# Technical Support Document for the 

# Minnesota State Implementation Plan for 

# Regional Haze

November 9, 2009

Prepared by:

520 Lafayette Road North St. Paul, Minnesota 55155-4194

Margaret McCourtney margaret.mccourtney@pca.state.mn.us 651-757-25582 

### Contents Page

I. Introduction 8 I.a. Establishment of RPGs 8 I.b. Determination of future Visibi lity—Pollutant Emission Levels and 11 Reductions Needed from Individual States I.c. Calculation of the Resulting Degree of Visibility Improvement that 15 would be Achieved at Each Class I Area I.d. Comparison of Visibility Improvement Between Proposed 17 Control Strategies. I.e. Conclusion that the Long-Term Strategy Provides for Reasonable 17 Progress II. Process for Developing Techni cal Support for Regional Haze 21 III. Description of Each Step in the Process 24 1.0 Model Year Selection 24 2.0 Emissions Inventory Development 24 2.1 The Base Year Inventory—2002 25 2.2 The Future Year Inventory—2018 31 2.3 Canada Emissions Inventory 47 3.0 Emissions Modeling 48 3.1 Temporal and Spatial Differences Between MRPO and CENRAP 52 2002 Base Year Inventories 3.2 MRPO Quality Assurance for Emissions Inventories 59 4.0 Meteorological Modeling 60 5.0 Atmospheric Chemistry and Transport Modeling 60 6.0 Model Performance Evaluation 62 6.1 Observed Data Available/Used for Comparison 63 6.2 Performance Tests Used and the Results 67 6.2.a Fractional bias and error 67 6.2.b Time Series Plots 70 6.2.c Observation and Prediction Comparison Bar Charts 70 6.3 Comparison of Minnesota (MRPO) and CENRAP Model Performance 72 Evaluation on Specific Individual Days 6.4 Implications of the Performance Evaluation 77 6.4.a. Grid Scale Needs 77 6.4.b Horizontal Extent of Domain 78 6.4.c Improvements to Emissions Inventory 78 6.4.d Modifications of Models 78 6.5 Diagnostic Tests: Accuracy of the model in characterizing the 78 sensitivity of PM 2.5 to changes in emissions 7.0 Uniform Rate of Progress Analysis 79 8.0 Control Strategy Development 83 8.1 Particulate Source Apportionment to Assess Contributions to 90 Visibility Impairment 8.2 Beyond On-the-Books Controls 983 8.2.a Minnesota Controls 99 8.2.b Other Contributing State Controls 111 8.3 Canadian Impacts and Control Strategies 113 IV. Data Access 113 V. References 114 VI. Glossary 116

Appendices

Appendix A: Base Year 2002 and Future Year 2018 Point Source Emissions for Minnesota in Minnesota (MRPO) Modeling

Appendix B: Post Emissions Modeling File Ch anges for Base Year 2002 and Future Year 2018 Estimates for Minnesota

Appendix C: Annual 2005 and 2108 Emissions in Tons in MRPO 2005 Case by Source Category for Minnesota and Surrounding States.

Appendix D: Meteorological Modeling Pr otocol and Performance Evaluation

Appendix E: MRPO Air Quality Modeling Protocol

Appendix F: Julian and Gregorian Cale ndar Cross-Reference for the Year 2002

Appendix G: Minnesota (MRPO) Air Quality Model Perf ormance Evaluation Plots

Appendix H: Particulate Source Apportionm ent Results for CENRAP and MRPO Cases

Tables Page

Introduction/ Table I.2 Annual Emissions in Tons fo r the Significant Cont ributing States 14 In 2002 and 2018

Section III/ Table 2.1 Summary of MRPO 2002 Case (used in Minnesota (MRPO) case) and 27 CENRAP Case Inventories Table 2.2 Annual 2002 Emissions in Minnesota (MRPO) case by 29 Source and Category for Minne sota and Surrounding States and Surrounding States Table 2.3 Annual Ammonia Emissions in Tons in the 33 MRPO 2002 and MRPO 2005 cases4 Table 2.4 Taconite Growth Factors from 2002 to 2018 Used by MRPO and 34 CENRAP Table 2.5 Minnesota Adjustment s and Corrections to IPM3.0 35 Table 2.6 IPM2.1.9(VISTAS) and IPM3.0 “base” and “will do” 38 Alterations Table 2.7 Growth Rates Applied to Mi nnesota EGUs based on Electricity 39 Generation by Electricity Market Module Region and Source in Billion Kilowatt Hours for the MAPP Region Table 2.8 Future Year Control Assumpti ons in IPM3.0 “will do” (with CAIR 41 and MRPO Case B 2018 (without CAIR) Table 2.9 Annual 2018 Emissions in Tons in Minnesota (MRPO) case by 44 Source Sector and Specie for Minnesota and Surrounding States Table 2.10 Difference Between Annual 2002 and 2018 Emissions in Tons in 45 Minnesota (MRPO) case by Source Category for Minnesota and Surrounding States Table 2.11 Canada Annual 2005 Emissions To tal in MRPO 4rpos Region in Tons 47 Table 3.1 General Emissions Modeling Selections by RPO 49 Table 3.2 Summary of Temporal and Spatial Allocation and Speciation 50 Profile Methodology by RPO Table 3.3 2002 Emissions for a Winter and Summer Day in Tons for 58 Minnesota (MRPO) and CENRAP Table 3.4 2005 Emissions in Tons for a Winter and Summer Day for 59 MRPO BaseM Table 6.1 Performance Evaluation Monitor to Model Definition of Pollutants 64 Evaluated at Minnesota Class I Areas Table 6.2 Species Data at Minnesota Class I Area Monitor Sites for 66 20% Worst Days in Micrograms per Cubic Meter Table 6.3 Performance Goals and Crite ria for Fractional Bias and Error 67 Table 6.4 Nitrate Fractional Bias for 36km and 12km(PiG) where 69 Results Significantly Differ Table 6.5 Canada Elevated Point S ource Emissions in 2005 Inventory— 74 Domain Differences Table 7.1 Monthly f S (RH) and f L (RH) Values for Boundary Waters and 81 Voyageurs Table 8.1 Uniform Rate of Progress An alysis for 20% Worst Days with 85 On-the-Books Controls Table 8.2 Uniform Rate of Progress An alysis for 20% Best Days with 85 On-the-Books Controls Table 8.3 Relative Response Factor s for 20% Worst Days with 86 On-the-Books Controls Table 8.4 Relative Response Factor s for 20% Best Days with 86 On-the-Books Controls Table 8.5 Winter Day NO X and NH 3 Emissions in Tons from 87 Base Year 2002 for CENRAP and Minnesota (MRPO) Table 8.6 Winter Day NO X and NH 3 Emissions in Tons from 2018, and 87 Difference from 2002 to 2018 for CENRAP and Minnesota (MRPO)5 Table 8.7 Minnesota Annual Emi ssions Change in Tons of NO X and NH 3 from 89 All Source Categories in Minnesota (MRPO) and MRPO 2005 Case Table 8.8 Minnesota Point Sources Evaluated with PSAT 103 Table 8.9 Northeast Minnesota Plan Re ductions to Non-EGU Point Sources 108 Table 8.10 Xcel Energy—Sherbur ne Plant Original Proposal 110 Table 8.11 Xcel Energy—Sher burne Plant Final Proposal 110 Table 8.12 Statewide Emissions Ratios for EGU IPM3.0 “will do” Scenario 111 Table 8.13 Adjustments Made to Contribu ting States to Reflect 0.25 lb/MMBtu 112 Emissions Rate Table 8.14 Annual 2018 Potentially Reasonabl e Control Measures, Emissions for 112 Point Sources in Tons

Figures Page

Introduction/ Figure I.1 Minnesota Class I Areas— Voyageurs and Boundary Waters 9 and Michigan Class I Area–Isle Royale Figure I.2 Regional Planning Organizations 11 Figure I.3 State Contributions to Amm onium Nitrate and Ammonium Sulfate 13 Light Extinction at Boundary Wate rs and Voyageurs for the Year 2002 Figure I.4 State Contributions to Amm onium Nitrate and Ammonium Sulfate 14 Light Extinction at Boundary Wate rs and Voyageurs for the Year 2018 Figure I.5 RPG at Boundary Waters for 2018 16 Figure I.6 RPG at Voyageurs for 2018 16

Section II/ Figure II.1 Procedural Flow for Demons trating Attainment of Air Quality 23 Goals for Regional Haze

Section III/ Figures 2.1—2.4 Annual 2002 SO 2 , NO X , NH 3 and Biogenic VOC 30 Emissions in Tons for Minnesota (MRPO) Case Figures 2.5—2.7 Annual 2018 SO 2 , NO X , NH 3 Emissions in Tons for 46 Minnesota (MRPO) Case. Figure 3.1 National Inter-RPO Doma in and MRPO 4rpos Domain 49 Figure 3.2 Estimated Seasonal Distribut ion of Ammonia Emissions from 53 Livestock and Fertilizer in Minnesota, Michigan and Wisconsin Combined (tons per day) Figure 3.3 2002 Winter Day and Summer Da y Ammonia Emissions in Tons for 55 Minnesota (MRPO) Case Figure 3.4 2002 Winter Day and Summer Da y Ammonia Emissions in Tons for 55 CENRAP Case Figure 3.5 2002 Winter Day and Summer Day NO X Emissions in Tons for 56 Minnesota (MRPO) Case6 Figure 3.6 2002 Winter Day and Summer Day NO X Emissions in Tons for 56 CENRAP Case. Figure 3.7 2002 Winter Day and Summer Day SO 2 Emissions in Tons for 57 Minnesota (MRPO) Case Figure 3.8 2002 Winter Day and Summer Day SO 2 Emissions in Tons for 57 CENRAP Case Figure 6.1 Locations of IMPROVE Monitors at Boundary Waters and Voyageurs 63 Figure 6.2 MRPO Fractional Bias for 2002 Monthly Average Concentrations 68 for 4rpos Domain Figure 6.3 Comparison of Observed and M odeled Pairs for 20% Worst Days as 71 Extinction—Boundary Waters and Voyageurs Figure 6.4 Observations and Predictions in Minnesota (MRPO) and CENRAP Cases— 72 Extinction by Species Averaged Over 20 Percent Worst Days Figure 6.5 EndPoint VOYA, January 5, 2002 74 Figure 6.6 EndPoint VOYA, January 11, 2002 74 Figure 6.7 EndPoint VOYA, October 26, 2002 75 Figure 6.8 EndPoint BOWA, December 10, 2002 75 Figure 6.9 EndPoint BOWA, December 13, 2002 75 Figure 6.10 EndPoint VOYA, April 14, 2002 76 Figure 6.11 EndPoint VOYA, August 30, 2002 76 Figure 6.12 EndPoint VOYA, September 2, 2002 76 Figure 6.13 MRPO Fractional Bias for 2005 Monthly Average Concentrations 77 for 4rpos Domain Figure 8.1 Boundary Waters 36-km Minnesota (MRPO) Position on the Glidepath 84 with On-the-Books Controls Figure 8.2 Voyageurs 36-km Minnesota (MRPO) Position on the Glidepath with 84 On-the-Books Controls Figure 8.3 CENRAP Winter Da y NOx Emissions in Tons 88 Figure 8.4 Geographic Regions Mo deled with PSAT in Minnesota (MRPO) Case 91 Figures 8.5—8.8 Boundary Waters Extin ction Contribution by Specie and 95 Sector for Each Region on the 20 Percent Worst Days Figures 8.9—8.12 Voyageurs Extinction C ontribution by Specie and Sector for 96 Each Region on the 20 Percent Worst Days Figures 8.13 and 8.14 Boundary Waters 2018 Extinction Contribution by 97 Sector for each Specie & by Speci e for each Sector on the 20 Percent Worst Days Figures 8.15 and 8.16 Voyageurs 2018 Extinct ion Contribution by Sector for each 97 Specie & by Specie for each Sector on the 20 Percent Worst Days Figure 8.17 State Contributions to Amm onium Nitrate and Ammonium Sulfate 99 Light Extinction at Boundary Waters for the Year 2018 after Implementation of On-the-Books Controls Figure 8.18 State Contributions to Amm onium Nitrate and Ammonium Sulfate 99 Light Extinction at Voyageurs for the Year 2018 after Implementation of On-the-Books Controls Figure 8.19 Receptor Locations for 12km Grid with PiG 100 Figure 8.20 Percentage Cont ribution of Northeast and Rest of Minnesota to 1017 Boundary Waters (BOWA1) and Voyageurs (VOYA2) on the 20 Percent Worst Days Figure 8.21 Percentage Cont ribution of Northeast and Rest of Minnesota to 101 Maximum (BOWA_13) and Minimum (ISLE_1) Receptors in the Class I Areas on the 20 Percent Worst Days Figure 8.22 Minnesota Contributions at Receptors Placed Throughout Boundary 102 Waters, Voyageurs, and the Tip of Isle Royale in 2018 by Species and Geographic Region on the 20 Percent Worst Days Figure 8.23 Locations of Individual Po int Sources Evaluated with PSAT and 104 Plume-in-Grid Figure 8.24 Minnesota Point Source Contributio ns to Nitrate, Sulf ate and Ammonium 105 Extinction at Receptors Placed Throughout Boundary Waters, Voyageurs and the Tip of Isle Royale in 2018 on the 20 Percent Worst Days Figure 8.25 Other Minnesota Individual Point Source Contributions on the 106 20 Percent Worst Days for 2002 Figure 8.26 Other Minnesota Individual Point Source Contributions on the 106 20 Percent Worst Days for 2018 Figure 8.27 Northeast Minnesota Indi vidual Point Source Contributions 107 on the 20 Percent Worst Days for 2002 Figure 8.28 Northeast Minnesota Indi vidual Point Source Contributions 107 on the 20 Percent Worst Days for 2018 Figure 8.29 RPG at Boundary Waters 36km Minnesota (MRPO) with On-the-Books 109 Controls + the Northeast Minnesota Plan Figure 8.29 RPG at Voyageurs 36km Minnesota (MRPO) with On-the-Books 109 Controls + the Northeast Minnesota Plan Figure 8.31 Boundary Waters 36-km Minnesota (MRPO) Position on Glidepath with 113 “Potentially Reasonable” Controls Figure 8.32 Voyageurs 36-km Minnesota (MRPO) Position on Glidepath with 113 “Potentially Reasonable” Controls

Appendices

Appendix A: Annual 2002 and 2018 Point Sour ce Emissions in Tons in Minnesota (MRPO) Case for Minnesota by Stack Appendix B: Post Emissions Modeling File Ch anges for Base Year 2002 and Future Year 2018 Estimates for Minnesota Appendix C: Annual 2005 and 2018 Emissions in Tons in MRPO 2005 Case by Source and Category for Minnesota and Surrounding States Appendix D: Meteorological Modeling Pr otocol and Performance Evaluation Appendix E: MRPO Air Quality Modeling Protocol Appendix F: Julian and Gregorian Cale ndar Cross-Reference for the Year 2002 Appendix G: Minnesota (MRPO) Air Quality Model Performance Evaluation Plots Appendix H: Particulate Source Apportionmen t Results for CENRAP and MRPO Cases8 

### I. Introduction

This document details the modeling analyses con ducted to support the polic y decisions made in Minnesota’s Regional Haze State Implementation Plan (RH SIP). The plan is available at http://www.pca.state.mn.us/air/regionalhaze.html

. To comport with the July 1, 1999 publication of the Regional Haze Rule in the Federal Register, modeling analyses support the:

• Establishment of Reasonable Progress Goals (RPG ) for Class I areas that ensure visibility on the worst impaired days improves toward natural visibility conditions experienced on those days, and that ensure no degradation of visibility occurs on the least impaired days;

• Determination of future visibility causi ng pollutant emissions levels needed, and reductions needed from individua l states, to meet the RPG;

• Calculation of the resulting degree of visibi lity improvement that would be achieved at each Class I area;

• Comparison of visibility improvement betw een proposed control strategies; and the

• Conclusion that the long-term strategy provides for reasonable progress.

I.a. Establishment of RPGs.

The Regional Haze rule requires States to “establ ish goals that provide for reasonable progress toward achieving natural visibility conditi ons for each Class I area within a State” 1 ; improving visibility on the most impaired days and not de grading visibility on the least impaired days. Minnesota has two federal Class I areas, th e Boundary Waters Canoe Area Wilderness (Boundary Waters) and Voyageurs National Park (V oyageurs). Both Minnesota Class I areas are located along the State’s Northern border, shared with Canada, as shown in Figure I.1. Also shown in the Figure is Isle Royale, a Michigan Class I area, in close proximity to Boundary Waters and Voyageurs.

Although Minnesota impacts visibility at Class I areas other than those within the State borders, for example Isle Royale, the focus of this documen t is on Boundary Waters and Voyageurs. This document shows that Minnesota has the greatest vi sibility impact on thes e two Class I areas. Any future emissions reductions in the State made to improve visibility in Boundary Waters and Voyageurs should have a commensurate affecton any other Class I areas impacted by Minnesota.

1 40 CFR 51.308(d)(1).9 Figure I.1. Minnesota Class I Areas—V oyageurs and Boundary Waters—and Michigan Class I Area—Isle Royale.

Minnesota

Wisconsin

Iowa South Dakota North Dakota

Michigan Ontario Manitoba

Boundary Waters Canoe Area Wilderness

Isle Ro y ale N Voyageurs NP

The core of the visibility assessment is the baseline and natural visibility conditions based on observed data collected at Interagency Mon itoring of Protected Visual Environments (IMPROVE) monitors, made available through the Vi sibility Information Exchange Web System (VIEWS) 2 . The baseline conditions are developed from five years of monitoring data, and represent the starting point from which reasonable progress is measured. The Regional Haze Rule prescribes the baseline period as the years 2000-2004 3 , and defines baseline visibility conditions as the average of the most—or the 20 percent worst—visibility impaired days, and the average of the least—or the 20 percent best—vis ibility impaired days, calculated from the monitoring data for each year of the baseline, and then averaged over the 5-year baseline period. The ultimate goal is to reach natu ral visibility conditions in 2 064. Reasonable progress goals are established as interim goals repr esenting progress toward that e nd. The year 2018 is the initial year for developing a reasonable progress goal. This document supports the reasonable progress goal for 2018.

Fine particles less than 2. 5 microns (μm) in size (PM 2.5 ) are primarily responsible for impaired visibility. 4 PM 2.5 is composed of several pollutant sp ecies; nitrate, sulf ate, organic carbon, elemental carbon, fine soil, sea salt (which is neg ligible in Minnesota Clas s I areas) and water.

2 http://vista.cira.colostate.edu/views/
3 40 CFR 51.308(d)(2).
4 Malm (2000)10 Coarse particulate mass ( > 2.5 μm, but ≤ 10 μm diameter) is also in cluded in the visibility equation, but—as we show in this document—i s an insignificant com ponent of visibility impairment at Boundary Waters and Voyageurs.

We calculate visibility usi ng the individual components desc ribed above in the IMPROVE algorithm adopted by the IMPROVE St eering Committee in December 2005 5 . Details on the equation and its use for developi ng the RPG at Boundary Waters and Voyageurs are provided in this document. The solution to the IMPROVE equation is in the form of extinction (b ext ). The Regional Haze rule requires visibi lity to be converted to, and e xpressed in, deciviews (dv). In the deciview scale, “a 1 to 2 deciview differe nce corresponds to a small, visibly perceptible change in scene appearance...” 6 by the human observer.

Models are used to establish an RPG by simulating the future visibility conditions that will result from future emission estimates. EPA guidelines 7 outline the methodology for modeling future conditions and applying modeled results to develop reasonable progress goals.

Emissions from a “base”, or known, year (i.e. 20 02) representing the base line period and from a year in the future (i.e. 2018) are each modeled. The model results are used to estimate the air concentration change from base year to future ye ar inventories. These air concentration changes are in the form of ratios of the future year air concentrations to the base year concentrations predicted near a monitor location and averaged over the same 20 percent worst and 20 percent best days in the base year, whic h were also used to establish ba seline visibility conditions. A ratio is developed for each specie comprising PM 2.5 concentration (sulfate, nitrate, organic carbon, elemental carbon, fine soil [ ≤ 2.5 μm diameter], and coarse particulate matter [ > 2.5 μm, but ≤ 10 μm diameter]). The ratio is called a Rela tive Response Factor (RRF). Applying the RRFs to baseline monitoring conditi ons, for each specie comprising PM 2.5 , provides the estimate of future visibility conditions, the RPG.

Cognizant of the intense resources required to conduct modeling analyses of this nature, EPA guidelines for regional haze do not suggest mode ling the multiple years comprising the 5-year baseline period, but discuss modeling one full ye ar as a “logical goal”. The methodology in the EPA guidelines attempts to take into account the year-to-year variability of the meteorology in the monitored baseline. The middle year (2002) w ill have more weight due to the fact that the 2002 emissions and the meteorology are used in th e modeling to develop the RRF applied to the baseline conditions. This application of the m odel results is intended to balance the resource limitations of conducting multiple years of modeling, and to “help reduce the impact of possible over-or under-estimations by the dispersion mode l due to emissions, meteorology, or general selection of other mode l input parameters”.

Modeled results differ depending on the meteorology used. In this document, we describe how the RRF—and hence the RPG—are sensitive to me teorology and therefore the locations in the United States where the modeled emission reduc tions occur. Using 2005 meteorology, the RPG for Boundary Waters and Voyageurs is a little more optimistic, apparently taking greater

5 http://vista.cira.colostate.edu/views/Web/RHR/RHR_Planning.aspx
6 Pitchford, et al. (1994)
7 EPA, OAQPS, (2007)11 advantage of emissions reductions occurring in stat es located Southeast of the Minnesota Class I

areas.

In this document we show that in the upper-Midwest Class I areas, most PM 2.5 causing visibility impairment is secondarily formed from emissions of sulfur dioxide from power plants and other industrial sources, nitr ogen oxides from motor vehicles a nd industrial sources, and ammonia from agricultural operations. These emissions le ad to the formation of ammonium nitrate and ammonium sulfate. Volatile orga nic compounds from natural sources like trees can contribute to secondary organic aerosols, or orga nic carbon. But wildfire s are the source that dictates days of worst visibility impairment due to organic ca rbon. Of 22-24 days in 2002 comprising the 20 percent worst visibility days at Boundary Waters and Voyageurs, five days are identified as worst-impaired visibility days in 2002 because of organic carbon from wildfires. The remaining worst-impaired days are due to amm onium nitrate and ammonium sulfate.

I.b. Determination of Future Visibility—P ollutant Emissions Levels, and Reductions Needed from Individual States.

The resource requirements for conducting regional scale modeling, and the need to coordinate emission estimates and control strategies, make it necessary to consolidat e resources and develop the modeling analyses through a Regional Planni ng Organization (RPO) process. RPOs were developed to address the need to work in concert with other States and Tribes. A ssociation in the various RPOs is shown in Figure I.2.

Figure I.2. Regional Planning Organizations

.

Although Minnesota is affiliated with the Central Regional Air Planning Association (CENRAP), the state also works closely with the Midwest Regional Planning Organization (MRPO) for the technical analysis. Visibility conditions in Isle Roya le (see Figure I.1) and Seney Class I areas, both within Michigan, are as sessed by the MRPO. Mich igan is a member of MRPO. The characteristics of regional haze have greater similarity among the MRPO and12 Minnesota’s Class I areas than among Minnesota’s and the more southern Class I areas in CENRAP. For this reason, Minnesota has recogn ized the benefit in fu rthering its technical analyses of the Upper Midwest Class I areas al ong with the MRPO. There are two additional reasons Minnesota emphasized collaboration with the MRPO in regard to modeling:

1. The MRPO supports contributing states in c onducting modeling within each state. This appeals to Minnesota’s desire to explore answers to questio ns that are too specific to Minnesota to be feasible to addres s through the larger RPO process; and 2. Along with the five member States of the MR PO, Minnesota is the only other State in the U.S. that is within EPA Region V. The staff of this region will review SIPs from the six States. Thus, it is useful to have some technical collaboration w ith the other Region V States.

In 2004, the MRPO and CENRAP informally agreed to work together to address the Minnesota Class I Areas with the MRPO taking the lea d. The MRPO supported Minnesota’s modeling capabilities to allow the State to investigate visibility issues that address more localized concerns, for example, the impacts of indivi dual sources located near Boundary Waters and Voyageurs. Ultimately, regional haze mode ling at Boundary Waters and Voyageurs was performed by CENRAP, MRPO and by Minneso ta; MRPO conducted modeling using both a 2002 base year and 2005 base year.

Minnesota conducted its modeling to specifically assess visibility impacts from Minnesota sources near the Class I areas, as this type of modeling falls ou tside the scope of RPO work. Minnesota supplemented the MRPO modeling by focusing on the two Minnesota Class I areas and the visibility impacts of nearby point sources located within Minneso ta. Because Minnesota would like to better correlate results with CENR AP, and because Minnesota did not have a final version of the State 2005 emissions inventor y at the time the new base year modeling commenced, Minnesota retains the MRPO-develope d emissions and meteorological data inputs from the 2002 base year and 2018 future year base case with some adjustments. These include some changes reflected in the development of the MRPO 2005 case and some modifications specific to Minnesota, and is referred to throughout this chapter as the Minnesota (MRPO) case.

Minnesota (MRPO) case modeling for the 2002 base year iden tifies Minnesota as by far the greatest contributor to impaired visibility in Boundary Waters and Voyageurs. Other significant— approximately five percent or more—contributor s are Illinois, Iowa, Missouri, North Dakota, and Wisconsin. Figure I.3 shows State contri butions to ammonium nitrate and ammonium sulfate in 2002. During the 16-year period between 2002 and 2018, several emission reduction programs will be implemented despite the Regional Haze Rule. These include Federal and State regulations, legislation and permit actions. Te rmed “on-the-books” cont rols, these measures comprise the bulk of the emissions reductions de monstrating reasonable pr ogress. Emissions in 2002 and 2018 for the significant contributing States are shown in Table I.2. From the six significant contributi ng states alone, SO 2 emissions are reduced by 456,000 tons per year, and NOx are reduced by 1,350,000 tons per year. Ammoni a emissions are estimated to increase by 339,000 tons per year, making more ammonia av ailable to form ammonium nitrate and ammonium sulfate.13 The emission changes based on on-the-books controls from all contributors, not just the six States listed above, result in an overall decrease in visibility impairment due to ammonium nitrate and ammonium sulfate of about 8 inverse megameters (Mm -1 ) at Boundary Waters and 5 Mm -1 at Voyageurs in 2018. Inverse megameters are the units for ex tinction. Although reasonable progress goals are depicted in decivi ews, contributions of individual components, source sectors and source regions are only evaluated in terms of extinction. An extinction coefficient less than 10 Mm -1 will produce a negative value in deciviews. Assessing contributions to visibility with negative values would be confusing. The resulting changes in State contribution to visibility due to ammoni um nitrate and ammonium sulfate are shown in Figure I.4.

Figure I.3. State Contributio ns to Ammonium Nitrate and Ammonium Sulfate Light Extinction at Boundary Waters and Voyageurs for the Year 2002.

Illinois

10%

Iowa

7%

Wisconsin 11% Canada 3% AllOthers 27% BC 9%

Minnesota 23%

NorthDakota 5% Missouri 5% Boundary

Waters

Total Extinction of NH 4 NO 3 & (NH 4 ) 2 S0 4 : 40 Mm -1

Illinois 5%

Minnesota 29%

Canada 4% AllOthers 20% BC 13% Iowa 7%

Wisconsin 7% NorthDakota 12% Missouri 3% Voyageurs

Total Extinction of NH 4 NO 3 & (NH 4 ) 2 S0 4 : 37 Mm -114 Table I.2. Annual Emissions in Tons for the Significant Contributing States in 2002 and 2018.

State 2002 2018 Difference (2018-2002)
SO 2 NO X NH 3 SO 2 NO X NH 3 SO 2 NO X NH 3 Illinois 536,000 936,000 136,000 262,000 440,000 196,000 -274,000 -496,000 60,000

Iowa 192,000 381,000 254,000 173,000 234,000 371,000 -19,000 -147,000 117,000 Minnesota 163,000 516,000 185,000 108,000 317,000 253,000 -55,000 -199,000 68,000 Missouri 394,000 545,000 133,000 416,000 278,000 190,000 22,000 -267,000 57,000

North Dakota 206,000 182,000 70,400 125,000 170,000 103,000 -81,000 -12,000 32,600

Wisconsin 266,000 427,000 123,000 217,000 203,000 127,000 -49,000 -224,000 4,000

Figure I.4. State Contributio ns to Ammonium Nitrate and Ammonium Sulfate Light Extinction at Boundary Waters and Voyageurs for the Year 2018 after Implementation of On-the-Books Controls.

Illinois

6%

Iowa

8%

Wisconsin 10% Canada 3% AllOthers 22% BC 11%

Minnesota 28%

NorthDakota 6% Missouri 6% Boundary

Waters

Total Extinction of NH 4 NO 3 & (NH 4 ) 2 SO 4 :

32 Mm -1

Illinois 3%

Minnesota 31%

Canada 5% AllOthers 16% BC 15% Iowa 7%

Wisconsin 6% NorthDakota 13% Missouri 4% Voyageurs

Total Extinction of NH 4 NO 3 & (NH 4 ) 2 SO 4 :

32 Mm -115 Minnesota, the host State to Boundary Waters an d Voyageurs, will always have the greatest contribution to extinction in the Class I Areas, despite reasonable emission reduction estimates. The percentage contribution from Minnesota an d bordering States increases as large emission reductions occur in distant States. A significant shift of contribution to the remaining extinction at Boundary Waters goes to Minnesota apparently due to large modeled SO 2 reductions in more distant States, especially in Illinois. Monito ring data shows us that the majority of worst visibility days at Boundary Waters in 2002 are due to sulfate. Although sulfate is formed all year around, most is formed in the warmer months of the year. Prevailing winds during this period are from the Southeast. Thus, Boundary Waters benefits from emission reductions occurring in those locations.

The contribution shift is also true for Voyageur s, but not as much. At Voyageurs, monitoring data shows us that the cause for most of the worst visibility days in 2002 are about equally divided between sulfate a nd nitrate. Ammonium nitrate is formed in the winter, when prevailing winds are from the West and Northwest. This may prevent Voyageurs from fully benefiting from the over 1,300,000 tons of NO X reductions mostly occurring in Minnesota and the four other significantly contributi ng States located to the East and Southeast.

Further analysis of Minnesota’ s contribution to impaired visibility at Boundary Waters and Voyageurs indicates that six counties—Car lton, Cook, Itasca, Koochi ching, Lake and St. Louis—in Northeast Minnesota contribute more th an half of the States total contribution of extinction at Boundary Waters, and just under half of the contributi on to extinction at Voyageurs. A plan to reduce SO 2 and NO X by 30% of 2002 emission levels was determined reasonable in that part of the State. About 20% of that emissions reduction appears in the on-the-books controls, based on measures implemented by the electric generating ut ility Minnesota Power— Boswell. The remaining 10% of the emi ssion reduction are applied in the Minnesota (MRPO) case to taconite mines based on permit limits, fu rnace modifications, fuel switching, new control equipment, newer rate information, and some in itial measures taken to comply with the Best Available Retrofit Technology (BART) ru le associated with the RH SIP.

The Northeast Minnesota Plan results in an additional reduction of 2,000 tons per year SO 2 and 7,000 tons per year NO X . Minnesota’s RPG is based on the 2018 on-the-books controls and the Northeast Minnesota Plan.

I.c. Calculation of the Resulting Degree of Vi sibility Improvement that would be Achieved at each Class I Area.

Applying the methodology described in I.a. with future emission es timates that reflect reasonable controls, provides the RPG. Minnesota’s RP G for Boundary Waters and Voyageurs based on Minnesota (MRPO) case modeling are shown in Figures I .5 and I.6. The results are shown in relation to a line representing a un iform rate of progress from th e baseline conditions to natural conditions. The top line represents the 20 per cent worst visibility days, and the bottom line represents the 20 percent be st visibility days.16 Overall, an estimated 1.3 deciview improve ment at Boundary Waters and a 0.6 deciview improvement at Voyageurs are expected in 2018. At Boundary Waters, a perceptible change in visibility is expected. At Voyageurs, the cha nge may not necessarily be visually perceptible, however, it is a step in the righ t direction toward reaching the vi sibility goals at the Minnesota Class I areas.

Figure I.5. RPG at Boundary Waters for 2018.

