October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-2 “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 list ed above (EPA, 2007a) di scusses the use of modeling results to project fu ture-year visibility. The se cond EPA guidance document (EPA, 2003a) focuses on monitored visibility, how to defi ne the visibility Baseline Conditions and how to track visibility goals. The third EPA gui dance 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 appr oaches for defining Natural Conditions, in

this section we use the EPA default Natural Conditions at Class I ar eas updated to the new IMPROVE equation as available on the VIEW S website. The final EPA guidance document

discusses how states should define their reasona ble 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 VIST AS visibility projec tions are based on the new IMPROVE equation, although projections ba sed on the original IMPROVE equation are also presented as an alternative approach in Chapter 5.

4.2 Calculation of Visibility and 2018 Unif orm Rate of Progress from IMPROVE

Measurements

EPA guidance recommends using the model in a re lative 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 ap proach are as follows (EPA, 2003a, b; 2007a):

• Monitored data are used to define curr ent visibility Baseline Conditions using IMPROVE monitoring data from the 2000-2004 five-year period.

• Monitored concentrations of PM 10 are divided into six major components (note that

Sea Salt is also included in the new IM PROVE equation and held constant between the current and future years), the firs t five of which are assumed to be PM 2.5 and the sixth is coarse mass (CM or PM 2.5-10 ).
3⁄4 SO4 (sulfate) that is assumed to be ammonium sulfate [(NH 4 ) 2 SO 4 ];
3⁄4 NO3 (particulate nitrate) that is a ssumed to be ammonium nitrate [NH 4 NO 3 ];
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).October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-3 • 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 concentr ations are consolidated to provide an estimate of future-year air quality and visi bility 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 evaluati ng the visibility improvements.

• It is assumed that all measured sulfat e is in the form of ammonium sulfate [(NH 4 ) 2 SO 4 ] and all particulate nitrate is in the form of ammonium nitrate [NH 4 NO 3 ].

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

for each Class I area:

Baseline Conditions : Baseline Conditions represent visibili ty 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 fi ve-year period and are the st arting point in 2004 for the Uniform Rate of Progress glidepath and 2018 visibility projections. Th e 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., visi bility 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 deciview s starting with the observed 2000-2004 Baseline Conditions in 2004 and ending at Natural Conditi ons in 2064 for each Class I area. Where the linear glidepath passes through 2018 is the 2018 Uniform Rate of Pr ogress in deciviews.

4.2.1 Calculation of Visibility fr om IMPROVE PM Measurements

Baseline Conditions for Class I areas are calcul ated 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 (PM 2.5 ) and total PM 2.5 and PM 10 mass concentrations from which visibility is obtained through the IMPROVE equation.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-4 Visibility conditions are estimated star ting with the IMPROVE 24-hour average mass measurements for six PM species:

• Sulfate [(NH 4 ) 2 SO 4 ];

• Particulate Nitrate [(NH 4 NO 3 ];

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

• Elemental Carbon [EC] or Li ght Absorbing Carbon [LAC];

• Other fine particulate [Soil]; and

• Coarse Matter or Coarse Mass [CM].

The IMPROVE monitors do not directly measure so me 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 e quation sulfate and particulate nitrate are assumed to be completely neutralize d by ammonium. In addition, only the fine mode (PM 2.5 ) 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 th e above six components of light extinction in the IMPROVE equation are obtained from the IM PROVE measured species using the mappings

shown in Table 4-1:

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

Sulfate 1.375 x (3 x S)

Nitrate 1.29 x NO 3 -

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 pr oton induced x-ray emissi ons (PIXE) analysis

of the IMPROVE Module A 1 . To estimate the mass of the sulfate ion (SO 4 = ), S is multiplied by 3 to account the presence of oxygen. If S is missing then the sulfate (SO 4 ) measured by ion chromatography analysis of th e 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 SO 4 ).

• NO 3 - is the particulate nitrate measured by ion chromatography analysis of the Module B.

For the IMPROVE aerosol extinction calculat ion, it is assumed to be completely neutralized by ammonium (1.29 x NO 3 - ).

• 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 PM 10 mass. (see http://vista.cira.colostat e.edu/improve/ fo r more details).October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-5 mass due to other elements in the OMC be sides Carbon (e.g., oxygen). IMPROVE also uses the term Particulate Organi c 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 masse s 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 fo rms. 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 PM 10 and PM 2.5 mass, respectively whose difference gives CM.

4.2.1.1 Original and New IMPROVE Equations

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

μ g/m 3 ) 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 rela tive 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 ha ve hygroscopic properties, but the IMPROVE equations assume OMC is non-hygroscopic.

There are currently two IMPROVE e quations that are used to convert PM concentrations to light extinction, the original (or old) a nd the new IMPROVE equations.

4.2.1.1.1 Original IMPROVE Equation

The original IMPROVE equation th at converts PM species concentr ations to light extinction is given as follows (Malm et al., 2000):

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

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

b EC = 10 x [EC]

b OMC = 4 x [OMC]

b Soil = 1 x [Soil]

b CM = 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/Regiona lHaze_2002FRHcurve/fRH_analysis/

.

The total light extinction (b ext ) is assumed to be the sum of the light extinction due to the six PM species listed above plus Ra yleigh (blue sky) background (b Ray ) that is assumed to be 10 Mm -1

.

b ext = b Ray + b Sulfate + b Nitrate + b EC +b OMC + b Soil + b CM

The total light extinction (b ext ) in Mm -1 is related to visual range (VR) in km using the following relationship:October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-6 VR = 3912 / b ext ,

for b ext in Mm -1

.

The Regional Haze Rule requires that visibility be expressed in terms of a haze index (HI) in units of deciviews (dv), whic h is calculated as follows:

HI = 10 ln(b ext /10)

4.2.1.1.2 New IMPROVE Equation

The new IMPROVE equation is nonlinear in SO 4, NO3 and OMC concentrations accounting for the different light scattering efficiency characteri stics as a function of concentrations for these three species (DeBell et al., 2006). It is expressed as follows:

b Sulfate = 2.2 x f S (RH) x [Small Sulfate] + 4.8 f S (RH) x [Large Sulfate]

b Nitrate = 2.4 x f S (RH) x [Small Nitrate] + 5.1 f S (RH) x [Large Nitrate]

b EC = 10 x [Elemental Carbon]

b OMC = 2.8 x [Small Organic Mass] + 6.1 x [Large Organic Mass]

b Soil = 1 x [Fine Soil]

b CM = 0.6 x [Coarse Mass]

b NaCl = 1.7 x f SS (RH) x [Sea Salt]

b NO2 = 0.33 x [NO 2 (ppb)]

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

size distributions of those co mponents. 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 IM PROVE equation. New te rms have been added for Sea Salt (important for coastal areas and po ssibly other areas) and for light absorption by NO2 (only used where NO2 observations are availa ble). 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 IMPR OVE monitoring site is used in the new IMPROVE equation, as compar ed to a constant 10 Mm -1 value assumed in the original IMPROVE equation.

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

done as follows:

[Large Sulfate] = [Total Sulfate] / 20 x [Total Sulfate], for [Total Sulfate] < 20 μ g/m 3

[Large Sulfate ] = [Total Sulf ate], for [Total Sulfate] > 20 μ g/m 3

[Small Sulfate] = [Total Su lfate] – [Large Sulfate]October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-7 The same equations are used to apportion To tal Nitrate and Total OMC among their Large and Small components.

The total extinction (b ext ) 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

NO 2 as noted above. In addition, site-specific Rayleigh bac kground is used with the new IMPROVE equation:

b ext = b Ray + b Sulfate + b Nitrate + b EC +b OMC + b Soil + b CM + b NaCl + b NO2

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 f itting reconstructed ligh t extinction based on

IMPROVE measured PM and NO2 concentrations with actual co-located measured light extinction (e.g., nephelometer measur ements). Figure 4-1 displays example comparisons of 24- hour light extinction using the original and ne w 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 towa rd understating light extinction at the high end and overstating it at the low end, whereas th e 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 mo re 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 VIST AS website (http://www.vistassesarm.org/documents/FinalDocs.asp). Result s using the original IMPROVE equation are presented in Section 5 as an alternative approach.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-8 Figure 4-1. Comparisons of observed light extinction wi th 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 20 00-2004 period for each Class I area following EPA’s guidance (EPA, 2003a). The basic procedur es 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 (E PA, 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 suff icient valid data, rank the visibility in

terms of extinction or decivi ew using either the original or new IMPROVE equation and monthly average f(RH) factors (EPA, 2003a).

3. For the worst 20 percent days, ex tract the 20% most impaired vi sibility 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 fr equency this would re sult 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 th e 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 pe rcent (or best 20 perc ent) 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-9 4.2.3 Data Filling for Sites with Insufficient Va lid Data to Calculate Baseline Conditions

Several Class I areas within and near the VI STAS region did not cont ain sufficient IMPROVE observations during the five-year 2000-2004 Baseline to have thr ee 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 th e VISTAS region, plus Breton Island (BRET), Louisiana, and Mingo (MING), Mi ssouri in the CENRAP region. For these Class I areas, Air Resource Specialists develope d substituted data sets ( ftp://ftp.air-resource.com/ VISTAS) so that at least three years of valid data were availa ble from which valid Baseline Conditions could be calculated using EPA’s minimal valid data re quirement (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 fo r 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 we re 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 gene rate the 2018 Uniform Rate of Progress. These

EPA default Natural Conditions are used as the an chor point for the glidepaths in 2064 that are provided in Appendix E for the VI STAS and nearby Class I areas.

4.2.5 2018 Uniform Rate of Progress

The Uniform Rate of Progress glidepath is c onstructed 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 IM PROVE 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 Baselin e 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 pe rcent days using the new IMPROVE equation. The linear glidepath crosses 2018 at 25.79 dv which becomes the 2018 Uniform Rate of Progress.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-10 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

YearHaziness 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 fo r 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 da ys 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 c oncentrations for the 2002 worst 20 percent days (i=1...N) at a give n Class I area for th e 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 ofmodeling 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 speci es do not exactly match up with the IMPROVE PM species, thus assumptions must be made to map the m odeled species to the IMPROVE PM species for the purpose of projecti ng 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-11 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 ae rosol (SOA) module in CMAQ V4.5 was enhanced (SOAmods) to include additional processes rela ted 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 include d in the standard version of CMAQ V4.5 (Morris et al., 2006):

• ASOC1 – SOA from biogenic so urces (e.g., terpenes and is oprene) that has become polymerized so is no longer volatile.

• ASOC2 – SOA from biogenic sesquiterpen e and higher reactivit y and higher yield monoterpene emissions.

• ASOC3 – SOA from bioge nic isoprene emissions.

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

version of the model used in VIST AS 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 ar ea and PM species specific. RRFs are applied to the Baseline Condition observ ed PM species to project future-year PM levels from which visibility can be assessed us ing the IMPROVE equations listed above. The following six steps are used to project future-yea r visibility for the worst 20 percent and best 20 percent visibility days (discussion is for worst 20 percent days but also a pplies to best 20 percent days):October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-12 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 th e 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 concentra tions for the worst 20 percent days and the five-year Baseline.

4. Compute the future-year daily extinction us ing the IMPROVE equatio n 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 a nd average the daily deciview values within each of the five years separately to obtain fi ve-years (or as many years with valid data in the 2000-2004 Baseline) of aver age deciview visibility fo r the worst 20 percent days.

6. Average the five-years of averag e deciview visibility to obta in 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 spatia l 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 gr id cells centered in the monito ring site when constructing the RRFs using the, respectively, 36 km a nd 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 pro cedure 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 percen t 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 visibilityOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-13 projections for the worst 20 percent days and th e 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 Projecti ons 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 A ppendix E and reproduced in Figure 4-3 below. Similar worst 20 percent days 2018 visibility pr ojections 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 proj ection 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 a nd the 2018 Uniform Rate of Pr ogress 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 visibi lity 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 vi sibility projections acro ss Class I areas it is useful to present the 2018 visibi lity projections as a percenta ge of the 2018 Uniform Rate of Progress; where 100% is meeting the 2018 Unif orm 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 improveme nt is achieved than th e 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 Progre ss 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 C onditions in 2064 a nd is designed to help states in selecting their 2018 reasonable progress go als. As clearly stated in EPA guidance “The glidepath is not a presumptive target, and Stat es may establish a reasonable progress goal that provides for greater, lesser, or equivalent im provement as that desc ribed 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. Th is 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 presen ted in Chapter 3 and Appendices B, C and D

that evaluated the 2002 Actual Base G2 CM AQ 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 th ere 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 observe d extinction reasonabl y 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 (e xcluding Rayleigh and Sea Salt) at SIPS isOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-14 173 Mm -1 , compared to a modeled value that is 27% (43 Mm -1 ) lower (127 Mm -1 ). This understatement is primarily due to the mode l 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 Ba se G2 12 km CMAQ simulations. SO4 is always the largest component of the extin ction that is reduced fr om 2002 to 2018 at SIPS for the worst 20 percent days. There are several da ys that exhibit very li ttle 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 (s ee Figure 4-3c). On worst 20 percent days when the model grossly unde restimates the observed extinction, the model is very stiff (i.e., shows little response to reductions in emissi ons, 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 rela tive changes in modeled PM species between the

2002 and 2018 emission scenarios aver aged across the worst 20 percen t days that are represented by the last bar in each figure. These results ar e summarized for SIPS in Table 4-2 below. Table 4-2 compares the average extinction across th e 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 200 2 emissions scenarios. Although the results in Table 4-2 are not RRFs (RRFs are based on ratios of concentra tions not extinction) they do show how the RRFs may magnify or deflate th e 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% increa se is applied to the higher observed CM extinction value it magnifies the modeled change s in CM in the 2018 visi bility projections.

