west virginia department of environmental protection

Appendix G

Modeling Results

and

Supplemental Analyses

Promoting a healthy environment.

WV Regional Haze SIP Appendix G - 1
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WV Regional Haze SIP Appendix G - 2
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 Concentration 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................................................................................................ 18

WV Regional Haze SIP Appendix G - 3
List of Figures 

Figure 3.6-1: Uniform rate of progress, or glidepath, 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 processed with the “new” IMPROVE 

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

diamonds) for VISTAS Class I areas, and Class I areas near the VISTAS states. 

Heavy black line represents the percent of reductions needed to reach reasonable 

progress goals........................................................................................................ 14 

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

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

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

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

VISTAS, CENRAP, and WRAP modeling efforts. Results calculated with the 

“new” IMPROVE equation................................................................................... 16 

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

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

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

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

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

Figure 4.2-5. Estimates by four methods of impacts of international emissions on annual 

average ammonium sulfate concentrations 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 algorithm (squares) and the old IMPROVE 

algorithm (diamonds). Solid symbols indicate results when all emissions are 

considered; open symbols indicate results when effects of international emissions 

are removed........................................................................................................... 21 

WV Regional Haze SIP Appendix G - 4
List of Tables 

Table 3.2-1: Baseline Visibility Conditions for DOSO1............................................................. 8 

Table 3.3-1: Base year concentrations, future year concentrations, and 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 deciview values and final future mean deciview values (dvfuture) 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 

WV Regional Haze SIP Appendix G - 5
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WV Regional Haze SIP Appendix G - 6
1 Regional Haze Rule Requirements 

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

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

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

results from manmade air pollution.” As a response to the mandate in the CAA, 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 reasonable progress goals (RPGs) for each Class I area - one for 

showing improvement for the 20% worst days and one for ensuring no degradation for the 20% 

best days over the period of the implementation plan. For the 20% best days, the goal should be 

set to reflect at least as good a level of visibility as the baseline conditions for 2000-2004, 

calculated per 308(d)(2)(i). 

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

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

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

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

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

The following document 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 three metrics of visibility that are part of the determination of 

reasonable progress: 

1) Natural conditions, 

2) Baseline conditions 

3) Current conditions. 

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

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

relative humidity factors. Total light extinction is then converted to deciviews (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 performed to determine what level of control would be 

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

result of these controls. After the potential control analysis, reasonable progress goals for the 

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

determination for West Virginia Class I areas is described in Appendix H. 

WV Regional Haze SIP Appendix G - 7
3 Model Application 

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

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

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

modeling is applied in a relative sense, similar to the particulate matter attainment test. The 

following sections outline the process to determine 2018 projections from regional modeling, as 

suggested in the “Guidance on the Use of Models and Other Analyses for Demonstrating 

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

referred to as “Attainment Guidance”). 

3.1 Determination of 20% Best and worst Days 

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

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

2004) for each Class I area. Day-specific observations for mass associated with SO4, NO3, OC, 

EC, soil, and CM, should be used to calculate bext for each day using climatological relative 

humidity adjustment factor(s) (f(rh)). Total bext for all components should be converted to 

deciviews for each day to get a daily deciview value. 

3.2 Determine Baseline Conditions 

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

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

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

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

determine the baseline values for the best days, or the dvbaseline values. Then the 5 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 baseline visibility conditions were estimated using 

sampling data collected at the IMPROVE monitoring site at one of the two Class I areas in West 

Virginia. A five year average (2000 to 2004) was calculated for each of the 20 percent worst and 

20 percent best visibility days in accordance with 40 CFR 51.308(d)(2) and the EPA Guidance 

for Tracking Progress Under the Regional Haze Rule. IMPROVE data records for Dolly Sods 

for the period 2000 to 2004 meet EPA requirements for data completeness (75 percent for the 

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

Wilderness and considers the IMPROVE monitor at Dolly Sods to be representative of visibility 

in the Otter Creek area. The light extinction and deciview visibility values for the 20 percent 

worst and 20 percent best visibility days at the Class I areas are based on data and calculations 

included in Appendix B of this SIP. Table 3.2-1 summarizes the mean yearly visibility 

conditions and the baseline visibility conditions for West Virginia class I areas on both the 20% 

best and 20% worst days. 

