Appendix A. 

Modeling and Emission Inventory Development; Review and 

Analysis for Louisiana’s Regional Haze State Implementation 

Plan Submittal 

Prepared and Reviewed by: 

Erik Snyder 

Michael Feldman 
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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 from IMPROVE Measurements ................................. 10

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

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

2.5 Uniform Rate of Progress (GlidePath) 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 Selection 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 and 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 (SO4) ................................................. 33

4.7.4.2 Model Performance for Nitrate (NO3) ................................................. 34
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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 Particulate 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 Changes Using Modeling Results ........................... 44

5.3 Reasonable Progress Goal and Path 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 .................................................................... 60
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 List of Tables 

Table 2.1. Uniform Rate of Progress for Breton National Wildlife Refuge and (worst 

quintile, western natural visibility conditions) .................................................................. 14

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

Table 3-2. Emission estimates for Louisiana sources in 2018 (tons/yr) .......................... 20

Table 5-1. Comparison of reasonable progress goal to uniform rate of progress for 2018 

(total extinction and deciviews) ........................................................................................ 46

Table 6-1. Projected light extinction for 20% worst days at Breton National Wildlife 

Refuge in 2002 (Mm-1) ..................................................................................................... 51

Table 6-2. Projected light extinction for 20% worst days at Breton National Wildlife 

Refuge in 2018 (Mm-1) ..................................................................................................... 51

Table 6-3. Projected light extinction for 20% worst days at Caney Creek Wilderness 

Area in 2002 (Mm-1) ......................................................................................................... 56

Table 6-4. Projected light extinction for 20% worst days at Caney Creek Wilderness 

Area in 2018 (Mm-1) ......................................................................................................... 56

Table 6-5. Percent contribution to total visibility impairment at Class I areas on 20% 

worst days from Louisiana Sources (contributions less than 1% are excluded) ............... 59

Table 7-1. BART-eligible facilities closest to Caney Creek ............................................ 61

Table 7-2. BART-eligible facilities closest to Breton ..................................................... 64

Table 7-3: Facilities Requested to either Screen or Perform Refined Modeling .............. 69

Table 7-4: CALPUFF/CALPOST Screening Results ....................................................... 70

Table 7-5 Facilities that LDEQ determined had units that were subject to BART .......... 72
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List of Figures 

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

Figure 4-1. National RPO 36-km modeling domain for CMAQ, CAMx, and SMOKE 

modeling. .......................................................................................................................... 24

Figure 4-2. CENRAP model performance (factional bias and error) of the Typ02f 

modeling scenario for sulfate (SO4). ................................................................................. 34

Figure 4-3. CENRAP model performance (factional bias and error) of the Typ02f 

modeling scenario for nitrate (NO3). ................................................................................ 35

Figure 4-4. CENRAP model performance (factional bias and error) of the Typ02f 

modeling scenario for organic carbon (OC). .................................................................... 36

Figure 4-5. CENRAP model performance (factional bias and error) of the Typ02f 

modeling scenario for elemental carbon (EC). ................................................................. 37

Figure 4-6. CENRAP model performance (factional bias and error) of the Typ02f 

modeling scenario for soil. ................................................................................................ 38

Figure 4-7. CENRAP model performance (factional bias and error) of the Typ02f 

modeling scenario for course particulate material (CM). ................................................. 39

Figure 4-8. Daily extinction model performance at Breton National Wildlife Refuge, LA 

for the worst (top) and best (bottom) 20% days during 2002 ........................................... 41

Figure 4-9. Daily extinction model performance 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 results by source region and source category 

for worst 20% days at Breton National Wildlife Refuge in 2002. .................................... 52

Figure 6-3. Source apportionment modeling results by source region and species for 

worst 20% days at Caney Creek Wilderness Area in 2002. ............................................. 53

Figure 6-4. Source apportionment modeling results by source region and source category 

for worst 20% days at Breton National Wildlife Refuge in 2018. .................................... 54

Figure 6-5. Source apportionment modeling results by source region and species for 

worst 20% days at Breton National Wildlife Refuge in 2018. ......................................... 55

Figure 6-6. Source apportionment modeling results 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 results by source region and source category 

for worst 20% days at Caney Creek Wilderness Area in 2018. ........................................ 58

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

sources............................................................................................................................... 58

Figure 6-9. Source apportionment modeling results by source region and species for 

worst 20% days at Caney Creek Wilderness Area in 2018. ............................................. 58

Figure 7-1. BART Source Screening Modeling 2001 ...................................................... 74

Figure 7-2. BART Source Screening Modeling 2002 ...................................................... 75

Figure 7-3. BART Source Screening Modeling 2003. ..................................................... 77
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Chapter 1: Background and Introduction 

1.1 BACKGROUND 

Regional haze is visibility impairment that is caused overwhelmingly by fine particulates 

(PM2.5). Visibility impairment occurs when PM2.5 in the atmosphere scatters and absorbs light, 

thereby creating haze. PM2.5 can be emitted into the atmosphere directly as primary 

particulates, or it can be produced in the atmosphere from photochemical 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, and because the ability of PM2.5 to scatter light 

depends on particle size, with light 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 remain suspended in the atmosphere for longer 

periods and is transported long distances, thereby contributing to regional-scale impacts of 

pollutant emissions on visibility. 

The sources of PM2.5 are difficult to quantify because of the complex nature of their formation, 

transport, and removal from the atmosphere. This makes it difficult to simply use emissions data 

to determine which pollutants should be controlled to most effectively improve visibility. 

Photochemical air quality models offer opportunity to better understand the sources of PM2.5 by 

simulating the emissions of pollutants and the formation, transport, and deposition of PM2.5. If 

an air quality model performs well for a historical 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 visibility goals. Although several types of air quality modeling systems 

are available, the gridded, three-dimensional, Eulerian models provide the most complete spatial 

representation and the most comprehensive representation of processes affecting PM2.5, 

especially for situations in which multiple pollutant sources interact to form PM2.5. 

In Section 169A of the 1977 Amendments to CAA, 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 Federal Class I areas which impairment results from 

manmade air pollution.” EPA promulgated a rule to address regional haze on July 1, 1999 (64 

FR 35713), the Regional Haze Rule (RHR). The 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 from sources located across broad 

geographic areas, EPA has encouraged the States and Tribes across the United States to address 

visibility impairment from a regional perspective. Five Regional Planning Organizations (RPOs) 

were developed to address regional haze and related 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 developing their regional haze plans. 
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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 federal agencies established to conduct data 

analyses, conduct pollutant transport modeling, and coordinate planning activities among the 

central States. CENRAP members include the State governments of Nebraska, Kansas, 

Oklahoma, Texas, Minnesota, Iowa, Missouri, Arkansas, and Louisiana and Tribal governments 

included in these states. 

1.2 TECHNICAL REQUIREMENTS FOR REGIONAL HAZE SIPS 

The RHR does not mandate specific milestones or rates of progress, but instead calls for States to 

establish goals that provide for “reasonable progress” toward achieving natural visibility 

conditions. In setting Reasonable 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 visibility 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 visibility 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. 
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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 Retrofit Technology (BART) 

v. Current and Future (2018) Emission Inventories 

vi. Source Contribution to Haze 

vii. Reasonable Progress Goals 

The purpose of this document is to supplement the main TSD and provide review of issues not 

covered in the main TSD dealing with the technical products developed by the Louisiana 

Department of Environmental Quality (LDEQ) and CENRAP for the central regional states, in 

support of their RH SIP. This document evaluated the methods and procedures used by LDEQ 

and CENRAP to develop the modeling and emission inventory products that assisted Louisiana 

and the central regional States in addressing the required elements of a RH SIP. Specifically, 

this document reviewed emission inventory, meteorological, photochemical, and BART 

modeling conducted by CENRAP and other screening modeling, evaluated the results and 

determined if these models met applicable guidelines or protocols, and met modeling standards 

at the time they were conducted. 
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Chapter 2: Development of Baseline and Natural Visibility Conditions and 

Glidepath 

2.1 INTRODUCTION

Under the Regional Haze Rule (RHR), each State is required to demonstrate reasonable progress 

in visibility conditions for each of its Class I areas. The State is to determine a uniform rate of 

progress ("glide path", "glide slope") toward the goal of natural visibility conditions in 2064. 