Most of the changes in emissions from 2002 to 2018 are reductions in So2 and NOx emissions, with very little changes in car bon, soil and coarse mass. Therefor e, 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 S pecies 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%October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-15 

### 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

YearHaziness 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% groupbEXT (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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-16 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 DayDelta 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 visib ility 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 ac hieved 126% and 127% of the vi sibility reducti on 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 plo tted at 126% and 127%, which are above the 100% line so the 2018 Base G2 base case modeling re sults 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 Mo untains 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 warm er 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 A ppalachian Mountains also achieve the 2018 Uniform Rate of Progress with visibility reduc tions of approximate ly 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 th e winter months with elevated NO3 levels (see Appendix E). These days show little change in SO4 and NO3 by 2018 so the average RRF areOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-17 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 co astal 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 la rge 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%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent 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 Expr ess 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 th e 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 resu lts (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 ar eas including GRSM, SHRO and EV ER. Figure 4-6 displays theOctober 2007

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 percen t days (i.e., the relative modele d 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 th e Class I areas. In particular, the relative increases in NO3 in 2018 for GRSM, SHRO a nd EVER are much higher using the 12 km modeling results (> 100%) than the 36 km modeli ng results (< 100%). These higher relative NO3 increases using the 12 km modeling resu lts 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 thes e three Class I areas ar e near zero (see Figures E-3c, E-9c and E-12c) for the 12 km modeling re sults 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, becau se 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 resu lts. To examine this issue in more detail, in Chapter 5 we look at alternative projection tech niques in which we only use the modeled derived RRFs for NO3 if the 2002 base case NO3 is greater than 0.1 μ g/m 3

.October 2007

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

C OH

U1 CADI 1 DOSO1 GRSM1

J A RI1 LIGO1 MA C A 1 SHEN1 SHR O1 SIPS1 CHAS1 EVER1 OK EF

1

R

OM

A1

S A M A1 SWA N 1

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.October 2007

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

C O H U1 CADI 1 DOSO1

G R S M1

J ARI

1

L IGO1 MAC A 1 SHEN1

S H RO1 SI PS

1 CHAS1

E V ER1 OKEF1 ROM A1

S A M A1 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-21 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 GR SM

1 JARI 1 LIGO 1

M ACA1 SHEN1

S HR

O 1
S IP S 1 CHAS1 EVER1 OKEF1 RO MA

1 SAMA1

S WA

N1

IMPROVE sitePercent 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 sitePercent 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-22 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 CA D I1 DOS O1 GRSM1

J ARI

1

L I G O 1 MACA1 SHEN1 SHR O 1 SIP S 1 CH AS1 EVER1

O KEF1 ROM A 1 SAMA1

S WA

N1

IMPROVE sitePercent 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 sitePercent 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-23 4.4.3 2018 Visibility Projections fo r the Best 20 Percent Days

Figure 4-7a displays the 2018 visibi lity projections for the Best 20 percent days at Sipsey, similar plots for other Class I areas are given in Appendi x 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 per cent 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 differ ences in the modeled extinction between the

2018 and 2002 Base G2 emission scenarios on the be st 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 reducti ons in SO4 is resulting in increases in NO3 that is likely due to “nitrate replacement” (see Ap pendix F). Nitrate replacement occurs when the

reductions in SO4 results in the release of am monium (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 [SO 4 (NR 4 ) 2- ], than nitrate replacement could potentially form two particulate NO3 [NO 3 NH 4 ] for every one SO4 molecule reduced, how ever this is unlikely in the real atmosphere due to aerosol thermodynamics and the fact that NO3 form ation is not always completely ammonia limited. We also see occa sions 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-pha se chemistry formation of SO4 which is pH sensitive. Reductions in NO3 will make water droplet s 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 201 8 visibility projections for the Best 20 percent days using the CMAQ 12 km and 36 km Base G2 2002/2018 modeli ng 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 excepti on, all of the 2018 visibility projections show improved visibility from current visibility conditi ons for the best 20 percent days. The exception is for Everglades that shows a worsening of visi bility 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 cl osely 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 sim ilar, 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 ex tinction increases by approximately 0.001 Mm -1 from 2002 to 2018 averaged across the best 20 percen t 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 relativeOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-24 change in extinction. When the modeled NO3 c oncentrations 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 μ g/m 3

.
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

YearHaziness 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% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-7b. Compared of predicted and observed da ily extinction for the best 20 percent days at the Sipsey, Alabama Class I area and the CMAQ 2002 Typical Base G2 12 km simulation.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-25 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 DayDelta Bext (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-7c. Modeled changes in daily extinction us ing 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% COHU1 DOSO1 GRSM1 JARI1 LIGO1 CADI1 MACA1 SHEN1 SHRO1 SIPS1 CHAS1 EVER1 OKEF1 ROMA1 SAMA1 SWAN1 BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1 Percent of target reduction achieved 12km

36km

VISTAS non-VISTASOctober 2007

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% groupbEXT (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% groupbEXT (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 DayDelta 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 0.5

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 DayDelta 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.doc 4-27 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%

C OHU

1 CADI1

D OS

O1

G RSM

1 JARI1
LI GO

1 MACA1 SHEN1 SHR O1 SIP S 1

C HAS

1 EV E R 1 OKEF1

R OMA

1

S A MA

1 SWAN1

IMPROVE sitePercent 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%

COHU 1
C AD

I1 DOSO 1

G R S M 1

J A RI1 LIGO1 MACA 1 SHE N 1 SH R O 1 SIP S1 CHAS1 EVER1

O KE

F1 ROMA1 SAMA1

S WA

N1

IMPROVE sitePercent 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_6_References2.doc 6-1 6.0 REFERENCES

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### A-1 APPENDIX A

Emission Processing Selected QA Summary Reports and Figures

### A-2 Figure A-1. VISTAS BaseG2 Onroad Mobile Source Emissions Summary – 12km Daily Emissions Density Plot (January 17, 2002)

### A-3 Figure A-2. VISTAS BaseG2 Area Source Emissions Summary – 12km Daily Emissions Density Plot (January 17, 2002)

### A-4 Figure A-3. VISTAS BaseG2 Biogenic Source Emissions Summary – 36km Daily Emissions Density Plot (January 17, 2002) – ISOP

