# 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 Con centration 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 glid epath, for GSMNP1 on the 20% best days (top) and the 20% worst days (bottom) ................................................................................................ . 14

Figure 3.6-2: Uniform rate of progress, or glid epath, for LIGO1 on the 20% best days (top) and the 20% worst days (bottom) .................................................................................................... .... 15

Figure 3.6-3: Uniform rate of progress, or glid epath, for SHRO1 on the 20% best days (top) and the 20% worst days (bottom) .................................................................................................... .... 16

Figure 3.6-4: Uniform rate of progress, or glid epath, 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 r easonable 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 Na tural 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 (dv future 

## ) 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 cal culated 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 vi sibility 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 Go als for Ozone, PM2.5, and Regional Haze”(from this point forward, referred to as “Attainment Guidance”).

3.1 Determination of 20% Best and worst Days

The first step in developing model projections is to rank visibility (in deciviews) on each day with observed speciated PM2.5 data for each of the 5 years comprising the base period (2000- 2004) for each Class I area. Day-specific observations for mass associated with sulfate (SO 4 

## ), nitrates (NO 3 

## ), organic carbon (OC), elemental carbon (EC), soil, and coarse matter (CM),
should be used to calculate b ext 

## for each day using climatological relative humidity adjustment factor(s) ( f(rh) ). Total light extinction (b ext 

## ) for all components should be converted to deciviews for each day to get a daily deciview value.

3.2 Determine Baseline Conditions

The ranked visibility days from the first step are then used to calculate the average baseline deciviews for the 20% best and worst days. First, the mean deciview value for the identified 20% worst- and best visibility days in each year is calculated (dv current 

## ). The 5 year mean deciview values reflecting mean visibility on the days with best visibility and then averaged to determine the baseline values for the best days, or the dv baseline 

## values. Then the 5 year mean deciview values reflecting worst visibility are averaged together. This represents the value subject to improvement to meet the glidepath for regional haze.

For each of the 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.

dv baseline Natural Condtions 

### dv baseline 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:

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

= RRF

To accomplish this step, concentration for each component of PM2.5 and coarse matter (the difference between particles less than 10 micrometers in diameter (PM10) and PM2.5) are extracted from the base year and future year modeling output near IMPROVE monitoring sites for the monitored best and worst days. The component concentrations are then averaged to develop base and future year average component concentrations. The future year average component concentrations are then 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 SO 4 

## , NO 3 

## , OC, EC, Soil and CM, for each of the “worst” and “best” visibility days.

3.5. Future Year Daily bext Calculation

Once future year concentrations are 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 * f s 

## (RH) * [Small Sulfate] + 4.8 * f L 

## (RH) * [Large Sulfate]
+ 2.4 * f s 

## (RH) * [Small Nitrate] + 5.1 * f L 

## (RH) * [Large Nitrate] + 2.8 * {Small Organic Mass] + 6.1 * [Large Organic Mass] + 10 * [Elemental Carbon] + 1 * [Fine Soil]
+ 1.7 * f ss 

## (RH) * [Sea Salt] + 0.6 * [Coarse Mass] + Rayleigh Scattering (site specific) + 0.33 * [NO 2 

## (ppb)]

Model estimated daily concentrations are used with the corresponding 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 b ext 

## for each day (calculated in the previous step, Section 3.5) to deciviews. This future year daily deciview value for each of the best and worst days is then averaged to 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 dv future 

## . 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 dv future



## .

Table 3.6-1: Final future mean deciview values (dv future 

## ) and Natural Conditions for the North Carolina Class I areas, based on 12km modeling results.

dv

f uture Natural Condtions 

### dv f 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 dv future 

## values for the “worst” and “best” visibility days can be compared to the
dv baseline 

## values calculated in step 2 (Section 3.2). If the resulting change in deciviews is a negative number (future - base), this represents an improvement in visibility. Table 3.6-2
summarizes these calculation for the IMPROVE monitors associated with 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 DV base 

### DV f uture 

### DV dif f 

### DV base 

### DV f uture 

### DV dif 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 Ass essment, new IMPROVE Algorithm, Great Smoky Mo untains - 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

YearHaziness 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

YearHaziness 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

YearHaziness 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

YearHaziness 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

YearHaziness Index (Deciviews)

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



## Uniform Rate of Reason able 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

YearHaziness 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

YearHaziness Index (Deciviews)

