Appendix G: 

Modeling Results 

And 

Supplemental Analysis 
Modeling Results & Supplemental Analysis Appendix G - 1 

Regional Haze SIP for NC Class I Areas December 17, 2007 
Modeling Results & Supplemental Analysis Appendix G - 2 

Regional Haze SIP for NC Class I Areas December 17, 2007 

Table of Contents

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

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

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

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

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

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

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

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

3.4. Future Year Daily Concentration Calculation ................................................................... 12 

3.5. Future Year Daily bext Calculation ................................................................................... 12 

3.6. Future Mean Deciview Values Calculation ....................................................................... 12 

4 Supplemental Analysis............................................................................................................... 18 

4.1 Refinements to Recommended Analysis ............................................................................ 18 

4.2 Additional Air Quality Modeling........................................................................................ 19 

4.2.1 Grid Resolution............................................................................................................ 19 

4.2.2 Other Modeling Results ............................................................................................... 21 

4.2.3 International Emissions................................................................................................ 23
Modeling Results & Supplemental Analysis Appendix G - 3 

Regional Haze SIP for NC Class I Areas December 17, 2007 

List of Figures 

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

and the 20% worst days (bottom) ................................................................................................. 14 

Figure 3.6-2: Uniform rate of progress, or glidepath, for LIGO1 on the 20% best days (top) and 

the 20% worst days (bottom) ........................................................................................................ 15 

Figure 3.6-3: Uniform rate of progress, or glidepath, for SHRO1 on the 20% best days (top) and 

the 20% worst days (bottom) ........................................................................................................ 16 

Figure 3.6-4: Uniform rate of progress, or glidepath, for GSMNP1 on the 20% best days (top) 

and the 20% worst days (bottom) ................................................................................................. 17 

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

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

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

reductions needed to reach reasonable progress goals.................................................................. 19 

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.............................................. 20 

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......................................................................................................................................... 21 

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. .......................................................................... 22 

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

sites. .............................................................................................................................................. 23 

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

neighboring Class I areas.............................................................................................................. 25 

Figure 4.2-6: PM component concentrations from US domestic sources on 20 percent worst 

visibility days in 2002 (left bars) and CMAQ-simulated international contributions (right bars) at 

Swanquarter, North Carolina. ....................................................................................................... 25 

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 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............................................................................. 26 
Modeling Results & Supplemental Analysis Appendix G - 4 

Regional Haze SIP for NC Class I Areas December 17, 2007 

List of Tables 

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

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

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

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

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

Table 3.3-2: Base year concentrations, future year concentrations, and RRF for Linville Gorge 

(LIGO1) on the monitored 20% best days (top) and 20% worst days (bottom) based on 12km 

modeling results.............................................................................................................................. 9 

Table 3.3-3: Base year concentrations, future year concentrations, and RRF for Shinning Rock 

(SHRO1) on the monitored 20% best days (top) and 20% worst days (bottom) based on 12km 

modeling results............................................................................................................................ 10 

Table 3.3-4: Base year concentrations, future year concentrations, and RRF for Swanquarter 

(SWAN1) on the monitored 20% best days (top) and 20% worst days (bottom) based on 12km 

modeling results............................................................................................................................ 11 

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

Carolina Class I areas, based on 12km modeling results.............................................................. 13 

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

deciview estimates. ....................................................................................................................... 13 
Modeling Results & Supplemental Analysis Appendix G - 5 

Regional Haze SIP for NC Class I Areas December 17, 2007 

1 Regional Haze Rule Requirements 

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

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

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

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

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

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

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

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

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

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

56.308(d)(2)(i). 

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

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

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

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

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

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

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

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

2 Reasonable Progress Goals 

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

reasonable progress: 

1) Natural conditions, 

2) Baseline conditions 

3) Current conditions. 

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

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

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

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

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

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

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

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

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

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

for North Carolina Class I areas is described in Appendix H. 
Modeling Results & Supplemental Analysis Appendix G - 6 

Regional Haze SIP for NC Class I Areas December 17, 2007 

3 Model Application 

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

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

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

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

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

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

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

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

3.1 Determination of 20% Best and worst Days 

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

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

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

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

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

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

for each day to get a daily deciview value. 

3.2 Determine Baseline Conditions 

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

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

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

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

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

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

subject to improvement to meet the glidepath for regional haze. 

For each of the North Carolina Class I areas, the baseline visibility conditions were estimated 

using sampling data collected at Interagency Monitoring of Protected Visual Environments 

(IMPROVE) monitoring sites at four of the five Class I areas in North Carolina. A five year 

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

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

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

and Linville Gorge 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). Shining Rock and 

Swanquarter had missing data in more than one year between 2000 and 2004. Data records for 

these sites were filled using data substitution procedures outlined in Appendix B. IMPROVE 

does not operate a monitor at Joyce Kilmer Wilderness Area and considers the IMPROVE 

monitor at Great Smoky Mountains to be representative of visibility in the Joyce Kilmer 

Wilderness Area. The light extinction and deciview visibility values for the 20 percent worst and 

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

Appendix B of this SIP. Table 3.2-1 summarizes the mean yearly visibility conditions and the 
Modeling Results & Supplemental Analysis Appendix G - 7 

Regional Haze SIP for NC Class I Areas December 17, 2007 

baseline visibility conditions for North Carolina Class I areas on both the 20% best and 20% 

worst days. 

Table 3.2-1: Baseline Visibility Conditions and Natural Conditions for the North Carolina 

Class I areas. 

dvbaseline Natural Condtions dvbaseline Natural Condtions

Great Smoky Mountains 

National Park (GSMNP) 30.28 11.05 13.58 4.54

Linville Gorge (LIGO) 28.77 11.19 11.11 4.08

Shinning Rock (SHRO) 28.46 11.47 7.69 2.51

Swanquarter (SWAN) 24.74 11.55 11.99 5.46

Worst Visiblity Days Best Visiblity Days Site

 

3.3 Relative Response Factor Calculation 

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

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

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

base year concentration or: 

AverageModeled Base Year Concentration 

AverageModeled Future Year Concentration RRF =

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

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

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

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

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

component concentrations are then divided by their respective base year component 

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

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

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

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

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

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

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

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

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

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

the same data for the 20% worst days. Tables of for each of the IMPORVE monitors of interest 

to North Carolina are provided, and abide by the same format. 
Modeling Results & Supplemental Analysis Appendix G - 8 

Regional Haze SIP for NC Class I Areas December 17, 2007 

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

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

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

20% Best Days at 

GSMNP1

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Mean 

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

Base Year Future Year RRFs

20% Worst Days 

at GSMNP1

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Mean 

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

Base Year Future Year RRFs
Modeling Results & Supplemental Analysis Appendix G - 9 

Regional Haze SIP for NC Class I Areas December 17, 2007 

Table 3.3-2: Base year concentrations, future year concentrations, and RRF for Linville 

Gorge (LIGO1) on the monitored 20% best days (top) and 20% worst days (bottom) based 

on 12km modeling results. 

20% Best Days at 

LIGO1

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

5 0.761 0.337 2.725 0.444 1.590 0.424 0.711 0.352 2.064 0.257 1.450 0.488 0.934 1.046 0.757 0.579 0.912 1.149

8 1.241 0.089 0.859 0.177 0.643 0.272 1.076 0.098 0.676 0.109 0.676 0.293 0.867 1.107 0.786 0.618 1.050 1.076

11 1.356 0.004 0.721 0.124 0.535 0.238 1.127 0.090 0.637 0.080 0.611 0.249 0.832 24.609 0.883 0.650 1.142 1.048

23 0.663 0.007 0.798 0.125 0.353 0.222 0.234 0.051 0.555 0.068 0.270 0.251 0.353 7.753 0.696 0.544 0.765 1.128

26 0.671 0.551 3.313 0.431 1.379 0.507 0.485 0.360 2.343 0.229 1.247 0.578 0.723 0.653 0.707 0.531 0.904 1.140

38 0.760 0.001 0.283 0.052 0.159 0.111 0.480 0.001 0.204 0.028 0.148 0.131 0.632 0.827 0.722 0.535 0.932 1.181

62 2.611 0.574 0.711 0.111 0.477 0.158 2.151 0.508 0.660 0.079 0.489 0.168 0.824 0.885 0.928 0.711 1.027 1.064

77 0.855 0.026 0.627 0.115 0.340 0.278 0.319 0.056 0.465 0.059 0.313 0.322 0.373 2.111 0.742 0.515 0.920 1.158

89 0.958 0.024 0.720 0.079 0.217 0.147 0.389 0.030 0.611 0.055 0.195 0.163 0.406 1.254 0.848 0.694 0.900 1.106

104 1.662 0.022 0.801 0.061 0.186 0.624 0.847 0.090 0.716 0.042 0.206 0.657 0.510 4.053 0.893 0.698 1.108 1.053

116 1.082 0.090 0.974 0.133 0.492 0.959 0.843 0.050 0.848 0.088 0.634 0.985 0.779 0.554 0.871 0.662 1.288 1.027

119 2.329 0.227 0.803 0.097 0.477 0.487 1.630 0.153 0.754 0.068 0.570 0.501 0.700 0.674 0.939 0.701 1.194 1.029

302 1.830 0.560 0.560 0.060 0.178 0.201 0.571 0.705 0.534 0.032 0.203 0.231 0.312 1.259 0.953 0.529 1.140 1.153

308 1.824 0.129 0.887 0.148 0.579 0.317 1.158 0.129 0.774 0.086 0.670 0.380 0.635 0.997 0.873 0.579 1.158 1.199

320 0.308 0.018 0.239 0.022 0.068 0.075 0.166 0.004 0.198 0.012 0.059 0.084 0.539 0.244 0.827 0.544 0.869 1.116

326 1.290 0.806 0.638 0.132 0.855 0.300 1.334 1.052 0.609 0.080 1.004 0.315 1.034 1.306 0.954 0.604 1.175 1.048

341 0.812 0.300 1.154 0.227 0.887 0.794 0.740 0.299 0.896 0.138 0.870 0.809 0.911 0.999 0.776 0.610 0.981 1.019

347 0.721 1.406 0.644 0.096 0.340 0.181 0.451 0.959 0.535 0.051 0.318 0.207 0.625 0.682 0.831 0.531 0.936 1.143

356 1.699 0.536 1.630 0.196 1.192 0.457 1.523 0.543 1.462 0.134 1.013 0.415 0.897 1.013 0.897 0.687 0.850 0.907

359 1.026 0.879 0.455 0.108 0.593 0.222 1.157 1.141 0.453 0.070 0.788 0.204 1.128 1.298 0.994 0.649 1.329 0.920

362 1.750 0.547 1.196 0.198 1.166 0.329 1.716 0.790 1.094 0.133 1.112 0.300 0.981 1.444 0.915 0.671 0.954 0.912

Mean 

Concentration 1.248 0.340 0.988 0.149 0.605 0.348 0.910 0.355 0.814 0.090 0.612 0.368 0.729 1.046 0.824 0.606 1.011 1.058

Base Year Future Year RRFs

20% Worst Days 

at LIGO1

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

122 3.452 0.135 2.730 0.272 0.313 0.373 1.939 0.020 2.646 0.241 0.336 0.376 0.562 0.148 0.969 0.887 1.072 1.006

128 6.202 0.000 2.753 0.191 0.484 0.614 2.683 0.009 2.423 0.128 0.586 0.678 0.433 0.000 0.880 0.669 1.210 1.103

137 4.729 0.074 3.393 0.197 0.804 0.779 2.435 0.088 3.190 0.124 0.975 0.852 0.515 1.187 0.940 0.631 1.212 1.094

149 4.118 0.502 2.579 0.183 0.574 1.240 2.097 0.130 2.246 0.115 0.716 1.308 0.509 0.258 0.871 0.631 1.247 1.055

152 8.343 0.000 2.665 0.147 0.822 0.410 3.752 0.005 2.482 0.088 0.954 0.434 0.450 0.000 0.931 0.597 1.160 1.058

170 7.436 0.081 4.054 0.173 0.748 0.638 3.206 0.221 3.806 0.097 0.978 0.700 0.431 2.743 0.939 0.562 1.307 1.097

191 4.599 0.000 3.830 0.227 0.509 0.340 1.995 0.007 3.603 0.180 0.628 0.372 0.434 0.000 0.941 0.796 1.233 1.093

203 7.280 0.000 5.224 0.199 0.704 0.499 3.096 0.028 4.785 0.138 1.096 0.543 0.425 63.659 0.916 0.691 1.555 1.088

206 1.630 0.000 0.856 0.044 0.128 0.214 0.565 0.002 0.775 0.022 0.185 0.252 0.346 0.000 0.905 0.508 1.451 1.181

209 5.654 0.000 2.674 0.107 0.535 0.318 2.966 0.001 2.473 0.073 0.611 0.347 0.525 0.000 0.925 0.685 1.141 1.093

212 7.889 0.000 1.093 0.105 0.544 0.467 4.394 0.000 1.403 0.138 0.732 0.544 0.557 0.000 1.284 1.312 1.345 1.165

215 5.532 0.042 4.569 0.175 0.609 0.521 2.152 0.035 4.095 0.104 0.836 0.563 0.389 0.854 0.896 0.592 1.372 1.081

218 5.730 0.000 3.083 0.195 0.607 0.343 2.664 0.000 2.921 0.147 0.837 0.372 0.465 0.000 0.948 0.751 1.379 1.086

221 3.366 0.455 5.651 0.406 0.863 0.678 1.288 0.136 5.160 0.306 1.230 0.763 0.383 0.300 0.913 0.754 1.426 1.126

224 15.465 0.000 8.567 0.373 1.199 0.575 6.197 0.000 8.024 0.274 1.548 0.668 0.401 0.000 0.937 0.734 1.291 1.163

233 6.542 0.001 2.324 0.174 0.534 0.525 2.824 0.006 2.096 0.109 0.726 0.599 0.432 4.902 0.902 0.631 1.359 1.141

236 4.564 0.000 2.176 0.097 0.453 0.235 2.081 0.000 1.975 0.061 0.525 0.251 0.456 0.000 0.908 0.626 1.158 1.071

239 8.170 0.208 2.027 0.101 0.437 0.319 2.694 0.123 1.958 0.060 0.538 0.350 0.330 0.590 0.966 0.599 1.230 1.098

248 2.352 0.015 2.397 0.139 0.574 0.480 1.132 0.006 1.963 0.075 0.841 0.523 0.481 0.381 0.819 0.538 1.466 1.090

251 4.540 0.263 3.654 0.173 0.619 0.544 2.257 0.273 3.293 0.113 0.852 0.583 0.497 1.038 0.901 0.652 1.376 1.072

254 10.061 0.000 3.781 0.221 1.365 0.571 4.824 0.031 3.637 0.136 1.764 0.620 0.479 0.000 0.962 0.615 1.292 1.086

260 14.405 0.000 3.703 0.193 1.301 0.800 7.789 0.000 3.330 0.112 1.452 0.799 0.541 0.000 0.899 0.582 1.116 0.998

Mean 

Concentration 6.457 0.081 3.354 0.186 0.669 0.522 2.956 0.051 3.104 0.129 0.861 0.568 0.458 0.632 0.925 0.695 1.286 1.088

Base Year Future Year RRFs
Modeling Results & Supplemental Analysis Appendix G - 10 

Regional Haze SIP for NC Class I Areas December 17, 2007 

Table 3.3-3: Base year concentrations, future year concentrations, and RRF for Shinning 

Rock (SHRO1) on the monitored 20% best days (top) and 20% worst days (bottom) based 

on 12km modeling results. 

20% Best Days at 

SHRO1

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

5 0.568 0.218 1.340 0.218 0.701 0.281 0.498 0.070 0.970 0.122 0.602 0.312 0.876 0.321 0.724 0.561 0.859 1.109

8 1.134 0.155 1.341 0.272 0.908 0.312 0.999 0.258 0.950 0.151 0.860 0.324 0.881 1.668 0.708 0.556 0.947 1.038

11 1.043 0.136 0.996 0.187 0.599 0.296 0.785 0.144 0.749 0.103 0.623 0.311 0.753 1.057 0.752 0.550 1.040 1.052

26 0.722 0.207 2.605 0.366 1.232 0.410 0.676 0.187 1.788 0.194 1.089 0.469 0.938 0.903 0.687 0.531 0.884 1.142

38 0.517 0.009 0.361 0.067 0.182 0.077 0.282 0.025 0.234 0.031 0.146 0.088 0.547 2.607 0.648 0.468 0.803 1.137

50 1.117 0.096 2.268 0.286 0.774 0.311 0.789 0.072 2.056 0.222 0.845 0.355 0.707 0.749 0.907 0.777 1.092 1.139

68 0.836 0.017 0.442 0.063 0.136 0.459 0.554 0.012 0.405 0.050 0.135 0.471 0.662 0.718 0.916 0.796 0.990 1.027

71 0.744 0.199 0.865 0.125 0.294 0.170 0.487 0.107 0.784 0.097 0.304 0.186 0.654 0.536 0.906 0.779 1.036 1.093

86 2.564 0.237 0.883 0.142 0.620 0.204 1.962 0.207 0.752 0.086 0.685 0.220 0.765 0.871 0.851 0.602 1.105 1.080

89 0.663 0.029 1.106 0.139 0.267 0.134 0.464 0.024 1.007 0.110 0.253 0.145 0.700 0.830 0.911 0.792 0.944 1.080

104 1.553 0.000 0.603 0.059 0.134 0.459 0.872 0.034 0.558 0.046 0.150 0.488 0.561 162.952 0.926 0.780 1.113 1.064

257 2.371 0.000 0.930 0.056 0.216 0.246 1.129 0.001 0.879 0.033 0.250 0.275 0.476 0.000 0.945 0.588 1.158 1.116

269 0.215 0.000 0.070 0.004 0.006 0.014 0.107 0.000 0.075 0.001 0.008 0.016 0.498 0.000 1.061 0.325 1.460 1.150

299 4.217 0.055 1.275 0.143 0.586 0.407 2.192 0.034 1.117 0.078 0.594 0.408 0.520 0.616 0.876 0.547 1.014 1.002

302 1.744 0.094 0.470 0.065 0.165 0.136 0.774 0.080 0.412 0.034 0.166 0.152 0.444 0.857 0.875 0.526 1.012 1.121

308 1.615 0.217 1.049 0.190 0.699 0.293 1.034 0.258 0.792 0.093 0.648 0.343 0.641 1.188 0.755 0.491 0.927 1.170

320 0.886 0.046 0.435 0.047 0.144 0.111 0.451 0.012 0.364 0.026 0.136 0.122 0.509 0.265 0.838 0.545 0.948 1.104

326 1.321 0.639 0.664 0.133 0.750 0.279 1.399 0.756 0.610 0.078 0.853 0.281 1.060 1.182 0.918 0.587 1.138 1.008

341 0.719 0.342 1.564 0.283 0.922 0.715 0.668 0.348 1.182 0.173 0.836 0.736 0.929 1.019 0.756 0.610 0.907 1.030

344 1.988 0.938 1.931 0.340 1.269 1.125 1.465 1.720 1.522 0.196 1.332 1.136 0.737 1.834 0.788 0.577 1.049 1.010

347 0.850 0.608 0.644 0.114 0.522 0.154 0.527 0.622 0.543 0.070 0.830 0.203 0.620 1.024 0.843 0.613 1.590 1.320

353 2.227 0.319 0.707 0.114 0.370 0.460 1.396 0.012 0.575 0.071 0.361 0.472 0.627 0.039 0.813 0.621 0.977 1.026

359 1.108 0.352 0.436 0.100 0.495 0.161 1.288 0.476 0.392 0.063 0.601 0.170 1.162 1.353 0.898 0.626 1.214 1.054

362 1.632 0.698 1.383 0.217 1.260 0.326 1.617 0.973 1.209 0.142 1.149 0.291 0.991 1.394 0.874 0.654 0.912 0.892

Mean 

Concentration 1.382 0.234 1.001 0.153 0.546 0.316 0.953 0.277 0.824 0.093 0.559 0.333 0.690 1.180 0.823 0.612 1.024 1.055

Base Year Future Year RRFs

20% Worst Days 

at SHRO1

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

128 8.165 0.001 3.036 0.189 0.449 0.512 2.770 0.001 2.784 0.136 0.559 0.559 0.339 0.827 0.917 0.720 1.247 1.091

137 4.578 0.033 2.738 0.163 0.640 0.828 2.378 0.057 2.587 0.104 0.778 0.896 0.520 1.731 0.945 0.636 1.214 1.082

152 7.954 0.001 3.006 0.163 0.777 0.408 3.459 0.006 2.757 0.096 0.950 0.450 0.435 6.372 0.917 0.591 1.222 1.102

155 3.008 0.004 3.318 0.117 0.567 0.507 1.622 0.021 3.074 0.064 0.625 0.555 0.539 6.000 0.926 0.548 1.101 1.096

161 2.119 0.056 2.633 0.153 0.508 0.658 0.892 0.024 2.436 0.085 0.715 0.723 0.421 0.429 0.925 0.557 1.409 1.099

164 3.664 0.001 4.384 0.184 0.612 0.481 1.292 0.011 4.258 0.141 0.793 0.550 0.353 12.033 0.971 0.766 1.295 1.145

170 5.552 0.104 2.613 0.130 0.526 0.496 2.456 0.040 2.430 0.071 0.717 0.539 0.442 0.387 0.930 0.546 1.363 1.087

182 8.958 0.000 4.940 0.199 0.794 0.617 4.066 0.006 4.420 0.113 1.082 0.652 0.454 0.000 0.895 0.567 1.363 1.058

185 6.212 0.000 2.077 0.119 0.486 0.377 2.399 0.000 1.945 0.060 0.640 0.419 0.386 0.000 0.936 0.509 1.317 1.112

188 5.925 0.001 3.534 0.184 0.615 0.430 2.685 0.013 3.275 0.126 0.862 0.475 0.453 22.241 0.927 0.681 1.403 1.105

191 4.814 0.000 3.314 0.214 0.508 0.350 2.173 0.007 3.104 0.164 0.626 0.382 0.451 35.550 0.937 0.769 1.232 1.092

197 8.823 0.001 2.114 0.132 0.615 0.400 2.717 0.002 1.891 0.068 0.778 0.462 0.308 1.268 0.895 0.510 1.265 1.155

203 6.731 0.000 4.387 0.190 0.634 0.581 3.112 0.008 4.051 0.118 0.844 0.633 0.462 16.170 0.923 0.622 1.331 1.088

206 4.025 0.041 1.575 0.078 0.227 0.235 1.501 0.001 1.536 0.048 0.304 0.279 0.373 0.024 0.975 0.622 1.341 1.186

212 7.087 0.000 1.422 0.118 0.724 0.409 3.384 0.000 1.286 0.068 0.824 0.417 0.477 0.000 0.904 0.571 1.139 1.020

215 6.377 0.000 4.772 0.209 0.831 0.546 2.251 0.034 4.308 0.121 1.107 0.612 0.353 0.000 0.903 0.576 1.333 1.119

218 6.591 0.000 3.948 0.219 0.899 0.445 2.925 0.003 3.686 0.145 1.158 0.487 0.444 0.000 0.934 0.663 1.289 1.095

221 4.493 0.037 5.858 0.369 0.951 0.681 1.520 0.042 5.517 0.251 1.228 0.767 0.338 1.151 0.942 0.680 1.292 1.127

224 10.988 0.000 8.733 0.441 1.194 0.554 4.094 0.040 8.270 0.330 1.516 0.645 0.373 0.000 0.947 0.748 1.269 1.164

233 4.173 0.000 3.000 0.227 0.765 0.621 2.050 0.007 2.761 0.149 1.002 0.702 0.491 0.000 0.920 0.656 1.310 1.130

248 6.627 0.021 3.330 0.202 1.101 0.691 3.971 0.001 3.013 0.113 1.322 0.746 0.599 0.039 0.905 0.558 1.200 1.080

251 4.285 0.001 3.720 0.222 0.755 0.521 2.377 0.344 3.425 0.138 1.085 0.576 0.555 284.620 0.921 0.622 1.437 1.106

254 10.488 0.000 4.726 0.276 1.556 0.631 4.661 0.056 4.480 0.174 1.898 0.669 0.444 0.000 0.948 0.630 1.220 1.060

260 12.828 0.009 2.653 0.139 0.912 0.549 6.885 0.000 2.587 0.093 1.010 0.547 0.537 0.000 0.975 0.666 1.107 0.995

266 5.086 0.017 0.887 0.099 0.648 0.240 3.821 0.009 0.798 0.053 0.785 0.267 0.751 0.509 0.899 0.536 1.211 1.110

Mean 

Concentration 6.383 0.013 3.519 0.191 0.748 0.497 2.883 0.029 3.274 0.121 0.951 0.546 0.452 2.297 0.930 0.636 1.271 1.099

Base Year Future Year RRFs
Modeling Results & Supplemental Analysis Appendix G - 11 

Regional Haze SIP for NC Class I Areas December 17, 2007 

Table 3.3-4: Base year concentrations, future year concentrations, and RRF for 

Swanquarter (SWAN1) on the monitored 20% best days (top) and 20% worst days 

(bottom) based on 12km modeling results. 

