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 

651-757-2558 
2

Contents 

Page 

I. Introduction 8 

 I.a. Establishment of RPGs 8 

 I.b. Determination of future Visibility—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 Technical 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 PM2.5 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 98 
3

 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 Changes 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 Protocol and Performance Evaluation 

Appendix E: MRPO Air Quality Modeling Protocol 

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

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

Appendix H: Particulate Source Apportionment Results for CENRAP and MRPO Cases 

 

Tables 

 Page 

Introduction/ 

Table I.2 Annual Emissions in Tons for the Significant Contributing 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 Minnesota and Surrounding States 

 and Surrounding States 

Table 2.3 Annual Ammonia Emissions in Tons in the 33 

 MRPO 2002 and MRPO 2005 cases 
4

Table 2.4 Taconite Growth Factors from 2002 to 2018 Used by MRPO and 34 

 CENRAP 

Table 2.5 Minnesota Adjustments 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 Minnesota 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 Assumptions 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 Total 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 Criteria 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 Source Emissions in 2005 Inventory— 74 

 Domain Differences 

Table 7.1 Monthly fS(RH) and fL(RH) Values for Boundary Waters and 81 

 Voyageurs 

Table 8.1 Uniform Rate of Progress Analysis for 20% Worst Days with 85 

 On-the-Books Controls 

Table 8.2 Uniform Rate of Progress Analysis for 20% Best Days with 85 

 On-the-Books Controls 

Table 8.3 Relative Response Factors for 20% Worst Days with 86 

 On-the-Books Controls 

Table 8.4 Relative Response Factors for 20% Best Days with 86 

 On-the-Books Controls 

Table 8.5 Winter Day NOX and NH3 Emissions in Tons from 87 

 Base Year 2002 for CENRAP and Minnesota(MRPO)

Table 8.6 Winter Day NOX and NH3 Emissions in Tons from 2018, and 87 

 Difference from 2002 to 2018 for CENRAP and Minnesota(MRPO)
5

Table 8.7 Minnesota Annual Emissions Change in Tons of NOX and NH3 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 Reductions to Non-EGU Point Sources 108 

Table 8.10 Xcel Energy—Sherburne Plant Original Proposal 110 

Table 8.11 Xcel Energy—Sherburne Plant Final Proposal 110 

Table 8.12 Statewide Emissions Ratios for EGU IPM3.0 “will do” Scenario 111 

Table 8.13 Adjustments Made to Contributing States to Reflect 0.25 lb/MMBtu 112 

 Emissions Rate 

Table 8.14 Annual 2018 Potentially Reasonable 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 Ammonium Nitrate and Ammonium Sulfate 13 

 Light Extinction at Boundary Waters and Voyageurs for the Year 2002 

Figure I.4 State Contributions to Ammonium Nitrate and Ammonium Sulfate 14 

 Light Extinction at Boundary Waters 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 Demonstrating Attainment of Air Quality 23 

 Goals for Regional Haze 

Section III/ 

Figures 2.1—2.4 Annual 2002 SO2, NOX, NH3 and Biogenic VOC 30 

 Emissions in Tons for Minnesota(MRPO) Case 

Figures 2.5—2.7 Annual 2018 SO2, NOX, NH3 Emissions in Tons for 46 

 Minnesota(MRPO) Case. 

Figure 3.1 National Inter-RPO Domain and MRPO 4rpos Domain 49 

Figure 3.2 Estimated Seasonal Distribution 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 Day Ammonia Emissions in Tons for 55 

 Minnesota(MRPO) Case 

Figure 3.4 2002 Winter Day and Summer Day Ammonia Emissions in Tons for 55 

 CENRAP Case 

Figure 3.5 2002 Winter Day and Summer Day NOX Emissions in Tons for 56 

 Minnesota(MRPO) Case 
6

Figure 3.6 2002 Winter Day and Summer Day NOX Emissions in Tons for 56 

 CENRAP Case. 

Figure 3.7 2002 Winter Day and Summer Day SO2 Emissions in Tons for 57 

 Minnesota(MRPO) Case 

Figure 3.8 2002 Winter Day and Summer Day SO2 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 Modeled 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 Day NOx Emissions in Tons 88 

Figure 8.4 Geographic Regions Modeled with PSAT in Minnesota(MRPO) Case 91 

Figures 8.5—8.8 Boundary Waters Extinction Contribution by Specie and 95 

 Sector for Each Region on the 20 Percent Worst Days 

Figures 8.9—8.12 Voyageurs Extinction Contribution 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 Specie for each Sector on the 20 Percent 

 Worst Days 

Figures 8.15 and 8.16 Voyageurs 2018 Extinction Contribution by Sector for each 97 

 Specie & by Specie for each Sector on the 20 Percent Worst Days 

Figure 8.17 State Contributions to Ammonium 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 Ammonium 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 Contribution of Northeast and Rest of Minnesota to 101 
7

 Boundary Waters (BOWA1) and Voyageurs (VOYA2) on the 

 20 Percent Worst Days 

Figure 8.21 Percentage Contribution 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 Point Sources Evaluated with PSAT and 104 

 Plume-in-Grid 

Figure 8.24 Minnesota Point Source Contributions to Nitrate, Sulfate 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 Individual Point Source Contributions 107 

 on the 20 Percent Worst Days for 2002 

Figure 8.28 Northeast Minnesota Individual 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 Source Emissions in Tons in Minnesota(MRPO) Case 

for Minnesota by Stack 

Appendix B: Post Emissions Modeling File Changes 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 Protocol and Performance Evaluation 

Appendix E: MRPO Air Quality Modeling Protocol 

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

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

Appendix H: Particulate Source Apportionment Results for CENRAP and MRPO Cases 

 
8

I. Introduction 

This document details the modeling analyses conducted to support the policy 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 causing pollutant emissions levels needed, and 

reductions needed from individual states, to meet the RPG; 

• Calculation of the resulting degree of visibility improvement that would be achieved at 

each Class I area; 

• Comparison of visibility improvement between 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 “establish goals that provide for reasonable progress 

toward achieving natural visibility conditions for each Class I area within a State” 1; improving 

visibility on the most impaired days and not degrading visibility on the least impaired days. 

Minnesota has two federal Class I areas, the Boundary Waters Canoe Area Wilderness 

(Boundary Waters) and Voyageurs National Park (Voyageurs). 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 document is on Boundary Waters and Voyageurs. This 

document shows that Minnesota has the greatest visibility impact on these two Class I areas. 

Any future emissions reductions in the State made to improve visibility in Boundary Waters and 

Voyageurs should have a commensurate affect on any other Class I areas impacted by 

Minnesota. 

 

1

 40 CFR 51.308(d)(1). 
9

Figure I.1. Minnesota Class I Areas—Voyageurs and Boundary Waters—and Michigan 

Class I Area—Isle Royale. 

Minnesota

Wisconsin

Iowa

South Dakota

North Dakota

Michigan

Manitoba Ontario

Boundary Waters 

Canoe Area Wilderness

Isle Royale N

Voyageurs NP

The core of the visibility assessment is the baseline and natural visibility conditions based on 

observed data collected at Interagency Monitoring of Protected Visual Environments 

(IMPROVE) monitors, made available through the Visibility 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 2000-20043

, 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—visibility 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 natural visibility conditions in 2064. Reasonable progress goals are 

established as interim goals representing progress toward that end. 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 (PM2.5) are primarily responsible for impaired 

visibility.4 PM2.5 is composed of several pollutant species; nitrate, sulfate, organic carbon, 

elemental carbon, fine soil, sea salt (which is negligible in Minnesota Class 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 included in the visibility 

equation, but—as we show in this document—is an insignificant component of visibility 

impairment at Boundary Waters and Voyageurs. 

We calculate visibility using the individual components described above in the IMPROVE 

algorithm adopted by the IMPROVE Steering Committee in December 20055

. Details on the 

equation and its use for developing the RPG at Boundary Waters and Voyageurs are provided in 

this document. The solution to the IMPROVE equation is in the form of extinction (bext ). The 

Regional Haze rule requires visibility to be converted to, and expressed in, deciviews (dv). In 

the deciview scale, “a 1 to 2 deciview difference 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 guidelines7

 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. 2002) representing the baseline 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 year 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, which were also used to establish baseline visibility conditions. A 

ratio is developed for each specie comprising PM2.5 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 Relative Response Factor (RRF). Applying the 

RRFs to baseline monitoring conditions, for each specie comprising PM2.5, 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 modeling the multiple years comprising the 5-year 

baseline period, but discuss modeling one full year 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) will have more weight due to the fact that the 

2002 emissions and the meteorology are used in the modeling to develop the RRF applied to the 

baseline conditions. This application of the model 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 model due to emissions, meteorology, or general 

selection of other model 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 meteorology and therefore the locations in the 

United States where the modeled emission reductions 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 states located Southeast of the Minnesota Class I 

areas. 

In this document we show that in the upper-Midwest Class I areas, most PM2.5 causing visibility 

impairment is secondarily formed from emissions of sulfur dioxide from power plants and other 

industrial sources, nitrogen oxides from motor vehicles and industrial sources, and ammonia 

from agricultural operations. These emissions lead to the formation of ammonium nitrate and 

ammonium sulfate. Volatile organic compounds from natural sources like trees can contribute to 

secondary organic aerosols, or organic carbon. But wildfires are the source that dictates days of 

worst visibility impairment due to organic carbon. 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 ammonium nitrate and ammonium sulfate. 

I.b. Determination of Future Visibility—Pollutant 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 consolidate resources and develop 

the modeling analyses through a Regional Planning Organization (RPO) process. RPOs were 

developed to address the need to work in concert with other States and Tribes. Association 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 Royale (see Figure I.1) and 

Seney Class I areas, both within Michigan, are assessed by the MRPO. Michigan is a member of 

MRPO. The characteristics of regional haze have greater similarity among the MRPO and 
12

Minnesota’s Class I areas than among Minnesota’s and the more southern Class I areas in 

CENRAP. For this reason, Minnesota has recognized the benefit in furthering its technical 

analyses of the Upper Midwest Class I areas along 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 conducting modeling within each state. This 

appeals to Minnesota’s desire to explore answers to questions that are too specific to 

Minnesota to be feasible to address through the larger RPO process; and 

2. Along with the five member States of the MRPO, 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 with 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 lead. 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 individual sources located near Boundary Waters and 

Voyageurs. Ultimately, regional haze modeling at Boundary Waters and Voyageurs was 

performed by CENRAP, MRPO and by Minnesota; 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 outside 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 Minnesota. Because Minnesota 

would like to better correlate results with CENRAP, and because Minnesota did not have a final 

version of the State 2005 emissions inventory at the time the new base year modeling 

commenced, Minnesota retains the MRPO-developed 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 identifies Minnesota as by far the greatest 

contributor to impaired visibility in Boundary Waters and Voyageurs. Other significant—

approximately five percent or more—contributors are Illinois, Iowa, Missouri, North Dakota, 

and Wisconsin. Figure I.3 shows State contributions 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. Termed “on-the-books” controls, these measures 

comprise the bulk of the emissions reductions demonstrating reasonable progress. Emissions in 

2002 and 2018 for the significant contributing States are shown in Table I.2. From the six 

significant contributing states alone, SO2 emissions are reduced by 456,000 tons per year, and 

NOx are reduced by 1,350,000 tons per year. Ammonia emissions are estimated to increase by 

339,000 tons per year, making more ammonia available 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 extinction. Although 

reasonable progress goals are depicted in deciviews, 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 ammonium nitrate and ammonium sulfate are shown in 

Figure I.4. 

Figure I.3. State Contributions 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 NH4NO3 

& (NH4)2S04: 

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 NH4NO3 

& (NH4)2S04: 

37 Mm-1
14

Table I.2. Annual Emissions in Tons for the Significant Contributing States in 

2002 and 2018. 

State 2002 2018 Difference (2018-2002) 

SO2 NOX NH3 SO2 NOX NH3 SO2 NOX NH3

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 Contributions 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 NH4NO3 

& 

(NH4)2SO4: 

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 NH4NO3 

& 

(NH4)2SO4: 

32 Mm-1
15

Minnesota, the host State to Boundary Waters and Voyageurs, will always have the greatest 

contribution to extinction in the Class I Areas, despite reasonable emission reduction estimates. 

The percentage contribution from Minnesota and 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 SO2 reductions in more 

distant States, especially in Illinois. Monitoring 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 Voyageurs, 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 and 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 NOX reductions mostly occurring in Minnesota and the four 

other significantly contributing 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—Carlton, Cook, Itasca, Koochiching, Lake and St. 

Louis—in Northeast Minnesota contribute more than half of the States total contribution of 

extinction at Boundary Waters, and just under half of the contribution to extinction at Voyageurs. 

A plan to reduce SO2 and NOX 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 utility Minnesota Power—

Boswell. The remaining 10% of the emission reduction are applied in the Minnesota(MRPO) case 

to taconite mines based on permit limits, furnace modifications, fuel switching, new control 

equipment, newer rate information, and some initial measures taken to comply with the Best 

Available Retrofit Technology (BART) rule associated with the RH SIP. 

The Northeast Minnesota Plan results in an additional reduction of 2,000 tons per year SO2 and 

7,000 tons per year NOX. 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 Visibility Improvement that would be Achieved 

at each Class I Area. 

Applying the methodology described in I.a. with future emission estimates that reflect reasonable 

controls, provides the RPG. Minnesota’s RPG 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 uniform rate of progress from the baseline conditions to natural 

conditions. The top line represents the 20 percent worst visibility days, and the bottom line 

represents the 20 percent best visibility days. 
16

Overall, an estimated 1.3 deciview improvement 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 change may not necessarily be visually perceptible, 

however, it is a step in the right direction toward reaching the visibility 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

Year

Deciviews

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

Year

Deciviews

 
17

I.d. Comparison of Visibility Improvement 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 thus are not reflected in the Minnesota RPG. As detailed in the 

RH SIP, Minnesota did request Iowa, Missouri, North Dakota and Wisconsin—along with 

Minnesota— to evaluate the reasonableness of emission limits on the electric generating sector 

that would result in an additional total annual reduction of 222,000 tons SO2 and 29,700 tons 

NOX from 2018 emission estimates. These measures would result in an additional 0.3 deciview 

improvement (18.3 dv) in visibility at Boundary Waters, and an additional 0.2 deciview 

improvement (18.7 dv) in visibility at Voyageurs. 

