## Appendix A.

Review of Modeling and Emissi on Inventory Development for the Regional Haze Implementa tion Plan for the State of

Arkansas

Prepared and Reviewed by:

Michael Feldman

Dayana Medina

Erik Snyder

### Table of Contents

Table of Contents ............................................................................................................. ... ii

List of Tables ................................................................................................................ ..... iv List of Figures ............................................................................................................... ...... v Chapter 1: Background and Introduction ..................................................................... 7

1.1 Background .......................................................................................................... 7

1.2 Technical Requirements for Regional Haze SIPs ................................................ 8 Chapter 2: Development of Baseline and Natural Visibility Conditions and Glidepath

10

2.1 Introduction ........................................................................................................ 10

2.2 Calculation of Visibility fr om IMPROVE Measurements ................................. 11

2.3 Baseline Visibility Conditions ........................................................................... 13

2.4 Natural Visibility Conditions ............................................................................. 14

2.5 Uniform Rate of Progress (G lidePath) Calculation ............................................ 14 Chapter 3: Emission Inventory Development ............................................................. 16

3.1 Introduction ........................................................................................................ 16

3.2 2002 emissions inventory ................................................................................... 17

3.2.1 Stationary Point-Source Emissions ........................................................... 18

3.2.2 On-Road Mobile Emissions ...................................................................... 19

3.2.3 Biogenic Emissions ................................................................................... 19

3.2.4 Non-Road Mobile Emissions .................................................................... 20

3.2.5 Area Source Emissions ............................................................................. 20

3.3 2018 Emissions Inventory .................................................................................. 20 Chapter 4: Modeling Protocol, Episode Selectio n and Modeling Set-up Overview .. 23

4.1 Introduction ........................................................................................................ 23

4.2 Quality Assurance Program Plan ....................................................................... 23

4.3 Episode Selection ............................................................................................... 23

4.4 Photochemical and Emissions Modeling Domain ............................................. 24

4.5 MM5 Meteorological Model .............................................................................. 25

4.5.1 Model Selection ........................................................................................ 25

4.5.2 Meteorological Modeling Domain a nd Vertical Layer Structure ............. 26

4.5.3 Model Configuration ................................................................................. 26

4.5.4 MM5 Processing and Application ............................................................ 27

4.5.5 Model Performance ................................................................................... 27

4.6 SMOKE Emissions Model ................................................................................. 29

4.7 Air Quality Model .............................................................................................. 29

4.7.1 Model Selection ........................................................................................ 30

4.7.1.1 CMAQ Air Quality Model .................................................................. 31

4.7.1.2 CAMx Air Quality Model ................................................................... 31

4.7.2 Vertical Modeling Domain ....................................................................... 32

4.7.3 Initial and Boundary Conditions ............................................................... 32

4.7.4 Base Case/ Baseline Model Performance ................................................. 33

4.7.4.1 Model Performance for Sulfate (SO 4 ) ................................................. 34

4.7.4.2 Model Performance for Nitrate (NO 3 ) ................................................. 35

A-ii4.7.4.3 Model Performance for Organic Carbon (OC) .................................... 36

4.7.4.4 Model Performance for Elemental Carbon (EC) ................................. 37

4.7.4.5 Model Performance for Soil ................................................................ 38

4.7.4.6 Model Performance for Course Pa rticulate Material (CM) ................. 39

4.7.4.7 Model Performance for Prediction of Total Extinction ....................... 41 Chapter 5: 2018 Future Year Modeling ...................................................................... 44

5.1 2018 Model Simulations .................................................................................... 44

5.2 Visibility Projections .......................................................................................... 44

5.2.1 Mapping Model Results to IMPROVE Measurements ............................ 45

5.2.2 Projecting Visibility Change s Using Modeling Results ........................... 45

5.3 Reasonable Progress Goal and Pa th to Natural Conditions ............................... 46 Chapter 6: Source Apportionment Modeling ............................................................. 52

6.1 Introduction ........................................................................................................ 52

6.2 Source Apportionment Results at Caney Creek Wilderness Area ..................... 53

6.3 Source Apportionment Results at Upper Buffalo Wilderness Area ................... 57

6.4 Contributions to visibility impairment at other Class I areas ............................. 61 Chapter 7: BART Determination ................................................................................ 63

7.1 Introduction ........................................................................................................ 63

7.2 BART Dispersion Modeling Protocol ................................................................ 68

7.2.1 Overview of Modeling Approach ............................................................. 68

7.2.2 Model Selection and Applicability ........................................................... 71

7.2.3 Modeling Domain and Meteorology Inputs .............................................. 72

7.2.4 Emissions Input ......................................................................................... 73

7.2.5 CALPUFF/CALMET Model Settings ...................................................... 74

7.3 BART Determinations ........................................................................................ 74

7.3.1 Issues identified with ADEQ BART analyses. ......................................... 75

7.3.1.1 Use of Pollutant-specific modeling ..................................................... 76

7.3.1.2 Adoption of Presumptive Limits ......................................................... 76

7.3.1.3 Selection of post-control scenarios to model ....................................... 76

7.3.2 ADEQ BART Results and Summary ........................................................ 77

7.3.2.1 PM BART for AEP Flint Creek No. 1 Boiler...................................... 79

7.3.2.2 SO2 and PM BART for natural gas firing scenario for Entergy Lake

Catherine Unit 4 .................................................................................................... 80

7.3.2.3 PM BART for Units 1 and 2 Entergy White Bluff .............................. 80

7.3.2.4 PM BART for Domtar Power Boiler No. 1 ......................................... 81

A-iii

### List of Tables
Table 2.5. Uniform Rate of Progress for Caney Creek Wilderness Area and (worst quintile, western natural visibility conditions) .................................................................. 15 Table 2.5. Uniform Rate of Progress for Uppe r Buffalo Wilderness Area (worst quintile,

western natural visib ility conditions) ................................................................................ 15

Table 3-1. Emissions from Ar kansas Sources (tons/yr)................................................... 18

Table 3-2. Emission estimates for Ar kansas sources in 2018 (tons/yr) ........................... 21 Table 5-1. Comparison of reas onable progress goal to uniform rate of progress for 2018 (total extinction and deciviews) ........................................................................................ 48 Table 6-1. Projected light ex tinction for 20% worst days at Caney Creek Wilderness Area in 2002 (Mm -1 ) ......................................................................................................... 54 Table 6-2. Projected light ex tinction for 20% worst days at Caney Creek Wilderness Area in 2018 (Mm -1 ) ......................................................................................................... 54 Table 6-3. Projected light ex tinction for 20% worst days at Upper Buffalo Wilderness Area in 2002 (Mm -1 ) ......................................................................................................... 58 Table 6-4. Projected light ex tinction for 20% worst days at Upper Buffalo Wilderness Area in 2018 (Mm -1 ) ......................................................................................................... 58

Table 6-5. Percent contributi on to total visibility impair ment at Class I areas on 20% worst days from Arkansas Sources (contri butions less than 1% are excluded) ................ 62

Table 7-1. Facilities with BART -eligible units in Arkansas ............................................ 64 Table 7-2. Facilities granted waivers fr om BART based on ADEQ screening modeling.

............................................................................................................................... ............ 66 Table 7-3. Sources in Arkansas determined to be subject to BART by ADEQ ............... 71

Table 7-4. BART Determinations Not Satisfying Section 51.308(e) .............................. 77

Table 7-5. BART Determinations Satisfying Section 51.308(e) ..................................... 79 Table 7-6. Percent contribution of PM (course and fine) to total visibility impairment on

1 st

High day. ADEQ pre-control screening m odeling (Appendix 9.2B), AEP-Flint Creek

............................................................................................................................... ............ 80 Table 7-7. Percent contribution of PM (course and fine) to total visibility impairment on

1 st

High day. ADEQ pre-control screening m odeling (Appendix 9.2B), Entergy White

Bluffs......................................................................................................................... ........ 81

A-iv

### List of Figures
Figure 1-1. Map of Regiona l Planning Organizations ........................................................ 8 Figure 4-1. National RPO 36-km modeli ng domain for CMAQ, CAMx, and SMOKE modeling. .......................................................................................................................... 25 Figure 4-2. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for sulfate (SO 4 ). ................................................................................. 35 Figure 4-3. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for nitrate (NO 3 ). ................................................................................ 36 Figure 4-4. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for organic carbon (OC). .................................................................... 37 Figure 4-5. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for elemental carbon (EC). ................................................................. 38 Figure 4-6. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for soil. ................................................................................................ 39 Figure 4-7. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for course particulate material (CM). ................................................. 40 Figure 4-8. Daily extinction model performan ce at Caney Creek Wilderness Area AR for the worst (top) and best (bottom) 20% days during 2002 ................................................. 42 Figure 4-9. Daily extinction model performan ce at Upper Buffalo Wilderness Area, AR for the worst (top) and best (bottom) 20% days during 2002 ........................................... 43 Figure 5-1. Projections of vi sibility impairment for 20% worst days at Caney Creek .... 48 Figure 5-2. Projections of vi sibility impairment for 20% worst days at Upper Buffalo . 49 Figure 5-3. Differences in modeled total extinction (Bext) between Base18G and Typ02g for 20% worst days at Caney Creek Wilderness Area. ..................................................... 50 Figure 5-4. Differences in modeled total extinction (Bext) between Base18G and Typ02g for 20% worst days at Upper Buffalo Wilderness Area. .................................................. 50 Figure 6-1. Source Regions used in CAMx PSAT PM source apportionment modeling 53 Figure 6-2. Source apportionment modeling resu lts by source region and source category for worst 20% days at Caney Creek Wilderness Area in 2002. ........................................ 55 Figure 6-3. Source apportionment modeling re sults by source region and species for worst 20% days at Caney Cr eek Wilderness Area in 2002. ............................................. 55 Figure 6-4. Source apportionment modeling resu lts by source region and source category for worst 20% days at Caney Creek Wilderness Area in 2018. ........................................ 56 Figure 6-5. Source apportionment modeling re sults by source region and species for worst 20% days at Caney Cr eek Wilderness Area in 2018. ............................................. 57 Figure 6-6. Source apportionment modeling resu lts by source region and source category for worst 20% days at Upper Buffalo Wilderness Area in 2002. ..................................... 59 Figure 6-7. Source apportionment modeling re sults by source region and species for worst 20% days at Upper Buffalo Wilderness Area in 2002. ........................................... 59 Figure 6-8. Source apportionment modeling resu lts by source region and source category for worst 20% days Upper Buffalo Wilderness Area in 2018. ......................................... 60 Figure 6-9. Source apportionment modeling re sults by source region and species for worst 20% days at Upper Buffalo Wilderness Area in 2018. ........................................... 61 Figure 7-1. Map showing Arkansas’s BART-e ligible sources and the 300 km radius buffer zones around five separate receptors (north, south, east, west, and center) located

A-vA-vi in the following Class I areas: Upper Buffal o (UPBU), Caney Creek (CACR), Hercules Glade (HEGL), Mingo (MING), and Sipsey (SIPS) ......................................................... 70 Figure 7-2. CENRAP CA LMET 6km meteorologi cal modeling domains ...................... 72

### Chapter 1: Background and Introduction

1.1 B ACKGROUND

Regional haze is visibility impairment that is caused overwhelmingly by fine particulates (PM2.5). Visibility impairment occurs when PM 2.5 in the atmosphere scatters and absorbs light, thereby creating haze. PM2.5 can be emitted in to the atmosphere directly as primary particulates, or it can be produced in the atmosphere from photoc hemical reactions of gas-phase precursors and subsequent condensation to form secondary particulates. Examples of primary

PM2.5 include crustal materials and elemental carbon; examples of secondary PM include ammonium nitrate, ammonium sulfates, and secondary organic aerosols (SOA). Secondary PM2.5 is generally smaller than primary PM2.5, a nd because the ability of PM2.5 to scatter light depends on particle size, with lig ht scattering for fine particles being greater than for coarse particles, secondary PM2.5 plays an especially important role in visibility impairment. Moreover, the smaller secondary PM2.5 can rema in suspended in the atmosphere for longer periods and is transported long distances, thereb y contributing to region al-scale impacts of pollutant emissions on visibility.

The sources of PM2.5 are difficult to quantify because of the comp lex nature of their formation, transport, and removal from the atmosphere. This makes it difficult to simply use emissions data to determine which pollutants s hould be controlled to most ef fectively improve visibility. Photochemical air quality models offer opportuni ty to better understand the sources of PM2.5 by simulating the emissions of pollutants and the fo rmation, transport, and deposition of PM2.5. If an air quality model performs well for a histori cal episode, the model may then be useful for identifying the sources of PM2.5 and helping to select the most effective emissions reduction strategies for attaining visibil ity goals. Although several type s of air quality modeling systems are available, the gridded, three-dimensional, Eu lerian models provide th e most complete spatial representation and the most comprehensive representation of pro cesses affecting PM2.5, especially for situations in which multiple pollutant sources inte ract to form PM2.5.

In Section 169A of the 1977 Amendments to CA A, Congress set forth a program for protecting visibility in the nation’s national parks and wilderness areas. This section of the CAA establishes as a national goal the “prevention of any future, and the remedying of any existing, impairment of visibility in mandatory Federa l Class I areas which impairment results from manmade air pollution.” EPA pr omulgated a rule to address regional haze on July 1, 1999 (64 FR 35713), the Regional Haze Rule (RHR). Th e RHR established the goal of achieving “natural” visibility conditions in all 156 Federal Class I areas by 2064.

Because the pollutants that lead to regional haze can originate fr om sources located across broad geographic areas, EPA has encouraged the States and Tribes across the Un ited States to address visibility impairment from a regional perspective. Five Regional Planni ng Organizations (RPOs) were developed to address regional haze and re lated issues (Figure 1- 1). One of the main objectives of the RPOs is to analyze available data and conduct pollutant transport modeling to assist the States in developi ng their regional haze plans.

A-7Figure 1-1. Map of Regional Planning Organizations

The Central Regional Air Planning Association (CENRAP) RPO is a collaborative effort of State governments, tribal governments, and various fe deral agencies established to conduct data analyses, conduct pollutant transport modeling, and coordinate planni ng activities among the

central States. CENRAP members include the State governments of Nebraska, Kansas, Oklahoma, Texas, Minnesota, Iowa, Missouri, Ar kansas, and Louisiana and Tribal governments

included in these states.

1.2 T ECHNICAL R EQUIREMENTS FOR R EGIONAL H AZE SIP S

The RHR does not mandate specific milestones or rates of progress, but instead calls for States to establish goals that pr ovide for “reasonable progress” to ward achieving natural visibility conditions. In setting Reasona ble Progress Goals (RPGs), States must provide for an improvement in visibility for the most impaired days over the ten-year period of the SIP, and ensure no degradation in visibili ty for the least impaired days over the same period. In setting the RPGs for each 10-year period covered by a SIP, States must also compare the RPGs to the uniform rate of progress needed to reach natural visibili ty conditions by 2064, referred to as the “glide path”, which is the linear rate of reduction in visibility impairment (in deciviews) needed to achieve natural conditions by 2064.

A-8According to the RHR, Regional Haze SIPs must specifically identify and address the following

elements:

i. Baseline Visibility Conditions

ii. Natural Visibility Conditions

iii. Uniform Rate of Progress

iv. Best Available Retrof it Technology (BART)

v. Current and Future (201 8) Emission Inventories

vi. Source Contribution to Haze

vii. Reasonable Progress Goals

The purpose of this document is to review th e technical products developed by the Arkansas Department of Environmental Quality (ADEQ) a nd CENRAP for the central regional states, in support of their RH SIP. This document evalua ted the methods and procedures used by ADEQ and CENRAP to develop the modeling and emissi on inventory products that assisted Arkansas and the central regional States in addressing the required elements of a RH SIP. Specifically,

this document reviewed emission inventor y, meteorological, photochemical, and BART modeling conducted by CENRAP, evaluated the resu lts and determined if these models met applicable guidelines or protocols, and met mode ling standards at the time they were conducted.

A-9

### Chapter 2: Development of Baseline and Natur al Visibility Conditions and Glidepath

2.1 I NTRODUCTION

Under the Regional Haze Rule (RHR), each State is required to demonstr ate reasonable progress in visibility conditions for each of its Class I area s. The State is to determine a uniform rate of progress ("glide path", "glide slope") toward th e goal of natural visibility conditions in 2064. Considering various statutory factor s, the State is also to define a reasonable rate of progress, and compare this to the benchmark uniform rate; if pr ojected progress is less than the uniform rate, then the State is to explain why. Procedures for assessing progress are de scribed in the Regional Haze Rule and EPA guidance documents.

In brief, the guidance defines a metric to quantif y visibility conditions, to gether with procedures for determining a starting point and an ending poin t, between which progress is to be made. The metric used is the Haze Index, measured in de civiews, and is designe d to correspond to human perception of visibility cha nges. It is defined as: 10*ln(b ext /10) (1)

where b ext is extinction, the fraction of light scattered out of a sight path due to pollutants over a given distance (with units of Mm -1 or "inverse megameters"); it is inversely related to visual range. A 24-hour average is used, so there is a deciview value for each day of the year; the average of the 20% most-impaired days, and the av erage of the 20% least-impaired days during a year are to be assessed. The Regional Haze Rule goal is to improve visibility on the worst 20% of days, while having no de gradation on the best 20%.

The starting point for progress is current or ba seline visibility conditions, as monitored by the

I nteragency M onitoring of PRO tected V isual E nvironments (IMPROVE) monitoring network (webpage and data access: http://vista.cira.colostate.edu/improve/Default.htm ). 24-hour samples are collected every three days and are sent to a laboratory facility for analysis to obtain dry concentrations of a wide variety of species that impact visibility. Monitored pollutant concentrations are converted to visibility ex tinction using the IMPROVE equation, which adds up the contribution of each pollutant to extincti on, while accounting for the effect of relative humidity. This total extinction is then conve rted to deciviews in the Haze Index through equation 1. For each of the years of the ba seline period (2000-2004), the average of the deciviews on the worst 20% of days is calculat ed; the five-year average of these defines the baseline. This procedure is described in deta il in EPA's "Guidance fo r Tracking Progress Under the Regional Haze Rule". 1 The guidance also makes provisions for dealing with missing data, since monitoring instrument maintenance and malf unctions mean that data is not available for every scheduled measurement.

The end point for progress is th e goal of natural visi bility conditions in 2064. The default approach for determining these is described in EPA's "Guidance for Estimating Natural Visibility

1 Hereafter “GTR”: EPA, 2003, Guidance for Tracking Progress Under the Regional Haze Rule , EPA-454/B-03- 004, September 2003, EPA OAQPS ; web page: http://www.epa.gov/ttn/oarpg/t1pgm.html

direct link: http://www.epa.gov/ttn/oarpg/t1/memoranda/rh_tpurhr_gd.pdf

A-10Conditions Under the Regional Haze Program". 2 Annual average natu ral background pollutant concentrations are estimated by Trijonis et al. 3 under NAPAP for the East and West parts of the country. Deciviews are calculated based on these natural background estimates with the IMPROVE equation, using the monthly relative humi dity for each specific Class I area. These annual averages are then translated into estima tes for the best 20% and worst 20% days needed for the progress assessment. Extinction was assumed to have a lognormal frequency distribution;

deciviews would then have a norma l distribution, and its 10th and 90th percentiles were used as estimates of the average of the best 20% and wo rst 20% of days, respec tively. The result is a

table of best and worst 20% deciview values fo r each Class I area, which appears in Appendix B of the guidance. The guidance also allows States to use a refined alternative to this default approach for estimating natural conditions.

Finally, the uniform rate of progress is calcul ated as the difference between the baseline and natural conditions, spread over the 60 years be tween 2004 and 2064: uniform deciviews per year improvement = (current 2004 deciviews - natura l 2064 deciviews) / 60. This rate is the benchmark against which visibility improvement is to be compared by the State; the first planning period envisaged by the Regional Haze Ru le is through 2018, so this uniform rate is multiplied by 14 to determine the first benchmark.