Uniform Rate of Progress - Boundary Waters, MN

3.4 18.6

11.6 19.9

6.4 6.5

0.0 5.0 10.0 15.0 20.0 25.0

1990 2000 2010 2020 2030 2040 2050 2060 2070

YearDeciviews

Figure I.6. RPG at Voyageurs for 2018.

Uniform Rate of Progress - Voyageurs, MN

4.3 18.9

12.1 19.5

7.1 7.1

0.0 5.0 10.0 15.0 20.0 25.0

1990 2000 2010 2020 2030 2040 2050 2060 2070

YearDeciviews17 I.d. Comparison of Visibility Improveme nt between Proposed Control Strategies.

Not all potential control strategies have been deemed reasonable, as described in the RH SIP, at the time of SIP preparation and t hus are not reflected in the Minneso ta RPG. As detailed in the RH SIP, Minnesota did request Iowa, Missouri, North Dakot a and Wisconsin—along with Minnesota— to evaluate the reas onableness of emission limits on the electric generating sector that would result in an additional total annual reduction of 222,000 tons SO 2 and 29,700 tons NO X from 2018 emission estimates. These measures would result in an additional 0.3 deciview improvement (18.3 dv) in visibility at Bounda ry Waters, and an additional 0.2 deciview improvement (18.7 dv) in vi sibility at Voyageurs.

I.e. Conclusion that the Long-Term St rategy Provides for Re asonable Progress.

The reasonableness of the long- term strategy—the controls implemented—falls beyond the scope of this technical support document. To be reasonable, factors such as the cost effectiveness of the control measures are asse ssed. The RH SIP provides discussion on the reasonableness of the long-term strategy. Th is document does, however, provide detail on emission unit level emissions changes that were modeled to reflect the strategy. The document also provides evidence that the RPG at Boundary Waters and Voyageurs appear to be somewhat conservative estimates. As part of the wei ght-of-evidence supporting the Minnesota reasonable progress goal, this document features a de tailed comparison among the modeling inputs and results by CENRAP, MRPO (2002) and MRPO (2005) as compared to the Minnesota (MRPO) case used to establish the RPG.

In summary, the various modeling results all show slightly varying levels of assurance that visibility on the worst impaired days will improve toward natu ral conditions. The MRPO 2005 case projects the greatest visibi lity improvements, showing at le ast a uniform rate of progress toward natural conditions on the worst impaired days. The CENRAP case projects the next greatest improvements, followed by the Minnesota (MRPO) , and lastly by the MRPO 2002 case.

The increased visibility improvement shown by the Minnesota (MRPO) case over the MRPO 2002 case can be explained by the use of improved future emissions projections for the electric generating utility sector and the incorporation of the Northeast Minnesota Plan. Because the Minnesota (MRPO) case uses the improved future emissions projections for the electric generating utility sector and the Northeast Minnesota Pl an—resulting in greater projected emission reductions for the future—it is somewhat unexpe cted that the CENRAP case projects greater improvements in future visibility. The CENRAP case has the same electric generating emissions as the MRPO 2002 case. The cause may be due to the estimated level of NO x and ammonia emissions.

The CENRAP case over predicts nitrate formati on at Boundary Waters and Voyageurs compared to observed values collected at monitoring statio ns in 2002. This is likely caused by additional NO x and a significant amount of available ammonia with which to react. Because the CENRAP case has a lot of available ammonia to react with NO x emissions, the model responds well to future projected reductions in NO x emissions, possibly ev en over-stating them.18 CENRAP has significantly more ammonia in the modeling system during the winter than the Minnesota (MRPO) case, which under predicts nitrate formation on the same days. The Minnesota (MRPO) case does not appear to have much availa ble ammonia in the winter to form the same level of nitrate from NO x emissions as observed. In the future projected case, NO x emissions in the Minnesota (MRPO) case are reduced while ammonia emissions significantly increase. In the future, the increased ammonia allows the model to form nitrate. Even though NO x emissions decrease, the increased ammoni a and nitrate formation may prevent model response to the reduction in NO x emissions. Thus, the CENRAP case would project greater improvements in visibility.

An additional important factor is that the CENRAP case considers significant emissions reductions from Canada from the base year to the future year. The Minnesota (MRPO) case keeps Canada emissions constant from the base year to the future year, due to concerns about the uncertainty of the Canada emissions.

The MRPO 2005 case has a somewhat similar situ ation regarding model response to ammonium nitrate formation. Although the MRPO 2005 case does not over predict nitrat e in the base case, it does have additional ammonia than the Minnesota (MRPO) case. Also, in the MRPO 2005 future case (2018), ammonia emissions do not incr ease nearly as much as the Minnesota (MRPO) case. Again, this scenario would allow the m odel to respond better to reductions in NO X emissions in the MRPO 2005 case than the Minnesota (MRPO) case.

In this document we quantitatively describe how the RRF—and hence the RPG—are sensitive to the available ammonia. Adding more ammonia in the Minnesota (MRPO) base case and estimating less of an increase in ammonia in the future cas e results in a more optimistic RPG of 0.2 dv (18.4 dv) at Boundary Waters and 0.3 dv (18.6 dv)Voya geurs. Livestock operations are the major source of ammonia emissions. The MRPO 2005 futu re year estimates were improved from that used in the Minnesota (MRPO) case. The reduced ammonia gr owth in the MRPO 2005 case was most impacted by a future estimated decline in dairy operations.

The greater visibility improvement in the MRPO 2005 case can also be attributed to meteorology. It uses 2005 meteorology rather than the 2002 meteorology used in the CENRAP and Minnesota (MRPO) cases. Thus, the MRPO 2005 projections for future visibility are based on different worst visibility days, di fferent visibility components that contribute to those days, and the predominant wind direction from which emission reductions occur.

The MRPO 2005 case shows significa ntly less contribution from Mi nnesota and Western States at both Boundary Waters and Voyageurs than the Minnesota (MRPO) case, and greater influence from states located to the East and Southeast of Minnesota, including Indiana, Iowa, Michigan, and Missouri. Modifying the Minnesota (MRPO) case with 2005 meteorol ogy results in a more optimistic RPG – 0.4 dv (18.2 dv) at Boundary Wate rs and 0.8 dv (18.1 dv) at Voyageurs – than the Minnesota (MRPO) case using 2002 meteorology, but not as optimistic as the MRPO 2005 case. Modifying the Minnesota (MRPO) case with 2005 meteorology and MRPO 2005/2018 ammonia emissions results in an even more optimistic RPG – 0.6 dv (18.0 dv) at Boundary Waters and 0.9 dv (18.0 dv) at Voyageurs.19 Overall, the evidence supports a conclusion that the Minnesota (MRPO) case projects a minimum expected future visibility improvement. The model responds better with more winter-time ammonia available in the base year, and less availa ble in the future year. We believe the future year estimates are better with less available amm onia in the future than that reflected in the Minnesota (MRPO) case. The quest for better ammonia em ission estimates continues. The model shows more optimistic goals using 2005 me teorology. Because 2002 meteorology emphasizes more contribution to visibility impariment from nitrate in the winter when winds originate form the West and Northwest, the model may show great er visibility improvements if the uncertainty in Canada emissions were resolved and any possible NO x emission reductions taken into account. As these efforts would en tail internationa l relations, it is prudent for the EPA to lead this effort.

The remainder of this document provides th e detail on the modeling conducted by Minnesota, and the comparison to the other modeling analyses. These include the emissions input to the model, description of the model options used, pe rformance evaluation of the model compared to observed, the uniform rate of progress analysis, a nd the assessment of the contributing States and source sectors to visibility impairment.

This part of the document is divided into sectio ns that correlate to the individual steps in the process for making a technical demo nstration of reasonable progress. For those with minimal or no direct involvement in the regional haze modeling activities, the document begins with a general explanation of the process by which the technical work was completed.IMPORTANT NOTE Regarding Future Year Emissions Estimates of Electric Generating Units :

The on-the-books controls for electric generati ng units (EGUs) used to establish the RPG in this document presume the Clean Air Intersta te Rule (CAIR) is in affect. On July 11, 2008, after the end of the public notice peri od for the Minnesota RH SIP but before submittal to the EPA, the D.C Circuit Court of Appeals issued an opinion pointing out several “fatal flaws” with CAIR, and vacated the rule. The EPA petitioned for rehearing which included changing the remedy from vacatur to a remand.

On December 23, 2008, the Court issued a pa nel opinion, remanding CAIR to the EPA to be rewritten to address the fl aws identified in the July ru ling. This action means that CAIR is in effect while the flaws are addressed.

One issue the EPA must address on remand is whether Minnesota shoul d continue to be included in CAIR. The Court ruled in July that the EPA did not adequately respond to claims made by Minnesota Power that data on Minnesota emissions were inaccurate, and that by using better data Minnesota would fall below the threshold impact on a nonattainment area that was used for inclusion.

On May 12, 2009 EPA published in the Federa l Register (74 FR 22147) a rule amending CAIR to stay the effectiveness of the rule wi th respect to sources located in the State of Minnesota. The administrative stay will re main in effect until such time as EPA determines through a rulemaking under the Cl ean Air Act whether Minnesota should be included in the CAIR region for fine particul ate matter. In a letter dated October 31, 2008, from the EPA to Minnesota Power counsel , the EPA indicated its belief “that in light of the Court’s decision, sources in Minne sota should not be required to make any additional expenditures to comply with CAIR prior to the ex piration of the administrative stay of the rule”.

Minnesota has determined that the known controls in 2018 with CAIR in place—as modeled to establish the RPG—and without CAIR in place are nearly identical for Minnesota. The resulting emission projections are also very similar. Dissimilarities in emissions projections are attri buted to differences in emi ssion projection methods. CAIR emissions were estimated with the Integr ated Planning Model (IPM), while emissions without CAIR were developed using electricity generation fo recasts by the Department of Energy “Electricity Market Module Supply Regions” by fuel type, and then applying known controls. IPM projections used to establish the RPG contain 6 percent less NO X (-2,900 tons from 52,400 tons ) and 9 percent more SO 2 (4,400 tons from 49,400 tons) from Minnesota EGUs. Without detailed docu mentation available for the IPM estimates, we can not definitively explain the differences, however, we believe the additional SO 2 may be attributed to IPM’s tendency to allow SO 2 emissions to rise close to the permitted SO 2 emissions rate. Detail on the control m easures and emission estimate methodologies are provided in this document.

Minnesota will continue to establish the RP G based on CAIR in place. Future electric generating unit emission projections will be re -evaluated after EPA re-writes the CAIR rule

.

### II. Process for Developing Tec hnical Support for Regional Haze

The modeling system is composed of an atmo spheric transport and ch emistry model, also known as the “air quality model”, an emissions model and a meteorological model. The emissions and meteorology models create inpu ts for use by the air quality model. In general, Minnesota chose to use the modeli ng system chosen by MRPO. The modeling system 8

, 9 used in the Minnesota (MRPO) case is made up of the following:

• Comprehensive Air Quality Model (CAMx ). CAMx simulates atmospheric and surface processes affecting the transport, chemical transformation and deposition of air pollutants and their precursors. Some advantages of CAMx are two-way nesting, a subgrid scale plume-in-grid m odule to treat the early dispersion and chemistry of point source plumes, a fast chemistry solver, and Particulate Source Apportionment Technology (PSAT), which tracks the original source of particulate species by geographic regi on and source category. CAMx is a Eulerian model that computes a numeric al solution on a fixed grid. Minnesota used version 4.42.

• The Pennsylvania State University/Nati onal Center for Atmospheric Research (PSU/NCAR) Mesoscale Meteorological Model (MM5). MM5 output data is used in the emissions model a nd in the air quality model.

• Emissions Modeling System (EMS-2003). EMS-2003 generates hourly speciated emissions on a gridded basis for mobile, non road, area, point, natural (biogenic) and fires. The emissions are input to an air quality model.

Many steps are required when using models to support a reasonable progress goal for Regional Haze. Although many parts of the process overlap in practice, for ease of discussion they are identified in this document as a series of steps. The first step in conducting the demonstration is to select the period of time to model. Visibility issues occur throughout the year, and modeling must coincide with a year scheduled for emissions inventory development. States develop emissions inventories every three
years.

The second step is to develop an emissions inventory of the primarily formed fine particulate and the precursors to the secondarily formed portion of fine particulate. These precursors include sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), volatile organic compounds (VOC), ammonia (NH 3 ), fine particulate (PM 2.5 ) and coarse particulate mass. Sources of these precursors are both huma n (anthropogenic) and natural (biogenic). Biogenic releases are those from vegetation, for example trees, and are primarily VOCs. Anthropogenic sources of PM 2.5 precursors are stationary “point” sources (industrial

8 The MRPO conducted modeling for a 2005 base year analysis using CAMx version 4.50, a combination of EMS-2003 and Concept (for mobile source), and MM5 run by Alpine Geophysics.
9 CENRAP chose the Community Multiscale Air Quality Model (CMAQ) as its air quality model, the Sparse Matrix Operator Kernel Emissions (SMOKE) model as its emissions model, and MM5 as its meteorological model.22 facilities), “mobile” sources (on-road and o ff-road vehicles, airp lanes locomotives and marine vessels) and “area” sources (i.e. residential wood burning).

That leads to steps 3 through 5; emi ssions modeling, meteorological modeling and atmospheric chemistry and transport (or ai r quality) modeling. Although meteorological modeling output is an input to emissions models, emissions modeling is identified as the third step in this document. Emissions m odels take the emissions inventory from the second step and prepare them for input to the air quality model.

The fourth step is to process meteorologica l data through meteorological models. Some aspects of the meteorological modeling are used both as input to the emissions model and as direct inputs to the air quality model.

In the fifth step, the air quality model simula tes transport of the emissions and chemical reactions in the atmosphere to produce am bient air concentrati ons of the individual components of PM 2.5 ; sulfate (SO 4 ), nitrate (NO 3 ), organic carbon (OC), elemental carbon (EC) ammonium (NH 4 ), and fine and coarse particulate. The results are evaluated against monitoring data of the same period in the sixth step, model performance evaluation. Sta tistical analyses and other means are used to compare modeled results with obs erved concentrations.

If the model/monitor evaluation indicates a poor correlation, adjustments are made to the modeling system. Adjustments could be made to the size, coverage and grid-resolution of the domain; the time period modeled; the inventory; the emissi ons modeling; and/or the meteorological modeling or the air quality model (i.e. revising code). The ambient monitors and how they collect samples also could be explored during a model/monitor performance assessment. Good model performance means that various permutations— such as attempts to predict emissions for future years—can be incorporated in the modeling with some degree of accuracy.

Successful development of a future year emi ssions inventory leads to the seventh step, the uniform rate of progress analysis. This analysis establishes where future visibility falls on a uniform rate of progress, or “glid epath” toward natural background visibility conditions. The modeling alone does not dictate reasonable progress goals, neither does it determine whether the goals have been met. It does, however, provide a deciview value that reflects reasonable control measures , which in turn is the goal. Potentially reasonable control measures ar e quantified in step eight.

The flow diagram in Figure II.1 shows the proc edural flow for demonstrating attainment of air quality goals for Regional Haze. The diagram illustrates the iterative nature of these analyses. Specifics on each step are described throughout the remainder of this document supporting reasonable progress goals in the Minnesota Cla ss I areas, Boundary Waters and Voyageurs.23Figure II.1 – Procedural Flow for Demonstrating Attainment of Air Quality Goals for Regional Haze.SelectedYearPoor PerformanceModify Base Year1.0Model YearSelection2.0EmissionsInventoryDevelopment3.0EmissionsModeling4.0MeteorologicalModeling5.0Atmos. Chemistry& TransportModeling6.0PerformanceEvaluation8.0Control StrategyDevelopment7.0Uniform Rate ofProgressAnalysesControl StrategyImplementationEmissionsInventoryEmissionsInputsMonitor DataSource CategoryReductionsOverallPercentage &Point ReductionsBase YearModeled AmbientConcentrationsFuture YearModeled AmbientConcentrationsSuggestedStrategiesSelected EffectiveStrategiesMeteorologyInputsGood PerformanceDevelop Future YearSelectedYearPoor PerformanceModify Base Year1.0Model YearSelection2.0EmissionsInventoryDevelopment3.0EmissionsModeling4.0MeteorologicalModeling5.0Atmos. Chemistry& TransportModeling6.0PerformanceEvaluation8.0Control StrategyDevelopment7.0Uniform Rate ofProgressAnalysesControl StrategyImplementationEmissionsInventoryEmissionsInputsMonitor DataSource CategoryReductionsOverallPercentage &Point ReductionsBase YearModeled AmbientConcentrationsFuture YearModeled AmbientConcentrationsSuggestedStrategiesSelected EffectiveStrategiesMeteorologyInputsGood PerformanceDevelop Future Year24 

### III. Description of Each Step in the Process

This section contains the detail on the data used for each step in the process flow diagram shown and described above. Each sub-section (or step) begins with a description of the modeling work conducted by Minnesota for the Minnesota (MRPO) case. As part of the weight-of-evidence the description is followed by a comparison w ith work conducted by CENRAP, and where applicable, with the MRPO 2005 case.



### 1.0: Model Year Selection

Regional haze issues appear thr oughout the year in the Minnesota Class I areas, which make it necessary to model a full year rather than a s horter episode period. A model year must coincide with a year scheduled for emissions inventory development. States develop full emissions inventories every three years, the latest being the fully co mpleted 2002 inventory and the 2005 inventory which was still in development at the time of this analysis. EPA guidance suggests choosing a model year that has monitoring data available that stradd les the model year. Minnesota has selected 2002 as the base year for modeling.

During the iterative process of regional scale modeling, the MR PO decided to switch to a 2005 base year for reasons unrelate d to regional haze. The MRPO uses the modeling system to address issues related to ozone and PM 2.5 nonattainment in member states in addition to regional haze. Although Minnesota still uses MRPO inputs in its Minnesota (MRPO) model analyses, they remain the 2002 base year; while incorporating so me aspects of the 2005 inventory, as described in section 2.0. The 2002 base year allows Minneso ta to better correlate results with CENRAP, which also uses 2002 as its base year, and allows for using monitoring data that straddles the inventory year (2000-2004) to establish baseline conditions.



### 2.0: Emissions Inventory Development

Emissions are lumped into sectors based on the si milarity of the techniques used to process the emissions. These sectors are:

• Point or industrial sources that are iden tified by locational coordinate and stack parameters (i.e. facilities with state permits);

• Mobile Onroad or automobile and truck traffic on paved roadways;

• Nonroad or mobile equipment not traveling on roadways (i.e. recreational vehicles, construction and agricultural equipment);

• Marine vessels, airplanes and locomotives (also considered “nonr oad” sources although emission estimation techniques vary);

• Area or stationary sources that are not iden tified by locational coordinate and stack parameters (i.e. agricultural opera tions, residential heating); and

• Biogenic or natural emissions (i.e. trees).

Emissions modeled for regional haze are sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), volatile organic compounds (VOC), ammonia (NH 3 ), fine particulate (PM 2.5 ) and coarse particulate (PM 2.5 – PM 10 ) mass.25 The base year inventory, based on actual emission s calculated and recorded, is developed first. The base year inventory is proc essed through an emissions model and air quality model. Once the base year model results pass performance evaluation goals, they are deemed ready to become the basis from which future year emissions pred ictions are developed. For regional haze, the future year modeling inventor y is for the year 2018.

This section is divided in two pa rts. The first part describes th e base year inventory development and the second part describes the fu ture year inventory development.

2.1. The Base Year Inventory—2002. For the most part, base year inventories are de veloped by each individual State. These are the same inventories States submit to the EPA for the National Emissions Inventory (NEI). For some sectors, methods initially available to stat es for developing an inventory are inadequate for air quality modeling.

For these sectors, both CENRAP and MRPO have independently, and in some cases cooperatively, hired contractors to develop emissi ons data to support Stat e-developed inventories where older methodology, insufficient for modeli ng purposes, was used. For example, the development of accurate ammonia emissions is important because ammonia combines with sulfuric and nitric ac id to form aerosol sulfate and n itrate, significant components of PM 2.5 and of visibility impairment. Considerable effort 10 has been expended by the RPOs to improve calculation methodologies for ammo nia emissions from livestock and agricultural practices, and to appropriately allocate these em issions to each month of the year . Also, States do not typically develop inventories for biogenics, so th ese inventories had to be created.

Both CENRAP and the MRPO incorporated the inve ntories developed by th e States within their respective RPOs and shared modeling inventories with one another and other RPOs. Due to the iterative nature of the work (illustrated in Fi gure II.1), a variety of em ission inventories have been developed and used by organizations c onducting haze modeling. Therefore, each RPO might have a different version of their member Stat es’ inventories. Each subsequent version of a modeling emissions inventory might include the addition of emission sources that were missed, corrections to location coordinate s and stack parameters of industr ial point sources, and revisions to the inventory methodology.

The CENRAP case incorporates its latest invent ory “baseG”, while the MRPO 2002 case and the Minnesota (MRPO) case incorporate the MRPOs latest 2002 inventory “baseK”. Unfortunately, based on each RPO’s timing in the creation of thes e modeling inventories, the latest base year inventory of one RPO is not necessarily included in another RPO’s base year 11 . For example, the MRPO 2002 case contains CENRAP’s baseC. Th e CENRAP case contains the MRPO baseK.

10 This effort is ongoing in MRPO.
11 CENRAP and WRAP retained the same contractors fo r emissions modeling and atmospheric chemistry and transport modeling. The CENRAP baseG contains the most recent WRAP Plan02b inventory. North and South Dakota, located immediately to the West of Minnesota, are in the WRAP RPO.26 Revisions to CENRAP baseC in baseG emissions include 12 :

• Some changes to the Oklahoma point source inventory;

• Some stack parameter and emissions update s from some CENRAP states, including removal of double-counting of electric gene rating unit emissions in Missouri that resulted in a reduction of about 11,000 tons per year;

• Updated Mexico emissions; and

• Increased Gulf of Mexico NO X /SO 2 emissions.

Of these changes, the change to the Missouri inve ntory would be relevant to the Minnesota Class I areas. However, the MRPO 2002 case does no t have this double-counting of emissions in Missouri. Other CENRAP inventory changes between the versions are related to the future year emissions and are described in section 2.2.

More recently, MRPO changed to a 2005 invent ory year, termed “baseM”. The MRPO2005 case incorporates VISTAS baseG except for the five MRPO states, Minnesota nonroad, mobile and point, and Iowa and Missouri agricultural nonroad. VISTAS base G contains CENRAP baseG, WRAP Plan02b and MANE-VU 3.1 with estimated growth from 2002 to 2005. The MRPO temporalized the EGU emissions from all States using CEM data.

Minnesota worked with both CENRAP and MRPO. Each organization has separately developed a Minnesota modeling inventory. Thus, MRPO developed a baseK inventory for Minnesota, rather than using the CENRAP baseC. CENR AP developed a baseG inventory for Minnesota rather than using the MRPO baseK. Table 2.1 summarizes the supplementary base year emissions inventory development activities fo r the MRPO 2002 case and the CENRAP case. More detail on the inventory used by each of the two RPOs can be found in their respective technical support documents.

Minnesota did not make alterations to th e MRPO 2002 case emissions in the Minnesota (MRPO) modeling. However, some changes were made to Minnesota stack locations and parameters as described in Appendix B.

A summary of the base year emissions for Mi nnesota and the surrounding states used in the Minnesota (MRPO) case are in Table 2.2. Unit by unit point source emissions for Minnesota are provided in Appendix A. Emissions totals for CENRAP are not included in this table. Further evaluation of information supplied by CENR AP indicates that CENRAP model inputs 13 do not support some of the annual emissions totals provided. The MRPO 2005 inventory methodology is not detailed in this docume nt, but a table containing annual em issions estimates is provided in Appendix C.

12 These changes exclude those made to Minnesota and Iowa, where MRPO developed their own inventory and CENRAP baseC was not used.
13 CENRAP model inputs for one winter day and one summer day, which Minnesota has in-house, were evaluated to make this determination. See Section 2.2 for detail on the CENRAP winter and summer day model inputs.27 Table 2.1. Summary of MRPO 2002 Case (used in Minnesota (MRPO) case) and CENRAP Case Inventories.

Source Sector MRPO 14 2002 Case CENRAP 15 Case

Point State prepared 2002 inventories for five MRPO states (Illinois, Indiana, Michigan, Ohio and Wisconsin) plus Iowa and Minnesota.

For other states, emissions are those reflected in other RPO work, VISTAS baseF CENRAP 6/24/05 (baseC excluding Minnesota and Iowa) WRAP pre02d MANE-VU 1/31/05—no version number available. State prepared 2002 inventories for nine CENRAP states (Arkansas, Iowa, Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma, and Texas )

For other states, emissions generated from EPA NEI02 v. 1 and other information from RPOs VISTAS baseG MRPO baseK WRAP plan02b MANE-VU base

Mobile Onroad MRPO calculated emissions for all U.S. States based on VMT and MOBILE6.2 obtained from the 2002 NEI. MRPO states, Minnesota and Iowa provided comments on these inputs. MOBILE6.2 was used as a module within the EMS-2003 emissions model system described further in Section 3.0. CENRAP calculated emissions based on VMT and MOBILE6 inputs forCENRAP, MRPO, VISTAS and MANEVU. MOBILE6 emissions were used for the WRAP states. MOBILE6 was used as a module within the SMOKE emissions model system described further in Section 3.0. Nonroad MRPO calculated monthly emissions for all nonroad categories using the NONROAD 2004 model for its five states and Minnesota. Monthly NONROAD emissions were also calculated for Iowa and Missouri agricultural equipment. RPO supplied data was used for all other states and for the remaining nonroad categories for Iowa and Missouri. CENRAP calculated 2002 annual emissions using the NONROAD model for all states, except Minnesota, and Iowa agricultural equipment, and California. The MRPO baseK monthly NONROAD2004 emissions were used for Minnesota and Iowa agricultural equipment. Marine, Aircraft & Rail MRPO contracted with ENVIRON to estimate emissions for marine, locomotive and aircraft for MRPO states. 16

, 17

Fire MRPO contracted with EC/R to develop a fire inventory 18 The University of Wisconsin conducted a wildfire emissions study for the period May through September 2002. Though calculated, neither prescribed CENRAP calculated fire emissions using the Emission Production Model (EPM)/CONSUME within the BlueSky framework. CENRAP characterized some fires as

14 MRPO (April 2005)
15 ENVIRON (September 2007)
16 ENVIRON (December 2004)
17 After baseK, it was discovered that marine emissions are overestimated in Lake Superior. New emission factors developed for Base M (2005), but were not incorporated into the Minnesota (MRPO) modeling.
18 MRPO (September 2004)28 (excluding elevated point sources in Minnesota) 19 nor wildfires were included in the air quality modeling. 20 area sources, and some fires (within CENRAP, WRAP, VISTAS and Canada) as point sources.

Area Ammonia MRPO used CMU Ammonia Model (version 3.6) with 2002 agricultural census data for monthly and diurnal fertilizer application and livestock related emissions. Emissions estimates from soils, humans, dogs, cats and deer were excluded. Data within the CMU model was modified to reflect improvements made in temporal profiles (see Section 3.0). CENRAP used the CMU Ammonia Model for 13 source categories

, with improvements to livestock and fertilizer activity data or emission factors. Some area source categories (i.e. landfills and ammonia refrigeration) were calculated outside the CMU model.

Other Area

Sources State direct supplied NEI submittals for MRPO states plus Minnesota and Iowa, EPA NEI for other states. EPA NEI02 v. 1 and other information from other RPO base year inventories

Biogenic (Natural) MRPO used BIOME3 (BEIS3 written in SAS within EMS-2003) with day-specificmeteorology from MM5 and hourly satellitebased photosynthetically activated radiation values. Biogenic Emissions Landcover Database version 3 (BELD3) was used for fractional land-use and vegetative speciation. Environ used SMOKE with BEIS3.12, with day-specific meteorology from MM5. BELD3 land use data was used, with BEIS3 summer and winter emission factors.

Dust Wind-blown and agricultural tilling dust emissions were eliminated from the modeling inventory due to concerns over the transportable fraction of fugitive dust. Road dust is included. CENRAP calculated windblown dust emissions using a process based model developed by ENIVRON. Fugitive dust emissions were eliminated from the inventory.

19 Prescribed burning emissions in Minnesota were inadverten tly left in the Minnesota base year and future year inventories.
20 MRPO conducted a sensitivity analysis to estimate the af fect of fire emissions. The result showed an impact a small impact (i.e. less than 0.2 ug/m3) in modeled organic carbon and elemental carbon concentrations

.29 Table 2.2. Annual 2002 Emissions in Tons 21 in Minnesota (MRPO) Case by Source and Category for Minnesota and Surrounding States.

M I N N E S O T A SrcGroup SO 2 NO X NH 3 PM 25 PM 10 VOC

Point 131,000 155,000 2,310 12,500 31,100 33,700

Area 22,800 58,100 175,000 19,500 72,200 133,000

Mobile On-road 29 172,000 7,200 2,200 2,200 97,600

Non-road 9,210 102,000 98 5,600 6,380 96,800

Biogenics 0 28,700 0 0 0 698,000

Minnesota TOTAL: 163,000 516,000 185,000 39,900 112,000 1,060,000

(no biogenics) TOTAL: 163,000 487,000 185,000 39,900 112,000 361,000

I O W A Point 179,000 120,000 3,220 10,900 25,000 45,400

Area 5,330 9,290 246,000 9,280 36,900 79,100

Mobile On-road 43 117,000 4,280 1,290 1,290 87,500

Non-road 7,600 93,900 80 5,450 6,240 65,700

Biogenics 0 40,700 0 0 0 227,000

Iowa TOTAL: 192,000 381,000 254,000 26,900 69,400 504,000

(no biogenics) TOTAL: 192,000 340,000 254,000 26,900 69,400 278,000

N D O A R K T O H T A Point 159,000 85,500 29 14 258 2,310

Area 45,800 15,500 69,400 3,400 16,700 43,100

Mobile On-road 16 26,600 887 318 318 14,200

Non-road 1,190 20,900 16 395 469 4,790

Biogenics 0 33,600 0 0 0 158,000

North Dakota TOTAL: 206,000 182,000 70,400 4,120 17,800 223,000

(no biogenics) TOTAL: 206,000 148,000 70,400 4,120 17,800 64,500

S D O A U K T O H T A Point 14,100 20,700 12 245 1,310 1,620

Area 18,500 6,140 106,000 3,510 15,800 26,800

Mobile On-road 21 33,200 1,120 403 403 16,800

Non-road 432 6,710 3 292 346 4,430

Biogenics 0 40,700 0 0 0 266,000

South Dakota TOTAL: 33,000 107,000 107,000 4,450 17,800 315,000

(no biogenics) TOTAL: 33,000 66,700 107,000 4,450 17,800 49,600

W I S C O N S I N Point 252,000 129,000 308 5,200 10,300 28,700

Area 6,150 21,700 114,000 7,950 10,300 140,000

Mobile On-road 411 181,000 8,600 1,860 1,860 92,700

Non-road 7,430 76,600 94 4,580 5,240 112,000
Biogenics 0 18,600 0 0 0 413,000

Wisconsin TOTAL: 266,000 427,000 123,000 19,600 27,700 787,000
(no biogenics) TOTAL: 266,000 408,000 123,000 19,600 27,700 374,000

21 Values are reported to three significant digits. Sum totals may not add up due to rounding.30Figures 2.1 through 2.4. Annual 2002 SO2, NOx, NH3and biogenic VOC emissions in Tons for Minnesota(MRPO)Case.Sulfur Dioxide:Nitrogen Oxides:AnnualEmissions(Tons)100 - 200201 - 300301 - 500501 - 700701 - 1,0001,001 - 2,0002,001 - 5,0005,001 - 10,00010,001 - 15,00015,001 - 220,000AnnualEmissions(Tons)100 - 200201 - 300301 - 500501 - 700701 - 1,0001,001 - 2,0002,001 - 5,0005,001 - 10,00010,001 - 15,00015,001 - 220,000Ammonia: Biogenic VolatileOrganic Compounds:AnnualEmissions(Tons)50 - 100101 - 200201 - 400401 - 600601 - 800801 - 1,0001,001 - 3,0003,001 - 5,0005,001 - 7,0007,001 - 35,000AnnualEmissions(Tons)100 - 1,0001,001 - 2,0002,001 - 3,0003,001 - 4,0004,001 - 6,0006,001 - 8,0008,001 - 10,00010,001 - 14,00014,001 - 18,00018,001 - 25,00031 Underestimation of Minnesota Nonroad Emissions. Some concerns have been raised about the potential impact of emissions from the fleet s of large trucks opera ted at taconite mines located in St. Louis County, which is in north eastern Minnesota, near the Minnesota Class I areas. Emissions from these sources were underestimated in both the CENRAP and MRPO inventories.

