## Appendix A.

Modeling and Emission Inventory Development; Review and Analysis for Louisiana’s Region al Haze State Implementation

Plan Submittal

Prepared and Reviewed by:

Erik Snyder

Michael FeldmanA-ii 

### Table of Contents

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

List of Tables ................................................................................................................ ..... iv List of Figures ............................................................................................................... ...... v Chapter 1: Background and Introduction ..................................................................... 6

1.1 Background .......................................................................................................... 6

1.2 Technical Requirements for Regional Haze SIPs ................................................ 7 Chapter 2: Development of Baseline and Natural Visibility Conditions and Glidepath

9

2.1 Introduction .......................................................................................................... 9

2.2 Calculation of Visibility fr om IMPROVE Measurements ................................. 10

2.3 Baseline Visibility Conditions ........................................................................... 12

2.4 Natural Visibility Conditions ............................................................................. 13

2.5 Uniform Rate of Progress (G lidePath) Calculation ............................................ 13 Chapter 3: Emission Inventory Development ............................................................. 15

3.1 Introduction ........................................................................................................ 15

3.2 2002 emissions inventory ................................................................................... 16

3.2.1 Stationary Point-Source Emissions ........................................................... 17

3.2.2 On-Road Mobile Emissions ...................................................................... 18

3.2.3 Biogenic Emissions ................................................................................... 18

3.2.4 Non-Road Mobile Emissions .................................................................... 19

3.2.5 Area Source Emissions ............................................................................. 19

3.3 2018 Emissions Inventory .................................................................................. 19 Chapter 4: Modeling Protocol, Episode Selectio n and Modeling Set-up Overview .. 22

4.1 Introduction ........................................................................................................ 22

4.2 Quality Assurance Program Plan ....................................................................... 22

4.3 Episode Selection ............................................................................................... 22

4.4 Photochemical and Emissions Modeling Domain ............................................. 23

4.5 MM5 Meteorological Model .............................................................................. 24

4.5.1 Model Selection ........................................................................................ 24

4.5.2 Meteorological Modeling Domain a nd Vertical Layer Structure ............. 25

4.5.3 Model Configuration ................................................................................. 25

4.5.4 MM5 Processing and Application ............................................................ 26

4.5.5 Model Performance ................................................................................... 26

4.6 SMOKE Emissions Model ................................................................................. 28

4.7 Air Quality Model .............................................................................................. 28

4.7.1 Model Selection ........................................................................................ 29

4.7.1.1 CMAQ Air Quality Model .................................................................. 30

4.7.1.2 CAMx Air Quality Model ................................................................... 30

4.7.2 Vertical Modeling Domain ....................................................................... 31

4.7.3 Initial and Boundary Conditions ............................................................... 31

4.7.4 Base Case/ Baseline Model Performance ................................................. 32

4.7.4.1 Model Performance for Sulfate (SO 4 ) ................................................. 33

4.7.4.2 Model Performance for Nitrate (NO 3 ) ................................................. 34A-iii 4.7.4.3 Model Performance for Organic Carbon (OC) .................................... 35

4.7.4.4 Model Performance for Elemental Carbon (EC) ................................. 36

4.7.4.5 Model Performance for Soil ................................................................ 37

4.7.4.6 Model Performance for Course Pa rticulate Material (CM) ................. 38

4.7.4.7 Model Performance for Prediction of Total Extinction ....................... 40 Chapter 5: 2018 Future Year Modeling ...................................................................... 43

5.1 2018 Model Simulations .................................................................................... 43

5.2 Visibility Projections .......................................................................................... 43

5.2.1 Mapping Model Results to IMPROVE Measurements ............................ 44

5.2.2 Projecting Visibility Change s Using Modeling Results ........................... 44

5.3 Reasonable Progress Goal and Pa th to Natural Conditions ............................... 45 Chapter 6: Source Apportionment Modeling ............................................................. 49

6.1 Introduction ........................................................................................................ 49

6.2 Source Apportionment Results at Breton National Wildlife Refuge ................. 50

6.3 Source Apportionment Results at Caney Creek Wilderness Area ..................... 55

6.4 Contributions to visibility impairment at other Class I areas ............................. 59 Chapter 7: BART Determination ................................................................................ 60

7.1 BART SCREENING ANALYSES .................................................................... 60A-iv 

### List of Tables
Table 2.1. Uniform Rate of Progress for Br eton National Wildlife Refuge and (worst quintile, western natural visibility conditions) .................................................................. 14

