Appendix G.2



Accounting for Transport of International Emissions in

CMAQ Modeling of the VISTAS Region 

Tennessee Department of Environment and Conservation 

Division of Air Pollution Control 

9th Floor L&C Annex 

401 Church Street 

Nashville, TN 37243

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Appendix G.2 - 1

January 18, 2008
Accounting for Transport of International Emissions in 

CMAQ Modeling of the VISTAS Region 

26 July 2007 

Anthropogenic and natural aerosols from emissions in Mexico, Canada, Central America, 

Asia, and Africa have been shown to affect air quality and regional haze in the 

continental United States. States may consider the fraction of the ambient aerosol that is 

beyond the regulatory control of the U.S as they evaluate emission control strategies to 

achieve reasonable progress in reducing regional haze in Class I areas. To support the 

VISTAS states in evaluating the contribution from international emissions, the VISTAS 

RPO carried out analyses to estimate how much of the haze at Class I areas in the 

VISTAS region in 2002 and 2018 (as projected by the CMAQ model) results from 

international emissions. 

CMAQ modeling of regional haze in the VISTAS region for 2002 and 2018 was carried 

out on a grid of 12x12-km cells that covers the VISTAS states (AL, GA, FL, MS, SC, 

NC, TN, KY, VA, WV) and states adjacent to them. This grid is nested within a larger 

national CMAQ modeling grid of 36x36-km grid cells that covers the continental United 

States, portions of Canada and Mexico, and portions of the Atlantic and Pacific Oceans 

along the east and west coasts. Transport of pollution from the national domain into the 

VISTAS modeling domain is reflected in hourly-varying boundary conditions around the 

VISTAS domain, which are derived from the CMAQ simulations in the 36-km national 

domain. The national domain incorporates emissions from northern Mexico and southern 

Canada, and therefore explicitly calculates the effects of these international emissions on 

concentrations at the boundaries of the VISTAS modeling domain. 

Effects of emissions from more distant sources that are outside of the 36-km national 

domain were estimated using outputs from a global air pollution model. The Big Bend 

Regional Aerosol and Visibility Observational (BRAVO) study had found that the 

performance of national-scale modeling was enhanced by using monthly averages of 

outputs from a global air pollution model, GOCART, to generate SO2 and sulfate 

boundary conditions for its 36-km national modeling grid (Schichtel, et al., 2005), instead 

of using the EPA default concentrations that are normally used. VISTAS elected to use a 

similar approach, using the outputs of the GEOS-Chem global air pollution model, which 

also contains a detailed representation of ozone-NOx-VOC-PM chemistry. 

Under EPRI sponsorship, Harvard University had already used the GEOS-Chem model, 

with 2°x2.5° horizontal resolution, to estimate natural conditions and transboundary 

influences on elemental and organic carbon aerosols in the United States in 1998 (Park et 

al., 2003). After some emission inventory and model improvements, similar information 

was developed for sulfate and nitrate aerosols in 2001 (Park et al., 2004). 

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Appendix G.2 - 2

January 18, 2008
2

Under contract to VISTAS (which was acting on behalf of all the RPOs), Harvard then 

simulated global air pollution for the year 2002 (the VISTAS modeling year) with 4°x5°

horizontal resolution and 3-hour temporal resolution (Jacob, 2005). The coarser grid scale 

was considered sufficient to provide boundary conditions outside of North America, 

largely over the oceans. Although the VISTAS simulation was generally similar to the 

2001 EPRI simulation mentioned above, the VISTAS modeling incorporated the 

following substantive changes in addition to the change in grid scale: (1) use of U.S. 

anthropogenic emissions from the EPA NEI 1999 inventory, except for ammonia; (2) use 

of forest fire information specific to 2002; (3) inclusion of the secondary organic aerosol 

(SOA) formation mechanism from Chung and Seinfeld (2002); and (4) inclusion of 

prototype soil dust and sea salt simulations. An additional model improvement, which has 

since been implemented in the standard version of GEOS-Chem, was the application of 

surface emissions and dry deposition to the entire mixed layer column diagnosed by 

GEOS (the Global Earth Observation System of NASA) rather than to just the surface 

layer of the model. Details of the emissions, meteorology, model configuration, and 

model performance evaluation for the VISTAS simulations are described in the report to 

VISTAS (Jacob, 2005). 