### A-5 Figure A-4

. VISTAS BaseG2 Area Source Emissions Summary – Daily Domainwide

Diurnal Distribution – ALD2

### A-6 Figure A-5. VISTAS BaseG2 Elevated Fire Source Emissions Summary – Daily Domainwide
Vertical Distribution – CO.A-7Figure A-6.VISTAS BaseG2 Elevated Fire Source Emissions Summary – 2002 Annual Domainwide Temporal Distribution – OLEA-8Table A.1– VISTAS BaseG2 Area Source Emissions Summary – 12km State Level Emission Totals (July 18, 2002).Stationary areaProcessed as Area sourcesBase inventory year 2002Gridding matrix applied for gridVISTA12_168X177No speciation matrix appliedTemporal factors applied for episode fromThursday July 18, 2002 at 000000 toThursday July 18, 2002 at 230000Annual total data basis in reportDate ; Region; State ; CO ; NOX ; VOC ; NH3 ; SO2 ; PM10 ; PM2_5 ; PMC; ; ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day]------------------------------------------------------------------------------------------------------------------------------------------------------------------07/18/2002; 001000;Alabama ; 0.44371E+02; 0.17017E+02; 0.35038E+03; 0.17273E+03; 0.10311E+03; 0.46289E+03; 0.73994E+02; 0.38889E+0307/18/2002; 005000;Arkansas ; 0.32214E+03; 0.59925E+02; 0.17541E+03; 0.39077E+03; 0.77625E+02; 0.93498E+03; 0.21273E+03; 0.72227E+0307/18/2002; 009000;Connecticut ; 0.74329E+01; 0.90859E+01; 0.12071E+03; 0.14411E+02; 0.53008E+00; 0.47080E+02; 0.78904E+01; 0.39190E+0207/18/2002; 010000;Delaware ; 0.57666E+01; 0.40146E+01; 0.23986E+02; 0.43931E+02; 0.12991E+01; 0.27233E+02; 0.43240E+01; 0.22909E+0207/18/2002; 011000;District of Columbia; 0.15917E+01; 0.15292E+01; 0.15007E+02; 0.22391E-01; 0.17404E+01; 0.83408E+01; 0.14891E+01; 0.68518E+0107/18/2002; 012000;Florida ; 0.67168E+02; 0.70148E+02; 0.10590E+04; 0.99305E+02; 0.90880E+02; 0.71195E+03; 0.93626E+02; 0.61833E+0307/18/2002; 013000;Georgia ; 0.13696E+03; 0.78878E+02; 0.73790E+03; 0.24723E+03; 0.15909E+03; 0.80410E+03; 0.13605E+03; 0.66805E+0307/18/2002; 017000;Illinois ; 0.64822E+02; 0.54397E+02; 0.49044E+03; 0.15470E+03; 0.10554E+02; 0.18652E+04; 0.36050E+03; 0.15047E+0407/18/2002; 018000;Indiana ; 0.58957E+02; 0.53330E+02; 0.32822E+03; 0.21196E+03; 0.16105E+03; 0.17266E+04; 0.34249E+03; 0.13841E+0407/18/2002; 019000;Iowa ; 0.40775E+02; 0.42057E+01; 0.22173E+03; 0.54576E+03; 0.14195E+01; 0.13372E+04; 0.25094E+03; 0.10863E+0407/18/2002; 020000;Kansas ; 0.28911E+03; 0.29621E+02; 0.88596E+02; 0.43985E+02; 0.88975E+01; 0.70323E+03; 0.15211E+03; 0.55112E+0307/18/2002; 021000;Kentucky ; 0.77191E+02; 0.10531E+03; 0.23546E+03; 0.11256E+03; 0.99659E+02; 0.42778E+03; 0.81534E+02; 0.34625E+0307/18/2002; 022000;Louisiana ; 0.16404E+04; 0.28481E+03; 0.29980E+03; 0.19668E+03; 0.23245E+03; 0.88853E+03; 0.28283E+03; 0.60571E+0307/18/2002; 024000;Maryland ; 0.21134E+03; 0.21519E+02; 0.20890E+03; 0.66855E+02; 0.16790E+02; 0.18854E+03; 0.47790E+02; 0.14075E+0307/18/2002; 025000;Massachusetts ; 0.57736E+01; 0.38607E+01; 0.20534E+02; 0.56228E+01; 0.26920E+01; 0.16076E+02; 0.37205E+01; 0.12356E+0207/18/2002; 026000;Michigan ; 0.30982E+02; 0.40472E+02; 0.31871E+03; 0.10240E+03; 0.71099E+02; 0.74950E+03; 0.13415E+03; 0.61535E+0307/18/2002; 027000;Minnesota ; 0.20006E+02; 0.14727E+02; 0.66111E+02; 0.11717E+03; 0.38086E+01; 0.61705E+03; 0.10937E+03; 0.50769E+0307/18/2002; 028000;Mississippi ; 0.42895E+01; 0.22679E+01; 0.30101E+03; 0.14251E+03; 0.57813E+00; 0.55921E+03; 0.81543E+02; 0.47766E+0307/18/2002; 029000;Missouri ; 0.20959E+03; 0.64235E+02; 0.27642E+03; 0.35350E+03; 0.12071E+03; 0.33762E+04; 0.58408E+03; 0.27922E+0407/18/2002; 031000;Nebraska ; 0.32840E+00; 0.66370E-01; 0.10041E+01; 0.17089E+01; 0.25804E-01; 0.11599E+02; 0.21084E+01; 0.94902E+0107/18/2002; 034000;New Jersey ; 0.14231E+02; 0.21007E+02; 0.30461E+03; 0.48417E+02; 0.79506E+01; 0.65704E+02; 0.13140E+02; 0.52564E+0207/18/2002; 036000;New York ; 0.48058E+02; 0.93906E+02; 0.58074E+03; 0.10722E+03; 0.13600E+03; 0.37438E+03; 0.64907E+02; 0.30947E+0307/18/2002; 037000;North Carolina ; 0.35788E+03; 0.69702E+02; 0.41336E+03; 0.47466E+03; 0.86454E+01; 0.36327E+03; 0.62859E+02; 0.30041E+0307/18/2002; 039000;Ohio ; 0.33951E+02; 0.40539E+02; 0.46160E+03; 0.19957E+03; 0.22287E+02; 0.10241E+04; 0.19312E+03; 0.83099E+0307/18/2002; 040000;Oklahoma ; 0.13292E+03; 0.56404E+02; 0.51844E+02; 0.10585E+03; 0.69006E+01; 0.68486E+03; 0.12595E+03; 0.55891E+0307/18/2002; 042000;Pennsylvania ; 0.31625E+03; 0.55051E+02; 0.54914E+03; 0.22572E+03; 0.73776E+02; 0.59936E+03; 0.11511E+03; 0.48427E+0307/18/2002; 044000;Rhode Island ; 0.81695E+01; 0.41245E+01; 0.69566E+02; 0.18260E+01; 0.41886E+01; 0.11126E+02; 0.25125E+01; 0.86136E+0107/18/2002; 045000;South Carolina ; 0.10702E+03; 0.46035E+02; 0.30912E+03; 0.76314E+02; 0.30258E+02; 0.31972E+03; 0.52325E+02; 0.26739E+0307/18/2002; 046000;South Dakota ; 0.00000E+00; 0.00000E+00; 0.35041E-02; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 047000;Tennessee ; 0.75720E+02; 0.30086E+02; 0.35511E+03; 0.82795E+02; 0.77852E+02; 0.38690E+03; 0.77792E+02; 0.30911E+0307/18/2002; 048000;Texas ; 0.28857E+03; 0.15916E+03; 0.36398E+03; 0.16190E+03; 0.91386E+02; 0.25000E+04; 0.42791E+03; 0.20721E+0407/18/2002; 051000;Virginia ; 0.16641E+03; 0.11720E+03; 0.32211E+03; 0.12067E+03; 0.29423E+03; 0.26361E+03; 0.48840E+02; 0.21477E+0307/18/2002; 053000;Washington ; 0.00000E+00; 0.00000E+00; 0.47969E-02; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 054000;West Virginia ; 0.36476E+02; 0.26373E+02; 0.14474E+03; 0.31362E+02; 0.26887E+02; 0.74822E+02; 0.20668E+02; 0.54154E+0207/18/2002; 055000;Wisconsin ; 0.37344E+02; 0.17641E+02; 0.21000E+03; 0.20515E+03; 0.53226E+01; 0.31806E+03; 0.64998E+02; 0.25306E+03##################################################################################################################################################################A-9Table A.2 –VISTAS BaseG2 Point Source Emissions Summary – Facility-Stack Level Emission Totals.Processed as Point sourcesBase inventory year 2002No gridding matrix appliedNo speciation matrix appliedNo temporal factors appliedAnnual total data basis in reportSource ID ; Region; SCC ; Plant ID; Char 1; Char 2; Char 3; Stk Ht; Stk Dm; Stk Tmp; Stk Vel; Plt Name; CO ; NOX ; VOC ; NH3 ; SO2 ; PM10 ; PM2_5 ; PMC; ; ; ; ; ; ; m ; m ; deg K ; m/s ; ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day]-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------1442; 012069;2610000500; 31536 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31536 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061443; 012069;2610000500; 31537 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31537 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061444; 012069;2610000500; 31538 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31538 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061445; 012069;2610000500; 31539 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31539 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061446; 012069;2610000500; 31540 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31540 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061447; 012069;2610000500; 31541 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31541 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061448; 012069;2610000500; 31542 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31542 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061449; 012069;2610000500; 31543 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31543 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061450; 012069;2610000500; 31544 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31544 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061451; 012069;2610000500; 31545 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31545 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061452; 012069;2610000500; 31546 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31546 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061453; 012069;2610000500; 31547 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31547 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061454; 012069;2610000500; 31548 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31548 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061455; 012069;2610000500; 31549 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31549 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061456; 012069;2610000500; 31550 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31550 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061457; 012069;2610000500; 31551 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31551 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061458; 012069;2610000500; 31552 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31552 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061459; 012069;2610000500; 31553 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31553 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061460; 012069;2610000500; 31554 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31554 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061461; 012069;2610000500; 31555 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31555 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061462; 012069;2610000500; 31556 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31556 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061463; 012069;2610000500; 31557 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31557 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061464; 012069;2610000500; 31558 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31558 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061465; 012069;2610000500; 31559 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31559 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061466; 012069;2610000500; 31560 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31560 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061467; 012069;2610000500; 31561 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31561 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061468; 012069;2610000500; 31562 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;31562 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061469; 012069;2801500000; 32004 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32004 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061470; 012069;2801500000; 32005 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32005 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061471; 012069;2801500000; 32006 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32006 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061472; 012069;2801500000; 32007 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32007 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061473; 012069;2801500000; 32008 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32008 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061474; 012069;2801500000; 32009 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32009 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061475; 012069;2801500000; 32010 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32010 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061476; 012069;2801500000; 32011 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32011 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061477; 012069;2801500000; 32012 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32012 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061478; 012069;2801500000; 32013 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32013 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061479; 012069;2801500000; 32014 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32014 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061480; 012069;2801500000; 32015 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32015 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061481; 012069;2801500000; 32016 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32016 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061482; 012069;2801500000; 32017 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32017 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061483; 012069;2801500000; 32018 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32018 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061484; 012069;2801500000; 32019 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32019 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061485; 012069;2801500000; 32020 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32020 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061486; 012069;2801500000; 32021 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32021 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061487; 012069;2801500000; 32022 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32022 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061488; 012069;2801500000; 32023 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32023 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061489; 012069;2801500000; 32024 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32024 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061490; 012069;2801500000; 32025 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32025 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061491; 012069;2801500000; 32026 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32026 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061492; 012069;2801500000; 32027 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32027 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061493; 012069;2801500000; 32028 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32028 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061494; 012069;2801500000; 32029 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32029 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061495; 012069;2801500000; 32030 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32030 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061496; 012069;2801500000; 32031 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32031 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061497; 012069;2801500000; 32032 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32032 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061498; 012069;2801500000; 32033 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32033 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061499; 012069;2801500000; 32034 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32034 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061500; 012069;2801500000; 32035 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32035 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061501; 012069;2801500000; 32036 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32036 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061502; 012069;2801500000; 32037 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32037 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061503; 