Considering various statutory factors, the State is also to define a reasonable rate of progress, and 

compare this to the benchmark uniform rate; if projected progress is less than the uniform rate, 

then the State is to explain why. Procedures for assessing progress are described in the Regional 

Haze Rule and EPA guidance documents. 

In brief, the guidance defines a metric to quantify visibility conditions, together with procedures 

for determining a starting point and an ending point, between which progress is to be made. The 

metric used is the Haze Index, measured in deciviews, and is designed to correspond to human 

perception of visibility changes. It is defined as: 

10*ln(bext/10) (1) 

where bext 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 average 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 degradation on the best 20%. 

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

Interagency Monitoring of PROtected Visual Environments (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 extinction using the IMPROVE equation, which adds 

up the contribution of each pollutant to extinction, while accounting for the effect of relative 

humidity. This total extinction is then converted to deciviews in the Haze Index through 

equation 1. For each of the years of the baseline period (2000-2004), the average of the 

deciviews on the worst 20% of days is calculated; the five-year average of these defines the 

baseline. This procedure is described in detail in EPA's "Guidance for Tracking Progress Under 

the Regional Haze Rule".1 The guidance also makes provisions for dealing with missing data, 

since monitoring instrument maintenance and malfunctions mean that data is not available for 

every scheduled measurement. 

The end point for progress is the goal of natural visibility conditions in 2064. The default 

approach for determining these is described in EPA's "Guidance for Estimating Natural Visibility 

 

1

 Hereafter “GTR”: EPA, 2003, Guidance for Tracking Progress Under the Regional Haze Rule, EPA-454/B-03-

004, September 2003, EPA OAQPS ; web page: http://www.epa.gov/ttn/oarpg/t1pgm.html 

direct link: http://www.epa.gov/ttn/oarpg/t1/memoranda/rh_tpurhr_gd.pdf 
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Conditions Under the Regional Haze Program".2

 Annual average natural 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 humidity for each specific Class I area. These 

annual averages are then translated into estimates for the best 20% and worst 20% days needed 

for the progress assessment. Extinction was assumed to have a lognormal frequency distribution; 

deciviews would then have a normal distribution, and its 10th and 90th percentiles were used as 

estimates of the average of the best 20% and worst 20% of days, respectively. The result is a 

table of best and worst 20% deciview values for 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 calculated as the difference between the baseline and 

natural conditions, spread over the 60 years between 2004 and 2064: uniform deciviews per year 

improvement = (current 2004 deciviews - natural 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 Rule is through 2018, so this uniform rate is 

multiplied by 14 to determine the first benchmark. 

2.2 CALCULATION OF VISIBILITY FROM IMPROVE MEASUREMENTS

The CENRAP procedure used for developing a uniform 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 revised IMPROVE algorithm was utilized rather 

than the original IMPROVE equation . The procedure used is described in the Technical 

Support Document for CENRAP Emissions and Air Quality Modeling 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 species, and then total them up, accounting for the 

effect of relative humidity, and including the Rayleigh scattering that occurs in pure air. The 

extinction total is then used to calculate deciviews 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 Historical Conditions-Causes and Effects", chapter 24 in 

NAPAP State of Science & Technology, Vol. III web page: 

http://vista.cira.colostate.edu/improve/Publications/Principle_pubs.htm 4

 Hereafter “CENRAP TSD”: Environ International 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. 
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Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze”6

 states 

that the use of either the IMPROVE or the revised IMPROVE equation is acceptable provided 

that the States supply documentation concerning the choice of equation and that the same 

algorithm is utilized for both the base and future extinction calculations. 

The IMPROVE program revised the IMPROVE equation after a scientific assessment of its 

implications for regional haze planning to reduce biases in light extinction estimates compared to 

the old algorithm.7 In particular, when compared to nephelometer direct measurements of 

visibility extinction, the original IMPROVE equation over-predicts 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 for coastal sites; 2) increased organic carbon mass 

estimate, based on more recent data for remote areas; 3) Rayleigh scattering using site-specific 

elevation and temperature, a refinement over the older network-wide constant; and 4) separate 

estimates for small and large particles of visibility impacts and humidity-dependent particle size 

growth rates, which could affect estimates at the low and high ends.8 The revised equation has 

an additional term for inclusion of NO2; however, none of the CENRAP Class I areas have 

monitors that provide observations of NO2 so this term was not used. 

The new equation shows broader scatter overall, but less bias in matching visibility 

measurements under high and low visibility conditions. That is, though it has a somewhat worse 

fit considering all the data, it has a better fit under visibility 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 changed (IMPROVE technical subcommittee for algorithm 

review, 2001, pp. 11-12), and so this makes little difference for assessing the contribution of 

emission sources to current conditions, and for projecting 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 IMPROVE 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/scram001/guidance_sip.htm direct link: 

http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf

7

 IMPROVE, 2006, Revised IMPROVE algorithm for Esimating Light Extinction from Particle Speciation Data, 

January 2006; http://vista.cira.colostate.edu/improve/Publications/GrayLit/gray_literature.htm; Hand, J.L.; Douglas, 

S.G., 2006, Review of the IMPROVE Equation for Estimating Ambient Light Extinction Coefficients – Final 

Report, 

http://vista.cira.colostate.edu/improve/Publications/GrayLit/016_IMPROVEEeqReview/IMPROVEeqReview.htm

8

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

use", The IMPROVE Newsletter, Volume 14, Number 4, Air Resource Specialists, Inc.; web 

page: http://vista.cira.colostate.edu/improve/Publications/news_letters.htm 

direct link: http://vista.cira.colostate.edu/improve/Publications/NewsLetters/IMPNews4thQtr2005.pdf
A-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 sites, fine sulfurous aerosol contributes the 

most to visibility impairment on the worst days during the baseline years, 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 distributions 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 considerations of relevance for the calculations 

needed for assessing progress under the RHR. EPA believes it is appropriate for the CENRAP 

states to use the revised IMPROVE equation. As the state of the science evolves, it is 

recommended that this procedure is reevaluated to apply more current and site specific data as it 

becomes available. CENRAP provides alternative calculations using the original IMPROVE 

equation for comparison with these visibility calculation results.

2.3 BASELINE VISIBILITY CONDITIONS

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 the 5 year period. These steps are mostly 

straightforward and are briefly discussed here with a more detailed discussion on the differences 

between EPA guidance and CENRAP procedures. 

We discuss the data filling for the Breton monitor 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. With the data substitution, this meets the minimum 

overall data completeness requirements for calculation 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 monitor 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 completeness 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 procedures can be found at the VIEWS website: 

(http://vista.cira.colostate.edu/views/). 
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The CENRAP followed EPA guidance for estimating baseline visibility conditions. 