Table 3-1. Emissions from L ouisiana Sources (tons/yr) .................................................. 17

Table 3-2. Emission estimates for Loui siana sources in 2018 (tons/yr) .......................... 20 Table 5-1. Comparison of reas onable progress goal to unifor m rate of progress for 2018 (total extinction and deciviews) ........................................................................................ 46 Table 6-1. Projected light ex tinction for 20% worst days at Breton National Wildlife Refuge in 2002 (Mm -1 ) ..................................................................................................... 51 Table 6-2. Projected light ex tinction for 20% worst days at Breton National Wildlife Refuge in 2018 (Mm -1 ) ..................................................................................................... 51 Table 6-3. Projected light ex tinction for 20% worst days at Caney Creek Wilderness Area in 2002 (Mm -1 ) ......................................................................................................... 56 Table 6-4. Projected light ex tinction for 20% worst days at Caney Creek Wilderness Area in 2018 (Mm -1 ) ......................................................................................................... 56

Table 6-5. Percent contributi on to total visibility impair ment at Class I areas on 20% worst days from Louisiana Sources (contri butions less than 1% are excluded) ............... 59 Table 7-1. BART-eligible faciliti es closest to Caney Creek ............................................ 61 Table 7-2. BART-eligible facili ties closest to Breton ..................................................... 64 Table 7-3: Facilities Reques ted to either Screen or Perform Refined Modeling .............. 69

Table 7-4: CALPUFF/CALPOS T Screening Results ....................................................... 70 Table 7-5 Facilities that LDEQ determined had units that were subject to BART .......... 72A-v 

### List of Figures
Figure 1-1. Map of Regional Pl anning Organizations ........................................................ 7 Figure 4-1. National RPO 36-km modeli ng domain for CMAQ, CAMx, and SMOKE modeling. ..................................................................................................................... ..... 24 Figure 4-2. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for sulfate (SO 4 ). ................................................................................. 34 Figure 4-3. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for nitrate (NO 3 ). ................................................................................ 35 Figure 4-4. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for organic carbon (OC). .................................................................... 36 Figure 4-5. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for elemental carbon (EC). ................................................................. 37 Figure 4-6. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for soil. ................................................................................................ 38 Figure 4-7. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for course pa rticulate material (CM). ................................................. 39 Figure 4-8. Daily extinction model performan ce at Breton National Wildlife Refuge, LA for the worst (top) and best (bottom) 20% days during 2002 ........................................... 41 Figure 4-9. Daily extinction model performan ce at Caney Creek Wilderness Area, AR for the worst (top) and best (bottom) 20% days during 2002 ................................................. 42 Figure 5-1. Projections of visibility impairment for 20% worst days at Breton Island ... 47 Figure 5-2. Differences in modeled total extinction (Bext) between Base18G and Typ02g for 20% worst days at Breton National Wildlife Refuge. ................................................. 48 Figure 6-1. Source Regions used in CAMx PSAT PM source apportionment modeling 50 Figure 6-2. Source apportionment modeling resu lts by source region and source category for worst 20% days at Breton National Wildlife Refuge in 2002. .................................... 52 Figure 6-3. Source apportionment modeling re sults by source region and species for worst 20% days at Caney Creek Wilderness Area in 2002. ............................................. 53 Figure 6-4. Source apportionment modeling resu lts by source region and source category for worst 20% days at Breton National Wildlife Refuge in 2018. .................................... 54 Figure 6-5. Source apportionment modeling re sults by source region and species for worst 20% days at Breton Nati onal Wildlife Refuge in 2018. ......................................... 55 Figure 6-6. Source apportionment modeling resu lts by source region and source category for worst 20% days at Caney Creek Wilderness Area in 2002. ........................................ 57 Figure 6-7. Source apportionment modeling results by source region and species for worst 20% days at Caney Creek Wilderness Area in 2002. ............................................. 57 Figure 6-8. Source apportionment modeling resu lts by source region and source category for worst 20% days at Caney Creek Wilderness Area in 2018. ........................................ 58 *2018 projections for Texas Point sources ar e divided into EGU and Non-EGU point

sources........................................................................................................................ ....... 58 Figure 6-9. Source apportionment modeling re sults by source region and species for worst 20% days at Caney Creek Wilderness Area in 2018. ............................................. 58 Figure 7-1. BART Source Sc reening Modeling 2001 ...................................................... 74 Figure 7-2. BART Source Sc reening Modeling 2002 ...................................................... 75 Figure 7-3. BART Source Sc reening Modeling 2003. ..................................................... 77A-6 