012069;2801500000; 32038 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32038 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061504; 012069;2801500000; 32039 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32039 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061505; 012069;2801500000; 32040 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32040 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061506; 012069;2801500000; 32041 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32041 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061507; 012069;2801500000; 32042 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32042 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061508; 012069;2801500000; 32043 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32043 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061509; 012069;2801500000; 32044 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32044 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061510; 012069;2801500000; 32045 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32045 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061511; 012069;2801500000; 32046 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32046 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061512; 012069;2801500000; 32047 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32047 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061513; 012069;2801500000; 32048 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32048 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061514; 012069;2801500000; 32049 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32049 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061515; 012069;2801500000; 32050 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32050 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061516; 012069;2801500000; 32051 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32051 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061517; 012069;2801500000; 32052 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32052 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061518; 012069;2801500000; 32053 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32053 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-061519; 012069;2801500000; 32054 ; 0 ; 1 ; ; 3.00; 0.20; 295.37; 4.00;32054 ; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.27397E-06; 0.54795E-06; 0.27397E-06; 0.27397E-06A-10Table A.3 –VISTAS BaseG2 Point Source Emissions Summary – Hourly SCC Level Emission Totals.Processed as Point sourcesBase inventory year 2002No gridding matrix appliedNo speciation matrix appliedTemporal factors applied for episode fromThursday July 18, 2002 at 000000 toFriday July 19, 2002 at 000000Annual total data basis in reportDate ; Hour; SCC ; CO ; NOX ; VOC ; NH3 ; SO2 ; PM10 ; PM2_5 ; PMC; ; ; [tons/hr] ; [tons/hr] ; [tons/hr] ; [tons/hr] ; [tons/hr] ; [tons/hr] ; [tons/hr] ; [tons/hr]------------------------------------------------------------------------------------------------------------------------------------------------------07/18/2002; 01;0000000000; 0.69632E-01; 0.10601E+00; 0.98348E-01; 0.14427E-03; 0.28078E+00; 0.67371E-01; 0.00000E+00; 0.67371E-0107/18/2002; 01;0010100101; 0.23717E+00; 0.64409E+00; 0.18311E+00; 0.86984E-05; 0.15537E+01; 0.36883E-01; 0.12885E-01; 0.23998E-0107/18/2002; 01;0010100102; 0.49310E-04; 0.29138E-03; 0.74712E-05; 0.00000E+00; 0.00000E+00; 0.89654E-05; 0.53643E-05; 0.36011E-0507/18/2002; 01;0010100201; 0.12337E+01; 0.18448E+02; 0.21384E+00; 0.11847E-02; 0.45404E+02; 0.11176E+01; 0.63193E+00; 0.48564E+0007/18/2002; 01;0010100202; 0.13432E+02; 0.13367E+03; 0.76286E+00; 0.13934E+00; 0.39127E+03; 0.77426E+01; 0.34779E+01; 0.43236E+0107/18/2002; 01;0010100203; 0.15106E+01; 0.41001E+02; 0.22772E+00; 0.10565E-01; 0.57875E+02; 0.10036E+01; 0.62171E+00; 0.38190E+0007/18/2002; 01;0010100204; 0.46840E+00; 0.11215E+01; 0.51058E-02; 0.18008E-03; 0.37646E+01; 0.13055E+00; 0.52570E-01; 0.77985E-0107/18/2002; 01;0010100205; 0.17514E+00; 0.40535E+00; 0.12817E-02; 0.18564E-04; 0.10426E+01; 0.49851E-01; 0.84878E-02; 0.41363E-0107/18/2002; 01;0010100211; 0.12032E+00; 0.71511E-01; 0.14337E-01; 0.00000E+00; 0.56604E+00; 0.80955E-01; 0.27104E-02; 0.78245E-0107/18/2002; 01;0010100212; 0.27195E+01; 0.35507E+02; 0.31254E+00; 0.25909E+00; 0.14595E+03; 0.26430E+01; 0.11402E+01; 0.15028E+0107/18/2002; 01;0010100217; 0.59853E+00; 0.73339E+00; 0.22997E-01; 0.78953E-03; 0.21109E+01; 0.78834E-01; 0.39504E-01; 0.39330E-0107/18/2002; 01;0010100218; 0.12248E+00; 0.29317E+00; 0.28785E-02; 0.81146E-03; 0.64975E+00; 0.10895E-01; 0.18197E-02; 0.90749E-0207/18/2002; 01;0010100221; 0.71317E+00; 0.93035E+01; 0.59317E-01; 0.17286E-03; 0.10284E+02; 0.17314E+00; 0.77331E-01; 0.95812E-0107/18/2002; 01;0010100222; 0.71637E+01; 0.65497E+02; 0.59207E+00; 0.11337E+00; 0.11145E+03; 0.29680E+01; 0.99985E+00; 0.19681E+0107/18/2002; 01;0010100223; 0.80563E+00; 0.18628E+02; 0.16394E+00; 0.58958E-02; 0.19546E+02; 0.33507E+00; 0.28768E+00; 0.47390E-0107/18/2002; 01;0010100224; 0.30668E+00; 0.59444E+00; 0.30663E-02; 0.42486E-05; 0.45436E+00; 0.27206E+00; 0.45352E-02; 0.26752E+0007/18/2002; 01;0010100225; 0.84370E-01; 0.80214E-01; 0.55180E-03; 0.00000E+00; 0.12985E+00; 0.36873E-01; 0.51351E-03; 0.36359E-0107/18/2002; 01;0010100226; 0.37955E+01; 0.49513E+02; 0.46960E+00; 0.55985E+00; 0.90938E+02; 0.36303E+01; 0.15562E+01; 0.20741E+0107/18/2002; 01;0010100238; 0.00000E+00; 0.37761E-03; 0.00000E+00; 0.19097E-05; 0.80068E-03; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 01;0010100301; 0.78828E+01; 0.54602E+01; 0.84065E-01; 0.34718E-02; 0.15707E+02; 0.39028E+00; 0.17150E+00; 0.21878E+0007/18/2002; 01;0010100302; 0.16107E+02; 0.12727E+02; 0.14625E+00; 0.25457E-01; 0.49491E+02; 0.13062E+01; 0.83217E+00; 0.47408E+0007/18/2002; 01;0010100303; 0.28142E+00; 0.56976E+01; 0.55583E-01; 0.16925E-01; 0.88036E+01; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 01;0010100306; 0.19797E-01; 0.00000E+00; 0.11458E-03; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 01;0010100317; 0.52071E-02; 0.00000E+00; 0.82754E-03; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 01;0010100318; 0.00000E+00; 0.11651E+00; 0.00000E+00; 0.81850E-03; 0.27784E+00; 0.00000E+00; 0.00000E+00; 0.00000E+0007/18/2002; 01;0010100401; 0.24586E+01; 0.76006E+01; 0.19600E+00; 0.61335E-01; 0.11806E+02; 0.46239E+00; 0.34598E+00; 0.11642E+0007/18/2002; 01;0010100404; 0.48699E+00; 0.33056E+01; 0.82921E-01; 0.47267E-01; 0.10960E+02; 0.32426E+00; 0.22947E+00; 0.94788E-0107/18/2002; 01;0010100405; 0.27744E-04; 0.31632E-03; 0.35943E-05; 0.19814E-05; 0.89624E-03; 0.46775E-04; 0.35290E-04; 0.11485E-0407/18/2002; 01;0010100501; 0.10665E+01; 0.98314E+01; 0.11157E+00; 0.14510E-01; 0.41097E+01; 0.35716E+00; 0.24833E+00; 0.10887E+0007/18/2002; 01;0010100504; 0.75882E-02; 0.66438E-01; 0.21897E-02; 0.10574E-06; 0.15788E-01; 0.20965E-02; 0.19289E-02; 0.16766E-0307/18/2002; 01;0010100505; 0.61880E-03; 0.25294E-02; 0.34996E-04; 0.14639E-04; 0.13508E-02; 0.44740E-03; 0.66140E-04; 0.38126E-0307/18/2002; 01;0010100600; 0.96495E-04; 0.10518E-03; 0.60310E-05; 0.00000E+00; 0.53856E-06; 0.60310E-05; 0.60310E-05; 0.00000E+0007/18/2002; 01;0010100601; 0.87846E+01; 0.21074E+02; 0.70139E+00; 0.15155E+00; 0.99403E+01; 0.14958E+01; 0.13850E+01; 0.11236E+0007/18/2002; 01;0010100602; 0.50297E+00; 0.70762E+00; 0.32526E-01; 0.11195E-01; 0.35912E+00; 0.11366E+00; 0.96447E-01; 0.18402E-0107/18/2002; 01;0010100604; 0.10116E+01; 0.56493E+01; 0.18644E+00; 0.65348E-01; 0.89274E+00; 0.61273E+00; 0.27600E+00; 0.33673E+0007/18/2002; 01;0010100701; 0.11273E+00; 0.82767E+00; 0.17431E-01; 0.32131E-05; 0.12597E-01; 0.93832E-01; 0.77319E-01; 0.16512E-0107/18/2002; 01;0010100702; 0.51429E-01; 0.64817E-01; 0.31871E-02; 0.00000E+00; 0.17190E+00; 0.13937E-01; 0.79361E-02; 0.60013E-0207/18/2002; 01;0010100711; 0.16037E-02; 0.12431E-01; 0.10496E-03; 0.60163E-04; 0.17647E-03; 0.15795E-03; 0.15795E-03; 0.00000E+0007/18/2002; 01;0010100801; 0.81893E-01; 0.22331E+00; 0.92392E-02; 0.00000E+00; 0.70142E+00; 0.28780E+00; 0.28444E+00; 0.44545E-0207/18/2002; 01;0010100901; 0.12543E+00; 0.73472E-01; 0.22990E-01; 0.00000E+00; 0.34409E-01; 0.25949E-01; 0.12759E-01; 0.13189E-0107/18/2002; 01;0010100902; 0.24309E+01; 0.91108E+00; 0.64300E-01; 0.88651E-02; 0.34429E+00; 0.10444E+00; 0.80967E-01; 0.23477E-0107/18/2002; 01;0010100903; 0.10181E+01; 0.52995E+00; 0.67198E-01; 0.33635E-03; 0.13256E+00; 0.11066E+00; 0.96558E-01; 0.14106E-0107/18/2002; 01;0010100911; 0.29619E+00; 0.16035E+00; 0.12301E-01; 0.12042E-03; 0.15444E-02; 0.10733E-01; 0.75779E-02; 0.31546E-0207/18/2002; 01;0010101001; 0.98770E-06; 0.19367E-05; 0.77818E-07; 0.00000E+00; 0.00000E+00; 0.21838E-09; 0.00000E+00; 0.21838E-09A-11Table A.4 –VISTAS BaseG2 Onroad Mobile Source Emissions Summary – Daily County Level Speciated Emission Totals.Processed as Mobile sourcesBase inventory year 2018No gridding matrix appliedNo speciation matrix appliedTemporal factors applied for episode fromThursday July 18, 2002 at 000000 toThursday July 18, 2002 at 230000Annual total data basis in reportDate ; Region; State ; County ; EXR__CO ; EXR__NOX ; EXR__TOG ; EXR__SO4 ; EXR__OCARB25 ; EXR__OCARBPMC; EXR__ECARB25 ;EXR__ECARBPMC; EXR__GASPM25 ; EXR__GASPMC ; EXR__SO2 ; EXR__NH3 ; EXS__CO ; EXS__NOX ; EXS__TOG ; HOT__TOG ; DNL__TOG ; RST__TOG ; EVR__TOG ;CRC__TOG ; BRK__BRAKE25 ; BRK__BRAKEPMC; TIR__TIRE25 ; TIR__TIREPMC ; RFL__TOG; ; ; ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ;[tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ;[tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day] ; [tons/day]-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------07/18/2002; 001001;Alabama ; Autauga Co ; 0.10683E+02; 0.11802E+01; 0.41604E+00; 0.11465E-02; 0.29400E-02; 0.25566E-03; 0.57158E-02;0.49703E-03; 0.85616E-02; 0.80122E-03; 0.20665E-01; 0.22136E+00; 0.32406E+01; 0.12053E+00; 0.23488E+00; 0.16280E+00; 0.82331E-02; 0.13731E+00; 0.17125E+00;0.19892E-01; 0.12934E-01; 0.17487E-01; 0.60055E-02; 0.18017E-01; 0.61950E-0107/18/2002; 001003;Alabama ; Baldwin Co ; 0.37965E+02; 0.43607E+01; 0.14322E+01; 0.39511E-02; 0.11914E-01; 0.10360E-02; 0.23174E-01;0.20151E-02; 0.31073E-01; 0.29143E-02; 0.75657E-01; 0.79900E+00; 0.11578E+02; 0.43130E+00; 0.83895E+00; 0.58222E+00; 0.29457E-01; 0.49208E+00; 0.51809E+00;0.71577E-01; 0.47153E-01; 0.63751E-01; 0.22368E-01; 0.67105E-01; 0.22387E+0007/18/2002; 001005;Alabama ; Barbour Co ; 0.80254E+01; 0.87307E+00; 0.38301E+00; 0.97693E-03; 0.20218E-02; 0.17580E-03; 0.39285E-02;0.34161E-03; 0.66920E-02; 0.62444E-03; 0.16034E-01; 0.17498E+00; 0.25495E+01; 0.94684E-01; 0.18480E+00; 0.12787E+00; 0.64618E-02; 0.10742E+00; 0.20953E+00;0.15740E-01; 0.10112E-01; 0.13671E-01; 0.45855E-02; 0.13757E-01; 0.48623E-0107/18/2002; 001007;Alabama ; Bibb Co ; 0.44676E+01; 0.54423E+00; 0.19802E+00; 0.50455E-03; 0.15706E-02; 0.13658E-03; 0.30556E-02;0.26571E-03; 0.39253E-02; 0.36840E-03; 0.96062E-02; 0.10113E+00; 0.14457E+01; 0.53894E-01; 0.10475E+00; 0.72701E-01; 0.36809E-02; 0.61371E-01; 0.85463E-01;0.90342E-02; 0.59898E-02; 0.80983E-02; 0.28642E-02; 0.85925E-02; 0.28245E-0107/18/2002; 001009;Alabama ; Blount Co ; 0.11195E+02; 0.13462E+01; 0.46810E+00; 0.12197E-02; 0.38412E-02; 0.33402E-03; 0.74729E-02;0.64982E-03; 0.95500E-02; 0.89674E-03; 0.23381E-01; 0.24570E+00; 0.35353E+01; 0.13179E+00; 0.25615E+00; 0.17783E+00; 0.90044E-02; 0.15025E+00; 0.18901E+00;0.21962E-01; 0.14563E-01; 0.19690E-01; 0.69726E-02; 0.20918E-01; 0.68770E-0107/18/2002; 001011;Alabama ; Bullock Co ; 0.28452E+01; 0.35569E+00; 0.13313E+00; 0.33076E-03; 0.10242E-02; 0.89058E-04; 0.19925E-02;0.17326E-03; 0.25570E-02; 0.24003E-03; 0.62610E-02; 0.65921E-01; 0.94260E+00; 0.35139E-01; 0.68297E-01; 0.47404E-01; 0.24047E-02; 0.39987E-01; 0.60101E-01;0.58884E-02; 0.39044E-02; 0.52788E-02; 0.18671E-02; 0.56014E-02; 0.18422E-0107/18/2002; 001013;Alabama ; Butler Co ; 0.11799E+02; 0.12890E+01; 0.43964E+00; 0.11955E-02; 0.32843E-02; 0.28560E-03; 0.63862E-02;0.55532E-03; 0.92877E-02; 0.86975E-03; 0.22418E-01; 0.23901E+00; 0.35146E+01; 0.13077E+00; 0.25471E+00; 0.17660E+00; 0.89172E-02; 0.14917E+00; 0.17458E+00;0.21468E-01; 0.14006E-01; 0.18937E-01; 0.65433E-02; 0.19630E-01; 0.66939E-0107/18/2002; 001015;Alabama ; Calhoun Co ; 0.48673E+02; 0.52443E+01; 0.25748E+01; 0.60386E-02; 0.12160E-01; 0.10574E-02; 0.23633E-01;0.20550E-02; 0.38282E-01; 0.35827E-02; 0.92508E-01; 0.10076E+01; 0.14941E+02; 0.55505E+00; 0.10829E+01; 0.74928E+00; 0.37719E-01; 0.63012E+00; 0.15867E+01;0.90452E-01; 0.58436E-01; 0.79006E-01; 0.26688E-01; 0.80063E-01; 0.27938E+0007/18/2002; 001017;Alabama ; Chambers Co ; 0.77831E+01; 0.86810E+00; 0.31296E+00; 0.85048E-03; 0.21927E-02; 0.19067E-03; 0.42629E-02;0.37068E-03; 0.65372E-02; 0.61100E-03; 0.15706E-01; 0.16869E+00; 0.24543E+01; 0.91266E-01; 0.17788E+00; 0.12324E+00; 0.62354E-02; 0.10375E+00; 0.12973E+00;0.15154E-01; 0.98375E-02; 0.13300E-01; 0.45506E-02; 0.13652E-01; 0.47135E-0107/18/2002; 001019;Alabama ; Cherokee Co ; 0.55946E+01; 0.68518E+00; 0.26212E+00; 0.64557E-03; 0.19666E-02; 0.17101E-03; 0.38260E-02;0.33269E-03; 0.48814E-02; 0.45847E-03; 0.11971E-01; 0.12606E+00; 0.18102E+01; 0.67482E-01; 0.13116E+00; 0.91037E-01; 0.46160E-02; 0.76815E-01; 0.12223E+00;0.11255E-01; 0.74697E-02; 0.10099E-01; 0.35749E-02; 0.10725E-01; 0.35201E-0107/18/2002; 001021;Alabama ; Chilton Co ; 0.13349E+02; 0.14880E+01; 0.49241E+00; 0.13590E-02; 0.38839E-02; 0.33773E-03; 0.75533E-02;0.65681E-03; 0.10541E-01; 0.98812E-03; 0.25564E-01; 0.27124E+00; 0.39766E+01; 0.14804E+00; 0.28817E+00; 0.19990E+00; 0.10106E-01; 0.16894E+00; 0.18268E+00;0.24336E-01; 0.15952E-01; 0.21566E-01; 0.75089E-02; 0.22527E-01; 0.76024E-0107/18/2002; 001023;Alabama ; Choctaw Co ; 0.35209E+01; 0.43252E+00; 0.15894E+00; 0.40062E-03; 0.12502E-02; 0.10872E-03; 0.24323E-02;0.21150E-03; 0.31251E-02; 0.29330E-03; 0.76462E-02; 0.80496E-01; 0.11505E+01; 0.42889E-01; 0.83360E-01; 0.57867E-01; 0.29386E-02; 0.48814E-01; 0.69629E-01;0.71905E-02; 0.47676E-02; 0.64457E-02; 0.22798E-02; 0.68393E-02; 0.22502E-0107/18/2002; 001025;Alabama ; Clarke Co ; 0.59226E+01; 0.68641E+00; 0.25458E+00; 0.67197E-03; 0.18723E-02; 0.16280E-03; 0.36413E-02;0.31663E-03; 0.50767E-02; 0.47544E-03; 0.12334E-01; 0.13127E+00; 0.18841E+01; 0.70154E-01; 0.13653E+00; 0.94672E-01; 0.47865E-02; 0.79823E-01; 0.11274E+00;0.11755E-01; 0.77142E-02; 0.10430E-01; 0.36290E-02; 0.10887E-01; 0.36618E-0107/18/2002; 001027;Alabama ; Clay Co ; 0.32368E+01; 0.39861E+00; 0.15935E+00; 0.38110E-03; 0.11363E-02; 0.98807E-04; 0.22106E-02;0.19223E-03; 0.28016E-02; 0.26332E-03; 0.68852E-02; 0.72518E-01; 0.10461E+01; 0.38997E-01; 0.75794E-01; 0.52605E-01; 0.26659E-02; 0.44385E-01; 0.78972E-01;0.64717E-02; 0.42994E-02; 0.58128E-02; 0.20594E-02; 0.61781E-02; 0.20236E-0107/18/2002; 001029;Alabama ; Cleburne Co ; 0.84659E+01; 0.97428E+00; 0.30666E+00; 0.83602E-03; 0.26955E-02; 0.23439E-03; 0.52440E-02;0.45600E-03; 0.65992E-02; 0.62058E-03; 0.16179E-01; 0.16928E+00; 0.24835E+01; 0.92582E-01; 0.17994E+00; 0.12497E+00; 0.63173E-02; 0.10589E+00; 0.10401E+00;0.15149E-01; 0.10055E-01; 0.13594E-01; 0.48311E-02; 0.14493E-01; 0.47565E-01

