Appendix G: 

Modeling Results 

And 

Supplemental Analysis 
Modeling Results & Supplemental Analysis Appendix G - 1 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
Table of Contents

List of Figures................................................................................................................................. 3 

List of Tables.................................................................................................................................. 4 

1 Regional Haze Rule Requirements.............................................................................................. 5 

2 Reasonable Progress Goals.......................................................................................................... 5 

3 Model Application ....................................................................................................................... 6 

3.1 Determination of 20% Best and worst Days......................................................................... 6 

3.2 Determine Baseline Conditions............................................................................................ 6 

3.3 Relative Response Factor Calculation .................................................................................. 7 

3.4. Future Year Daily Concentration Calculation ..................................................................... 9 

3.5. Future Year Daily bext Calculation ..................................................................................... 9 

3.6. Future Mean Deciview Values Calculation ......................................................................... 9 

4 Supplemental Analysis............................................................................................................... 12 

4.1 Refinements to Recommended Analysis............................................................................ 13 

4.2 Additional Air Quality Modeling........................................................................................ 14 

4.2.1 Grid Resolution............................................................................................................ 14 

4.2.2 Other Modeling Results............................................................................................... 16 

4.2.3 International Emissions................................................................................................ 18 

Modeling Results & Supplemental Analysis Appendix G - 2 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
List of Figures 

Figure 3.6-1: Uniform rate of progress, or glidepath, for GSMNP1 on the 20% best days (top) 

and the 20% worst days (bottom) ................................................................................................. 11 

Figure 3.6-2: Uniform rate of progress, or glidepath, for JOKI on the 20% best days (top) and the 

20% worst days (bottom).............................................................................................................. 12 

Figure 4.1-1: Plot of 2018 modeling results as processed with the “new” IMPROVE algorithm 

(pink squares) and with the “old” IMPROVE algorithm (blue diamonds) for VISTAS Class I 

areas, and Class I areas near the VISTAS states. Heavy black line represents the percent of 

reductions needed to reach reasonable progress goals.................................................................. 14 

Figure 4.2-1: VISTAS modeling result for IMPORVE monitors within the 12km modeling 

domain. Estimates of reductions are presented from the both the “old” and ”new” IMPROVE 

equation applied to both the 36 and 12 km grid resolutions results.............................................. 15 

Figure 4.2–2: Comparison of percent of Uniform Rate of Progress haze index reduction from 

VISTAS, CENRAP, and WRAP modeling efforts. Results calculated with the “new” IMPROVE 

equation......................................................................................................................................... 16 

Figure 4.2–3: Comparison of percent of Uniform Rate of Progress haze index reduction from 

VISTAS, CENRAP, Midwest RPO (MwRPO) and WRAP modeling efforts. Results were 

calculated with the “old” IMPROVE equations. .......................................................................... 17 

Figure 4.2–4: Preliminary MANE-VU final future mean deciview values at VISTAS IMPROVE 

sites. .............................................................................................................................................. 18 

Figure 4.2-5: Estimated international emissions contributions to sulfate at VISTAS and 

neighboring Class I areas.............................................................................................................. 20 

Figure 4.2-6. Percent of Uniform Rate of Progress haze index reduction achieved in 2018 

according to the VISTAS inventory. Results are given for improvements calculated using the 

new IMPROVE algorithm (squares) and the old IMPROVE algorithm (diamonds). Solid 

symbols indicate results when all emissions are considered; open symbols indicate results when 

effects of international emissions are removed............................................................................. 21 

Modeling Results & Supplemental Analysis Appendix G - 3 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
List of Tables 

Table 3.2-1: Baseline Visibility Conditions and Natural Conditions for the Tennessee Class I 

areas. ............................................................................................................................................... 7 

Table 3.3-1: Base year concentrations, future year concentrations, and RRF for Great Smoky 

Mountain National Park (GSMNP1) on the monitored 20% best days (top) and 20% worst days 

(bottom), based on 12km modeling results..................................................................................... 8 

Table 3.6-1: Final future mean deciview values (dvfuture) and Natural Conditions for the 

Tennessee Class I areas, based on 12km modeling results........................................................... 10 

Table 3.6-2: Comparison of future deciview projections for 2018 compared to 2002 baseline 

deciview estimates. ....................................................................................................................... 10 

Modeling Results & Supplemental Analysis Appendix G - 4 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
1 Regional Haze Rule Requirements 

Visibility improvement regulations originate from language in the Clean Air Act (CAA). 

Section 169A of the CAA declared a national goal to prevent “any future, and the remedying of 

any existing, impairment of visibility in mandatory class I Federal areas which impairment 

results from manmade air pollution.” As a response to the mandate in the CAA, the United 

States Environmental Protection Agency (USEPA) promulgated the regional haze rule (RHR) in 

July of 1999. Regulations at 40 CFR 56.308(d)(1) of the RHR require that a State set two 

reasonable progress goals (RPGs) for each Class I area - one for showing improvement for the 

20% worst days and one for ensuring no degradation for the 20% best days over the period of the 

implementation plan. For the 20% best days, the goal should be set to reflect at least as good a 

level of visibility as the baseline conditions for 2000-2004, calculated per 40 CFR 

56.308(d)(2)(i). 