The trucks used for hauling material at the mi ne sites are nonroad mobile sources and operate throughout the year. In the base line inventory, nonroad mobile source emissions were estimated by county based on population. Because St. Lo uis County is relatively low in population, NO X emissions from the nonroad sector are likely underestimated in this county.

One facility, which uses newer model trucks, estimates its truck emissions at about 170 tons per year. Based on this number, Minnesota estimates th at the mining trucks at a total of six taconite facilities could add between 1200 – 2500 tons per year of NO X . Because this underestimation was noted late in the modeling process, the inve ntory has not been modified to include these emissions.

2.2 The Future Year Inventory—2018. The base year inventory must be processed through the emissions model (see section 3.0) and the air quality model (see section 5.0). The base year model results must pass the performance evaluation goals (see section 6.0) be fore it is ready to become th e basis from which future year emissions predictions are devel oped. For regional haze SIP, the future year modeling inventory is developed for the year 2018.

Both CENRAP and MRPO primarily used the Economic Growth Analysis System (EGAS) model to estimate the growth of emissions fr om 2002 to 2018 for all source categories, except onroad mobile sources and EGUs. Onroad mo bile sources were gr own using the MOBILE6 model. EGUs were grown using th e Integrated Planning Model (IPM).

MOBILE6 . On-road mobile sources were grown us ing MOBILE6 within the emissions model EMS-2003 (see section 3.0). The MRPO used St ate Metropolitan Planning Organization and Department of Transportation modeled future year networks and local MOBILE6 inputs to generate emissions estimates for the five MR PO states and Minneso ta. Alpine Geophysics provided the information for other states.

EGAS5

. Emissions for all source sectors, ex cept on-road mobile sources and EGUs, were grown using EGAS version 5. EGAS is a forecast mo del used to predict national and regional economic activity in order to estimate air qualit y levels. EGAS is based on the premise that growth in emissions largely depends on the growth in economic activity, particularly changes in sales forecasts, in an area. Therefore, the gr owth factors are based on things like projected changes in fuel consumption, or increases in pop ulation that may result in a greater use of a product or service, increases in pr oduction of a product, or the clos ing of industrial facilities.

Growth factors are applied to 2002 emissions from existing units by SCC code, and are meant to represent total growth for that particular SCC including the addition of new sources, retirement32 of existing sources, and output changes from existi ng sources. Growth f actors cannot predict the development of new facilities and do not take in to account future controls on new units. They merely grow emissions from existing facilities w ithin an SCC in order to capture the overall emissions growth by 2018 for that SCC. The growth factors assume constant growth rates each
year.

The MRPO capped growth rates for the MRPO stat es and Minnesota, Iowa and Missouri. It was assumed there was no growth for residential w ood combustion and pesticides All other area source categories were capped at 3 percent pe r year based on 1.5 times the growth in the Midwest Manufacturing Index over the seven years 1998-2004, which is 21 percent over seven years. Minnesota requested that CENRAP use th e same cap for similar SCCs for the State, and provided a list of specific SCC with gr owth factor modifi cations by specie (NO X and SO 2 ).

For several categories where the EGAS-supplie d growth factors were suspect, the MRPO contracted for the development of alternative grow th factors. Alternative growth factors were developed for 22 priority area source categories, which mainly included fuel combustion point sources. E.H. Pechan 22 developed growth and control factor s for all area, and point sources. They developed growth rates for fuel combus tion categories based on Department of Energy historical and projected fuel consumption data fo r industrial sources, for the five MRPO states. Minnesota and Iowa were included by developing a composite SCC-by-SCC data set analyzed to determine more appropriate growth rates. Ot her categories, for example ammonia forecasts, were based on the Regional Economic Models Incorporate (REMI) State-level economic model and sector-specific equations rela ting emission activity trends to trends associated with a REMI socioeconomic variable.

MRPO 2005 Growth Factor Refinements. While Minnesota-specific hi storical and forecast growth indicator data was not sp ecifically included in the 22 pr iority source category review in the MRPO 2002 case, Minnesota-specific data wa s included in the MRPO 2005 update. Also, the overall MRPO 2002 case growth factors were revised to reflect more updated growth indicators. Of particular inte rest for this TSD is the revi sion in ammonia growth methodology from agricultural livestock sources because of the demonstrated model sensitivity to ammonia levels in the development of the RPG. Rather than the REMI model and the regression equations used for this category in the MRPO 2002 case, gr owth from agricultural livestock sources was based on interpolated SCC animal count pr ojections by State from the EPA ammonia inventory 23 . The reduced ammonia growth in th e MRPO 2005 case was most impacted by a future estimated decline in dairy operations. Th e resulting difference in estimated ammonia area source emissions between the MRPO 2002 case and the MRPO 2005 case is shown in Table 2.3.

22 E.H. Pechan (December 2005)
23 EPA (January 2004)33 Table 2.3 Annual Ammonia Emissions in Tons in the MRPO 2002 and MRPO 2005 cases.

Case Emission Category Annual Emissions (tons) 2002/2005 Percent Growth Annual Emissions (tons) 2018

MRPO

2002 Total Area Ammonia 175,000 239,000 Fertilizer 65,600 49% 97,600 Livestock 109,000 29% 141,000

MRPO 2005* Total Area Ammonia 187,000 218,000 Fertilizer 70,100 37% 95,900 Livestock 117,000 4% 122,000 * Note that about seven percent growth is assu med between the MRPO 2002 and MRPO 2005 base cases. Adjusting for growth in the MRPO 2005 case—from 2002 to 2005—results in 46 percent growth in emissions from fertilizer application and 12 percent growth in emissions from livestock from 2002 to 2018

. Taconite Facility Adjustments. Minnesota made further changes to the growth estimates in the Minnesota (MRPO) case for taconite mining sources located in Northeastern Minnesota. Due to the general proximity of ma ny facilities within Mi nnesota’s mining industr y (largely taconite extraction and processing) to Minn esota’s Class I Areas, particular attention was paid to future year emission projections from these sources.

While investigating the 2018 taconite industry emissions, it was discove red that Minnesota’s 2002 emissions inventory often used SCCs for tacon ite indurating furnaces that are outside of the taconite-specific SCCs. Ideally the SCC used for the taconite indurating furnaces would be 303023xx (Industrial processes, primary metal producti on, taconite ore processing). However, Minnesota emission inventory staff has us ed other SCCs (such as 39000699 - Industrial processes, in-process fuel us e, natural gas, general, or 10200802 – Industrial External Combustion boiler, petroleum coke) as a means to keep track of fuel usage.

Because emissions at some facilities have, in so me cases, been assigned to more general SCCs, different growth factors were assi gned to different facilities for essentially the same process. The growth factor used for indurating furnaces (and all SCC related to taconite mining) should be the same. Starting with the 2006 point source inventory, Minnesota em issions inventory staff began assigning emissions from taconite f acilities only to tacon ite-specific SCCs.

As shown in Table 2.4, for any given taconite indurating furnace SCC (except 39000699) MRPO and CENRAP have used different growth factors. In addition, MR PO has used different growth factors for non-MRPO and MRPO states. The grow th factor used for indurating furnaces (and all SCC related to taconite mining) should be the same for all taconite facilities.

Minnesota does not expect any growth in emissi ons from existing taconi te mining plants from 2002 levels, however, some growth is expect ed due to new sources. Therefore, the Minnesota (MRPO) modeling assigned a growth factor of 1.0, and revised control factors, to all existing taconite mines, and added location info rmation and projected emissions for new mining34 facilities. Two facilities were added as propos ed “east mine” and “west mine” projects. The emissions projections reflect those for Polymet and Minnesota Steel, and lines at UTAC (EVTAC) 24 line 1 and Northshore Mining Silver Ba y Furnace 5, that did not operate during 2002. Emissions for the Mesabi Nugget taconite plant were also added to the future year inventory.

Table 2.4. Taconite Growth Factors from 2002 to 2018 Used By MRPO and CENRAP

SCC SCC Description CENRAP Growth Factor. 25 MRPO Growth Factor for MRPO states 26 MRPO Growth Factor for MN 27

10200802 Industrial External Combustion Boilers, Petroleum Coke 1.126 1.095 1.126

30302312 Industrial Process, Primary Metal Production, TaconiteOre Processing, GasFired Indurating Furnace 1.357 1.275 1.357

39000699 Industrial Processes, In-Process Fuel Use, Natural Gas 1.0 1.0 1.0

Changes made to the future year inventory used in the Minnesota (MRPO) case are in Appendix B. 28 Overall, these changes result in less NO x (original 83 tons/day, revised 77 tons/day) and less SO 2 (original 13 tons/day, revised 10 tons/day) emissions from these facilities in 2018.

Integrated Planning Model (IPM). All RPOs agreed to predict future EGU emissions with IPM, which is a model developed by ICF that EPA uses to evaluate future impact of policies on EGUs in combination with projected energy needs. For example, the EPA used IPM to support the Clean Air Interstate Rule (CAIR) a nd the Clean Air Mercury Rule (CAMR).

IPM version 2.1.9(VISTAS) was used in bot h CENRAP and MRPO 2002 future year inventories. The IPM model output presumes CAIR and CAMR are implemented. While developing its 2005 future year modeling inve ntory, the MRPO switched to IPM3.0. IPM3.0 also presumes CAIR and CAMR are in place, bu t makes different assumptions (i.e. fuel cost)

24 Although United Taconite LLC (UTAC) is the current name of the facility, this document retains the name “EVTAC” to maintain consistency with the naming conventio n of the 2002 modeling inventory. Other facilities that retain former facility names include: ArcelorMittal Minorca Mine, which retains the name “Ispat” Inland Steel, and Ainsworth Engineered (USA) LLC, which retains the name “Potlatch” Corp.
25 Obtained from CENRAP spreadsheet provided by Lee Wa rden on Dec. 6, 2006 of top non-EGU facilities (250 TPY in 2018 or greater) of SO 2 and NO x 26 Obtained from MI/WI growth factor spreadsheets done by MRPO – overall percent growth.(http://www.ladco.or g/tech/emis/basek/reports/ control/argnc.scc.all4one26.0 20712.r4s1_none gu_2018.lst)
27 Obtained from “argnc.gnc .all4one” from MRPO website.
28 Appendix B also includes changes made to some stack parameters and locations.35 when estimating future EGU emissions. Minnesota ’s review of the IPM results concluded that IPM3.0 better reflects Minnesota’s estimation of the future EGU scenario. Although the IPM3.0 predictions are improved, they could still use some adjustments. MRPO requested its member states plus Minnesota, North Dakota, Iowa and Missouri to make corrections or adjustments to the IPM3.0 outputs. For example, Minnesot a made a correction to the Minnesota Power-Boswell EGU emissions to acc ount for an underestimation of the facility’s capacity in IPM. Adjustments were limited to co mmitted control projects that occurred after the scheduled deadline for submission to EPA for th e IPM3.0 base model run. All corrections and adjustments made to Minnesota EGUs were done in consultation with industry representatives. Table 2.5 summarizes Minnesota’s adjust ments and corrections to IPM3.0.

Table 2.5. Minnesota Adjustments and Corrections to IPM3.0

. Facility

Name Basis for Correction/Adjustment Unit # IPM base Total Emissions (Mton) IPM “will do” Total Emissions (Mton) NO x SO 2 NO x SO 2 MinnesotaPowerBoswell Unit 3 adjusted to reflect NO X permit limit, SO 2 decreased with addition of FGD* Unit 4 capacity was increased from 425 MW to 535 MW 3

4 0.79

3.75 6.11

4.00 0.93

4.72 1.19

3.22

Xcel—A.S. King Adjusted based on permit limit. 1 1.43 1.87 2.08 2.49

Xcel—Black Dog Adjusted based on current performance in response to Xcel Energy comments. 3 4 1.17 1.63 3.92 5.47

MinnesotaPowerTaconite Harbor Retrofit SO 2 FSI** on Units 1 through 3. 1 2 3 1.56 1.39 1.37 0.67 0.68 0.67

*Fabric Filter/Flue Gas Desulfurization. **Furnace Sorbent Injection.

The modifications made to the IPM3.0 base m odel run, are called the IPM3.0 “will do” run. Table 2.6, created by MRPO, shows the overall difference in emissions among IPM2.1.9(VISTAS), the IPM3.0 base, and the IPM 3.0 “will do” scenario for each state in the upper mid-West.

Concerns have been raised as to whether adjusting IPM output would compromise the integrity of the IPM model predications. IPM assumes an energy balance throughout the sector, and the concern is that modifications at a handful of facilities can throw the system off-balance. However, Minnesota views IPM as one method fo r predicting future EGU emissions and if States and affected industry be lieve that the predicted emissions are incorrect, they should change them in order to get the most accurate es timation of future emissions in the state. The corrections and adjustments made by Minnesota should not throw the system off-balance36 because they generally address only changes in the performance of equipment that result in emission changes, and not overall energy balance.

VISTAS states have also made post-IPM model adjustments for their states to the IPM2.1.9— VISTAS output. CENRAP has not made any adjustments to the IPM2.1.9—VISTAS output used in their modeling. CENRAP intended to switch to IPM3.0, but did not due to timing and financial reasons.

Although the Minnesota (MRPO) case was conducted with the MPRO 2002/2018 case emissions from the non-utility source categories (with m odifications described above), the IPM3.0 EGU emissions were used for the future year inventor y. This substitution is possible because, unlike other sectors, future year EGU emissions calculated with IPM are independent of the base year inventory. They are not based on a growth factor applied to ba se year emissions.

Controls on Future Year Inventory. Control is applied after gr owth and may be a due to the addition of physical controls to a process. Thes e controls may be voluntar y or due to regulatory requirements. Controls also reflect Federal and State regulat ions and legislation and permit actions. MRPO contracted with E.H. Pechan and Associates to iden tify control implemented— termed “on-the-books” control—between 2002 and 2018 source sectors except EGUs. In the MRPO emissions, the control on all sectors, including EGUs, include 29 :

On-Highway Mobile Sources

• Tier II/Low sulfur fuel;

• Inspection/Maintenance programs in nonattainment areas (does not apply in Minnesota); and

• Reformulated gasoline in nonattainment areas (does not apply in Minnesota);

Off-Highway Mobile

• Federal control programs incorporated into NONR OAD model (e.g. nonroad di esel rule), and the evaporative Large Spark Ignition an d Recreational Vehicle standards;

• Heavy-duty diesel (2007) engine standard/Low sulfur fuel;

• Federal railroad/locomotive standards; and

• Federal commercial marine vessel engine standards.

Electric Generating Units 30

• Title IV Acid Rain Program (Phases I and II);

• NOx SIP Call (does not apply in Minnesota);

• Clean Air Interstate Rule; and

• Clean Air Mercury Rule.

Other Point Sources

• VOC 2-, 4-, 7-, and 10-year MACT standards;

• Combustion turbine MACT; and

• Industrial boile r/process heater/RICE MACT.

29 MRPO (April 2008)
30 These controls are included in the IPM3.0 projections.37 • The MRPO also included c ontrol factors to reflect settlement agreements for petroleum refineries and other non-EGU sources in MRPO states plus Minnesota. 31

31 MACTEC (January 2006)38 Table 2.6. IPM2.1.9 (VISTAS), and IPM3.0 “base” and “will do” Alterations 32

State Heat Input (MMBTU/year) Scenario SO2 (tons/year) SO2 (lb/MMBTU) NOx (tons/year) NOx (lb/MMBTU)

IL 980,197,198 2001 - 2003 (average) 362,417 0.74 173,296 0.35 IPM 2.1.9 241,000 73,000 1,310,188,544 IPM3.0 (base) 277,337 0.423 70,378 0.107 IPM3.0 - will do 140,296 0.214 62,990 0.096

IN 1,266,957,401 2001 - 2003 (average) 793,067 1.25 285,848 0.45 IPM 2.1.9 377,000 95,000 1,509,616,931 IPM3.0 (base) 361,835 0.479 90,913 0.120 IPM3.0 - will do 628,286 0.832 128,625 0.170

IA 390,791,671 2001 - 2003 (average) 131,080 0.67 77,935 0.40 IPM 2.1.9 147,000 51,000 534,824,314 IPM3.0 (base) 115,938 0.434 59,994 0.224 IPM3.0 - will do 115,938 0.434 59,994 0.224

MI 756,148,700 2001 - 2003 (average) 346,959 0.92 132,995 0.35 IPM 2.1.9 399,000 100,000 1,009,140,047 IPM3.0 (base) 244,151 0.484 79,962 0.158 IPM3.0 - will do 244,151 0.484 79,962 0.158

MN 401,344,495 2001 - 2003 (average) 101,605 0.50 85,955 0.42 IPM 2.1.9 86,000 42,000 447,645,758 IPM3.0 (base) 61,739 0.276 41,550 0.186 IPM3.0 - will do 54,315 0.243 49,488 0.221

MO 759,902,542 2001 - 2003 (average) 241,375 0.63 143,116 0.37 IPM 2.1.9 281,000 78,000 893,454,905 IPM3.0 (base) 243,684 0.545 72,950 0.163 IPM3.0 - will do 237,600 0.532 72,950 0.163

ND 339,952,821 2001 - 2003 (average) 145,096 0.85 76,788 0.45 IPM 2.1.9 109,000 72,000 342,685,501 IPM3.0 (base) 41,149 0.240 44,164 0.258 IPM3.0 - will do 56,175 0.328 58,850 0.343

SD 39,768,357 2001 - 2003 (average) 12,545 0.63 15,852 0.80 IPM 2.1.9 12,000 15,000 44,856,223 IPM3.0 (base) 4,464 0.199 2,548 0.114 IPM3.0 - will do 4,464 0.199 2,548 0.114

WI 495,475,007 2001 - 2003 (average) 191,137 0.77 90,703 0.36 IPM 2.1.9 155,000 46,000 675,863,447 IPM3.0 (base) 127,930 0.379 56,526 0.167 IPM3.0 - will do 150,340 0.445 55,019 0.163

32 MRPO (April 2008)39 Alternative EGU Emissions Projections. At the time CAIR was vacated by the courts, the MRPO developed future year EGU emissions wit hout CAIR in place. Rather than use the IPM model, which was used to model CAIR, the em issions without-CAIR were developed using electricity generation forecasts by the Department of Energy “Electricity Market Module Supply Regions by fuel type, and then applying known controls.

There are 13 supply regions. Minnesota is in Region 5, Mid-Continent Area Power Pool (MAPP) in the National Energy Modeling System that projects energy needs in billions of kilowatt hours. There are six fuel categories: coal, petroleum, natural gas, nuclear, pumped storage, and renewables. The MR PO used only coal, natural gas and petroleum in the future year estimates. The ratio of future year to base year for each fuel category was applied to the appropriate Source Classificat ion Codes (SCCs) in the 2007 EGU base year emissions by emission unit. Major SCCs are 101, 102, 103, 201 and 202. Minor SCCs 001, 002, and 003 were assigned coal; 004, 005, 008 were assigne d petroleum; and 006, 007 and 009 were assigned natural gas. Where a minor code does not fit into any of these cat egories, the default is coal.

Future year projections were estimated based on 2007 EGU emissions. Minnesota reviewed the 2007 emissions and found that not a ll facilities operated throughout th e year. For example, Xcel Energy—Allen S. King was installing controls duri ng that year and did not operate all of 2007. In this case, future year estimates were adjusted to reflect 2018 as if the facility operated the full year. Table 2.7 shows the growth rates applie d to Minnesota EGUs. Growth factors were applied to all facilities in the State.

Table 2.7. Growth Rates Applied to Minnesota EGUs based on Electricity Generation by

Electricity Market Module Region and So urce in Billion Kilowatt Hours for the MAPP Region 33

.

Mid-Continent Area Power Pool Base Year 2007 (Bkwh) Future Year 2018

(Bkwh) Percent Change

2005- 2030 Growth Factor 2007- 2018

Coal 120.81 139.14 0.8% 1.152 Petroleum 0.77 0.62 0.8% 0.805 Natural Gas 1.74 3.47 -1.0% 1.997

Known legally enforceable controls were then applied to the emissions grown to 2018. These controls include BART on all applicable E GUs in Minnesota including Power Boiler #2 at Northshore Mining-Silver Bay, whose BART determ ination was not complete at the time the emission projections were made. The legally enforceable BART controls on Power Boiler #2 includes the Low NO X burners in the preliminary stages of the BART determination, but does not include the Dry Sorbent Injection to control SO 2 which completes the BART determination for that unit.

33 http://www.eia.doe.gov/oiaf/archive/aeo08/supplement , Table 76.40 The resulting controls were nearly identical to those assumed in the IPM version 3.0 “will do” scenario described above and used to estab lish the RPG for the RH SIP. The control assumptions in the IPM 3.0 “will do” scenario a nd the controls without-CAIR are shown in Table 2.8.

A review of the total Minnesota EGU SO 2 and NO X for 2018 shows very similar emissions projections using the different methodologies. Emission projections used to establish the RPG with IPM 3.0 “will do” contain 6 percent less NO X (-2,900 tons from 52,400 tons) and 9 percent more SO 2 (4,400 tons from 49,400 tons) from Mi nnesota EGUs. Without detailed documentation available for the IPM estimates, the differences can not be definitively explained. However, the overall increased SO 2 projection by IPM may be due to its tendency to allow SO 2 emissions to rise close to the permitted SO 2 emissions rate. Some examples include Rochester Public Utilities, Hibbing Public Utilities and Virginia Public Utilities, where IPM allows emissions to rise to 4 lbs/MMBtu permitted rate when actuals are less than 1 lb/MMBtu. IPM v3.0 assumes no control changes at Northshore Mi ning-Silver Bay in 2018, however, the model predicts a low annual emissions ra te for 2018 regardless. It is unc lear why this is the case.

Since these alternative EGU projections were de veloped, the Court remanded the CAIR rule to EPA. Thus, CAIR is in effect while EPA addresse s the flaws in the rule specified by the Court. Included in that effort, EPA will determine whethe r or not CAIR applies in Minnesota. Whether or not CAIR applies has no effect on the known fu ture year controls pr ojected to 2018 already modeled for the RH SIP (with CAIR in place). Al so, at this time it is not possible to project future year emissions for other States without knowing how EPA will fix the flaws in the rule. Thus, Minnesota will continue to establish th e RPG using the IPM 3.0 “will do” projections for EGUs as described above.41 Table 2.8. Future Year Control Assumpti ons in IPM 3.0 “will do” (with CAIR) and MRPO Case B 2018 (without CAIR).

Facility Name Facility ID Stack ID Emission Unit ID Specie Controls Without CAIR Controls With CAIR Case B: Legally

Enforceable Modeled in SIP (IPM v3.0"will do")

2018 Control

EF Control Type 2018 Control

EF Control Type*

Xcel Energy -

Riverside Generating Plant 2705300015 SV001 EU001 NO X 100% removed 100% removed
SO 2 100% removed 100% removed

EU002 NO X 100% removed 100% removed
SO 2 100% removed 100% removed

SV003 EU003 NO X 100% removed 100% removed
SO 2 100% removed 100% removed

EU009 NO X * new unit natural gas * new unit natural gas

SO 2 * new unit natural gas * new unit natural gas

SV008 EU010 NO X * new unit natural gas * new unit natural gas

SO 2 * new unit natural gas * new unit natural gas

SV009 EU011 NO X 100% removed 100% removed
SO 2 100% removed 100% removed Minnesota Power Inc -

Boswell Energy Ctr 2706100004 SV003 EU003 NO X 80% SCR 80% SCR

SO2 85% FGD 85% FGD Rochester Public Utilities

- Silver Lake 2710900011 SV003 EU004 NO X 40% SNCR ** **

SO 2 85% SCRUBBER 95% SCRUBBER

Xcel Energy - High Bridge Generating 2712300012 SV001 EU001 NO X 100% removed 100% removed
SO 2 100% removed 100% removed

EU002 NO X 100% removed 100% removed
SO 2 100% removed 100% removed

EU003 NO X 100% removed 100% removed
SO 2 100% removed 100% removed

EU004 NO X 100% removed 100% removed
SO 2 100% removed 100% removed

SV008 EU010 NO X * new unit natural gas * new unit natural gas

SO 2 * new unit natural gas * new unit natural gas

SV009 EU011 NO X * new unit natural gas * new unit natural gas

SO 2 * new unit natural gas * new unit natural gas42 Facility Name Facility ID Stack ID Emission Unit ID Specie Controls Without CAIR Controls With CAIR Case B: Legally

Enforceable Modeled in SIP (IPM v3.0"will do") 2018 Control

EF Control Type 2018 Control

EF Control Type*

NSP - Sherburne Generating Plant 2714100004 SV001 EU001 NO X 50% LNB ** **
SO 2 85% SCRUBBER  

EU002 NO X 50% LNB **  **
SO 2 85% SCRUBBER  
SV002 EU003 NO X 50% LNB **  ** Xcel Energy - Allen S King

Generating 2716300005 SV001 EU001 NO X 80% SCR 90% SCR

SO 2 82% SCRUBBER 82% SCRUBBER

Minnesota Power Inc -

Taconite Harbor Ctr 2703100001 SV001 EU001 NO X 50% ROFA/SNCR 50% ROFA/SNCR
SO 2 40% FSI 40% FSI

SV002 EU002 NO X 50% ROFA/SNCR 50% ROFA/SNCR
SO 2 40% FSI 40% FSI

SV003 EU003 NO X 50% ROFA/SNCR 50% ROFA/SNCR
SO 2 40% FSI 40% FSI

Northshore Mining Silver Bay 2707500003 SV001 EU001 NO X 40% LNB
SO 2 20%*** biomass***

SV002 EU002 NO X 40% LNB
SO 2 20%*** biomass*** * Additional emissions for new units in the without-CAIR case were projected to be comparable to the IPM 3.0 projections ** These projects became legally enforceable af ter the IPM 3.0 "will do" case was developed. ***This control, which is part of the BART determination for the unit, was not included in the final “Controls without CAIR” because the BART determination was incomplete at that time.

Biomass: Co-firing biomass with existing fuel FGD: Fabric Filter/Fl ue Gas Desulfurization FSI: Furnace Sorb ent Injection LNB: Low NOx Burner ROFA: Rotating Opposed Fire Air System SCR: Selective Catalytic Reduction SNCR: Selective Non-Catalytic Reduction

Comparison Between MRPO and CENRAP Future Year Emission Estimates. Because the MRPO 2002 and Minnesota (MRPO) cases include the CENRAP baseC2018 inventory, it is imperative to understand the changes made to the CENRAP future year inventory between baseC2018 and baseG2018. These changes include:

• Modified growth and control fact ors for point sources in CENRAP:

o BART on American Electric Power (SWEPCO)/Gentry in Arkansas;

o Cement kiln emissions held constant (NO X SIP Call), early CAIR controls for Ameren with changed SO 2 and stack parameters, shut-down of Doe Run Glover Smelter and held remaining smelter to 2002 emission levels in Missouri;43 o BART for OPPD Nebraska Unit #1 and OPPD Gerald Gentleman, Units 1 and 2 in Nebraska;

o BART controls for Westar Energy and Kans as City Power and Light BART affected units, and MACT control assumptions in Kansas;

o Added several new points and modified emi ssions at Lehigh Cement Company, Lafarge North America, ADM Corn Processing Clint on and ADM Corn Processing Cedar Rapids in Iowa;

o BART for PSO Comanche, Northeastern, Riverside and Jenks and OG&E Sooner and Muskogee in Oklahoma.

• Added new emissions for non-platforms in the Gulf of Mexico;

• Corrected onroad mobile inputs for some Texas counties; and

• Included refinery settlements in Oklahoma, Texas and Louisiana.

None of the above states have significant visibility impacts on Boundary Waters and Voyageurs except Iowa and Missouri. Modifications made to Iowa sources reduced up to 23,000 tons per year of SO 2 (some to EGUs as well as nonEGUs) in the 2018 inventory. An emissions summary provided by CENRAP suggests that the CENRAP case contains annually 37,000 tons more point source SO 2 than the Minnesota (MRPO) case for Iowa. EGU SO 2 emissions from Iowa decreased significantly between the IPM2.1.9 and IPM3.0, whic h conceals any other changes and makes it unclear whether the Minnesota (MRPO) inventory includes the Iowa non-utility point emissions changes. For the same reason, it is also unclear whether the Missouri changes are included. However, for both Iowa and Missouri, the Minnesota (MRPO) inventory contains a conservative growth estimate for SO 2
. A summary of the projected 2018 em issions for Minnesota and the su rrounding states used in the Minnesota (MRPO) case are in Table 2.9. Unit by unit point source emissions for Minnesota are provided in Appendix A. Emissions totals for CENRAP are not included in this table. Further evaluation of information supplied by CENR AP indicates that CENRAP model inputs 34 do not support some of the annual emissions totals provided. The MRPO 2005 inventory methodology is not detailed in this docume nt, but a table containing annual em issions estimates is provided in Appendix C.

Table 2.10 shows the difference between the 2002 and 2018 projected emissions modeled in the Minnesota (MRPO) case. The largest reductions are associated with NO X from mobile sources, which is the main source of NO X in each of the States except North Dakota and South Dakota in the base year. The most significant reductions in SO 2 emissions are from point sources, which are the main source of SO 2 . Ammonia significan tly increases from 2002 to 2018 in all States except Wisconsin, where State-specific historical and forecast growth indicator data was used.

34 CENRAP model inputs for one winter day and one summer day, which Minnesota has in-house, were evaluated to make this determination. See Section 2.2 for detail on the CENRAP winter and summer day model inputs.44 Table 2.9. Annual 2018 Emissions in Tons 35 in Minnesota (MRPO) Case by Source and Category for Minnesota and Surrounding States.

M I N N E S O T A SrcGroup SO 2 NO X NH 3 PM 25 PM 10 VOC

Point 83,500 117,000 3,420 25,100 47,900 42,800

Area 22,700 62,100 239,000 19,500 72,400 129,000

Mobile On-road 2 31,400 10,100 514 514 20,000

Non-road 2,170 76,900 125 4,410 5,030 86,700

Biogenics 0 28,700 0 0 0 698,000

Minnesota TOTAL: 108,000 317,000 253,000 49,600 126,000 977,000

(no biogenics) TOTAL: 108,000 288,000 253,000 49,600 126,000 279,000

I O W A Point 167,000 101,000 6,100 12,300 22,100 58,600

Area 6,100 10,700 359,000 10,900 44,600 84,200

Mobile On-road 3 18,200 6,000 302 302 12,200

Non-road 842 63,900 105 3,120 3,570 30,300

Biogenics 0 40,700 0 0 0 227,000

Iowa TOTAL: 173,000 234,000 371,000 26,700 70,600 412,000

(no biogenics) TOTAL: 173,000 193,000 371,000 26,700 70,600 185,000

N D O A R K T O H T A Point 79,900 70,800 400 5,300 6,600 2,600

Area 44,200 17,400 102,000 3,910 19,400 47,500

Mobile On-road 1 4,020 1,230 73 73 2,620

Non-road 803 44,600 69 2,240 2,570 13,400

Biogenics 0 33,600 0 0 0 158,000

North Dakota TOTAL: 125,000 170,000 103,000 11,500 28,700 224,000

(no biogenics) TOTAL: 125,000 137,000 103,000 11,500 28,700 66,100

S D O A U K T O H T A Point 6,480 9,540 66 478 1,700 1,700

Area 18,900 6,620 153,000 4,040 18,500 27,800

Mobile On-road 1 4,950 1,570 95 95 3,080

Non-road 543 27,900 44 1,670 1,910 11,200

Biogenics 0 40,700 0 0 0 266,000

South Dakota TOTAL: 25,900 89,700 155,000 6,280 22,200 309,000

(no biogenics) TOTAL: 25,900 49,000 155,000 6,280 22,200 43,700

W I S C O N S I N Point 210,000 89,100 1,080 12,100 18,400 37,300

Area 6,280 23,400 114,000 7,630 10,300 127,000

Mobile On-road 16 27,200 12,100 428 428 16,700

Non-road 1,410 45,000 127 2,810 3,240 71,700
Biogenics 0 18,600 0 0 0 413,000

Wisconsin TOTAL: 217,000 203,000 127,000 22,900 32,400 666,000
(no biogenics) TOTAL: 217,000 185,000 127,000 22,900 32,400 253,000

35 Values are reported to three significant digits. Sum totals may not add up due to rounding.45 Table 2.10. Difference Between A nnual 2002 and 2018 Emissions in Tons 36 in Minnesota (MRPO) Case by Source and Category for Mi nnesota and Surrounding States.