2.2 C ALCULATION OF V ISIBILITY FROM IMPROVE M EASUREMENTS

The CENRAP procedure used for developing a uni form rate of progress (URP, also known as "glide path" or "glide slope") for the State of Arkansas followed EPA guidance contained in the GTR and GENVC with the exception that the revised IMPROVE algorithm was utilized rather than the original IMPROVE equation . The pr ocedure used is described in the Technical Support Document for CENRAP Em issions and Air Quality Mode ling to Support Regional Haze State Implementation Plans. 4

CENRAP used the approach of Pitchford et al. 5 The equation utilized is referred to as the “revised” IMPROVE algorithm or equation and was used for estimates of both baseline and natural conditions. The revised IMPROVE equation is used to convert measured concentrations into extinction for each pollutant chemical sp ecies, and then total them up, accounting for the effect of relative humidity, and including the Rayleigh scattering that occurs in pure air. The

extinction total is then used to calculate deci views for use in visibility progress assessments through equation 1. EPA’s 2007 “Guidance on the Use of Models and Other Analyses for

2 Hereafter “GENVC”: EPA, 2003, Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Program , EPA-454/B-03-005, September 2003, EPA OAQPS; web page: http://www.epa.gov/ttn /oarpg/t1pgm.html direct link: http://www.epa.gov/ttn/oarpg/t1/memoranda/rh_envcurhr_gd.pdf

3 Trijonis, J.C., et al., 1990, "Visibility: Existing and Hi storical Conditions-Causes and Effects", chapter 24 in

NAPAP State of Science & Techno logy, Vol. III web page: http://vista.cira.colostate.edu/improve/Publications/Principle_pubs.htm

4 Hereafter “CENRAP TSD”: Environ Inte rnational Corp. and University of California at Riverside, September

2007.

5 Pitchford, Marc; William Malm, Bret Schichtel, Nare sh Kumar, Douglas Lowenthal, and Jenny Hand, 2007: Revised algorithm for estimating light extinction from IMPROVE particle speciation data. J. Air & Waste Manage. Assoc., 57, 1326-1336.

A-11Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze” 6 states
that the use of either the IM PROVE or the revised IMPROVE equation is acceptable provided that the States supply documentation concerni ng the choice of equation and that the same algorithm is utilized for both the base a nd future extinction calculations.

The IMPROVE program revised the IMPROVE equa tion after a scientific assessment of its implications for regional haze planning to reduce bias es in light extinction estimates compared to the old algorithm. 7 In particular, when compared to nephelometer direct measurements of visibility extinction, the origin al IMPROVE equation over-predict s for low extinction conditions and under-predicts for high extinction. These biases have direct relevance for estimates for the best 20% and worst 20% visibility days that are used to assess progress.

The revised equation used by CENRAP has four changes: 1) greater completeness though the inclusion of sea salt, which can be important fo r coastal sites; 2) incr eased organic carbon mass estimate, based on more recent data for remote areas; 3) Rayleigh scatte ring using site-specific elevation and temperature, a refinement over the older network-wide cons tant; and 4) separate estimates for small and large particles of visibi lity impacts and humidity-dependent particle size growth rates, which could affect es timates at the low and high ends. 8 The revised equation has

an additional term for inclus ion of NO2; however, none of th e CENRAP Class I areas have monitors that provide observations of NO2 so this term was not used.

The new equation shows broader scatter overa ll, but less bias in matching visibility measurements under high and low visibility cond itions. That is, though it has a somewhat worse fit considering all the data, it ha s a better fit under visibi lity conditions most relevant to regional haze planning, the best and worst 20% of days. The looser overall fit can cause a slightly different set of days to be the ones chosen as the 20% worst, but the chemical species composition for such days is little change d (IMPROVE technical subcommittee for algorithm review, 2001, pp. 11-12), and so th is makes little difference for assessing the contribution of emission sources to current conditions, and for pr ojecting the effect of emission controls. The split between small and large particles was the main factor in reducing the biases.

The organic carbon (OC) measured by the IM PROVE network does not include all organic matter (OM); based on 1970's urban data, a scaling factor of 1.4 is embedded in the old equation

to account for the full mass. Based on recent data more relevant to relatively remote Class I areas, the revised IMPROVE equation embeds an OM/OC factor of 1.8. At the Caney Creek

6 Hereafter “GOPMRH”: EPA, 2007, Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze , EPA-454/B-07-002, April 2007, EPA OAQPS; web page: http://www.epa.gov/scram001/guidance_sip.htm direct link: http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf

7 IMPROVE, 2006, Revised IMPROVE algorithm for Esimating Ligh t Extinction from Particle Speciation Data

, January 2006; http://vista.cira.colostate.edu/improve /Publications/GrayLit/gray_literature.htm ; Hand, J.L.; Douglas, S.G., 2006, Review of the IMPROVE Equation for Estimating Ambient Light Extinction Coefficients – Final Report, http://vista.cira.colostate.edu/improve/Publications/GrayLit/016_IMPROVEEeqReview/IMPROVEeqReview.htm

8 Pitchford, Marc, 2006, "New IMPROVE algorithm for estimating light extinction approved for use", The

IMPROVE Newsletter , Volume 14, Number 4, Air Resource Specialists, Inc.; web page: http://vista.cira.colostate.edu/impr ove/Publications/news_letters.htm

direct link: http://vista.cira.colostate.edu/improve/Pub lications/NewsLetters/IMPNews4thQtr2005.pdf

A-12Wilderness Area and Upper Buffalo Wilderness Area si tes, fine sulfurous aerosol contributes the most to visibility impairment on the worst days during the baseline year s, although a few of the worst days are dominated by nitrates. . The largest difference in results between the two algorithms is related to the separation of total concentrations of sulfate, nitrate, and organic carbon into small and large size distri butions in the revised equation.

The revised IMPROVE equation has less bias, is more refined, accounts for more pollutants, incorporates more recent data, and is based on co nsiderations of relevance for the calculations needed for assessing progress under the RHR. EP A believes it is approp riate for the CENRAP states to use the revised IMPROVE equation.

2.3 B ASELINE V ISIBILITY C ONDITIONS

Section 2 of the EPA's "Guidance for Tracking Progress Under the Regional Haze Rule" ("GTP") describes a step-by-step process for calculating the visibility metric for the baseline period 2000-2004. The steps involve (1) assembly of daily species concentration data from the IMPROVE network, (2) inclusion of substitutions for missing data; (3) assessment of site data completeness (4) calculation of extinction via the IMPROVE equation; (5) calculation of the deciview Haze Index; (6) calculation of average deciviews for the 20% best and 20% worst days for each year; and (7) averaging these over th e 5 year period. These steps are mostly straightforward and are briefly di scussed here with a more detail ed discussion on the differences between EPA guidance and CENRAP procedures.

For data substitution, the EPA guidance describes two procedures. The first is the use of quarterly median concentrations. This is the median concentration from the quarter the data value is missing from, averaged with similar medians from the preceding four years, using only quarters with having at least 50% of the days available and no more than 10 consecutive missing days (GTP, "Step 3: Determine Quarterly Median Concentrations for Missi ng Variables", p.2-5). The second substitution involves using quarterly averages, as long as the substitution changes extinction values by less than 10% in 90% of th e data (GRP, "Step 5 - Evaluate Feasibility of Substituting Average Values", pp. 2-7 - 2-8).

Completeness requirements are also given in the EPA guidance: to be complete and included in the progress calculation, a year must have data for 50% of the sampling days in every quarter, 75% of the days for the year overall, and no more than 10 consecutive missing sampling days; the overall 5-year period must have at least 3 comp lete years of data (GTP, "Step 7 - Check Data Completeness", pp. 2-8 - 2-9).

The RHR defines the baseline period as th e five year span from 2000-2004. IMPROVE monitors were established at the Caney Creek Wilderness Area site located on Eagle Mountain, Polk County, Arkansas and at the Upper Buffa lo Wilderness Area located in Deer, Newton County, Arkansas and collected samples for the two Class I areas in the state of Arkansas. Data for the Caney Creek Wilderness Area site that meet the completeness requirements are available for the 2002-2004 portion of this baseline period. Th erefore, baseline visi bility was calculated based on the average of the worst (best) 20% of da ys for each of these three years. This meets the minimum overall data completeness requirements for calculation of the baseline visibility

A-13conditions detailed in the GTP. Data for th e Upper Buffalo Wilderness Area from 2000 through 2004 were averaged for the worst (best) 20% of da ys to calculate baselin e visibility conditions

for the site.

Every Class I area within the CENRAP states has an associated IMPROVE monitor. Results from analysis of samples collected at each monito r site are used to calculate extinction and haze index using the procedure desc ribed above. For those CENRAP sites (Breton Island (BRET), Louisiana; Boundary Waters (BOWA), Minnesota and Mingo (MING), Missouri) that did not have three valid years that met the completene ss requirements for inclusion in the baseline visibility calculations, data filling was used to create at least three years of valid data. These data filled IMPROVE databases were prepared and made available on the VIEWS website. More information on the data filling procedur es can be found at the VIEWS website: (http://vista.cira.colo state.edu/views/).

The CENRAP followed EPA guidance for estimating baseline visibi lity conditions.

2.4 N ATURAL V ISIBILITY C ONDITIONS

EPA guidance set out a default procedure for esti mating natural conditions, but also describes circumstances when States might want to use a more refined approach, such as to reduce uncertainty when baseline visibil ity is already near na tural conditions, or when there is marked seasonality; these might be accomplished via altern ative estimates of natural concentrations, or use of temporally varying estimates (GENV sec. 3.1 and 3.2).

ADEQ opted to use the revised IMPROVE equation to calculate the “refined” natural visibility conditions. This is an acceptable approach unde r our 2003 Natural Visibili ty Guidance. This approach uses the revised IMPROVE equation so that progress between ba seline conditions and

natural conditions can be calculated on a c onsistent basis.

The procedure used has several acknowledged lim itations. 1) each chemical species can have one of only two possible backgr ound concentrations, one for the East and one for the West. Future efforts may provide for a larger number of geographic zones with differing concentrations A second potential limitation is that the same approach is used for both natural- and anthropogenic-dominated species co mponents; EPA guidance mentions the possibility of treating these separately (GENV sec. 3.4).

The majority of visibility impairment at th e Caney Creek Wilderness Area and Upper Buffalo Wilderness Area site is currently from anthropogen ic sources. As measures are taken to improve visibility and decrease emissions, the ability to identify natural sources and background concentrations of PM will improve. The curre nt approach used by ADEQ follows EPA methods and is acceptable. As additional information and more site-specific data become available, ADEQ is encouraged to pursue refi nements in this approach to be tter quantify natural visibility

conditions.

2.5 U NIFORM R ATE OF P ROGRESS (G LIDE P ATH ) C ALCULATION

A-14The uniform rate of progress is calculated as th e linear rate of progress (decrease in deciviews per year) required to reach natural visibility conditions in 2064, starting from the baseline conditions in 2004. The first benchmark year is 2018 and the calculated improvement required

to attain the desired rate of progress is 3.45 deciviews for Caney Creek and 3.43 dv for Upper

Buffalo. Tables 2.5 and 2.6 summarize the ca lculations performed by ADEQ.

Table 2.5. Uniform Rate of Progress for Cane y Creek Wilderness Area and (worst quintile,

western natural visi bility conditions)

Conditions Total extinction (Mm -1 ) Haze Index (deciviews) Baseline (2002-2004) conditions 134.1 26.36 Natural (for 2064) conditions 21.16 11.58 Observed impairment above natural conditions 112.94 14.78 Progress (2004-2018) at unifo rm rate .246 per year Improvement needed by 2018 assuming uniform rate of progress 26.35 3.45

Table 2.6. Uniform Rate of Progress for Uppe r Buffalo Wilderness Area (worst quintile,

western natural visi bility conditions)

Conditions Total extinction (Mm -1 ) Haze Index (deciviews) Baseline (2002-2004) conditions 131.95 26.27 Natural (for 2064) conditions 21.54 11.57 Observed impairment above natural conditions 110.41 14.70 Progress (2004-2018) at unifo rm rate .245 per year Improvement needed by 2018 assuming uniform rate of progress 25.76 3.43

A-15

### Chapter 3: Emission Inventory Development

3.1 I NTRODUCTION

In support of the CENRAP Regional Haze air quality modeling efforts, air quality modeling inputs including annual meteorology and emissions inventories for a 2002 actual emissions base case, a planning case to repres ent the 2000-04 regional haze baseli ne period using averages for key emissions categories, and a 2018 base case of projected emissions are needed. All emission inventories were developed using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system (See section 3.6 ). Each of these inventor ies has undergone a number of revisions throughout the developmen t process to arrive at the fi nal versions used in CMAQ and CAMx air quality modeling. In general, update d 2002 emissions data for the U.S. developed by the Regional Planning Organizati ons (RPOs), updated emissions data for Mexico from the

BRAVO 1999 emissions inve ntory, and version 2 of the 2000 em issions data for Canada were used to generate a 2002 annual emissions data base. The 2002 and 2018 emissions inventories and ancillary modeling data were provided by CENRAP emissions inventory contractors, 9 other RPOs and EPA. Emission modeling and quali ty assurance (QA) work was based on the Quality Assurance Project Plan (QAPP) CENRAP Emissions and Air Quality Modeling 10 and Protocol for the CENRAP 2002 Annual Emissions and Air Quality Modeling 11 (hereafter referred to as the “Modeling Protocol”). These prot ocols were reviewed by the EPA Regions at the time they were developed.

The development of each of these emission scenarios are as follows:

• The 2002 base case emissions scenario was deve loped to represent th e actual conditions in calendar year 2002 with respect to ambient air quality and the associated sources of criteria and particulate matter air pollutants. This emission inventory is used to validate the air quality model and associated databases and to demonstrate acceptable model performance with respect to re plicating observed particulate matter air quality. The base case includes actual day-specific emissions of SO 2 and NOx emissions for large stationary point sources based on measured continuous emissions monitoring (CEM) data along with actual 2002 fire emissions.

9 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and Associates, Inc. and Carolina En vironmental Program (CEP), University of North Carolina(UNC), (http://www.cenrap.org/html/projects.php?mode=subcatdownl oad&id=50); Pechan and CEP. 2005. Refinements of CENRAP’s 2002 Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and Associates, Inc. and CEP, UNC. ( http://www.dnr.mo.gov/env/apcp/docs/appendixh-3.pdf ); Reid, S.B. et al. 2004. Emission Inventory Development for Mobile Sources and Agricultural Dust Sources for the Central Stat es. Sonoma Technology, Inc. ( http://cenrap.sonomatech.com/CENRAP_Mobile/FinalReport.pdf) .; Reid, S.B et al. 2004. Research and Development of Planned Burning Emission Inventories for the Central States Regional Air Planning Association. Sonoma Technology, Inc. ( http://cenrap.sonomatech.com/CENRAP_ PlannedBurnData/FinalReport.pdf) .; Coe, D.L. and S.B. Reid. 2003. Research and Development of Ammonia Emission Inventories for the Central States Regional Air Planning Association. Sonoma Technology, Inc. (http://cenrap.sonomatech.com/CENRA P_Ammonia_NIF/FinalReport.pdf).

10 Morris, R.E. and G. Tonnesen. 2006. Quality Assuranc e Project Plan (Draft) for Ce ntral Regional Air Planning Association (CENRAP) Emissions and Air Quality Modeling. (http://pah.cert.ucr.edu/aqm/cenrap/doc s/CENRAP_QAPP_Rev3_Mar_29_2006.pdf )

11 Morris, R.E. et al. Modeling Protocol for the CENR AP 2002 Annual Emissions and Air Quality Modeling, Draft

2.0. Web: http://pah.cert.ucr.edu/aqm/cenrap/docs/CENRAP_Draft2.0_Modeling_Protocol_120804.pdf ).

A-16• The 2000-04 baseline period planning case em issions scenario is referred to as “Typ02G”. The purpose of the Typ02G invent ory is to represent baseline emission patterns based on average, or “typical”, condi tions. This inventory provides a basis for comparison with the future year 2018 projecte d emissions, as well as to gauge reasonable progress with respect to future year visibili ty. 5-years of CEM data were analyzed and typical seasonal and diurnally vary ing emissions were defined.

• The 2018 future-year base case emissions scenar io is referred to as “2018 Base Case” or

“Base18G”. These emissions are used to re present conditions in future year 2018 with respect to sources of criter ia and particulate matter air pollutants, taking into consideration growth and contro ls. Modeling results based on this emission inventory are used to define the future year ambient air quality and visibility metrics.

Emission inventory data from five general categor ies are needed to support air quality modeling: stationary point-source emissions, stationary area-source emissions (a lso called nonpoint), mobile emissions for on-road sources, mobile em issions for nonroad source s (including aircraft, railroad, and marine vessels), and biogenic em issions. The emission inventory development and emissions modeling steps can be different for each of these categories. The Technical Support Document for CENRAP Emissions and Air Q uality Modeling to Support Regional Haze State Implementation Plans 12 (hereafter referred to as the “CENRAP TSD”) describes the development of each source category inventory is detail. Appendix B of the CENRAP TSD lists the file names, data source, type and a desc ription of emissions used in the 2002 typical (Typ02G) emissions inventory. Emissions invent ories for each source category are described briefly in the following secti on. The CENRAP TSD is included as Appendix 48.1of the ADEQ Regional Haze Implementation Plan Revision.

3.2 2002 EMISSIONS INVENTORY

ADEQ developed the 2002 point source emissions inventory in-house with Emission Inventory
Questionnaires and used the biogenic source inventory developed by EPA.ADEQ contracted
with ENVIRON to develop an em ission inventory for three invent ory source classifications: onroad and non-road mobile sources and nonpoi nt sources for the baseline year of 2002. 13

The nonpoint, or area source, inventory includes emitters of ozone pollutants (i.e., NOx and
VOCs) such as devices that combust fuel (e.g., dry cleaners, degreasing, and industrial surface
coating), gasoline distribution, asphalt paving, and fires and open bur ning (e.g., agricultural
burning, structural fires, wildfires, prescribed burning). In addition, area source categories
contributing to visibility po llutants (i.e., PM10, PM2.5, and NH3) are also included in the area
source emissions inventory (e.g., fugitive dust, agri cultural operations, livestock ammonia, etc.).

12 Environ International Corp. and University of California at Riverside, 2007. Technical Support Document for CENRAP Emissions and Air Quality Modeling to Supp ort Regional Haze State Implementation Plans. (http://www.cenrap.org/html/projects.php?mode=download&id=87)

13 Final Report: Arkansas 2002 Emission Inventory, prepared by ENVIRON and Eastern Research Group, May 13, 2004 (Appendix 7.1A of the RH SIP)

A-17The contractor reviewed all emissi on factors used in the inventory to ensure they were the most
appropriate and up-to-date emission factors available and checked a ll calculations for accuracy.

The 2002 national emissions inventory (2002 NEI), compiled from submitted inventories from states, tribal and local agencies was the orig inal basis for the CENRAP emission inventory. Sonoma Technology supplemented the 2002 NEI da ta with non-point source inventories to address agricultural and prescribed burning, on-road and non-road mobile sources, agricultural tilling and livestock dust, and agricultur al ammonia for the CENRAP inventory. 14

Table 3-1. Emissions from Arkansas Sources (tons/yr)

VOC NOx PM2.5 PM10 NH3 CO SO2

Point 44,329 72,419 7,837 12,406 1 56,366 92,205

Area 93,548 24,450 68,000 148,433 152,436 436,525 29,889

Non-road

mobile 54,785 62,472 5,220 5,673 49 272,627 5,490

On-road

mobile 48,599 141,894 3,021 3,784 2,480 669,214 3,902

Biogenic 1,385,666 18,960 136,688

Total 1,626,927 323,195 84,078 170,296 154,967 1,571,419 131,485

3.2.1 Stationary Point-Source Emissions Point sources are typically regulated and informa tion on emissions and locations are available in regulatory reports. Larger permitted point sour ces in Arkansas are required to submit annual emissions inventories via Emission Inventory Ques tionnaires (EIQ), and a ll other point sources have a reporting frequency of every 3 years, be ginning with the 2002 base inventory. This data, along with similar data available fr om other states make the basis of the point source inventory. The CENRAP stationary-point inventory consisted of annual county-level and tribal data provided in August of 2005. 15 Point source inventories were developed by the other RPOs and

shared with CENRAP. These inventories are typically further divided into EGU and non-EGU sources. For EGU sources, continuous emissions monitoring (CEM) data is available to create day and hour-specific emission inventories for i nput into the Base02F inventory. The Typ02G

14 Reid, S.B. et al. 2004. Emission Inventory Development for Mobile Sources and Agricultural Dust Sources for the Central States. Sonoma Technology, Inc. ( http://cenrap.sonomatech.com/CENRAP_Mobile/FinalReport.pdf) .; Reid, S.B et al. 2004. Research and Development of Planned Burning Emission Inventories for the Central States Regional Air Planning Association. Sonoma Technology, Inc. ( http://cenrap.sonomatech.com/CENRAP_ PlannedBurnData/FinalReport.pdf) .; Coe, D.L. and S.B. Reid. 2003. Research and Development of Ammonia Emission Inventories for the Central States Regional Air Planning Association. Sonoma Technology, Inc.

