Road Dust 3%

On-Road Mobile 2% Area 36% Point 9%

Non-Road Mobile

5% Ag Fire

1%

Rx Fire 27%

Wildfire 14% 

# PM -10 Emissions
1996 2018 3,559,201 tons 3,684,776 tons Increase = 125,574 tons (+4% ) from 1996

Unpaved Road Dust 10%

W ildfire

21% Rx Fire

1% Paved Road Dust 3% Point 6%

Non-R oad Mobile 3%

On-R oad M obile 2 % Area 54% Paved Road Dust 4% Unpaved Road Dust 9%

W ildfire

7% Rx Fire 15% Ag Fire

0% Point 7%

Non-Roa d Mobile 2%

O n-Road Mobile 1% Area 55%

Final §309 Regional TSD Page 24 of 250 December 15, 2003 Revised May 7, 20081.1.2. Stationary Source Emissions

Emissions inventories for stationary sources we re developed as inputs to various technical analyses as needed for the requirements of §309, consisting of air quality modeling described later in this chapter, and separate emissions control analyses by the WRAP Market Trading Forum, largely described in other reports. The WRAP Emissions Forum developed a 1996 base case of known emissions, and several future versions of 2018 emissions inventories (a base case [expected changes in emissions with no additional control programs], a command and control [BART] inventory, a BART with an uncertainty factor inventory, and an emissions cap and backstop trading program inventory [SO 2 Annex Milestones]). The results of those inventories are presented next. The terms “stationary source emissions” and “point source emissions” are used interchangeably in this document.

Base Year (1996)

The 1996 base year stationary point source emission estimates that were used in this analysis were prepared by Pacific Environmental Serv ices, Inc. (PES) and Eastern Research Group, Inc., under contract to the Western Gover nors’ Association (WGA) (PES, 2001). The WRAP Emissions Forum contracted with PES to compile the 1996 WRAP region base year emission inventory, utilizing the 1996 U.S. EPA National Emission Inventory (NEI) as the initial basis for this inventory. PES augmented and updated the NEI with information solicited directly from State, local, and tribal agencies in the WRAP region. The 1996 point source file used in this analysis had the filename WGA_PT96.DBF and was retrieved from the WRAP website on August 15, 2001. This file contains information on unit-level criteria pollutant emissions (plus ammonia) for 1996 operations, along with stack coordinates, stack parameters, and operating schedules (hours per day, days per week, weeks per year). While each State may use different criteria to determine which sources to include in their point source file, the official definition of a point source at the time of inventory development was actual emissions greater than: 100 tpy for SO

, NO 2 x , VOCs, and particulate matter with an aerodynamic diameter of 10 microns or less (PM 10 ), 1,000 tpy for CO, and 5 tpy for lead (Pb).

Table 1.1.2.1. 1996 point source emissions by St ate for the 22-state modeling domain

. 1996 Emissions (tpy)

State VOC NO CO SO PM PM NH x 2 10 2.5 3 Arizona 10,473 108,736 17,991 195,851 22,243 11,670 20
Arkansas 14,863 72,440 101,790 112,096 31,855 19,324 15,031
California 69,564 134,844 96,397 43,353 30,135 17,535 15,304
Colorado 37,791 130,552 34,323 106,003 19,932 11,943 245
Idaho 482 6,292 4,522 23,957 12,976 8,035 2
Iowa 11,087 106,311 11,724 243,494 11,333 6,314 8,122
Kansas 26,547 195,309 81,756 131,192 14,632 9,900 12,592
Louisiana 127,270 349,255 700,613 262,457 37,308 27,690 62,797
Minnesota 39,137 169,842 82,626 127,067 81,954 36,691 1,006
Missouri 60,194 214,365 107,128 482,636 49,385 19,407 21,884

Final §309 Regional TSD Page 25 of 250 December 15, 2003 Revised May 7, 2008Final §309 Regional TSD Page 26 of 250 December 15, 2003 Revised May 7, 2008 Montana 7,682 42,517 48,553 45,925 14,366 7,635 405
Nebraska 11,221 61,532 13,934 72,303 8,976 3,867 19
Nevada 1,718 65,005 14,403 55,858 16,235 6,153 54
New Mexico 16,493 146,023 38,374 158,646 10,149 3,045 83
North Dakota 1,128 114,229 13,222 243,670 4,992 3,328 22
Oklahoma 56,949 208,954 221,065 140,951 11,883 7,489 16,614 Oregon 16,179 26,005 76,001 15,211 11,384 8,439 15
South Dakota 1,386 22,022 366 16,077 906 485 1
Texas 272,914 920,366 470,866 962,042 57,873 40,596 2,907
Utah 16,936 100,434 38,405 43,091 19,381 11,545 1,185 Washington 21,242 56,771 171,132 120,436 12,706 8,716 4,618 Wyoming 23,460 131,552 57,827 128,470 32,565 19,535 1,050

Point Source Totals 844,716 3,383,356 2,403,018 3,730,786 513,169 289,342 163,976

Forecast Year (2018)

This section describes the methods and results used to estimate 2018 emissions for the 2018 base case, which includes controls applied after 1996.

Table 1.1.2.2. 2018 Base Case Scenario - Point Source Emissions - 22-State Region 2018 Emissions (tpy)

VOC NO x CO SO 2 PM 10 PM 2.5 NH 3 Arizona 17,152 138,583 32,723 131,499 38,211 20,117 33

Arkansas 19,068 73,001 124,170 108,738 34,346 18,471 25,423

California 104,337 106,812 152,122 32,079 45,419 24,399 26,900

C olorado 44,386 130,922 54,980 91,684 31,371 18,252 180

Idaho 275 7,295 6,410 14,510 6,979 4,314 2

Iowa 13,496 106,820 19,088 194,510 14,126 7,967 13,764

Kansas 34,474 196,081 87,634 120,444 20,990 14,507 19,302

Louisiana 152,153 317,031 593,941 222,884 38,862 27,971 73,139

Minnesota 46,047 175,765 103,413 109,321 131,675 58,398 1,288

Missouri 93,122 154,590 207,781 451,579 80,213 30,962 36,398

Montana 7,856 44,566 60,608 37,062 15,241 7,944 298

Nebraska 13,468 61,242 25,235 70,107 12,163 5,112 27

Nevada 2,109 52,859 29,814 22,763 22,260 8,362 89

New Mexico 23,936 154,192 59,770 148,352 11,677 3,476 132

N orth Dakota 1,646 115,180 20,277 252,125 5,196 3,538 31

Oklahoma 76,633 206,228 255,821 130,218 13,948 7,930 27,325 Oregon 23,715 33,226 111,960 26,828 14,117 10,142 19

South Dakota 1,553 22,493 615 15,236 770 432 2Texas 347,864 624,122 585,932 867,479 66,132 44,982 4,338

Utah 21,548 122,277 63,845 43,438 30,706 18,589 1,715 Washington 26,510 65,291 276,092 39,305 14,438 9,753 6,660 Wyoming 28,711 139,554 70,587 130,978 32,040 19,720 1,153

Totals 1,100,062 3,048,131 2,942,819 3,261,141 680,878 365,337 238,217

The Integrated Assessment System (IAS) model was developed for the GCVTC to evaluate the visibility benefits of air pollution emission control strategies. This model started with a 1990 base year and made emissions forecasts in ten-year increments. The objective for this analysis was to prepare a 2018 emission forecast from a 1996 base year. Because the 2018 scenario results needed to be in the appropriate form to provide inputs to regional visibility models like the Community Multiscale Air Quality (CMAQ) model, and the Regulatory Modeling System for Aerosols and Deposition (REMSAD), if the IAS model was used directly for projecting 2018 emissions, an additional reporting step would be needed to take the aggregated source category level/IAS region results and allocate them to specific emission units. It was decided that it would be preferable to use the IAS algorithms and apply them to the unit-level information in the 1996 WRAP point source data base in order to provide 2018 emission forecasts at the same level of detail.

At the same time that the IAS model algorithms were being adapted for use with the 1996 point source file, a number of model upgrades were accomplished. These included determining the initial year of operation for large point sources in the core WRAP States in order to better simulate source retirements, and replacement with sources that emit at lower, new source rates. Another initiative was to review federal, state, and local regulations and incorporate up-to-date information about wester n state control programs in the 2018 forecast. In addition, information on unit lifetimes by source type was collected and evaluated, and the IAS model unit lifetime assumptions were changed. New source technology information about controlled pollutants and control efficiencies from the IAS model was also reviewed and updated with current information. Finally, growth factors were updated using recent REMI model projections, with State-supp lied projections substituted when provided.

Projections for two sectors were performed outside the IAS model framework – those for electric utilities and copper smelters. Estimates of 2018 SO emissions for the major electric utility plants use 2018 forecasts of electricity generation by unit that were developed for the WRAP Market Trading Forum. Because the WRAP-sponsored work only included SO emission estimates, E.H. Pechan & Associates, Inc. (Pechan) used 1999 Emissions Tracking System/Continuous Emission Monitoring (ETS/CEM) data, if available, or the emissions computed based on EIA-767 data to estimate 1999 NO emissions. Then, the same unit level growth assumptions (2018 versus 1999 generation estimates) that were applied to estimate 2018 SO emissions were used to estimate 2018 NO levels. Information provided by individual utility companies about known future NO emission limits was compared with 1999 emission rates, and it was found that 1999 pounds per million British thermal units (lbs/MMBtu) emission rates are at or below future limits, with the exception of the Cherokee plant in Colorado. Additional known NO controls were applied to all four units at Cherokee. These reductions ranged from 14 to 28 percent. 2

2

x

2 x

x

x

Final §309 Regional TSD Page 27 of 250 December 15, 2003 Revised May 7, 2008The 2018 forecasts of future utility PM and PM emissions (and all other criteria pollutants plus ammonia) use the 1996 WRAP point source inventory emission estimates and estimates of 1996 generation by unit to establish base year conditions, and the ratio of 2018 generation to 1996 generation by unit to estimate activity changes. 10 2.5

For copper smelters, 2018 SO emissions are set at the levels defined by the stepped reduction milestones through 2018 established by the Annex to the GCVTC’s recommendations. The current year allocation for the six copper smelters in the 9-State region is 86,000 tons. This allocation is reduced to 78,000 tons by 2018. The plant-level emissions difference between 86,000 tons and 78,000 tons SO was simulated by subtracting 2,000 tons each from the four largest smelters, which are ASARCO-Hayden, BHP-San Manuel, Phelps Dodge-Chino Mines, and Phelps Dodge-Hidalgo. 2

2

SO Annex Milestone Case (2018) 2
The Annex Milestones case emission forecast begins with a base case simulation that accounts for the expected controls and activity changes by State and source category that are expected to occur between 1996 and 2018. For utilities, estimates of 2018 SO emissions were provided by the WRAP Market Trading Forum from Integrated Planning Model simulations of the effects of a backstop regional (9 State) cap-and-trade program. Total utility SO emissions in the 9-State Commission Transport Region are 286,000 tpy. 2

2
The WRAP submitted an Annex to the Report of the GCVTC (SO Annex) to EPA on October 2, 2000, and EPA has since adopted the Annex in rule. The Annex shows the details of regional SO milestones and a backstop cap-and-trade program that would be triggered if the milestones were not met. The milestones were designed to show steady and continuing progress towards reducing SO emissions, and to show greater reasonable progress than the application of BART for the purposes of regional haze visibility impairment. 2

2

2

Table 1.1.2.3. 2018 SO Annex point source emissions by State for the modeling domain 2 2018 Emissions (tpy)

State VOC NO CO SO PM PM NH x 2 10 2.5 3 Arizona 17,152 138,583 32,723 109,877 38,211 20,117 33
Arkansas 19,068 73,001 124,170 80,821 34,346 18,471 25,423
California 104,337 106,812 152,122 32,650 45,419 24,400 26,900
Colorado 44,386 130,922 54,980 56,929 31,371 18,252 180
Idaho 275 7,295 6,410 14,769 6,979 4,314 2

Iowa 13,496 106,820 19,088 144,377 14,126 7,967 13,764

Kansas 34,474 196,081 87,634 89,505 20,990 14,507 19,302
Louisiana 152,153 317,031 593,941 167,373 38,862 27,971 73,139
Minnesota 46,047 175,765 103,413 82,930 131,675 58,398 1,288
Missouri 93,122 154,590 207,781 335,137 80,213 30,962 36,398

Montana 7,856 44,566 60,608 27,802 15,241 7,944 298
Nebraska 13,468 61,242 25,235 52,180 12,163 5,112 27

Final §309 Regional TSD Page 28 of 250 December 15, 2003 Revised May 7, 2008Nevada 2,109 52,859 29,814 23,167 22,260 8,362 89
New Mexico 23,936 154,192 59,770 135,280 11,677 3,476 131
North Dakota 1,646 115,180 20,277 186,751 5,196 3,538 31
Oklahoma 76,633 206,228 255,821 97,121 13,948 7,930 27,325 Oregon 23,715 33,226 111,960 12,129 14,117 10,142 19
South Dakota 1,553 22,493 615 11,337 770 432 2

Texas 347,864 624,122 585,932 645,980 66,132 44,982 4,338
Utah 21,548 122,277 63,845 35,506 30,706 18,589 1,715 Washington 26,510 65,291 276,092 32,410 14,438 9,753 6,660 Wyoming 28,711 139,554 70,587 89,693 32,040 19,720 1,153
Point Source Totals 1,100,062 3,048,131 2,942,819 2,463,724 680,878 365,337 238,217

BART with Uncertainty Case (2018)

For this scenario, emission reductions that would be expected to be achieved by applying controls to BART-eligible sources are applied within the 9-State Commission Transport Region, and then 10 percent of the Base Case SO emissions were added back into the point source inventory emissions from the 2018 Command and Control Case prorated across the 9- State Commission Transport Region in proportion to the Base Case inventory. The purposeof this analysis is to show the potential variability of emissions under the command-andcontrol scenario compared with a regional emissions cap, where regulatory consequences occur if total emissions exceed milestones. In addition, this 2018 scenario examines uncertainties in the command-and-control scenario that need to be accounted for in order to make a fair comparison with the market trading program (which includes an absolute cap on emissions). These uncertainties might include assumed retirements, the level of control applied to BART sources, new source emissions, utility unit capacity factors, and whether existing sources increase their actual emissions to be closer to permitted levels. 2

BART Case SO emission estimates for utilities in the 9-State Commission Transport Region are taken from column J (BART Case) from the utilin2.xls file provided by the WRAP Market Trading Forum. SO emission reductions from BART-eligible non-utility sources were estimated using the WRAP Market Trading Forum provided allstat7.xls file. States with non-utility BART sources and associated SO emission reductions in this scenario included Arizona, Colorado, New Mexico, Or egon, and Wyoming. Eight non-utility BART sources were expected to be retired by 2018, so no BART-estimated SO emission reductions were applied to these units. 2

2

2

2

The net result of the BART application is that modest SO emission reductions were estimated for BART controls applied to non-utility units in the 9-State Commission Transport Region. The largest SO emission reductions are estimated to occur in Colorado, and these are mostly attributable to BART controls applied to Trigen-Colorado Energy Corporation. For the 8 retired BART sources, SO emission reductions that might have been attributed to BART application were already captured in the 2018 emission forecast. 2

2

2

Final §309 Regional TSD Page 29 of 250 December 15, 2003 Revised May 7, 2008Within the GCVTC Transport Region States, increasing SO emissions by 10 percent brings the GCVTC point source SO emission total to 550 thousand tons, which is 40 thousand tons above the milestone amount. 2

2

Table 1.1.2.4. 2018 BART plus Uncertainty case results for point sources 2018 Annual Emissions (tpy)
State VOC NO CO SO PM PM NH x 2 10 2.5 3 Arizona 17,152 138,583 32,723 110,887 38,211 20,117 33 Arkansas 19,068 73,001 124,170 80,821 34,346 18,471 25,423 California 104,337 106,812 152,122 35,287 45,419 24,400 26,900 Colorado 44,386 130,922 54,980 63,648 31,371 18,252 180 Idaho 275 7,295 6,410 15,962 6,979 4,314 2 Iowa 13,496 106,820 19,088 144,377 14,126 7,967 13,764 Kansas 34,474 196,081 87,634 89,505 20,990 14,507 19,302 Louisiana 152,153 317,031 593,941 167,373 38,862 27,971 73,139 Minnesota 46,047 175,765 103,413 82,930 131,675 58,398 1,288 Missouri 93,122 154,590 207,781 335,137 80,213 30,962 36,398 Montana 7,856 44,566 60,608 27,802 15,241 7,944 298 Nebraska 13,468 61,242 25,235 52,180 12,163 5,112 27 Nevada 2,109 52,859 29,814 25,039 22,260 8,362 89 New Mexico 23,936 154,192 59,770 147,631 11,677 3,476 131 North Dakota 1,646 115,180 20,277 186,751 5,196 3,538 31 Oklahoma 76,633 206,228 255,821 97,121 13,948 7,930 27,325 Oregon 23,715 33,226 111,960 13,302 14,117 10,142 19 South Dakota 1,553 22,493 615 11,337 770 432 2 Texas 347,864 624,122 585,932 645,980 66,132 44,982 4,338 Utah 21,548 122,277 63,845 37,426 30,706 18,589 1,715 Washington 26,510 65,291 276,092 32,410 14,438 9,753 6,660 Wyoming 28,711 139,554 70,587 101,054 32,040 19,720 1,153

Totals 1,100,062 3,048,131 2,942,819 2,503,960 680,878 365,337 238,217

1.1.3. Area Source Emissions

Emissions inventories for area sources were deve loped as inputs to various technical analyses as needed for the requirements of §309, consisting of air quality modeling described later in this chapter. The WRAP Emissions Forum developed a 1996 base case of known area source emissions, and a 2018 base case, accounting for growth resulting from the expected increases in the population of the region, and including federal, state, and local area source emissions control programs already adopted.

Base Year (1996)

Area sources are generally described as those sources that are too small, numerous, or difficult to be inventoried individually. The EPA 1996 NEI was selected as the starting point for estimating area source emissions. EPA prepares the NEI with input from State, local, and tribal organizations. EPA uses the NEI to track long-term emission trends on a national

Final §309 Regional TSD Page 30 of 250 December 15, 2003 Revised May 7, 2008scale, to evaluate the effectiveness of national emission standards, and to satisfy some international obligations. The 1996 base year was selected because it was the most recent, quality assured inventory that was available when the analysis began. The 1996 data summaries presented here are based on Version 3.12 of the 1996 NEI.

A detailed description of the area source emission estimation methods used in the NEI is described in the EPA Procedures Document (EPA, 2001). This report is available through the EPA website www.epa.gov/ttn/chief (Clearinghouse for Emission Inventories and Factors).

The 1996 NEI included State-supplied area source emission inventories for Washington, California, Oklahoma, Texas, Missouri, and Louisiana. For other States, the 1996 area source emission estimates were those prepared by EPA. For the purposes of the WRAP analysis, additional area source data were provided by Oregon, Utah, and Colorado.

The 1996 area source emission estimates used in this analysis had the file name WGA_AR96.DBF. This file includes emission estimates for all source categories traditionally inventoried as area sources by the States, with the following exceptions: highway vehicles, nonroad engines/vehicles, dust from either paved or unpaved roads, or emissions from wild fire, prescribed burning, and agricultural burning. Emission estimates for geogenic wind blown dust from undisturbed natural soils have been set to zero for all States in this file.

The WRAP Mobile Sources Forum prepared estimates of on-road and off-road vehicle emissions and paved and unpaved road dust separately. The Fire Emissions Forum prepared estimates of fire-related emissions separately.

Forecast Year (2018)

The 2018 area source emission forecasts were developed using methods consistent with those used to develop the point source emission proj ections. This includes using algorithms from the GCVTC IAS. The original IAS model had 10 source categories (scc_ids) that were used to represent expected growth and controls affecting western State area source emissions. This relatively small number of source categories provides a reasonable aggregate picture of how area source emissions might be expected to change in future years because the focus of WRAP has been on SO , PM 2 10 , and NO x emissions. There are a relatively small number of important area source categories that emit these pollutants. However, because VOC emissions are also included in the regional modeling efforts, and are important in both ozone and PM formation, it was decided to expand the source category-level modeling process to the source classification code (SCC) level in order to capture VOC controls likely to occur via maximum achievable control technology (MACT) standards, or nonattainment area level ozone control plans. In making this change, what was formerly included in the area source IAS algorithms as new source control technology c ontrol efficiencies was incorporated in the control factors, rule effectiveness, and rule penetration values by SCC that are used to estimate 2018 area source emissions.

Final §309 Regional TSD Page 31 of 250 December 15, 2003 Revised May 7, 2008In order to develop area source control factors by pollutant, area-specific PM 10 control plans and information were collected and compile d from EPA Regional Offices, and State and local agencies for each of the selected nonattainment areas. PM 10 control factors were developed for PM 10 emitting source categories such as construction activity, residential wood combustion, vacant land/improved lots, open burning, and agricultural tilling. These control factors are applied in the nonattainment areas where such PM 10 controls are applied.

Growth surrogate data were compiled for this effort from contacts with the WRAP States, Regional Economics Model, Inc. (REMI) economic models incorporated into Version 4.0 of the Economic Growth Analysis System (EGAS) (Pechan, 2001), U.S. Department of Agriculture agricultural baseline projections (USDA, 2001), and the U.S. Department of Energy’s Annual Energy Outlook (DOE, 2000).

Growth factors used in the 2018 emission forecasts for most sectors are based on constant dollar output data that correspond directly with REMI industry sectors. Population is used as the growth surrogate for most non-combustion area source categories. Western States were given an opportunity to provide their own growth forecasts to be used for their States. Each of the 13 WRAP States and the 9 Tier 1/Tier 2 States were asked to submit their projections for constant dollar output, population, and personal income data. Population data were the most readily available. Colorado, Kansas, Louisiana, Minnesota, Missouri, Montana, Texas, and Washington provided comprehensive population data. Four States provided personal income projections: California, Montana, Nevada, and South Dakota.

Area Source Inventory Results

Table 1.1.3.1. 1996 Base Case - Area Source Emissions 1996 Annual Emissions (tpy)
State State VOC NO CO SO PM PM NH X 2 10 2.5 3 Arizona AZ 115,376 49,266 69,193 3,191 72,047 23,022 31,491 Arkansas AR 129,757 37,375 152,507 19,797 219,684 58,064 136,874 California CA 351,245 108,202 393,657 10,746 223,455 74,308 167,725 Colorado CO 90,573 11,004 65,578 1,915 215,931 44,747 96,298 Idaho ID 54,195 12,737 73,152 7,557 100,419 26,661 63,124 Iowa IA 137,257 29,594 79,613 13,663 356,507 77,668 290,821 Kansas KS 119,378 67,755 68,309 3,376 435,444 91,497 215,345 Louisiana LA 126,405 97,348 79,348 93,437 214,683 50,892 64,537 Minnesota MN 193,447 23,872 134,465 6,108 344,244 82,268 183,513 Missouri MO 167,505 13,904 205,511 32,006 287,545 74,964 180,035 Montana MT 49,669 11,312 62,958 1,180 175,511 39,796 89,797 Nebraska NE 79,048 14,546 43,159 9,912 272,469 56,246 228,745 Nevada NV 37,915 6,998 19,135 3,494 34,366 9,985 14,966 New Mexico NM 55,225 24,667 44,438 7,836 72,329 18,517 46,655 North Dakota ND 57,178 18,512 17,142 56,891 274,090 55,229 88,223 Oklahoma OK 109,069 30,147 83,788 5,023 249,239 56,701 187,370 Oregon OR 328,414 15,103 406,351 2,239 188,803 82,644 59,517 South Dakota SD 39,237 6,485 26,094 19,288 240,226 49,011 129,468 Texas TX 557,354 30,908 93,708 8,409 1,044,558 217,054 459,674

Final §309 Regional TSD Page 32 of 250 December 15, 2003 Revised May 7, 2008Utah UT 41,701 5,067 58,529 8,148 41,647 12,511 31,641 Washington WA 171,261 19,160 246,298 3,419 229,051 74,832 46,498 Wyoming WY 19,906 64,109 30,949 15,882 53,514 11,926 49,084

Area Source Totals 3,031,118 698,072 2,453,881 333,518 5,345,761 1,288,542 2,861,401

Table 1.1.3.2. 2018 Base Case - Area Source Emission s 2018 Annual Emissions (tpy)
State State VOC NO CO SO PM PM NH X 2 10 2.5 3 Arizona AZ 140,467 79,000 81,181 5,347 73,467 25,236 33,212 Arkansas AR 136,601 53,446 134,873 29,937 221,657 58,875 143,387 California CA 421,653 111,355 350,340 12,216 216,950 72,048 177,918 Colorado CO 90,443 11,896 69,713 2,057 217,316 44,747 102,820 Idaho ID 59,355 17,739 73,613 8,904 102,654 28,273 66,613 Iowa IA 163,616 37,901 74,653 17,778 357,862 77,880 308,253 Kansas KS 141,587 94,609 69,057 4,346 437,677 92,211 227,985 Louisiana LA 115,005 132,163 78,712 132,514 217,330 52,200 66,680 Minnesota MN 223,912 28,098 124,882 7,349 345,612 83,190 192,281 Missouri MO 176,763 19,171 181,748 43,352 290,796 75,975 190,720 Montana MT 52,131 15,145 53,349 1,326 177,201 39,904 94,708 Nebraska NE 93,879 19,126 41,996 13,931 275,017 56,735 243,144 Nevada NV 43,408 9,008 19,568 4,729 35,137 10,401 15,946 New Mexico NM 59,634 35,501 47,839 12,017 74,529 19,942 49,760 North Dakota ND 71,041 22,504 16,822 61,483 275,213 55,347 91,618 Oklahoma OK 110,837 41,001 76,844 7,408 252,046 57,527 198,103 Oregon OR 275,647 22,729 712,906 3,230 233,328 126,271 62,501 South Dakota SD 45,266 8,057 24,905 25,496 242,520 49,479 137,329 Texas TX 668,175 36,030 90,137 11,412 1,053,273 220,122 488,403 Utah UT 45,883 5,540 65,035 8,219 43,008 13,298 33,703 Washington WA 183,733 26,020 253,564 4,425 235,008 80,864 48,920 Wyoming WY 19,617 85,065 32,126 17,975 54,730 12,225 52,286

Area Source Totals 3,338,657 911,104 2,673,863 435,450 5,432,331 1,352,751 3,026,289

As a general rule, regional area source emissions for all of the criteria pollutants plus ammonia are expected to be higher in 2018 than they were in 1996. For example, total VOC emissions in the modeling domain are expected to be 10 percent higher in 2018 than they were in 1996.

Table 1.1.3.3. Regional area source emissions changes, 1996 to 2018.

Pollutant Regional Percentage Change (1996 to 2018) VOC +10 NO +30 x CO +9 SO +31 2 PM +2 10 PM +5 2.5

Final §309 Regional TSD Page 33 of 250 December 15, 2003 Revised May 7, 2008NH +6 3

1.1.4. Mobile Source Emissions

Emissions inventories for mobile sources were developed as inputs to various technical analyses as needed for the requirements of §309, consisting of air quality modeling described later in this chapter, and originally, the separate “determination of significance” requirement. The WRAP Mobile Sources Forum developed a 1996 base case of known emissions, and future cases for 2003, 2008, 2013, and 2018, accounting for growth resulting from the expected increases in the population and vehicle miles traveled of the region, and including federal, state, and local programs for contro l technologies and fuel formulation changes already adopted.

Emissions Inventoried Under Mobile Sources

Mobile sources include on-road and off-road vehicles and engines. On-road mobile sources include vehicles certified for highway use – cars, buses, trucks, and motorcycles. For reporting on-road mobile source emissions, vehicles are divided into two major classes – light-duty and heavy-duty. Light-duty vehicles include passenger cars, light-duty trucks (up to 8500 lbs gross vehicle weight [GVW]), and mo torcycles. Heavy-duty vehicles are trucks of more than 8500 lbs GVW.

Off-road mobile equipment encompasses a wide variety of equipment types that either move under their own power or are capable of being moved from site to site. Off-road mobile equipment sources are defined as those that move or are moved within a 12-month period and are covered under the EPA’s emissions re gulations for nonroad mobile sources. Offroad mobile sources are vehicles and engines in the following categories:

• Agricultural equipment, such as tractors, combines, and balers;

• Aircraft, jet and piston engines;

• Airport ground support equipment, such as terminal tractors;

• Commercial and industrial equipment, such as fork lifts and sweepers;

• Construction and mining equipment, such as graders and back hoes;

• Lawn and garden equipment, such as leaf and snow blowers;

• Locomotives, switching and line-haul trains;

• Logging equipment, such as shredders and large chain saws;

• Pleasure craft, such as power boats and personal watercraft;

• Railway maintenance equipment, such as rail straighteners;

• Recreational equipment, such as all-terrain vehicles and off-road motorcycles; and

• Underground mining and oil field equipment, such as mechanical drilling engines.

Scope Of The Mobile Sources Inventory

The scope of the WRAP mobile sources emission inventories is as follows:

Final §309 Regional TSD Page 34 of 250 December 15, 2003 Revised May 7, 2008Geographic domain : Emissions were estimated by county for all counties in 13 states: Arizona, California, Colorado, Idaho, Montan a, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming. Alaska was not a WRAP member at the time the emissions were estimated, and so is not included in the mobile sources emission inventories.

Temporal resolution : Emissions were estimated for an average weekday in each of the four seasons, and for an average annual weekday. Seasons are defined as three-month periods: Spring (March–May), Summer (June-August), Fall (September-November), and Winter(December-January). In addition to the 1996 base year and 2018 future modeling year, onroad and off-road mobile sources were estimated for the intermediate years 2003, 2008, and 2013.

Pollutants : Emissions were estimated for PM 10 , PM 2.5 , NO , SO x x , VOCs, carbon monoxide (CO), NH 3 , elemental and organic carbon (EC/OC), and sulfate (SO4). For all pollutants, emissions were estimated separately for gasoline and diesel-fueled engines.

General Approach For Estimating On-Road & Off-Road Mobile Emissions

As with most emissions sources, on-road and off-road mobile source emissions are estimated as the products of emission factors and activity estimates. Except for California, the on-road mobile sources emission factors were derive d from the EPA MOBILE6 and PART5 models (the latter modified for this project). Activity for on-road mobile sources is vehicle miles traveled (VMT). States were provided defa ult modeling inputs and VMT levels for base and future years for review and update; several stat es provided revised data. California provided on-road emissions estimates by county directly.

EPA’s NONROAD2000 model was used to estimate so-called traditional off-road sources, all sources listed above except aircraft, commercial marine, and locomotives. The NONROAD model includes estimates of emission factors, activity levels, and growth factors for all traditional off-road sources. The default activity levels were provided to state agencies for input and update; however, no state provided updated off-road activity data. Emissions estimation methods for aircraft, commercial marine, and locomotives were similar to approaches EPA has recently used in devel oping national emission inventories. California also provided off-road emissions estimates by county directly.

Models for estimating on-road mobile source emissions

EPA on-road emission factor models were used as the basis for estimating on-road emissions. EPA’s MOBILE6 model was used to estimate vehicle VOC, NO X , and CO emissions. For WRAP on-road emissions estimates, the latest available version of draft MOBILE6 that was available at the time was used. Specifically, the March 2001 draft version was used for estimating 1996 base year on-road emissions, and the September 2001

Final §309 Regional TSD Page 35 of 250 December 15, 2003 Revised May 7, 2008draft version was used for estimating on-road emissions for all future years. The model was officially released in January of 2002 1

. The MOBILE6 model includes the effects of all Federal motor vehicle control programs:

• Tier 1 light-duty vehicle standards, beginning with the 1996 model year;

• National Low Emission Vehicle (NLEV) sta ndards for light-duty vehicles, beginning with model year 2001;

• Tier 2 light-duty vehicle standards, begi nning with model year 2005, with low sulfur gasoline beginning in the summer of 2004;

• Heavy-duty vehicle standards, beginning with model year 2004; and

• Heavy-duty vehicle standards (with low sulfur diesel), beginning with model year 2007.

EPA’s PART5 model was the basis for estimating PM 10 , PM 2.5 , SO , NH 4 3 , and EC/OC emissions, but was modified for use in this project. For PM, the model estimates exhaust, tire wear, and brake wear emission factors. At the time of inventory development, the PART5 model was the only publicly available tool from EPA with which to generate particulate matter emissions estimates for on-road motor vehicles. The PART5 emission factors are outdated, and were revised to incorporate the results of more recent testing programs. Changes were made to both light -duty vehicle and heavy-duty vehicle exhaust emission factors, and some of the non-exhaust processes (e.g., brake wear) were updated as well. Where new data existed, particle size distributions for estimating PM 2.5 from PM 10 were also updated. Fleet characteristic data (e.g., registration fractions, mileage accumulation rates, etc.) were updated to be consistent with MOBILE6. Finally, the model was modified to estimate ammonia emission factors for all vehicle classes, and to estimate EC/OC emission factors as fractions of PM emi ssion factors. All of these model revisions and the data upon which they are based are fully described in the ENVIRON (2003) report. After the PART5 modifications were made and the WRAP on-road emissions inventories were generated, EPA incorporated the PART5 emission factors into an updated version of MOBILE6 (version 6.2), which is now the regulatory model for on-road emissions.

The California Air Resources Board (CARB) has developed its own model for estimating California on-road emissions, EMFAC. The m odel includes both emission factors and VMT activity data. The version of the model that was used to generate the WRAP base and future year on-road emissions was EMFAC2000. CARB ran the models for WRAP and provided the emissions estimates in the geographic, temporal, and source category detail as needed for WRAP.

Models For Estimating Traditional Off-Road Mobile Source Emissions

1 - The September 2001 draft model differs from the March 2001 draft version only in VOC refueling emissions (by a few percent). There were several changes from the September 2001 draft version to th e January 2002 public release (described in ENVIRON, 2003); effects of these changes vary by count y and year and depend on fleet composition, Inspection and Maintenance program parameters, a nd speed and temperature inputs.

Final §309 Regional TSD Page 36 of 250 December 15, 2003 Revised May 7, 2008Emissions for traditional nonroad sources are estimated with EPA’s NONROAD model. Until late June 2003, the draft version of the model that was publicly available was the June 2000 version; this is the version that was originally used to develop the WRAP off-road emissions estimates. The June 2000 NONROAD model incorporates the effects of off-road equipment regulations that were in effect at the time:

• Phase 1 and 2 emission standards for new nonr oad spark-ignition engines at or below 25 horsepower (hp);

• Emission standards for new gasoline spark-ignition marine engines;

• Tier 1 and Tier 2 emission standards for new nonroad compression-ignition engines below 50 hp, including recreational marine engines less than 50 hp; and

• Tier 1 through Tier 3 emission standards for new nonroad compression-ignition engines at or above 50 hp, not including recr eational marine engines greater than 50 hp.

In December 2002, EPA confidentially released an updated version of the model, NONROAD2002, to WRAP and other air quality planning agencies. The NONROAD2002 model was publicly released on the NONROAD model website in late June 2003, along with an updated User Guide and technical reports 1 . The NONROAD2002 model incorporates updated population data, load factors, and median life; increased PM deterioration rates; revised PM and SO x calculations (correcting coding in the prior version); revised equipment growth and scrappage; revised activity data for all terrain vehicles; and revised algorithms for allocating national to county-level equipment populations.

With all of these changes, the emission inventory estimates from the NONROAD2002 model are substantially different from the June 2000 draft version. Total U.S. emissions for 1996 are 13 percent lower for VOC in NONROAD2002, 35 percent lower for NO X , 28 percent lower for PM 2.5 , and 72 percent lower for SO 2 . The percent changes vary by equipment type and fuel, as shown in Figure 10. Construction and mining equipment are the largest source of emissions for NO X , PM 2.5 , and SO 2 . Diesel emissions for construction and mining and other equipment types are reduced in NONROAD2002 because the population numbers are lower, and also because lower average equipmen t lifetimes result in the age distribution with more newer equipment. Pleasure craft are the largest source of VOC emissions. Pleasure craft and other SI equipment are on average lower by about 10 percent in NONROAD2002, though recreational equipment VOC emissions increased primarily because the activity rates and increased. Note that these are 1996 U.S. total comparisons. Comparisons for individual states or counties will be different because of changes in geographical allocation and differences in fuel properties. The differences between NONROAD2000 and NONROAD2002 over time will also vary in amount, but not in direction. Comparisons of the two models for U.S. total emissions for years 2000, 2010, 2020, and 2030 may be found in an EPA memo released along with the confidential December 2002 model release (Harvey, 2002).

1 - http://www.epa.gov/oms/nonrdmdl.htm

Final §309 Regional TSD Page 37 of 250 December 15, 2003 Revised May 7, 2008Figure 1.1.4.1. Changes in 1996 U.S. emissions from NONROAD2000 to NONROAD2002 by source category and fuel.

0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 Agricultural Equipment

Airport Equipment Commercial Equipment Construction and Mining Equipment Industrial Equipment

Lawn and Garden Equipment (Com) Lawn and Garden Equipment (Res) Logging Equipment

Pleasure Craft Railroad Equipment Recreational Equipment

1996 US NOx Emissions (tons) 0 200,000 400,000 600,000 800,000 1,000,000 Agricultural Equipment

Airport Equipment Commercial Equipment Construction and Mining Equipment Industrial Equipment

Lawn and Garden Equipment (Com) Lawn and Garden Equipment (Res) Logging Equipment

Pleasure Craft Railroad Equipment Recreational Equipment

1996 US VOC Emissions (tons) NONROAD2000 SI
NONROAD2000 Diesel
NONROAD2002 SI
NONROAD2002 Diesel NONROAD2000 SI
NONROAD2000 Diesel
NONROAD2002 SI
NONROAD2002 Diesel

0 50,000 100,000 150,000 200,000 250,000 300,000 Agricultural Equipment

Airport Equipment Commercial Equipment Construction and Mining Equipment Industrial Equipment

Lawn and Garden Equipment (Com) Lawn and Garden Equipment (Res) Logging Equipment

Pleasure Craft Railroad Equipment Recreational Equipment

1996 US SO2 Emissions (tons) 0 30,000 60,000 90,000 120,000 150,000 Agricultural Equipment

Airport Equipment Commercial Equipment Construction and Mining Equipment Industrial Equipment

Lawn and Garden Equipment (Com) Lawn and Garden Equipment (Res) Logging Equipment

Pleasure Craft Railroad Equipment ecreational Equipment

1996 US PM2.5 Emissions (tons) R NONROAD2000 SI NONROAD2000 SI NONROAD2000 Diesel NONROAD2000 Diesel NONROAD2002 SI NONROAD2002 SI NONROAD2002 Diesel NONROAD2002 Diesel

The NONROAD2002 model does not incorporate the effects of the September 2002 off-road equipment regulations for large (> 25 hp) spark-ignition equipment, recreational equipment, and compression-ignition (diesel) pleasure craft above 50 hp. ENVIRON obtained updated NONROAD2002 model input files from EPA that incorporate these new exhaust standards (the effects of the evaporative standards are not yet modeled). Emissions for traditional offroad equipment were estimated with the NONROAD2002 model, with the updates for the September 2002 rulemaking.

The NONROAD2002 emission factors for future years incorporate the effects of all promulgated off-road equipment regulations (except as noted above). The NONROAD2002 model also includes equipment population growth factors, but they are based on a confidential and undocumented data source, and they are national-level only. ENVIRON developed state-level growth factors so that st ate-to-state growth differences could be taken into account for estimating future year emissions. The growth factors were based on

Final §309 Regional TSD Page 38 of 250 December 15, 2003 Revised May 7, 2008analyses of historical Gross State Product (GSP) data by economic sector available from the Bureau of Economic Affairs (BEA), Census population data, or other appropriate activity trend data; full details are in the ENVIRON (2003) report.

In April 2003, EPA proposed new standards for off-road compression-ignition (diesel) equipment. The proposal is for new engine standards to phase in between 2008 and 2013, with smaller engine standards phasing in earlier than for larger engines. Emissions standards for the largest engines, between 175 and 750 horsepower, phase in from 2011 to 2013. With these so-called Tier 4 emission standards, fuel sulfur levels need to be reduced to 15 ppm to enable the PM and NO X emission control technology. Nonroad diesel sulfur is proposed to be 500 ppm in 2008 and 15 ppm in 2010.

The NONROAD2002 modeling was completed prior to the release of these proposed new standards. While the NONROAD2002 emissions modeling performed for WRAP did not include the anticipated emissions effects of the Tier 4 technology, the diesel fuel sulfur levels were set to 15 ppm in the NONROAD2002 modeling for years 2008, 2013, and 2018. The emission estimates were adjusted to back out th e effects of the low sulfur diesel, by applying the known state averages of diesel fuel sulfur to the NONROAD2002 data.

For many years, CARB has been developing its own off-road emissions model, called OFFROAD. The model has not yet been released, though CARB has developed many of the emission factor and population/activity inputs and assumptions as part of their analyses in support of their off-road equipment regulati ons. For this project, CARB provided countylevel emissions estimates for all off-road sources by fuel type for the 1996 base and future years; these estimates were derived from an internal working version of the OFFROAD model.

Emissions Estimation Methods - Aircraft, Commercial Marine, & Locomotive

Aircraft, commercial marine vessel, and locomotive emissions are not addressed in EPA’s NONROAD model. Emissions from these sources were estimated using EPA guidance and/or EPA methodology as used in the 1996 and 1999 national emission inventories. The analysis approach for each of these sources is summarized briefly here; complete details of the methods and assumptions are provided in the ENVIRON (2003) report. The methods described here were used to estimate emissions for these three source categories in all states except California; for California the ARB pr ovided county-level emission inventories.

Aircraft

For the 1996 base year, aircraft emissions were estimated per airport, and then assigned to counties. For the nine large “hub” airports in the region (outside California), emissions for commercial air carriers were estimated using detailed data on monthly landings and take-offs (LTO) by airframe model, and emission factors by airframe model from the Federal Aviation Administration’s (FAA) Emissions and Disper sion Modeling System (EDMS). Commercial air carrier emissions from other airports were estimated using total air carrier LTO data and the fleet average emission factor for air carriers. For all airports, emissions from air taxis,

Final §309 Regional TSD Page 39 of 250 December 15, 2003 Revised May 7, 2008general aviation, and military aircraft were estimated using total LTO data by aircraft group and EPA fleet average emission factors for these sm aller aircraft. The base and future years’ emissions estimates account only for exhaust below 10,000 feet above sea level, and do not include cruising emissions.

Future year aircraft emissions projections were estimated by applying state-level growth factors to the 1996 aircraft emissions estimates. The state-level growth factors for commercial aircraft (air carriers and air taxi s) are based on historical FAA LTO data; for military and general aviation, the growth factors are based on BEA state-level air transportation GSP data. There were no reducti ons in emission factors from 1996 base year values for commercial aircraft.

Commercial marine

Base year commercial marine vessel activity was estimated by vessel type – ocean-going, tug, ferries, dredges, and fishing vessels. For each vessel type, the activity estimates were derived from a number of data sources, incl uding EPA contractor reports, Army Corps of Engineers data, State Implementation Plan (SIP) emission inventory submittals, and locally derived information from ferry schedules and conversations with Harbor Pilots. The activity estimates were converted to engine activity parameters in terms of work or fuel consumed, and then multiplied by EPA engine emission factor estimates.

For future year emissions estimates, historical freight tonnage data from the US Army Corps of Engineers were used to forecast growth factor s by state. EPA projections of the effects of commercial marine emissions standards were incorporated into emission factors for future
years.

Locomotives

The methodology used to estimate emissions from railroad locomotives is similar to what EPA used to generate the locomotive inventory for the 1996 National Emission Trends (NET96) project. For the 1996 base year, annual estimates of railroad locomotive fuel consumption generated as part of the 1996 NET project were used to define locomotive activity levels. Emissions were estimated as the product of these activity levels and EPA emission factors for uncontrolled line-haul and switch locomotives. National activity allocations of fuel consumption used in line-haul and switch engines were used to allocate line-haul and switch engine activity within each county (as no local estimates were available).

Future year locomotive emissions were projected from the 1996 base year emission estimates. State-level growth factors were estimated from historical railroad fuel consumption data. EPA projections of the e ffects of locomotive emissions standards were used to determine future year locomotive emission factors.

1.1.5. Dust emissions from paved and unpaved roads

Final §309 Regional TSD Page 40 of 250 December 15, 2003 Revised May 7, 2008Fugitive dust from paved and unpaved roads is a significant source of PM emissions. For the 1996 base case WRAP modeling and the early 2018 modeling, estimates of road dust emissions estimates were initially generated using methodologies defined in EPA’s AP-42. However, significant inconsistencies in the resulting road dust emissions were observed between states, and revised road dust emissions were estimated. This section describes both the original road dust calculations, and the revised calculations.

Original road dust estimates - standard EPA approach

Unpaved Roads

For the WRAP 1996 base case modeling, the EPA 1996 NEI estimates of paved and unpaved road dust emissions were used. Road dust emissions for 2018 from paved and unpaved roadways were estimated for WRAP based on the NET96 emissions estimates.

These road dust emissions estimates are based on the standard AP-42 method. The following equation, from AP-42, was used to calculate emission factors for unpaved road dust for each month:

E = [k * (s/12)a * (W/3)b / (M

ext dry /0.2)c] * [(365 – (p*12))/365]

where:

E ext = monthly PM 10 emission factor extrapolated for natural mitigation (lb/mile) k = empirical constant (2.6 lb/mile) 2000 = conversion factor, number of pounds per ton s = surface material silt content (%) a = empirical constant (0.8) W = mean vehicle weight (tons) b = empirical constant (0.4) = surface material moisture content under dry, uncontrolled conditions (%) M dry c = empirical constant (0.3) p = number of days in a given month with greater than 0.01 inches of precipitation

This equation is representative of a fleet average emission factor rather than a vehiclespecific emission factor. A default value of 2.2 tons was used nationally as the mean vehicle weight, as recommended in the AP-42 documentation for travel on publicly accessible unpaved roads. A value of 1.0 percent was applied nationally for the surface material moisture content under dry, uncontrolled conditions as a conservative estimate based on available data. Silt content values were from a database developed for the 1985 National Acid Precipitation Assessment Program (NAPAP) inventory with silt contents of over 200 unpaved roads from over 30 states. Average silt contents of unpaved roads were calculated for each state that had three or more samples; for other states the national average across all samples was used. The number of days per month with more than 0.01 inches of rain for each state was estimated from ten years of data from a meteorological station in each state selected to be representative of rural areas within the state.

Final §309 Regional TSD Page 41 of 250 December 15, 2003 Revised May 7, 2008Vehicle miles traveled (VMT) on unpaved roads in the NET96 were estimated using data reported to the Federal Highway Administration (FHWA). For the 2018 unpaved road dust emissions, two factors were applied to the NET96 VMT to estimate changes in unpaved road VMT from 1996 to 2018. First, a population growth factor was calculated at the state level as the projected 2018 population divided by th e 1996 population. The second factor was a regional unpaved roadway mileage growth factor , developed based on historical trends in unpaved roadway mileage, to account for regional differences in the counter-acting trends to increase unpaved road mileage in some areas while paving over existing unpaved roads in other areas.

Unpaved road fugitive dust emissions were estimated for 2018 at the state and roadway type level by multiplying the state/roadway type-specific emission factors by the corresponding VMT estimate. The state-level emissions were then allocated to the county using the ratio of each county’s rural population to the state rural population.

Paved Roads

PM emission factors for reentrained road dust from paved roads were calculated using EPA’s PART5 model. The PART5 equation, from AP-42, is:

PAVED = PSDPVD * (PVSILT/2) 0.65 * (WEIGHT/3) 1.5

where:

PAVED = paved road dust emission factor for all vehicle classes combined (grams per mile), PSDPVD = base emission factor for particles of less than 10 microns in diameter from paved road dust (7.3 g/mi for PM 10 ), PVSILT = road surface silt loading (g/m 2 ), and WEIGHT = average weight of all vehicle types combined (tons).

Paved road silt loadings were assigned by roadway type based on traffic volume. For local roads, a silt loading of 1 gm/m 2 was assigned; for all other roadway types, 0.20 or 0.04 gm/m 2 was assigned for roadway types that had an estimated average daily traffic volume (ADTV) of less than or more than 5,000 vehicles per day, respectively. ADTV was calculated by dividing annual VMT by state and roadway type by state-specific mileage by roadway type.

These emission factors were modified to account for the number of days with a sufficient amount of precipitation to prevent road dust resuspension, using ten-year monthly average meteorological data from a selected urban mete orological station in each state. The PART5 emission factors were multiplied by the fraction of days in a month with less than 0.01 inches of precipitation, reduced by 50 percent (i.e., the rain correction factor is calculated as: [365 – (p * 12 * 0.5)] / 365, where p represents the number of days in a given month with greater

Final §309 Regional TSD Page 42 of 250 December 15, 2003 Revised May 7, 2008than 0.01 inches of precipitation).

Paved road fugitive dust emissions for 2018 were estimated at the state and roadway type level by multiplying the state/roadway type-specific emission factors by the corresponding VMT estimate. The state-level fugitive dust emissions were then allocated to the county level in a three-step process. First, the total VMT (from paved and unpaved roads combined) was summed by county and roadway type and by state and roadway type. Next, the ratio of county to state total VMT by roadway type was calculated for each county and roadway type combination. Finally, the paved road emissions at the state and roadway type level were multiplied by the ratio of the county to state total VMT for the given roadway type. The result of this calculation was a database of 2018 paved road emissions at the county and roadway type level of detail.

Revised road dust emissions estimates

Unexpected inconsistencies were observed in the road dust emissions estimates developed using the standard AP-42 approach. There was large variation in emission estimates from state to state, and in adjacent counties. In particular, there is unexpectedly large variation in the unpaved road dust emissions among the Colorado Plateau states. The underlying data used to estimate the road dust emissions were reviewed, and significant variation was found in both the emission factors and the activity data.

Variability in the state-level emission factors is largely because of variability in the assumed state-level silt content values. For the revised road dust emissions estimates, changes were made to the state-level silt content vales. An updated database was obtained, and an extremely high outlying value (for Montana) was deleted from the database. The state-level and Western state averages were recalculated, and the Western states average was used for those states with inadequate data (less than three measurements), rather than the national
average.

Changes were also made to the ADTV assumptions. The unpaved road VMT data are the product of road mileage and the assumed ADTV, obtained from FHWA reporting systems. The FHWA data were submitted by each state, with varying data collection methods across states and counties — some jurisdictions performed surveys; others relied on road owners/managers (usually Federal agencies). The ADTV estimates were revised based on survey work done for the Clark County, NV June 2001 PM 10 State Implementation Plan (SIP), and traffic volume estimates from National Forest Service unpaved roads (NFS is the largest manager of unpaved roads in the west). Complete details of all of these changes are described in the ENVIRON (2003) report.

For air quality modeling, the revised road dust emission estimates were multiplied by a factor to account for deposition and other removal mechanisms that tend to lower the amount of dust that is transported on a regional basis (i.e., outside the 36 km grid cells in the modeling domain). In the past, EPA and others have reduced the road dust emissions by a factor of four to account for these effects. For this project, county-specific transport fractions, developed by members of the WRAP Dust Expert Panel, were applied. The transport

Final §309 Regional TSD Page 43 of 250 December 15, 2003 Revised May 7, 2008fractions depend on the vegetative characteristics of each county, and were calculated as the weighted average of vegetation-specific transport fractions in each county. The transport fractions for each vegetation type are as follows: barren and water, 97%; agricultural, 85%; grasses, 70%; urban, 40%; scrub and sparse wooded, 30%; and forested, 5%.

The effect of all of these changes was an overall 89 percent reduction in road dust emissions estimates in the Western states. Revision of the silt loading values resulted in a 15 percent reduction; revisions to the ADTV data resulted in a 68 percent reduction; and application of the transport fractions resulted in a 60 percent reduction. The WRAP Expert Panel is reviewing these road dust estimates, as well as other potential modeling changes for estimating road dust transport.

