## CENRAP Modeling TSD

Review of Modeling and Emission Inventory Development for the Regional Haze Implementation Plan for the State of Texas

Prepared and Reviewed by:

Erik Snyder

Michael Feldman

November, 2014CENRAP Modeling TSD

- ii 

### Table of Contents

Table of Contents

................................ ................................ ................................ .............. ii

List of Tables

................................ ................................ ................................ .................... iv List of Figures

................................ ................................ ................................ .................... v Chapter 1: Background and Introduction

................................ ................................ . 7

1.1 Background

................................ ................................ ................................ ......... 7

1.2 Technical Requirements for Regional Haze SIPs

................................ ........... 8 Chapter 2: Development of Baseline and Natural Visi bility Conditions and Glidepath ........................................................................................... 10

2.1 Introduction

................................ ................................ ................................ ...... 10

2.2 Calculation of Visibility from IMPROVE Measurements

........................... 11

2.3 Baseline Visibility Conditions

................................ ................................ ......... 13

2.4 Natural Visibility Conditions and Uniform Rate of Progress (GlidePath)

Calculation

................................ ................................ ................................ ................... 14 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

................................ ................................ .. 18

3.2.5 Area Source Emissions

................................ ................................ ........... 19

3.3 2018 Emissions Inventory

................................ ................................ ................ 19 Chapter 4: Modeling Protocol, Episode Selection and Modeling Set

- up Overview

23

4.1 Introduction

................................ ................................ ................................ ...... 23

4.2 Quality Assurance Program Plan

................................ ................................ ... 23

4.3 Episode Selection

................................ ................................ .............................. 23

4.4 Photochemical and Emissions Model ing Domain

................................ ......... 24

4.5 MM5 Meteorological Model

................................ ................................ ............ 25

4.5.1 Model Selection

................................ ................................ ....................... 25

4.5.2 Meteorological Modeling Domain and Vertical Layer Structure

...... 26

4.5.3 Model Configuration

................................ ................................ .............. 26

4.5.4 MM5 Processing and Application

................................ ......................... 27

4.5.5 Model Performance

................................ ................................ ................ 27

4.6 SMOKE Emissions Model

................................ ................................ ............... 29

4.7 Air Quality Model

................................ ................................ ............................ 29

4.7.1 Model Selection

................................ ................................ ....................... 29

4.7.1.1 CMAQ Air Quality Model

................................ ................................ 31

4.7.1.2 CAMx Air Quality Model

................................ ................................ . 31

4.7.2 Vertical Modeling Domain

................................ ................................ ..... 32

4.7.3 Initial and Boundary Conditions

................................ ........................... 32

4.7.4 Base Case/ Baseline Model Performance

................................ .............. 33

4.7.4.1 Model Performance for Sulfate (SO 4 )

................................ .............. 34CENRAP Modeling TSD

- iii 4.7.4.2 Model Perfo rmance for Nitrate (NO 3 )

................................ ............. 35

4.7.4.3 Model Performance for Organic Carbon (OC)

.............................. 36

4.7.4.4 Model Performance for Elemental Carbon (EC)

........................... 37

4.7.4.5 Model Performance for Soil

................................ .............................. 38

4.7.4.6 Model Performance for Course Particulate Material (CM)

.......... 39

4.7.4.7 Model Performance for Prediction of Total Extinction

................. 41 Chapter 5: 2018 Future Year Modeling

................................ ................................ ... 44

5.1 2018 Model Simulations

................................ ................................ ................... 44

5.2 Visibility Projections

................................ ................................ ........................ 44

5.2.1 Mapping Model Results to IMPROVE Measurements

....................... 45

5.2.2 Projecting Visibility Changes Using Modeling Results

....................... 45

5.3 Reasonable Progress Goal and Path to Natural Conditions

........................ 46 Chapter 6: Source Apportionment Modeling

................................ .......................... 53

6.1 Introduction

................................ ................................ ................................ ...... 53

6.2 Source Apportionment Results at Big Bend National Park

......................... 54

6.3 Source Apportionment Results at Guadalupe Mountain National Park

.... 58

6.4 Contributions to visibility impairment at other Class I areas

..................... 62CENRAP Modeling TSD

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### List of Tables

Table 3

- 1. CENRAP 2002 Base Year Emissions from Texas Sources (tons/yr)

............ 17

Table 3

- 2. Emission estimates for Texas sources in 2018 (tons/ yr)

................................ 20

Table 3

- 3. Change in Texas emissions from 2002 to 2018

................................ ................ 20

Tab le 5

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

2018 (TCEQ estimated natural visibility conditions)

................................ .... 47

Table 5

- 2. Comparison of r easonable progress goal to uniform rate of progress for

2018 (EPA default estimated natural visibility conditions)

.......................... 49

Table 6

- 1. Projected light extinction for 20% worst days at Big Bend National Park in

2002 (Mm

- 1 )

................................ ................................ ................................ ....... 55

Table 6

- 2. Projected light extinction for 20% worst days at Big Bend National Park in

2018 (Mm

- 1 )

................................ ................................ ................................ ....... 55

Table 6

- 3. Projected light extinction for 20% worst days at Guadalupe Mountain

National Park in 2002 (Mm

- 1 )

................................ ................................ ......... 58

Table 6

- 4. Projected light extinction for 20% worst days at Guadalupe Mountain

National Park in 2018 (Mm

- 1 )

................................ ................................ .......... 59

Tab le 6

- 5. Contribution from Texas Emissions to Visibility Impairment at Class I areas

on 20% Worst Days in 2002(Mm

- 1 )

................................ ................................ 62

Table 6

- 6. Contribution from Texas Emissions to Visibility Impairment at Class I areas

on 20% Worst Days in 2018 (Mm

- 1 )

................................ ............................... 63CENRAP Modeling TSD

- v 

### List of Figures
Figure 1

- 1. Map of Regional Planning Organizations

................................ ................... 8 Figure 4

- 1. National RPO 36

- km modeling domain for CMAQ, CAMx, and

SMOKE modeling.

................................ ................................ ....................... 25 Figure 4

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

modeling scenario for sulfate (SO 4 ).

................................ ........................... 35 Figure 4

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

modeling scenario for nitrate (NO 3 ).

................................ .......................... 36 Figure 4

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

modeling scenario for organic carbon (OC).

................................ ............. 37 Figure 4

- 5. CENRAP model performance (fact ional bias and error) of the Typ02f

modeling scenario for elemental carbon (EC).

................................ .......... 38 Figure 4

- 6. CENRAP model performance (factiona l bias and error) of the Typ02f

modeling scenario for soil.

................................ ................................ .......... 39 Figure 4

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

modeling scenario for course particulate material (CM).

........................ 40 Figure 4

- 8. Daily extinction model performance at Big Bend National Park for the

worst (top) and best (bottom) 20% days during 2002

.............................. 42 Figure 4

- 9. Daily extinction model performance at Guadalupe Mountains National

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

........ 43 Figure 5

- 1. Projections of visibility impairment for 20% worst days for Big Bend

(TCEQ estimate of natural conditions, Figure 10

- 1 of t he TX RH SIP) 48 Figure 5

- 2. Projections of visibility impairment for 20% worst days for Guadalupe

Mountatins (TCEQ estimate of natural conditions, Figure 10

- 2 of the TX RH SIP)

................................ ................................ ................................ ......... 48 Figure 5

- 3. Projections of visibility impairment for 20% worst days for Big Bend

(EPA default estimate of natural conditions, Figure D

- 9a in Appendix D to the CENRAP’S TSD)

................................ ................................ .............. 49 Figure 5

- 4. Projections of visibility impairment for 20% worst days for Guadalupe

Mountains (EPA default estimate of natural conditions, Figure D

- 10a in Appendix D to the CENRAP’S TSD)

................................ ......................... 50 Figure 5

- 5. Differences in modeled total extinction (Bext) between Base18G and

Typ02g for 20% worst days at Big Bend.

................................ .................. 51 Figure 5

- 6. Differences in modeled total extinction (Bext) between Base18G and

Typ02g for 20% worst days at Guadalupe Mountains.

........................... 51 Figure 6

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

modeling

................................ ................................ ................................ ........ 54 Figure 6

- 2. Source apportionment modeling results by source region and source

category for worst 20% days at Big Bend National Park in 2002.