I.e. Conclusion that the Long-Term Strategy Provides for Reasonable 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 assessed. The RH SIP provides discussion on the 

reasonableness of the long-term strategy. This 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 weight-of-evidence supporting the Minnesota reasonable 

progress goal, this document features a detailed 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 natural conditions. The MRPO 2005 

case projects the greatest visibility improvements, showing at least 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 Plan—resulting in greater projected emission 

reductions for the future—it is somewhat unexpected 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 NOx and ammonia 

emissions. 

The CENRAP case over predicts nitrate formation at Boundary Waters and Voyageurs compared 

to observed values collected at monitoring stations in 2002. This is likely caused by additional 

NOx and a significant amount of available ammonia with which to react. Because the CENRAP 

case has a lot of available ammonia to react with NOx emissions, the model responds well to 

future projected reductions in NOx emissions, possibly even 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 available ammonia in the winter to form the 

same level of nitrate from NOx emissions as observed. In the future projected case, NOx

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 

NOx emissions decrease, the increased ammonia and nitrate formation may prevent model 

response to the reduction in NOx 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 situation regarding model response to ammonium 

nitrate formation. Although the MRPO 2005 case does not over predict nitrate 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 increase nearly as much as the Minnesota(MRPO) case. 

Again, this scenario would allow the model to respond better to reductions in NOX 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 case results in a more optimistic RPG of 0.2 dv (18.4 

dv) at Boundary Waters and 0.3 dv (18.6 dv)Voyageurs. Livestock operations are the major 

source of ammonia emissions. The MRPO 2005 future year estimates were improved from that 

used in the Minnesota(MRPO) case. The reduced ammonia growth 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, different visibility components that contribute to those days, and 

the predominant wind direction from which emission reductions occur. 

The MRPO 2005 case shows significantly less contribution from Minnesota 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 meteorology results in a more 

optimistic RPG – 0.4 dv (18.2 dv) at Boundary Waters 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 available in the future year. We believe the future 

year estimates are better with less available ammonia in the future than that reflected in the 

Minnesota(MRPO) case. The quest for better ammonia emission estimates continues. The model 

shows more optimistic goals using 2005 meteorology. 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 greater visibility improvements if the uncertainty 

in Canada emissions were resolved and any possible NOx emission reductions taken into 

account. As these efforts would entail international relations, it is prudent for the EPA to lead 

this effort. 

The remainder of this document provides the 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, performance evaluation of the model compared to 

observed, the uniform rate of progress analysis, and the assessment of the contributing States and 

source sectors to visibility impairment. 

This part of the document is divided into sections that correlate to the individual steps in the 

process for making a technical demonstration 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 generating units (EGUs) used to establish the RPG 

in this document presume the Clean Air Interstate Rule (CAIR) is in affect. On July 11, 

2008, after the end of the public notice period 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 panel opinion, remanding CAIR to the EPA to 

be rewritten to address the flaws identified in the July ruling. This action means that 

CAIR is in effect while the flaws are addressed. 

One issue the EPA must address on remand is whether Minnesota should 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 Federal Register (74 FR 22147) a rule amending 

CAIR to stay the effectiveness of the rule with respect to sources located in the State of 

Minnesota. The administrative stay will remain in effect until such time as EPA 

determines through a rulemaking under the Clean Air Act whether Minnesota should be 

included in the CAIR region for fine particulate 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 Minnesota should not be required to make any 

additional expenditures to comply with CAIR prior to the expiration 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 attributed to differences in emission projection methods. CAIR 

emissions were estimated with the Integrated Planning Model (IPM), while emissions 

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. IPM projections used to establish the RPG contain 6 percent less NOX 

(-2,900 tons from 52,400 tons) and 9 percent more SO2 (4,400 tons from 49,400 tons) 

from Minnesota EGUs. Without detailed documentation available for the IPM estimates, 

we can not definitively explain the differences, however, we believe the additional SO2

may be attributed to IPM’s tendency to allow SO2 emissions to rise close to the permitted 

SO2 emissions rate. Detail on the control measures and emission estimate methodologies 

are provided in this document. 

Minnesota will continue to establish the RPG 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 Technical Support for Regional Haze

The modeling system is composed of an atmospheric transport and chemistry model, also 

known as the “air quality model”, an emissions model and a meteorological model. The 

emissions and meteorology models create inputs for use by the air quality model. In 

general, Minnesota chose to use the modeling system chosen by MRPO. The modeling 

system8,

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 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 original source of 

particulate species by geographic region and source category. CAMx is a 

Eulerian model that computes a numerical solution on a fixed grid. Minnesota 

used version 4.42. 

• The Pennsylvania State University/National Center for Atmospheric Research 

(PSU/NCAR) Mesoscale Meteorological Model (MM5). MM5 output data is 

used in the emissions model and in the air quality model. 

• Emissions Modeling System (EMS-2003). EMS-2003 generates hourly speciated 

emissions on a gridded basis for mobile, nonroad, 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 (SO2), nitrogen oxides (NOx), volatile organic 

compounds (VOC), ammonia (NH3), fine particulate (PM2.5) and coarse particulate mass. 

Sources of these precursors are both human (anthropogenic) and natural (biogenic). 

Biogenic releases are those from vegetation, for example trees, and are primarily VOCs. 

Anthropogenic sources of PM2.5 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 off-road vehicles, airplanes locomotives and 

marine vessels) and “area” sources (i.e. residential wood burning). 

That leads to steps 3 through 5; emissions modeling, meteorological modeling and 

atmospheric chemistry and transport (or air quality) modeling. Although meteorological 

modeling output is an input to emissions models, emissions modeling is identified as the 

third step in this document. Emissions models take the emissions inventory from the 

second step and prepare them for input to the air quality model. 

The fourth step is to process meteorological 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 simulates transport of the emissions and chemical 

reactions in the atmosphere to produce ambient air concentrations of the individual 

components of PM2.5; sulfate (SO4), nitrate (NO3), organic carbon (OC), elemental 

carbon (EC) ammonium (NH4), and fine and coarse particulate. The results are 

evaluated against monitoring data of the same period in the sixth step, model 

performance evaluation. Statistical analyses and other means are used to compare 

modeled results with observed 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 emissions 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 emissions 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 “glidepath” 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 are quantified in step eight. 

The flow diagram in Figure II.1 shows the procedural 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 Class I areas, Boundary 

Waters and Voyageurs. 
Figure II.1 – Procedural Flow for Demonstrating Attainment of Air Quality Goals for Region

Selected

Year

Poor Performance

Modify Base Year

1.0

Model Year 

Selection

2.0

Emissions

Inventory

Development

3.0

Emissions

Modeling

4.0

Meteorological

Modeling

5.0

Atmos. Chemistry 

& Transport

Modeling 

6.0

Performance

Evaluation

8.0

Control Strategy

Development

Emissions

Inventory

Emissions

Inputs

Source Category

Reductions

Overall

Percentage &

Point Reductions

Base Year

Modeled Ambient

Concentrations

Future Year

Modeled Ambie

Concentration

Meteorology

Inputs

Good Performance

Develop Future Year

Selected

Year

Poor Performance

Modify Base Year

1.0

Model Year 

Selection

2.0

Emissions

Inventory

Development

3.0

Emissions

Modeling

4.0

Meteorological

Modeling

5.0

Atmos. Chemistry 

& Transport

Modeling 

6.0

Performance

Evaluation

8.0

Control Strategy

Development

Emissions

Inventory

Emissions

Inputs

Source Category

Reductions

Overall

Percentage &

Point Reductions

Base Year

Modeled Ambient

Concentrations

Future Year

Modeled Ambie

Concentration

Meteorology

Inputs

Good Performance

Develop Future Year
24

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 with work conducted by CENRAP, and where 

applicable, with the MRPO 2005 case. 

1.0: Model Year Selection 

Regional haze issues appear throughout the year in the Minnesota Class I areas, which make it 

necessary to model a full year rather than a shorter 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 completed 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 straddles the model year. 

Minnesota has selected 2002 as the base year for modeling. 

During the iterative process of regional scale modeling, the MRPO decided to switch to a 2005 

base year for reasons unrelated to regional haze. The MRPO uses the modeling system to 

address issues related to ozone and PM2.5 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 some aspects of the 2005 inventory, as described 

in section 2.0. The 2002 base year allows Minnesota 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 similarity of the techniques used to process the 

emissions. These sectors are: 

• Point or industrial sources that are identified 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 “nonroad” sources although 

emission estimation techniques vary); 

• Area or stationary sources that are not identified by locational coordinate and stack 

parameters (i.e. agricultural operations, residential heating); and 

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

 

Emissions modeled for regional haze are sulfur dioxide (SO2), nitrogen oxides (NOx), volatile 

organic compounds (VOC), ammonia (NH3), fine particulate (PM2.5) and coarse particulate 

(PM2.5 – PM10) mass. 
25

The base year inventory, based on actual emissions calculated and recorded, is developed first. 

The base year inventory is processed 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 predictions are developed. For regional haze, the 

future year modeling inventory is for the year 2018. 

This section is divided in two parts. The first part describes the base year inventory development 

and the second part describes the future year inventory development. 

2.1. The Base Year Inventory—2002. 

For the most part, base year inventories are developed 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 states 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 emissions data to support State-developed inventories 

where older methodology, insufficient for modeling purposes, was used. For example, the 

development of accurate ammonia emissions is important because ammonia combines with 

sulfuric and nitric acid to form aerosol sulfate and nitrate, significant components of PM2.5 and of 

visibility impairment. Considerable effort10 has been expended by the RPOs to improve 

calculation methodologies for ammonia emissions from livestock and agricultural practices, and 

to appropriately allocate these emissions to each month of the year. Also, States do not typically 

develop inventories for biogenics, so these inventories had to be created. 

Both CENRAP and the MRPO incorporated the inventories developed by the 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 Figure II.1), a variety of emission inventories have 

been developed and used by organizations conducting haze modeling. Therefore, each RPO 

might have a different version of their member States’ inventories. Each subsequent version of a 

modeling emissions inventory might include the addition of emission sources that were missed, 

corrections to location coordinates and stack parameters of industrial point sources, and revisions 

to the inventory methodology. 

The CENRAP case incorporates its latest inventory “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 these modeling inventories, the latest base year 

inventory of one RPO is not necessarily included in another RPO’s base year11. For example, the 

MRPO 2002 case contains CENRAP’s baseC. The CENRAP case contains the MRPO baseK. 

 

10 This effort is ongoing in MRPO. 

11 CENRAP and WRAP retained the same contractors for 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 include12: 

• Some changes to the Oklahoma point source inventory; 

• Some stack parameter and emissions updates from some CENRAP states, including 

removal of double-counting of electric generating unit emissions in Missouri that 

resulted in a reduction of about 11,000 tons per year; 

• Updated Mexico emissions; and 

• Increased Gulf of Mexico NOX/SO2 emissions. 

Of these changes, the change to the Missouri inventory would be relevant to the Minnesota Class 

I areas. However, the MRPO 2002 case does not 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 inventory 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 baseG 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. CENRAP developed a baseG inventory for Minnesota 

rather than using the MRPO baseK. Table 2.1 summarizes the supplementary base year 

emissions inventory development activities for 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 the 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 Minnesota 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 CENRAP indicates that CENRAP model inputs13 do not 

support some of the annual emissions totals provided. The MRPO 2005 inventory methodology 

is not detailed in this document, but a table containing annual emissions 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 MRPO14 2002 Case CENRAP15 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 for 

CENRAP, 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 inventory18 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-specific 

meteorology 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 inadvertently left in the Minnesota base year and future year 

inventories. 20 MRPO conducted a sensitivity analysis to estimate the affect 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 Tons21 in Minnesota(MRPO) Case by Source and 

Category for Minnesota and Surrounding States. 

M 

I 

N 

N 

E 

S 

O 

T 

A 

SrcGroup SO2 NOX NH3 PM25 PM10 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. 
Figures 2.1 through 2.4. Annual 2002 SO2, NOx, NH3 and biogenic VOC emissions in Tons for Minne

Sulfur Dioxide: Nitrogen Oxides: 

Annual

Emissions

(Tons)

100 - 200201 - 300301 - 500501 - 700

701 - 1,000

1,001 - 2,0002,001 - 5,000

5,001 - 10,000

10,001 - 15,000

15,001 - 220,000

E

1

Ammonia: Biogenic Volatile Organic Compounds: 

Annual

Emissions

(Tons)

50 - 100

101 - 200201 - 400401 - 600601 - 800

801 - 1,000

1,001 - 3,0003,001 - 5,0005,001 - 7,000

7,001 - 35,000
31

Underestimation of Minnesota Nonroad Emissions. Some concerns have been raised about 

the potential impact of emissions from the fleets of large trucks operated at taconite mines 

located in St. Louis County, which is in northeastern 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 mine sites are nonroad mobile sources and operate 

throughout the year. In the baseline inventory, nonroad mobile source emissions were estimated 

by county based on population. Because St. Louis County is relatively low in population, NOX

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 that the mining trucks at a total of six taconite 

facilities could add between 1200 – 2500 tons per year of NOX. Because this underestimation 

was noted late in the modeling process, the inventory 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) before it is ready to become the basis from which future year 

emissions predictions are developed. 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 from 2002 to 2018 for all source categories, except 

onroad mobile sources and EGUs. Onroad mobile sources were grown using the MOBILE6 

model. EGUs were grown using the Integrated Planning Model (IPM). 

MOBILE6. On-road mobile sources were grown using MOBILE6 within the emissions model 

EMS-2003 (see section 3.0). The MRPO used State Metropolitan Planning Organization and 

Department of Transportation modeled future year networks and local MOBILE6 inputs to 

generate emissions estimates for the five MRPO states and Minnesota. Alpine Geophysics 

provided the information for other states. 

EGAS5. Emissions for all source sectors, except on-road mobile sources and EGUs, were grown 

using EGAS version 5. EGAS is a forecast model used to predict national and regional 

economic activity in order to estimate air quality 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 growth factors are based on things like projected 

changes in fuel consumption, or increases in population that may result in a greater use of a 

product or service, increases in production of a product, or the closing 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, retirement 
32

of existing sources, and output changes from existing sources. Growth factors cannot predict the 

development of new facilities and do not take into account future controls on new units. They 

merely grow emissions from existing facilities within 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 states and Minnesota, Iowa and Missouri. It was 

assumed there was no growth for residential wood combustion and pesticides All other area 

source categories were capped at 3 percent per 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 the same cap for similar SCCs for the State, and 

provided a list of specific SCC with growth factor modifications by specie (NOX and SO2). 