1.1.6. Pollution Prevention Emissions Inventory

Two key recommendations from the GCVTC focused on the development of renewable energy sources and promotion of energy conservation. Labeled the 10/20 goals, the recommendation on development of renewable energy sources encouraged States and tribes in the Transport Region to undertake steps that would increase the use of renewable energy to 10 percent of the regional power needs by 2005 and 20 percent of the regional power needs by 2015. For energy conservation, the Commission supported the continued development of energy efficiency standards and suggested that the emphasis on energy conservation be maintained within the changing electric power markets. In addition to the 10/20 goals and energy conservation recommendations, the GCVTC suggested that future modeling work be conducted to analyze the potential emission reductions, cost savings, and secondary benefits associated with the use of renewable energy, energy efficiency, and pollution prevention.

The Air Pollution Prevention Forum of the WRAP has been charged with implementing the air pollution prevention recommendations of the GCVTC. The Air Pollution Prevention Forum commissioned the ICF Consulting Group to analyze the potential emission reductions, costs, and secondary regional economic impacts of meeting the 10/20 goals and energy efficiency recommendations (ICF, 2002). The analysis of this case incorporates the results of the ICF analysis for the Air Pollution Prevention Forum in a scenario that includes 2018 milestone case emission estimates for non-utility point sources.

The estimated SO and NO 2 x emissions by utility unit for existing facilities, and by State for new sources, were provided by the ICF Consulting Group. The percentage changes in SO 2 and NO x emissions by unit were applied to the 2018 Milestone Case emissions to estimate air pollution prevention case emissions for this analysis. The ICF model also provided estimated SO and NO 2 x reductions for new sources. These new source emission reductions were applied to the utility units in each State in proportion to 2018 milestone case emissions. Because of the regional SO trading program, the regional SO 2 2 emissions total is the same in the air pollution prevention case as it was for the milestone case. There is some shifting of SO emissions among units and States, though. Regional NO 2 x emissions decline by about 14 thousand tons (air pollution prevention case versus milestone case). A State-level summary of utility emission changes by State from the 2018 milestone case is provided below. The

Final §309 Regional TSD Page 44 of 250 December 15, 2003 Revised May 7, 2008tribal new source changes were allocated to Arizona. States not listed had no emissions change.

Table 1.1.6.1. State-Level Emissions Changes fr om Regional Pollution Prevention Program. Air Pollution Prevention Case Emissions Chan g e State NO t py SO t py x 2 Arizona * -3

, 267 5

, 558 Colorado -1,370 -1,119 Nevada -430 -307 New Mexico -7,053 -5,135 Utah -780 -595 Wyoming -1,374 1,598
Re g ional Chan g es -14

, 274 0 * - All new Tribal source emissions reductions (–990 for NO x and –430 for SO 2 ) were assigned to Arizona.

This 2018 scenario is described separately from the other 2018 emission forecasts because it was developed from a revised 1996 base year em issions database. The 1996 point source file used in this analysis includes revisions made by Pechan during the summer of 2002 after quality control checks were made on the original PES data file. The revised point source file used in this analysis is the September 2002 revised WGA_96pt.zip file, which is posted at www.wrapair.org . This revised file includes some changes to SO 2 emission estimates so that facility-level emissions would be as close as possible to those in the true-up inventory. The true-up inventory contains the plant-level SO 2 emission estimates used to measure the region’s progress in meeting emission milestones. In addition, corrections were made to problems found with point source coordinates, stack parameters, and SCC assignments. Thus, Table 1.1.6.2 below shows the resulting emissions, after changing from version 1 to version 2 of the 1996 WRAP point source data base, and the following summary table the net effect on 2018 Annex/Milestone case emission estimates.

Table 1.1.6.2. 2018 Regional Pollution Prevention Case Point Source Emissions for the Annex Milestones Case – State Totals (22 State Region) SO 2 2018 Emissions (tpy)
State VOC NO CO SO PM PM NH x 2 10 2.5 3 Arizona 10,775 134,615 31,917 113,915 26,736 9,524 30 Arkansas 19,068 73,001 124,170 80,468 34,346 18,471 25,423 California 104,337 106,707 152,122 31,197 45,419 24,400 26,900 Colorado 44,386 129,551 54,980 57,619 31,371 18,252 180 Idaho 275 7,295 6,410 13,613 6,979 4,314 2 Iowa 13,496 107,005 19,088 145,785 14,140 7,971 13,764 Kansas 34,474 195,204 87,634 86,662 20,967 14,491 19,302 Louisiana 152,153 317,031 593,941 164,979 38,862 27,971 73,139 Minnesota 46,047 177,235 103,413 83,048 131,699 58,412 1,288 Missouri 93,122 155,670 207,781 338,407 80,224 30,968 36,398 Montana 7,856 44,566 60,608 27,440 15,241 7,944 298 Nebraska 13,468 61,705 25,235 52,548 12,177 5,121 27 Nevada 2,109 52,426 29,814 22,648 22,260 8,362 89 New Mexico 23,941 148,706 59,834 133,121 11,736 3,477 131

Final §309 Regional TSD Page 45 of 250 December 15, 2003 Revised May 7, 2008North Dakota 1,967 120,051 23,747 183,811 5,585 3,534 31 Oklahoma 76,633 206,228 255,821 96,390 13,948 7,930 27,325 Oregon 23,634 31,888 108,312 8,388 13,638 9,740 19 South Dakota 1,553 22,076 615 11,275 770 432 2 Texas 347,864 624,359 585,932 642,106 66,132 44,982 4,338 Utah 13,250 103,310 60,882 36,233 20,977 12,244 1,162 Washington 26,510 64,816 275,981 31,283 14,320 10,007 6,660 Wyoming 28,711 138,180 70,587 93,264 32,040 19,757 1,153

Totals 1,085,632 3,021,623 2,938,824 2,454,201 659,566 348,303 237,661

The primary result of the above-mentioned changes to the 1996 WRAP point source database is to reduce GCVTC transport region criteria pollutant emissions. The table below shows how much lower the 2018 Milestone Case emissions are after the 1996 point source emission file revisions were made. SO 2 emissions are, by definition, 510,000 tpy in the Milestone Case, so the SO 2 emissions difference is zero. Regional emissions for all of the other pollutants are lower when the revised 1996 point source database was used to generate 2018 emission forecasts. These emissions were not included in the air quality modeling of the §309 control strategies.

Table 1.1.6.3. Regional Changes in Emissions in 2018 Base Case resulting from changing the 1996 Base Case from Version 1 to Version 2. Pollutant 2018 Regional Emission Reductions in the Milestone Case (tpy) VOC 14,751 NO 18,768 x CO 7,354 SO 0 2 PM 21,624 10 PM 17,301 2.5 556 NH 3

1.1.7. Fire Emissions

The Emissions Task Team (ETT) of the FEJF pr epared a historical fire emission inventory for 1996 and fire emission projections for 2018. This section is a summary of the two final technical reports for these WRAP emission inventory projects. The reports are entitled: “1996 Fire Emission Inventory”, and “Integrated Assessment Update and 2018 Emissions Inventory for Prescribed Fire, Wildfire, and Agricultural Burning”. The complete technical reports are listed in Appendix M. For detailed discussion of the topics summarized in the fire portions of the TSD, please refer to the appropriate section in the technical reports.

Basic concepts of the WRAP fire emission inventories

Final §309 Regional TSD Page 46 of 250 December 15, 2003 Revised May 7, 2008The FEJF inventoried fire emissions for 1996 and projected fire emissions for 2018. The term fire refers inclusively to wildfire, wildland fire managed for resource benefits (formerly prescribed natural fire), prescribed fire and agricultural fire.

The WRAP Air Quality Modeling and Emissions Forums provided the FEJF specific data resolution requirements for the fire emission inventories. For spatial resolution, each fire event needed a specific latitude and longitude in order to satisfy the spatial resolution goal of one minute of latitude and longitude. For temporal resolution, the hourly emission estimates for each fire event were required.

Estimating emissions from fire events involves considerable scientific uncertainty. For the 1996 inventory, historic data were of varying quality and for some areas unavailable. For the 2018 prescribed fire projections, future activity levels were estimates based on an emissions inventory product of the GCVTC, called the Fire Emissions Project (FEP). Detailed historical agricultural residue burning data proved to be particularly challenging to compile. Additional scientific uncertainty in emission es timates of all fire types can be introduced by emission factors, fuel loadings, and plume rise equations.

The specifications required by the Emissions and Modeling Forums combined with the limitations of existing data and emission estimation methods shaped the emission inventory development. Fire, traditionally considered an "area" source, is treated as "point" sources in the model. In both the 1996 and 2018 inventories, fire emissions are placed at a latitude/longitude coordinate location for each day. From the daily and spatially resolved emission inventory, hourly consumption and plume rise are estimated.

The 1996 fire emissions inventory is summarized in the following section. This emission inventory was based on data collected by state and federal agencies. The 1996 inventory included separate calculations for wildfire and prescribed burning. No estimate of agricultural residue burning was included.

The 2018 fire emission projections are summarized following the section dedicated to the 1996 inventory. The 2018 effort produced three control scenarios (two of which were used for the WRAP modeling effort) for prescribed burning and agricultural burning. A single "typical year" wildfire emission inventory was prepared to complete the suite of fire emission inventory files for the 2018 project.

The 2018 fire projections should not be comp ared directly with the 1996 fire emission inventories. The nature of inventories and the methods utilized to derive them are fundamentally different. For example, the historic inventory was based on recorded activity data while the activity estimates of the 2018 projections were developed in an independent effort and based largely on expert opinion. Importantly, the 2018 projections were not "grown" from the 1996 inventory. A more appropria te use of the fire emission inventories is to evaluate the 2018 emission reduction estimates resulting from the implementation of the different emission reduction scenarios.

Final §309 Regional TSD Page 47 of 250 December 15, 2003 Revised May 7, 2008All emission inventory datasets were made available to the WRAP digitally as database files and text files formatted for input to the Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System.

The 1996 fire emission inventory

The FEJF's stated objective for producing the 1996 inventories was to provide the Air Quality Modeling and Emissions Forums with wildfire and prescribed fire emission inventories that could be used to establish the performance of the WRAP's dispersion model to estimate the air quality impact of smoke from fire. Calculation methods and data quality standards were devised with this objective in mind. The FEJF emission inventory reflected consistent methods and specific fire event data. Data quality standards were set for the historic activity data collected. Consistent calculation methods and calculation parameters (such as literature-based fuel loadings) were implemented.

The ETT established a number of fire event data objectives that would be used to identify and utilize fire event data suitable for the 1996 fire emissions inventory. Each individual entry in the “raw” fire activity database was analyzed to determine if all of the fire event data objectives were met in order for the record to be included in the fire emissions inventory. The data objectives are:

• A specific location for each fire event;

• A specific calendar day in 1996 for each fire event;

• A specific size for each fire event; and

• Sufficient information to assign a fuel loading for each fire event.

The FEJF determined that, as a whole, available agricultural residue burning activity data did not meet these data quality objectives. As a result, no historical agricultural burning emission inventory for 1996 was produced.

The wildfire and prescribed burning emission i nventories were developed with parallel data gathering and emission calculation techniques. The same basic inventory process was utilized with a few specific variations implemented. A summary of the technical methods follows.

Activity data

Wildfire activity data were collected using a tiered process. Only wildfires greater than 100 acres in size were considered. Detailed activity data with sufficient spatial and temporal resolution were contained in the National Interagency Fire Center’s (NIFC) daily National Situation Report publication and ICS-209 wildfire forms. The National Situation Report database and ICS-209 databases were merged. Procedures were implemented to avoiding including duplicate records in the merged dataset.

In a companion effort, 1996 records from the Department of the Interior's Wildland Fire Management Data and the Department of Agriculture’s Forest Service National Interagency

Final §309 Regional TSD Page 48 of 250 December 15, 2003 Revised May 7, 2008Fire Management Integrated Database were appended to form an independent "federal database" of fire events. Fire events in the National Situation Report/ICS-209 database werethen paired with fire events in the "federal database." The National Situation Report/ICS209 database was supplemented on a record-by-record basis with location, fire size, and fuel loading information from the "federal database" where necessary. Lastly, Wyoming State Forestry provided additional wildfire information for Wyoming.

For the prescribed fire activity database, the FEJF set no de-minimus activity level for prescribed burning. The FEJF made requests of air quality officials of each state the WRAP region to provide wildland prescribed fire activity conducted by state and federal agencies for 1996. Individual fire records would be needed to satisfy rather precise temporal, spatial, and activity criteria for WRAP modeling purposes. Therefore, the FEJF requested:

1. Any information that could be used to ascertain a prescribed fire’s location (i.e., legal location, latitude/longitude coordinates, and county); 2. Timing information (burn date or season) and/or fire duration; and 3. Such fuels information as vegetation type, acres burned, tons burned, burn type (piles or broadcast).

1996 prescribed fire information was received from each of the WRAP states except for Nevada and North Dakota. Generally, inform ation received from interagency or statefacilitated smoke management programs encompassed prescribed fire data for multiple federal and state land management. Some activity data were received independently from federal agencies. Fire records having sufficient spatial, temporal, and activity components were formatted into a single region-wide prescribed fire activity inventory. Many of the records in the prescribed fire activity dataset did not have latitude and longitude coordinates but did have location information in the form of a Township Range and Section (TRS). A geographic information system (GIS) algorithm was developed to convert, or geo-reference, TRS codes to a latitude and longitude coordinate pair. Fire records ultimately not meeting the data quality objectives were dropped from the dataset.

Fire events in the wildfire and prescribed burning activity databases that did not contain fuel type in the source data were assigned a fuel model using GIS techniques. The fire's location was plotted on the National Fire Danger Rating System (NFDRS) fuel model map and the corresponding fuel model code was recorded to the activity record.

Fuel loading and emission factors

In the event that fire event specific fuel loading values were not contained in the database record, fuel loading values for the wildfire inventory were assigned using the NFDRS fuel model codes (see above) and a table of fuel loading values for NFDRS fuel model categories (Cohen and Deeming, 1985). In addition to the default NFDRS fuel loadings, additional fuel loading was added to each category to adjust for fuel present as duff and tree crowns. Similarly, for prescribed fire events for which no fuel loading value was available, the prescribed fire fuel loading values were the same as those used for the wildfire inventory.

Final §309 Regional TSD Page 49 of 250 December 15, 2003 Revised May 7, 2008“Adjusted" NFDRS fuel loading assignments for wildfire and prescribed burning differ by the percent consumption assumed for live fuels, duff and crown components.

An emission factor suite was developed to apply to wildfire and prescribed fire activity data. The emission factor suite included one lookup table for wildfire and prescribed broadcast burns and one table for prescribed pile burns. The twelve pollutants included were total suspended particulate matter (TSP), particulate matter less than 10 microns in diameter (PM 10 ), particulate matter less than 2.5 microns in diameter (PM 2.5 ), elemental carbon (EC), organic carbon (OC), non-methane volatile organic compounds (VOC), methane (CH 4 ), ammonia (NH ), oxides of nitrogen (NO ), carbon monoxide (CO), sulfur dioxide (SO 3 x 2 ) and coarse particulate matter– defined as the difference between PM 10 and PM 2.5 (PMC). The emission factor suite consists of two lookup tables. Two emission factor references were drawn upon: the US Environmental Protection Agency’s AP-42 section 13.1 and an emission inventory methods survey report (Battye, 2001) funded by the US EPA Office of Air Quality Planning and Standards. The emission factor suite is a compilation of emission factors and emission factor relationships (multipliers) from both documents.

Emission calculations

For wildfire and prescribed burning, daily emissions were calculated as fuel consumed (tons of fuel) multiplied by each emission factor (pounds of pollutant per ton of fuel). Total fuel consumed was either extracted directly from the activity data (as was often the case with prescribed fire) or was calculated as the size of the fire (acres burned) multiplied by the "adjusted" NFDRS fuel loading value (tons fuel per acre burned). For wildfires and prescribed broadcast burns of particular "h eavy" fuel model types, additional smoldering emissions were assigned in that location on the following calendar day.

A plume profile tailored for wildland fire was assigned to each daily fire event. The plume values included the top and bottom of the plume and the percent of emissions fumigated to the surface layer of the atmosphere. These three plume parameters were assigned for each of the 24 hours of each daily fire event. Normally, plume rise is predicted using hourly pyrotechnical and meteorological informati on. However, given the unique physical characteristics of wildland fire events and previous experience with dispersion models that indicated poor performance with regard to dispersing smoke plumes, the FEJF utilized expert opinion to assign plume characteristics to each fire event. Five plume classes were defined with increasing potential plume heights to reflect the range of "heat releases" possible in wildland fires. Plume bottom heights and percent of the plume fumigated to the first layer of the atmosphere were also developed for the five plume classes. Fire size and fuel loading from the activity data were used to estimate heat release, categorize the fire event into one of the five plume classes, and assign the corresponding plume parameters for each daily fire event. In this fashion, physical plume characteristics were explicitly assigned to each daily fire event. The plume characteristics are used by the WRAP dispersion model in lieu of calculated plume dimensions.

Final §309 Regional TSD Page 50 of 250 December 15, 2003 Revised May 7, 2008A diurnal fuel consumption table was created to allocate daily wildland fire emissions by hour. The table, consisting of a percent of fuel consumed for each hour of the day– summing to 100, was submitted to the Modeling Forum for implementation within SMOKE.

Emission Inventory Summaries

A total of 1,348 wildfires are included in the 1996 wildfire emissions inventory with a mean duration for each fire of 3.6 days. These fires represent a total of 4,902 reported fire days and 5,311 fire days when an extra smoldering day was included for a subset of the fire events (based on the NFDRS fuel model for the fire event). An estimated total of approximately five million acres and 48 million tons of fuel were consumed by wildfire in 1996. Wildfire activity was highest during the summer months and peaked in the month of August. Wildfire activity differed widely by state with the highest activities in California, Idaho and Oregon.

The 1996 prescribed fire emission inventory is comprised of significantly more events than the wildfire inventory with 14,696 events and 16,603 fire days after smoldering days were added. The total acreage consumed by prescribed burns was 555,000 acres and the total fuel consumed was approximately 5.2 million tons. Over 80% of the prescribed burning activity records are piled fuels. Temporally, prescribed burning had two peaks: one in the Spring and a higher peak in the Fall.

When expressed as fire days, about 25% of the fire activity occurred as wildfires and 75% as prescribed fires. However, emission estimates for wildfire were an order of magnitude higher than for prescribed fire due to a combination of larger (acres) events and higher fuel consumption estimates.

2018 fire emission projections

The FEJF produced 2018 emission projections for wildfire, prescribed burning and agricultural burning. Control scenarios were developed for each source. Prescribed fire and agricultural burning each have "base," "no control" and "maximum control" scenarios. (Note: Only the “base” and “maximum control” scenarios are used in the WRAP modeling.) A single wildfire dataset was submitted to represent constant "natural" emissions for application in every scenario. The FEJF 2018 control scenarios are described in more detail in Chapter 6.

The Air Quality Modeling and Emissions Forums' spatial and temporal resolution requirements apply to the 2018 projections for fi re. The FEJF's 2018 wildfire projection was derived from the 1996 fire emissions inventory. Wildfire events retain their location and timing and only their emissions are adjusted for the future case.

Prescribed burning and agricultural burning projections are based on a 50-kilometer (km) gridded inventory and county-level inventory, respectively. The temporal scale of the source data for these two fire types was also in aggr egate; seasonal for prescribed fire and monthly for agricultural burning. Because the activity data was of coarse scale yet the modeling required fine resolution, the FEJF decided to "simulate" event-based emission inventories

Final §309 Regional TSD Page 51 of 250 December 15, 2003 Revised May 7, 2008from the available datasets. This is to say the technical methods, for prescribed and agricultural emission projections, included spatial and temporal refinements to allocate emissions to specific coordinates and dates. The objective was to create a "representative" and resolved emission inventory from coarse projections.

Prescribed fire

The emission estimates used in the 2018 prescribed fire emissions inventory were extracted from the Fire Emissions Project (FEP) emissions inventories prepared for the Grand Canyon Visibility Transport Commission in 1995. The FEP emissions inventories were based on a survey of land managers' projections of future prescribed burning activity. Emission estimates for three fire emission control strategies were developed from this survey information and technical execution of an emission-estimating model developed for the FEP.

The resulting FEP emission inventories are regarded to be “possible” inventories for prescribed fire in 2018. Any number of other i nventories for prescribed fire in 2018 are also possible. Many factors that went into the assumptions underlying FEP (e.g., budget (or other resource) constraints, land management priorities, etc.) may have changed since the FEP was originally developed. Those who utilize the data in the WRAP’s fire emission inventories or the dispersion modeling results produced using these data should recognize the potential year-to-year variability in all types of fire activity (wildfire, prescribed fire, and agricultural burning). They should also be mindful that the 2018 burning activity levels are hypothetical and that emission estimates are imprecise.

The FEP developed emission projections for ten states: Arizona, California, Colorado, Idaho, New Mexico, Nevada, Oregon, Utah, Wa shington, and Wyoming. The FEP emissions inventory included seasonal (fall, winter, spring, summer) emission totals for all of the WRAP pollutants of interest except for SO . Emissions factors for SO 2 2 were estimated by multiplying the PM 2.5 emissions factor by a factor of 0.07054 which is the ratio of the SO 2 to PM 2.5 emission factors used in the FEJF 1996 em ission inventory (1.7 and 24.1 lbs per ton of fuel consumed, respectively). In the FEP database, the emission estimates were summarized as the mass of pollutant (tons) per land cover type, season, and FEP grid point (coordinate).

“FEP-like” emissions were estimated for the three WRAP-region states not included in the original GCVTC study: Montana (MT), North Dakota (ND), and South Dakota (SD). Emissions were assigned to these three states by summarizing fire emissions by land cover classification in neighboring GCVTC states, then applying those emission rates to land cover in the new states. That is, the average seasonal emission per land cover (ton per square km) across representative states was assigned to each same classification of land cover (square km) in the new states. Oregon, Washington, Idaho, and Wyoming were the representative states for ND and MT, while Colorado and Wyoming were the representative states for SD. The new prescribed fire emissions inventory fo r these states was produced without the use of any true fire activity data. The resulting levels of activity and smoke management controls for MT, ND, and SD are the average of the activity levels and controls for the representative
states.

Final §309 Regional TSD Page 52 of 250 December 15, 2003 Revised May 7, 2008Seasonal FEP emissions totals (in tons) were refined to daily fire-event-based emissions using a combination of rules and stochastic processes. The FEJF created several "burn calendars" to sub-divide seasonal emissions into two-week periods. The calendars, developed from expert opinion, were intended to typify intra-seasonal activity in different ecological regions within the WRAP. Once individual fire events were simulated (described below), each event was randomly assigned to a specific day in the two-week period. This temporal allocation achieved the daily "resolution" required by the modelers.

The FEP emissions were also spatially disaggregated from total emissions for the square 50- km grid to latitude/longitude locations within the grid cell. The original FEP grid-wide emission estimates could be broken down by owner and land cover classification. For every FEP grid cell, emissions were aggregated by land management agency. Using a geographic information system to map the federal land management for every 1 km pixel in the 13-state domain, emissions were assigned to the respective land-manager portions of the 50-km grid. Once individual fire events were simulated, they were randomly assigned to a latitude and longitude within the land manager's boundary. This spatial allocation achieved the coordinate "resolution" required for the WRAP modeling.

With the seasonal and 50-km gridded FEP emissi on projections systematically disaggregated in time (two-week period) and to areas (management portion of grid cell), realistically sized fire events were created. Consistent with the effort to produce a "representative" 2018 emission inventory for modeling, the FEJF str ove to produce 2018 emission inventories that contain a representative variety of fire sizes. The distribution of fire sizes in the 1996 prescribed fire inventory was used as a template for breaking up the two-week period totals into realistically sized daily events. A nominal fire size (in tons of emissions of PM 2.5 ) was assigned to each of the five plume classes. The frequency of the different plume classes governed the size and number of "fire events" created out of each 2018 subtotal.

Wildfire

The FEJF produced a wildfire emission inventory for the year 2018 that represents a “typical year” of wildfire activity within the 13-state WRAP region. The 2018 wildfire activity projection is a modification of the 1996 wildfire activity database. The FEJF came to consensus that 1996 was a high wildfire-occurrence year in the West. To reflect more typical conditions for 2018, the FEJF developed a methodology to normalize the 1996 wildfire inventory to a longer running historical average. Fire occurrence statistics were collected from the USDA Forest Service Wildfire Statistics Database for 1971 through 1990. The 20 years of wildfire events were classified into the five fire size/plume parameter classes used elsewhere in FEJF inventory development. Av erage fire size per class was calculated within each state for this long-term period. Next, the ratio of the mean 1971-1990 wildfire acreage to the 1996 wildfire acreage was calculated by state and by fire size class. This ratio was used to scale acreages appearing in the 1996 inventory to a more typical projection for 2018, on a fire-by-fire basis. Since the average 1971-1990 fire size was usually smaller than the average fire sizes in 1996, the ratio generally served to scale fire sizes down.

Final §309 Regional TSD Page 53 of 250 December 15, 2003 Revised May 7, 2008The wildfire normalizing process projected acres burned for a typical wildfire year and this was used to represent wildfire events in 2018. The time, location, and fuel loading of each of the fire events were carried over directly from the 1996 inventory. With updated acreage, 2018 emissions and plume characteristics were then calculated using the same methodology as used for the original 1996 inventory.

The total number of wildfire acres in the 2018 emission projections was considerably lower than for 1996, with 1.2 million acres (2018) compared to 5.0 million acres (1996). The larger fire size classes tended to contribute most to the higher wildfire acreage in the 1996 emissions inventory. Compared to the original 1996 emissions inventory, the 2018 projections show a decrease in the frequency and acreage of wildfires greater than 1,000 acres and relatively little change in fires in the size class from 100 to 999 acres.

Agricultural Burning

The FEJF produced agricultural burning emission projections for 2018 using simulation methods comparable to those for prescribed burning. However, rather than creating the emissions inventory from emission projections fr om FEP, historical agricultural burning data were used. The agricultural activity database commissioned by the FEJF (see the report titled “Non-Burning Management Alternatives on Agricultural Lands in the Western United States”, Eastern Research Group/Enviro-Tech Communications, February 2002, included in Appendix D) was by-crop at the county-level. It was an "annual" inventory of residue burned (in tons) that, in an effort to be more complete in terms of coverage of the 13-State WRAP domain, incorporated agricultural burning data from calendar years 1996 through 2001. The FEJF came to consensus that this historic activity database, and resulting emissions, would serve as the 2018 "no control" emission projection.

The agricultural activity data included utilization of a "gap filling" approach to acquire as much crop data for as many counties in the WRAP region as feasible. The source data fell into three categories: permits issued or other mechanisms for determining actual burn activity, emissions inventory estimates, and anecdotal information. For some counties daily resolved activity data was gathered. For the majority of the 13-state WRAP domain only monthly totals were available. The agricultural burning activity database did not include quantification of burning on tribal lands. Activ ity records without valid date, state, county, or residue-loading data were not used in fi nal agricultural burning emission inventories for 2018.

Emission factors were identified and compiled by the ETT and the Alternatives to Agricultural Burning Task Team of the FEJF. To estimate emissions, the pollutant-specific emission factors (in pounds of pollutant per ton of residue burned) were multiplied by the residue loading values (tons of residue burned) in the agricultural burning activity database. For single fire events in the database (as opposed to monthly totals), plume characteristics were assigned using the same five-category sc heme developed for wildland fire. Due to lower fuel loadings and fire size, agricultural burning events fell into only the smallest three plume classes.

Final §309 Regional TSD Page 54 of 250 December 15, 2003 Revised May 7, 2008The FEJF resolved the by-county by-month agricultural burning data to a temporally and spatially resolved inventory in a similar way as the 2018 prescribed fire projections. Approximately 48,000 records in the agricultural burning database were daily fire event records and 2,000 records were monthly records. The monthly tonnages were disaggregated to "simulated" fire events. The FEJF intended to represent a variety of fire sizes with these events. Plume classes were determined for the 48,000 daily events. The frequency of occurrence of the three possible classes was recorded along with a nominal emission (in tons PM 2.5 ) for each class. This frequency table was used to break the monthly emission totals into a plausible distribution of individual fires.

Simulated agricultural fire events were then merged with the true daily events to complete the refined emission projection. All fire events that did not have a specific date (but were keyed to a month) were randomly assigned a day. Inferring "black-out" periods from the historic daily data, fires were not assigned to particular dates during harvest time or considered a holiday. All fire events, true and simulated, came to the FEJF spatially resolved to the county. Using a geographic information system to map agricultural land cover in every county of the WRAP, fire events were concentrated to the portion of the county classified as agricultural. Fire events were assigned a random latitude and longitude coordinate within the agricultural delineation.

The FEJF devised, through expert opinion, an hourly consumption template for agricultural burning that apportioned daily emissions to the hour of the day. Using the smallest three plume categories and accompanying methods developed for wildland fire, plume top height, bottom height, and percent fumigated into the first layer was assigned to every agricultural fire.

Emission estimates

Averaging across the three 2018 projection scenarios, wildfire represents about one-third, prescribed fire two-thirds, and agricultural burning between one and three percent of all fire emissions. For 2018, PM 2.5 emissions from wildfire are projected to be most prevalent from late spring to early fall with the majority of the emissions occurring in the month of August. PM 2.5 emissions from prescribed fire are projected to peak in the spring and fall. Agricultural burning tends to be more equally distributed over the whole year, but has minor peaks in the spring and late summer. The optimal control scenario results in a 13 percent reduction of PM 2.5 emissions from prescribed burning. The projected PM 2.5 emissions from agricultural burning differ considerably across the three control scenarios.

Interpretation of the fire emissions data

The 1996 fire emission inventory and 2018 fire em ission projections were developed to best satisfy WRAP modeling requirements. The FEJF endeavored to deliver a complete and timely emission inventory product given imperfect science and limited data over the large WRAP domain. There are important caveats and limitations of the inventories. Activity data from jurisdictions without data reporting systems are omitted or may be under-represented. For example, the 1996 emission inventory of prescribed burning lacks data from various

Final §309 Regional TSD Page 55 of 250 December 15, 2003 Revised May 7, 2008counties and rangeland burning. Similarly, agricultural burning activity was not readily and consistently available. Wildfire activity in 1996 was believed to be a drought year "high." A compounding error may be that, as a consequence of wildfire conditions, prescribed fire activity was therefore "low" in 1996. An understanding of the data sources, quality assurance, and correction of some errors not withstanding, source data is incorporated into the emissions databases "as is."

The 2018 projections are based on theoretical policy and technical assumptions. 2018 projections are not grown from the 1996 inventories. Additional uncertainties were introduced in the process of distributing the quarterly FEP emissions and county agricultural emissions over time and space. The FEJF employed custom fire size and plume characteristic algorithms throughout the 1996 and 2018 inventory development. Designed to be reasonable and representative classification schemes, these methods were based on a combination of empirical data and expert judgment. In conclusion, the efforts of the Fire Emissions Joint Forum were dedicated to using professional judgment to select the best or most appropriate methods and parameters to estimate emissions from fire. However, the 1996 and 2018 emission inventories for wildfire, prescribed fires, and agricultural burning are not advanced by the FEJF as being “right” or “correct” in an absolute sense.

1.1.8. Biogenics

MCNC prepared the model-ready biogenic emi ssions inventory using the SMOKE version of the Biogenic Emission Inventory System, version 2 (BEIS2), available at: www.epa.gov/asmdnerl/beis_bkgrnd.html . BEIS2 modeling begins with gridded biogenic land use data, biogenic emission factors, and meteorology data. In this section, we first provide a summary of the biogenic emissions i nventory development process and input files. Then, we document the land use data file. Lastly, we provide county-total emissions summaries and describe what data are available for states and counties to use for getting further understanding of their §309 biogenic inventories.

Summary of biogenic inventory generation

Biogenic inventories depend heavily on the mete orological conditions of each day and hour. Temperature and solar radiation data are com ponents of the biogenic inventory calculation. For this §309 modeling effort, we used the prognostic meteorological data provided by MCIP for input to the CMAQ. Since the inventory is really a modeling exercise, the inventory is computed by the SMOKE system during emissions processing, and not as a separate inventory preparation process. The main preparation activity is creating the gridded land use data, which in this case was done in another EPA modeling project.

Figure 11 shows the major steps involved in preparation of the biogenic inventory. First, the annual meteorology data are scanned to determine the first and last freeze dates of the year for each grid cell. Then, then land use data are combined with winter emission factors and summer emission factors to create winter and summer normalized emissions of isoprene (ISOP), terpenes (TERP), and other volatile organic compounds (OVOC). These emissions are time independent and do not contain any adjustments for meteorology; this is what is

Final §309 Regional TSD Page 56 of 250 December 15, 2003 Revised May 7, 2008meant by the “ normalized” nomenclature. For each hour of the year, SMOKE makes adjustments to the normalized emissions using temperature and solar radiation data from the MCIP meteorology data used by CMAQ. During these adjustments, SMOKE uses the summer normalized emissions during dates between the last freeze date in the spring, and the first freeze date in the fall; otherwise, the winter normalized emissions are the starting point for this calculation. These calculations convert the time-independent normalized emissions to hour-specific emissions of VOC and NO. Lastly, SMOKE applies a single speciation profile for VOC emissions, to compute the chemical species needed for the air quality model.

Figure 1.1.8.1. Creating biogenic model -ready inventories using SMOKE

Winter Land Use Import Winter Normalized Emissions Model-ready Emissions Meteorology

Scanning

Summer Land

Use Import Summer Normalized Emissions

Winter/ Summer Switch

Meteorology Adjustments

Program Shows input or output File Winter Land Use Import Winter Normalized Emissions Model-ready Emissions Meteorology

Scanning

Summer Land

Use Import Summer Normalized Emissions

Winter/ Summer Switch

Meteorology Adjustments

Program Shows input or output File

The names and origins of the data files that we used for preparing the biogenic inventory are listed next. The CMAQ input meteorology files were used as well, but these are not included in the list because there are too many files to list, and are listed elsewhere in this document.

Table 1.1.8.1. Input files used for creati ng the biogenic emissions inventory. File description File name Source Date Ellen Kinnee, DynCorp, RTP NC BEIS2 gridded land use data cmaq36beld.5_nhdr July 2000
Tom Pierce, U.S. EPA, ORD November 1998 Winter emission factors bfac.winter.txt
Tom Pierce, U.S. EPA, ORD November 1998 Summer emission factors bfac.summer.txt gspro.cmaq.cb-iv.091101.txt, profile 0000 Tom Pierce, U.S. EPA, ORD November 1998 Speciation profiles

Of these files, the only non-standard file is the land use data. The winter and summer emission factors (Pierce et al., 1998) are taken directly from the BEIS2 system available at EPA’s web site ( http://www.epa.gov/ttn/chief/emch/models/beis/index.html ), although different files names are different in the EPA installation. The winter data are based on the assumption that deciduous vegetation can be mostly ignored and the summer data are based on the assumption of full leaf biomass conditions.

The speciation profile 0000 in the main SMOKE profile file used for this project (and listed above) was simply created from the factors w ithin the BEIS2 code, after having confirmed

Final §309 Regional TSD Page 57 of 250 December 15, 2003 Revised May 7, 2008with Tom Pierce at the U.S. EPA during the development of SMOKE for the Models-3 system. This profile converts the ISOP, TERP, and OVOC emissions available from the emission factors to the model-ready species for the CB-IV chemical mechanism for isoprene (ISOP), aldehydes (ALD2), paraffins (PAR), olefins (OLE), nonreactive VOC (NR), and terpenes (TERPB) needed by CMAQ. The “mole factor” converts the mass-based normalized emissions to emissions in moles by incorporating the molecular weights of isoprene (68.12 g/mole), other VOC (148 g/mole), and terpenes (136.23 g/mole). The “mass factor” is simply the mass fraction of the CMAQ species. Please note that the mole factor is not simply the mass factor divided by the molecular weight.

Table 1.1.8.2. SMOKE-BEIS2 speciation profile 0000 factors BEIS2 pollutant BEIS2 species Mole factor Mass factor ISOP ISOP 1.46799E-02 1 OVOC NR 3.33784E-03 0.05 OVOC OLE 3.33784E-03 0.10 OVOC PAR 5.74324E-02 0.85 TERP ALD2 1.10108E-02 0.30 TERP OLE 3.67026E-03 0.1 TERP PAR 4.40432E-02 0.6 TERP TERPB 7.34053E-03 1

Land use data

One of the main input files to preparing a biogenic emission inventory is the land use data. The emissions provided for the WRAP cover both of the modeling domains, including all regions of Mexico and Canada within the domain. The land use data are used by SMOKE to compute normalized emissions using Biogenic Emission Inventory System, version 2 (BEIS2) emission factors. DynCorp prepared the land use data used in this project for an EPA project known as the “Proof-of-Concept” modeling project. The file prepared by Dyncorp has an extensive metadata header, which cannot be used in a SMOKE input file. We have included the contents of the header here to document the biogenic land use data used in this project. The following indented text is this header.

Introduction:

This file was designed for use with the Biogenic Emissions Inventory System (BEIS -2) and is being adapted for meteorological processing with the EPA's Third Generation Air Quality Modeling System (MODELS-3).

Grid Definition:

CMAQ 36 km national domain has 132 columns and 90 rows. The longitude and latitude of the lower left corner are 132.730 west and 21.350 north, and the upper right corner is 62.609 west and 54.373 north. The size of each grid cell is 36km by 36km.

Final §309 Regional TSD Page 58 of 250 December 15, 2003 Revised May 7, 2008File Description:

The ASCII data file is free formatted with land use data given for every column and row as shown below.

COL ROW TOT_AREA <repeated for every COL ROW> FOR_AREA NREC1 SPEC_ID AREA <repeated NREC1> URBFOR_AREA NREC2 SPEC_ID AREA <repeated NREC2> AGR_AREA NREC3 SPEC_ID AREA <repeated NREC3> OTH_AREA NREC4 SPEC_ID AREA <repeated NREC4>

Variable list: COL column (integer) ROW row number (integer) TOT_AREA grid area (hectares) FOR_AREA non-urban forest area (hectares) NRECx n of records for each x type, where x is each major group SPEC_ID 4 char id enclosed in single quotes (such as 'Quer') AREA area (hectares) for SPEC_ID in group x UFOR_AREA total urban forest area (hectares) AGR_AREA total agriculture area (hectares) OTH_AREA total of remaining areas (hectares)

Notes: (1) COL and ROW depend on grid definition. (2) VEGID has mixed upper case and lower case, such as 'Quer'. Descriptions of all current land use classes are given below. However, file structure is designed to allow additional VEGIDs depending on land use classification system. (3) Sum of areas for each of 4 major groups should equal total area in grid cell. Sum of individual SPEC_IDs should also equal total area in grid cell. (4) If a major group has NRECx = 0, it will not have any records following.

The remainder of this section of the header that was provided with the original Data from DynCorp is available in Appendix A. It contains a list of VEGIDs values that can be used in the above file format for the biogenic land use file, along with a brief description.

Source of Data:

This land use data set is documented in a paper by Kinnee, et. al., Ecological Applications, 7(1), 1997 pp. 46-58. The source of the data was county resolved data

Final §309 Regional TSD Page 59 of 250 December 15, 2003 Revised May 7, 2008in the US, based on an amalgamation of US Forest Service tree coverage (circa 1990), US Agricultural Census (1987), US urbanized areas (1990 Census Bureau), USGS AVHRR classification (1990), and Environm ent Canada 1.1 km land use data. As indicated, the data have been resolved to the county level in the US and similar census tract levels in Canada.

Version: July 2000

Biogenic inventory totals

th rd Below are state summaries of January 5 , 1996 and July 3 , 1996. These emissions totals were used in the inventory summaries provided later in this document. The totals include only those emissions that are inside the modeling grid, because they are computed from model-ready, gridded emissions. This will not impact the totals for any of the WRAP states listed in these tables, because these are all entirely within the CMAQ modeling grid.

Table 1.1.8.3. Biogenic emission totals within CMAQ grid for January 5, 1996.
State NO ALD2 ISOP NR OLE PAR TERPB [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] Arizona 88.16 29.59 28.57 5.40 20.66 150.97 98.63 California 72.39 142.63 126.66 26.46 100.47 735.16 475.45 Colorado 28.09 23.30 6.55 3.83 15.43 111.72 77.67

Idaho 25.07 23.70 5.37 4.19 16.29 118.70 78.99 Montana 2.17 14.40 2.27 2.54 9.87 71.92 48.01 Nevada 63.84 4.11 1.46 0.83 3.03 22.33 13.71 New Mexico 93.81 22.32 19.59 4.13 15.70 114.85 74.41 North Dakota 0.00 0.01 0.00 0.00 0.01 0.04 0.03 Oregon 29.86 81.56 20.56 14.83 56.85 415.25 271.87 South Dakota 0.40 0.76 0.08 0.14 0.54 3.97 2.54 Utah 40.51 12.97 4.30 2.22 8.77 63.77 43.24 Washington 16.44 57.44 10.57 10.13 39.40 287.02 191.46

Wyoming 20.12 6.92 0.94 1.12 4.54 32.84 23.05

Table 1.1.8.4. Biogenic emission totals within CMAQ grid for July 3, 1996.
State NO ALD2 ISOP NR OLE PAR TERPB [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] Arizona 243.84 333.70 1205.77 53.61 218.46 1578.79 1112.32 California 170.40 689.31 3684.06 136.96 503.69 3706.96 2297.70 Colorado 207.78 361.21 2344.45 66.94 254.29 1860.44 1204.04

Idaho 125.00 461.61 2746.10 89.00 331.86 2436.17 1538.69 Montana 349.11 606.15 3164.16 108.41 418.86 3055.21 2020.49 Nevada 198.76 141.43 280.42 18.37 83.87 595.07 471.44 New Mexico 254.98 292.20 1114.19 44.96 187.32 1348.74 974.00 North Dakota 271.44 55.58 278.08 6.72 31.96 225.34 185.27 Oregon 100.10 446.38 1871.24 86.71 322.22 2366.85 1487.93 South Dakota 303.56 123.58 335.88 17.59 76.38 546.27 411.92 Utah 141.85 227.12 1283.95 38.35 152.41 1106.23 757.05

Final §309 Regional TSD Page 60 of 250 December 15, 2003 Revised May 7, 2008Washington 72.68 387.18 1424.72 77.23 283.51 2087.22 1290.61 Wyoming 187.08 281.26 1275.03 45.65 185.06 1338.60 937.54

In addition to these data for sample days, day-specific summaries of biogenic emissions by model-species can be obtained by contacting the Regional Modeling Center through their website at http://pah.cert.ucr.edu/rmc . These totals are created in SMOKE starting with hourly, model-ready data files. SMOKE uses the “cell area” spatial surrogate to determinethe fraction of each county within each grid cell, which in turn is used to estimate the countytotal and state-total emissions for each day. These values are estimates because the variation within each grid cell is not accounted for when computing these totals.

1.1.9. Windblown Dust

Due to the inaccuracy and unreliability of windblown dust emissions estimation techniques for use in regional-scale air quality modeling, the WRAP Technical Oversight Committee and Technical Forums did not attempt to develop a windblown dust emissions inventory for 1996 or 2018, or to model it for §309. Specifically, the Emission Forum determined that data for the windblown dust component was so unreliable that it would be detrimental to the modeling effort to try to include it. As described earlier, road dust and area source dust were inventoried, and used in the air quality modeling described later in this chapter. No specific control strategy evaluations or determinations of the contribution of windblown dust to Class I area visibility were identified specifically, or required in §309. The WRAP Technical Oversight Committee and Technical Forums understand the importance of properly and accurately characterizing windblown dust in future technical analyses, and they have undertaken several projects to accomplish that task.

The WRAP convened a Dust Expert Panel, and this panel has published a report on various technical issues associated with the characterization of dust emissions, available at www.wrapair.org . The WRAP has created and budgeted for a Dust Emissions Joint Forum (DEJF), with technical and policy development responsibilities within the WRAP organizational structure. The DEJF has a number of projects underway to characterize the ambient constituents of dust as collected from IMPROVE and other aerosol monitors, better understand activity levels and spatial/temporal patterns of the construction and agriculture source sectors, and to classify natural and manmade dust emissions sources. Directly related to windblown dust emissions, the DEJF has cont racted ENVIRON Corporation to develop an algorithm and computer code, to generate PM 2.5 and PM 10 windblown dust emissions inventories using the wind fields in the regional air quality model. This project will be completed for testing in the air quality model by Fall 2003.

1.2. Overview of Analytical Approach – Modeling

The WRAP Air Quality Modeling Forum selected staff from the University of California at Riverside, ENVIRON Corporation, and Carolin a Environmental Programs (formerly MCNC) to work together to set up a Regional Modeling Center (RMC) for the WRAP region. The organizations involved in the RMC had a large number of challenging tasks to accomplish

Final §309 Regional TSD Page 61 of 250 December 15, 2003 Revised May 7, 2008over the past 2 years, including characterizi ng 1996 base year and 2018 projection year air quality and visibility, for a variety of §309 scenarios. Their efforts are described next.

1.2.1. Processing of emissions inventories in SMOKE

The emissions inventories used in the WRAP modeling summarize the mass of pollutants emitted annually into US counties, Canadian provinces, and Mexican states. Covering each of the major air pollution source categories: on-road mobile, non-road mobile, stationary point, and area sources, the inventories contai n emission information for individual pollution generating processes. Emissions models associate the inventories with information about the nature and timing of polluting activities and allocate the pollution data spatially in 3- dimensions. Integrating meteorology, chemistry, and geospatial data, emissions models produce daily estimates of pollution emissions that are resolved in both space and time. MCNC used the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system to prepare daily CMAQ and REMSAD emissions inputs for the entire modeling years of 1996 and 2018 for the respective WRAP modeling domains. WRAP emissions forum contractors provide d MCNC with SMOKE-ready inventories and tabulated summaries of the inventory data for quality assurance (QA) purposes. MCNC provided all of the SMOKE ancillary input files, such as the chemical and temporal allocation inputs, used for the WRAP emissions modeling. Most of these non-inventory SMOKE input files originated from the US EPA Office of Air Quality Planning and Standards (OAQPS). MCNC made some slight modification to these files based on the inventory data used for the WRAP modeling. Details on all of the input files, the scripts used to run SMOKE, the configuration and set-up of SMOKE on the RMC computers, and pitfalls encountered during the emissions generation process are provided in the following sections. Discussion about the QA procedures taken to ensure the accuracy of the emissions generation process is provided below.

Final §309 Regional TSD Page 62 of 250 December 15, 2003 Revised May 7, 2008Table 1.2.1.1. WRAP emissions scenarios used in most current modeling +

Description (includes inventory files used in the simulation) Release

Date Version Tag Source category Simulation

01/21/02 1996 REMSAD Base case f Us36f The inventory configuration for CMAQ base case f applies to these emissions; final set of 1996 REMSAD emissions
Typical-year average wildfi res, agricultural burning, and prescribed fires repl ace the actual 1996 fires, and revised road dust inventor y replaces the NEI96 road dust; current 1996 base case emissions used for CMAQ modeling 06/20/02 1996 CMAQ Base case g wrap96g

ar96_wrap_073001.ida ar96_wrapTier2.nei96.060801.ida

area ar96_eastus+can.nei96_nwrap.060801.ida ar96_Mexico_060701.ida armb96_Canada_060801.ida pt96_wrap_102901a.ida pt96_wrap_102901b.ida pt96_wrap_102901c.ida point pt96_wrap_102901d.ida ptinv_eastus.net96.ida_nwrap.txt pt96_Mexico_061101.ida ca_or96_{season}.ida wrap_or96_{season}ida Vmt9631x.nei_Tier1.ida Vmt9631x.nei_Tier2.ida on-road mobile Vmt9631x.nei_east.ida mb96h{MON}_Tier1.ida mb96h{MON}_Tier2.122101.ida mb96h{MON}_east.122101.ida , NH (NOTE: inadvertent double-counting of SO 2 3 , PM , and PM for all WRAP states except CA) 2.5 10 wrap_nr96_{season}.ida nonroad mobile nr96_wrapTier1.nei96.ida nr96_wrapTier2.nei96.060801.ida nr96_east.nei96.060801.ida unp_96emis{season}_env.ida road dust pvd_rd96emis_env.ida ptinv_wrap96.wf.043002.{month}.ida wildfire ptday_wf_043002.mod.{month}.ida pthour_wf_043002.mod.{month}.ida ptinv_wrap96.agbase.043002.{month}.ida agricultural fire ptday_agbase_043002.mod.{month}.ida pthour_agbase_043002.mod.{month}.ida

ptinv_wrap96.rxbase.043002.{month}.ida prescribed fire ptday_rxbase_043002.mod.{month}.ida pthour_rxbase_043002.mod.{month}.ida

Final §309 Regional TSD Page 63 of 250 December 15, 2003 Revised May 7, 2008Description (includes inventory files used in the simulation) Release

Date Version Tag Source category Simulation

biogenic cmaq36beld.5_nhdr

Typical-year average wildfi res, agricultural burning, and prescribed fires repl ace the actual 1996 fires, and revised road dust inventor y replaces the NEI96 road dust; current 2018 base case emissions used for CMAQ modeling. 06/06/02 2018 CMAQ Base case g wrap2018g

ar2018_wrap_021102.ida arinv_g+c.east_nei.wrap96f_2018.ida area ar96_g+c_Mexico_wrap96f_2018.ida armb96_g+c_Canada_wrap96f_2018.ida pt2018_wrap_noSO2_02112.ida ptinv_g+c.nei_east_noSO2.wrap96f_2018.ida point pt2018_wrap_SO2_02112.ida ptinv_g+c.nei_east_SO2.wrap96f_2018.ida ca_or18_{season}.ida wrap_or18_{season}ida Vmt9631x.nei_Tier1.wrap96f_2018.ida on-road mobile Vmt9631x.nei_Tier2.wrap96f_2018.ida Vmt9631x.nei_east.wrap96f_2018.ida mbinv.nei_Tier1.wrap96f_2018.{month}.ida mbinv.nei_Tier2.wrap96f_2018.{month}.ida mbinv.nei_east.wrap96f_2018.{month}.ida wrap_nr18_season.ida nonroad mobile nrinv.wrapTier1.nei.wrap96f_2018.ida nrinv.wrapTier2.nei.wrap96f_2018.ida nrinv.east_nei.wrap96f_2018.ida rd.v4_2018emis{season}.wrap.tfrac.ida rd.v4_2018emis{season}.Tier1.tfrac.ida road dust rd.v4_2018emis{season}.Tier2.tfrac.ida rd.v4_2018emis{season}.eastUS.tfrac.ida ptinv_wrap96.wf.043002.{month}.ida wildfire ptday_wf_043002.mod.{month}.ida pthour_wf_043002.mod.{month}.ida ptinv_wrap96.agbase.043002.{month}.ida agricultural fire ptday_agbase_043002.mod.{month}.ida pthour_agbase_043002.mod.{month}.ida ptinv_wrap96.rxbase.043002.{month}.ida prescribed fire ptday_rxbase_043002.mod.{month}.ida pthour_rxbase_043002.mod.{month}.ida biogenic cmaq36beld.5_nhdr
02/18/03 2018 Base case g2 wrap2018_ g2 CMAQ base case wrap2018g with corrected layer fractions for point sources

03/03/03 2018 Base case g3 wrap2018_ g3 CMAQ base case g2 with agricultural fires from July 31, 2002, distribution

Final §309 Regional TSD Page 64 of 250 December 15, 2003 Revised May 7, 2008Description (includes inventory files used in the simulation) Release

Date Version Tag Source category Simulation

Case wrap2018g emissions with milestone stationary source SO 2 controls in the 9 Grand Canyon states, and BART controls in the rest of the WRAP, tier 1, and tier 2 states applied 06/01/02

point pt2018_wrap_noSO2_02112.ida ptinv_g+c.nei_east_noSO2.wrap96f_2018.ida milestone_smk_pt_051402.wrap_gc.ida commd_cntrl_smk_pt_2018_052302.wrap_other.ida commd_cntrl_smk_pt_2018_052302.wrap_t1.ida commd_cntrl_smk_pt_2018_052302.wrap_t2.ida ptinv_g+c.nei_east_SO2.wrap96f_2018.ida 2108 CMAQ Milestone Point SO wrap2018g_ ptSO2_ milestone

2 sensitivity

all other

sources same as 2018 CMAQ Base case wrap2018g

Case w36g emissions with BART stationary source SO 2 controls in the WRAP, tier 1, and tier 2 states applied 06/01/02

point pt2018_wrap_noSO2_02112.ida ptinv_g+c.nei_east_noSO2.wrap96f_2018.ida commd_cntrl_smk_pt_2018_040302.wrap_other.ida ptinv_g+c.nei_east_SO2.wrap96f_2018.ida 2018 CMAQ BART Point SO wrap2018g_ ptSO2_ BART

2 sensitivity

all other

sources same as 2018 CMAQ Base case wrap2018g
Case w36g emissions with BART with uncertainty stationary source SO 2 controls in the 9 Grand Canyon states, and BART controls in the rest of the WRAP, tier 1, and Tier2 states applied 06/01/02

point pt2018_wrap_noSO2_0211-2.ida ptinv_g+c.nei_east_noSO2.wrap96f_2018.ida commd_cntrl_uncertainty_smk_pt_2018_052302.wrap _gc.ida commd_cntrl_smk_pt_2018_052302.wrap_other.ida commd_cntrl_smk_pt_2018_052302.wrap_t1.ida commd_cntrl_smk_pt_2018_052302.wrap_t2.ida ptinv_g+c.nei_east_SO2.wrap96f_2018.ida 2018 CMAQ BART with Uncertainty Point SO wrap2018g_ ptSO2_ BART Uncrty

2 sensitivity

all other

sources same as 2018 CMAQ Base case wrap2018g

Case wrap2018g emissions with all on-road and nonroad mobile emissions in the 9 Grand Canyon states reduced to zero 10/07/02 2018 CMAQ WRAP mobile sensitivity wrap2018g_ NoGCMb

+ all sources same as 2018 CMAQ Base case wrap2018g

Case wrap2018g emissions with all on-road and nonroad mobile emissions in California reduced to zero 10/07/02 2018 CMAQ CA mobile sensitivity wrap2018g_ NoCAMb

+ all sources same as 2018 CMAQ Base case wrap2018g

Final §309 Regional TSD Page 65 of 250 December 15, 2003 Revised May 7, 2008Description (includes inventory files used in the simulation) Release

Date Version Tag Source category Simulation

Case wrap2018g emissions with all on-road and nonroad mobile emissions in Maricopa County, Arizona, reduced to zero 10/07/02 2018 CMAQ Phoenix mobile sensitivity wrap2018g_ NoPhnxAZ Mb

+ all sources same as 2018 CMAQ Base case wrap2018g

Case wrap2018g emissions with all on-road and nonroad mobile emissions in Clark County, Nevada, reduced to zero 10/07/02 2018 CMAQ Las Vegas mobile sensitivity wrap2018g_ NoVegas Mb

+ all sources same as 2018 CMAQ Base case wrap2018g

Case wrap2018g emissions with Grand Canyon state point sources emitting greater than 100 TPY NOx have their NOx emissions reduced by 50% 12/03/02 2018 Grand Canyon point source NO wrap2018g_ GCptNOx ctl

x sensitivity ‡ all sources same as 2018 CMAQ Base case wrap2018g

Case wrap2018g emissions with Grand Canyon state point sources emitting greater than 100 TPY PM10 have their PM10 emissions reduced by 50% 11/27/02 2018 Grand Canyon point source PM wrap2018g_ GCptPM10 ctl

10 sensitivity ‡ all sources same as 2018 CMAQ Base case wrap2018g

Case wrap2018g emissions with all Grand Canyon state point source PM 10 and NO x increased by 25% 12/12/02 2018 Grand Canyon point source sensitivity wrap2018g_ GCptNOx PMinc ‡ all sources same as 2018 CMAQ Base case wrap2018g
Case wrap2018g emissions with Pollution Prevention (P2) point sources and optimal smoke management agricultural and prescribed fires 01/10/03

point pt2018_wrap_P2.112702.ida ptinv_g+c.nei_east_noSO2.wrap96f_2018.ida ptinv_g+c.nei_east_SO2.wrap96f_2018.ida 2018 All control sensitivity wrap2018g_ P2all

ptinv_wrap96.agosm.073102.{month}.ida ptday_agosm_073102.mod.{month}.ida agricultural fire pthour_agosm_073102.mod.{month}.ida (NOTE: fires dropped from optimal SMOKE management inadvertently from May 6-31) ptinv_wrap96.rxosm.043002.{month}.ida prescribed fire ptday_rxosm_043002.mod.{month}.ida pthour_rxosm_043002.mod.{month}.ida all other

sources same as 2018 CMAQ Base case wrap2018g

* D etails on state and local control program assumptions for all WRAP emissions modeling scenarios are listed

in Appendix A

+ Mobile-source controls were applied to the SMOKE intermediate files; no manipulation of the raw

inventories occurred.