.......... 56 Figure 6

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

worst 20% days at Big Bend National Park in 2002.

................................ 56 Figure 6

- 4. Source apportionment modeling results by source region and source

category for worst 20% days at Big Bend National Park in 2018.

.......... 57 Figure 6

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

worst 20% days at Big Bend National Park in 2018.

................................ 57CENRAP Modeling TSD

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Figure 6

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

category for worst 20% days at Guadalupe Mountain National P ark in

2002.

................................ ................................ ................................ .............. 60 Figure 6

- 7. Source apportionment modeling results by source region and species for

worst 20% days at Guadalupe Mountain National Park in 2002.

.......... 60 Figure 6

- 8. Source apportionment modeling results by source region and source

category for wor st 20% days at Guadalupe Mountain National Park in

2018.

................................ ................................ ................................ .............. 61 Figure 6

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

worst 20% days at Guadalupe Mountains National Park in 2018.

......... 61 Figure 6

- 10. Projected 2018 total extinction and Te xas contribution at Class I areas

in Texas and other states

................................ ................................ ............. 64CENRAP Modeling TSD

- 7 

### Chapter 1: Background and Introduction

1.1 B ACKGROUND

Regional haze is visibility impairment that is caused overwhelmingly by fine particulates (PM2.5). Visibility impairment occurs when PM2.5 in the atmosphere scatters and absorbs light, thereby creating haze. PM2.5 can be emitted into the atmosphere direc tly 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 sc attering 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 transp orted 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 m akes 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 emissi ons 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 r eduction 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 represent ation 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 tha t 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.CENRAP Modeling TSD

- 8 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

includ ed in these states.

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

The RHR does not mandate specific milestones or rates of progress, but instead calls for States to establish goals that provide for “reasonable progress” toward achieving natural visibili ty 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 s ame 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 o f reduction in visibility impairment (in deciviews) needed to achieve natural conditions by 2064.

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

elements:

i. Baseline Visibility Conditions

ii. Natural Visibility ConditionsCENRAP Modeling TSD

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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 review the technical products developed by the Texas Department of Environmental Quality ( TCEQ ) and CENRAP for the central regional states, in support of their RH SIP. This document evaluate s the methods and procedures used by TCEQ and CENRAP to develop the modeling and emission inventory produ cts that assisted Texas and the central regional States in addressing the required elements of a RH SIP. Specifically, this

document reviewed emission inventory, meteorological, and photochemical modeling conducted by CENRAP, evaluated the results and det ermined if these models met applicable guidelines or protocols, and met modeling standards at the time they were conducted.CENRAP Modeling TSD

- 10



### Chapter 2: Development of Baseline and Natural Visibility Conditions and Glidepath

2.1 I NTRODUCTION

Under the Regional Haze Rule (RHR), each Sta te 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.

Conside ring 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 prog ress 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(b ext /10) (1)

where b ext is e xtinction, 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 a re 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

I nteragency M onitoring of PRO tected V isual E nvironments (IMPROVE) monitoring network (webpage and data access: http://vista.cira.colostate.edu/improve/Default.htm ). 24

- hour samples are collected every three days and are sent to a laboratory facility for analysis to obtain dry concentrations of a wide variety of species that impact visibility. Monitored pollutant concentrations are converted to visibility extinction using the IMPROVE equation, which add s 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

- 200 4), 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.pdfCENRAP Modeling TSD

- 11

Conditions Under the Regional Haze Program". 2 Annual average natural background pollutant concentrations are estimated by Trijonis et al. 3 under N APAP 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 translate d 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

est imates of the average of the best 20% and worst 20% of days, respecti vely. 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 al ternative 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 th is uniform rate is multiplied by 14 to determine the first benchmark.

2.2 C ALCULATION OF V ISIBILITY FROM IMPROVE M EASUREMENTS

The CENRAP procedure used for developing a uniform rate of progress (URP, also known as "glide path" or "glide slope") for the State of Texas followed EPA guidance contained in the GTR and GENVC with the exception that the revised IMPROVE algorithm was uti lized 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 H aze State Implementation Plans

. 4

CENRAP used the approach of Pitchfor d et al. 5 . The equation utilized is referred to as the

“revised” or “new” 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 exti nction 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 visibili ty progress

2 H ereafter “GENVC”: EPA, 2003, Guidance for Estimating Natural Vi sibility 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’S TSD”: Environ International Corp. and University of California at Riverside, September

2007.

5 Pitchford, Ma rc; 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.CENRAP Modeling TSD

- 12

assessments through equation 1. EPA’s 2007 “Guidance on the Use of Models and Other Analyses for 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 IMPRO VE 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 af ter 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 origin al 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 equati on 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 NO 2 ; however, none of the CENRAP Class I areas have monitors that provide observations of NO 2 so this term was not used.

The new equation shows broader scatter overall, but l ess 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, 200 1, 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 th e 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

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: h ttp://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_literat ure.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 Re source 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.pdfCENRAP Modeling TSD

- 13

to account for the full mass. Based on recent data more releva nt to relatively remote Class I areas, the revised IMPROVE equation embeds an OM/OC factor of 1.8. The largest difference in results between the two algorithms is related to the separation of total concentrations of sulfate, nitrate, and organic carbon int o 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

need ed for assessing progress under the RHR. EPA believes it is appropriate for the CENRAP states to use the revised IMPROVE equation.

2.3 B ASELINE V ISIBILITY C ONDITIONS

Section 2 of the EPA's "Guidance for Tracking Progress Under the Regional Haze Rule" ("GTP") describes a step

- by

- step process for calculating the visibility metric for the baseline period 2000

- 2004. The steps involve (1) assembly of daily species concentration data from the IMPROVE network, (2) inclusion of substitutions for missing data; (3) assess ment 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 ov er 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 procedure s.

For data substitution, the EPA guidance describes two procedures. The first is the use of quarterly median concentrations. This is the median concentration from the quarter the data value is missing from, averaged with similar medians from the preceding four years, using only quarters with having at least 50% of the day s available and no more than 10 consecutive missing days (GTP, "Step 3: Determine Quarterly Median Concentrations for Missing Variables", p.2

- 5). The second substitution involves using quarterly averages, as long as the substitution changes

extinction va lues by less than 10% in 90% of the data (GRP, "Step 5

- Evaluate Feasibility of Substituting Average Values", pp. 2

- 7

- 2

- 8).

Completeness requirements are also given in the EPA guidance: to be complete and included in the progress calculation, a year mu st have dat a for 50% of the sampling days in every quarter, 75% of the days for the year overall, and no more than 10 consecutive missing sampling days;

the overall 5

- year period must have at least 3 complete years of data (GTP, "Step 7

- Check Data Comple teness", pp. 2

- 8

- 2

- 9).

The RHR defines the baseline period as the five year span from 2000

- 2004. An IMPROVE monitor was established at the Big Bend National Park located in Brewster County and the Guadalupe M ountains National Park in Culberson County a nd collected samples for the only

Class I area s in the state of Texas. Data for the Big Bend National Park site that meet the completeness requirements are available for the 2001

- 2004 portion of this baseline period. Therefore, baseline visibility was ca lculated based on the average of the worst (best) 20% of days for each of these four years. This meets the minimum overall data completeness requirements for calculation of the baseline visibility conditions detailed in the GTP. Data for

the Guadalupe Mo untains National Park from 2000 through 2004 were averaged for the worstCENRAP Modeling TSD

- 14

(best) 20% of days to calculate baseline visibility conditions for the site. See Table 5

- 1 of the Texas Regional Haze SIP for baseline visibility data for each year.

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 Island ( 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/).

The CENRAP followed EPA guidance for estimating baseline visibility conditions.

2.4 N ATURAL V ISIBILITY C ONDITIONS AND U NIFORM R ATE OF P ROGRESS (G LIDE P ATH )

C ALCULATION

Natural visibility conditions must be calculated by estimating the degree of visibility i mpairment existing under natural conditions for the most impaired and least impaired days, based on available monitoring information and appropriate data analysis techniques. See Section C.2. of the TX TSD for our review of the TCEQ’s calculation of natur al visibility conditions for Big Bend and Guadalupe Mountains.

