November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-17 

Average Sulfate Boundary Conditions 

GEOS-CHEM for July 1, 2001 

Figure 5-6. Example 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. 

Using the boundary conditions provided by the GEOS-CHEM global simulations, VISTAS 

calculated the impacts of international anthropogenic emissions on haze at U.S. Class I areas in 

two different ways, which are summarized in Table 5-2: 

• First, as the difference between the results of two GEOS-CHEM simulations: (1) with 

2002 international emissions (natural and anthropogenic) but only natural U.S. 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 U.S., the result is an 

estimate of what the international contribution would be if the U.S. 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 5-2 for portrayal of this 

calculation.) 

• Second, as the difference between the results of two CMAQ 36-km grid simulations of 

the U.S., 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 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-18 

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 5-2 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, that is that there are no 

international fire emissions due to anthropogenic burning. 

Table 5-2. 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 

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 (ENVIRON and UCR, 2007). 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 5-7 and 5-8 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 four of the 

methods described above. Two sets of GEOS-CHEM results are shown: one for 2002 with the 

VISTAS 4°x5° grid and one for 2001 with the EPRI 1°x1° grid. 

 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-19 

Figure 5-7. Estimates by four methods of impacts of international emissions on annual average 

ammonium sulfate [(NH4)2SO4] concentrations at Class I areas.

Transboundary Man-Made Impacts

0.00

0.05

0.10

0.15

0.20

0.25

0.30

ACAD

LYBR

BRIG

BOWA

ISLE

BIBE

HEGL

CACR

MING

UPBU

BRET

EVER

CHAS

SAMACOHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

OCM Conc. (

_g/m3

)

GEOS_4x5 GEOS_1x1 PSAT CMAQ

Figure 5-8. Estimates by four methods of impacts of international emissions on annual average 

organic carbonaceous mass (OCM) or organic mass carbon (OMC) 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. 

Transboundary Man-Made Impacts

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

ACAD

LYBRBRIG

BOWA

ISLEBIBE

HEGL

CACRMINGUPBUBRETEVERCHASSAMACOHU

OKEF

ROMA

SIPS

SHROGRSM

LIGOMACASWAN

JARI

DOSO

SHEN

GEOS_4x5 GEOS_1x1 PSAT CMAQ

(NH4)2SO4 Conc. (_g/m3) 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-20 

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

in 2002 are compared to annual average contributions in Figures 5-9 and 5-10. Results obtained 

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

projected slightly higher international contributions to sulfate on the 20 percent worst visibility 

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

international contribution, CAMx/PSAT generally showed lower contribution to sulfate on the 

20 percent 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 percent 

worst and annual average days in either the CAMx/PSAT or CMAQ results. 

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 5-9. Estimates by the PSAT and CMAQ models of the impacts of international emissions 

on ammonium sulfate mass concentrations at Class I areas on the 20% worst visibility days and 

on the annual average in 2002. 

Transboundary Man-Made Impacts

0.00

0.05

0.10

0.15

0.20

0.25

0.30

BRIG

HEGL

CACR

MING

UPBU

BRET

EVER

CHAS

SAMACOHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

OCM Conc. (

_g/m3

)

PSAT_20 PSAT CMAQ_20 CMAQ

Figure 5-10. 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. 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-21 

Figures 5-11 and 5-12 put the international impacts into the context of current measured 

concentrations. They compare the international impact estimates with current monitored 

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

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 5-13 and 5-14, where all the 

haze index values were calculated using the new IMPROVE algorithm. In each plot, the open 

triangle at 2018 indicates the estimated haze index, in deciviews, as predicted by the CMAQ 

model using the Base G2 emission inventory. The open diamond 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 open diamond 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 5-11. For the 20 percent 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).
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-22 

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 10/1710/20 10/23 12/7

 Mass (μg/m3) 

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

Figure 5-12. For the 20 percent worst visibility days in 2002 at St. 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

28.11

25.12

22.13

19.14

16.15

13.16

11.37

22.11

20.47

0

5

10

15

20

25

30

35

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

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Method 1 Prediction w/o Intl. Trans.

Figure 5-13. 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; open diamond = estimate with international emissions removed. 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-23 

Uniform Rate of Progress Glide Path

Saint Marks - 20% Worst Days

26.31 25.33

22.89

20.45

18.01

15.57

13.13

11.67

22.89

21.82

0

5

10

15

20

25

30

35

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

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Method 1 Prediction w/o Intl. Trans.

Figure 5-14. 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; open diamond = estimate with international emissions removed. 

At Shenandoah, Figure 5-13 shows that the modeled 2018 haze index (22.11 dv) is already 

below the 2018 Uniform Rate of Progress (25.12 dv). Removing the international contribution 

would lower the 2018 haze index by another 1.64 dv. Although the international contribution is 

small today, it becomes increasingly important as U.S. emissions are reduced. (For these 

analyses, the international emissions are assumed to remain constant from the baseline period to 

2018). 

At Saint Marks, Figure 5-14 shows that the modeled 2018 haze index (22.89) is right at the 2018 

Uniform Rate of Progress (22.89). Removing the international contribution would reduce the 

2018 haze index by 1.07 dv, to a value that lies just below (indicating greater reduction than) the 

2018 Uniform Rate of Progress. 

Information of the type portrayed for 2018 in Figures 5-13 and 5-14 is summarized in Figure 5-

15 for all VISTAS Class I areas and Class I areas in adjacent states using the DotPlot format 

presented previously that represents the 2018 visibility projection as a percentage of achieving 

the 2018 Uniform Rate of Progress. Figure 5-15 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 eliminated, all Class I areas 

exceed the Uniform Rate of Progress in 2018. 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-24 

CMAQ 2018g2b/2002gt2a 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 New IMPROVE Algorithm w/ Intl Trans.

Mountain Coastal non-VISTAS

Figure 5-15. Percent of Uniform Rate of Progress haze index reduction achieved in 2018 

according to the CMAQ modeling for the Base G2 inventory. Results are given for 

improvements calculated using the new IMPROVE algorithm. Solid symbol indicate results 

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

emissions are removed from the 2018 visibility projection. 

5.5 Visibility Trends 

Figure 5-16 displays trends in visibility impairment at the VISTAS Class I areas using the period 

of record of measurements at the associated IMPROVE monitor and the new IMPROVE 

equation. Trends are included for the worst 20 percent days, the best 20 percent days and all 

IMPROVE sampled days during a year. The EPA guidance procedures were used to construct 

the worst and best 20 percent days that includes a minimum data capture requirement (EPA, 

2003a), whereas no such minimum data capture was applied when looking at the “annual 

average” of all IMPROVE sampled days trends. So care must be taken when analyzing trends 

for the all sampled IMPROVE days trends as there could be large missing periods with high or 

low extinction that are not being account for. The WRAP Technical Support System (TSS) 

website was used to calculate the visibility trends at the VISTAS Class I areas that includes 

IMPROVE data from start of recording through 2004 and includes no data filling (see: 

http://vista.cira.colostate.edu/TSS/Default.aspx) . 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-25 

The IMPROVE monitor at the SIPS Class I area has been operating for 13 years, however only 

five years of trends are available for the worst and best 20 percent days due to the valid data 

capture requirements of these regional haze metrics (EPA, 2003a). Although there is a lot of 

year-to-year variability in the visibility due to meteorology, fires and other factors, there does 

appear to be a downward trend in visibility impairment at SIPS for the worst 20 percent and all 

IMPROVE sampled days (Figure 5-16a). 

The visibility trends at CHAS are given in Figure 5-16b. Again there is lots of year-to-year 

variability but there also appears to be a general downward trend in visibility impairment at 

CHAS for the worst 20 percent and all sampled days. 

The trends at EVER are confounded by poor valid data capture and what appears to be impacts 

due to biomass burning in 2003 that enhance the OMC and EC contributions for the worst 20 

percent days (Figure 5-16c). Also changes in IMPROVE measurement protocols may affect the 

interpretation of the visibility trends at EVER. In any event, there is no indication of any 

downward trend in visibility impairment at EVER. 

SAMA (Figure 5-16d) and COHU (Figure 5-16e) only have two years of sufficient valid data to 

construct trends for the worst and best 20 percent days which is insufficient to form any opinion 

on trends given the year-to-year variability in visibility. 

There is a fairly long term record of IMPROVE measurements at the OKEF Class I area (14 

years) with 11 years of sufficient data capture to construct the worst and best 20 percent days 

regional haze metric (Figure 5-16g). For the worst 20 percent days, there appears to be a slight 

trend in improved visibility impairment that is driven by sulfate, although the high sulfate 

extinction in 2003 counters that argument. For the best 20 percent days, there is so much yearto-year variability that no trend can be distinguished. There is also a slight trend towards 

improvements in visibility for the all sampled days metric. 

Visibility at LIGO for the four years of sufficient valid data (2001-2004) is fairly flat for the 

worst and all sampled days metrics (Figure 5-16h). 

SHRO has sufficient valid data capture for the older four years of 1995-1998 and two more 

recent years of 2003-2004 to develop best/worst haze metrics (Figure 5-16i). 2004 has the least 

impaired worst 20 percent days of record at SHRO which suggests a trend toward improvements 

in visibility. However, 2004 was also a very wet year with cleaner than normal pollution 

conditions and removing it from the SHRO trend plots would make the more recent visibility 

within the range of year-to-year variability in the past. 

SWAN has only two years with sufficient data capture so no discussions on trends can be made 

(Figure 5-16j). 

ROMA has a fairly rich trend history dating back to 1995 with the cleanest three years occurring 

recently in 2001-2003 (Figure 5-16k). Although 2004 is dirtier, the occurrence of the recent 

cleaner years suggests a downward trend in visibility impairment at ROMA with the more 

impaired conditions of 2004 likely due to year-to-year variability. 

GRSM has one of the longest periods of record with the IMPROVE monitor operating for 17 

years and collecting sufficient valid data to produce 15 years of best and worst 20 percent days 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-26 

regional haze metrics (Figure 5-16l). Again there is lots of year-to-year variability in the 

visibility impairment, but it is encouraging that over the 5 most recent years light extinction for 

the worst 20 percent days is approximately 210 Mm-1 or less a level achieved only once in the 

previous ten years. A trend toward improvements in visibility appears to occur in the worst 20 

percent and all sampled days metrics. 

Although the two latest years (2003-2004) are the lowest visibility years in the four-year record 

at JARI, it is too short a period to infer a trend (Figure 5-16m). 

Like GRSM, SHEN has a 17 year history of IMPROVE measurements that include sufficient 

data capture to produce 14 years of best and worst 20 percent days regional haze metrics (Figure 

5-16n). There is a clear downward trend in visibility impairment for the worst 20 percent days 

with the values for the first five years of the 1990s ranging from 240 to 285 Mm-1, whereas the 

values for the first five years of the 2000s ranging from 160 to 210 Mm-1. There also appears to 

be a distinct trend in improved visibility in the all sampled days metric and, although confounded 

by a lot of year-to-year variability, even the best 20 percent days metric. 

The DOSO Class I area also has a long record of IMPROVE measurements (Figure 5-16o). 

There is a slight trend toward visibility improvements in the worst 20 percent and all sampled 

days metrics. Although this is driven in part by the high visibility years of 1994-1995 which if 

removed suggests a more flat trend in visibility. 

. 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-27 

Figure 5-16a. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Sipsey (SIPS), Alabama for the average of the Worst 20 Percent days (top), Best 20 Percent 

days (middle) days and all IMPROVE sampling days during the period of record (bottom). 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-28 

Figure 5-16b. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Chassahowitzka (CHAS), Florida for the average of the Worst 20 Percent days 

(top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period 

of record (bottom). 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-29 

Figure 5-16c. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) at 

Everglades (EVER), Florida for the average of the Worst 20 Percent days (top), Best 20 Percent days 

(middle) days and all IMPROVE sampling days during the period of record (bottom). 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-30 

Figure 5-16d. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) at 

St. Marks (SAMA), Florida for the average of the Worst 20 Percent days (top), Best 20 Percent days 

(middle) days and all IMPROVE sampling days during the period of record (bottom). 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-31 

Figure 5-16e. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) at 

Cohutta (COHU), Georgia for the average of the Worst 20 Percent days (top), Best 20 Percent days 

(middle) days and all IMPROVE sampling days during the period of record. 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-32 

Figure 5-16f. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Okefenokee (OKEF), Georgia for the average of the Worst 20 Percent days (top), Best 20 

Percent days (middle) days and all IMPROVE sampling days during the period of record. 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-33 

Figure 5-16g. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Mammoth Cave (MACA), Kentucky for the average of the Worst 20 Percent 

days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the 

period of record (bottom). 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-34 

Figure 5-16h. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Linville Gorge (LIGO), North Carolina the average of the Worst 20 Percent days 

(top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of 

record (bottom). 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-35 

Figure 5-16i. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Shinning Rock (SHRO), North Carolina for the average of the Worst 20 Percent 

days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the 

period of record (bottom). 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-36 

Figure 5-16j. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Swanquarter (SWAN), North Carolina for the average of the Worst 20 Percent 

days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the 

period of record (bottom). 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-37 

Figure 5-16k. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Cape Romain (ROMA), South Carolina for the average of the Worst 20 Percent 

days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the 

period of record (bottom). 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-38 

Figure 5-16l. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Great Smokey Mountains (GRSM), Tennessee-North Carolina for the average 

of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE 

sampling days during the period of record (bottom). 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-39 

Figure 5-16m. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at James River Face (JARI), Virginia for the average of the Worst 20 Percent days 

(top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of 

record (bottom). 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-40 

Figure 5-16n. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Shenandoah (SHEN), Virginia for the average of the Worst 20 Percent days 

(top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of 

record (bottom). 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-41 

Figure 5-16o. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Dolly Sods (DOSO), West Virginia for the average of the Worst 20 Percent days 

(top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of 

record (bottom). 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-42 

5.6 Geographic Areas of Influence of Visibility Impairment for Class I Areas 

VISTAS has determined that SO2 emission reductions from EGU and non-EGU point sources in 

the VISTAS and nearby states would be the most effective sources to control to improve 

visibility at the Class I areas in the VISTAS region and vicinity. One of the next steps was to 

identify the specific geographic areas that most likely influence visibility in each Class I area, 

and then to identify the major SO2 point sources located in those geographic areas. An SO2 

Area of Influence was defined for each Class I area to represent the geographic area containing 

sources that would likely have the greatest impact on visibility at that Class I area. All SO2 

point sources within these Areas of Influence were identified and ranked by their 2018 

emissions. The following sections contain a broad overview of the steps in the Area of Influence 

analyses and the final results. See Appendix H for a more detailed discussion of how these 

analyses were developed. The Area of Influence analyses were conducted by Joe Adhloch and 

Cassie Archuleta of Air Resources Specialists, Gerry Mansell of ENVIRON, Greg Stella of 

Alpine Geophysics and Pat Brewer of VISTAS and the reader is referred to additional 

documentation for more details at the analyses (Adhloch et al., 2007; Mansell, 2007). 

5.6.1 Back Trajectory Analyses 

The first step in the Area of Influence analyses was to generate meteorological back trajectories 

for IMPROVE monitoring sites in VISTAS region and neighboring Class I areas for the 2000-

2004 worst 20 percent days baseline period. Back trajectory analyses use interpolated measured 

or modeled meteorological fields to estimate the most likely central path of air masses that arrive 

at a receptor at a given time. The method essentially follows a parcel of air backward in hourly 

steps for a specified length of time. Trajectories were started at 100 meters and 500 meters 

above the surface and run backward from the site for 72-hours. Figure 5-17 is an example of a 

back trajectory analysis for Great Smoky Mountains National Park for the worst 20 percent days 

in 2002. 

Figure 5-17. Example back trajectories for worst 20 percent visibility days in 2002 for Great 

Smoky Mountains National Park. 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-43 

Figure 5-18. Example residence time plot for 20 percent worst visibility days in 2000-2004 for 

Great Smoky Mountains National Park based on trajectories with 100m start height. 

5.6.2 Residence Time Plots 

The next step was to plot residence time for each Class I area using five years of back trajectories 

for the worst 20 percent visibility days in 2000-2004. Residence time is the frequency that winds 

pass over a specific geographic area on the path to a Class I area. Separate residence time plots 

were generated using back trajectories with 100m and 500m start heights. As illustrated in 

Figure 5-18, winds influencing Great Smoky Mountains on the worst 20 percent days come from 

all directions and there is no single predominant wind direction influencing the 20 percent worst 

visibility days. In contrast, at Swanquarter, the residence time plot indicates a clear north-south 

gradient of influence along the eastern coast of the United States (Figure 5-19). 

GRSM1

2000-04

20% Worst Monitored Days

100m Residence Times (%)

< 0.01

0.01 - 0.02

0.02 - 0.04

0.04 - 0.08

0.08 - 0.16

0.16 - 0.32

> 0.32
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F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-44 

Figure 5-19. Example residence time plot for 20 percent worst visibility days in 2000-2004 for 

Swanquarter based on trajectories with 100m start height. 

5.6.3 SO2 Areas of Influence 

The next step was to develop sulfate extinction weighted residence time plots to define the 

geographic area with highest probability of influencing the receptor on the worst 20 percent days 

in 2000-2004 that were dominated by sulfate. Each back trajectory was weighted by sulfate 

extinction for that day. This allows the focus to be on the worst 20 percent days that are 

influenced by sulfate and places less importance on days influenced by, for example, organic 

carbon from fires. Sulfate-weighted back trajectories for the 20 percent worst days were 

combined for the 5 years of data. The resulting sulfate extinction-weighted residence time plots 

were used to define the geographic Area of Influence for sources of SO2 emissions. In Figure 5-

20 the area representing 10 percent or greater residence time is outlined in red and the area 

representing 5 percent or greater residence time is outlined in gray. The VISTAS states focused 

their analyses on the Area of Influence defined by 5 percent or greater sulfate extinctionweighted residence time. 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-45 

Figure 5-20. Example SO2 Area of Influence plot for sulfate extinction weighted residence time 

for 20 percent worst visibility days in 2000-2004 for Great Smoky Mountains National Park 

based on trajectories with 100m start height. 

5.6.4 Emissions Sources within SO2 Areas of Influence 

Residence time plots were then combined with geographically-gridded emission data based for 

the 2002 baseline and 2018 emissions inventories. Plots were generated for the Areas of 

Influence defined by trajectories with 100m and 500 m start heights. As a way of incorporating 

the effects of transport, deposition, and chemical transformation of point source emissions along 

the path of the trajectories, these data were weighted by 1/d, where d was calculated as the 

distance, in kilometers, between the center of the grid cell in which a source is located and the 

center of the grid cell in which the IMPROVE monitor is located. The distance-weighted point 

source SO2 emissions are then combined with the gridded extinction-weighted back-trajectory 

residence times at a spatial resolution of 36 km. 

The final step was to combine the residence times and gridded emissions data in plots and data 

sets. The distance weighted (1/d) gridded point source SO2 emissions were multiplied by the 

total extinction-weighted back-trajectory residence times on a grid cell by grid cell basis. These 

results were then normalized by the domain-wide total and displayed as a percentage. The 

analysis was done using both the 2002 and 2018 emissions inventories. 

Figure 5-21 illustrates the 2002 and 2018 distance weighted gridded emissions multiplied by 

sulfate extinction weighted residence time for Great Smoky Mountains National Park. These 

maps help visualize where the emissions reductions will be occurring between 2002 and 2018. 

SO2 Area of Influence for Great Smoky Mountains

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%

Orange line perimeter indicate Area of Influence with Residence Time > 5%.

SO2 Area of Influence for Great Smoky Mountains

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%

Orange line perimeter indicate Area of Influence with Residence Time > 5%.
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-46 

The change in SO2 emissions between 2002 and 2018 can be seen by comparing emissions 

source strengths in the two plots. Note the emissions from each source are normalized by the 

total emissions in the domain. Sources that reduce SO2 emissions by 2018 will show a lower 

contribution to emissions in the domain. On the 2018, map the grid cells with these sources will 

show a lighter color gradient than on the 2002 map. For example, SO2 reductions from EGU in 

western and central North Carolina under the North Carolina Clean Smokestacks Act can be seen 

by comparing the 2002 and 2018 maps. Because the total emissions in the domain are smaller in 

2018, a source that does not change emissions between 2002 and 2018 may actually appear to 

increase in importance in 2018 compared to 2002. 

Figure 5-21. Great Smoky Mountains 2002 (left) and 2018 (right) SO2 distance weighted 

emissions times SO4 extinction-weighted residence time plots. 

5.6.5 Specific Source Types in the Areas of Influence 

The next step in the analysis was to review the emissions inventories to determine the source 

categories, as well as specific sources, found to have the greatest impact on visibility in VISTAS 

and nearby Class I areas. Lists of SO2 point sources within the Areas of Influence for each Class 

I areas were developed using the VISTAS 2002 base year and 2018 future year emissions. For 

this purpose, the Area of Influence was defined as the counties with maximum sulfate extinction 

weighted residence time greater than five. For SO2 sources within each Area of Influence, the 

following attributes were defined for each individual unit: 

• State, county, and source (plant), and industry identification codes 

• SO2 emissions for 2002 and 2018 

• 2018 control efficiency 

• Distance to Class I areas (defined by distance to the monitor at the Class I area) 

2002 vs 2018 SO2 Emissions weighted by Residence Time

Great Smoky Mtn., TN 

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%.

Orange line perimeter indicate Area of Influence with Residence Time > 5%.

Max Value = 46%

2002 vs 2018 SO2 Emissions weighted by Residence Time

Great Smoky Mtn., TN 

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%.

Orange line perimeter indicate Area of Influence with Residence Time > 5%.

Max Value = 46%

2002 vs 2018 SO2 Emissions weighted by Residence Time

Great Smoky Mtn., TN 

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%.

Orange line perimeter indicate Area of Influence with Residence Time > 5%.

Max Value = 46%
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-47 

• Emissions divided by distance (Q/d), a metric that accounts for the dispersion of 

emissions over distance 

• Maximum sulfate extinction weighted residence time (RTmax) 

Our review was conducted in a top down fashion starting with an analysis of the major source 

categories in each SO2 Area of Influence to determine which major categories had the highest 

residual contribution to the area in 2018. It was also important to identify reductions that are 

projected to occur between 2002 and 2018 within each category or at specific units. This 

allowed VISTAS States to determine if certain source categories or units that had yet to be 

controlled under the future year base case had the potential for reduction. Once the highest 

source types were identified, subcategories within those source types were reviewed. The 

contributions from major source categories to the 2018 inventory for the SO2 Areas of Influence 

for the Great Smokey Mountains Class I area is listed in Table 5-3. In this table, the source 

categories are broken out by the USEPA’s Tier 1 report categories and are defined below: 

• Fuel Comb Elec Utility Emissions from all fuel combustions at utility boilers 

• Fuel Comb Industrial Emissions from all fuel combustions at industrial boilers 

• Fuel Comb Other Emissions from all fuel combustions from commercial/institutional and 

residential sources (i.e., fireplaces, natural gas stoves, oil heaters, etc.) 

• Chemical & Allied 

Product Mfg Emissions from chemical manufacturing processes 

• Metal Processing Emissions from metal processing operations 

• Petroleum & Related 

Industries Emissions from petroleum refineries & related industries 

• Other Industrial 

Processes All other industrial processing not previously mentioned 

• Solvent Utilization Emissions from solvent utilization such as degreasing operations, surface 

coating operations, etc. 

• Storage & Transport Emissions from storage and transport of petroleum, organic and inorganic 

products 

• Waste Disposal & 

Recycling 

Emissions from open burning, incineration, landfills, publicly owned 

treatment works, treatment storage and/or disposal facilities, wastewater 

treatment facilities 

• Highway Vehicles Emissions from on-road mobile sources 

• Off-highway Emissions from off-road mobile sources 

• Miscellaneous (Ag, Fires) Emissions from agricultural operations, wildland fires, and other emissions 

sources not previously mentioned 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-48 

Table 5-3. 2018 Emissions Contributions from Major Source Categories in the Area of 

Influence for Great Smoky Mountains.

Table 5-3 indicate that for the Great Smoky Mountains Class I area, EGUs and industrial boilers 

are the two major sources categories contributing to 2018 SO2 emissions in the Areas of 

Influence, even after implementation of the CSA and CAIR. Together these two source 

categories contribute 84 percent of the 2018 SO2 emissions for the Area of Influence of the 

Great Smokey Mountains Class I area. Other fuel combustion and other industrial processes 

comprise another 8 percent of the 2018 SO2 emissions. 

These types of tables can also be used to evaluate the major source categories contributing to 

emissions of NOx, NH3, and PM emissions in 2018. For instance, highway vehicles and off 

road vehicles are major sources of NOx emissions, in addition to electric utilities and industrial 

boilers. The source category “miscellaneous” (which includes agricultural sources and fires) is 

the major contributor to NH3 and primary PM. 

The contributions to SO2 emissions in 2018 from the three highest source categories, electric 

utilities, industrial boilers, and other fuel combustion have been further broken out into 

subcategories. Table 5-4 indicates subcategories for the Areas of Influence for the Great 

Smokey Mountains Class I areas. Within electric utilities, all the SO2 emissions are attributable 

to coal fired power plants. Within industrial boilers, most emissions are attributable to coal fired 

boilers with lesser contributions from oil and gas boilers. Commercial and institutional coal and 

oil boilers have smaller contributions. 

Miscellaneous (Ag, Fires) 1% 1% 5% 0% 59% 26% 86%

Off-highway 11% 18% 35% 1% 2% 4% 0%

Highway Vehicles 18% 25% 48% 0% 2% 2% 11%

Waste Disposal & Recycling 4% 2% 3% 0% 5% 11% 0%

Storage & Transport 6% 0% 0% 0% 0% 1% 0%

Solvent Utilization 44% 1% 0% 0% 0% 1% 0%

Other Industrial Processes 6% 5% 1% 3% 8% 11% 0%

Petroleum & Related Industries 0% 0% 0% 1% 0% 0% 0%

Metals Processing 1% 1% 2% 3% 2% 5% 0%

Chemical & Allied Product Mfg 2% 0% 0% 2% 1% 1% 0%

Fuel Comb. Other 5% 6% 3% 6% 5% 12% 1%

Fuel Comb. Industrial 1% 18% 2% 30% 3% 5% 0%

Fuel Comb. Elec. Util. 0% 22% 1% 54% 11% 21% 1%

Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3

Miscellaneous (Ag, Fires) 1% 1% 5% 0% 59% 26% 86%

Off-highway 11% 18% 35% 1% 2% 4% 0%

Highway Vehicles 18% 25% 48% 0% 2% 2% 11%

Waste Disposal & Recycling 4% 2% 3% 0% 5% 11% 0%

Storage & Transport 6% 0% 0% 0% 0% 1% 0%

Solvent Utilization 44% 1% 0% 0% 0% 1% 0%

Other Industrial Processes 6% 5% 1% 3% 8% 11% 0%

Petroleum & Related Industries 0% 0% 0% 1% 0% 0% 0%

Metals Processing 1% 1% 2% 3% 2% 5% 0%

Chemical & Allied Product Mfg 2% 0% 0% 2% 1% 1% 0%

Fuel Comb. Other 5% 6% 3% 6% 5% 12% 1%

Fuel Comb. Industrial 1% 18% 2% 30% 3% 5% 0%

Fuel Comb. Elec. Util. 0% 22% 1% 54% 11% 21% 1%

Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-49 

Table 5-4. 2018 Emissions Contributions from Major Source Categories in the Area of 

Influence for Great Smoky Mountains Class I area. 

5.6.6 Evaluating the Four Statutory Factors for Specific SO2 Emissions Sources in Each 

Area of Influence 

The next step in the Area of Influence analyses was to identify emission reductions that have 

already occurred within each source category and at specific units. Unit level tables of emission 

comparisons from 2002 to 2018 were developed, allowing VISTAS States to review existing 

emission reductions. These tables assigned future year control technology from IPM forecasting 

and State modification for EGU and from control efficiency tables for non-EGU point sources. 

Once emission control profiles for specific units were defined, the next step is to determine what, 

if any, additional control measures would feasibly be available, and to assign costs to those 

control measures. For EGUs, the 2018 IPM file used by VISTAS was obtained and matched to 

the 2018 base case inventory of EGU sources. This step was conducted to ensure that 

incremental controls assigned to these source types did not duplicate existing base case 

assumptions. 

VISTAS used the USEPA’s AirControlNET database, modified for the VISTAS emission 

inventories, for the non-EGUs. The core of AirControlNET is a relational database system in 

which control technologies are linked to sources within the USEPA emissions inventories. The 

system contains a database of control measure applicability, efficiency, and cost information for 

reducing emissions. The control measure data file in AirControlNET includes not only the 

technology's control efficiency, and calculated emission reductions for that source, but also 

estimates the costs (annual and capital) for application of the control measure. 