Section 51.308(d)(1) of the RHR also established that state goals for regional haze must be 

expressed in deciviews (dv). Deciviews are calculated directly from light extinction using a 

logarithmic scale. The deciview is a useful measure for tracking progress in improving visibility, 

because each deciview change is an equal incremental change in visibility perceived by the 

human eye. Most people can detect a one deciview change in visibility. 

The following document sets to provide an overview of the process for defining RPGs and how 

to apply modeling to predict if these goals will be met in the future. Additional evidence to 

support modeling results, or weight of evidence, will also be presented in this document. 

2 Reasonable Progress Goals 

For each Class I area, there are three metrics of visibility that are part of the determination of 

reasonable progress: 

1) Natural conditions, 

2) Baseline conditions 

3) Current conditions. 

Each of the three metrics includes the concentration data of the visibility impairing pollutants as 

different terms in the light extinction algorithm, with respective extinction coefficients and 

relative humidity factors. Total light extinction is then converted to deciviews, and an average 

value for both the 20 percent best and 20 percent worst visibility days is calculated. 

After the natural, baseline, and current conditions are calculated, a uniform rate of progress, or 

glidepath, can be created for the 20% worst days for each Class I area. Next, an analysis of 

potential control measures is performed to determine what level of control would be achievable 

in the given timeframe and what the potential improvement to visibility would be as a result of 

these controls. After the potential control analysis, reasonable progress goals for the Class I 

areas are determined after consultation with surrounding states. The entire RPG determination 

for Tennessee Class I areas is described in Appendix H. 

Modeling Results & Supplemental Analysis Appendix G - 5 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
3 Model Application 

The purpose of a modeling assessment of uniform rate of progress is to determine if a proposed 

control strategy will result in uniform rate of progress being met when measured concentrations 

of particulate matter are used to estimate visibility impairment at some future date. The 

modeling is applied in a relative sense, similar to the fine particulate matter (PM25) attainment 

test. The following sections outline the process to determine 2018 projections from regional 

modeling, as suggested in the “Guidance on the Use of Models and Other Analyses for 

Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze”(from 

this point forward, referred to as “Attainment Guidance”). 

3.1 Determination of 20% Best and worst Days 

The first step in developing model projections is to rank visibility (in deciviews) on each day 

with observed speciated PM2.5 data for each of the 5 years comprising the base period (2000-

2004) for each Class I area. Day-specific observations for mass associated with sulfate (SO4), 

nitrates (NO3), organic carbon (OC), elemental carbon (EC), soil, and coarse matter (CM), 

should be used to calculate bext for each day using climatological relative humidity adjustment 

factor(s) (f(rh)). Total light extinction (bext)for all components should be converted to deciviews 

for each day to get a daily deciview value. 

3.2 Determine Baseline Conditions 

The ranked visibility days from the first step are then used to calculate the average baseline 

deciviews for the 20% best and worst days. First, the mean deciview value for the identified 

20% worst- and best visibility days in each year is calculated (dvcurrent). The 5 year mean 

deciview values reflecting mean visibility on the days with best visibility and then averaged to 

determine the baseline values for the best days, or the dvbaseline values. Then the 5 year mean 

deciview values reflecting worst visibility are averaged together. This represents the value 

subject to improvement to meet the glidepath for regional haze. 

For each of the Tennessee Class I areas, the baseline visibility conditions were estimated using 

sampling data collected at Interagency Monitoring of Protected Visual Environments 

(IMPROVE) monitoring sites at one of the two Class I areas in Tennessee. A five year average 

(2000 to 2004) was calculated for each of the 20 percent worst and 20 percent best visibility days 

in accordance with 40 CFR 51.308(d)(2) and the USEPA’s Guidance for Tracking Progress 

Under the Regional Haze Rule. IMPROVE data records for Great Smoky Mountains for the 

period 2000 to 2004 meet the USEPA’s requirements for data completeness (75 percent for the 

year and 50 percent for each quarter). IMPROVE does not operate a monitor at Joyce Kilmer 

Wilderness Area and considers the IMPROVE monitor at Great Smoky Mountains to be 

representative of visibility in the Joyce Kilmer Wilderness Area. The light extinction and 

deciview visibility values for the 20 percent worst and 20 percent best visibility days at the Class 

I areas are based on data and calculations included in Appendix B of this SIP. Table 3.2-1 

summarizes the mean yearly visibility conditions and the baseline visibility conditions for 

Tennessee Class I areas on both the 20% best and 20% worst days. 

Modeling Results & Supplemental Analysis Appendix G - 6 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
Table 3.2-1: Baseline Visibility Conditions and Natural Conditions for the Tennessee Class 

I areas. 

dvbaseline Natural Condtions dvbaseline Natural Condtions

Great Smoky Mountains 

National Park (GSMNP) 30.28 11.05 13.58 4.54

Joyce Kilmer (JOKI) 30.28 11.05 13.58 4.54

 

 

Worst Visiblity Days Best Visiblity Days Site

 

3.3 Relative Response Factor Calculation 

The next step is to use base year and future year modeling results to estimate a relative response 

factor (RRF) for each component of PM2.5 on the 20% best and worst monitoring days. Simply 

put, the RRF is the average future year concentration near the monitor divided by the average 

base year concentration or: 

Year Base Modeled Average Concentration 

Year Future Modeled Average Concentration RRF =

To accomplish this step, concentration for each component of PM2.5 and coarse matter (the 

difference between particles less than 10 micrometers in diameter (PM10) and PM2.5) are 

extracted from the base year and future year modeling output near IMPROVE monitoring sites 

for the monitored best and worst days. The component concentrations are then averaged to 

develop base and future year average component concentrations. The future year average 

component concentrations are then divided by their respective base year component 

concentrations to develop RRFs for each component of PM2.5 and coarse matter. 