A s part of its RPGs determination, the TCEQ analyzed and determined the uniform rate of progress ( URP ) needed to reach natural visibility conditions by the year 2064. Having calculated

the b aseline and natural visibility conditions, t he TCEQ constructed the URP by plotting a straight graphical line from the baseline level of visibility impairment to the level of visibility conditions representing no anthropogenic impairment in 2064 for both B ig Bend and the Guadalupe Mountains. See Section B.3 of the TX TSD for our review of the TCEQ’s uniform rate of progress calculation.CENRAP Modeling TSD

- 15 

### Chapter 3: Emission Inventory Development

3.1 I NTRODUCTION

In support of the CENRAP Regional Haze air quality modeling efforts, air quality modeling inputs including annual meteorology and emissions inventories for a 2002 actual emissions base case, a planning case to 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 developme nt 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 emis sions 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 othe r RPOs and EPA. Emission modeling and quality assurance (QA) work was based on the Quality Assurance Project Plan (QAPP) CENRAP Emissions and Air Quality Modeling 10 and Protocol for the CENRAP 2002 Annual Emissions and Air Quality Modeling 11 (hereafter referred to as the “Modeling Protocol”). These 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 develop ed 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 SO 2 and NOx emissions for large

9 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and

Associa tes, 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 T echnology, 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 Centr al 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

Assoc iation. Sonoma Technology, Inc. ( http://cenrap.sonomatech.com/CENRAP_Ammonia_NIF/FinalReport.pdf ).

10 Morris, R.E. and G. Tonnesen. 200 6 . 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 ).CENRAP Modeling TSD

- 16

stationary point sources based on measur ed continuous emissions monitoring (CEM) data along with actual 2002 fire emissions.

 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

patter ns 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 m odeling: 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 Emission s and Air Quality Modeling to Support Regional Haze State Implementation Plans 12 (hereafter referr ed to as the “CENRAP ’s TSD”) describes the development of each source category inventory is detail. Appendix B of the CENRAP’S 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’S TSD is included as Appendix 8

- 1 the Texas Regional Haze Impl ementation Plan Revision.

3.2 2002 EMISSIONS INVENTORY

TCEQ developed an emission inventory for 2002 quantifying emissions of sulfur dioxide (SO 2 ), ammonia (NH3), nitrogen oxides (NOx), volatile organic compounds (VOCs), and particulate matter of aerodynamic diameters less than 10 μm (PM10) and less than 2.5 μm (P M2.5) for the

State of Texas , as described in Chapter 7 and Appendix &

- 1 of the Texas RH SIP submittal . 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 agricultur al ammonia for the CENRAP inventory. 13

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=downloa d&id=87 )

13 Reid, S.B. et al. 2004 . Emission Inventory Development for Mobile Sources and Agricultural Dust Sources for the Central States. Sonoma Technology, Inc.CENRAP Modeling TSD

- 17

Table 3

- 1. CENRAP 2002 Base Year Emissions from Texas Sources (tons/yr)

CO NOx SO2 TOG* PM2.5 PM10 NH3

Area 908,407 280,811 111,853 1,163,549 347,490 1,552,824 380,057

Point 498,467 600,725 821,961 207,695 46,789 80,947 2,609

Non

- road

mobile 1,210,158 242,551 21,828 148,952 15.089 15,556 56

On

- road

mobile 4,098,391 664,163 18,814 309,707 11,275 15,476 21,599

Total 6,715,423 1,788,250 974,457 1,829,902 420,642 1,664,803 404,321

3.2.1 S TATIONARY P OINT

- S OURCE E MISSIONS

Point sources are typically regulated and information on emissions and locations are available in regulatory reports. The Texas point sources are defined as industrial, commercial, or institutional sites that meet the reporting requirements of 30 Texas Administrative Code (TAC) §101.10 Permitted point sources in Texas are required to submit annual emissions inventories. The reported data are drawn from TCEQ’s computer

- based State of Texas Air Retrieval system (STARS). 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. 14 Point source inventories were developed by

the othe r 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 inventory includes further processing of EGU emissions to develop a typical emission l evels 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. 15 More specific speciation profiles should be utilized as they become

( http://cenrap.s onomatech.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

Asso c iation. Sonoma Technology, Inc.

14 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and

Associa tes, 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 )

15 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.CENRAP Modeling TSD

- 18

available to accurately describe the speciation of PM2.5 from combustion of different types of

coal utilized in Texas

.

3.2.2 O N

- R OAD M OBILE E MISSIONS

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 MOBILE6 16 inputs for calculation in SMOKE/MOBILE6. Annual mobile emissions were pre

- computed as part of the 1999 Mexico inventory and 2000 Canada i nventory. 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 Texas counties, county

- level Highway

Performance Monit oring System (HPMS) annual average VMT data were used . For those

counties with available local traf fic demand model (TDM) data, VMT estimates were calibrated against HPMS data

. For the CENRAP states, Sonoma Technology provided monthly VMT data

and MOBILE 6 input files for the months of Janu ary and July for all counties in the CENRAP region. 17 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 emission s from on

- road mobile sources.

3.2.3 B IOGENIC E MISSIONS

The BEIS3 system is utilized by CENRAP 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 ar e 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. 18 The EPA approves of the use of BEIS3 by

CENRAP in this SIP.

3.2.4 N ON

- R OAD M OBILE E MISSIONS

Emissions from aircraft operations, commercial and recreational marine vessels, and railroad locomotives and other sources were developed by the EPA for the 2002 NEI. Emissions fr om

non

- road engines sources are based on available information on engine speicifications, emission

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

17 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)

18 Tonnesen, G., et al . 2005. Final Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) for the Project Pe riod March 1, 2004 through February 28, 2005. UCR

. ( http://pah.cert.ucr.edu/aqm/308/reports/final/2004_RMC_final_report_main_body.pdf ).CENRAP Modeling TSD

- 19

factors, eqiuipment population, and annual usage. The EPA NONROAD 19 model was utilized by Sonoma Technology to develop a non

- road emissions inventory for the CENRAP states. 17
Texas provided estimates for 2002 non

- road emissions in lieu of the CENRAP prepared inventory. Texas provided annual and daily emissions for several oil and gas field equipment

non

- road categories. EPA and CENRAP emissions were consolidated by Pechan and CEP. 20

3.2.5 A REA S OURCE E MISSIONS

Area sources include commercial, small

- scale industrial, and residential categories of sources. Emissions from these sources are typically estimated on a source category or group basis. The area source inventory includes data from the EPA 2002 NEI and in ventories prepared by TCEQ

, CENRAP and other CENRAP states. Sonoma Technology prepared additional inventories of prescribed burning, agricultural dust, and soil agricultural ammonia for the CENRAP region. 21
TCEQ also prepared an inventory of oil and gas p roduction area sources based on county

- level production data and emission estimates for gasoline distribution, dry cleaning and wildfires. 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. 22 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 appor tionment modeling with CAMx.

The TCEQ noted that the area source SO 2 emissions used by the CENRAP in their modeling are significantly higher than the 15,633 tons per year (tpy) reported by the TCEQ for 2002 . The difference is industrial and residential coal combustion which was erroneously included in the CENRAP inventory. Texas determined that CENRAP’s modeled emissions estimate s are not expected to significantly impact visibility estimates for 2018 because of the relatively small

contribution from these Texas sources on Class I areas. 23

3.3 2018 E MISSIONS I NVENTORY

19 NONROAD is availabl e at http://www.epa.gov/otaq/nonrdmdl.htm

20 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and

Associa tes, Inc. and CEP

, UNC

, ( http://www.cenrap.org/ html/projects.php?mode=subcatdownload&id=50 )

21 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 Deve lopment of Ammonia Emission Inventories for the Central States Regional Air Planning

Assoc iation. Sonoma Technology, Inc.