WV Regional Haze SIP Appendix G - 8
Table 3.2-1: Baseline Visibility Conditions for DOSO1 

 

3.3 Relative Response Factor Calculation 

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

factor (RRF) for each component of PM25 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: 

AverageModeled Base Year Concentration 

AverageModeled Future Year Concentration 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 monitored best and worst days. The component 

concentrations are then averaged to develop base and future year average component 

concentrations. The future year average component concentrations are then divided by their 

respective base year component concentrations to develop RRFs for each component of PM25 

and coarse matter. 

For a numerical example, consider Table 3.3-1, which has all the pertinent data for the DOSO1 

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

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

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

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

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

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

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

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

the same data for the 20% worst days. 

DOSO1 

Mean dvcurrent 

Worst Visiblity Days

Mean dvcurrent 

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

dvbaseline 29.05 12.28

Natural Condtions 10.45 12.28

WV Regional Haze SIP Appendix G - 9
Table 3.3-1: Base year concentrations, future 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 RRFs

WV Regional Haze SIP Appendix G - 10
3.4. Future Year Daily Concentration Calculation 

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

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

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

results in daily future year species concentrations data. 

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

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

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

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

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

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

visibility days. 

3.5. Future Year Daily bext Calculation 

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

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

base years. This is achieved by applying the “new” IMPROVE equation: 

bext ≈ 2.2 * fs(RH) * [Small Sulfate] + 4.8 * fL(RH) * [Large Sulfate] 

+ 2.4 * fs(RH) * [Small Nitrate] + 5.1 * fL(RH) * [Large Nitrate] 

+ 2.8 * {Small Organic Mass] + 6.1 * [Large Organic Mass] 

+ 10 * [Elemental Carbon] 

+ 1 * [Fine Soil] 

+ 1.7 * fss(RH) * [Sea Salt] 

+ 0.6 * [Coarse Mass] 

+ Rayleigh Scattering (site specific) 

+ 0.33 * [NO2 (ppb)] 

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

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

3.6. Future Mean Deciview Values Calculation 

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

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

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

for the worst and best days. 

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

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

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

“best” visibility days for each year. This leads to 5 future estimated mean deciview values for the 

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

WV Regional Haze SIP Appendix G - 11
yearly values for the best and worst days are then averaged to produce the final future mean 

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

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

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

Table 3.6-1: Yearly daily deciview values and final future mean deciview values (dvfuture) 

for DOSO1 based on 12km modeling results. 

The resulting future dv value (dvfuture) for the “worst” and “best” visibility days can be compared 

to the baseline mean deciview (dvbaseline) values calculated in step 2 (Section 3.2). If the resulting 

change in deciviews is a negative number (future - base), this represents an improvement in 

visibility. Table 3.6-2 summarizes these calculation for the IMPROVE monitor 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 deciview projections for 2018 compared to 2002 

baseline deciview estimates. 

 20% Worst Days 20% Best Days 

Site DVbase DVfuture DVdiff DVbase DVfuture DVdiff

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 Sods / Otter Creek Class I area (Figure 3.6-1). 

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

future shows an improvement that is well below the uniform rate of progress for the only Class I 

monitoring site in West Virginia. 

DOSO1 

Mean dvfuture 

Worst Visibility Days

Mean dvfuture 

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

dvfuture 21.78 10.66

WV Regional Haze SIP Appendix G - 12
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

Year

Haziness Index (Deciviews)

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

Uniform Rate of Reasonable 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

Year

Haziness 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 glidepath, forDOSO on the 20% best days (top) 

and the 20% worst days (bottom) 

WV Regional Haze SIP Appendix G - 13
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 

consider modeling outputs other than the results of the attainment test. If the attainment test 

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

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

may be inconclusive. 

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

air quality as with ozone or fine particle pollution. The 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/WOE for regional haze goals. 

