### Kentucky Energy and Environment Cabinet Kentucky Division for Air Quality



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Appendix G Modeling Results and Supplemental Analysis[This page intentionally left blank.]

iiTable of Contents

List of Figures

............................................................................................................................... . iv

List of Tables

............................................................................................................................... ... v
1. Regional Haze Rule Requirements

............................................................................................. 1

2. Reasonable Progress Goals

......................................................................................................... 1

3. Model Application

...................................................................................................................... 2 3.1 Determination of 20% Best and worst Days

......................................................................... 2 3.2 Determine Baseline Conditions

............................................................................................ 2 3.3 Relative Response Factor Calculation

.................................................................................. 3 3.4. Future Year Daily C oncentration Calculation

..................................................................... 5 3.5. Future Year Daily bext Calculation

..................................................................................... 5 3.6. Future Mean Deciview Values Calculation

......................................................................... 5

4. Supplemental Analysis

................................................................................................................ 8 4.1 Refinements to Recommended Analysis

.............................................................................. 8 4.2 Additional Air Quality Modeling

.......................................................................................... 9 4.2.1 Grid Resolution

.............................................................................................................. 9 4.2.2 Other Modeling Results

............................................................................................... 11 4.2.3 International Emissions

................................................................................................ 13

iiiList of Figures Figure 3.6-1: Uniform rate of progress, or glid epath, for MACA1 on the 20% best days (top) and the 20% worst days (bottom ) ........................................................................................................ 11
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 th e VISTAS states. Heavy black line represents the percent of reductions needed to reach reasonable progress goals. ................................................................... 9 Figure 4.2-1: VISTAS modeling result for IM PORVE monitors within the 12km modeling domain. Estimates of reductions are presente d from the both the “old” and ”new” IMPROVE equation applied to both the 36 and 12 km grid resolutions results. ............................................. 10
Figure 4.2–2: Comparison of percen t of Uniform Rate of Progr ess haze index reduction from VISTAS, CENRAP, and WRAP modeling efforts. Results calcu lated with the “new” IMPROVE equation.

............................................................................................................................... ......... 11
Figure 4.2–3: Comparison of percen t of Uniform Rate of Progr ess haze index reduction from VISTAS, C ENRAP, Midwest RPO (MwRPO) and WRAP modeling efforts. Results were calculated with the “old” IMPROVE equations. .......................................................................... 12
Figure 4.2–4: Preliminary MANE-VU final future m ean deciview values at VISTAS IMPROVE sites.

............................................................................................................................... ............... 13
Figure 4.2-5: Estimated international emission s contributions to su lfate at VISTAS and neighboring Class I areas.

............................................................................................................. 15
Figure 4.2-6: PM component conc entrations from US domestic sources on 20 percent worst visibility days in 2002 (left bars) and CMAQ-simula ted international contribu tions (right bars) at Mammoth Cave, Kentucky. .......................................................................................................... 15
Figure 4.2-7. Percent of Uniform Rate of Pr ogress haze index reduction achieved in 2018 accord ing 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. ............................................................................ 16

ivList of Tables Table 3.2-1: Baseline Visibility Conditions and Natural Con ditions for Kentucky’s Class I area 3
Table 3.3-1: Base year concentrations, future year concentrations, and RRF for Mammoth Cave National Park (MACA1) on the m onitored 20% best days (top) and 20% worst days (bottom), based on 12km modeling results. .................................................................................................... 4
Table 3.6-1: Final future mean deciview values (dv future ) and Natural Conditions for the Kentucky’s Class I area, based on 12km modeling results. ............................................................ 6 Table 3.6-2: Comparison of future deciview pr ojections for 2018 compared to 2002 baseline deciview estim ates. ......................................................................................................................... 6

v1. 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 ma ndatory class I Federal areas which impairment results from manmade air pollution.” As a res ponse 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 se t 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 al so established that state goals for regional haze m ust be expressed in deciviews (dv). Deciviews are calc ulated 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 m odeling 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 m etrics of visibility that ar e part of the determination of reasonable progress:

1) Natural con ditions, 2) Baseline co nditions 3) Current conditions.

Each of the three metrics includes the concentration data of the vi sib ility impairing pollutants as different terms in the light ex tinction algorithm, with respectiv e 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 unif orm 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 would be as a result of these controls. After the potential control analys is, reasonable progress goals for the Class I area are determined after consultati on with surrounding states. The entire RPG determination for Kentucky’s Class I area is de scribed in Appendix H.