Final §309 Regional TSD Page 66 of 250 December 15, 2003 Revised May 7, 2008‡ Point-source controls were applied to the SMOKE intermediate files; no manipulation of the raw inventories occurred.

Emission Inventories

The WRAP provided modified versions of the National Emissions Inventory 1996 (NEI96) to MCNC for use in generating emissions for the CMAQ and REMSAD modeling. MCNC used the unmodified NEI96 to provide emissions data for the areas of the domain not covered by the WRAP inventories. The four major regions of the WRAP CMAQ modeling domain

are: Region States WRAP AZ, CA, CO, ID, MT, NV, NM, ND, OR, SD, UT, WA, WY Tier 1 KS, NE, OK, TX Tier 2 AR, IA, LA, MN, MO East US IL, MI, MS, TN, WI

Table 12 lists the inventory data sources for the four regions of the WRAP modeling domain. “WRAP” refers to the modified WRAP invent ories and “NEI96” refers to the unmodified National Emissions Inventory for 1996. Table 1.2.1.2. Sources of emissions inventory da ta for the WRAP modeling domain regions

Year Inventory WRAP Tier 1 Tier 2 East US

Area WRAP WRAP WRAP NEI96 Non-Road Mb WRAP NEI96 NEI96 NEI96 On-Road Mb WRAP NEI96 NEI96 NEI96 1996 CA On-Road Mb WRAP (CA) Point WRAP WRAP WRAP NEI96 Fire WRAP
Area WRAP WRAP WRAP NEI96 Road Dust WRAP WRAP WRAP WRAP Non-Road Mb WRAP NEI96 NEI96 NEI96 On-Road Mb WRAP NEI96 NEI96 NEI96 2018
CA On-Road Mb WRAP (CA) Point WRAP WRAP WRAP NEI96 Fire WRAP

Details on the exact nomenclature, creation dates, included pollutants, the number of records, and the name of the contractor who prepared the data for all of the emissions inventory files are tabulated in Appendix C of the Final Re port: WRAP Regional Modeling Center – ShortTerm Modeling Analysis contract (Houyoux, 2003). The files named in Appendix C are consistent with the files listed in Table 2 above.

Tables 1.2.1.3a-c through 1.2.1.4a-c contain pie charts of the 1996 and 2018 emissions by source category for all inventory pollutants for the WRAP modeling domain. Appendices F and G of the Final Report: WRAP Regional Modeling Center – Short-Term Modeling

Final §309 Regional TSD Page 67 of 250 December 15, 2003 Revised May 7, 2008Analysis contract (Houyoux, 2003) contain the annual inventory totals for 1996 and 2018 respectively for each of the major source categories.

Table 1.2.1.3a. 1996 CO, NO , and VOC base emissions contributions by source category x

Fire Nonroad Mobile On-road Mobile

Utility Point Road Dust Area

Non-Utility Point BiogenicJuly 17 January 17 

### CO

13% 2% 0.2%

10%

75%

9% 1%

6% 3%

45% 36% 

### NO x

10% 4%

10% 12.6%

23%

40%

1% 18%

11%

9% 23%

27% 11% 

### VOC

29%

32% 4% 0.11%

8% 27%

90% 5% 1% 0.05% 2% 2%

Final §309 Regional TSD Page 68 of 250 December 15, 2003 Revised May 7, 2008Table 1.2.1.3b.1996 PM 10 , PM 2.5 , and PMC base emissions contributions by source category

Fire Nonroad Mobile On-road Mobile

Utility Point Road Dust Area

Non-Utility Point BiogenicJuly 17January 17 

### PM 10

59% 31% 5% 1% 2% 2%

53% 31% 7% 3% 2%

3% 1% 

### PM 2.5

41% 17%

14% 11%
4%

11% 2%

41% 17%

14% 11%
4%

11% 2% 

### PMC

59% 37% 3% 1% 0.2%

58% 36% 4% 0.5% 2%

0.4% 0.3%

Final §309 Regional TSD Page 69 of 250 December 15, 2003 Revised May 7, 2008Table 1.2.1.3c. 1996 NH and SO emissions contributions by source category 3 2

Fire Nonroad Mobile On-road Mobile

Utility Point Road Dust Area

Non-Utility Point Biogenic



### NH 3July 17 January 17

89% 4% 1% 6%

94% 2% 1% 0.3% 3% 

### SO 2

11%

33% 44% 9.8%

2%

9%

19% 1.1%

46% 25%

0.4%

Final §309 Regional TSD Page 70 of 250 December 15, 2003 Revised May 7, 2008, and VOC base emissions contributions by source category Table 1.2.1.4a. 2018 CO, NO x

Fire Non-road Mobile On-road Mobile

Utility Point Road Dust Area

Non-Utility Point BiogenicJuly 17 January 17 

### CO

22% 5% 2% 0.5%

19% 51%

19%

1%

5% 3%

40% 32% 

### NO x

18% 6%

13 1% 20%

24%
18% %

4%

18%

12 % 8% 21%

26% 11% 

### VOC

40% 37% 6% 0.2%

7% 10%

89% 5% 2% 2% 1% 1%

Final §309 Regional TSD Page 71 of 250 December 15, 2003 Revised May 7, 2008Table 1.2.1.4b.2018 PM 10 , PM 2.5 , and PMC base emissions contributions by source category

Fire Non-road Mobile On-road Mobile

Utility Point Road Dust Area

Non-Utility Point BiogenicJuly 17January 17 

### PM 10

73% 15% 7% 1% 1%

2% 1%

11%

57% 2%

7% 18%

4% 1% 

### PM 2.5

92% 6% 0.4% 1% 0.3% 1%

29%

13% 42%

2.8%

11% 2% 

### PMC

75% 19% 5% 0.3% 1%

0.3% 0.4%

69% 2% 1% 5% 23% 0.5% 0.4%

Final §309 Regional TSD Page 72 of 250 December 15, 2003 Revised May 7, 2008and SO emissions contributions by source category Table 1.2.1.4c. 2018 NH 3 2

Fire Non-road Mobile On-road Mobile

Utility Point Road Dust Area

Non-Utility Point Biogenic



### NH 3July 17 January 17

83% 10%

1% 0.1% 5%

92% 4% 0.3% 2% 2% 

### SO 2

15%

27% 0.2%

44% 13%

1%

1% 9%

44%

22% 24%

0.4%

Processing with SMOKE

MCNC installed SMOKE at the WRAP RMC for large-scale batch processing. The operational scripts contained in the WRAP installation are organized by the number of times that each SMOKE program is used in creating an annual emissions data set. An alternative set-up is to organize the scripts by source category. The operational scripts reduce the interaction between the modeler and the software, thus automating more of the tasks and

Final §309 Regional TSD Page 73 of 250 December 15, 2003 Revised May 7, 2008reducing the sources of human error. The following subsection provides details about each of the operational scripts used in creating the emissions datasets for WRAP modeling. The base SMOKE installation directory for the scripts on the WRAP RMC system is:

/home/aqm2/edss/subsys/smokev1.3.2/scripts/run

Details on the exact SMOKE settings used in these scripts are included in Appendix H of the Final Report: WRAP Regional Modeling Center – Short-Term Modeling Analysis contract (Houyoux, 2003). Appendix I includes instructions on how to use the SMOKE scripts installed on the RMC computers to create an annual emissions dataset for CMAQ or REMSAD.

SMOKE Scripts

The scripts are the interface that emissions modelers use to run SMOKE, and are therefore the items of practical importance for anyone wanting to simply reproduce the work performed as part of this contract. For this project, we created many SMOKE scripts to run the required emissions modeling cases, which we describe in this subsection, and in the following table. There are five types of scripts used in the SMOKE installation created for the WRAP RMC:

• Scripts that run programs once per model year

• Scripts that run programs once per month

• Scripts that run programs for each day

• Scripts that run programs for holidays

• Scripts that grow from 1996 to 2018

Table 1.2.1.5. Summary of SMOKE scripts

Path under $SCRIPTS/

run Source categories Year and case Script names

1996 REMSAD Base, us36e All remsad smkinven_wrap.remsad_us36.mole.cmd monthly_wrap.remsad_us36.mole.cmd daily_mrg_wrap.remsad_us36.cmd ptsrce_wrap96_remsad_us36.job 1996 CMAQ Base case f, w36f All base_96 smkinven_wrap96.cmaq_w36.mole.cmd monthly_wrap96.cmaq_w36.mole.cmd daily_mrg_wrap96.cmaq_w36.cmd

In addition, the following scripts were derived from the base distribution files for processing specific inventory categories. They were created when we had to do reruns of some source categories. mobile/ monthly_mb_wrap96.cmaq_w36.mole.cmd

Final §309 Regional TSD Page 74 of 250 December 15, 2003 Revised May 7, 2008Path under $SCRIPTS/

run Source categories Year and case Script names

point/ smkinven_wrap96.cmaq_w36.mole.cmd_pt point/ monthly_pt_wrap1996.cmaq_w36.mole.cmd point/ daily_pt_wrap1996.cmaq_w36.cmd area/ smkinven_wrap96.cmaq_w36.mole.rpt_ar All base_2018 smkinven_wrap2018.cmaq_w36.mole.cmd monthly_wrap2018.cmaq_w36.mole.cmd daily_wrap2018.cmaq_w36.mole.cmd 2018 CMAQ Base, w36g
stationary area, road dust, area smkinven_ar_wrap2018.cmaq_w36.mole.cmd monthly_ar_wrap2018.cmaq_w36.mole.cmd daily_ar_wrap2018.cmaq_w36.cmd smkinven_ar_wrap2018.cmaq_w36.mole.rpt nonroad mobile on-road mobile mobile monthly_mb_wrap2018.cmaq_w36.mole.cmd daily_mb_wrap2018.cmaq_w36.cmd point

sources, SO point smkinven_pt_wrap2018.cmaq_w36.mole.cmd monthly_pt_wrap2018.cmaq_w36.mole.cmd daily_pt_wrap2018.cmaq_w36.cmd 2 and no-SO smkinven_pt_wrap2018.cmaq_w36.mole.rpt
2 files wildfire, agricultural fire, prescribed fire FireScens smkinven_agbsm_wrap2018.cmaq_w36.mole.cmd smkinven_agosm_wrap2018.cmaq_w36.mole.cmd smkinven_rxbsm_wrap2018.cmaq_w36.mole.cmd smkinven_rxosm_wrap2018.cmaq_w36.mole.cmd smkinven_wf_wrap2018.cmaq_w36.mole.cmd daily_pt_wrap2018.cmaq_w36.cmd.agbsm daily_pt_wrap2018.cmaq_w36.cmd.agosm daily_pt_wrap2018.cmaq_w36.cmd.rxbsm daily_pt_wrap2018.cmaq_w36.cmd.rxosm daily_pt_wrap2018.cmaq_w36.cmd.wf daily_mrggrid_wrap2018.cmaq_w36.bsm daily_mrggrid_wrap2018.cmaq_w36.osm Point 2018 w36g_ptSO2_ milestone PtControls smkinven_wrap2018.cmaq_w36.mole.cmd_milestone monthly_wrap2018.cmaq_w36.mole.cmd_milestone daily_merge_wrap2018.cmaq_w36.milestone (Smkmerge) daily_mrggrid_wrap2018.cmaq_w36.milestone (Mrggrid) Point 2018 w36g_ptSO2_ BART PtControls smkinven_wrap2018.cmaq_w36.mole.cmd_BART monthly_wrap2018.cmaq_w36.mole.cmd_BART daily_merge_wrap2018.cmaq_w36.BART daily_mrggrid_wrap2018.cmaq_w36.BART Point 2018 w36g_ptSO2_ PtControls smkinven_wrap2018.cmaq_w36.mole.cmd_BARTwUncrty monthly_wrap2018.cmaq_w36.mole.cmd_BARTwUncrty daily_merge_wrap2018.cmaq_w36.BARTwUncrty BART Uncrty daily_mrggrid_wrap2018.cmaq_w36.BARTwUncrty

2018 w36g_NoGCMb Mobile MbControls smkinven_wrap2018.cmaq_w36.mole.cmd_noGCMb monthly_wrap.cmaq_w36.mole.cmd_noGCMb daily_wrap2018.cmaq_w36.cmd_noGCMb daily_mrggrid_wrap2018.cmaq_w36.base_noGCMb

Final §309 Regional TSD Page 75 of 250 December 15, 2003 Revised May 7, 2008Path under $SCRIPTS/

run Source categories Year and case Script names

2018 w36g_NoCAMb Mobile MbControls smkinven_wrap2018.cmaq_w36.mole.cmd_noCAMb monthly_wrap.cmaq_w36.mole.cmd_noCAMb daily_wrap2018.cmaq_w36.cmd_noCAMb daily_mrggrid_wrap2018.cmaq_w36.base_noCAMb

2018 w36g_ Mobile MbControls smkinven_wrap2018.cmaq_w36.mole.cmd_NoPhnxAZMb monthly_wrap.cmaq_w36.mole.cmd_ NoPhnxAZMb NoPhnxAZMb daily_wrap2018.cmaq_w36.cmd_ NoPhnxAZMb daily_mrggrid_wrap2018.cmaq_w36.base_ NoPhnxAZMb 2018 w36g_ Mobile MbControls smkinven_wrap2018.cmaq_w36.mole.cmd_NoVegasMb monthly_wrap.cmaq_w36.mole.cmd_ NoVegasMb NoVegasMb daily_wrap2018.cmaq_w36.cmd_ NoVegasMb daily_mrggrid_wrap2018.cmaq_w36.base_ NoVegasMb 2018 w36g_ Point PtControls smkinven_wrap2018.cmaq_w36.mole.cmd_GCNOxCtl monthly_wrap2018.cmaq_w36.mole.cmd_GCNOxCtl GCNOxCtl daily_merge_wrap2018.cmaq_w36.GCNOxCtl daily_mrggrid_wrap2018.cmaq_w36.GCNOxCtl 2018 w36g_ Point PtControls smkinven_wrap2018.cmaq_w36.mole.cmd_GCPM10Ctl monthly_wrap2018.cmaq_w36.mole.cmd_GCPM10Ctl GCPM10Ctl daily_merge_wrap2018.cmaq_w36.GCPM10Ctl daily_mrggrid_wrap2018.cmaq_w36.GCPM10Ctl 2018 w36g_ Point PtControls smkinven_wrap2018.cmaq_w36.mole.cmd_GCNOxPMinc monthly_wrap2018.cmaq_w36.mole.cmd_GCNOxPMinc GCNOxPMinc daily_merge_wrap2018.cmaq_w36.GCNOxPMinc daily_mrggrid_wrap2018.cmaq_w36.GCNOxPMinc 20918 w36g_P2 Point P2 smkinven_wrap2018.cmaq_w36.mole.cmd_P2 monthly_pt_wrap2018.cmaq_w36.mole.cmd.P2 daily_pt_wrap2018.cmaq_w36.cmd.P2 daily_mrggrid_wrap2018.cmaq_w36.cmd_cntl_all

The following section explains the four types of scripts in more detail. The scripts are presented in the order in which they should be run. For more details on how to use these scripts, please refer to Appendix J.

Scripts that run programs once per model year

The scripts in Table 15 with names starting with “smkinven” run SMOKE processing steps that are needed only once per model year. These scripts run the following processing steps:

• Import the raw stationary area, nonroad mobile, and/or point inventories (uses Smkinven program)

• Import the biogenic land use (for 1996 base case only) (uses Rawbio program)

• Compute matrices for chemical speciation (program Spcmat) and gridding (program Grdmat) for stationary area, nonroad mobile, and point sources

Final §309 Regional TSD Page 76 of 250 December 15, 2003 Revised May 7, 2008• Create import QA reports to summarize inventories by state, county, SCC, and various other combinations (uses Smkreport program)

• For 2018 cases, compute matrices for growth and controls (uses Cntlmat and Grwinven programs)

Scripts that run programs once per month

The scripts in Table 1.2.1.5 with names st arting with “monthly” run SMOKE processing steps, which need to be run once per model month. These scripts run the following processing steps:

• Import the raw on-road mobile-source precomputed emissions inventory and VMT (uses Smkinven program)

• Import any fire data for each month (only fo r scripts that include fire processing) (uses Smkinven program)

• Compute matrices for chemical speciation (Spcmat program) and gridding (Grdmat program) for on-road precomputed emissions mobile sources

• Compute matrices for chemical speciation (Spcmat program) and gridding (Grdmat program) for fire data (only for scripts that include fire processing)

• Calculate hourly emissions for representative Mondays, weekdays, Saturdays, and Sundays for stationary area, nonroad mobile, and point sources; Table 16 lists the Julian dates in each month used as representative days (uses Temporal program)

• Create import QA reports to summarize the on-road mobile precomputed emissions inventory by state, county, SCC, and vari ous other combinations (uses Smkreport program)

• Create additional QA reports of hourly emissions for stationary area, nonroad mobile, on-road mobile, and point sources for representative Mondays, weekdays, Saturdays, and Sundays; these reports include inventory summaries by state, county, SCC and other combinations (uses Smkreport program).

Final §309 Regional TSD Page 77 of 250 December 15, 2003 Revised May 7, 2008Table 1.2.1.6. Julian dates of representative Saturday, Sunday, Weekday, Monday days

Month Saturday Sunday Weekday Monday
January 1996006 1996007 1996005 1996008 February 1996034 1996035 1996037 1996036
March 1996062 1996063 1996065 1996064 April 1996097 1996098 1996094 1996093 May 1996125 1996126 1996128 1996127
June 1996160 1996161 1996156 1996155 July 1996188 1996189 1996185 1996190 August 1996216 1996217 1996219 1996218 September 1996252 1996252 1996254 1996253
October 1996279 1996280 1996282 1996281
November 1996307 1996308 1996310 1996309
December 1996342 1996343 1996345 1996344

Scripts that run programs for each day

There are two types of scripts that run SMOKE programs for each day. These all start with the name “daily”. The daily scripts other than those starting with “daily_mrggrid” (which are a special case, described below the bulleted list) run the following processing steps:

• Compute hourly on-road emissions for every day for precomputed emissions and VMT (uses Premobl, Emisfac, and Temporal programs)

• Compute hourly fire emissions (for fire -specific scripts only) (uses Temporal program)

• Compute point-source plume rise for point sources and fire sources (uses Laypoint program)

• Combine hourly emissions with matrices to create hourly, gridded, and chemically speciated emissions; a separate file is created for each day and source category for stationary area, nonroad mobile, on-road mobile, point, and all fire sources (uses Smkmerge program)

• Calculate hourly, gridded, and chemically speciated biogenic emissions based on previously imported land use data and meteorology data (base year only) (uses Tmpbio program of SMOKE/BEIS2) Once all of the hourly, gridded, and chemically speciated files have been created for either CMAQ or REMSAD, the “daily_mrggrid” script performs the following step:

• Combine separate hourly, gridded, and chemically speciated emissions files for each source category into the single 3-D file needed for CMAQ or the low-level emissions and ASCII point source elevated file needed for REMSAD

Final §309 Regional TSD Page 78 of 250 December 15, 2003 Revised May 7, 2008Scripts that run programs for holidays

Because we are using a Monday-weekday-Saturday-Sunday approach, we must also make sure to run holidays separately. This is because the holidays are assigned Sunday diurnal profiles, regardless of the day of the week on which the holiday falls. Because our inventory covers five time zones but all output uses the same GMT time zone, we must also run for the day after each holidays to capture those holiday emissions at the end of the holiday in the local time zone that appear in the next day’s model input file in GMT. Holidays must be run for all cases that use the MWDSS approach, which excludes only biogenic sources. On-road mobile sources that use MOBILE5b (or MOBILE6 in newer versions of SMOKE) are also excluded from this step. The holiday script needs to be run only once to allocate holiday emissions to Sunday temporal profiles because it runs the necessary SMOKE programs for all of the holiday days.

Table 1.2.1.7. 1996 holidays for Task 3 Holiday Dates Julian Dates
New Year’s Day January 1 1996001 Good Friday April 14 1996096 Memorial Day May 29 1996148 Independence Day July 4 1996186 Labor Day September 4 1996246 Thanksgiving Weekend November 23, 24 1996333, 1996334
Christmas Eve December 24 1996359
Christmas December 25 1996360

SMOKE Ancillary Inputs

Appendix D of the Final Report: WRAP Regiona l Modeling Center – Short-Term Modeling Analysis (Houyoux, 2003) lists the final set of ancillary input files used to create the CMAQ and REMSAD emissions with SMOKE. We started with SMOKE version 1.3 ancillary files, and made some modifications based on (1) changes requested by the WRAP Modeling Forum or required because of the needs of the modeling effort, and (2) errors found in the files. Some of these files went through several versions before the creation of the final file. Other than some modifications to the files to accommodate new inventory sources (e.g., fires), we maintained consistency in the ancillary files throughout all of the emissions modeling.

In this subsection, we provide further description of changes that we made to SMOKE version 1.3 files. We document how the spatial surrogates were provided by EMC, and how we updated the temporal assignments and speciation assignments. In addition, we also identify files that we did not update after erro rs had been found because the files had already been used in the base case modeling and needed to continue to be used “as is” for the sake of consistency with all of the modeling runs.

Final §309 Regional TSD Page 79 of 250 December 15, 2003 Revised May 7, 2008MCNC created the SMOKE spatial surrogates for specific parts of the domain for both the REMSAD and CMAQ emissions modeling. For the REMSAD domain, we used a Geographic Information System (GIS)-based tool to overlay a 36-km resolution grid over the map of the WRAP region and assign the 20-category Models-3 spatial surrogate fractions to the grid. The table below lists the data sources that we used to obtain GIS shape files for deriving the SMOKE surrogate data. We created spatial surrogates for Canada and Mexico from the same database, but used only a single surrogate, population, to spatially allocate these emissions. The spatial surrogate names, codes, and data sources are available in the Final Report: WRAP Regional Modeling Center – Short-Term Modeling Analysis (Houyoux, 2003). We modified the spatial cross-reference file for the California on-road mobile inventory. Since the inventory does not include road types as part of the identification of emissions, and since the default SMOKE spatial cross-reference assigns surrogates based on road type, we had to add entries so that the emissions would not be dropped. Population was the only available surrogate that we could use to assign on-road mobile emissions that are defined only by their vehicle type. Had road class been a part of the emissions inventory, we could have used the road-class-specific surrogates, which would certainly have provided a more robust spatial allocation approach for California on-road emissions.

MCNC added information to the EPA temporal a llocation cross-reference and profile files to accommodate the WRAP inventories. Temporal information for Canada, in the form of region-specific profiles, was added to the files. We used Canadian profile data developed by the EPA for the CMAQ Proof-of-Concept modeling study (EPA, 2000). The WRAP Fire Forum supplied daily temporal profiles for wildfires, agricultural fires, and prescribed fire sources that capture the differences in the diurnal distribution of emissions for the three types of fires. We added these fire profiles to the SMOKE temporal input files. The profiles can be found in the Final Report: WRAP Regional Modeling Center – Short-Term Modeling Analysis (Houyoux, 2003).

For the seasonal mobile inventories, the cross-references for several on-road and nonroad mobile SCCs for the WRAP states were converted to uniform monthly profiles to account for the inherent seasonality in the inventories. The Final Report: WRAP Regional Modeling Center – Short-Term Modeling Analysis (Houyoux, 2003) lists the on-road and nonroad mobile SCCs for the WRAP states that received uniform monthly profiles. We did this because it is unnecessary to apply a monthly profile to these emissions when the emissions are already being provided for each month or season.

On July 10, 2002, MCNC was notified of a bug in the temporal profiles used for the WRAP modeling that affects the monthly allocation of stationary point source emissions. The temporal profiles used in the WRAP modeling contain incorrect monthly profile codes for some point source SCCs. These incorrect codes affect the seasonal and monthly allocation of the emissions from some point sources. The magn itude of the effects from this bug by SCC is available on the website for the Community Modeling and Analysis System (CMAS) center

Final §309 Regional TSD Page 80 of 250 December 15, 2003 Revised May 7, 2008at http://www.emc.mcnc.org/product_qa/bugzilla/show_bug.cgi?id=403 . The “bug report 403” attachment can be downloaded for review by interested parties.

In addition, MCNC modified the chemical allocation profiles to account for the information contained in the WRAP inventories. Since the WRAP on-road mobile inventories used prespeciated PM 2.5 emissions, the chemical allocation profiles for both CMAQ and REMSAD had to be modified to speciate these emissions correctly. Table 1.2.1.8 lists the pollutants contained in the prespeciated mobile inventories and the CMAQ and REMSAD input pollutants to which they were mapped. Based on recommendations from the Fire Forum, EMC also slightly modified the PM 2.5 chemical profiles for fire sources. Table 1.2.1.9 presents the chemical allocation profiles used for fire sources in the WRAP Jumpstart modeling.

Table 1.2.1.8. WRAP mobile-source PM chemical species

Nonspeciated Pollutant WRAP Mobile PM Pollutant CMAQ Input Pollutant Nonspeciated Pollutant PMC (PM10-PM2_5) PMC_PRE PMC PMCOARS PM2_5 EC2_5 PEC PEC OC2_5 POA POA SO4_2_5 PSO4 GSO4 OTHER2_5 PMFINE PMFINE

Table 1.2.1.9. WRAP fire-source PM chemical profiles

Source Profile # PEC PMFINE PNO3 POA PSO4

Agricultural Fire 22070 0.09 0.274 0 0.636 0

Wildfire/Prescribed Fire 22080 0.262 0.2663 0.0063 0.45 0.0154

General processing issues

SMOKE has four different processing approaches: processing for area, on-road mobile, point, and biogenic sources. The WRAP source categories map to these processing approaches is shown next. Table 1.2.1.10. WRAP source categories mapped to processing approaches in SMOKE

WRAP Source category SMOKE processing approach Stationary area Area Nonroad mobile Area Road dust Area

Final §309 Regional TSD Page 81 of 250 December 15, 2003 Revised May 7, 2008On-road mobile On-road mobile Point Point Fire (wildfire, agricultural fire, and prescribed fire) Point

Biogenics Biogenics

Each SMOKE processing approach is for a specific SMOKE source category, which has its own source characteristics. These correspond to the identifiers used in creating the emission inventory (e.g. state/county FIPS code and S CC). The source categories also have source attributes, which are the other useful data in the emission inventories that SMOKE uses for processing them (e.g., point-source flue ga s exit height and temperature). Source characteristics define the sources as area, mobile, or point sources for SMOKE processing and also distinguish one source in the inve ntory from another. Source attributes are additional data about the source that do not contribute to the source’s uniqueness in SMOKE. In SMOKE, each source category is defined by source characteristics, as follows:

• Area sources are defined by (1) country, state, and county codes, (2) SCC codes, or optionally (3) grid cell only.

• On-road mobile sources are defined by (1) country, state, and county codes, (2) SCC codes, and optionally (3) link codes.

• Point sources are defined by (1) country, state, and county codes, (2) plant codes, and (3) characteristics 1 through 5, one of which should be the SCC code.

• Biogenic sources are defined differently depending on the type of processing that you are using. They can be defined either by (1) country, state, and county codes and (2) land use code, or by (1) grid cell and (2) land use code.

In the following subsections, we describe the processing that we performed for each of the WRAP source categories. A great deal of additional information on the processing for each of these source categories is available in the Final Report: WRAP Regional Modeling Center – Short-Term Modeling Analysis (Houyoux, 2003). This additional information includes a list of all scripts, the major steps taken during processing, and figures describing the connections between SMOKE programs and files.

Area source emissions processing

MCNC divided the stationary area source invent ory into area sources, road dust sources, and Mexico sources to facilitate the creation of emissions sensitivities by the WRAP. The area source inventory contains US area sources and Canadian area, non-road, and on-road mobile sources. WRAP inventory contractors supplied area inventories for the WRAP, Tier 1, and Tier 2 states. MCNC used the NEI96 for the inve ntories in the Eastern US tier of states. The road dust inventory being used in the final m odeling is the fourth version of the inventory and is composed of paved and unpaved road emissions. An improvement over the default road dust emissions of the NEI96, the WRAP road dust inventory uses a more robust method to account for the transportable fraction of the emissions as compared to the flat 75% reduction used for the NEI96 (ENVIRON, 2002). WRAP inventory contractors supplied the

Final §309 Regional TSD Page 82 of 250 December 15, 2003 Revised May 7, 2008road dust inventory for the entire WRAP domain. The Mexico inventory is a combination of stationary, area, nonroad, and on-road mobile sources for the three northwestern Mexican states, Chihuahua, Baja Norte, and Sonora. All of these area source components are combined at the final step in the processing to represent the WRAP area source emissions.

Nonroad mobile source emissions processing

Consistent with the technique of dividing th e area source inventory into several specific inventories, MCNC processed the non-road m obile inventory as a stand-alone inventory component. The WRAP mobile source inventory contractor supplied seasonal, non-road emissions for the 13 WRAP states; MCNC used the NEI96 to represent the inventory for the rest of the domain. The inventory developed by the inventory contractor contained 4-digit SCCs for the non-road sources, rather than the standard 10-digit SCCs normally used. While the 4-digit SCCs capture the engine types (i.e. 4-stroke vs. 2-stroke) of the non-road sources, they do not capture the specific vehicle types (i.e. motorcycles vs. snowmobiles). Before processing the non-road mobile inventory, MCNC explored the effects of using general, 4- digit SCC descriptions of the non-road sources. It was determined and documented in the email titled “WRAP NONROAD emissions, 2-stroke versus 4-stroke speciation” sent on February 13, 2002 by Marc Houyoux, that the effects of the 4-digit SCCs are of less concern on speciation and temporal allocation than are the effects of using antiquated modeling profiles. Although prespeciated, particulate, nonroad mobile data were provided by the nonroad mobile contractor, we are using the non-prespeciated data only with speciation profiles. Future configurations should consider using the prespeciated particulate emissions data as is done with on-road mobile sources.

On-road mobile source emissions processing

MCNC received monthly on-road mobile invent ories from the WRAP inventory contractor for the 13 WRAP states. The inventory cont ained two files per season, a California-only inventory and an inventory for the “other-12” WRAP states. The California inventory does not associate road-types with vehicle classes while the “other-12” on-road inventory does. MCNC combined these WRAP inventories with monthly NEI96 heavy-duty diesel inventories for the Tier 1, Tier 2, and Eastern US states to get coverage for the entire WRAP domain. The WRAP on-road mobile PM inventor y differed from the rest of the inventories by containing pre-speciated PM 2.5 emissions. MCNC created a new set of speciation profiles that simply mapped the pre-speciated emissions directly to CMAQ-ready pollutants. MCNC hybridized both the temporal and chemical allocation profiles to account for the combination of the WRAP and NEI inventories. The app lication of uniform monthly temporal profiles accounted for the inherent temporal variability contained in the seasonal and monthly inventories. MCNC created new speciation profiles that combined the standard PM 2.5 profiles with the pre-speciated PM 2.5 mapping.

Point source emissions processing including utilities

Final §309 Regional TSD Page 83 of 250 December 15, 2003 Revised May 7, 2008Point-source processing for the WRAP includes emissions for the point-source inventory, wildfire inventory, prescribed (Rx) burni ng inventory, and agricultural (ag) burning inventory. The point-source inventory included Mexican point sources but no Canadian point sources. The processing approaches differed for 1996 and 2018; below we describe the 1996 approach, then the 2018 approach.

For 1996, the approach and data changed as we proceeded. Initially, we processed all point sources and wildfire sources together in a single run. These wildfires were a 1996-specific set of fires, and the other fire sources were not available. By the end of the project, we separated the point inventories from the fire pro cessing, so that is what we present here; this is the 1996 “base g” case. This case uses the 1996 point sources, the “typical year” wildfires provided in April 2002, the Base Smoke Management (BSM) agricultural fires, and BSM prescribed fires. The files and processing approaches are described in further detail in the Final Report: WRAP Regional Modeling Center – Short-Term Modeling Analysis (Houyoux, M., 2003). For the many point-source control strategies, we configured the point-source processing in many groups to facilitate reusing as much previous work as possible in creating the controlled inventories. In SMOKE, one of the major factors distingui shing stationary-source inventory processing from the other source categories is the calculation by SMOKE of plume rise and subsequentallocation of the emissions vertically. In addition, point-source processing can include dayspecific and hour-specific emission inventorie s and hour-specific precomputed plume rise. Also, the elevated point sources are treated differently for CMAQ and REMSAD processing.

Fire emissions processing

MCNC instrumented SMOKE to treat fire em issions as point sources. The technique developed for the WRAP modeling differs from the traditional fire emissions processing methods of treating fires in the area source i nventory. The new method locates the fires using a latitude-longitude coordinate and imports pre-computed plume rise information to determine the vertical allocation of the fire emissions. SMOKE uses daily fire inventory data and newly developed hourly temporal profiles to model the fires. In addition, hourly plume rise information, supplied to MCNC by the fi re inventory contractor, provides data for allocating the emissions to the different vertical model layers. There were a few issues that MCNC resolved with the fire inventories and ancillary input data before the final set of fire emissions was created.

Biogenic emissions processing

Please refer to the earlier section about the biogenic emission inventory development, which covers the methods for processing the data.

Emissions processing quality assurance

Final §309 Regional TSD Page 84 of 250 December 15, 2003 Revised May 7, 2008The two primary areas of focus in the emissions QA process are the input data and the emissions software. We employed three types of QA checks throughout the emissions generation process for the WRAP Jumpstart modeling to address these areas:

• Qualitative analyses of different visualizations of spatially and temporally allocated pollutants are useful in determining whether there were problems with the input data. The location of urban centers and highways, temporal profiles for the different source categories, and the omission of areas within the domain are all checked in this analysis.

• Quantitative analyses of the inventory reports confirm that SMOKE correctly processed the information contained in the raw inventories. Comparisons of the inventory reports with the reports generated by the Smkreport module allow QA checks of the emissions generation process from the SCC level up to the level of fully merged emissions for the entire domain.

• Random and frequent checks of the SMOKE log files can uncover problems with the SMOKE inputs, the processing, and scripting.

The Final Report: WRAP Regional Modeling Ce nter – Short-Term Modeling Analysis WRAP Jumpstart (Houyoux, 2003) discusses these steps in more detail. The proper combination of the three steps leads to a high-quality emissions data set in the context of SMOKE processing. While errors in the raw inventory and/or SMOKE inputs may manifest themselves in the CMAQ-ready emissions, thoroughly performing these steps will ensure that SMOKE correctly processed the inventory information and reproduced the input data.

Qualitative Analysis

Importing the model-ready emissions into the Package for Analysis and Visualization of Environmental data (PAVE) and looking at both th e spatial and temporal distributions of the emissions provides insight into the quality and accuracy of the emissions inputs. By visualizing the model-ready emissions with the scale on the plots set to a very low value, one can determine whether data for sections of the modeling domain are omitted from the raw inventory. Checking for point, area, and bioge nic emissions over water cells, and confirming the presence of elevated emissions over urban areas, are checks for correct spatial allocation of the emissions. Comparing pollutant time-seri es in different source categories for different days of the week and different months checks the accuracy of the diurnal, weekend/weekday, and seasonal temporal allocation in the emissions. Several QA analyses based on visualizations of the emissions are possible, but as we were concerned more with the accuracy of SMOKE than with the accuracy of the input data, we performed only cursory qualitative checks using PAVE visualizations.

Quantitative Analysis

Throughout the §309 modeling effort, the WRAP inventory contractors supplied us with reports summarizing the raw inventories from which SMOKE generated the CMAQ-ready emissions. We used the state totals in these reports to check how accurately SMOKE reproduced the inventories. By comparing these totals to the Smkreport outputs after each

Final §309 Regional TSD Page 85 of 250 December 15, 2003 Revised May 7, 2008step in the emissions generation process, we tracked the performance of SMOKE and were able to discover problems as they arose. We are confident that SMOKE imported the raw inventory correctly. Additionally, we are confident that the temporal, spatial, and chemical allocation occurred in conjunction with the SMOKE input files used in the Section 309 modeling. Our standard quantitative QA procedures described above do not cover the allocation of point-source emissions to the vertical model layers. As this step in the emissions modeling process introduced errors in the model-ready emissions, it has now been added to the standard QA package. The Final Report: WRAP Regional Modeling Ce nter – Short-Term Modeling Analysis (Houyoux, 2003) contains examples of QA plots that represent the products that we used to check that SMOKE correctly processed the raw emissions inventories. SMOKE generates reports following each major step in the process of converting annual average emissions inventories to temporally, chemically, and spatially allocated model-ready emissions. These reports are compared back to the inventory contractor totals qualitatively through normalized differences using graphical plots. Creating these plots for every pollutant for each new inventory quickly helps to ensure that SMOKE correctly processes the inventory data and the corresponding input files.

SMOKE Log Files

The first step in troubleshooting problems enc ountered while creating model-ready emissions with SMOKE is to inspect the log files generated during each step in the process. If any step in the SMOKE processing fails, the log files contain the information needed to track down the reasons for the failure. The warnings and errors printed in the SMOKE log files offer a wealth of information for determining the causes of most problems encountered during SMOKE processing. Errors in SMOKE processing occur as both “show-stoppers”, problems that prevent SMOKE from completing successfully, and as the assimilation of incorrect input files. SMOKE handles showstoppers by printing out detailed descriptions of the cause of the errors in the log files. Unexpected results in SMOKE outputs are often due to either lack of information in the SMOKE input files and the subsequent assignment of default profiles, or the assignment of wrong input files. For example, after adding the new set of temporal profiles for wildfire sources to the SMOKE temporal allocation inputs and reprocessing the fire sources, the temporal profile for wildfires did not change in the applicable grid cells. Inspection of the log files uncovered that the revised temporal allocation file had not been applied. We corrected this problem by updating the setting for the point-source temporal allocation inputs and rerunning SMOKE. This last type of QA check is not as specific as thequalitative and quantitative checks and is most often used in determining the cause of runtime errors. There are times, however, when it becomes necessary to confirm that we used the correct input files to generate of a set of emissions. The SMOKE log files present a concise summary of the entire set of information used to create the model-ready emissions.

WRAP Emissions QA

Final §309 Regional TSD Page 86 of 250 December 15, 2003 Revised May 7, 2008Appendices E and F of the Final Report: WRAP Regional Modeling Center – Short-Term Modeling Analysis (Houyoux, 2003) list the annual totals for all of the states in the WRAP domain for each of the following base inventory scenarios:

• 1996 Base scenario point, area, on-road mobile, non-road mobile, road dust

• 2018 Base scenario point, area, on-road mobile, non-road mobile, road dust

Appendix G of the same report lists the annual totals for all of the states in the WRAP domain for each of the following sensitivity inventory scenarios:

• 2018 SO Milestone/Annex scenario point 2

• 2018 BART scenario point

• 2018 BART with Uncertainty scenario point

• 2018 Zero Grand Canyon States Mobile scenario on-road and nonroad mobile

• 2018 Zero California Mobile scenario on-road and nonroad mobile

• 2018 Zero Phoenix, AZ, MSA Mobile scenario on-road and nonroad mobile

• 2018 Zero Las Vegas, NV, MSA Mobile scenario on-road and nonroad mobile

• 2018 Stationary source 50% NO reductions x

• 2018 Stationary source 50% PM 10 reductions

• 2018 Stationary source 25% NO + PM x 10 increases

• 2018 Pollution Prevention point We used the inventory summaries in Appendices E through G to generate regression plots for checking that SMOKE processed these inventories correctly. Appendix H of the Final Report: WRAP Regiona l Modeling Center – Short-Term Modeling Analysis contract (Houyoux, 2003) contains emi ssions density plots. These plots are best viewed in color.

Problems encountered and remaining processing issues

The emissions processing required for this project has been the most complicated effort performed by the authors to date. The complexity arose from the necessity of having to merge several different inventories (e.g., WRAP, NEI, California) provided by several different sources (e.g., PES, Pechan, ENVIRON, EPA) at several different time resolutions (e.g., day-specific, hour-specific, annual, seasonal, and monthly). Furthermore, the inventories were frequently in error themselves, were provided at different times and in numerous different versions, and were often pr ovided without detailed descriptions of what the inventories contained or how the inventories were intended to be different from previous versions. In some cases, the formats of the inputs were not acceptable, requiring our having to reformat them before using them. Needless to say, such unprecedented complexity resulted in discovery of many shortcomings with our emissions processing system and quality assurance techniques. These problems resulted in wasted modeling and analysis time when it was discovered, for example, that the emissions data being used in CMAQ were invalid for a critical case. In this subsection, we document the critical problems that we encountered as well as problems remaining at the end of the modeling effort.

Final §309 Regional TSD Page 87 of 250 December 15, 2003 Revised May 7, 2008A number of problems were found and resolved as part of the modeling effort. These aredocumented in Section 3.4.1 of the Final Report: WRAP Regional Modeling Center – ShortTerm Modeling Analysis (Houyoux, 2003). In addition, some of the problems found were not resolved by the completion of the Jumpstart project, and these are documented in Section 3.4.2 of that same report. It is important for future WRAP modeling efforts that these issues be considered and addressed.

1.2.2. MM5/MCIP meteorological processing

Models-3 Community Multiscale Air Quality (CMAQ) modeling system is designed to simulate multiscale (urban and regional) and multi-pollutant (oxidants, acid deposition, and particulates) air quality problems. Before running the CMAQ Chemical Transport Model (CCTM), information about the coordinates and grid as well as the meteorological data has to be processed and provided. In addition, CCTM-ready meteorological data are needed to process emissions files.

The Regional Modeling Center (RMC) uses the Meteorology-Chemistry Interface Processor (MCIP) version 1 provided in the CMAQ modeling system to link Pennsylvania State University/National Center for Atmospheri c Research (PSU/NCAR) Mesoscale Modeling System Generation 5 (MM5) with CCTM to provi de a complete set of meteorological data needed for air quality simulation. Some other necessary parameters not available from the meteorological model are estimated with appr opriate diagnostic algorithms in the MCIP processor. The key functions of MCIP include:

1. Reading in meteorological model (MM5) output files 2. Extracting meteorological data for CCTM window domain 3. Collapsing of meteorological profile data if coarse vertical resolution data is requested 4. Computation or passing through surface and PBL parameters 5. Diagnosing of cloud parameters 6. Computation of species-specific dry deposition velocities 7. Generation of coordinate dependent me teorological data for the generalized coordinate CCTM simulation 8. Output meteorological data in Models-3 I/O API format which is required for operations of Models-3 CMAQ processors

MM5

The dynamic meteorology model selected w ith the Community Multiscale Air Quality (CMAQ) Model is the Fifth-Generation Pennsyl vania State University/National Center for Atmospheric Research (NCAR) Mesoscale Mode l (Grell et. al., 1994). The MM5 is a threedimensional prognostic meteorological model av ailable not only for meteorology studies but also for air quality studies. The MM5 was originally developed in the early 70's and has undergone many changes to increase and broaden its capabilities. It was used to simulate meteorology at 108-km and 36-km resoluti ons for calendar year 1996 over the entire

Final §309 Regional TSD Page 88 of 250 December 15, 2003 Revised May 7, 2008continental United States and portions of Ca nada, Mexico, and the Atlantic and Pacific Oceans (Olerud et al., 1999).

The version of MM5 used to simulate th e 1996 meteorology is MM5 version 2.12 with modifications to allow the output of vertical ex change coefficient (Kv) for use in air quality models. The domain for MM5 covers the entire United States at 108-km and 36-km resolutions, and the western half of the United States at 12-km resolution. Some of thephysics used in the simulation include one-way nesting; nonhydrostatic dynamics; fourdimensional data assimilation of wind, temper ature, and mixing ratio; explicit treatment of moisture; cumulus subgrid cloud parameterization with Anthes-Kuo scheme in the 108-km grid and Kain-Fritsch scheme in the 36-km gr id; vertical mixing of momentum in the mixed layer; planetary boundary layer (PBL) paramete rization; atmospheric radiation; sea ice treatment; and snow cover. Atmospheric radiation was adjusted for cloud effects.

The MM5 version 2-12 was run to generate a set of annual meteorological data for 1996. The simulations consisted of multiple nest ed domains of 108-km and 36-km horizontal resolution on a Lambert Conformal Projection (L CP) coordinate system with 23 vertical sigma layers extending from the surface to the 100 mb pressure level (Table 21). In addition to the standard MM5 output, a supplemental out put data file was generated providing the Planetary Boundary Layer (PBL) heights and the ve rtical exchange coefficients, for both heat and momentum. The total file size for 1996 MM5 36-km horizontal resolution output files are 195 Gb. The extent of the MM5 36-km reso lution domain is shown below (domain D02). This MM5 36-km domain is configured using a grid with 168 columns and 114 rows.

Table 1.2.2.1. Vertical structure of the MM5 modeling system

Level Sigma Height (m) Pressure (mb) Thickness (m)
0 1.000 0.0 1000.0 0.0
1 0.995 38.0 995.5 38.0
2 0.988 91.5 989.2 53.5 3 0.980 152.9 982.0 61.4
4 0.970 230.3 973.0 77.3 5 0.956 339.5 960.4 109.2
6 0.938 481.6 944.2 142.1
7 0.916 658.1 924.4 176.4 8 0.893 845.8 903.7 187.8 9 0.868 1053.9 881.2 208.1 10 0.839 1300.7 855.1 246.8
11 0.808 1571.4 827.2 270.7
12 0.777 1849.6 799.3 278.2 13 0.744 2154.5 769.6 304.9
14 0.702 2556.6 731.8 402.1 15 0.648 3099.0 683.2 542.4

Final §309 Regional TSD Page 89 of 250 December 15, 2003 Revised May 7, 2008Level Sigma Height (m) Pressure (mb) Thickness (m)
16 0.582 3805.8 623.8 706.8 17 0.500 4763.7 550.0 957.9 18 0.400 6082.5 460.0 1318.8 19 0.300 7627.9 370.0 1545.5 20 0.200 9510.5 280.0 1882.6
21 0.120 11465.1 208.0 1954.6
22 0.052 13750.2 146.0 2285.1 23 0.000 16262.4 100.0 2512.1

Figure 1.2.2.1. MM5 domains for the 1996 simulations

After the simulation was completed, statistical measures of surface variables for the entire analysis domain were examined. Point-specifi c performance using time series was also examined. Overall, for the entire year, MM5 performed reasonably well. It does a good job in replicating the mean flow on a cell-to-cell basis. However, the 36-km resolution used in this modeling is clearly insufficient to resolve th e complicated orographically-induced flows near the surface over the western United States. The wind fields aloft are modeled well everywhere. The surface moisture fields ar e modeled exceptionally well. Major synoptic features were captured, and only a couple e rrors stood out (Olerud et al., 1999). Finally,

Final §309 Regional TSD Page 90 of 250 December 15, 2003 Revised May 7, 2008because MM5 is not built for air quality mode ling purposes, MCIP processor was used to provide a complete set of meteorological data needed for air quality simulations.