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



## Uniform Rate of Reasona ble 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

YearHaziness 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 m odeling 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 suggest s 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 extin ction 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%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm

VISTAS non-VISTAS 

## Figure 4.1-1: Plot of 2018 modeling results as processed with the “new” IMPROVE algorithm (pink squares) and with the “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%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved CMAQ New IMPROVE Algorithm 12km
CMAQ Old IMPROVE Algorithm 12km
CMAQ New IMPROVE Algorithm 36km
CMAQ Old IMPROVE Algorithm 36km

VISTAS non-VISTAS 

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

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

VISTAS non-VISTAS 

## Figure 4.2-1: VISTAS modeling result for 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 comp aring VISTAS final results to the other RPO modeling, essentially an emissions sensitivity run is created.

Preliminary modeling from Central Regional Ai r Planning Association (CENRAP) and Western Regional Air Partnership (WRAP) were obtaine d 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%CACR1UPBU1BRET1HEGL1MING1SIPS1MACA1GRSA1ROMO1SACR1WHIT1WHPE1LOST1THRO1BADL1WICA1Percent 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 effort s. 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 Orga nization (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%CACR1UPBU1BRET1SIPS1HEGL1MING1MACA1BOWA1VOYA2ISLE1GRSA1ROMO1SACR1WHIT1WHPE1LOST1THRO1BADL1WICA1Percent of target reduction CENRAP-base 18f

VISTAS-base G2
MwRPO-base g

WRAP-base 18b

VISTAS WRAP MwRPO



## Figure 4.2–3: Comparison of percent of 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 2020Deciview Brigantine Dolly Sods

Shenandoah Brig BOTW_BaseB4 Dolly BOTW_BaseB4

Shen BOTW_BaseB4



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

As more modeling work becomes available 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 domai n 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. ConcurrentlyHarvard 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 1.2BRIGHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHEN(NH4)2SO4Conc. (g/m3)



### GEOS_4x5 GEOS_1x1 CMAQ



## Figure 4.2-5: Estimated international emission s 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 35DomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternational

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 12/7Mass (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 35DomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternational

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 12/7Mass (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-sim ulated 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 s hows 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%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved

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

V ISTAS non-VISTA S



## Figure 4.2-7. 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 emi ssions 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 J ennifer A. Logan, Harvard University

June 24, 2005

### 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. J acob, 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

### 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-NO

x 

### -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 (4 o 

### x5 o 

### horizontal resolution vs. 2 o 

### x2.5 o 

### ), updated anthropogenic emissions from the EPA NEI 1999, and 2002-sp ecific 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. anthropogenic 1 

### 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-NO

x 

### -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.

### 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 1 o 

### x1 o 

### and 55 vertical layers. The data after August 2002 are from the updated GEOS-4 assimilation, with horizontal
resolution of 1 o 

### x1.25 o 

### 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 2 o 

### x2.5 o



### horizontal resolution. Continental-scale simulations for North America have been
conducted with the native 1 o 

### x1 o 

### resolution of the GEOS-3 data [Li et al., 2005; Park et al., 2005b]. The VISTAS simulations used a coarser horizontal resolution of 4 o 

### latitude x

5 o 

### 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

4 o 

### x5 o 

### and 2 o 

### x2.5 o 

### resolution, and the MAQSIP regional model with 36x36 km 2 

### resolution. We found that using the 4 o 

### x5 o 

### 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 additionalmodification 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 avery shallow surface layer (only 10-m deep in GEOS-3). The effect is significant for fastdepositing gases such as HNO 3 

### and NH 3 

### ; 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 us es 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

### 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-NO

x 

### -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 c ontributions 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, NO

x 

### , and ammonia, are given by Park et al. [2004]. Anthropogenic emissions are from the Global Emission Inventory Activity (GEIA) with 1 o 

### x1 o 

### 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 0.25 o 

### x0.25 o

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

### x5 o 

### 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 GE IA climatology. Emission of dimethylsulfide (DMS) by phytoplankton uses a global distributi on of DMS seawater concentrations from Kettle et al. [1999] and a standard sea-air exchange parameterization driven by the local
wind. Soil emissions of NO

x 

### are from GEIA and are function of local temperature and precipitation history. Lightning emissions of NO

x 

### are computed globally with a standard

### 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). An thropogenic 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; emi ssion 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) formati on from biogenic hydrocarbons follows the scheme of Chung and Seinfeld [2002] developed for application in global models (the Park et al. [2003] simulations simply scal ed the SOA source to 10% of monoterpene emission). The Chung and Seinfeld [2002] de scribes SOA formation from oxidation ofseveral 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 PM 2.5 

