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“Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule” 

(EPA, 2003b). 

“Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program” 

(EPA, 2007b). 

The first EPA modeling guidance document listed above (EPA, 2007a) discusses the use of 

modeling results to project future-year visibility. The second EPA guidance document (EPA, 

2003a) focuses on monitored visibility, how to define the visibility Baseline Conditions and how 

to track visibility goals. The third EPA guidance document (EPA, 2003b) discusses procedures 

for defining Natural Conditions for a Class I area. Natural Conditions are the visibility goal for 

2064. Although states may propose alternative approaches for defining Natural Conditions, in 

this section we use the EPA default Natural Conditions at Class I areas updated to the new 

IMPROVE equation as available on the VIEWS website. The final EPA guidance document 

discusses how states should define their reasonable progress goals and their relationship to the 

2018 Uniform Rate of Progress. 

The EPA documents discussed above are followed for the visibility projections presented in this 

section with one notable exception. Some of the EPA documents are based on the original 

IMPROVE equation (e.g., EPA, 2003a,b). The VISTAS visibility projections are based on the 

new IMPROVE equation, although projections based on the original IMPROVE equation are 

also presented as an alternative approach in Chapter 5. 

4.2 Calculation of Visibility and 2018 Uniform Rate of Progress from IMPROVE 

Measurements 

EPA guidance recommends using the model in a relative sense to project future-year visibility 

conditions (EPA, 2007a). This projection is made using Relative Response Factors (RRFs) that 

are defined as the ratio of the future-year to the base-year modeling results. The RRFs are 

applied to the baseline visibility conditions to project future-year visibility. The major features 

of EPA’s recommended visibility projection approach are as follows (EPA, 2003a, b; 2007a): 

• Monitored data are used to define current visibility Baseline Conditions using 

IMPROVE monitoring data from the 2000-2004 five-year period. 

• Monitored concentrations of PM10 are divided into six major components (note that 

Sea Salt is also included in the new IMPROVE equation and held constant between 

the current and future years), the first five of which are assumed to be PM2.5 and the 

sixth is coarse mass (CM or PM2.5-10). 

3⁄4 SO4 (sulfate) that is assumed to be ammonium sulfate [(NH4)2SO4]; 

3⁄4 NO3 (particulate nitrate) that is assumed to be ammonium nitrate [NH4NO3]; 

3⁄4 OC (organic carbon) that is assumed to be total organic mass carbon (OMC) 

3⁄4 EC (elemental carbon); 

3⁄4 IP (other fine inorganic particulate or Soil); and 

3⁄4 CM (coarse mass). 

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• Models are used in a relative sense to develop RRFs between baseline and future 

predicted concentrations of each component. 

• PM component-specific RRFs are multiplied by observed Baseline monitored values 

to estimate future-year PM component concentrations. 

• Estimates of future-year component concentrations are consolidated to provide an 

estimate of future-year air quality and visibility using either the original or new 

IMPROVE equation. 

• Future-year model projected visibility is compared with the 2018 Uniform Rate of 

Progress to assist in evaluating the visibility improvements. 

• It is assumed that all measured sulfate is in the form of ammonium sulfate 

[(NH4)2SO4] and all particulate nitrate is in the form of ammonium nitrate [NH4NO3]. 

In order to facilitate tracking visibility progress, three important visibility concepts are required 

for each Class I area: 

Baseline Conditions: Baseline Conditions represent visibility for the 20 percent best (B20%) 

and 20 percent worst (W20%) visibility days for the initial five-year baseline period of the 

regional haze program. Baseline Conditions are calculated using IMPROVE monitor data 

collected during the 2000-2004 five-year period and are the starting point in 2004 for the 

Uniform Rate of Progress glidepath and 2018 visibility projections. The baseline data are 

available on the VIEWS website (http://vista.cira.colostate.edu/views/). 

Natural Conditions: Estimates of natural visibility conditions for the best 20 percent and 

worst 20 percent days at a Class I area (i.e., visibility conditions that would be experienced in 

the absence of human-caused impairment). EPA has defined a set of default Natural 

Conditions (EPA, 2003b) that have been updated for the new IMPROVE equation on the 

VIEWS website and are used in this section. 

2018 Uniform Rate of Progress: The 2018 Uniform Rate of Progress is defined by 

constructing a linear glidepath in deciviews starting with the observed 2000-2004 Baseline 

Conditions in 2004 and ending at Natural Conditions in 2064 for each Class I area. Where 

the linear glidepath passes through 2018 is the 2018 Uniform Rate of Progress in deciviews. 

4.2.1 Calculation of Visibility from IMPROVE PM Measurements 

Baseline Conditions for Class I areas are calculated using the procedures in EPA’s guidance 

documents (EPA, 2003a; 2007a) and fine and coarse particulate matter concentrations measured 

at IMPROVE monitors (Malm et al., 2000; Debell et al., 2006). Currently, each Class I area in 

the VISTAS region has an associated IMPROVE monitor. The IMPROVE monitors do not 

directly measure visibility, but instead measure speciated fine particulate (PM2.5) and total PM2.5

and PM10 mass concentrations from which visibility is obtained through the IMPROVE equation. 

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Visibility conditions are estimated starting with the IMPROVE 24-hour average mass 

measurements for six PM species: 

• Sulfate [(NH4)2SO4]; 

• Particulate Nitrate [(NH4NO3]; 

• Organic Matter Carbon or Organic Mass by Carbon [OMC]; 

• Elemental Carbon [EC] or Light Absorbing Carbon [LAC]; 

• Other fine particulate [Soil]; and 

• Coarse Matter or Coarse Mass [CM]. 

The IMPROVE monitors do not directly measure some of these species so assumptions are made 

as to how the IMPROVE measurements can be adjusted and combined to obtain these six 

components of light extinction. For example, in the IMPROVE equation sulfate and particulate 

nitrate are assumed to be completely neutralized by ammonium. In addition, only the fine mode 

(PM2.5) of PM is speciated by the IMPROVE monitor to obtain sulfate and nitrate measurements 

(that is, any coarse mode sulfate and nitrate in the real atmosphere may be present in the CM 

IMPROVE measurement). Concentrations for the above six components of light extinction in 

the IMPROVE equation are obtained from the IMPROVE measured species using the mappings 

shown in Table 4-1: 

Table 4-1. Definition of IMPROVE PM Components from Measured IMPROVE Species. 

IMPROVE Component IMPROVE Measured Species 

Sulfate 1.375 x (3 x S) 

Nitrate 1.29 x NO3 

-

OMC 1.4*OC (original IMPROVE) and 1.8*OC (new IMPROVE) 

LAC EC 

Soil 2.2*AL + 2.49*SI + 1.63*CA + 2.42*FE + 1.94*TI 

CM MT – MF 

Where: 

• S is elemental sulfur as determined from proton induced x-ray emissions (PIXE) analysis 

of the IMPROVE Module A1

. To estimate the mass of the sulfate ion (SO4

=

), S is 

multiplied by 3 to account the presence of oxygen. If S is missing then the sulfate (SO4) 

measured by ion chromatography analysis of the Module B is used to replace (3 x S). For 

the IMPROVE aerosol extinction calculation, Sulfate is assumed to be completely 

neutralized by ammonium (1.375 x SO4). 

• NO3

-

 is the particulate nitrate measured by ion chromatography analysis of the Module B. 

For the IMPROVE aerosol extinction calculation, it is assumed to be completely 

neutralized by ammonium (1.29 x NO3

-

). 

• The IMPROVE Organic Carbon (OC) measurements are multiplied by 1.4 to obtain 

Organic Mass Carbon (OMC) using the original IMPROVE equation and multiplied by 

1.8 for the new IMPROVE equation. This adjustment of the measured OC accounts for 

 

1

 The IMPROVE sampler consists of four independent modules (A, B, C and D). Each module incorporates a 

separate inlet, filter pack and pump assembly and are controlled by a common timing mechanism. Module A 

measures fine PM mass and elements. Module B measures sulfate and nitrate ions. Module C measures EC and 

OC. Module D measures PM10 mass. (see http://vista.cira.colostate.edu/improve/ for more details). 

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mass due to other elements in the OMC besides Carbon (e.g., oxygen). IMPROVE also 

uses the term Particulate Organic Matter (POM) to describe OMC. 

• Elemental Carbon (EC) is also referred to as Light Absorbing Carbon (LAC). 

• Soil is determined as a sum of the masses of those elements (measured by PIXE) 

predominantly associated with soil (Al, Si, Ca, Fe, K and Ti), adjusted to account for 

oxygen associated with the common oxide forms. Since K and FE are products of the 

combustion of vegetation, they are both represented in the formula by 0.6 x Fe and K is 

not shown explicitly. 

• MT and MF are total PM10 and PM2.5 mass, respectively whose difference gives CM. 

4.2.1.1 Original and New IMPROVE Equations 

Associated with each PM species is an extinction efficiency that converts concentrations (in 

μg/m3

) to light extinction (in inverse megameters, Mm-1). Sulfate and nitrate are hygroscopic 

which means that they can absorb water from the atmosphere which changes their extinction 

efficiency. This is accounted for through relative humidity adjustment factors [f(RH)] that 

increase the particle’s extinction efficiency with increasing RH to account for the particles taking 

on water Note that some OMC may also have hygroscopic properties, but the IMPROVE 

equations assume OMC is non-hygroscopic. 

There are currently two IMPROVE equations that are used to convert PM concentrations to light 

extinction, the original (or old) and the new IMPROVE equations. 

4.2.1.1.1 Original IMPROVE Equation 

The original IMPROVE equation that converts PM species concentrations to light extinction is 

given as follows (Malm et al., 2000): 

bSulfate = 3 x f(RH) x [Sulfate] 

bNitrate = 3 x f(RH) x [Nitrate] 

bEC = 10 x [EC] 

bOMC = 4 x [OMC] 

bSoil = 1 x [Soil] 

bCM = 0.6 x [CM] 

Monthly average f(RH) factors are used as recommended in EPA’s guidance (EPA, 2003a). 

These values are available in the final EPA guidance document (EPA, 2003a) and at: 

ftp://ftp.saic.com/raleigh/RegionalHaze_2002FRHcurve/fRH_analysis/. 

The total light extinction (bext) is assumed to be the sum of the light extinction due to the six PM 

species listed above plus Rayleigh (blue sky) background (bRay) that is assumed to be 10 Mm-1. 

 bext = bRay + bSulfate + bNitrate + bEC +bOMC + bSoil + bCM

The total light extinction (bext) in Mm-1 is related to visual range (VR) in km using the following 

relationship: 

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 VR = 3912 / bext, 

for bext in Mm-1. 

The Regional Haze Rule requires that visibility be expressed in terms of a haze index (HI) in 

units of deciviews (dv), which is calculated as follows: 

 HI = 10 ln(bext/10) 

4.2.1.1.2 New IMPROVE Equation 

The new IMPROVE equation is nonlinear in SO4, NO3 and OMC concentrations accounting for 

the different light scattering efficiency characteristics as a function of concentrations for these 

three species (DeBell et al., 2006). It is expressed as follows: 

bSulfate = 2.2 x fS(RH) x [Small Sulfate] + 4.8 fS(RH) x [Large Sulfate] 

bNitrate = 2.4 x fS(RH) x [Small Nitrate] + 5.1 fS(RH) x [Large Nitrate] 

bEC = 10 x [Elemental Carbon] 

bOMC = 2.8 x [Small Organic Mass] + 6.1 x [Large Organic Mass] 

bSoil = 1 x [Fine Soil] 

bCM = 0.6 x [Coarse Mass] 

bNaCl = 1.7 x fSS(RH) x [Sea Salt] 

bNO2 = 0.33 x [NO2 (ppb)] 

The total Sulfate, Nitrate and OMC are each split into two fractions, representing small and large 

size distributions of those components. As noted in Table 4-1, the OMC is 1.8 times the 

IMPROVE OC measurement in the new IMPROVE algorithm, compared to 1.4 times the 

IMPROVE OC measurement in the original IMPROVE equation. New terms have been added 

for Sea Salt (important for coastal areas and possibly other areas) and for light absorption by 

NO2 (only used where NO2 observations are available). Consistent with IMPROVE guidance, 

VISTAS used Cl- mass from ion chromatography to define sea salt concentrations. As NO2 is 

rarely measured at IMPROVE monitoring sites and, except for conditions of plume blight has 

little effect on regional haze, this component of the new IMPROVE equations was not used. 

Site-specific Rayleigh scattering for each IMPROVE monitoring site is used in the new 

IMPROVE equation, as compared to a constant 10 Mm-1 value assumed in the original 

IMPROVE equation. 