20% Best Days at 

SWAN1

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

14 1.023 3.377 2.199 0.222 1.119 0.481 0.872 1.870 2.094 0.166 1.244 0.485 0.852 0.554 0.952 0.749 1.112 1.010

26 1.083 0.070 1.596 0.182 0.609 0.697 0.942 0.092 1.824 0.191 0.960 0.710 0.870 1.309 1.142 1.048 1.577 1.019

68 0.434 0.000 0.108 0.015 0.046 2.285 0.439 0.000 0.159 0.025 0.048 2.290 1.013 0.000 1.470 1.664 1.045 1.002

104 0.330 0.000 0.142 0.014 0.041 1.809 0.335 0.000 0.181 0.021 0.046 1.816 1.014 0.000 1.274 1.487 1.123 1.004

125 0.116 0.001 0.255 0.013 0.071 0.687 0.084 0.001 0.263 0.012 0.069 0.683 0.722 0.779 1.031 0.917 0.974 0.994

173 0.120 0.000 0.074 0.006 0.022 1.154 0.125 0.000 0.116 0.013 0.029 1.158 1.042 0.000 1.556 2.289 1.332 1.003

176 0.382 0.000 0.536 0.028 0.096 1.374 0.362 0.000 0.540 0.025 0.124 1.441 0.948 1.667 1.007 0.876 1.292 1.049

194 1.041 0.000 3.122 0.554 0.247 1.791 0.830 0.000 3.119 0.548 0.273 1.783 0.798 0.000 0.999 0.989 1.102 0.995

221 2.198 0.011 6.398 0.765 0.724 1.204 1.521 0.011 6.272 0.691 1.073 1.196 0.692 0.975 0.980 0.902 1.482 0.994

227 0.903 0.000 0.799 0.073 0.113 1.129 0.632 0.000 0.795 0.068 0.128 1.130 0.699 0.000 0.996 0.927 1.134 1.001

242 0.257 0.000 0.932 0.039 0.268 0.563 0.225 0.000 0.885 0.025 0.283 0.533 0.874 1.214 0.950 0.635 1.055 0.947

251 0.981 0.000 0.481 0.052 0.062 2.418 1.002 0.000 0.500 0.057 0.257 2.413 1.022 0.000 1.040 1.086 4.117 0.998

263 1.149 0.000 1.114 0.057 0.185 1.306 0.964 0.000 1.119 0.053 0.346 1.273 0.840 0.000 1.004 0.928 1.872 0.975

269 0.652 0.000 0.302 0.022 0.097 1.158 0.556 0.000 0.338 0.022 0.201 1.310 0.853 0.000 1.118 0.990 2.086 1.131

284 1.747 0.000 0.441 0.037 0.148 3.361 1.094 0.000 0.440 0.032 0.156 3.348 0.626 0.000 0.998 0.867 1.056 0.996

290 2.947 0.561 1.680 0.125 0.677 0.550 1.447 0.289 1.614 0.079 0.918 0.539 0.491 0.515 0.961 0.630 1.356 0.980

314 1.355 0.000 0.315 0.046 0.164 2.322 0.926 0.000 0.303 0.037 0.191 2.323 0.683 0.000 0.963 0.798 1.163 1.001

320 0.669 0.000 0.267 0.025 0.113 1.063 0.440 0.000 0.266 0.021 0.127 1.049 0.657 2.000 0.998 0.807 1.126 0.987

332 1.516 0.580 1.030 0.218 0.920 0.625 1.264 0.687 0.996 0.145 1.169 0.566 0.834 1.184 0.967 0.663 1.271 0.906

335 0.918 0.197 1.002 0.132 0.732 0.250 0.975 0.172 0.982 0.096 0.965 0.269 1.062 0.875 0.981 0.732 1.319 1.076

344 0.397 0.001 0.338 0.025 0.102 0.772 0.382 0.000 0.342 0.027 0.286 0.766 0.960 0.250 1.012 1.069 2.815 0.992

353 2.027 0.000 0.553 0.069 0.299 4.218 1.516 0.000 0.518 0.057 0.314 4.182 0.748 0.000 0.937 0.816 1.051 0.992

359 0.800 0.763 0.679 0.109 0.558 0.194 0.685 0.789 0.675 0.069 0.723 0.195 0.856 1.034 0.995 0.630 1.296 1.002

Mean 

Concentration 1.002 0.242 1.059 0.123 0.322 1.366 0.766 0.170 1.058 0.108 0.432 1.368 0.764 0.703 0.999 0.875 1.340 1.002

Base Year Future Year RRFs

20% Worst Days 

at SWAN1

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

5 2.023 9.021 3.557 0.587 2.634 0.720 1.916 6.940 3.496 0.408 3.268 0.832 0.947 0.769 0.983 0.695 1.240 1.156

53 2.530 3.532 5.863 0.486 1.853 0.895 1.984 1.401 5.846 0.410 2.067 1.009 0.784 0.397 0.997 0.843 1.115 1.128

56 1.551 0.065 1.761 0.204 0.658 2.128 1.346 0.052 1.642 0.211 0.878 2.119 0.868 0.797 0.932 1.036 1.333 0.996

110 3.626 0.068 3.651 0.159 0.710 0.447 1.951 0.024 3.543 0.129 0.859 0.473 0.538 0.350 0.970 0.815 1.209 1.057

119 3.207 0.024 2.443 0.172 0.608 0.996 1.901 0.016 2.423 0.154 0.836 1.008 0.593 0.667 0.992 0.900 1.375 1.012

122 2.987 0.001 2.260 0.177 0.433 3.151 2.060 0.001 2.252 0.160 0.502 3.166 0.690 2.140 0.997 0.902 1.159 1.004

128 2.748 0.000 1.024 0.075 0.296 1.235 1.477 0.002 1.012 0.059 0.405 1.236 0.537 3.000 0.989 0.790 1.368 1.001

146 8.768 0.131 1.982 0.165 0.725 1.851 4.708 0.098 1.952 0.129 0.912 1.848 0.537 0.744 0.985 0.784 1.258 0.998

185 5.509 0.000 2.493 0.103 0.486 0.911 2.866 0.000 2.453 0.075 0.638 0.918 0.520 0.000 0.984 0.724 1.313 1.008

188 2.273 0.010 6.714 0.931 0.631 0.676 1.706 0.012 6.666 0.908 0.981 0.676 0.751 1.229 0.993 0.975 1.553 0.999

197 7.111 0.000 2.908 0.160 0.573 0.493 3.171 0.005 2.779 0.107 0.753 0.502 0.446 0.000 0.956 0.672 1.314 1.020

200 4.495 0.001 1.648 0.077 0.301 2.366 1.916 0.000 1.644 0.057 0.385 2.371 0.426 0.000 0.998 0.749 1.279 1.002

209 10.401 0.001 4.669 0.155 0.866 0.618 5.335 0.007 4.532 0.108 1.108 0.630 0.513 8.434 0.971 0.698 1.280 1.019

212 7.968 0.005 3.441 0.194 0.883 0.917 3.900 0.002 3.150 0.122 1.259 0.929 0.489 0.379 0.915 0.627 1.426 1.013

233 3.171 0.000 1.674 0.081 0.329 1.963 1.596 0.000 1.623 0.060 0.584 1.955 0.503 0.100 0.969 0.741 1.773 0.996

236 9.070 0.005 5.272 0.214 0.913 0.895 4.272 0.003 5.133 0.152 1.216 0.937 0.471 0.607 0.974 0.709 1.332 1.048

254 4.602 0.006 2.824 0.179 0.841 1.156 2.433 0.010 2.756 0.119 1.222 1.160 0.529 1.781 0.976 0.664 1.453 1.004

260 9.302 0.038 2.354 0.178 0.812 0.831 4.392 0.000 2.246 0.125 1.057 0.826 0.472 0.011 0.954 0.704 1.303 0.994

278 7.173 0.000 2.544 0.134 0.526 0.906 3.668 0.000 2.471 0.103 0.634 0.908 0.511 0.000 0.971 0.769 1.205 1.002

293 6.691 1.607 4.018 0.254 1.735 1.015 3.971 0.523 3.859 0.165 2.160 1.049 0.593 0.325 0.960 0.649 1.245 1.033

326 4.570 1.005 2.083 0.194 1.169 0.742 3.873 0.918 2.323 0.189 1.372 0.752 0.847 0.913 1.115 0.975 1.173 1.014

329 2.419 6.280 5.921 0.388 2.314 1.425 1.821 2.915 5.696 0.281 2.839 1.475 0.753 0.464 0.962 0.723 1.227 1.035

341 1.900 3.013 1.583 0.273 1.210 0.616 1.812 3.144 1.592 0.191 1.734 0.612 0.954 1.043 1.006 0.701 1.434 0.993

Mean 

Concentration 4.961 1.079 3.160 0.241 0.935 1.172 2.786 0.699 3.091 0.192 1.203 1.191 0.562 0.648 0.978 0.799 1.286 1.016

Base Year Future Year RRFS
Modeling Results & Supplemental Analysis Appendix G - 12 

Regional Haze SIP for NC Class I Areas December 17, 2007 

3.4. Future Year Daily Concentration Calculation 

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

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

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

This results in daily future year species concentrations data. 

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

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

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

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

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

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

visibility days. 

3.5. Future Year Daily bext Calculation 

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

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

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

more information on the new IMPROVE equation): 

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

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

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

+ 10 * [Elemental Carbon] 

+ 1 * [Fine Soil] 

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

+ 0.6 * [Coarse Mass] 

+ Rayleigh Scattering (site specific) 

+ 0.33 * [NO2 (ppb)] 

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

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

3.6. Future Mean Deciview Values Calculation 

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

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

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

for the worst and best days. 

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

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

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

“best” visibility days for each year. This leads to 5 future estimated mean deciview values for the 
Modeling Results & Supplemental Analysis Appendix G - 13 

Regional Haze SIP for NC Class I Areas December 17, 2007 

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

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

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

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

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

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

North Carolina Class I areas, based on 12km modeling results. 

 

dvf uture Natural Condtions dvf uture Natural Condtions

Great Smoky Mountains 

National Park (GSMNP) 23.66 11.05 12.15 4.54

Linville Gorge (LIGO) 21.98 11.19 9.64 4.08

Shinning Rock (SHRO) 22.11 11.47 6.91 2.51

Swanquarter (SWAN) 20.37 11.55 10.96 5.46

Site Worst Visiblity Days Best Visiblity Days

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

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

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

summarizes these calculation for the IMPROVE monitors associated with North Carolina Class I 

areas. From the table, it is apparent that all North Carolina Class I areas are showing visibility 

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

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

deciview estimates. 

Site DVbase DVf uture DVdif f DVbase DVf uture DVdif f

Great Smoky Mountains 

National Park (GSMNP) 30.28 23.66 -6.62 13.58 12.15 -1.43

Linville Gorge (LIGO) 28.77 21.98 -6.79 11.11 9.64 -1.47

Shinning Rock (SHRO) 28.46 22.11 -6.35 7.69 6.91 -0.78

Swanquarter (SWAN) 24.74 20.37 -4.37 11.99 10.96 -1.03

20% Worst Days 20 % Best Days

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

uniform rate of progress values for each Class I area (Figures 3.6-1 through 3.6-4). When 

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

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

sites in North Carolina. 
Modeling Results & Supplemental Analysis Appendix G - 14 

Regional Haze SIP for NC Class I Areas December 17, 2007 

Reasonable Progress Assessment, new IMPROVE 

Algorithm, Great Smoky Mountains - Best 20% Days

13.58 13.58 13.58

12.15

4.54

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)

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

Uniform Rate of Reasonable Progress Glide Path

Great Smoky Mountains - 20% Worst Days

New IMPROVE Equation

30.28

28.67

25.79

22.59

19.38

16.18

12.97

11.05

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

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

(top) and the 20% worst days (bottom) 
Modeling Results & Supplemental Analysis Appendix G - 15 

Regional Haze SIP for NC Class I Areas December 17, 2007 

Reasonable Progress Assessment, new IMPROVE 

Algorithm, Linville Gorge - Best 20% Days

11.11 11.11 11.11

9.64

4.08

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)

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

Uniform Rate of Reasonable Progress Glide Path

Linville Gorge - 20% Worst Days

New IMPROVE Equation

28.77

27.31

24.67

21.74

18.81

15.88

12.95

11.19

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

Figure 3.6-2: Uniform rate of progress, or glidepath, for LIGO1 on the 20% best days (top) 

and the 20% worst days (bottom) 
Modeling Results & Supplemental Analysis Appendix G - 16 

Regional Haze SIP for NC Class I Areas December 17, 2007 

Reasonable Progress Assessment

Shining Rock - 20% Best Days

New IMPROVE Equation, 12 km grid

7.69 7.69

6.91

2.51

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)

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

Uniform Rate of Reasonable Progress Glide Path

Shining Rock - 20% Worst Days

New IMPROVE Equation

28.46

27.04

24.50

21.66

18.83

16.00

13.17

11.47

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

Figure 3.6-3: Uniform rate of progress, or glidepath, for SHRO1 on the 20% best days 

(top) and the 20% worst days (bottom) 
Modeling Results & Supplemental Analysis Appendix G - 17 

Regional Haze SIP for NC Class I Areas December 17, 2007 

Reasonable Progress Assessment

Swanquarter - 20% Best Days

New IMPROVE Equation, 12 km grid

11.99 11.99

10.96

5.46

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)

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

Uniform Rate of Reasonable Progress Glide Path

Swanquarter - 20% Worst Days

New IMPROVE Equation

24.74 23.64

21.66

19.46

17.26

15.07

12.87

11.55

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

Figure 3.6-4: Uniform rate of progress, or glidepath, for GSMNP1 on the 20% best days 

(top) and the 20% worst days (bottom) 
Modeling Results & Supplemental Analysis Appendix G - 18 

Regional Haze SIP for NC Class I Areas December 17, 2007 

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 USEPA believes a weight of evidence 

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

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

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

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

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

of progress analysis. 

All North Carolina 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 North Carolina Class I areas. 

4.1 Refinements to Recommended Analysis 

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

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

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

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

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

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

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

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

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

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

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

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

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

four IMPROVE sites associated with North Carolina Class I areas show visibility improvement 

beyond what is required by the set reasonable progress goals. This is further evidence that North 

Carolina Class I areas should reach visibility improvement goals, with a margin of safety. 
Modeling Results & Supplemental Analysis Appendix G - 19 

Regional Haze SIP for NC Class I Areas December 17, 2007 

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 modeling results as processed with the “new” IMPROVE 

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

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

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

4.2 Additional Air Quality Modeling 

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

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

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

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

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

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

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

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

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

4.2.1 Grid Resolution 

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

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

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

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

resolution may prove too costly for some states. 
Modeling Results & Supplemental Analysis Appendix G - 20 

Regional Haze SIP for NC Class I Areas December 17, 2007 

VISTAS modeling was performed on a 12km resolution grid nested within a 36km resolution 

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

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

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

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

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

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

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

processed with the old IMPROVE equations. 

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

together. The SHRO1 site has perhaps the largest spread across all four modeling combinations. 

However, the 12 and 36km results with the new IMPROVE equation are similar, suggesting the 

choice of IMPROVE algorithm at SHRO1 make the largest difference in final values. Overall, 

North Carolina Class I area IMPROVE monitors exceed 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 North Carolina Class I areas. 

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

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

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

modeling result at the 12km grid resolution. 

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

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

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

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

IMPROVE equation applied to both the 36 and 12 km grid resolutions results. 
Modeling Results & Supplemental Analysis Appendix G - 21 

Regional Haze SIP for NC Class I Areas December 17, 2007 

4.2.2 Other Modeling Results 

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

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

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

based on these different options. 

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

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

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

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

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

modeling, essentially an emissions sensitivity run is created. 

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

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

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

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

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

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

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

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

Visibility Projections with New Algorithm for CENRAP Class I areas

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%

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. 
Modeling Results & Supplemental Analysis Appendix G - 22 

Regional Haze SIP for NC Class I Areas December 17, 2007 

Additionally Midwest Regional Planning Organization (MwRPO) has provided preliminary 

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

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

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

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

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

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

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

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

the model projections are similar across the United States. 

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

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

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

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

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

results. 

Visibility Projections with Old Algorithm for CENRAP Class I areas

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%

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. 
Modeling Results & Supplemental Analysis Appendix G - 23 

Regional Haze SIP for NC Class I Areas December 17, 2007 

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 MwRPO. The modeling for the MwRPO 

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

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

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

4.2.3 International Emissions 

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

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

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

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

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

(GEOS-Chem) and CMAQ. 

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

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

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

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

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

found in Appendix M. 

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

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

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

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

as part of the boundary conditions outside the national modeling domain, as appropriate. 
Modeling Results & Supplemental Analysis Appendix G - 24 

Regional Haze SIP for NC Class I Areas December 17, 2007 

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

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

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

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

underestimated in these projections 

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

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

and international anthropogenic emissions were removed. The difference is attributable 

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

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

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

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

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

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

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

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

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

than natural background conditions. 

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

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

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

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

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

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

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

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

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

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

Appendix H. 

In Figure 4.2-6, CMAQ projections of contributions from international emissions to PM mass on 

20 percent worst visibility days in 2002 at Swanquarter, North Carolina are compared to United 

States domestic contributions to PM components at the site on those days. 
Modeling Results & Supplemental Analysis Appendix G - 25 

Regional Haze SIP for NC Class I Areas December 17, 2007 

Transboundary Man-Made Impacts

0.0

0.2

0.4

0.6

0.8

1.0

1.2

BRIG

HEGL

CACR

MINGUPBU

BRETEVER

CHAS

SAMA

COHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACA

SWAN

JARI

DOSO

SHEN

(NH4) SO 2 4 Conc. (

g/m3

)

GEOS_4x5 GEOS_1x1 CMAQ

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

neighboring Class I areas. 

Swanquarter, NC

Domestic and International Aerosol Mass by Species

20% Worst Days in 2002

0

5

10

15

20

25

30

35

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

1/5 2/22 2/25 4/20 4/29 5/2 5/8 5/26 7/4 7/7 7/16 7/19 7/28 7/31 8/21 8/24 9/11 9/17 10/5 10/20 11/22 11/25 12/7

Mass (μg/m3)

(NH4)2SO4 NH4NO3 POM LAC Soil CM Sea Salt

Swanquarter, NC

Domestic and International Aerosol Mass by Species

20% Worst Days in 2002

0

5

10

15

20

25

30

35

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

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Domestic

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Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

International

Domestic

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Domestic

International

1/5 2/22 2/25 4/20 4/29 5/2 5/8 5/26 7/4 7/7 7/16 7/19 7/28 7/31 8/21 8/24 9/11 9/17 10/5 10/20 11/22 11/25 12/7

Mass (μg/m3)

(NH4)2SO4 NH4NO3 POM LAC Soil CM Sea Salt

Figure 4.2-6: PM component concentrations from US domestic sources on 20 percent worst 

visibility days in 2002 (left bars) and CMAQ-simulated international contributions (right 

bars) at Swanquarter, North Carolina. 
Modeling Results & Supplemental Analysis Appendix G - 26 

Regional Haze SIP for NC Class I Areas December 17, 2007 

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 4.2-7 shows the estimates from VISTAS modeling of 

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

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

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

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

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

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

the haze index 

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

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

220%

240%

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 4.2-7. Percent of Uniform Rate of Progress haze index reduction achieved in 2018 

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

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

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

results when effects of international emissions are removed. 

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

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

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

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

setting reasonable progress goals with this SIP. 
Modeling Results & Supplemental Analysis Appendix G - 27 

Regional Haze SIP for NC Class I Areas December 17, 2007 

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

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

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

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

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

regional haze work. 
Appendix G.1 

GEOS-CHEM Report 
DOCUMENTATION AND EVALUATION OF THE GEOS-CHEM SIMULATION 

FOR 2002 PROVIDED TO THE VISTAS GROUP 

Final Report 

Principal Investigator: Daniel J. Jacob, Harvard University 

(djacob@fas.harvard.edu) 

Co-Investigators: Rokjin Park and Jennifer A. Logan, Harvard University 

June 24, 2005 

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 Appendix G.1 - 1 

December 17, 2007
2

TABLE OF CONTENTS 

1. PROJECT OBJECTIVE AND PRODUCTS DELIVERED 3 

2. DESCRIPTION OF GEOS-CHEM SIMULATIONS FOR VISTAS 3 

2.1 The GEOS-Chem Model 3 

2.2 Model configuration used for VISTAS 4 

2.3 PM sources used in VISTAS simulations 5 

2.3.1 Sulfate-nitrate ammonium 5 

2.3.3 Carbonaceous aerosols 6 

2.3.4 Soil dust 6 

2.3.5 Sea salt 6 

2.4 Biomass burning emission inventory for 2002 7 

2.4.1 The United States and Canada 7 

2.4.2 Russia and Kazakhstan 8 

2.4.3 Summary 9 

3. MODEL PERFORMANCE EVALUATION 9 

4. REFERENCES 11 

FIGURES 13 

Appendix A: Rokjin J. Park, Daniel J. Jacob, Mian Chin, and Randall V. Martin (2003), 

Sources of carbonaceous aerosols over the United States and implications for natural 

visibility, J. Geophys. Res., 108(D12), 4355, doi:10.1029/2002JD003190. 22 

Appendix B: Rokjin J. Park, Daniel J. Jacob, Brendan D. Field, Robert M. Yantosca, and 

Mian Chin (2004), Natural and transboundary pollution influences on sulfate-nitrateammonium aerosols in the United States: implications for policy, J. Geophys. Res., 109, 

D15204, doi:10.1029/2003JD004473. 48 

Modeling Results & Supplemental Analysis 

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 Appendix G.1 - 2 

December 17, 2007
3

1. PROJECT OBJECTIVE AND PRODUCTS DELIVERED 

The objective of this project was to provide chemical boundary conditions with 

synoptic-scale resolution from the GEOS-Chem global chemical transport model (CTM) 

to serve as continental-scale CMAQ regional simulations for 2002 conducted by the 

VISTAS group. The GEOS-Chem simulations were to include a detailed representation 

of ozone-NOx-VOC-PM chemistry as described by Park et al. [2003, 2004] (appended to 

this report). They were to replicate the Park et al. [2003, 2004] simulations but with 

coarser resolution (4ox5o horizontal resolution vs. 2ox2.5o), updated anthropogenic 

emissions from the EPA NEI 1999, and 2002-specific biomass burning emissions. They 

were also to include preliminary simulations of soil dust and sea salt. We conducted three 

full-year simulations for 2002: 

1. A baseline simulation with best estimates of 2002 emissions; 

2. A background simulation modified from the baseline by shutting off U.S. 

anthropogenic1

 emissions; 

3. A natural simulation modified from the baseline by shutting off anthropogenic 

emissions worldwide. 

3-D concentration fields with 3-hour temporal resolution were archived from each 

simulation to serve as boundary conditions for CMAQ. A model performance evaluation 

(MPE) was conducted through comparisons of the baseline simulation to IMPROVE and 

CASTNET observations in the United States, using the same metrics as in Park et al. 

[2003, 2004]. 

2. DESCRIPTION OF GEOS-CHEM SIMULATIONS FOR VISTAS 

2.1. The GEOS-Chem model 

The GEOS-Chem model (http://www-as.harvard.edu/chemistry/trop/geos) is a 

cooperative global CTM used by 21 institutions in North America and Europe, and 

centrally managed by Daniel Jacob’s group at Harvard. It is driven by assimilated 

meteorological observations from the Global Earth Observation System (GEOS) of the 

NASA Global Modeling and Assimilation Office (GMAO). It is presently being applied 

to a wide range of atmospheric composition problems including greenhouse gases, 

oxidants, PM, mercury, and other species. The coupled ozone-NOx-VOC-PM version of 

GEOS-Chem is described in Park et al. [2004] (appendix B). The GEOS-Chem model is 

documented in over 100 papers in the refereed literature (see above web site for the 

 

1

 “Anthropogenic” here includes all fuel, industrial, and agricultural sources; it does not include biomass 

burning. See Park et al. [2004] for detail on source specifications. 

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 Appendix G.1 - 3 

December 17, 2007
4

publication list). Evaluations of the global PM simulation are presented in Park et al. 

[2003, 2004, 2005ab], Li et al. [2005], Alexander et al. [2005], and Heald et al. [2005]. 

2.2. Model configuration used for VISTAS 

The GEOS-Chem simulations for VISTAS used GEOS meteorological 

observations for the year 2002. These were obtained from GMAO as a 6-hourly archive 

(3-hour for surface quantities such as mixing depths). The data through August 2002 are 

from the GEOS-3 assimilation, with horizontal resolution of 1o

x1o and 55 vertical layers. 

The data after August 2002 are from the updated GEOS-4 assimilation, with horizontal 

resolution of 1o

x1.25o and 48 vertical layers. 

GEOS-Chem simulations can be conducted either with the native resolution of the 

GEOS meteorological data, or with degraded horizontal resolution to reduce 

computational expense. The Park et al. [2003, 2004] simulations used a 2ox2.5o

horizontal resolution. Continental-scale simulations for North America have been 

conducted with the native 1o

x1o resolution of the GEOS-3 data [Li et al., 2005; Park et 

al., 2005b]. The VISTAS simulations used a coarser horizontal resolution of 4o

 latitude x 

5o longitude, as this was considered sufficient to provide boundary conditions outside of 

North America for use in continental-scale CMAQ simulations. In Fiore et al. [2003], we 

previously compared ozone simulations for North America using GEOS-Chem with 

4o

x5o and 2ox2.5o resolution, and the MAQSIP regional model with 36x36 km2

resolution. We found that using the 4o

x5o resolution of GEOS-Chem significantly 

degraded the ability of the model to reproduce the observed variability of concentrations 

over North America, but still maintained the synoptic-scale structure and did not incur a 

significant continental-scale mean bias. 

Significant modifications to the representation of PM sources were made in the 

VISTAS simulations relative to the work of Park et al. [2003, 2004] and are described in 

more detail below. They include (1) use of U.S. anthropogenic emissions from the EPA 

NEI 1999 inventory; (2) use of forest fire information specific to 2002; (3) inclusion of 

the secondary organic aerosol (SOA) formation mechanism from Chung and Seinfeld 

[2002]; (4) inclusion of prototype soil dust and sea salt simulations. An additional 

modification was the application of surface emissions and dry deposition to the GEOSdiagnosed mixed layer column rather than to the surface layer of the model. This was 

introduced to correct for the effect of 1-hour operator splitting between transport and 

chemistry (including emissions and dry deposition) in the model, when dealing with a 

very shallow surface layer (only 10-m deep in GEOS-3). The effect is significant for fastdepositing gases such as HNO3 and NH3; correcting it is an objective model improvement 

that has since been implemented in the standard version of GEOS-Chem. 

