### B-1 APPENDIX B

Emissions Modeling for the HGB Attainment Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard

### B-2 APPENDIX B

Emissions Modeling for the HGB Attainment Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard

INDEX

Description Page

Section 1.0 Introduction – General M odeling E missions Development B-3

B-5 1.1 Modeling Domain and Nested Grid Structure
1.2 HGB Modeling EpisodeS B-9
1.3 Emissions Processing System B-9

Section 2.0 Point Source Modeling missions Development B-14

B-14 2.1 2005 & 2006 Base Case Point Source Modeling E missions Development

B-65 2.2 2006 Baseline Point Source Modeling Emissions Development

B-78 2.3 2018 Future Year Point Source Modeling Emissions Development

B-117 2.4 2018 Future Year Point Sources Control Strategy Modeling Emissions Develop ment

Section 3.0 On-Road Mobile Source Modeling Emissions Development B-120

B-120 3.1 On-Road Mobile Source Emissions Inventories for Eight-County HGB

B-140 3.2 On-Road Mobile Source Emissions Inventories for Non-Eight-County

HGB Areas

Section 4.0 Non-Road, Off-Road, and Area Source Modeling Emissions B-142

B-143 4.1 Non-Road Mobile Source Modeling Emissions for Eight-County HGB

B-159 4.2 Aircraft and Ground Support Equipment Modeling Emissions for Eight-Cou nty HGB

B-164 4.3 Locomotive Modeling Emissions for Eight-County HGB

B-168 4.4 Commercial Marine Modeling Emissions for Eight-County HGB

B-173 4.5 Area Source Modeling Emissions for Eight-County HGB

B-181 4.6 Off-Shore Oil and Gas Production Emissions

B-182 4.7 Non-Road, Off-Road, and Area Source Emissions Outside of Eight-County

HGB

Section 5.0 Biogenic Modeling Emissions B-183

ATTACHMENT 1 Reconciling Reported VOC Emissions with Ambient Measurements B-187

### B-3 1.0 INTRODUCTION – GENERAL MODELING EMISSIONS DEVELOPMENT
The EPA’s Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze (EPA, 2007) specifies a procedure for demonstrating attainment through modeling. Inst ead of using the model results in an absolute sense, the eight-hour ozone procedure uses the modeli ng results in a relative sense. This relative approach is based on how the model responds to the reduction in emissions between a baseline and a future year. Therefore, the photochemi cal modeling process for attainment demonstration requires four modeling emissions data sets:

 base case emissions;

 baseline emissions;

 future year emissions; and

 future year control strategy emissions.

1.0.1 Base Case Modeling Emissions In order for the photochemical model to be used in the attainment demonstration, the model needs to be capable of adequately rep licating historical episodes (base cases) for which high daily eighthour ozone was measured. To maximize m odel performance, base case emission inputs are estimated as accurately as possible. In the de velopment of the base case modeling emissions, a number of quality assurance techniques are used to evaluate the reasonableness of the emission magnitudes, along with their spatial distributio n and temporal profile. Using the quality assured episode-specific emissions along with other modeling inputs (e.g., meteorology), the photochemical model is run and th e simulated concentrations of both ozone and ozone precursors (e.g., NO X , VOC) are compared to the measured concentr ations to evaluate the adequacy of the photochemical model in replicating the base case. If the evaluation indicates that the base case is not adequately replicated, then diagnostics ar e conducted to determine which modeling inputs are insufficient. When the emissions are implic ated, the modeling emissions are reviewed and pertinent revisions are made as appropriate. If e ither the evaluation implicated other inputs or once the photochemical model adequately replicat es the base case, then the modeling emissions are considered to be sufficiently representative of the episode.

A summary of the primary data sources for the development of the base case modeling emissions is provided in Table 1.0-1: Summary of Base Case Emissions Data Sources . This table is not intended to be exhaustive because episodes in both 2005 and 2006 are being modeled for this Houston-Galveston-Brazoria (HGB) State Implementation Plan (SIP) revision.

### B-4 Table 1.0-1: Summary of Ba se Case Emissions Data Sources Category Region Data Source

Texas 2005, 2006 STARS LA, AR, OK 2004, 2005 individual NEIs Other (Regional) 2002 CenRAP/RPOs EI

All States 2005, 2006 EPA hourly Acid Rain data

Texas 2005, 2006 hourly Special Inventory surveys

Texas 2005, 2006 hourly Harris County Tank Landing Loss

surveys
Texas 2005, 2006 HGB HRVOC reconciliation (PSCF)

Offshore 2005 MMS GWEI platforms of western Gulf of Mexico

Mexico 1999 Phase III Mexico NEI Point

Source
Emissions

Canada 1995 from EPA’s CAIR 2001 base modeling

HGB & BPA 2005, 2006 based on MOBILE6.2 and local travel demand model (TDM) for vehicle miles traveled (VMT)

Other Texas 2005, 2006 based on MOBILE6.2 and Highway Performance Monitoring System (HPMS) for VMT On-road
Mobile
Emissions

Outside Texas 2005, 2006 based on EPA’s Na tional Mobile Inventory Model (NMIM)

HGB 2005 TexAER, Texas NONROAD (TexN) model

Texas 2005 TexAER, TexN model Non-road

Mobile
Emissions Outside Texas 2005, 2006 NMIM

Texas 2005 TexAER Area Source

Emissions Outside Texas 2002 EPA NEI

Texas GloBEIS3 with TCEQ LULC data and drought adjustment Biogenic
Emissions Outside Texas GloBEIS3 with BELD3 LULC data

Emissions are developed for the ozone precursors of NO X , VOC, and CO. The emission inventories (EIs) are prepared for photochemi cal modeling input usin g Version 3 of the Emissions Processing System (EPS3), which is discussed in more detail in Section 1.3.

1.0.2 Baseline Modeling Emissions As indicated above, the EPA procedure for demons trating attainment requires the development of modeling emissions for a baseline year to be used with similarly developed future year emissions. In order to keep the baseline and future year modeling emissions commensurate, more generic non-episodic ozone season day (OSD) emissions are developed for the baseline year. The OSD modeling emissions for the baseline and future y ears are developed using the same averaging and estimating procedures, which provides an appropriate basis for assessing the photochemical model response to emission reductions.

The major difference between the base case and base line modeling emissions is the treatment of the hourly-specific emissions for point sources, such as electric generating units (EGUs). With the exception of on-road mobile sources within the eight-county HGB area, emissions for the other source categories are identical between th e base cases and baseline modeling emissions. 2006 was chosen as the baseline year and Section 2.2 describes the averag ing processes used in the development of the baseline inventory.

1.0.3 Future Year Modeling Emissions With a classification of severe, the eight-hour ozone attainment date for HGB is June 15, 2019, but the year for modeling attainment is 2018 because it is the full ozone season prior to the attainment date. Modeling emissions for the 2018 fu ture year were estimated by applying growth projections and control measures to the 2006 baseline modeling emissions. In addition to newly proposed control measures, the 2018 modeling em issions include the benefits of the Federal

### B-5 Motor Vehicle Control Program (FMVCP), Mass Emissions Cap and Tr ade (MECT) Program, and the Highly Reactive VOC Emission Cap and Trade (HECT) Program.

1.1 MODELING DOMAIN AND NESTED GRID STRUCTURE The emissions modeling and the photochemical mode l use the same horizontal grid configuration based on a Lambert Conformal Conic (LCC) map projection as defined in Table 1.1-1: Lambert Conformal Conic Map Projection Parameters . This coordinate system has been used for several other photochemical modeling studies in the central United States (e.g., CENRAP).

Table 1.1-1: Lambert Conforma l Conic Map Projection Parameters LCC Projection Parameter LCC Projection Value First True Latitude (Alpha) 30 degrees north Second True Latitude (Beta) 60 degrees north Central Longitude (Gamma) 100 degrees west Projection Origin 100 degrees west, 40 degrees north Spheroid Perfect sphere with radius of 6,370 kilometers

The horizontal modeling domain structure consists of a 36 km by 36 km coarse regional grid for the Eastern U.S. and three sequentially nested fine grid subdomains:

 a 12 km by 12 km subdomain that includes Eastern Texas and adjacent states;

 a 4 km by 4km subdomain that includes HGB, BPA, and Lake Charles, Louisiana; and

 a 2 km by 2 km subdomain that includes portions of the HGB and BPA areas.

A graphical representation of the emissions mode ling domains is provided in Figure 1.1-1: Emissions Modeling Domains , and the parameters for each of these domains are summarized in
Table 1.1-2: Nested Grid Structure

.Figure 1.1-1: Emissions Modeling Domains

Table 1.1-2: Nested Grid Structure Range (km) Number of Cells Cell Size (km) Domain

Name Easting Northing Easting Northing Easting Northing CAMx
Code

East

US (-108, 2376) (-1584, 828) 69 67 36 36 eus_36km

East

Texas (-12, 1056) (-1488, -420) 89 89 12 12 etx_12km

HGB/

BPA (356, 688) (-1288, -968) 83 65 4 4 hgbp_04km

HGB (394, 542) (-1154, -1042) 74 56 2 2 hgb_02km

The vertical configuration for the 2 km and 4 km horizontal domains contains 28 layers of varying depth above ground level ( AGL) as shown in Table 1.1-3: CAMx Vertical Layer Structure for 2 km and 4 km Fine Grids . The vertical configuration for the 12 km and 36 km horizontal domains contains 17 layers of varying depth as shown in Table 1.1-4: CAMx Vertical Layer Structure for Interm ediate and Coarse Grids . The additional layers in the fine grid domains are needed, particularly near the ground level, because of:

 the unique mixture of industrial sources in the HGB and BPA area that release pollutants across a wide range of elevations; and



### B-6

### B-7  the unique meteorology induced the close proximity of land to Galveston Bay and the
Gulf of Mexico.

Table 1.1-3: CAMx Vertical Layer Structure for 2 km and 4 km Fine Grids

CAMx Layer MM5 Layer Top (meters AGL) Center (meters AGL) Thickness (meters)

28 38 15179.1 13637.9 3082.5

27 36 12096.6 10631.6 2930.0

26 32 9166.6 8063.8 2205.7

25 29 6960.9 6398.4 1125.0

24 27 5835.9 5367 937

23 25 4898 4502.2 791.6

22 23 4106.4 3739.9 733

21 21 3373.5 3199.9 347.2

20 20 3026.3 2858.3 335.9

19 19 2690.4 2528.3 324.3

18 18 2366.1 2234.7 262.8

17 17 2103.3 1975.2 256.2

16 16 1847.2 1722.2 256.3

15 15 1597.3 1475.3 249.9

14 14 1353.4 1281.6 243.9

13 13 1209.8 1139 143.6

12 12 1068.2 998.3 141.6

11 11 928.5 859.5 137.8

10 10 790.6 745.2 90.9

9 9 699.7 654.7 90.1

8 8 609.5 564.9 89.3

7 7 520.2 476.0 88.5

6 6 431.7 387.8 87.8

5 5 343.9 300.4 87.0

4 4 256.9 213.7 86.3

3 3 170.5 127.7 85.6

2 2 84.9 59.4 51.0

1 1 33.9 16.9 33.9

### B-8 Table 1.1-4: CAMx Vertical Layer Stru cture for Intermediate and Coarse Grids

CAMx Layer MM5 Layer Top (meters AGL) Center (meters AGL) Thickness (meters)

17 38 15179.1 12172.9 6012.5

16 32 9166.6 7501.3 3330.7

15 27 5835.9 4970.9 1730

14 23 4105.9 3565.9 1080

13 20 3025.9 2564.5 922.9

12 17 2103 1728.1 749.8

11 14 1353.2 1210.6 285.2

10 12 1068.2 929.3 277.5

9 10 790.6 700.0 181.0

8 8 609.5 564.9 89.3

7 7 520.2 476.0 88.5

6 6 431.7 387.8 87.8

5 5 343.9 300.4 87.0

4 4 256.9 213.7 86.3

3 3 170.5 127.7 85.6

2 2 84.9 59.4 51.0

1 1 33.9 16.9 33.9

All modeling emissions for the 4 km HGB/BPA domain were developed at a 2 km grid cell resolution and aggregated up to 4 km. In th e emissions modeling, the sources are separated between those emitting at the surface (e.g., mob ile and low-level point) and those emitted at elevation (e.g., high-level points and ships). Elevat ed emissions are typically from a source with a fixed location, so a single emissions modeling file can be used for each of the 4 km, 12 km, and 36 km domain inputs. To account for necessary spatial resolution, separate emission modeling files must be developed for the 4 km, 12 km, and 36 km surface level inputs. To facilitate reporting and quality assurance, modeling emission s files were developed specifically eightcounty HGB and surrounding areas, which area shown below in Figure 1.1-2: HGB and BPA

Subdomain

.Figure 1.1-2: HGB and BPA Subdomain

1.2 HGB MODELING EPISODES Three episodes from 2005 and three from 2006 were m odeled for this attainment demonstration,
as shown in Table 1.2-1: Modeled Episodes . Each episode day was modeled with a unique set of low-level and elevated emissions for the biogenic and point source categories.

Table 1.2-1: Modeled Episodes Episode

Name Common

Name Modeling
Period bc05ep0 May-June 2005 May 19 – June 3, 2005 bc05ep1 June 2005 June 17-30, 2005 bc05ep2 July-August 2005 July 26 – August 8, 2005 bc06ep0 June 2006 May 31 – June 15, 2006 bc06aqs1 2006 AQS-1 August 13 – September 15, 2006 bc06aqs2 2006 AQS-2 Septem ber 16 – October 11, 2006

1.3 EMISSIONS PROCESSING SYSTEM Emission inventory totals for area, biogenic, on -road, non-road, and point sources are not simply input to photochemical models such as CAMx. After the emissions inventories are developed with tools such as MOBILE6.2, NONROAD, etc., the data sets must be:

 spatially allocated to grid cells or assigned to fixed points;

 temporally allocated by hour of day, day of week, etc.; and

 chemically speciated into groups of compounds with similar reactivity for the formation
of ozone.



### B-9

### B-10 This process is typically referred to as either emissions modeling or emissions processing and is distinctly separate from emissions inventory deve lopment. The TCEQ currently uses Version 3 of the Emissions Processing System (EPS3) devel oped by Environ to apply the necessary spatial, temporal, and chemical species allocation. Environ’s EPS3 User’s Guide provides step-by-step details for emissions modeling with this software , including all the steps necessary for generating a photochemical model-ready emissions input file. A copy of the EPS3 User’s Guide is available

at ftp://ftp.tceq.state.tx.us/pub/OEPAA/ TAD/Modeling/HGB8H2/ei/EPS3_m anual/

.

This document contains a thorough summary of the needs, requirements, and general processing methods for emissions modeling. The remainder of this section is an excerpt from pages 1-2 through 1-5 of Environ’s EPS3 User’s Guide.

“A photochemical model simulates the hour-by-hour photochemistry occurring for each grid cell in the modeling domain; consequently, the input emissions data must contain a comparable level of resolution. Total VOC and NO X emissions must be chemically allocated into the ch emical classes employed by the model. Additionally, the emissions data must be spatially allocated by grid cell for each hour of the modeling episode. If photoche mical modeling is being performed to evaluate potential control strategies, future-year inventories must be projected which incorporate anticipated changes in emissions levels. The following
information was extracted from EPA's Procedures for the Preparation of Emission Inventories for Carbon Monoxide and Precursors of Ozone, Volume II: Emission Inventory Requirements for P hotochemical Air Quality Simulation

Models (EPA-450/4-91-014, May 1991). Refer to Table 2-2 of Section 2 for an internet link to emission guidance documents.

Spatial Resolution of Emissions In order for a photochemical model to provide spatially resolved predictions of ozone and various other pollutants at the gr id cell level, it must be supplied with emission data that have the same degree of spatial resolution (i.e., by grid cell). The amount of effort required to implem ent this resolution will vary depending on the type of source. Most existing data bases of point source emissions will contain location data for each source (typica lly reported to within a fraction of a kilometer), allowing direct assignment of emissions to the appropriate grid cells.

By contrast, spatial resolution of area source emissions requires substantially
more effort. Two basic methods can be used to apportion area source emissions to grid cells. The most accurate (and r esource-intensive) approach is to obtain area source activity levels for each grid cell. The alternative approach, more commonly employed, is to apportion the county-level emissions from the existing inventory to grid cells using some spatial surrogate indicator of emission levels or activity (e.g., population, censu s tract data, or type of land use). The major assumption underlying this method is that emissions from each area source behave spatially in the same manner as th e surrogate indicator. In developing spatial apportioning factors, the emissi ons modeler should focus on determining accurate factors for the more significant s ources. In most large urban areas local planning agencies can provide the emissions modeler with detailed land use, population, or in some cases, employment statistics at the subcounty level. The U.S. Census Bureau also gathers and re ports this type of data for the nation (generally at the subcounty level).

Because of the relative importance of highway motor vehicle emissions (which usually comprise a large fraction of total VOC and NO X emissions for most urban areas), these sources should be considered separately from other area sources in the modeling inventory. Instead of using county vehicle miles

### B-11 traveled (VMT) or gasoline sales to estimate highway vehicle emissions (as
annual and seasonal inventories sometim es do), urban transportation planning models should be employed to generate VMT on an individual link basis whenever possible. The emissions for each link could then be assigned to the appropriate grid cells based on link location coordinates.

Planning, land use, and transportation mode ls are already in use in many urban areas, and can provide the emissions modeler with much of the data necessary to
allocate area source emissions and de velop emission estimates by link for highway motor vehicles. Such models may also be capable of forecasting emissions for use in the development of projected inventories. Local agencies (particularly metropolitan planning orga nizations, or MPOs) should always be contacted during the invent ory planning process to determine what planning models are being used and how the data from these models can be used in developing an emission inventory. Inde pendently developed information such as growth forecasts not only may be unnecessary but, when used in photochemical modeling, may be the basis for challenges to the modeling results because of alleged nonconformity with other proj ections available to the public.

Temporal Resolution of Emissions In order to accurately predict hourly c oncentrations of ozone, a photochemical model requires hourly estimates of emissi ons for each grid cell in the modeling domain. The existing inventory from which the modeling inventory is developed will generally contain annual average or , for the peak ozone season, daily emission rates; the emissions modeler must adjust the emissions in the baseline inventory to reflect the conditions of the ozone episode being modeled, including seasonal adjustments for temperature or activity levels (if baseline emissions are reported as annual averages) as well as adju stments for the day of the week. In addition, they must be allocated to the hours of the modeling episode.

The most accurate approach for tempor al distribution of emissions involves determining emission rates (or activity) for specific sources for each hour of a typical day in the time period being modeled. This approach is sometimes employed for point sources, but often proves impractical for all but the major sources in the region. More commonly, point source emissions are allocated by hour using the operating schedule informa tion provided in the existing base-year inventory. Operating schedules for point sources are typically reported only in terms of hours per day, days per week, and weeks per year in operation, with seasonal variations reported in terms of percent of annual throughput. Consequently, the emissions modeler must determine the most representative allocation of emissions to the hours of the modeling episode. If possible, operational hours for major sources in the point source inventory should be obtained to supplement the operational information in the baseline inventory.

For area and mobile sources, the emissions modeler usually develops typical hourly patterns of activity levels for each source category. Typical temporal distribution profiles and adjustment factors for area and mobile source categories
are available in the EPS3 software distri bution, however, these data may not be representative of a given region, which may differ significantly from national average profiles due to the effects of regional meteorological conditions or activity patterns.

Ideally, locale-specific temporal informa tion should be collected for all sources that contribute significantly to the inventory. Regional weekday/weekend

### B-12 activity levels and diurnal variations by source category can be determined through special surveys or estimated using engineering judgment.

Chemical Resolution of Emissions Since every reaction of all of the organic compounds found in an urban atmosphere cannot be considered, p hotochemical models generally group pollutants to limit the number of reactions and species to a reasonable level while permitting reasonable accuracy in predicting ozone formation. Photochemical models employ a chemical mechanism with a predefined set of air quality modeling compounds. Accordingly, VOC emissions must be disaggregated into chemical mechanism compounds prior to model application. These models also require that NO X emissions be disaggregated into NO and NO 2

.

In the absence of more detailed information, VOC emissions can be speciated using the VOC speciation profiles by source type contained in EPA's SPECIATE 4.0, Volatile Organic Compound (VOC)/Particulate Matter (PM) Speciation Data System (EPA/600/R-06/161, 2006). In some cases, however, source-specific speciation profiles or (less commonly) region-specific speciation profiles by source type may be available. If the latt er are available, the splits associated with that source category can be modified to more accurately reflect the profiles of
local sources. Issues the emissions mode ler should consider when developing or modifying mechanism compound split factors include how hydrocarbon emissions are reported in the inventor y (e.g., as VOCs, total hydrocarbons, reactive hydrocarbons, total non-methane hydrocarbons, etc.) and the compatibility of the speciation profile d esignations with the source classification scheme used in the inventory.

In some instances, data on VOC com pounds for individual facilities may be available (perhaps through source tests or material composition considerations), and the emissions modeler may prefer to use these in the modeling inventory instead of an assumed VOC-compound distribution. Generally, however, most industries cannot provide reliable VOC or NO X compound data or accurately
apportion their emissions into appropriate chemical classes. Thus, typical VOC
and NO X distributions for various source categories are usually used. If speciation data are available for some facilities within the modeling domain, the emissions modeler must identify the correct chemical mechanism speciation of each individual hydrocarbon compound in the data. If splits for specific compounds in the speciated emissions data are unavailabl e, a photochemical modeling specialist familiar with both photochemistry and the chemical
mechanism should be consulted to determ ine appropriate split factors for those compounds.

Development of Projected Inventories There are basically two types of projections: baseline projections and control strategy projections. Baseline projections are estimates of future-year emissions that account for both expected growth in an area and air pollution control regulations that are in effect at the time the projections are made . If provisions in existing control regulations are to take effect at some future date, the baseline projections should include the effects of these expected changes. By contrast, control strategy projections also include the expected impact of proposed control regulations.

Two general methods can be employed for predicting future-year emissions levels: source-specific projections a nd aggregate projections. Information regarding projected increases in activity at existing facilities or proposed

### B-13 construction can be obtained directly fro m operators. Permit applications also provide a source of information a bout proposed expa nsions and new construction. Difficulties encountered in incorporating facility-specific projections into a modeling emissions inventory include differentiating between increased activity at existing facilities and increased activity due to new construction; for proposed facilities, the probable location, stack parameters, and operating schedules, as well as emissions, w ill need to be estimated for each point source within the facility.

Aggregate growth projections are more commonly employed in estimating future-year inventories. In some cases projection information may be available for types of point sources. Similarly, growth indicators may be employed to estimate future-year emissions for groups of sources. Local metropolitan planning offices regularly project demographic and economic parameters for planning purposes; such data can be used in place of or to supplement projections
derived from national data.

Point source projections may be more difficult due to the problem of appropriately representing increased activity at each source. The emissions modeler has three basic options for allo cating projected emissions changes for point sources:

1. Apply projected growth in each source category (e.g., electric utilities) equally across all existing plants in the county;

2. Apply projected growth in each source category across existing plants, weighing the growth more heavily in areas of the county targeted for greater industrial activity; or

3. Identify areas where new plant activities are scheduled to reflect growth due to the startup of new facilities as well as increased activity at existing
facilities.

If the emissions modeler chooses to allo cate projected emissions changes in industrially zoned land by grid cell rath er than assuming that all increased activity will occur at existing facilities, hypothetical emission records for the proposed point sources must be develope d. For area and mobile sources, the
emissions modeler should review the su rrogate indicators used to spatially allocate emissions in the base-year inventory and make any necessary modifications to account for anticipated changes in land use patterns or the existing transportation link network (e.g ., proposed highway, railway, or airport construction). The user should refer to EPA's Emission Inventory Improvement Program (EIIP) Document Series - Volume 10 Emissions Projections (EPA, 1992) for additional guidance. Refer to Ta ble 2-2 of Section 2 for an internet link to emission guidance documents.”

### B-14 2.0 POINT SOURCE MODELING EMISSIONS DEVELOPMENT
The various data sources that went into the de velopment of the point source modeling emissions
are summarized in Table 2.0-1: Sources of Point Source Emissions Data . The TCEQ compiled and formatted the data to generate modeling dataset s for the base case, the b aseline, and the future case studies as detailed in Sections 2.1, 2.2, and 2.3, respectively.

Table 2.0-1: Sources of Point Source Emissions Data

Sources of Data Calendar Year(s)

Used TCEQ State of Texas Air Reporting System (STARS) 2005, 2006

TCEQ Special Inventory (SI) Surveys 2005, 2006 hourly TCEQ Consolidated Compliance Enforcement Data System (CCEDS) of Emissions Event / Maintenance Startup Shutdown (EE/MMS) 2005, 2006

events TCEQ Floating Roof Tank Landing Loss (TLL) Surveys 2005, 2006 hourly TCEQ-derived Highly Reactive VOC (HRVOC) Reconciliation using Potential Source Contribution Factor (PSCF) Methodology 2005, 2006

EPA Clean Air Markets Division (CAMD) Acid Rain Program Continuous Emission Monitors (CEMs) for all states 2005, 2006,

2007 EPA / Texas Clean Air Interstate Rule (CAIR) allocations for entire modeling domain 2009-2014,

2015+ U.S. Department of Energy (DOE) Energy Information Administration (EIA) Annual Energy Outlook Report 2008

Louisiana Department of Environmental Quality (LDEQ) 2004

Oklahoma Department of Environmental Quality (ODEQ) 2005

Arkansas Department of Environmental Quality (ADEQ) 2005

U.S. Department of the Interior Minerals Management Service (MMS) Gulf-Wide Emissions Inventory (GWEI) of Offshore Platforms 2005

Central States Regional Air / Regional Planning Organizations (CenRAP / RPO) 2002, 2018

Canada from
The EPA’s CAIR 2001 base case 1995

Mexican NEI
Phase III 1999

2.1 2005 & 2006 BASE CASE POIN T SOURCE MODELING EMISSIONS
DEVELOPMENT The point source emissions inventories are composed of information from several databases. The following subsections describe development of th e base case point source modeling emissions for all portions of the domain and for all of the epis odes described in Section 1.2. Unless otherwise noted, the same general procedures are used to gather, process, and quality assure (QA) the modeling emissions for each episode.

2.1.1 Texas Point Sources For Texas point sources, ozone season daily em issions data from STARS and hourly emissions data from the EPA’s acid rain database (ARD) pr ovided the basis for modeling the 2005 and 2006 base case episodes. Additionally, a supplemental “extra olefins” file was developed to account
for reconciled HRVOC emissions in the HGB 4 km fine grid modeling domain. HRVOC include ethylene, propylene, 1,3- butadie ne, and all isomers of butene. Hourly emissions data from reporting surveys were used for specific episodes when available. Episode-specific survey results

### B-15 of HGB floating roof tank landing losses (TLL) were compiled and used to develop files of hourly emissions for both 2005 and 2006 episodes. An hourly Special Inventory (SI) survey during the intensive period of the Texas Ai r Quality Study II (TexAQS II) from August 15 through September 15, 2006, was used to de velop modeling emissions for selected Texas nonEGUs (NEGUs). In all cases, where more de tailed, episode-specific or hourly data were available, hourly emissions were used in lieu of the daily emissions data from STARS. The following subsections describe the development of modeling emissions for all of these
components.

2.1.1.1 State of Texas Air Reporting System (STARS) Point source emissions and industrial process operating data are collected annually from sites that meet the reporting requirements of 30 Texas Administrative Code (TAC) §101.10. To collect data, the TCEQ mails annual emissions inventor y questionnaires (EIQs) to all sources identified as meeting the reporting requirements. Subject en tities are required to report levels of emissions subject to regulation from all emissions-generating units and emissions points, and also must provide representative samples of calculations used to estimate the emissions. Descriptive information is also required on process equipm ent, including operating schedules, emission control devices, abatement device control efficiencies, and emission point discharge parameters such as location, height, temperature, and exhaus t gas flow rate. All data submitted in the EIQ are subjected to QA procedures. The data are th en stored in the STARS database. The TCEQ reports point source emissions data to the EPA fo r inclusion in the National Emissions Inventory (NEI).

Annually, the TCEQ collects emissions info rmation from approximately 2000 major point sources. In nonattainment areas, major point s ources are defined for inventory reporting purposes as industrial, commercial, or institutional sources that emit actual levels of criteria pollutants at or above the following amounts: 10 tpy of VOC; 25 tpy of NO X ; or 100 tpy of any of the other criteria pollutants including CO, SO 2 , PM 10 , or lead. For the attainme nt areas of the state, any company that emits a minimum of 100 tpy of any criteria pollutant must submit an inventory. Additionally, any source that either generates or has the potential to generate at least 10 tpy of any single Hazardous Air Pollutant (HAP) or 25 tpy of aggregate HAPs is required to report emissions to the TCEQ. The reporting requireme nts, guidance documents, trends, and summaries of the most recently QAed year of reported data can be found at http://www.tceq.state.tx.us/implementation/air/industei/psei/psei.html

.



### Development of the Texas point source emission modeling files begins with queries of the STARS database. Separate queries were performed for 2005 and 2006 after the reported data were quality assured and appr oved for inclusion in STARS. Updated modeling query reports are typically run when significant STARS updates are completed. The STARS extract is always a snapshot of Texas emissions, si nce emissions fromprevious years can be updated by the regulated entities. The query report is a pipedelimited ASCII text flat file from the relatio nal STARS database. Each line of this text file typically contains one or two pieces of new information, so that many lines are needed to make up one complete modeling reco rd or “path” of emissions. For one year of emissions for all pollutants, the complete STARS extract query report contains more than 10 million lines. A sample 2006 extract from STARS is provided in Figure 2.1-1: Sample of STARS Extract Output

.

### B-16 E|ACCOUNT-SITE|RN100216977|LAST EI DATE|20071231| E|ACCOUNT-SITE|RN100216977|EPA ACCOUNT NUMBER|182| E|ACCOUNT-SITE|RN100216977|OWNER OPERATOR TYPE|BOTH| E|ACCOUNT-SITE|RN100216977|MAJOR SOURCE TYPE|B| E|ACCOUNT-SITE|RN100216977|COUNTY STATUS|NONATTAIN| E|ACCOUNT-SITE|RN100216977|REGION CODE|10| E|ACCOUNT-SITE|RN100216977|COUNTY NAME|JEFFERSON| E|ACCOUNT-SITE|RN100216977|SPRING PERCENTAGE|25| E|ACCOUNT-SITE|RN100216977|SUMMER PERCENTAGE|25| E|ACCOUNT-SITE|RN100216977|FALL PERCENTAGE|25| E|ACCOUNT-SITE|RN100216977|WINTER PERCENTAGE|25| E|ACCOUNT-SITE|RN100216977|TOTAL OPERATING HOURS|8760| E|ACCOUNT-SITE|RN100216977|HOURS PER DAY|24| E|ACCOUNT-SITE|RN100216977|DAYS PER WEEK|7| E|ACCOUNT-SITE|RN100216977|WEEKS PER YEAR|52| E|ACCOUNT-SITE|RN100216977|LATITUDE|295704.15| E|ACCOUNT-SITE|RN100216977|LONGITUDE|935306.01|

. . . E|EMISSION|B-7240 N-14 567502006|ACCOUNT|RN100216977| E|EMISSION|B-7240 N-14 567502006|DETERMINATION|S| E|EMISSION|B-7240 N-14 567502006|CONTAM NAME|PENTANE| E|EMISSION|B-7240 N-14 567502006|CAS NUMBER|109660| E|EMISSION|B-7240 N-14 567502006|ANNUAL|0.9134| E|EMISSION|B-7240 N-14 567502006|OZONE|5.7463| E|EMISSION|B-7240 N-14 567502006|EE/SMSS|0| E|EMISSION|B-7240 N-14 567502006|UPSET|0| E|EMISSION|B-7240 N-14 567752006|ACCOUNT|RN100216977| E|EMISSION|B-7240 N-14 567752006|DETERMINATION|S| E|EMISSION|B-7240 N-14 567752006|CONTAM NAME|PROPANE| E|EMISSION|B-7240 N-14 567752006|CAS NUMBER|74986| E|EMISSION|B-7240 N-14 567752006|ANNUAL|0.5619| E|EMISSION|B-7240 N-14 567752006|OZONE|3.5349| E|EMISSION|B-7240 N-14 567752006|EE/SMSS|0| E|EMISSION|B-7240 N-14 567752006|UPSET|0| Figure 2.1-1: Sample STARS Extract Output

Custom Statistical Analysis System (SAS) computer programming code was written that performs multiple steps on the STARS modeling extract report:

 parses the extract data file,

 makes datasets of specific variables,

 makes necessary units conversions,

 performs various logical checks and comparisons,

 assigns defaults for missing data,

 applies rule effectiveness to VOC paths with control devices,

 arranges the data into an AIRS Fac ility Subsystem (AFS) file format, and

 creates a composition file for total VOC emissions from reported pollutants.

STARS contains all the data reported by the regulated entity (RN) on the annual EIQ. The modeling extract query report that is based on SAS has been refined over the years, but is always a snapshot of the dataset. The STARS extract contains all four types of emission rates: annual, ozone season daily (OSD), emission events / scheduled maintenance startup shutdown (EE/SMSS), and upsets. Typically, only the OSD emissions are retained for the resultant AFS file that is developed and further processed into model-ready emissions files. The OSD emission rate is representative of average daily emissi ons during the summer, where TCEQ defines ozone season for EI reporting purposes as June through August. This is generally the time of the year that monitored ozone concentrations are highest. The annual emission rate is used in cases were OSD rates are not reported. The EE/SMSS and upset emissions are a reported total for the entire year, so a daily rate that could be developed is , in general, not very useful without specific knowledge of the timeframe associated with such events. Instead, a review of data in TCEQ’s emission events reporting database was conducted, as described below in Section 2.1.1.8.1.

### B-17 2.1.1.2 Rule Effectiveness (RE) As noted in the list above, rule effectiveness (RE) is applied to the STARS VOC emissions where appropriate. RE is a term that describes a method to account for the reality that not all facilities covered by a rule are in compliance with the rule 100% of the time. RE also accounts for the fact that control equipment does not always operate at its assumed control efficiency.

The EPA has provided updated guidance on this topic via Appendix B: Revised Rule Effectiveness of the EPA’s August 2005 EI Guidance document, " Emissions Inventory Guidance for Implementation of Ozone and Particulate Matt er National Ambient Air Quality Standards (NAAQS) and Regional Haze Regulations. " This document can be found at http://www.epa.gov/ttn/chief/eidocs/eiguid/eiguidfinal_nov2005.pdf Again, the relevant part of this document is Appendix B, which reiterates previous guidance that RE is required of EIs for SIP modeling purposes, including eight-hour ozone, particulate, and regional haze EIs.

The updated guidance also suggests improved methodology for arriving at RE values, by taking into account several factors including compliance hist ory and inspection intervals. To gather this facility-specific info, conducting local surveys is pr eferred, but if that is not feasible, analyses performed by others may be compared to simila r source types. The EPA guidance suggests RE ranges with proposed criteria being met to move from one range of values to another.

The TCEQ continues to use the EPA-approved RE procedure that it has used for SIP modeling since 1995, which assumes the 80% default on all controlled VOC sources, unless a different RE value for the source category was developed from the 1993 survey. Since a new survey, using the suggested updated EPA criteria, is resource intensive, the TCEQ has and will continue to use the existing RE method until all or some of the s ource categories RE values have been updated.

Table 2.1-1: VOC Rule Effectiveness Summary demonstrates the effect of the application of RE upon STARS VOC emissions for the nonattainment areas of the State and the State as a whole. Note that HGB has the largest amount of VOC and the largest number of controlled sources, resulting in the largest increase due to RE. DF W has the least, with BPA somewhat in the middle, and the Statewide average being 26% a nd 22% increase in VOC emissions due to the application of RE, for years 2005 and 2006, respectively.

Table 2.1-1: VOC Rule Effectiveness Summary

Inventory

Year HGB DFW BPA Statewide

STARS VOC (tpd) 141 31 44 434
Added RE (tpd) 60 3 14 111
Total VOC (tpd) 201 34 58 546

% Added 2005

42% 9% 32% 26%

STARS VOC (tpd) 119 35 56 406
Added RE (tpd) 44 3 12 90
Total VOC (tpd) 163 38 68 495

% Added 2006

37% 8% 22% 22%

While the current TCEQ RE algorithms are likely over-predicting actual VOC emissions with rule effectiveness, based on reported emissions trends, ambient monitoring of many VOC compounds indicates that the reported rates should be even greater. Hen ce, until the TCEQ has collected better data that meet or exceed the EPA’s suggested RE criteria, the existing algorithms
will continue to be used.

### B-18 2.1.1.3 Preparation of AFS File for EPS3 Input The resultant AFS file is in a format ready for input into Version 3 of the Emissions Processor System (EPS3). The STARS-derived AFS file for all criteria pollutants typically has more than 200,000 records. Each point source emissions path contains references for the TCEQ account (RN), equipment (FIN), and exhaust point (EPN ). For ozone modeling purposes, values for the ozone precursors of NO X , VOC, and CO are retained in the AFS file for EPS3 input. An example of a single AFS record for VOC is provided as Figure 2.1-2: Example of AFS Formatted Record for EPS3 Input.

B 06 06 AC 48201 HGB 2869 40703622 14 131 107

AD 06060100 06090100 481.0641 -1105.9330 12.2 0.915 294 0.003 25

25 25 25 24 0 7 8736 43104 0.00693

RN102574803 HG0229F TK3055 TK3055 N S T TANKS VERTICAL FIXED

EXXONMOBIL CHEMICAL CO BAYTOWN CHEMICAL PLANT HARRIS BAYTOWN

VOC A Pt STAR v4b Figure 2.1-2: Example of AFS Formatted Record for EPS3 Input

EPS3 directly uses the information in columns 1 through 219. Columns beyond 219, starting with “RN” in the Figure 2.1-2 example, provide u seful identifying data fields from STARS. The AFS file format (field descriptions and options) used by the TCEQ for this modeling can be found on the public TCEQ ftp modeling site, ftp://ftp.tceq.state.tx.us/pub/OEPAA/T AD/Modeling/HGB8H2/ei/point/AFS- EPS3-v3.doc

.

2.1.1.4 STARS-AFS Quality Assurance (QA) A QA check is performed where the 100 highest emitting NO X and VOC sources from the AFS file are compared against the 100 highest emitting NO X and VOC sources from an independent STARS query. This step is done to ensure that no systematic errors were included in either the original STARS extract or in the subsequent SAS code processing. Both the order of the top 100
sources as well as the emission totals should be id entical. The only exceptio ns occur when some of the VOC figures in the AFS file have rule effectiveness applied to them.

Another quality assurance step is conducted wh ere the number of point sources and overall emission totals of the AFS file is compared ag ainst sequential STARS extracts of the same EIQ reporting year. STARS emissions data for a given year are generally fairly stable within 18 months after the end of an EIQ year. For example, the 2006 STARS extract changed only a trivial amount after June of 2008. Different extracts of the same year are incremented by both version number and letter. For example, the number “4” in “v4b” at the end of the line in Figure
2.1-2 indicates that this record was from the four th STARS extract, and the “b” indicates that this was the second SAS run for production of an AFS fi le. Multiple extracts of the same year occur when there have been changes or additions to STARS, and multiple SAS runs of the same extract occur when there have been changes in either the SAS processing code or the manner in which the data are being used.

2.1.1.5 Preparation of Photochemical Model Ready Files With EPS3 EPS3 is used to process the emissions in the AFS file into a format ready for photochemical model input. Photochemical model inputs require that the emissions be (in order performed by TCEQ):

 chemically speciated into groups of compounds with similar reactivity for the formation of ozone;

 temporally allocated by hour of day, day of week, etc.; and

 spatially allocated to grid cells or assigned to fixed points.

The EPS3 User’s Guide referenced in Section 1.3, a bove, provides additional details for processing the point source emissions for photochemical model input. The remainder of this section discusses some of the specific point source emissions processing procedures with excerpts of the data provided as appropriate fo r a better understanding of the process.

### B-19 2.1.1.5.1 Chemical Speciation With EPS3 VOC Emissions in STARS can be reported as individual compounds, mixtures, classes of compounds, total VOC, and unclassified VOC. The VOC values that are included in the AFS file must be speciated into compound groups of simila r ozone reactivity that will be recognized by the photochemical model. For operational effici ency, photochemical models are not based on separate algorithms for each individual reaction. Instead, groups of compounds with similar reactivity for the formation of ozone are groupe d together and input into the photochemical model. The current speciation mechanism used the TCEQ for this purpose is Carbon Bond 2005 (CB05).

When the composition of the VOC reported for a specific source is unknown, a default speciation profile is applied based on the source classificati on code (SCC). The majority of EIQ responses include constituent VOC emission rates that are used to develop more accurate point-specific speciation profiles. More detail on the source-speci fic speciation approach taken by the TCEQ is available in a report and presentation given at the 17 th Annual International Emission Inventory Conference ( http://www.epa.gov/ttn/chief/conference/ei17/ ) held in Portland, Oregon in 2008. The report is entitled Emissions Modeling of Specific Highly Reactive Volatile Organic Compounds (HRVOC) in the Houston-Galvest on-Brazoria Ozone Nonattainment Area , and is
available at http://www.epa.gov/ttn/chief/conference/ei17/session6/thomas.pdf

.

Two primary improvements to the overall speciation procedure were made for this SIP revision. The first is based on the premise that sources which are part of the HRVOC Emission Cap and Trade (HECT) program in Harris County monitor a ll of their sources of HRVOC, measure them accurately, and report them completely, as require d by the HRVOC rules of Chapter 115. The
SAS code was modified to ensure that no a dditional speciation is performed on the reported HRVOC for HECT sources. For example, if a HECT source reported 0.6 tpd of butenes and 0.3 tpd of unclassified VOC, only the unclassified portion needed further speciation. The second speciation improvement was for situations where th e VOC constituents of a specific source varied on an hourly basis. More detail on this improveme nt is described below in Section 2.1.1.7.2, 2006 Special Inventory speciation.

When applying speciation to a VOC emissions record with EPS3, a cross-reference file is needed that links the AFS emissions record to a specific profile that provides the VOC composition. An example for the Figure 2.1-2 AFS record is shown below in Figure 2.1-3: Sample Speciation Cross-Reference Record

.

48201 14 131 107 40703622 HG14__107_131_ Figure 2.1-3: Sample Speciation Cross-Reference Record

The unique 14-digit speciation profile code of HG1 4__107_131_ in this example links this record to the VOC constituent profile contained in a sep arate file, as shown below in Figure 2.1-4: Sample Speciation Profiles

.

HG14__107_131_ 45102 0.8799
HG14__107_131_ 45202 0.0626
HG14__107_131_ 45203 0.0575 Figure 2.1-4: Sample Speciation Profiles

Note that the HG14__107_131_ profile code is the common link between the profile speciation and cross-reference files. In this example, the Storage and Retrieval of Aerometric Data (SAROAD) contaminant codes of 45102, 45202, and 45203 are for xylene, toluene, and ethyl benzene, respectively. The values in the righ t-hand column are the weight fraction of each contaminant, either as reported for that path or as speciated for that path if a portion of the emissions were reported as unclassified VOC. Note that these are the only three VOC constituents for this path, since the weight frac tions add to 1.0. The SAS code does not make cross-reference or profile records for paths that have negligible VOC emissions quantities, and

### B-20 this omission causes EPS3 to delete these paths. This approach does not result in a loss in accuracy, but is mentioned here to explain wh y very small reported VOC emissions are not photochemically modeled.

2.1.1.5.2 Temporal Allocation With EPS3 Even though ozone season day (OSD) is typically used for processing of photochemical modeling emissions, EPS3 can temporally distribute emissions by month, week, day, and hour of a specific episode, provided that sufficient detail is provided in the EIQ. EPS3 writes temporal information in both profile code and cross-refe rence files. The cross-reference file attaches a temporal profile code for each path. An example is shown below in Figure 2.1-5: Sample of Temporal CrossReference Records

.

00000 00000 1 7 24

48001 ETX 0012 0018 0009 31000203 00000 1 7 25

48001 ETX 0012 0018 0010 31000203 00000 1 7 25

48001 ETX 0012 0018 0011 31000203 00000 1 7 25

48001 ETX 0012 0018 0012 31000203 00000 1 7 26

48001 ETX 0012 0020 0025 40899999 00000 1 7 25

48001 ETX 0012 0021 0026 40899999 00000 1 7 25

48001 ETX 0024 0002 0002 30900201 00000 1 7 27

48001 ETX 0024 0002 0018 40200802 00000 1 7 28

48001 ETX 0024 0008 0007 40282001 00000 1 7 29

48001 ETX 0024 0009 0008 40200101 00000 1 7 27

48001 ETX 0024 0009 0016 40200802 00000 1 7 28

48001 ETX 0024 0010 0009 40200101 00000 1 7 27

48001 ETX 0024 0010 0016 40200802 00000 1 7 28 Figure 2.1-5: Sample of Temporal Cross-Reference Records

For the second record in this example, 48001 is the county FIPS code and 0012, 0018, and 0009 are the plant, stack, and point identifiers, respectively.

The entries of 1, 7, and 25 in the three right-h and columns are the codes for the monthly, daily, and hourly temporal profiles, respectively. During processing with the EPS3 TMPRL module, each point source is matched to the cross-reference file that best fits the combination of county FIPS, plant, stack, and point identifiers.

The first line of the cross-reference file with a ll zeros is for situations when a specific point source does not match any of the other entries in the cross-reference file. In such instances, default profiles of 1, 7, and 24 are applied for th e monthly, daily, and hourly temporal allocation, respectively. This designation corresponds to a non-varying or “flat” distribution of emissions for every hour of the year. An example of this approach is shown in Figure 2.1-6: Excerpt of Temporal Profile Codes File

.

### B-21 /MONTHLY/

1 1 1 1 1 1 1 1 1 1 1 1 1 12
/END/

/WEEKLY/

7 14 14 14 14 14 14 14 98

8 2 2 2 2 2 0 0 14

9 0 0 0 0 0 5 5 35

10 16 16 16 16 16 16 0 112
/END/

/DIURNAL WEEKDAY/

24 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 96

25 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

26 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

27 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 10

28 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 10

29 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10

30 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 10

31 0 0 0 0 0 0 0 11 11 11 11 11 11 11 11 11 0 0 0 0 0 0 0 0 99
/END/ Figure 2.1-6: Excerpt of Temporal Profile Codes File

The numeric character in the first two column s (e.g., 1 in the MONTHLY section, 7 in the WEEKLY section, etc.) corresponds to the monthly, daily, and hourly codes from the temporal cross-reference example in Figure 2.1-5. A path with cross-references of 1, 7, and 24 receives an even temporal distribution of annual emissions by month, day, and hour, respectively. In this case, a path would simply have its OSD emissions divided by 24 to represent the hourly emissions provided to the photochemical model. Most of the point source records from STARS fall into this category, particularly the large continuous processes.

When sufficient data are available, a non-even tem poral distribution of emissions is applied. For example, a source that only operates from Monday through Friday would have a WEEKLY temporal code of “8” applied, while one that only operated on Saturday and Sunday would have a WEEKLY temporal code of “9” applied. A source that only operates ten hours per day from 8:00 AM through 6:00 PM would have the DIURNAL WEEKDAY code of “27” applied. The DIURNAL WEEKDAY codes are derived by th e EPS3 TMPFAC module based on both the start time and reported hours of operation for that path. Note that the last column of each profile is always the sum of all reported integers. The per cent allocation per month, day, or hour for each profile code is obtained by dividi ng the reported integer by this sum.

2.1.1.5.3 Spatial Allocation With EPS3 Photochemical models generally rely on a three- dimensional Eulerian system where emissions are allocated to individual grid cells. Emissions occur at the surface for most source categories such as area, biogenic, on-road, and non-road, and are classified as low-level. Numerous point
sources also fall into the low-level surface cate gory, but large combustion sources such as power plants are categorized as elevat ed because their hot exhaust gases can rise several hundred meters into the atmosphere.

Low-level point sources are allocated to grid cells and merged with the other source categories prior to photochemical model input. Elevated point sources are kept at their reported X-Y
locations and assumed to emit from the calculated ef fective plume height of Z to better simulate physical mixing in the elevated layers of the photochemical model. As with other advanced emissions processors, EPS3 processing of point source emissions is divided into low-level and elevated streams. The advantage of this appro ach is better simulation of how elevated emissions are distributed prior to mixing and reacting with surface emissions, but the drawbacks are more complicated EPS3 processing and longer photochemical model run times.

### B-22 The photochemical model inputs for point sources consist of a single low-level gridded merged file and a single file of elevated sources. A plume cutoff height of 30 meters was chosen to divide the point sources into low-level and elev ated categories. The emissions from elevated sources can be individually tracked, and NO X reaction chemistry can be enhanced by treating these plumes as Lagrangian puffs by use of the optional Plume-in-Grid (PiG) treatment. The TCEQ uses the Greatly Reduced Execution and Simplified Dynamics (GREASD) PiG option in CAMx, which is most applicable to large NO X plumes. More detail on application of the GREASD PiG approach is provided below in Section 2.1.3

2.1.1.6 Hourly Acid Rain Program Database

NO X emission estimates for electric generating units (EGUs), also known as power plants, were obtained from EPA’s Clean Air Markets Division (CAMD) Acid Rain Program Database (ARPDB). EPA’s Acid Rain Program affe cts both new EGUs and existing ones serving generators with an output capacity of greater than 25 megawatts. Under the Acid Rain Program, each unit must continuously measure and record its emissions of sulfur dioxide (SO 2 ), NO X , and carbon dioxide (CO 2 ), as well as volumetric flow and capacity. In most cases, a continuous emission monitoring (CEM) system must be used. EGUs report hourly emissions data to the EPA on a quarterly basis. These data are then stored in the Emissions Tracking System which serves as a repository of data for the utility indus try. The EPA quality assures the raw hourly data and provides both datasets and a query wizard on the CAMD website for downloading the data. Missing or invalid hourly data that arise from CEM equipment problems are provided by the EPA using specific substitution criteria. This results in some instances wher e the EGU-reported data
does not match that from EPA’s CAMD. The data can be downloaded by or ispl (site) and unitid (point) for any state for any quarter. The user has several dataset options, as well as output options within some datasets. The website for these data is http://camddataandmaps.epa.gov/gdm/index.cfm?fuseaction=emissions.wizard

.

Hourly data were downloaded from ARPDB for both Texas and all other states within the modeling domain for the quarters covering the 2005 and 2006 episodes. An example of two consecutive hourly records with a header is provided below in Figure 2.1-7: Sample Hourly

ARPDB Records

.

"STATE","FACILITY_NAME","ORISPL_CODE","UNITID","OP_DATE","OP_HOUR", "OP_TIME","GLOAD","SLOAD","SO2_MASS","SO2_MASS_MEASURE_FLG","SO2_RATE",
"SO2_RATE_MEASURE_FLG","NOX_RATE","NOX_RATE_MEASURE_FLG","NOX_MASS",
"NOX_MASS_MEASURE_FLG","CO2_MASS","CO2_MASS_MEASURE_FLG","CO2_RATE",
"CO2_RATE_MEASURE_FLG","HEAT_INPUT"

"TX","Sam Seymour","6179","2","07-01-2005","0","1","611","","4082.9",
"Measured",".649","Calculated",".087","Measured","546.978","Calculated",
"645.1","Measured",".103","Calculated","6287.1"

"TX","Sam Seymour","6179","2","07-01-2005","1","1","605","","4042",
"Measured",".645","Calculated",".086","Measured","538.79","Calculated",
"642.8","Measured",".103","Calculated","6265" Figure 2.1-7: Sample Hourly ARPDB Records

Using the header for reference purposes, this example shows that:

 the Sam Seymour power plant in Texas is ORISPL 6179;

 Unit #2 emitted 546.978 pounds of NO X from 12-1 AM on July 1, 2005; and

 Unit #2 emitted 538.790 pounds of NO X from 1-2 AM on July 1, 2005.

Use of the hourly emissions data from th e ARPDB instead of OSD averages from STARS requires that the unique orispl and unitid identifie rs be matched in the AFS file. Use of the ARPDB hourly NO X data also requires that hourly VOC and CO emissions be estimated separately because these are not reported.

