# Appendix G: Modeling Results And Supplemental AnalysisModeling Results & Supplemental Analysis Appendix G - 1 Pre-Hearing Draft November 6, 2007 Regional Haze SIP for TN Class I AreasTable of Contents
List of Figures

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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 C oncentration 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 AreasList of Figures Figure 3.6-1: Uniform rate of progress, or glid epath, for GSMNP1 on the 20% best days (top) and the 20% worst days (bottom) ................................................................................................. 11 Figure 3.6-2: Uniform rate of progress, or glidep ath, for JOKI on the 20% be st days (top) and the 20% worst days (bottom) .............................................................................................................. 12 Figure 4.1-1: Plot of 2018 mode ling 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 th e 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 IM PORVE monitors within the 12km modeling domain. Estimates of reducti ons 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 su lfate at VISTAS and neighboring Class I areas. ............................................................................................................. 20 Figure 4.2-6. Percent of Uniform Rate of Pr ogress haze index reduction achieved in 2018 according to the VISTAS inventory. Results ar e given for improvements calculated using the new IMPROVE algorithm (squares) and the ol d IMPROVE algorithm (diamonds). Solid symbols indicate results when all emissions are c onsidered; 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 AreasList of Tables Table 3.2-1: Baseline Visibility Conditions a nd 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 monitore d 20% best days (top) and 20% worst days (bottom), based on 12km modeling results. .................................................................................... 8

Table 3.6-1: Final future m ean deciview values (dv future ) 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 Areas1 Regional Haze Rule Requirements

Visibility improvement regulat ions originate from language in the Clean Air Act (CAA). Section 169A of the CAA declared a national goal to prevent “a ny future, and the remedying of any existing, impairment of vi sibility in mandatory class I Federal areas which impairment results from manmade air pollution.” As a resp onse 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 degradati on 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 co nditions 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). D eciviews are calculated directly from light extinction using a logarithmic scale. The deciview is a useful m easure for tracking progress in improving visibility, because each deciview change is an equal incr emental 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 overv iew 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 thr ee metrics of visibility that ar e part of the determination of reasonable progress:

1) Natural conditions, 2) Baseline conditions 3) Current conditions.

Each of the three metrics includes the concentrati on data of the visibility impairing pollutants as different terms in the light extinction algorithm, with respective extinc tion 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 dete rmine what level of control would be achievable in the given timeframe and what the potential impr ovement to visibility w ould be as a result of these controls. After the potential control an alysis, 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 Areas3 Model Application

The purpose of a modeling assessment of uniform ra te 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 visi bility 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 pro cess to determine 2018 projections from regional modeling, as suggested in the “Guidance on th e Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Go als 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 obser vations for mass associated with sulfate (SO 4 ), nitrates (NO 3 ), organic carbon (OC), elemental car bon (EC), soil, and coarse matter (CM), should be used to calculate b ext for each day using climatological relative humidity adjustment factor(s) ( f(rh) ). Total light extinction (b ext ) 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. Firs t, the mean deciview value for the identified 20% worst- and best visibility da ys in each year is calculated (dv current ). The 5 year mean deciview values reflecting mean vi sibility on the days with best vi sibility and then averaged to determine the baseline values for the best days, or the dv baseline 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 base line visibility conditions were estimated using sampling data collected at Interagency Mon itoring 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 pe rcent 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 qua rter). 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 Kilm er Wilderness Area. The light extinction and deciview visibility values for th e 20 percent worst and 20 percent best visibility days at the Class I areas are based on data and calculations incl uded in Appendix B of this SIP. Table 3.2-1 summarizes the mean yearly visibility conditi ons 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 AreasTable 3.2-1: Baseline Visibility Conditions a nd Natural Conditions for the Tennessee Class I areas.

dv baseline Natural Condtions dv baseline 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 y ear modeling results to es timate 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:

ion Concentrat Year Base Modeled Average ion Concentrat Year Future Modeled Average

= 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 divide d by their respective ba se year component concentrations to develop RRFs for each component of PM2.5 and coarse matter.

For a numerical example, consider Table 3.3-1, wh ich has all the pertinen t data for the GSMNP1 IMPROVE monitor. Looking at th e top table, the first column identifies the 20% best days identified in step 1 (Section 3.1). The next si x columns have the 12km modeled base year (2002) concentration values near the monitor correspondi ng to the 20% best days. The next six columns have the 12km modeled future ye ar (2018) component concentrati ons near the monitor for the same set of days. The final six columns c ontain the daily RRF values for each component species, which is simply the future concentrati on divided by the base year concentration. The bottom row of the table contains th e average values for the base y ear 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 AreasTable 3.3-1: Base year concentrations, futu re 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 Areas3.4. 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 th e previous step, Secti on 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 ye ar species concentrations data.