For North America, the VISTAS GEOS-Chem modeling used monthly-average 

emissions, so the model results do not reflect diurnal or weekday-weekend variability in 

emissions, and also diminish the impacts of episodic emissions, such as the July 2002 

wildfire in Ontario, Canada. The Mexican emissions were from the inventory of the 

BRAVO Study, while Canadian emissions for the year 2000 were used. Emissions from 

Caribbean islands were not included, which could impact model estimates at the 

Everglades. 

The surface meteorological data inputs to GEOS-Chem had 3-hr temporal resolution and 

the upper air meteorological inputs had 6-hr resolution, with a horizontal resolution of 

1°x1° or better and 48 layers vertically. 

The output of the GEOS-Chem modeling, as delivered to VISTAS, contained 

concentrations of all visibility-related components of particulate matter with 3-hr 

temporal resolution and 4°x5° horizontal resolution over multiple layers of the 

atmosphere. 

Even though the coarse spatial resolution degraded the performance of the model over 

North America, relative to results from prior modeling for other years using finer grids, 

the concentrations simulated by GEOS-Chem maintained the synoptic-scale structure and 

did not have a continental-scale bias. At a finer scale, the concentrations simulated by 

GEOS-Chem for the surface layer in 2002 were generally within a factor of two of 

regional-averages (~500 km scale) of measurements of the various chemical components 

of particulate matter in the United States and, except for organic carbon, satisfactorily 

simulated the seasonal variability in the observations. 

In the meantime, Harvard University had also carried out new finer-grid simulations for 

2001 for EPRI, using a 1°x1° grid over North America, nested in a coarser grid that 

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Appendix G.2 - 3

January 18, 2008
3

covered the globe. The final version of these simulations included modifications to the 

modeling grid to improve delineation between domestic US emissions and those of 

foreign sources at the national boundaries, and revision (reduction) of the Canadian and 

Mexican sulfur emissions. These GEOS-Chem 1°x1° grid scale simulations for 2001 and 

their results are described in the paper by Park et al. (2006). Over appropriate geographic 

and temporal scales, the results of these simulations for 2001were similar to those of the 

2002 VISTAS simulations, which gave confidence in the VISTAS GEOS-Chem results. 

The concentration fields that resulted from the GEOS-Chem simulations were not 

directly usable as boundary conditions for the 36-km grid scale CMAQ simulations. 

GEOS-Chem and CMAQ use different horizontal and vertical grid scales and different 

map projections, and have different labels for chemical constituents. The GEOS-Chem 

outputs were processed by Daewon Byun at the University of Houston to adjust for these 

discrepancies and to develop boundary conditions for the 36-km CMAQ modeling. Since 

the CMAQ modeling has temporal resolution of one hour, the boundary conditions were 

given for every hour. However, since the GEOS-Chem outputs had 3-hr resolution, the 

hourly boundary conditions were the same for three consecutive hours. An example of 

the resulting boundary conditions for sulfate at the surface is shown in Figure 1, 

Average Sulfate Boundary Conditions 

GEOS-CHEM for July 1, 2001 

Figure 1. GEOS-CHEM projections of sulfate boundary conditions for the national 

CMAQ modeling grid on July 1, 2001, as indicated by the colors along the boundary of 

the modeling domain.

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Appendix G.2 - 4

January 18, 2008
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 

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Appendix G.2 - 5

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5

The CENRAP RPO applied a third method to estimate the impacts of international 

emissions. They used the PSAT (PM Source Apportionment Technology) module of the 

CAMx regional air quality model to track the 2002 contributions of three non-domestic 

source categories (Canadian emissions in the CAMx modeling domain, Mexican 

emissions in the CAMx modeling domain, and boundary conditions) at all Class I areas. 

The boundary conditions applied at the boundaries of the CAMx 36-km scale grid were 

based on the outputs of the GEOS-Chem modeling described above. The international 

impacts in 2002 were then calculated as the sums of the PSAT-determined contributions 

of these three source categories. One major difference between this approach and the 

VISTAS approaches is that the PSAT international impacts included the contributions 

from fire emissions. This will result in larger OC and EC international impacts compared 

to the VISTAS approach. 

To illustrate the magnitudes of the international contributions to two of the principal 

components of the visibility-impairing aerosol, Figures 2 and 3 show the annual averages 

of estimates of international contributions to ammonium sulfate and organic 

carbonaceous mass (OCM) at Class I areas in the VISTAS region and in adjacent states. 

Results are presented for all three of the methods described above. Two sets of GEOSChem results are shown – for 2002 with the VISTAS 4°x5° grid and for 2001 with the 

EPRI 1°x1° grid. 

