Bryan W. Shaw, Ph.D., Chairman Buddy Garcia, Commissioner
Carlos Rubinstein, Commissioner 

### - 

### .

:**~~·.->** .:.\ \ .: I·**!d:~** 

## fJLr.;·u:i: . !·

Mark R. Vickery, P.G., Executive Director 

## 10 APR -9 Pii 

## 1:55

TEXAS COMMISSION ON ENVIRONMENTAL QUALITY

Protecting Texas bg Reducing and Preventing Pollution 

## April6,2010

Docket No. 2009-0802-SIP, Project No. 2009-017-SIP-NR**~** 

### Docket No. 2009-0932-RUL, Rule Project No. 2009-006-101-EN · Docket No. 2009-0867-RUL, Rule Project No. 2009-019-101-EN

A1 Armendariz, PhD, Regional Administrator U.S. Environmental Protection Agency- Region 6 

### 1445 Ross Avenue Dallas, Texas 75202-2733

.

Dear Dr. Armendariz:

On March 10, 2010, the Texas Commission on Environmental Quality (Commission) adopted revisions to 

### the State Implementation Plan (SlP) for the control of ozone air pollution for the Houston-Galveston-Brazoria (HGB) 

### area.

The Commission adopted amendments to 

### 30 Texas Administrative Code (TAC) Chapter 101, Subchapter A, General Rules, Section 101.1; and Subchapter H, Emissions Banking and Trading, Division 6, Highly Reactive Volatile Organic Compound Emissions Cap and 

### Trade Program, 

### Sections 101.390

- 101.394, 101.396, and 

### 101.399

- 101.401; 30 TAC Chapter 101, Subchapter H, 

### Emissions Banking and Trading, Division 3, Mass Emissions C and 

### Trade Pro 

### Sections 101.350 101.351 and 

### 101.353 and co · revisions to the SlP; and 

### GB Attsinment Demonstration SlP Revision for the 1997 Ei t-Hour Ozone Standard. These rule changes and SIP revisions demonstrate that the HGB area 

### will attain the 

### 1997 eight-hour ozone National Ambient Air 

### Quality Standard by June 15, 2019.

Enclosed are 

### the proposed revisions to the SlP, a public hearing certification, a 

### complete record of the public hearings, and the accompanying orders.· I 

### look forward to your expeditious approval of these SlP revisions.

Sincerely, 

# B**~,;ktw.--·**

# B~haw,PhD.

Chairman

BWS/jmm

Enclosures

cc: The Honorable Rick Perry, Governor ofTexas Mr. Toby Baker, Office ofBudget, Planning and Policy, Office of the Governor Mr. Mark 

### R Vickery, P.G., Executive Director, Texas Cor!unission on Environmental Quality

P.O. Box 13087 Austin, Texas 78711-3087 512-239'1000 1ntemet address: www.tceq.state.tx.usTexas Commission on Environmental Quality Public Hearings Odober 28-29, 2009

Houston-Galveston-Brazoria (HGB) Attainment Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard Projed Number 2009-017-SIP-NR Volume 2 of2REVISION TO THE STATE IMPLEMENTATION PLAN FOR THE CONTROL OF OZONE AIR POLLUTION

HOUSTON-GALVESTON-BRAZORIA 1997 EI GHT-HOUR OZONE STANDARD NONATTAINMENT AREA

TEXAS COMMISSION ON ENVIRONMENTAL QUALITY P.O. BOX 13087 AUSTIN, TEXAS 78711-3087

Houston-Galveston-Brazoria Attainment Demonstration State Implementation Plan Revision for the 1997 Eight-Hour Ozone Standard

PROJECT NO. 2009-017-SIP-NR

Adopted March 10, 2010xv LIST OF APPENDICES

Appendix Appendix Name

Appendix A Meteorological Modeling for the HGB Attainment Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard

Appendix B Emissions Modeling for th e HGB Attainment Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard

Appendix C Photochemical Modeling for the HGB Attainment Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard

Appendix D Reasonably Available Control Technology Analysis

Appendix E Reasonably Available Control Measure Analysis

Appendix F Evaluation of Mobile Sour ce Control Strategies for the HoustonGalveston-Brazoria State Implementation Plan (With Detailed Strategies), Prepared for Houston-Galveston Area Council, Prepared by ENVIRON International Corporation

Appendix G Transportation Control Measures for the HGB Attainment Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard (Excerpt from Evaluation of Mobile Source Control Strategies for the Houston-Galveston-Brazoria State Implementation Plan), Prepared for Houston-Galveston Area Council, Prepared by ENVIRON International Corporation

Appendix H Voluntary Mobile Emission Reduction Program for the HGB Attainment Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard (Excerpt from Evaluation of Mobile Source Control Strategies for the Houston-Galveston-Brazoria State Implementation Plan), Prepared for Houston-Galveston Area Council, Prepared by ENVIRON International Corporation

Appendix I Corroborative Analysis for th e HGB Attainment Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard

Appendix J Recommendations for Best Ma nagement Practices to Control and Reduce Inadvertent Cargo Vapor Emissions in the Tank Barge Community

### APPENDIX I:

CORROBORATIVE ANALYSIS FO R THE HGB ATTAINMENT DEMONSTRATION SIP REVISION FOR THE 1997 EIGHT-HO UR OZONE STANDARD

### This page is intentionally left blankAppendix I: Corroborative Analysis for the HGB Attainment Demonstrati o n SIP Revision for the 1997 Eight-Hour Ozone Standard

The appendix to Chapter 5 includes three sections. The first section is a detailed description of the Chem ical Process Analyses that have been performed for the TCEQ photochemical grid modeling for 2005-2006. The second section is a detailed model performance evaluation for five well-performing episode days. The third section s hows the projected future design values when using a 2005 baseline emissions inventory.

Chemical Process Analysis (CPA) of TCEQ Photochemical Grid Modeling One of the advanced analyses that can b e pe rfor med upon photochemi cal grid modeling is chemical process analysis. Standard model eval uations examine the concentrations of reaction products of series of chemical reactions, such as ozone, formaldehyde, nitr ic acid, and others. Process analysis allows for examination of the reactions that create these products. The processes of radical initiation, propagation and termination can be examined in depth, which can provide insights into the model’s sensitivity to VOC and NO X concentrations (Jang et al. 1995, Zhang et al. 2004, CAMx User’s Guide, 2008).

Due to the computational expense and storage de mands of process analysis, a limited number of days were selected for CPA analysis. Table 1 shows which days were chosen.

I- 

### 3Table 1. Days Chosen for Chem ic al Process Analysis; Color-Coded by Flow Regime.

Date of high

ozone modeled Observed areawide peak 8-hr O 3 average Location of daily observed peak Number of sites exceeding

85 ppb 8-hr O 3 NAAQS Flow regime 25-May-05 92.2 WALV 2 Flow reversal 26-May-05 88 TOMB 1 Steady flow (NE) 27-May-05 116.8 HNWA 10 Typical veering 1-Jun-05 92.2 TXCT 3 Steady flow (N) 2-Jun-05 125.8 CRBL 20 Flow reversal 21-Jun-05 102.4 TOMB 12 Typical veering 22-Jun-05 104.4 KATP 17 Typical veering 23-Jun-05 96.4 HNWA 4 Typical veering 1-Aug-05 93.7 KATP 6 Flow reversal 2-Aug-05 104.6 WHOU 10 Flow reversal 3-Aug-05 92 MEYE 2 Flow reversal 4-Jun-06 111.7 H07H 13 Flow reversal 5-Jun-06 109.1 H04H 16 Flow reversal 6-Jun-06 110.5 CNR2 9 Flow reversal 8-Jun-06 122.9 BYWC 19 Flow reversal 9-Jun-06 106.5 BUHV 13 Flow reversal 10-Jun-06 94.1 CRBL 7 Flow reversal 12-Jun-06 101.8 DRPK 9 Flow reversal 13-Jun-06 91.2 MSTG 3 Flow reversal 14-Jun-06 120.4 TOMB 17 Typical veering 16-Aug-06 95.5 MERC 6 Flow reversal 17-Aug-06 113.6 TOMB 14 Typical veering 18-Aug-06 89.7 KATP 1 Typical veering 31-Aug-06 127.1 SHWH 14 Typical veering 1-Sep-06 121.8 DRPK 27 Flow reversal 6-Sep-06 87.9 CLHS 1 Flow reversal 7-Sep-06 110.5 BAYP 12 Typical veering 13-Sep-06 89 MACP 1 Flow reversal 14-Sep-06 118.9 WHOU 8 Typical veering 26-Sep-06 87.8 DNCG 1 Flow reversal 27-Sep-06 90.5 MEYE 2 Flow reversal 6-Oct-06 95.3 TOMB 3 Steady flow (NE) 7-Oct-06 87 MACP 1 Steady flow (NE)

Process rate values wer e derived by invoking th e CPA module in CAMx version 4.53. For each receptor site, values were averaged through the depth of the PBL. PBL depth was diagnosed by a vertical averaging algorithm developed by En viron, which bases its diagnosed PBL depth on changes in the vertical mixing parameter K V . In some circumstances, the effective PBL depth in the model will differ somewhat from the diagnosed PBL depth, but diagnosed PBL depth is often an acceptable approximation. For the Moody Towe r site, the value associated with model layer two, in which the top of the tower resides, is used instead of the PBL-averaged value.