M I N N E S O T A SrcGroup SO 2 NO X NH 3 PM 25 PM 10 VOC

Point -47,000 -38,000 1,120 12,600 16,900 9,040

Area -150 3,970 63,400 13 254 -3,800

Mobile On-road -26 -140,000 2,890 -1,700 -1,700 -78,000

Non-road -7,000 -25,000 27 -1,200 -1,400 -10,000

Minnesota TOTAL: -54,000 -200,000 67,500 9,690 14,100 -82,000

I O W A Point -13,000 -20,000 2,870 1,430 -2,800 13,200

Area 772 1,400 113,000 1,630 7,780 5,130

Mobile On-road -40 -99,000 1,720 -990 -990 -75,000

Non-road -6,800 -30,000 26 -2,300 -2,700 -35,000

Iowa TOTAL: -19,000 -150,000 117,000 -260 1,280 -92,000

N D O A R K T O H T A Point -79,000 -15,000 371 5,290 6,350 287

Area -1,600 1,890 32,100 512 2,670 4,400

Mobile On-road -15 -23,000 348 -250 -250 -12,000

Non-road -390 23,700 53 1,850 2,100 8,570

North Dakota TOTAL: -81,000 -12,000 32,900 7,410 10,900 1,630

S D O A U K T O H T A Point -7,600 -11,000 54 233 391 74

Area 412 480 47,300 527 2,750 973

Mobile On-road -19 -28,000 454 -310 -310 -14,000

Non-road 111 21,200 40 1,380 1,570 6,750

South Dakota TOTAL: -7,100 -18,000 47,900 1,830 4,400 -5,900

W I S C O N S I N Point -43,000 -40,000 767 6,860 8,090 8,570

Area 130 1,640 65 -320 -21 -13,000

Mobile On-road -400 -150,000 3,530 -1,400 -1,400 -76,000

Non-road -6,000 -32,000 33 -1,800 -2,000 -41,000

Wisconsin TOTAL: -49,000 -220,000 4,390 3,340 4,640 -120,000

36 Values are reported to three significant digits. Sum totals may not add up due to rounding.46Figures 2.5 through 2.7. Annual 2018 SO2, NOx, NH3emissions in Tons for Minnesota(MRPO)Case.Sulfur Dioxide:Nitrogen Oxides:AnnualEmissions(Tons)100 - 200201 - 300301 - 500501 - 700701 - 1,0001,001 - 2,0002,001 - 5,0005,001 - 10,00010,001 - 15,00015,001 - 220,000AnnualEmissions(Tons)100 - 200201 - 300301 - 500501 - 700701 - 1,0001,001 - 2,0002,001 - 5,0005,001 - 10,00010,001 - 15,00015,001 - 220,000Ammonia:AnnualEmissions(Tons)50 - 100101 - 200201 - 400401 - 600601 - 800801 - 1,0001,001 - 3,0003,001 - 5,0005,001 - 7,0007,001 - 35,00047 

### 2.3 Canada Emissions Inventory In addition to including emissions from the indi vidual States in the United States, CENRAP and MRPO included emissions from Canada in an at tempt to reflect the base year 2002. Canada’s provinces report an emissions inventory to Envi ronment Canada, similar to the States reporting the NEI to the EPA. The subset of the Canadi an inventory—or Pollutant Release and Transfer Register (PRTR)—that includes the relevant pollutants is the Criteria Air Contaminants Emission Inventory. Canadians do not report thei r emissions on the same schedule as the United States, and did not provide a 2002 inventory. Both the CENRAP case and MRPO 2002 case include the Canadian 2000 inventor y. The Canadian inventory is confidential and specifics are not shared freely outside the countr y. This makes it difficult to qua lity assure the data and fully understand whether the estimation methods are comp arable. The MRPO are collaborating with staff in Ontario and are becoming more familiar with the emissions in Ontario.

During the iterative modeling process, it was de termined that much of the emissions from Canada were assigned no stack heig ht and as a result were modeled as if released in the surface layer of the atmosphere. These included some ve ry large sources, whose emissions in reality are released at much higher elevations. Because of meteorological (i.e. wind direction and speed) differences throughout the various layers, revisions to the elevation from which the Canadian sources emit were required. CENRAP made these changes to the Canadian 2000 inventory. MRPO, which was in the process of switching to a 2005 base year, chose to use the Canadian 2005 inventory, and with the help of Ontario envi ronmental staff, fixed the problems in that province. The Minnesota (MRPO) replaced the 2000 Canadian inventory with the Canadian 2005 inventory. A summary of annual 2005 Canada emissions totaled over the MRPO 4rpos domain is in Table 2.11.

Table 2.11 Canada Annual 2005 Emissions Total in MRPO 4rpos Region in Tons 37

C A N A D A SrcGroup SO 2 NO X NH 3 PM 25 PM 10 VOC

Point 948,000 298,000 7,700 28,800 96,600 108,000

Area 36,300 115,000 360,000 0 0 1,400,000

Mobile (nonroad + onroad) 26,200 535,000 9,470 2 2 412,000 Biogenics 80,200 4,650,000

TOTAL: 1,010,000 1,030,000 377,000 28,800 96,600 6,570,000

CENRAP used model-ready projected Canada em issions for the year 2020 from the EPA to represent 2018. CENRAP notes in its TSD that the EPA did not confirm whether EPA corrected any Canadian stack parameters.

Because of the large uncertainti es in the Canadian inventory, Minnesota elected to use the Canadian 2005 inventory for both the base year and the future year. Thus, no credit is taken for any possible reduction (or increase) in emissions from Canada. The previous sentence contains

37 These emissions are calculated from hourly CAMx model inpu t files and reported to three significant digits. Sum totals may not add up do to rounding.48 an emphasis on reduction in emissions becau se the 2000 and 2020 Canada emission summaries 38 for the entire Inter-RPO National Domain pr ovided by CENRAP show annual reductions in Canadian SO 2 of -2,150,000 tons, which equates to an 88 % reduction—nearly all of which consists of point source SO 2 (-2,130,000 tons, -97%)—and in Canadian NO X of -731,000 tons, which equates to 25% reduction, most of which consists of point source NO X (-630,000 tons, - 95%). Although the actual values may be questionabl e, it is safe to say the Canadian inventory predicts a reduction in emissions.



### 3.0: Emissions Modeling

The MRPO uses the EMS-2003 model, and CENR AP uses the SMOKE model, to develop emissions ready for import into atmospheric ch emistry and transport models. The emissions inventory described in Section 2.0 is not immediately ready for input into the air quality models, (and in some cases, the emissions are calculated using the emissions models). State developed emissions are reported as total emissions ove r the year, whereas modeling requires hourly emissions. Although State developed inventories contain specific locati onal (longitude/latitude) information for industrial point sources, all ot her emissions are reported on a county-basis. Emissions modeling will allocate em issions as specifically to each 36km (or less) grid square as possible.

In addition to spatially and temporally allocati ng emissions, the models are used to speciate the emissions for each of the source categories, wher e applicable. For example, if the state NEI submittal reports emissions as NO x , these emissions are speciated into NO and NO 2 . Point sources are allocated to grid ce lls based on their locational (lat itude/longitude) coordinates, and are allocated to individual hour s using reported operating sche dule information. County-level area source emissions are spatia lly allocated to grid cells using surrogates, for example population or land use, and are a llocated to specific hours using hour ly profiles representative of the area source. Both EMS and SMOKE directly allocate mobile and biogenic source emissions to grid cell by hour.

The MRPO generated modeling em issions estimates for a weekday, Saturday and Sunday for each month for point, mobile on-road, mobile no n-road, and area sources. Biogenic emissions were developed for every day of the year. General choices in emissions modeling for both MRPO and CENRAP are shown in Table 3.1. Summ aries of the methodologies are in Table 3.2.

38 Obtained from emis_smry_cenrap_typ02f_b18f_021407.xls49 Table 3.1. General Emissions Modeling Selections by RPO

. MRPO CENRAP Emissions Model Biogenics model: EMS version 2003 BEIS3 SMOKE version 2.1 and 2.3 BEIS3 Domain Extent 4rpos National Grid Size 36km 36km Emission Categories

• Point

• Mobile Onroad

• Nonroad

• Area

• Biogenic Weekday, Sat, Sun per Month Weekday, Sat, Sun per Month Weekday, Sat, Sun per Month Weekday, Sat, Sun per Month Daily Daily Daily Mon, weekday, Sat, Sun (MWSS) Average weekday; MWSS Daily Species SO 2 , NO x , VOC, PM 25 , CM and NH 3 SO 2 , NO x , VOC, PM 25 , CM and NH 3
Emissions were generated for every hour and a llocated to 36km grids over the National InterRPO Domain. This domain was agreed upon by all th e RPOs as the basic domain from which to model. The MRPO uses a subset of the natio nal RPO domain, called the 4rpos domain, to focus on an area that includes the United States and Ca nada extending east of a line dissecting the United States at the western-most tip of Texas. Both the Na tional Inter-RPO Domain and the MRPO 4rpos domain are shown in Figure 3.1. CENRAP generated modeling emissions in a similar fashion, but encompassing the entire National Inter-RPO 36km domain. Minnesota created a 12km flexi-nested domain over Minnesota to take a closer l ook at Minnesota nearby source impacts.

Figure 3.1. National Inter-RPO Domain and MRPO 4rpos Domain.

National Inter-RPO 36km Domain

MRPO "4rpos" 36km Domain

Minnesota 12km Nested Domain50 Table 3.2. Summary of Temporal and Spati al Allocation and Speciation Profile Methodology by RPO

Source Category MRPO 39 (baseK) CENRAP 40 (baseG) Point EGU Temporal Profiles: EGU SO 2 and NO X emissions were temporalized using heat input, in lb/MMBtu from CEM data to create temporal profileswith month-of-year and day-ofweek, and hour-of-day variations by emissions unit. Three years of CEM data were obtained, and a median value for each weekday, Saturday and Sunday for each Month was used for projections to the future. The median value was used to apportion the annual (SO 2 ) emissions and summer/winter (NO X ) IPM results 41 CENRAP used CEM temporal profiles for both VISTAS and CENRAP, based on 2000 – 2003 data. Data representative of Saturday, Sunday, weekday and Monday emissions were used as surrogates.

Non-Utility

Temporal

Profiles: No temporal profiles were used. Emissions allocated evenly over the year. If CEM data available, SMOKE was configured to explicitly represent daily conditions with hourly meteorology and daily/hourly emissions. Otherwise, representative Saturday, Sunday, weekday and Monday emissions were used as surrogates. Spatial Allocation: Point source locations specified in data provided by States and RPOs. Locations were quality assured and modified as recommended by States. Revised EPA Spatial Surrogates

Speciation Profiles EPA’s SPECIATE data base Version 3.0, augmented with EC/R developed speciation profiles for a few large non-utility sccs, specified by MRPO. 42 Applied these speciation profiles to all U.S. States. NCOAL for lignite-burning EGUs in North Dakota and Texas

CMU profile developed forMRPO for all other coalburning EGUs

39 MRPO (April 2005)
40 ENVIRON (September 2007)
41 EGU temporalization: Both SO 2 and NO X were temporalized using heat input, in lb/MMBtu. They correlated well (note: in 2018, the temporalization was like that of 2005 where the temporalization was not done by heat input. This is because the NOx SIP call was implemented in some States. So, they turn on their SCRs in the summer and turn them off in the winter. Thus, emissions were used for temporalization, rather than heat input).
42 EC/R Incorporated (February 2005)51 Revised CB4 Chemical Speciation Mobile Onroad Temporal Profiles: Used default EPA profiles base on the NEI. Hourly meteorology and daily/hourly emissions were used in SMOKE. Spatial Allocation: Used population for lesser roads, used fraction of road miles within a grid cell for arterials, freeway and expressways. Speciation

Profiles: Updated EC/R profiles. 32 WRAP emissions prespeciated. SMOKE PM speciation module for other states CB4 Nonroad Temporal Profiles: Wisconsin DNR ran NMIM to create monthly profiles for MRPO states plus Minnesota for all categories, and added Iowa and Missouri Agricultural Equipment.

Pechan developed local data for MRPO states to improve on EPA defaults for Construction. Pechan prepared local data for Ag equipment for MRPO states plus Minnesota and Iowa. 43 Seasonal temporal coverage for WRAP non-road mobile and aircraft, with uniform monthly temporal profiles.

Annual temporal coverage for WRAP locomotive, in-port and all CENRAP non-road,VISTAS and MANE-VU nonroad sources with nonuniform monthly temporal profiles.

Monthly temporal coverage for MRPO and MN non-road sources and IA non-road agricultural sources, with uniform monthly temporal profiles. Marine, Locomotive Aircraft & Recreational Vehicles Spatial Allocation: Marine is allocated to all but winter months. Area-like using spatial surrogates

Speciation

Profiles: Carbon Bond IV (CB4) CB4

Area Fire Temporal Profiles: Not applicable Diurnal temporal profiles were applied to most states, and monthly and flat weekly profiles to WRAP states. Area Ammonia Ammonia temporal profiles specific for hogs, beef and dairy farms (hogs defined poultry) were Uniform monthly temporal profiles used.

43 E.H. Pechan (September 2004)52 developed by UC-Riverside based on UC-Davis ammonia model (beta version). 44
Other Area

Sources Temporal Profiles: Monthly, daily and hourly EC/R profiles. 45

Spatial Allocation: Spatial profile data from EPA; spatial surrogates. Spatial profile data from EPA; spatial surrogates

Speciation

Profiles: EPA’s SPECIATE database Version 3.0 augmented with EC/R developed speciation profiles or various SCCs. Applied to all SCCs throughout the country. 32 Revised CBM-IV Chemical Speciation

Biogenic Temporal Profiles:

Spatial Allocation: MRPO used BIOME3 (BEIS3)within EMS-2003 with dayspecific meteorology and hourly satellite-based photosynthetically activated radiation values. Biogenic Emissions Landcover Database version 3 (BELD3) was used for fractional land-use and vegetative speciation. Environ used SMOKE with BEIS3.12, with day-specific meteorology from MM5. BELD3 land use data was used.

3.1 Temporal and Spatial Differences be tween MRPO and CENRAP 2002 Base Year Inventories Large differences can occur between modeli ng studies on how the annual emissions get distributed to each month. These differences w ould likely vary most in the months comprising Spring and Fall because profiles are often developed for a Summe r and Winter day. Depending on the category, this can be significant. For non-road sources, Minneso ta found that emissions would be significantly over-estim ated were monthly profiles not used. The State of Wisconsin, which was creating monthly non-road temporal profiles for MRPO states, agreed to create similar profiles for Minnesota. MRPO used th e Minnesota monthly non-ro ad profiles, and asked CENRAP to do the same.

Temporalization for ammonia can also be very important. Assigning more of the total ammonia emissions in the colder months will make it more available to interact with NO x to form ammonium nitrate (although pref erentially interacting with SO 2 to form ammonium sulfate). Assigning ammonia emissions to warmer months will make them more available to form ammonium sulfate.

MRPO and CENRAP took di fferent approaches to compile es timates of ammonia emissions for the 2002 base year emission inventory (see Section 2.0). Not only are the CENRAP estimates of

44 MRPO (April 2005)
45 EC/R Incorporated (February 2005)53 livestock and agricultural ammonia emissions hi gher than MRPO estimates, the distribution of those emissions among categories of livestock and fertilization met hods, and the temporal distribution, vary as well.

An example of the temporal profiles for th e ammonia inventory developed by the MRPO is shown in Figure 3.2. The Figure was developed for the MRPO 2005 inventory and illustrates the profile for the combined States Minnesota, Wi sconsin and Michigan. The Figure depicts the same temporal profile as the Minnesota (MRPO) case, however, there is more total ammonia estimated in 2005 than in 2002 (~7 % in Minnesota).

Figure 3.2: Estimated Seasonal Distribution of Ammonia Em issions from Livestock and Fertilizer in Minnesota, Michigan a nd Wisconsin Combined (tons per day) 46

.

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

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec Avg Animals Fertilizers Overall

Although it does not provide a complete picture, emissions for one summer day and one winter day from MRPO and CENRAP were compared in order to get a sense for the temporal and spatial differences between MRPO and CENR AP. Emissions for both the CENRAP baseF 47 (the immediate predecessor of baseG) and the MRPO 2002 case for a winter and summer day in tons were obtained from CAMx model input files for each organization 48 . Thus, no differences between values from the two organizations ar e attributable to calcu lation methodology. Table 3.3 contains winter and summer day emissions of each specie s for Minnesota, surrounding States and Canada. Table 3.4 contains similar winter and summer day emissions from the MRPO 2005 model input files.

46 EC/R Incorporated (November 2007)
47 The main difference between CENRAP baseF and baseG was updated emissions from Mexico.
48 Although CENRAP used the CMAQ model for most a pplications, CAMx was used for source apportionment (see Section 8.0). Minnesota obtained one winter day and one summer day model input file from ENVIRON.54 Ammonia . As shown in the tables, CENRAP has significantly more ammonia in the model system than MRPO for Minnesota, Iowa, North Dakota, South Dakota, Wisconsin and Canada combined This occurs in both winter and su mmer. Some of the additional ammonia in the CENRAP inventory is allocated to northern Minnesota, near the two Minnesota Class I areas, as shown in Figures 3.3 and 3.4. These figures contai n total emissions from all source categories. As mentioned in Section 2, mo re ammonia present results in more ammonium nitrate and ammonium sulfate formation. Levels of ammonia emissions in the MRPO 2005 case are more similar to Minnesota (MRPO) , with growth from the 2002 to 2005 base year.

Nitrogen Oxides . NO x emissions are of greater importan ce in the colder months, and for a winter day, it appear s that the Minnesota (MRPO) , and CENRAP cases have similar amounts of NO X at this time of the year for Minnesota and surrounding states combined. The spatial distribution of those emissions varies. On th e winter day, CENRAP has additional Minnesota and North Dakota NO X than Minnesota (MRPO) . CENRAP also has significantly more Canada NO X using the Canada 2000 inventory than Minnesota (MRPO) has using the 2005 Canada inventory. Spatially, so me of the increased NO X from Canada appears directly along the border shared with Minnesota, and al ong the northern border of North Dakota, directly North and Northwest of the Minnesota Class I areas, as shown in Figures 3.3 and 3.4. As shown in the model performance in Section 6, winds during the 20 percent worst days at Boundary Waters and Voyageurs during 2002 often come from the West/Northwest of the Class I areas.

The MRPO 2005 winter day contains less NO X in the region than both CENRAP or Minnesota (MRPO) mainly due to the use of a differe nce calculation methodology for biogenic NO X, and emission reductions for mobile and nonroad sources from 2002 to 2005.

Sulfur Dioxide . There are no significant differences between the winter and summer day SO 2 emissions. Between the two organizations, the em issions in the U.S are quite comparable. The exception is for Canada emissions, where CENRAP SO 2 emissions are significantly higher. However, impaired visibility due to SO 2 from the north on the 20 pe rcent worst days are not prevalent. Thus, this emissions difference from Canada does not appear to be as relevant for those days. Some of the CENRAP increased SO 2 from Canada occurs directly north of the two Class I areas, but most of the elevated SO 2 is due to a single 1,100 ton per winter day source in Ontario, just north of Lake Huron.

Fine and Coarse Particulates, and Volatile Organic Compounds. As discussed in Section 2.0, the CENRAP case has significantly more of th ese components of haze than the Minnesota (MRPO) case. In regard to particulat es, this is mainly due to MRPO excluding wind-blown dust and agricultural equipment dust because of concerns about the transportable fraction. Primary particulates are not a major component in the visibility equation for Boundary Waters and Voyageurs.

The reason for the difference in VOCs between Minnesota (MRPO) and CENRAP—associated with biogenics—is not readily apparent. The MRPO 2005 has somewhat more biogenic VOC than Minnesota (MRPO) , due to the use of a different calcula tion methodology, and more is allocated to55 summer than winter. However, because control measures are not proposed for biogenic sources, the analyses can be more forgiving in differe nces of VOC emissions for regional haze purposes.

Figure 3.3. 2002 Winter Day and Summer Day Ammonia Emissions in Tons for Minnesota (MRPO) Case.

Minnesota (MRPO) —Winter Day : Minnesota (MRPO) —Summer Day :

Daily Emissions

(Tons)

0.5 - 1.0
1.0 - 2.0
2.0 - 4.0
4.0 - 6.0
6.0 - 8.0
8.0 - 10.0
10.0 - 14. 0
14.0 - 20. 0
20.0 - 26. 0

26.0 +

Figure 3.4 2002 Winter Day and Summer Day Ammonia Emissions in Tons for CENRAP Case.

CENRAP—Winter Day : CENRAP—Summer Day :

Daily Emissions

(Tons)

0.5 - 1.0
1.0 - 2.0
2.0 - 4.0
4.0 - 6.0
6.0 - 8.0
8.0 - 10.0
10.0 - 14. 0
14.0 - 20. 0
20.0 - 26. 0

26.0 +56 Figure 3.5. 2002 Winter Day and Summer Day NO X Emissions in Tons for Minnesota (MRPO) Case.

Minnesota (MRPO) —Winter Day : Minnesota (MRPO) —Summer Day :

Dail y Emissions

(Tons)

1 - 5
5 - 10
10 - 20
20 - 30
30 - 50
50 - 75
75 - 125
125 - 17 5
175 - 30 0

300 +

Figure 3.6 2002 Winter Day and Summer Day NO X Emissions in Tons for CENRAP case.

CENRAP—Winter Day : CENRAP—Summer Day :

Dail y Emissions

(Tons)

1 - 5
5 - 10
10 - 20
20 - 30
30 - 50
50 - 75
75 - 125
125 - 17 5
175 - 30 0

300 +57 Figure 3.7. 2002 Winter Day and Summer Day SO 2 Emissions in Tons for Minnesota (MRPO) Case.

Minnesota (MRPO) —Winter Day : Minnesota (MRPO) —Summer Day :

Figure 3.8 2002 Winter Day and Summer Day SO 2 Emissions in Tons for CENRAP case.

CENRAP—Winter Day : CENRAP—Summer Day :58Table 3.3. 2002 Emissions for a Winter and Summer Day in Tons for Minnesota(MRPO)and CENRAP.SO2NOXNH3PM25PM10VOC SO2NOXNH3PM25PM10VOCMinnesota458 1,378 158 113 304 1,552 477 1,461 345 282 1,349 1,615Iowa533 942 237 64 175 814 469 910 269 232 1,231 1,006North Dakota617 498 23 11 47 279 647 627 74 264 1,744 601South Dakota91 250 72 12 47 292 50 280 214 427 3,706 380Wisconsin785 1,298 131 68 91 1,347 675 1,093 122 74 208 1,507Canada*2,491 2,055 997 78 256 3,236 4,046 4,080 1,023 680 3,576 11,535total:(only U.S)2,484 4,365 621 269 663 4,284 2,319 4,371 1,024 1,280 8,238 5,109total:(incl Canada)4,975 6,421 1,618 347 919 7,520 6,365 8,451 2,047 1,960 11,814 16,644SO2NOXNH3PM25PM10VOC SO2NOXNH3PM25PM10VOCMinnesota461 1,506 666 99 296 6,483 476 1,668 437 293 1,362 8,822Iowa564 1,135 1,027 70 189 1,927 495 1,102 1,058 218 982 2,757North Dakota549 636 122 10 50 1,607 695 995 925 190 836 2,891South Dakota89 390 314 10 45 1,983 80 595 541 129 573 3,236Wisconsin797 1,237 571 33 57 3,883 796 1,144 553 63 105 5,246Canada*2,036 3,257 994 73 237 48,198 3,187 5,215 1,047 661 3,233 85,214total:(only U.S)2,460 4,904 2,700 222 638 15,883 2,542 5,505 3,514 894 3,857 22,952total:(incl Canada)4,496 8,161 3,694 295 875 64,082 5,729 10,720 4,561 1,555 7,091 108,166Winter Day Emissions in Tons -- Base Year 2002Minnesota(MRPO)CENRAPRegionSummer Day Emissions in Tons -- Base Year 2002Minnesota(MRPO)CENRAPRegion* Canada emissions are the total included over the entire extent of the 4rpos domain; not delineated by Province. The Minnesota(MRPO) emissions include theCanada 2005 inventory,while the CENRAP emissions include the Canada 2000 inventory.* Canada emissions are the total included over the entire extent of the 4rpos domain; not delineated by Province. The Minnesota(MRPO) emissions include theCanada 2005 inventory,while the CENRAP emissions include the Canada 2000 inventory.59 Table 3.4. 2005 Emissions in Tons for a Winter and Summer Day for MRPO BaseM



### 3.2 MRPO Quality Assurance for Emissions Inventories While developing the emissions inventory and conducting emissions modeling, Minnesota participated in the MRPO quality assurance fo r emissions inventories. MPRO assured the quality of the data through the review of emissions reports and spatial analysis 49 as follows:

• Emission Reports : EMS performs a number of checks and generates several reports, as documented in the EMS User’s Guide 50 . The QA checks for point sources, for example, duplicate or missing keys (stid, cyid, fcid, stkid, dvid, prid, po lid), missing UTM coordinates and mismatched UTM zone, missing or invalid FIPS state and county codes, missing facility name missing or invalid SIC, and missing or out-of-range st ack parameters. The reports include tabular summaries of the state- and county-level emissions for point, area, and mobile sources; and various spatial plots of emissions.

49 MRPO (June 2004)

50 Janssen, M., Hua, C. (1998) SO 2 NO X NH 3 PM 25 PM 10 VOC Minnesota 494 1,303 162 83 314 1,236 Iowa 543 764 250 62 171 599 North Dakota 456 449 32 12 16 274 South Dakota 69 192 77 11 20 179 Wisconsin 765 1,023 136 84 127 1,139 Canada* 2,652 2,416 1,013 78 257 5,399 total: (only U.S) 2,327 3,730 658 252 647 3,426 total: (incl Canada) 4,485 4,843 1,509 247 590 7,590

SO 2 NO X NH 3 PM 25 PM 10 VOC Minnesota 463 1,711 703 65 293 10,145 Iowa 559 1,326 1,110 65 178 3,933 North Dakota 519 1,233 141 13 19 1,930 South Dakota 122 948 340 9 19 2,417 Wisconsin 748 1,227 564 20 65 8,499 Canada* 2,099 3,735 1,022 73 238 66,277 total: (only U.S) 2,411 6,445 2,858 172 574 26,923 total: (incl Canada) 4,047 8,469 3,176 180 519 83,056 Winter Day Emissions in Tons -- Base Year 2005 MRPO

Summer Day Emissions in Tons -- Base Year 2005 Region MRPO Region

* Canada emissions are the total included over the entire extent of the 4rpos domain; not delineated b y Province.60 • Spatial Analysis : A second level of quality assuran ce is performed by the photochemical modelers The additional checks include: eval uating spatial tile plots of total daily SO 2 , NO X , VOC, ammonia, PM 2.5 , and coarse mass emissions, and evaluating plots that show the variation in emissions from mont h to month, and from hour to hour.



### 4.0: Meteorological Modeling

Meteorological data inputs we re prepared using the Fift h-Generation NCAR/Penn State Mesoscale Model (MM5). Appendix B contains a protocol titled “Meteorological Modeling Protocol for Application to PM2.5/Haze/Oz one Modeling Projects”, December 2004, for conducting meteorological modeling with MM5.

Both the MRPO and CENRAP use the same 36k m modeled meteorological data for their 2002 and 2018 simulations. The data was prepared by Matthew Johnson of the State of Iowa.

Appendix D contains the detail on the meteorol ogical inputs, including the definition of the meteorological data grid, pro cessing of input data to MM5, model execution and the model performance evaluation. There are two model performance documents. One was prepared by Kirk Baker of MRPO, titled “M eteorological Modeli ng Performance Summary for Application to PM2.5/Haze/Ozone Modeling Projects”, Februa ry 18, 2005, and the other was prepared by Matthew Johnson of Iowa, titled “Meteorological Model Performa nce Evaluation of an Annual 2002 MM5 (version 3.6.3) Simulation, 2004-2007, v2.0.3. Model performance was deemed good.



### 5.0: Atmospheric Chemis try and Transport Modeling

Guidelines for conducting regional-scale modeli ng for particulate matter and visibility are provided in 40 CFR § 51, Appendi x W. The EPA recommends the use of one of the three following models to simulate pollutants impairin g visibility: Community Multiscale Air Quality (CMAQ), the Comprehensive Air Quality Mode l (CAMx), and Regional Modeling System for Aerosols and Deposition (REMSAD). These are Eu lerian models that compute the numerical solution of partial differential equations of pl umes on a fixed grid. The models simulate atmospheric and surface processes affecting th e transport, chemical transformation and deposition of air pollutants and their precursors. CAMx also a llows two-way grid nesting, a subgrid scale Plume-in-Grid (PiG ) module to treat the early dispersion and chemistry of point source plumes, a fast chemistry solver, and Particulate Source Apportionment Technology (PSAT), which tracks the origin of particulate species by geographic region (i.e. States) and by source category (i.e. point sources, mobile sources) 51

The EPA sponsored the development of CM AQ. For regional haze modeling, CENRAP contractors used CMAQ and CAMx (for PSAT) , and the MRPO and its member States use CAMx. Minnesota, using MRPO model inputs, also uses CAMx. Both CENRAP and MRPO limited regional haze modeling to the 36km grid s cale. Both organizations found this grid resolution sufficient for regional haze. However, Minnesota has an inte rest in evaluating the

51 ENVIRON (September 2006).61 impacts of sources close in to Boundary Waters and Voyageurs. Minne sota has conducted an evaluation using the MRPO inputs, creating a 12km nested grid covering the State of Minnesota, and using PiG treatment for those sources nearest the Minnesota Class I areas. Minnesota used CAMx version 4.42.

A modeling protocol formalizes the procedur es for conducting the modeling analyses. Minnesota did not prepare its own protocol, inst ead relying on the meteorological modeling and air quality modeling protocol s prepared by the MRPO.

MRPO Modeling Protocol

. Appendix E contains the MRPO protocol, “2002 Basecase Modeling Protocol: Technical Details”, October 15, 2007. Minnesota conducted modeling using the MRPO inputs and defers to the MRPO modeling protocol for the technical details:

1. The methodology for conducting the regional scale modeling, including details on

a. the geographic coverage of the modeling domain (i.e. covering an area that includes the United States and Canada extending east of a line dissecting the United States at the western-most tip of Texas) and the size of i ndividual grid cells (i.e 36km X 36km grids); b. details on the meteorological data input to the CAMx modeling;

c. a summary of the emissions inputs, landus e inputs, drought stress and snow cover, photolysis rates, initia l and boundary conditions; d. quality assurance measures taken on the model inputs; and

e. the configuration of the CAMx model, the gas phase chemistry, deposition, nesting, PiG treatment of point sources, and the use of probing tools (i.e. source apportionment); 2. Steps taken to evaluate the model predictions co mpared to observations (monitored values) at the same location over the same time period; and 3. Conversion of the data to visi bility metrics; and the method for conducting the reasonable progress test (estimate of position on the uniform rate of progress toward natural visibility).

Minnesota explored answers to questions specif ic to Minnesota beyond the scope covered by the air quality modeling protocol prep ared by the MRPO. For example, Minnesota is interested in the visibility impact of sources located near Bounda ry Waters and Voyageurs. This resulted in a modeling analysis using a 12km nested grid and a PiG tool for individual elevated point sources located within a certain distance of the Class I ar eas. PiG tracks individual plume segments from each point source, rather than immediately disp ersing the individual point source emissions in the grid cell. In order to us e the PiG module properly, some m odifications to the air quality modeling inputs files were required. For example, stack pa rameter and location information has greater importance when PiG is employed. These changes are outlined in Section 2.0.

The meteorology and all emissions in the Minnesota 12km grid are “flexi-nested”. This means that no emissions were developed with an emissions model to allocate emissions appropriately to each 12km grid (excluding point sources). Thus, area source emissions for a 36km grid cell were evenly distributed to each of the nine 12km gr id cells contained within the 36km grid cell. However, a specific 12km landuse file was created and input to CAMx. The 12km landuse file was created with MRPO methodology. The main pur pose of the 12km nest is to better locate, and concentrate emissions, from point sources to a smaller grid and to allow a better segue for invoking PiG treatment for larger point sources lo cated near the Minnesota Class I areas. The62 GREASD PiG treatment was applied to point sour ces with emissions greater than 100 tons per year of SO 2 or NO x and located within the six Minnesota counties nearest the Boundary Waters and Voyageurs Class I areas.