Final §309 Regional TSD Page 91 of 250 December 15, 2003 Revised May 7, 2008CCTM-ready Meteorological Data Preparation

The RMC uses the Meteorology-Chemical Interface Processor (MCIP) to derive the CCTMready meteorological data for the WRAP dom ain for the entire year of 1996. The CCTMready meteorological input files were deri ved from the Penn State/NCAR Mesoscale Meteorological Model (MM5) runs developed by the EPA. In running MCIP, offsets for the CMAQ domain relative to the MM5 domain (in te rms of number of columns and rows) are specified to be 11 columns and 21 rows. (It is de sirable that the offsets be at least 4-6 grid cells to avoid any boundary interferences.) The WRAP modeling domain is large enough to address the needs of stakeholders and cons ists of 85 columns and 95 rows. The WRAP CMAQ modeling domain is limited by the extent of the MM5 modeling domain.

The CCTM-ready meteorological input files derived from the MM5 data include threedimensional gridded fields of u- and v-wi nd components, temperatures, water vapor, cloud water content, rain water content and vertical exchange coefficients. The MCIP processor also developed two-dimensional gridded fields of terrain and land use information, surface temperatures and pressures as well as rainfall rates.

The vertical domain used in the MM5 extends from the surface to the 100-mb pressure surface and is discretized using 23 layers of variable thickness. The first layer has a thickness of approximately 38 m. The vertical st ructure is described below in further detail, where the heights are calculated using standard atmospheric conditions.

Table 1.2.2.2. Vertical Layer Structure – Sigma Coordinates and Layer Thickness

Sigma Coordinates Layer Thickness (m)

Layers Layer_23 Layer_18 Layer_23 Layer_18

23 0 0 16262.4 16262.4
22 0.052 13750.2

21 0.12 11465.1

20 0.2 0.2 9510.5 9510.5

19 0.3 7627.9

18 0.4 6082.5

17 0.5 0.5 4763.7 4763.7

16 0.582 3805.8

15 0.648 0.648 3099 3099

14 0.702 0.702 2556.6 2556.6

13 0.744 0.744 2154.5 2154.5

12 0.777 0.777 1849.6 1849.6

11 0.808 0.808 1571.4 1571.4

10 0.839 0.839 1300.7 1300.7

Final §309 Regional TSD Page 92 of 250 December 15, 2003 Revised May 7, 20089 0.868 0.868 1053.9 1053.9

8 0.893 0.893 845.8 845.8

7 0.916 0.916 658.1 658.1

6 0.938 0.938 481.6 481.6

5 0.956 0.956 339.5 339.5

4 0.97 0.97 230.3 230.3

3 0.98 0.98 152.9 152.9

2 0.988 0.988 91.5 91.5

1 0.995 0.995 38 38
0 1 1 0 0

Vertical layer collapsing has been used in a number of modeling studies to reduce computational costs associated with using larg er number of vertical layers. However, as meteorological data from various layers are collapsed into a single layer, consistency problems may occur for the resulting profile a nd the model results will vary considerably. While the layer collapsing procedure raises data quality issues such as different cloud parameterization, change in vertical distributi on of emissions, and different plume rise from point sources, RMC understands the need for comp romises, given the stringent timelines and budget for this study. The WRAP Modeling Forum has suggested collapsing the data from 23 layers to 16 layers. The RMC had conducted a to tal of seven sensitivity studies to examine the impact on layer collapsing to model simulati ons. The entire month of July was simulated in each sensitivity study. The seven sensitivity studies include collapsing the 23 MM5 layers into 8, 12, 16 (two different configura tions), 17 18, and 23 CMAQ layers. The CMAQ emissions input files are processed according to each layer structure.

The RMC compared the differences of the CCTM results from the various vertical layer structures and looked at differences in tile pl ots and four statistical measurements including the maximum, minimum, mean, and standard deviation of the differences of each grid cell in the surface layer. We also examined the mean normalized bias error and the mean normalized gross error. Based upon the results from the seven sensitivity runs, the RMC has chosen a 18-layer structure (Table 1.2.2.3) capable of adequately resolving diurnal variations in boundary layer growth and mixing processes therein, wind shear, as well as transport to and from the free troposphere and consequent eff ects of long-range transport processes. The 18-layer structure also gives us good model simulation results (compared with 23-layer structure) with less computation time required.

Final §309 Regional TSD Page 93 of 250 December 15, 2003 Revised May 7, 2008Table 1.2.2.3. Vertical Layer Structure – Sigma Coordinates and Layer Thickness

Sigma Coordinates Layer Thickness (m)

Layers Layer_23 Layer_18 Layer_23 Layer_18

23 0 0 16262.4 16262.4
22 0.052 13750.2

21 0.12 11465.1

20 0.2 0.2 9510.5 9510.5

19 0.3 7627.9

18 0.4 6082.5

17 0.5 0.5 4763.7 4763.7

16 0.582 3805.8

15 0.648 0.648 3099 3099

14 0.702 0.702 2556.6 2556.6

13 0.744 0.744 2154.5 2154.5

12 0.777 0.777 1849.6 1849.6

11 0.808 0.808 1571.4 1571.4

10 0.839 0.839 1300.7 1300.7

9 0.868 0.868 1053.9 1053.9

8 0.893 0.893 845.8 845.8

7 0.916 0.916 658.1 658.1

6 0.938 0.938 481.6 481.6

5 0.956 0.956 339.5 339.5

4 0.97 0.97 230.3 230.3

3 0.98 0.98 152.9 152.9

2 0.988 0.988 91.5 91.5

1 0.995 0.995 38 38
0 1 1 0 0

Recently, EPA has announced that various al gorithmic bugs had been discovered in the MCIP versions 1 and 2. The most severe problem is the incorrect layer collapsing for UHAT_S and VHAT_T (horizontal wind component s coupled with map-scaled factors on the Arakawa-C grid). In MCIP v.1 that the RMC used to process MM5 files and MCIP v.2 that EPA released well after the WRAP modeling is under way, the two fields were inadvertently filled directly from the lowest NL AYS layers of the input met data set without collapsing ( ftp://falcon.emc.mcnc.org/MODELS/MCIP/RELEASE_NOTES ). By conducting the seven vertical layer collapsing sensitivity studies prior to select the final vertical structure for WRAP CMAQ modeling, the RMC was able to avoid the impacts of the bugs.

Final §309 Regional TSD Page 94 of 250 December 15, 2003 Revised May 7, 2008By collapsing the vertical structure from 23 into 18 layers, the computation costs for CCTM (per day) are reduced from approximately 4 hour (23-layers) to 3.5 hours (18-layers). The half an hour reduction for each model day may not seem significant, but it translates into 7.6 calendar days (183 hours) when the RMC runs CCTM for a whole year. The total file size for 1996 CCTM-ready meteorology files (entire year) is about 133 Gb.

MCIP Configurations

The default configuration was used for MCIP (Table 1.2.2.4), which comes with the February 1, 2001 released version of CMAQ. We also configured MCIP to be consistent with the CMAQ grid, vertical layer structure, and episode. Table 24 also lists the other MCIP processor options used in preparing CCTM-ready meteorological files.

Table 1.2.2.4. MCIP default configuration Module or option Values or setting Additional Information
Land use module RADM Currently the only module available
PBL module Models-3 Currently the only module available Dry deposition module RADM Currently the only module available
Convective cloud module RADM/Kuo Currently the only module available
MM5-PX Includes cloud cover and radiation parameters as output Cloud cover, solar radiation

and other cloud parameters module PBL value computation option Yes Do not use MM5 PBL Similarity algorithm option PBL Dry deposition option RADM Cloud computation option Yes Do not use MM5 clouds Land use input option MM5 direct Hydrostatic conversion option No Do not convert from nonhydrostatic to hydrostatic Check output parameter range

option Yes

Number of iterations for

vertical wind field correction 0 No correction will be made to the vertical wind fields I/O API output file type Time-dependent
Maximum boost rate for urban

area 2 Will treat a cell with any urban component as all urban Number of deposition layers 1
Check I/O API file headers? No

Final §309 Regional TSD Page 95 of 250 December 15, 2003 Revised May 7, 2008Modeling Domain

Figure 1.2.2.2. 36 km grid cell modeling domain used for the §309 modeling.

1.2.3. Use of CMAQ for §309 Visibility Modeling

Regional haze and poor visibility are caused by particulate matter (PM) in the atmosphere, which scatters and absorbs light. PM can be classified as fine particulate matter with aerodynamic diameter of less than 2.5 micrometers (PM 2.5 ) and coarser particles with aerodynamic diameter of less than 10 micrometers (PM 10 ). Particulates can also be classified by formation mechanism; either by primary emissions or by secondary production from chemical conversion of gas phase precursors and subsequent mass transfer to the aerosol phase. Sources of primary particulate emissions in the coarse size mode include geological

Final §309 Regional TSD Page 96 of 250 December 15, 2003 Revised May 7, 2008processes such as wind-blown dust and sea salt particles. Combustion processes produce primary emissions of soot or elemental carbon (EC) in the PM 2.5 size mode. Formation of secondary particulates begins with chemical conversion of NO , SO , NH x x 3 and VOC to ammonium nitrate and sulfates and low volatility organic compounds. Transfer from the gas phase to particulate phase can occur by nucleation of new particles when gas phase concentrations exceed their saturation vapor pressure or by absorption of the gas phase to existing particles. Particulates also undergo a number of dynamic processes including coagulation or aggregation of small particulates to former larger particulates, deposition to surfaces, and fine particulates act as sites for cloud condensation.

The rate of formation and the fate of secondary PM 2.5 are non-linear functions of the rate of precursor emissions, complex chemical transformations, and meteorological parameters. Thus, sophisticated numerical simulation models must be used to represent the formation of PM 2.5 and the transport and fate of both PM 2.5 and PM 10 . Typically, air quality models are used to simulate an historical air pollution episode, and, after the model has been validated for the historical episode, it is used to predict the effects of emissions changes in future scenarios. For the case of the WRAP §309 modeling, out historical episode was calendar year 1996, and we then simulated future emissions and controls strategies for calendar year 2018. This section discusses the science components and the model performance validation of the air quality modeling including the following topics:

• Model setup for 1996 base case and 2018 control case simulations

• 1996 model performance evaluation

• Overview for 2018 projection cases

• Table of control strategies and SMOKE emissions files applied in each model run.

• QA for each model run

• Use of relative reduction factors using EPA guidance

CMAQ Model Configuration Description

The Models-3 Community Multiscale Air Quality (CMAQ; USEPA, 1999) model was used for all §309 modeling. CMAQ is a state-of-the-a rt “one atmosphere” Eulerian photochemical air quality modeling system (Dennis et al., 1998). The system is described as “one atmosphere” because it is designed to address all atmospheric pollutants of interest including O 3 , PM, regional haze, and air toxics. It is described as a “modeling system” because CMAQ includes several different models or prep rocessors, which must be operated together to simulate air quality. The components of CMAQ include the following:

• ICON A preprocessor for creating a model input data for initial conditions;

• BCON A preprocessor for creating a model input data for boundary conditions;

• JPROC A preprocessor for creating a look up tables of photolysis rates;

• MCIP The meteorological preprocessor fo r converting MM5 files into CMAQ model ready input files.

• SMOKE Preprocessor for preparing emissions inventories.

Final §309 Regional TSD Page 97 of 250 December 15, 2003 Revised May 7, 2008• CCTM The CMAQ Chemical Transport Model used for predicting 3-dimensional concentration fields of trace gas and aerosol species and extinction coefficients. Each of these interface processors incorporates raw data into CMAQ and performs functions such as calculating parameters and interpolating or converting data. CMAQ’s CCTM has a modular design such that the user can select from alternative science algorithms by selecting the appropriate module when compiling the executable program. This also facilitates the incorporation of new or alternative science algorithms into the modeling system. Currently, nine science modules are included:

• DRIVER controls model data flows and synchronizes fractional time steps;

• HADV computes the effects of horizontal advection;

• VADV computes the effects of vertical advection;

• ADJCON adjusts mixing ratio conservation property of advection processes;

• HDIFF computes the effects of horizontal diffusion;

• VDIFF computes the effects of vertical diffusion and deposition;

• CHEM computes the effects of gas-phase chemical reactions;

• CLOUD computes the effects of aqueous-phase reactions and cloud mixing;

• AERO computes aerosol dynamics and size distributions; and

• PING computes the effects of plume chemistry. The default options for each module are summarized in Table 1.2.3.1. CMAQ also uses the concurrent versions system (CVS) software to manage the source code. Although the use of modular structure and CVS software increases the complexity of compiling and running the air quality model, this approach provides several convenient features that are not available in other modeling systems including code management and archiving capabilities.

Final §309 Regional TSD Page 98 of 250 December 15, 2003 Revised May 7, 2008Table 1.2.3.1. CMAQ CCTM Science Module Options (Default)

Module Name Process Description Algorithm Used in Default Configuration

Driver Chemistry-transport coupling control execution Chemical Tracer Model (CTM)

Hadv Horizontal advection Piece- wise parabolic method (PPM)

Vadv Vertical advection Piece- wise parabolic method (PPM)

ADJc Mass conservation/adjust ment Density adjustment to ensure ma ss conservation in vertical advection solver

Hdiff Horizontal diffusion K-theory, Uniform

Vdiff Vertical diffusion K-theory, Blackadar scheme

Chem Chemical mechanism CB4 w/ aerosol version 2 and aqueous species

Chem Photolysis rate calcu lation Table look-up (from JPROC)

Chem Gas-phase chemical solver Modified Euler Backward Integration (MEBI)

PinG Plume-in-grid No

Cloud Convective / nonconvective cloud mixing schemes RADM-Kuo / resolvable-scale clouds

Cloud Scavenging / wet deposition Henry’s Law for washout; RADM scheme for rainout; aerosols scavenged according to Binkowski and Shankar [1994], and Shankar and Binkowski [1994]

Cloud Aqueous chemistry Walcek and Taylor [1986]

Aero2 Speciation Speciated PM 2.5 from emissions

Aero2 Thermodynamics RPMARES

Aero2 Aerosol dynamics Whitby’s Modal Aero sol Dynamics size distribution formulation (RPM)

Aero2 Secondary Organic Aerosols Pandis et al. [ 1992] methods for secondary organic aerosol formation (SOA) formation

Aero2 Visibility IMPROVE algorithm Adepv Deposition velocities Surface-layer resistance method (RPM) based on approach used in RADM

Treatment of Aerosols

The approach used to treat aerosol size and dyna mics is of special importance to visibility modeling. Particulate matter can be classified by particle size or diameter. For example, in the CMAQ model, particulates are classified in 3 different size modes (with approximate size range shown in parentheses) as follows



### •

•

• Aitken nuclei (0.03 to 0.5 μm) Accumulation mode (0.5 to 2.5 μm) Coarse mode (greater than 2.5 μm)

Accurate simulation of aerosol physics and the prediction of visibility impairment require that the model must represent the full range of particle sizes from 0.03 to 10 μm. Two common approaches have been employed to represent aerosol size distributions: a sectional

Final §309 Regional TSD Page 99 of 250 December 15, 2003 Revised May 7, 2008approach in which particles are represented by a finite number of discrete bin sizes, and a modal approach in which size distributions ar e represented as the moments of log normal distributions. The advantage of the sectional approach is its inherent simplicity, but its computational cost increases rapidly as the numbe r of bins is increased to improve particle size resolution and to achieve greater accuracy. The advantage of the modal approach is that it provides a representation of the entire particle size distribution. The CMAQ model adopts the modal approach in which distributions of particle number, diameter, and mass are represented for the Aitken nuclei, the accumu lation mode and the coarse mode, while the REMSAD adopts a sectional approach.

The aerosol modal size distributions are also used in the calculation of dynamic processes include coagulation of particles within and betw een the modes, binary nucleation of sulfuric acid and water, and condensation and evaporati on of condensable vapor on/off particles, as well as size-dependent wet and dry removal a nd in-cloud scavenging of interstitial aerosol. CMAQ includes an alternative approach to the IMPROVE visibility algorithm involving a size-dependent and computationally efficient Mie-based algorithm.

Chemical Mechanism and Species Treatment

All major regulatory modeling studies employ condensed photochemical mechanisms that use a small set of artificial surrogate species to represent the complex mixture of ambient VOC (Dodge, 2000). In particular, three different condensed, gas phase chemical mechanisms are widely used in air quality modeling. These include the Carbon Bond Mechanisms IV (CB4, Gery et al., 1989), the Regional Acid Deposition Mechanism (RADM2, Stockwell et al., 1990), and the SA PRC99 mechanism (Carter, 2000). The CB4 mechanism was used in the WRAP §309 modeling because it provides the greatest computational efficiency. The CB4 was developed for high NO x , typically urban conditions, but a number of “patches” have been made to the CB4 over the years to attempt to address the concern for regional, low NO x modeling. Both the RADM2 and SAPRC99 gas phase mechanisms were specifically developed for regional, low NO x conditions and therefore these mechanisms include a more deta iled representation of the fate of NO x and of the chemistry of peroxyl radical species. The errors introduced by the use of the CB4 are expected to be considerably less than other errors or uncertainties associated with the emissions and meteorology inputs. The errors in CB4 are most likely to affect SOA formation due to CB4’s more condensed representation of organic intermediates.

Secondary Organic Aerosol (SOA) Treatment

There are two SOA algorithms implemented in CMAQ that are based on the methods of Pandis et al. (1992) and Odum et al. (1996). Th e Pandis approach is the default and current choice in CMAQ for calculating SOA yields in the 2001 CMAQ release. The CMAQ chemical mechanism includes OH, NO , and O 3 3 oxidation reactions for six lumped VOC groups, which lead to SOA production. These are (1) the C8 and higher alkanes, (2) anthropogenic internal alkenes, (3) xylenes, (4) toluene, (5) cresols, and (6) monoterpenes. Also, through a user-defined option, CMAQ can use the alternative Odum et al. [1996]

Final §309 Regional TSD Page 100 of 250 December 15, 2003 Revised May 7, 2008approach to provide a more detailed dependence of aerosol yields on the existing organic aerosol mass.

Operating System

CMAQ has been ported and operated successfully on a LINUX platform, providing more reliability, easy accessibility and much less computational expense than other operating systems.

Model Predictions of Visibility Impairment

Impairment of visibility can occur both by s cattering and absorption of light, where these effects can be represented as a scattering coe fficient and an absorption coefficient. The sum of these is defined as the extinction coefficient ( β ext ). In a clean atmosphere, the extinction coefficient from Rayleigh scattering by gas molecules provides an upper limit for maximum visibility, where a standard value of Rayleigh scattering is defined as: β = 1/(100 km). 0
Several different metrics are used to describe visibility. These include visual range, extinction coefficient, and a hazi ness scale defined in units of deciviews (Pitchford and Malm, 1994). Visual range is intuitively simple a nd is a useful measure for applications such as air operations and recreational activities. Ex tinction coefficients are a useful measure for scientists in performing budget analysis of the contribution of atmospheric constituents to haziness. However, neither visual range nor ex tinction coefficients have a linear relationship to perceived haziness and therefore are not eas ily used for analyzing changes in visibility. For example, the effect of a change in visibility of 1 km can only be meaningfully interpreted in references to a base case: a reduction from 50 km to 49 km would be insignificant, while a reduction from 2 km to 1 km w ould be of great importance. For this reason, Pitchford and Malm (1994) recommend a haziness scale defined in units of deciviews that provides a linear measure of perceived changes in visibility.

The deciview scale is defined in terms of a relationship of the visibility to a perfectly clean atmosphere, where this is calculated as:

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

=

0

ln10 β β

ext deciview

where β ext is the extinction coefficient of ambient air, and this is calculated as a function of the total particulate load, its size distribution, and the refractive index of the particles. The Regional Haze Rule requires that calculations of baseline and natural visibility conditions must be expressed in units of deciviews.

1996 model performance evaluation case

This section describes the procedures used to evaluate the performance of CMAQ using 1996 base case study. As part of the WRAP re gional visibility modeling using the ModelsFinal §309 Regional TSD Page 101 of 250 December 15, 2003 Revised May 7, 20083/CMAQ model, the observed PM concentrations collected at the IMPROVE monitors were matched up with the model predictions as part of the model performance evaluation. The RMC and the jumpstart team engaged in a revi ew process with members of WRAP modeling forum to select the mapping of ambient data to the model predicted species. The RMC also developed software tools available for performing model evaluation.

Ambient Data used for Evaluation

Model simulations were evaluated by comparing with ambient monitoring data for PM from the IMPROVE sites and from the CASTNET site s. Approximately 60 IMPROVE sites were available in the western US domain during 1996. Data were collected at the IMPROVE sites every 3 rd day, and approximately 100 days of PM data were available for the evaluation. IMPROVE data are reported as the 24 hour averag e. The species measured at the IMPROVE sites do not correspond exactly with the species as represented in the CMAQ model. We used the groupings of model species shown in Table 26 to compare the model to the monitored species. CASTNet data are reported as 7-day averages, and we used the groupings shown in the following table to co mpare the model species with the CASTNet data. Detailed plots showing the comparison of model to data for each monitoring site are available at the project website ( www.cert.ucr.edu/rmc ) as described below.

Table 1.2.3.2. Mapping of CMAQ species to IMPROVE species.

Compound IMPROVE Species CMAQ Mapping

SO4 SO4 ASO4J + ASO4I

NO3 NO3 ANO3J + ANO3I

1.4*(OC1+OC2+OC3+OC4+O P) AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI OC

EC EC1+EC2+EC3-OP AECJ + AECI

2.2*Al + 2.49*Si + 1.63*Ca + 2.42*Fe + 1.94*Ti SOIL A25I +A25J

CM MT – FM ACORS + ASEAS + ASOIL

1.375*(ASO4J + ASO4I) + 1.29*(ANO3J + ANO3I) + AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI + AECJ + AECI + A25J + A25I PM25 a FM

1.375*SO4 + 1.29*NO3 + EC

+ OC + SOIL RCFM Same for PM25

Final §309 Regional TSD Page 102 of 250 December 15, 2003 Revised May 7, 20081.375*(ASO4J + ASO4I) + 1.29*(ANO3J + ANO3I) + AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI + AECJ + AECI + A25J + A25I + ACORS + ASEAS + ASOIL PM10 MT

b c + 3*f(RH) 10 [1.375*(ASO4J + ASO4I) + 1.29*(ANO3J + ANO3I)] + 4*1.4*(AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI) + 10*(AECJ + AECI) + 1*(A25J + A25I) + 0.6*(ACORS + ASEAS + ASOIL) b c + 3*f(RH) 10 (1.375*SO4 + 1.29*NO3) + 4* OC + 10* EC

+ SOIL + 0.6* CM Bext_Recon (1/Mm)

a b

Measured; Rayleigh scattering correction; c f(RH), monthly relative humidity

Although O does not affect visibility directly, O 3 3 play a central role in the oxidation of precursors that form PM and affect visib ility. In particular, concentration of O 3 and related oxidants (NO , OH, and HO 3 2 ) affect the rate of formation of nitric acid, sulfuric acid and low volatility organics. We performed a limited comparison of model predictions to O 3 monitoring data for the month of July 1996. We did not expect good model performance for O 3 in urban areas because of the coarse (36 km ) grid resolution is unable to resolve major point source plumes and urban NO x and VOC gradients. Indeed, the model typically underestimates peak O 3 levels in southern CA by 10 to 30 ppb. The model generally compared well with O 3 data in central CA and in other pa rts of the domain, typically within 10 to 20 ppb of observed O . Increased attention to mode l performance evaluations for O 3 3 will be a high priority as we attempt to improve model performance for future §308 modeling.

Development of model evaluation tools

This section discusses the approach for matching up the CMAQ model estimates with the IMPROVE PM data. CMAQ treats particles, based on their size distributions, as the superposition of three lognormal subdistributions, called modes. The three modes are:

1. fine particle mode, the i -mode, representing the smaller (nuclei or Aitken) particles from nucleation or from direct emission with diameters < 0.1 μm; 2. larger particle mode, the j -mode, representing particles with diameters between 0.1 μm and 1-2 μm, known as the accumulation range; and 3. coarse particles, representing the difference between the masses in PM 10 and PM 2.5
.
While the first two modes, i - and j- mode, are two interacting modes treated as two subdistributions in PM 2.5 , the addition of coarse particles makes up the total amount of PM 10
. The chemical species treated in the aerosol component of CMAQ are listed in the next table. Among them, the fine particle species include sulfates, nitrates, ammonium, water, anthropogenic and biogenic organic carbon. The coarse mode species include sea salt, windblown dust, and other unspecified material of anthropogenic origin.

Table 1.2.3.3. CMAQ aerosol species list. All units are in mass concentration μ g m -3

.

Final §309 Regional TSD Page 103 of 250 December 15, 2003 Revised May 7, 2008CMAQ Species Name Description

ASO4J Accumulation mode sulfate mass

ASO4I Aitken mode sulfate mass

ANH4J Accumulation mode ammonium mass

ANH4I Aitken mode ammonium mass

ANO3J Accumulation mode nitrate mass

ANO3I Aitken mode aerosol nitrate mass

AORGAJ Accumulation mode anthropogenic secondary organic mass

AORGAI Aitken mode anthropogenic secondary organic mass

AORGPAJ Accumulation mode primary organic mass

AORGPAI Aitken mode primary organic mass

AORGBJ Accumulation mode secondary biogenic organic mass

AORGBI Aitken mode secondary biogenic organic mass

AECJ Accumulation mode elemental carbon mass

AECI Aitken mode elemental carbon mass

A25J Accumulation mode unspecified anthropogenic mass

A25I Aitken mode unspecified anthropogenic mass

ACORS Coarse mode unspecified anthropogenic mass

ASEAS Coarse mode marine mass

ASOIL Coarse mode soil-derived mass

AH2OJ Accumulation mode water mass

AH2OI Aitken mode water mass

The IMPROVE monitoring network ( http://vista.cira.colostate.edu/improve ) reports detailed chemical species (raw database) in its measurements of visibility-reducing aerosol species on a twice-a-week basis. Next are the PM fine mass species being used in the evaluation :

• Sulfates (SO ), as sulfate ion; 4

• Nitrates (NO ), as nitrate ion; 3

• Organic carbon (OC), as organic carbon mass;

• Elemental carbon (EC), as light absorbing carbon or carbon soot;

• Soil (SOIL), as fine soil and is sum of several inorganic elements such as Al, Si, Ca, Fe and Ti.

Final §309 Regional TSD Page 104 of 250 December 15, 2003 Revised May 7, 2008These species are all measured using a 2.5-micron cut point inlet. The IMPROVE monitors also measure total PM 10 and PM 2.5 mass. These values are reported as the PM 2.5 fine matter (FM) portion of the mass and the coarse matter (CM) portion, as PM 10 - PM 2.5 . The mapping of the CMAQ species to the IMPROVE species counterparts is summarized in Table 24, above. Note that in CMAQ water as fine particle species is not included among the mapping of IMPROVE species, because IMPROVE measures only dry particles. In addition, IMPROVE defines SOIL as fine soil concentration, which is the sum concentrations of several inorganic species. Although fine soil is not specifically defined in CMAQ, it is taken as unspeciated portion of PM 2.5 emitted species. Therefore, model species, A25J+A25I, are used as surrogates for the IMPROVE fine soil concentration. For the visibility comparisons of CMAQ model predictions and IMPROVE measurements, IMPROVE network uses either direct transmissometer measurement or reconstructed light extinction from aerosol species measurements, while CMAQ uses two approaches to calculation light extinction coefficient. The first is based on theoretical calculation (known as Mie theory) of extinction coefficient from the sum of scattering and absorption coefficients. The second approach is based on modified aerosol species mass concentrations known as reconstructed extinction. This is an empirical approach and uses the similar equation used in the IMPROVE measurement for visibility calculation:

β ext(1/Mm) = 3*f(rh)*([ammonium sulfate]+[ammonium nitrate]) + 4*[organic mass] + 10*[light absorbing carbon] + 1*[fine soil] + 0.6*[coarse mass] + β Rayleigh Eq (1)

where f(rh) is relative humidity correction factor, and β Rayleigh is the extinction for Rayleigh scattering with a value of 10 Mm -1 for clean background environment. In CMAQ, the relative humidity factor, f(rh), is obtained froma table of corrections with entries at onepercent intervals (CMAQ protocol), whereas in IMPROVE measurement it is based on monthly site-specific relative humidity adjustment factors obtained from the document -- “Draft Guidance for Tracking Progress Under the Regional Haze Program”. For model evaluation, however, monthly site-specific relative humidity factors are used to calculate the extinction coefficients both in CMAQ and IMPROVE ambient measurement (Table 24).

The RMC has developed a software package capable of producing model versus observation graphs in an automated process to help model evaluation. The programs in this package extract information from both CMAQ outputs and IMPROVE observation data sets and combine which with proper species mappings, and produce the following graphs:

• Example scatter plots for CMAQ seasonal performance evaluation for all sites and all days (see Figures 1.2.3.1 through 1.2.3.5)

• Example scatter plots of all days at one site (see Figure 1.2.3.6)

• Example scatter plots of all sites for one day (see Figure 1.2.3.7)

• Example time series plots at a given site (see Figure 1.2.3.8)

Final §309 Regional TSD Page 105 of 250 December 15, 2003 Revised May 7, 2008Summer Spring

Winter Fall

Figure 1.2.3.1. Seasonal comparison plots of model predictions versus all available observation data in all monitoring sites within modeling domain for SO

. 4

Final §309 Regional TSD Page 106 of 250 December 15, 2003Spring Summer

Winter Fall

Figure 1.2.3.2. Seasonal comparison plots of model predictions versus all available observation data in all monitoring sites within modeling domain for NO

. 3

Final §309 Regional TSD Page 107 of 250 December 15, 2003Summer Spring

Winter Fall

Figure 1.2.3.3. Seasonal comparison plots of model predictions versus all available observation data in all monitoring sites within modeling domain for OC.

Final §309 Regional TSD Page 108 of 250 December 15, 2003Summer Spring

Winter Fall

Figure 1.2.3.4. Seasonal comparison plots of model predictions versus all available observation data in all monitoring sites within modeling domain for PM 2.5
.

Final §309 Regional TSD Page 109 of 250 December 15, 2003Summer Spring

Fall Winter

Figure 1.2.3.5. Seasonal comparison plots of model predictions versus all available observation data in all monitoring sites within modeling domain for B ext .

Final §309 Regional TSD Page 110 of 250 December 15, 2003Figure 1.2.3.6. Example comparison plots of m odel predictions versus available observation data (all days) from one monitoring site (one site). Top: SO 4 at Bridger Wilderness, WY. Bottom: PM 2.5 at Redwood National Park, CA.

Final §309 Regional TSD Page 111 of 250 December 15, 2003Figure 1.2.3.7. Example comparison plots of model predictions versus available observation data from all monitoring sites within modeling domain (one day, all sites). Top: PM 2.5 for all monitoring sites on Julian day 188 (July 6), 1996. Bottom: B ext for all monitoring sites on Julian day 202 (July 20), 1996.

Final §309 Regional TSD Page 112 of 250 December 15, 2003Figure 1.2.3.8. Example time series plots of m odel predictions vs. available observation data (on days for which measurements are reported) from one monitoring site (one site). Top: PM 2.5 concentration profiles in 1996 at Grand Canyon National Park, AZ (GRCA). Bottom: B

B

ext

profiles in 1996 at Redwood National Park, CA (REDW).

Final §309 Regional TSD Page 113 of 250 December 15, 2003Along the graphs, several statistical measures, including regression (r 2 ), mean normalized bias (MNB,%) and mean normalized error (MNE, %), are also provided. The statistical measures used in the evaluation have the following equations: Mean Normalized Bias (MNB, %). The mean normalized bias is given by:

− 

## ∑

=

obs obs predict C CC

N MNB 1 (%)

where N equals the number of observations from all monitoring stations on days with available data. Mathematically, the bias is derived from the average signed deviation of the concentration residuals and is calculated using all pairs of predicted and observations. Mean Normalized Error (MNE, %) The mean normalized error is calculated in a similar way to the bias, and is given by: 

## ∑ −

=

obs obs predict C CC

N MNE 1 (%)

The MNE quantifies the mean absolute deviation of the residuals.

All example plots were obtained using 1996 CMAQ annual simulations and compared with available IMPROVE measurements. Full sets of plots, including monthly and annual results, are posted on the RMC project website under 1996 model evaluation results:

http://pah.cert.ucr.edu/rmc/aerosol_webpage/index.shtml

Programs in this package are written in C++ and Perl script programming languages, and are set up to execute on all Linux machines. The whole evaluation package can also be downloaded from the RMC website:

http://pah.cert.ucr.edu/rmc/download.shtml

1.2.4. Model Performance Results

The WRAP annual and monthly evaluation statistics for the following PM species based on the IMPROVE monitoring data are shown in Table 1.2.4.1 and Figures 1.2.4.1 and 1.2.4.2. The species used in the model performance evaluation include the following: sulfate (SO 4 ); nitrate (NO 3 ); elemental carbon (EC); particulate organic carbon (OC); coarse mass (CM); soil (SOIL); PM 2.5 ; PM 10 ; and extinction coefficient (B ext ). While it is not easy to summarize annual model performance, some consistent trends do appear. The model substantially underpredicts soil and coarse mass. This most likely can be attributed to the lack of a fugitive dust emissions inventory, which was excluded because the uncertainty in fugitive dust emissions was so large. For other species the model performance is best for the summer months, and it has large positive bias in the winter months. Because the model has positive bias for most species, including EC, this suggests the possibility that the boundary layer height is too shallow for winter months. Howe ver, this hypothesis has not been investigated and this is a high priority for ongoing work.

Final §309 Regional TSD Page 114 of 250 December 15, 2003Table 1.2.4.1. Summary statistics for speciated aerosol data and visibility for all monitoring stations within WRAP on days with available data.

Annual Error Statistics Species Mean Normalized Bias (%) Mean Normalized Error (%)

SO 140.8 165.3 4

NO 1519.6 1579.2 3

OC 88.4 125.0

EC 146.5 191.1

SOIL 524.0 553.7

CM -69.6 87.8

PM 78.7 123.1 2.5

-8.7 79.8 PM 10

B 236.9 268.7

B ext

Final §309 Regional TSD Page 115 of 250 December 15, 2003Figure 1.2.4.1. Monthly summary statistics for speciated aerosol data for all monitoring stations within WRAP region on days with available data.

Final §309 Regional TSD Page 116 of 250 December 15, 2003Mean Normalized Bias _ Bext

0 100 200 300 400 500 600

1 2 3 4 5 6 7 8 9 10 11 12

Month(%)

Mean Normalized Error _ Bext

0 100 200 300 400 500 600

1 2 3 4 5 6 7 8 9 10 11 12

Month(%)

Figure 1.2.4.2. Monthly summary statistics for light extinction (B ext ) for all monitoring stations within the WRAP region on days with available data.

Final §309 Regional TSD Page 117 of 250 December 15, 2003The model positive bias is by far the largest for NO 3 . We hypothesize that this may be a result of overestimates of NH emissions in the winter months. Although the original NH 3 3 emissions inventory included a small seasonal adjustment, current research at EPA/ORD suggests that a much larger reduction in NH 3 emissions may be appropriate for winter months. Based on these EPA/ORD results, we reduced the NH 3 emissions by 50% for the months of December, January and February. While this resulted in large reduc tions of nitrate, the model still overpredicted NO 3 in winter months, although the overprediction wa s closer in magnitude to that of the other species. There are several alternative hypotheses that could explain the model NO 3 over prediction:

1. Ambient NO may be transferred to the coarse PM fraction in the form of CaNO 3 3 or NaNO . Coarse mass NO 3 3 would not be detected in the fine PM fractions at the IMPROVE sites, and, because the model does not represent CaNO and NaNO 3 3 , the model would partition all NO 3 to the fine fraction, thereby causing it to overestimate fine NO

. 3

2. IMPROVE monitoring sites may underestimate actual ambient NO 3 due to sampling errors. This hypothesis is supported by the model comparison to CASTNet sites for which there was a smaller positive bias in the model NO predictions. 3

3. The model may underestimate the deposition of NH or HNO 3 3 . In fact, there was an error in the model deposition velocity that accounted for part of the NO over prediction 3

4. It is possible that the model incorrectly represents the conversion of NO to HNO x 3 by nighttime N O 2 5 hydrolysis. There is very large uncerta inty in this chemistry, and future updates to the model could either increase or decrease the model positive bias for NO 3 in future simulations.

Several efforts are underway to improve the model performance for NO 3 in modeling for §308 requirements. The cause of the NO 3 overpredictions could not be determined in the time frame of the §309 modeling. Therefore, for the model se nsitivity simulations that were concerned with NO and NO x 3 , the model results were scaled to the ambient data. In the case of the mobile source sensitivity simulations, the analysis was re stricted to the April-October period for which the model performed comparatively well.

1.2.5. Model Uncertainties and Quality Assurance

In comparison to traditional model performance evaluations for short-term O 3 episodes, the 1996 base case CMAQ simulation has large error and positive bias for most species, especially for winter months. However, two key issues mu st be considered when assessing the model performance for use in the §309 analysis. First, there do not exist well-established criteria for acceptable model performance for long-term model simulations. The model performance statistics used to judge episodic modeling may not be appropriate for use in long term modeling because in episodic modeling it is possible to fine -tune the model inputs. For example, observed meteorological data are typically used to nudge the meteorological model predictions toward the correct simulation of the temperature and wind fields in episodic modeling, and much more

Final §309 Regional TSD Page 118 of 250 December 15, 2003detail attention and adjustments to emissions inventories are typically applied in episodic modeling. Secondly, model performance criteria have not yet been established for evaluating PM and haze modeling. In the case of O 3 modeling, filters are typically applied to screen out low O 3 data, and the model performance assessment is focused on sites and times with relatively high O 3 concentrations. In the case of PM modeling, th e best visibility days as well as poor visibility days must be analyzed, so filters have not been applied to eliminate low PM concentration data. Moreover, the formulations used in computi ng conventional model performance statistics of error and bias may be inappropriate for evalua ting models under very clean conditions (Eder et al., 2002).

In the case of the 1996 CMAQ modeling uncertainty or errors in the meteorological modeling may have had an especially large effect on the model performance. Limited analysis and QA was performed for the 1996 MM5 simulation. Underestim ates in inversion heights and PBL heights could cause large over predictions for all PM species. Moreover, errors in the modeled wind direction may cause CMAQ to advect pollutant plumes in the wrong direction and to predict high PM concentrations in the incorrect grid ce ll. In other words, it is possible that CMAQ may have over predicted PM in some cells and under pred icted PM in other cells because of errors in wind direction. When calculating model error a nd bias a matched in time and space comparison of model predictions to ambient data was used , and this may have been an overly stringent criteria for judging model performance given the co arse grid resolution and uncertainty in met fields. Future model performance can also be evaluated in different ways, such as using unpaired-in-time-and-space metrics.

In spite of the caveats in the model performance ev aluation, there are large errors or uncertainties in the key emissions inputs. The uncertainty in emissions combined with uncertainty in the meteorology and chemistry means that there is limited confidence in the useful of the 1996 CMAQ modeling for prescribing additional emissi ons control strategies. Therefore, the 1996 CMAQ results should be limited in their use. Appr opriate uses include sensitivity studies, as in the PM, NO x and mobile source sensitivity studies described in this document, where the results or these sensitivity studies represent a first atte mpt to estimate to gauge the relative importance of different emissions sources. In addition, because the sulfate chemistry and emissions are subject to less uncertainty than that of other species such as nitrate, OC and dust, the 1996 CMAQ modeling is useful for comparing the relative benefits of alterative SO x emissions control strategies. However, these modeling results should not be solely relied upon to prescribe acceptable SO x emissions. In addition to establishi ng more rigorous criteria for judging PM model performance, future additional control decisions for SO x and other species should be based on weight of evidence approach that integrat es data from other sources such as trajectory analysis, chemical mass balance modeling, and other source-receptor modeling approaches.

Efforts are currently underway to improve the mode l input data and model performance, and this should produce better model scenarios for use in analyzing emissions control strategies and options as part of the §308 process for developing future state and tribal implementation plans. Updates being carried out to improve the future §308 modeling work include the following:

• New emissions models and datasets for fugitive dust and ammonia.

• New meteorological data for 2002.

Final §309 Regional TSD Page 119 of 250 December 15, 2003• Model simulations using finer resolution grids at 12 km and 4 km.

• Model simulations for the planning base year (calendar year 2002) that has a more extensive ambient monitoring database and improved emissions inventory data.

• More extensive model performance evaluati ons using AIRS, PAMS and PM supersite data in addition to the IMPROVE and CASTNet data.

• Development of criteria for model performance evaluations for PM and for seasonal and annual modeling that includes clean conditions.

Overall, the model performed best in the summer months, and for the alternative SO x control strategy options that required evaluation. Also , the 7-month period (April-October) spanning the Summer months captures virtually all of the average 20% worst visibility days for Class I areas on the Colorado Plateau. Although there are limita tions in the WRAP §309 modeling, it is fully adequate for its primary intended purpose of ev aluating §309 control strategies and assessment of programs recommended by the GCVTC. The WRAP §309 technical assessments and modeling analyses also represent the best available science for regional haze at this time.

1.2.6. 1996 base cases and 2018 projection cases

After completing the model performance evaluation for the 1996 base case several additional annual model simulations to support requirements of §309 were performed. The goals of these modeling were to perform the following evaluations:

• Evaluate changes in visibility from 1996 to 2018 using a 2018 “base case” emissions inventory that includes growth and projected changes in emissions based on control measures already in place.

• Compare the SO x Annex Milestones control strategy to a Best Available Retrofit Technology (BART) scenario and a “BART with uncertainty” scenario.

• Evaluate the sensitivity of visibility to changes in mobile source and road dust emissions from the GCVTC region, California, La s Vegas (Clark County), NV and Phoenix (Maricopa County), AZ.

• Evaluate the effects of a fire emissions “Optimal Smoke Management” strategy.

• Evaluate the sensitivity of visibility to changes in emissions from major point sources of NO and PM x 10
.

• Evaluate the progress by 2018 when all §309 control measures are implemented.

Sixteen (16) different emissions scenarios were modeled to perform these evaluations. For most of these scenarios annual model simulations were performed. In the case of the mobile source sensitivity, 7-month simulations were performed, that included the period from April to October. Winter months were excluded because of the hi gh model bias for nitrate during the winter. Because of errors and updates in emissions invent ories during the course of the project, some of these model simulations were repeated 2 or 3 times. Table 1.2.6.1 lists the inputs and purposes of the modeling runs.

Final §309 Regional TSD Page 120 of 250 December 15, 2003Table 1.2.6.1. Control strategies and SMOKE files applied in each modeling run.

Scenario Name Purpose Emissions components included
Point; Area; MOBILE6 and EMFAC2000 for CA; Old non-road

mobile; 1996 BESI2 biogenics; actual 1996 wild fire; no windblown fugitive dust; no prescribed or agriculture burning 1996 Base Case Model performance evaluation

Same as 1996 base case EXCEPT: New non-road mobile, Typical year wild fire; 2018 base cases for prescribed and agriculture burning 1996 Base with Typical Wildfire Comparison to 2018 runs for calculating progress

Point & Area Emissions grown to 2018; MOBILE6 and EMFAC2000, New non-road mobile, 1996 BESI2 biogenics, Typical year wild fire; 2018 ba se cases for prescribed and agriculture burning 2018 Base Case Base case for 2018

Best Available Retrofit Technology ; (all else same as 2018 base case) Command and Control SOx control strategy

SOx Inventory with BART uncertainty; (all else same as 2018 base case) Command and Control with Uncertainty SOx control strategy

Annex Milestones SOx inventory; (a ll else same as 2018 base case) Milestone/Annex SO 2 Annex Milestones emissions cap scenario

Separate “Zero out” runs for GC VTC states’ mobile sources and road dust; (all else same as 2018 base case) Mobile and Road Dust Sensitivity Evaluate mobile source and road dust contributions at 16 Colorado Plateau Class I areas

Zero out California mobile sources; (all else same as 2018 base case) California Mobile Sensitivity Evaluate mobile source contribution

Zero out Phoenix area mobile sour ces; (all else same as 2018 base case) Phoenix Mobile Sensitivity Evaluate mobile source contribution

Zero out Clark Co. mobile sour ces; (all else same as 2018 base case) Las Vegas Mobile Sensitivity Evaluate mobile source contribution

Across the board 50% reduction in major point source NOx (facility >100t/day (all else same as 2018 Annex Milestones case) 50% Point Source NOx reduction Evaluate contribution of major NOx point sources

Across the board 50% reduction in major point source PM (facility >100t/day (all else same as 2018 Annex Milestones case) 50% Point Source PM reduction Evaluate contribution of major PM point sources

Across the board simultaneous 25% increase in major point source NOx and PM (facility >100t/day (all else same as 2018 Annex Milestones case) Evaluate sensitivity to NOx and PM increases 25% Combined Point Source NOx & PM Increase

Annex SOx inventory, Pollution Prevention Scenario (P2); Base Smoke Management; (all else same as 2018 base case) Base Smoke Management Visibility Improvement from 1996 to 2018 from §309 Control Strategies (Scenario 1)

Annex SOx inventory, Pollution Pr evention Scenario (P2); Optimal Smoke Management; (all else same as 2018 base case) All Control Case Visibility Improvement from Base to Optimal Smoke Management Programs in 2018 (Scenario 2)

Final §309 Regional TSD Page 121 of 250 December 15, 2003In total, approximately 50 years of model simulation were performed, which generated well over 20 Terabytes of output data. Summaries of this data have been placed on the website and in various technical memorandums that describe the effects of these emissions scenarios at the 9 GCVTC Class 1 sites and the 16 Colorado Plateau sites. These results are discussed in detail in subsequent chapters.

1.2.7. Projection of Future-Year Visibility Using Relative Reduction Factors

The CMAQ modeling results for the 1996 Base Case and 2018 emission scenarios were processed following EPA’s guidance for projecting visibility changes for demonstrating visibility goals of the Regional Haze Rule (EPA, 1997; 2001).

Future-year visibility is estimated starting with the IMPROVE reconstructed particulate matter (PM) mass measurements for six PM species:

• Sulfate [(NH ) SO ]; 4 2 4

• Particulate Nitrate [NH NO ]; 4 3

• Organic Matter [OM];

• Elemental Carbon [EC];

• Other Fine Particulate [Soil]; and

• Coarse Matter [CM].

Associated with each PM species is an extinction coefficient that converts concentrations (in
μ g/m 3 ) to light extinction (in inverse megameters, Mm -1 ). Sulfate and nitrate are hygroscopic so relative humidity adjustment factors [f(RH)] are us ed to modify the extinction coefficients that increase the particle’s extinction efficiency with increasing RH to account for the particles taking on water and having higher light scattering properties.

B

B Sulfate = 3 x f(RH) x [(NH ) SO ] 4 2 4 B

B

Nitrate = 3 x f(RH) x [NH NO ] 4 3 B = 10 x [EC]

B EC B

B OM = 4 x [OM] B

B Soil = 1 x [Soil] B

B

CM = 0.6 x [CM]

Monthly average f(RH) factors are used as recommended in EPA’s guidance (EPA, 1997; 2001). These values have been recently updated (SAIC, 2003) and are available at:

ftp://ftp.saic.com/raleigh/RegionalHaze_2002FRHcurve/fRH_analysis/

The total light extinction (B ext ) is assumed to be the sum of the light extinction due to the six PM species listed above plus Rayleigh (blue sky) background (B Ray ) that is assumed to be 10 Mm -1

.

B ext = B

B Ray + B Sulfate +B Nitrate + B EC +B OM + B Soil +B CM

Final §309 Regional TSD Page 122 of 250 December 15, 2003The total light extinction (B ext ) in Mm -1 is related to visual range (VR) in km using the following relationship:

VR = 3912 / B

B ext
The Regional Haze Rule requires that visibility be expressed in terms of deciview (dV) that uses natural logarithms of the extinction as follows:

dV = 10 ln(B ext /10)

It is generally believed that a perceptible change in visibility is approximately 1-2 dV.

The CMAQ modeling results are used in a relative fashion to project future-year visibility using relative reduction factors (RRFs). RRFs are expresse d of the ratio of the modeling results for the future-year (2018) scenarios to the results of the base year (1996) and are Class I area and PM species specific. RRFs are applied to the base year observed PM species to project future-year PM levels from which visibility can be assessed using the extinction equations listed above. The specific steps used to project future-year visibility are as follows:

1. Map observed IMPROVE particulate matter (PM) measurements from the IMPROVE monitoring network to each Class I area using the Class I area clustering approach and proximity. 2. For each Class I area, identify the observed PM components for the days during 1996 that represent the Worst 20% and Best 20% visibility days using the extinction equations listed above and generate average observed PM species concentrations for the Worst 20% and Best 20% days and the six PM components of light extinction 3. Run the CMAQ model for the 1996 base-year base case and 2018 future-year emission scenarios, extract the PM species concentrations at the Class I areas for the observed Worst 20% and Best 20% days and generate model estimated average PM concentrations for the observed Worst 20% and Best 20% visibility days from 1996. 4. Develop Class I area and PM species specific Relative Reduction Factors (RRFs) of the average PM concentrations for the Worst 20% and Best 20% observed visibility days that are the ratio of the future-year to base-year modeling results. For example, the RRF for sulfate (SO 4 ) concentrations for the Worst 20% (W20%) days at the Grand Canyon (GC) for the 2018 Base Case (BC) would be obtained as follows:

RRF (BC, GC, W20%) = 2018Model (BC, GC, W20%) / 1996Model (BC, GC, W20%) SO4 SO4 SO4

5. The future-year PM species estimates at each Class I area and the Worst 20% and Best 20% days are obtained by applying the model estimated RRFs to the observed values. For example, for projecting sulfate concentrations for the 2018 Base Case and the Grand Canyon:

2018 (BC, GC, W20%) = Observed (BC, GC, W20%) x RRF (BC, GC, W20%) SO4 SO4 SO4

Final §309 Regional TSD Page 123 of 250 December 15, 20036. Two different sets of observed average PM concentrations for the Worst 20% and Best 20% days are used whether projecting improvements in visibility (Chapter 2) or comparing the relative visibility changes due to alternative stationary source control strategies (Chapter 4):

Projection of Improvement : When projecting improvements in visibility at the 16 Colorado Plateau Class I areas in 2018, the EPA guidance for future-year visibility projections to be used in the future §308 plans were followed as closely as possible. The baseline observed visibility for the Worst 20% and Best 20% days is based on the latest five-year period for which IMPROVE measurements are available, which is 1997-2001. This was done for the following reasons: (1) to follow the EPA guidance as closely as possible; and (2) to take advantage of the availability of more IMPROVE monitors at Class I areas that started to become available in 2000.

Relative Comparison of Alternative Stationary Source Control Strategies : For §309 the specific evaluation of the relative changes in alternative stationary source control strategies (e.g., comparison of the SO 2 Annex Milestone and BART with Uncertainty control scenarios) the observed average 1996 Worst 20% and Best 20% visibility days was used as the observed baseline. This was done for the following reasons: (1) these analyses are §309-specific; (2) the modeled RRFs and observed visibility baseline would be consistent and based on the same set of visibility days from 1996; and (3) these analyses compare the relative visibility benefits of these control strategies, so the observed baseline is not as important.

7. The future-year visibility estimate is obtained from the future-year PM species estimates using the extinction equation listed above from which visual range and deciview can be estimated also using the equations listed previously.

The EPA procedures (EPA, 2001) for projecting future-year visibility using model estimated RRFs were followed for 4 of the six PM components of light extinction. For the other fine particulate (Soil) and Coarse Matter (CM) components of PM the RRFs were set equal to unity:

RRF Soil = RRF CM = 1.0

This was done because the WRAP emissions inventory did not include any windblown fugitive dust emissions that is a component of the Soil and CM PM species. Thus, the relative change in the modeling results for these two species (i.e., the RRFs) would be incorrect. This assumes that the Soil and CM components for the Worst 20% and Best 20% visibility days in 2018 remain unchanged from the observed baseline levels.