The apportionment of the Small and Large components of Sulfate, Nitrate and Organic Mass is 

done as follows: 

[Large Sulfate] = [Total Sulfate] / 20 x [Total Sulfate], for [Total Sulfate] < 20 μg/m3

[Large Sulfate ] = [Total Sulfate], for [Total Sulfate] > 20 μg/m3

[Small Sulfate] = [Total Sulfate] – [Large Sulfate] 

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The same equations are used to apportion Total Nitrate and Total OMC among their Large and 

Small components. 

The total extinction (bext) in the new IMPROVE equations is the sum of all the extinction 

components associated with each PM species. The new IMPROVE equation adds Sea Salt and 

NO2 as noted above. In addition, site-specific Rayleigh background is used with the new 

IMPROVE equation: 

bext = bRay + bSulfate + bNitrate + bEC +bOMC + bSoil + bCM + bNaCl + bNO2

The Haze Index (HI) and Visual Range (VR) are calculated from the total extinction from the 

new IMPROVE equation using the same formulas as given above for the original IMPROVE 

equation. 

4.2.1.1.3 Justification for Using the New IMPROVE Equation 

The new IMPROVE equation was developed using the latest scientific information on PM 

species extinction properties combined with fitting reconstructed light extinction based on 

IMPROVE measured PM and NO2 concentrations with actual co-located measured light 

extinction (e.g., nephelometer measurements). Figure 4-1 displays example comparisons of 24-

hour light extinction using the original and new IMPROVE equations compared against 24-hour 

nephelometer measurements of light extinction at the VISTAS Great Smoky Mountains Class I 

area IMPROVE monitor. The original IMPROVE equation has a bias toward understating light 

extinction at the high end and overstating it at the low end, whereas the new IMPROVE equation 

does a better job in estimating light extinction from measured PM at all extinction levels. 

Because the new IMPROVE equation is based on more recent science and fits the observed light 

extinction values better, the VISTAS states have elected to perform their primary visibility 

projections using the new IMPROVE equation. Further justification of the new IMPROVE 

equation is provided in other VISTAS documents by Dr. Ivar Tombach and Air Resource 

Specialists available on the VISTAS website (http://www.vistassesarm.org/documents/FinalDocs.asp). Results using the original IMPROVE equation are 

presented in Section 5 as an alternative approach. 

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Figure 4-1. Comparisons of observed light extinction with reconstructed light extinction using the 

new (left) and original (right) IMPROVE equations at the Great Smoky Mountains National Park. 

4.2.2 Calculation of the Baseline Conditions 

The visibility Baseline Conditions for the worst 20 percent and best 20 percent days is calculated 

from the IMPROVE observations from the 2000-2004 period for each Class I area following 

EPA’s guidance (EPA, 2003a). The basic procedures for calculating the Baseline Conditions are 

as follows: 

1. Determine whether the observed IMPROVE data for each site and year satisfies EPA’s 

minimal data capture criteria (EPA, 2003a). If there are less than three years with valid 

data capture for the 2000-2004 Baseline, then the Baseline Conditions can not be 

calculated and data filling is needed. 

2. For each year in the 2000-2004 period with sufficient valid data, rank the visibility in 

terms of extinction or deciview using either the original or new IMPROVE equation and 

monthly average f(RH) factors (EPA, 2003a). 

3. For the worst 20 percent days, extract the 20% most impaired visibility days for each year 

(similarly for best 20 percent days extract 20% cleanest days). With a complete yearly 

data capture of IMPROVE 1:3 day sampling frequency this would result in 24 worst 20 

percent and 24 best 20 percent days in a year. 

4. For each worst 20 percent (or best 20 percent) day in each year, calculate 24-hour average 

visibility extinction using the IMPROVE measurements and either the original or new 

IMPROVE equation. Convert the daily extinction to daily deciview and then average 

across each year to get yearly average deciview extinction for the worst 20 percent (or best 

20 percent) days for each valid year from the 2000-2004 period. 

5. Average the annual average deciview worst 20 percent (or best 20 percent) days deciview 

across each valid year in the 2000-2004 period (minimum of 3 valid years required) to get 

the worst 20 percent (or best 20 percent) Baseline Conditions. 

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4.2.3 Data Filling for Sites with Insufficient Valid Data to Calculate Baseline Conditions 

Several Class I areas within and near the VISTAS region did not contain sufficient IMPROVE 

observations during the five-year 2000-2004 Baseline to have three valid years of data from 

which Baseline Conditions could be constructed: Chassahowitzka (CHAS), Florida, Cohutta 

(COHU), Georgia, St. Marks (SAMA), Florida, Shining Rock (SHRO), North Carolina, and 

Swan Quarter (SWAN), North Carolina within the VISTAS region, plus Breton Island (BRET), 

Louisiana, and Mingo (MING), Missouri in the CENRAP region. For these Class I areas, Air 

Resource Specialists developed substituted data sets (ftp://ftp.air-resource.com/VISTAS) so that 

at least three years of valid data were available from which valid Baseline Conditions could be 

calculated using EPA’s minimal valid data requirement (EPA, 2003a). These substituted 

IMPROVE databases are also available on the VIEWS website. More information on the data 

filling procedures can be found at the VIEWS website: (http://vista.cira.colostate.edu/views/). 

4.2.4 Natural Conditions 

EPA has published default Natural Conditions for Annual Average and the worst 20 percent and 

best 20 percent days using the old IMPROVE equation (EPA, 2003b). These EPA default worst 

20 percent Natural Conditions were updated to the new IMPROVE equation and are available on 

the VIEWS website (http://vista.cira.colostate.edu/views/). The default Natural Conditions were 

used in constructing the linear glidepaths to generate the 2018 Uniform Rate of Progress. These 

EPA default Natural Conditions are used as the anchor point for the glidepaths in 2064 that are 

provided in Appendix E for the VISTAS and nearby Class I areas. 

4.2.5 2018 Uniform Rate of Progress 

The Uniform Rate of Progress glidepath is constructed by generating a linear glidepath in 

deciviews from the Baseline Conditions in 2004 to Natural Conditions in 2064. Where the linear 

glidepath crosses 2018 is the 2018 Uniform Rate of Progress. Figure 4-2 displays an example 

linear glidepath for the Great Smoky Mountains Class I area in Tennessee-North Carolina. 

There are five years of sufficient valid IMPROVE data during the 2000-2004 Baseline with 

annual average worst 20 percent values of 30.71, 30.60, 29.94, 30.51 and 29.62 dv (blue 

diamonds) resulting in worst 20 percent Baseline Conditions of 30.28 dv that is placed as the 

starting point in 2004 for the glidepath. The ending point for the glidepath is 11.05 dv which is 

the default Natural Conditions for the worst 20 percent days using the new IMPROVE equation. 

The linear glidepath crosses 2018 at 25.79 dv which becomes the 2018 Uniform Rate of 

Progress. 

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Uniform Rate of Progress Glide Path

Great Smoky Mountains - 20% Worst Days

30.28 28.99

25.79

22.59

19.38

16.18

12.97

11.05

0

5

10

15

20

25

30

35

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

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation

Figure 4-2. Uniform Rate of Progress glidepath for Great Smoky Mountains (GRSM), 

Tennessee-North Carolina Class I area that linearly connects the 30.28 dv Baseline Conditions 

in 2004 with the 11.05 dv Natural Conditions in 2064 resulting in a 2018 Uniform Rate of 

Progress of 25.79 dv. 

4.3 EPA Default Approach for Visibility Projections 

For VISTAS model application for a single year (2002), EPA’s regional haze modeling guidance 

recommends developing Class I area-specific and PM species-specific RRFs based on the 

average concentrations for the worst 20 percent days from 2002 (EPA, 2007a). Thus, this is the 

methodology used to project 2018 visibility estimates in this section. For example, if 

SO4(2002)i and SO4(2018)i are the model estimated sulfate concentrations for the 2002 worst 20 

percent days (i=1...N) at a given Class I area for the 2002 and 2018 emission scenarios then the 

RRF for sulfate and this Class I area is given by: 

 RRF(SO4)i = ∑SO4(2018)i / ∑SO4(2002)i

4.3.3 Mapping of Modeling Results to the IMPROVE Measurements 

As noted above, to project future-year visibility at Class I areas the modeling results are used in a 

relative sense to scale current observed visibility for the worst 20 percent and best 20 percent 

visibility days using RRFs that are the ratio of modeling results for the future-year to currentyear. This scaling is done separately for each of the six components of light extinction in the 

IMPROVE equations. The CMAQ modeled species do not exactly match up with the 

IMPROVE PM species, thus assumptions must be made to map the modeled species to the 

IMPROVE PM species for the purpose of projecting visibility improvements. For example, 

CMAQ explicitly simulates ammonium and sulfate may or may not be fully neutralized in the 

model by ammonium. In contrast the IMPROVE equations assume sulfate is fully neutralized by 

ammonium. For the CMAQ Version 4.5 (September 15, 2005 release) model, the mapping of 

modeled species to IMPROVE equation PM species is listed in Table 4-2. 

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Table 4-2. Mapping of CMAQ V4.5 modeled species concentrations to IMPROVE PM 

components. 

IMPROVE 

Component 

CMAQ V4.3 Species 

Sulfate 1.375 x (ASO4J + ASO4I) 

Nitrate 1.29 x (ANO3J + ANO3I) 

LAC AECJ + AECI 

OMC AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI 

Soil A25J + A25I 

CM ACORS + ASEAS + ASOIL 

For the VISTAS visibility projections using the 2002 Typical and 2018 base case Base G2 

emission scenarios, the secondary organic aerosol (SOA) module in CMAQ V4.5 was enhanced 

(SOAmods) to include additional processes related to the generation of SOA from biogenic 

emissions. In particular, three new species have been added that represent SOA products from 

biogenic emission compounds that are not included in the standard version of CMAQ V4.5 

(Morris et al., 2006): 

• ASOC1 – SOA from biogenic sources (e.g., terpenes and isoprene) that has become 

polymerized so is no longer volatile. 

• ASOC2 – SOA from biogenic sesquiterpene and higher reactivity and higher yield 

monoterpene emissions. 

• ASOC3 – SOA from biogenic isoprene emissions. 

Thus, the species mapping for Organic Mass Carbon (OMC) and the CMAQ V4.5 SOAmods 

version of the model used in VISTAS visibility projections is as given in Table 4-2 only with the 

addition of the three new biogenic SOA species to OMC as follows: 

OMC = AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI + 

ASOC1 + ASOC2 + ASOC3 

4.3.4 Using Modeling Results to Project Changes in Visibility 

Modeling results are used in a relative fashion to project future-year visibility using relative 

response factors (RRFs). RRFs are expressed as the ratio of the modeling results for the futureyear to the results of the base year (2018/2002) and are Class I area and PM species specific. 

RRFs are applied to the Baseline Condition observed PM species to project future-year PM 

levels from which visibility can be assessed using the IMPROVE equations listed above. The 

following six steps are used to project future-year visibility for the worst 20 percent and best 20 

percent visibility days (discussion is for worst 20 percent days but also applies to best 20 percent 

days): 

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1. For each Class I area and each monitored day, daily visibility is ranked using IMPROVE 

observations and the IMPROVE equation (either original or new IMPROVE equation) 

for each year from the five-year baseline period (2000-2004) to identify the worst 20 

percent visibility days for each year (see Baseline Conditions discussion above). 

2. Use an air quality model to simulate a base year period (ideally the five-year Baseline 

period of 2000-2004, but for VISTAS just the 2002 annual period was simulated) and a 

future-year (e.g., 2018) and use the resulting information to develop Class I area-specific 

RRFs for each of the six components of light extinction in the IMPROVE equation (SO4, 

NO3, EC, OMC, Soil and CM). 

3. Multiply the RRF times the measured 24-hour PM concentration data for each day from 

the worst 20 percent days in each year from the five-year Baseline period to obtain 

projected future-year 24-hour PM concentrations for the worst 20 percent days and the 

five-year Baseline. 

4. Compute the future-year daily extinction using the IMPROVE equation and the projected 

PM concentrations for each of the worst 20 percent days in the five-year baseline from 

Step 3. 

5. For each of the worst 20 percent days within each year of the five-year baseline, convert 

the future-year daily extinction to deciview and average the daily deciview values within 

each of the five years separately to obtain five-years (or as many years with valid data in 

the 2000-2004 Baseline) of average deciview visibility for the worst 20 percent days. 

6. Average the five-years of average deciview visibility to obtain the future-year visibility 

Haze Index estimate that is the future-year estimated visibility. 

In calculating the RRFs, EPA draft guidance recommends selecting estimated PM species 

concentrations “near” the monitor by taking a spatial average of PM concentrations across a grid 

cell resolution dependent NX by NY array of cells centered on the grid cell containing the 

monitor. The NX x NY array of cells is grid resolution dependent with EPA recommending that 

NX=NY=1 for 36 km grids, NX=NY=3 for 12 km grids and NX=NY=7 for 4 km grids (EPA, 

2007a). VISTAS performed modeling using both a 36 km and 12 km grids, so used the grid cell 

containing the monitor or the 9 grid cells centered in the monitoring site when constructing the 

RRFs using the, respectively, 36 km and 12 km CMAQ modeling results. 