### 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-7 Figure 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-8 According 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 supplement th e main TSD and provide review of issues not covered in the main TSD dealing with the technical products deve loped by the Louisiana Department of Environmental Quality (LDEQ) a nd CENRAP for the central regional states, in support of their RH SIP. This document evalua ted the methods and procedures used by LDEQ and CENRAP to develop the modeling and emissi on inventory products that assisted Louisiana 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 and other scr eening modeling, evalua ted the results and determined if these models met applicable guidelines or protocols, and met modeling 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.pdfA-10 Conditions 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. Ex tinction 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 Louisiana followed EPA guidance contained in the GTR and GENVC with the exception that the re vised 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 Rayl eigh 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 (2007) Pitchford, Marc; William Malm, Bret Schichtel, Naresh 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-11 Demonstrating Attainment of Air Qualit y 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

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/scr am001/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/Publicati ons/GrayLit/016_IMPROVEEeqReview/IMPROVEeqReview.htm

8 Pitchford, Marc, 2006, "New IMPR OVE 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 /improve/Publications/news_letters.htm

direct link: http://vista.cira.colostate.edu/improve/Pub lications/NewsLetters/IMPNews4thQtr2005.pdfA-12 areas, the revised IMPROVE equation embeds an OM/OC factor of 1.8. At the Caney Creek Wilderness 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. As the state of the scie nce evolves, it is recommended that this procedure is reevaluated to apply more current and site specific data as it becomes available. CENRAP provides alternat ive calculations using the original IMPROVE equation for comparison with thes e visibility calculation results.

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.

We discuss the data filling for the Breton monito r and the acceptability of the data that was generated with assistance from the IMPROVE committee and utilized in the CENRAP and LDEQ RH SIP in the main TSD for this action.

The RHR defines the baseline period as the five year span from 2000-2004. As discussed in the main TSD. LDEQ has calculated a baseline visibility based on the average of the worst (best) 20% of days for each of these three years. W ith the data substitution, this meets the minimum overall data completeness requirements for calcu lation of the baseline visibility conditions

detailed in the GTP.

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 described above. For those CENRAP sites ( Breton (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.colostate.edu/views/ ).A-13 The CENRAP followed EPA guidance for estima ting 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 es timates (GENV sec. 3.1 and 3.2).

LDEQ 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 the Breton National Wildlife Refuge site is currently from anthropogenic sources. As measures are taken to improve visibility and decrease emissions, the ability to identify natural sour ces and background concentrations of PM will improve. The current approach used by LDEQ follows EPA methods and is acceptable. As

additional information and more s ite-specific data become available, LDEQ is encouraged to pursue refinements in this approach to be tter quantify natural vi sibility conditions.

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

The 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 Breton Island. Table 2.5 summarize the calculations performed by LDEQ.A-14 Table 2.1. Uniform Rate of Progress for Breton National Wildlife Refuge and (worst quintile,

western natural visi bility conditions)

Conditions Total extinction (Mm -1 ) Haze Index (deciviews) Baseline (2002-2004) conditions 131.05 25.73 Natural (for 2064) conditions 32.97 11.93 Observed impairment above natural conditions 98.08 13.8 Progress (2004-2018) at unifo rm rate 0.23 per year Improvement needed by 2018 assuming uniform rate of progress 22.885 3.22A-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/CEN RAP_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 section. The CENRAP TSD is included as Appendix B of the LDEQ Regional Haze Implementation Plan Revision.

3.2 2002 EMISSIONS INVENTORY

LDEQ developed the 2002 point source emission s inventory in-house with Emission Inventory
Questionnaires and used the biogenic source inve ntory developed by EPA.LDEQ contracted with
ENVIRON to develop an emission inventory for three inventory source classifications: on-road
and non-road mobile sources and nonpoint 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-17 The 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.
LDEQ.

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 Louisiana Sources (tons/yr)

SO 2 NH 3 NO X VOCs PM 10 PM 2.5

Point 286,050 9,237 312,634 89,025 73,333 60,899

Area 81,153 75,381 99,060 124,311 245,162 84,068

Non-road

mobile 14,324 563 117,250 109,598 10,663 9,791

On-road

mobile 4,653 3,748 15,137 64,643 3,563 2,689

Total 386,180 88,929 544,081 387,577 332,721 157,447

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 Louisiana 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/CEN RAP_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-18 inventory 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 speciation 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 Louisiana.