Final §309 Regional TSD Page 124 of 250 December 15, 2003

## Chapter 2 - Projection of Improvement

2.1. 2018 visibility improvement scenarios using §309 control strategies

Improvement in visibility for the 16 Colorado Plateau Class I areas was evaluated for two scenarios that anticipate implementation of regional emissions management programs identified in §309. Scenario 1 is designed to assess the effect of the GCVTC-recommended control strategies promulgated in §309, comparing the 1996 modeled base case to the visibility improvement resulting from the implementation of the following strategies: the SO 2 Annex Milestones, the regional pollution prevention program, maintenance of existing base smoke management (BSM) programs, and accounting for the 2018 base case emissions (known and adopted federal, tribal, state, and local control programs in the contiguous WRAP region). Visibility changes resulting from regional implementation of state pollution prevention programs were modeled by the Regional Modeling Center, as part of the other §309 control strategies. Visibility changes resulting from implementation of pollution prevention programs by individual states or tribes were not modeled. Emissions changes from state or tribal pollution prevention programs, and the resulting visibility changes are small, based on the regional pollution prevention emissions analysis, but are accounted for in the regional modeling.

Scenario 2 is designed to assess the effect of the implementation of Enhanced Smoke Management Programs (ESMP), as reflected in the Fire Emissions Joint Forum’s 2018 Optimal Smoke Management (OSM) inventory. ESMP s were recommended by GCVTC and are required in §309. This scenario uses the emissions inventories from Scenario 1, except the OSM inventory was substituted for fire emissions. Thus, the results for Scenario 2 are a comparison of visibility changes resulting from emission reductions between the 2018 BSM and 2018 OSM fire inventories.

2.2. Projected visibility improvement in 2018

Presented in the next two subsections are the results of modeling analyses showing the projected visibility improvements at the 16 Class I areas on the Colorado Plateau. For comparison of model-predicted changes in visibility, the absolute modeling results are shown for the average 20% best and worst visibility days are described in Section 2.2.1. Following that in Section 2.2.2, the modeling results for the average 20% best and worst visibility days are shown, calculated using relative reduction factors, compared to the 1997-2001 baseline monitoring data, following the procedures and draft EPA guidance discussed in Chapter 1. The tables in each of the following sections present the projected visibility improvement for the 16 Class I areas on the Colorado Plateau in 2018.

2.2.1. Absolute Modeling Results

The absolute modeling results are shown in Tables 2.2.2.1 and 2.2.1.2, for the 1996 and 2018 base cases, and 2018 Scenarios 1 and 2. Absolute modeling results are not the method recommended in draft EPA guidance for demons trating reasonable progress (EPA, 2001), but allow direct comparison of model results between years. Given that absolute modeling results

Final §309 Regional TSD Page 125 of 250 December 15, 2003Final §309 Regional TSD Page 126 of 250 December 15, 2003 have not had relative reduction factors applied, variation in the performance of the model in evaluating different emissions scenarios affec ting each Class I area is apparent. Overall performance of the model was discussed in Chapte r 1. Thus, the lack of a correlation between the downward-trending emissions reduction scenarios, i.e., 2018 base case to 2018 Scenario 1, and uniformly improving visibility at the 16 Class I areas is not unexpected. Other reasons for changes in visibility at specific Class I areas are discussed in Section 2.2.2.

On the 20% average worst visibility days, there is improving visibility projected between the 1996 and the 2018 base cases, and between the 2018 Scenarios 1 and 2, for all 16 Class I areas. This means that the model is likely accounting well for the noticeable emissions reductions expected from on-road mobile sources and specific stationary sources between 1996 and 2018, and for the anticipated fire emissions reductions expected from implementation of regional ESMPs. Between the 2018 base case and Scenario 1, projected visibility on the worst days stays about the same or degrades slightly, well within model uncertainty, which may be related to a number of factors; model performance at specifi c Class I areas, and/or emissions location and magnitude changes. Reasons for visibility changes between the 2018 Base Case and Scenario 1 are best evaluated using modeling results normalized using relative reduction factors, discussed in the next section.

On the 20% average best visibility days, projected visibility stays the same or improves between the 1996 and the 2018 base cases, and between the 2018 Scenarios 1 and 2. As above, this means that the model is likely accounting well for the noticeable emissions reductions expected from on-road mobile sources and specific stationary sources between 1996 and 2018, and for the anticipated fire emissions reductions expected from implementation of regional ESMPs. Between the 2018 base case and Scenario 1, projected visibility on the best days degrades slightly, well within model uncertainty, again which may be related to a number of factors; model performance at specific Class I areas, and/ or emissions location and magnitude changes. Again, reasons for visibility changes between the 2018 Base Case and Scenario 1 are best evaluated using modeling results normalized using relative reduction factors, discussed in the next section.Table 2.2.1.1.AbsoluteModeling Results for Projected Visibility Improvement at the 16 Colorado Plateau Class I areas on theAverage 20% Worst Visibility Days, for the 1996 Base Case, the 2018 Base Case, Scenario 1, and Scenario 2.AbsoluteModeling Results (deciviews)2018 Scenario 1Colorado PlateauClass I areaState1996 BaseCase(20% WorstDays’Visibility)2018 Base Case(20% Worst Days’Visibility for allcontrols “on thebooks” as of 2002)2018 Scenario 2(20% Worst Days’ Visibilityfor all §309 ControlStrategies (SO(20% Worst Days’ Visibilityfor all §309 ControlStrategies (SO2AnnexMilestones and PollutionPrevention) with BaseSmoke Management)2AnnexMilestones and PollutionPrevention) with OptimalSmoke Management)Grand Canyon NPAZ10.869.9910.5810.23Mount Baldy WildernessAZ13.0611.6511.9611.81Petrified Forest NPAZ12.0311.1911.2011.24Sycamore Canyon WildernessAZ10.049.249.669.44Black Canyon of Gunnison NPCO12.0811.1811.3211.39Flat Tops WildernessCO12.5811.3311.6411.49Maroon Bells-Snowmass WACO9.989.159.449.39Mesa Verde NPCO11.2910.0410.3010.14West Elk WildernessCO12.4011.0811.2711.17Weminuche WildernessCO14.2613.5513.7513.79San Pedro Parks WildernessNM11.179.8810.029.96Arches NPUT10.119.339.779.62Bryce Canyon NPUT12.7412.0012.3112.29Canyonlands NPUT11.3710.0010.3010.17Capitol Reef NPUT8.627.898.118.04Zion NPUT12.3110.9311.0311.01Final §309 Regional TSD Page 127 of 250 December 15, 2003Final §309 Regional TSD Page 128 of 250 December 15, 2003Table 2.2.1.2.AbsoluteModeling Results for Projected Visibility Improvement at the 16 Colorado Plateau Class I areas on theAverage 20% Best Visibility Days, for the 1996 Base Case, the 2018 Base Case, Scenario 1, and Scenario 2.AbsoluteModeling Results (deciviews)Colorado PlateauClass I areaState1996 BaseCase(20% BestDays’Visibility)2018 Base Case(20% Best Days’Visibility for allcontrols “on thebooks” as of 2002)2018 Scenario 1(20% Best Days’ Visibilityfor all §309 ControlStrategies (SO2AnnexMilestones and PollutionPrevention) with BaseSmoke Management)2018 Scenario 2(20% Best Days’ Visibilityfor all §309 ControlStrategies (SO2AnnexMilestones and PollutionPrevention) with OptimalSmoke Management)Grand Canyon NPAZ9.868.909.279.10Mount Baldy WildernessAZ13.1711.7912.1712.04Petrified Forest NPAZ9.818.929.249.22Sycamore Canyon WildernessAZ9.258.358.638.52Black Canyon of Gunnison NPCO9.999.189.379.37Flat Tops WildernessCO12.6311.2011.5811.47Maroon Bells-Snowmass WACO10.8110.2010.3510.28Mesa Verde NPCO12.4811.1611.5411.41West Elk WildernessCO13.2412.2612.7512.54Weminuche WildernessCO15.0214.1814.4514.30San Pedro Parks WildernessNM13.0311.7912.0911.91Arches NPUT11.5010.1710.9810.77Bryce Canyon NPUT11.5710.5410.7110.59Canyonlands NPUT12.5011.2011.6211.44Capitol Reef NPUT12.1111.0011.4411.26Zion NPUT12.8911.2810.3110.412.2.2. Visibility Modeling Results Using Relative Reduction Factors

Using the procedures from draft EPA guidance (EPA, 2001) discussed in Chapter 1, Tables 2.2.2.1 and 2.2.2.2 display the improvements in visibility from the 1997-2001 baseline period to 2018 under Scenario 1 and 2 conditions for, respectively, the Worst 20% and Best 20% visibility days. On the average 20% Worst visibility days, projected improvement from the 1997-2001 baseline data to 2018 Scenario 1 at the 16 Class I areas on the Colorado Plateau range from a maximum reduction of 3.89 dV at Sycamore Canyon National Park in Arizona to a maximum increase of 1.42 dV at San Pedro Parks Wilderness in New Mexico. On the Worst 20% days, Scenario 1 shows improving visibility at half and degradation in visibility for the other half of the 16 Colorado Plateau Class I areas.

On the average 20% Best visibility days, projected change from the 1997-2001 baseline data to 2018 Scenario 1 ranged from a maximum reduction of 2.11 dV at Zion National Park in Utah to a maximum increase of 1.51 dV at San Pedro Parks Wilderness Area in New Mexico. On the Best 20% days, Scenario 1 improves visibility cond itions at three-quarters of the Class I areas on the Colorado Plateau. A comparison of the visibility estimates for 2018 Scenarios 1 and 2 at the 16 Class I areas on the Colorado Plateau for the Worst 20% and Best 20% days reveals that 2018 Scenario 2 always estimated reduced (improved) visibility as compared to 2018 Scenario 1. That is, the regional implementation of Enhanced Smoke Management Programs produces visibility improvements over the existing Base Smoke Management Programs across all 16 Class I areas for both the Worst 20% and Best 20% days.

The reason why visibility is projected to improve in some areas and degrade in others is due tothe assumptions regarding the growth of emissions and the implementation of all controls “onthe-books” in 2002, as well as artifacts of the June 2000 version of the EPA NONROAD model. Figure 2.2.2.1 displays the differences in SO 2 emissions between the 1996 and 2018 Base Case emissions scenarios. Due to the implementation of SO 2 controls on the Navajo and Mojave power plants between 1996 and 2018, there are projected to be large reductions in SO 2 emissions in the counties in Arizona and Nevada that contain these two point sources. However, in many counties where there are not reductions in point source SO emissions, SO 2 2 emissions are projected to increase. As discussed in more detail in Chapter 4 and assumed in the modeling results reported here, this is due in part to increased activity in the nonroad mobile source sector, the continued use of high sulfur diesel fuel in nonroad sources, and bugs in the June 2000 version of the EPA NONROAD emissions model, that overstates nonroad equipment activity as well as associated SO emissions. 2
Visibility is projected to improve on the Worst 20% and Best 20% days for Class I areas in Arizona and southern Utah in close proximity to the large SO 2 reductions expected from controls on the Navajo plant, and downwind from the large SO 2 reductions at the Mojave plant in southern Nevada, as well as from emissions reduc tions from California. Similarly, the Class I areas where visibility is projected to degrade are near counties where SO 2 emissions are forecast to increase due to the assumed increases in SO 2 emissions from the nonroad mobile source sector. For example, the San Pedro Parks Wilderness Area in New Mexico lies in and near counties that are projected to have increases in SO 2 emissions under the 2018 Base Case conditions, and it is not surprising that the modeling results project that visibility would degrade

Final §309 Regional TSD Page 129 of 250 December 15, 2003Final §309 Regional TSD Page 130 of 250 December 15, 2003 at this Class I area. Future visibility modeling of the emissions projected by the updated NONROAD2002 model, accounting for proposed low sulfur diesel and emission control regulations for nonroad sources, and anticipati ng other local (e.g., 8-hour ozone and fine particulate) would likely produce improving visibility at all 16 Class I areas.Table 2.2.2.1. Projected Visibility Improvement at the 16 Colorado Plateau Class I areas on the Average 20% Worst Visibility Days,comparing the 1997-2001 Monitoring Data to the 2018 Base Case, Scenario 1, and Scenario 2 Modeling Results, usingRelative Reduction Factors.Modeling Results (deciviews)2018 Scenario 1Colorado PlateauClass I areaState1997-2001MonitoringData(20% WorstDays’ Visibility- deciviews)2018 Base Case(20% WorstDays’ Visibilityfor all controls“on the books” asof 2002)2018 Scenario 2(20% Worst Days’ Visibilityfor all §309 ControlStrategies (SO(20% Worst Days’ Visibilityfor all §309 ControlStrategies (SO2AnnexMilestones and PollutionPrevention) with BaseSmoke Management)2AnnexMilestones and PollutionPrevention) with OptimalSmoke Management)Grand Canyon NPAZ12.3011.6211.5611.51Mount Baldy WildernessAZ14.3012.2212.0211.96Petrified Forest NPAZ13.0011.9911.8211.74Sycamore Canyon WildernessAZ15.4011.6311.5111.48Black Canyon of Gunnison NPCO11.3010.9010.7610.60Flat Tops WildernessCO10.5011.0410.9110.73Maroon Bells-Snowmass WACO10.6011.1511.0010.84Mesa Verde NPCO13.1012.2412.0311.84West Elk WildernessCO10.6011.1910.9910.84Weminuche WildernessCO11.3011.0810.8910.72San Pedro Parks WildernessNM10.7012.3312.1211.71Arches NPUT12.1012.4112.2912.15Bryce Canyon NPUT11.8012.2612.2411.95Canyonlands NPUT12.1012.4112.3112.18Capitol Reef NPUT12.1012.5112.4912.36Zion NPUT13.6012.1312.0912.03Final §309 Regional TSD Page 131 of 250 December 15, 2003Final §309 Regional TSD Page 132 of 250 December 15, 2003Table 2.2.2.2. Projected Visibility Improvement at the 16 Colorado Plateau Class I areas on the Average 20% Best Visibility Days,comparing the 1997-2001 Monitoring Data to the 2018 Base Case, Scenario 1, and Scenario 2 Modeling Results, usingRelative Reduction Factors.Modeling Results (deciviews)Colorado PlateauClass I areaState1997-2001MonitoringData(20% BestDays’ Visibility- deciviews)2018 Base Case(20% Best Days’Visibility for allcontrols “on thebooks” as of 2002)2018 Scenario 1(20% Best Days’ Visibilityfor all §309 ControlStrategies (SO2AnnexMilestones and PollutionPrevention) with BaseSmoke Management)2018 Scenario 2(20% Best Days’ Visibilityfor all §309 ControlStrategies (SO2AnnexMilestones and PollutionPrevention) with OptimalSmoke Management)Grand Canyon NPAZ4.804.764.724.64Mount Baldy WildernessAZ5.505.495.465.36Petrified Forest NPAZ6.505.185.145.10Sycamore Canyon WildernessAZ6.304.854.824.75Black Canyon of Gunnison NPCO4.603.893.833.75Flat Tops WildernessCO3.103.963.903.81Maroon Bells-Snowmass WACO3.103.903.853.80Mesa Verde NPCO5.504.404.384.33West Elk WildernessCO3.103.893.833.74Weminuche WildernessCO4.603.973.923.82San Pedro Parks WildernessNM4.005.595.515.36Arches NPUT5.504.854.724.61Bryce Canyon NPUT4.303.913.923.89Canyonlands NPUT5.604.874.764.67Capitol Reef NPUT5.604.854.854.75Zion NPUT5.903.813.793.75Figure 2.2.2.1. Differences in count average SO emissions between the 1996 Base Case and the 2 2018 Base Case emissions scenarios.

Final §309 Regional TSD Page 133 of 250 December 15, 2003

## Chapter 3 – Assessment of Clean Air Corridors

3.1. Clean Air Corridor (CAC) Requirements

The requirements of the regional haze rule regarding clean air corridors are found in §309(d)(3): The plan must describe and provide for implementation of comprehensive emission tracking strategies for clean-air corridors to ensure that the visibility does not degrade on the least impaired days at any of the 16 Class I areas.

More specifically, the rule requires that the §309 SIP:

1. Identify clean air corridors, with EPA evaluating the identification based on the work of the Meteorology Subcommittee of the Grand Canyon Visibility Transport Commission; 2. Within the clean air corridors, identify patte rns of growth that could cause significant emissions increases that could impair visibility at one or more of the 16 Class I areas; 3. Identify significant emissions growth outside th e clean air corridors that could impair the quality of the air in the corridor; 4. If impairment is identified, analyze the e ffects of increased emissions and provide for implementation of additional emissions reductions where necessary; and 5. Determine whether any other clean air corridors exist for any of the 16 Class I areas and identify any measures necessary to protect them against future degradation.

3.2. CAC Definition

The Clean Air Act Amendments of 1990 specifically require that visibility transport commissions address “the establishment of clean air corridors, in which additional restrictions on increases in emissions may be appropriate to protect visibility in affected class I areas.” 1 The Grand Canyon Visibility Transport Commission (GCTVC) in its recommendations 2 found that clean air corridors exist and that, generally, clean air comes to the Colorado Plateau from the northwest. Using one of the proposed corridor alignments examined by the Meteorology Subcommittee, a corridor that would protect the 30% cleanest days on the Colorado Plateau, BBC Research and Consulting conducted an economic and demographic evaluation of the corridor to determine whether emissions increases expected by 2040 would approach 25%.

3 According to its projections, emissions are not expected to increase 25% by 2040. The boundaries of the corridor defined in the report are shown in Figure 25. The WRAP adopts this boundary because of the extensive demographic, economic, and air quality impact analysis performed on this corridor and the subsequent review and approval, including the consensus reached by the Grand Canyon Visibility Transport Co mmission. This is a slight modification of the boundary used in the BBC Report. The grid cells used by the GCVTC did not follow state or

1 42 U.S.C. 2169B(d)(2)(A). 2 Grand Canyon Visibility Transport Commission. “R ecommendations for Improving Western Vistas”. Western Governors’ Association. Denver, CO. June 1996. 3 BBC Report, page III-5

Final §309 Regional TSD Page 134 of 250 December 15, 2003county boundaries, and for ease of administration, the WRAP has removed small areas of southern Washington and southwestern Montana from the corridor. These areas are far from the Colorado Plateau and it is unlikely that emissions increases in these small areas would affect the Class I areas on the Plateau. Also, the WRAP boundary includes all of Box Elder, Tooele and Grand Counties in Utah, Wasco and Sherman Counties in Oregon, and Cassia and Lemhi Counties in Idaho; these counties were not included within the BBC boundary.

Figure 25: Clean Air Corridor – WRAP (blu e) – GCVTC/BBC (red) and Colorado Plateau Class I areas (green)

Final §309 Regional TSD Page 135 of 250 December 15, 20033.3. Emission Changes

Emission changes within the clean air corridor between the 1996 base year and the projection year of 2018, including the SO Annex Milestones case are shown in Table 32. PM 2 10 and PM 2.5 emissions are expected to increase about 7% and 18%, respectively. NO x and VOC, however, are expected to decrease about 15% and 26%, respectively. SO 2 emissions are expected to increase about 5% within the corridor, even with the declining milestones of the backstop emissions cap program. Overall, SO 2 emissions are expected to decline by 17% in the 13-state GCVTC region by 2018, and the fact that the projections show a 5% increase in SO 2 within the clean air corridor is a result of non-road sources burning high-sulfur diesel fuel. This source of SO 2 is expected to drastically reduce (e.g., from a fuel sulfur content of more than 3,000 ppm to 15 ppm) before 2018 according to announcements by EPA to develop new engine certification standards for non-road vehicles and equipment. Thus, 5% should be viewed as an upper bound on the possible increase.

Table 32: Changes in CAC Emissions by 2018 (including milestones) from 1996
Point Area On Road Non Road Paved Unpaved Total SO 1996 51,413 9,260 2,065 10,838 0 0 73,576 2 2018 45,330 10,614 413 21,596 0 0 77,954 2018-1996 -6,082 1,354 -1,652 10,758 0 0 4,378

NO 1996 85,782 12,935 93,581 64,462 0 0 256,762 x 2018 109,863 17,576 28,692 62,557 0 0 218,689 2018-1996 24,080 4,641 -64,889 -1,905 0 0 -38,072

PM 1996 27,055 142,776 3,872 5,952 5,740 47,733 233,128 10 2018 32,748 154,966 2,640 6,763 12,402 38,828 248,347 2018-1996 5,692 12,190 -1,232 811 6,662 -8,904 15,219

PM 1996 11,987 41,595 3,495 5,487 1,435 7,160 71,160 2.5 2018 14,583 52,069 2,058 6,228 3,101 5,824 83,863 2018-1996 2,595 10,474 -1,438 740 1,665 -1,336 12,702

VOC 1996 5,993 95,921 69,899 38,535 0 0 210,349 2018 7,921 95,515 22,651 29,233 0 0 155,321 2018-1996 1,927 -406 -47,248 -9,301 0 0 -55,029

3.4. CAC emissions tracking using the WRAP Emissions Data Management System

The preamble of the RHR defines a CAC as “a region that generally brings clean air to a receptor region”, and also says, “the requirement to track emissions will enable states to quickly determine if changes in patterns of emissions will reduce the number of clean air days (defined as the average of the 20% clearest days) in any of the 16 Class I areas.” The actual requirements state that the §309 SIP or TIP must describe and provide for implementation of comprehensiveemission tracking strategies for CAC to ensure that the visibility does not degrade on the leastimpaired days at any of the 16 Class I areas.

Final §309 Regional TSD Page 136 of 250 December 15, 2003Using the most recent emission inventory data available through the Emissions Data Management System (EDMS), WRAP will produce a report for each five-year implementation plan revision (2007-8, 2013, and 2018) on the current and projected emissions in the CAC and in areas surrounding the corridor and compare these emissions to a 1996 baseline, as part of a larger attribution of haze project managed by the Technical Oversight Committee (described in the next section).

The EDMS will have the capability to produce the following special reports in tabular and simple plots (i.e. bar graph and pie chart) formats and allow queries of the same information including presentation in GIS format, in addition to the standard reports:

• A summary report of the annual summed total emissions for all six source categories and

all of the pollutants by county/state and tribal lands, as well as for the entire CAC.

• A summary report of the annual summed total emissions for all six source categories and

all of the pollutants for the same types of political boundaries surrounding the CAC.

• A summary report of the comparison of the annual summed total emissions for all six

source categories and all of the pollutants for the same types of political boundaries, as well as the entire CAC and the corresponding base year total emissions.

The EDMS to be developed is described in a draft technical report to the Emissions Forum: Needs Assessment for Evaluation and Design of an Emissions Data Reporting, Management, and Tracking System , (EA Engineering, Science, and Technology, June 26, 2003).

3.5. Process to analyze emissions growth in, and surrounding, the CAC

As part of the next round of analysis and prep aration for regional haze SIPs due in 2007-08, the Technical Oversight Committee will be conducting two separate haze attribution exercises (discussed in the WRAP 2003-08 Strategic Plan), integrating analytical results from aerosol and meteorological monitoring, air quality modeling, and preparation of emissions inventories. These haze attribution exercises will identify the source regions and categories causing visibility impairment at Class I areas. As part of those haze attribution exercises, the TOC will analyze the changes in emissions for the counties and tribal lands in the CAC, as well as those surrounding the CAC. Better emissions inventory data expected to be available each time, as the TOC iterates through these 2 exercises. Specific results from each of the haze attribution exercises will address emissions growth both inside and surrounding the CAC, as well as the impact on visibility at affected Class I areas.

3.6. Other Clean Air Corridors

Other than the various options for selection of a clean air corridor for Grand Canyon National Park, shown above, no other corridors have been iden tified. If the growth of visibility-impairing emissions, in the corridor identified, remain protective of Grand Canyon National Park, then it should be protective of the other Colorado Plateau Class I areas. Localized emissions near the Class I areas within the Clean Air Corridor, however, may have more effect on those Class I areas. Similarly, disproportionate emissions grow th in the southern portion of the corridor may have more effect on Grand Canyon National Park.

Final §309 Regional TSD Page 137 of 250 December 15, 2003

## Chapter 4 – Assessment of Stationary Sources

4.1. Visibility Improvements of the SO 2 Milestone Annex versus the BART with Uncertainty Control Scenarios

One of the elements of §309 of the Regional H aze Rule (RHR) requires the demonstration that the SO 2 Annex Milestone stationary source control strategy recommended by the GCVTC is better than Best Available Retrofit Technology (BART) controls for SO 2 on stationary sources, for improving visibility at the 16 Class I Areas on the Colorado Plateau. This demonstration has been made in the SO 2 Annex submitted by WRAP, and adopted in rule by EPA. The modeling analyses discussed next serve to verify the original Annex analysis, and also provides estimates of visibility changes due to the SO 2 Annex and BART scenarios at other Class I areas as well. The CMAQ modeling system was applied to estimate fine particulate concentrations and visibility in the western U.S., using the 1996 MM5 meteorological data for three future-year emission scenarios:

• 2018 Base Case;

• 2018 SO Annex Milestones; and 2

• 2018 BART with Uncertainty.

Chapter 1 of the TSD discusses the assumptions and emissions summaries of the 2018 Base Case, 2018 SO Annex Milestones, and 2018 BART with Uncertainty emission scenarios. 2
The RHR uses two metrics to judge changes in visibility at Class I areas:

• The 5-year mean visibility (expressed as extinction or deciview) of the observed 20% worst visibility days; and

• The 5-year mean visibility of the observed 20% best visibility days.

For the future §308 regional haze implementation plans, the RHR requires demonstration of reasonable progress toward achieving natural visibility conditions (i.e., no man-made impairment) by 2064 for the mean of the Worst 20% observed days and show no degradation in visibility for the mean of the Best 20% observed days. To assess whether the 2018 SO 2 Annex control strategy is “better than” the 2018 BART with Uncertainty control scenario at the 16 Class I areas in the Colorado Plateau, the average 20% Worst and 20% Best visibility days' metrics are used. The RHR observed visibility baseline is the five-year 2000-2004 period. The §309 SIPs are due in 2003, so the 2000-2004 data are not yet available. Thus, a 1996 baseline period is used to assess the visibility impacts of the 2018 Base Case, 2018 SO 2 Annex, and 2018 BART with Uncertainty emission scenarios as the data for the RHR 2000-2004 baseline period are not yet available.

Future-year visibility estimates were generated for the 16 Class I areas on the Colorado Plateau using 1996 IMPROVE observations and the 1996 and 2018 CMAQ model estimates and the procedures discussed in Chapter 1 following EPA guidance (EPA, 1997; 2001). These procedures use the model in a relative sense to scale the observed visibility components using

Final §309 Regional TSD Page 138 of 250 December 15, 2003relative reduction factors (RRFs) to project future-year visibility estimates. IMPROVE measurements were not available at all of the 16 Class I areas during the 1996 baseline period. So for those Class I areas without IMPROVE obs ervations the IMPROVE data from the closest most representative monitoring site were mapped to the Class I area as shown in Table 32. Note that although IMPROVE measurement data had to be mapped to some of the 16 Class I areas, the modeling results at the actual location of each Class I area were used to project future-year visibility estimates.

Table 4.1.1. Mapping of 1996 IMPROVE PM measurement data to the 16 Class I areas on the Colorado Plateau Class I Area Mapped IMPROVE Monitor Arches NP Canyonlands NP Black Canyon of Gunnison NP Weminuche Wilderness Bryce Canyon NP Bryce Canyon NP Canyonlands NP Canyonlands NP Capitol Reef NP Canyonlands NP Flat Tops Wilderness Mount Zirkel Wilderness Grand Canyon NP Grand Canyon NP Maroon Bells-Snowmass Wilderness Weminuche Wilderness Mesa Verde NP Mesa Verde NP Mount Baldy Wilderness Petrified Forest NP Petrified Forest NP Petrified Forest NP San Pedro Parks Wilderness Bandelier National Monument Sycamore Canyon Wilderness Grand Canyon NP West Elk Wilderness Weminuche Wilderness Weminuche Wilderness Weminuche Wilderness Zion NP Bryce Canyon NP

Table 4.1.2 displays the 2018 visibility estimates for the Worst 20% days at the 16 Class I areas on the Colorado Plateau and the 2018 Base Case, 2018 SO 2 Annex Milestone, and 2018 BART with Uncertainty emission scenarios. The visibility estimates are presented for the three 2018 emissions scenarios in terms of deciview (dV). Also shown in Table 33 are the differences in visibility for the Worst 20% days between the 2018 control scenarios and the 2018 Base Case. Based on the results in Table 33 we can draw the following conclusions regarding visibility changes due to the SO Annex Milestone versus the BART with Uncertainty control scenarios: 2

• The two 2018 stationary source SO 2 control scenarios result in small improvements in visibility over the 2018 Base Case that are less than can be perceived (< 1 dV);

• The SO 2 Annex Milestone results in more visibility benefits than the BART with Uncertainty at 15 of the 16 (~94%) Class I areas on the Colorado Plateau.

The one Class I area of the 16 on the Colorado Plateau where more visibility improvements are estimated in the 2018 BART with Uncertainty scenario than the 2018 SO 2 Annex Milestone strategy is Petrified Forest National Park where the BART with Uncertainty estimates slightly more visibility benefits over the 2018 Base Case (-0.26 dV) than estimated for the SO 2 Annex Milestone (-0.24 dV) control strategy.

Final §309 Regional TSD Page 139 of 250 December 15, 2003Table 4.1.2. Estimated visibility levels in deciviews (dV) at the 16 Class I areas on the Colorado Plateau for the Worst 20% days and the 2018 Base Case, 2018 SO 2 Annex Milestones, and 2018 BART with Uncertainty emission scenarios and the differences (improvements) in visibility due to the 2018 control scenarios from the 2018 Base Case.
2018 2018 Difference Difference 2018 Class I Areas Base Case (dV) SO 2 Annex (dV) BART w/Uncert. (dV) Annex – BART– Base Base (dV) (dV) Arches NP 12.42 12.32 12.37 -0.10 -0.05 Black Canyon of Gunnison NP 10.95 10.79 10.85 -0.16 -0.10 Bryce Canyon NP 12.26 12.23 12.24 -0.03 -0.02 Canyonlands NP 12.27 12.17 12.21 -0.09 -0.05 Capitol Reef NP 12.50 12.46 12.49 -0.03 -0.01 Flat Tops Wilderness 10.89 10.74 10.79 -0.15 -0.10 Grand Canyon NP 11.77 11.69 11.71 -0.08 -0.06 Maroon Bells-Snowmass Wilderness 11.23 11.04 11.10 -0.19 -0.13 Mesa Verde NP 12.04 11.85 11.92 -0.18 -0.12 Mount Baldy Wilderness 12.26 12.09 12.14 -0.17 -0.13 Petrified Forest NP 11.99 11.76 11.73 -0.24 -0.26 San Pedro Parks Wilderness 12.32 12.11 12.16 -0.21 -0.16 Sycamore Canyon Wilderness 12.11 12.01 12.04 -0.10 -0.07 West Elk Wilderness 11.12 10.90 10.96 -0.22 -0.16 Weminuche Wilderness 11.01 10.80 10.87 -0.20 -0.14 Zion NP 12.03 12.00 12.02 -0.03 -0.01

Table 4.1.3 summarizes the changes in visibility at the 16 Class I areas for the Best 20% visibility days from 2018 Base Case levels for the 2018 SO 2 Annex Milestone and the 2018 BART with Uncertainty control scenarios. Both the 2018 SO 2 Annex and BART with Uncertainty estimate very small (hundredths of dV) improvements in visibility at the 16 Class I areas for the Best 20% days. For all 16 Class I areas on the Colorado Plateau and the Best 20% visibility days, the 2018 SO 2 Annex Milestone control strategy estimates more visibility improvement (12 Class I areas) or the same visibility improvement (4 Class I areas) from the 2018 Base Case than the 2018 BART with Uncertainty control scenario.

In conclusion, when looking at all 16 Class I areas on the Colorado Plateau and the Worst 20% and Best 20% visibility days, the 2018 SO 2 Annex Milestone control strategy estimate more visibility improvements than the 2018 BART with Uncertainty control scenario.

Final §309 Regional TSD Page 140 of 250 December 15, 2003Table 4.1.3. Estimated visibility levels in deciviews (dV) at the 16 Class I areas on the Colorado Plateau for the Best 20% days and the 2018 Base Case, 2018 SO 2 Annex Milestones and 2018 BART with Uncertainty emission scenarios and the changes in visibility due to the 2018 control scenarios from the 2018 Base Case.

Class I Area 2018 Base Case (dV) 2018 SO 2 Annex (dV) 2018 Difference Difference BART w/Uncert. (dV) Annex – BART– Base Base (dV) (dV) Arches NP 4.85 4.77 4.80 -0.08 -0.05 Black Canyon of Gunnison NP 3.87 3.83 3.85 -0.05 -0.02 Bryce Canyon NP 3.24 3.22 3.23 -0.01 -0.01 Canyonlands NP 4.78 4.71 4.74 -0.06 -0.04 Capitol Reef NP 4.84 4.81 4.82 -0.03 -0.02 Flat Tops Wilderness 3.89 3.83 3.85 -0.06 -0.03 Grand Canyon NP 4.76 4.69 4.71 -0.07 -0.05 Maroon Bells-Snowmass Wilderness 3.89 3.83 3.85 -0.06 -0.04 Mesa Verde NP 4.20 4.16 4.18 -0.03 -0.01 Mount Baldy Wilderness 5.46 5.42 5.42 -0.04 -0.04 Petrified Forest NP 5.19 5.14 5.15 -0.04 -0.04 San Pedro Parks Wilderness 5.55 5.49 5.52 -0.06 -0.04 Sycamore Canyon Wilderness 4.86 4.81 4.82 -0.05 -0.03 West Elk Wilderness 3.88 3.84 3.86 -0.05 -0.03 Weminuche Wilderness 3.93 3.89 3.91 -0.05 -0.03 Zion NP 3.80 3.78 3.79 -0.02 -0.01

4.1.1. Stationary Source SO 2 Control Scenario Modeling Results at the 16 Class I Areas on the Colorado Plateau

Although the modeling results suggest that overall the 2018 SO 2 Annex Milestone strategy is better than BART with Uncertainty for improving visibility at the 16 Class I areas on the Colorado Plateau, the reasons why the improvements are so small and why the SO 2 Annex estimates less improvement in visibility than BART with uncertainty for the 20% Worst days at Petrified Forest National Park need to be understood.

Figure 4.1.1.1 displays the difference in SO 2 emissions between the 1996 Base Case and the 2018 Base Case emissions scenario by county for th e 9 western states that may opt-in to §309. Table 4.1.1.1 displays the SO 2 emissions for the 13 “WRAP” states (includes Nevada but not Alaska) and the 1996 and 2018 Base Cases and 2018 SO 2 Annex and BART with Uncertainty emissions scenarios. Figure 4.1.1.2 displays the differences in SO 2 emissions between the 2018 Base Case and 2018 SO 2 Annex Milestone control scenario, whereas Figure 4.1.1.3 displays the differences in SO 2 emissions between the 2018 SO 2 Annex and 2018 BART with Uncertainty emission control strategies. Observations on these figures and tables are as follows:

Final §309 Regional TSD Page 141 of 250 December 15, 2003• Between the 1996 and 2018 Base Cases there is an approximately 210 thousand ton per year (TTPY) (-18%) reduction in SO 2 emissions from point sources that is partly offset by a 60 TTPY and 25 TTPY increase in SO 2 emissions from the non-road mobile and area source sectors, respectively, resulting in a net reduction in SO 2 emissions across the WRAP states of only 148 TTPY (-9%).

• The largest reductions in SO 2 emissions between the 1996 Base Case and the 2018 Base Case occur in Coconino County, Arizona (~60,000 TPY) and Clark County, Nevada (~25,000 TPY) that are mainly due to implementation of SO 2 controls on the Navajo and Mojave generating stations, respectively.

• In contrast to the controls on Navajo and Mojave electrical generating units (EGUs) between the 1996 and 2018 Base Cases that occur adjacent to or upwind of the 16 Class I areas on the Colorado Plateau, the additional SO 2 controls on EGUs due to the SO 2 Annex Milestone control scenario occur at locations more downwind from the 16 Class I areas, which explains the small impacts they have on visibility (Table 36).

• The differences in SO 2 emissions between the 2018 SO 2 Annex and 2018 BART with uncertainties (Figure 4.1.1.3) show counties with increases and decreases with the SO 2 Annex scenario generally having lower SO 2 emissions with a net reduction of approximately 40,000 TPY.

• The county in which the SO 2 Annex emissions scenario has the highest amount of SO 2 emissions compared to the BART with Uncertainty scenario (~7,000 TPY higher) is Navajo County, Arizona, which is due to the Cholla EGU. The effects of the market trading program estimates that Cholla would be a buyer of emission credits so would have greater emissions under the SO 2 Annex emission scenario than under a more traditional command and control BART with Uncertainty control strategy. As the Cholla EGU is nearby and upwind of the Petrified Forest National Park, these results explain why the BART with Uncertainty scenario pr oduces slightly more visibility improvements than the SO 2 Annex Milestone control strategy.

Table 4.1.1.1. Summary of SO 2 emissions (thousand tons per year, TTPY) in the 13 WRAP states for the 1996 and 2018 Base Cases and 2018 SO 2 Annex Milestones and 2018 BART with Uncertainty emission control scenarios. % Diff % Difference 1996 Base (TTPY) 2018 Base (TTPY) 2018- 1996 (%) 2018 Annex (TTPY) 2018 BART (TTPY) Annex- BARTBase Base (%) (%) Point 1197 985 -18% 768 809 -22% -18% Mobile 28 7 -77% 7 7 0% 0% Non-Road 213 272 +28% 272 272 0% 0% Area 142 167 +18% 167 167 0% 0% Wildfire 25 25 0% 25 25 0% 0% Ag Fire 1 1 0% 1 1 0% 0% Rx Fire 31 31 0% 31 31 0% 0% Total 1638 1490 -9% 1272 1312 -15% -12%

Final §309 Regional TSD Page 142 of 250 December 15, 2003Figure 4.1.1.1. Changes in SO 2 emissions between the 1996 Base Case and 2018 Base Case emissions scenarios by county for the 9 potential §309 states.

Final §309 Regional TSD Page 143 of 250 December 15, 2003Figure 4.1.1.2. Changes in SO 2 emissions between the 2018 Base Case and 2018 SO 2 Annex Milestones control scenario by county for the 9 potential §309 states.

Final §309 Regional TSD Page 144 of 250 December 15, 2003Figure 4.1.1.3. Changes in SO 2 emissions between the 2018 SO 2 Annex Milestones and 2018 BART with Uncertainty control scenarios by county for the 9 potential §309
states.

Final §309 Regional TSD Page 145 of 250 December 15, 20034.1.2. Stationary Source SO 2 Control Scenario Modeling Results for Western Class I Areas

Tables 4.1.2.1 and 4.1.2.2 list the changes in visibility from the 2018 Base Case for the 2018 SO 2 Annex Milestone and 2018 BART with Uncertainty control scenarios and the Worst 20% and Best 20% visibility days, respectively, for all Class I areas in the WRAP modeling domain (roughly west of the Mississippi River) ordered by state. The results can be summarized as follows:

Arizona : For the Worst 20% days, the SO 2 Annex Milestone estimates more visibility improvements than BART with Uncertainty for 13 of the 14 Class I areas in Arizona. The one exception is Petrified Forest National Park that has slightly less visibility improvements in the SO 2 Annex scenario due to the higher SO 2 emissions at the Cholla EGU described above. For the Best 20% days, the SO 2 Annex scenario estimates the same or more visibility improvements that BART with Uncertainty for 12 of the 14 Class I areas with the only exceptions being the Chiricahua Wilderness and National Monument near the Mexican border where very small -0.01 dV (SO 2 Annex) and –0.03 dV (BART with Uncertainty) improvements in visibility are estimated.

California : Both the SO 2 Annex and BART with Uncertainty scenarios both have essentially no effect on visibility at the 29 Class I areas in California on the Worst 20% and Best 20% days. This is due to the fact that there are very few large stationary SO 2 sources in California that would be affected by the two strategies.

Colorado : At the 12 Class I areas in Colorado, the 2018 SO 2 Annex scenario is estimated to have the same or more visibility improvements than the 2018 BART with Uncertainty scenario at each Class I area.

Idaho : The 2018 SO 2 Annex Milestone control scenario estimates approximately the same visibility benefits as the 2018 BART with Uncertainty control scenario at each of the four Class I areas in Idaho.

Michigan : The Isle Royal National Park is the only Class I area in Michigan and the SO 2 Annex and BART with Uncertainty control strategies estim ate almost identical visibility benefits for the Worst 20% and Best 20% days.

Minnesota : The two 2018 control scenarios also estimate essentially the same visibility benefits at the two Class I areas in Minnesota.

Missouri : Almost identical visibility benefits are estimated for the SO 2 Annex and BART with Uncertainty control scenarios at the Mingo and Hercules-Glade Class I areas in Missouri.

Montana : For the 10 Class I areas in Montana and both the 20% Worst and 20% Best visibility days, the 2018 SO 2 Annex scenario estimates the same or better visibility improvements than the 2018 BART with Uncertainty control scenario.

Final §309 Regional TSD Page 146 of 250 December 15, 2003North Dakota : The visibility benefits of the two 2018 c ontrol scenarios are essentially the same at the two Class I areas in North Dakota with exactly the same visibility improvements at Lostwood Wilderness and slightly more benefits exhibited by the SO 2 Annex scenario at Theodore Roosevelt National Park.

New Mexico : The visibility benefits due to the 2018 SO 2 Annex control scenario are estimated to be better than the 2018 BART with Uncertainty control scenario at all 9 Class I areas in New Mexico for both the Worst 20% and Best 20% visibility days.

Nevada : The SO 2 Annex scenario estimates slightly better visibility on both the Worst 20% and Best 20% days at the Jarbidge Wilderness Area, the only Class I area in Nevada.

Oklahoma : The estimated visibility improvements for the SO 2 Annex scenario are better than BART with Uncertainty for both the Worst 20% and Best 20% days at the Wichita Mountains Wilderness Area, the only Class I area in Oklahoma.

Oregon : At the 11 Class I areas in Oregon, the SO 2 Annex scenario estimates more visibility improvements than the BART with Uncertainty scenario on the Worst 20% days. For the Best 20% days, both 2018 scenarios estimate there will be no change in visibility over the 2018 Base Case conditions at most of the Class I areas, but when there are changes visibility is improved and the SO 2 Annex scenario estimates more improvements than the BART with Uncertainty scenario.

South Dakota : More improvements in visibility are estimated by the 2018 SO 2 Annex scenario than the 2018 BART with Uncertainty for the Wors t 20% days at the two Class I areas in South Dakota. For the Best 20% days, the two 2018 control scenarios estimate essentially the same level of visibility improvements.

Texas : Nearly equivalent visibility benefits are estimated for both the Worst 20% and Best 20% days and both Class I areas in Texas, with the SO 2 Annex scenario always exhibiting the same or better improvements than BART with Uncertainty.

Utah : All five Class I areas in Utah are part of the 16 Class I areas on the Colorado Plateau. The SO 2 Annex Milestone scenario estimates more visibility improvements than the BART with Uncertainty scenario at all five Class I areas for the Worst 20% days and at 4 of the 5 Class I areas for the Best 20% days, with identical improvements at the other Class I area for the Best 20% days.

Washington : Essentially the same visibility improvements are estimates for the 2018 SO 2 Annex and 2018 BART with Uncertainty emission control scenarios for the Worst 20% and Best 20% days at the 8 Class I areas in Washington.

Wyoming : For the 7 Class I areas in Wyoming, the 2018 SO 2 Annex Milestones emissions scenario always estimated more visibility improvements than the 2018 BART with Uncertainty control scenario for both the Worst 20% and Best 20% days.

Final §309 Regional TSD Page 147 of 250 December 15, 2003Final §309 Regional TSD Page 148 of 250 December 15, 2003 Table 4.1.2.1. Estimated visibility levels in deciviews (dV) at all western Class I areas in the WRAP modeling domain for the Worst 20% days and the 2018 Base Case, 2018 SO 2 Annex Milestones and 2018 BART with Uncertainty emission scenarios and the changes in visibility resulting from the 2018 control scenarios, from the 2018 Base Case.

Class I Area (Worst 20%) State 2018 Base Case (dV) 2018 SO 2 Annex (dV) 2018 BART w/Uncert. (dV) Difference Annex –

Base (dV) Difference BART–

Base (dV) Caney Creek Wilderness AR 27.12 26.32 26.32 -0.80 -0.79 Upper Buffalo Wilderness AR 27.04 26.26 26.26 -0.78 -0.77 Chiricahua NM AZ 12.93 12.84 12.85 -0.09 -0.08 Chiricahua Wilderness AZ 12.91 12.82 12.83 -0.09 -0.08

Galiuro Wilderness AZ 12.43 12.38 12.42 -0.06 -0.01 Grand Canyon NP AZ 11.77 11.69 11.71 -0.08 -0.06 Mazatzal Wilderness AZ 14.38 14.26 14.28 -0.13 -0.10 Mount Baldy Wilderness AZ 12.26 12.09 12.14 -0.17 -0.13 Petrified Forest NP AZ 11.99 11.76 11.73 -0.24 -0.26 Pine Mountain Wilderness AZ 14.44 14.31 14.33 -0.13 -0.10 Saguaro Wilderness AZ 12.33 12.26 12.29 -0.07 -0.03 Sierra Ancha Wilderness AZ 14.40 14.26 14.29 -0.14 -0.11 Superstition Wilderness AZ 14.03 13.93 13.95 -0.10 -0.07 Sycamore Canyon Wilderness AZ 12.11 12.01 12.04 -0.10 -0.07 Agua Tibia Wilderness CA 22.44 22.44 22.45 0.00 0.01

Caribou Wilderness CA 12.46 12.45 12.45 -0.01 -0.01 Cucamonga Wilderness CA 21.17 21.17 21.20 0.00 0.03 Desolation Wilderness CA 22.03 22.03 22.04 0.00 0.00 Dome Land Wilderness CA 21.08 21.06 21.07 -0.02 0.00

Emigrant Wilderness CA 18.54 18.53 18.54 0.00 0.00 Hoover Wilderness CA 18.56 18.56 18.56 0.00 0.00 John Muir Wilderness CA 22.24 22.23 22.24 -0.01 0.00 Joshua Tree NP CA 20.76 20.76 20.78 0.00 0.01 Kaiser Wilderness CA 22.12 22.12 22.13 0.00 0.01 Kings Canyon NP CA 21.99 21.98 21.99 -0.01 0.00 Lava Beds Wilderness CA 13.09 13.08 13.08 -0.01 -0.01

Lassen Volcanic NP CA 12.45 12.43 12.44 -0.01 -0.01 Marble Mountain Wilderness CA 15.71 15.69 15.70 -0.02 -0.01 Minarets Ansel Adams WA CA 18.53 18.53 18.54 -0.01 0.00

Mokelumne Wilderness CA 21.96 21.96 21.96 0.00 0.00 Pinnacles NM CA 16.19 16.17 16.20 -0.02 0.01 Point Reyes NS CA 18.54 18.49 18.51 -0.04 -0.03

Redwood NP CA 17.23 17.21 17.22 -0.02 -0.01 San Gabriel Wilderness CA 21.42 21.43 21.45 0.00 0.02 San Gorgonio Wilderness CA 21.90 21.90 21.91 0.00 0.01

San Jacinto Wilderness CA 21.85 21.85 21.87 0.00 0.02 San Rafael Wilderness CA 16.57 16.56 16.58 -0.01 0.01 Sequoia NP CA 21.65 21.63 21.64 -0.02 -0.01Final §309 Regional TSD Page 149 of 250 December 15, 2003 Class I Area (Worst 20%) State 2018 Base Case (dV) 2018 SO 2 Annex (dV) 2018 BART w/Uncert. (dV) Difference Annex –

Base (dV) Difference BART–

Base (dV)
South Warner Wilderness CA 12.43 12.41 12.41 -0.02 -0.01 Thousand Lakes Wilderness CA 12.61 12.60 12.61 -0.01 -0.01 Ventana Wilderness CA 16.98 16.95 16.96 -0.03 -0.02 Yolla Bolly Middle Eel WA CA 13.31 13.32 13.32 0.01 0.01 Yosemite NP CA 18.41 18.40 18.41 -0.01 0.00 Black Canyon of Gunnison NP CO 10.95 10.79 10.85 -0.16 -0.10 Eagles Nest Wilderness CO 12.79 12.57 12.57 -0.21 -0.21

Flat Tops Wilderness CO 10.89 10.74 10.79 -0.15 -0.10 Great Sand Dunes NM CO 11.97 11.72 11.75 -0.24 -0.21 La Garita Wilderness CO 11.80 11.61 11.66 -0.19 -0.14 Maroon Bells-Snowmass WA CO 11.23 11.04 11.10 -0.19 -0.13 Mesa Verde NP CO 12.04 11.85 11.92 -0.18 -0.12 Mount Zirkel Wilderness CO 11.74 11.55 11.60 -0.19 -0.14 Rawah Wilderness CO 13.17 12.99 13.05 -0.18 -0.12 Rocky Mountain NP CO 13.16 12.99 13.05 -0.17 -0.11 West Elk Wilderness CO 11.12 10.90 10.96 -0.22 -0.16 Weminuche Wilderness CO 11.01 10.80 10.87 -0.20 -0.14 Craters of The Moon Wilderness ID 14.71 14.66 14.67 -0.04 -0.04 Hells Canyon Wilderness ID 14.94 14.85 14.85 -0.09 -0.08

Sawtooth Wilderness ID 14.45 14.41 14.41 -0.04 -0.04 Selway-Bitterroot Wilderness ID 15.13 15.07 15.08 -0.05 -0.05 Isle Royale NP MI 26.90 26.52 26.53 -0.38 -0.37 Boundary Waters Canoe Area MN 19.95 19.78 19.79 -0.17 -0.16 Voyageurs NP MN 19.30 19.10 19.10 -0.20 -0.20 Hercules-Glades Wilderness MO 26.87 26.14 26.15 -0.73 -0.73 Mingo Wilderness MO 26.92 26.33 26.33 -0.59 -0.59 Anaconda-Pintler Wilderness MT 17.59 17.47 17.48 -0.12 -0.11

Bob Marshall Wilderness MT 17.81 17.73 17.74 -0.09 -0.07 Cabinet Mountains Wilderness MT 17.88 17.78 17.79 -0.11 -0.10

Gates of the Mountain WA MT 17.04 16.92 16.94 -0.11 -0.10 Glacier NP MT 18.55 18.40 18.41 -0.15 -0.15 Medicine Lake Wilderness MT 18.30 18.11 18.11 -0.20 -0.19 Mission Mountain Wilderness MT 17.66 17.58 17.59 -0.08 -0.07 Red Rock Lakes Wilderness MT 14.90 14.83 14.83 -0.07 -0.06 Scapegoat Wilderness MT 17.35 17.26 17.27 -0.09 -0.08 UL Bend Wilderness MT 17.55 17.22 17.25 -0.32 -0.30 Lostwood Wilderness ND 18.40 18.22 18.22 -0.18 -0.18 Theodore Roosevelt NP ND 18.06 17.82 17.84 -0.23 -0.22 Bandelier NM NM 12.34 12.14 12.18 -0.19 -0.16 Bosque del Apache Wilderness NM 14.46 14.20 14.28 -0.26 -0.18 Carlsbad Caverns NP NM 14.57 14.40 14.42 -0.18 -0.16 Gila Wilderness NM 15.02 14.81 14.85 -0.20 -0.16 Pecos Wilderness NM 12.40 12.21 12.24 -0.20 -0.16Final §309 Regional TSD Page 150 of 250 December 15, 2003 Class I Area (Worst 20%) State 2018 Base Case (dV) 2018 SO 2 Annex (dV) 2018 BART w/Uncert. (dV) Difference Annex –

Base (dV) Difference BART–

Base (dV)
Salt Creek Wilderness NM 14.72 14.54 14.59 -0.18 -0.13 San Pedro Parks Wilderness NM 12.32 12.11 12.16 -0.21 -0.16 White Mountain Wilderness NM 14.55 14.40 14.44 -0.15 -0.11

Wheeler Peak Wilderness NM 12.51 12.28 12.33 -0.24 -0.18 Jarbidge Wilderness NV 14.89 14.86 14.87 -0.03 -0.02 Wichita Mountains Wilderness OK 26.03 25.13 25.16 -0.90 -0.88 Crater Lake NP OR 15.39 15.37 15.38 -0.02 -0.02 Diamond Peak Wilderness OR 15.64 15.59 15.60 -0.05 -0.04

Eagle Cap Wilderness OR 14.93 14.81 14.82 -0.12 -0.11 Gearhart Mountain Wilderness OR 15.06 15.03 15.04 -0.03 -0.02 Kalmiopsis Wilderness OR 15.77 15.74 15.75 -0.03 -0.02 Mount Hood Wilderness OR 14.84 14.79 14.80 -0.05 -0.05 Mount Jefferson Wilderness OR 15.38 15.32 15.33 -0.06 -0.05 Mountain Lakes Wilderness OR 15.18 15.14 15.15 -0.03 -0.02 Mount Washington Wilderness OR 15.37 15.32 15.33 -0.04 -0.04 Strawberry Mountain Wilderness OR 14.60 14.52 14.53 -0.08 -0.07 Three Sisters Wilderness OR 14.88 14.86 14.86 -0.03 -0.02 Badlands NM SD 18.04 17.73 17.77 -0.31 -0.26 Wind Cave NP SD 17.79 17.51 17.56 -0.27 -0.23 Big Bend NP TX 18.94 18.90 18.90 -0.05 -0.04 Guadalupe Mountains NP TX 14.66 14.50 14.51 -0.16 -0.14 Arches NP UT 12.42 12.32 12.37 -0.10 -0.05 Bryce Canyon NP UT 12.26 12.23 12.24 -0.03 -0.02 Canyonlands NP UT 12.27 12.17 12.21 -0.09 -0.05 Capitol Reef NP UT 12.50 12.46 12.49 -0.03 -0.01 Zion NP UT 12.03 12.00 12.02 -0.03 -0.01 Alpine Lakes Wilderness WA 15.07 14.99 14.99 -0.08 -0.08 Glacier Peak Wilderness WA 15.12 15.04 15.05 -0.07 -0.07 Goat Rocks Wilderness WA 16.95 16.88 16.88 -0.07 -0.07 Mount Adams Wilderness WA 17.06 16.99 17.00 -0.07 -0.06 Mount Rainier NP WA 17.38 17.30 17.30 -0.08 -0.08 North Cascades NP WA 15.26 15.19 15.20 -0.07 -0.07 Olympic NP WA 15.72 15.69 15.69 -0.03 -0.03 Pasayten Wilderness WA 15.21 15.14 15.14 -0.07 -0.07 Bridger Wilderness WY 12.05 11.79 11.81 -0.27 -0.24 Fitzpatrick Wilderness WY 12.13 11.97 11.99 -0.16 -0.14 Grand Teton NP WY 14.74 14.66 14.67 -0.08 -0.07 North Absaroka Wilderness WY 14.87 14.78 14.79 -0.09 -0.08 Teton Wilderness WY 14.82 14.74 14.75 -0.08 -0.07 Washakie Wilderness WY 14.79 14.71 14.71 -0.08 -0.07 Yellowstone NP WY 14.92 14.85 14.86 -0.08 -0.07Final §309 Regional TSD Page 151 of 250 December 15, 2003 Table 4.1.2.2. Estimated visibility levels in deciviews (dV) at all western Class I areas in the WRAP modeling domain for the Best 20% days and the 2018 Base Case, 2018 SO 2 Annex Milestones and 2018 BART with Uncertainty emission scenarios and the changes in visibility resulting from the 2018 control scenarios, from the 2018 Base Case.