Unlike the MRPO, who uses the Particulate So urce Apportionment Tool (PSAT) to track the contributions of all the i ndividual components of PM 25 , Minnesota limited contribution assessment to the sulfate ion, nitrate ion, and ammonium ion because th ese are the components most likely to be involved in control measur es. Also, MRPO PSAT results show that the excluded components—elemental carbon, and primar y emissions of organic aerosol, soil and coarse mass—are not significant cont ributors to visibili ty in contrast to ammonium nitrate and ammonium sulfate, especially because fires are not included in the modeled emissions inventory. The MRPO did not employ source apportionment for secondary organic aerosols because of the long simulation run times. However, Minneso ta has reported seconda ry organic aerosol contributions specific to biogeni c and anthropogenic s ources from the standard CAMx output. Separate geographic regions a nd source groups tracked by Minneso ta are detailed in Section 8.0—Control Strategy Development.



### 6.0: Model Perfor mance Evaluation

Prior to proceeding to the development of future year emissions, it is desirable to show the air quality model’s ability to simulate observed measur ements on the same days that are selected for evaluation. EPA guidance calls this an “ope rational evaluation” of regional haze model performance.

Model performance is conducted on the base mo del year, which was developed based on the 2002 emissions inventory and meteorology. The base case period used to evaluate model performance is the same as that applied to the baseline period in the modeled attainment test. Several iterations of the 2002 base inventory were conducted by MRPO culminating in the latest baseK inventory used as the backbone for the Minnesota (MRPO) modeling. Model performance evaluations of these iterations allowed for improving the emissions, meteorology and the CAMx model, resulting in improved model performance.

Both CENRAP and MRPO have evaluated mode l performance over the entire domains they modeled. Because Minnesota is using MRPO 2002 inputs with emissions modifications described in Section 2.0, the State focused its own model performance on Boundary Waters and Voyageurs. Particular attention was placed on the 20 percent worst da ys in the two Class I areas.

In addition to the more specific perfor mance evaluation results for the Minnesota (MRPO) modeling, a summary of the CENR AP and MRPO model performan ce results are provided. All performance results provided are for the 36km gr id. Minnesota also evaluated performance of the Minnesota (MRPO) 12km grid with PiG in this Sec tion. The results are discussed, but corresponding plots and charts are not provided as part of this tech nical support document.

Model performance evaluation was conducted on the several components of fine particulate (PM 2.5 )—sulfate ion (SO 4 ), nitrate ion (NO 3 ), ammonium ion (NH 4 ), organic carbon (OC), elemental carbon (EC) and crusta l/soil material. These major components that comprise the63 mixture PM 2.5 are evaluated separately in order to avoi d compensating errors that might indicate good model performance were the compounded PM 2.5 evaluated.

The remainder of the discussion for this Secti on includes a summary of the observational data available for observed/predicted co mparisons, an identification of the performance tests used and their results, a description of th e ability of the model to reprod uce observed temporal and spatial patterns, and an overall assessment of what the performance evaluation implies.

6.1 Observed Data Available/Used for Comparison

. The speciated components used in the Minnesota (MRPO) evaluation are collected with monitors in the Interagency Monitoring of Protected Visu al Environments (IMPROVE) network. This network collects individual PM 2.5 components (excluding ammonium ) at Class I areas, such as at Boundary Waters (BOWA1) and Voyageurs (VOYA2). The loca tions of these monitors in relation to Boundary Waters and Voyageurs are pr ovided in Figure 6.1. The Isle Royale monitor is not located on the island, but further southwes t, on the Upper Peninsula of Michigan. The species collected at the monitor and the corres ponding species in the CAMx model are shown in Table 6.1.

Figure 6.1. Locations of IMPROVE Moni tors at Boundary Waters and Voyageurs

.

G F G F

Minnesota

Wisconsin

Iowa South Dakota North Dakota

Michigan Ontario Manitoba

Boundary Waters Canoe Area Wilderness

Isle Royale N Voyageurs NP64 Table 6.1 – Performance Evaluation Mo nitor to Model Definition of Pollutants Evaluated at Minnesota Class I Areas 52

. Component IMPROVE CAMx4 species Sulfate Aerosol SO4f PSO4 Nitrate Aerosol NO3f PNO3 Organic Aerosol OCf * FACTOR

FACTOR = 1.6 rural 2.1 urban SOA1 + SOA2 + SOA3 + SOA4 + SOA5 + POA

Elemental Carbon ECf PEC Soil/Crustal 2.2 * ALf + 2.49*SIf + 1.63*CAf + 2.42*FEf + 1.94*TIf FCRS

Monitoring data used for model performance wa s obtained from the Visibility Information Exchange Web System (VIEWS), the repository of data to support the Re gional Haze Rule. This data is available at http://vista.cira.colostate.edu/views/ and is maintained by the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University. Performance evaluation is conducted using the raw data set. PM 2.5 and PM 2.5 component species at the two IMPROVE sites are collected over 24-hour periods once every three days.

VIEWS has a substitute data set for Boundary Wate rs because of a lack of complete data for a few years. An equipment malfunction in 2002, 2003 and 2004 caused the loss of the following data:

• “Module A” – PM 2.5 particle mass;

• “Module C” – Elemental and organic carbon mass, and

• “Module D” – PM 10 particle mass.

This data loss invalidated three out of every se ven samples from these modules. “Module B” has a denuder that collects nitrate, chloride, sulfate and nitrite. Ac cording to CIRA, the “Module B” data from Boundary Waters during this period are va lid. In order to utilize the valid data from Boundary Waters, Scott Copeland of CIRA s ubstituted the missing components with a linear regression analysis from corresponding valid data collected at Voyageur s. Data substitution reports are available on the VIEWS website. 53 Substituted data at Boundary Waters is identified in this Section with the acronym BOWAV.

The performance evaluation relies on a different set of 20 percent worst days at Boundary Waters than those currently available on VIEWS. The MRPO identified some days at Upper Midwest Class I areas where data was exclude d from the 20 percent worst days on VIEWS because of incomplete capture of insignificant co mponents of visibility in those Class I areas. For example, coarse mass and soil/crustal material are missing, while the remaining components—notably sulfate and nitrate—are present at levels that would cause those days to be on the list of 20 percent worst. 54 More detail on the implications of this is provided in Section 7.0.

52 MRPO (October 2007)
53 http://vista.cira.colostate.edu/view s/Web/Documents/SubstituteData.aspx
54 MRPO (June 2007)65 For the year 2002, the inclusion of this previously excluded data re sults in the displacement of two days from the 20 percent worst days in Boundary Waters. January 11 and March 27 are replaced with July 22 and September 8. The 20 pe rcent worst days in 2002 at Voyageurs remain the same. The data for the previously excluded da ys are in the raw data set from VIEWS and as long as model performance programs do not hard-c ode which days are the 20 percent worst days, the evaluation will include days otherwise excluded because of incomplete capture of insignificant components. Table 6.2 contains th e observed values of each component for the 20 percent worst days in this performance evaluation. The “v” next to the Julian date means the day is one of the 20 percent worst for Voyageurs only, the “b” means the day is one of the 20 percent worst for Boundary Waters only. Days w ithout a letter mean the day is one of the 20 percent worst for both Class I areas. In mode ling analyses, days are identified by their Julian date. Throughout the rest of th is document, days modeled may be identified either based on the Gregorian or Julian Calendar. Appendix F cont ains a cross-reference table for Gregorian and Julian calendar days in 2002.66Table 6.2 Species Data at Minnesota Class I Area Monitor Sites for 20% Worst daysin Micrograms per Cubic Meter5v11v26b 71v86v104v131 146149b 152v 161bBOWAVSO4no data 1.074 2.892 no data 1.518 1.425 2.262 1.9793.866 no data 6.938NO3no data 2.192 0.670 no data 1.721 1.207 0.636 0.4130.216 no data 0.189ECno data 0.117 0.129 0.228 0.119 0.245 0.292 0.2800.287 no data 0.501OCno data 0.522 0.687 0.822 0.597 1.169 1.586 2.5231.523 no data 2.543NH4no data no data no data no data no data no data no data no data no data no data no dataCRUSTALno data 0.256 0.275 0.437 0.442 0.673 0.740 0.7660.454 no data 0.738VOYA2SO41.911 0.863 2.420 2.002 1.562 1.542 2.283 1.8341.390 0.786no dataNO33.292 3.360 0.742 2.789 2.818 2.418 0.762 0.2680.203 0.210no dataEC0.224 0.204 0.202 0.258 0.187 0.309 0.228 0.2560.183 0.818no dataOC0.811 0.733 1.057 0.851 0.752 1.120 1.593 2.3271.553 8.925no dataNH4no data no data no data no data no data no data no data no data no data no data no dataCRUSTAL0.217 0.114 0.159 0.378 0.477 0.404 0.385 0.5080.384 0.552no data170 176b 179 188197200203b221242 245 251bBOWAVSO45.287 3.658 2.140 1.850 4.829 0.857 3.415 2.2004.415 3.0547.839NO30.535 0.283 0.108 0.243 0.254 0.096 no data 0.1740.177 0.0940.208EC0.560 0.278 0.575 0.221 0.331 0.713 0.225 0.2660.365 0.2290.443OC3.299 2.445 7.394 2.435 1.543 11.4361.492 1.3121.820 1.3142.981NH4no data no data no data no data no data no data no data no data no data no data no dataCRUSTAL0.867 0.808 0.867 1.269 0.938 0.408 0.548 0.5250.564 0.453no dataVOYA2SO44.602 1.739 1.733 2.095 7.645 1.163 0.229 2.1025.054 4.101no dataNO30.522 0.092 0.102 0.241 0.192 0.107 0.029 0.1730.231 0.245no dataEC0.707 0.179 0.585 0.298 0.408 0.842 0.034 0.2960.417 0.256no dataOC2.742 1.301 7.650 2.248 1.938 15.1870.682 1.3361.984 1.332no dataNH4no data no data no data no data no data no data no data no data no data no data no dataCRUSTAL0.422 0.292 0.533 0.867 1.245 0.708 0.123 0.4610.495 0.391no data254v 260v 272v 275b 284v 299314b332344 347BOWAVSO41.365 0.669 1.545 3.319 0.218 1.459 1.465 1.6270.865 1.958NO30.080 0.065 0.158 1.025 0.024 4.197 1.482 3.3724.023 2.816EC0.137 0.305 0.066 0.471 0.350 0.363 0.191 0.2420.294 0.316OC1.312 1.659 0.523 1.738 1.475 1.493 0.955 0.7911.194 1.334NH4no data no data no data no data no data no data no data no data no data no dataCRUSTAL0.280 1.102 0.131 0.588 0.435 0.208 0.113 0.5970.326 0.377VOYA2SO41.045 1.959 2.510 0.306 2.496 1.771 0.819 1.4590.694 1.458NO30.182 0.228 0.184 0.016 1.172 4.486 0.388 2.6413.193 4.387EC0.269 0.349 0.171 0.028 0.394 0.353 0.125 0.2450.324 0.353OC3.116 1.772 1.031 0.388 1.642 1.674 0.676 0.8531.310 1.482NH4no data no data no data no data no data no data no data no data no data no dataCRUSTAL0.232 1.000 0.230 0.060 0.412 0.228 0.017 0.2990.297 0.351Julian dateStationPM2.5componentJulian dateJulian dateStationPM2.5componentStationPM2.5component67 6.2 Performance Tests Used and their Results

. EPA guidance recommends considering various st atistical assessments and other techniques of modeled versus observed pairs when conducting a performance evaluation for regional haze. The other techniques include spatia l plots, timeseries plots and qualitative descriptions.

6.2.a Fractional Bias and Error. Focus for the statistical assessment is on mean fractional bias and error, although MRPO also looks at mean bias and gross error. Performance goals and performance criteria for mean fractional bias and error are equal to or less than the values in Table 6.3.

Table 6.3 Performance Goals and Criter ia for Fractional Bias and Error. 55
Fractional Bias Fraction al Error Goal/Criteria ± 30% 50% Goal—Best that a m odel is expected to achieve. ± 60% 75% Criteria—Acceptable for standard modeling applications.

The goals and criteria shown above apply to more abundant concentrations of PM 2.5 components in the atmosphere. At zero concentration, the goal and criteria ra nge from ±200% mean fractional bias to +200% mean fr actional error. The goal and criter ia asymptotically approach the goals and criteria in Table 6.3. Thus, a higher percentage error and bi as are allowed at low concentrations. This is because at lower con centrations the models have greater difficulty predicting concentrations.

The statistics typically look better at greater averaging times. T hus, the fractional bias and error likely will show a better f it within the goals and criteria at an annual average than a monthly average, and at a monthly av erage than a 24-hour average.

As mentioned in earlie r sections, the MRPO 2002 case is the backbone for the Minnesota (MRPO) case. The MRPO evaluated performance of th e MRPO 2002 case encompassing a significant portion of the 4rpos domain. The analysis included monitor site s in Minnesota (most notably Boundary Waters and Voyageurs), but also extended as far East as Maine and as far South as to include some sites in South Caro lina, Georgia, Alabama, Arkansas and Oklahoma. In addition to the IMPROVE sites, the MRPO evaluation also included Speciation Trends Network (STN) and CASTNet visibility sites.

Figure 6.2 depicts fractional bias and error for th e monthly average concentrations in the MRPO 2002 case. These results show very good performance for sulfate and elemental carbon; good performance for soils and nitrate; and an under prediction of organic carbon.

The MRPO also conducted performance analysis 56 for 24-hour average mean bias, gross error, fractional bias and fractional error for each mont h of 2002 over the same domain. That analysis concludes that model performance for sulfate is good in the summer months when concentrations are highest; nitrate is best in winter when concentrations ar e highest; organic carbon is poor

55 Boylan, J.W. and Russell, A.G. (2006)

56 Baker, Kirk, (undated)68 especially in the summer months when concen trations are highest and in urban areas; soil (crustal material) is poor; and elemental carbon is good. Not much soil is collected on the filters at the Boundary Waters and Voyageurs IMPROVE monitors, so model performance is not as important for this specie as it is for sulfate, nitrate and organic carbon. Ammonium is not collected at the IMPROVE monitors.

Figure 6.2 MRPO Fractional Bias for 2002 Monthly Average Concentrations for 4rpos Domain

The statistical evaluation of the Minnesota (MRPO) modeling performance focuses attention on the IMPROVE sites at the Minnesota Class I areas (Boundary Waters and V oyageurs). Evaluation was done for 24-hour averaging times. Fractiona l bias and error are evaluated for 36km and 12km (w/ PiG) grid scale modeling. The evalua tion compares observed and modeled values for the 20 percent worst days for one or the other (o r both) Class I area. There are thirty-two 20- percent worst days total. Ei ght days are Boundary waters onl y, 10 days are Voyageurs only, and 14 days are shared between the two Class I areas.

Fractional bias and error plots for the 20 percent worst days at Boundary Waters and Voyageurs for the 36 km grid are shown in Figures 1 through 12 in Appendix G. Because Boundary Waters has substituted data for all components but sulf ate and nitrate, it is appropriate to test performance only on sulfate and nitrate at Bounda ry Waters. However, including the substituted data in the performance provides some insight into how the substituted data aligns with modeled predictions.

Sulfate fractional bias and error fit the criteria and goals, except the four days January 26, May 11, September 17 and October 11. Nitrate fractional bias and error show poor model performance. There are nearly th e same number of days not meeting the criteria as are meeting the criteria. These days are scattered throughout the year, although most of the poor performing69 days are at very low observed concentr ations—between 0.03-0.52 μg/m3—in the summer months when modeled concentrations are zero.

Organic carbon performance is good with several days not meeting the criteria. They are May 26, June 1, June 28, July 19 and October 2. Good pe rformance may somewhat be associated with prescribed burning emissions inadvertently included in the Minnesota non-uti lity point file. In a paper titled “Establishing Reasonable Progress Goal s for the Northern Class I Areas: Treatment of Organic Carbon”, April 2, 2007, the MRPO addre sses impact from wild fire activity. MRPO identifies the days with bad organic carbon model performance as days in which the Minnesota Class I areas were impacted by Canadian wildfires.

Elemental carbon performance is good with only two days not meeting the criteria, June 1 and July 19. Elemental carbon is produced by fires, a nd these two days are impacted by the Canadian wildfires. Soil/crustal material performance is good. Coarse mass performs poorly, which is expected because coarser primary particles (large ly composed of wind borne dust) do not travel far and are influenced by very nearby sources. The 36km grid modeling cannot account for these local influences. Also, wind bor ne dust was excluded from the em issions inventory, for reasons described in Section 2.0.

As mentioned earlier in this document, Minnesota ha s an interest in the impact of local emissions to regional haze in Boundary Waters and Voyage urs, so fractional bias and error were also evaluated for the Minnesota (MRPO) case with a 12km grid covering the State of Minnesota with PiG invoked for point sources in Northeast Minn esota. Fractional bias and error look very similar between the 36km and 12km(PiG) modeling resu lts for all components except for nitrate. Poor at 36km, nitrate performance is worse at 12km(PiG), especially when concentrations are highest. Three days that are just outside the criteria for fractional bi as and just within the criteria for fractional error exhibit poor performance for the 12km(PiG) run. These three days are January 5, December 10 and December 13. Table 6.4 illustrates the difference on these three days.

Table 6.4 Nitrate Fractional Bias for 36km and 12km(PiG) where Results Significantly Differ. Date Average Observation Fractional Bias 36km 12km January 5 3.29 -73.6 -106.8 December 10 3.61 -69.2 -104.6 December 13 3.61 -61.5 -125.8

These results suggest that nitrat e either is more a local point source issue at Boundary Waters and Voyageurs and the 36km meteor ology is too coarse to transpor t the point source plumes in the “true” direction; or dispersing the emissions from Northeast Minnesota sources immediately into 36km grid cells near the monitors is maski ng underlying issues in the modeling system that are causing poor nitrate performance at the two Class I areas.

6.2.b Time Series Plots . Time series plots help determ ine how well the model performs each day of the week over the year modeled as compared to observed for each Class I area. Appendix70 G, Figures 13 through 24, contains time series plots by specie for both Boundary Waters and Voyageurs. Observed values are depicted by dots, as this data is collected on the monitor filters every three days. Modeled values are depicted as a line, as modeled estimates are continuous.

The time series plots are divided in to quarters of the year. Because concentrations of species can vary significantly at different times of the year, the scales on the y-axis may differ in each plot by quarter for the same specie. For example, th e timeseries plots for nitrate are on a scale of 0- 10 μg/m 3 in the 4 th quarter, but on a scale of 0-1 μg/m3 in the 3 rd quarter because there is so little nitrate formed during that quarter.

The model is following the patterns (ups and dow ns) of the observed pe aks and valleys quite well. This means that when an observed value is higher, the modeled value also is higher. At times, the modeled estimate is not high e nough. The model misses some peaks of NO 3 in the 1 st quarter, specifically in January and March. The model does not pr edict the very low concentrations of NO 3 in the summer. The model also misses some SO 4 peaks in the first quarter and over predicts SO 4 a few days in both the 1 st and 4 th quarters. Some of these days with over predicted winter SO 4 correspond with days with under predicted NO 3 . Ammonia preferentially reacts with SO 2 to form SO 4, so limited ammonia availabl e to further react with NO x to form NO 3 could explain this modeled response.

Elemental carbon looks good, organic carbon looks good ex cept a few days attributed to fires. Crustal/soil follows the pattern of observed quite well except for the 2 nd quarter. As expected, the model does not capture the patterns of obser ved coarse mass because coarse mass deposits out of the air very near the source, and can not be captured by 36km grid scale modeling. Also, wind-blown dust was excluded from the modeled i nventory for reasons described in Section 2.0.

6.2.c Observation and Prediction Comparison Bar Charts. The bar charts in Figure 6.3 compare each of the 20 percent worst day Minnesota (MRPO) modeled predicted values to observed values for each Class I area in terms of extinct ion, as calculated with the IMPROVE algorithm. The column labeled “o” is the observed value. The column labeled “m” is the model predicted value.

The bar charts illustrate that elemental carbon and crustal material are not significant components of light extinction in either the observed or the modeled values. Organic carbon (shown as a green bar) is a significant compone nt in extinction at Boundary Waters and Voyageurs as illustrated in the observed. The model does not show appreciable amounts of organic carbon on days where fire influences the level of organic carbon, because wild fires were excluded from the model inventory as further discu ssed in other sections of this document. The charts also illustrate the signi ficant roles nitrate and sulfate have in the extinction equation, and the times of the year when nitrate and sulfat e claim greater importanc e. In 2002, nitrate has greater importance from the end of Oct ober to the end of the year.71 Figure 6.3 Comparison of Observed and Mode led Pairs for 20% Wors t Days as Extinction

0 20 40 60 80 100 120 140 160

omomomomomomomomomomomomomomomomomomomomomomom 26 131 146 149 161 170 176 179 188 197 200 203 221 242 245 251 275 299 314 332 344 347 Avg

Julian Date in 2002Extinction (Mm-1) SO4 NO3 OC EC CRUSTAL

0 20 40 60 80 100 120 140 160 180 200

omomomomomomomomomomomomomomomomomomomomomomomomom 5 11 71 86 104 131 146 152 170 179 188 197 200 221 242 245 254 260 272 284 299 332 344 347 Avg

Julian Date in 2002Extinction (Mm-1) SO4 NO3 OC EC CRUSTAL Voyageurs Boundary Waters72 6.3 Comparison of Minnesota (MRPO) and CENRAP Model Performance Evaluation on Specific Individual Days. CENRAP conducted its performance evaluati on as described in its Technical Support Document 57

. The evaluation was conducted on the CENRAP baseF and over a different domain than the MRPO. The CENRAP perfor mance evaluation was conducted at monitor locations throughout the CENRAP region. Becau se the coverages do not overlap it is not possible to directly compare MRPO and CE NRAP model performance evaluation. Also, CENRAP focused its performance on four months of 2002; Januar y, April, July and October. Except for nitrate, the general c onclusions for both organizations are very similar. Both the MRPO 2002 and CENRAP cases generally show good performance for sulfate and elemental carbon and poor performance for soil and coarse mass. Both studies have passable nitrate performance. The MRPO 2002 case found nitrate to be under predicted, but best in the winter months when the concentrations are highest. The CENRAP case found n itrate performance was variable over their domain with under estimati on in the summer and over estimation in the winter. CENRAP organic carbon mass performa nce was variable, but better than MRPO over its domain. This could be attributed to an updated secondary organic aerosol module in CMAQ that was not available in the version of CAMx used in the MRPO 2002 and Minnesota (MRPO) cases. It is more likely attri buted to a fire inventory include d in the CENRAP modeling, which was not included in the MRPO and Minnesota (MRPO) modeling. Because organic carbon performance at Boundary Waters and Voyageurs is okay except for a few days likely influenced by fire it does not appear that the secondary organic aerosol modifications would make much difference at the two Minnesota Class I areas. Nitrate at Boundary Waters and Voyageurs. On average—over the 20 percent worst days at each Class I area—the Minnesota (MRPO) case under predicts nitrate by 5 Mm -1 at Boundary Waters and 9 Mm -1 at Voyageurs; while the CENRAP case over predicts nitrate by 10 Mm -1 at Boundary Waters and 1 Mm -1 at Voyageurs. Minnesota (MRPO) matches observed for sulfate at Boundary Waters and over predicts sulfate at Voyageurs by 8 Mm -1 . The CENRAP case under predicts sulfate by 14 Mm -1 at Boundary Waters and by 8 Mm -1 at Voyageurs. The 20 percent worst day average for the observed, Minnesota (MRPO) and CENRAP predictions are shown in Figure 6.4. Figure 6.4 Observations and Predictions in Minnesota (MRPO) and CENRAP Cases— Extinction by Species Averaged Over 20 Percent Worst Days. Voyageurs: Boundary Waters:

0 10 20 30 40 50 60 70 80

obs MN(mrpo) CENRAP
Avg
Julian dateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM

0 10 20 30 40 50 60 70 80

obs MN(mrpo) CenRAP
Avg
Julian DateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM

57 ENVIRON (September 2007)73 Minnesota has compared observed and predic ted results from the CENRAP and Minnesota (MRPO) modeling studies for nitrate and sulfate in orde r to get a clearer picture on the differences between the results on individual 20 percent worst days at Boundary Waters and Voyageurs. Back trajectories are included along with the results in order to provide some evidence of the direction winds originated on th e days with large differences between the two studies. Back trajectories are not assumed to mimic the MM5 wind flows. Some back trajectories were difficult to interpret, so only obs erved/modeled pairs where backtraj ectories indicate air parcels travel in a clear path are presented here.

The backtrajectories were created usi ng HYSPLIT4 (September 2007), with EDAS meteorological data and a starting level of 500 meters. Four trajectory end times were chosen, tracking air parcels arriving at the Minnesota Class I areas at midnight, 6 am, noon, and 6 pm. Trajectories extend backward in time for 48 hour s, but were truncated if they extended beyond the northern limit of the EDAS data (approximately 400 kilometers north of the United States border).

On most individual days, the CENRAP and Minnesota (MRPO) cases similarly predict observed nitrate. Days of similarity are in the warmer months when nitrate concentrations are low. Differences occur in the colder months, when ni trate concentrations ar e highest. The day of greatest difference is at Boundary Wate rs on October 26, when the Minnesota (MRPO) case under predicts nitrate by 24 Mm -1 , while the CENRAP case ove r predicts nitrate by 46 Mm -1 . Other days with significant differences between the studies for nitrate are January 5, January 11, December 10 and December 13. All the air parcels on these days di stinctly originate and end in northern Minnesota and surroundi ng states and Canada.

On days such as January 5 and 11 where the Minnesota (MRPO) case under predicts nitrate and the CENRAP case either simulates observed quite well or under predicts less, the air parcels appear to originate in western Canada and do not travel across the United States. The trajectories do get truncated at the northern boundary of the EDAS dom ain, so it is not possible to see the entire path of the air parcels. However, the performan ce difference could be inte rpreted to suggest that the Minnesota (MRPO) case is missing emission sources in Albe rta Canada that the greater western extent of the CENRAP domai n (see Figure 3.1) captures.

Although similar data from CENRAP is not availa ble (for the 2000 Canada inventory), Table 6.5 contains the difference in Canada 2005 elevated point source emissions between the 4rpos and inter-RPO national domain. To provide some pers pective on the level of “missing” Canada NO x emissions, they are relatively clos e to the combined point source NO x emissions from Minnesota and South Dakota (see Table 2.2), bu t from much further away. Th e emissions in Alberta are at a distance of about 1,600 kilometers from Boundary Waters and Voyageurs; about the same distance as to the lower third of Missouri.74 Table 6.5. Canada Elevated Point Source Emissions in 2005 Inventory—Domain Differences

Domain Source Sector SO 2 NO X PM 25 PM 10 VOC National Elevated Point 1,790,000 406,000 24,000 80,500 38,300

4rpos Elevated Point 881,000 227,000 16,100 51,700 13,700
difference: 909,000 179,000 7,900 28,800 24,600

A look at other days suggests the answer may be closer to home. On all days where the CENRAP case over predicts nitrat e, air parcels originate from the west/northwest. The more time the air parcel appears to spend just north west of the Minnesota border and in northern Minnesota, the greater the over prediction in the CENRAP case . The CENRAP inventory has more wintertime NO x and ammonia in those regions than the Minnesota (MRPO). case. Section 3.0 contains plots demonstrating the spatia l difference between CENRAP and Minnesota (MRPO) case emissions for a January and July weekday.

The more time the air parcel originating in the west/northwest appears to spend in the United States, the closer the Minnesota (MRPO) results compare with the obs erved. In these cases, the under prediction of nitrate is likely due to insu fficient ammonia available in the model in the winter to react with NO x to form ammonium nitrate. Figure 6.5 EndPoint VOYA, January 5, 2002

Figure 6.6 EndPoint VOYA, January 11, 2002 1/05/02 End Point VOYA

0 20 40 60 80 100 120

obs MN(mrpo) CENRAP

5
Julian dateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM 1/05/02 End Point VOYA

0 20 40 60 80 100 120

obs MN(mrpo) CENRAP

5
Julian dateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM

1 /11/02 End Point VOYA

0 20 40 60 80 100 120

obs MN(mrpo) CENRAP

11
Julian dateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM 1 /11/02 End Point VOYA

0 20 40 60 80 100 120

obs MN(mrpo) CENRAP

11
Julian dateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM75 Figure 6.7 EndPoint VOYA, October 26, 2002

Figure 6.8 EndPoint BOWA, December 10, 2002

Figure 6.9 EndPoint BOWA, December 13, 2002.

For sulfate, the day of greatest difference is at Voyageurs on September 2, when the Minnesota (MRPO) case over predicts sulfate by 59 Mm -1 , while the CENRAP case under predicts sulfate by 43 Mm -1 . On that day, air parcels originated to the west, nor thwest and in the Midwest 0 20 40 60 80 100 120

obs MN(mrpo) CenRAP

299
Julian DateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM 1 0/26/02 End Point BOWA

0 20 40 60 80 100 120

obs MN(mrpo) CenRAP

299
Julian DateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM 1 0/26/02 End Point BOWA

12/10/02 End Point BOWA

0 20 40 60 80 100 120

obs MN(mrpo) CenRAP

344
Julian DateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM 12/10/02 End Point BOWA

0 20 40 60 80 100 120

obs MN(mrpo) CenRAP

344
Julian DateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM

12/13/02 End Point BOWA

0 20 40 60 80 100 120

obs MN(mrpo) CenRAP

347
Julian DateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM 12/13/02 End Point BOWA

0 20 40 60 80 100 120

obs MN(mrpo) CenRAP

347
Julian DateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM76 (starting at Chicago/Gary area and looping down through Missouri and up through Iowa and Minnesota). On other days where the Minnesota (MRPO) case over predicts, and the CENRAP case under predicts sulfate, th ere is a strong southerly component where air parcels spend time in southern Minnesota. All the back trajectories ar e difficult to interpret. It appears that the Minnesota (MRPO) over prediction and CENRAP under pr ediction may be due to higher point source SO 2 emissions estimates for the MRPO states in the Minnesota (MRPO) case and greater summer ammonia emissions estimates for Minneso ta (i.e. +200 tons/day in July) in the Minnesota (MRPO) modeling.

Figure 6.10 EndPoint VOYA, April 14, 2002

Figure 6.11 EndPoint VOYA, August 30, 2002

Figure 6.12 EndPoint VOYA, September 2, 2002 4/14/02 End Point VOYA

0 20 40 60 80 100 120

obs MN(mrpo) CENRAP

104
Julian dateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM 4/14/02 End Point VOYA

0 20 40 60 80 100 120

obs MN(mrpo) CENRAP

104
Julian dateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM

8/30/02 End Point VOYA

0 20 40 60 80 100 120

obs MN(mrpo) CENRAP

242
Julian dateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM 8/30/02 End Point VOYA

0 20 40 60 80 100 120

obs MN(mrpo) CENRAP

242
Julian dateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM

9/2/02 End Point VOYA

0 20 40 60 80 100 120

obs MN(mrpo) CENRAP

245
Julian dateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM 9/2/02 End Point VOYA

0 20 40 60 80 100 120

obs MN(mrpo) CENRAP

245
Julian dateExtinction (Mm-1) SO4 NO3 OC EC CRUSTAL CM77 The performance evaluation of the MRPO 2005 ca se shows similar performance with improved performance for nitrate. The MRPO 2005 case ha s more ammonia in the model system with similar profiles as the MRPO 2002 and Minnesota (MRPO) cases. It is difficult to conclude whether the improved performance is due to the increas ed ammonia, because the MRPO 2005 case was conducted using different meteorology—culminati ng in a different set of 20 percent worst days—than the Minnesota (MRPO) and CENRAP cases. Figure 6.13 shows the MRPO 2005 case model performance results.

Figure 6.13. MRPO Fractional Bias for 2005 Monthly Average Concentrations for 4rpos Domain.

6.4 Implications of the Performance Evaluation. The model performance among the different cases at Boundary Waters and Voyageurs varies significantly on individual 20 percent worst days. The EPA guidance attempts to mitigate uncertainties in model performance by using the m odel results in a “relative” sense, meaning that the future concentrations are anchored to an observed measurement value. Thus, problems posed by less than ideal model performance—as show n for individual days above—are reduced. However, these differences do appear to aff ect how the model responds to reductions in emissions, which is described in Section 7.0.