13. Model Application

The purpose of a modeling assessment of uniform ra te of progress is to determ ine 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 proc ess 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 (i n 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. Da y-specific observations 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 ar e then used to calcu late 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 days 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 Kentucky’s Class I area, the baseline visib ility conditions were estim ated using sampling data collected at Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring site at four of the Class I area in Kentucky. A five year average (2000 to 2004) was calculated for each of the 20 percent worst and 20 pe rcent best visibility da ys 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 Mammoth Cave 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). The light extinction a nd deciview visibility values for the 20 percent worst and 20 percent best visibility days at Mammoth Cave is based on data and calculations included in Appendix B of this SIP. Table 3.2-1 summarizes the mean yearly visibili ty conditions and the baseline visibility conditions for Kentucky’s Cl ass I area on both the 20% best and 20% worst days.

2Table 3.2-1: Baseline Visibility Conditions a nd Natural Conditions for Kentucky’s Class I

area. Worst Visibility Days Best Visibility Days Site dv baseline Natural Conditions (dv) dv baseline Natural Conditions (dv) 2000 32.10 2000 17.09 2001 31.54 2001 15.17 2002 30.98 2002 17.41 2003 31.82 2003 16.66 2004 30.42 2004 16.22 Mammoth Cave

National Park

(MACA1), Kentucky

Mean 31.37 11.08

Mean 16.51 4.99

3.3 Relative Response Factor Calculation

The next step is to use base year and future year modeling results to estim ate 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 concentr ation 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 le ss than 10 m icrometers in diam eter (PM10) and PM2.5) are extracted from the base year and future year modeling output near IM PROVE monitoring sites for the monitored best and worst days. The co mponent concentrations are then averaged to develop base and future year average componen t concentrations. The future year average component concentrations are then divide d 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, wh ich has all the pertinent data for the Mammoth Cave IMPROVE monitor. Looking at the top table, the first colum n identif ies the 20% best days identified in step 1 (Section 3.1). The next si x columns have the 12km mo deled base year (2002) concentration values near the m onitor corresponding to the 20% best days. The next six columns have the 12km modeled future year (2018) com ponent concentrations near the monitor for the same set of days. The final six columns cont ain 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 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. Tables for the IMPROVE monitor of interest for Kentucky’s Class I area are provided, and abide by the same format.

3Table 3.3-1: Base year concentrations, futu re year concentrations, and RRF for Mammoth Cave National Park (MACA1) on the monitored 20% best days (top) and 20% worst days (bottom), based on 12km modeling results.