For several categories where the EGAS-supplied growth factors were suspect, the MRPO 

contracted for the development of alternative growth factors. Alternative growth factors were 

developed for 22 priority area source categories, which mainly included fuel combustion point 

sources. E.H. Pechan22 developed growth and control factors for all area, and point sources. 

They developed growth rates for fuel combustion categories based on Department of Energy 

historical and projected fuel consumption data for 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. Other categories, for example ammonia forecasts, 

were based on the Regional Economic Models Incorporate (REMI) State-level economic model 

and sector-specific equations relating emission activity trends to trends associated with a REMI 

socioeconomic variable. 

MRPO 2005 Growth Factor Refinements. While Minnesota-specific historical and forecast 

growth indicator data was not specifically included in the 22 priority source category review in 

the MRPO 2002 case, Minnesota-specific data was included in the MRPO 2005 update. Also, 

the overall MRPO 2002 case growth factors were revised to reflect more updated growth 

indicators. Of particular interest for this TSD is the revision 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, growth from agricultural livestock sources was 

based on interpolated SCC animal count projections by State from the EPA ammonia 

inventory23. The reduced ammonia growth in the MRPO 2005 case was most impacted by a 

future estimated decline in dairy operations. The 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 assumed 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 many facilities within Minnesota’s mining industry (largely taconite 

extraction and processing) to Minnesota’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 discovered that Minnesota’s 

2002 emissions inventory often used SCCs for taconite 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 production, taconite ore processing). However, 

Minnesota emission inventory staff has used other SCCs (such as 39000699 - Industrial 

processes, in-process fuel use, 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 some cases, been assigned to more general SCCs, 

different growth factors were assigned 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 emissions inventory staff 

began assigning emissions from taconite facilities only to taconite-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, MRPO has used different growth 

factors for non-MRPO and MRPO states. The growth 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 emissions from existing taconite mining plants from 

2002 levels, however, some growth is expected 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 information and projected emissions for new mining 
34

facilities. Two facilities were added as proposed “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 Bay 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 states26

MRPO Growth 

Factor for MN27

10200802 

Industrial External 

Combustion Boilers, 

Petroleum Coke 

1.126 1.095 1.126 

30302312 

Industrial Process, 

Primary Metal 

Production, Taconite 

Ore 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 NOx (original 83 tons/day, revised 77 tons/day) and less SO2

(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) and the Clean Air Mercury Rule (CAMR). 

IPM version 2.1.9(VISTAS) was used in both CENRAP and MRPO 2002 future year 

inventories. The IPM model output presumes CAIR and CAMR are implemented. While 

developing its 2005 future year modeling inventory, the MRPO switched to IPM3.0. IPM3.0 

also presumes CAIR and CAMR are in place, but 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 convention 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 Warden on Dec. 6, 2006 of top non-EGU facilities (250 

TPY in 2018 or greater) of SO2 and NOx 26 Obtained from MI/WI growth factor spreadsheets done by MRPO – overall percent 

growth.(http://www.ladco.org/tech/emis/basek/reports/control/argnc.scc.all4one26.020712.r4s1_nonegu_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, Minnesota made a correction to 

the Minnesota Power-Boswell EGU emissions to account for an underestimation of the facility’s 

capacity in IPM. Adjustments were limited to committed control projects that occurred after the 

scheduled deadline for submission to EPA for the 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 adjustments 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) 

NOx SO2 NOx SO2

Minnesota 

PowerBoswell 

Unit 3 adjusted to reflect 

NOX permit limit, SO2

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 

Minnesota 

PowerTaconite 

Harbor 

Retrofit SO2 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 model 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 IPM3.0 “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 for predicting future EGU emissions and if 

States and affected industry believe that the predicted emissions are incorrect, they should 

change them in order to get the most accurate estimation of future emissions in the state. The 

corrections and adjustments made by Minnesota should not throw the system off-balance 
36

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 modifications described above), the IPM3.0 EGU 

emissions were used for the future year inventory. 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 base year emissions. 

Controls on Future Year Inventory. Control is applied after growth and may be a due to the 

addition of physical controls to a process. These controls may be voluntary or due to regulatory 

requirements. Controls also reflect Federal and State regulations and legislation and permit 

actions. MRPO contracted with E.H. Pechan and Associates to identify 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, include29: 

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 NONROAD model (e.g. nonroad diesel rule), and the 

evaporative Large Spark Ignition and 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 Units30

• 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 boiler/process heater/RICE MACT. 

 

29 MRPO (April 2008) 

30 These controls are included in the IPM3.0 projections. 
37

• The MRPO also included control 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” Alterations32

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 without CAIR in place. Rather than use the IPM 

model, which was used to model CAIR, the emissions 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 MRPO 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 Classification 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 assigned petroleum; and 006, 007 and 009 were assigned 

natural gas. Where a minor code does not fit into any of these categories, the default is coal. 

Future year projections were estimated based on 2007 EGU emissions. Minnesota reviewed the 

2007 emissions and found that not all facilities operated throughout the year. For example, Xcel 

Energy—Allen S. King was installing controls during 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 applied 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 Source in Billion Kilowatt Hours for the MAPP 

Region33. 

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 EGUs in Minnesota including Power Boiler #2 at 

Northshore Mining-Silver Bay, whose BART determination was not complete at the time the 

emission projections were made. The legally enforceable BART controls on Power Boiler #2 

includes the Low NOX burners in the preliminary stages of the BART determination, but does 

not include the Dry Sorbent Injection to control SO2 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 establish the RPG for the RH SIP. The control 

assumptions in the IPM 3.0 “will do” scenario and the controls without-CAIR are shown in 

Table 2.8. 

A review of the total Minnesota EGU SO2 and NOX 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 NOX (-2,900 tons from 52,400 tons) and 9 percent 

more SO2 (4,400 tons from 49,400 tons) from Minnesota EGUs. Without detailed 

documentation available for the IPM estimates, the differences can not be definitively explained. 

However, the overall increased SO2 projection by IPM may be due to its tendency to allow SO2

emissions to rise close to the permitted SO2 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 Mining-Silver Bay in 2018, however, the model 

predicts a low annual emissions rate for 2018 regardless. It is unclear why this is the case. 

Since these alternative EGU projections were developed, the Court remanded the CAIR rule to 

EPA. Thus, CAIR is in effect while EPA addresses the flaws in the rule specified by the Court. 

Included in that effort, EPA will determine whether or not CAIR applies in Minnesota. Whether 

or not CAIR applies has no effect on the known future year controls projected to 2018 already 

modeled for the RH SIP (with CAIR in place). Also, 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 the RPG using the IPM 3.0 “will do” projections for 

EGUs as described above. 
41

Table 2.8. Future Year Control Assumptions 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 NOX 100% removed 100% removed 

SO2 100% removed 100% removed 

EU002 NOX 100% removed 100% removed 

SO2 100% removed 100% removed 

SV003 

EU003 NOX 100% removed 100% removed 

SO2 100% removed 100% removed 

EU009 NOX * 

new unit 

natural gas * 

new unit 

natural gas 

SO2 * 

new unit 

natural gas * 

new unit 

natural gas 

SV008 EU010 NOX * 

new unit 

natural gas * 

new unit 

natural gas 

SO2 * 

new unit 

natural gas * 

new unit 

natural gas 

SV009 EU011 NOX 100% removed 100% removed 

SO2 100% removed 100% removed 

Minnesota 

Power Inc - 

Boswell 

Energy Ctr 

2706100004 SV003 EU003 

NOX 80% SCR 80% SCR 

SO2 85% FGD 85% FGD 

Rochester 

Public Utilities 

- Silver Lake 

2710900011 SV003 EU004 

NOX 40% SNCR ** ** 

SO2 85% SCRUBBER 95% SCRUBBER 

Xcel Energy - 

High Bridge 

Generating 

2712300012 

SV001 

EU001 NOX 100% removed 100% removed 

SO2 100% removed 100% removed 

EU002 NOX 100% removed 100% removed 

SO2 100% removed 100% removed 

EU003 NOX 100% removed 100% removed 

SO2 100% removed 100% removed 

EU004 NOX 100% removed 100% removed 

SO2 100% removed 100% removed 

SV008 EU010 NOX * 

new unit 

natural gas * 

new unit 

natural gas 

SO2 * 

new unit 

natural gas * 

new unit 

natural gas 

SV009 EU011 NOX * 

new unit 

natural gas * 

new unit 

natural gas 

SO2 * 

new unit 

natural gas * 

new unit 

natural gas 
42

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 NOX 50% LNB ** ** 

SO2 85% SCRUBBER   

EU002 NOX 50% LNB **  ** 

SO2 85% SCRUBBER   

SV002 EU003 NOX 50% LNB **  ** 

Xcel Energy - 

Allen S King 

Generating 

2716300005 SV001 EU001 

NOX 80% SCR 90% SCR 

SO2 82% SCRUBBER 82% SCRUBBER 

Minnesota 

Power Inc - 

Taconite 

Harbor Ctr 

2703100001 

SV001 EU001 NOX 50% ROFA/SNCR 50% ROFA/SNCR 

SO2 40% FSI 40% FSI 

SV002 EU002 NOX 50% ROFA/SNCR 50% ROFA/SNCR 

SO2 40% FSI 40% FSI 

SV003 EU003 NOX 50% ROFA/SNCR 50% ROFA/SNCR 

SO2 40% FSI 40% FSI 

Northshore 

Mining Silver 

Bay 

2707500003 

SV001 EU001 

NOX 40% LNB 

SO2 20%*** biomass*** 

SV002 EU002 

NOX 40% LNB 

SO2 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 after 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/Flue Gas Desulfurization 

FSI: Furnace Sorbent 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 factors for point sources in CENRAP: 

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

o Cement kiln emissions held constant (NOX SIP Call), early CAIR controls for Ameren 

with changed SO2 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 Kansas City Power and Light BART affected 

units, and MACT control assumptions in Kansas; 

o Added several new points and modified emissions at Lehigh Cement Company, Lafarge 

North America, ADM Corn Processing Clinton 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 SO2 (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 SO2 than the Minnesota(MRPO) case for Iowa. EGU SO2 emissions from Iowa decreased 

significantly between the IPM2.1.9 and IPM3.0, which 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 SO2. 

A summary of the projected 2018 emissions for Minnesota and the surrounding 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 CENRAP indicates that CENRAP model inputs34 do not 

support some of the annual emissions totals provided. The MRPO 2005 inventory methodology 

is not detailed in this document, but a table containing annual emissions 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 NOX from mobile sources, 

which is the main source of NOX in each of the States except North Dakota and South Dakota in 

the base year. The most significant reductions in SO2 emissions are from point sources, which 

are the main source of SO2. Ammonia significantly 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 Tons35 in Minnesota(MRPO) Case by Source and Category 

for Minnesota and Surrounding States. 

M 

I 

N 

N 

E 

S 

O 

T 

A 

SrcGroup SO2 NOX NH3 PM25 PM10 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 Annual 2002 and 2018 Emissions in Tons36 in Minnesota(MRPO)

Case by Source and Category for Minnesota and Surrounding States. 

M 

I 

N 

N 

E 

S 

O 

T 

A 

SrcGroup SO2 NOX NH3 PM25 PM10 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. 
Figures 2.5 through 2.7. Annual 2018 SO2, NOx, NH3 emissions in Tons for Minnesota(MRPO

Sulfur Dioxide: Nitrogen Oxides: 

Annual 

Emissions

(Tons)

100 - 200201 - 300301 - 500501 - 700

701 - 1,000

1,001 - 2,0002,001 - 5,000

5,001 - 10,000

10,001 - 15,000

15,001 - 220,000

A

Em

2

5

10

15

Ammonia: 

Annual 

Emissions

(Tons)

50 - 100

101 - 200201 - 400401 - 600601 - 800

801 - 1,000

1,001 - 3,0003,001 - 5,0005,001 - 7,000

7,001 - 35,000
47

2.3 Canada Emissions Inventory 

In addition to including emissions from the individual States in the United States, CENRAP and 

MRPO included emissions from Canada in an attempt to reflect the base year 2002. Canada’s 

provinces report an emissions inventory to Environment Canada, similar to the States reporting 

the NEI to the EPA. The subset of the Canadian inventory—or Pollutant Release and Transfer 

Register (PRTR)—that includes the relevant pollutants is the Criteria Air Contaminants 

Emission Inventory. Canadians do not report their 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 inventory. The Canadian inventory is confidential and specifics are 

not shared freely outside the country. This makes it difficult to quality assure the data and fully 

understand whether the estimation methods are comparable. 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 determined that much of the emissions from 

Canada were assigned no stack height and as a result were modeled as if released in the surface 

layer of the atmosphere. These included some very 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 environmental 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 Tons37

C 

A 

N 

A 

D 

A 

SrcGroup SO2 NOX NH3 PM25 PM10 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 emissions 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 uncertainties 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 input files and reported to three significant digits. Sum 

totals may not add up do to rounding. 
48

an emphasis on reduction in emissions because the 2000 and 2020 Canada emission summaries38

for the entire Inter-RPO National Domain provided by CENRAP show annual reductions in 

Canadian SO2 of -2,150,000 tons, which equates to an 88 % reduction—nearly all of which 

consists of point source SO2 (-2,130,000 tons, -97%)—and in Canadian NOX of -731,000 tons, 

which equates to 25% reduction, most of which consists of point source NOX (-630,000 tons, -

95%). Although the actual values may be questionable, 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 CENRAP uses the SMOKE model, to develop 

emissions ready for import into atmospheric chemistry 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 over the year, whereas modeling requires hourly 

emissions. Although State developed inventories contain specific locational (longitude/latitude) 

information for industrial point sources, all other emissions are reported on a county-basis. 

Emissions modeling will allocate emissions as specifically to each 36km (or less) grid square as 

possible. 

In addition to spatially and temporally allocating emissions, the models are used to speciate the 

emissions for each of the source categories, where applicable. For example, if the state NEI 

submittal reports emissions as NOx, these emissions are speciated into NO and NO2. Point 

sources are allocated to grid cells based on their locational (latitude/longitude) coordinates, and 

are allocated to individual hours using reported operating schedule information. County-level 

area source emissions are spatially allocated to grid cells using surrogates, for example 

population or land use, and are allocated to specific hours using hourly 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 emissions estimates for a weekday, Saturday and Sunday for 

each month for point, mobile on-road, mobile non-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. Summaries of the methodologies are in Table 3.2. 