Other aspects of the simulation (transport, chemistry, deposition) are as described 

in Park et al. [2004]. Briefly, transport uses the advection scheme of Lin and Rood 

[1998], with instantaneous vertical mixing through the local mixing depth, and 

convective transport computed from GEOS-3 archived convective mass fluxes by 

replicating the convection algorithm of the parent GEOS general circulation model 

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 Appendix G.1 - 4 

December 17, 2007
5

(GCM). Natural sources are calculated within the GEOS-Chem simulation as a function 

of local values of meteorological variables (temperature, insolation, soil moisture, 

precipitation, wind speed, convective cloud tops). The description of ozone-NOx-VOCPM chemistry includes ~100 chemical species and ~400 chemical reactions. A full 

documentation of this mechanism is posted as a pdf document on the GEOS-Chem web 

site (http://www.env.leeds.ac.uk/%7Emat/GEOS-CHEM/geoschem_mech.pdf). Coupling 

of PM with ozone-NO x-VOC oxidant chemistry takes place through sulfate, nitrate, and 

SOA formation and thermodynamics, aerosol effects on UV actinic fluxes, and 

heterogeneous radical chemistry. Wet deposition of soluble gases and PM follows the 

scheme of Liu et al. [2001] and includes contributions from scavenging in convective 

updrafts, rainout and washout in convective and large-scale precipitation, and partial or 

total release during re-evaporation below cloud base. Dry deposition is computed with a 

standard resistance-in-series scheme [Wang et al., 1998]. 

All simulations were conducted from September 1, 2001 to December 31, 2002. 

The first four months were used to achieve proper initialization. Results delivered to 

VISTAS are from the 12-month 2002 simulation. 

2.3. PM sources used in VISTAS simulations 

We describe here briefly the PM sources used in the VISTAS simulations. 

Description of the biomass burning emission inventory for 2002 is presented in section 

2.4. 

2.3.1. Sulfate-nitrate-ammonium 

Global GEOS-Chem budgets of sulfate, nitrate, and ammonium aerosols, 

including breakdown by source types of emissions for sulfur, NOx, and ammonia, are 

given by Park et al. [2004]. Anthropogenic emissions are from the Global Emission 

Inventory Activity (GEIA) with 1o

x1o spatial resolution and seasonal temporal resolution, 

and are scaled for individual countries to the year 1998 on the basis of national emission 

inventories and fuel use statistics. Park et al. [2004] give global totals for non-U.S. 

anthropogenic emissions computed in this manner. They used the same procedure also 

for U.S. emissions. For the VISTAS baseline simulation, we used monthly mean 

anthropogenic U.S. emissions from the NEI 1999 inventory produced by EPA. An 

archive of monthly mean NEI 1999 emissions from that inventory with 0.25o

x0.25o

horizontal resolution was generated for us by Alice Gilliland of EPA/ORD, and was regridded to 4o

x5o for application to VISTAS. The U.S. emission of ammonia in the NEI99 

inventory (3.6 Tg N yr-1) is known to be too high [Gilliland et al., 2004]. Therefore we 

retained for that species the U.S. emission inventory of Park et al. [2004] (2.2 Tg N yr-1) 

Natural emissions except for biomass burning are as given by Park et al. [2004]. 

Volcanic sulfur emissions are from the GEIA climatology. Emission of dimethylsulfide 

(DMS) by phytoplankton uses a global distribution of DMS seawater concentrations from 

Kettle et al. [1999] and a standard sea-air exchange parameterization driven by the local 

wind. Soil emissions of NOx are from GEIA and are function of local temperature and 

precipitation history. Lightning emissions of NOx are computed globally with a standard 

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 Appendix G.1 - 5 

December 17, 2007
6

algorithm based on convective cloud tops [Wang et al., 1998] and are scaled to yield a 

global source of 6 Tg N yr-1 [Martin et al., 2002]. Emissions of ammonia from soils and 

oceans are from GEIA. 

2.3.2. Carbonaceous aerosols 

Detailed discussion of the EC and OC emission source processes in the model is 

given in Park et al. [2003] (appendix A). Anthropogenic emissions of EC and OC outside 

the U.S. are from the Cooke et al. [1999] inventory. For the VISTAS baseline simulation 

we used U.S. anthropogenic emissions from Park et al. [2003], who optimized EC and 

OC sources using monthly IMPROVE observations for 1998. Both EC and OC have 

major sources from biomass burning; emission factors are from Andreae and Merlet 

[2001] and are applied to the 2002 biomass burning inventory described in section 2.4. 

Secondary organic aerosol (SOA) formation from biogenic hydrocarbons follows the 

scheme of Chung and Seinfeld [2002] developed for application in global models (the 

Park et al. [2003] simulations simply scaled the SOA source to 10% of monoterpene 

emission). The Chung and Seinfeld [2002] describes SOA formation from oxidation of 

several classes of biogenic hydrocarbons through gas-aerosol partitioning of the semivolatile products as a function of local temperature and pre-existing OC mass 

concentration. 

2.3.3. Soil dust 

We included in the VISTAS simulations a preliminary representation of soil dust 

using the global dust mobilization scheme of Zender et al. [2003]. Dust particles in four 

different size classes were transported as separate tracers with different source and 

settling properties, thus allowing in particular some segregation of PM2.5 and PM10-2.5. A 

global evaluation of this preliminary dust simulation in GEOS-Chem was presented by 

Fairlie [2004]. Asian and African dust sources are simulated without obvious bias. There 

is a large overestimate at many IMPROVE sites in fall due to spurious local dust 

generation from seasonally dry and vegetation-deprived prairie ecosystems. 

2.3.4. Sea salt 

The sea salt concentrations in the VISTAS simulations are from a new GEOSChem capability developed by Alexander et al. [2005]. The simulation uses the standard 

source scheme of Monahan [1986] which is function of surface wind speed over the 

oceans, and transports sea salt PM in two size classes (0.1-1 and 1-10 μm). The resulting 

global source of sea salt in GEOS-Chem is 5400 Tg yr-1, consistent with earlier literature 

(3500-7600 Tg yr-1). Alexander et al. [2005] present further discussion of the global sea 

salt budget and distributions in GEOS-Chem, including comparisons to observations and 

previous global models. 

Modeling Results & Supplemental Analysis 

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 Appendix G.1 - 6 

December 17, 2007
7

2.4. Biomass burning emission inventory for 2002 

2.4.1. The United States and Canada 

We developed an inventory for emissions from fires in the United States using 

data for areas burned that are reported by several federal agencies, by regional 

interagency coordination centers such as the Pacific Northwest, Western Great Basin, 

Eastern Great Basin, and Southwest (www.or.blm.gov/nwcc/, www.nv.blm.gov/wgbcc/, 

www.blm.gov/utah/egbcc/, www.fs.fed.us/r3/fire/) and by various states. We also 

obtained a data base of fires in the southeastern United States from G. Stella of Alpine 

Geophysics. In the western half of the U.S. and Alaska, most of the land is federally 

owned, so the fire information is thought to be comprehensive. Reports from the Bureau 

of Indian Affairs, Bureau of Land Management (BLM), National Park Service, Fish and 

Wildlife Service, and U.S. Forest Service are available on-line 

(http://famweb.nwcg.gov/weatherfirecd). These reports give the fire name, start and end 

date, their location (latitude and longitude), and area burned. We analyzed these data to 

spatially and temporally allocate the fires. A simple concatenation of data was not 

possible as different agencies sometimes reported the same fires, so we ensured that 

duplicate reports for the same fire were eliminated. The Daily Incident Management 

Reports (www.cidi.org/wildfire) of the National Interagency Coordination Center (NICC) 

were consulted to corroborate incidence, location and sizes of major fires, and to 

determine whether these fires were surface or crown fires. 

Our database of all fires with areas larger than 100 acres gives a total of 1626 

fires, of which 338 are located in the southeastern states. These fires consumed 2.65 x 

106

 ha in the United States in 2002. The data provided by Alpine Geophysics for the 

southeast gave a total area of 0.044 x 106

 ha. Six states accounted for over 80% of the 

national area burned, namely Alaska, Oregon, Colorado, California, New Mexico, and 

Arizona. Alaska accounted for one third of the area burned in the United States, and the 

seven largest fires in Alaska contributed 55% of the area burned for the state. For the 

entire United States, the largest 20 fires accounted for half the area burned nationally. 

The area burned in the United States in 2002 was the second highest for the 

previous ten years. To put the area burned in a larger context, the area burned in Canada 

was similar to that in the United States, 2.76 x 106

 ha, while 2.8 x 106 ha burned in 

Kazakhstan, and 11 x 106

 ha in Asiatic Russia. It was also a very high fire year in Russia. 

For our initial estimate of the amount of dry matter burned (used in the GEOSCHEM simulations) we adopted a loading 2.6 kg DM/m2

 for Alaskan fires, and 1.8 kg 

DM/m2

 for the lower states. We found that 47 Tg of dry matter was consumed by fires in 

the United States, producing CO emissions of 5 Tg in about 3 months. We developed 

preliminary maps of dry matter burned on a 1ox1o grid by month by assuming that each 

fire burned at the same rate during each day of the burn period. 

For Canada, we relied on a product provided by David Lavoue (Environment 

Canada). He is developing an inventory for Canada using a detailed data-base on the 

location of the size and position of the fires; that data-base is not yet publicly available. 

He gave us an interim product that consists of monthly totals of fuel consumed on a map 

Modeling Results & Supplemental Analysis 

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 Appendix G.1 - 7 

December 17, 2007
8

with resolution of 1ox1o latitude by longitude. Lavoue estimates that 58 Tg of dry matter 

was consumed by fires in 2002, which gives rise to CO emissions of 7 Tg.. 

2.4.2. Russia and Kazakhstan 

The most detailed fire information was available for eastern Russia. The IFFN 

report (http://www.fire.uni-freiburg.de/iffn/iffn_28/Russia-1.pdf ) gives estimates by 

province of the total areas burned in Asian Russia for the fire season of 2002. The total 

area burned was 11 x 106

 ha. The Fire Laboratory of the Sukachev Institute of Forest, 

Krasnoyarsk, provides maps showing the locations of large fires for 10-day periods 

derived from NOAA AVHRR data (http://www.fire.uni-freiburg.de/current/ 

archive/archive.htm). We used these maps to spatially and temporally apportion the area 

burned in each province among all of the fires logged for the province; burn scars at the 

end of the fire season were approximated as rectangles. The areas burned were put on a 

grid of 1ox1o (lat. x long.) for each 10 day period. The Kamchatka Peninsula was not 

included on the burning maps for eastern Russia. Areas burned there were confined to 

June and July (IFFN report). Maps of ATSR fire counts were used to locate the fires. 

Most of the burning in Russia was in July-August in the northern province of 

Yakutia. There was a smaller peak in the burning in May, primarily in the provinces of 

southern Siberia. We relied on a detailed vegetation map of Russia to determine the 

dominant vegetation type in each gridbox, and applied appropriate fuel loads to 

determine the amount of biomass burned. 

Table 1. Loadings used for fires in Russia, in kg dry matter (DM) m-2 

 

Vegetation Type kg DM m-2 

 Tundra 1.8 

 Wooded Tundra 2.0 

 Taiga 2.5 

 Boreal Forest 3.0 

 Grasslands 0.3 

 Steppe 0.3 

 Desert 0.3 

 Farmlands 0.3 

Recent work indicates the importance of including the burning of peat as a 

component of boreal fires. We track peat burning separately from burning of other forest 

fuels as it has a higher moisture content, hence less efficient combustion, and higher 

emission factors for CO. The construction of peatland map is in progress for Russia (L. 

Pozdnyakova, personal communication), there is not one currently available. As a 

surrogate, we used maps of wetlands and soil drainage to give the distribution of boggy 

or poorly drained soils, as advised by S. Conard and M. Turetsky. The locations of 

individual fires given by the remote sensing data were superimposed on this map; 

burning of peat was confined to the area defined by the perimeter of the fires. Few 

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 Appendix G.1 - 8 

December 17, 2007
9

experiments have been reported which describe measurements of fuel consumed and 

emissions from peat fires in boreal forests. We assumed that 2.2 kg C m-2 was burned. 

For western Russia, wildfires other than peat bog fires were not reported in 

sufficient detail to ascertain their whereabouts. Qualitative descriptions of major peat 

bog fires in July to September around Moscow, St. Petersburg, Nizhny Novgorod, and 

other cities were abundant, but few estimates of areas burned and depth of peat burned 

were found. From the IFFN news and CNN reports, we used quantitative and qualitative 

information to approximate the areas burned in the peat bogs around four of the major 

cities. We distributed the burning homogeneously in the oblast (province) that contained 

each city and applied a fuel consumption estimate for peat burning as above. 

Maps of fires for each month and the annual amount burned (2.8 x 106

 ha) were 

available for Kazakhstan. Here also, ATSR fire counts were used to locate the fires on a 

monthly basis. We used a vegetation map for Kazakhstan 

[http://www.fire.unifreiburg.de/iffn/country/ kz/kz_2_1b.gif] and an agricultural map of 

the CIS (Major World Crop Areas and Climatic Profiles) to determine the fuel loads to 

use. Based on these maps, we postulated that the fires in the southern regions were 

burning of agricultural residues, for which we adopted a loading of 1.2 kg DM m-2; for 

the pine forests in the north, we adopted a loading recommended for Kazakhstan’s pine 

forests, 2 kg DM m-2. 

2.4.3. Summary 

We find that 409 Tg dry matter was consumed in Russia, 52 Tg in Kazakhstan, and 47 Tg 

in the United States. For Canada, Lavoue’s estimate of 58 Tg of dry matter consumed in 

2002 is low in comparison to our estimate for Russia, given the ratio of areas. Lavoue did 

not include burning of peat in Canada as he believes it in not an important component of 

the Canadian fires (personal communication). These estimates were used in the GEOSCHEM simulations to provide monthly emissions from biomass burning. In other 

regions we replied on the work of Van der Werf et al. (Science, 2003), based on VIRS 

satellite data. 

3. MODEL PERFORMANCE EVALUATION 

Model performance evaluation (MPE) of the baseline VISTAS simulation focused 

on the annual and seasonal PM concentration statistics previously reported by Park et al. 

[2003, 2004] using observations from IMPROVE and CASTNET sites. The Park et al. 

simulations were conducted for 1998 and 2001, whereas the VISTAS simulation was for 

2002. We use here observations from 145 IMPROVE sites and 84 CASTNET sites 

available for 2002. 

The limitations of using U.S. concentration data to evaluate a global PM 

simulation with 4o

x5o horizontal resolution should be stressed. Any variability on scales 

less than ~500 km cannot be resolved. This compromises the evaluation for individual 

sites, particularly in urban, industrial, and coastal regions. We focus therefore our 

evaluation on the large-scale spatial distribution and on seasonal statistics for the national 

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ensemble of sites. The evaluation figures shown here reproduce similar figures presented 

by Park et al. [2003, 2004] for their 1998 and 2001 simulations with 2o

x2.5o resolution. 

Direct comparison to these figures can therefore be made to assess the relative quality of 

the simulations. We use the slopes of the regression lines in the simulated vs. observed 

scatterplots to diagnose mean biases in the model. Regression lines are computed with the 

reduced major axis method [Hirsch and Gilroy, 1984]. 

Figure 1 compares simulated and observed annual mean sulfate concentrations at 

the ensemble of IMPROVE and CASTNET sites for the year 2002, plotted on the 4o

x5o

model grid. Values are highest in the industrial midwest, in both model and observations, 

reflecting the distribution of anthropogenic emissions. Figure 2 shows scatterplots of 

simulated vs. observed annual and seasonal sulfate concentrations for the ensemble of 

(left) IMPROVE and (middle) CASTNET sites. The right column in Figure 2 compares 

simulated and observed sulfate precipitation data for 2002 at NADP sites. The correlation 

between model and observations is high for the annual mean values (R2

 = 0.82 for the 

concentration data and 0.71 for the deposition data) and also for the seasonal means (R2 = 

0.63–0.87 for the concentration data). An exception is the deposition data in summer, for 

which correlation between model and observations is low (R2 = 0.18). Overall, the 

correlations presented here are consistent with those presented by Park et al. [2004] for 

2001 (see Figures 3 and 4 of appendix B) although that simulation was more successful 

in capturing the variance in summer deposition. 

Figure 3 compares simulated and observed annual mean concentrations of 

ammonium at CASTNET sites. Observed concentrations are higher in the east than in the 

west and are highest in the midwest, reflecting agricultural operations. The model 

reproduces this spatial distribution. Scatterplots of simulated vs. observed annual and 

seasonal ammonium concentrations are shown in Figure 4 for the ensemble of sites. The 

model reproduces the variability of observed ammonium concentrations, both in an 

annual mean sense (R2

 = 0.81) and in different seasons (R2 = 0.72-0.81). The R2 values 

are slightly lower than the values from the Park et al. [2004] simulation (Figure 6 of 

appendix B). That simulation showed a factor of 2 high bias in fall due to too high 

seasonal ammonia emission. Despite the same ammonia emission, the VISTAS 

simulation shows much reduced bias (50% for ammonium concentration in fall). This 

reflects in part the increase in the effective ammonia deposition velocity resulting from 

application of the dry deposition sink throughout the mixed layer column, as described in 

section 2.2. 

Figure 5 compares simulated and observed annual mean nitrate concentrations at 

the IMPROVE and CASTNET sites. Maximum concentrations are in the Midwest, 

reflecting the limitation of ammonium nitrate formation by the availability of ammonia. 

The model captures the observed spatial distribution of nitrate concentrations but tends to 

be high on an annual mean basis (slope = 1.41-1.80 in Figure 4). This is however also an 

improvement over Park et al. [2004], which found a factor of 2 high bias (slope = 1.87 – 

2.43 in Figure 6 of appendix B), and reflects the improved treatment of HNO3 dry 

deposition. The high bias in the VISTAS simulation is mainly driven by the fall months, 

when the model appears to produce too much ammonium nitrate due to too high 

ammonia emission [Park et al., 2004]. 

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Figure 6 compares simulated and observed annual mean concentrations of EC at 

IMPROVE sites, and the scatterplots of Figure 7 compare seasonal mean concentrations. 

The model reproduces about half of the observed spatial variability in different seasons 

(R2

 = 0.42-0.58) except in summer (R2 = 0.18). Simulated concentrations tend to be 

higher than observed (slope = 1.58-1.74) except in summer (slope = 0.76). The high bias 

is mostly driven by IMPROVE sites in the northeastern corridor. 

Figure 8 compares simulated and observed annual mean concentrations of organic 

carbon mass (OMC) at IMPROVE sites, and Figure 9 shows the seasonal scatterplots. 

Although the ability of the model to reproduce variability in the observations is poor, 

particularly in summer, the simulated seasonal mean concentrations are generally within 

a factor of two of observations. The simulation is significantly worse than that described 

by Park et al. [2003]; this reflects the use of the Chung and Seinfeld [2002] SOA 

parameterization, which appears to underestimate the source from vegetation. 

Overall, the MPE conducted for the VISTAS simulation for the U.S. shows the 

level of agreement that can be expected considering the state of the science in large-scale 

aerosol modeling and the intrinsic limitations of a simulation with 4o

x5o horizontal 

resolution. Simulated concentrations are mainly within a factor of 2 of observations on 

regional and seasonal scales, and can generally account (except for OC) for most of the 

observed variability on those scales. 

 

4. REFERENCES 

Alexander, B., R. J. Park, D. J. Jacob, Q. B. Li, R. M. Yantosca, J. Savarino, C. C. W. 

Lee, and M. H. Thiemens (2005), Sulfate formation in sea-salt aerosols: Constraints 

from oxygen isotopoes, J. Geophys. Res., 110, D10307, doi:10.1029/2004JD005659. 

Andreae, M, O. and P. Merlet (2001), Emission of trace gases and aerosols from biomass 

burning, Global Biogeochem. Cycles, 15(4), 955-966. 

Chung, S. H. and J. H. Seinfeld (2002), Global distribution and climate forcing of 

carbonaceous aerosols, J. Geosphy. Res., 107(D19), 4407, 

doi:10.1029/2001JD001397. 

Cooke, W.F., C. Liousse, H. Cachier, and J. Feichter (1999), Construction of a 1o

 x 1o

fossil fuel emission data set for carbonaceous aerosol and implementation and 

radiative impact in the ECHAM-4 model, J. Geophys. Res., 104, 22,137-22,162. 

Fairlie T. D. (2004), Transpacific Transport of Mineral Dust from Asia During Spring 

2001, presented at the American Geophysical Union Spring Meeting in Montreal, 

May 19, 2004. 

Fiore, A. M., D. J. Jacob, R. Mathur, and R. V. Martin (2003), Application of empirical 

orthogonal functions to evaluate ozone simulations with regional and global models, 

J. Geophys. Res., 108, 4431, doi: 10.1029/2002JD003151. 

Heald, C.L., D.J. Jacob, R.J. Park, L.M. Russell, B.J. Huebert, J.H. Seinfeld, H. Liao and 

R.J. Weber (2005), A large organic aerosol source in the free troposphere missing 

from current models, submitted to Geophys. Res. Lett.. 

Hirsch, R. M., and E. J. Gilroy (1984), Methods of fitting a straight line to data: 

examples in water resourses, Water Res. Bull., 20, 705-711. 

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Kettle, A. J., et al. (1999), A global database of sea surface dimethylsulfide (DMS) 

measurements and a procedure to predict sea surface DMS as a function of latitude, 

longitude, and month, Glob. Biogeochem. Cycle, 13(2), 399-444. 

Li, Q., D. J. Jacob, R. Park, Y. Wang, C. L. Heald, R. Hudman, R. M. Yantosca, R. V. 

Martin, and M. Evans (2005), North American pollution outflow and the trapping of 

convectively lifted pollution by upper-level anticyclone, J. Geophys. Res., 110, 

D10301, doi:10.1029/2004JD005039. 

Lin, S,-J., and R. B. Rood (1996), Multidimensional flux-form semi-Lagrangian transport 

schemes, Mon. Wea. Rev., 124, 2046-2070. 

Liu, H., D. J. Jacob, I. Bey, and R. M. Yantosca (2001), Constraints from 210Pb and 7

Be 

on wet deposition and transport in a global three-dimensional chemical tracer model 

driven by assimilated meteorological fields, J. Geophys. Res., 106, 12,109-12,128. 

Martin, R.V., K. Chance, D.J. Jacob, T.P. Kurosu, R.J.D. Spurr, E. Bucsela, J.F. Gleason, 

P.I. Palmer, I. Bey, A.M. Fiore, Q.B. Li, R.M. Yantosca, and R.B.A. Koelemeijer 

(2002), An improved retrieval of tropospheric nitrogen dioxide from GOME, J. 

Geophys. Res., 107(D20), 4437, 10.1029/2001JD001027. 

Monahan, E. C., D. E. Spiel, and K. L. Davidson (1986), A model of marine aerosol 

generation via whitecaps and wave disruption, in Oceanic Whitecaps and Their Role 

in Air-Sea Exchange Processes, edited by E. C. Monahan and G. M. Niocaill, pp. 

167-174, Springer, New York. 

Park, R. J., D. J. Jacob, M. Chin and R. V. Martin (2003), Sources of carbonaceous 

aerosols over the United States and implications for natural visibility, J. Geophys. 

Res., 108(D12), 4355, doi:10.1029/2002JD003190. 

Park, R. J., D. J. Jacob, B. D. Field, R. M. Yantosca, and M. Chin (2004), Natural and 

transboundary pollution influences on sulfate-nitrate-ammonium aerosols in the 

United States: implications for policy, J. Geophys. Res., D15204, 

10.1029/2003JD004473. 

Park, R. J., D.J. Jacob, P.I. Palmer, A.D. Clarke, R.J. Weber, M.A. Zondlo, F.L. Eisele, 

A.R. Bandy, D.C. Thornton, G.W. Sachse, and T.C. Bond (2005a), Export efficiency 

of black carbon aerosol in continental outflow: global implications, J. Geophys. Res., 

110, D11205, doi:10.1029/2004JD005432. 

Park, R. J., D. J. Jacob, et al. (2005b), Natural and transboundary pollution influences on 

variability of regional visibility in the United States, manuscript in preparation. 

Wang, Y., D. J. Jacob, and J. A. Logan (1998), Global simulation of tropospheric O3-

NOx-hydrocarbon chemistry, 1. Model formulation, J. Geophys. Res., 103(D9), 

10,713-10,726. 

Zender, C. S., H. Bian, and D. Newman (2003), Mineral Dust Entrainment and 

Deposition (DEAD) model: Description and 1990s dust climatology, J. Geophys. 

Res., 108(D14), 4416, doi:10.1029/2002JD002775. 

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Figure 1. Annual mean concentrations of sulfate in surface air over the United States in 

2002. The top panel shows results from the GEOS-CHEM model. The middle and bottom 

panels show the observations from the IMPROVE and CASTNET networks, 

respectively, averaged over the model 4o

 × 5o grid. 

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Figure 2. Scatterplot of simulated versus observed sulfate concentrations at the 

IMPROVE and CASTNET sites, and sulfate deposition fluxes at NADP sites. Values are 

annual means (top panels) and seasonal means for 2002. Sites in the western and eastern 

United States (separated at 95o

W) are shown as pluses and open circles, respectively. 

Thick solid lines are reduced major axis regressions for the ensemble of the data; 

regression equations and R2

 are shown inset. Thin solid lines show the y=x relationship. 

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Figure 3. Annual mean concentrations of ammonium in surface air over the United States 

in 2002. The top panel shows results from the GEOS-CHEM model. The bottom panel 

shows the observations from the CASTNET network. 

