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 anthropoge nic 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 (nat ural 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 anthropogeni c emissions in the U.S., the result is an
estimate of what the international contributio n 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 betw een the results of two CMAQ 36-km grid simulations of the U.S., both using 2018 OTW Base F em issions, with boundary conditions based on GEOS-CHEM simulations: (1) with all global sources active; and (2) with U.S. anthropogenic emissions and all global natu ral emissions (Mexican and CanadianNovember 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 emissi ons, the result of this calculation is
an estimate of what the inte rnational contribution would be in 2018 if international emissions remain at 2002 levels. (See the lowe r half of Table 5-2 fo r portrayal of this calculation.)

Both cases underestimate the total interna tional contribution because the GEOS-CHEM
simulations assumed that all fire s 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 thir d method to estimate the impact s of international emissions. They used the PSAT (PM Source Apportionment T echnology) module of the CAMx regional air quality model to track the 2002 co ntributions of three non-domes tic source categories (Canadian
emissions in the CAMx modeli ng 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 c ontributions of these th ree source categories. One major difference between this approach a nd 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 th e international contributions to two of the principal components of the visibility-impairing aerosol, Figures 5-7 an d 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 stat es. Results are presented for all four of the
methods described above. Two sets of GEOS-C HEM 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 [(NH 4 

### ) 2 

### SO 4 

### ] concentrations at Class I areas.



## Transboundary Man-Made Impacts

0.00 0.05 0.10 0.15 0.20 0.25 0.30ACADLYBRBRIGBOWAISLEBIBEHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHENOCM Conc. (_g/m3)

GEOS_4x5 GEOS_1x1 PSAT CMAQ



### Figure 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 agr eement (better than a factor of two or so everywhere except at Everglades) between all pairs of methods, while the results are more variable for OCM. The impacts estimated by the VISTAS 4 ° x5 ° GEOS-CHEM simulations tend
to be smaller than those of the other methods . Not surprisingly, the biggest impacts from
international emissions tend to occur at Class I areas that are near the Canadian and Mexican
borders. 

## Transboundary Man-Made Impacts

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4ACADLYBRBRIGBOWAISLEBIBEHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHEN

GEOS_4x5 GEOS_1x1 PSAT CMAQ(NH4)2SO4Conc. (_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 contribu tions 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 co rresponding annual average international contribution, CAMx/PSAT generally showed lower contribu tion to sulfate on the 20 percent worst days at the moun tain sites and equal or slightly higher contributions at the coastal sites. For organic carbonaceous mass, there were no clear tre nds 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 1.2BRIGHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHEN(NH4)2SO4Conc.(_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 0.30BRIGHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHENOCM Conc. (_g/m3)

PSAT_20 PSAT CMAQ_20 CMAQ



### Figure 5-10. Estimates by the PSAT and CMAQ model s 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 internationa l 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 (S AMA). At both locations, the international impacts on concentrations on such days are sma ll, 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 es timated haze index, in decivi ews, 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 si mulation, so the difference between the open triangle and th e open diamond represents th e international contribution.

SHEN1 20% Worst Days

0 5 10 15 20 25 30 35 40 45

2/28 5/26 5/29 6/4 6/10 6/13 6/19 6/25 7/1 7/4 7/16 7/19 7/22 7/31 8/3 8/12 8/21 8/24 8/27 9/11 9/14 9/17 9/29 10/2Mass (μg/m3)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt


### Figure 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 12/7Mass (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



### YearHaziness 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



### YearHaziness 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 internati onal 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 m odeled 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 ju st below (indicating greater reduction than) the 2018 Uniform Rate of Progress.

Information of the type portrayed for 2018 in Fi gures 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 repres ents the 2018 visibil ity 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 c ontributions 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%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent 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 Progres s 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 IMPROV E 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 vi sibility impairment at the VISTAS Class I areas using the period
of record of measurements at the associ ated IMPROVE monitor and the new IMPROVE equation. Trends are included fo r 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 min imum 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 th ere could be large missing periods with high or
low extinction that are not be ing account for. The WRAP Technical Support System (TSS) website was used to calculate the visibility tren ds 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 operati ng 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 me trics (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. Ag ain there is lots of year-to-year variability but there also appears to be a gene ral downward trend in visi bility impairment at CHAS for the worst 20 percent and all sampled days.

The trends at EVER are confounded by poor valid da ta capture and what appears to be impacts due to biomass burning in 2003 th at enhance the OMC and EC c ontributions for the worst 20 percent days (Figure 5-16c). Also changes in IMPROVE measurement pr otocols 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 (Fi gure 5-16e) only have two years of sufficient valid data to construct trends for the worst and best 20 percen t 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 IMPR OVE measurements at th e 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 ar gument. 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 h aze 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 pl ots would make the more recent visibility within the range of year-to-y ear variability in the past.

SWAN has only two years with suffi cient data capture so no disc ussions 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 impairme nt at ROMA with the more impaired conditions of 2004 likely due to year-to-year variability.

GRSM has one of the longest periods of reco rd with the IMPROVE monitor operating for 17 years and collecting sufficient valid data to pro duce 15 years of best and worst 20 percent daysNovember 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 y ears light extinction for the worst 20 percent days is approximately 210 Mm -1 

### or less a level achi eved only once in the previous ten years. A trend to ward improvements in vi sibility 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 measurem ents that include sufficient data capture to produce 14 years of best and wo rst 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 y ears 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 al l 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 ha s a long record of IMPROVE m easurements (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 tre nd 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 rec onstructed 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 recons tructed 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 recons tructed 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 recons tructed 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 rec onstructed 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 Virgin ia 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 Visi bility Impairment for Class I Areas

VISTAS has determined that SO2 emission reduc tions from EGU and non- EGU point sources in the VISTAS and nearby states would be the mo st 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 sour ces located in those geographic areas. An SO2
Area of Influence was defined for each Class I ar ea to represent the geographic area containing sources that would likely have th e greatest impact on visibility at that Class I area. All SO2 point sources within these Areas of Influe nce 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 Influe nce analyses were condu cted by Joe Adhloch and
Cassie Archuleta of Air Resources Specialists, Gerry Mansell of ENVIRON, Greg Stella of Alpine Geophysics and Pat Brew er of VISTAS and the reader is referred to additional documentation for more details at the analys es (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 re gion and neighboring Class I areas for the 2000- 2004 worst 20 percent days baseline period. Back tr ajectory 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 essentia lly follows a parcel of air backward in hourly steps for a specified length of tim e. 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 Cl ass 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 10 0m and 500m start heights. As illustrated in Figure 5-18, winds influencing Gr eat Smoky Mountains on the wors t 20 percent days come from all directions and there is no single predominan t wind direction influencing the 20 percent worst visibility days. In contrast, at Swanquarter, th e residence time plot indi cates a clear north-south gradient of influence along the eastern co ast 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

> 0.32November 2007

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

### 5-44 Figure 5-19. Example residence time plot for 20 perc ent 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 su lfate extinction weighted reside nce time plots to define the geographic area with highest probability of infl uencing the receptor on the worst 20 percent days
in 2000-2004 that were dominated by sulfate. Each back traj ectory 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 importan ce on days influenced by, for example, organic carbon from fires. Sulfate-weighted back traj ectories for the 20 per cent worst days were combined for the 5 years of data. The resulti ng sulfate extinction-weight ed residence time plots were used to define the geogra phic Area of Influence for sources of SO2 emissions. In Figure 5- 20 the area representing 10 percen t or greater residence time is outlined in red and the area representing 5 percent or greate r residence time is outlined in gr ay. The VISTAS states focused their analyses on the Area of Influence define d 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 em ission data based for
the 2002 baseline and 2018 emissi ons 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 m onitor is located. The distance-weighted point
source SO2 emissions are then combined with th e gridded extinction-wei ghted back-trajectory residence times at a spatia l 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 poin t source SO2 emissions were multiplied by the total extinction-weighted back-tra jectory 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 a nd 2018 emissions inventories.

Figure 5-21 illustrates the 2002 and 2018 distance weighted gridded emissions multiplied by sulfate extinction weighted residence time fo r Great Smoky Mountains National Park. These maps help visualize where the emissions redu ctions 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 a nd 2018 can be seen by comparing emissions source strengths in the two plots. Note the emissions from each sour ce 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 w ith 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 Nort h 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 be tween 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 sour ces within the Areas of Influence for each Class I areas were developed using the VISTAS 2002 ba se 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 th an 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 iden tification codes

• SO2 emissions for 2002 and 2018

• 2018 control efficiency

• Distance to Class I areas (defined by dist ance 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 metr ic 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 st arting with an analysis of the major source categories in each SO2 Area of Influence to de termine 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 ye ar 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 th e 2018 inventory for the SO2 Areas of Influence for the Great Smokey Mountains Cla ss 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 co mbustions 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, nat ural 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 pr ocessing not previously mentioned

• Solvent Utilization Emissions from solvent utilization su ch 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 mentionedNovember 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 contribut ing to 2018 SO2 emissions in the Areas of Influence, even after implementation of th e 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 eval uate 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. W ithin electric u tilities, all the SO2 emi ssions are attributable to coal fired power plants. Within industrial boilers, most emissions are attributable to coal fired
boilers with lesser contri butions from oil and gas boilers. Co mmercial and institutional coal and
oil boilers have smaller contributions. 86% 26% 59% 0% 5% 1% 1% Miscellaneous (Ag, Fires) 0% 4% 2% 1% 35% 18% 11% Off-highway 11% 2% 2% 0% 48% 25% 18% Highway Vehicles 0% 11% 5% 0% 3% 2% 4% Waste Disposal & Recycling 0% 1% 0% 0% 0% 0% 6% Storage & Transport 0% 1% 0% 0% 0% 1% 44% Solvent Utilization 0% 11% 8% 3% 1% 5% 6% Other Industrial Processes 0% 0% 0% 1% 0% 0% 0% Petroleum & Related Industries 0% 5% 2% 3% 2% 1% 1% Metals Processing 0% 1% 1% 2% 0% 0% 2% Chemical & Allied Product Mfg 1% 12% 5% 6% 3% 6% 5% Fuel Comb. Other 0% 5% 3% 30% 2% 18% 1% Fuel Comb. Industrial 1% 21% 11% 54% 1% 22% 0% Fuel Comb. Elec. Util. NH3 PM-2.5 PM-10 SO2 CO NOX VOC Tier

86% 26% 59% 0% 5% 1% 1% Miscellaneous (Ag, Fires) 0% 4% 2% 1% 35% 18% 11% Off-highway 11% 2% 2% 0% 48% 25% 18% Highway Vehicles 0% 11% 5% 0% 3% 2% 4% Waste Disposal & Recycling 0% 1% 0% 0% 0% 0% 6% Storage & Transport 0% 1% 0% 0% 0% 1% 44% Solvent Utilization 0% 11% 8% 3% 1% 5% 6% Other Industrial Processes 0% 0% 0% 1% 0% 0% 0% Petroleum & Related Industries 0% 5% 2% 3% 2% 1% 1% Metals Processing 0% 1% 1% 2% 0% 0% 2% Chemical & Allied Product Mfg 1% 12% 5% 6% 3% 6% 5% Fuel Comb. Other 0% 5% 3% 30% 2% 18% 1% Fuel Comb. Industrial 1% 21% 11% 54% 1% 22% 0% Fuel Comb. Elec. Util. NH3 PM-2.5 PM-10 SO2 CO NOX VOC TierNovember 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 fo r Specific SO2 Emissions Sources in Each
Area of Influence

The next step in the Area of Influence analys es 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, a llowing VISTAS States to review existing emission reductions. These tables assigned futu re year control technology from IPM forecasting
and State modification for EGU and from control e fficiency 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 sour ce types did not duplic ate existing base case assumptions.

VISTAS used the USEPA’s AirControlNET database, modified for the VISTAS emission inventories, for the non-EGUs. The core of Ai rControlNET 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 a pplicability, 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

1% Fuel Comb. Other-Residential Other 0% Fuel Comb. Other-Residential Wood 0% Fuel Comb. Other-Misc. (Except Residential) 0% Fuel Comb. Other-Comm/Institutional Gas 3% Fuel Comb. Other-Comm/Institutional Oil 2% Fuel Comb. Other-Comm/Institutional Coal 0% Fuel Comb. Industrial-Internal Combustion 1% Fuel Comb. Industrial-Other 2% Fuel Comb. Industrial-Gas 5% Fuel Comb. Industrial-Oil 22% Fuel Comb. Industrial-Coal 0% Fuel Comb. Elec. Util.-Internal Combustion 0% Fuel Comb. Elec. Util.-Other 0% Fuel Comb. Elec. Util.-Gas 0% Fuel Comb. Elec. Util.-Oil 54% Fuel Comb. Elec. Util.-Coal GRSM Tier S

1% Fuel Comb. Other-Residential Other 0% Fuel Comb. Other-Residential Wood 0% Fuel Comb. Other-Misc. (Except Residential) 0% Fuel Comb. Other-Comm/Institutional Gas 3% Fuel Comb. Other-Comm/Institutional Oil 2% Fuel Comb. Other-Comm/Institutional Coal 0% Fuel Comb. Industrial-Internal Combustion 1% Fuel Comb. Industrial-Other 2% Fuel Comb. Industrial-Gas 5% Fuel Comb. Industrial-Oil 22% Fuel Comb. Industrial-Coal 0% Fuel Comb. Elec. Util.-Internal Combustion 0% Fuel Comb. Elec. Util.-Other 0% Fuel Comb. Elec. Util.-Gas 0% Fuel Comb. Elec. Util.-Oil 54% Fuel Comb. Elec. Util.-Coal GRSM TierNovember 2007

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

### 5-50 Using the modified inventories identified a bove, 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 VI STAS 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 spreadsh eet 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/d 2 

### ), 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 th eir review of the statutory factor s for reasonable further progress.

The regional haze rule requires th at states consider the followi ng factors and demonstrate how
these factors were taken into considerati on in selecting the reasonable progress goal:

• the costs of compliance

• the time necessary for compliance

• the energy and non-air quality environm ental 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-fir ed 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. Indus trial boilers using o il and commercial and institutional boilers using coal or oil had small cont ributions to SO2 in 2018.

Sulfur dioxide emissions from utility, industria l, commercial, or inst itutional 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 re quire source specific analyses for accuracy.

Bituminous coal is commonly burned in boilers in the eastern US. Switching to another
bituminous coal with lower su lfur content or blending b ituminous coals can reduce SO2 emissions with least impact to boiler performance. While sub-b ituminous coal has a lower sulfur content than bituminous coal, it al so 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 sulf ur sub-bituminous coal without affecting boiler operations. Costs of boiler modi fications 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, transpor tation costs, and modifica tion 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-combus tion control for coal-fir ed 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 cal cium sulfate by-product is produced that may be further processed to produce gypsum as a commercial byproduct. Costs of flue-gasNovember 2007

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

### 5-51 desulfurization include initial construction costs a nd ongoing operational and maintenance costs for the absorber tower, sorbent handling, and wa ste product handling facilities. Costs per ton vary with boiler size, type, and f acility 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 n ecessary to terminate existing fu el contracts and initiate new
contracts needs to be considere d. 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-combus tion 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 add itional 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 im pacts 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 pr ogress. 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.or g/documents/FinalDocs.asp

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

## .

. . .