I- 

### 4

## Progression of ozone formation across the HGB area

TexAQS 2000 researchers showed that the highest ozone concentrations observed in the HGB area can invariably be linked to the industrial pl umes emitted in the vicinity of the Houston Ship Channel (Daum et al., 2003,2004; Ryerson et al., 20 03). These results are consistent with TCEQ case studies of ozone episodes in the HGB area (TCEQ Significant Events web page, 2009), and with subsequent analyses during TexAQS II (Gilman et al., 2009; de Gouw et al., 2009). In the HGB area, one common flow regime during ozone episodes is the typical veering pattern (TCEQ Conceptual Model, 2009). During a typical veering episode, the day begins with light winds from the northeast, which carry the industrial plumes from the Houston Ship Channel area to the southwest. Ozone begins forming in these concentr ated plumes. During the day, the winds shift to the southeast, and then to the south or sout h-southwest. Consequently, the ozone is carried across southern and southwestern Harris County, across western Houston, to northwest Harris County. This typical flow pattern results in high ozone along a chain of monitors, beginning in the Houston Ship Channel area, and ending in w estern Harris County. The monitors experience the ozone plume during different times in its development, with the easternmost monitors observing fresh emissions and rapid ozone forma tion, and the westernmost observing the highest ozone concentrations, but the lowest ozone forma tion rates. This progression of ozone formation can be witnessed with process analysis of the model output.

Three high ozone days have been chosen to illus trate the evolution of high ozone in the HGB area, June 21-23, 2005. All three of these days ar e classified as typical veering days, but there are some important differences among them. Figures 1-3 show the 12-hour forward trajectories for all three days, starting at 0800. The trajectori es on June 21 have the southernmost path, June 22 is slightly more northerly, and June 23 is almost northwest in orientation, pushing the industrial plumes through the urban core on the way to northwestern Harris County. As the process analysis will show, the different pathways affect the ozone formation process in important ways.

The differing ozone behavior on these three days is first illustrated by examining the diurnal variation in ozone concentration for each day at all HGB area monitoring sites. Figures 4-6 show 5-minute ozone averages for the three days. June 21 and June 22 are relatively similar, though the background ozone appears to be slightly higher on June 22. June 23, however, has some large ozone peaks that do not occur on the other two days . The largest of these peaks is observed at the West Houston (WHOU) monitor, and the other large peaks are near that monitor. Also note that the WHOU peak occurs earlier in the day in co mparison to the peak concentrations observed on June 21-22. All three days have some early peaks at monitors in the vicinity of the Houston Ship Channel.

I- 

### 5I- 

### 6 

### ' '.,

\


### ... Daily Max 8hr 03 ppb

ar monitors 

# • 1)0 + 

# • 95 to< 1)0 

# • 90 to < 95 

# • 85 to < 90 

# • 80 to < 85 

# • 75 to < 80 0 < 75
MetStat: ObseNed MetStat: ep1.eta.dbemis.fddats.newuhsstnewutcsriulc.grell

Forward Trajectories 2005-06-21 Max 8hr 03 (monitored): 102.4 at: TOMB

MetStat: ObseNed Start time: 08:00 8Hr frame: 1000-1700

Daily Max 8hr 03 ppb

ar monitors 

# • 1)0 + 

# • 95 to< 1)0 

# • 90 to < 95 

# • 85 to < 90 

# • 80 to < 85 

# • 75 to < 80 0 < 75

MetStat: ep1.eta.dbemis.fddats.newuhsstnewutcsriulc.grell

Forward Trajectories 2005-06-22 Max 8hr 03 (monitored): 104.4 at: KATP Start time: 08:00 8Hr frame: 1100-1800Figures 1-3. Transport in the HGB area on June 21-23, 2005. Modeled transport is in blue, observed is in red. The forward trajectories begin at 0800 and proceed for 12 hours.

I- 

### 7June 21, 2005 5-min ozone

0 20 40 60 80 100 120 140 160 180

0:00 4:00 8:00 12:00 16:00 20:00ppb

June 22, 2005 5-min ozone

0 20 40 60 80 100 120 140 160 180

0:00 4:00 8:00 12:00 16:00 20:00ppb

Figures 4-6. Five-m inute ozone data fo r June 21-23, 2005, for all HGB area ozone monitors. June 23, 2005 5-min ozone

0 20 40 60 80 100 120 140 160 180

0:00 4:00 8:00 12:00 16:00 20:00ppb WHOU

DRPK

I- 

### 8To study t he evolution of ozone formation during June 21-23, 2005, CPA data for three different areas in Houston have been examined. The first area is in eastern Harris County, in the vicinity of the industrial emission sources that apparently are the source of the highest plumes of ozone observed in Houston on these days. The second area is in central Houston, between the emission source area and the peak ozone area. Much of the oz one is formed in this area during June 21-23. The third area is where the peak ozone concentr ations were observed, in western Houston.

Figure 7. Map of ozon e monitoring sites in Houst on. The typical veering pattern of wind flow is approximately indicated by the arrow.

Figure 8 shows one-hour ozone concentration observe d at the Deer Park (DRPK) site in eastern Harris County, at the Houston Monroe (HSMA) and Houston Regional Office (HROC) sites in central Houston, and at the Tom Bass (TOMB), West Houston (WHOU) and Katy Park (KATP)

I- 

### 9sites in weste rn Houston. The progression of ozone peaks differs slightly on each day. On June 21, ozone peaks successively at DRPK, HSMA, TOMB, WHOU and KATP. On June 22, ozone peaks again successively at DRPK, HSMA, TOMB, WHOU and KATP, though the DRPK, HSMA, and TOMB peaks occur almost simultaneously. On June 23, ozone peaks successively at DRPK, HROC, HSMA, TOMB, WHOU and KATP, with the HROC, HSMA and TOMB peaks occurring almost simultaneously. In addition, th e WHOU peak is much greater on June 23 than any other peak during the three days, and is much greater than the peaks earlier in the day, in spite of the lower background ozone on this day. The ozone data presented here suggests that it would be useful to examine ozone produc tion at these selected monitoring sites.



### 0 20 40 60 80 100 120 140 160

0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00Ozone, ppb 

### DRPK HROC
HSMA
WHOU
TOMB
KATP June 21 June 22 June 23



### Figure 8. One-hour ozone concentrations obs erved at selected sites, June 21-23, 2005.

CPA can be used to examine the rate at which O3 is being created and destroyed, and to examine the environment in which O3 creation occurs. CPA will allow examination of the sensitivity of ozone formation to NOX or VOC availability along the entire path from emission area to peak ozone area. Figures 9-15 present modeled diurnal O3 production for June 21-23, 2005, for the selected sites in eastern, central, and west ern Harris County. Because these figures present unfamiliar information, a detailed explanation of Fi gure 9 (the Deer Park site) is presented below.

I- 

### 10

### DRPK, Ox production, June 2005 CPA

-10

-5 0 5 10 15 20 25 30036912151821036912151821036912151821ppb/hr OxProd

OxLoss
PO3_net
PO3_VOCsns
PO3_NOxsns

Figure 9. Odd oxygen (Ox) production at the Deer Park monitor, June 21-23, 2005.

OxProd refers to production of odd oxyge n, which is approximately equal to O 3 + NO 2 . In the ozone production cycle, O 3 and NO 2 essentially trade an oxygen atom back and forth, creating and destroying O 3 , NO 2 , and NO, until a radical termination reaction pulls the odd oxygen out of the cycle.

OxLoss refers to destruction of odd oxygen, i.e., th e sum of all radical termination reactions that pull the odd oxygen out of the ozone production cycle.

PO3net refers to the net production of ozone. This quantity is usually positive during daylight hours, but can be negative at night.

PO3_VOCsns refers to the amount of ozone prod uced under VOC-sensitive conditions, i.e., in an environment rich in NOx. If VOC were removed from this environment, ozone formation would decrease.

PO3_NOxsns refers to the amount of ozone prod uced under NOx-sensitive conditions, i.e., in a NOx-poor environment. If NOx were removed fro m this environment, ozone formation would decrease.

The ozone formation environment, VOC-sensitive or NO X -sensitive, is diagnosed in CAMx by the Chemical Process Analysis algorithms by de termining the ratio of hydrogen peroxide production to nitric acid production (Sillman, 1995; CAMx User’s Guide, 2008). If this ratio exceeds 0.35, the environment is usually NO X -sensitive, because the VOC and/or radicals are in such abundance that the HO 2 radicals formed by VOC oxidation are reacting vigorously with each other. If the ratio is less than 0.35, th e environment is usually VOC-sensitive, because NO 2 is in such abundance that it is destroying OH radicals to form nitric acid. In either case, the radicals are being removed from the ozone formation pathway.

I- 

### 11The ozone production sensitivit y determined by PO3_VOCsns and PO3_NOxsns reflect what has occurred during the preceding hour. When examined at a single site or a single hour, these data alone cannot give fully accurate directional gui dance on control strategies for the nonattainment area, because the ozone behavior at a single site may not be related to the peak areawide ozone.

The Deer Park monitoring site encountered industr ial plumes shortly after they had been emitted on June 21-23, and VOC-sensitive ozone production is quite vigoro us in the morning hours, but ozone production shifts to NOX-sensitive by noon. Figures 10-12 show Ox production for three monitors in central Houston (Park Place, Houst on Monroe, and Houston Re gional Office), which encountered the plumes after they had aged for a few hours. Figures 13-15 show Ox production from three western Houston monitors that observed high ozone (Tom Bass, Katy Park, West Houston).

PRKP, Ox production, June 2005 CPA

-10

-5 0 5 10 15 20 25 30036912151821036912151821036912151821ppb/hr OxProd

OxLoss
PO3_net
PO3_VOCsns
PO3_NOxsns

Figure 10. Modeled Ox production at Park Place site, June 21-23, 2005.

