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APPENDIX B 

Emissions Modeling for the HGB Attainment Demonstration SIP Revision for the 1997 

Eight-Hour Ozone Standard 
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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 Modeling Emissions Development B-3

1.1 Modeling Domain and Nested Grid Structure B-5

1.2 HGB Modeling EpisodeS B-9

1.3 Emissions Processing System B-9

Section 2.0 Point Source Modeling missions Development B-14

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

2.2 2006 Baseline Point Source Modeling Emissions Development B-65

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

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2.4 2018 Future Year Point Sources Control Strategy Modeling Emissions 

 Development

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

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

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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

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

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4.2 Aircraft and Ground Support Equipment Modeling Emissions for 

 Eight-County HGB

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

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

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

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

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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
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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. Instead of using the model results in an absolute 

sense, the eight-hour ozone procedure uses the modeling 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 photochemical 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 replicating historical episodes (base cases) for which high daily eighthour ozone was measured. To maximize model performance, base case emission inputs are 

estimated as accurately as possible. In the development 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 distribution and temporal profile. Using the quality assured 

episode-specific emissions along with other modeling inputs (e.g., meteorology), the 

photochemical model is run and the simulated concentrations of both ozone and ozone precursors 

(e.g., NOX, VOC) are compared to the measured concentrations 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 are conducted to determine which modeling inputs are 

insufficient. When the emissions are implicated, the modeling emissions are reviewed and 

pertinent revisions are made as appropriate. If either the evaluation implicated other inputs or 

once the photochemical model adequately replicates 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. 
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Table 1.0-1: Summary of Base 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 National 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 

Area Source Texas 2005 TexAER 

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 NOX, VOC, and CO. The emission 

inventories (EIs) are prepared for photochemical modeling input using 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 demonstrating 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 years 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 baseline 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 the base cases and baseline modeling emissions. 

2006 was chosen as the baseline year and Section 2.2 describes the averaging 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 future 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 emissions include the benefits of the Federal 
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Motor Vehicle Control Program (FMVCP), Mass Emissions Cap and Trade (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 model 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 Conformal 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 modeling 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 

Domain Range (km) Number of Cells Cell Size (km)

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 Intermediate 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 

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 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
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Table 1.1-4: CAMx Vertical Layer Structure 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 the emissions modeling, the sources are separated 

between those emitting at the surface (e.g., mobile and low-level point) and those emitted at 

elevation (e.g., high-level points and ships). Elevated 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 emissions 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 modeled 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 September 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. 

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This process is typically referred to as either emissions modeling or emissions processing and is 

distinctly separate from emissions inventory development. The TCEQ currently uses Version 3 

of the Emissions Processing System (EPS3) developed 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_manual/. 

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 NOX emissions 

must be chemically allocated into the chemical classes employed by the model. 

Additionally, the emissions data must be spatially allocated by grid cell for each 

hour of the modeling episode. If photochemical 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 Photochemical 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 grid 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 implement 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 (typically 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 resource-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, census 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 the surrogate indicator. In developing 

spatial apportioning factors, the emissions modeler should focus on determining 

accurate factors for the more significant sources. 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 reports 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 NOX 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 
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traveled (VMT) or gasoline sales to estimate highway vehicle emissions (as 

annual and seasonal inventories sometimes 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 models 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 develop 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 organizations, or MPOs) should always be 

contacted during the inventory 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. Independently 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 projections available to the public. 

Temporal Resolution of Emissions 

In order to accurately predict hourly concentrations of ozone, a photochemical 

model requires hourly estimates of emissions 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 adjustments for the day of the week. In 

addition, they must be allocated to the hours of the modeling episode. 

The most accurate approach for temporal 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 information 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 distribution, 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 information should be collected for all sources 

that contribute significantly to the inventory. Regional weekday/weekend 
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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, photochemical 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 NOX emissions be disaggregated into NO and NO2. 

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 latter are available, the splits associated with 

that source category can be modified to more accurately reflect the profiles of 

local sources. Issues the emissions modeler should consider when developing or 

modifying mechanism compound split factors include how hydrocarbon 

emissions are reported in the inventory (e.g., as VOCs, total hydrocarbons, 

reactive hydrocarbons, total non-methane hydrocarbons, etc.) and the 

compatibility of the speciation profile designations with the source classification 

scheme used in the inventory. 

In some instances, data on VOC compounds 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 NOX compound data or accurately 

apportion their emissions into appropriate chemical classes. Thus, typical VOC 

and NOX 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 unavailable, a photochemical 

modeling specialist familiar with both photochemistry and the chemical 

mechanism should be consulted to determine 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 and aggregate projections. Information 

regarding projected increases in activity at existing facilities or proposed 
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construction can be obtained directly from operators. Permit applications also 

provide a source of information about proposed expansions 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, will 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 allocating 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 allocate projected emissions changes in 

industrially zoned land by grid cell rather than assuming that all increased 

activity will occur at existing facilities, hypothetical emission records for the 

proposed point sources must be developed. For area and mobile sources, the 

emissions modeler should review the surrogate 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 Table 2-2 of Section 2 for an internet 

link to emission guidance documents.” 
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2.0 POINT SOURCE MODELING EMISSIONS DEVELOPMENT

The various data sources that went into the development 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 datasets for the base case, the baseline, 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 POINT SOURCE MODELING EMISSIONS 

DEVELOPMENT 

The point source emissions inventories are composed of information from several databases. The 

following subsections describe development of the base case point source modeling emissions for 

all portions of the domain and for all of the episodes 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 emissions data from STARS and hourly emissions 

data from the EPA’s acid rain database (ARD) provided 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- butadiene, and all isomers of butene. Hourly emissions data from 

reporting surveys were used for specific episodes when available. Episode-specific survey results 
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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 Air Quality Study II (TexAQS II) from August 15 

through September 15, 2006, was used to develop modeling emissions for selected Texas nonEGUs (NEGUs). In all cases, where more detailed, 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 inventory questionnaires (EIQs) to all sources identified 

as meeting the reporting requirements. Subject entities 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 equipment, including operating schedules, emission 

control devices, abatement device control efficiencies, and emission point discharge parameters 

such as location, height, temperature, and exhaust gas flow rate. All data submitted in the EIQ 

are subjected to QA procedures. The data are then stored in the STARS database. The TCEQ 

reports point source emissions data to the EPA for inclusion in the National Emissions Inventory 

(NEI). 

Annually, the TCEQ collects emissions information from approximately 2000 major point 

sources. In nonattainment areas, major point sources 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 NOX; or 100 tpy of any of the other 

criteria pollutants including CO, SO2, PM10, or lead. For the attainment 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 requirements, 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 approved 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, since emissions from 

previous years can be updated by the regulated entities. The query report is a pipedelimited ASCII text flat file from the relational 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 record 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 Facility 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 emissions 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 Matter 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 history and inspection intervals. To gather this 

facility-specific info, conducting local surveys is preferred, but if that is not feasible, analyses 

performed by others may be compared to similar 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 source 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. DFW has the least, with BPA somewhat in the 

middle, and the Statewide average being 26% and 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. Hence, 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 NOX, 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 ST 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 useful 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/TAD/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 NOX and VOC sources from the AFS 

file are compared against the 100 highest emitting NOX 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 identical. The only exceptions occur when some 

of the VOC figures in the AFS file have rule effectiveness applied to them. 

Another quality assurance step is conducted where the number of point sources and overall 

emission totals of the AFS file is compared against 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 fourth STARS extract, and the “b” indicates that this 

was the second SAS run for production of an AFS file. 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, above, 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 for 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 similar ozone reactivity that will be recognized by the 

photochemical model. For operational efficiency, 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 grouped 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 classification 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-specific speciation approach taken by the TCEQ is 

available in a report and presentation given at the 17th 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-Galveston-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 all of their sources of HRVOC, measure them 

accurately, and report them completely, as required by the HRVOC rules of Chapter 115. The 

SAS code was modified to ensure that no additional 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 the VOC constituents of a specific source varied 

on an hourly basis. More detail on this improvement 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 HG14__107_131_ in this example links this record 

to the VOC constituent profile contained in a separate 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 right-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 fractions 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 why 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-reference 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-hand 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 all 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 the 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 columns (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 temporal 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 the 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 percent allocation per month, day, or hour for each 

profile code is obtained by dividing 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 category, but large combustion sources such as power 

plants are categorized as elevated 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 effective 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 approach 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 elevated categories. The emissions from elevated 

sources can be individually tracked, and NOX 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 NOX 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

NOX 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 affects 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 (SO2), NOX, and 

carbon dioxide (CO2), 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 industry. 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 where the EGU-reported data 

does not match that from EPA’s CAMD. The data can be downloaded by orispl (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 NOX from 12-1 AM on July 1, 2005; and 

 Unit #2 emitted 538.790 pounds of NOX from 1-2 AM on July 1, 2005. 

Use of the hourly emissions data from the ARPDB instead of OSD averages from STARS 

requires that the unique orispl and unitid identifiers be matched in the AFS file. Use of the 

ARPDB hourly NOX 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-reference 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 Seymour 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 the 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 substituting ARPDB emissions into the path’s AFS 

record, adjusting the date and hour of the emissions, 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”) NOX 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 STARS data represents ozone season average daily 

emissions, whereas substituting hourly emission records generate 24 records for each day of the 

episode. For modeling purposes, paths that have hourly NOX emissions receive hourly VOC and 

CO records by multiplying the STARS OSD VOC-to-NOX and CO-to-NOX emissions by the 

hourly ARPDB NOX 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 points. 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) records 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 used 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 peaking units. For example, Figure 2.1-10: Hourly 

ARD NOX Emissions for W.A. Parish EGU on August 16, 2006 shows hourly NOX emissions for 

the eight individual units of the W.A. Parish power 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 approximately 10 AM and shut down 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 and 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

Hour

NOx Emissions, tons/hour

WAP1

WAP2

WAP3

WAP4

WAP5

WAP6

WAP7

WAP8

Figure 2.1-10: Hourly ARD NOX Emissions for W.A. Parish EGU on August 16, 2006

Figure 2.1-11: Tileplot of HGB ARD NOX Emissions for August 16, 2006 is a tileplot of ARD 

NOX emissions in the eight counties of HGB area 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/shaded tile represents the 

ARD EGU NOX tons for a modeled 2km-by-2km grid cell within the HGB eight-county area. 

B-24
Figure 2.1-11: Tileplot of HGB ARD NOX 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 detail 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. 

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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 captured 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 previous 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) compile the results into a consolidated place 

automatically, to make its use much easier and quicker. 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 Inventory Reporting System (HEIRS) which allowed 

regulated entities (SI respondents) to enter data into 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 report 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 NOX, 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 ozone 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 test study, came the ultimate test for the new HEIRS software, 

interface (including a web data entry option), and STARS capability. An SI request was made for 

the intensive period of TexAQS II: August 15 through 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/implementation/air/airmod/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 NOX, but all species for all hours of the 32-day 

period were expected to be reported. Again, 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 combination, denote that the unit was idle (“UI”), 

reporting zero emissions of unclassified VOC (TCEQ 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 (September 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 NOX 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 NOX

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 NOX 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, 2006 

Total VO C Em issions, tph

for entire SI area, not just HGB 

Special Inventory

Ozone Season Average
Figure 2.1-14: Tileplot of SI NOX for September 2, 2006 

B-29
Figure 2.1-15: Tileplot of SI VOC for September 2, 2006 

Again, the goal was to replace the corresponding 2006 OSD records with these hourly records for 

the period August 15 through September 15, 2006. Not only did the 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 SIP 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 for every hour for every path. Other than this 

improvement, the speciation process is essentially the same as that for the OSD, in which pointspecific profiles are generated for each source, retaining 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-205 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 the 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, the 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 links 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 affected 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 are 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, 2006 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 for 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 with unique path identifiers for each record (for speciation), 

 hourly SI NOX emission records, 

 hourly ARD records with SI NOX 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 NOX 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 records, there is a difference between the SI survey respondent 

reporting the value of zero emissions and not providing an emission rate at all. While a missing 

emission rate might be intended as zero, it remains 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 terminal facilities and crude oil breakout stations) 

in the Houston Ship Channel (HSC) area were found to be landing their floating roofs (internal 

and external) on the tank legs and not reporting those vapor space losses. As a result, a TCEQ 

Chapter 115 rule was written that limits the number of permissible “convenience” roof landings. 

Additionally, these sources of additional VOC emissions 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 emissions from floating roof storage tanks have two components: standing 

idle losses and filling losses. When some types 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 displaces the vapor under the roof. These are 

called “filling losses.” 

B-33
B-34

As part of the Second Texas Air Quality Study (TexAQS II), in July 2005 the TCEQ 

conducted the Remote Sensing Volatile Organic Compound Project in Southeast Texas. 

For this project, the TCEQ used passive infrared 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 requested 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 emissions were developed for the modeled 2005 

and 2006 base case ozone episodes. Before the 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 accounted 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 Pipeline LP Seaway Texas City Station Galveston 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 Oil 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 formally added to STARS, as part of the effort to 

model 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 regulated 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 for 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 floating 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 days 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 05062712, 05062712 05062713, ...) 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 speciation 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 across 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 emissions 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 

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

2.1.1.8 Emission Events Inventories

Generally, the more detailed (i.e., temporally, spatially, and chemically resolved) the input data, 

the more likely the model will appropriately replicate 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 measurements 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 the 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. 

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2.1.1.8.1 Consolidated Compliance and Enforcement Database System (CCEDS) 

CCEDS is one source of data the TCEQ reviewed 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 business 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 

more 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 §101.1 of this title (relating to Definitions)"

These data are electronically submitted by the responsible company representative through the 

State of Texas Environmental Electronic Reporting 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 modeling 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 beginning 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. 
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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 NOX, CO, CO2, SOX, 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, others reporting ”VOC”, and others reporting 

“unclassified VOC”. Similarly, modelers assigned NOX to the various reported compounds: 

“nitric oxide”, “nitrogen monoxide”, “nitrogen dioxide”, “nitrogen 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 event, 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, NOX, VOC species, and unclassified VOC, were formatted for modeling. Where 

unclassified VOC was reported, propylene was substituted to create a worst case reactivity. The 

TCEQ generated hourly AFS records for subsequent EPS3 processing. 
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In 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 and SI where overlap existed. The cursory examination found 

that emissions reported to CCEDS were also included 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 performed 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 modeling sensitivity runs did not see 

enough of a change to justify incorporating CCEDS 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 staff reviewed as a potential improvement to the 

modeled point source EI. EMRS is a tool that the TCEQ uses to capture detail regarding 

excessive emission measurements at monitoring stations, focused in the Houston Ship Channel 

(HSC) area. It is primarily a monitoring system which notifies upwind entities, who voluntarily 

participate, when excessive emission measurements are recorded on ambient monitors, and 

prompts the regulated entity (via email or digital 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. 