Hercules Glade,
MO



## .

. 

# VISTAS Class INovember 2007

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

### 6-1 6.0 REFERENCES

Alpine Geophysics, 2003a. “VISTA S Emissions and Air Quality Modeling – Phase I Subtask 3c-1 Report: MM5 Meteorology QA/QC Summary Repor t for Episode 1: 2-20 January 2002.” Alpine Geophysics LLC, ENVIRON International Corporation and University of California at Riverside. Ft. Wright, Kentucky (available at: http://pah.cert.ucr.edu/vistas/docs.shtml ). September 10.

Alpine Geophysics, 2003b. “VISTAS Emissions and Air Quality Modeling – Phase I Subtask 3c-1 Report: MM5 Meteorology QA/QC Summary Re port for Episode 1: 13-22 July 1999.” Alpine Geophysics LLC, ENVIRON International Corporation and University of California at Riverside. Ft. Wright, Kentucky (available at: http://pah.cert.ucr.edu/vistas/docs.shtml ). September 15.

Alpine Geophysics, 2003c. “VISTA S Emissions and Air Quality Modeling – Phase I Subtask 3c-1 Report: MM5 Meteorology QA/QC Summary Re port for Episode 1: 11-27 July 2001.” Alpine Geophysics LLC, ENVIRON International Corporation and University of California at Riverside. Ft. Wright, Kentucky (available at: http://pah.cert.ucr.edu/vistas/docs.shtml ). September 16.

Anthes, R.A. 1977. “A Cumulus Parameteriza tion Scheme Utilizing a One-Dimensional Cloud Model”, Mon. Wea. Rev., Vol. 105, pp. 270-286.

Anthes, R.A. and T.T. Warner. 1978. “The Deve lopment of Mesoscale Models Suitable for Air Pollution and Other Mesometeorological Studies”, Mon. Wea. Rev. , Vol. 106, pp, 1045-

1078.

Archuleta, C., J. Adlhoch, G. Mans ell, G. Stella, P. Brewer. VISTAS Area of Influence Analyses for Sulfur Dioxide. Prepared for VISTAS. Air Resource Specialists, Fort Collins, Colorado. February 28.

Arnold, J.R.; R.L. Dennis, and G.S. Tonnesen. 1998. Advanced techniques fo r evaluating Eulerian air quality models: background and methodology. In: Preprints of the 10th Joint Conference on the Applications of Air Pollution Meteorology with the Air & Waste Management Association, January 11-16, 1998, Phoenix, Ariz ona. American Meteorological Society, Boston, Massachusetts, paper no. 1.1, pp. 1-5.

Arnold, J.R., R.L. Dennis, and G.S. Tonnesen. 2003. “Diagnostic Evaluation of Numerical Air Quality Models with Specialized Ambient Observations: Testing the Community Multiscale Air Quality Modeling System (CMAQ) at Selected SOS 95 Ground Sites”, Atmos. Environ.

, Vol. 37, pp. 1185-1198.

ARS. 2002a. Analysis of IMPROVE and SEARCH Speciated PM 2.5 

### Data. Air Resource Specialists,

Inc. Report to VISTAS, August 28, 2002. http://www.vistas-sesarm.org

.

ARS. 2002b. Analysis of Alternative Methods. Air Resource Specialists, Inc. Report to VISTAS, August 28, 2002. http://www.vistas-sesarm.org

.November 2007

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

### 6-2 ARS. 2002c. Review of Selected Factors Res ponsible for High and Low Extinction. Air Resource Specialists, Inc. Report to VISTAS, October 14, 2002. http://www.vistas-sesarm.org

.

Barchet, W.R., and R.L. Dennis. 1990. “ANAP AP Model Evaluation, Volume 1: Protocol”, prepared for the U.S. Environmental Protection Agency, prepared by Battelle Pacific Northwest Laboratories, Richland, WA.

Binkowski, F.S. and S.J. Roselle. 2003. Models-3 Community Multiscale Air Quality (CMAQ) Model Aerosol Component -- 1. Model Description. Journal of Geophysical Research

, Vol. 108(D6), 4163, DOI:10.1029/2001JD001409. March 27.

Boylan, J. W. 2003. “Comparisons of 36 km and 12 km Grid Resolutions”, PowerPoint presentation prepared for December 19, 2003 VISTAS modeling conference call.
Available at: http://pah.cert.ucr.edu/vistas/ppt_files/12_36.ppt

.

Boylan, J. W. 2004. “Calculating Statistics: Concentration Related Performance Goals”, paper presented at the EPA PM Model Performance Workshop , Chapel Hill, NC. 11 February.

Brewer, P., S. Holman and J. Hornback. 2003. “Vistas Analyses of Regional Haze in the
Southeastern United States.” Presented at Annual Air and Waste Management Association Meeting and Exhibition, San Diego, CA. June.

Byun, D.W., and J.K.S. Ching. 1999. “Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System”, EPA/600/R-99/030.

Byun, D.W. 2004. Quality Assurance Activities for VISTAS BC Processing. University of
Houston. December 31.

Carter, W.P.L. 1999. Documentation of the SA PRC-99 Chemical Mechanism for VOC Reactivity Assessment, Draft report to the California Air Resources Board, Contracts 92-329 and 95- 308, 9/13/99.

Chung, S. H. and Seinfeld, J.H. (2002), Global di stribution and climate forcing of carbonaceous aerosols, Journal of Geophysical Research, Vol. 107 (D19), 4407,
doi:10.1029/2001JD001397.

Chow, J. 2005. Memorandum: EPA Chemical Profiles for Coal Fired Power Station Emissions. Prepared for TCEQ. July 2005.

Coella, P., and P. L., Woodward. 1984. “The Piecewise Parabolic Method (PPM) for GasDynamical Simulations”, J. Comp. Physics , Vol. 54, pp. 174-201.

Dennis, R. L., et al. 1990. "Evaluation of Regional Acid Deposition Models", State-ofScience/Technology Report No. 5, National Acid Precipitation Assessment Program, Washington, D.C.November 2007

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

### 6-3 Dentener, F. J., and P. J. Crutzen. 1993. Reaction of N 2 

### O 5 

### on tropospheric aerosols: Impact on global distributions of NO

x 

### , O 3 

### , and OH, J. Geophys. Res., 98 , 7149-7163.

Donahue, N.M., M.K. Dubey, R. Mohrschladt, K.L. Demerjian, and J.G. Anderson. 1997. Highpressure flow study of the reactions OH+NO

x 

### → HONO

x 

### : Errors in the falloff region. J. Geophys. Res. 102 , 6159-6168.

Douglas, S., B. Hudischewskyj and Y.H. Wei. Characterization of Meteorology and Its Relationships to Fine Particulate Mass a nd Visibility in the VISTAS Region. ICF International, San Rafael, California. December 8.

Dudhia, J. 1993. "A Non-hydrostatic Versi on of the Penn State/NCAR Mesoscale Model:
Validation Tests and Simulation of an Atlantic Cyclone and Cold Front", Mon. Wea. Rev.

, Vol. 121. pp. 1493-1513.

Edgerton, E. S., S ources of PM2.5 Carbon in the Southeast US , presentation at the MARAMA Science Meeting, Baltimore, MD, January 22, 2003. http://www.marama.org

.

ENVIRON. 2002. “User’s Guide Comprehensive Air Quality Model With Extensions (CAMx)
Version 3.10.” ENVIRON International Cor poration, Novato, California (available at

www.camx.com ) April.

ENVIRON, Alpine Geophysics and UC Riverside. 2003a. “VISTAS Emissions and Air Quality Modeling – Phase I Task 2 Report: Recommende d Model Configurations and Evaluation Methodology for Phase I Modeling.” Prepared by ENVIRON International Corporation, Alpine Geophysics, LLC and University of Ca lifornia at Riverside. Novato, California. (available at: http://pah.cert.ucr.edu/vistas/docs.shtml ). August 4.

ENVIRON, Alpine Geophysics and UC Riversid e. 2003b. “VISTAS Emissions and Air Quality Modeling – Phase I Task 3 Report: Review and Assessment of Available Ambient Air Quality Modeling and Performance Evaluation for the Three VISTAS Phase I Episodes.” Prepared by ENVIRON International Corpora tion, Alpine Geophysics, LLC and University
of California at Riverside. N ovato, California. (available at: http://pah.cert.ucr.edu/vistas/docs.shtml ). July 22.

ENVIRON, Alpine Geophysics and UCR. 2003c. “VISTAS Emissions and Air Quality Modeling – Phase I Task 4a/b Report: Review of Model Sensitivity Simulations and Recommendations of Initial CMAQ Model Configurations and Sensitivity Tests.” Prepared by ENVIRON International Corporation, Alpine Geophysics, LLC and University of California at Riverside. Novato, California. (available at: http://pah.cert.ucr.edu/vistas/docs.shtml ). July

25.

ENVIRON. 2003d. “Development of an Advanced Photochemical Model for Particulate Matter: PMCAMx.” ENVIRON International Corporati on, Novato, CA. Prepared for Coordinating Research Council, Inc. Project A-30 (available at www.crcao.com ).November 2007

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

### 6-4 ENVIRON, Alpine Geophysics, UC Riverside and UC Davis. 2004. “VISTAS Emissions and Air Quality Modeling — Phase I Task 6 Report: M odeling Protocol for the VISTAS Phase II Regional Haze Modeling.” Second Draft May 6.

ENVIRON and UCR. 2007. Technical Support Docu ment for CENRAP Emissions and Air Quality Modeling to Support Regional haze State Implem entation Plans. ENVIRON International Corporation, Novato, California. September 12. (available at: http://www.cenrap.org/projects.asp# ).

EPA. 1991. "Guidance for Regulatory Application of the Urban Airshed Model (UAM), "Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, N.C.

EPA. 1999. “Draft Guidance on the Use of M odels and Other Analyses in Attainment Demonstrations for the 8-hr Ozone NAAQS”. Draft (May 1999), U.S. Environmental Protection Agency, Office of Air Quality Pla nning and Standards, Research Triangle Park,

N.C

EPA. 2001. “Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 

### and Regional Haze”, Draft Report, U.S. Environmental Prot ection Agency, Research Triangle Park, NC.

EPA. 2003a. “Guidance for Tracking Progress Unde r the Regional Haze Rule”, U.S. Environmental Protection Agency, Research Triangle Park, NC.

EPA. 2003b. “Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule”, U.S. Environmental Protection Agency, Research Triangle Park, NC.

EPA. 2003c. A Conceptual Model to Adjust Fugitive Du st Emissions to Account for Near Source Particle Removal in Grid Model Applications , prepared by Tom Pace, U.S. EPA, August 22,

2003. http://www.epa.gov/ttn/chief/emch/invent/statusfugdustemissions_082203.pdf

.

EPA. 2003d. National Emission Inventory QA and Augmentation Memo , prepared by Anne Pope, U.S. EPA, June 2003. http://www.epa.gov/ttn/chief/emch/invent/qaaugmementationmemo_99nei_60603.pdf

EPA. 2004. Technical Support Document for the Interstate Air Quality Rule – Air Quality Modeling Analysis. January 2004. Docket Number OAR-2003-0053-0162.

EPA. 2005c. Technical Support Document for the Final Clean Air Interstate Rule – Air Quality Modeling. U.S. Environmental Protection Ag ency, Office of Air Quality and Planning Standards, Research Triangle Park, North Carolina. March.

EPA. 2007a. Guidance on the Use of Models and Ot her Analyses for Demons trating Attainment of Air Quality Goals for Ozone, PM 2.5 

### and Regional Haze. U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA-454/B-07-002. April.

EPA. 2007b. Guidance for Setting Reasonable Progr ess Goals Under the Regional Haze Program. U.S. Environmental Protection Agency, Office of Air Quality and Planning Standards, AirNovember 2007

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

### 6-5 Policy Division, Geographic Strategies Group, Research Triangle Park, NC. June 1. ( http://epa.gov/ttn/oarpg/t1/memoranda/reasonable_progress_guid071307.pdf ).

ERG. 2006a. WRAP Point and Area Source Emi ssions Projections for the 2018 Base Case. Eastern Research Group, Inc., Sacramento, California. January 25. ( http://www.wrapair.org/forums/ssjf/doc uments/eictts/docs/WRAP_2018_EI-Version_1- Report_Jan2006.pdf ).

ERG. 2006b. Mexico National Emissions Inve ntory, 1999. Eastern Research Group, Inc, Sacramento, California and TransEngin eering, El Paso, Texas. October 11. ( http://www.epa.gov/ttn/chief/net/mexico/1999_mexico_nei_final_report.pdf ).

FLAG. 2000. Federal Land Manager’s Air Quality Related Values Workgroup (FLAG) Phase I Report. ( http://www2.nature.nps.gov/ard/flagfiee/AL.doc ).

Frank, N. 2005. Composition of the FRM PM 2.5 

### for Modeling and NAAQS Implementation. Presented at May 18-20, 2005 EPA PM/Reg ional Haze/Ozone Modeling Workshop ( http://cleanairinfo.com/modelingworkshop/presentations/FRM2_Frank.pdf ).

Gery, M. W., G.Z. Whitten, J.P. Killus, and M. C. Dodge. 1989. A photochemical mechanism for urban and regional-scale computer modeling. J. Geophys. Res. 94, 12925-12956.

Grell, G. A., J. Dudhia, and D. R. Stauffer. 1994. "A Description of the Fifth Generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, NCAR TN-398-STR, 138 pp.

Hanson, D.A.; Edgerton, E.S.; Hartsell, B.E.; Jansen, J.J.; Kandasamy, K.; Blanchard, C.
Southeastern Aerosol Research and Char acterization Study (SEARCH): 1. Overview.
Submitted to JAWM for publication.

Jacob, D. J. 1999. Heterogeneous chemistry and tropospheric ozone. Atmos. Environ. , special issue, 1999 NARSTO Assessment.

Jacob, D.J., R. Park and J.A. Logan. 2005. Do cumentation and Evaluation of the GEOS-CHEM Simulation for 2002 Provided to the VISTAS Group. Harvard University. June 24.

Jang, M., N.M. Czoschke, S. Lee, and R.M. Kamens. 2002. Heterogeneous Atmospheric Aerosol Production by Acid-Catalyzed Particle Phase Reactions. Science , 298,814.

Jang, M., N.M. Czoschke and A.L. Northcross. 2005. Semiempirical Model for Organic Aerosol Growth by Acid-Catalyzed Heterogeneous Reactions of Organic Carbonyls. Environ. Sci.
Technol ., 39, 164-174.