15 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and Associates, Inc. and CEP, UNC, (http://www.cenrap.org/html/projects. php?mode=subcatdownload&id=50); Pechan and CEP. 2005. Refinements of CENRAP’s 2002 Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and Associates, Inc. and CEP, UNC. ( http://www.dnr.mo.gov/env/apcp/docs/appendixh-3.pdf )

A-18inventory includes further processi ng of EGU emissions to devel op a typical emission levels and temporal profiles.

Coal-fired point sources within the CENRAP states use a PM 2.5 speciation profile recently developed for MRPO by Carnegie Mellon that is representative of combustion of eastern

bituminous coal. Texas and North Dakota source s that burn lignite coal used a modified NCOAL speciation. 16 More specific specia tion profiles should be utilized as they become available to accurately describe the speciation of PM2.5 from combustion of different types of

coal utilized in Arkansas.

3.2.2 On-Road Mobile Emissions Emissions from mobile, on-road sources are pr epared for CENRAP mo deling in one of two ways: 1) pre-computed emissions supplied by an RPO or other group or 2) supplied vehicle miles traveled (VMT), meteor ological data and other MOBILE6 17 inputs for calculation in

SMOKE/MOBILE6. Annual mobile emissions were pre-computed as part of the 1999 Mexico inventory and 2000 Canada inventor y. Seasonal mobile emissions calculated in MOBILE6 were provided for all 13 WRAP states. For all ot her RPOs, including CENRAP, county-level VMT were prepared and input into SMOKE/MOBILE6. . For all Arkansas counties, county-level Highway Performance Monitoring System annual average VMT data were used. Data for 2007 and 2010 were extrapolated back to 2002. Annua l average data was adjusted using seasonal factors to arrive at month-specific estimates . Weekday VMT for summer and winter were estimated from monthly values using Texas st atewide average weekday/annual average daily

factors. 18 For the other CENRAP states, Sonoma Technology provided monthly VMT data and MOBILE6 input files for the months of January and July for all counties in the CENRAP region. 19 MOBILE6 input files for the remaining m onths of 2002 had to be generated. The EPA MOBILE6 was state-of-the-science at the time the modeling was conducted and deemed acceptable at that time. EPA Office of Trans portation and Air Quality has developed a new model, Motor Vehicle Emission Simulator (MOV ES), which will replace the MOBILE6 model for estimating emissions from on-road mobile sources.

3.2.3 Biogenic Emissions The BEIS3 system is utilized to estimate emissi ons from biogenic sources. BEIS3 is integrated into SMOKE for deriving biogenic emissions es timates given land use information, emissions factors for different plant species, and hourly, gr idded meteorology data. Land use data is from

the BELD3 land use database and emission factor s used are version 0.98 of the BELD emissions

factors. These land use data and emission f actors were developed by the WRAP during their

16 Chow, J et al. 2004. Source Profiles for Industrial, Mobile, and Area Sources in the Big Bend Regional Aerosol Visibility and Observational Study. Chemosphere 54, 185-208.

17 EPA’s MOBILE6 model is available at http://www.epa.gov/OMSWWW/m6.htm

18 Final Report: Arkansas 2002 Emission Inventory, prepared by ENVIRON and Eastern Research Group, May 13, 2004 (Appendix 7.1A of the RH SIP)

19 Reid, S.B. et al. 2004. Emission Inventory Development for Mobile Sources and Agricultural Dust Sources for the Central States. Sonoma Technology, Inc. ( http://cenrap.sonomatech.com/CENRAP_Mobile/FinalReport.pdf)

A-19preliminary modeling efforts. BEIS modeling pr oduces gridded, hourly emi ssions for input into CMAQ and CAMx. 20 The EPA approves of the use of BEIS3 by CENRAP in this SIP.

3.2.4 Non-Road Mobile Emissions Emissions from aircraft operati ons, commercial and recreational marine vessels, and railroad

locomotives and other sources were devel oped by the EPA for the 2002 NEI. The EPA

NONROAD 21 model was utilized by Sonoma Tec hnology to develop a non-road emissions inventory for the CENRAP states. Error! Bookmark not defined. EPA and CENRAP emissions were consolidated by Pechan and CEP. 22

3.2.5 Area Source Emissions

The area source inventory includes data from the EPA 2002 NEI and inventories prepared by ADEQ, CENRAP and other CENRAP states. Sonoma Technology prepared additional inventories of prescribed bur ning, agricultural dust, and soil agricultural a mmonia for the CENRAP region. 23 ADEQ used 2001 EIQ data for fuel use to estimate area source fuel combustion emissions. The Western Regional Air Partnership (WRAP) pr ovided an oil and gas production inventory for states within the WRAP that included a number of states in the CENRAP modeling domain. These emissions were consolidated by Pechan and CEP. 24 UCR processed this inventory further to separate the inventory into s ubcategories (general area, fire, ammonia, road dust, fugitive dust, uncategorized) to assist in particulate source apportionment modeling with CAMx.

3.3 2018 E MISSIONS I NVENTORY

An emission inventory for 2018 including antici pated changes due to population growth, emission controls and development of industry, energy, and natural resour ces is required to project the net effect on visi bility conditions by 2018. CENRAP developed an emission inventory for 2018 (Base18G) using a combinati on of EPA Economic Growth Analysis System (EGAS 5), MOBILE 6, NONROAD, and the Inte grated Planning Model (IPM) of ICF

20 Tonnesen, G., et al. 2005. Final Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) for the Project Period March 1, 2004 through February 28, 2005. UCR. ( http://pah.cert.ucr.edu/aqm/308/reports/final/2004_RMC_final_report_main_body.pdf ).

21 NONROAD is available at http://www.epa.gov/otaq/nonrdmdl.htm

22 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and Associates, Inc. and CEP, UNC, (http://www.cenrap.org/html/projects. php?mode=subcatdownload&id=50)

23 Reid, S.B. et al. 2004. Emission Inventory Development for Mobile Sources and Agricultural Dust Sources for the Central States. Sonoma Technology, Inc. ( http://cenrap.sonomatech.com/CENRAP_Mobile/FinalReport.pdf) .; Reid, S.B et al. 2004. Research and Development of Planned Burning Emission Inventories for the Central States Regional Air Planning Association. Sonoma Technology, Inc. ( http://cenrap.sonomatech.com/CENRAP_ PlannedBurnData/FinalReport.pdf) .; Coe, D.L. and S.B. Reid. 2003. Research and Development of Ammonia Emission Inventories for the Central States Regional Air Planning Association. Sonoma Technology, Inc.

24 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and Associates, Inc. and Carolina En vironmental Program (CEP), University of North Carolina(UNC), (http://www.cenrap.org/html/projects.php?mode=subcatdownl oad&id=50); Pechan and CEP. 2005. Refinements of CENRAP’s 2002 Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and Associates, Inc. and CEP, UNC. ( http://www.dnr.mo.gov/env/apcp/docs/appendixh-3.pdf )

A-20International for EGUs to project emissions fr om 2002 to 2018. Emission projections for most source categories are based on growth and control factors compiled by Pechan and detailed in the Development of Growth and Contro l Inputs for CENRAP 2018 Emissions draft technical support

document. 25

Table 3-2. Emission estimates for Arkansas sources in 2018 (tons/yr)

VOC NOx PM2.5 PM10 NH3 CO SO2

Point 55,603 71,107 13,775 19,799 2,575 75,708 106,461

Area 107,387 31,531 69,585 148,592 201,722 448,760 31,169

Non-road

mobile 31,475 34,305 3,387 3,678 49 293,734 211

On-road

mobile 19,924 33,460 949 949 3,412 367,152 443

Biogenic 1,385,666 18,960 136,688

Total 1,600,055 189,542 87,695 173,019 207,758 1,322,043 138,283

Table 3-3. Change in Arkansas emissions from 2002 to 2018

VOC NOx PM2.5 PM10 NH3 CO SO2

Point 11,274

‐ 1,312 5,938 7,393 2,574 19,342 14,256

Area 13,839 7,081 1,585 159 49,286 12,235 1,280

Non-road

mobile

‐ 23,310

‐ 28,167

‐ 1,833

‐ 1,995 0 21,107

‐ 5,279

On-road

mobile

‐ 28,675

‐ 108,434

‐ 2,072

‐ 2,835 932

‐ 302,062

‐ 3,459

Biogenic 0 0 0 0 0 0 0

Net change

‐ 26,872

‐ 133,653 3,617 2,723 52,791

‐ 249,376 6,798

% change

‐ 2%

‐ 41% 4% 2% 34%

‐ 16% 5%

Pechan used the following alternative data sources to replace EGAS default projections:

• County-level population projec tions for CENRAP states;

• Annual Energy Outlook (AEO) projections for oil and gas production emissions;

• average historical values rather th an 2002 data for prescribed burning;

• Extrapolation of historical trends for unpaved roads;

• United States Department of Agriculture (U SDA) projections of planted acreage; for major crops for crop tilling emissions;

• Onroad vehicle miles traveled projections for paved road fugitive dust emissions;

• USDA livestock projections

25 Pechan 2005. Development of Growth and Control Inputs for CENRAP 2018 Emissions, Draft Technical Support Document. E.H. Pechan and Associates, Inc. (http://www.dnr.mo.gov/env/apcp/docs/appendixh-4.pdf)

A-21All control strategies expected to take effect prior to 2018 are included in the projected emission inventory. Maximum Achievable Control Technology (MACT) re gulations were applied to those engines subject to MACT rules. Emi ssions for Canada are based on a shared 2020 emission inventory. 2018 EGU emissions were ba sed on the run 2.1.9 of the Integrated Planning Model (IPM) updated by the CENRAP states. Re ductions anticipated from BART controls for EGUs in Oklahoma, Arkansas, Kansas, and Ne braska were included in projections of 2018 emissions. These anticipated reductions were based on actual operating c onditions and estimated

control efficiencies from util ities. In the State’s September 27, 2011 supplemental submission, ADEQ clarified that it provided the CENRAP Modeling Workgroup with the controlled BART

source emission limits contained in the Stat e’s RH Rule, the APC& E Commission Regulation 19, Chapter 15, for inclusion in the CENRAP’s 2018 future case modeling. Newly permitted coal-fired utilities were included in 2018 pr ojections. Conservatively, no IPM projected new

units were removed from the simulation with the addition of the permitted facilities. Appendix B of the CENRAP TSD lists the file names, data s ource, type and a description of emissions used in the 2018 (Base18G) emissions inventory. We note that emissions of sulfate from point sources in Arkansas are projected to increase by over 14,000 tons/y r in 2018 from 2002 levels.

The following sources were assumed to remain constant between the 2002 and 2018 base case

simulations:

• Biogenic VOC and NOx emissions from the BEIS3 biogenic emissions model;

• Wind blown dust associated with non-agricul tural sources (i.e., natural wind blown fugitive dust);

• Off-shore emissions associated with off- shore marine and oi l and gas production activities;

• Emissions from wildfires;

• Emissions from Mexico; and

• Global transport (i.e., emissions due to BCs from the 2002 GEOS-CHEM global chemistry model.

The last future runs (2018G) util ized an inventory that had assumptions about BART controls in

the CENRAP states.

A-22

### Chapter 4: Modeling Protocol, Episode Selection and Modeling Set-up

Overview

4.1 I NTRODUCTION

Meteorological, emission and photochemical models are essential tools in examining factors that impact visibility and for devel opment of effective control stra tegies to meet the goals and requirements of the RHR. CENRAP selected the team of ENVIRON and UCR to perform the needed emissions and air quality modeling. The team performed regional haze analyses by operating regional scale, three-dime nsional air quality models to si mulate the transport and fate of key species that affect visibility in Class I Ar eas in the central U.S. This work included the development of meteorological data for input in to the model as well as creation and processing

of emission estimates for use in the model. The Modeling Protocol 26 describes the model selection, configuration, episode selection, and model evaluation used in support of the Arkansas

RHR SIP.

4.2 Q UALITY A SSURANCE P ROGRAM P LAN

The modeling team developed a qualit y assurance program plan (QAPP) 27 to develop clearly defined data quality objectives, documentation, and procedures. This QAPP was developed incorporating the following elements as describe d in the EPA guidance document for modeling:

• A systematic planning process including id entification of assessments and related performance criteria;

• Peer reviewed theory and equations;

• A carefully designed life-cycle developm ent process that minimizes errors;

• Documentation of any change s from original plans;

• Clear documentation of assumptions, theory, an d parameterization that is detailed enough so others can understand the model output;

• Input data and parameters that are accura te and appropriate for the problem; and

• Output data that can be used to help inform decision makers. The plan describes the data management and qual ity assurance/quality cont rol measures taken to assure high quality emission inve ntories and air quality modeli ng results for use in the RH analysis.

4.3 E PISODE S ELECTION

EPA guidance 28 describes the criteria that should be us ed to select a modeling episode. The modeling episode should: 1) reflect a variety of meteorological c onditions that are representative of the 20% worst and 20% best days in the Class I areas being modeled, 2) be representative of

26 Morris, R.E. et al. Modeling Protocol for the CENRAP 2002 Annual Emissions and Air Quality Modeling, Draft

2.0. Web: http://pah.cert.ucr.edu/aqm/cenrap/docs/CENRAP_Draft2.0_Modeling_Protocol_120804.pdf ).

27 Morris, R.E. and G. Tonnesen. 2004. Quality Assurance Project Plan (Draft ) for Central Regional Air Planning Association (CENRAP) Emissions and Air Quality Modeling. (http://pah.cert.ucr.edu/aqm/cenrap/docs/CE NRAP_QAPP_Nov _24_2004.pdf ). December 23.

28 EPA, 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze , EPA-454/B-07-002, April 2007, EPA OAQPS; ( http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf )

A-23the baseline period of 2000-2004, 3) cover a period where extensiv e air quality/meteorological data are available, 4) cover a long enough period so that relative response factors (RRF) can be averaged over a period several days (> 10 da ys). For regional haze modeling, the preferred approach is to simulate an entire representative y ear. This allows the states to base RRF values on the 20% best and 20% worst days of the year.

CENRAP selected the entire calendar year of 2002 for regional haze modeling. This is consistent with the EPA guidance and has the added benefit of the ba se case and baseline inventories covering the same y ear. Other RPOs selected the same modeling year, allowing for more direct comparison of modeling results and sh aring of modeling inputs. The availability of 2002 NEI also provides an additiona l resource in the development of emission inventories for the modeling episode. 2002 appears to be a more re presentative year when compared to 2003 and 2004. The EPA approves of the selection of 20 02 for the regional haze modeling episode.

4.4 P HOTOCHEMICAL AND E MISSIONS M ODELING D OMAIN

CENRAP conducted emissions and air quality mode ling on the 36-km national regional planning organization (RPO) domain. This domain consis ts of a 148 × 112 array of 36-km × 36-km grid

cells and covers the continental United States . Additional photochemical modeling runs were performed on a 12-km domain covering the central states to examine the sensitivity of model

results to domain resolution. These results were similar to the 36 km results so CENRAP determined that the 36-km modeling domain was sufficient for the 2002 annual modeling. 29

CENRAP’s choice of 36 km horizontal resolution was appropriate given the lack of improved performance at 12 km resolution and the additiona l computational resources required to run the model at the higher resolution. The use of highe r spatial resolution modeling should be revisited in future modeling efforts as computational efficiency improves.

29 Morris, R.E. et al. 2006. CENRAP Modeling: Need for 36 km versus 12 km Grid Resolution. Presented at CENRAP Modeling Work Group Meeting, Baton Rouge, Louisiana. (http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/ Morris_36vs12km_Feb6-8_2006.ppt ).

A-24Figure 4-1. National RPO 36-km modeling dom ain for CMAQ, CAMx, and SMOKE modeling.

4.5 MM5 M ETEOROLOGICAL M ODEL

4.5.1 Model Selection

Photochemical grid models, such as CMAQ a nd CAMx, require inputs of three-dimensional gridded meteorological data, including wind, temperature, humi dity, cloud/precipitation, and boundary layer parameters. The Fifth-Genera tion Penn State/NCAR Mesoscale Model (MM5) was used to develop these input fields for the CENRAP visibility modeling as well as inputs for the SMOKE emissions processing tool. MM5 is a state-of-the-science atmosphere model that has proven useful for air quality a pplications and has been used exte nsively in past local, state, regional, and national modeling efforts. MM5 ha s undergone extensive peer -review, with all of its components continually undergoing developm ent and scrutiny by the modeling community. In-depth descriptions of MM5 30 can be found in Dudhia (1993) 31 and Grell et al. (1994). 32 All meteorological data used for the CENRAP air quality modeling efforts are derived from MM5

model simulations.

30 http://www.mmm.ucar.edu/mm5

31 Dudhia, J., 1993. “A non-hydrostatic version of the Penn State/NCAR Mesoscale Model: validation tests and simulation of an Atlantic cyclone and cold front.” Mon. Wea. Rev . 121, pp.1493-1513.

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

A-25In addition to development of meteorological inputs for CMAQ and CAMx, MM5 was also used to develop meteorological inputs for the CA LMET/CALPUFF modeling sy stem. As discussed further in Section 6 of this review, CALM ET/CALPUFF was used to determine whether a BART eligible source contributes to visibility impairment at a Cla ss I area. Refer to Section 6 of

this review for further information on the use of MM5 for BART modeling.

The CENRAP meteorological modeling used for input to photochemical modeling and emission processing was performed by the Iowa Depart ment of Natural Resources (IDNR) and is described fully in the report entitled Meteorological Model Perf ormance Evaluation of an Annual 2002 MM5 (version 3.6.3) Simulation (hereafter referred to as the Meteorological Model Performance Evaluation report) . 33

4.5.2 Meteorological Modeling Domain and Vertical Layer Structure

In the IDNR 36-km meteorological modeling, MM 5 was configured to run on the standard continental-scale Regional Planning Organizatio n (RPO) National Grid with 36-km grid point spacing. The RPO National Grid is defined on a Lambert conformal projection, with true latitudes at 33°N and 45°N, and the centra l latitude and longitude at 40°N and 97°W, respectively. The grid point spacing is 36-km. The continental expanse of this domain results in a grid of 165 (east-west) by 129 (north-south) dot points, and 164 (eas t-west) by 128 (northsouth) cross points. Overall, the domain covers 5904 km by 4608 km. The MM5 domain provides overlap of the CMAQ and CAMx air quality modeling grid (described in section 3.3) to alleviate any numerical boundary artifacts that may be present in the MM5 output fields. Meteorology modeling was also completed on a regional-scale dom ain with 12 km grid spacing covering the central states by EPA Region V II and the Texas Commission on Environmental Quality to examine model prediction sensitivity to grid resolution. The vert ical layer structure of the CENRAP meteorological modeling domain consis ts of 34 layers, a top level at 100 millibars, and increasing layer thickness with a ltitude. The vertical layer stru cture is further detailed in the Modeling Protocol.