S

Fuel Comb. Other-Residential Other 1%

Fuel Comb. Other-Residential Wood 0%

Fuel Comb. Other-Misc. (Except Residential) 0%

Fuel Comb. Other-Comm/Institutional Gas 0%

Fuel Comb. Other-Comm/Institutional Oil 3%

Fuel Comb. Other-Comm/Institutional Coal 2%

Fuel Comb. Industrial-Internal Combustion 0%

Fuel Comb. Industrial-Other 1%

Fuel Comb. Industrial-Gas 2%

Fuel Comb. Industrial-Oil 5%

Fuel Comb. Industrial-Coal 22%

Fuel Comb. Elec. Util.-Internal Combustion 0%

Fuel Comb. Elec. Util.-Other 0%

Fuel Comb. Elec. Util.-Gas 0%

Fuel Comb. Elec. Util.-Oil 0%

Fuel Comb. Elec. Util.-Coal 54%

Tier GRSM S

Fuel Comb. Other-Residential Other 1%

Fuel Comb. Other-Residential Wood 0%

Fuel Comb. Other-Misc. (Except Residential) 0%

Fuel Comb. Other-Comm/Institutional Gas 0%

Fuel Comb. Other-Comm/Institutional Oil 3%

Fuel Comb. Other-Comm/Institutional Coal 2%

Fuel Comb. Industrial-Internal Combustion 0%

Fuel Comb. Industrial-Other 1%

Fuel Comb. Industrial-Gas 2%

Fuel Comb. Industrial-Oil 5%

Fuel Comb. Industrial-Coal 22%

Fuel Comb. Elec. Util.-Internal Combustion 0%

Fuel Comb. Elec. Util.-Other 0%

Fuel Comb. Elec. Util.-Gas 0%

Fuel Comb. Elec. Util.-Oil 0%

Fuel Comb. Elec. Util.-Coal 54%

Tier GRSM
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-50 

Using the modified inventories identified above, VISTAS ran every available SO2 control 

strategy in AirControlNET against the EGU and non-EGU point source inventories to develop a 

master list of incremental control strategies for each unit in the VISTAS 36 km domain. 

For the sources within the Area of Influences for each VISTAS and nearby Class I area, the 

master list of incremental control measures was sorted to determine the costs of incremental 

control measures. These data were combined in a master spreadsheet with the distance from the 

emission release point to the Class I area IMPROVE monitor (in km), the 2018 residual 

emissions and distance (Q/d) or squared distance (Q/d2

), and the normalized 2018 SO2 point 

source emissions times distance-weighted residence time (RTMax) values for the county in 

which the emission release point was located. Each VISTAS state evaluated these control 

measures and costs as part of their review of the statutory factors for reasonable further progress. 

The regional haze rule requires that states consider the following factors and demonstrate how 

these factors were taken into consideration in selecting the reasonable progress goal: 

• the costs of compliance 

• the time necessary for compliance 

• the energy and non-air quality environmental impacts of compliance, and 

• the remaining useful life of any potentially affected sources. 

5.6.6.1 Cost of Compliance 

 

As defined in Section 5.6.5, coal-fired electric utilities and coal-fired industrial boilers were the 

largest source categories contributing to SO2 in 2018 in the Areas of Influence defined for the 

Great Smoky Mountains Class I area. Industrial boilers using oil and commercial and 

institutional boilers using coal or oil had small contributions to SO2 in 2018. 

Sulfur dioxide emissions from utility, industrial, commercial, or institutional boilers can be 

controlled either by switching the fuel source to a lower sulfur fuel content or by installing postcombustion controls. Costs vary by fuel source and boiler type and require source specific 

analyses for accuracy. 

Bituminous coal is commonly burned in boilers in the eastern US. Switching to another 

bituminous coal with lower sulfur content or blending bituminous coals can reduce SO2 

emissions with least impact to boiler performance. While sub-bituminous coal has a lower sulfur 

content than bituminous coal, it also has a lower heat rate and so more coal has to be burned to 

generate the same energy output. For boilers that are designed for bituminous coal, only a small 

fraction of the fuel can be switched to low sulfur sub-bituminous coal without affecting boiler 

operations. Costs of boiler modifications to accommodate low sulfur sub-bituminous coal may 

make switching to sub-bituminous coal impractical. Contract initiation and termination costs, 

differential fuel prices and heat rates, transportation costs, and modification to boiler operations, 

fuel handling and waste handling systems will have to be considered specific to each source. 

Flue-gas desulfurization is the common post-combustion control for coal-fired boilers. Flue gas 

is passed through an absorbent for sulfur dioxide (generally limestone or lime) in either a wet 

scrubber, dry scrubber, or spray dryer. A calcium sulfate by-product is produced that may be 

further processed to produce gypsum as a commercial byproduct. Costs of flue-gas 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-51 

desulfurization include initial construction costs and ongoing operational and maintenance costs 

for the absorber tower, sorbent handling, and waste product handling facilities. Costs per ton 

vary with boiler size, type, and facility siting considerations. 

For oil-fired boilers, lower sulfur oil may be an option. Costs need to consider differential fuel 

prices and heat rate, boiler modifications, fuel handling costs, and maintenance costs. 

Conceptually, post-combustion controls can be used for oil boilers, but there is little precedence 

for such installations. 

5.6.6.2 Time Necessary for Compliance 

For fuel switching, the time necessary to terminate existing fuel contracts and initiate new 

contracts needs to be considered. Generally two to three years may be required. Installation of 

post-combustion controls will require 3 or more years depending on market availability of labor 

and materials and utility system-wide priorities. Time necessary for compliance will need to be 

refined for specific sources. 

5.6.6.3 Energy and Non-Air Environmental Impacts 

Switching to lower sulfur fuel or installing post-combustion controls may reduce boiler heat rate 

and energy output. Scrubbers and spray dryers will require additional safeguards for fuel 

handling and waste handling systems to avoid additional non-air environmental impacts such as 

increased effluents in waste water discharges and storm water runoff. These factors will need to 

be considered specific to individual sources. Carbon dioxide is emitted as a by-product of flue 

gas desulfurization, therefore impacts of increased carbon emissions will need to be considered, 

particularly if carbon emissions are limited in the future under climate change mitigation 

strategies. 

5.6.6.4 Remaining Useful Life 

The useful remaining life is specific to the unit for which controls are considered. 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_5_AddAnal3.doc 5-52 

5.7 Area of Influence Analyses Results for VISTAS and Nearby Class I Areas 

VISTAS grouped the Area of Influence (AOI) results by Class I area for discussion of the four 

factors to be determined in defining reasonable progress. Figure 5-22 displays the Class I area 

groupings used by VISTAS for the AOI and four factors analysis that can be found at: 

http://www.vistas-sesarm.org/documents/FinalDocs.asp

Figure 5-22. Class I area groupings for VISTAS AOI displays and four factor analysis. 

. 

. 

. 

. 

Hercules 

Glade, 

MO . 

. 

VISTAS Class I 
 November 2007 

F:\VISTAS Phase II\TSD\draft#3\Chapter_6_References3.doc 6-1 

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APPENDIX A 

Emission Processing 

Selected QA Summary Reports and Figures 
 A-2 

Figure A-1. VISTAS BaseG2 Onroad Mobile Source Emissions Summary – 12km Daily 

Emissions Density Plot (January 17, 2002)
 A-3 

Figure A-2. VISTAS BaseG2 Area Source Emissions Summary – 12km Daily Emissions 

Density Plot (January 17, 2002)
 A-4 

Figure A-3. VISTAS BaseG2 Biogenic Source Emissions Summary – 36km Daily Emissions 

Density Plot (January 17, 2002) – ISOP
 A-5 

Figure A-4. VISTAS BaseG2 Area Source Emissions Summary – Daily Domainwide 

Diurnal Distribution – ALD2 
 A-6 

Figure A-5. VISTAS BaseG2 Elevated Fire Source Emissions Summary – Daily Domainwide 

Vertical Distribution – CO.
Figure A-6. VISTAS BaseG2 Elevated Fire Source Emissions Summary – 2002 Annual Domainwide Temporal 
Table A.1 – VISTAS BaseG2 Area Source Emissions Summary – 12km State Level Emission Totals (July 18, 2Stationary area Processed as Area sources Base inventory year 2002 Gridding matrix applied for gridVISTA12_168X177 No speciation matrix applied Temporal factors applied for episode from  Thursday July 18, 2002 at 000000 to  Thursday July 18, 2002 at 230000 Annual total data basis in report Date ; Region; State ; CO ; NOX ; VOC ; NH3 ; SO2 ; PM10 ; PM2_5 ; PM ; ; ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [t------------------------------------------------------------------------------------------------------------------------------------------------------ 07/18/2002; 001000;Alabama ; 0.44371E+02; 0.17017E+02; 0.35038E+03; 0.17273E+03; 0.10311E+03; 0.46289E+03; 0.73994E+02;  07/18/2002; 005000;Arkansas ; 0.32214E+03; 0.59925E+02; 0.17541E+03; 0.39077E+03; 0.77625E+02; 0.93498E+03; 0.21273E+03;  07/18/2002; 009000;Connecticut ; 0.74329E+01; 0.90859E+01; 0.12071E+03; 0.14411E+02; 0.53008E+00; 0.47080E+02; 0.78904E+01;  07/18/2002; 010000;Delaware ; 0.57666E+01; 0.40146E+01; 0.23986E+02; 0.43931E+02; 0.12991E+01; 0.27233E+02; 0.43240E+01;  07/18/2002; 011000;District of Columbia; 0.15917E+01; 0.15292E+01; 0.15007E+02; 0.22391E-01; 0.17404E+01; 0.83408E+01; 0.14891E+01;  07/18/2002; 012000;Florida ; 0.67168E+02; 0.70148E+02; 0.10590E+04; 0.99305E+02; 0.90880E+02; 0.71195E+03; 0.93626E+02;  07/18/2002; 013000;Georgia ; 0.13696E+03; 0.78878E+02; 0.73790E+03; 0.24723E+03; 0.15909E+03; 0.80410E+03; 0.13605E+03;  07/18/2002; 017000;Illinois ; 0.64822E+02; 0.54397E+02; 0.49044E+03; 0.15470E+03; 0.10554E+02; 0.18652E+04; 0.36050E+03;  07/18/2002; 018000;Indiana ; 0.58957E+02; 0.53330E+02; 0.32822E+03; 0.21196E+03; 0.16105E+03; 0.17266E+04; 0.34249E+03;  07/18/2002; 019000;Iowa ; 0.40775E+02; 0.42057E+01; 0.22173E+03; 0.54576E+03; 0.14195E+01; 0.13372E+04; 0.25094E+03;  07/18/2002; 020000;Kansas ; 0.28911E+03; 0.29621E+02; 0.88596E+02; 0.43985E+02; 0.88975E+01; 0.70323E+03; 0.15211E+03;  07/18/2002; 021000;Kentucky ; 0.77191E+02; 0.10531E+03; 0.23546E+03; 0.11256E+03; 0.99659E+02; 0.42778E+03; 0.81534E+02;  07/18/2002; 022000;Louisiana ; 0.16404E+04; 0.28481E+03; 0.29980E+03; 0.19668E+03; 0.23245E+03; 0.88853E+03; 0.28283E+03;  07/18/2002; 024000;Maryland ; 0.21134E+03; 0.21519E+02; 0.20890E+03; 0.66855E+02; 0.16790E+02; 0.18854E+03; 0.47790E+02;  07/18/2002; 025000;Massachusetts ; 0.57736E+01; 0.38607E+01; 0.20534E+02; 0.56228E+01; 0.26920E+01; 0.16076E+02; 0.37205E+01;  07/18/2002; 026000;Michigan ; 0.30982E+02; 0.40472E+02; 0.31871E+03; 0.10240E+03; 0.71099E+02; 0.74950E+03; 0.13415E+03;  07/18/2002; 027000;Minnesota ; 0.20006E+02; 0.14727E+02; 0.66111E+02; 0.11717E+03; 0.38086E+01; 0.61705E+03; 0.10937E+03;  07/18/2002; 028000;Mississippi ; 0.42895E+01; 0.22679E+01; 0.30101E+03; 0.14251E+03; 0.57813E+00; 0.55921E+03; 0.81543E+02;  07/18/2002; 029000;Missouri ; 0.20959E+03; 0.64235E+02; 0.27642E+03; 0.35350E+03; 0.12071E+03; 0.33762E+04; 0.58408E+03;  07/18/2002; 031000;Nebraska ; 0.32840E+00; 0.66370E-01; 0.10041E+01; 0.17089E+01; 0.25804E-01; 0.11599E+02; 0.21084E+01;  07/18/2002; 034000;New Jersey ; 0.14231E+02; 0.21007E+02; 0.30461E+03; 0.48417E+02; 0.79506E+01; 0.65704E+02; 0.13140E+02;  07/18/2002; 036000;New York ; 0.48058E+02; 0.93906E+02; 0.58074E+03; 0.10722E+03; 0.13600E+03; 0.37438E+03; 0.64907E+02;  07/18/2002; 037000;North Carolina ; 0.35788E+03; 0.69702E+02; 0.41336E+03; 0.47466E+03; 0.86454E+01; 0.36327E+03; 0.62859E+02;  07/18/2002; 039000;Ohio ; 0.33951E+02; 0.40539E+02; 0.46160E+03; 0.19957E+03; 0.22287E+02; 0.10241E+04; 0.19312E+03;  07/18/2002; 040000;Oklahoma ; 0.13292E+03; 0.56404E+02; 0.51844E+02; 0.10585E+03; 0.69006E+01; 0.68486E+03; 0.12595E+03;  07/18/2002; 042000;Pennsylvania ; 0.31625E+03; 0.55051E+02; 0.54914E+03; 0.22572E+03; 0.73776E+02; 0.59936E+03; 0.11511E+03;  07/18/2002; 044000;Rhode Island ; 0.81695E+01; 0.41245E+01; 0.69566E+02; 0.18260E+01; 0.41886E+01; 0.11126E+02; 0.25125E+01;  07/18/2002; 045000;South Carolina ; 0.10702E+03; 0.46035E+02; 0.30912E+03; 0.76314E+02; 0.30258E+02; 0.31972E+03; 0.52325E+02;  07/18/2002; 046000;South Dakota ; 0.00000E+00; 0.00000E+00; 0.35041E-02; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00;  07/18/2002; 047000;Tennessee ; 0.75720E+02; 0.30086E+02; 0.35511E+03; 0.82795E+02; 0.77852E+02; 0.38690E+03; 0.77792E+02;  07/18/2002; 048000;Texas ; 0.28857E+03; 0.15916E+03; 0.36398E+03; 0.16190E+03; 0.91386E+02; 0.25000E+04; 0.42791E+03;  07/18/2002; 051000;Virginia ; 0.16641E+03; 0.11720E+03; 0.32211E+03; 0.12067E+03; 0.29423E+03; 0.26361E+03; 0.48840E+02;  07/18/2002; 053000;Washington ; 0.00000E+00; 0.00000E+00; 0.47969E-02; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00;  07/18/2002; 054000;West Virginia ; 0.36476E+02; 0.26373E+02; 0.14474E+03; 0.31362E+02; 0.26887E+02; 0.74822E+02; 0.20668E+02;  07/18/2002; 055000;Wisconsin ; 0.37344E+02; 0.17641E+02; 0.21000E+03; 0.20515E+03; 0.53226E+01; 0.31806E+03; 0.64998E+02; 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Table A.2 – VISTAS BaseG2 Point Source Emissions Summary – Facility-Stack Level Emission Totals. Processed as Point sources Base inventory year 2002 No gridding matrix applied No speciation matrix applied No temporal factors applied Annual total data basis in report Source ID ; Region; SCC ; Plant ID; Char 1; Char 2; Char 3; Stk Ht; Stk Dm; Stk Tmp; Stk Vel; Plt Name; CO ; NOX ; VOC ; NH3 ; SO2 ; PM10 ;  ; ; ; ; ; ; ; m ; m ; deg K ; m/s ; ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1442; 012069;2610000500; 31536 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31536 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1443; 012069;2610000500; 31537 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31537 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1444; 012069;2610000500; 31538 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31538 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1445; 012069;2610000500; 31539 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31539 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1446; 012069;2610000500; 31540 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31540 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1447; 012069;2610000500; 31541 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31541 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1448; 012069;2610000500; 31542 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31542 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1449; 012069;2610000500; 31543 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31543 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1450; 012069;2610000500; 31544 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31544 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1451; 012069;2610000500; 31545 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31545 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1452; 012069;2610000500; 31546 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31546 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1453; 012069;2610000500; 31547 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31547 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1454; 012069;2610000500; 31548 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31548 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1455; 012069;2610000500; 31549 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31549 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1456; 012069;2610000500; 31550 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31550 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1457; 012069;2610000500; 31551 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31551 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1458; 012069;2610000500; 31552 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31552 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1459; 012069;2610000500; 31553 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31553 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1460; 012069;2610000500; 31554 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31554 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1461; 012069;2610000500; 31555 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31555 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1462; 012069;2610000500; 31556 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31556 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1463; 012069;2610000500; 31557 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31557 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1464; 012069;2610000500; 31558 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31558 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1465; 012069;2610000500; 31559 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31559 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1466; 012069;2610000500; 31560 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31560 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1467; 012069;2610000500; 31561 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31561 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1468; 012069;2610000500; 31562 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31562 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1469; 012069;2801500000; 32004 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32004 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1470; 012069;2801500000; 32005 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32005 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1471; 012069;2801500000; 32006 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32006 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1472; 012069;2801500000; 32007 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32007 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1473; 012069;2801500000; 32008 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32008 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1474; 012069;2801500000; 32009 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32009 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1475; 012069;2801500000; 32010 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32010 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1476; 012069;2801500000; 32011 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32011 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1477; 012069;2801500000; 32012 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32012 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1478; 012069;2801500000; 32013 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32013 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1479; 012069;2801500000; 32014 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32014 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1480; 012069;2801500000; 32015 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32015 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1481; 012069;2801500000; 32016 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32016 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1482; 012069;2801500000; 32017 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32017 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1483; 012069;2801500000; 32018 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32018 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1484; 012069;2801500000; 32019 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32019 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1485; 012069;2801500000; 32020 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32020 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1486; 012069;2801500000; 32021 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32021 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1487; 012069;2801500000; 32022 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32022 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1488; 012069;2801500000; 32023 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32023 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1489; 012069;2801500000; 32024 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32024 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1490; 012069;2801500000; 32025 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32025 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1491; 012069;2801500000; 32026 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32026 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1492; 012069;2801500000; 32027 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32027 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1493; 012069;2801500000; 32028 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32028 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1494; 012069;2801500000; 32029 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32029 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1495; 012069;2801500000; 32030 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32030 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1496; 012069;2801500000; 32031 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32031 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1497; 012069;2801500000; 32032 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32032 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1498; 012069;2801500000; 32033 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32033 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1499; 012069;2801500000; 32034 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32034 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1500; 012069;2801500000; 32035 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32035 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1501; 012069;2801500000; 32036 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32036 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1502; 012069;2801500000; 32037 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32037 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1503; 012069;2801500000; 32038 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32038 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1504; 012069;2801500000; 32039 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32039 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1505; 012069;2801500000; 32040 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32040 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1506; 012069;2801500000; 32041 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32041 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1507; 012069;2801500000; 32042 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32042 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1508; 012069;2801500000; 32043 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32043 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1509; 012069;2801500000; 32044 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32044 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1510; 012069;2801500000; 32045 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32045 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1511; 012069;2801500000; 32046 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32046 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1512; 012069;2801500000; 32047 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32047 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1513; 012069;2801500000; 32048 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32048 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1514; 012069;2801500000; 32049 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32049 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1515; 012069;2801500000; 32050 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32050 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1516; 012069;2801500000; 32051 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32051 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1517; 012069;2801500000; 32052 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32052 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1518; 012069;2801500000; 32053 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32053 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06;  1519; 012069;2801500000; 32054 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32054 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 
Table A.3 – VISTAS BaseG2 Point Source Emissions Summary – Hourly SCC Level Emission Totals.Processed as Point sources Base inventory year 2002 No gridding matrix applied No speciation matrix applied Temporal factors applied for episode from  Thursday July 18, 2002 at 000000 to  Friday July 19, 2002 at 000000 Annual total data basis in report Date ; Hour; SCC ; CO ; NOX ; VOC ; NH3 ; SO2 ; PM10 ; PM2_5  ; ; ; [tons/hr] ; [tons/hr] ; [tons/hr] ; [tons/hr] ; [tons/hr] ; [tons/hr] ; [tons/hr--------------------------------------------------------------------------------------------------------------------------------- 07/18/2002; 01;0000000000; 0.69632E-01; 0.10601E+00; 0.98348E-01; 0.14427E-03; 0.28078E+00; 0.67371E-01; 0.000 07/18/2002; 01;0010100101; 0.23717E+00; 0.64409E+00; 0.18311E+00; 0.86984E-05; 0.15537E+01; 0.36883E-01; 0.128 07/18/2002; 01;0010100102; 0.49310E-04; 0.29138E-03; 0.74712E-05; 0.00000E+00; 0.00000E+00; 0.89654E-05; 0.536 07/18/2002; 01;0010100201; 0.12337E+01; 0.18448E+02; 0.21384E+00; 0.11847E-02; 0.45404E+02; 0.11176E+01; 0.631 07/18/2002; 01;0010100202; 0.13432E+02; 0.13367E+03; 0.76286E+00; 0.13934E+00; 0.39127E+03; 0.77426E+01; 0.347 07/18/2002; 01;0010100203; 0.15106E+01; 0.41001E+02; 0.22772E+00; 0.10565E-01; 0.57875E+02; 0.10036E+01; 0.621 07/18/2002; 01;0010100204; 0.46840E+00; 0.11215E+01; 0.51058E-02; 0.18008E-03; 0.37646E+01; 0.13055E+00; 0.525 07/18/2002; 01;0010100205; 0.17514E+00; 0.40535E+00; 0.12817E-02; 0.18564E-04; 0.10426E+01; 0.49851E-01; 0.848 07/18/2002; 01;0010100211; 0.12032E+00; 0.71511E-01; 0.14337E-01; 0.00000E+00; 0.56604E+00; 0.80955E-01; 0.271 07/18/2002; 01;0010100212; 0.27195E+01; 0.35507E+02; 0.31254E+00; 0.25909E+00; 0.14595E+03; 0.26430E+01; 0.114 07/18/2002; 01;0010100217; 0.59853E+00; 0.73339E+00; 0.22997E-01; 0.78953E-03; 0.21109E+01; 0.78834E-01; 0.395 07/18/2002; 01;0010100218; 0.12248E+00; 0.29317E+00; 0.28785E-02; 0.81146E-03; 0.64975E+00; 0.10895E-01; 0.181 07/18/2002; 01;0010100221; 0.71317E+00; 0.93035E+01; 0.59317E-01; 0.17286E-03; 0.10284E+02; 0.17314E+00; 0.773 07/18/2002; 01;0010100222; 0.71637E+01; 0.65497E+02; 0.59207E+00; 0.11337E+00; 0.11145E+03; 0.29680E+01; 0.999 07/18/2002; 01;0010100223; 0.80563E+00; 0.18628E+02; 0.16394E+00; 0.58958E-02; 0.19546E+02; 0.33507E+00; 0.287 07/18/2002; 01;0010100224; 0.30668E+00; 0.59444E+00; 0.30663E-02; 0.42486E-05; 0.45436E+00; 0.27206E+00; 0.453 07/18/2002; 01;0010100225; 0.84370E-01; 0.80214E-01; 0.55180E-03; 0.00000E+00; 0.12985E+00; 0.36873E-01; 0.513 07/18/2002; 01;0010100226; 0.37955E+01; 0.49513E+02; 0.46960E+00; 0.55985E+00; 0.90938E+02; 0.36303E+01; 0.155 07/18/2002; 01;0010100238; 0.00000E+00; 0.37761E-03; 0.00000E+00; 0.19097E-05; 0.80068E-03; 0.00000E+00; 0.000 07/18/2002; 01;0010100301; 0.78828E+01; 0.54602E+01; 0.84065E-01; 0.34718E-02; 0.15707E+02; 0.39028E+00; 0.171 07/18/2002; 01;0010100302; 0.16107E+02; 0.12727E+02; 0.14625E+00; 0.25457E-01; 0.49491E+02; 0.13062E+01; 0.832 07/18/2002; 01;0010100303; 0.28142E+00; 0.56976E+01; 0.55583E-01; 0.16925E-01; 0.88036E+01; 0.00000E+00; 0.000 07/18/2002; 01;0010100306; 0.19797E-01; 0.00000E+00; 0.11458E-03; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.000 07/18/2002; 01;0010100317; 0.52071E-02; 0.00000E+00; 0.82754E-03; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.000 07/18/2002; 01;0010100318; 0.00000E+00; 0.11651E+00; 0.00000E+00; 0.81850E-03; 0.27784E+00; 0.00000E+00; 0.000 07/18/2002; 01;0010100401; 0.24586E+01; 0.76006E+01; 0.19600E+00; 0.61335E-01; 0.11806E+02; 0.46239E+00; 0.345 07/18/2002; 01;0010100404; 0.48699E+00; 0.33056E+01; 0.82921E-01; 0.47267E-01; 0.10960E+02; 0.32426E+00; 0.229 07/18/2002; 01;0010100405; 0.27744E-04; 0.31632E-03; 0.35943E-05; 0.19814E-05; 0.89624E-03; 0.46775E-04; 0.352 07/18/2002; 01;0010100501; 0.10665E+01; 0.98314E+01; 0.11157E+00; 0.14510E-01; 0.41097E+01; 0.35716E+00; 0.248 07/18/2002; 01;0010100504; 0.75882E-02; 0.66438E-01; 0.21897E-02; 0.10574E-06; 0.15788E-01; 0.20965E-02; 0.192 07/18/2002; 01;0010100505; 0.61880E-03; 0.25294E-02; 0.34996E-04; 0.14639E-04; 0.13508E-02; 0.44740E-03; 0.661 07/18/2002; 01;0010100600; 0.96495E-04; 0.10518E-03; 0.60310E-05; 0.00000E+00; 0.53856E-06; 0.60310E-05; 0.603 07/18/2002; 01;0010100601; 0.87846E+01; 0.21074E+02; 0.70139E+00; 0.15155E+00; 0.99403E+01; 0.14958E+01; 0.138 07/18/2002; 01;0010100602; 0.50297E+00; 0.70762E+00; 0.32526E-01; 0.11195E-01; 0.35912E+00; 0.11366E+00; 0.964 07/18/2002; 01;0010100604; 0.10116E+01; 0.56493E+01; 0.18644E+00; 0.65348E-01; 0.89274E+00; 0.61273E+00; 0.276 07/18/2002; 01;0010100701; 0.11273E+00; 0.82767E+00; 0.17431E-01; 0.32131E-05; 0.12597E-01; 0.93832E-01; 0.773 07/18/2002; 01;0010100702; 0.51429E-01; 0.64817E-01; 0.31871E-02; 0.00000E+00; 0.17190E+00; 0.13937E-01; 0.793 07/18/2002; 01;0010100711; 0.16037E-02; 0.12431E-01; 0.10496E-03; 0.60163E-04; 0.17647E-03; 0.15795E-03; 0.157 07/18/2002; 01;0010100801; 0.81893E-01; 0.22331E+00; 0.92392E-02; 0.00000E+00; 0.70142E+00; 0.28780E+00; 0.284 07/18/2002; 01;0010100901; 0.12543E+00; 0.73472E-01; 0.22990E-01; 0.00000E+00; 0.34409E-01; 0.25949E-01; 0.127 07/18/2002; 01;0010100902; 0.24309E+01; 0.91108E+00; 0.64300E-01; 0.88651E-02; 0.34429E+00; 0.10444E+00; 0.809 07/18/2002; 01;0010100903; 0.10181E+01; 0.52995E+00; 0.67198E-01; 0.33635E-03; 0.13256E+00; 0.11066E+00; 0.965 07/18/2002; 01;0010100911; 0.29619E+00; 0.16035E+00; 0.12301E-01; 0.12042E-03; 0.15444E-02; 0.10733E-01; 0.757 07/18/2002; 01;0010101001; 0.98770E-06; 0.19367E-05; 0.77818E-07; 0.00000E+00; 0.00000E+00; 0.21838E-09; 0.000
Table A.4 – VISTAS BaseG2 Onroad Mobile Source Emissions Summary – Daily County Level Speciated EmisProcessed as Mobile sources Base inventory year 2018 No gridding matrix applied No speciation matrix applied Temporal factors applied for episode from  Thursday July 18, 2002 at 000000 to  Thursday July 18, 2002 at 230000 Annual total data basis in report Date ; Region; State ; County ; EXR__CO ; EXR__NOX ; EXR__TOG ; EXR__SO4 ; EXR__OCARB25 ; EXR__OCARBEXR__ECARBPMC; EXR__GASPM25 ; EXR__GASPMC ; EXR__SO2 ; EXR__NH3 ; EXS__CO ; EXS__NOX ; EXS__TOG ; HOT__TOG ; DNL__TOG ; CRC__TOG ; BRK__BRAKE25 ; BRK__BRAKEPMC; TIR__TIRE25 ; TIR__TIREPMC ; RFL__TOG  ; ; ; ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  07/18/2002; 001001;Alabama ; Autauga Co ; 0.10683E+02; 0.11802E+01; 0.41604E+00; 0.11465E-02; 0.29400E-02; 0.255660.49703E-03; 0.85616E-02; 0.80122E-03; 0.20665E-01; 0.22136E+00; 0.32406E+01; 0.12053E+00; 0.23488E+00; 0.16280E+00; 0.82331E-02; 0.19892E-01; 0.12934E-01; 0.17487E-01; 0.60055E-02; 0.18017E-01; 0.61950E-01  07/18/2002; 001003;Alabama ; Baldwin Co ; 0.37965E+02; 0.43607E+01; 0.14322E+01; 0.39511E-02; 0.11914E-01; 0.103600.20151E-02; 0.31073E-01; 0.29143E-02; 0.75657E-01; 0.79900E+00; 0.11578E+02; 0.43130E+00; 0.83895E+00; 0.58222E+00; 0.29457E-01; 0.71577E-01; 0.47153E-01; 0.63751E-01; 0.22368E-01; 0.67105E-01; 0.22387E+00  07/18/2002; 001005;Alabama ; Barbour Co ; 0.80254E+01; 0.87307E+00; 0.38301E+00; 0.97693E-03; 0.20218E-02; 0.175800.34161E-03; 0.66920E-02; 0.62444E-03; 0.16034E-01; 0.17498E+00; 0.25495E+01; 0.94684E-01; 0.18480E+00; 0.12787E+00; 0.64618E-02; 0.15740E-01; 0.10112E-01; 0.13671E-01; 0.45855E-02; 0.13757E-01; 0.48623E-01  07/18/2002; 001007;Alabama ; Bibb Co ; 0.44676E+01; 0.54423E+00; 0.19802E+00; 0.50455E-03; 0.15706E-02; 0.136580.26571E-03; 0.39253E-02; 0.36840E-03; 0.96062E-02; 0.10113E+00; 0.14457E+01; 0.53894E-01; 0.10475E+00; 0.72701E-01; 0.36809E-02; 0.90342E-02; 0.59898E-02; 0.80983E-02; 0.28642E-02; 0.85925E-02; 0.28245E-01  07/18/2002; 001009;Alabama ; Blount Co ; 0.11195E+02; 0.13462E+01; 0.46810E+00; 0.12197E-02; 0.38412E-02; 0.334020.64982E-03; 0.95500E-02; 0.89674E-03; 0.23381E-01; 0.24570E+00; 0.35353E+01; 0.13179E+00; 0.25615E+00; 0.17783E+00; 0.90044E-02; 0.21962E-01; 0.14563E-01; 0.19690E-01; 0.69726E-02; 0.20918E-01; 0.68770E-01  07/18/2002; 001011;Alabama ; Bullock Co ; 0.28452E+01; 0.35569E+00; 0.13313E+00; 0.33076E-03; 0.10242E-02; 0.890580.17326E-03; 0.25570E-02; 0.24003E-03; 0.62610E-02; 0.65921E-01; 0.94260E+00; 0.35139E-01; 0.68297E-01; 0.47404E-01; 0.24047E-02; 0.58884E-02; 0.39044E-02; 0.52788E-02; 0.18671E-02; 0.56014E-02; 0.18422E-01  07/18/2002; 001013;Alabama ; Butler Co ; 0.11799E+02; 0.12890E+01; 0.43964E+00; 0.11955E-02; 0.32843E-02; 0.285600.55532E-03; 0.92877E-02; 0.86975E-03; 0.22418E-01; 0.23901E+00; 0.35146E+01; 0.13077E+00; 0.25471E+00; 0.17660E+00; 0.89172E-02; 0.21468E-01; 0.14006E-01; 0.18937E-01; 0.65433E-02; 0.19630E-01; 0.66939E-01  07/18/2002; 001015;Alabama ; Calhoun Co ; 0.48673E+02; 0.52443E+01; 0.25748E+01; 0.60386E-02; 0.12160E-01; 0.105740.20550E-02; 0.38282E-01; 0.35827E-02; 0.92508E-01; 0.10076E+01; 0.14941E+02; 0.55505E+00; 0.10829E+01; 0.74928E+00; 0.37719E-01; 0.90452E-01; 0.58436E-01; 0.79006E-01; 0.26688E-01; 0.80063E-01; 0.27938E+00  07/18/2002; 001017;Alabama ; Chambers Co ; 0.77831E+01; 0.86810E+00; 0.31296E+00; 0.85048E-03; 0.21927E-02; 0.190670.37068E-03; 0.65372E-02; 0.61100E-03; 0.15706E-01; 0.16869E+00; 0.24543E+01; 0.91266E-01; 0.17788E+00; 0.12324E+00; 0.62354E-02; 0.15154E-01; 0.98375E-02; 0.13300E-01; 0.45506E-02; 0.13652E-01; 0.47135E-01  07/18/2002; 001019;Alabama ; Cherokee Co ; 0.55946E+01; 0.68518E+00; 0.26212E+00; 0.64557E-03; 0.19666E-02; 0.171010.33269E-03; 0.48814E-02; 0.45847E-03; 0.11971E-01; 0.12606E+00; 0.18102E+01; 0.67482E-01; 0.13116E+00; 0.91037E-01; 0.46160E-02; 0.11255E-01; 0.74697E-02; 0.10099E-01; 0.35749E-02; 0.10725E-01; 0.35201E-01  07/18/2002; 001021;Alabama ; Chilton Co ; 0.13349E+02; 0.14880E+01; 0.49241E+00; 0.13590E-02; 0.38839E-02; 0.337730.65681E-03; 0.10541E-01; 0.98812E-03; 0.25564E-01; 0.27124E+00; 0.39766E+01; 0.14804E+00; 0.28817E+00; 0.19990E+00; 0.10106E-01; 0.24336E-01; 0.15952E-01; 0.21566E-01; 0.75089E-02; 0.22527E-01; 0.76024E-01  07/18/2002; 001023;Alabama ; Choctaw Co ; 0.35209E+01; 0.43252E+00; 0.15894E+00; 0.40062E-03; 0.12502E-02; 0.108720.21150E-03; 0.31251E-02; 0.29330E-03; 0.76462E-02; 0.80496E-01; 0.11505E+01; 0.42889E-01; 0.83360E-01; 0.57867E-01; 0.29386E-02; 0.71905E-02; 0.47676E-02; 0.64457E-02; 0.22798E-02; 0.68393E-02; 0.22502E-01  07/18/2002; 001025;Alabama ; Clarke Co ; 0.59226E+01; 0.68641E+00; 0.25458E+00; 0.67197E-03; 0.18723E-02; 0.162800.31663E-03; 0.50767E-02; 0.47544E-03; 0.12334E-01; 0.13127E+00; 0.18841E+01; 0.70154E-01; 0.13653E+00; 0.94672E-01; 0.47865E-02; 0.11755E-01; 0.77142E-02; 0.10430E-01; 0.36290E-02; 0.10887E-01; 0.36618E-01  07/18/2002; 001027;Alabama ; Clay Co ; 0.32368E+01; 0.39861E+00; 0.15935E+00; 0.38110E-03; 0.11363E-02; 0.988070.19223E-03; 0.28016E-02; 0.26332E-03; 0.68852E-02; 0.72518E-01; 0.10461E+01; 0.38997E-01; 0.75794E-01; 0.52605E-01; 0.26659E-02; 0.64717E-02; 0.42994E-02; 0.58128E-02; 0.20594E-02; 0.61781E-02; 0.20236E-01  07/18/2002; 001029;Alabama ; Cleburne Co ; 0.84659E+01; 0.97428E+00; 0.30666E+00; 0.83602E-03; 0.26955E-02; 0.234390.45600E-03; 0.65992E-02; 0.62058E-03; 0.16179E-01; 0.16928E+00; 0.24835E+01; 0.92582E-01; 0.17994E+00; 0.12497E+00; 0.63173E-02; 0.15149E-01; 0.10055E-01; 0.13594E-01; 0.48311E-02; 0.14493E-01; 0.47565E-01 
 