2.4 NATURAL VISIBILITY CONDITIONS

EPA guidance set out a default procedure for estimating natural conditions, but also describes 

circumstances when States might want to use a more refined approach, such as to reduce 

uncertainty when baseline visibility is already near natural conditions, or when there is marked 

seasonality; these might be accomplished via alternative estimates of natural concentrations, or 

use of temporally varying estimates (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 under our 2003 Natural Visibility Guidance. This 

approach uses the revised IMPROVE equation so that progress between baseline conditions and 

natural conditions can be calculated on a consistent basis. 

The procedure used has several acknowledged limitations. 1) each chemical species can have 

one of only two possible background 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 components; 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 sources and background concentrations of PM will 

improve. The current approach used by LDEQ follows EPA methods and is acceptable. As 

additional information and more site-specific data become available, LDEQ is encouraged to 

pursue refinements in this approach to better quantify natural visibility conditions. 

2.5 UNIFORM RATE OF PROGRESS (GLIDEPATH) CALCULATION

The uniform rate of progress is calculated as the 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. 

 
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Table 2.1. Uniform Rate of Progress for Breton National Wildlife Refuge and (worst quintile, 

western natural visibility 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 uniform rate 0.23 per year 

Improvement needed by 2018 assuming uniform 

rate of progress 

22.885 3.22 

 
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Chapter 3: Emission Inventory Development 

3.1 INTRODUCTION

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 represent the 2000-04 regional haze baseline 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 inventories has undergone a number of 

revisions throughout the development process to arrive at the final versions used in CMAQ and 

CAMx air quality modeling. In general, updated 2002 emissions data for the U.S. developed by 

the Regional Planning Organizations (RPOs), updated emissions data for Mexico from the 

BRAVO 1999 emissions inventory, and version 2 of the 2000 emissions data for Canada were 

used to generate a 2002 annual emissions database. 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 quality assurance (QA) work was based on the Quality 

Assurance Project Plan (QAPP) CENRAP Emissions and Air Quality Modeling10 and Protocol 

for the CENRAP 2002 Annual Emissions and Air Quality Modeling11 (hereafter referred to as the 

“Modeling Protocol”). These protocols 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 developed to represent the 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 replicating observed particulate matter air quality. The base 

case includes actual day-specific emissions of SO2 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 Environmental Program (CEP), University of North Carolina(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); Reid, S.B. et al. 2004. Emission Inventory 

Development for Mobile Sources and Agricultural Dust Sources for the Central States. Sonoma Technology, Inc. 

(http://cenrap.sonomatech.com/CENRAP_Mobile/FinalReport.pdf).; Reid, S.B et al. 2004. Research and 

Development of Planned Burning Emission Inventories for the Central States Regional Air Planning 

Association. Sonoma Technology, Inc. 

(http://cenrap.sonomatech.com/CENRAP_PlannedBurnData/FinalReport.pdf).; Coe, D.L. and S.B. Reid. 2003. 

Research and Development of Ammonia Emission Inventories for the Central States Regional Air Planning 

Association. Sonoma Technology, Inc. 

(http://cenrap.sonomatech.com/CENRAP_Ammonia_NIF/FinalReport.pdf). 10 Morris, R.E. and G. Tonnesen. 2006. 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/CENRAP_QAPP_Rev3_Mar_29_2006.pdf ) 11 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).
A-16

 

• The 2000-04 baseline period planning case emissions scenario is referred to as 

“Typ02G”. The purpose of the Typ02G inventory is to represent baseline emission 

patterns based on average, or “typical”, conditions. This inventory provides a basis for 

comparison with the future year 2018 projected emissions, as well as to gauge reasonable 

progress with respect to future year visibility. 5-years of CEM data were analyzed and 

typical seasonal and diurnally varying emissions were defined. 

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

“Base18G”. These emissions are used to represent conditions in future year 2018 with 

respect to sources of criteria and particulate matter air pollutants, taking into 

consideration growth and controls. 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 categories are needed to support air quality modeling: 

stationary point-source emissions, stationary area-source emissions (also called nonpoint), 

mobile emissions for on-road sources, mobile emissions for nonroad sources (including aircraft, 

railroad, and marine vessels), and biogenic emissions. 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 Quality Modeling to Support Regional Haze State 

Implementation Plans12 (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 description of emissions used in the 2002 typical 

(Typ02G) emissions inventory. Emissions inventories 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 emissions inventory in-house with Emission Inventory 

Questionnaires and used the biogenic source inventory 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 burning (e.g., agricultural 

burning, structural fires, wildfires, prescribed burning). In addition, area source categories 

contributing to visibility pollutants (i.e., PM10, PM2.5, and NH3) are also included in the area 

source emissions inventory (e.g., fugitive dust, agricultural 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 Support 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) 
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The contractor reviewed all emission factors used in the inventory to ensure they were the most 

appropriate and up-to-date emission factors available and checked all calculations for accuracy. 

LDEQ. 

 The 2002 national emissions inventory (2002 NEI), compiled from submitted inventories from 

states, tribal and local agencies was the original basis for the CENRAP emission inventory. 

Sonoma Technology supplemented the 2002 NEI data with non-point source inventories to 

address agricultural and prescribed burning, on-road and non-road mobile sources, agricultural 

tilling and livestock dust, and agricultural ammonia for the CENRAP inventory.14 

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

SO2 NH3 NOX VOCs PM10 PM2.5

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 information on emissions and locations are available in 

regulatory reports. Larger permitted point sources in Louisiana are required to submit annual 

emissions inventories via Emission Inventory Questionnaires (EIQ), and all other point sources 

have a reporting frequency of every 3 years, beginning with the 2002 base inventory. This data, 

along with similar data available from 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 input into the Base02F inventory. The Typ02G 

 

14 Reid, S.B. et al. 2004. Emission Inventory Development for Mobile Sources and Agricultural Dust Sources for 

the Central States. Sonoma Technology, Inc. 

(http://cenrap.sonomatech.com/CENRAP_Mobile/FinalReport.pdf).; Reid, S.B et al. 2004. Research and 

Development of Planned Burning Emission Inventories for the Central States Regional Air Planning 

Association. Sonoma Technology, Inc. 

(http://cenrap.sonomatech.com/CENRAP_PlannedBurnData/FinalReport.pdf).; Coe, D.L. and S.B. Reid. 2003. 

Research and Development of Ammonia Emission Inventories for the Central States Regional Air Planning 

Association. Sonoma Technology, Inc. 15 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and 

Associates, Inc. and CEP, UNC, (http://www.cenrap.org/html/projects.php?mode=subcatdownload&id=50); 

Pechan and CEP. 2005. Refinements of CENRAP’s 2002 Emissions Inventories (Schedule 9; Work Item 3). 

E.H. Pechan and Associates, Inc. and CEP, UNC. (http://www.dnr.mo.gov/env/apcp/docs/appendixh-3.pdf) 
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inventory includes further processing of EGU emissions to develop a typical emission levels and 

temporal profiles. 

Coal-fired point sources within the CENRAP states use a PM2.5 speciation profile recently 

developed for MRPO by Carnegie Mellon that is representative of combustion of eastern 

bituminous coal. Texas and North Dakota sources 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 prepared for CENRAP modeling in one of two 

ways: 1) pre-computed emissions supplied by an RPO or other group or 2) supplied vehicle 

miles traveled (VMT), meteorological data and other MOBILE617 inputs for calculation in 

SMOKE/MOBILE6. Annual mobile emissions were pre-computed as part of the 1999 Mexico 

inventory and 2000 Canada inventory. Seasonal mobile emissions calculated in MOBILE6 were 

provided for all 13 WRAP states. For all other RPOs, including CENRAP, county-level VMT 

were prepared and input into SMOKE/MOBILE6. For all Louisiana parishes, parish-level 

Highway Performance Monitoring System annual 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 files 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 emissions from on-road mobile sources. 