For a numerical example, consider Table 3.3-1, which has all the pertinent data for the GSMNP1 

IMPROVE monitor. Looking at the top table, the first column identifies the 20% best days 

identified in step 1 (Section 3.1). The next six columns have the 12km modeled base year (2002) 

concentration values near the monitor corresponding to the 20% best days. The next six columns 

have the 12km modeled future year (2018) component concentrations near the monitor for the 

same set of days. The final six columns contain the daily RRF values for each component 

species, which is simply the future concentration divided by the base year concentration. The 

bottom row of the table contains the average values for the base year components, the future year 

components, and the RRFs for each of the average component values. The bottom table contains 

the same data for the 20% worst days. 

Modeling Results & Supplemental Analysis Appendix G - 7 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
Table 3.3-1: Base year concentrations, future year concentrations, and RRF for Great 

Smoky Mountain National Park (GSMNP1) on the monitored 20% best days (top) and 

20% worst days (bottom), based on 12km modeling results. 

20% Best Days at 

GSMNP1

Julian Date SO4 NO3 OC EC SOIL CM SO4 NO3 OC EC SOIL CM SO4 NO3 OC EC SOIL CM

8 1.732 0.444 1.453 0.247 1.102 0.771 1.706 0.685 1.391 0.172 1.293 0.844 0.985 1.543 0.958 0.697 1.173 1.095

11 1.090 1.050 1.780 0.415 2.115 1.279 0.945 1.169 1.794 0.264 2.446 1.547 0.867 1.113 1.008 0.635 1.156 1.209

14 2.091 0.377 3.074 0.353 1.583 0.489 1.743 0.364 2.899 0.263 1.602 0.495 0.834 0.966 0.943 0.744 1.012 1.014

23 3.095 0.002 0.632 0.070 0.223 0.249 1.164 0.000 0.571 0.050 0.256 0.278 0.376 0.000 0.903 0.713 1.146 1.116

32 2.654 0.778 1.378 0.229 1.293 0.482 1.968 0.724 1.349 0.154 1.309 0.533 0.742 0.931 0.979 0.674 1.012 1.105

38 0.523 0.430 0.575 0.101 0.416 0.307 0.344 0.426 0.547 0.058 0.448 0.386 0.658 0.991 0.951 0.577 1.076 1.255

50 1.178 0.041 2.334 0.271 0.791 0.294 0.820 0.026 2.172 0.217 0.905 0.348 0.696 0.620 0.930 0.799 1.144 1.182

59 1.555 0.073 0.890 0.142 0.903 0.508 1.502 0.130 0.843 0.102 0.934 0.508 0.966 1.789 0.947 0.720 1.034 1.000

62 2.530 2.313 1.043 0.173 0.959 0.247 2.926 2.091 1.048 0.127 1.030 0.266 1.156 0.904 1.004 0.735 1.075 1.075

68 2.013 0.156 1.256 0.184 0.396 0.527 1.116 0.125 1.235 0.166 0.425 0.552 0.554 0.804 0.983 0.902 1.071 1.046

89 0.571 0.450 1.074 0.116 0.713 0.294 0.508 0.301 1.029 0.092 0.728 0.351 0.888 0.668 0.958 0.792 1.022 1.192

116 1.319 0.211 1.964 0.238 1.297 1.176 0.914 0.072 1.848 0.157 1.446 1.297 0.693 0.340 0.941 0.660 1.115 1.103

194 1.990 0.005 2.088 0.145 0.360 0.387 1.000 0.019 1.807 0.112 0.498 0.433 0.503 3.632 0.865 0.774 1.383 1.120

257 2.486 0.000 0.861 0.045 0.182 0.201 0.899 0.000 0.760 0.028 0.213 0.228 0.362 3.286 0.883 0.625 1.171 1.137

269 0.423 0.042 0.633 0.059 0.368 0.282 0.257 0.018 0.618 0.033 0.391 0.352 0.608 0.434 0.976 0.567 1.063 1.247

299 5.879 0.658 3.823 0.278 1.602 0.846 3.330 1.063 3.643 0.172 1.841 0.972 0.566 1.617 0.953 0.619 1.149 1.150

302 3.034 0.000 0.718 0.047 0.182 0.222 1.321 0.000 0.659 0.032 0.198 0.255 0.435 0.000 0.917 0.688 1.088 1.146

308 1.052 0.851 1.769 0.325 1.746 1.036 0.887 0.675 1.830 0.208 1.906 1.297 0.843 0.794 1.034 0.639 1.092 1.252

320 1.250 0.376 1.079 0.221 1.290 0.508 0.700 0.644 1.135 0.161 1.356 0.631 0.560 1.712 1.052 0.727 1.051 1.243

326 1.696 2.036 1.200 0.289 1.966 0.840 1.860 1.934 1.187 0.176 2.306 0.966 1.097 0.950 0.990 0.611 1.173 1.151

341 1.745 3.191 3.016 0.609 4.148 1.241 1.732 3.831 2.941 0.394 4.458 1.433 0.992 1.201 0.975 0.647 1.075 1.155

347 0.705 0.071 0.883 0.130 0.768 0.292 0.452 0.241 0.811 0.079 0.773 0.356 0.641 3.405 0.918 0.607 1.006 1.219