### B-23 When the STARS and ARPDB datasets are for the same time period, then a STARS record should exist for every ARPDB record. A cross-re ference file was developed to link the ARPDB
and STARS datasets because the former identifies sources by orispl/unit codes, while the latter uses codes for county FIPS, plant, stack, and point. Additionally, the ARPDB identifies emissions by either boiler or unitid, rather than by the exhaust stack reference in STARS. An example of this cross-reference for the Sam Se ymour plant from Figure 2.1-7 is provided below in Figure 2.1-8: Sample ARPDB-STARS Cross-Reference Record

.

6179,2,48149,5,8,8,FC0018G,Sam Seymour,1.00,FAYETTE Figure 2.1-8: Sample ARPDB-STARS Cross-Reference Record

In some cases, one ARPDB record must be matched to two STARS paths because ARPDB tracks units (e.g., a boiler or turbine), but STARS does not always have a one-to-one relationship
between an emission unit and an emission stack. Such situations occur when two sources (FINs) discharge through a common stack (EPN), or when one source discharges through multiple stacks. In these situations, the ARPDB emissions are split evenly between th e multiple paths. In the Figure 2.1-8 example above, Sam Seymour Unit #2 from ARPDB (6179,2) links to only one STARS path (48149,5,8,8) as denoted by a “1.00” emissions unit factor.

2.1.1.6.1 Hourly ARPDB Substitution Hourly emissions records are generated by s ubstituting ARPDB emissions into the path’s AFS record, adjusting the date and hour of the em issions, converting to AFS-required tons of emissions, and changing the “period of emission” indicator. The two records in Figure 2.1-9: Sample Hourly AFS Records After ARPDB Substitution are consecutive hourly emission records for the acid rain data of Figure 2.1-7.

B 05 05 AC 48149 ETX 4911 10100226 0005 0008 0008

S 05070100 05070101 312.80280 -1094.9120 83 7.012 426 28.96 24

22 29 25 24 0 7 8736 42603 0.273489

RN100226844 FC0018G FPP-2 FPP-2 N ST COMBUSTN BOILER-ELECTRIC 6179 2 LOWER COLORADO RIVER AUTH Sam Seymour FAYETTE

LA GRANGE NOx D STAR v4a

B 05 05 AC 48149 ETX 4911 10100226 0005 0008 0008

S 05070101 05070102 312.80280 -1094.9120 83 7.012 426 28.96 24

22 29 25 24 0 7 8736 42603 0.269395

RN100226844 FC0018G FPP-2 FPP-2 N ST COMBUSTN BOILER-ELECTRIC 6179 2 LOWER COLORADO RIVER AUTH Sam Seymour FAYETTE

LA GRANGE NOx D STAR v4a Figure 2.1-9: Sample Hourly AFS Records After ARPDB Substitution

The two records of Figure 2.1-9 are hourly (period of emission = “S”) NO X emissions for the first two hours of July 17, 2005 (begin and end times = 05070100 05070101 and 05070101 05070102, respectively). The typical AFS record of STA RS data represents ozone season average daily emissions, whereas substituting hourly emission reco rds generate 24 records for each day of the episode. For modeling purposes, paths that have hourly NO X emissions receive hourly VOC and CO records by multiplying the STARS OSD VOC-to-NO X and CO-to-NO X emissions by the hourly ARPDB NO X emissions. If the path has ARPDB emissions but does not have a corresponding STARS record (missing, new point source, or different years), then the TCEQ used a default ratio, e.g., an average of similar poi nts. The hourly VOC records link to the same speciation profile used by the STARS OSD record.

In past SIP revisions, the TCEQ divided the point source EI (Texas and the rest of the domain) into EGUs and non-EGUs (NEGUs). Now, the TCEQ has divided the point source EI into hourly and non-hourly files, where OSD is the standard daily (non-hourly) record s created from STARS. The ARPDB is only one example of hourly records that the TCEQ is modeling for these current 2005 and 2006 episodes. The acronym “ARD” is u sed in the filenames to denote hourly “Acid Rain Data” files, and will be used hereafter.ARD emissions greatly improve the temporal resolution of the point source inventory, because the ARD distinguishes between sources that operate throughout the day as baseload units and sources that operate more intermittently as p eaking units. For example, Figure 2.1-10: Hourly

ARD NO X Emissions for W.A. Parish EGU on August 16, 2006 shows hourly NO X emissions for the eight individual units of the W.A. Parish pow er plant. The hourly ARD accounts for emission changes throughout the day. Figure 2.1-10 shows that W.A. Parish EPNs WAP1, WAP2, WAP3 and WAP4 commence operation at a pproximately 10 AM and shut dow n in the late evening. The four remaining EPNs operate throughout the day at nearly a constant emission rate – these are the baseload coal units. Without these hourly ARD, the TCEQ would be modeling these units at their reported STARS ozone season daily averages a nd with their reported operating schedule.



### ARDB Emissions for W A Parish on August 16, 2006

0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.016 0.018 0.020

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

HourNOx Emissions, tons/hour WAP1

WAP2

WAP3

WAP4

WAP5

WAP6

WAP7

WAP8

Figure 2.1-10: Hourly ARD NO X Emissions for W.A. Parish EGU on August 16, 2006

Figure 2.1-11: Tileplot of HGB ARD NO X Emissions for August 16, 2006 is a tileplot of ARD

NO X emissions in the eight counties of HGB ar ea for a specific day modeled in 2006. The tileplot is a valuable tool for modelers to graphically QA the modeled emissions. Note the emissions totals by county in the lower left hand corner and the combined diurnal profile of the sources in the lower right corner of the graphic. The colored/shaded tiles on this map represent the quantity of emissions within that grid cell. In this case, a colored/sh aded tile represents the

ARD EGU NO X tons for a modeled 2km-by-2km grid cell within the HGB eight-county area.



### B-24Figure 2.1-11: Tileplot of HGB ARD NO X Emissions for August 16, 2006

2.1.1 .7 Hourly Special Inventories As with previous attainment demonstration SIP revisions, the TCEQ conducted hourly Special Inventories (SIs) in order to obtain better de tail about emissions during potential modeling episodes. The TCEQ conducted a 2005 SI, a 2006 SI, a Tank Landing Loss (TLL) SI, and a 2006 HRVOC SI. All but the HRVOC SI were incorporated into the base case episodic modeling EIs, because the HRVOC SI was less complete than data already available from STARS. The following subsections describe each of these in more detail.



### B-25

### B-26 2.1.1.7.1 2005 Special Inventory (SI) The TCEQ expected high ozone in HGB during May of 2005, because there had been such a trend, and the month of May had not been captu red in any Special Inventories previously. Additionally, this timeframe provided an opportunity to test new SI handling procedures prior to the 2006 TexAQS II intensive period. In previ ous SI requests, the TCEQ consolidated many versions of submitted spreadsheets into the modeled SI, after much labor.

For this SI request, there were several new goals: (1) make reporting easier for respondents, (2) limit the number of hours and days of reporting, (3) make reporting and data capture near realtime, (4) capture variability and highly-resolved data, (5) target specific regulated entities that would like to test new software, and (6) comp ile the results into a consolidated place automatically, to make its use much easier and qui cker. After selecting URS as the contractor to assist TCEQ, the goal quickly became to automate the entire SI submittal and retrieval process via software that ultimately created a file that could be uploaded directly into STARS. The contractor developed the Hourly Emission Inve ntory Reporting System (HEIRS) which allowed regulated entities (SI respondents) to enter data in to this new database system online, perform QA checks, and output a file that could be imported by STARS. The TCEQ could then use the existing modeling extract query to generate the re port described in Section 2.1.1.1 above. This process took URS and the TCEQ an entire year or more to work out all the bugs.

The 2005 SI request was limited to 22 accounts in Harris County; it addressed VOC, HRVOC,
and non-EGU NO X , and covered only the days which met criteria set by TCEQ modelers to
obtain the best data in the shortest amount of time, resulting in less data gathering for the respondent and TCEQ. The reporting criteria were based on eight-hour ozone thresholds, such that as daily ozone levels anywhere in the County approached the standard, data collection began,
and data collection ended once the monitored oz one levels everywhere in the County dropped
below a lower threshold value. This resulted in SI data being collected for May 3-7 and May 12- June 4, 2005. These periods overlapped with the modeled May 19-June 3, 2005 episode. Ultimately, the 2005 SI data was dropped from modeling files, as the 2005 SI had limited participation (new procedure scared off some respondents), and some of the reported emissions had obvious errors that TCEQ could not resolve with the respondent in a timely manner. The victory of the 2005 SI came in the form of a successful test case study.

2.1.1.7.2 2006 Special Inventory (SI)
On the heels of the 2005 SI t est study, came the ultimate t est for the new HEIRS software, interface (including a web data entry option), an d STARS capability. An SI request was made for the intensive period of TexAQS II: August 15 thro ugh September 15, 2006. This request targeted non-EGUs that likely had CEMs or PEMs to provide highly-variable and highly-resolved data in near real-time. Additionally, some HRVOC monitoring systems were beginning to come online as respondents were meeting the requirements of the new Chapter 115 HRVOC rules. The webpage, http://www.tceq.state.tx.us/implementati on/air/air mod/texaqs/texaqs_data.html

, contains information regarding the SI, including data file download.

The TCEQ made this 2006 Special Inventory request to 141 accounts (sites) in 24 counties of Texas. A total of 49 of those accounts were in the HGB eight-county area. The request was for hourly emissions from a specific list of 405 paths (process unit, emission point combinations) that generally have regulatory monitors of some type on their equipment. The emissions requested were specifically HRVOC, other VOC, and NO X , but all species for all hours of the 32-day period were expected to be reported. Agai n, the accounts had the option to use the HEIRS
software to consolidate and format the data for input to STARS; but, regardless, the accounts had to submit the data in STARS upload format.

STARS was queried for the 2006 hourly SI data, a report was extracted, and a record for each path-hour-contaminant combination was generated. The SI extract pulls data for all SI records in STARS (2005 and 2006), for a total of 15 million lines of pipe-delimited data. The extract SAS

### B-27 code was modified to filter for the year 2006. Figure 2.1-12: Sample STARS SI Extracted Data provides an excerpt of these data.

E|ACCOUNT-SITE|RN102204211|ACCT_NUM|HG9625W| E|SPECIAL EMISSION|14 14 50001200609151|REASON CODE|UI| E|SPECIAL EMISSION|14 14 50001200609151|QUANTITY|0| E|SPECIAL EMISSION|14 14 50001200609152|REASON CODE|UI| E|SPECIAL EMISSION|14 14 50001200609152|QUANTITY|0|

. . . E|SPECIAL EMISSION|9A 16 500012006091417|QUANTITY|0| E|SPECIAL EMISSION|9A 16 500012006091419|REASON CODE|UI| E|SPECIAL EMISSION|9A 16 500012006091419|QUANTITY|0| E|SPECIAL EMISSION|9A 16 524202006091416|REASON CODE|N| E|SPECIAL EMISSION|9A 16 524202006091416|QUANTITY|10.05| E|SPECIAL EMISSION|9A 16 524202006091415|REASON CODE|N| E|SPECIAL EMISSION|9A 16 524202006091415|QUANTITY|20.1| E|SPECIAL EMISSION|9A 16 524202006091414|REASON CODE|N| E|SPECIAL EMISSION|9A 16 524202006091414|QUANTITY|10.05| E|SPECIAL EMISSION|9A 16 500012006091424|REASON CODE|UI| E|SPECIAL EMISSION|9A 16 500012006091424|QUANTITY|0| E|SPECIAL EMISSION|9A 16 500012006091423|REASON CODE|UI| E|SPECIAL EMISSION|9A 16 500012006091423|QUANTITY|0| E|SPECIAL EMISSION|9A 16 500012006091422|REASON CODE|UI| E|SPECIAL EMISSION|9A 16 500012006091422|QUANTITY|0| Figure 2.1-12: Sample STARS SI Extracted Data

Figure 2.1-12, above, shows Special Inventory emissions for two paths of site RN102204211, identified by FIN 14/EPN 14 and FIN 9A/EPN 16. Note that the first two “SPECIAL EMISSION” records of the excerpt, in combinat ion, denote that the unit was idle (“UI”), reporting zero emissions of unclassified VOC (T CEQ Contam code “50001”) for Hour 1 of September 15, 2006 (200609151).

Figure 2.1-13: Hourly SI vs. OSD VOC for September 2, 2006 , below, demonstrates the difference in the hourly SI versus the reported 2006 OSD for all of the SI paths (not just in HGB) for total VOC emissions for a particular day (Septemb er 2, 2006) of the SI period. Note that the reported OSD has a flat diurnal profile, and the SI is generally slightly less than the average OSD, except for hour 11 and hours 22 and 23. In fact , a huge spike, approximately three times the average hour was reported in the SI for hour 22.

### B-28 Figure 2.1-13: Hourly SI vs. OSD VOC for September 2, 2006

Figure 2.1-14: Tileplot of SI NO X for September 2, 2006 and Figure 2.1-15: Tileplot of SI VOC for September 2, 2006 are tileplots of the same SI day as represented in Figure 2.1-13 for NO X and VOC, respectively. Note the one hour time shift most notably in the spike in Figure 2.1-15 as the temporal module of EPS3 converts the daylight time to standard time. Standard time is required input for the photochemical model, in order to match the meteorology. The NO X SI diurnal profile bobs up and down around an average, not varying by more than ten percent. Comparison of Special Inventory Hourly Emissions with Ozone Season Daily Average

0 1 2 3 4 5 6 7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time on September 2, 2006Total VO C Em issions, tph for entire SI area, not just HGB

Special Inventory

Ozone Season AverageFigure 2.1-14: Tileplot of SI NO X for September 2, 2006



### B-29Figure 2.1-1 5: Tileplot of SI VOC for September 2, 2006

Again, the goal was to replace the corresponding 20 06 OSD records with these hourly records for the period August 15 through Sept ember 15, 2006. Not only did th e TCEQ anxiously await these data, but many TexAQS II scientific participants sought these data, for comparison purposes with their ambient measurements form ships, aircraft, and ground and elevated monitors. To generate the EPS3-ready AFS file, the TCEQ wrote specialized SAS code to parse the extracted data and format for an AFS file, because each line in the emissions record of the extract file represented a single hourly emissions record, as illustrated in Figure 2.1-16: Example of SI Parsed Extract

Emissions Records

.



### B-30

### B-31 RN000000000 HG0000A M2A-D-1-5 ES-205 06081520

10.0000 RM 55176

RN000000000 HG0000A M2A-D-1-5 ES-205 06081521

20.0000 RM 55176 Figure 2.1-16: Example of SI Parsed Extract Emissions Records

The records in Figure 2.1-16 are butene (TCEQ contam code 55176) emissions for two consecutive hours (20 and 21) of August 15, 2006, from path RN=RN000000000, FIN=M2A-D1-5 and EPN=ES-205. As indicated by the records, there is an emission rate change between the two hours (10.0000 to 20.0000 pounds per hour). If there are other contaminants or other hours reported for the two streams, then there will be additional records.

Speciation of the Special Inventory The TCEQ modeling of the 2000 SI in previous SI P revisions used a calculated episode average composition of the VOC constituents of a reported SI path. In reality, each path can report any number of contaminants in any given hour; so in order to retain as much detail as possible, the TCEQ now generates a unique speciation profile fo r every hour for every path. Other than this improvement, the speciation process is essentially th e same as that for the OSD, in which pointspecific profiles are generated for each source, re taining the specific constituents as reported, and applying the SCC default profile (after removing non-VOC and common species from the profiles) for the unspeciated portion.

To generate complete AFS records, the TCEQ matched the extracted hourly SI records with STARS OSD records of the same path. To uniquely identify each VOC emission record, the TCEQ numbered the sites and assigned that number in sequence to the “plant” identifier. The date and hour of the emissions were incorporated into the “stack” and “point” identifiers. Figure

2.1-17: Example of SI AFS Records shows two consecutive hours of SI VOC records.

B 06 06 AC 48201 HGB 2821 30101802 0999 0815 0020

S 06081519 06081520 474.98390 -1108.9180 69.0 0.1 811 0.01 25

25 25 25 24 0 7 8736 43104 1.200000

RN000000000 HG0000A M2A-D-1-5 ES- 2 05 FL VOCPROCESS OTHER VOC PROCE

TOTAL PETROCHEMICALS USA LA PORTE PLANT HARRIS DEER PARK

VOC E EPA SI06 v3a V001

B 06 06 AC 48201 HGB 2821 30101802 0999 0815 0021

S 06081520 06081521 474.98390 -1108.9180 69.0 0.1 811 0.01 25

25 25 25 24 0 7 8736 43104 2.000000

RN000000000 HG0000A M2A-D-1-5 ES-205 FL VOCPROCESS OTHER VOC PROCE

TOTAL PETROCHEMICALS USA LA PORTE PLANT HARRIS DEER PARK

VOC E EPA SI06 v3a V001 Figure 2.1-17: Example of SI AFS Records

The two records of Figure 2.1-17 are for th e same path in Figure 2.1-16, identified by RN=000000000, FIN=M2A-D-1-5 and EPN=ES-205. In STARS, the path would have different plant, stack and point identifiers. In the SI, th e site is uniquely identified by plant=0999. Other sites in the SI are uniquely identified by plant -- “0001”, “0002”, and so on. Stack = “0815” identifies the date (August 15) and point = “0020” in the first record identifies the hour (19:00 through 20:00). Each hourly emission record lin ks to a speciation cross reference, as shown in Figure 2.1-18: Example of SI Speciation Cross Reference File.

### B-32 48201 0999 0815 0017 30101802 SIV99908150017

48201 0999 0815 0018 30101802 SIV99908150018

48201 0999 0815 0019 30101802 SIV99908150019

48201 0999 0815 0020 30101802 SIV99908150020

48201 0999 0815 0021 30101802 SIV99908150021

48201 0999 0815 0022 30101802 SIV99908150022

48201 0999 0815 0023 30101802 SIV99908150023

48201 0999 0815 0024 30101802 SIV99908150024

48201 0999 0816 0001 30101802 SIV99908160001

48201 0999 0816 0002 30101802 SIV99908160002

48201 0999 0816 0003 30101802 SIV99908160003

48201 0999 0816 0004 30101802 SIV99908160004 Figure 2.1-18: Example of SI Speciation Cross Reference File

The cross reference file of Figure 2.1-18 links point sources to their composition profile. The profile code entries of Figure 2.1-19: Examle of Speciation Profile for One SI Path shows the composition for the point source referenced by SIV999081500 from Figure 2.1-18. In this particular example, the (hypothetical) survey respondent did not include unclassified VOC emissions in the inventory that could have aff ected the final component and composition profile. (Unclassified VOC get a default composition, and the profile code drops components that do not have a threshold weight fraction.)

SIV99908150020 43203 0.2000

SIV99908150020 43204 0.2000

SIV99908150020 43205 0.2000

SIV99908150020 43212 0.1500

SIV99908150020 43213 0.1000

SIV99908150020 43231 0.1500

SIV99908150021 43203 0.2000

SIV99908150021 43204 0.2000

SIV99908150021 43205 0.2000

SIV99908150021 43212 0.2000

SIV99908150021 43213 0.1200

SIV99908150021 43214 0.0400

SIV99908150021 43231 0.0400 Figure 2.1-19: Example of Speciation Profile for One SI Path

Values in the second column of Figure 2.1-19 are the AIRS/SAROAD pollutant code (e.g., butene=43213), and the values in the last column ar e weight fractions. Note that the six records for Hour 20 add to 1.0, as well as the seven hours for Hour 21.

Creating the Composite AFS Files The complete August 15 through September 15, 2 006 AFS files consist of the SI AFS records, along with ARD and OSD records (and Tank Landing Losses – discussed in the next subsection). To ensure no double counting of records, the TCEQ used RN, FIN and EPN path identifiers to link the SI emission records to ARD (none in the 2006 SI) or OSD counterparts. Next, the OSD records having SI were expanded into hourly emission records, and each ARD or OSD emission record was deleted, if there was an SI emission fo r that path and hour. Thus, the complete set of ARD and OSD records with SI consists of the following files:

 hourly SI VOC emission records w ith unique path identifiers for each record (for speciation),

 hourly SI NO X emission records,

 hourly ARD records with SI NO X records removed,

 daily OSD records with all SI VOC paths removed,

 hourly OSD and ARD emission records for SI paths where the survey respondent did not provide emissions for particular hours.

There were more than 30,000 hourly records prior to introducing the hourly SI records. The SI added approximately 350,000 hourly NO X records and approximately 420,000 hourly VOC records. These values do not include the Tank Landing Losses discussed in the next subsection.No Acid Rain EGU sources were included in this SI data request, so there happened to be no SI ARD records. For hourly OSD r ecords, there is a difference betw een the SI survey respondent reporting the value of zero emissions and not providi ng an emission rate at all. While a missing emission rate might be intended as zero, it remain s an unreported value and, in this case, the emission rate is allowed to default back to its hourly OSD or ARD value. To distinguish between these two situations, the SI dataset carried zero emission records, which are normally deleted. Such records do not have profiles (composition) and, even though there is a path, they do not
receive cross references.

2.1.1.7.3 2005 & 2006 Tank Landing Loss (TLL) Surveys As a result of a remote sensing VOC project in July 2005, as part of TexAQS II, large storage tanks in specific service (e.g., tanks-for-hire at te rminal facilities and crude oil breakout stations) in the Houston Ship Channel (HSC) area were f ound to be landing their floating roofs (internal and external) on the tank legs and not reporting th ose vapor space losses. As a result, a TCEQ Chapter 115 rule was written that limits the num ber of permissible “convenience” roof landings. Additionally, these sources of additional VOC emissi ons were rarely reported in annual EIQs and were rarely permitted; both of these situations have been remedied.

Figure 2.1-20: Landed Floating Roof Emissions is a graphical representation of a floating roof storage tank with its roof landed on its legs, and the subsequent vapor losses.

Figure 2.1-20: Landed Floating Roof Emissions Source: API, 2004

Landing loss em issions from floating roof stor age tanks have two components: standing idle losses and filling losses. When some ty pes of maintenance activities are performed or for some changes in tank liquid, the tank is drained of its contents. When the liquid drops to a level near the bottom of the tank, the roof lands on legs that prevent it from dropping any further as the liquid continues to drain. In this configuration, emissions called “standing idle losses” occur from the vapor space under the landed roof because of a small amount of liquid remaining in the tank. When the tank is refilled and the roof refloated, emissions occur as the liquid di splaces the vapor under the roof. These are called “filling losses.”



### B-33

### B-34 As part of the Second Texas Air Quality St udy (TexAQS II), in July 2005 the TCEQ conducted the Remote Sensing Volatile Organi c Compound Project in Southeast Texas. For this project, the TCEQ used passive infra red cameras in aerial and ground surveys in areas including the Houston Ship Channel and Texas City. The results of this study indicated that landing loss VOC emissions from floating roof storage tanks generally had been unreported in the TCEQ emissions inventory.

As a result of this study, the TCEQ request ed Emissions Inventory Questionnaire (EIQ) revisions accounting for TLLs from 52 sites in the HGB ozone nonattainment area, with a focus on bulk terminals, tank leasing terminals, and pipeline breakout stations. The inventory revisions were requested for the years 2002-2004 and companies were required to incorporate landing loss emissions into their EIQs for subsequent years.

For this SIP revision, hourly TLL VOC emis sions were developed for the modeled 2005 and 2006 base case ozone episodes. Before th e hourly emissions for the episodes were developed, the TCEQ determined which of the 52 sites in the HGB area had accounted for the majority of landing loss emissions during the period 2002–2004. This screening
showed that twelve of these sites had account ed for approximately 97 percent of landing loss emissions reported during those years. The TCEQ thus considered only these twelve sites, listed in Table 2.1-2: TLL Sites Modeled , in the development of the hourly episodic landing loss inventory.

Table 2.1-2: TLL Sites Modeled
Regulated Entity
Reference

Number Company Site Name County Standard
Industrial
Classification (SIC)

RN101948883 TEPPCO Crude Oil Pipeline LLC Seaway Jones Creek Station Brazoria 4612

RN102180486 Magellan Terminals Holdings LP Galena Park Terminal Harris 4612

RN102560182 TEPPCO Crude Pi peline LP Seaway Texas Cit y Station Ga lveston 4612

RN101041598 LBC Houston LP L.B. C. Houston, L.P. Harris 4226

RN100224815 Kinder Morgan Operating LP Pasadena Terminal Harris 4226

RN100237452 Kinder Morgan Liquids Terminals LLC Galena Park Terminal Harris 5171

RN100210806 Intercontinental Terminals Co. Intercontinental Terminal Harris 4226

RN101851517 Marathon Ashland Pipe line LLC Marathon Ashland Pipe Ln Harris 4613

RN100224740 Oil Tanking Houston LP O il Tanking Houston Terminal Harris 4226

RN100225093 Vopak Terminal Deer Park Terminal Harris 4226

RN100219591 TEPPCO Baytown Terminal Harris 4789

RN101921781 TEPPCO Crude Oil Pipeline LLC Seaway Galena Park Station Harris 4612

Of the twelve sites listed in the table above, one (Marathon Ashland) was not included in the hourly landing loss inventory since the company’ s records did not show any landings occurring on the ozone episode days of interest.

In the meantime, before these emissions were formal ly added to STARS, as part of the effort tomodel the real world emissions, the TCEQ included these events as a portion of the underreported VOC in HGB. The TCEQ organized and summarized the data in Excel spreadsheets for all of the modeled 2005 and 2006 episodes. In reality, part of these emissions were doublecounted in the base cases, as re gulated entities also reported these in their 2005 and 2006 annual and ozone season emissions. For the base cases, these TLL emissions were modeled in addition to what was ultimately reported in the 2005 and 2006 EIs, entered into STARS, and subsequently extracted for modeling. This was corrected for the baseline modeling. For the baseline modeling,

### B-35 the TLL paths were removed from the OSD file and an average of the hourly TLL emissions were modeled. Figure 2.1-21: Sample of Two TLL and Refilling Events shows a typical entry documenting landing and refilling losses fo r one of the survey respondents.

Company Account # FIN EPN Type of Floating
Roof Tank Landing
Start

Date and
Time Landing

End Date
and Time

TEPPCO BL0571U SW02TANK01 SW02TANK01 External Floating

Roof Tank 6/27/20

05 2:26 6/27/2005

11:08

TEPPCO BL0571U SW02TANK01 SW02TANK01 External Floating

Roof Tank 6/27/20

05

20:37 6/28/2005

14:03 Landing

Product
Contaminant

Name Refilling
Start Date
and Time Refilling End
Date and Time Refilling

Pollutant
Contam Code Refilling Vapors -

Pollutant

Name Standing

Idle

Losses (lb/hr) Refilling

Losses (lb/hr)

Crude Oil 6/27/200

5 11:08 6/27/2005

14:46 59001 Crude Oil 37.5197 1572.9691

Crude Oil 6/28/200

5 14:03 6/28/2005

17:41 59001 Crude Oil 31.9739 1572.9691 Figure 2.1-21: Sample of Two TLL and Refilling Events

Temporal Allocation The two data entries (records) of Figure 2.1-21 detail two events during which a floating roof tank was emptied and then refilled. After the fl oating roof lands (comes to rest on supports when the tank is empty), the tank is subject to standing idle (breathing) losses. As the tank refills, there are refilling vapor losses as the refilling liquid fills the vapor space prior to refloating the roof. The tank FIN (facility identification number) and EPN (equipment point number) represent the STARS path. Matching the survey results to the STARS AFS records, modelers generated a sequence of hourly emissions records for the da ys of each of the modeling episodes in which respondents provided data.

The TCEQ allocated tank breathing losses to the eight hours between 8:00 AM and 3:00 PM – the sunny hours. Figure 2.1-22: Sample TLL Event AFS Record is an AFS record for the first hour of event one of Figure 2.1-21.

B 05 05 AC 48039 HGB 4612 40301132 131 1 1

S 05062711 05062712 446.07150 -1197.1010 15 0.915 298 0.003 25

25 25 25 24 7 7 8736 43104 0.786485

RN101948883 BL0571U SW02TANK01 SW02TANK01 N ST TANKS EXT FL ROOF: DB TEPPCO Crude Pipelin Jones Creek Terminal BRAZORIA JONES
CREEK VOC 59001 Crude Oil (R LAND Figure 2.1-22: Sample TLL Event AFS Record

The entire history of a TLL event is a sequence of hourly AFS records. Figure 2.1-23: Sample of Hourly Consecutive TLL Event AFS Records is an excerpt from consecutive AFS records, showing the progression of start/end times ( 05062711 0506271 2, 05062712 05062 713, ...) and emission rates (0.786485, 0.786485, ...) for path SW02TANK01/SW02TANK01. Each of the records is an hour of VOC emissions. Note that “05062711” is the eleventh hour of June 27,

2005.

### B-36 05062711 05062712 43104 0.786485 SW02TANK01 SW02TANK01 VOC Crude Oil

05062712 05062713 43104 0.786485 SW02TANK01 SW02TANK01 VOC Crude Oil

05062713 05062714 43104 0.786485 SW02TANK01 SW02TANK01 VOC Crude Oil

05062714 05062715 43104 0.786485 SW02TANK01 SW02TANK01 VOC Crude Oil Figure 2.1-23: Sample of Hourly Consecutive TLL Event AFS Records

Speciation When modeling these TLL VOC emissions, the TCEQ was expecting to need to use the same procedures that were used for the SI, above, for sp eciation of these events. Each event in the TLL dataset could have its own speciation cross reference and profile. The 2005 and 2006 TLL surveys, however, did not have a composition change in any of the tanks, so a single composition was applicable for every hour acr oss the events for the year for a given tank. Hence, the STARS OSD point-specific profiles were suitable for the TLL AFS file.

Figure 2.1-24: TLL Tileplot for June 23, 2005 and Figure 2.1-25: TLL Tileplot for June 28, 2005 are modeling QA tileplots showing total VOC emis sions from TLLs for two different days in June of 2005. Note how different these two days are, both in diurnal profile (distribution of emissions across the hours of the day) and total tons of VOC.Figure 2.1-24: TLL Tileplot for June 23, 2005



### B-37Figure 2.1-25: TLL Tileplot for June 28, 2005

2.1.1 .8 Emission Events Inventories Generally, the more detailed (i.e., temporally, spa tially, and chemically resolved) the input data, the more likely the model will appropriately re plicate ozone episodes, especially as modelers push the resolution envelope. Additionally, in HGB, recent air quality studies, such as TexAQS II have provided an abundance of ambient measur ements of air quality constituents to use in evaluating the photochemical modeling. These measurements indicated the need for a higher temporal resolution of EIs. The addition of th e hourly inventories described in the previous sections contributes to this goal. TCEQ modelers tried incorporating at least two other TCEQ databases, as described in the following subsections.



### B-38

### B-39 2.1.1.8.1 Consolidated Compliance and Enforcement Database System (CCEDS) CCEDS is one source of data the TCEQ revi ewed to potentially improve the point source modeling inventory. CCEDS allows the TCEQ to track, monitor, report and implement enforcement programs across the agency in all busin ess areas. It contains data for maintenance, startups, shutdowns, emission events, and other reports. Reporting entities enter data into the system based on events that are officially declared “reportable”:

 30 TAC 101 Subchapter A 101.1 (28): "unauthorized emissions of air contaminants from one or

m ore emissions points at a regulated entity"

 30 TAC 101 Subchapter F Division 2 101.211 (a): "any scheduled maintenance, startup, or shutdown activity that is expected to cause an unauthorized emission that equals or exceeds the reportable quantity (RQ) as defined in §10 1.1 of this title (relating to Definitions)"

These data are electronically submitted by the responsible company representative through the State of Texas Environmental Electronic Reporti ng System (STEERS) web interface and stored in an Oracle database. The TCEQ staff query the CCEDS database for the period of time and
areas of the State that are of interest.

Queries were made for all of 2005 and 2006, and subsequently reduced to the area of interest (HGB 8-county area), pollutants of interest, and mo deling episode dates. Reported data includes, among others, details about the reporting entity, the facility or emission point identifier (not necessarily the FIN or EPN from STARS), the begi nning and ending times of the event, and the pollutant and quantity released. Figure 2.1-26: Sample CCEDS Data is an excerpt of a CCEDS query (seven, 19-field-long records), covering two different days, for a single HGB point source
in 2005.

### B-40 REG_ENT_NUM REG_ENT_NAME CITY COUNTY REGION SIC

RN1025xxxxx CHEMICAL PLANT TEXAS CITY GALVESTON REGION 12 - HOUSTON 2869

RN1025xxxxx CHEMICAL PLANT TEXAS CITY GALVESTON REGION 12 - HOUSTON 2869

RN1025xxxxx CHEMICAL PLANT TEXAS CITY GALVESTON REGION 12 - HOUSTON 2869

RN1025xxxxx CHEMICAL PLANT TEXAS CITY GALVESTON REGION 12 - HOUSTON 2869

RN1025xxxxx CHEMICAL PLANT TEXAS CITY GALVESTON REGION 12 - HOUSTON 2869

RN1025xxxxx CHEMICAL PLANT TEXAS CITY GALVESTON REGION 12 - HOUSTON 2869

RN1025xxxxx CHEMICAL PLANT TEXAS CITY GALVESTON REGION 12 - HOUSTON 2869

FIN_EPN _NUM UNIT_TYPE UNIT_NAME EVENT_TRA CK_NUM EVENT_TYPE EVENT_START _DT EVENT_ START_
TM

FL-401 EPCN PX-2 Flare 56,855.00 AIR SHUTDOWN 04/15/2005 9:50

FL-401 EPCN PX-2 Flare 56,855.00 AIR SHUTDOWN 04/15/2005 9:50

FL-401 EPCN PX-2 Flare 56,855.00 AIR SHUTDOWN 04/15/2005 9:50

FL-401 EPCN PX-2 Flare 56,855.00 AIR SHUTDOWN 04/15/2005 9:50

FL-401 EPCN PX-2 FLARE 65,361.00 AIR SHUTDOWN 09/21/2005 12:00

FL-401 EPCN PX-2 FLARE 65,361.00 AIR SHUTDOWN 09/21/2005 12:00

FL-401 EPCN PX-2 FLARE 65,361.00 AIR SHUTDOWN 09/21/2005 12:00

EVENT_END_
DT EVENT_END_
TM CONTAMINANT QUANTITY_
RELEASED UNIT_OF_MEASURE NOTIF_TYP_
CD
04/22/2005 17:14 P-Xylene 29.00 POUNDS FINAL

04/22/2005 17:14 Ethylbenzene 0.40 POUNDS FINAL

04/22/2005 17:14 O-Xylene 0.60 POUNDS FINAL

04/22/2005 17:14 M-Xylene 2.00 POUNDS FINAL

10/04/2005 12:00 Benzene 0.22 POUNDS FINAL

10/04/2005 12:00 Propane 1,592.01 POUNDS FINAL

10/04/2005 12:00 Ethylene (gaseous) 526.48 POUNDS FINAL Figure 2.1-26: Sample CCEDS Data

Pollutants include NO X , CO, CO2, SO X , PM, various hydrocarbon species, unclassified VOC, and other chemical compounds and elements. For VOC, reports varied in level of detail, with some reporting speciated VOC compounds, othe rs reporting ”VOC”, and others reporting “unclassified VOC”. Similarly, modelers assigned NO X to the various reported compounds:

“n itric oxide”, “n itrogen monoxide”, “nitrogen dioxide”, “n itrogen oxides”, “oxides of nitrogen”, “nitrogen” and “nitrous oxides”. Hence, there was much labor poured into the process of formatting these data into modeling files.

The TCEQ processed the data to create hourly-specific emission records from the reported eventspecific records. For each even t, the start and end times defined the time frame for which the emissions would be distributed. In some instances the event time frame extended beyond that of the episode; consequently the episode would then limit the event time frame, reducing the actual event length, resulting in an approximate worst case scenario. Once the time frame was established, the emissions were evenly distributed across all hours, for lack of better temporal resolution, resulting in a flat temporal profile for the modeled event. Only records with the pollutants CO, NO X , VOC species, and unclassified VOC, were formatted for modeling. Where unclassified VOC was reported, propylene was subs tituted to create a worst case reactivity. The TCEQ generated hourly AFS records for subsequent EPS3 processing.

### B-41In an effort to provide the most highly-resolved data for modeling, point-specific and hourlyspecific emissions were generated. To accomplish this, assumptions were made where reported data were unclear or vague. As the data carried only one field for the facility and emission point identifier, in some cases, the TCEQ staff took a best guess approach to assign the emissions to a specific STARS path. The path had to be known, so that the TCEQ staff would know which STARS OSD records to potentially replace with these higher-resolved CCEDS records. The temporal resolution of CCEDS events was often too vague for building precise hourly modeling files. The reported data included a beginning and ending time, which often extended for many days or weeks. With the given information, the TCEQ staff assumed an even distribution of emissions over the reported period, thus creating the flat temporal profiles.

As CCEDS and SI datasets both carry emissions for the same paths and pollutants, the TCEQ compared data between CCEDS a nd SI where overlap existed. The cursory examination found that emissions reported to CCEDS were also incl uded in the more resolute SI; therefore no additional modeling information was easily gleaned from CCEDS during the SI periods. In fact, for the sensitivity model runs that were pe rformed with some of these data, there was insignificant change in ozone production when these data were added. This is not to say that under the right conditions, and with a large enough exceedence that was well defined in CCEDS,
that additional ozone could not be generated. Preliminary m odeling sensitivity runs did not see enough of a change to justify incorporating CCE DS data for each of the modeled episodes. Figure 2.1-27: CCEDS VOC Tileplot is a tileplot of one of the preliminary days with the largest
estimated emissions.Figure 2.1-27: CCEDS VOC Tileplot

2.1.1.8.2 Environmental Monitoring and Response System (EMRS) EMRS is another data source that the TCEQ st aff reviewed as a potential improvement to the modeled point source EI. EMRS is a tool th at the TCEQ uses to capture detail regarding excessive emission measurements at monitoring st ations, focused in the Houston Ship Channel (HSC) area. It is primarily a monitoring system which notifies upwind entities, who voluntarily participate, when excessive emission measurem ents are recorded on ambient monitors, and prompts the regulated entity (via email or digita l page) to submit a report. Once notified, these respondents have 48 hours to report information about any releases of the four HRVOC species: ethylene, propylene, 1,3-butadiene and butenes. Information submitted in the report includes date of notification, response date, name and account number, quantity (threshold or range) of pollutant discharged, and various activity descriptions.



### B-42EMRS reports were generated from a webpage. An excerpt of the database query is provided as Figure 2.1-28: Sample EMRS Data

.

date of time notification response response pams account company

notification cdt id no no. date site no. name

9/3/2006 3:00 2344 2085 9/3/2006 Deer Park xxxxx xxxxxxxxxx 9/3/2006 3:00 2345 2042 9/5/2006 Deer Park xxxxx xxxxxxxxxx 9/3/2006 3:00 2346 2044 9/6/2006 Deer Park xxxxx xxxxxxxxxx 9/15/2006 12:00 2347 2065 9/19/2006 Milby Park xxxxx xxxxxxxxxx

hrvoc quantity EE/ SSM eqpt maint loading/ flaring flaring CTower remarks released clearing activities unload >100 #s ? >100 #s ? Propylene 10-<100 LB N Y N N N N N Propylene <10 LB N N N N N N N Propylene <10 LB N N N N N N N 1,3 Butadiene <10LB NYNYNNN Figure 2.1-28: Sample EMRS Data

As with the CCEDS data, data from EMRS reports were scoured for information that could add to the detail already modeled in HGB. The TCEQ de termined that EMRS, as currently structured, did not offer modeling EI improvements; the data reported does not include specific parameters required for detailed (high resolution) modeling:

 The reports do not incl ude facility and point identification necessary to define unique STARS emission paths.

 A specific emission rate is not given, rather a thres hold or range such as “gr eater than 10 lbs.”, or “greater than 10 lbs. but less than 100 lbs.”

 The event timeframe recorded in the report are not specific start and end times for emissions; instead it is a time in which the previous 48 hour cumulative emissions totals are solicited.

Hence, as the reports do not add resolution, the TCEQ did not incorporate EMRS data into the modeling.

2.1.1.9 Emissions Inventory Reconciliation “EI Reconciliation” is a term the TCEQ uses to identify the process by which the reported EI is adjusted so that modeled emissions more closely match the measured (monitored) VOC concentrations during the 2005 and 2006 modeled episodes. TexAQS II confirmed the need for this, as TexAQS 2000 first affirmed. VOC, a nd especially HRVOC, continue to be underreported in the annual EIQs, according to monitors and aircraft measurements.

In previous Attainment Demonstration SIP revisi ons, the TCEQ generated an “extra olefins” file that was added to the modeling EI to account for the under-reporting, in an effort to true-up the reported EI. The current procedure for emissions reconciliation is a significant departure from the procedure used in previous SIP revision modeling. The details of this Potential Source Contribution Function (PSCF) technique, as app lied to the modeled HGB EI, are provided in
Attachment 1: Reconciling Reported VOC Emissions with Ambient Measurements.

These reconciled HRVOC emissions were modeled as an additional file for the HGB area. Rather than placing the reconciled extra emissions at the locations of existing point sources, the TCEQ placed a single pseudo point in each aff ected modeling cell (i.e., modeling cells which contain actual point sources), and assigned an emis sion rate for each HRVOC to best offset the
difference between modeled and calculated concen trations. The placement meant creation of a new VOC AFS record for each pseudo point source. The TCEQ placed each pseudo point source geographically in the middle of each affected cell and assigned default stack parameters (e.g., 5.0 meter stack height). Since these reconciled point s do not exist in the STARS database, modelers assigned unique plant, stack and point identifiers that link to new speciation cross reference and profile files. The profiles were individually determined during the PSCF procedure, and EPS3 processing was performed for this set of data separately. Figure 2.1-29: Sample Reconciliation



### B-43

### B-44 Record and Profile is an example of a single inventory reconciliation (“extra alkene” emissions) AFS record, along with its cross reference and profile code, in which VOC emissions are 100% trans-2-butene (43216) released at a (default) height of 5.0 meters.

M 05 05 EX 48039 HGB 0000 30119705 0407 1175

5600 S 06080100 06080124 407.00000 -1175.0000

5.0 0.01 294 0.01 43104 0.001104
BL0000X VOC Extra alkene emissions

48039 0407 1175 5179 30119705

BL040711755179

BL040711755179 43216 1.0000 Figure 2.1-29: Sample Reconciliation Record and Profile

2.1.1.10 Splitting Texas into 8-county HGB and non-HGB To facilitate easier summarization of modeled pieces of the EI, the TCEQ split all parts of the Texas EI into 8-county HGB files and non-HGB files, including OSD and ARD sources. This allows for easier tracking of changes to the area of greatest interest – HGB. As many controls have been applied to HGB in the past, this allo ws for easier QA of the application of those to HGB. This results in nearly twice as many poin t source EI files that were processed with EPS3 and subsequently merged prior to CAMx modeling. The tables (Tables 2.1-11 and 2.1-12) at the
end of this section that list the AFS files th at were modeled do not list the “hgb-8co” and “no_hgb_8co” separately for conciseness. The EPS3 processed and merged files that are input to CAMx do have separate files for “hgb-8co” and “no_hgb_8co.”

This concludes the discussion of Texas point sour ces included in the modeling. Tables at the end
of this section summarize the Texas da tasets used for each base case episode.

2.1.2 Regional (Outside of Texas) Point Sources This section and its subsections discuss the point source modeling emissions development for all
areas outside of Texas within the modeled CA Mx domain. The modeled “Regional” area (in order of increasing distance from HGB) includes the following parts:

 Offshore (Gulf of Mexico);

 Oklahoma, Arkansas, and Louisiana ( three st ates adjacent within the modeling domain);

 Remaining U.S. states (within the modeling domain);

 Mexico;

 Canada.

The level of detail applied to the development of the modeling emissions for each of these regions decreases with its distance from Texas and HGB, in general. Each region was modeled using a different source of EI data; each subsection below addresses these data sources.

2.1.2.1 States Adjacent to Texas The states adjacent to Texas (and within the m odeling domain) include Oklahoma, Arkansas, and Louisiana. It is expected that the influen ce of emissions on HGB from outside of Texas would decrease with distance from HGB, so the TCEQ models with more detail from those sources
closest to HGB. As has become custom with TCEQ modeling, TCEQ contacted each of these adjacent states to individually obtain their point source emissions data files.

### B-45 In general, the emissions files that can be obtained directly from a state are more current than can be obtained from the National Emission Inventory (NEI) maintained by the EPA. Hence, the TCEQ queried the EI staff of the three states independently to ask for their point source EIs that most closely matched TCEQ’s episodes to be m odeled. The timing was fairly good, as the three states were in the process of QAing their 2005 EIs for electronic submittal to the EPA for compilation into the 2005 NEI. Rather than wait for the official 2005 NEI to be released by the EPA, the TCEQ obtained the most current data that the adjacent states would release to us. Emissions data from other states are generally provided given as annual emissions, whereas the Texas STARS emissions are average ozone season day. When using data from other states, the TCEQ used ozone season data, if provided. When using annual data, however, the TCEQ typically creates daily emission records from the annual data, to be consistent with other datasets. Thus, “daily emissions” may refer to the annua l average, if only annual data was provided.

The Louisiana Department of Environmental Quality (LDEQ) provided a 2004 point source annual emissions inventory in AFS EPS2 format ; the TCEQ converted the records to EPS3 format and grew the daily emissions to 2005 and 2006 using EPA’s Economic Growth Analysis System (EGAS5) growth factors. The LDEQ did not feel comfortable releasing their 2005 EI to the TCEQ, due to the chaos that Hurricane Katrin a caused in late August 2005 and the remainder of the year. The LDEQ staff explained to th e TCEQ staff that the 2004 emissions were more representative than any 2005 that the LDEQ may have developed later.

The Oklahoma of Environmental Quality (ODEQ) and the Arkansas Department of Environmental Quality (ADEQ) each provi ded their 2005 point source annual emission inventories in NEI format (as comma delimited and text formatted files, respectively). NEI data is provided in six data files:

 site (SI)

 emission unit (EU)

 emission process (EP)

 emission release point (ER)

 transmittal (TR)

 control equipment (CE)

The data files were provided in the NEI Input Fo rmat version 3.0 (NIF3.0) which is documented on the EPA website, http://www.epa.gov/ttn/chief/nif/index.html . These six files can also be downloaded as “.mdb” files for linking in a relational database, such as MS-Access. The SI, EU,
EP and ER files contain information to construct AFS formatted emission records. The SI file, for example, provides fips, ORISPL (if that field is filled in), facility identification, and facility name. Using fips and facility as keys, SI records link to unit identification, SIC, and BLRID (if that field is filled in) in the EU file, and so on. Thus, the TCEQ staff constructed AFS-formatted emission records for every NEI point source in the provided EI.

The TCEQ updated their NEI-to-AFS conversion SA S program and tested the results. The TCEQ developed the 2006 datasets for each state using the latest version of EPA’s Economic Growth Analysis System (EGAS5, also referred to as si mply EGAS). EGAS estimates future growth, based on fips (state and county/pa rish) and SCC. Table 2.1-3: Excerpt of EGAS Factors for Base

Year=2002 is an excerpt of EGAS growth factors, sorted by SCC, as generated by the TCEQ after running EGAS5. Entries where the fips is truncated are assigned factors by best fit. For example, fips=”22” is a fit to all Louisiana parishes.

### B-46 Table 2.1-3: Excerpt of EGAS5 Factors for Base Year=2002

SCC FIPS 2004 2005 2006 2018

10100101 1 1.01162 1.03728 1.07100 1.22343

10100101 5 0.98884 1.01519 1.04091 1.01559

10100101 9 1.06889 1.09699 1.12094 1.14163

10100101 10 1.02806 1.06091 1.10250 1.24438

10100101 11 1.02806 1.06091 1.10250 1.24438

10100101 12 1.02806 1.06091 1.10250 1.24438

10100101 13 1.02806 1.06091 1.10250 1.24438

10100101 17 1.04758 1.07592 1.09051 1.25736

10100101 18 1.04758 1.07592 1.09051 1.25736

10100101 19 1.03659 1.07712 1.11602 1.20097

10100101 20 1.03659 1.07712 1.11602 1.20097

10100101 21 1.01162 1.03728 1.07100 1.22343

10100101 22 0.98884 1.01519 1.04091 1.01559

10100101 23005 1.06889 1.09699 1.12094 1.14163

10100101 23017 1.06889 1.09699 1.12094 1.14163

10100101 23023 1.06889 1.09699 1.12094 1.14163

10100101 23031 1.06889 1.09699 1.12094 1.14163

10100101 24 1.02806 1.06091 1.10250 1.24438

10100101 25 1.06889 1.09699 1.12094 1.14163

10100101 26 1.04758 1.07592 1.09051 1.25736

10100101 27 1.03659 1.07712 1.11602 1.20097

10100101 28 1.01162 1.03728 1.07100 1.22343

10100101 29 1.03659 1.07712 1.11602 1.20097

10100101 31 1.03659 1.07712 1.11602 1.20097

10100101 34 0.96887 0.98556 1.02163 1.13020

10100101 36 0.96887 0.98556 1.02163 1.13020

10100101 37 1.02806 1.06091 1.10250 1.24438

10100101 38 1.03659 1.07712 1.11602 1.20097

10100101 39 1.04758 1.07592 1.09051 1.25736

10100101 40 0.98884 1.01519 1.04091 1.01559

10100101 42 0.96887 0.98556 1.02163 1.13020

10100101 44 1.06889 1.09699 1.12094 1.14163

10100101 45 1.02806 1.06091 1.10250 1.24438

10100101 46 1.03659 1.07712 1.11602 1.20097

10100101 47 1.01162 1.03728 1.07100 1.22343

10100101 48 0.98884 1.01519 1.04091 1.01559

10100101 50 1.06889 1.09699 1.12094 1.14163

10100101 51 1.02806 1.06091 1.10250 1.24438

10100101 54 1.02806 1.06091 1.10250 1.24438

10100101 55 1.04758 1.07592 1.09051 1.25736

10100102 1 1.01162 1.03728 1.07100 1.22343

10100102 5 0.98884 1.01519 1.04091 1.01559

10100102 9 1.06889 1.09699 1.12094 1.14163

10100102 10 1.02806 1.06091 1.10250 1.24438

10100102 11 1.02806 1.06091 1.10250 1.24438

10100102 12 1.02806 1.06091 1.10250 1.24438

10100102 13 1.02806 1.06091 1.10250 1.24438

10100102 17 1.04758 1.07592 1.09051 1.25736

10100102 18 1.04758 1.07592 1.09051 1.25736

. . .

The base year (factor=1.00000) is 2002 in Table 2.1- 3, since this dataset was also used for other purposes. Where the table does not have the complete five character fips code, which is common

### B-47 with EGAS, a best fit of data is applied (51 = 51 xxx, the entire state). Thus, from Table 2.1-3, all point sources having SCC=10100101 in Louisiana (state=22) have 2004 an d 2006 growth factors of 0.98884 and 1.04091, respectiv ely, for a 2002 base year (factor=1.00000). To start with a base year of 2004 (Louisiana, for example), the growth to 2006 would be the 2006 factor divided by the 2004 factor, or 1.05266.

2.1.2.1.1 Hourly Acid Rain Substitution As with the Texas EI, the TCEQ replaced the daily (or annual) emission records with hourly
records for all the Acid Rain E GUs in the adjacent states, as well as the nation. This replacement required matching the ARD identifiers (orispl and unitid) with the daily/annual records that were provided by each state. The AFS records that the LDEQ supplied did not have the acid rain identifiers in it, so the TCEQ used the 2002 NE I as the cross reference fo r Louisiana, since the NEI contains fields (not always filled in, t hough) for these identifiers. Since Oklahoma and Arkansas provided their NEI files to us, the match was somewhat easier.

The TCEQ downloaded all the hourly acid rain data from EPA’s CAMD query for each of these adjacent states, for the necessary quarters of 2005 and 2006, as it did for Texas. Because the 2002 NEI was used for EGU identifiers for Louisiana, the 2005 and 2006 CAMD queries included some new EGUs (i.e., post 2002). For new Acid Rain EGUs that showed up in the 2005 and 2006 queries, the TCEQ staff was able to manually match some of the new EGUs by site name. For the remainder, the TCEQ created AFS records and assigned new EGUs with default stack parameters to the near est town. The corresponding ho urly VOC and CO records for matched EGUs were genera ted using their VOC-to-NO X and CO-to-NO X ratios. For unmatched points in each state, the TCEQ used VOC and CO ratios larger than roughly two-thirds of the
ratios across the state.