6.4.a Grid Scale Needs . The performance evaluation at the m onitor locations is slightly better at 36km than for the Minnesota (MRPO) 12km(PiG) grid for nitrate. As discussed above, these results suggest that nitrate eith er is more a local point source issu e at Boundary Waters and Voyageurs and the 36km meteorology is too coarse to tr ansport the point source plumes in the “true” direction; or dispersing the emissions from No rtheast Minnesota sources immediately into 36km grid cells near the monitors is masking underlying issues in the modeling system that are causing78 poor nitrate performance at the two Class I areas. However, as shown in Section 7.0, the 12km grid (with PiG) and the 36km grid modeling re sult in the same future visibility estimate.

6.4.b Horizontal Extent of Domain . Minnesota (MRPO) modeling performance for nitrate suggests the modeling could bene fit from extending the domain further west to encompass additional sources in Canada. However, there is enough uncertainty in Canada emissions and the model response to ammonia levels, that this conclusion is not substa ntiated. Minnesota has elected to keep Canada emissions constant be tween the base and future year. Effectively, Canada emissions (whether increases or decreas es) have no relevance in developing the RPG at Boundary Waters and Voyageurs. Further disc ussion on Canada contributions to regional haze are discussed in Section 8.5.

6.4.c Improvements to Emissions Inventory . Continued emissions/modeling and corresponding performance evaluations conducte d by the RPOs has resulted in the best emissions inventory possible to da te. Efforts continue to impr ove the inventory, including the ammonia emissions inventory. Section 8.5 provid es information on the models sensitivity to ammonia emissions in establishing the RPG.

6.4.d Modifications of Models . Model performance evaluati ons over entire domains have resulted in changes to models, but were made for some reason other than achieving regional haze goals in Boundary Waters and Voyageurs. An example would be the s econdary organic aerosol module improvements in CMAQ, and more rece ntly in CAMx version 4.5. Although overall model performance for organic carbon may improve, model resu lts without those improvements are acceptable in Boundary Waters and Voyageur s. Also, improved secondary formed organic carbon will not affect reasonabl e progress in Boundary Waters and Voyageurs as natural biogenic emissions—which are th e main contributor to SOA formation at the Class I areas— remain constant in the baseline and future ye ar. Obviously, no controls are proposed for trees, thus they are not accounted for in development of the relative reduction factors. MRPO sensitivity tests show between 1-2 μg/m3 difference in organic carbon mass in northern Minnesota occurring in the summer mont hs between CAMx 4.2 (without SOA module improvements) and the beta version of CAMx 4.5.

6.5 Diagnostic tests: Accuracy of the mode l in characterizing the sensitivity of PM 2.5 to changes in emissions. Simulations can be performed to determine the sensitivity of model predictions to various inputs to the model. The purpose for di agnostic tests is to assess how various changes to the model inputs affect PM 2.5 concentrations. It may be that reductions in one input component may still result in similar overall PM 2.5 concentrations beca use reduction of one precursor may free up another for additional chemical reactions formulating PM 2.5 concentrations. An example might be an ex amination of the extent to which sulfate concentration reductions might increase nitrate concentrations by freeing-up ammonia. No specific diagnostic tests were conduc ted to test performance of th e models for this analysis. However, sensitivity to changes in ammonia emi ssions in the base and future year and their impact on the RPG is documented in Section 8.079 

### 7.0: Uniform Rate of Progress Analysis

The Regional Haze Rule, promulgated by the EP A July 1, 1999, requires states to “establish goals (expressed in deciviews) that provide for reasonable progr ess towards achieving natural visibility conditions for each Cl ass I area within a State”; im proving visibility on the most impaired days and not degrading vi sibility on the least impaired days 58 . The ultimate goal of natural visibility is to be met in 2064, and reasonable pr ogress goals are interim goals representing progress toward th at end. The year 2018 is the in itial year for developing a reasonable progress goal.

EPA guidance describes the method for determining the reasonable rate of progress goal for each Class I area. There are several steps, summarized as follows:

1. Calculate average baseline visibility conditions for the 20 percent worst and 20 percent best visibility days at each Class I area using five years—2000 through 2004—of observed values that straddle the modeled base year (i.e. 2002); 2. Calculate natural conditions using observed values; 3. Model a base year ambient air concentration , in micrograms per cubic meter (μg/m3), with modeled values that meet performance goals and criteria; 4. Project future ambient air concentration in μg/m3; 5. Calculate relative response factors for the main components of PM 2.5 from the modeled base year and projected future year con centrations and apply them to the observed concentration data (by indi vidual component) for the 20 pe rcent worst and 20 percent best days; 6. Calculate projected future year visibility in terms of extinction in inverse megameters (Mm -1 ) using the IMPROVE algorithm; and 7. Convert extinction to deciviews (dv).

The core of the visibility assessment is the baseline and natural visibility conditions based on observed data collected at IMPROVE monitors and made available through VIEWS. The baseline visibility conditions are developed from five years of monitoring data and represent the starting point from which reasona ble progress is measured. The Regional Haze Rule prescribes the baseline period as the years 2000-2004, and defi nes baseline visibil ity conditions as the average of the 20 percent worst visibility impair ed days, calculated from the monitoring data for each year of the baseline, and then aver aged over the 5-year baseline period.

The monitoring data used in the baseline visibil ity conditions is the re gulatory version obtained from VIEWS, including the substituted values for Boundary Waters desc ribed in Section 6.0. The 20 percent worst days in Boundary Waters and Voyageurs used here differ from those currently available on VIEWS. The MRPO iden tified some days at Upper Mid-West Class I areas that were excluded from the 20 percent worst days on VIEWS because of incomplete capture of insignificant components of visibility . For example, coarse mass and soil/crustal material are missing, while the remaining compon ents—notably sulfate a nd nitrate—are present

58 (40 CFR 51.308(d)(1))80 at levels that would cause those days to be on the list of 20 percent worst. 59 Over the five-year period used to calculate the base line visibility conditions, this affects six days at Boundary Waters and three days at Voyage urs. The baseline increases by 0.3 dv at Boundary Waters and 0.2 dv at Voyageurs. The MRPO treatment doe s not affect the 20 percent best days.

Using the monitored data over the 5-year baseline period, the baseline visibility for each day is ranked for each day based on the extinction coe fficient. Extinction was calculated using PM 10 and speciated PM 2.5 measurements at Boundary Waters and at Voyageurs in the IMPROVE equation adopted by the IMPROVE St eering Committee in December 2005 60 :

b ext = 2.2 * f S (RH) * [small sulfate] + 4.8 * f L (RH) * [large sulfate] + 2.4 * f S (RH) * [small nitrate] + 5.1 * f L (RH) * [large nitrate] + 2.8 * [small organic mass] + 6.1 * [large organic mass] + 10 * [elemental carbon] + 1 * [fine soil] + 1.7 * f SS (RH) * [sea salt] + 0.6 * [coarse mass] + Rayleigh scattering (site sp ecific—BOWA1= 11, VOYA2 = 12) + 0.33 * [NO 2 (ppb)]

where: b ext is calculated total light exti nction in inverse megameters f

S (RH) is the relative humidity adjustment factor for small particles; f

L (RH) is the relative humidity adjustment factor for large particles; f SS (RH) is the relative humidity adjustment factor for sea salt; and

The apportionment of the total concentr ation of sulfate compounds into the concentrations of the small and large size fractions is accomplished using the following equations:

[large sulfate] = ([total sulfate]/20μg/m3) * [total sulfate], for [total sulfate] < 20 μg/m3;

[large sulfate] = [total su lfate], for [total sulfate] ≥ 20 μg/m3; and

[small sulfate] = [total su lfate] – [large sulfate]

The same equations above for large sulfate, are also used to apportion total nitrate and total organic mass concentrations into the large and small size fractions.

59 MRPO (June 2007)
60 VIEWs web site. http://vista.cira.colost ate.edu/views/Web/RHR/RHR_Planning.aspx81 NO 2 is not currently measured at the IMPROVE monitors 61 , so this factor is not included. The IMPROVE equation assumes sulfate is in the form of ammonium sulfate and nitrate is in the form of ammonium nitrate.

Monthly f S (RH) and f L (RH) values are presented in Table 7.1 62

, 63

. Table 7.1. Monthly f S (RH) and f L (RH) values for Boundary Waters and Voyageurs ClassI f (RH) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

BOWA1 f S (RH) 3.24 2.84 2.99 2.64 2.93 3.21 3.44 3.67 3.80 3.07 3.50 3.49 f

L (RH) 2.50 2.26 2.32 2.09 2.22 2.42 2.57 2.69 2.76 2.37 2.65 2.65 f

SS (RH) 3.74 3.37 3.34 2.92 3.03 3.43 3.68 3.85 3.95 3.44 3.89 3.92

VOYA2 f S (RH) 3.16 2.77 2.82 2.59 2.65 3.28 3.25 3.48 3.66 3.02 3.37 3.32 f

L (RH) 2.46 2.22 2.22 2.07 2.09 2.46 2.46 2.59 2.70 2.35 2.58 2.55 f

SS (RH) 3.69 3.31 3.20 2.90 2.89 3.46 3.55 3.71 3.87 3.42 3.83 3.80

The solution to this equation is in the form extinction (b ext ). The Regional Haze rule requires visibility to be expressed in deciview s (dv). The following equation converts b ext to deciviews (dv):

Haze Index (dv) = 10 ln (b ext /10)

Where: b ext and light scattering due to Rayleigh scattering (i.e. the “10” in the denominator) are both expresse d in inverse megameters (Mm -1 ). In order to be consistent across all Class I areas, the EPA prescrib ed that the Rayleigh scattering in the denominator should always be 10 instead of using site-specific Rayleigh scattering values. 64

Values for natural conditions— visibility conditions that would exist in the absence of manmade impairment—were obtained from CIRA. 65 The missing data described above are not included in the natural conditions du e to the relatively late discovery of these data in the regional haze process (early 2007) and the workload and st aff shortage at CIRA for updating VIEWS . The resulting change in natural c onditions will be small due to the fact that natural conditions in deciviews are small and the changes due to the addition of these days had a relatively small effect on the 20 percent best and worst days in the baseline period. A proportionate change to natural visibility would be less. According to Scott Copeland of CIRA, “Including the (missing) days has a demonstrable effect on the baseline values, but the natural conditions two values are normalized to the Trijonis annual mean estimate. So, for example, adding a few extra high sulfate days increases the annual sulfate mean, which increases th e sulfate scaling factor which reduces all the values in the distribution, somewhat offsetting the larger va lues. In the specific case of (Boundary Waters) and (Voyageurs), only 6 and 3 sample dates resp ectively are added to

61 According to Scott Copeland of CIRA, “NO 2 is not a normal part of the IMPROVE program. [He] would expect slight changes to both natural and baseline conditions, perhaps adding very roughly 1-3 Mm -1 to the 20% worst baseline and 0.5-1.5 Mm -1 to the 20% worst natural. This would have a small effect on glide path calculations.”
62 VIEWs web site. http://vis ta.cira.colostate.edu/views
63 Hand, et al. (March 2006)
64 EPA (April 2007).
65 Copeland, Scott (April 2008)82 the distribution of roughly 120 obser vations that are in the 5 year s’ worth of 20 (percent) worst days, so there is ...no way to move the mean very much.”

A straight line connecting the baseline visibil ity average (2000-2004) and natural conditions (2064) form the uniform rate of progress or “glidepath”. Placement relative to the line determines whether estimated fu ture visibility (i.e. 2018) moves in a downward direction at a rate that natural conditions are likely reached in 2064. Voyageurs would be on the glidepath in 2018 if visibility impairment on the 20 percent worst visibility days were reduced by 1.7 dv ([19.5 – 12.1]*[(2018-2004)/(2064-2004)]) from base line conditions. Boundary Waters would be on the glidepath in 2018 if visibility impair ment on the 20 percent worst days were reduced by 2.0 deciviews from baseline conditions.

Base year and future year ambient air concentrations were modeled using emissions and meteorology inputs as described in Sections 1.0 through 5.0. The Relative Response Factors (RRF) are the ratio of the future year and base year ambient air concentrations, calculated as follows:

RRF [X] = Modeled Future Mean [X] /Modeled Base Year Mean [X]

Where: RRF is the relative response factor (unitless); Future Mean and Base Year Mean ar e the modeled base year (2002) and the future year (2018) concentrations at the Class I area monitor location averaged for the 20 percent worst days (and 20 percent best days) as determined by the base year (2002) monitor data; and [X] is the species concentr ation (i.e. sulfate, nitrat e, organic carbon, elemental carbon, fine soil and coarse particulate matter).

Applying the RRFs to baseline monitoring conditions, for each species comprising PM 2.5 , provides the estimate of future year visibility conditions, described below:

A. Multiply each species specific RRF, developed from the 2002 and 2018 modeling data, by the corresponding meas ured species concentration for all of the 20 percent worst (and 20 percent best) days ov er the 5-year baseline period;

[X]future = RRF (X) * [X]baseline (daily value)

B. Estimate extinction coefficient for each of the 20 percent worst (and 20 percent best) days using the IMPROVE equati on, convert to deciviews; and

C. Calculate the average future year deciview for the 20 percent worst (and 20 percent best) days. 1. Calculate the arithmetic mean deciview value for the 20 percent worst and best visibility values for each ye ar in the baseline period; and 2. Average the resulting 5-year mean decivi ew values (for the 20 percent worst, and for the 20 percent best).83 Applying the above methodology with future emissi on estimates that reflect reasonable controls provides the RPG based on modeling.

The final deciview value represents the reasonable progr ess goal, which is established using a projected future ambient concentration resulti ng from emissions that reflect a four-factor analysis. The Clean Air Act requires states to consider:

• Costs of compliance;

• Time necessary for compliance;

• Energy and non-air quality environmen tal impacts of compliance; and

• Remaining useful life of existing sources th at contribute to vi sibility impairment.

Minnesota used the EPA Modeled Attainment Te st Software (MATS) program, version 1.1.2, to calculate the baseline for the average 20 percen t worst days, the RRFs using the modeling work from each organization, the resulting projected futu re year visibility conditions in extinction, and to convert extinction to deciviews. Section 8.0 discusses the development of Minnesota’s RPG.



### 8.0: Control Strategy Development

The MRPO, CENRAP and Minnesota (MRPO) cases all initially consid ered on-the-books controls, meaning controls expected due to other program s despite the Regional Haze Rule, to evaluate future year placement relative to the glidepat h. On-the-books controls are summarized in Section 2.0. Position relative to the glidepath in 2018, estimated with on-the-books controls in the Minnesota (MRPO) case, is illustrated in Figures 8.1 and 8.2 for Boundary Waters and Voyageurs, respectively. These re sults indicate that future year visibility is on a path toward natural visibility conditions, how ever, above the glidepath.

Tables 8.1 and 8.2 contain the Minnesota (MRPO) deciview values for the 20 percent worst and best days at Boundary Waters and Voyageurs along with the re sults of other modeling work conducted by CENRAP, and by MRPO (2002 and 2005 cases). In order to do an equitable comparison, the visibility conditions and the futu re year values for each organization were calculated using the same monitoring data esta blishing the baseline ( 2000-2004) and ran through MATS. The only difference lies with the modele d base year and future year concentrations, which are used to calculate RRFs applied to the baseline monitoring data.

The results among the modeling analyses differ by tenths of a deciview. Plotting the various results on Figure 8.1 and 8.2 would show a very tight overlapped groupin g, thus they are not shown. The CENRAP case shows a future projected visibility 0.1 dv closer to the glidepath at Boundary Waters and 0.4 dv closer to the g lidepath at Voyageurs than the Minnesota (MRPO) case. This is somewhat unexpected because the Minnesota (MRPO) case uses improved future emissions projections for the EGU sector, which the CENR AP case does not. See Section 2.0 for details on the EGU emissions.

The MRPO 2002 case shows future projected visibility 0.2 dv furt her from the glidepath at Boundary Waters and at Voyageurs than the Minnesota (MRPO) case. This is expected and due to the improved future emissions projecti ons for EGU sector in the Minnesota (MRPO) case.84 Figure 8.1. Boundary Waters 36-km Minnesota (MRPO) Position on Glide Path with On-the-Books Controls

Uniform Rate of Progress - Boundary Waters, MN

3.4 18.7

11.6 19.9

6.4 6.5

0.0 5.0 10.0 15.0 20.0 25.0

1990 2000 2010 2020 2030 2040 2050 2060 2070

YearDeciviews

Figure 8.2. Voyageurs 36-km Minnesota (MRPO) Position on Glide Path with On-the-Books Controls

Uniform Rate of Progress, Voyageurs MN

4.3 19.0

12.1 19.5

7.1 7.1

0.0 5.0 10.0 15.0 20.0 25.0

1990 2000 2010 2020 2030 2040 2050 2060 2070

YearDeciviews

The MRPO 2005 results for the 20 percent worst days, in Table 8.1, are on the glidepath at Boundary Waters, and below the glidepath at Voyageurs. These results show 0.8 dv less visibility impact at Boundary Waters and 1.3 dv less visibility impact at Voyageurs than the Minnesota (MRPO) case.85 Table 8.1 Uniform Rate of Progress An alysis for 20% Worst Days with On-the-Books Controls.

Class I Area Name Organization Grid Resolution Base Year Baseline 2018 URP 2018 Projected

(dv) (dv) (dv) difference (dv) Boundary Waters CENRAP 36 2002 19.9 17.9 18.6 0.7 Boundary Waters Minnesota 36 2002 19.9 17.9 18.7 0.8 Boundary Waters MRPO 36 2002 19.9 17.9 18.9 1.0 Boundary Waters MRPO 36 2005 19.9 17.9 17.9 0.0 Voyageurs CENRAP 36 2002 19.5 17.8 18.6 0.8 Voyageurs Minnesota 36 2002 19.5 17.8 19.0 1.2 Voyageurs MRPO 36 2002 19.5 17.8 19.2 1.4 Voyageurs MRPO 36 2005 19.5 17.8 17.7 -0.1

Table 8.2—Uniform Rate of Progress An alysis for 20% Best Days with On-the-Books Controls.

Class I Area Name Organization Grid Resolution Base Year Baseline 2018 URP 2018 Projected

(dv) (dv) (dv) difference (dv) Boundary Waters CENRAP 36 2002 6.4 6.4 6.4 0.0 Boundary Waters Minnesota 36 2002 6.4 6.4 6.5 0.1 Boundary Waters MRPO 36 2002 6.4 6.4 6.9 0.5 Boundary Waters MRPO 36 2005 6.4 6.4 6.1 -0.3 Voyageurs CENRAP 36 2002 7.1 7.1 7.0 -0.1 Voyageurs Minnesota 36 2002 7.1 7.1 7.1 0.0 Voyageurs MRPO 36 2002 7.1 7.1 7.3 0.2 Voyageurs MRPO 36 2005 7.1 7.1 6.8 -0.3

Before proceeding with the evaluation of additio nal control strategies, it is prudent to better understand the reasons behind the varying results between the organizations which all presumed essentially the same on-the-books controls. This includes the sensitivity of the model to varying emissions and meteorology and the resulting affect on the RPG.

Because the baseline and natural visibility cond itions are the same among the various analyses, the modeled RRFs can help explain the differe nce. Tables 8.3 and 8.4 contain the RRFs by visibility component, or species, for each modeli ng study for the 20 percent worst and best days, respectively. An RRF above 1.000 means the m odeled concentration increases from 2002 to 2018. A factor below 1.000 means the modeled c oncentration decreases from 2002 to 2018.

Evaluation of the RRFs for Boundary Waters and V oyageurs focuses on sulfate and nitrate. Both of these components figure prominently in the extinction calculation described in Section 7.0. Crustal/soil and coarse mass ar e not prominent components in the extinction calculation, nor are they significant in the extinction calculated at Boundary Waters and Voyageurs on the 20 percent worst days, so changes in these components will not affect the resulting future year projection. Elemental carbon has low measured values at the two Class I areas; ev en though the extinction calculation multiplies the observed concentration of elemental carbon by a factor of 10, it still86 does not feature prominently in the future year projections at Boundary Wa ters and Voyageurs. The RRFs for organic carbon are similar between the various analyses, so no further discussion is warranted for that component.

Table 8.3 Relative Response Factors for 20% Worst Days with On-the-Books Controls.

sulfate nitrate organic carbon elemental carbon crustal/ soil coarse

mass Boundary Waters CenRAP 36 2002 0.870 0.790 0.947 0.756 1.102 1.062 Boundary Waters Minnesota 36 2002 0.798 0.936 0.945 0.786 1.402 1.127 Boundary Waters MRPO 36 2002 0.877 0.929 0.949 0.788 1.265 1.112 Boundary Waters MRPO 36 2005 0.746 0.849 0.990 0.800 1.269 0.596 Voyageurs CenRAP 36 2002 0.932 0.817 0.954 0.796 1.101 1.091 Voyageurs Minnesota 36 2002 0.855 1.035 0.956 0.834 1.275 1.069 Voyageurs MRPO 36 2002 0.949 1.054 0.956 0.830 1.200 1.064 Voyageurs MRPO 36 2005 0.761 0.822 0.976 0.772 1.239 0.637

Class I Area Name Organization Grid Base Year Relative Response Factors

Table 8.4—Relative Response Factors for 20% Best Days with On-the-Books Controls.

sulfate nitrate organic carbon elemental carbon crustal/ soil coarse

mass Boundary Waters CenRAP 36 2002 1.006 0.855 0.961 0.950 1.157 1.082 Boundary Waters Minnesota 36 2002 0.995 1.171 0.989 0.971 1.091 1.038 Boundary Waters MRPO 36 2002 1.022 2.181 0.991 0.972 1.046 1.039 Boundary Waters MRPO 36 2005 0.937 0.964 0.977 0.922 1.024 0.730 Voyageurs CenRAP 36 2002 1.001 0.854 0.973 0.859 1.175 1.139 Voyageurs Minnesota 36 2002 0.989 1.100 0.989 0.956 1.078 1.027 Voyageurs MRPO 36 2002 1.015 1.639 0.993 0.954 1.060 1.030 Voyageurs MRPO 36 2005 0.942 0.848 0.986 0.901 0.965 0.724 Relative Response Factors

Class I Area Name Organization Grid Base Year

Because the MRPO 2002 case is the basis for the Minnesota (MRPO) case, it is easier to compare these two analyses. According to the RRFs, th e most noticeable difference between the MRPO 2002 and Minnesota (MRPO) cases is the response to sulfat e reductions. As expected, the Minnesota (MRPO) case shows greater reductions due to the updated EGU emission projections associated with IPM version 3.0 “will do”. The MRPO 2002 case inco rporates IPM version 2.1.9 VISTAS.

The noticeable difference in the RRFs for sulfate between the CENRAP and Minnesota (MRPO) cases are also mainly due to the differences in future year EGU emission estimates. This conclusion is drawn from the similarity in the MRPO 2002 case and CENRAP 2002 case sulfate RRFs; both cases incorporate IPM version 2.1.9VISTAS.

Even more noticeable than the differences in su lfate RRFs are the differences in RRFs for nitrate between the CENRAP, MRPO 2002 and Minnesota (MRPO) cases. The RRFs for the 20 percent worst days, show that the CENRAP case estimat es a greater reduction in nitrate than the Minnesota (MRPO) case. In fact, Minnesota (MRPO) case RRFs show a slight increase in nitrate at Voyageurs from 2002 to 2018. MRPO 2005 case shows the largest decrease in nitrate from 2002 to 2018. These differences in nitrate RRF s overcome the sulfate decreases in the Minnesota (MRPO) case attributable to the change from IP M2.1.9 to IPM3.0. This explains why the87 position of the CENRAP case in relation to th e glidepath is the same as the Minnesota (MRPO) case at Boundary Waters and is closer to the glidepath than the Minnesota (MRPO) case at Voyageurs, which would see greater influence from NO X reductions in Canada.

To elaborate further, Section 6.0 of this docum ent shows that the CENR AP case over predicts nitrate formation in 2002 at Boundary Waters and Voyageurs compared to observed values collected at monitoring stations. Th is is likely caused by additional NO X and a significant amount of available ammonia with which to reac t. This information, al ong with the greater projected decrease in NO X emissions (i.e. Canada), and the RRFs for nitrate, suggests that the excess free ammonia in the CENRAP inventory a llows for the model to respond well to future projected reductions in NO x emissions, possibly even over-stati ng them. Conversel y, the lack of free ammonia in the Minnesota (MRPO) case for winter months in the base year combined with a significant increase in available ammonia in the future year might under-s tate the effects of reducing the nitrate ion du e to reductions of NO x emissions.

Tables 8.5 and 8.6 summar ize the winter day NO X and NH 3 changes in emissions for the Minnesota (MRPO) and CENRAP cases from 2002 to 2018 for Minnesota, North Dakota, South Dakota and Canada. These Tables focus on Minnes ota and areas to the West only because winds appear to originate in that direction on th e days with a discrepancy on model performance between the CENRAP and Minnesota (MRPO) cases (see Section 6.0). The emissions summary for Canada in Tables 8.5 and 8.6 encompasses the entir e portion of that Country within the 4rpos domain. While the Minnesota (MRPO) case does not contain a change in Canada NO X emissions, the CENRAP case does, by about 44 percent on a wi nter day over the portion of Canada covered by the 4rpos domain. However, the emissions summary can not detect where these emission reductions because the emissions summary tool s used do not separate Canada emissions by province. The spatial plot in Figure 8.3 illustrates that the reduction in NO X is distributed throughout the country within the domain, but mostly in the East , rather than the West. The Tables and the Figure also do not address the ad ditional Canada emissions further West, outside the 4rpos domain, included in the CENRAP case.

Results from modeling conducted with the PSAT t ool in CAMx supports the above interpretation on the model response to nitrate ch anges in the CENRAP and Minnesota (MRPO) cases. This tool tracks the original source of particulate sp ecies by geographic regi on and source category. CENRAP, MRPO and Minnesota all used this tool to assess the main contributors to visibility at the Class I areas in 2002 and 2018 for 36km modeling. The description of the PSAT analyses is provided in Section 8.1.88 Table 8.5. Winter Day NO X and NH 3 Emissions in Tons from Base Year 2002 for CENRAP and Minnesota (MRPO)

NO X NH 3 NO X NH 3 NO X NH 3 Minnesota 1,461 345 1,378 158 83 187 North Dakota 627 74 498 23 129 51 South Dakota 280 214 250 72 31 143 Canada* 4,080 1,023 2,055 997 2,025 26 total: (only U.S) 2,368 633 2,125 253 242 381 total: (incl Canada) 6,448 1,656 4,181 1,250 2,267 407 Region CENRAP Minnesota (MRPO) Difference (CENRAP - Minnesota (MRPO) ) Winter Day Emissions in Tons -- Base Year 2002

Table 8.6. Winter Day NO X and NH 3 Emissions in Tons from Base Year 2018, and Difference from 2002 to 2018, for CENRAP and Minnesota (MRPO)

NO X NH 3 NO X NH 3 NO X NH 3 NO X NH 3 NO X NH 3 Minnesota 849 431 768 208 81 223 -42% 25% -44% 32% North Dakota 558 75 345 33 213 42 -11% 1% -31% 43% South Dakota 207 218 139 102 68 116 -26% 2% -44% 42% Canada* 2,286 991 2,219 1,016 67 (24) -44% -3% 8% 2% total: (only U.S) 1,613 724 1,251 343 362 381 -32% 14% -41% 36% total: (incl Canada) 3,899 1,715 3,470 1,359 429 356 -40% 4% -17% 9% Region Winter Day Emissions in Tons -- Base Year 2018 Difference (CENRAP -

Minnesota (MRPO) ) CENRAP Minnesota (MRPO) CENRAP 2018 - 2002 Minnesota (MRPO) 2018 - 2002

* Minnesota (MRPO) modeling used the same 2005 Canada inventory to reflect the base year (2002) and the future

year (2018). The difference in emissions shown in the abov e tables reflects that Canada area sources (other than ammonia sources) were inadvertently excluded from the base year files.

Figure 8.3. CENRAP Winter Day NO X Emissions in Tons 2002: 2018:89 The MRPO 2005 case uses 2005 meteorology rather than the 2002 meteorology used in the CENRAP and Minnesota (MRPO) cases. Thus, the MRPO 2005 projec tions for future visibility are based on different worst visibility days, different visibility com ponents that contribute to those days, and the predominant wind direction from wh ich emission reductions occur. There are also the rather significant differences in ammonia emissions. Sect ion 6 describes improved model performance for nitrate in the MRPO 2005 base year.

Overall, the MRPO 2005 case contains more a mmonia in the base year (~24% domain-wide; ~7% in Minnesota) than the 2002 Minnesota (MRPO) case. The MRPO 2005 case also projects significantly less ammonia in 2018 than the Minnesota (MRPO) case, as described in Section 2, and shown in Table 8.7 for Minnesota This combin ation may counter-balance the model response to NO x emission reductions seen in the Minnesota (MRPO) case.

The following exercise supports this hypothesis. The ammoni a inventory in the Minnesota (MRPO) case was replaced with the ammonia inventor y in the MRPO 2005 case and modeled. Adding more ammonia in just the Minnesota (MRPO) base case and keeping the future year the same, resulted in a 6 percent change in the nitrate RRF at Boundary Waters and a 2 percent change at Voyageurs. Estimating less of an increase in ammonia in the future case using the MRPO 2005 case future year emissions results in an 8 percen t change in the nitrate RRF at Boundary Waters, and 9 percent at Voyageurs. Ultimately, this re sults in a more optimistic projected RPG in 2018 by 0.2 dv at Boundary Waters a nd by 0.3 dv at Voyageurs.

Because the Minnesota (MRPO) case under predicts ammonia in th e base year (see Section 2.0), and because of improvements made by MRPO to th e future year ammonia inventory since the inventory used in the Minnesota (MRPO) case, the model sensitivity te st also suggests that the RPG established in the Minnesota (MRPO) case may be a slightly conservative estimate.

Table 8.7 Minnesota Annual Emi ssions Change in Tons of NO X and NH 3 from All Source Categories in Minnesota (MRPO), and MRPO 2005 Case
Specie Minnesota ( MRPO ) MRPO 2005 Case 2002 2018 Difference 2005 2018 difference NO X 516,000 317,000 -199,000 464,000 278,000 -186,000 NH 3 185,000 253,000 +68,000 196,000 227,000 +31,000

More important is the difference in the model sensitivity to meteorology. Even though the EPA methodology for establishing the RPG attempts to ta ke into account the y ear-to-year variability of the meteorology in the monitored 5-year baselin e, the RPG still is sensitive to meteorology. In this case, use of 2005 meteorology provide s a more optimistic RPG. Modifying the Minnesota (MRPO) case with only the 2005 meteorology results in a more optimistic projected RPG in 2018 by 0.4 dv at Boundary Waters and by 0.8 dv at Voyageurs.

Results from modeling using PSAT in CAMx also supports the above interpretation on how the model responds to changes in ammonia in the fo rmation of nitrate, and the meteorology, between the MRPO 2005 and Minnesota (MRPO) cases. See section 8.1 for more discussion.

Another factor to take into account when comparing RRFs among organizations are the modifications made to the Minnesota (MRPO) case to, for example, revise inappropriate growth90 factors and include additional sources in north eastern Minnesota. These changes were only made to the Minnesota (MRPO) case and likely result in future projections further from the glidepath than were th e corrections not made.

Uniform Rate of Progress Analysis with 12km(PiG) model results. In addition to the 36km results, Minnesota assessed placement relative to the uniform rate of progress line using 12km(PiG) results. Various 12km grid cells were evaluated, as pseudo monitors, throughout the Class I areas using the baseline m onitoring values from the monito r within the Class I area being evaluated. Although the monitor lo cation for Isle Royale is not on the Island but in the upper peninsula of Michigan, the observed values were still used as surrogates.

Minnesota (MRPO) values for the 12km grid assessed for several receptors th roughout the Class I areas, indicated a range of pr ojected values from 18.3 – 19.0 dv, with an average value of 18.7 dv in Boundary Waters—the largest Class I area—f or the 20 percent worst days. The average value is the same as the 36km result at the monito r location. The same is true for the 20 percent best days. It does not appear necessary to se t separate goals for various locations across the Class I area based on the 12km results. Only one receptor was placed at Is le Royale and the two Voyageurs receptors showed the same result, so separate goals for these areas are not needed either.

8.1 Particulate Source Apportionment to Asse ss Contributions to Visibility Impairment. As mentioned above, PSAT in CAMx tracks the original source of pa rticulate species by geographic region and source category. CENRAP, MR PO and Minnesota all used this tool to assess the main contributors to visibility at the Class I areas in 2002 and 2018 36km modeling. The geographical and source ca tegory groupings were somewhat different among the PSAT analyses conducted by the three organizations, depending on the purpose of each.