Julian Date SO4 NO3 OC EC SOIL CM SO4 NO3 OC EC SOIL CM SO4 NO3 OC EC SOIL CM 14 1.333 1.114 2.704 0.295 2.032 0.585 1.262 1.111 2.637 0.215 1.923 0.506 0. 947 0.998 0.975 0.730 0.947 0.865 23 1.277 0.026 0.434 0.105 0.506 0.428 1.073 0.062 0.401 0.065 0.488 0.434 0. 840 2.364 0.925 0.622 0.964 1.014 32 1.813 1.613 1.030 0.157 1.193 0.322 2.166 1.525 1.047 0.117 1.227 0.286 1. 194 0.945 1.017 0.746 1.029 0.888 35 1.158 2.206 1.067 0.166 1.209 0.381 1.241 2.118 1.064 0.129 1.311 0.317 1. 072 0.960 0.997 0.778 1.084 0.831 44 0.864 0.733 0.787 0.160 1.060 0.650 0.816 0.600 0.774 0.120 1.362 0.648 0. 944 0.819 0.983 0.754 1.285 0.996 62 3.107 2.970 0.707 0.156 0.925 0.282 4.104 2.586 0.725 0.125 0.982 0.253 1. 321 0.871 1.025 0.799 1.062 0.898 68 1.710 0.771 1.751 0.242 0.805 0.325 1.339 0.582 1.712 0.192 0.903 0.325 0. 783 0.755 0.978 0.794 1.122 1.002 83 2.419 3.953 2.882 0.447 1.930 1.334 1.737 1.796 2.747 0.300 2.104 1.419 0. 718 0.454 0.953 0.671 1.090 1.063 89 2.409 3.564 1.012 0.205 1.232 1.014 2.012 2.235 0.981 0.127 1.487 1.093 0. 835 0.627 0.970 0.617 1.208 1.078 113 2.864 2.676 1.180 0.207 1.208 1.074 2.577 1.505 1.117 0.134 1.508 1.108 0. 900 0.562 0.947 0.650 1.249 1.032 116 1.409 0.983 1.055 0.150 0.898 1.381 1.083 0.419 1.016 0.103 1.105 1.388 0. 769 0.426 0.963 0.687 1.229 1.005 227 2.081 0.025 0.880 0.119 0.605 0.850 1.797 0.020 0.834 0.062 0.627 0.889 0. 863 0.794 0.948 0.520 1.036 1.047 266 1.727 0.174 0.991 0.195 1.218 0.779 1.588 0.114 1.006 0.120 1.634 0.840 0. 920 0.654 1.015 0.614 1.342 1.078 269 0.512 0.151 0.301 0.023 0.139 0.072 0.274 0.042 0.315 0.013 0.171 0.074 0. 535 0.280 1.047 0.569 1.229 1.031 278 1.414 0.279 1.010 0.233 2.118 0.965 1.347 0.172 1.016 0.154 2.676 1.066 0. 952 0.618 1.006 0.659 1.264 1.106 281 1.796 0.403 1.448 0.297 1.882 1.109 1.335 0.219 1.420 0.160 2.299 1.223 0. 743 0.542 0.981 0.539 1.221 1.102 287 1.315 0.729 1.047 0.261 1.823 0.997 1.056 0.429 1.054 0.148 2.259 1.090 0. 803 0.588 1.007 0.568 1.239 1.093 302 1.512 0.960 0.633 0.213 1.011 0.664 1.170 1.012 0.649 0.119 1.202 0.694 0. 774 1.054 1.025 0.560 1.188 1.044 308 1.797 6.417 1.126 0.293 2.056 1.366 1.938 6.005 1.144 0.181 2.436 1.452 1. 079 0.936 1.017 0.618 1.185 1.063 323 1.990 2.207 1.724 0.289 2.193 1.353 2.134 1.778 1.692 0.186 2.206 1.274 1. 072 0.806 0.981 0.644 1.006 0.942 338 0.768 0.568 0.449 0.125 0.634 0.181 0.614 0.609 0.448 0.091 0.883 0.181 0. 800 1.072 0.999 0.730 1.393 1.000 347 1.174 0.327 0.342 0.069 0.386 0.137 0.899 0.434 0.353 0.046 0.480 0.153 0. 766 1.326 1.031 0.667 1.244 1.111 356 1.620 1.728 1.419 0.214 1.518 0.628 1.589 1.729 1.406 0.165 1.468 0.570 0. 981 1.001 0.991 0.767 0.967 0.908

1.655 1.503 1.129 0.201 1.243 0.734 1.528 1.178 1.111 0.134 1.424 0.751 0. 923 0.784 0.984 0.665 1.146 1.024 Mean Concentration RRFs Future Year Base Year 20% Best Days