APPENDIX B 

Air Quality Model Performance Evaluation: 

Regional Model Evaluation across the VISTAS Region
 B-2 

B.1 OVERVIEW 

The discussion of model performance evaluation in this Appendix focuses on the evaluation of 

the 2002 Base G2 Actual base case modeling results from the Community Multiscale Air Quality 

(CMAQ) modeling system Version 4.5 with SOAmods enhancement across the VISTAS states 

and VISTAS 12 km grid. The CMAQ results are compared with observational data from the 

Interagency Monitoring of PROtected Visual Environments (IMPROVE), Speciated Trends 

Network (STN), Clean Air Status Trends Network (CASTNet), Federal Reference Method 

(FRM) PM2.5 mass, National Acid Deposition Program (NADP) and South East Aerosol 

Research and CHaracterization (SEARCH) study monitoring networks. The evaluation will 

primarily focus on the air quality model’s performance with respect to individual components of 

fine particulate matter (PM2.5), as good model performance of the PM component species will 

dictate good model performance of total or reconstituted fine particulate matter (i.e., visibility). 

The FRM networks only collects total PM2.5 mass and these monitors are almost exclusively 

located in urban areas so are not as relevant for judging how well the model is predicting the 

components of light extinction at the more rural Class I areas. However, model performance of 

the total fine particulate matter and the resulting total light extinction will also be provided as a 

means to discuss the overall model performance. 

B.2 CMAQ Evaluation Methodology 

EPA’s integrated ozone, PM2.5 and regional haze modeling guidance calls for a comprehensive, 

multi-layered approach to model performance testing, consisting of the four major components: 

operational, diagnostic, mechanistic (or scientific) and probabilistic (EPA, 2007a). 

B.2.1 Evaluation Approach 

The CMAQ model performance evaluation effort focused on the first two components, namely: 

• Operational Evaluation: Tests the ability of the model to estimate PM concentrations 

(both fine and coarse) and the components at PM10 and PM2.5 including the quantities 

used to characterize visibility (i.e., sulfate, nitrate, ammonium, organic carbon, elemental 

carbon, other PM2.5, and coarse matter (PM2.5-10). This evaluation examines whether the 

measurements are properly represented by the model predictions but does not necessarily 

ensure that the model is getting “the right answer for the right reason”; and 

• Diagnostic Evaluation: Tests the ability of the model to predict visibility and extinction, 

PM chemical composition including PM precursors (e.g., SOx, NOx, and NH3) and 

associated oxidants (e.g., ozone and nitric acid); PM size distribution; temporal variation; 

spatial variation; mass fluxes; and components of light extinction (i.e., scattering and 

absorption). 
 B-3 

The diagnostic evaluation also includes the performance of diagnostic tests to better understand 

model performance and identify potential flaws in the modeling system that can be corrected. 

The diagnostic evaluation may also includes the use of “probing tools” to understand why the 

model obtains a given prediction; probing tools include Process Analysis (PA), decoupled direct 

method (DDM) and source apportionment (SA). 

In this final model performance evaluation for the VISTAS 2002 Actual Base G2 CMAQ 36/12 

km base case simulation, the operational evaluation has been given the greatest attention since 

this is the primarily thrust of EPA’s modeling guidance. However, we have also examined 

certain diagnostic features dealing with the model’s ability to simulate sub-regional and 

monthly/diurnal gas phase and aerosol concentration distributions. In the course of the VISTAS 

study numerous diagnostic sensitivity tests were performed to investigate and improve model 

performance. Key diagnostic tests performed are discussed and the results for the rest are 

available on the VISTAS modeling website: http://pah.cert.ucr.edu/vistas/vistas2/. 

B.2.2 Particulate Matter and Component Species 

Regional haze is calculated by estimating light scattering and absorption by components of 

PM2.5. Regional haze is measured by an extinction coefficient (bext), which represents the light 

attenuation resulting from scattering and absorption of light from ambient particulate matter plus 

scattering of light due to gas molecules in the air (i.e., Rayleigh scattering). 

Some scattering does occur from air molecules; however, the bulk of light extinction is caused 

by the presence of suspended particles, particularly fine particles (aerodynamic mass <2.5 μm). 

Fine particulate matter can be composed of varying amounts of several different species, 

including: 

• Sulfate (SO4) 

• Nitrate (NO3) 

• Ammonium (NH4) 

• Organic Matter Carbon (OMC) 

• Elemental Carbon (EC) 

• Coarse particulate matter or Coarse Mass (i.e., PM10 - PM2.5 or CM). 

• Soil (also known as crustal material, fine soil, major metal oxides, inorganic particulates, 

or other PM) 

• Sea Salt (NaCl) 

B.2.3 Ambient Air Quality Data for VISTAS Model Evaluation 

A ground-level model evaluation database for 2002 was compiled by the modeling team using 

several routine and research-grade databases. The focus of the regional evaluation of the CMAQ 

model for use in simulating visibility in the southeastern is on the PM components that can cause 

visibility impairment. The primary monitoring networks available to evaluate this component of 

the CMAQ are: (a) Interagency Monitoring of Protected Visual Environments (IMPROVE); (b) 
 B-4 

Clean Air Status and Trends Network (CASTNET); (c) Southeastern Aerosol Research and 

Characterization (SEARCH); (d) EPA Federal Reference Method PM2.5 and PM10 Mass 

Networks (EPA-FRM); (e) EPA Speciation Trends Network (STN) of PM2.5 species; and (f) 

National Acid Deposition Network (NADP). These PM monitoring networks may also provide 

ozone and other gas phase precursors and product species, and visibility measurements at some 

sites. There are also gas-phase criteria pollutant measures from the EPA’s Air Quality System 

(AQS) that are available for use. However, these sites are urban-oriented and not as useful for 

the regional evaluation of the CMAQ modeling system. The study team has performed a 

detailed evaluation of the CMAQ 2002 base case simulation using the AQS that is available on 

the modeling website: http://pah.cert.ucr.edu/vistas/vistas2/mpe2.shtml. Table B-1 and Figure 

B-1 summarizes the species collected and locations of the monitoring sites for the IMPROVE, 

STN, CASTNet, NADP and SEARCH monitoring networks use in the VISTAS model 

evaluation. 

Table B-1. Ambient monitoring data available in the VISTAS region during 2002. 

Monitoring 

Network Chemical Species Measured 

Sampling Frequency; 

Duration 

IMPROVE Speciated PM2.5 and PM10 1 in 3 days; 24 hr 

CASTNET Speciated PM2.5, Ozone Hourly, Weekly; 1 hr, Week 

SEARCH 

24-hr PM25 (FRM Mass, OC, BC, SO4, NO3, 

NH4, Elem.); 24-hr PM coarse (SO4, NO3, 

NH4, elements); Hourly PM2.5 (Mass, SO4, 

NO3, NH4, EC, TC); and Hourly gases (O3, 

NO, NO2, NOy, HNO3, SO2, CO) Daily, Hourly; 

NADP WSO4, WNO3, WNH4 Weekly 

EPA-FRM Only total fine mass (PM2.5) 1 in 3 days; 24 hr 

EPA-STN Speciated PM2.5 Varies; Varies 

AIRS/AQS CO, NO, NO2, NOx, O3 Hourly; Hourly 
 B-5 

250 500 750 1000 1250 1500 1750 2000

-1500

-1250

-1000

-750

-500

-250

0

250

500

IMPROVE

CASTNet

SEARCH

STN

NADP

AQS

Figure B-1. IMPROVE, STN, CASTNet, NADP and SEARCH monitoring sites located in the 

VISTAS region and the VISTAS 12 km grid. 

B.3 Model Performance Statistics 

To quantify model performance, several statistical measures were calculated and evaluated for 

all the monitors within the VISTAS region and within each VISTAS state. The statistical 

measures selected were based on the recommendations outlined in section 18.4.1 of the 

USEPA’s Guidance On The Use Of Models And Other Analyses for Demonstrating Attainment 

of Air Quality Goals for Ozone, PM2.5, and Regional Haze (EPA, 2007a). In 2004, VISTAS 

established model performance goals and criteria for components of fine particle mass based on 

previous model performance for ozone and fine particles. EPA modeling guidance for fine 

particulate matter at the time noted that PM models might not be able to achieve the same level 

of performance as ozone models. VISTAS’ evaluation considered several statistical 

performance measures and displays. 
 B-6 

B.4 Modeling Performance Across the VISTAS Region 

In the following discussions we use selected monthly scatter plots, time series plots and model 

performance statistical measures from the UCR Analysis Tool application to the 2002 CMAQ 

Base G2 Actual base case simulation in an operational evaluation of the model for PM species. 

We focus on the six main components of PM that are used to project visibility. 

B.4.1 Sulfate (SO4) Monthly Model Performance in the VISTAS Region 

B.4.1.1 SO4 in January 2002 

Figure B-2a displays scatter plots of predicted and observed SO4 concentrations or wet 

depositions for January 2002, sites in the VISTAS region using observations from the 

IMPROVE, STN, CASTNet and NADP monitoring networks and the CMAQ Base G2 Actual 

base case 36 km (red) and 12 km (blue) modeling results. The IMPROVE and STN SO4 

concentrations are 24-hour averages whereas the CASTNet SO4 concentrations and NADP SO4 

wet deposition are weekly averages. The January SO4 performance at the IMPROVE and STN 

networks in the VISTAS region is quite good with low fractional bias (-14%) and gross error 

(34%-38%) that almost achieves the most stringent model performance evaluation goals (Table 

C-1XXX). There is a larger SO4 concentration underestimation bias across the CASTNet 

network (-21% to -23%) with less scatter so there is lower gross error (30% to 32%). The lower 

scatter and error is likely due to the longer sampling period for CASTNet (weekly) versus 

IMPROVE/STN networks (24-hour) that reduces the variability in both the predicted and 

observed SO4 concentrations. Surprisingly the model exhibits low SO4 wet deposition bias 

across the VISTAS region (-7% to -8%) but lots of scatter so produces a high bias than for the 

concentrations (67% to 70%). This is surprising because obtaining good SO4 model 

performance requires the simultaneous occurrence of correct depiction of SO4 concentrations 

and precipitation model predictions. As noted by Olerud (2003c,d), the MM5 simulation does a 

respectable job in reproducing precipitation during synoptic rain events as occur in the winter, 

however it exhibited less skill in predicting precipitation during the summer convective events. 

Figure B-2b present quantile-quantile (Q-Q) plots for January 2002 SO4 concentration and wet 

deposition model performance. Q-Q plots compare the frequency distribution between the 

predicted and observed SO4 concentrations/depositions. Across the IMPROVE network the 

model slightly underestimates the frequency distribution of the lower SO4 below approximately 

5 ug/m3 and overpredictions the values above this value. Across the more urban STN network 

the model exhibits a slight underprediction of the observed frequency distribution across the 

VISTAS region during January 2002. Across the mixed urban/non-urban SEARCH network the 

model reproduces the observed frequency distribution of SO4 concentrations quite well, except 

for overestimating the highest values above approximately 4 ug/m3. Finally, the observed wet 

SO4 deposition in the VISTAS region and January 2002 is matched exactly by the model except 

for the very highest events that are underpredicted. It is unclear whether the model 

overestimation of the highest observed concentrations and underestimation of the highest 

observed wet deposition events are connected.
 B-7 

Figure B-2a. Scatter plots of predicted and observed sulfate (SO4) concentrations for January 2002 

and sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and 

NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-8 

Figure B-2b. Q-Q plots of predicted and observed sulfate (SO4) concentrations or depositions for 

January 2002 and sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), 

SEARCH (bottom left) and NADP monitoring networks and the CMAQ 2002 12 km Base G2 Actual 

base case simulation
 B-9 

B.4.1.2 SO4 in April 2002 

In April CMAQ still exhibits reasonable good SO4 model performance although with an 

underestimation bias of -20% to -22% across the IMPROVE and STN networks. The scatter in 

the SO4 performance across these two networks is very tight with low fractions error values of 

30% to 35% (Figure B-3a). Across the CASTNet network in April 2002, both the 36 km and 12 

km versions of the model exhibits a higher underestimations bias (-25% to -26%), but low error 

(27% to 28%), which is due to the less variability in the weekly samples. Wet SO4 deposition 

performance in April across the VISTAS region is much worse than seen for January with an 

overprediction bias of 48% (12 km) and 50% (36 km) and fractions errors of approximately 

100%. The Q-Q plots of SO4 concentrations and deposition model performance in April shown 

in Figure C-3b indicates that the model does a reasonable job in reproducing the observed 

frequency distribution, although with a slight underprediction bias except for the highest values 

that are either reproduced fairly well (IMPROVE and STN) or underpredicted (SEARCH and 

NADP).. 
 B-10 

Figure B-3a. Scatter plots of predicted and observed sulfate (SO4) concentrations for April 2002 

and sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and 

NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-11 

Figure B-3b. Q-Q plots of predicted and observed sulfate (SO4) concentrations for April 2002 and 

sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) 

and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-12 

B.4.1.3 SO4 in July 2002 

The SO4 model performance in July 2002 across the VISTAS region is also fairly good. The 

CMAQ Base G2 12 km model simulation produces slight lower fractions bias than the 36 km 

model for the IMPROVE (-18% vs. -20%), STN (-13% vs. -16%) and CASTNet (-17% vs. -

23%) networks (Figure B-4a). The fractional error for July SO4 concentrations across these 

same networks ranges from 27% to 48% with the 12 km model exhibiting slight lower error than 

the 36 km model. The July SO4 wet deposition exhibits an overestimation bias of 39% to 45% 

and fractions errors of ~90%. The overestimation of the July 2002 SO4 wet deposition in the 

VISTAS region is consistent with the MM5 meteorological model evaluation that found that 

MM5 overstated the amount of summer precipitation due to convective activity. 

The model reproduces the observed frequency distribution of SO4 concentrations across the 

IMPROVE monitors in the VISTAS region during July 2002 very well (Figure B-4b). The Q-Q 

plots of July SO4 performance for the STN and CASTNet monitoring network across the 

VISTAS region also exhibits fairly good model performance, although with an underestimation 

bias for STN and overestimation bias for SEARCH at the high end. The observed frequency 

distribution of SO4 wet deposition across the VISTAS region is greatly overestimated by the 

model. This summer wet SO4 overestimation bias undoubtedly contributes to the 

underprediction of the SO4 concentrations and extinction for the Worst 20 percent days as 

discussed in Appendix C. 
 B-13 

Figure B-4a. Scatter plots of predicted and observed sulfate (SO4) concentrations for July 2002 and 

sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and 

NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-14 

Figure B-4b. Q-Q plots of predicted and observed sulfate (SO4) concentrations for July 2002 and 

sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) and 

NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-15 

B.4.1.4 SO4 in October 2002 

In October 2002, CMAQ is doing a better job of reproducing the observed SO4 concentrations 

with much lower fractional bias values across the IMPROVE (0% to 4%) and STN (-6% to -

15%) and fractional errors of 33% to 48% (Figure C-5a). Good SO4 concentrations model 

performance for October 2002 and the VISTAS region is also seen across the CASTNet monitors 

with zero bias and fractions error of approximately 30% (i.e., meeting the most stringent ozone 

model performance goals). Although the SO4 wet deposition model performance bias is fairly 

low (~20%) there is lots of scatter producing large errors (~85%). 

The Q-Q plot comparisons (Figure C-5b) indicate that the model overstates both the SO4 

concentrations and deposition distribution in October 2002 with the overstatement increasing at 

the higher values. 
 B-16 

Figure B-5a. Scatter plots of predicted and observed sulfate (SO4) concentrations for October 2002 

and sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and 

NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-17 

Figure B-5b. Q-Q plots of predicted and observed sulfate (SO4) concentrations for October 2002 

and sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) 

and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-18 

B.4.1.5 SO4 Monthly Bias and Error 

Figure B-6a compares the monthly SO4 concentration fractional bias and error across the 

VISTAS region for the IMPROVE, STN, CASTNet, SEARCH Daily and SEARCH Hourly 

monitoring networks. Note that these monthly model performance statistics were calculated 

across the VISTAS 12 km grid versus just the VISTAS states for used in the monthly 

performance evaluation in Figures B-2 through B-5. The underprediction bias in SO4 

concentrations is clearly evident across most of the year and monitoring networks with the 

lowest SO4 bias in October. However, this underprediction bias is not severe usually not going 

lower than -20% and never going lower than -25%. The exception to this is the SEARCH 

Hourly network that exhibits a positive overprediction SO4 bas for several months. 

The SO4 monthly fractional error values are usually under 40% and, with one exception, always 

under 50% (Figure B-6a). The exception is the SEARCH Hourly network with error values 

ranging from 50% to 70%. 

Figure B-6b displays Bugle Plots of monthly SO4 fractional bias and error. Bugle Plots compare 

the monthly bias and error values with a performance goal and performance criteria and accounts 

for the concept that better performance is needed (i.e., more stringent model performance goals 

and criteria) for species with higher concentrations/extinctions and that performance goals and 

criteria can be relaxed as the species concentrations become low and are not longer an important 

component of the PM or extinction budget (see Chapter 3 and Appendix C for more discussion). 

The Bugle Plots in Figure B-6b confirm the previous analysis that SO4 model performance tends 

to be good across all months and, with one exception, always achieving the model performance 

goal for both bias and error. The one exception is for fractional error across the SEARCH 

Hourly network that is above the performance goal, but still achieves the performance criteria 

(Figure B-6b, bottom). 
 B-19 

Figure B-6a. Monthly SO4 fractional bias (top) and fractional gross error (bottom) statistical 

measures for CASTNet, IMPROVE, STN and SEARCH (daily and hourly) monitoring sites in the 

VISTAS 12 km grid. 
 B-20 

Figure B-6b. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for SO4 in 

the VISTAS 12 km grid and comparison to model performance goals (blue) and criteria (yellow). 
 B-21 

B.4.2 Nitrate (NO3) Monthly Model Performance in the VISTAS Region 

The regional model performance evaluation for nitrate (NO3) follows the same format as given 

above for SO4. 

B.4.2.1 NO3 in January 2002 

The January NO3 performance (Figure B-7a) is not as good as seen for SO4. The January NO3 

fractional bias is in the -20% to -25% and very similar for the 36 km and 12 km CMAQ Base G2 

simulations for the STN and CASTNet monitoring networks across the VISTAS region, with 

fractional errors that range from 30% to 37%. However, across the IMPROVE network in the 

VISTAS region the CMAQ 12 km run exhibits a higher underprediction bias (-39%) than the 36 

km run (-23%), although it has lower error (90%) than the 36 km run (96%). The January NO3 

wet deposition performance for the 36 km and 12 km simulations are very similar with an 

approximate -7% bias and 36% gross error. 

The Q-Q plots comparing the predicted and observed January NO3 frequency distributions 

indicate that although there is a net underestimation bias in NO3 when averaged across the 

monitoring networks as shown IN Figure B-7a, the model overestimates the highest observed 

NO3 concentrations. This overestimation of the highest observed NO3 concentrations in January 

occurs for the IMPROVE, CASTNet and NADP networks, but not the more urban-oriented STN 

network.
 B-22 

Figure B-7a. Scatter plots of predicted and observed nitrate (NO3) concentrations for January 2002 

and sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and 

NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-23 

Figure B-7b. Q-Q plots of predicted and observed nitrate (NO3) concentrations for January 2002 

and sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) 

and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-24 

B.4.2.2 NO3 in April 2002 

The monthly NO3 performance degrades in April 2002 with the model exhibiting an 

underprediction bias of approximately -60% and -160% across the IMPROVE and STN 

monitoring in the VISTAS region and errors of over 100% and 160%, respectively. Better April 

NO3 performance is seen across the CASTNet monitoring network with bias values of -9% and -

18% for the CMAQ 36 km and 12 km base case simulations and errors of approximately 80%. 

April NO3 wet deposition is overestimated by 36% to 31% with errors of approximately 95%. 

Despite the -60% underestimation bias for April monthly NO3 across the IMPEOVE network, 

the Q-Q plot indicates an overestimation bias for the highest NO3 values (Figure B-8b). This is 

because most of the modeled NO3 values are near zero so show up as an underprediction bias for 

the very lowest observed NO3 values in the Q-Q bias, but there are some modeled values higher 

than observed that are accentuated in the Q-Q plot. 
 B-25 

Figure B-8a. Scatter plots of predicted and observed nitrate (NO3) concentrations for April 2002 and 

sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and 

NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-26 

Figure B-8b. Q-Q plots of predicted and observed nitrate (NO3) concentrations for April 2002 and 

sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) and 

NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-27 

B.4.2.3 NO3 in July 2002 

NO3 monthly model performance in July is characterized by a large underprediction bias with 

fractional bias values of -150% to -160% across the IMPROVE and STN networks and of 

approximately -100% across the CASTNet monitoring network (Figure B-8a), This is due to the 

model most of the time predicting near zero NO3 concentrations in July as the hot temperatures 

in the southeastern U.S. will volatilize the particulate NO3 in the model to gaseous nitric acid. 