### B-1 APPENDIX B

Air Quality Model Performance Evaluation: Regional Model Evaluation across the VISTAS Region

### B-2 B.1 OVERVIEW

The discussion of model performance evaluation in this Appendix focuses on the evaluation of the 2002 Base G2 Actual base case modeling resu lts from the Community Multiscale Air Quality (CMAQ) modeling system Versi on 4.5 with SOAmods enhancement across the VISTAS states and VISTAS 12 km grid. The CMAQ results are compared with observational data from the

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

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

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

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

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

### mass and these monitors are almost exclusively

located in urban areas so are not as relevant for judging how well the model is predicting the components of light extinction at the more rural Class I areas. However, model performance of the total fine particulate matter and the resulting to tal light extinction will also be provided as a means to discuss the overall model performance.

B.2 CMAQ Evaluation Methodology

EPA’s integrated ozone, PM 2.5 

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

B.2.1 Evaluation Approach

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

• Operational Evaluation : Tests the ability of the model to estimate PM concentrations (both fine and coarse) and the components at PM 10 

### and PM 2.5 

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

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

### ). This evaluation examines whether the measurements are properly represented by th e model predictions but does not necessarily ensure that the model is getting “the right answer for the right reason”; and

• Diagnostic Evaluation: Tests the ability of the model to predict visibility and extinction, PM chemical composition including PM precursors (e.g., SOx, NOx, and NH 3 

### ) and associated oxidants (e.g., ozone and nitric acid ); PM size distribution; temporal variation; spatial variation; mass fluxes; and components of light extinction (i .e., scattering and absorption).

### B-3 The diagnostic evaluation also includes the perf ormance of diagnostic tests to better understand model performance and identify potential flaws in the modeling system that can be corrected. The diagnostic evaluation may al so includes the use of “probing tools” to understand why the model obtains a given prediction; probing tools in clude Process Analysis (PA), decoupled direct method (DDM) and source apportionment (SA).

In this final model performa nce evaluation for the VISTAS 2002 Actual Base G2 CMAQ 36/12 km base case simulation, the operational evaluati on has been given the gr eatest attention since this is the primarily thrust of EPA’s modeling guidance. However, we have also examined certain diagnostic features dealing with the model’s ability to simulate sub-regional and monthly/diurnal gas phase and aero sol concentration distributions. In the course of the VISTAS study numerous diagnostic sensitiv ity tests were performed to investigate and improve model performance. Key diagnostic tests performed ar e discussed and the results for the rest are available on the VISTAS modeling website: http://pah.cert.ucr.edu/vistas/vistas2/.

B.2.2 Particulate Matter and Component Species Regional haze is calculated by estimating light scattering and absorption by components of

PM 2.5 

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

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

Some scattering does occur from air molecules; ho wever, the bulk of light extinction is caused by the presence of suspended particles, partic ularly fine particle s (aerodynamic mass <2.5 μ m). Fine particulate matter can be composed of varying amounts of several different species, including:

• Sulfate (SO4)

• Nitrate (NO3)

• Ammonium (NH4)

• Organic Matter Carbon (OMC)

• Elemental Carbon (EC)

• Coarse particulate matter or Coarse Mass (i.e., PM 10 

### - PM 2.5 

### or CM).

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

• Sea Salt (NaCl)

B.2.3 Ambient Air Quality Data for VISTAS Model Evaluation

A ground-level model evaluation database for 2002 was compiled by the modeling team using several routine and research-grade databases. The focus of the regional evaluation of the CMAQ model for use in simulating visibility in the sout heastern is on the PM components that can cause visibility impairment. The prim ary monitoring networks available to evaluate this component of the CMAQ are: (a) Interagency Monitoring of Protected Visual Environments (IMPROVE); (b)

### B-4 Clean Air Status and Trends Network (CASTNET); (c) Southeastern Aerosol Research and Characterization (SEARCH); (d) EPA Federal Reference Method PM 2.5 

### and PM 10 

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

### species; and (f) National Acid Deposition Network (NADP). Thes e PM monitoring networks may also provide ozone and other gas phase precursor s and product species, and visi bility measurements at some sites. There are also gas-phase criteria polluta nt measures from the EPA’s Air Quality System (AQS) that are available for use. However, thes e sites are urban-oriented and not as useful for the regional evaluation of the CMAQ modeli ng system. The study team has performed a detailed evaluation of the CMAQ 2002 base case si mulation using the AQS that is available on the modeling website: http://pah.cert.ucr.edu/vistas/vistas2/mpe2.shtml . Table B-1 and Figure B-1 summarizes the species collected and loca tions of the monitoring sites for the IMPROVE, STN, CASTNet, NADP and SEARCH monitori ng networks use in the VISTAS model

evaluation.

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

Monitoring

Network Chemical Species Measured Sampling Frequency;

Duration

IMPROVE Speciated PM 2.5 and PM 10 1 in 3 days; 24 hr

CASTNET Speciated PM 2.5 , Ozone Hourly, Weekly; 1 hr, Week

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

NADP WSO 4 , WNO 3 , WNH 4 Weekly

EPA-FRM Only total fine mass (PM 2.5 ) 1 in 3 days; 24 hr

EPA-STN Speciated PM 2.5 Varies; Varies
AIRS/AQS CO, NO, NO 2 , NO

x , O 3 Hourly; Hourly

### B-5 

## 250 500 750 1000 1250 1500 1750 2000 -1500 -1250 -1000

-750 -500 -250 0 250 500

IMPROVE

CASTNet

SEARCH

STN

NADP

AQS

Figure B-1. IMPROVE, STN, CASTNet, NADP and SEARCH monitoring sites located in the VISTAS region and the VISTAS 12 km grid.



### B.3 Model Performance Statistics

To quantify model performance, several statistical measures we re calculated and evaluated for all the monitors within the VISTAS region and within each VISTAS state. The statistical

measures selected were based on the reco mmendations outlined in section 18.4.1 of the

USEPA’s Guidance On The Use Of Models And Ot her Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze (EPA, 2007a). In 2004, VISTAS established model performance goals and criteria for components of fine particle mass based on previous model performance for ozone and fine particles. EPA modeling guidance for fine particulate matter at the time noted that PM models might not be able to achieve the same level of performance as ozone models. VISTAS ’ evaluation considered several statistical performance measures and displays.

### B-6 B.4 Modeling Performance Across the VISTAS Region

In the following discussions we use selected mont hly scatter plots, time series plots and model performance statistical measures from the U CR Analysis Tool appli cation to the 2002 CMAQ Base G2 Actual base case simulation in an opera tional evaluation of the model for PM species. We focus on the six main components of PM that are used to project visibility.

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

B.4.1.1 SO4 in January 2002

Figure B-2a displays scatter pl ots of predicted and observed SO4 concentrations or wet depositions for January 2002, sites in the VI STAS region using observations from the IMPROVE, STN, CASTNet and NADP monitoring networks and the CMAQ Base G2 Actual base case 36 km (red) and 12 km (blue) m odeling results. The IMPROVE and STN SO4 concentrations are 24-hour averages whereas the CASTNet SO4 concen trations and NADP SO4 wet deposition are weekly averages. The January SO4 performance at the IMPROVE and STN networks in the VISTAS region is quite good w ith low fractional bias (-14%) and gross error (34%-38%) that almost achieves the most string ent model performance ev aluation goals (Table C-1XXX). There is a larger SO4 concentration underestima tion bias across the CASTNet network (-21% to -23%) with less scatter so there is lower gross error (30% to 32%). The lower scatter and error is likely due to the longer sampling period for CASTNet (weekly) versus IMPROVE/STN networks (24-hour) that reduces the variability in both the predicted and observed SO4 concentrations. Surprisingly the model exhibits low SO4 wet deposition bias across the VISTAS region (-7% to -8%) but lots of scatter so pr oduces a high bias than for the concentrations (67% to 70%). This is surprising because obtaining good SO4 model performance requires the simultaneous occurrence of correct depiction of SO4 concentrations and precipitation model predictions. As noted by Olerud (2003c,d), the MM5 simulation does a respectable job in reproducing pr ecipitation during synoptic rain events as o ccur in the winter, however it exhibited less skill in predicting prec ipitation during the summer convective events.

Figure B-2b present quantile-qua ntile (Q-Q) plots for January 2002 SO4 concentration and wet deposition model performance. Q-Q plots co mpare the frequency distribution between the predicted and observed SO4 concentrations/d epositions. Across the IMPROVE network the model slightly underestimates the frequency dist ribution of the lower SO4 below approximately 5 ug/m3 and overpredictions the values above this value. Across the more urban STN network the model exhibits a slight underprediction of the observed frequency distribution across the VISTAS region during January 2002. Across th e mixed urban/non-urban SEARCH network the model reproduces the observed frequency distribution of SO4 concentra tions quite well, except for overestimating the highest values above ap proximately 4 ug/m3. Finally, the observed wet SO4 deposition in the VISTAS region and Janu ary 2002 is matched exactly by the model except for the very highest events that are underpre dicted. It is unclear whether the model overestimation of the highest observed concentrations and underestimation of the highest observed wet deposition events are connected.

### B-7 Figure B-2a. Scatter plots of predicted and observed sulfate (SO4) concentrations for January 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-8 Figure B-2b. Q-Q plots of predicted and observed sulfate (SO4) concentrations or depositions for January 2002 and sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) and NADP monitoring networks and the CMAQ 2002 12 km Base G2 Actual

base case simulation

### B-9 B.4.1.2 SO4 in April 2002

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

underestimation bias of -20% to -22% across th e IMPROVE and STN networks. The scatter in the SO4 performance across these two networks is very tight with low fractions error values of 30% to 35% (Figure B-3a). Across the CASTNe t network in April 2002, both the 36 km and 12 km versions of the model exhibits a higher undere stimations bias (-25% to -26%), but low error (27% to 28%), which is due to the less variabi lity in the weekly samples. Wet SO4 deposition performance in April across the VISTAS region is much worse than seen for January with an overprediction bias of 48% (12 km) and 50% ( 36 km) and fractions errors of approximately 100%. The Q-Q plots of SO4 con centrations and deposition mode l performance in April shown in Figure C-3b indicates that the model does a reasonable job in re producing the observed frequency distribution, although with a slight underprediction bias except for the highest values that are either reproduced fairly well (IMPR OVE and STN) or underpredicted (SEARCH and NADP)..

### B-10 Figure B-3a. Scatter plots of predicted and observed sulfate (SO4) concentrations for April 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-11 Figure B-3b. Q-Q plots of predicted and observed sulfate (SO4) concentrations for April 2002 and sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-12 B.4.1.3 SO4 in July 2002

The SO4 model performance in July 2002 across the VISTAS regi on is also fairly good. The CMAQ Base G2 12 km model simulation produces slight lower fractions bias than the 36 km model for the IMPROVE (-18% vs. -20%), ST N (-13% vs. -16%) and CASTNet (-17% vs. - 23%) networks (Figure B-4a). The fractional error for July SO4 concentrations across these same networks ranges from 27% to 48% with the 12 km model exhibiting slight lower error than the 36 km model. The July SO4 wet deposition e xhibits an overestimation bias of 39% to 45%

and fractions errors of ~90%. The overestima tion of the July 2002 SO4 wet deposition in the VISTAS region is consistent with the MM5 me teorological model evaluation that found that MM5 overstated the amount of summer pr ecipitation due to convective activity.

The model reproduces the observed frequency di stribution of SO4 con centrations across the

IMPROVE monitors in the VISTAS region during July 2002 very well (Figure B-4b). The Q-Q plots of July SO4 performance for the STN and CASTNet monitoring network across the VISTAS region also exhibits fairly good mode l performance, although with an underestimation

bias for STN and overestimation bias for SEA RCH at the high end. The observed frequency distribution of SO4 wet deposi tion across the VISTAS region is greatly overestimated by the model. This summer wet SO4 overestimation bias undoubtedly contributes to the underprediction of the SO4 concentrations and extinction for the Worst 20 percent days as discussed in Appendix C.

### B-13 Figure B-4a. Scatter plots of predicted and observed sulfate (SO4) concentrations for July 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-14 Figure B-4b. Q-Q plots of predicted and observed sulfate (SO4) concentrations for July 2002 and sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-15 B.4.1.4 SO4 in October 2002

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

with much lower fractional bias values acro ss the IMPROVE (0% to 4%) and STN (-6% to - 15%) and fractional errors of 33% to 48% (Fi gure C-5a). Good SO4 concentrations model performance for October 2002 and the VISTAS region is also seen across the CASTNet monitors with zero bias and fractions error of approximate ly 30% (i.e., meeting the most stringent ozone model performance goals). Alt hough the SO4 wet deposition model performance bias is fairly low (~20%) there is lots of sca tter producing large errors (~85%).

The Q-Q plot comparisons (Figure C-5b) indi cate that the model overstates both the SO4 concentrations and deposition distribution in Oc tober 2002 with the overstatement increasing at the higher values.

### B-16 Figure B-5a. Scatter plots of predicted and observed sulfate (SO4) concentrations for October 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-17 Figure B-5b. Q-Q plots of predicted and observed sulfate (SO4) concentrations for October 2002 and sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-18 B.4.1.5 SO4 Monthly Bias and Error

Figure B-6a compares the monthly SO4 concen tration fractional bias and error across the VISTAS region for the IMPROVE, STN, CAST Net, SEARCH Daily and SEARCH Hourly monitoring networks. Note that these monthly model performance statistics were calculated across the VISTAS 12 km grid versus just th e VISTAS states for used in the monthly performance evaluation in Figures B-2 thr ough B-5. The underprediction bias in SO4 concentrations is clearly evid ent across most of the year a nd monitoring networks with the lowest SO4 bias in October. However, this unde rprediction bias is not severe usually not going lower than -20% and never going lower than -25%. The exception to this is the SEARCH Hourly network that exhibits a positive ove rprediction SO4 bas for several months.

