west virginia department of environmental protection



# Appendix G Modeling Results and Supplemental Analyses

Promoting a healthy environment.[This page intentionally left blank.]Table of Contents

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

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

1 Regional Haze Rule Requirements .............................................................................................. 6

2 Reasonable Progress Goals.................................................................................................... ...... 6

3 Model Application ............................................................................................................ ........... 7 3.1 Determination of 20% Best and worst Days......................................................................... 7 3.2 Determine Baseline Conditions ............................................................................................ 7 3.3 Relative Response Factor Calculation .................................................................................. 8 3.4. Future Year Daily Con centration Calculation ................................................................... 10 3.5. Future Year Daily bext Calculation ................................................................................... 10 3.6. Future Mean Deciview Values Calculation ....................................................................... 10

4 Supplemental Analysis........................................................................................................ ....... 13 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................................................................................................ 18List of Figures

Figure 3.6-1: Uniform rate of progress, or glid epath, forDOSO on the 20% best days (top) and the 20% worst days (bottom) ................................................................................ 12
Figure 4.1-1: Plot of 2018 Base G2 modeling results as proces sed with the “new” IMPROVE algorithm (pink squares) and with the “old” IMPROVE algorithm (blue diamonds) for VISTAS Class I areas, and Cl ass I areas near the VISTAS states. Heavy black line represents the percent of reductions needed to reach reasonable progress goals........................................................................................................ 14
Figure 4.2-1: VISTAS modeling result for IMPORVE monitors within the 12km modeling domain. Estimates of reduc tions are presented from th e both the “old” and ”new” IMPROVE equation applied to both the 36 a nd 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 effo rts. 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 th e “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. Estimates by four methods of im pacts of international emissions on annual average ammonium sulfate concen trations at Class I areas.................................. 20
Figure 4.2-6. Percent of Uniform Rate of Progress haze index reduction achieved in 2018 according to the VISTAS Base G1 inventory. Results are given for improvements calculated using the new IMPROVE algor ithm (squares) and the old IMPROVE algorithm (diamonds). Solid symbols indi cate results when all emissions are considered; open symbols indi cate results when effects of international emissions are removed........................................................................................................... 21List of Tables

Table 3.2-1: Baseline Visibil ity Conditions for DOSO1............................................................. 8
Table 3.3-1: Base year concentrations, future year concentrations, a nd RRF for DOSO1 on the monitored 20% best days (top) and 20% worst days (bottom), based on 12km modeling results...................................................................................................... 9
Table 3.6-1: Yearly daily deci view values and final future mean deciview values (dv future ) for DOSO1 based on 12km modeling results............................................................. 11
Table 3.6-2: Comparison of future deciview projections for 2018 compared to 2002 baseline deciview estimates. ............................................................................................... 11[This page intentionally left blank.]1 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 response to the mandate in the CAA, EPA promulgated the regional haze rule (RHR) in July of 1999. Regulations at 308(d)(1) of the RHR require that a State set two r easonable progress goals (RPGs) for each Class I area - one for showing improvement for the 20% worst days a nd one for ensuring no degradation for the 20% best days over the period of the implementation plan. For the 20% best days, the goal should be set to reflect at least as g ood a level of visibility as the baseline conditions for 2000-2004, calculated per 308(d)(2)(i).

Section 51.308(d)(1) of the RHR al so established that state goal s 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 provides an overview of the process for defining RPGs and how to apply modeling to predict if these goals will be met in the future. Additional evidence to support modeling results, or weight of evidence, is also 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 convert ed to deciviews (dv), 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% best and worst days for each Class I area. Next, an analysis of potential control measures is perfor med to determine what le vel of control would be achievable in the given timeframe and what the pote ntial improvement to visibility would be as a result of these controls. After the potential c ontrol analysis, reasonabl e progress goals for the Class I areas are determined after consultation with surround ing states. The entire RPG determination for West Virginia Class I areas is described in Appendix H.3 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 particulate matter attainment test. The following sections outline the pr ocess to determine 2018 projectio ns 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, PM 2.5, 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 PM 2.5 data for each of the 5 years comprising the base period (2000- 2004) for each Class I area. Day-specific observations for mass associated with SO 4 , NO 3 , OC, EC, soil, and CM, should be used to calculate b ext for each day using climatological relative humidity adjustment factor(s) ( f(rh) ). Total 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 yearly 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 yearly mean deciview values reflecting worst visibility are averaged together . This represents the value subject to improvement to meet the glidepath for regional haze.