22 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and

Associa tes, Inc. and Carol ina 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 Ass ociates, Inc. and CEP, UNC

. ( http://www.dnr.mo.gov/env/apcp/docs/appendixh

- 3.pdf )

23 See page 7

- 1 of the Texas Regional Haze SIPCENRAP Modeling TSD

- 20

An emission inventory for 2018 including anticipated changes due to population growth, emission controls and development of industry, energ y, 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), MOBILE 6, NONROAD, and the Inte grated 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 the Development of Growth and Cont rol Inputs for CENRAP 2018 Emissions draft technical support

document. 24

Table 3

- 2. Emission estimates for Texas sources in 2018 (tons/yr)

CO NOx SO2 TOG* PM2.5 PM10 NH3

Area 899,497 274,663 114,138 1,420,681 354,712 1,557,089 562,379

Point 542,128 525,174 625,068 283,290 80,577 121,733 6,790

Non

- road

mobile 1,921,674 167,451 6,988 119,855 10,588 11,498 239

On

- road

mobile 2,710,631 148,387 2,925 125,234 5,337 5,337 32,191

Total 6,073,930 1,115,676 749,119 1,949,060 451,214 1,695,657 601,598

Table 3

- 3. Change in Texas emissions from 2002 to 2018

CO NOx SO2 TOG* PM2.5 PM10 NH3

Area

- 8,910

- 6,148 2,285 257,132 7,222 4,265 182,322

Percent Change

- 1%

- 2% 2% 22% 2% 0% 48%

Point 43,661

- 75,551

- 196,893 75,595 33,788 40,786 4,181

Percent Change 9%

- 13%

- 24% 36% 72% 50% 160%

Non

- road

mobile 711,516

- 75,100

- 14,840

- 29,097

- 4,501

- 4,058 183

Percent Change 59%

- 31%

- 68%

- 20%

- 30%

- 26% 327%

On

- road

mobile - 1,387,760

- 515,776

- 15,889

- 184,473

- 5,938

- 10,139 10,592

Percent Change

- 34%

- 78%

- 84%

- 60%

- 53%

- 66% 49%

Inventory

Total for 2002 6,715,423 1,788,250 974,457 1,829,902 420,642 1,664,803 404,321

24 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 )CENRAP Modeling TSD

- 21

Estimate Total

for 2018 6,073,930 1,115,676 749,119 1,949,060 451,214 1,695,657 601,598

Net change

- 641,493

- 672,574

- 225,338 119,158 30,572 30,854 197,277

Percent Change

- 9.55%

- 37.61%

- 23.12% 6.51% 7.27% 1.85% 48.79%

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; f or 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

inv entory. 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 Pl anning Model (IPM) updated by the CENRAP states. In developing the IPM emission projections, EGU

controls in Texas associated with the Clean Air Interstate Rule (CAIR) were accounted for in addition to any existing state programs, such as Texas Senate Bil l 7, unit specific NOx emission

limits in the Dallas

- Forth Worth ozone non

- attainment area, and NOx emission caps in the

Houston

- Galveston

- Brazoria ozone non

- attainment area. 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 projection s. Conservatively, no IPM projected new units were removed from the simulation with the addition of the permitted facilities . Appendix B of the CENRAP’S TSD lists the file names, data source, type and a description of emissions used in the 2018 (Base18G) emissions inventory.

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.CENRAP Modeling TSD

- 22

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

the CENRAP states.CENRAP Modeling TSD

- 23



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

- up Overview

4.1 I NTRODUCTION

Meteorological, emission and photochemical models are essential tools in examining factors that impact visibility and for development of effective control strategies to meet the goals and requirements of the RHR. CENRAP selected the team of ENVIRON and UC R 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 Are as 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 Protocol 25 describes the model selection, configuration, episode selection, and model evaluation used in su pport of the Texas

RH SIP.

4.2 Q UALITY A SSURANCE P ROGRAM P LAN

The modeling team developed a quality assurance program plan (QAPP) 26 to develop clearly defined data quality objectives, documentation, and pro cedures. 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 mo del 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 ta ken to

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

4.3 E PISODE S ELECTION

25 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 ). Appendix 8

- 2 of the TX RH SIP.

26 Morris, R.E. and G. Tonn esen. 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. Appendix 8

- 3 of the TX RH SIPCENRAP Modeling TSD

- 24

EPA guidance 27 describes the criteria that should be used to select a modeling episode. The modeling episode should: 1) reflect a variety o f meteorological conditions that are representative of the 20% worst and 20% best days in the Class I areas being modeled, 2) be representative of the baseline period of 2000

- 2004, 3) cover a period where extensive air quality/meteorological

data are avail able, 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 a nd 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

inven tories 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 emi ssion inventori es 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 P HOTOCHEMICAL AND E MISSIONS M ODELING D OMAIN

CE NRAP 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 photochemica l 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. 28 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 u se of higher spatial resolution modeling should be revisited in future modeling efforts as computational efficiency improves.

27 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 )

28 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 ).CENRAP Modeling TSD

- 25

Figure 4

- 1. National RPO 36

- km modeling domain for CMAQ, CAMx, and SMOKE modeling.

4.5 MM5 M ETEOROLOGICAL M ODEL

4.5.1 M ODEL S ELECTION

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 M esoscale 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 an d 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 description s of MM5 29 can be found in Dudhia (1993) 30 and Grell et al. (1994). 31 All meteorological data used for the CENRAP air quality modeling efforts are derived from MM5

model simulations.

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

30 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.

31 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.CENRAP Modeling TSD

- 26

The CENRAP meteorological modeling used for input to photochemical model ing 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 a s the Meteorological Model Performance Evaluation report)

. 32

4.5.2 M ETE OROLOGICAL M ODELING D OMAIN AND V ERTICAL L AYER S TRUCTURE

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, wi th 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 1 64 (east

- west) by 128 (north

south) cross points. Overall, the do main 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 s ensitivity 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 al titude. The vertical layer structure is further detailed in the Modeling Protocol.

4.5.3 M ODEL C ONFIGURATION

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 th e State of Iowa and the Lake Michigan Air Directors Consortium (LADCO) and further sensitivit y 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 meteorological modeling for VISTAS. 33 The combination of all of these studies led to the

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

. 33 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.pdfCENRAP Modeling TSD

- 27

configuration used by CENRAP for MM5 modeling detailed in the CENRAP’S TSD and the Modeling Protocol.

4.5.4 MM5 P ROCESSING AND A PPLICATION

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 US GS 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

measure ments, 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 spin

up period, the simulation started at 12/16/2001 12Z

.

4.5.5 M ODEL P ERFORMANCE

Model performance evaluation was performed by IDNR through comparison with observations of surface and upper

- air meteorological conditions and precipitation. 34 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. 35 Details on this comparison can

be found in the CENRAP’S TSD , Appendix A.

The goal of the evaluation was to determine whether the meteorological fields are sufficientl y 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 w ill be severely hampered

34 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.evalua tion.v204.p df )

35 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_W RAP_2002_36km_MM5_eval.pptCENRAP Modeling TSD

- 28

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;

 Mesosc ale 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 che mical 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

m odel’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 p atterns.

The ID N R evaluation of the MM5 model per formance 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 accurat e. The operational evaluation essentially tests w hether 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 t o 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

benchmark s for acceptable MM5 model performance

. 36 The IDNR concluded , based on the 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 wit h similar modeling efforts by WRAP and VISTAS revealed comparable performance across all three simulations. The three simulations

36 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 C orporation.CENRAP Modeling TSD

- 29

showed similar performance for prediction of surface wind speed, wind direction and humidity.

The use of surface data assimi lation 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

model ing protocol. The 2002 VISTAS MM5 simulations showed the best performance and the CENRAP performance more closely resembled that of th e VISTAS than the WRAP.

The 2002 CENRAP MM5 model results are within the bound s of other meteorological databases

used f or 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 meteorologic al 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 R esearch 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 me teorological modeling efforts.

4.6 SMOKE E MISSIONS M ODEL

CENRAP selected the Sparse Matrix Operator Kernel Emissions model 37 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 s patial 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 th rough SMOKE with the Biogenic Emission Inventory System, version 3 (BEIS3) 38 using the Biogenic Emissions Landcover Database (BELD3) vegetative database. Mobile emissions can be calculated by

SMOKE from mobile

- source activity data, using emission factors f rom the MOBILE6 model

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

models.

4.7 A IR Q UALITY M ODEL

Photochemical air quality models offer opportunity to better understand the sources of particulate matter that impair v isibility 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 processes affecting particulate matter, especially for situations in which multiple pollutant sources interact to form particulate matter.