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EMRS 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 determined 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 include facility and point identification necessary to define unique STARS 

emission paths. 

 A specific emission rate is not given, rather a threshold or range such as “greater 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, and especially HRVOC, continue to be underreported in the annual EIQs, according to monitors and aircraft measurements. 

In previous Attainment Demonstration SIP revisions, 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 applied 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 affected modeling cell (i.e., modeling cells which 

contain actual point sources), and assigned an emission rate for each HRVOC to best offset the 

difference between modeled and calculated concentrations. 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 points 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 

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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 allows for easier QA of the application of those to 

HGB. This results in nearly twice as many point 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 that 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 sources included in the modeling. Tables at the end 

of this section summarize the Texas datasets 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 CAMx 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 states 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 modeling domain) include Oklahoma, Arkansas, and 

Louisiana. It is expected that the influence 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 modeled. 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 annual 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 Katrina caused in late August 2005 and the remainder 

of the year. The LDEQ staff explained to the 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 provided 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 Format 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 SAS 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 simply EGAS). EGAS estimates future growth, 

based on fips (state and county/parish) 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. 
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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 
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with EGAS, a best fit of data is applied (51 = 51xxx, the entire state). Thus, from Table 2.1-3, all 

point sources having SCC=10100101 in Louisiana (state=22) have 2004 and 2006 growth factors 

of 0.98884 and 1.04091, respectively, 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 EGUs 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 NEI as the cross reference for Louisiana, since the 

NEI contains fields (not always filled in, though) 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 nearest town. The corresponding hourly VOC and CO records for 

matched EGUs were generated using their VOC-to-NOX and CO-to-NOX 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 NOX Emissions for 

June 22, 2005 has tileplots of ARD NOX 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 NOX

Emissions for June 22, 2005 

2.1.2.2 Remaining States in Modeling Domain

The 2005 and 2006 EIs for the remaining states (states beyond Texas, Louisiana, Arkansas and 

Oklahoma) within the modeling domain were developed from 2002 data files provided by the 

Central states Regional Air Planning association (CenRAP) and included all of the Regional 

Planning Organizations (RPOs) data for the nation, 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 Matrix Operator Kernel Emissions (SMOKE) 

processing. The TCEQ extracted data from the CenRAP harddrive that represent the “final 2002 

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Base g Typical (typ02f)” annual emissions. Figure 2.1-30: Tileplots of Louisiana, Arkansas, 

Oklahoma and remaining states ARD NOX Emissions for June 22, 2005 in the above adjacent 

states subsection has a tileplot of ARD NOX 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 concatenating. The TCEQ extracted the point source 

data from the EI files, along with the three pollutants of interest for ozone modeling. Emission 

rates were provided as annual total tons for all RPOs, 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, default 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 required 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 NOX 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 prevented 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 code 4911 (Electric Services). 

 Record had an ORIS and a SIC code 4931 (Electric 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 and weekend diurnal variations were converted 

from SMOKE format via SAS programming, and formatted for EPS3 processing. For modeling 

emissions development from the CenRAP/RPO files, 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 Minerals 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 and gas exploration and production platform (point) 

sources and non-platform (area) sources. The 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/environ/airquality/gulfwide_emission_inventory/20

05GulfwideEmissionInventory.html

The TCEQ reviewed and compared the differences between the 2000 GWEI and the 2005 GWEI. 

The comparisons show an overall increase in emissions in CO and NOX 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 include any decline in emissions due to Hurricane 

Katrina (end of August 2005) and any other hurricanes that moved into the Gulf of Mexico, such 

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as Hurricane Rita. With the significant overall increase in NOX 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 specific 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 joined, 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. Data 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 format, including all necessary and relevant modeling 

parameters for EPS3 processing. Modelers created AFS files with monthly emission rates for 

each point source for each month, May through October, but only modeled a representative June 

day, as discussed below in this subsection. 

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 data existed, default values 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 NOX 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 Oct

Tons

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 NOX (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 the year, due to the effects of Hurricane Katrina. 

From the above figure and table, the TCEQ ascertained 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 offshore and be more conservative, 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 data files themselves; however, the documentation 

did provide rudimentary temporal information for 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 

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documentation. 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 assignments, 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 modeling episodes, also. Figure 2.1-32: Tileplot of 

Offshore NOX 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. 

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Figure 2.1-32: Tileplot of Offshore NOX for a representative June day 

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Figure 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 the 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 northern 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 northern 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 without growth, due to a lack of information 

on growth and controls for Mexican point sources. No temporal allocation or speciation data 

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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”). The 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 NOX (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 NOX 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 NOX 

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Figure 2.1-35: Tileplot of representative 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 Canadian point source dataset. This was still 

based on a 1995 Canadian EI. The TCEQ extracted 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 

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he TCEQ. 

defaults were used. This is the same Canadian dataset used for modeling in the May 2007 DFW 

SIP revision. The only part of Canada within the 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. NOX 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 NOX plumes. The 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 NOX 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 NOX 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 NOX emissions on an episode day were tracked as a 

PiG source. The TCEQ chose a co-location distance for NOX of 200 meters, indicating that if 

multiple stacks were close enough together for their plumes to merge (within 200 meters of each 

other), and the aggregate NOX emission rate for the cluster exceeded the threshold value, a 

new source was created with the combined NOX 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 sources 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 Case Point Source Data Files 

Snapshots of the point source emission files that 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 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 2.1-11: AFS Files for the 2005 Episodes 

Episode Area AFS Point Source Emissions File Hourly 

May-June 2005 Texas afs.ard_19May_to_03Jun05_TX_episode_v8 X  (bc05ep0 ) TX_osd_2005_all_episodes 

afs.aggVOC_extra_alkenes_for_2005_v2 afs.landing_losses_19May_to_08Aug05 X 

Regional afs.REGIONAL_ard_minus_TXOKARLA_19May_to_03Jun05_episode_v4 X 

afs.Arkansas_ard_19May_to_03Jun05_episode_v4 X afs.Louisiana_ard_19May_to_03Jun05_episode_v4 X afs.Oklahoma_ard_19May_to_03Jun05_episode_v4 X USA_osd_2005_for_3Q05_generic_from_2005CENRAP.v3 afs.gwei2005.May_Oct_edit.3pol.lcp afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1 afs.CAIRCanada.2001.v1a.latlong.lcp 

June 2005 Texas afs.ard_15Jun_to_30Jun05_TX_episode_v11 X  (bc05ep1) TX_osd_2005_all_episodes 

afs.aggVOC_extra_alkenes_for_2005_v2 afs.landing_losses_19May_to_08Aug05 X 

Regional afs.REGIONAL_ard_minus_TXOKARLA_15Jun_to_30Jun05_episode_v3 X 

afs.Arkansas_ard_15Jun_to_30Jun05_episode_v5 X afs.Louisiana_ard_15Jun_to_30Jun05_episode_v5 X afs.Oklahoma_ard_15Jun_to_30Jun05_episode_v5 X USA_osd_2005_for_3Q05_generic_from_2005CENRAP.v3 afs.gwei2005.May_Oct_edit.3pol.lcp afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1 afs.CAIRCanada.2001.v1a.latlong.lcp 

July-August 2005 Texas afs.ard.26Jul_to_08Aug05_TX_episode_v8 X  (bc05ep2) TX_osd_2005_all_episodes 

afs.aggVOC_extra_alkenes_for_2005_v2 afs.landing_losses_19May_to_08Aug05 X 

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Episode Area AFS Point Source Emissions File Hourly 

Regional afs.REGIONAL_ard_minus_TXOKARLA_26Jul_to_08Aug05_episode_v4 X 

afs.Arkansas_ard_26Jul_to_08Aug05_episode_v6 X 

afs.Louisiana_ard_26Jul_to_08Aug05_episode_v8 X afs.Oklahoma_ard_26Jul_to_08Aug05_episode_v6 X USA_osd_2005_for_3Q05_generic_from_2005CENRAP.v3 afs.gwei2005.May_Oct_edit.3pol.lcp afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1 afs.CAIRCanada.2001.v1a.latlong.lcp 

NOTES: 1 Aggregated VOC records require cross reference and profile code files for composition/spe

2 Same for all 2005 episodes 3 AFS file contains monthly emissions; only June was modeled; see temporal profiles; see S4 AFS file contains annual emissions 

Table 2.1-12: AFS Files for the 2006 Episodes 

Episode Area AFS Point Source Emissions File Hourly 

June 2006 Texas afs.ard.29May_to_16Jun06_TX_episode_v5 X  (bc06ep0) afs.osd_2006_STARS_extract_for_29May06_episode_v5 

afs.aggVOC_extra_alkenes_for_2006_v2 afs.landing_losses_all_2006_episodes_v1 X 

Regional afs.REGIONAL_ard_minus_TXOKARLA_29May_to_16Jun06_episode_v3 X 

afs.Arkansas_ard_29May_to_16Jun06_episode_v4 X afs.Louisiana_ard_29May_to_16Jun06_episode_v4 X afs.Oklahoma_ard_29May_to_16Jun06_episode_v4 X USA_osd_2006_for_3Q06_generic_from_2006CENRAP.v1 afs.gwei2005.May_Oct_edit.3pol.lcp afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1 afs.CAIRCanada.2001.v1a.latlong.lcp 

2006 AQS-1 Texas afs.ard_minus_SI_for_15Aug2006_episode_v8 X 

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Episode Area AFS Point Source Emissions File Hourly 

(SI period) afs.osd_minus_SI_for_15Aug2006_episode_v8 (bc06aqs1) afs.osd_n_ard_SI_paths_for_15Aug2006_episode_v8 X 

afs.aggVOC_SI_for_15Aug2006_episode_v9 X afs.NOx_SI_for_15Aug2006_episode_v9 X afs.aggVOC_extra_alkenes_for_2006_v2 afs.landing_losses_all_2006_episodes_v1 X 

Regional afs.REGIONAL_ard_minus_TXOKARLA_15Aug_to_15Sep06_episode_v4 X 

afs.Arkansas_ard_15Aug_to_15Sep06_episode_v4 X afs.Louisiana_ard_15Aug_to_15Sep06_episode_v4 X afs.Oklahoma_ard_15Aug_to_15Sep06_episode_v4 X USA_osd_2006_for_3Q06_generic_from_2006CENRAP.v1 afs.gwei2005.May_Oct_edit.3pol.lcp afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1 afs.CAIRCanada.2001.v1a.latlong.lcp 

2006 AQS-2 Texas afs.ard.16Sep_to_16Oct06_TX_episode_v4 X  (bc06aqs2) afs.osd_2006_STARS_extract_for_16Sep06_episode_v4 

afs.aggVOC_extra_alkenes_for_2006_v2 afs.landing_losses_all_2006_episodes_v1 X 

Regional afs.REGIONAL_ard_minus_TXOKARLA_16Sep_to_16Oct06_episode_v2 X 

afs.Arkansas_ard_16Sep_to_15Oct06_episode_v4 X afs.Louisiana_ard_16Sep_to_15Oct06_episode_v4 X afs.Oklahoma_ard_16Sep_to_15Oct06_episode_v4 X USA_osd_2006_for_3Q06_generic_from_2006CENRAP.v1 afs.gwei2005.May_Oct_edit.3pol.lcp afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1 afs.CAIRCanada.2001.v1a.latlong.lcp 

NOTES: 1 Aggregated VOC records require cross reference and profile code files for composition/spe

2 Same for all 2006 episodes 3 AFS file contains monthly emissions; only June was modeled; see temporal profiles; see S4 AFS file contains annual emissions 

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2.1.5 Emissions Summary for Each Base Case Episode

The TCEQ chose a “representative day” in each episode for which to report base case emissions totals for point sources. A W

each episode that was also an ozone exceedence day for each of the base case episodes in 2005 and 2006. Table 2.1-13: PoinEpisodes Emissions Reporting Days provides a summary of the base case episodes. The emissions summary for these episode

14: 2005 & 2006 Base Case Episode Day Emissions Summary. Table 2.1-13: Point Source Base Case Episodes Emissions Reporting Days 

HGB Modeled Episodes 

Episode Name 

Common Name Days 

Reported Point Source Day 

bc05ep0 May-Jun 2005 May 19 - June 3, 2005 June 1, 2005 bc05ep1 June 2005 June 17 - 30, 2005 June 22, 2005 bc05ep2 Jul-Aug 2005 July 26 - August 8, 2005 August 3, 2005 bc06ep0 June 2006 May 31 - June 15, 2006 June 14, 2006 bc06aqs1 2006 AQS-1 August 13 - September 15, 2006 August 16, 2006 bc06aqs2 2006 AQS-2 September 16 - October 11, 2006 September 27, 2006 

Table 2.1-14: 2005 & 2006 Base Case Episode Day Emissions Summary 

HGB Texas minus HGB USA minus Te

Reported 

Representative Day Emissions Source 

NOX

(tpd) 

VOC 

(tpd) 

NOX

(tpd) 

VOC 

(tpd) 

NOX

(tpd) 

VO

(tp

June 1, 2005 NEGUs (OSD) 147.5 199.2 653.8 332.9 6782.3 3

EGUs (ARD) 43.9 2.2 459.0 10.3 5626.5 Tank Landing Losses 0.0 49.5 0.0 0.0 0.0 HRVOC Reconciliation 0.0 26.2 0.0 0.0 0.0 

TOTALS = 191.4 277.1 1112.8 369.3 12408.8 

June 22, 2005 NEGUs (OSD) 147.5 199.2 653.8 332.9 6782.3 3

EGUs (ARD) 43.0 2.1 531.8 0.0 6968.0 Tank Landing Losses 0.0 17.8 0.0 0.0 0.0 HRVOC Reconciliation 0.0 26.2 0.0 0.0 0.0 

TOTALS = 190.5 245.3 1185.6 345.9 13750.3 

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HGB Texas minus HGB USA minus Te

Reported 

Representative Day Emissions Source 

NOX

(tpd) 

VOC 

(tpd) 

NOX

(tpd) 

VOC 

(tpd) 

NOX

(tpd) 

VO

(tp

August 3, 2005 NEGUs (OSD) 147.5 199.2 653.8 332.9 6782.3 3

EGUs (ARD) 55.3 2.9 528.6 0.0 8428.7 Tank Landing Losses 0.0 33.1 0.0 0.0 0.0 HRVOC Reconciliation 0.0 26.2 0.0 0.0 0.0 

TOTALS = 202.8 261.4 1182.4 346.0 15211.0 

June 14, 2006 NEGUs (OSD) 124.1 161.4 617.8 320.6 7644.6 3

EGUs (ARD) 45.1 1.7 476.7 15.2 6156.0 Tank Landing Losses 0.0 17.4 0.0 0.0 0.0 HRVOC Reconciliation 0.0 20.2 0.0 0.0 0.0 

TOTALS = 169.2 200.7 1094.5 335.8 13800.6 

August 16, 2006 NEGUs (OSD) 118.9 152.7 605.3 320.2 7644.6 3 (SI day) EGUs (ARD) 60.6 2.6 560.2 16.5 6772.9 

Tank Landing Losses 0.0 10.4 0.0 0.0 0.0 HRVOC Reconciliation 0.0 20.2 0.0 0.0 0.0 

TOTALS = 179.5 185.9 1165.5 336.7 14417.5 

September 27, 2006 NEGUs (OSD) 124.1 161.4 617.8 320.6 7644.6 3

EGUs (ARD) 43.4 1.9 475.0 14.4 5384.4 Tank Landing Losses 0.0 0.3 0.0 0.0 0.0 HRVOC Reconciliation 0.0 20.2 0.0 0.0 0.0 

TOTALS = 167.5 183.8 1092.8 335.0 13029.0 

NOTES: These tabulated emissions are AFS (input to EPS3) totals. CAMx input values may differ. 