Johnson, M. 2007. Meteorological Model Perf ormance Evaluation of an Annual 2002 MM5 (Version 3.6.3) Simulation. Iowa Department of natural Resources, Air Quality Bureau. November. ( http://www.iowadnr.gov/air/prof/progdev/modeling.html ).

Johnson, J., Y. Jia, C. Emery, R. Morris, Z. Wang and G. Tonnesen. 2006. Comparison of
CENRAP 36 km and 12 km MM5 Model Runs for 2002. Prepared for CENRAP ModelingNovember 2007

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

### 6-6 Work Group. May 23. ( http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/CENRAP_2002_36km_vs_12km_MM5_May2 2_2006.ppt ).

Kalberer M., D. Paulson, M. Sax, M. Steinbacher , J. Dommen, A.S. H. Prevot, R. Fisseha, E. Weingartner, V. Franhevich, R. Zenob: and U. Balternsperger. 2004. Identification of Polymers as Major Components of Atmospheric Organic Aerosols. Science , Vol. 303, 1 ,659 – 1,662. March 12.

Kemball-Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang and G. Tonnesen. 2004a. Comparison of CENRAP, VISTAS and WRAP 36 km MM5 Model Runs for 2002, Task 3: Meteorological Gatekeeper Report. ( http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/CENRAP_VISTAS_WRAP_2002_36km_ MM5_eval.ppt ). December 14.

Khasibatla, P. et al. 1997. Impact of inert or ganic nitrate formation on ground-level ozone in a regional air quality model using the Carbon Bond Mechanism 4. Geophys. Res. Letters

, 24

, 3205-3208.

Lindhjem, C. and Hoats, A. 2006. Memorandum: Descri ption of Spatial Alloca tion of Marine Vessel Emissions. September 2006.

MACTEC. 2003. Memorandum: Expansion of Existing PM 2.5 

### Split Factor Background Documentation. Prepared for EPA. September 2003.

MACTEC. 2006. Documentation of the Ba se G 2002 Base Year, 2009 and 2018, Emission Inventories for VISTAS. Prepared for Visibility Improvement State and Tribal Association of the Southeast (VISTAS). MACTEC, Inc., Gainesville, Florida.

Malm, W.C.; Pitchford, M.L; Scruggs, M.; Sisler , J.F.; Ames, R.; Cepeland, S.; Gebhart, K.; Day,

D.E. 2000. Spatial and Seasonal Patterns and Tempor al Variability of Haze and Its Constituents in the United States. Report III. Cooperative Institute for Research in the Atmosphere, May.

Matthias-Maser, S. and R. Jaenicke. 1995. "The size distribution of primary biological aerosol particles with radii >0.2 mm in an urban-rural influenced region." Atmos. Res ., 39, 279-286.

Matsunaga, S., C. Wiedinmyer, A. Guenther, J. Or lando, T. Karl, J. Greenberg, and Y. Kajii. 2005. Isoprene oxidation products area a significant atmo spheric aerosol component. Submitted to Geophysical Research letters.

Matsunaga, S., M. Mochida, and K. Kawamura. 2003. Growth of organic aerosols by semivolatile carbonyls in the forestal atmosphere, Atmospheric Environment, 35 2045-2050.

Mansell, G., S. Lau, J. Russell and M. Omary. Fugitive Wind Blown Dust Emissions and Model
Performance Evaluation – Phase II. ENVI RON International Corporation, Novato California and University of Ca lifornia at Riverside. May 5.November 2007

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

### 6-7 ( http://www.wrapair.org/forums/dejf/docum ents/WRAP_WBD_PhaseII_Final_Report_0505 06.pdf ).

Mansell, G. 2007. Procedures for Development and Displaying AoI Back-trajectories Residence Time GIS Data. Prepared for VISTAS. ENVIRON International Corporation, Novato, California. August 29.

McNally, D.E., and T.W. Tesche. 1994. "MA PS2.3 User's Guide", Alpine Geophysics, LLC, Golden, CO.

MMS. 2006. Gulfwide Emission Inventory Study for the Regional Haze and Ozone Modeling Effort. May 2006

Morris, R.E., B. Koo, S. Lau, T.W. Tesche, D. Mc Nally, C. Loomis, G. Stella, G. Tonnesen and Z. Wang. 2004a. “VISTAS Emissions and Air Quality Modeling – Phase I Task 4cd Report:
Model Performance Evaluation and Model Sens itivity Tests for Three Phase I Episodes”, prepared for the VISTAS Technical An alysis Committee, prepared by ENVIRON International Corporation, Alpine Geophysics, LLC, and the University of California, Riverside (CE-CERT).

Morris, R.E., B. Koo, S. Lau, D. McNally, T.W. Tesche, C. Loomis, G. Stella, G. Tonnesen and C-J.
Chien. 2004b. “VISTAS Phase II Emissions and Air Quality Modeling – Task 4a Report: Evaluation of the Initial CMAQ 2002 Annual Simulation”, prepared for the VISTAS Technical Analysis Committee, prepared by ENVIRON International Corporation, Alpine Geophysics, LLC, and the University of Califor nia, Riverside (CE-CERT). September 27.

Morris, R.E., B. Koo, S. Lau, D. McNally, T.W. Tesche, G. Tonnesen, Z. Wang and C-J. Chien. 2004c. Initial VISTAS Phase II 2002 CMAQ Operational Model Performance Evaluation. ENVIRON International Corporation, Novato, California. September 21. (http://pah.cert.ucr.edu/vistas/vistas2/ppt_files/Sept_2004_tawg/358,1,Slide 1)

Morris R.E., T.W. Tesche and G.S. Tonnesen. 2004. Quality Assurance Project Plan (Draft) for the Visibility Improvements for State and Tribal Associations for the Southeastern States (VISTAS) Emissions and Air Quality Mode ling. Revision 0. ENVIRON International Corporation, Novato, California. November 17.

Morris R.E., D.E. McNally, T.W. Tesche, G.S. Tonnesen, J.W. Boylan and P. Brewer. 2005. Preliminary Evaluation of the Community Multiscale Air Quality Model for 2002 over the

Southeastern United States. J. Air and Waste Manage. Assoc. 55 :1694-1708. November.

Morris R.E. and G.M. Stella. 2005. Quality Assura nce Project Plan (Draft) for the Association for Southeastern Integrated Planning (AIP) Emi ssions and Air Quality Modeling to Address 8-
Hour Ozone and PM 2.5 Nonattainment in the Southeastern States. Revision 0. ENVIRON International Corporation, Novato, California. November 28.

Morris, R.E., B. Koo, A. Guenther, G. Yarwood, D. McNally, T.W. Tesche, G. Tonnesen, J. Boylan and P. Brewer. 2006. Model Sensitivity Ev aluation for Organic Carbon using Two Multi-November 2007

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

### 6-8 Pollutant Air Quality Models that Simulate Regional Haze in the Southeastern United States.

Atmos. Env. 40 (2006) 4960-4972.

Odman, M. T.; Boylan, J.W.; Wilkinson, J.G.; and Russell, A.G. SAMI Air Quality Modeling Final Report. Southern Appalachian Mountains Initiative. 2002. http://www.saminet.org

.

Olerud, D. 2003a. “Evaluation Methodologies for Meteorological Modeling in Support of VISTAS (Visibility Improvement – State and Tribal Association): Task 1 Deliverables” (Draft) Baron Advanced Meteorological Systems, LLC, Re search Triangle Park, NC. (available at: http://www.baronams.com/projects/VISTAS/#reports ). April 16.

Olerud, D. 2003b. “Protocol for Annual MM5 Mode ling of VISTAS (Visibility Improvement – State and Tribal Association): Task 7c De liverable.” Baron Advanced Meteorological Systems, LLC, Research Tria ngle Park, NC. (available at: http://www.baronams.com/projects/VISTAS/#reports ). March 27.

Olerud, D. 2003c. “MM5 Sensitivity Modeling in Support of VISTAS (Visibility Improvement – State and Tribal Association): Task 2e Deliv erable” (Draft) Baron Advanced Meteorological Systems, LLC, Research Triangle Park, NC. 4 December 2003

Olerud, D. 2003d. “VISTAS Meteorological M odeling Sensitivity Results”, PowerPoint presentation at the 29-30 September 2003 VIST AS Technical Review Meeting (Research Triangle Park, NC), prepared by Baron Advan ced Meteorological Systems, LLC, Research Triangle Park, NC. 29 September 2003

Olerud, D and A. Sims. 2004. “Episode Selection from a Meteorological Perspective for VISTAS (Visibility Improvement – State and Tribal A ssociation): Task 3a Deliverable.” Baron Advanced Meteorological Systems, LLC, Re search Triangle Park, NC. (available at: http://www.baronams.com/projects/VISTAS/#reports ). February 9.

Park. R.J., Jacob, D.J., Chin, M., and Martin, R.V. (2003). Sources of carbonaceous aerosols over the United States and implications for natural visibility. Journal of Geophysical Research, Vol. 108 (D12), 4355, doi:10.1029/2002JD003190.

Park. R.J., Jacob, D.J., Field, B.D., Yantos ca, R.M., and Chin, M. (2004). Natural and transboundary pollution influences on sulfate-nitrate-ammonium aerosols in the United States: Implications for policy. Journal of Geophysical Research, Vol. 109, D15204,
doi:10.1029/2003JD004473.

Park. R.J., Jacob, D.J., Kumar, N., and Yantosca, R.M. (2006). Regional visibility statistics in the United States: Natural and transboundary pollution influences, and implications for the Regional Haze Rule. Atmospheric Environment, Vol. 40, pp. 5405-5423.

Pitchford, M. 2006. Natural Haze Levels II: Application of the New IMPROVE Algorithm to Natural Species. Final Report by the Natu ral Haze Levels II Committee to the RPO Monitoring/Data Analysis Workgroup. ( Marck.Pitchford@NOAA.gov ).November 2007

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

### 6-9 Pollack, A.K, L. Chan, P. Chandraker, J. Grant, C. Lindhjem, S. Rao, J. Russell and C. Tran. 2006.
WRAP Mobile Source Emission Inventories U pdate. ENVIRON International Corporation, Novato, California. May. ( http://www.wrapair.org/forums/ef/UMSI/ 0606_WRAP_Mobile_Source_EI_Final_Report.p

df

)

Russell, J., C. Lindhjem, B. Koo, R. Morris, C. Loomis, and G. Stella. 2006. Addition of Off-shore Marine Emissions to VISTAS Modeling. May 2006.

Schichtel, B.A., Gebhart, K.A., Malm, W.C., Barna, M.G., Pitchford, M.L., Knipping, E.M., and Tombach, I.H. (2005). Reconciliation and Interpretation of Big Bend National Park Sulfur Source Apportionment: Results from the Big Bend Regional Aerosol and Visibility Observational Study – Part I. Journal of the Air & Waste Management Assoc. Vol. 55, pp. 1709-1725.

Seigneur, C., et al. 2000. “Gui dance for the Performance Evaluation of Three-Dimensional Air Quality Modeling Systems for Particulate Matter and Visibility”, J. Air & Waste Manage.

Assoc. Vol. 50, pp. 588-599.

Solomon, P.S., T. Klamser-Williams , P. Egeghy, D. Crumpler and J. Rice. 2004. "STN/IMPROVE Comparison Study Preliminary Results”. Pres ented at PM Model Performance Workshop. Chapel Hill, NC. February 10.

Timin, B. 2002. “PM 2.5 

### and Regional Haze Modeling Guidance”. Prepared by the U.S. EPA/OAQPS. April 24.

Timin, B. 2004. “PM 2.5 

### Model Performance Evaluation: Purpose and Goals”. Presented at PM Model Evaluation Workshop, Chapel Hill, NC. February.

Timin, B. 2007. Final Ozone/PM2.5/Regional H aze Modeling Guidance Summary. Presented at Region IV Modeling Workshop. U.S. Environm ental Protection Agency, Office of Air Quality and Planning Standards, RTP, NC. March 28. ( http://www.epa.gov/region04/air/modeli ng/Wed%203-28-07/Timin%20-%20Finalguidance-summary-R4-v2.pdf )

Tombach, I. 2007a. Accounting for Transport of International Emissions in CMAQ Modeling of the VISTAS Region. July 26.

Tombach, I. 2007b. Application of GEOS -CHEM for 2002 Boundary Conditions for CMAQ Modeling of the VISTAS Region. October 5.

Xiu, A., and J.E. Pleim. 2000. Development of a land surface model. Part I: Application in a mesoscale meteorology model. Journal of Applied Meteorology , 40, 192-209.