 

38 Obtained from emis_smry_cenrap_typ02f_b18f_021407.xls 
49

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 SO2, NOx, VOC, PM25, CM and 

NH3

SO2, NOx, VOC, PM25, CM and 

NH3

Emissions were generated for every hour and allocated to 36km grids over the National InterRPO Domain. This domain was agreed upon by all the RPOs as the basic domain from which to 

model. The MRPO uses a subset of the national RPO domain, called the 4rpos domain, to focus 

on 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. Both the National 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 look 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 Domain
50

Table 3.2. Summary of Temporal and Spatial Allocation and Speciation Profile 

Methodology by RPO 

Source Category MRPO39

(baseK) 

CENRAP40

(baseG) 

Point EGU Temporal 

Profiles: 

EGU SO2 and NOX emissions 

were temporalized using heat 

input, in lb/MMBtu from CEM 

data to create temporal profiles 

with 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 (SO2) 

emissions and summer/winter 

(NOX) 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 for 

MRPO for all other coalburning EGUs 

 

39 MRPO (April 2005) 

40 ENVIRON (September 2007) 

41 EGU temporalization: Both SO2 and NOX 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 between MRPO and CENRAP 2002 Base Year 

Inventories 

Large differences can occur between modeling studies on how the annual emissions get 

distributed to each month. These differences would likely vary most in the months comprising 

Spring and Fall because profiles are often developed for a Summer and Winter day. Depending 

on the category, this can be significant. For non-road sources, Minnesota found that emissions 

would be significantly over-estimated 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 the Minnesota monthly non-road 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 NOx to form 

ammonium nitrate (although preferentially interacting with SO2 to form ammonium sulfate). 

Assigning ammonia emissions to warmer months will make them more available to form 

ammonium sulfate. 

MRPO and CENRAP took different approaches to compile estimates 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 higher than MRPO estimates, the distribution of 

those emissions among categories of livestock and fertilization methods, and the temporal 

distribution, vary as well. 

An example of the temporal profiles for the 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, Wisconsin 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 Emissions from Livestock and 

Fertilizer in Minnesota, Michigan and 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 CENRAP. Emissions for both the CENRAP baseF47 (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 organization48. Thus, no differences 

between values from the two organizations are attributable to calculation methodology. Table 

3.3 contains winter and summer day emissions of each species 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 applications, 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 summer. 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 contain total emissions from all source categories. 

As mentioned in Section 2, more 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. NOx emissions are of greater importance in the colder months, and for a 

winter day, it appears that the Minnesota(MRPO), and CENRAP cases have similar amounts of 

NOX at this time of the year for Minnesota and surrounding states combined. The spatial 

distribution of those emissions varies. On the winter day, CENRAP has additional Minnesota 

and North Dakota NOX than Minnesota(MRPO). CENRAP also has significantly more Canada 

NOX using the Canada 2000 inventory than Minnesota(MRPO) has using the 2005 Canada 

inventory. Spatially, some of the increased NOX from Canada appears directly along the border 

shared with Minnesota, and along 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 NOX in the region than both CENRAP or 

Minnesota(MRPO) mainly due to the use of a difference calculation methodology for biogenic 

NOX, 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 SO2

emissions. Between the two organizations, the emissions in the U.S are quite comparable. The 

exception is for Canada emissions, where CENRAP SO2 emissions are significantly higher. 

However, impaired visibility due to SO2 from the north on the 20 percent 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 SO2 from Canada occurs directly north of the two 

Class I areas, but most of the elevated SO2 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 these components of haze than the Minnesota(MRPO)

case. In regard to particulates, 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 calculation methodology, and more is allocated to 
55

summer than winter. However, because control measures are not proposed for biogenic sources, 

the analyses can be more forgiving in differences 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 NOX Emissions in Tons for 

Minnesota(MRPO) Case. 

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

Daily

Emissions

(Tons)

1 - 5

5 - 10

10 - 20

20 - 30

30 - 50

50 - 75

75 - 125

125 - 175

175 - 300

300 +

Figure 3.6 2002 Winter Day and Summer Day NOX Emissions in Tons for 

CENRAP case. 

CENRAP—Winter Day: CENRAP—Summer Day: 

Daily

Emissions

(Tons)

1 - 5

5 - 10

10 - 20

20 - 30

30 - 50

50 - 75

75 - 125

125 - 175

175 - 300

300 +
57

Figure 3.7. 2002 Winter Day and Summer Day SO2 Emissions in Tons for 

Minnesota(MRPO) Case. 

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

Figure 3.8 2002 Winter Day and Summer Day SO2 Emissions in Tons for 

CENRAP case. 

CENRAP—Winter Day: CENRAP—Summer Day: 
Table 3.3. 2002 Emissions for a Winter and Summer Day in Tons for Minnesota(MRPO) and CE

SO2 NOX NH3 PM25 PM10 VOC SO2 NOX NH3 PM25 

Minnesota 458 1,378 158 113 304 1,552 477 1,461 345 28Iowa 533 942 237 64 175 814 469 910 269 23North Dakota 617 498 23 11 47 279 647 627 74 26South Dakota 91 250 72 12 47 292 50 280 214 42Wisconsin 785 1,298 131 68 91 1,347 675 1,093 122 7Canada* 2,491 2,055 997 78 256 3,236 4,046 4,080 1,023 68

total:

(only U.S) 2,484 4,365 621 269 663 4,284 2,319 4,371 1,024 1,2

total: 

(incl Canada) 4,975 6,421 1,618 347 919 7,520 6,365 8,451 2,047 1,9

SO2 NOX NH3 PM25 PM10 VOC SO2 NOX NH3 PM25 

Minnesota 461 1,506 666 99 296 6,483 476 1,668 437 29Iowa 564 1,135 1,027 70 189 1,927 495 1,102 1,058 21North Dakota 549 636 122 10 50 1,607 695 995 925 19South Dakota 89 390 314 10 45 1,983 80 595 541 12Wisconsin 797 1,237 571 33 57 3,883 796 1,144 553 6Canada* 2,036 3,257 994 73 237 48,198 3,187 5,215 1,047 66

total:

(only U.S) 2,460 4,904 2,700 222 638 15,883 2,542 5,505 3,514 8

total: 

(incl Canada) 4,496 8,161 3,694 295 875 64,082 5,729 10,720 4,561 1,5

Winter Day Emissions in Tons -- Base Year 2002

Minnesota (MRPO) CENRAP

Region 

Summer Day Emissions in Tons -- Base Year 2002

Minnesota (MRPO) CENRAP

Region 

 * Canada emissions are the total included over the entire extent of the 4rpos domain; not delineated by Province. The Minnesota(MRPO) em Canada 2005 inventory, while the CENRAP emissions include the Canada 2000 inventor

y.

 * Canada emissions are the total included over the entire extent of the 4rpos domain; not delineated by Province. The Minnesota(MRPO) em Canada 2005 inventory, while the CENRAP emissions include the Canada 2000 inventor

y.
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 for emissions inventories. MPRO assured the 

quality of the data through the review of emissions reports and spatial analysis49 as follows: 

 

• Emission Reports: EMS performs a number of checks and generates several reports, as 

documented in the EMS User’s Guide50. The QA checks for point sources, for example, 

duplicate or missing keys (stid, cyid, fcid, stkid, dvid, prid, polid), 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 stack 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)

SO2 NOX NH3 PM25 PM10 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 

SO2 NOX NH3 PM25 PM10 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 by Province. 
60

• Spatial Analysis: A second level of quality assurance is performed by the photochemical 

modelers The additional checks include: evaluating spatial tile plots of total daily SO2, NOX, 

VOC, ammonia, PM2.5, and coarse mass emissions, and evaluating plots that show the 

variation in emissions from month to month, and from hour to hour. 

4.0: Meteorological Modeling 

Meteorological data inputs were prepared using the Fifth-Generation NCAR/Penn State 

Mesoscale Model (MM5). Appendix B contains a protocol titled “Meteorological Modeling 

Protocol for Application to PM2.5/Haze/Ozone Modeling Projects”, December 2004, for 

conducting meteorological modeling with MM5. 

Both the MRPO and CENRAP use the same 36km 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 meteorological inputs, including the definition of the 

meteorological data grid, processing 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 “Meteorological Modeling Performance Summary for Application 

to PM2.5/Haze/Ozone Modeling Projects”, February 18, 2005, and the other was prepared by 

Matthew Johnson of Iowa, titled “Meteorological Model Performance Evaluation of an Annual 

2002 MM5 (version 3.6.3) Simulation, 2004-2007, v2.0.3. Model performance was deemed 

good. 

5.0: Atmospheric Chemistry and Transport Modeling 

Guidelines for conducting regional-scale modeling for particulate matter and visibility are 

provided in 40 CFR § 51, Appendix W. The EPA recommends the use of one of the three 

following models to simulate pollutants impairing visibility: Community Multiscale Air Quality 

(CMAQ), the Comprehensive Air Quality Model (CAMx), and Regional Modeling System for 

Aerosols and Deposition (REMSAD). These are Eulerian models that compute the numerical 

solution of partial differential equations of plumes on a fixed grid. The models simulate 

atmospheric and surface processes affecting the transport, chemical transformation and 

deposition of air pollutants and their precursors. CAMx also allows 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 CMAQ. 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 scale. Both organizations found this grid 

resolution sufficient for regional haze. However, Minnesota has an interest in evaluating the 

 

51 ENVIRON (September 2006). 
61

impacts of sources close in to Boundary Waters and Voyageurs. Minnesota 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 procedures for conducting the modeling analyses. 

Minnesota did not prepare its own protocol, instead relying on the meteorological modeling and 

air quality modeling protocols 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 individual 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, landuse inputs, drought stress and snow cover, 

photolysis rates, initial 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 compared to observations (monitored values) at 

the same location over the same time period; and 

3. Conversion of the data to visibility 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 specific to Minnesota beyond the scope covered by the 

air quality modeling protocol prepared by the MRPO. For example, Minnesota is interested in 

the visibility impact of sources located near Boundary 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 areas. PiG tracks individual plume segments from 

each point source, rather than immediately dispersing the individual point source emissions in 

the grid cell. In order to use the PiG module properly, some modifications to the air quality 

modeling inputs files were required. For example, stack parameter 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 grid 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 purpose 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 located near the Minnesota Class I areas. The 
62

GREASD PiG treatment was applied to point sources with emissions greater than 100 tons per 

year of SO2 or NOx and located within the six Minnesota counties nearest the Boundary Waters 

and Voyageurs Class I areas. 

Unlike the MRPO, who uses the Particulate Source Apportionment Tool (PSAT) to track the 

contributions of all the individual components of PM25, Minnesota limited contribution 

assessment to the sulfate ion, nitrate ion, and ammonium ion because these are the components 

most likely to be involved in control measures. Also, MRPO PSAT results show that the 

excluded components—elemental carbon, and primary emissions of organic aerosol, soil and 

coarse mass—are not significant contributors to visibility 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, Minnesota has reported secondary organic aerosol 

contributions specific to biogenic and anthropogenic sources from the standard CAMx output. 

Separate geographic regions and source groups tracked by Minnesota are detailed in Section 

8.0—Control Strategy Development. 

6.0: Model Performance 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 measurements on the same days that are selected for 

evaluation. EPA guidance calls this an “operational evaluation” of regional haze model 

performance. 

Model performance is conducted on the base model 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 model 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 days in the two Class I areas. 

In addition to the more specific performance evaluation results for the Minnesota(MRPO)

modeling, a summary of the CENRAP and MRPO model performance results are provided. All 

performance results provided are for the 36km grid. Minnesota also evaluated performance of 

the Minnesota(MRPO) 12km grid with PiG in this Section. The results are discussed, but 

corresponding plots and charts are not provided as part of this technical support document. 

 

Model performance evaluation was conducted on the several components of fine particulate 

(PM2.5)—sulfate ion (SO4), nitrate ion (NO3), ammonium ion (NH4), organic carbon (OC), 

elemental carbon (EC) and crustal/soil material. These major components that comprise the 
63

mixture PM2.5 are evaluated separately in order to avoid compensating errors that might indicate 

good model performance were the compounded PM2.5 evaluated. 

The remainder of the discussion for this Section includes a summary of the observational data 

available for observed/predicted comparisons, an identification of the performance tests used and 

their results, a description of the ability of the model to reproduce 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 Visual Environments (IMPROVE) network. This 

network collects individual PM2.5 components (excluding ammonium) at Class I areas, such as 

at Boundary Waters (BOWA1) and Voyageurs (VOYA2). The locations of these monitors in 

relation to Boundary Waters and Voyageurs are provided in Figure 6.1. The Isle Royale monitor 

is not located on the island, but further southwest, on the Upper Peninsula of Michigan. The 

species collected at the monitor and the corresponding species in the CAMx model are shown in 

Table 6.1. 

Figure 6.1. Locations of IMPROVE Monitors at Boundary Waters and Voyageurs. 

GF

GF

Minnesota

Wisconsin

Iowa

South Dakota

North Dakota

Michigan

Manitoba Ontario

Boundary Waters 

Canoe Area Wilderness

Isle Royale N

Voyageurs NP
64

Table 6.1 – Performance Evaluation Monitor to Model Definition of 

Pollutants Evaluated at Minnesota Class I Areas52. 

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 was obtained from the Visibility Information 

Exchange Web System (VIEWS), the repository of data to support the Regional 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. PM2.5 and PM2.5 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 Waters 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” – PM2.5 particle mass; 

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

• “Module D” – PM10 particle mass. 