4.7.1 M ODEL S ELECTION

37 Available at http://www.smoke

- model.org/index.cfm

38 Available at http://www.epa.gov/ttn/chief/software.html#pcbeisCENRAP Modeling TSD

- 30

Guidance from the EPA requires that the air quality m odel 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 mode l’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 res ponse 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 app lications. (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 o n 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 a nd other RPOs, as well as comparisons with CAMx model results. 39 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 Reg ional 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 model ing 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 compared to 36

- km runs. With this possible exc eption, CENRAP noted little benefit in overall

39 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 )CENRAP Modeling TSD

- 31

model performance with use of the 12

- km grid. Therefore, the 36km domain was selected for all CENRAP CMAQ and CAMx runs. 40

These air quality models are discussed in more detail be low.

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

fram e 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 o n 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 me chanisms 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) c oncentrations. 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 Vers ion 4.5 model. CENRAP decided to use the CMAQ Version 4.5 with the “SOAmods enhancement” 41 for CENRAP modeling. Details of the CMAQ model configuration used by CENRAP can be found in the

CENRAP’S TSD and the Modeling Protocol.

4.7.1.2 CAMx Air Quality Model

40 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 )

41 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.CENRAP Modeling TSD

- 32

The Comprehensive Air Quality Model with extensions (CAMx) model 42 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 t here 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 sta tes. The CAMx model is based

on well

- established treatments of advection, diffusio n, 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 Pa rticulate 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 CEN RAP can be found in the CENRAP’S TSD and the Modeling

Protocol.

4.7.2 V ERTICAL M ODELING D OMAIN

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 VI STAS 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 structur e 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, C ENRAP selected the 19 layer vertical structure described in the CENRAP’S

TSD . This selection improves computational efficiency and produces results almost identical to

the full vertical structure runs.

4.7.3 I NITIAL AND B OUNDARY C ONDITIONS

Initial c onditions (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 a bout 7 da ys. A model spin

- up period is included in each simulation to eliminate any effects from the ICs. For the CENRAP modeling, the annual simula tion is divided into four quarters, and included a 15

- day spin

- up period for the quarters beginning in Apri l, 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 sufficien t to minimize the effects of the IC on model results given

th e 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

42 ENVIRON, 2006. “User’s Guide

– Comprehensive Air

- quality Model with extensions, Version 4.30.” ENVIRON International Corpor ation, Novato, California . (a vailable at http://www.camx.com ).CENRAP Modeling TSD

- 33

a much larger effect on model simulations than do ICs. For some areas in the CENRAP region and for clean conditions, the BCs can be a substan tial contributor to visibility impairment. For this study BC data generated in an annual simulation of the global

- scale GEOS

- Chem model for calendar year 2002 were applied. 43 The BCs employed by CENRAP were state

- of

- the

- science at the time they were imple mented

.

4.7.4 B ASE C ASE / B ASELINE M ODEL P ERFORMANCE

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

2 002 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 Typ0 2F emission inve ntory.

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 simula ting 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 larg e 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

Ca rbon (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 m odels 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 mea n 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 wor st

- 20% visibility days at each site; the higher the light extinction, the lower the visibility

43 Jacob, D.J., R. Park and J.A. Logan. 2005. Documentation and Evaluation of the GEOS

- CHEM Simulation for

2002 Provided to the VI STAS Group. Harvard Unive rsity (http://www.vistas

sesarm.org/documents/Harvard_GEOS

- CHEM_FinalReport_20050624.doc )CENRAP Modeling TSD

- 34

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 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 (c urves) 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 crit eria 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/m 3

.

Model performance at IMPROVE monitors is of highest importance, because these monitors are sited to be representative of the visibility conditions impacting each Class 1 Area. The CASTNET monitoring network is more sparse 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 sh ould be given less importance.

The mo del 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 agree s with the CENRAP model performance procedures and analysis. Detailed resul ts of the model performance evaluation can be found in Appendix C of the CENRAP’S TSD and on the University of California, Riverside CENRAP visibility modeling website ( http ://pah.cert.uc r.edu/aqm/cenrap/cmaq.shtml#cmaq_typ02f_mpe ).

4.7.4.1 Model Performance for Sulfate (SO 4 )

Figure 4

- 2 shows the monthly SO 4 fractional error and bias for the STN, IMPROVE and CASTNET monitoring networks as well as the proposed performance goals and criteria. In general, there is an under

- prediction bias that is more pronounced during the spring and

summer months. For the ST N 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

IMPROV E sites with the exception of two months that lie within the criteria but beyond the goal

for fractional error.CENRAP Modeling TSD

- 35

Figure 4

- 2. CENRAP model performance (factional bias and error) of the Typ02f modeling scenario for sulfate (SO 4 ). The 12 symbols for each monit or represent monthly average model performance for the year 2002, averaging all monitors in the CENRAP region.

Solid lines represent CEN RAP modeling goals and criteria.

4.7.4.2 Model Performance for Nitrate (NO 3 )

Figure 4

- 3 shows the monthly NO 3 fractional error and bias for the STN, IMPROVE and CASTNET monitoring networks as well as the proposed performance goals and criteria. NO3 model performance is variable. There is an underprediction during the summer months, approaching a fractional bia s of

- 140% in June and July and an overprediction with bias of

app roximately 50% in the winter. The winter bias is more significant because NO 3 concentrations tend to be a large component of visibility impairment during the winter months.CENRAP Modeling TSD

- 36

In general, win ter model performance does not meet the performance goals and in some cases does not meet the criteria, predicting concentrations of NO 3 much highe r than observed

.

Figure 4

- 3. CENRAP model performance (factional bias and error) of the Typ02f modeling scenario for nitrate (NO 3 ). The 12 symbols for each monitor represent monthly average model performance for the year 2002, averaging all monitors in th e CENRAP region.

Solid lines represent CENRAP modeling goals and criteria.

4.7.4.3 Model Performance for Org anic Carbon (OC)

Figure 4

- 4 shows the monthly OC fractional error and bias for the STN and IMPROVE monitoring networks as well as the proposed perf ormance goals and criteria. For the IMPROVE network, model performance for all months is within the goals for both fractional bias and error. The STN monitors in urban areas measured higher concentrations of OC then the ruralCENRAP Modeling TSD

- 37

IMPROVE monitors. Model performance for STN sites shows a negative bias throughout the year that fall within the model criteria for bo th 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 moni tors 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 sh ows 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 sm all with a fractional error around 50%. There is a large model underprediction during the summer atCENRAP Modeling TSD

- 38

the IMPROVE sites. However, EC concentrations at these sites are low putting the model performance within th e goals for low concentrations.

Figure 4

- 5. C ENRAP 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. Soli d 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 pe rformance 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 e ffects near the monitor and difficulties in capturing emissions accurately in theCENRAP Modeling TSD

- 39

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 underp redictions of CM concentrations throughout the year. This may be due to localized emissions near the monitor and difficulties in capturing these emissions accurately in theCENRAP Modeling TSD

- 40

inventory. This is an area of concern, especially in areas where CM contributes s ignificantly 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 t he year 2002, averaging all monitors in the CENRAP region. Solid lines represent CENRAP modeling goals and criteria.CENRAP Modeling TSD

- 41

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 per formance 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% wo rst da ys 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 Island (BRET), LA, Big Bend (BIBE), TX and Guadalupe Mountains (GUMO), TX Class I areas for the worst 20 percent days is particularly poor. At GUMO, visibility impairment is primarily due to high soil and CM which are not well pr edicted 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 pu rposes. In general, model performance is acceptable for SO 4 , NO 3 , OMC and EC at the Class I areas. The model was not able to accurately predict CM and soil conce ntrations in the CENRAP region.

In order to address this model performance issue, CENRAP inv estigated 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’S TSD for results o f this sensitivity analysis.