Geographical groupings in the Minnesota (MRPO) case were designed by breaking out smaller regions nearest Boundary Waters and Voyageurs that may have the greatest contributions to impaired visibility. The geographical groupi ngs are shown in Figure 8.4 and described as follows:

• Individual States Arkansas, Illinois, Indiana, Io wa, Kansas, Louisiana, Michigan, Minnesota, Missouri, North Dakota, Nebras ka, Oklahoma, South Dakota, Texas and Wisconsin; the

• Northeast States consolidated as one geographic region comprised of Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Ohio, Pennsylvania and Rhode Island; the

• Southeast States consolidated as one geographic regi on comprised of Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Caroli na, Tennessee, Virginia and West Virginia; the

• West States consolidated as one geographic regi on comprised of Eastern portions of Colorado, Montana, New Mexico and Wyoming; and

• Canada all provinces consolidated as one geographic region. The remainder of the western United States and Canada is outside the modeling domain.91 Source category groupings in the Minnesota (MRPO) case are described as follows:

• Electric Generating Units ( EGU )

• Point sources that are not EGUs ( nonEGU );

• Onroad mobile ( onroad );

• Nonroad mobile including commercial mari ne, airport and recreational vehicles ( nonrd_mar );

• Agricultural ammonia sources ( nh3 );

• Other area sources ( other ); and

• Initial and boundary conditions ( ICBC ). Boundary Conditions are air concentrations coming into the domain from the East, West, North, South and above—over the top—of the domain (i.e. intrusion of stratospheric ozone ). Initial conditions are the inputs at the start-up of the model run and should not appear in the results if the model run accounts for appropriate spin-up time. For example, the Minnesota (MRPO) case has a spin-up period of two weeks prior to the date for wh ich usable model results are desired.

In order to compare the PSAT results among the Minnesota (MRPO) , CENRAP and MRPO 2005 cases, Minnesota grouped and plotted the results fr om each organization so they all appear on the same scale, order and colo r scheme as the Minnesota (MRPO) case. The plots for the Minnesota (MRPO) case are in Figures 8.5 through 8.16. The plots for the CENRAP and MRPO 2005 cases are in Appendix H.

Figure 8.4 Geographic Regions Modeled with PSAT in Minnesota (MRPO) Case.92 The CENRAP and MRPO 2005 cases include PSAT re sults for the species, ammonium nitrate, ammonium sulfate, organic ae rosols, elemental carbon, soil and coarse matter. The Minnesota (MRPO) case only includes ammonium nitrate and ammonium sulfate because it was determined that monitored and modeled extinc tion values for the 20 percent worst days at Boundary Waters and Voyageurs were predominan tly associated with ammonium nitrate and ammonium sulfate on days not im pacted by wildfires (see Secti on 6.0). Other considerations include the additional computati onal times associated with mode ling the other species, coupled with their relative unimportance for the developm ent of control strategies. The exclusion of organic aerosol, elemental carbon, soil and coarse matter led to the use of a variation of the IMPROVE equation in the Minnesota (MRPO) case PSAT analysis from that used for model performance and the attainment test.

The IMPROVE equation does not contain ammoni um, assuming sulfate and nitrate are fully neutralized. The PSAT re sults in the Minnesota (MRPO) and MRPO 2005 cases both include ammonium as a separate species in the extinction equation, indi cating that ammonia sources are viable targets for control meas ure consideration. Without ammoni a, no ammonium sulfate and no ammonium nitrate are formed. PSAT can pr ovide source and geogra phic location where the ammonia that forms ammonium originates. The inclusion of ammonium calls for an additional modification to the IMPROVE equation. The modi fied IMPROVE equation for sulfate, nitrate and ammonium for use in the PSAT analysis is as follows:

Extinction (Mm -1 ) = (2.2* f S (RH) *[small sulfate]) + (4.8 * f L (RH) *[large sulfate]) + (2.4 * f S (RH) *[small nitr ate]) + (5.1* f L (RH) *[large nitrate]) + (2.2* f S (RH) *[small ammonium assoc. sulfate ]) + (4.8 * f L (RH) *[large ammonium assoc. sulfate ]) + (2.4 * f S (RH) *[small ammonium assoc. nitrate ]) + (5.1* f L (RH) *[small ammonium assoc. nitrate ])

Where: f S (RH) = water growth factor as a function of relative humidity for small particles; f

L (RH) = water growth factor as a function of relative humidity for large particles; and [nitrate], [sulfate] and [ammonium] = concen tration of these components in μg/m3.

Although reasonable progress goals are depicted in deciviews, contributions of individual species, source sectors and source regions are on ly evaluated in terms of extinction. An extinction value less than 10 Mm -1 will produce a negative value in deciviews. An extinction coefficient of B ext = 10 Mm -1 will result in a deciview value of 0 dv (10 * ln (B ext /10)). Assessing contributions to visibility with nega tive values would be confusing.

Figures 8.5 through 8.8 illustrate th e geographic contribution assessm ent, in extinction, on the 20 percent days at Boundary Waters in the Minnesota (MRPO) case. Figures 8.9 through 8.12 illustrate the geographic contribution assessment at Voya geurs. In 2018 with on-the-books controls implemented, the greatest contribu tors to ammonium sulfate and ammonium nitrate at both Class I areas are Minnesota, Wisconsin, Iowa, Illi nois, Missouri, North Dakota and Canada. Ammonium sulfate contributes most of the visibility impairment on the 20 percent days at both Class I areas.93 Figures 8.6, 8.8, 8.10 and 8.12 illustrate the difference in contribution between the 2002 and 2018 PSAT analyses. These Figures show very l ittle modeled contribution change in nitrate from the base year to the future year. This further supports the conc lusion that a lack of ammonia in the base year and excess ammonia in the future year can dampen the effect of NO X controls.

Caution should be taken when dr awing conclusions from the differe nce charts in these Figures. PSAT does not provide answers to model response; meaning if emissions are reduced in a particular sector or regi on, PSAT can not tell what effect that ch ange will have on the results. In other words, it would be inappropr iate to conclude that total emission reductions in Minnesota equates to about 0.8 Mm -1 contribution reductions from Minneso ta to visibility impairment. Instead, one can simply see how the 2018 PSAT results compar e with the 2002 PSAT results on what species, regions and sectors ar e contributing to extinction.

Much less visibility impairment is attributed to EGUs in 2018 than in 2002. Between 2002 and 2018, the contribution from EGUs decreases fr om nearly all geographic regions. The contribution to nitrate formation from mobile sources also decreases in Minnesota, and to a lesser extent, Wisconsin and North Dakota. Th ese decreases in contribution from EGUs and mobile sources cause a shift in contributi on to nonEGU point sour ces in Minnesota, and somewhat to area sources, in 2018.

Figures 8.13 through 8.16 illustrate the source ca tegory contribution assessment for the entire domain on the 20 percent worst days at Bounda ry Waters and Voyageurs in the Minnesota (MRPO) case in 2018 with on-the-books controls implemente d. Overall, point sources are the greatest contributors to extinction due to sulfate and nitrate. While in Boundary Waters EGUs contribute more than nonEGU sources, in Voyageurs EGU and nonEGU sources are nearly equal contributors. As expected, the largest contributor to ammonium is agricultural operations.

After point sources, boundary conditi ons are the next greatest cont ributor to extinction in 2018 at Boundary Waters and Voyageurs. Boundary cond itions are source contributions that originate outside, and transfer into and out, of the modeling domain. Source apportionment techniques can only account for the total contribution of boundary conditions to the overall visibility conditions, accounting for conservation of mass in the modeling apportionment. Although it is not possible to specifically attribute the bounda ry condition contributi on to a specific source grouping, the domain used in the Minnesota (MRPO) case extends far enough from Boundary Waters and Voyageurs to fully account for the c ontributions from the most significant States (listed above). If the emissions originating in Alberta Canada—which is outside the modeling domain—are accurate, more contribution may be at tributed to Canada than depicted in the Minnesota (MRPO) case. However, the CENRAP domain extends beyond Alberta Canada, and yet has a similar contribution level attributed to boundary conditions as the Minnesota (MRPO) case.

Appendix H contains the 2018 PSAT results for ge ographic contribution in the CENRAP case. Results for Boundary Waters are in Figures H. 1 through H.4, and for Voyageurs are in Figures H.5 through H.8. In comparison, the CENRAP case shows overall more light extinction (+11 Mm -1 at Boundary Waters, +6 Mm -1 at Voyageurs) due to ammonium sulfate and ammonium94 nitrate than Minnesota (MRPO) case. The relative contribution by State is quite comparable. As expected, the CENRAP case has significantly more contribution attributed to Canada. Only Minnesota contributes more than Canada in the CENRAP case, whereas in the Minnesota (MRPO) case Canada barely fits criteria for a main geogra phic source contri butor (i.e. ~5% or more). The Figures also show that the CENRAP case has more nitrate formation relative to sulfate formation than the Minnesota (MRPO) case.

The Figures also show how extinction contributi on changes between the base year and 2018. In the Minnesota (MRPO) case, overall extinction of ammonium nitrate and ammonium sulfate decreased by 8 Mm -1 at Boundary Waters and 5 Mm -1 at Voyageurs with nearly all of the reduction associated with ammonium sulfate. In the CENRAP case, overall extinction of ammonium nitrate and ammoni um sulfate decreased by 10 Mm -1 at Boundary Waters and 6 Mm -1 at Voyageurs, with a large portion of the reduction associated with ammonium nitrate, especially from Minnesota and Canada. This sup ports earlier conclusions that the excess free ammonia in the CENRAP case allows for the model to respond well to future projected reductions in NO x emissions, possibly even over-stating them. The very large contribution shift between 2002 and 2018 associated with mobile source NO X in the CENRAP case appears to support this conclusion.

Appendix H also contains the 2018 PSAT results fo r geographic contribution in the MRPO case. Results for Boundary Waters are in Figures H. 9 through H.12, and for Voyageurs are in Figures H.13 through H.16. In comparison, the MRPO 2005 case shows overall nearly the same extinction at Boundary Waters (+2 Mm -1 ) and Voyageurs (-1 Mm -1 ) in 2018 due to ammonium sulfate and ammonium n itrate as the Minnesota (MRPO) case. The relative c ontribution by State is quite comparable at Boundary Waters, however, V oyageurs is more influenced by States located to the East and Southeast of the Class I area than in the Minnesota (MRPO) case. As discussed above, this difference in the geographic contributi on to Voyageurs is associated with the use of 2005 meteorology and hence a different set of 20 percent worst days.

Comparing contributions between 2018 a nd 2005, the MRPO 2005 case shows much less contribution from States to the East and S outheast of both Class I areas, than in the Minnesota (MRPO) case (2018-2002). This supports the c onclusion that the MRPO 2005 modeling shows more influence from emission changes that occur in those geograp hic regions. Boundary Waters and Voyageurs experience much less cont ribution to sulfate in 2018 than 2005. The MRPO 2005 case also shows less contribution to extinction from nitr ate in 2018 than 2005, which also supports the conclusion that the model is responding more to NO X reductions. The MRPO 2005 case has less contribution from point , mobile and ammonia source categories from Minnesota in 2018 than in 2005, as well. The MRPO 2005 case does not include the modifications made to nonEGU point source grow th, and it inadvertently excludes the mining operations at Northshore Mining-Silver Ba y, that are included in the Minnesota (MRPO) case. It is not possible to tell how the MRPO 2005 PSAT resu lts might change were these modifications made.95Boundary Waters:Extinction Contribution by Specie and Sector for Each Region on the 20 Percent Worst DaysFigures 8.5 and 8.7: 2018 Figures 8.6 and 8.8: 2018 minus 20020 1 2 3 4 5 6 7 8 9 10BCCanadaWestSouthEastNorthEastWisconsinTexasSouthDakotaOklahomaNorthDakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)PSO4PNO3PNH4Total Extinction:32 Mm-1-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5BCCanadaWestSouthEastNorthEastWisconsinTexasSouthDakotaOklahomaNorthDakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)PSO4PNO3PNH4Total ExtinctionDiff: -8.3 Mm-10 1 2 3 4 5 6 7 8 9 10BCCanadaWestSouthEastNorthEastWisconsinTexasSouthDakotaOklahomaNorthDakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)EGUnonEGUonroadnonrd_marothernh3BCTotal Extinction:32 Mm-1-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5BCCanadaWestSouthEastNorthEastWisconsinTexasSouthDakotaOklahomaNorthDakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)EGUnonEGUonroadnonrd_marothernh3BCTotal ExtinctionDiff: -8.3 Mm-196Voyageurs:Extinction Contribution by Specie and Sector for Each Regionon the 20 Percent Worst DaysFigures 8.9 and 8.11: 2018 Figures 8.10 and 8.12: 2018 minus 20020 1 2 3 4 5 6 7 8 9 10BCCanadaWestSouthEastNorthEastWisconsinTexasSouthDakotaOklahomaNorthDakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)PSO4PNO3PNH4Total Extinction:32 Mm-1-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5BCCanadaWestSouthEastNorthEastWisconsinTexasSouthDakotaOklahomaNorthDakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)PSO4PNO3PNH4Total ExtinctionDiff: - 4.7 Mm-10 1 2 3 4 5 6 7 8 9 10BCCanadaWestSouthEastNorthEastWisconsinTexasSouthDakotaOklahomaNorthDakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)EGUnonEGUonroadnonrd_marothernh3BCTotal Extinction:32 Mm-1-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5BCCanadaWestSouthEastNorthEastWisconsinTexasSouthDakotaOklahomaNorthDakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)EGUnonEGUonroadnonrd_marothernh3BCTotal ExtinctionDiff: - 4.7 Mm-197Figures 8.13 and 8.14 Boundary Waters2018 Extinction Contribution by Sector for eachSpecie & by Specie foreach Sector on the20 Percent Worst Days.0246810121416182022PSO4 PNO3 PNH4 ASOA BSOA

Extinction (Mm -1 )EGUnonEGUOnroadNonroadotherAreaAmmoniaBCASOABSOATotal Extinction:35 Mm-10123456789101112 EGU nonEGU Onroad Nonroad otherArea Ammonia BC ASOA BSOA

Extinction (Mm -1 )PSO4PNO3PNH4BSOAASOATotal Extinction:35 Mm-1Figures 8.15 and 8.16 Voyageurs2018 Extinction Contribution by Sector for eachSpecie & by Specie for each Sector on the20 Percent Worst Days.0246810121416182022PSO4 PNO3 PNH4 BSOA ASOA

Extinction (Mm -1 )EGUnonEGUOnroadNonroadotherAreaAmmoniaBCASOABSOATotal Extinction:34 Mm-10123456789101112 EGU nonEGU Onroad Nonroad otherArea Ammonia BC ASOA BSOA

Extinction (Mm -1 )PSO4PNO3PNH4BSOAASOATotal Extinction:34 Mm-198 8.2 Beyond On-the-Books Controls. The PSAT results provide guidance to what geog raphic regions and source categories to target for emission reductions in 2018 beyond those associ ated with on-the-books controls. While the model does not respond as well as envisioned to changes in NO X in the Minnesota (MRPO) case, it is sufficient for providing guidance on e xploring control measures, understanding its uncertainties. The response to changes in NO X emissions has been shown to rely on the amount of ammonia in the system. Sensitivity of the model response to improvements in ammonia emission forecasts (less ammonia in 2018 than projected in the Minnesota (MRPO) case) shows that visibility in the Class I areas will benefit with NO X emission reductions. Also, as ammonium sulfate levels decline du e to reductions in SO 2 emissions, more ammonia is free to react with NO X to form ammonium nitrate. Thus, reductions in NO X emissions should be explored as well as for SO 2 emissions.

The model response to NO X changes in the Minnesota (MRPO) case does not impact the source regions and categories on which to focus for additional controls. The model was also found to be sensitive to meteorology. When Boundary Waters and Voyageurs are more impacted by sources to the Southeast and East—as is the case using 2005 meteorology—the model responds better to emission changes in those regions; the RPG r eaches the glidepath. Use of 2002 meteorology in the Minnesota (MRPO) case may be preferred then, to explor e model response to emissions changes in other regions.

In developing the RH SIP, Minnesota decided to focus attention on Stat es that contribute 5 percent or more to either Boundary Waters or Voyageurs in 2018. The percentage breakdown contribution to ammonium nitrate and amm onium sulfate projected for 2018 by geographic region are shown in Figures 8.17 and 8.18. Minnesota contributes 28 percent (8.6 Mm -1 ), at Boundary Waters and 31 percent (9.9 Mm -1 ) at Voyageurs. The next largest individual state contributors at Boundary Waters are Wisconsin (10 percent, 3.2 Mm -1 ), Iowa (8 percent, 2.7 Mm -1 ), Illinois (6 percent, 2.0 Mm -1 ), Missouri (6 percent, 2.0 Mm -1 ) and North Dakota (6 percent, 2.0 Mm -1 ) 66 . The next largest indi vidual state contributors at Voyageurs are North Dakota (13 percent, 4.0 Mm -1 ) 48 , Iowa (7 percent, 2.4 Mm -1 ), Wisconsin (6 percent, 1.8 Mm -1 ) and Canada (5 percent, 1.7 Mm -1 ). Recall that Canada emissions in the Minnesota (MRPO) case remain constant between the base year and 2018.

66 The state “mask” files that tell the model how to assign geographic regions for PSAT to track (see Figure 8.1) were made such that about 65,000 tpy SO 2 and 25,000 tpy NO x from grid cells in Canada at the Canada/North Dakota border were inadvertently assigned to North Dakota. Thus, less contribution shou ld be attributed to North Dakota, and more to Canada. In 2018, the total U.S. to tal North Dakota point source emissions were 77,200 SO 2 and 70,500 NO x .99 Figure 8.17. State Contributions to Ammoni um Nitrate and Ammonium Sulfate Light Extinction at Boundary Waters for the Year 2018 after Implementati on of On-the-Books Controls

Illinois 6%

Iowa

8%

Wisconsin 10% Canada 3% AllOthers 22% BC 11%

Minnesota 28%

NorthDakota 6% Missouri 6% Boundary

Waters

Total Extinction of NH 4 NO 3 & (NH 4 ) 2 SO 4 :

32 Mm -1

Figure 8.18. State Contributions to Ammoni um Nitrate and Ammonium Sulfate Light Extinction at Voyageurs for the Year 2018 aft er Implementation of On-the-Books Controls

Illinois

3%

Minnesota 31%

Canada 5% AllOthers 16% BC 15% Iowa 7%

Wisconsin 6% NorthDakota 13% Missouri 4% Voyageurs

Total Extinction of NH 4 NO 3 & (NH 4 ) 2 SO 4 :

32 Mm -1

8.2.a Minnesota Controls. In order to determine where to focu s potential control strategies in Minnesota, PSAT model runs were conducted to better understand c ontributions of the Northeast part of Minnesota (near Boundary Waters and Voyageurs) where a significant amount of point source NO x and SO 2 is emitted, as compared to the contribution from the rest of the State. The size of Boundary Waters, 1.3 million acres stretchi ng nearly 150 miles along the international100 boundary adjacent to Canada’s Quetico Provincial Park, 67 and the proximity of large point source NO X and SO 2 emitters in Northeast Minnesota, compelled Minnesota to model the Minnesota (MRPO) case with a 12km flexi-nested grid and apply PiG treatment to point sources of interest. For the same reasons, Minnesota chose to evaluate contributi ons to various receptor points within Boundary Waters and Voyageurs in addition to the monitor location used in the establishment of reasonable progress goals. Minnesota also added a receptor point on Isle Royale, as the monitor for that Class I area is not physically located on the Island. Figure 8.19 shows the location of the receptors evaluated.

Figure 8.19. Receptor Location s for 12km Grid with PiG.

G F G F

G F G F G F G F G F G F G F

G F G F

G F G F

Minnesota Michigan Ontario

Boundary Waters Canoe Area Wilderness

I sle Royale N Voyageurs NP VOYA2

BOWA1 ISRO_1 BOWA_6

BOWA_1 VOYA_12

BOWA_36 BOWA_31 BOWA_29 BOWA_26

BOWA_13

The 12km PSAT results show that Northeast Mi nnesota contributes 14 percent and the rest-ofMinnesota contributes 12 percent of total ammonium nitrate and ammonium sulfate extinction at the Boundary Waters monitor loca tion. Northeast Minnesota contri butes 15 percent and the restof-Minnesota contributes 17 percent of total ex tinction at the Voyageur s monitor location.

At the receptors placed at various points thr oughout the Class I areas, Northeast Minnesota contributions range from 3 – 19 percent of total ex tinction. The 3 percent is at the western tip of Isle Royale, and the 19 percent c ontribution is at a receptor with in Boundary Waters. The restof-Minnesota contributions range from 9 – 17 percent. The 9 percent is at the western tip of Isle Royale and the 17 percent is at a receptor within Voyageurs. Figure 8.20 illustrates the extinction contribution be tween Northeast Minnesota and the rest-of-Minnesota relative to contributions from all other geographic areas. Figure 8.21 shows results at the receptor with the

67 www.bwcaw.org101 maximum (Boundary Waters, receptor 13) and the mi nimum (Western tip of Isle Royale) impact from Northeast Minnesota

Figure 8.20 Percentage Contribution of Northeas t and Rest of Minnesota to Boundary Waters (BOWA1) and Voyageurs (VOYA2) on the 20 Percent Worst Days

Boundary Waters: Total Extinction 30 Mm -1

AllOther

62% BC 12%

NE Minnesota 14% Rest of Minnesota 12%

MN Total: 26% Voyageurs: Total Extinction 30 Mm -1

AllOther

53% BC 15% Rest of Minnesota 17%

NE Minnesota 15% MN Total: 32%

Figure 8.21 Percentage Contribution of Nort heast and Rest-of-Minnesota to Maximum (BOWA_13) and Minimum (ISLE)R eceptors in the Class I Areas on the 20 Percent Worst Days

AllOther

57% BC 11% Rest of Minnesota 13%

NE Minnesota 19% MN Total: 32% Boundary Waters: Total Extinction 34 Mm -1 (BOWA_13) Isle Royale: Total Extinction 47 Mm -1 (ISLE_1)

AllOther

78% BC 10% Rest of Minnesota 9%

NE Minnesota 3% MN Total: 12%102 Furthermore, the PSAT results i ndicate sulfate as the major co mponent contributed by Northeast Minnesota point sources, while the rest of the stat e is more evenly divided between sulfate from point sources and ammonium fr om agricultural sources. Figu re 8.22 illustrates the species contributions across the Cla ss I areas. Emissions of NO X in Northeast Minnesota are much higher than SO 2 . Point source emissions of NO X in Northeast Minnesota are estimated at 37,500 tons per year, while SO 2 emissions are 8,000 tons per year.

One explanation for this disc repancy in contribution of SO 2 and NO X to the 20 percent worst days is that nitrate is an i ssue on fewer days and is formed in the colder months. Viewing animated spatial plots of the source apportionmen t results on days with high nitrate show that winds on several of the 20 percent worst days during this period appear to be coming from the West and Northwest of Minnesota. Thus, the NO X emissions are not moving North toward the Class I areas to form ammonium nitrate, but ar e moving to the East, S outheast and South.

Figure 8.22: Minnesota Contributions at R eceptors Placed throughout Boundary Waters,

Voyageurs and the tip of Isle Royale in 2018 by Species and Geographic Region on the 20 Percent Worst Days

The contribution of select indi vidual Minnesota point sources, listed in Table 8.8, was evaluated with PSAT. The sources identified as located in Northeast Minnesota were further evaluated with PiG due to their proximity to the Class I ar eas. In Northeast Minneso ta, the point sources in the list account for nearly all the SO 2 and NO X emissions in that part of the state. This is not the case for the individual point sources listed above for the rest-of-Minnesota. The Minnesota point sources listed above for the rest-of-Minnesota wher e chosen because they ar e some of the largest emitters in the State. The locations of the sources are shown in Figure 8.23. 0 1 2 3 4 5 6

SO4 NO3 NH4 SO4 NO3 NH4

NE Minnesota Rest of MinnesotaExtinction (Mm-1) lowerquartile minimum median maximum upperquartile

Rest MN Ext:
2.7 - 5.1 Mm -1 NE MN Ext:
1.6 - 6.5 Mm -1103 Table 8.8 Minnesota Point Sources Evaluated with PSAT.

Northeast Minnesota Other Minnesota Northeast Minnesota Other Minnesota Blandin Paper/Rapids Energy Austin Utilities - NE Power St Blandin Paper/Rapids Energy Austin Utilities - NE Power St Boise Cascade Corp - Intl Flint Hills Resources LP - Pine Bend Boise Cascade Corp - Intl Flint Hills Resources LP - Pine Bend Duluth Steam Cooperative Assoc International Paper - Sartell Duluth Steam Cooperative Assoc International Paper - Sartell EVTAC Mining - Fairlane Plant Marathon Ashland Petroleum LLC EVTAC Mining - Fairlane Plant Marathon Ashland Petroleum LLC Georgia-Pacific - Duluth Hardboard Otter Tail Power Co - Hoot Lake Georgia-Pacific - Duluth Hardboard Otter Tail Power Co - Hoot L ake Hibbing Public Utilities Commission Rochester Public Utilities Hibbing Public Utilities Commission Rochester Public Utilities Hibbing Taconite Co Xcel - Sherburne Generating Plant Hibbing Taconite Co Xcel - Sherburne Generating Plant Ispat Inland Mining Co Xcel Energy - Allen S King Ispat Inland Mining Co Xcel Energy - Allen S King Keewatin Taconite Operations Xcel Energy - Black Dog Keewatin Taconite Operations Xcel Energy - Black Dog Minnesota Power - Taconite Harbor Xcel Energy - High Bridge Minnesota Power - Taconite Harbor Xcel Energy - High Bridge Minnesota Power Inc - Boswell Xcel Energy - Riverside Minnesota Power Inc - Boswell Xcel Energy - Riverside Minnesota Power Inc - Laskin Minnesota Power Inc - Laskin Minnesota Power Inc - ML Hibbard Minnesota Power Inc - ML Hibbard Northshore Mining Co - Silver Bay Northshore Mining Co - Silver Bay Potlatch - Cook Potlatch - Cook Potlatch - Grand Rapids Potlatch - Grand Rapids Sappi Cloquet LLC Sappi Cloquet LLC US Steel Corp - Minntac US Steel Corp - Minntac Virginia Dept of Public Utilities Virginia Dept of Public Utilities East Mine projected Mesabi Nugget West Mine projected 2002 2018

* The Northeast Minnesota sources were modeled using PiG.

Overall, the point sources listed in Table 8.8 contribute a range of 2.5-6.7 Mm -1 in 2002 to 1.8- 6.3 Mm -1 in 2018 at all receptors. Figure 8.24 illustra tes the contribution for the base year and the future year for all the listed point sources , the Northeast Minnesota sources and the other Minnesota sources. Clearly the list of other-Minnesota point sources does not encompass the entire impact from the rest-of-Minnesota. Howe ver, the impact from the individual Northeast Minnesota sources listed in Table 8.8 does account for nearly all of the contribution from Northeast Minnesota.

Figures 8.25 through 8.28 illustrate the contribu tion break-out by individual point source modeled for both 2002 and 2018. Figures 8.25 and 8.26 show the contributions of the Other Minnesota point sources. Decreas es in individual point source contributions from 2002 to 2018 for Xcel Energy—A.S. King, —High Bridge and —R iverside plants are due to implementation of the Metropolitan Reduction Project (MERP).

Figures 8.27 and 8.28 illustrate the contribution break-out for the Northeast Minnesota point sources. Reduced contribution from Minnesota Power –Boswell, and –Taconite Harbor, are due to projects reflected in the IPM3.0 “will do” scen ario, described in Section 2.2. The IPM model projected increased emissions from Virginia Public Utilities and Hibbing Public Utilities. For both these facilities, IPM allowed emissions to ri se to 4 lbs/MMBtu permitted rate when actuals are less than 1 lb/MMBtu. Most of the other contributions from existi ng sources remained the same from the base year to the future year, wi th very slight increases in contribution from EVTAC startup of line 1, and also some EGAS pr edicted growth at non-mine facilities such as Boise Cascade. Additional contributions are a ssociated with the three new taconite mines.104 Figure 8.23 Locations of Individual Point sou rces evaluated with PSAT and Plume-in-Grid

VirgPU

RochPU Xcel-ASK

AustinPU MNPWR-TH

Xcel-SHER HibbingPU MNPWR-Las

Xcel-HiBr OtterHoot NShore-SB MNPWR-Bos

Xcel-Bdog Xcel-Rivr Eastmine

Westmine MessabiNug

EVTAC Ispat HibTac BoiseC

SappiC

MaraAsh Blandin Pot-Cook

IntlP-Sar MNPWR-Hib Pot-Grand

FlintH-PB Keetac Minntac

CPacificD DuluthStm

PSAT & Plume-in-Grid for Point Sources Plume-in-Grid Applied Electric Generating Units Non-Utility Non-Utility Added to 2018105 Figure 8.24 Minnesota Point Source Contributio ns to Nitrate, Sulfate and Ammonium Extinction at Receptors Placed Throughout Boundary Waters, Voyageurs and the Tip of Isle Royale in 2018 on the 20 Percent Worst Days

0 1 2 3 4 5 6 7 8

All MN 2002 NE MN 2002 Other MN 2002 All MN 2018 NE MN 2018 Other MN 2018Extinction (Mm-1) lowerquartile minimum median maximum upperquartile106 Figure 8.25 Other Minnesota Individua l Point Source Contributions in 2002 on the 20 Percent Worst Days

0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8

2AustinPUFlintH-PBIntlP-SarMaraAshOtterHootRochPUXcel-ASKXcel-BDogXcel-HiBrXcel-RivrXcel-SHERExtinction (Mm-1) lowerquartile minimum median maximum upperquartile

Other MN Point Total Extinction:
1.4 - 2.6 Mm -1

Figure 8.26 Other Minnesota Individua l Point Source Contributions in 2018 on the 20 Percent Worst Days

0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8

2AustinPUFlintH-PBIntlP-SarMaraAshOtterHootRochPUXcel-ASKXcel-BDogXcel-HiBrXcel-RivrXcel-SHERExtinction (Mm-1) lowerquartile minimum median maximum upperquartile

Other MN Point Total Extinction:
0.7 - 1.3 Mm -1107 Figure 8.27 Northeast Minnesota Indivi dual Point Source Contributions in 2002 on the 20 Percent Worst Days

0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8

2BlandinBoiseCDuluthStmEVTACGPacificDHibbingPUHibTacIspatKeeTacMinntacMNPWR-BosMNPWR-HibMNPWR-LasMNPWR-THNShore-SBPot-CookPot-GrandSappiCVirgPUExtinction (Mm-1) lowerquartile minimum median maximum upperquartile

NE MN Point Total Extinction:
1.2 - 4.4 Mm -1

Figure 8.28 Northeast Minnesota Indivi dual Point Source Contributions in 2018 on the 20 Percent Worst Days

0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8

2BlandinBoiseCDuluthStmEastMineEVTACGPacificDHibbingPUHibTacIspatKeeTacMesabiNugMinntacMNPWR-BosMNPWR-HibMNPWR-LasMNPWR-THNShore-SBPot-CookPot-GrandSappiCVirgPUWestMineExtinction (Mm-1) lowerquartile minimum median maximum upperquartile

NE MN Point Total Extinction:
1.1 - 5.2 Mm -1108 The above information supports consideration of a control strategy that focuses on point sources in Northeast Minnesota. Pla nning staff determined that a 30 percent reduction in SO 2 and NO X emissions from 2002 levels for the six countie s comprising “Northeast Minnesota” would be reasonable. The plan is coined the “Northeast Minnesota Plan”.

Northeast Minnesota Plan . In this plan, the six counties in northeast Minnesota would maintain a 30 percent reduction in NO x and SO 2 from 2002 emissions levels. About 21 percent of that reduction is already associated with the Mi nnesota Power—Boswell and –Taconite Harbor projects described above and includ ed in the on-the-books controls. In order to model this plan in the uniform rate of progress analysis, the re maining approximately 10 percent was applied to taconite industry sources. The emission reductions were based on permit limits, furnace modifications in 2006 and 2007, fuel switching, a new scrubber, newer rate information, and some reductions due to BART.

The CAMx input files for the Minnesota (MRPO) case were modified to reflect the 10 percent reduction from 2002 emission levels by applying factors to unit/stack specific emissions at the taconite facilities. Table 8.9 lists the affected facility, the affect ed stack, the factors applied and the resulting emissions. All these controls, ex cept those associated with BART, are in place.