USA minus Texas includes some points outside the modeling domain Texas minus HGB includes some points in Texas outside the modeling domain August 16, 2006 emissions data includes Special Inventory 
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2.2 2006 BASELINE POINT SOURCE MODELING EMISSIONS DEVELOPMENT 

The 2006 point sources used in the base case, described 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 baseline 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) ozone concentrations are predicted at each 

monitor and tabulated for comparison to the modeled 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 flare exit (initial dispersion) parameters, and 

(2) the 2006 baseline includes the addition of STARS reported annual upsets and emission events 

(EE) and scheduled maintenance, startup 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 calculated an “equivalent diameter” for each flare stack, 

based on VOC combustion rate. Then, based on typical 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 reasonable 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 assist 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. 
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Table 2.2-1: 2006 Baseline OSD Emissions in Texas 

NOX 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 Reconciliation emissions 

remain unchanged from the 2006 base case. As described 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 summary 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 NOX for each hour of the 

day for each unit for the third quarter of 2006 (July 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 individual units. Corresponding hourly average CO and 

VOC emissions were calculated from STARS ozone season daily (OSD), stack-specific emissions 

by multiplying CO:NOX and VOC:NOX ratios by the hourly NOX 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. 
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Table 2.2-3: 2006 Baseline Hourly Texas Acid Rain EGU Emissions 

NOX 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 into hourly emissions for each of the base case 

modeling episodes. The 2006 baseline was calculated 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 emissions 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 total 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. 
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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 through 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, the 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 STARS 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 NOX 

Figure 2.2-2: Elevated VOC

Figure 2.2-3: Low-level NOX

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 elevated 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 NOX, (includes ships)

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

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Figure 2.2-3: Low-level NOX 

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Figure 2.2-4: Low-level VOC

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Figure 2.2-5: HRVOC Reconciliation 

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Figure 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 the 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 states provided to TCEQ (2004 for LA, 2005 for OK and 

AR) with the Acid Rain units removed. For 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. 

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Table 2.2-5: 2006 Baseline Emissions Summary for Non-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 remaining states within the modeling domain, excluding 

LA, OK AR, and TX. The photochemical model may not use all of the records in the non-Acid 

Rain 2006 base case, since some states have a fraction 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 

NOX 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 having 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 NOX emissions and the VOC:NOX and CO:NOX ratios, computed from the daily 

emissions data, for each Acid Rain point. 

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

summarizes the 2006 baseline Acid Rain emissions 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 western Kansas. All hourly values were rounded to 

one decimal place. 

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

NOX 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 
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NOX 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 emissions (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 they 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 that 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 change 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 Baseline 

Area AFS Point Source Emissions File Hourly 

Texas afs.ard_generic_episode_3Q2006_aver_day_v1 X 

afs.osd_2006_STARS_generic_with_upsets_and_routine_TLL afs.landing_losses_3Q06_aver_day_episode_v1 X afs.aggVOC_extra_alkenes_for_2006_v3 

Regional afs.USA_minusTx_ard_generic_day_v1 X 

USA_osd_2006_for_3Q06_generic_from_2006CENRAP.v1 afs.gwei2005.May_Oct_edit.3pol.lcp afs.Mexico_from_updated_1999_NEI.lcp_3pols_v1 afs.CAIRCanada.2001.v1a.latlong.lcp 

Notes 1 Aggregated VOC records require cross reference and profile code files for composition/speciation 

2 Unchanged from 2006 base case 3 AFS file contains monthly emissions; only June was modeled; see temporal profiles; see Section 2.1.2.3 4 AFS file contains annual emissions 

Table 2.2-8 2006 Baseline Point Source Emissions Summary 

HGB Texas minus HGB USA minus Texas 

Emissions Source 

NOX

(tpd) 

VOC 

(tpd) 

NOX

(tpd) 

VOC 

(tpd) 

NOX

(tpd) 

VOC 

(tpd) 

NEGUs (OSD) 124.8 175.2 619.8 342.2 7644.6 3331.2EGUs (ARD) 48.1 2.0 501.5 14.3 6524.5 47.1Tank Landing Losses 0.0 6.5 0.0 0.0 na na HRVOC Reconciliation 0.0 19.3 0.0 0.0 na na 

TOTALS = 172.9 203.0 1121.3 356.5 14169.0 3378.3

Notes: These tabulated emissions are AFS (input to EPS3) totals. CAMx input values may differ.  USA minus Texas includes some points outside the modeling domain  Texas minus HGB includes some points in Texas outside the modeling domain 

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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 future 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 SIP 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 provides 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 strategies 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 baseline 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 future 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 Arthur (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 similar 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 for 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 NOX point sources at EGU facilities are Acid Rain 
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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/news/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/permitting/air/memos/turbine_lst.pdf

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

document 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 permits. Reconciliation between all data sources was 

made to ensure all units were accounted for, and 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 changes subsequent to 2007 (“post 2007”). 

While the above sources of data provide information 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 Western 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) projection 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-CAIR” scenario a sensitivity test and has not 

provided any documentation for that scenario. During the process of developing the 2018 future 
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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 completed 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) are provided in Section 2.3.1.2.1 below. 

For the newly-permitted (since 2007) EGUs, emissions were calculated based on their permit 

allowables from the permit Maximum Allowable Emission Rates Table (MAERT). For ozone 

modeling, pollutants acquired from the permits were NOX, 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) and/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 solid fuel-fired units. These limitations on some 

MAERTs represent a good compromise between the standard short-term allowable pounds per 

hour (that sometimes includes maintenance, startup and shutdown emissions) and the standard 

long-term permit allowable ton per year. The short 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 could 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 during 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 modeling 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 peaking 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 ambiguous 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 basis, 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. 
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 Incorporate MSS emissions where applicable and available. 

The emission rates calculated for these units represent 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 developing 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 newly-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 November 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 2009

Emission Rate

Area Site Name Calculated from Perm

2 County Boiler Capacity (MW) Fuel1 NOX VOC C

HGB Dow Chemical Cogen Brazoria B56CR96 180 G 0.66 0.66 4

Green Power Unit 1 Brazoria GT-2 35 G 0.14 0.07 0Green Power Unit 1 Brazoria GT-3 35 G 0.14 0.07 0Cedar Bayou Chambers CBY41 300 G 0.69 0.15 6Cedar Bayou Chambers CBY42 300 G 0.77 0.15 6NRG Cedar Bayou Chambers 

CBY51 350 G 0.57 0.20 6

NRG Cedar Bayou Chambers 

CBY52 350 G 0.57 0.20 6

Deer Park Energy Center Harris ST-5 265 G 0.42 0.87 6Deer Park Energy Center Harris ST-6 265 G 0.42 0.87 6TECO Central Plant Harris CHP-1 50 G 0.32 0.10 1TECO Central Plant Harris CHP-2 50 G 0.32 0.10 1Lewis Creek Montgomery STCK3A 250 G 0.27 0.09 1Lewis Creek Montgomery STCK3B 250 G 0.27 0.09 1

BPA East TX Elec Coop Hardin Hardin HCCT1 73 G 0.43 0.12 0

Hardin Facility Hardin HCCT2 73 G 0.43 0.12 0

ATTAIN3 Lufkin Generating Plant Angelina LBLR01 45 R 0.62 0.08 0

Panda Temple Power Bell CTG-1 275 G 0.45 0.60 5Panda Temple Power Bell CTG-2 275 G 0.45 0.60 5Panda Temple Power Bell CTG-3 275 G 0.45 0.60 5Panda Temple Power Bell CTG-4 275 G 0.45 0.60 5J K Spruce Unit 2 Bexar U-6 750 C 6.62 0.35 53VH Braunig 5 Bexar CGT5 50 G 0.32 0.04 1VH Braunig 6 Bexar CGT6 50 G 0.32 0.04 1VH Braunig 7 Bexar CGT7 50 G 0.32 0.04 1VH Braunig 8 Bexar CGT8 50 G 0.32 0.04 1Dansby Power Plant Brazos 6 50 G 0.30 0.03 1E.S. Joslin Power Station Calhoun ESJ-1A 300 P 2.23 0.16 4Formosa Pt.Comfort Calhoun CFB-S1 150 C P 1.26 0.10 1Formosa Pt.Comfort Calhoun CFB-S2 150 C P 1.26 0.10 1La Palma Power Station Cameron LAP8 273 G 0.74 0.19 4

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Emission Rate

Area Site Name Calculated from Perm

2 County Boiler Capacity (MW) 1 Fuel NOX VOC C

Quail Run Energy Center Ector CTDB2A 138 G 0.26 0.04 0Quail Run Energy Center Ector CTDB2B 138 G 0.26 0.04 0Quail Run Energy Center Ector CTDB3A 113 G 0.13 0.04 0Quail Run Energy Center Ector CTDB3B 113 G 0.13 0.04 0Global Alternative Fuels El Paso EPN1_25 55 R 0.68 0.19 0Newman Unit 6 El Paso CC-S6A 144 G 1.42 0.22 5Newman Unit 6 El Paso CC-S6B 144 G 1.42 0.22 5Pattillo Branch Fannin S-1 350 G 0.86 0.33 3Pattillo Branch Fannin S-2 350 G 0.86 0.33 3Pattillo Branch Fannin S-3 350 G 0.86 0.33 3Pattillo Branch Fannin S-4 350 G 0.86 0.33 3Winchester PowerPark Fayette CGT1 50 G 0.11 0.05 0Winchester PowerPark Fayette CGT2 50 G 0.11 0.05 0Winchester PowerPark Fayette CGT3 50 G 0.11 0.05 0Winchester PowerPark Fayette CGT4 50 G 0.11 0.05 0J L Bates Power Station Hidalgo JLBCT1 290 G 0.74 0.19 5Cobisa-Greenville Hunt GTDB1 300 G 0.69 1.24 5Cobisa-Greenville Hunt GTDB2 300 G 0.69 1.24 5Cobisa-Greenville Hunt GTDB3 300 G 0.69 1.24 5Cobisa-Greenville Hunt GTDB4 300 G 0.68 1.11 4Cobisa-Greenville Hunt GTDB5 300 G 0.68 1.11 4Cobisa-Greenville Hunt GTDB6 300 G 0.68 1.11 4Jack Co Gen Facility Jack HRSG-3 330 G 1.82 0.53 7Jack Co Gen Facility Jack HRSG-4 330 G 1.82 0.53 7Lamar Power Partners II Lamar GT-HRSG5 310 G 0.54 0.10 0Lamar Power Partners II Lamar GT-HRSG6 310 G 0.54 0.10 0Madison Bell Partners Madison CTDB1-A 138 G 1.00 0.13 2Madison Bell Partners Madison CTDB1-B 138 G 1.00 0.13 2Madison Bell Partners Madison CTDB2-A 138 G 1.00 0.13 2Madison Bell Partners Madison CTDB2-B 138 G 1.00 0.13 2Sandy Creek McLennan S01 800 C 6.88 0.35 29

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Emission Rate

Area Site Name Calculated from Perm

2 County Boiler Capacity (MW) Fuel1 NOX VOC C

Sandow 5 Milam 17CFB 282 C 3.55 0.18 3Sandow 5 Milam 17CFB 282 C 3.55 0.18 3Nacogdoches Power Nacogdoches BFB-1 100 C P 1.65 0.24 2Nacogdoches Power Nacogdoches HRSG-1 330 G 3.07 0.34 2Barney M Davis Nueces BMD3 350 G 1.25 0.47 6Barney M Davis Nueces BMD4 350 G 1.25 0.47 6Lon C. Hill Unit 5 Nueces LCH5 290 G 0.74 0.19 1Nueces Bay WLE Nueces NB8 350 G 1.25 0.47 6Nueces Bay WLE Nueces NB9 350 G 1.25 0.47 6Oak Grove 1 Robertson OGU1 800 C 8.61 0.56 36Oak Grove 2 Robertson OGU2 800 P 8.61 0.56 36San Jacinto Facility San Jacinto SJCCT1 73 G 0.43 0.12 0San Jacinto Facility San Jacinto SJCCT2 73 G 0.43 0.12 0Sand Hill Energy Ctr Travis SH-6 48 G 0.30 0.06 1Sand Hill Energy Ctr Travis SH-7 48 G 0.30 0.06 1Victoria Elec Gen Station Victoria VIC7 289 G 0.74 0.19 5Laredo Power Station Webb LPSCT4 100 G 0.29 0.05 0Laredo Power Station Webb LPSCT5 100 G 0.29 0.05 0Colorado Bend Energy Center Wharton CTDB2A 138 G 0.28 0.04 0Colorado Bend Energy Center Wharton CTDB2B 138 G 0.28 0.04 0Colorado Bend Energy Center Wharton CTDB3A 113 G 0.14 0.04 0Colorado Bend Energy Center Wharton CTDB3B 113 G 0.14 0.04 0Wise County Power Plant Wise CT21 82 G 0.86 0.07 3Wise County Power Plant Wise CT22 82 G 0.86 0.07 3Wise County Power Plant Wise CT31 82 G 0.86 0.07 3Wise County Power Plant Wise CT32 82 G 0.86 0.07 3

TOTAL = 18856 93.00 24.33 41

1 Fuel Types: G – Natural Gas, P – Petroleum, C – Coal, R – Renewables (Biomass)2 Some of the Newly Permitted EGUs are located beyond the modeling domain and excluded from the photochemical modeling 3 “ATTAIN” refers to the attainment areas of Texas.
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orst case). 