 

4.4 EPA Default 2018 Visibility Projections at VISTAS and Nearby Class I areas and 

Comparisons to 2018 Uniform Rate of Progress 

Using the EPA default visibility projection procedure described in Section 4.3 and the VISTAS 

2002 Typical Base G2 and 2018 Base G2 36 km and 12 km CMAQ modeling results, 2018 

visibility projections were made for VISTAS and nearby Class I areas. Appendix E details the 

2018 visibility projections for the worst 20 percent days and each Class I area in and nearby the 

VISTAS region using the new IMPROVE equation. 2018 visibility projections for the best 20 

percent days are given in Appendix F. Results for the Sipsey (SIPS) Alabama Class I area and 

the worst 20 percent days area are discussed in Section 4.4.1 below. Displays for 2018 visibility 

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projections for the worst 20 percent days and the other VISTAS Class I areas are provided in 

Appendix E and summarized in Section 4.4.2 

4.4.1 Example 2018 Base G2 Visibility Projections for Worst 20% Days Sipsey, Alabama 

The 2018 visibility projections for the Sipsey (SIPS), Alabama Class I area and the worst 20 

percent days are shown in Figure E-1 in Appendix E and reproduced in Figure 4-3 below. 

Similar worst 20 percent days 2018 visibility projections and glidepaths for all VISTAS and 

nearby Class I areas are provided in Appendix E. 

4.4.1.1 EPA Default 2018 Visibility Projections 

The 2018 Base G2 CMAQ 12 km visibility projection using the EPA default method (EPA, 

2007a) and comparison with the 2018 Uniform Rate of Progress for the worst 20 percent days for 

the SIPS Class I area is shown in Figure 4-3a (and Figure E-1a). The 2000-2004 Baseline 

Condition for SIPS is 29.03 dv and the 2018 Uniform Rate of Progress is 24.80 dv so that a 4.23 

dv reduction in visibility for the worst 20 percent days is needed to meet the 2018 Uniform Rate 

of Progress. The 2018 Base G2 12 km CMAQ projected visibility is 23.69 dv so the modeling 

predicts more visibility improvement (5.34 dv reduction) than the 2018 Uniform Rate of 

Progress (4.23 dv reduction). When looking at visibility projections across Class I areas it is 

useful to present the 2018 visibility projections as a percentage of the 2018 Uniform Rate of 

Progress; where 100% is meeting the 2018 Uniform Rate of Progress, greater than 100% 

surpassing the visibility improvements needed for the 2018 Uniform Rate of Progress and less 

than 100% means that less visibility improvement is achieved than the 2018 Uniform Rate of 

Progress. For 2018 Base G2 12 km CMAQ modeling at SIPS, 126% of the visibility reduction 

needed to meet the 2018 Uniform Rate of Progress is projected. Note that meeting the 2018 

Uniform Rate of Progress is not a requirement of the RHR SIPs, rather it just serves as a 

benchmark to compare progress toward Natural Conditions in 2064 and is designed to help states 

in selecting their 2018 reasonable progress goals. As clearly stated in EPA guidance “The 

glidepath is not a presumptive target, and States may establish a reasonable progress goal that 

provides for greater, lesser, or equivalent improvement as that described by the glidepath”

(EPA, 2007b). 

Figure 4-3b displays “StackedBar Chart” plots of observed and model estimated extinction for 

each of the worst 20 percent days in 2002 and the 2002 Typical Base G2 CMAQ simulation and 

the average across the worst 20 percent days. This figure allows a comparison of how well the 

model is reproducing the observed extinction at SIPS for the worst 20 percent days in 2002 

(more details on model performance were presented in Chapter 3 and Appendices B, C and D 

that evaluated the 2002 Actual Base G2 CMAQ 36 and 12 km simulations). 2002 worst 20 

percent days at SIPS are dominated by SO4 days (yellow) with OMC being the next most 

important PM component. During the winter there are also several days with high observed 

and/or modeled NO3 as well (Julian Days 80, 296, 338 and 341). For most of the worst 20 

percent days at SIPS the model reproduces the observed extinction reasonably well, although it 

does tend to understate SO4 and total extinction on several days. The observed average 

extinction across the 2002 worst 20 percent days (excluding Rayleigh and Sea Salt) at SIPS is 

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173 Mm-1, compared to a modeled value that is 27% (43 Mm-1) lower (127 Mm-1). This 

understatement is primarily due to the model understating the observed SO4 extinction 

(130 Mm-1) by 42 Mm-1. 

Figure 4-3c displays “Boxplots” of differences in modeled extinction for the 2002 worst 20 

percent days between the 2018 Base G2 and 2002 Typical Base G2 12 km CMAQ simulations. 

SO4 is always the largest component of the extinction that is reduced from 2002 to 2018 at SIPS 

for the worst 20 percent days. There are several days that exhibit very little extinction reduction 

going from 2002 to 2018 (Julian Days 80, 122, 260, 338, 341). These are either high NO3 days 

or days in which the observed SO4 extinction is greatly underestimated (see Figure 4-3c). On 

worst 20 percent days when the model grossly underestimates the observed extinction, the model 

is very stiff (i.e., shows little response to reductions in emissions, see Figure 4-3d). However, 

since the modeled RRFs used to project future-year visibility are based on the average model 

changes between 2018 and 2002 across all the 2002 worst 20 percent days, then the average 

RRFs are driven more by the higher modeled days and are less affected by the days with gross 

under predictions, as long as there are only a few days with such poor performance. 

The absolute modeling results are not used directly in the visibility projections, rather they are 

used to develop RRFs specific to each PM-species for each Class I area. That is, important 

attributes of Figures 4-3c and 4-3d are the relative changes in modeled PM species between the 

2002 and 2018 emission scenarios averaged across the worst 20 percent days that are represented 

by the last bar in each figure. These results are summarized for SIPS in Table 4-2 below. Table 

4-2 compares the average extinction across the 2002 worst 20 percent days at SIPS from the 

measured IMPROVE data (2000-2004), the modeled 2002 values and the modeled change in 

extinction between the 2018 and 2002 emissions scenarios. Although the results in Table 4-2 are 

not RRFs (RRFs are based on ratios of concentrations not extinction) they do show how the 

RRFs may magnify or deflate the importance of a modeled PM species when the RRFs are 

applied to the observed values in the projections. For example, the model estimates that CM is 

three times lower than observed and predicts a small increase (+10%) between 2002 to 2018 of 

less than 0.1 Mm-1. However, when this small 10% increase is applied to the higher observed 

CM extinction value it magnifies the modeled changes in CM in the 2018 visibility projections. 

Most of the changes in emissions from 2002 to 2018 are reductions in So2 and NOx emissions, 

with very little changes in carbon, soil and coarse mass. Therefore, visibility improvements are 

primarily due to reductions in SO4 and NO3, with very little change in EC, OMC, Soil and CM. 

Table 4-2. Observed and Modeled Extinction by Species Averaged Across the Worst 20 

Percent Days in 2002 at SIPS using 2002/2018 Base G2 12 km CMAQ results. 

2002 

Average 

Observed 

W20% 

(Mm-1) 

2002 

Average 

Modeled 

W20% 

(Mm-1) 

2018 

Average 

Modeled 

W20% 

(Mm-1)

2018-2002 

Reduction 

(Mm-1) 

2018-2002 

Reduction 

(%) 

bSO4 130.0 88.1 40.0 -48.1 -54% 

bNO3 10.1 9.5 7.7 -1.7 -19% 

bOMC 23.3 25.6 21.6 -3.0 -12% 

bEC 5.9 2.6 1.8 -0.8 -31% 

bSOIL 1.0 1.4 1.7 +0.3 +18% 

bCM 3.1 0.6 0.7 +0.07 -10% 

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Uniform Rate of Progress Glide Path

Sipsey - Worst 20% Days

29.03

27.52

24.80

21.78

18.76

15.74

12.72

10.90

26.43

23.69

0

5

10

15

20

25

30

35

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

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Figure 4-3a. 2018 Visibility Projections and 2018 Uniform Rate of Progress Glidepaths in 

Deciview for Sipsey (SIPS), Alabama and Worst 20 Percent (W20%) days Using 2002/2018 

Base G2 CMAQ 12 km Modeling Results. 

Worst 20% Obs (left) vs 2002gt2a (right) at SIPS1

0

50

100

150

200

250

300

350

80 122 131 143 152 155 161 185 188 191 215 218 221 224 251 254 260 263 284 296 338 341 _ _ _ Avg

Julian Day in Worst 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-3b. Comparison of Observed (left) and 2002 Base G2 CMAQ 12 km Modeled (right) 

Daily Extinction for Sipsey (SIPS), Alabama and Worst 20 Percent (W20%) days in 2002. 

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Bext Response (2018g2b - 2002gt2a) at SIPS1 on Worst 20% Days

-180

-160

-140

-120

-100

-80

-60

-40

-20

0

20

80 122 131 143 152 155 161 185 188 191 215 218 221 224 251 254 260 263 284 296 338 341 Avg

Julian Day

Delta Bext (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-3c. Differences in Modeled 2002 and 2018 Base G2 12 km CMAQ Results (2018-

2002) Daily Extinction for Sipsey (SIPS), Alabama and Worst 20 Percent (W20%) Days in 2002. 

4.4.2 Summary 2018 Visibility Projections for Worst 20 Percent Days Across Class I 

Areas 

Figure 4-4 displays a “DotPlot” of 2018 visibility projections using the 2002 Typical and 2018 

Base G2 CMAQ 12 km and 36 km modeling results. DotPlots present the 2018 visibility 

projections as a percentage of achieving the 2018 Uniform Rate of Progress . For example, at 

SIPS the 2018 Base G2 modeling achieved 126% and 127% of the visibility reduction needed to 

meet the 2018 Uniform Rate of Progress using the 12 km and 36 km CMAQ modeling results, 

respectively. So the dots under SIPS are plotted at 126% and 127%, which are above the 100% 

line so the 2018 Base G2 base case modeling results achieve the 2018 Uniform Rate of Progress 

at SIPS. Class I areas’ with dots above 100% achieve more visibility improvements than the 

2018 Uniform Rate of Progress, whereas Class I areas’ with dots that are under 100% do not 

meet the 2018 Uniform Rate of Progress. 

The Class I areas within the Appalachian Mountains all achieve the 2018 Uniform Rate of 

Progress, typically by 150% or more. Essentially most of the 20% worst days at these sites are 

dominated by sulfate and occur during the warmer months; reductions in SO2 under the 2018 

Base G2 emissions scenario results in large sulfate reductions. Mammoth Cave (MACA) and 

Sipsey (SIPS) in the interior of the eastern U.S. west of the Appalachian Mountains also achieve 

the 2018 Uniform Rate of Progress with visibility reductions of approximately 120% to 125% of 

the Uniform Rate of Progress. The smaller average improvements at MACA and SIPS are 

attributed to some 20% worst days occurring in the winter months with elevated NO3 levels (see 

Appendix E). These days show little change in SO4 and NO3 by 2018 so the average RRF are 

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smaller than at those the Appalachian Mountains Class I areas. The Swanquarter (SWAN) 

coastal site in North Carolina achieves 140% of the 2018 Uniform Rate of Progress whereas the 

Cape Romain (ROMA) coastal site in South Carolina achieves 112% of the 2018 Uniform Rate 

of Progress. The Florida and Georgia Class I coastal areas are projected to be close to the 2018 

Uniform Rate of Progress. Chassahowitzka (CHAS) is estimating more visibility improvements 

than the 2018 Uniform Rate of Progress (113%-115%), with St. Marks (SAMA) right on the 

2018 Uniform Rate of Progress (100% and 106%). Okefenokee (OKEF) and Everglades 

(EVER) are estimate to achieve less visibility improvement than the 2018 Uniform Rate of 

Progress. 

For a several sites, GRSM, SHRO, and EVER, there are fairly large differences between the 12 

km and 36 km CMAQ 2018 visibility projections; these differences are discussed in more detail 

below. 

CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

COHU1DOSO1

GRSM1

JARI1

LIGO1CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent of target reduction achieved

12km NIA

36km NIA

VISTAS non-VISTAS

Figure 4-4. 2018 Base G2 CMAQ 36 km and 12 km Visibility Projections for VISTAS and 

Nearby Class I areas Using DotPlots that Express 2018 Visibility as a Percentage of Meeting 

the 2018 URP Goal On the Deciview Linear Glidepath. 