Class I Area (Best 20%) State 2018 Base Case (dV) 2018 SO 2 Annex (dV) 2018 BART w/Uncert. (dV) Difference Annex –

Base (dV) Difference BART–

Base (dV) Caney Creek Wilderness AR 12.40 12.09 12.10 -0.31 -0.30 Upper Buffalo Wilderness AR 12.26 11.96 11.98 -0.30 -0.28 Chiricahua NM AZ 5.77 5.76 5.74 -0.01 -0.03 Chiricahua Wilderness AZ 5.75 5.74 5.72 -0.01 -0.03

Galiuro Wilderness AZ 5.66 5.65 5.67 -0.01 0.01 Grand Canyon NP AZ 4.76 4.69 4.71 -0.07 -0.05 Mazatzal Wilderness AZ 7.26 7.21 7.21 -0.05 -0.05 Mount Baldy Wilderness AZ 5.46 5.42 5.42 -0.04 -0.04 Petrified Forest NP AZ 5.19 5.14 5.15 -0.04 -0.04 Pine Mountain Wilderness AZ 7.32 7.27 7.27 -0.05 -0.05 Saguaro Wilderness AZ 5.62 5.61 5.62 -0.01 0.00 Sierra Ancha Wilderness AZ 7.40 7.33 7.35 -0.07 -0.05 Superstition Wilderness AZ 7.18 7.12 7.15 -0.06 -0.03 Sycamore Canyon Wilderness AZ 4.86 4.81 4.82 -0.05 -0.03 Agua Tibia Wilderness CA 5.84 5.83 5.84 0.00 0.00

Caribou Wilderness CA 3.31 3.31 3.31 -0.01 -0.01 Cucamonga Wilderness CA 5.56 5.56 5.56 0.00 0.01 Desolation Wilderness CA 12.24 12.24 12.25 0.00 0.00 Dome Land Wilderness CA 7.62 7.62 7.63 0.00 0.01

Emigrant Wilderness CA 4.97 4.95 4.96 -0.02 -0.01 Hoover Wilderness CA 4.91 4.89 4.90 -0.02 -0.01 John Muir Wilderness CA 7.87 7.87 7.87 0.00 0.00 Joshua Tree NP CA 5.69 5.68 5.69 0.00 0.00 Kaiser Wilderness CA 7.23 7.23 7.23 0.00 0.00 Kings Canyon NP CA 7.82 7.82 7.83 0.00 0.00 Lava Beds Wilderness CA 3.43 3.42 3.43 0.00 0.00

Lassen Volcanic NP CA 3.30 3.30 3.30 -0.01 0.00 Marble Mountain Wilderness CA 4.15 4.05 4.05 -0.10 -0.10 Minarets Ansel Adams WA CA 4.88 4.86 4.87 -0.02 -0.01

Mokelumne Wilderness CA 12.10 12.09 12.10 0.00 0.00 Pinnacles NM CA 6.75 6.75 6.77 0.01 0.02 Point Reyes NS CA 7.59 7.59 7.59 0.00 0.00

Redwood NP CA 4.38 4.38 4.38 0.00 0.00 San Gabriel Wilderness CA 5.63 5.63 5.64 0.00 0.01 San Gorgonio Wilderness CA 5.76 5.76 5.76 0.00 0.00

San Jacinto Wilderness CA 5.24 5.24 5.24 0.00 0.00 San Rafael Wilderness CA 6.92 6.92 6.94 0.00 0.03 Sequoia NP CA 7.68 7.68 7.69 0.00 0.01Final §309 Regional TSD Page 152 of 250 December 15, 2003 Class I Area (Best 20%) State 2018 Base Case (dV) 2018 SO 2 Annex (dV) 2018 BART w/Uncert. (dV) Difference Annex –

Base (dV) Difference BART–

Base (dV)
South Warner Wilderness CA 3.29 3.28 3.28 -0.01 -0.01 Thousand Lakes Wilderness CA 3.30 3.30 3.30 0.00 0.00 Ventana Wilderness CA 6.76 6.76 6.76 0.00 0.00 Yolla Bolly Middle Eel WA CA 3.29 3.29 3.29 0.00 0.00 Yosemite NP CA 4.97 4.95 4.96 -0.02 -0.01 Black Canyon of Gunnison NP CO 3.87 3.83 3.85 -0.05 -0.02 Eagles Nest Wilderness CO 3.85 3.80 3.82 -0.05 -0.03

Flat Tops Wilderness CO 3.89 3.83 3.85 -0.06 -0.03 Great Sand Dunes NM CO 4.38 4.31 4.32 -0.07 -0.06 La Garita Wilderness CO 4.28 4.23 4.24 -0.05 -0.04 Maroon Bells-Snowmass WA CO 3.89 3.83 3.85 -0.06 -0.04 Mesa Verde NP CO 4.20 4.16 4.18 -0.03 -0.01 Mount Zirkel Wilderness CO 4.76 4.69 4.72 -0.08 -0.04 Rawah Wilderness CO 4.01 3.96 3.98 -0.04 -0.03 Rocky Mountain NP CO 3.84 3.80 3.82 -0.04 -0.02 West Elk Wilderness CO 3.88 3.84 3.86 -0.05 -0.03 Weminuche Wilderness CO 3.93 3.89 3.91 -0.05 -0.03 Craters of The Moon Wilderness ID 5.29 5.25 5.26 -0.04 -0.03 Hells Canyon Wilderness ID 5.73 5.62 5.63 -0.11 -0.10

Sawtooth Wilderness ID 3.58 3.56 3.57 -0.01 -0.01 Selway-Bitterroot Wilderness ID 5.63 5.51 5.52 -0.12 -0.11 Isle Royale NP MI 10.39 10.39 10.39 0.00 0.00 Boundary Waters Canoe Area MN 7.26 7.24 7.24 -0.02 -0.02 Voyageurs NP MN 6.70 6.69 6.69 -0.01 -0.01 Hercules-Glades Wilderness MO 12.20 11.92 11.94 -0.28 -0.27 Mingo Wilderness MO 12.17 11.93 11.94 -0.24 -0.23 Anaconda-Pintler Wilderness MT 7.00 6.90 6.91 -0.10 -0.09

Bob Marshall Wilderness MT 7.14 7.09 7.09 -0.05 -0.05 Cabinet Mountains Wilderness MT 7.20 7.15 7.15 -0.05 -0.04

Gates of the Mountain WA MT 6.74 6.68 6.69 -0.06 -0.05 Glacier NP MT 7.65 7.56 7.57 -0.09 -0.08 Medicine Lake Wilderness MT 7.36 7.29 7.30 -0.06 -0.06 Mission Mountain Wilderness MT 7.02 6.96 6.96 -0.06 -0.05 Red Rock Lakes Wilderness MT 5.25 5.19 5.20 -0.06 -0.05 Scapegoat Wilderness MT 6.94 6.88 6.89 -0.05 -0.05 UL Bend Wilderness MT 7.05 7.00 7.00 -0.05 -0.05 Lostwood Wilderness ND 7.48 7.38 7.38 -0.10 -0.10 Theodore Roosevelt NP ND 7.24 7.16 7.16 -0.08 -0.07 Bandelier NM NM 5.49 5.44 5.45 -0.05 -0.04 Bosque del Apache Wilderness NM 6.83 6.71 6.75 -0.12 -0.08 Carlsbad Caverns NP NM 6.84 6.74 6.76 -0.10 -0.08 Gila Wilderness NM 7.45 7.39 7.42 -0.06 -0.04 Pecos Wilderness NM 4.82 4.78 4.79 -0.04 -0.03Final §309 Regional TSD Page 153 of 250 December 15, 2003 Class I Area (Best 20%) State 2018 Base Case (dV) 2018 SO 2 Annex (dV) 2018 BART w/Uncert. (dV) Difference Annex –

Base (dV) Difference BART–

Base (dV)
Salt Creek Wilderness NM 6.78 6.67 6.69 -0.11 -0.08 San Pedro Parks Wilderness NM 5.55 5.49 5.52 -0.06 -0.04 White Mountain Wilderness NM 6.85 6.77 6.80 -0.09 -0.05

Wheeler Peak Wilderness NM 5.61 5.54 5.57 -0.06 -0.04 Jarbidge Wilderness NV 3.62 3.62 3.62 -0.01 0.00 Wichita Mountains Wilderness OK 11.79 11.58 11.61 -0.21 -0.18 Crater Lake NP OR 4.69 4.69 4.69 0.00 0.00 Diamond Peak Wilderness OR 4.39 4.39 4.39 0.00 0.00

Eagle Cap Wilderness OR 5.73 5.64 5.64 -0.09 -0.08 Gearhart Mountain Wilderness OR 4.20 4.20 4.20 0.00 0.00 Kalmiopsis Wilderness OR 4.50 4.50 4.50 -0.01 -0.01 Mount Hood Wilderness OR 4.74 4.73 4.74 0.00 0.00 Mount Jefferson Wilderness OR 4.57 4.57 4.57 0.00 0.00 Mountain Lakes Wilderness OR 4.29 4.29 4.30 0.00 0.00 Mount Washington Wilderness OR 4.32 4.32 4.32 0.00 0.00 Strawberry Mountain Wilderness OR 3.97 3.97 3.97 -0.01 0.00 Three Sisters Wilderness OR 4.33 4.32 4.33 0.00 0.00 Badlands NM SD 7.35 7.25 7.26 -0.10 -0.09 Wind Cave NP SD 7.29 7.18 7.19 -0.10 -0.10 Big Bend NP TX 7.75 7.69 7.71 -0.05 -0.04 Guadalupe Mountains NP TX 6.89 6.79 6.80 -0.10 -0.09 Arches NP UT 4.85 4.77 4.80 -0.08 -0.05 Bryce Canyon NP UT 3.24 3.22 3.23 -0.01 -0.01 Canyonlands NP UT 4.78 4.71 4.74 -0.06 -0.04 Capitol Reef NP UT 4.84 4.81 4.82 -0.03 -0.02 Zion NP UT 3.80 3.78 3.79 -0.02 -0.01 Alpine Lakes Wilderness WA 6.03 5.99 6.00 -0.03 -0.03 Glacier Peak Wilderness WA 6.04 5.97 5.97 -0.07 -0.07 Goat Rocks Wilderness WA 5.75 5.73 5.73 -0.03 -0.02 Mount Adams Wilderness WA 5.90 5.87 5.88 -0.03 -0.02 Mount Rainier NP WA 5.61 5.59 5.60 -0.02 -0.01 North Cascades NP WA 5.90 5.85 5.85 -0.05 -0.05 Olympic NP WA 6.34 6.35 6.34 0.01 0.00 Pasayten Wilderness WA 5.82 5.78 5.78 -0.04 -0.04 Bridger Wilderness WY 3.23 3.17 3.18 -0.05 -0.05 Fitzpatrick Wilderness WY 3.31 3.28 3.30 -0.02 -0.01 Grand Teton NP WY 4.79 4.76 4.77 -0.03 -0.02 North Absaroka Wilderness WY 5.08 5.03 5.04 -0.05 -0.04 Teton Wilderness WY 5.12 5.09 5.11 -0.03 -0.02 Washakie Wilderness WY 4.74 4.69 4.70 -0.05 -0.04 Yellowstone NP WY 5.16 5.09 5.10 -0.07 -0.064.2. Stationary Source NO x and PM sensitivity modeling

The modeling performed for the draft Market Trading Forum report entitled Review of Stationary Source NO x and PM Emissions in the WRAP Region: An Initial Assessment of Emissions, Controls, and Air Quality Impacts (WRAP, 2003) is best described as a “sensitivity analysis”. The intent is to get a preliminary assessment of the general atmospheric response to changes in NO x and PM emissions from stationary sources. A secondary objective is to “practice” this type of modeling to get a better understanding of the key technical issues and to identify the most effective ways at evaluating and displaying model results. The results presented here are the best available predictions at this time, but forthcoming improvements to the modeling system may affect the results in ways that alter the policy implications. For this reason, results are discussed in a fairly broad and qualitative manner – i.e., spatial patterns and relative changes. As the modeling system improves and specific strategies are contemplated, additional emission scenarios will be designed and modeled.

The WRAP’s regional-scale air quality modeling system used to support other aspects of the §309 plans was also used to provide information for this report. A description of the modeling system, in addition to model performance statistics, input files, and detailed model results, is available at:

http://pah.cert.ucr.edu/rmc

4.2.1. Emission Scenarios

Three emission scenarios were simulated:

 A 50 percent decrease in NO x emissions from plants with NO x emissions > 100 tons per year (tpy),

 A 50 percent decrease in PM 10 emissions from plants with PM 10 emission > 100 tpy, and

 A simultaneous 25 percent increase in NO x and PM 10 emissions from all stationary sources.

The first two scenarios are meant to address the §309 requirement to “assess emissions control strategies for stationary source NO x and PM, and the degree of visibility improvement that would result from such strategies”. As discussed in Section VI of the is report, many commercially-available technologies (and various combinations of such technologies) are capable of achieving a 50% or greater NO x emission reduction without having to switch fuels. Hence, the 50% reduction, although intended primarily to gauge the general atmospheric response to NO x reductions, is not an unreasonable level of control to assume for this exercise in terms of technical feasibility. Again with technical (and administrative) feasibility in mind, emission reductions were limited to plants with emissions greater than 100 tpy, similar to the approach in the Annex. The third scenario is meant to address the rule’s requirement to “evaluate and discuss the need to establish emission milestones for NO x and PM to avoid any net increase in these pollutants from stationary sources within the transport region.” Hence, a 25 percent increase from all stationary sources was assumed to simulate potential growth in the

Final §309 Regional TSD Page 154 of 250 December 15, 2003economy and/or disproportionate growth in high- emitting sectors such as energy development, fossil-fueled electricity generation, and mineral processing.

For reasons implied in the rule, the emission changes in the scenarios described above were limited to the nine-state GCVTR 1 . Also, the emission changes were applied to the 2018 inventory, which includes reductions expected from full implementation of the Annex. This provides a basis for comparing results to other strategies being modeled by the WRAP.

4.2.2. Model Performance and Future Improvements

Nitrate concentrations are poorly predicted by the current modeling system, especially in the winter. For this reason, results for nitrate (and all other species) for the NO x and PM sensitivity runs are only presented for the three-month period of July-September.

Several aspects of the modeling system are being improved and/or evaluated, which should improve confidence in future model predictions, both in the summer and winter. These improvements and evaluations involve the chemical mechanisms, the ammonia inventory, an offroad inventory based on NONROAD2002, a more r obust meteorological database (2002 versus 1996), enhanced grid resolution (12 km versus 36 km), plume-in-grid capabilities, the introduction of an inventory for wind-blown dust emissions, and better temporal allocation and chemical speciation of point and area source em issions. A source apportionment mechanism is also expected to be included with the model.

4.2.3. Modeling Results

As stated above, results are presented in a fairly broad and qualitative manner – i.e., spatial patterns and relative changes. Relative (percent) changes are of particular interest because their errors are believed to be smaller than those of the absolute concentrations. It is not clear how the seasonal limitation of this analysis (July-September) may affect the relative changes, but it is likely to reduce them to some extent. First, nitrate concentrations tend to be lower in the summer than in the winter, especially in areas where nitrate concentrations are highest and the potential for change the greatest. Second, results are averaged over a full three-month period. Typically, visibility effects are measured by averaging conditions over the worst 20 percent of the days observed per year at an ambient monitoring site, which is approximately 22 days. But in this analysis, because it is limited to the Ju ly-September timeframe, the results are averaged over 92 consecutive days and do not represent a measure of the worst conditions, again when the potential for change is the greatest. Thus, while there are many uncertainties surrounding the model’s nitrate predictions, the limitation of this study to July-September will tend to limit the apparent impacts from the NO x (and to some extent) PM 10 emission changes.

On a ton-per-ton basis, reductions in stationary source PM 10 emissions appear to yield greater regional haze benefits than reductions in NO x emissions. For instance, when stationary source PM 10 emissions are reduced by 98,000 tpy (a 50 percent reduction from GCVTR facilities

1 - In 1996, stationary sources in the Grand Canyon Visibility Transport Region (GCVTR) emitted about 75 percent and 83 percent of the NO x and PM 10 emissions, respectively, in the 13-state WRAP region.

Final §309 Regional TSD Page 155 of 250 December 15, 2003> 100 tpy), the average summer-time visibility improvement across all Class I areas in the GCVTR (in Mm -1 ) is about 0.4 percent. When stationary source NO x emissions are reduced by 412,000 tpy (a 50 percent reduction from GCVTR facilities > 100 tpy), the visibility improvement is only somewhat greater, at 0.5 percent. 1 Hence, on a purely technical basis (without considering existing controls, costs, or other implementation issues), reductions in PM emissions might be more effective at improving regional haze than reductions in NO x emissions.

Nevertheless, the 50 percent NO x reduction scenario tends to produce slightly greater regional haze benefits than the 50 percent PM 10 reduction scenario. This is because stationary sources comprise 33 percent of the total NO x inventory but only 7 percent of the total PM 10 inventory. So even though much of the NO x is never converted to the particulate phase, the sheer volume of NO x emission reductions relative to PM 10 reductions and the fact that nitrate scatters light more efficiently than primary PM make the NO x reduction scenario more meaningful in terms of regional haze benefits than the PM 10 reduction scenario. The fact that stationary source NOx emissions are not as well controlled as stationary source PM 10 emissions in the West actually lends some relevance to the outcome that NO x emissions are altered more in the sensitivity analysis than PM 10 emissions.

For the three-month summer period examined in this analysis, NO x changes have very little effect on aerosol concentrations beyond changes in n itrate. Other species that could be indirectly affected – e.g., ozone concentrations and subsequent oxidation of SO 2 and organic gases into the particulate phase – do not appear influenced by the levels of NO x reductions (16 percent of the total inventory) assumed in this analysis. This finding may change after implementing all the model improvements noted above, but since nitrate currently appears as the largest responder to NO x changes, and given the information above regarding the NO x and PM scenarios, the maps, tables, and discussion below place somewhat more emphasis on nitrate and the results of the 50 percent NO x reduction scenario than on other species and scenarios.

Figures 4.2.3.1 and 4.2.3.2 show the model-predicted 2018 base case (Annex included) surfacelayer concentrations of ammonium nitrate (NH 4 NO 3 ) and PM 10, respectively, averaged over the three- month period of July-September. The values in these maps should not be construed as the expected ammonium nitrate and PM 10 concentrations in 2018, which are determined by scaling the ambient monitoring data by the relative changes predicted by the model. Rather, these maps are intended to provide a sense of the spatial variability and span of concentrations, which are useful for interpreting the following maps of relative (percent) changes – e.g., a high percentage change in a low-concentration area may be less meaningful than a moderate percentage change in a high concentration area.

Figures 4.2.3.3 and 4.2.3.4 show the absolute and percentage change, respectively, in NH 4 NO 3 concentrations from a 50 percent reduction in stationary source NO x emissions from facilities in the GCVTR greater than 100 tpy. The largest absolute changes occur in southern CA, where concentrations in Class I areas are predicted to decrease by 0.15 to 0.25 ug/m 3 . A second area of reductions is predicted in the central-east Rocky Mountains, especially in north-central CO.

1 - In some Class I areas, the visibility improvement can be two to five percent on some days.

Final §309 Regional TSD Page 156 of 250 December 15, 2003Although the reductions are not as large as in southern CA (0.04 to 0.11 ug/m 3 ), they are larger than average across the domain and exhibit the largest percentage reduction (10 to 20 percent).

It is interesting to compare these results with those simulating the effects of the SO 2 backstop emissions trading program, or Annex. In the case of the Annex, an SO 2 emission reduction of 15 percent (132,000 tons) in the GCVTR produced a sulfate reduction of 4 percent averaged across all Class I areas in the GCVTR on the 20% worst modeled days. In the case of the NO x sensitivity run, a NO x emission reduction of 15 percent (412,000 tons) in the GCVTR produced anitrate reduction of 5 percent averaged across all Class I areas in the GCVTR on the JulySeptember modeled days. The nitrate reduction does not produce as much visibility benefit at most Class I areas because its concentrations are much smaller, but the response of nitrate to NO x reductions is similar in proportion to the response of sulfate to SO 2 reductions.

Figures 4.2.3.5 and 4.2.3.6 show the absolute and percentage change, respectively, in NH 4 NO 3 concentrations from a 25 percent increase in stationary source NO x and PM 10 emissions from all stationary sources in the GCVTR. The spatial pattern of changes is very similar to that in the 50 percent NO x reduction scenario, although the magnitude of changes are about half. Again, it is interesting to see some proportionality in the modeling results – i.e., an emission change that is half as large produces aerosol changes that are about half as large. The percent increase in NH 4 NO 3 concentrations and visibility impairment (in Mm -1 ) in this scenario is 2 percent and0.5 percent, respectively, when averaged over all Class I areas in the GCVTR for JulySeptember.

Figures 4.2.3.7 and 4.2.3.8 show the absolute and percentage change, respectively, in PM 10 concentrations from a 50 percent reduction in stationary source PM 10 emissions from facilities in the GCVTR greater than 100 tpy. Maximum reductions in PM 10 are about 0.1 to 0.5 ug/m 3 , or about 4 to 8 percent. Compared to the NO x reduction scenario, reductions in ambient PM 10 are more dispersed, with a greater number of local maximums. This may reflect the fact that there are a fewer number of large PM 10 sources than large NO x sources and that much of the PM 10 emissions are coarse particles, with shorter transport distances.

Figures 4.2.3.9 and 4.2.3.10 show the absolute and percentage change, respectively, in PM 10 concentrations from a 25 percent increase in stationary source NO x and PM 10 emissions from all stationary sources in the GCVTR. The spatial pattern of changes reflects where both relatively large NH 4 NO 3 changes (southern CA and central-east Rockies) and PM 10 changes (additional areas) are predicted. The largest PM 10 increases are about 0.1 to 0.3 ug/m 3 , or 2 to 3 percent. Less than half of this is NH 4 NO 3
. Table 4.2.3.1 shows the predicted change in light extinction and NH 4 NO 3 at each Class I area in the GCVTR averaged over the July-September period as a result of reducing NO x emissions by 50 percent from stationary sources with emissi ons greater than 100 tpy in the GCVTR. As shown in the maps, the greatest impacts occur in southern CA, followed by areas in CO. The average improvement in light extinction in these areas is about 0.3 to 1.5 Mm -1 (1 to 2.5 percent). The average improvement in NH 4 NO 3 is about 0.05 to 0.25 ug/m 3 (3 to 20 percent).

Final §309 Regional TSD Page 157 of 250 December 15, 2003Figure 4.2.3.1. Base Case Ammonium Nitrate Concentrations ( μ g/m 3 ).

Figure 4.2.3.2. Base Case PM 10 Concentrations ( μ g/m 3 ).

Final §309 Regional TSD Page 158 of 250 December 15, 2003Figure 4.2.3.3. Change in Ammonium Nitrate C oncentrations Resulting from a 50% Reduction in Stationary Source NO x Emissions > 100 tpy.

Figure 4.2.3.4. Relative Change in Ammonium Nitrate Concentrations Resulting from a 50% Reduction in Stationary Source NO x Emissions > 100 tpy.

Final §309 Regional TSD Page 159 of 250 December 15, 2003Figure 4.2.3.5. Change in Ammonium Nitrate Concentrations Resulting from a 25% Increase in Stationary Source NO x and PM 10 Emissions.

Figure 4.2.3.6. Relative Change in Ammonium Nitrate Concentrations Resulting from a 25% Increase in Stationary Source NOx and PM 10 Emissions.

Final §309 Regional TSD Page 160 of 250 December 15, 2003Figure 4.2.3.7. Change in PM 10 Concentrations Resulting from a 50% Reduction in Stationary Source PM 10 Emissions > 100 tpy.

Figure 4.2.3.8. Relative Change in PM 10 Concentrations Resulting from a 50% Reduction in Stationary Source PM 10 Emissions > 100 tpy.

Final §309 Regional TSD Page 161 of 250 December 15, 2003Figure 4.2.3.9. Change in PM 10 Concentrations Resulting from a 25% Increase in Stationary Source NO x and PM 10 Emissions.

Figure 4.2.3.10. Relative Change in PM 10 Concentrations Resulting from a 25% Increase in

Stationary Source NO x and PM 10 Emissions.

Final §309 Regional TSD Page 162 of 250 December 15, 2003Final §309 Regional TSD Page 163 of 250 December 15, 2003 Table 4.2.3.1. Light Extinction and Ammoni um Nitrate Changes Resulting from a 50% Reduction in Stationary Source NO x Emissions > 100 tpy, Sorted by Average Light Extinction.

Li g ht Extinction NH 4 NO 3

State GCVTR Class I Area Mm -1 Change % Change

 μ g/m 3 Change % Change

 CA Cucamon g a Wilderness -1.59 -1.37 -0.25 -3.25 CA San Jacinto Wilderness -1.13 -1.18 -0.19 -2.97 CA San Gabriel Wilderness -0.83 -0.82 -0.13 -3.06 CA Agua Tibia Wilderness -0.81 -1.05 -0.12 -2.77 CA San Gorgonio Wilderness -0.80 -0.93 -0.16 -2.65 CO Rawah Wilderness -0.69 -2.41 -0.11 -16.84 CO Mount Zirkel Wilderness -0.61 -2.28 -0.09 -20.86 CO Rocky Mountain NP -0.57 -1.68 -0.09 -14.14 CA Joshua Tree NP -0.47 -0.77 -0.13 -3.69 CO Eagles Nest Wilderness -0.45 -1.41 -0.07 -11.97 CO Great Sand Dunes NM -0.43 -1.57 -0.06 -13.87 NM White Mountain Wild. -0.36 -1.11 -0.05 -10.51 CO Flat Tops Wilderness -0.34 -1.28 -0.05 -13.82 CO La Garita Wilderness -0.34 -1.27 -0.05 -12.15 CO West Elk Wilderness -0.33 -1.19 -0.05 -12.09 CO Black Canyon of Gunnison -0.31 -0.97 -0.04 -14.83 CO Weminuche Wilderness -0.29 -1.14 -0.04 -13.02 CO Maroon Bells-Snowmass -0.29 -1.00 -0.04 -10.62 CA Dome Land Wilderness -0.27 -0.46 -0.04 -4.48 CA Pinnacles NM -0.26 -0.86 -0.04 -5.93 NM Wheeler Peak Wilderness -0.24 -0.91 -0.03 -8.94 AZ Mount Baldy Wilderness -0.22 -0.64 -0.03 -6.25 NM Salt Creek Wilderness -0.22 -0.71 -0.02 -7.75 AZ Petrified Forest NP -0.21 -0.73 -0.01 -6.88 WY Bridger Wilderness -0.20 -0.77 -0.03 -7.51 CA Hoover Wilderness -0.20 -0.19 -0.04 -2.60 CA Emigrant Wilderness -0.19 -0.25 -0.03 -3.08 NM Gila Wilderness -0.18 -0.34 -0.02 -3.81 CA Minarets -0.18 -0.23 -0.03 -2.71 OR Mount Jefferson Wild. -0.17 -0.28 -0.02 -2.59 NM San Pedro Parks Wild. -0.17 -0.64 -0.02 -10.43 NM Bandelier NM -0.17 -0.58 -0.02 -7.42 AZ Superstition Wilderness -0.16 -0.40 -0.02 -2.04 OR Mount Washington Wild. -0.16 -0.30 -0.02 -2.55 OR Mount Hood Wilderness -0.14 -0.22 -0.03 -1.83 CA Kaiser Wilderness -0.14 -0.19 -0.02 -2.63 CA Kings Canyon NP -0.14 -0.22 -0.02 -2.83 CA John Muir Wilderness -0.14 -0.23 -0.02 -2.69 CA San Rafael Wilderness -0.14 -0.32 -0.01 -5.40 AZ Sierra Ancha Wilderness -0.13 -0.35 -0.01 -1.76 CA Sequoia NP -0.13 -0.24 -0.02 -4.56 CA Yosemite NP -0.13 -0.17 -0.02 -2.63 UT Arches NP -0.13 -0.51 -0.01 -14.82Final §309 Regional TSD Page 164 of 250 December 15, 2003 Li g ht Extinction NH 4 NO 3

State GCVTR Class I Area Mm -1 Change % Change

 μ g/m 3 Change % Change

 N M Pecos Wilderness -0.12 -0.44 -0.03 -7.29 WY Fitzpatrick Wilderness -0.12 -0.46 -0.02 -4.83 NM Bosque del Apache Wild. -0.12 -0.44 -0.01 -8.65 OR Kalmiopsis Wilderness -0.11 -0.34 -0.01 -3.05 OR Eagle Cap Wilderness -0.11 -0.31 -0.02 -4.29 OR Three Sisters Wilderness -0.11 -0.24 -0.02 -2.55 AZ Grand Canyon NP -0.11 -0.40 -0.01 -7.36 UT Capitol Reef NP -0.11 -0.45 -0.01 -8.21 WY Grand Teton NP -0.11 -0.36 -0.02 -3.47 WY Teton Wilderness -0.10 -0.36 -0.02 -3.56 OR Crater Lake NP -0.10 -0.21 -0.01 -2.09
ID Hells Canyon Wilderness -0.10 -0.13 -0.02 -3.87 OR Strawberry Mountain Wild. -0.10 -0.15 -0.01 -2.89 AZ Sycamore Canyon Wild. -0.10 -0.32 -0.01 -5.25 CA Marble Mountain Wild. -0.10 -0.23 -0.01 -2.57 AZ Chiricahua NM -0.10 -0.36 0.00 -6.65 AZ Chiricahua Wilderness -0.10 -0.36 0.00 -6.65 AZ Galiuro Wilderness -0.10 -0.30 -0.01 -4.30 UT Canyonlands NP -0.09 -0.42 -0.01 -10.61 OR Diamond Peak Wild. -0.09 -0.18 -0.01 -2.20 AZ Saguaro Wilderness -0.09 -0.28 -0.01 -6.84 UT Bryce Canyon NP -0.08 -0.32 -0.01 -6.14 AZ Pine Mountain Wild. -0.08 -0.24 -0.01 -2.82 AZ Mazatzal Wilderness -0.08 -0.23 -0.01 -2.82 NM Carlsbad Caverns NP -0.08 -0.26 -0.01 -4.03 OR Mountain Lakes Wild. -0.07 -0.18 -0.01 -2.43 UT Zion NP -0.07 -0.21 -0.01 -7.22 CO Mesa Verde NP -0.07 -0.21 -0.03 -17.68 CA Lava Beds Wilderness -0.06 -0.15 -0.01 -2.09 WY Yellowstone NP -0.06 -0.20 -0.01 -2.50 CA South Warner Wilderness -0.06 -0.19 -0.01 -3.77
ID Selway-Bitterroot Wild. -0.05 -0.12 -0.01 -2.19 WY North Absaroka Wild. -0.05 -0.19 -0.01 -2.43 WY Washakie Wilderness -0.05 -0.19 -0.01 -2.43 CA Point Reyes NS -0.05 -0.15 0.00 -2.80
ID Craters of The Moon Wild. -0.04 -0.14 -0.01 -3.89 OR Gearhart Mountain Wild. -0.04 -0.13 0.00 -2.24 CA Caribou Wilderness -0.04 -0.11 0.00 -3.38 CA Thousand Lakes Wild. -0.03 -0.09 0.00 -2.30 CA Lassen Volcanic NP -0.03 -0.07 0.00 -2.28 CA Yolla Bolly Middle Eel Wild. -0.03 -0.09 0.00 -1.13 NV Jarbidge Wilderness -0.03 -0.13 0.00 -4.49 CA Ventana Wilderness -0.02 -0.12 0.00 -5.24 CA Redwood NP -0.02 -0.06 0.00 -2.92
Avera g e -0.21 -0.51 -0.03 -5.794.3. Tracking pre-trigger stationary source SO 2 emissions using the WRAP EDMS

The SO 2 Annex program, as proposed by WRAP and adopted by EPA, requires the tracking of SO 2 emissions from eligible stationary sources within states or tribal reservations participating in §309, to determine if the regional SO 2 emissions cap has been exceeded. This is known as “pretrigger” tracking. Beginning with the 2003 calendar year and continuing through 2018, each state and tribe participating in the program will submit an annual SO 2 emissions report to the WRAP EDMS for the sources covered by the program. These annual reports will contain the following information:

• Identification and explanation for new/additional SO 2 sources which emissions are greater

than100 tpy that were not contained in the previous year’s emissions report.

• Explanation for sources shut down or removed from the previous year’s emissions report.

• Explanation for emissions variations at any covered source that exceeds +/- 20% from the

previous year.

• Identification and explanation of new em issions reporting methods at any source.

The Emissions Forum will compile these annual emissions reports for the program, using the EDMS, as submitted by the participating states and tribes, to provide a regional emission report for SO 2 . By December 31 of the year following the applicable compliance year, WRAP will prepare a regional emission report that will include the following information:

• Summary of regional SO 2 emissions (tpy).

• Identification of any paper emission increases and decreases that have occurred due to

changes in emission inventory techniques since the last revision of the regional haze implementation plan. The report will contain a running regional total, as well as supporting documentation identifying the specific changes that have occurred at individual sources.

• Average emissions for the last three (3) years (if applicable) for comparison to the

regional milestone.

• Regional milestone for the compliance period.

• Draft determination that the milestone has either been met, or has been exceeded thereby

triggering the backstop trading program.

The EDMS will have the capability to produce the following special reports in tabular and simple plots (i.e. bar graph and pie chart) formats and allow queries of the same information including presentation in GIS format, in addition to the standard reports.

• A summary report of the annual WRAP region emissions from the stationary sources

emitting more than 100 tpy of SO 2 in the base year for each state, tribe, and the entire region.

• A summary report of the new stationary sources emitting more than 100 tpy of SO 2 that

were not contained in the previous year’s inventory for each state, tribe, and the entire region.

Final §309 Regional TSD Page 165 of 250 December 15, 2003• A summary report of the stationary sources emitting more than 100 tpy of SO 2 that are retired compared to the previous year’s inventory for each state, tribe, and the entire region.

• A summary report of the regional average SO 2 emissions from stationary sources emitting

more than 100 tpy of SO 2 for the last three (3) years and comparison to the regional milestone for the compliance period.

• A summary report of the stationary sources emitting more than 100 tpy of SO 2 which

emissions exceed +/- 20% compared to the previous year’s inventory for each state, tribe, and the entire region.

• A summary report identifying all the stationary sources emitting more than 100 tpy of

SO 2 that choose to opt in the program for each state, tribe, and the entire region.

• A summary report identifying all the stationary sources emitting more than 100 tpy of

SO 2 that were not included in the base year for each state, tribe, and the entire region.

The EDMS to be developed is described in a draft technical report to the Emissions Forum: Needs Assessment for Evaluation and Design of an Emissions Data Reporting, Management, and Tracking System , (EA Engineering, Science, and Technology, June 26, 2003).

Final §309 Regional TSD Page 166 of 250 December 15, 2003

## Chapter 5 – Assessment of Mobile Sources

5.1. Mobile source emissions inventory requirements

Revisions to the requirements for analyzing a nd tracking mobile source emissions under §309 of the Regional Haze Rule were proposed in the Federal Register by EPA, using simultaneous proposals of a rule revision and a direct final rule (39842 Federal Register, Volume 68, No. 128, Thursday, July 3, 2003). The rule change will be effective September 2, 2003, unless adverse comments are received. The changes are a result of a WRAP Board recommendation. Following are the requirements of the revised rule, from the Federal Register.

PART 51—REQUIREMENTS FOR PREPARATION, ADOPTION, AND SUBMITTAL OF IMPLEMENTATION PLANS 1. The authority citation for part 51 continues to read as follows: Authority: 42 U.S.C. 7410, 7414, 7421, 7470–7479, 7492, 7601, and 7602.

Subpart P—Protection of Visibility 2. Section 51.309 is amended by revising paragraphs (b)(6) and (d)(5)(i), deleting paragraphs (d)(ii) and (d)(iii), and renumbering (d)(iv) to (d)(ii), to read as follows:

§ 51.309 Requirements related to the Grand Canyon Visibility Transport Commission.

(b)(6) Continuous decline in total mobile source emissions means that the projected level of emissions from mobile sources of each listed pollutant in 2008, 2013, and 2018, are less than the projected level of emissions from mobile sources of each listed pollutant for the previous period (i.e., 2008 less than 2003; 2013 less than 2008; and 2018 less than 2013).

(d)(5)(i) Statewide inventories of onroad and nonroad mobile source emissions of VOC, NO X , SO 2 , PM 2.5 , elemental carbon, and organic carbon for the years 2003, 2008, 2013, and 2018.

(A) The inventories must demonstrate a continuous decline in total mobile source emissions (onroad plus nonroad; tailpipe and evaporative) of VOC, NO X , PM 2.5 , elemental carbon, and organic carbon, evaluated separately. If the inventories show a continuous decline in total mobile source emissions of each of these pollutants over the period 2003–2018, no further action is required as part of this plan to address mobile source emissions of these pollutants. If the inventories do not show a continuous decline in mobile source emissions of one or more of these pollutants over the period 2003–2018, the plan submission must provide for an implementation plan revision by no later than December 31, 2008 cont aining any necessary long-term strategies to achieve a continuous decline in total mobile source emissions of the pollutant(s), to the extent practicable, considering economic and technological reasonableness and Federal preemption of vehicle standards and fuel standards under title II of the CAA.

(B) The plan submission must also provide for an implementation plan revision by no later than December 31, 2008 containing any long-term strate gies necessary to reduce emissions of SO 2 from nonroad mobile sources, consistent with the goal of reasonable progress. In assessing the need for such long-term strategies, the State may consider emissions reductions achieved or

Final §309 Regional TSD Page 167 of 250 December 15, 2003anticipated from any new Federal standards for sulfur in nonroad diesel fuel.

(ii) [text of (iv) retained same as before]

The original rule text for analyzing and tracking mobile source emissions under §309 of the Regional Haze Rule was as follows:

(5) Mobile sources. The plan submission must provide for:

(i) Statewide inventories of current annual emissions and projected future annual emissions of VOC, NO x , SO 2 , elemental carbon, organic carbon, and fine particles from mobile sources for the years 2003 to 2018. The future year inventories must include projections for the year 2005, or an alternative year that is determined by the State to represent the year during which mobile source emissions will be at their lowest levels within the State.

(ii) A determination whether mobile source emissions in any areas of the State contribute significantly to visibility impairment in any of the 16 Class I Areas, based on the statewide inventory of current and projected mobile source emissions.

(iii) For States with areas in which mobile source emissions are found to contribute significantly to visibility impairment in any of the 16 Class I areas:

(A) The establishment and documentation of a mobile source emissions budget for any such area, including provisions requiring the State to restrict the annual VOC, NO x , SO 2 , elemental and organic carbon, and/or fine particle mobile source emissions to their projected lowest levels, to implement measures to achieve the budget or cap, and to demonstrate compliance with the budget.

(B) An emission tracking system providing for reporting of annual mobile source emissions from the State in the periodic implementation plan revisions required by paragraph (d)(10) of this section. The emission tracking system must be sufficient to determine the States’ contribution toward the Commission’s objective of reducing em issions from mobile sources by 2005 or an alternate year that is determined by the State to represent the year during which mobile source emissions will be at their lowest levels within the State, and to ensure that mobile source emissions do not increase thereafter.

(iv) Interim reports to EPA and the public in years 2003, 2008, 2013, and 2018 on the implementation status of the regional and lo cal strategies recommended by the Commission Report to address mobile source emissions.

Technical analysis work on mobile source emissions and modeling for the visibility significance requirement was conducted by the WRAP Mobile Sources and Air Quality Modeling Forums. Emissions analyses are described in a WRAP technical memorandum: ( http://64.27.97.126/forums/ef/inventories/mobile/FinalMobile_Emissions_Memo_Nov26.doc ), and the visibility modeling results in another WRAP memorandum: ( http://64.27.97.126/forums/ef/inventories/mobile/FinalMobile_Sig_Mem_Nov4.doc ). After

Final §309 Regional TSD Page 168 of 250 December 15, 2003review by the WRAP Technical and Initiatives Oversight Committees, respectively, and the WRAP Board of Directors, a letter from the Board requesting that EPA modify the mobile source requirements through a rule change was sent ( http://64.27.97.126/forums/msf/documents/whitman5-6-03.pdf ). The rule change was proposed by EPA on July 3, 2003, and is cited at the beginning of this section ( http://64.27.97.126/forums/msf/documents/Proposed_Rule.pdf ).

5.2. Mobile source emissions inventory trends

The revisions to mobile source emissions portion of §309 of the Regional Haze Rule described above require the demonstration of a continuous decline in total mobile source emissions. As above, this means that the projected level of emissions from mobile sources of each listed pollutant (statewide inventories of on-road and non-road mobile source emissions of VOC, NO X , SO 2 , PM 2.5 , elemental carbon, and organic carbon for the years 2003, 2008, 2013, and 2018) in 2008, 2013, and 2018, are to be shown to be less than the projected level of emissions from mobile sources of each listed pollutant for the previous period (i.e., 2008 less than 2003; 2013 less than 2008; and 2018 less than 2013). Also reported are 1996 on-road mobile source emissions data. Estimates of on-road mobile source SO 2 emissions, based on already adopted federal and state rules, are reported for each of the 5 years. The trend data start with 1996; these on-road emissions were used in the air quality “base case” modeling discussed in Chapter 2.

5.2.1. On-road emissions trends

As discussed earlier, WRAP on-road emission inventories are available for five years: the 1996 base year, and projection years 2003, 2008, 2013, and 2018 [Pollack, 2003]. The projection year inventories include all promulgated on-road control programs, as listed in Chapter 1. Neither EPA nor CARB is currently developing any further on-road regulations for criteria or visibilityrelated pollutants. On-road mobile source emissions are a major contributor to the total anthropogenic NO x (35% in 1996, 14% in 2018) and VOC (38% in 1996, 15% in 2018) emissions; they contribute only negligibly to PM 2.5 and SO 2 emissions.

Table 5.2.1.1 shows the net emissions change from on-road sources by pollutant, in 2018. Elemental and organic carbon emissions are fixed fractions of the PM 2.5 emissions, and vary by gas and diesel engine distributions. These values will be published as this document is updated. Figures 5.2.1.1 through 5.2.1.4 show the VOC, NO X , SO 2 , and PM 2.5 on-road mobile source emissions inventory data for the GCVTC 9-st ate region, for the 1996 through 2018 timeframe. On-road VOC, NO X , and PM 2.5 emissions in the 9-state region are projected to decline consistently from 1996 to 2018 as a result of fleet turnover; i.e., older vehicles will be scrapped and replaced with newer vehicles that meet tighter emissions standards (for both light-duty and heavy-duty vehicles). On-road SO 2 emissions will decrease significantly between 2003 and 2008 with the introduction of low sulfur gasoline in 2004 and low sulfur diesel fuel in 2006; from 2008 onward SO 2 emissions will increase as the fuel sulfur levels stay constant but fuel consumption increases.

Table 5.2.1.1. Net emissions change from 2003 to 2018 for on-road sources, by pollutant.

Final §309 Regional TSD Page 169 of 250 December 15, 2003Reduction from 2003 (%) 2018 On

- road Emissions (tons per day) State VOC NO x PM 2.5 * SO 2 VOC NO x PM 2.5 SO 2 152.3 129.5 4.6 2.1 55% 63% 47% 78% Arizona 453.7 577.8 27.5 6.0 61% 64% 16% 44% California 84.7 100.8 4.8 1.8 60% 70% 53% 84% Colorado 32.1 34.5 1.7 0.6 59% 68% 47% 87%

Idaho 23.4 26.8 1.4 0.4 59% 68% 46% 90% Montana 38.4 40.4 1.8 0.7 56% 64% 51% 64% Nevada 59.2 59.5 3.3 1.0 57% 67% 51% 87% New Mexico 16.0 17.6 0.9 0.3 62% 69% 48% 87% North Dakota 91.7 93.3 3.6 1.4 61% 64% 43% 87% Oregon 19.5 22.2 1.1 0.3 59% 68% 46% 86% South Dakota 54.2 61.1 3.7 1.0 58% 69% 49% 81% Utah 175.3 145.8 5.7 1.9 62% 66% 44% 88% Washington 17.5 19.8 1.0 0.3 60% 69% 47% 88%

Wyoming 1218.0 1329.3 61.1 17.9 13 States 983.8 1116.9 51.9 14.9 9 GC States * - exhaust emissions only, includes elemental and organic carbon.

Final §309 Regional TSD Page 170 of 250 December 15, 2003

### GCVTC 9-State Region On-Road Em issions Annual Average Daily VOC

0 500 1000 1500 2000 2500 3000 3500Annual Average Day (Tons Per Day)

Arizona 411 337 242 187 152
California 1,792 1,149 834 604 454
Colorado 298 210 157 113 85
Idaho 111 78 54 39 32
Nevada 108 87 66 48 38
New Mexico 194 137 95 70 59 Oregon 316 234 161 121 92
Utah 172 128 88 65 54
W y omin g 66 44 29 21 17 1996 2003 2008 2013 2018

Figure 5.2.1.1. Trends in on-road mobile VOC emissions by GCVTC state.

Final §309 Regional TSD Page 171 of 250 December 15, 2003

### GCVTC 9-State Region On-Road Em issions Annual Average Daily NOx

0 500 1000 1500 2000 2500 3000 3500 4000Annual Average Day (Tons Per Day)

Arizona 384 346 257 186 129
California 2,159 1,594 1,188 831 578
Colorado 374 332 252 159 101
Idaho 130 109 79 52 35
Nevada 118 113 86 60 40
New Mexico 208 179 132 87 60 Oregon 313 261 192 132 93
Utah 218 200 147 96 61
W y omin g 78 64 45 30 20 1996 2003 2008 2013 2018

Figure 5.2.1.2. Trends in on-road mobile NO x emissions by GCVTC state.

Final §309 Regional TSD Page 172 of 250 December 15, 2003

### GCVTC 9-State Region On-Road Em issions Annual Average Daily SO2

0 10 20 30 40 50 60 70Annual Average Day (Tons Per Day)

Arizona 8.0 9.6 1.6 1.8 2.1
California 12.0 10.7 5.3 5.6 6.0
Colorado 9.6 11.1 1.4 1.6 1.8
Idaho 3.4 4.8 0.5 0.5 0.6
Nevada 2.0 2.0 0.6 0.6 0.7
New M exico 5.7 7.6 0.8 0.9 1.0 Oregon 9.2 10.5 1.1 1.3 1.4
Utah 4.4 5.5 0.8 0.9 1.0
W y omin g 1.9 2.6 0.3 0.3 0.3 1996 2003 2008 2013 2018

Figure 5.2.1.3. Trends in on-road mobile SO 2 emissions by GCVTC state.

Final §309 Regional TSD Page 173 of 250 December 15, 2003

### GCVTC 9-State Region On-Road Em issions Annual Average Daily PM2.5

0 10 20 30 40 50 60 70 80 90 100Annual Average Day (Tons Per Day)

Arizona 11.2 8.8 8.2 5.8 4.6
California 36.7 32.7 30.6 28.4 27.5
Colorado 13.7 10.2 10.4 6.1 4.8
Idaho 4.1 3.2 3.1 2.1 1.7
Nevada 4.1 3.7 3.5 2.3 1.8
New M exico 6.6 6.7 6.3 4.3 3.3 Oregon 8.2 6.3 5.9 4.4 3.6
Utah 8.0 7.1 6.7 4.7 3.7
W y omin g 2.4 1.9 1.8 1.2 1.0 1996 2003 2008 2013 2018

Figure 5.2.1.4. Trends in on-road mobile PM 2.5 emissions by GCVTC state.

Final §309 Regional TSD Page 174 of 250 December 15, 20035.2.2. Non-road emissions trends

As discussed earlier, WRAP non-road emission inventories are available for five years: the 1996 base year, and projection years 2003, 2008, 2013, and 2018. The projection year inventories include all promulgated on-road and non-road controls, as listed in Chapter 1. Further non-road controls, however, have been proposed. As discussed in Chapter 1, in April 2003, EPA proposed further emissions controls for large non-road diesel equipment, along with a requirement for low sulfur non-road diesel fuel to enable the em issions control technology. The projection year emission inventories do not include anticipated eff ects of these proposed controls. As discussed in Chapter 1, the NONROAD2002 model was run with ultra low sulfur ( < 15 ppm) diesel fuel for non-road equipment in the development of the 2008, 2013, and 2018 non-road emissions estimates, and the emission estimates were adjusted to back out the effects of the low sulfur diesel. These emissions estimates were calculated by applying the known state averages of diesel fuel sulfur to the NONROAD2002 data. Table 5.2.2.1 shows the net emissions change from non-road sources by pollutant, in 2018. Elemental and organic carbon emissions are fixed fractions of the PM 2.5 emissions, and vary by gas and diesel engine distributions.