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 Louisiana parishes, parish-level Highway Performance Monitoring System annu al average VMT data were used. Annual 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 statewide average weekday/annual average daily factors. 18 For the other CENRAP states, Sonoma Technology provided monthly VMT data and MOBILE6 input file s for the months of January and July for all counties in the CENRAP region. 19 MOBILE6 input files for the remaining months 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 Transportation and Air Quality has developed a new model, Motor Vehicle Emission Simulator (MOVES), which will replace the MOBILE6 model for estimating emi ssions 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 preliminary 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.

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)

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 ).A-19 3.2.4 Non-Road Mobile Emissions Emissions from airport/aircraft operations, co mmercial and recreationa l marine vessels, and railroad locomotives, farm equipment, lawn equi pment, and other sources were developed by the

EPA for the 2002 NEI. The EPA NONROAD 21 (NONROAD 2004 at the time) model was utilized by Sonoma Technology 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 LDEQ, CENRAP and other CENRAP states. Area sources include small sources that combust fuel (small heaters, water heaters, etc.) and ot her sources such as dr y cleaning, degreasing and industrial surface coating. Sonoma Technology prepared additional inventories of prescribed burning, agricultural dust, and soil agri cultural ammonia for the CENRAP region. 23 The Western Regional Air Partnershi p (WRAP) provided 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 subcategories (gener al area, fire, ammonia, road dust, fugitive dust, uncategorized) to assist in particulate source apportionm ent 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 5/6), MOBILE 6, NONROAD, and the In tegrated Planning Model (IPM) of ICF International for EGUs to project emissions fr om 2002 to 2018. Emission projections for most source categories are based on growth and contro l factors compiled by Pechan and detailed in

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/CEN RAP_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-20 Chapter 7 of the Louisiana RH SIP and in Pechan’s Development of Growth and Control Inputs for CENRAP 2018 Emissions draft technical support document. 25

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

SO 2 NH 3 NO X VOCs PM 10 PM 2.5

Point 354,087 14,435 269,215 187,741 73,136 60,899

Area 87,538 36,896 114,374 117,600 16,936 14,536

Non-road

mobile 11,584 72 106,685 64,294 8,670 7,955

On-road mobile 561 5,436 44,806 30,340 1,191 1,191

Total 453,770 56,839 535,080 399,975 99,933 84,581

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

All 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 utilities. Newly perm itted coal-fired utilities were included in 2018 projections. Conservatively, no IP M projected new units were rem oved from the simulation with the addition of the permitted facilities. Appendi x B of the CENRAP TSD lists the file names, data source, type and a description of emi ssions used in the 2018 (Base18G) emissions inventory. The Access Database th at includes facility specific and day specific emission rates is available upon request due to the size of the file.

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-21 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 evalua tion used in support of the

Louisiana 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-23 the 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-24 Figure 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-25 In 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 diur nal 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-26 meteorological 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 distri bution 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.pptA-27 The 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 observati ons. 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 ar e 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-28 results 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#pcbeisA-29 processes 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 th e 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-30 compared 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-31 The 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 sma ll 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-32 calendar 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-33 from 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-34 Figure 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-35 In 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 ruralA-36 IMPROVE 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 atA-37 the 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 difficu lties in capturing emissions accurately in theA-38 inventory. This is an area of c oncern, especially in areas where so il contributes significantly to visibility impairment.

Figure 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 theA-39 inventory. This is an area of c oncern, especially in areas where CM contributes significantly to visibility impairment.

Figure 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-40 4.7.4.7 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-region s. 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 th e Breton (BRET), LA, Big Bend (BIBE), TX and Guadalupe Mountains (GUMO), TX Class I areas for the worst 20 pe rcent days is particularly poor. At GUMO, visibility impairment is primar ily due to high soil and CM which are not well predicted by the model across th e CENRAP area. At the BRET a nd BIBE sites, all components are under-predicted, leading to an under-predicti on in total extinction. Model predictions at

these sites are less reliable than at other CENRAP sites for 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 Louisiana, Breton National W ildlife Refuge is the only Class I area. Model performance at predicting total extinction at Breton and Caney Creek Wilderness Area during the worst 20% and best 20% days are shown in Figures 4-8 and 4-9.