356 1.903 0.823 2.267 0.267 1.610 0.941 1.796 0.696 2.181 0.190 1.473 0.909 0.944 0.845 0.962 0.712 0.915 0.966

Mean 

Concentration 1.848 0.625 1.556 0.215 1.131 0.583 1.300 0.662 1.491 0.148 1.228 0.662 0.703 1.060 0.958 0.688 1.085 1.136

Base Year Future Year RRFs

20% Worst Days 

at GSMNP1

Julian Date SO4 NO3 OC EC SOIL CM SO4 NO3 OC EC SOIL CM SO4 NO3 OC EC SOIL CM

56 4.456 0.154 5.656 0.605 1.734 0.399 2.398 0.348 5.021 0.474 1.957 0.482 0.538 2.253 0.888 0.784 1.128 1.210

122 2.416 0.002 3.134 0.321 0.291 0.474 1.710 0.005 3.054 0.303 0.332 0.494 0.708 2.034 0.975 0.944 1.144 1.043

128 7.068 0.000 3.328 0.192 0.529 0.606 3.117 0.001 2.942 0.145 0.688 0.687 0.441 0.000 0.884 0.757 1.302 1.132

137 5.661 0.003 2.704 0.173 0.636 0.636 2.653 0.003 2.479 0.115 0.805 0.688 0.469 1.101 0.917 0.664 1.265 1.082

152 8.223 0.000 5.054 0.212 1.075 0.665 3.632 0.014 4.470 0.130 1.311 0.771 0.442 0.000 0.884 0.614 1.220 1.159

164 3.700 0.009 6.481 0.186 0.857 0.842 1.562 0.008 5.831 0.120 1.050 1.002 0.422 0.900 0.900 0.644 1.225 1.190

170 5.701 0.000 3.934 0.160 0.762 0.506 2.988 0.002 3.610 0.096 0.948 0.565 0.524 0.000 0.918 0.602 1.245 1.117

173 4.181 0.002 2.586 0.155 0.497 0.497 2.045 0.003 2.291 0.106 0.632 0.539 0.489 1.284 0.886 0.686 1.270 1.083

182 8.517 0.000 5.780 0.232 1.174 0.887 4.106 0.001 5.141 0.147 1.460 1.033 0.482 0.000 0.890 0.634 1.244 1.165

188 5.631 0.000 3.792 0.173 0.615 0.301 2.433 0.001 3.323 0.125 0.835 0.333 0.432 5.389 0.876 0.720 1.359 1.108

191 5.882 0.000 4.062 0.215 0.731 0.649 2.722 0.003 3.551 0.161 0.886 0.740 0.463 0.000 0.874 0.750 1.212 1.140

197 11.592 0.001 4.508 0.243 1.258 0.899 3.622 0.004 3.822 0.137 1.453 1.102 0.312 4.958 0.848 0.566 1.155 1.226

203 5.851 0.000 4.326 0.132 0.475 0.472 2.566 0.001 3.909 0.088 0.619 0.532 0.439 0.000 0.904 0.669 1.304 1.128

212 8.142 0.009 3.423 0.237 1.506 1.070 5.302 0.006 2.957 0.149 1.639 1.206 0.651 0.681 0.864 0.630 1.088 1.127

215 4.647 0.000 4.268 0.202 0.622 0.405 1.832 0.001 3.774 0.137 0.781 0.452 0.394 0.000 0.884 0.679 1.256 1.115

218 7.146 0.000 5.856 0.309 1.515 0.777 2.675 0.004 5.290 0.213 1.842 0.901 0.374 0.000 0.903 0.689 1.216 1.160

221 5.120 0.004 6.957 0.378 1.030 0.597 1.890 0.004 6.373 0.275 1.380 0.721 0.369 1.021 0.916 0.726 1.340 1.208

224 9.523 0.000 10.352 0.511 1.313 0.657 3.163 0.014 9.558 0.422 1.678 0.799 0.332 0.000 0.923 0.825 1.278 1.215

227 6.679 0.000 3.450 0.164 0.438 0.518 2.243 0.002 2.929 0.117 0.580 0.630 0.336 0.000 0.849 0.713 1.325 1.217

233 2.498 0.000 4.201 0.262 0.798 0.777 1.280 0.001 3.732 0.211 0.931 0.933 0.513 4.739 0.888 0.804 1.168 1.200

248 4.788 0.000 4.428 0.223 1.359 0.835 2.454 0.012 3.802 0.131 1.565 0.951 0.512 53.000 0.858 0.587 1.152 1.139

251 7.636 0.000 5.041 0.254 1.101 0.332 3.318 0.011 4.551 0.165 1.499 0.378 0.434 88.583 0.903 0.651 1.362 1.137

254 12.383 0.000 8.373 0.443 2.635 1.371 5.267 0.038 7.808 0.267 3.047 1.581 0.425 0.000 0.933 0.603 1.157 1.153

260 15.114 0.001 5.427 0.206 1.334 1.012 7.367 0.005 4.721 0.118 1.398 1.102 0.487 7.116 0.870 0.570 1.048 1.089

Mean 

Concentration 6.874 0.001 4.846 0.243 0.980 0.686 3.041 0.006 4.344 0.169 1.190 0.789 0.442 4.571 0.896 0.695 1.213 1.149

Base Year Future Year RRFs

Modeling Results & Supplemental Analysis Appendix G - 8 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
3.4. Future Year Daily Concentration Calculation 

The fourth step in using model output to estimate the future visibility impairment is to multiply 

the relative response factors (determined in the previous step, Section 3.3) by the measured 

species concentration data during the base period (for the measured 20% best and worst days). 