Within the state of Texas , the Big Ben d National Park and Guadalup e Mountains National Park

are the only Class I area s . Model performance at predicting total extinction at these site s during the worst 20% and best 20% days for 2002 are shown in Figure 4

- 8 and 4

- 9. For the 20% worst days at Big Bend, total extinction is dominated by sulfate and OMC. SO 4 and O M C

underestimates results in an under

- prediction (

- 72 % bias) on total extinction. Every component is underestimated with the bias ranging from

- 24% for OMC, to

- 94% for SO 4 , and

- 162 % for

CM. Prediction on the 20% best days is better with a slight positive bias (13%). For Guadalupe Mountains, most of the 20% worst days are dominated by high dust days. As discussed above, Soil and CM are not captured well by the model. Performanc e on the best 20% days is better but the model still underestimates soil and CM.CENRAP Modeling TSD

- 42

Figure 4

- 8. Daily extinction model performance at Big Bend National Park for the worst (top) and best (bottom) 20% days during 2002

.CENRAP Modeling TSD

- 43

Figure 4

- 9 . Daily extinction model performance at Guadalupe Mountains National Park for the

worst (top) and best (bottom) 20% days during 2002

.CENRAP Modeling TSD

- 44 

### Chapter 5: 2018 Future Year Modeling

5.1 2018 M ODEL S IMULATIONS

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 particulat e 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 simulati ons 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 an cillary 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 pr edictions 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 V ISIBILITY P ROJECTIONS

The Regional Haze Rule (RHR) goals include achieving natural visibility conditions at 156

f ederally mandated Class I areas by 2064. In more spec ific 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 v isibility (“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 (200 0

- 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 guidance 44 that recommends applying the modeling results in a relative sense to project future

- year visibility conditions. Projections are made using relative response factors (RRFs), which are defined as the ratio of the future

- year modeling results for

44 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/B

0 7

- 002, EPA, April 2007, ( http://www.epa.gov/ttn/scram/guidance/guide/final

- 03

- pm

- rh

- guidance.pdf)CENRAP Modeling TSD

- 45

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 use d 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 carbo n)

o Soil (other fin e 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 wou ld result in the goal being met.

5.2.1 M APPING M ODEL R ESULTS TO IMPROVE M EASUREMENTS

Each of the six PM components of light extinction in the revised IMPROVE mass extinction equation 45 is scaled separately. Because the modeled species do not exactly match up w ith 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’S TSD shows the assumptions used to relate modeled s pecies 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’S TSD ) resulting from the modified SOA module used by CENRAP are al so included in the OC term.

5.2.2 P ROJEC TING V ISIBILITY C HANGES U SING M ODELING R ESULTS

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

45 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 )CENRAP Modeling TSD

- 46

revised IMPROVE equation. The following six steps found in the modeling guidanc e 46
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 o f 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 value s. 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 res ponse 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 fu ture visibility conditions. These methods were appropriate

at the ti me the modeling was performed.

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

A linear URP from the Baseline Conditions in 2004 to Natural Conditions in 2064 is assumed,

and th e value on the glide path at 2018 is the presumptive URP visibility target that the modeled 2018 projections are compared against to judge progress. Table 5

- 1 and figure s 5

- 1 and 5

- 2 compare the URP using the TCEQ derived natural conditions described in s ection C. 2 of the TX

TSD to the modeled reasonable progress goal for Big Bend and Guadalupe Mountains . The baseline visibility (2002

- 2004) is 17.3 dv for Big Bend and 17.2 dv for Guadalupe Mountains

and the estimated 2018 URP is 15.6 dv for Big Bend and 1 6 dv for Guadalupe Mountains . The modeling predicts a visibility improvement of 0.7 dv for Big Bend and 0.9 dv for Guadalupe

Mountains , compared to the URP improvement of 1.7 and 1.2 dv by 2018. Table 5

- 2 and figures 5

- 3 and 5

- 4 compare the URP using t he EPA default natural conditions to the modeled reasonable progress goal for Big Bend and Guadalupe Mountains. The baseline visibility (2002

- 2004) is 17.3 dv for Big Bend and 17.2 dv for Guadalupe Mountains and the estimated 2018 URP is 14.9 dv for Big B end and 14.7dv for Guadalupe Mountains. The modeling predicts a visibility improvement of 0.7 dv for Big Bend and 0.9 dv for Guadalupe Mountains, compared to the URP improvement of 2.36 and 2.42 dv by 2018

. Achieving the 2018 URP point is not a

46 US EPA, 2006. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality

Goals for Ozon e, PM2.5, and Regional Haze, US EPA, Office of Air Quality and Planning Standards, EPA

- 454/B

0 7

- 002, EPA, April 2007, ( http://www.epa.gov/ttn/scram/guidance/guide/final

- 03

- pm

- rh

- guidance.pdf)CENRAP Modeling TSD

- 47

requireme nt 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 Region al Haze Program 47 , “The glidepath is not a presumptive target, and States may establish a RPG that provides for greater, lesser, or equivalent improvement as tha t described by the glidepath.”

The estimated visibility impairment in 2018 (reasonable progress goal) is higher than the calculated URP (Section 2). The URP acts as a benchmark for evaluating the reasonable progress towards reaching natural conditions by 2064. In determining reasonable progress, section 169A(g) of the Clean Air Ac t require s 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.

The reasonable progress goal s established by TCEQ for the 20% best days shows an improvement in visibility of 0. 2 deciviews by 2018 at both Big Bend and Guadalupe Mountains

. This is consistent with the requirement of no degradation of visibility on t he best days at Class I

sites

.

Table 5

- 1. Comparison of reasonable progress goal to uniform rate of progress for 2018 ( TCEQ estimated natural visibility conditions )

Big Bend Guadalupe Mountains
Baseline Conditions (2000

- 2004) 17.3 dv 17.2 dv Change by 2018 (reasonable progress go al) 0.7 dv 0.9 dv Change by 2018 at uniform rate of progress 1.7 dv 1.2 dv Projected rate of change (2004

- 2018) 0.05 dv/yr 0.06 dv/yr Change needed to reach natural conditions 7.2 dv 4.9 dv Progress at uniform rate 0.12 dv/yr 0.08 dv/yr

47 US EPA, 2007, Guidance for Setting Reasonable Progress Goals U nder the Regional Haze Program, EPA, June

2007. http://www.epa.gov/ttncaaa1/t1/memoranda/reasonable_progress_guid071307.pdfCENRAP Modeling TSD

- 48

Figure 5

- 1. Projections of visibility impairment for 20% worst days for Big Bend (TCEQ

estimate of natural conditions , Figure 10

- 1 of the TX RH SIP )

Figure 5

- 2. Projections of visibility impairment for 20% worst days for Guadalupe Mountatins

(TCEQ estimate of nat ural conditions , Figure 10

- 2 of the TX RH SIP )CENRAP Modeling TSD

- 49

Table 5

- 2 . Comparison of reasonable progress goal to uniform rate of progress for 2018 ( EPA

default estimated natural visibility conditions)

Big Bend Guadalupe

Mountains

Baseline Conditions (2000

- 2004) 17.3 dv 17.2 dv Change by 2018 (reasonable progress goal) 0.7 dv 0.9 dv Change by 2018 at uniform rate of progress 2.36 dv 2.42 dv Projected rate of change (2004

- 2018) 0.05 dv/yr 0.06 dv/yr Change needed to reach natural conditions 10.1 dv 10.39 dv Progress at uniform rate 0.17 dv/yr 0.17 dv/yr

Figure 5

- 3 . Projections of visibility impairment for 20% worst days for Big Bend ( EPA default estimate of natural conditions, Figure D

- 9a in Appendix D to the CENRAP’S TSD )CENRAP Modeling TSD

- 50

Figure 5

- 4. Projections of visibility impairment for 20% worst days for Guadalupe Mountains

(EPA default estimate of natural conditions, Figure D

- 10a in Appendix D to the

CENRAP’S TSD )

Figures 5

- 5 and 5

- 6 show the differences in model results of total extinction between the Base18g and Typ02 g model predictions, including the contributions from each component specie of the IMPROVE algorithm. On most days, visibility improvements are due to reductions in

s ulfate.CENRAP Modeling TSD

- 51

Figure 5

- 5 . Differences in modeled total extinction (Bext) between Base18 G and Typ02 g for 20% worst days at Big Bend

.