For the West Virginia Class I areas, the baseli ne visibility conditions were estimated using sampling data collected at the IM PROVE monitoring site at one of the two Class I areas in West Virginia. A five year averag e (2000 to 2004) was calculated for each of the 20 percent worst and 20 percent best visibility days in acco rdance with 40 CFR 51.308(d)(2) and the EPA Guidance for Tracking Progress Under the Regional Haze Rule. IMPROVE data records for Dolly Sods for the period 2000 to 2004 meet EPA requirement s for data completeness (75 percent for the year and 50 percent for each qua rter). IMPROVE does not oper ate a monitor at Otter Creek Wilderness and considers the IMPROVE monitor at Do lly Sods to be repres entative of visibility in the Otter Creek area. The li ght extinction and deci view visibility valu es for the 20 percent worst and 20 percent best visibility days at the Class I areas are based on data and calculations included in Appendix B of this SIP. Table 3.2-1 summarizes the mean yearly visibility conditions and the baseline visibi lity conditions for West Virginia class I areas on both the 20% best and 20% worst days.Table 3.2-1: Baseline Visibili ty Conditions for DOSO1

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 PM25 on the 20% best and worst monitoring days. Simply put, the RRF is the average future year concen tration 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 PM25 and coarse matter (the difference between PM10 and PM25) are extracted from the base year and future year modeling output near IMPROVE monitoring sites for the m onitored best and worst days. The component concentrations are then averaged to devel op base and future year average component concentrations. The future year average compon ent concentrations are then divided by their respective base year component concentrations to develop RRFs for each component of PM25 and coarse matter.

For a numerical example, consider Table 3.3-1, wh ich has all the pertinent data for the DOSO1 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 ye ar 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. DOSO1 Mean dv current Worst Visiblity Days Mean dv current Best Visiblity Days

2000 29.03 12.96
2001 28.24 13.30
2002 28.47 11.91
2003 29.73 11.54
2004 29.76 11.67

dv

baseline 29.05 12.28

Natural Condtions 10.45 12.28Table 3.3-1: Base year concentrations, futu re year concentrations, and RRF for DOSO1 on the monitored 20% best days (top ) and 20% worst days (bottom), based on 12km modeling results.