4.5.3 Model Configuration

The final CENRAP MM5 modeling system conf iguration for the 2002 annual simulation is provided in the Modeling Prot ocol and the Meteorological Model Performance Evaluation report. Early MM5 simulations by the State of Iowa and the Lake Michigan Air Directors Consortium (LADCO) and further sensitivity tests were performed to identify an MM5 configuration for annual runs.

The initial 2002 36-km IDNR simulation results showed that MM5 result s showed an extreme cold bias over the central U.S and unnatural diurnal profiles near shorelines. A number of sensitivity tests were performed by IDNR to reso lve performance issues identified in the initial simulation. At the same time, sensitivity tests were performed in support of the development of

33 Johnson, M. 2007. Meteorological Model Performance Evaluation of an Annual 2002\ MM5 (version 3.6.3) Simulation. Iowa Department of Natural Resources, Air Quality Bureau. ( http://www.iowadnr.gov/air/prof/progdev/files/IDNR.2002mm5v363.evaluation.v204.p f )

.

A-26meteorological modeling for VISTAS. 34 The combination of all of these studies led to the configuration used by CENRAP for MM5 mode ling detailed in the CENRAP TSD and the Modeling Protocol.

4.5.4 MM5 Processing and Application

Several preprocessing steps are necessary to prepare input data for an MM5 simulation. The MM5 modeling system provides all of the tools n ecessary to prepare topo graphic, vegetative, initial condition, boundary conditi on, and FDDA nudging input files.

Global topographic data at 2-minute (latitude/longitude) resolution were used to define terrain elevations on the 36-km grid. Land use distribution on the MM5 domains was defined from the 24-category USGS vegetation data w ith a resolution of 2 minutes.

The 3-hour Eta analysis and surface fields availa ble from the National Center for Atmospheric Research (NCAR) were taken fr om the Eta Data Assimilation System (EDAS) and used to supply initial and boundary conditio ns to MM5, and for analysis nudging in the FDDA package. The EDAS analyses are devel oped from a wide variety of obs ervational sources, including standard surface and upper air measurements, prof iler networks, radar- and satellite-derived measurements, and ship and aircra ft reports. The wide array of data sources, coupled with the high time- and spatial resolution provided by E DAS, result in an anal ysis product that far

exceeds the level of detail found in tr aditional global-scale analyses.

Sea surface temperatures (SSTs) were a pproximated by ETA skin temperatures The annual simulation was generated from 95 inde pendent simulations in itialized at 12Z and integrated through five days. To allow for a pproximately a two week photochemical model spinup period, the simulation st arted at 12/16/2001 12Z.

4.5.5 Model Performance

Model performance evaluation was performed by IDNR through comparison with observations of surface and upper-air meteorolog ical conditions and precipitation. 35 Additional performance evaluation was done by CENRAP by compari ng the 2002 CENRAP MM5 simulation with the

2002 VISTAS MM5 and the interim 2002 WRAP simulations. 36 Details on this comparison can be found in the CENRAP TSD, Appendix A.

34 Olerud, D., Sims, A., 2004. MM5 2002 Modeling in Support of VISTAS (Visibility Improvement—State and Tribal Association). Baron Advanced Meteorologi cal Systems, LLC, Research Triangle Park, NC. http://www.baronams.com/projects/VISTAS /reports/VISTAS_TASK3f_final.pdf

35 Johnson, M. 2007. Meteorological Model Performance Evaluation of an Annual 2002\ MM5 (version 3.6.3) Simulation. Iowa Department of Natural Resources, Air Quality Bureau. (http://www.iowadnr.gov /air/prof/progdev/files/IDNR. 2002mm5v363.evaluation.v204.pdf)

36 Kemball-Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang and G. Tonnesen, 2005. Comparison of CENRAP, VISTAS and WRAP 36 km MM5 Model Runs fo r 2002, Task 3: Met Gatekeeper Report. http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/ CENRAP_VISTAS_WRAP_2002_36km_MM5_eval.ppt

A-27The goal of the evaluation was to determine whethe r the meteorological fields are sufficiently accurate to properly characterize the trans port, chemistry, and removal processes in CMAQ/CAMx. If errors in the meteorological fiel ds are too large, the ability of the air quality model to replicate regional polluta nt levels over the entire base y ear will be severely hampered and the predicted impacts from future year growth and controls will be highly questionable. To provide a reasonable meteorological characteriza tion to the photochemical/visibility model, MM5 must represent with some fidelity the:

• Large-scale weather patterns (i.e., synoptic patterns depicted in the 850-300 mb height fields), as these are key forci ngs for mesoscale circulations;

• Mesoscale and regional wind, temperature, PB L height, humidity, and cloud/precipitation

patterns;

• Mesoscale circulations such as sea br eezes and mountain/drainage circulations;

• Diurnal cycles in PBL depth, temperature, and humidity.

For visibility applications, the moisture and conden sate fields are particularly important as they significantly impact PM chemical formation, rem oval, and light scattering efficiency. In addition, cloud and precipitation fi elds are a good measure of the integrated performance of the model since these are model-derived quantities and not nudged to observations. Because of the model’s coarse resolution of 36-km, the model ca nnot be expected to faithfully simulate the pattern or variability of th e convective precipitation, but should reproduce the synoptic precipitation and cloud patterns.

The IDNR evaluation of the MM5 model performa nce was limited to operational testing of the model, and not to a scientific evaluation. Previous peer-reviewed documentation of MM5 formulation, testing, and evaluati on provide the basis for its scie ntific validity. An operational

evaluation entails an assessment of the model's ability to correctly estimate surface and boundary layer wind, temperature, and moisture largel y independent of whet her the actual process descriptions in the model are ac curate. The operational evaluati on essentially tests whether the predicted meteorological fields are reasonable, consistent, and ag ree adequately with available observations in time and space. The process pr ovides only limited information about whether

the results are correct from a scie ntific perspective or whether th ey are the fortuitous product of compensating errors; thus a “successful” operatio nal evaluation is a nece ssary but insufficient condition for achieving a sound, reliabl e performance testing exercise.

The basis for the IDNR operational performan ce assessment entailed a comparison of the predicted meteorological fields to available surf ace and aloft data that are collected, analyzed, and disseminated by the National Weather Service. It was carried out both graphically and statistically to evaluate m odel performance for winds, temperatures, humidity, and the placement, intensity, and evolution of key weather phenomena. The MM5 results were compared to a specific set of statistics that have been identified for use in establishing benchmarks for acceptable MM5 model performance. 37 The IDNR concluded, based on the

37 Emery, C.A., E. Tai, and G. Yarwood. 2001. “Enhanced meteorological modeling and performance evaluation for two Texas ozone episodes.” Prepared for the Texas Natural Resource Conservation Commission, by ENVIRON International Corporation.

A-28results of the performance evaluation, that th e final 36 km CENRAP MM5 simulations exhibit reasonably good performance for the central U.S.

Comparison of CENRAP MM5 performance w ith similar modeling efforts by WRAP and VISTAS revealed comparable performance across all three simulations. The three simulations showed similar performance for prediction of surface wind speed, wind direction and humidity.

The use of surface data assimilati on of temperature in the interi m WRAP simulation resulted in the best performance in prediction of surface temperatures but the poorest performance for vertical temperature profiles. Surface data a ssimilation has since been dropped from the WRAP modeling protocol. The 2002 VISTAS MM5 simula tions showed the best performance and the CENRAP performance more closely resembled that of the VISTAS than the WRAP.

The 2002 CENRAP MM5 model results are within the bounds of other me teorological databases used for prior air quality modeling efforts. It is therefore de emed reasonable to proceed with its use as inputs for visibility modeling. The EPA accepts the use of MM5 in this configuration and selected modeling domain and recognizes that the MM5 meteorological model used by CENRAP

was state-of-the-science at the time the modeli ng was conducted. The performance of the model was adequate for the purposes for which it was used and on par with other st udies at the time. A new meteorological model, the Weather Research Forecast model (WRF), has been developed to

address the some of the limitations of the MM5 model and should be considered as a possible alternative for future meteorological modeling efforts.

4.6 SMOKE E MISSIONS M ODEL

CENRAP selected the Sparse Matrix Operator Kernel Emissions model 38 to generate gridded hourly speciated emission estimates for mobile, non-road, area, point, fire and biogenic emission sources for use as inputs for photochemical grid models. The purpose of SMOKE is to convert the spatial and temporal resolution of the ava ilable emission inventory data to the resolution needed by the air quality model. SMOKE also ha s the ability to compute emissions for mobile on-road and biogenic sources. Biogenic emissi on modeling is perfor med through SMOKE with the Biogenic Emission Inventory System, version 3 (BEIS3) 39 using the Biogenic Emissions Landcover Database (BELD3) vege tative database. Mobile em issions can be calculated by

SMOKE from mobile-source activ ity data, using emission factor s from the MOBILE6 model. SMOKE supports the emission input formats required by the CAMX and CMAQ air quality

models.

4.7 A IR Q UALITY M ODEL

Photochemical air quality models offer opportunity to better unders tand the sources of particulate matter that impair visibility by simulating the em issions, formation, transport, and deposition of these pollutants. If an air qua lity model performs well for a hi storical episode, the model may then be useful for identifying the sources of pa rticulate matter and helping to select the most

effective emissions reduction stra tegies for attaining visibility goals. Although several types of air quality modeling systems are available, the gridded, three-di mensional, Eulerian models provide the most complete spatial representation and the most comprehensive representation of

38 Available at http://www.smoke-model.org/index.cfm

39 Available at http://www.epa.gov/ttn/chief/software.html#pcbeis

A-29processes affecting particulate matter, especial ly for situations in which multiple pollutant sources interact to form particulate matter.

4.7.1 Model Selection

Guidance from the EPA requires that the air quality model should be selected based on intended application and must be freely dow nloadable to all stakeholders. Furthermore, the user must be able to revise the code to perf orm diagnostic analyses and/or to improve the model’s ability to

describe observations in a credible manner. Se veral additional prerequisi tes should be met for a model to be used to support an attainment demonstration or unif orm rate of progress assessment.

• It should have received and been revised in response to a scie ntific peer review.

• It should be appropr iate for the specific applic ation on a theoretical basis.

• It should be used with a data base which is adequate to supp ort its application.

• It should be shown to have performed we ll in past modeling applications. (If the application is the first for a particular m odel, then the State sh ould note why it believes the new model is expected to perform sufficiently.)

• It should be applied consis tently with a protocol on methods and procedures.

The Guideline on Air Quality Models (GAQM - 40 CFR Part 51 Appendix W) does not indicate a preferred photochemical grid model for Re gional Haze applications. The CMAQ and CAMx models have been accepted by EPA for numerous regulatory air quality modeling applications and were considered by CENRAP for use in regional haze modeling. CENRAP selected CMAQ

Version 4.5 with “SOAmod enhancements” as the primary air quality model for regional haze modeling and the CAMx Version 4.40 model, app lied using similar options as used by CMAQ, as a secondary corroborative model. CAMx was also utilized with its Particulate Source Apportionment Technology (PSAT) tool to provide source apportionment with both the baseline and future case emissions inventories (See Sect ion 5). EPA concurred with the selection of CAMx for the CENRAP regional haze modeling as it has been extensively used within the region and has been proven to be an acceptable model. The selection of CMAQ was based on review of previous and concurrent studies within CENRAP and other RPOs, as well as comparisons with CAMx model results. 40 Major differences between the two models that still exist are in the basic model code, in the tr eatment of horizontal diffusion SOA formation mechanisms, and in grid nesting. EPA accep ts the choice of CMAQ as it satisfies the requirements and guidelines detailed above. The versions of CMAQ and CAMx used by CENRAP in its visibility modeling were the state-of-the-science at the time they were implemented and are acceptable to EP A for this Regional Haze selection.

Both air quality models were set up and r un on the RPO national 36–km modeling domain described in section 3.3. This modeling domain is also used by WRAP and VISTAS. Sensitivity runs performed by CENRAP for CMAQ run on a 12km modeling domain revealed limited improvement over the 36-km runs and a large incr ease in computer resources and time. CAMx

runs at 12-km resolution redu ced the sulfate under-prediction bias in the summertime when

40 Morris, R.E., et al. 2006. CENRAP Modeling Update: CMAQ versus CAMx Model Performance Evaluation. Presented at CENRAP Modeling Work Group Meeting, Baton Rouge, Louisiana. (http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/ Morris_MPE_Feb6-8_2006.ppt )

A-30compared to 36-km runs. With this possible ex ception, CENRAP noted little benefit in overall model performance with use of the 12-km grid. Therefore, the 36km domain was selected for all CENRAP CMAQ and CAMx runs. 41

These air quality models are discussed in more detail below.

4.7.1.1 CMAQ Air Quality Model EPA initially developed the Community Multi-S cale Air Quality (CMAQ) modeling system in

the late 1990s. The model source code and supporting data can be downloaded from the Community Modeling and Analysis System (CMAS) Center ( http://www.cmascenter.org/ ), which is funded by EPA to distribute and provi de limited support for CMAQ users. CMAQ was designed as a “one atmosphere” modeling system to encompass modeling of multiple pollutants and issues, including ozone, PM, visibi lity, and air toxics. This is in contrast to many earlier air quality models that focused on single-pollutant issues (e .g., ozone modeling by the Urban Airshed Model). CMAQ is an Eulerian model—that is, it is a grid-based model in which the frame of reference is a fixed, three-dimensional (3-D) grid with uniformly sized horizontal grid cells and variable vertical layer thicknesses. The number and size of gr id cells and the number and thicknesses of layers are defined by the user, based in pa rt on the size of the modeling domain to be used for each modeling project. The key science processes included in CMAQ are emissions, advection and dispersion, photochemical transformation, aerosol thermodynamics and phase transfer, aqueous chemistry, and wet and dry deposition of trace species. CMAQ offers a variety of choices in the nume rical algorithms for treating many of these processes, and it is designed so that new algorithms can be included in the model. CMAQ offers a choice of three photochemical mechanisms for solving gas-pha se chemistry: the Regional Acid Deposition Mechanism version 2 (RADM2), a fixed coeffici ent version of the SAPRC90 mechanism, and the Carbon Bond IV mechanism (CB-IV).

CENRAP used CMAQ Version 4.5 with a “SOAm ods enhancement” for 2002 base case (actual emissions), 2002 baseline (typi cal emissions) and 2018 future case (projected emissions) modeling. The “SOAmods enhancement” was the result of work by VISTAS investigating the model’s underestimate of organic mass car bon (OMC) concentrati ons. The updated CMAQ secondary organic aerosol (SOA) module led to improved estimation of OMC in VISTA modeling. CENRAP examined the use of the enhanced SOA module and found similar improvements in model performance over the original CMAQ Version 4.5 model. CENRAP decided to use the CMAQ Version 4.5 with the “SOAmods enhancement” 42 for CENRAP modeling. Details of the CMAQ model configur ation used by CENRAP can be found in the CENRAP TSD and the Modeling Protocol.

4.7.1.2 CAMx Air Quality Model

41 Morris, R.E., et al. 2006. CENRAP Modeling: Need for 36 km versus 12 km Grid Resolution. Presented at CENRAP Modeling Work Group Meeting, Baton Rouge, Louisiana. (http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/ Morris_36vs12km_Feb6-8_2006.ppt )

42 Morris, R.E., B. Koo, A. Guenther, G. Yarwood, D. McNally, T.W. Tesche, G. Tonnesen, J. Boylan and P. Brewer. 2006. Model Sensitivity Evaluation for Organic Carbon using Two MultiPollutant Air Quality Models that Simulate Regional Haze in the Southeastern United States. Atmos. Env. 40 (2006) 4960-4972.

A-31The Comprehensive Air Quality Model with extensions (CAMx) model 43 was initially developed by ENVIRON in the late 1990s as a nested-grid, gas-phase, Eulerian photochemical grid model. ENVIRON later revised CAMx to treat PM, visi bility, and air toxics. While there are many similarities between the CMAQ and CAMx systems, there are also some significant differences in their treatment of advection, dispersion, ae rosol formation, and dry and wet deposition. CAMx has seen extensive use within many of th e CENRAP states. The CAMx model is based

on well-established treatments of advecti on, diffusion, deposition, and chemistry. CENRAP used CAMx Version 4.40, applied using simila r options as used by CMAQ, as a secondary

corroborative model and utilized CAMx with its Particulate Source Apportionment Technology (PSAT) tool to provide source apportionment of nitrate and sulfate ae rosol with both the 2002

baseline and 2018 future case emission inventories (See section 5). Details of the CAMx model configuration used by CENRAP can be found in the CENRAP TSD and the Modeling Protocol.

4.7.2 Vertical Modeling Domain CMAQ and CAMx have the ability to collapse th e 34 layer vertical st ructure used in MM5 modeling to a smaller set of vertical layers . Sensitivity studie s by WRAP and VISTAS examined model performance looking at a variet y of vertical modeling domains ranging from modeling all 34 vertical layers to collapsing the structure down to 12 vertical layers. Results of this study showed that collapsi ng the vertical structure down to 19 layers while matching the 8 bottom most vertical layers produced results nearly identical to the full 34 layer runs. The more aggressive layer collapsing scheme of 12 layers produced substantially different results. Based on these results, CENRAP selected the 19 layer vertical structure described in the CENRAP TSD. This selection improves computational effi ciency and produces results almost identical to

the full vertical structure runs.

4.7.3 Initial and Boundary Conditions Initial conditions (ICs) are spec ified by the user for the first day of a model simulation. For continental-scale modeling using the RPO 36-km do main, the ICs can affect model results for as many as 15 days, although the eff ect typically becomes very small after about 7 days. A model spin-up period is included in each simulation to eliminate any effects from the ICs. For the CENRAP modeling, the annual simu lation is divided into four quarters, and included a 15-day spin-up period for the quarters beginning in April, July, and October. For the quarter beginning in January 2002, a spin-up period covering December 16-31, 2001, using meteorology and emissions data developed for CENRAP were us ed. We agree that the 15 day spin-up period employed by CENRAP was sufficient to minimize the effects of the IC on model results given the size of the modeling domain.

Boundary conditions (BCs) specify the concentrations of gas and PM species at the four lateral

boundaries of the model domain. BCs determine the amounts of gas and PM species that are transported into the model domain when winds fl ow into the domain. Boundary conditions have a much larger effect on model simulations than do ICs. For some areas in the CENRAP region and for clean conditions, the BCs can be a substan tial contributor to visib ility impairment. For this study BC data generated in an annual simu lation of the global-scal e GEOS-Chem model for

43 ENVIRON, 2006. “User’s Guide – Comprehensive Air-qua lity Model with extensions, Version 4.30.” ENVIRON International Corporation, Novato, California. (available at http://www.camx.com ).

A-32calendar year 2002 were applied. 44 The BCs employed by CENRAP we re state-of-the-science at the time they were implemented

.

4.7.4 Base Case/ Baseline Model Performance The 2002 Base Case modeling efforts were used to evaluate air quality/visibility modeling systems for a historical episode—in this cas e, for calendar year 2002—to demonstrate the suitability of the modeling systems for subse quent planning, sensitivity, and emissions control strategy modeling. Comparisons between the 2 002 Base F actual emissions model performance with the 2002 typical emissions (Typ02F) revealed little difference in model performance. The 2002 F model predictions are nearly identical to 2002 G results so model performance evaluation performed with 2002 Base F emissions is repres entative of the final model performance. Therefore, model performan ce was evaluated using the T yp02F emission inventory.

Model performance evaluation is performed by co mparing output from model simulations with ambient air quality data for the same time peri od to determine whether the model’s performance is sufficiently accurate to justify using the model for simulating future conditions. There are a number of challenges in completing an annual MPE for regional haze. The model must be compared to ambient data from several differe nt monitoring networks for both PM and gaseous species, for an annual time period, and for a large number of sites. The fo cus of the performance evaluation is on the six components of particulate matter that are used to characterize visibility at Class I areas: Sulfate (SO4); Particulate Nitrat e (NO3); Elemental Carbon (EC); Organic Mass Carbon (OMC); Other inorganic fi ne particulate (IP or Soil); a nd Coarse Matter (CM). The

model must be evaluated for both the worst visi bility conditions and for very clean conditions. Finally, final guidance on how to perform an MPE for fine-particulate models is not available from EPA. Therefore, the CENRAP experiment ed with many different approaches for showing model performance results.