On June 21, Figures 9-15 show that ozone form ation is more vigorous along a southerly path, from DRPK to HSMA to TOMB to KATP. The northerly path, through PRKP, HROC, and WHOU, shows lower overall ozone production rat es. Each site shows VOC-sensitive ozone formation in the morning hours, and NOX-sensitive formation in the afternoon. However, the largest ozone production rates are associated w ith VOC-sensitive ozone formation. At PRKP, TOMB, and WHOU, the net ozone production ra te drops when the chemistry switches from VOC-sensitive to NOX-sensitive ozone formation. By the time the ozone reaches KATP, ozone formation is much slower, and is dominated by NOX-sensitive formation.

On June 22, ozone formation progresses in a simila r manner to June 21, except that the PRKP and TOMB monitors both show large VOC-sensitive ozone formation peaks. Ozone formation at the WHOU and KATP monitors is modest, and domin ated by NOX-sensitive ozone formation.

On June 23, ozone formation behavior is somewh at different from June 21-22. The HSMA and TOMB monitors show relatively low ozone form ation rates, with NOX-sensitive formation for

I- 

### 12HSMA, Ox production, June 2005 CPA

-10

-5 0 5 10 15 20 25 30036912151821036912151821036912151821ppb/hr OxProd

OxLoss
PO3_net
PO3_VOCsns
PO3_NOxsns

Figure 11. Modeled Ox production at H ouston Monroe site, June 21-23, 2005.

HROC, Ox production, June 2005 CPA

-10

-5 0 5 10 15 20 25 30036912151821036912151821036912151821ppb/hr OxProd

OxLoss
PO3_net
PO3_VOCsns
PO3_NOxsns



### Figure 12. Modeled Ox produc tion at Houston Regional O ffice site, June 21-23, 2005.

I- 

### 13TOMB, Ox production, June 2005 CPA

-10

-5 0 5 10 15 20 25 30036912151821036912151821036912151821ppb/hr OxProd

OxLoss
PO3_net
PO3_VOCsns
PO3_NOxsns

Figure 13. Modeled Ox production at Tom Bass site, June 21-23, 2005.


### WHOU, Ox production, June 2005 CPA

-10

-5 0 5 10 15 20 25 30036912151821036912151821036912151821ppb/hr OxProd

OxLoss
PO3_net
PO3_VOCsns
PO3_NOxsns

Figure 14. Modeled Ox production at West Houston site, June 21-23, 2005.

I- 

### 14KATP, Ox production, June 2005 CPA

-10

-5 0 5 10 15 20 25 30036912151821036912151821036912151821ppb/hr OxProd

OxLoss
PO3_net
PO3_VOCsns
PO3_NOxsns

Figure 15. Modeled Ox production at Katy Park site, June 21-23, 2005.

much of the day. The WHOU and KATP sites show much higher ozone formation rates than the two previous days, and at the HROC site, ozone formation is dominated by VOC-sensitive formation rather than NOX-sensitive.

One of the causes for these ozone formation diffe rences can be seen by examining Figures 1-3, which show how emissions from different parts of the HGB area are transported during 8:00am8:00pm on each day, respectively. On June 21-22, the industrial plumes are carried southwesterly, to the south of the urban core . Consequently, high ozone and high VOC-sensitive ozone production are seen along the trajectory paths that originated in the industrial areas. On June 23, however, the industrial emissions are carried due west, through the urban core, passing over the HROC, WHOU, and KATP sites. In both cases, the sites that encounter the industrial plumes have the highest ozone formation rates, and those that do not have lower ozone formation rates. The modeling on June 23 suggests that wh en the industrial plume mixes with the urban core plume, vigorous VOC-sensitive ozone formation can occur. Figures 4-6 show the consequences of higher ozone formation rat es—ozone concentrations at WHOU and KATP are much higher on June 23 than any other ob servations during the other two days.

Figures 16-20 show the modeled formaldehyde fo rmation rates at sites with enhanced ozone formation. Formaldehyde formation is higher at sites closer to the industrial areas, and decreases as the emissions age. For sites unaffected by industrial plumes, HCHO formation is much lower, and is dominated by biogenic species. For sit es affected by industrial plumes, even on the western side of Houston, HCHO formation is high, and anthropogenic HRVOC (ETH, OLE, and IOLE) are the major source of HCHO.

Figures 21-25 compare the observed formaldehyde concentrations at three sites to the model output. At Moody Tower, the simulated formaldehyde matches the observed diurnal variations and the absolute magnitude most of the time, but misses the highest peaks, which is similar to the model’s ozone performance. At LYNF and H03H , the simulated formalde hyde is consistently underestimated. At LYNF, the diurnal variations match the observations relatively well, but at

I- 

### 15H03H, the modeled concentrations peak at rush hour, whereas the observed concentrations peak in the afternoon. The m o deled formaldehyde appears to be associated with primary mobile source emissions, but the observed formaldehyde is consistent with secondary formation. Note also that the formaldehyde performance is cons iderably better at Moody Tower, an elevated location, than at the two ground-based sites, sugges ting that vertical mixing may be playing a role in these differences.

I- 

### 16

## DRPK HCHO production from VOCs

0 0.5

1 1.5

2 2.5036912151821036912151821036912151821ppb/hr HCHOp_ispd HCHOp_isop HCHOp_terp HCHOp_iole HCHOp_ole HCHOp_eth

Figure 16. Formaldehyde production (HCHOp) from HRVOC and biogenic CB05 species at eastern site DRPK for June 21-23, 2005.


## HSMA HCHO production from VOCs

0 0.5

1 1.5

2 2.5036912151821036912151821036912151821ppb/hr HCHOp_ispd HCHOp_isop HCHOp_terp HCHOp_iole HCHOp_ole HCHOp_eth

Figure 17. Formaldehyde production (HCHOp) from HRVOC and biogenic CB05 species at central site HSMA for June 21-23, 2005.

I- 

### 17

## HROC HCHO production from VOCs

0 0.5

1 1.5

2 2.5036912151821036912151821036912151821ppb/hr HCHOp_ispd HCHOp_isop HCHOp_terp HCHOp_iole HCHOp_ole HCHOp_eth

Figure 18. Formaldehyde production (HCHOp) from HRVOC and biogenic CB05 species at central site HROC for June 21-23, 2005.


## TOMB HCHO production from VOCs

0 0.5

1 1.5

2 2.5036912151821036912151821036912151821ppb/hr HCHOp_ispd HCHOp_isop HCHOp_terp HCHOp_iole HCHOp_ole HCHOp_eth

Figure 19. Formaldehyde production (HCHOp) from HRVOC and biogenic CB05 species at western site TOMB for June 21-23, 2005.

I- 

### 18

## WHOU HCHO production from VOCs

0 0.5

1 1.5

2 2.5036912151821036912151821036912151821ppb/hr HCHOp_ispd HCHOp_isop HCHOp_terp HCHOp_iole HCHOp_ole HCHOp_eth

Figure 20. Formaldehyde production (HCHOp) from HRVOC and biogenic CB05 species at western site WHOU for June 21-23, 2005.

Figures 21-22. Modeled and observed formaldehy de concentrations at Moody Tower (MDTW) during the August-October 2006 episode days.

I- 

### 19Figures 23-25. Modeled and observed formaldehy de data at LYNF and H03H for the AugustOctober 2006 episode days. Data for the August 15 - September 14 period at H03H are not shown, because only one complete day of data is available for that period at H03H.

I- 

### 20Another method of examining the characteristics of the air m asses associated with high ozone formation rates is to look at the rate of OH oxi dation rates, also known at OH reactivity. Figures 26-28 show the OH reactivity for eastern, central and western sites in the HGB area. At each site, the OH reactivity is highest when ozone production is the highest, and when the site is encountering the industrial plumes. The site near est the industrial areas, DRPK, shows the largest proportion of OH reactivity from alkenes of the three sites.

DRPK June 21-23, 2005

0 1 2 3 4 5 6 7 8 9 10

0 6 12 18 0 6 12 18 0 6 12 18OH reactivity OVOCs

aromatics

alkenes

alkanes

NOx

CO

Figure 26. OH reactivity (second -1 ) modeled at eastern site DR PK on June 21-23, 2005.

TOMB June 21-23, 2005

0 1 2 3 4 5 6 7 8 9 10

0 6 12 18 0 6 12 18 0 6 12 18OH reactivity OVOCs

aromatics

alkenes

alkanes

NOx

CO

Figure 27. OH reactivity (second -1 ) modeled at western site TOMB on June 21-23, 2005.

I- 

### 21WHOU June 21-23, 2005

0 2 4 6 8 10 12 14 16

0 6 12 18 0 6 12 18 0 6 12 18OH reactivity OVOCs

aromatics

alkenes

alkanes

NOx

CO

Figure 28. OH reactivity (second -1 ) modeled at western site WH OU on June 21-23, 2005.

For the case study of June 21-23, 2005, the CPA tool is useful in examining why ozone formation on these three days behaved differently. Based upon this case study, and examination of CPA data for other days (not shown here), several conclusions can be reached about the model behavior.

 Ozone formation occurs in both VOC-s ensitive and NOX-sensitive conditions each episode day at many sites within the HGB area, with VOC-sensitive conditions dominating in the morning hours, and NOX-sensitive conditions dominating in the afternoon.

 Vigorous ozone formation in VOC-sensitive conditions is closely associated with industrial plumes, both in the immediate vici nity of the emission sources, and downwind in the areas that experience the highest ozone. Relatively high formaldehyde formation rates from anthropogenic alkenes and high OH reactivity from alkenes are also associated with industrial plumes.