20% Best Days at DOSO1
Julian Date SO4 NO3 OC EC SOIL CM SO4 NO3 OC EC SOIL CM SO4 NO3 OC EC SOIL CM
5 1.072 0.436 0.849 0.156 1.009 0.248 0.959 0.768 0.850 0.115 1.165 0.229 0.894 1.762 1.001 0.734 1.155 0.924 14 1.035 0.271 1.231 0.143 0.692 0.162 0.771 0.243 1.065 0.095 0.742 0.148 0.745 0.897 0.865 0.663 1.072 0.912 23 0.358 0.040 0.266 0.034 0.153 0.039 0.213 0.034 0.229 0.020 0.148 0.039 0.595 0.843 0.861 0.596 0.970 0.999 26 0.666 0.000 0.523 0.069 0.503 0.130 0.496 0.005 0.494 0.053 0.607 0.128 0.745 0.000 0.946 0.766 1.207 0.989 50 1.214 0.055 1.297 0.152 0.668 0.195 0.793 0.043 1.143 0.107 0.774 0.208 0.654 0.778 0.882 0.706 1.158 1.065 68 3.263 0.441 1.425 0.163 0.448 0.657 1.669 0.322 1.309 0.129 0.478 0.669 0.511 0.731 0.919 0.794 1.067 1.018 89 1.543 0.014 0.722 0.078 0.519 0.290 0.946 0.016 0.690 0.067 0.624 0.290 0.613 1.135 0.955 0.860 1.202 0.998 104 1.753 0.000 0.596 0.032 0.129 0.227 0.976 0.001 0.554 0.025 0.151 0.237 0.557 3.000 0.931 0.776 1.166 1.042 119 1.571 0.277 0.315 0.051 0.319 0.258 1.257 0.485 0.305 0.031 0.419 0.265 0.800 1.748 0.969 0.613 1.315 1.029 158 2.872 0.110 0.530 0.048 0.195 0.267 1.319 0.043 0.492 0.028 0.246 0.274 0.459 0.386 0.930 0.584 1.260 1.028 284 0.066 0.063 0.172 0.002 0.007 0.009 0.031 0.022 0.152 0.001 0.008 0.010 0.470 0.350 0.884 0.498 1.158 1.037 293 1.500 0.761 0.568 0.110 1.045 0.393 1.252 1.123 0.617 0.075 1.242 0.401 0.834 1.476 1.085 0.679 1.189 1.021 299 2.134 0.015 0.185 0.029 0.232 0.109 1.212 0.038 0.202 0.021 0.356 0.120 0.568 2.587 1.092 0.719 1.531 1.097 302 1.208 4.482 0.580 0.108 0.618 0.417 1.308 2.957 0.585 0.064 0.726 0.404 1.083 0.660 1.009 0.592 1.173 0.970 320 0.960 1.842 0.613 0.080 0.308 0.131 1.091 1.052 0.590 0.044 0.339 0.143 1.136 0.571 0.962 0.554 1.101 1.095 326 1.291 0.518 0.359 0.074 0.532 0.218 1.094 0.834 0.354 0.048 0.624 0.199 0.848 1.610 0.987 0.650 1.174 0.911 338 0.905 1.455 0.527 0.107 0.492 0.464 0.652 1.012 0.495 0.076 0.629 0.423 0.720 0.000 0.939 0.708 1.278 0.912 341 1.317 1.036 0.621 0.130 0.923 0.634 1.270 1.513 0.650 0.103 1.103 0.583 0.965 1.461 1.046 0.789 1.195 0.920 347 1.748 1.571 0.369 0.042 0.202 0.199 1.323 1.174 0.366 0.030 0.251 0.199 0.757 0.747 0.992 0.700 1.242 0.999 356 1.685 0.588 0.937 0.119 0.890 0.327 1.270 0.773 0.929 0.092 0.946 0.289 0.754 1.314 0.991 0.776 1.063 0.885 359 0.817 0.848 0.344 0.082 0.484 0.240 0.699 0.973 0.381 0.064 0.688 0.146 0.855 1.147 1.106 0.785 1.422 0.607
Mean Concentration 1.380 0.706 0.620 0.086 0.494 0.267 0.981 0.639 0.593 0.061 0.584 0.257 0.711 0.906 0.956 0.712 1.183 0.963 Base Year Future Year RRFs