### and PM 10-2.5 

### . 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 μ m). 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.

### 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 th e 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 southeaste rn 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 Manageme nt (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 Nationa l 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

10 6 

### ha in the United States in 2002. The data provided by Alpine Geophysics for the southeast gave a total area of 0.044 x 10 6 

### ha. Six states accounted for over 80% of the national area burned, namely Alaska, Ore gon, 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 10 6 

### ha, while 2.8 x 10 6 

### ha burned in Kazakhstan, and 11 x 10 6 

### 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/m 2 

### for Alaskan fires, and 1.8 kg
DM/m 2 

### 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

### 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-fre iburg.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 10 6 

### 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 bur ning 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

### 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 10 6 

### 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 ofthe 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 4 o 

### x5 o 

### 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 national10 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 2 o x2.5 o 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 4 o x5 o 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 (R 2 = 0.82 for the concentration data and 0.71 for the deposition data) and also for the seasonal means (R 2 = 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 (R 2 = 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 (R 2 = 0.81) and in different seasons (R 2 = 0.72-0.81). The R 2 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 HNO 3 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].11 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 (R 2 = 0.42-0.58) except in summer (R 2 = 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 simulati on 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 4 o x5 o 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.

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, 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: Descrip tion and 1990s dust climatology, J. Geophys.

Res. , 108(D14), 4416, doi:10.1029/2002JD002775.13 Figure 1. Annual mean concentrations of sulf ate 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 4 o × 5 o grid.14 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 95 o 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 R 2 are shown inset. Thin solid lines show the y=x relationship.15 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.16 Figure 4. Scatterplot of simulated versus observed ammonium concentrations at the CASTNET sites (left column), and nitrat e 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 95 o 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 R 2 are shown inset. Thin solid lines show the y=x relationship.17 Figure 5. Same as Figure 3 but for nitrate.18 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.19 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 95 o 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 R 2 are shown inset. Thin solid lines show the y=x relationship. Dotted lines show the y=2x and y=0.5x relationships.20 Figure 8. Same as Figure 6 but for organic carbon mass (OMC)21 Figure 9. Same as Figure 7 but for organic carbon mass (OMC)22 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 (R 2 = 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 a nd 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 com ponent 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 directly23 to the atmosphere (primary OC) and formed in situ by condensation of low-volatility products of the photooxidation of hydrocarbons (s econdary 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 regi ons, 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), 2 o latitude by 2.5 o 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 foss il fuel, biofuel, and biomass burning, plus an OC component of biogenic origin, resulting in a total of 13 tracers transported by the
model.24 Simulation of aerosol wet and dry deposition follows the schemes used by Liu et al. [2001] in previous GEOS-CHEM simulations of 210 Pb 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 210 Pb 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 1 o x1 o 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 St ates 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 f actors 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 1 o x1 o 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 southern25 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 evaluati on 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 aeros ol 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 2 o x 2.5 o 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.26 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 furt her 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 ( R 2 =0.84 for
EC and R 2 =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,27 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 R 2 (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 R 2 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 μ g m -3 for EC, 1.23 μ g 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 R 2 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 to28 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. On29 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 μ g/m 3 and 1.09 μ g/m 3 , respectively, for the western United States (west of 95 o W)
and 0.06 μ g/m 3 and 0.95 μ g/m 3 , 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 μ g/m 3 and 1.33 μ g/m 3 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

μ g/m 3 for EC, and 0.47 μ g/m 3 (west) and 1.40 μ g/m 3 (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 standa rd 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 continental30 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 μ g/m 3

and 1.25 μ g/m 3 , respectively, for the western United States and 0.04 μ g/m 3 and 1.17

μ g/m 3 , 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 (R 2 = 0.84 for EC, R 2 = 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 indi cate that Canadian fires in 1995 enhanced the31 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 μ g/m 3 in the western United States (west of 95 o W) and 0.04 μ g/m 3 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 μ g/m 3 in the west and 1.17 μ g/m 3 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.

References

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Figures Captions

Figure 1. Yearly biomass burning OC em ission 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 2 o × 2.5 o grid. The bottom panel shows the difference between the two.