As in Texas, this procedure resulted in a daily “OSD” (either provided or estimated from annual) AFS file for each state and an hourly ARD AFS file for each state. The TCEQ compared AFS emission totals to the ARD for each state, making sure all emissions for each were assigned to points within each state. Next, the TCEQ compared AFS and ARD emissions for each day of the episode to make sure that all the emissions were distributed over the entire episode. Figure 2.1-

30: Tileplots of Louisiana, Arkansas, Oklahoma and remaining states ARD NO X Emissions for June 22, 2005 has tileplots of ARD NO X emissions for the adjacent states for a specific day
modeled in 2005.Figure 2.1-30: Tileplots of Louisiana, Arkansas, Oklahoma and remaining states ARD NO X Emissions for June 22, 2005

2.1.2 .2 Remaining States in Modeling Domain The 2005 and 2006 EIs for the remaining states (sta tes beyond Texas, Louisiana, Arkansas and Oklahoma) within the modeling domain were de veloped from 2002 data files provided by the Central states Regional Air Planning associati on (CenRAP) and included all of the Regional Planning Organizations (RPOs) data for the nati on, as used for Regional Haze SIP modeling. The TCEQ received the inventory data from CenRAP on a hard drive, in Inventory Data Analyzer (IDA) format, a format suitable for Sparse Ma trix Operator Kernel Emissions (SMOKE) processing. The TCEQ extracted data from the Ce nRAP harddrive that represent the “final 2002



### B-48

### B-49 Base g Typical (typ02f)” annual emissions. Figure 2.1-30: Tileplots of Louisiana, Arkansas, Oklahoma and remaining states ARD NO X Emissions for June 22, 2005 in the above adjacent
states subsection has a tileplot of ARD NO X emissions for the remaining states for a specific day
modeled in 2005.

2.1.2.2.1 AFS File Creation Each of the RPO files was read in and processed with a SAS program that the TCEQ created for converting IDA/SMOKE files to AFS files. This included reading in each of the files, performing minor quality checks and adjustments, and concaten ating. The TCEQ extracted the point source data from the EI files, along with the three pollu tants of interest for ozone modeling. Emission rates were provided as annual total tons for all RP Os, and tons per average day for some RPOs. Hourly emissions were provided for sources with continuous emission monitors (CEMs) in some RPOs (which was not used since it was for 2002). Because many records did not include average day emissions, but temporal profiles were provided, the TCEQ elected to use the annual emission rates for modeling.

Quality checks included:

 excessive or missing values for stack height, diameter and exit temperature

 erroneous latitude-longitude values

o plotted the (x,y) to note those that were obviously out of place (only a few were)

Where missing or outlier stack data existed, defa ult values were substituted. Necessary defaults and adjustments included:

 assigned minimum stack height 0.01m,

 assigned minimum stack diameter 0.05m,

 assigned minimum exhaust temperature 294K,

 assigned minimum exit velocity 0.01m/s,

 assigned maximum exit velocity 100m/s,

 corrected lat/long values,

 removed records with 10 digit SCC codes, as these are non-point source units.

Emissions were reported as typical for 2002, so they had to be grown to 2005 and 2006. The TCEQ used EPA’s EGAS5 growth factors with a FIPS SCC configuration as was performed for the adjacent states (see the subsection above). An AFS-formatted file, including all necessary and relevant modeling parameters was produced for EPS3 processing.

2.1.2.2.2 Hourly Acid Rain Substitution As with the Texas and the adjacent states, the TCEQ replaced the annual records with hourly records for all the Acid Rain EGUs. This requi red matching the ARD identifiers (oris and unitid) with the annual records identifiers. The TCEQ acquired the hourly Acid Rain data from EPA’s CAMD query webpage for the states within the modeling domain. The ARD download provided records for approximately 3400 EGUs reporting hourly NO X emissions in 2005 and 2006. Because parts of some states are outside the modeling domain, some of these points were later appropriately discarded by the EPS3 processor.

It was necessary to remove from the annual RPO data those emissions which are ARD to avoid double-counting emissions. Because data came from various RPO sources, inconsistencies in identifiers between the RPO and Acid Rain data pr evented a suitable cross reference to be used for removing duplicate emission sources, i.e., names did not match and Acid Rain identifiers were not always provided. Therefore, suitable criteria were established to remove the RPO records that have hourly emissions records in the ARD. Meeting any of the following criteria resulted in
removal from the annual AFS file for 2005 and 2006: Record had both an Office of the Regulatory Information System identifier (ORIS) and a unit identifier (UNITID).

 Record had an ORIS and a SIC co de 4911 (Electric Services).

 Record had an ORIS and a SIC code 4931 (El ectric and Other Services Combined, Cogeneration Facility).

2.1.2.2.3 Speciation and Temporal Allocation The CenRAP/RPO data included files for temporal profiles, and the associated cross references. Profiles for monthly, weekly, weekday diurnal a nd weekend diurnal varia tions were converted from SMOKE format via SAS programming, and formatted for EPS3 processing. For modeling emissions development from the CenRAP/RPO file s, the TCEQ chose to use default speciation assignments, since the conversions from the SMOKE format were not straightforward. In addition, a number of the SMOKE formatted speciation assignments are likely based on default speciation assignments, so any over all differences should be minor.

2.1.2.3 Offshore Point Sources The TCEQ obtained the 2005 Gulf-Wide Emissions Inventory (GWEI), developed by Eastern Research Group (ERG) under contract to the Mine rals Management Service (MMS). This was a significant update to the 2000 GWEI, which the TCEQ modeled in previous SIP revisions. The report and data are divided into two parts: oil a nd gas exploration and production platform (point) sources and non-platform (area) sources. Th e TCEQ obtained the 2005 GWEI data and documentation from MMS in Microsoft Access (zipped) and PDF format, respectively. These can all be downloaded from the MMS webpage, http://www.gomr.mms.gov/homepg/regulate/envir on/airqualit y/gulfwide_emission_inventory/20 05GulfwideEmissionInventory.html

The TCEQ reviewed and compared the differen ces between the 2000 GWEI and the 2005 GWEI. The comparisons show an overall increase in emissions in CO and NO X in 2005, and a slight
decline for VOC. Table 2.1-4: Comparison of GWEI for Platform and Non-Platform Sources
shows the differences for all GWEI sources. Table 2.1-5: Comparison of GWEI for Platform (Point) Sources Only shows results for just the platform sources, and Table 2.1-6: Comparison of GWEI for Non-Platform (Area) Sources Only shows results for just the non-platform sources.

Table 2.1-4: Comparison of GWEI for Platform and Non-Platform Sources

Base Year CO (tpy) NOx (tpy) VOC (tpy)

2000 113,303 203,349 85,008

2005 122,295 392,961 80,933
Percent Difference 8% 93% -5%

Table 2.1-5: Comparison of GWEI for Platform (Point) Sources Only

Base Year CO (tpy) NOx (tpy) VOC (tpy)

2000 92,144 78,049 59,536

2005 89,813 82,581 51,241
Percent Difference -3% 6% -14%

Table 2.1-6: Comparison of GWEI for Non-Platform (Area) Sources Only

Base Year CO (tpy) NOx (tpy) VOC (tpy)

2000 21,159 125,300 25,472

2005 32,482 310,380 29,692
Percent Difference 54% 148% 17%

These emissions totals are annual totals and incl ude any decline in emissions due to Hurricane Katrina (end of August 2005) and any other hurrican es that moved into the Gulf of Mexico, such



### B-50

### B-51 as Hurricane Rita. With the significant overall increase in NO X emissions, in concert with the large changes shown in the 2005 non-platform data, the TCEQ decided this was a significant upgrade to the GWEI and opted to use the 2005 GWEI for modeling both platform emissions and non-platform emissions.

ERG used a structure similar to that of the EPA’s National Emissions Inventory (NEI) database to compile the 2005 GWEI platform files. The sp ecific data structure used for the 2005 GWEI is based on NEI Input Format (NIF) Version 3.0 for point sources (platform).

The MS-Access database (.mdb files) was comprised of several tables, each with a primary key(s) to create relationships. The NIF tables were join ed, and pollutants for ozone modeling were kept. Adjustments and quality checks on modeling parameters were performed on the data to ensure the emissions would be modeled correctly. Emission release points were forced to be stack, flare, or fugitive, so that appropriate default parameters could be assigned to each record in which zero values, missing values or outliers were found. Da ta retained include monthly records for May through October (spanning the months of the 2005- 06 modeling episodes). TCEQ converted the tables to the “flat file” (non-relational) AFS fo rmat, including all necessary and relevant modeling parameters for EPS3 processing. Modelers crea ted AFS files with monthly emission rates for each point source for each month, May through Oc tober, but only modeled a representative June day, as discussed below in this subsection.

Quality checks included:

 excessive or missing values for stack he ight, diameter and exit temperature;

 erroneous latitude-longitude values;

o plotted the (x,y) to note those that were obviously out of place (only a few were).

Where missing or outlier data existed, default va lues were substituted. Defaults and adjustments
included:

 Fugitive defaults:

o stack height 15m

o stack diameter 0.01m

o exhaust temperature 294K

o minimum exit velocity 0.01m/s

o maximum exit velocity 300m/s

 Flare defaults:

o minimum stack height 15m

o maximum stack height 100m

o stack diameter 0.1m

o minimum exhaust temperature 294K

o maximum exhaust temperature 1273K

o minimum exit velocity 0.01m/s

o maximum exit velocity 20m/s

 Stack defaults:

o stack height 15m

o stack diameter 0.1m

o exhaust temperature 294K

o minimum exit velocity 0.01m/s

o maximum exit velocity 300m/s

Figure 2.1-31: 2005 GWEI Platform Monthly Emissions from Raw Data Files shows an emissions summary for the months May through October from the MS-Access database files.
Table 2.1-7: 2005 GWEI Platform Monthly Emissions from EPS3 Processing is a summary of the tons per day of NO X and VOC that could be modeled for the same months.2005 GWEI Offshore Emissions

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

May Jun Jul Aug Sep OctTons NOx

VOC

Figure 2.1-31: 2005 GWEI Platform Monthly Emissions from Raw Data Files

Table 2.1-7: 2005 GWEI Platform Monthly Emissions from EPS3 Processing

yymmdd Month NO X (tpd) VOC (tpd)

050522 May 269.7 168

050622 June 271.4 166.8

050722 July 246.7 164.7

050825 August 206.5 151.0

050922 September 151.3 97.9

051026 October 155.5 95.3

A significant caveat that MMS insists every user of the data keep in mind is the use of the 2005 GWEI data for late August 2005 and the rest of th e year, due to the effect s of Hurricane Katrina. From the above figure and table, the TCEQ ascer tained that using a representative day in June 2005 would be an appropriate ozone season day to model. June had the highest emissions, so it should generate more ozone from o ffshore and be more conservati ve, plus modeling with a single day for this category saved hard drive space and emissions processing time. The June VOC value (166.8) in Table 2.1.7, does not precisely match the value in the tileplot of Figure 2.1-33 (163.08), because tileplots represent modeled CB-05 species in methane equivalent.

No temporal profiles were provided with the da ta files themselves; however, the documentation did provide rudimentary temporal information fo r groups of offshore activities. Each emission source was categorized into one of these five activity groups provided in the documentation:

 Constant Activity Sources (CAS)

 Boilers/Heaters/Burners (BHB)

 Natural Gas Turbines and Internal Combustion Engines (NGT)

 Temperature Dependent Sources (TDS)

 Helicopters and Supply Vessels (can apply to non-platform, non-point sources)

To facilitate the creation of temporal profiles, an assignment for each emission record was made to one of the groups via its SCC. In some cases, SCCs existed in the data, but not in the temporal



### B-52documentation. These were hard-coded as in Table 2.1-8: Specifically-Assigned SCCs Not

Documented

.

Table 2.1-8: Specifically-Assigned SCCs Not Documented SCC Assigned Diurnal Code procid (Process ID) 10201701 BHB D<600g
20200102 NGT D>600d

20200251 NGT NGE-2R
20200252 NGT NGE-2L
20200253 NGT NGE-4L

20200254 NGT NGE-4R
20200255 NGT NGE-2C
20200256 NGT NGE-4C
31000123 CAS VEN

31000146 CAS FOEo 31000229 CAS FOEg
31088811 CAS FFLAo
40400321 TDS STO-CO

40600248 TDS LOA

Temporal profiles were made based on these assign ments, and a cross reference file was created via SAS programming to join the AFS file to the profiles file.

The 2005 GWEI was used as is for the 2006 m odeling episodes, also. Figure 2.1-32: Tileplot of Offshore NO X for a representative June day and Figure 2.1-33: Tileplot of Offshore VOC for a representative June day are tileplots of the 2005 GWEI for a representative June 2005 day.



### B-53Figure 2.1-32: Tileplot of Offshore NO X for a representative June day



### B-54Figure 2.1-33: Tileplot of Offshore VOC for a representative June day

2.1.2 .4 Mexican Point Sources The TCEQ used the data from Phase III of th e 1999 Mexico NEI, which was an update from the Mexican EI that TCEQ was using in previous SIP revisions (Phase II). Phase III included all of Mexico, whereas Phase II was just the six north ern Mexican states. Even though the TCEQ modeling domain is fully within the Phase II states, TCEQ upgraded to Phase III, because there were updates to some of the data in norther n Mexico. TCEQ downloaded the NIF format
versions of the files from http://www.epa.gov/ttn/chief/net/mexico.html and parsed the files into
AFS files as described in sections above.

The 1999 data was used for 2005 and 2006 episodes w ithout growth, due to a lack of information on growth and controls for Mexican point sources. No temporal allocation or speciation data



### B-55

### B-56 were available, so defaults were used. Table 2.1-9: Comparison of Mexican NEI Phases II & III shows a comparison of the Mexican emissions as modeled by the TCEQ for Phase II (denoted with “BRAVO”) and Phase III (“updated 1999”). Th e emissions totals in the table represent an area of Mexico that is much larger than the area covered by the modeling domain.

Table 2.1-9: Comparison of Mexican NEI Phases II & III

File NO X (tpd) VOC (tpd)

afs.bravo.Mexico.1999_v1a.lcp.3pols.eps3 549.5 37
afs.Mexico_from_updated1999NEI_lcp_3pols_v1 611.9 230.1

Figure 2.1-34: Tileplot of representative Mexican OSD NO X and Figure 2.1-35: Tileplot of representative Mexican OSD VOC are tileplots of Phase III of the 1999 Mexico NEI for a representative ozone season day.Figure 2.1-34: Tileplot of representative Mexican OSD NO X



### B-57Figure 2.1-35: Tileplot of repre sentative Mexican OSD VOC

2.1.2.5 Canadian Point Sources As described on the EPA webpage for North American EIs, http://www.epa.gov/ttn/chief/net/canada.html , Canada has not publicly released any updates to their stationary point source EI, due to “Environment Canada’s data privacy concerns.” In EPA’s CAIR 2001 base case modeling, the EPA used a Cana dian point source dataset. This was still based on a 1995 Canadian EI. The TCEQ extr acted this dataset from EPA’s IDA-formatted
SMOKE data files that the EPA released with its CAIR modeling files. The TCEQ converted these data to AFS records for further processing with EPS3.

As with Mexico, the TCEQ used these 1995 data for 2005 and 2006 episodes without any adjustment for growth or controls. No temporal allocation or speciation data were available, so



### B-58

### B-59 he TCEQ. defaults were used. This is the same Canadi an dataset used for modeling in the May 2007 DFW SIP revision. The only part of Canada within th e modeling domain is the southern part of Ontario province.

2.1.3 Plume-in-Grid (PiG) Source Selection As mentioned in Section 2.1.1.5.3, above, CAMx provides the option to model selected point sources with a PiG algorithm. NO X reaction chemistry can be enhanced by treating these selected point source plumes as Lagrangian puffs via the optional use of the PiG. TCEQ

continues to use the 

### Greatly Reduced Execution and Simplified Dynamics (GREASD) PiG option in CAMx, which is most applicable to large NO X 

### plumes. T he GREASD PiG option was used for all point sources that met the criteria in Table 2.1-10: Summary of PiG Thresholds Chosen, as assigned by t

Table 2.1-10: Summary of PiG Thresholds Chosen

Modeled Area NO X Threshold (tpd)

Texas 5.0 Adjacent States (LA, AR, OK) & Mexico 7.5 Next ring of States (MS, etc) 10.0 Next ring of States (AL, etc) 15.0 Other States, Canada & Offshore 25.0

The NO X threshold of 5.0 tons in Texas denotes that any individual stack or co-located group of nearby stacks that total 5.0 or more tons of NO X emissions on an episode day were tracked as a PiG source. The TCEQ chose a co-location distance for NO X of 200 meters, indicating that if multiple stacks were close enough together for thei r plumes to merge (within 200 meters of each other), and the aggregate 

### NO X 

### emission rate for the cluster exceeded the threshold value, a
new source was created with the combined NO X 

### emission rate of the cluster, and this source was flagged for PiG treatment. The stack parameters of the new source became an “average” of the stack parameters of all of the sour ces in the cluster. The TCEQ modeled both
individual PiGs and combined PiGs within each of the modeled areas of Table 2.1-10. The EPS3 module, PiGEMS, provides a summary of the PiG treatment that showed a total of 117 PiG sources chosen for the entire domain, 91 of them are from co-located combined new
stacks.

2.1.4 Summary of Base Ca se Point Source Data Files Snapshots of the point source emission files th at were processed with EPS3 for CAMx in each episode are presented in Table 2.1-11: AFS Files for the 2005 Episodes and Table 2.1-12: AFS Files for the 2006 Episodes . The version number on each dataset indicates a change from the previous version (e.g., “v8”). The FTP downloa d site for these files or their successors is ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/HGB8H2/ei/point/ Outdated files are generally not retained on the FTP site.Table 2.1-11: AFS Files for the 2005 EpisodesEpisodeAreaAFS Point Source Emissions FileHourlyDailySeeNoteMay-June 2005Texasafs.ard_19May_to_03Jun05_TX_episode_v8X(bc05ep0 )TX_osd_2005_all_episodesX1,2afs.aggVOC_extra_alkenes_for_2005_v2X1afs.landing_losses_19May_to_08Aug05X1,2Regionalafs.REGIONAL_ard_minus_TXOKARLA_19May_to_03Jun05_episode_v4Xafs.Arkansas_ard_19May_to_03Jun05_episode_v4Xafs.Louisiana_ard_19May_to_03Jun05_episode_v4Xafs.Oklahoma_ard_19May_to_03Jun05_episode_v4XUSA_osd_2005_for_3Q05_generic_from_2005CENRAP.v3X2afs.gwei2005.May_Oct_edit.3pol.lcp2,3afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1X2afs.CAIRCanada.2001.v1a.latlong.lcp2,4June 2005Texasafs.ard_15Jun_to_30Jun05_TX_episode_v11X(bc05ep1)TX_osd_2005_all_episodesX1,2afs.aggVOC_extra_alkenes_for_2005_v2X1afs.landing_losses_19May_to_08Aug05X1,2Regionalafs.REGIONAL_ard_minus_TXOKARLA_15Jun_to_30Jun05_episode_v3Xafs.Arkansas_ard_15Jun_to_30Jun05_episode_v5Xafs.Louisiana_ard_15Jun_to_30Jun05_episode_v5Xafs.Oklahoma_ard_15Jun_to_30Jun05_episode_v5XUSA_osd_2005_for_3Q05_generic_from_2005CENRAP.v3Xafs.gwei2005.May_Oct_edit.3pol.lcp2,3afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1X2afs.CAIRCanada.2001.v1a.latlong.lcp2,4July-August 2005Texasafs.ard.26Jul_to_08Aug05_TX_episode_v8X(bc05ep2)TX_osd_2005_all_episodesX1,2afs.aggVOC_extra_alkenes_for_2005_v2X1afs.landing_losses_19May_to_08Aug05X1,2B-60EpisodeAreaAFS Point Source Emissions FileHourlyDailySeeNoteRegionalafs.REGIONAL_ard_minus_TXOKARLA_26Jul_to_08Aug05_episode_v4Xafs.Arkansas_ard_26Jul_to_08Aug05_episode_v6Xafs.Louisiana_ard_26Jul_to_08Aug05_episode_v8Xafs.Oklahoma_ard_26Jul_to_08Aug05_episode_v6XUSA_osd_2005_for_3Q05_generic_from_2005CENRAP.v3X2afs.gwei2005.May_Oct_edit.3pol.lcp2,3afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1X2afs.CAIRCanada.2001.v1a.latlong.lcp2,4NOTES:1Aggregated VOC records require cross reference and profile code files for composition/speciation2Same for all 2005 episodes3AFS file contains monthly emissions; only June wasmodeled; see temporal profiles; see Section 2.1.2.34AFS file contains annual emissionsTable 2.1-12: AFS Files for the 2006 EpisodesEpisodeAreaAFS Point Source Emissions FileHourlyDailySeeNoteJune 2006Texasafs.ard.29May_to_16Jun06_TX_episode_v5X(bc06ep0)afs.osd_2006_STARS_extract_for_29May06_episode_v5X1,2afs.aggVOC_extra_alkenes_for_2006_v2X1afs.landing_losses_all_2006_episodes_v1X1,2Regionalafs.REGIONAL_ard_minus_TXOKARLA_29May_to_16Jun06_episode_v3Xafs.Arkansas_ard_29May_to_16Jun06_episode_v4Xafs.Louisiana_ard_29May_to_16Jun06_episode_v4Xafs.Oklahoma_ard_29May_to_16Jun06_episode_v4XUSA_osd_2006_for_3Q06_generic_from_2006CENRAP.v1X2afs.gwei2005.May_Oct_edit.3pol.lcp2,3afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1X2afs.CAIRCanada.2001.v1a.latlong.lcp2,42006 AQS-1Texasafs.ard_minus_SI_for_15Aug2006_episode_v8XB-61EpisodeAreaAFS Point Source Emissions FileHourlyDailySeeNote(SI period)afs.osd_minus_SI_for_15Aug2006_episode_v8X1,2(bc06aqs1)afs.osd_n_ard_SI_paths_for_15Aug2006_episode_v8Xafs.aggVOC_SI_for_15Aug2006_episode_v9Xafs.NOx_SI_for_15Aug2006_episode_v9Xafs.aggVOC_extra_alkenes_for_2006_v2X1afs.landing_losses_all_2006_episodes_v1X1,2Regionalafs.REGIONAL_ard_minus_TXOKARLA_15Aug_to_15Sep06_episode_v4Xafs.Arkansas_ard_15Aug_to_15Sep06_episode_v4Xafs.Louisiana_ard_15Aug_to_15Sep06_episode_v4Xafs.Oklahoma_ard_15Aug_to_15Sep06_episode_v4XUSA_osd_2006_for_3Q06_generic_from_2006CENRAP.v1Xafs.gwei2005.May_Oct_edit.3pol.lcp2,3afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1X2afs.CAIRCanada.2001.v1a.latlong.lcp2,42006 AQS-2Texasafs.ard.16Sep_to_16Oct06_TX_episode_v4X(bc06aqs2)afs.osd_2006_STARS_extract_for_16Sep06_episode_v4X1,2afs.aggVOC_extra_alkenes_for_2006_v2X1afs.landing_losses_all_2006_episodes_v1X1,2Regionalafs.REGIONAL_ard_minus_TXOKARLA_16Sep_to_16Oct06_episode_v2Xafs.Arkansas_ard_16Sep_to_15Oct06_episode_v4Xafs.Louisiana_ard_16Sep_to_15Oct06_episode_v4Xafs.Oklahoma_ard_16Sep_to_15Oct06_episode_v4XUSA_osd_2006_for_3Q06_generic_from_2006CENRAP.v1X2afs.gwei2005.May_Oct_edit.3pol.lcp2,3afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1X2afs.CAIRCanada.2001.v1a.latlong.lcp2,4NOTES:1Aggregated VOC records require cross reference and profile code files for composition/speciation2Same for all 2006 episodes3AFS file contains monthly emissions; only June wasmodeled; see temporal profiles; see Section 2.1.2.34AFS file contains annual emissionsB-622.1.5 Emissions Summaryfor Each Base Case EpisodeThe TCEQ chose a “representative day” ineach episode for which to report base case emissions totals for point sources. A Wednesday was found ineach episode that was also an ozone exceedence day for each of the base case episodes in 2005 and 2006. Table 2.1-13:Point Source Base CaseEpisodes Emissions Reporting Daysprovides a summary of the base case episodes. The emissions summary for these episodes is provided as Table 2.1-14:2005 & 2006 Base Case Episode Day Emissions Summary.Table 2.1-13: Point Source Base Case Episodes Emissions Reporting DaysHGB Modeled EpisodesEpisodeNameCommonNameDaysReported PointSource Daybc05ep0May-Jun 2005May 19 - June 3, 2005June 1, 2005bc05ep1June 2005June 17 -30, 2005June 22, 2005bc05ep2Jul-Aug 2005July 26 - August 8, 2005August 3, 2005bc06ep0June 2006May 31 - June 15, 2006June 14, 2006bc06aqs12006 AQS-1August 13 - September 15, 2006August 16, 2006bc06aqs22006 AQS-2September 16 - October 11, 2006September 27, 2006Table 2.1-14: 2005 & 2006 Base Case Episode Day Emissions SummaryHGBTexas minus HGBUSA minus TexasReportedRepresentative DayEmissions SourceNOX(tpd)VOC(tpd)NOX(tpd)VOC(tpd)NOX(tpd)VOC(tpd)June 1, 2005NEGUs (OSD)147.5199.2653.8332.96782.33221.5EGUs (ARD)43.92.2459.010.35626.541.9Tank Landing Losses0.049.50.00.00.00.0HRVOC Reconciliation0.026.20.00.00.00.0TOTALS =191.4277.11112.8369.312408.83263.4June 22, 2005NEGUs (OSD)147.5199.2653.8332.96782.33221.5EGUs (ARD)43.02.1531.80.06968.050.6Tank Landing Losses0.017.80.00.00.00.0HRVOC Reconciliation0.026.20.00.00.00.0TOTALS =190.5245.31185.6345.913750.33272.1B-63B-64HGBTexas minus HGBUSA minus TexasReportedRepresentative DayEmissions SourceNOX(tpd)VOC(tpd)NOX(tpd)VOC(tpd)NOX(tpd)VOC(tpd)August 3, 2005NEGUs (OSD)147.5199.2653.8332.96782.33221.5EGUs (ARD)55.32.9528.60.08428.767.3Tank Landing Losses0.033.10.00.00.00.0HRVOC Reconciliation0.026.20.00.00.00.0TOTALS =202.8261.41182.4346.015211.03288.8June 14, 2006NEGUs (OSD)124.1161.4617.8320.67644.63331.2EGUs (ARD)45.11.7476.715.26156.046.2Tank Landing Losses0.017.40.00.00.00.0HRVOC Reconciliation0.020.20.00.00.00.0TOTALS =169.2200.71094.5335.813800.63377.4August 16, 2006NEGUs (OSD)118.9152.7605.3320.27644.63331.2(SI day)EGUs (ARD)60.62.6560.216.56772.946.2Tank Landing Losses0.010.40.00.00.00.0HRVOC Reconciliation0.020.20.00.00.00.0TOTALS =179.5185.91165.5336.714417.53377.4September 27, 2006NEGUs (OSD)124.1161.4617.8320.67644.63331.2EGUs (ARD)43.41.9475.014.45384.440.9Tank Landing Losses0.00.30.00.00.00.0HRVOC Reconciliation0.020.20.00.00.00.0TOTALS =167.5183.81092.8335.013029.03372.1NOTES:These tabulated emissions are AFS (input to EPS3) totals. CAMx input values may differ.USA minus Texas includes some points outside the modeling domainTexas minus HGB includes some points in Texas outside the modeling domainAugust 16, 2006 emissions data includes Special Inventory

### B-65 2.2 2006 BASELINE POINT SOURCE MODELING EMISSIONS DEVELOPMENT The 2006 point sources used in the base case, descri bed above, can be very specific to individual days and hours, especially for the Acid Rain EGU portion of the EI. Therefore, the TCEQ created files that represent a typical summer day in 2006. Again, the baselin e is the basis for the Relative Response Factor (RRF) calculation: (1 ) these baseline emissions, along with the base case meteorology are modeled with CAMx, (2) oz one concentrations are predicted at each monitor and tabulated for comparison to the m odeled future case, and (3) baseline predicted
ozone is the denominator in the RRF calculation.

The subsections that follow discuss the parts of the inventory and how they may differ from the
base case.

2.2.1 Texas Point Sources 2.2.1.1 Ozone Season Daily (OSD) The OSD point source emissions for the typical 2006 baseline day are, in general, the same as the 2006 base case OSD emissions, as these are the average ozone season daily emissions, as extracted from STARS. There are two exceptions (improvements to the base case) that were modeled: (1) the 2006 baseline includes improved fl are exit (initial dispersion) parameters, and (2) the 2006 baseline includes the addition of STA RS reported annual upsets and emission events (EE) and scheduled maintenance, startu p and shutdown (SMSS) emissions.

The first improvement provided a closer review of modeled flare exhaust parameters due to the renewed scrutiny applied by the TCEQ Flare Task Force Stakeholder Group (see http://www.tceq.state.tx.us/implementation/air/rules/flare_stakeholder.html ). Additionally, TCEQ photochemical modelers sought consistent treatment of flares with TCEQ permit dispersion modeling guidance. Modelers calculate d an “equivalent diameter” for each flare stack, based on VOC combustion rate. Then, based on typi cal combustion efficiency and stoichiometry, and on a 20 meters/sec tip velocity , modelers estimated that the exit velocity equivalent for a flare will be 1.0 meters/sec (the 20 mps velocity is a r easonable velocity to avoid stack tip downwash). The new diameter is typically much larger than the internal diameter of the actual flare tip as reported to STARS. Likewise, the exit velocity used in modeling will be much smaller than the velocity of the uncombusted gas as it exits the flare tip. These new modeling parameters are important, because they affect the plume rise of the flare emissions.

The second improvement added the annual EE/SMSS and upset emissions fields from STARS in order to capture upsets and emission events that could not be readily discerned from CCEDS (as discussed in Section 2.1.1.8.1 above) and to assi st in the prevention of potential double-counting of tank landing loss emissions (see Section 2.1. 1.7.3 above). In past years, these annual emissions fields in STARS had been sparsely populated and of nearly negligible emissions upon conversion to daily emissions (dividing by 365). Adding these converted EE/SMSS and upset emissions to the OSD emissions provided 0.7 tpd more NOx and 19.4 tpd more VOC to the HGB emissions. Over the entire State, adding these fields provided 4.4 tpd more NOx and 41.2 tpd

more VOC.

These two improvements have been incorporated into the STARS modeling extract SAS program and are now the default method for processing all future STARS extracts, until revised again. These improvements were not worked back into the base case modeling due to time contraints, but will be included in base case modeling in the future.

Table 2.2-1 : 2006 Baseline OSD Texas Emissions shows the modeled ozone precursor 2006 baseline totals for point sources in the HGB eight -county nonattainment area (NAA), in the other NAAs of the State, and in the balance of the State.

### B-66 Table 2.2-1: 2006 Baseline OSD Emissions in Texas

NO X VOC CO Area #points tpd #points tpd #points tpd

HGB 5189 124.77 26699 175.16 5537 94.49

BPA 1229 58.23 6217 76.08 1244 36.22

DFW 1515 41.53 4767 37.28 1576 38.61

Rest of TX 8921 520.01 24055 228.88 9237 415.03

TX Totals 744.54 517.40 584.35

Note that some points in Texas are outside the modeling domain (e.g., points in west Texas). The “#points” entry in Table 2.2-1 is the total number of point sources, summed to give the area total tons per day (tpd), including points with zero emissions. The TCEQ typically eliminates zero emissions records, and, in any case, the EPS3 processor will drop VOC records with zero emissions, because those records will not have a speciation cross reference.

2.2.1.2 Emissions Inventory Reconciliation (aka, HRVOC Reconciliation, Extra Olefins ) As with the OSD point source emissions, the added 2006 HRVOC EI Re conciliation emissions remain unchanged from the 2006 base case. As desc ribed in Section 2.1.1.9, above, the PSCF technique used to ascribe the extra HRVOC placed the additional HRVOC in the center of grid cells, and was already an average day for the grid cell. A total of 19.29 tpd of extra HRVOC were assigned to 230 grid cells in HGB. A su mmary of the reconciled PSCF extra HRVOC is provided as Table 2.2-2: Summary of HGB Reconciled HRVOC . The emissions are tabulated in speciated VOC totals, not EPS3-processed CB-05.

Table 2.2-2: Summary of HGB Reconciled HRVOC

HRVOC #points tpd

Ethene 124 5.58 Propene 179 11.52

1-butene 68 0.43

c-2-butene 104 0.32

t-2-butene 136 1.04

1,3-butadiene 65 0.40

Total Emissions = 19.29

2.2.1.3 Hourly Acid Rain Data (ARD) Point Sources In the 2006 base case, the Acid Rain EGUs were modeled for each hour of the episodes. To develop an Acid Rain EGU baseline, the TCEQ averaged the Acid Rain NO X for each hour of the day for each unit for the third quarter of 2006 (J uly 1 through September 30). These data records represent a 92-day period of 2006 that simulate a typical ozone season (summer) period, yet they maintain the temporal profile of the indivi dual units. Corresponding hourly average CO and VOC emissions were calculated from STARS ozone season daily (OSD), stack-specific emissions by multiplying CO:NO X and VOC:NO X ratios by the hourly NO X rate for each acid rain unit. A summary of the ARD emissions for the State are presented in Table 2.2-3: 2006 Baseline Hourly

Texas Acid Rain EGU Emissions . The tabulated hourly emissions values in the table are the sum of emissions for all point sources in each NAA and the remainder of the State, although some of the point sources in the attainment areas of Texas are outside of the modeling domain. All hourly values were rounded to one decimal place.

### B-67 Table 2.2-3: 2006 Baseline Hourly Texas Acid Rain EGU Emissions

NO X Emissions (tph) VOC Emissions (tph) CO Emissions (tph)

hour HGB BPA DFW rest
of

TX HGB BPA DFW rest
of

TX HGB BPA DFW rest
of

TX

1 1.50 0.38 0.24 17.00 0.05 0.02 0.01 0.44 1.16 0.16 0.12 34.43

2 1.47 0.34 0.22 16.32 0.05 0.02 0.01 0.42 1.15 0.15 0.11 33.82

3 1.45 0.32 0.20 15.98 0.05 0.02 0.01 0.41 1.13 0.14 0.10 33.46

4 1.45 0.33 0.20 16.01 0.05 0.02 0.01 0.41 1.13 0.15 0.10 33.54

5 1.57 0.35 0.23 16.62 0.06 0.02 0.01 0.43 1.28 0.15 0.10 33.77

6 1.57 0.38 0.21 16.45 0.06 0.02 0.01 0.44 1.22 0.16 0.10 33.52

7 1.59 0.38 0.21 18.41 0.06 0.02 0.01 0.47 1.23 0.16 0.11 34.58

8 1.59 0.39 0.25 17.73 0.06 0.02 0.01 0.48 1.23 0.17 0.13 35.30

9 1.69 0.40 0.32 18.65 0.06 0.02 0.01 0.51 1.29 0.17 0.16 36.30

10 1.84 0.44 0.36 19.53 0.07 0.03 0.02 0.54 1.39 0.19 0.18 37.07

11 2.08 0.53 0.40 20.39 0.08 0.03 0.02 0.56 1.59 0.22 0.21 37.63

12 2.32 0.62 0.45 21.46 0.10 0.04 0.03 0.60 1.83 0.25 0.26 38.03

13 2.61 0.73 0.52 22.65 0.12 0.04 0.03 0.63 2.10 0.30 0.32 38.32

14 2.75 0.78 0.57 23.81 0.13 0.04 0.04 0.66 2.24 0.32 0.34 38.63

15 2.84 0.81 0.62 24.78 0.13 0.05 0.04 0.69 2.32 0.34 0.37 39.11

16 2.82 0.81 0.63 25.22 0.13 0.05 0.04 0.71 2.30 0.34 0.38 39.37

17 2.74 0.75 0.61 24.65 0.12 0.04 0.04 0.69 2.19 0.32 0.36 39.13

18 2.57 0.65 0.55 23.55 0.11 0.04 0.04 0.65 1.96 0.27 0.32 38.72

19 2.38 0.56 0.49 22.27 0.10 0.03 0.03 0.62 1.79 0.24 0.29 38.27

20 2.25 0.54 0.45 21.52 0.09 0.03 0.03 0.59 1.74 0.23 0.27 37.90

21 2.08 0.48 0.41 20.72 0.09 0.03 0.02 0.57 1.62 0.21 0.24 37.55

22 1.79 0.42 0.37 19.96 0.07 0.03 0.02 0.55 1.39 0.18 0.20 37.20

23 1.62 0.41 0.30 18.76 0.06 0.02 0.01 0.51 1.26 0.17 0.15 36.39

24 1.55 0.41 0.27 17.75 0.06 0.03 0.01 0.47 1.22 0.18 0.13 35.43

Total 48.09 12.23 9.09 480.17 1.98 0.72 0.51 13.06 37.76 5.18 5.03 877.46

# pts 74 15 49 231 74 15 49 231 74 15 49 231

2.2.1.4 Hourly Tank Landing Loss (TLL) Survey The floating roof TLL surveys were compiled in to hourly emissions for each of the base case modeling episodes. The 2006 baseline was calculate d as the average of the hourly emissions for each tank point source of the survey for all of the modeled episode days of 2006. The TLL emissions occur during tank landings. A TLL point source with intervals (hours) of missing losses may be operating routinely with other emi ssions that were not reported in the survey because they were not due to a tank landing. This distinction is important, because the intermittent tank landing losses are only a part of the total emissions for each of these tanks and
need to be subtracted from the to tal to prevent double counting. There were a total of 218 point source paths reporting TLLs. A summary of the averaged hourly TLL total VOC emissions data in tons per hour (tph) is presented as Table 2.2-4: 2006 Baseline Hourly Tank Landing Loss

Emissions in HGB.

### B-68 Table 2.2-4: 2006 Baseline Hourly Tank Landing Loss Emissions in HGB

hour #points VOC (tph)

1 58 0.1768

2 54 0.1476

3 44 0.0819

4 44 0.0953

5 37 0.1150

6 41 0.1454

7 61 0.3150

8 58 0.2762

9 199 0.5293

10 200 0.4968

11 201 0.4860

12 201 0.4708

13 202 0.3999

14 204 0.4401

15 202 0.4467

16 204 0.4230

17 50 0.1848

18 50 0.1837

19 56 0.2601

20 54 0.2348

21 55 0.2086

22 60 0.1194

23 56 0.1457

24 59 0.1661 VOC Emissions, tpd = 6.5491

Point source counts (“#points” in Table 2.2-4) less than 218 occur for most hours, because not all tanks reported TLL for each hour of the modeled 2006 episodes. Emissions were assigned to be higher between 8:00 am and 4:00 pm (hours 9 th rough 16) due to tank breathing losses. This averaged baseline value is much less than the base case episodic emissions -- see Table 2.1-14. Table 2.1-14 also shows that, in general, th e TLL emissions were tapering off from Spring 2005
to Fall 2006. r

2.2.1.5 Hourly Special Inventory The 2006 Special Inventory (SI) was not carried into the baseline, because these emissions are not “typical;” they were hourly substitutes for STA RS OSD emissions for a specific time period of
the base case. The same could be said for the CCEDS data, but they were not modeled in the
base case.

2.2.1.6 2006 Baseline Tileplots for Texas Following are tileplots of 2006 baseline modeling emissions. Included are: Figure 2.2-1: Elevated NO X Figure 2.2-2: Elevated VOC Figure 2.2-3: Low-level NO X Figure 2.2-4: Low-level VOC Figure 2.2-5: HRVOC Reconciliation Figure 2.2-6: Tank Landing Losses VOC

Of note is the fact that the elevated NOx and elevated VOC tileplots include ships, which are an area source. TCEQ chooses to include these as el evated point sources since many ship stack heights and emission plume rises exceed the first layer of the modeling domain, and therefore will be captured with the plume-in-grid algorithm of CAMx.Figure 2.2-1: Elevated NO X , (includes ships)



### B-69Figure 2.2-2: Elevated VOC, (includes ships)



### B-70Figure 2.2-3: Low-level NO X



### B-71Figure 2.2-4: Low-level VOC



### B-72Figure 2.2-5: HRVOC Reconciliation



### B-73Figure 2.2-6: Tank Landing Losses VOC

2.2.2 Outside Texas 2.2.2.1 Adjacent States and Regional States Non-Acid Rain As with Texas OSD sources, the TCEQ used th e 2006 OSD (or calculated average day if only annual emissions were provided) base case records for the non-ARD 2006 baseline for states outside of Texas. For the three adjacent states, these data were from the 2006 base case as grown
from the base data that the individual stat es provided to TCEQ (2004 for LA, 2005 for OK and AR) with the Acid Rain units removed. Fo r the Regional states, the 2006 baseline (and base case) was the base data (2002 CENRAP/RPO) grown to 2006, with the Acid Rain units removed.



### B-74

### B-75 Table 2.2-5: 2006 Baseline Emissions Summary for N on-ARD Points Outside of Texas summarizes the non-Acid Rain emissions for the 2006 baseline (same as 2006 base case). Emissions in Louisiana, Oklahoma and Arkansas are segregated to illustrate that the composite modeling file has emissions data for all states in the modeling domain. The “Other States” entry
in Table 2.2-5 is emissions data for the remain ing states within the modeling domain, excluding LA, OK AR, and TX. The photochemical model ma y not use all of the records in the non-Acid Rain 2006 base case, since some states have a fr action of their area outside of the modeling domain (e.g., the Oklahoma panhandle). EPS3 processing drops these from the modeling EI.

Table 2.2-5: 2006 Baseline Emissions Summary for Non-ARD Points Outside of Texas

NO X VOC CO Area #points tpd #points tpd #points tpd

AR 1036 82 2063 101 1061 157

LA 4090 658 7538 234 3763 389

OK 2338 216 5425 107 2340 155

Other States 78431 6688 159614 2889 73940 7513

2.2.2.2 Adjacent States and Regional States Hourly Acid Rain As with Texas Acid Rain units, the 2006 baseline for the ARD sources of the states of the rest of the nation is a calculated typical summer day havi ng hourly emissions that are the average for each Acid Rain point source during the third quarter (July 1 through September 30) of 2006. As with Texas, VOC and CO emissions for each hour of the typical summer day come from the hourly NO X emissions and the VOC:NO X and CO:NO X ratios, computed from the daily emissions data, for each Acid Ra in point.

Table 2.2-6: 2006 Baseline Hourly Emissions Summa ry for ARD Points Outside of Texas
summarizes the 2006 baseline Acid Rain emissi ons within the TCEQ regional modeling domain outside of Texas. Emissions in the adjacent states of Arkansas, Louisiana and Oklahoma are segregated to illustrate that the composite modeling file has emissions for all states in the domain. Only a fraction of the point sources emissions in some states is modeled if part of that state’s area is outside the modeling domain, e.g., parts of w estern Kansas. All hourly values were rounded to one decimal place.

Table 2.2-6: 2006 Baseline Hourly Emission s Summary for ARD Points Outside of Texas

NO X Emissions (tph) VOC Emissions (tph) CO Emissions (tph)

hour AR LA OK Other AR LA OK Other AR LA OK Other

1 4.6 6.2 9.2 212.4 0.1 0.1 0.1 1.3 0.5 1.9 1.1 57.4

2 4.3 6.0 8.8 203.0 0.1 0.1 0.1 1.3 0.5 1.9 1.0 54.9

3 4.0 5.9 8.6 196.8 0.1 0.1 0.1 1.2 0.4 1.8 1.0 53.2

4 4.0 5.9 8.6 195.6 0.1 0.1 0.1 1.2 0.4 1.8 1.0 52.9

5 4.1 6.0 8.8 202.4 0.1 0.1 0.1 1.3 0.4 1.9 1.0 54.7

6 4.2 6.4 9.0 214.1 0.1 0.1 0.1 1.4 0.5 2.0 1.1 57.9

7 4.4 6.4 9.2 223.9 0.1 0.1 0.1 1.4 0.5 2.0 1.1 60.5

8 4.9 6.5 9.5 236.3 0.1 0.1 0.1 1.5 0.6 2.1 1.1 63.9

9 5.2 6.7 10.0 249.9 0.1 0.1 0.1 1.6 0.6 2.1 1.2 67.6

10 5.2 6.9 10.7 261.9 0.1 0.1 0.1 1.7 0.6 2.2 1.4 71.0

11 5.1 7.4 11.5 270.5 0.1 0.1 0.2 1.8 0.6 2.3 1.6 73.3

12 5.2 7.8 12.2 276.3 0.1 0.2 0.2 1.9 0.6 2.3 1.8 74.9

13 5.2 8.3 12.9 280.1 0.1 0.2 0.2 1.9 0.6 2.5 2.0 76.0

14 5.2 8.6 13.4 282.4 0.1 0.2 0.2 1.9 0.6 2.5 2.2 76.6

15 5.3 8.7 13.6 283.5 0.1 0.2 0.2 1.9 0.6 2.6 2.3 77.0

16 5.2 8.6 13.9 283.8 0.1 0.2 0.2 1.9 0.6 2.5 2.3 77.0

17 5.1 8.3 13.6 282.8 0.1 0.2 0.2 1.9 0.6 2.5 2.3 76.7

### B-76 NO X Emissions (tph) VOC Emissions (tph) CO Emissions (tph)

hour AR LA OK Other AR LA OK Other AR LA OK Other

18 5.1 7.9 13.1 279.0 0.1 0.2 0.2 1.9 0.6 2.4 2.1 75.7

19 5.0 7.5 12.5 275.4 0.1 0.2 0.2 1.8 0.6 2.3 2.0 74.7

20 5.0 7.3 12.1 274.5 0.1 0.2 0.2 1.8 0.6 2.2 1.8 74.4

21 4.9 7.0 11.7 269.0 0.1 0.1 0.2 1.7 0.5 2.2 1.7 72.9

22 4.7 6.7 11.0 256.4 0.1 0.1 0.1 1.6 0.5 2.1 1.5 69.4

23 4.7 6.5 10.3 240.9 0.1 0.1 0.1 1.5 0.5 2.0 1.3 65.1

24 4.7 6.4 9.7 224.9 0.1 0.1 0.1 1.4 0.5 2.0 1.2 60.8

Totals 115 170 264 5976 2 3 3 39 13 52 37 1619

#points 41 96 71 1351 41 96 71 1351 41 96 71 1351

Note that, as in Texas, the peak hours of emissi ons (coinciding with peak electrical demand) for
the Acid Rain EGUs are between hours 14 and 16, for the summation of the entire country.

2.2.2.3 Offshore, Mexico, and Canada The Offshore 2005 GWEI, the 1999 Mexican NEI, and the 1995 Canadian baseline point source files are the same as the base case files, since th ey are already being modeled as an average day.

2.2.3 Summary of 2006 Baseline Point Source Data Files Snapshots of the point source emission files th at were processed with EPS3 for CAMx for the baseline (typical summer day) are presented in Table 2.2-7: AFS Files for the 2006 Baseline

. The version number on each dataset indicates a cha nge from the previous version (e.g., “v2”). The FTP download site for the point source files or their successors is ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/HGB8H2/ei/point/ . Outdated files are generally not retained on the FTP site. Table 2.2.8: 2006 Baseline Point Source Emissions Summary summarizes the baseline emissions.Table 2.2-7: AFS Files for the 2006 BaselineAreaAFS Point Source Emissions FileHourlyDailySeeNoteTexasafs.ard_generic_episode_3Q2006_aver_day_v1Xafs.osd_2006_STARS_generic_with_upsets_and_routine_TLLX1afs.landing_losses_3Q06_aver_day_episode_v1X1afs.aggVOC_extra_alkenes_for_2006_v3X1,2Regionalafs.USA_minusTx_ard_generic_day_v1XUSA_osd_2006_for_3Q06_generic_from_2006CENRAP.v1Xafs.gwei2005.May_Oct_edit.3pol.lcp2,3afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1X2afs.CAIRCanada.2001.v1a.latlong.lcp2,4Notes1 Aggregated VOC records require cross referenceand profile code files for composition/speciation2 Unchanged from 2006 base case3 AFS file contains monthly emissions; only June was modeled; see temporal profiles; see Section 2.1.2.34 AFS file contains annual emissionsTable 2.2-8 2006 Baseline Point Source Emissions SummaryHGBTexas minus HGBUSA minus TexasEmissions SourceNOX(tpd)VOC(tpd)NOX(tpd)VOC(tpd)NOX(tpd)VOC(tpd)NEGUs (OSD)124.8175.2619.8342.27644.63331.2EGUs (ARD)48.12.0501.514.36524.547.1Tank Landing Losses0.06.50.00.0nanaHRVOC Reconciliation0.019.30.00.0nanaTOTALS =172.9203.01121.3356.514169.03378.3Notes: These tabulated emissions are AFS (input to EPS3) totals. CAMx input values may differ.USA minus Texas includes some points outside the modeling domainTexas minus HGB includes some points in Texas outside the modeling domainB-77

### B-78 2.3 2018 FUTURE YEAR POINT SOURCE MODELING EMISSIONS DEVELOPMENT This section describes the development of the 2018 future year point source EI. The eight-hour ozone attainment date for HGB is June 15, 2019 (severe classification). The modeled attainment year is 2018 -- the ozone season prior to the attainment date.

The 2018 case that this section describes is the futu re base EI development, in which a baseline is projected (”grown”) to the attainment year with on-the-books controls applied that will be in place after the baseline year yet prior to the attainment year. The on-the-books controls are
controls for which enforceable emissions reductions rules have been written already; they are not additional proposed rules that result from this SI P proposal and adoption. Additional proposed or adopted rules that would be modeled would be developed in a 2018 control strategy EI (that would be described in a future section) or as part of Reasonably Available Control Measure (RACM) analyses. This 2018 future base EI pr ovides the basis from which to begin testing
model sensitivities to determine how sensitive the model is (how much ozone responds) to various EI changes, to determine which control st rategies may be needed to reach attainment or
can advance the attainment date. The 2018 future base EI is the starting point for any 2018 control strategy modeling.

The TCEQ has prepared a single 2018 future year point source EI for the entire State. In this SIP revision, emphasis is placed on HGB receiving the most detailed attention. Since the baseline year for the future design value calculation is 2006 , this future year point source EI has been projected primarily from the 2006 point source basel ine EI, as described in Section 2,2 above. As discussed below, not all parts of the 2018 EI were based on 2006 (some based on 2007). The 2018 future base needs to parallel the baseline for an apples-to-apples comparison for the future design value calculation; therefore, the 2018 futu re base EI needs to represent a typical summer day in 2018.

The format of this section is similar to that of the previous sections, in that we describe the procedures used to develop the EI for Texas, with all the details for the nonattainment areas (NAAs) of the State, then working our way outward to Canada.

2.3.1 Attainment Areas of Texas The attainment areas of Texas include all of Texas, excluding the ozone NAAs of the State: HGB, Dallas-Fort Worth (DFW), and Beaumont-Port Ar thur (BPA). The subsections below address growth projections to 2018 and existing controls separately.

2.3.1.1 Attainment Area 2018 Growth Projections Growth projections were applied to the baseline EI in the attainment areas of the state to obtain a 2018 grown EI. Different techniques were applied to the Electric Generating Units (EGUs) that have Acid Rain data (ARD) and the non-EGUs (NEGUs) that generally rely on ozone season daily (OSD) emissions. These techniques are sim ilar to EPA projection methods and Regional Planning Organization (RPO) projection methods for modeling future cases.

2.3.1.1.1 EGUs
As discussed in the base case and baseline sections above, the ARD provide hourly emissions for EGUs that must comply with the EPA’s Acid Rain Program. The typical summer Acid Rain day is a single day in which every modeled hour of each ARD point source is the average of all days in the third quarter of 2007 (3Q2007) for that hour. The same procedure (see Section 2.2.1.3 above) used to generate the 2006 baseline EGUs fo r Texas was used for 2007. The TCEQ chose this dataset from which to project, because it is newer, containing more of the actual emissions growth from newer units. Projecting EGUs from the 2006 baseline would inherently have a bit more error in it, since it would begin farther away from the attainment year of 2018. Not all
EGUs are Acid Rain sources and not all NO X point sources at EGU facilities are Acid Rain

### B-79 sources. The non-Acid Rain EGUs were modeled at their 2006 baseline emissions along with the non-EGU point sources.