 In general, VOC-sensitive ozone formati on appears more vigorous than NOX-sensitive formation. When ozone formation shif ts from VOC-sensitive to NOX-sensitive, the formation rate often drops considerably.

 On June 23, the industrial plumes are adv ected through the urban core; the mixing of urban core emissions and industrial emissi ons appear to increase VOC-sensitive ozone formation in western Houston.

I- 

### 22

## Comparison of CAMx grid modeling to TexAQS II box modeling

During TexAQS II, researchers monitored many species not routinely measured in Houston, and these data are useful in model evaluation, including OH, HO 2 , HONO, HCHO, HNO 3 and other species (Mao et al., 2009; Chen et al., 2009). The researchers used a constrained photochemical steady-state box model to calculate the radical budgets at the Moody Tower site for each hour, based upon measured concentrations for August 17 – September 28, 2006. Mao et al. (2009) investigated the radical production rates fo r the Carbon Bond 2005 (CB05) and Regional Atmospheric Chemical Modeling (RACM) chemic al mechanisms; Chen et al. (2009) built upon their work, and added modeling with four more chemical mechanisms: the Master Chemical Mechanism (MCM), State Air Pollution Research Center (SAPRC) mechanisms from 1999 and 2007, and the Langley Research Center (LaRC) mechanism.

Presented below is a comparison of the CAMx CPA radical budget to the radical budget calculated by Mao et al. and Chen et al. These comparisons should be considered with several important qualifications. First, the Mao et al. and Chen et al. results are based upon point measurements, which have been fed into a box model to constrain the chemistry to the observations, whereas the CPA results have been obtained by extracting the values from Layer 2 for the 2km x 2km grid cell containing the Mood y Tower site. The results from these two types of modeling probably have unexa mined commensurability problems. Second, the results from the journal articles have been averaged for th e entire sampling period, August 17 – September 28, which includes high and low ozone days, whereas the CPA results are from specific high ozone days only. Third, these results are based upo n a photochemical steady-state box model, and constrained by the observed concentrations at th e Moody Tower. The chemical environment at the Moody Tower, however, may not always be in a photochemical steady-state, which could bias the results of the model.

Figure 29 shows the CPA radical budget for August 16 , 2006. Figure 30 shows the same radical reactions for the CB05 constrained steady-state bo x modeling results, averaged over the TexAQS II measurement period (Mao et al., 2009).



## Radical Production Radical production rates (PHO X ) are similar in magnitude and timing for both the CPA and Mao et al. results, with CPA rates slightly higher. The Mao box modeling indicates that HONO + h  , O1D + H 2 O, and HCHO + h  have similar rates, whereas the CPA results indicate that O1D +
H 2 O rates are much higher than the other two reactions. The HCHO + h  rate is about the same in both models, but the HONO + h  rate is much lower in CPA. Therefore, underestimation of HONO photolysis is probably due to underestimat ed HONO concentrations, not errors in the predicted solar radiation. Figure 31 shows that at the Moody Tower, where HONO was measured during TexAQS II, the CAMx model consisten tly underestimated HONO concentrations. Modeled daytime OH production from photolysis of HONO does not exceed 0.2 ppb, well below the observed concentrations. Since modele d concentrations of HONO are consistently underestimated, it is not surprising that little OH radical is produced by this pathway. The standard model chemistry removes HONO rapidly by photolysis, and does not produce it in large enough quantities to explain the observed concentr ations. Other researchers have found similar results (Sarwar et al., 2008; Kleffman et al., 2003, 2005; Acker et al., 2006; Zhou et al., 2003). Ziemba et al. (2009) have shown that daytime HONO concentrations at the Moody Tower during TexAQS II exceeded the expected concentrations based upon the current chemical mechanisms, and suggested that a HONO formation path h as been left out of the existing chemical mechanisms. They hypothesize a new path that involves reactions of nitric acid on the surfaces

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### 23of aerosols, and photoly s is, to generate HONO. The proposed mechanism for HONO production is still in the early stages of investigation; it ca nnot be used to quantitatively estimate the missing source of HONO.

Figure 32 shows the predicted OH and HO2 con centrations from seven different chemical mechanisms, including CB05; all of the mechanisms underestimate midday radical concentrations. While adding the missing HONO formation path(s) may help alleviate the underestimations in the mechanisms, note that ther e are many other hypotheses for the shortfall in radical production. These other hypotheses for the shortfall in radical formation include isoprene production of hydroxyl radical (OH) (Lelieveld et al., 2008; North and Ghosh, 2009); formation and decomposition of electronically excited nitrogen dioxide (NO 2 * ) (Li et al., 2008); nitryl chloride (ClNO 2 ) chemistry (Osthoff et al., 2008; Simon et al., 2008); alternative aromatic chemistry (Faraji et al., 2008; Hu et al., 2007); and molecular chlorine reactions (Chang et al., 2002; Tanaka et al., 2003; Chang and Allen, 200 6; Sarwar and Bhave, 2007). None of these hypotheses have been adequately tested yet, and many of the proposed mechanisms for correcting the model chemistry are still speculative. TCEQ has refrained from experimenting with any of these hypotheses in its current round of modeling.



## Radical termination
The relative rates at which radicals are terminated have been used to indicate whether the ozone formation is sensitive to NO X or to VOC (or radical) availability (Sillman, 1995). The radical termination rate OH+NO 2 is indicative of nitric acid fo rmation and VOC-sensitive ozone formation; the terminations HO 2 +HO 2 and HO 2 +RO 2 are indicative of peroxide formation and NO

t

ive

AMx m odeling.

X -sensitive ozone formation. These reactions are depicted on Figures 29 and 30. Both models indicate that OH+NO 2 termination is higher between sunrise and 1300, when OH+NO 2 rates drop and HO 2 +HO 2 rates increase. The trans ition between VOC-sensitive and NO X sensitive ozone formation occurs when the ratio (HO 2 +HO 2 )/(OH+NO 2 ) is greater than abou 0.35 (Sillman, 1995). The transition appears to occur for both the model and monitored observations at about noon. For most monitoring sites in Houston, the shift from VOC-sensit to NO X -sensitive ozone formation occurs every day. The Moody Tower box modeling indicates that the apparent shift from VOC-sensitive to NO X -sensitive conditions is not an artifact of the C

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### 24-4 -2 0 2 4 6

0 61 2 1 8107molecules / cm3/ sec PHOx

O1D+H2O

HONO+hv

O3+alkenes

HCHO+hv

HO2+HO2

LHOx

HO2+RO2

OH+NO2

Figures 29 and 30. Modele d radical budget from CAMx CPA results for August 16, 2006 (top). Median CB05 results at Moody Tower, August 17 – September 28, 2006 (bottom). Units on this graph are molecules per cm 3 per second. Bottom figure is excerpted from Mao et al., 2009.

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### 25Figure 31. Observed nitrous acid (HONO) concentr ations, compared to modeled concentrations at the Moody Tower site during August 15 - September 14, 2006.

Figure 32. Diurnal Variation in OH and HO 2 Radical Concentrations on September 2, 2006. Concentrations observed at Moody Tower during TexAQS II, and modeled by CB05, RACM, LaRC, SAPRC-99, SAPRC-07, and MCMv3.1 chem ical mechanisms run in a constrained box model. Figure reproduced from Chen et al., 2009.

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### 26-6 -4 -2 0 2 4 6 8

0 6 12 18 0 6 12 18 0 6 12 18 0 6 12 18 0 6 12 18 0 6 12 18 0 6 12 18 0 6 12 18 0 6 12 18107molecules / cm3/ sec PHOx

O1D+H2O

HONO+hv

O3+alkenes

HCHO+hv

HO2+HO2

LHOx

HO2+RO2

OH+NO2 A ug 16 A ug 17 A ug 18 A ug 31 Sep 1 Sep 6 Sep 7 Sep 12 Sep 14

Figure 33. Modeled radical budget for Moody To wer for high ozone days during TexAQS II.

Figure 34. Comparisons of Radical Budgets Estimated from Measurements of OH, HO 2 , and Other Species During Field Campaigns in Houston, New York City, and Mexico City.

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### 27Figure 33 sho w s the diurnal variations in radical budget at the Moody Tower site for nine days modeled by TCEQ. The ozone production and loss rates vary by about a factor of two during these days. Most of the days show a tran sition from VOC-sensitive to NOX-sensitive ozone formation at about noon, based upon the relatively large OH+NO2 termination rates early in the day, and the relatively large HO2+HO2 and HO2+RO2 rates in the afternoon. Note that Moody Tower did not encounter the industrial plumes every day, so that the production rates and sensitivities differ according to the origin of the plumes. The absolute magnitudes compare well to those calculated by Mao et al. in Figures 30 and 34.

Figure 34 shows the radical behavior in Hous ton in 2000 (TexAQS 2000) and 2006 (TRAMP 2006). It isn’t immediately apparent from this analysis whether the differences between the two years is due to the differing locations of the mon itoring sites in 2000 and 2006, or due to areawide emission or meteorological differences between th e two years. In 2000, radical measurements were made at La Porte, which is a site very near the heavily-industrialized areas of the Houston Ship Channel and Galveston Bay. Figure 34 shows that VOC-sensitive ozone formation conditions prevailed at La Porte for the entire day, on average, in 2000. The VOC-sensitive conditions are characterized by the dominance of the OH+NO 2 termination reaction in the upper left panel of Figure 34. By contrast, the lower right panel shows the radical termination behavior during TexAQS II in 2006, at the Moody Tower. The Moody Tower site is located on the University of Houston campus, in close proximity to major highways, and to the central business district. It is, however, close enough to the Ship Channel to be affected by industrial plumes fairly often. Its radical behavior is somewhat di fferent from that observed at La Porte in 2000, in that the ozone formation conditions begin as VOC-sensitive and switch to NO X -sensitive about noon. The CPA results presented in the June 21-23, 2005 case study above, and other CAMx simulations for 2005-2006 show that VOC-sensitive ozone formation are closely associated with areas that encounter the industrial plumes, which w ould occur more frequently at La Porte than at Moody Tower. While it is possible that this difference between the M oody Tower 2006 data and the La Porte 2000 data represents a change in the chemical environment due to emission reductions since 2000, it seems as likely that sampling within a different chemical environment in Houston is responsible for the difference.