Figure 4-5 displays the model estimated absolute change in extinction (Mm-1) averaged across 

the 2002 worst 20 percent days at Class I areas in and near the VISTAS region. The largest 

modeled reductions are in SO4 extinction. There are some differences in the modeled extinction 

reductions between the 12 km grid modeling results (Figure 4-5a) and the 36 km grid modeling 

results (Figure 4-5b). In particular, the 12 km modeling results exhibit much higher NO3 

increases at several Class I areas including GRSM, SHRO and EVER. Figure 4-6 displays the 

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F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-18 

percent change in the projected PM extinction by PM species for each VISTAS and nearby Class 

I area for the worst 20 percent days (i.e., the relative modeled change). These results are 

analogous to the RRFs used in the visibility projections. There are some significant differences 

in the relative changes in the 12 km (Figure 4-6a) and 36 km (Figure 4-6b) modeling results 

between 2002 and 2018 NO3 values for several of the Class I areas. In particular, the relative 

increases in NO3 in 2018 for GRSM, SHRO and EVER are much higher using the 12 km 

modeling results (> 100%) than the 36 km modeling results (< 100%). These higher relative 

NO3 increases using the 12 km modeling results explain why the 12 km 2018 visibility 

projections are not as optimistic as the 36 km modeling results (see Figure 4-4). The modeled 

NO3 concentrations for the 2002 base case at these three Class I areas are near zero (see Figures 

E-3c, E-9c and E-12c) for the 12 km modeling results and lower than the 2002 base case NO3 

for the 36 km results. Thus, although the model is estimating extremely small increases in NO3 

in the 2018 scenario at 12 km, because it is starting with near zero NO3 in the 2002 base case it 

estimates a relatively larger percentage change in NO3 than in the 36 km scenario. This larger 

percentage change for the 12 km results is then applied to higher observed NO3 concentrations 

from the 2000-2004 Baseline in the 2018 projections. When the modeled NO3 (or other species) 

concentrations approach zero, there is less confidence in the modeled relative changes in 

concentrations as model noise may affect the results. To examine this issue in more detail, in 

Chapter 5 we look at alternative projection techniques in which we only use the modeled derived 

RRFs for NO3 if the 2002 base case NO3 is greater than 0.1 μg/m3

. 

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F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-19 

 

Average change in extinction components from 2002 baseline to 2018 projected

at VISTAS sites using 2018g2b/2002gt2a RRFs

-120.0

-100.0

-80.0

-60.0

-40.0

-20.0

0.0

20.0

COHU1

CADI1

DOSO1

GRSM1

JARI1

LIGO1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

IMPROVE site

[2018 project Bext - (2000-2004 baseline Bext)] (1/Mm)

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Average change in extinction components from 2002 baseline to 2018 projected

at non-VISTAS sites using 2018g2b/2002gt2a RRFs

-70.0

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

10.0

BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1

IMPROVE site

[2018 project Bext - (2000-2004 baseline Bext)] (1/Mm)

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-5a. Absolute Model Estimated Changes in Extinction (Mm-1) by PM Species for Class 

I Areas in the VISTAS region (top) and Near VISTAS (bottom) using CMAQ 2002/2018 Base G2 

12 km modeling results. 

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F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-20 

Average change in extinction components from 2002 baseline to 2018 projected

at VISTAS sites using 2018g2a/2002gt2a RRFs

-120.0

-100.0

-80.0

-60.0

-40.0

-20.0

0.0

20.0

COHU1

CADI1

DOSO1

GRSM1

JARI1

LIGO1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

IMPROVE site

[2018 project Bext - (2000-2004 baseline Bext)] (1/Mm)

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Average change in extinction components from 2002 baseline to 2018 projected

at non-VISTAS sites using 2018g2a/2002gt2a RRFs

-80.0

-70.0

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

10.0

BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1

IMPROVE site

[2018 project Bext - (2000-2004 baseline Bext)] (1/Mm)

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-5b. Absolute Model Estimated Changes in Extinction (Mm-1) by PM Species for Class 

I Areas in the VISTAS region (top) and Near VISTAS (bottom) using CMAQ 2002/2018 Base G2 

36 km modeling results.

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Percent change in extinction components from 2002 baseline to 2018 projected

at VISTAS sites using 2018g2b/2002gt2a RRFs

-100%

-50%

0%

50%

100%

150%

200%

COHU1

CADI1

DOSO1

GRSM1

JARI1

LIGO1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Percent change in extinction components from 2002 baseline to 2018 projected

at non-VISTAS sites using 2018g2b/2002gt2a RRFs

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-6a. Percent Change In Modeled Extinction by PM Species Averaged Across the 2002 

Worst 20 Percent Days for Class I areas in the VISTAS region (top) and near VISTAS (bottom) 

using 2002/2018 Base G2 12 km CMAQ results. 

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Percent change in extinction components from 2002 baseline to 2018 projected

at VISTAS sites using 2018g2a/2002gt2a RRFs

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

COHU1

CADI1

DOSO1

GRSM1

JARI1

LIGO1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Percent change in extinction components from 2002 baseline to 2018 projected

at non-VISTAS sites using 2018g2a/2002gt2a RRFs

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-6b. Percent Change In Modeled Extinction by PM Species Averaged Across the 2002 

Worst 20 Percent Days for Class I areas in the VISTAS region (top) and near VISTAS (bottom) 

using 2002/2018 Base G2 36 km CMAQ results.

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F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-23 

4.4.3 2018 Visibility Projections for the Best 20 Percent Days 

Figure 4-7a displays the 2018 visibility projections for the Best 20 percent days at Sipsey, similar 

plots for other Class I areas are given in Appendix F. Recall that the RHR goal for the Best 20 

percent days is a no worsening in visibility impairment from current conditions. Thus the 2018 

visibility projection 14.37 dv) is compared with the current observed visibility for the Best 20 

percent days from the Baseline (16.67 dv). Also shown in Figure 4-7a is the natural conditions 

for the Best 20 percent days that is plotted at 2064 (5.03 dv). Figure 4-7b compares the predicted 

and observed daily extinction for the best 20 percent days at Sipsey. In general the model 

predicts total extinction on the clean days fairly well, although the observed extinction is 

overstated on Julian Days 26 and 176. The differences in the modeled extinction between the 

2018 and 2002 Base G2 emission scenarios on the best 20 percent days at Sipsey are shown in 

Figure 4-7c. Visibility improvements on the best 20 percent days are also primarily due to SO4 

and NO3 reductions, although it is not completely dominated by SO4 as seen for the worst 20 

percent days and reductions in extinction due to EC are also evident. Although not as significant 

at Sipsey, at many of the Class I areas the reductions in SO4 is resulting in increases in NO3 that 

is likely due to “nitrate replacement” (see Appendix F). Nitrate replacement occurs when the 

reductions in SO4 results in the release of ammonium (NH4) molecules that were bonded to the 

SO4 that then react with gaseous HNO3 to form particulate NO3. As SO4 can bind up to two 

NH4 molecules [SO4 (NR4) 2-], than nitrate replacement could potentially form two particulate 

NO3 [NO3 NH4] for every one SO4 molecule reduced, however this is unlikely in the real 

atmosphere due to aerosol thermodynamics and the fact that NO3 formation is not always 

completely ammonia limited. We also see occasions when SO4 increases when NO3 is reduced 

even thought SO2 emissions are reduced as well. One potential chemical reason for increased 

SO4 when NO3 is reduced is through aqueous-phase chemistry formation of SO4 which is pH 

sensitive. Reductions in NO3 will make water droplets less acidic that in turn can result in faster 

conversion of SO2 to SO4 via aqueous-phase chemistry. 

Figure 4-8 displays a DotPlot of 2018 visibility projections for the Best 20 percent days using the 

CMAQ 12 km and 36 km Base G2 2002/2018 modeling results. For the Best 20 percent days 

2018 projections, the DotPlots display the percent reductions from current visibility conditions 

for the 20 percent best days. With one exception, all of the 2018 visibility projections show 

improved visibility from current visibility conditions for the best 20 percent days. The exception 

is for Everglades that shows a worsening of visibility for the best 20 percent days when the 36 

km CMAQ modeling results are used, but improved visibility using the 12 km CMAQ modeling 

results. This issue was examined more closely using the side-by-side model/observed 

StackedBar charts and BoxPlots of extinction differences between modeled 2018 and 2002 

values for the best 20 percent days at EVER (Figure 4-9). Although the 36 km and 12 km 

modeling results at EVER look similar, the relative changes are quite different as shown by the 

percent changes in the average extinction across the 2002 worst 20 percent days between the 

CMAQ 2018 and 2002 simulation using the 36 and 12 km grids (Figure 4-10). Using the 12 km 

NO3 averaged across the worst 20 percent days is projected to increase approximately 30 percent 

from 2002 to 2018. However, when the 36 km grid is used the projected NO3 increase is 

approximately 550%. An examination of the results more closely reveals that CMAQ 36 km and 

12 km modeling both project that NO3 extinction increases by approximately 0.001 Mm-1 from 

2002 to 2018 averaged across the best 20 percent days. However, the CMAQ 36 km CMAQ 

results is starting with a ~0.001 Mm-1 NO3 extinction in 2002 compared to a ~0.01 Mm-1 NO3 

extinction using the 12 km grid so the 36 km modeling results estimates a much larger relative 

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change in extinction. When the modeled NO3 concentrations are so low, the reactive changes 

are less reliable which is why one alternative projection approach is not to use the modeled RRFs 

when concentrations are less than 0.1 μg/m3

.

Reasonable Progress Assessment Base G2, new IMPROVE Algorithm

Sipsey - Best 20% Days

15.57 15.57 15.57

14.37

5.03

0

5

10

15

20

25

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

Year

Haziness Index (Deciviews)

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

Figure 4-7a. 2018 visibility projections for Best 20 percent days at Sipsey, Alabama using Base 

G2 12 km CMAQ results and comparisons with current (2000-2004) observed Best 20 percent 

days and natural conditions for the best days (plotted at 2064). 

Best 20% Obs (left) vs 2002gt2a (right) at SIPS1

0

20

40

60

80

100

120

11 14 26 32 59 77 89 113 116 167 176 179 212 230 257 302 308 347 356 359 362 _ _ _ _ Avg

Julian Day in Best 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-7b. Compared of predicted and observed daily extinction for the best 20 percent days 

at the Sipsey, Alabama Class I area and the CMAQ 2002 Typical Base G2 12 km simulation. 

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Bext Response (2018g2b - 2002gt2a) at SIPS1 on Best 20% Days

-60

-50

-40

-30

-20

-10

0

10

20

11 14 26 32 59 77 89 113 116 167 176 179 212 230 257 302 308 347 356 359 362 Avg

Julian Day

Delta Bext (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-7c. Modeled changes in daily extinction using CMAQ 12 km results between the 2018 

and 2002 emission scenarios for the best 20 percent days at the Sipsey, Alabama Class I area. 

Figure 4-8. 2018 visibility projections at VISTAS and nearby Class I areas for the Best 20 

percent days using the CMAQ 2002/2018 Base G2 12 km and 36 km modeling results. 

CMAQ 2018g2a/2002gt2a Visibility Projection for VISTAS+ sites on Best 20% 

days -50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50% COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1

Percent of target reduction achieved

12km

36km

VISTAS non-VISTAS

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F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-26 

Best 20% Obs (left) vs 2002gt2a (right) at EVER1

0

5

10

15

20

25

30

23 29 32 71 80 104 134 137 140 164 176 191 206 212 242 245 248 251 254 284 287 296 299 302 _ Avg

Julian Day in Best 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs 2002gt2a (right) at EVER1

0

5

10

15

20

25

23 29 32 71 80 104 134 137 140 164 176 191 206 212 242 245 248 251 254 284 287 296 299 302 _ Avg

Julian Day in Best 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Bext Response (2018g2b - 2002gt2a) at EVER1 on Best 20% Days

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

1

23 29 32 71 80 104 134 137 140 164 176 191 206 212 242 245 248 251 254 284 287 296 299 302 Avg

Julian Day

Delta Bext (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Bext Response (2018g2a - 2002gt2a) at EVER1 on Best 20% Days

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

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23 29 32 71 80 104 134 137 140 164 176 191 206 212 242 245 248 251 254 284 287 296 299 302 Avg

Julian Day

Delta Bext (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-9. Comparison of StackedBar predicted and observed extinction (top) and BoxPlots 

modeled 2018-2002 extinction difference plots for Everglades using the 12 km (left) and 36 km 

(CMAQ) modeling results. 

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Percent change in extinction components from 2002 baseline to 2018 projected

at VISTAS sites using 2018g2b/2002gt2a RRFs on Best 20% days

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

COHU1

CADI1

DOSO1

GRSM1

JARI1

LIGO1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Percent change in extinction components from 2002 baseline to 2018 projected

at VISTAS sites using 2018g2a/2002gt2a RRFs on Best 20% days

-100%

0%

100%

200%

300%

400%

500%

600%

COHU1

CADI1

DOSO1

GRSM1

JARI1

LIGO1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-10. Percent Change In Modeled Extinction by PM Species Averaged Across the 2002 

Best 20 Percent Days for Class I areas in the VISTAS region using the CMAQ 12 km (top) and 

36 km (bottom) modeling results.