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

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

### B-19 Figure B-6a. Monthly SO4 fractional bias (top) and fractional gross error (bottom) statistical measures for CASTNet, IMPROVE, STN and SEA RCH (daily and hourly) monitoring sites in the VISTAS 12 km grid.

### B-20 Figure B-6b . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for SO4 in the VISTAS 12 km grid and comparison to model performance goals (blue) and criteria (yellow).

### B-21 B.4.2 Nitrate (NO3) Monthly Model P erformance in the VISTAS Region

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

above for SO4.

B.4.2.1 NO3 in January 2002

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

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

simulations for the STN and CASTNet monitori ng networks across the VISTAS region, with fractional errors that range from 30% to 37%. However, across the IMPROVE network in the VISTAS region the CMAQ 12 km run exhibits a higher underprediction bi as (-39%) than the 36 km run (-23%), although it has lower error (90%) than the 36 km run (96%). The January NO3 wet deposition performance for the 36 km and 12 km simulations are very similar with an approximate -7% bias and 36% gross error.

The Q-Q plots comparing the predicted and obs erved January NO3 fre quency distributions indicate that although th ere is a net underestimation bias in NO3 when averaged across the monitoring networks as shown IN Figure B-7a, the model overestimates the highest observed

NO3 concentrations. This overest imation of the highest observed NO3 concentrations in January occurs for the IMPROVE, CASTNet and NADP netw orks, but not the more urban-oriented STN

network.

### B-22 Figure B-7a. Scatter plots of predicted and observed nitrate (NO3) concentrations for January 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-23 Figure B-7b. Q-Q plots of predicted and observed nitrate (NO3) concentrations for January 2002 and sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-24 B.4.2.2 NO3 in April 2002

The monthly NO3 performance degrades in April 2002 with the model exhibiting an underprediction bias of approximately -60% and -160% across the IMPROVE and STN monitoring in the VISTAS region and errors of over 100% and 160%, respectively. Better April NO3 performance is seen across the CASTNet mon itoring network with bias values of -9% and - 18% for the CMAQ 36 km and 12 km base case simu lations and errors of approximately 80%. April NO3 wet deposition is overestimated by 36% to 31% with errors of approximately 95%.

Despite the -60% underestimation bias for Ap ril monthly NO3 across the IMPEOVE network, the Q-Q plot indicates an overestimation bias for the highest NO3 values (Figure B-8b). This is because most of the modeled NO3 values are near zero so show up as an underprediction bias for the very lowest observed NO3 values in the Q-Q bias, but there are some modeled values higher than observed that are accentuated in the Q-Q plot.

### B-25 Figure B-8a. Scatter plots of predicted and observed nitrate (NO3) concentrations for April 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-26 Figure B-8b. Q-Q plots of predicted and observed nitrate (NO3) concentrations for April 2002 and sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-27 B.4.2.3 NO3 in July 2002

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

fractional bias values of - 150% to -160% across the IMPROV E and STN networks and of approximately -100% across the CASTNet monitoring network (Figure B-8a), This is due to the model most of the time predicting near zero NO3 concentrations in July as the hot temperatures in the southeastern U.S. will volatilize the partic ulate NO3 in the model to gaseous nitric acid. Even so, there are occurrences of overestimated model NO3 concentrations in July and for the IMPROVE network the highest modeled NO3 values exceed the measured values as shown in the Q-Q plot in Figure B-8b.

Figure B-9a. Scatter plots of predicted and observed nitrate (NO3) concentrations for July 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-28 Figure B-9b. Q-Q plots of predicted and observed nitrate (NO3) concentrations for July 2002 and sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-29 B.4.2.4 NO3 in October 2002

The NO3 scatter plots for October 2002 suggest that the model is overestimating the observed NO3 but there is a net negative fraction bias of approximately -30% to 0-40% for the IMPROVE and STN networks and the VISTAS 12 km grid (Fi gure B-10a). This negative bias is due to the model predicting very low near zero NO3 concen trations when the observed NO3 is less than approximately a 0.5 ug/m3. However, when the observed value is greater than approximately 0.5 ug/m3 at the IMPROVE and STN networks it a ppears that the model mostly overstates the

observed value. Across the CASTNet network a positive bias is seen (32% to 42%) which may be due in part to volatilization of the particul ate NO3 in the weekly sample. The magnitude of the NO3 wet deposition observed values is reprodu ced reasonable well with low bias (6% to 8%) but with lots of scatter around the 1:1 line producing errors of approximately 85%.

The overprediction of the highest observed NO3 concentrations and depo sition in October is clearly evident in the Q-Q plots (Figure B-10b).

### B-30 Figure B-10a. Scatter plots of predicted and observed nitrate (NO3) concentrations for October 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), CASTNet (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-31 Figure B-10b. Q-Q plots of predicted and observed nitrate (NO3) concentrations for October 2002 and sites in the VISTAS region using IMPROVE (top left), CASTNet (top right), SEARCH (bottom left) and NADP monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-32 B.4.2.5 NO3 Monthly Bias and Error

The monthly fractional bias time series plot clearly shows the seasonal dependence of this performance measure with severe summer underp rediction bias of - 100% to -150% in the summer and bias values generally within ±50% in the winter (Figure B-11a). The time series of

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

to a 160% error in the summer with a bell-like distribution. At fi rst glance it appears that the summer underprediction bias would a dversely affect the regional haze modeling as that is when a vast majority of the worst 20 percent days o ccur at Class I areas in the VISTAS region. However, as shown in the Bugle Plots in Fi gure B-11b, these large summer underprediction bias

values occur when NO3 concentrations are lo w (< 1.0 ugm3) and in fact when the average observed NO3 concentration exceeds 1 ug/m3 the model mostly achieves the model performance goal and almost always achieves the model performan ce criteria for bias (Figure B-11a top). The model performance goal and criteria is achieved less often for fractional error and NO3 (Figure B-11b, bottom), but the goal is always achieved across the CASTNet and usually achieved across the IMPROVE monitoring networks.

### B-33 Figure B-11a. Monthly NO3 fractional bias (top) and fractional gross error (bottom) statistical measures for CASTNet, IMPROVE, STN and SEA RCH daily and hourly monitoring sites in the VISTAS region.

### B-34 Figure B-11b . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for NO3 in the VISTAS region and comparison to model performance goals (blue) and criteria (yellow).

### B-35 B.4.3 Organic Matter Carbon (O MC) Monthly Model Performanc e in the VISTAS Region

The CMAQ model simulates organic matter carbo n (PMC) that includes both primary and secondary organic aerosol (OA) that incl udes carbon, oxygen and other compounds. However, the monitoring networks measure just the orga nic carbon (OC) portion of the OMC. Thus, the

OC measurements need to be converted to OM C for the model evaluation. OMC/OC ratios tend to vary between 1.2 to 2.2 depending on the organic compounds and the amount of photochemistry the compounds have undergone, with newer OMC tending to have lower OMC/OC ratios than aged OMC. The origin al IMPROVE equation uses a 1.4 OMC/OC ratio, whereas the new IMPROVE equation uses a 1.8 OM C/OC ratio. In this model performance

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

concern with the STN OC measurements because they are not black corrected. In fact the differences in OC sampling met hodologies results in differences in measurements that exceeds our performance goals (Solomon et al., 1994). Thus, these uncertainties in the OMC

measurements need to be accounted fo r in the model performance evaluation.

B.4.3.1 OMC in January 2002

In January the CMAQ 12 km and 36 km Base G2 2002 Actual base case simulation exhibit a 11% and 18% overprediction bias across the ru ral IMPROVE monitors and a -40% and -46% underprediction bias across the urban STN monitors in the VISTAS region with errors of approximately 50% (Figure B-12a). For the mi xed urban/rural SEARCH monitoring network an OMC underprediction bias of -24% and -16% occurs using the 12 km and 36 km modeling

results with errors of 40% to 50%. The SEARCH monitors also collected hourly measurements of total carbon mass (TCM=EC+OC) which wa s overestimated by the 12 km and 36 km modeling results by 20% and 12% with errors of approximately 70%. The Q-Q plots indicate the model is reproducing the low end of the observed OMC frequency distributions fairly well across

the IMPROVE and STN networks but overestimat es the high end, whereas the high end is

underestimated across the SEARCH network.

### B-36 Figure B-12a. Scatter plots of predicted and observed organic matter carbon (OMC) concentrations for January 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and SEARCH Daily (bottom left) monitoring networks and for total carbon mass (TCM) for the SEARCH Hourly network (bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-37 Figure B-12b. Q-Q plots for January 2002 and the VISTAS 12 km grid of predicted and observed organic matter carbon (OMC) and elemental carbon (EC) across the IMPROVE network (top left and right) and SEARCH Daily OCM (bottom left) and SEARCH Hourly total carbon mass (TCM) (bottom right) using the CMAQ 2002 12 km Base G2 Actual base case simulation.

### B-38 B.4.3.2 OMC in April 2002

In April 2002, the CMAQ Base B2 modeling results has a near zero bias across the IMPROVE monitors with a 39% error, and a -59% (12 km ) and -54% (36 km) underprediction bias across the STNB monitors. There is also very low OMC bias across the SEARCH network in April, although the errors are higher (~60%). The SE ACH hourly TCM also shows low OMC bias in April (11%-16%) and errors of 60%. The April OMC Q-Q plots are similar to January with the model overstating the observed OMC frequency distribution at the high end for IMPRVE and STN, but understating it for SEARCH.

Figure B-13a. Scatter plots of predicted and observed organic matter carbon (OMC) concentrations for April 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and SEARCH Daily (bottom left) monitoring networks and for total carbon mass (TCM) for the SEARCH Hourly network (bottom right) usi ng the CMAQ 2002 36 and 12 km Base G2 Actual

base case simulation.

### B-39 Figure B-13b. Q-Q plots for January April and the VISTAS 12 km grid of predicted and observed organic matter carbon (OMC) and elemental carbon (EC) across the IMPROVE network (top left and right) and SEARCH Daily OCM (bottom left) and SEARCH Hourly total carbon mass (TCM) (bottom right) using the CMAQ 2002 12 km Base G2 Actual base case simulation.

### B-40 B.4.3.3 OMC in July 2002

In July the model underestimates OMC across the IMPOREVE (-13%), STN (-54% to -59%) and SEARCH networks (-34% to -40%) monitori ng networks in the VISTAS states (Figure B14a). The scatter plots show that the model is exhibiting some skill in predicting the observed OMC, albeit with an underpredic tion bias. Unlike January and Apr il, the observed high end of

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

Figure B-14a. Scatter plots of predicted and observed organic matter carbon (OMC) concentrations for July 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and SEARCH Daily (bottom left) monitoring networks and for total carbon mass (TCM) for the SEARCH Hourly network (bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-41 Figure B-14b. Q-Q plots for July 2002 and the VISTAS 12 km grid of predicted and observed organic matter carbon (OMC) and elemental carbon (EC) across the IMPROVE network (top left and right) and SEARCH Daily OCM (bottom left) and SEARCH Hourly total carbon mass (TCM) (bottom right) using the CMAQ 2002 12 km Base G2 Actual base case simulation.

### B-42 B.4.3.4 OMC in October 2002

In October the model exhibits near zero bias across the IMPROVE, large underprediction bias across STN and smaller underprediction bias acro ss the SEARCH networks (Figure B-15a). The highest observed OMC frequency distributions are overstated at the IMPROVE and STN but matched across the SEARCH monitors (Figure B-15b)

Figure B-15a. Scatter plots of predicted and observed organic matter carbon (OMC) concentrations for October 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and SEARCH Daily (bottom left) monitoring networks and for total carbon mass (TCM) for the SEARCH Hourly network (bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-43 Figure B-15b. Q-Q plots for October 2002 and the VISTAS 12 km grid of predicted and observed organic matter carbon (OMC) and elemental carbon (EC) across the IMPROVE network (top left and right) and SEARCH Daily OCM (bottom left) and SEARCH Hourly total carbon mass (TCM) (bottom right) using the CMAQ 2002 12 km Base G2 Actual base case simulation.

### B-44 B.4.3.5 OMC Monthly Bias and Error

The monthly fractional bias and error plot fo r the 12 km CMAQ base case simulation in Figure B-16a displays fairly good performance across th e IMPROVE monitors with bias usually within ±15% and errors between 40% and 50%. However, at the urban STN sites the model exhibits a large underprediction bias of -35% to -60%. Pa rt of the underprediction bias may be due to measurement uncertainties (e.g., blank corrections) and part may be due to the model over diluting the urban OMC emissions through the co arse 36/12 km grid. The underprediction of urban OMC is a common problem in PM modeling and likely also points to uncertainties in the OMC and SOA precursor emission inventories.

The Bugle Plots of monthly fractional bias and gross error (Figure B-16b) concurs that the OMC performance across the IMPROVE monitoring sites is fairly good achieving the model performance goals. But the OMC underprediction bias for the STN monitors is significant enough so that always falls out of the model performance goals and is right at the model performance criteria. The OMC performance across the SEARCH network is mixed usually achieving the model performance goal and always achieving the model performance criteria.