For Breton Island, the worst 20% days are heavil y dominated by sulfate extinction. Nitrates do have a sizeable component on a few days but th e sulfate extinction components are still higher on these few days. Sulfate, OMC and Soil under-pr ediction results in an under-prediction (-50 to -70%) and EC and CM are on the order of -100% . Worst observed extinction is 90-170 Mm-1 but modeled values drop to as low as 15 Mm-1. This underestimation results in more uncertainty for the Breton projections, but the monitoring an d modeled data both conclude a high impact

from sulfates on total extinction. Overall observed vs. mode l performance is relatively good on a

bias level but there is a lot of scatter on indi vidual days. On averag e, the low bias looks reasonable, but day specific performa nce is more questionable.

On most of the worst 20 % days at Caney Cr eek, 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 under-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

.A-41 Figure 4-8. Daily extinction model performance at Breton National Wildlife Refuge, LA for the worst (top) and best (bottom) 20% days during 2002.A-42 Figure 4-9. Daily extinction model performance at Caney Creek 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 ambien t air quality and vi sibility metrics.

Input data used for the 2018 Base Case model si mulations consisted of the same meteorology as

for the 2002 Base Case and the Base18 emission inventories described under the Emissions Modeling section (Section 3). The setup of the CMAQ model (i ncluding science options, run scripts, simulation periods, and ancillary data) fo r the Base18 cases was identical to that used in the Typ02G modeling.

The purpose of modeling 2018 visibility is to comp are 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-44 response 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-45 revised 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 de ciview 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 slightly less than the ca lculated URP for 2018 (Section 2). The URP acts as a benchmark for evaluating the reasonable progress towards 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 Breton Island,

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-46 the baseline visibility (2 002-2004) is 25.73 dv, and the estimated 2018 URP is 22.51dv. The modeling predicts a visibility improveme nt of 3.01 dv by 2018, compared to the URP improvement of 3.22 dv by 2018. The modeling pred icts a visibility improvement of 3.01 dv, compared to the URP improvement of 3.22 dv by 2018, less than 10% gap is left. Achieving the 2018 URP point is not a requirement of the RHR SI Ps, but it serves as a benchmark to compare progress toward natural visibility conditions in 2064 and is designe d to help states in selecting

their 2018 RPGs. As stated in EPA Guidance fo r Setting Reasonable Progress Goals Under the Regional Haze Program 48 , “The glidepath is not a presumptiv e target, and States may establish a RPG that provides for greater, lesser, or equiva lent improvement as that described by the glidepath.”

The modeling also shows visibility impairme nt in 2018 for the 20% best days show an improvement in visibility of 0.90 dv at Breton by 2018. This is consistent with the requirement of no degradation of visibility on the best days at Class I sites.

LDEQ adopted the modeled 2018 visibility c onditions as the Reasonable Progress Goal

for Breton Class I area. We ar e proposing to partially appr ove and partially disapprove Louisiana’s Reasonable Progress Go als because of the linkage to EPA’s CAIR and the Transport

Rule. See the Notice and the main TSD for more details. CENRAP and LDEQ’s projections

indicate that Sulfate emissi ons in Louisiana are projecte d to increase from 2002 to 2018. Louisiana 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 as addi tional analyses are conducted for BA RT on sources as discussed in the BART sections of the main TSD and in the FRN proposal.

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

Breton Island Avg. for 20% Worst Days (Baseline 2000-04) 25.73 dv

2018 URP Goal 22.51 dv

RPG 22.72 dv Change by 2018 (reasonable progress goal) -3.22 dv Change by 2018 at uniform rate of progress -3.45 dv Projected rate of change (2004-2018) -0.32 dv/yr Change needed to reach natural conditions -14.78 dv

Avg. for r20% Best Days (Baseline 2000-04) 13.12 dv

RPG 12.22 dv

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.pdfA-47 Figure 5-1. Projections of vi sibility impairment for 20% worst days at Breton Island

Figure 5-2. shows the differences in model resu lts of total extinction between the Base18g and Typ02g model predictions, including the contri butions from each component specie of the IMPROVE algorithm. On most days , visibility improvements are due to reductions in sulfate. A few days exhibit differences in nitrate concentr ations being the most si gnificant contribution to improved visibility.A-48 Figure 5-2. Differences in modeled total ex tinction (Bext) between Base18G and Typ02g for 20% worst days at Breton National Wildlife Refuge.