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Figure 4. Scatterplot of simulated versus observed ammonium concentrations at the 

CASTNET sites (left column), and nitrate concentrations at the CASTNET and 

IMPROVE sites (right two columns). Values are annual means (top panels) and seasonal 

means for 2002. Sites in the western and eastern United States (separated at 95o

W) are 

shown as pluses and open circles, respectively. Thick solid lines are reduced major axis 

regressions for the ensemble of the data; regression equations and R2

 are shown inset. 

Thin solid lines show the y=x relationship. 

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Figure 5. Same as Figure 3 but for nitrate. 

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Figure 6. Annual mean concentrations of elemental carbon (EC) aerosol in surface air 

over the United States in 2002. The top panel shows results from the GEOS-CHEM 

model. The bottom panel shows the observations from the IMPROVE network. 

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Figure 7. Scatterplot of simulated versus observed EC aerosol concentrations at the 

IMPROVE sites. Values are seasonal means for 2002. Sites in the western and eastern 

United States (separated at 95o

W) are shown as pluses and open circles, respectively. 

Thick solid lines are reduced major axis regressions for the ensemble of the data; 

regression equations and R2 are shown inset. Thin solid lines show the y=x relationship. 

Dotted lines show the y=2x and y=0.5x relationships.

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Figure 8. Same as Figure 6 but for organic carbon mass (OMC) 

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Figure 9. Same as Figure 7 but for organic carbon mass (OMC) 

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Appendix A: Rokjin J. Park, Daniel J. Jacob, Mian Chin, and Randall V. Martin 

(2003), Sources of carbonaceous aerosols over the United States and implications for 

natural visibility, J. Geophys. Res., 108(D12), 4355, doi:10.1029/2002JD003190. 

Abstract. We use a global 3-D model (GEOS-CHEM) to better quantify the sources of 

elemental carbon (EC) and organic carbon (OC) aerosols in the United States through 

simulation of year-round observations for 1998 at a network of 45 sites (IMPROVE). 

Simulation with our best a priori understanding of sources, including global satellite data 

to constrain fire emissions, captures most of the variance in the observations (R2

 = 0.84 

for EC, 0.67 for OC) with a low bias of 15% for EC and 26% for OC. Multiple linear 

regression to fit the IMPROVE data yields best estimates of 1998 U.S. sources of 0.60 Tg 

yr-1 EC and 0.52 Tg yr-1 OC from fossil fuel; 0.07 Tg yr-1 EC and 0.89 Tg yr-1 OC from 

biofuel; 0.08 Tg yr-1 EC and 0.60 Tg yr-1 OC from wildfires; and 1.10 Tg yr-1 OC from 

vegetation. We find that fires in Mexico and Canada contributed 40-70% of annual mean 

natural EC in the United States for 1998, and 20-30% of annual mean natural OC. 

Transpacific transport from Asian pollution sources amounted to less than 10% of the 

natural EC and less than 2% of the natural OC; in contrast to ozone, we find that 

intercontinental transport of anthropogenic carbonaceous aerosols does not enhance 

significantly the natural background. IMPROVE observations and model simulations for 

the summer of 1995 show that Canadian fire emissions can produce large events of 

elevated EC and OC in the southeastern United States. Our best estimates of mean natural 

concentrations of EC and OC in the United States, using a model simulation with 

climatological monthly mean fire emissions, are 2-3 times higher than the default values 

recommended by the U.S. Environmental Protection Agency (EPA) for visibility 

calculations, except for OC in the eastern United States (16% lower). 

1. Introduction 

Carbonaceous aerosol is one of the least understood components of fine particulate 

matter (PM). It is usually divided in two fractions, elemental carbon (EC) and organic 

carbon (OC). OC is the second most abundant component of the aerosol in the United 

States after sulfate, and the dominant component of the natural continental aerosol [Malm 

et al., 2000]. EC is the dominant component of the light-absorbing aerosol. 

Carbonaceous aerosol is presently the subject of intense scrutiny because of its impact on 

human health, visibility, and climate. 

We present here an assessment of the sources of EC and OC in the United States by 

using a global 3-D model (GEOS-CHEM) simulation of observations from the 

Interagency Monitoring of Protected Visual Environments (IMPROVE) network. Our 

focus is on quantifying the anthropogenic and natural sources of these aerosols, the role 

of transboundary transport, and the implications for visibility. The U.S. Environmental 

Protection Agency Regional Haze Rule [U.S. EPA, 2001] mandates a schedule of 

increasing emission controls to achieve “natural visibility conditions” in national parks 

and other wilderness areas by 2064. The ambiguity in defining “natural visibility 

conditions” requires better information on natural PM concentrations and the perturbing 

effects from fires and from sources outside the United States. 

Elemental carbon is emitted to the atmosphere by combustion. Major sources in the 

United States include coal burning and diesel engines. Organic carbon is emitted directly 

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to the atmosphere (primary OC) and formed in situ by condensation of low-volatility 

products of the photooxidation of hydrocarbons (secondary OC). Primary sources of OC 

in the United States are wood fuel, coal burning, and wild fires [Seinfeld and Pandis, 

1998; Cabada et al., 2002]. Secondary OC includes an anthropogenic component from 

oxidation of aromatic hydrocarbons, and a biogenic component from oxidation of 

terpenes [Griffin et al., 1999]. 

Our approach is to conduct a 3-D model simulation of EC and OC concentrations in 

the United States for 1998, with best a priori sources, compare results with observations 

from the IMPROVE network, and use the constraints from the comparison to optimize 

our treatment of sources by multiple linear regression. Our treatment of fire emissions 

accounts for year-to-year variability through satellite observations; 1998 was a 

particularly active fire year, thus offering good constraints on emissions from that source. 

We also present a case study for the summer of 1995 to demonstrate the large-scale 

enhancements of EC and OC concentrations in the United States that can arise from 

Canadian fires. We go on to quantify mean natural EC and OC concentrations in the 

United States for different seasons and regions, using climatological fire emissions and 

sources from vegetation, and to assess the enhancement of EC and OC background 

concentrations resulting from transpacific transport of Asian pollution. 

2. Model Description 

2.1 General 

We use the GEOS-CHEM global 3-D model of tropospheric chemistry [Bey et al., 

2001] to simulate EC and OC aerosols for 1998 (1 year) and 1995 (summer). The model 

(version 4.23, http://www-as.harvard.edu/chemistry/trop/geos/index.html) uses 

assimilated meteorological data from the NASA Goddard Earth Observing System 

(GEOS) including winds, convective mass fluxes, mixed layer depths, temperature, 

precipitation, and surface properties. Meteorological data for 1995 and 1998 are 

available with 6-hour temporal resolution (3-hour for surface variables and mixing 

depths), 2o

 latitude by 2.5o longitude horizontal resolution, and 20 (GEOS1 for 1995) or 

48 (GEOS3 for 1998) sigma vertical layers. We retain this spatial resolution in the 

GEOS-CHEM simulation. The lowest model levels are centered at approximately 50, 

250, 600, 1100, and 1750 m above the local surface in GEOS1 and 10, 50, 100, 200, 400, 

600, 900, 1200, and 1700 m in GEOS3. 

The simulation of carbonaceous aerosols in GEOS-CHEM follows that of the Georgia 

Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) 

model [Chin et al., 2002], with a number of modifications described below. The model 

resolves EC and OC, with a hydrophobic and a hydrophilic fraction for each (i.e., four 

aerosol types). Combustion sources emit hydrophobic aerosols that then become 

hydrophilic with an e-folding time of 1.2 days following Cooke et al. [1999] and Chin et 

al. [2002]. We assume that 80% of EC and 50% of OC emitted from all primary sources 

are hydrophobic [Cooke et al., 1999; Chin et al., 2002; Chung and Seinfeld, 2002]. All 

secondary OC is assumed to be hydrophilic. The four aerosol types in the model are 

further resolved into contributions from fossil fuel, biofuel, and biomass burning, plus an 

OC component of biogenic origin, resulting in a total of 13 tracers transported by the 

model. 

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Simulation of aerosol wet and dry deposition follows the schemes used by Liu et al.

[2001] in previous GEOS-CHEM simulations of 210Pb and 7

Be aerosol tracers. Wet 

deposition includes contributions from scavenging in convective updrafts, rainout from 

convective anvils, and rainout and washout from large-scale precipitation. Wet 

deposition is applied only to the hydrophilic component of the aerosol. Dry deposition of 

aerosols uses a resistance-in-series model [Walcek et al., 1986] dependent on local 

surface type and meteorological conditions; it is small compared to wet deposition. Liu 

et al. [2001] found no systematic biases in their simulations of 210Pb and 7

Be with GEOSCHEM. 

2.2 A priori sources of EC and OC 

We use global anthropogenic emissions of EC (6.4 Tg yr-1) and OC (10.5 Tg yr-1) 

from the gridded Cooke et al. [1999] inventory for 1984. This inventory includes 

contributions from domestic, vehicular, and industrial combustion of various fuel types. 

In the GOCART simulation of Chin et al. [2002], the Cooke et al. [1999] inventory was 

used with no seasonal variation. However, the source from heating fuel should vary with 

season [Cabada et al., 2002]. Cooke et al. [1999] do not resolve the contributions to EC 

and OC emissions from heating fuel. We assume these contributions to represent 8% 

(EC) and 35% (OC) of total anthropogenic emissions, based on data for the Pittsburgh 

area from Cabada et al. [2002] and apply local seasonal variations of emissions using the 

heating degree days approach [EIA, 1997; Cabada et al., 2002]. In this manner we find 

that anthropogenic EC emission in the United States in winter is 15% higher than in 

summer. For OC the anthropogenic winter emission is twice that in summer. 

The Cooke et al. [1999] inventory does not include biofuels, which provide however 

an important source of heating in rural households and are also used in agroindustrial 

factories. We use a global biofuel use inventory with 1o

x1o spatial resolution from 

Yevich and Logan [2002] with emission factors of 1.0 g EC and 5 g OC per dry mass 

burned [Street et al., 2001; Dickerson et al., 2002]. For the United States and Canada, we 

supersede that inventory with data on wood fuel consumption for residential and 

industrial sectors available for individual states and provinces [EIA, 2001] and which we 

distribute on a rural population map. Emission factors for this North American wood fuel 

source are 0.2 g EC and 3.0 g OC per kg dry wood burned [Cabada et al., 2002]. 

Seasonal variation in biofuel emissions is included for the United States only and is 

estimated according to the heating degree-days approach. 

Biomass burning emissions of EC and OC are calculated using the global biomass 

burning inventory of Duncan et al. [2002]. This inventory uses a fire climatology 

compiled on a 1o

x1o grid by Lobert et al. [1999], and applies monthly and interannual 

variability to that climatology from satellite observations. Emission factors are 2g EC 

and 14 g OC per kg dry mass burned [Chin et al., 2002], higher than for biofuels because 

combustion is less efficient. For boreal forest fires, which are of particular interest here, 

emission factors reported in the literature range from 0.38 to 2.55 g EC per kg dry mass 

burned [Lavoué et al., 2000, and references therein], consistent with the value assumed 

here. The OC/EC emission ratio of 7 is within the range of 6.9 to 8.2 used by Liousse et 

al. [1996]. Figure 1 shows the resulting annual OC emissions from biomass burning in 

North and Central America for 1997-2000 as well as the climatological mean. An 

ENSO-related drought resulted in catastrophic wildfires in the tropical forests of southern 

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Mexico and Central America in 1998 [Peppler et al., 2000]. Canadian fire emissions 

were also unusually large in 1998. Fire emissions in the United States were 38% higher 

than the climatological mean. 

Figure 2 shows the spatial and seasonal distribution of biomass burning OC emission 

from our model in 1998. Fires in Mexico and Central America were most intense in May 

[Peppler et al., 2000, Cheng and Lin, 2001]. Canadian fires peaked in July-September. 

In the United States, most fires occurred in the northwest (Idaho, Montana) in summer; 

additional fires occurred in spring in Florida, due to the ENSO-induced drought. 

Secondary formation of OC from oxidation of large hydrocarbons is an important 

source but uncertainties are large [Griffin et al., 1999; Kanakidou et al., 2000; Chung and 

Seinfeld, 2002]. Chung and Seinfeld [2002] find that biogenic terpenes are the main 

source of secondary OC aerosols. We assume a 10% carbon yield of OC from terpenes 

[Chin et al., 2002], and apply this yield to a global terpene emission inventory dependent 

on vegetation type, monthly adjusted leaf area index, and temperature [Guenther et al., 

1995]. 

Table 1 shows a summary of a priori EC and OC emissions used in the GEOSCHEM simulation for 1998. The most important global source for both is biomass 

burning. In the United States, EC is mostly emitted from the combustion of fossil fuel 

and OC originates mostly from vegetation (but with large seasonal variation, as discussed 

below). 

3. Model evaluation 

A global evaluation of the EC and OC aerosol simulation was done by Chin et al.

[2002] as part of a more general evaluation of aerosol optical depth using ground and 

satellite observations. Our simulation of aerosol sources and meteorological processes is 

similar to that of Chin et al. [2002] and our global distributions of EC and OC 

concentrations are comparable. We focus here our model evaluation on the United 

States, using observations at the IMPROVE sampling sites. The IMPROVE monitoring 

program was initiated in 1987 in national parks and other protected environments to 

identify the contribution of different aerosol components to visibility degradation [Malm 

et al., 1994]. The data for 1995 and 1998 consist of 24-h speciated aerosol 

concentrations measured twice a week. The EC and OC concentrations are determined 

using the Thermal Optical Reflectance (TOR) method, which is state of the science but is 

subject to uncertainties that are difficult to quantify [Chow et al., 1993; Malm et al., 

1994]. In the present paper we take the data at face value. There are 45 IMPROVE sites 

with continuous measurements for 1998 (Figure 3). 

Figure 4 compares simulated and observed annual mean EC and OC concentrations at 

the 45 IMPROVE sites for the year 1998. The IMPROVE measurements are plotted on 

the 2o

 x 2.5o model grid. The bottom panels show the differences (model bias). A 

general objection to evaluating model results with 24-hr averaged concentrations in 

continental surface air is the inability of models to resolve nighttime stratification [Jacob 

et al., 1993]. This is not an issue in our case because of high vertical resolution of the 

model near the surface and because the IMPROVE sites are not in the vicinity of large 

sources. We verified that the 24-h average concentrations simulated by the model in 

layers 1 (0-10m), 2 (10-50m), and 3 (50-100m) are not significantly different. 

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Observed concentrations of EC and OC are generally higher in the eastern than the 

western United States, reflecting higher anthropogenic and vegetative (OC) emissions in 

the east. The OC maximum is shifted south relative to the EC maximum, and shows a 

secondary maximum along the west coast, reflecting the vegetative source. The model 

captures well this large-scale spatial distribution of EC and OC. Fires in the model also 

lead to high concentrations over Central America and western Canada. 

Site-to-site comparisons reveal however some major discrepancies between model 

and observations, as shown in the bottom panel of Figure 4 and in the scatterplot of 

Figure 5. Some of these discrepancies appear to reflect inadequate spatial resolution in 

the model. Model overestimates at coastal sites with large local urban or fire sources 

(BRIG in New Jersey; OKEF in Georgia; REDW, PORE, and PINN in California) are 

due to the inability of the model to simulate steep subgrid land-to-sea gradients in mixing 

depth [Fiore et al., 2002]. Model overestimates at SEQU (California) and GLAC 

(Montana) are due to local fire emissions (Figure 1) for which averaging over the grid 

scale may induce large errors in the simulation of local observations. We exclude these 

seven sites in further statistical data analysis. 

The model overestimates OC concentrations at THSI (Oregon) and MORA 

(Washington) sites due to a particularly large vegetative source in the model in summer 

that is apparently not seen in the observations. The discrepancy is local in nature (it is 

not found at nearby sites). As discussed further below, our specification of the vegetative 

OC source appears inadequate to describe OC concentrations at these two sites, and 

therefore we exclude them from further statistical analysis. 

Figure 5 shows that the model generally reproduces the annual mean EC and OC 

concentrations to within a factor of two and captures the spatial pattern well (R2

=0.84 for 

EC and R2

=0.67 for OC). However, the slope of the reduced major axis line [Hirsch and 

Gilroy, 1984] is 0.85±0.06 for EC and 0.74±0.08 for OC, reflecting a low bias in the 

model. We will correct for this model bias by adjusting the sources, as discussed below. 

Figure 6 and Figure 7 compare seasonal variations of simulated and observed EC and 

OC concentrations at selected IMPROVE sites. Contributions from individual sources to 

the model concentrations are shown. Seasonal variations for EC differ considerably from 

site to site, and the model has significant success in capturing these differences. Fossil 

fuel is the dominant source for EC at most sites, but seasonal maxima in May-September 

over the western United States are due to forest fires. The OC concentrations are 

generally highest in summer and lowest in winter, both in the model and in the 

observations; this seasonal variation is mostly due to the biogenic source. Peaks in OC in 

May-September in the western United States are seen both in the model and in the 

observations and are due to wildfires, as for EC. Wintertime OC is higher in the eastern 

than the western United States, and includes contributions of comparable importance 

from biofuels and fossil fuels. 

Rogers and Bowman [2001] used satellite measurements and air parcel trajectory 

calculations to illustrate the transport of the 1998 fire plumes from Central America to the 

central and southern United States. Our model successfully captures the corresponding 

peaks of EC and OC observed in May at the IMPROVE sites (e.g., BIBE in Texas, CHIR 

in Arizona, CANY in Utah, MOZI in Colorado, UPBU in Arkansas, GRSM in 

Tennessee). The enhancement in concentrations is much stronger for OC than for EC, 

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both in the model and in the observations, reflecting high OC/EC fire emission ratios and 

the relatively large fossil fuel source of EC in the United States. 

The model has also some success in reproducing the influences from fire emissions 

within the United States. For example, the high OC in April-June at CHAS in Florida is 

well captured in the model. Fires in the western United States result in peak EC and OC 

concentrations in September at several sites (MORA, Washington; THSI, Oregon; 

LAVO, California; JARB, Nevada). 

Figure 8 compares simulated and observed monthly mean concentrations for the 

ensemble of IMPROVE sites and for separate seasons. The model simulation with a 

priori sources has success in reproducing the variability of observed EC and OC for 

winter and spring, as measured by the high R2

 (0.67-0.79) correlation between model and 

observations. The slope of the regression line (0.84-0.98) is close to one for both EC and 

OC. The R2

 is lower in summer and fall, particularly for OC (0.37-0.40) and the slope of 

the regression line is off from one (0.72-0.74 for EC and 0.74-1.06 for OC). The slope of 

the OC regression line in fall is close to one only because high model bias from wildfire 

sources at western sites offsets the low model bias at eastern sites. 

4. Top-down emission estimates 

The statistical model biases apparent in Figure 8 could reflect errors in the a priori

sources. We examine what adjustments in the sources would be needed for least-squares 

minimization of the bias between simulated and observed monthly mean EC and OC 

concentrations. We identify for this purpose four source components: fossil fuel, biofuel, 

biomass burning, and vegetation (the latter for OC only). We use a multiple linear 

regression to fit the annual mean U.S. source for each component to the monthly mean 

IMPROVE observations. In order to give equal weight to EC and OC concentrations in 

the least-squares minimization, we normalize them by their respective annual mean 

concentrations for the ensemble of IMPROVE sites (0.29 μg m-3 for EC, 1.23 μg m-3 for 

OC). 

We find in this manner that fossil fuel and biofuel emissions should be increased by 

15% and 65% respectively from a priori levels, while biomass burning emissions should 

be decreased by 17% and the biogenic source for OC should be increased by 11%. We 

consider these adjustments to be well within the uncertainties on the a priori estimates. 

The a posteriori values of our adjusted sources are given in Table 1. The increase in the 

biofuel source is largely determined by the model underestimate of observed OC for the 

cold season. 

Figure 9 presents annual mean surface air concentrations of EC and OC in the model 

using a posteriori sources. Relative to the simulation with a priori sources (Figure 4), 

there are 15-20% increases in EC and OC concentrations in the eastern United States. 

Changes in the western United States are smaller because the decrease in the biomass 

burning source offsets the increase in the biogenic OC source. 

The effect of source adjustment on the ability of the model to fit observed EC and OC 

concentrations is shown by the scatterplots in Figure 8. Compared to the simulation with 

a priori sources, the R2

 correlation coefficients are slightly higher and the slopes of the 

regression lines are closer to unity. Figures 6 and 7 show the effect of the a posteriori

sources on the simulation at individual sites. The adjustments are generally too small to 

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correct site-specific discrepancies, which would require modifying the geographic 

distributions of the sources. 

Figures 10 and 11 show the contributions of individual a posteriori sources to EC and 

OC for winter and summer. Fossil fuel is the most important source of EC everywhere in 

the United States, except in some areas in the west in summer where wildfires make a 

more important contribution. For OC, the anthropogenic sources (fossil and biofuel) 

dominate in winter, while the natural sources (fires and vegetation) are more important in 

summer. The fossil fuel OC is mostly concentrated in the northeastern corridor, the 

industrial Midwest and Southern California, whereas the biofuel OC is more widely 

distributed. Biogenic OC in summer is highest in the southeast and along the west coast. 

We previously discussed in the context of Figure 7 the large OC enhancements in the 

southern United States due to fires in Central America, but these enhancements are in 

spring (cf. Figure 2) and thus not apparent in Figure 11. Figure 11 shows a large 

enhancement in OC concentrations over the north-central United States due to Canadian 

fires, but the IMPROVE sites are not well situated to observe this enhancement (Figure 

3). We present below a case study for summer 1995 demonstrating Canadian fire 

influence over the eastern United States. 

5. Canadian fire influence: a case study for the summer of 1995 

Previous studies [Wotawa and Trainer, 2000; Fiore et al., 2002; McKeen et al., 2002] 

have shown that major Canadian wildfires in June-July 1995 caused large enhancements 

of CO and smaller enhancements of ozone in the southeastern United States. The 

Canadian fire plumes were carried by northerly flows associated with high pressure 

systems on the back side of cold fronts. We use here a GEOS-CHEM simulation for the 

summer 1995 to demonstrate large aerosol EC and OC enhancements from these fires at 

IMPROVE sites in Arkansas (UPBU), Tennessee (GRSM) and Kentucky (MACA). 

Our simulation of the 1995 Canadian wildfires uses daily, geographically resolved 

emission data estimated from the area burned in each province. Those data are given by 

Wotawa and Trainer [2000] for CO, and are scaled here to our climatological biomass 

burning emission inventory for CO [Lobert et al., 1999] to derive corresponding EC and 

OC emissions. The resulting EC and OC emissions from the fires are 0.34 and 2.41 Tg, 

respectively, and are distributed in five areas (Northwest Territories, Alberta, 

Saskatchewan, Manitoba, and Ontario) for four burning periods from 17 June to 13 July. 

Figure 12 shows the time series of simulated and observed EC and OC concentrations 

at three sites in the southeastern United States: UPBU in Arkansas, MACA in Kentucky, 

and GRSM in Tennessee. There are two large peaks in the observations, for July 1 and 

July 8, which are captured by the model and are due to the Canadian fires (compare solid 

and dashed lines in Figure 12). The timing of those peaks is consistent with those 

concurrently observed for CO at nearby sites [McKeen et al., 2002]. Our simulation of 

the magnitude of the July 7-9 event is improved in a sensitivity simulation where we 

assume initial lifting of the fire emissions up to 4 km altitude (Figure 12, dotted line). 

Such lifting can be expected from buoyancy, particularly for large crown fires [Liousse et 

al., 1996; 1997; Lavoué et al., 2000]. 

Our model simulation allows us to assess the influence of Canadian fire emissions on 

seasonal aerosol concentrations in the United States for the summer of 1995. We find 

that the events associated with Canadian fire plumes persisted typically for 3-5 days. On 

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a seasonal basis, they caused the mean June-August 1995 natural EC to increase by 80% 

(east) and 36% (west) and the mean OC to increase by 23% (east) and 16% (west), 

relative to a sensitivity simulation with no Canadian fires. 

6. Implications for natural visibility in the United States 

We use results from our model to estimate the role of natural carbonaceous aerosols 

in visibility reduction and compare to the default values recommended by EPA [2001] for 

application of the Regional Haze Rule. Our 1998 simulation with a posteriori sources 

yields annual average concentrations of natural EC and OC from fires and vegetation of 

0.09 μg/m3 and 1.09 μg/m3

, respectively, for the western United States (west of 95oW) 

and 0.06 μg/m3 and 0.95 μg/m3, respectively, for the eastern United States. In order to 

compute the light extinction by OC we need to multiply the OC mass by 1.4 to obtain an 

Organic Carbon Mass (OMC) that accounts for the non-carbon additional mass attached 

to OC aerosols [Malm et al., 1994]. The resulting annual average for natural OMC is 

1.52 μg/m3

 and 1.33 μg/m3 for the west and east, respectively. Except for OMC in the 

eastern United States, our best estimates of natural concentrations for EC and OMC are 

significantly higher than the default values recommended by EPA [2001] which are 0.02 

μg/m3

 for EC, and 0.47 μg/m3 (west) and 1.40 μg/m3 (east) for OMC. 

Several issues need to be addressed in this comparison to the EPA default values. 

First, 1998 had unusually high fire emissions, principally from Mexico and Canada, as 

shown in Figure 1. Second, it is important to quantify the contribution of transboundary 

transport to natural EC and OC concentrations in the United States. Third, there is 

ambiguity from a U.S. policy standpoint as to whether intercontinental transport of 

anthropogenic pollution (as from Asia) should be considered part of the “natural” 

background. To address these issues we conducted three sensitivity simulations, with 

sources modified from those in our standard 1998 simulation. The first includes no EC 

and OC sources in the United States to quantify the contributions from transboundary 

transport, mostly from Canada and Mexico. The second includes EC and OC sources 

from Asia only, to quantify the transpacific transport. The third uses climatological 

biomass burning emissions as shown in Figure 1 in order to derive mean default values of 

natural EC and OC concentrations in the United States. The results are summarized in 

Table 2. 