Table 5.2.2.1. Net emissions change from 2003 to 2018 for non-road sources, by pollutant. 2018 Non-road Emissions (tons per day) Reduction from 2003 (%) State VOC NO x PM 2.5 * SO 2 ** SO 2 *** VOC NO x PM 2.5 69.7 107.8 13.4 8.4 8.1 38% 28% 5% Arizona 266.0 602.1 52.2 77.5 82.6 37% 37% 19% California 55.5 138.4 11.6 22.7 10.9 40% 28% 20% Colorado 27.8 56.1 4.9 7.5 4.4 41% 28% 23%

Idaho 19.2 123.0 8.3 13.3 14.2 39% 26% 24% Montana 31.5 136.7 8.3 11.6 15.8 35% 22% 8% Nevada 16.6 20.9 3.6 5.8 0.4 38% 34% 17% New Mexico 18.6 121.1 9.4 24.5 8.4 46% 33% 42% North Dakota 64.7 300.3 12.5 17.7 24.1 35% 28% 14% Oregon 14.9 62.8 6.9 21.6 0.1 46% 31% 38% South Dakota 37.6 94.6 8.3 20.6 5.8 38% 28% 17% Utah 79.4 207.4 15.8 22.8 31.3 39% 32% 17% Washington 16.9 113.5 5.2 4.9 16.8 32% 24% 16%

Wyoming 718.4 2084.6 160.3 258.9 222.8 13 States 586.3 1570.3 119.9 176.6 168.7 9 GC States * - exhaust emissions only, includes elemental and organic carbon. ** - SO 2 at present diesel fuel sulfur levels from land-based sources (no planes, trains, or ships). *** - SO 2 from planes, trains, and ships.

Final §309 Regional TSD Page 175 of 250 December 15, 20035.2.3. Total mobile source emissions trends

Total mobile source emissions trends are reported next.

Table 5.2.3.1. Net emissions change from 2003 to 2018 for total mobile sources, by pollutant. 2018 Total Mobile Source Emissions Reduction from 2003 (%) (tons per day) State VOC NO x PM 2.5 * SO 2 VOC NO x PM 2.5 SO 2 Arizona 222.0 237.3 18.1 10.3 51% 52% 21% 51% California 719.7 1179.9 79.7 97.3 54% 54% 18% 42% Colorado 140.2 239.2 16.4 12.8 54% 54% 33% 66%

Idaho 59.9 90.6 6.6 5.0 52% 51% 31% 69% Montana 42.6 149.8 9.7 14.7 52% 40% 28% 44% Nevada 69.9 177.1 10.1 16.6 49% 38% 20% 27% New Mexico 75.8 80.4 6.9 1.4 54% 62% 38% 89% North Dakota 34.7 138.7 10.3 8.8 55% 42% 42% 71% Oregon 156.5 393.6 16.1 25.5 53% 42% 23% 43% South Dakota 34.3 85.0 8.0 0.5 54% 47% 39% 97% Utah 91.7 155.7 12.0 6.9 51% 53% 30% 73% Washington 254.7 353.2 21.6 33.4 57% 52% 27% 51%

Wyoming 34.4 133.3 6.2 17.1 50% 37% 23% 14% 13 States 1936.4 3413.9 221.4 250.5 9 GC States 1570.1 2687.1 171.9 193.1 * - exhaust emissions only, includes elemental and organic carbon.

Data showing continuous declines in total mobile source emissions are presented next. Elemental and organic carbon emissions are fixed fractions of the PM 2.5 emissions, and vary by gas and diesel engine distributions. Most of the data used to develop these fractional carbon estimates are from a 1999 Desert Research Institute study by Gillies and Gertler, prepared for EPA, and used by ENVIRON for the WRAP mobile source emissions inventories (Pollack,2003). Elemental and organic carbon emissions shown here are averages of the gasoline, lightduty diesel, and heavy-duty diesel exhaust fractions of PM 2.5 emissions. On average, exhaust emissions of elemental and organic carbon total just over 87% of PM 2.5 exhaust emissions. Average organic carbon exhaust emissions are 33.7% of PM 2.5 exhaust emissions, and average elemental carbon exhaust emissions are 53.4% of PM 2.5 exhaust emissions.

Table 5.2.3.2. Continuous declines in total mobile source emissions. 

### 2003-08 Emissions Changes (tons per day) Organic Carbon Elemental Carbon State VOC NO x PM 2.5 *

Arizona -128.8 -115.3 -0.1 -0.3 -0.5 California -367.1 -537.4 -0.7 -1.9 -3.0 Colorado -79.7 -113.2 -0.1 -0.3 -0.6

Idaho -34.2 -42.2 -0.1 -0.2 -0.3

Final §309 Regional TSD Page 176 of 250 December 15, 2003Montana -24.7 -51.8 -0.1 -0.4 -0.6 Nevada -35.2 -55.8 -0.1 -0.2 -0.3 New Mexico -49.3 -52.6 -0.1 -0.2 -0.4 North Dakota -22.2 -46.9 -0.3 -0.9 -1.4 Oregon -96.9 -130.4 -0.2 -0.4 -0.7 South Dakota -21.8 -29.5 -0.2 -0.5 -0.8 Utah -54.0 -74.1 -0.2 -0.4 -0.7 Washington -191.0 -173.1 -0.3 -0.9 -1.4

Wyoming -18.1 -47.0 -0.1 -0.2 -0.3 

### 2008-13 Emissions Changes (tons per day) Organic Carbon Elemental Carbon State VOC NO x PM 2.5 *

Arizona -63.1 -84.5 -0.4 -1.0 -1.6 California -291.0 -492.8 -0.9 -2.4 -3.8 Colorado -51.8 -109.2 -0.7 -1.9 -3.0

Idaho -20.6 -34.0 -0.2 -0.6 -0.9 Montana -14.6 -30.6 -0.3 -0.7 -1.0 Nevada -20.3 -34.0 -0.2 -0.5 -0.9 New Mexico -27.2 -48.6 -0.3 -0.8 -1.3 North Dakota -13.3 -32.8 -0.4 -1.2 -1.9 Oregon -48.0 -96.6 -0.3 -0.8 -1.3 South Dakota -13.1 -28.5 -0.3 -0.9 -1.4 Utah -28.8 -63.8 -0.4 -1.0 -1.6 Washington -95.1 -116.3 -0.5 -1.3 -2.1

Wyoming -10.7 -21.3 -0.1 -0.3 -0.5 

### 2013-18 Emissions Changes (tons per day) Organic Carbon Elemental Carbon State VOC NO x PM 2.5 *

Arizona -34.8 -59.4 -0.1 -0.4 -0.6 California -194.1 -335.1 -0.6 -1.5 -2.4 Colorado -31.4 -63.4 -0.2 -0.5 -0.8

Idaho -10.7 -19.8 -0.1 -0.2 -0.4 Montana -6.9 -17.7 -0.1 -0.3 -0.4 Nevada -10.4 -20.5 -0.0 -0.1 -0.2 New Mexico -11.2 -29.5 -0.1 -0.4 -0.6 North Dakota -6.5 -20.2 -0.2 -0.5 -0.8 Oregon -31.6 -60.0 -0.1 -0.3 -0.5 South Dakota -6.1 -17.8 -0.1 -0.4 -0.6 Utah -14.4 -37.8 -0.1 -0.3 -0.5 Washington -46.8 -85.8 -0.2 -0.5 -0.7

Wyoming -5.5 -11.4 -0.1 -0.1 -0.2 * - exhaust emissions only

Final §309 Regional TSD Page 177 of 250 December 15, 2003

## Chapter 6 – Assessment of Fire Programs

6.1. Fire emissions inventories for 2018 Smoke Management Scenarios

The Fire Emissions Joint Forum (FEJF) of the Western Regional Air Partnership (WRAP) addressed three different emission reduction scenarios for the 2018 projected emission inventory for fire, "no control," "base," and "optimal." The base and optimal scenarios were utilized in the modeling and are briefly compared and summarized here. For the full documentation and discussion of the 2018 fire emission projections, please follow the reference in Appendix D to the contractor technical report entitled "Integrated Assessment Update and 2018 Emissions Inventory for Prescribed Fire, Wildfire, and Agricultural Burning," Air Sciences Inc., March 2003. A pollutant summary of the “base” emission inventories for prescribed fire, wildfire, and agricultural burning is shown next.

Figure 6.1.1. Pollutant summary of the base emi ssion inventories for prescribed fire, wildfire, and agricultural burning. Pollutant Summary WRAP-wide for All Burning Sources

5,598,679 2,780,020

- 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000Emissions inTons

Prescribed (Base) 216,187 391,906 5,598,679 36,008 602,321 510,441 78,375
Wildfire 130,825 59,641 2,780,020 16,353 270,307 231,829 12,506 Agricultural 20,415 10,165 217,717 1,355 22,153 21,020 4,390 VOC NOx CO SO2 PM 10 PM 2.5 NH3

6.1.1. Wildfire scenario comparison

A single emission projection for wildfire was submitted by the FEJF for use in all three modeling scenarios. The WRAP Modeling and Emissions Forums called on the FEJF to produce a wildfire projection that represented a "typical year" of wildfire activity within the 13-state WRAP region, which were used in all control strategy modeling.

Final §309 Regional TSD Page 178 of 250 December 15, 20036.1.2. Prescribed burning scenario comparison

For prescribed burning, base and optimal scenar ios originated in the Fire Emissions Project (FEP) commissioned by the Grand Canyon Visibility Transport Commission (GCVTC). The GCVTC solicited quantitative activity and burn-practices estimates from land managers in the West and synthesized this information to emission projection scenarios. The FEP 2015 projections were taken by the FEJF to represent 2018 WRAP projections.

The FEP database application calculated emissions given various forest types, activity levels, pre-burn fuel treatments, and smoke management techniques. The base scenario was calculated using the mix of emission reduction techniques defined by the land managers or by default values. The optimal scenario included the same fire treatments as the base scenario but applied a set of optimal smoke management practices (emission reduction techniques) to further reduce emissions. The set and effect of optimal smoke management practices were defined by expert opinion and essentially replaced the surveyed practices. A schematic showing the development of the prescribed fire smoke management scenarios is shown next.

Figure 6.1.2.1. Prescribed Fire Smoke Management Scenario Flowchart. 

# Rx NSM

FEP Output Status quo Smoke Mgmt removed. 

# Rx Base

FEP Output Status quo Smoke Mgmt maintained. 

# Rx OSM

FEP Output

Rules for optimal control. 

# Rx NSM

FEP Output Status quo Smoke Mgmt removed. 

# Rx Base

FEP Output Status quo Smoke Mgmt maintained. 

# Rx OSM

FEP Output

Rules for optimal control.

Data tables were created for the three different prescribed fire smoke management scenarios. The spatial and temporal refinement documented in Chapter 1 was performed on the base smoke management scenario. Refined emission invent ories for the two other cases were created by scaling the emissions of the base scenario. In this way, the event-based emission inventories forall three scenarios are identical except for emission mass. By comparing the FEP data tables bygrid, by-owner, by-pollutant, and by-season a percent increase or decrease was identified and applied. The prescribed burning optimal smoke management scenario resulted in an overall 13 percent reduction of PM 2.5 emissions relative to base. A summary of PM 2.5 emissions from prescribed fire for the three smoke management scenarios are presented next.

Final §309 Regional TSD Page 179 of 250 December 15, 2003Table 6.1.2.1. Summary of PM 2.5 emissions from prescribed burning by state and smoke management scenario.

No Smoke Management Base Smoke Management Optimal Smoke Management

State Absolute (tons x 10 3 ) Relative (%) Absolute (tons x 10 3 ) Relative (%) Absolute (tons x 10 3 ) Relative (%)

Arizona 77.0 15% 69.5 14% 65.2 15%
California 110.3 21% 109.7 21% 95.1 21%
Colorado 25.0 5% 24.8 5% 21.6 5%
Idaho 47.1 9% 47.1 9% 39.9 9%

Montana 40.0 8% 39.1 8% 34.6 8%
Nevada 5.9 1% 5.8 1% 5.1 1%

New Mexico 74.5 14% 74.4 15% 63.6 14%
North Dakota 1.8 0% 1.8 0% 1.6 0% Oregon 48.1 9% 46.7 9% 39.7 9%
South Dakota 3.7 1% 3.6 1% 3.3 1%
Utah 46.4 9% 45.7 9% 38.5 9% Washington 25.8 5% 25.2 5% 20.5 5% Wyoming 17.1 3% 16.9 3% 16.2 4%

TOTAL 522.6 100% 510.4 100% 444.8 100%

Figure 6.1.2.2. Summary of PM 2.5 emissions from prescribed burning by state and smoke management scenario.

Sm oke M anagem ent Scenario Sum m ary for Prescribed Fire (PM 2.5 )

0 2 0 4 0 6 0 8 0 100 120

Ar

i z o na Ca l if o rn i a Co l o r ad

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a
Ne

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t a Oregon South Dakota Utah Was

h i n gt

o n W y omi

ngEmissions PM2.5in Thousand Tons

No Smoke Mana g e m Base SM O ptim al S M

Final §309 Regional TSD Page 180 of 250 December 15, 2003In the course of conducting QA/QC on the disp ersion modeling results and fire emissions inventories, it was discovered that in limited cases the optimal smoke management scenario in FEP yielded greater emissions than base. Through consultation with the developers of FEP, the Emissions Task Team (ETT) of the FEJF determined that any instances of OSM producing greater emissions than base are unintended. The FEJF will investigate the cause for the unintended outputs from FEP and may modify the code in FEP to remedy the problem. In the short term, the ETT elected to employ data processing techniques to prepare OSM data files that include prescribed fire events that in all cases are less than or equal to base. This data processing resulted in the removal of 468 tons of PM 2.5 (1% of the total PM 2.5 in the prescribed fire inventory) from 3,077 fire events (2% of the total number of events in the prescribed fire inventory) of the optimal smoke management emission inventory.

The three figures below illustrate the effects of the data processing on the prescribed fire inventory. Figure 53 shows the number of events modified and the quantity of PM 2.5 emissions removed by fire size categories. Figure 54 displays the number of events modified and quantity of PM 2.5 emissions removed by state. Figure 55 presents the number of events modified and quantity of PM 2.5 emissions removed by month.

Figure 6.1.2.3. Effect of OSM Data Pro cessing – by Plume Class / Fire Size Category

Instances of OSM Greater than Base

by Plume Class/Fire Size Category

0 50 100 150 200 250

Plume Class / Fire Size CategoryTons PM2.5Removed

0 200 400 600 800 1000 1200 1400 1600 1800 2000NumberofEventsAffected

Tons PM2.5 39 102 204 124 0

Num. Events 1853 992 218 14 0 12345

Final §309 Regional TSD Page 181 of 250 December 15, 2003Figure 6.1.2.4. Effect of OSM Data Processing – by State

Instances of OSM Greater than Base by State

0 50 100 150 200 250 300 350 400 450

StateTons PM2.5Removed

0 200 400 600 800 1000 1200 1400 1600 1800NumberofEventsAffected

Tons PM2.5 3 9 76 0 0 0 0 0 0 0 0 1 4 5 0 1

Num. Events 1 6 7 4 4 8 0 1 0 0 1 0 0 03 8 4 8 8 2 8 7 AZ CA CO ID MT NV NM ND OR SD UT WA WY

Figure 6.1.2.5. Number of events modified and quantity of PM 2.5 emissions removed by month.

Instances of OSM Greater than Base by Month

0 20 40 60 80 100 120 140 160 180 200

MonthTons PM2.5Removed

0 200 400 600 800 1000 1200NumberofRecordsAffected

Tons PM2.5 0 0 0 4 5 5 0 12 0 1 8 7 1 5 7 8 9

Num. Events 0 0 0 158 132 136 27 55 322 983 756 508 1 2 3 4 5 6 7 8 91 0 1 1 1 2

Final §309 Regional TSD Page 182 of 250 December 15, 20036.1.3. Findings of Initial Investigation of FEP Results

The FEJF charged the fire emissions contractor with investigating the Base and OSM output from FEP in order to guide the process of revising the FEP input or code to eliminate the occurrences of OSM emissions being greater than Base emissions. FEP is an intricate emissions model with many layers of input data and algorithms and this investigative process was intended to make any necessary modifications to FEP more efficient.

FEP was queried for as many of the characteristics of the OSM data that could possibly lead to the surprising results of OSM greater than Base. These data were reviewed using automated scripts and cross tabulations to identify patterns or trends in the data. In addition, the summary of the methods used to develop FEP (included as Appendix A of the report on the 2018 fire emissions inventories, Integrated Assessm ent Update and 2018 Emissions Inventory for Prescribed Fire, Wildfire, and Agricultural Burning) was reviewed. Several potential reasons (all pertaining to the application of smoke management rules as input to FEP) for the surprising OSM versus Base outputs of FEP are provided below:

• A set of smoke management / emission control “rules” were prepared in the development of FEP. These rules were applied to the Base and OSM scenarios independently. That is, there was no inherent mechanism to ensure that OSM emissions would be less than Base. Because smoke management rules were applied independently – and often in complex combinations – different emission factors, control factors, and treatment application rates resulted causing varying emission reduction effects across the landscape.

• In some instances, smoke management application levels as input to FEP were strictly higher in Base than in OSM. That is, for the same smoke management applied in Base and in OSM, the emission reduction factors are lower (i.e., the resulting emission estimates are higher) in OSM.

Certain smoke management treatments (specifically, “Mass Ignition / Shortened Fire Duration” and “Rapid Mop-Up”) triggered the smoldering flag to be turned on in FEP. An increased application of “Rapid Mop-Up” is disproportionately represented in the FEP records where OSM is greater than Base.

6.1.4. Agricultural burning scenario comparison

The FEJF prepared three agricultural burning em ission inventories representing three emission control scenarios. The no control scenario is based upon the FEJF's 1996 agricultural burning activity database (as complied by Eastern Research Group (ERG)) without adjustment. The base projection was developed through post-processing the spatially and temporally refined no control inventory. Adjustments for the base case reflected existing regulatory programs that will serve to reduce emissions from "uncontrolled levels" in 2018. The optimal scenario is derived based upon adjustments made to the base inventory. Adjustments for the optimal smoke management scenario reflect the full implementation of reasonably known emission control techniques, alternatives to agricultural burning, and burn permitting programs. A flowchart showing the development of the agricultural burning smoke management scenarios is shown next. The Ag

Final §309 Regional TSD Page 183 of 250 December 15, 2003OSM database is a subset of the Ag Base data, which is subset of the Ag NSM database. For the Ag Base, the ETT developed a set of calculations to convert the legislative programs in place to emission reduction targets. To derive the Ag Base database, agricultural burning events were removed from Ag NSM until the total of the emissions removed equaled the Ag Base emission reduction target. Similarly, the ETT developed a set of calculations to convert the full implementation of available emission reduction techniques WRAP-wide to emission reduction targets for Ag OSM and burning events were removed from Ag Base until the total of the emissions removed equaled the Ag OSM emission reduction target.

Figure 6.1.4.1. Agricultural Smoke Management Scenario Flowchart. 

# Ag NSM

ERG activity data. 

# Ag Base

NSM adjusted to reflect legislative programs in place. 

# Ag OSM

Base adjusted to reflect full implementation of available emission reduction techniques and alternatives. 

# Ag NSM

ERG activity data. 

# Ag Base

NSM adjusted to reflect legislative programs in place. 

# Ag Base

NSM adjusted to reflect legislative programs in place. 

# Ag OSM

Base adjusted to reflect full implementation of available emission reduction techniques and alternatives. 

# Ag OSM

Base adjusted to reflect full implementation of available emission reduction techniques and alternatives.

To form the base scenario, the FEJF identified three jurisdictions in the WRAP with existing emission reduction programs already codified into laws and/or regulations. These programs are assumed to have their full regulatory effect in 2018: California's Sacramento Valley has a program focused on reducing the amount of rice stubble burned. Washington State has established a goal to reduce emissions from wheat stubble burning and Oregon's Willamette Valley limits open-field burning of seed grass. For each case, emission reduction targets were developed on a per-crop and per-county basis and applied to selectively shrink the 2018 no control dataset.

Optimal, or "maximum" control is considered to be the aggressive implementation of emissioncontrol measures currently recognized to be effective and available. The FEJF quantified cropspecific emission reduction factors through expert opinion and applied them uniformly across the WRAP region. Reductions to three crop categories were devised under this "maximum" hypothesis. Orchard crops would have a 90 percen t reduction in emissions due to utilization of prunings for renewable energy. Cereal crops would experience a 75 percent reduction due to imposed emissions control. Grass seed fields would see a 50 percent reduction via emission controls and increased availability of alternatives to burning. These "rules" were applied to the base scenario by randomly removing fire events (of the appropriate crop-type) until the emission reduction target was reflected in the dataset. The remaining inventory records thereby comprised the 2018 optimal control emissions projection for the WRAP region.

Compared to the base scenario, the optimal smoke management scenario yielded an overall 55 percent decrease in PM 2.5 emissions. A summary of the PM 2.5 emissions for agricultural burning by state and smoke management scenario are presented next.

Final §309 Regional TSD Page 184 of 250 December 15, 2003Table 6.1.4.1. Summary of PM 2.5 emissions from agricultural burning by state and smoke management scenario.

No Smoke Management Base Smoke Management Optimal Smoke Management

State Absolute (tons x 10 3 ) Relative (%) Absolute (tons x 10 3 ) Relative (%) Absolute (tons x 10 3 ) Relative (%) Arizona 0.21 1% 0.21 1% 0.06 1% California 8.05 30% 7.00 33% 2.87 31% Colorado 0.01 0% 0.01 0% 0.00 0% Idaho 5.60 21% 5.60 27% 2.42 26% Montana 0.03 0% 0.03 0% 0.01 0% Nevada 0.00 0% 0.00 0% 0.00 0% New Mexico 0.04 0% 0.04 0% 0.01 0% North Dakota 2.23 8% 2.23 11% 0.56 6% Oregon 6.78 25% 2.58 12% 1.05 11% South Dakota 0.56 2% 0.56 3% 0.14 1% Utah 0.21 1% 0.21 1% 0.06 1% Washington 2.91 11% 2.35 11% 2.16 23% Wyoming 0.19 1% 0.19 1% 0.05 1% 26.83 100% 21.02 100% 9.40 100%

Figure 6.1.4.2. Summary of PM 2.5 emissions from agricultural burning by state and smoke management scenario.



### Smoke Management Scenario Summary for Agricultural Burning (PM 2.5 

### )

0 1 2 3 4 5 6 7 8 9

Arizo n a Ca

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New Mexic o

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S

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a Utah Washingto n Wyomi n gEmissions PM2.5in Thousand Tons

No Smoke Manageme n Base SM Optimal SM

Final §309 Regional TSD Page 185 of 250 December 15, 20036.2. Tracking fire emissions using the WRAP EDMS

For fire emissions, §309 specifically calls for a statewide inventory and emissions tracking system (spatial and temporal) of VOC, NO X , EC, OC, and PM 2.5 emissions from all fire sources. Existing emissions inventories prepared by the Fire Emissions Joint Forum described in this TSD satisfy that requirement, and also inventory SO 2 , PM 10 , CO, and NH 3 emissions. Under §309, states and tribes must identify a method, or a timeline to develop a method, to track fire activity data and calculate the resulting required emissions inventory, in their SIP or TIP. Tracking of fire activity data and calculation of the resulting emissions through the EDMS will provide information critical to the successful implementation of other requirements under §309, including the development, adoption, and implementation of enhanced smoke management programs, the establishment of annual emission goals, and future projections of fire emissions. In order to support the development of an annual emissions goal, the FEJF will provide methods to calculate the benefits of emissions reduction techniques.

The EDMS will track activity data as reported by states and tribes participating in §309, as well as the same type of data provided by other WRAP region state, tribal, and local air agencies, and federal/state/private sources using prescribed a nd/or agricultural burning techniques. The EDMS will calculate the resulting emissions for fire source types including prescribed fire, wildfire, and agricultural burning. Open burning activities on residential, commercial, or industrial properties are not included in the fire source category, and will be inventoried as area sources by the EDMS. The WRAP Fire Tracking System policy identifies seven essential components of a fire tracking system that represent the minimum spatial and temporal fire activity information necessary to consistently calculate emissions and to uniformly assess impacts on regional haze. These are:

• date of burn

• burn location

• area of burn

• fuel type

• pre-burn fuel loading

• type of burn, and

• anthropogenic or natural classification.

Specifications for calculating emissions from the reported fire activity data will be provided to the Emissions Forum, for use in the EDMS, at a future date by the Fire Emissions Joint Forum. This will allow county/state and tribal reservati on tracking of fire emissions, and the expected emissions reductions from the application of emissions reduction techniques.

The EDMS will have the capability to produce a special report in tabular and simple plots (i.e. bar graph and pie chart) formats and to allow queries of the same information including presentation in GIS format. The EDMS to be de veloped is described in a draft technical report to the Emissions Forum: Needs Assessment for Evaluation and Design of an Emissions Data

Final §309 Regional TSD Page 186 of 250 December 15, 2003Reporting, Management, and Tracking System , (EA Engineering, Science, and Technology, June 26, 2003).

Final §309 Regional TSD Page 187 of 250 December 15, 2003

## Chapter 7 – Assessment of Road Dust

7.1. Emissions inventories for re-entrained road dust from paved and unpaved roads

Paved and unpaved road dust emissions in the WRAP states were estimated using the methods described in Chapter 1. In brief, paved road dust emissions were estimated using the standard EPA approach, while unpaved road dust emissions were estimated using revised silt contents, revised activity data, and transport fractions. Paved and unpaved road dust PM 10 and PM 2.5 emissions by state for 1996 and 2018 are provided in the following tables. Paved road dust emissions increase by about three percent per year from 1996 to 2018, per the increase in vehicle miles traveled. The increase varies by state, with the largest projected growth in vehicle travel in Washington, Idaho, and Utah. Unpaved road dust emissions are projected to decrease between 1996 and 2018, by about 0.75% per year, because of reductions in unpaved road mileage over time as more roads are paved. As a result, unpaved road dust emissions are about 80% of road dust PM 10 emissions in 1996, and about 65% of road dust PM 10 emissions in 2018. Overall, road dust PM 10 emissions increase by about 6% from 1996 to 2018.

Table 7.1.1. 1996 WRAP States Fugitive Road Dust Emissions (TPY) State Paved Road Dust Unpaved Road Dust Total Road Dust PM 10 PM 2.5 PM 10 PM 2.5 PM 10 PM 2.5 Arizona * 7,318 1,830 18,605 2,791 25,923 4,620 California * 35,643 8,911 86,474 14,090 122,117 23,001 Colorado * 7,897 1,974 30,601 4,590 38,498 6,564

Idaho * 3,502 876 28,304 4,246 31,806 5,121 Montana 2,909 727 15,796 2,369 18,705 3,097 Nevada * 2,528 632 9,460 1,449 11,988 2,081 New Mexico * 5,395 1,349 27,972 4,196 33,367 5,545 North Dakota 2,461 615 34,419 5,163 36,880 5,778

Oregon * 8,067 2,017 19,078 2,862 27,144 4,878 South Dakota 2,296 574 46,199 6,930 48,495 7,504 Utah * 3,680 920 16,040 2,406 19,721 3,326 Washington 7,804 1,951 34,365 5,155 42,169 7,106 Wyoming * 1,823 456 3,449 517 5,271 973
13 states total 91,322 22,831 370,762 56,763 462,084 79,594 * 9 GCVTC States 75,853 18,963 239,983 37,146 315,835 56,109

Table 7.1.2. 2018 WRAP States Fugitive Road Dust Emissions (TPY) State Paved Road Dust Unpaved Road Dust Total Road Dust PM 10 PM 2.5 PM 10 PM 2.5 PM 10 PM 2.5 Arizona * 12,618 3,154 12,976 1,945 25,594 5,099 California * 59,794 14,950 56,447 9,198 116,242 24,148 Colorado * 13,771 3,442 36,956 5,544 50,728 8,986

Idaho * 7,548 1,887 17,976 2,697 25,524 4,584 Montana 4,796 1,201 17,907 2,686 22,703 3,887 Nevada * 5,019 1,256 7,198 1,102 12,217 2,358 New Mexico * 10,198 2,551 32,730 4,909 42,928 7,461

Final §309 Regional TSD Page 188 of 250 December 15, 2003North Dakota 3,723 931 34,255 5,139 37,978 6,070 Oregon * 14,684 3,672 12,158 1,825 26,842 5,497 South Dakota 3,723 931 50,239 7,537 53,962 8,468 Utah * 7,541 1,883 20,487 3,073 28,028 4,957 Washington 18,681 4,668 22,991 3,449 41,672 8,118 Wyoming * 3,019 756 3,719 558 6,738 1,314
13 states total 165,115 41,282 326,040 49,666 491,155 90,947 * 9 GCVTC States 134,192 33,551 200,648 30,853 334,840 64,404

7.2. Road Dust significance analysis for the Colorado Plateau 16 Class I areas

§309 requires the following for Road Dust: “ Area sources of dust emissions from paved and unpaved roads. The plan must include an assessment of the impact of dust emissions from paved and unpaved roads on visibility conditions in the 16 Class I Areas. If such dust emissions are determined to be a significant contributor to visibility impairment in the 16 Class I areas, the State must implement emissions management strategies to address the impact as necessary and appropriate.” As described above, road dust emissions inventories were developed, and the significance of road dust was then tested using the regional air quality model at the Regional Modeling Center, for review by the Air Quality Modeling Forum, and other WRAP Forums and Committees. The modeling results are presented next:

Table 7.2.1. Road Dust Significance Modeling Results
EPA DRAFT default Natural Conditions 2018 2018 Bext change Bext change dv Change No Road

Dust Colorado Plateau Base No Road Dust No Road Dust No Road Dust (dv) Class I Areas Case (Mm -1 ) (Mm -1 ) Worst 20% Days (Mm -1 ) (Mm -1 ) (%)

25.91 25.63 20.12 0.29 1.42 0.14 Arches NP Black Canyon of

Gunnison NP 32.84 32.20 20.26 0.63 3.13 0.31

24.24 23.99 20.12 0.25 1.24 0.12 Bryce Canyon NP 23.89 23.71 20.08 0.18 0.89 0.09 Canyonlands NP 25.56 25.30 20.18 0.26 1.29 0.13 Capitol Reef NP 28.55 28.37 20.28 0.19 0.92 0.09 Flat Tops Wilderness 27.78 27.53 20.08 0.25 1.22 0.12

Grand Canyon NP

Maroon BellsSnowmass Wilderness 31.78 31.46 20.30 0.32 1.57 0.16

34.47 34.21 20.18 0.26 1.30 0.13 Mesa Verde NP

Mount Baldy Wilderness 41.63 41.26 20.04 0.37 1.87 0.19

32.46 32.22 20.08 0.24 1.20 0.12 Petrified Forest NP

San Pedro Parks

Wilderness 28.90 28.65 20.18 0.24 1.21 0.12
Sycamore Canyon Wilderness 35.34 34.95 20.34 0.39 1.92 0.19

31.15 30.85 20.26 0.30 1.47 0.15 West Elk Wilderness 29.93 29.77 20.20 0.16 0.80 0.08 Weminuche Wilderness 25.57 24.95 20.06 0.61 3.05 0.30 Zion NP

Final §309 Regional TSD Page 189 of 250 December 15, 2003In the regional model, the road dust inventory was set to zero for the entire modeling domain, other 2018 base case emissions were included as normal, and the resulting modeled effect of road dust emissions were calculated in terms of the impact on visibility at each of the 16 Colorado Plateau Class I areas, on the predicted worst 20% days. The modeled regional impact of road dust emissions ranged from 0.31 deciviews (3.1% of EPA DRAFT default natural conditions to be reached by 2064) at the Black Canyon of the Gunnison National Park to 0.08 deciviews (0.8% of EPA DRAFT default natural conditions to be reached by 2064) at the Weminuche Wilderness. While EPA draft default natural conditions were used for the purposes of this regional significance analysis, the EPA natural conditions estimates are still in draft form, and do not represent the same levels that WRAP would necessarily use in future analyses. The modeling data were presented and discussed by the Modeling and Mobile Sources Forums, the Oversight Committees, and the Board of Directors, and all agreed that the regional impact of road dust emissions were not significant at the 16 Colorado Plateau Class I areas at this time.

The Board directed the Technical Forums to continue to track and evaluate road dust emissions, as dust aerosols have been shown to be an important contributor to visibility impairment, using IMPROVE aerosol filter data, at many WRAP region Class I areas. The finding of no significance is a rigorous test, as no windblown dust emissions are included in the model, as discussed earlier, so the modeled difference between having road dust in, and road dust out of the model is not masked by a large mass of other dust emissions. The regional model has a grid cell size of 36km 2 , so the impacts of intra-grid cell road dust emissions are not as well characterized. The Air Quality Modeling Forum will be developing a finer grid(s) to better address the localized versus regional impacts road dust and other emissions categories, and other WRAP Forums will review representative natural conditions estimates for individual Class I areas, both for use in the §308 modeling and analysis.

7.3. Tracking road dust emissions using the WRAP EDMS

For road dust, the §309 SIP and TIP submissions mu st provide for statewide inventories of paved and unpaved road dust; these have been prepared for the years 1996 and 2018, and are described in this TSD.

The EDMS will have the capability to produce the following special report in tabular and simple plots (i.e. bar graph and pie chart) formats and to allow queries of the same information including presentation in GIS format, in addition to the standard reports. The special report would include a comparison of annual WRAP region total emissions of paved and unpaved road dust emissions by state, tribal reservation, as well as the entire region, and the corresponding previous period’s total emissions.

The EDMS to be developed is described in a draft technical report to the Emissions Forum: Needs Assessment for Evaluation and Design of an Emissions Data Reporting, Management, and Tracking System , (EA Engineering, Science, and Technology, June 26, 2003).

Final §309 Regional TSD Page 190 of 250 December 15, 2003

## Chapter 8 – Assessment of Pollution Prevention

Two key recommendations from the Grand Canyon Visibility Transport Commission (GCVTC) focused on the development of renewable energy sources and promotion of energy conservation. Labeled the 10/20 goals, the recommendation on development of renewable energy sources encouraged states and tribes in the Transport Region to take steps that would increase the use of renewable energy to 10% of the regional power needs by 2005 and 20% of the regional power needs by 2015. For energy conservation, the GCVTC supported the continued development of energy efficiency standards and suggested that the emphasis on energy conservation be maintained within the changing electric power markets. In addition to the 10/20 goals and energy conservation recommendations, the GCVTC suggested that future modeling work be conducted to analyze the potential emission reductions, cost savings, and secondary benefits associated with the use of renewable energy, energy efficiency, and pollution prevention.

The WRAP Air Pollution Prevention Forum has been charged with implementing the air pollution prevention recommendations of the GCVTC. The Air Pollution Prevention Forum commissioned the ICF Consulting Group to analyze the potential emission reductions, costs, and secondary regional economic impacts of meeting the 10/20 goals and energy efficiency recommendations (ICF, 2002). The analysis of this case incorporates the results of the ICF analysis for the Air Pollution Prevention Forum in a scenario that includes 2018 milestone case emission estimates for non-utility point sources.

The estimated SO 2 and NO x emissions by utility unit for existing facilities, and by State for new sources, were provided by the ICF Consulting Group. The percentage changes in SO 2 and NO x emissions by unit were applied to the 2018 Milestone Case emissions to estimate air pollution prevention case emissions for this analysis. The ICF model also provided estimated SO 2 and NO x reductions for new sources. These new source emission reductions were applied to the utility units in each State in proportion to 2018 milestone case emissions. Because of the regional SO 2 trading program, the regional SO 2 emissions total is the same in the air pollution prevention case as it was for the milestone case. There is some shifting of SO 2 emissions among units and States, though. Regional NO x emissions decline by about 14,000 tons (air pollution prevention case versus milestone case). A State-level summary of utility emission changes by State from the 2018 milestone case is provided below. The tribal new source changes were allocated to Arizona. States not listed had no emissions change.

Air Pollution Prevention Case Emissions Change

State NO x tpy SO 2 tpy
Arizona -3,267 5,558
Colorado -1,370 -1,119

Nevada -430 -307

New Mexico -7,053 -5,135

Utah -780 -595 Wyoming -1,374 1,598 Regional Changes -14,274 0

Final §309 Regional TSD Page 191 of 250 December 15, 2003Final §309 Regional TSD Page 192 of 250 December 15, 2003 Table 8.1 presents the air pollution prevention case results for utility sources in the GCVTC transport region States. In the air pollution prevention case, GCVTC transport region point source SO 2 emissions are 510,000 tons.

Table 8.1. 2018 Air Pollution Prevention Case Scenario – Utility Point Source Emissions (GCVTC transport region States)

2018 Emissions (tpy) MTF Ftype FIPSST State Code State VOC NO x CO SO 2 PM 10 PM 2.5 NH 3

1 Utility 04 AZ Arizona 700 91,331 7,205 67,085 3,434 1,659 8 08 CO Colorado 602 80,785 7,122 46,918 1,624 1,006 6 32 NV Nevada 369 43,825 3,073 19,294 5,607 2,506 3 35 NM New Mexico 783 84,762 7,077 65,756 9,161 2,707 4 41 OR Oregon 51 10,094 424 3,935 257 177 0 49 UT Utah 430 75,229 5,317 23,763 2,561 1,135 3 56 WY Wyoming 1,238 102,439 11,346 69,383 11,100 6,612 0

Totals 4,173 488,465 41,564 296,134 33,744 15,802 24

The 22-State region utility criteria pollutant emission summaries for the air pollution prevention case are presented next.

Table 8.2. 2018 Air Pollution Prevention Case Scenario – Utility Emissions by State (22 -State Region ) 2018 Emissions (tpy) MTF Ftype FIPSST State Cod

e State VOC NO x CO SO 2 PM 10 PM 2.5 NH 3

1 Utility 04 AZ Arizona 700 91,331 7,205 67,085 3,434 1,659 8 1 Utility 05 AR Arkansas 918 50,964 7,248 71,496 1,654 1,140 98 1 Utility 08 CO Colorado 602 80,785 7,122 46,918 1,624 1,006 6 1 Utility 19 IA Iowa 943 76,811 8,537 107,006 3,141 1,803 18 1 Utility 20 KS Kansas 1,192 91,664 9,207 81,153 2,166 1,370 65 1 Utility 22 LA Louisiana 1, 973 75,803 17,077 75,680 3,116 1,933 925 1 Utility 27 MN Minnesota 1, 007 84,056 7,833 61,025 4,172 2,024 21 1 Utility 29 MO Missouri 2,053 113,515 13,678 261,759 3,061 2,014 26 1 Utility 30 MT Montana 405 26,030 3,382 12,862 4,136 2,062 5 1 Utility 31 NE Nebraska 514 46,631 4,070 45,946 1,014 703 8 1 Utility 32 NV Nevada 369 43,825 3,073 19,294 5,607 2,506 3 1 Utility 35 NM New Mexico 783 84,762 7,077 65,756 9,161 2,707 4

1 Utility 38 ND North Dakota 1,357 108,558 15,808 132,836 3,604 2,095 15

1 Utility 40 OK Oklahoma 1,614 88,543 15,797 79,824 2,389 1,682 314 1 Utility 41 OR Oregon 51 10,094 424 3,935 257 177 0 1 Utility 46 SD South Dakota 130 17,542 615 11,102 55 40 1

1 Utility 48 TX Texas 11,173 219,850 127,621 471,544 23,362 17,182 2,583 1 Utility 49 UT Utah 430 75,229 5,317 23,763 2,561 1,135 3 1 Utility 53 WA Washington 257 19,152 2,133 8,721 2,955 2,033 0 1 Utility 56 WY Wyoming 1,238 102,439 11,346 69,383 11,100 6,612 0 Totals 27,708 1,507,582 274,569 1,717,088 88,569 51,884 4,105Table 8.3 shows the electricity generating units in the GCVTC Transport Region with predicted SO 2 and NO x emission changes in the air pollution prevention case.

Table 8.3. Air Pollution Prevention Case Electricity Generating Unit Emissions
Pollution Prevention Case 2018 Percentage

Change* Milestone Case 2018 Difference
State County Unit ID ORISID Plant NOx SO 2 NOx SO 2 NOx SO 2 NOx SO 2
AZ Navajo 1 113 APS Cholla -40. 52% -40.51% 2,066 1,050 1,229 625 -837 -425

AZ Navajo 3 113 APS Cholla 0 344.45 4,081 2,292 4,081 10,189 0 7,896

CO Montrose 1 527 Nucla 0 -2.36 1,038 1,530 1,038 1,494 0 -36

NM San Juan 1 2451 San Juan Generating

- 36.25 -36.25 6,926 7,170 4,415 4,571 -2,511 -2,599

NM San Juan 3 2451 San Juan Generating

- 40.48 -25.07 9,911 8,459 5,899 6,338 -4,012 -2,121

WY Sweetwater BW72 8066 Pacificorp-Jim Bridge r -0.04 55.42 10,171 4,838 10,167 7,519 -4 2,681
34,193 25,338 26,829 30,734 -7,364 5,396 *Compared with the 2018 Milestone Case.

These model-estimated changes occur at 6 units at 4 plants in 4 States (Arizona, Colorado, New Mexico, and Wyoming). Expected SO 2 emission increases occur at Cholla #2 and Pacific CorpJim Bridger. These emission increases are offset by decreases in SO 2 emissions at Cholla #1, 2 San Juan Generating Station units, and at Nucla. NO x emissions in the air pollution prevention case are always lower than or equal to those in the milestone case. The largest NO x emission reductions in the pollution prevention case occur in New Mexico at San Juan Generating Station.

Final §309 Regional TSD Page 193 of 250 December 15, 2003

## Appendix A: Methods Used to Incorporate State and Local control programs in WRAP Emission Inventories

The purpose of this appendix is to provide a list of state and local emission control programs and assumptions included in the emissions inventor ies prepared by WRAP contractors for §309. Documentation about the details and assumptions for each emissions inventory are contained in the individual contractors’ reports, listed as refere nces in Appendix C of this document. Federal control programs and actions are published in the Federal Register, and are incorporated into EPA emissions models in most cases, and are not listed in this appendix.

A.1. Area Sources

This information is from Chapter IV “Existing Source State Regulation Analyses, Western Regional Air Partnership Emission Forecasts For 2018 - Final Report”, E.H. Pechan & Associates, Inc., December 2002, Pechan Rpt. No. 02.12.003/9409.000.

This chapter describes analyses of State and Local regulations affecting criteria pollutant emissions between 1996 and 2018. Results of these analyses are organized by pollutant: PM 10 , followed by NO x regulations, followed by SO 2 . These analyses were performed in order to update the IAS model control factors so that they would reflect the expected pollution reduction effects of State and local regulations.

PM 10

Many PM 10 nonattainment areas are located in the Western United States. Federal, State, and local air pollution regulations and other initiatives likely to affect point and area PM 10 sources were analyzed. The focus was on PM 10 sources in nonattainment areas and the control measures that areas are implementing to bring their areas into attainment. It is not expected that attainment areas would implement post-1996 control measures for PM 10 and that any pre-1996 regulation effects would already be inco rporated in their 1996 emission estimates.

Using EPA’s web site Classifications of PM-10 Nonattainment Areas , a group of twelve nonattainment areas were selected for analyses (EPA, 2001b). The selected areas included all of the listed serious classification nonattainment ar eas – Clark County, NV; Coachella Valley, CA; Los Angeles/South Coast Air Basin, CA; Owens Valley, CA; Phoenix, AZ; and San Joaquin Valley, CA . The selected areas also included a sampling of moderate classification nonattainment areas in the WRAP States. For the moderate classification areas, selection was also based on availability of the needed information. The selected moderate classification nonattainment areas included Aspen, CO; Anthony, NM; Klamath Falls, OR; Salt Lake County, UT; Spokane County, WA; and Sheridan, WY.

Area-specific PM 10 control plans and information were collected and compiled from EPA Regional Offices, and State and local agencies for each of the selected nonattainment areas. Often the information was available via the Internet and the agency was able to provide the

Final §309 Regional TSD Page 194 of 250 December 15, 2003web site address. Agency staff were also interviewed to gain insight into an area ’s particular nonattainment situation and learn about novel or unique control measures. EPA’s web site Federal Register Notices Related to PM-10 Designations and Classifications was used to identify recent actions related to the selected nonattainment areas (EPA, 2001c).

Pechan reviewed the gathered documents and prepared a series of tables to summarize the control measure information for each nonattainment area. This information is summarized in Tables IV-3 through IV-9. Each table presents adopted measures for a different source category. Source categories include construction, residential wood combustion, vacant land/unpaved lots, open burning, agricultural tilling, salting/sanding of paved roads, and miscellaneous sources. For use in this analysis, the information about PM 10 control measures by PM 10 nonattainment area was translated into a set of PM 10 control efficiencies by area that were applied as PM 10 control factors in the 2018 emissions forecast. Each table identifies the nonattainment area and names the types of measures that the area uses to control emissions of PM 10 . The assumed degree of control of road dust emissions in each PM 10 nonattainment area is described in the mobile sources emissions inventory report (E NVIRON, 2003). For road dust emissions, PM control measures were applied to fugitive dust emissions from paved and unpaved roads in all PM 10 nonattainment areas, with the control factors reflecting a higher control level in serious PM 10 nonattainment areas than was applied in moderate PM 10 nonattainment areas.

Final §309 Regional TSD Page 195 of 250 December 15, 2003Final §309 Regional TSD Page 196 of 250 December 15, 2003Table IV-10 lists the control factors that were applied to the 2018 PM 10 emissions in the listed PM 10 nonattainment areas in the Western States. Some of the source categories that are included in the prior tables in this chapter are not incl uded in the control factor file because their PM 10 emissions are not accounted for in the point and area source inventories.

Final §309 Regional TSD Page 197 of 250 December 15, 2003The control efficiencies and rule penetration values shown below are based on control measure evaluations performed by Pechan for EPA’s regulat ory analysis of the PM National Ambient Air Quality Standard. Control factor development is described by source category below:

Construction Activity - the numerous measures adopted to reduce fugitive dust PM emissions from construction activity were condensed in to two primary measures: a dust control plan and chemical stabilization. A typical dust control plan includes water treatment of disturbed soil and vacuum street sweeping of nearby paved areas. Cont rol efficiency and rule penetration values are as follows:

Agricultural Tilling - the typical measure in the PM 10 nonattainment area plan s is soil conservation plans. A 20 percent control efficiency is applied to both PM 10 and PM 2.5 emissions in areas that have these plans. This 20 percent control efficiency may be conservative for estimating emission reductions for areas like Maricopa County, Arizona where agricultural best management practices have been adopted.

Final §309 Regional TSD Page 198 of 250 December 15, 2003A.2. Prescribed Forest/Range and Agricultural Fire Smoke Management Programs

The following information is from “Integrated Assessment Update and 2018 Emissions Inventory for Prescribed Fire, Wildfire, and Agricultural Burning”, Air Sciences Inc., originally published August 27, 2002, revisions in press, Project # 178-2.

Final §309 Regional TSD Page 199 of 250 December 15, 2003Final §309 Regional TSD Page 200 of 250 December 15, 2003A.3. On-Road Mobile Sources

This information is from “Development Of WRAP Mobile Source Emission Inventories”, Pollack, 2003, in press.

A.3.1. 1996 Control Programs

MOBILE6/PART5 inputs related to several on-road control programs were also included in the modeling. These control programs are area-specific (i.e., not applied nationally or regionwide), generally based on an area’s ozone or CO nonattain ment status. These programs include vehicleinspection and maintenance (I/M) programs, oxygenated fuel programs, and Stage II (at-thepump) vehicle refueling controls. Note that reformulated gasoline is not included in this list because none of the WRAP states had implemented a reformulated gasoline program by 1996. The default control program parameters were those in the 1996 NET. These were updated by the state and local air agencies in some cases. As described in Section 2, federal control programs are included in MOBILE6 and no additional inputs are needed to model these programs.

A.3.2. Inspection and Maintenance (I/M) Programs

I/M program inputs are specific to each state or area implementing such a program. The default I/M program inputs were those from the 1996 N ET, converted to MOBILE6 input format, along with the county coverage of these programs in the 1996 NET. Updated information on these programs was provided by Arizona, Colorado, Neva da, Oregon, Utah, and Washington. Table 3- 2 lists the counties modeled with an I/M program in place.

Table 3-2 . Counties modeled with an inspection and maintenance program in 1996. State County AZ Maricopa AZ Pima CO Adams CO Arapahoe CO Boulder CO Douglas CO Jefferson CO Denver CO El Paso CO Larimer CO Weld ID Ada N M Bernalillo N V Clark N V Washoe OR Clackamas OR Jackson OR Multnomah

Final §309 Regional TSD Page 201 of 250 December 15, 2003Final §309 Regional TSD Page 202 of 250 December 15, 2003 OR Washington UT Davis UT Salt Lake UT Weber UT Utah WA Clark WA King WA Snohomish WA Spokane WA Pierce

A.3.3. Oxygenated Fuel

For the WRAP modeling, the program in place in each of the mid-months of the seasons was used (i.e., the program in place in January for the November to February winter season). Table 3-3 lists the counties that were modeled with oxygenated fuels and the inputs used to model these programs. The information in this table includes updated information on these programs provided by the states.

Table 3-3. Oxygenated fuel inputs. January Oxygenated Fuel Inputs October Oxygenated Fuel Inputs Market Share (%) Oxygen Content (%) Market Share (%) Oxygen Content (%)

State County Ether Blend Alcohol Blend Ether Blend Alcohol Blend Ether Blend Alcohol Blend Ether Blend Alcohol Blend

AZ Maricopa 17 83 2.7 3.5 17 83 2.7 3.5 AZ Pima * 17 83 2.7 3.5 17 83 2.7 3.5 CO Adams 25 75 2.7 3.3 CO Arapahoe 25 75 2.7 3.3 CO Boulder 25 75 2.7 3.3 CO Denver 25 75 2.7 3.3 CO Douglas 25 75 2.7 3.3 CO El Paso 0 100 2.7 2.7 0 100 2.7 2.7 CO Jefferson 25 75 2.7 3.3 CO Larimer 0 100 2.7 2.7 0 100 2.7 2.7 CO Weld 25 75 2.7 3.3 MT Missoula 0 100 2.7 3.5 0 100 2.7 3.5 N V Clark 24 76 2.7 3.5 24 76 2.7 3.5 N V Washoe 95 5 2.7 3.5 95 5 2.7 3.5 N M Bernalillo 15 85 2.7 3.5 15 85 2.7 3.5 OR Clackamas 0 100 0 3.5 0 100 0 3.5 OR Jackson 0 100 0 3.5 0 100 0 3.5 OR Josephine 0 100 0 3.5 0 100 0 3.5 OR Klamath 0 100 0 3.5 0 100 0 3.5January Oxygenated Fuel Inputs October Oxygenated Fuel Inputs Market Share (%) Oxygen Content (%) Market Share Oxygen Content (%) (%)

State County Ether Blend Alcohol Blend Ether Blend Alcohol Blend Ether Blend Alcohol Blend Ether Alcohol Blend Blend OR Multnomah 0 100 0 3.5 0 100 0 3.5 OR Washington 0 100 0 3.5 0 100 0 3.5 OR Yamhill 0 100 0 3.5 0 100 0 3.5 UT Utah 0 100 0 3.5 0 100 0 3.5 WA Clark 0 100 0 2.7 WA King 0 100 0 2.7 WA Pierce 0 100 0 2.7 WA Snohomish 0 100 0 2.7 WA Spokane 0 100 0 3.2 0 100 0 3.5 * - Pima County inputs were provided by ADEQ. Actual Pima County winter oxygenate is 1.8% rather than 3.5%.