at MACA1

Julian Date SO4 NO3 OC EC SOIL CM SO4 NO3 OC EC SOIL CM SO4 NO3 OC EC SOIL CM 86 4.032 0.955 0.897 0.211 1.075 0.616 3.808 1.060 0.861 0.128 1.375 0.624 0.944 1.110 0.960 0.608 1.279 1.012 149 8.467 0.011 2.428 0.185 0.813 1.066 3.177 0.027 2.322 0.118 0.965 1.163 0.375 2.526 0.956 0.637 1.188 1.091 152 7.113 0.089 3.259 0.250 1.968 1.248 3.835 0.041 3.099 0.151 1.886 1.169 0.539 0.459 0.951 0.603 0.958 0.936 155 7.376 0.465 6.626 0.471 2.134 2.134 3.453 0.067 6.343 0.236 2.422 2.365 0.468 0.143 0.957 0.500 1.135 1.108 170 6.363 0.157 4.860 0.280 1.681 1.703 3.246 0.043 4.626 0.155 1.931 1.825 0.510 0.273 0.952 0.554 1.149 1.072 173 4.051 0.056 3.160 0.215 0.965 1.116 1.896 0.017 2.966 0.125 1.135 1.181 0.468 0.298 0.939 0.583 1.176 1.058 185 8.509 0.070 3.030 0.291 2.014 1.479 4.598 0.051 2.883 0.150 2.349 1.563 0.540 0.723 0.952 0.517 1.166 1.057 191 7.778 0.044 1.247 0.133 0.923 1.070 3.902 0.010 1.175 0.079 0.991 1.033 0.502 0.221 0.942 0.593 1.073 0.965 197 17.596 0.007 1.774 0.199 1.088 1.327 5.861 0.009 1.706 0.105 1.345 1.435 0.333 1.197 0.962 0.526 1.237 1.081 203 10.273 0.022 3.173 0.268 1.613 1.442 5.905 0.057 3.015 0.136 1.775 1.530 0.575 2.573 0.950 0.510 1.101 1.061 206 13.985 0.009 2.295 0.232 1.186 1.237 4.969 0.008 2.147 0.128 1.504 1.270 0.355 0.913 0.936 0.549 1.268 1.026 212 18.100 0.144 1.997 0.205 1.977 1.533 12.875 0.055 1.902 0.130 2.279 1.534 0.711 0.384 0.952 0.635 1.153 1.001 215 8.354 0.004 4.092 0.249 1.272 1.266 3.720 0.013 3.784 0.138 1.588 1.365 0.445 3.429 0.925 0.553 1.249 1.078 218 8.082 0.002 3.373 0.321 1.695 1.256 4.559 0.061 3.273 0.203 2.052 1.299 0.564 25.596 0.970 0.633 1.211 1.034 224 7.074 0.387 9.775 0.672 1.846 1.819 2.768 0.080 9.381 0.514 2.187 1.991 0.391 0.206 0.960 0.764 1.185 1.095 233 7.909 0.011 2.708 0.303 1.462 1.239 4.053 0.016 2.552 0.183 1.760 1.345 0.512 1.369 0.942 0.603 1.204 1.086 236 5.494 0.034 1.250 0.102 0.851 0.841 3.858 0.006 1.154 0.066 0.794 0.711 0.702 0.192 0.924 0.652 0.933 0.845 239 8.273 0.000 2.837 0.225 1.121 0.899 4.279 0.054 2.707 0.134 1.555 0.948 0.517 0.000 0.954 0.596 1.387 1.055 245 9.657 0.631 2.767 0.278 1.536 1.623 4.412 0.241 2.625 0.137 1.828 1.792 0.457 0.382 0.949 0.493 1.190 1.104 251 5.684 0.287 5.616 0.281 1.484 1.020 2.018 0.037 5.257 0.163 1.787 1.062 0.355 0.130 0.936 0.579 1.204 1.041 254 7.366 0.056 4.206 0.293 2.119 1.280 4.047 0.096 4.073 0.181 2.655 1.383 0.549 1.707 0.968 0.617 1.253 1.081 260 19.632 0.110 3.750 0.293 2.248 1.721 12.085 0.040 3.642 0.167 2.346 1.644 0.616 0.364 0.971 0.570 1.044 0.955 263 6.566 0.000 2.507 0.134 0.651 0.397 2.768 0.008 2.450 0.081 0.793 0.443 0.422 62.231 0.977 0.602 1.218 1.117 341 2.379 8.944 2.535 0.675 3.884 2.352 2.819 8.714 2.569 0.419 4.001 2.397 1.185 0.974 1.013 0.621 1.030 1.019

8.755 0.521 3.340 0.282 1.567 1.320 4.538 0.450 3.188 0.168 1.804 1.378 0.518 0.865 0.955 0.595 1.152 1.044 RRFs

Mean Concentration 20% Worst Days at MACA1 Base Year Future Year

43.4. Future Year Daily Concentration Calculation

The fourth step in using model output to estimate the f uture visibility impairment is to multiply the relative response factors (d etermined in the previous ste p, Section 3.3) by the measured species concentration data during the base period (for the measur ed 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 attainment test, where base year concentrations, or the cur rent year design value (DVC) is multipli ed 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 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 concentratio ns are calculated (Section 3.4) the future daily extinction coefficients are then calculated for the 20% best and 20% worst visi bility days in each of the five base years. This is achieved by applying th e 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 concentrations are used with the corresponding ba se line daily relative hum idity 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 da ily deciview values from the to tal daily extincti on 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 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 th e 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 “best” visibility days for each year. This leads to 5 future estimated mean deciview values for the

5“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 3.2-1 summarizes the mean yearly future visibility conditions and the baseline visibility conditions for Kentuc ky’s Class I area on both the 20% best and 20% worst days.

Table 3.6-1: Final future mean deciview values (dv future ) and Natural Conditions for Kentucky’s Class I area, ba sed on 12km modeling results.