As discussed in the following chapter on source apportionment, visibility impairment at Breton National Wildlife Refuge is due to emissions and transport from outside of Louisiana as well as

in state sources.A-49 

### 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 natura l 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-50 average 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 B RETON N ATIONAL W ILDLIFE R EFUGE

Tables 6-1 and 6-2 show the modeled contributi ons 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 Breton National Wildlife Refuge site in 2002 on the worst 20% days is largely due to sulfate from point sources that cont ributes over half (96.83 Mm

1 ) of the total extinction of 132.52 Mm -1 . The largest contributions of sulfate come from Louisiana ( 15.48 Mm -1 from all source categories) and the eastern United States (22.88 Mm -1 ). Overall, the largest source regi on contributions to visibility impairment in 2002 are from the eastern United States, Louisiana, Alabama, and Indiana).

52 available at http://www.cenrap.org/html/projects.phpA-51 In 2018, Louisiana sources contribute the most to visibility impairment at Breton, as large reductions in impairment from point sources in the Indiana, Alabama and the eastern U.S. occur

while sulfate emissions increase in Louisiana.

. The 2018 projection shows the total extinction at Breton for the worst 20 % days is estimated to be 102.5 Mm -1 , a reduction of approximately 23%.

Reductions of sulfate emissions from point sources in Alabama, the eastern United States, Indiana, and Ohio make up the lionshare of the decr ease in total light extin ction. Even with such large reductions in SO4 from point sources in 201 8, extinction 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. There is an under-prediction bias in the model that must be considered when examining source apportionment results for sulfat e. Use of a 12km resolution modeling grid in

CAMX reduced the summertime sulfate bias but re quired large computational expense. The use of higher resolution modeling should be r econsidered in future modeling efforts.

Table 6-1. Projected light extinc tion for 20% worst days at Bret on National Wildlife Refuge in 2002 (Mm -1 )

Total 1 Point Natural On-Road Non-Road Area

SO4 96.83 78.92 0.08 1.31 2.95 8.74

NO3 8.29 2.53 0.48 1.44 1.29 1.03

POA 4.71 1.03 0.49 0.13 0.46 2.00

EC 5.40 0.35 0.34 0.70 2.08 1.41

SOIL 0.95 0.25 0.03 0.01 0.01 0.60

CM 3.70 0.30 0.18 0.02 0.04 2.14

Sum 132.52 83.38 1.60 3.61 6.82 15.93

1 Totals include contributions from boundary conditions and secondary organic matter

Table 6-2. Projected light extinc tion for 20% worst days at Bret on National Wildlife Refuge in 2018 (Mm -1 )

Total 1 Point Natural On-Road Non-Road Area

SO4 68.63 51.59 0.04 0.15 1.57 10.61

NO3 8.20 2.53 0.49 0.53 1.22 1.85

POA 4.37 1.21 0.21 0.05 0.38 1.93

EC 3.92 0.34 0.15 0.14 1.43 1.34

SOIL 1.16 0.43 0.03 0.01 0.01 0.63

CM 3.95 0.31 0.15 0.02 0.04 2.40

Sum 102.50 56.43 1.05 0.90 4.64 18.76

1 Totals include contributions from boundary conditions and secondary organic matterA-52 Figure 6-2. Source apportionment modeling results by source re gion and source category for worst 20% days at Breton Na tional Wildlife Refuge in 2002.A-53 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-54 Figure 6-4. Source apportionment modeling results by source regi on and source category for worst 20% days at Breton Na tional Wildlife Refuge in 2018.

*2018 projections for Texas Point sources are divided into EGU and Non-EGU point sourcesA-55 Figure 6-5. Source apportionment modeling results by source regi on and species for worst 20% days at Breton National Wildlife Refuge in 2018.

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

Tables 6-3 and 6-4 show the modeled contributi ons 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 Louisiana (13.57 Mm -1 ).

In 2018, Louisiana 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 Louisiana. 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 largeA-56 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 Louisiana sources decreases 2.32 Mm -1 , almost entirely due to reductions from mobile sources. Total reductions in mobile sources of NO3

contribute a decrease in total extinction of approximately 9 Mm -1 . There is an under-prediction

bias in the model that must be considered when examining source apportionment results for sulfate. Use of a 12km resolution modeling grid in CAMX reduced the summertime sulfate bias but required large computational expense. Th e use of higher resolution modeling should be reconsidered in future modeling efforts.

Table 6-3. 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 6-4. 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 matterA-57 Figure 6-6. Source apportionment modeling results by source re gion and source category for worst 20% days at Caney Cr eek Wilderness Area in 2002.