3.2.3 Biogenic Emissions 

The BEIS3 system is utilized to estimate emissions from biogenic sources. BEIS3 is integrated 

into SMOKE for deriving biogenic emissions estimates given land use information, emissions 

factors for different plant species, and hourly, gridded meteorology data. Land use data is from 

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

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

preliminary modeling efforts. BEIS modeling produces gridded, hourly emissions 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).
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3.2.4 Non-Road Mobile Emissions 

Emissions from airport/aircraft operations, commercial and recreational marine vessels, and 

railroad locomotives, farm equipment, lawn equipment, and other sources were developed by the 

EPA for the 2002 NEI. The EPA NONROAD21 (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 other sources such as dry cleaning, degreasing and 

industrial surface coating. Sonoma Technology prepared additional inventories of prescribed 

burning, agricultural dust, and soil agricultural ammonia for the CENRAP region.23 The 

Western Regional Air Partnership (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 (general area, fire, ammonia, road dust, fugitive dust, 

uncategorized) to assist in particulate source apportionment modeling with CAMx. 

3.3 2018 EMISSIONS INVENTORY

An emission inventory for 2018 including anticipated changes due to population growth, 

emission controls and development of industry, energy, and natural resources is required to 

project the net effect on visibility conditions by 2018. CENRAP developed an emission 

inventory for 2018 (Base18G) using a combination of EPA Economic Growth Analysis System 

(EGAS 5/6), MOBILE 6, NONROAD, and the Integrated Planning Model (IPM) of ICF 

International for EGUs to project emissions from 2002 to 2018. Emission projections for most 

source categories are based on growth and control 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/CENRAP_Mobile/FinalReport.pdf).; Reid, S.B et al. 2004. Research and 

Development of Planned Burning Emission Inventories for the Central States Regional Air Planning 

Association. Sonoma Technology, Inc. 

(http://cenrap.sonomatech.com/CENRAP_PlannedBurnData/FinalReport.pdf).; Coe, D.L. and S.B. Reid. 2003. 

Research and Development of Ammonia Emission Inventories for the Central States Regional Air Planning 

Association. Sonoma Technology, Inc. 24 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and 

Associates, Inc. and Carolina Environmental Program (CEP), University of North Carolina(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) 
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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 Louisiana sources in 2018 (tons/yr) 

SO2 NH3 NOX VOCs PM10 PM2.5

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 projections for CENRAP states; 

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

• average historical values rather than 2002 data for prescribed burning; 

• Extrapolation of historical trends for unpaved roads; 

• United States Department of Agriculture (USDA) 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) regulations were applied to 

those engines subject to MACT rules. Emissions for Canada are based on a shared 2020 

emission inventory. 2018 EGU emissions were based on the run 2.1.9 of the Integrated Planning 

Model (IPM) updated by the CENRAP states. Reductions anticipated from BART controls for 

EGUs in Oklahoma, Arkansas, Kansas, and Nebraska were included in projections of 2018 

emissions. These anticipated reductions were based on actual operating conditions and estimated 

control efficiencies from utilities. Newly permitted coal-fired utilities were included in 2018 

projections. Conservatively, no IPM projected new units were removed from the simulation with 

the addition of the permitted facilities. Appendix B of the CENRAP TSD lists the file names, 

data source, type and a description of emissions used in the 2018 (Base18G) emissions 

inventory. The Access Database that 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) 
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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-agricultural sources (i.e., natural wind blown 

fugitive dust); 

• Off-shore emissions associated with off-shore marine and oil 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) utilized an inventory that had assumptions about BART controls in 

the CENRAP states. 
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Chapter 4: Modeling Protocol, Episode Selection and Modeling Set-up 

Overview 

4.1 INTRODUCTION

Meteorological, emission and photochemical models are essential tools in examining factors that 

impact visibility and for development of effective control strategies 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-dimensional air quality models to simulate the transport and fate 

of key species that affect visibility in Class I Areas in the central U.S. This work included the 

development of meteorological data for input into the model as well as creation and processing 

of emission estimates for use in the model. The Modeling Protocol26 describes the model 

selection, configuration, episode selection, and model evaluation used in support of the 

Louisiana RHR SIP.

 

4.2 QUALITY ASSURANCE PROGRAM PLAN

The modeling team developed a quality assurance program plan (QAPP)27 to develop clearly 

defined data quality objectives, documentation, and procedures. This QAPP was developed 

incorporating the following elements as described in the EPA guidance document for modeling: 

• A systematic planning process including identification of assessments and related 

performance criteria; 

• Peer reviewed theory and equations; 

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

• Documentation of any changes from original plans; 

• Clear documentation of assumptions, theory, and parameterization that is detailed enough 

so others can understand the model output; 

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

• Output data that can be used to help inform decision makers. 

The plan describes the data management and quality assurance/quality control measures taken to 

assure high quality emission inventories and air quality modeling results for use in the RH 

analysis. 

4.3 EPISODE SELECTION

EPA guidance28 describes the criteria that should be used to select a modeling episode. The 

modeling episode should: 1) reflect a variety of meteorological conditions 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/CENRAP_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) 
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the baseline period of 2000-2004, 3) cover a period where extensive 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 days). For regional haze modeling, the preferred 

approach is to simulate an entire representative year. 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 base case and baseline 

inventories covering the same year. Other RPOs selected the same modeling year, allowing for 

more direct comparison of modeling results and sharing of modeling inputs. The availability of 

2002 NEI also provides an additional resource in the development of emission inventories for the 

modeling episode. 2002 appears to be a more representative year when compared to 2003 and 

2004. The EPA approves of the selection of 2002 for the regional haze modeling episode. 

4.4 PHOTOCHEMICAL AND EMISSIONS MODELING DOMAIN

CENRAP conducted emissions and air quality modeling on the 36-km national regional planning 

organization (RPO) domain. This domain consists 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 additional computational resources required to run the 

model at the higher resolution. The use of higher 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). 
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Figure 4-1. National RPO 36-km modeling domain for CMAQ, CAMx, and SMOKE modeling. 

4.5 MM5 METEOROLOGICAL MODEL 

4.5.1 Model Selection 

Photochemical grid models, such as CMAQ and CAMx, require inputs of three-dimensional 

gridded meteorological data, including wind, temperature, humidity, cloud/precipitation, and 

boundary layer parameters. The Fifth-Generation 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 applications and has been used extensively in past local, state, 

regional, and national modeling efforts. MM5 has undergone extensive peer-review, with all of 

its components continually undergoing development and scrutiny by the modeling community. 

In-depth descriptions of MM530 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. 
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In addition to development of meteorological inputs for CMAQ and CAMx, MM5 was also used 

to develop meteorological inputs for the CALMET/CALPUFF modeling system. As discussed 

further in Section 6 of this review, CALMET/CALPUFF was used to determine whether a 

BART eligible source contributes to visibility impairment at a Class 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 Department of Natural Resources (IDNR) and is 

described fully in the report entitled Meteorological Model Performance 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, MM5 was configured to run on the standard 

continental-scale Regional Planning Organization (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 central 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 (east-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 domain with 12 km grid spacing 

covering the central states by EPA Region VII and the Texas Commission on Environmental 

Quality to examine model prediction sensitivity to grid resolution. The vertical layer structure of 

the CENRAP meteorological modeling domain consists of 34 layers, a top level at 100 millibars, 

and increasing layer thickness with altitude. The vertical layer structure is further detailed in the 

Modeling Protocol. 