### B-45 Figure B-16a. Monthly OMC fractional bias (top) and fractional gross error (bottom) statistical measures for IMPROVE, STN and SEARCH monitoring sites in the VISTAS region.

### B-46 Figure B-16b . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for OMC in the VISTAS region and comparison to model performance goals (blue) and criteria (yellow).

### B-47 B.4.4 Elemental Carbon (EC) Monthly Mode l Performance in the VISTAS Region

Elemental carbon is measured at the IMPR OVE, STN and SEARCH monitoring networks. SEARCH included 24-hur and hourly TCM measurements. In the OMC performance plots in Figures B-12 through B-15 the SEARCH hourly TCM results were plotted. In the EC performance plots in Figures B-17 through B-20 we display the SEARCH daily average TCM performance.

B.4.4.1 EC in January 2002

Elemental Caron (EC) in January 2002 is fairly good with fractions bi as < ±15% across the

IMPROVE and STN networks and errors of ~45% (Figure B-17). A larger underprediction bias and higher errors are seen across th e SEARCH network for OMC and TCM.

### B-48 Figure B-17. Scatter plots of predicted and observed elemental carbon (EC) concentrations for January 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and SEARCH Daily (bottom left) monitoring networks and for total carbon mass (TCM) for the SEARCH Daily network (bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-49 B.4.4.2 EC in April 2002

Reasonable performance is seen for OMC in April, but with an underprediction bias that ranges

from -30% to -50% across the IMPORVE and ST N monitoring networks in the VISTAS region with errors of 50% to 60% (Figure B-18) . The OMC and TCM performance across the SEARCH network in April exhibits near zero bias but with a lot of scatter resulting in errors on

~60%.

Figure B-18. Scatter plots of predicted and observed elemental carbon (EC) concentrations for April 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and SEARCH Daily (bottom left) monitoring networks and for tota l carbon mass (TCM) for the SEARCH Daily network (bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-50 B.4.4.3 EC in July 2002

EC is underestimated in July 2002 in the VIST AS region across all monitoring networks. With the underestimation bias greater at the more rural IMPROVE (-41% and -53%) than urban STN (2% and -23%) networks (Figure B-19). It is in teresting to note that EC model performance at the urban STN and partially urban SEARCH netw orks is much better for the 12 km modeling results (bias of 2% and -60%) than the 36 km results (-23% and -86%).

Figure B-19. Scatter plots of predicted and observed elem ental carbon (EC) concentrations for July 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and SEARCH Daily (bottom left) monitoring networks and for tota l carbon mass (TCM) for the SEARCH Daily network (bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-51 B.4.4.4 EC in October 2002

The EC underprediction bias also occurs in Octobe r (Figure B-20). Again lower bias is seen at the STN networks with the 12 km results (-10%) producing lower bias than the 36 km results (- 31%).

Figure B-20. Scatter plots of predicted and observed elemental carbon (EC) concentrations for October 2002 and sites in the VISTAS region using IMPROVE (top left), STN (top right), and SEARCH Daily (bottom left) monitoring networks and for total carbon mass (TCM) for the SEARCH Daily network (bottom right) using the CMAQ 2002 36 and 12 km Base G2 Actual base case

simulation.

### B-52 B.4.4.5 EC Monthly Bias and Error

The monthly bias and error performance statistic s for EC across the VISTAS region are shown in Figure B-21a. Bias and error are lower in the first quarter of the year, and then the IMPROVE and SEARCH networks begin to exhibit an underp rediction bias that peaks in the summer and gets lower again with the onset of winter. Surprisingly, the STN network exhibits very low EC bias during the summer to fall.

The Bugle Plot in Figure B-21b shows that a lthough the EC underprediction produces large fractional bias values, because the EC concentrat ions are low the EC performance achieves the model performance goal for all months and networks since it is in the flar ed horn section of the Bugle Plot.

### B-53 Figure B-21a. Monthly EC fractional bias (top) and fractional gross error (bottom) statistical measures for IMPROVE, STN and SEARCH daily and hourly monitoring sites in the VISTAS region.

### B-54 Figure B-21b . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for EC in the VISTAS region and comparison to model performance goals (blue) and criteria (yellow).

### B-55 B.4.5 Other PM 2.5 

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

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

### category that is emissi ons not identified as SO4, NO3, OMC or EC.

B.4.5.1 Soil in January, April, July and October 2002

Soil model performance is poor in January with a systematic overprediction bias of 134% to 157% with similar magnitude of errors (Figure B-22) . The overprediction bi as is not as large in April and no longer systematic (i.e., doesn’t oc cur across all predicted-observed pairs) with fraction bias values of ~20% (IMPROVE) and ~80% (SEARCH) (Figure B-23). Higher

observed values in the measured Soil values in July lowers the model overprediction bias to approximately 40% across the SEARCH network with a -30% underprediction bias across the IMPORVE monitors (Figure B-24). The overprediction bias return s in October with fractional bias of ~100% (IMPROVE) to ~140% (SEARCH).

### B-56 Figure B-22. Comparison of predicted and observed other PM 2.5 (Soil) concentrations for January 2002 and sites in the VISTAS region using scatter plots (top) and Q-Q plots (bottom) and observations from the IMPROVE (left) and SEARCH Da ily (left) monitoring networks for the CMAQ
2002 12 km Base G2 Actual base case simulation.

### B-57 Figure B-23. Comparison of predicted and observed other PM 2.5 (Soil) concentrations for April 2002 and sites in the VISTAS region using scatter plots (top) and Q-Q plots (bottom) and observations from the IMPROVE (left) and SEARCH Daily (left) monitoring networks for the CMAQ 2002 12 km
Base G2 Actual base case simulation.

### B-58 Figure B-24. Comparison of predicted and observed other PM 2.5 (Soil) concentrations for July 2002 and sites in the VISTAS region using scatter plots (top) and Q-Q plots (bottom) and observations from the IMPROVE (left) and SEARCH Daily (left) monitoring networks for the CMAQ 2002 12 km
Base G2 Actual base case simulation.

### B-59 Figure B-25. Comparison of predicted and observed other PM 2.5 (Soil) concentrations for October 2002 and sites in the VISTAS region using scatter plots (top) and Q-Q plots (bottom) and observations from the IMPROVE (left) and SEARCH Da ily (left) monitoring networks for the CMAQ
2002 12 km Base G2 Actual base case simulation.

### B-60 B.4.5.2 Soil Monthly Bias and Error

The seasonal dependence of the Soil model performance across the IMPROVE and STN monitors in the VISTAS region is clearly evident in the monthly bias time series plot in Figure B-26a. As noted in the scatter plots in Figur e B-22 through B-25, the modeled Soil values tend

to be between 0 and 2 μ g/m 3 

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

### ) and comparable to the modeled values in the summer. These results suggest that the poor Soil model performance in the winter, spring and fall is likely due to incorrect emission temporal adjust ment factors. For example, the effects of wetted surfaces on fugitive dust emissions may not be properly characterized in the emissions inventory.

The Bugle plots of bias and error also display the seasona l dependence of the Soil model performance with the model achieving the mode l performance goals for Soil at the IMPROVE monitoring network for approximately half the year and not achieving both the model performance goals and criteria at the IMPROVE network for the other half (Figure B-26b).

Performance for Soil across the SEARCH network is worse with the model not achieving either the performance goals or criteria for most months.

### B-61 Figure B-26a. Monthly Soil fractional bias (top) and fractional gross error (bottom) statistical measures for IMPROVE and SEARCH Daily mo nitoring sites in the VISTAS region.

### B-62 Figure B-26b . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for Soil in the VISTAS region and comparison to model performance goals (blue) and criteria (yellow).

### B-63 B.4.6 Coarse Mass (CM) and PM 10 

### Mass Monthly Model Performance in the VISTAS Region

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

### mass and

PM 2.5 

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

### fraction of PM, then any coarse SO4 or NO3 would be in the CM measurement. In the model, on the other hand, CM is due solely to primary PM emissions that have been speciated to the coarse fractions (PM 2.5-10 

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

concentrations.

In addition, CM has a much shorter transport dist ances than fine PM. In the modeling this is accounted for though fugitive dust transport factor s (FDTF) that adjust the fugitive dust emissions downward (by ~75% on average in the VI STAS region) to account for the fact that these emissions are deposited locally and are not transported out of the grid cell where they are emitted. However, such local dust emissions can impact an IMPROVE monitor. Thus, much of

the measured CM at the IMPROVE monitors is li kely local in nature an d subgrid-scale to the

model.

B.4.6.1 CM and PM 10 

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

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

### concentrations across the IMPROVE monitors in the VISTAS region for, respectively, January, April, July and October. CM e xhibits an underprediction bias of approximately -50% in January and approximately -100% for the other three mont hs with errors from 100% to 130%. The PM 10 performance in January across the IMPORVE monitors is quite good with near zero bias and ~30% error so achieves the most stringent ozone model performance goals (Figure B-27). However, an underprediction bias becomes larger in April (-35%) a nd even larger in July (-60%) only to come back to fairly good model performance again in October.

Figure B-27. Scatter plots of predicted and observed coarse mass (CM) (left) and PM 10 (right) concentrations for January 2002 and observations fr om the IMPROVE monitoring network for the
CMAQ 2002 12 km Base G2 Actual base case simulation.

### B-64 Figure B-28. Scatter plots of predicted and observed coarse mass (CM) (left) and PM 10 (right) concentrations for April 2002 and observations from the IMPROVE monitoring network for the CMAQ
2002 12 km Base G2 Actual base case simulation.

Figure B-29. Scatter plots of predicted and observed coarse mass (CM) (left) and PM 10 (right) concentrations for July 2002 and observations from the IMPROVE monitoring network for the CMAQ
2002 12 km Base G2 Actual base case simulation.

### B-65 Figure B-30. Scatter plots of predicted and observed coarse mass (CM) (left) and PM 10 (right)
concentrations for October 2002 and observations fr om the IMPROVE monitoring network for the
CMAQ 2002 12 km Base G2 Actual base case simulation.



### B.4.6.5 CM Monthly Bias and Error

Figure B-31a displays the monthly fraction bias and gross error values for CM across the IMPROVE monitors in the VISTAS region. The underprediction bias ranges from -60% to - 120% with errors of 80% to 140%. The Bugle Pl ot (Figure B-31b) shows that these performance statistics are either right at or exceeds th e performance criteria indicating poor CM model performance.

### B-66 Figure B-31a. Monthly coarse mass (CM) fractional bias (top) and fractional gross error (bottom) statistical measures for IMPROVE monitoring sites in the VISTAS region.

### B-67 Figure B-31b . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for coarse mass in the VISTAS region and comparison to model performance goals (blue) and criteria (yellow).

### B-68 B.4.7 PM 2.5 

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

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

### mass. In the model, PM 2.5 

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

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

### could exclude some SO4 and NO3 if they are

in the coarse mode that would be included in the modeled PM .5 that assumes all SO4 and NO3

are fine.

B.4.7.1 PM2.5 in January 2002

PM 2.5 

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

### is slightly overestimated across the IMPROVE, underestimated across the STN and near zero bias across the SEARXCH and FRM monitoring sites.

Figure B-32a. Scatter plots of predicted and observed PM 2.5 mass concentrations for January 2002 and sites in the VISTAS region using IMPROVE (t op left), SEARCH Daily (top right), STN (bottom left) and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual

base case simulation.

### B-69 Figure B-32a. (concluded) Scatter plots of predicted and observed 

### PM 2.5 

### mass concentrations for January 2002 and sites in the VISTAS region using IMPROVE (top left), SEARCH Daily (top right), STN (bottom left) and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km
Base G2 Actual base case simulation.

Figure B-32b. Q-Q plots of predicted and observed 

### PM 2.5 

### mass concentrations for January 2002 and sites in the VISTAS region using IMPROVE (left) and SEARCH Daily (right) monitoring networks and
the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-70 B.4.7.2 PM 2.5 

### in April 2002

In April the PM 2.5 

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

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

### PM 2.5 

### mass concentrations for April 2002 and sites in the VISTAS region using IMPROVE (top left), SEARCH Daily (top right), STN (bottom left) and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual

base case simulation.

### B-71 Figure B-33b. Q-Q plots of predicted and observed 

### PM 2.5 

### mass concentrations for April 2002 and sites in the VISTAS region using IMPROVE (left) and SEARCH Daily (right) monitoring networks and
the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-72 B.4.7.3 PM 2.5 

### in July 2002

The underprediction bias is higher in July (-35% to -50%) with errors higher as well (45% to 55%).

Figure B-34a. Scatter plots of predicted and observed 

### PM 2.5 

### mass concentrations for July 2002 and sites in the VISTAS region using IMPROVE (top left), SEARCH Daily (top right), STN (bottom left) and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual

base case simulation.

### B-73 Figure B-34b. Q-Q plots of predicted and observed 

### PM 2.5 

### mass concentrations for July 2002 and sites in the VISTAS region using IMPROVE (left) and SEARCH Daily (right) monitoring networks and
the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-74 B.4.7.4 PM 2.5 

### in October 2002

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

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

### PM 2.5 

### mass concentrations for October 2002 and sites in the VISTAS region using IMPROVE (t op left), SEARCH Daily (top right), STN (bottom left) and FRM (bottom right) monitoring networks and the CMAQ 2002 36 and 12 km Base G2 Actual

base case simulation.