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F:\VISTAS Phase II\TSD\draft#2\Chapter_6_References2.doc 6-1 

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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. 

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APPENDIX A 

Emission Processing 

Selected QA Summary Reports and Figures 

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

Emissions Density Plot (January 17, 2002)

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

Density Plot (January 17, 2002)

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

Density Plot (January 17, 2002) – ISOP

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

Diurnal Distribution – ALD2 

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

Vertical Distribution – CO.

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Figure A-6. VISTAS BaseG2 Elevated Fire Source Emissions Summary – 2002 Annual Domainwide Temporal Distribution – OLE 

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Table A.1 – VISTAS BaseG2 Area Source Emissions Summary – 12km State Level Emission Totals (July 18, 2002). Stationary area Processed as Area sources Base inventory year 2002 Gridding matrix applied for gridVISTA12_168X177 No speciation matrix applied Temporal factors applied for episode from  Thursday July 18, 2002 at 000000 to  Thursday July 18, 2002 at 230000 Annual total data basis in report Date ; Region; State ; CO ; NOX ; VOC ; NH3 ; SO2 ; PM10 ; PM2_5 ; 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+03  07/18/2002; 005000;Arkansas ; 0.32214E+03; 0.59925E+02; 0.17541E+03; 0.39077E+03; 0.77625E+02; 0.93498E+03; 0.21273E+03; 0.72227E+03  07/18/2002; 009000;Connecticut ; 0.74329E+01; 0.90859E+01; 0.12071E+03; 0.14411E+02; 0.53008E+00; 0.47080E+02; 0.78904E+01; 0.39190E+02  07/18/2002; 010000;Delaware ; 0.57666E+01; 0.40146E+01; 0.23986E+02; 0.43931E+02; 0.12991E+01; 0.27233E+02; 0.43240E+01; 0.22909E+02  07/18/2002; 011000;District of Columbia; 0.15917E+01; 0.15292E+01; 0.15007E+02; 0.22391E-01; 0.17404E+01; 0.83408E+01; 0.14891E+01; 0.68518E+01  07/18/2002; 012000;Florida ; 0.67168E+02; 0.70148E+02; 0.10590E+04; 0.99305E+02; 0.90880E+02; 0.71195E+03; 0.93626E+02; 0.61833E+03  07/18/2002; 013000;Georgia ; 0.13696E+03; 0.78878E+02; 0.73790E+03; 0.24723E+03; 0.15909E+03; 0.80410E+03; 0.13605E+03; 0.66805E+03  07/18/2002; 017000;Illinois ; 0.64822E+02; 0.54397E+02; 0.49044E+03; 0.15470E+03; 0.10554E+02; 0.18652E+04; 0.36050E+03; 0.15047E+04  07/18/2002; 018000;Indiana ; 0.58957E+02; 0.53330E+02; 0.32822E+03; 0.21196E+03; 0.16105E+03; 0.17266E+04; 0.34249E+03; 0.13841E+04  07/18/2002; 019000;Iowa ; 0.40775E+02; 0.42057E+01; 0.22173E+03; 0.54576E+03; 0.14195E+01; 0.13372E+04; 0.25094E+03; 0.10863E+04  07/18/2002; 020000;Kansas ; 0.28911E+03; 0.29621E+02; 0.88596E+02; 0.43985E+02; 0.88975E+01; 0.70323E+03; 0.15211E+03; 0.55112E+03  07/18/2002; 021000;Kentucky ; 0.77191E+02; 0.10531E+03; 0.23546E+03; 0.11256E+03; 0.99659E+02; 0.42778E+03; 0.81534E+02; 0.34625E+03  07/18/2002; 022000;Louisiana ; 0.16404E+04; 0.28481E+03; 0.29980E+03; 0.19668E+03; 0.23245E+03; 0.88853E+03; 0.28283E+03; 0.60571E+03  07/18/2002; 024000;Maryland ; 0.21134E+03; 0.21519E+02; 0.20890E+03; 0.66855E+02; 0.16790E+02; 0.18854E+03; 0.47790E+02; 0.14075E+03  07/18/2002; 025000;Massachusetts ; 0.57736E+01; 0.38607E+01; 0.20534E+02; 0.56228E+01; 0.26920E+01; 0.16076E+02; 0.37205E+01; 0.12356E+02  07/18/2002; 026000;Michigan ; 0.30982E+02; 0.40472E+02; 0.31871E+03; 0.10240E+03; 0.71099E+02; 0.74950E+03; 0.13415E+03; 0.61535E+03  07/18/2002; 027000;Minnesota ; 0.20006E+02; 0.14727E+02; 0.66111E+02; 0.11717E+03; 0.38086E+01; 0.61705E+03; 0.10937E+03; 0.50769E+03  07/18/2002; 028000;Mississippi ; 0.42895E+01; 0.22679E+01; 0.30101E+03; 0.14251E+03; 0.57813E+00; 0.55921E+03; 0.81543E+02; 0.47766E+03  07/18/2002; 029000;Missouri ; 0.20959E+03; 0.64235E+02; 0.27642E+03; 0.35350E+03; 0.12071E+03; 0.33762E+04; 0.58408E+03; 0.27922E+04  07/18/2002; 031000;Nebraska ; 0.32840E+00; 0.66370E-01; 0.10041E+01; 0.17089E+01; 0.25804E-01; 0.11599E+02; 0.21084E+01; 0.94902E+01  07/18/2002; 034000;New Jersey ; 0.14231E+02; 0.21007E+02; 0.30461E+03; 0.48417E+02; 0.79506E+01; 0.65704E+02; 0.13140E+02; 0.52564E+02  07/18/2002; 036000;New York ; 0.48058E+02; 0.93906E+02; 0.58074E+03; 0.10722E+03; 0.13600E+03; 0.37438E+03; 0.64907E+02; 0.30947E+03  07/18/2002; 037000;North Carolina ; 0.35788E+03; 0.69702E+02; 0.41336E+03; 0.47466E+03; 0.86454E+01; 0.36327E+03; 0.62859E+02; 0.30041E+03  07/18/2002; 039000;Ohio ; 0.33951E+02; 0.40539E+02; 0.46160E+03; 0.19957E+03; 0.22287E+02; 0.10241E+04; 0.19312E+03; 0.83099E+03  07/18/2002; 040000;Oklahoma ; 0.13292E+03; 0.56404E+02; 0.51844E+02; 0.10585E+03; 0.69006E+01; 0.68486E+03; 0.12595E+03; 0.55891E+03  07/18/2002; 042000;Pennsylvania ; 0.31625E+03; 0.55051E+02; 0.54914E+03; 0.22572E+03; 0.73776E+02; 0.59936E+03; 0.11511E+03; 0.48427E+03  07/18/2002; 044000;Rhode Island ; 0.81695E+01; 0.41245E+01; 0.69566E+02; 0.18260E+01; 0.41886E+01; 0.11126E+02; 0.25125E+01; 0.86136E+01  07/18/2002; 045000;South Carolina ; 0.10702E+03; 0.46035E+02; 0.30912E+03; 0.76314E+02; 0.30258E+02; 0.31972E+03; 0.52325E+02; 0.26739E+03  07/18/2002; 046000;South Dakota ; 0.00000E+00; 0.00000E+00; 0.35041E-02; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00  07/18/2002; 047000;Tennessee ; 0.75720E+02; 0.30086E+02; 0.35511E+03; 0.82795E+02; 0.77852E+02; 0.38690E+03; 0.77792E+02; 0.30911E+03  07/18/2002; 048000;Texas ; 0.28857E+03; 0.15916E+03; 0.36398E+03; 0.16190E+03; 0.91386E+02; 0.25000E+04; 0.42791E+03; 0.20721E+04  07/18/2002; 051000;Virginia ; 0.16641E+03; 0.11720E+03; 0.32211E+03; 0.12067E+03; 0.29423E+03; 0.26361E+03; 0.48840E+02; 0.21477E+03  07/18/2002; 053000;Washington ; 0.00000E+00; 0.00000E+00; 0.47969E-02; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00  07/18/2002; 054000;West Virginia ; 0.36476E+02; 0.26373E+02; 0.14474E+03; 0.31362E+02; 0.26887E+02; 0.74822E+02; 0.20668E+02; 0.54154E+02  07/18/2002; 055000;Wisconsin ; 0.37344E+02; 0.17641E+02; 0.21000E+03; 0.20515E+03; 0.53226E+01; 0.31806E+03; 0.64998E+02; 0.25306E+03 ################################################################################################################################################################## 

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Table A.2 – VISTAS BaseG2 Point Source Emissions Summary – Facility-Stack Level Emission Totals. Processed as Point sources Base inventory year 2002 No gridding matrix applied No speciation matrix applied No temporal factors applied Annual total data basis in report Source ID ; Region; SCC ; Plant ID; Char 1; Char 2; Char 3; Stk Ht; Stk Dm; Stk Tmp; Stk Vel; Plt Name; CO ; NOX ; VOC ; NH3 ; SO2 ; PM10 ; 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-06  1443; 012069;2610000500; 31537 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31537 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1444; 012069;2610000500; 31538 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31538 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1445; 012069;2610000500; 31539 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31539 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1446; 012069;2610000500; 31540 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31540 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1447; 012069;2610000500; 31541 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31541 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1448; 012069;2610000500; 31542 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31542 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1449; 012069;2610000500; 31543 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31543 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1450; 012069;2610000500; 31544 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31544 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1451; 012069;2610000500; 31545 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31545 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1452; 012069;2610000500; 31546 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31546 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1453; 012069;2610000500; 31547 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31547 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1454; 012069;2610000500; 31548 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31548 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1455; 012069;2610000500; 31549 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31549 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1456; 012069;2610000500; 31550 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31550 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1457; 012069;2610000500; 31551 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31551 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1458; 012069;2610000500; 31552 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31552 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1459; 012069;2610000500; 31553 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31553 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1460; 012069;2610000500; 31554 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31554 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1461; 012069;2610000500; 31555 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31555 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1462; 012069;2610000500; 31556 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31556 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1463; 012069;2610000500; 31557 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31557 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1464; 012069;2610000500; 31558 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31558 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1465; 012069;2610000500; 31559 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31559 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1466; 012069;2610000500; 31560 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31560 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1467; 012069;2610000500; 31561 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31561 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1468; 012069;2610000500; 31562 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31562 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1469; 012069;2801500000; 32004 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32004 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1470; 012069;2801500000; 32005 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32005 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1471; 012069;2801500000; 32006 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32006 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1472; 012069;2801500000; 32007 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32007 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1473; 012069;2801500000; 32008 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32008 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1474; 012069;2801500000; 32009 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32009 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1475; 012069;2801500000; 32010 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32010 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1476; 012069;2801500000; 32011 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32011 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1477; 012069;2801500000; 32012 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32012 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1478; 012069;2801500000; 32013 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32013 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1479; 012069;2801500000; 32014 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32014 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1480; 012069;2801500000; 32015 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32015 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1481; 012069;2801500000; 32016 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32016 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1482; 012069;2801500000; 32017 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32017 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1483; 012069;2801500000; 32018 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32018 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1484; 012069;2801500000; 32019 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32019 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1485; 012069;2801500000; 32020 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32020 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1486; 012069;2801500000; 32021 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32021 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1487; 012069;2801500000; 32022 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32022 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1488; 012069;2801500000; 32023 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32023 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1489; 012069;2801500000; 32024 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32024 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1490; 012069;2801500000; 32025 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32025 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1491; 012069;2801500000; 32026 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32026 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1492; 012069;2801500000; 32027 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32027 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1493; 012069;2801500000; 32028 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32028 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1494; 012069;2801500000; 32029 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32029 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1495; 012069;2801500000; 32030 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32030 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1496; 012069;2801500000; 32031 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32031 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1497; 012069;2801500000; 32032 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32032 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1498; 012069;2801500000; 32033 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32033 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1499; 012069;2801500000; 32034 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32034 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1500; 012069;2801500000; 32035 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32035 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1501; 012069;2801500000; 32036 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32036 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1502; 012069;2801500000; 32037 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32037 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1503; 012069;2801500000; 32038 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32038 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1504; 012069;2801500000; 32039 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32039 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1505; 012069;2801500000; 32040 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32040 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1506; 012069;2801500000; 32041 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32041 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1507; 012069;2801500000; 32042 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32042 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1508; 012069;2801500000; 32043 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32043 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1509; 012069;2801500000; 32044 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32044 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1510; 012069;2801500000; 32045 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32045 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1511; 012069;2801500000; 32046 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32046 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1512; 012069;2801500000; 32047 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32047 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1513; 012069;2801500000; 32048 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32048 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1514; 012069;2801500000; 32049 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32049 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1515; 012069;2801500000; 32050 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32050 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1516; 012069;2801500000; 32051 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32051 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1517; 012069;2801500000; 32052 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32052 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1518; 012069;2801500000; 32053 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32053 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06  1519; 012069;2801500000; 32054 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32054 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06 