4.5.3 Model Configuration 

The final CENRAP MM5 modeling system configuration for the 2002 annual simulation is 

provided in the Modeling Protocol 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 results showed an extreme 

cold bias over the central U.S and unnatural diurnal profiles near shorelines. A number of 

sensitivity tests were performed by IDNR to resolve 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) 

. 
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meteorological modeling for VISTAS.34 The combination of all of these studies led to the 

configuration used by CENRAP for MM5 modeling 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 necessary to prepare topographic, vegetative, 

initial condition, boundary condition, and FDDA nudging input files. 

Global topographic data at 2-minute (latitude/longitude) resolution were used to define terrain 

elevations on the 36-km grid. Land use distribution on the MM5 domains was defined from the 

24-category USGS vegetation data with a resolution of 2 minutes. 

The 3-hour Eta analysis and surface fields available from the National Center for Atmospheric 

Research (NCAR) were taken from the Eta Data Assimilation System (EDAS) and used to 

supply initial and boundary conditions to MM5, and for analysis nudging in the FDDA package. 

The EDAS analyses are developed from a wide variety of observational sources, including 

standard surface and upper air measurements, profiler networks, radar- and satellite-derived 

measurements, and ship and aircraft reports. The wide array of data sources, coupled with the 

high time- and spatial resolution provided by EDAS, result in an analysis product that far 

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

Sea surface temperatures (SSTs) were approximated by ETA skin temperatures 

The annual simulation was generated from 95 independent simulations initialized at 12Z and 

integrated through five days. To allow for approximately a two week photochemical model spinup period, the simulation started 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 meteorological conditions and precipitation.35 Additional performance 

evaluation was done by CENRAP by comparing 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 Meteorological 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 for 2002, Task 3: Met Gatekeeper Report. 

http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/CENRAP_VISTAS_WRAP_2002_36km_MM5_eval.ppt 
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The goal of the evaluation was to determine whether the meteorological fields are sufficiently 

accurate to properly characterize the transport, chemistry, and removal processes in 

CMAQ/CAMx. If errors in the meteorological fields are too large, the ability of the air quality 

model to replicate regional pollutant levels over the entire base year will be severely hampered 

and the predicted impacts from future year growth and controls will be highly questionable. To 

provide a reasonable meteorological characterization 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 forcings for mesoscale circulations; 

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

patterns; 

• Mesoscale circulations such as sea breezes and mountain/drainage circulations; 

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

For visibility applications, the moisture and condensate fields are particularly important as they 

significantly impact PM chemical formation, removal, and light scattering efficiency. In 

addition, cloud and precipitation fields are a good measure of the integrated performance of the 

model since these are model-derived quantities and not nudged to observations. Because of the 

model’s coarse resolution of 36-km, the model cannot be expected to faithfully simulate the 

pattern or variability of the convective precipitation, but should reproduce the synoptic 

precipitation and cloud patterns. 

The IDNR evaluation of the MM5 model performance was limited to operational testing of the 

model, and not to a scientific evaluation. Previous peer-reviewed documentation of MM5 

formulation, testing, and evaluation provide the basis for its scientific validity. An operational 

evaluation entails an assessment of the model's ability to correctly estimate surface and boundary 

layer wind, temperature, and moisture largely independent of whether the actual process 

descriptions in the model are accurate. The operational evaluation essentially tests whether the 

predicted meteorological fields are reasonable, consistent, and agree adequately with available 

observations in time and space. The process provides only limited information about whether 

the results are correct from a scientific perspective or whether they are the fortuitous product of 

compensating errors; thus a “successful” operational evaluation is a necessary but insufficient 

condition for achieving a sound, reliable performance testing exercise. 

The basis for the IDNR operational performance assessment entailed a comparison of the 

predicted meteorological fields to available surface and aloft data that are collected, analyzed, 

and disseminated by the National Weather Service. It was carried out both graphically and 

statistically to evaluate model 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. 
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results of the performance evaluation, that the final 36 km CENRAP MM5 simulations exhibit 

reasonably good performance for the central U.S. 

Comparison of CENRAP MM5 performance with 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 assimilation of temperature in the interim WRAP simulation resulted in 

the best performance in prediction of surface temperatures but the poorest performance for 

vertical temperature profiles. Surface data assimilation has since been dropped from the WRAP 

modeling protocol. The 2002 VISTAS MM5 simulations 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 meteorological databases 

used for prior air quality modeling efforts. It is therefore deemed 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 modeling was conducted. The performance of the model 

was adequate for the purposes for which it was used and on par with other studies 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 EMISSIONS MODEL 

CENRAP selected the Sparse Matrix Operator Kernel Emissions model38 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 available emission inventory data to the resolution 

needed by the air quality model. SMOKE also has the ability to compute emissions for mobile 

on-road and biogenic sources. Biogenic emission modeling is performed through SMOKE with 

the Biogenic Emission Inventory System, version 3 (BEIS3)39 using the Biogenic Emissions 

Landcover Database (BELD3) vegetative database. Mobile emissions can be calculated by 

SMOKE from mobile-source activity data, using emission factors from the MOBILE6 model. 

SMOKE supports the emission input formats required by the CAMX and CMAQ air quality 

models. 

 

4.7 AIR QUALITY MODEL

Photochemical air quality models offer opportunity to better understand the sources of particulate 

matter that impair visibility by simulating the emissions, formation, transport, and deposition of 

these pollutants. If an air quality model performs well for a historical episode, the model may 

then be useful for identifying the sources of particulate matter and helping to select the most 

effective emissions reduction strategies for attaining visibility goals. Although several types of 

air quality modeling systems are available, the gridded, three-dimensional, Eulerian models 

provide the most complete spatial representation and the most comprehensive representation of 

 

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

39Available at http://www.epa.gov/ttn/chief/software.html#pcbeis
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processes affecting particulate matter, especially 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 downloadable to all stakeholders. Furthermore, the user must be 

able to revise the code to perform diagnostic analyses and/or to improve the model’s ability to 

describe observations in a credible manner. Several additional prerequisites should be met for a 

model to be used to support an attainment demonstration or uniform rate of progress assessment. 

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

• It should be appropriate for the specific application on a theoretical basis. 

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

• It should be shown to have performed well in past modeling applications. (If the 

application is the first for a particular model, then the State should note why it believes 

the new model is expected to perform sufficiently.) 

• It should be applied consistently 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 Regional Haze applications. The CMAQ and CAMx 

models have been accepted by EPA for numerous regulatory air quality modeling applications 

and were considered by CENRAP for use in regional haze modeling. CENRAP selected CMAQ 

Version 4.5 with “SOAmod enhancements” as the primary air quality model for regional haze 

modeling and the CAMx Version 4.40 model, applied 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 Section 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 treatment of horizontal diffusion SOA formation 

mechanisms, and in grid nesting. EPA accepts 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 EPA for this Regional Haze selection. 

Both air quality models were set up and run 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 increase in computer resources and time. CAMx 

runs at 12-km resolution reduced 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) 
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compared to 36-km runs. With this possible exception, 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-Scale 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 provide 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, visibility, 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 grid cells and the number 

and thicknesses of layers are defined by the user, based in part 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 numerical 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-phase chemistry: the Regional Acid Deposition 

Mechanism version 2 (RADM2), a fixed coefficient version of the SAPRC90 mechanism, and 

the Carbon Bond IV mechanism (CB-IV). 