Even so, there are occurrences of overestimated model NO3 concentrations in July and for the 

IMPROVE network the highest modeled NO3 values exceed the measured values as shown in 

the Q-Q plot in Figure B-8b. 

Figure B-9a. Scatter plots of predicted and observed nitrate (NO3) concentrations for July 2002 and 

sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and 

NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-28 

Figure B-9b. Q-Q plots of predicted and observed nitrate (NO3) concentrations for July 2002 and 

sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) and 

NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-29 

B.4.2.4 NO3 in October 2002 

The NO3 scatter plots for October 2002 suggest that the model is overestimating the observed 

NO3 but there is a net negative fraction bias of approximately -30% to 0-40% for the IMPROVE 

and STN networks and the VISTAS 12 km grid (Figure B-10a). This negative bias is due to the 

model predicting very low near zero NO3 concentrations when the observed NO3 is less than 

approximately a 0.5 ug/m3. However, when the observed value is greater than approximately 

0.5 ug/m3 at the IMPROVE and STN networks it appears that the model mostly overstates the 

observed value. Across the CASTNet network a positive bias is seen (32% to 42%) which may 

be due in part to volatilization of the particulate NO3 in the weekly sample. The magnitude of 

the NO3 wet deposition observed values is reproduced reasonable well with low bias (6% to 8%) 

but with lots of scatter around the 1:1 line producing errors of approximately 85%. 

The overprediction of the highest observed NO3 concentrations and deposition in October is 

clearly evident in the Q-Q plots (Figure B-10b). 
 B-30 

Figure B-10a. Scatter plots of predicted and observed nitrate (NO3) concentrations for October 

2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom 

left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-31 

Figure B-10b. Q-Q plots of predicted and observed nitrate (NO3) concentrations for October 2002 

and sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) 

and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-32 

B.4.2.5 NO3 Monthly Bias and Error 

The monthly fractional bias time series plot clearly shows the seasonal dependence of this 

performance measure with severe summer underprediction bias of -100% to -150% in the 

summer and bias values generally within ±50% in the winter (Figure B-11a). The time series of 

fraction error values also exhibit a seasonal dependence with an 80% error in the winter peaking 

to a 160% error in the summer with a bell-like distribution. At first glance it appears that the 

summer underprediction bias would adversely affect the regional haze modeling as that is when a 

vast majority of the worst 20 percent days occur at Class I areas in the VISTAS region. 

However, as shown in the Bugle Plots in Figure B-11b, these large summer underprediction bias 

values occur when NO3 concentrations are low (< 1.0 ugm3) and in fact when the average 

observed NO3 concentration exceeds 1 ug/m3 the model mostly achieves the model performance 

goal and almost always achieves the model performance criteria for bias (Figure B-11a top). The 

model performance goal and criteria is achieved less often for fractional error and NO3 (Figure 

B-11b, bottom), but the goal is always achieved across the CASTNet and usually achieved across 

the IMPROVE monitoring networks. 
 B-33 

Figure B-11a. Monthly NO3 fractional bias (top) and fractional gross error (bottom) statistical 

measures for CASTNet, IMPROVE, STN and SEARCH daily and hourly monitoring sites in the 

VISTAS region. 
 B-34 

Figure B-11b. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for NO3 in 

the VISTAS region and comparison to model performance goals (blue) and criteria (yellow). 
 B-35 

B.4.3 Organic Matter Carbon (OMC) Monthly Model Performance in the VISTAS Region 

The CMAQ model simulates organic matter carbon (PMC) that includes both primary and 

secondary organic aerosol (OA) that includes carbon, oxygen and other compounds. However, 

the monitoring networks measure just the organic carbon (OC) portion of the OMC. Thus, the 

OC measurements need to be converted to OMC for the model evaluation. OMC/OC ratios tend 

to vary between 1.2 to 2.2 depending on the organic compounds and the amount of 

photochemistry the compounds have undergone, with newer OMC tending to have lower 

OMC/OC ratios than aged OMC. The original IMPROVE equation uses a 1.4 OMC/OC ratio, 

whereas the new IMPROVE equation uses a 1.8 OMC/OC ratio. In this model performance 

evaluation we used a 1.4 OMC/OC ratio to convert the measured OC to OMC. There is also 

concern with the STN OC measurements because they are not black corrected. In fact the 

differences in OC sampling methodologies results in differences in measurements that exceeds 

our performance goals (Solomon et al., 1994). Thus, these uncertainties in the OMC 

measurements need to be accounted for in the model performance evaluation. 

B.4.3.1 OMC in January 2002 

In January the CMAQ 12 km and 36 km Base G2 2002 Actual base case simulation exhibit a 

11% and 18% overprediction bias across the rural IMPROVE monitors and a -40% and -46% 

underprediction bias across the urban STN monitors in the VISTAS region with errors of 

approximately 50% (Figure B-12a). For the mixed urban/rural SEARCH monitoring network an 

OMC underprediction bias of -24% and -16% occurs using the 12 km and 36 km modeling 

results with errors of 40% to 50%. The SEARCH monitors also collected hourly measurements 

of total carbon mass (TCM=EC+OC) which was overestimated by the 12 km and 36 km 

modeling results by 20% and 12% with errors of approximately 70%. The Q-Q plots indicate the 

model is reproducing the low end of the observed OMC frequency distributions fairly well across 

the IMPROVE and STN networks but overestimates the high end, whereas the high end is 

underestimated across the SEARCH network. 
 B-36 

Figure B-12a. Scatter plots of predicted and observed organic matter carbon (OMC) concentrations 

for January 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and 

SEARCH Daily (bottom left) monitoring networks and for total carbon mass (TCM) for the SEARCH 

Hourly network (bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-37 

Figure B-12b. Q-Q plots for January 2002 and the VISTAS 12 km grid of predicted and observed 

organic matter carbon (OMC) and elemental carbon (EC) across the IMPROVE network (top left and 

right) and SEARCH Daily OCM (bottom left) and SEARCH Hourly total carbon mass (TCM) (bottom 

right) using the CMAQ 2002 12 km Base G2 Actual base case simulation. 
 B-38 

B.4.3.2 OMC in April 2002 

In April 2002, the CMAQ Base B2 modeling results has a near zero bias across the IMPROVE 

monitors with a 39% error, and a -59% (12 km) and -54% (36 km) underprediction bias across 

the STNB monitors. There is also very low OMC bias across the SEARCH network in April, 

although the errors are higher (~60%). The SEACH hourly TCM also shows low OMC bias in 

April (11%-16%) and errors of 60%. The April OMC Q-Q plots are similar to January with the 

model overstating the observed OMC frequency distribution at the high end for IMPRVE and 

STN, but understating it for SEARCH. 

Figure B-13a. Scatter plots of predicted and observed organic matter carbon (OMC) 

concentrations for April 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top 

right), and SEARCH Daily (bottom left) monitoring networks and for total carbon mass (TCM) for 

the SEARCH Hourly network (bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual 

base case simulation. 
 B-39 

Figure B-13b. Q-Q plots for January April and the VISTAS 12 km grid of predicted and observed 

organic matter carbon (OMC) and elemental carbon (EC) across the IMPROVE network (top left 

and right) and SEARCH Daily OCM (bottom left) and SEARCH Hourly total carbon mass (TCM) 

(bottom right) using the CMAQ 2002 12 km Base G2 Actual base case simulation. 
 B-40 

B.4.3.3 OMC in July 2002 

In July the model underestimates OMC across the IMPOREVE (-13%), STN (-54% to -59%) 

and SEARCH networks (-34% to -40%) monitoring networks in the VISTAS states (Figure B14a). The scatter plots show that the model is exhibiting some skill in predicting the observed 

OMC, albeit with an underprediction bias. Unlike January and April, the observed high end of 

the OMC measurements is underestimated by the model across all three networks. 

Figure B-14a. Scatter plots of predicted and observed organic matter carbon (OMC) concentrations 

for July 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and 

SEARCH Daily (bottom left) monitoring networks and for total carbon mass (TCM) for the SEARCH 

Hourly network (bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-41 

Figure B-14b. Q-Q plots for July 2002 and the VISTAS 12 km grid of predicted and observed 

organic matter carbon (OMC) and elemental carbon (EC) across the IMPROVE network (top left and 

right) and SEARCH Daily OCM (bottom left) and SEARCH Hourly total carbon mass (TCM) (bottom 

right) using the CMAQ 2002 12 km Base G2 Actual base case simulation. 
 B-42 

B.4.3.4 OMC in October 2002 

In October the model exhibits near zero bias across the IMPROVE, large underprediction bias 

across STN and smaller underprediction bias across the SEARCH networks (Figure B-15a). The 

highest observed OMC frequency distributions are overstated at the IMPROVE and STN but 

matched across the SEARCH monitors (Figure B-15b) 

Figure B-15a. Scatter plots of predicted and observed organic matter carbon (OMC) concentrations 

for October 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and 

SEARCH Daily (bottom left) monitoring networks and for total carbon mass (TCM) for the SEARCH 

Hourly network (bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-43 

Figure B-15b. Q-Q plots for October 2002 and the VISTAS 12 km grid of predicted and observed 

organic matter carbon (OMC) and elemental carbon (EC) across the IMPROVE network (top left and 

right) and SEARCH Daily OCM (bottom left) and SEARCH Hourly total carbon mass (TCM) (bottom 

right) using the CMAQ 2002 12 km Base G2 Actual base case simulation. 
 B-44 

B.4.3.5 OMC Monthly Bias and Error 

The monthly fractional bias and error plot for the 12 km CMAQ base case simulation in Figure 

B-16a displays fairly good performance across the IMPROVE monitors with bias usually within 

±15% and errors between 40% and 50%. However, at the urban STN sites the model exhibits a 

large underprediction bias of -35% to -60%. Part of the underprediction bias may be due to 

measurement uncertainties (e.g., blank corrections) and part may be due to the model over 

diluting the urban OMC emissions through the coarse 36/12 km grid. The underprediction of 

urban OMC is a common problem in PM modeling and likely also points to uncertainties in the 

OMC and SOA precursor emission inventories. 

The Bugle Plots of monthly fractional bias and gross error (Figure B-16b) concurs that the OMC 

performance across the IMPROVE monitoring sites is fairly good achieving the model 

performance goals. But the OMC underprediction bias for the STN monitors is significant 

enough so that always falls out of the model performance goals and is right at the model 

performance criteria. The OMC performance across the SEARCH network is mixed usually 

achieving the model performance goal and always achieving the model performance criteria. 
 B-45 

Figure B-16a. Monthly OMC fractional bias (top) and fractional gross error (bottom) statistical 

measures for IMPROVE, STN and SEARCH monitoring sites in the VISTAS region. 
 B-46 

Figure B-16b. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for OMC in 

the VISTAS region and comparison to model performance goals (blue) and criteria (yellow). 
 B-47 

B.4.4 Elemental Carbon (EC) Monthly Model Performance in the VISTAS Region 

Elemental carbon is measured at the IMPROVE, STN and SEARCH monitoring networks. 

SEARCH included 24-hur and hourly TCM measurements. In the OMC performance plots in 

Figures B-12 through B-15 the SEARCH hourly TCM results were plotted. In the EC 

performance plots in Figures B-17 through B-20 we display the SEARCH daily average TCM 

performance. 

B.4.4.1 EC in January 2002 

Elemental Caron (EC) in January 2002 is fairly good with fractions bias < ±15% across the 

IMPROVE and STN networks and errors of ~45% (Figure B-17). A larger underprediction bias 

and higher errors are seen across the SEARCH network for OMC and TCM. 
 B-48 

Figure B-17. Scatter plots of predicted and observed elemental carbon (EC) concentrations for 

January 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and 

SEARCH Daily (bottom left) monitoring networks and for total carbon mass (TCM) for the SEARCH 

Daily network (bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-49 

B.4.4.2 EC in April 2002 

Reasonable performance is seen for OMC in April, but with an underprediction bias that ranges 

from -30% to -50% across the IMPORVE and STN monitoring networks in the VISTAS region 

with errors of 50% to 60% (Figure B-18). The OMC and TCM performance across the 

SEARCH network in April exhibits near zero bias but with a lot of scatter resulting in errors on 

~60%. 

Figure B-18. Scatter plots of predicted and observed elemental carbon (EC) concentrations for April 

2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and SEARCH Daily 

(bottom left) monitoring networks and for total carbon mass (TCM) for the SEARCH Daily network 

(bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-50 

B.4.4.3 EC in July 2002 

EC is underestimated in July 2002 in the VISTAS region across all monitoring networks. With 

the underestimation bias greater at the more rural IMPROVE (-41% and -53%) than urban STN 

(2% and -23%) networks (Figure B-19). It is interesting to note that EC model performance at 

the urban STN and partially urban SEARCH networks is much better for the 12 km modeling 

results (bias of 2% and -60%) than the 36 km results (-23% and -86%). 

Figure B-19. Scatter plots of predicted and observed elemental carbon (EC) concentrations for July 

2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and SEARCH Daily 

(bottom left) monitoring networks and for total carbon mass (TCM) for the SEARCH Daily network 

(bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-51 

B.4.4.4 EC in October 2002 

The EC underprediction bias also occurs in October (Figure B-20). Again lower bias is seen at 

the STN networks with the 12 km results (-10%) producing lower bias than the 36 km results (-

31%). 

Figure B-20. Scatter plots of predicted and observed elemental carbon (EC) concentrations for 

October 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and 

SEARCH Daily (bottom left) monitoring networks and for total carbon mass (TCM) for the SEARCH 

Daily network (bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case 

simulation. 
 B-52 

B.4.4.5 EC Monthly Bias and Error 

The monthly bias and error performance statistics for EC across the VISTAS region are shown in 

Figure B-21a. Bias and error are lower in the first quarter of the year, and then the IMPROVE 

and SEARCH networks begin to exhibit an underprediction bias that peaks in the summer and 

gets lower again with the onset of winter. Surprisingly, the STN network exhibits very low EC 

bias during the summer to fall. 

The Bugle Plot in Figure B-21b shows that although the EC underprediction produces large 

fractional bias values, because the EC concentrations are low the EC performance achieves the 

model performance goal for all months and networks since it is in the flared horn section of the 

Bugle Plot. 
 B-53 

Figure B-21a. Monthly EC fractional bias (top) and fractional gross error (bottom) statistical 

measures for IMPROVE, STN and SEARCH daily and hourly monitoring sites in the VISTAS 

region. 
 B-54 

Figure B-21b. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for EC in 

the VISTAS region and comparison to model performance goals (blue) and criteria (yellow). 
 B-55 

B.4.5 Other PM2.5 (Soil) Monthly Model Performance in the VISTAS Region 

There are potential model-measurement incommensurability issues with the Soil model 

performance. The IMPROVE and SEARCH networks builds up Soil from the elements, whereas 

in the model Soil is mapped to the other PM2.5 category that is emissions not identified as SO4, 

NO3, OMC or EC. 

B.4.5.1 Soil in January, April, July and October 2002 

Soil model performance is poor in January with a systematic overprediction bias of 134% to 

157% with similar magnitude of errors (Figure B-22). The overprediction bias is not as large in 

April and no longer systematic (i.e., doesn’t occur across all predicted-observed pairs) with 

fraction bias values of ~20% (IMPROVE) and ~80% (SEARCH) (Figure B-23). Higher 

observed values in the measured Soil values in July lowers the model overprediction bias to 

approximately 40% across the SEARCH network with a -30% underprediction bias across the 

IMPORVE monitors (Figure B-24). The overprediction bias returns in October with fractional 

bias of ~100% (IMPROVE) to ~140% (SEARCH). 
 B-56 

Figure B-22. Comparison of predicted and observed other PM2.5 (Soil) concentrations for January 

2002 and sites in the VISTAS region using scatter plots (top) and Q-Q plots (bottom) and 

observations from the IMPROVE (left) and SEARCH Daily (left) monitoring networks for the CMAQ 

2002 12 km Base G2 Actual base case simulation. 
 B-57 

Figure B-23. Comparison of predicted and observed other PM2.5 (Soil) concentrations for April 2002 

and sites in the VISTAS region using scatter plots (top) and Q-Q plots (bottom) and observations 

from the IMPROVE (left) and SEARCH Daily (left) monitoring networks for the CMAQ 2002 12 km 

Base G2 Actual base case simulation. 
 B-58 

 

Figure B-24. Comparison of predicted and observed other PM2.5 (Soil) concentrations for July 2002 

and sites in the VISTAS region using scatter plots (top) and Q-Q plots (bottom) and observations 

from the IMPROVE (left) and SEARCH Daily (left) monitoring networks for the CMAQ 2002 12 km 

Base G2 Actual base case simulation. 
 B-59 

 

Figure B-25. Comparison of predicted and observed other PM2.5 (Soil) concentrations for October 

2002 and sites in the VISTAS region using scatter plots (top) and Q-Q plots (bottom) and 

observations from the IMPROVE (left) and SEARCH Daily (left) monitoring networks for the CMAQ 

2002 12 km Base G2 Actual base case simulation. 
 B-60 

B.4.5.2 Soil Monthly Bias and Error 

The seasonal dependence of the Soil model performance across the IMPROVE and STN 

monitors in the VISTAS region is clearly evident in the monthly bias time series plot in Figure 

B-26a. As noted in the scatter plots in Figure B-22 through B-25, the modeled Soil values tend 

to be between 0 and 2 μg/m3

 year round, however the observed values are much lower in the 

winter (0 to 0.5 μg/m3) and comparable to the modeled values in the summer. These results 

suggest that the poor Soil model performance in the winter, spring and fall is likely due to 

incorrect emission temporal adjustment factors. For example, the effects of wetted surfaces on 

fugitive dust emissions may not be properly characterized in the emissions inventory. 

The Bugle plots of bias and error also display the seasonal dependence of the Soil model 

performance with the model achieving the model performance goals for Soil at the IMPROVE 

monitoring network for approximately half the year and not achieving both the model 

performance goals and criteria at the IMPROVE network for the other half (Figure B-26b). 

Performance for Soil across the SEARCH network is worse with the model not achieving either 

the performance goals or criteria for most months. 
 B-61 

Figure B-26a. Monthly Soil fractional bias (top) and fractional gross error (bottom) statistical 

measures for IMPROVE and SEARCH Daily monitoring sites in the VISTAS region. 
 B-62 

Figure B-26b. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for Soil in 

the VISTAS region and comparison to model performance goals (blue) and criteria (yellow). 
 B-63 

B.4.6 Coarse Mass (CM) and PM10 Mass Monthly Model Performance in the VISTAS 

Region 

Coarse Mass (CM) measurements are defined as the difference between the PM10 mass and 

PM2.5 mass measurements. Since IMPROVE network only speciates the PM2.5 fraction of PM, 

then any coarse SO4 or NO3 would be in the CM measurement. In the model, on the other hand, 

CM is due solely to primary PM emissions that have been speciated to the coarse fractions 

(PM2.5-10). Thus, there are potential incompatibility issues with the measured and modeled CM 

concentrations. 

In addition, CM has a much shorter transport distances than fine PM. In the modeling this is 

accounted for though fugitive dust transport factors (FDTF) that adjust the fugitive dust 

emissions downward (by ~75% on average in the VISTAS region) to account for the fact that 

these emissions are deposited locally and are not transported out of the grid cell where they are 

emitted. However, such local dust emissions can impact an IMPROVE monitor. Thus, much of 

the measured CM at the IMPROVE monitors is likely local in nature and subgrid-scale to the 

model. 

B.4.6.1 CM and PM10 in January, April, July and October 2002 

Figures B-27 through B30 display scatter plots of predicted and observed 24-hour CM and PM10

concentrations across the IMPROVE monitors in the VISTAS region for, respectively, January, 

April, July and October. CM exhibits an underprediction bias of approximately -50% in January 

and approximately -100% for the other three months with errors from 100% to 130%. The PM 

10 performance in January across the IMPORVE monitors is quite good with near zero bias and 

~30% error so achieves the most stringent ozone model performance goals (Figure B-27). 

However, an underprediction bias becomes larger in April (-35%) and even larger in July (-60%) 

only to come back to fairly good model performance again in October. 

Figure B-27. Scatter plots of predicted and observed coarse mass (CM) (left) and PM10 (right) 

concentrations for January 2002 and observations from the IMPROVE monitoring network for the 

CMAQ 2002 12 km Base G2 Actual base case simulation. 
 B-64 

Figure B-28. Scatter plots of predicted and observed coarse mass (CM) (left) and PM10 (right) 

concentrations for April 2002 and observations from the IMPROVE monitoring network for the CMAQ 

2002 12 km Base G2 Actual base case simulation. 

Figure B-29. Scatter plots of predicted and observed coarse mass (CM) (left) and PM10 (right) 

concentrations for July 2002 and observations from the IMPROVE monitoring network for the CMAQ 

2002 12 km Base G2 Actual base case simulation. 
 B-65 

Figure B-30. Scatter plots of predicted and observed coarse mass (CM) (left) and PM10 (right) 

concentrations for October 2002 and observations from the IMPROVE monitoring network for the 

CMAQ 2002 12 km Base G2 Actual base case simulation. 

B.4.6.5 CM Monthly Bias and Error 

Figure B-31a displays the monthly fraction bias and gross error values for CM across the 

IMPROVE monitors in the VISTAS region. The underprediction bias ranges from -60% to -

120% with errors of 80% to 140%. The Bugle Plot (Figure B-31b) shows that these performance 

statistics are either right at or exceeds the performance criteria indicating poor CM model 

performance. 
 B-66 

Figure B-31a. Monthly coarse mass (CM) fractional bias (top) and fractional gross error 

(bottom) statistical measures for IMPROVE monitoring sites in the VISTAS region. 
 B-67 

Figure B-31b. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for coarse 

mass in the VISTAS region and comparison to model performance goals (blue) and criteria 

(yellow). 
 B-68 

B.4.7 PM2.5 Mass Monthly Model Performance in the VISTAS Region 

Below we present the CMAQ 2002 Base G2 12km and 36 km model performance for PM2.5

mass. In the model, PM2.5 mass is obtained by summing all of the PM species except CM. In 

the measurement, PM2.5 is obtained by the total PM on a filter collected by a sampler with a 2.5 

micron inlet cut point. Thus, the measured PM2.5 could exclude some SO4 and NO3 if they are 

in the coarse mode that would be included in the modeled PM .5 that assumes all SO4 and NO3 

are fine. 

B.4.7.1 PM2.5 in January 2002 

PM2.5 performance in January is quite good achieving the most stringent 1-hour ozone 

<±15%/<35% 1-hour ozone bias/error performance goal (Figure B-32a). PM2.5 is slightly 

overestimated across the IMPROVE, underestimated across the STN and near zero bias across 

the SEARXCH and FRM monitoring sites. 

Figure B-32a. Scatter plots of predicted and observed PM2.5 mass concentrations for January 2002 

and sites in the VISTAS region using IMPROVE (top left), SEARCH Daily (top right), STN (bottom 

left) and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual 

base case simulation. 
 B-69 

Figure B-32a. (concluded) Scatter plots of predicted and observed PM2.5 mass concentrations for 

January 2002 and sites in the VISTAS region using IMPROVE (top left), SEARCH Daily (top right), 

STN (bottom left) and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km 

Base G2 Actual base case simulation. 

Figure B-32b. Q-Q plots of predicted and observed PM2.5 mass concentrations for January 2002 and 

sites in the VISTAS region using IMPROVE (left) and SEARCH Daily (right) monitoring networks and 

the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-70 

B.4.7.2 PM2.5 in April 2002 

In April the PM2.5 model performance is still fairly good with errors in the 30% to 40% range, 

but with an underprediction bias of from -15% (IMPROPVE) to -35% (STN). 

Figure B-33a. Scatter plots of predicted and observed PM2.5 mass concentrations for April 2002 and 

sites in the VISTAS region using IMPROVE (top left), SEARCH Daily (top right), STN (bottom left) 

and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual 

base case simulation. 
 B-71 

Figure B-33b. Q-Q plots of predicted and observed PM2.5 mass concentrations for April 2002 and 

sites in the VISTAS region using IMPROVE (left) and SEARCH Daily (right) monitoring networks and 

the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-72 

B.4.7.3 PM2.5 in July 2002 

The underprediction bias is higher in July (-35% to -50%) with errors higher as well (45% to 

55%). 

Figure B-34a. Scatter plots of predicted and observed PM2.5 mass concentrations for July 2002 and 

sites in the VISTAS region using IMPROVE (top left), SEARCH Daily (top right), STN (bottom left) 

and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual 

base case simulation. 
 B-73 

Figure B-34b. Q-Q plots of predicted and observed PM2.5 mass concentrations for July 2002 and 

sites in the VISTAS region using IMPROVE (left) and SEARCH Daily (right) monitoring networks and 

the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-74 

B.4.7.4 PM2.5 in October 2002 

By October the performance has improved with lower bias and errors across all the networks. 

Figure B-35a. Scatter plots of predicted and observed PM2.5 mass concentrations for October 2002 

and sites in the VISTAS region using IMPROVE (top left), SEARCH Daily (top right), STN (bottom 

left) and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual 

base case simulation. 
 B-75 

Figure B-35b. Q-Q plots of predicted and observed PM2.5 mass concentrations for October 2002 and 

sites in the VISTAS region using IMPROVE (left) and SEARCH Daily (right) monitoring networks and 

the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 

B.4.7.5 PM2.5 Monthly Bias and Error 

Figure B-26a displays the monthly time series of fractional bias and error for PM2.5 mass 

concentrations for the IMPROVE, STN, SEARCH Daily, SEARCH Hourly and FRM networks 

in the VISTAS region. For the first three and last three months of the year the model performs 

well for PM2.5 with low bias (<±20%) and error (<40% except for SEARCH Hourly). During 

April through July the underprediction bias becomes larger and is in the -20% to -45%) and the 

errors are also larger. 

The Bugle Plots confirm the good performance for PM2.5 mass concentrations that achieves the 

model performance goals most of the time and always achieves the model performance criteria 

(Figure B-36b). 
 B-76 

Figure B-36a. Monthly PM2.5 fractional bias (top) and fractional gross error (bottom) statistical 

measures in the VISTAS region. 
 B-77 

Figure B-36b. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for PM2.5 in 

the VISTAS region and comparison to model performance goals (blue) and criteria (yellow). 
 B-78 

B.4.8 Sea Salt in the VISTAS Region 

Figure B-37 compares scatter plots of predicted and observed “Sea Salt” for IMPROVE monitors 

in the VISTAS region and January, April, July and October 2002. The IMPROVE “Sea Salt” is 

inferred from the chlorine ion measurement [Cl-], whereas CMAQ internally generates its Sea 

Salt emissions. The model is predicting near zero Sea Salt resulting in poor performance. As 

Sea Salt is not directly used in the 2018 visibility projections this is not a big cause for concern, 

but can affect aerosol concentrations and is an area that should be investigated further, 

Figure B-37. Scatter plots of predicted and observed Sea Salt concentrations in the VISTAS region 

using IMPROVE monitoring data and for January (top left), April (top right), July (bottom left) and 

October (bottom right) and the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-79 

B.5 Diagnostic Model Evaluation for Gas-Phase and Precursor Species 

The CASTNet and SEARCH networks also measure gas-phase species that are PM precursor or 

related species. The diagnostic evaluation of the 2002 36/12 km Base G2 CMAQ base case 

simulation for these compounds and the four seasonal months presented previously is provided 

below. Note that the EPA Air Quality Stations (AQS) compliance network also provides routine 

measurements of many gas-phase precursor (e.g., NOx and SO2) and product (e.g., O3) species 

as well. However, these sites tend to be urban-oriented and designed to measure maximum 

potential concentrations of criteria pollutants so would not provide a good measure of a model’s 

ability to estimate regional concentrations at the more rural Class I areas. Consequently the gasphase precursor and product species diagnostic evaluation used measured data from the 

CASTNet and SEARCH networks that included some more rural monitoring sites. 