### 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.A-7Figure A-6.VISTAS BaseG2 Elevated Fire Source Emissions Summary – 2002 Annual Domainwide Temporal Distribution – OLEA-8Table A.1– VISTAS BaseG2 Area Source Emissions Summary – 12km State Level Emission Totals (July 18, 2002).Stationary areaProcessed as Area sourcesBase inventory year 2002Gridding matrix applied for gridVISTA12_168X177No speciation matrix appliedTemporal factors applied for episode fromThursday July 18, 2002 at 000000 toThursday July 18, 2002 at 230000Annual total data basis in reportDate ; Region; State ; CO ; NOX ; VOC ; NH3 ; SO2 ; PM10 ; PM2_5 ; PMC; ; ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day]------------------------------------------------------------------------------------------------------------------------------------------------------------------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; 0.38889E+0307/18/2002; 005000;Arkansas ; 0.32214E+03; 0.59925E+02; 0.17541E+03; 0.39077E+03; 0.77625E+02; 0.93498E+03; 0.21273E+03; 0.72227E+0307/18/2002; 009000;Connecticut ; 0.74329E+01; 0.90859E+01; 0.12071E+03; 0.14411E+02; 0.53008E+00; 0.47080E+02; 0.78904E+01; 0.39190E+0207/18/2002; 010000;Delaware ; 0.57666E+01; 0.40146E+01; 0.23986E+02; 0.43931E+02; 0.12991E+01; 0.27233E+02; 0.43240E+01; 0.22909E+0207/18/2002; 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; 0.68518E+0107/18/2002; 012000;Florida ; 0.67168E+02; 0.70148E+02; 0.10590E+04; 0.99305E+02; 0.90880E+02; 0.71195E+03; 0.93626E+02; 0.61833E+0307/18/2002; 013000;Georgia ; 0.13696E+03; 0.78878E+02; 0.73790E+03; 0.24723E+03; 0.15909E+03; 0.80410E+03; 0.13605E+03; 0.66805E+0307/18/2002; 017000;Illinois ; 0.64822E+02; 0.54397E+02; 0.49044E+03; 0.15470E+03; 0.10554E+02; 0.18652E+04; 0.36050E+03; 0.15047E+0407/18/2002; 018000;Indiana ; 0.58957E+02; 0.53330E+02; 0.32822E+03; 0.21196E+03; 0.16105E+03; 0.17266E+04; 0.34249E+03; 0.13841E+0407/18/2002; 019000;Iowa ; 0.40775E+02; 0.42057E+01; 0.22173E+03; 0.54576E+03; 0.14195E+01; 0.13372E+04; 0.25094E+03; 0.10863E+0407/18/2002; 020000;Kansas ; 0.28911E+03; 0.29621E+02; 0.88596E+02; 0.43985E+02; 0.88975E+01; 0.70323E+03; 0.15211E+03; 0.55112E+0307/18/2002; 021000;Kentucky ; 0.77191E+02; 0.10531E+03; 0.23546E+03; 0.11256E+03; 0.99659E+02; 0.42778E+03; 0.81534E+02; 0.34625E+0307/18/2002; 022000;Louisiana ; 0.16404E+04; 0.28481E+03; 0.29980E+03; 0.19668E+03; 0.23245E+03; 0.88853E+03; 0.28283E+03; 0.60571E+0307/18/2002; 024000;Maryland ; 0.21134E+03; 0.21519E+02; 0.20890E+03; 0.66855E+02; 0.16790E+02; 0.18854E+03; 0.47790E+02; 0.14075E+0307/18/2002; 025000;Massachusetts ; 0.57736E+01; 0.38607E+01; 0.20534E+02; 0.56228E+01; 0.26920E+01; 0.16076E+02; 0.37205E+01; 0.12356E+0207/18/2002; 026000;Michigan ; 0.30982E+02; 0.40472E+02; 0.31871E+03; 0.10240E+03; 0.71099E+02; 0.74950E+03; 0.13415E+03; 0.61535E+0307/18/2002; 027000;Minnesota ; 0.20006E+02; 0.14727E+02; 0.66111E+02; 0.11717E+03; 0.38086E+01; 0.61705E+03; 0.10937E+03; 0.50769E+0307/18/2002; 028000;Mississippi ; 0.42895E+01; 0.22679E+01; 0.30101E+03; 0.14251E+03; 0.57813E+00; 0.55921E+03; 0.81543E+02; 0.47766E+0307/18/2002; 029000;Missouri ; 0.20959E+03; 0.64235E+02; 0.27642E+03; 0.35350E+03; 0.12071E+03; 0.33762E+04; 0.58408E+03; 0.27922E+0407/18/2002; 031000;Nebraska ; 0.32840E+00; 0.66370E-01; 0.10041E+01; 0.17089E+01; 0.25804E-01; 0.11599E+02; 0.21084E+01; 0.94902E+0107/18/2002; 034000;New Jersey ; 0.14231E+02; 0.21007E+02; 0.30461E+03; 0.48417E+02; 0.79506E+01; 0.65704E+02; 0.13140E+02; 0.52564E+0207/18/2002; 036000;New York ; 0.48058E+02; 0.93906E+02; 0.58074E+03; 0.10722E+03; 0.13600E+03; 0.37438E+03; 0.64907E+02; 0.30947E+0307/18/2002; 037000;North Carolina ; 0.35788E+03; 0.69702E+02; 0.41336E+03; 0.47466E+03; 0.86454E+01; 0.36327E+03; 0.62859E+02; 0.30041E+0307/18/2002; 039000;Ohio ; 0.33951E+02; 0.40539E+02; 0.46160E+03; 0.19957E+03; 0.22287E+02; 0.10241E+04; 0.19312E+03; 0.83099E+0307/18/2002; 040000;Oklahoma ; 0.13292E+03; 0.56404E+02; 0.51844E+02; 0.10585E+03; 0.69006E+01; 0.68486E+03; 0.12595E+03; 0.55891E+0307/18/2002; 042000;Pennsylvania ; 0.31625E+03; 0.55051E+02; 0.54914E+03; 0.22572E+03; 0.73776E+02; 0.59936E+03; 0.11511E+03; 0.48427E+0307/18/2002; 044000;Rhode Island ; 0.81695E+01; 0.41245E+01; 0.69566E+02; 0.18260E+01; 0.41886E+01; 0.11126E+02; 0.25125E+01; 0.86136E+0107/18/2002; 045000;South Carolina ; 0.10702E+03; 0.46035E+02; 0.30912E+03; 0.76314E+02; 0.30258E+02; 0.31972E+03; 0.52325E+02; 0.26739E+0307/18/2002; 046000;South Dakota ; 0.00000E+00; 0.00000E+00; 0.35041E-02; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 047000;Tennessee ; 0.75720E+02; 0.30086E+02; 0.35511E+03; 0.82795E+02; 0.77852E+02; 0.38690E+03; 0.77792E+02; 0.30911E+0307/18/2002; 048000;Texas ; 0.28857E+03; 0.15916E+03; 0.36398E+03; 0.16190E+03; 0.91386E+02; 0.25000E+04; 0.42791E+03; 0.20721E+0407/18/2002; 051000;Virginia ; 0.16641E+03; 0.11720E+03; 0.32211E+03; 0.12067E+03; 0.29423E+03; 0.26361E+03; 0.48840E+02; 0.21477E+0307/18/2002; 053000;Washington ; 0.00000E+00; 0.00000E+00; 0.47969E-02; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 054000;West Virginia ; 0.36476E+02; 0.26373E+02; 0.14474E+03; 0.31362E+02; 0.26887E+02; 0.74822E+02; 0.20668E+02; 0.54154E+0207/18/2002; 055000;Wisconsin ; 0.37344E+02; 0.17641E+02; 0.21000E+03; 0.20515E+03; 0.53226E+01; 0.31806E+03; 0.64998E+02; 0.25306E+03##################################################################################################################################################################A-9Table A.2 –VISTAS BaseG2 Point Source Emissions Summary – Facility-Stack Level Emission Totals.Processed as Point sourcesBase inventory year 2002No gridding matrix appliedNo speciation matrix appliedNo temporal factors appliedAnnual total data basis in reportSource 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 ; PM2_5 ; PMC; ; ; ; ; ; ; m ; m ; deg K ; m/s ; ; [tons/day] ; [tons/day] ; [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; 0.27397E-06; 0.27397E-061443; 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; 0.27397E-06; 0.27397E-061444; 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; 0.27397E-06; 0.27397E-061445; 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; 0.27397E-06; 0.27397E-061446; 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; 0.27397E-06; 0.27397E-061447; 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; 0.27397E-06; 0.27397E-061448; 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; 0.27397E-06; 0.27397E-061449; 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; 0.27397E-06; 0.27397E-061450; 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; 0.27397E-06; 0.27397E-061451; 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; 0.27397E-06; 0.27397E-061452; 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; 0.27397E-06; 0.27397E-061453; 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; 0.27397E-06; 0.27397E-061454; 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; 0.27397E-06; 0.27397E-061455; 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; 0.27397E-06; 0.27397E-061456; 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; 0.27397E-06; 0.27397E-061457; 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; 0.27397E-06; 0.27397E-061458; 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; 0.27397E-06; 0.27397E-061459; 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; 0.27397E-06; 0.27397E-061460; 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; 0.27397E-06; 0.27397E-061461; 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; 0.27397E-06; 0.27397E-061462; 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; 0.27397E-06; 0.27397E-061463; 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; 0.27397E-06; 0.27397E-061464; 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; 0.27397E-06; 0.27397E-061465; 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; 0.27397E-06; 0.27397E-061466; 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; 0.27397E-06; 0.27397E-061467; 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; 0.27397E-06; 0.27397E-061468; 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; 0.27397E-06; 0.27397E-061469; 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; 0.27397E-06; 0.27397E-061470; 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; 0.27397E-06; 0.27397E-061471; 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; 0.27397E-06; 0.27397E-061472; 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; 0.27397E-06; 0.27397E-061473; 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; 0.27397E-06; 0.27397E-061474; 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; 0.27397E-06; 0.27397E-061475; 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; 0.27397E-06; 0.27397E-061476; 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; 0.27397E-06; 0.27397E-061477; 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; 0.27397E-06; 0.27397E-061478; 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; 0.27397E-06; 0.27397E-061479; 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; 0.27397E-06; 0.27397E-061480; 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; 0.27397E-06; 0.27397E-061481; 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; 0.27397E-06; 0.27397E-061482; 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; 0.27397E-06; 0.27397E-061483; 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; 0.27397E-06; 0.27397E-061484; 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; 0.27397E-06; 0.27397E-061485; 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; 0.27397E-06; 0.27397E-061486; 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; 0.27397E-06; 0.27397E-061487; 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; 0.27397E-06; 0.27397E-061488; 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; 0.27397E-06; 0.27397E-061489; 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; 0.27397E-06; 0.27397E-061490; 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; 0.27397E-06; 0.27397E-061491; 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; 0.27397E-06; 0.27397E-061492; 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; 0.27397E-06; 0.27397E-061493; 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; 0.27397E-06; 0.27397E-061494; 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; 0.27397E-06; 0.27397E-061495; 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; 0.27397E-06; 0.27397E-061496; 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; 0.27397E-06; 0.27397E-061497; 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; 0.27397E-06; 0.27397E-061498; 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; 0.27397E-06; 0.27397E-061499; 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; 0.27397E-06; 0.27397E-061500; 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; 0.27397E-06; 0.27397E-061501; 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; 0.27397E-06; 0.27397E-061502; 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; 0.27397E-06; 0.27397E-061503; 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; 0.27397E-06; 0.27397E-061504; 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; 0.27397E-06; 0.27397E-061505; 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; 0.27397E-06; 0.27397E-061506; 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; 0.27397E-06; 0.27397E-061507; 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; 0.27397E-06; 0.27397E-061508; 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; 0.27397E-06; 0.27397E-061509; 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; 0.27397E-06; 0.27397E-061510; 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; 0.27397E-06; 0.27397E-061511; 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; 0.27397E-06; 0.27397E-061512; 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; 0.27397E-06; 0.27397E-061513; 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; 0.27397E-06; 0.27397E-061514; 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; 0.27397E-06; 0.27397E-061515; 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; 0.27397E-06; 0.27397E-061516; 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; 0.27397E-06; 0.27397E-061517; 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; 0.27397E-06; 0.27397E-061518; 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; 0.27397E-06; 0.27397E-061519; 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; 0.27397E-06; 0.27397E-06A-10Table A.3 –VISTAS BaseG2 Point Source Emissions Summary – Hourly SCC Level Emission Totals.Processed as Point sourcesBase inventory year 2002No gridding matrix appliedNo speciation matrix appliedTemporal factors applied for episode fromThursday July 18, 2002 at 000000 toFriday July 19, 2002 at 000000Annual total data basis in reportDate ; Hour; SCC ; CO ; NOX ; VOC ; NH3 ; SO2 ; PM10 ; PM2_5 ; PMC; ; ; [tons/hr] ; [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; 0.00000E+00; 0.67371E-0107/18/2002; 01;0010100101; 0.23717E+00; 0.64409E+00; 0.18311E+00; 0.86984E-05; 0.15537E+01; 0.36883E-01; 0.12885E-01; 0.23998E-0107/18/2002; 01;0010100102; 0.49310E-04; 0.29138E-03; 0.74712E-05; 0.00000E+00; 0.00000E+00; 0.89654E-05; 0.53643E-05; 0.36011E-0507/18/2002; 01;0010100201; 0.12337E+01; 0.18448E+02; 0.21384E+00; 0.11847E-02; 0.45404E+02; 0.11176E+01; 0.63193E+00; 0.48564E+0007/18/2002; 01;0010100202; 0.13432E+02; 0.13367E+03; 0.76286E+00; 0.13934E+00; 0.39127E+03; 0.77426E+01; 0.34779E+01; 0.43236E+0107/18/2002; 01;0010100203; 0.15106E+01; 0.41001E+02; 0.22772E+00; 0.10565E-01; 0.57875E+02; 0.10036E+01; 0.62171E+00; 0.38190E+0007/18/2002; 01;0010100204; 0.46840E+00; 0.11215E+01; 0.51058E-02; 0.18008E-03; 0.37646E+01; 0.13055E+00; 0.52570E-01; 0.77985E-0107/18/2002; 01;0010100205; 0.17514E+00; 0.40535E+00; 0.12817E-02; 0.18564E-04; 0.10426E+01; 0.49851E-01; 0.84878E-02; 0.41363E-0107/18/2002; 01;0010100211; 0.12032E+00; 0.71511E-01; 0.14337E-01; 0.00000E+00; 0.56604E+00; 0.80955E-01; 0.27104E-02; 0.78245E-0107/18/2002; 01;0010100212; 0.27195E+01; 0.35507E+02; 0.31254E+00; 0.25909E+00; 0.14595E+03; 0.26430E+01; 0.11402E+01; 0.15028E+0107/18/2002; 01;0010100217; 0.59853E+00; 0.73339E+00; 0.22997E-01; 0.78953E-03; 0.21109E+01; 0.78834E-01; 0.39504E-01; 0.39330E-0107/18/2002; 01;0010100218; 0.12248E+00; 0.29317E+00; 0.28785E-02; 0.81146E-03; 0.64975E+00; 0.10895E-01; 0.18197E-02; 0.90749E-0207/18/2002; 01;0010100221; 0.71317E+00; 0.93035E+01; 0.59317E-01; 0.17286E-03; 0.10284E+02; 0.17314E+00; 0.77331E-01; 0.95812E-0107/18/2002; 01;0010100222; 0.71637E+01; 0.65497E+02; 0.59207E+00; 0.11337E+00; 0.11145E+03; 0.29680E+01; 0.99985E+00; 0.19681E+0107/18/2002; 01;0010100223; 0.80563E+00; 0.18628E+02; 0.16394E+00; 0.58958E-02; 0.19546E+02; 0.33507E+00; 0.28768E+00; 0.47390E-0107/18/2002; 01;0010100224; 0.30668E+00; 0.59444E+00; 0.30663E-02; 0.42486E-05; 0.45436E+00; 0.27206E+00; 0.45352E-02; 0.26752E+0007/18/2002; 01;0010100225; 0.84370E-01; 0.80214E-01; 0.55180E-03; 0.00000E+00; 0.12985E+00; 0.36873E-01; 0.51351E-03; 0.36359E-0107/18/2002; 01;0010100226; 0.37955E+01; 0.49513E+02; 0.46960E+00; 0.55985E+00; 0.90938E+02; 0.36303E+01; 0.15562E+01; 0.20741E+0107/18/2002; 01;0010100238; 0.00000E+00; 0.37761E-03; 0.00000E+00; 0.19097E-05; 0.80068E-03; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 01;0010100301; 0.78828E+01; 0.54602E+01; 0.84065E-01; 0.34718E-02; 0.15707E+02; 0.39028E+00; 0.17150E+00; 0.21878E+0007/18/2002; 01;0010100302; 0.16107E+02; 0.12727E+02; 0.14625E+00; 0.25457E-01; 0.49491E+02; 0.13062E+01; 0.83217E+00; 0.47408E+0007/18/2002; 01;0010100303; 0.28142E+00; 0.56976E+01; 0.55583E-01; 0.16925E-01; 0.88036E+01; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 01;0010100306; 0.19797E-01; 0.00000E+00; 0.11458E-03; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 01;0010100317; 0.52071E-02; 0.00000E+00; 0.82754E-03; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 01;0010100318; 0.00000E+00; 0.11651E+00; 0.00000E+00; 0.81850E-03; 0.27784E+00; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 01;0010100401; 0.24586E+01; 0.76006E+01; 0.19600E+00; 0.61335E-01; 0.11806E+02; 0.46239E+00; 0.34598E+00; 0.11642E+0007/18/2002; 01;0010100404; 0.48699E+00; 0.33056E+01; 0.82921E-01; 0.47267E-01; 0.10960E+02; 0.32426E+00; 0.22947E+00; 0.94788E-0107/18/2002; 01;0010100405; 0.27744E-04; 0.31632E-03; 0.35943E-05; 0.19814E-05; 0.89624E-03; 0.46775E-04; 0.35290E-04; 0.11485E-0407/18/2002; 01;0010100501; 0.10665E+01; 0.98314E+01; 0.11157E+00; 0.14510E-01; 0.41097E+01; 0.35716E+00; 0.24833E+00; 0.10887E+0007/18/2002; 01;0010100504; 0.75882E-02; 0.66438E-01; 0.21897E-02; 0.10574E-06; 0.15788E-01; 0.20965E-02; 0.19289E-02; 0.16766E-0307/18/2002; 01;0010100505; 0.61880E-03; 0.25294E-02; 0.34996E-04; 0.14639E-04; 0.13508E-02; 0.44740E-03; 0.66140E-04; 0.38126E-0307/18/2002; 01;0010100600; 0.96495E-04; 0.10518E-03; 0.60310E-05; 0.00000E+00; 0.53856E-06; 0.60310E-05; 0.60310E-05; 0.00000E+0007/18/2002; 01;0010100601; 0.87846E+01; 0.21074E+02; 0.70139E+00; 0.15155E+00; 0.99403E+01; 0.14958E+01; 0.13850E+01; 0.11236E+0007/18/2002; 01;0010100602; 0.50297E+00; 0.70762E+00; 0.32526E-01; 0.11195E-01; 0.35912E+00; 0.11366E+00; 0.96447E-01; 0.18402E-0107/18/2002; 01;0010100604; 0.10116E+01; 0.56493E+01; 0.18644E+00; 0.65348E-01; 0.89274E+00; 0.61273E+00; 0.27600E+00; 0.33673E+0007/18/2002; 01;0010100701; 0.11273E+00; 0.82767E+00; 0.17431E-01; 0.32131E-05; 0.12597E-01; 0.93832E-01; 0.77319E-01; 0.16512E-0107/18/2002; 01;0010100702; 0.51429E-01; 0.64817E-01; 0.31871E-02; 0.00000E+00; 0.17190E+00; 0.13937E-01; 0.79361E-02; 0.60013E-0207/18/2002; 01;0010100711; 0.16037E-02; 0.12431E-01; 0.10496E-03; 0.60163E-04; 0.17647E-03; 0.15795E-03; 0.15795E-03; 0.00000E+0007/18/2002; 01;0010100801; 0.81893E-01; 0.22331E+00; 0.92392E-02; 0.00000E+00; 0.70142E+00; 0.28780E+00; 0.28444E+00; 0.44545E-0207/18/2002; 01;0010100901; 0.12543E+00; 0.73472E-01; 0.22990E-01; 0.00000E+00; 0.34409E-01; 0.25949E-01; 0.12759E-01; 0.13189E-0107/18/2002; 01;0010100902; 0.24309E+01; 0.91108E+00; 0.64300E-01; 0.88651E-02; 0.34429E+00; 0.10444E+00; 0.80967E-01; 0.23477E-0107/18/2002; 01;0010100903; 0.10181E+01; 0.52995E+00; 0.67198E-01; 0.33635E-03; 0.13256E+00; 0.11066E+00; 0.96558E-01; 0.14106E-0107/18/2002; 01;0010100911; 0.29619E+00; 0.16035E+00; 0.12301E-01; 0.12042E-03; 0.15444E-02; 0.10733E-01; 0.75779E-02; 0.31546E-0207/18/2002; 01;0010101001; 0.98770E-06; 0.19367E-05; 0.77818E-07; 0.00000E+00; 0.00000E+00; 0.21838E-09; 0.00000E+00; 0.21838E-09A-11Table A.4 –VISTAS BaseG2 Onroad Mobile Source Emissions Summary – Daily County Level Speciated Emission Totals.Processed as Mobile sourcesBase inventory year 2018No gridding matrix appliedNo speciation matrix appliedTemporal factors applied for episode fromThursday July 18, 2002 at 000000 toThursday July 18, 2002 at 230000Annual total data basis in reportDate ; Region; State ; County ; EXR__CO ; EXR__NOX ; EXR__TOG ; EXR__SO4 ; EXR__OCARB25 ; EXR__OCARBPMC; EXR__ECARB25 ;EXR__ECARBPMC; EXR__GASPM25 ; EXR__GASPMC ; EXR__SO2 ; EXR__NH3 ; EXS__CO ; EXS__NOX ; EXS__TOG ; HOT__TOG ; DNL__TOG ; RST__TOG ; EVR__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] ; [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; 0.25566E-03; 0.57158E-02;0.49703E-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; 0.13731E+00; 0.17125E+00;0.19892E-01; 0.12934E-01; 0.17487E-01; 0.60055E-02; 0.18017E-01; 0.61950E-0107/18/2002; 001003;Alabama ; Baldwin Co ; 0.37965E+02; 0.43607E+01; 0.14322E+01; 0.39511E-02; 0.11914E-01; 0.10360E-02; 0.23174E-01;0.20151E-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; 0.49208E+00; 0.51809E+00;0.71577E-01; 0.47153E-01; 0.63751E-01; 0.22368E-01; 0.67105E-01; 0.22387E+0007/18/2002; 001005;Alabama ; Barbour Co ; 0.80254E+01; 0.87307E+00; 0.38301E+00; 0.97693E-03; 0.20218E-02; 0.17580E-03; 0.39285E-02;0.34161E-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; 0.10742E+00; 0.20953E+00;0.15740E-01; 0.10112E-01; 0.13671E-01; 0.45855E-02; 0.13757E-01; 0.48623E-0107/18/2002; 001007;Alabama ; Bibb Co ; 0.44676E+01; 0.54423E+00; 0.19802E+00; 0.50455E-03; 0.15706E-02; 0.13658E-03; 0.30556E-02;0.26571E-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; 0.61371E-01; 0.85463E-01;0.90342E-02; 0.59898E-02; 0.80983E-02; 0.28642E-02; 0.85925E-02; 0.28245E-0107/18/2002; 001009;Alabama ; Blount Co ; 0.11195E+02; 0.13462E+01; 0.46810E+00; 0.12197E-02; 0.38412E-02; 0.33402E-03; 0.74729E-02;0.64982E-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; 0.15025E+00; 0.18901E+00;0.21962E-01; 0.14563E-01; 0.19690E-01; 0.69726E-02; 0.20918E-01; 0.68770E-0107/18/2002; 001011;Alabama ; Bullock Co ; 0.28452E+01; 0.35569E+00; 0.13313E+00; 0.33076E-03; 0.10242E-02; 0.89058E-04; 0.19925E-02;0.17326E-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; 0.39987E-01; 0.60101E-01;0.58884E-02; 0.39044E-02; 0.52788E-02; 0.18671E-02; 0.56014E-02; 0.18422E-0107/18/2002; 001013;Alabama ; Butler Co ; 0.11799E+02; 0.12890E+01; 0.43964E+00; 0.11955E-02; 0.32843E-02; 0.28560E-03; 0.63862E-02;0.55532E-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; 0.14917E+00; 0.17458E+00;0.21468E-01; 0.14006E-01; 0.18937E-01; 0.65433E-02; 0.19630E-01; 0.66939E-0107/18/2002; 001015;Alabama ; Calhoun Co ; 0.48673E+02; 0.52443E+01; 0.25748E+01; 0.60386E-02; 0.12160E-01; 0.10574E-02; 0.23633E-01;0.20550E-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; 0.63012E+00; 0.15867E+01;0.90452E-01; 0.58436E-01; 0.79006E-01; 0.26688E-01; 0.80063E-01; 0.27938E+0007/18/2002; 001017;Alabama ; Chambers Co ; 0.77831E+01; 0.86810E+00; 0.31296E+00; 0.85048E-03; 0.21927E-02; 0.19067E-03; 0.42629E-02;0.37068E-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; 0.10375E+00; 0.12973E+00;0.15154E-01; 0.98375E-02; 0.13300E-01; 0.45506E-02; 0.13652E-01; 0.47135E-0107/18/2002; 001019;Alabama ; Cherokee Co ; 0.55946E+01; 0.68518E+00; 0.26212E+00; 0.64557E-03; 0.19666E-02; 0.17101E-03; 0.38260E-02;0.33269E-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; 0.76815E-01; 0.12223E+00;0.11255E-01; 0.74697E-02; 0.10099E-01; 0.35749E-02; 0.10725E-01; 0.35201E-0107/18/2002; 001021;Alabama ; Chilton Co ; 0.13349E+02; 0.14880E+01; 0.49241E+00; 0.13590E-02; 0.38839E-02; 0.33773E-03; 0.75533E-02;0.65681E-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; 0.16894E+00; 0.18268E+00;0.24336E-01; 0.15952E-01; 0.21566E-01; 0.75089E-02; 0.22527E-01; 0.76024E-0107/18/2002; 001023;Alabama ; Choctaw Co ; 0.35209E+01; 0.43252E+00; 0.15894E+00; 0.40062E-03; 0.12502E-02; 0.10872E-03; 0.24323E-02;0.21150E-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; 0.48814E-01; 0.69629E-01;0.71905E-02; 0.47676E-02; 0.64457E-02; 0.22798E-02; 0.68393E-02; 0.22502E-0107/18/2002; 001025;Alabama ; Clarke Co ; 0.59226E+01; 0.68641E+00; 0.25458E+00; 0.67197E-03; 0.18723E-02; 0.16280E-03; 0.36413E-02;0.31663E-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; 0.79823E-01; 0.11274E+00;0.11755E-01; 0.77142E-02; 0.10430E-01; 0.36290E-02; 0.10887E-01; 0.36618E-0107/18/2002; 001027;Alabama ; Clay Co ; 0.32368E+01; 0.39861E+00; 0.15935E+00; 0.38110E-03; 0.11363E-02; 0.98807E-04; 0.22106E-02;0.19223E-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; 0.44385E-01; 0.78972E-01;0.64717E-02; 0.42994E-02; 0.58128E-02; 0.20594E-02; 0.61781E-02; 0.20236E-0107/18/2002; 001029;Alabama ; Cleburne Co ; 0.84659E+01; 0.97428E+00; 0.30666E+00; 0.83602E-03; 0.26955E-02; 0.23439E-03; 0.52440E-02;0.45600E-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; 0.10589E+00; 0.10401E+00;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 resu lts from the Community Multiscale Air Quality (CMAQ) modeling system Versi on 4.5 with SOAmods enhancement across the VISTAS states and VISTAS 12 km grid. The CMAQ results are compared with observational data from the