## Radical Propagation OH chain length is a measure of how many times OH is recreated before it is consumed by a termination reaction with NO 2 . It is an indication of the strength of radical propagation. Figure 35 shows that OH chain length is particularly l ong at the Moody Tower site during TexAQS II. Long OH chain length implies that ozone production can occur very rapidly and effectively. However, long chain length is also associated with low-pollution environments. Figure 36 shows three days during June 2006 at the Bayland Park monitor. On the first two days, OH chain length peaks between 10 and 20, as ozone concentrations reach greater than 120 ppb. However, on June 10, the OH chain length peaks at over 45, whereas th e ozone concentrations peak at only about 80 ppb. This pattern is common in Houston m odeling; the very highest OH chain length is associated with air that is less polluted. The high median OH chain length measured by Mao et al. may be a sign that the air in Houston simp ly was less polluted during TexAQS II than during the TexAQS 2000 field study in Houston. Concentration data for ozone, NOx, formaldehyde, ethene, and other species confirm that the air was less polluted in Houston in 2006 than in 2000 (Mao et al., 2009; TCEQ Conceptual Model, 2009; de Gouw et al., 2009).

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### 28Figure 35. OH Chain Length During TexAQS II, as Measured at the Moody Tower (TRAMP2006). The long chain length implies that OH propagation was particularly efficient at Moody Tower during TexAQS II, compared to Tex AQS 2000 at La Porte, or New York City, or Mexico City.

BAYP, O3 production, June 2006 CPA

-10

0 10 20 30 40 50

0 6 12:00 June 8 18 0 6 12:00 June 9 18 0 6 12:00 June 10 18ppb/hr PO3_VOCsns PO3_NOxsns

0 20 40 60 80 100 120 140 Mao CL
modeled O3
observed O3

Figure 36. Ozone Production and Concentrations atthe Bayland Park Monitor, June 8-10, 2006. The “Mao CL” variable is the OH chain length, as calculated in Mao et al., 2009. The axis on the right is for OH chain length (unitless), and ozone production rates (ppb/hour); the axis on the left is for modeled and observed oz one concentrations (ppb).

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### 29BAYP, Ox production, June 21-23, 2005

-10

-5 0 5 10 15 20 25 30036912151821036912151821036912151821ppb/hr

0 5 10 15 20 25 30 35 40 OxProd
PO3_net
PO3_VOCsns
PO3_NOxsns

JNO2

F igure 37. Ozone Production Rates, and NO 2 Photolysis Rates, for BAYP, June 21-23, 2005.

hows

iazar

The ver, i n dicates that an accurate epiction of clouds is importa nt to the model performance.


## indings from chemical process analysis

e ozon e

d urban ive conditions appears to be more

related to the shortfall in pe ak m odeled ei ght-hour ozone concentrations. Due to the 

## Photolysis Process analysis shows the importance of accurate ly modeling photolysis rates. Figure 37 s how ozone production rates are directly correlated with NO 2 photolysis. In this case, NO 2 photolysis probably is correlated with all of the ot her relevant photolysis rates as well, so that the modeled dependence reflects sensitivity to all photolysis reactions, not just JNO 2 . This finding is consistent with the findings of Koo et al., 200 8, who used higher-order decoupled direct method (HDDM) analysis to test the sensitivity of the mode led ozone to all photolysis rates. Pour-B et al. (2006) found in their modeling of Hous ton that modeled ozone performance could be substantially improved by assimilating satellite-deriv ed cloud cover data into the modeling. sensitivity of the model to photolysis, and hence, to cloud co d



## F

 Both VOC-sensitive ozone formation and NO X -sensitive ozone formation occur in Houston every day, with VOC-sensitivity tending to occur in the morning and NO X sensitivity in the afternoon. The radical data collected at the Moody Tower during TexAQS II and the constrained steady-state box modeling indicate that th f ormation behavior modeled in CAMx is actually occurring in Houston.

 VOC-sensitive conditions occur more often and more strongly in the industrial an core plumes. Ozone production in VOC- sensit
p roductive than in NO X -sensitive conditions.

 The modeled shortfall in OH radical producti on is common to more than one chemical mechanism, not just Carbon Bond 05. The shortfall in radical production is probably

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### 30 Radical production from photol y sis of HONO very low in the CAMx modeling, but not in the box modeling. The current chemical mechanisms do not have a path for producing the observed quantities of HONO during the daylight hours. The lack of daytime HONO could be one of the missing radical sources needed to improve the OH radical budget, but further studies in Houston are needed to describe the missing HONO formation pathway.

 OH chain length was much longer in 2006 than in 2000, possibly because the air was cleaner in 2006.

 Decreases in ozone production rates and other reaction rates correlate with decreases in NO 2 photolysis, implying that most of the oz one formation chemistry is highly sensitive to photolysis, and hence, highly sensitive to cloud cover errors.

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### 31

## Intensive Model Performance Evaluation
To examine the model performance in greater depth, case study analyses have been done for several high ozone days: June 2, 2005; June 23, 2005; June 5, 2006; August 16, 2006; and September 14, 2006. On these days, the m odel appears to have good meteorological performance, and therefore difficulties with chem istry or emissions can perhaps be diagnosed more easily on these days than on days that have meteorological difficulties as well.


## June 2, 2005 Episode
On Thursday, June 2, 2005, high concentrations of ozone were observed throughout the Houston area, but were particularly high in northeast Harris and Montgomery Counties. One-hour maximum ozone concentrations of 148 ppb were observed at Crosby Library (CRBL) at 1500; six additional sites exceeded 125 ppb on this day. An areawide eight-hour maximum ozone concentration of 125.8 ppb was observed at CRBL from 1000-1700; 20 other sites exceeded the 85-ppb eight-hour ozone standard on this day. A flow reversal occurred on this day, with stagnant conditions prevailing early, and then wi nds picking up from the south and then shifting to southeast.

According to the TCEQ Conceptual Model analyses, daily flow regi me in Houston is one of the factors that determines whether an ozone exceedance occurs, where the highest ozone concentrations are observed, and ho w high the concentrations reach.

Figure 1 shows the flow occurring in Houston on the afternoon and evening of June 1, 2005, before the large exceedance on the next day. Tr ajectories indicate that winds from the previous day are from the north, carrying the day’s emissions to the south of Harris County. The next morning is almost completely stagnant. Figure 2 shows that the winds shift direction and begin blowing onshore. Due to the wind shift, it is likely that pollutants from the previous day are carried back into Houston. Winds continue fro m the S for much of the afternoon, carrying the urban core and industrial plumes to the north, an d creating very high ozone in NE Harris County and in Montgomery County.

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### 32Figure 1. Trajectories Calculated from MM5 Output and Observed Surface Wind Monitors. MM5 output is shown in blue; monitored observatio ns are shown in red. Winds on the afternoon of June 1 are northerly, but become stagnant toward the end of the trajectory period, as indicated by endpoints that are very close together.

Figure 2. Trajectories Calculated from MM5 Output and Observed Surface Wind Monitors. MM5 output is shown in blue; monitored observatio ns are shown in red. The emissions from the previous day are carried north, back over Hous ton, creating a large area of high ozone both upwind and downwind of Houston.

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### 33Figure 3. Scatter Plot for Areawide Averag e Wind Speeds in the Houston-Galveston Area for June 2, 2005.

Figure 3 displays simulated and observed areawid e average wind speeds for monitoring sites in the Houston area. Banta et al. (2007) and Hardesty et al. (2007) found that one of the most important factors in determining the intensity of ozone in Houston is wind speed. Errors in modeling wind speed can lead to errors in prediction of concentrations of ozone and its precursors; simulated winds that are too strong can simply advect precursors out of the area before they have had a chance to make ozone, and can prevent ozone from accumulating. Figure 5-53 shows that simulated wind speeds for June 2, 2005 were very close to the observed; the slope indicates an average under-prediction of only five percent.

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### 34Figure 4. Daily Maximum Eight-Hour Ozone by Grid Cell for the HGB Area. Monitoring sites are designated by circles. Ozone concentra tions are designated for both model output and monitored observations by the color scale at right.

Figure 4 shows that the model successfully places ozone in the correct locations, but that at some sites, including the peak sites, mode led concentrations are too low.

Note that observed and modeled peak ozone o ccurs on the northeast edge of the monitoring network, and that the maximum modeled ozone occurs between the CRBL site and the CNR2 site, in a region of sparse monitoring. B ackground ozone, observed at the upwind sites in Brazoria County (DNCG and LKJK) appear to be too low, suggesting that one possible cause of the underestimates of high ozone in northeast Harris County is underestimated background ozone.

Figure 5 shows that when additional HRVOC a nd formaldehyde are added to modeled flare emissions, eight-hour ozone concentrations are enha nced, so that the degree of underestimation is much smaller. Areas in which higher concentrati ons appear, and the intensity of the enhanced ozone, suggest that flare emissions may play a role on this particular day. Note that much of the increase occurs in sparsely monitored areas, so that model performance assessed at the monitoring sites only would not detect the change. High ozone observed at sites nearest the industrial source region is still underestimated.