 

Transboundary Man-Made Impacts

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

ACAD

LYBR

BRIG

BOWA

ISLE

BIBE

HEGL

CACR

MING

UPBU

BRET

EVER

CHAS

SAMACOHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

(NH4)2SO4 Conc. 

(_g/m3

)

GEOS_4x5 GEOS_1x1 PSAT CMAQ

Figure 2. Estimates by four methods of impacts of international emissions on annual 

average ammonium sulfate concentrations at Class I areas. 

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Appendix G.2 - 6

January 18, 2008
6

Transboundary Man-Made Impacts

0.00

0.05

0.10

0.15

0.20

0.25

0.30

ACAD

LYBR

BRIG

BOWA

ISLE

BIBE

HEGL

CACR

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UPBU

BRET

EVER

CHAS

SAMACOHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

OCM Conc. (

_g/m3

)

GEOS_4x5 GEOS_1x1 PSAT CMAQ

Figure 3. Estimates by four methods of impacts of international emissions on annual 

average organic carbonaceous mass concentrations at Class I areas. 

For ammonium sulfate there is reasonable agreement (better than a factor of two or so 

everywhere except at Everglades) between all pairs of methods, while the results are 

more variable for OCM. The impacts estimated by the VISTAS 4°x5° GEOS-Chem 

simulations tend to be smaller than those of the other methods. Not surprisingly, the 

biggest impacts from international emissions tend to occur at Class I areas that are near 

the Canadian and Mexican borders. 

The projected contributions from international emissions on the 20% worst visibility days 

in 2002 are compared to annual average contributions in Figures 4 and 5. Results 

obtained using both the PSAT and CMAQ models are shown. At the VISTAS sites, 

CMAQ projected slightly higher international contributions to sulfate on the 20% worst 

visibility days than the annual average contributions. Compared to the corresponding 

annual average international contribution, PSAT generally showed lower contribution to 

sulfate on the 20% worst days at the mountain sites and equal or slightly higher 

contributions at the coastal sites. For organic carbonaceous mass, there were no clear 

trends between 20% worst and annual average days in either the PSAT or CMAQ results. 

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Appendix G.2 - 7

January 18, 2008
7

Transboundary Man-Made Impacts

0.0

0.2

0.4

0.6

0.8

1.0

1.2

BRIG

HEGL

CACR

MING

UPBU

BRET

EVER

CHAS

SAMACOHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

(NH4)2SO4 Conc. 

(_g/m3

)

PSAT_20 PSAT CMAQ_20 CMAQ

Figure 4. Estimates by the PSAT and CMAQ models of the impacts of international 

emissions on ammonium sulfate mass concentrations at Class I areas on the 20% worst 

visibility days and on the annual average in 2002.

Transboundary Man-Made Impacts

0.00

0.05

0.10

0.15

0.20

0.25

0.30

BRIG

HEGL

CACR

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OKEF

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GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

OCM Conc. (

_g/m3

)

PSAT_20 PSAT CMAQ_20 CMAQ

Figure 5. Estimates by the PSAT and CMAQ models of the impacts of international 

emissions on organic carbonaceous mass concentrations at Class I areas on the 20% 

worst visibility days and on the annual average in 2002.

Figures 6 and 7 put the international impacts into the context of current measured 

concentrations. They compare the international impact estimates with current monitored 

concentrations on the 20% worst visibility days at two VISTAS Class I areas chosen as 

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Appendix G.2 - 8

January 18, 2008
8

examples, Shenandoah (SHEN) and St. Marks (SAMA). At both locations, the 

international impacts on concentrations on such days are small, typically less than 5% of 

the measured concentrations. 

At the same two Class I areas, the magnitudes of international contributions relative to 

the uniform rate of progress (URP) goal for 2018 are shown in Figures 8 and 9, where all 

the haze index values were calculated using the new IMPROVE algorithm. The 

numerical value given at 2004 (e.g., 29.31 dv in Figure 8) is the baseline period haze 

index value (the average over the five years from 2000 to 2004). Because the CMAQ 

results are scaled with Relative Response Factors (RRFs), the initial CMAQ haze index 

matches the measured baseline value. In each plot, the open triangle at 2018 indicates the 

estimated haze index, in deciviews, as predicted by the CMAQ model using the Base G1 

emission inventory. The X indicates the haze index in 2018 if international emissions are 

removed from the 2018 CMAQ simulation, so the difference between the open triangle 

and the X represents the international contribution. 