Figure 5

- 6 . Differences in modeled total extinction (Bext) between Base18 G and Typ02 g for 20% worst days at Guadalupe Mountains

.CENRAP Modeling TSD

- 52

As discussed in the following chapter on source apportionment, visibility impairment at Big Bend and Guadalupe Mountains is due to emissions and transport from outside of Texas as well as in state sources. Efforts to meet the goal of natura l visibility by 2064 will require further

emission reductions within Texas and other states in the region. As additional emission control strategies are implemented nationally and across other states in the region, future visibility projections will be ev aluated to determine the future necessary reductions in Texas emissions

needed to satisf

y the 2064 regional haze goal.CENRAP Modeling TSD

- 53 

### Chapter 6: Source Apportionment Modeling

6.1 I NTRODUCTION

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 eff ective 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 opt ions and inputs as the CAMQ modeling with

both the 2002 baseline and 2018 future case emission inventories. The CAM x 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’S 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 fo r the contributions of user specified source regions and categories to PM concentrations throughout the modeling domain. Details on the formulation of the CAMx PSAT

sour ce apportionment can be found in the CAMx user’s guidance. 48 The CAMx PSAT analysis

ha s been tested and evaluated against other apportionment techniques

. 49

, 50 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 pro jected 2018 case in order

to identify those regions and source categories that, if controlled, would produce the greate st improvements in visibility.

CENRAP defined 30 geographical source regions (Fig 5

- 1) consisting of CENRAP and nearby states, with Texa s divided into 3 regions (East Texas, West Texas, and Texas Gulf Coast) , the remainder of the western and eastern United States, the Gulf of Mexico, Canada and Mexico. Six source categories (elevated point sources; low

- level point sources, on

- road mobile, non

- road mobile, area and natural or non

- anthropogenic sou rces) 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 e xtracted from the PSAT results. The original IMPROVE equation was used to calcul ate 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 t he CENRAP class I areas. Extinction due to soil and coarse material

48 “User’s Guide Comprehensive Air Quality Model With Extensions (CAMx) Version 4.30.” ENVIRON International Corporation, Novato, Cal ifornia , 2006 (available at www.camx.com).

49 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

Conferenc e, Minneapolis, MN. June.

50 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)CENRAP Modeling TSD

- 54

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 regio ns and categories to visibility impairment for the average of the worst 20 percent and best 20 percent days at each CENRAP and nearby Class I areas. 51 EPA believes the selection and application of CAMx for source apportionment analysis is appropri ate.

Figure 6

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

6.2 S OURCE A PPORTIONMENT R ESULTS AT B IG B END N ATIONAL P ARK

Tables 6

- 1 and 6

- 2 show the CENRAP 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 Big Bend National Park site in 2002 on the worst 20% days is largely due to sulfate that contributes 26.10 Mm

- 1 ( 17.70 Mm

- 1 from point sources)

of the total extinction of 47.49 Mm

- 1 . The largest contributions of sulfate in 2002 come from Texas sources (5.50 Mm

- 1 , 3.33 Mm

- 1 from East Texas) , from areas outside the modeling domain captured as boundary conditions ( 5.82 Mm

- 1 ), and from Mexico (8.28 Mm

- 1 from all source categories)

. Overall, the largest source region contributions to visibility impairment in 2002 are

from Mexico (12.75 Mm

- 1 from all source categories), from areas outside the modeling domain captured as boundary conditions (12.27 Mm

- 1 ), and from Texas sources (11.87 Mm

- 1 , 6.23 Mm

- 1

from West Texas and 3.73 Mm

- 1 from East Texas).

51 Downloaded at http://www.cenrap.org/html/projects.php

. And available in the docket for this action as CENRAP_PSAT_Tool_ENVIRON_Aug27_2007.mdbCENRAP Modeling TSD

- 55

In 2018, impacts from Mexico (28.4% of total visibility impairm ent) and BC (27.8%) remain the largest contributors, followed by Texas sources (22.8%)

. The next largest contributing source region is Louisiana, which contributes 3.3% or 1.47 Mm

- 1 from all sources and pollutants

combined in the state. The 2018 projec tion shows the total extinction at Big Bend National Park for the worst 20 % days is estimated to be 44.23 Mm

- 1 , a reduction of approximately 8 %. Anticipated reductions of SO 2 emissions primarily from point sources in Texas, the Eastern United States, Indiana, Illinois, Kansas, Alabama and Ohio will account for a decrease of 2.73

Mm

- 1 in total light extinction (1.55 Mm

- 1 decrease from Texas point sources).Even with these

expect ed reductions in SO 2 emissions from point sources in 2018, extinction due to point sources will continue to be the highest contributor to visibility impairment on the worst 20% days, accounting for over one third 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 Big Bend National Park in 2002 (Mm

- 1 )

Total 1 Point Natural On

- Road Non

- Road Area

SO4 26.10 17.70 0.02 0.28 0.45 1.82

NO3 2.05 0.55 0.33 0.36 0.23 0.30

POA 5.81 0.10 0.08 0.04 0.09 0.83

EC 2.12 0.01 0.03 0.10 0.32 0.45

SOIL 2.54 0.28 1.14 0.01 0.00 1.00

CM 7.03 0.02 5.42 0.00 0.07 1.23

Sum 47.79 18.66 7.12 0.80 1.16 5.63

1 Totals include contributions from boundary conditions and secondary organic matter

Table 6

- 2. Projected light extinction for 20% worst days at Big Bend National Park in 2018 (Mm

- 1 )

Total 1 Point Natural On

- Road Non

- Road Area

SO4 23.00 15.15 0.01 0.04 0.20 1.84

NO3 1.99 0.63 0.38 0.12 0.16 0.35

POA 5.61 0.14 0.06 0.02 0.08 0.67

EC 1.81 0.02 0.02 0.02 0.23 0.29

SOIL 2.56 0.33 1.15 0.01 0.00 0.98

CM 7.17 0.02 5.54 0.00 0.07 1.36

Sum 44.23 16.28 7.15 0.20 0.74 5.50

1 Totals include contributions from boundary conditions and secondary organic matterCENRAP Modeling TSD

- 56

Figure 6

- 2. Source apportionment modeling results by source region and source category for

worst 20% days at Big Bend National Park in 2002.

Figure 6

- 3. Source apportionment modeling results by source region and species for worst 20% days at Big Bend National Park in 2002. 0.00E+00 2.00E+00 4.00E+00 6.00E+00 8.00E+00 1.00E+01 1.20E+01 1.40E+01 Extinction (1/Mm)

Source Region

CENRAP PSAT Projected W20% 2002 BEXT at Site BIBE1

[Total=47.79,Rayleigh=9,SS=0.1] Elevated Point Low-Level Point Natural On-Road Mobile

Non-Road Mobile Area EGU Point Non-EGU Point

SOAA SOAB Initial Concentration Boundary Condition

0.00E+00 2.00E+00 4.00E+00 6.00E+00 8.00E+00 1.00E+01 1.20E+01 1.40E+01 Extinction (1/Mm)

Source Region

CENRAP PSAT Projected W20% 2002 BEXT at Site BIBE1

[Total=47.79,Rayleigh=9,SS=0.1] SO4 NO3 POA EC SOIL CM SOAA SOABCENRAP Modeling TSD

- 57

F igure 6

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

worst 20% days at Big Bend National Park in 2018.

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

- EGU point s ources Figure 6

- 5. Source apportionment modeling results by source region and species for worst 20% days at Big Bend National Park in 2018.

0.00E+00 2.00E+00 4.00E+00 6.00E+00 8.00E+00 1.00E+01 1.20E+01 1.40E+01
Extinction (1/Mm)

Source Region

CENRAP PSAT Projected W20% 2018 BEXT at Site BIBE1

[Total=44.23,Rayleigh=9,SS=0.1] SO4 NO3 POA EC SOIL CM SOAA SOAB 0.00E+00 2.00E+00 4.00E+00 6.00E+00 8.00E+00 1.00E+01 1.20E+01 1.40E+01 Extinction (1/Mm)

Source Region

CENRAP PSAT Projected W20% 2018 BEXT at Site BIBE1

[Total=44.23,Rayleigh=9,SS=0.1] Elevated Point Low-Level Point Natural On-Road Mobile

Non-Road Mobile Area EGU Point Non-EGU Point

SOAA SOAB Initial Concentration Boundary ConditionCENRAP Modeling TSD

- 58

6.3 S OURCE A PPORTIONMENT R ESULTS AT G UADALUPE M OUNTAIN N ATIONAL P ARK

Tables 6

- 3 and 6

- 4 show the contributions to total extinction for each source category and species for 2002 and 2018, respectively. Figures 6

- 6 , 6

- 7 , 6

- 8 , and 6

- 9 show the geographical source apportionment by source category and species for the 20% worst days in 2002 and 2 018. Visibility impairment at the Guadalupe Mountain National Park site in 2002 on the worst 20% days is largely due to sulfate and course material that contributes 15.94 Mm

- 1 and 16.04 Mm

- 1

, respectively of the total extinction of 47.80 Mm

- 1 . The large st contributions of sulfate come from Texas (4.28 Mm

- 1 ), Mexico (3.2 Mm

- 1 ) , the eastern United States ( 1.45 Mm

- 1 ), and from areas outside the modeling domain captured as boundary conditions (1.90 Mm

- 1 )

. As expected, the largest contributions of CM are from the nearby source regions of Texas, New Mexico, and

Mexico. Overall, the largest source region contributions to visibility impairment in 2002 are

from Texas ( 16.62 Mm

- 1 )
, and Mexico ( 7.90 Mm

- 1 ), follow ed by boundary conditions (4.16 Mm

- 1 ) and New Mexico (3.49 Mm

- 1 )

.