This is similar to the final step in the ozone at tainment 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, Se ction 3.2) using an RRF for each PM component, for both the best days and the worst days. This produces future concentration estimates for SO 4 , NO 3 , 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 calculat ed (Section 3.4) the future daily extinction coefficients are then calculated for the 20% best a nd 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 * f s (RH) * [Small Sulfate] + 4.8 * f L (RH) * [Large Sulfate] + 2.4 * f s (RH) * [Small Nitrate] + 5.1 * f L (RH) * [Large Nitrate] + 2.8 * {Small Organic Mass] + 6.1 * [Large Organic Mass] + 10 * [Elemental Carbon] + 1 * [Fine Soil] + 1.7 * f ss (RH) * [Sea Salt] + 0.6 * [Coarse Mass] + Rayleigh Scattering (site specific) + 0.33 * [NO 2 (ppb)]

Model estimated daily concentra tions are used with the corres ponding 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 aver age mean deciviews for the worst and best days for each year. The 5 years are then averaged togeth er 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 b ext 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 determ ine 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 va lues 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 dv future . Table 3.6-1 displays the yearly da ily 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 dv future
. Table 3.6-1: Final future mean deciview values (dv future ) and Natural Conditions for the Tennessee Class I areas, based on 12km modeling results.

dv

future Natural Condtions dv future 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 dv future values for the “worst” and “best” visibility days can be compared to the dv baseline 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 a pparent that the two Tennessee Cla ss 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 DV base DV future DV diff DV base DV future DV diff 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 Cl ass I area (Figures 3.6-1 through 3.6-2). When plotted against the glidepath, one can see the cha nge in deciview between the baseline and future shows an improvement that is well below the unifo rm 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 AreasReasonable 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 5 10 15 20 25 30 35

200 0 200 4 200 8 201 2 201 6 202 0 202 4 202 8 203 2 203 6 204 0 204 4 204 8 205 2 205 6 206 0 206 4

YearHaziness Index (Deciviews)

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



### 0 5 10 15 20 25 30 35

200 0 200 4 200 8 201 2 201 6 202 0 202 4 202 8 203 2 203 6 204 0 204 4 204 8 205 2 205 6 206 0 206 4

YearHaziness Index (Deciviews)

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

Figure 3.6-1: Uniform rate of progress, or gl idepath, 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 AreasReasonable Progress Assessment Base G2 inventory, new IMPROVE Algorithm Joyce Kilmer-Slickrock - Best 20% Days

13.58 13.58 13.58

13.11 12.11 4.54 

### 0 5 10 15 20 25 30 35

200 0 200 4 200 8 201 2 201 6 202 0 202 4 202 8 203 2 203 6 204 0 204 4 204 8 205 2 205 6 206 0 206 4

YearHaziness Index (Deciviews)

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

Uniform Rate of Reasonable Progress Glide Path Joyce Kilmer-Slickrock - W20% Data Days

30.28 28.67 25.79
22.59
19.38
16.18
12.97 11.05 26.72
23.50



### 0 5 10 15 20 25 30 35

200 0 200 4 200 8 201 2 201 6 202 0 202 4 202 8 203 2 203 6 204 0 204 4 204 8 205 2 205 6 206 0 206 4

YearHaziness Index (Deciviews)

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

Figure 3.6-2: Uniform rate of pr ogress, or glidepath, for JOKI 1 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 Areasconsider 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 attain ment 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 pollu tion. 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 “Attainme nt Guidance”, USEPA gives recommendation of additional analysis that can be used for supplement analysis/W OE for regional haze goals. These suggestions include performing additiona l 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 corro borate modeling results, than a WOE analysis to show attainment. In the following sections we e xplore 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 recomme nds exploring refinement s to the uniform rate of progress. One refinement th e USEPA suggests is using an alternative or area specific IMPROVE equations. One altern ative 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 understand ing of light extinction calculation. Results presented in Section 3 reflect the use of the new IM PROVE equation, as described in Section 3.5. As supplemental analysis, results were also calculated with th e “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 with in the VISTAS 12km modeling domain.