This results in daily future year species concentrations data. 

This is similar to the final step in the ozone attainment test, where base year concentrations, or 

the current year design value (DVC) is multiplied by the RRF to determine the future design 

value (i.e. DVF = RRF * DVC). For regional haze, this calculation is performed for each best 

and worst day for the five-year base line period (calculated in step 2, Section 3.2) using an RRF 

for each PM component, for both the best days and the worst days. This produces future 

concentration estimates for SO4, NO3, OC, EC, Soil and CM, for each of the “worst” and “best” 

visibility days. 

3.5. Future Year Daily bext Calculation 

Once future year concentrations are calculated (Section 3.4) the future daily extinction 

coefficients are then calculated for the 20% best and 20% worst visibility days in each of the five 

base years. This is achieved by applying the new IMPROVE equation (see Appendix B for a 

more information on the new IMPROVE equation): 

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) 

+ 0.33 * [NO2 (ppb)] 

Model estimated daily concentrations are used with the corresponding base line daily relative 

humidity adjustment factor(s) (f(rh)) identified in step 1 (Section 3.1). 

3.6. Future Mean Deciview Values Calculation 

The final step is to calculate daily deciview values from the total daily extinction calculated in 

the previous step, then compute the future average mean deciviews for the worst and best days 

for each year. The 5 years are then averaged together to get the final future mean deciview value 

for the worst and best days. 

The first step in this process is to convert the total daily bext for each day (calculated in the 

previous step, Section 3.5) to deciviews. This future year daily deciview value for each of the 

best and worst days is then averaged to determine the future deciview value for the “worst” and 

Modeling Results & Supplemental Analysis Appendix G - 9 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
“best” visibility days for each year. This leads to 5 future estimated mean deciview values for the 

“worst” and 5 future estimated mean deciview values for the “best” visibility days. The five 

yearly values for the best and worst days are then averaged to produce the final future mean 

deciview value, or dvfuture. Table 3.6-1 displays the yearly daily deciview value for each year in 

the baseline period for both the 20% worst days (center column) and the 20% best days (right 

column). The last row in the table displays the final future mean deciview value, or dvfuture. 

Table 3.6-1: Final future mean deciview values (dvfuture) and Natural Conditions for the 

Tennessee Class I areas, based on 12km modeling results. 

 

dvfuture Natural Condtions dvfuture Natural Condtions

Great Smoky Mountains 

National Park (GSMNP) 23.50 11.05 12.11 4.54

Joyce Kilmer (JOKI) 23.50 11.05 12.11 4.54

 8

 

Site Worst Visiblity Days Best Visiblity Days

The resulting dvfuture values for the “worst” and “best” visibility days can be compared to the 

dvbaseline values calculated in step 2 (Section 3.2). If the resulting change in deciviews is a 

negative number (future - base), this represents an improvement in visibility. Table 3.6-2 

summarizes these calculation for the IMPROVE monitors associated with Tennessee Class I 

areas. From the table, it is apparent that the two Tennessee Class I areas are showing visibility 

improvement on both the 20% best and 20% worst days. 

Table 3.6-2: Comparison of future deciview projections for 2018 compared to 2002 baseline 

deciview estimates. 

Site DVbase DVfuture DVdiff DVbase DVfuture DVdiff

Great Smoky Mountains 

National Park (GSMNP) 30.28 23.50 -6.78 13.58 12.11 -1.47

Joyce Kilmer (JOKI) 30.28 23.50 -6.78 13.58 12.11 -1.47

 

 

20% Worst Days 20 % Best Days

These changes in visibility can also be compared to the previously calculated (in Appendix B) 

uniform rate of progress values for each Class I area (Figures 3.6-1 through 3.6-2). When 

plotted against the glidepath, one can see the change in deciview between the baseline and future 

shows an improvement that is well below the uniform rate of progress for all Class I monitoring 

sites in Tennessee. 

Modeling Results & Supplemental Analysis Appendix G - 10 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
Reasonable Progress Assessment Base G2 inventory, new IMPROVE 

Algorithm

Great Smoky Mountains - Best 20% Days

13.58 13.58 13.58

13.11 12.11

4.54

0

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Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction

Uniform Rate of Reasonable Progress Glide Path

Great Smoky Mountains - W20% Data Days

30.28 28.67

25.79

22.59

19.38

16.18

12.97 11.05

26.72

23.50

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Haziness Index (Deciviews)

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

Figure 3.6-1: Uniform rate of progress, or glidepath, for GSMNP1 on the 20% best days 

(top) and the 20% worst days (bottom) 

Modeling Results & Supplemental Analysis Appendix G - 11 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
Reasonable Progress Assessment Base G2 inventory, new IMPROVE 

Algorithm

Joyce Kilmer-Slickrock - Best 20% Days

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Figure 3.6-2: Uniform rate of progress, or glidepath, for JOKI1 on the 20% best days (top) 

and the 20% worst days (bottom) 

4 Supplemental Analysis 

The Attainment Guidance asserts that all attainment demonstrations should be accompanied by 

supplemental analysis that further supports the modeling conclusions. This supplemental 

analysis can include additional analyses of air quality, emissions and meteorological data, and 

Modeling Results & Supplemental Analysis Appendix G - 12 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
consider modeling outputs other than the results of the attainment test. If the attainment test 

results fall short of the standard, the results of corroboratory analyses may be used in a weight of 

evidence determination (WOE) to show that attainment is likely despite modeled results, which 

may be inconclusive. 