20% Worst Days at DOSO1
Julian Date SO4 NO3 OC EC SOIL CM SO4 NO3 OC EC SOIL CM SO4 NO3 OC EC SOIL CM
122 3.142 0.292 1.271 0.112 0.507 0.343 1.729 0.161 1.226 0.083 0.528 0.315 0.550 0.552 0.964 0.736 1.041 0.918 128 7.105 0.488 0.592 0.063 0.324 0.281 3.258 0.223 0.571 0.040 0.477 0.295 0.459 0.457 0.964 0.643 1.474 1.051 137 3.248 0.060 2.022 0.110 0.584 0.229 1.571 0.036 1.927 0.076 0.695 0.238 0.484 0.000 0.953 0.693 1.188 1.039 146 5.698 0.001 2.526 0.111 0.543 0.326 2.630 0.030 2.402 0.080 0.639 0.334 0.462 51.431 0.951 0.718 1.176 1.023 149 4.190 0.038 1.653 0.103 0.262 0.620 2.623 0.056 1.522 0.073 0.360 0.636 0.626 0.000 0.920 0.706 1.374 1.027 152 3.117 0.000 0.780 0.078 0.606 0.534 1.944 0.003 0.758 0.051 0.723 0.550 0.624 40.429 0.972 0.650 1.194 1.030 155 4.519 0.029 2.809 0.114 0.565 0.408 2.529 0.020 2.681 0.080 0.601 0.405 0.560 0.000 0.955 0.700 1.065 0.992 161 18.547 0.296 3.345 0.139 0.853 0.376 7.597 0.059 3.112 0.090 1.159 0.414 0.410 0.200 0.930 0.644 1.359 1.100 164 3.086 0.023 0.419 0.026 0.168 0.129 1.183 0.001 0.396 0.018 0.196 0.130 0.383 0.000 0.943 0.673 1.168 1.007 170 3.432 0.374 2.140 0.106 0.445 0.444 1.822 0.164 1.952 0.063 0.546 0.475 0.531 0.438 0.912 0.595 1.226 1.070 173 3.962 0.240 2.415 0.171 0.428 0.635 1.757 0.096 2.202 0.105 0.546 0.671 0.444 0.000 0.912 0.616 1.275 1.056 176 10.410 0.000 2.887 0.116 0.706 0.408 4.480 0.000 2.824 0.083 0.950 0.431 0.430 0.000 0.978 0.714 1.345 1.058 182 20.920 0.008 4.477 0.165 0.835 0.442 8.515 0.000 4.120 0.113 1.175 0.445 0.407 0.000 0.920 0.682 1.408 1.006 191 5.345 0.020 1.012 0.091 0.231 0.281 2.268 0.003 0.927 0.071 0.308 0.233 0.424 0.164 0.916 0.785 1.337 0.831 206 3.503 0.172 0.865 0.082 0.232 0.226 1.556 0.251 0.816 0.053 0.309 0.240 0.444 0.000 0.943 0.651 1.332 1.065 224 11.074 0.214 2.808 0.129 0.726 0.239 4.535 0.029 2.618 0.097 0.842 0.215 0.410 0.000 0.932 0.750 1.160 0.899 227 6.317 0.000 4.221 0.184 0.584 0.290 2.560 0.005 3.949 0.136 0.745 0.320 0.405 0.000 0.936 0.740 1.274 1.105 254 5.361 0.001 2.603 0.160 0.867 0.500 2.535 0.060 2.478 0.106 1.211 0.440 0.473 0.000 0.952 0.662 1.397 0.881 257 6.969 0.003 3.510 0.132 0.660 0.340 3.335 0.021 3.282 0.081 0.845 0.360 0.479 0.000 0.935 0.610 1.281 1.058 260 8.060 0.392 1.146 0.081 0.638 0.349 4.318 0.108 1.061 0.051 0.781 0.356 0.536 0.276 0.926 0.627 1.224 1.022 263 11.603 0.194 3.057 0.127 0.675 0.233 5.027 0.047 2.898 0.070 0.817 0.246 0.433 0.245 0.948 0.556 1.209 1.057 272 9.393 0.623 2.975 0.186 1.032 0.459 5.169 0.535 2.729 0.117 1.312 0.390 0.550 0.859 0.917 0.630 1.271 0.850 275 16.977 0.691 2.883 0.135 0.765 0.329 6.941 0.115 2.819 0.102 0.955 0.350 0.409 0.000 0.978 0.759 1.248 1.066
Mean Concentration 7.651 0.181 2.279 0.118 0.576 0.366 3.473 0.088 2.142 0.080 0.727 0.369 0.454 0.487 0.940 0.676 1.263 1.009 Base Year Future Year RRFs3.4. Future Year Daily Concentration Calculation

The fourth step in using model output to estimate future visibility impairment is to multiply the relative response factors (determi ned in the previous step, Section 3.3) by the measured species concentration data during the base period (for the measured 20% best and worst days). This results in daily future year species concentrations data.