Table 2.3-1, above, includes two types of emission rates: (1) calculated NOX, VOC and CO from 

permit applications and MAERTs, representing realistic average day emissions prior to meeting 

CAIR Phase II and MECT requirements as discussed below in later subsections; and (2) modeled 

NOX rates incorporating CAIR Phase II and MECT. It is assumed NOX controls, offsets, or 

credit purchases will be used to meet these NOX 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, including the reserve margin, projected by ERCOT 

(in its CDR report) to be required by 2018, is satisfied by existing units, planned units, and these 

newly-permitted units (provided they can run at full load) that are all expected to be operating by 

2011 or 2012. Most of the permitted units are expected to be operational by the end of 2010. 

Historically, some permitted units are never built and some postpone construction; hence, 2011-

2012 is simply an estimate. All (or nearly all) of these newly-permitted EGUs, except perhaps 

the cogeneration (cogen) units, have “interconnect 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 those 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 modeling-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 the 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 NOX emissions are reduced for modeling, the MW assessment considers only 

those units whose generation is in the ERCOT 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 applications 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 Phase 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 Classification 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 State. These average profiles were then used for 

the new EGUs. For some units on the new EGU 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. 
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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 Hour

Percent 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 described previously, the TCEQ is referring to these 

in the shorthand as non-EGUs (NEGUs). The basis 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 based 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 index, TIPI provided unexpectedly low emission rates 

for some point sources. The TCEQ chose to use the 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 indicates a decline rather than a growth. Also note that the TIPI factor 

varies only by the two-digit (first two) SIC. 
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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 

... 
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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 NOX Growth Projections to 2018. 

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

Base 2018 Growth Projection 

EI 

Source # 

points 

NOX

(tpd) 

GROWTH METHOD # 

points 

NOX

(tpd) 

% 

Growth 

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 new facilities at locations where the facilities are 

permitted, not by growing emissions from existing facilities. The net reduction in EGU NOX

emissions is a testament to Best Available Control Technology (BACT) as implemented by the 

New Source Review (NSR) permitting program of the TCEQ. CAIR Phase II limitations may 

further reduce NOX emissions. 

Again, only a subset of the Texas attainment area sources received growth from the procedures 

above. Any source that had an existing rule applied 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 into 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 path of 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 describe 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 (SO2) and NOX EGU emissions, where the definition of an “EGU” for the 

CAIR program is approximately the same definition 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 program, with each of the CAIR-applicable states 

given a calculated NOX budget and a calculated SO2 budget by the EPA. The EPA modeled all 
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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 the nonattainment of the particulate matter of 2.5 

microns and less (PM2.5) standard of two counties in Illinois; therefore, Texas was included in 

CAIR for the transport of PM2.5. Texas was not covered under the CAIR program for eight-hour 

ozone contribution. Texas is not part of the ozone season (May through September) NOX budget 

part of CAIR, only the annual NOX 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 NOX caps, and Oklahoma is not a CAIR state. 

CAIR is implemented in two phases: for NOX, Phase I covers the years 2009-2014 and 

Phase II is for the years 2015 and later; for SO2, Phase I covers the years 2010-2014 and 

Phase II is for the years 2015 and later. The Phase I NOX budget calculated and assigned to 

Texas was 181,014 tons per year, and the Phase II NOX budget was 150,845. The Phase II 

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

NOX 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 NOX

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 relevant states can be found from the CAMD 

Allowances data query wizard website, 

http://camddataandmaps.epa.gov/gdm/index.cfm?fuseaction=allowances.wizard. 

s of actual emissions) used for the allocation of CAIR NOX 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 implementing EPA’s CAIR program 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 the “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 NOX 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 receive 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 NOX 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 unit 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 (3rd quarter) Acid Rain NOX 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 NOX emissions during the summer (peak electricity 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 the 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 needs more allowances (e.g., increased its load or 

capacity). If an existing EGU site wants to add a new unit to that site, it will have to operate that 
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 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 only 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 allocation), a site that commenced operation after January 1, 2001 must complete and 

submit (by May 1 of each year) the appropriate TCEQ 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 allowances from the new unit pool in any one year 

exceeds the pool allocation (9.5%, or 14,330 tons 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 distributed proportionally to the units

p

Under CAIR, EGUs have the option to hold, transfer or sell allowances, 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 NOX 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 emissions credits than it needs, or wheth

w X

For modeling purposes, the 9.5% set-aside cap for 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 allowances 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’ NOX tons reported to the EPA’s CAMD for 20

th

A small amount of new unit pool CAIR allowance remained after the 2001-2007 new unit 

distribution, and the TCEQ spread the remaining allowance among all the newly-permitted (post2007) EGU permits according (proportional) to the modeling permitted rate, compressing their 

permitted NOX rates to approximately one-quarter in order to fit within the new unit pool. Again, 

in actuality, the TCEQ CAIR SIP revision provides 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 then. 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 subsection. A list of the CAIR Phase II allocations by 

site, in descending order of allocated tons of NOX, is provided as Table 2.3-5: Texas NOX 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 NOX CAIR Phase II Allocations 

TCEQ RN# Site Name ORISPL #Units NOX 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 NOX 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 NOX 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 modeled 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 NOX CAIR Allowances in Texas. 

Table 2.3-6: Distribution of NOX 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 modeled as a redistribution addition to CAIR 

EGUs. Also note that the NOX 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 NOX emissions are 20% higher than the annual average. 

2.3.1.2.2 NEGUs 

Several existing control programs are still expecting reductions between the baseline and 2018 in 

the attainment areas of the State. These include: 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 NOX

rule was shown to benefit DFW in a recent SIP revision. Almost three-quarters 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 earlier, 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 reductions due to agreed orders and/or consent decrees were 

incorporated into the 2018 point source EI. The 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 refineries 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 for Hazardous Air Pollutants (NESHAP) for Benzene Waste 

Operation, 40 CFR 61, Subpart FF 

In the interest of settling these allegations, without 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 approximately 80,000 tons per year of nitrogen oxides 

(NOX) and 235,000 tons per year of sulfur dioxide (SO2) upon full implementation. See EPA’s 

webpage at http://www.epa.gov/compliance/resources/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 NOX reductions, and most of them lack enforceable requirements of 

quantified reductions. The TCEQ can verify that 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 modeled, due to the number of TCEQ programs that 

apply to HGB. As with all the other parts of the 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 that 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 NEGUs. 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 future base for EGUs in the NAAs of the State is the 

typical summer Acid Rain day having hourly emissions that are the average of all days in the 

third quarter of 2007 (3Q2007). As described for 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 permits. As with previous SIP 

revisions, TCEQ assumes that the EGU growth in the State comes from the TCEQ newlypermitted EGUs. This is a very similar assumption 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, some of the newly-permitted EGUs are planned to 

operate 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 NOX 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 (NOX and VOC) from major source modifications or new major sources. These 

emissions would need to be offset during the 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 the 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 NOX . For each new MECT-applicable source added to HGB, the 

available MECT NOX 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, their 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 NOX emissions of Table 2.3-1 reflect MECT, absorbing 

part of that cap, while also absorbing their share of the CAIR cap. 
B-96

NEGU Projections 

The basis for future growth for NEGUs in the 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 emission credits in the bank (described in the following 

paragraphs), except for HGB NOX, which is tightly regulated under the MECT program. 

Again, technically, ozone nonattainment counties can not grow (expand facilities) in major 

sources of emissions of ozone precursors, NOX 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 previous 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 NOX. 

The procedure for incorporating the banked emissions 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/index.cfm?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 longer 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 

 

NOX 

(tpy) 

VOC

(tpy) 

NOX

(tons) 

VOC 

(tons) 

CO 

(tons) 

NOX

(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 

NOX 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 Table 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 purchaser of ERCs must buy 11.5 ton per year of 

credits for each 10 tons per year of increase proposed. 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 NOX from all applicable sources with a cap. Yet, 

in HGB, DERCs can be used to comply with MECT 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 NOX (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 NOX 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 

NOX is not capped. Minor modifications can still occur to these MECT-exempt sources, and 

permits by rule (PBRs) may still allow new sources to come online within the NAAs. By adding 

the banked emissions back into the airshed and assuming the banked emissions can reappear in 

the airshed during the attainment year, this procedure is very likely overpredicting emissions 

growth. For HGB NOX, the TCEQ has assumed that this bank (both ERCs and DERCs) growth 

will be limited by the amount of TIPI or REMI-EGAS growth predicted by the larger of the 

indexes. Table 2.3-7, above, represents this overall growth in HGB. Hence, the 9.6 tpd is the 

sum of the ERCs and MECT-convertible DERCs divided by 365, plus the remainder of DERCs 

limited by the predicted TIPI/REMI-EGAS growth. 

For the other HGB pollutants and the rest of the NAAs, the TCEQ modeled even more margin of 

safety, erring on the side of more growth is better 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 

NOX (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 programming. 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 

NOX 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 INCINERATOR 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 NOX example for selected paths in HGB, the same procedure 

applies to other paths in HGB, paths in BPA and 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 NOX 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 NOX 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. (Exempt 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 applied 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 NOX rules for HGB, most notably the Chapter 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 Emission Cap and Trade (HECT) program, where 

HRVOC is defined in Ch. 115 to be the Highly Reactive VOC compounds: ethylene, propylene, 

1,3-butadiene, and the butenes. As in the above sections, these control programs are on-thebooks rules for which the final compliance dates are 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 NOX sources at an EGU account, and MECT 

defines a lower NOX standard that must be met. MECT NOX annual allowances are provided on 

an emissions unit (FIN) basis. Essentially, every NOX-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 NOX-emitting unit subject to the program 

emits more NOX for the year than it had allowances for that year, and when another MECTapplicable unit did not use its allowances of NOX 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 NOX each 

FIN could ultimately emit. 

Since the CAIR NOX 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 NOX “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 CAIR credit will not be equally portioned out, 
B-100

because of trading and differing NOX 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 Cogeneration, RN100239672, in Harris County receives 

474.6 NOX tons of MECT allowances each year. This site will also receives a total of 840 NOX

tons of CAIR annual allowances from 2009 through 2014. Because the MECT program is more 

stringent, the 474.6 number was modeled. The additional CAIR annual allowances (365.4 NOX

tons) are then available for trade or retained 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 for Attainment Demonstration). Hence, this 

generation of CAIR NOX 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 NOX 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 (or 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 NOX 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 that 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 applied. 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 forcing 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 NOX 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 for 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 excess 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 all 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 that 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-butadiene, 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 17th

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 procedure 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 that 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., result of combustion byproducts) HRVOC emissions. 

HECT allowances were allocated to applicable sites in proportion to the site's level of activity, 

determined from each site's selection of a twelve-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 sources. 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 calculating 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 allowances. 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 HRVOC 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 reported 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 accounts have been allocated to all the HECT 

accounts that did report VOC emissions in 2006. 
Table 2.3-10: HECT Accounts 

Total VOC Emissions 

(tpd) 

HRVOC Emissions 

(tpd) RN Owner HECT Cap 

(tpd) 

# HECT 

allocated 

paths Reported 

2006 2018 

Reported 

2006 2018 

RN100210319 EQUISTAR LYONDELL CH 0.2782 11 1.2338 0.8016 0.7170 0.2849RN100210806 INTERCONTINENTAL TER 0.0364 4 0.0346 0.0372 0.0346 0.0372RN100211879 SHELL OIL DEER PARK 0.9601 60 1.0938 1.5443 0.5326 0.9831RN100212109 TOTAL PETROCHEMICALS 0.3226 120 2.1981 1.0517 1.4767 0.3303RN100213958 Georgia Gulf Pasaden 0.0505 4 0.0243 0.0643 0.0117 0.0517RN100214212 Targa Galena Park 0.0433 2 0.1246 0.1203 0.0486 0.0443RN100216761 Basell Bayport 0.2057 5 0.5367 0.4134 0.3339 0.2107RN100218130 Lyondell Citgo Houst 0.3046 42 0.8973 1.0801 0.1290 0.3119RN100218247 Albemarle 0.0139 9 0.1014 0.0575 0.0581 0.0142RN100218411 Odjfell Terminals 0.0139RN100218841 Kaneka Texas 0.0139 4 0.1418 0.1272 0.0288 0.0142RN100219237 Johann Haltermann 1 0.0139 2 0.0033 0.0157 0.0018 0.0142RN100219310 Valero Houston Refin 0.3268 24 0.3461 0.5680 0.1127 0.3346RN100219526 Texas Petrochemicals 0.5275 6 0.2110 0.6602 0.0909 0.5401RN100221589 Lubrizol Corporation 0.0264 10 0.0142 0.0370 0.0042 0.0270RN100224450 Millenium LaPorte 0.0289 3 0.0643 0.0538 0.0401 0.0296RN100225085 DuPont LaPorte 0.0455 6 0.0889 0.0891 0.0464 0.0466RN100225689 BASF Pasadena 0.0236 4 0.0951 0.0983 0.0210 0.0242RN100227016 Celanese Clear Lake 0.0719 16 0.1547 0.1055 0.1229 0.0736RN100229905 INEOS Polyethylene 0.1788 9 0.4669 0.4122 0.2378 0.1831RN100524008 Sunoco Bayport Polyp 0.0494 9 0.3349 0.1406 0.2448 0.0506RN100542224 Nova Chemicals Baypo 0.0322 1 0.0162 0.0491 0.00002 0.0330RN100542281 Equistar Channelview 1.2092 13 2.7993 3.0360 1.0013 1.2381RN100633650 Lyondell Channelview 0.3207 11 0.5241 0.7567 0.0957 0.3283RN100683952 NATURAL GAS ODORIZIN 0.0139 2 0.0119 0.0142 0.0119 0.0142RN100716661 Pasadena Refining 0.1666 20 1.4590 1.3745 0.2551 0.1706RN100870898 Goodyear Houston Che 0.0361 9 0.2595 0.2796 0.0168 0.0370RN100909373 Total Bayport HDPE 0.0805 14 0.1536 0.2015 0.0345 0.0824