For regional haze progress goals address trends in air quality rather than some absolute level of 

air quality as with ozone or fine particle pollution. The USEPA believes a weight of evidence 

analysis focusing on the air quality trends is an appropriate option for States to use when 

examining visibility trend. In the “Attainment Guidance”, USEPA gives recommendation of 

additional analysis that can be used for supplement analysis/WOE for regional haze goals. 

These suggestions include performing additional air quality modeling, a review of trends, 

making use of observational models, and applying refinements to the recommended uniform rate 

of progress analysis. 

All Tennessee Class I areas meet uniform rate of progress trends, making the following section 

an examination of supplemental analysis to corroborate modeling results, than a WOE analysis to 

show attainment. In the following sections we explore both refinements to the uniform rate of 

progress, and additional modeling preformed by VISTAS as part of a supplemental analysis for 

the Tennessee Class I areas. 

4.1 Refinements to Recommended Analysis 

In the Attainment Guidance, the USEPA recommends exploring refinements to the uniform rate 

of progress. One refinement the USEPA suggests is using an alternative or area specific 

IMPROVE equations. One alternative to examine is the use of the old IMPROVE equation. 

VISTAS opted to move to the new IMPROVE when it was first suggested, as it represented the 

most current scientific understanding of light extinction calculation. Results presented in Section 

3 reflect the use of the new IMPROVE equation, as described in Section 3.5. As supplemental 

analysis, results were also calculated with the “old” IMPROVE equation during step 5 of the 

modeling analysis. Figure 4.1-1 presents the modeling results from both the old and the new 

IMPROVE equation for IMPROVE sites within the VISTAS 12km modeling domain. 

The plot shows the percentage of target reduction achieved at each site. Points on or above the 

bold 100% line met target reduction goals determined by their site-specific reasonable progress 

goals. Points below the line fail to meet their visibility improvement goals. For most of the 

sites, the reduction estimates from both the old and new IMPROVE equation track closely. The 

one IMPROVE sites associated with Tennessee Class I areas (GRSM) show visibility 

improvement beyond what is required by the set reasonable progress goals. This is further 

evidence that Tennessee Class I areas should reach visibility improvement goals, with a margin 

of safety. 

Modeling Results & Supplemental Analysis Appendix G - 13 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
CMAQ 2018g2a/2002gt2a Method 1 predictions for VISTAS+ sites

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Percent of target reduction achieved

CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm

VISTAS non-VISTAS

Figure 4.1-1: Plot of 2018 modeling results as processed with the “new” IMPROVE 

algorithm (pink squares) and with the “old” IMPROVE algorithm (blue diamonds) for 

VISTAS Class I areas, and Class I areas near the VISTAS states. Heavy black line 

represents the percent of reductions needed to reach reasonable progress goals. 

4.2 Additional Air Quality Modeling 

Another means of providing supplemental analysis is to complete additional air quality 

modeling. The Attainment Guidance suggests several additional modeling exercises that can be 

performed as part of supplemental/WOE analysis. Suggestions include modeling at finer grid 

resolutions, or modeling alternative set ups and emissions as part of sensitivity. Additional 

modeling analysis undertaken by the southern regional planning organization (RPO) Visibility 

Improvement State and Tribal Association of the Southeast (VISTAS) include, looking at both 

36 and 12km grid resolutions, the comparison of VISTAS results to those from modeling efforts 

by other RPOs, and undertaking an emissions sensitivity to assess the importance of international 

emissions. Each of theses items will be discussed in the following sections. 

4.2.1 Grid Resolution 

In the Attainment Guidance, the USEPA suggests a grid cell resolution of 36km due to the 

rural/remote nature of Class I areas and the fact regional haze is generally not dominated by local 

sources, which necessitate a regional scale modeling. Given that computation needs increase 

with finer resolutions, the resources needed to model on a regional scale using a finer grid 

resolution may prove too costly for some states. 

Modeling Results & Supplemental Analysis Appendix G - 14 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
VISTAS modeling was performed on a 12km resolution grid nested within a 36km resolution 

grid. This allowed for future mean deciview values, and subsequently the percentage of 

reduction, to be calculated at both a 36 and 12km resolution. As an additional measure, the 12 

and 36km results were also processed with both the old and new IMPROVE equations. Figure 

4.2-1 presents the 12 and 36km results in percentage of target reduction achieved. Pink squares 

represent the 12km results processed with the new IMPROVE equations, and blue diamonds as 

the 12km results processed with the old IMPROVE equations. Burgundy triangles represent 

36km results processed with the new IMPROVE equations, with blue Xs noting the 36km results 

processed with the old IMPROVE equations. 

From the plot, one can see the results from both grid resolutions and equations track fairly close 

together. The 12 and 36km results with the new IMPROVE equation are similar, suggesting the 

choice of IMPROVE algorithm at GRSM1 make a significant difference in final values. Overall, 

the Tennessee Class I area IMPROVE monitor exceeds target reductions with both grid 

resolution modeling results when applied to either the new or old IMPROVE equation. The 

similarity between the 36km and 12km results further supports attainment of visibility 

improvement goals for Tennessee Class I areas. 