The complete set of 2018 EGUs consists of the 3Q2007 ARD EGUs, the 2006 non-Acid Rain EGUs, and post-2007 EGUs that have approved TCEQ permits. As with previous SIP revisions, the TCEQ assumes that the EGU growth in the State comes from the TCEQ newly-permitted

EGUs.

Newly-Permitted EGUs Growth in EGUs in Texas is accomplished with the addition of all newly-permitted EGUs since the baseline, all within the constraint of the EPA ’s Clean Air Interstate Rule (CAIR), which is
described in the controls subsection below. This subsection describes the procedures for developing the newly-permitted EGU EI.

Texas’ EGU emissions for 2018 were developed by researching and compiling data from various
sources. These sources include:

 Electric Reliability Council of Texas (ERCOT) http://www.ercot.com/content/ne ws/ presentations/2008/2008_Capacity,_Demand,_Reserves_Rep ort_FINAL.xls

 Public Utility Commission (PUC) http://www.puc.state.tx.us/electric/maps/gentable.pdf

 Energy Information Administration (EIA). Form 860 is the most useful: http://www.eia.doe.gov/cneaf/electricity/page/eia860.html

 TCEQ Permit Engineers and link to projects with combustion turbines: http://www.tceq.state.tx.us/assets/public/p ermitting/air/memos/turbine_lst.pdf

 TCEQ New Source Review Permits Information Management System (NSRP IMS) internal
doc ument server

Information from these sources include individual units’ disposition (i.e., operating status), new units coming online, units to be mothballed, and units to be shut down and retired. The TCEQ assumed that by 2018, all current mothballed and reliability-must-run (RMR) units would be retired. For new units planned to come online, a distinction is made for those that have received a permit, versus those that have pending permit s. Reconciliation between all data sources was made to ensure all units were accounted for, a nd their most current status, as of February 2009,
was modeled. As the most recent EGU emissions data for modeling is from 3Q2007 Acid Rain,
new EGUs are based on additions and cha nges subsequent to 2007 (“post 2007”).

While the above sources of data provide informa tion on current and planned electric generation, the TCEQ used the ERCOT demand projections to determine a suitable methodology to model
2018 EGU emissions. ERCOT controls the electric grid for approximately 85% of the Texas customers, with the remaining areas controlled by Southwest Power Pool (SPP), Southeastern Electric Reliability Council (SERC), and West ern Systems Coordinating Council (WSCC). ERCOT’s Capacity, Demand and Reserves (CDR) report (see first bullet above) forecasts a five year demand (load) and capacity (generation) pr ojection to 2013. The TCEQ extrapolated the demand to 2018 by continuing the average growth of approximately 2% per year, based on the Summer Summary portion of the report. This 2018 extrapolated value was also verified by an ERCOT long-term projection chart in the CDR. ERCOT also includes a reserve margin (12.5% is preferred) for capacity, which is accounted for in modeled emissions from future electric generation.

The TCEQ performed a highly detailed study of the EGU growth, capacity, and demand under the scenario that CAIR was vacated – the constraint of CAIR cap and controls was lifted, temporarily. The TCEQ considers the “non-CA IR” scenario a sensitiv ity test and has not provided any documentation for that scenario. During the process of developing the 2018 future

### B-80 case EI, the Clean Air Interstate Rule (CAIR) was vacated by the Courts. The TCEQ quickly went down the path of developing a “non-CAIR” version of the 2018 EI for the EGUs in Texas and outside of Texas. As the TCEQ comp leted the non-CAIR 2018 EI, the Court remanded CAIR back to the EPA. The EPA provided guidance that CAIR is still in effect, as promulgated, until future revision or replacement. Hence, this version of 2018 EI incorporates CAIR Phase II (post 2014 step-down of CAIR). This is handled separately for Texas and states outside of Texas. Details of CAIR (a control program) ar e provided in Section 2.3.1.2.1 below.

For the newly-permitted (since 2007) EGUs, emi ssions were calculated based on their permit allowables from the permit Maximum Allowabl e Emission Rates Table (MAERT). For ozone modeling, pollutants acquired from the permits were NO X , VOC, and CO. Methods of determining these allowables differ between the types of units and their primary purpose for being constructed. Hence, methods for assigning modeled allowable emissions differ between the types of units. For example, coal plants may have a 30 day rolling average, while gas turbines may have a short term allowable (pounds per hour) a nd/or a long term allowable (tons per year). In some cases, a unit may have a combination of the above, in addition to maintenance, startup and shutdown (MSS) emission limits.

Whenever available from a permit MAERT, the 30-day pounds-per-hour emissions limitation was
used. These were most often available for so lid fuel-fired units. These limitations on some MAERTs represent a good compromise between th e standard short-term allowable pounds per hour (that sometimes includes ma intenance, startup and shutdown emissions) and the standard long-term permit allowable ton per year. The sh ort term, when converted to tpd, is often substantially more than a unit would realistically emit in any day; the long-term, when converted to tpd may often under-represent what a unit coul d emit during any one day, especially during a summer day during the ozone season. Therefore, a 30-day average pounds-per-hour limitation is a reasonable representation of the emissions duri ng the modeled ozone episode. Again, the 30- day limit was available generally only from the MAERTs of solid fuel-fired units.

MSS permitting is fairly new, but the TCEQ mode ling staff saw this as an opportunity to capture a more realistic operating scenario than simply the maximum of the short-term or long-term emission rates alone. This is especially important for those units that have many MSS events during a typical summer, most notably, the peaki ng units that attempt to capture only the peak demand times. MSS limits vary between permits , with some units having discrete and specific quantities on MAERTs, while others are more ambi guous in the permit Special Conditions or footnotes, usually due to the variety of needs and operating parameters of the equipment. Examples include:

 3 hour limit per MSS event

 2500 hours/yr

 MSS event maximum: cold start= 5 hours; warm start= 3 hours; shutdown= 1 hour

 none given

An attempt was made to maintain consistent MSS limits across the different types of units where the assignments were vague. MSS limits were not used for baseload coal units, as it is assumed these operate generally nonstop throughout summer ozone season.

To model the newly-permitted EGU emissions in a somewhat consistent manner, keeping in mind that permitting is performed on a case-by-case basi s, a preferred order (given below) was prescribed for using appropriate emission limits, and adherence to this order is kept in most
circumstances:

 Use 30 day rolling average emission limit if provided in permit.

 Use higher of the short term or long term limits, and converted to tons per day.

### B-81  Incorporate MSS emissions wher e applicable and available.

The emission rates calculated for these units represe nt worst case for some units, but for most, it represents a typical summer day during the ozone season, which corresponds to some of the highest days of electricity demand. In developi ng a future EI, the TCEQ has historically modeled only those units that (1) have been issued a permit, and (2) are expected to be operational prior to the attainment date. The complete list of ne wly-permitted EGUs (permitted for startup after 2007) that were added as the EGU growth in the State, by area, is provided as Table 2.3-1: Newly Permitted EGUs (post 2007) in Texas as of November 2009 . This table represents the very most up-to-date list of newly-permitted EGUs, as of N ovember 2009. The version modeled may not be as comprehensive as this list.Table 2.3-1: Newly-Permitted EGUs (post-2007) in Texas, as of November 2009Emission Rates (tpd)Calculated from PermitModeledAreaSite Name2CountyBoilerCapacity(MW)Fuel1NOXVOCCONOXHGBDow Chemical CogenBrazoriaB56CR96180G0.660.664.320.16Green Power Unit 1BrazoriaGT-235G0.140.070.800.03Green Power Unit 1BrazoriaGT-335G0.140.070.800.03Cedar BayouChambersCBY41300G0.690.156.500.16Cedar BayouChambersCBY42300G0.770.156.500.18NRG Cedar BayouChambersCBY51350G0.570.206.220.14NRG Cedar BayouChambersCBY52350G0.570.206.220.14Deer Park Energy CenterHarrisST-5265G0.420.876.680.10Deer Park Energy CenterHarrisST-6265G0.420.876.680.10TECO Central PlantHarrisCHP-150G0.320.101.630.08TECO Central PlantHarrisCHP-250G0.320.101.630.08Lewis CreekMontgomerySTCK3A250G0.270.091.070.06Lewis CreekMontgomerySTCK3B250G0.270.091.070.06BPAEast TX Elec Coop HardinHardinHCCT173G0.430.120.800.10Hardin FacilityHardinHCCT273G0.430.120.800.10ATTAIN3Lufkin Generating PlantAngelinaLBLR0145R0.620.080.620.14Panda Temple PowerBellCTG-1275G0.450.605.780.10Panda Temple PowerBellCTG-2275G0.450.605.780.10Panda Temple PowerBellCTG-3275G0.450.605.780.10Panda Temple PowerBellCTG-4275G0.450.605.780.10J K Spruce Unit 2BexarU-6750C6.620.3553.761.47VH Braunig 5BexarCGT550G0.320.041.430.07VH Braunig 6BexarCGT650G0.320.041.430.07VH Braunig 7BexarCGT750G0.320.041.430.07VH Braunig 8BexarCGT850G0.320.041.430.07Dansby Power PlantBrazos650G0.300.031.030.07E.S. Joslin Power StationCalhounESJ-1A300P2.230.164.780.49Formosa Pt.ComfortCalhounCFB-S1150C P1.260.101.980.28Formosa Pt.ComfortCalhounCFB-S2150C P1.260.101.980.28La Palma Power StationCameronLAP8273G0.740.194.820.16B-82Emission Rates (tpd)Calculated from PermitModeledAreaSite Name2CountyBoilerCapacity(MW)1FuelNOXVOCCONOXQuail Run Energy CenterEctorCTDB2A138G0.260.040.820.06Quail Run Energy CenterEctorCTDB2B138G0.260.040.820.06Quail Run Energy CenterEctorCTDB3A113G0.130.040.560.03Quail Run Energy CenterEctorCTDB3B113G0.130.040.560.03Global Alternative FuelsEl PasoEPN1_2555R0.680.190.420.15Newman Unit 6El PasoCC-S6A144G1.420.225.060.31Newman Unit 6El PasoCC-S6B144G1.420.225.060.31Pattillo BranchFanninS-1350G0.860.333.870.19Pattillo BranchFanninS-2350G0.860.333.870.19Pattillo BranchFanninS-3350G0.860.333.870.19Pattillo BranchFanninS-4350G0.860.333.870.19Winchester PowerParkFayetteCGT150G0.110.050.500.02Winchester PowerParkFayetteCGT250G0.110.050.500.02Winchester PowerParkFayetteCGT350G0.110.050.500.02Winchester PowerParkFayetteCGT450G0.110.050.500.02J L Bates Power StationHidalgoJLBCT1290G0.740.195.390.16Cobisa-GreenvilleHuntGTDB1300G0.691.245.900.15Cobisa-GreenvilleHuntGTDB2300G0.691.245.900.15Cobisa-GreenvilleHuntGTDB3300G0.691.245.900.15Cobisa-GreenvilleHuntGTDB4300G0.681.114.430.15Cobisa-GreenvilleHuntGTDB5300G0.681.114.430.15Cobisa-GreenvilleHuntGTDB6300G0.681.114.430.15Jack Co Gen FacilityJackHRSG-3330G1.820.537.670.40Jack Co Gen FacilityJackHRSG-4330G1.820.537.670.40Lamar Power Partners IILamarGT-HRSG5310G0.540.100.890.12Lamar Power Partners IILamarGT-HRSG6310G0.540.100.890.12Madison Bell PartnersMadisonCTDB1-A138G1.000.132.100.22Madison Bell PartnersMadisonCTDB1-B138G1.000.132.100.22Madison Bell PartnersMadisonCTDB2-A138G1.000.132.100.22Madison Bell PartnersMadisonCTDB2-B138G1.000.132.100.22Sandy CreekMcLennanS01800C6.880.3529.471.52B-83B-84Emission Rates (tpd)Calculated from PermitModeledAreaSite Name2CountyBoilerCapacity(MW)Fuel1NOXVOCCONOXSandow 5Milam17CFB282C3.550.183.550.79Sandow 5Milam17CFB282C3.550.183.550.79Nacogdoches PowerNacogdochesBFB-1100C P1.650.242.720.37Nacogdoches PowerNacogdochesHRSG-1330G3.070.342.630.68Barney M DavisNuecesBMD3350G1.250.476.090.28Barney M DavisNuecesBMD4350G1.250.476.090.28Lon C. Hill Unit 5NuecesLCH5290G0.740.191.330.16Nueces Bay WLENuecesNB8350G1.250.476.090.28Nueces Bay WLENuecesNB9350G1.250.476.090.28Oak Grove 1RobertsonOGU1800C8.610.5636.601.91Oak Grove 2RobertsonOGU2800P8.610.5636.601.91San Jacinto FacilitySan JacintoSJCCT173G0.430.120.800.10San Jacinto FacilitySan JacintoSJCCT273G0.430.120.800.10Sand Hill Energy CtrTravisSH-648G0.300.061.410.07Sand Hill Energy CtrTravisSH-748G0.300.061.410.07Victoria Elec Gen StationVictoriaVIC7289G0.740.195.380.16Laredo Power StationWebbLPSCT4100G0.290.050.340.06Laredo Power StationWebbLPSCT5100G0.290.050.340.06Colorado Bend Energy CenterWhartonCTDB2A138G0.280.040.890.06Colorado Bend Energy CenterWhartonCTDB2B138G0.280.040.890.06Colorado Bend Energy CenterWhartonCTDB3A113G0.140.040.610.03Colorado Bend Energy CenterWhartonCTDB3B113G0.140.040.610.03Wise County Power PlantWiseCT2182G0.860.073.620.19Wise County Power PlantWiseCT2282G0.860.073.620.19Wise County Power PlantWiseCT3182G0.860.073.620.19Wise County Power PlantWiseCT3282G0.860.073.620.19TOTAL =1885693.0024.33410.9820.691Fuel Types: G – Natural Gas, P – Petroleum, C – Coal, R – Renewables (Biomass)2Some of the Newly Permitted EGUs are located beyond the modeling domain and excluded from the photochemical modeling3“ATTAIN” refers to the attainment areas of Texas.

### B-85 orst case). Table 2.3-1, above, includes two types of emission rates: (1) calculated NO X , VOC and CO from permit applications and MAERTs, representing rea listic average day emissions prior to meeting CAIR Phase II and MECT requirements as discussed below in later subsections; and (2) modeled

NO X rates incorporating CAIR Phase II and MECT. It is assumed NO X controls, offsets, or credit purchases will be used to meet these NO X emissions rates, leaving VOC and CO rates modeled at their permitted levels (an artifact modelers are aware of, but w

All of the 87,290 megawatt (MW) demand, includi ng the reserve margin, projected by ERCOT (in its CDR report) to be required by 2018, is sa tisfied by existing units, planned units, and these newly-permitted units (provided they can run at fu ll load) that are all expected to be operating by 2011 or 2012. Most of the perm itted units are expected to be operational by the end of 2010. Historically, some permitted units are never bu ilt and some postpone cons truction; hence, 2011- 2012 is simply an estimate. All (or nearly a ll) of these newly-permitted EGUs, except perhaps the cogeneration (cogen) units, have “inter connect agreements” (put up bond money with ERCOT to connect to the grid), so these are genuine projects acknowledged by ERCOT, PUC, and EIA. The cogen units of Table 2.3-1 include Dow, Formosa, Deer Park Energy, and BP Amoco. The TCEQ has assumed that any remaining deficit to the demand between 2012 and
2018 will be filled with renewable resources that do not involve combustion sources, such as wind and nuclear, which both have huge expansions planned between 2007 and 2018 (see ERCOT CDR Report).

Modeling new (post-2007) EGUs for 2018 requires that their emissions be reduced (about 78%) to comply with CAIR Phase II (and MECT for th ose in HGB). The corresponding reduction in capacity would result in an approximate 12,000 MW deficit to the 2012 demand. Conceptually, these newly-permitted units will operate at the m odeling-restrictive 22% capacity (another artifact of modeling), however, they could operate at a much higher capacity by purchasing credits or providing other offsets. TCEQ maintains that th e remaining capacity deficit will likely be filled with renewable resources, some of which are mandated by State legislative action. Note that of the 18,856 MW of new capacity, a portion of it is generated outside of the ERCOT grid. While
all new EGU’s NO X emissions are reduced for modeling, the MW assessment considers only those units whose generation is in the ERCO T grid, and only the gridded portion of cogen facilities. For areas outside of the ERCOT grid, TCEQ assumes similar growth rationale to meet the expected demand as for areas in ERCOT’s grid (new units and renewable resources).

Pending permits (permit applicati ons under TCEQ review, but not yet granted permits) were not included in this CAIR scenario for several reasons, including:

 These permits are more speculative.

 Adding more units under CAIR does not affect the total modeled EGU emissions, just where the
emissions occur.

 These would have been squeezed under the CAIR Ph ase II 9.5% set-aside, as currently modeled, along with all the other post-2000 EGUs.

See the CAIR discussion below, in Section 2.3.1.2.1. CAIR is technically a control, but it is a serious constraint on the EGU growth in the attainment counties.

The temporal distributions of the newly-permitted EGUs are based on those of existing units of similar equipment type or Source Classificati on Codes (SCCs). For each SCC included in the newly-permitted EGU list, an average temporal distribution was calculated for these SCCs, based on diurnal profiles of existing units within the St ate. These average profiles were then used for
the new EGUs. For some units on the new EG U list, a corresponding SCC did not exist for existing units. In these cases, the default profile (flat) was assigned. An example diurnal profile of a natural gas turbine is provided as Figure 2.3-1: Example EGU Diurnal Profile

.

### B-86 Diurnal Weekday Profile - Natural Gas Turbine EGU

SCC 20100201

0% 1% 2% 3% 4% 5% 6%

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Beginning HourPercent of Daily Em issions

Figure 2.3-1: Example EGU Diurnal Profile

2.3.1.1.2 Non-EGUs (NEGUs) When the Acid Rain units are removed from the point source EI for hourly treatment, the remainder is ozone season daily (OSD). As descri bed previously, the TCEQ is referring to these in the shorthand as non-EGUs (NEGUs). The basi s for future growth for NEGUs in the Texas attainment areas was the 2006 OSD emissions, already an average ozone season day from

STARS.

Emissions from NEGUs in the attainment areas of the state were projected to 2018 using a combination of projection factors. Projection factors derived from the Dallas Federal Reserve Bank’s Texas Industrial Production Index (TIPI) are b ased on an industry’s Standard Industrial Classification (SIC) – the kind of business they are in, e.g., petrochemical manufacturing. TIPI does not cover all of the SICs, so for those not covered, projection factors from EPA’s Economic Growth Analysis System version 5.0 (EGAS5) with a Texas-specific version of the Regional Economic Models, Inc (REMI) update were used. This version of EGAS with Texas-specific
REMI is hereafter referred to as REMI-EGAS. No individual new permits were modeled as growth for NEGUs.

While there is an argument for using either inde x, TIPI provided unexpectedly low emission rates for some point sources. The TCEQ chose to use th e index that provided the most growth for each point source. Again, there is not a TIPI factor available for all sources (only select SICs), but there is a REMI-EGAS factor for each source (every SCC). For the SICs with missing TIPI factors, a value of 1.2 was assigned (the average of the non-missing factors). Examples of TIPI and REMI-EGAS growth factors are provided as Table 2.3-2: Sample TIPI Growth Factors for

Texas and Table 2.3-3: Sample REMI-EGAS Growth Factors for Texas , respectively. Note that a growth factor less than 1.0 indicat es a decline rather than a growth. Also note that the TIPI factor varies only by the two-digit (first two) SIC.

### B-87 Table 2.3-2: Sample TIPI Growth Factors for Texas

SIC Source Growth

Factor
2006-2018 SIC Source Growth

Factor
2006-2018

1311 TIPI 0.80

. . . 1321 TIPI 0.80 3082 TIPI 1.24

1381 TIPI 0.80 3084 TIPI 1.24

1382 TIPI 0.80 3086 TIPI 1.24

1422 TIPI 1.77 3087 TIPI 1.24

1459 TIPI 1.77 3088 TIPI 1.24

2011 TIPI 1.30 3089 TIPI 1.24

2013 TIPI 1.30 3143 default 1.14

2032 TIPI 1.30 3149 default 1.14

2041 TIPI 1.30 3211 TIPI 1.05

2046 TIPI 1.30 3221 TIPI 1.05

2051 TIPI 1.30 3229 TIPI 1.05

2061 TIPI 1.30 3231 TIPI 1.05

2074 TIPI 1.30 3241 TIPI 1.05

2077 TIPI 1.30 3251 TIPI 1.05

2082 TIPI 1.30 3253 TIPI 1.05

2095 TIPI 1.30 3261 TIPI 1.05

2096 TIPI 1.30 3272 TIPI 1.05

2099 TIPI 1.30

. . .

...

Table 2.3-3: Sample REMI-EGAS Growth Factors for Texas

Growth Factors SCC fips 2006 2018 NOTES

10100101 48 1.04091 1.01559 fips = 48, best fit is entire state (48xxx)

10100102 48 1.04091 1.01559

10100201 48 1.04091 1.01559

10100202 48 1.04091 1.01559

10100203 48 1.04091 1.01559

10100204 48 1.04091 1.01559

10100205 48 1.04091 1.01559

... 10100604 48 0.78445 1.17224 Growth 2006 to 2018 = 1.1722/0.7845=1.4943

10100701 48 0.92453 1.09418

10100702 48 0.92453 1.09418

10100703 48 0.92453 1.09418

10100704 48 0.92453 1.09418

10100707 48 0.92453 1.09418

10100711 48 1.33333 1.40741

10100712 48 0.92453 1.09418

10100801 48 1.31050 1.40910

...

### B-88 Projection factors were assigned individually to each NEGU path based on its SIC-SCC combination, with the larger of either its SIC- derived factor (TIPI) or its SCC-derived factor (REMI-EGAS) applied. The same factor was applied to all three modeled pollutants. A summary of the EGU and NEGU growth in the Texas attainment areas is provided as Table 2.3-4: Summary of Texas Attainment Area NO X Growth Projections to 2018

.

Table 2.3-4: Summary of Texas Attainment Area Growth Projections to 2018

Base 2018 Growth Projection

EI
Source # points NO X (tpd) GROWTH METHOD # points NO X (tpd) %

G rowth

NEGUs 2006 STARS 8914 519 Apply TIPI or REMI-EGAS growth factor to existing point
sources that don’t have existing rule limitations 8914 608 17.1

EGUs 2007
ARD 221 379 Addition of newly-permitted point sources 293 398 5.1

As explained in the controls section below, in reality, the TCEQ took this EGU growth (newlypermitted) from the CAIR cap (along with the 2001- 07 new units), which affects the entire State. The TCEQ accounts for EGU growth by adding ne w facilities at locations where the facilities are permitted, not by growing emissions from ex isting facilities. The net reduction in EGU NO X emissions is a testament to Best Available C ontrol Technology (BACT) as implemented by the New Source Review (NSR) permitting program of the TCEQ. CAIR Phase II limitations may
further reduce NO X emissions.

Again, only a subset of the Texas attainment area sources received growth from the procedures above. Any source that had an existing rule app lied to it was capped at that rule level for the future, so only the “no rule” sources had growth factors applied to them. See the next subsection
for the discussion of controls.

2.3.1.2 Texas Attainment Areas 2018 Existing Controls Existing on-the-books controls are incorporated in to the 2018 future base. For the EGUs of the State, the control modeled was the EPA’s Clean Air Interstate Rule (CAIR) program. For the NEGUs in the attainment areas of the State, several existing controls were modeled.

2.3.1.2.1 EGUs During the process of applying CAIR in Texas to simulate 2018, the TCEQ learned that CAIR was vacated by the Court (July 2008). The TCEQ then went down the pathof developing a “nonCAIR” version of the 2018 EI for the EGUs in Texas and outside of Texas, after numerous
conversations with the EPA and other RPOs. As modeling staff completed the non-CAIR 2018 EI, the Court remanded CAIR back to the EPA (late December 2008). The EPA provided guidance that CAIR would still be in effect, as promulgated, until future revision or replacement. For more information on the ruling, see the EPA’s CAIR webpage, http://www.epa.gov/cair/ , or the TCEQ CAIR/CAMR webpage, http://www.tceq.state.tx.us/implementation/air/sip/caircamr.html . Hence, CAIR Phase II was applied for 2018. The following paragraphs descr ibe this federal program and how the TCEQ is dealing with CAIR.

Under CAIR, 28 eastern states (plus the District of Columbia) are required to comply with a cap on sulfur dioxide (SO 2 ) and NO X EGU emissions, where the definition of an “EGU” for the CAIR program is approximately the same definiti on as that for a Federal Clean Air Act Title IV Acid Rain unit (i.e., larger than 25 MW and more than one-third of its generation going to the public grid for sale). CAIR is a cap-and-trade pr ogram, with each of the CAIR-applicable states given a calculated NO X budget and a calculated SO 2 budget by the EPA. The EPA modeled all

### B-89 of these states in order to test the effectiveness of controls. A result of the EPA’s CAIR modeling was that Texas “significantly contributed” to th e nonattainment of the particulate matter of 2.5 microns and less (PM 2.5 ) standard of two counties in Illinois; therefore, Texas was included in CAIR for the transport of PM 2.5 . Texas was not covered under the CAIR program for eight-hour ozone contribution. Texas is not part of the ozone season (May through September) NO X budget part of CAIR, only the annual NO X budget program. For more details on these CAIR issues, see EPA’s CAIR Texas webpage, http://www.epa.gov/cair/tx.html . Arkansas and Louisiana have

ozone season NO X caps, and Oklahoma is not a CAIR state.



### CAIR is implemented in two phases: for NO X 

### , Phase I covers the years 2009-2014 and Phase II is for the years 2015 and later; for SO 2 

### , Phase I covers the years 2010-2014 and Phase II is for the years 2015 and later. The Phase I NO X 

### budget calculated and assigned to Texas was 181,014 tons per year, and the Phase II NO X 

### budget was 150,845. The Phase II
annual NO X 

### budgets for all states was originally assigned to be five-sixths of the Phase I

NO X 

### budget. Because 2018 is the HGB ozone attainment year, this SIP revision incorporates CAIR Phase II (post 2014 step-down of CAIR) which provides for a Texas state-wide NO X budget of 150,845 tons per year, or 413.3 tons per day. Even though Phase II begins in 2015, allocations have to be provided to the EPA to populate the accounts in 2011. The CAIR allocations (and past transactions) for all re levant states can be found from the CAMD Allowances data query wizard w ebsite,

ttp://camddataandmaps.epa.gov/gdm/in dex.cf m?fuseaction=allowances.wizard h

.

s of actual emissions) used for the allocation of CAIR NO X allowances om the general pool.

ol cap; EGUs would use more of the cap during ozone season and less in the

on-ozone season. For im plementing EPA’s CAIR pr ogram in Texas, the State Legislature mandated that TCEQ allocate 90.5% of the CAIR budget to the EGUs in the State that were operating on or before January 1, 2001 (call these “existing” units in th e “general pool”) for control periods 2009-2014. Those EGUs that reported emissions in 2000 and earlier were given, in aggregate, 90.5% of
Texas’ allocation of the CAIR NO X budget for control periods 2009-2014. Each “existing” EGU was apportioned its allowance based on fuel type and historical (2000-2004) heat input. The remaining 9.5% of the State CAIR budget cap was set aside for “new” (post-2000) EGUs, in the “new unit pool”. New units are eligible to rece ive allowances from the general pool starting with the 2015 control period if the new units have an appropriate baseline, as determined under 30 TAC §101.506, CAIR NO X Allowance Allocations. The following paragraphs discuss the modeling of the “existing” EGUs and the “new” EGUs separately. Section 2.3.1.1.1, above,discussed the newly-permitted units that were added on top of these. Ultimately the newlypermitted EGUs obtain allowances from the “new un it pool” until these units have the appropriate baseline (i.e., five year

fr

For existing Acid Rain EGUs, the TCEQ applied the assigned allowance cap for each unit in the “general pool” to the hourly average day 3Q2007 data file, scaling HGB emissions by 120% and the rest of the State by 112%. This scaling is required, because the CAIR cap is an annual limit, yet summer (3 rd quarter) Acid Rain NO X emissions that are modeled in the 3Q2007 for HGB and the remainder of Texas are 20% and 12% higher than the annual average, respectively. In other words, TCEQ expects that EGUs will continue to emit these higher percentages of their total
annual NO X emissions during the summer (peak electric ity demand) of 2018, which happens to be the time of the year that we are modeling (ozone season) and when we have the vast majority of monitored ozone exceedences; hence, we expect the EGUs to use these higher percentages of the cap during these times. Note that scaling th e emissions affects neither the general pool cap nor the new unit po

n

Under CAIR, when an EGU shuts down or curtails its use, the allowances for that unit can be
banked for later use or sold to an EGU that n eeds more allowances (e.g., increased its load or capacity). If an existing EGU site wants to add a ne w unit to that site, it will have to operate that

### B-90 in the general ool. Once allowances are allocated, trading can occur between any CAIR EGUs.

er Texas ill need to purchase emission credits from the federal CAIR annual NO bank in 2018.

08. Hence, e TCEQ could o nly model an estimate of the CAIR program and the new unit pool.

eneral pool). The 9.5% new pool is not allocated specifically, and not included in Table 2.3-5. new unit within the new unit pool. To obtain allowances from the new unit pool (9.5% of total
Texas allocat ion), a site that commenced operation after January 1, 2001 must complete and submit (by May 1 of each year) the appropriate TC EQ application form each year until the new unit starts to receive its allowances from the general pool. It receives allowances from the general pool once it has established a “baseline”, determined from the first five years of operation. The program’s intent is for a unit to move seamlessly from the new unit pool to the general pool. If the requested amount of allowa nces from the new unit pool in any one year exceeds the pool allocation (9.5%, or 14,330 t ons per year for 2015 and beyond), then the allowances are proportioned according to each unit’s requested amount. Hence, the total new unit pool is always used completely. If the requested amount for any one year is less than the pool allocation, then those leftover allowances are dist ributed proportionally to the units

p

Under CAIR, EGUs have the option to hold, transfer or sell allo wances, but at the end of each year’s reconciliation period, each EGU must have enough allowances in its compliance account to cover emissions during the previous control period (previous calendar year). Hence, in reality,
Texas can trade NO X emissions with any other CAIR state, since CAIR is a federal trading program. Yet, the TCEQ assumed that Texas will not be trading with other states, because we can not predict whether Texas will have more emi ssions credits than it needs, or wheth

w X

For modeling purposes, the 9.5% set-aside cap fo r the “new” EGUs was applied to all post-2000 point sources that began operation in the 2001-2007 time period (as evidenced by their Acid Rain emissions) and to post-2007 EGUs that have been permitted by TCEQ (see the “newly-permitted” units of Section 2.3.1.1.1). The TCEQ distributed the new unit pool to the 2001-2007 startups at their average 3Q2007 Acid Rain emission rates divided by 1.12 for attainment county EGUs (ozone season scaling factor as described above). In actuality, according to the TCEQ CAIR rules, the first allocation of new unit allowa nces must be requested by July 2009, at approximately the same time the TCEQ had to finalize the modeling for these sources, and the allowances will likely equal the units’ NO X tons reported to the EP A’s CAMD for 20
th

A small amount of new unit pool CAIR allo wance remained after the 2001-2007 new unit distribution, and the TCEQ spread the remaini ng allowance among all the newly-permitted (post2007) EGU permits according (proportional) to the modeling permitted rate, compressing their permitted NO X rates to approximately one-quarter in orde r to fit within the new unit pool. Again, in actuality, the TCEQ CAIR SIP revision provid es conversion timeline tables that denote that units commencing operation between January 1, 2001 and December 31, 2008 will all be in the general pool in the year 2018, assuming all units submit the required data. Yet, even newer units will be allocated new unit pool allowances by th en. So, for 2018 modeling, it does not matter which pool the emissions will be in, and the TCEQ could only estimate this complex program. This modeling of the CAIR Phase II cap is made even more complicated by the MECT program in HGB; this will be discussed in a later subsecti on. A list of the CAIR Phase II allocations by site, in descending order of allocated tons of NO X , is provided as Table 2.3-5: Texas NO X CAIR
Allocations . These are the only units for which the TCEQ has provided allowances (the 90.5%

g

### B-91 Table 2.3-5: Texas NO X CAIR Phase II Allocations

TCEQ RN# Site Name ORISPL #Units NO X CAIR Cap (tpy)

RN100888312 WA Parish 3470 8 11648

RN102583093 MARTIN LAKE 6146 3 10475

RN102285921 MONTICELLO 6147 3 8722

RN100213370 WELSH POWER PLANT 6139 3 7104

RN100226844 SAM SEYMOUR 6179 3 7000

RN100542927 LIMESTONE 298 2 6699

RN101198059 BIG BROWN 3497 2 5568

RN100224849 HARRINGTON STATION 6193 3 5453

RN100224534 TOLK STATION 6194 2 4909

RN100217975 JT DEELY 6181 2 3897

RN102147881 SANDOW 6648 1 2882

RN100214287 PIRKEY 7902 1 2870

RN100217975 JK SPRUCE 7097 1 2753

RN101062255 OKLAUNION 127 1 2694

RN100226919 COLETO CREEK 6178 1 2577

RN102513041 Sabine Plant 3459 5 2563

RN100226539 SAN MIGUEL 6183 1 2006

RN100825371 CEDAR BAYOU 3460 3 1897

RN101062826 PH ROBINSON 3466 4 1872

RN100214550 GIBBONS CREEK 6136 1 1843

RN100226570 TWIN OAKS POWER (TNP 7030 2 1456

RN100217033 SWEENY COGENERATION 55015 4 1422

RN102566494 TRADINGHOUSE 3506 2 1370

RN100210863 COGEN LYONDELL 50815 6 1153

RN102336906 HANDLEY 3491 6 1092

RN100664812 DeCordova 8063 5 1083

RN100218882 Lamar Power Project 55097 4 1028

RN100222041 Pasadena Cogeneratio 55047 3 1003

RN102547957 GREGORY POWER FAC 55086 2 984

RN100245539 Tenaska Frontier Gen 55062 3 966

RN100642040 BARNEY M DAVIS 4939 2 922

RN100224245 Texas City Cogenerat 52088 3 917

RN100217835 VH BRAUNIG 3612 5 892

RN100233998 Bayou Cogeneration P 10298 4 881

RN102183969 Permian Basin 3494 7 861

RN100226877 LEWIS CREEK SES 3457 2 849

RN100239672 Clear Lake Cogenerat 10741 3 840

RN102285855 VALLEY 3508 3 796

RN100224765 JONES STATION 3482 2 779

RN102596400 MIDLOTHIAN ENERGY 55091 4 777

RN102285848 Morgan Creek 3492 10 757

RN100673490 LAKE HUBBARD 3452 2 728

RN100211309 NEWMAN STATION 3456 5 702

RN101286433 Mustang Station 55065 2 697

RN100225820 GUADALUPE POWER PART 55153 3 696

RN100542885 TH Wharton 3469 14 693

RN100217298 Black Hawk Station 55064 2 685

RN100542596 WILKES 3478 3 674

RN100219872 Decker Creek 3548 6 670

RN101621449 Stryker Creek 3504 2 596

RN101559235 MOUNTAIN CREEK 3453 6 583

### B-92 TCEQ RN# Site Name ORISPL #Units NO X CAIR Cap (tpy)

RN100217975 OW SOMMERS 3611 2 548

RN100552181 NUECES BAY 3441 3 544

RN102038486 SIM GIDEON 3601 3 527

RN101559854 NORTH LAKE 3454 3 520

RN100542901 SAN JACINTO SES 7325 2 516

RN102563426 GRAHAM 3490 2 514

RN100224989 Hidalgo Energy Cente 55545 2 481

RN100825389 Sam Bertron 3468 4 438

RN102033891 RW Miller 3628 5 433

RN100224419 PLANT X 3485 4 414

RN100216555 Tenaska III TX Partn 50109 2 407

RN100223312 Ponderosa Pine Energ 54817 1 407

RN100215557 FORT PHANTOM 4938 2 393

RN100215896 CR Wing Cogeneration 52176 2 392

RN100224641 NICHOLS STATION 3484 3 363

RN100215979 LON C HILL 3440 4 358

RN100219468 TC FERGUSON 4937 1 355

RN100693308 EAGLE MOUNTAIN 3489 3 343

RN100219203 RAY OLINGER PLANT - 3576 3 341

RN102344645 Frontera Generation 55098 2 341

RN102156916 KNOX LEE 3476 4 337

RN100223395 SWEETWATER 50615 3 326

RN100216837 AES Deepwater Incorp 10670 1 278

RN100220045 HOLLY STREET 3549 4 274

RN100209766 Sabine Cogeneration 55104 2 258

RN100213909 Laredo Power Station 3439 3 250

RN100542851 Greens Bayou 3464 7 238

RN100214980 VICTORIA 3443 3 219

RN102565843 JL BATES 3438 2 216

RN101698520 Lake Creek 3502 2 211

RN101531226 SAN_ANGELO 3527 1 209

RN102688835 Rio Pecos 3526 2 150

RN100226638 ES JOSLIN 3436 1 146

RN100804301 PARKDALE 3455 3 145

RN100542992 Webster 3471 1 127

RN100542885 TH WHARTON 3469 1 124

RN101943868 Trinidad 3507 1 120

RN100215300 PAINT CREEK 3524 4 118

RN102166576 ROLAND C DANSBY POWE 6243 1 109

RN100226232 BOSQUE COUNTY PEAKIN 55172 2 103

RN102688439 Oak Creek Power Stat 3523 1 85

RN102545142 HOLLY AVE PLANT 3602 2 75

RN100214766 Spencer Station 4266 2 72

RN100784735 COLLIN 3500 1 72

RN100217611 WB TUTTLE 3613 4 65

RN100217678 J Robert Massengale 3604 1 59

RN100542877 DEEPWATER 3461 1 48

RN100215490 Copper 9 1 36

RN100219450 Silas Ray Power Plan 3559 2 30

RN102764933 NORTH MAIN 3493 1 29

RN101983344 RIVER CREST 3503 1 24

RN101612851 Bryan 3561 1 20

### B-93 TCEQ RN# Site Name ORISPL #Units NO X CAIR Cap (tpy)

RN100217439 LEON CREEK 3609 2 17

RN100224955 MOORE COUNTY 3483 1 16

RN100223023 POWERLANE UNITS 2&3 4195 2 13

RN102560687 La Palma 3442 1 13

RN100217173 MISSION ROAD 3610 1 9

RN100542620 LONE_STAR 3477 1 9

RN102528510 CE NEWMAN 5 3574 1 7

RN100226554 NEWGULF POWER 50137 1 5

RN100216993 NORTH TX STATION 3627 1 3

SHUTDOWN SITES 29 1941

“ SHUTDOWN SITES ” are facilities that received an original CAIR allocation from the general pool, but have since officially shutdown. Those allocations are available for distribution among the other sites in the general pool. Additionally, other facilities that are no longer operating (e.g., have zero Acid Rain emissions), similarly, had their allocations re-distributed. Table 2.3-5, above, does not represent the emissions modele d for each of these sites, because it does not include the ozone season scaling factor for HGB or the State, nor does it include the redistribution from the shutdown units. A summary of the distribution of the general and new CAIR pools to areas of Texas is provided as Table 2.3-6: Distribution of NO X CAIR Allowances in Texas

.

Table 2.3-6: Distribution of NO X CAIR Allowances in Texas Modeled Allowances (tyd) Area Base Existing 2001-2007 post2007

HGB Acid Rain (hourly) 25.0 STARS OSD (daily) 4.9 1.3

BPA Acid Rain (hourly) 10.0 1.3 STARS OSD (daily) 0.0 0.3 0.2

DFW Acid Rain (hourly) 17.4 4.2 STARS OSD (daily) 0.0 0.0 0.0

Attain Acid Rain (hourly) 361.0 17.5 STARS OSD (daily) 0.0 0.0 19.2

Note that the EGU portion of HGB MECT program also counts against the CAIR budget. MECT is more restrictive than CAIR (lower controlled allowable emission rate, ignoring trading), so the
difference between CAIR and MECT was also mo deled as a redistribution addition to CAIR
EGUs. Also note that the NO X emissions in the table do not total to the CAIR allowance, because the cap is an annual limit, and emissions are higher during ozone season. Thus, a point source should use more of its cap during the summer than in other times of the year. In the HGB region, for example, acid rain NO X emissions are 20% higher than the annual av erage.

2.3.1.2.2 NEGUs Several existing control programs are still expec ting reductions between th e baseline and 2018 in
the attainment areas of the State. These incl ude: the East Texas Combustion Rule, Agreed Orders, and the Refinery Initiative.

East Texas Combustion Rule The East Texas Combustion rule was applied to rich-burn stationary gas-fired point source and area source engines in 33 identified attainment counties of East Texas. This Chapter 117 NO X rule was shown to benefit DFW in a recent SIP re vision. Almost three-qua rters of the reductions came from point sources. Its full compliance date is not until March 2009; therefore it qualifies as an existing rule with a future (post-baseline) compliance date. If a rule has a complete (all

### B-94 applicable sources) compliance date of 2006 or ear lier, then compliance with the rule is assumed to have been incorporated in the baseline. To model this rule for this HGB SIP revision, the TCEQ assumed the rule was essentially a cap on the applicable sources and modeled those point
sources at the 2009 rule emissions level.

Agreed Orders Federally-enforceable emissions re ductions due to agreed orders and/or consent decrees were incorporated into the 2018 point source EI. Th e included the Eastman and ALCOA agreed orders as were modeled in previous SIP revisions.

Refinery Initiative Since the late 1990s, petroleum refineries have been the focus of an EPA enforcement initiative. This initiative alleges that, in general, petroleum re fineries violated and/or continue to violate one or more of the following regulatory Clean Air Act provisions, or parts thereof:

 Prevention of Significant Deterioration (PSD) requirements, 40 CFR 51-52

 New Source Performance Standards (NSPS) requirements, 40 CFR 60

 Leak Detection and Repair (LDAR) requirements, 40 CFR 60,61,63

 National Emission Standards fo r Hazardous Air Pollutants (N ESHAP) for Benzene Waste Operation, 40 CFR 61, Subpart FF

In the interest of settling these allegations, w ithout admitting to the alleged violations, many petroleum refiners have entered into consent decrees with EPA. Since March 2000, the EPA has entered into settlements with petroleum refiners that, collectively, represent nearly 80 percent of U.S. petroleum refining capacity. According to the EPA, these settlements, covering 86 refineries in 25 states, will result in a reduction of approxi mately 80,000 tons per y ear of nitrogen oxides (NO X ) and 235,000 tons per year of sulfur dioxide (SO 2 ) upon full implementation. See EPA’s webpage at http://www.epa.gov/compliance/resour ces/cases/ civil/caa/oil/index.html for this and
more details.

The TCEQ has not modeled any reductions for these consent decrees, because most of the agreements do not require NO X reductions, and most of them lack enforceable requirements of quantified reductions. The TCEQ can verify th at several refineries (some in HGB and BPA NAAs) have modified permits to comply with their consent decrees, but permit reductions do not always result in actual reported emission reductions.

2.3.2 Nonattainment Areas (NAAs) of Texas The section above discussed the general growth and controls for the attainment counties of the State; this section describes the specific growth and controls applied to the three ozone NAAs: HGB, DFW, and BPA. There is much commonality between the three NAAs in the way that they were modeled; much of this section discusses the procedures used in modeling all three NAAs, with emphasis on HGB.

2.3.2.1 HGB The area of the modeling domain demanding the highest level of detail is the HGB NAA. It also happens to be the most complex of the areas m odeled, due to the number of TCEQ programs that apply to HGB. As with all the other parts of th e 2018 EI, we describe the growth projections to 2018 and any existing controls that will affect the area between the baseline and 2018. DFW and
BPA NAAs are discussed in subsections th at follow this HGB modeling description.

2.3.2.1.1 HGB 2018 Growth Projections Growth projections were applied to the baseline EI in HGB to obtain a 2018 grown EI. Different techniques were applied to the EGUs and the NE GUs. Some of these techniques are very similar to those applied to the attainment areas of the State as described in the previous section.

### B-95 EGU Projections As with the attainment areas of the State, the fu ture base for EGUs in the NAAs of the State is the typical summer Acid Rain day having hourly emissi ons that are the average of all days in the third quarter of 2007 (3Q2007). As described fo r the attainment areas, not all EGUs are Acid
Rain units. The non-Acid Rain EGUs were modeled at their 2006 ozone season daily (OSD) emissions, as if they were NEGUs.

The complete set of 2018 EGUs consists of the 3Q2007 Acid Rain EGUs, the 2006 non-Acid Rain EGUs, and post-2007 EGUs that have approved TCEQ perm its. As with previous SIPrevisions, TCEQ assumes that the EGU growth in the State comes from the TCEQ newlypermitted EGUs. This is a very similar assump tion that the Integrated Planning Model (IPM) would make, as the EPA has used in all of its recent regional modeling studies, e.g., CAIR. In Texas, the TCEQ knows where the growth is likely to occur, via permits.

Newly-Permitted EGUs in Nonattainment Areas (NAAs) As with the attainment areas, discussed above, so me of the newly-permitted EGUs are planned tooperate in the NAAs of HGB, DFW, and BPA. Table 2.3-1, above, lists all of the newlypermitted EGUs in each of the areas of the State. In fact there are thirteen newly-permitted units in HGB, two in BPA, and no units in DFW. In these areas, CAIR limitations are superceded by more stringent nonattainment NO X rules (TCEQ Chapter 117), although all EGU emissions must fit under the CAIR cap and absorb a share of the CAIR cap. Technically, NAAs cannot grow in ozone precursors (NO X and VOC) from major source modifications or new major sources. These emissions would need to be offset during th e permitting process, either by contemporaneous creditable reductions already performed at that site, or by purchasing emissions credits from the bank. If emissions were purchased from the bank, such were included in the permit application and already removed from the bank by th e time the TCEQ modeled the bank. If contemporaneous reductions were taken, then the TCEQ assume these were already accounted for in the 2006 EI or 3Q2007 Acid Rain. If the reductions were not already accounted for in a reported EI, then a worst case is being modeled by keeping those emissions in the EI and adding emissions from newly-permitted EGUs.

Specifically, in HGB, adding newly-permitted EGUs is more complex, since all EGUs (and almost all combustion sources) must comply with the HGB Chapter 117 Mass Emission Cap and Trade (MECT) program for NO X . For each new MECT-applicable source added to HGB, the
available MECT NO X cap gets smaller, to the point that the TCEQ squeezed new MECT sources under the cap and reallocated. In reality, the MECT cap is not reallocated for every new source, but rather as some sources shutdown or reduce, th eir part of the MECT cap is made available for use; yet, there is no easy way to predict or model how that occurs. Instead, the TCEQ has assumed it is more important to place the new sources at the correct location with approximately the correct emissions, rather than not include them in the model at all (and assume that the cap is the cap as it existed years ago, looking back from 2018). Yet, to maintain the integrity of the cap, and model the MECT cap, the TCEQ reallocated the cap slightly to make room for the new

sources.

The newly-permitted post-2007 EGUs in HGB are all MECT sources, as well as CAIR sources, and emissions from these points are reconciled with the MECT. MECT is technically a control (that will be addressed in more detail in the controls subsection), but it is a serious constraint on the growth in HGB. The HGB modeled NO X emissions of Table 2.3-1 reflect MECT, absorbing part of that cap, while also abso rbing their share of the CAIR cap.

### B-96 NEGU Projections The basis for future growth for NEGUs in th e Texas NAAs was the 2006 OSD emissions, already an average ozone season day – the 2006 baseline. No individual new permits were modeled as growth for NEGUs, except in BPA (see Section 2.3.2.2.1). Emissions from NEGUs in the NAAs of the state were projected to 2018 using the higher of the TIPI-REMI-EGAS factors (as described in the previous section) or the emi ssion credits in the bank (described in the following paragraphs), except for HGB NO X , which is tightly regulated under the MECT program.

Again, technically, ozone nonattainment counti es can not grow (expand facilities) in major sources of emissions of ozone precursors, NO X and VOC, except by purchasing from the bank to obtain emissions offsets during New Source Review of permit applications or by sitewide contemporaneous period reductions. The TCEQ has assumed that all of the emissions in the bank can come back into the airshed. As in previ ous TCEQ SIP modeling, the TCEQ has assumed the worst case -- that all the emissions in the TCEQ Emissions Banking and Trading (EBT) Registry can be emitted into the airshed during the ozone season of 2018, without expiration of any of the credits in the bank. The exception to this is HGB NO X

.

The procedure for incorporating the banked emissi ons in the future base is as follows. Banked emissions were extracted from the TCEQ’s EBT Emission Reduction Credit (ERC) and Discrete Emission Reduction Credit (DERC) Registries, which can be found on EBT’s Registry webpage: http://www5.tceq.state.tx.us/airperm/inde x.cf m?fuseaction=registry.registry

. ERC and DERC totals for each of the NAAs, as of October 2009, were extracted, tabulated, and summarized in a spreadsheet. According to 30 TAC §101.378, DERCs generated from shutdown strategies prior to September 30, 2002 are no longe r available after September 8, 2010. Hence, these retiring DERCs were removed from the HGB modelable total for 2018. Table 2.3-7: Banked Emissions as of October 2009 summarizes these results.

Table 2.3-7: Banked Emissions as of October 2009

NAA ERCs DERCs Total Bank Modelable

NO X (tpy) VOC (tpy) NO X (tons) VOC (tons) CO (tons) NO X (tpd) VOC (tpd) CO (tpd)
HGB Registry 41.5 1044.9 48765.1 1050.0 1488.4

HGB Modelable 36.1 908.6 1000.0 945.0 1339.6 9.6 5.1 3.7

BPA Registry 1179.1 134.0 2926.4 0 2591.8

BPA Modelable 1071.9 121.8 2633.8 0.0 2332.6 10.2 0.3 6.4

DFW Registry 616.1 203.9 9084.7 5.0 0

DFW Modelable 535.7 177.3 8176.2 4.5 0.0 23.9 0.5 0.0

“Modelable” emissions in Table 2.3-7 refers to the maximum amount of emissions that could be added as growth to the area from banked emissions (with the exception of HGB as noted below).

Chapter 101 of 30 TAC requires that an ERC must be surplus to any federal, state or local rule. Also, the Chapter 101 MECT program DERC-use restrictions for HGB were incorporated in the

NO X total. The recent DFW DERC-use restrictions were not modeled, as we have no idea what will be necessary in 2018, as that rule dealt with 2009, so the full DERC registry was modeled for
DFW as worst case. The totals indicated in Ta ble 2.3-7 incorporate the originally-designated eight-hour ozone offset ratios (for worst case) for each of the NAAs, where an offset ratio of 1.15:1 for moderate areas indicates that the purchas er of ERCs must buy 11.5 ton per year of credits for each 10 tons per year of increase pr oposed. Additionally, the modeled bank took into account the requirement of Chapter 101 to retire 10 percent of the DERCs, as an environmental contribution, upon DERC usage.

### B-97 The HGB MECT program limits the amount of NO X from all applicable sources with a cap. Yet, in HGB, DERCs can be used to comply with ME CT during the control period, up to a maximum of 10,000 but at a 10:1 usage ratio -- hence the 1000 tons of modelable HGB DERCs listed in Table 2.3-7. This provides for some growth in the MECT Cap, up to 2.74 tpd NO X (1000/365). This growth in MECT was attributed back to all MECT sources based on their portion of the overall cap.

There are many MECT-exempt sources, sources of NO X within HGB that are specifically exempt from the MECT program. These were separated into an AFS file processing stream of their own. These MECT-exempt sources were allowed to grow as part of the “no rules” sources, since their

NO X is not capped. Minor modifications can s till occur to these MECT-exempt sources, and permits by rule (PBRs) may still allow new source s to come online within the NAAs. By adding
the banked emissions back into the airshed a nd assuming the banked emissions can reappear in the airshed during the attainment year, this pr ocedure is very likely overpredicting emissions growth. For HGB NO X , the TCEQ has assumed that this bank (both ERCs and DERCs) growth will be limited by the amount of TIPI or REM I-EGAS growth predicted by the larger of the indexes. Table 2.3-7, above, represents this ove rall growth in HGB. Hence, the 9.6 tpd is the
sum of the ERCs and MECT-convertible DERCs di vided by 365, plus the remainder of DERCs limited by the predicted TIPI/REMI-EGAS growth.