A.3.4. Stage II Refueling Controls

Stage II controls were applied in the following counties: Maricopa County, AZ; Clark and Washoe Counties, NV; Multnomah County, OR; and Clark, King, and Pierce Counties, WA. The Oregon and Washington counties were modeled with a 95 percent Stage II control efficiency for light-duty gasoline vehicles and trucks and an 80 percent Stage II control efficiency for heavy-duty gasoline vehicles. Maricopa County, Clark County (NV), and Washoe County were modeled with a 50 percent control efficiency, 95 percent control efficiency, and 85 percent control efficiency, respectively, applied to both light and heavy vehicles.

A.3.5. Processing of California Data

California has different on-road mobile source control programs from the rest of the country. CARB has its own model that estimates the effects of these control programs. CARB provided 1996 on-road emissions estimates from EMFAC2000 model runs by vehicle class, county, and season, with all applicable controls incorporated.

A.3.6. Future Control Programs for 2003, 2008, 2013, and 2018

The effects of Federal on-road control programs are included in the MOBILE6 and modified PART5 models. The Federal control programs that started in or after 1996 that are treated as defaults in the MOBILE6/PART5 modeling ar e: National Low Emission Vehicle (NLEV) emission standards starting with the 2001 model year ; Tier 2 emission standards starting with the 2004 model year; two phases of new heavy duty ve hicle emission standards—one starting in the 2004 model year and the other starting in the 2007 model year; onboard diagnostics; and the Supplemental Federal Test Procedure (SFTP) rule. As discussed above, the low sulfur gasoline fuel corresponding with the Tier 2 emission st andards and the low sulfur diesel fuel corresponding with the heavy-duty vehicle 2007 emission standards were also modeled throughout the WRAP region. Also modeled as part of the default conditions in MOBILE6 are

Final §309 Regional TSD Page 203 of 250 December 15, 2003estimates of excess NO x emissions resulting from the use of defeat devices in heavy-duty diesel vehicles as well as the provisions to offset these excess emissions through early pull-ahead of the 2004 heavy-duty diesel emission standards a nd through low emission rebuilds of existing engines. All of these control programs were modeled using the MOBILE6 defaults and the modified PART5 model defaults, with no additional user input.

In addition to the national on-road control programs, several area-specific control programs were included in the MOBILE6 modeling for the projection years. These include I/M and ATP programs, oxygenated fuel programs, and Stage II refueling control programs. These were modeled as follows:

• I/M and ATP Programs – County coverage of the I/M and ATP programs did not change from the 1996 base year modeling to the projection years. The counties with I/M and/or ATP programs are listed in Table 3-2 (above). The States of Colorado, Oregon, Utah, and Washington provided updates to the I/M or ATP program inputs for the projection years. For the remaining States with I/M or ATP programs modeled in the 1996 base year modeling (Arizona, Idaho, New Mexico, and Nevada), the same I/M and ATP program inputs were modeled in the projection years. It should be noted, however, that these programs did already include projecti on years in the inputs, with OBD testing starting with the 1996 model year. In both the base year modeling and the projection year modeling, the I/M programs in Washington were only applied to a fraction of the VMT in each of the five counties with an I/M program. These fractions that the I/M emission factors apply to were provided by Washington, and emission factors without I/M programs applied were modeled for the remainder of the VMT in each of these counties.

• Oxygenated Fuel Programs – Table 3-3 (above) lists the counties that were modeled with oxygenated fuel in the 1996 base year, as well as the corresponding inputs used to model the oxygenated fuel program in each county with MOBILE6. Several changes were made to these base year oxygenated fuel inputs for the projection years. For Utah County, Utah, the oxygen content of the oxygenated fuel was changed from 3.5 percent to 2.7 percent. For the counties with oxygenated fuel in Oregon, the oxygenated fuel program was eliminated from the 2008, 2013, a nd 2018 projection years. In Clark, King, Pierce, and Snohomish Counties, Washington, the oxygenated fuel program was discontinued after 1996, so no oxygenated fuel was modeled for these counties in any of the projection years.

• Stage II Refueling Controls – In the 1996 base year modeling, Stage II controls were applied in the following counties: Maricopa County, AZ; Clark and Washoe Counties, NV; Multnomah County, OR; and Clark, King, and Pierce Counties, WA. The only changes made for the projection year modeling were to add Stage II controls in Clackamas County and Washington Counties, in Oregon. The MOBILE6 inputs for modeling Stage II controls applied to these two counties were the same as those applied to Multnomah County in the 1996 base year modeling - a 95 percent Stage II control efficiency for light-duty gasoline vehicles and trucks and an 80 percent Stage II control efficiency for heavy-duty gasoline vehicles.

Final §309 Regional TSD Page 204 of 250 December 15, 2003A.3.7. Processing of Future California Data

For California, CARB provided on-road emissions estimates from EMFAC2000 model runs for all four future years by vehicle class, county, and season with all applicable control programs incorporated.

A.4. Non-Road Mobile Sources

For non-road sources, 1996 emissions estimates are directly controlled by fuel input, as control technologies were not required for these sources. 1996 state-level off-road fuel sulfur averages are shown below; there are some differences by counties within states and the county-specific sulfur contents were used in developing the 1996 emissions estimates. The fuel sulfur inputs were adjusted to reflect federal rules for gasoline and highway diesel fuels that become effective between 1997 and 2018. No additional control technologies were assumed for 2018.

1996 State Averages Highway Off-Highway Gasoline Sulfur (ppm) Diesel Sulfur (ppm) Diesel Sulfur (ppm) Arizona 213 338 2005 California 23 135 135 Colorado 195 335 4100 Idaho 285 380 3075 Montana 375 320 4100 Nevada 91 310 3400 New Mexico 303 310 4100 North Dakota 266 312 4175 Oregon 293 299 3400 South Dakota 238 320 4186 Utah 186 366 3955 Washington 281 301 3400 Wyoming 285 380 4100

California has somewhat different off-road mobile source control programs from the rest of the country, and CARB has its own internal model that estimates the effects of these control programs. CARB provided 1996 off-road emissi ons estimates from their OFFROAD model by equipment type, county, and season, with all applicable controls incorporated.

Final §309 Regional TSD Page 205 of 250 December 15, 2003A.5. Stationary Sources - Existing Source State Regulation Analyses

This information is from Chapter IV “Existing Source State Regulation Analyses, Western Regional Air Partnership Emission Forecasts For 2018 - Final Report”, E.H. Pechan & Associates, Inc., December 2002, Pechan Rpt. No. 02.12.003/9409.000.

A.5.1. NO x
The analysis of NO x emission regulations primarily examined ozone nonattainment areas. These are limited to California and Maricopa County (Phoenix), Arizona.

Arizona

Portions of Maricopa County are (were) nonattainment for both ozone and PM 10 . The primary ozone control measure adopted in Maricopa County was a 15 percent rate VOC emission reduction requirement of the CAA. This emission reduction has no direct impact on SO 2 , NO x and PM 10 emissions. There are a limited number of NO x control requirements.

California

In California, the thirty-five (35) air pollution control districts have jurisdiction in imposing emission limits on point sources. The following sections present the district NO x emission limits for turbines, boilers, internal combustion engines, and petroleum refineries. The fuel combustion sources (boilers, internal combustion engines, and turbines) are of particular interest in this study because they are the largest stationary source NO x emitters in California.

The impact of these regulatory requirements was estimated as follows. Uncontrolled emission rates were estimated u sing EPA AP-42 uncontrolle d emission factors, which are primarily listed in units of pounds per million British thermal units (lbs/MMBtu). EPA guidance was followed to convert these EPA emission factors into parts per million (ppm). This was done for comparison to the California district rules and Maricopa County rules that regulate emissions from these emission units in ppm. This method was used to estimate the likely level of control required by the California Air Pollution Control District (CAPCD) regulations and Maricopa County, Arizona rules. The CAPCD point source regulations also apply to existing units, except as noted. Several CAPCD regulations impose different NO x limits for units larger than 10 megawatts (MW) depending on whether they have an SCR control device. Since it is not clear whether units in those districts with two sets of rules have installed SCR, to be conservative, the less restrictive emission limit is imposed (assuming no SCR).

Gas Turbines

The first row of Table IV-11 lists the NO x emission factors for uncontrolled turbine units. They are provided for comparison with emission limits permitted from gas turbines as found by CAPCD. In some cases, CAPCDs impose different NO x emission limits on units with identical

Final §309 Regional TSD Page 206 of 250 December 15, 2003power ratings that differ only in whether they ar e equipped with SCR control technology. In all of these cases, those units without SCR control technology are allowed a higher NO x emission limit. Since it is not clear whether most gas turbines are equipped with SCR or not, to be conservative the less restrictive emission limit assuming no SCR control is being used applies. With this information, the control effectiveness of the NO x emission limits imposed in each CAPCD is identified. The control effectiven ess is obtained by dividing the CAPCD imposed

Final §309 Regional TSD Page 207 of 250 December 15, 2003NO x emission limits by the corresponding and applicable EPA AP-42 uncontrolled emission factor. The CAPCD turbine regulations also apply to existing units, except as noted.

Industrial Boilers

The IAS separately tracks emissions from industrial coal (incobo), natural gas (inngbo), oil (inoibo), and wood (inwobo) boilers. Table IV-12 lists the EPA NO x uncontrolled emission factors used for these boilers. Also listed in Table IV-12 are the NO x emission factor limits imposed on these boilers as found for some CAPCDs. These CAPCD regulations also apply to steam generators and process heaters, except as noted. The control effectiveness of these regulations is obtained by dividing the CAPCD imposed NO x emission limits by the corresponding and applicable EPA AP-42 uncontrolled emission factor.

Internal Combustion Engines

Table IV-13 lists the NO x emission factors appearing in EPA AP-42 applicable to uncontrolled internal combustion units. Also listed in Table IV-13 are the emission limits imposed on these units within Maricopa County, Arizona and by CAPCD. With this information, one is able to identify the control effectiveness of the NO x emission limits imposed within Maricopa County, Arizona and in each CAPCD. The control eff ectiveness is obtained by dividing the Maricopa County or CAPCD imposed NO x emission limits by the corresponding and applicable EPA AP42 uncontrolled emission factor. The CAPCD regula tions also apply to existing units, except as noted.

As previously noted, the base case emission inventory for this study is 1996. Because some CAPCD regulations go into effect after 1996, it is expected that these post-1996 regulations will result in a corresponding emission reduction in those areas for these sources relative to 1996. This is captured by reporting the NO x emission reduction expected in each region relative to 1996, where data are available to perform this task. We have also been able to identify the control effectiveness of the NO x emission limits imposed in Maricopa County, Arizona and within each CAPCD. The control effectiveness is obtained by dividing the Maricopa County, Arizona and CAPCD imposed NO x emission limits by the corresponding and applicable EPA AP-42 uncontrolled emission factor. The CAPCD regulations also apply to existing units, except as noted.

Final §309 Regional TSD Page 208 of 250 December 15, 2003Final §309 Regional TSD Page 209 of 250 December 15, 2003Final §309 Regional TSD Page 210 of 250 December 15, 2003Final §309 Regional TSD Page 211 of 250 December 15, 2003Industrial Reciprocating Engines, Including Natural Gas

Table IV-14 lists the NO x emission factors permitted from natural gas and other fuels used in reciprocating engines as reported by CAPCD. As shown below , only Santa Barbara County and San Diego County Air Pollution Cont rol Districts apply specific NO x emission factor limits from these types of units.

Final §309 Regional TSD Page 212 of 250 December 15, 2003Industrial Petroleum Refineries

The California Bay Area District imposed regulations limiting NO x emissions from boilers, steam generators, and process heaters in petroleum refineries. The limits imposed were 0.2 pounds per MMBtu in 1995 and 0.033 pounds per MM Btu in 1997. In other words, the Bay Area District decreased the allowable NO x emission factor from petroleum refineries by 83.5 percent from 1995 to 1997 (see Table IV-15).

Oil and Gas Production Facilities

None of the documents checked on-line included any information about regulated NO x or PM emissions. The documents related to oil and gas production had to do with leak detection and repair, which affects VOC emissions.

Missouri

Missouri is included in this analysis because its emissions are within the WRAP Region modeling domain. EPA’s (1999b) Regional Transport NO x State Implementation Plan (SIP) proposed to reduce NO x emissions within many States east of the Rocky Mountains, including Missouri, in an effort to reduce trans ported ozone concentrations in eastern States. The primary focus for reducing NO x emission s was from electric gene rating units (EGUs).

For EGU point sources, base year 1995/1996 NO x emissions were used to develop an Integrated Planning Model (IPM) Year 2007 emission inventory. For Missouri, the IPM Year 2007 summer emission inventory for EGU point s ources equaled 82,097 tons. The EPA 2007 NO x control case was then developed by unit by applying IPM growth factors to the unit emission rate for the 1995/1996 base year. Emissions from EGUs greater than 25 MW equivalents were then limited to 0.15 lbs NOx/MMBtu. Units 25 MW equivalents or smaller were left at their 2007 base case NO x emission rate. For Missouri, the resulting IPM NO x control Year 2007 summer emission inventory for EGU point sources equale d 24,216 tons. Thus, the EPA analysis called

Final §309 Regional TSD Page 213 of 250 December 15, 2003for a 70 percent reduction in EGU 2007 NO x emissions relative to the IPM base case Year 2007 Missouri inventory (see Table IV-16).

Texas

Texas is included in this analysis because its emissions are within the WRAP Region modeling domain. Recent revisions to the SIPs for the major ozone nonattainment areas in Texas have added many regulations that require stationary source NO x emitters to reduce their future year emissions.

The Texas SIPs developed by the Texas Natu ral Resource Conservation Commission (TNRCC) to reduce ozone concentrations in ambient air are very source-specific. There are three ozone nonattainment areas of note in Texas: (1) Beaumont/Port Arthur; (2) Houston/Galveston; and (3) Dallas/Fort Worth. The SIPs developed for these areas require a reduction in NO x emissions from specific point sources or uniformly acro ss a source category as described below. In addition, TNRCC entered into orders requiring Alcoa and Eastman Chemical to reduce NO x and VOC emissions for the purpose of revising its SIP for ozone. The effect of these orders in terms of NO x emission reductions is also included in this analysis. There is also a TNRCC SIP requirement that utility and grandfathered non-utility sources in Eastern and Central counties of Texas reduce emissions. The recommended implementation of this requirement is presented below.

Beaumont/Port Arthur

The Beaumont/Port Arthur ozone nonattainment area includes Hardin, Jefferson, and Orange counties. TNRCC (2000a) believes Tier 1 reductions in NO x emissions from these three counties will be enough for Beaumont/Port Arthur to attain the 1-hour ozone standard.

The Tier 1 reductions amount to a 40.6 percent, 61.9 percent, and 36.5 percent reduction in NO x emissions from point sources in Hardin, Jeffe rson, and Orange counties (see Table IV-17). TNRCC (2000) reports that these reductions are equivalent to requiring a 50 percent emission reduction from utility sources and a 20 percent emission reduction from four (4) refineries and fifteen (15) chemical plants. These NO x reductions of 40.6 percent, 61.9 percent, and 36.5 percent from point sources in Hardin, Jefferson, and Orange counties were uniformly applied to all point sources in this ozone nonattainment area.

Final §309 Regional TSD Page 214 of 250 December 15, 2003Houston/Galveston

The Houston/Galveston ozone nonattainment area includes Brazoria, Chambers, Fort Bend, Galveston, Harris, Liberty, Montgomery, a nd Waller counties. For point sources, TNRCC compiled a 2007 future year NO x emission inventory equal to 564 and 641 tpy (TNRCC, 2000b) for Phase II and Phase III base cases. TNRCC also compiled a 2007 future year control case NO x inventory. This control case inventory contained 64 and 67 tpy (TNRCC, 2000b) of point source NO x emissions, respectively, for Phase II and Phase III scenarios. The difference in the 2007 base case and control case amounts to a 90 percent reduction in NO x emissions from point sources within Houston/Galveston ozone nonattainment area counties (see Table IV-17). (The 90 percent reduction is calculated from the Phase III scenario as follows: 90 percent = 100 percent x (1 – 67 t/ 641 t).) This 90 percent reduction was applied uniformly to all point sources in the Houston/Galveston area counties shown in Table IV-17.

Final §309 Regional TSD Page 215 of 250 December 15, 2003Dallas/Fort Worth

Appendix F of the Dallas/Fort Worth ozone nonattainment demonstration (TNRCC, 1999a) identifies NO x control factors proposed for specific industrial boilers and engines and EGUs in that area. These unit specific reductions were applied to estimate 2018 NO x emissions.

Alcoa

Alcoa operates a plant in Milam County, Texas. A TNRCC order with Alcoa limits future maximum NO x emissions from Alcoa’s 3 boilers to 13,622.4 tpy. This equals a 19.6 percent NO x emission reduction relative to the emission inventory for these three boilers in the WRAP database for 1996. These reductions were applied in the forecast year.

Cement Kilns

Appendix F of the Dallas/Fort Worth ozone nonattainment demonstration (TNRCC, 1999a) identifies 11 cement kilns modeled as part of the proposed Dallas/Fort Worth NO x emission reduction strategy. The level of NO x controls required by TNRCC ranged by unit from 6 to 66 percent. These controls were applied on a unit-by-unit basis as reported by TNRCC. However, one of the four Texas Industries (Ellis County) cement kilns identified by TNRCC as requiring control was not listed in the WRAP 1996 emission inventory. It is unclear whether the WRAP emission inventory missed counting emissions from a cement kiln, or whether there is a typo in the Dallas/Fort Worth ozone SIP strategy.

Eastman Chemical

Eastman Chemical operates a chemical plant in Ha rris County, Texas. Harris County is part of the Houston/Galveston ozone nonattainment ar ea. A TNRCC order requires this Eastman Chemical plant to reduce NO x emission s from 14 units by 1,671.5 tpy. Thirteen of the 14 units are to be retired. Because the retirement of th ese units would also reduce emissions of other pollutants, these specific units in the WRAP database for Eastman Chemical were retired. Because the unit specific codes in the WRAP database and the TNRCC unit identifiers for Eastman Chemical did not match, this required some judgment to determine which units in the WRAP database best matched those identified by TNRCC.

Industry and Utility Units in Central and Eastern Texas

As part of the Houston/Galveston ar ea SIP, TNRCC (1999b) added the following NO x emission reduction requirements applicable outside the Houston/Galveston area nonattainment counties and within Central and Eastern Texas:

• 50 percent reduction of NO x emissions from all utility stationary sources, and

• 30 percent reduction of NO x emissions from remaining grandfathered sources.

Final §309 Regional TSD Page 216 of 250 December 15, 2003The 50 percent reduction was applied uniformly to all utility stationary sources in Central and Eastern Texas. The 30 percent NO x reduction requirement from grandfathered sources is difficult to simulate, because the identity of the grandfathered sources was not provided by TNRCC. An analysis was made to determine how this information could be adapted and applied uniformly. The analysis made use of a NO x emissions data file for grandfathered and nongrandfathered sources. The Alcoa boilers (3) mentioned above are thought to represent a part of the non-utility grandfathered sources in Central and Eastern counties of Texas. When the Alcoa boilers emission reduction requirement is removed, the 30 percent reduction required by TNRCC from grandfathered non-utility sources equates to a 7.3 percent emission reduction requirement from all non-utility sources in Central and Eastern Texas. The 7.3 percent reduction was applied uniformly to all non-utility point sources, except for Alcoa.

A.5.2. SO 2
The analysis of existing source State regulations affecting SO 2 emissions in the WRAP States focused on identifying the regulations that were recent enough that existing sources would not have responded to them by 1996. It was also recognized that regulations affecting the largest point source SO 2 emitters would be most important to the forecast. This evaluation focuses on non-utility sources. Utility units are affected by the Federal Acid Rain Program, but as is explained in Chapter VII, future year utility SO 2 and NO x emission estimates incorporate 2018 utility unit values that were prepared under a separate study. The tables in the following pages report the recent SO 2 emission regulations for the WRAP States that have SO 2 nonattainment areas, or regulations that affect the major sources in their States.

California

Table IV-18 lists the SO 2 emission factor limits found on-line as reported by CAPCD. The emission limits found cover a range of unit operations or in some cases cover all unit operations possible.

Arizona

Arizona air pollution control regulations restrict copper smelter SO 2 emissions by facility asshown below. Of the listed Arizona copper smelters, only ASARCO-Hayden and Phelps DodgeMiami are currently operating.

Final §309 Regional TSD Page 217 of 250 December 15, 2003Final §309 Regional TSD Page 218 of 250 December 15, 2003Final §309 Regional TSD Page 219 of 250 December 15, 2003Montana

Lewis and Clark County (East Helena) (County Code: 30-049)

These SO 2 emission limits were part of the SIP submitted by the State of Montana, and have been included in the Federally (EPA) approved SIP (SMAQCIP, 1995).

Yellowstone County (County Code: 30-111)

These SO 2 emission limits were part of SIPs submitted by the State of Montana but have not been approved by EPA. Therefore, these limits are State-enforceable only. In addition, the following emission limits will apply whenever the Yellowstone Energy Limited Partnership (YELP) facility receives Exxon Coker unit flue gas, or whenever the Exxon Coker unit is not in operation (SMAQCIP, 2000a).

Final §309 Regional TSD Page 220 of 250 December 15, 2003Final §309 Regional TSD Page 221 of 250 December 15, 2003Nevada

Nevada State SO 2 regulations were summarized as follows:

Final §309 Regional TSD Page 222 of 250 December 15, 2003New Mexico

Coal Burning Equipment (After December 31, 1984, the owner or operator of a coal burning station that has two or more units o f ex isting coal burning equipment that have a rated heat capacity greater than 250 MMBtus per hour has an SO2 emission limit of 17,900 pounds per hour, which is averaged over any three-hour period and determined on a total station basis (NMED, 1995).)

Natural Gas Processing Plants

Petroleum Refineries

Sulfur Recovery Plants (This limit applies to plants where fabrication, erection, or installation commenced before August 14, 1974.

Final §309 Regional TSD Page 223 of 250 December 15, 2003Sulfuric Acid Production Units

Nonferrous Smelters

Utah

The SIP for Utah was last approved by EPA on July 8, 1994, except for the Amoco Oil Company submission.

After gathering the above information about State regulations, the SO 2 emission limits were compared with the SO 2 emissions in the WRAP 1996 point source file for affected facilities. In all cases, it was found that emission points/facilities were in compliance with these SO 2 regulations. Therefore, no additional SO 2 controls were placed on point sources in the 2018 emission forecast.

Final §309 Regional TSD Page 224 of 250 December 15, 2003A.6. Stationary Sources – Retirement Factors, Unit Lifetime Analysis

This information is from Chapter V “Retirement Factors – Unit Lifetime Analysis, Western Regional Air Partnership Emission Forecasts For 2018 - Final Report”, E.H. Pechan & Associates, Inc., December 2002, Pechan Rpt. No. 02.12.003/9409.000.

In the original IAS model, future year forecasts of electric utility emissions used estimates of the date of initial operation and expected unit lifetimes in years to determine when existing source emission rates were likely to be replaced with new source emission rates. So, for example, if an oil-fired utility boiler began operating in 1970, it would be expected to be replaced by a new boiler that emits at NSPS/BACT level emission rates in 2000 at the end of its 30-year lifetime. For non-utility units, the IAS model includes the effects of retirements using an annual rate. So, each unit in any source category has the same annual retirement rate applied. For example, the annual retirement rate for industrial boilers in the IAS model has been 0.6 percent per year. If this retirement rate were applied to the 1996 to 2018 forecast horizon that is being used for this project, then 12.4 percent of industrial boiler capacity would be retired during this 22-year period. One of the objectives of this project was to establish projection methods for the largest non-utility units that parallel those used for utilities. This requires gathering and using information about the year of initial operation for individual non-utility units and expressing non-utility unit lifetimes in years. The year of initial operation data gathering activity is described in Chapter II. This chapter describes the effort to establish appropriate lifetime estimates for the source categories (scc_ ids) in the IAS model.

A.6.1. Industrial Sources

This section deals with estimating the lifetimes of the IAS industrial sources listed in Table V-1. The IAS annual retirement rates for each sector were converted into the lifetime years listed above by the following formula:

We consulted several other data sources, such as Internal Revenue Service Publications, Bureau of Economic Analysis (BEA) depreciation schedul es, other industry publications, and estimates provided by authorities in different sectors, to estimate the actual lifetimes of the different industrial sector units or plants. The following sub sections describe how the lifetimes of the different industrial sector units or plants were calculated or estimated.

Final §309 Regional TSD Page 225 of 250 December 15, 2003A.6.2. Industrial Boilers

The annual retirement rates used in the original IAS model for industrial fuel combustors or industrial boilers are taken from a U.S. energy model named the ICE model. The ICE model was developed and applied as part of the Nationa l Acid Precipitation Assessment Program (NAPAP) emission and control techniques evaluation process. The assumed IAS annual industrial boiler retirement rate of 0 .6 percent converts into a lifetime of 167 years. However, other data sources present boiler lifetimes that are much lower, and these estimates are presented next.

According to Steam/its generation and use , the degree of pressure and heat associated with a boiler, along with its design, function, and opera tion affect boiler lifetime. Industrial boilers operating at pressures above 1,200 psi (pounds per square inch, absolute or difference) and 900 F (482 C) final steam temperature undergo more complicated aging mechanisms than lower temperature boilers (Stultz, 1992). The high pressures and associated high furnace wall temperatures make these units more susceptible to water side corrosion. Table V-2 presents the component replacement sequence for a typical high pressure, high temperature boiler (Stultz, 1992).

Final §309 Regional TSD Page 226 of 250 December 15, 2003In the case of a typical high temperature, high-pressure boiler, most boiler pressure part components have been replaced after 40 years of operation. However, the aging process and rate of component degradation differ from boiler to boiler. Moreover, the actual component life of a boiler is highly variable depending on the specific design, operation, maintenance, and fuel (Stultz, 1992). In another analysis, Teknekron Research Inc. assumed a 30-year boiler lifetime when calculating the retirement rate of a boiler in its report “Review of Modeling Activities Related to New Source Performance Standards for Industrial Boilers” (Placet, 1980). However, it was also found that some boilers over 70 years old were still in use, with no plans to retire them. Therefore, Teknekron suggested an approximate boiler lifetime of 40 years as a reasonable estimate of the lifetime of an industrial boiler (Placet, 1980).

The Internal Revenue Service’s “Publication 946: How to Depreciate Property” lists lifetimes of industrial boilers from a depreciation point of view. The IRS uses a system called Modified Accelerated Cost Recovery System (MACRS) to de preciate assets. According to this system, a class life of 28 years is estimated for the asset category “Central Steam Utility Production and Distribution.” In addition, 20-year and 28-year recovery periods are estimated for the General Depreciation System (GDS) and Alternative De preciation System (ADS), respectively (IRS, 2000). The lifetime years used in the depreciation schedules in this publication may not be directly representative of the actual lifetime of a boiler. Therefore, we presume that these lifetimes represent a minimum lifetime estimate for industrial boilers. This same issue arose in interpreting the BEA ’s depreciation schedules. These schedules estimate a service life of 32 years for “Steam Engines and Turbines” (Fraumeni, 1997). Again, since this depreciation lifetime may not directly represent the actual lifetime of a boiler, these lifetimes might represent a minimum lifetime estimate for industrial boilers.

Discussions w ere held with Bob Bessette of the Council of Industrial Boiler Owners (CIBO), Randall Rawson of the American Boiler Manufacturer s Association, Ian Lutes of Foster Wheeler Corporation, and Brian Moore of the Hartford Steam Boiler Company. The opinion among this group was that while industrial boiler lifetimes could range from 30 to 100 years, the majority of these boilers stay in service from 35 to 60 years. Industrial boilers generally have less focus on

Final §309 Regional TSD Page 227 of 250 December 15, 2003maintenance than utility boilers. Utility boilers, as a rule, are optimally maintained. In some cases, industrial boiler owners are reticent to pe rform maintenance on their units for fear of triggering new source review. Therefore, it would be expected that the average lifetime of an industrial boiler would be less than that of a comparable utility boiler. There are exceptions, of course, especially when industrial boilers are well maintained and operated at lower pressures. Field erected units tend to have higher lifetimes than package boilers for a variety of reasons. Through discussions with staff at the U.S. Depa rtment of Energy, it was determined that the most comprehensive data source about expected unit lifetimes by source type was Energy and Environmental Analysis’s Industrial Sector Technology Use Mod el (ISTUM). The estimated lifetimes by industrial sector technology from ISTUM (EEA, 2001) range from 20 years for refinery heaters and distillation units to 30 years for industrial boilers. However, there is evidence that the equipment turnover in these industries is not nearly as rapid as ISTUM predicts.

Pechan’s recommendation based on the evidence provided by the boiler industry representatives is that a 45-year lifetime be used for all industrial boilers in the emission forecasts to 2018. This is 1.5 times the lifetime used by the ISTUM model. It is also recommended that the IAS model lifetimes for other industrial sector technologies be 1.5 times the ISTUM values. This makes the lifetimes for most refinery equipment 30 years, and makes the cement kiln lifetimes 37.5 y ears. Making these changes provides a more conservative estimate of future year WRAP State emissions. A summary of estimated unit lifetimes by industrial source category is provided in Table V-3.

Final §309 Regional TSD Page 228 of 250 December 15, 2003Example Calculations

The IAS model algorithms are applied to estimate 2018 emissions given the primary variables affecting emissions in that year, which are: 1996 emissions, unit date of initial operation, expected unit lifetime or retirement rate, new source control efficiency, and growth rates/factors. The base IAS algorithm for performing emission forecasts to 2018 at the unit level is shown in the equation below.

In the point source emission projections, there are three cases that all of the sources fall into. These three cases are listed below:

Example calculations of 2018 emissions are provided below for each of these three cases:

In this example, because the unit is expected to still be operating in 2018, the existing source portion of the SO 2 emissions (5,437 tpy) remains the same as in 1996. Any increase in activity at this facility is estimated to occur at new source emission rate levels, which are 3 percent of existing source rates.

Because this unit has an expected retirement date before 2018, all of the 2018 emissions are at new source rates, which are 28 percent of existing source rates. The growth factor that is applied to the new source emission rates incorporates 1996 activity, plus expected activity increases from 1996 to 2018.

Final §309 Regional TSD Page 229 of 250 December 15, 2003With no specific start date/retirement date available, the retirement rate is applied in a way to capture the percentage o f existing capacity in this industry that is expected to retire each year over the 22-year forecast horizon. In this example, 73 percent of the 1996 capacity is estimated to have been retired by 2018. While, in reality, units do not retire a fraction of their capacity each year, this calculation is expected to provide a reasonable simulation of existing source retirement, new source growth when spread over a broad geographic region, like the WRAP States.

Implications of Retirement Assumptions in IAS

The practical result of using the revised estimates of unit lifetimes by source category and technology is that future emissions are lower fo r source categories with significant differences between new and existing source emission rates. Figure V-1 presents an example 1996 to 2018 SO 2 emissions path using the previous industrial boiler IAS retirement rate of 0.6 percent per year compared with the new retirement rate of 2.2 percent per year. This is a source category where the new source SO 2 control efficiency is 90 percent, so the faster the existing units retire, the more rapid the decline in future SO 2 emissions. A 2.0 percent per year new source growth rate is used in this example. So, a 1,000 tpy SO 2 source in 1996 would be estimated to have 2018 emissions of 936 tpy if the prior IAS retirement rate was used. The emission forecasting methods applied in this study yield a 2018 emissions estimate of 619 tpy. This is a significant reduction in future emissions from this source category compared with prior methods.

Final §309 Regional TSD Page 230 of 250 December 15, 2003

## Appendix B: Use of EPA Guidance and best practices

The WRAP and its contractors followed EPA emi ssions inventory guidance and best emissions inventory development practices, EPA modeling guidance, and used professional judgment in preparing technical analyses for §309. Specific references to EPA guidance documents are listed throughout the earlier chapters, usually as web addresses and report titles, as well as in Appendix M of this document.

Final §309 Regional TSD Page 231 of 250 December 15, 2003

## Appendix C: 1996 Base Case Emissions Used in Air Quality Modeling

The following spreadsheet details emissions used in the 1996 base case modeling run. These are not the emissions used in the 1996 base case model performance evaluation run, where the model-predicted results are compared to monitoring data. The emissions following are used as the basis of “projected visibility improvement”, requirement of §309, for the 16 Colorado Plateau Class I areas. Fire emissions in the sp readsheet below are typical wildfire, and the 2018 base case emissions for forest/range prescribed fire and agricultural fire.

The 1996 base case modeling run emissions spreadsheet is found on the WRAP website at:

http://wrapair.org/309/documents/FinalDocs/1996_Base_Case_Final.xls

.

Final §309 Regional TSD Page 232 of 250 December 15, 2003

## Appendix D: 2018 Base Case Emissions Used in Air Quality Modeling

The following spreadsheet details emissions used in the 2018 base case modeling run. The emissions following are based on “rules and regulations on the books” as of 2001. Fire emissions in the spreadsheet below are typical wildfire, and the 2018 base case emissions for forest/range prescribed fire and agricultural fire.

The 2018 base case modeling run emissions spreadsheet is found on the WRAP website at:

http://wrapair.org/309/documents/FinalDocs/2018_Base_Case_Final.xls

.

Final §309 Regional TSD Page 233 of 250 December 15, 2003

## Appendix E: 2018 Scenario 1 Emissions Used in Air Quality Modeling

The following spreadsheet details emissions used in the 2018 Scenario 1 modeling run. The emissions following are based on “rules and regulations on the books” from the 2018 base case, plus adoption of the SO 2 Annex Milestones and Pollution Prevention programs across the Grand Canyon Visibility Transport Commission 9-state regi on. Fire emissions in the spreadsheet below are typical wildfire, and the 2018 base case em issions for forest/range prescribed fire and agricultural fire. Scenario 1 is the first of two projections of visibility improvement from implementation of §309 programs.

The 2018 Scenario 1 modeling run emissions spreadsheet is found on the WRAP website at:

http://wrapair.org/309/documents/FinalDocs/2018_Scenario_1_Final.xls

.

Final §309 Regional TSD Page 234 of 250 December 15, 2003

## Appendix F: 2018 Scenario 2 Emissions Used in Air Quality Modeling

The following spreadsheet details emissions used in the 2018 Scenario 2 modeling run. The emissions following are based on “rules and regulations on the books” from the 2018 base case, adoption of the SO 2 Annex Milestones and Pollution Prevention programs across the Grand Canyon Visibility Transport Commission 9-state region, as well as adoption of the Enhanced Smoke Management Programs across the Grand Canyon Visibility Transport Commission 9- state region, based on the 2018 Optimal Smoke Management emissions inventories for forest/range prescribed fire and agricultural fire. Typical wildfire emissions were used in this scenario. Scenario 2 is the second of two projections of visibility improvement from implementation of §309 programs.

The 2018 Scenario 2 modeling run emissions spreadsheet is found on the WRAP website at:

http://wrapair.org/309/documents/FinalDocs/2018_Scenario_2_Final.xls

.

Final §309 Regional TSD Page 235 of 250 December 15, 2003

## Appendix G: 2018 BART with Uncertainty Scenario Emissions Used in Air Quality Modeling

The following spreadsheet details emissions used in the 2018 BART with Uncertainty Scenario modeling run. The emissions following are based on analyses described in Chapter 1 of this document. Fire emissions in the spreadsheet below are typical wildfire, and the 2018 base case emissions for forest/range prescribed fire and agricultural fire. The BART with Uncertainty Scenario was compared using air quality modeling to the SO 2 Annex Milestones Scenario, and the results are discussed in Chapter 4 of this document.

The 2018 BART with Uncertainty Scenario modeling run emissions spreadsheet is found on the WRAP website at:

http://wrapair.org/309/documents/Final Docs/2018_BARTwUncertainty_Final.xls

.

Final §309 Regional TSD Page 236 of 250 December 15, 2003

## Appendix H: 2018 SO 2 

## Annex Milestones Scenario Emissions Used in Air Quality Modeling

The following spreadsheet details emissions used in the 2018 SO 2 Annex Milestones Scenario modeling run. The emissions following are based on analyses described in Chapter 1 of this document. Fire emissions in the spreadsheet below are typical wildfire, and the 2018 base case emissions for forest/range prescribed fire and agricultural fire. The SO 2 Annex Milestones Scenario was compared using air quality modeling to the BART with Uncertainty Scenario, and the results are discussed in Chapter 4 of this document.

The 2018 SO 2 Annex Milestones Scenario modeling run emissions spreadsheet is found on the WRAP website at:

http://wrapair.org/309/documents/FinalDocs/2018_SO2_Annex_Milestones_Final.xls

.

Final §309 Regional TSD Page 237 of 250 December 15, 2003

## Appendix I: 2018 Stationary Source 50% NO x 

## Reduction Scenario Emissions Used in Air Quality Modeling

The following spreadsheet details emissions used in the 2018 Stationary Source 50% NO x Reduction Scenario modeling run. The emissions following are based on analyses described in Chapter 4 of this document. Fire emissions in the spreadsheet below are typical wildfire, and the 2018 base case emissions for forest/range prescribed fire and agricultural fire. The 2018 Stationary Source 50% NO x Reduction Scenario was modeled to satisfy requirement of §309, and the results are discussed in Chapter 4 of this document.

The 2018 Stationary Source 50% NO x Reduction Scenario modeling run emissions spreadsheet is found on the WRAP website at:

http://wrapair.org/309/documents/FinalDocs /2018_NOx_50percent_Decrease_Stationary_Sourc es_Final.xls

.

Final §309 Regional TSD Page 238 of 250 December 15, 2003

## Appendix J: 2018 Stationary Source 50% PM 10 

## Reduction Scenario Emissions Used in Air Quality Modeling

The following spreadsheet details emissions used in the 2018 Stationary Source 50% PM 10 Reduction Scenario modeling run. The emissions following are based on analyses described in Chapter 4 of this document. Fire emissions in the spreadsheet below are typical wildfire, and the 2018 base case emissions for forest/range prescribed fire and agricultural fire. The 2018 Stationary Source 50% PM 10 Reduction Scenario was modeled to satisfy requirement of §309, and the results are discussed in Chapter 4 of this document.

The 2018 Stationary Source 50% PM 10 Reduction Scenario modeling run emissions spreadsheet is found on the WRAP website at:

http://wrapair.org/309/documents/FinalDocs /2018_PM10_50percent_Decrease_Stationary_Sour ces_Final.xls

.

Final §309 Regional TSD Page 239 of 250 December 15, 2003

## Appendix K: 2018 Stationary Source Simultaneous 25% NO x 

## and 25% PM 10 

## Increase Scenario Emissions Used in Air Quality Modeling

The following spreadsheet details emissions used in the 2018 Stationary Source Simultaneous 25% NO x and PM 10 Increase Scenario modeling run. The emissions following are based on analyses described in Chapter 4 of this document. Fire emissions in the spreadsheet below are typical wildfire, and the 2018 base case emissions for forest/range prescribed fire and agricultural fire. The 2018 Stationary Source Simultaneous 25% NO x and PM 10 Increase Scenario was modeled to satisfy requirement of §309, and the results are discussed in Chapter 4 of this document.

The 2018 Stationary Source Simultaneous 25% NO x and PM 10 Increase Scenario modeling run emissions spreadsheet is found on the WRAP website at:

http://wrapair.org/309/documents/FinalDocs/2018_PM10_25percent_and_NOx_25percent_Incre ase_Stationary_Sources_Final.xls

.

Final §309 Regional TSD Page 240 of 250 December 15, 2003

## Appendix L: Clean Air Corridor Change in Emissions 1996 to 2018

The projected changes in emissions, within the clean air corridor, are show spatially in the following figures for SO 2 , NO 2 , VOC, PM 10 , and PM 2.5
.

SO 2 emission difference, by county, 2018 projections with WRAP SO 2 Annex Milestones and 1996 base.

Final §309 Regional TSD Page 241 of 250 December 15, 2003NO X emission difference, by county, 2018 projections and 1996 base

Final §309 Regional TSD Page 242 of 250 December 15, 2003VOC emission difference, by county, 2018 projections and 1996 base

Final §309 Regional TSD Page 243 of 250 December 15, 2003PM 10 emission difference, by county, 2018 projections and 1996 base

Final §309 Regional TSD Page 244 of 250 December 15, 2003PM 2.5 emission difference, by county, 2018 projections and 1996 base

Final §309 Regional TSD Page 245 of 250 December 15, 2003

## Appendix M: References

Air Sciences, Inc. 2002a: “1996 Fire Emission Inventory”, prepared for the Emissions Task Team of the WRAP Fire Emissions Joint Forum, March 2002.

Air Sciences, Inc. 2002b: “Integrated Assessment Update and 2018 Emissions Inventory for Prescribed Fire, Wildfire, and Agricultural Burning”, pr epared for the Emissions Task Team of the WRAP Fire Emissions Joint Forum, March 2002.

Carter, W. P. L., 1999: “Documentation of the SAPRC- 99 Chemical Mechanism for VOC Reactivity Assessment”, Draft report to the California Air Resources Board, Contracts 92-329 and 95-308, 9/13/99.

Dennis R.L., Byun D.W., Novak J.H., Galluppi K.J., Coat s C.J. and Vouk M.A., 1996: “The next generation of integrated air quality modeling: EPA's M odels-3”, Atmos. Environ., 30, 1925-1938.

DOE, 2000: U.S. Department of Energy, “Annual Energy Outlook 2001, with Projections through 2020”, DOE/EIA-0383 (2001), Energy Informati on Administration, Office of Integr ated Analysis and Forecasting, Washington, DC, December 2000.

EA Engineering, Science, and Technology, 2003: “Needs Assessment for Evaluation and Design of an Emissions Data Reporting, Management, and Tracking System”, draft report to the Emissions Forum, June 26.

EPA, 1997: “Guidance for Demonstrating A ttainment of Air Quality Goals for PM 2.5 and Regional Haze.” Draft 1.1. March 27.

EPA, 1999: “Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System” (eds. Byun, D., and Ching, J.), EPA Tech. Rep. EPA-600/R-99/030. Available from EPA/ORD, Washington, D. C., 20460.

EPA, 2001: U.S. Environmental Protection Agency, “Procedures Document for National Emission Inventory Criteria Air Pollutants, 1985-1999,” EPA- 454/R-01-006, Office of Air Quality Pl anning and Standards, Research Triangle Park, NC.

EPA, 2001: “Draft Guidance for Tracking Progress Under th e Regional Haze Rule.” Offi ce of Air Quality Planning and Standards, Research Triangle Park, NC. September 27.

EPA, 2001: “Draft Guidance for Estima ting Natural Visibility Conditions Under the Regional Haze Rule.” Office of Air Quality Planning and Standards, Res earch Triangle Park, NC. September 27.

Federal Register, 1999: 40 CFR Part 51. Regional Haze Regulations, Final Rule . Volume 64, No. 126. July 1.

Gery M. W., Whitten G. Z., Killus J. P. and Dodge M. C. , 1989: “A photochemical mechanism for urban and regional-scale computer modeling”. Journal of Geophysical Research 94, 12925-12956. Grell, A. G., J. Dudhia, and D. R. Stauffer, 1994: “A description of the fifth-generation Penn State/NCAR mesoscale model (MM5)”. NCAR Tech. Note NCAR/TN 398+STR, National Center for Atmospheric Research, Boulder, CO. Houyoux, M.R., Vukovich, J. M. ; Coats, C. J., Jr. ; Wheeler, N. J. M. ; Kasibhatla, P. S., 2000: “Fast emissions modeling with the sparse matrix operator kernel emissions modeling system”. J. Geophys Res., 105: 9079-9090.

Harvey, Craig. 2002. “Comparison of Draft NONROAD2002 to Prior Draft Version.” Memo to Record, provided with confidential December 2002 NONROAD2002 model release. December 9.

Houyoux, M., et. al., 2003. “Final Report: WRAP Regional Modeling Center – Short-Term Modeling Analysis”, UNC – Chapel Hill, Carolina Environmental Program, March 31.

Final §309 Regional TSD Page 246 of 250 December 15, 2003ICF, 2002: ICF Consulting Group, “Ec onomic Assessment of Implementing the 10/20 Goals and Ener gy Efficiency Recommendations,” prepared for the WRAP Air Pollution Prevention Forum, October 2002.

Odum, J.R.; Hoffman, T.; Bowman, F. ; Collins, D.; Flagan, R.C.; and Se infeld, J.H., 1996: “Gas/particle partitioning and secondary aerosol formation” . Environ. Sci. and Technol

. 30: 2580-2585. Pechan, 2002: E.H. Pechan & Associates, Inc., “WRAP Em issions Forecasts for 2018 – Draft Report”, prepared for the WRAP Emissions Forum May 2002 (Revised), Pechan Rpt. No. 01.12.002/9409.000 (Rev.).

Pechan, 2001: E.H. Pechan & Associates, Inc., “Economic Growth Analysis System, Version 4.0 Reference Manual, Final Draft,” prepared for U.S. Environmenta l Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, January 26, 2001.

Pechan, 2002: Technical Memorandum: Revisions to the 1996 WRAP Point Source Emissions Data Base, E.H. Pechan & Associates, Inc., prepared for the WRAP Emissions Forum, August 2002.

PES, 2001: Pacific Environmental Services, Inc., and Eastern Research Group, Inc., “Development of the 1996 Base Year Emission Inventory for Regional Haze Analyses ,” Research Triangle Park, NC and Sacramento, CA, prepared for WRAP Emissions Forum, August 2001.

Pierce, T., C. Geron, L. Bender, R. Dennis, G. Tonnesen, and A. Guenther, 1998: “Influence of increased isoprene emissions on regional ozone modeling”, Journal of Geophysical Research, vol. 103, pp. 25611-25629.

Pollack, et. al., 2003: “Development Of WRAP Mobile Source Emission Inventories – Draft Report” ENVIRON Corporation, et. al., Prepared for Western Governors’ Association, November 26.

SAIC. 2001. “Interpolating Relative Humidity Weighting Factors to Calc ulate Visibility Impairment and the Effects of Improve Monitor Outliers.” EPA No. 68-D-98-113, WA No. 3-39.

SAIC, 2003: “EPA Contract No. 68-D-98-113, Work Assignment No. 5-78, SAIC Project No. 1-0825-08-2566- 000, Application/Utilization of GVS T echniques in Analyzing Particulate Matter (PM) and Regional Haze (RH) Policy and Control Issues”. Letter from Richard Gardner, Sc ience Applications International Corporation (SAIC) to Thomas E. Rosenthal, Integrated Policy & Strategies Group, OAQPS, AQSSD (MD-15), U.S. Environmental Protection Agency Research Triangle Park, North Carolina 27711, July 3.

USDA, 2001: U.S. Department of Agriculture, “Agricu ltural Baseline Projection Tables to 2010,” Staff Report WAOB-2001-1, Office of the Chief Economist, prepared by the Interagency Agricultural Projections Committee, available from http://usd a.mannlib.cornell.edu/, accessed December 2001.

WRAP, 2003: Western Governors’ Association, “Stationary Source NO x and PM Emissions in the WRAP Region: An Initial Assessment of Emissions, Controls, and Air Qu ality Impacts”, Market Trading Forum draft, May 30.

Final §309 Regional TSD Page 247 of 250 December 15, 2003Glossary (after Malm, W. C., Introduction to Visibility , National Park Service, May 1999)

absorption: a class of processes by which one material is taken up by another.

absorption coefficient: a measure of the ability of particles or gases to absorb photons; a number that is proportional to the number of photons removed from the sight path by absorption per unit length.

aerosol: a dispersion of microscopic solid or liquid particles in a gaseous medium, such as smoke and fog.

air parcel: a volume of air that tends to be transported as a single entity.

anthropogenic: produced by human activities.

apportionment: to distribute or divide and assign proportionately.

attenuation: the diminuation of quantity. In the case of visibility, attenuation or extinction refers to the loss of image-forming light as it passes from an object to the observer.

coagulation: the process by which small particles collide with and adhere to one another to form larger particles.

condensation: the process by which molecules in the atmosphere collide and adhere to small particles.

condensation nuclei: the small nuclei or particles with which gaseous constituents in the atmosphere (e.g., water vapor) collide and adhere.

deciview: a unit of visibility proportional to the logarithm of the atmospheric extinction, an index of haziness. Under many circumstances a change in one deciview will be perceived to be the same on clear and hazy days.

extinction: the attenuation of light due to scattering and absorption as it passes through a medium.

extinction coefficient: a measure of the ability of particles or gases to absorb and scatter photons from a beam of light; a number that is proportional to the number of photons removed from the sight path per unit length. See absorption.

haze: an atmospheric aerosol of sufficient concentration to be visible. The particles are so small that they cannot be seen individually, but are still e ffective in visual range restriction. See visual
range.

Final §309 Regional TSD Page 248 of 250 December 15, 2003homogenous nucleation: process by which gases interact and combine with droplets made up of their own kind. For instance, the collision and subsequent adherence of water vapor to a water droplet is homogenous nucleation. See nucleation.

hydrocarbons: compounds containing only hydrogen and carbon. Examples: methane, benzene, decane, et cetera.

hygroscopic: readily absorbing moisture, as from the atmosphere.

IMPROVE: Interagency Monitoring of PROtected Visual Environments.

isopleth: a line drawn on a map through all points having the same numerical value.

LAC: See Light-Absorbing Carbon.

light-absorbing carbon: carbon particles in the atmosphere that absorb light. Black carbon.

light extinction budget: the percent of total atmospheric extinction attributed to each aerosol and gaseous component of the atmosphere.

micron: a unit of length equal to one millionth of a meter; the unit of measure for wavelength.

nitrogen dioxide: a gas (NO2) consisting of one nitrogen and two oxygen atoms. It absorbs blue light and therefore has a reddish-brown color associated with it.

NO 2 : See nitrogen dioxide.

nucleation: process by which a gas interacts and combines with droplets. See homogenous nucleation.

Perceived Visual Air Quality (PVAQ): an index that relates directly to how human observers perceive changes in visual air quality.

photon: a bundle of electromagnetic energy that exhibits both wave-like and particle-like characteristics.

plume blight: visual impairment of air quality that manifests itself as a coherent plume.

point source: 1) generally, any stationary source for which individual records are maintained for emission inventory purposes; distinguished from area source, often by a criterion involving emission rate, such as 100 tons per year. 2) A source of pollution that is point-like in nature. An example is the smoke stack of a coal-fired power plant or smelter. See source.

precursor emissions: emissions from point or regional sources that transform into pollutants with varied chemical properties.

Final §309 Regional TSD Page 249 of 250 December 15, 2003Rayleigh scattering: the scattering of light by particles much smaller than the wavelength of the light. In the ideal case, the process is one of a pure dipole interaction with the electric field of the light wave.

relative humidity: the ratio of the partial pressure of water to the saturation vapor pressure, also called saturation ratio; often expressed as a percentage.

scattering (light): an interaction of a light wave with an object that causes the light to be redirected in its path. In elastic scattering, no energy is lost to the object.

scattering coefficient: a measure of the ability of particles or gases to scatter photons out of a beam of light; a number that is proportional to the amount of photons scattered per unit length.

secondary aerosols: aerosol formed by the interaction of two or more gas molecules and/or primary aerosols.

SO 2 : See sulfur dioxide.

source: in atmospheric chemistry, the place, places, group of sites, or areas where a substance is injected into the atmosphere. Can include point sources, elevated sources, area sources, regional sources, multiple sources, et cetera.

sulfates: those aerosols that have origins in the gas- to-aerosol conversion of sulfur dioxide; of primary interest are sulfuric acid and ammonium sulfates.

sulfur dioxide: a gas (SO 2 ) consisting of one sulfur and two oxygen atoms. Of interest because sulfur dioxide converts to an aerosol that very e fficiently scatters light. Also, it can convert into acid droplets consisting primarily of sulfuric acid.

visual range: the distance at which a large black object just disappears from view.

VOC: Volatile Organic Compound - gaseous hydrocarb on.

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