Pre-Hearing Draft

Regional Haze SIP for TN Class I Areas

Appendix M.1 - 193

January 18, 2008
A-10 

Table A.3 – VISTAS BaseG2 Point Source Emissions Summary – Hourly SCC Level Emission Totals.Processed as Point sources Base inventory year 2002 No gridding matrix applied No speciation matrix applied Temporal factors applied for episode from  Thursday July 18, 2002 at 000000 to  Friday July 19, 2002 at 000000 Annual total data basis in report Date ; Hour; SCC ; CO ; NOX ; VOC ; NH3 ; SO2 ; PM10 ; PM2_5 ; 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-01  07/18/2002; 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-01  07/18/2002; 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-05  07/18/2002; 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+00  07/18/2002; 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+01  07/18/2002; 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+00  07/18/2002; 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-01  07/18/2002; 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-01  07/18/2002; 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-01  07/18/2002; 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+01  07/18/2002; 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-01  07/18/2002; 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-02  07/18/2002; 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-01  07/18/2002; 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+01  07/18/2002; 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-01  07/18/2002; 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+00  07/18/2002; 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-01  07/18/2002; 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+01  07/18/2002; 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+00  07/18/2002; 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+00  07/18/2002; 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+00  07/18/2002; 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+00  07/18/2002; 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+00  07/18/2002; 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+00  07/18/2002; 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+00  07/18/2002; 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+00  07/18/2002; 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-01  07/18/2002; 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-04  07/18/2002; 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+00  07/18/2002; 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-03  07/18/2002; 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-03  07/18/2002; 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+00  07/18/2002; 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+00  07/18/2002; 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-01  07/18/2002; 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+00  07/18/2002; 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-01  07/18/2002; 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-02  07/18/2002; 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+00  07/18/2002; 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-02  07/18/2002; 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-01  07/18/2002; 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-01  07/18/2002; 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-01  07/18/2002; 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-02  07/18/2002; 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-09 

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Table A.4 – VISTAS BaseG2 Onroad Mobile Source Emissions Summary – Daily County Level Speciated Emission Totals. Processed as Mobile sources Base inventory year 2018 No gridding matrix applied No speciation matrix applied Temporal factors applied for episode from  Thursday July 18, 2002 at 000000 to  Thursday July 18, 2002 at 230000 Annual total data basis in report Date ; Region; State ; County ; EXR__CO ; EXR__NOX ; EXR__TOG ; EXR__SO4 ; EXR__OCARB25 ; EXR__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-01  07/18/2002; 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+00  07/18/2002; 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-01  07/18/2002; 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-01  07/18/2002; 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-01  07/18/2002; 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-01  07/18/2002; 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-01  07/18/2002; 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+00  07/18/2002; 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-01  07/18/2002; 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-01  07/18/2002; 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-01  07/18/2002; 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-01  07/18/2002; 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-01  07/18/2002; 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-01  07/18/2002; 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 

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APPENDIX B 

Air Quality Model Performance Evaluation: 

Regional Model Evaluation across the VISTAS Region

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B.1 OVERVIEW 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

means to discuss the overall model performance. 

B.2 CMAQ Evaluation Methodology 

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

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

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

B.2.1 Evaluation Approach 

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

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

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

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

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

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

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

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

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

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

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

absorption). 

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

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

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

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

method (DDM) and source apportionment (SA). 

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

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

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

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

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

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

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

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

B.2.2 Particulate Matter and Component Species 

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

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

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

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

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

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

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

including: 

• Sulfate (SO4) 

• Nitrate (NO3) 

• Ammonium (NH4) 

• Organic Matter Carbon (OMC) 

• Elemental Carbon (EC) 

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

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

or other PM) 

• Sea Salt (NaCl) 

B.2.3 Ambient Air Quality Data for VISTAS Model Evaluation 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

evaluation. 

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

Monitoring 

Network Chemical Species Measured 

Sampling Frequency; 

Duration 

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

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

SEARCH 

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

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

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

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

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

NADP WSO4, WNO3, WNH4 Weekly 

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

EPA-STN Speciated PM2.5 Varies; Varies 

AIRS/AQS CO, NO, NO2, NOx, O3 Hourly; Hourly 

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250 500 750 1000 1250 1500 1750 2000

-1500

-1250

-1000

-750

-500

-250

0

250

500

IMPROVE

CASTNet

SEARCH

STN

NADP

AQS

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

VISTAS region and the VISTAS 12 km grid. 

B.3 Model Performance Statistics 

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

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

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

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

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

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

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

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

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

performance measures and displays. 

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B.4 Modeling Performance Across the VISTAS Region 

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

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

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

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

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

B.4.1.1 SO4 in January 2002 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

performance requires the simultaneous occurrence of correct depiction of SO4 concentrations 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

overestimation of the highest observed concentrations and underestimation of the highest 

observed wet deposition events are connected.

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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. 

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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

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B.4.1.2 SO4 in April 2002 

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

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

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

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

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

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

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

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

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

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

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

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

NADP).. 

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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. 

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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. 

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B.4.1.3 SO4 in July 2002 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

discussed in Appendix C. 

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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. 

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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. 

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B.4.1.4 SO4 in October 2002 

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

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

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

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

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

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

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

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

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

the higher values. 

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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. 

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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. 

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B.4.1.5 SO4 Monthly Bias and Error 

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

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

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

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

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

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

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

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

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

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

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

ranging from 50% to 70%. 

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

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

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

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

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

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

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

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

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

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

(Figure B-6b, bottom). 

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

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

VISTAS 12 km grid. 

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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). 

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

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

above for SO4. 

B.4.2.1 NO3 in January 2002 

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

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

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

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

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

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

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

approximate -7% bias and 36% gross error. 

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

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

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

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

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

network.

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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. 

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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. 

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B.4.2.2 NO3 in April 2002 

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

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

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

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

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

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

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

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

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

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

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

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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. 

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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. 

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B.4.2.3 NO3 in July 2002 

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

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

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

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

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

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

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

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

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

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

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

simulation. 

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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. 

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B.4.2.4 NO3 in October 2002 

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

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

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

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

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

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

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

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

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

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

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

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

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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. 

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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. 

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B.4.2.5 NO3 Monthly Bias and Error 

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

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

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

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

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

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

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

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

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

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

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

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

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

the IMPROVE monitoring networks. 

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

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

VISTAS region. 

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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). 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

B.4.3.1 OMC in January 2002 

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

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

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

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

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

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

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

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

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

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

underestimated across the SEARCH network. 

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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. 

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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. 

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B.4.3.2 OMC in April 2002 

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

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

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

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

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

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

STN, but understating it for SEARCH. 

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

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

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

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

base case simulation. 

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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. 

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B.4.3.3 OMC in July 2002 

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

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

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

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

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

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

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

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

simulation. 

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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. 

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B.4.3.4 OMC in October 2002 

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

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

highest observed OMC frequency distributions are overstated at the IMPROVE and STN but 

matched across the SEARCH monitors (Figure B-15b) 

Figure B-15a. Scatter plots of predicted and observed organic matter carbon (OMC) concentrations 

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

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

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

simulation. 

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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. 

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B.4.3.5 OMC Monthly Bias and Error 

The monthly fractional bias and error plot for the 12 km CMAQ base case simulation in Figure 

B-16a displays fairly good performance across the IMPROVE monitors with bias usually within 

±15% and errors between 40% and 50%. However, at the urban STN sites the model exhibits a 

large underprediction bias of -35% to -60%. Part of the underprediction bias may be due to 

measurement uncertainties (e.g., blank corrections) and part may be due to the model over 

diluting the urban OMC emissions through the coarse 36/12 km grid. The underprediction of 

urban OMC is a common problem in PM modeling and likely also points to uncertainties in the 

OMC and SOA precursor emission inventories. 

The Bugle Plots of monthly fractional bias and gross error (Figure B-16b) concurs that the OMC 

performance across the IMPROVE monitoring sites is fairly good achieving the model 

performance goals. But the OMC underprediction bias for the STN monitors is significant 

enough so that always falls out of the model performance goals and is right at the model 

performance criteria. The OMC performance across the SEARCH network is mixed usually 

achieving the model performance goal and always achieving the model performance criteria. 

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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. 

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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). 

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B.4.4 Elemental Carbon (EC) Monthly Model Performance in the VISTAS Region 

Elemental carbon is measured at the IMPROVE, STN and SEARCH monitoring networks. 

SEARCH included 24-hur and hourly TCM measurements. In the OMC performance plots in 

Figures B-12 through B-15 the SEARCH hourly TCM results were plotted. In the EC 

performance plots in Figures B-17 through B-20 we display the SEARCH daily average TCM 

performance. 

B.4.4.1 EC in January 2002 

Elemental Caron (EC) in January 2002 is fairly good with fractions bias < ±15% across the 

IMPROVE and STN networks and errors of ~45% (Figure B-17). A larger underprediction bias 

and higher errors are seen across the SEARCH network for OMC and TCM. 

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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. 

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B.4.4.2 EC in April 2002 

Reasonable performance is seen for OMC in April, but with an underprediction bias that ranges 

from -30% to -50% across the IMPORVE and STN monitoring networks in the VISTAS region 

with errors of 50% to 60% (Figure B-18). The OMC and TCM performance across the 

SEARCH network in April exhibits near zero bias but with a lot of scatter resulting in errors on 

~60%. 

Figure B-18. Scatter plots of predicted and observed elemental carbon (EC) concentrations for April 

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

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

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

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B.4.4.3 EC in July 2002 

EC is underestimated in July 2002 in the VISTAS region across all monitoring networks. With 

the underestimation bias greater at the more rural IMPROVE (-41% and -53%) than urban STN 

(2% and -23%) networks (Figure B-19). It is interesting to note that EC model performance at 

the urban STN and partially urban SEARCH networks is much better for the 12 km modeling 

results (bias of 2% and -60%) than the 36 km results (-23% and -86%). 

Figure B-19. Scatter plots of predicted and observed elemental carbon (EC) concentrations for July 

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

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

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

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B.4.4.4 EC in October 2002 

The EC underprediction bias also occurs in October (Figure B-20). Again lower bias is seen at 

the STN networks with the 12 km results (-10%) producing lower bias than the 36 km results (-

31%). 

Figure B-20. Scatter plots of predicted and observed elemental carbon (EC) concentrations for 

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

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

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

simulation. 

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B.4.4.5 EC Monthly Bias and Error 

The monthly bias and error performance statistics for EC across the VISTAS region are shown in 

Figure B-21a. Bias and error are lower in the first quarter of the year, and then the IMPROVE 

and SEARCH networks begin to exhibit an underprediction bias that peaks in the summer and 

gets lower again with the onset of winter. Surprisingly, the STN network exhibits very low EC 

bias during the summer to fall. 

The Bugle Plot in Figure B-21b shows that although the EC underprediction produces large 

fractional bias values, because the EC concentrations are low the EC performance achieves the 

model performance goal for all months and networks since it is in the flared horn section of the 

Bugle Plot. 

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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. 

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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). 

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B.4.5 Other PM2.5 (Soil) Monthly Model Performance in the VISTAS Region 

There are potential model-measurement incommensurability issues with the Soil model 

performance. The IMPROVE and SEARCH networks builds up Soil from the elements, whereas 

in the model Soil is mapped to the other PM2.5 category that is emissions not identified as SO4, 

NO3, OMC or EC. 

B.4.5.1 Soil in January, April, July and October 2002 

Soil model performance is poor in January with a systematic overprediction bias of 134% to 

157% with similar magnitude of errors (Figure B-22). The overprediction bias is not as large in 

April and no longer systematic (i.e., doesn’t occur across all predicted-observed pairs) with 

fraction bias values of ~20% (IMPROVE) and ~80% (SEARCH) (Figure B-23). Higher 

observed values in the measured Soil values in July lowers the model overprediction bias to 

approximately 40% across the SEARCH network with a -30% underprediction bias across the 

IMPORVE monitors (Figure B-24). The overprediction bias returns in October with fractional 

bias of ~100% (IMPROVE) to ~140% (SEARCH). 

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Figure B-22. Comparison of predicted and observed other PM2.5 (Soil) concentrations for January 

2002 and sites in the VISTAS region using scatter plots (top) and Q-Q plots (bottom) and 

observations from the IMPROVE (left) and SEARCH Daily (left) monitoring networks for the CMAQ 

2002 12 km Base G2 Actual base case simulation. 