This data loss invalidated three out of every seven samples from these modules. “Module B” has 

a denuder that collects nitrate, chloride, sulfate and nitrite. According to CIRA, the “Module B” 

data from Boundary Waters during this period are valid. In order to utilize the valid data from 

Boundary Waters, Scott Copeland of CIRA substituted the missing components with a linear 

regression analysis from corresponding valid data collected at Voyageurs. 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 excluded from the 20 percent worst days on VIEWS 

because of incomplete capture of insignificant components 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/views/Web/Documents/SubstituteData.aspx 54 MRPO (June 2007) 
65

For the year 2002, the inclusion of this previously excluded data results 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 percent worst days in 2002 at Voyageurs remain 

the same. The data for the previously excluded days are in the raw data set from VIEWS and as 

long as model performance programs do not hard-code 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 the 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 without a letter mean the day is one of the 20 

percent worst for both Class I areas. In modeling analyses, days are identified by their Julian 

date. Throughout the rest of this document, days modeled may be identified either based on the 

Gregorian or Julian Calendar. Appendix F contains a cross-reference table for Gregorian and 

Julian calendar days in 2002. 
Table 6.2 Species Data at Minnesota Class I Area Monitor Sites for 20% Worst days in Micrograms p

5v 11v 26b 71v 86v 104v 131 146 149b 

BOWAV SO4 no data 1.074 2.892 no data 1.518 1.425 2.262 1.979 3.866 

NO3 no data 2.192 0.670 no data 1.721 1.207 0.636 0.413 0.216 EC no data 0.117 0.129 0.228 0.119 0.245 0.292 0.280 0.287 OC no data 0.522 0.687 0.822 0.597 1.169 1.586 2.523 1.523 NH4 no data no data no data no data no data no data no data no data no data CRUSTAL no data 0.256 0.275 0.437 0.442 0.673 0.740 0.766 0.454 

VOYA2 SO4 1.911 0.863 2.420 2.002 1.562 1.542 2.283 1.834 1.390 

NO3 3.292 3.360 0.742 2.789 2.818 2.418 0.762 0.268 0.203 EC 0.224 0.204 0.202 0.258 0.187 0.309 0.228 0.256 0.183 OC 0.811 0.733 1.057 0.851 0.752 1.120 1.593 2.327 1.553 NH4 no data no data no data no data no data no data no data no data no data 

CRUSTAL 0.217 0.114 0.159 0.378 0.477 0.404 0.385 0.508 0.384 

170 176b 179 188 197 200 203b 221 242 

BOWAV SO4 5.287 3.658 2.140 1.850 4.829 0.857 3.415 2.200 4.415 

NO3 0.535 0.283 0.108 0.243 0.254 0.096 no data 0.174 0.177 EC 0.560 0.278 0.575 0.221 0.331 0.713 0.225 0.266 0.365 OC 3.299 2.445 7.394 2.435 1.543 11.436 1.492 1.312 1.820 

NH4 no data no data no data no data no data no data no data no data no data 

CRUSTAL 0.867 0.808 0.867 1.269 0.938 0.408 0.548 0.525 0.564 

VOYA2 SO4 4.602 1.739 1.733 2.095 7.645 1.163 0.229 2.102 5.054 

NO3 0.522 0.092 0.102 0.241 0.192 0.107 0.029 0.173 0.231 EC 0.707 0.179 0.585 0.298 0.408 0.842 0.034 0.296 0.417 OC 2.742 1.301 7.650 2.248 1.938 15.187 0.682 1.336 1.984 

NH4 no data no data no data no data no data no data no data no data no data 

CRUSTAL 0.422 0.292 0.533 0.867 1.245 0.708 0.123 0.461 0.495 

254v 260v 272v 275b 284v 299 314b 332 344 

BOWAV SO4 1.365 0.669 1.545 3.319 0.218 1.459 1.465 1.627 0.865 

NO3 0.080 0.065 0.158 1.025 0.024 4.197 1.482 3.372 4.023 EC 0.137 0.305 0.066 0.471 0.350 0.363 0.191 0.242 0.294 OC 1.312 1.659 0.523 1.738 1.475 1.493 0.955 0.791 1.194 NH4 no data no data no data no data no data no data no data no data no data 

CRUSTAL 0.280 1.102 0.131 0.588 0.435 0.208 0.113 0.597 0.326 

VOYA2 SO4 1.045 1.959 2.510 0.306 2.496 1.771 0.819 1.459 0.694 

NO3 0.182 0.228 0.184 0.016 1.172 4.486 0.388 2.641 3.193 EC 0.269 0.349 0.171 0.028 0.394 0.353 0.125 0.245 0.324 OC 3.116 1.772 1.031 0.388 1.642 1.674 0.676 0.853 1.310 NH4 no data no data no data no data no data no data no data no data no data 

CRUSTAL 0.232 1.000 0.230 0.060 0.412 0.228 0.017 0.299 0.297 

Julian date

Station

PM2.5 

component

Julian dateJulian date

Station

PM2.5 

component

Station

PM2.5 

component
67

6.2 Performance Tests Used and their Results. 

EPA guidance recommends considering various statistical assessments and other techniques of 

modeled versus observed pairs when conducting a performance evaluation for regional haze. 

The other techniques include spatial 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 Criteria for Fractional Bias and Error.55

Fractional Bias Fractional Error Goal/Criteria 

± 30% 50% Goal—Best that a model is expected to achieve. 

± 60% 75% Criteria—Acceptable for standard modeling 

applications. 

The goals and criteria shown above apply to more abundant concentrations of PM2.5 components 

in the atmosphere. At zero concentration, the goal and criteria range from ±200% mean 

fractional bias to +200% mean fractional error. The goal and criteria asymptotically approach the 

goals and criteria in Table 6.3. Thus, a higher percentage error and bias are allowed at low 

concentrations. This is because at lower concentrations the models have greater difficulty 

predicting concentrations. 

The statistics typically look better at greater averaging times. Thus, the fractional bias and error 

likely will show a better fit within the goals and criteria at an annual average than a monthly 

average, and at a monthly average than a 24-hour average. 

As mentioned in earlier sections, the MRPO 2002 case is the backbone for the Minnesota(MRPO)

case. The MRPO evaluated performance of the MRPO 2002 case encompassing a significant 

portion of the 4rpos domain. The analysis included monitor sites 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 Carolina, 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 the 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 analysis56 for 24-hour average mean bias, gross error, 

fractional bias and fractional error for each month 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 are 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 concentrations 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 Voyageurs). Evaluation 

was done for 24-hour averaging times. Fractional bias and error are evaluated for 36km and 

12km (w/ PiG) grid scale modeling. The evaluation compares observed and modeled values for 

the 20 percent worst days for one or the other (or both) Class I area. There are thirty-two 20-

percent worst days total. Eight days are Boundary waters only, 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 sulfate and nitrate, it is appropriate to test 

performance only on sulfate and nitrate at Boundary 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 the 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 performing 
69

days are at very low observed concentrations—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 performance may somewhat be associated with 

prescribed burning emissions inadvertently included in the Minnesota non-utility point file. In a 

paper titled “Establishing Reasonable Progress Goals for the Northern Class I Areas: Treatment 

of Organic Carbon”, April 2, 2007, the MRPO addresses impact from wildfire 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, and 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 (largely 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 borne dust was excluded from the emissions inventory, for reasons 

described in Section 2.0. 

As mentioned earlier in this document, Minnesota has an interest in the impact of local emissions 

to regional haze in Boundary Waters and Voyageurs, 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 Minnesota. Fractional bias and error look very 

similar between the 36km and 12km(PiG) modeling results 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 bias 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 nitrate either is more a local point source issue at Boundary Waters 

and Voyageurs and the 36km meteorology is too coarse to transport 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 masking 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 determine how well the model performs each 

day of the week over the year modeled as compared to observed for each Class I area. Appendix 
70

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 into 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, the timeseries plots for nitrate are on a scale of 0-

10 μg/m3

 in the 4th quarter, but on a scale of 0-1 μg/m3 in the 3rd quarter because there is so little 

nitrate formed during that quarter. 

The model is following the patterns (ups and downs) of the observed peaks 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 enough. The model misses some peaks of NO3 in the 1st

quarter, specifically in January and March. The model does not predict the very low 

concentrations of NO3 in the summer. The model also misses some SO4 peaks in the first quarter 

and over predicts SO4 a few days in both the 1st and 4th quarters. Some of these days with over 

predicted winter SO4 correspond with days with under predicted NO3. Ammonia preferentially 

reacts with SO2 to form SO4, so limited ammonia available to further react with NOx to form NO3

could explain this modeled response. 

Elemental carbon looks good, organic carbon looks good except a few days attributed to fires. 

Crustal/soil follows the pattern of observed quite well except for the 2nd quarter. As expected, 

the model does not capture the patterns of observed 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 inventory 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 extinction, 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 component 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 discussed in other sections of this document. The 

charts also illustrate the significant roles nitrate and sulfate have in the extinction equation, and 

the times of the year when nitrate and sulfate claim greater importance. In 2002, nitrate has 

greater importance from the end of October to the end of the year. 
71

Figure 6.3 Comparison of Observed and Modeled Pairs for 20% Worst 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 2002

Extinction (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 2002

Extinction (Mm-1)

SO4 NO3 OC EC CRUSTAL Voyageurs 

Boundary Waters 
72

6.3 Comparison of Minnesota(MRPO) and CENRAP Model Performance Evaluation on 

 Specific Individual Days. 

CENRAP conducted its performance evaluation as described in its Technical Support 

Document57. The evaluation was conducted on the CENRAP baseF and over a different 

domain than the MRPO. The CENRAP performance evaluation was conducted at monitor 

locations throughout the CENRAP region. Because the coverages do not overlap it is not 

possible to directly compare MRPO and CENRAP model performance evaluation. Also, 

CENRAP focused its performance on four months of 2002; January, April, July and October. 

Except for nitrate, the general conclusions 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 nitrate performance was 

variable over their domain with under estimation in the summer and over estimation in the 

winter. CENRAP organic carbon mass performance 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 attributed to a fire inventory included 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 date

Extinction (Mm-1)

SO4 NO3 OC EC CRUSTAL CM

 

0

10

20

30

40

50

60

70

80

obs MN(mrpo) CenRAP

Avg

Julian Date

Extinction (Mm-1)

SO4 NO3 OC EC CRUSTAL CM

 

57 ENVIRON (September 2007) 
73

Minnesota has compared observed and predicted results from the CENRAP and Minnesota(MRPO)

modeling studies for nitrate and sulfate in order 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 the 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 observed/modeled pairs where backtrajectories indicate air parcels 

travel in a clear path are presented here. 

The backtrajectories were created using 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 hours, 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 nitrate concentrations are highest. The day of 

greatest difference is at Boundary Waters on October 26, when the Minnesota(MRPO) case under 

predicts nitrate by 24 Mm-1, while the CENRAP case over 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 distinctly originate and end in 

northern Minnesota and surrounding 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 domain, so it is not possible to see the entire 

path of the air parcels. However, the performance difference could be interpreted to suggest that 

the Minnesota(MRPO) case is missing emission sources in Alberta Canada that the greater western 

extent of the CENRAP domain (see Figure 3.1) captures. 

Although similar data from CENRAP is not available (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 perspective on the level of “missing” Canada NOx

emissions, they are relatively close to the combined point source NOx emissions from Minnesota 

and South Dakota (see Table 2.2), but from much further away. The 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 SO2 NOX PM25 PM10 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 nitrate, air parcels originate from the west/northwest. The more 

time the air parcel appears to spend just northwest of the Minnesota border and in northern 

Minnesota, the greater the over prediction in the CENRAP case. The CENRAP inventory has 

more wintertime NOx and ammonia in those regions than the Minnesota(MRPO). case. Section 3.0 

contains plots demonstrating the spatial 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 observed. In these cases, the 

under prediction of nitrate is likely due to insufficient ammonia available in the model in the 

winter to react with NOx 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 date

Extinction (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 date

Extinction (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 date

Extinction (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 date

Extinction (Mm-1)

SO4 NO3 OC EC CRUSTAL CM
75

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, northwest and in the Midwest 

0

20

40

60

80

100

120

obs MN(mrpo) CenRAP

299

Julian Date

Extinction (Mm-1)

SO4 NO3 OC EC CRUSTAL CM 10/26/02

End Point BOWA

0

20

40

60

80

100

120

obs MN(mrpo) CenRAP

299

Julian Date

Extinction (Mm-1)

SO4 NO3 OC EC CRUSTAL CM 10/26/02

End Point BOWA

12/10/02

End Point BOWA

0

20

40

60

80

100

120

obs MN(mrpo) CenRAP

344

Julian Date

Extinction (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 Date

Extinction (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 Date

Extinction (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 Date

Extinction (Mm-1)

SO4 NO3 OC EC CRUSTAL CM
76

(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, there is a strong southerly component where air parcels spend time in 

southern Minnesota. All the back trajectories are difficult to interpret. It appears that the 

Minnesota(MRPO) over prediction and CENRAP under prediction may be due to higher point 

source SO2 emissions estimates for the MRPO states in the Minnesota(MRPO) case and greater 

summer ammonia emissions estimates for Minnesota (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 date

Extinction (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 date

Extinction (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 date

Extinction (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 date

Extinction (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 date

Extinction (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 date

Extinction (Mm-1)

SO4 NO3 OC EC CRUSTAL CM
77

The performance evaluation of the MRPO 2005 case shows similar performance with improved 

performance for nitrate. The MRPO 2005 case has 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 increased ammonia, because the MRPO 2005 case was 

conducted using different meteorology—culminating 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 model 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 shown for individual days above—are reduced. 

However, these differences do appear to affect 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 monitor locations is slightly better at 

36km than for the Minnesota(MRPO) 12km(PiG) grid for nitrate. As discussed above, these results 

suggest that nitrate either is more a local point source issue at Boundary Waters and Voyageurs 

and the 36km meteorology is too coarse to transport 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 masking underlying issues in the modeling system that are causing 
78

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 result in the same future visibility estimate. 

6.4.b Horizontal Extent of Domain. Minnesota(MRPO) modeling performance for nitrate 

suggests the modeling could benefit 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 substantiated. Minnesota has 

elected to keep Canada emissions constant between the base and future year. Effectively, 

Canada emissions (whether increases or decreases) have no relevance in developing the RPG at 

Boundary Waters and Voyageurs. Further discussion 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 conducted by the RPOs has resulted in the best 

emissions inventory possible to date. Efforts continue to improve the inventory, including the 

ammonia emissions inventory. Section 8.5 provides information on the models sensitivity to 

ammonia emissions in establishing the RPG. 

6.4.d Modifications of Models. Model performance evaluations 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 secondary organic aerosol 

module improvements in CMAQ, and more recently in CAMx version 4.5. Although overall 

model performance for organic carbon may improve, model results without those improvements 

are acceptable in Boundary Waters and Voyageurs. Also, improved secondary formed organic 

carbon will not affect reasonable progress in Boundary Waters and Voyageurs as natural 

biogenic emissions—which are the main contributor to SOA formation at the Class I areas—

remain constant in the baseline and future year. 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 months between CAMx 4.2 (without SOA module 

improvements) and the beta version of CAMx 4.5. 