Figure 5. Scatterplot of simulated (G EOS-CHEM) vs. observed (IMPROVE) annual
mean EC and OC concentrations for the da ta shown in Figure 4. The pluses and the
circles indicate data in the western and eastern United States (separated at 95 o 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 were34 discarded in statistical analysis for OC. The thin solid and dotted lines represent the y = x
relation and a factor of 2 deviation. The thic k solid line represents the reduced major-axis linear regression [ Hirsch and Gilroy , 1984], excluding sites A-I. The Pearson correlation
coefficients R 2 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 lin es with symbols: asterisks (fossil fuel combustion), diamonds (biomass burning), and squares (biofuel use).

Figure 7. Same as in Fi gure 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 95 o W) are shown as pluses and open circles, respectively. Thin solid lin es 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 regressi on. The Pearson correlation coefficients R 2 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 s ources types to EC concentrations ( μ g 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 th ree 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.35 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 ( μ g m -3 ) in the United

States a

. 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

OMC b
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

95 o W. 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 μ g m -3 (West) and 1.40 μ g m -3 (East) for OMC, and 0.02 μ g m 3 for EC.

b Organic carbon mass (OMC), defined as 1.4 times the OC mass to account for noncarbon contributions to the organic aerosol.36 Figures

Figure 1. Yearly biomass burning OC em ission in 1997-2000 for North and Central America, and climatological mean value (see text).

### 37 Figure 2. Annual biomass burning OC emission over North and Central America in 1998 (top) and seasonal variations for different regions (bottom).38 Figure 3. IMPROVE sampling sites with continuous records for 1998.39 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 2 o × 2.5 o grid. The bottom panel shows the difference between the two.40 Figure 5. Scatterplot of simulated (G EOS-CHEM) vs. observed (IMPROVE) annual
mean EC and OC concentrations for the da ta shown in Figure 4. The pluses and the
circles indicate data in the western and eastern United States (separated at 95 o 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 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 R 2 and regression equations are indicated.41 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 lin es with symbols: asterisks (fossil fuel combustion), diamonds (biomass burning), and squares (biofuel use).42 Figure 7. Same as in Fi gure 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).43 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 95 o W) are shown as pluses and open circles, respectively. Thin solid lin es 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 regressi on. The Pearson correlation coefficients R 2 are indicated.

### 44 Figure 9. Annual mean concentrations ( μ g 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.45 Figure 10. Contribution from different s ources types to EC concentrations ( μ g m -3 ) in surface air for DJF and JJA. Values are model results for 1998 using a posteriori sources (Table 1).46 Figure 11. Same as Figure 10 but for OC.47 Figure 12. Concentrations of EC and OC at th ree 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.48 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 bot h 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 SO 2 and nitrogen oxides (NO

x ≡ NO + NO 2 ) to H 2 SO 4 and HNO 3 , respectively [ NARSTO

,49 2003]. Fossil fuel combustion is the dominant source of SO 2 and NO

x in the United States. Important natural sources include volcanoes and atmospheric oxidation of oceanic dimethylsulfide (DMS) for SO 2 ; and lightning, soils, and wildfires for NO

x [ NARSTO

, 2003]. The low vapor pressure of H 2 SO 4 over H 2 SO 4 -H 2 O solutions implies that all of sulfate is in the aerosol phase. The sulfate aerosols can be partly or totally neutralized by ammonia (NH 3 ) emitted from livestock, fertilizer use, and other less important sources. If excess ammonia is available beyond that required for sulfate neutralization to ammonium sulfate (NH 4 ) 2 SO 4 , then ammonium nitrate (NH 4 NO 3 ) aerosol can form; otherwise, and except for cloudy conditions, nitric acid remains in the gas phase. This simple H 2 SO 4 - HNO 3 -NH 3 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 Europeanobservations, as 2001 observations were not available in a timely manner. The GEOS50 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), 1 o latitude by 1 o longitude (1 o × 1 o ) horizontal resolution, and 48 sigma vertical layers. We degrade the horizontal resolution to 2 o × 2.5 o 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-NO