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Figure B-23. Comparison of predicted and observed other PM2.5 (Soil) concentrations for April 2002 

and sites in the VISTAS region using scatter plots (top) and Q-Q plots (bottom) and observations 

from the IMPROVE (left) and SEARCH Daily (left) monitoring networks for the CMAQ 2002 12 km 

Base G2 Actual base case simulation. 

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Figure B-24. Comparison of predicted and observed other PM2.5 (Soil) concentrations for July 2002 

and sites in the VISTAS region using scatter plots (top) and Q-Q plots (bottom) and observations 

from the IMPROVE (left) and SEARCH Daily (left) monitoring networks for the CMAQ 2002 12 km 

Base G2 Actual base case simulation. 

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Figure B-25. Comparison of predicted and observed other PM2.5 (Soil) concentrations for October 

2002 and sites in the VISTAS region using scatter plots (top) and Q-Q plots (bottom) and 

observations from the IMPROVE (left) and SEARCH Daily (left) monitoring networks for the CMAQ 

2002 12 km Base G2 Actual base case simulation. 

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B.4.5.2 Soil Monthly Bias and Error 

The seasonal dependence of the Soil model performance across the IMPROVE and STN 

monitors in the VISTAS region is clearly evident in the monthly bias time series plot in Figure 

B-26a. As noted in the scatter plots in Figure B-22 through B-25, the modeled Soil values tend 

to be between 0 and 2 μg/m3

 year round, however the observed values are much lower in the 

winter (0 to 0.5 μg/m3) and comparable to the modeled values in the summer. These results 

suggest that the poor Soil model performance in the winter, spring and fall is likely due to 

incorrect emission temporal adjustment factors. For example, the effects of wetted surfaces on 

fugitive dust emissions may not be properly characterized in the emissions inventory. 

The Bugle plots of bias and error also display the seasonal dependence of the Soil model 

performance with the model achieving the model performance goals for Soil at the IMPROVE 

monitoring network for approximately half the year and not achieving both the model 

performance goals and criteria at the IMPROVE network for the other half (Figure B-26b). 

Performance for Soil across the SEARCH network is worse with the model not achieving either 

the performance goals or criteria for most months. 

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Figure B-26a. Monthly Soil fractional bias (top) and fractional gross error (bottom) statistical 

measures for IMPROVE and SEARCH Daily monitoring sites in the VISTAS region. 

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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). 

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B.4.6 Coarse Mass (CM) and PM10 Mass Monthly Model Performance in the VISTAS 

Region 

Coarse Mass (CM) measurements are defined as the difference between the PM10 mass and 

PM2.5 mass measurements. Since IMPROVE network only speciates the PM2.5 fraction of PM, 

then any coarse SO4 or NO3 would be in the CM measurement. In the model, on the other hand, 

CM is due solely to primary PM emissions that have been speciated to the coarse fractions 

(PM2.5-10). Thus, there are potential incompatibility issues with the measured and modeled CM 

concentrations. 

In addition, CM has a much shorter transport distances than fine PM. In the modeling this is 

accounted for though fugitive dust transport factors (FDTF) that adjust the fugitive dust 

emissions downward (by ~75% on average in the VISTAS region) to account for the fact that 

these emissions are deposited locally and are not transported out of the grid cell where they are 

emitted. However, such local dust emissions can impact an IMPROVE monitor. Thus, much of 

the measured CM at the IMPROVE monitors is likely local in nature and subgrid-scale to the 

model. 

B.4.6.1 CM and PM10 in January, April, July and October 2002 

Figures B-27 through B30 display scatter plots of predicted and observed 24-hour CM and PM10

concentrations across the IMPROVE monitors in the VISTAS region for, respectively, January, 

April, July and October. CM exhibits an underprediction bias of approximately -50% in January 

and approximately -100% for the other three months with errors from 100% to 130%. The PM 

10 performance in January across the IMPORVE monitors is quite good with near zero bias and 

~30% error so achieves the most stringent ozone model performance goals (Figure B-27). 

However, an underprediction bias becomes larger in April (-35%) and even larger in July (-60%) 

only to come back to fairly good model performance again in October. 

Figure B-27. Scatter plots of predicted and observed coarse mass (CM) (left) and PM10 (right) 

concentrations for January 2002 and observations from the IMPROVE monitoring network for the 

CMAQ 2002 12 km Base G2 Actual base case simulation. 

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Figure B-28. Scatter plots of predicted and observed coarse mass (CM) (left) and PM10 (right) 

concentrations for April 2002 and observations from the IMPROVE monitoring network for the CMAQ 

2002 12 km Base G2 Actual base case simulation. 

Figure B-29. Scatter plots of predicted and observed coarse mass (CM) (left) and PM10 (right) 

concentrations for July 2002 and observations from the IMPROVE monitoring network for the CMAQ 

2002 12 km Base G2 Actual base case simulation. 

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Figure B-30. Scatter plots of predicted and observed coarse mass (CM) (left) and PM10 (right) 

concentrations for October 2002 and observations from the IMPROVE monitoring network for the 

CMAQ 2002 12 km Base G2 Actual base case simulation. 

B.4.6.5 CM Monthly Bias and Error 

Figure B-31a displays the monthly fraction bias and gross error values for CM across the 

IMPROVE monitors in the VISTAS region. The underprediction bias ranges from -60% to -

120% with errors of 80% to 140%. The Bugle Plot (Figure B-31b) shows that these performance 

statistics are either right at or exceeds the performance criteria indicating poor CM model 

performance. 

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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. 

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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). 

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B.4.7 PM2.5 Mass Monthly Model Performance in the VISTAS Region 

Below we present the CMAQ 2002 Base G2 12km and 36 km model performance for PM2.5

mass. In the model, PM2.5 mass is obtained by summing all of the PM species except CM. In 

the measurement, PM2.5 is obtained by the total PM on a filter collected by a sampler with a 2.5 

micron inlet cut point. Thus, the measured PM2.5 could exclude some SO4 and NO3 if they are 

in the coarse mode that would be included in the modeled PM .5 that assumes all SO4 and NO3 

are fine. 

B.4.7.1 PM2.5 in January 2002 

PM2.5 performance in January is quite good achieving the most stringent 1-hour ozone 

<±15%/<35% 1-hour ozone bias/error performance goal (Figure B-32a). PM2.5 is slightly 

overestimated across the IMPROVE, underestimated across the STN and near zero bias across 

the SEARXCH and FRM monitoring sites. 

Figure B-32a. Scatter plots of predicted and observed PM2.5 mass concentrations for January 2002 

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

left) and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual 

base case simulation. 

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Figure B-32a. (concluded) Scatter plots of predicted and observed PM2.5 mass concentrations for 

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

STN (bottom left) and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km 

Base G2 Actual base case simulation. 

Figure B-32b. Q-Q plots of predicted and observed PM2.5 mass concentrations for January 2002 and 

sites in the VISTAS region using IMPROVE (left) and SEARCH Daily (right) monitoring networks and 

the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 

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B.4.7.2 PM2.5 in April 2002 

In April the PM2.5 model performance is still fairly good with errors in the 30% to 40% range, 

but with an underprediction bias of from -15% (IMPROPVE) to -35% (STN). 

Figure B-33a. Scatter plots of predicted and observed PM2.5 mass concentrations for April 2002 and 

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

and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual 

base case simulation. 

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Figure B-33b. Q-Q plots of predicted and observed PM2.5 mass concentrations for April 2002 and 

sites in the VISTAS region using IMPROVE (left) and SEARCH Daily (right) monitoring networks and 

the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 

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B.4.7.3 PM2.5 in July 2002 

The underprediction bias is higher in July (-35% to -50%) with errors higher as well (45% to 

55%). 

Figure B-34a. Scatter plots of predicted and observed PM2.5 mass concentrations for July 2002 and 

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

and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual 

base case simulation. 

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Figure B-34b. Q-Q plots of predicted and observed PM2.5 mass concentrations for July 2002 and 

sites in the VISTAS region using IMPROVE (left) and SEARCH Daily (right) monitoring networks and 

the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 

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B.4.7.4 PM2.5 in October 2002 

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

Figure B-35a. Scatter plots of predicted and observed PM2.5 mass concentrations for October 2002 

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

left) and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual 

base case simulation. 

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Figure B-35b. Q-Q plots of predicted and observed PM2.5 mass concentrations for October 2002 and 

sites in the VISTAS region using IMPROVE (left) and SEARCH Daily (right) monitoring networks and 

the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 

B.4.7.5 PM2.5 Monthly Bias and Error 

Figure B-26a displays the monthly time series of fractional bias and error for PM2.5 mass 

concentrations for the IMPROVE, STN, SEARCH Daily, SEARCH Hourly and FRM networks 

in the VISTAS region. For the first three and last three months of the year the model performs 

well for PM2.5 with low bias (<±20%) and error (<40% except for SEARCH Hourly). During 

April through July the underprediction bias becomes larger and is in the -20% to -45%) and the 

errors are also larger. 

The Bugle Plots confirm the good performance for PM2.5 mass concentrations that achieves the 

model performance goals most of the time and always achieves the model performance criteria 

(Figure B-36b). 

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Figure B-36a. Monthly PM2.5 fractional bias (top) and fractional gross error (bottom) statistical 

measures in the VISTAS region. 

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Figure B-36b. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for PM2.5 in 

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

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B.4.8 Sea Salt in the VISTAS Region 

Figure B-37 compares scatter plots of predicted and observed “Sea Salt” for IMPROVE monitors 

in the VISTAS region and January, April, July and October 2002. The IMPROVE “Sea Salt” is 

inferred from the chlorine ion measurement [Cl-], whereas CMAQ internally generates its Sea 

Salt emissions. The model is predicting near zero Sea Salt resulting in poor performance. As 

Sea Salt is not directly used in the 2018 visibility projections this is not a big cause for concern, 

but can affect aerosol concentrations and is an area that should be investigated further, 

Figure B-37. Scatter plots of predicted and observed Sea Salt concentrations in the VISTAS region 

using IMPROVE monitoring data and for January (top left), April (top right), July (bottom left) and 

October (bottom right) and the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 

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B.5 Diagnostic Model Evaluation for Gas-Phase and Precursor Species 

The CASTNet and SEARCH networks also measure gas-phase species that are PM precursor or 

related species. The diagnostic evaluation of the 2002 36/12 km Base G2 CMAQ base case 

simulation for these compounds and the four seasonal months presented previously is provided 

below. Note that the EPA Air Quality Stations (AQS) compliance network also provides routine 

measurements of many gas-phase precursor (e.g., NOx and SO2) and product (e.g., O3) species 

as well. However, these sites tend to be urban-oriented and designed to measure maximum 

potential concentrations of criteria pollutants so would not provide a good measure of a model’s 

ability to estimate regional concentrations at the more rural Class I areas. Consequently the gasphase precursor and product species diagnostic evaluation used measured data from the 

CASTNet and SEARCH networks that included some more rural monitoring sites. 

The CASTNet network measures weekly average samples of SO2, SO4, NO2, HNO3, NO3 and 

NH4. Because the CASTNet samples are collected in a cartridge and sampled at a later date 

some of the NO3 may volatize into gaseous HNO3. Thus, we also evaluate against total nitrate 

(TNO3) that is defined as the sum of NO3 and HNO3. The SEARCH Hourly network collects 

hourly measurements of gaseous SO2, NO, NO2, NOy, O3 and CO and particulate SO4, NO3, 

and NH4. A comparison of the SO2 and SO4 performance provides insight into whether the 

SO4 formation rate may be too slow or fast. For example, if SO4 is underestimated and SO2 is 

overestimated, that may indicate too slow chemical conversion rate. Analyzing the performance 

for SO4, HNO3, NO3, Total NO3 and NH4 provides insight into the equilibrium of these 

species. For example, if TNO3 performs well but HNO3 and NO3 do not, then there may be 

issues associated with the partitioning between the gaseous and particle phases of nitrate (e.g., 

ammonia may be off). 

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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. 

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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. 

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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. 

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Figure B-40. Scatter plots of predicted and observed gaseous NO (top left), NO2 (top right), NOy 

(bottom left) and O3 (bottom right) from the SEARCH Hourly monitoring network and January 2002 

for the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 

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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. 

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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. 

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Figure B-42. Scatter plots of predicted and observed gaseous SO2 (left) and particulate SO4 (right) 

from the SEARCH Hourly (top) and CASTNet (bottom) monitoring networks and April 2002 for the 

CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 

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Figure B-43. Scatter plots of predicted and observed gaseous NO (top left), NO2 (top right), NOy 

(bottom left) and O3 (bottom right) from the SEARCH Hourly monitoring network and April 2002 for 

the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 

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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. 

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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. 