I nteragency M onitoring of PRO tected V isual E nvironments (IMPROVE), S peciated T rends

N etwork (STN), C lean A ir S tatus T rends Net work (CASTNet), F ederal R eference M ethod (FRM) PM 2.5 

### mass, N ational A cid D eposition P rogram (NADP) and S outh E ast A erosol

R esearch and CH aracterization (SEARCH) study monitori ng networks. The evaluation will primarily focus on the air quality model’s performance with respect to individual components of fine particulate matter (PM 2.5 

### ), as good model performance of the PM component species will dictate good model performance of total or reconstitu ted fine particulate matter (i.e., visibility). The FRM networks only collects total PM 2.5 

### 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 to tal light extinction will also be provided as a means to discuss the overall model performance.

B.2 CMAQ Evaluation Methodology

EPA’s integrated ozone, PM 2.5 

### and regional haze modeling guida nce calls for a comprehensive, multi-layered approach to model performance testi ng, consisting of the four major components: operational, diagnostic, mechanistic (or sc ientific) and probabilistic (EPA, 2007a).

B.2.1 Evaluation Approach

The CMAQ model performance evaluation effort fo cused 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 PM 10 

### and PM 2.5 

### including the quantities used to characterize visibility (i.e., sulfate, nitrate, ammonium, organic carbon, elemental carbon, other PM 2.5 

### , and coarse matter (PM 2.5-10 

### ). This evaluation examines whether the measurements are properly represented by th e 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 NH 3 

### ) 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 perf ormance of diagnostic tests to better understand model performance and identify potential flaws in the modeling system that can be corrected. The diagnostic evaluation may al so includes the use of “probing tools” to understand why the model obtains a given prediction; probing tools in clude Process Analysis (PA), decoupled direct method (DDM) and source apportionment (SA).

In this final model performa nce evaluation for the VISTAS 2002 Actual Base G2 CMAQ 36/12 km base case simulation, the operational evaluati on has been given the gr eatest 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 aero sol concentration distributions. In the course of the VISTAS study numerous diagnostic sensitiv ity tests were performed to investigate and improve model performance. Key diagnostic tests performed ar e 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

PM 2.5 

### . Regional haze is measured by an extinction coefficient (b ext 

### ), which represents the light attenuation resulting from scatte ring and absorption of light from ambient particulate matter plus scattering of light due to gas molecules in the air (i.e., Raylei gh scattering).