The CASTNet network measures weekly average samples of SO2, SO4, NO2, HNO3, NO3 and 

NH4. Because the CASTNet samples are collected in a cartridge and sampled at a later date 

some of the NO3 may volatize into gaseous HNO3. Thus, we also evaluate against total nitrate 

(TNO3) that is defined as the sum of NO3 and HNO3. The SEARCH Hourly network collects 

hourly measurements of gaseous SO2, NO, NO2, NOy, O3 and CO and particulate SO4, NO3, 

and NH4. A comparison of the SO2 and SO4 performance provides insight into whether the 

SO4 formation rate may be too slow or fast. For example, if SO4 is underestimated and SO2 is 

overestimated, that may indicate too slow chemical conversion rate. Analyzing the performance 

for SO4, HNO3, NO3, Total NO3 and NH4 provides insight into the equilibrium of these 

species. For example, if TNO3 performs well but HNO3 and NO3 do not, then there may be 

issues associated with the partitioning between the gaseous and particle phases of nitrate (e.g., 

ammonia may be off). 
 B-80 

B.5.1 Diagnostic Model Performance in January 2002 

Figure B-38a. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom 

left) and TNO3 (bottom right) from the SEARCH Hourly monitoring network and January 2002 for the 

CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-81 

Figure B-38b. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom 

left) and TNO3 (bottom right) from the CASTNet monitoring network and January 2002 for the CMAQ 

2002 36 and 12 km Base G2 Actual base case simulation. 
 B-82 

Figure B-39. Scatter plots of predicted and observed gaseous SO2 (left) and particulate SO4 (right) 

from the SEARCH Hourly (top) and CASTNet (bottom) monitoring networks and January 2002 for the 

CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-83 

Figure B-40. Scatter plots of predicted and observed gaseous NO (top left), NO2 (top right), NOy 

(bottom left) and O3 (bottom right) from the SEARCH Hourly monitoring network and January 2002 

for the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-84 

B.5.2 Diagnostic Model Performance in April 2002 

Figure B-41a. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom 

left) and TNO3 (bottom right) from the SEARCH Hourly monitoring network and April 2002 for the 

CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-85 

Figure B-41b. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom 

left) and TNO3 (bottom right) from the CASTNet monitoring network and April 2002 for the CMAQ 

2002 36 and 12 km Base G2 Actual base case simulation. 
 B-86 

Figure B-42. Scatter plots of predicted and observed gaseous SO2 (left) and particulate SO4 (right) 

from the SEARCH Hourly (top) and CASTNet (bottom) monitoring networks and April 2002 for the 

CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-87 

Figure B-43. Scatter plots of predicted and observed gaseous NO (top left), NO2 (top right), NOy 

(bottom left) and O3 (bottom right) from the SEARCH Hourly monitoring network and April 2002 for 

the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-88 

B.5.3 Diagnostic Model Performance in July 2002 

Figure B-44a. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom 

left) and TNO3 (bottom right) from the SEARCH Hourly monitoring network and July 2002 for the 

CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-89 

Figure B-44b. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom 

left) and TNO3 (bottom right) from the CASTNet monitoring network and July 2002 for the CMAQ 

2002 36 and 12 km Base G2 Actual base case simulation. 
 B-90 

Figure B-45. Scatter plots of predicted and observed gaseous SO2 (left) and particulate SO4 (right) 

from the SEARCH Hourly (top) and CASTNet (bottom) monitoring networks and July 2002 for the 

CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-91 

Figure B-46. Scatter plots of predicted and observed gaseous NO (top left), NO2 (top right), NOy 

(bottom left) and O3 (bottom right) from the SEARCH Hourly monitoring network and July 2002 for 

the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-92 

B.5.4 Diagnostic Model Performance in October 2002 

Figure B-47a. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom 

left) and TNO3 (bottom right) from the SEARCH Hourly monitoring network and October 2002 for the 

CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-93 

Figure B-47b. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom 

left) and TNO3 (bottom right) from the CASTNet monitoring network and October 2002 for the CMAQ 

2002 36 and 12 km Base G2 Actual base case simulation. 
 B-94 

Figure B-48. Scatter plots of predicted and observed gaseous SO2 (left) and particulate SO4 (right) 

from the SEARCH Hourly (top) and CASTNet (bottom) monitoring networks and October 2002 for 

the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 B-95 

Figure B-49. Scatter plots of predicted and observed gaseous NO (top left), NO2 (top right), NOy 

(bottom left) and O3 (bottom right) from the SEARCH Hourly monitoring network and October 2002 

for the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 
 

APPENDIX C 

Air Quality Model Performance Evaluation: 

Evaluation at VISTAS and Nearby Class I Areas 
 C-1 

C.1 OVERVIEW 

The discussion of model performance in this Appendix focuses on the comparison of 

observational data from the Interagency Monitoring of PROtected Visual Environments 

(IMPROVE) monitors and model output data from the 2002 VISTAS BaseG2 Actual annual air 

quality modeling. The evaluation will primarily focus on the air quality model’s performance 

with respect to individual components of fine particulate matter (PM2.5), as good model 

performance of the component species will dictate good model performance of total or 

reconstituted fine particulate matter from which visibility is constructed. Model performance of 

the total fine particulate matter and the resulting total light extinction will also be provided as a 

means to discuss the overall model performance for this Implementation Plan. 

C.1.1 IMPROVE Monitoring Sites 

There are eighteen (18) Class I areas within the VISTAS States (VISTAS region) as shown in 

Figure C-1. There are also several Class I areas adjacent to the VISTAS region where we also 

project future-year visibility conditions. Figure C-2 displays the IMPROVE monitoring sites 

within and nearby the VISTAS region. The 18 VISTAS Class I areas and nearby Class I areas 

where the CMAQ Base G2 modeling results are evaluated in this Appendix are as follows: 

VISTAS Class I Areas

• Sipsey Wilderness Area (SIPS), Alabama 

• Chassahowitzka Wildlife Refuge (CHAS), Florida 

• Everglades National Park (EVER), Florida 

• St. Marks Wildlife Refuge (SAMA), Florida 

• Cohutta Wilderness Area (COHU), Georgia 

• Wolf Island Wildlife Refuge (WOIS), Georgia 

• Okefenokee Wildlife Refuge (OKEF), Georgia 

• Mammoth Cave National Park (MACA), Kentucky 

• Linville Gorge Wilderness Area (LIGO), North Carolina 

• Shining Rock Wilderness Area (SHRO), North Carolina 

• Swanquarter Wildlife Refuge (SWAN), North Carolina 

• Joyce Kilmer-Slickrock Wilderness Area (JKSR), North Carolina 

• Cape Romain Wildlife Refuge (ROMA), South Carolina 

• Great Smoky Mountains National Park (GRSM), Tennessee-North Carolina 

• James River Face Wilderness Area (JARI), Virginia 

• Shenandoah National Park (SHEN), Virginia 

• Dolly Sods Wilderness Area (DOSO), West Virginia 

• Otter Creek Wilderness Area (OTCR), West Virginia 
 C-2 

Nearby Class I Areas

• Breton Island Wildlife Refuge (BRET), Louisiana 

• Caney Creek Wilderness Area (CACR), Arkansas 

• Upper Buffalo Wilderness Area (UPBU), Arkansas 

• Mingo Wildlife Refuge (MING), Missouri 

• Hercules Glade Wilderness Area (HEGL), Missouri 

• Brigantine Wildlife Refuge (BRIG), New Jersey 

Note that not all Class I areas have their own IMPROVE monitor. The Wolf Island Class I area 

shares the Okefenokee IMPROVE monitor in Georgia and in West Virginia, the Otter Creek 

Class I area shares the Dolly Sods IMPROVE monitor and the Joyce Kilmer-Slickrock Class I 

area shares the IPROVE monitor at Great Smokey Mountains National Park. So when 

presenting model performance evaluation and visibility projections for Okefenokee, Dolly Sods 

and Great Smokey Mountains IMPROVE monitors they are also represented of, respectively, 

Wolf Island, Otter Creek and Joyce Kilmer-Slickrock Class I areas, respectively. Also note that 

there are some IMPROVE sites not located in Class I areas. Although data at these sites are used 

in the regional evaluation discussed in Appendix B, the performance at the individual IMPROVE 

monitors not located at a Class I area are not discussed in this section. For the VISTAS region, 

the Cadiz (CADI) site is one such IMPROVE monitor. 
 C-3 

Figure C-1. Class I areas within the VISTAS region. 
 C-4 

250 500 750 1000 1250 1500 1750 2000

-1500

-1250

-1000

-750

-500

-250

0

250

500

EVER1

CHAS1

BRET1

SAMA1

OKEF1 SIKE1

ROMA1 SIPS1 CACR1

COHU1

SHRO1

SWAN1

GRSM1

UPBU1

LIGO1

HEGL1

MING1 CADI1

MACA1

JARI1 JEFF1

ELDO1

SHEN1

LIVO1

WASH1 DOSO1

BRIG1 AREN1

QUCI1

BOND1

LASU1

MKGO1

MOMO1

ADPI1

COHI1

GRRI1

Figure C-2. IMPROVE monitoring sites within and near the VISTAS region. 

C.1.2 Particulate Matter and Component Species 

Regional haze is calculated by estimating light scattering and absorption by components of 

PM2.5. Regional haze is measured by an extinction coefficient (bext), which represents the light 

attenuation resulting from scattering and absorption of light from ambient particulate matter plus 

scattering of light due to gas molecules in the air (i.e., Rayleigh scattering). 

Some scattering does occur from air molecules; however, the bulk of light extinction is caused 

by the presence of suspended particles, particularly fine particles (aerodynamic mass <2.5 μm). 

Fine particulate matter can be composed of varying amounts of several different species, 

including: 

• Sulfates 

• Nitrates 

• Ammonium 

• Organic carbon (OC) 
 C-5 

• Elemental carbon (EC) 

• Coarse particulate matter (i.e., PM10 - PM2.5) 

• Soil (also known as crustal material, fine soil, major metal oxides, inorganic particulates, 

or other PM) 

• Sea Salt (NaCl) [new IMPROVE equation] 

The composition of particulate matter can vary from day-to-day, allowing a day with a large 

concentration of sulfates to be closely followed by a day dominated by organic carbon. This is 

an important fact as some of the components of fine particulate matter are more effective at light 

scattering than others. Since there can be such a large variability in the concentrations of the 

component species, the model will actually be evaluated on a per species basis, followed by a an 

evaluation of total reconstructed fine particle mass. This is also a more stringent test of the 

model, as simply looking at particle pollution performance may provide good results, even 

though the underlying constituent concentrations are incorrect. Only a brief overview of total 

fine particles after component evaluation is necessary, since the accuracy of the model in 

predicting the component species will dictate the accuracy of the total fine particulate mass 

prediction. 

C.1.3 20% Best and Worst Days 

Projections of light extinction for the evaluation of regional haze are based on a subset of days 

for which there is monitored data. IMPROVE monitors collect 24-hour PM samples every third 

day. Observations of component species on these days are used to calculate light extinction, 

which is the basis on which these days are ranked. These rankings are then used to identify the 

20% most impaired days, or “worst days”, and the 20% least impaired days, or “best days”, for 

use in visibility projections. Table C-1 lists of the 20% best and worst days for all of Class I 

areas within and nearby of the VISTAS region. . 

Since the ultimate future year projections are based on these 20% best and worst days, the 

following performance evaluation of the model has mainly focused on the best and worst days 

for VISTAS and nearby Class I areas. 
Date 

Julian 

Day SIPS CHAS EVER SAMA COHU OKEF MACA LIGO SHRO SWAN ROMA GRSM JARI SHEN DOSO BRET 

01/05/02 5 W W B B W B B 01/08/02 8 B B B B B 01/11/02 11 B B B B B B B 01/14/02 14 B B B B B B B B B 01/17/02 17 W W W 01/20/02 20 B B B 01/23/02 23 B B W B B B B B B B 01/26/02 26 B W B B B B B B B 01/29/02 29 B W 02/01/02 32 B B B B B B B 02/04/02 35 W W B B 02/07/02 38 B B B B B B B B B 02/10/02 41 W 02/13/02 44 W W W B W B W B W 02/16/02 47 W W W W 02/19/02 50 B B B B 02/22/02 53 W W 02/25/02 56 W W W W W W W 02/28/02 59 B B B B W 03/03/02 62 W B B B B B B B 03/06/02 65 B 03/09/02 68 B B B B B B B B B B B 03/12/02 71 B B B 03/15/02 74 W W 03/18/02 77 B B B B B 03/21/02 80 W B B B 03/24/02 83 B 03/27/02 86 W B 03/30/02 89 B W W B W B B B B B 04/02/02 92 B 04/05/02 95 W W W 04/08/02 98 W W 04/11/02 101 B B 04/14/02 104 B B B B B B B B B 04/17/02 107 W B 04/20/02 110 W W 
Date 

Julian 

Day SIPS CHAS EVER SAMA COHU OKEF MACA LIGO SHRO SWAN ROMA GRSM JARI SHEN DOSO BRET 

04/23/02 113 B W B B B 04/26/02 116 B W W B B B B B 04/29/02 119 B B W B 05/02/02 122 W W W W W W W W W 05/05/02 125 W W B B W 05/08/02 128 W W W W W W W W W 05/11/02 131 W W 05/14/02 134 B B 05/17/02 137 B W W W W W 05/20/02 140 B 05/23/02 143 W W 05/26/02 146 B W W W W 05/29/02 149 W W W W B 06/01/02 152 W W W W W W W W W 06/04/02 155 W W W W W W W W W W W 06/07/02 158 W B W B B B B 06/10/02 161 W B B W B W W W 06/13/02 164 B B W W W W 06/16/02 167 B 06/19/02 170 B B W W W B W W W B 06/22/02 173 B B B B W B B W W 06/25/02 176 B B B B B B B W W B 06/28/02 179 B B B W 07/01/02 182 B W W W W W W 07/04/02 185 W W W W W W W W W 07/07/02 188 W W W W W W W W 07/10/02 191 W B B W B W W W B W W W B 07/13/02 194 B B B B W 07/16/02 197 W W W W W W W W W W W 07/19/02 200 W W W W W W W W 07/22/02 203 B W W W W W W 07/25/02 206 W B B W W W W W B W W 07/28/02 209 B W W B 07/31/02 212 B B W W W W W W W W 08/03/02 215 W W W W W W W W W 08/06/02 218 W W B W W W W W W W 
Date 

Julian 

Day SIPS CHAS EVER SAMA COHU OKEF MACA LIGO SHRO SWAN ROMA GRSM JARI SHEN DOSO BRET 

08/09/02 221 W W W W B W B 08/12/02 224 W B B W W W W W W W W 08/15/02 227 B W B B B W W B 08/18/02 230 B 08/21/02 233 W W W W W W W W B 08/24/02 236 W W W W W W W W W B 08/27/02 239 B B B W W W W W 08/30/02 242 B B B B B 09/02/02 245 B W B B B B 09/05/02 248 B B W W W B W W 09/08/02 251 W B W W W W B W W 09/11/02 254 W B W W W W W W W W W W W 09/14/02 257 B W B B B W B W W W W 09/17/02 260 W W W W W W W W W W W W W W 09/20/02 263 W W W W B B W W W 09/23/02 266 B B W B B W B W 09/26/02 269 B B B B B B B 09/29/02 272 W W W W 10/02/02 275 B W W W 10/05/02 278 W B W 10/08/02 281 W W W W B W 10/11/02 284 W B B B B B B W 10/14/02 287 B B W 10/17/02 290 W W W B 10/20/02 293 W W W B 10/23/02 296 W W B W W W 10/26/02 299 B B W B B B B 10/29/02 302 B B B B B B B B B B 11/01/02 305 W W 11/04/02 308 B B W B B B B 11/07/02 311 W W 11/10/02 314 B 11/13/02 317 11/16/02 320 B B B B B B B B 11/19/02 323 B 11/22/02 326 B B W B B B 
Date 

Julian 

Day SIPS CHAS EVER SAMA COHU OKEF MACA LIGO SHRO SWAN ROMA GRSM JARI SHEN DOSO BRET 

11/25/02 329 W W 11/28/02 332 W W B B 12/01/02 335 B B B B B 12/04/02 338 W W B B B 12/07/02 341 W W W W B W W B B W W B B B 12/10/02 344 B B B B B B 12/13/02 347 B B B B B B B B B B 12/16/02 350 W W W 12/19/02 353 B B B 12/22/02 356 B B B B B B B B B B B 12/25/02 359 B B B B B B B B B 12/28/02 362 B W B B 

12/31/02* 365 B B B B 
 C-10 

C.2 Model Performance Statistics 

To quantify model performance, several statistical measures were calculated and evaluated for 

all the IMPROVE monitors within the VISTAS 12 km domain and individually for each 

IMPROVE monitor associated with a VISTAS or nearby Class I areas. The statistical measures 

selected were based on the recommendations outlined in section 18.4.1 of the USEPA’s 

Guidance On The Use Of Models And Other Analyses for Demonstrating Attainment of Air 

Quality Goals for Ozone, PM2.5, and Regional Haze (EPA, 2007a). 

In 2004, VISTAS established model performance goals and criteria for components of fine 

particle mass (Table C-2) based on previous model performance for ozone and fine particles. 

EPA modeling guidance for fine particulate matter at the time noted that PM models might not 

be able to achieve the same level of performance as ozone models. VISTAS’ evaluation 

considered several statistical performance measures and displays. 

Table C-2. Model performance goals and criteria for components of fine particle mass. 

Fractional Bias Fractional Error Comment 

<15% <35% Goal for PM model performance based on ozone model 

performance, considered excellent performance 

<30% <50% Goal for PM model performance, considered good 

performance 

<60% <75% Criteria for PM model performance, considered average 

performance. Exceeding this level of performance 

indicates fundamental concerns with the modeling 

system and triggers diagnostic evaluation. 

The statistical measures were calculated for all of the component species of particulate matter 

responsible for light extinction, and for total light extinction. For convenience, these statistical 

measures or metrics, along with a variety of additional statistical measures, are summarized in 

Table C-3. 
 C-11 

Table C-3. Statistical metric calculations. 

Statistical Measure Shorthand 

Notation 

Mathematical Expression Notes 

Accuracy of Paired 

Peak 

Ap

peak

peak

O

− OP

Coefficient of 

Determination 

R2

( )( )

∑ ∑

∑

= =

=

− −

⎥

⎦

⎤ ⎢

⎣

⎡ −−

N

i

N

i

i i

N

i

i i

OOPP

OOPP

1 1

2 2

2

1

)()(

Pi

= prediction at time and 

 location i; 

Oi = observation at time and 

 location i; 

P = arithmetic average of Pi, 

 i = 1, 2, . . ., N; 

O = arithmetic average of Oi, 

 i = 1, 2, . . ., N; 

Normalized Mean 

Error 

NME 

∑

∑

=

=

−

N

i

i

N

i

ii

O

OP

1

1

Reported as % 

Root Mean Square 

Error 

RMSE 

( ) 2

1

1

1 2

⎥

⎦

⎤ ⎢

⎣

⎡ ∑ − =

N

i

OP ii N

Reported as % 

Mean Fractional 

Error 

MFE 

∑= +

− N

i ii

ii

OP

OP

N 1

2 Reported as % 

Mean Absolute 

Gross Error 

MAGE ∑=

−

N

i

OP ii N 1

1

Mean Normalized 

Gross Error 

MNGE 

∑=

N −

i i

ii

O

OP

N 1

1 Reported as % 

Mean Biased MB ∑( ) =

−

N

i

OP ii N 1

1 Reported as concentration 

Mean Normalized 

Bias 

MNB ( ) ∑=

− N

i i

ii

O

OP

N 1

1 Reported as % 

Mean Fractionalized 

Bias (Fractional Bias) 

MFB 

∑= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

− N

i ii

ii

OP

OP

N 1

2 Reported as % 

Normalized Mean 

Bias 

NMB ( )

∑

∑

=

=

−

N

i

i

N

i

ii

O

OP

1

1

Reported as % 

Bias Factor BF 

∑= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ N

i i

i

O

P

N 1

1 Reported as BF:1 or 1:BF or in 

fractional notation (BF/1 or 1/BF) 
 C-12 

C.2.1 Statistical Tables 

The statistical data is calculated collectively for all of the IMPROVE monitors within the 

VISTAS 12 km domain, as well as individually for each of the IMPROVE monitors associated 

with a VISTAS Class I area along with the IMPROVE monitors associated with nearby Class I 

areas. These statistics have been calculated separately for the 20% Best days (Section C.2.1.1) 

and 20% Worst days (Section C.2.1.2). 

C.2.1.1 Model Performance Statistics for the 20% Best Days 

Table C-4 displays the model performance statistical measures for the Best 20 percent visibility 

days during 2002. Table C-4a contains statistical performance metrics for the Best 20 percent 

days across all IMPROVE monitors in the VISTAS 12 km domain, whereas the remainder of the 

tables contain statistics for each individual IMPROVE monitor associated with a Class I area 

within and near the VISTAS region. 

C.2.1.2 Model Performance Statistics for the 20% Worst Days 

Statistical performance measures for the Worst 20 percent days in 2002 and the CMAQ Base G2 

12 km base case are shown in Table C-5. 

Table C-4a. Model performance statistics for the Best 20% Days across Class I areas in the 12 

km VISTAS grid. 

12km bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -115.20 -110.06 -25.88 -27.46 78.49 148.33 -81.50 -81.14

MFE (%) 122.49 132.75 57.01 55.29 114.38 184.31 104.25 88.50

Ap 2.48 1.09 2.16 1.54 1.73 4.23 -0.18 1.85

NMB (%) -61.28 -39.82 -3.89 -12.78 213.72 551.54 -62.01 -47.74

NME (%) 75.88 86.49 55.78 52.03 266.13 711.04 70.48 59.48

MNB (%) -61.79 -40.68 0.75 8.92 682.14 2051.72 -38.65 -48.42

MNE (%) 77.67 91.64 58.34 68.09 704.19 2147.79 78.76 60.38

MB (μg/m3) -9.52 -1.77 -0.18 -0.25 0.63 2.88 -1.34 -19.42

MAGE (μg/m3) 11.79 3.84 2.64 1.02 0.78 3.71 1.52 24.20

RMSE (μg/m3) 14.76 6.23 5.04 1.97 1.18 7.45 2.07 27.92

Obs Max (μg/m3) 41.57 30.49 21.39 9.96 2.95 15.38 9.86 65.36

Pred Max (μg/m3) 144.46 63.78 67.55 25.33 8.05 80.50 8.10 186.22

Mean_Obs 15.54 4.44 4.74 1.95 0.29 0.52 2.15 40.69

Mean_Pred 6.02 2.68 4.55 1.70 0.92 3.40 0.82 21.27

Std_Dev_Obs 7.06 3.93 2.50 1.09 0.32 1.78 1.68 10.12

Std_Dev_Pre 10.74 6.01 5.33 1.91 0.97 6.47 0.92 20.63

Variance 217.83 38.82 25.35 3.88 1.40 55.56 4.29 779.65

R2 0.06 0.11 0.12 0.06 0.00 0.01 0.14 0.09

BF 0.38 0.59 1.01 1.08 7.82 4.57 0.60 0.52

N 490 490 490 490 490 490 490 490
 C-13 

Table C-4b. Model performance statistics for the Best 20% Days and the Breton Island Class I 

area.

BRET bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -152.90 -179.64 -82.67 -69.08 14.14 27.20 -80.87 -139.99

MFE (%) 152.90 181.21 83.72 69.19 126.36 166.60 91.94 139.99

Ap -0.74 -0.69 -0.54 -0.46 -0.25 -0.47 -0.46 -0.66

NMB (%) -86.23 -94.40 -54.53 -50.86 -14.55 -47.92 -56.39 -81.07

NME (%) 86.23 95.50 55.38 50.96 132.14 111.15 59.69 81.07

MNB (%) -85.67 -90.70 -53.78 -48.24 454.91 -71.19 -41.48 -81.47

MNE (%) 85.67 92.44 54.89 48.36 519.05 76.78 71.91 81.47

MB (μg/m3) -15.48 -2.76 -1.40 -0.81 -0.05 -0.39 -1.98 -32.99

MAGE (μg/m3) 15.48 2.80 1.43 0.82 0.43 0.90 2.10 32.99

RMSE (μg/m3) 16.76 3.21 1.79 1.04 0.57 1.25 2.40 33.48

Obs Max (μg/m3) 28.83 7.28 6.17 2.75 1.61 3.81 7.45 48.75

Pred Max (μg/m3) 7.43 2.23 2.86 1.48 1.21 2.03 4.00 16.64

Mean_Obs 17.95 2.93 2.57 1.60 0.33 0.81 3.51 40.69

Mean_Pred 2.47 0.16 1.17 0.79 0.28 0.42 1.53 7.70

Std_Dev_Obs 7.00 1.50 1.40 0.76 0.41 1.00 1.66 6.09

Std_Dev_Pre 2.24 0.49 0.90 0.46 0.31 0.51 1.06 4.97

Variance 280.94 10.28 3.21 1.08 0.32 1.57 5.76 1120.72

R2 0.12 0.04 0.34 0.25 0.08 0.05 0.31 0.22

BF 0.14 0.09 0.46 0.52 5.55 0.15 0.59 0.19

N 23 23 23 23 23 23 23 23

Table C-4c. Model performance statistics for the Best 20% Days and the Brigantine Class I 

area.

BRIG bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -118.49 -85.82 1.36 36.59 68.09 88.47 -126.19 -75.43

MFE (%) 126.68 116.82 57.68 75.89 107.44 176.33 126.19 94.46

Ap 0.14 0.09 7.20 4.77 2.48 -0.61 -0.70 1.14

NMB (%) -66.46 -29.38 124.33 131.07 204.17 -11.47 -76.73 -39.27

NME (%) 75.91 76.29 157.87 159.04 240.37 164.42 76.73 67.56

MNB (%) -65.18 -24.93 99.46 309.60 278.12 3530.32 -75.55 -35.94

MNE (%) 75.82 86.26 138.10 336.38 303.32 3647.08 75.55 69.99

MB (μg/m3) -10.86 -1.07 5.36 2.69 0.48 -0.23 -3.00 -17.47

MAGE (μg/m3) 12.41 2.77 6.81 3.26 0.56 3.30 3.00 30.05

RMSE (μg/m3) 13.51 3.51 16.44 6.75 0.74 4.55 3.49 34.13

Obs Max (μg/m3) 27.59 11.88 8.23 4.39 0.62 12.00 8.61 52.91

Pred Max (μg/m3) 31.45 12.90 67.55 25.33 2.17 4.65 2.62 113.43

Mean_Obs 16.35 3.63 4.31 2.05 0.23 2.00 3.90 44.48

Mean_Pred 5.48 2.56 9.67 4.74 0.71 1.77 0.91 27.01

Std_Dev_Obs 5.74 2.26 1.88 1.17 0.13 3.83 1.82 6.00

Std_Dev_Pre 7.08 3.74 16.57 6.23 0.54 1.28 0.76 28.02

Variance 182.58 12.31 270.43 45.58 0.55 20.74 12.15 1165.02

R2 0.04 0.19 0.16 0.00 0.03 0.29 0.04 0.06

BF 0.35 0.75 1.99 3.91 3.78 14.85 0.24 0.64

N 22 22 22 22 22 22 22 22
 C-14 

Table C-4d. Model performance statistics for the Best 20% Days and the Caney Creek Class I 

area.

CACR bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -98.27 -48.24 12.50 -32.03 121.94 192.93 -80.27 -54.72

MFE (%) 115.70 107.35 51.24 50.47 127.27 192.93 89.10 68.41

Ap 0.74 0.60 -0.05 -0.05 2.68 10.90 -0.73 0.75

NMB (%) -49.86 -8.82 17.09 -26.56 422.09 6340.14 -64.97 -32.38

NME (%) 75.74 92.86 46.05 37.07 428.84 6340.14 67.42 50.84

MNB (%) -48.65 38.51 36.27 -15.41 2407.02 24672.54 -48.65 -32.00

MNE (%) 71.78 132.02 63.94 43.59 2411.82 24672.54 60.37 49.96

MB (μg/m3) -4.15 -0.52 0.77 -0.45 1.23 3.55 -1.32 -10.93

MAGE (μg/m3) 6.30 5.44 2.07 0.63 1.25 3.55 1.37 17.16

RMSE (μg/m3) 7.49 7.61 2.34 0.73 1.45 4.64 1.92 19.73

Obs Max (μg/m3) 15.58 18.62 9.59 2.61 0.76 1.20 5.41 42.26

Pred Max (μg/m3) 27.18 29.87 9.11 2.48 2.80 14.24 1.45 73.85

Mean_Obs 8.32 5.86 4.49 1.70 0.29 0.06 2.04 33.75

Mean_Pred 4.17 5.34 5.25 1.25 1.52 3.61 0.71 22.82

Std_Dev_Obs 3.75 5.22 2.15 0.67 0.20 0.25 1.41 7.14

Std_Dev_Pre 6.91 7.23 2.63 0.61 0.74 3.03 0.35 17.54

Variance 56.13 57.90 5.47 0.53 2.09 21.58 3.69 389.25

R2 0.17 0.06 0.32 0.34 0.00 0.01 0.01 0.09

BF 0.51 1.39 1.36 0.85 25.07 45.04 0.51 0.68

N 22 22 22 22 22 22 22 22

Table C-4e. Model performance statistics for the Best 20% Days and the Chassahowitzka 

Class I area.