We find that the transboundary transport of anthropogenic sources makes only a small 

contribution (less than 10%) to the total anthropogenic concentrations of EC and OC in 

the United States. However, the transboundary transport of natural sources, mostly from 

fires in Canada and Mexico, makes a large contribution to annual mean natural 

concentrations in the United States for 1998 (44% in the west and 67% in the east for EC; 

28% in the west and 37% in the east for OC). 

Transpacific transport from Asian sources is found to make little contribution to EC 

and OC concentrations in the United States, even in the context of the natural 

background. The concentrations generated in the simulation with anthropogenic and 

natural Asian sources only (Table 2) amount to less than 2% of the natural OC 

concentrations from the standard simulation, and less than 10% of the natural EC. The 

small role of intercontinental transport in contributing to background EC and OC 

concentrations over the United States reflects the short lifetime of these species against 

wet deposition, particularly considering that the lifting of air from the continental 

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boundary layer to the free troposphere involves wet processes [Stohl, 2001]. This can be 

contrasted to ozone, for which transport from outside North America makes a large 

contribution to the U.S. background [Fiore et al., 2002]. 

Our best estimates of mean natural EC and OC concentrations for comparison to the 

EPA default values are obtained from the simulation using mean climatological fire 

emissions. We find annual average concentrations of natural EC and OMC of 0.06 μg/m3 

and 1.25 μg/m3, respectively, for the western United States and 0.04 μg/m3 and 1.17 

μg/m3, respectively, for the eastern United States (Table 2). These are higher by a factor 

of 2-3 than the EPA default values except for OMC in the eastern United States which is 

lower by 16%. 

The implications of our results for natural visibility estimates are substantial, 

particularly in the western United States. Our higher natural OMC component relative to 

EPA’s default estimates results in lower natural visibility. For example, EPA [2001] uses 

its default natural PM concentrations to derive mean light extinctions of 15.60×10-6 m-1

and 15.78×10-6 m-1 at Bandelier National Monument (BAND, New Mexico) and at 

Yellowstone National Park (YELL, Wyoming). Applying the EPA [2001] visibility 

formula with our best estimates of natural EC and OMC (from the simulation with 

climatological mean fires), and using EPA default values for the other PM components, 

we find natural light extinctions of 19.13×10-6 m-1 and 19.31×10-6 m-1 at BAND and 

YELL, respectively, about 22% higher than EPA values. 

7. Conclusions 

We used the GEOS-CHEM global 3-D model to simulate observed concentrations of 

elemental carbon (EC) and organic carbon (OC) from a network of 45 sites in relatively 

remote regions of the United States (IMPROVE network). Our focus was to better 

quantify the anthropogenic and natural sources of EC and OC in the United States, and 

the role of transboundary and intercontinental transport, in the context of assessing the 

effect of these aerosols on visibility. 

We conducted a 1-year simulation for 1998 using best a priori estimates of EC and 

OC sources, including global satellite observations of fires, and compared the results to 

observed concentrations at the IMPROVE sites. Wildfire emissions were from a gridded 

climatological inventory, scaled to monthly fire emissions for 1998 using satellite fire 

count data. The model reproduces well the spatial pattern in the observations (R2

 = 0.84 

for EC, R2

 = 0.67 for OC) but is biased low by 15% for EC and 26% for OC. From a 

multiple linear regression fit we concluded that fossil fuel and biofuel emissions for EC 

and OC in the United States should be increased by 15%, and 65% respectively from a 

priori levels, while biomass burning emissions for both EC and OC should be decreased 

by 17% and the biogenic source for OC should be increased by 11%. Our best a 

posteriori estimates are given in Table 1. 

Canadian fire influence on the United States in 1998 was largely confined to the 

upper Midwest, where no IMPROVE data are available. We conducted an additional 

simulation for the summer of 1995, for which large CO enhancements in the southeastern 

United States from Canadian fires had previously been reported [Wotawa and Trainer, 

2000]. We find correspondingly large EC and OC enhancements in the IMPROVE 

observations for this region, which the model captures and diagnoses as being due to 

Canadian fire emissions. Model results indicate that Canadian fires in 1995 enhanced the 

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31

mean June-August natural EC and OC concentrations in the eastern United States by 80% 

and 23%, respectively. 

Our 1998 and 1995 simulations lead confidence in the representation of fire emissions 

of EC and OC in the model. We used a simulation with climatological monthly mean fire 

emissions, together with our best estimate of the biogenic OC source, to estimate natural 

concentrations of carbonaceous aerosols in the United States for purpose of natural 

visibility assessments and application of the EPA Regional Haze Rule [EPA, 2001]. Our 

best estimates of natural annual mean concentrations for EC are 0.06 μg/m3

 in the 

western United States (west of 95o

W) and 0.04 μg/m3 in the east; for organic carbon mass 

(OMC = 1.4 OC, to account for the non-carbon contribution to OC aerosols), they are 

1.25 μg/m3 in the west and 1.17 μg/m3 in the east. These values are 2-3 times higher than 

the default values recommended by EPA [2001] for application of the Regional Haze 

Rule, except for OMC in the east (16% lower). Our higher estimates of the natural OMC 

concentrations relative to EPA’s default estimates result in higher natural light extinction 

(and hence lower natural visibility) by 22% in the western United States. We also find a 

large seasonal variability in natural light extinction from EC and OC, with highest values 

in summer due to sources from wildfires and vegetation. 

We further investigated the contribution from transboundary transport to EC and OC 

concentrations in the United States. A sensitivity simulation with no EC and OC sources 

in the United States shows that fires in Mexico and Canada made a large contribution to 

annual mean natural concentrations of EC (40-70%) and OC (30-40%) in the United 

States in 1998. A sensitivity simulation with Asian sources only shows that transpacific 

transport contributes less than 10% of the natural background EC over the United States, 

and less than 2% of the natural background OC. 

Acknowledgments. This research was supported by the Electric Power Research 

Institute (EPRI) and the U.S. Environmental Protection Agency (EPA). The authors are 

grateful to R. Yevich for providing her annual biofuel emission data.

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Figures Captions 

Figure 1. Yearly biomass burning OC emission in 1997-2000 for North and Central 

America, and climatological mean value (see text). 

Figure 2. Annual biomass burning OC emission over North and Central America in 1998 

(top) and seasonal variations for different regions (bottom). 

Figure 3. IMPROVE sampling sites with continuous records for 1998. 

Figure 4. Annual mean concentrations of EC (left) and OC (right) in surface air over the 

United States in 1998. The top panel shows results from the GEOS-CHEM model using 

a priori sources. The middle panel shows the IMPROVE observations plotted on the 

model 2o

 × 2.5o grid. The bottom panel shows the difference between the two. 

Figure 5. Scatterplot of simulated (GEOS-CHEM) vs. observed (IMPROVE) annual 

mean EC and OC concentrations for the data shown in Figure 4. The pluses and the 

circles indicate data in the western and eastern United States (separated at 95o

W), 

respectively. The asterisks with letter labels indicate sites discarded in the statistical 

analysis (see text): REDW(A), PORE(B), PINN(C), SEQU(D), GLAC(E), OKEF(F), and 

BRIG(G). The squares indicate OC data at MORA(H) and THSI(I) sites which were 

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34

discarded in statistical analysis for OC. The thin solid and dotted lines represent the y = x 

relation and a factor of 2 deviation. The thick solid line represents the reduced major-axis 

linear regression [Hirsch and Gilroy, 1984], excluding sites A-I. The Pearson correlation 

coefficients R2 and regression equations are indicated. 

Figure 6. Seasonal variation of monthly mean EC concentrations in 1998 at selected 

IMPROVE sites. Site locations are shown in Figure 1. Values are monthly means. Closed 

circles indicate the observations. Dashed and solid lines represent the model simulations 

with a priori and a posteriori sources, respectively. The a priori model components by 

source types are indicated as thin solid lines with symbols: asterisks (fossil fuel 

combustion), diamonds (biomass burning), and squares (biofuel use). 

Figure 7. Same as in Figure 6 but for OC. The a priori model results by source types are 

represented as thin solid line with asterisks (fossil fuel), diamonds (biomass burning), 

squares (biofuel), and triangles (biogenic terpenes). 

Figure 8. Scatterplots of monthly mean EC (left two columns) and OC (right two 

columns) simulated vs. observed concentrations with a priori (left) and a posteriori

(right) sources, for the ensemble of IMPROVE sites and for individual seasons in 1998. 

Sites in the western and eastern United States (separated at 95o

W) are shown as pluses 

and open circles, respectively. Thin solid lines indicate a perfect match of the model 

results with observations, and dotted lines denote a factor of 2 departure. Thick solid lines 

represent the reduced major axis regression. The Pearson correlation coefficients R2

 are 

indicated. 

Figure 9. Annual mean concentrations of EC (left) and OC (right) in surface air over the 

United States in 1998 from the GEOS-CHEM model using a posteriori sources. 

Figure 10. Contribution from different sources types to EC concentrations (μg m-3) in 

surface air for DJF and JJA. Values are model results for 1998 using a posteriori sources 

(Table 1). 

Figure 11. Same as Figure 10 but for OC. 

Figure 12. Concentrations of EC and OC at three southeastern U.S. sites (UPBU, MACA, 

and GRSM) in June-July 1995. Observations (24-h averages, twice a week) are shown as 

asterisks. The solid line shows results from the standard model simulation. Results from 

sensitivity simulations without Canadian fire emissions (dashed line) and with fire 

emissions initially mixed to 600 hPa (dotted line) are also shown. 

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Tables 

Table 1. Carbonaceous aerosol sources in the GEOS-CHEM model (1998). 

Aerosol Source type Global (Tg yr-1)

a

 United States (Tg yr-1) 

 A priori A posteriori

EC 22.0 0.66 0.75 

 Fossil fuel 6.6 0.52 0.60 

 Biofuel 1.4 0.04 0.07 

 Biomass burning 14.0 0.10 0.08 

OC 129.8 2.70 3.11 

 Fossil fuel 10.6 0.45 0.52 

 Biofuel 7.6 0.54 0.89 

 Biomass burning 97.9 0.72 0.60 

 Biogenic 13.7 0.99 1.10 

a Including a posteriori emissions for the United States. 

Table 2. Natural and anthropogenic EC and OC concentrations (μg m-3) in the United 

Statesa

. 

 Natural concentrations Anthropogenic concentrations 

 West East West East 

EC 

 1998 emissions (base) 0.09 0.06 0.21 0.62 

 No U.S. sources 0.04 0.04 0.02 0.02 

 Asian sources only 0.003 0.001 0.005 0.003 

 Climatological fire emissions 0.06 0.04 0.21 0.62 

OMCb 

 1998 emissions (base) 1.52 1.33 0.52 1.90 

 No U.S. sources 0.43 0.49 0.05 0.05 

 Asian sources only 0.022 0.013 0.013 0.007 

 Climatological fire emissions 1.25 1.17 0.52 1.90 

a

Values are annual means from the standard 1998 simulation (base) and from the 

sensitivity simulations described in section 5. Partition between West and East is at 

95oW. The natural concentrations from the simulation with climatological fire emissions 

can be compared to the default estimates suggested by EPA [2001] for application of the 

Regional Haze Rule: 0.47 μg m-3 (West) and 1.40 μg m-3 (East) for OMC, and 0.02 μg m3 for EC. 

b

Organic carbon mass (OMC), defined as 1.4 times the OC mass to account for noncarbon contributions to the organic aerosol. 

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Figures 

Figure 1. Yearly biomass burning OC emission in 1997-2000 for North and Central 

America, and climatological mean value (see text). 

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Figure 2. Annual biomass burning OC emission over North and Central America in 1998 

(top) and seasonal variations for different regions (bottom). 

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Figure 3. IMPROVE sampling sites with continuous records for 1998. 

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Figure 4. Annual mean concentrations of EC (left) and OC (right) in surface air over the 

United States in 1998. The top panel shows results from the GEOS-CHEM model using 

a priori sources. The middle panel shows the IMPROVE observations plotted on the 

model 2o

 × 2.5o grid. The bottom panel shows the difference between the two. 

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Figure 5. Scatterplot of simulated (GEOS-CHEM) vs. observed (IMPROVE) annual 

mean EC and OC concentrations for the data shown in Figure 4. The pluses and the 

circles indicate data in the western and eastern United States (separated at 95oW), 

respectively. The asterisks with letter labels indicate sites discarded in the statistical 

analysis (see text): REDW(A), PORE(B), PINN(C), SEQU(D), GLAC(E), OKEF(F), and 

BRIG(G). The squares indicate OC data at MORA(H) and THSI(I) sites which were 

discarded in statistical analysis for OC. The thin solid and dotted lines represent the y = x 

relation and a factor of 2 deviation. The thick solid line represents the reduced major-axis 

linear regression [Hirsch and Gilroy, 1984], excluding sites A-I. The Pearson correlation 

coefficients R2 and regression equations are indicated. 

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Figure 6. Seasonal variation of monthly mean EC concentrations in 1998 at selected 

IMPROVE sites. Site locations are shown in Figure 1. Values are monthly means. Closed 

circles indicate the observations. Dashed and solid lines represent the model simulations 

with a priori and a posteriori sources, respectively. The a priori model components by 

source types are indicated as thin solid lines with symbols: asterisks (fossil fuel 

combustion), diamonds (biomass burning), and squares (biofuel use). 

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Figure 7. Same as in Figure 6 but for OC. The a priori model results by source types are 

represented as thin solid line with asterisks (fossil fuel), diamonds (biomass burning), 

squares (biofuel), and triangles (biogenic terpenes). 

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Figure 8. Scatterplots of monthly mean EC (left two columns) and OC (right two 

columns) simulated vs. observed concentrations with a priori (left) and a posteriori

(right) sources, for the ensemble of IMPROVE sites and for individual seasons in 1998. 

Sites in the western and eastern United States (separated at 95o

W) are shown as pluses 

and open circles, respectively. Thin solid lines indicate a perfect match of the model 

results with observations, and dotted lines denote a factor of 2 departure. Thick solid lines 

represent the reduced major axis regression. The Pearson correlation coefficients R2

 are 

indicated. 

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Figure 9. Annual mean concentrations (μg m-3) of EC (left) and OC (right) in surface air 

over the United States in 1998 from the GEOS-CHEM model using a posteriori sources. 

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Figure 10. Contribution from different sources types to EC concentrations (μg m-3) in 

surface air for DJF and JJA. Values are model results for 1998 using a posteriori sources 

(Table 1). 

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46

Figure 11. Same as Figure 10 but for OC. 

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Figure 12. Concentrations of EC and OC at three southeastern U.S. sites (UPBU, MACA, 

and GRSM) in June-July 1995. Observations (24-h averages, twice a week) are shown as 

asterisks. The solid line shows results from the standard model simulation. Results from 

sensitivity simulations without Canadian fire emissions (dashed line) and with fire 

emissions initially mixed to 600 hPa (dotted line) are also shown. 

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Appendix B: Rokjin J. Park, Daniel J. Jacob, Brendan D. Field, Robert M. 

Yantosca, and Mian Chin (2004), Natural and transboundary pollution influences 

on sulfate-nitrate-ammonium aerosols in the United States: implications for policy, 

J. Geophys. Res., 109, D15204, doi:10.1029/2003JD004473. 

Abstract. 

We use a global 3-D coupled oxidant-aerosol model (GEOS-CHEM) to estimate 

natural and transboundary pollution influences on sulfate-nitrate-ammonium aerosol 

concentrations in the United States. This work is motivated in part by the Regional Haze 

Rule of the U.S. Environmental Protection Agency (EPA), which requires immediate 

action to improve visibility in U.S. wilderness areas along a linear trajectory towards an 

endpoint of “natural visibility conditions” by 2064. We present full-year simulations for 

1998 and 2001 and evaluate them with nationwide networks of observations in the United 

States and Europe (IMPROVE, CASTNET, NADP, EMEP) and with Asian outflow 

observations from the NASA TRACE-P aircraft mission. Shutting off U.S. anthropogenic 

emissions in the model defines “background” aerosol concentrations representing 

contributions from both natural and transboundary pollution sources. We find that 

transboundary transport of pollution from Canada, Mexico, and Asia dominates over 

natural influences for both sulfate and nitrate. Transpacific transport of Asian pollution 

accounts for 30% of background sulfate in both the western and eastern United States. 

Our best estimates of natural concentrations for ammonium sulfate and ammonium 

nitrate in the United States are either consistent with or lower than the default values 

recommended by EPA for natural visibility calculations. However, the large 

transboundary pollution influence in our calculation suggests that a natural visibility 

objective cannot be approached without international emission controls. 

1. Introduction 

Visibility degradation in the United States is mostly due to fine aerosols [Malm et 

al., 2000] including carbonaceous (elemental and organic), sulfate, nitrate, and soil dust 

components. These aerosols originate from both anthropogenic and natural sources. The 

U.S. Environmental Protection Agency Regional Haze Rule [U.S. EPA, 2003a] mandates 

a schedule of increasing emission controls to achieve “natural visibility conditions” in 

national parks and other wilderness areas of the United States by 2064. Defining this 

natural visibility endpoint requires better information on natural aerosol concentrations 

and on the perturbing effects from transboundary transport of anthropogenic pollution. 

We previously examined this issue in a global 3-D model simulation of carbonaceous 

aerosols [Park et al., 2003] and found that quantification of wildfire emissions was of 

critical importance. Transboundary transport of anthropogenic pollution was relatively 

unimportant for carbonaceous aerosols because of the large natural sources from 

wildfires and vegetation. We apply here the same analysis to sulfate and nitrate aerosols, 

which are other important components of visibility degradation and hence prime targets 

for regulation. As we will see, transboundary transport of pollution including 

intercontinental transport from Asia emerges in this case as a critical concern. 

The main sources of sulfate and nitrate aerosols are atmospheric oxidation of SO2

and nitrogen oxides (NOx ≡ NO + NO2) to H2SO4 and HNO3, respectively [NARSTO, 

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2003]. Fossil fuel combustion is the dominant source of SO2 and NOx in the United 

States. Important natural sources include volcanoes and atmospheric oxidation of oceanic 

dimethylsulfide (DMS) for SO2; and lightning, soils, and wildfires for NOx [NARSTO, 

2003]. The low vapor pressure of H2SO4 over H2SO4-H2O solutions implies that all of 

sulfate is in the aerosol phase. The sulfate aerosols can be partly or totally neutralized by 

ammonia (NH3) emitted from livestock, fertilizer use, and other less important sources. If 

excess ammonia is available beyond that required for sulfate neutralization to ammonium 

sulfate (NH4)2SO4, then ammonium nitrate (NH4NO3) aerosol can form; otherwise, and 

except for cloudy conditions, nitric acid remains in the gas phase. This simple H2SO4-

HNO3-NH3 thermodynamic framework provides a remarkably successful general 

description of sulfate and nitrate aerosols in the United States [Seinfeld and Pandis, 1998; 

NARSTO, 2003]. Sulfate and nitrate can also be incorporated in soil dust or sea salt 

particles, but these contributions appear to be significant only in desert and coastal areas. 

Transboundary transport of pollution could compromise the objective of “natural 

visibility” in the Regional Haze Rule. We define here an aerosol “background” following 

U.S. EPA [2003b] as the aerosol concentration that would be present over the United 

States in the absence of domestic anthropogenic emissions. It includes contributions from 

natural sources but also from transboundary transport of pollution. If the latter are 

significant, then a “natural visibility” objective can be approached only through 

international emission controls. Alternatively, one should replace this objective by a 

“background visibility” objective that allows for uncontrollable emissions outside U.S. 

borders. 

Intercontinental transport of Asian pollution is of particular interest for our study. 

Previous studies have shown that Asian pollution makes a significant (2-6 ppbv) 

contribution to background ozone concentrations in surface air in the United States 

[Berntsen et al., 1999; Jacob et al., 1999; Fiore et al., 2002], principally by enhancing 

the northern hemispheric ozone background [Fiore et al., 2003b]. Export and transpacific 

transport of Asian aerosol pollution is expected to be far less efficient than for ozone 

because the lifting of Asian air to the free troposphere involves wet processes 

(convection, warm conveyor belts) [Liu et al., 2003] that scavenge aerosols with high 

efficiency [Koike et al., 2003]. Most previous studies of transpacific transport of aerosols 

have focused on dust events, where the Asian source is very large and the lifting to the 

free troposphere takes place by dry processes [Husar et al., 2001; McKendry et al., 2001; 

Vaughan et al., 2001]. However, Jaffe et al. [1999, 2003] and Bertschi et al. [2003] also 

showed significant aerosol enhancements in Asian pollution plumes sampled over the 

west coast of the United States in spring. As we will see, our model results suggest that 

transpacific Asian pollution is a major contributor to the sulfate background over the 

United States on an annual average basis. 

2. Model description 

2.1 General description 

We use the GEOS-CHEM chemical transport model (CTM) [Bey et al., 2001a] to 

conduct full-year simulations for 1998 and 2001 of the sulfate-nitrate-ammonium 

inorganic aerosol system coupled to oxidant chemistry. Most of our analysis focuses on 

the 2001 simulation. The 1998 simulation is used for evaluation with European 

observations, as 2001 observations were not available in a timely manner. The GEOSModeling Results & Supplemental Analysis 

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CHEM model (version 5.03, http://www-as.harvard.edu/chemistry/trop/geos) uses 

assimilated meteorological data from the NASA Goddard Earth Observing System 

(GEOS) including winds, convective mass fluxes, mixed layer depths, temperature, 

clouds, precipitation, and surface properties. Meteorological data for 1998 and 2001 are 

available with 6-hour temporal resolution (3-hour for surface variables and mixing 

depths), 1o

 latitude by 1o longitude (1o × 1o) horizontal resolution, and 48 sigma vertical 

layers. We degrade the horizontal resolution to 2o × 2.5o for computational expediency. 

The lowest model levels are centered at approximately 10, 50, 100, 200, 400, 600, 900, 

1200, and 1700 m above the local surface. 

The GEOS-CHEM simulation of tropospheric oxidant chemistry includes a 

detailed ozone-NOx-hydrocarbon chemical mechanism (~80 species, ~300 reactions). 

Results from this simulation have been reported in a number of papers [Bey et al.,

2001ab; Li et al., 2001, 2002ab; Liu et al., 2002; Martin et al., 2002] including focused 

studies of surface ozone in North America and North American outflow [Fiore et al.,

2002, 2003ab; Li et al., 2004]. GEOS-CHEM simulations of aerosols have been reported 

previously for radionuclides [Liu et al., 2001] and carbonaceous species [Park et al.,

2003]. The H2SO4-HNO3-NH3 aerosol simulation is a new capability for GEOS-CHEM 

and is described in more detail below. The aerosol and oxidant simulations are coupled 

through formation of sulfate and nitrate, HNO3(g)/NO3

-

 partitioning of total inorganic 

nitrate, heterogeneous chemistry [Jacob, 2000], and aerosol effects on photolysis rates 

[Martin et al., 2003]. Partitioning of total ammonia and nitric acid between the gas and 

aerosol phases is calculated using the MARS-A thermodynamic equilibrium model 

[Binkowski and Roselle, 2003]. 

The wet deposition scheme for aerosols is described by Liu et al. [2001]. It 

includes contributions from scavenging in convective updrafts, rainout and washout from 

convective anvils and large-scale precipitation, and it allows for return to the atmosphere 

following evaporation. We extend it here to soluble gases on the basis of their effective 

Henry’s law partitioning in warm clouds, retention efficiency upon droplet freezing in 

mixed clouds, and surface coating or co-condensation of ice crystals in cold clouds [Mari 

et al., 2000]. Scavenging of SO2 is limited by the local availability of H2O2 as a fast 

aqueous-phase oxidant converting SO2 to sulfate [Chin et al., 1996, 2000a]. Dry 

deposition of aerosols and gases uses a standard resistance-in-series model dependent on 

local surface type and meteorological conditions [Wesely, 1989], and implemented as 

described by Wang et al. [1998]. 

We conducted five different simulations for 2001 including one standard 

simulation as described above, and four sensitivity simulations excluding anthropogenic 

emissions (1) globally, (2) in the United States, (3) in North America, and (4) in Asia. 

From these we quantify the influences of natural, transboundary, and intercontinental 

pollution sources on sulfate-nitrate-ammonium aerosol concentrations in the United 

States. 

Each simulation was carried out as follows. We first conducted a fully coupled 

oxidant-aerosol simulation at 4o

 × 5o horizontal resolution for computational expediency. 

Oxidant concentration fields (OH, O3, NO3), H2O2 production rates and photolysis 

frequencies, and total inorganic nitrate concentrations (gas-phase nitric acid plus aerosol 

nitrate) were archived from this simulation and used to conduct an aerosol-only 

simulation at finer 2o

 × 2.5o horizontal resolution. The aerosol-only simulation includes 9 

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prognostic chemical species: dimethylsulfide (DMS), SO2, sulfate, methane sulfonic acid 

(MSA), HNO3(g), NO3

-

, NH3(g), NH4

+

, and H2O2. The 2001 and 1998 simulations were 

initialized on October 1, 2000 and October 1, 1997, respectively, and conducted for 15 

months. The first three months were used to achieve proper initialization, and we focus 

our attention on the following 12 months. 