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### 35Figure 5. Daily Maximum Eight-Hour Ozone by Grid Cell for the Sensitivity Run that Includes Additional HRVOC and Formaldehyde Emissions fro m Flares. Monitoring sites are designated by circles. Ozone concentrations are designate d for both model output and observations by the color scale at right.

To further examine model performance for ozone and its precursors, modeled and observed concentrations were examined along two transects. Since the main ozone plume is oriented approximately north-south on the east side of the urban core, sites were chosen starting upwind of the urban core and Ship Channel, moving northwar d toward the area of highest ozone in northeast Harris and in Montgomery Counties.

For the western transect, which sampled upwind-to-downwind of the urban core and the western Ship Channel, ozone, NO 2 , ETH and OLE performance was ge nerally very good. For the upwind-to-downwind transect that intersected th e eastern Ship Channel, peak ozone in the afternoon was generally underest imated substantially, though in the morning hours, observed ozone increases of 30-50 ppb were well simulated. NO 2 performance was relatively good, with no major over- or under-estimation at any site . All sites except WALV showed good ETH performance; at WALV, ETH was overestimated by a factor of two at about 0600. OLE was underestimated at LYNF by a factor of four in the morning hours, and underestimated by a factor of two at WALV at about the same time. The addition of HRVOC flares with HCHO did not change either the OLE or ozone performance at th ese two sites. These results suggest that there may be chemical and/or meteorological features occurring in the eastern Ship Channel that the

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### 36model has no t si mulated well on this day. Figur es 4 and 5 show that on a larger scale, the model simulated the extent, location, and relati ve intensity of ozone well on this day.

Given the relatively good performance on this day, it seems useful to evaluate the model’s dynamic response to emission changes, since the l ack of major flaws in the modeling for this day suggest it can be effectively used for evaluating emission changes.

Figure 6 shows the model’s response on this day to the typical emission reductions that occur from Wednesday to Sunday, i.e., substantial d ecrease in mobile source NOx. The eight-hour maximum daily ozone shows strong NOx-sensitivity for the entire domain on this day, with the largest change occurring near the CNR2 site where some of the highest ozone was observed. Maximum eight-hour ozone decrease at CNR2 is about 20 ppb. There is a large area with ozone decreases of greater than five ppb. Note that much of this area is outside the current ozone monitoring network, suggesting that the response to emission changes may be greater outside the network than within it. This finding may have implications for control strategy evaluation.

Figure 6. Change in Ozone when Emissions are Decreased on the Weekend (Sunday minus Wednesday).

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### 37Figure 7. The Change in Eight-Hour Ozone Concentration by Switching from the Base Case reg10 EI to an HRVOC+HCHO Adjustme nt to the Flare Emissions.

Figure 7 shows the change in eight-hour ozone on June 2, 2005 when HRVOC flares with HCHO are added. The maximum change in eight-hour oz one is over 10 ppb, and it occurs in one of the areas where ozone is being underestimated somewhat in the base case. The implication is that a change from a general emission reconciliation ( PSCF VOC emission reconciliation) to a flaretargeted reconciliation (HRVOC+HCHO Adjustme nt), with an addition of the oxidized combustion product HCHO, may reflect the emissions more accurately.

Figure 8 shows how ozone concentrations change when year 2000 emissions are substituted for 2006 baseline emissions on June 2, 2005. This analysis demonstrates the difficulty of showing how the model responds to historical emission cha nges. Figure 9 shows the monitoring network, as it existed in 2000 and in 2006. As Figure 8 show s, many of the largest changes in ozone occur outside the monitoring network as it existed in 2000. Therefore, dynamic model evaluations may not always be able to assess the largest ozone changes in the domain, due to limited monitor coverage. Note that TCEQ added many mon itors to the NE Harris County area, at the recommendation of TexAQS 2000 scientists, who found that unmonitored high ozone often occurred in this area (Daum et al., 2003; Ryerson et al., 2003).

The blue streak of reduced ozone stretching from Fort Bend County into Harris County is the Parish power plant plume; TexAQS II data, a nd continuous emissions monitoring data at the stack, have shown that the NOx emissions fro m Parish have dropped 80% since 2000.

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### 38Figure 8: Change in Eight-Hour Ozone when Year 2000 Emissions are Substituted for 2006 Emissions.

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### 39Figure 9. M onitoring Network for Ozone in th e HGB Nonattainment Area. Monitors labeled in green were operating in 2000; monitors labeled in red were operating in 2005-2006. The blue box indicates the area where monitors have been a dded to fill in a gap in monitoring coverage since 2000.

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### 40June 2 Figure 10. Eight-ho ur ozone concentrations (s imulated and observed) at the WALV monitoring site. The model run using the standard rec onciled inventory is in green, the HRVOC+HCHO flare run is in blue, and the observations are in red.

Figure 10 shows the eight-hour ozone perform ance for the base case modeling and the HRVOC+HCHO sensitivity. Note that the HRVOC_HCHO flare run duplicates the results of the standard reconciled inventory, except that it pr oduces higher peaks, without degrading the model performance at other times. Since the high oz one peaks are underestimated at times, the HRVOC+HCHO flare sensitivity has better model performance on some days.



## June 5, 2006 On Monday, June 5, 2006, eight-hour ozone ex ceedances were observed at 16 monitoring stations throughout the HGB area, with the highest eight-hour concentration of 109 ppb observed at the H04H site in the Channelview area. The sa me monitoring site observed a one-hour ozone concentration of 130 ppb. Exceedances of the ei ght-hour standard occurred in the northern halfof Harris County. Winds were from the west-southwest in the morning, switching to eastsoutheast, resulting in a flow reversal. Fi gures 11-12 show the observed plume streamlines (TCEQ Significant Events website, 2006). Figure 13 shows forward trajectories calculated from the surface wind observations and from model output for June 5. Note that two different methods were used for the streamlines and the trajectories; the two techniques are not expected to yield identical results. Figure 13 shows that the trajectories derived from model results (in blue) and observations (in red) agree relatively well for this date, indicating that any differences in chemical concentrations between the model and the observati ons are more likely to caused by factors other than transport errors.

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### 41Figures 11 an d 12. Plum e streamlines for June 5, 2006.

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### 42Figure 13. Forward trajectories calculate d from surface wind observations and model output.

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### 43Figure 14. Comp arison of modeled and observe d average wind speed data for the HGB area, June 5, 2006.

In addition to the trajectory data, the model’s ab ility to simulate the wind flow can be examined by comparing to the wind speed observations for the HGB area. Figure 14 shows that for the June 5, 2006 simulation, the wind speeds during the day were simulated quite accurately (in red); the wind speeds during the evening hours were biased slightly high, but 100% of the modeled evening averages fell within  2 meters per second of the observed averages.

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### 44

## June 5, 2006 5-min Ozone

0 20 40 60 80 100 120 140 160 180

0:00 4:00 8:00 12:00 16:00 20:00ppb 

### WALV HNW A

Figure 15. Fi ve-m inute observations of ozone at every operational monitoring site in the HGB area for June 5, 2006.

Figure 15 shows five-minute ozone average concentrations observed at every HGB area monitoring site for June 5, 2006. From this figure, the background ozone concentrations in the HGB area can be deduced from the lower boundary of ozone concentrations, as well as the approximate degree of maximum local contributi on to the observed ozone. For this date, the background ozone is approximately 60 ppb, with an estimated local contribution of an additional 60-70 ppb. Note that the lowest value is usua lly not used as the background, because it often represents an urban core monitoring site that has low ozone due to titration by fresh mobile source NO emissions. The morning peak ozone was observed at the Wallisville Road site, in far eastern Harris County, and the afternoon peak was observed at the Northwest Harris County site, but many sites observed high ozone in midday.

Figures 16 and 17 show maps of the modeled and observed peak ozone concentrations for onehour and eight-hour averages. At both temporal scales, the extent, location, and relative magnitude of ozone concentrations is simulate d accurately, but the peak concentrations are underestimated. The background concentrations on this day for the sites on the periphery of the urban area are simulated very well.

Figures 18 and 19 show the diurnal variations of wind speed and direction, and planetary boundary layer depth at two different sites, Hunt sville and Jefferson County Airport. Both sites show good agreement for PBL evolution, and the wind speeds and directions throughout the PBL, indicating that the vertical mixing in the m odel is simulated accurate ly on June 5, 2006.

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### 45Figures 20 and 21 sh ow th e ozone and NOx concen trations observed and modeled for June 5, 2006 for the Aldine monitoring site, north of th e urban core. This site observed high ozone during the midday hours. Ozone was simulated very well on this date, even for the peak concentrations. NOx was also simulated well. Figure 22 shows the modeled and monitored OLE concentrations for the Wallisville Road site. OLE represents the alkenes with greater than 2 carbon atoms, i.e., larger than ethene. The ob servations show that OLE concentrations were elevated at the Wallisville Road site on this day. The model underestimates the peak OLE concentrations in the early morning hours, but simulates the late morning and afternoon concentrations relatively well. As Figure 15 shows, the Wallisville Road site observed high ozone concentrations earlier than any other site in the HGB area on this day. The high OLE and high ozone concentrations suggest that industria l emissions upwind of WALV may have played a role in the ozone formation on this date. At the time of highest observed OLE concentrations, winds were from the west-southwest, suggesting that the Baytown or eastern Ship Channel area may have been the source of the high OLE c oncentrations. None of the other auto-GC monitoring sites observed high concentrations of ETH or OLE on this date.