Figure 6-7. Source apportionmen t modeling results by source re gion and species for worst 20% days at Caney Creek Wilderness Area in 2002.A-58 Figure 6-8. Source apportionment modeling results by source regi on 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 Figure 6-9. Source apportionment modeling results by source regi on and species for worst 20% days at Caney Creek Wilderness Area in 2018.A-59 6.4 C ONTRIBUTIONS TO VISIBILITY IMPAIRMENT AT OTHER C LASS I AREAS

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

sources are modeled to have cont ributions to the Class I areas in Oklahoma with impairment % increasing from 3.55 to 4.99%. Outside of Oklaho ma, the next largest contributions from

Louisiana sources are on Class I areas in Ar kansas (Upper Buffalo), Texas (Big Bend and Guadalupe Mtns.) and Missouri (Hercules-Gl ades and Mingo). The growth in % of apportionment is partially due to the increase in emissions projected for 2018 from Louisiana sources, especially the SO2 emissions.

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

Class I area 2002 2018

UPBU1 2.42% 2.99%

CACR1 2.87% 4.36%

HEGL1 2.24% 3.05%

MING1 0.22% 0.34%

WIMO1 3.55% 4.99%

GUMO1 2.00% 2.48%

BIBE1 2.42% 2.85%A-60 

### Chapter 7: BART Determination

7.1 BART SCREENING ANALYSES

The discussion that follows is a descripti on of the process used to determine BART Sources. LDEQ conducted an eval uation to support just evaluating the two closest Class I areas.

The two Class I areas closest to Louisiana s ources are Breton National Wildlife Refuge and Caney Creek Wilderness Area. We concur with LDEQ’s decision to fo cus on these two Class I

areas.

First, LDEQ sorted the BART-eligib le facilities in Louisiana with visibility impairing pollutants by distances to the nearest Cla ss I area. Second, LDEQ evaluated the ratios of the total of visibility impairing emissions to the dist ance to the Class I area was calculated on the spreadsheet. See Tables 7-2 and 7-3 for this information. Third, the facilities with the higher

emissions to distance ratios were modeled with the CALPUFF scr eening model using the following methodology:

● EPA regulatory approved model, CALPUFF version 5.711a;

● CENRAP 6 km spacing resolu tion domains with no observation

● CALMET met data of 2001, 2002 and 2003; and,

● Ozone data for 2001, 2003 Louisiana state ozone data and 2002 CENRAP southern region ozone data were used in the screening process.

● The 24 hour maximum pollutant emissions of NOx, SO2 and pa rticulate collected in the BART survey were used for the model emissions inputs.

● POSTUTIL was used in calculation of repartitioning of NO3/HNO3 without

ammonia data.

● CALPOST version 5.51 was used to determ ine the visibility impact on the Class

I area of interest.

We concur with the use of this version of CALPUFF at the ti me and the methodology that LDEQ utilized for using specific facili ties with a high Q/D of visibil ity impairing pollutants as model plants for screening of sources.

In accordance with the Guidelines , LDEQ chose to use a contribu tion threshold of 0.5 deciviews (98 th percentile) for determining which sources were subject to BART. To be more conservative,

due to some of the uncertainties with this approach, LDEQ used the maximum impact value

instead of the 98 th Percentile. Therefore, LDEQ used a screening evaluation criterion was a maximum deciview impact of greater than 0.5 de civiews to require a re fined analysis. We concur with this approach.

The two (2) existing facilities that had the highest emission divide d by distance ratios with respect to the Caney Creek Class I area we re Smurfit Stone in J ackson Parish, Louisiana and Chemtrade Refining in Caddo Parish, Louisian a. Results of the faci lity’s screening areA-61 shown in table 7-4. Modeled results indicated th at there was no visibility impact at Caney Creek, with the exception of Ch emtrade in 2002 and the average of the maximums were below 0.5 dv impact.. Model outputs are listed below: Smurfit Stone, Jackson Parish, Louisian a; distance from Caney Creek equals

263km SSE o 2001 inputs indicated 0.188 dv impact o 2002 inputs indicated 0.259 dv impact o 2003 inputs indicated 0.183 dv impact Chemtrade Refining, Caddo Parish, Loui siana; distance from Caney Creek equals 226.6km almost due south o 2001 inputs indicate 0.043 dv impact o 2002 inputs indicate 0.052 dv impact o 2003 in puts indicate 0.042 dv impact.

Graphics Packaging International (see Facility 1 in Table 7-4) reported revised BART eligible emissions after the screening modeling had begun, so this facility was requested to perform its own screening. The remaining facilities listed in Table 7-2, were eliminated from

BART consideration as their emissions were le ss than either Smurfit Stone or Chemtrade Refining and they were farther away from th e Caney Creek Class I ar ea. As a check, LDEQ modeled a carbon black plant, Cabot Company in Evangeline Parish and a coal-fired EGU, Big Cajun 2 in Pointe Coupee Parish that were over 300 kms from Caney Creek and emitted high amounts of visibility impairing pollutant s from tall stacks. The modeling indicated there was no impact to visibility at Ca ney Creek from these two additional sources.