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

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

of progress analysis. 

All 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 using an alternative or area specific IMPROVE 

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

opted to move to the “new” IMPROVE when it was first suggested, as it represented the most 

current scientific understanding of light extinction calculation. Results presented in section three 

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

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

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

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

The plot shows the percentage of target reduction achieved at the 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. 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. 

WV Regional Haze SIP Appendix G - 14
CMAQ 2018g2a/2002gt2a Method 1 predictions for VISTAS+ sites

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

COHU1

DOSO1

GRSM1

JARI1

LIGO1

CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent of target reduction achieved

CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm

VISTAS non-VISTAS

Figure 4.1-1: Plot of 2018 Base G2 modeling results as processed with the “new” 

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

algorithm (blue diamonds) for VISTAS Class I areas, and Class I areas near 

the VISTAS states. Heavy black line represents the percent of reductions 

needed to reach reasonable progress goals. 

4.2 Additional Air Quality Modeling 

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

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

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

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

modeling analysis undertaken by VISTAS include, looking at both 36 and 12km grid resolutions, 

the comparison of VISTAS results to those from modeling efforts by other regional planning 

organizations (RPOs), and undertaking an emissions sensitivity to assess the importance of 

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

4.2.1 Grid Resolution 

In the Attainment Guidance, the 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 modeling. Given that computation needs increase 

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

resolution may prove too costly for some states. 

WV Regional Haze SIP Appendix G - 15
CMAQ 2018g1a/Typ02g Method 1 predictions for VISTAS+ sites

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

COHU1DOSO1

GRSM1

JARI1

LIGO1

CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent 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%

COHU1DOSO1

GRSM1

JARI1

LIGO1

CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent 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 subsequently the percentage of 

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

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

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

squares represent the 12km results processed with the “new” IMPROVE equations, and blue 

diamonds as the 12km results processed with the “old” IMPROVE equations. Burgundy 

triangles represent 36km results processed with the “new” IMPROVE equations, with blue Xs 

noting the 36km results processed with the “old” IMPROVE equations. 

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

together. 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 supports attainment of 

visibility improvement goals for West Virginia Class I areas. 

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

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

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

modeling results at the 12km grid resolution. 

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

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

“new” IMPROVE equation applied to both the 36 and 12 km grid 

resolutions results. 

WV Regional Haze SIP Appendix G - 16
4.2.2 Other Modeling Results 

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

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

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

based on these different options. 

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

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

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

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

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

modeling, essentially an emissions sensitivity run is created. 

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

Regional Air Partnership (WRAP) were obtained by VISTAS contractors are compared to 

VISTAS modeling in Figure 4.2-2. Again, the graph display the percent of the target reduction 

attained at each site per the modeling projection based on the “new” IMPROVE equations. 

Though data for all VISTAS sites was not available from CENRAP or WRAP, projection for 

sites in multiple modeling domains agree well and are generally less than 10 percentage points 

off from one another. This is encouraging support for VISTAS modeling suggesting that even 

with some differences in emission inventories, final results of the modeling are very similar. 

Visibility Projections with New Algorithm for CENRAP Class I areas

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%

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CACR1

UPBU1

BRET1

HEGL1

MING1

SIPS1

MACA1

GRSA1

ROMO1

SACR1

WHIT1

WHPE1

LOST1

THRO1

BADL1

WICA1

Percent of target reduction

CENRAP-base 18f

VISTAS-base G2

WRAP-base 18b

VISTAS WRAP

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

from VISTAS, CENRAP, and WRAP modeling efforts. Results calculated 

with the “new” IMPROVE equation. 

WV Regional Haze SIP Appendix G - 17
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 comparison, CENRAP, WRAP, and VISTAS data were 

recalculated with the “old” IMPROVE equation. As with the previous comparison, not all 

VISTAS sites are modeled in the other RPOs domains. The sites that do overlap are presented in 

Figure 4.2-3. The plot reveals the modeling results are very similar across RPOs. For example, 

modeling results for Mingo Wilderness area are very similar, with CENRAP modeling predicting 

an 83% of target reduction, MwRPO predicting 90%, and VISTAS projecting a 96% of target 

reduction. This further corroborates VISTAS modeling, as even using the “old” IMPROVE 

equation and varying emission inventories, the model projections are similar across the US. 