B-102
B-103

Total VOC Emissions 

(tpd) 

HRVOC Emissions 

(tpd) RN Owner HECT Cap 

(tpd) 

# HECT 

allocated 

paths Reported 2006 2018 Reported 2006 2018 

RN101041598 LBC Houston Bayport 0.0139RN101058410 Lubrizol Corporation 0.0139 1 0.0005 0.0147 0.00003 0.0142RN101379287 American Acryl Pasad 0.0139RN102018322 Cheveron Phillips 0.2488 56 1.0478 0.9894 0.3132 0.2547RN102204211 Kirby Inland Marine 0.0230 1 0.0959 0.0533 0.0661 0.0236RN102212925 ExxonMobil Baytown O 0.7933 30 2.4220 2.1283 1.1058 0.8122RN102523107 Lyondell Chemical Ba 0.1083 5 0.3474 0.3873 0.0709 0.1109RN102537289 INEOS Polypropylene 0.1088 3 0.3851 0.2227 0.2738 0.1114RN102574803 ExxonMobil Baytown C 0.5317 32 0.5216 0.7667 0.2993 0.5444RN102579307 ExxonMobil Baytown R 1.1759 142 4.0044 4.7524 0.4560 1.2039RN102580834 Enterprise HSC 0.0139RN102608932 GOODYEAR TIRE & RUBB 0.0139 1 0.0012 0.0153 0.0001 0.0142RN102610912 HALTERMANN 0.0139RN102887270 Nisseki Bayport 0.0139RN102888328 Sunoco LaPorte 0.1116 4 0.1261 0.1232 0.1172 0.1143RN102926920 Equistar Bayport 0.0514 3 0.0368 0.0696 0.0198 0.0526RN102940103 ENTERPRISE PRODUCTS 0.0139RN103773206 EQUISTAR (Sunoco) Ba 0.0572RN103919817 Chevron Phillips Ced 0.6739 21 1.1841 1.3288 0.5453 0.6900RN104150123 Dow Chemical Clear Lake 0.0661

TOTALS = 9.4562 23.5619 24.0415 8.9765 9.4562
B-104

2018 Reconciled HRVOC

2006 was the first full year for reporting of monitored HRVOC, as required by 30 TAC Chapter 

115. Additionally, 2006 was the year of the HRVOC survey. Section 2.2.1.2 above showed that 

a total of 19.29 tpd of HRVOC were added to grid cells in HGB as the reconciled HRVOC (extra 

olefins). Of this, 9.03 tpd of the reconciled 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 reconciled (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 “extra 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 number 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 “convenient” floating roof tank landings, as witnessed from 

TLL decreasing trends reported for the years 2004-2007, 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” improvements 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 future 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 composed 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 NOX and VOC 2018 Emissions 

Summary by Area provides a 2018 emissions summary for HGB. 
B-105

Table 2.3-11: Detailed NOX 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 NOX 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 NOX 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 NOX 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 (NOX/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 (NOX/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 (NOX/VOC ratio) 0.43 0.43

 

ATTAIN Growth from TIPI 134.40 182.24

 “ from EGAS 94.47 111.96

 CAIR ARDs (NOX/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 refers 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 growth 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 basis for future growth for NEGUs in BPA and DFW 

NAAs was the 2006 OSD emissions, already an average 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 quantified 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 just prior to the attainment date, this procedure 

likely overpredicts emissions growth, especially in DFW, where the NOX 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 source that had an existing rule applied to it was 

capped at that rule level for the future, so only the “no rule” sources had growth factors applied to 

them. 

Known Refinery Expansions in BPA 

Three refineries in BPA have proposed increasing 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 Premcor 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 Arthur refinery by 50,000 barrels per day. The 

following paragraphs and resultant 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 decrease compared to the 2005 allowable emissions, no 

change from 2005 actual emissions was projected. These 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 for the sites. Future allowable data associated 

with the Motiva and Total refineries were obtained 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 provided 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, modeling 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 NOX VOC NOX 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 NOX rules for BPA and DFW, most notably Chapter 117 NOX rules 

that were previously implemented via SIP rules in each NAA for attainment demonstration. 

Additionally, there are Agreed Orders for BPA and a Cement Kiln cap to apply for DFW. The 

most recent BPA attainment demonstration SIP revision 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 final 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 NOX rules. Yet, all of these 

rules for EGUs were already in place with a compliance date prior to the 3Q2007 baseline for the 

Acid Rain EGUs. The exception to this is a recent NOX rule for DFW EGUs that was expected 

to achieve only a small amount of reductions. For 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 actual emissions reductions, not just reductions in 

permit allowables. See 

http://www.tceq.state.tx.us/assets/public/implementation/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 provided “shutdown equipment” language in the 

Agreed Order. These were the same ones modeled 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 2005, so these were modeled as 

reductions that will occur prior to 2018. 

BPA Chapter 117 Rules 

The TCEQ applied Chapter 117 NOX emissions limits to BPA point sources over 25 tpy, as 

specified in the most recent BPA Attainment Demonstration with rules package. 
B-109

DFW 5% IOP Rules 

The DFW 5% Increment of Progress (IOP) SIP revision 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 NOX reductions from ICI (Industrial, 

Commercial, Institutional) reciprocating gas-fired engines in the nine-county area, but the NOX

rules were generally made more strict by the Major Source Rule (see next paragraph). So only 

the VOC controls were specifically modeled in this “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 (Industrial, Commercial, Institutional) NEGU point 

sources. It approximates the Chapter 117 NOX 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 regulated 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 ERG “Study”, as described in more detail in the 

May 2007 (most recent) DFW SIP revision. Additionally, site-wide (by account) “alternative” 

NOX 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 extremely insignificant for impact upon HGB ozone 

concentrations and was not corrected. The compliance 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 the remainder of the point sources in these 

NAAs. The 2018 future base is built upon these datasets by incorporating the changes expected 

due to growth/bank and emissions budgets/controls for certain source categories that were 

developed for previous SIP revisions. See Table 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, “regional” 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 adjacent 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 identified. 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 between EGUs and Acid Rain units. Not all EGUs 

(SIC 4911 and 4939) are Acid Rain units, e.g., true cogeneration units are 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 quarter (3Q2007) hourly data 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 downloaded data consisted of hourly NOX 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 NOX-to-CO and NOX-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 newest 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 been 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 state filled in the ORIS/UNITID field during their 

NEI submittal. For Acid Rain EGUs in Louisiana, Arkansas and Oklahoma that did not match, 

the TCEQ manually created ARD records using “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 records in each state, so that all of the emissions 

would be accounted for. As was done for the 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 NOX 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 that 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 NOX emissions 

level for each state. As with Texas, no trading was assumed. The TCEQ went to the trouble of 

“growing” the EGUs first with the AEO data to assist with projecting the fuel types that may be 

present in 2018, to get the mix of fuels as accurate 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 Regional Haze modeling. The TCEQ gathered and 

converted the IDA-formatted files for the nation (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 TCEQ. 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 scenarios based on projected fuel type prices/demand, 

plus CAIR trading that included Texas buying CAIR 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 NOX 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 NOX 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 expected to be less than the totals above. 
B-114

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

NOX 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 after 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 would be expected to be less than the totals 

tabulated here. 
B-115

Table 2.3-16: 2018 Regional States NEGU Emissions 

NOX 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 modeling domain, outside of Texas. Again, parts 

of some states may extend outside the modeling 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 

 

NOX

(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 farther 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 the offshore area (Gulf of Mexico), Mexico, and 

Canada were the same as those used in the 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 that 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_growth_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_CAIR_based_on_pt_caps.v1 X 

 afs.osd_NEGUs_split_out_of_2018_CENRAP X 

Canada afs.CAIRCanada.2001.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 newly-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 via 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 allocation used in the initial 2018 modeling studies differs 

from the allocations in later studies, although the 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-board 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 sources 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 the 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 Section 2.1, and a model run was performed to 

determine the benefit to ozone at monitors 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 Reduction 

Total VOC Emissions (tpd) HRVOC Emissi

RN Owner HECT Cap (tpd) # HECT allocated paths Reported 2006 2018 2018 with 25% Reduction Reported 2006 2018 

RN100210319 EQUISTAR LYONDELL CH 0.4222 11 1.2338 0.9567 0.8435 0.7170 0.4399 RN100210665 Enterprise Products 0.0274 RN100210806 INTERCONTINENTAL TER 0.0528 4 0.0346 0.0550 0.0409 0.0346 0.0550 RN100211879 SHELL OIL DEER PARK 1.2535 60 1.0938 1.8673 1.5314 0.5326 1.3061 RN100212109 TOTAL PETROCHEMICALS 0.3783 120 2.1981 1.1156 1.0142 1.4767 0.3942 RN100212216 Kuraray America Inc 0.0608 1 0.0364 0.0691 0.0528 0.0307 0.0633 RN100213958 Georgia Gulf Pasaden 0.0274 4 0.0243 0.0411 0.0413 0.0117 0.0285 RN100214212 Targa Galena Park 0.0610 2 0.1246 0.1395 0.1232 0.0486 0.0635 RN100216761 Basell Bayport 0.2755 5 0.5367 0.4897 0.4159 0.3339 0.2870 RN100218130 Lyondell Citgo Houst 0.0432 42 0.8973 0.8133 0.8017 0.1290 0.0450 RN100218247 Albemarle 0.1416 9 0.1014 0.1908 0.1529 0.0581 0.1475 RN100218411 Odjfell Terminals 0.0274 RN100218841 Kaneka Texas 0.0274 4 0.1418 0.1416 0.1417 0.0288 0.0285 RN100219237 Johann Haltermann 1 0.0274 2 0.0033 0.0300 0.0302 0.0018 0.0285 RN100219310 Valero Houston Refin 0.1491 24 0.3461 0.3887 0.3487 0.1127 0.1553 RN100219526 Texas Petrochemicals 0.2124 6 0.2110 0.3414 0.2845 0.0909 0.2213 RN100221589 Lubrizol Corporation 0.0274 10 0.0142 0.0385 0.0387 0.0042 0.0285 RN100224450 Millenium LaPorte 0.0466 3 0.0643 0.0728 0.0603 0.0401 0.0486 RN100225085 DuPont LaPorte 0.1617 6 0.0889 0.2110 0.1677 0.0464 0.1685 RN100225689 BASF Pasadena 0.0492 4 0.0951 0.1254 0.1122 0.0210 0.0513 RN100227016 Celanese Clear Lake 0.0971 16 0.1547 0.1330 0.1070 0.1229 0.1012 RN100229905 INEOS Polyethylene 0.3171 9 0.4669 0.5595 0.4745 0.2378 0.3304 RN100524008 Sunoco Bayport Polyp 0.2088 9 0.3349 0.3076 0.2516 0.2448 0.2176 RN100542224 Nova Chemicals Baypo 0.0274 1 0.0162 0.0447 0.0449 0.00002 0.0285 RN100542281 Equistar Channelview 0.7348 13 2.7993 2.5636 2.3667 1.0013 0.7657 RN100633650 Lyondell Channelview 0.1059 11 0.5241 0.5387 0.5103 0.0957 0.1103 RN100683952 NATURAL GAS ODORIZIN 0.0274 2 0.0119 0.0285 0.0287 0.0119 0.0285 

B-118
B-119

Total VOC Emissions (tpd) HRVOC Emissi

RN Owner HECT Cap (tpd) # HECT allocated paths Reported 2006 2018 2018 with 25% Reduction Reported 2006 2018 

RN100716661 Pasadena Refining 0.1651 20 1.4590 1.3759 1.3317 0.2551 0.1721 RN100870898 Goodyear Houston Che 0.0274 9 0.2595 0.2712 0.2714 0.0168 0.0285 RN100909373 Total Bayport HDPE 0.1746 14 0.1536 0.3011 0.2543 0.0345 0.1820 RN101041598 LBC Houston Bayport 0.0274 RN101058410 Lubrizol Corporation 0.0274 1 0.0005 0.0290 0.0292 0.00003 0.0285 RN101379287 American Acryl Pasad 0.0274 RN102018322 Cheveron Phillips 0.6226 56 1.0478 1.3834 1.2166 0.3132 0.6487 RN102204211 Kirby Inland Marine 0.0497 1 0.0959 0.0816 0.0682 0.0661 0.0518 RN102212925 ExxonMobil Baytown O 0.7043 30 2.4220 2.0500 1.8613 1.1058 0.7339 RN102523107 Lyondell Chemical Ba 0.2088 5 0.3474 0.4940 0.4381 0.0709 0.2176 RN102528197 BP Amoco Chemical Co 0.0274 7 0.0234 0.0326 0.0328 0.0193 0.0285 RN102537289 INEOS Polypropylene 0.3802 3 0.3851 0.5075 0.4056 0.2738 0.3962 RN102574803 ExxonMobil Baytown C 0.4467 32 0.5216 0.6877 0.5680 0.2993 0.4654 RN102576063 PL Propylene LLC* 0.0699 RN102579307 ExxonMobil Baytown R 0.3028 142 4.0044 3.8639 3.7828 0.4560 0.3155 RN102580834 Enterprise HSC 0.0274 RN102887270 Nisseki Bayport 0.0274 RN102888328 Sunoco LaPorte 0.2989 4 0.1261 0.3204 0.2403 0.1172 0.3114 RN102926920 Equistar Bayport 0.0685 3 0.0368 0.0884 0.0700 0.0198 0.0714 RN103773206 Sunoco Bayport Polye 0.1193 RN103919817 Chevron Phillips Ced 0.6349 21 1.1841 1.3004 1.1302 0.5453 0.6616 RN104150123 Dow Chemical Clear L 0.0274 

TOTALS = 9.4562 23.6205 24.0502 21.6862 9.0265 9.4562 
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 brief overview of the eight-county Houston-GalvestonBrazoria (HGB) area on-road mobile source emissions inventory data that were input into the 

photochemical model for both the 2005 and 2006 base case episodes, along with the 2018 future 

case. These inventory data were developed under contract by the 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 because 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, NOx, VOC, & CO Summary for 2005 HGB On-Road Inventory, Table 3.1-2: 

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

NOx, VOC, & CO Summary for 2018 HGB On-Road Inventory provide summaries of the total 

VMT, NOx, 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 highest total VMT of the week, while Saturdays and 

Sundays have the lowest total daily VMT. Greater detail on the activity differences with respect 

to school and summer seasons can be found 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/implementation/air/airmod/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 older, 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 maintenance (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, NOx, VOC, & CO Summary for 2005 HGB On-Road Inventory 

Season On-Road Emissions (tpd)

Type 

Day 

Type 

Eight-County 

VMT Total NOx 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, NOx, VOC, & CO Summary for 2006 HGB On-Road Inventory 

Season On-Road Emissions (tpd)

Type 

Day 

Type 

Eight-County 

VMT Total NOx 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, NOx, VOC, & CO Summary for 2018 HGB On-Road Inventory 

Season On-Road Emissions (tpd)

Type 

Day 

Type 

Eight-County 

VMT Total NOx 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 again 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-road inventory combinations were used, only the 

summer weekday will be detailed here. For the 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, NOx, 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 

HGB Vehicle Miles Traveled On-Road Emissions (tpd)

County Total Distribution NOx 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 

HGB Vehicle Miles Traveled On-Road Emissions (tpd)

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 

HGB Vehicle Miles Traveled On-Road Emissions (tpd)

County Total Distribution NOx 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, NOx, 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 

MOBILE6.2 Vehicle Miles Traveled On-Road Emissions (tpd)

Vehicle Type Total Distribution NOx 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 

MOBILE6.2 Vehicle Miles Traveled On-Road Emissions (tpd)

Vehicle Type Total Distribution NOx 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 

MOBILE6.2 Vehicle Miles Traveled On-Road Emissions (tpd)

Vehicle Type Total Distribution NOx 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 these inventories, along with detailed reports and 

tab-delimited summary output data, can be found 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 “readable” 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 Process 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, NO2, Olefins, Parrafins, etc. 