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Figure B-45. Scatter plots of predicted and observed gaseous SO2 (left) and particulate SO4 (right) 

from the SEARCH Hourly (top) and CASTNet (bottom) monitoring networks and July 2002 for the 

CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 

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Figure B-46. Scatter plots of predicted and observed gaseous NO (top left), NO2 (top right), NOy 

(bottom left) and O3 (bottom right) from the SEARCH Hourly monitoring network and July 2002 for 

the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 

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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. 

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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. 

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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. 

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Figure B-49. Scatter plots of predicted and observed gaseous NO (top left), NO2 (top right), NOy 

(bottom left) and O3 (bottom right) from the SEARCH Hourly monitoring network and October 2002 

for the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation. 

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APPENDIX C 

Air Quality Model Performance Evaluation: 

Evaluation at VISTAS and Nearby Class I Areas 

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C.1 OVERVIEW 

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

observational data from the Interagency Monitoring of PROtected Visual Environments 

(IMPROVE) monitors and model output data from the 2002 VISTAS BaseG2 Actual annual air 

quality modeling. The evaluation will primarily focus on the air quality model’s performance 

with respect to individual components of fine particulate matter (PM2.5), as good model 

performance of the component species will dictate good model performance of total or 

reconstituted fine particulate matter from which visibility is constructed. Model performance of 

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

means to discuss the overall model performance for this Implementation Plan. 

C.1.1 IMPROVE Monitoring Sites 

There are eighteen (18) Class I areas within the VISTAS States (VISTAS region) as shown in 

Figure C-1. There are also several Class I areas adjacent to the VISTAS region where we also 

project future-year visibility conditions. Figure C-2 displays the IMPROVE monitoring sites 

within and nearby the VISTAS region. The 18 VISTAS Class I areas and nearby Class I areas 

where the CMAQ Base G2 modeling results are evaluated in this Appendix are as follows: 

VISTAS Class I Areas

• Sipsey Wilderness Area (SIPS), Alabama 

• Chassahowitzka Wildlife Refuge (CHAS), Florida 

• Everglades National Park (EVER), Florida 

• St. Marks Wildlife Refuge (SAMA), Florida 

• Cohutta Wilderness Area (COHU), Georgia 

• Wolf Island Wildlife Refuge (WOIS), Georgia 

• Okefenokee Wildlife Refuge (OKEF), Georgia 

• Mammoth Cave National Park (MACA), Kentucky 

• Linville Gorge Wilderness Area (LIGO), North Carolina 

• Shining Rock Wilderness Area (SHRO), North Carolina 

• Swanquarter Wildlife Refuge (SWAN), North Carolina 

• Joyce Kilmer-Slickrock Wilderness Area (JKSR), North Carolina 

• Cape Romain Wildlife Refuge (ROMA), South Carolina 

• Great Smoky Mountains National Park (GRSM), Tennessee-North Carolina 

• James River Face Wilderness Area (JARI), Virginia 

• Shenandoah National Park (SHEN), Virginia 

• Dolly Sods Wilderness Area (DOSO), West Virginia 

• Otter Creek Wilderness Area (OTCR), West Virginia 

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Nearby Class I Areas

• Breton Island Wildlife Refuge (BRET), Louisiana 

• Caney Creek Wilderness Area (CACR), Arkansas 

• Upper Buffalo Wilderness Area (UPBU), Arkansas 

• Mingo Wildlife Refuge (MING), Missouri 

• Hercules Glade Wilderness Area (HEGL), Missouri 

• Brigantine Wildlife Refuge (BRIG), New Jersey 

Note that not all Class I areas have their own IMPROVE monitor. The Wolf Island Class I area 

shares the Okefenokee IMPROVE monitor in Georgia and in West Virginia, the Otter Creek 

Class I area shares the Dolly Sods IMPROVE monitor and the Joyce Kilmer-Slickrock Class I 

area shares the IPROVE monitor at Great Smokey Mountains National Park. So when 

presenting model performance evaluation and visibility projections for Okefenokee, Dolly Sods 

and Great Smokey Mountains IMPROVE monitors they are also represented of, respectively, 

Wolf Island, Otter Creek and Joyce Kilmer-Slickrock Class I areas, respectively. Also note that 

there are some IMPROVE sites not located in Class I areas. Although data at these sites are used 

in the regional evaluation discussed in Appendix B, the performance at the individual IMPROVE 

monitors not located at a Class I area are not discussed in this section. For the VISTAS region, 

the Cadiz (CADI) site is one such IMPROVE monitor. 

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Figure C-1. Class I areas within the VISTAS region. 

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250 500 750 1000 1250 1500 1750 2000

-1500

-1250

-1000

-750

-500

-250

0

250

500

EVER1

CHAS1

BRET1

SAMA1

OKEF1 SIKE1

ROMA1 SIPS1 CACR1

COHU1

SHRO1

SWAN1

GRSM1

UPBU1

LIGO1

HEGL1

MING1 CADI1

MACA1

JARI1 JEFF1

ELDO1

SHEN1

LIVO1

WASH1 DOSO1

BRIG1 AREN1

QUCI1

BOND1

LASU1

MKGO1

MOMO1

ADPI1

COHI1

GRRI1

Figure C-2. IMPROVE monitoring sites within and near the VISTAS region. 

C.1.2 Particulate Matter and Component Species 

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

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

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

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

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

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

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

including: 

• Sulfates 

• Nitrates 

• Ammonium 

• Organic carbon (OC) 

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• Elemental carbon (EC) 

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

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

or other PM) 

• Sea Salt (NaCl) [new IMPROVE equation] 

The composition of particulate matter can vary from day-to-day, allowing a day with a large 

concentration of sulfates to be closely followed by a day dominated by organic carbon. This is 

an important fact as some of the components of fine particulate matter are more effective at light 

scattering than others. Since there can be such a large variability in the concentrations of the 

component species, the model will actually be evaluated on a per species basis, followed by a an 

evaluation of total reconstructed fine particle mass. This is also a more stringent test of the 

model, as simply looking at particle pollution performance may provide good results, even 

though the underlying constituent concentrations are incorrect. Only a brief overview of total 

fine particles after component evaluation is necessary, since the accuracy of the model in 

predicting the component species will dictate the accuracy of the total fine particulate mass 

prediction. 

C.1.3 20% Best and Worst Days 

Projections of light extinction for the evaluation of regional haze are based on a subset of days 

for which there is monitored data. IMPROVE monitors collect 24-hour PM samples every third 

day. Observations of component species on these days are used to calculate light extinction, 

which is the basis on which these days are ranked. These rankings are then used to identify the 

20% most impaired days, or “worst days”, and the 20% least impaired days, or “best days”, for 

use in visibility projections. Table C-1 lists of the 20% best and worst days for all of Class I 

areas within and nearby of the VISTAS region. . 

Since the ultimate future year projections are based on these 20% best and worst days, the 

following performance evaluation of the model has mainly focused on the best and worst days 

for VISTAS and nearby Class I areas. 

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C-7 

Date 

Julian 

Day SIPS CHAS EVER SAMA COHU OKEF MACA LIGO SHRO SWAN ROMA GRSM JARI SHEN DOSO BRET CACR UPBU MING HEGL BRIG1 

01/05/02 5 W W B B W B B W 01/08/02 8 B B B B B 01/11/02 11 B B B B B B B B 01/14/02 14 B B B B B B B B B B B B B 01/17/02 17 W W W 01/20/02 20 B B B W W 01/23/02 23 B B W B B B B B B B 01/26/02 26 B W B B B B B B B B B B 01/29/02 29 B W 02/01/02 32 B B B B B B B B B 02/04/02 35 W W B B 02/07/02 38 B B B B B B B B B 02/10/02 41 W 02/13/02 44 W W W B W B W B W B B 02/16/02 47 W W W W B 02/19/02 50 B B B B 02/22/02 53 W W 02/25/02 56 W W W W W W W 02/28/02 59 B B B B W 03/03/02 62 W B B B B B B B B 03/06/02 65 B 03/09/02 68 B B B B B B B B B B B B B B 03/12/02 71 B B B B 03/15/02 74 W W W 03/18/02 77 B B B B B W B 03/21/02 80 W B B B W 03/24/02 83 B 03/27/02 86 W B 03/30/02 89 B W W B W B B B B B B B B 04/02/02 92 B 04/05/02 95 W W W W 04/08/02 98 W W B B 04/11/02 101 B B 04/14/02 104 B B B B B B B B B 04/17/02 107 W B W 04/20/02 110 W W 

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Date 

Julian 

Day SIPS CHAS EVER SAMA COHU OKEF MACA LIGO SHRO SWAN ROMA GRSM JARI SHEN DOSO BRET CACR UPBU MING HEGL BRIG1 

04/23/02 113 B W B B B B B 04/26/02 116 B W W B B B B B B B B B 04/29/02 119 B B W B B B B 05/02/02 122 W W W W W W W W W W 05/05/02 125 W W B B W B 05/08/02 128 W W W W W W W W W W W 05/11/02 131 W W 05/14/02 134 B B B B 05/17/02 137 B W W W W W B B W 05/20/02 140 B B 05/23/02 143 W W W W 05/26/02 146 B W W W W 05/29/02 149 W W W W B W W 06/01/02 152 W W W W W W W W W W W 06/04/02 155 W W W W W W W W W W W W 06/07/02 158 W B W B B B B W W B 06/10/02 161 W B B W B W W W 06/13/02 164 B B W W W W B B 06/16/02 167 B B B 06/19/02 170 B B W W W B W W W B W W W W 06/22/02 173 B B B B W B B W W W W W 06/25/02 176 B B B B B B B W W B B B W 06/28/02 179 B B B W W W W 07/01/02 182 B W W W W W W 07/04/02 185 W W W W W W W W W W W 07/07/02 188 W W W W W W W W W W W W 07/10/02 191 W B B W B W W W B W W W B W W W W W 07/13/02 194 B B B B W W B 07/16/02 197 W W W W W W W W W W W B W W W 07/19/02 200 W W W W W W W W W B 07/22/02 203 B W W W W W W W W 07/25/02 206 W B B W W W W W B W W W W W B 07/28/02 209 B W W B W 07/31/02 212 B B W W W W W W W W 08/03/02 215 W W W W W W W W W W W W W 08/06/02 218 W W B W W W W W W W W W W W 

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Date 

Julian 

Day SIPS CHAS EVER SAMA COHU OKEF MACA LIGO SHRO SWAN ROMA GRSM JARI SHEN DOSO BRET CACR UPBU MING HEGL BRIG1 

08/09/02 221 W W W W B W B W W W W B 08/12/02 224 W B B W W W W W W W W W W W 08/15/02 227 B W B B B W W B B B B 08/18/02 230 B W 08/21/02 233 W W W W W W W W B 08/24/02 236 W W W W W W W W W B 08/27/02 239 B B B W W W W W W W W W 08/30/02 242 B B B B B W W W W 09/02/02 245 B W B B B B W W 09/05/02 248 B B W W W B W W W W W 09/08/02 251 W B W W W W B W W W W W W B 09/11/02 254 W B W W W W W W W W W W W W W 09/14/02 257 B W B B B W B W W W W W W W 09/17/02 260 W W W W W W W W W W W W W W W W W 09/20/02 263 W W W W B B W W W B B 09/23/02 266 B B W B B W B W B B B B 09/26/02 269 B B B B B B B W W B 09/29/02 272 W W W W W 10/02/02 275 B W W W W 10/05/02 278 W B W B B B B W 10/08/02 281 W W W W B W B B B B 10/11/02 284 W B B B B B B W B B 10/14/02 287 B B W B B B B 10/17/02 290 W W W B B B B 10/20/02 293 W W W B B 10/23/02 296 W W B W W W W W W W 10/26/02 299 B B W B B B B B 10/29/02 302 B B B B B B B B B B B B B B B 11/01/02 305 W W 11/04/02 308 B B W B B B B B B 11/07/02 311 W W B B B 11/10/02 314 B 11/13/02 317 B B 11/16/02 320 B B B B B B B B W W W 11/19/02 323 B B B 11/22/02 326 B B W B B B 

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Date 

Julian 

Day SIPS CHAS EVER SAMA COHU OKEF MACA LIGO SHRO SWAN ROMA GRSM JARI SHEN DOSO BRET CACR UPBU MING HEGL BRIG1 

11/25/02 329 W W B W 11/28/02 332 W W B B W W B 12/01/02 335 B B B B B B B B B 12/04/02 338 W W B B B B 12/07/02 341 W W W W B W W B B W W B B B W W W W 12/10/02 344 B B B B B B 12/13/02 347 B B B B B B B B B B W 12/16/02 350 W W W 12/19/02 353 B B B B B B B 12/22/02 356 B B B B B B B B B B B B B B 12/25/02 359 B B B B B B B B B B B 12/28/02 362 B W B B W 

12/31/02* 365 B B B B B B 

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