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### 46Figures 16 an d 17. Daily p eak 1-hour and 8-hour ozone concentrations for June 5, 2006.

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### 47Figures 18 and 19. Vertical profiles of wind sp eed and direction measured at two sites in southeastern Texas. Modeled are in blue, observed are in red. Measurements were made by radar profiler.

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### 48Figures 20, 21, and 22. Observed and modeled ozone concentrations (top) at Aldine, NOx concentrations (middle) at Aldine, and alkenes (bottom) at Wallisville Road on June 5, 2006 (enclosed in red box).

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### 49Figure 23. M odel response to the degree of m ob ile source NOx reduction typical for a weekend (Sunday eight-hour peak ozone minus Wednesday eight-hour peak ozone).

Figure 23 shows that the highest ozone on June 5, 2006 is sensitive to NOx reductions. A small area that had high ozone on this day showed a small increase in eight-hour ozone (1 ppb - 2.5 ppb) in response to NOx decreases, suggesting that the ozone in this area may be more sensitive to VOC changes than to NOx changes. Figure 24 shows how the HGB area responded to a large increase in flare VOC emissions for the June 5, 2 006 meteorology. The ozone in the area of NOx disbenefit as noted in Figure c13 shows the largest increase in response to added VOC in Figure 24. The increase in eight-hour ozone of 13.6 ppb in response to the HRVOC+FORM substitution is one of the largest for any day during the six episodes modeled. Based upon these dynamic model evaluations, it appears that the high ozone observed on June 5, 2006 is likely sensitive to both VOC and NOx emissions, and to both mobile and industrial emissions. Since the meteorological performance on this day is exce llent, these conclusions are likely to be robust.

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### 50Figure 24. Model response to the large increas e in HRVOC and FORM flare e m issions in the industrial areas of the HGB area.

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### 51

## August 16, 2006

On Wednesday, August 16, 2006, eight-hour ozon e exceedances were observed at six monitoring sites in the HGB area. The peak eight-hour ozone concentration of 95.5 ppb for the HGB area was observed at the Mercer Arboretum site in north-northwestern Harris County. Figure 25 and 26 show the plume streamlines for this day. Note that the flow reversal pattern observed on this day is fairly similar to the pattern observed for June 5, 2006. The winds in the morning blew from the west-southwest, then switched to sout heast. Figures 27 and 28 contrast the forward trajectories calculated from the model output (blue) and from surface wind observations (red). These trajectories confirm the flow reversal, and show that the model simulates the reversal relatively well. In addition, the wind speeds ar e relatively well simulated (Figure 29), with 96% of modeled wind speed averages within 1 meter/second of the observed average.

Wind profiles in the vertical are shown in Figures 30 and 31. The La Porte radar profiler site is located just south of the Houston Ship Channel, and it shows that the PBL depth was relatively well simulated for most hours, though the modele d data show a drop in PBL depth at 1500 hour that doesn’t match the diagnosed PBL depth from the profiler data. However, both the modeled and observed wind speed and direction data show a discontinuity at about 0.25 km at 1500, suggesting that the bay breeze has penetrated to La Porte at that hour. The PBL depth at the Huntsville site is higher, but it is also modeled relatively well.

Based upon the trajectories, wind speed scatterplots , estimated PBL depths, and wind profiles, the meteorology appears to be well simulated for August 16, 2006, so that meteorological errors are not likely to be the cause of problems.

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### 52Figures 25 and 26. Plum e streamlines observed on August 16, 2006, showing a flow reversal.

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### 53Figures 27 and 28. Forward trajectories calcula ted fro m surface wind observations and model output, starting at midnight (top) and 0800 (bottom).

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### 54Figure 29. Scatterplot of HGB area average wind speeds, modeled and observed, for August 16, 2006.

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### 55Figure 30 and 31. Modeled and m e asured winds at La Porte and Huntsville, August 16, 2006. Observed wind profiles were derived from ra dar profiler data at these two sites.

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### 56Ozone concentrations were high across the norther n portions of Harris Count y. Figure 32 and 33 show the one-hour peak ozone and eight-hour peak ozone, respectively for August 16, 2006. The modeled and observed high ozone plumes from th e Houston Ship Channel and the urban core areas match well for both time scales. The peak co ncentrations are slightly underestimated for one-hour averages, but are unbiased for the eight-hour averages. Background ozone concentrations are slightly overestimated. Wh en examining all ozone data for the HGB area (Figure 34), the background ozone appears to be re latively low, only about 40 ppb, but the model simulates background at about 55 ppb. Local ozone production on this day is fairly large, averaging about 80 ppb for many high ozone sites, with a few sites even higher.

Figure 35-38 show ozone concentrations for th e aqs1 episode; the August 16, 2006 data are enclosed in the dotted-line red box. Data fro m the WALV, HALC, MERC, and CNR2 are shown to illustrate the approximate boundaries of the high ozone observed on this day. Ozone concentrations at HALC and WALV are well si mulated, but ozone at MERC and CNR2 are slightly underestimated. Ozone observed at Moody Tower (not shown) peaked at only about 60 ppb, but the model showed ozone peaking at about 90 ppb, much higher than observed. The Moody Tower did not encounter the industrial plum e on this day, and could even be considered an upwind site, so the overestimated ozone is due at least in part to the overestimation of the background ozone. In spite of the fact that the ozone performance at the downwind sites was very good, the model still did not have enough dy namic range; it did not make as much locally produced ozone as was observed.

The dynamic response of the model to emission changes was tested with the weekend effect sensitivity and the flare sensitivity. Figure 39 shows the model response to the Sunday mobile NOx decrease; Figure 40 shows the model response to substituting large HRVOC and formaldehyde flares for the standard emission r econciliation. The mobile NOx decrease resulted in a large eight-hour ozone decrease in Montgomery and far northeastern Harris Counties, dropping as much as 11 ppb. However, it resulted in a relatively large increase (8 ppb) in Harris County north of the Houston Ship Channel, sugges ting that the ozone in th at area is sensitive to changes in VOC emissions. The trajectories (Fi gure 28) indicate that the urban core plume was transported into the Houston Ship Channel area before the flow reversal occurred, which may have allowed the urban core and i ndustrial emissions to mix. Other days for which the urban core and industrial plumes mix together show a similar response to mobile NOx decreases. Figure 40 shows that the area of NOx disbenefit is indeed sensitive to VOC emission changes; the addition of large quantities of HRVOC and formaldehyde to the flares increases eight-hour ozone concentrations by up to 16 ppb, in the sam e area where the NOx di sbenefit occurred.

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### 57Figures 32 an d 33. Daily p eak 1-hour and 8-hour ozone for August 16, 2006.

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### 58

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0 20 40 60 80 100 120 140 160 180

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### 59Figures 35-38. Ozone concentrations fo r August 16, 2006 (enclosed in red box).

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i'77n Mcd e led. 7x7-ce !l m axi minFigure 39. M odel response to the degree of m ob ile source NOx reduction typical for a weekend (Sunday eight-hour peak ozone minus Wednesday eight-hour peak ozone).

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### 61Figure 40. Changes in 8-h our ozone due to the substitution of HRVOC + formaldehyde flares for PSCF emissions for August 16, 2006.

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### 62

## September 14, 2006

On Thursday, September 14, 2006, a severe ozone episode occurred in the Houston area, with thehighest concentrations observed in western Houston and western Harris County. The peak onehour ozone concentration observed was 165.1 p pb at the West Houston (WHOU) monitor at 1400. Three other monitors exceeded 125 ppb one-h our ozone concentrations on this day as well. The peak eight-hour ozone concentration observed was 118.9 ppb at WHOU from 1100-1800. Eight other monitoring sites also observed peak eight-hour ozone exceedances. The flow regime on this day was the typical veering pattern, with light northeast winds in the morning, veering to east and southeast later in the day. Figure 41 shows simulated and observed trajectories for this day. The model accurately simulates the typical veering pattern, though the model places the trajectories slightly further south than the observations.

Figure 41. Trajectories for September 14, 2006. Trajectories were calculated from MM5 output (blue) and from observed surface wind monitors (red). Winds veer from NE to E to SE during the course of the day, and emissions from the industrial areas are carried into west Houston.

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### 63Figure 42. Scatter Plot for Areawide Average Wind Speeds in the Houston-Galveston Area for September 14, 2006.

Figure 42 shows the comparison of wind speeds for the Houston-Galveston domain on this date. Note that the slope of the line is 1.033, i.e., ve ry close to unity. The results presented in Figures 41 and 42 indicate that the simulated transport for this date is excellent.

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### 64Figure 43. Daily Maximum Eight-Hour Ozone by Grid Cell for the Houston-Galveston Area on September 14, 2006. Monitoring sites are designa ted by circles. Ozone concentrations are designated for both model output and monitore d observations by the color scale at right.

Figure 43 shows peak eight-hour ozone concentrati ons in each grid cell for this day of model simulation. The measured areawide peak is under estimated by the model, even though the eighthour background concentrations in Galveston, Brazo ria, and southeast Harris Counties appear to be overestimated.

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### 65Figure 44. Daily Maximum One-Hour Ozone by Grid Cell for the Houston-Galveston Area on September 14, 2006. Monitoring sites are designa ted by circles. Ozone concentrations are designated for both model output and obser vations by the color scale at right.

Figure 44 shows peak one-hour ozone concentrati ons. In this case, background concentrations along the coastlines appear to be less severely over estimated, but they are still biased high. Peak one-hour ozone observed in western Harris County is underestimated by the model. Note that ozone greater than about 110 ppb is confined to the southwest portion of Harris County; this is probably due to the trajectory error shown in Figure 41, which keeps plumes from the Ship Channel area in southwest Harris, instead of spr eading them into northwest Harris County as the observed trajectories would indicate.