GRDEM Sum emissions by grid cell 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 NOX 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 NOx emissions for only six of the 28 total vehicle types. These vehicle types are the 

MOBILE6.2 light-duty gasoline vehicle (LDGV), light-duty gasoline trucks 1-4 (LDGT1-4), and 

motorcycle (MC) classes. There is no temperature/humidity NOx 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. Under contract to the Houston Advanced Research 

Center (HARC), Environ worked with the Southwest Research Institute (SwRI) to develop 

temperature/humidity NOx correction equations to apply to both the 13 diesel and nine heavyduty gasoline vehicle classes in MOBILE6.2. These equations reflect the fact that as ambient 

temperature increases, tailpipe NOx emissions increase. However, as ambient humidity 

increases, tailpipe NOx emissions decrease. Greater detail on the development of these correction 

equations 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. 

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

NOx, VOC, and/or CO correction for each hour, episode day, county, and vehicle type 

combination. TCEQ modeling staff developed Statistical Analysis Software (SAS) code to 

calculate the appropriate NOX adjustment factors for each county and vehicle type using hourly 

inputs for temperature, relative humidity, and barometric 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 NOx Correction, Table 3.1-13: 

Summary of 2006 Temperature/Humidity On-Road NOx Correction, and Table 3.1-14: Summary 
B-127

of 2018 Temperature/Humidity On-Road NOx 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. For each episode day, there are greater NOx 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 NOx correction either decreases NOx

very slightly or increases it somewhat, depending upon the specific conditions for that hour. 

Overall, the temperature/humidity NOx correction procedure allows not only for improved 

estimates of the total on-road NOx emissions, but also for improved spatial and temporal 

allocation of those emissions. 

Table 3.1-12: Summary of 2005 Temperature/Humidity On-Road NOx Correction

Season 

Type 

Day 

Type 

Temperature/Humidity NOX

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 NOx Correction 

Season 

Type 

Day 

Type 

Temperature/Humidity NOX

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 NOx Correction

Season 

Type 

Day 

Type 

Temperature/Humidity NOX

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 benefits 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

HGB Emission Benefits (tpd)

County NOX 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 

HGB Emission Benefits (tpd)

County NOX 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 

HGB Emission Benefits (tpd)

County NOX 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 

HGB Emission Benefits (tpd)

County NOX 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 

HGB Emission Benefits (tpd)

County NOX 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

HGB Emission Benefits (tpd)

County NOX 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 NOx TxLED benefit should be claimed for 2002-and-newer 

diesel vehicles and a 6.2 percent NOx 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 NOx emissions rates by model year. By using these data, the 4.8 

percent and 6.2 percent reduction factors were weighted according to NOx model year 

contributions for each vehicle type. The resulting 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 NOx emission rates. 
B-130

he 2005 on-road inventory. 

Since the TxLED rule was not in effect in the summer of 2005, adjustment factors were not 

applied to t

Table 3.1-21: Summary of 2006 Summer Weekday TxLED Benefits by Vehicle Type 

Diesel 2006 TxLED Benefits 

Vehicle 

Type 

NOX

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 Weekday TxLED Benefits by Vehicle Type

Diesel 2018 TxLED Benefits 

Vehicle 

Type 

NOX

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 strategies, 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 run with conventional gasoline and a run with 

RFG. For both runs, the I/M program was not modeled and all other inputs were held constant. 

Then, the I/M program benefits were determined by calculating the difference between a 

MOBILE6.2 run with RFG and a run with both RFG 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. Since RFG and I/M affect 

gasoline-powered vehicles only, it does not matter 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 

2005 On-Road Emission Benefits (tpd)

Control Strategy NOX VOC CO

RFG 1.75 17.40 112.84

I/M 13.47 11.86 205.72

Total 15.21 29.26 318.56

Table 3.1-24: Summary of 2006 Summer Weekday On-road Control Strategy Benefits 

2006 On-Road Emission Benefits (tpd)

Control Strategy NOX VOC CO

RFG 2.07 15.97 107.45

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 

2018 On-Road Emission Benefits (tpd)

Control Strategy NOX 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 used to re 4 percent of rly NOx, 

VOC, and CO s from the “running” emissions 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 

NOx

ion. The summer weekday extended idling 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

 emissio for 3.4 p

sions calcu DV8a and HDD

move 3. the hou

emission 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

HGB Extended Idling Emissions (tpd)

County NOx 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 1.8163 0.0605 0.3380

Liberty 0.0468 0.0015 0.0086

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

HGB Extended Idling Emissions (tpd)

County NOx 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

HGB Extended Idling Emissions (tpd)

County NOx 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 2.5359 0.0946 0.5462

Table 3.1-28: 201 HDDV8b Extended Idling Emissions for Eight-County 

HGB 

8 HDDV8a &

HGB Extended Idling Emissions (tpd)

County NOx VOC CO 

Brazoria 0.0019 0.0003 0.0006

C s hamber 0.0231 0.0044 0.0069

Fort Bend 0.0378 0.0071 0.0110

Galveston 0.0064 0.0012 0.0019

Harris 0.2335 0.0438 0.0676

L iberty 0.0061 0.0011 0.0018

Montgomery 0.0510 0.0094 0.0146

Waller 0.0295 0.0054 0.0084

Total 0.3893 0.0727 0.1128

The summer w -road emissions b nty that 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

Ox corre

 summar 3, and 3.

n-Road Mobile Source Inventory

HGB On-Road E ns missio (tpd)

County NOx VOC CO 

Brazoria 9.19 3.92 53.41

C s 5.5 hamber 5 1.98 34.10

Fort Bend 12.74 4.91 69.27

Galveston 9.26 4.17 54.18

Harris 158.29 69.44 882.75

Liberty 4.75 2.38 32.32

M ontgomery 17.60 6.51 92.24

Waller 4.31 1.83 26.68

Total 221.68 95.14 1,244.95

Table 3.1-30: 2006 Summer Weekday On-Road Mobile Source Inventory by County 

HGB On-Road Em s ission (tpd)
B-134

County NOx 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 29.30

Montgomery 15.87 6.18 82.91

Waller 3.88 1.79 24.63

Total 197.29 90.71 1,115.26

Table 018 Sum kday On-R 3.1-31: 2 mer Wee oad Mobile Source Inventory by County 

HGB On-Road Emissions (tpd)

County NOx VOC CO 

Brazoria 2.50 2.17 36.59

Chambers 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 1.41 23.64

Montgomery 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

Season On-Road Emissions (tpd)

Type 

Day 

Type NOx VOC CO 

Weekday 221.68 95.14 44.95 1,2

Friday 223.69 103.76 1,359.37

S 1 aturday 80.74 73.07 1,032.64

Summer 

Sunday 143.18 61.86 873.70

Weekday 233.36 100.61 1,307.38

Friday 241.07 112.59 1,457.81

S 1 aturday 92.68 78.00 1,099.40

School 

Sunday 148.69 64.32 906.88

Table 3.1-33: 2006 On-Road Mobile Source Inventory by Season and Day Type 

Season On-Road Emissions (tpd)

Type 

Day 

Type NOx 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 

Sunday 127.69 58.76 778.75

Weekday 207.66 95.96 1,171.29

Friday 215.44 107.41 05.79 1,3

Saturday 171.38 74.10 17 980.

School 

Sunday 132.60 61.12 41 808.

Table 3.1-34: 2018 On-Road Mobile Source Inventory by Season and Day Type 

Season On-Road Emissions (tpd)

Type 

Day 

Type NOx 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

Weekday 53.02 48.25 764.81

Friday 56.46 53.89 853.13

Saturday 43.25 36.48 621.40

School 

Sunday 34.32 30.12 513.33

An addition for the 2018 emission inventories was necessary to app e 

1.55 NO emission reduction program (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

X source enefit

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

. 

Table 3.1-35: 2018 Distribution of Summe day On- ning Em

Vehicle On-Road NOX (tpd)

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 NOX 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 NOX B ts 

o split the regional traffic 

h u eavy-d ative commuting benefits w

rtion. These VMEP benefits 

ule b OX ad

: Allocation enefi

On-Road VMEP On-Road NOX Emissions (tpd)

Parameter Light-Duty Heavy-Duty Total 

Regional Traffic Flow Improvements 0.03 0.02 0.05

Alternative Commuting 0.20 0.00 0.20

Vehicle Retrofit and Replacement 0.00 1.30 1.30
B-136

On-Road VMEP On-Road NOX Emissions (tpd)

Parameter Light-Duty Heavy-Duty Total

Total On-Road VMEP 0.23 1.32 1.55

R missions W VMEP unning E ithout 31.53 18.85 50.37

Running Emissions With VMEP 31.29 17.53 48.82

Relative NOX Reduction 0.73% 7.00% 3.08%

EPS3 CNTLEM NOX Adjustment 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, which 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

f ummer n-Road

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 

HGB On-Road Emissions (tpd)

County NOx VOC CO 

Brazoria 2.42 2.17 36.59

Chambers 1.67 1.25 24.85

Fort 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

Waller 1.23 1.01 17.52

Total 49.21 45.97 733.18

Table 3.1-38: 2018 On-Road Mobile Source Inventory by Season and Day Type With 

VMEP 

Season On-Road Emissions (tpd)

Type 

Day 

Type NOx VOC CO 

Weekday 49.21 45.97 733.18

Friday 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 subdomain, the EPS3 message files along 

with the gridded files input into the photochemical model for 2005, 2006, and 2018 are available 

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 gridded files for the Texas-only portion of the larger 

12 km modeling domain are available for 2005, 2006, and 2018, respectively, on the following 

FTP sites: 

 ftp://ftp.tceq.state.tx.us/pub/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 gridded files for he Texas and exas 

portio are available for 2006, and 2 

respectively,

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/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/TAD/Modeling/Mobile_EI/US/eps3/2006/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/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 meteorological data are reported and modeled. However, 

emissions inventory files are typically developed in CDT.

When governmental organizations need to demonstrate conformity to the MVEB, they will not be 

developing photochemical modeling 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 NOx correction are applied outside of EPS3, but in a manner consistent 

with the descriptions included above. This approach 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 NOx 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, gridded, 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 involves the use of Central Standard Time (CST) 

instead of Central Daylight Time (CDT). All photochemical modelin

C

Eight-County Total Emissions (tpd)

HGB Area NOx VOC 

On-Road Inventory From TTI (Table 3.1-3) 

Includes I/M, RFG, & TxLED 52.55 45.97

Temperature/Humidity NOx 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 NOX 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 NOX 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 2018 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, and Orange, a similar link-based inventory 

development approach was taken by TTI based on TDM output available from local 

transportation agencies. Similar 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 development 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-road 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 using 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/TAD/Modeling/Mobile_EI/US/eps3/2005/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/Mobile_EI/US/eps3/2006/

 ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/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 the 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 17th 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 Development 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 NOX

Correction Yes Yes No 

“18-Wheeler” Idling 

Emissions Separation Yes No No 
B-142

4.0 NON-ROAD, OFF-ROAD, AND AREA SOURCE MODELING EMISSIONS 

For the purposes of this Houston-Galveston-Brazoria (HGB) modeling emissions summary, nonroad vehicles and equipment refer to those categories 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 cover 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 contained within the following sections. 