Table 8.9 Northeast Minnesota Plan Reductions to Non-EGU Point Sources Facility

Name Facility ID Stack ID 2018 NO x Emissions NO x factor NE MN Plan Emissions 2018 SO 2 Emissions SO 2 Factor NE MN Plan Emissions Comments

US Steel Minntac 2713700005 SV103 2,195 0.81 1,778 324 1.00 324 PSD permit

limit SV118 2,521 0.81 2,042 380 1.00 380 SV127 2,015 0.81 1,632 380 1.00 380 SV144 2,557 0.81 2,071 380 1.00 380 SV151 1,767 0.81 1,431 343 1.00 343

Hibbing Taconite 2713700061 SV024 1,797 0.80 1,438 197 1.00 197 Furnace Mod. in ‘06/’07 SV028 1,682 0.80 1,345 185 1.00 185 SV029 1,916 0.80 1,533 211 1.00 211 Ispat Inland Mining 2713700062 SV017 3,254 0.70 2,278 155 1.00 155 Furnace Mod. in Fall ‘07

Keewatin Taconite 2713700063 SV030 6,049 0.70 4,234 464 1.00 464 Switched from NG to NG/coal b lend + New Scrubber EVTAC Mining – Fairlane

Plant 2713700113 SV049 1,764 0.86 1,517 3,222 0.41 1,321 ’05 Rate for 1 and 2,

BART on line 2 SV046 2,626 0.86 2,258 53 1.00 53

Including the changes to the Northeast Minnesota EGUs and the taconite adjustment factors, the overall percent reduction from Northeast Minne sota point sources is about 31 percent. A109 uniform progress analysis was developed for th is scenario, and Minneso ta chose the resulting deciview values at Boundary Waters (18.6 dv worst days, 6.5 dv best days) and at Voyageurs (18.9 dv worst days, 7.1 best days) for the RPG, shown in Figures 8.29 and 8.30.

Figure 8.29. RPG at Boundary Waters 36-km Minnesota (MRPO) with On-the-Books Controls + the Northeast Minnesota Plan

Uniform Rate of Progress - Boundary Waters, MN

3.4 18.6

11.6 19.9

6.4 6.5

0.0 5.0 10.0 15.0 20.0 25.0

1990 2000 2010 2020 2030 2040 2050 2060 2070

YearDeciviews

Figure 8.30. RPG at Voyageurs 36-km Minnesota (MRPO) with On-the-Books Controls + the Northeast Minnesota Plan

Uniform Rate of Progress - Voyageurs, MN

4.3 18.9

12.1 19.5

7.1 7.1

0.0 5.0 10.0 15.0 20.0 25.0

1990 2000 2010 2020 2030 2040 2050 2060 2070

YearDeciviews

Northshore Mining – Silver Bay. Other recent controls finalized after establishment of the RPG are those associated with the BART determina tion for the power boilers (EGUs) at Northshore110 Mining—Silver Bay. The controls are LNB on Power Boilers 1 & 2 (emission unit i.d. EU001 & EU002) to control NO X , and co-firing of biomass to disp lace coal on Power Boilers 1 & 2 to control SO 2 . These controls were not explicitly included in IPM, however, the IPM model projected low annual emissions for these units in 2018. Effectively, these controls are accounted for in the RPG in Figures 8.29 and 8.30.

Xcel Energy – Sherburne County . During the time controls were evaluated for the Minnesota (MRPO) case, other proposed control measur es were considered, including Xcel Energy—Sherburne County. Xcel energy had subm itted a potential project that involved retrofit SCRs, fabric filters and dry scrubbers on Units 1 and 2, and SCR on Unit 3, as shown in Table 8.10. However, Xcel Energy later withdrew the pr oposal for SCR on all three units due to cost concerns. Instead, they committed to low NO X burners (LNB) on Units 1, 2 and 3 68 . The new proposal (in Table 8.11) projects less SO 2 emitted than IPM base total emissions based on a change in projected fuel use. Th e LNB results in more projected NO X than the original proposal with SCR. Units 1 & 2 are subject to BART, an d the new proposal has since been determined to be BART. The RPG in Figures 8.29 and 8.30 does not reflect the controls in either proposal because the modeling was completed before th e project was finalized. LNB was installed 2008/2009; scrubber upgrade (retrof itting sparger tubes and instal ling limestone injection) is expected to occur upon EPA approval of the SIP. The original proposal was included in an analysis of potential controls explored for Minne sota and other significan t contributing States. That analysis is described further in Section 8.2.b.

Table 8.10. Xcel Energy –Sherburne Plant Original Proposal

Facility

Name Basis for Correction/Adjustment Unit # IPM base Total Emissions (Mton) Original Proposal Total Emissions (Mton) NO x SO 2 NO x SO 2 Xcel— Sherburne Co SCR+Fabric Filter+Dry Scrubber on Units 1 and 2; SCR on unit 3. 1 2 3 4.58 6.99 8.31 4.82 4.87

-- 2.78 2.80 3.55 3.05 3.08

--
Table 8.11. Xcel Energy –Sherburne Plant Final Proposal

Facility

Name Basis for Correction/Adjustment Unit # IPM base Total Emissions (Mton) New Proposal Total Emissions (Mton) NO x SO 2 NO x SO 2

Xcel— Sherburne Co LNB+Upgraded Venturi Scrubber and Lime Injection+Wet ESP on Units 1, 2; LNB+Fabric Filter+Dry Scrubber on Unit 3 1 2

3 4.58 6.99

8.31 4.82 4.87

-- 4.16 4.20

8.17 3.33 3.36

--

68 Letter to Anne Jackson (MPCA) from Jim Alders (Xcel Energy), “Request for Withdr awal in the Matter of the Other Environmental Improvements Plan at the Sherburne County and A.S. King Generating Plants”, MPUC Docket No. E002/M-08-739, November 24, 2008.111 8.2.b Other Contributing State Controls. Control measures thought to be potentially reasonable—based on initial cost estimates by CENR AP and initial four-factor analyses results from MRPO—were applied to the Minnesota (MRPO) case in order to evaluate what additional improvements to visibility might result. The regions and sour ce categories targeted are those believed to be contributing most to the 20 percent worst visibility days.

The potentially reasonable control measures are a 0.25 lb/MMBtu emission rate from Iowa, Missouri, North Dakota and Wisconsin. These rates were applied to EGUs in those states that did not already have additional controls in place. The MRPO assisted in developing this scenario by adjusting the IPM “will do” emissions used to establish the RPG in Figures 8.29 and 8.30. This emissions scenario also included the or iginal Xcel Energy—Sherburne County proposal described above, and emission re duction requests from MANE-VU for all five MRPO states (Illinois, Indiana, Michigan, Ohio and Wiscons in). The MANE-VU reduction request involves scaling EGU SO 2 emissions for non-scrubbed units on a list of 167 emission units by 0.10 (90% control), and scaling nonEGU point (and nonroad, MAR, and on-road) SO 2 emissions by 0.72.

In response to the Minnesota 0.25 lb/MMBtu emi ssion rate request from neighboring states and Missouri, MRPO applied the emissions ra te to the IPM3.0 “will do” by scaling SO 2 and NO X emissions for the top emitting power plants by 0.10 (90% control). In establishing an emissions target, MRPO used the emissions summary for each specified state fr om the “will do” and applied ratios to the emissions in the respective States modeling inventory to estimate a “target” value. Table 8.12 contains the state-wide ratios.

Table 8.12 Statewide Emissions Ratios for EGU IPM3.0 “will do” Scenario

State SO 2 (tons/year) SO 2 (lb/MMBtu) NO X (tons/year) NO X (lb/MMBtu) Statewide Ratio

SO 2 Statewide Ratio NO X Iowa 116,000 0.434 60,000 0.224 0.58 n/a Missouri 238,000 0.532 73,000 0.163 0.47 n/a North Dakota 56,200 0.328 58,900 0.343 0.76 0.73

Wisconsin 150,000 0.445 55,000 0.163 0.56 n/a

The ratios were applied to the largest emitti ng facilities using MRPO emissions-over-distance “Q/d” lists for 2002 emissions. MRPO de termined that 90% control (scaling SO 2 and NO X emissions by 0.10) would get close to the “targe t”. The facilities chos en, the targets, and emission reductions should 2018 “will do” emi ssions get scaled are shown in Table 8.13.112 Table 8.13 Adjustments Made to Contributing States to Reflect 0.25 lb/MMBtu Emissions Rate

State Pollutant Facility Name 2018 Total (tons/day) Reduction (tons/day) New 2018 Total (tons/day)

Iowa SO 2 George Neal North Ottumwa George Neal South 60.28

48.97 41.15 -54.25

-44.07 -37.04 6.03

4.90 4.12

Missouri SO 2 Labadie AE Hill Rush Island Meramac 153.51 70.73 76.63 49.06 -138.16 -63.66 -68.97 -44.16 15.35 7.07 7.66 4.91

North Dakota SO 2 Antelope Valley 38.91 -35.02 3.89

NO X Antelope Valley Coal Creek 36.58 23.85 -33.28 -20.80 3.30 3.05

Wisconsin SO 2 Dairyland Stoneman Alliant WP & L Alliant 37.22 17.58 78.39 44.87 -13.94 -15.82 -71.30 -40.39 23.28 1.76 7.10 4.49

Table 8.14 summarizes the resul ting 2018 emissions projections fo r point sources associated with the potentially reasonabl e control measures and the emissions change from the 2018 Minnesota (MRPO) emissions projections used for establ ishing the reasonable progress goal. The emission reductions associated w ith Minnesota point sources in Ta ble 8.14 reflects the original Xcel Energy—Sherburne County proposal.

Table 8.14 Annual 2018 Potentially Reasonable Control Measures Emissions for Point Sources in Tons

Region Potentially Reasonable Control Measure Emission (ton/year) Reduction from 2018 RPG Emissions (ton/year) Reduction from 2018 RPG Emissions (percent)

SO 2 NO X SO 2 NO X SO 2 NO X Iowa 66,700 58,500 -47,200 0 -41% Minnesota 51,000 38,200 0 0 Missouri 127,000 72,700 -2,980 -10,700 -1% North Dakota 46,000 39,900 -1,900 -6,550 -3% -11% Wisconsin 96,300 51,900 -111,000 0 -77%113 The resulting position on the glidepath at Boundar y Waters and Voyageurs, due to the emissions reductions summarized above, is shown in Figur es 8.31 and 8.32. The results show an additional 0.3 dv reduction at Boundary Waters and an additi onal 0.2 dv reduction at Voyageurs than in the RPG (see Figures 8.29 and 8.30), but still above the glidepath. As mentioned above, these results are not the RPG because none of the control measur es from other states, nor the original Xcel Energy—Sherburne County proposal, have been de termined reasonable at the time of RH SIP development.

Figure 8.31. Boundary Waters 36-km Minnesota (MRPO) Position on Glide Path with “Potentially Reasonable” Controls.

Uniform Rate of Progress - Boundary Waters, MN

3.4 18.3 19.9

11.6

6.4 6.5

0.0 5.0 10.0 15.0 20.0 25.0

1990 2000 2010 2020 2030 2040 2050 2060 2070

YearDeciviews

Figure 8.32. Voyageurs 36-km Minnesota (MRPO) Position on Glide Path with “Potentially Reasonable” Controls.

Uniform Rate of Progress, Voyageurs MN

4.3 18.7

7.1 19.5

12.1

7.1

0.0 5.0 10.0 15.0 20.0 25.0

1990 2000 2010 2020 2030 2040 2050 2060 2070

YearDeciviews114 8.3 Canadian Impacts and Control Strategies. Many approaches have been proposed on how to quantify international im pacts, specifically Canada’s contribution to visibi lity in the Minnesota Class I areas. Many of those proposed include developing alternate glidepaths, assuming Canada emissions are zero. These assumptions are problematic because Canada emi ssions would still be included in the baseline observed values. Some have attempted to estima te how much Canada emissions might be in the baseline and try to remove it. Manipulating the observed values into something that is no longer the observed creates additional problems. Minne sota has avoided taking these approaches.

Including Canada emissions in the modeling has the advantage of setting the Minnesota RPG in relation to an observed baseline that includes Canada contributions to visibility. Keeping the emissions constant between the base year and th e future year establishe s a reasonable progress goal based on known, publicly available informa tion from the United States. There is enough uncertainty in the Canada emissions that in cluding their emissions changes from 2002 to 2018 could provide misleading results. There is enough compelling evidence from the PSAT results to show that Canada has a significant enough imp act on visibility that co llaboration with Canada on emissions and modeling could help bette r assess the RPG at Boundary Waters and Voyageurs. MRPO has begun such collaboration with Ontario; but it ap pears that Canadian impact at Boundary Waters and Voyageurs also comes from the more western provinces of Manitoba, Saskatchewan and Alberta, so addition al collaboration would be useful from those provinces as well. As this woul d entail international relations, it is prudent for the EPA to lead this effort.



### IV. Data Access

All data files used to support this TSD and th e accompanying SIP are archived at the Minnesota offices and that provision has been made to ma intain them. The MRPO and CENRAP maintain their own files for their work. The Minnesota (MRPO) files are generated and read on a Linux operating platform. Model output are processed with a series of Fortran programs invoked by Cshell scripts. To obtain files used in the an alyses contact Margaret McCourtney at 651-757-2558 or margaret.mccourtney@pca.state.mn.us.115 

### V. References

Baker, Kirk, (undated) “CAMx Model Performan ce for Three Annual Applications (2001-2003) Supporting Regional Haze and PM2.5 Regulations”

Baker, Kirk (June 2004). “Processing and Quality Assura nce of Emissions Files for Photochemical Model Input”

Baker, Kirk (December 2004) “Meteorological Modeling Protocol for Application to PM 2.5 /Haze/Ozone Modeling Projects”

Baker, Kirk, and Matthew Johnson, Steven King, Wusheng Ji (February 2005), “Meteorological Modeling Performance Summary for Application to PM 2.5 /Haze/Ozone Modeling Projects”

Baker, Kirk (October 2007) “Modeling Prot ocol: 2002 Basecase Technical Details”

Battye, William and Jeff Harris, EC/R Incorporat ed (February 2005) “Improving Modeling Inventory Data: Speciation Profiles”, Draft Technical Report

Boundary Waters Canoe Area Wilderness. http://www.bwcaw.org

Boylan, J.W. and Russell, A.G. (2006) PM and Ligh t Extinction Model Performance Metrics, Goals, and Criteria for Three-Dimensional Air Quality Mode ls; Atmos. Environ., Volume 40, pp. 4946-4959.

Boyer, K., Battye, W., Fudge, S., and Barrows, R. (September 2004). “Fire Emissions Inventory Development for the Midwest Regional Planni ng Organization: Final Report” EC/R, Inc.

CENRAP, Modeling Emissions Inventory Summ ary. http://www.cenrap.org/projects.asp

Copeland, Scott (April 2008) “Calculation Method for Natural Conditions with the New IMPROVE algorithm” Air and Waste Management Associati on specialty conference “Aerosol and Atmospheric Optics: Visual Air Quality and Radiation”, Moab, Utah.

EC/R Incorporated (November 2007), “Interim White Paper – Midwest RPO Candidate Control Measures”

E.H Pechan (September 2004) “MRPO Nonroad Emissi ons Inventory Project – Development of Local Data for Construction and Agricu ltural Equipment”, Final Report

E.H. Pechan (October 2004) “Development of Growth and Control Factors for Lake Michigan Air Directors Consortium”

E.H. Pechan (December 2005) “Development of Update d Growth and Control Factors for Lake Michigan Air Directors Consortium”

ENVIRON Corp (September 2006). “CAMx User’s Guide, Version 4.40”

ENVIRON Corp (September 2007) “Technical Support Document for CENRAP Emissions and Air Quality Modeling to Support Regional Haze State Implementation Plans”116 EPA (January 2004), “National Emissions Inventory—Ammonia Em issions from Animal Husbandry Operations, Draft Report”

EPA (April 2007) “Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 and Regional Haze” Hand, et al. (March 2006) Hand, J.L, W.C. Malm , “Review of the IMPROVE Equation for Estimating Ambient Light Extinction Coefficients”

Janssen, M., Hua, C., (1999), “Emissions Modeling System-95 User’s Guide”

Johnson, Matthew (2007) “Meteorological Model Performance Evaluation of an Annual 2002 MM5 (version 3.6.3) Simulation”

Jones, Donna Lee, EC/R Incorporated (February 20 05) “Improving Modeling Inventory Data: Temporal Profiles”, Final Technical Report

Kenski, Donna, (March 2007, revised June 2007) “Impact of Missing Data on Worst Days at Midwest Northern Class I Areas”

Lindhjem, Chris, ENVIRON Corp (December 2004), “MRPO Nonroad Emissions Inventory Project for Locomotive, Commercial Marine, and R ecreational Marine Emission Sources”

MACTEC (January 2006) “Documentation for MAC TEC NonEGU ‘on-the-books’ Control Factor File”

Malm, William C.(April 2000), “Introduction to Visibility”

MRPO (June 2004), “Processing and Quality Assurance of Emissions Files for Photochemical Model Input”

MRPO (April 2005), “BaseI/Round 1 Modeling Inventory”

MRPO (April 2005), “Development of an Improved Process Based Ammonia Emission Model for Agricultural Sources”

MRPO (November 2005), “BaseJ/Round 3 Strategy Modeling: Emissions”

MRPO (May 2006), “BaseK/Round 4 Strategy Modeling: Emissions”

MRPO (April 2007), “Estimating Reasonable Progress Goals for the Northern Class I areas: Treatment of Organic Carbon”

MRPO (June 2007) “Impact of Missing Data on Worst Days at Midwest Northern Class I Areas”, March 12, 2007 (revised June, 19, 2007).

MRPO (April 2008), “Regional Air Quality Analyses for Ozone, PM 2.5 , and Regional Haze: Final Technical Support Document”

Pitchford (1994), Pitchford, Marc L., Malm, William C. “Development and Application of a Standard Visual Index. Atmospheric Environment”, Volume 28: pp. 1049-1054.

VIEWS. http://vista.cira.colostate.edu/views/117 

### VI. Glossary

• 20 Percent Best Days – The 20% of days with the best visibility during each year in the baseline period.

• 20 Percent Worst Days – The 20% of days with the worst visibility during each year in the baseline period.

• Ainsworth Engineered (USA) LLC – formerly Potlatch Corp.

• Air Parcel – a volume of air that tends to be transpor ted about the earth as an intact entity and can be tracked.

• Anthropogenic – Caused by humans (i.e. pollutant em issions from industrial processes, automobiles and trucks).

• Apportionment – Proportional distribut ion or allocation.

• ArcelorMittal Minorca Mine Inc . – formerly Ispat Inland Inc.

• Area Source – an emisions source that are not iden tified by locational c oordinate and stack parameters (i.e. agricultural ope rations, residential heating). Emissions are assigned to a grid cell.

• Backtrajectory – Tracking backward in time from wher e air parcels arriving at a particular destination of interest came.

• BART – Best Available Retrofit Technology – Regional Haze rule (70 FR 39104-39172) required controls on certain types of sour ces built between 1962 – 1977 and generally grandfathered under most Clean Air Act programs.

• BaseC – 2002 emissions inventory de veloped by CENRAP prior to Base G. BaseC is the version of the CENRAP inventor y used in Minnesota modeling.

• BaseF – 2002 emissions inventory developed by CENRAP, final sour ce contribution modeling with CAMx and model performance ev aluation was conducted with this version of the CENRAP case; not BaseG.

• BaseG –2002 emissions inventory developed by CE NRAP. BaseG is the final CENRAP emissions inventory for establishing reasona ble progress goals for regional haze SIPs.

• BaseK – 2002 emissions inventory developed by MRPO which is the backbone for the Minnesota (MRPO) case. BaseK is the final 2002 MRPO emissions inventory for source contribution modeling, performance evaluation and for establishing reasonable progress goals.

• BaseM – 2005 emissions inventory developed by MRPO. BaseM is the final 2005 MRPO emissions inventory used for source contribu tion modeling, performance evaluation and for establishing reasonable progress goals.

• Baseline Conditions – Average visibility conditions (in d eciviews) that exist for each Class I area over the 20 percent best and 20 percent worst days in the 5- year baseline period.

• Baseline Period – The years 2000 – 2004, as prescr ibed by the Regional Haze Rule.

• Base Year – The individual years comprising the Baselin e Period. It also refers to the year modeled to establish the RPG.

• BC – Air concentrations coming into the domain from the East, West, North, South and above (i.e. intrusion of strato spheric ozone) of the domain.

• B ext – Extinction Coefficient. Provides a direct , but non-linear, measure of the correlation between air concentrations of visibility impairing pollutants and visibility conditions.

• Biogenic – Caused by natural processes (i.e. em issions from respiration of trees).118 • Biomass – Co-firing biomass with existing fuel.

• Bkwh – Billion kilowatt hours.

• CAIR – Clean Air Interstate Rule – A federal rule establishing a cap and trade program covering 28 Eastern States to reduce emissions of NO X and SO 2 primarily from power plants. Initially finalized March 2005, vacated by the D. C. Circuit Court of Appeals July 2008, and remanded to EPA December 2008. EPA proposed to stay CAIR in Minnesota January 2009.

• CAMx – Comprehensive Air quality Model with exte nsions - an Eularian air quality grid model that simulates atmoshperic and surface pr ocesses affecting the transport, chemical transformation and deposition of air pollutant s and their precursors; a contemporary of CMAQ. Used by MRPO and Minnesota to esta blish reasonable progress goals, and for source apportionment by these and ot her organizations (i.e. CENRAP).

• CASTnet – Clean Air Status and Trends Networ k. A regional long-term monitoring program established in 1987 and administer ed and operated by EPA’s Clean Air Markets Division and collects deposition data from air pollution. The National Park Service sponsors 27 monitor sites located in Class I areas.

• CENRAP – Central Regional Air Planning Associ ation – Regional planning organization covering the central portion of the U.S, including states and tribal areas of Nebraska, Kansas, Oklahoma, Texas, Minnesota, Iowa, Missouri, Arkansas, and Louisiana.

• CENRAP Case – 2002 BaseG inventory developed by CENR AP for use in establishing the 2018 reasonable progress goals for regional haze SIPs. See BaseG.

• CIRA – Cooperative Institute for Research in the Atmosphere – Research institute contracted by the National Park Service to work on visi bility information, including IMPROVE and VIEWS.

• Class I Area – Areas of special national or region al value, whether natural, scenic, recreational or historic, for which the Clean Air Act provide s special protection. Mandatory Class I areas are managed either by the Forest Se rvice, the National Park Service or Fish and Wildlife Service.

• CMAQ – Community Multiscale Air Quality model - an Eularian air quali ty grid model that simulates atmoshperic and surface processes aff ecting the transport, chemical transformation and deposition of air pollutants and their pr ecursors; a contemporary of CAMx. An EPA supported moded used to support CAIR, and us ed regional planning organizations (i.e. CENRAP) to establish r easonable progress goals.

• Coarse Particulate Mass – Particulate mass with a diamet er between 2.5 and 10 microns, between PM 2.5 – PM 10
.

• CONCEPT – CONsolidated Community Emissions Processing Tool. A model that calculates air pollutant emi ssion inventories generating hour ly speciated emissions on a gridded basis for input to an air quality m odel. A evolution of the EMS-2003 model, but with open source software and multiple levels of quality control.

• Deciview – A standard visual index defined in te rms of the extinction coefficient that is linear with perceived changes in visibility. On e to two deciviews is the smallest change in visibility that is percep tive to the human eye.

• Extinction – Attenuation of light due to scattering and absorption as it en counters a particle.

• Extinction Coefficient – See B ext .

• Dv – see Deciview.119 • EDAS – Eta Data Assimilation System – the data assimilation system associated with the Eta mesoscale weather forecast model.

• EGU – Electric Generating Unit. Any device that combusts soli d, liquid, or gaseous fuel for the purpose of producing electricity for sale or for use onsite.

• EC – Elemental Carbon

• EMS – Emissions Modeling System – A model that calculates air pollutant emission inventories generating hourly speciated emissions on a gridde d basis for input to an air quality model. The most current ve rsion of EMS available is EMS-2003.

• EPA – United States Environmental Protection Agency.

• EVTAC – Currently United Taconite LLC (UTAC).

• FGD – Fabric Filter/Flue Gas Desulfurizat ion. A scrubber device to control SO 2 that uses an alkaline reagent to absorb and react to produce a solid compound. Typically applied to stationary coal- and oil-fired combustion units (i.e. utility an d inductrial boilers)

• FIPS – Federal Information Processing Standards, a standard set of numeric codes issued by the National Insittute of Standars and Tec hnology to ensure uniform identification of geographic entities throughout all federal government agencies.

• FSI – Furnace Sorbent Injection. A control technology that reduces SO 2 emissions by injecting a dry sorbent into th e upper part of the furnace, wher e the sorbent reacts to form salts that are then removed by a fabric filter or electrostatic precipitator. This technology can reduce SO 2 by varying amounts depending on the applica tion. In applications in Minnesota within the SIP, this technology can reduce SO 2 by 40%.

• Glidepath – another term for the Uniform Rate of Progress. See URP.

• GREASD PiG – An option in CAMx designed to treat the early chemical evolution of large NO X point source plumes before oxidant producti on becomes important to facilitate source apportionment of particulate matter.

• HYSPLIT4 - HYbrid Single-Particle Lagrangian Integrated Trajectory – Model for computing simple air parcel trajectories.

• IC –Initial conditions are the input s at the start-up of the model run and should not appear in the results if the model run account s for appropriate spin-up time.

• Integrated Planning Model – a model developed by ICF that EPA uses to evaluate future impact of pollution control policies on EGUs in combination with pr ojected energy needs.

• IPM3.0 – See Integrated Pla nning Model (version 3.0)

• IMPROVE – Interagency Monitoring of Protected Visual Environments – Cooperative program to monitor visibili ty in the Class I areas.

• Ispat Inland Inc – Currently ArcelorMittal Minorca Mine Inc.

• LADCO – Lake Michigan Air Directors Consortiu m – Air quality planning organization for Illinois, Indiana, Michigan, Oh io, and Wisconsin; conducts air quality technical assessments for and assists its member States on air quality issues.

• Lb/MMBtu – Pounds per million British thermal units , the measurement of heat created by burning any material

• LNB – Low NO X Burner. A control t echnology that reduces NO X by controlling fuel and air mixing in the burner, reducing peak flame temp erature. Over-fire air (OFA) and SNCR can be added to LNB to increase NO X removal efficiency. Depending on the application in Minnesota within the SIP, LNB/OFA can reduce NO X by 40-50%.120 • MAR – Marine (i.e. ore boats on Lake Superior), aircraft and recreational (i.e. snowmobiles) vehicles. See Nonroad.

• Minnesota (MRPO) Case – Minnesota modified MRPO 2002 Ca se (BaseK) inventory for use in source contribution mode ling, performance evaluation and for the 2018 reasonable progress goals at Boundary Waters and Voyage urs for Minnesota’s regional haze SIP.

• MM -1 – Inverse megameters. The un its for extinction coefficient.

• MM5 – Mesoscale Meteorological Model – A numerical model for weather prediction from one kilometer to continental scales, develope d by Pennsylvania State University/National Center for Atmospheric Research.

• MRPO – Midwest Regional Planning Organiza tion – Regional planning organization for regional haze, covering the Midwest states (Illinois, Indiana, Michigan, Ohio, and Wisconsin). See LADCO.

• MRPO 2002 Case – The 2002 inventory developed by MR PO for use in establishing the 2018 reasonable progress goals for regional haze SIPs. See BaseK. Serves as the backbone of the Minnesota (MRPO) Case.

• MRPO 2005 Case – The 2005 inventory developed by MR PO for use in establishing the 2018 reasonable progress goals for regional haze SIPs. See BaseM.

• Mton – Megaton

• μg/m 3 – micrograms (or microns) per cubic meter

• Natural Conditions – Estimation of visibility in th e absence of man-made influence.

• NEI – National Emission Inventory – Compilation of annual emissions by pollutant, source category by County for each State; EPA requires Stat es to submit to EPA on a 3-year cycle.

• NH 3 – Ammonia

• NH 4 – Ammonium ion

• NO 3 – Nitrate ion

• NO X – Nitrogen oxides

• Nonattainment – Areas of the country designated by EP A where pollutant levels exceed national ambient air standards.

• NonEGU – an industrial point source th at is not an EGU. See EGU.

• Nonroad – mobile equipment not traveling on ro adways (i.e. recreational vehicles, construction and agricultural equipment). Mari ne vessels, airplanes and locomotives are also considered nonroad sources, however, emission estimation techniques vary from those of other nonroad equipment. Emissi ons are assigned to grid cells

• NO X – Nitrogen Oxides. The sum of nitric oxide and nitrogen dioxide.

• OC – Organic Carbon

• Onroad – mobile sources, automobiles and trucks that travel on paved roadways. Emissions are assigned to a grid cell

• PiG – Plume in Grid, a module w ithin CAMx to treat the earl y dispersion and chemistry of point source plumes.

• Plume-in-Grid – See PiG

• PM 2.5 – Fine particulate mass with a diameter less than or equal to 2.5 microns.

• PM 10 – Particulate mass with a diameter less than or equal to 10 microns.

• Point Sources – industrial sources that are identifie d by locational coordinate and stack parameters (i.e. facilities with state permits). Emissions are assigned to a grid cell based on121 the locational coordinate of the stacks, excep t in cases where PiG is applied (emissions directly released from the stack).

• Potlatch Corp – currently Ainsworth Engineered (USA) LLC.

• PSAT – Particulate Matter Source Apportionment Technology, a tool within CAMx that tracks the original source of particulate sp ecies by geographic regi on and source category.

• PSD – Prevention of Significant Deterioration; a program established by the Clean Air Act that limits the amount of additiona l air pollution that is allowed in Class I and Class II areas.

• Rayleigh Scattering – The scattering of light by partic les smaller than the wavelength of light that is scattered.

• Reasonable Progress Goal – State established interim goa ls, expressed in deciviews, representing incremental visibility improvement over time toward the ultimate goal of natural conditions.

• Regional Haze – A cloud of aerosols extending up to hundreds of miles across a region impairing visibility.

• Relative Response Factor – the ratio of the future year PM 2.5 air concentrations to the base year concentrations predicted near a monitor location and averaged over the 20 percent best or 20 percent worst days.

• RH – Relative Humidity

• ROFA – Rotating Opposed Fire Air System. A boos ted injection of ove r-fire air in the boiler or furnace to reduce NO X . Flue gas mixes with added air in the presence of heat, improving particle burnout in the upper furnace therefore reducing NO X . Typically applied to coal fired power plants. ROFA can reduce NO X about 50%.

• RPG – See Reasonable Progress Goal

• RPO – Regional Planning Organization

• RRF – See Relative Response Factor

• SCC - Source Classification Code – Code used by EPA to classify different types of anthropogenic emission activities.

• SCR – Selective Catalyic Reduction. Flue gases are direceted through a reaction unit containing a catalyst while ammonia is injected into the inlet gas stream. The ammonia reacts with NO X in the presence of oxygen to form nitrogen and water. This technology can achieve from 70-90% control of NO X depending on the application.

• SIC – Standard Industrial Classification codes are standardized codes that indicate a company’s type of business.

• SIP – State Implementation Plan

• SMOKE – Sparse Matrix Object Kernel Emissi on – EPA supported model to process and prepare emission data for air quality model input.

• SNCR – Selective Non-Catalytic Reduction. An add-on technology to control NO X . When applied to boilers, ammonia is injected into the furnace abov e the combustion zone, where it reacts with NO X to reduce it to N 2 and water. In applications in Minnesota within the SIP, this technology can reduce NO X by 40%.

• SO 2 – sulfur dioxide.

• SO 4 – sulfate ion.

• STN – Speciation Trends Network. EPA mon itoring network that began in 1999 and provides speciated, urban fine particle data.

• TPY – Tons per year122 • TSD – Technical Support Document

• URP – Uniform Rate of Progress – The linear ra te of visibility improvement from the 2000- 2004 baseline period to natural conditions in 2064 at each Class I area.

• UTAC – United Taconite LLC, formerly EVTAC.

• UTM – Universal Transverse Mercator

• VIEWS – Visibility Information Exchange Web System – Web repository of visibility information. http://vista.cira.colostate.edu/views/

• VISTAS – Visibility Improvement States and Tribal Association of the Southeast. The RPO consisting of the States and Trib al areas within Alabama, Tennesee, Virginia, West Virginia, Georgia, Kentucky, Florida, Mississippi, North Carolina and South Carolina.

• VOCs – Volatile Organic Compounds.

• WRAP – Western Regional Air Partnership. The RPO consisting of the States and Tribal areas within Arizona, California, Colorado, Idaho, Montana, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington and Wyoming.