6.5 Diagnostic tests: Accuracy of the model in characterizing the sensitivity of PM2.5 to 

changes in emissions. Simulations can be performed to determine the sensitivity of model 

predictions to various inputs to the model. The purpose for diagnostic tests is to assess how 

various changes to the model inputs affect PM2.5 concentrations. It may be that reductions in one 

input component may still result in similar overall PM2.5 concentrations because reduction of one 

precursor may free up another for additional chemical reactions formulating PM2.5

concentrations. An example might be an examination of the extent to which sulfate 

concentration reductions might increase nitrate concentrations by freeing-up ammonia. No 

specific diagnostic tests were conducted to test performance of the models for this analysis. 

However, sensitivity to changes in ammonia emissions in the base and future year and their 

impact on the RPG is documented in Section 8.0 
79

7.0: Uniform Rate of Progress Analysis 

The Regional Haze Rule, promulgated by the EPA July 1, 1999, requires states to “establish 

goals (expressed in deciviews) that provide for reasonable progress towards achieving natural 

visibility conditions for each Class I area within a State”; improving visibility on the most 

impaired days and not degrading visibility on the least impaired days58. The ultimate goal of 

natural visibility is to be met in 2064, and reasonable progress goals are interim goals 

representing progress toward that end. The year 2018 is the initial 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 PM2.5 from the modeled 

base year and projected future year concentrations and apply them to the observed 

concentration data (by individual component) for the 20 percent 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 reasonable progress is measured. The Regional Haze Rule prescribes 

the baseline period as the years 2000-2004, and defines baseline visibility conditions as the 

average of the 20 percent worst visibility impaired days, calculated from the monitoring data for 

each year of the baseline, and then averaged over the 5-year baseline period. 

The monitoring data used in the baseline visibility conditions is the regulatory version obtained 

from VIEWS, including the substituted values for Boundary Waters described 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 identified 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 components—notably sulfate and 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 baseline visibility conditions, this affects six days at Boundary 

Waters and three days at Voyageurs. The baseline increases by 0.3 dv at Boundary Waters and 

0.2 dv at Voyageurs. The MRPO treatment does 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 coefficient. Extinction was calculated using PM10

and speciated PM2.5 measurements at Boundary Waters and at Voyageurs in the IMPROVE 

equation adopted by the IMPROVE Steering Committee in December 200560: 

bext = 2.2 * fS(RH) * [small sulfate] + 4.8 * fL(RH) * [large sulfate] 

 + 2.4 * fS(RH) * [small nitrate] + 5.1 * fL(RH) * [large nitrate] 

 + 2.8 * [small organic mass] + 6.1 * [large organic mass] 

 + 10 * [elemental carbon] 

 + 1 * [fine soil] 

 + 1.7 * fSS(RH) * [sea salt] 

 + 0.6 * [coarse mass] 

 + Rayleigh scattering (site specific—BOWA1= 11, VOYA2 = 12) 

 + 0.33 * [NO2 (ppb)] 

where: bext is calculated total light extinction in inverse megameters 

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

fL(RH) is the relative humidity adjustment factor for large particles; 

 fSS(RH) is the relative humidity adjustment factor for sea salt; and 

The apportionment of the total concentration 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 sulfate], for [total sulfate] ≥ 20 μg/m3; and 

[small sulfate] = [total sulfate] – [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.colostate.edu/views/Web/RHR/RHR_Planning.aspx
81

 NO2 is not currently measured at the IMPROVE monitors61, 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 fS(RH) and fL(RH) values are presented in Table 7.162,

63. 

Table 7.1. Monthly fS(RH) and fL(RH) values for Boundary Waters and Voyageurs 

ClassI f(RH) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

BOWA1 

fS(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 

fL(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 

fSS(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 

fS(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 

fL(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 

fSS(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 (bext ). The Regional Haze rule requires 

visibility to be expressed in deciviews (dv). The following equation converts bext to deciviews 

(dv): 

Haze Index (dv) = 10 ln(bext /10) 

Where: bext and light scattering due to Rayleigh scattering (i.e. the “10” in the 

denominator) are both expressed in inverse megameters (Mm-1). In order to be consistent 

across all Class I areas, the EPA prescribed 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 due to the relatively late discovery of these data in the regional 

haze process (early 2007) and the workload and staff shortage at CIRA for updating VIEWS . 

The resulting change in natural conditions 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 the sulfate scaling factor which 

reduces all the values in the distribution, somewhat offsetting the larger values. In the specific 

case of (Boundary Waters) and (Voyageurs), only 6 and 3 sample dates respectively are added to 

 

61 According to Scott Copeland of CIRA, “NO2 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://vista.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 observations that are in the 5 years’ worth of 20 (percent) worst 

days, so there is ...no way to move the mean very much.” 

A straight line connecting the baseline visibility average (2000-2004) and natural conditions 

(2064) form the uniform rate of progress or “glidepath”. Placement relative to the line 

determines whether estimated future 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 baseline conditions. Boundary Waters would 

be on the glidepath in 2018 if visibility impairment 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 are 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 concentration (i.e. sulfate, nitrate, organic carbon, elemental 

carbon, fine soil and coarse particulate matter). 

Applying the RRFs to baseline monitoring conditions, for each species comprising PM2.5, 

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 measured species concentration for all of the 20 percent 

worst (and 20 percent best) days over 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 equation, 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 year in the baseline period; and 

2. Average the resulting 5-year mean deciview values (for the 20 percent worst, 

and for the 20 percent best). 
83

Applying the above methodology with future emission estimates that reflect reasonable controls 

provides the RPG based on modeling. 

The final deciview value represents the reasonable progress goal, which is established using a 

projected future ambient concentration resulting 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 environmental impacts of compliance; and 

• Remaining useful life of existing sources that contribute to visibility impairment. 

Minnesota used the EPA Modeled Attainment Test Software (MATS) program, version 1.1.2, to 

calculate the baseline for the average 20 percent worst days, the RRFs using the modeling work 

from each organization, the resulting projected future 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 considered on-the-books controls, 

meaning controls expected due to other programs despite the Regional Haze Rule, to evaluate 

future year placement relative to the glidepath. 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 results indicate that future year visibility is on a path toward 

natural visibility conditions, however, 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 results 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 future year values for each organization were 

calculated using the same monitoring data establishing the baseline (2000-2004) and ran through 

MATS. The only difference lies with the modeled 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 grouping, 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 glidepath 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 CENRAP case does not. See Section 2.0 for details on 

the EGU emissions. 

The MRPO 2002 case shows future projected visibility 0.2 dv further from the glidepath at 

Boundary Waters and at Voyageurs than the Minnesota(MRPO) case. This is expected and due to 

the improved future emissions projections 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

Year

Deciviews

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

Year

Deciviews

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 Analysis 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 Analysis 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 additional 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 conditions are the same among the various analyses, 

the modeled RRFs can help explain the difference. Tables 8.3 and 8.4 contain the RRFs by 

visibility component, or species, for each modeling study for the 20 percent worst and best days, 

respectively. An RRF above 1.000 means the modeled concentration increases from 2002 to 

2018. A factor below 1.000 means the modeled concentration decreases from 2002 to 2018. 

Evaluation of the RRFs for Boundary Waters and Voyageurs 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 are 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; even though the extinction 

calculation multiplies the observed concentration of elemental carbon by a factor of 10, it still 
86

does not feature prominently in the future year projections at Boundary Waters 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 GridBase

 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 GridBase

 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, the most noticeable difference between the MRPO 

2002 and Minnesota(MRPO) cases is the response to sulfate 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 incorporates 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 sulfate 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 estimates 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 RRFs overcome the sulfate decreases in the 

Minnesota(MRPO) case attributable to the change from IPM2.1.9 to IPM3.0. This explains why the 
87

position of the CENRAP case in relation to the 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 NOX reductions in Canada. 

To elaborate further, Section 6.0 of this document shows that the CENRAP case over predicts 

nitrate formation in 2002 at Boundary Waters and Voyageurs compared to observed values 

collected at monitoring stations. This is likely caused by additional NOX and a significant 

amount of available ammonia with which to react. This information, along with the greater 

projected decrease in NOX emissions (i.e. Canada), and the RRFs for nitrate, suggests that the 

excess free ammonia in the CENRAP inventory allows for the model to respond well to future 

projected reductions in NOx emissions, possibly even over-stating them. Conversely, 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-state the effects of 

reducing the nitrate ion due to reductions of NOx emissions. 

Tables 8.5 and 8.6 summarize the winter day NOX and NH3 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 Minnesota and areas to the West only because winds 

appear to originate in that direction on the 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 entire portion of that Country within the 4rpos 

domain. While the Minnesota(MRPO) case does not contain a change in Canada NOX emissions, 

the CENRAP case does, by about 44 percent on a winter 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 tools used do not separate Canada emissions by 

province. The spatial plot in Figure 8.3 illustrates that the reduction in NOX 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 additional Canada emissions further West, outside 

the 4rpos domain, included in the CENRAP case. 

Results from modeling conducted with the PSAT tool in CAMx supports the above interpretation 

on the model response to nitrate changes in the CENRAP and Minnesota(MRPO) cases. This tool 

tracks the original source of particulate species by geographic region 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 NOX and NH3 Emissions in Tons from Base Year 2002 for 

CENRAP and Minnesota(MRPO) 

 NOX NH3 NOX NH3 NOX NH3 

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 NOX and NH3 Emissions in Tons from Base Year 2018, and 

Difference from 2002 to 2018, for CENRAP and Minnesota(MRPO)

 NOX NH3 NOX NH3 NOX NH3 NOX NH3 NOX NH3 

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 above tables reflects that Canada area sources (other than 

ammonia sources) were inadvertently excluded from the base year files. 

Figure 8.3. CENRAP Winter Day NOX 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 projections for future visibility are 

based on different worst visibility days, different visibility components that contribute to those 

days, and the predominant wind direction from which emission reductions occur. There are also 

the rather significant differences in ammonia emissions. Section 6 describes improved model 

performance for nitrate in the MRPO 2005 base year. 

Overall, the MRPO 2005 case contains more ammonia 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 combination may counter-balance the model response to 

NOx emission reductions seen in the Minnesota(MRPO) case. 

The following exercise supports this hypothesis. The ammonia inventory in the Minnesota(MRPO)

case was replaced with the ammonia inventory 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 percent change in the nitrate RRF at Boundary Waters, 

and 9 percent at Voyageurs. Ultimately, this results in a more optimistic projected RPG in 2018 

by 0.2 dv at Boundary Waters and by 0.3 dv at Voyageurs. 

Because the Minnesota(MRPO) case under predicts ammonia in the base year (see Section 2.0), and 

because of improvements made by MRPO to the future year ammonia inventory since the 

inventory used in the Minnesota(MRPO) case, the model sensitivity test also suggests that the RPG 

established in the Minnesota(MRPO) case may be a slightly conservative estimate. 

Table 8.7 Minnesota Annual Emissions Change in Tons of NOX and NH3 from 

All Source Categories in Minnesota(MRPO), and MRPO 2005 Case 

Specie Minnesota(MRPO) MRPO 2005 Case 

2002 2018 Difference 2005 2018 difference 

NOX 516,000 317,000 -199,000 464,000 278,000 -186,000 

NH3 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 take into account the year-to-year variability 

of the meteorology in the monitored 5-year baseline, the RPG still is sensitive to meteorology. 

In this case, use of 2005 meteorology provides 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 formation 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 growth 
90

factors and include additional sources in northeastern Minnesota. These changes were only 

made to the Minnesota(MRPO) case and likely result in future projections further from the 

glidepath than were the 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 monitoring values from the monitor within the Class I area being 

evaluated. Although the monitor location 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 throughout the Class I 

areas, indicated a range of projected values from 18.3 – 19.0 dv, with an average value of 18.7 

dv in Boundary Waters—the largest Class I area—for the 20 percent worst days. The average 

value is the same as the 36km result at the monitor location. The same is true for the 20 percent 

best days. It does not appear necessary to set separate goals for various locations across the 

Class I area based on the 12km results. Only one receptor was placed at Isle 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 Assess Contributions to Visibility Impairment. 

As mentioned above, PSAT in CAMx tracks the original source of particulate species by 

geographic region 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 36km modeling. 

The geographical and source category 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 groupings are shown in Figure 8.4 and described as 

follows: 

• Individual States Arkansas, Illinois, Indiana, Iowa, Kansas, Louisiana, Michigan, 

Minnesota, Missouri, North Dakota, Nebraska, 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 region comprised of Alabama, Florida, 

Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee, Virginia and 

West Virginia; the 

• West States consolidated as one geographic region 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 marine, 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 which 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 from each organization so they all appear on the 

same scale, order and color 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 results for the species, ammonium nitrate, 

ammonium sulfate, organic aerosols, 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 extinction values for the 20 percent worst days at 

Boundary Waters and Voyageurs were predominantly associated with ammonium nitrate and 

ammonium sulfate on days not impacted by wildfires (see Section 6.0). Other considerations 

include the additional computational times associated with modeling the other species, coupled 

with their relative unimportance for the development 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 ammonium, assuming sulfate and nitrate are fully 

neutralized. The PSAT results in the Minnesota(MRPO) and MRPO 2005 cases both include 

ammonium as a separate species in the extinction equation, indicating that ammonia sources are 

viable targets for control measure consideration. Without ammonia, no ammonium sulfate and no 

ammonium nitrate are formed. PSAT can provide source and geographic location where the 

ammonia that forms ammonium originates. The inclusion of ammonium calls for an additional 

modification to the IMPROVE equation. The modified IMPROVE equation for sulfate, nitrate 

and ammonium for use in the PSAT analysis is as follows: 

Extinction(Mm

-1

) = (2.2* fS(RH)*[small sulfate]) + (4.8 * fL(RH)*[large sulfate]) 

 + (2.4 * fS(RH)*[small nitrate]) + (5.1* fL(RH)*[large nitrate]) 

 + (2.2* fS(RH)*[small ammoniumassoc. sulfate]) + 

 (4.8 * fL(RH)*[large ammoniumassoc. sulfate]) 

 + (2.4 * fS(RH)*[small ammoniumassoc. nitrate]) + 

(5.1* fL(RH)*[small ammoniumassoc. nitrate]) 

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

fL(RH) = water growth factor as a function of relative humidity for large particles; and 

 [nitrate], [sulfate] and [ammonium] = concentration of these components in μg/m3. 