Some scattering does occur from air molecules; ho wever, the bulk of light extinction is caused by the presence of suspended particles, partic ularly fine particle s (aerodynamic mass <2.5 μ m). 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., PM 10 

### - PM 2.5 

### 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 sout heastern is on the PM components that can cause visibility impairment. The prim ary 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 PM 2.5 

### and PM 10 

### Mass Networks (EPA-FRM); (e) EPA Speci ation Trends Network (STN) of PM 2.5 

### species; and (f) National Acid Deposition Network (NADP). Thes e PM monitoring networks may also provide ozone and other gas phase precursor s and product species, and visi bility measurements at some sites. There are also gas-phase criteria polluta nt measures from the EPA’s Air Quality System (AQS) that are available for use. However, thes e sites are urban-oriented and not as useful for the regional evaluation of the CMAQ modeli ng system. The study team has performed a detailed evaluation of the CMAQ 2002 base case si mulation 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 loca tions of the monitoring sites for the IMPROVE, STN, CASTNet, NADP and SEARCH monitori ng 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 PM 2.5 and PM 10 1 in 3 days; 24 hr
CASTNET Speciated PM 2.5 , Ozone Hourly, Weekly; 1 hr, Week

SEARCH 24-hr PM 25 (FRM Mass, OC, BC, SO 4 , NO 3 , NH 4 , Elem.); 24-hr PM coarse (SO 4 , NO 3 , NH 4 , elements); Hourly PM 2.5 (Mass, SO 4 , NO 3 , NH 4 , EC, TC); and Hourly gases (O 3 , NO, NO 2 , NO y , HNO 3 , SO 2 , CO) Daily, Hourly;

NADP WSO 4 , WNO 3 , WNH 4 Weekly

EPA-FRM Only total fine mass (PM 2.5 ) 1 in 3 days; 24 hr
EPA-STN Speciated PM 2.5 Varies; Varies AIRS/AQS CO, NO, NO 2 , NO
x , O 3 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 we re calculated and evaluated for all the monitors within the VISTAS region and within each VISTAS state. The statistical
measures selected were based on the reco mmendations outlined in section 18.4.1 of the
USEPA’s Guidance On The Use Of Models And Ot her 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 mont hly scatter plots, time series plots and model performance statistical measures from the U CR Analysis Tool appli cation to the 2002 CMAQ Base G2 Actual base case simulation in an opera tional 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 pl ots of predicted and observed SO4 concentrations or wet depositions for January 2002, sites in the VI STAS 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) m odeling results. The IMPROVE and STN SO4 concentrations are 24-hour averages whereas the CASTNet SO4 concen trations 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 w ith low fractional bias (-14%) and gross error (34%-38%) that almost achieves the most string ent model performance ev aluation goals (Table C-1XXX). There is a larger SO4 concentration underestima tion 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 pr oduces 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 pr ecipitation during synoptic rain events as o ccur in the winter, however it exhibited less skill in predicting prec ipitation during the summer convective events.

Figure B-2b present quantile-qua ntile (Q-Q) plots for January 2002 SO4 concentration and wet deposition model performance. Q-Q plots co mpare the frequency distribution between the predicted and observed SO4 concentrations/d epositions. Across the IMPROVE network the model slightly underestimates the frequency dist ribution 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 th e mixed urban/non-urban SEARCH network the model reproduces the observed frequency distribution of SO4 concentra tions quite well, except for overestimating the highest values above ap proximately 4 ug/m3. Finally, the observed wet SO4 deposition in the VISTAS region and Janu ary 2002 is matched exactly by the model except for the very highest events that are underpre dicted. 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 th e 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 CASTNe t network in April 2002, both the 36 km and 12 km versions of the model exhibits a higher undere stimations bias (-25% to -26%), but low error (27% to 28%), which is due to the less variabi lity 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 con centrations and deposition mode l performance in April shown in Figure C-3b indicates that the model does a reasonable job in re producing the observed frequency distribution, although with a slight underprediction bias except for the highest values that are either reproduced fairly well (IMPR OVE 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 regi on 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%), ST N (-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 e xhibits an overestimation bias of 39% to 45%
and fractions errors of ~90%. The overestima tion of the July 2002 SO4 wet deposition in the VISTAS region is consistent with the MM5 me teorological model evaluation that found that MM5 overstated the amount of summer pr ecipitation due to convective activity.

The model reproduces the observed frequency di stribution of SO4 con centrations 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 mode l performance, although with an underestimation
bias for STN and overestimation bias for SEA RCH at the high end. The observed frequency distribution of SO4 wet deposi tion 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 acro ss the IMPROVE (0% to 4%) and STN (-6% to - 15%) and fractional errors of 33% to 48% (Fi gure 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 approximate ly 30% (i.e., meeting the most stringent ozone model performance goals). Alt hough the SO4 wet deposition model performance bias is fairly low (~20%) there is lots of sca tter producing large errors (~85%).

The Q-Q plot comparisons (Figure C-5b) indi cate that the model overstates both the SO4 concentrations and deposition distribution in Oc tober 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 concen tration fractional bias and error across the VISTAS region for the IMPROVE, STN, CAST Net, SEARCH Daily and SEARCH Hourly monitoring networks. Note that these monthly model performance statistics were calculated across the VISTAS 12 km grid versus just th e VISTAS states for used in the monthly performance evaluation in Figures B-2 thr ough B-5. The underprediction bias in SO4 concentrations is clearly evid ent across most of the year a nd monitoring networks with the lowest SO4 bias in October. However, this unde rprediction 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 ove rprediction SO4 bas for several months.

The SO4 monthly fractional error values are usually under 40% a nd, with one exception, always under 50% (Figure B-6a). The exception is th e SEARCH Hourly network with error values ranging from 50% to 70%.

Figure B-6b displays Bugle Plots of monthly SO4 fractional bias a nd error. Bugle Plots compare the monthly bias and error values with a perf ormance goal and performa nce criteria and accounts for the concept that better performance is needed (i.e., more stringent model performance goals and criteria) for species with hi gher concentrations/ext inctions and that performance goals and criteria can be relaxed as the species concentra tions become low and are not longer an important component of the PM or extinction budget (see Ch apter 3 and Appendix C for more discussion). The Bugle Plots in Figure B-6b confirm the previ ous analysis that SO4 model performance tends to be good across all months and, with one ex ception, always achieving the model performance goal for both bias and error. The one excepti on 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 SEA RCH (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 P erformance in the VISTAS Region

The regional model performance evaluation for nitr ate (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 ve ry similar for the 36 km and 12 km CMAQ Base G2
simulations for the STN and CASTNet monitori ng 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 bi as (-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 obs erved January NO3 fre quency distributions indicate that although th ere 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 overest imation of the highest observed NO3 concentrations in January occurs for the IMPROVE, CASTNet and NADP netw orks, 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 mon itoring network with bias values of -9% and - 18% for the CMAQ 36 km and 12 km base case simu lations 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 Ap ril 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 char acterized by a large underprediction bias with
fractional bias values of - 150% to -160% across the IMPROV E 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 partic ulate 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 (Fi gure B-10a). This negative bias is due to the model predicting very low near zero NO3 concen trations 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 a ppears 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 particul ate NO3 in the weekly sample. The magnitude of the NO3 wet deposition observed values is reprodu ced 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 depo sition 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 underp rediction 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 exhibi t a seasonal dependence with an 80% error in the winter peaking
to a 160% error in the summer with a bell-like distribution. At fi rst glance it appears that the summer underprediction bias would a dversely affect the regional haze modeling as that is when a vast majority of the worst 20 percent days o ccur at Class I areas in the VISTAS region. However, as shown in the Bugle Plots in Fi gure B-11b, these large summer underprediction bias
values occur when NO3 concentrations are lo w (< 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 performan ce 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 SEA RCH 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 (O MC) Monthly Model Performanc e in the VISTAS Region

The CMAQ model simulates organic matter carbo n (PMC) that includes both primary and secondary organic aerosol (OA) that incl udes carbon, oxygen and other compounds. However, the monitoring networks measure just the orga nic carbon (OC) portion of the OMC. Thus, the
OC measurements need to be converted to OM C 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 origin al IMPROVE equation uses a 1.4 OMC/OC ratio, whereas the new IMPROVE equation uses a 1.8 OM C/OC ratio. In this model performance
evaluation we used a 1.4 OMC/OC ratio to conver t 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 met hodologies 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 fo r 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 ru ral 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 mi xed 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 wa s 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 overestimat es 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 SE ACH 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) usi ng 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%) monitori ng 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 underpredic tion bias. Unlike January and Apr il, 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 acro ss 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 fo r the 12 km CMAQ base case simulation in Figure B-16a displays fairly good performance across th e 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%. Pa rt 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 co arse 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 Mode l Performance in the VISTAS Region

Elemental carbon is measured at the IMPR OVE, 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 bi as < ±15% across the
IMPROVE and STN networks and errors of ~45% (Figure B-17). A larger underprediction bias and higher errors are seen across th e 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 ST N 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 tota l 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 VIST AS 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 in teresting to note that EC model performance at the urban STN and partially urban SEARCH netw orks 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 elem ental 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 tota l 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 Octobe r (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 statistic s 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 underp rediction 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 a lthough the EC underprediction produces large fractional bias values, because the EC concentrat ions are low the EC performance achieves the model performance goal for all months and networks since it is in the flar ed 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 PM 2.5 

### (Soil) Monthly Model Perfor mance in the VISTAS Region

There are potential model-measurement inco mmensurability issues with the Soil model performance. The IMPROVE and SEARCH networ ks builds up Soil from the elements, whereas in the model Soil is mapped to the other PM 2.5 

### category that is emissi ons 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 bi as is not as large in April and no longer systematic (i.e., doesn’t oc cur 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 return s in October with fractional bias of ~100% (IMPROVE) to ~140% (SEARCH).

### B-56 Figure B-22. Comparison of predicted and observed other PM 2.5 

### (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 Da ily (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 PM 2.5 

### (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 PM 2.5 

### (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 PM 2.5 

### (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 Da ily (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 Figur e B-22 through B-25, the modeled Soil values tend
to be between 0 and 2 μ g/m 3 

### year round, however the observed values are much lower in the winter (0 to 0.5 μ g/m 3 

### ) 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 adjust ment 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 seasona l dependence of the Soil model performance with the model achieving the mode l 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 mo nitoring 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 PM 10 

### Mass Monthly Model Performance in the VISTAS Region

Coarse Mass (CM) measurements are defined as the difference between the PM 10 

### mass and

PM 2.5 

### mass measurements. Since IMPR OVE network only speciates the PM 2.5 

### 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 (PM 2.5-10 

### ). Thus, there are potential incompatibility issues with the measured and modeled CM
concentrations.

In addition, CM has a much shorter transport dist ances than fine PM. In the modeling this is accounted for though fugitive dust transport factor s (FDTF) that adjust the fugitive dust emissions downward (by ~75% on average in the VI STAS 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 li kely local in nature an d subgrid-scale to the
model.

B.4.6.1 CM and PM 10 

### in January, April, July and October 2002

Figures B-27 through B30 display scatter plot s of predicted and observed 24-hour CM and PM 10 

### concentrations across the IMPROVE monitors in the VISTAS region for, respectively, January, April, July and October. CM e xhibits an underprediction bias of approximately -50% in January and approximately -100% for the other three mont hs 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%) a nd 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 PM 10 

### (right) concentrations for January 2002 and observations fr om 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 PM 10 

### (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 PM 10 

### (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 PM 10 

### (right)
concentrations for October 2002 and observations fr om 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 Pl ot (Figure B-31b) shows that these performance statistics are either right at or exceeds th e 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 PM 2.5 

### Mass Monthly Model Performa nce in the VISTAS Region

Below we present the CMAQ 2002 Base G2 12km and 36 km model performance for PM 2.5 

### mass. In the model, PM 2.5 

### mass is obtained by summing all of the PM species except CM. In the measurement, PM 2.5 

### is obtained by the total PM on a f ilter collected by a sampler with a 2.5 micron inlet cut point. Thus, the measured PM 2.5 

### 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

PM 2.5 

### performance in January is quite good ach ieving the most stringent 1-hour ozone <±15%/<35% 1-hour ozone bias/error pe rformance goal (Figure B-32a). PM 2.5 

### 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 PM 2.5 

### mass concentrations for January 2002 and sites in the VISTAS region using IMPROVE (t op 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 

### PM 2.5 

### 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 

### PM 2.5 

### 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 PM 2.5 

### in April 2002

In April the PM 2.5 

### model performance is still fairly good with errors in the 30% to 40% range, but with an underprediction bias of fr om -15% (IMPROPVE) to -35% (STN).

Figure B-33a. Scatter plots of predicted and observed 

### PM 2.5 

### 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 

### PM 2.5 

### 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 PM 2.5 

### 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 

### PM 2.5 

### 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 

### PM 2.5 

### 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 PM 2.5 

### in October 2002

By October the performance has improved with lo wer bias and errors across all the networks.

Figure B-35a. Scatter plots of predicted and observed 

### PM 2.5 

### mass concentrations for October 2002 and sites in the VISTAS region using IMPROVE (t op 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 

### PM 2.5 

### 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 PM 2.5 

### Monthly Bias and Error

Figure B-26a displays the monthly time seri es of fractional bias and error for PM 2.5 

### mass concentrations for the IMPROVE, STN, SEARCH Daily, SEARCH Hourly and FRM networks in the VISTAS region. For the first three and lastthree months of the year the model performs
well for PM 2.5 

### with low bias (<±20%) and error (<40% except for SEARCH Hourly). During April through July the underpredi ction bias becomes larger and is in the -20% to -45%) and the errors are also larger.

The Bugle Plots confirm the good performance for PM 2.5 

### 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 

### PM 2.5 

### 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 

### PM 2.5 

### 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 predicte d 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 ze ro Sea Salt resulting in po or performance. As Sea Salt is not directly used in the 2018 visibility projections this is no t a big cause for concern,
but can affect aerosol concentr ations and is an area that s hould be investig ated 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 presen ted 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 goo d 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 incl uded some more rural monitoring sites.

The CASTNet network measures weekly aver age samples of SO2, SO4, NO2, HNO3, NO3 and NH4. Because the CASTNet samples are collected in a cartridge and samp led 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 a nd 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 pe rformance provides insi ght into whether the SO4 formation rate may be too sl ow or fast. For example, if SO4 is underestimated and SO2 is overestimated, that may indicate too slow chemi cal conversion rate. An alyzing the performance for SO4, HNO3, NO3, Total NO3 and NH4 provides insight into the equilibrium of these species. For example, if TNO3 performs we ll but HNO3 and NO3 do not, then there may be issues associated with the par titioning between the gaseous and pa rticle 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 gas eous 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 (bo ttom) 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 gas eous 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 (bo ttom) 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 gas eous 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 gas eous 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 a nd Nearby Class I Areas

### C-1 C.1 OVERVIEW

The discussion of model performance in th is Appendix focuses on the comparison of
observational data from the I nteragency M onitoring of PRO tected V isual E nvironments (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 component s of fine particulate matter (PM 2.5 

### ), as good model performance of the component species will dictate good model performance of total or reconstituted fine particulate ma tter from which visibility is c onstructed. Model performance of the total fine particulate matter and the resulting to tal 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 th e VISTAS States (VISTAS region) as shown in Figure C-1. There are also seve ral Class I areas adjacent to the VISTAS region where we also project future-year visibility conditions. Fi gure C-2 displays the IMPROVE monitoring sites within and nearby the VISTAS region. The 18 VI STAS Class I areas and nearby Class I areas where the CMAQ Base G2 modeling results are ev aluated in this Append ix 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 Re fuge (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 Ref uge (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 We st Virginia, the Otter Creek Class I area shares the Dolly Sods IMPROVE m onitor 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 visibi lity projections for Okefenokee, Dolly Sods and Great Smokey Mountains IMPROVE monitors they are also repres ented of, respectively, Wolf Island, Otter Creek and Joyce Kilmer-Slickroc k Class I areas, respec tively. Also note that
there are some IMPROVE sites not located in Class I areas. Alt hough data at these sites are used in the regional evaluation disc ussed in Appendix B, the performa nce at the individual IMPROVE
monitors not located at a Class I area are not di scussed 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 CADI1 MING1 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

PM 2.5 

### . Regional haze is measured by an extinction coefficient (b ext 

### ), which represents the light attenuation resulting from scatte ring and absorption of light from ambient particulate matter plus scattering of light due to gas molecules in the air (i.e., Raylei gh scattering).