The plot shows the percentage of target reducti on achieved at each site. Points on or above the bold 100% line met target reduction goals determ ined 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 equa tion track closely. The one IMPROVE sites associated with Tenness ee Class I areas (GRSM) show visibility improvement beyond what is required by the set r easonable 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 AreasCMAQ 2018g2a/2002gt2a Method 1 predictions for VISTAS+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent 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 “ol d” IMPROVE algorithm (blue diamonds) for VISTAS Class I areas, and Class I areas ne ar 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 analys is is to complete additional air quality modeling. The Attainment Guidance suggests seve ral 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 th e Southeast (VISTAS) in clude, looking at both 36 and 12km grid resolutions, the comparison of VI STAS results to those from modeling efforts by other RPOs, and undertaking an emissions sensitiv ity to assess the importa nce 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 suggest s 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 mode ling. 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 AreasVISTAS modeling was performed on a 12km resolution grid nested within a 36km resolution grid. This allowed for future mean deciview values, and subsequen tly 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 bot h the old and new IMPROVE equations. Figure 4.2-1 presents the 12 and 36km results in percentage of target re duction achieved. Pink squares represent the 12km results pro cessed with the new IMPROVE equations, and blue diamonds as the 12km results processed with the old IMPR OVE equations. Burgundy triangles represent 36km results processed with the new IMPROVE e quations, with blue Xs noting the 36km results processed with the old IMPROVE equations.

From the plot, one can see the results from both gr id resolutions and equati ons track fairly close together. The 12 and 36km results with the ne w IMPROVE equation are similar, suggesting the choice of IMPROVE algorithm at GRSM1 make a sign ificant difference in final values. Overall, the Tennessee Class I area IMPROVE monitor exceeds target reductions with both grid resolution modeling results when applied to ei ther 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 demo nstration, 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 mo deling 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

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved CMAQ New IMPROVE Algorithm 12km CMAQ Old IMPROVE Algorithm 12km CMAQ New IMPROVE Algorithm 36km CMAQ Old IMPROVE Algorithm 36km

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

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved CMAQ New IMPROVE Algorithm 12km CMAQ Old IMPROVE Algorithm 12km CMAQ New IMPROVE Algorithm 36km CMAQ Old IMPROVE Algorithm 36km

VISTAS non-VISTAS Figure 4.2-1: VISTAS modeling result for IM PORVE monitors within the 12km modeling domain. Estimates of reductions are pres ented 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 Areas4.2.2 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 modeli ng 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 sh ared amongst the RPOs, modeling work with preliminary versions of the emission inventor ies 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 in ventories, or have completed preliminary runs based on older emission inventorie s. By comparing VISTAS fi nal results to the other RPO modeling, essentially an emissions sensitivity run is created.

Preliminary modeling from Central Regional Ai r Planning Association (CENRAP) and Western Regional Air Partnership (WRAP) were obtained and are compar ed 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 IMPR OVE 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

105% 104%

92% 92% 113% 128%

119%

16% 41%

24% 28%

6% 5% 8% 24% 19% 117% 120%

83% 96% 111% 128%
122%

15% 30%

13% 14% 14% 34%

8%

0% 20% 40% 60% 80% 100% 120% 140%CACR1UPBU1BRET1HEGL1MING1SIPS1MACA1GRSA1ROMO1SACR1WHIT1WHPE1LOST1THRO1BADL1WICA1Percent of target reduction CENRAP-base 18f
VISTAS-base G2
WRAP-base 18b

VISTAS WRAP

Figure 4.2–2: Comparison of percent of Unif orm Rate of Progress haze index reduction from VISTAS, CENRAP, and WRAP modeling effo rts. 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 AreasAdditionally 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 w ith the old IMPROVE equation. As with the previous comparison, not a ll VISTAS sites are modeled in the other RPOs domains. The sites that do overl ap are presented in Figure 4.2-3. The plot reveals the modeling results are very similar across RPOs. For exam ple, modeling results for Mingo Wilderness area are very similar, with CENRAP modeling pr edicting an 83% of target reduction, MwRPO predicting 90%, and VISTAS proj ecting a 96% of target reducti on. This further corroborates VISTAS modeling, as even usi ng the old IMPROVE equation and varying emission inventories, the model projections are similar across the United States.