CHAS bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -97.65 -185.48 -62.20 -34.33 75.31 179.65 -77.10 -71.12

MFE (%) 117.54 185.48 67.73 48.95 85.36 190.74 79.50 77.64

Ap 1.16 -0.84 -0.23 0.11 0.30 1.00 -0.54 0.48

NMB (%) -49.52 -95.94 -46.59 -25.06 104.88 1176.77 -58.67 -45.69

NME (%) 77.32 95.94 51.53 41.99 124.45 1297.43 59.97 53.66

MNB (%) -46.09 -95.88 -41.14 -19.10 338.45 472.23 -51.69 -46.07

MNE (%) 79.70 95.88 47.39 43.56 346.64 522.75 54.46 53.71

MB (μg/m3) -8.50 -4.25 -2.65 -0.59 0.36 6.75 -2.10 -20.64

MAGE (μg/m3) 13.27 4.25 2.94 1.00 0.43 7.45 2.15 24.23

RMSE (μg/m3) 15.07 4.48 3.50 1.22 0.53 8.82 2.61 26.26

Obs Max (μg/m3) 24.80 8.90 8.38 3.78 1.14 10.05 7.40 52.76

Pred Max (μg/m3) 53.53 1.41 6.45 4.19 1.48 20.13 3.39 77.91

Mean_Obs 17.16 4.43 5.70 2.37 0.34 0.57 3.58 45.17

Mean_Pred 8.66 0.18 3.04 1.78 0.70 7.33 1.48 24.53

Std_Dev_Obs 5.32 1.45 1.58 0.59 0.29 2.15 1.49 6.38

Std_Dev_Pre 12.45 0.31 1.36 0.85 0.26 4.94 0.54 17.53

Variance 227.25 20.04 12.27 1.48 0.29 77.76 6.81 689.51

R2 0.02 0.03 0.07 0.02 0.01 0.05 0.00 0.10

BF 0.54 0.04 0.59 0.81 4.38 0.78 0.48 0.54

N 22 22 22 22 22 22 22 22
 C-15 

Table C-4f. Model performance statistics for the Best 20% Days and the Cohutta Class I area.

COHU bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -87.92 -80.72 -5.85 -24.92 92.95 198.40 -43.63 -64.47

MFE (%) 106.08 104.25 41.26 44.71 108.35 198.40 89.95 70.27

Ap 0.57 -0.18 0.54 0.03 1.38 23.19 -0.88 -0.01

NMB (%) -43.70 -38.79 8.59 -16.07 189.67 16676.44 -71.13 -41.05

NME (%) 69.09 67.47 40.33 33.25 204.65 16676.44 82.18 47.02

MNB (%) -45.05 -35.48 8.54 -12.98 451.68 10913.35 -9.19 -41.72

MNE (%) 71.18 67.60 45.10 38.67 462.35 10913.35 84.08 48.37

MB (μg/m3) -7.46 -1.79 0.39 -0.30 0.51 2.40 -0.91 -16.67

MAGE (μg/m3) 11.80 3.12 1.82 0.61 0.55 2.40 1.05 19.10

RMSE (μg/m3) 12.95 4.15 2.32 0.70 0.73 2.97 2.24 21.31

Obs Max (μg/m3) 24.33 14.54 6.69 3.25 0.84 0.28 8.34 51.42

Pred Max (μg/m3) 38.12 11.93 10.33 3.34 1.99 6.75 0.99 50.69

Mean_Obs 17.08 4.62 4.52 1.84 0.27 0.01 1.27 40.62

Mean_Pred 9.62 2.83 4.91 1.55 0.78 2.41 0.37 23.94

Std_Dev_Obs 4.94 3.06 1.37 0.73 0.22 0.06 2.05 6.33

Std_Dev_Pre 10.77 3.50 2.79 0.84 0.51 1.80 0.23 14.43

Variance 167.74 17.20 5.39 0.49 0.53 8.81 5.01 453.92

R2 0.04 0.10 0.30 0.44 0.01 0.02 0.03 0.12

BF 0.55 0.65 1.09 0.87 5.52 11.01 0.86 0.58

N 20 20 20 20 20 20 20 20

Table C-4g. Model performance statistics for the Best 20% Days and the Dolly Sods Class I 

area.

DOSO bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -107.37 -62.64 -78.22 -73.22 104.58 200.00 -84.35 -97.79

MFE (%) 109.37 98.95 78.34 76.44 110.05 200.00 127.36 97.79

Ap -0.04 0.45 -0.52 -0.47 2.47 -999.00 -0.79 -0.21

NMB (%) -62.79 -24.54 -57.40 -49.56 317.65 -999.00 -79.16 -61.85

NME (%) 65.70 64.67 57.44 51.87 321.71 -999.00 84.37 61.85

MNB (%) -64.90 -18.19 -52.99 -48.25 926.54 -999.00 20.22 -63.26

MNE (%) 67.01 69.59 53.11 52.13 931.32 -999.00 143.07 63.26

MB (μg/m3) -8.23 -0.74 -2.55 -0.92 0.45 0.82 -0.56 -20.58

MAGE (μg/m3) 8.61 1.96 2.55 0.96 0.46 0.82 0.60 20.58

RMSE (μg/m3) 9.90 2.68 3.16 1.10 0.59 1.02 0.80 21.54

Obs Max (μg/m3) 23.24 9.02 9.02 3.44 0.43 0.00 1.93 43.68

Pred Max (μg/m3) 22.29 13.11 4.36 1.82 1.49 2.76 0.41 34.63

Mean_Obs 13.11 3.03 4.43 1.85 0.14 0.00 0.71 33.27

Mean_Pred 4.88 2.29 1.89 0.93 0.59 0.82 0.15 12.69

Std_Dev_Obs 5.34 2.39 2.10 0.76 0.11 0.00 0.61 7.00

Std_Dev_Pre 5.28 3.02 1.13 0.52 0.40 0.63 0.10 7.57

Variance 98.09 7.20 9.97 1.20 0.34 1.05 0.64 464.10

R2 0.19 0.30 0.18 0.35 0.05 -999.00 0.11 0.36

BF 0.35 0.82 0.47 0.52 10.27 0.00 1.20 0.37

N 21 21 21 21 21 21 21 21
 C-16 

Table C-4h. Model performance statistics for the Best 20% Days and the Cohutta Class I area.

EVER bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -165.78 -199.82 -8.55 -6.99 -58.20 -10.33 -119.13 -131.93

MFE (%) 165.78 199.82 43.15 52.61 105.18 127.64 119.13 131.93

Ap -0.61 -1.00 -0.18 -0.17 -0.85 -0.67 -0.64 -0.55

NMB (%) -88.57 -99.95 -5.12 -12.84 -69.39 -58.59 -76.84 -78.61

NME (%) 88.57 99.95 40.75 47.33 84.41 98.24 76.84 78.61

MNB (%) -89.70 -99.95 3.63 -2.76 -1.43 -56.94 -72.48 -78.57

MNE (%) 89.70 99.95 40.75 41.91 96.80 57.89 72.48 78.57

MB (μg/m3) -8.06 -2.04 -0.15 -0.12 -0.27 -0.74 -1.80 -23.62

MAGE (μg/m3) 8.06 2.04 1.21 0.45 0.33 1.24 1.80 23.62

RMSE (μg/m3) 8.36 2.15 1.47 0.64 0.52 1.75 2.03 23.93

Obs Max (μg/m3) 14.39 3.74 5.72 2.89 1.84 4.83 3.91 32.94

Pred Max (μg/m3) 5.54 0.02 4.71 2.39 0.27 1.57 1.40 14.82

Mean_Obs 9.10 2.04 2.96 0.95 0.39 1.26 2.35 30.05

Mean_Pred 1.04 0.00 2.81 0.83 0.12 0.52 0.54 6.43

Std_Dev_Obs 2.57 0.68 0.96 0.63 0.43 1.44 0.89 2.39

Std_Dev_Pre 1.26 0.00 1.05 0.60 0.06 0.39 0.30 3.22

Variance 69.82 4.61 2.16 0.41 0.27 3.07 4.13 572.47

R2 0.23 0.00 0.01 0.22 0.12 0.14 0.00 0.00

BF 0.10 0.00 1.04 0.93 0.99 0.30 0.28 0.21

N 24 24 24 24 24 24 24 24

Table C-4i. Model performance statistics for the Best 20% Days and the Great Smoky 

Mountains Class I area.

GRSM bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -94.17 -85.46 4.96 -7.50 138.59 178.02 -92.16 -63.98

MFE (%) 106.13 121.52 35.87 46.24 146.55 192.47 100.92 73.86

Ap 1.26 0.42 0.48 0.80 5.01 1.79 -0.80 1.12

NMB (%) -46.57 -28.06 17.14 5.51 631.35 909.08 -69.24 -38.72

NME (%) 73.85 89.51 44.67 47.10 648.27 1026.45 72.85 55.67

MNB (%) -43.83 -18.32 18.89 8.54 1821.12 -1.90 -53.08 -37.82

MNE (%) 82.10 96.66 43.55 48.94 1826.58 88.88 64.36 58.76

MB (μg/m3) -8.72 -1.12 0.73 0.11 0.97 2.19 -0.79 -16.09

MAGE (μg/m3) 13.83 3.57 1.91 0.96 1.00 2.48 0.83 23.13

RMSE (μg/m3) 17.59 4.96 2.88 1.22 1.31 3.29 1.09 27.06

Obs Max (μg/m3) 31.91 10.95 8.95 3.38 0.69 3.59 3.75 52.59

Pred Max (μg/m3) 72.10 15.57 13.26 6.09 4.17 10.01 0.77 111.51

Mean_Obs 18.72 3.99 4.27 2.03 0.15 0.24 1.14 41.55

Mean_Pred 10.00 2.87 5.00 2.15 1.13 2.43 0.35 25.46

Std_Dev_Obs 8.30 3.09 1.77 0.72 0.17 0.84 0.78 9.77

Std_Dev_Pre 14.66 4.40 3.10 1.33 0.89 2.27 0.21 21.63

Variance 309.58 24.64 8.30 1.48 1.70 10.85 1.19 732.11

R2 0.03 0.03 0.18 0.16 0.01 0.02 0.03 0.03

BF 0.56 0.82 1.19 1.09 19.21 0.09 0.47 0.62

N 23 23 23 23 23 23 23 23
 C-17 

Table C-4j. Model performance statistics for the Best 20% Days and the Hercules Glade Class 

I area.

HEGL bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -132.82 -81.46 -11.53 -30.28 144.10 200.00 -32.67 -47.04

MFE (%) 132.82 103.95 40.05 41.82 144.10 200.00 57.88 60.54

Ap -0.63 1.76 0.17 -0.04 8.46 -999.00 -0.35 0.89

NMB (%) -77.98 -20.11 -4.87 -21.83 720.14 -999.00 -28.19 -28.48

NME (%) 77.98 82.73 35.75 33.02 720.14 -999.00 48.77 46.61

MNB (%) -78.28 -32.69 0.82 -20.20 911.74 -999.00 -11.82 -29.64

MNE (%) 78.28 78.59 39.89 33.88 911.74 -999.00 51.51 47.04

MB (μg/m3) -9.24 -1.15 -0.27 -0.53 2.20 8.53 -0.67 -11.19

MAGE (μg/m3) 9.24 4.73 2.01 0.80 2.20 8.53 1.16 18.31

RMSE (μg/m3) 9.68 7.54 2.51 0.94 2.56 10.14 1.47 20.11

Obs Max (μg/m3) 17.95 13.33 9.21 3.73 0.57 0.00 5.14 44.20

Pred Max (μg/m3) 6.62 36.84 10.79 3.57 5.40 22.46 3.33 83.40

Mean_Obs 11.85 5.71 5.61 2.41 0.31 0.00 2.39 39.28

Mean_Pred 2.61 4.56 5.34 1.88 2.50 8.53 1.71 28.09

Std_Dev_Obs 3.36 3.53 1.88 0.74 0.13 0.00 1.10 3.87

Std_Dev_Pre 1.80 8.16 2.56 0.86 1.32 5.60 0.81 18.23

Variance 93.67 56.85 6.31 0.88 6.57 102.75 2.15 404.22

R2 0.23 0.13 0.14 0.27 0.02 -999.00 0.00 0.15

BF 0.22 0.67 1.01 0.80 10.12 0.00 0.88 0.70

N 23 23 23 23 23 23 23 23

Table C-4k. Model performance statistics for the Best 20% Days and the James River Face 

Class I area.

JARI bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -91.09 -83.18 -65.51 -65.84 84.39 199.75 -132.42 -83.60

MFE (%) 100.63 118.95 69.44 67.77 84.97 199.75 132.42 87.18

Ap -0.09 0.75 -0.67 -0.72 0.88 330.57 -0.77 0.09

NMB (%) -47.44 -45.84 -49.87 -54.56 146.02 291595.78 -80.40 -52.48

NME (%) 59.51 92.91 53.49 55.58 146.61 291595.78 80.40 56.50

MNB (%) -51.16 -4.56 -44.70 -45.74 216.63 6626.39 -78.95 -52.91

MNE (%) 63.28 112.94 49.69 47.91 217.19 6626.39 78.95 57.42

MB (μg/m3) -10.04 -2.26 -3.11 -1.88 0.35 2.00 -1.05 -25.36

MAGE (μg/m3) 12.59 4.59 3.34 1.91 0.35 2.00 1.05 27.30

RMSE (μg/m3) 14.16 5.71 4.56 2.62 0.43 2.45 1.18 29.72

Obs Max (μg/m3) 41.57 10.56 21.39 9.96 0.69 0.02 2.90 60.15

Pred Max (μg/m3) 37.76 18.46 6.97 2.82 1.29 5.24 0.67 65.41

Mean_Obs 21.16 4.94 6.24 3.44 0.24 0.00 1.31 48.33

Mean_Pred 11.12 2.67 3.13 1.56 0.59 2.00 0.26 22.97

Std_Dev_Obs 7.70 2.48 3.83 1.96 0.15 0.00 0.61 7.18

Std_Dev_Pre 11.63 4.04 1.82 0.58 0.26 1.45 0.15 16.35

Variance 200.48 32.55 20.77 6.87 0.18 6.01 1.39 883.03

R2 0.25 0.10 0.21 0.10 0.09 0.02 0.36 0.08

BF 0.49 0.95 0.55 0.54 3.17 2.92 0.21 0.47

N 23 23 23 23 23 23 23 23
 C-18 

Table C-4l. Model performance statistics for the Best 20% Days and the Linville Gorge Class I 

area.

LIGO bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -115.63 -95.20 -45.21 -44.22 104.52 181.01 -105.22 -97.19

MFE (%) 116.61 115.72 51.50 50.16 115.41 181.18 119.65 97.19

Ap -0.44 -0.72 0.31 -0.09 1.23 2.10 -0.83 -0.42

NMB (%) -69.37 -53.30 -31.24 -31.42 230.32 1115.81 -77.59 -63.14

NME (%) 69.82 71.17 42.01 38.50 251.11 1117.78 78.96 63.14

MNB (%) -69.20 -43.30 -30.96 -29.43 510.03 884.70 -45.89 -63.32

MNE (%) 70.23 74.23 38.11 36.49 519.16 885.91 88.23 63.32

MB (μg/m3) -9.06 -1.22 -1.42 -0.68 0.42 0.97 -0.72 -21.62

MAGE (μg/m3) 9.12 1.63 1.90 0.83 0.46 0.97 0.74 21.62

RMSE (μg/m3) 10.48 2.33 2.46 1.05 0.59 1.28 1.00 22.72

Obs Max (μg/m3) 25.33 11.62 8.77 4.76 0.70 0.98 3.43 41.91

Pred Max (μg/m3) 14.31 3.29 11.47 4.35 1.56 3.06 0.57 24.48

Mean_Obs 13.05 2.29 4.53 2.16 0.18 0.09 0.93 34.24

Mean_Pred 4.00 1.07 3.12 1.48 0.60 1.05 0.21 12.62

Std_Dev_Obs 5.92 2.24 2.03 0.95 0.16 0.27 0.80 6.50

Std_Dev_Pre 3.72 1.15 2.65 1.07 0.42 0.82 0.14 6.37

Variance 109.79 5.41 6.07 1.10 0.35 1.65 1.01 516.35

R2 0.20 0.19 0.41 0.46 0.02 0.00 0.43 0.14

BF 0.31 0.57 0.69 0.71 6.10 1.41 0.54 0.37

N 21 21 21 21 21 21 21 21

Table C-4m. Model performance statistics for the Best 20% Days and the Mammoth Cave 

Class I area.

MACA bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -121.24 -61.27 -66.90 -38.39 96.54 200.00 -53.22 -83.33

MFE (%) 121.24 98.70 67.80 46.35 106.03 200.00 106.30 83.33

Ap -0.45 0.12 -0.29 -0.27 0.55 -999.00 -0.80 -0.04

NMB (%) -73.51 -18.48 -47.74 -32.06 228.66 -999.00 -61.68 -53.43

NME (%) 73.51 84.20 48.54 37.77 248.33 -999.00 76.41 53.43

MNB (%) -72.87 -13.78 -46.23 -24.86 315.06 -999.00 -6.34 -53.97

MNE (%) 72.87 81.80 47.16 34.63 322.26 -999.00 97.75 53.97

MB (μg/m3) -18.73 -1.78 -3.18 -0.94 0.86 3.45 -0.71 -30.58

MAGE (μg/m3) 18.73 8.09 3.24 1.11 0.94 3.45 0.88 30.58

RMSE (μg/m3) 20.15 10.57 3.79 1.47 1.10 4.21 1.21 34.66

Obs Max (μg/m3) 39.05 30.49 13.25 6.05 1.41 0.00 4.08 65.36

Pred Max (μg/m3) 21.37 34.29 9.43 4.41 2.19 8.01 0.83 63.00

Mean_Obs 25.48 9.61 6.67 2.94 0.38 0.00 1.15 57.23

Mean_Pred 6.75 7.83 3.49 2.00 1.24 3.45 0.44 26.65

Std_Dev_Obs 6.70 6.69 2.68 1.11 0.27 0.00 1.02 6.69

Std_Dev_Pre 5.16 9.91 2.19 0.90 0.60 2.47 0.25 17.74

Variance 405.94 111.65 14.33 2.17 1.22 17.73 1.47 1201.52

R2 0.04 0.05 0.42 0.13 0.03 -999.00 0.03 0.12

BF 0.27 0.86 0.54 0.75 4.15 0.00 0.90 0.46

N 23 23 23 23 23 23 23 23
 C-19 

Table C-4n. Model performance statistics for the Best 20% Days and the Mingo Class I area.

MING bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -122.89 -71.02 -31.76 -7.09 138.37 197.99 24.07 -20.17

MFE (%) 122.89 108.52 46.62 37.98 138.37 197.99 58.30 47.25

Ap -0.59 4.78 -0.27 0.22 4.57 53.62 -0.18 2.28

NMB (%) -73.17 15.70 -26.09 -2.91 508.18 20904.07 14.53 0.62

NME (%) 73.17 124.27 39.88 37.18 508.18 20904.07 64.88 48.50

MNB (%) -73.66 10.81 -17.69 3.48 1070.38 3119.60 89.76 -1.41

MNE (%) 73.66 118.63 38.69 38.50 1070.38 3119.60 114.86 46.76

MB (μg/m3) -12.68 0.74 -1.74 -0.07 2.61 21.82 0.41 0.29

MAGE (μg/m3) 12.68 5.88 2.66 0.88 2.61 21.82 1.84 22.58

RMSE (μg/m3) 13.73 13.02 3.40 1.10 3.11 28.77 2.72 35.59

Obs Max (μg/m3) 32.46 11.03 12.57 3.80 1.45 1.47 9.86 56.75

Pred Max (μg/m3) 13.20 63.78 9.23 4.66 8.05 80.50 8.10 186.22

Mean_Obs 17.33 4.73 6.68 2.38 0.51 0.10 2.83 46.57

Mean_Pred 4.65 5.48 4.93 2.31 3.12 21.93 3.24 46.86

Std_Dev_Obs 6.18 3.01 2.76 0.77 0.38 0.35 2.51 7.41

Std_Dev_Pre 3.83 13.49 2.45 1.07 1.71 19.37 1.84 38.39

Variance 188.61 169.45 11.59 1.21 9.67 827.57 7.42 1266.49

R2 0.25 0.03 0.12 0.09 0.00 0.27 0.05 0.13

BF 0.26 1.11 0.82 1.03 11.70 2.93 1.90 0.99

Num_Points 22 22 22 22 22 22 22 22

Table C-4o. Model performance statistics for the Best 20% Days and the Okefenokee Class I 

area.

OKEF bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -128.51 -179.19 31.20 -51.96 15.38 151.32 -127.01 -83.50

MFE (%) 128.51 179.19 43.66 66.15 92.26 162.36 127.01 83.50

Ap -0.57 -0.56 0.83 -0.13 -0.23 0.04 -0.71 -0.33

NMB (%) -76.36 -92.60 53.82 -31.70 5.19 217.14 -80.36 -56.54

NME (%) 76.36 92.60 61.43 49.69 109.76 270.83 80.36 56.54

MNB (%) -76.48 -92.69 52.43 -31.75 160.17 -8.23 -75.22 -56.79

MNE (%) 76.48 92.69 62.30 51.45 208.59 31.64 75.22 56.79

MB (μg/m3) -14.61 -3.72 3.79 -0.69 0.02 0.59 -1.57 -26.00

MAGE (μg/m3) 14.61 3.72 4.33 1.09 0.41 0.74 1.57 26.00

RMSE (μg/m3) 15.31 4.06 5.77 1.22 0.55 0.88 1.81 27.22

Obs Max (μg/m3) 26.45 8.07 11.63 4.86 1.77 2.07 3.93 52.70

Pred Max (μg/m3) 11.29 3.56 21.29 4.24 1.36 2.15 1.12 35.48

Mean_Obs 19.13 4.02 7.05 2.18 0.37 0.27 1.96 45.99

Mean_Pred 4.52 0.30 10.85 1.49 0.39 0.86 0.38 19.99

Std_Dev_Obs 4.27 1.56 2.04 0.85 0.39 0.61 0.94 5.57

Std_Dev_Pre 3.31 0.77 5.18 1.11 0.32 0.52 0.27 8.41

Variance 234.25 16.49 33.34 1.48 0.31 0.78 3.27 740.97

R2 0.07 0.01 0.28 0.23 0.07 0.09 0.06 0.13

BF 0.24 0.07 1.52 0.68 2.60 0.21 0.25 0.43

Num_Points 22 22 22 22 22 22 22 22
 C-20 

Table C-4p. Model performance statistics for the Best 20% Days and the Cape Romain Class I 

area.

ROMA bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -124.88 -181.06 -94.53 -46.13 12.09 -31.75 -43.14 -122.25

MFE (%) 135.54 181.06 101.08 72.68 84.22 150.25 63.91 126.73

Ap 0.56 -0.57 0.10 -0.25 1.56 -0.80 -0.58 0.06

NMB (%) -59.52 -93.83 -50.21 -35.42 27.07 -75.42 -45.43 -69.84

NME (%) 78.50 93.83 61.05 49.83 104.69 108.25 53.72 74.60

MNB (%) -60.36 -93.35 -56.56 -35.41 71.98 -4.48 -19.27 -70.13

MNE (%) 87.92 93.35 64.79 46.91 115.30 120.19 52.90 76.16

MB (μg/m3) -8.44 -2.51 -1.72 -0.37 0.06 -2.52 -1.23 -27.68

MAGE (μg/m3) 11.13 2.51 2.09 0.53 0.23 3.62 1.46 29.56

RMSE (μg/m3) 12.73 2.73 2.45 0.75 0.37 5.48 2.27 30.48

Obs Max (μg/m3) 26.12 4.50 8.19 5.01 0.65 15.38 8.77 50.02

Pred Max (μg/m3) 40.73 1.93 9.04 3.75 1.66 3.04 3.67 53.00

Mean_Obs 14.19 2.68 3.43 1.06 0.22 3.35 2.71 39.63

Mean_Pred 5.74 0.17 1.71 0.68 0.28 0.82 1.48 11.95

Std_Dev_Obs 6.77 1.02 1.81 1.08 0.18 4.48 2.03 7.68

Std_Dev_Pre 9.02 0.43 2.23 0.93 0.37 0.88 0.88 12.36

Variance 161.99 7.47 5.98 0.56 0.13 30.00 5.16 928.75

R2 0.07 0.00 0.40 0.63 0.05 0.23 0.09 0.05

BF 0.40 0.07 0.43 0.62 1.72 0.75 0.81 0.30

Num_Points 23 23 23 23 23 23 23 23

Table C-4q. Model performance statistics for the Best 20% Days and the St. Marks Class I 

area.

SAMA bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -126.89 -188.67 0.59 -14.74 1.18 160.72 -97.03 -97.60

MFE (%) 137.86 188.67 63.77 63.21 119.22 174.90 100.19 102.93

Ap 0.37 -0.80 1.33 0.82 -0.69 3.18 -0.64 0.28

NMB (%) -68.20 -96.21 45.71 21.88 -42.60 502.57 -67.55 -57.30

NME (%) 80.63 96.21 84.58 78.78 103.88 537.83 69.19 63.73

MNB (%) -60.48 -96.73 40.44 14.39 195.14 3985.28 -60.84 -57.58

MNE (%) 88.12 96.73 85.00 69.07 259.81 4022.29 64.66 64.99

MB (μg/m3) -12.62 -2.70 2.03 0.34 -0.20 0.93 -1.72 -23.78

MAGE (μg/m3) 14.92 2.70 3.76 1.23 0.48 1.00 1.76 26.45

RMSE (μg/m3) 15.81 2.84 5.77 1.86 0.70 1.37 2.07 28.55

Obs Max (μg/m3) 24.51 4.76 9.53 3.98 2.05 1.13 4.89 50.25

Pred Max (μg/m3) 33.63 0.96 22.22 7.25 0.64 4.74 1.75 64.45

Mean_Obs 18.50 2.80 4.45 1.57 0.47 0.19 2.54 41.51

Mean_Pred 5.88 0.11 6.48 1.91 0.27 1.12 0.83 17.73

Std_Dev_Obs 4.41 0.96 1.96 1.04 0.58 0.37 1.09 6.13

Std_Dev_Pre 8.14 0.25 5.95 2.16 0.19 1.16 0.45 16.11

Variance 249.94 8.05 33.28 3.45 0.49 1.87 4.27 814.88

R2 0.02 0.14 0.14 0.25 0.23 0.26 0.00 0.03

BF 0.40 0.03 1.40 1.14 2.95 11.14 0.39 0.42

Num_Points 22 22 22 22 22 22 22 22
 C-21 

Table C-4r. Model performance statistics for the Best 20% Days and the Shenandoah Class I 

area.

SHEN bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -74.36 25.46 35.69 15.95 133.54 200.00 -62.78 -25.64

MFE (%) 93.26 75.95 50.96 36.84 133.54 200.00 73.10 53.35

Ap 5.97 1.09 5.98 4.44 3.08 -999.00 -0.54 3.27

NMB (%) 4.37 74.12 91.62 50.01 420.15 -999.00 -52.15 6.69

NME (%) 99.60 103.04 107.36 67.27 420.15 -999.00 56.76 56.93

MNB (%) -9.26 79.51 87.61 49.86 901.25 -999.00 -39.38 2.07

MNE (%) 96.56 110.24 100.76 66.81 901.25 -999.00 54.53 57.60

MB (μg/m3) 0.52 3.61 2.55 0.81 0.57 3.12 -0.47 2.16

MAGE (μg/m3) 11.86 5.02 2.99 1.09 0.57 3.12 0.51 18.34

RMSE (μg/m3) 28.13 6.76 7.50 2.78 0.65 3.91 0.61 34.08

Obs Max (μg/m3) 20.73 11.73 5.74 2.68 0.35 0.00 1.92 42.00

Pred Max (μg/m3) 144.46 24.52 40.06 14.58 1.41 8.41 0.89 179.34

Mean_Obs 11.90 4.87 2.78 1.62 0.14 0.00 0.90 32.21

Mean_Pred 12.42 8.48 5.33 2.43 0.71 3.12 0.43 34.37

Std_Dev_Obs 4.19 2.96 1.49 0.69 0.09 0.00 0.49 6.21

Std_Dev_Pre 29.54 6.95 7.90 2.83 0.33 2.40 0.22 36.39

Variance 791.22 45.64 56.28 7.74 0.42 15.25 0.37 1161.58

R2 0.07 0.31 0.28 0.08 0.20 -999.00 0.37 0.12

BF 0.91 1.80 1.88 1.50 10.01 0.00 0.61 1.02

Num_Points 23 23 23 23 23 23 23 23

Table C-4s. Model performance statistics for the Best 20% Days and the Shining Rock Class I 

area.