Although reasonable progress goals are depicted in deciviews, contributions of individual 

species, source sectors and source regions are only evaluated in terms of extinction. An 

extinction value less than 10 Mm-1 will produce a negative value in deciviews. An extinction 

coefficient of Bext = 10 Mm-1 will result in a deciview value of 0 dv (10 * ln(Bext/10)). Assessing 

contributions to visibility with negative values would be confusing. 

Figures 8.5 through 8.8 illustrate the geographic contribution assessment, 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 Voyageurs. In 2018 with on-the-books controls 

implemented, the greatest contributors to ammonium sulfate and ammonium nitrate at both Class 

I areas are Minnesota, Wisconsin, Iowa, Illinois, 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 little modeled contribution change in nitrate 

from the base year to the future year. This further supports the conclusion that a lack of 

ammonia in the base year and excess ammonia in the future year can dampen the effect of NOX

controls. 

Caution should be taken when drawing conclusions from the difference charts in these Figures. 

PSAT does not provide answers to model response; meaning if emissions are reduced in a 

particular sector or region, PSAT can not tell what effect that change will have on the results. In 

other words, it would be inappropriate to conclude that total emission reductions in Minnesota 

equates to about 0.8 Mm-1 contribution reductions from Minnesota to visibility impairment. 

Instead, one can simply see how the 2018 PSAT results compare with the 2002 PSAT results on 

what species, regions and sectors are 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 from 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. These decreases in contribution from EGUs and 

mobile sources cause a shift in contribution to nonEGU point sources in Minnesota, and 

somewhat to area sources, in 2018. 

Figures 8.13 through 8.16 illustrate the source category contribution assessment for the entire 

domain on the 20 percent worst days at Boundary Waters and Voyageurs in the Minnesota(MRPO)

case in 2018 with on-the-books controls implemented. 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 conditions are the next greatest contributor to extinction in 2018 at 

Boundary Waters and Voyageurs. Boundary conditions 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 boundary condition contribution 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 contributions 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 attributed 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 geographic 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 ammonium 
94

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 geographic source contributor (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 contribution 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 ammonium 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 supports earlier conclusions that the excess free 

ammonia in the CENRAP case allows for the model to respond well to future projected 

reductions in NOx emissions, possibly even over-stating them. The very large contribution shift 

between 2002 and 2018 associated with mobile source NOX in the CENRAP case appears to 

support this conclusion. 

Appendix H also contains the 2018 PSAT results for 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 nitrate as the Minnesota(MRPO) case. The relative contribution by State is 

quite comparable at Boundary Waters, however, Voyageurs 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 contribution to Voyageurs is associated with the use of 

2005 meteorology and hence a different set of 20 percent worst days. 

Comparing contributions between 2018 and 2005, the MRPO 2005 case shows much less 

contribution from States to the East and Southeast of both Class I areas, than in the 

Minnesota(MRPO) case (2018-2002). This supports the conclusion that the MRPO 2005 modeling 

shows more influence from emission changes that occur in those geographic regions. Boundary 

Waters and Voyageurs experience much less contribution to sulfate in 2018 than 2005. The 

MRPO 2005 case also shows less contribution to extinction from nitrate in 2018 than 2005, 

which also supports the conclusion that the model is responding more to NOX 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 growth, and it inadvertently excludes the mining 

operations at Northshore Mining-Silver Bay, that are included in the Minnesota(MRPO) case. It is 

not possible to tell how the MRPO 2005 PSAT results might change were these modifications 

made. 
Boundary Waters: Extinction Contribution by Specie and Sector for Each Region on the 20 Percen

Figures 8.5 and 8.7: 2018 Figures 8.6 and 8.8: 2018 m

0 1 2 3 4 5 6 7 8 9 10

BC

Canada

West

SouthEast

NorthEast

Wisconsin

Texas

SouthDakota

Oklahoma

NorthDakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm-1)

PSO4 PNO3 PNH4

Total Extinction: 32 Mm-1 

-2.5 -2 -1.5 -1 -0.5 0 

BC

Canada

West

SouthEast

NorthEast

Wisconsin

Texas

SouthDakota

Oklahoma

NorthDakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm

PSO4 PNO3 

0 1 2 3 4 5 6 7 8 9 10

BC

Canada

West

SouthEast

NorthEast

Wisconsin

Texas

SouthDakota

Oklahoma

NorthDakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm-1)

EGU nonEGU onroad nonrd_mar other nh3 BC

Total Extinction: 32 Mm-1 

-2.5 -2 -1.5 -1 -0.5 0 

BC

Canada

West

SouthEast

NorthEast

Wisconsin

Texas

SouthDakota

Oklahoma

NorthDakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm

EGU nonEGU onroad nonrd_
Voyageurs: Extinction Contribution by Specie and Sector for Each Region on the 20 Percent W

Figures 8.9 and 8.11: 2018 Figures 8.10 and 8.12: 2018 

0 1 2 3 4 5 6 7 8 9 10

BC

Canada

West

SouthEast

NorthEast

Wisconsin

Texas

SouthDakota

Oklahoma

NorthDakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm-1)

PSO4 PNO3 PNH4

Total Extinction: 32 Mm-1 

-2.5 -2 -1.5 -1 -0.5 0 

BC

Canada

West

SouthEast

NorthEast

Wisconsin

Texas

SouthDakota

Oklahoma

NorthDakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm

PSO4 PNO3 

0 1 2 3 4 5 6 7 8 9 10

BC

Canada

West

SouthEast

NorthEast

Wisconsin

Texas

SouthDakota

Oklahoma

NorthDakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm-1)

EGU nonEGU onroad nonrd_mar other nh3 BC

Total Extinction: 32 Mm-1 

-2.5 -2 -1.5 -1 -0.5 0 

BC

Canada

West

SouthEast

NorthEast

Wisconsin

Texas

SouthDakota

Oklahoma

NorthDakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm

EGU nonEGU onroad nonrd_
Figures 8.13 and 8.14 Boundary Waters 2018 Extinction Contribution by Sector for each Specie & by Specie 

20 Percent Worst Days. 

02468

10121416182022

PSO4 PNO3 PNH4 ASOA BSOA

Extinction (Mm-1)

EGU nonEGU Onroad Nonroad otherArea Ammonia BC ASOA BSOA

Total Extinction: 35 Mm-1 

0123456789

101112

EGU

nonEGU

Onroad

Nonroad

otherArea

Ammonia

Extinction (Mm-1

)

PSO4 PNO3 PNH4 BSOA 

Figures 8.15 and 8.16 Voyageurs 2018 Extinction Contribution by Sector for each Specie & by Specie for 

20 Percent Worst Days. 

02468

10121416182022

PSO4 PNO3 PNH4 BSOA ASOA

Extinction (Mm-1)

EGU nonEGU Onroad Nonroad otherArea Ammonia BC ASOA BSOA

Total Extinction: 34 Mm-1 

0123456789

101112

EGU

nonEGU

Onroad

Nonroad

otherArea

Ammonia

Extinction (Mm-1

)

PSO4 PNO3 PNH4 BSOA 
98

8.2 Beyond On-the-Books Controls. 

The PSAT results provide guidance to what geographic regions and source categories to target 

for emission reductions in 2018 beyond those associated with on-the-books controls. While the 

model does not respond as well as envisioned to changes in NOX in the Minnesota(MRPO) case, it 

is sufficient for providing guidance on exploring control measures, understanding its 

uncertainties. The response to changes in NOX 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 NOX emission reductions. Also, as ammonium 

sulfate levels decline due to reductions in SO2 emissions, more ammonia is free to react with 

NOX to form ammonium nitrate. Thus, reductions in NOX emissions should be explored as well 

as for SO2 emissions. 

The model response to NOX 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 reaches the glidepath. Use of 2002 meteorology in 

the Minnesota(MRPO) case may be preferred then, to explore model response to emissions changes 

in other regions. 

In developing the RH SIP, Minnesota decided to focus attention on States that contribute 5 

percent or more to either Boundary Waters or Voyageurs in 2018. The percentage breakdown 

contribution to ammonium nitrate and ammonium 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 individual 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 SO2 and 25,000 tpy NOx from grid cells in Canada at the Canada/North 

Dakota border were inadvertently assigned to North Dakota. Thus, less contribution should be attributed to North 

Dakota, and more to Canada. In 2018, the total U.S. total North Dakota point source emissions were 77,200 SO2

and 70,500 NOx. 
99

Figure 8.17. State Contributions to Ammonium Nitrate and Ammonium Sulfate Light 

Extinction at Boundary Waters 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 NH4NO3 

& 

(NH4)2SO4: 

32 Mm-1

Figure 8.18. State Contributions to Ammonium Nitrate and Ammonium Sulfate Light 

Extinction at Voyageurs for the Year 2018 after 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 NH4NO3 

& 

(NH4)2SO4: 

32 Mm-1

8.2.a Minnesota Controls. In order to determine where to focus potential control strategies in 

Minnesota, PSAT model runs were conducted to better understand contributions of the Northeast 

part of Minnesota (near Boundary Waters and Voyageurs) where a significant amount of point 

source NOx and SO2 is emitted, as compared to the contribution from the rest of the State. The 

size of Boundary Waters, 1.3 million acres stretching nearly 150 miles along the international 
100

boundary adjacent to Canada’s Quetico Provincial Park,67 and the proximity of large point source 

NOX and SO2 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 contributions 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 Locations for 12km Grid with PiG. 

GF

GF

GF

GF GF

GF GF GF GF

GF

GF

GF

GF

Minnesota

Michigan

Ontario

Boundary Waters 

Canoe Area Wilderness

Isle 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 Minnesota contributes 14 percent and the rest-ofMinnesota contributes 12 percent of total ammonium nitrate and ammonium sulfate extinction at 

the Boundary Waters monitor location. Northeast Minnesota contributes 15 percent and the restof-Minnesota contributes 17 percent of total extinction at the Voyageurs monitor location. 

At the receptors placed at various points throughout the Class I areas, Northeast Minnesota 

contributions range from 3 – 19 percent of total extinction. The 3 percent is at the western tip of 

Isle Royale, and the 19 percent contribution is at a receptor within 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 between 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.org 
101

maximum (Boundary Waters, receptor 13) and the minimum (Western tip of Isle Royale) impact 

from Northeast Minnesota 

Figure 8.20 Percentage Contribution of Northeast 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 Northeast and Rest-of-Minnesota to Maximum 

(BOWA_13) and Minimum (ISLE)Receptors 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 indicate sulfate as the major component contributed by Northeast 

Minnesota point sources, while the rest of the state is more evenly divided between sulfate from 

point sources and ammonium from agricultural sources. Figure 8.22 illustrates the species 

contributions across the Class I areas. Emissions of NOX in Northeast Minnesota are much 

higher than SO2. Point source emissions of NOX in Northeast Minnesota are estimated at 37,500 

tons per year, while SO2 emissions are 8,000 tons per year. 

One explanation for this discrepancy in contribution of SO2 and NOX to the 20 percent worst 

days is that nitrate is an issue on fewer days and is formed in the colder months. Viewing 

animated spatial plots of the source apportionment 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 NOX emissions are not moving North toward the 

Class I areas to form ammonium nitrate, but are moving to the East, Southeast and South. 

Figure 8.22: Minnesota Contributions at Receptors 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 individual 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 areas. In Northeast Minnesota, the point sources in 

the list account for nearly all the SO2 and NOX 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 where chosen because they are 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 Minnesota

Extinction (Mm-1) lowerquartile minimum median maximum upperquartile

Rest MN Ext:

2.7 - 5.1 Mm-1

NE MN Ext:

1.6 - 6.5 Mm-1
103

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 Lake

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 illustrates 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. However, 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 contribution 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. Decreases in individual point source contributions from 2002 to 2018 

for Xcel Energy—A.S. King, —High Bridge and —Riverside 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” scenario, 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 rise to 4 lbs/MMBtu permitted rate when actuals 

are less than 1 lb/MMBtu. Most of the other contributions from existing sources remained the 

same from the base year to the future year, with very slight increases in contribution from 

EVTAC startup of line 1, and also some EGAS predicted growth at non-mine facilities such as 

Boise Cascade. Additional contributions are associated with the three new taconite mines. 
104

Figure 8.23 Locations of Individual Point sources 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

DuluthStm CPacificD

PSAT & Plume-in-Grid

for Point Sources Plume-in-Grid Applied

Electric Generating Units

Non-Utility 

Non-Utility Added to 2018
105

Figure 8.24 Minnesota Point Source Contributions 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

2018

Extinction (Mm-1)

lowerquartile minimum median maximum upperquartile
106

Figure 8.25 Other Minnesota Individual 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

2

AustinPU

FlintH-PB

IntlP-Sar

MaraAsh

OtterHoot

RochPU

Xcel-ASK

Xcel-BDog

Xcel-HiBr

Xcel-Rivr

Xcel-SHER

Extinction (Mm-1)

lowerquartile minimum median maximum upperquartile

Other MN 

Point Total 

Extinction: 

1.4 - 2.6 Mm-1

Figure 8.26 Other Minnesota Individual 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

2

AustinPU

FlintH-PB

IntlP-Sar

MaraAsh

OtterHoot

RochPU

Xcel-ASK

Xcel-BDog

Xcel-HiBr

Xcel-Rivr

Xcel-SHER

Extinction (Mm-1)

lowerquartile minimum median maximum upperquartile

Other MN 

Point Total 

Extinction: 

0.7 - 1.3 Mm-1
107

Figure 8.27 Northeast Minnesota Individual 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

2

Blandin

BoiseC

DuluthStm

EVTAC

GPacificD

HibbingPU

HibTac

Ispat

KeeTac

Minntac

MNPWR-Bos

MNPWR-Hib

MNPWR-Las

MNPWR-TH

NShore-SB

Pot-Cook

Pot-Grand

SappiC

VirgPU

Extinction (Mm-1)

lowerquartile minimum median maximum upperquartile

NE MN 

Point Total 

Extinction: 

1.2 - 4.4 Mm-1

Figure 8.28 Northeast Minnesota Individual 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

2

Blandin

BoiseC

DuluthStm

EastMine

EVTAC

GPacificD

HibbingPU

HibTac

Ispat

KeeTac

MesabiNug

Minntac

MNPWR-Bos

MNPWR-Hib

MNPWR-Las

MNPWR-TH

NShore-SB

Pot-Cook

Pot-Grand

SappiC

VirgPU

WestMine

Extinction (Mm-1)

lowerquartile minimum median maximum upperquartile

NE MN 

Point Total 

Extinction: 