### B-75 Figure B-35b. Q-Q plots of predicted and observed 

### PM 2.5 

### mass concentrations for October 2002 and sites in the VISTAS region using IMPROVE (left) and SEARCH Daily (right) monitoring networks and
the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.



### B.4.7.5 PM 2.5 

### Monthly Bias and Error

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

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

well for PM 2.5 

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

The Bugle Plots confirm the good performance for PM 2.5 

### mass concentrations that achieves the model performance goals most of the time and always achieves the model performance criteria (Figure B-36b).

### B-76 Figure B-36a. Monthly 

### PM 2.5 fractional bias (top) and fractional gross error (bottom) statistical measures in the VISTAS region.

### B-77 Figure B-36b . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for 

### PM 2.5 in the VISTAS region and comparison to model performance goals (blue) and criteria (yellow).

### B-78 B.4.8 Sea Salt in the VISTAS Region

Figure B-37 compares scatter plots of predicte d and observed “Sea Salt” for IMPROVE monitors in the VISTAS region and January, April, July and October 2002. The IMPROVE “Sea Salt” is inferred from the chlorine ion measurement [Cl-], whereas CMAQ internally generates its Sea Salt emissions. The model is predicting near ze ro Sea Salt resulting in po or performance. As Sea Salt is not directly used in the 2018 visibility projections this is no t a big cause for concern,

but can affect aerosol concentr ations and is an area that s hould be investig ated further,

Figure B-37. Scatter plots of predicted and observed Sea Salt concentrations in the VISTAS region using IMPROVE monitoring data and for January (top left), April (top right), July (bottom left) and October (bottom right) and the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-79 B.5 Diagnostic Model Evaluation for Gas-Phase and Precursor Species

The CASTNet and SEARCH networks also measure gas-phase species that are PM precursor or related species. The diagnostic evaluation of the 2002 36/12 km Base G2 CMAQ base case simulation for these compounds and the four seasonal months presen ted previously is provided below. Note that the EPA Air Quality Stations (AQS) compliance network also provides routine measurements of many gas-phase precursor (e .g., NOx and SO2) and product (e.g., O3) species as well. However, these sites tend to be urban-oriented and designed to measure maximum potential concentrations of criteria pollutants so would not provide a goo d measure of a model’s ability to estimate regional concentrations at the more rural Class I areas. Consequently the gasphase precursor and product species diagnostic evaluation used measured data from the

CASTNet and SEARCH networks that incl uded some more rural monitoring sites.

The CASTNet network measures weekly aver age samples of SO2, SO4, NO2, HNO3, NO3 and NH4. Because the CASTNet samples are collected in a cartridge and samp led at a later date some of the NO3 may volatize into gaseous HNO3. Thus, we also evaluate against total nitrate (TNO3) that is defined as the sum of NO3 a nd HNO3. The SEARCH Hourly network collects hourly measurements of gaseous SO2, NO, NO2, NOy, O3 and CO and particulate SO4, NO3, and NH4. A comparison of the SO2 and SO4 pe rformance provides insi ght into whether the SO4 formation rate may be too sl ow or fast. For example, if SO4 is underestimated and SO2 is overestimated, that may indicate too slow chemi cal conversion rate. An alyzing the performance for SO4, HNO3, NO3, Total NO3 and NH4 provides insight into the equilibrium of these species. For example, if TNO3 performs we ll but HNO3 and NO3 do not, then there may be issues associated with the par titioning between the gaseous and pa rticle phases of nitrate (e.g., ammonia may be off).

### B-80 B.5.1 Diagnostic Model Performance in January 2002

Figure B-38a. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom left) and TNO3 (bottom right) from the SEARCH Hourly monitoring network and January 2002 for the
CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-81 Figure B-38b. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom left) and TNO3 (bottom right) from the CASTNet monitoring network and January 2002 for the CMAQ
2002 36 and 12 km Base G2 Actual base case simulation.

### B-82 Figure B-39. Scatter plots of predicted and observed gaseous SO2 (left) and particulate SO4 (right) from the SEARCH Hourly (top) and CASTNet (bottom) monitoring networks and January 2002 for the
CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-83 Figure B-40. Scatter plots of predicted and observed gas eous NO (top left), NO2 (top right), NOy (bottom left) and O3 (bottom right) from the SEARCH Hourly monitoring network and January 2002
for the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-84 B.5.2 Diagnostic Model Performance in April 2002

Figure B-41a. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom left) and TNO3 (bottom right) from the SEARCH Hourly monitoring network and April 2002 for the
CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-85 Figure B-41b. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom left) and TNO3 (bottom right) from the CASTNet monitoring network and April 2002 for the CMAQ
2002 36 and 12 km Base G2 Actual base case simulation.

### B-86 Figure B-42. Scatter plots of predicted and observed gaseous SO2 (left) and particulate SO4 (right) from the SEARCH Hourly (top) and CASTNet (bo ttom) monitoring networks and April 2002 for the
CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-87 Figure B-43. Scatter plots of predicted and observed gas eous NO (top left), NO2 (top right), NOy (bottom left) and O3 (bottom right) from the SEARCH Hourly monitoring network and April 2002 for
the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-88 B.5.3 Diagnostic Model Performance in July 2002

Figure B-44a. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom left) and TNO3 (bottom right) from the SEARCH Hourly monitoring network and July 2002 for the
CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-89 Figure B-44b. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom left) and TNO3 (bottom right) from the CASTNet monitoring network and July 2002 for the CMAQ
2002 36 and 12 km Base G2 Actual base case simulation.

### B-90 Figure B-45. Scatter plots of predicted and observed gaseous SO2 (left) and particulate SO4 (right) from the SEARCH Hourly (top) and CASTNet (bo ttom) monitoring networks and July 2002 for the
CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-91 Figure B-46. Scatter plots of predicted and observed gas eous NO (top left), NO2 (top right), NOy (bottom left) and O3 (bottom right) from the SEARCH Hourly monitoring network and July 2002 for
the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-92 B.5.4 Diagnostic Model Performance in October 2002

Figure B-47a. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom left) and TNO3 (bottom right) from the SEARCH Hourly monitoring network and October 2002 for the
CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-93 Figure B-47b. Scatter plots of predicted and observed NO3 (top left), HNO3 (top right), NH4 (bottom left) and TNO3 (bottom right) from the CASTNet monitoring network and October 2002 for the CMAQ
2002 36 and 12 km Base G2 Actual base case simulation.

### B-94 Figure B-48. Scatter plots of predicted and observed gaseous SO2 (left) and particulate SO4 (right) from the SEARCH Hourly (top) and CASTNet (bottom) monitoring networks and October 2002 for
the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

### B-95 Figure B-49. Scatter plots of predicted and observed gas eous NO (top left), NO2 (top right), NOy (bottom left) and O3 (bottom right) from the SEARCH Hourly monitoring network and October 2002
for the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.C-1 APPENDIX C

Air Quality Model Performance Evaluation:

Evaluation at VISTAS a nd Nearby Class I Areas

### C-2 C.1 OVERVIEW

The discussion of model performance in th is Appendix focuses on the comparison of

observational data from the I nteragency M onitoring of PRO tected V isual E nvironments (IMPROVE) monitors and model output data from the 2002 VISTAS BaseG2 Actual annual air quality modeling. The evaluation will primarily focus on the air quality model’s performance with respect to individual component s of fine particulate matter (PM 2.5 

### ), as good model performance of the component species will dictate good model performance of total or reconstituted fine particulate ma tter from which visibility is c onstructed. Model performance of the total fine particulate matter and the resulting to tal light extinction will also be provided as a means to discuss the overall model performance for this Implementation Plan.

C.1.1 IMPROVE Monitoring Sites

There are eighteen (18) Class I areas within th e VISTAS States (VISTAS region) as shown in Figure C-1. There are also seve ral Class I areas adjacent to the VISTAS region where we also project future-year visibility conditions. Fi gure C-2 displays the IMPROVE monitoring sites within and nearby the VISTAS region. The 18 VI STAS Class I areas and nearby Class I areas where the CMAQ Base G2 modeling results are ev aluated in this Append ix are as follows:

VISTAS Class I Areas

• Sipsey Wilderness Area (SIPS), Alabama

• Chassahowitzka Wildlife Refuge (CHAS), Florida

• Everglades National Park (EVER), Florida

• St. Marks Wildlife Refuge (SAMA), Florida

• Cohutta Wilderness Area (COHU), Georgia

• Wolf Island Wildlife Refuge (WOIS), Georgia

• Okefenokee Wildlife Refuge (OKEF), Georgia

• Mammoth Cave National Park (MACA), Kentucky

• Linville Gorge Wilderness Area (LIGO), North Carolina

• Shining Rock Wilderness Area (SHRO), North Carolina

• Swanquarter Wildlife Refuge (SWAN), North Carolina

• Joyce Kilmer-Slickrock Wilderness Area (JKSR), North Carolina

• Cape Romain Wildlife Refuge (ROMA), South Carolina

• Great Smoky Mountains National Park (GRSM), Tennessee-North Carolina

• James River Face Wilderness Area (JARI), Virginia

• Shenandoah National Park (SHEN), Virginia

• Dolly Sods Wilderness Area (DOSO), West Virginia

• Otter Creek Wilderness Area (OTCR), West Virginia

### C-3 Nearby Class I Areas

• Breton Island Wildlife Re fuge (BRET), Louisiana

• Caney Creek Wilderness Area (CACR), Arkansas

• Upper Buffalo Wilderness Area (UPBU), Arkansas

• Mingo Wildlife Refuge (MING), Missouri

• Hercules Glade Wilderness Area (HEGL), Missouri

• Brigantine Wildlife Ref uge (BRIG), New Jersey

Note that not all Class I areas have their own IMPROVE monitor. The Wolf Island Class I area

shares the Okefenokee IMPROVE monitor in Georgia and in We st Virginia, the Otter Creek Class I area shares the Dolly Sods IMPROVE m onitor and the Joyce Kilmer-Slickrock Class I

area shares the IPROVE monitor at Great Smokey Mountains National Park. So when presenting model performance evaluation and visibi lity projections for Okefenokee, Dolly Sods and Great Smokey Mountains IMPROVE monitors they are also repres ented of, respectively, Wolf Island, Otter Creek and Joyce Kilmer-Slickroc k Class I areas, respec tively. Also note that

there are some IMPROVE sites not located in Class I areas. Alt hough data at these sites are used in the regional evaluation disc ussed in Appendix B, the performa nce at the individual IMPROVE

monitors not located at a Class I area are not di scussed in this section. For the VISTAS region, the Cadiz (CADI) site is one such IMPROVE monitor.

### C-4 Figure C-1. Class I areas within the VISTAS region.

### C-5 

### 250 500 750 1000 1250 1500 1750 2000 -1500 -1250 -1000

-750 -500 -250 0 250 500

EVER1 CHAS1

BRET1 SAMA1 OKEF1 SIKE1 ROMA1 SIPS1 CACR1 COHU1 SHRO1 SWAN1

GRSM1

UPBU1 LIGO1
HEGL1 CADI1 MING1 MACA1 JARI1 JEFF1

ELDO1 SHEN1

LIVO1 WASH1 DOSO1 BRIG1 AREN1

QUCI1

BOND1 LASU1 MKGO1 MOMO1

ADPI1

COHI1
GRRI1

Figure C-2. IMPROVE monitoring sites within and near the VISTAS region.



### C.1.2 Particulate Matter and Component Species

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

PM 2.5 

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

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

Some scattering does occur from air molecules; ho wever, the bulk of light extinction is caused by the presence of suspended particles, partic ularly fine particle s (aerodynamic mass <2.5 μ m). Fine particulate matter can be composed of varying amounts of several different species, including:

• Sulfates

• Nitrates

• Ammonium

• Organic carbon (OC)

### C-6 • Elemental carbon (EC)

• Coarse particulate matter (i.e., PM 10 

### - PM 2.5 

### )

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

• Sea Salt (NaCl) [new IMPROVE equation]

The composition of particulate matter can vary fr om day-to-day, allowing a day with a large concentration of sulfates to be closely followe d by a day dominated by organic carbon. This is an important fact as some of the components of fi ne particulate matter are more effective at light scattering than others. Since there can be such a large variability in the concentrations of the component species, the model will actually be ev aluated on a per species basis, followed by a an evaluation of total reconstructed fine particle mass. This is also a more stringent test of the model, as simply looking at particle pollu tion performance may provide good results, even though the underlying constituent concentrations are incorrect. Only a brief overview of total fine particles after component evaluation is necessary, since the accuracy of the model in predicting the component species will dictate th e accuracy of the total fine particulate mass prediction.

C.1.3 20% Best and Worst Days

Projections of light extinction for the evaluation of regional haze are based on a subset of days

for which there is monitored data. IMPROVE m onitors collect 24-hour PM samples every third day. Observations of component species on these days are used to calculate light extinction, which is the basis on which these days are ranked. These rankings are then used to identify the 20% most impaired days, or “wor st days”, and the 20% least impa ired days, or “best days”, for use in visibility projections. Table C-1 lists of the 20% best and worst days for all of Class I areas within and nearby of the VISTAS region. .

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