For the other HGB pollutants and the rest of th e NAAs, the TCEQ modeled even more margin of safety, erring on the side of more growth is bette r than not enough (an approach EPA prefers), by including as growth the greater of the banked emissions or the combined TIPI/REMI-EGAS factors for each path for each of the NAAs , excluding MECT sources, where TIPI/REMI-EGAS is the larger of the TIPI or REMI-EGAS growth factors, as described in the previous section.
Table 2.3-8: Texas NEGU Growth Summary shows the growth projected in each of the NAAs from each of the indexes. Again, the growth was only applied to the sources that were not already limited by existing TCEQ SIP rules.

Table 2.3-8: Texas NEGU “No-Rules” Growth Summary

NO X (tpd) VOC (tpd)

Area Projection

Index 2006
STARS 2018 2006
STARS 2018

HGB from EGAS 3.1 3.7 22.2 27.8

from TIPI 15.7 21.8 135.0 166.4

BPA from EGAS 7.9 9.1 3.0 3.7

from TIPI 31.5 45.3 73.0 102.7

DFW from EGAS 8.3 9.9 24.5 30.8

from TIPI 2.6 3.2 11.0 15.5

Attain from EGAS 315.8 357.4 94.5 112.0

from TIPI 193.8 248.5 134.4 182.2

Again, the comparison between the bank and the TIPI/REMI-EGAS was performed on a path-bypath basis, and was automated with SAS program ming. The bank growth in each NAA was the bank divided by the total emissions in the NAA. A path’s share of the bank was based on its fractional emissions of the total, and was added to that path. Again, only the “no rule” paths were allowed to grow via the bank or TIPI/REMI-EGAS. A few examples of the results of this process for HGB are provided in Table 2.3-9: Sample Growth Applied to HGB Paths

.Table 2.3-9: Sample Growth Applied to HGB Paths

NO X Emissions (tpd)

2006 to 2018 Growth Projection Owner/Site

Name FIN EPN FIN
Profile 2006

TIPI EGAS BANK 2018

TEXAS PETROLEUM
INVESTMENT
ANAHUAC
COMPRESSOR
STATION RB1 RB1 BOILER 0.0023 0.0002 0.0002 0.0002 0.0002

TEXAS PETROLEUM
INVESTMENT
ANAHUAC
COMPRESSOR
STATION RB2 RB2 BOILER 0.0023 0.0002 0.0002 0.0002 0.0002

TEXAS EASTERN
TRANSMISSION

MT. BELVIEU
COMPRESSOR ST UNIT-6 UNIT-6 I.C.
ENGINE 0.0004 0.0004 0.0004 0.0004 0.0004

TEXAS
INSTRUMENTS INC
TEXAS
INSTRUMENTS
STAFFORD THERM
OX 428 INCINER
ATOR 0.0490 0.0783 0.0782 0.0490 0.0783

TEXAS
INSTRUMENTS INC
TEXAS
INSTRUMENTS
STAFFORD BOIL4 129 BOILER 0.0020 0.0032 0.0021 0.0020 0.0032

TEXAS
INSTRUMENTS INC
TEXAS
INSTRUMENTS
STAFFORD HFTREA
T 419 HEATER 0.0001 0.0001 0.0001 0.0001 0.0001

TEXAS
INSTRUMENTS INC

TEXAS
INSTRUMENTS
STAFFORD DFP 448 I.C.
ENGINE 0.0003 0.0005 0.0005 0.0003 0.0005

TEXAS
INSTRUMENTS INC
TEXAS
INSTRUMENTS
STAFFORD B1GEN 451 I.C.
ENGINE 0.0015 0.0024 0.0016 0.0015 0.0024

TEXAS
INSTRUMENTS INC
TEXAS
INSTRUMENTS
STAFFORD CES
GEN 453 I.C.
ENGINE 0.0005 0.0008 0.0005 0.0005 0.0008

TEXAS
INSTRUMENTS INC
TEXAS
INSTRUMENTS
STAFFORD B2GEN 226 I.C.
ENGINE 0.0009 0.0014 0.0010 0.0009 0.0014

TEXAS
INSTRUMENTS INC
TEXAS
INSTRUMENTS
STAFFORD BOIL8 332 BOILER 0.0003 0.0004 0.0003 0.0003 0.0004



### B-98

### B-99 Although Table 2.3-9 provides a NO X example for selected paths in HGB, the same procedure applies to other paths in HGB, paths in BPA a nd DFW, and to VOC and CO emissions in the
three nonattainment areas. In attainment areas (outside HGB, DFW and BPA), growth was based on the larger of TIPI or REMI-EGAS, as described in the subsection above. Results of this procedure include:

 DFW NO X growth came almost exclusively (all but one path) from the bank, as alluded to earlier, due to the many accumulated DERCs.

 DFW VOC growth was split between TIPI and the bank.

 BPA and HGB growth was split between TIPI and the bank.

 MECT NO X was capped (not grown, except as noted above).

 VOC and CO for MECT paths was allowed to grow.

 HECT HRVOC was capped (not grown).

 MECT-exempt paths were allowed to grow. (Exe mpt paths are point sources in MECT accounts, but are not one of the points limited by the cap.)

The HRVOC Reconciliation emissions (via the PSCF technique) were not grown to 2018. See
the 2018 Reconciled HRVOC subsection below.

The Tank Landing Loss emissions in HGB are only expected to become less with time and were not grown to 2018.

Again, any source that had an existing rule app lied to it was capped at that rule level for the future, so only the “no rule” sources had growth factors applied to them.

2.3.2.1.2 HGB Existing (Future Base) Controls for 2018 The existing NO X rules for HGB, most notably the Ch apter 117 MECT program, has been
discussed in some context in the above sections , and will be discussed a bit more here. The primary existing VOC program is the HRVOC Em ission Cap and Trade (HECT) program, where HRVOC is defined in Ch. 115 to be the Highl y Reactive VOC compounds: ethylene, propylene, 1,3-butadiene, and the butenes. As in the a bove sections, these control programs are on-thebooks rules for which the final compliance dates ar e after the baseline year and prior to 2018. Again, these will be discussed in terms of their effects on EGUs and NEGUs.

EGUs As discussed above, CAIR applies to all the EGUs in the entire state, but in HGB, the Mass Emission Cap and Trade (MECT) program also applies to EGUs. MECT is more restrictive than CAIR, since MECT applies to almost all of the NO X sources at an EGU account, and MECT
defines a lower NO X standard that must be met. MECT NO X annual allowances are provided on an emissions unit (FIN) basis. Essentially, every NO X -emitting FIN at an EGU account is covered under the MECT program, except for maybe the smallest of engines.

Trades can also be conducted under MECT, when a NO X -emitting unit subject to the program
emits more NO X for the year than it had allowances for that year, and when another MECTapplicable unit did not use its allowances of NO X and wishes to sell some of its allowances. A modeling snapshot of the EI might indicate that not all the MECT cap is being used, so the TCEQ increased all the modeled MECT units to the MECT cap, since that’s the amount of NO X each FIN could ultimately emit.

Since the CAIR NO X allocations are generally so much greater than the MECT allowances on the same units, a surplus CAIR allowance is actually generated by applying the more restrictive
MECT limit for units in HGB. This CAIR NO X “credit” can then made available for use by other CAIR EGUs in the State outside HGB, allocated proportionally by their CAIR allowance for modeling purposes; whereas, in reality this CA IR credit will not be equally portioned out,

### B-100 because of trading and differing NO X control strategies. These weren’t actually reallocated by the TCEQ, since the CAIR cap was applied after the MECT cap was applied. The caps were modeled. For example, Clear Lake Cogene ration, RN100239672, in Harris County receives

474.6 NO X tons of MECT allowances each year. This site will also receives a total of 840 NO X tons of CAIR annual allowances from 2009 thro ugh 2014. Because the MECT program is more stringent, the 474.6 number was modeled. Th e additional CAIR annual allowances (365.4 NO X tons) are then available for trade or reta ined for use in future control periods.

The MECT allowances were allocated on a unit basis, because each type of equipment has a specific Chapter 117 ESAD (Emission Standard fo r Attainment Demonstration). Hence, this generation of CAIR NO X credits from HGB (and the other NAAs also, but to a lesser extent) will help Texas attain the CAIR Phase II cap, because units in the less-strictly-regulated attainment
areas of the state will be able to use (purchase) these.

As discussed in previous subsections, for the Acid Rain EGUs (“ARD”), the TCEQ used the hourly average 3Q2007 NO X emissions (and scaled the VOC and CO) for the entire State, including HGB. For the non-Acid Rain units , the TCEQ used the 2006 OSD base emissions as the starting point. The MECT compliance date for all the MECT-specific 11 EGF (Electric Generating Facilities) accounts was May 2005 (o r after the reconciliation period for 2004); therefore, all of these units were assumed to be already complying with MECT. The non-11 EGF accounts technically had until April 2008, the date of the final MECT NO X phased step-down reconciliation period, to comply with MECT, but likely all were actually complying by the third quarter of 2007, or were complying in aggregate (since it is a cap and trade program).

Again, a modeling snapshot of the EI indicated th at not all the MECT cap was being used, so the TCEQ assigned the MECT cap to each path, with the left-over cap applied to the post-2006 MECT paths. The EGU growth was first applie d. All of the Newly-Permitted (post-2007) EGUs in HGB will have to comply with MECT, as well as CAIR, and emissions from these points were reconciled with (credited against) MECT by fo rcing these thirteen newly-permitted units (see Table 2.3-1) under MECT, and reallocating the MECT cap. After the addition of these newlypermitted EGUs under the MECT cap, there was a very small amount of MECT cap still left (about 6.7 NO X tpd) that was distributed between approximately 1950 NEGU (non-ARD) MECT paths.

NEGU Future Base Controls in HGB MECT applies to many more NEGU paths in HGB than EGU paths. The future base emissions for all of the NEGUs (except HECT sources – see next paragraph) were taken from their 2006 OSD emission rates. The final phase (after reconciliation) of the MECT cap step-down for NEGUs was April 2008, so there was still time fo r the units to reach overall compliance levels; therefore, the TCEQ could not assume that MECT had total compliance by 2006. Instead, the TCEQ brought the overall non-Acid Rain MECT units to the level of the MECT cap by scaling the 2006 emissions for each MECT-applicable path, according to its final MECT compliance allowance, resulting in each path being placed at its MECT cap, and the left-over allocated between those units as MECT growth. These exces s MECT allotments are available for purchase by one or more of the existing MECT cap owners, or the allotment can be purchased in the future by a new point source. Distributing the cap among existing points spreads the excess over the entire area while assuring that TCEQ is modeling the MECT cap. In reality, the final MECT amount for each FIN in not known, since there are still flexible and allowable allowances that are being converted to actuals (certifying five years of actual operating emissions for a MECT baseline). The TCEQ modeling can only simulate the MECT program (as with CAIR) with a snapshot of data, yet modeling has incorporated a ll of the allowables-to-actuals “recertifications” from the TCEQ EBT program as of November 2009.

### B-101 The other cap-and-trade program within HGB th at applies to NEGUs is the HRVOC Emissions Cap and Trade (HECT) program. The compounds specifically regulated by HECT are the four HRVOC species: ethylene, propylene, 1,3-butadie ne, and the butenes, where butenes includes all
isomers of butene.

The background and speciation procedure for the HRVOC (see the base case speciation of Section 2.1) and the HECT cap have been provided in a presentation that was given at the 17 th
Annual International EI Conference, in Portland, Oregon, in 2008. The presentation can be found at the conference webpage, http://www.epa.gov/ttn/chief/conference/ei17/session6/thomas_pres.pdf

, and the detailed paper from the conference proceedings can be found at http://www.epa.gov/ttn/chief/conference/ei17/session6/thomas.pdf

The TCEQ recently improved the speciation procedur e for the HECT sources, as described in the base case modeling in Section 2.1.

HECT-applicable units include units in Harris County that emit HRVOC from point sources that are flares, process vent stacks, or cooling towers th at have the potential to emit more than ten tons per year of HRVOC. Smaller sites in Harris County could opt-in to the HECT program. These are all primary sources, not secondary (e.g., r esult of combustion byproducts) HRVOC emissions. HECT allowances were allocated to applicable sites in proportion to the s ite's level of activity,
determined from each site's selection of a tw elve-consecutive-month baseline from 2000 through 2004. HECT sites were given the greater of 5.0 tons of HECT allowances or the allocation from the site, determined from using the equation listed in 30 TAC §101.394(a)(1). This procedure has recently been updated (“reallocated”) via rule changes brought forth to the Commissioners for proposal. These rule changes will be discussed in Section 2.4, below. HECT participants report the total HRVOC emissions from qualifying point s ources. Again, this is a trading program, so HECT-applicable sites may bank or trade HRVOC between other HECT sites, as long as the
account is reconciled at or below its allowance level before the end of the HECT reconciliation period each year.

Modeling the 2018 future case HECT involved calcu lating the HRVOC emissions for each source based on composition and total flow rate, allocating the cap to points in accounts that have an allowance, and distributing any unused portion (due to shutdowns, primarily) of the cap to points in other HECT-qualifying accounts that do not have a cap. The first step was to match the 2006 modeled HECT paths to the tabulated HECT a llowances. For HECT cap accounts, the TCEQ distributed the HRVOC cap between the qualifying HECT paths within the account, based on their total (all four species) HRVOC emission rate. Only the HRVOC components of the VOC emissions records were “controlled”; the flow rate of all other (non-HRVOC) constituents remained unchanged. Thus, a change in HRVO C emissions – due to “control” to the HECT allocation limit -- required a change in the VOC record of the modeled AFS file and the speciation profile (composition) for each of the HECT-applicable path of all of the HECT accounts. For vacated HECT accounts – accounts shutdown or otherwise not reporting HRVOC emissions – the unused cap is available to distribute (“sell”) to 2006 HECT accounts that exceeded their allowance for that control period (calendar year). The TCEQ distributed these left-over HRVOC emissions to paths based (proportionally) on the reported 2006 HRVOC emissions rate. A summary of the 2006 reporte d HRVOC emissions and the HECT Cap for all of the HECT accounts (applicable in Harris County only) is provided as Table 2.3-10: HECT

Accounts . Note that the totals at the bottom of the table for HECT Cap and modeled HRVOC Emissions are identical. Also note that each HECT account is actually modeled at somewhat higher HRVOC than allocated by the Cap, due to the fact that the HRVOC allowances for those accounts that reported zero emissions in 2006 acco unts have been allocated to all the HECT accounts that did report VOC emissions in 2006.Table 2.3-10: HECT AccountsTotal VOC Emissions(tpd)HRVOC Emissions(tpd)RNOwnerHECTCap(tpd)# HECTallocatedpathsReported20062018Reported20062018RN100210319EQUISTAR LYONDELL CH0.2782111.23380.80160.71700.2849RN100210806INTERCONTINENTAL TER0.036440.03460.03720.03460.0372RN100211879SHELL OIL DEER PARK0.9601601.09381.54430.53260.9831RN100212109TOTAL PETROCHEMICALS0.32261202.19811.05171.47670.3303RN100213958Georgia Gulf Pasaden0.050540.02430.06430.01170.0517RN100214212Targa Galena Park0.043320.12460.12030.04860.0443RN100216761Basell Bayport0.205750.53670.41340.33390.2107RN100218130Lyondell Citgo Houst0.3046420.89731.08010.12900.3119RN100218247Albemarle0.013990.10140.05750.05810.0142RN100218411Odjfell Terminals0.0139RN100218841Kaneka Texas0.013940.14180.12720.02880.0142RN100219237Johann Haltermann 10.013920.00330.01570.00180.0142RN100219310Valero Houston Refin0.3268240.34610.56800.11270.3346RN100219526Texas Petrochemicals0.527560.21100.66020.09090.5401RN100221589Lubrizol Corporation0.0264100.01420.03700.00420.0270RN100224450Millenium LaPorte0.028930.06430.05380.04010.0296RN100225085DuPont LaPorte0.045560.08890.08910.04640.0466RN100225689BASF Pasadena0.023640.09510.09830.02100.0242RN100227016Celanese Clear Lake0.0719160.15470.10550.12290.0736RN100229905INEOS Polyethylene0.178890.46690.41220.23780.1831RN100524008Sunoco Bayport Polyp0.049490.33490.14060.24480.0506RN100542224Nova Chemicals Baypo0.032210.01620.04910.000020.0330RN100542281Equistar Channelview1.2092132.79933.03601.00131.2381RN100633650Lyondell Channelview0.3207110.52410.75670.09570.3283RN100683952NATURAL GAS ODORIZIN0.013920.01190.01420.01190.0142RN100716661Pasadena Refining0.1666201.45901.37450.25510.1706RN100870898Goodyear Houston Che0.036190.25950.27960.01680.0370RN100909373Total Bayport HDPE0.0805140.15360.20150.03450.0824B-102B-103Total VOC Emissions(tpd)HRVOC Emissions(tpd)RNOwnerHECTCap(tpd)# HECTallocatedpathsReported20062018Reported20062018RN101041598LBC Houston Bayport0.0139RN101058410Lubrizol Corporation0.013910.00050.01470.000030.0142RN101379287American Acryl Pasad0.0139RN102018322Cheveron Phillips0.2488561.04780.98940.31320.2547RN102204211Kirby Inland Marine0.023010.09590.05330.06610.0236RN102212925ExxonMobil Baytown O0.7933302.42202.12831.10580.8122RN102523107Lyondell Chemical Ba0.108350.34740.38730.07090.1109RN102537289INEOS Polypropylene0.108830.38510.22270.27380.1114RN102574803ExxonMobil Baytown C0.5317320.52160.76670.29930.5444RN102579307ExxonMobil Baytown R1.17591424.00444.75240.45601.2039RN102580834Enterprise HSC0.0139RN102608932GOODYEAR TIRE & RUBB0.013910.00120.01530.00010.0142RN102610912HALTERMANN0.0139RN102887270Nisseki Bayport0.0139RN102888328Sunoco LaPorte0.111640.12610.12320.11720.1143RN102926920Equistar Bayport0.051430.03680.06960.01980.0526RN102940103ENTERPRISE PRODUCTS0.0139RN103773206EQUISTAR (Sunoco) Ba0.0572RN103919817Chevron Phillips Ced0.6739211.18411.32880.54530.6900RN104150123Dow Chemical Clear Lake0.0661TOTALS =9.456223.561924.04158.97659.4562

### B-104 2018 Reconciled HRVOC 2006 was the first full year for reporting of m onitored HRVOC, as required by 30 TAC Chapter 115. Additionally, 2006 was the year of the HRVO C survey. Section 2.2.1.2 above showed that a total of 19.29 tpd of HRVOC were added to gr id cells in HGB as the reconciled HRVOC (extra olefins). Of this, 9.03 tpd of the reconcile d HRVOC were covered by the HECT program in Harris County.

TCEQ analysis showed that approximately 60% of the HRVOC in Harris County are covered by HECT (non-fugitive). If we assume that HECT will control (or has already controlled) all the non-fugitive HRVOC in Harris County, then 60% of the Harris Co reconc iled (extra olefin) HRVOC will be gone in the future, given that we're already modeling the HECT cap in Harris County. Putting all of this together, the TCEQ modeled the reconciled HRVOC in the 2018 future case by multiplying emissions in the 2006 “ext ra olefins” file by 0.4065 to remove the estimated 60% that come from HECT point sources. Thus, there is no 2018 extra olefins AFS modeling file, just the 2006 file and the reduction factor that converts the emissions to 2018.

2018 Tank Landing Losses (TLL) The emissions used for the hourly TLL for this 2018 future base is the same as the baseline: the sum of the three 2006 episodes divided by the numb er of days of the three episodes, for each
hour.

It is likely that future VOC reductions will be actualized from the Chapter 115 rules (compliance date of January 1, 2009) that limit “convenien t” floating roof tank landings, as witnessed from TLL decreasing trends reported for the years 20 04-2007, as sites were made aware of the reporting problem and the rule. Yet it is difficult to quantify the emissions reductions expected from these “best practices” improve ments at these accounts. The number of landings per year will decrease, but not necessarily the magnitude of the emissions on any given day. Hence, the TCEQ decided to take no “credit” for any futu re convenience landing reductions that may be
made at the accounts discussed in Section 2.1.1.7.3.

HGB 2018 EI Summary In summary, for HGB, the future base EI is com posed of two datasets: hourly average 3Q2007 for the ARDs and 2006 OSD for the HECT sources and the remainder of the point sources in HGB. The 2018 future base is built upon these datasets by incorporating the changes expected due to growth and emission caps. Table 2.3-11: Detailed NO X and VOC 2018 Emissions Summary by Area provides a 2018 emissions summary for HGB.

### B-105 Table 2.3-11: Detailed NO X and VOC 2018 Emissions Summary by Area
Detailed NOx Emissions (tpd)

Region Projection Affected Sources 2006
STARS 2007

ARD 2018

HGB MECT OSDs getting MECT CAP 105.99 84.22

“ ARDs getting MECT CAP 49.65 36.52

Permitted Growth Post-2007 EGUs in MECT 1.32

“ Cap available for growth 6.75

nonMECT Growth from TIPI 15.69 21.85

“ from EGAS 3.07 3.69

“ from BANK 0.01 0.01

BPA CAIR ARDS, existing in 2000 9.36 9.97

“ OSDs, existing in 2000 0.26 0.26

“ ARDs, 2000-2007 1.31 1.31
Permitted Growth Post-2007 EGUs 0.19

Controls BPA NO X Controls 18.61 19.28
nonCAIR Growth from TIPI 31.46 45.28

“ from EGAS 7.90 9.12

“ from BANK 0.00 0.00

DFW CAIR ARDs, existing in 2000 5.00 17.44

“ ARDS, 2000-2007 4.23 4.23

Permitted Growth Post-2007 EGUs 0.00
Controls DFW major NO X 8.69 4.34

“ Midlothian Kilns Cap 21.99 16.37
nonCAIR Growth from TIPI 2.57 3.18

“ from EGAS 8.28 9.86

“ from BANK 0.00 0.00

ATTAIN CAIR ARDS, existing in 2000 429.84 360.96

“ ARDs, 2000-2007 17.54 17.54

“ OSDs in CAIR 4.31 1.14

Permitted Growth Post-2007 EGUs 19.19
Controls ETX Combustion NO X 5.48 1.02

nonCAIR Growth from TIPI 193.84 248.47

“ from EGAS 315.77 357.41

TOTAL = 1260.86 1300.92

### B-106 Detailed VOC Emissions (tpd)

Region Projection Affected Sources 2006 STARS 2007
ARD 2018

HGB HECT Harris Co. HRVOC 23.56 24.04 HRVOC
Reconciliation Extra Alkenes (PSCF) 19.29 7.84
Growth Tank Landing Losses 6.55

Growth from TIPI 135.03 166.44

“ from EGAS 22.18 27.82

“ from BANK 0.05 0.05
CAIR ARDs (NO X /VOC ratio) 2.76 2.76

Permitted Growth Post-2007 EGUs 3.63

BPA Growth from TIPI 73.03 102.66

“ from EGAS 3.05 3.74

“ from BANK 0.00 0.00
CAIR ARDs (NO X /VOC ratio) 0.79 0.79

Permitted Growth Post-2007 EGUs 0.24

DFW Controls Midlothian Kilns Cap 1.81 2.00

Growth from TIPI 11.01 15.46

“ from EGAS 24.45 30.84

“ from BANK 0.00 0.00
CAIR ARDs (NO X /VOC ratio) 0.43 0.43

ATTAIN Growth from TIPI 134.40 182.24

“ from EGAS 94.47 111.96
CAIR ARDs (NO X /VOC ratio) 11.49 11.49

Permitted Growth Post-2007 EGUs 20.44

TOTAL = 557.87 719.05

2.3.2.2 BPA and DFW Nonattainment Areas As stated in the previous subsection, there is much commonality in how HGB, BPA, and DFW were modeled, so much of this subsection refe rs to the procedural descriptions above. This subsection describes the growth projections to 2018 and any existing controls that affect the BPA
and DFW NAAs between the baseline and 2018.

2.3.2.2.1 BPA and DFW 2018 Growth Projections Growth projections were applied to the baseline EI for BPA and DFW to obtain a 2018 grown EI. Different techniques were applied to the EGUs/ARDs and the NEGUs.

EGU Projections The complete set of 2018 EGUs consists of the 3Q2007 ARD EGUs, the 2006 non-Acid Rain EGUs, and post-2007 EGUs that have approved TCEQ permits. As documented in previous sections above, TCEQ assumes that the EGU grow th in the State comes from the TCEQ newlypermitted EGUs. Table 2.3-1, above, lists all of the newly-permitted EGUs in the NAAs -- none in DFW and one project (two units) in BPA. As in all areas of the State, these units must fit within the CAIR cap (discussed below as a control).

### B-107 NEGU Projections As with HGB and the rest of the State, the b asis for future growth for NEGUs in BPA and DFW NAAs was the 2006 OSD emissions, already an averag e day in the ozone season. As with HGB, no individual new permits were modeled as growth for NEGUs, except the known refinery expansions for BPA that were already quantifie d in a recent SIP revision (see discussion below). Additionally, as with HGB, the other NAAs were projected to 2018 using the greater of the
TIPI/REMI-EGAS factors or the emission credits in the bank. Table 2.3-7, above, provides the banked emissions for BPA and DFW individually. Table 2.3-8, above, provides the TIPI/REMIEGAS growth factors applicable to BPA and DFW individually. See Section 2.3.2.1.1 for discussion and results of these procedures.

Note that by adding the banked emissions back into the airshed and assuming all of the banked emissions can reappear in the airshed during and ju st prior to the attainment date, this procedure likely overpredicts emissions growth, especially in DFW, where the NO X bank credits (refer to Table 2.3-7) represents approximately a 50percent growth factor for the NEGUs. The DFW DERC-use restrictions, developed for the recent DFW SIP revision and codified in Chapter 101, were not accounted-for in this modeling, since we do not know what will be necessary in 2018; hence, the full DERC registry was modeled for DFW as worst case.

As with HGB and the rest of the State, any sour ce that had an existing rule applied to it was capped at that rule level for the future, so only th e “no rule” sources had growth factors applied to
them.

Known Refinery Expansions in BPA Three refineries in BPA have proposed increasi ng daily production levels, according to recent permit actions. Motiva Enterprises, LLC (Motiva) is doubling the capacity at its Port Arthur refinery to 600,000 barrels per day, the Pr emcor Refining Group (Valero) is expanding its capacity to 420,000 from 300,000 barrels per day, and Total Petrochemicals USA Incorporated (Total) is increasing the capacity of its Port Ar thur refinery by 50,000 barrels per day. The following paragraphs and resulta nt projections were implemented first in the December 2008 BPA SIP revision and are applicable to this SIP revision as well. The details can be found in Appendix E of that BPA SIP revision, at http://www.tceq.state.tx.us/assets/public/implementation/air/sip/bpa/App_E_pro.pdf

.

To account for the expansion at these refineries, projected emissions were developed by multiplying the 2005 actual emissions by the ratio of the future allowable emissions to the 2005
allowable emissions. This is consistent with the recent BPA Maintenance Plan. For sites where future allowable emissions appeared to decrea se compared to the 2005 allowable emissions, no change from 2005 actual emissions was projected. Th ese data were arrived at via detailed permit reviews to collect 2005 allowable emissions data from available New Source Review (NSR) permits that authorize ozone precursor emissions fo r the sites. Future allowable data associated
with the Motiva and Total refineries were obtaine d from a TCEQ contract with Environ (contract number 582-07-84005-FY08-08). For Valero, sitewide emissions were determined from flexible permit 6825A and a table of exempted units provide d by the Air Permits Division. While it is true that permit allowable increases percentages generally will not be the same as the future actual emissions increase percentages, modeli ng these percentage increases should provide a good estimate. Table 2.3-12: Permitted Refinery Expansions is a summary of the findings that were modeled, where “Allowable ratio” is the growth factor applied to that account. These emissions were not further grown or controlled for this 2018 case.

### B-108 Table 2.3-12: Permitted Refinery Expansions
2005 tpd Allowable ratio Company NO X VOC NO X VOC

Motiva 6.73 3.69 1.00 1.30

Total 2.19 1.39 1.04 1.23

Valero 5.42 1.10 1.00 1.18

2.3.2.2.2 BPA and DFW Existing (Future Base) Controls for 2018 There are several existing NO X rules for BPA and DFW, most notably Chapter 117 NO X rules that were previously implemented via SIP rules in each NAA for attainment demonstration. Additionally, there are Agreed Orders for BPA a nd a Cement Kiln cap to apply for DFW. The
most recent BPA attainment demonstration SIP revi sion and its associated rules were adopted by the Commission in September 2005. The most recent DFW attainment demonstration SIP revision was adopted by the Commission in May 2007. As in the above sections, these control programs are on-the-books rules for which the fina l compliance dates are after the baseline year and prior to 2018. Again, these will be discussed in terms of their effects on EGUs/ARDs and

NEGUs.

EGUs As discussed above, CAIR applies to all the EGUs in the entire state, but in BPA and DFW, more restrictive emissions limits were placed on EGUs via Chapter 117 NO X rules. Yet, all of these rules for EGUs were already in place with a comp liance date prior to the 3Q2007 baseline for the Acid Rain EGUs. The exception to this is a recent NO X rule for DFW EGUs that was expected to achieve only a small amount of reductions. Fo r the non-Acid Rain EGUs, the baseline is 2006. The TCEQ has assumed no EGU reductions for BPA and DFW.

NEGUs This subsection discusses the modeling of two sets of existing control programs for BPA and three sets for DFW. These were modeled as essentially a cap on the specific sources affected.
We start with the oldest and work to the most recent.

BPA Agreed Orders A sites-specific SIP revision adopted by the Commission in December 2004 was approved to make federally enforceable certain voluntary emissions reductions, projects, or activities by six BPA companies at eight sites. Most of these were through permit revisions. The TCEQ determined the ones that were likely to produce act ual emissions reductions, not just reductions in permit allowables. See http://www.tceq.state.tx.us/assets/public/impleme ntati on/air/sip/agreements/bpa_ao_sip_041217. pdf and http://www.tceq.state.tx.us/implementation/air/sip/dec2004bpa.html

.

The reductions modeled were the ones that pr ovided “shutdown equipment” language in the Agreed Order. These were the same ones mode led for the most recent DFW SIP revision that represented Agreed Orders for three companies in Jefferson County: Premcor, Exxon Chemicals,
and Motiva. These reductions were generally not in place by 20 05, so these were modeled as reductions that will occur prior to 2018.

BPA Chapter 117 Rules The TCEQ applied Chapter 117 NO X emissions limits to BPA point sources over 25 tpy, as specified in the most recent BPA Attainme nt Demonstration with rules package.

### B-109 DFW 5% IOP Rules The DFW 5% Increment of Progress (IOP) SIP revi sion identified required emissions reductions in the added five counties for the DFW eight -hour ozone NAA. These included VOC reductions from the DFW Surface Coating rule. There were also NO X reductions from ICI (Industrial, Commercial, Institutional) reciprocating gas-fi red engines in the nine-county area, but the NO X rules were generally made more strict by the Ma jor Source Rule (see next paragraph). So only the VOC controls were specifically modeled in th is “control packet”, as was done for the most
recent DFW SIP revision. These rules were adopted by the Commission in April 2005.

DFW 2009 Ch. 117 Major Source Rule The DFW major source rule controls ICI (Indus trial, Commercial, Institutional) NEGU point sources. It approximates the Chapter 117 NO X Emission Standards for Attainment Demonstration (ESADs) that are in place in HGB, plus some new ESADs for source categories that didn’t exist or were otherwise not regulat ed in HGB. The compliance dates for these were March 2009. The paths affected by this rules are those modeled for the most recent DFW SIP
revision.

DFW 2009 Cement Kiln Cap The level of controls applied to the Midlothian (Ellis County) cement kilns are those recommended as the low level of controls by the ER G “Study”, as described in more detail in the May 2007 (most recent) DFW SIP revision. A dditionally, site-wide (by account) “alternative”

NO X caps were part of this rule, as modified by baseline changes reported by the sites. These updated ozone season (March 1 through October 1) caps, totaling 17.6 tpd for the ten kilns at the three sites, should have been modeled, but as Table 2.3-11 shows, only 16.37 tpd NOx were modeled for these ten kilns (accidentally reverted back to the pre-adoption rule file). This
difference in DFW emissions is considered ex tremely insignificant for impact upon HGB ozone
concentrations and was not corrected. The co mpliance date for this rule was March 2009.

BPA and DFW 2018 EI Summary In summary, for BPA and DFW, the 2018 future base EI is composed of two datasets: hourly average 3Q2007 for the ARDs and 2006 OSD for th e remainder of the point sources in these NAAs. The 2018 future base is built upon these da tasets by incorporating the changes expected due to growth/bank and emissi ons budgets/controls for certain source categories that were developed for previous SIP revisions. See Ta ble 2.3-11, above, for the 2018 emissions summary
for BPA and DFW.

2.3.3 Regional (Outside of Texas) Point Sources To parallel the base case and baseline sections, “reg ional” includes the states adjacent to Texas, the remaining eastern half of the USA within our modeling domain, offshore (Gulf of Mexico), and the parts of Mexico and Canada within our modeling domain. Since the same procedure was used to model 2018 adjacent states as the remainder of the states beyond Texas, these areas have
been combined in the subsection below.

2.3.3.1 States Outside of Texas Since the 2018 projections of the states adjacen t to Texas (Louisiana, Arkansas, and Oklahoma) were treated just as the remainder of the United States, they are discussed in this subsection as a whole, with any differences specifically identif ied. To parallel the other sections above, the development of the 2018 EGU EI is discussed first, then NEGUs.

2.3.3.1.1 EGUs Here again, the TCEQ makes the distinction betw een EGUs and Acid Rain units. Not all EGUs (SIC 4911 and 4939) are Acid Rain units, e.g., tr ue cogeneration units ar e not Acid Rain units, but large cogens that supply more than one-third of their electricity to the public electrical grid
are Acid Rain units. Non-Acid Rain EGUs are treated as NEGUs. This is the same definition

### B-110 that the EPA applies to EGUs for CAIR purposes, i. e., an EGU, as defined by CAIR, is an Acid Rain EGU, which we could also call a CAIR EGU.

The TCEQ built future case 2018 emission records for Acid Rain EGUs using the same data source as used for Texas: the 2007 third quart er (3Q2007) hourly da ta downloaded from the EPA’s Clean Air Markets Division (CAMD) website (see Section 2.1.2.1). A brief description is given here again. The downloa ded data consisted of hourly NO X emission rates for point sources identified by ORISPL and UNITID. To develop modeling AFS records, these emissions were joined to the 2002 NEI point source records that provided location, stack, and other parameters
needed for AFS records. In addition, the NO X -to-CO and NO X -to-VOC emission ratios from the 2002 NEI provided a way to estimate hourly CO and VOC emissions for each point.

The goal is to substitute the new est Acid Rain data (ARD) into the 2018 NEI to retain the average hourly emissions profile for each EGU, even though the emission rates will change in the future
case. The CenRAP/RPO AFS records would have b een used if they had contained the ORISPL and UNITID identifiers that are necessary to link the ARD to the AFS. For EGUs, for which the goal is substituting hourly data anyway, only the stack parameter data are necessary, and these should not vary from NEI to CenRAP/RPO files. Hence, the NEI was used for purpose, because it contained the identifiers necessary.

Less than 5% of Acid Rain EGUs did not match an NEI source record. Again, the TCEQ were using 2007 ARD and comparing it to the 2002 NEI (grown to 2018 using EGAS). Since the TCEQ did not have time to develop and QA an updated 2007 Acid Rain-to-NEI cross reference,
all new EGUs that came online between 2002 and 2007 had to be dealt with. Add to this the issue that not every Acid Rain EGU in every st ate filled in the ORIS/UNITID field during their
NEI submittal. For Acid Rain EGUs in Louisi ana, Arkansas and Oklahoma that did not match, the TCEQ manually created ARD records usi ng “nearest town” for the location, default parameters for the stack exhaust, and averages for the CO and VOC emission ratios. For the other states in the USA within the modeling domain, the TCEQ distributed the unmatched
emissions of each state to the matched ARD record s in each state, so that all of the emissions
would be accounted for. As was done for th e baseline, the TCEQ built a single 2007 typical summer acid rain day having average hourly emissions for each Acid Rain EGU.

Next, the 3Q2007 emissions were “grown” to 2018 using the Department of Energy’s (DOE’s) Energy Information Administration (EIA) 2008 Annual Energy Outlook (AEO) report. Specifically, the TCEQ referred to EIA’s 2008 AEO spreadsheet, April 2008, Table 76 , which provided electricity generation by electricity market region of the country and fuel type for the years 2005-2030; this can be found at http://www.eia.doe.gov/oiaf/archive/aeo08/supplement/index.html . The growth from 2007 to 2018 for areas outside of Texas was derived from this table by taking the 2018 value and dividing by the 2007 value for each region of the country for each fuel type. The results of this are shown
in Table 2.3-13: 2007 to 2018 EGU Growth Factors, EIA’s 2008 AEO , broken out by each state in the modeling domain.

### B-111 Table 2.3-13: 2007 to 2018 EGU Growth Factors, EIA’s 2008 AEO

Fuel State Coal Natural_Gas Oil

AL 4.59 -6.44 -6.15

AR 7.57 1.97 -39.6

CT 19.73 -4.78 -6.69
DC 2.37 -32.28 15.34

DE 2.37 -32.28 15.34

FL 61.79 1.18 7.81

GA 4.59 -6.44 -6.15

IA 13.52 -6.08 -32.89

IL 13.52 -6.08 -32.89

IN 8.89 21.67 -3.07

KS 7.57 1.97 -39.6

KY 8.89 21.67 -3.07

LA 4.59 -6.44 -6.15

MA 19.73 -4.78 -6.69

MD 2.37 -32.28 15.34

ME 19.73 -4.78 -6.69

MI 8.89 21.67 -3.07

MN 15.17 99.43 -19.48

MO 13.52 -6.08 -32.89
MS 4.59 -6.44 -6.15

NC 4.59 -6.44 -6.15

ND 15.17 99.43 -19.48

NE 15.17 99.43 -19.48

NH 19.73 -4.78 -6.69

NJ 2.37 -32.28 15.34

NY -1.39 14.73 7.14

OH 8.89 21.67 -3.07
OK 7.57 1.97 -39.6

PA 2.37 -32.28 15.34

RI 19.73 -4.78 -6.69

SC 4.59 -6.44 -6.15
SD 15.17 99.43 -19.48

TN 4.59 -6.44 -6.15

VA 4.59 -6.44 -6.15

VT 19.73 -4.78 -6.69

WI 13.52 -6.08 -32.89

WV 8.89 21.67 -3.07

Note: includes all other
solid fuels includes all other vapor fuels includes all other liquid fuels

Note that many of the growth factors in Table 2.3-13 are negative. This indicates negative growth, or a projected shrinking of demand for that fuel in that region of the country.

After “growing” these regional state EGUs, the TCEQ applied controls to them to complete the projection to 2018. The controls assumed are the CAIR NO X budgets for each state for 2009-14, as defined by EPA’s Notice of Data Availability (NODA) allocations spreadsheet, obtained from EPA’s website, http://www.epa.gov/airmarkt/progsregs/cair/noda.html

. If the state had an ozone season budget, then th at was the assumed budget; otherwise, the annual budget was converted to a ton per day limit. These state allocations were then multiplied by the presumptive five-sixths to obtain the Phase II budgets for 2015 and beyond.

### B-112 The TCEQ then “controlled” the grown non-Texas EGUs to the CAIR Phase II NO X emissions level for each state. As with Texas, no tradi ng was assumed. The TCEQ went to the trouble of “growing” the EGUs first with the AEO data to ass ist with projecting the fuel types that may be present in 2018, to get the mix of fuels as accu rate as possible. Then the CAIR Phase II caps were applied.

A CenRAP and other RPOs EI was available for 2018 in SMOKE files format (IDA). These files were built by a contractor for the purpose of Re gional Haze modeling. The TCEQ gathered and
converted the IDA-formatted files for the nati on (actually, from just the RPOs within the modeling domain) into AFS files and associated temporal files. The TCEQ used these data for NEGUs. Yet, ultimately, for the EGUs, TCEQ only used these files to compare with the aforementioned 2018 EGU files generated by the TC EQ. In comparing the two, the AEO/CAIR approach that was modeled does have slightly higher emissions. The CenRAP/RPO EGU files incorporated IPM with growth and control scenar ios based on projected fuel type prices/demand, plus CAIR trading that included Texas buying CA IR credits. Since the TCEQ can not predict the form of final CAIR or its replacement program, the TCEQ felt validated by using the AEO/CAIR approach that it modeled, because it did provide slightly higher emissions for the modeling
domain as a whole.

The TCEQ applied CAIR Phase II to all states, as for Texas -- distributing the cap between point sources according to their grown averaged hourly 3Q2007 Acid Rain emissions. When modeling the CAIR cap, the ozone season cap applies (if one exists), otherwise the annual cap applies. States not in the CAIR program were modeled at their grown 2018 emissions. Table 2.3-14;

Modeled CAIR Phase II NO X Allocations for States in the Modeling Domain is a listing of states in the modeling domain with their associated presumptive annual and/or ozone season CAIR Phase II allocations (EPA’s NODA). The last column of Table 2.3-14 is the 3Q2007 with 2008 AEO growth applied to it.

### B-113 Table 2.3-14: Modeled CAIR Phase II NO X Allocations for States in the Modeling Domain

Allocated CAIR Cap (tpy) Modeled CAIR Cap (tpd)

State Annual Ozone
Season Annual Ozone
Season Projected 2018 Emissions, without CAIR (tpd)

AL 65569 30573 179.64 199.82 279.56
AR 10939 71.50 129.01
CT 2431 15.89 16.43
DC 137 106 0.38 0.69 0.55
DE 3958 2115 10.84 13.82 39.55
FL 94473 45516 258.83 297.49 689.39
GA 63005 172.62 238.20
IA 31057 13550 85.09 88.56 172.94
IL 72419 29166 198.41 190.63 276.35
IN 103488 43654 283.53 285.32 411.87
KS 197.95
KY 79045 34243 216.56 223.81 298.79
LA 33736 16231 92.43 106.08 172.59
MA 7173 46.88 27.60
MD 26338 12192 72.16 79.69 114.14
MD 26338 12192 72.16 79.69 114.14
ME 1.58
MI 62039 27522 169.97 179.88 282.51
MN 29871 81.84 243.05
MO 56877 25344 155.83 165.65 221.72
MS 16917 8278 46.35 54.10 175.00
NC 59074 26972 161.85 176.29 203.88
ND 225.99
NE 143.18
NH 15.74
NJ 12037 6321 32.98 41.31 58.56
NY 43336 19600 118.73 128.10 175.90
OH 103234 43381 282.83 283.54 428.95
OK 252.60
PA 94097 40062 257.80 261.84 396.36
RI 1.32
SC 31029 14487 85.01 94.69 131.28
SD 42.49
TN 48424 21700 132.67 141.83 163.86
VA 34270 15194 93.89 99.31 166.66
VT 1.19
WI 38721 17088 106.08 111.69 166.04

WV 70509 25516 193.18 166.77 206.80
Note: An empty cell indicates no CAIR cap (not applicable) for that state.

Table 2.3-15 : Modeled 2018 Typical Day Acid Rain EGU Emissions for Regional States summarizes the Acid Rain EGU emissions data for point sources outside Texas. The “bal USA” totals are for all states in the modeling domain, excluding Texas, Arkansas, Louisiana and Oklahoma, although parts of some states may extend outside the modeling domain. Hence, photochemical model (CAMx) inputs would be e xpected to be less than the totals above.

### B-114 Table 2.3-15: Modeled 2018 Typical Day Acid Rain EGU Emissions for Regional States

NO X Emissions (tph) VOC Emissions (tph) CO Emissions (tph)
hour

AR LA OK Rest of
USA AR LA OK Rest of
USA AR LA OK Rest of
USA

1 2.5 3.1 8.4 133.9 0.1 0.1 0.1 1.8 0.6 0.7 1.0 59.0

2 2.3 3.1 8.1 128.4 0.1 0.1 0.1 1.7 0.5 0.6 1.0 56.2
3 2.2 3.1 8.0 125.0 0.1 0.1 0.1 1.7 0.5 0.6 0.9 54.3

4 2.2 3.1 8.0 124.7 0.1 0.1 0.1 1.7 0.5 0.6 0.9 53.9

5 2.2 3.1 8.2 129.0 0.1 0.1 0.1 1.7 0.5 0.6 1.0 55.9
6 2.4 3.1 8.5 135.6 0.1 0.1 0.1 1.8 0.5 0.7 1.0 59.1

7 2.4 3.1 8.9 141.2 0.1 0.1 0.1 1.9 0.5 0.7 1.1 61.7
8 2.5 3.2 9.0 148.1 0.1 0.1 0.1 2.0 0.6 0.7 1.1 65.1
9 2.5 3.3 9.5 155.3 0.1 0.1 0.1 2.1 0.6 0.7 1.2 68.7
10 2.5 3.5 10.3 160.7 0.1 0.1 0.1 2.2 0.6 0.8 1.4 71.8

11 2.6 3.8 11.2 165.6 0.1 0.1 0.1 2.3 0.6 0.9 1.6 74.5

12 2.6 4.0 12.0 169.0 0.1 0.1 0.1 2.4 0.6 1.1 1.8 76.4
13 2.6 4.3 12.8 171.6 0.1 0.1 0.2 2.4 0.6 1.2 2.0 77.7

14 2.6 4.4 13.2 173.2 0.1 0.1 0.2 2.5 0.6 1.3 2.1 78.5
15 2.6 4.5 13.6 174.0 0.1 0.1 0.2 2.5 0.6 1.4 2.2 78.8
16 2.6 4.8 13.8 173.8 0.1 0.1 0.2 2.5 0.6 1.4 2.3 78.8

17 2.6 4.4 13.4 172.8 0.1 0.1 0.2 2.4 0.6 1.3 2.2 78.5
18 2.6 4.5 12.8 170.5 0.1 0.1 0.2 2.4 0.6 1.2 2.0 77.4
19 2.6 4.5 12.1 168.0 0.1 0.1 0.2 2.4 0.6 1.0 1.8 76.4
20 2.6 3.8 11.7 166.6 0.1 0.1 0.1 2.3 0.6 1.0 1.7 75.8

21 2.5 3.6 11.0 163.3 0.1 0.1 0.1 2.3 0.6 0.9 1.5 74.1

22 2.5 3.5 10.0 156.8 0.1 0.1 0.1 2.2 0.6 0.8 1.3 70.7
23 2.5 3.4 9.2 149.2 0.1 0.1 0.1 2.1 0.6 0.7 1.2 66.6

24 2.5 3.2 8.8 141.2 0.1 0.1 0.1 1.9 0.6 0.7 1.1 62.5

Totals 59.6 88.4 252.6 3697.6 1.6 2.3 3.1 51.1 13.4 21.4 35.5 1652.5 #points 17 61 69 1201 17 61 69 1201 17 61 69 1201
NOTE: "bal USA" denotes the remainder of the states in the region, regardless of whether the EGU is located within the modeling domain

2.3.3.1.2 NEGUs The 2018 NEGU file for states beyond Texas was the remainder of the 2018 CenRAP/RPO (described in the subsection above) file afte r the EGUs were removed. Table 2.3-16: 2018 Regional States NEGU Emissions represents the NEGU emissions for states beyond Texas, but within our modeling domain. Again, parts of some states may extend outside the modeling domain; hence, photochemical model (CAMx) inputs w ould be expected to be less than the totals
tabulated here.

### B-115 Table 2.3-16: 2018 Regional States NEGU Emissions

NO X VOC CO State #points tpy #points tpy #points tpy

AL 1805 83819 3192 56018 1817 216573
AR 1075 38304 2106 41815 1101 69153
CT 854 8981 1203 4331 832 11582
DE 386 4166 1009 1994 383 8988
DC 44 744 61 90 43 485
FL 1766 82572 3154 44255 1611 140447
GA 1749 67655 2509 41129 1651 198301
IL 6175 98249 15080 69608 5898 99252
IN 3154 83037 7674 74111 3059 448868
IA 2907 51382 6683 43886 2744 52498
KS 2698 96929 4391 37005 2610 123990
KY 2812 43327 8320 55949 2717 141027
LA 4073 242029 7489 108589 3748 182196
ME 807 26532 1180 6640 797 26934
MD 715 26472 1263 6411 675 150020
MA 5551 29841 8596 10843 4502 25487
MI 3439 95577 8935 42526 2866 101156
MN 4847 74426 8593 46968 4604 59488
MS 1458 64020 2437 46094 1441 79548
MO 4152 58336 8384 44560 3878 187043
NB 911 22363 1172 10461 852 9185
NH 207 1696 233 1246 206 1572
NJ 5120 28192 10508 18457 4650 19601
NY 2092 58717 2644 8187 2074 100171
NC 2619 56736 4992 71322 2630 71341
ND 126 92098 96 2493 130 22589
OH 2067 71948 3244 33370 1871 290157
OK 2379 95719 5475 48085 2385 86566
PN 6990 133641 11807 44645 6716 167483
RI 139 2479 259 1798 140 3337
SC 2729 54672 4541 44091 2676 79704
SD 79 24726 88 3523 67 6853
TN 1744 65556 4901 93366 1661 141943
VT 89 991 73 1734 85 842
VA 2248 76898 3898 53637 2106 87985
WV 1167 46877 2728 16621 1206 120640

WI 4373 43770 9666 43692 4097 45403

Table 2.3-17: 2018 Regional States Emissions Summary , below, provides an overall summary of
the 2018 emissions for all the states within the m odeling domain, outside of Texas. Again, parts of some states may extend outside the mode ling domain; hence, photochemical model (CAMx) inputs would be expected to be less than the totals tabulated here.

Table 2.3-17: 2018 Regional States Emissions Summary

NO X (tpd) VOC (tpd) CO (tpd)

2006 Baseline EGU 6524 47 1720
NEGU 7645 3331 8213

2018 Future Case EGU 4089 58 1723
NEGU 5900 3506 9804

### B-116 2.3.4 Offshore, Mexico, and Canada The GWEI 2005 report did mention declines in the number of platforms between 2000 and 2005, and platforms have been tending to be located fart her offshore in deeper water, so the impact to onshore areas may be decreasing. Yet MMS has not provided projections for the offshore EI. For lack of projections data, the 2018 EIs for th e offshore area (Gulf of Mexico), Mexico, and
Canada were the same as those used in th e 2006 baseline and the 2005-06 base cases, as described in previous sections.

2.3.5 Summary of Future Case Point Source Data Files Snapshots of the point source emission files th at were processed with EPS3 for CAMx in each episode are presented in Table 2.3-18: AFS Files for the 2018 Future Case Episode. The version number on each dataset indicates a change from the previous version (e.g., “v8”). The FTP
download site for these files or their successors is ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/HGB8H2/ei/point/ . Outdated files are generally not retained on the FTP site.

Table 2.3-18: AFS Files for the 2018 Future Case Episode

Area AFS Point Source Emissions Dataset Hourly Daily See

Note

Texas afs.ard_MECT_with_season_adjustment_v12 X

afs.ard_2007_sans_mhc_no_NOx_recs_v9 X

afs.ard_CAIR_with_season_adjustment_v11 X

afs.osd_MECT_for_HGB_NOx_pts_v10 X

afs.osd_MECT_exempt _growth_to_2018.v8 X

afs.osd_growth_under_MECT_v15b X

afs.2018_HECT_HarrisCo_2006_basis_all_pts_0.75sens_v11 X

afs.osd_CAIR_with_pathway_allowances_v10 X

afs.00to07_egus_getting_new_CAIR_allowances_v13 X X

afs.osd_post07_egus_getting_CAIR_from_permit_v12b X

afs.osd_2009_controls_v4 X

afs.osd_no_rules_growt h_to_2018_routTLL.v12 X

afs.landing_losses_3Q06_aver_day_episode_v1 X

afs.aggVOC_extra_alkenes_for_2006_v3 X

Regional afs.ard_USAmTX_generic_CAI R_based_on_pt_caps.v1 X

afs.osd_NEGUs_split_out_of_2018_CENRAP X

Canada afs.CAIRCanada.20 01.v1a.latlong.lcp 1

Mexico afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1 X

Offshore afs.gwei2005.May_Oct_edit.3pol.lcp 2

Notes: 1 Records are tons per year (tpy)

2 Records are tons per month

### B-117 2.4 2018 FUTURE YEAR POINT SOURCES CONTROL STRATEGY MODELING
EMISSIONS DEVELOPMENT This section describes the modeling of the final newl y-proposed rules/controls that were applied to the 2018 future point source EI, necessary /assisting for attainment demonstration.