Table 4.0-1: 2005 HGB Non-Road, Off-Road, and Area Source Modeling Emissions 

Source Category Summer Weekday Emissions (tpd)

Description NOX 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 

Source Category Summer Weekday Emissions (tpd)

Description NOX 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 

Source Category Summer Weekday Emissions (tpd)

Description NOX 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
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4.1 Non-Road Mobile Source Modeling Emissions for Eight-County HGB 

Non-road emissions for 2005, 2006, and 2018 for the 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/OEPAA/TAD/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, Miscellaneous, & Equipment Under 25 Horsepower 

2005, 2006, and 2018 summer weekday scenarios were run with the TexN model for all eight 

counties in the HGB area. 25 DCE subcategories for 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 NOX, 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 
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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 

County Summer Weekday Emissions (tpd)

Name 

Equipment 

Population NOX 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 

County Summer Weekday Emissions (tpd)

Name 

Equipment 

Population NOX 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 

County Summer Weekday Emissions (tpd)

Name 

Equipment 

Population NOX 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 NOX and VOC emissions decrease due to the more 

stringent emissions standards for new equipment purchases, combined with the simultaneous 
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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

Aggregate Equipment Summer Weekday Emissions (tpd)

Category 

Equipment 

Population NOX 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

Aggregate Equipment Summer Weekday Emissions (tpd)

Category 

Equipment 

Population NOX 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

Aggregate Equipment Summer Weekday Emissions (tpd)

Category 

Equipment 

Population NOX 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
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Aggregate Equipment Summer Weekday Emissions (tpd)

Category 

Equipment 

Population NOX 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

Fuel/Engine Summer Weekday Emissions (tpd)

Type 

Equipment 

Population NOX 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

Fuel/Engine Summer Weekday Emissions (tpd)

Type 

Equipment 

Population NOX 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

Fuel/Engine Summer Weekday Emissions (tpd)

Type 

Equipment 

Population NOX 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 summarized 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. 
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Table 4.1-11: 2005 HGB Summer Weekday Non-Road Inventory by DCE Subcategory

Non-Road DCE Summer Weekday Emissions (tpd)

Subsector Code 

Equipment 

Population NOX 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
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Table 4.1-12: 2006 HGB Summer Weekday Non-Road Inventory by DCE Subcategory

Non-Road DCE Summer Weekday Emissions (tpd)

Subsector Code 

Equipment 

Population NOX 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
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Table 4.1-13: 2018 HGB Summer Weekday Non-Road Inventory by DCE Subcategory

Non-Road DCE Summer Weekday Emissions (tpd)

Subsector Code 

Equipment 

Population NOX 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 EPS3, the inventory data 

are in a “readable” text-based format. However, 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 Process 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 NOX correction 

TMPRL Apply profiles to temporally allocate daily emission totals 

CHMSPL Chemically speciate emissions into NO, NO2, Olefins, Parrafins, etc. 

GRDEM Spatially allocate emissions using surrogates and prepare photochemical inputs 

MRGUAM Merge and adjust multiple GRDEM output files for photochemical model input 
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its. 

The 2006 and 2018 non-road NOX emission totals presented above in Tables 4.1-3 through 4.1-

13 exclude the benefits of Texas Low Emission Diesel (TxLED) fuel. Instead, TxLED benefits 

were accounted for by applying a NOX reduction to the diesel non-road equipment categories 

through use of the EPS3 CNTLEM module. The specific 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 NOX

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 the 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

Calendar Summer Weekday TxLED Benefits (tpd)

Year NOX 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 hourly variation in temperature and humidity on 

NOX 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 NOX 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 NOX Correction. 

Table 4.1-17: HGB Summer Weekday Non-Road Temperature/Humidity NOX Correction

Calendar Temperature/Humidity NOX Reduction (tpd)

Year NOX 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 NOX adjustments with the CNTLEM 

module, the non-road emissions are spatially allocated with surrogates and prepared for 

photochemical model input using the EPS3 GRDEM 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 NOX totals are reduced by the 
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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

Aggregate Equipment Summer Weekday Emissions (tpd)

Category 

Equipment 

Population NOX 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

Aggregate Equipment Summer Weekday Emissions (tpd)

Category 

Equipment 

Population NOX 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
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Table 4.1-20: 2018 HGB Adjusted Non-Road Emissions by Equipment Category

Aggregate Equipment Summer Weekday Emissions (tpd)

Category 

Equipment 

Population NOX 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 for the 2018 modeling emissions 

was necessary to apply the 0.7 NOX 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 

Calendar Summer Weekday Oil & Gas Emissions (tpd)

Year NOX 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 NOX and VOC emissions for the 

HGB area. These plots are respectively entitled Figure 4.1-1: 2005 HGB Summer Weekday NonRoad NOX Emissions, Figure 4.1-2: 2006 HGB Summer Weekday Non-Road NOX Emissions, 

Figure 4.1-3: 2018 HGB Summer Weekday Non-Road NOX 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 NOX 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 categories 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 concentration 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. For example, if a grid cell is evenly divided 
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se counties. 

between two counties, the plotting routine will evenly 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 NOX Emissions 
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Figure 4.1-2: 2006 HGB Summer Weekday Non-Road NOX Emissions 
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Figure 4.1-3: 2018 HGB Summer Weekday Non-Road NOX Emissions 
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Figure 4.1-4: 2005 HGB Summer Weekday Non-Road VOC Emissions 
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Figure 4.1-5: 2006 HGB Summer Weekday Non-Road VOC Emissions 
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Figure 4.1-6: 2018 HGB Summer Weekday Non-Road VOC Emissions 
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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 (EDMS) by Jacobs Consultancy under contract to 

the Houston Airport System (HAS). The results 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

HGB 2005 Ozone Season Day (tpd)

Area Airport NOX 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 

HGB 2006 Ozone Season Day (tpd)

Area Airport NOX 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 

HGB 2018 Ozone Season Day (tpd)

Area Airport NOX 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 developed 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-Continental 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 between available estimates for 2005 and 2011. 
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Table 4.2-4: 2005 Ozone Season Day GSE Emissions by HGB Airport

HGB Area 2005 Ozone Season Day (tpd)

Airport Airline NOX VOC CO 

Hobby Southwest 0.15 0.01 0.09

Airport Other 0.08 0.01 0.25

Intercontinental Continental 1.19 0.61 20.96

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 NOX VOC CO 

Hobby Southwest 0.15 0.01 0.10

Airport Other 0.08 0.01 0.23

Intercontinental Continental 1.17 0.60 20.52

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 NOX VOC CO 

Hobby Southwest 0.18 0.02 0.11

Airport Other 0.02 0.00 0.04

Intercontinental Continental 0.55 0.25 8.70

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 and 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 NOX

and 0-1 pounds of VOC. General 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 

Calendar General Aviation Emissions (tpd)

Year NOX VOC 

2005 0.024 0.410 

2006 0.025 0.420 

2018 0.037 0.610 

The TCEQ developed special surrogates to spatially allocate the aircraft and GSE emissions to 

grid cells covering the Ellington Field, Hobby, and Intercontinental airports. The following pages 

contain graphical plots of the 2005, 2006, and 2018 airport summer weekday NOX emissions for 

the HGB area. These plots are respectively entitled Figure 4.2-1: 2005 HGB Summer Weekday 
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Airport NOX Emissions, Figure 4.2-2: 2006 HGB Summer Weekday Airport NOX Emissions, and 

Figure 4.2-3: 2018 HGB Summer Weekday Airport NOX Emissions. 

Figure 4.2-1: 2005 HGB Summer Weekday Airport NOX Emissions 
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Figure 4.2-2: 2006 HGB Summer Weekday Airport NOX Emissions 
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Figure 4.2-3: 2018 HGB Summer Weekday Airport NOX Emissions
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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 emissions 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 

Calendar Summer Weekday Locomotive Emissions (tpd)

Year NOX VOC 

2005 30.32 1.83

2006 28.53 1.79

2018 34.22 2.35

The locomotive emissions were spatially allocated 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 NOX emissions for the 

HGB area. These plots are respectively entitled Figure 4.3-1: 2005 HGB Summer Weekday 

Locomotive NOX Emissions, Figure 4.3-2: 2006 HGB Summer Weekday Locomotive NOX

Emissions, and Figure 4.3-3: 2018 HGB Summer Weekday Locomotive NOX Emissions. 
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Figure 4.3-1: 2005 HGB Summer Weekday Locomotive NOX Emissions 
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Figure 4.3-2: 2006 HGB Summer Weekday Locomotive NOX Emissions 
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Figure 4.3-3: 2018 HGB Summer Weekday Locomotive NOX Emissions
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4.4 Commercial Marine Modeling Emissions for Eight-County HGB 

When modeling emissions were being 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/OEPAA/TAD/Modeling/Nonroad_EI/Marine/. A summary of this 

work is available in Table 4.4-1: HGB Commercial Marine NOX Emissions Inventory for 1997 

and 2007. 

Table 4.4-1: HGB Commercial Marine NOX Emissions Inventory for 1997 and 2007 

Commercial Marine Vessels NOX 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 Houston from 1996-2005 was reported on their website 

(http://www.portofhouston.com/busdev/tradedevelopment/tradestatistics.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 data 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 estimates for 2005, 2006, and 2018. An additional 
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set of adjustments was applied to the 2018 harbor 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 

Diesel-Powered 2007 to 2018 Emission Rate Adjustments 

Harbor Vessels NOX 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 described above resulted in the commercial 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 

Calendar Commercial Marine Emissions (tpd)

Year NOX 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 along the shipping lanes, major waterways, and 

docks within both Galveston Bay and the Houston Ship Channel. The exhaust stack parameters 

assigned to each of these sequential point sources varied by the different vessel types shown in 

Table 4.4-1. The magnitude of emissions allocated to each sequential point is proportional to 

specific trips from the off-shore sea buoy to the various docks located 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 NOX emissions for the 

HGB area. These plots are respectively entitled Figure 4.4-1: 2005 HGB Summer Weekday 

Commercial Marine NOX Emissions, Figure 4.4-2: 2006 HGB Summer Weekday Commercial 

Marine NOX Emissions, and Figure 4.4-3: 2018 HGB Summer Weekday Commercial Marine 

NOX Emissions. Grid cells in dark red indicate the highest concentration of shipping emissions 

where the greatest amount of activity was identified by the Starcrest study referenced above. 
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Figure 4.4-1: 2005 HGB Summer Weekday Commercial Marine NOX Emissions
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Figure 4.4-2: 2006 HGB Summer Weekday Commercial Marine NOX Emissions 
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Figure 4.4-3: 2018 HGB Summer Weekday Commercial Marine NOX Emissions
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4.5 Area Source Modeling Emissions for Eight-County HGB 

Area sources include commercial, small-scale industrial, and residential categories of sources that 

use materials or operate processes that can generate emissions. These sources of emissions fall 

below the point source reporting levels and are either 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 either hydrocarbon evaporation or fuel combustion. 

Examples of sources of evaporative losses include printing operations, industrial coatings, 

degreasing solvents, house paints, leaking underground storage tanks, underground tank filling at 

gasoline service stations, and vehicle refueling operations. Fuel combustion sources include 

stationary source fossil fuel combustion at residences 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 the 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 primarily on data from the 2005 periodic emissions 

inventory (PEI) that is available in the TCEQ 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 Analysis 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 NOX 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 NOX Emissions for 2005, 2006, and 2018 

Area Source Summer Weekday NOX Emissions (tpd)

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
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Table 4.5-2: Eight-County HGB Area Source VOC Emissions for 2005, 2006, and 2018

Area Source Summer Weekday VOC Emissions (tpd)

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 EPS3 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 NOX and VOC 

emissions. The TCEQ developed special surrogates to spatially allocate the oil and gas 

production emissions using well-head location data obtained from the Texas Railroad 

Commission. 

The following pages contain graphical plots of the 2005, 2006, and 2018 area source summer 

weekday NOX and VOC emissions for the HGB area. These plots are respectively entitled Figure 

4.5-1: 2005 HGB Summer Weekday Area Source NOX Emissions, Figure 4.5-2: 2006 HGB 

Summer Weekday Area Source NOX Emissions, Figure 4.5-3: 2018 HGB Summer Weekday Area 

Source NOX 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. 
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Figure 4.5-1: 2005 HGB Summer Weekday Area Source NOX Emissions
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Figure 4.5-2: 2006 HGB Summer Weekday Area Source NOX Emissions
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Figure 4.5-3: 2018 HGB Summer Weekday Area Source NOX Emissions
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Figure 4.5-4: 2005 HGB Summer Weekday Area Source VOC Emissions
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Figure 4.5-5: 2006 HGB Summer Weekday Area Source VOC Emissions
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Figure 4.5-6: 2018 HGB Summer Weekday Area Source VOC Emissions 
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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 distribution 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 NOX Emissions. 

Figure 4.6-1: 2005 Gulf of Mexico Oil and Gas Production NOX Emissions
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4.7 Non-Road, Off-Road, and Area Source Emissions Outside of Eight-County HGB 

Photochemical modeling inputs were 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 discussed above in Section 4.1. Similar to the 

approach taken with HGB, summer weekday emission 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 Inventory 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 estimates for the non-Texas portions of the modeling 

domain were obtained from EPA’s 2005 National Emissions 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 2006 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 Sources for Non-HGB Areas 

Source Category Data Source(s) 

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 
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5.0 BIOGENIC MODELING EMISSIONS

For development of the biogenic emissions, the TCEQ used version 3.1 of the Global Biosphere 

Emissions and Interactions System (GloBEIS3.1), 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 radiation 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 temperature data generated from weather station data. 

Land-use data for the 12 km and 36 km non-Texas portions of the modeling domain were 

obtained from Version 3 of the Biogenic Emissions Landuse Data (BELD3; Kinnee et al., 1997), 

which is a vegetation database for the entire North 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 effort 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 National 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 GloBEIS3.1 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) Continental 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 obtained from weather stations throughout the U.S., 

including data from the National Weather Service (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 organizations 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 estimated 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. 
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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 NOX, 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 lists the various GloBEIS3.1 input settings. Since 

biogenic emissions are dependent upon the meteorological 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: Total 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 

Ozone Biogenic Emissions (tpd)

Episode 

Day Isoprene Total 

Monoterpenes 

Other 

VOC NOX

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
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Ozone Biogenic Emissions (tpd)

Episode 

Day Isoprene Total 

Monoterpenes 

Other 

VOC NOX

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 NOX

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. 
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Biogenic VOC and NOX Emissions

August 2, 2005

Figure 5.0-1: Representative Biogenic VOC and NOX Emissions for August 2, 2005 
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ATTACHMENT 1 

Reconciling Reported VOC Emissions 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 under-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 extensive network of Automatic Gas Chromatographs, 

which measure ambient concentrations of individual 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 reconcile the reported emissions with measured 

concentrations. 

Emissions data (in units of tons per day, or tpd) are fundamentally incommensurate with ambient 

concentration data (in units of parts/billion carbon, 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 emission 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 photochemical 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 depth, ambient temperature, and stability class. 

Emission rates can be specified as constant throughout the simulation, or can be hour-specific. 

The model assumes steady-state conditions, straight-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. 
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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 the winds have been relatively constant. To 

exclude trajectories that are too “curvy”, we examined 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. The TCEQ chose a relatively tight criterion of 

sinuosity ≥ 0.95. 

 A maximum variation of wind direction, in degrees. 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. 
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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 experimented 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, reducing 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 for rule-effectiveness (RE) during this process, which increased 

emissions 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 the 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 photochemical 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 regulated as an HRVOC since it is primarily of 

biogenic origin). 