Worst Visibility Days Best Visibility Days Site dv future Natural Conditions (dv) dv future Natural Conditions (dv) 2000 26.27 2000 16.16 2001 25.73 2001 14.23 2002 25.20 2002 16.46 2003 25.89 2003 15.69 2004 24.72 2004 15.29 Mammoth Cave

National Park

(MACA1), Kentucky

Mean 25.56 11.08

Mean 15.57 4.99

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 resulti ng change in deciviews is a negative number (future - base), this represents an improvem ent in visibility. Table 3.6-2 summarizes these calculation for the IMPROVE m onitor associated with Kentucky’s Class I area. From the table, it is apparent that Kentucky’s Class I area is 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.

Worst Visibility Days Best Visibility Days Site dv base dv future dv diff dv base dv future dv diff Mammoth Cave

National Park

(MACA1), Kentucky 31.37 25.56 -5.81 16.51 15.57 -0.94

These changes in visibility can also be compared to the previously calculated (in Appendix B) uniform rate of progress values for Kentucky’s Class I area (Figur e 3.6-1). When plotted against the glidepath, one can see the change in decivi ew between the baseline and future shows an improvement that is well below the uniform rate of progress for the Class I monitoring site at Kentucky’s Mammoth Cave.

6

### Reasonable Progres s Assessment Mammoth Cave - 20% Best Days New IMPROVE Equation, 12 km grid

16.51 16.51 16.51

15.57

4.99

0 5 10 15 20 25 30 35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064 YearHaziness Index (Deciviews)

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



### Reasonable Progr ess Assessment Mammoth Cave - 20% Worst Days New IMPROVE Equation, 12 km grid

31.37 29.68

26.64

23.26

19.87

16.49

13.11
11.08 25.56

0 5 10 15 20 25 30 35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064 YearHaziness Index (Deciviews)

Glide Path Natural Conditio n (Worst Days) Observation Method 1 Prediction Figure 3.6-1: For Mammoth Cave, Reasonable pr ogress assessment for 20% best days (top) and Reasonable progress assessment compared to Uniform Rate of Progress for 20% worst days (bottom).

74. Supplemental Analysis

The Attainment Guidance asserts that all attain ment demonstrations sh ould be accompanied by supplemental analysis that further supports th e modeling conclusions. This supplemental analysis can include additional analyses of ai r quality, emissions and meteorological data, and consider modeling outputs other than the results of the attainment test. If the attainment test results fall short of the standard, the results of co rroboratory analyses may be used in a weight of evidence determination (WOE) to show that atta inment is likely despit e 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/W OE for regional haze goals. These suggestions include performing additiona l air quality m odeling, a review of trends, making use of observational models, and applying refinements to the recommended uniform rate of progress analysis.

Kentucky’s Class I area meets uniform rate of pr ogress trends, making the following section an examination of supplemental anal ysis to corroborate modeling re sults, 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 Kentucky’s Class I area.

4.1 Refinements to Recommended Analysis

In the Attainment Guidance, the USEPA recommends exploring refinem ents to the uniform rate of progress. One refinement the USEPA suggests is using an alternative or area specific IMPROVE equations. One alternative to examin e 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 li ght extinction calculation. Re sults 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 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 with in 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 ol d and new IMPROVE equation track closely. The one IMPROVE site associated with Kentucky’s Class I area shows visibility improvement beyond what is required by the set reasonable pr ogress goals. This is further evidence that Kentucky Class I area should reach visibility im provement goals, with a margin of safety.

8CMAQ 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 w ith 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 com plete 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 alternat ive set ups and emissions as pa rt of sensitivity. Additional modeling analysis undertaken by the southern regional planning organization (RPO) Visibility Improvement State and Tribal A ssociation of the Sout heast (VISTAS) include , 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 sugges ts a grid cell resolution of 36km due to the rural/rem ote nature of Class I areas and the fact regional haze is genera lly not dominated by local sources, which necessitate a regional scale m odeling. Given that computation needs increase with finer resolutions, the resources needed to model on a regional scal e using a finer grid resolution may prove too costly for some states.

9VISTAS modeling was performed on a 12km resolution grid nest ed 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 re solution. 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 percenta ge of target reduction achieved. Pink squares represent the 12km results pro cessed with the new IMPROVE e quations, and blue diamonds as the 12km results processed with the old IMPR OVE 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 gr id resolutions and equati ons trac k fairly close together. Overall, the Kentucky Class I area IMPROVE monitor exceeds target reductions with both grid resolution modeling results when applie d to either the new or old IMPROVE equation. The similarity between the 36km and 12km results further supports attainment of visibility improvement goals for Kentucky’s Class I area.

It should be noted that for purposes of this dem onstration, final attainment values in Section 3 have been presented based on m odeling 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

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 w ithin 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.