CENRAP used CMAQ Version 4.5 with a “SOAmods enhancement” for 2002 base case (actual 

emissions), 2002 baseline (typical 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 carbon (OMC) concentrations. 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 configuration 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. 
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The Comprehensive Air Quality Model with extensions (CAMx) model43 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, visibility, 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, aerosol formation, and dry and wet deposition. 

CAMx has seen extensive use within many of the CENRAP states. The CAMx model is based 

on well-established treatments of advection, diffusion, deposition, and chemistry. CENRAP 

used CAMx Version 4.40, applied using similar 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 aerosol 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 the 34 layer vertical structure used in MM5 

modeling to a smaller set of vertical layers. Sensitivity studies by WRAP and VISTAS 

examined model performance looking at a variety 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 collapsing 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 efficiency and produces results almost identical to 

the full vertical structure runs. 

4.7.3 Initial and Boundary Conditions 

Initial conditions (ICs) are specified by the user for the first day of a model simulation. For 

continental-scale modeling using the RPO 36-km domain, the ICs can affect model results for as 

many as 15 days, although the effect typically becomes very small after about 7 days. A model 

spin-up period is included in each simulation to eliminate any effects from the ICs. For the 

CENRAP modeling, the annual simulation 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 used. 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 flow 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 substantial contributor to visibility impairment. For 

this study BC data generated in an annual simulation of the global-scale GEOS-Chem model for 

 

43 ENVIRON, 2006. “User’s Guide – Comprehensive Air-quality Model with extensions, Version 4.30.” ENVIRON 

International Corporation, Novato, California. (available at http://www.camx.com). 
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calendar year 2002 were applied.44 The BCs employed by CENRAP were 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 case, for calendar year 2002—to demonstrate the 

suitability of the modeling systems for subsequent planning, sensitivity, and emissions control 

strategy modeling. Comparisons between the 2002 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 representative of the final model performance. 

Therefore, model performance was evaluated using the Typ02F emission inventory. 

 

Model performance evaluation is performed by comparing output from model simulations with 

ambient air quality data for the same time period 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 different monitoring networks for both PM and gaseous 

species, for an annual time period, and for a large number of sites. The focus 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 Nitrate (NO3); Elemental Carbon (EC); Organic Mass 

Carbon (OMC); Other inorganic fine particulate (IP or Soil); and Coarse Matter (CM). The 

model must be evaluated for both the worst visibility 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 experimented 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 and model-predicted species concentrations 

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

• Spatial analysis plots with ambient data overlaid on model predictions 

• Bar plots comparing the mean fractional bias (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 contributions 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 performance for CENRAP CMAQ modeling using 

the Typ02F emissions inventory. Below are six 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 for 

2002 Provided to the VISTAS Group. Harvard University (http://www.vistassesarm.org/documents/Harvard_GEOS-CHEM_FinalReport_20050624.doc) 
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from a monitoring network with the corresponding model output. The monitoring networks used 

for comparison are IMPROVE, CASTNET, and STN, and are treated separately because each 

monitoring network has different goals, siting criteria, and data collection protocols. The model 

performance plots depicted here are “bugle plots”, and depict model performance (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 performance goals or criteria (i.e., the ≤30%/50% and 

≤60%/75% bias/error levels) as concentrations become greater than 2.5 μg/m3

.

 

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 than the IMPROVE network, but is also mostly 

sited at Class 1 Areas and as such, model performance at CASTNET sites should also be 

considered important. The STN monitoring network is an urban network, and model 

performance relative to this network should be given less importance. 

The model performance goals and criteria used by CENRAP were appropriate at the time the 

modeling was conducted and consistent with the methods adopted by VISTAS and WRAP. The 

EPA agrees with the CENRAP model performance procedures 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/cenrap/cmaq.shtml#cmaq_typ02f_mpe). 

4.7.4.1 Model Performance for Sulfate (SO4) 

 

Figure 4-2 shows the monthly SO4 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 performance for all months is within the goals for 

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

fractional error and within the criteria for fractional 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. 
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Figure 4-2. CENRAP model performance (factional bias and error) of the Typ02f modeling 

scenario for sulfate (SO4). 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. 

4.7.4.2 Model Performance for Nitrate (NO3) 

 

Figure 4-3 shows the monthly NO3 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 during 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 winter bias is more significant because NO3

concentrations tend to be a large component of visibility impairment during the winter months. 
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In general, winter model performance does not meet the performance goals and in some cases 

does not meet the criteria, predicting concentrations of NO3 much higher than observed. 

Figure 4-3. CENRAP model performance (factional bias and error) of the Typ02f modeling 

scenario for nitrate (NO3). 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. 

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 performance 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 higher concentrations of OC then the rural 
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IMPROVE monitors. Model performance for STN sites shows a negative bias throughout the 

year that fall within the model criteria for both bias and error. 

Figure 4-4. CENRAP model performance (factional bias and error) of the Typ02f modeling 

scenario for organic carbon (OC). 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. 

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 performance 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 large model underprediction during the summer at 
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the IMPROVE sites. However, EC concentrations at these sites are low putting the model 

performance within the goals for low concentrations.

Figure 4-5. CENRAP model performance (factional 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 CENRAP 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 and 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 getting worse in the fall and spring. This may be 

due to local effects near the monitor and difficulties in capturing emissions accurately in the 
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inventory. This is an area of concern, especially in areas where soil contributes significantly to 

visibility impairment. 

Figure 4-6. CENRAP model performance (factional bias and error) of the Typ02f modeling 

scenario for soil. 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. 

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 throughout the year. This may be due to localized 

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

visibility impairment. 

Figure 4-7. CENRAP model performance (factional bias and error) of the Typ02f modeling 

scenario for course particulate 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. 
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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 diverse geographic area may mask model performance 

issues occurring in smaller geographic sub-regions. CENRAP also evaluated model performance 

in predicting total extinction on the 20% best and 20% worst days at each Class I site. 

Performance for the worst 20% days at the CENRAP Class I areas is generally characterized by 

an underestimation bias. Performance at the Breton (BRET), LA, Big Bend (BIBE), TX and 

Guadalupe Mountains (GUMO), TX Class I areas for the worst 20 percent days is particularly 

poor. At GUMO, visibility impairment is primarily due to high soil and CM which are not well 

predicted by the model across the CENRAP area. At the BRET and BIBE sites, all components 

are under-predicted, leading to an under-prediction in total extinction. Model predictions at 

these sites are less reliable than at other CENRAP sites for planning purposes. In general, model 

performance is acceptable for SO4, NO3, 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 investigated the assumption that all 

CM and soil are natural and 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 various 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 Wildlife 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 heavily dominated by sulfate extinction. Nitrates do 

have a sizeable component on a few days but the sulfate extinction components are still higher 

on these few days. Sulfate, OMC and Soil under-prediction 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 and modeled data both conclude a high impact 

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

bias level but there is a lot of scatter on individual days. On average, the low bias looks 

reasonable, but day specific performance is more questionable. 

On most of the worst 20 % days at Caney Creek, total extinction is dominated by sulfate 

extinction with some extinction due to OMC. On four of the worst 20% days extinction is 

dominated by nitrate. Sulfate is underestimated 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 NO3. 
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Figure 4-8. Daily extinction model performance at Breton National Wildlife Refuge, LA for the 

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

worst (top) and best (bottom) 20% days during 2002. 
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Chapter 5: 2018 Future Year Modeling 

5.1 2018 MODEL SIMULATIONS

The 2018 future-year base case scenario is referred to as “2018 Base Case” or “Base18G”. The 

purpose of the Base18G scenario is to simulate the air quality representative of conditions in 

future year 2018 with respect to sources of criteria and particulate matter air pollutants, taking 

into consideration growth and controls. Modeling results based on this emission inventory are 

used to define the future year ambient air quality and visibility metrics. 