2.4.1 HECT Reallocation and 25% Cap Reduction Rule Since a portion of the original HECT cap was not being utilized, and the TCEQ confirmed such directly with the HECT allowance holders vi a the 2006 HRVOC Special Inventory, the TCEQ proposed a rule that would reallocate the HECT cap and limit the overall cap to 75% of its reallocated value. (That is, the HECT alloca tion used in the initial 2018 modeling studies differs from the allocations in later studies, although th e total cap is unchanged. The 25% HECT cap reduction comes off the more recent allocation.) The TCEQ must include the full cap allowances in the future case, because the emissions level in the future could technically reach the full cap in a given year.

The new rule was modeled with an across-the-boa rd reduction of HRVOC, except that accounts were constrained at a minimum of 10 tpy. As with the previous HECT modeling, any cap assigned to accounts without qualifying point sourc es is distributed proportionally (by emissions)
to the remainder of the accounts. This treatment explains why the emissions from some of the
accounts exceed their HECT allocation. For th e new control strategy, each HRVOC species was reduced by 25% within the 2006 STARS reported HECT accounts. This necessitated the respeciation of the VOC distribution within the HECT accounts. New speciation profiles were generated using the procedure outlined in Sec tion 2.1, and a model run was performed to
determine the benefit to ozone at mon itors in the area. Table 2.4-1: HECT Accounts with Cap Reallocation and 25% Cap Reduction shows the resulting VOC and HRVOC emissions.Table 2.4-1: HECT Accounts with Cap Reallocation and 25% Cap ReductionTotal VOC Emissions (tpd)HRVOC Emissions (tpd)RNOwnerHECTCap (tpd)# HECTallocatedpathsReported200620182018 with25%ReductionReported200620182018 with25%ReductionRN100210319EQUISTAR LYONDELL CH0.4222111.23380.95670.84350.71700.43990.3268RN100210665Enterprise Products0.0274RN100210806INTERCONTINENTAL TER0.052840.03460.05500.04090.03460.05500.0409RN100211879SHELL OIL DEER PARK1.2535601.09381.86731.53140.53261.30610.9702RN100212109TOTAL PETROCHEMICALS0.37831202.19811.11561.01421.47670.39420.2928RN100212216Kuraray America Inc0.060810.03640.06910.05280.03070.06330.0471RN100213958Georgia Gulf Pasaden0.027440.02430.04110.04130.01170.02850.0287RN100214212Targa Galena Park0.061020.12460.13950.12320.04860.06350.0472RN100216761Basell Bayport0.275550.53670.48970.41590.33390.28700.2132RN100218130Lyondell Citgo Houst0.0432420.89730.81330.80170.12900.04500.0334RN100218247Albemarle0.141690.10140.19080.15290.05810.14750.1096RN100218411Odjfell Terminals0.0274RN100218841Kaneka Texas0.027440.14180.14160.14170.02880.02850.0287RN100219237Johann Haltermann 10.027420.00330.03000.03020.00180.02850.0287RN100219310Valero Houston Refin0.1491240.34610.38870.34870.11270.15530.1154RN100219526Texas Petrochemicals0.212460.21100.34140.28450.09090.22130.1644RN100221589Lubrizol Corporation0.0274100.01420.03850.03870.00420.02850.0287RN100224450Millenium LaPorte0.046630.06430.07280.06030.04010.04860.0361RN100225085DuPont LaPorte0.161760.08890.21100.16770.04640.16850.1252RN100225689BASF Pasadena0.049240.09510.12540.11220.02100.05130.0381RN100227016Celanese Clear Lake0.0971160.15470.13300.10700.12290.10120.0752RN100229905INEOS Polyethylene0.317190.46690.55950.47450.23780.33040.2454RN100524008Sunoco Bayport Polyp0.208890.33490.30760.25160.24480.21760.1616RN100542224Nova Chemicals Baypo0.027410.01620.04470.04490.000020.02850.0287RN100542281Equistar Channelview0.7348132.79932.56362.36671.00130.76570.5687RN100633650Lyondell Channelview0.1059110.52410.53870.51030.09570.11030.0819RN100683952NATURAL GAS ODORIZIN0.027420.01190.02850.02870.01190.02850.0287B-118B-119Total VOC Emissions (tpd)HRVOC Emissions (tpd)RNOwnerHECTCap (tpd)# HECTallocatedpathsReported200620182018 with25%ReductionReported200620182018 with25%ReductionRN100716661Pasadena Refining0.1651201.45901.37591.33170.25510.17210.1278RN100870898Goodyear Houston Che0.027490.25950.27120.27140.01680.02850.0287RN100909373Total Bayport HDPE0.1746140.15360.30110.25430.03450.18200.1352RN101041598LBC Houston Bayport0.0274RN101058410Lubrizol Corporation0.027410.00050.02900.02920.000030.02850.0287RN101379287American Acryl Pasad0.0274RN102018322Cheveron Phillips0.6226561.04781.38341.21660.31320.64870.4819RN102204211Kirby Inland Marine0.049710.09590.08160.06820.06610.05180.0385RN102212925ExxonMobil Baytown O0.7043302.42202.05001.86131.10580.73390.5451RN102523107Lyondell Chemical Ba0.208850.34740.49400.43810.07090.21760.1616RN102528197BP Amoco Chemical Co0.027470.02340.03260.03280.01930.02850.0287RN102537289INEOS Polypropylene0.380230.38510.50750.40560.27380.39620.2943RN102574803ExxonMobil Baytown C0.4467320.52160.68770.56800.29930.46540.3457RN102576063PL Propylene LLC*0.0699RN102579307ExxonMobil Baytown R0.30281424.00443.86393.78280.45600.31550.2344RN102580834Enterprise HSC0.0274RN102887270Nisseki Bayport0.0274RN102888328Sunoco LaPorte0.298940.12610.32040.24030.11720.31140.2314RN102926920Equistar Bayport0.068530.03680.08840.07000.01980.07140.0530RN103773206Sunoco Bayport Polye0.1193RN103919817Chevron Phillips Ced0.6349211.18411.30041.13020.54530.66160.4914RN104150123Dow Chemical Clear L0.0274TOTALS =9.456223.620524.050221.68629.02659.45627.0922

### B-120 3.0 ON-ROAD MOBILE SOURCE MODELING EMISSIONS DEVELOPMENT

3.1 On-Road Mobile Source Emissions Inventories for Eight-County HGB The purpose of this section is to provide a br ief overview of the eight-county Houston-GalvestonBrazoria (HGB) area on-road mobile source emissi ons inventory data that were input into the photochemical model for both the 2005 and 2006 b ase case episodes, along with the 2018 future case. These inventory data were developed under contract by th e Texas Transportation Institute (TTI) based on travel demand model output from the Houston-Galveston Area Council (H-GAC). For each of the eight HGB counties, TTI combined MOBILE6.2 emissions rate output with vehicle miles traveled (VMT) estimates from H-GAC’s travel demand model. The net result is referred to as a “link-based” inventory becau se both hourly VMT and emissions estimates are developed for each roadway segment or “link.” For each year, both school and summer (i.e., non-school) season on-road emissions inventories were developed for the four “day types” of weekday (i.e., Monday-Thursday), Friday, Saturday, and Sunday.

Table 3.1-1: VMT, NO

x , VOC, & CO Summary for 2005 HGB On-Road Inventory , Table 3.1-2:
VMT, NO

x , VOC, & CO Summary for 2006 HGB On-Road Inventory , and Table 3.1-3: VMT,

NO

x , VOC, & CO Summary for 2018 HGB On-Road Inventory provide summaries of the total VMT, NO

x , VOC, and CO emissions for the entire eight-county HGB area for each combination of season and day type for the 2005 base case, the 2006 base case, and the 2018 future case, respectively. For a specific season in both the base and future cases, the weekday inventories have similar VMT totals. Fridays have the high est total VMT of the week, while Saturdays and Sundays have the lowest total daily VMT. Great er detail on the activity differences with respect
to school and summer seasons can be f ound in an August 2007 TTI report entitled Development and Production of VMT Profiles for Non-School , Memorial Day, and Labor Day Weekends

, which is available at http://www.tceq.state.tx.us/implementati on/air/airm od/project/pj_report_mob.html

.

While overall VMT increases with future growth, total emissions decrease from 2005/2006 to 2018 as a result of more stringent emissions standards for newer vehicles entering the fleet,
combined with the simultaneous attrition of ol der, higher-emitting vehicles. Consistent with current federal and state rules, the on-road inventories from TTI include the benefits of reformulated gasoline (RFG), the inspection and ma intenance (I/M) Program in five of the eight HGB counties, and the use of Texas Low Emission Diesel (TxLED) for 2006 and 2018.

Table 3.1-1: VMT, NO

x , VOC, & CO Summary for 2005 HGB On-Road Inventory

On-Road Emissions (tpd) Season

Type Day Type Eight-County

VMT Total NO

x VOC CO
Weekday 131,238,085 233.48 95.14 1,244.97

Friday 141,870,236 233.10 103.75 1,353.90
Saturday 114,562,063 190.03 73.15 1,031.97 Summer

Sunday 96,699,512 151.49 61.90 880.50
Weekday 138,466,395 245.76 100.62 1,307.40

Friday 153,208,899 251.08 112.57 1,451.45
Saturday 122,299,838 202.54 78.10 1,098.61 School

Sunday 100,639,776 157.50 64.39 914.70

### B-121 Table 3.1-2: VMT, NO

x , VOC, & CO Summary for 2006 HGB On-Road Inventory

On-Road Emissions (tpd) Season

Type Day Type Eight-County

VMT Total NO

x VOC CO
Weekday 133,868,661 206.74 90.71 1,115.28

Friday 144,714,731 207.36 98.94 1,212.59
Saturday 116,859,389 168.18 69.49 920.02 Summer

Sunday 98,638,659 134.48 58.79 784.73
Weekday 141,240,451 217.59 95.96 1,171.31

Friday 156,278,685 223.32 107.40 1,300.20
Saturday 124,750,764 179.23 74.21 979.50 School

Sunday 102,656,939 139.81 61.16 815.29

Table 3.1-3: VMT, NO

x , VOC, & CO Summary for 2018 HGB On-Road Inventory

On-Road Emissions (tpd) Season

Type Day Type Eight-County

VMT Total NO

x VOC CO
Weekday 180,993,087 52.55 45.97 733.18

Friday 197,299,034 54.70 50.59 804.87
Saturday 157,889,621 42.95 35.07 597.65 Summer

Sunday 132,325,767 34.82 29.39 505.01
Weekday 189,613,165 54.88 48.25 764.81

Friday 209,417,787 57.87 53.86 849.66
Saturday 164,569,357 44.65 36.52 620.84 School

Sunday 136,020,863 35.73 30.18 517.86

For photochemical modeling applications, the HGB on-road emission inventories were matched to specific ozone episodes by season and day type. Table 3.1-4: School Year Start & Stop Days for the Houston Independent School District provides a summary of the dates in 2005 and 2006 when school ended in May, and then started up ag ain in August. This information was obtained from the Houston Independent School District (HISD) website at http://www.houstonisd.org/

.

Table 3.1-4: School Year Start & Stop Days for the Houston Independent School District School Year Start/Stop Date

2004-2005 School Year Ends Thursday May 26, 2005
2005-2006 School Year Begins Monday August 15, 2005

2005-2006 School Year Ends Thursday May 25, 2006
2006-2007 School Year Begins Monday August 14, 2006

Even though all of the season and day type on-ro ad inventory combinations were used, only the summer weekday will be detailed here. For th e 2005 base case, 2006 base case, and 2018 future case, Table 3.1-5: Summary of 2005 HGB Summer Weekday On-Road Inventory by County

, Table 3.1-6: Summary of 2006 HGB Summer Weekday On-Road Inventory by County , and Table

3.1-7: Summary of 2018 HGB Summer Weekday On-Road Inventory by County present respective summaries of the VMT, NO

x , VOC, and CO emissions for each of the eight counties
in the HGB area

.

### B-122 Table 3.1-5: Summary of 2005 HGB Summer Weekday On-Road Inventory by County

Vehicle Miles Traveled On-Road Emissions (tpd) HGB County Total Distribution NO

x VOC CO

Brazoria 5,632,467 4.29% 9.81 3.92 53.43

Chambers 2,463,364 1.88% 5.78 1.98 34.08

Fort Bend 7,770,902 5.92% 13.41 4.90 69.26

Galveston 5,799,633 4.42% 9.94 4.17 54.20

Harris 95,386,290 72.68% 166.90 69.45 882.83
Liberty 2,476,652 1.89% 4.97 2.39 32.32
Montgomery 9,817,032 7.48% 18.35 6.50 92.20

Waller 1,891,745 1.44% 4.32 1.82 26.65

Total 131,238,085 100.00% 233.48 95.14 1,244.97

Table 3.1-6: Summary of 2006 HGB Summer Weekday On-Road Inventory by County

Vehicle Miles Traveled On-Road Emissions (tpd) HGB County Total Distribution NOx VOC CO

Brazoria 5,728,510 4.28% 8.70 3.66 47.04

Chambers 2,519,042 1.88% 5.07 1.82 29.62

Fort Bend 7,994,337 5.97% 11.97 4.66 62.02

Galveston 5,850,101 4.37% 8.70 3.89 47.37

Harris 97,155,857 72.58% 147.48 66.43 792.44
Liberty 2,529,328 1.89% 4.50 2.29 29.30
Montgomery 10,141,863 7.58% 16.42 6.17 82.88

Waller 1,949,623 1.46% 3.90 1.79 24.60

Total 133,868,661 100.00% 206.74 90.71 1,115.28

Table 3.1-7: Summary of 2018 HGB Summer Weekday On-Road Inventory by County

Vehicle Miles Traveled On-Road Emissions (tpd) HGB County Total Distribution NO

x VOC CO

Brazoria 9,116,171 5.04% 2.61 2.17 36.60

Chambers 3,998,297 2.21% 1.76 1.24 24.85

Fort Bend 14,058,858 7.77% 3.82 3.24 53.11

Galveston 6,750,271 3.73% 1.89 1.70 26.46

Harris 125,484,043 69.33% 35.10 31.62 491.05
Liberty 3,751,678 2.07% 1.71 1.41 23.64
Montgomery 15,273,310 8.44% 4.39 3.59 59.96

Waller 2,560,459 1.41% 1.27 1.00 17.52

Total 180,993,087 100.00% 52.55 45.97 733.18

Table 3.1-8: Summary of 2005 HGB On-Road Inventory by Vehicle Type , Table 3.1-9: Summary of 2006 HGB On-Road Inventory by Vehicle Type , and Table 3.1-10: Summary of 2018 HGB OnRoad Inventory by Vehicle Type present respective summaries for 2005, 2006, and 2018 of the VMT, NO

x , VOC, and CO emissions for each of the 28 vehicle classes in MOBILE6.2.

### B-123 Table 3.1-8: Summary of 2005 HGB Summer Weekday On-Road Inventory by Vehicle Type

Vehicle Miles Traveled On-Road Emissions (tpd) MOBILE6.2 Vehicle Type Total Distribution NO

x VOC CO

LDGV 78,139,639 59.54% 56.12 58.67 762.11

LDGT1 7,026,183 5.35% 5.07 5.44 74.83

LDGT2 23,390,024 17.82% 23.01 18.88 266.82

LDGT3 7,179,960 5.47% 6.16 3.60 63.98

LDGT4 3,301,919 2.52% 3.99 1.80 30.36

HDGV2b 916,044 0.70% 3.86 0.64 7.64

HDGV3 321,103 0.24% 1.63 0.29 3.32

HDGV4 127,139 0.10% 0.61 0.11 1.13

HDGV5 45,637 0.03% 0.26 0.07 0.79

HDGV6 123,880 0.09% 0.75 0.20 2.00

HDGV7 40,749 0.03% 0.29 0.07 0.68

HDGV8a 47,270 0.04% 0.39 0.12 1.30

HDGV8b 8,148 0.01% 0.08 0.02 0.22

LDDV 104,876 0.08% 0.16 0.06 0.17

LDDT12 17,269 0.01% 0.05 0.05 0.08

HDDV2b 1,978,078 1.51% 7.11 0.35 1.87

HDDV3 565,868 0.43% 2.61 0.13 0.62

HDDV4 339,522 0.26% 1.91 0.09 0.44

HDDV5 241,108 0.18% 1.41 0.07 0.34

HDDV6 723,328 0.55% 6.13 0.31 1.09

HDDV7 354,283 0.27% 3.91 0.20 0.70

HDDV8a 718,404 0.55% 13.32 0.44 2.52

HDDV8b 4,766,120 3.63% 86.91 2.52 14.04

MC 131,238 0.10% 0.16 0.37 2.00

HDGB 55,530 0.04% 0.53 0.29 4.45

HDDBT 155,519 0.12% 3.14 0.09 0.68

HDDBS 269,621 0.21% 3.75 0.20 0.70

LDDT34 149,626 0.11% 0.17 0.06 0.11

Total 131,238,085 100.00% 233.48 95.14 1,244.97

### B-124 Table 3.1-9: Summary of 2006 HGB Summer Weekday On-Road Inventory by Vehicle Type

Vehicle Miles Traveled On-Road Emissions (tpd) MOBILE6.2 Vehicle Type Total Distribution NO

x VOC CO

LDGV 79,713,513 59.55% 51.28 55.48 673.55

LDGT1 7,166,631 5.35% 4.80 5.28 67.15

LDGT2 23,857,629 17.82% 22.48 18.42 244.17

LDGT3 7,324,561 5.47% 5.98 3.45 59.33

LDGT4 3,368,346 2.52% 3.94 1.74 28.21

HDGV2b 934,741 0.70% 3.41 0.58 6.71

HDGV3 327,657 0.24% 1.50 0.27 2.87

HDGV4 129,734 0.10% 0.59 0.11 1.01

HDGV5 46,569 0.03% 0.24 0.07 0.66

HDGV6 126,408 0.09% 0.69 0.18 1.64

HDGV7 41,581 0.03% 0.28 0.07 0.60

HDGV8a 48,234 0.04% 0.38 0.12 1.07

HDGV8b 8,315 0.01% 0.08 0.02 0.20

LDDV 92,401 0.07% 0.11 0.05 0.13

LDDT12 15,280 0.01% 0.05 0.04 0.07

HDDV2b 2,018,614 1.51% 6.42 0.34 1.91

HDDV3 577,464 0.43% 2.32 0.12 0.63

HDDV4 346,479 0.26% 1.71 0.09 0.44

HDDV5 246,049 0.18% 1.26 0.07 0.35

HDDV6 738,151 0.55% 5.43 0.29 1.08

HDDV7 361,543 0.27% 3.50 0.19 0.69

HDDV8a 733,126 0.55% 11.56 0.42 2.38

HDDV8b 4,863,149 3.63% 71.54 2.37 13.68

MC 133,869 0.10% 0.16 0.37 1.84

HDGB 50,184 0.04% 0.47 0.25 3.42

HDDBT 158,782 0.12% 2.86 0.08 0.65

HDDBS 281,531 0.21% 3.55 0.20 0.72

LDDT34 158,122 0.12% 0.16 0.06 0.11

Total 133,868,661 100.00% 206.74 90.71 1,115.28

### B-125 Table 3.1-10: Summary of 2018 HGB Summer Weekday On-Road Inventory by Vehicle Type

Vehicle Miles Traveled On-Road Emissions (tpd) MOBILE6.2 Vehicle Type Total Distribution NO

x VOC CO

LDGV 108,995,558 60.22% 17.35 25.85 439.58

LDGT1 8,888,147 4.91% 1.72 2.67 42.13

LDGT2 29,588,480 16.35% 8.41 9.98 160.85

LDGT3 11,625,768 6.42% 2.29 2.33 47.41

LDGT4 5,346,330 2.95% 1.60 1.17 22.84

HDGV2b 1,487,577 0.82% 0.47 0.24 7.41

HDGV3 383,753 0.21% 0.27 0.09 2.57

HDGV4 107,797 0.06% 0.05 0.02 0.70

HDGV5 38,805 0.02% 0.03 0.02 0.29

HDGV6 77,613 0.04% 0.07 0.03 0.60

HDGV7 21,562 0.01% 0.02 0.01 0.19

HDGV8a 34,496 0.02% 0.14 0.05 0.34

HDGV8b 4,315 0.00% 0.01 0.00 0.06

LDDV 97,920 0.05% 0.01 0.01 0.06

LDDT12 0 0.00% 0.00 0.00 0.00

HDDV2b 3,390,752 1.87% 1.18 0.36 0.40

HDDV3 820,990 0.45% 0.43 0.10 0.13

HDDV4 387,133 0.21% 0.34 0.06 0.10

HDDV5 340,412 0.19% 0.26 0.05 0.07

HDDV6 780,943 0.43% 0.95 0.16 0.21

HDDV7 300,363 0.17% 0.54 0.08 0.12

HDDV8a 654,123 0.36% 2.07 0.21 0.51

HDDV8b 6,567,971 3.63% 10.83 1.94 2.80

MC 180,993 0.10% 0.12 0.25 2.86

HDGB 23,282 0.01% 0.09 0.03 0.25

HDDBT 220,737 0.12% 1.03 0.05 0.22

HDDBS 433,371 0.24% 2.25 0.18 0.41

LDDT34 193,899 0.11% 0.03 0.02 0.06

Total 180,993,087 100.00% 52.55 45.97 733.18

The MOBILE6.2 input files used to develop th ese inventories, along with detailed reports and tab-delimited summary output data, can be f ound on the following FTP sites for 2005, 2006, and 2018, respectively:

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/Mobile_EI/HGB/m62/2005/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/Mobile_EI/HGB/m62/2006/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/Mobile_EI/HGB/m62/2018/

The on-road emissions inventory data provided by TTI were prepared for input into the photochemical model using Version 3 of the Emissions Processing System (EPS3). When input into EPS3, the inventory data are in a “readabl e” text-based format. However, once within EPS3, the emissions data are maintained in a binary format. Table 3.1-11: EPS3 Modules Used to

### B-126 Process Eight-County HGB On-Road Emissions Data summarizes the EPS3 modules that were used to process the eight-county HGB on-road inventories.

Table 3.1-11: EPS3 Modules Used to Pro cess Eight-County HGB On-Road Emissions Data

EPS3 Module Description

LBASE Spatially allocate link-based emissions among grid cells

PREAM Prepare non-link “roadway type” emissions for further processing

PREPNT Prepare stationary extended idling emissions for further processing

CNTLEM Apply controls to model strategies, apply adjustments, etc.

TMPRL Apply temporal profiles to extended idling emissions

CHMSPL Chemically speciate emissions into NO, NO 2 , Olefins, Parrafins, etc.

GRDEM Sum emissions by grid ce ll for photochemical model input

MRGUAM Merge and adjust multiple gridded files for photochemical model input

The EPS3 CNTLEM module was used to:

 Remove 3.4 percent of the HDDV8a and HDDV8b (“18-wheeler”) emissions for separate processing as “extended idling” emissions in accordance with the January 2004 EPA Guidance for Quantifying and Using Long Duration Truck Idling Emission Reductions in State Implementation Plans and Transportation Conformity ; and

 Apply a temperature/humidity NO X correction to both diesel and heavy-duty gasoline
vehicles.

The MOBILE6.2 model accounts for the effects that changes in hourly temperature and humidity
have on NO

x emissions for only six of the 28 total ve hicle types. These vehicle types are the MOBILE6.2 light-duty gasoline vehicle (LDGV), li ght-duty gasoline trucks 1-4 (LDGT1-4), and motorcycle (MC) classes. There is no temperature/humidity NO

x correction in MOBILE6.2 for the remaining 22 vehicle classes, which include all 13 of the diesel-powered vehicle classes and the nine heavy-duty gasoline vehicle classes. Unde r contract to the Houston Advanced Research Center (HARC), Environ worked with the Sout hwest Research Institute (SwRI) to develop temperature/humidity NO

x correction equations to apply to both the 13 diesel and nine heavyduty gasoline vehicle classes in MOBILE6.2. Th ese equations reflect the fact that as ambient temperature increases, tailpipe NO

x emissions increase. However, as ambient humidity increases, tailpipe NO

x emissions decrease. Greater detail on the development of these correction equations can be found in Appendices F.4 a nd F.5 of the December 2004 HGB SIP revision,
which can be found at http://www.tceq.state.tx.us/implementation/air/sip/dec2004hgb _mcr.html

.

The EPS3 CNTLEM module referenced above in Table 3.1-11 allows the user to apply a different

NO

x , VOC, and/or CO correction for each hour, episode day, county, and vehicle type combination. TCEQ modeling staff developed St atistical Analysis Software (SAS) code to calculate the appropriate NO X adjustment factors for each county and vehicle type using hourly inputs for temperature, relative humidity, and ba rometric pressure. The hourly temperature, relative humidity, and barometric pressure inputs used by the SAS software were also used by TTI in its development of the 2005, 2006, and 2018 HGB on-road inventories. These meteorological data were obtained from National Weather Service (NWS) and TCEQ monitors in the HGB area for the time periods of May 15-September 15, 2005 and May 15-September 15, 2006. The spreadsheets that were developed to obtain the final hourly inputs are available at:

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/Mobile_EI/HGB/m62/2005/ ; and

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/Mobile_EI/HGB/m62/2006/

.

Table 3.1-12: Summary of 2005 Temperature/Humidity On-Road NO

x Correction , Table 3.1-13: Summary of 2006 Temperature/Humidity On-Road NO

x Correction , and Table 3.1-14: Summary

### B-127 of 2018 Temperature/Humidity On-Road NO

x Correction present the temperature and humidity corrections applied to each combination of season and day type for the 2005 base case, 2006 base case, and 2018 future case, respectively. Fo r each episode day, there are greater NO

x reductions during the overnight and early morning hours when the temperature is at its minimum and the relative humidity is at its maximum. However, during the hottest hours of the afternoon when the relative humidity is at its lowest, the temperature/humidity NO

x correction either decreases NO

x very slightly or increases it somewhat, depending upon the specific conditions for that hour. Overall, the temperature/humidity NO

x correction procedure allows not only for improved
estimates of the total on-road NO

x emissions, but also for improved spatial and temporal
allocation of those emissions.

Table 3.1-12: Summary of 2005 Temperature/Humidity On-Road NO

x Correction

Season

Type Day Type Temperature/Humidity NO X Reduction (tpd)

Weekday 11.79

Friday 11.11
Saturday 9.28 Summer

Sunday 6.73
Weekday 12.39

Friday 11.96
Saturday 9.86 School

Sunday 6.97

Table 3.1-13: Summary of 2006 Temperature/Humidity On-Road NO

x Correction

Season

Type Day Type Temperature/Humidity NO X Reduction (tpd)

Weekday 9.45

Friday 8.91
Saturday 7.40 Summer

Sunday 5.40
Weekday 9.93

Friday 9.59
Saturday 7.87 School

Sunday 5.59

Table 3.1-14: Summary of 2018 Temperature/Humidity On-Road NO

x Correction

Season

Type Day Type Temperature/Humidity NO X Reduction (tpd)

Weekday 1.78

Friday 1.70
Saturday 1.37 Summer

Sunday 0.99
Weekday 1.86

Friday 1.80
Saturday 1.43 School

Sunday 1.01

### B-128 The 2005, 2006, and 2018 on-road inventories for the eight-county HGB area include the benefits of RFG and I/M. The summer weekday RFG bene fits for 2005, 2006, and 2018 are provided in
Table 3.1-15: Summary of 2005 Summer Weekday RFG Benefits by County , Table 3.1-16: Summary of 2006 Summer Weekday RFG Benefits by County , and Table 3.1-17: Summary of 2018 Summer Weekday RFG Benefits by County , respectively.

Table 3.1-15: Summary of 2005 Summer Weekday RFG Benefits by County

Emission Benefits (tpd) HGB County NO X VOC CO

Brazoria 0.08 0.69 4.79

Chambers 0.04 0.28 2.40

Fort Bend 0.09 0.90 6.08

Galveston 0.08 0.74 4.61

Harris 1.31 12.93 81.13
Liberty 0.03 0.39 2.87
Montgomery 0.11 1.18 8.62

Waller 0.02 0.29 2.34

Total 1.75 17.40 112.84

Table 3.1-16: Summary of 2006 Summer Weekday RFG Benefits by County

Emission Benefits (tpd) HGB County NO X VOC CO

Brazoria 0.08 0.62 4.50

Chambers 0.04 0.25 2.14

Fort Bend 0.11 0.82 5.88

Galveston 0.09 0.67 4.33

Harris 1.54 11.92 77.42
Liberty 0.04 0.36 2.67
Montgomery 0.14 1.08 8.26

Waller 0.03 0.27 2.24

Total 2.07 15.97 107.45

Table 3.1-17: Summary of 2018 Summer Weekday RFG Benefits by County

Emission Benefits (tpd) HGB County NO X VOC CO

Brazoria 0.04 0.39 3.63

Chambers 0.02 0.15 1.58

Fort Bend 0.05 0.64 5.51

Galveston 0.03 0.31 2.46

Harris 0.57 6.18 50.37
Liberty 0.02 0.20 1.82
Montgomery 0.05 0.68 6.15

Waller 0.01 0.13 1.31

Total 0.78 8.69 72.84

### B-129 The summer weekday I/M program benefits for 2005, 2006, and 2018 are provided in Table 3.1-

18: Summary of 2005 Summer Weekday I/ M Program Benefits by County , Table 3.1-19: Summary of 2006 Summer Weekday I/ M Program Benefits by County , and Table 3.1-20: Summary of 2018 Summer Weekday I/M Program Benefits by County , respectively.

Table 3.1-18: Summary of 2005 Summer Weekday I/M Program Benefits by County

Emission Benefits (tpd) HGB County NO X VOC CO

Brazoria 0.60 0.52 9.36

Fort Bend 0.76 0.66 11.58

Galveston 0.62 0.55 9.48

Harris 10.47 9.28 159.57
Montgomery 1.02 0.85 15.74

Total 13.47 11.86 205.72

Table 3.1-19: Summary of 2006 Summer Weekday I/M Program Benefits by County

Emission Benefits (tpd) HGB County NO X VOC CO

Brazoria 0.62 0.51 9.03

Fort Bend 0.80 0.68 11.60

Galveston 0.62 0.55 9.14

Harris 10.75 9.39 156.22
Montgomery 1.06 0.86 15.53

Total 13.86 11.99 201.52

Table 3.1-20: Summary of 2018 Summer Weekday I/M Program Benefits by County

Emission Benefits (tpd) HGB County NO X VOC CO

Brazoria 1.01 0.72 13.98

Fort Bend 1.49 1.09 20.29

Galveston 0.73 0.54 10.08

Harris 14.30 10.45 192.09
Montgomery 1.61 1.13 22.03

Total 19.14 13.93 258.47

Based on an EPA memorandum entitled Texas Low Emission Diesel (LED) Fuel Benefits

, September 27, 2001, a 4.8 percent NO

x TxLED benefit should be claimed for 2002-and-newer diesel vehicles and a 6.2 percent NO

x TxLED benefit should be claimed for 2001-and-older diesel vehicles. In order to determine the specific TxLED adjustment factors that should apply to each of the 13 diesel vehicle types, MOBILE6.2 runs were performed for the HGB area to
determine both VMT and NO

x emissions rates by model year. By using these data, the 4.8 percent and 6.2 percent reduction fact ors were weighted according to NO

x model year contributions for each vehicle type. The resultin g TxLED adjustment factors and benefits for
both 2006 and 2018 are summarized in Table 3.1-21: Summary of 2006 Summer Weekday TxLED Benefits by Vehicle Type and Table 3.1-22: Summary of 2018 Summer Weekday TxLED Benefits by Vehicle Type , respectively. The TxLED adjustment factors were incorporated by TTI into the on-road inventories by post-processing the MOBILE6.2 diesel NO

x emission rates.

### B-130 he 2005 on-road inventory. Since the TxLED rule was not in effect in th e summer of 2005, adjustment factors were not applied to t

Table 3.1-21: Summary of 2006 Summer W eekday TxLED Benefits by Vehicle Type

2006 TxLED Benefits Diesel
Vehicle

Type NO X Reduction Adjustment

Factor Benefit (tpd)

LDDV 6.14% 0.9386 0.0069

LDDT12 6.20% 0.9380 0.0027

HDDV2b 5.13% 0.9487 0.3215

HDDV3 5.45% 0.9455 0.1235

HDDV4 5.58% 0.9442 0.0937

HDDV5 5.47% 0.9453 0.0676

HDDV6 5.69% 0.9431 0.3032

HDDV7 5.75% 0.9425 0.1979

HDDV8a 5.95% 0.9405 0.6760

HDDV8b 5.78% 0.9422 4.0481

HDDBT 5.87% 0.9413 0.1647

HDDBS 5.87% 0.9413 0.2040

LDDT34 5.53% 0.9447 0.0082

Total Diesel 5.75% 0.9425 6.2180

Table 3.1-22: Summary of 2018 Summer W eekday TxLED Benefits by Vehicle Type

2018 TxLED Benefits Diesel
Vehicle

Type NO X Reduction Adjustment

Factor Benefit (tpd)

LDDV 5.39% 0.9461 0.0003

LDDT12 6.20% 0.9380 0.0000

HDDV2b 4.88% 0.9512 0.0556

HDDV3 4.90% 0.9510 0.0206

HDDV4 4.96% 0.9504 0.0163

HDDV5 5.08% 0.9492 0.0126

HDDV6 5.05% 0.9495 0.0464

HDDV7 5.09% 0.9491 0.0263

HDDV8a 5.30% 0.9470 0.1063

HDDV8b 4.96% 0.9504 0.5179

HDDBT 5.05% 0.9495 0.0499

HDDBS 5.63% 0.9437 0.1231

LDDT34 4.91% 0.9509 0.0016

Total Diesel 5.08% 0.9492 0.9769

The on-road control strategy benefits referenced above are summarized below in Table 3.1-23: Summary of 2005 Summer Weekday On -Road Control Strategy Benefits , Table 3.1-24: Summary of 2006 Summer Weekday On-Road Control Strategy Benefits , and Table 3.1-25: Summary of 2018 Summer Weekday On-Road Control Strategy Benefits . When determining the amount of benefit assigned to specific on-road control stra tegies, the sequence in which they are modeled

### B-131 with MOBILE6.2 is important. For example, the RFG benefit for each county was determined by calculating the difference between a MOBILE6.2 r un with conventional gasoline and a run with RFG. For both runs, the I/M program was not m odeled and all other inputs were held constant. Then, the I/M program benefits were determ ined by calculating the difference between a
MOBILE6.2 run with RFG and a run with both RF G and I/M. Please note that if the benefits were calculated in a different order, the benefit assigned to each individual strategy would vary, but the magnitude of the combined benefits would not change. Sin ce RFG and I/M affect gasoline-powered vehicles only, it does not ma tter where in the sequence TxLED is modeled because it only affects diesel-powered vehicles.

Table 3.1-23: Summary of 2005 Summer Weekday On-road Control Strategy Benefits
Emission Benefits (tpd) 2005 On-Road

Control Strategy NO X VOC CO

RFG 1.75 17.40 112.84

13 5.72 I/M .47 1.86 1 20

Total 15.21 29.26 318.56

T able 3.1-24: Summary of 2006 Summer Weekday On-road Control Strategy Benefits
Emission Benefits (tpd) 2006 On-Road

Control Strategy NO X VOC CO

RFG 2.07 1 5.97 10 7.45

I/M 13.86 11.99 201.52

TxLED 6.22 0.00 0.00

Total 22.14 27.96 308.96

Table 3.1-25: Summary of 2018 Summer Weekday On-road Control Strategy Benefits
Emission Benefits (tpd) 2018 On-Road

Control Strategy NO X VOC CO

RFG 0.78 8.69 72.84

### B-132 0.00

Total 20.90 22.62 331.31

EPA issued a t in January 2004 entitled Guidance for Quantifying and Using Long

Duration Tru mission Reduc State Implementation Plans an portation Conformity. uidance states nded idling ns account ercent of
the total emis lated with MOBILE6.2 for the HD V8b vehicle classes. As previously stated, the CNTLEM module was u sed to re 4 percent of rly NO

x , VOC, and CO s from the “running” emissi ons prepared for photochem input from the HDDV8a and HDDV8b classes. Using a combin SAS and UNIX code, these
extended id were aggre to an eight 24-hour total and spatially assigned to stop locations extended idling emissions were then processed through EPS3 a were stationary l point sources. The emissions were temporally
allocated as the inverse of HDDV8a/HD MT. Consequently, more of the extended idling

missions were allocated during overnight hours rather than daytime hours. The extended idling

NO

x ion. The summer weekday extended idli ng emissions by county are presented below in 2005 HDDV8a & HDDV8b Extended Idling Emissions for the Eight-County HGB

, Table 3.1-27: V8a & HDDV8b , and
Table 3.1-28: DV8a & HDDV8b Extended Idling Emissions for Eight-County HGB

. Greater detail duty vehicle idl vity specific to Texas metropolitan areas can be found in repo n the following e: http://www.tceq.state.tx.us/implementati irmod/project/pj_report_mob.htm I/M 19.14 13.93 258.47

TxLED 0.98 0.00

documen ck Idling E This EPA g tions in
that exte d Trans for 3.4 p emissio
sions calcu DV8a and HDD

move 3. the hou

em ission ical model
ation of ling emissions
known truck gated in
. The -county

s if they low-leve
DV8b V

e emissions were also run through the CNTLEM module to receive a temperature/humidity

correct
Table 3.1-26:

2006 HDD

2018 HD Extended Idling Emissions for Eight-County HGB

on heavy- ing acti
rts located o web sit
on/air/a l

Table 3.1-26 V8a & HDDV ended Idlin ions for Eig nty
HGB : 2005 HDD 8b Ext g Emiss ht-Cou

Exten ded Idling Emissions (tpd) HGB County NO

x VOC CO

B

razoria 0.0145 0.0005 0.0027

C s

hamber 0.1793 0.0061 0.0343

Fort Bend 0.2940 0.0098 0.0547

Galveston 0.0502 0.0016 0.0093

Harris 1 0.3380 .8163 0.0605
Liberty 0. 0468 0015 .0086 0. 0
M y

ontgomer 0.3967 0.0131 0.0730

Waller 0.2297 0.0075 0.0421

Total 3.0275 0.1006 0.5627

Table 3.1-27 V8a & HDDV ended Idlin ions for Eig nty
HGB : 2006 HDD 8b Ext g Emiss ht-Cou

Exten ded Idling Emissions (tpd) HGB County NO

x VOC CO

B

razoria 0.0122 0.0005 0.0026

C s

hamber 0.1506 0.0058 0.0333

Fort Bend 0.2462 0.0092 0.0532

### B-133 Extended Idling Emissions (tpd) HGB County NO

x VOC CO

Galveston 0.0419 0.0015 0.0090

Harris 1.5224 0.0569 0.3280
Liberty 0.0393 0.0014 0.0083
Montgomery 0.3327 0.0122 0.0708

Waller 0.1906 0.0071 0.0410

Total 2.53 0.5462 59 0.0946

Table 3.1-28: 201 HDDV8b Extended Idling Emissions for Eight-County
HGB 8 HDDV8a &

Extended Idl ing Emissions (tpd) HGB County NO

x V OC CO

Brazoria 0 003 0 .0019 0.0 .0006

C s 044

hamber 0.0231 0.0 0.0069

Fort Bend 0 071 0 .0378 0.0 .0110

Galveston 0.0064 0.0 012 0 .0019

Harris 0 438 0 .2335 0.0 .0676

L 011

iberty 0.0061 0.0 0.0018
Montgomery 0.0510 0.0094 0.0146

Waller 0.0295 0.0084 0.0054

Total 0 .3893 0.1128 0.0727

The summer w -road emissions b nty th at were input into the photo model
are summari in Table 3.1-29 05 Summer Weekday On-Road e Source Inventory by le 3.1-30: 2006 Summer Weekday On-Road Mobile Source Inventory by County , and Table 3.1-31: 2018 Summer Weekday On-Road Mobile Source Inventory by County

. The on-road i mmaries in Ta -29, 3.1-3 1-31 are a com n of both running and extended idling emissions. ion, the t ture/humidity N ction has been applied as ized above in Tables 3.1-12, 3.1-1 1-14.

Table 3.1-29: 2005 Summer Weekday O by County eekday on y cou chemical
zed below : 20 Mobil County , Tab

nventory su bl
In addit

es 3.1 0

empera

, and 3. binatio

O

x corre

summar 3, and 3.

n-R oad Mobile Source Inventory

On -Road E ns (tpd) missio HGB County NO

x VOC CO

Brazoria 9.19 3.92 53.41

C s 5.5 34.10

hamber 5 1.98

Fort Bend 12.74 91 69.27 4.

Galveston 9.26 4 .17 54.18

Harris 1 69 8 58.29 .44 82.75
Liberty 4.75 2 .38 32.32

M 6

ontgomery 17.60 .51 92.24

Waller 4.31 1.83 26.68

Total 2 95 1,2 21.68 .14 44.95

Table 3.1-30: 2006 Summer Weekday On-R oad Mobile Source Inventory by County

HGB O n-Road Em s (tpd) ission

### B-134 County NO

x VOC CO

Brazoria 8.24 3.66 47.02

Chambers 4.90 1.82 29.63

Fort Bend 11.46 4.66 62.03

Galveston 8.16 3.89 47.36

Harris 140.45 66.42 792.36
Liberty 4.34 2.29 29.3 0

M ontgomery 15.87 2.91 6.18 8

Waller 3.88 1.79 24. 63

Total 197.29 90.71 1,115.26

Table 018 Sum kday On-R oad Mobile Source Inventory by Coun 3.1-31: 2 m er Wee ty

On-Road Emissions (tp d) HGB County NO

x VO C C O

Brazoria 2.50 2.17 36.59

C hambers 1.72 1.25 24.85

Fort Bend 3.67 3.24 53.11

Galveston 1.80 1.69 26.46

Harris 33.90 31.62 491.04
Liberty 1.67 3.64 1.41 2

M ontgomery 4.25 3.59 59. 97

Waller 1.26 1.01 17.52

Total 50.76 45.97 733.18

The total eight-county HG emissions in e photoche odel by com of season and day type a rized below in Table 3.1-32: 2005 On-Road Mobil e Inventory by Season and , Table 3.1-33 On-Road Mobile Source Inventory by Season and Day Type , an 2018 On-Road Source I by Season y Type

.

able 3.1-32: 2005 On-Road Mobile Source Inventory by Season and Day Type B on-road put to th mical m bination

re summa e Sourc Day Type : 2006

d 3-34: Mobile nventory and Da

T

On-Road Emissions (tpd) Season

Type Day Type NO

x VOC CO
Weekday 221.68 .14 44.95 95 1,2
Friday 2 1 1 23.69 03.76 ,359.37

S 1 1

aturday 80.74 73.07 ,032.64 Summer

1 Sunday 43.18 61.86 873.70

W 2 1 1 eekday 33.36 00.61 ,307.38
Friday 2 1 1 41.07 12.59 ,457.81

S 1 1

aturday 92.68 78.00 ,099.40 School

1 Sunday 48.69 64.32 906.88

able 3.1-33: 2006 On-Road Mobile Source Inventory by Season and Day Type T

On-Road Emissions (tpd) Season

Type Day Type NO

x VOC CO

### B-135 Weekday 197.29 90.71 1,115.26

Friday 199.93 98.94 1,217.38
Saturday 160.78 69.41 920.60 Summer

778.75 Sunday 127. 69 58.76
Weekday 207. 6 95.9 ,171.29 6 6 1
Friday 215.44 7.41 05.79 10 1,3
Saturday 171.38 4.10 17 7 980. School

132.60 1.12 41 Sunday 6 808.

Table 3.1-34: 2018 On-Road Mobile Source Inventory by Season and Day Type

On-Road Emissions (tpd) Season

Type Day Type NO

x VOC CO
Weekday 50.76 45.97 733.18

Friday 53.35 50.61 808.09
Saturday 41.59 35.03 597. 98 Summer

Sunday 33.48 29.34 500.97

Weekda y 53. 764.81 02 48.25

Fri day .13 56.46 53.89 853
Saturday 43.2 36.48 62 5 1.40 School

Sunday 34.3 30.12 51 2 3.33

An addition for the 2018 emis sion inventories was necessary to app e

1.55 NO emission reduction pro gram (V on-road b s
subm X emissions are broken into bot y and - duty c 8 Distribution mmer Week -Road R g

Emissions

r Week Road Run issions al post-processing step tpd of voluntary mobile ly th
enefit X source itted by H-GAC. The non-idling NO M h light-dut EP) heavy ategories in Table 3.1-35: 201 of Su day On unnin

.

T able 3.1-35: 2018 Distribution of Summe day On- ning Em

On-Road NO X (tpd) Vehicle Grouping Emissions Contribution
Light-Duty 31.53 62.58%
Heavy-Duty 18.85 37.42%
Total "Running" Emissions 50.37 100.00%

Table 3.1-36: Allocation of 2018 On-Road VMEP NO X Benefits demonstrates how the relative
contributions from Table 3.1-35 were used t flow improvements into light-duty and ty categories. Altern ere applied only to the light-duty portion of the fleet, while retrofit and replacement benefits were applied only to the heavy-duty po were incorporated into the on-road emission inventories with the EPS3 CNTLEM mod y using the N justment factors shown.

Table 3.1-36 of 2018 On-Road VMEP NO X B ts o split the regional traffic
ativ e commuting benefits w h u

eavy-d

rtion. These VMEP benefits
ule b O X ad

: Allocation enefi

On-Road NO X Emission s (tpd) On-Road V MEP

Param eter Light-Duty Heavy-Duty T otal
Regional Traffic Flow Improvements 0.0 0.02 0.05 3
Alternative Commuting 0.2 0.00 0.20 0
Vehicle Retrofit and Replacement 0.0 1.30 1.30 0

### B-136 On-Road NO X Emissions (tpd) On-Road VMEP

Parameter Light-Duty Heavy-Duty Total

Total On-Road VMEP 0.23 1.32 1.55

R missions W VMEP 50.37

unning E ithout 31.53 18.85
Running Emissions With VMEP 31.29 17.53 48.82

Relative NO X 3% 0% Reduction 0.7 7.0 3.08%

EPS3 CNTLEM NO X A djustment Factors 0.99266 0.93004

After application of the V ustment factors, 18 county-level emissions figu
Table 3.1-31 were modi esented in Tabl 37: 2018 S Weekday O Mobile Source Inventory by County With VMEP . T e set of VM ustment fact e applied to all of the seaso types, wh ich resulted in the 2018 ons from Table 3.1- 34 modified as presented in Table 3.1-38: 2018 On-Road Mobile S Inventory by Season and Day Type With VMEP MEP adj ied as pr the 20

e 3.1- res from

n-Road f ummer he sam EP adj ors wer n and day emissi

ource

.

Table 3.1-37: 2018 Summer Weekday On-Road Mobile Inventory by County With VMEP

On-Road Emissions (tpd) HGB County NO

x VOC CO

Brazoria 2.42 2.17 36.59

C hambers 1.67 1.25 24.85

F ort Bend 3.55 3.24 53.11

Galveston 1.74 1.69 26.46

Harris 32.86 31.62 491.04
Liberty 1.63 1.41 23.64
Montgomery 4.11 3.59 59.97

W aller 1.23 1.01 17.52

T otal 49.21 45.97 733.18

Table 3.1-38: 2018 On-Road Mobile Source Inventory by Season and Day Type With

VMEP

On-Road Emissions (tpd) Season

Type Day Type NO

x VOC CO
Weekday 49.21 45.97 733.18

F riday 51.84 50.61 808.09
Saturday 40.37 35.03 597.98 Summer

Sunday 32.56 29.34 500.97
Weekday 51.40 48.25 764.81

Friday 54.86 53.89 853.13
Saturday 41.98 36.48 621.40 School

Sunday 33.37 30.12 513.33

For the on-road mobile inventory portion of the HGB sub domain, the EPS3 message files along

ith the gridded files input into the photoche mical model for 2005, 2006, and 2018 are available w on the following FTP sites, respectively:

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/ TAD/Modeling/Mobile_EI/ HGB/eps3/2005/

### B-137  ftp://ftp.tceq.state.tx.us/pub/OEPAA/ TAD/Modeling/Mobile_EI/ HGB/eps3/2006/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/ TAD/Modeling/Mobile_EI/ HGB/eps3/2018/

Similar on-road mobile EPS3 message and gridde d files for the Texas-only portion of the larger 12 km modeling domain are available for 2005, 20 06, and 2018, respectively, on the following
FTP sites:

 ftp://ftp.tceq.state.tx.us/p ub/OEPAA/TAD/ Modeling/Mobile_EI/ Statewide/eps3/2005/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/ Modeling/Mobile_EI/ Statewide/eps3/2006/

/  ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/ Modeling/Mobile_EI/Statewide/eps3/2018

Similar on-road mob ssage and gr idded files for he Texas and exas portio are available for 2006, and 2 respectively,

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/ T AD/Modeling/Mobile_EI/US/eps3/2005/ ile EPS3 me both t non-T ns of the larger 12 km modeling domain 2005, 018, on the following FTP sites:

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/ T AD/Modeling/Mobile_EI/US/eps3/2006/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/ T AD/Modeling/Mobile_EI/US/eps3/2018/

By definition, the f -road emissions inventory input i final atta t

emonstration photochemical modeling run should establish the motor vehicle emissions budget

g inventory files must be in ST to be consistent with the way meteorologi cal data are reported and modeled. However, emissions inventory files are typically developed in CDT.

When governmental organiza tions need to demonstrate conformity to the MVEB, they will not be developing photochemical m odeling inventories and therefore will not apply this time-shift step. Consequently, the 2018 MVEB for the eight-county HGB area will start with the summer weekday on-road inventory as received from TTI in CDT format. Then, adjustments for the temperature/humidity NO

x correction are applied outside of EPS3, but in a manner consistent with the descriptions included above. This a pproach is summarized below in Table 3.1-39: 2018
Attainment Demonstration Motor Vehicle Emissions Budget for Eight-County HGB Area . The slight differences between the MVEB and the eight-county NO

x and VOC totals in Tables 3.1-37 and 3.1-38 are due solely to the manner in which the EPS3 system converts text-based, nonspeciated inventory data in CDT into a binary, gr idded, and speciated format in CST appropriate for photochemical model input.

Table 3.1-39: 2018 Attainment Demonstration MVEB for the Eight-County HGB Area uture case on nto the inmen
d (MVEB). However, use of the EPS3 processor introduces unique adjustments to the on-road emissions inventory that are necessary for photochemical modeling efforts. An EPS3 processing step necessary for photochemical model input i nvolves the use of Central Standard Time (CST) instead of Central Daylight Time (CDT). All photochemical modelin

C

Total Emissions (tpd) Eight-County

HGB Area NO

x VOC On-Road Inventory From TTI (Table 3.1-3) Includes I/M, RFG, & TxLED 52.55 45.97

Temperature/Humidity NO

x Correction (Table 3.1-14) 1.78 0.00

On-Road VMEP Benefits (Table 3.1-36) 1.55 0.00

Final Eight-County

HGB MVEB 49.22 45.97

### B-138 The following pages contain graphical plots of the 2018 on-road summer weekday NO X and VOC emissions for a combination of the HGB and Beaumont/Port Arthur (BPA) areas. These plots are respectively entitled Figure 3.1-1: 2018 HGB & BPA Summer Weekday NOx Emissions and Figure 3.1-2: 2018 HGB & BPA Summer Weekday VOC Emissions

.