The plot types that were found to be the most useful are the following:

• Time-series plots comparing the measured a nd model-predicted species concentrations

• Scatter plots showing model predictions on th e y-axis and ambient data on the x-axis

• Spatial analysis plots with ambien t data overlaid on model predictions

• Bar plots comparing the mean fractional bi as (MFB) or mean fractional error (MFE) performance metrics

• “Bugle plots” showing how model performance varies as a function of the PM species

concentration

• Stacked-bar plots of contribut ions to light extinction for the average of the best-20% visibility days or the worst-20% visibility days at each site; the higher the light extinction, the lower the visibility

The following plots depict summary model pe rformance for CENRAP CMAQ modeling using the Typ02F emissions inventory. Below are si x sets of model fractional bias and model fractional error plots. Each set of plots compares the measured chemically speciated aerosol data

44 Jacob, D.J., R. Park and J.A. Logan. 2005. Documentation and Evaluation of the GEOS-CHEM Simulation for2002 Provided to the VISTAS Group. Harvard University (http://www.vistassesarm.org/documents/Harvard_GEOS-CHEM_FinalReport_20050624.doc)

A-33from a monitoring network with the corresponding mode l output. The monitoring networks used for comparison are IMPROVE, CASTNET, and STN, and are treated separately because each monitoring network has different go als, siting criteria, and data co llection protocols. The model performance plots depicted here are “bugle plot s”, and depict model pe rformance (symbols) and model performance standards (curves) on the y axis relative to measured concentration on the x axis. Model performance standards are of greater latitude at lower concentrations because of the higher relative uncertainties in the data at lower concentrations. Performance goals or criteria approach 200% error and ≤ 200% bias as observed concentrations approach zero and asymptotically approach the proposed pe rformance goals or criteria (i.e., the ≤ 30%/50% and

≤ 60%/75% bias/error levels) as concentrations become greater than 2.5 μg/m 3

.

Model performance at IMPROVE monitors is of highest importance, because these monitors are sited to be representative of the visibility conditions impacting each Class 1 Area. The CASTNET monitoring network is more sparse th an the IMPROVE network, but is also mostly sited at Class 1 Areas and as such, model pe rformance at CASTNET sites should also be considered important. The STN monitoring network is an urban network, and model performance relative to this networ k should be given less importance.

The model performance goals and criteria used by CENRAP were approp riate at the time the modeling was conducted and consistent with th e methods adopted by VI STAS and WRAP. The EPA agrees with the CENRAP model performance pr ocedures and analysis. Detailed results of the model performance evaluation can be found in Appendix C of the CENRAP TSD and on the University of California, Riverside CENRAP visibility modeling website ( http://pah.cert.ucr.edu/aqm/cen rap/cmaq.shtml#cmaq_typ02f_mpe ).

4.7.4.1 Model Performance for Sulfate (SO 4 )

Figure 4-2 shows the monthly SO 4 fractional error and bias for the STN, IMPROVE and CASTNET monitoring networks as well as the proposed performance goals and criteria. In general, there is an under-prediction bias that is more pronounced during the spring and summer months. For the STN network, model perfor mance for all months is within the goals for

both fractional bias and error. Model performance for CASTN ET sites is within goals for

fractional error and within the criteria for fr actional bias as is model performance for the IMPROVE sites with the exception of two months that lie within the criteria but beyond the goal

for fractional error.

A-34Figure 4-2. CENRAP model performance (factiona l bias and error) of the Typ02f modeling scenario for sulfate (SO 4 ). The 12 symbols for each monitor represent monthly average model performance for the year 2002, averag ing all monitors in the CENRAP region. Solid lines represent CENRAP mo deling goals and criteria.

4.7.4.2 Model Performance for Nitrate (NO 3 )

Figure 4-3 shows the monthly NO 3 fractional error and bias for the STN, IMPROVE and CASTNET monitoring networks as well as the proposed performance goals and criteria. NO3 model performance is variable. There is an underprediction duri ng the summer months, approaching a fractional bias of -140% in June and July and an overprediction with bias of approximately 50% in the winter. The wi nter bias is more significant because NO 3 concentrations tend to be a larg e component of visibility impair ment during the winter months.

A-35In general, winter model performance does not meet the performance goals and in some cases does not meet the criteria, pr edicting concentrations of NO 3 much higher than observed.

Figure 4-3. CENRAP model performance (factiona l bias and error) of the Typ02f modeling scenario for nitrate (NO 3 ). The 12 symbols for each monitor represent monthly average model performance for the year 2002, averag ing all monitors in the CENRAP region. Solid lines represent CENRAP mo deling goals and criteria.

4.7.4.3 Model Performance for Organic Carbon (OC)

Figure 4-4 shows the monthly OC fractional error and bias for the STN and IMPROVE monitoring networks as well as the proposed perf ormance goals and criteria. For the IMPROVE network, model performance for all months is within the goals for both fractional bias and error.

The STN monitors in urban areas measured hi gher concentrations of OC then the rural

A-36IMPROVE monitors. Model performance for ST N sites shows a negati ve bias throughout the year that fall within the model cr iteria for both bias and error.

Figure 4-4. CENRAP model performance (factiona l bias and error) of the Typ02f modeling scenario for organic carbon (OC). The 12 sy mbols for each monitor represent monthly average model performance for the year 2002, averaging all monitors in the CENRAP region. Solid lines represent CENR AP modeling goals and criteria.

4.7.4.4 Model Performance for Elemental Carbon (EC)

Figure 4-5 shows the monthly EC fractional error and bias for the STN and IMPROVE monitoring networks as well as the proposed perf ormance goals and criteria. Model performance for EC falls within the proposed performance goals . Fractional bias for the STN sites is small

with a fractional error around 50%. There is a la rge model underprediction during the summer at

A-37the IMPROVE sites. However, EC concentrat ions at these sites are low putting the model performance within the goals for low concentrations.

Figure 4-5. CENRAP model performance (factiona l bias and error) of the Typ02f modeling scenario for elemental carbon (EC). The 12 symbols for each monitor represent monthly average model performance for the year 2002, averaging all monitors in the CENRAP region. Solid lines represent CENR AP modeling goals and criteria.

4.7.4.5 Model Performance for Soil

Figure 4-6 shows the monthly soil fractional error and bias for the monitoring network as well as the proposed performance goals an d criteria. Model performance for the winter months is poor with large overpredictions of soil concentrations . The summer months are within the goals for both fractional bias and error with performance gett ing worse in the fall and spring. This may be

due to local effects near the monitor and diffi culties in capturing emissions accurately in the inventory. This is an area of c oncern, especially in areas where so il contributes significantly to visibility impairment.

A-38Figure 4-6. CENRAP model performance (factiona l bias and error) of the Typ02f modeling

scenario for soil. The 12 symbols for each monitor re present monthly average model performance for the year 2002, averaging all monitors in the CENRAP region. Solid lines represent CENRAP m odeling goals and criteria.

4.7.4.6 Model Performance for Course Particulate Material (CM)

Figure 4-7 shows the monthly CM fractional error and bias for IMPROVE and monitoring network as well as the proposed performance goals and criteria. Model performance is poor with large underpredictions of CM concentrations thro ughout the year. This may be due to localized

emissions near the monitor and difficulties in capturing these emissions accurately in the inventory. This is an area of c oncern, especially in areas where CM contributes significantly to visibility impairment.

A-39Figure 4-7. CENRAP model performance (factiona l bias and error) of the Typ02f modeling scenario for course par ticulate material (CM). The 12 symbols for each monitor represent monthly average model performance for the year 2002, averaging all monitors in the CENRAP region. Solid lines represent CENRAP modeling goals and criteria.

A-404.7.4.7 Model Performance for Prediction of Total Extinction

The above model performance summary includes all sites within the CENRAP. However, a model performance summary over such a divers e geographic area may mask model performance issues occurring in smaller geographic sub-regions. CENRAP also evaluated model performance in predicting total extinction on the 20% best and 20% worst days at each Class I site. Performance for the worst 20% days at the CENR AP Class I areas is generally characterized by

an underestimation bias. Performance at the Breton Island (BRET), LA, Big Bend (BIBE), TX and Guadalupe Mountains (GUMO), TX Class I areas for the worst 20 percent days is particularly poor. At GUMO, vi sibility impairment is primarily due to high soil and CM which are not well predicted by the m odel across the CENRAP area. At the BRET and BIBE sites, all components are under-predicted, leading to an under-prediction in total extinction. Model predictions at these sites are less reliable than at other CENRAP sites fo r planning purposes. In general, model performance is acceptable for SO 4 , NO 3 , OMC and EC at the Class I areas. The model was not able to accurately predict CM and soil concentrations in the CENRAP region.

In order to address this model performance issue, CENRAP invest igated the assumption that all

CM and soil are natural a nd their concentrations remain constant for future projections as well as assuming that only a portion of the soil was from natural sources. Results of this sensitivity analysis showed that these vari ous projections of CM and soil had little effect on visibility predictions at the CENRAP class I areas. See section 5.5.1 of the CENRAP TSD for results of this sensitivity analysis.

Within the state of Arkansas, the Caney Creek Wilderness Area and Upper Buffalo Wilderness Area are the only Class I areas. Model perfor mance at predicting total extinction at Caney Creek and Upper Buffalo during the worst 20% and best 20% days are shown in Figures 4-8 and 4-9. On most of the worst 20 % days at Caney Creek, total extinction is dominated by sulfate

extinction with some extinction due to OMC. On four of the worst 20% days extinction is dominated by nitrate. Sulfate is underestimate d and results in an unde r-prediction (-33% bias)

on total extinction. There is an overestimate of extinction (+44% bias) on the 20% best days due to an over-prediction of NO 3 . For Upper Buffalo, the worst 20% days are dominated by sulfate extinction, however two of these worst days are dominated by nitrate. Sulfate and OMC underprediction results in an under-p rediction (-40%) on total extinc tion. Overall performance is good on the 20% best days at Upper Buffalo, however there are large over-predictions of nitrate on some days.

A-41Figure 4-8. Daily extinction model performance at Caney Creek Wilderness Area AR for the worst (top) and best (bottom) 20% days during 2002.

A-42Figure 4-9. Daily extinction model performance at Upper Buffalo Wilderness Area, AR for the worst (top) and best (bottom) 20% days during 2002.

A-43

### Chapter 5: 2018 Future Year Modeling

5.1 2018 M ODEL S IMULATIONS

The 2018 future-year base case scenar io is referred to as “2018 Base Case” or “Base18G”. The purpose of the Base18G scenario is to simulate the air quality represen tative of conditions in future year 2018 with respect to sources of crit eria and particulate matte r air pollutants, taking into consideration growth and controls. Modeli ng results based on this emission inventory are used to define the future year ambient air quality and vi sibility metrics.

Input data used for the 2018 model simulations consisted of the same meteorology as for the

2002 Base Case and the Base18 emission invent ories described under the Emissions Modeling section (Section 3). The setup of the CMAQ mo del (including science options, run scripts, simulation periods, and ancillary data) for the 201 8 cases was identical to that used in the Typ02G modeling.

The purpose of modeling 2018 visibility is to compare the 2018 visibility predictions to the 2002 typical-year visibility modeli ng results and compare 2018 visibili ty predictions to the URP goal for 2018, as discussed below. Some improvement s in visibility by 2018 are expected because of reductions in emissions due to currently planne d regulations and techno logy improvements. The methodology used by CENRAP in developing visi bility projections for 2018 and described below is consistent with EPA guidance.

5.2 V ISIBILITY P ROJECTIONS

The Regional Haze Rule (RHR) goals include ac hieving natural visibility conditions at 156 federally mandated Class I areas by 2064. In more specific terms, that RHR goal is defined as (1) visibility improvement toward natural conditions for the 20% of days that have the worst visibility (termed “20% worst” visibility days) and (2) no worsening in vi sibility for the 20% of days that have the best visibility (“20% best” visibility days). One component of the states’ demonstration to EPA that they are making r easonable progress toward this 2064 goal is the comparison of modeled visibility projections for the first milestone year of 2018 with what is termed a uniform rate of progress (URP) goal. As explained in detail in Section 2, the 2018 URP goal is obtained by constructing a “linear glide path” (in deciviews) th at has at one end the observed visibility conditions during the mandated five-year ( 2000-2004) baseline period and at the other end natural visibility c onditions in 2064; the visibility value that occurs on the glide path at year 2018 is the URP goal.

CENRAP has made 2018 visibility projecti ons using Typ02G and Base18G CMAQ 36-km modeling results following EPA guidance 45 that recommends applying the modeling results in a relative sense to project future-year visibility conditions. Projections are made using relative

45 US EPA, 2006. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze, US EPA, Office of Air Quality and Planning Standards, EPA-454/B07-002, EPA, April 2007, (http://www.epa.gov/ttn/s cram/guidance/guide/final-03-pm-rh-guidance.pdf)

A-44response factors (RRFs), which are defined as th e ratio of the future-y ear modeling results for each component that affects visibility to the current-year modeling results. The calculated RRFs are applied to the baseline obs erved visibility conditions to project future-year observed visibility. These projections can then be used to assess the effectiveness of the simulated emission control strategies that were included in the future-year modeling. The major features of our recommended visibility projections and guidance are as follows:

• Monitoring data should be used to define current air quality.

• Monitored concentrations of PM10 are divided in to six major components; the first five are

assumed to be PM2.5 and the sixth is PM2.5-10.

o SO4 (sulfate)

o NO3 (particulate nitrate)

o OC (organic carbon)

o EC (elemental carbon)

o Soil (other fine particulate or soil)

o CM (coarse matter).

• Models are used in a relative sense to devel op RRFs between future and current predicted concentrations of each component.

• Component-specific RRFs are multiplied by curr ent monitored values to estimate future component concentrations.

• Estimates of future component concentrations are consolidated to pr ovide an estimate of future air quality.

• Future estimated air quality is compared with the goal for re gional haze to see whether the simulated control strategy would result in the goal being met.

5.2.1 Mapping Model Results to IMPROVE Measurements

Each of the six PM components of light exti nction in the revised IMPROVE mass extinction equation 46 is scaled separately. Because the modele d species do not exactly match up with the IMPROVE measured PM species, assumptions must be made to map the modeled PM species to the IMPROVE measured species for the purpose of projecting visibility improvements. Table 4-

2 of the CENRAP TSD shows the assumptions used to relate m odeled species in CMAQ Version 4.5 to the species used in the equation to estimate visibility. Some additional species (described in section 4.3.1 of the CENRAP TSD) resul ting from the modified SOA module used by

CENRAP are also included in the OC term.

5.2.2 Projecting Visibility Chan ges Using Modeling Results

RRFs are calculated as the ratio of the 2018 mode ling results to the 2002 modeling results, and are specific to each Class I area and each PM species. These RRFs are applied to the baseline period visibility conditions calculated from observed PM species levels to project future-year PM levels. The projected PM levels are used to estimate visibility conditions in 2018 through the

46 IMPROVE technical subcommittee fo r algorithm review, 2006. Revised IMPROVE Algorithm for Estimating Light Extinction from Particle Speciation Data. ( http://vista.cira.colostate.edu/improve/Publications /GrayLit/019_RevisedIMPROVEeq/RevisedIMPROVEAlgorith

m3.doc )

A-45revised IMPROVE equation. The following six steps found in the modeling guidance 47
summarize the general procedure used to project fu ture-year visibility fo r the 20% best and 20% worst visibility days:

1) For each Class I area, rank visibility (in d eciviews) on each day with observed speciated

PM data for each of the 5 years of the base period. 2) For each of the 5 years comprising the base pe riod, calculate the mean deciviews for the 20% worst and 20% best visibility days. For each Class I area, calculate the 5 year mean deciviews for the worst and best days from the 5 year-specific values. 3) Use an air quality model to simulate base pe riod emissions and future emissions. Use the resulting information to develop relativ e response factors fo r each component of particulate matter identified in the IMPROVE equation. 4) Multiply the relative response factors times the measured species concentration data during the base period (for the 20% best and worst days). This results in daily future year species concentrations data. 5) Using the results in Step 4 and the IMPR OVE algorithm calculate the future daily extinction coefficients for the 20% best and worst visibility days in each of the five base

years. 6) Calculate daily deciview values (from total daily extinction) and then compute the future average mean deciviews for the best and wors t days of each year. Then average the 5 years together to get the final future mean d eciview value for the best and worst days.

The six steps listed above from national EPA modeling guidance for regional haze were followed by CENRAP to estimate projected future visibili ty conditions. These methods were appropriate at the time the modeling was performed.

5.3 R EASONABLE P ROGRESS G OAL AND P ATH TO N ATURAL C ONDITIONS

A linear URP from the Baseline Conditions in 2004 to Natural Conditions in 2064 is assumed, and the value on the glide path at 2018 is the presumptive URP visibility target that the modeled 2018 projections are compared agains t to judge progress. The estimat ed visibility impairment in 2018 is less than the calculated URP for 2018 (S ection 2). The URP acts as a benchmark for evaluating the reasonable progress toward s reaching natural conditions by 2064.

In determining reasonable progress, section 169A (g) of the Clean Air Ac t requires that four

factors be considered:

• Cost of compliance

• Time necessary for compliance

• Energy and non-air quality environm ental impacts of compliance

• Remaining useful life of existing sources that contribute to visibility impairment. Table 5-1 and figures 5-1 and 5-2 compares the URP using the natural conditions described in

section 2 to the modeled visibili ty conditions in 2018 for each Class I area. For Caney Creek, the

47 US EPA, 2006. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze, US EPA, Office of Air Quality and Planning Standards, EPA-454/B07-002, EPA, April 2007, (http://www.epa.gov/ttn/s cram/guidance/guide/final-03-pm-rh-guidance.pdf)

A-46baseline visibility (2002-2004) is 26.36 dv, and the estimated 2018 URP is 22.91dv. The modeling predicts a visibility improvement of 3.88dv by 2018, compared to the URP improvement of 3.45dv by 2018. For Upper Buffalo, the baseline visibility (2002-2004) is 26.27 dv, and the estimated 2018 URP is 22.84 dv. The mode ling predicts a visibility improvement of 3.75 dv, compared to the URP improvement of 3.43 dv by 2018. Achieving the 2018 URP point is not a requirement of the RHR SIPs, but it serves as a benchm ark to compare progress toward natural visibility conditions in 2064 and is designed to help states in selecting their 2018 RPGs. As stated in EPA Guidance for Setting Reas onable Progress Goals Under the Regional Haze Program 48 , “The glidepath is not a presumptive target, and States may establish a RPG that provides for greater, lesser, or eq uivalent improvement as that de scribed by the glidepath.”

ADEQ adopted the modeled 2018 visibility c onditions as the Reasonable Progress Goals for Caney Creek and Upper Buffalo Class I areas . We are proposing to disapprove Arkansas’s Reasonable Progress Goals because the State did not establish the RPGs for Caney Creek and Upper Buffalo in accordance with the requirements of the RH Ru le. Section 169A(g)(1) of the CAA and section 51.308(d)(1)(i )(A) of the RH Rule re quire states to take into account certain factors in establishing its reas onable progress goals and to demonstrate how those factors were taken into consideration in selecting the goals. ADEQ did not do so. As a result, ADEQ’s RH SIP fails to ensure reasonable progress toward m eeting the national visibil ity goal. As discussed in the following chapter, projected improvements in visibility conditions in 2018 at Caney Creek and Upper Buffalo are primarily due to reductions of sulfate emissions in other states. Sulfate emissions in Arkansas are projected to in crease from 2002 to 2018. Arkansas sources are projected to remain significant contributors to visibility impairment in 2018, thus providing further support that additional analysis should have be performed according to the statutory factors. We note that there are at least two poi nt sources in Arkansas not subject to the BART requirements that contribute to visi bility impairment at Arkansas’ Cl ass I areas. This conclusion is based on the information in the RH SIP (a ppendix 9.2B) indicating that these sources have predicted visibility impacts ex ceeding the 0.5 dv threshold used by ADEQ to determine whether BART sources contribute to visibility impairment . These two sources are potential candidates

for emissions controls under reasonable progress, given thei r contribution to visibility impairment, as may be other sources in Arkans as whose visibility impact was not evaluated by ADEQ.