Some scattering does occur from air molecules; ho wever, the bulk of light extinction is caused by the presence of suspended particles, partic ularly fine particle s (aerodynamic mass <2.5 μ m). 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., PM 10 

### - PM 2.5 

### )

• 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 fr om day-to-day, allowing a day with a large concentration of sulfates to be closely followe d by a day dominated by organic carbon. This is an important fact as some of the components of fi ne 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 ev aluated 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 pollu tion 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 th e 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 m onitors 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 “wor st days”, and the 20% least impa ired 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 ar e 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.C-6DateJulianDaySIPSCHASEVERSAMACOHUOKEFMACALIGOSHROSWANROMAGRSMJARISHENDOSOBRETCACRUPBUMINGHEGLBRIG101/05/02 5WWBBWBBW01/08/02 8BBBBB01/11/02 11BBBBBBBB01/14/02 14BBBBBBBBBBBBB01/17/02 17WWW01/20/02 20BBBWW01/23/02 23BBWBBBBBBB01/26/02 26BWBBBBBBBBBB01/29/02 29BW02/01/02 32BBBBBBBBB02/04/02 35WWBB02/07/02 38BBBBBBBBB02/10/02 41W02/13/02 44WWWBWBWBWBB02/16/02 47WWWWB02/19/02 50BBBB02/22/02 53WW02/25/02 56WWWWWWW02/28/02 59BBBBW03/03/02 62WBBBBBBBB03/06/02 65B03/09/02 68BBBBBBBBBBBBBB03/12/02 71BBBB03/15/02 74WWW03/18/02 77BBBBBWB03/21/02 80WBBBW03/24/02 83B03/27/02 86WB03/30/02 89BWWBWBBBBBBBB04/02/02 92B04/05/02 95WWWW04/08/02 98WWBB04/11/02 101BB04/14/02 104BBBBBBBBB04/17/02 107WBW04/20/02 110WWC-7DateJulianDaySIPSCHASEVERSAMACOHUOKEFMACALIGOSHROSWANROMAGRSMJARISHENDOSOBRETCACRUPBUMINGHEGLBRIG104/23/02 113BWBBBBB04/26/02 116BWWBBBBBBBBB04/29/02 119BBWBBBB05/02/02 122WWWWWWWWWW05/05/02 125WWBBWB05/08/02 128WWWWWWWWWWW05/11/02 131WW05/14/02 134BBBB05/17/02 137BWWWWWBBW05/20/02 140BB05/23/02 143WWWW05/26/02 146BWWWW05/29/02 149WWWWBWW06/01/02 152WWWWWWWWWWW06/04/02 155WWWWWWWWWWWW06/07/02 158WBWBBBBWWB06/10/02 161WBBWBWWW06/13/02 164BBWWWWBB06/16/02 167BBB06/19/02 170BBWWWBWWWBWWWW06/22/02 173BBBBWBBWWWWW06/25/02 176BBBBBBBWWBBBW06/28/02 179BBBWWWW07/01/02 182BWWWWWW07/04/02 185WWWWWWWWWWW07/07/02 188WWWWWWWWWWWW07/10/02 191WBBWBWWWBWWWBWWWWW07/13/02 194BBBBWWB07/16/02 197WWWWWWWWWWWBWWW07/19/02 200WWWWWWWWWB07/22/02 203BWWWWWWWW07/25/02 206WBBWWWWWBWWWWWB07/28/02 209BWWBW07/31/02 212BBWWWWWWWW08/03/02 215WWWWWWWWWWWWW08/06/02 218WWBWWWWWWWWWWWC-8DateJulianDaySIPSCHASEVERSAMACOHUOKEFMACALIGOSHROSWANROMAGRSMJARISHENDOSOBRETCACRUPBUMINGHEGLBRIG108/09/02 221WWWWBWBWWWWB08/12/02 224WBBWWWWWWWWWWW08/15/02 227BWBBBWWBBBB08/18/02 230BW08/21/02 233WWWWWWWWB08/24/02 236WWWWWWWWWB08/27/02 239BBBWWWWWWWWW08/30/02 242BBBBBWWWW09/02/02 245BWBBBBWW09/05/02 248BBWWWBWWWWW09/08/02 251WBWWWWBWWWWWWB09/11/02 254WBWWWWWWWWWWWWW09/14/02 257BWBBBWBWWWWWWW09/17/02 260WWWWWWWWWWWWWWWWW09/20/02 263WWWWBBWWWBB09/23/02 266BBWBBWBWBBBB09/26/02 269BBBBBBBWWB09/29/02 272WWWWW10/02/02 275BWWWW10/05/02 278WBWBBBBW10/08/02 281WWWWBWBBBB10/11/02 284WBBBBBBWBB10/14/02 287BBWBBBB10/17/02 290WWWBBBB10/20/02 293WWWBB10/23/02 296WWBWWWWWWW10/26/02 299BBWBBBBB10/29/02 302BBBBBBBBBBBBBBB11/01/02 305WW11/04/02 308BBWBBBBBB11/07/02 311WWBBB11/10/02 314B11/13/02 317BB11/16/02 320BBBBBBBBWWW11/19/02 323BBB11/22/02 326BBWBBBC-9DateJulianDaySIPSCHASEVERSAMACOHUOKEFMACALIGOSHROSWANROMAGRSMJARISHENDOSOBRETCACRUPBUMINGHEGLBRIG111/25/02 329WWBW11/28/02 332WWBBWWB12/01/02 335BBBBBBBBB12/04/02 338WWBBBB12/07/02 341WWWWBWWBBWWBBBWWWW12/10/02 344BBBBBB12/13/02 347BBBBBBBBBBW12/16/02 350WWW12/19/02 353BBBBBBB12/22/02 356BBBBBBBBBBBBBB12/25/02 359BBBBBBBBBBB12/28/02 362BWBBW12/31/02* 365BBBBBB

### C-10 C.2 Model Performance Statistics

To quantify model performance, several statistical measures we re 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 secti on 18.4.1 of the USEPA’s
Guidance On The Use Of Models And Other Anal yses for Demonstrati ng 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 mode l 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 perf ormance 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 A p

peak peak


### O OP

−

Coefficient of
Determination R 2


# ( ) 

# ( )

∑ ∑ ∑

= = =



### − − ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

−−

N

i N

i i i N

i i i



### OOPP OOPP

1 1 2 2 2

1


### )()( P i = prediction at time and location i;
O i = observation at time and location i;



### P = arithmetic average of P i , i = 1, 2, . . ., N;



### O = arithmetic average of O i , 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 2 

### 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− 

# ∑

= N

i ii 

### OP 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 ii 

### OP N 1 

### 1

Mean Normalized
Gross Error MNGE 

# ∑

= 

### − N

i i ii



### O OP

N 1 

### 1 Reported as %

Mean Biased MB 

# ( ) 

# ∑

= 

### − N

i ii 

### OP 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 collectivel y 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 sepa rately 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 perf ormance statistical measures for the Best 20 percent visibility days during 2002. Table C-4a c ontains 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 IM PROVE 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 Wors t 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

A p 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 ( μ g/m 3 ) -9.52 -1.77 -0.18 -0.25 0.63 2.88 -1.34 -19.42 MAGE ( μ g/m 3 ) 11.79 3.84 2.64 1.02 0.78 3.71 1.52 24.20 RMSE ( μ g/m 3 ) 14.76 6.23 5.04 1.97 1.18 7.45 2.07 27.92 Obs Max ( μ g/m 3 ) 41.57 30.49 21.39 9.96 2.95 15.38 9.86 65.36 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -15.48 -2.76 -1.40 -0.81 -0.05 -0.39 -1.98 -32.99 MAGE ( μ g/m 3 ) 15.48 2.80 1.43 0.82 0.43 0.90 2.10 32.99 RMSE ( μ g/m 3 ) 16.76 3.21 1.79 1.04 0.57 1.25 2.40 33.48 Obs Max ( μ g/m 3 ) 28.83 7.28 6.17 2.75 1.61 3.81 7.45 48.75 Pred Max ( μ g/m 3 ) 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

R 2 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

A p 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 ( μ g/m 3 ) -10.86 -1.07 5.36 2.69 0.48 -0.23 -3.00 -17.47 MAGE ( μ g/m 3 ) 12.41 2.77 6.81 3.26 0.56 3.30 3.00 30.05 RMSE ( μ g/m 3 ) 13.51 3.51 16.44 6.75 0.74 4.55 3.49 34.13 Obs Max ( μ g/m 3 ) 27.59 11.88 8.23 4.39 0.62 12.00 8.61 52.91 Pred Max ( μ g/m 3 ) 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

R 2 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

A p 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 ( μ g/m 3 ) -4.15 -0.52 0.77 -0.45 1.23 3.55 -1.32 -10.93 MAGE ( μ g/m 3 ) 6.30 5.44 2.07 0.63 1.25 3.55 1.37 17.16 RMSE ( μ g/m 3 ) 7.49 7.61 2.34 0.73 1.45 4.64 1.92 19.73 Obs Max ( μ g/m 3 ) 15.58 18.62 9.59 2.61 0.76 1.20 5.41 42.26 Pred Max ( μ g/m 3 ) 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

R 2 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

A p 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 ( μ g/m 3 ) -8.50 -4.25 -2.65 -0.59 0.36 6.75 -2.10 -20.64 MAGE ( μ g/m 3 ) 13.27 4.25 2.94 1.00 0.43 7.45 2.15 24.23 RMSE ( μ g/m 3 ) 15.07 4.48 3.50 1.22 0.53 8.82 2.61 26.26 Obs Max ( μ g/m 3 ) 24.80 8.90 8.38 3.78 1.14 10.05 7.40 52.76 Pred Max ( μ g/m 3 ) 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

R 2 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

A p 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 ( μ g/m 3 ) -7.46 -1.79 0.39 -0.30 0.51 2.40 -0.91 -16.67 MAGE ( μ g/m 3 ) 11.80 3.12 1.82 0.61 0.55 2.40 1.05 19.10 RMSE ( μ g/m 3 ) 12.95 4.15 2.32 0.70 0.73 2.97 2.24 21.31 Obs Max ( μ g/m 3 ) 24.33 14.54 6.69 3.25 0.84 0.28 8.34 51.42 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -8.23 -0.74 -2.55 -0.92 0.45 0.82 -0.56 -20.58 MAGE ( μ g/m 3 ) 8.61 1.96 2.55 0.96 0.46 0.82 0.60 20.58 RMSE ( μ g/m 3 ) 9.90 2.68 3.16 1.10 0.59 1.02 0.80 21.54 Obs Max ( μ g/m 3 ) 23.24 9.02 9.02 3.44 0.43 0.00 1.93 43.68 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -8.06 -2.04 -0.15 -0.12 -0.27 -0.74 -1.80 -23.62 MAGE ( μ g/m 3 ) 8.06 2.04 1.21 0.45 0.33 1.24 1.80 23.62 RMSE ( μ g/m 3 ) 8.36 2.15 1.47 0.64 0.52 1.75 2.03 23.93 Obs Max ( μ g/m 3 ) 14.39 3.74 5.72 2.89 1.84 4.83 3.91 32.94 Pred Max ( μ g/m 3 ) 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

R 2 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

A p 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 ( μ g/m 3 ) -8.72 -1.12 0.73 0.11 0.97 2.19 -0.79 -16.09 MAGE ( μ g/m 3 ) 13.83 3.57 1.91 0.96 1.00 2.48 0.83 23.13 RMSE ( μ g/m 3 ) 17.59 4.96 2.88 1.22 1.31 3.29 1.09 27.06 Obs Max ( μ g/m 3 ) 31.91 10.95 8.95 3.38 0.69 3.59 3.75 52.59 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -9.24 -1.15 -0.27 -0.53 2.20 8.53 -0.67 -11.19 MAGE ( μ g/m 3 ) 9.24 4.73 2.01 0.80 2.20 8.53 1.16 18.31 RMSE ( μ g/m 3 ) 9.68 7.54 2.51 0.94 2.56 10.14 1.47 20.11 Obs Max ( μ g/m 3 ) 17.95 13.33 9.21 3.73 0.57 0.00 5.14 44.20 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -10.04 -2.26 -3.11 -1.88 0.35 2.00 -1.05 -25.36 MAGE ( μ g/m 3 ) 12.59 4.59 3.34 1.91 0.35 2.00 1.05 27.30 RMSE ( μ g/m 3 ) 14.16 5.71 4.56 2.62 0.43 2.45 1.18 29.72 Obs Max ( μ g/m 3 ) 41.57 10.56 21.39 9.96 0.69 0.02 2.90 60.15 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -9.06 -1.22 -1.42 -0.68 0.42 0.97 -0.72 -21.62 MAGE ( μ g/m 3 ) 9.12 1.63 1.90 0.83 0.46 0.97 0.74 21.62 RMSE ( μ g/m 3 ) 10.48 2.33 2.46 1.05 0.59 1.28 1.00 22.72 Obs Max ( μ g/m 3 ) 25.33 11.62 8.77 4.76 0.70 0.98 3.43 41.91 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -18.73 -1.78 -3.18 -0.94 0.86 3.45 -0.71 -30.58 MAGE ( μ g/m 3 ) 18.73 8.09 3.24 1.11 0.94 3.45 0.88 30.58 RMSE ( μ g/m 3 ) 20.15 10.57 3.79 1.47 1.10 4.21 1.21 34.66 Obs Max ( μ g/m 3 ) 39.05 30.49 13.25 6.05 1.41 0.00 4.08 65.36 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -12.68 0.74 -1.74 -0.07 2.61 21.82 0.41 0.29 MAGE ( μ g/m 3 ) 12.68 5.88 2.66 0.88 2.61 21.82 1.84 22.58 RMSE ( μ g/m 3 ) 13.73 13.02 3.40 1.10 3.11 28.77 2.72 35.59 Obs Max ( μ g/m 3 ) 32.46 11.03 12.57 3.80 1.45 1.47 9.86 56.75 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -14.61 -3.72 3.79 -0.69 0.02 0.59 -1.57 -26.00 MAGE ( μ g/m 3 ) 14.61 3.72 4.33 1.09 0.41 0.74 1.57 26.00 RMSE ( μ g/m 3 ) 15.31 4.06 5.77 1.22 0.55 0.88 1.81 27.22 Obs Max ( μ g/m 3 ) 26.45 8.07 11.63 4.86 1.77 2.07 3.93 52.70 Pred Max ( μ g/m 3 ) 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