SHRO bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -75.84 -81.41 13.51 27.00 123.07 194.49 -1.93 -68.69

MFE (%) 86.97 107.46 56.47 58.31 131.08 194.49 144.41 74.39

Ap 1.86 -0.12 0.39 0.57 3.68 36.95 -0.76 0.70

NMB (%) -32.22 -52.02 30.21 41.98 483.48 9454.52 -69.54 -42.24

NME (%) 62.92 80.62 67.11 68.10 499.53 9454.52 93.21 51.49

MNB (%) -43.15 -17.86 61.34 130.27 1099.73 1414.30 21.60 -44.28

MNE (%) 62.34 93.68 91.14 155.33 1105.94 1414.30 142.80 51.78

MB (μg/m3) -2.64 -0.81 0.74 0.46 0.46 0.92 -0.43 -10.14

MAGE (μg/m3) 5.16 1.25 1.63 0.74 0.47 0.92 0.58 12.36

RMSE (μg/m3) 7.24 1.75 2.33 1.02 0.59 1.32 0.82 14.01

Obs Max (μg/m3) 13.73 4.65 6.13 2.31 0.28 0.10 2.87 31.05

Pred Max (μg/m3) 39.24 4.09 8.51 3.63 1.29 3.81 0.68 52.94

Mean_Obs 8.19 1.55 2.43 1.09 0.09 0.01 0.62 24.00

Mean_Pred 5.55 0.75 3.17 1.55 0.55 0.93 0.19 13.86

Std_Dev_Obs 4.17 1.30 1.31 0.58 0.08 0.03 0.70 5.96

Std_Dev_Pre 8.28 1.00 2.05 0.98 0.38 0.97 0.14 10.91

Variance 52.35 3.08 5.44 1.05 0.35 1.75 0.68 196.33

R2 0.31 0.00 0.02 0.14 0.02 0.00 0.00 0.19

BF 0.57 0.79 1.61 2.30 12.00 2.52 0.86 0.56

N 24 24 24 24 24 24 24 24
 C-22 

Table C-4t. Model performance statistics for the Best 20% Days and the Sipsey Class I area.

SIPS bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -106.85 -109.16 -9.15 -54.86 77.60 200.00 -117.27 -69.04

MFE (%) 120.51 109.41 40.17 64.97 113.99 200.00 117.27 78.81

Ap 1.69 -0.33 0.70 -0.04 -0.14 -999.00 -0.74 1.04

NMB (%) -47.35 -56.06 8.56 -36.07 120.89 -999.00 -76.03 -41.33

NME (%) 84.16 56.35 41.07 46.35 198.21 -999.00 76.03 57.32

MNB (%) -53.46 -61.27 2.84 -34.17 389.14 -999.00 -71.89 -42.53

MNE (%) 82.66 61.52 39.22 48.53 410.41 -999.00 71.89 57.28

MB (μg/m3) -9.62 -4.46 0.59 -0.98 0.65 3.74 -1.76 -21.39

MAGE (μg/m3) 17.11 4.49 2.83 1.26 1.06 3.74 1.76 29.67

RMSE (μg/m3) 21.62 6.66 3.79 1.53 1.30 4.54 2.07 32.63

Obs Max (μg/m3) 36.05 21.36 11.04 4.69 2.95 0.00 4.24 61.55

Pred Max (μg/m3) 97.08 14.24 18.78 4.49 2.52 9.09 1.12 125.72

Mean_Obs 20.32 7.96 6.89 2.72 0.54 0.00 2.31 51.75

Mean_Pred 10.70 3.50 7.48 1.74 1.19 3.74 0.55 30.36

Std_Dev_Obs 6.48 5.30 2.40 1.08 0.67 0.00 1.17 6.11

Std_Dev_Pre 20.91 4.28 5.23 1.17 0.75 2.64 0.29 26.57

Variance 467.50 44.35 14.40 2.35 1.70 20.58 4.28 1064.78

R2 0.10 0.21 0.54 0.19 0.10 -999.00 0.08 0.11

BF 0.47 0.39 1.03 0.66 4.89 0.00 0.28 0.57

N 21 21 21 21 21 21 21 21

Table C-4u. Model performance statistics for the Best 20% Days and the Swanquarter Class I 

area.

SWAN bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -136.66 -161.55 -38.76 -28.09 46.67 34.79 -118.19 -118.69

MFE (%) 138.38 170.70 62.73 58.61 109.65 173.91 118.19 120.57

Ap -0.13 1.44 2.83 1.96 0.27 -0.67 -0.60 0.18

NMB (%) -74.90 -69.17 11.83 17.63 96.96 -46.99 -72.45 -68.09

NME (%) 77.12 92.56 65.71 77.26 189.16 134.86 72.45 70.44

MNB (%) -76.02 -77.95 -12.66 83.61 335.71 -81.74 -73.26 -69.58

MNE (%) 77.94 92.54 56.88 146.06 375.39 81.74 73.26 71.69

MB (μg/m3) -9.68 -1.72 0.37 0.18 0.16 -0.66 -2.15 -24.60

MAGE (μg/m3) 9.97 2.30 2.06 0.80 0.31 1.89 2.15 25.45

RMSE (μg/m3) 10.90 2.57 4.25 1.71 0.42 3.11 2.37 26.66

Obs Max (μg/m3) 21.09 4.21 6.60 2.59 0.87 11.33 6.25 42.25

Pred Max (μg/m3) 18.25 10.24 25.28 7.65 1.11 3.69 2.53 49.97

Mean_Obs 12.93 2.48 3.14 1.04 0.16 1.40 2.97 36.13

Mean_Pred 3.24 0.77 3.51 1.22 0.32 0.74 0.82 11.53

Std_Dev_Obs 3.88 0.85 1.44 0.75 0.19 2.68 1.42 3.96

Std_Dev_Pre 4.36 2.22 5.35 1.84 0.32 1.02 0.60 11.96

Variance 118.84 6.61 18.09 2.91 0.18 9.70 5.63 710.89

R2 0.05 0.23 0.60 0.11 0.03 0.08 0.62 0.26

BF 0.24 0.22 0.87 1.84 4.36 0.09 0.27 0.30

N 23 23 23 23 23 23 23 23
 C-23 

Table C-4v. Model performance statistics for the Best 20% Days and the Upper Buffalo Class I 

area.

UPBU bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -138.41 -121.02 -23.25 -46.69 93.32 199.90 -110.88 -88.95

MFE (%) 139.43 121.02 44.59 56.99 108.59 199.90 126.30 90.01

Ap -0.07 -0.37 -0.11 0.36 2.20 1232.47 -0.85 0.00

NMB (%) -77.41 -71.02 -13.97 -31.38 262.69 767627.06 -78.16 -58.54

NME (%) 79.10 71.02 36.99 43.46 275.35 767627.06 81.63 59.75

MNB (%) -78.49 -70.21 -11.36 -29.34 1615.30 17510.96 -57.49 -57.86

MNE (%) 79.56 70.21 37.29 42.69 1625.86 17510.96 84.47 58.99

MB (μg/m3) -8.11 -3.62 -0.67 -0.54 0.95 3.28 -2.71 -21.59

MAGE (μg/m3) 8.28 3.62 1.76 0.74 1.00 3.28 2.83 22.04

RMSE (μg/m3) 9.10 4.57 2.03 0.88 1.19 4.05 3.42 23.65

Obs Max (μg/m3) 19.65 12.68 8.70 2.65 0.81 0.01 8.94 44.16

Pred Max (μg/m3) 18.27 7.98 7.73 3.60 2.58 11.59 1.31 44.17

Mean_Obs 10.47 5.10 4.77 1.71 0.36 0.00 3.47 36.88

Mean_Pred 2.36 1.48 4.10 1.17 1.32 3.28 0.76 15.29

Std_Dev_Obs 4.10 3.60 1.70 0.47 0.17 0.00 2.13 5.26

Std_Dev_Pre 3.75 1.87 2.09 0.74 0.63 2.44 0.33 8.88

Variance 82.79 20.90 4.14 0.78 1.41 16.42 11.68 559.29

R2 0.18 0.38 0.23 0.12 0.20 0.02 0.00 0.01

BF 0.22 0.30 0.89 0.71 17.15 8.00 0.43 0.42

N 22 22 22 22 22 22 22 22

Table C-5a. Model performance statistics for the Worst 20% Days across Class I areas in the 

12 km VISTAS grid. 

12km bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -63.82 -121.18 -32.08 -63.17 22.51 188.69 -87.88 -49.58

MFE (%) 74.05 146.07 50.78 70.50 69.91 191.96 115.76 60.44

Ap -0.50 0.44 -0.83 -0.28 -0.43 8.80 -0.65 -0.43

NMB (%) -39.35 -18.22 -29.24 -42.51 36.38 18564.68 -69.20 -32.51

NME (%) 50.22 87.96 48.66 52.21 97.47 18666.07 74.92 44.58

MNB (%) -38.14 -39.83 -15.63 -41.16 156.91 7919.56 -39.56 -29.87

MNE (%) 51.57 102.75 43.37 51.71 189.98 7956.43 88.73 44.15

MB (μg/m3) -48.14 -1.38 -6.15 -2.24 0.38 12.46 -2.26 -55.75

MAGE (μg/m3) 61.44 6.66 10.23 2.75 1.01 12.52 2.44 76.45

RMSE (μg/m3) 82.20 13.69 28.78 3.59 1.79 20.70 3.53 101.79

Obs Max (μg/m3) 475.66 107.80 614.69 41.23 16.92 11.70 20.96 735.80

Pred Max (μg/m3) 235.90 154.76 102.23 29.67 9.61 114.64 7.23 419.01

Mean_Obs 122.36 7.57 21.03 5.26 1.04 0.07 3.26 171.51

Mean_Pred 74.22 6.19 14.88 3.03 1.42 12.52 1.01 115.76

Std_Dev_Obs 68.78 14.02 31.42 3.03 1.23 0.57 2.77 72.23

Std_Dev_Pre 59.70 18.80 12.73 3.01 1.23 16.52 1.03 74.73

Variance 6757.49 187.38 828.03 12.88 3.20 428.51 12.47 10360.90

R2 0.22 0.48 0.20 0.32 0.00 0.00 0.06 0.11

BF 0.62 0.60 0.84 0.59 2.57 4.23 0.58 0.70

N 512 512 510 510 512 512 512 512
 C-24 

Table C-5b. Model performance statistics for the Worst 20% Days at the Breton Island Class I 

area.

BRET bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -108.00 -197.37 -85.96 -103.52 -39.07 138.89 -103.81 -108.13

MFE (%) 113.52 197.37 88.07 103.52 106.38 165.39 103.81 109.77

Ap -0.13 -0.96 -0.75 -0.68 -0.84 1.71 -0.37 -0.25

NMB (%) -58.22 -98.65 -60.76 -69.59 -60.95 572.95 -61.85 -62.38

NME (%) 64.22 98.65 62.79 69.59 99.61 650.61 61.85 63.98

MNB (%) -60.00 -99.31 -53.74 -66.26 39.75 76.21 -63.36 -62.99

MNE (%) 66.37 99.31 56.03 66.26 126.72 130.85 63.36 64.73

MB (μg/m3) -42.55 -8.04 -5.76 -3.05 -0.78 1.64 -2.74 -69.94

MAGE (μg/m3) 46.94 8.04 5.95 3.05 1.28 1.87 2.74 71.73

RMSE (μg/m3) 52.63 10.62 10.17 3.77 2.59 2.49 3.16 79.19

Obs Max (μg/m3) 126.22 29.70 46.34 12.96 11.47 1.93 10.72 171.55

Pred Max (μg/m3) 109.49 1.16 11.69 4.20 1.84 5.21 6.78 129.27

Mean_Obs 73.10 8.15 9.47 4.38 1.28 0.29 4.44 112.12

Mean_Pred 30.54 0.11 3.72 1.33 0.50 1.93 1.69 42.17

Std_Dev_Obs 18.79 7.30 10.41 2.76 2.32 0.55 2.01 19.64

Std_Dev_Pre 34.94 0.30 3.36 0.93 0.52 1.69 1.72 40.19

Variance 2770.25 112.76 103.42 14.21 6.70 6.18 9.96 6270.32

R2 0.19 0.55 0.44 0.43 0.08 0.07 0.42 0.13

BF 0.40 0.01 0.46 0.34 1.40 0.51 0.37 0.37

N 24 24 24 24 24 24 24 24

Table C-5c. Model performance statistics for the Worst 20% Days at the Brigantine Class I 

area.

BRIG bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -68.36 -54.70 -42.36 -16.55 25.67 197.35 -96.66 -44.58

MFE (%) 76.35 101.20 51.78 34.66 67.60 197.35 133.02 55.90

Ap -0.28 0.60 -0.83 -0.28 -0.44 17.73 -0.92 -0.70

NMB (%) -44.01 58.77 -63.48 -14.87 38.85 16306.94 -81.39 -38.91

NME (%) 53.83 103.33 67.06 30.43 98.91 16306.94 84.05 48.12

MNB (%) -43.87 -1.06 -27.55 -7.43 133.28 486.75 -76.23 -28.70

MNE (%) 54.42 87.49 40.23 34.29 162.56 486.75 76.23 42.52

MB (μg/m3) -53.74 7.87 -31.03 -1.17 0.43 8.35 -3.53 -81.61

MAGE (μg/m3) 65.73 13.84 32.78 2.40 1.08 8.35 3.64 100.93

RMSE (μg/m3) 77.68 25.55 110.38 3.47 1.68 9.45 5.34 154.13

Obs Max (μg/m3) 275.18 71.94 614.69 41.23 6.83 1.13 20.65 735.80

Pred Max (μg/m3) 197.92 115.45 102.23 29.67 3.80 21.08 1.62 223.13

Mean_Obs 122.10 13.40 48.89 7.88 1.10 0.05 4.34 209.75

Mean_Pred 68.36 21.27 17.86 6.71 1.52 8.40 0.81 128.14

Std_Dev_Obs 60.77 16.68 127.43 7.80 1.30 0.24 4.16 133.57

Std_Dev_Pre 52.55 37.68 20.47 5.76 1.05 4.52 0.31 56.24

Variance 6034.93 652.70 12184.42 12.05 2.83 89.35 28.56 23754.77

R2 0.24 0.74 0.88 0.85 0.00 0.01 0.04 0.04

BF 0.56 0.99 0.72 0.93 2.33 0.27 0.22 0.71

N 22 22 22 22 22 22 22 22
 C-25 

Table C-5d. Model performance statistics for the Worst 20% Days at the Caney Creek Class I 

area.

CACR bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -69.34 -45.08 -7.66 -68.17 31.62 190.50 -113.64 -43.67

MFE (%) 73.49 94.78 32.17 68.17 57.31 190.50 122.51 52.94

Ap -0.46 0.69 -0.12 -0.34 -0.26 20.76 -0.81 -0.25

NMB (%) -46.53 23.99 -3.37 -49.45 29.65 24425.54 -79.04 -30.60

NME (%) 50.44 86.91 28.24 49.45 81.70 24425.54 79.74 40.54

MNB (%) -45.19 17.81 0.41 -48.37 140.50 1964.48 -61.55 -26.81

MNE (%) 50.01 99.08 30.62 48.37 158.09 1964.48 79.67 40.75

MB (μg/m3) -50.95 2.45 -0.66 -2.16 0.42 14.33 -3.40 -49.12

MAGE (μg/m3) 55.22 8.88 5.55 2.16 1.16 14.33 3.43 65.08

RMSE (μg/m3) 78.07 23.14 7.96 2.46 1.88 16.37 3.94 92.23

Obs Max (μg/m3) 281.03 70.57 51.07 7.26 6.70 1.35 7.89 324.07

Pred Max (μg/m3) 151.79 119.16 44.88 4.82 4.98 29.35 1.46 244.51

Mean_Obs 109.50 10.22 19.65 4.38 1.43 0.06 4.30 160.52

Mean_Pred 58.54 12.67 18.99 2.21 1.85 14.38 0.90 111.40

Std_Dev_Obs 61.21 17.65 10.63 1.38 1.34 0.28 1.98 59.45

Std_Dev_Pre 44.67 27.86 10.96 1.04 1.01 8.19 0.32 65.64

Variance 6095.27 535.44 63.35 6.06 3.54 267.87 15.53 8506.03

R2 0.15 0.30 0.52 0.29 0.07 0.13 0.01 0.04

BF 0.55 1.18 1.00 0.52 2.41 0.90 0.38 0.73

N 23 23 23 23 23 23 23 23

Table C-5e. Model performance statistics for the Worst 20% Days at the Chassahowitzka 

Class I area.

CHAS bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -56.60 -120.68 -35.75 -30.28 103.28 200.00 -20.70 -16.94

MFE (%) 65.36 143.24 47.42 50.30 115.65 200.00 41.49 34.42

Ap 0.63 1.18 -0.34 0.35 0.04 -999.00 -0.48 0.51

NMB (%) -30.52 -46.96 -29.64 -18.68 199.83 -999.00 -24.95 -9.49

NME (%) 45.19 90.60 40.89 44.17 239.31 -999.00 41.42 31.54

MNB (%) -36.55 -38.40 -24.84 -16.13 522.15 -999.00 -9.21 -10.29

MNE (%) 47.47 100.64 38.58 45.44 531.13 -999.00 37.57 31.36

MB (μg/m3) -23.98 -2.50 -6.10 -1.16 1.31 28.97 -0.67 -11.87

MAGE (μg/m3) 35.52 4.83 8.42 2.73 1.57 28.97 1.11 39.44

RMSE (μg/m3) 40.90 6.12 11.22 3.14 1.84 30.72 1.65 44.81

Obs Max (μg/m3) 121.53 9.87 71.19 10.37 3.58 0.00 6.40 169.84

Pred Max (μg/m3) 198.53 21.47 46.83 14.02 3.71 58.76 3.31 256.55

Mean_Obs 78.59 5.33 20.60 6.19 0.66 0.00 2.69 125.05

Mean_Pred 54.61 2.83 14.49 5.03 1.97 28.97 2.02 113.18

Std_Dev_Obs 22.96 2.23 14.55 1.97 0.74 0.00 1.34 19.06

Std_Dev_Pre 46.76 5.23 10.49 3.27 0.87 10.47 0.50 50.42

Variance 1673.04 37.40 125.95 9.83 3.38 943.77 2.73 2007.87

R2 0.53 0.00 0.56 0.19 0.11 -999.00 0.07 0.25

BF 0.63 0.62 0.75 0.84 6.22 0.00 0.91 0.90

N 23 23 23 23 23 23 23 23
 C-26 

Table C-5f. Model performance statistics for the Worst 20% Days at the Cohutta Class I area.

COHU bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -64.44 -114.37 -8.34 -64.29 4.78 190.48 45.44 -51.06

MFE (%) 73.26 131.32 41.25 76.94 46.01 190.48 141.50 59.52

Ap -0.48 1.00 -0.17 -0.03 -0.15 -999.00 -0.85 -0.44

NMB (%) -48.01 -40.98 -3.97 -41.16 6.48 -999.00 -65.26 -39.23

NME (%) 54.69 87.80 50.87 57.36 50.78 -999.00 100.69 46.26

MNB (%) -40.29 -55.39 9.05 -38.89 40.67 -999.00 -25.45 -33.00

MNE (%) 50.79 83.95 45.41 58.09 72.18 -999.00 70.16 42.53

MB (μg/m3) -88.61 -1.87 -1.03 -2.07 0.07 10.91 -1.11 -91.72

MAGE (μg/m3) 100.93 4.01 13.15 2.88 0.56 10.91 1.71 108.16

RMSE (μg/m3) 126.52 5.37 21.92 3.25 0.81 12.79 3.03 138.10

Obs Max (μg/m3) 376.51 16.47 96.66 9.78 3.19 0.00 9.23 507.89

Pred Max (μg/m3) 194.78 32.90 80.36 9.48 2.69 29.03 1.37 282.09

Mean_Obs 184.56 4.57 25.85 5.03 1.10 0.00 1.70 233.80

Mean_Pred 95.95 2.70 24.83 2.96 1.17 10.91 0.59 142.08

Std_Dev_Obs 81.83 3.53 18.12 1.51 0.59 0.00 2.77 91.26

Std_Dev_Pre 52.37 7.17 17.61 2.35 0.55 6.84 0.28 66.55

Variance 16006.81 28.82 480.69 10.57 0.66 163.56 9.17 19072.43

R2 0.01 0.54 0.04 0.03 0.00 -999.00 0.13 0.02

BF 0.60 0.45 1.09 0.61 1.41 0.00 0.43 0.67

N 21 21 21 21 21 21 21 21

Table C-5g. Model performance statistics for the Worst 20% Days at the Dolly Sods Class I 

area.

DOSO bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -72.68 -116.04 -46.04 -106.64 -18.55 191.30 -133.53 -70.32

MFE (%) 79.52 127.19 65.52 106.64 42.44 191.30 133.53 75.20

Ap -0.45 0.19 -0.37 -0.73 -0.37 -999.00 -0.93 -0.46

NMB (%) -47.87 -60.19 -34.19 -69.89 -17.51 -999.00 -86.58 -47.83

NME (%) 54.04 77.85 49.85 69.89 34.44 -999.00 86.58 52.21

MNB (%) -45.39 -58.71 -26.38 -68.33 3.88 -999.00 -77.99 -45.29

MNE (%) 53.23 75.27 50.74 68.33 49.74 -999.00 77.99 50.65

MB (μg/m3) -73.37 -1.72 -4.04 -2.89 -0.14 3.03 -1.36 -88.24

MAGE (μg/m3) 82.83 2.23 5.89 2.89 0.28 3.03 1.36 96.32

RMSE (μg/m3) 113.35 2.55 6.91 3.11 0.38 3.37 1.78 129.42

Obs Max (μg/m3) 404.99 5.69 26.51 7.50 1.78 0.00 4.85 451.54

Pred Max (μg/m3) 223.28 6.78 16.74 2.06 1.11 5.67 0.34 243.89

Mean_Obs 153.28 2.86 11.82 4.14 0.82 0.00 1.57 184.51

Mean_Pred 79.91 1.14 7.78 1.25 0.68 3.03 0.21 96.26

Std_Dev_Obs 78.10 1.10 5.71 1.22 0.32 0.00 1.21 80.44

Std_Dev_Pre 63.83 1.73 4.41 0.43 0.29 1.51 0.07 68.63

Variance 12847.43 6.53 47.69 9.70 0.14 11.38 3.18 16750.30

R2 0.06 0.02 0.15 0.07 0.10 -999.00 0.32 0.03

BF 0.55 0.41 0.74 0.32 1.04 0.00 0.22 0.55

Num_Points 23 23 23 23 23 23 23 23
 C-27 

Table C-5h. Model performance statistics for the Worst 20% Days at the Everglades Class I 

area.

EVER bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -78.63 -193.20 -20.53 -23.81 -1.47 146.26 -106.69 -72.38

MFE (%) 92.21 193.20 60.07 60.39 102.54 182.68 106.69 81.65

Ap 0.30 -0.92 -0.77 0.09 -0.84 0.02 -0.77 -0.24

NMB (%) -34.44 -97.70 -40.91 -20.12 -37.65 365.97 -75.50 -42.18

NME (%) 56.66 97.70 68.37 46.07 111.98 526.54 75.50 54.59

MNB (%) -42.02 -98.16 3.60 -16.04 116.22 27.90 -66.00 -41.14

MNE (%) 60.29 98.16 52.64 41.20 179.95 127.20 66.00 52.69

MB (μg/m3) -13.47 -3.75 -7.39 -0.95 -0.50 2.67 -2.44 -34.61

MAGE (μg/m3) 22.15 3.75 12.35 2.18 1.50 3.84 2.44 44.79

RMSE (μg/m3) 26.53 4.08 29.80 3.20 3.59 5.05 3.57 58.45

Obs Max (μg/m3) 79.12 8.02 134.42 10.81 16.92 11.70 13.75 196.06

Pred Max (μg/m3) 103.14 0.67 31.29 11.74 2.74 11.92 3.18 149.58

Mean_Obs 39.10 3.84 18.06 4.74 1.34 0.73 3.23 82.04

Mean_Pred 25.63 0.09 10.67 3.79 0.84 3.40 0.79 47.43

Std_Dev_Obs 17.43 1.72 27.39 2.70 3.37 2.41 2.64 28.80

Std_Dev_Pre 28.94 0.17 8.82 3.09 0.81 3.01 0.63 42.20

Variance 704.09 16.68 887.81 10.22 12.91 25.47 12.75 3416.20

R2 0.35 0.19 0.01 0.18 0.05 0.09 0.01 0.01

BF 0.58 0.02 1.04 0.80 2.16 0.27 0.34 0.59

N 24 24 24 24 24 24 24 24

Table C-5i. Model performance statistics for the Worst 20% Days at the Great Smokey 

Mountains Class I area.

GRSM bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -63.76 -163.78 -1.34 -63.89 1.23 200.00 -100.86 -52.84

MFE (%) 65.31 197.12 37.84 64.35 43.61 200.00 124.35 54.90

Ap -0.31 -0.85 0.42 -0.25 0.74 -999.00 -0.83 -0.20

NMB (%) -43.46 -97.29 -0.61 -46.94 5.71 -999.00 -80.08 -38.18

NME (%) 44.65 97.64 39.61 47.41 43.59 -999.00 83.61 39.87

MNB (%) -43.44 -99.12 13.10 -46.37 48.53 -999.00 -27.41 -37.69

MNE (%) 45.09 99.12 44.16 46.84 80.70 -999.00 107.85 39.86

MB (μg/m3) -73.15 -2.67 -0.11 -2.25 0.06 7.32 -1.63 -79.67

MAGE (μg/m3) 75.14 2.68 7.46 2.28 0.42 7.32 1.70 83.20

RMSE (μg/m3) 92.01 3.23 10.13 2.54 0.57 8.15 2.15 100.92

Obs Max (μg/m3) 308.51 10.16 33.88 7.71 1.52 0.00 4.89 347.18

Pred Max (μg/m3) 212.34 1.54 47.97 5.81 2.66 17.08 0.82 277.94

Mean_Obs 168.29 2.75 18.83 4.80 0.97 0.00 2.03 208.67

Mean_Pred 95.15 0.07 18.71 2.55 1.02 7.32 0.40 129.00

Std_Dev_Obs 64.74 2.07 7.77 1.29 0.37 0.00 1.42 65.71

Std_Dev_Pre 58.12 0.31 8.98 1.16 0.53 3.68 0.15 66.87

Variance 8466.67 10.42 102.61 6.47 0.32 66.48 4.61 10185.05

R2 0.33 0.56 0.06 0.27 0.04 -999.00 0.00 0.30

BF 0.57 0.01 1.13 0.54 1.49 0.00 0.73 0.62

N 24 24 24 24 24 24 24 24
 C-28 

Table C-5j. Model performance statistics for the Worst 20% Days at the Hercules Glade Class I 

area.

HEGL bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -62.64 -64.72 -31.35 -67.72 88.94 192.00 -37.50 -27.40

MFE (%) 73.75 95.79 40.17 67.72 93.38 192.00 51.55 43.36

Ap -0.54 -0.19 -0.33 -0.31 0.80 -999.00 -0.58 -0.45

NMB (%) -39.63 -21.48 -26.27 -48.11 176.06 -999.00 -36.57 -19.99

NME (%) 52.05 55.67 35.45 48.11 190.77 -999.00 47.96 36.40

MNB (%) -37.25 -18.60 -22.76 -48.54 330.59 -999.00 -23.65 -14.88

MNE (%) 52.07 75.95 33.30 48.54 334.07 -999.00 40.40 34.41

MB (μg/m3) -41.28 -3.14 -4.77 -2.39 1.84 28.38 -1.05 -31.35

MAGE (μg/m3) 54.21 8.13 6.44 2.39 1.99 28.38 1.38 57.08

RMSE (μg/m3) 82.66 16.53 8.68 2.63 2.53 30.85 1.93 90.89

Obs Max (μg/m3) 339.47 99.26 42.42 9.36 4.42 0.00 8.18 377.55

Pred Max (μg/m3) 155.36 80.27 28.32 6.45 7.97 47.45 3.41 207.61

Mean_Obs 104.15 14.60 18.16 4.96 1.04 0.00 2.88 156.80

Mean_Pred 62.87 11.47 13.39 2.57 2.88 28.38 1.83 125.46

Std_Dev_Obs 61.34 27.70 8.58 1.45 0.82 0.00 1.57 52.29

Std_Dev_Pre 49.77 22.81 6.10 1.33 1.52 12.33 0.63 59.02

Variance 6832.22 273.09 75.41 6.90 6.43 951.43 3.74 8260.55

R2 0.02 0.64 0.29 0.46 0.01 -999.00 0.00 0.05

BF 0.63 0.81 0.77 0.51 4.31 0.00 0.76 0.85

N 25 25 25 25 25 25 25 25

Table C-5k. Model performance statistics for the Worst 20% Days at the James Rover Face 

Class I area.