It should be noted that for purposes of this demonstration, final attainment values in Section 3 

have been presented based on modeling with 12km horizontal grid resolution. Though final 

results from the 12km and 36km modeling are similar, the decision was made to present the final 

modeling result at the 12km grid resolution. 

CMAQ 2018g1a/Typ02g Method 1 predictions for VISTAS+ sites

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VISTAS non-VISTAS

CMAQ 2018g1a/Typ02g Method 1 predictions for VISTAS+ sites

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CMAQ Old IMPROVE Algorithm 12km

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CMAQ Old IMPROVE Algorithm 36km

VISTAS non-VISTAS

Figure 4.2-1: VISTAS modeling result for IMPORVE monitors within the 12km modeling 

domain. Estimates of reductions are presented from the both the “old” and ”new” 

IMPROVE equation applied to both the 36 and 12 km grid resolutions results. 

Modeling Results & Supplemental Analysis Appendix G - 15 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
4.2.2 Other Modeling Results 

One way to acquire modeling sensitivity runs is to examine the modeling results from other 

RPOs. Other RPOs may use different physical and chemical modeling options for their 

meteorological and air quality modeling runs, which would provide a comparison or sensitivity 

based on these different options. 

Though emissions inventories are generally shared amongst the RPOs, modeling work with 

preliminary versions of the emission inventories are run and available for comparison. For 

example, VISTAS has completed a final modeling run with inventories available at the time. 

However, other RPOs are still finalizing their inventories, or have completed preliminary runs 

based on older emission inventories. By comparing VISTAS final results to the other RPO 

modeling, essentially an emissions sensitivity run is created. 

Preliminary modeling from Central Regional Air Planning Association (CENRAP) and Western 

Regional Air Partnership (WRAP) were obtained and are compared to VISTAS modeling in 

Figure 4.2-2. Again, the graph display the percent of the target reduction attained at each site per 

the modeling projection based on the new IMPROVE equations. Though data for all VISTAS 

sites was not available from CENRAP or WRAP, projection for sites in multiple modeling 

domains agree well and are generally less than 10 percentage points off from one another. This 

is encouraging support for VISTAS modeling suggesting that even with some differences in 

emission inventories, final results of the modeling are very similar. 

Visibility Projections with New Algorithm for CENRAP Class I areas

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CENRAP-base 18f

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VISTAS WRAP

Figure 4.2–2: Comparison of percent of Uniform Rate of Progress haze index reduction

from VISTAS, CENRAP, and WRAP modeling efforts. Results calculated with the “new” 

IMPROVE equation. 

Modeling Results & Supplemental Analysis Appendix G - 16 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
Additionally Midwest Regional Planning Organization (MwRPO) has provided preliminary 

results to VISTAS. These results were based on the old IMPROVE equations. For consistency 

in comparison, CENRAP, WRAP, and VISTAS data were recalculated with the old IMPROVE 

equation. As with the previous comparison, not all VISTAS sites are modeled in the other RPOs 

domains. The sites that do overlap are presented in Figure 4.2-3. The plot reveals the modeling 

results are very similar across RPOs. For example, modeling results for Mingo Wilderness area 

are very similar, with CENRAP modeling predicting an 83% of target reduction, MwRPO 

predicting 90%, and VISTAS projecting a 96% of target reduction. This further corroborates 

VISTAS modeling, as even using the old IMPROVE equation and varying emission inventories, 

the model projections are similar across the United States. 

Limited data has been attained from Mid-Atlantic/Northeast Visibility Union (MANE-VU); 

however projections for Shenandoah National Park (SHEN1), Brigantine (BRIG1), and Dolly 

Sods (DOSO1) are comparable to VISTAS modeling results (See Figure 4.2-4). As with 

previous model comparisons, the similar model results produced by MAN-VU, despite 

difference in modeling set up and inventories, further bolsters confidence in VISTAS modeling 

results. 

Visibility Projections with Old Algorithm for CENRAP Class I areas

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VISTAS MwRPO WRAP

Figure 4.2–3: Comparison of percent of Uniform Rate of Progress haze index reduction

from VISTAS, CENRAP, Midwest RPO (MwRPO) and WRAP modeling efforts. Results 

were calculated with the “old” IMPROVE equations. 

Modeling Results & Supplemental Analysis Appendix G - 17 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
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Figure 4.2–4: Preliminary MANE-VU final future mean deciview values at VISTAS 

IMPROVE sites. 

As more modeling work becomes available additional examination of result will be preformed. 

Of particular interest to VISTAS, are results from the MwRPO. The modeling for the MwRPO 

differs from VISATS modeling in that they have chosen to use the CAMx model in lieu of 

CMAQ for their air quality modeling as well as using a base year of 2005. VISTAS also hope to 

obtain MANE-VU model output, so the results can be added to Figure 4.2-2. 

4.2.3 International Emissions 

An integral part to accurately modeling visibility is accurately quantifying emissions from 

sources outside the continental United States. Emission from areas such as Mexico, Canada, 

Central America, and beyond have been shown to affect air quality in the United States. To 

evaluate the affect of the emission from areas outside the United States, VISTAS conducted a 

study of modeling results generated from the global three-dimensional chemical transport model 

(GEOS-Chem) and CMAQ. 