This is similar to the final step in the ozone 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:

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 “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 fiveyearly 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: Yearly daily deciview values and final future mean deciview values (dv future ) for DOSO1 based on 12km modeling results.

The resulting future dv value (dv future ) for the “worst” and “best” visibility days can be compared to the baseline mean deciview (dv baseline ) values calculated in step 2 (Section 3.2). If the resulting change in deciviews is a negativ e number (future - base), this represents an improvement in visibility. Table 3.6-2 summarizes these calcul ation for the IMPROVE m onitor associated with West Virginia Class I areas. From the table, it is apparent that all West Virginia Class I areas are showing visibility improvement on both the 20% best and 20% worst days.

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

20% Worst Days 20% Best Days Site DV base DV future DV diff DV base DV future DV diff DOSO 29.05 21.78 -7.26 12.28 10.66 -1.61

These changes in visibility can also be compared to the previously calculated (in Appendix B) uniform rate of progress values for the Dolly S ods / Otter Creek Class I area (Figure 3.6-1). When plotted against the glidepath, one can see th e change in deciview between the baseline and future shows an improvement that is well below th e uniform rate of progress for the only Class I monitoring site in West Virginia. DOSO1 Mean dv future Worst Visibility Days Mean dv future Best Visibility Days
2000 21.96 11.41 2001 21.20 11.48 2002 21.22 10.32 2003 22.17 10.02 2004 22.37 10.08
dv

future 21.78 10.66

### Reasonable Progress Assessment Base G2 inventory, new IMPROVE Algorithm

Dolly Sods & Otter Creek - Best 20% Days

12.3 12.3 12.3

11.4 10.8

3.6

0 2 4 6 8 10 12 14

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



### Uniform Rate of Reasona ble Progress Glide Path Base G2 inventory, new IMPROVE Algorithm Dolly Sods & Otter Creek - Worst 20% Days

29.0 27.5

24.7

21.6

18.5

15.3

12.2 10.4 24.8

21.6

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 Condition (Worst Days) Observation Method 1 Prediction Uniform Rate of Progress = 4.3 dv by 2018

Figure 3.6-1: Uniform rate of progress, or glid epath, forDOSO on the 20% best days (top) and the 20% worst days (bottom)4 Supplemental Analysis

The Attainment Guidance asserts that all attain ment 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 consider modeling outputs other than the results of the attainment test. If the attainment test results fall short of the standard, the results of corroboratory analyses may be used in a weight of evidence determination (WOE) to show that 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 pol lution. The EPA 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”, EPA 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 West Virginia Class I areas meet uniform rate of progress trends, making the following section an examination of supplemental analysis to corroborate modeling results, than a WOE analysis to show attainment. In the following sections we explore both refinements to the uniform rate of progress, and additional modeling preformed by VISTAS as part of a supplemental analysis for the West Virginia Class I areas.

4.1 Refinements to Recommended Analysis

In the Attainment Guidance, the EPA recommends exploring refinements to the uniform rate of progress. One refinement the EPA suggests is us ing an alternative or area specific IMPROVE equations. One alternative to examine is th e use of the “old” IMPROVE equation. VISTAS opted to move to the “new” IMPROVE when it wa s first suggested, as it represented the most current scientific understa nding of light extinction calculation. Results presented in section three reflect the use of the “new” IMPROVE 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 fr om 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 the 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 thei r visibility improvement goals. The reduction estimates from both the “old” and “new” IMPROVE equation track closely. The IMPROVE site associated with West Virginia Class I areas shows visibility improvement beyond what is required by the set reasonable progress goals. This is further evidence that West Virginia Class I areas should reach visibility improvement goals, with a margin of safety.CMAQ 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 Base G2 modelin g results as processed with the “new” IMPROVE algorithm (pink squares) and with the “old” IMPROVE algorithm (blue diamonds) for VISTAS Cl ass I areas, and Class I areas near the VISTAS states. Heavy black line represents the percent of reductions needed to reach reasonable progress goals.