Figure 3.1-1: 2018 HGB & BPA Summer Weekday NO X Emissions

### B-139 Figure 3.1-2: 2018 HGB & BPA Summer Weekday VOC Emissions

### B-140 3.2 On-Road Mobile Source Emissions Inventories for Non-Eight-County HGB Areas

On-road emission inventories for 2005, 2006, and 20 18 were also developed for portions of the modeling domain outside of the eight-county HGB area. For the three Beaumont-Port Arthur (BPA) area counties of Hardin, Jefferson, a nd Orange, a similar link-based inventory development approach was taken by TTI based on TDM output available from local transportation agencies. Simila r to the HGB on-road emissions development, both school and summer season on-road inventories were developed for the four day types of weekday, Friday, Saturday, and Sunday. More detail on the de velopment of these BPA inventories with the MOBILE6.2 model is available on the following FTP sites:

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/ TAD/Modeling/Mobile_EI/ BPA/m62/2005/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/ TAD/Modeling/Mobile_EI/ BPA/m62/2006/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/ TAD/Modeling/Mobile_EI/ BPA/m62/2018/

For the Texas counties outside of the eight-county HGB and three-county BPA areas, on-road emissions were developed by TTI using Highway Performance Monitoring System (HPMS) data as the basis for VMT estimates. Summer season emission estimates were developed for the four day types of weekday, Friday, Saturday, and Sunday. Hourly emission rates from MOBILE6.2 were coupled with county-level VMT estimates by roadway type for 2005, 2006, and 2018. More detail on the development of these HPMS-based on-ro ad inventories is available on the following
FTP sites:

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/Mobile_EI/Statewide/m62/2005/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/Mobile_EI/Statewide/m62/2006/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/Mobile_EI/Statewide/m62/2018/

On-road emission estimates for non-Texas states within the photochemical modeling domain were developed for 2005, 2006, and 2018 usi ng the EPA National Mobile Inventory Model (NMIM), which is available at http://www.epa.gov/otaq/nmim.htm and can be used to develop on-road county-level emission estimates for every year from 1999-2050. However, the NMIM activity database is only populated with county-level VMT figures for 1999 and 2002. Historical annual VMT figures by state from 1980-2006 were obtained from the Federal Highway Administration (FHWA) Highway Statistics Series webpage, which is available at ( http://www.fhwa.dot.gov/policy/ohpi/hss/index.htm ). Linear trends were established with these data to project 2018 VMT for each state and roadway type. NMIM was run for 2005, 2006, and
2018 for all non-Texas states after inclusion of these FHWA-based VMT figures into the NMIM activity tables, and the results are available at:

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/ T AD/Modeling/Mobile_EI/US/eps3/2005/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/ T AD/Modeling/Mobile_EI/US/eps3/2006/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/ T AD/Modeling/Mobile_EI/US/eps3/2018/

Since only average weekday emissions can easily be obtained with NMIM, Friday, Saturday, and Sunday day type emissions were estimated for th e non-Texas area by applying adjustment factors with the EPS3 TMPRL module. These adjustment factors were based on the ratios of Friday/weekday, Saturday/weekday, and Sunday/weekday emissions from the statewide inventories developed by TTI for Texas. More detail on this approach is documented in a report
entitled Use of the National Mobile Inventory Model for Photochemical Modeling Applications in

Texas , which was presented at the 17 th International Emissions Inventory Conference and is
available at ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/Mobile_EI/NMIM/

.

### B-141 A summary of the different emission estimation approaches taken for this HGB SIP revision is provided in Table 3.1-40: On-Road Emission Inventory Devel opment by Area Within the Modeling Domain

.

Table 3.2-1: On-Road Inventory Development by Area Within the Modeling Domain

On-Road Inventory Development

Parameter HGB and

BPA Non-HGB and
Non-BPA Non-Texas
States/Counties

VMT

Source Travel Demand Models (TDMs) HPMS Data

Sets NMIM Database/
FHWA Statistics

VMT
Resolution Roadway Links

From TDM 19 Roadway Types 12 Roadway Types

Season Types School and Summer (i.e., non-School) Summer Only Summer Only Day Types Weekday, Friday, Saturday, and Sunday Weekday, Friday, Saturday, and Sunday Weekday, Friday, Saturday, and Sunday Hourly

VMT? Yes Yes No

VMT Mix Variation By Day/Time Period? Yes Yes No Roadway Speed

Distribution Varies by
Hour and Link Varies by Hour and Roadway Type MOBILE6.2
Default Spatial
Resolution Excellent Very Good Good Temporal
Resolution Excellent Very Good Good

MOBILE6.2 Vehicle Types 28 28 12 Temperature/Humidity
Diesel NO X Correction Yes Yes No

“18-Wheeler” Idling Emissions Separation Yes No No

### B-142 4.0 NON-ROAD, OFF-ROAD, AND AREA SOURCE MODELING EMISSIONSFor the purposes of this Houston-Galveston-Brazoria (HGB) modeling emissions summary, nonroad vehicles and equipment refer to those cate gories whose emissions can be estimated with EPA’s NONROAD model ( http://www.epa.gov/otaq/nonrdmdl.htm ). Off-road vehicles and equipment refer to the aircraft, locomotive, and commercial marine categories that are not covered by the NONROAD model. Area sources c over a wide range of remaining categories.
Emission estimates are summarized below in Table 4.0-1: 2005 HGB Non-Road, Off-Road, and Area Source Modeling Emissions , Table 4.0-2: 2006 HGB Non-Road, Off-Road, and Area Source Modeling Emissions , and Table 4.0-3: 2018 HGB Non-Road, Off-Road, and Area Source Modeling Emissions . Greater detail on each category is c ontained within the following sections.

Table 4.0-1: 2005 HGB Non-Road, Off-Road, and Area Source Modeling Emissions Summer Weekday Emissions (tpd) Source Category Description NO X VOC
Non-Road Equipment 77.74 58.29 Oil and Gas Exploration 6.49 0.25

Aircraft 7.55 1.90
Airport Ground Support Equipment 1.89 0.76

Locomotives 30.32 1.83

Commercial Marine 34.47 0.97

Area Sources 36.20 427.79

Grand Total 194.66 491.79

Table 4.0-2: 2006 HGB Non-Road, Off-Road, and Area Source Modeling Emissions Summer Weekday Emissions (tpd) Source Category Description NO X VOC
Non-Road Equipment 73.47 54.58 Oil and Gas Exploration 5.87 0.24

Aircraft 7.77 1.96
Airport Ground Support Equipment 1.83 0.73

Locomotives 28.53 1.79

Commercial Marine 35.10 0.99

Area Sources 36.36 431.66

Grand Total 188.93 491.95

Table 4.0-3: 2018 HGB Non-Road, Off-Road, and Area Source Modeling Emissions Summer Weekday Emissions (tpd) Source Category Description NO X VOC
Non-Road Equipment 30.58 27.17
Oil and Gas Exploration 2.34 0.14

Aircraft 11.36 2.44
Airport Ground Support Equipment 0.86 0.30

Locomotives 34.22 2.35

Commercial Marine 39.24 1.18

Area Sources 42.06 530.25

Grand Total 160.66 563.83

### B-143 4.1 Non-Road Mobile Source Modeling Emissions for Eight-County HGB Non-road emissions for 2005, 2006, and 2018 for th e eight-county HGB area were estimated with a customized version of EPA’s NONROAD model called Texas NONROAD (TexN), which is
available at ftp://ftp.tceq.state.tx.us/pub/OEP AA/T AD/Modeling/Nonroad_EI/TexN/ . For each county specified in a TexN scenario, 25 separate runs of the NONROAD model are performed for the following non-road categories outlined in Table 4.1-1: Summary of TexN Model Diesel Construction Equipment Subcategories . Runs 1-25 (excluding 24) are for specific diesel construction equipment (DCE) categories, while the numeric code of 0 is for all non-DCE categories.

Table 4.1-1: Summary of TexN Model Diesel Construction Equipment Subcategories

Numeric

Code NONROAD Model Subsector Description (Diesel Construction Equipment for 1-24)

0 Other - Non-Diesel Construction Equipment

1 DCE - Agricultural Activities

2 DCE - Boring and Drilling Equipment

3 DCE - Brick and Stone Operations

4 DCE - City and County Road Construction

5 DCE - Commercial Construction

6 DCE - Concrete Operations

7 DCE - County-Owned Construction Equipment

8 DCE - Cranes

9 DCE - Heavy Highway Construction

10 DCE - Landfill Operations

11 DCE - Landscaping Activities

12 DCE - Manufacturing Operations

13 DCE - Municipal-Owned Construction Equipment

14 DCE - Transportation/Sales/Services

15 DCE - Residential Construction

16 DCE - Rough Terrain Forklifts

17 DCE - Scrap/Recycling Operations

18 DCE - Skid Steer Loaders

19 DCE - Special Trades Construction

20 DCE - Trenchers

21 DCE - TxDOT Construction Equipment

22 DCE - Utility Construction

23 DCE - Mining and Quarry Operation

25 DCE - Off-Road Tractors, Miscellaneou s, & Equipment Under 25 Horsepower

2005, 2006, and 2018 summe r weekday scenarios were run with the TexN model for all eight counties in the HGB area. 25 DCE subcategories fo r each of the eight counties resulted in a total of 200 NONROAD model runs for each calendar year. The NONROAD input files, output files, and Excel spreadsheet summary tables for these analyses are available at ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/Nonroad_EI/HGB/

.

The NO X , VOC, and CO emissions by county along with associated non-road equipment population figures are presented below in Table 4.1-2: 2005 HGB Summer Weekday Non-Road

### B-144 Equipment and Emissions by County , Table 4.1-3: 2006 HGB Summer Weekday Non-Road Equipment and Emissions by County , and Table 4.1-4: 2018 HGB Summer Weekday Non-Road Equipment and Emissions by County

.

Table 4.1-2: 2005 HGB Summer Weekday Non-Road Equipment and Emissions by County Summer Weekday Emissions (tpd) County

Name Equipment Population NO X VOC CO

Brazoria 97,840 5.14 3.89 29.25

Chambers 21,159 1.29 1.18 6.05

Fort Bend 111,040 5.79 3.24 38.38

Galveston 129,000 3.18 4.01 25.14

Harris 1,252,383 58.47 39.70 462.16 Liberty 24,140 1.99 0.73 6.69
Montgomery 134,457 4.70 5.00 37.55

Waller 13,420 1.39 0.54 5.02

Total 1,783,439 81.96 58.29 610.23

Table 4.1-3: 2006 HGB Summer Weekday Non-Road Equipment and Emissions by County Summer Weekday Emissions (tpd) County

Name Equipment Population NO X VOC CO

Brazoria 99,911 4.94 3.63 26.97

Chambers 21,565 1.26 1.11 5.68

Fort Bend 113,816 5.89 2.96 35.28

Galveston 131,905 3.39 3.79 23.09

Harris 1,279,801 56.84 37.20 429.22 Liberty 24,790 1.88 0.67 6.17
Montgomery 137,521 4.55 4.72 34.47

Waller 13,798 1.36 0.50 4.60

Total 1,823,106 80.11 54.58 565.47

Table 4.1-4: 2018 HGB Summer Weekday Non-Road Equipment and Emissions by County Summer Weekday Emissions (tpd) County

Name Equipment Population NO X VOC CO

Brazoria 122,941 2.22 1.76 19.14

Chambers 25,611 0.48 0.52 3.61

Fort Bend 143,242 2.76 1.60 26.38

Galveston 159,796 1.50 1.98 18.70

Harris 1,599,823 24.05 18.03 317.57 Liberty 31,257 0.71 0.38 4.19
Montgomery 170,929 2.19 2.62 28.74

Waller 17,488 0.53 0.28 3.29

Total 2,271,087 34.46 27.17 421.63

Even with overall growth in the non-road equipment population from roughly 1.8 million in 2005/2006 to 2.3 million in 2018, total NO X and VOC emissions decrease due to the more stringent emissions standards for new equipment purchases, combined with the simultaneous

### B-145 attrition of older, higher-emitting pieces of non -road equipment. The eight-county non-road emissions are summarized by aggregate equipment category in Table 4.1-5: 2005 HGB Summer Weekday Non-Road Inventory by Equipment Category , Table 4.1-6: 2006 HGB Summer Weekday Non-Road Inventory by Equipment Category , and Table 4.1-7: 2018 HGB Summer Weekday Non-Road Inventory by Equipment Category

.

Table 4.1-5: 2005 HGB Summer Weekday Non-Road Inventory by Equipment Category Summer Weekday Emissions (tpd) Aggregate Equipment Category Equipment Population NO X VOC CO
Agricultural 6,335 6.76 0.85 9.98

Commercial 183,218 8.27 11.98 238.20

Construction 69,450 35.30 6.95 57.30

Industrial 30,241 28.59 7.83 118.48

Lawn and Garden 1,323,636 1.83 15.77 146.82 Logging 1,538 0.28 0.17 1.18

Pleasure Craft 117,863 0.68 10.21 22.53 Railway Maintenance 90 0.08 0.01 0.06

Recreational 51,068 0.16 4.52 15.66

Total 1,783,439 81.96 58.29 610.23

Table 4.1-6: 2006 HGB Summer Weekday Non-Road Inventory by Equipment Category Summer Weekday Emissions (tpd) Aggregate Equipment Category Equipment Population NO X VOC CO
Agricultural 6,443 6.41 0.68 8.86

Commercial 188,842 8.44 11.46 217.19

Construction 72,021 34.69 6.39 52.71

Industrial 30,929 27.36 7.39 118.17

Lawn and Garden 1,349,719 1.92 14.06 131.63 Logging 1,586 0.26 0.15 1.09

Pleasure Craft 118,745 0.77 9.75 20.79 Railway Maintenance 93 0.07 0.01 0.06

Recreational 54,728 0.18 4.70 14.99

Total 1,823,106 80.11 54.58 565.47

Table 4.1-7: 2018 HGB Summer Weekday Non-Road Inventory by Equipment Category Summer Weekday Emissions (tpd) Aggregate Equipment Category Equipment Population NO X VOC CO
Agricultural 7,957 2.42 0.30 2.28

Commercial 256,241 5.60 6.35 201.05

Construction 92,055 16.72 3.64 36.34

Industrial 39,145 7.51 1.35 30.83

Lawn and Garden 1,664,135 1.15 7.96 113.48 Logging 2,154 0.05 0.13 1.13

Pleasure Craft 129,479 0.81 4.35 17.72 Railway Maintenance 126 0.05 0.01 0.04

Recreational 79,794 0.15 3.08 18.77

### B-146 Summer Weekday Emissions (tpd) Aggregate Equipment Category Equipment Population NO X VOC CO

Total 2,271,087 34.46 27.17 421.63

The eight-county non-road emissions are summarized by fuel and engine type in Table 4.1-8: 2005 HGB Summer Weekday Non-Road Inventory by Fuel and Engine Type, Table 4.1-9: 2006 HGB Summer Weekday Non-Road Inventory by Fuel and Engine Type , and Table 4.1-10: 2018 HGB Summer Weekday Non-Road Inventory by Fuel and Engine Type

.

Table 4.1-8: 2005 HGB Summer Weekday Non-Road Inventory by Fuel and Engine Type Summer Weekday Emissions (tpd) Fuel/Engine Type Equipment Population NO X VOC CO

Two-Stroke Gasoline 590,850 0.38 22.61 41.88

Four-Stroke Gasoline 1,081,799 4.77 22.70 426.59

Diesel 84,555 51.87 5.62 26.28 Compressed Natural Gas 1,543 1.33 0.94 8.83 Liquefied Petroleum Gas 24,692 23.61 6.42 106.65

Total 1,783,439 81.96 58.29 610.23

Table 4.1-9: 2006 HGB Summer Weekday Non-Road Inventory by Fuel and Engine Type Summer Weekday Emissions (tpd) Fuel/Engine Type Equipment Population NO X VOC CO

Two-Stroke Gasoline 602,224 0.44 20.50 37.83

Four-Stroke Gasoline 1,105,617 5.07 21.76 385.19

Diesel 88,256 50.98 5.45 25.78 Compressed Natural Gas 1,556 1.15 0.73 8.23 Liquefied Petroleum Gas 25,452 22.46 6.14 108.45

Total 1,823,106 80.11 54.58 565.47

Table 4.1-10: 2018 HGB Summer Weekday Non-Road Inventory by Fuel and Engine Type Summer Weekday Emissions (tpd) Fuel/Engine Type Equipment Population NO X VOC CO

Two-Stroke Gasoline 732,797 0.61 12.11 42.00

Four-Stroke Gasoline 1,380,034 2.35 10.79 334.84

Diesel 121,853 25.97 3.02 13.05 Compressed Natural Gas 1,909 0.26 0.08 1.22
Liquefied Petroleum Gas 34,493 5.27 1.17 30.51

Total 2,271,087 34.46 27.17 421.63

The eight-county non-road emissions are summari zed by the 25 DCE subcategory codes from
Table 4.1-1 in Table 4.1-11: 2005 HGB Summer Weekday Non-Road Inventory by DCE Subcategory , Table 4.1-12: 2006 HGB Summer Weekday Non-Road Inventory by DCE Subcategory , and Table 4.1-13: 2018 HGB Summer Weekday Non-Road Inventory by DCE Subcategory

.

### B-147 Table 4.1-11: 2005 HGB Summer Weekday Non-Road Inventory by DCE Subcategory Summer Weekday Emissions (tpd) Non-Road DCE
Subsector Code Equipment Population NO X VOC CO

0 1,738,334 47.33 54.63 592.51

1 823 1.18 0.13 0.63

2 215 0.22 0.02 0.08

3 110 0.36 0.03 0.14

4 678 0.26 0.02 0.11

5 11,913 2.83 0.27 1.25

6 71 0.18 0.01 0.08

7 254 0.20 0.02 0.09

8 2,107 6.56 0.41 1.45

9 429 0.58 0.05 0.27

10 123 0.90 0.05 0.31

11 1,049 0.53 0.11 0.47

12 132 0.30 0.02 0.13

13 1,075 0.62 0.11 0.49

14 1,203 2.42 0.22 1.15

15 2,235 4.06 0.31 1.60

16 2,294 2.28 0.24 1.39

17 160 0.51 0.04 0.16

18 8,087 2.59 0.78 3.24

19 77 0.05 0.01 0.03

20 5,965 4.75 0.49 3.02

21 191 0.08 0.01 0.04

22 1,741 0.68 0.05 0.29

23 110 0.55 0.04 0.17

25 4,063 1.96 0.23 1.14

Total 1,783,439 81.96 58.29 610.23

### B-148 Table 4.1-12: 2006 HGB Summer Weekday Non-Road Inventory by DCE Subcategory Summer Weekday Emissions (tpd) Non-Road DCE
Subsector Code Equipment Population NO X VOC CO

0 1,775,504 46.09 51.02 548.13

1 817 1.10 0.12 0.60

2 233 0.23 0.02 0.08

3 105 0.32 0.02 0.12

4 688 0.25 0.02 0.11

5 13,580 3.11 0.29 1.37

6 72 0.17 0.01 0.08

7 258 0.19 0.02 0.09

8 2,336 6.79 0.42 1.50

9 411 0.53 0.04 0.25

10 125 0.84 0.05 0.30

11 1,077 0.52 0.11 0.47

12 130 0.28 0.02 0.12

13 1,093 0.61 0.10 0.48

14 1,264 2.40 0.22 1.16

15 2,408 4.15 0.32 1.60

16 2,551 2.43 0.24 1.47

17 162 0.48 0.04 0.16

18 8,879 2.77 0.81 3.45

19 80 0.05 0.01 0.03

20 4,879 3.73 0.37 2.35

21 191 0.08 0.01 0.04

22 2,026 0.76 0.06 0.32

23 72 0.32 0.02 0.11

25 4,164 1.92 0.22 1.10

Total 1,823,106 80.11 54.58 565.47

### B-149 Table 4.1-13: 2018 HGB Summer Weekday Non-Road Inventory by DCE Subcategory Summer Weekday Emissions (tpd) Non-Road DCE
Subsector Code Equipment Population NO X VOC CO

0 2,204,337 17.97 25.15 412.73

1 744 0.29 0.04 0.17

2 346 0.17 0.01 0.05

3 75 0.05 0.01 0.02

4 817 0.14 0.01 0.05

5 20,431 2.40 0.22 1.01

6 91 0.05 0.01 0.03

7 311 0.10 0.01 0.04

8 3,428 2.23 0.29 0.56

9 489 0.22 0.02 0.10

10 149 0.11 0.03 0.04

11 1,375 0.32 0.05 0.26

12 175 0.09 0.01 0.04

13 1,318 0.34 0.04 0.22

14 2,320 1.14 0.16 0.63

15 2,301 1.16 0.13 0.48

16 4,024 1.17 0.13 0.78

17 187 0.09 0.02 0.03

18 12,869 2.32 0.45 2.56

19 101 0.03 0.00 0.02

20 6,387 2.18 0.16 0.87

21 191 0.04 0.00 0.02

22 3,162 0.60 0.05 0.24

23 79 0.05 0.01 0.02

25 5,381 1.19 0.14 0.64

Total 2,271,087 34.46 27.17 421.63

The non-road emissions data output from TexN were prepared for photochemical model using Version 3 of the Emissions Processing System (EPS3). When input into EPS 3, the inventory data
are in a “readable” text-based format. Howeve r, once within EPS3, the emissions data are maintained in a binary format. Table 4.1-14: EPS3 Modules Used to Process Eight-County HGB

Non-Road Emissions Data summarizes the steps that were taken to process the 2005, 2006, and
2018 non-road inventories.

Table 4.1-14: EPS3 Modules Used to Pr ocess Eight-County HGB Non-Road Emissions

EPS3

Module Emissions Processing Description

PREAM Convert text-based input files to binary format for further processing

CNTLEM Apply adjustments for TxLED and temperature/humidity NO X correction

TMPRL Apply profiles to temporally allocate daily emission totals

CHMSPL Chemically speciate emissions into NO, NO 2 , Olefins, Parrafins, etc.

GRDEM Spatially allocate emissions using surrogates and prepare photochemical inputs

MRGUAM Merge and adjust multiple GRDEM out put files for photochemical model input

### B-150 its

. The 200 6 and 2018 non-road NO X emission totals presented above in Tables 4.1-3 through 4.1-
13 exclude the benefits of Texas Low Emission Di esel (TxLED) fuel. Instead, TxLED benefits were accounted for by applying a NO X reduction to the diesel non-road equipment categories through use of the EPS3 CNTLEM module. The speci fic adjustment factors vary by horsepower range and certification standard, as summarized in Table 4.1-15: Non-Road TxLED Adjustment Factor Summary . More detail on development of these post-processing adjustments can be found in a September 27, 2001 EPA memorandum Texas Low Emission Diesel (LED) Fuel Benef

Table 4.1-15: Non-Road TxLED Adjustment Factor Summary Non-Road Diesel Equipment Standard/Category TxLED NO X Reduction Factor Under 50 Horsepower 0.0% Base, Tier 0, Tier 1, Tier 2 6.2% Tier 3, Tier 4 4.8%

Since the TxLED rule was not in effect in th e summer of 2005, adjustment factors were not applied to the 2005 non-road inventory. The 2006 and 2018 non-road TxLED benefits for the eight-county HGB area are presented in Table 4.1-16: HGB Summer Weekday Non-Road TxLED Benefits by Calendar Year.

Table 4.1-16: HGB Summer Weekday Non-Road TxLED Benefits by Calendar Year Summer Weekday TxLED Benefits (tpd) Calendar

Year NO X VOC CO

2006 3.00 0.00 0.00

2018 1.29 0.00 0.00

When running a summer weekday scenario, the NONROAD model reports 24-hour emission
totals and does not account for the effects of hour ly variation in temperature and humidity on

NO X emissions. Greater detail on the development of correction equations to account for these effects can be found in Appendices F.4 and F.5 of the December 2004 HGB SIP revision, which
can be found at http://www.tceq.state.tx.us/implementation/air/sip/dec2004hgb_mcr.html

. During EPS3 processing, the CNTLEM module is also used to apply an hourly temperature/humidity NO X correction and the impacts on the 2005, 2006, and 2018 non-road inventories are presented in Table 4.1-17: HGB Summer Weekday Non-Road Temperature/Humidity NO X Correction

.

Table 4.1-17: HGB Summer Weekday Non-Road Temperature/Humidity NO X Correction Temperature/Humidity NO X Reduction (tpd) Calendar

Year NO X VOC CO

2005 4.21 0.00 0.00

2006 3.64 0.00 0.00

2018 1.88 0.00 0.00

After application of the TxLED and temperature/humidity NO X adjustments with the CNTLEM module, the non-road emissions are spatially allocated with surrogates and prepared for photochemical model input using the EPS3 GRDE M module. The summer weekday emissions that were output from GRDEM are summarized by aggregate equipment category in Table 4.1-

18: 2005 HGB Adjusted Non-Road Inventory by Equipment Category , Table 4.1-19: 2006 HGB Adjusted Non-Road Inventory by Equipment Category , and Table 4.1-20: 2018 HGB Adjusted Non-Road Inventory by Equipment Category . Comparison with Tables 4.1-2 through 4.1-13 will indicate that the VOC and CO totals remain unchanged, while the NO X totals are reduced by the

### B-151 TxLED and temperature/humidity adjustments presented in Tables 4.1-16 and 4.1-17, respectively.

Table 4.1-18: 2005 HGB Adjusted Non-Road Emissions by Equipment Category Summer Weekday Emissions (tpd) Aggregate Equipment Category Equipment Population NO X VOC CO
Agricultural 6,335 6.29 0.85 9.98

Commercial 183,218 7.83 11.98 238.20

Construction 69,450 32.49 6.95 57.30

Industrial 30,241 28.16 7.83 118.48

Lawn and Garden 1,323,636 1.79 15.77 146.82 Logging 1,538 0.27 0.17 1.18

Pleasure Craft 117,863 0.68 10.21 22.53 Railway Maintenance 90 0.07 0.01 0.06

Recreational 51,068 0.16 4.52 15.66

Total 1,783,439 77.74 58.29 610.23

Table 4.1-19: 2006 HGB Adjusted Non-Road Emissions by Equipment Category Summer Weekday Emissions (tpd) Aggregate Equipment Category Equipment Population NO X VOC CO
Agricultural 6,443 5.67 0.68 8.86

Commercial 188,842 7.80 11.46 217.19

Construction 72,021 30.21 6.39 52.71

Industrial 30,929 26.68 7.39 118.17

Lawn and Garden 1,349,719 1.85 14.06 131.63 Logging 1,586 0.24 0.15 1.09

Pleasure Craft 118,745 0.77 9.75 20.79 Railway Maintenance 93 0.06 0.01 0.06

Recreational 54,728 0.18 4.70 14.99

Total 1,823,106 73.47 54.58 565.47

### B-152 Table 4.1-20: 2018 HGB Adjusted Non-Road Emissions by Equipment Category Summer Weekday Emissions (tpd) Aggregate Equipment Category Equipment Population NO X VOC CO
Agricultural 7,957 2.14 0.30 2.28

Commercial 256,241 5.13 6.35 201.05 Construction and Mining 92,055 14.68 3.64 36.34

Industrial 39,145 7.20 1.35 30.83

Lawn and Garden 1,664,135 1.09 7.96 113.48 Logging 2,154 0.04 0.13 1.13

Pleasure Craft 129,479 0.81 4.35 17.72 Railway Maintenance 126 0.04 0.01 0.04

Recreational 79,794 0.15 3.08 18.77

Pre-VMEP Total 2,271,087 31.28 27.17 421.63

H-GAC Non-Road VMEP 0.70

Total 2,271,087 30.58 27.17 421.63

As shown in Table 4.1-20, an additional post-processing step fo r the 2018 modeling emissions was necessary to apply the 0.7 NO X tpd of voluntary mobile source emission reduction program (VMEP) non-road benefits submitted by H-GAC.

Drilling rig emissions from oil and gas exploration equipment were estimated separately from the TexN model and are available in the Texas Air Emissions Repository (TexAER) system, which is
available at http://texaerweb.tceq.state.tx.us/texaer/index.cfm . These estimates are summarized
below in Table 4.1-21: HGB Oil and Gas Exploration Emissions for 2005, 2006, and 2018

.

Table 4.1-21: HGB Oil and Gas Exploration Emissions for 2005, 2006, and 2018 Summer Weekday Oil & Gas Emissions (tpd) Calendar

Year NO X VOC CO

2005 6.49 0.25 7.35

2006 5.87 0.24 7.23

2018 2.34 0.14 6.20

The non-road emissions were spatially allocated using the EPS3 GRDEM module with surrogates that vary as a function of source category code (SCC). The following pages contain graphical plots of the 2005, 2006, and 2018 non-road summer weekday NO X and VOC emissions for the HGB area. These plots are respectively entitled Figure 4.1-1: 2005 HGB Summer Weekday NonRoad NO X Emissions , Figure 4.1-2: 2006 HGB Summer Weekday Non-Road NO X Emissions

, Figure 4.1-3: 2018 HGB Summer Weekday Non-Road NO X Emissions , Figure 4.1-4: 2005 HGB Summer Weekday Non-Road VOC Emissions , Figure 4.1-5: 2006 HGB Summer Weekday NonRoad VOC Emissions , and Figure 4.1-6: 2018 HGB Summer Weekday Non-Road VOC

Emissions

.

For two primary reasons, the eight-county HGB NO X and VOC totals in these plots do not exactly match the totals in the tables shown above . First, these graphical plots report Carbon Bond 2005 (CB-05) mass, while the data tables show VOC mass. The photochemical model requires that VOC emissions be speciated into cat egories of differing reactivity with a mechanism such as CB-05. Second, the reported emissions for the entire domain on the plots are correct, but the county totals are only approximate. A concentr ation of emissions near county borders leads to some inaccuracy in the reported totals on these plots because emissions in a grid cell are allocated among counties based on relative area. Fo r example, if a grid cell is evenly divided

### B-153 se counties. between two counties, the plotting routine will even ly divide the reported emissions even if the EPS3 spatial surrogate correctly allocated all of the emissions to just one of tho

Figure 4.1-1: 2005 HGB Summer Weekday Non-Road NO X Emissions

### B-154 Figure 4.1-2: 2006 HGB Summer Weekday Non-Road NO X Emissions

### B-155 Figure 4.1-3: 2018 HGB Summer Weekday Non-Road NO X Emissions

### B-156 Figure 4.1-4: 2005 HGB Summer Weekday Non-Road VOC Emissions

### B-157 Figure 4.1-5: 2006 HGB Summer Weekday Non-Road VOC Emissions

### B-158 Figure 4.1-6: 2018 HGB Summer Weekday Non-Road VOC Emissions

### B-159 4.2 Aircraft and Ground Support Equipment Modeling Emissions for Eight-County HGB The aircraft inventory was developed using the U.S. Federal Aviation Administration (FAA) Emissions and Dispersion Modeling System (EDM S) by Jacobs Consultancy under contract to the Houston Airport System (HAS). The r esults of these analyses by HGB area airport are presented below in Table 4.2-1: 2005 Ozone Season Day Aircraft Emissions by HGB Airport

, Table 4.2-2: 2006 Ozone Season Day Aircraft Emissions by HGB Airport , and Table 4.2-3: 2018 Ozone Season Day Aircraft Emissions by HGB Airport . The 2006 figures were linearly interpolated between available estimates for 2005 and 2011.

Table 4.2-1: 2005 Ozone Season Day Aircraft Emissions by HGB Airport 2005 Ozone Season Day (tpd) HGB Area Airport NO X VOC CO
Ellington Field 0.17 0.34 3.25
Hobby 1.46 0.48 2.54

Intercontinental 5.91 1.08 7.42

Total 7.55 1.90 13.20

Table 4.2-2: 2006 Ozone Season Day Aircraft Emissions by HGB Airport 2006 Ozone Season Day (tpd) HGB Area Airport NO X VOC CO
Ellington Field 0.18 0.35 3.33
Hobby 1.45 0.48 2.50

Intercontinental 6.14 1.13 7.56

Total 7.77 1.96 13.39

Table 4.2-3: 2018 Ozone Season Day Aircraft Emissions by HGB Airport 2018 Ozone Season Day (tpd) HGB Area Airport NO X VOC CO
Ellington Field 0.23 0.45 4.23
Hobby 1.55 0.55 2.90

Intercontinental 9.58 1.44 7.74

Total 11.36 2.44 14.87

Airport ground support equipment (GSE) emission estimates were devel oped using the EDMS model by the following entities:

 Landrum & Brown for Continental Airlines operations at Intercontinental Airport;

 Southwest Airlines and the TCEQ for Southwest operations at Hobby Airport; and

 Jacobs Consultancy for all non-Continen tal and non-Southwest operations at Intercontinental and Hobby Airports, respectively.

These GSE estimates are presented below in Table 4.2-4: 2005 Ozone Season Day GSE Emissions by HGB Airport , Table 4.2-5: 2006 Ozone Season Day GSE Emissions by HGB Airport , and Table 4.2-6: 2018 Ozone Season Day GSE Emissions by HGB Airport . The 2006 figures were linearly interpolated betw een available estimates for 2005 and 2011.

### B-160 Table 4.2-4: 2005 Ozone Season Day GSE Emissions by HGB Airport

HGB Area 2005 Ozone Season Day (tpd)
Airport Airline NO X VOC CO

Southwest 0.15 0.01 0.09 Hobby Airport Other 0.08 0.01 0.25

Continental 1.19 0.61 20.96 Intercontinental Airport Other 0.46 0.12 3.10

Total 1.89 0.76 24.41

Table 4.2-5: 2006 Ozone Season Day GSE Emissions by HGB Airport

HGB Area 2006 Ozone Season Day (tpd)
Airport Airline NO X VOC CO

Southwest 0.15 0.01 0.10 Hobby Airport Other 0.08 0.01 0.23

Continental 1.17 0.60 20.52 Intercontinental Airport Other 0.43 0.11 2.89

Total 1.83 0.73 23.73

Table 4.2-6: 2018 Ozone Season Day GSE Emissions by HGB Airport

HGB Area 2018 Ozone Season Day (tpd)
Airport Airline NO X VOC CO

Southwest 0.18 0.02 0.11 Hobby Airport Other 0.02 0.00 0.04

Continental 0.55 0.25 8.70 Intercontinental Airport Other 0.11 0.03 0.72

Total 0.86 0.30 9.57

For more detail, ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/Nonroad_EI/Airports/HGB/ can be consulted for the electronic EDMS output files for the HGB area analyses, along with a detailed report from Jacobs Consultancy an d a memorandum from Landrum & Brown. Additional estimates for GSE equipment were made for small regional airports outside of Harris County, but those figures are not reported here because they range from only 1-2 pounds of NO X and 0-1 pounds of VOC. Genera l aviation aircraft emissions for the smaller regional airports were also developed and presented below in Table 4.2-7: General Aviation Emissions for 2005, 2006, and 2018

.

Table 4.2-7: General Aviation Emissions for 2005, 2006, and 2018

General Aviation Emissions (tpd) Calendar

Year NO X VOC

2005 0.024 0.410

2006 0.025 0.420

2018 0.037 0.610

The TCEQ developed special surrogates to spatia lly allocate the aircraft and GSE emissions to grid cells covering the Ellington Field, Hobby, an d Intercontinental airports. The following pages contain graphical plots of the 2005, 2006, and 2018 airport summer weekday NO X emissions for the HGB area. These plots are respectively entitled Figure 4.2-1: 2005 HGB Summer Weekday

### B-161 Airport NO X Emissions , Figure 4.2-2: 2006 HGB Summer Weekday Airport NO X Emissions , and Figure 4.2-3: 2018 HGB Summer Weekday Airport NO X Emissions

.

Figure 4.2-1: 2005 HGB Summer Weekday Airport NO X Emissions

### B-162 Figure 4.2-2: 2006 HGB Summer Weekday Airport NO X Emissions

### B-163 Figure 4.2-3: 2018 HGB Summer Weekday Airport NO X Emissions

### B-164 4.3 Locomotive Modeling Emissions for Eight-County HGB 2005, 2006, and 2018 line-haul and switcher locomotive emission estimates for the eight-county HGB area were obtained from the Texas Air Emissions Repository (TexAER) system, which is
available at http://texaerweb.tceq.state.tx.us/texaer/index.cfm . These locomotive emission estimates available in TexAER were originally developed with the Texas Railroad Emission Inventory Model (TREIM), which is a Microsoft Access application developed by Eastern Research Group (ERG) under contract to the TCEQ . These locomotive emissions are separate from the railway maintenance equipment emis sions that are estimated with the NONROAD model. A summary is provided in Table 4.3-1: Eight-County HGB Locomotive Emissions for 2005, 2006, and 2018

.

Table 4.3-1: Eight-County HGB Locomotive Emissions for 2005, 2006, and 2018 Summer Weekday Locomotive Emissions (tpd) Calendar

Year NO X VOC

2005 30.32 1.83

2006 28.53 1.79

2018 34.22 2.35

The locomotive emissions were spatially allocat ed with the EPS3 GRDEM module to grid cells containing known railway tracks in the eight-county HGB area. The following pages contain graphical plots of the 2005, 2006, and 2018 locomotive summer weekday NO X emissions for the HGB area. These plots are respectively entitled Figure 4.3-1: 2005 HGB Summer Weekday

Locomotive NO X Emissions , Figure 4.3-2: 2006 HGB Summer Weekday Locomotive NO X Emissions , and Figure 4.3-3: 2018 HGB Summer Weekday Locomotive NO X Emissions

.

### B-165 Figure 4.3-1: 2005 HGB Summer Weekday Locomotive NO X Emissions

### B-166 Figure 4.3-2: 2006 HGB Summer Weekday Locomotive NO X Emissions

### B-167 Figure 4.3-3: 2018 HGB Summer Weekday Locomotive NO X Emissions

### B-168 4.4 Commercial Marine Modeling Emissions for Eight-County HGB When modeling emissions were bei ng developed for this SIP revision, the latest available detailed commercial marine inventories were for the 1997 and 2007 calendar years. Development of these data sets is documented in a Starcrest Consulting Group report entitled Houston-Galveston Area Vessel Emissions Inventory, which is available at ftp://ftp.tceq.state.tx.us/pub/OEP AA/T AD/Modeling/Nonroad_EI/Marine/ . A summary of this
work is available in Table 4.4-1: HGB Commercial Marine NO X Emissions Inventory for 1997

and 2007

.

Table 4.4-1: HGB Commercial Marine NO X Emissions Inventory for 1997 and 2007

Commercial Marine Vessels NO X Emissions (tpd)
Description Source Category Code 1997 2007
Assist Tugboats 2280002021 2.01 2.28

Ferries 2280002022 0.88 1.16

Towboats 2280002023 9.51 8.66
Dredging Emissions 2280002025 0.88 0.65
Barge Pumps 2280002026 1.55 1.20
Ocean Going Vessels 2280003010 3.80 4.77
Bulk Cargo Vessels 2280003011 1.92 2.52
General Cargo Vessels 2280003012 3.20 4.21
Container Ships 2280003013 1.07 1.40
Liquefied Gas Carriers 2280003014 1.36 1.80

Oil/Chemical Tankers 2280003015 8.13 10.71
Other Ocean Going Vessels 2280003016 0.73 0.96
Cruise Ships 2280003017 0.00 1.34

Grand Total 35.04 41.65

Total tonnage handled by the Port of Hous ton from 1996-2005 was reported on their website ( http://www.portofhouston.com/busdev/tr adedevelopment/tradest atistics.html ) as shown in Table

4.4-2: Annual Tonnage Handled by Port of Houston

.

Table 4.4-2: Annual Tonnage Handled by Port of Houston

Calendar

Year Annual Tonnage (short tons)

1996 148,183

1997 165,456

1998 169,070

1999 158,828

2000 186,567

2001 185,050

2002 177,561

2003 190,923

2004 202,047

2005 215,000

Trends were established with the 1996-2005 da ta to project annual tonnage from 2006-2018. Ratios of these annual tonnage figures were then applied to the reported 2007 figures from Table
4.4-1 to establish commercial marine emission estim ates for 2005, 2006, and 2018. An additional

### B-169 set of adjustments was applied to the 2018 har bor vessel emission estimates to account for a Federal Register notice entitled Control of Emissions of Air Pollution From New Compression Ignition Marine Engines at or Above 37 Kilowatts , December 11, 1998, which is available at http://www.epa.gov/fedrgstr/EPA-AIR/1998/December/Day-11/a32304.htm . The results of this approach are shown in Table 4.4-3: Harbor Vessel Emission Rate Adjustments From 2007 to

2018

.

Table 4.4-3: Harbor Vessel Emission Rate Adjustments From 2007 to 2018 2007 to 2018 Emission Rate Adjustments Diesel-Powered

Harbor Vessels NO X VOC CO
Assist Tugboats

Ferries

Towboats
Dredging Emissions

Barge Pumps 17.70 percent
reduction with

adjustment

factor of

0.8230 3.63 percent
reduction with adjustment
factor of

0.9637 1.55 percent
reduction with adjustment
factor of

0.9845

Application of the steps describ ed above resulted in the comme rcial marine modeling emission
estimates shown in Table 4.4-4: HGB Commercial Marine Modeling Emissions for 2005, 2006,

and 2018

.

Table 4.4-4: HGB Commercial Marine Modeling Emissions for 2005, 2006, and 2018

Commercial Marine Emissions (tpd) Calendar

Year NO X VOC

2005 34.47 0.97

2006 35.10 0.99

2018 39.24 1.18

The exhaust emissions from all vessels were processed through EPS3 as if they were coming from a series of elevated point sources located al ong the shipping lanes, major waterways, and docks within both Galveston Bay and the Houston Sh ip Channel. The exhaust stack parameters assigned to each of these sequential point sources va ried by the different vessel types shown in Table 4.4-1. The magnitude of emissions allocat ed to each sequential point is proportional to specific trips from the off-shore sea buoy to the various docks lo cated within Galveston Bay and along the Houston Ship Channel. A similar approach has been taken in the past and is documented in Appendix F of the HGB SIP revision ( http://www.tceq.state.tx.us/implementation/air/sip/dec2000hgb.html ) dated December 6, 2000.

This custom spatial allocation approach can be seen on the following pages that contain graphical plots of the 2005, 2006, and 2018 commercial marine summer weekday NO X emissions for the HGB area. These plots are respectively entitled Figure 4.4-1: 2005 HGB Summer Weekday
Commercial Marine NO X Emissions , Figure 4.4-2: 2006 HGB Summer Weekday Commercial

Marine NO X Emissions , and Figure 4.4-3: 2018 HGB Summer Weekday Commercial Marine

NO X Emissions . Grid cells in dark red indicate the hi ghest concentration of shipping emissions where the greatest amount of activity was identif ied by the Starcrest study referenced above.

### B-170 Figure 4.4-1: 2005 HGB Summer Weekday Commercial Marine NO X Emissions

### B-171 Figure 4.4-2: 2006 HGB Summer Weekday Commercial Marine NO X Emissions

### B-172 Figure 4.4-3: 2018 HGB Summer Weekday Commercial Marine NO X Emissions

### B-173 4.5 Area Source Modeling Emissions for Eight-County HGB Area sources include commercial, small-scale industria l, and residential categories of sources that use materials or operate processes that can gene rate emissions. These sources of emissions fall below the point source reporting levels and are ei ther too numerous or too small to identify individually. Emissions from these sources are estimated on either a source category or group
basis. Area source emissions result from eith er hydrocarbon evaporation or fuel combustion. Examples of sources of evaporative losses include printing operations, industrial coatings, degreasing solvents, house paints, leaking undergro und storage tanks, underground tank filling at gasoline service stations, and vehicle refueli ng operations. Fuel combustion sources include stationary source fossil fuel combustion at resi dences and businesses, along with outdoor burning and structural fires. With some exceptions, area source emission estimates are obtained by multiplying an established emission factor by th e appropriate activity or activity surrogate responsible for generating the emissions. Human population is the most commonly used activity surrogate for many area source categories, while other activity data include the amount of gasoline sold in an area, employment by industry type, acres of cropland, etc.

Area source modeling estimates were based prim arily on data from the 2005 periodic emissions inventory (PEI) that is available in the TC EQ TexAER database, which is available at http://texaerweb.tceq.state.tx.us/texaer/index.cfm . The area source emissions inventories were compiled by using the EPA Economic Growth An alysis System (EGAS) model with Texasspecific growth factors for each area source category supplied by Regional Economic Models, Inc. (REMI). Projections to 2006 and 2018 were accomplished with individual growth factors for each area source category by county and year available with the REMI-EGAS system. The
results of these efforts are summarized in Table 4.5-1: HGB Area Source NO X Emissions for 2005, 2006, and 2018 and Table 4.5-2: HGB Area Source VOC Emissions for 2005, 2006, and

2018

.

Table 4.5-1: Eight-County HGB Area Source NO X Emissions for 2005, 2006, and 2018
Summer Weekday NO X Emissions (tpd) Area Source

Category 2005 2006 2018
Architectural Coatings 0.00 0.00 0.00
Asphalt Paving 0.00 0.00 0.00
Auto Refinishing 0.00 0.00 0.00

Dry Cleaning 0.00 0.00 0.00
Food / Brewing 0.00 0.00 0.00

Graphic Arts 0.00 0.00 0.00

Industrial Fuel Use 9.13 9.13 9.13
Leaking Underground Storage Tanks 0.00 0.00 0.00

Oil and Gas Production 24.28 24.43 30.05
Open Burning 0.50 0.51 0.59

Pesticide Use 0.00 0.00 0.00

Petroleum / Gasoline 0.00 0.00 0.00

Residential Fuel Use 2.29 2.29 2.29

Solvent Use 0.00 0.00 0.00
Surface Cleaning 0.00 0.00 0.00
Surface Coating 0.00 0.00 0.00
Traffic Marking 0.00 0.00 0.00

Waste Treatment 0.00 0.00 0.00

Grand Total 36.20 36.36 42.06

### B-174 Table 4.5-2: Eight-County HGB Area So urce VOC Emissions for 2005, 2006, and 2018 Summer Weekday VOC Emissions (tpd) Area Source

Category 2005 2006 2018
Architectural Coatings 30.35 31.24 40.07
Asphalt Paving 0.92 0.95 1.31
Auto Refinishing 3.32 3.41 4.64

Dry Cleaning 2.40 2.41 2.97
Food / Brewing 0.73 0.72 0.87

Graphic Arts 0.65 0.65 0.80

Industrial Fuel Use 0.39 0.39 0.39
Leaking Underground Storage Tanks 2.34 2.26 2.37

Oil and Gas Production 238.48 239.82 295.02
Open Burning 7.06 7.25 8.48

Pesticide Use 0.01 0.01 0.02

Petroleum / Gasoline 61.02 60.45 68.74

Residential Fuel Use 0.13 0.13 0.13

Solvent Use 49.22 50.80 60.70
Surface Cleaning 0.90 0.94 1.35
Surface Coating 22.51 22.81 33.32
Traffic Marking 0.43 0.42 0.44

Waste Treatment 6.93 7.00 8.63

Total 427.79 431.66 530.25

These area source categories were processed with E PS3 for photochemical model input. Spatial allocation was performed with surrogates appropriate for each category. For example, residential fuel use was spatially allocated to grid cells as a function of residential area. As Tables 4.5-1 and 4.5-2 demonstrate, oil and gas production is a dominant contributor of area source NO X and VOC emissions. The TCEQ developed special surrogates to spatially allocate the oil and gas production emissions using well-head locatio n data obtained from the Texas Railroad
Commission.

The following pages contain graphical plots of the 2005, 2006, and 2018 area source summer weekday NO X and VOC emissions for the HGB area. These plots are respectively entitled Figure

4.5-1: 2005 HGB Summer Weekday Area Source NO X Emissions , Figure 4.5-2: 2006 HGB Summer Weekday Area Source NO X Emissions , Figure 4.5-3: 2018 HGB Summer Weekday Area

Source NO X Emissions , Figure 4.5-4: 2005 HGB Summer Weekday Area Source VOC Emissions

, Figure 4.5-5: 2006 HGB Summer Weekday Area Source VOC Emissions , and Figure 4.5-6: 2018 HGB Summer Weekday Area Source VOC Emissions

.

### B-175 Figure 4.5-1: 2005 HGB Summer Weekday Area Source NO X Emissions

### B-176 Figure 4.5-2: 2006 HGB Summer Weekday Area Source NO X Emissions

### B-177 Figure 4.5-3: 2018 HGB Summer Weekday Area Source NO X Emissions

### B-178 Figure 4.5-4: 2005 HGB Summer Weekday Area Source VOC Emissions

### B-179 Figure 4.5-5: 2006 HGB Summer Weekday Area Source VOC Emissions

### B-180 Figure 4.5-6: 2018 HGB Summer Weekday Area Source VOC Emissions

### B-181 4.6 Off-Shore Oil and Gas Production Emissions 2005 emission estimates from oil and gas production in the Gulf of Mexico is detailed in a Minerals Management Service (MMS) study. Environ provided TCEQ with non-platform surrogates that allow for improved spatial distribu tion of this off-shore emissions activity. These
2005 estimates were held constant for 2006 and 2018 modeling and are graphically presented in Figure 4.6-1: 2005 Gulf of Mexico Oil and Gas Production NO X Emissions

.

Figure 4.6-1: 2005 Gulf of Mexico Oil and Gas Production NO X Emissions

### B-182 4.7 Non-Road, Off-Road, and Area Source Emissions Outside of Eight-County HGB Photochemical modeling inputs we re also developed for the non -road, off-road, and area source categories outside of the eight-county HGB area. All non-road emission estimates within Texas were estimated with the TexN model already di scussed above in Section 4.1. Similar to the approach taken with HGB, summer weekday emi ssion estimates were developed with TexN for every Texas county, and the results have been posted to the TCEQ FTP site at ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/Nonroad_EI/ . Non-road emission estimates for non-Texas states within the photochemical modeling domain were developed for 2005, 2006, and 2018 using the EPA National Mobile Invent ory Model (NMIM), which is available at http://www.epa.gov/otaq/nmim.htm and can be used to develop non-road county-level emission estimates for every year from 1999-2050.

In general, 2005 non-HGB county emission estimates within Texas for the off-road and area source categories were obtained from the TCEQ TexAER system. 2006 and 2018 projections of these 2005 figures were done with the REMI-EGAS projection factors discussed above in Section
4.5. Off-road and area source emission estimat es for the non-Texas portions of the modeling
domain were obtained from EPA’s 2005 National Emi ssions Inventory (NEI), which is available

at http://www.epa.gov/ttn/chief/net/2005inventory.html . Growth factors from EGAS were used to project the non-Texas 2005 NEI data to both 20 06 and 2018. A summary of these data sources for the non-HGB areas is provided in Table 4.7-1: Non-Road, Off-Road, are Area Source Emission Sources for Non-HGB Area

.

Table 4.7-1: Non-Road, Off-Road, and Area Source Emission Sou rces for Non-HGB Areas
Data Source(s) Source Category Description Non-HGB Texas Non-Texas
Non-Road Equipment TexN Model NMIM
Oil and Gas Exploration TexAER NMIM

Aircraft TexAER NMIM
Airport Ground Support Equipment TexAER NMIM

Locomotives TexAER NEI

Commercial Marine TexAER / Starcrest NEI

Area Sources TexAER NEI

### B-183 5.0 BIOGENIC MODELING EMISSIONS For development of the biogenic emissions, the TC EQ used version 3.1 of the Global Biosphere Emissions and Interactions System (G loBEIS3.1), which is available at http://www.globeis.com/

. GloBEIS3.1 incorporates detailed locality-specific land-use data to generate the mix and density of vegetative species. In addition, solar ra diation data from Geostationary Operational Environmental Satellite (GOES) satellite imagery, which is used to generate the photosynthetically-active solar radiation (PAR), can be input to the GloBEIS3.1 model. Further, the GloBEIS3.1 model can accept hourly temperatur e data generated from weather station data.

Land-use data for the 12 km and 36 km non-Te xas portions of the modeling domain were obtained from Version 3 of the Biogenic Emissi ons Landuse Data (BELD3; Kinnee et al., 1997), which is a vegetation database for the entire Nort h American continent prepared specifically for creating biogenic emission inventories. Land- use data for the Mexico portion of the 12 km modeling domain were obtained from a joint effo rt between the University of Monterrey and Georgia Tech (Mendoza-Dominguez et al., 2000). Land-use data for the Texas portion of the 12 km domain were obtained from the Texas vegetation database (Wiedinmyer et al., 2001), which
was derived from:

 Texas Parks and Wildlife vegetation data;

 agricultural statistics from the Nationa l Agricultural Statistics Survey; and

 field surveys carried out in 1999.

Within the 4 km nested domain surrounding the Houston-Galveston-Brazoria (HGB) area, a new land-cover database from the University of Texas Center for Space Research (UT-CSR) was used (Feldman et al., 2007) that was developed from:

 classification of recent Landsat 7 data;

 Shuttle Radar Topography Mission and National Elevation datasets to identify wetlands;
and

 U.S. Department of Agriculture Common Land Unit (CLU) data for identification of agricultural lands.