The modeled visibility conditions for 2018 for th e 20% best days show an improvement in visibility of 0.89 dv and 0.91dv for Caney Creek a nd Upper Buffalo, respectively, by 2018. This is consistent with the requirement of no degradati on of visibility on the best days at Class I sites.

48 US EPA, 2007, Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program, EPA, June 2007. http://www.epa. gov/ttncaaa1/t1/memoranda/reasonabl e_progress_guid 071307.pdf

A-47Table 5-1. Comparison of reasona ble progress goal to uniform ra te of progress for 2018 (total extinction and deciviews)

Caney Creek Upper Buffalo Change by 2018 (reasonable progress goal) -3.88 dv -3.75 dv Change by 2018 at uniform rate of progress -3.45 dv -3.43 dv Projected rate of change (2004-2018) -0.32 dv/yr -0.17 dv/yr Change needed to reach natural conditions -14.78 dv -14.70 dv

Figure 5-1. Projections of vi sibility impairment for 20% worst days at Caney Creek

A-48Figure 5-2. Projections of visibility impairment for 20% worst days at Upper Buffalo

Figures 5-3 and 5-4 show the differences in model results of total extinction between the Base18g and Typ02g model predictions, including th e contributions from each component specie of the IMPROVE algorithm. On most days, visi bility improvements are due to reductions in sulfate. A few days exhibit differences in n itrate concentrations being the most significant contribution to improved visibility.

A-49Figure 5-3. Differences in modeled total ex tinction (Bext) between Base18G and Typ02g for 20% worst days at Caney Creek Wilderness Area.

Figure 5-4. Differences in modeled total ex tinction (Bext) between Base18G and Typ02g for 20% worst days at Upper Buffalo Wilderness Area.

A-50As discussed in the following chapter on source apportionment, visibility impairment at Caney Creek Wilderness Area and Upper Buffalo Wilderne ss Area is due to emissions and transport

from outside of Arkansas as well as in state sources.

A-51

### Chapter 6: Source Apportionment Modeling

6.1 I NTRODUCTION

Visibility impairment in Class I areas is the re sult of local air pollution as well as transport of regional pollution across long distance s. The relative contributions to visibility impairment from each source region and category is needed to deve lop effective control strategies to improve visibility. CENRAP used CAMx Version 4.40 with its Particulate Source Apportionment Technology (PSAT) tool to provide source a pportionment by geographic regions and major source category. CAMx was run with similar opt ions and inputs as the CAMQ modeling with

both the 2002 baseline and 2018 future case emi ssion inventories (Base F). The CAMX model selection and performance are disc ussed briefly in section 3.7 of this document and details of the CAMx model configuration used by CENRAP can be found in the CENRAP TSD and the Modeling Protocol. PSAT uses reactive tracers th at operate in parallel to the CAMx host model using the same emissions, transport, chemical transformation and deposition rates as the host

model to account for the contributions of user specified source regions and categories to PM concentrations throughout the modeling domain. Details on the formulation of the CAMx PSAT source apportionment can be found in the CAMx user’s guidance. 49 The CAMx PSAT analysis has been tested and evaluated agai nst other apportionment techniques. 50

, 51 The goals of the

PSAT assessment are to evaluate the contributions of different geographi c regions and source categories to visibility impair ment at Class I areas in 2002 an d the projected 2018 case in order to identify those regions and s ource categories that, if contro lled, would produce the greatest improvements in visibility.

CENRAP defined 30 geographical source regions (Fig 5-1) consisting of CENRAP and nearby states, with Texas divided into 3 regions, the remainder of the we stern and eastern United States, the Gulf of Mexico, Canada and Mexico. Six source categories (eleva ted point sources; lowlevel point sources, on-road mobile, non-road mobile, area and natural or non-anthropogenic sources) were tracked separately. The CENRAP PSAT 2002 and 2018 applica tions used three of the PSAT families of tracers: 1) sulfate, 2) nitrate and ammonium, and 3) secondary organic aerosols (SOA). SOA was portioned into an anthropogenic (SOAA) and biogenic (SOAB) components. Contributions for the 20% worst an d 20% best days at e ach CENRAP and nearby

Class I area were extracted from the PSAT result s. The original IMPROVE equation was used

to calculate extinction coefficients from modele d concentrations. Modeling performance is poor

for soil and coarse material. As discussed in Section 3 of this document, results of various projections of CM and soil had littl e effect on visibility predictions at the CENRAP class I areas.

Extinction due to soil and coarse material ch anges very little between 2002 and 2018. A PSAT Visualization Tool was developed that can be used by States, Tr ibes and others to generate displays of the contributions of source regions and categories to visibility impairment for the

49 “User’s Guide Comprehensive Air Quality Model W ith Extensions (CAMx) Version 4.30.” ENVIRON International Corporation, Novato, Calif ornia, 2006 (available at www.camx.com).

50 Morris, R.E., G.Y., C.E., G.W., B. K. 2005. “Recent Advances in On e-Atmospheric Modeling Using the Comprehensive Air-quality Model with Extensions.” Presented at the 98th Annual Air and Waste Management Conference, Minneapolis, MN. June.

51 Yarwood, G., R.E. Morris, G. Wilson. 2004. “Particulate Matter Source Apportionment Technology (PSAT) in the CAMx Photochemical Grid Model." Presented at the ITM 27th NATO Conference- Banff Centre, Canada, October. (http://www.camx.com/publ/pdfs/___Yarwood_ITM_paper.pdf)

A-52average of the worst 20 percent and best 20 perc ent days at each CENRAP and nearby Class I

areas. 52 The 2002 projected results apply the 2002 PSAT modeled source apportionment to the

observed 2000-2004 Baseline extincti on keeping the relative contri butions of source groups to each PM species (e.g., SO4, NO3, etc.) the same av eraged across the 2002 worst 20 percent days but scaling their magnitudes up or down base d on the ratio of the 2000-2004 Baseline to the 2002 modeling results. Similarly, the 2018 projected results use the relative contributions of the 2018 PSAT results from each source group and scales them according to the differences in the 2018 projected PM species to th e 2018 modeled PM species for the average of the worst 20 percent days. EPA believes the selection and application of CAMx for source apportionment analysis is appropriate.

Figure 6-1. Source Regions used in CAMx PSAT PM source apportionment modeling

6.2 S OURCE A PPORTIONMENT R ESULTS AT C ANEY C REEK W ILDERNESS A REA

Tables 6-1 and 6-2 show the modeled contributions to total extinction for each source category and species for 2002 and 2018, respectively. Fi gures 6-2, 6-3, 6-4, and 6-5 show the geographical source apportionment by source cate gory and species for the 20% worst days in 2002 and 2018. Visibility impairment at the Cane y Creek Wilderness Area site in 2002 on the worst 20% days is largely due to sulfate from point sources that cont ributes over half (75.1 Mm

1 ) of the total extinction of 133.93 Mm -1 . The largest contributions of sulfate come from Texas (

11.55 Mm -1 from all source categories) and the eastern United States (17.98 Mm -1 ). Overall, the largest source region contributions to visibility impairment in 2002 are from the eastern United States (19.16 Mm -1 ) , Texas (14.89 Mm -1 )and Arkansas (13.57 Mm -1 ).

52 available at http://www.cenrap.org/html/projects.php

A-53In 2018, Arkansas sources contribute the most to visibility impairment at Caney Creek, as large reductions in impairment from point sources in East Texas and the eastern U.S. occur while

sulfate emissions increase in Arkansas. The 2018 projection shows the to tal extinction at Caney

Creek Wilderness Area for the worst 20 % days is estimated to be 85.84 Mm -1 , a reduction of approximately 36%. Reductions of sulfate emissi ons from point sources in Texas, the eastern United States, Indiana, and Ohio account for a decrease of 24.41 Mm -1 in total light extinction, approximately half of the to tal reduction betwee n 2002 and 2018. Even with such large reductions in SO4 from point sources in 2018, extinc tion due to point sources is still the highest contributor to visibility impairment on the worst 20% days, accounting for over half of the total extinction. Visibility impairment from all Arkansas sources decreases 2.32 Mm -1 , almost entirely

due to reductions from mobile sources. Total reductions in mob ile sources of NO3 contribute a

decrease in total extinct ion of approximately 9 Mm -1 . There is an under-prediction bias in the model that must be considered when examining s ource apportionment results for sulfate. Use of a 12km resolution modeling grid in CAMX redu ced the summertime sulfate bias but required large computational expense. The use of higher resolution modeling should be reconsidered in future modeling efforts.

Table 6-1. Projected light exti nction for 20% worst days at Caney Creek Wilderness Area in 2002 (Mm -1 )

Total 1 Point Natural On-Road Non-Road Area

SO4 87.05 75.10 0.09 1.19 1.70 5.66

NO3 13.78 4.06 0.64 4.70 2.45 1.37

POA 10.50 1.29 1.33 0.46 1.34 5.32

EC 4.80 0.19 0.33 0.86 1.79 1.40

SOIL 1.12 0.19 0.01 0.01 0.01 0.87

CM 3.73 0.21 0.04 0.03 0.02 3.19

Sum 133.93 81.04 2.45 7.26 7.31 17.81

1 Totals include contributions from boundary conditions and secondary organic matter

Table 6-2. Projected light exti nction for 20% worst days at Caney Creek Wilderness Area in 2018 (Mm -1 )

Total 1 Point Natural On-Road Non-Road Area

SO4 48.95 39.83 0.07 0.12 0.44 5.31

NO3 7.57 2.84 0.53 0.97 1.33 1.37

POA 9.93 1.76 1.18 0.14 1.03 5.09

EC 3.17 0.24 0.30 0.16 0.94 1.31

SOIL 1.29 0.35 0.01 0.01 0.01 0.87

CM 3.58 0.24 0.04 0.03 0.01 3.02

Sum 85.84 45.27 2.12 1.44 3.76 16.96

1 Totals include contributions from boundary conditions and secondary organic matter

A-54Figure 6-2. Source apportionment modeling results by source re gion and source category for worst 20% days at Caney Cr eek Wilderness Area in 2002.

Figure 6-3. Source apportionment modeling results by source regi on and species for worst 20% days at Caney Creek Wilderness Area in 2002.

A-55Figure 6-4. Source apportionment modeling results by source re gion and source category for worst 20% days at Caney Cr eek Wilderness Area in 2018.

*2018 projections for Texas Point sources are divided into EGU and Non-EGU point sources

A-56Figure 6-5. Source apportionment modeling results by source regi on and species for worst 20% days at Caney Creek Wilderness Area in 2018.

6.3 S OURCE A PPORTIONMENT R ESULTS AT U PPER B UFFALO W ILDERNESS A REA

Tables 6-3 and 6-4 show the contributions to total extinction for each source category and species for 2002 and 2018, respectively. Figures 6-6, 6-7, 6-8, and 6-9 show the geographical source apportionment by source category and speci es for the 20% worst days in 2002 and 2018. Visibility impairment at the Upper Buffalo Wilderness Area site in 2002 on the worst 20% days is largely due to sulfate from point sources that contributes over half (72.17 Mm -1 ) of the total

extinction of 131.79 Mm -1 . The largest contributions of su lfate come from the eastern United States (18.56 Mm -1 ), Indiana (9.79 Mm -1 ), Illinois (8.06 Mm -1 ), and Kentucky (6.93 Mm -1 ). Overall, the largest source regi on contributions to visibility impairment in 2002 are from the eastern United States (20.00 Mm -1 ) , Arkansas (13.47 Mm -1 ), Indiana (10.20 Mm -1 ), Illinois (9.64

Mm -1 ), and Missouri (9.60 Mm -1 ).

In 2018, Arkansas sources contribute the most to vi sibility impairment at Upper Buffalo, as large reductions in impairment from point sources in In diana, Illinois, Ohio and the eastern U.S. occur

while sulfate emissions increase in Arkansas. The 2018 projection shows the total extinction at Upper Buffalo Wilderness Area for the worst 20 % days is estimated to be 86.16 Mm -1 , a reduction of approximately 35%. Reductions of sulfate emissions from point sources in the eastern United States, Indiana, Illinois, Kentucky and Ohio account fo r a decrease of 28.43 Mm -1 in total light extinction, more than 60% of the total reduction in impa irment between 2002 and 2018. Even with such large reductions in SO4 from point sources in 2018, extinction due to point sources is still the highest contributor to visibility im pairment on the worst 20% days,

A-57accounting for approximately half of the total extinction. Visibility impairment from all

Arkansas sources decreases 1.45 Mm -1 , due to reductions from mobile sources. Total reductions

in mobile sources of NO3 contribute a decrea se in total extinction of approximately 8.5 Mm -1

. There is an under-prediction bias in the model th at must be considered when examining source apportionment results for sulfate. Use of a 12k m resolution modeling grid in CAMX reduced the summertime sulfate bias but requir ed large computational expense. The use of higher resolution modeling should be reconsidered in future modeling efforts.

Table 6-3. Projected light extinc tion for 20% worst days at U pper Buffalo Wilderness Area in 2002 (Mm -1 )

Total 1 Point Natural On-Road Non-Road Area

SO4 83.18 72.17 0.08 1.15 1.67 5.24

NO3 13.30 3.93 0.61 4.14 2.71 1.23

POA 10.85 1.06 1.33 0.47 1.38 5.75

EC 4.72 0.16 0.31 0.80 1.93 1.30

SOIL 1.21 0.20 0.02 0.01 0.01 0.93

CM 6.85 0.29 0.05 0.05 0.02 6.02

Sum 131.79 77.80 2.39 6.62 7.72 20.46

1 Totals include contributions from boundary conditions and secondary organic matter

Table 6-4. Projected light extinc tion for 20% worst days at U pper Buffalo Wilderness Area in 2018 (Mm -1 )

Total 1 Point Natural On-Road Non-Road Area

SO4 45.38 37.09 0.06 0.12 0.42 4.95

NO3 9.22 3.48 0.63 1.10 1.81 1.48

POA 10.17 1.48 1.20 0.14 1.01 5.49

EC 3.07 0.21 0.28 0.15 0.99 1.21

SOIL 1.40 0.40 0.01 0.01 0.01 0.93

CM 6.53 0.36 0.05 0.04 0.02 5.65

Sum 86.16 43.02 2.24 1.57 4.25 19.71

A-581 Totals include contributions from boundary conditions and secondary organic matter

Figure 6-6. Source apportionment modeling results by source re gion and source category for worst 20% days at Upper Buff alo Wilderness Area in 2002.

Figure 6-7. Source apportionment modeling results by source regi on and species for worst 20% days at Upper Buffalo Wilderness Area in 2002.

A-59Figure 6-8. Source apportionment modeling results by source region and source category for worst 20% days Upper Buffalo Wilderness Area in 2018.

A-60* 2018 projections for Texas Point sources are divided into EGU and Non-EGU point sources Figure 6-9. Source apportionment modeling results by source regi on and species for worst 20% days at Upper Buffalo Wilderness Area in 2018.

6.4 C ONTRIBUTIONS TO VISIBILITY IMPAIRMENT AT OTHER C LASS I AREAS

CAMx PSAT results are also utilized to evaluate the impact of Arkansas emission sources in 2002 and 2018 on visibility impairment at Class I areas outside of the state. Arkansas

sources are modeled to have cont ributions to the Class I areas in Missouri (Hercules-Glades and Mingo). Outside of Arkansas and Missouri, the larg est contribution from Ar kansas sources is at

the Wichita Mountains Class I area in Oklahoma th at amounts to 2.0 % of visibility impairment in 2002 and 2.3% in 2018. We note that any ad ditional reductions that may occur due to ADEQ

refinements in BART determinations would likel y be beneficial in re ducing Arkansas sources impacts on Class I areas with Arka nsas and outside of Arkansas.

A-61Table 6-5. Percent contribution to total visibility impairment at Class I areas on 20% worst days from Arkansas Sources (contributio ns less than 1% are excluded)

Class I area 2002 2018

UPBU1 10.2% 14.0%

CACR1 10.1% 13.1%

HEGL1 5.9% 7.6%

MING1 3.3% 4.4%

WIMO1 2.0% 2.3%

MACA1 1.0% 1.8%

BOND1 1.2% 1.5%

BRET1 1.1% 1.3%

CADI1 0.9% 1.2%

A-62

### Chapter 7: BART Determination

7.1 I NTRODUCTION

The Clean Air Act establishes the national goal of eliminating man-made visibility impairment from all Class 1 areas. As a part of the plan for achieving this goal Se ction 169A(b)(2)(A) of the act requires certain major stati onary sources in existence betw een 1962 and 1977 to be reviewed for Best Available Retrofit Technology (BART). Arkansas DEQ identified facilities in the affected source categories with potential emissions over the BART rule threshold of 250 tons per year for any visibility-i mpairing pollutant (NOx, SO 2 , and PM) from any unit that was in existence on August 7, 1977 and began operatio n after August 7, 1962. Table 7-1 shows all facilities within Arkansas that have units that were determined to be BART-eligible by ADEQ.

A-63Table 7-1. Facilities with BA RT-eligible units in Arkansas

BART source category Facility name County Number

of units

Fossil fuel-fired boilers

of more than 250 MMBTU/hr heat input AEP Flint Creek Power Plant Benton 1

Arkansas Electric Cooperative Corporation Carl E. Bailey Generating

Station Woodruff 1

Arkansas Electric Cooperative Corporation John L. McClellan Generating Station Ouachita 1

Entergy Lake Catherine Plant Hot Spring 1

Entergy Robert E. Ritchie Plant Phillips 1

Entergy White Bluff Plant Jefferson 3

Kraft pulp mills Domtar Ashdown Mill Little River 2

Delta Natural Kraft Jefferson 1

Evergreen Packaging/International Paper Jefferson 1

Georgia Pacific Crossett Mill Ashley 1

Green Bay Packaging Conway 1

Potlach Forest Products/Clearwater Paper Corporation Cypress Bend Mill Desha 1

Petroleum refineries Lion Oil Company Union 1

Sulfur recovery plant Albermarle Corporation South Plant Columbia 1

Sintering plants Big River Indus tries- Arkalite Crittenden 1

Chemical processing plants Albermarle Corporation S outh Plant Columbia 2

Future Fuels/Eastman Chemical Company Independence 3

El Dorado Chemical Company Union 3

We note that in Chapter 15 of APC&E Regulatio n 19, contained in the submittal we received on September 23, 2008, and as revised by the submittal we received on August 3, 2010, ADEQ identified one more unit (not listed in Table 7- 1), the 6A Boiler at Geor gia-Pacific Crossett Mill, as being BART eligible. ADEQ did not identify th e 6A Boiler as BART eligible in the RH SIP narrative. Appendix 9.1A states the 6A Bo iler began operation prior to August 7, 1962, and

therefore falls out of the eligibility criteria. On September 27, 2011, ADEQ submitted supplemental information clarifying that the Georgia-Pacific Crosse tt Mill provided ADEQ a copy of a boiler inspection report for the 6A Boiler , which states that the inspection of the new boiler took place on August 6, 1962, to determine if the boiler complied with the State and

A-64American Society of Mechanical Engineers (ASME) codes. 53 However, ADEQ stated it cannot say with certainty whether the 6A boiler was in operation as of August 6, 1962, or at a later

date. 54 Since there is not sufficient information to determine the date of start of operations of the 6A Boiler, we cannot make the determination that the boiler is not BART eligible. Therefore, we are proposing to find that the 6A Boiler at the Georgia-Pacific Crossett Mi ll is BART eligible

In the RH SIP, ADEQ identified one unit (t he No. 4 recovery boiler) at International Paper/ Evergreen Packaging as BART eligible (shown in Table 7-1 ADEQ included two other units (the No. 1 and 2 Power Boilers) at Inte rnational Paper/Evergreen Packaging in its evaluation to determine what sources are subject to BART The Intern ational Paper/Evergreen Packaging No. 1 and No. 2 Power Boilers are not BART-eligible because they were constructed and were in operation prior to August 7, 1962. 55 We agree that the No. 1 and 2 Power Boilers at International Paper/Evergreen Packaging are not BART-eligible

.