1.1 - 5.2 Mm-1
108

The above information supports consideration of a control strategy that focuses on point sources 

in Northeast Minnesota. Planning staff determined that a 30 percent reduction in SO2 and NOX

emissions from 2002 levels for the six counties 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 NOx and SO2 from 2002 emissions levels. About 21 percent of that 

reduction is already associated with the Minnesota Power—Boswell and –Taconite Harbor 

projects described above and included in the on-the-books controls. In order to model this plan 

in the uniform rate of progress analysis, the remaining 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 affected stack, the factors applied and 

the resulting emissions. All these controls, except 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 

NOx

Emissions

NOx

factor

NE MN 

Plan 

Emissions

2018 

SO2

Emissions

SO2

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 

SV028 1,682 0.80 1,345 185 1.00 185 ‘06/’07

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 

blend + 

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 

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

Including the changes to the Northeast Minnesota EGUs and the taconite adjustment factors, the 

overall percent reduction from Northeast Minnesota point sources is about 31 percent. A 
109

uniform progress analysis was developed for this scenario, and Minnesota 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

Year

Deciviews

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

Year

Deciviews

Northshore Mining – Silver Bay. Other recent controls finalized after establishment of the RPG 

are those associated with the BART determination for the power boilers (EGUs) at Northshore 
110

Mining—Silver Bay. The controls are LNB on Power Boilers 1 & 2 (emission unit i.d. EU001 & 

EU002) to control NOX, and co-firing of biomass to displace coal on Power Boilers 1 & 2 to 

control SO2. 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 measures were considered, including Xcel 

Energy—Sherburne County. Xcel energy had submitted 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 proposal for SCR on all three units due to cost 

concerns. Instead, they committed to low NOX burners (LNB) on Units 1, 2 and 368. The new 

proposal (in Table 8.11) projects less SO2 emitted than IPM base total emissions based on a 

change in projected fuel use. The LNB results in more projected NOX than the original proposal 

with SCR. Units 1 & 2 are subject to BART, and 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 the project was finalized. LNB was installed 

2008/2009; scrubber upgrade (retrofitting sparger tubes and installing 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 Minnesota and other significant 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) 

NOx SO2 NOx SO2

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) 

NOx SO2 NOx SO2

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 Withdrawal 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 CENRAP 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 source 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 original Xcel Energy—Sherburne County proposal 

described above, and emission reduction requests from MANE-VU for all five MRPO states 

(Illinois, Indiana, Michigan, Ohio and Wisconsin). The MANE-VU reduction request involves 

scaling EGU SO2 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) SO2 emissions by 0.72. 

In response to the Minnesota 0.25 lb/MMBtu emission rate request from neighboring states and 

Missouri, MRPO applied the emissions rate to the IPM3.0 “will do” by scaling SO2 and NOX

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 from 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 SO2

(tons/year) 

SO2

(lb/MMBtu) 

NOX

(tons/year) 

NOX

(lb/MMBtu) 

Statewide 

Ratio 

SO2

Statewide 

Ratio 

NOX

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 emitting facilities using MRPO emissions-over-distance 

“Q/d” lists for 2002 emissions. MRPO determined that 90% control (scaling SO2 and NOX

emissions by 0.10) would get close to the “target”. The facilities chosen, the targets, and 

emission reductions should 2018 “will do” emissions 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 SO2

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 SO2

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 

SO2 Antelope Valley 38.91 -35.02 3.89

NOX

Antelope Valley

Coal Creek 

36.58

23.85

-33.28 

-20.80 

3.30

3.05

Wisconsin SO2

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 resulting 2018 emissions projections for point sources associated 

with the potentially reasonable control measures and the emissions change from the 2018 

Minnesota(MRPO) emissions projections used for establishing the reasonable progress goal. The 

emission reductions associated with Minnesota point sources in Table 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) 

 SO2 NOX SO2 NOX SO2 NOX

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 Boundary Waters and Voyageurs, due to the emissions 

reductions summarized above, is shown in Figures 8.31 and 8.32. The results show an additional 

0.3 dv reduction at Boundary Waters and an additional 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 measures from other states, nor the original Xcel 

Energy—Sherburne County proposal, have been determined 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

Year

Deciviews

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

Year

Deciviews
114

8.3 Canadian Impacts and Control Strategies. 

Many approaches have been proposed on how to quantify international impacts, specifically 

Canada’s contribution to visibility 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 emissions would still be included in the baseline 

observed values. Some have attempted to estimate 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. Minnesota 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 the future year establishes a reasonable progress 

goal based on known, publicly available information from the United States. There is enough 

uncertainty in the Canada emissions that including 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 impact on visibility that collaboration with Canada 

on emissions and modeling could help better assess the RPG at Boundary Waters and 

Voyageurs. MRPO has begun such collaboration with Ontario; but it appears that Canadian 

impact at Boundary Waters and Voyageurs also comes from the more western provinces of 

Manitoba, Saskatchewan and Alberta, so additional collaboration would be useful from those 

provinces as well. As this would 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 the accompanying SIP are archived at the Minnesota 

offices and that provision has been made to maintain 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 analyses contact Margaret McCourtney at 651-757-2558 

or margaret.mccourtney@pca.state.mn.us. 
115

V. References 

 

Baker, Kirk, (undated) “CAMx Model Performance for Three Annual Applications (2001-2003) 

Supporting Regional Haze and PM2.5 Regulations” 

Baker, Kirk (June 2004). “Processing and Quality Assurance of Emissions Files for Photochemical Model 

Input” 

Baker, Kirk (December 2004) “Meteorological Modeling Protocol for Application to PM2.5 /Haze/Ozone 

Modeling Projects” 

Baker, Kirk, and Matthew Johnson, Steven King, Wusheng Ji (February 2005), “Meteorological 

Modeling Performance Summary for Application to PM2.5 /Haze/Ozone Modeling Projects” 

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

Battye, William and Jeff Harris, EC/R Incorporated (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 Light Extinction Model Performance Metrics, Goals, and 

Criteria for Three-Dimensional Air Quality Models; 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 Planning Organization: Final Report” EC/R, Inc. 

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

Copeland, Scott (April 2008) “Calculation Method for Natural Conditions with the New IMPROVE 

algorithm” Air and Waste Management Association 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 Emissions Inventory Project – Development of Local 

Data for Construction and Agricultural 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 Updated 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 Emissions 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, PM2.5 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 2005) “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 Recreational Marine Emission Sources” 

MACTEC (January 2006) “Documentation for MACTEC 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, PM2.5, 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 transported about the earth as an intact entity 

and can be tracked. 

• Anthropogenic – Caused by humans (i.e. pollutant emissions from industrial processes, 

automobiles and trucks). 

• Apportionment – Proportional distribution or allocation. 

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

• Area Source – an emisions source that are not identified by locational coordinate and stack 

parameters (i.e. agricultural operations, residential heating). Emissions are assigned to a grid 

cell. 

• Backtrajectory – Tracking backward in time from where 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 sources built between 1962 – 1977 and generally 

grandfathered under most Clean Air Act programs. 

• BaseC – 2002 emissions inventory developed by CENRAP prior to Base G. BaseC is the 

version of the CENRAP inventory used in Minnesota modeling. 

• BaseF – 2002 emissions inventory developed by CENRAP, final source contribution 

modeling with CAMx and model performance evaluation was conducted with this version of 

the CENRAP case; not BaseG. 

• BaseG –2002 emissions inventory developed by CENRAP. BaseG is the final CENRAP 

emissions inventory for establishing reasonable 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 contribution modeling, performance evaluation and for 

establishing reasonable progress goals. 

• Baseline Conditions – Average visibility conditions (in deciviews) 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 prescribed by the Regional Haze Rule. 

• Base Year – The individual years comprising the Baseline 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 stratospheric ozone) of the domain. 

• Bext – 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. emissions 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 NOX and SO2 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 extensions - an Eularian air quality grid 

model that simulates atmoshperic and surface processes affecting the transport, chemical 

transformation and deposition of air pollutants and their precursors; a contemporary of 

CMAQ. Used by MRPO and Minnesota to establish reasonable progress goals, and for 

source apportionment by these and other organizations (i.e. CENRAP).

• CASTnet – Clean Air Status and Trends Network. A regional long-term monitoring 

program established in 1987 and administered 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 Association – 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 CENRAP 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 visibility information, including IMPROVE and 

VIEWS. 

• Class I Area – Areas of special national or regional value, whether natural, scenic, 

recreational or historic, for which the Clean Air Act provides special protection. Mandatory 

Class I areas are managed either by the Forest Service, the National Park Service or Fish and 

Wildlife Service. 

• CMAQ – Community Multiscale Air Quality model - an Eularian air quality grid model that 

simulates atmoshperic and surface processes affecting the transport, chemical transformation 

and deposition of air pollutants and their precursors; a contemporary of CAMx. An EPA 

supported moded used to support CAIR, and used regional planning organizations (i.e. 

CENRAP) to establish reasonable progress goals.

• Coarse Particulate Mass – Particulate mass with a diameter between 2.5 and 10 microns, 

between PM2.5 – PM10. 

• CONCEPT – CONsolidated Community Emissions Processing Tool. A model that 

calculates air pollutant emission inventories generating hourly speciated emissions on a 

gridded basis for input to an air quality model. 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 terms of the extinction coefficient that is 

linear with perceived changes in visibility. One to two deciviews is the smallest change in 

visibility that is perceptive to the human eye. 

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

• Extinction Coefficient – See Bext. 

• 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 solid, 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 gridded basis for input to an air 

quality model. The most current version of EMS available is EMS-2003. 

• EPA – United States Environmental Protection Agency. 

• EVTAC – Currently United Taconite LLC (UTAC). 

• FGD – Fabric Filter/Flue Gas Desulfurization. A scrubber device to control SO2 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 and inductrial boilers) 

• FIPS – Federal Information Processing Standards, a standard set of numeric codes issued by 

the National Insittute of Standars and Technology to ensure uniform identification of 

geographic entities throughout all federal government agencies. 

• FSI – Furnace Sorbent Injection. A control technology that reduces SO2 emissions by 

injecting a dry sorbent into the upper part of the furnace, where the sorbent reacts to form 

salts that are then removed by a fabric filter or electrostatic precipitator. This technology can 

reduce SO2 by varying amounts depending on the application. In applications in Minnesota 

within the SIP, this technology can reduce SO2 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 

NOX point source plumes before oxidant production 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 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. 

• Integrated Planning Model – a model developed by ICF that EPA uses to evaluate future 

impact of pollution control policies on EGUs in combination with projected energy needs. 

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

• IMPROVE – Interagency Monitoring of Protected Visual Environments – Cooperative 

program to monitor visibility in the Class I areas. 

• Ispat Inland Inc – Currently ArcelorMittal Minorca Mine Inc. 

• LADCO – Lake Michigan Air Directors Consortium – Air quality planning organization for 

Illinois, Indiana, Michigan, Ohio, 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 NOX Burner. A control technology that reduces NOX by controlling fuel and air 

mixing in the burner, reducing peak flame temperature. Over-fire air (OFA) and SNCR can 

be added to LNB to increase NOX removal efficiency. Depending on the application in 

Minnesota within the SIP, LNB/OFA can reduce NOX by 40-50%. 
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• 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 Case (BaseK) inventory for use 

in source contribution modeling, performance evaluation and for the 2018 reasonable 

progress goals at Boundary Waters and Voyageurs for Minnesota’s regional haze SIP. 

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

• MM5 – Mesoscale Meteorological Model – A numerical model for weather prediction from 

one kilometer to continental scales, developed by Pennsylvania State University/National 

Center for Atmospheric Research. 

• MRPO – Midwest Regional Planning Organization – 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 MRPO 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 MRPO for use in establishing the 

2018 reasonable progress goals for regional haze SIPs. See BaseM. 

• Mton – Megaton 

• μg/m3 – micrograms (or microns) per cubic meter 

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

• NEI – National Emission Inventory – Compilation of annual emissions by pollutant, source 

category by County for each State; EPA requires States to submit to EPA on a 3-year cycle. 

• NH3 – Ammonia 

• NH4 – Ammonium ion 

• NO3 – Nitrate ion 

• NOX – Nitrogen oxides 

• Nonattainment – Areas of the country designated by EPA where pollutant levels exceed 

national ambient air standards. 

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

• Nonroad – mobile equipment not traveling on roadways (i.e. recreational vehicles, 

construction and agricultural equipment). Marine vessels, airplanes and locomotives are also 

considered nonroad sources, however, emission estimation techniques vary from those of 

other nonroad equipment. Emissions are assigned to grid cells 

• NOX – 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 within CAMx to treat the early dispersion and chemistry of 

point source plumes. 

• Plume-in-Grid – See PiG 

• PM2.5 – Fine particulate mass with a diameter less than or equal to 2.5 microns. 

• PM10 – Particulate mass with a diameter less than or equal to 10 microns. 

• Point Sources – industrial sources that are identified by locational coordinate and stack 

parameters (i.e. facilities with state permits). Emissions are assigned to a grid cell based on 
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the locational coordinate of the stacks, except 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 species by geographic region and source category. 

• PSD – Prevention of Significant Deterioration; a program established by the Clean Air Act 

that limits the amount of additional air pollution that is allowed in Class I and Class II areas. 

• Rayleigh Scattering – The scattering of light by particles smaller than the wavelength of 

light that is scattered. 

• Reasonable Progress Goal – State established interim goals, 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 PM2.5 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 boosted injection of over-fire air in the 

boiler or furnace to reduce NOX. Flue gas mixes with added air in the presence of heat, 

improving particle burnout in the upper furnace therefore reducing NOX. Typically applied 

to coal fired power plants. ROFA can reduce NOX 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 NOX in the presence of oxygen to form nitrogen and water. This technology can 

achieve from 70-90% control of NOX 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 Emission – 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 NOX. When 

applied to boilers, ammonia is injected into the furnace above the combustion zone, where it 

reacts with NOX to reduce it to N2 and water. In applications in Minnesota within the SIP, 

this technology can reduce NOX by 40%. 

• SO2 – sulfur dioxide. 

• SO4 – sulfate ion. 

• STN – Speciation Trends Network. EPA monitoring network that began in 1999 and 

provides speciated, urban fine particle data. 

• TPY – Tons per year 
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• TSD – Technical Support Document 

• URP – Uniform Rate of Progress – The linear rate 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 Tribal 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.