In 2018, Texas sources contribute the most to visibility impairment at Guadalupe Mountain

National Park. The 2018 projection shows the total extinction at Guadalupe Mountain National

Park fo r the worst 20 % days is estimated to be 44.43 Mm

- 1 , a reduction of approximately 7 %. Reductions of sulfate emissions from point sources account for a decrease of 1.99 Mm

- 1 in total light extinction

. Anticipated reductions of SO 2 emissions primarily from point sources in Texas,

the E astern United States, Indiana, Alabama and Ohio will account for a decrease of 2.02 Mm

- 1
in total light extinction (0.68 Mm

- 1 decrease from Texas point sources). Even with these expected reductions i n SO 2 emissions from point sources in 2018, extinction due to point sources will still be a significant contributor to visibility impairment on the worst 20% days, accounting

for over one fourth of the total extinction. Total reductions from mobile sourc es contribute a decrease in total extinction of approximately 8.5 Mm

- 1

. Reductions in mobile sources contribute a decrease in total extinction of approximately 1.42 Mm

- 1

. Visibility impairment from all Texas sources will decrease by 1.29 Mm

- 1, primarily due to expected reductions from point sources.

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 Guadalupe Mountain National Park in 2002 (Mm

- 1 )

Total 1 Point Natural On

- Road Non

- Road Area

SO4 15.94 12.10 0.02 0.22 0.33 1.36

NO3 3.67 1.09 0.40 0.79 0.55 0.52

POA 2.75 0.24 0.19 0.10 0.16 1.61

EC 1.19 0.01 0.04 0.15 0.34 0.51

SOIL 4.37 0.41 1.29 0.02 0.00 2.41

CM 16.04 0.19 7.75 0.02 0.39 6.60

Sum 47.80 14.05 9.68 1.31 1.76 13.00CENRAP Modeling TSD

- 59 1 Totals include contributions from boundary conditions and secondary organic matter

Table 6

- 4 . Projected light extinction for 20% worst days at Guadalupe Mountain National Park in 2018 (Mm

- 1 )

Total 1 Point Natural On

- Road Non

- Road Area

SO4 13.65 10.11 0.02 0.03 0.10 1.40

NO3 3.32 1.18 0.44 0.27 0.37 0.65

POA 2.38 0.29 0.15 0.05 0.13 1.30

EC 0.86 0.02 0.04 0.04 0.23 0.37

SOIL 4.37 0.51 1.29 0.02 0.00 2.31

CM 16.13 0.20 7.74 0.03 0.39 6.69

Sum 44.43 12.31 9.67 0.43 1.22 12.73

1 Totals include contributions from boundary conditions and secondary organic matterCENRAP Modeling TSD

- 60

Figure 6

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

worst 20% days at Guadalupe Mountain National Park in 2002.

Figure 6

- 7. Source apportionment modeling results by source region and species for worst 20% days at Guadalupe Mountain National Park in 2002.CENRAP Modeling TSD

- 61

Figure 6

- 8. Source apportionment modeling results by source region and source category for

worst 20% days at G uadalupe Mountain National Park in 2018.

Figure 6

- 9. Source apportionment modeling results by source region and species for worst 20%

days at Guadalupe Mountains National Park in 2018.CENRAP Modeling TSD

- 62

6.4 C ONTRIBUTIONS TO VISI BILITY IMPAIRMENT AT OTHER C LASS I AREAS

CAMx PSAT results are also utilized to evaluate the impact of Texas emission sources in 2002 and 2018 on visibility impairment at Class I areas outside of the state. Tables 6

- 5 and 6

- 6 summarize the contribution from Texas emissions on sulfate, nitrate and tot al visibility degradation at nearby state’s Class I areas for the 20% worst days in 2002 and 2018, as modeled by CENRAP and shown in Section 11.2 of the Texas RH SIP. 52 Additional information on

source apportionment results are available in the CENRAP’S TSD and Chapter 11 of the Texas

RH SIP

.

Table 6

- 5. Contribution from Texas Emissions to Visibility Impairme nt at Class I areas on 20% Worst Days in 2002(Mm

- 1 )

Sulfate Nitrate Total

Class I area State Texas Total, All

source

areas Texas Total, All

source

areas Texas Total, All

source

areas Carlsbad

Caverns New

Mexico 4.28 15.94 0.78 3.67 16.62 47.80

Salt Creek New

Mexico 4.79 16.75 3.05 11.15 13.41 52.50

White

Mountain New

Mexico 2.78 10.51 0.53 3.05 7.40 32.91

Wheeler

Peak New

Mexico 0.76 5.27 0.22 1.64 1.85 21.96

Wichita

Mountains Oklahoma 13.98 49.12 7.89 23.72 28.15 100.03

Great Sand

Dunes Colorado 0.66 5.84 0.02 1.94 1.25 27.88

Rocky

Mountains Colorado 0.30 7.69 0.08 5.17 0.58 32.13

Caney Creek Arkansas 11.55 87.05 1.49 13.78 14.89 133.93

Upper

Buffalo Arkansas 4.41 83.18 0.27 13.30 5.19 131.79

Hercules

Glades Missouri 3.48 87.94 2.56 17.91 6.59 140.05

Mingo Missouri 0.69 102.52 1.18 27.24 2.01 159.83

Breton Louisiana 3.55 96.83 0.15 8.29 4.20 123.99

52 See Appendix E of the Technical Support Document for CENRAP Emissions and Air Quality Modeling to Support Regional Haze SIP, included as Appendix 8

- 1 of the Texas RH SIP for PSAT modeling resultsCENRAP Modeling TSD

- 63

Table 6

- 6. Contribution from Texas Emissions to Visibility Impairment at Class I areas on 20% Worst Days in 2018 (Mm

- 1 )

Sulfate Nitrate Total

Class I area State Texas Total, All

source

areas Texas Total, All

source

areas Texas Total, All

source

areas
Carlsbad

Caverns New

Mexico 3.65 13.65 0.68 3.32 15.39 44.43

Salt Creek New

Mexico 3.50 13.75 2.43 9.81 10.24 46.67

White

Mountain New

Mexico 2.37 8.92 0.47 2.68 6.22 29.80

Wheeler

Peak New

Mexico 0.79 5.00 0.19 1.48 1.59 20.80

Wichita

Mountains Oklahoma 9.68 33.33 6.08 18.10 20.79 75.56

Great Sand

Dunes Colorado 0.65 5.32 0.02 1.83 1.11 26.77

Rocky

Mountains Colorado 0.30 6.52 0.06 4.28 0.51 29.41

Caney Creek Arkansas 7.24 48.95 0.83 7.57 9.74 85.84

Upper

Buffalo Arkansas 2.74 45.38 0.18 9.22 3.38 86.16

Hercules

Glades Missouri 2.51 50.63 1.51 12.35 4.45 92.49

Mingo Missouri 0.53 54.45 0.64 19.14 1.28 99.24

Breton Louisiana 2.66 68.63 0.16 8.20 3.23 94.06CENRAP Modeling TSD

- 64

Figure 6

- 10. Projected 2018 total extinction and Texas contribution at Class I areas in Texas and

other states