Input data used for the 2018 Base Case model simulations 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 (including science options, run 

scripts, simulation periods, and ancillary data) for the Base18 cases was identical to that used in 

the Typ02G modeling. 

The purpose of modeling 2018 visibility is to compare the 2018 visibility predictions to the 2002 

typical-year visibility modeling results and compare 2018 visibility predictions to the URP goal 

for 2018, as discussed below. Some improvements in visibility by 2018 are expected because of 

reductions in emissions due to currently planned regulations and technology improvements. The 

methodology used by CENRAP in developing visibility projections for 2018 and described 

below is consistent with EPA guidance. 

 

5.2 VISIBILITY PROJECTIONS

The Regional Haze Rule (RHR) goals include achieving 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 visibility 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 reasonable 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) that has at one end the 

observed visibility conditions during the mandated five-year (2000-2004) baseline period and at 

the other end natural visibility conditions in 2064; the visibility value that occurs on the glide 

path at year 2018 is the URP goal. 

CENRAP has made 2018 visibility projections using Typ02G and Base18G CMAQ 36-km 

modeling results following EPA guidance45 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/scram/guidance/guide/final-03-pm-rh-guidance.pdf) 
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response factors (RRFs), which are defined as the ratio of the future-year modeling results for 

each component that affects visibility to the current-year modeling results. The calculated RRFs 

are applied to the baseline observed 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 into 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 develop RRFs between future and current predicted 

concentrations of each component. 

• Component-specific RRFs are multiplied by current monitored values to estimate future 

component concentrations. 

• Estimates of future component concentrations are consolidated to provide an estimate of 

future air quality. 

• Future estimated air quality is compared with the goal for regional 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 extinction in the revised IMPROVE mass extinction 

equation46 is scaled separately. Because the modeled 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 modeled 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) resulting from the modified SOA module used by 

CENRAP are also included in the OC term. 

5.2.2 Projecting Visibility Changes Using Modeling Results 

RRFs are calculated as the ratio of the 2018 modeling 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 for 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) 
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revised IMPROVE equation. The following six steps found in the modeling guidance47

summarize the general procedure used to project future-year visibility for the 20% best and 20% 

worst visibility days: 

1) For each Class I area, rank visibility (in deciviews) 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 period, 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 period emissions and future emissions. Use the 

resulting information to develop relative response factors for 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 IMPROVE 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 worst days of each year. Then average the 5 

years together to get the final future mean deciview 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 visibility conditions. These methods were appropriate 

at the time the modeling was performed.

5.3 REASONABLE PROGRESS GOAL AND PATH TO NATURAL CONDITIONS

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 against to judge progress. The estimated visibility impairment in 

2018 is slightly less than the calculated 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 Act requires that four 

factors be considered: 

• Cost of compliance 

• Time necessary for compliance 

• Energy and non-air quality environmental 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 visibility 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/scram/guidance/guide/final-03-pm-rh-guidance.pdf) 
A-46

the baseline visibility (2002-2004) is 25.73 dv, and the estimated 2018 URP is 22.51dv. The 

modeling predicts a visibility improvement of 3.01 dv by 2018, compared to the URP 

improvement of 3.22 dv by 2018. The modeling predicts 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 SIPs, but it serves as a benchmark to compare 

progress toward natural visibility conditions in 2064 and is designed to help states in selecting 

their 2018 RPGs. As stated in EPA Guidance for Setting Reasonable Progress Goals Under the 

Regional Haze Program48, “The glidepath is not a presumptive target, and States may establish a 

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

glidepath.” 

The modeling also shows visibility impairment 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 conditions as the Reasonable Progress Goal 

for Breton Class I area. We are proposing to partially approve and partially disapprove 

Louisiana’s Reasonable Progress Goals 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 emissions in Louisiana are projected 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 additional analyses are conducted for BART on sources as discussed in 

the BART sections of the main TSD and in the FRN proposal. 

 

Table 5-1. Comparison of reasonable progress goal to uniform rate 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/reasonable_progress_guid071307.pdf 
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Figure 5-1. Projections of visibility impairment for 20% worst days at Breton Island 

Figure 5-2. shows the differences in model results of total extinction between the Base18g and 

Typ02g model predictions, including the contributions 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 concentrations being the most significant contribution to 

improved visibility. 
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Figure 5-2. Differences in modeled total extinction (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. 
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Chapter 6: Source Apportionment Modeling 

6.1 INTRODUCTION

Visibility impairment in Class I areas is the result of local air pollution as well as transport of 

regional pollution across long distances. The relative contributions to visibility impairment from 

each source region and category is needed to develop effective control strategies to improve 

visibility. CENRAP used CAMx Version 4.40 with its Particulate Source Apportionment 

Technology (PSAT) tool to provide source apportionment by geographic regions and major 

source category. CAMx was run with similar options and inputs as the CAMQ modeling with 

both the 2002 baseline and 2018 future case emission inventories (Base F). The CAMX model 

selection and performance are discussed 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 that 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 against other apportionment techniques.50,51 The goals of the 

PSAT assessment are to evaluate the contributions of different geographic regions and source 

categories to visibility impairment at Class I areas in 2002 and the projected 2018 case in order 

to identify those regions and source categories that, if controlled, 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 western and eastern United States, 

the Gulf of Mexico, Canada and Mexico. Six source categories (elevated point sources; lowlevel point sources, on-road mobile, non-road mobile, area and natural or non-anthropogenic 

sources) were tracked separately. The CENRAP PSAT 2002 and 2018 applications 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 and 20% best days at each CENRAP and nearby 

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

to calculate extinction coefficients from modeled 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 little effect on visibility predictions at the CENRAP class I areas. 

Extinction due to soil and coarse material changes very little between 2002 and 2018. A PSAT 

Visualization Tool was developed that can be used by States, Tribes 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 With Extensions (CAMx) Version 4.30.” ENVIRON 

International Corporation, Novato, California, 2006 (available at www.camx.com). 

50 Morris, R.E., G.Y., C.E., G.W., B.K. 2005. “Recent Advances in One-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) 
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average of the worst 20 percent and best 20 percent 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 extinction keeping the relative contributions of source groups to 

each PM species (e.g., SO4, NO3, etc.) the same averaged across the 2002 worst 20 percent days 

but scaling their magnitudes up or down based 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 the 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 SOURCE APPORTIONMENT RESULTS AT BRETON NATIONAL WILDLIFE REFUGE

Tables 6-1 and 6-2 show the modeled contributions to total extinction for each source category 

and species for 2002 and 2018, respectively. Figures 6-2, 6-3, 6-4, and 6-5 show the 

geographical source apportionment by source category 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 contributes over half (96.83 Mm1) of the total extinction of 132.52 Mm-1. The largest contributions of sulfate come from 

Louisiana ( 15.48 Mm-1from all source categories) and the eastern United States (22.88 Mm-1). 

Overall, the largest source region 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.php
A-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 decrease in total light extinction. Even with such 

large reductions in SO4 from point sources in 2018, 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 sulfate. Use of a 12km resolution modeling grid in 

CAMX reduced the summertime sulfate bias but required large computational expense. The use 

of higher resolution modeling should be reconsidered in future modeling efforts. 

Table 6-1. Projected light extinction for 20% worst days at Breton National Wildlife Refuge in 

2002 (Mm-1) 

Total1 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 extinction for 20% worst days at Breton National Wildlife Refuge in 

2018 (Mm-1) 

Total1 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 matter 
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Figure 6-2. Source apportionment modeling results by source region and source category for 

worst 20% days at Breton National Wildlife Refuge in 2002. 