SHEN1

20% Worst Days 

0

5

10

15

20

25

30

35

40

45

2/28 5/26 5/29 6/4 6/10 6/13 6/19 6/25 7/1 7/4 7/16 7/19 7/22 7/31 8/3 8/12 8/21 8/24 8/27 9/11 9/14 9/17 9/29 10/2

Mass (μg/m3

)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

Figure 6. For the 20% worst visibility days in 2002 at Shenandoah, CMAQ-simulated 

international contributions (right bars) and domestic contribution to measured PM 

concentrations (measured component minus international contribution) (left bars). 

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Appendix G.2 - 9

January 18, 2008
9

SAMA1

20% Worst Days (2002)

0

5

10

15

20

25

30

35

2/4 2/13 2/16 2/25 3/30 4/5 4/8 4/17 4/26 5/2 5/5 5/8 5/11 6/4 7/4 7/7 7/16 7/19 9/17 10/17 10/20 10/23 12/7

 Mass (μg/m3) 

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

Figure 7. For the 20% worst visibility days in 2002 at Saint Marks, CMAQ-simulated 

international contributions (right bars) and domestic contribution to measured PM 

concentrations (measured component minus international contribution) (left bars). 

Uniform Rate of Progress Glide Path

Shenandoah - 20% Worst Days

29.31 28.12

25.14

22.16

19.18

16.20

13.23

11.44

22.00

19.74

0

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15

20

25

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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.

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Appendix G.2 - 10

January 18, 2008
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

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25

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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. 

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Appendix G.2 - 11

January 18, 2008
11

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

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

220%

240%

COHU1DOSO1

GRSM1

JARI1

LIGO1CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent of target reduction achieved

CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm

CMAQ New IMPROVE Algorithm w/ Int'l Trans Removed

CMAQ Old IMPROVE Algorithm w/ Int'l Trans Removed

VISTAS non-VISTAS

Figure 10. Percent of Uniform Rate of Progress haze index reduction achieved in 2018 

according to the CMAQ modeling with the Base G1 inventory. Results are given for 

improvements calculated using the new IMPROVE algorithm (squares) and the old 

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

considered; open symbols indicate results when effects of international emissions are 

removed. 

Figure 10 shows that removing the international contribution is sufficient to raise the 

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

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

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

used calculate the haze index. 

Figures 11through14 display the Uniform Rate of Progress for the four Class I areas in 

North Carolina and show the modeled predictions for 2018 with all emissions considered 

and without the contribution from international emissions. These figures have the same 

format as Figures 8 and 9. 

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Appendix G.2 - 12

January 18, 2008
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

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15

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25

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35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

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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

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10

15

20

25

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35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

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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. 

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Appendix G.2 - 13

January 18, 2008
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

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10

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25

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35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

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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

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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. 

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Appendix G.2 - 14

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14

References 

Chung, S. H. and Seinfeld, J.H. (2002), Global distribution and climate forcing of 

carbonaceous aerosols, Journal of Geophysical Research, Vol. 107 (D19), 4407, 

doi:10.1029/2001JD001397. 

Jacob, D.J. (2005), Documentation and Evaluation of the GEOS-Chem Simulation for 

2002 Provided to the VISTAS Group. Final report, Harvard University. June 24. 

Park. R.J., Jacob, D.J., Chin, M., and Martin, R.V. (2003). Sources of carbonaceous 

aerosols over the United States and implications for natural visibility. Journal of 

Geophysical Research, Vol. 108 (D12), 4355, doi:10.1029/2002JD003190. 

Park. R.J., Jacob, D.J., Field, B.D., Yantosca, R.M., and Chin, M. (2004). Natural and 

transboundary pollution influences on sulfate-nitrate-ammonium aerosols in the United 

States: Implications for policy. Journal of Geophysical Research, Vol. 109, D15204, 

doi:10.1029/2003JD004473. 

Park. R.J., Jacob, D.J., Kumar, N., and Yantosca, R.M. (2006). Regional visibility 

statistics in the United States: Natural and transboundary pollution influences, and 

implications for the Regional Haze Rule. Atmospheric Environment, Vol. 40, pp. 5405-

5423. 

Schichtel, B.A., Gebhart, K.A., Malm, W.C., Barna, M.G., Pitchford, M.L., Knipping, 

E.M., and Tombach, I.H. (2005). Reconciliation and Interpretation of Big Bend National 

Park Sulfur Source Apportionment: Results from the Big Bend Regional Aerosol and 

Visibility Observational Study – Part I. Journal of the Air & Waste Management Assoc.

Vol. 55, pp. 1709-1725. 

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