Limited data has been attained from Mid-At lantic/Northeast Visibi lity Union (MANE-VU); however projections for Shenandoah National Park (SHEN1), Brigantine (BRIG1), and Dolly Sods (DOSO1) are comparable to VISTAS mo deling 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

98%

86% 81% 116%

76% 83% 112%

18% 26% 76%

14% 36%

11% 26%

6% 6% 7% 19% 15% 111% 110%

66% 124%

90% 96% 113%

90% 120%

39%

11% 50%

0% 20% 40% 60% 80% 100% 120% 140%CACR1UPBU1BRET1SIPS1HEGL1MING1MACA1BOWA1VOYA2ISLE1GRSA1ROMO1SACR1WHIT1WHPE1LOST1THRO1BADL1WICA1Percent of target reduction CENRAP-base 18f
VISTAS-base G2 MwRPO-base g
WRAP-base 18b

VISTAS WRAP MwRPO

Figure 4.2–3: Comparison of percent of Unif orm 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 Areas22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0 30.0

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020Deciview Brigantine Dolly Sods Shenandoah Brig BOTW_BaseB4 Dolly BOTW_BaseB4 Shen BOTW_BaseB4

Figure 4.2–4: Preliminary MANE-VU final fu ture mean deciview values at VISTAS IMPROVE sites.

As more modeling work becomes available additi onal examination of result will be preformed. Of particular interest to VI STAS, are results from the MwRP O. 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 us ing a base year of 2005. VISTAS also hope to obtain MANE-VU model output, so the resu lts can be added to Figure 4.2-2.

4.2.3 International Emissions

An integral part to accurately modeling visibi lity 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 em ission from areas outside the Un ited States, VISTAS conducted a study of modeling results generated from the globa l three-dimensional chemical transport model (GEOS-Chem) and CMAQ.

GEOS-Chem was run for the 2002 modeling year usi ng 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 variabil ity in emissions. Details of the international em issions study are described in a report, which is included here as Attachment G1. Discussion of the GEOS-Chem runs as they pertain to the bounda ry 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 include d 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 AreasTwo complementary methods were used to calcul ate the impact of inte rnational emissions at Class I areas. Because the international emissions inventory used in GEOS-CHEM did not distinguish between wildfires a nd anthropogenic fires, all intern ational fire emissions were assumed to be wildfire, and the contributions attr ibutable to international anthropogenic fires are underestimated in these projections

1. International emissions as the difference be tween 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 differe nce is attributable to international anthropogeni c 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 inte rnational contribution on 4 x 5 degree grid. Concurrently Harvard modeling for the Electric Power Re search Institute (EPRI) provided GEOSChem results for 2001 international contri bution on a 1 x 1 degree grid scale.

2. International emissions as the differen ce between two CMAQ 36-km simulations, both using 2018 emissions and boundary conditions from GEOS-Chem. In the first CMAQ run, all global natura l and anthropogenic emissions in 2018 are active. In the second CMAQ run, only global (United Stat es and international) natu ral emissions are active. Here the impacts of internat ional emissions are compared against 2018 conditions rather than natural background conditions.

VISTAS has compared its results to results fr om other RPOs on the impact of international emissions and boundary conditions on vi sibility at Class I areas in 2002; the results were similar.

As illustrated in Figure 4.2-5 for annual averag e contributions to sulfate at VISTAS and neighboring Class I areas, the estim ated international c ontributions 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 GE OS-Chem runs because the grid scale is finer (more accurate dispersion of emissions) and because the background atmosphere includes loadings from current United States anthropogeni c emissions (greater photochemical activity). Similar charts for nitrate and organic carbon ma ss, 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

0.0 0.2 0.4 0.6 0.8 1.0 1.2BRIGHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHEN(NH4)2SO4Conc. (g/m3)

GEOS_4x5 GEOS_1x1 CMAQ
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 sh ows the estimates from VISTAS modeling of what the estimated percent of target reduction in vi sibility impairment is achieved at each of the VISTAS Class I areas. Calcul ations were preformed with both the new and old IMPROVE equations. Figure 4.2-7 shows that removing the in ternational cont ribution 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 Cl ass 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 AreasCMAQ 2018g1a/Typ02g Method 1 predictions for VISTAS+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200% 220% 240%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved

CMAQ New IMPROVE Algorithm CMAQ Old IMPROVE Algorithm CMAQ New IMPROVE Algorithm w/ Int'l Trans Removed CMAQ Old IMPROVE Algorithm w/ Int'l Trans Removed

V ISTAS non-VISTA S

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 emis sions are considered; open symbols indicate results when effects of intern ational emissions are removed.

For the Tennessee Class I areas, accounting for th e international contribu tion do not change the conclusion that reductions of United States an thropogenic 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 in ternational emission study results when setting reasonable progress goals with this SIP.

The results of this study do reit erate the significance of international emissions in the United States to the attainment of future visibility im provement 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 re sults 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