In the RH SIP, ADEQ did not identify Boile rs SN-301A and SN-302A at the Great Lakes Chemical Plant as BART-eligible, but since these units were at one point believed to be BART eligible, ADEQ included these units in its evalua tion to determine what sources are subject to BART. EPA reviewed the federally enforceable operating permit for the Boilers SN-301A and

SN-302A at the Great Lakes Chemical Plant and determined that Boilers SN-301A and SN-302A are not BART eligible because th ey are boilers with a heat input rating less than 250 MMBtu/hr and are not integral to the proce ss, as the permit states they su pply heat to the process. The BART Guidelines provide that an individual fo ssil fuel boiler smaller than 250 MMBtu/hr that does not fall into source Category 1 (i.e., Fossil-fu el fired steam electric plants of more than 250 MMBtu/hr heat input), falls into one of the source categories for BA RT eligibility only if it is an integral part of a process descrip tion at a plant. If th e boiler is integral to the process description at a plant, it falls into the source category of the process which it serves. In general, if the boiler serves the process in any way beyond contributing h eat, it is integral to the process. Based on information in the current operating air permit for the Great Lakes Chemical Plant, we agree that

Boilers SN-301A and SN-302A are not BART-eligible. 56

As discussed above, there is a discrepancy between the BART eligible sour ces identified in the RH SIP narrative, and those identified in th e State’s RH Rule. Because ADEQ submitted supplemental information on September 27, 2011, clar ifying that it did not know with certainty the startup date of operations of the 6A Boile r at the Georgia-Pacific Crossett Mill, we are proposing to find that the 6A Boiler is BART eligible. We are proposing to approve ADEQ’s identification of the remaining BART-eligible sources.

Pursuant to federal regulations, states have th e option of exempting a BA RT-eligible source from the BART requirements based on dispersion m odeling demonstrating that the source cannot reasonably be anticipated to cause or contribute to visibility im pairment in a Class I area.

53 A copy of the boiler inspection report for the 6A Boile r at the Georgia-Pacific Crossett Mill can be found in the docket for this proposed rulemaking.

54 The BART Guidelines define “in operation” as “engage d in activity related to the primary design function of the

source.”

55 Letter from ADEQ (William Swafford) to International Paper Company (Kelly Bryant), RE: BART eligibility of Power Boilers 1 and 2, SN-13 and SN-15, August 30, 2006

56 The current air permit for the Great La kes Chemical Plant can be found in the docket for this rulemaking.

A-65According to 40 CFR Part 51, Appendix Y 57 , a BART eligible s ource is considered to “contribute” to visibility impairme nt in a Class I area if the m odeled 98th percentile change in deciviews is equal to or greater than the “contribution threshold.” The original meteorological databases generated by CENRAP did not include observations as EPA guidance recommends. Therefore, in their evaluation of sources for BART purposes, states used the 1st high values (i.e., maximum value) of modeled visibility im pacts instead of the 8th high values (98 th percentile value). The use of the 1st high modeled values was agreed to by EPA, representatives of the Federal Land Managers, and CENRAP stakeholde rs. Any BART-eligible source determined to cause or contribute to visibility impairment in any Class I area is subject to BART. The EPA

BART Guidelines state that the contribution threshold used to dete rmine whether a source “contributes” to visibility impairment should not be higher than 0.5 deciview. States have the option to establish alternative, lower thresholds . ADEQ applied the 0.5 deci view threshold to the BART-eligible sources. To determine whet her a source exceeds the BART contribution threshold, EPA recommends use of the CA LMET/CALPUFF modeling system; the main components of this modeling system are CALMET (a diagnostic three-dimensional meteorological model), CALPUFF (an air qual ity dispersion model), and CALPOST (a postprocessing package). CENRAP developed a BART modeling protocol in 2005 with input from EPA Region 6, 7 and representa tives of the Federal Land Mana gers. Table 7-2 shows those facilities that were granted waivers from BA RT based on dispersion modeling results.

Table 7-2. Facilities granted waivers from BART based on ADEQ screening modeling.

Facility name Maximum impacted Class

I area Maximum visibility impact

Delta Natural Kraft HEGL 0.02

Big River Industries- Arkalite MING 0.09

Entergy Robert E. Ritchie Plant SIPS 0.10

Clearwater Paper Corporati on Cypress Bend Mill CACR 0.12

Green Bay Packaging CACR 0.17

Lion Oil Company HEGL 0.19

Albermarle Corporation South Plant CACR 0.30

El Dorado Chemical Company CACR 0.31

Future Fuels Chemical Company HEGL 0.71

In Appendix 9.2B of the RH SIP, ADEQ provide d screening modeling re sults for all sources identified in the RH SIP as BART-eligible so urces, as well as for Boilers SN-301A and SN302A at Great Lakes Chemical and the No. 1 and No. 2 Power Boilers at International Paper/Evergreen Packaging, and the 6A and 9A Bo ilers at the Georgia-P acific Crossett Mill (as discussed above). Our evaluation of these results s howed that four BART e ligible facilities that

A-66were not identified as subject-to-BART by ADEQ had modeled visibility impacts that exceed the

chosen threshold of 0.5 dv.

• ADEQ included the No. 1 and No. 2 Power Bo ilers at Internati onal Paper/Evergreen Packaging and Boilers SN-301A and SN-302A at Great Lakes Chemical in its modeling evaluation to determine what sources are subj ect to BART. As discussed above, we are proposing to approve ADEQ’s identification of these two sources as not BART-eligible and not subject to BART.

• The original meteorologi cal databases generated by CENRAP did not include observations as EPA guidance recommends. Ther efore, in their eval uation of sources for BART purposes, states used the 1st high values (i.e., maximum value) of modeled visibility impacts instead of the 8th high values (98 th percentile value). The use of the 1st high modeled values was agreed to by EP A, representatives of the Federal Land Managers, and CENRAP stakeholders. ADE Q’s modeling shows that Future Fuels/

Eastman Chemical has a mode led visibility impact of 0.711 dv at Hercules-Glade. Further examination of the modeling results re veal that only one day of the three years

modeled exceeds the threshold value at any Class I area. Si nce only one day is projected above the threshold, we believe it is very unlikely that a refined modeling approach, using updated meteorological data whic h would allow for the use of the 98 th percentile modeled visibility impact rather than the maximum impact, would show modeled impacts above the threshold. Therefore, we are proposing that this facility is not subject-toBART.

• The visibility modeling provided in Appendix 9.2B of the Arkansas RH SIP shows that the 9A Boiler of the Georgia- Pacific Crossett Mill has visi bility impacts exceeding the 0.5 dv contribution threshold, w ith a visibility impact above 1 dv at Caney Creek and Hercules-Glade. EPA also reviewed ADEQ’ s revised modeling for this source, which looked at the visibility impacts of both the 6A and 9A Boilers at the Georgia-Pacific Crossett Mill. Using updated emission rate s, the revised modeling showed projected visibility impacts of the tw o boilers combines below the 0.5 dv threshold. For SO 2 , the

modeled emission rate for the 9A Boiler was re vised from the permit limit of 613.3 lbs/hr to 306.7 lbs/hr, based on the assumption that the scrubber in use will reduce emissions by

50%. 58 Georgia-Pacific also states that stack test data was used to give a more accurate

emission factor for SO 2 emissions and that this factor was used with worst-case actual monthly fuel usage to determine an emission rate. 59 For NOx, the facility proposed good combustion practices and reduced the mode led emissions of the 9A Boiler to 244.4

lbs/hr. 60 We note that the current permit limits for the 9A Boiler for SO 2 is 502.5 lb/hr

58 Letter from Georgia-Pacific (James Cutbirth) to ADEQ (Michael Watt), RE : Proposed Best Available Retrofit Technology (BART) Emission Reductions from Georgia-Paci fic Corporation Crossett Mill 9A Boiler, November 3,

2006

59 Two letters from Georgia-Pacific (J ames Cutbirth) to ADEQ (Mike Bate s), RE: Georgia-Pacific LLC CrossettPaper BART Emissions AFIN: 02-00013 Permit Number: 597-AOP-R8, Janurary 26, 2007; Letter from GeorgiaPacific (Karen Dickinson) to ADEQ (M ary Pettyjohn) , RE: Georgia-Pacific Corporation Crossett BART Emissions

AFIN: 02-00013 Permit Number: 597-AOP-R8, December 11, 2006

60 Letter from Georgia-Pacific (James Cu tbirth) to ADEQ (Michael Watt), November 3, 2006; ; RE: Proposed Best Available Retrofit Technology (BART) Emission Reductions from Georgia-Pacific Corporation Crossett Mill 9A

Boiler

A-67and 218 lb/hr for NOx. 61 From the data provided, it is unclear if the modeled emissions are representative of the actual maximum 24 hour emissions from the highest emitting day over the modeled period. There is no s upporting technical anal ysis explaining how

the stack test data were used to estima te maximum emissions nor is fuel usage information provided for the modeled period. We are proposing to disapprove ADEQ’s determination that Georgia Pacific Crossett Mi ll’s 6A and 9A boilers are not subject-toBART because ADEQ has not modeled the visi bility impact of the 6A and 9A Boilers using acceptable estimates of maximum 24 hour emissions, and as a result we do not

know if the boilers have a combined visi bility impact below the 0.5 dv contribution threshold or not. Based on the original mode led visibility impact resulting when permit

allowables for SO 2 are modeled (Appendix 9.2B of th e RH SIP), the two boilers are subject-to-BART. and require a full BART analysis.

We are proposing to approve ADEQ’s identification of subject-to-BART sources, except for ADEQ’s determination that the Ge orgia-Pacific Crossett Mill 6A and 9A Boilers are not subject

to BART.

7.2 BART D ISPERSION M ODELING P ROTOCOL

7.2.1 Overview of Modeling Approach

In the CENRAP region, the states themselves took the lead in the BART modeling, but CENRAP provided modeling guidance 62 and readily available mode ling data bases that were used by states and/or source opera tors in many cases to conduct th eir analyses. In development of these guidelines, CENRAP fo llowed the EPA BART guidelines 63 and the applicable CALMET/CALPUFF modeling guidance 64 in effect at the time the analysis was conducted. ADEQ performed source-specific dispersion m odeling using CALPUFF to determine if the

source causes or contributes to visibi lity impairment in any Class I area.

Using the procedures in the modeling guideli nes, ADEQ conducted initial subject-to-BART screening to determine whether or not the sour ce contributes significantly (above the 0.5 dv

61 ADEQ Operating Air Permit, Permit No. : 0597-AOP-RI 3, issued to: Georgia-P acific LLC - Crossett Paper Operations, August 4, 2011

62 CENRAP BART Modeling Guidelines, Tesche, T. W., Dennis E. McNally, and George J. Schewe, Alpine Geophysics, LLC, 2005.

63 EPA. 2005. “Regional Haze Regulations and Guidelines for Best Available Technology (BART) Determinations”.Fed. Reg./Vol. 70, No. 128/Wed. July 6, 2005, Rules and Regulations, pp. 39104-39172. 40 CFR Part 51, FRL7925-9, RIN AJ31.

64 Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeling Long-Range Transport and Impacts on Regional Visibility, EPA- 454/R-98-019, IWAQM, 1998; “Federal Land Managers’ Air Quality Related Values Workgroup (FLAG)”: Phase I Report, FLAG, USDI – National Park Service, Air Resources Division, Denver, CO., 2000. http://www.nature.nps.gov/air/Pubs/pdf/flag/FlagFinal.pdf ;. EPA, 2003. “Revisions to the Guideline on Air Quality Models: Adoption of a Preferred Long Range Transport Model and Other Resources”; Final Rule. Fed. Reg./Vol. 68, No. 72/Tuesday April 15, 2003/Rules and Regulations. 40 CFR51.

A-68threshold) to visibility impairment at any Class I area. Visibility impairment is calculated in relation to the natural background visibility conditions described in the GENV. ADEQ’s BART Modeling Protocol (Appendix 9.2A of the AR RH SIP ) is consistent with the CENRAP BART screening modeling protocol. We note that th e original meteorological databases generated by CENRAP and used by ADEQ did not include ob servations as EPA guidance recommends, therefore sources were evaluated using the 1st Hi gh values instead of the 8th High values. The use of the 1st High modeling values was agreed to by EPA, representatives of the Federal Land Managers, and CENRAP stakeholders. Sources modeled below 0.5 deciviews impact at any Class I area were determined to be exempt from BART. Each facility was modeled individually; evaluating the visibility impacts at the five Class I areas within 300k m of any BART-eligible source in Arkansas (Figure 7-1). Appendix 9.2B of the AR RH SIP includes the modeling files for the BART-eligible sources. ADEQ used those results, in c onjunction with other information,

to determine whether or not the individual BA RT-eligible source will be subject to BART control requirements. ADEQ notifie d those facilities that were determined to be subject-toBART (Table 7-3) and provided guidance on how to conduct the BART analyses.

ADEQ also performed post-contr ol CALPUFF modeling to determ ine the amount of visibility improvement anticipated from the controls dete rmined to be BART by the facilities and ADEQ (Appendix 9.3C of the AR RH SIP). ADEQ’s summary of each of these facilities’ BART determinations can be found in Chapter 9 of th e AR RH SIP. The BA RT analysis conducted by the facility for each subject-to-BART source is in cluded in Appendix 9.3A of the AR RH SIP.

A-69Figure 7-1. Map showing Arkansas’s BART-eligible sources and the 300 km radius buffer zones around five separate receptor s (north, south, east, west, and center) located in the following Class I areas: Upper Buffalo (UPB U), Caney Creek (CACR), Hercules Glade (HEGL), Mingo (MING), and Sipsey (SIPS)

A-70Table 7-3. Sources in Arkansas determined to be subject to BART by ADEQ

Facility Name BART

Emission Units Source Category Pollutants

Evaluated

Arkansas Electric Cooperative Corporation Carl E. Bailey Generating Station Unit 1 fossil fuel-fired

steam electric plants SO 2

NOx

PM 10 Arkansas Electric Cooperative Corporation John L. McClellan Generating Station Unit 1 fossil fuel-fired

steam electric plants SO 2

NOx

PM 10

American Electric Power Flint Creek

Power Plant Boiler No. 1 fossil fuel-fired

steam electric plants SO 2

NOx

PM 10 Entergy Lake Catherine Plant Unit 4 fossil fuel-fired

steam electric plants SO 2

NOx

PM 10 Entergy White Bluff Plant Units 1, 2, and Auxiliary Boiler fossil fuel-fired

steam electric plants SO 2

NOx

PM 10

Domtar Ashdown Mill Boilers No. 1 and

2 kraft pulp mill SO 2

NOx

PM 10

7.2.2 Model Selection and Applicability

Relevant guidance 65 states that the CALPUFF model is gene rally applicable at distances from 50

km to at least 200-300 km downwind of a source. BART screening analyses were performed

with the 5.53a/5.753 versions of CALMET/CALPU FF, which were acceptable versions at the time the modeling was performed. Visibility imp acts were modeled at Ca ney Creek Wilderness Area (CACR), Upper Buffalo Wilderness Ar ea (UPBU), Mingo National Wildlife Refuge

65 Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeling Long-Range Transport and Impacts on Regional Visibility, EPA- 454/R-98-019, IWAQM, 1998; “Federal Land Managers’ Air Quality Related Values Workgroup (FLAG)”: Phase I Report, FLAG, USDI – National Park Service, Air Resources Division, Denver, CO., 2000. http://www.nature.nps.gov/air/Pubs/pdf/flag/FlagFinal.pdf ;. EPA, 2003. “Revisions to the Guideline on Air Quality Models: Adoption of a Preferred Long Range Transport Model and Other Resources”; Final Rule. Fed. Reg./Vol. 68, No. 72/Tuesday April 15, 2003/Rules and Regulations. 40 CFR51.

A-71(MING), Hercules-Glade Wilderness Area (HEGL) , and Sipsey Wilderness Area (SIPS). These

are the closest Class I areas to th ese facilities and each facility is w ithin 300 km of at least one of

these sites.

7.2.3 Modeling Domain and Meteorology Inputs The CENRAP developed a set of three overl apping CALMET/CALPUFF modeling domains in

the central United States coveri ng the southern, central and nor thern portion of the CENRAP region (Fig. 7-1). The dimensions of the domain we re selected to include the State of interest, plus Class I areas in nearby stat es, and to provide a sufficient buffer between the Class I areas and the domain boundaries (e.g., 50 km) to assure th at CALPUFF puffs are not eliminated that may temporarily leave the domain and later reemer ge and cause visibility impacts at a Class I area. The operator/owner may elect to restrict the computational CALPUF F domain to a subset

of the CALMET domain to include the facilities of interest and only areas where significant impacts are feasible, extending at least 50 km in all directions beyond the class I areas within

300km of a source.

Figure 7-2. CENRAP CA LMET 6km meteorologi cal modeling domains

For BART screening analyses, CALPUFF modeli ng was performed by ADEQ consistent with the procedures described in section 6 of the CENRAP BART modeling guidance. 66 The CALMET modeling used terrain data from the Shuttle Radar Topography Mission (SRTM)- GTOPO 30 second (~1 km) resoluti on dataset and land use data fr om United States Geological Survey (USGS) Global Land Cover Characterizati on (GLCC) version 2.0 database at 30 second

66 CENRAP BART Modeling Guidelines, Tesche, T. W., Dennis E. McNally, and George J. Schewe, Alpine Geophysics, LLC, 2005.

A-72(~1km) resolution. CENRAP used MM5 meteorology data for the years 2001 67 , 2002 68 and

2003 69 (at 36 km resolution to create 6-km re solution CALMET meteorological fields.

Observational data was not blended with the MM5 data in the CALMET modeling, therefore sources were evaluated using the 1st High values instead of the 8th High values. The use of the 1st High modeling values was agreed to by EPA, representatives of the Federal Land Managers,

and CENRAP stakeholders. EPA recommends performing three years of CALMET/CALPUFF modeling when using MM5 data and 2001 through 2003 were the most recent three years with MM5 data available at the time this modeling was initiated. The model-ready meteorological data sets for each domain were provided by CENR AP to expedite the screening analysis and provide for a consistent approach. To evalua te impacts at each Class I area, receptor data including coordinates and eleva tions that has been developed by the National Park Service.

7.2.4 Emissions Input

According to the EPA BART Guidelines: “The emissions estimates used in the models are intended to reflect steady-state operating conditions during periods of high capacity utilization. We do not generally recommend that emission s reflecting periods of start-up, shutdown, and malfunction be used, as such emission rate s could produce higher t han normal effects than would be typical of most facilities. We reco mmend that States use the 24 hour average actual emission rate from the highest emitting day of the meteorological peri od modeled, unless this rate reflects periods start- up, shutdown, or malfunction.” 70

The CENRAP BART modeling guidance reco mmends the following prioritization for

identification of the maximum 24-hr actual emissi ons rates for the most recent 3 or 5 years, according to the following prioritization:

• Continuous Emissions Monitoring (CEM) data;

• Facility emissions tests;

• Emissions factors;

• Permit limits; or lastly,

• Potential to emit.

67 “Annual Application of MM5 for Calendar Year 2001”. McNally, D. E., Alpine Geophysics, LLC, Arvada, CO. 178 pp., 2003 (http://www.epa.gov/scra m001/reports/2001_36_mm5_summary_fin al.pdf); “Annua l Meteorological Modeling Protocol: Annual Application of the MM5 to the Continental United States”, McNally, D. E., and T. W. Tesche, 2002. (http://www.epa.gov/scram001/meteor ology/metgridmodeling/an nual_prot_1.1.pdf).