 
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Figure 6-3. Source apportionment modeling results by source region and species for worst 20% 

days at Caney Creek Wilderness Area in 2002. 

 
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Figure 6-4. Source apportionment modeling results by source region and source category for 

worst 20% days at Breton National Wildlife Refuge in 2018. 

*2018 projections for Texas Point sources are divided into EGU and Non-EGU point sources 
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Figure 6-5. Source apportionment modeling results by source region and species for worst 20% 

days at Breton National Wildlife Refuge in 2018. 

6.3 SOURCE APPORTIONMENT RESULTS AT CANEY CREEK WILDERNESS AREA

Tables 6-3 and 6-4 show the modeled contributions to total extinction for each source category 

and species for 2002 and 2018, respectively. Figures 6-2, 6-3, 6-4, and 6-5 show the 

geographical source apportionment by source category and species for the 20% worst days in 

2002 and 2018. Visibility impairment at the Caney Creek Wilderness Area site in 2002 on the 

worst 20% days is largely due to sulfate from point sources that contributes over half (75.1 Mm1) of the total extinction of 133.93 Mm-1. The largest contributions of sulfate come from Texas ( 

11.55 Mm-1from 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 total 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 emissions from point sources in Texas, the eastern 

United States, Indiana, and Ohio account for a decrease of 24.41 Mm-1in total light extinction, 

approximately half of the total reduction between 2002 and 2018. Even with such large 
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reductions in SO4 from point sources in 2018, 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. 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. The use of higher resolution modeling should be 

reconsidered in future modeling efforts. 

Table 6-3. Projected light extinction for 20% worst days at Caney Creek Wilderness Area in 

2002 (Mm-1) 

Total1 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 extinction for 20% worst days at Caney Creek Wilderness Area in 

2018 (Mm-1) 

Total1 Point Natural On-Road Non-Road Area

SO4 48.95 39.83 0.07 0.12 0.44 5.31 

NO3 7.57 2.84 0.53 0.97 1.33 1.37 

POA 9.93 1.76 1.18 0.14 1.03 5.09 

EC 3.17 0.24 0.30 0.16 0.94 1.31 

SOIL 1.29 0.35 0.01 0.01 0.01 0.87 

CM 3.58 0.24 0.04 0.03 0.01 3.02 

Sum 85.84 45.27 2.12 1.44 3.76 16.96 1

Totals include contributions from boundary conditions and secondary organic matter 
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Figure 6-6. Source apportionment modeling results by source region and source category for 

worst 20% days at Caney Creek Wilderness Area in 2002. 

Figure 6-7. Source apportionment modeling results by source region and species for worst 20% 

days at Caney Creek Wilderness Area in 2002. 
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Figure 6-8. Source apportionment modeling results by source region and source category for 

worst 20% days at Caney Creek 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 region and species for worst 20% 

days at Caney Creek Wilderness Area in 2018. 
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6.4 CONTRIBUTIONS TO VISIBILITY IMPAIRMENT AT OTHER CLASS 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 contributions to the Class I areas in Oklahoma with impairment % 

increasing from 3.55 to 4.99%. Outside of Oklahoma, the next largest contributions from 

Louisiana sources are on Class I areas in Arkansas (Upper Buffalo), Texas (Big Bend and 

Guadalupe Mtns.) and Missouri (Hercules-Glades 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 (contributions 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% 
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Chapter 7: BART Determination 

7.1 BART SCREENING ANALYSES 

The discussion that follows is a description of the process used to determine BART 

Sources. LDEQ conducted an evaluation to support just evaluating the two closest Class I areas. 

The two Class I areas closest to Louisiana sources are Breton National Wildlife Refuge and 

Caney Creek Wilderness Area. We concur with LDEQ’s decision to focus on these two Class I 

areas. 

First, LDEQ sorted the BART-eligible facilities in Louisiana with visibility impairing pollutants 

by distances to the nearest Class I area. Second, LDEQ evaluated the ratios of the total of 

visibility impairing emissions to the distance 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 screening model using the 

following methodology: 

● EPA regulatory approved model, CALPUFF version 5.711a; 

● CENRAP 6 km spacing resolution 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 particulate 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 determine the visibility impact on the Class 

I area of interest. 

We concur with the use of this version of CALPUFF at the time and the methodology that LDEQ 

utilized for using specific facilities with a high Q/D of visibility impairing pollutants as model 

plants for screening of sources. 

In accordance with the Guidelines, LDEQ chose to use a contribution threshold of 0.5 deciviews 

(98th 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 98th Percentile. Therefore, LDEQ used a screening evaluation criterion was a 

maximum deciview impact of greater than 0.5 deciviews to require a refined analysis. We 

concur with this approach. 

The two (2) existing facilities that had the highest emission divided by distance ratios 

with respect to the Caney Creek Class I area were Smurfit Stone in Jackson Parish, Louisiana 

and Chemtrade Refining in Caddo Parish, Louisiana. Results of the facility’s screening are 

 
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shown in table 7-4. Modeled results indicated that there was no visibility impact at Caney 

Creek, with the exception of Chemtrade in 2002 and the average of the maximums were below 

0.5 dv impact.. Model outputs are listed below: 

Smurfit Stone, Jackson Parish, Louisiana; 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, Louisiana; 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 less than either Smurfit Stone or Chemtrade 

Refining and they were farther away from the Caney Creek Class I area. 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 pollutants from tall stacks. The modeling indicated 

there was no impact to visibility at Caney Creek from these two additional sources. 

Table 7-1. BART-eligible facilities closest to Caney Creek 
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LDEQ altered their methods for determining visibility impairment for the Breton Class I area 

from the analysis methods they used for Caney Creek. LDEQ chose to model two 

facilities: ConocoPhillips Alliance Refiner 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 facilities 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/distance ration was greater than 0.0898678 then the 

facility was requested to conduct its own modeling exercise. Facilities 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 Michoud 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 facilities were found to have an impact on visibility 

at Breton, and both were requested to perform the refined modeling. (Facilities 11 and 12 in 

Table 9.4) Facility 13, Sid Richardson, was requested to perform refined modeling also 

because its emissions/distance ratio was slightly greater than of Murphy Oil (0.0891079). 

Looking at BART-eligible facilities further 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 Evangeline 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 impact from this facility at Breton. 

To be conservative due to the uncertainties of LDEQ’s BART-eligible screening 
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analysis, LDEQ formally requested other BART-eligible 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, because their emissions approached 5 tons and 

both facilities are within 150 km of Breton. 

Table 7-2. BART-eligible facilities closest to Breton 

 
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Table 7-3: Facilities Requested to either Screen or Perform Refined Modeling 
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 The results of the individual screening and refined modeling analyses for each 

source that could not be eliminated from BART consideration are included in Table 9.5. 

Each modeling exercise was reviewed and approved by LDEQ, FLM, and EPA. 

Table 7-4: CALPUFF/CALPOST Screening Results 
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Table 7-5 Facilities that LDEQ determined had units that were subject to BART 

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

which facilities in Louisiana have units that are 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 model plant analysis, modeling files for individual 

facilities, and modeling reports are available on request. Due to the 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 depict specific BART-eligible sources, their modeled 

deciview impact, location and distance from the two Class I areas for 2001, 2002, and 

2003. 

 
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Figure 7-1. BART Source Screening Modeling 2001 

 
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Figure 7-2. BART Source Screening Modeling 2002 
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Figure 7-3. BART Source Screening Modeling 2003. 

 
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