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

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

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

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

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

results. 

Visibility Projections with Old Algorithm for CENRAP Class I areas

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%

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CACR1

UPBU1

BRET1

SIPS1

HEGL1

MING1

MACA1

BOWA1

VOYA2

ISLE1

GRSA1

ROMO1

SACR1

WHIT1

WHPE1

LOST1

THRO1

BADL1

WICA1

Percent 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 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. 

WV Regional Haze SIP Appendix G - 18
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 2020

Deciview

Brigantine

Dolly Sods

Shenandoah

Brig BOTW_BaseB4

Dolly BOTW_BaseB4

Shen BOTW_BaseB4

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

IMPROVE sites. 

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

Of particular interest to VISTAS, are results from the Midwest Regional Planning Organization. 

The modeling for the Midwest RPO differs from VISATS modeling in that they have chosen to 

use the CAMx model in lieu of CMAQ for their air quality modeling as well as using a base year 

of 2005. VISTAS also hope to obtain MANE-VU model output, so the results can be added to 

Figure 4.2-2. 

4.2.3 International Emissions 

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

sources outside the continental US. Emission from areas such as Mexico, Canada, Central 

America, and beyond have been shown to affect air quality in the United 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 progress 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 using a 4 x 5 degree horizontal grid resolution 

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

not capture the episodic variability in emissions. Details of the emissions, meteorology, model 

configuration, and model performance evaluation for 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 domain included portions of Canada and 

WV Regional Haze SIP Appendix G - 19
Mexico, so emissions for these countries were included within the national CMAQ modeling 

domain or as part of the boundary conditions outside the national modeling domain, as 

appropriate. 

Two complementary methods were used to calculate the impact of international emissions at 

Class I areas. All international fires are treated as natural emissions in these runs so both cases 

underestimate the contributions from international fires that are due to anthropogenic burning. 

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

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

and international anthropogenic emissions were removed. The difference is attributable 

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

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

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

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

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

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

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

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

active. Here the impacts of international emissions are compared against 2018 conditions 

rather than natural background conditions. 

Separate from VISTAS, the CENRAP RPO used PM Source Apportionment Technology 

(PSAT) in the CAMx regional air quality model 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 average contributions to sulfate at VISTAS and 

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

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

contribution is higher using CMAQ 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 international emissions on calculated light extinction are 

included in Attachment G2 to this appendix. 

WV Regional Haze SIP Appendix G - 20
Transboundary Man-Made Impacts

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

ACAD

LYBR

BRIG

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ISLE

BIBE

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UPBU

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CHAS

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OKEF

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GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

(NH4)2SO4 Conc. 

(_g/m3

)

GEOS_4x5 GEOS_1x1 PSAT CMAQ

Figure 4.2-5. Estimates by four methods of impacts of international emissions on annual 

average ammonium sulfate concentrations 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 impairment is achieved at each of the 

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

equations. Figure 4.2-6 shows that removing the international contribution is sufficient to raise 

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

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

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

the haze index. 

WV Regional Haze SIP Appendix G - 21
CMAQ 2018g1a/Typ02g Method 1 predictions for VISTAS+ sites

0%

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220%

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COHU1DOSO1

GRSM1

JARI1

LIGO1CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent 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

VISTAS non-VISTAS

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 algorithm (squares) and 

the old IMPROVE algorithm (diamonds). Solid symbols indicate results 

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

of international emissions are removed 

For the West Virginia Class I areas, accounting for the international contribution 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 the attainment of future visibility improvement 

goals. In the future, as the atmosphere becomes closer to natural background conditions, the 

incremental contribution from international emissions will become more important. As the 

results of this study do not directly affect this current attainment demonstration, the details are 

included here to provide a reference for future assessments of reasonable progress. 

WV Regional Haze SIP Appendix G - 22