104.2.2 Other Modeling Results

One way to acquire modeling sensitivity runs is to exam ine the modeling results from other RPOs. Other RPOs may use different physical and chemical modeling options for their meteorological and air quality modeling runs, wh ich would provide a comparison or sensitivity based on these different options.

Though emissions inventories are generally shared am ongst th e 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 inventories, or have completed preliminary runs based on older emission inventories. By comp aring VISTAS final 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 atta ined 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 WR AP, 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 sugge sting 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 P rogress haze index reduction from VISTAS, CENRAP, and WRAP modeling effo rts. Results calculated with the “new” IMPROVE equation.

11Additionally 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 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 projecting a 96% of target reduction. This further corroborates VISTAS modeling, as even usin g 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 Pa rk (SHEN1), Brigantine (BRIG1), and Dolly Sods (DOSO1) are comparable to VISTAS mode ling results (See Figur e 4.2-4). As with previous model comparisons, the similar model results produced by MAN-VU, despite difference in modeling set up an d 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 P rogress haze index reduction from VISTAS, CENRAP, Midwest RPO (MwRPO) and WRAP modeling efforts. Results were calculated with the “old” IMPROVE equations.

1222.0 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 av ailable additional exam ination 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 us e the CAMx model in lieu of CMAQ for their air quality modeling as well as usi ng 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 visibility is accurately quantifying emissions from

sources outs ide 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 generate d from the global three-dimens ional chemical transport model (GEOS-Chem) and CMAQ.

GEOS-Chem was run for the 2002 modeling year usi ng a 4 x 5 degree horizo ntal grid resolution and a 3-hour temporal resolution. Emissions we re 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 nation al CMAQ modeling domain for the base year and future year modeling, as well as the international emissions impact study. The national CMAQ doma in included portions of Canada and Mexico, so emissions for these countries were includ ed within the national CMAQ modeling domain or as part of the boundary conditions outside the national modeling domain, as appropriate.

13Two complementary methods were used to calculate the impact of international emissions at Class I areas. Because the international em issions 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. Intern ational emissions as the difference betw een 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 abse nce of United Stat es anthropogenic emissions (e.g. compared to 2064 levels). Harvard University provided GEOS-Chem results to VISTAS for 2002 international cont ribution 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. Intern ational 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 an thropogenic emissions in 2018 are active. In the second CMAQ run, only global (United St ates 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 con tributions to sulfate at VISTAS and neighboring Class I areas, the estimated interna tional 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 b ecause the grid scale is finer (more accurate dispersion of emissions) and b ecause 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 perc ent worst visibility days, and for impacts of intern ational emissions on calculated light extinction are included in Appendix H.

In Figure 4.2-6, CMAQ projections of contributions from intern ational emissions to PM mass on 20 percent worst visibility days in 2002 at Ma mmoth Cave, Kentucky are compared to United States domestic contributions to PM co mponents at the site on those days.

14

### 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.

Domestic and International Aerosol Mass by Species 20% Worst Days in 2002 Mammoth Cave, Kentucky

0 5 10 15 20 25 30 35 40DomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternational

3/27 5/29 6/1 6/4 6/19 6/22 7/4 7/10 7/16 7/22 7/25 7/31 8/3 8/6 8/12 8/21 8/24 8/27 9/2 9/8 9/11 9/17 9/20 12/7Mass (μg/m3)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

Figure 4.2-6: PM component concentrations fr om US domestic sources on 20 percent w orst

visibility days in 2002 (left bars) and CMAQ-s imulated international contributions (right bars) at Mammoth Cave, Kentucky.

15In addition to looking at the indi vidual com ponent 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 4.2-7 shows the estimates from VISTAS modeling of what the estimated percent of ta rget reduction in visibility impai rment 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 in ternational contribution is sufficient to raise the reductions ratios at all Cl ass I areas in the figure above 100%. This means that when the international contributions are c onsidered, all Class I areas excee d the Uniform Rate of Progress in 2018. This conclusion holds whether the old or new IMPROVE algorithm is used calculate the haze index

CMAQ 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-VISTAS

Figure 4.2-7. Percent of Uniform Rate of Progress haze index reduction achieved in 2018 according to the VISTAS inventory. Results are given for improvements calcu lated 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 Kentucky’s Class I area, accounting for the interna tional contributi on 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, Kentucky has elected

16not to take into account these internationa l emission study results when setting reasonable progress goals with this SIP.

The results of this study do reiterate the signifi cance 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 contribu tion 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.

17