The episode-specific PAR data input to Gl oBEIS3.1 were obtained from the website ( http://metosrv2.umd.edu/~srb/gcip/cgi-bin/historic.cgi ) operated by the Global Energy and Water Cycle Experiment (GEWEX) Continenta l International Project (GCIP) and GEWEX Americas Prediction Project (GAPP). These data were available at half-degree resolution and were reprocessed to spatially match the grid structure of the modeling domain. These data are derived from hourly GOES satellite imagery of cloud cover, which have been processed with a solar irradiation model (Pinker and Laszlo, 1992).

The episode-specific temperature data were obtai ned from weather stations throughout the U.S., including data from the National Weather Serv ice (NWS), the EPA Aerometric Information Retrieval System (AIRS) air quality database, the National Buoy Data Center, the Texas A&M Crop Weather Program, the Louisiana Agricultural Information Service, and the Texas Coastal Oceanographic Observation Network. The organizat ions providing these meteorological data sets typically run thorough quality assurance checks, but a further review for possible anomalies was done by the TCEQ. Kriging algorithms were used with Statistical Analysis Software (SAS) to prepare the hourly temperature fields. The estim ated hourly values were interpolated with a variogram that appropriately fits the inherent degree of variation.

The GloBEIS3.1 model was run for each of the ozone episode days listed in Table 5.0-1: 2005 and 2006 Ozone Episode Days for Emissions Modeling . The date ranges that coincide with the 2006 Texas Air Quality Study (TexAQS) are appropriately noted.

### B-184 Table 5.0-1: 2005 and 2006 Ozone Episode Days for Emissions Modeling

Common

Name Modeling
Period Number of Ozone Exceedance Days May-June 2005 May 19 – June 3, 2005 8

June 2005 June 15-30, 2005 9 July-August 2005 July 26 – August 8, 2005 9

June 2006 May 31 – June 15, 2006 12 2006 TexAQS-1 August 15 – September 14, 2006 10 2006 TexAQS-2 September 19 – October 11, 2006 5

For quality assurance purposes, an emissions summary file was created showing the hourly
domain-wide total emissions of NO X , CO, isoprene, monoterpenes, and other VOCs to allow quick comparisons over different episode days. An additional quality assurance step involved review of the model configuration file that lis ts the various GloBEIS3.1 input settings. Since biogenic emissions are dependent upon the mete orological conditions on a given day, the same episode-specific emissions for the 2006 baseline were used in the 2018 future case modeling scenarios. A demonstration of how total biogenic emissions vary as a function of temperature and other meteorological conditions is provided in Table 5.0-2: T otal Biogenic Emissions for 2005 Episode Days in the HGB Fine Grid Domain

.

Table 5.0-2: Total Biogenic Emissions for 2005 Episode Days in the HGB Fine Grid
Domain Biogenic Emissions (tpd) Ozone Episode

Day Isoprene Total Monoterpenes Other

VOC NO X

5/19/2005 990.52 251.84 319.54 24.80

5/20/2005 1,109.71 278.04 352.36 26.44

5/21/2005 1,541.05 348.85 440.39 30.20

5/22/2005 1,532.87 344.62 435.81 30.16

5/23/2005 1,354.11 322.04 405.24 28.59

5/24/2005 1,192.72 292.19 369.07 27.09

5/25/2005 1,195.11 279.15 356.74 27.05

5/26/2005 881.18 246.16 312.06 24.20

5/27/2005 1,113.46 270.14 343.22 25.98

5/28/2005 1,022.86 260.69 334.74 25.88

5/29/2005 890.56 243.62 310.22 24.26

5/30/2005 695.74 219.55 280.17 23.04

5/31/2005 967.94 255.76 327.19 25.44

6/1/2005 736.14 231.56 294.77 23.64

6/2/2005 1,276.95 296.01 375.71 27.39

6/3/2005 1,227.64 305.24 386.17 27.74

6/15/2005 1,610.00 367.55 464.54 31.40

6/16/2005 1,587.98 353.77 451.58 31.34

6/17/2005 1,528.45 354.31 450.87 31.04

6/18/2005 1,301.66 314.68 405.57 29.89

6/19/2005 1,345.59 316.52 405.68 29.54

6/20/2005 1,304.66 298.36 382.29 28.23

### B-185 Biogenic Emissions (tpd) Ozone Episode

Day Isoprene Total Monoterpenes Other

VOC NO X

6/21/2005 1,405.17 317.71 403.96 28.68

6/22/2005 1,458.86 339.87 430.24 29.90

6/23/2005 1,465.39 342.09 433.44 30.21

6/24/2005 1,516.43 346.50 437.62 30.17

6/25/2005 1,365.38 332.40 421.48 29.71

6/26/2005 1,342.27 326.56 414.27 29.42

6/27/2005 1,431.97 341.17 430.62 29.87

6/28/2005 1,565.05 363.93 459.58 31.14

6/29/2005 1,645.34 383.84 484.11 32.17

6/30/2005 1,719.80 406.90 513.57 33.42

7/26/2005 1,477.01 358.21 452.81 30.87

7/27/2005 1,461.15 356.60 451.20 30.75

7/28/2005 1,485.12 353.40 450.08 30.80

7/29/2005 1,331.62 325.84 413.26 29.06

7/30/2005 1,373.65 323.38 411.48 29.36

7/31/2005 1,486.39 346.78 441.26 30.47

8/1/2005 1,448.97 347.40 439.20 30.12

8/2/2005 1,317.70 326.88 417.80 29.75

8/3/2005 1,317.06 324.84 415.99 29.74

8/4/2005 1,232.77 313.11 400.39 28.70

8/5/2005 1,272.09 321.72 405.66 28.44

8/6/2005 1,455.58 345.82 437.99 30.02

8/7/2005 1,362.22 317.55 407.52 29.54

8/8/2005 1,016.61 281.15 360.45 27.35

A sample plot showing the magnitude and spatial distribution of biogenic emissions for the 4 km fine grid modeling domain is available in Figure 5.0-1: Representative Biogenic VOC and NO X Emissions for August 2, 2005 . Please note that the total VOC reported in Figure 5.0-1 will not match the total VOC reported from Table 5.0-2 because the figure contains emission speciated for photochemical model input with the Carbon Bond 2005 (CB-05) chemical mechanism.

### B-186 

## Biogenic VOC and NO X 

## Emissions August 2, 2005

Figure 5.0-1: Representative Biogenic VOC and NO X Emissions for August 2, 2005

### B-187 ATTACHMENT 1

Reconciling Reported VOC Emission s with Ambient Measurements

### B-188 Background Historically, reported emissions of VOC have been insufficient to explain concentrations measured in the HGB area. Specifically, Highly-Reactive VOC (HRVOC – Ethylene, Propylene, Butenes, and 1,3-Butadiene) may have been unde r-reported in some areas by as much as an order of magnitude. TexAQS-II data suggest that the discrepancy between reported emissions and observed concentrations of ethylene are smaller than was the case in 2000. The discrepancy for propylene appears to have changed little.

Since 2003, the HGB area has had an extensiv e network of Automatic Gas Chromatographs,
which measure ambient concentrations of indi vidual hydrocarbon species. In 2005 and 2006, twelve sites operated in Harris (8), Galveston (1), and Brazoria (3) counties. This work uses the Auto-GC data from 2005 and 2006 to attempt to rec oncile the reported emissions with measured
concentrations.

Emissions data (in units of tons per day, or t pd) are fundamentally incommensurate with ambient concentration data (in units of parts/billion car bon, or ppbC), i.e. they cannot be directly compared. One way to make such a comparison is to use a dispersion model which estimates ambient concentrations resulting from specified emis sion rates. A wide variety of models could be employed, ranging from simple steady-state models such as the Industrial Source Complex (ISC) model to three-dimensional photochemica l grid models including the Community Model for Air Quality (CMAQ) and the Comprehensive Air Quality Model with extensions (CAMx). The TCEQ chose the ISCST3 (ISC Short Term, Version 3) model for the analyses reported here since this model has been widely tested and used for Air Permit Applications. A large number of model runs was required and the development of input data and the running of ISC were done with relative ease compared to CAMx or CMAQ. These latter models and their input data would have been too unwieldy for the scope of this project.

The ISC-ST3 Model The ISC-ST3 (EPA, 1992) model is widely used in modeling for air permit applications. For each hour simulated, it takes as input emission rates from point, area, and mobile sources, stack parameters, wind speed and direction, mixing dept h, ambient temperature, and stability class. Emission rates can be specified as constant throug hout the simulation, or can be hour-specific. The model assumes steady-state conditions, strai ght-line winds, no atmospheric chemistry, and Gaussian dispersion of airborne contaminants. ISC outputs hourly average pollutant concentrations at specified locations, in this case at the location of the 12 Auto-GCs operating in the HGB area, shown in Figure 1: HGB Auto-GC Sites and Reported 2006 Daily Propylene

Emissions . For reference purposes, the figure also shows 2006 reported daily emissions of propylene.

### B-189 Figure 1: HGB Auto-GC Sites and Reported 2006 Daily Propylene Emissions

The ISC model was run for every hour in both 2005 and 2006, producing predicted concentrations of six HRVOC species: ethylene, propylene, 1,3-butadiene, 1- butene, t-2-butene, and c-2-butene, along with two species of relatively low reactivity: isobutane and n-butane. The resulting modeled concentrations could then be compared directly with the concentrations measured by the 12 auto-GCs at the same hours.

Selecting Hours for Comparison Since ISC is a steady-state model that assumes straight-line winds, it is necessary to limit the comparison with measurements to those hours where th e winds have been relatively constant. To exclude trajectories that are too “curvy”, we exam ined five-minute back trajectories terminating at the auto-GC sites and calculated two parameters:

 A sinuosity value, which is the straight-line distance from the start of the trajectory to its endpoint, divided by the actual trajectory length. For perfectly straight trajectories this value is unity, and is < 1 for all others. Th e TCEQ chose a relatively tight criterion of sinuosity ≥ 0.95.

 A maximum variation of wind direction, in de grees. This latter condition is needed to avoid situations where the sinuosity would otherwise be acceptable, but the trajectory makes a sharp turn near the monitoring site. In this case, the sinuosity could be close to unity, yet the sources along the path may lie in a direction markedly different from the wind direction measured at the monitor. For this work, we required all trajectory points to lie within a 15° “wedge” with its apex at the monitor site.

### B-190 Another assumption of ISC is that species are conserved chemically, clearly not the case for HRVOC, which break down quickly in sunlight and also tend to react with other atmospheric contaminants even at night. The TCEQ experime nted with using back trajectories of up to six hours in length, but ultimately chose two-hour trajectories for several reasons: first, beyond two hours significant fractions of the emitted HRVOC will have reacted, and second, longer trajectories tend to develop more curvature, reduc ing the number of hours with relatively straight trajectories available for comparison. Third, the farther the source is from the receptor, the greater the uncertainty in the wind trajectories.

Emissions for ISC Modeling HGB-area 2005 and 2006 point source emissions in tpd were extracted from the State of Texas Air Reporting System (STARS) and formatted for input into ISC. The emissions were processed into the EPS3 input format known as AFS (AIRS facility system). It is important to note that these emissions are adjusted fo r rule-effectiveness (RE) during this process, which increasedemissions from many sources for modeling purposes. Because there is almost no routinelycollected information available about hourly point source emissions, each hour’s emissions for each source are just the daily tons/day divided by 24. Area, on-road mobile, and non-road mobile source emissions previously developed for th e upcoming HGB eight-hour ozone attainment
demonstration SIP revision were allocated to a 2 km grid using the SPLTEM program. This program provides hourly emissions of each component chemical species by grid cell, using the spatial and temporal allocations developed for use in photoc hemical modeling conducted in support of the SIP revision. The program also adjusts daily emissions by day-of-week. Biogenic emissions were not considered, since these sources are not associated with HRVOC emissions (isoprene, although highly reactive, is not regul ated as an HRVOC since it is primarily of biogenic origin).

ISC Results Figure 2: Measured and Modeled Median Concentrati ons by Wind Direction and Emissions for Propylene in the 2005 Ship Channel Area shows a comparison of mode led concentrations (what would be expected, based on the RE-adjusted re ported emissions) with measured concentrations (what was actually seen) for 2005 in the Houst on Ship Channel area. The radar plots at each monitor location convey both measured and mode led propylene concentrations as a function of wind direction. Note that the scales are loga rithmic, with the darker circles representing concentrations of 1, 10, and 100 ppbC. The red pie slices represent the median measured concentration of propylene (propylene) coming from the indicated direction (each wedge represents ten degrees) over all trajectories that me t the straightness criteria described above. The corresponding blue slices represent median modele d concentrations. Slices shown in lighter shading indicate no significant difference between the measured and modeled medians, using a simple sign test [Conover, 1971]. Some directi ons are not represented, having fewer than 15 trajectories meeting the criteria for inclusion. Because of its close proximity with the Clinton Drive and Caesar Chavez monitors, the plot for the Milby Park site is omitted from the figure, but is included in all subsequent analyses. To illu strate the relationship between the sources and receptors, reported 2005 point source propylene emissions are represented as circles of varying
size and color.

### B-191 Figure 2: Measured and Modeled Median Concentrations by Wind Direction and Emissions for Propylene in the 2005 Ship Channel Area

Figure 3: Measured and Modeled Median Concentrati ons by Wind Direction and Emissions for Propylene in the 2005 Gal veston and Brazoria Counties below is similar to Figure 2, except it shows monitoring sites in Galveston and Brazoria c ounties. In both figures, it is clear that the longer slices generally point towards inventoried point sources, sometimes with several monitors pointing to the same source. In the case of the Galveston and Brazoria county monitors, there are lobes pointing towards local sources, but each also has lobe pointing towards the Ship Channel. With a few exceptions, the red slices are usually longer than the blue slices, indicating that median monitored propylene concentrations are gr eater than can be explained by the RE-adjusted reported propylene emissions in STARS. In fact, the discrepancy may be ev en greater than seen on the plots, since some fraction of the emitted propylene will react before it reaches the monitor
locations.

### B-192 Figure 3: Measured and Modeled Median Concentrations by Wind Direction and Emissions for Propylene in the 2005 Galveston and Brazoria Counties

The ISC comparison illustrated in this secti on provides a means for quantifying possible discrepancies between reported emissions and actual emissions; if a red pie slice is significantly longer than the corresponding blue pie slice, then there is evidence that the emissions from that direction may be under-reported. Conversely, if a blue slice is longer than it’s red counterpart, the source may be over-reported, but this could also result because some fraction of the emitted pollutant reacted away en route to the monitor. Simply ratioing the lengths of the red and blue slices provides a factor that could be applied to upwind sources to reconcile reported emissions with observed atmospheric concentrations. This factor is referred to as an Emission Reconciliation Factor (ERF).

The above technique would be a ppropriate for isolated sources, but not in areas like the Houston Ship Channel which has hundreds of sources clum ped together along its length and in nearby industrial areas. One option in this case would be to apply some weighted average of individual ERFs, but this approach becomes unwieldy when accounting for the number of observations and source-receptor distances. A more elegant appr oach based on the Poten tial Source Contribution Function (Ashbaugh, et al 1985, H opke et al 1995) that is described in the following section.

Potential Source Contribution Function The Potential Source Contribution Function (PSCF) is a technique which uses back-trajectories to attempt to pinpoint source regions for observed atmospheric contaminants. While generally applied over regional scales, PSCF is an ideal technique for use in complex urban-scale applications like the HGB area. First, a grid is placed over the source region, then points along the back-trajectories which fall into each grid cell are counted (in the case of HGB, each point represents a five-minute interval along the trajectory track). Of these points, some will be from trajectories which are associated with particul arly large concentrations (e.g., above the 75 th

### B-193 percentile of all observations). Grid cells with unusually high percentage of these highconcentration trajectory points are good candidates to contain emission sources. Xie and Berkowitz (2006) used this technique to identify potential source regions in the Houston Ship Channel area, and, not unexpectedly, these source regions largely coincided with reported
emission sources.

An illustration of the PSCF as applied by Xie a nd Berkowitz is shown below in Figure 4: PSCF
Illustration . In the figure three trajectories pass thr ough the grid cell outlined in blue, two associated with low concentration readings at the monitor (Auto GC), shown in green, and one ending in a high concentration (shown in red). Si nce one of the five trajectory points is from a “high” trajectory, the PSCF score for the grid cell outlined in blue is 1/5 = 0.2.

Auto GC

“High” Traj. “Low” Traj.

“Low” Traj. PSCF =
= 1/5 =

= .2 Auto GC

“Low” Traj.

“Low” Traj. PSCF =
= 1/5 =

= .2

“H igh” Traj.
Figure 4: PSCF Illustration

One feature of this technique is that it is actua lly self-weighting in terms of wind speed; slower moving trajectories will spend more time in each grid cell than faster-moving ones, which implies that more trajectory points will accrue for the slower ones. So in effect the trajectories (or more accurately, the concentrations associated with the tr ajectories) are weighted proportionately to the amount of time the trajectory spends in a grid cell.

Figure 5: Summer Daytime PSCF Probabilities for Propy lene at Channelview and Combined for

Five Sites , reproduced from work performed under contr act to the TCEQ by Xie and Berkowitz, shows the PSCF analysis for propylene at the Channelview auto-GC, (right), and a combined analysis for five auto-GCs (lef t) for 2003 summer daytime observations. The shading in the plots represents the empirical probability that an air parcel passing through a specific location will deliver a high (i.e. greater than the 75 th percentile) concentration of propylene to the Channelview
site.

The right-hand picture shows that most of the high-probability trajectories at Channelview come from the east-to-northeast and southwest-to-southeast directions, which, not surprisingly, correspond with locations of reported propylene sources. The long tails in most cases do not represent distant emission sources; rather, they re present locations traversed by a large number of six-hour trajectories that eventually passed over a source near the monitor and terminated at the monitor. The left-hand picture, using the co mbined PSCF analyses for five sites, still show evidence of artificially long tails like those seen in the right-hand picture, but use of multiple sites has clearly focused the highest PSCF probabilities into areas that more closely correspond with known propylene sources.

### B-194 Figure 5: Summer Daytime PSCF Probabilities for Propylene at Channelview (r) and
Combined for Five Sites

Modified Point Source Contribution Function While the PSCF technique is seen to successfu lly locate potential emission sources, it does not provide quantification for the emission rates. In pursuit of a means to quantify emissions, the PSCF approach was modified as follows: instead of simply computing the fraction of trajectories associated with high concentrations in each grid cell, the median of the concentrations associated with each trajectory point was calculated. Th is value will be referred to as a pseudoconcentration, since it is not the concentration in the grid cell but rather represents the median concentration at the endpoint of the trajectories passing through the cell.

Figure 6: Modified PSCF Technique illustrates the modified PSCF technique. The left-hand figure is the same as Figure 4 except that instead of simply labeling the trajectories as “high” and “low”, the recorded concentrati ons are maintained when the traj ectories arrive at the monitoring location. The right-hand portion of the figure expa nds the grid cell marked in blue and shows the
five concentration values associated with the thr ee trajectories with points inside the cell. The
median of these five values is the modified PSCF va lue for this cell. Note that the median was
used instead of the mean due to the highly skewed nature of the c oncentration data (an alternative would be to use the geometric mean).

0.21 ppbC 0.68 ppbC

2.35 ppbC Auto GC

2.35 pp b C2.35 pp b C Auto GC

2.35 0.68

0.68

0.21 0.21 0.68 0.68

0.68

0.68 0.21

0.21 2.35

2.35 0.21

0.21

0.68

0.68 Auto GC

2. 35 PSCF Value = 0.68

2.35 0.68

0.68

0.21 0.21 0.68 0.68

0.68

0.68 0.21

0.21 2.35

2.35 2.35 0.68

0.68

0.21 0.21 0.68

0.21

0.21

0.68

0.68 PSCF Value = 0.68

2.35 2. 35

0.68 2.35

0.68 0.21

Figure 6: Modified PSCF Technique

Figure 7: 2005 Propylene Pseudo-Concen trations Using Channelview Observed Concentrations presents the results of applying the modified PSC F analysis to back trajectories ending at the

### B-195 Channelview Auto-GC, using the same back traj ectories used to create Figure 2 (i.e., 2005 twohour back-trajectories with sinuosity 2km grid cell having at least 100 trajectory points.

Figure 7: 2005 Propylene Pseudo-Concentrations Using Channelview Observed
Concentrations

Note the similarity between Figure 7 and the right -hand side of Figure 5. Both figures identify
sources in the north-to-northeast and southwest-t o-southeast sectors, even though the tails in Figure 5 are much longer because of the longer trajectories.

As was done in the original PSCF analysis, the PSCF analysis was re-run, but this time all 12 auto-GC sites were included together. In this case, the interpretation of the pseudo concentrations changes slightly – instead of representing the me dian concentration associated with points of trajectories ending at a single monitor, the pseudo-concentrations shown in Figure 8: 2005 Propylene Pseudo-Concentrations Using Observed Concentrations at 12 Auto-GC Sites represent the median concentration associated with poi nts of trajectories ending at any monitor.

### B-196 Figure 8: 2005 Propylene Pseudo-Concentratio ns Using Observed Concentrations at 12
Auto-GC Sites

At this point, the median concentrations can be quantified for air parcels moving over the monitoring locations identified on the graphic, but still have no means to quantify emissions themselves. But the same analysis depicted in Figure 8 can be performed using the modeled concentrations at the monitor locations, instead of the measured concentrations. Figure 9: 2005 Pseudo-Concentrations of Propylene Using Modeled Concentrations at 12 Auto-GC Sites thus
shows the median concentrations over the sources that we would expect to see based on the REadjusted reported emissions.

While the darker areas in Figure 9 correspond well with those in Figure 8, clearly the areas are generally much lighter in Figure 9. This impli es that reported emissions, even adjusted for RE, are not sufficient to explain the concentrations of propylene seen at the Auto-GC sites.

### B-197 Figure 9: 2005 Pseudo-Concentrations of Propylene Using Modeled Concentrations at 12
Auto-GC Sites

The comparison of the modeled and measured PSCF pseudo-concentration values leads naturally to a means for quantifying any discrep ancies between them. Figure 10: Ratios of 2005 Propylene Pseudo-Concentrations Based on Meas urements to Pseudo-Concentrations Based on Modeling presents the ratio of median measured PSCF pseudo-concentrations by grid cell to their median modeled counterparts. Red areas indicate source regions where reported emissions are not sufficient to explain the measured concentrations, while blue areas indicate the converse. White areas indicate relatively close agreement.

### B-198 Figure 10: Ratios of 2005 Propylene Pseudo -Concentrations Based on Measurements to Pseudo-Concentrations Based on Modeling

Emissions Reconciliation After calculating the pseudo-concentration ratios depi cted in Figure 10, the next logical step is to apply the ratios to the RE-adjusted reported emissi ons by grid cell. This approach is valid because of the assumption of steady-state, non-reac tive conditions. Point sources reported in the 2005 STARS database were identified by grid cell, and their RE-adjusted emissions were multiplied by the indicated ratio. Even though the ratios for a small number of sources were less than unity, only reconciliation factors greater than one were applied since some reduction in propylene concentration would be expected due to reactions in the atmosphere. Some early applications of the technique a pplied reconciliation factors to ot her types of sources, but this procedure was rejected because it resulted in spuriously large increases to area and mobile source emissions which happened to be near point sour ces. In 2005, the emissions reconciliation added 15 tpd of propylene emissions to the RE-adjusted reported total of 10 tpd, resulting in an increase of 150 percent.

After performing the reconciliation, it is natural to re-run the analysis using the reconciled point
source emissions to see if the reconciled emissions really are more congruent with measurements than were the reported emissions. Figure 11: Modified PSCF Analysis Using Observed and

Modeled Concentrations at 12-Auto GC-Sites shows a six-panel comparison between the beforeand-after reconciliation PSCF plots. The top row reproduces the plots in Figures 8-10 using REadjusted reported emissions, and the bottom row shows the same plots using the reconciled emissions (the leftmost plots in each row are identic al to each other). It is apparent that the PSCF pseudo concentrations based on reconciled emis sions (bottom center) match the observationbased PSCF pseudo-concentrations in the left panel much better than do those based on REadjusted reported emissions (top center), and th e ratios based on reconciled emissions (bottom right) show ratios generally much closer to unity th an do those in the row above. It can then be

### B-199 concluded that the reconciled emissions do a bette r job of explaining the observed concentrations than do the RE-adjusted reported emissions.

Figure 11: Modified PSCF Analysis Using Observed and Modeled Concentrations at 12- Auto GC-Sites. Top Row: (L) PSCF pseudo-con centrations of propylene using monitored concentrations as 12 auto-GC sites; (C) PSCF pseudo-concentrations of propylene using modeled concentrations at 12 auto-GC sites, calculated using RE-adjusted reported emissions; (R) Ratio of monitoring-based PSCF pseudo-concentrations to model-based pseudo-concentrations using RE-adjusted rep orted emissions. Bottom Row: (L) PSCF pseudo-concentrations of propylene using moni tored concentrations at 12 auto-GC sites; (C) PSCF pseudo-concentrations of propylene using modeled concentrations at 12 auto-GC sites, calculated using reconciled emissions; (R) Ratio of monitoring-based PSCF pseudoconcentrations to model-based pseudo-concentrations using reconciled emissions

After performing a second round of PSCF analysis, it would, of course, be possible to make a second round of emission adjustments, and repeat th e process again ad infinitum. In this work,
additional iterations were found to add little value and so multiple iterations are not being run at this time, although this may be investigated in future work.

Summary of Reconciled Emissions
Table 1: RE-Adjusted 2005 and 2006 Emissions of Six HRVOC Species, Reconciled Emission Totals, and Percent Change below shows totals of emissions before and after reconciliation for the six regulated HRVOC for 2005 and 2006. Co mparing the two years shows that all species except t-2-butene required smaller relative adjust ments in 2006 than in 2005, consistent with enhanced monitoring and control of previ ously unaccounted-for emission sources.

### B-200 Table 1: RE-Adjusted 2005 and 2006 Emis sions of Six HRVOC Species, Reconciled Emission Totals, and Percent Change

2005 2006

HRVOC Species REAdjusted Reported
Emissions Reconciled
Emissions Percent Change REAdjusted Reported
Emissions Reconciled
Emissions Percent Change Propylene 10.03 25.07 149.8% 9.65 21.92 127.1% Ethylene 10.96 18.72 70.8% 10.10 15.80 56.4% 1,3-Butadiene 2.22 3.02 35.7% 1.53 1.97 28.3%

1-Butene 2.59 3.96 52.8% 2.70 3.13 16.2%

t-2-Butene 0.58 1.38 135.4% 0.50 1.55 210.9%

c-2-Butene 0.33 0.74 126.8% 0.54 0.86 58.1%

TOTAL 26.72 52.89 97.9% 25.02 45.23 80.7%

### B-201 Model Performance Evaluation

This section presents model performance comp arisons between CAMx runs using the unreconciled and reconciled emissions. Since the current base case, Reg 10 already included the PSCF-based emission reconciliation, a special version of Reg 10 without the emission reconciliation, Reg 10 NOPSCF, was created for purposes of this evaluation.

HRVOC Performance This section compares modeled and observed conc entrations of the CB-05 species ETH, OLE,
and IOLE before and afte r emission reconciliation 1 . The observed data consists of observations made at four Ship Channel auto-GC sites: Deer Park, Wallisville, HRM-3, and Lynchburg Ferry, as well as aircraft observations made by the NOAA P3 in the Houston urban and industrial areas. Comparisons for surface data are shown for thr ee episodes: June 2005, June 2006, and AQS-1. Scatter plots for a complete set of CB-05 hydrocarbon species at all twelve HGB auto-GC sites for each episode are available at ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/HGB8H2/

. Note that concentrations in this section are disp layed in ppb instead of ppbC, since the output CAMx concentrations are always in ppb.

The scatter plots displayed in this section use a log- log scale, which is useful when analyzing data that is approximately log-normally distributed. Also note the red dots plot the ordered values of observed and modeled concentrations against each other (e.g., smallest observed concentration is paired with smallest modeled concentration). This quantile-quantile (Q-Q) plot shows how well
the distribution of modeled concentrations matc hes that of the observations (i.e., the model generates the correct proportions of small, medium, and large values). The closer the red dots lie to the one-one line, the better the distributions match.

Figure 12: Scatter Plots Comparing Reg10 Base Case Modeled ETH Concentrations with

Observed Concentrations at Deer Park for Three Episodes shows that the PSCF-based emission reconciliation improves the comparison between modeled and measured ethylene (ETH) concentrations at Deer Park for all three epis odes, except for slightly increasing the positive bias for concentrations less than 1 ppb during the AQS-1 episode. Figure 13: Scatter Plots Comparing Reg10 Base Case Modeled ETH Concentrations with Observed Concentrations at Wallisville for Three Episodes shows that at Wallisville, the reconc iliation improves the comparison between measured and modeled ethylene for the June , 2005 episode, but increases the positive bias seen during the other two episodes. At HRM-3 (Figure 14: Scatter Plots Comparing Reg10 Base Case
Modeled ETH Concentrations with Observed Co ncentrations at HRM-3 for Three Episodes ), reconciliation improves the comparison during the June 2005 episode. For the June 2006 episode, the reconciliation does not visibly improve nor degrade the model’s performance, but it does add to the positive bias seen during the AQS-1 episode. At Lynchburg Ferry (Figure 15: Scatter Plots Comparing Reg10 Base Case M odeled ETH Concentrations with Observed Concentrations at Lynchburg Ferry for Three Episodes ), the reconciliation improves the comparison between modeled and ob served ethylene for concentrations above 1 ppb for the June

1 Because of the vast number of hydrocarbon species and potential r eactions, the CB-05 mechanism dealsonly with a limited number of species classified by carbon bonds. Emissions are transformed during preprocessing into these CB-05 species before being in put into CAMx. For comp arison purposes, measured concentrations of sampled hydrocarbon species may also be transformed into CB-05 species. There is a close (though not exact) correspond ence between the TCEQ’s list of HRVOCs (ethylene, propylene, 1,3- butadiene, and butenes) and the three CB-05 species ETH, OLE, and IOLE. Ethylene is treated explicitly by CB-05, so the ETH CB-05 species compares directly with measured ethylene concentrations. The OLE species represents a large collection of olefins (alkenes), but in practice (at least in the industrial areas in the HGB region) largely consists of propylene. The IOLE species represents a relatively small number of olefins with internal double bonds, including butenes.

### B-202 2005 and June 2006 episodes, and a bove 2 ppb for the AQS-1 episode. The reconciliation adds to the positive bias below 2 ppb for the AQS-1 episode.



## Deer Park ETH

Unreconciled ReconciledJune 2005June 2006AQS-1

Figure 12: Scatter Plots Comparing Reg10 Ba se Case Modeled ETH Co ncentrations with Observed Concentrations at Deer Park for Three Episodes - (L) Without PSCF-Based Emissions Reconciliation; (R) With Emission s Reconciliation: (top to bottom row) June 2005, June 2006, and AQS-1

### B-203 

## Wallisville ETH

Unreconciled ReconciledJune 2005June 2006AQS-1

Figure 13: Scatter Plots Comparing Reg10 Ba se Case Modeled ETH Co ncentrations with
Observed Concentrations at Wallisville fo r Three Episodes - (L) Without PSCF-Based Emissions Reconciliation; (R) With Emission s Reconciliation: (top to bottom row) June 2005, June 2006, and AQS-1

### B-204 

## HRM-3 ETH

Unreconciled ReconciledJune 2005June 2006AQS-1

Figure 14: Scatter Plots Comparing Reg10 Ba se Case Modeled ETH Co ncentrations with Observed Concentrations at HRM-3 for Three Episodes - (L) Without PSCF-Based Emissions Reconciliation; (R) With Emission s Reconciliation: (top to bottom row) June 2005, June 2006, and AQS-1

### B-205 

## Lynchburg Ferry ETH

Unreconciled ReconciledJune 2005June 2006AQS-1

Figure 15: Scatter Plots Comparing Reg10 Base Case Modeled ETH Concentrations withObserved Concentrations at Lynchburg Ferry for Three Episodes - (L) Without PSCFBased Emissions Reconciliation; (R) With Em issions Reconciliation: (top to bottom row) June 2005, June 2006, and AQS-1

### B-206 Figure 16: Scatter Plots Comparing Reg10 Base Case Modeled ETH Concentrations with Observed Concentrations Sampled by the P3 Aircraft in the HG 2 Km X 2 Km Grid compares modeled ethylene concentrations with ethylene concentrations observed by the NOAA WP-3D Orion (P3) aircraft within the 2 km X 2 km HG fine grid shown in Figure 1.1-1. The aircraft sampled ethylene continuously along its route and for purposes of comparison with modeled output, the data were averaged over one-minute intervals. The mid-point in space and time of each interval was located within the modeling grid, and the modeled value fo r that grid cell and hour was paired with the observation; no spatial or temporal interpolati on was performed. The P3 flew missions between August 31, 2006 and October 13, 2006, but most of the observations were taken during the AQS2 episode period September 19 - October 12, 2006.

The figure shows that the model generally under-estimated the P3-observed ethylene
concentrations within the Hous ton urban and industrial areas. Adding in the PSCF-based emissions reconciliation improves the compar ison, but the model still under-predicts the observations by about a factor of two. The aircra ft data gives a somewhat different picture from that portrayed by the surface monitors, where in some cases the unreconciled model predicted the observations well or even over-predicted concentrations in some cases.

Figure 16: Scatter Plots Comparing Reg10 Base Case Modeled ETH Concentrations with Observed Concentrations Sampled by the P3 Aircraft in the HG 2 Km X 2 Km Grid - (L) Without PSCF-Based Emissions Reconciliation; (R) With Emissions Reconciliation.

Figures 17-20 compare modeled and measured concentrations of the CB-05 OLE species at the same four monitors and for the same three episodes depicted above. Here, the picture is fairly consistent, with the unreconciled model under-pre dicting the observed concentrations in the majority of cases, often by a factor of twp to three, and showing no tendency towards overpredicting the observations in any case. The reconciliation improves the comparison with few exceptions, and in some cases causes the distribution of modeled values to match almost exactly
that of the observations.

At Deer Park (Figure 17: Scatter Plots Comparing Reg10 Base Case Modeled OLE

Concentrations with Observed Concentra tions at Deer Park for Three Episodes ), the reconciliation improved, but did not completely correct, the model’s under-prediction of observed concentrations less than one ppb, and causes a sm all over-prediction for observations in the 2-10 ppb range during the June, 2005 and AQS-1 ep isodes. At Wallisville (Figure 18: Scatter Plots Comparing Reg10 Base Case Modeled OLE Concentr ations with Observed Concentrations at Wallisville for Three Episodes ), the reconciliation caused some over-prediction between about 0.5 and 10 ppb for June, 2005, but improved the comparison for June, 2006 (although still under-

### B-207 predicting), and for the AQS-1 episode, the di stribution of modeled concentrations after reconciliation matched the observed distribution extremely well. At HRM-3 (Figure 19: Scatter Plots Comparing Reg10 Base Case Modeled OLE Con centrations with Observed Concentrations at HRM-3 for Three Episodes ), the comparison between the unreconciled model and the observations was fairly good to begin with, with only a small under-predictive bias evident. After reconciliation, though, the compar isons were very good for all th ree episodes. For Lynchburg Ferry (Figure 20: Scatter Plots Comparing Reg10 Base Case Modeled OLE Concentrations with Observed Concentrations at Lynchburg Ferry ), the reconciliation caused a small over-predictive bias between 0.2 and 5 ppb during the June, 2005 episode, but improved concentrations in all ranges during the June, 2006 episode and delivered a very good comparison between the modeled and measured concentration distributions during the AQS-1 episode.

### B-208 

## Deer Park OLEJune 2005June 2006AQS-1 

### Unreconciled Reconciled

Figure 17: Scatter Plots Comparing Reg10 Base Case Modeled OLE Concentrations with observed concentrations at Deer Park for Three Episodes - (L) Without PSCF-Based Emissions Reconciliation; (R) With Emission s Reconciliation: (top to bottom row) June 2005, June 2006, and AQS-1

### B-209 

## Wallisville OLE

Unreconciled ReconciledJune 2005June 2006AQS-1

Figure 18: Scatter Plots Comparing Reg10 Base Case Modeled OLE Concentrations with
observed concentrations at Wallisville fo r Three Episodes - (L) Without PSCF-Based Emissions Reconciliation; (R) With Emission s Reconciliation: (top to bottom row) June 2005, June 2006, and AQS-1

### B-210 

## HRM-3 OLE

Unreconciled ReconciledJune 2005June 2006AQS-1

Figure 19: Scatter Plots Comparing Reg10 Base Case Modeled OLE Concentrations with Observed Concentrations at HRM-3 for Three Episodes - (L) Without PSCF-Based Emissions Reconciliation; (R) With Emission s Reconciliation: (top to bottom row) June 2005, June 2006, and AQS-1

### B-211 

## Lynchburg Ferry OLE

Unreconciled ReconciledJune 2005June 2006AQS-1

Figure 20: Scatter Plots Comparing Reg10 Base Case Modeled OLE Concentrations withObserved Concentrations at Lynchburg Ferry for Three Episodes - (L) Without PSCFBased Emissions Reconciliation; (R) With Em issions Reconciliation: (top to bottom row) June 2005, June 2006, and AQS-1

### B-212 Figure 21: Scatter Plots Comparing Reg10 Base Case Modeled OLE Concentrations with Observed Concentrations Sampled by the P3 Aircraft in the HG 2 Km X 2 Km Grid compares modeled concentrations of OLE with concentra tions observed by the NOAA P3 aircraft within the 2 km X 2 km HG fine grid. The aircraft collected hundreds of Whole-Air Samples (WAS) in canisters which were analyzed to derive c oncentrations of hydrocarbon species. These
concentrations were then transformed into CB-05 species concentrations in the same fashion that reported emissions are transformed in preparation for input to CAMx. In most circumstances, especially in the Houston industrial areas, OLE cons ists largely of propylene. The mid-point in space and time of each sample (usually about 7 or 12 seconds) was located within the modeling grid, and the modeled value for that grid cell a nd hour was paired with the observation; as with the continuously-sampled ethylene data, no spatia l or temporal interpolation was performed, and again most of the observations were taken during the AQS-2 episode period September 19 - October 12, 2006.

The figure shows that the model under-estimated th e aircraft-observed OLE concentrations within the Houston urban and industrial areas by about a factor of three. Adding in the PSCF-based emissions reconciliation noticeably improves the co mparison, but even after the reconciliation the observations are still substantially under-predicted by about a factor of two for most of the observed range. While the surface observations also were under-predicted, the magnitude of the under-prediction was considerably smaller than seen by the aircraft, and in that case the reconciliation produced modeled con centrations distributions very co mparable to those observed.

### B-213 Figure 21: Scatter Plots Comparing Reg10 Base Case Modeled OLE Concentrations with Observed Concentrations Sampled by the P3 Aircraft in the HG 2 Km X 2 Km Grid - (L) Without PSCF-Based Emissions Reconciliation; (R) With Emissions Reconciliation.

Figures 22-25 show effects of emission reconciliation on the IOLE species. First note that for all four sites, the June 2005 observations are almost all greater than 0.1 ppb, probably indicating that some of the compounds that are allocated to th e IOLE composite species were being reported at threshold quantities. This effect is particularly dramatic at Wallisville, where it appears that the model dramatically under-predicts very low concentr ations of IOLE, but in reality probably is just an artifact. For later episodes, the measured IO LE concentrations appear to represent a more reasonable concentration range, but comparisons fo r very low observed concentrations are still questionable.

For Deer Park (Figure 22: Scatter Plots 

### Comparing Reg8 Base Case Modeled IOLE

Concentrations at Deer Park Us ing RE-Adjusted Reported Emissions for Three Episodes ), the model under-predicts the observed IOLE concentr ations except for the range of around 0.2 to 0.6 during the AQS-1 episode. Adding the em ission reconciliation only changes the modeled concentrations very subtly. Similarly, at Wallisville (Figure 23: Scatter Plots 

### Comparing Reg8
Base Case Modeled IOLE Concentrations at Wallisville Using RE-Adjusted Reported

Emissions for Three Episodes ), the reconciliation makes little difference, except for improving the predictions for observations between 0.1 and 0.3 ppb during the June, 2006 episode. At both HRM-3 (Figure 24: Scatter Plots 

### Comparing Reg8 Base Case Modeled IOLE Concentrations at HRM-3 Using RE-Adjusted Reported Emissions for Three Episodes )) and Lynchburg Ferry (Figure 25: Scatter Plots 

### Comparing Reg8 Base Case Modeled IOLE Concentrations at Lynchburg Ferry Using RE-Adjusted Reported Emissions for Three Episodes )), the reconciliation makes a slight improve ment during the June 2005 episode, but makes little noticeable change elsewhere.

### B-214 

## Deer Park IOLE

Unreconciled ReconciledJune 2005June 2006AQS-1

Figure 20: Scatter Plots Comparing Reg8 Base Case Modeled IOLE Concentrations at Deer Park Using RE-Adjusted Reported Emissions for Three Episodes - (L) Without PSCF-Based Emissions Reconciliation; (R) With Emission s Reconciliation: (top to bottom row) June 2005, June 2006, and AQS-1

### B-215 

## Wallisville IOLE

Unreconciled ReconciledJune 2005June 2006AQS-1

Figure 21: Scatter Plots Comparing Reg8 Base Case Modeled IOLE Concentrations at Wallisville Using RE-Adjusted Reported Emissions for Three Episodes - (L) Without PSCF-Based Emissions Reconciliation; (R) With Emission s Reconciliation: (top to bottom row) June 2005, June 2006, and AQS-1

### B-216 

## HRM-3 IOLE

Unreconciled ReconciledJune 2005June 2006AQS-1

Figure 22: Scatter Plots Comparing Reg8 Base Case Modeled IOLE Concentrations atHRM-3 Using RE-Adjusted Reported Emissions for Three Episodes - (L) Without PSCFBased Emissions Reconciliation; (R) With Em issions Reconciliation: (top to bottom row) June 2005, June 2006, and AQS-1

### B-217 

## Lynchburg Ferry IOLE

Unreconciled ReconciledJune 2005June 2006AQS-1

Figure 23: Scatter Plots Comparing Reg8 Ba se Case Modeled IOLE Concentrations at Lynchburg Ferry Using RE-Adjusted Reported Emissions for Three Episodes - (L) Without PSCF-Based Emissions Reconciliation; (R) With Emissions Reconciliation: (top to bottom row) June 2005, June 2006, and AQS-1

### B-218 Figure 24: Scatter Plots Comparing Reg10 Base Case Modeled IOLE Concentrations with Observed Concentrations Sampled by the P3 Aircraft in the HG 2 Km X 2 Km Grid compares modeled IOLE concentrations with concentra tions calculated from the WAS samples taken aboard the NOAA P3 aircraft in the HG 2 km X 2 km grid. Like the surface observations, the effect of adding in the emission reconciliation is quite subtle, with only a slight improvement in the comparison noticeable for measured concentrations of around 0.01 ppb.

Figure 24: Scatter Plots Comparing Reg10 Base Case Modeled IOLE Concentrations with Observed Concentrations Sampled by the P3 Aircraft in the HG 2 Km X 2 Km Grid - (L) Without PSCF-Based Emissions Reconciliation; (R) With Emissions Reconciliation.

Formaldehyde Performance

Formaldehyde is produced in the photochemical oxidation of ethylene and other VOCs, and is in turn a significant source of radicals that lead to ozone formation. Figure 25: Scatter Plots Comparing Reg10 Base Case Modeled Formaldehy de (FORM) Concentrations with Observed Concentrations Sampled by the P3 Aircraft in the HG 2 Km X 2 Km Grid compares scatter plots
of modeled vs. observed concentrations of fo rmaldehyde (FORM in CB-05 parlance) from the continuous monitoring instrume nt aboard the NOAA P3 aircraft, before and after the PSCF-based emission reconciliation. Though subtle, there is a distinguishable improvement in the concentrations in the Reg 10 base case compared to the case without reconciliation, particularly for concentrations above 5 ppb. Note the use of linear instead of logarithmic axes on the plots; linear scales are appropriate for species whose con centration distributions are close to normal (as opposed to lognormal). Since ozone and (most) formaldehyde are formed through atmospheric reactions, their concentrations generally do not va ry across several orders of magnitude as is the case with primarily-emitted species sa mpled close to their sources.

At three surface sites where formaldehyde data were collected during the TexAQS-II (Moody Tower, HRM-3, and Lynchburg Ferry), a comparis on of the scatter plots with and without the PSCF-based reconciliation reveals very minor differences. Instead, Figure 26: Time Series Plot Comparing Reg10 Base Case Modeled Formaldehy de (FORM) Concentrations with Observed Concentrations at Moody Tower during the AQS-1 Episode shows a time-series plot of modeled vs. observed formaldehyde at the Moody Tower fo r the AQS-1 episode. The with the two model
runs shown: the blue line shows modeled form aldehyde concentrations before reconciliation and the green line shows modeled concentrations afte r reconciliation; observaed concentrations are shown in red. For most hours, the green line is hidden, indicating only ve ry small differences in
concentrations between the two runs. But on Au gust 20 and 21, and to a lesser degree September 14, the reconciliation does noticeably improve the model’s replication of large daytime peaks.

### B-219 Figure 25: Scatter Plots Comparing Reg10 Base Case Modeled Formaldehyde (FORM) Concentrations with Observed Concentrations Sampled by the P3 Aircraft in the HG 2 Km X 2 Km Grid - (L) Without PSCF-Based Emis sions Reconciliation; (R) With Emissions
Reconciliation.

Figure 26: Time Series Plot Comparing Re g10 Base Case Modeled Formaldehyde (FORM) Concentrations with Observed Concentrations at Moody Tower during the AQS-1 Episode: Blue Line – Reg 10 Base Case Before PSCF-Based Emission Reconciliation; Green Line Reg 10 Base Case After Reconciliation; Red Squares: Observed Concentrations

Ozone Performance

Figure 27: Daily Maximum One-Hour Modeled Ozone Concentrations for August 16, 2006 shows an example of a plume of ozone traveling northeast from Houston, with the most intense concentrations downwind from the Ship Channel ar ea. The left-hand plot shows daily maximum
modeled one-hour ozone concentr ation by grid cell without em issions reconciliation (since the local maxima do not occur simultaneously across the domain, the plot represents a composite across several hours), and the center plot shows the same image but with emissions reconciliation. The right-hand plot shows the difference. While the differences between the first two plots are subtle, they are important. The reconciled emis sions produced higher concentrations than the unreconciled emissions (139.2 ppb vs. 136.3 ppb, with the observed maximum at 141.9) and the maximum (‘+’ sign on the plot) occurred closer to the observed maximum (the orange circle just SSE of the modeled maximum).

### B-220 Figure 27: Daily Maximum One-Hour Modeled Ozone Concentrations for August 16, 2006, No Emissions Reconciliations (L), With Emis sions Reconciliation (C), and The Difference Between The Two (L)

Figure 28: 1-Hour Ozone Mean Relative Bias, Reg 8 Base Case With and Without PSCF-Based

Emission Reconciliation and Figure 29: 1-Hour Ozone Mean Relative Error, Reg 8 Base Case

With and Without PSCF-Based Emission Reconciliation provide side-by-side comparisons of one-hour model relative bias and relative error, su mmarized over all sites used in the MPE across all days in each episode, and also summarized by year. These plots show that in every case but July of 2005, model relative bias improves with the emission reconciliation. On average, model relative bias for 2005 improved by 0.5 ppb, and by nearly 0.4 ppb for 2006. Model relative error improves marginally in the May and June 2005 episodes and increases slightly in July 2005, yielding a very modest overall improvement for 2005 of 0.08 ppb. For 2006, model relative error for two of the three episodes improved margina lly, and for one (AQS-1) model relative error showed an equally marginal increase, leaving ove rall 2006 relative error with a fairly negligible improvement of less than 0.05 ppb.

1-Hour Ozone Mean Relative Bias, Reg 8 Base Case With

and Without PSCF-Based Emission Reconciliation-10.088-7.9904.817-4.730-7.147-3.272-3.682-4.975-9.584-7.5145.354-4.231-6.775-2.913-3.273-4.601

-15 -10 -5 0 5 10 15

May 05 Jun 05 Jul 05 2005 Avg. Jun 06 AQS-1 AQS-2 2006 Avg.

EpisodeMean Relative Bias (% )

Reg 8 Base Case Reg 8 Base Case w/ PSCF Emissions ReconciliationFigure 28: 1-Hour Ozone Mean Relative Bias, Reg 8 Base Case With and Without PSCFBased Emission Reconciliation

### B-221 1-Hour Ozone Mean Relative Error, Reg 8 Base Case With

and Without PSCF-Based Emission Reconciliation16.59013.06816.19114.69913.09914.25513.66313.65416.42912.90716.29714.61613.01614.27413.57313.609

0 5 10 15 20 25 30

May 05 Jun 05 Jul 05 2005 Avg. Jun 06 AQS-1 AQS-2 2006 Avg.

EpisodeMean Relative Error (% )

Reg 8 Base Case Reg 8 Base Case w/ PSCF Emissions Reconciliation Figure 29: 1-Hour Ozone Mean Relative E rror, Reg 8 Base Case With and Without PSCFBased Emission Reconciliation

Conclusions and Future Research

The PSCF-based reconciliation process produces em issions which are more congruent with ambient measurements, as evidenced by comp aring the pseudo-concentrations created from
modeled and observed concentrations at the auto -GC sites. Direct comparisons between CAMx
modeled concentrations with auto-GC observations for a subset of monitors and episodes shows
that modeled concentration distributions distin ctly improved for OLE, which was underestimated
in the model without reconciliation. Addi ng in the reconciliation increased modeled concentrations and improved performance almost uni versally at the sites and episodes shown in the comparison. Comparing observed OLE concentra tions with those sampled by the P3 aircraft showed even greater under-prediction. Af ter reconciling the emissions, the modeled concentrations increased somewhat, but still were a factor of two to three lower than observed
concentrations

Results for ETH were somewhat mixed. For several site/episode comb inations, the modeled concentrations exceeded the observed concentrations for some or all of the range of observations, and in these cases reconciling the emissions degraded performance. But in at least as many instances, the model under-predicted ethylene con centrations before reconciliation, and in these cases the resonciliation improved model performances. The aircraft data, however, shows that at the flight altitude the model under-predicted th e observations, and even after reconciliation the
modeled concentrations were lower than observed by half.

In most, but not all, cases, IOLE concentrati ons were under-predicted by the model, and the reconciliation made some barely discernable impr ovements in model performance in some cases, including the aircraft data.

The emissions reconciliation noticeably reduced the model’s under-prediction of formaldehyde concentrations in the aircraft data, and improve d the model’s ability to replicate daytime peak formaldehyde concentrations in some cases.

Overall ozone performance improved slightly with the emissions r econciliation, more so in 2005 than in 2006. While it may seem like the m odest improvement seen in ozone performance obviates the need for emission reconciliation, it is still important to develop the best possible set of model inputs to help insure the model “gets the right answer for the right reason”, and to

### B-222 provide more confidence in the model’s future predictions. Additionally, TCEQ plans to continue to investigate ways to improve the emissions reconciliati on methodology which may lead to more substantive impr ovements in ozone performance.

As noted in the Model Performance Evaluation section of Appendix C: Photochemical Modeling for the HGB Attainment Demonstration SIP R evision for the 1997 Eight-Hour Ozone Standard

, adding HRVOC emissions to flares can signifi cantly improve performance of these species concentrations compared with aircraft observati ons. Future research plan s include investigating application of the PSCF-based reconciliation technique specifically to flares. Additional goals include applying the technique to additional pollutants (in particular, NO X ) and looking at additional years beyond 2005 and 2006.

References Ashbaugh, L.L., Malm, W.C., Sadeh, W.Z., (1985) . A residence time probability analysis of sulfur concentrations at Grand Canyon Nationa l Park. Atmospheric Environment 19 (8), 1263–

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Hopke, P.K., (2003). Recent development in recep tor modeling. Journal of Chemometrics 17, 1–

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Conover, W. J. (1971) Practical Nonpara metric Statistics, John Wiley & Sons.

EPA (1992) User’s Guide for the Industrial Source Complex (ISC2) Dispersion Models,
EPA450/4-92-008a.

Xie Yulong and Berkowitz, Carl M. (2006), The u se of conditional probability functions and potential source contribution functions to identify source regions and advection pathways of hydrocarbon emissions in Houston, Texas, Atmospheric Environment 41 (2007) 5831–5847.