GEOS-Chem was run for the 2002 modeling year using a 4 x 5 degree horizontal grid resolution 

and a 3-hour temporal resolution. Emissions were based on monthly averages so the model does 

not capture the episodic variability in emissions. Details of the international emissions study are 

described in a report, which is included here as Attachment G1. Discussion of the GEOS-Chem 

runs as they pertain to the boundary conditions for the base year and future year modeling can be 

found in Appendix M. 

The GEOS-Chem outputs were used to calculate initial and boundary conditions for the national 

CMAQ modeling domain for the base year and future year modeling, as well as the international 

emissions impact study. The national CMAQ domain included portions of Canada and Mexico, 

so emissions for these countries were included within the national CMAQ modeling domain or 

as part of the boundary conditions outside the national modeling domain, as appropriate. 

Modeling Results & Supplemental Analysis Appendix G - 18 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
Two complementary methods were used to calculate the impact of international emissions at 

Class I areas. Because the international emissions inventory used in GEOS-CHEM did not 

distinguish between wildfires and anthropogenic fires, all international fire emissions were 

assumed to be wildfire, and the contributions attributable to international anthropogenic fires are 

underestimated in these projections 

1. International emissions as the difference between two GEOS-Chem runs. In the first run 

United States anthropogenic emissions were removed, in the second run United States 

and international anthropogenic emissions were removed. The difference is attributable 

to international anthropogenic emissions, in the absence of United States anthropogenic 

emissions (e.g. compared to 2064 levels). Harvard University provided GEOS-Chem 

results to VISTAS for 2002 international contribution on 4 x 5 degree grid. Concurrently 

Harvard modeling for the Electric Power Research Institute (EPRI) provided GEOSChem results for 2001 international contribution on a 1 x 1 degree grid scale. 

2. International emissions as the difference between two CMAQ 36-km simulations, both 

using 2018 emissions and boundary conditions from GEOS-Chem. In the first CMAQ 

run, all global natural and anthropogenic emissions in 2018 are active. In the second 

CMAQ run, only global (United States and international) natural emissions are active. 

Here the impacts of international emissions are compared against 2018 conditions rather 

than natural background conditions. 

VISTAS has compared its results to results from other RPOs on the impact of international 

emissions and boundary conditions on visibility at Class I areas in 2002; the results were similar. 

As illustrated in Figure 4.2-5 for annual average contributions to sulfate at VISTAS and 

neighboring Class I areas, the estimated international contributions are higher at Class I areas 

near the Canadian and Mexican borders and along the eastern coast. The estimated international 

contribution is higher using CMAQ than in the GEOS-Chem runs because the grid scale is finer 

(more accurate dispersion of emissions) and because the background atmosphere includes 

loadings from current United States anthropogenic emissions (greater photochemical activity). 

Similar charts for nitrate and organic carbon mass, for impacts on 20 percent worst visibility 

days, and for impacts of international emissions on calculated light extinction are included in 

Appendix H. 

Modeling Results & Supplemental Analysis Appendix G - 19 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
Transboundary Man-Made Impacts

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Figure 4.2-5: Estimated international emissions contributions to sulfate at VISTAS and 

neighboring Class I areas. 

In addition to looking at the individual component concentrations at each Class I sites, efforts 

were made to examine how international emissions affect the ability of a Class I areas to meet 

uniform rate of progress goals. Figure 4.2-6 shows the estimates from VISTAS modeling of 

what the estimated percent of target reduction in visibility impairment is achieved at each of the 

VISTAS Class I areas. Calculations were preformed with both the new and old IMPROVE 

equations. Figure 4.2-7 shows that removing the international contribution is sufficient to raise 

the reductions ratios at all Class I areas in the figure above 100%. This means that when the 

international contributions are considered, all Class I areas exceed the Uniform Rate of Progress 

in 2018. This conclusion holds whether the old or new IMPROVE algorithm is used calculate 

the haze index 

Modeling Results & Supplemental Analysis Appendix G - 20 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas 
CMAQ 2018g1a/Typ02g Method 1 predictions for VISTAS+ sites

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CMAQ Old IMPROVE Algorithm

CMAQ New IMPROVE Algorithm w/ Int'l Trans Removed

CMAQ Old IMPROVE Algorithm w/ Int'l Trans Removed

VISTAS non-VISTAS

Figure 4.2-6. Percent of Uniform Rate of Progress haze index reduction achieved in 2018 

according to the VISTAS inventory. Results are given for improvements calculated using 

the new IMPROVE algorithm (squares) and the old IMPROVE algorithm (diamonds). 

Solid symbols indicate results when all emissions are considered; open symbols indicate 

results when effects of international emissions are removed. 

For the Tennessee Class I areas, accounting for the international contribution do not change the 

conclusion that reductions of United States anthropogenic emissions will result in visibility 

improvement greater than the uniform rate of progress by 2018. Therefore the state of Tennessee 

has elected not to take into account these international emission study results when setting 

reasonable progress goals with this SIP. 

The results of this study do reiterate the significance of international emissions in the United 

States to the attainment of future visibility improvement goals. In the future, as the atmosphere 

becomes closer to natural background conditions, the incremental contribution from international 

emissions will become more important. As the results of this study do not directly affect this 

current attainment demonstration, the details are included here to provide a reference for future 

regional haze work. 

Modeling Results & Supplemental Analysis Appendix G - 21 

Pre-Hearing Draft November 6, 2007 

Regional Haze SIP for TN Class I Areas