R 2 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

A p 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 ( μ g/m 3 ) -8.44 -2.51 -1.72 -0.37 0.06 -2.52 -1.23 -27.68 MAGE ( μ g/m 3 ) 11.13 2.51 2.09 0.53 0.23 3.62 1.46 29.56 RMSE ( μ g/m 3 ) 12.73 2.73 2.45 0.75 0.37 5.48 2.27 30.48 Obs Max ( μ g/m 3 ) 26.12 4.50 8.19 5.01 0.65 15.38 8.77 50.02 Pred Max ( μ g/m 3 ) 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

R 2 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

A p 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 ( μ g/m 3 ) -12.62 -2.70 2.03 0.34 -0.20 0.93 -1.72 -23.78 MAGE ( μ g/m 3 ) 14.92 2.70 3.76 1.23 0.48 1.00 1.76 26.45 RMSE ( μ g/m 3 ) 15.81 2.84 5.77 1.86 0.70 1.37 2.07 28.55 Obs Max ( μ g/m 3 ) 24.51 4.76 9.53 3.98 2.05 1.13 4.89 50.25 Pred Max ( μ g/m 3 ) 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

R 2 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

A p 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 ( μ g/m 3 ) 0.52 3.61 2.55 0.81 0.57 3.12 -0.47 2.16 MAGE ( μ g/m 3 ) 11.86 5.02 2.99 1.09 0.57 3.12 0.51 18.34 RMSE ( μ g/m 3 ) 28.13 6.76 7.50 2.78 0.65 3.91 0.61 34.08 Obs Max ( μ g/m 3 ) 20.73 11.73 5.74 2.68 0.35 0.00 1.92 42.00 Pred Max ( μ g/m 3 ) 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

R 2 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

A p 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 ( μ g/m 3 ) -2.64 -0.81 0.74 0.46 0.46 0.92 -0.43 -10.14 MAGE ( μ g/m 3 ) 5.16 1.25 1.63 0.74 0.47 0.92 0.58 12.36 RMSE ( μ g/m 3 ) 7.24 1.75 2.33 1.02 0.59 1.32 0.82 14.01 Obs Max ( μ g/m 3 ) 13.73 4.65 6.13 2.31 0.28 0.10 2.87 31.05 Pred Max ( μ g/m 3 ) 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

R 2 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 -9.1 5 -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

A p 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 ( μ g/m 3 ) -9.62 -4.46 0.59 -0.98 0.65 3.74 -1.76 -21.39 MAGE ( μ g/m 3 ) 17.11 4.49 2.83 1.26 1.06 3.74 1.76 29.67 RMSE ( μ g/m 3 ) 21.62 6.66 3.79 1.53 1.30 4.54 2.07 32.63 Obs Max ( μ g/m 3 ) 36.05 21.36 11.04 4.69 2.95 0.00 4.24 61.55 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -9.68 -1.72 0.37 0.18 0.16 -0.66 -2.15 -24.60 MAGE ( μ g/m 3 ) 9.97 2.30 2.06 0.80 0.31 1.89 2.15 25.45 RMSE ( μ g/m 3 ) 10.90 2.57 4.25 1.71 0.42 3.11 2.37 26.66 Obs Max ( μ g/m 3 ) 21.09 4.21 6.60 2.59 0.87 11.33 6.25 42.25 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -8.11 -3.62 -0.67 -0.54 0.95 3.28 -2.71 -21.59 MAGE ( μ g/m 3 ) 8.28 3.62 1.76 0.74 1.00 3.28 2.83 22.04 RMSE ( μ g/m 3 ) 9.10 4.57 2.03 0.88 1.19 4.05 3.42 23.65 Obs Max ( μ g/m 3 ) 19.65 12.68 8.70 2.65 0.81 0.01 8.94 44.16 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -48.14 -1.38 -6.15 -2.24 0.38 12.46 -2.26 -55.75 MAGE ( μ g/m 3 ) 61.44 6.66 10.23 2.75 1.01 12.52 2.44 76.45 RMSE ( μ g/m 3 ) 82.20 13.69 28.78 3.59 1.79 20.70 3.53 101.79 Obs Max ( μ g/m 3 ) 475.66 107.80 614.69 41.23 16.92 11.70 20.96 735.80 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -42.55 -8.04 -5.76 -3.05 -0.78 1.64 -2.74 -69.94 MAGE ( μ g/m 3 ) 46.94 8.04 5.95 3.05 1.28 1.87 2.74 71.73 RMSE ( μ g/m 3 ) 52.63 10.62 10.17 3.77 2.59 2.49 3.16 79.19 Obs Max ( μ g/m 3 ) 126.22 29.70 46.34 12.96 11.47 1.93 10.72 171.55 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -53.74 7.87 -31.03 -1.17 0.43 8.35 -3.53 -81.61 MAGE ( μ g/m 3 ) 65.73 13.84 32.78 2.40 1.08 8.35 3.64 100.93 RMSE ( μ g/m 3 ) 77.68 25.55 110.38 3.47 1.68 9.45 5.34 154.13 Obs Max ( μ g/m 3 ) 275.18 71.94 614.69 41.23 6.83 1.13 20.65 735.80 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -50.95 2.45 -0.66 -2.16 0.42 14.33 -3.40 -49.12 MAGE ( μ g/m 3 ) 55.22 8.88 5.55 2.16 1.16 14.33 3.43 65.08 RMSE ( μ g/m 3 ) 78.07 23.14 7.96 2.46 1.88 16.37 3.94 92.23 Obs Max ( μ g/m 3 ) 281.03 70.57 51.07 7.26 6.70 1.35 7.89 324.07 Pred Max ( μ g/m 3 ) 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

R 2 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

A p 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 ( μ g/m 3 ) -23.98 -2.50 -6.10 -1.16 1.31 28.97 -0.67 -11.87 MAGE ( μ g/m 3 ) 35.52 4.83 8.42 2.73 1.57 28.97 1.11 39.44 RMSE ( μ g/m 3 ) 40.90 6.12 11.22 3.14 1.84 30.72 1.65 44.81 Obs Max ( μ g/m 3 ) 121.53 9.87 71.19 10.37 3.58 0.00 6.40 169.84 Pred Max ( μ g/m 3 ) 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

R 2 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 -8.3 4 -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

A p -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 ( μ g/m 3 ) -88.61 -1.87 -1.03 -2.07 0.07 10.91 -1.11 -91.72 MAGE ( μ g/m 3 ) 100.93 4.01 13.15 2.88 0.56 10.91 1.71 108.16 RMSE ( μ g/m 3 ) 126.52 5.37 21.92 3.25 0.81 12.79 3.03 138.10 Obs Max ( μ g/m 3 ) 376.51 16.47 96.66 9.78 3.19 0.00 9.23 507.89 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -73.37 -1.72 -4.04 -2.89 -0.14 3.03 -1.36 -88.24 MAGE ( μ g/m 3 ) 82.83 2.23 5.89 2.89 0.28 3.03 1.36 96.32 RMSE ( μ g/m 3 ) 113.35 2.55 6.91 3.11 0.38 3.37 1.78 129.42 Obs Max ( μ g/m 3 ) 404.99 5.69 26.51 7.50 1.78 0.00 4.85 451.54 Pred Max ( μ g/m 3 ) 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

R 2 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

A p 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 ( μ g/m 3 ) -13.47 -3.75 -7.39 -0.95 -0.50 2.67 -2.44 -34.61 MAGE ( μ g/m 3 ) 22.15 3.75 12.35 2.18 1.50 3.84 2.44 44.79 RMSE ( μ g/m 3 ) 26.53 4.08 29.80 3.20 3.59 5.05 3.57 58.45 Obs Max ( μ g/m 3 ) 79.12 8.02 134.42 10.81 16.92 11.70 13.75 196.06 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -73.15 -2.67 -0.11 -2.25 0.06 7.32 -1.63 -79.67 MAGE ( μ g/m 3 ) 75.14 2.68 7.46 2.28 0.42 7.32 1.70 83.20 RMSE ( μ g/m 3 ) 92.01 3.23 10.13 2.54 0.57 8.15 2.15 100.92 Obs Max ( μ g/m 3 ) 308.51 10.16 33.88 7.71 1.52 0.00 4.89 347.18 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -41.28 -3.14 -4.77 -2.39 1.84 28.38 -1.05 -31.35 MAGE ( μ g/m 3 ) 54.21 8.13 6.44 2.39 1.99 28.38 1.38 57.08 RMSE ( μ g/m 3 ) 82.66 16.53 8.68 2.63 2.53 30.85 1.93 90.89 Obs Max ( μ g/m 3 ) 339.47 99.26 42.42 9.36 4.42 0.00 8.18 377.55 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -48.04 -3.12 -11.73 -4.71 -0.28 6.09 -2.62 -71.76 MAGE ( μ g/m 3 ) 65.98 4.16 12.36 4.71 0.39 6.09 2.62 84.45 RMSE ( μ g/m 3 ) 78.63 4.57 23.32 5.01 0.79 6.47 3.13 97.61 Obs Max ( μ g/m 3 ) 274.58 8.70 119.43 11.72 4.58 0.00 10.28 324.44 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -58.86 -1.63 -3.65 -2.69 -0.13 5.25 -2.09 -71.19 MAGE ( μ g/m 3 ) 66.23 1.88 5.14 2.69 0.23 5.25 2.09 76.47 RMSE ( μ g/m 3 ) 75.30 2.08 6.40 2.81 0.29 5.68 2.19 87.22 Obs Max ( μ g/m 3 ) 290.29 4.53 31.89 6.06 1.27 2.03 4.03 343.89 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -83.29 -5.79 -9.61 -2.75 0.29 13.40 -0.32 -96.83 MAGE ( μ g/m 3 ) 89.11 6.58 10.02 2.75 0.93 13.40 1.31 102.89 RMSE ( μ g/m 3 ) 120.48 10.17 11.30 3.15 1.58 15.17 1.60 136.14 Obs Max ( μ g/m 3 ) 347.31 107.80 40.53 9.05 8.22 0.00 4.63 405.74 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) 5.35 6.29 -4.29 -1.12 2.99 69.04 -1.12 69.50 MAGE ( μ g/m 3 ) 67.43 13.36 6.87 2.00 3.30 69.04 1.91 111.22 RMSE ( μ g/m 3 ) 83.82 21.83 9.85 2.64 3.77 76.00 2.60 136.16 Obs Max ( μ g/m 3 ) 263.55 105.09 40.46 10.36 6.65 1.56 11.69 287.91 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -47.55 -4.38 -6.64 -3.20 0.24 4.11 -2.14 -69.45 MAGE ( μ g/m 3 ) 48.02 4.51 17.90 4.72 0.72 4.11 2.16 69.45 RMSE ( μ g/m 3 ) 57.10 5.46 37.27 7.22 0.93 4.49 2.47 88.19 Obs Max ( μ g/m 3 ) 271.57 15.83 156.35 29.54 1.57 1.36 4.79 341.01 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -33.72 -0.69 -7.33 -2.27 0.38 6.16 -0.91 -47.20 MAGE ( μ g/m 3 ) 49.93 7.06 8.97 2.53 0.85 6.16 1.62 62.73 RMSE ( μ g/m 3 ) 67.15 12.07 14.36 2.93 1.06 7.11 2.10 79.95 Obs Max ( μ g/m 3 ) 234.53 13.18 70.65 14.42 1.77 0.00 7.17 270.34 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -0.06 -0.53 0.24 0.61 -0.16 1920.06 -0.34 -0.03 NMB (%) -40.40 -74.41 0.20 -4.6 3 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 ( μ g/m 3 ) -37.22 -3.58 0.04 -0.28 0.23 6.90 -1.93 -47.55 MAGE ( μ g/m 3 ) 41.48 3.58 10.40 3.39 0.98 6.90 1.98 53.27 RMSE ( μ g/m 3 ) 51.84 4.16 14.75 5.69 1.33 7.80 2.30 65.75 Obs Max ( μ g/m 3 ) 174.11 17.40 76.59 17.16 4.06 0.01 7.80 207.54 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -86.47 3.04 -8.89 -3.12 -0.18 8.24 -2.65 -99.38 MAGE ( μ g/m 3 ) 98.39 7.64 12.11 3.20 0.57 8.24 2.65 113.84 RMSE ( μ g/m 3 ) 117.52 10.84 31.39 4.09 1.17 9.41 3.88 135.13 Obs Max ( μ g/m 3 ) 354.08 32.37 150.90 15.51 6.59 0.00 15.30 375.09 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -71.73 -3.00 -3.26 -0.57 -0.31 5.56 -1.51 -80.99 MAGE ( μ g/m 3 ) 79.06 3.00 5.30 0.77 0.41 5.56 1.51 86.54 RMSE ( μ g/m 3 ) 106.33 3.22 6.88 0.95 0.57 5.84 1.73 114.15 Obs Max ( μ g/m 3 ) 475.66 6.49 23.94 3.86 2.55 0.06 3.68 518.88 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -51.76 -3.37 3.35 -3.26 0.43 11.39 -2.45 -53.35 MAGE ( μ g/m 3 ) 64.47 9.37 14.63 3.40 0.76 11.39 2.45 77.22 RMSE ( μ g/m 3 ) 76.05 17.21 18.18 3.72 0.99 13.95 2.68 90.66 Obs Max ( μ g/m 3 ) 279.55 63.69 41.03 11.07 2.28 0.03 5.08 325.68 Pred Max ( μ g/m 3 ) 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

R 2 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

A p 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 ( μ g/m 3 ) -20.67 0.29 -12.17 -2.42 0.31 3.93 -2.95 -42.55 MAGE ( μ g/m 3 ) 48.38 6.98 12.17 2.65 0.62 3.99 2.95 62.51 RMSE ( μ g/m 3 ) 58.52 10.49 24.00 2.90 0.87 4.51 3.25 76.28 Obs Max ( μ g/m 3 ) 209.85 45.16 128.63 7.73 1.34 1.20 7.26 243.51 Pred Max ( μ g/m 3 ) 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

R 2 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

A p -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 ( μ g/m 3 ) -40.13 -2.13 -3.40 -2.13 0.87 15.10 -7.97 -48.64 MAGE ( μ g/m 3 ) 47.45 9.67 4.53 2.13 1.27 15.10 7.97 59.30 RMSE ( μ g/m 3 ) 71.44 20.14 8.32 2.49 1.81 17.18 9.76 84.24 Obs Max ( μ g/m 3 ) 273.55 98.02 52.31 7.21 5.34 0.00 20.96 311.47 Pred Max ( μ g/m 3 ) 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

R 2 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 VI STAS 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 observ ed SO4, NO3, OMC, EC, Soil, CM, Sea
Salt and PM2.5 mass across all monitoring days at Si psey, Alabama for the CMAQ 2002 Base
G2 12 km base case simulation.

### C-42 Figure C-7. 

### (concluded) Time series of pred icted 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.