x -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 H 2 SO 4 -HNO 3 -NH 3 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, HNO 3 (g)/NO 3 - 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 c onvective 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 SO 2 is limited by the local availability of H 2 O 2 as a fast aqueous-phase oxidant converting SO 2 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 4 o × 5 o horizontal resolution for computational expediency. Oxidant concentration fields (OH, O 3 , NO 3 ), H 2 O 2 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 2 o × 2.5 o horizontal resolution. The aerosol-only simulation includes 951 prognostic chemical species: dimethylsulfide (DMS), SO 2 , sulfate, methane sulfonic acid (MSA), HNO 3 (g), NO 3 - , NH 3 (g), NH 4 + , and H 2 O 2 . 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 SO 2 except for a small fraction as sulfate, 5% in Europe and 3% elsewhere [ Chin et al. , 2000a]. Other anthropogenic sources of SO 2 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 1 o × 1 o spatial resolution from Yevich and Logan [2003] and apply an emission factor of 0.0015 moles SO 2 per mole CO [ Andreae
and Merlet , 2001]. Seasonal variations in biofuel em issions 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 SO 2 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 SO 2 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 SO 2 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 SO 2 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 c ontribute 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 SO 2 and MSA, by nitrate radicals (NO 3 ) to form SO 2 , and SO 2 oxidation by OH to form sulfate. Reaction rates are from DeMore et al. [1997] and the yields of52 SO 2 and MSA from DMS oxidation are from Chatfield and Crutzen [1990]. Aqueousphase oxidation of SO 2 by O 3 and H 2 O 2 in clouds to form sulfate is included using kinetic
data from Jacob [1986] and assuming a pH of 4.5 for the oxidation by O 3 . 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 1 o × 1 o 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 NH 3 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 (NH 4 + ) 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-NO

x -hydrocarbon chemical mechanism (see section 2.1). Table 1c gives a summary of global and contiguous U.S. NO

x 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 budgets53 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 aer osol 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 re ductions 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 contri butions 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 pollution54 [ 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 2 o × 2.5 o model grid. Values are higher in the eastern than the western United States and are highest in the industrial midwest, reflecti ng 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 (R 2 = 0.91-0.94 for the concentration data, 0.75 for the deposition data) and also for the seasonal means (R 2 = 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 R 2 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 SO 2 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 SO 2 in convective updrafts as a titration reaction limited solely by the supply of H 2 O 2 entrained in the updraft. However, kinetic limitations in the aqueousphase reaction of SO 2 with H 2 O 2 , as well as scavenging of H 2 O 2 , can greatly reduce the

SO 2 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 (R 2 = 0.90) and in different seasons (R 2 = 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 not55 adequately describe the seasonal variation of this dominant source. For the subset of
western sites alone the model has a lower R 2 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

4 3

T T

HNO SO NH GR −

−

= , (1)

where concentrations are in molar units, [NH 3 T ] is the sum of gas-phase ammonia and aerosol-phase ammonium concentrations, [HNO 3 T ] is the total inorganic nitrate concentration (sum of gas-phase nitric acid and aerosol nitrate), and [SO 4 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 [NH 4 + ] vs. (2[SO 4 2- ] + [NO 3 - ]) 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., Ca 2+ ) 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 (R 2 = 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]. Simulated56 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 out flow by using sulfate observations from
the TRACE-P aircraft mission conducted off th e 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, CO 2 , CH 4 , 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-SO 4 2- ) on average was incorporated in dust particles [ Jordan et al. , 2003]. Figure 11 compares mean vertical profiles of simulated and
observed nss-SO 4 2- concentrations for the ensemble of DC-8 flights over the NW Pacific
west of 177 o E and at 30 o -45 o N latitude [ Liu et al. , 2003]. Monthly mean concentrations in the model were sampled along the flight track s. 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 influenceWe 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 95 o 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 na tural 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 μ g m -3 , respectively, for the western
United States and 4.11 and 3.26 μ g 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 μ g m -3 ammonium sulfate and 0.03 μ g 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 μ g57 m -3 ) in the west but is factor of 2 lower than that (0.23 μ g 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 μ g 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 μ g m -3 for the west and 0.38 and 0.37 μ g 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 SO 2 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 [NH 3 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 No rth America shut off. We find a sulfate
enhancement > 0.1 μ g m -3 in surface air in Western Europe and northern Africa, comparable in magnitude to Asian pollution in fluence over North America. As in the case of Asian pollution, we find that export of a mmonium 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 by58 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 SO 2 over H 2 O 2 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 ( b ext /10) , (2)
where b ext is atmospheric light extinction in units of inverse megameters (Mm -1 =10 -6 m -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 ])[(3]))[((3 34 424