4.2 Additional Air Quality Modeling

Another means of providing supplemental 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 VISTAS include , looking at both 36 and 12km grid resolutions, the comparison of VISTAS results to those fr om modeling efforts by ot her regional planning organizations (RPOs), and undertaking an emissi ons sensitivity to assess the importance of international emissions. Each of theses items will be discussed in the following sections.

4.2.1 Grid Resolution

In the Attainment Guidance, the EPA suggests a grid cell resolution of 36km due to the rural/remote nature of Class I areas and the fact regional haze is generally not dominated by local sources, which necessitate a regional scale 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.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 VISTAS 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 both the “old” and “new” IMPROVE equations. Figure 4.2-1 presents the 12 and 36km results in percentage of ta rget reduction achieved. Pink squares represent the 12km results processed with the “new” IMPROVE equations, and blue diamonds as the 12km results processed with the “old” IMPROVE equations. Burgundy triangles represent 36km results processed with the “new” IMPROVE equations, with blue Xs noting the 36km results processed with the “old” IMPROVE equations.

From the plot, one can see the results from both gr id resolutions and equati ons track fairly close together. Overall, the West Virginia Class I area IMPROVE monitor exceeds target reductions with both grid resolution modeling results when applied to either the “new” or “old” IMPROVE equation. The similarity between the 36km and 12km results further supp orts attainment of visibility improvement goals for West Virginia 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 final modeling results at the 12km grid resolution.

Figure 4.2-1: VISTAS modeling result for IM PORVE monitors within the 12km modeling domain. Estimates of reductions are p resented from the both the “old” and “new” IMPROVE equation applied to both the 36 and 12 km grid resolutions results.4.2.2 Other Modeling Results

One way to acquire modeling sensitivity runs is to examine the modeling results from other RPOs. Other RPOs may use different physical and chemical 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 by VISTAS c ontractors are compared to VISTAS modeling in Figure 4.2-2. Again, the graph display the percent of the target reduction attained at each site per the modeling proj ection based on the “new” IMPROVE equations. Though data for all VISTAS sites was not ava ilable 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, fi nal results of the modeli ng 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 mode ling efforts. Results calculated with the “new” IMPROVE equation.Additionally Midwest Regional Planning Organization (MwRPO) has provided preliminary results to VISTAS. At the time of printing, MwRPO was proceeding using the “old” IMPROVE equations. For consistency in comparis on, 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 domai ns. The sites that do overlap are presented in Figure 4.2-3. The plot reveals th e modeling results are very sim ilar across RPOs. For example, modeling results for Mingo Wilderness area are ve ry similar, with CENR AP modeling predicting an 83% of target reduction, Mw RPO predicting 90%, and VISTAS projecting a 96% of target reduction. This further corrobor ates VISTAS modeling, as even using the “old” IMPROVE equation and varying emission inve ntories, the model projections are similar across the US.

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 wi th the “old” IMPROVE equations.22.0 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 VIST AS, are results from the Midwes t Regional Planning Organization. The modeling for the Midwest RPO differs from VI SATS modeling in that they have chosen to use the CAMx model in lieu of CMAQ for their air quality modeling as well as using a base year of 2005. VISTAS also hope to obtain MANE-VU m odel output, so the results 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 US. Emission from areas such as Mexico, Canada, Central America, and beyond have been shown to affect air quality in the Unite d States. States may consider the fraction of the ambient aerosol that is beyond the regulatory control of the U.S as they evaluate emission control strategies to achieve reasonable progr ess in reducing regional haze in Class I areas. To evaluate the affect of the emission from areas outside the US, VISTAS conducted a study of modeling results generated from the global three-dimensional chemical transport model (GEOS-Chem) and CMAQ.