ISC Results

Figure 2: Measured and Modeled Median Concentrations by Wind Direction and Emissions for 

Propylene in the 2005 Ship Channel Area shows a comparison of modeled concentrations (what 

would be expected, based on the RE-adjusted reported emissions) with measured concentrations 

(what was actually seen) for 2005 in the Houston Ship Channel area. The radar plots at each 

monitor location convey both measured and modeled propylene concentrations as a function of 

wind direction. Note that the scales are logarithmic, 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 met the straightness criteria described above. The 

corresponding blue slices represent median modeled concentrations. Slices shown in lighter 

shading indicate no significant difference between the measured and modeled medians, using a 

simple sign test [Conover, 1971]. Some directions 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 illustrate the relationship between the sources and 

receptors, reported 2005 point source propylene emissions are represented as circles of varying 

size and color. 
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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 Concentrations by Wind Direction and Emissions for 

Propylene in the 2005 Galveston and Brazoria Counties below is similar to Figure 2, except it 

shows monitoring sites in Galveston and Brazoria counties. 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 greater than can be explained by the RE-adjusted 

reported propylene emissions in STARS. In fact, the discrepancy may be even greater than seen 

on the plots, since some fraction of the emitted propylene will react before it reaches the monitor 

locations. 
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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 section 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 appropriate for isolated sources, but not in areas like the Houston 

Ship Channel which has hundreds of sources clumped 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 approach based on the Potential Source Contribution 

Function (Ashbaugh, et al 1985, Hopke 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 particularly large concentrations (e.g., above the 75th
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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 and Berkowitz is shown below in Figure 4: PSCF 

Illustration. In the figure three trajectories pass through 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). Since 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

“High” Traj.

Figure 4: PSCF Illustration 

One feature of this technique is that it is actually 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 trajectories) are weighted proportionately to the 

amount of time the trajectory spends in a grid cell. 

Figure 5: Summer Daytime PSCF Probabilities for Propylene at Channelview and Combined for 

Five Sites, reproduced from work performed under contract 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 (left) 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 75th 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 represent 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 combined 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. 
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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 successfully 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. This 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 concentrations are maintained when the trajectories arrive at the monitoring 

location. The right-hand portion of the figure expands the grid cell marked in blue and shows the 

five concentration values associated with the three trajectories with points inside the cell. The 

median of these five values is the modified PSCF value for this cell. Note that the median was 

used instead of the mean due to the highly skewed nature of the concentration 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-Concentrations Using Channelview Observed Concentrations

presents the results of applying the modified PSCF analysis to back trajectories ending at the 
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Channelview Auto-GC, using the same back trajectories 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-to-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 median 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 points of trajectories ending at any monitor. 
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Figure 8: 2005 Propylene Pseudo-Concentrations 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 implies that reported emissions, even adjusted for RE, 

are not sufficient to explain the concentrations of propylene seen at the Auto-GC sites. 
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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 discrepancies between them. Figure 10: Ratios of 2005 

Propylene Pseudo-Concentrations Based on Measurements 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. 
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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 depicted in Figure 10, the next logical step is to 

apply the ratios to the RE-adjusted reported emissions by grid cell. This approach is valid 

because of the assumption of steady-state, non-reactive 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 applied reconciliation factors to other 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 sources. 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 identical to each other). It is apparent that the PSCF 

pseudo concentrations based on reconciled emissions (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 the ratios based on reconciled emissions (bottom 

right) show ratios generally much closer to unity than do those in the row above. It can then be 
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concluded that the reconciled emissions do a better 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-concentrations 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 reported emissions. Bottom Row: (L) PSCF 

pseudo-concentrations of propylene using monitored 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 the 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. Comparing the two years shows that all species 

except t-2-butene required smaller relative adjustments in 2006 than in 2005, consistent with 

enhanced monitoring and control of previously unaccounted-for emission sources. 
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Table 1: RE-Adjusted 2005 and 2006 Emissions 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 comparisons 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 concentrations of the CB-05 species ETH, OLE, 

and IOLE before and after emission reconciliation1

. 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 three 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 displayed 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 matches 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 episodes, 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 reconciliation 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 Concentrations 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 Modeled ETH Concentrations with Observed 

Concentrations at Lynchburg Ferry for Three Episodes), the reconciliation improves the 

comparison between modeled and observed ethylene for concentrations above 1 ppb for the June 

 

1

 Because of the vast number of hydrocarbon species and potential reactions, the CB-05 mechanism deals 

only with a limited number of species classified by carbon bonds. Emissions are transformed during preprocessing into these CB-05 species before being input into CAMx. For comparison purposes, measured 

concentrations of sampled hydrocarbon species may also be transformed into CB-05 species. There is a 

close (though not exact) correspondence 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. 
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2005 and June 2006 episodes, and above 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 Reconciled

June 2005 June 2006 AQS-1

Figure 12: Scatter Plots Comparing Reg10 Base Case Modeled ETH Concentrations with 

Observed Concentrations at Deer Park for Three Episodes - (L) Without PSCF-Based 

Emissions Reconciliation; (R) With Emissions Reconciliation: (top to bottom row) June 

2005, June 2006, and AQS-1 
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Wallisville ETH

Unreconciled Reconciled

June 2005 June 2006 AQS-1

Figure 13: Scatter Plots Comparing Reg10 Base Case Modeled ETH Concentrations with 

Observed Concentrations at Wallisville for Three Episodes - (L) Without PSCF-Based 

Emissions Reconciliation; (R) With Emissions Reconciliation: (top to bottom row) June 

2005, June 2006, and AQS-1 
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HRM-3 ETH

Unreconciled Reconciled

June 2005 June 2006 AQS-1

Figure 14: Scatter Plots Comparing Reg10 Base Case Modeled ETH Concentrations with 

Observed Concentrations at HRM-3 for Three Episodes - (L) Without PSCF-Based 

Emissions Reconciliation; (R) With Emissions Reconciliation: (top to bottom row) June 

2005, June 2006, and AQS-1 
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Lynchburg Ferry ETH

Unreconciled Reconciled

June 2005 June 2006 AQS-1

Figure 15: Scatter Plots Comparing Reg10 Base Case Modeled ETH Concentrations with 

Observed Concentrations at Lynchburg Ferry for Three Episodes - (L) Without PSCFBased Emissions Reconciliation; (R) With Emissions Reconciliation: (top to bottom row) 

June 2005, June 2006, and AQS-1 
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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 for that grid cell and hour was paired with the 

observation; no spatial or temporal interpolation 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 Houston urban and industrial areas. Adding in the PSCF-based 

emissions reconciliation improves the comparison, but the model still under-predicts the 

observations by about a factor of two. The aircraft 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-predicting 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 Concentrations 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 small over-prediction for observations in the 2-10 

ppb range during the June, 2005 and AQS-1 episodes. At Wallisville (Figure 18: Scatter Plots 

Comparing Reg10 Base Case Modeled OLE Concentrations 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-
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predicting), and for the AQS-1 episode, the distribution of modeled concentrations after 

reconciliation matched the observed distribution extremely well. At HRM-3 (Figure 19: Scatter 

Plots Comparing Reg10 Base Case Modeled OLE Concentrations 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 comparisons were very good for all three 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. 
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Deer Park OLE

June 2005 June 2006 AQS-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 Emissions Reconciliation: (top to bottom row) June 

2005, June 2006, and AQS-1 
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Wallisville OLE

Unreconciled Reconciled

June 2005 June 2006 AQS-1

Figure 18: Scatter Plots Comparing Reg10 Base Case Modeled OLE Concentrations with 

observed concentrations at Wallisville for Three Episodes - (L) Without PSCF-Based 

Emissions Reconciliation; (R) With Emissions Reconciliation: (top to bottom row) June 

2005, June 2006, and AQS-1 
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HRM-3 OLE

Unreconciled Reconciled

June 2005 June 2006 AQS-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 Emissions Reconciliation: (top to bottom row) June 

2005, June 2006, and AQS-1 
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Lynchburg Ferry OLE

Unreconciled Reconciled

June 2005 June 2006 AQS-1

Figure 20: Scatter Plots Comparing Reg10 Base Case Modeled OLE Concentrations with 

Observed Concentrations at Lynchburg Ferry for Three Episodes - (L) Without PSCFBased Emissions Reconciliation; (R) With Emissions Reconciliation: (top to bottom row) 

June 2005, June 2006, and AQS-1 
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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 concentrations 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 concentrations 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 consists 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 and hour was paired with the observation; as with 

the continuously-sampled ethylene data, no spatial 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 the 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 comparison, 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 concentrations distributions very comparable to those observed. 
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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 the 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 concentrations of IOLE, but in reality probably is just 

an artifact. For later episodes, the measured IOLE concentrations appear to represent a more 

reasonable concentration range, but comparisons for very low observed concentrations are still 

questionable. 

For Deer Park (Figure 22: Scatter Plots Comparing Reg8 Base Case Modeled IOLE 

Concentrations at Deer Park Using RE-Adjusted Reported Emissions for Three Episodes), 

the model under-predicts the observed IOLE concentrations except for the range of around 0.2 to 

0.6 during the AQS-1 episode. Adding the emission 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 improvement during the June 2005 episode, but 

makes little noticeable change elsewhere. 
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Deer Park IOLE

Unreconciled Reconciled

June 2005 June 2006 AQS-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 Emissions Reconciliation: (top to bottom row) June 

2005, June 2006, and AQS-1 
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Wallisville IOLE

Unreconciled Reconciled

June 2005 June 2006 AQS-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 Emissions Reconciliation: (top to bottom row) June 

2005, June 2006, and AQS-1 
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HRM-3 IOLE

Unreconciled Reconciled

June 2005 June 2006 AQS-1

Figure 22: Scatter Plots Comparing Reg8 Base Case Modeled IOLE Concentrations at 

HRM-3 Using RE-Adjusted Reported Emissions for Three Episodes - (L) Without PSCFBased Emissions Reconciliation; (R) With Emissions Reconciliation: (top to bottom row) 

June 2005, June 2006, and AQS-1 
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Lynchburg Ferry IOLE

Unreconciled Reconciled

June 2005 June 2006 AQS-1

Figure 23: Scatter Plots Comparing Reg8 Base 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 
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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 concentrations 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 Formaldehyde (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 formaldehyde (FORM in CB-05 parlance) from the 

continuous monitoring instrument 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 concentration distributions are close to normal (as 

opposed to lognormal). Since ozone and (most) formaldehyde are formed through atmospheric 

reactions, their concentrations generally do not vary across several orders of magnitude as is the 

case with primarily-emitted species sampled 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 comparison 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 Formaldehyde (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 for the AQS-1 episode. The with the two model 

runs shown: the blue line shows modeled formaldehyde concentrations before reconciliation and 

the green line shows modeled concentrations after reconciliation; observaed concentrations are 

shown in red. For most hours, the green line is hidden, indicating only very small differences in 

concentrations between the two runs. But on August 20 and 21, and to a lesser degree September 

14, the reconciliation does noticeably improve the model’s replication of large daytime peaks. 
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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 Emissions Reconciliation; (R) With Emissions 

Reconciliation. 

Figure 26: Time Series Plot Comparing Reg10 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 area. The left-hand plot shows daily maximum 

modeled one-hour ozone concentration by grid cell without emissions 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 emissions 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). 
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Figure 27: Daily Maximum One-Hour Modeled Ozone Concentrations for August 16, 2006, 

No Emissions Reconciliations (L), With Emissions 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, summarized 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 marginally, and for one (AQS-1) model relative error 

showed an equally marginal increase, leaving overall 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

-7.990

4.817

-4.730

-7.147

-3.272

-3.682

-4.975

-9.584

-7.514

5.354

-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.

Episode

Mean Relative Bias (% )

Reg 8 Base Case Reg 8 Base Case w/ PSCF Emissions Reconciliation

Figure 28: 1-Hour Ozone Mean Relative Bias, Reg 8 Base Case With and Without PSCFBased Emission Reconciliation 
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1-Hour Ozone Mean Relative Error, Reg 8 Base Case With 

and Without PSCF-Based Emission Reconciliation

16.590

13.068

16.191

14.699

13.099

14.255

13.66313.654

16.429

12.907

16.297

14.616

13.016

14.274

13.573

13.609

0

5

10

15

20

25

30

May 05 Jun 05 Jul 05 2005 Avg. Jun 06 AQS-1 AQS-2 2006 Avg.

Episode

Mean Relative Error (% )

Reg 8 Base Case Reg 8 Base Case w/ PSCF Emissions Reconciliation

Figure 29: 1-Hour Ozone Mean Relative Error, Reg 8 Base Case With and Without PSCFBased Emission Reconciliation 

 

Conclusions and Future Research 

The PSCF-based reconciliation process produces emissions which are more congruent with 

ambient measurements, as evidenced by comparing 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 distinctly improved for OLE, which was underestimated 

in the model without reconciliation. Adding in the reconciliation increased modeled 

concentrations and improved performance almost universally at the sites and episodes shown in 

the comparison. Comparing observed OLE concentrations with those sampled by the P3 aircraft 

showed even greater under-prediction. After 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 combinations, 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 concentrations 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 the observations, and even after reconciliation the 

modeled concentrations were lower than observed by half. 

In most, but not all, cases, IOLE concentrations were under-predicted by the model, and the 

reconciliation made some barely discernable improvements 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 improved the model’s ability to replicate daytime peak 

formaldehyde concentrations in some cases. 

Overall ozone performance improved slightly with the emissions reconciliation, more so in 2005 

than in 2006. While it may seem like the modest 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 
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provide more confidence in the model’s future predictions. Additionally, TCEQ plans to 

continue to investigate ways to improve the emissions reconciliation methodology which may 

lead to more substantive improvements in ozone performance. 

 

As noted in the Model Performance Evaluation section of Appendix C: Photochemical Modeling 

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

adding HRVOC emissions to flares can significantly improve performance of these species 

concentrations compared with aircraft observations. Future research plans include investigating 

application of the PSCF-based reconciliation technique specifically to flares. Additional goals 

include applying the technique to additional pollutants (in particular, NOX) 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 National Park. Atmospheric Environment 19 (8), 1263–

1270. 

Hopke, P.K., (2003). Recent development in receptor modeling. Journal of Chemometrics 17, 1–

12. 

 

Conover, W. J. (1971) Practical Nonparametric 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 use 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.