JARI bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -44.05 -161.63 -45.20 -99.63 -17.57 200.00 -151.62 -46.37

MFE (%) 55.27 173.62 50.38 99.63 35.59 200.00 151.62 52.93

Ap -0.23 0.64 -0.74 -0.70 -0.76 -999.00 -0.96 -0.27

NMB (%) -29.70 -72.08 -45.42 -66.94 -25.82 -999.00 -88.49 -33.54

NME (%) 40.79 96.06 47.83 66.94 36.46 -999.00 88.49 39.47

MNB (%) -27.68 -77.76 -32.72 -65.82 -5.07 -999.00 -85.78 -32.04

MNE (%) 41.58 96.60 38.51 65.82 36.00 -999.00 85.78 39.68

MB (μg/m3) -48.04 -3.12 -11.73 -4.71 -0.28 6.09 -2.62 -71.76

MAGE (μg/m3) 65.98 4.16 12.36 4.71 0.39 6.09 2.62 84.45

RMSE (μg/m3) 78.63 4.57 23.32 5.01 0.79 6.47 3.13 97.61

Obs Max (μg/m3) 274.58 8.70 119.43 11.72 4.58 0.00 10.28 324.44

Pred Max (μg/m3) 212.68 14.24 30.79 3.53 1.11 9.46 0.44 237.97

Mean_Obs 161.76 4.33 25.83 7.03 1.07 0.00 2.96 213.98

Mean_Pred 113.71 1.21 14.10 2.32 0.79 6.09 0.34 142.22

Std_Dev_Obs 44.65 1.96 23.46 1.98 0.80 0.00 1.76 52.76

Std_Dev_Pre 62.85 3.39 6.34 0.64 0.21 2.23 0.07 65.78

Variance 6182.14 20.87 543.67 25.14 0.62 41.85 9.82 9528.45

R2 0.11 0.08 0.31 0.22 0.13 -999.00 0.00 0.13

BF 0.72 0.22 0.67 0.34 0.95 0.00 0.14 0.68

N 24 24 24 24 24 24 24 24
 C-29 

Table C-5l. Model performance statistics for the Worst 20% Days at the Linville Gorge Class I 

area.

LIGO bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -62.93 -150.69 -30.70 -87.63 -18.24 196.89 -153.13 -57.00

MFE (%) 67.74 159.27 40.91 87.63 31.41 196.89 153.13 60.02

Ap -0.19 -0.02 0.15 -0.33 0.08 3.84 -0.82 -0.23

NMB (%) -39.98 -71.27 -23.15 -59.18 -15.85 5689.19 -86.95 -38.66

NME (%) 44.99 82.09 32.56 59.18 28.30 5689.19 86.95 41.53

MNB (%) -41.43 -74.75 -21.29 -59.63 -11.88 384.10 -86.53 -39.51

MNE (%) 46.79 85.40 33.45 59.63 27.35 384.10 86.53 42.81

MB (μg/m3) -58.86 -1.63 -3.65 -2.69 -0.13 5.25 -2.09 -71.19

MAGE (μg/m3) 66.23 1.88 5.14 2.69 0.23 5.25 2.09 76.47

RMSE (μg/m3) 75.30 2.08 6.40 2.81 0.29 5.68 2.19 87.22

Obs Max (μg/m3) 290.29 4.53 31.89 6.06 1.27 2.03 4.03 343.89

Pred Max (μg/m3) 235.90 4.41 36.79 4.06 1.37 9.82 0.75 266.44

Mean_Obs 147.22 2.29 15.77 4.55 0.81 0.09 2.40 184.14

Mean_Pred 88.35 0.66 12.12 1.86 0.68 5.34 0.31 112.95

Std_Dev_Obs 49.16 0.94 6.92 0.97 0.26 0.43 0.74 52.20

Std_Dev_Pre 60.94 1.28 7.33 0.84 0.30 2.38 0.14 65.76

Variance 5669.60 4.34 41.00 7.88 0.09 32.28 4.81 7607.62

R2 0.41 0.10 0.51 0.36 0.29 0.18 0.29 0.41

BF 0.59 0.25 0.79 0.40 0.88 0.22 0.13 0.60

N 22 22 22 22 22 22 22 22

Table C-5m. Model performance statistics for the Worst 20% Days at the Mammoth Cave 

Class I area.

MACA bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -65.12 -134.11 -64.77 -68.22 37.17 183.33 73.57 -57.57

MFE (%) 68.90 147.55 66.39 68.22 55.41 183.33 156.43 60.57

Ap -0.38 -0.37 0.07 -0.25 -0.53 -999.00 -0.69 -0.39

NMB (%) -46.69 -57.44 -44.40 -49.42 22.76 -999.00 -28.66 -42.27

NME (%) 49.96 65.21 46.28 49.42 72.16 -999.00 117.76 44.92

MNB (%) -41.05 -64.03 -45.70 -48.73 103.82 -999.00 23.87 -37.74

MNE (%) 45.22 84.58 47.41 48.73 117.45 -999.00 124.55 41.05

MB (μg/m3) -83.29 -5.79 -9.61 -2.75 0.29 13.40 -0.32 -96.83

MAGE (μg/m3) 89.11 6.58 10.02 2.75 0.93 13.40 1.31 102.89

RMSE (μg/m3) 120.48 10.17 11.30 3.15 1.58 15.17 1.60 136.14

Obs Max (μg/m3) 347.31 107.80 40.53 9.05 8.22 0.00 4.63 405.74

Pred Max (μg/m3) 216.80 68.26 43.56 6.76 3.90 25.46 1.41 246.32

Mean_Obs 178.37 10.09 21.64 5.57 1.28 0.00 1.11 229.07

Mean_Pred 95.08 4.29 12.03 2.82 1.58 13.40 0.79 132.24

Std_Dev_Obs 68.00 21.11 7.98 1.75 1.55 0.00 1.56 65.54

Std_Dev_Pre 56.54 13.85 8.66 1.42 0.69 7.28 0.27 65.53

Variance 14514.27 103.51269 127.72 9.90 2.51 230.24 2.55 18532.89

R2 0.00 0.93 0.54 0.29 0.03 -999.00 0.00 0.01

BF 0.59 0.36 0.54 0.51 2.04 0.00 0.62 0.62

N 24 24 24 24 24 24 24 24
 C-30 

Table C-5n. Model performance statistics for the Worst 20% Days at the Mingo Class I area.

MING bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -20.59 -53.58 -22.20 -18.82 107.61 190.54 -13.23 20.38

MFE (%) 66.65 111.83 49.34 43.70 113.87 190.54 37.67 54.93

Ap -0.13 0.07 -0.22 -0.25 0.45 72.50 -0.38 0.46

NMB (%) 4.74 30.45 -27.80 -22.91 206.49 87137.63 -21.72 40.81

NME (%) 59.76 64.63 44.49 40.91 227.86 87137.63 36.98 65.30

MNB (%) 6.00 58.25 5.73 1.02 587.75 13729.83 -3.73 47.74

MNE (%) 65.16 150.88 56.28 48.24 592.40 13729.83 36.45 69.83

MB (μg/m3) 5.35 6.29 -4.29 -1.12 2.99 69.04 -1.12 69.50

MAGE (μg/m3) 67.43 13.36 6.87 2.00 3.30 69.04 1.91 111.22

RMSE (μg/m3) 83.82 21.83 9.85 2.64 3.77 76.00 2.60 136.16

Obs Max (μg/m3) 263.55 105.09 40.46 10.36 6.65 1.56 11.69 287.91

Pred Max (μg/m3) 228.22 112.07 31.38 7.75 9.61 114.64 7.23 419.01

Mean_Obs 112.84 20.67 15.43 4.89 1.45 0.08 5.15 170.33

Mean_Pred 118.19 26.96 11.14 3.77 4.44 69.12 4.03 239.83

Std_Dev_Obs 63.96 30.73 10.19 2.42 1.37 0.33 2.83 43.37

Std_Dev_Pre 88.99 38.04 6.24 1.47 1.73 32.57 1.04 111.15

Variance 7025.62 476.67 97.08 6.99 14.18 5776.49 6.75 18538.89

R2 0.17 0.68 0.22 0.08 0.02 0.03 0.32 0.00

BF 1.06 1.58 1.06 1.01 6.88 12.57 0.96 1.48

N 22 22 20 20 22 22 22 22

Table C-5o. Model performance statistics for the Worst 20% Days at the Okefenokee Class I 

area.

OKEF bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -79.19 -151.20 -7.49 -66.68 18.36 193.37 -117.48 -60.64

MFE (%) 79.72 153.54 42.51 78.30 75.40 193.37 121.37 60.64

Ap -0.23 -0.62 -0.49 -0.21 0.42 5.42 -0.71 -0.10

NMB (%) -49.55 -78.69 -18.89 -42.37 30.38 6970.71 -79.40 -43.75

NME (%) 50.03 80.92 50.95 62.56 90.15 6970.71 80.10 43.75

MNB (%) -52.76 -80.49 6.44 -39.32 296.99 62.41 -68.22 -42.38

MNE (%) 53.31 83.19 39.20 60.73 338.55 62.41 72.60 42.38

MB (μg/m3) -47.55 -4.38 -6.64 -3.20 0.24 4.11 -2.14 -69.45

MAGE (μg/m3) 48.02 4.51 17.90 4.72 0.72 4.11 2.16 69.45

RMSE (μg/m3) 57.10 5.46 37.27 7.22 0.93 4.49 2.47 88.19

Obs Max (μg/m3) 271.57 15.83 156.35 29.54 1.57 1.36 4.79 341.01

Pred Max (μg/m3) 208.50 6.03 80.47 23.21 2.23 8.70 1.41 308.25

Mean_Obs 95.97 5.57 35.14 7.54 0.79 0.06 2.69 158.76

Mean_Pred 48.41 1.19 28.50 4.35 1.04 4.17 0.55 89.30

Std_Dev_Obs 57.98 3.49 32.48 6.19 0.45 0.28 1.33 60.33

Std_Dev_Pre 44.25 1.86 19.09 5.04 0.59 1.75 0.33 57.69

Variance 3260.75 29.83 1388.71 52.15 0.86 20.15 6.12 7777.06

R2 0.69 0.12 0.00 0.10 0.30 0.06 0.09 0.31

BF 0.47 0.20 1.06 0.61 3.97 0.07 0.32 0.58

N 23 23 23 23 23 23 23 23
 C-31 

Table C-5p. Model performance statistics for the Worst 20% Days at the Cape Romain Class I 

area.

ROMA bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -56.37 -143.06 -55.60 -71.38 19.21 200.00 -17.45 -51.44

MFE (%) 73.61 169.89 59.90 73.52 93.27 200.00 88.22 62.26

Ap -0.31 3.18 -0.25 -0.08 0.66 -999.00 -0.16 -0.30

NMB (%) -38.82 -13.25 -36.31 -41.50 54.48 -999.00 -35.45 -35.48

NME (%) 57.49 135.58 44.40 46.11 122.86 -999.00 63.08 47.16

MNB (%) -29.54 -30.23 -36.69 -48.41 204.20 -999.00 327.16 -30.91

MNE (%) 53.90 131.34 42.01 50.87 254.18 -999.00 396.06 45.14

MB (μg/m3) -33.72 -0.69 -7.33 -2.27 0.38 6.16 -0.91 -47.20

MAGE (μg/m3) 49.93 7.06 8.97 2.53 0.85 6.16 1.62 62.73

RMSE (μg/m3) 67.15 12.07 14.36 2.93 1.06 7.11 2.10 79.95

Obs Max (μg/m3) 234.53 13.18 70.65 14.42 1.77 0.00 7.17 270.34

Pred Max (μg/m3) 162.03 55.10 52.73 13.30 2.93 13.62 6.04 189.41

Mean_Obs 86.86 5.21 20.20 5.48 0.69 0.00 2.57 133.01

Mean_Pred 53.14 4.52 12.86 3.21 1.07 6.16 1.66 85.82

Std_Dev_Obs 49.18 2.74 15.93 3.04 0.40 0.00 1.72 46.84

Std_Dev_Pre 39.67 12.76 12.71 3.08 0.82 3.62 1.15 49.11

Variance 4508.59 145.67 206.27 8.57 1.12 50.58 4.39 6392.06

R2 0.01 0.07 0.40 0.66 0.08 -999.00 0.02 0.00

BF 0.70 0.70 0.63 0.52 3.04 0.00 4.27 0.69

N 24 24 24 24 24 24 24 24

Table C-5q. Model performance statistics for the Worst 20% Days at the St. Marks Class I 

area.

SAMA bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -65.33 -155.89 -5.70 -36.19 7.23 199.98 -92.52 -53.12

MFE (%) 69.16 155.89 53.89 56.79 89.12 199.98 97.18 56.84

Ap -0.06 -0.53 0.24 0.61 -0.16 1920.06 -0.34 -0.03

NMB (%) -40.40 -74.41 0.20 -4.63 26.17 1762079.63 -62.69 -34.34

NME (%) 45.02 74.41 50.55 56.59 110.11 1762079.63 64.18 38.46

MNB (%) -42.17 -83.45 18.88 -10.13 268.57 113272.12 -57.20 -35.23

MNE (%) 46.35 83.45 58.03 56.61 322.15 113272.12 62.93 39.24

MB (μg/m3) -37.22 -3.58 0.04 -0.28 0.23 6.90 -1.93 -47.55

MAGE (μg/m3) 41.48 3.58 10.40 3.39 0.98 6.90 1.98 53.27

RMSE (μg/m3) 51.84 4.16 14.75 5.69 1.33 7.80 2.30 65.75

Obs Max (μg/m3) 174.11 17.40 76.59 17.16 4.06 0.01 7.80 207.54

Pred Max (μg/m3) 162.80 8.26 95.29 27.58 3.41 17.29 5.13 200.98

Mean_Obs 92.14 4.81 20.59 5.99 0.89 0.00 3.08 138.50

Mean_Pred 54.92 1.23 20.63 5.71 1.12 6.90 1.15 90.94

Std_Dev_Obs 34.96 3.53 18.04 3.86 0.76 0.00 1.60 30.80

Std_Dev_Pre 40.81 2.25 20.11 7.30 0.89 3.73 1.14 51.92

Variance 2687.22 17.32 217.69 32.39 1.76 60.83 5.31 4323.43

R2 0.29 0.65 0.48 0.37 0.09 0.04 0.37 0.22

BF 0.58 0.17 1.19 0.90 3.69 49.29 0.43 0.65

N 23 23 23 23 23 23 23 23
 C-32 

Table C-5r. Model performance statistics for the Worst 20% Days at the Shenandoah Class I 

area.

SHEN bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -76.03 12.44 -21.00 -77.27 13.36 175.00 -119.18 -69.71

MFE (%) 84.20 97.89 50.51 78.63 50.24 175.00 119.18 77.03

Ap -0.46 -0.13 -0.84 -0.60 -0.75 -999.00 -0.94 -0.36

NMB (%) -49.46 51.63 -43.57 -57.04 -16.01 -999.00 -80.94 -44.97

NME (%) 56.28 129.69 59.35 58.47 50.78 -999.00 80.94 51.51

MNB (%) -45.12 149.15 -2.40 -52.83 59.10 -999.00 -72.31 -41.96

MNE (%) 55.84 198.87 48.56 54.30 86.79 -999.00 72.31 50.86

MB (μg/m3) -86.47 3.04 -8.89 -3.12 -0.18 8.24 -2.65 -99.38

MAGE (μg/m3) 98.39 7.64 12.11 3.20 0.57 8.24 2.65 113.84

RMSE (μg/m3) 117.52 10.84 31.39 4.09 1.17 9.41 3.88 135.13

Obs Max (μg/m3) 354.08 32.37 150.90 15.51 6.59 0.00 15.30 375.09

Pred Max (μg/m3) 192.70 28.15 24.29 6.22 1.63 16.00 0.91 240.70

Mean_Obs 174.83 5.89 20.40 5.48 1.12 0.00 3.27 221.00

Mean_Pred 88.37 8.93 11.51 2.35 0.94 8.24 0.62 121.62

Std_Dev_Obs 74.90 6.47 29.56 2.49 1.30 0.00 2.91 66.32

Std_Dev_Pre 63.95 8.06 6.22 1.10 0.33 4.63 0.18 79.02

Variance 13811.26 117.59460 985.14 16.77 1.37 88.53 15.09 18260.59

R2 0.10 0.00 0.01 0.00 0.23 -999.00 0.01 0.03

BF 0.55 2.49 0.98 0.47 1.59 0.00 0.28 0.58

N 24 24 24 24 24 24 24 24

Table C-5s. Model performance statistics for the Worst 20% Days at the Shining Rock Class I 

area.

SHRO bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -64.10 -187.59 -31.54 -28.66 -29.64 199.31 -132.94 -57.06

MFE (%) 68.47 187.59 39.71 36.19 41.93 199.31 132.94 59.79

Ap -0.55 -0.85 0.57 0.14 -0.39 151.98 -0.86 -0.52

NMB (%) -44.23 -96.56 -20.83 -23.28 -29.58 50992.11 -83.16 -41.26

NME (%) 48.75 96.56 33.83 31.40 38.94 50992.11 83.16 44.09

MNB (%) -42.37 -96.50 -21.93 -21.05 -19.64 12340.48 -78.61 -39.81

MNE (%) 47.60 96.50 33.53 29.33 33.81 12340.48 78.61 42.98

MB (μg/m3) -71.73 -3.00 -3.26 -0.57 -0.31 5.56 -1.51 -80.99

MAGE (μg/m3) 79.06 3.00 5.30 0.77 0.41 5.56 1.51 86.54

RMSE (μg/m3) 106.33 3.22 6.88 0.95 0.57 5.84 1.73 114.15

Obs Max (μg/m3) 475.66 6.49 23.94 3.86 2.55 0.06 3.68 518.88

Pred Max (μg/m3) 214.07 0.96 37.53 4.41 1.56 8.90 0.50 250.07

Mean_Obs 162.18 3.11 15.66 2.45 1.05 0.01 1.82 196.28

Mean_Pred 90.45 0.11 12.40 1.88 0.74 5.57 0.31 115.28

Std_Dev_Obs 77.95 1.22 4.55 0.73 0.51 0.02 0.88 80.56

Std_Dev_Pre 56.81 0.21 7.07 0.80 0.28 1.82 0.08 61.81

Variance 11306.41 10.38869 47.33 0.90 0.33 34.06 2.98 13029.82

R2 0.11 0.03 0.26 0.24 0.11 0.09 0.21 0.13

BF 0.58 0.03 0.78 0.79 0.80 24.88 0.21 0.60

N 25 25 25 25 25 25 25 25
 C-33 

Table C-5t. Model performance statistics for the Worst 20% Days at the Sipsey Class I area.

SIPS bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -70.46 -117.95 -10.05 -80.92 22.60 199.72 -129.72 -48.58

MFE (%) 80.31 143.41 59.66 83.49 59.52 199.72 129.72 60.40

Ap -0.26 -0.05 0.81 -0.45 0.37 919.30 -0.72 -0.12

NMB (%) -39.82 -33.47 14.40 -55.11 43.23 738981.75 -79.24 -28.95

NME (%) 49.60 93.13 62.85 57.44 76.60 738981.75 79.24 41.90

MNB (%) -41.83 -47.12 12.97 -54.30 71.45 6291.68 -77.36 -29.54

MNE (%) 53.89 95.39 63.20 57.30 99.81 6291.68 77.36 44.20

MB (μg/m3) -51.76 -3.37 3.35 -3.26 0.43 11.39 -2.45 -53.35

MAGE (μg/m3) 64.47 9.37 14.63 3.40 0.76 11.39 2.45 77.22

RMSE (μg/m3) 76.05 17.21 18.18 3.72 0.99 13.95 2.68 90.66

Obs Max (μg/m3) 279.55 63.69 41.03 11.07 2.28 0.03 5.08 325.68

Pred Max (μg/m3) 208.05 60.69 74.21 6.10 3.12 31.20 1.41 286.87

Mean_Obs 129.97 10.07 23.28 5.92 0.99 0.00 3.09 184.32

Mean_Pred 78.21 6.70 26.63 2.66 1.42 11.39 0.64 130.96

Std_Dev_Obs 65.73 16.76 8.98 1.85 0.45 0.01 1.08 62.72

Std_Dev_Pre 64.39 15.57 20.92 1.40 0.82 8.24 0.33 87.85

Variance 5783.50 296.22 330.57 13.84 0.98 194.58 7.16 8218.50

R2 0.38 0.19 0.24 0.16 0.00 0.06 0.01 0.30

BF 0.58 0.53 1.13 0.46 1.71 2.91 0.23 0.70

N 22 22 22 22 22 22 22 22

Table C-5u. Model performance statistics for the Worst 20% Days at the Swanquarter Class I 

area.

SWAN bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -56.96 -122.20 -58.99 -76.45 31.37 187.73 -131.45 -49.02

MFE (%) 81.41 156.01 58.99 79.31 74.32 194.88 131.45 62.35

Ap 0.03 0.45 -0.79 0.21 1.01 6.38 -0.74 0.00

NMB (%) -24.66 3.84 -52.47 -50.51 50.46 7507.01 -80.76 -31.38

NME (%) 57.70 93.70 52.47 55.29 100.20 7623.59 80.76 46.10

MNB (%) -28.04 -19.11 -41.81 -51.65 183.63 -58.29 -77.44 -29.30

MNE (%) 59.96 125.41 41.81 55.08 214.89 58.29 77.44 45.20

MB (μg/m3) -20.67 0.29 -12.17 -2.42 0.31 3.93 -2.95 -42.55

MAGE (μg/m3) 48.38 6.98 12.17 2.65 0.62 3.99 2.95 62.51

RMSE (μg/m3) 58.52 10.49 24.00 2.90 0.87 4.51 3.25 76.28

Obs Max (μg/m3) 209.85 45.16 128.63 7.73 1.34 1.20 7.26 243.51

Pred Max (μg/m3) 216.04 65.32 26.40 9.32 2.68 8.88 1.89 244.55

Mean_Obs 83.84 7.44 23.19 4.79 0.62 0.05 3.65 135.60

Mean_Pred 63.17 7.73 11.02 2.37 0.93 3.98 0.70 93.05

Std_Dev_Obs 43.76 9.31 24.50 1.68 0.34 0.25 1.34 39.17

Std_Dev_Pre 60.46 16.51 6.37 1.92 0.62 2.16 0.41 60.38

Variance 3425.07 110.06 576.21 8.44 0.75 20.33 10.56 5818.45

R2 0.21 0.63 0.38 0.35 0.19 0.12 0.00 0.04

BF 0.72 0.81 0.58 0.48 2.84 0.02 0.23 0.71

N 23 23 23 23 23 23 23 23
 C-34 

Table C-5v. Model performance statistics for the Worst 20% Days at the Upper Buffalo Class I 

area.

UPBU bextSO4 bextNO3 bextOC bextEC bextSOIL bextSS bextCM totalBEXT

MFB (%) -63.22 -94.84 -21.14 -69.02 52.35 191.67 -150.24 -45.58

MFE (%) 70.32 123.99 27.94 69.02 70.13 191.67 150.24 52.57

Ap -0.40 -0.20 -0.56 -0.30 0.18 -999.00 -0.92 -0.34

NMB (%) -39.96 -21.91 -20.56 -50.26 75.18 -999.00 -89.44 -32.00

NME (%) 47.24 99.30 27.44 50.26 110.32 -999.00 89.44 39.01

MNB (%) -39.30 -4.26 -15.33 -48.75 150.41 -999.00 -84.89 -29.86

MNE (%) 47.64 119.52 22.99 48.75 162.88 -999.00 84.89 37.75

MB (μg/m3) -40.13 -2.13 -3.40 -2.13 0.87 15.10 -7.97 -48.64

MAGE (μg/m3) 47.45 9.67 4.53 2.13 1.27 15.10 7.97 59.30

RMSE (μg/m3) 71.44 20.14 8.32 2.49 1.81 17.18 9.76 84.24

Obs Max (μg/m3) 273.55 98.02 52.31 7.21 5.34 0.00 20.96 311.47

Pred Max (μg/m3) 163.12 78.69 23.01 5.03 6.32 29.04 1.68 204.34

Mean_Obs 100.43 9.74 16.53 4.24 1.15 0.00 8.91 152.01

Mean_Pred 60.30 7.61 13.13 2.11 2.02 15.10 0.94 103.37

Std_Dev_Obs 57.29 21.23 9.64 1.24 0.96 0.00 5.76 53.33

Std_Dev_Pre 47.58 18.03 5.84 0.95 1.15 8.38 0.31 57.74

Variance 5103.16 405.65 69.21 6.20 3.27 295.24 95.35 7097.14

R2 0.12 0.22 0.35 0.09 0.03 -999.00 0.00 0.04

BF 0.61 0.96 0.85 0.51 2.50 0.00 0.15 0.70

N 24 24 24 24 24 24 24 24
 C-35 

C.2.3 Class I Area Model Performance Evaluation 

Below we summarize the CMAQ 2002 Base G2 12 km Actual base case model performance at 

each of the Class I area within and nearby the VISTAS region for the best and worst 20% days 

and then for all days during 2002. 

C.2.3.1 Sipsey Wilderness Area (SIPS), Alabama 

C.2.3.1.1 SIPS Best 20 Percent Days 

Figure C-3a. CMAQ 2002 Base G2 12 km Actual base case model performance for the Best 20 

percent days at Sipsey (SIPS), Alabama for SO4 (top left), NO3 (top right), OMC (bottom left) and 

EC (bottom right). 
 C-36 

Figure C-3b. CMAQ 2002 Base G2 12 km Actual base case model performance for the Best 20 

percent days at Sipsey (SIPS), Alabama for Soil (top left), CM (top right), Sea Salt (bottom left) 

and Total Bext (bottom right). 
 C-37 

Figure C-4. Bugle Plots of Fractional Bias and Fractional Gross Error for Sipsey, 

Alabama and the Best 20 Percent Days. 
 C-38 

2.3.1.2 SIPS Worst 20 Percent Days 

Figure C-5a. CMAQ 2002 Base G2 12 km Actual base case model performance for the Worst 

20 percent days at Sipsey (SIPS), Alabama for SO4 (top left), NO3 (top right), OMC (bottom left) 

and EC (bottom right). 
 C-39 

Figure C-5b. CMAQ 2002 Base G2 12 km Actual base case model performance for the Worst 

20 percent days at Sipsey (SIPS), Alabama for Soil (top left), CM (top right), Sea Salt (bottom 

left) and Total Bext (bottom right). 
 C-40 

Figure C-6. Bugle Plots of Fractional Bias and Fractional Gross Error for Sipsey, 

Alabama and the Worst 20 Percent Days. 
 C-41 

2.3.1.3 SIPS for All Days 

Figure C-7. Time series of predicted and observed SO4, NO3, OMC, EC, Soil, CM, Sea 

Salt and PM2.5 mass across all monitoring days at Sipsey, Alabama for the CMAQ 2002 Base 

G2 12 km base case simulation.
 C-42 

Figure C-7. (concluded) Time series of predicted and observed SO4, NO3, OMC, EC, 

Soil, CM, Sea Salt and PM2.5 mass across all monitoring days at Sipsey, Alabama for the 

CMAQ 2002 Base G2 12 km base case simulation.
 C-43 

2.3.2 Chassahowitzka Wildlife Refuge (CHAS), Florida 

2.3.2.1 CHAS Best 20 Percent Days 

Figure C-8a. CMAQ 2002 Base G2 12 km Actual base case model performance for the Best 20 

percent days at Chassahowitzka (CHAS), Florida for SO4 (top left), NO3 (top right), OMC 

(bottom left) and EC (bottom right). 
 C-44 

Figure C-8b. CMAQ 2002 Base G2 12 km Actual base case model performance for the Best 20 

percent days at Chassahowitzka (CHAS), Florida for Soil (top left), CM (top right), Sea Salt 

(bottom left) and Total Bext (bottom right). 
 C-45 

Figure C-9. Bugle Plots of Fractional Bias and Fractional Gross Error for CHAS and 

the Best 20 Percent Days. 
 C-46 

2.3.2.2 CHAS Worst 20 Percent Days 

Figure C-10a. CMAQ 2002 Base G2 12 km Actual base case model performance for the Worst 

20 percent days at Chassahowitzka (CHAS), Florida for SO4 (top left), NO3 (top right), OMC 

(bottom left) and EC (bottom right). 
 C-47 

Figure C-10b. CMAQ 2002 Base G2 12 km Actual base case model performance for the Worst 

20 percent days at Chassahowitzka (CHAS), Florida for Soil (top left), CM (top right), Sea Salt 

(bottom left) and Total Bext (bottom right). 
 C-48 

Figure C-11. Bugle Plots of Fractional Bias and Fractional Gross Error for CHAS and 

the Worst 20 Percent Days.