68 Meteorological modeling protocol: IDNR 2002 annual MM 5 application, Johnson, M. T., Iowa Department of Natural Resources, Air Quality Bureau, Des Moines, IA, 2003;. Iowa DNR 2002 Annual MM5 Modeling Project. Johnson, M. T., Presented at the August 11th, 2003 CENRAP Workgroup Meeting in Bloomington, Minnesota, 2003. (http://www.epa.gov/scram001/adhoc/johnson_2003.pdf)

69 Baker, K., 2005. MM5 Model Performance: Great Lakes Region: 2002-04. Ad-Hoc Meteorological Modelers Group Meeting , Lakewood, Colorado. 11 June. (http://www.epa.gov /scram001/adhoc/baker_2005 .pdf); Baker, K., M. Johnson, et al. 2005. Meteorol ogical modeling performance summary for application to PM2.5/haze/ozone modeling projects, Prepared by the Lake Michigan Air Directors Consortium and the Midwest Regional Planning Organization, Des Plaines, IL. (http://www.deq.state.va.us/export/sites/default/info/pdf/vchec/BoardBook/attachments/Attachment_8.pdf)

70 EPA. 2005. “Regional Haze Regulations and Guidelines for Best Available Technology (BART) Determinations”.Fed. Reg./Vol. 70, No. 128/Wed. July 6, 2005, Rules and Regulations, pp. 39104-39172. 40 CFR Part 51, FRL7925-9, RIN AJ31.

A-73Sources determined to be BART eligible were generally modeled using emission estimates based on the maximum 24 hour emission rates during th e 2001-2003 meteorological period modeled. In-stack continuous emission monitoring (C EM) data were used where available.

For sources determined to be subject-to-BART, A fi ve step BART analysis is required. Step 5 of the BART analysis (describ ed in the BART guidelines 71 ) requires an evalua tion of the visibility impacts or degree of visibility improvement from the use of potential BART control technology. For this visibility analysis in support of a BART determinatio n, the model should be run at the pre-control emission rates discus sed above and the post-control emission rates determined from

an evaluation of the control effectiveness of the available a nd technically f easible control technologies. The visibility improvement is asse ssed based on the modeled change in visibility impacts between the pre-control and post- control emission scenarios.

7.2.5 CALPUFF/CALMET Model Settings

The CALMET and CALPUFF model settings used by CENRAP and included in the CENRAP modeling protocol complied with our recommen dations for regulatory application of the CALPUFF modeling system in effe ct at the time of the analysis with a few exceptions. Appendix A of the CENRAP BART modeling gu idance details the CALMET settings used and the EPA default values, and Appendix B detail s the recommended CALPUFF settings used for screening analyses and the EPA default sett ings. Development of the regional CALMET meteorological fields from MM5 data for sc reening applications was conducted in NoObservations (“No-Obs”) mode. Some of the differences between the CENRAP and EPA default CALMET modeling are detailed below.

i. The EPA default assumes no MM5 data will be used (IPROG=0). In the CENRAP BART screening analysis, MM5 data were used as an initial-guess field (IPROG=14);

ii. Gridded cloud data were inferred from the MM5 relative humidity fields (ICLOUD=3);

iii. CENRAP used the hourly 36-km MM5 data to define the upper-level winds, thus the extrapolation of the surface wind data al oft was not needed (IEXTRP= -1);

iv. The beginning and ending water land use categories were changed to 55, rather than using the EPA default (999) that a ssumes no water land use categories.

ADEQ’s modeling used a constant value of 3 ppb background concentration of ammonia for the domain during the modeling period. Hourly oz one observational data provided by ADEQ over

the 2001-2003 timeframe were used to de fine background ozone concentrations.

7.3 BART D ETERMINATIONS

The third step of a BART evaluation is to pe rform the BART analysis. The BART Guidelines describe the BART analysis as consis ting of the following five basic steps:

• Step 1: Identify All Availabl e Retrofit Control Technologies,

71 70 FR 39164.

A-74• Step 2: Eliminate Technically Infeasible Options, • Step 3: Evaluate Control Effectiven ess of Remaining Control Technologies, • Step 4: Evaluate Impacts and Document the Results, and • Step 5: Evaluate Visibility Impacts.

The BART analysis includes engineering and modeling methods and procedures used to determine the appropriate controls for the subject-to-BART units to reduce the source’s contribution to pollutant concentr ations that result in visibil ity impairment in the surrounding

Class I areas. The final factor to consider under EPA's BART Guidelines is the degree of visibility improvement from the BART control options 72 . The BART guidelines recommend use of the CALPUFF air quality dispersion model to estimate the visibility improvements at each Class I area, typically within roughly a 300 km (186 mile) or larg er radius of the source, of alternative control technologies, and to compare these to each other and to the impact of the baseline, or current, source configuration.

The subject-to-BART facilities conducted modeling or relied on ADEQ post-control modeling that followed the ADEQ modeling protocol. Th e modeling analyses generally followed the BART modeling protocol developed by CENRAP. AECC (Ba iley and McClellan) 73 and AEP

Flint Creek 74 relied on post-control m odeling performed by ADEQ. Domtar contracted with Trinity consultants to perform the BART analysis for this facility. Trinity consultants utilized ADEQ modeling files, adjusting only the emissi on rates to be modeled for their post-control modeling scenario. 75 Entergy Lake Catherine and Enter gy White Bluff BART analyses were performed by ENSR Corporation and were conduct ed in a manner generally consistent with the ADEQ approach, but did deviate in some ways by using a different CALPUFF version and used

the 98 th percentile (8 th highest day) in this analysis inst ead of the maximum visibility impact. 76
The BART analyses conducted by each facility for subject-to-B ART sources are included in Appendix 9.3A of the AR RH SIP. As describe d below, we have identified several problems with the BART analyses perfor med by ADEQ.

7.3.1 Issues identified with ADEQ BART analyses.

ADEQ made BART determinations for th e subject-to-BART sources for NOx, SO 2 , and PM (when we discuss PM we are referring to pr imary PM and not secondary). We have found several problems in these BART analyses, whic h lead us to propose disapproval of some of ADEQ’s BART determinations. These problems ar e discussed in detail in our proposed action and in the accompanying TSD. Some general i ssues with the visibility impact modeling are

summarized below as well as a discussion of the vi sibility analysis performed for each source.

72 59 FR 39104, 39170 (July 6, 2005).

73 Arkansas Electric Cooperative Corpora tion Best Available Retrofit Technology Engineering Analysis prepared by Stephen Cain, October 20, 2006

74 Letter from Southern Electric Power Company (SWPECO, T. Brian Bond) to ADEQ (Mike Bates), October 26,

2006

75 Best Available Retrofit Technology Determination, Domtar Industries, Inc., Ashdown Mill, Prepared by Trinity Consultants, Revised March 26, 2007

76 BART Analysis for the White Bluff Steam Electric Stati on, ENSR Corporation, December 2006; BART Analysis for Lake Catherine Plant-Unit 4, ENSR Corporation, December 2006

A-757.3.1.1 Use of Pollutant-specific modeling

Pollutant specific modeling was performed by AD EQ for those sources subject-to-BART. The results of this modeling was used to determine which pollutants (NOx, SO 2 , and/or PM ) are impacting visibility. For some sources, a BART vi sibility analysis and ev aluation of controls for one or more pollutants was not performed based on the assumption that if pre-control modeling conducted on the basis of a single pollutant showed that the source’s emissions of the pollutant in question did not “contribute” to visibility impa irment then further BART analysis for that pollutant was unnecessary. This approach is unac ceptable. Due to the nonlinear nature and complexity of atmospheric chemistry and chemical transformation among pollutants, all relevant pollutants should be modeled together to predict the total visibility impact at each Class I area

receptor. 77 NOx and SO2 emissions should be mode led together to determine the visibility impacts attributable to these pollutants and in evaluation of controls and combinations of controls in determining BART for a source.

Predicting the impacts of PM on visibility is re latively straight-forward, unlike predicting the impacts of SO2 and NOx. Using CALPUFF on a pollutant specific basis to model only the impact of PM emissions on visibility may be an acceptable approach to determine whether a source should be subject to review for PM controls, or alternativel y, that the source is not subject to BART for PM. ADEQ applied a threshol d of 0.5 dv for determining whether a source “contributes” to visibility impairment on a per-po llutant basis. As discussed above, the State

selected a threshold of 0.5 dv fo r the initial screening modeling that included all pollutants. Clearly, a lower threshold value is needed in evaluating pollutant-speci fic modeling for sources

that emit more than one visibili ty impairing pollutant. Furt hermore, this approach is only acceptable for PM-specific modeling. We note that a State may establish de minimis levels of emissions (applicable on a plant-wide basis) of visibility impairing pollu tants to exclude some

sources from further evaluation when the emissi ons are so minimal that they are unlikely to contribute to regional haze.

7.3.1.2 Adoption of Presumptive Limits

For some facilities a BART visi bility analysis was not perfor med, and instead the presumptive limits found in the BART Guidelines were adopted. This approach is unacceptable. We note that the BART Rule does not suggest the presum ptive limits should be vi ewed as establishing a safe harbor from more stringe nt regulation under the BART pr ovisions, and adoption of the presumptive limits does not exempt a source from doing a BART five fact or analysis. Only when a source has controls already in place that are the most stringent controls available, or

when a source commits to a BART determination th at consists of the most stringent controls available, can the source forego a BART five factor analysis, includi ng a visibility analysis of the anticipated visibility be nefits due to the use of available a nd technically feasib le controls.

7.3.1.3 Selection of post-control scenarios to model

77 Memo from Joseph Paisie (Geographic Strategies Group, OAQPS) to Kay Prince (Branch Chief EPA Region 4) on Regional Haze Regulations and Guidelines for Best Ava ilable Retrofit Technology (BART) Determinations, July 19, 2006

A-76For the BART determinations for which a BA RT visibility analysis was performed, the

determination of the control tec hnologies and the control efficienci es to model was inadequate. Source-specific BART modeling perf ormed to determine the visibility improvement that would

result from installation of controls failed to properly take into cons ideration the technology

available and/or the maximum leve l of control each control option is capable of achieving. As part of the BART analysis, the facility must ev aluate the visibility impacts of all technically feasible control options consid ered before making a BART determination. In most cases, the facility made a BART determination without evaluating the visibility improvement anticipated due to the use of all technically feasible control options and performed visibility modeling only after a control technology was selected as BART. This approach is unacceptable and does not allow for a comparison of the effectiveness of available controls in reducing visibility impacts to be considered as part of the BART determination.

7.3.2 ADEQ BART Results and Summary

We have reviewed ADEQ’s BART determinatio ns for the sources listed in Table 7-3,

above. For the reasons above and as discussed in more detail in the proposed rulemaking and TSD, we are proposing to find that ADEQ has partially satisfied the BART requirement of section 51.308(e). We are proposing to find that the BART determinations listed in Table 7-4 do not satisfy the BART requirement of section 51. 308(e). We are proposing to find that the BART

determinations listed in Table 7- 5 and discussed below satisfy th e BART requirement of section 51.308(e). We are also proposing to find that Georgia-Pacific Cro ssett Mills is subject-to-BART and requires a full BART analysis to satisfy the BART requirement of section 51.308(e).

Table 7-4. BART Determinati ons Not Satisfying Section 51.308(e)

Facility Name BART

Emission

Unit Pollutant BART emission

78 limit

Arkansas Electric Cooperative Corporation Carl E. Bailey Generating Station Unit 1 SO 2 Use of fuel oil with 1%

sulfur content

NOx No BART

Determination

PM No BART

Determination

Arkansas Electric Cooperative Corporation

John L. McClellan Generating Station Unit 1 SO 2 Use of fuel oil with 1%

sulfur content

NOx No BART

Determination

PM No BART

Determination

American Electric Boiler No. 1 SO 2 0.15 lb/MMBtu

78 Emission limits are based on a 30-day rolling average.

A-77Facility Name BART

Emission

Unit Pollutant BART emission

limit 78

Power Flint Creek

Power Plant NOx 0.23 lb/MMBtu

Entergy Lake Catherine

Plant

Unit 4 natural gas firing NOx 0.15 lb/MMBtu

fuel oil firing SO2 0.562 lb/MMBtu

NOx 0.25 lb/MMBtu

PM 0.037 lb/MMBtu

Entergy White Bluff

Plant

Unit 1 bituminous coal firing SO 2 0.15 lb/MMBtu

NOx 0.28 lb/MMBtu

subbituminous coal firing SO 2 0.15 lb/MMBtu

NOx 0.15 lb/MMBtu

Unit 2 bituminous coal firing SO 2 0.15 lb/MMBtu

NOx 0.28 lb/MMBtu

subbituminous coal firing SO 2 0.15 lb/MMBtu

NOx 0.15 lb/MMBtu

Auxiliary

Boiler All Boiler to be operated

no more than 4360 hrs annually

Domtar Ashdown Mill No. 1 Power

Boiler SO 2 1.12 lb/MMBtu

NOx 0.46 lb/MMBtu

No. 2 Power

Boiler SO 2 1.20 lb/MMBtu

NOx 0.45 lb/MMBtu

PM 10 0.10 lb/MMBtu

A-78Table 7-5. BART Determinati ons Satisfying Section 51.308(e)

Facility Name BART

Emission Unit Pollutant BART emission

79 limit

American Electric

Power Flint Creek

Power Plant Boiler No. 1 PM 10 0.10 lb/MMBtu

Entergy Lake Catherine

Plant Unit 4 natural gas firing SO 2 No BART

Determination

PM 10 existing PM emission limit (45 lb/hr)

Entergy White Bluff

Plant

Unit 1 bituminous coal firing PM 10 existing PM emission limit (0.10 lb/MMBtu)

subbituminous coal firing PM 10 existing PM emission limit (0.10 lb/MMBtu)

Unit 2 bituminous coal firing PM 10 existing PM emission limit (0.10 lb/MMBtu)

subbituminous coal firing PM 10 existing PM emission limit (0.10 lb/MMBtu)

Domtar Ashdown Mill No. 1 Power

Boiler PM 10 0.07 lb/MMBtu

7.3.2.1 PM BART for AEP Flint Creek No. 1 Boiler

ADEQ conducted pre-control CALFUFF modeli ng for the AEP Flint Creek No. 1 Boiler showing that PM 10 and PM 2.5 emissions from the source have mi nimal visibility impacts at each Class I area within 300 km. Therefore, ADEQ dete rmined that the existing PM emission limit in the operating air permit achievable through the use of the existing electrostatic precipitator (ESP)

is BART for PM for AEP Flint Creek No. 1 Bo iler. We reviewed the CALPUFF visibility modeling submitted by ADEQ for AEP Flint Cr eek No. 1 Boiler, and agree that PM 10 and PM 2.5 emissions from the source have minimal visibil ity impacts at each Class I area within 300 km. Table 7-6 shows the percent c ontribution to visibility impacts due to PM for the 1 st High days. Less impacted days (8 th High) show similar contributions from PM. We have found that the visibility impact due to PM em issions alone is so minimal such that the installation of any additional PM controls on the unit would likely achieve very low emissions reductions, have minimal visibility benefit, and not be cost-eff ective. Therefore, we propose to approve ADEQ’s determination that PM BART for AEP Flint Creek No. 1 Boiler is the existing PM emission

79 Emission limits are based on a 30-day rolling average.

A-79limit. ADEQ did not specify in the RH SIP what the existing PM emission limit for the source is, but the current operating air permit for the source indicates the PM emission limit for the unit

is 0.1 lb/MMBtu

Table 7-6. Percent contribution of PM (course a nd fine) to total visibility impairment on 1 st High day. ADEQ pre-control screening mode ling (Appendix 9.2B), AEP-Flint Creek

CACR HEGL MINGO SIPS UPBU

%_PMC %PMF %_PMC %PMF %_PMC %PMF %_PMC %PMF %_PMC %PMF

2001 0.06 0.16 0.23 0.14 0.03 0.15 0.03 0.09 1.33 0.65

2002 0.16 0.18 0.29 0.17 0.08 0.13 0.01 0.08 0.24 0.18

2003 0.06 0.11 0.15 0.15 0.02 0.1 0.03 0.09 1.25 0.62

7.3.2.2 SO2 and PM BART for natural gas firing scen ario for Entergy Lake Catherine Unit 4

Since Unit 4 is permitted to burn both natural gas and No. 6 fuel oil, ADEQ made BART determinations for both natural gas firing and fuel oil firing scenarios. The Arkansas RH SIP contains the CALPUFF pre-cont rol modeling files for the natu ral gas firing scenario.

SO 2 and PM emissions from natural gas-fired boil ers are generally very low. We agree with ADEQ’s decision not to make a BART determination for SO 2 for the natural gas firing scenario

for Unit 4. Revisions to the State’s RH Rule , Chapter 15 of APC&E Commission Regulation 19, which were submitted to us on August 3, 2010, state the existing PM emission limit as of October 15, 2007 is PM BART for the natural gas firing scenario for Entergy Lake Catherine Unit 4. This corresponds to an emission limit of 45 lb/hr PM. Since we have found that the visibility impact due to PM em issions alone is so minimal such that the installation of any additional PM controls on the unit would likely achieve very low emissions reductions, have minimal visibility benefits, and not be cost-eff ective, we are also proposing to approve ADEQ’s

determination that BART for PM is the exis ting PM emission limit as of October 15, 2007, or 45.0 lb/hr, for the natural gas firing scenario for Unit 4.

7.3.2.3 PM BART for Units 1 and 2 Entergy White Bluff

ADEQ’s determined that most of the visibility- causing emissions from Units 1 and 2 are due to

SO 2 and NOx, and determined that PM 10 emissions are well-controlled with existing electrostatic precipitators (ESPs). We reviewed the pre- control screening CALPU FF visibility modeling submitted by ADEQ for Entergy White Bluff, and agr ee that PM emissions from the source have minimal visibility impacts at each Class I ar ea within 300 km. Table 7-7 shows the percent contribution to visibility impacts due to PM for the 1 st High days. Less impacted days (8 th High)

show similar contributions from PM. We have found that the visibili ty impact due to PM

emissions alone is so minimal such that the inst allation of any additional PM controls on the unit would likely achieve very low emissions reductions, have minimal visibility benefit, and not be cost-effective. We propose to approve ADEQ’ s determination that PM BART for both the bituminous and sub-bituminous coal firing scenar ios is the existing PM emission limit for Units 1 and 2. The operating air permit states the PM emi ssions from the two units are controlled with ESPs and requires that the two units comply with a PM emission standard of 0.10 lb/MMBtu.

A-80A-81 Table 7-7. Percent contribution of PM (course a nd fine) to total visibility impairment on 1 st High day. ADEQ pre-control screening modeling (Appendix 9.2B), Entergy White Bluffs

CACR HEGL MINGO SIPS UPBU

%_PMC %PMF %_PMC %PMF %_PMC %PMF %_PMC %PMF %_PMC %PMF

2001 0.11 0.27 0.04 0.3 0.03 0.16 0.03 0.16 0.05 0.21

2002 0.04 0.17 0.04 0.17 0.06 0.32 0.03 0.14 0.05 0.19

2003 0.04 0.21 0.09 0.26 0.06 0.26 0.08 0.21 0.04 0.17

7.3.2.4 PM BART for Domtar Power Boiler No. 1

ADEQ stated the Domtar No. 1 Power Boiler was at the time subject to the Boiler Maximum Achievable Control Technology (MACT) 80 PM emission standard of 0.07 lb/MMBtu. A wet

ESP was installed at the No. 1 Power Boiler to meet the 0.07 lb/MMB tu Boiler MACT PM emission standard. The BART guidelines state that for PM sources subject to MACT standards, States may streamline the analysis by including a discussion of the MACT controls and whether any major new technologies have been develope d subsequent to the MACT standards. Concerning Power Boiler No. 1, ADEQ provided a di scussion of other PM control technologies available at the time, and determined that a wet ESP with a PM emission limit of 0.07 lb/MMBtu on a 30-day rolling average is BART for Power Boiler No. 1. We agree that ADEQ’s streamlined approach and determination for BART for PM for Power Boiler No. 1 is consistent

with the BART Guidelines a nd propose to approve it.

80 The MACT standards are part of the National Emission Standards for Hazardous Air Pollutants for Source Categories (NESHAP), provided under 40 CFR 63.