2.2 Sulfur simulation 

The sulfur simulation in GEOS-CHEM is based on the Georgia Tech/Goddard 

Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model [Chin et 

al., 2000a], with a number of modifications described below. Our fossil fuel and 

industrial emission inventory is for 1999-2000 and is obtained by scaling the gridded, 

seasonally resolved inventory from the Global Emission Inventory Activity (GEIA) for 

1985 [Benkovitz et al., 1996] with updated national emission inventories and fuel use data 

[Bey et al., 2001a]. The emissions for the United States and Canada are from U.S. EPA

[2001], and the emissions for European countries are from EMEP [2003]. Asian sulfur 

emission in the model is 20 Tg S yr-1, which can be compared to year 2000 estimates of 

17 Tg S yr-1 by Streets et al. [2003] and 25 Tg S yr-1 by IPCC [2001]. Anthropogenic 

sulfur is emitted as SO2 except for a small fraction as sulfate, 5% in Europe and 3% 

elsewhere [Chin et al., 2000a]. 

Other anthropogenic sources of SO2 in the model include gridded monthly aircraft 

emissions (0.07 Tg S yr-1) taken from Chin et al. [2000a] and biofuel use. We use a 

global biofuel CO emission inventory with 1o × 1o

 spatial resolution from Yevich and 

Logan [2003] and apply an emission factor of 0.0015 moles SO2 per mole CO [Andreae 

and Merlet, 2001]. Seasonal variations in biofuel emissions are specified from the heating 

degree-days approach [Park et al., 2003]. 

Natural sources of sulfur in the model include DMS from oceanic phytoplankton 

and SO2 from volcanoes and biomass burning. The oceanic emission of DMS is 

calculated as the product of local seawater DMS concentration and sea-to-air transfer 

velocity. The seawater DMS concentrations are gridded monthly averages from Kettle et 

al. [1999], and the transfer velocity of DMS is computed using an empirical formula from 

Liss and Merlivat [1986] as a function of the surface (10m) wind speed. The GEOS 

surface winds used here assimilate remote sensing data from the Special Sensor 

Microwave Imager (SSM/I) instrument. Volcanic emissions of SO2 from continuously 

active volcanoes are included from the database of Andres and Kasgnoc [1998]. 

Emissions from sporadically erupting volcanoes show large year-to-year variability and 

are not included in the model. No major volcanic eruptions occurred in 2001. Biomass 

burning emissions of SO2 are calculated using a gridded monthly biomass burning 

inventory of CO constrained from satellite observations in 2001 by Duncan et al. [2003] 

with an emission factor of 0.0026 moles SO2 per mole CO [Andreae and Merlet, 2001]. 

Table 1a summarizes global and contiguous U.S. (excluding Alaska and Hawaii) 

sulfur emissions for 2001. The United States contribute 10% of the global source (15% of 

the global anthropogenic source). Natural sources contribute 27% globally and are 

negligible within the contiguous United States. 

The gas-phase sulfur oxidation chemistry in the model includes DMS oxidation 

by OH to form SO2 and MSA, by nitrate radicals (NO3) to form SO2, and SO2 oxidation 

by OH to form sulfate. Reaction rates are from DeMore et al. [1997] and the yields of 

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SO2 and MSA from DMS oxidation are from Chatfield and Crutzen [1990]. Aqueousphase oxidation of SO2 by O3 and H2O2 in clouds to form sulfate is included using kinetic 

data from Jacob [1986] and assuming a pH of 4.5 for the oxidation by O3. Cloud liquid 

water content is not available in the GEOS data and we specify it instead in each cloudy 

grid box by using a temperature-dependent parameterization [Somerville and Remer, 

1984]. The cloud volume fraction in a given grid box is specified as an empirical function 

of the relative humidity following Sundqvist et al. [1989]. 

2.3 Ammonia simulation 

Ammonia emissions in the model are based on annual data for 1990 from the 1o

 × 

1o

 GEIA inventory of Bouwman et al. [1997]. Source categories in that inventory include 

domesticated animals, fertilizers, human bodies, industry, fossil fuels, oceans, crops, 

soils, and wild animals. We view the first five as anthropogenic and the last four as 

natural. Additional emissions from biomass burning and biofuel use are computed using 

the global inventories of Duncan et al. [2003] and Yevich and Logan [2003], with an 

emission factor of 1.3 g NH3 per kg dry mass burned [Andreae and Merlet, 2001]. The 

resulting total annual source of ammonia for the United States is reduced by 10% to 

match that derived by Gilliland et al. [2003] from an inverse model analysis of monthly 

precipitation chemistry (NH4

+

) data. 

Table 1b shows a summary of global and contiguous U.S. ammonia emissions for 

2001. The United States account for 5% of the global source (6% of the global 

anthropogenic source). Natural sources amount to 37% of global ammonia emissions and 

21% of contiguous U.S. emissions. 

Several ammonia sources in Table 1b have strong seasonal variations. For the 

emissions from domesticated animals and soils we use exponential dependences on 

temperature reported by Aneja et al. [2000] and Roelle and Aneja [2002], respectively. 

Ammonia emissions from crops and fertilizers are assumed to vary seasonally with the 

number of daylight hours [Adams et al., 1999]. Seasonal variations in biomass burning 

and biofuel emissions are specified from satellite observations [Duncan et al., 2003] and 

the heating degree-days approach [Park et al., 2003], respectively. Figure 1 shows the 

resulting seasonal variation of ammonia emission in the United States. The summer 

maximum is driven mainly by domesticated animals. Compared to the results of the 

Gilliland et al. [2003] inverse model analysis, also shown in Figure 1, our seasonal cycle 

lags in phase by 1-2 months and emission in October is a factor of 2 higher. 

2.4 Nitrate simulation 

Production of total inorganic nitrate (gas-phase nitric acid and aerosol nitrate) in 

the model is computed from the ozone-NOx-hydrocarbon chemical mechanism (see 

section 2.1). Table 1c gives a summary of global and contiguous U.S. NOx emissions; 

details on these sources are in Bey et al. [2001a] and Martin et al. [2002]. The United 

States account for 17% of global emissions (25% of global anthropogenic emissions). 

Natural sources from lightning, soils, and biomass burning account for 38% of global 

emissions and 9% in the contiguous United States. 

2.5 Global budgets 

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Tabulated summaries of the global sulfate, nitrate, and ammonium aerosol 

budgets in GEOS-CHEM are given by Martin et al. [2004], who applied the model to an 

investigation of phase transition effects on aerosol radiative forcing. The global sulfate, 

nitrate and ammonium burdens for 2001 are 0.40 Tg S, 0.07 Tg N, and 0.32 Tg N, 

respectively. The lifetimes against deposition are 3.9, 3.2, and 3.8 days, respectively. Wet 

deposition accounts for 80 - 90% of total deposition. 

Our global sulfate burden is lower than those (0.54-1.03 Tg S) from models that 

participated in the COmparison of large-scale atmospheric Sulfate Aerosol Models 

(COSAM) [Barrie et al., 2001]. Our anthropogenic emission (57 Tg S yr-1) is lower than 

that used in COSAM (67 Tg S yr-1) because our emission inventory is for 1999-2000 (vs. 

1985 in COSAM) and accounts for emission reductions in Europe (by 61% since 1985) 

and the United States (by 22% since 1985). The major natural sulfur sources in our model 

from oceans (15 Tg S yr-1) and volcanoes (5 Tg S yr-1) are also lower than those used in 

COSAM (29 and 10 Tg S yr-1, respectively). These natural sources contribute 

disproportionately to the global atmospheric sulfate burden because their sulfur can be 

delivered efficiently to the free troposphere where precipitation is infrequent [Chin and 

Jacob, 1996]. The lifetime of sulfate in our simulation (3.9 days) is at the low end of the 

3.6 – 7.5 days found in COSAM. 

Our annual average tropospheric ammonium burden (0.32 Tg N) is consistent 

with values from previous model studies (0.30 – 0.33 Tg N) [Dentener and Crutzen, 

1994; Adams et al., 1999] and the lifetime of ammonium is also similar (4.2 – 4.5 days). 

Our annual average tropospheric nitrate burden (0.07 Tg N) is within the range of 0.03 to 

0.09 Tg N found in the previous model study by Adams et al. [1999, 2001]. 

3. Model evaluation

 We focus our model evaluation on surface networks of sulfate-nitrate-ammonium 

aerosol observations in the United States and Europe. We also use sulfate wet deposition 

data in the United States as a test of the sulfur budget, and aircraft observations off the 

Asian Pacific Rim as a test of Asian export. Previous evaluation with sulfate observations 

at remote sites has been presented by Chin et al. [2000b] using the GOCART model 

which is similar to ours. Previous evaluations of GEOS-CHEM with aerosol 

radionuclides globally, and with carbonaceous aerosols in the United States, have been 

presented by Liu et al. [2001] and Park et al. [2003], respectively. Other GEOS-CHEM 

studies have evaluated the simulation of ozone and nitrogen oxides in the United States 

[Fiore et al., 2002, 2003ab; Li et al., 2004] and the transpacific transport of Asian ozone 

and CO pollution [Jaeglé et al., 2003; Heald et al., 2003]. 

3.1 United States 

We use aerosol observations for the year 2001 at 141 IMPROVE and 79 

CASTNET sites, and wet deposition data at 226 NADP sampling sites (Figure 2). The 

IMPROVE monitoring program was initiated in 1987 in national parks and other 

protected environments to identify the contributions of different aerosol components to 

visibility degradation [Malm et al., 1994]. The data for 2001 consist of 24-h sulfate and 

nitrate concentrations measured every third day by Particle Induced X-ray Emission 

(PIXE) and ion chromatography (IC), respectively. There are no ammonium data. The 

CASTNET network of rural sites was initiated in 1990 to monitor regional air pollution 

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[Lavery et al., 2002]. It provides weekly average concentrations of sulfate, nitrate, and 

ammonium measured by IC. The NADP network provides weekly chemical precipitation 

data [NADP, 2002]. Sites are predominantly located in rural areas and away from point 

sources of pollution. Weekly precipitation samples are analyzed for sulfate using IC. 

Figure 3 compares simulated and observed annual mean sulfate concentrations at 

the 141 IMPROVE and 79 CASTNET sites for the year 2001, plotted on the 2o × 2.5o

model grid. Values are higher in the eastern than the western United States and are 

highest in the industrial midwest, reflecting the distribution of anthropogenic emissions. 

Figure 4 shows scatterplots of simulated vs. observed annual and seasonal sulfate 

concentrations for the ensemble of IMPROVE (left) and CASTNET sites (center). The 

right column in Figure 4 compares simulated and observed sulfate precipitation data at 

NADP sites. The correlation between model and observations is high for the annual mean 

values (R2

 = 0.91-0.94 for the concentration data, 0.75 for the deposition data) and also 

for the seasonal means (R2

 = 0.79-0.90 for the concentration data, 0.58-0.74 for the 

deposition data). Western sites in the scatterplots are represented with “+” symbols and 

should be in general most representative of background conditions. The R2 coefficients 

between model and observations for the subset of western sites alone are 0.35-0.39 for 

the annual mean concentrations at the IMPROVE and CASTNET sites, lower than for the 

ensemble of U.S. sites, although this could largely reflect the weaker dynamic range. 

There is no significant bias in the simulation of concentrations at the cleanest western 

sites.

Regression lines are computed here and elsewhere with the reduced major axis 

method, which minimizes the area of the right triangle formed by vertical and horizontal 

lines running from the observed point to the regression line. It is the most appropriate 

linear regression to characterize a relationship between two data sets with uncertainties 

[Hirsch and Gilroy, 1984]. Results in Figure 4 show no significant model bias in the 

simulation of annual mean concentrations (slope = 0.91-0.95) but a 30% low bias in 

summer (slope = 0.71-0.74). Loss of SO2 in convective updrafts accounts for about 50% 

of sulfate wet deposition in summer in the model, and much less in other seasons. Our 

algorithm scavenges SO2 in convective updrafts as a titration reaction limited solely by 

the supply of H2O2 entrained in the updraft. However, kinetic limitations in the aqueousphase reaction of SO2 with H2O2, as well as scavenging of H2O2, can greatly reduce the 

SO2 scavenging efficiency [Mari et al., 2000; Kreidenweis et al, 2003]. Accounting for 

these limitations might correct the model bias but was not attempted here. 

Figure 5 compares simulated and observed annual mean concentrations of 

ammonium at CASTNET sites. Observed concentrations are higher in the east than in the 

west and are highest in the midwest, reflecting agricultural operations. The model 

reproduces this spatial distribution but is too high in the midwest. Scatterplots of 

simulated vs. observed annual and seasonal ammonium concentrations are shown in 

Figure 6 (left column) for the ensemble of sites. The model reproduces the variability of 

observed ammonium concentrations, both in an annual mean sense (R2

 = 0.90) and in 

different seasons (R2

 = 0.82-0.85). It shows a 30% high bias in annual mean 

concentrations which is mainly driven by the fall (slope = 2.0). Comparison with results 

from the Gilliland et al. [2003] inverse model analysis suggests that our ammonia 

emissions are excessive in the fall (Figure 1). It appears that a simple exponential 

temperature dependence of emissions from livestock, as assumed here, does not 

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adequately describe the seasonal variation of this dominant source. For the subset of 

western sites alone the model has a lower R2

 coefficient (0.53) between the simulated and 

observed annual mean concentration than that for the ensemble of U.S. sites but no 

apparent high bias (slope = 1.02). 

Figure 7 compares simulated and observed annual mean nitrate concentrations at 

the 141 IMPROVE and 79 CASTNET sites for 2001. The spatial distribution is similar to 

that of ammonium concentrations in both the observations and the model, reflecting the 

limitation of ammonium nitrate formation by the availability of ammonia as discussed 

further below. The model tends to be too high, by a factor of 2 on an annual basis as 

shown by the scatterplots of Figure 6. Most of the bias is driven by summer and fall. 

Nitrate formation is determined by the availability of ammonia beyond that required for 

sulfate neutralization; we find in a sensitivity analysis that the summer high bias for 

nitrate can be explained in large part by the low bias of sulfate. High nitrate in fall is 

likely caused by excessive ammonia emissions. However, the factor of 2 high bias for the 

simulated annual mean nitrate concentration relative to the observation is not apparent for 

the subset of western sites alone (slope = 1.09 - 1.34). 

Figure 8 shows the simulated Gas Ratio (GR) defined as 

 ][

][2][

3

2

3 4

T

T

HNO

NH SO GR

− − = , (1) 

where concentrations are in molar units, [NH3

T

] is the sum of gas-phase ammonia and 

aerosol-phase ammonium concentrations, [HNO3

T

] is the total inorganic nitrate 

concentration (sum of gas-phase nitric acid and aerosol nitrate), and [SO4

2-] is the sulfate 

concentration. The value of GR diagnoses the limiting reactant (ammonia or nitric acid) 

for the formation of ammonium nitrate [Ansari and Pandis, 1998]. We find that 

ammonium nitrate formation in most of North America is generally limited by the supply 

of ammonia (GR < 1). Exceptions are the upper Midwest and Mexico, where GR > 1 

indicates that nitrate formation is limited by the supply of nitric acid. Negative GR 

values, indicating an acidic sulfate aerosol, are mainly confined to the oceans. This 

neutralization of the aerosol is further illustrated in Figure 9, which compares the 

simulated (top) vs. observed (bottom) acidity of aerosols at CASTNET sites for different 

seasons as the regression slopes of the [NH4

+

] vs. (2[SO4

2-] + [NO3-

]) scatterplots. The 

observations show an annual mean slope of 0.84, i.e., within 16% of neutralization, and 

varying from 0.79 in summer to 0.93 in winter. The higher acidity in summer reflects the 

faster sulfate formation. The model is slightly less acidic than the observations on an 

annual mean basis (slope 0.90) but has the same seasonal trend (0.84 in summer, 0.98 in 

winter). The weaker apparent model acidity reflects at least in part the association of 

sulfate and nitrate in the observations with other alkaline cations (e.g., Ca2+) not included 

in the model. 

3.2 Europe 

Figure 10 compares model results to annual and seasonal mean observations of 

sulfate, nitrate, and ammonium at 93 European EMEP sites in 1998. Sulfate in the model 

reproduces the variability in the observations (R2

 = 0.60 - 0.78) with no systematic bias 

(the regression slope for the annual mean data is 0.98). There is a slight underestimate in 

winter (slope = 0.84), possibly caused by seasalt sulfate included in the EMEP 

observations but not in the model [Chin et al., 2000b; Gong et al., 2002]. Simulated 

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nitrate and ammonium compare less well with observations, with 40-60% overestimates 

of ammonium in summer and fall, likely due to excessive ammonia emission. 

3.3 Asia 

We evaluate our simulation of Asian outflow by using sulfate observations from 

the TRACE-P aircraft mission conducted off the Asian Pacific Rim from bases in Hong 

Kong and Japan during February-April 2001 [Jacob et al., 2003]. Previous applications 

of GEOS-CHEM to simulation of TRACE-P observations for ozone, CO, CO2, CH4, and 

nitriles indicate a good simulation of Asian outflow pathways [Liu et al., 2004; Heald et 

al., 2003; Palmer et al., 2003; Kiley et al., 2003; Suntharalingam et al., 2003; Xiao et al., 

2003; Li et al., 2003]. Bulk aerosol measurements from the DC-8 aircraft indicate that 

40% of non–seasalt sulfate (nss-SO4

2-) on average was incorporated in dust particles 

[Jordan et al., 2003]. Figure 11 compares mean vertical profiles of simulated and 

observed nss-SO4

2- concentrations for the ensemble of DC-8 flights over the NW Pacific 

west of 177oE and at 30o-45oN latitude [Liu et al., 2003]. Monthly mean concentrations in 

the model were sampled along the flight tracks. The observations shows strong outflow in 

the 0-5 km column. The model also shows an enhancement in that column but is lower 

than observations, by up to a factor of two. Targeted sampling of Asian outflow in the 

observations [Jacob et al., 2003] could account for part of this discrepancy. In any case, 

the comparison argues that the model does not overestimate the outflow of sulfate from 

Asia. This is an important point for our later discussion of transpacific pollution 

influence. 

 

4. Background aerosol in the United States: transboundary pollution influence

We now apply our model simulations to quantify background sulfate-nitrateammonium aerosol concentrations in the United States, and to separate the contributions 

to this background from natural sources and from transboundary pollution. We use for 

this purpose a sequence of four sensitivity simulations excluding anthropogenic 

emissions of both oxidant and aerosol precursors (1) globally, (2) in the United States, (3) 

in North America, and (4) in Asia. The results are summarized in Table 2 as annual 

averages for the western (west of 95o

W) and eastern United States. The EPA Regional 

Haze Rule document [EPA, 2003a] recommends “default average natural concentrations 

of ammonium sulfate and ammonium nitrate” in these two regions to serve as 2064 

endpoints for application of the Rule. For purpose of comparison we present our model 

results for sulfate and nitrate in Table 2 as those of the corresponding ammonium salts; in 

the model, almost all of the sulfate and nitrate are indeed associated with ammonium 

(Figure 9). Model results for the sum of natural and transboundary pollution contributions 

do not exactly add up to the independently calculated background concentrations because 

of chemical nonlinearities [Chin and Jacob, 1996; West et al., 1999]. 

Our 2001 base simulation yields annual average concentrations of ammonium 

sulfate and ammonium nitrate of 1.52 and 1.53 μg m-3, respectively, for the western 

United States and 4.11 and 3.26 μg m-3, respectively, for the eastern United States. We 

use the sensitivity simulation with anthropogenic emissions shut off globally to estimate 

natural concentrations. They are 0.11 μg m-3 ammonium sulfate and 0.03 μg m-3

ammonium nitrate for both the western and eastern United States. Our estimate of natural 

concentrations for ammonium sulfate is consistent with the EPA default value (0.12 μg 

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57

m-3) in the west but is factor of 2 lower than that (0.23 μg m-3) in the east. Oxidation of 

DMS is the major natural source of sulfate in the United States in the model. Our 

estimate of natural ammonium nitrate is three times lower than the EPA default value 

(0.1 μg m-3); it is not clear how that default value was obtained. 

Let us now examine the background concentrations from the sensitivity 

simulation including anthropogenic emissions only outside of the United States. The 

mean annual concentrations of background ammonium sulfate and nitrate in surface air 

over the United States are 0.43 and 0.27 μg m-3 for the west and 0.38 and 0.37 μg m-3 for 

the east. These values are several-fold higher than the natural concentrations because of 

the influence from transboundary pollution. Background sulfate is slightly higher in the 

west than the east, because of Asian pollution influence as discussed further below, while 

background nitrate is higher in the east because of Canadian pollution influence. 

We thus find that transboundary pollution influence dominates over natural 

sources in contributing to sulfate and nitrate background concentrations in the United 

States. Transboundary transport of anthropogenic emissions from Canada and Mexico is 

most important for nitrate, but for sulfate transpacific transport of Asian pollution is of 

comparable importance (Table 2). Remarkably, we find that this transpacific pollution 

source accounts for 30% of the sulfate aerosol background in the United States. 

Figure 12a shows the global distribution of Asian pollution influence on sulfatenitrate-ammonium aerosol concentrations in surface air, as determined by difference 

between the standard simulation and the sensitivity simulation with anthropogenic Asian 

emissions shut off. Transpacific transport from Asia to the United States mostly involves 

lifting of Asian air to the free troposphere by wet processes (convection, warm conveyor 

belts), followed by rapid advection in the westerlies and subsidence over the United 

States, generally behind cold fronts. Ammonium aerosol as well as gas-phase ammonia 

are scavenged in this wet lifting and we see therefore that transpacific transport of 

ammonium is negligible. In contrast, significant transpacific transport of sulfate can 

occur as SO2 partly escapes scavenging during lifting [Mari et al., 2000; Koike et al., 

2003; Tu et al., 2003]. Subsidence over the United States takes place mainly in the 

downwelling regions of the west and east, less in the upwelling region in the center of the 

country. Ammonium nitrate as we have seen is largely determined by difference between 

the total ammonium [NH3

T

] and the sulfate concentration, and the preferential export of 

sulfate relative to ammonium from Asia leads to a slight negative effect of Asian 

pollution on nitrate concentrations in the United States. 

It is of interest to compare the transpacific influence of Asian pollution on North 

America to the transatlantic influence of North American pollution on Europe. Figure 

12b shows the latter as the difference between the standard simulation and the sensitivity 

simulation with anthropogenic emissions in North America shut off. We find a sulfate 

enhancement > 0.1 μg m-3 in surface air in Western Europe and northern Africa, 

comparable in magnitude to Asian pollution influence over North America. As in the case 

of Asian pollution, we find that export of ammonium from North American pollution is 

far less efficient than for sulfate, resulting in small negative influences on nitrate aerosol 

concentrations over Europe and Asia. 

We show also in Table 2 the natural and background concentrations of elemental 

carbon (EC) and organic carbon mass (OMC) from our previous work [Park et al., 2003]. 

In that work we derived optimized estimates of individual EC and OMC sources by 

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58

fitting model results to observations from the IMPROVE sites. We concluded that the 

EPA default natural estimates were a factor of 3 too low in the west due to underestimate 

of wildfire influences. Our values for the east were more consistent with EPA. In contrast 

to sulfate and nitrate, transboundary transport of anthropogenic carbonaceous aerosols is 

insignificant relative to the large natural influences from wildfires and vegetation. We 

further find that transpacific transport of carbonaceous aerosols from Asian pollution is 

less efficient than for sulfate because of scavenging in the wet lifting processes involved 

in Asian outflow. The excess of SO2 over H2O2 in the Asian outflow allows part of the 

sulfur to escape scavenging [Koike et al., 2003; Tu et al., 2003]. This result is consistent 

with Jaffe et al. [2003] who found a larger increase in sulfate concentrations relative to 

carbonaceous aerosol at three IMPROVE sites in the western United States during a 

transpacific transport event of Asian pollution. 

5. Policy implications: the Regional Haze Rule 

The U.S. EPA Regional Haze Rule [U.S. EPA, 2003a] requires states to develop 

plans for achieving natural visibility conditions in national parks and other wilderness 

areas by 2064. Visibility degradation is measured by the deciview index 

 dv = 10 ln (bext/10) , (2) 

where bext is atmospheric light extinction in units of inverse megameters (Mm-1=10-6m-1). 

In the phase 1 implementation of the Regional Haze Rule, states have to show how they 

will decrease anthropogenic emissions over the 2004-2018 period in order to achieve a 

linear trajectory of decreasing deciviews towards the natural visibility endpoint of 2064. 

A linear decrease in deciviews implies an exponential decrease in aerosol extinction; as a 

result, and as we will see, the definition of the 2064 endpoint has important implications 

for determining the level of emission controls required during the 2004-2018 phase 1 

implementation. 

The EPA Regional Haze Rule document [U.S. EPA, 2003a] recommends a simple 

formula to estimate aerosol extinction by using dry mass concentrations of individual 

aerosol components (ammonium sulfate, ammonium nitrate, OMC, EC, soil dust, and 

coarse mass (CM)), as follows: 

 10][6.0][][10][4

424 34 ])[(3]))[((3

+++++

= +

OMC soilEC CM

NONHRHfSONHRHf extb

, (3) 

where bext is in units of Mm-1, aerosol concentrations are in units of μg m-3, and f(RH) is 

a correction factor for hygroscopic growth as a function of relative humidity (RH). The 

constant of 10 Mm-1 describes the scattering by air molecules and is such that an aerosolfree atmosphere would have a deciview index of zero. “Soil” in equation (3) is the fine 

component of soil dust (diameter < 2.5 μm) and “coarse mass” is the total mass of 

particles with diameter > 2.5 μm, mostly contributed by dust and sea salt. Recommended 

values of f(RH) for individual wilderness areas are given in the Regional Haze Rule 

document [U.S. EPA, 2003a]. In what follows we use typical f(RH) values of 2 and 3 for 

the west and east, respectively. 