Table 7-1. BART-eligible facili ties closest to Caney CreekA-62A-63 LDEQ altered their methods for determining visi bility impairment for the Breton Class I area from the analysis methods they used for Caney Creek. LDEQ chose to model two facilities: ConocoPhillips Alliance Refine r in St. Bernard Parish, Louisiana and the Big Cajun 2 power plant in Pointe Coupee Parish, Louisiana. Because Louisiana was a CAIR state at the time, only the particulate matter (PM10) component was used when performing the modeling for Big Cajun 2.

Model results from both facili ties indicated an impact of visibility at Breton. LDEQ

used as its criteria an emissions/distance ratio equal to or greater than Big Cajun 2 (0.0898678). If a facility’s emissions/distan ce ration was greater than 0.0898678 then the facility was requested to conduc t its own modeling exercise. F acilities 2 through 10 in Table 7-4 were above this ratio (0.0898678).

LDEQ then performed screening models on Murphy Oil USA, Meraux Refinery, St. Bernard Parish, Louisiana and the Entergy Mich oud facility in Orleans Parish, Louisiana. Once again, because Louisiana was a CAIR state at the time, the Entergy Michoud facility was screened only for particulates. Both of these faci lities were found to have an impact on visibility at Breton, and both were requested to perfor m the refined modeling. (Facilities 11 and 12 in Table 9.4) Facility 13, Sid Richardson, was re quested to perform refined modeling also

because its emissions/distance ratio was sli ghtly greater than of Murphy Oil (0.0891079). Looking at BART-eligible facilities fu rther to the west from Breton, LDEQ performed the screening model on the Dupont Ponchartrain Diamines Unit, St. John the Baptist Parish, Louisiana. The results of this run showed no impact on visibility at Breton.

Using established guidelines, LDEQ removed all of the remaining facilities listed in Table 7-3 that were a greater distance from Breton from BART consideration with exceptions listed below. LDEQ then modeled, as a double check on the analysis, Cabot Corporation, which is a carbon-black, facility located 332.3 km west of Breton in Evange line Parish, Louisiana. This facility was chosen because it emits high amounts of visibility impairing pollutants from a tall stack. The modeling indicated there was no im pact from this facility at Breton.

To be conservative due to the uncertain ties of LDEQ’s BART-eligible screeningA-64 analysis, LDEQ formally requested other BART-e ligible facilities that had emissions greater than 5 tons and within 250 kms to perform a screening analysis. That action added facilities 15, 16, and 17 and 19 through 27 in Table 7-4. LDEQ also added Chalmette Refining, facility 14, and Union Carbide, facility 18, b ecause their emissions approached 5 tons and

both facilities are with in 150 km of Breton.

Table 7-2. BART-eligible faci lities closest to BretonA-65A-66A-67A-68A-69 Table 7-3: Facilities Requested to eith er Screen or Perform Refined ModelingA-70 The results of the individual screeni ng and refined modeling analyses for each

source that could not be eliminated from BA RT consideration are included in Table 9.5. Each modeling exercise was reviewed and approved by LDEQ, FLM, and EPA.

Table 7-4: CALPUFF/CALP OST Screening ResultsA-71A-72 Table 7-5 Facilities that LDEQ determined had units that were subject to BART

With the exception of the Mosaic facility, we ar e in concurrence with LDEQ’s assessment of

which facilities in Louisiana have units that ar e subject to BART. For the our detailed review and analysis on these subject to BART sources and the Mosaic facility, see the main TSD.

The modeling files and reports for the BART mode l plant analysis, modeli ng files for individual facilities, and modeling reports ar e available on request. Due to th e combined file size of several Gigabytes, we cannot post to the Docket directly. Contact the person identified in the FRN to

obtain the materials.

Figures 7-9, 7-10, and 7-11 de pict specific BART-eligible sources, their modeled deciview impact, lo cation and distance from the tw o Class I areas for 2001, 2002, and

2003.A-73A-74 Figure 7-1. BART Source Screening Modeling 2001A-75 Figure 7-2. BART Source Screening Modeling 2002A-76A-77 Figure 7-3. BART Source Screening Modeling 2003.A-78