+++++ + =

CM soilEC OMC NONHRHfSONHRHf ext b , (3)

where b ext is in units of Mm -1 , aerosol concentrations are in units of μ g 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 μ m) and “coarse mass” is the total mass of particles with diameter > 2.5 μ m, 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 μ g m -3 ) and coarse mass (3.0 μ g 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)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.60 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 μ g m -3 , respectively, for the western United States
and 4.11 and 3.26 μ g m -3 , respectively, for the eastern United States. Our best estimates
of mean annual natural concentrations are 0.11 μ g m -3 ammonium sulfate and 0.03 μ g 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.61 Our best estimates of background concentrations for ammonium sulfate and
ammonium nitrate are 0.43 and 0.27 μ g m -3 for the west and 0.38 and 0.37 μ g 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 SO 2 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 μ g 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 we st, 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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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 NH 4 + data.67 Figure 2. Sampling sites from the I[M]PR OVE, [C]ASTNET, and NA[D]P networks in

2001.

Figure 3. Annual mean concentrations of sulf ate 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 2 o × 2.5 o 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 95 o 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 R 2 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 2 o × 2.5 o 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 nitrat e 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 95 o 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 R 2 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 [NH 4 + ] vs. (2[SO 4 2- ]+[NO 3 - ]) at CASTNET sitesin 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 axis68 regressions for the ensemble of the data; regression equations and R 2 are shown inset. Thin solid lines show the y=x relationship.

Figure 11. Simulated vs. observed mean vertical profiles of non-sea-salt sulfate (nssSO 4 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 30 o N (30-45 o N, 124-177 o E), 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 an thropogenic 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 95 o 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.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 0.0170 Table 1c. NO

x 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 States a

. 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
defaults b 0.12 0.23 0.1 0.1 0.02 0.02 0.47 1.40

a Values 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 95 o 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.71 Table 3. Visibility degradation (deciviews) in the United States a

. West East Baseline (2001) 14 23 Background b

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.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 NH 4 + data.73 Figure 2. Sampling sites from the I[M]PR OVE, [C]ASTNET, and NA[D]P networks in

2001.74 Figure 3. Annual mean concentrations of sulf ate 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 2 o × 2.5 o grid.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 95 o W) are shown as pluses and open circles, respectively. Thick solid lines are reduced major axis regressions for the ensemble of the76 data; regression equations and R 2 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 2 o × 2.5 o grid (ammonium is not measured at the IMPROVE sites).77 Figure 6. Scatterplot of simulated versus observed ammonium concentrations at the CASTNET sites (left column), and nitrat e 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 95 o W) are shown as pluses and open circles, respectively. Thick solid lines are reduced major axis78 regressions for the ensemble of the data; regression equations and R 2 are shown inset. Thin solid lines show the y=x relationship.

Figure 7. Same as in Figure 3 but for nitrate.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 < 0.80 Figure 9. Scatterplot of seasonal mean [NH 4 + ] vs. (2[SO 4 2- ]+[NO 3 - ]) at CASTNET sitesin 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.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 R 2 are shown inset. Thin solid lines show the y=x relationship.82Figure 11. Simulated vs. observed mean vertical profiles of non-sea-salt sulfate (nssSO 4 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 30 o N (30-45 o N, 124-177 o E), with standard deviations represented by horizontal bars. The dashed line shows the corresponding monthly mean model values along the flight tracks.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.84 Figure 12b. Same as in Figure 12a but for an thropogenic emissions from North America.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 95 o 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.

# 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 emissi ons 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 contributi on 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 emi ssions 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 sour ces 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 SO 2 

## 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-NO

x 

## -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).

### 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 re solution (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 th e 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

### 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, a nd 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.

### 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

### 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 1.4ACADLYBRBRIGBOWAISLEBIBEHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHEN(NH4)2SO4Conc.(_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.

### 6 

## Transboundary Man-Made Impacts

0.00 0.05 0.10 0.15 0.20 0.25 0.30ACADLYBRBRIGBOWAISLEBIBEHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHENOCM 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 agr eement (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 mode ls 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.

### 7 

## Transboundary Man-Made Impacts

0.0 0.2 0.4 0.6 0.8 1.0 1.2BRIGHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHEN(NH4)2SO4Conc.(_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 0.30BRIGHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHENOCM 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

### 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 10/2Mass (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).

### 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 12/7Mass (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.

### 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.

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

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

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

V ISTAS non-VISTA S



### Figure 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.

### 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.

### 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.

### 14 References

Chung, S. H. and Seinfeld, J.H. (2002), Gl obal 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 Ma rtin, 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., Yantos ca, 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.