Applying equation (3) to aerosol concentrations given in Table 2, and assuming 

EPA natural default values for fine soil dust (0.5 μg m-3) and coarse mass (3.0 μg m-3), 

we compute deciview index values for baseline (present-day) conditions and for different 

definitions of the 2064 natural or background visibility endpoint. We use the results 

(Table 3) to estimate the implications of our results for phase 1 (2004-2018) 

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59

implementation of the Regional Haze Rule. Under the EPA Regional Haze Rule, the 

linear improvement in visibility is to be applied to the 20% most impaired visibility days 

and at specific locations. Here we use visibility calculated from annual mean aerosol 

extinctions averaged over the western and eastern United States. Nevertheless, the results 

serve to illustrate the sensitivity of the required 2004-2018 emission controls to the 

choice of 2064 endpoint. 

Table 3 gives a baseline (current) visibility degradation of 14 and 23 deciviews 

for the western and the eastern United States, respectively. The natural visibility 

degradation is 6.3 and 6.2 deciviews, respectively. Organic aerosols are the dominant 

contributors to natural visibility degradation. Our estimate for natural visibility 

degradation as expressed in deciviews is 37% higher in the west and 17% lower in the 

east than the values computed using the natural default aerosol concentrations 

recommended by EPA (4.6 and 7.5 deciviews). We have previously argued in Park et al.

[2003] that the EPA natural default concentrations underestimate the influence of 

wildfires in the west. 

Figure 13 shows the trajectories of linear visibility improvement towards a 2064 

endpoint of natural visibility defined either from our results (dashed line) or from the 

EPA defaults (dotted line). Following these trajectories, we find that visibility 

degradation during the 2004-2018 phase 1 implementation of the Regional Haze Rule 

should be reduced by 1.8 deciviews (west) and 3.9 deciviews (east) if our estimate of the 

2064 natural visibility endpoint is used, and by 2.2 deciviews (west) and 3.6 deciviews 

(east) if the EPA default endpoint is used. The corresponding reductions in light 

extinction are 6.7 Mm-1 (west) and 32.2 Mm-1 (east) if our estimate of the 2064 natural 

visibility endpoint is used, and 8.1 Mm-1 (west) and 30.1 Mm-1 (east) if the EPA default 

endpoint is used. 

Let us now estimate the required percentage reductions in U.S. anthropogenic 

emissions needed to achieve such improvements in light extinction. We assume a linear 

correspondence between aerosol extinction, aerosol concentrations, and emissions. The 

current aerosol extinction from U.S. anthropogenic emissions can be calculated from the 

data in Table 2 by subtracting the background from the baseline aerosol concentrations, 

and applying equation (3). We obtain values of 18.1 Mm-1 in the west and 73 Mm-1 in the 

east. The resulting percentage decreases of U.S. anthropogenic emissions over 2004-2018 

are shown in Figure 13. They are 37% and 44% for the western and eastern United States, 

respectively, using our natural visibility endpoint, and 44% and 41%, respectively, using 

the EPA natural visibility endpoint. 

These differences are significant, but looking beyond the 2018 horizon exposes a 

more fundamental problem with the Regional Haze Rule. Continued linear decrease 

towards a 2064 natural visibility endpoint would require total shutdown of U.S. 

anthropogenic emissions by 2041-2049 (west) or 2053-2058 (east), as shown in Figure 

13. Because of transboundary pollution (assumed here to be unchanged in the future), 

natural visibility cannot be achieved even with total suppression of U.S. anthropogenic 

emissions. It will be therefore necessary to either impose emission controls on an 

international level or to amend the 2064 endpoint to allow for uncontrollable 

transboundary pollution influences. Such an amendment should define the 2064 endpoint 

as a background rather than natural visibility. One would then have to make estimates of 

future trends in foreign emissions. 

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However, amendment of the Regional Haze Rule to target a background visibility 

endpoint has major implications for phase 1 (2004-2018) emission controls in the west. 

Using the background deciview values in Table 3 as 2064 endpoint, the required 2004-

2018 decrease in visibility degradation is 1.4 deciviews (west) and 3.3 deciviews (east). 

The corresponding percentage decrease of U.S. anthropogenic emissions in the west is 

29%, much lower than 37% if a natural visibility endpoint from our results is used or 

44% if the natural visibility endpoint from the EPA defaults is used. 

6. Conclusions

We used a global 3-D coupled oxidant-aerosol model (GEOS-CHEM) to quantify 

natural and transboundary pollution influences on sulfate-nitrate-ammonium aerosol 

concentrations in the United States. The U.S. EPA Regional Haze Rule requires 

immediate action to improve visibility in U.S. national parks and other wilderness areas 

along a linear trajectory towards an endpoint of “natural visibility conditions” by 2064. 

We need to better quantify the natural aerosol concentrations defining this natural 

visibility, and to determine if transboundary transport of pollution not amenable to 

domestic emission controls elevates background aerosol concentrations in the United 

States significantly above the natural values. If they do, then the Regional Haze Rule 

must either involve international emission controls or be amended to an endpoint of 

“background” as opposed to “natural” visibility. “Background” is defined here following 

EPA [2003b] as the aerosol concentrations that would be present in the absence of U.S. 

anthropogenic emissions, but allowing for contributions from transboundary pollution. 

We conducted full-year simulations for 1998 and 2001. Results were evaluated 

with observations from surface networks in the United States and Europe (IMPROVE, 

CASTNET, NADP, EMEP) and with Asian outflow observations from the NASA 

TRACE-P aircraft mission over the northwest Pacific. The model reproduces well the 

spatial pattern and variability of sulfate observations in the United States and Europe 

across all seasons, with no systematic biases. Comparison with the TRACE-P 

observations indicates that Asian outflow of sulfate is if anything underestimated. Nitrate 

and ammonium aerosol concentrations in the model are highly correlated with 

observations but are too high in summer and fall, a problem that we attribute to seasonal 

overestimate of ammonia emissions [Gilliland et al., 2003]. We find that the availability 

of ammonia limits the formation of ammonium nitrate in most of North America. The 

aerosol is typically 80-100% neutralized, both in the model and in the observations, with 

maximum acidity in summer. 

We used a sequence of sensitivity simulations to quantify background sulfatenitrate-ammonium aerosol concentrations in the United States, and to separate the 

contributions to this background from natural sources and from transboundary pollution. 

Our 2001 base simulation yields annual average concentrations of ammonium sulfate and 

ammonium nitrate of 1.52 and 1.53 μg m-3, respectively, for the western United States 

and 4.11 and 3.26 μg m-3, respectively, for the eastern United States. Our best estimates 

of mean annual natural concentrations are 0.11 μg m-3 ammonium sulfate and 0.03 μg m-3

ammonium nitrate for both the western and eastern United States. Our values are 

consistent with or lower than the default values recommended by EPA for natural 

visibility calculations in the context of the Regional Haze Rule. 

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Our best estimates of background concentrations for ammonium sulfate and 

ammonium nitrate are 0.43 and 0.27 μg m-3 for the west and 0.38 and 0.37 μg m-3 for the 

east. These values are considerably higher than the natural concentrations, pointing to the 

dominance of transboundary pollution in defining the background. Transpacific transport 

of Asian pollution is of comparable importance to transport from Canada and Mexico in 

contributing to the background sulfate enhancement over the United States. A significant 

enhancement of sulfate relative to other aerosols in the Asian outflow can occur as SO2

partly escapes scavenging during wet lifting processes. In the case of ammonium nitrate, 

the transboundary pollution enhancement is mostly from Canada, and transpacific Asian 

pollution actually causes a slight depression (less than 0.1 μg m-3) due to the added 

sulfate. 

We assessed the implications of our results for implementation of the Regional 

Haze Rule. For this purpose we used our model to define the linear trend of visibility 

from present (2004) to natural or background (2064) conditions. We found that 

transboundary pollution prevents natural visibility from being achieved even with total 

suppression of U.S. anthropogenic emissions, implying the need for either international 

emission controls or for amendment of the 2064 endpoint to allow for uncontrollable 

transboundary pollution influences. The latter would require some estimates of future 

trends in transboundary pollution influences but these have large uncertainties. 

Projections by IPCC [2001] for 2060 anthropogenic sulfur emissions from Asia range 

from 30% to 160% of present-day levels depending on the socioeconomic scenario. 

Consideration of a background rather than natural visibility 2064 endpoint would have 

immediate implications for phase 1 implementation (2004-2018) of the Regional Haze 

Rule. It would imply, at least in the west, a significantly slower schedule of U.S. 

anthropogenic emission reductions. 

Our results are only a first attempt to quantify natural and transboundary pollution 

influences in the United States using a global 3-D model analysis. In future work we plan 

to examine in more detail the observational constraints on aerosol background 

concentrations in the United States, including site-by-site analysis and frequency 

distributions of aerosol concentrations. Specification of natural and background aerosol 

concentrations for regulatory purposes will require formal uncertainty bounds to be 

placed on model estimates, and again this will require more extensive evaluation with 

observations as well as higher-resolution simulations with a nested regional model. 

Acknowledgments. This work was supported by the Electric Power Research 

Institute (EPRI) and by the EPA Intercontinental transport and Climatic effects of Air 

Pollutants (ICAP) program. Development of the GEOS-CHEM model is supported by the 

Atmospheric Chemistry Modeling and Analysis Program (ACMAP) of the National 

Aeronautics and Space Administration (NASA).

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transport of air pollutants, Atmos. Environ., 37(3), 391-404, 2003. 

Jordan, C. E., J. E. Dibb, B. E. Anderson, and H. E. Fuelberg, Uptake of nitrate and 

sulfate on dust aerosols during TRACE-P, J. Geophys. Res., 108(D21), 8817, 

doi:10.1029/2002JD003101, 2003. 

Kettle, A. J., et al., A global database of sea surface dimethylsulfide (DMS) 

measurements and a procedure to predict sea surface DMS as a function of latitude, 

longitude, and month, Glob. Biogeochem. Cycle, 13(2), 399-444, 1999. 

Koike, M., et al., Expor of anthropogenic reactive nitrogen and sulfur compounds from 

the East Asia region in spring, J. Geophys. Res., 108(D20), 8789, 

doi:10.1029/2002JD003284, 2003. 

Kreidenweis S. M., C. J. Walcek, G. Feingold, W. Gong, M. Z. Jacobson, C.-H. Kim, X. 

Liu, J. E. Penner, A. Nenes, and J. H. Seinfeld, Modification of aerosol mass and size 

distribution due to aqueous-phase SO2 oxidation in clouds: Comparisons of several 

models, J. Geophys. Res., 108(D7), 4213, doi:10.1029/2002JD002697, 2003. 

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Geophys. Res., 108(D21), 8819, doi:10.1029/2002JD003089, 2003. 

Lavery, T. F., C. M. Rogers, H. Kemp Howell, M. C. Burnett, C. A. Wanta, M. O. 

Stewart, Clean Air Status and Trends Network (CASTNet) 2001 Annual Report, 

2002. 

Li, Q., D. J. Jacob, J. A. Logan, I. Bey, R. M. Yantosca, H. Liu, R. V. Martin, A. M. 

Fiore, B. D. Field, B. N. Duncan, and V. Thouret, A tropospheric ozone maximum 

over the Middle East, Geophys. Res. Lett., 28, 3235-3238, 2001. 

Li, Q., D. J. Jacob, I. Bey, P. I. Palmer, B. N. Duncan, B. D. Field, R. V. Martin, A. M. 

Fiore, R. M. Yantosca, D. D. Parrish, P. G. Simmonds, and S. J. Oltmans, 

Transatlantic transport of pollution and its effects on surface ozone in Europe and 

North America, J. Geophys. Res., 107(D13), doi:10.1029/2001JD001422, 2002a. 

Li, Q., D. J. Jacob, T. D. Fairlie, H. Liu, R. V. Martin, and R. M. Yantosca, Stratospheric 

versus pollution influences on ozone at Bermuda: Reconciling past analyses, J. 

Geophys. Res., 107(D22), 4611, doi:10.1029/2002JD00213, 2002b. 

Li Q., D. J. Jacob, R. Yantosca, C. Heald, H. Singh, M. Koike, Y. Zhao, G. W. Sachse, 

and D. Streets, A global three-dimensional model analysis of the atmospheric budgets 

of HCN and CH3CN: constraints from aircraft and ground measurements, J. 

Geophys. Res., 108(D21), 8827, doi:10.1029/2002JD003075, 2003. 

Li, Q. B., D. J. Jacob, R. M. Yantosca, J. W. Munger, and D. D. Parrish, Export of NOy 

from the North American Boundary Layer: Reconciling Aircraft Observations and 

Global Model Budgets, J. Geophys. Res., 109(D2), D02109, 10.1029/2003JD003912, 

2004. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 64 

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65

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Liu, H., D. J. Jacob, I. Bey, and R. M. Yantosca, Constraints from 210Pb and 7

Be on wet 

deposition and transport in a global three-dimensional chemical tracer model driven 

by assimilated meteorological fields, J. Geophys. Res., 106, 12,109-12,128, 2001. 

Liu, H., D. J. Jacob, L. Y. Chan, S. J. Oltmans, I. Bey, R. M. Yantosca, J. M. Harris, B. 

N. Duncan, and R. V. Martin, Sources of tropospheric ozone along the Asian Pacific 

Rim: An analysis of ozonesonde observations, J. Geophys. Res., 106, 

10.1029/2001JD002005, 2002. 

Liu, H., D. J. Jacob, I. Bey, R. M. Yantosca, B. N. Duncan, and G. W. Sachse, Transport 

pathways for Asian pollution outflow over the Pacific: Interannual and seasonal 

variations, J. Geophys. Res., 108(D20), 8786, doi:10.1029/2002JD003102, 2003. 

Liu, H., D. J. Jacob, J. E. Dibb, A. M. Fiore, and R. M. Yantosca, Constraints on the 

sources of tropospheric ozone from 210Pb-7Be-O3 correlations, J. Geophys. Res., in 

press, 2004. 

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seasonal trends in particle concentration and optical extinction in the United States, J. 

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Gebhart, and D. E. Day, Spatial and seasonal Patterns and Temporal Variability of 

Haze and Its consituents in the United States: Report III, Cooperative Institute for 

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deep convective cloud, J. Geophys. Res., 105(D17), 22255-22267, 2000. 

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observations of tropical tropospheric ozone with a global model and in-situ 

observations, J. Geophys. Res., 107(D18), 4351, 10.1029/2001JD001480, 2002. 

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aircraft observations over the western Pacific, J. Geophys. Res., 108(D21), 8828, 

doi:10.1029/2003JD003397, 2003. 

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the United States and implications for natural visibility, J. Geophys. Res., 108(D12), 

4355, doi:10.1029/2002JD003190, 2003. 

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Chinese carbon fluxes using CO2/CO correlations in Asian outflow, J. Geophys. Res., 

submitted, 2003. 

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Yantosca, G. W. Sachse, D. R. Blake, D. G. Streets, Constraints on Asian and 

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waste in the developing world, Global Biogeochem. Cycles, 17(4), 1095, 

doi:10.1029/2002GB001952, 2003. 

 

Figures Captions 

Figure 1. Monthly ammonia emissions in the contiguous United States. The values used 

in this work, broken down by source type (left bars), are compared to the values reported 

by Gilliland et al. [2003] (right bars) from inverse modeling of eight months of 

precipitation NH4

+

 data. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 66 

December 17, 2007
67

Figure 2. Sampling sites from the I[M]PROVE, [C]ASTNET, and NA[D]P networks in 

2001. 

Figure 3. Annual mean concentrations of sulfate in surface air over the United States in 

2001. The top panel shows results from the GEOS-CHEM model. The middle and bottom 

panels show the observations from the IMPROVE and CASTNET networks, 

respectively, averaged over the model 2o

 × 2.5o grid. 

Figure 4. Scatterplot of simulated versus observed sulfate concentrations at the 

IMPROVE and CASTNET sites, and sulfate deposition fluxes at NADP sites (Figure 2). 

Values are annual means (top panels) and seasonal means for 2001. Sites in the western 

and eastern United States (separated at 95o

W) are shown as pluses and open circles, 

respectively. Thick solid lines are reduced major axis regressions for the ensemble of the 

data; regression equations and R2

 are shown inset. Thin solid lines show the y=x 

relationship. 

Figure 5. Annual mean concentrations of ammonium in surface air over the United States 

in 2001. The top panel shows results from the GEOS-CHEM model. The bottom panel 

shows the observations from the CASTNET networks averaged over the model 2o

 × 2.5o

grid (ammonium is not measured at the IMPROVE sites). 

Figure 6. Scatterplot of simulated versus observed ammonium concentrations at the 

CASTNET sites (left column), and nitrate concentrations at the CASTNET and 

IMPROVE sites (right two columns). Values are annual means (top panels) and seasonal 

means for 2001. Sites in the western and eastern United States (separated at 95o

W) are 

shown as pluses and open circles, respectively. Thick solid lines are reduced major axis 

regressions for the ensemble of the data; regression equations and R2

 are shown inset. 

Thin solid lines show the y=x relationship. 

Figure 7. Same as in Figure 3 but for nitrate. 

Figure 8. Simulated gas ratio (GR; equation (1)) defined as the available ammonia 

concentration beyond that required for sulfate neutralization, divided by the total 

inorganic nitrate concentration (gas + aerosol) [Ansari and Pandis, 1998]. Values are 

computed from annual mean concentrations in surface air. Formation of ammonium 

nitrate aerosol is limited by the availability of nitric acid if GR > 1, by the availability of 

ammonia if 0 < GR < 1, and is totally suppressed if GR < 0. 

Figure 9. Scatterplot of seasonal mean [NH4

+

] vs. (2[SO4

2-]+[NO3-

]) at CASTNET sites 

in 2001, in the GEOS-CHEM model (top) and in observations (bottom). The reducedmajor–axis regression slopes (given on the Figure) indicate the degree of acid 

neutralization. 

Figure 10. Scatterplot of simulated versus observed sulfate (left), nitrate (middle) and 

ammonium (right) concentrations at 93 European EMEP sites. Values are annual means 

(top panels) and seasonal means for 1998. Thick solid lines are reduced major axis 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 67 

December 17, 2007
68

regressions for the ensemble of the data; regression equations and R2

 are shown inset. 

Thin solid lines show the y=x relationship. 

Figure 11. Simulated vs. observed mean vertical profiles of non-sea-salt sulfate (nssSO4

2-) concentrations over the NW Pacific from the TRACE-P aircraft mission in 

February-April 2001. The observations are binned vertically in 1-km intervals. The solid 

line shows mean observed values from Jordan et al. [2003] for the ensemble of DC-8 

flights north of 30o

N (30-45oN, 124-177oE), with standard deviations represented by 

horizontal bars. The dashed line shows the corresponding monthly mean model values 

along the flight tracks. 

Figure 12a. Enhancements of sulfate-nitrate-ammonium aerosol concentrations in surface 

air due to anthropogenic emissions from Asia. Values are annual means for 2001 and 

were obtained by difference between the standard model simulation and a sensitivity 

simulation with Asian anthropogenic sources shut off. 

Figure 12b. Same as in Figure 12a but for anthropogenic emissions from North America. 

Figure 13. Illustrative example of required visibility improvements (top) and domestic 

emission reductions (bottom) over the 2004-2064 period for the western and the eastern 

United States (separated at 95o

W) under the EPA Regional Haze Rule [U.S. EPA, 2003a]. 

The visibility endpoints are as given in Table 3. The required percentage decrease in U.S. 

anthropogenic emissions corresponding to a given visibility improvement is computed by 

assuming a linear correspondence between aerosol extinction and emissions. Results are 

shown for different choices for the 2064 endpoint: (1) EPA natural default visibility 

(dotted lines), (2) our estimate of natural visibility (dashed lines), and (3) our estimate of 

background visibility (solid lines). Background includes contributions from both natural 

and transbounary pollution sources. Year 2018 (thin vertical line) is the target date for 

phase 1 implementation of the Regional Haze Rule. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 68 

December 17, 2007
69

Tables 

Table 1a. Sulfur emissions for 2001 (Tg S yr-1). 

Source type Globe Contiguous 

United States 

Total 78 8.3 

 Anthropogenic source total 57 8.3 

 Industrial activity 56 8.3 

 Biofuel use 0.27 < 0.01 

 Aircraft 0.07 0.02 

 Natural source total 21 0.01 

 Ocean (DMS) 15 0 

 Volcanoes 4.8 0 

 Biomass burning 1.3 0.01 

Table 1b. Ammonia emissions for 2001 (Tg N yr-1). 

Source type Globe Contiguous 

United States

Total 55 2.8 

 Anthropogenic source total 35 2.2 

 Domesticated animals 21 1.3 

 Fertilizers 9.0 0.49 

 Human bodies 2.6 0.13 

 Biofuel use 1.6 0.18 

 Industry 0.2 0.03 

 Fossil fuel use 0.1 0.06 

 Natural source total 20 0.59 

 Ocean 8.2 0 

 Biomass burning 5.9 0.04 

 Crop 3.5 0.44 

 Soil 2.4 0.1 

 Wild animals 0.1 0.01 

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Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 69 

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70

Table 1c. NOx emissions for 2001 (Tg N yr-1). 

Source type Globe Contiguous 

United States 

Total 43 7.4 

 Anthropogenic source total 27 6.8 

 Fossil fuel use 24 6.7 

 Biofuel use 2.2 0.02 

 Fertilizer 0.47 0.07 

 Natural source total 17 0.66 

 Biomass burning 6.5 0.05 

 Natural soil 5.3 0.36 

 Lightning 4.7 0.25 

Table 2. Background aerosol concentrations (μg m-3) in the United Statesa

. 

 Ammonium 

sulfate 

Ammonium 

nitrate 

Elemental 

carbon 

Organic 

carbon mass 

West East West East West East West East 

Baseline (2001) 1.52 4.11 1.53 3.26 0.27 0.66 1.77 3.07 

Background 0.43 0.38 0.27 0.37 0.08 0.06 1.30 1.22 

 Natural 0.11 0.11 0.03 0.03 0.06 0.04 1.25 1.17 

 Transboundary 

 pollution 

0.28 0.26 0.18 0.23 0.02 0.02 0.05 0.05 

 Canada and 

 Mexico 

0.15 0.14 0.2 0.25 0.02 0.02 0.04 0.04 

 Asia 0.13 0.12 -0.02 -0.02 <0.01 <0.01 0.01 <0.01

EPA natural 

defaultsb

0.12 0.23 0.1 0.1 0.02 0.02 0.47 1.40 

 aValues are annual and spatial means from the standard 2001 simulation (baseline) and 

from the sensitivity simulations described in section 2.1. Partitioning between west and 

east is at 95o

W. Background and natural concentrations are obtained from the sensitivity 

simulations without U.S. and global anthropogenic emissions, respectively. 

Transboundary pollution influences from Canada and Mexico are determined by 

difference between two sensitivity simulations with anthropogenic emissions shut off in 

the United States versus in all of North America. Transpacific pollution influences from 

Asia are determined by difference between the standard simulation and the sensitivity 

simulation with anthropogenic sources shut off in Asia. Results for elemental carbon 

(EC) and organic carbon mass (OMC) are from our previous work [Park et al., 2003] in a 

simulation using climatological emissions from wildfires. 

 b

”Default average natural concentrations” recommended by U.S. EPA [2003a] for 

estimating natural visibility conditions as 2064 endpoint in the application of the EPA 

Regional Haze Rule.

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 70 

December 17, 2007
71

Table 3. Visibility degradation (deciviews) in the United Statesa

. 

 West East 

Baseline (2001) 14 23 

Backgroundb

Natural 

 This work 

 EPA default 

8.1 

6.3 

4.6 

9.0 

6.2 

7.5 

 a

Visibility degradation in deciviews (equation (2)) calculated from mean annual 

aerosol extinction as given by equation (3). Aerosol concentrations for use in equation (3) 

are from Table 2, with in addition EPA default natural values for soil (0.5 μg m-3) and 

coarse mass (CM) (3.0 μg m-3). Values of f(RH) in equation (3) are 2 in the west and 3 in 

the east. 

 b

Including contributions to visibility degradation from both natural and transboundary 

pollution sources. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 71 

December 17, 2007
72

Figures 

Figure 1. Monthly ammonia emissions in the contiguous United States. The values used 

in this work, broken down by source type (left bars), are compared to the values reported 

by Gilliland et al. [2003] (right bars) from inverse modeling of eight months of 

precipitation NH4

+

 data. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 72 

December 17, 2007
73

Figure 2. Sampling sites from the I[M]PROVE, [C]ASTNET, and NA[D]P networks in 

2001. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 73 

December 17, 2007
74

Figure 3. Annual mean concentrations of sulfate in surface air over the United States in 

2001. The top panel shows results from the GEOS-CHEM model. The middle and bottom 

panels show the observations from the IMPROVE and CASTNET networks, 

respectively, averaged over the model 2o

 × 2.5o grid. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 74 

December 17, 2007
75

Figure 4. Scatterplot of simulated versus observed sulfate concentrations at the 

IMPROVE and CASTNET sites, and sulfate deposition fluxes at NADP sites (Figure 2). 

Values are annual means (top panels) and seasonal means for 2001. Sites in the western 

and eastern United States (separated at 95o

W) are shown as pluses and open circles, 

respectively. Thick solid lines are reduced major axis regressions for the ensemble of the 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 75 

December 17, 2007
76

data; regression equations and R2

 are shown inset. Thin solid lines show the y=x 

relationship. 

Figure 5. Annual mean concentrations of ammonium in surface air over the United States 

in 2001. The top panel shows results from the GEOS-CHEM model. The bottom panel 

shows the observations from the CASTNET networks averaged over the model 2o

 × 2.5o

grid (ammonium is not measured at the IMPROVE sites). 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 76 

December 17, 2007
77

Figure 6. Scatterplot of simulated versus observed ammonium concentrations at the 

CASTNET sites (left column), and nitrate concentrations at the CASTNET and 

IMPROVE sites (right two columns). Values are annual means (top panels) and seasonal 

means for 2001. Sites in the western and eastern United States (separated at 95o

W) are 

shown as pluses and open circles, respectively. Thick solid lines are reduced major axis 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 77 

December 17, 2007
78

regressions for the ensemble of the data; regression equations and R2

 are shown inset. 