GEOS-Chem was run for the 2002 modeling year 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 variab ility in emissions. Details of the emissions, meteorology, model configuration, and model performance evaluation fo r the VISTAS simulations are described in a report, which is included here as Attachment G1.

The GEOS-Chem outputs were used to calculate initial and boundary conditions for the national CMAQ modeling domain. The national CMAQ do main included portions of Canada andMexico, so emissions for thes e countries were included with in the national CMAQ modeling domain or as part of the boundary conditions outside the national modeling domain, as appropriate.

Two complementary methods were used to calcul ate the impact of inte rnational emissions at Class I areas. All international fires are treated as natural emissions in these runs so both cases underestimate the contributions fr om international fire s that are due to anthropogenic burning.

1. International 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 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 difference between two CMAQ 36-km simulations, both using 2018 Base F emissions and boundary cond itions from GEOS-Chem. In the first CMAQ run, all global natural and anthropogeni c emissions in 2018 are active. In the second CMAQ run, only global (Un ited States and in ternational) natu ral emissions are active. Here the impacts of international emissions are co mpared against 2018 conditions rather than natural background conditions.

Separate from VISTAS, the CENRAP RP O used PM Source Apportionment Technology (PSAT) in the CAMx regional air quality mode l to calculate the impact of Canadian and Mexican emissions and boundary conditions on visibility at Class I areas in 2002.

Figure 4.2-5 illustrates that for annual averag e contributions to su lfate 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 and PSAT than in the GEOS-Chem runs because the grid scale is finer (more accurate dispersion of emissions) and because the background atmosphere includes loadings from current United States anthropogenic emissions (greater photochemical activity). Similar charts for nitrate and organic carbon mass, for impacts on 20 percent worst visibility days, and for impacts of internationa l emissions on calculated light extinction are included in Attachment G2 to this appendix.

### Transboundary Man-Made Impacts

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4ACADLYBRBRIGBOWAISLEBIBEHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHEN(NH4)2SO4Conc.(_g/m3)

GEOS_4x5 GEOS_1x1 PSAT CMAQ
Figure 4.2-5. Estimates by four methods of impa cts of international emissions on annual average ammonium sulfate concen trations at Class I areas.

In addition to looking at the individual component concentrations at each Class I sites, efforts were made to examine how international emissions affect the ability of a class I areas to meet uniform rate of progress goals. Figure 4.2-6 shows the estimates form base G1 modeling of what the estimated percent of target reduction in visibility impairme nt is achieved at each of the VISTAS Class I areas. Calcula tions were preformed with bot h the “new” and “old” IMPROVE equations. Figure 4.2-6 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.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-VISTA S

Figure 4.2-6. Percent of Uniform Rate of Progr ess haze index reduction achieved in 2018 according to the VISTAS Base G1 inventory. Results are given for improvements calculated using the ne w IMPROVE algorithm (squares) and the old IMPROVE algorithm (diamonds). Solid symbols indicate results when all emissions are cons idered; open symbols indica te results when effects of international emissions are removed

For the West Virginia Class I areas, accounting for the international co ntribution further support the attainment of uniform rate of progress goals with a margin of safety with currently proposed reductions of United States anthropogenic emissions . The results do reiterate the significance of international emissions in the United States to th e attainment of future visibility improvement goals. In the future, as the atmosphere become s closer to natural background conditions, the incremental contribution from international emi ssions will become more important. As the results of this study do not directly affect this current attainment demons tration, the details are included here to provide a reference for fu ture assessments of reasonable progress.