Thin solid lines show the y=x relationship. 

Figure 7. Same as in Figure 3 but for nitrate. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 78 

December 17, 2007
79

Figure 8. Simulated gas ratio (GR; equation (1)) defined as the available ammonia 

concentration beyond that required for sulfate neutralization, divided by the total 

inorganic nitrate concentration (gas + aerosol) [Ansari and Pandis, 1998]. Values are 

computed from annual mean concentrations in surface air. Formation of ammonium 

nitrate aerosol is limited by the availability of nitric acid if GR > 1, by the availability of 

ammonia if 0 < GR < 1, and is totally suppressed if GR < 0. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 79 

December 17, 2007
80

Figure 9. Scatterplot of seasonal mean [NH4

+

] vs. (2[SO4

2-]+[NO3-

]) at CASTNET sites 

in 2001, in the GEOS-CHEM model (top) and in observations (bottom). The reducedmajor–axis regression slopes (given on the Figure) indicate the degree of acid 

neutralization. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 80 

December 17, 2007
81

Figure 10. Scatterplot of simulated versus observed sulfate (left), nitrate (middle) and 

ammonium (right) concentrations at 93 European EMEP sites. Values are annual means 

(top panels) and seasonal means for 1998. Thick solid lines are reduced major axis 

regressions for the ensemble of the data; regression equations and R2

 are shown inset. 

Thin solid lines show the y=x relationship. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 81 

December 17, 2007
82

Figure 11. Simulated vs. observed mean vertical profiles of non-sea-salt sulfate (nssSO4

2-) concentrations over the NW Pacific from the TRACE-P aircraft mission in 

February-April 2001. The observations are binned vertically in 1-km intervals. The solid 

line shows mean observed values from Jordan et al. [2003] for the ensemble of DC-8 

flights north of 30o

N (30-45oN, 124-177oE), with standard deviations represented by 

horizontal bars. The dashed line shows the corresponding monthly mean model values 

along the flight tracks. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 82 

December 17, 2007
83

Figure 12a. Enhancements of sulfate-nitrate-ammonium aerosol concentrations in surface 

air due to anthropogenic emissions from Asia. Values are annual means for 2001 and 

were obtained by difference between the standard model simulation and a sensitivity 

simulation with Asian anthropogenic sources shut off. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 83 

December 17, 2007
84

Figure 12b. Same as in Figure 12a but for anthropogenic emissions from North America. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 84 

December 17, 2007
85

Figure 13. Illustrative example of required visibility improvements (top) and domestic 

emission reductions (bottom) over the 2004-2064 period for the western and the eastern 

United States (separated at 95o

W) under the EPA Regional Haze Rule [U.S. EPA, 2003a]. 

The visibility endpoints are as given in Table 3. The required percentage decrease in U.S. 

anthropogenic emissions corresponding to a given visibility improvement is computed by 

assuming a linear correspondence between aerosol extinction and emissions. Results are 

shown for different choices for the 2064 endpoint: (1) EPA natural default visibility 

(dotted lines), (2) our estimate of natural visibility (dashed lines), and (3) our estimate of 

background visibility (solid lines). Background includes contributions from both natural 

and transbounary pollution sources. Year 2018 (thin vertical line) is the target date for 

phase 1 implementation of the Regional Haze Rule. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas 

 Appendix G.1 - 85 

December 17, 2007
Appendix G.2 

International Emissions Impact 
Accounting for Transport of International Emissions in 

CMAQ Modeling of the VISTAS Region 

26 July 2007 

Anthropogenic and natural aerosols from emissions in Mexico, Canada, Central America, 

Asia, and Africa have been shown to affect air quality and regional haze in the 

continental 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 support the 

VISTAS states in evaluating the contribution from international emissions, the VISTAS 

RPO carried out analyses to estimate how much of the haze at Class I areas in the 

VISTAS region in 2002 and 2018 (as projected by the CMAQ model) results from 

international emissions. 

CMAQ modeling of regional haze in the VISTAS region for 2002 and 2018 was carried 

out on a grid of 12x12-km cells that covers the VISTAS states (AL, GA, FL, MS, SC, 

NC, TN, KY, VA, WV) and states adjacent to them. This grid is nested within a larger 

national CMAQ modeling grid of 36x36-km grid cells that covers the continental United 

States, portions of Canada and Mexico, and portions of the Atlantic and Pacific Oceans 

along the east and west coasts. Transport of pollution from the national domain into the 

VISTAS modeling domain is reflected in hourly-varying boundary conditions around the 

VISTAS domain, which are derived from the CMAQ simulations in the 36-km national 

domain. The national domain incorporates emissions from northern Mexico and southern 

Canada, and therefore explicitly calculates the effects of these international emissions on 

concentrations at the boundaries of the VISTAS modeling domain. 

Effects of emissions from more distant sources that are outside of the 36-km national 

domain were estimated using outputs from a global air pollution model. The Big Bend 

Regional Aerosol and Visibility Observational (BRAVO) study had found that the 

performance of national-scale modeling was enhanced by using monthly averages of 

outputs from a global air pollution model, GOCART, to generate SO2 and sulfate 

boundary conditions for its 36-km national modeling grid (Schichtel, et al., 2005), instead 

of using the EPA default concentrations that are normally used. VISTAS elected to use a 

similar approach, using the outputs of the GEOS-Chem global air pollution model, which 

also contains a detailed representation of ozone-NOx-VOC-PM chemistry. 

Under EPRI sponsorship, Harvard University had already used the GEOS-Chem model, 

with 2°x2.5° horizontal resolution, to estimate natural conditions and transboundary 

influences on elemental and organic carbon aerosols in the United States in 1998 (Park et 

al., 2003). After some emission inventory and model improvements, similar information 

was developed for sulfate and nitrate aerosols in 2001 (Park et al., 2004). 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 1 

December 17, 2007
2

Under contract to VISTAS (which was acting on behalf of all the RPOs), Harvard then 

simulated global air pollution for the year 2002 (the VISTAS modeling year) with 4°x5°

horizontal resolution and 3-hour temporal resolution (Jacob, 2005). The coarser grid scale 

was considered sufficient to provide boundary conditions outside of North America, 

largely over the oceans. Although the VISTAS simulation was generally similar to the 

2001 EPRI simulation mentioned above, the VISTAS modeling incorporated the 

following substantive changes in addition to the change in grid scale: (1) use of U.S. 

anthropogenic emissions from the EPA NEI 1999 inventory, except for ammonia; (2) use 

of forest fire information specific to 2002; (3) inclusion of the secondary organic aerosol 

(SOA) formation mechanism from Chung and Seinfeld (2002); and (4) inclusion of 

prototype soil dust and sea salt simulations. An additional model improvement, which has 

since been implemented in the standard version of GEOS-Chem, was the application of 

surface emissions and dry deposition to the entire mixed layer column diagnosed by 

GEOS (the Global Earth Observation System of NASA) rather than to just the surface 

layer of the model. Details of the emissions, meteorology, model configuration, and 

model performance evaluation for the VISTAS simulations are described in the report to 

VISTAS (Jacob, 2005). 

For North America, the VISTAS GEOS-Chem modeling used monthly-average 

emissions, so the model results do not reflect diurnal or weekday-weekend variability in 

emissions, and also diminish the impacts of episodic emissions, such as the July 2002 

wildfire in Ontario, Canada. The Mexican emissions were from the inventory of the 

BRAVO Study, while Canadian emissions for the year 2000 were used. Emissions from 

Caribbean islands were not included, which could impact model estimates at the 

Everglades. 

The surface meteorological data inputs to GEOS-Chem had 3-hr temporal resolution and 

the upper air meteorological inputs had 6-hr resolution, with a horizontal resolution of 

1°x1° or better and 48 layers vertically. 

The output of the GEOS-Chem modeling, as delivered to VISTAS, contained 

concentrations of all visibility-related components of particulate matter with 3-hr 

temporal resolution and 4°x5° horizontal resolution over multiple layers of the 

atmosphere. 

Even though the coarse spatial resolution degraded the performance of the model over 

North America, relative to results from prior modeling for other years using finer grids, 

the concentrations simulated by GEOS-Chem maintained the synoptic-scale structure and 

did not have a continental-scale bias. At a finer scale, the concentrations simulated by 

GEOS-Chem for the surface layer in 2002 were generally within a factor of two of 

regional-averages (~500 km scale) of measurements of the various chemical components 

of particulate matter in the United States and, except for organic carbon, satisfactorily 

simulated the seasonal variability in the observations. 

In the meantime, Harvard University had also carried out new finer-grid simulations for 

2001 for EPRI, using a 1°x1° grid over North America, nested in a coarser grid that 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 2 

December 17, 2007
3

covered the globe. The final version of these simulations included modifications to the 

modeling grid to improve delineation between domestic US emissions and those of 

foreign sources at the national boundaries, and revision (reduction) of the Canadian and 

Mexican sulfur emissions. These GEOS-Chem 1°x1° grid scale simulations for 2001 and 

their results are described in the paper by Park et al. (2006). Over appropriate geographic 

and temporal scales, the results of these simulations for 2001were similar to those of the 

2002 VISTAS simulations, which gave confidence in the VISTAS GEOS-Chem results. 

The concentration fields that resulted from the GEOS-Chem simulations were not 

directly usable as boundary conditions for the 36-km grid scale CMAQ simulations. 

GEOS-Chem and CMAQ use different horizontal and vertical grid scales and different 

map projections, and have different labels for chemical constituents. The GEOS-Chem 

outputs were processed by Daewon Byun at the University of Houston to adjust for these 

discrepancies and to develop boundary conditions for the 36-km CMAQ modeling. Since 

the CMAQ modeling has temporal resolution of one hour, the boundary conditions were 

given for every hour. However, since the GEOS-Chem outputs had 3-hr resolution, the 

hourly boundary conditions were the same for three consecutive hours. An example of 

the resulting boundary conditions for sulfate at the surface is shown in Figure 1, 

Average Sulfate Boundary Conditions 

GEOS-CHEM for July 1, 2001 

Figure 1. GEOS-CHEM projections of sulfate boundary conditions for the national 

CMAQ modeling grid on July 1, 2001, as indicated by the colors along the boundary of 

the modeling domain.

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 3 

December 17, 2007
4

Using the boundary conditions provided by the global simulations, VISTAS calculated 

the impacts of international anthropogenic emissions on haze at US Class I areas in two 

different ways, which are summarized in Table 1: 

• First, as the difference between the results of two GEOS-Chem simulations: (1) with 

2002 international emissions (natural and anthropogenic) but only natural US 

emissions, and (2) with only natural emissions worldwide. Such differences could be 

calculated with the 4°x5° grid of the VISTAS simulations or the 1°x1° grid of the 

EPRI simulations. Since the chemical reactions in these GEOS-Chem simulations do 

not reflect the contribution to the chemistry from anthropogenic emissions in the US, 

the result is an estimate of what the international contribution would be if the US 

emissions were only from natural sources (the 2064 goal) while the emissions of the 

rest of the world remained at 2002 levels. (See the upper half of Table 1 for portrayal 

of this calculation.) 

• Second, as the difference between the results of two CMAQ 36-km grid simulations 

of the US, both using 2018 OTW Base F emissions, with boundary conditions based 

on GEOS-Chem simulations: (1) with all global sources active, and (2) with U.S. 

anthropogenic emissions and all global natural emissions (Mexican and Canadian 

anthropogenic emissions within the 36-km CMAQ domain were also removed). Since 

the GEOS-Chem simulations are based on 2002 emissions, the result of this 

calculation is an estimate of what the international contribution would be in 2018 if 

international emissions remain at 2002 levels. (See the lower half of Table 1 for 

portrayal of this calculation.) 

Both cases underestimate the total international contribution because the GEOS-Chem 

simulations assumed that all fires outside of the US were natural, i.e., that there are no 

international fire emissions due to anthropogenic burning. 

Table 1. Approaches for estimating international contributions using GEOS-Chem 

results. 

Case Natural Sources Anthropogenic Sources 

GEOS-Chem 

(1) International and US International 

(2) International and US -- 

Difference = (1) – (2) -- International 

CMAQ with GEOS-Chem boundary conditions

(1) International and US International and US 

(2) International and US US 

Difference = (1) – (2) -- International 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 4 

December 17, 2007
5

The CENRAP RPO applied a third method to estimate the impacts of international 

emissions. They used the PSAT (PM Source Apportionment Technology) module of the 

CAMx regional air quality model to track the 2002 contributions of three non-domestic 

source categories (Canadian emissions in the CAMx modeling domain, Mexican 

emissions in the CAMx modeling domain, and boundary conditions) at all Class I areas. 

The boundary conditions applied at the boundaries of the CAMx 36-km scale grid were 

based on the outputs of the GEOS-Chem modeling described above. The international 

impacts in 2002 were then calculated as the sums of the PSAT-determined contributions 

of these three source categories. One major difference between this approach and the 

VISTAS approaches is that the PSAT international impacts included the contributions 

from fire emissions. This will result in larger OC and EC international impacts compared 

to the VISTAS approach. 

To illustrate the magnitudes of the international contributions to two of the principal 

components of the visibility-impairing aerosol, Figures 2 and 3 show the annual averages 

of estimates of international contributions to ammonium sulfate and organic 

carbonaceous mass (OCM) at Class I areas in the VISTAS region and in adjacent states. 

Results are presented for all three of the methods described above. Two sets of GEOSChem results are shown – for 2002 with the VISTAS 4°x5° grid and for 2001 with the 

EPRI 1°x1° grid. 

 

Transboundary Man-Made Impacts

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

ACAD

LYBR

BRIG

BOWA

ISLE

BIBE

HEGL

CACR

MING

UPBU

BRET

EVER

CHAS

SAMACOHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

(NH4)2SO4 Conc. 

(_g/m3

)

GEOS_4x5 GEOS_1x1 PSAT CMAQ

Figure 2. Estimates by four methods of impacts of international emissions on annual 

average ammonium sulfate concentrations at Class I areas. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 5 

December 17, 2007
6

Transboundary Man-Made Impacts

0.00

0.05

0.10

0.15

0.20

0.25

0.30

ACAD

LYBR

BRIG

BOWA

ISLE

BIBE

HEGL

CACR

MING

UPBU

BRET

EVER

CHAS

SAMACOHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

OCM Conc. (

_g/m3

)

GEOS_4x5 GEOS_1x1 PSAT CMAQ

Figure 3. Estimates by four methods of impacts of international emissions on annual 

average organic carbonaceous mass concentrations at Class I areas. 

For ammonium sulfate there is reasonable agreement (better than a factor of two or so 

everywhere except at Everglades) between all pairs of methods, while the results are 

more variable for OCM. The impacts estimated by the VISTAS 4°x5° GEOS-Chem 

simulations tend to be smaller than those of the other methods. Not surprisingly, the 

biggest impacts from international emissions tend to occur at Class I areas that are near 

the Canadian and Mexican borders. 

The projected contributions from international emissions on the 20% worst visibility days 

in 2002 are compared to annual average contributions in Figures 4 and 5. Results 

obtained using both the PSAT and CMAQ models are shown. At the VISTAS sites, 

CMAQ projected slightly higher international contributions to sulfate on the 20% worst 

visibility days than the annual average contributions. Compared to the corresponding 

annual average international contribution, PSAT generally showed lower contribution to 

sulfate on the 20% worst days at the mountain sites and equal or slightly higher 

contributions at the coastal sites. For organic carbonaceous mass, there were no clear 

trends between 20% worst and annual average days in either the PSAT or CMAQ results. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 6 

December 17, 2007
7

Transboundary Man-Made Impacts

0.0

0.2

0.4

0.6

0.8

1.0

1.2

BRIG

HEGL

CACR

MING

UPBU

BRET

EVER

CHAS

SAMACOHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

(NH4)2SO4 Conc. 

(_g/m3

)

PSAT_20 PSAT CMAQ_20 CMAQ

Figure 4. Estimates by the PSAT and CMAQ models of the impacts of international 

emissions on ammonium sulfate mass concentrations at Class I areas on the 20% worst 

visibility days and on the annual average in 2002.

Transboundary Man-Made Impacts

0.00

0.05

0.10

0.15

0.20

0.25

0.30

BRIG

HEGL

CACR

MING

UPBU

BRET

EVER

CHAS

SAMACOHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

OCM Conc. (

_g/m3

)

PSAT_20 PSAT CMAQ_20 CMAQ

Figure 5. Estimates by the PSAT and CMAQ models of the impacts of international 

emissions on organic carbonaceous mass concentrations at Class I areas on the 20% 

worst visibility days and on the annual average in 2002.

Figures 6 and 7 put the international impacts into the context of current measured 

concentrations. They compare the international impact estimates with current monitored 

concentrations on the 20% worst visibility days at two VISTAS Class I areas chosen as 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 7 

December 17, 2007
8

examples, Shenandoah (SHEN) and St. Marks (SAMA). At both locations, the 

international impacts on concentrations on such days are small, typically less than 5% of 

the measured concentrations. 

At the same two Class I areas, the magnitudes of international contributions relative to 

the uniform rate of progress (URP) goal for 2018 are shown in Figures 8 and 9, where all 

the haze index values were calculated using the new IMPROVE algorithm. The 

numerical value given at 2004 (e.g., 29.31 dv in Figure 8) is the baseline period haze 

index value (the average over the five years from 2000 to 2004). Because the CMAQ 

results are scaled with Relative Response Factors (RRFs), the initial CMAQ haze index 

matches the measured baseline value. In each plot, the open triangle at 2018 indicates the 

estimated haze index, in deciviews, as predicted by the CMAQ model using the Base G1 

emission inventory. The X indicates the haze index in 2018 if international emissions are 

removed from the 2018 CMAQ simulation, so the difference between the open triangle 

and the X represents the international contribution. 

SHEN1

20% Worst Days 

0

5

10

15

20

25

30

35

40

45

2/28 5/26 5/29 6/4 6/10 6/13 6/19 6/25 7/1 7/4 7/16 7/19 7/22 7/31 8/3 8/12 8/21 8/24 8/27 9/11 9/14 9/17 9/29 10/2

Mass (μg/m3

)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

Figure 6. For the 20% worst visibility days in 2002 at Shenandoah, CMAQ-simulated 

international contributions (right bars) and domestic contribution to measured PM 

concentrations (measured component minus international contribution) (left bars). 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 8 

December 17, 2007
9

SAMA1

20% Worst Days (2002)

0

5

10

15

20

25

30

35

2/4 2/13 2/16 2/25 3/30 4/5 4/8 4/17 4/26 5/2 5/5 5/8 5/11 6/4 7/4 7/7 7/16 7/19 9/17 10/17 10/20 10/23 12/7

 Mass (μg/m3) 

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

Figure 7. For the 20% worst visibility days in 2002 at Saint Marks, CMAQ-simulated 

international contributions (right bars) and domestic contribution to measured PM 

concentrations (measured component minus international contribution) (left bars). 

Uniform Rate of Progress Glide Path

Shenandoah - 20% Worst Days

29.31 28.12

25.14

22.16

19.18

16.20

13.23

11.44

22.00

19.74

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

Glide Path Natural Condition (Worst Days) Observation Base G Prediction Base G w/o Intl. Trans.

Figure 8. Illustration of the effect of removing the international emissions contribution to 

the CMAQ-modeled haze index on 20% worst days at Shenandoah in 2018. Open 

triangle = estimate with all emissions; X = estimate with international emissions 

removed.

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 9 

December 17, 2007
10

Uniform Rate of Progress Glide Path

Saint Marks - 20% Worst Days

26.31 25.33

22.88

20.44

17.99

15.55

13.10

11.64

23.38

22.06

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

Glide Path Natural Condition (Worst Days) Observation Base G Prediction Base G w/o Intl. Trans.

Figure 9. Illustration of the effect of removing the international emissions contribution to 

the CMAQ-modeled haze index on 20% worst days at St. Marks in 2018. Open triangle = 

estimate with all emissions; X = estimate with international emissions removed in 2018.

At Shenandoah, Figure 8 shows that the modeled 2018 haze index is already below the 

Uniform Rate of Progress line. Removing the international contribution would lower the 

2018 haze index by another 2.26 dv. Although the international contribution is small 

today, it becomes increasingly important as US emissions are reduced. (For these 

analyses, the international emissions are assumed to remain constant from the baseline 

period to 2018). 

At Saint Marks, Figure 9 shows that the modeled 2018 haze index is above the Uniform 

Rate of Progress line. Removing the international contribution would reduce the 2018 

haze index by 1.32 dv, to a value that lies just below (indicating greater reduction than) 

the Uniform Rate of Progress line. 

Information of the type portrayed for 2018 in Figures 8 and 9 is summarized in Figure 10 

for all VISTAS Class I areas and Class I areas in adjacent states. The quantity represented 

is the ratio (in percent) of two metrics of improvement in the haze index from the 

baseline period to 2018: the CMAQ-modeled improvement in haze index divided by the 

improvement reflected by the Uniform Rate of Progress line (the magenta line in Figures 

8 and 9). For example, in Figure 9 the ratio at St Marks with all emissions considered is 

(26.31-23.38)/(26.31-22.88) = 0.85 = 85%. A ratio of less than 100% means that the 

model-predicted improvement is not sufficient to meet the goal of the Uniform Rate of 

Progress; i.e., the predicted haze index in 2018 is above the Uniform Rate of Progress 

line. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 10 

December 17, 2007
11

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

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

220%

240%

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 10. Percent of Uniform Rate of Progress haze index reduction achieved in 2018 

according to the CMAQ modeling with the 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. 

Figure 10 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. 

Figures 11through14 display the Uniform Rate of Progress for the four Class I areas in 

North Carolina and show the modeled predictions for 2018 with all emissions considered 

and without the contribution from international emissions. These figures have the same 

format as Figures 8 and 9. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 11 

December 17, 2007
12

Uniform Rate of Progress Glide Path

Great Smoky Mountains - 20% Worst Days

30.28 29.01

25.85

22.69

19.53

16.37

13.20

11.31

23.92

21.87

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

Glide Path Natural Condition (Worst Days) Observation Base G Prediction Base G w/o Intl. Trans.

Figure 11. Illustration of the effect of removing the international emissions contribution 

to the CMAQ-modeled haze index on 20% worst days at Great Smoky Mountains in 

2018. Open triangle = estimate with all emissions; X = estimate with international 

emissions removed in 2018. Great Smoky Mtns. also represents Joyce Kilmer-Slickrock. 

Uniform Rate of Progress Glide Path

Linville Gorge - 20% Worst Days

28.77 27.61

24.69

21.78

18.86

15.95

13.03

11.28

22.03

20.32

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

Glide Path Natural Condition (Worst Days) Observation Base G Prediction Base G w/o Intl. Trans.

Figure 12. Illustration of the effect of removing the international emissions contribution 

to the CMAQ-modeled haze index on 20% worst days at Linville Gorge. Open triangle = 

estimate with all emissions; X = estimate with international emissions removed in 2018. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 12 

December 17, 2007
13

Uniform Rate of Progress Glide Path

Shining Rock - 20% Worst Days

28.66 27.52

24.69

21.85

19.01

16.18

13.34

11.64

22.21

20.25

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

Glide Path Natural Condition (Worst Days) Observation Base G Prediction Base G w/o Intl. Trans.

Figure 13. Illustration of the effect of removing the international emissions contribution 

to the CMAQ-modeled haze index on 20% worst days at Shining Rock. Open triangle = 

estimate with all emissions; X = estimate with international emissions removed in 2018. 

Uniform Rate of Progress Glide Path

Swanquarter - 20% Worst Days

24.71 23.87

21.75

19.64

17.52

15.41

13.29 12.02

20.28

18.45

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

Glide Path Natural Condition (Worst Days) Observation Base G Prediction Base G w/o Intl. Trans.

Figure 14. Illustration of the effect of removing the international emissions contribution 

to the CMAQ-modeled haze index on 20% worst days at Swanquarter. Open triangle = 

estimate with all emissions; X = estimate with international emissions removed in 2018. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 13 

December 17, 2007
14

References 

Chung, S. H. and Seinfeld, J.H. (2002), Global distribution and climate forcing of 

carbonaceous aerosols, Journal of Geophysical Research, Vol. 107 (D19), 4407, 

doi:10.1029/2001JD001397. 

Jacob, D.J. (2005), Documentation and Evaluation of the GEOS-Chem Simulation for 

2002 Provided to the VISTAS Group. Final report, Harvard University. June 24. 

Park. R.J., Jacob, D.J., Chin, M., and Martin, R.V. (2003). Sources of carbonaceous 

aerosols over the United States and implications for natural visibility. Journal of 

Geophysical Research, Vol. 108 (D12), 4355, doi:10.1029/2002JD003190. 

Park. R.J., Jacob, D.J., Field, B.D., Yantosca, R.M., and Chin, M. (2004). Natural and 

transboundary pollution influences on sulfate-nitrate-ammonium aerosols in the United 

States: Implications for policy. Journal of Geophysical Research, Vol. 109, D15204, 

doi:10.1029/2003JD004473. 

Park. R.J., Jacob, D.J., Kumar, N., and Yantosca, R.M. (2006). Regional visibility 

statistics in the United States: Natural and transboundary pollution influences, and 

implications for the Regional Haze Rule. Atmospheric Environment, Vol. 40, pp. 5405-

5423. 

Schichtel, B.A., Gebhart, K.A., Malm, W.C., Barna, M.G., Pitchford, M.L., Knipping, 

E.M., and Tombach, I.H. (2005). Reconciliation and Interpretation of Big Bend National 

Park Sulfur Source Apportionment: Results from the Big Bend Regional Aerosol and 

Visibility Observational Study – Part I. Journal of the Air & Waste Management Assoc.

Vol. 55, pp. 1709-1725. 

Modeling Results & Supplemental Analysis 

Regional Haze SIP for NC Class I Areas

 Appendix G.2 - 14 

December 17, 2007