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### 66Figure 45. Difference in Simulated Peak On e-Hour Ozone Concentrations Resulting from Substituting HRVOC and Formaldehyde-Laden Flare Emissions for reg10 (PSCF-based) Emissions. The ozone increase west of the urban core improves model performance in that area, without degrading performance elsewhere.

Figure 45 shows how substituting flare emissions spiked with formaldehyde can increase the ozone concentrations compared to the PSCF-based industrial source emissions inventory. This shows that even though the flares are located in eastern Harris and western Chambers Counties, they can still greatly affect ozone concentra tions throughout the Houston metropolitan area.

Examination of time series for September 14, 2006 shows that the monitoring sites along the southern edge of Harris County, i.e., TO MB, MACP, HCQA, BAYP, SHWH, show ozone overestimation by the model, whereas sites locat ed a little further north, i.e., WHOU, HLAA, BUHV, HNWA, KATP, show ozone is underest imated. Moving upstream, sites along the southern route (HSMA, PRKP, FWCB) are also somewhat overestimated, whereas HALC and HOEA are both slightly underestimated. Th ese patterns of over- and under-estimation are consistent with the too-southerly misalignment of plumes shown in Figure 41. At many of the west and central Houston sites, NO 2 shows major spikes (up to 40 ppb) in the hours before noon, which appear to be simulated relatively well by the model. NO 2 provided by this plume appears to effectively supply the fuel for high ozone late r in the day. Three auto-GC sites on the western end of the Ship Channel (C35C, CECH, MIPK), DRPK, and WALV all observed spikes of ETH and OLE up to 20 ppb in the morning hours, which were usually simulated well by the model. Moody Tower at the UH campus observed a very large HCHO spike (30 ppb) during the midday

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### 67hours, which was not well sim u lated by the model, though the model did simulate a HCHO peak earlier in the day near the Moody Tower site. When the HRVOC+HCHO flares were substituted into the modeling, the HCHO performance at Moody Tower improved slightly.

Based upon the time series, it appears that industr ial emissions from the Ship Channel area laden with HRVOC and NO X were pushed to the west-southwest by morning winds. The plumes generated HCHO and NO 2 along the way, then produced larg e quantities of ozone as they entered the west Houston area. While modeled wind speeds were approximately correct, the orientation of the industrial plumes was too far south, r esulting in overestimated ozone in southerly areas, and underestimated ozone in northerly areas. The WHOU site experienced very high ozone concentrations that were not si mulated anywhere in the domain, indicating that a transport error alone is not the cause of the missed peak at WHOU. Addition of flare emissions alleviated some but not all of the underestimation problems; th e observed peak ozone was still not simulated anywhere in the domain. Although the highest oz one peaks were not well simulated, the location, extent, and relative intensity of high ozone ar e depicted relatively well (Figure 44).

Figure 46. September 14, 2006: Change in Ozone When Emissions are Decreased on the Weekend (Sunday Minus Wednesday).

Figure 46 shows how the model responds to the em ission reductions that occur on the weekends. The four to ten ppb decrease in maximum eight- hour ozone in the western portion of the domain suggests that the highest ozone is somewhat sensitive to decreases in NO X . Note that the largest

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### 68changes in ozone occur outside the m onitoring network, in Waller County. A very small area of NO X disbenefit is found in the center of the urban core, which is likely due to the lessening of ozone titration by decreasing mobile NO emissions.



## June 23, 2005 Episode

June 23, 2005 had similar flow characteristics to September 14, 2006. The flow pattern is characterized as typical veering, though on this day, the veering is more subtle, giving trajectories that are nearly straight (Figure 47). Trajectori es that originate in the eastern Ship Channel area pass directly through the urban core, and create high ozone in western Harris County. Modeled transport is excellent on this day. Figure 48 shows that modeled wind speeds perform well on this day, too.

Figure 47. Trajectories for June 23, 2005. Trajectories were calculated from MM5 output (blue) and from observed surface wind monitors (red). Winds veer from ENE to E to SE during the course of the day, and emissions from the industrial areas are carried into west Houston.

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### 69Figure 48. Scatterplots of Areawide Average Wind Speeds in the Houston-Galveston Area for June 23, 2005.

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### 70Figure 49. Daily Maximum Eight-Hour Ozone by Grid Cell on June 23, 2005. Monitoring sites are designated by circles. Ozone concentra tions are designated for both model output and monitored observations by the color scale at right.

Simulated peak eight-hour ozone concentrations are shown in Figure 49. The location, extent, and relative intensity of high oz one are well-depicted by the model simulations. The highest ozone is observed in the proper area (northwest Harris County), but it is slightly lower than observed. In addition, ozone concentrations al ong the trajectory pathways from the Ship Channel area to NW Harris County are also underestimat ed. Substitution of HRVOC- and HCHO-laden flares in the industrial areas improves model perfo rmance somewhat (Figure 50). Note that in both Figures 49 and 50, some of the highest simulated ozone occurs outside the monitoring network.

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### 71Figure 50. Daily Maximum Eight-Hour Ozone by Grid Cell on June 23, 2005, with the Substitution of HRVOC+HCHO Flares. Monitoring sites are designated by circles. Ozone concentrations are designated for both model out put and monitored observations by the color scale at right.

Given the relatively good model performance on this date, it is appropriate to examine how the simulation responds to emission changes. Figure 51 shows how weekend mobile NO X reductions change peak eight-hour ozone for this simulation day.

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### 72Figure 51. June 23, 2005: Change in Ozone When Emissions are Decreased on the Weekend (Sunday Minus Wednesday).

The large area of ozone increase in response to NO X decreases suggests that peak eight-hour ozone in west Houston on this day is sensitiv e to VOC emissions. To further examine this possibility, the model response to flare emissions is examined in Figure 52.

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### 73Figure 52. Peak One-Hour Ozone Response to Substituting HRVOC+HCHO Flares for PSCFBased Emissions on June 23, 2005.

Figure 52 shows that the June 23, 2005 episode responds vigorously to the substitution of HRVOC and formaldehyde flares in the emissi ons inventory. The June 23, 2005 episode enhances the peak one-hour ozone by about 30 pp b, and enhances peak eight-hour ozone by about 6 ppb (not shown). The area of extremely high enhancement occurs in approximately the same area that shows increases in ozone with NO X cuts. This phenomenon is quite interesting, especially given that the two previous days, whic h had similar flow, did not show this degree of enhancement.

All modeling days were examined to find other days for which ozone concentrations increased when the weekend mobile NO X decrease was applied. We f ound that these days were not common; out of 95 modeled days, only 13 days showed ozone increases of five ppb or more in the presence of weekend mobile NO X decreases. Table 1 shows a list of these days, along with the maximum ozone increase resulting from the weekend mobile source NO X decreases.

Table 1. Modeling Days for Which Weekend Mobile NO X Decreases Yielded Ozone Increases

Date Maximum 1-hr ozone increase Maximum 8-hr ozone increase Location of VOCsensitive ozone Flow regime May 20, 2005 11 ppb 9 ppb NE of urban core Flow reversal May 22, 2005 15 ppb 5 ppb E and NE of urban core Flow reversal May 25, 2005 6 ppb 4 ppb ENE of urban core Flow reversal June 23, 2005 8 ppb 4 ppb W of urban core Typical veering June 26, 2005 6 ppb 3 ppb W of urban core Typical veering

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### 74June 28, 2 0 0 5 11 ppb 2 ppb W of urban core Typical veering June 29, 2005 13 ppb 5 ppb W of urban core Typical veering August 3, 2005 17 ppb 6 ppb NNE of urban core Flow reversal June 12, 2006 11 ppb 11 ppb SW of urban core Flow reversal August 16, 2006 18 ppb 8 ppb NE of urban core Flow reversal August 18, 2006 6 ppb 2 ppb SW of urban core Flow reversal August 20, 2006 10 ppb 10 ppb W of urban core Typical veering August 21, 2006 11 ppb 5 ppb W of urban core Typical veering Note: all other modeling days had 1-hr ozone decreases, or increases of less than 5 ppb.

Trajectory analysis of all modeling days suggests that the days listed in Table 1 have something in common: on these days, the urban core plume mixes with the industrial plume. The flow conditions that allow these plumes to mix are not particularly common; most of the local flow patterns in Houston do not allow these plumes to mix. On days dominated by the coastal oscillation, wind shifts tend to occur throughout the domain at approximately the same time, allowing urban and industrial plumes to remain on parallel courses rather than intersecting. On days dominated by bay or Gulf breezes, cooler air from the breezes can push under the urban air, so that the air masses do not mix well (see the TOPAZ data discussion).



## Conclusions from case study analyses

This section has examined a number of high ozone days for which the meteorological modeling has performed well, so that the chemistry, emissions, and response to emission changes can be examined without the uncertainty added by meteor ological errors. The following conclusions can be reached from these analyses.

 The model is capable of accurately simulati ng the wind patterns for both typical veering and flow reversal days.

 The extent, location, and relative magnitude of ozone are well simulated on each of these selected days.

 Peak ozone concentrations are often under estimated; background ozone concentrations are sometimes overestimated. The model’s dynamic range of ozone variation is not as large as the observed dynamic range.

 Some of the peak underestimation problem may be alleviated by allocating additional VOC emissions to the flares. The addition of formaldehyde to HRVOC flares can increase modeled one-hour ozone c oncentrations by up to 25 ppb, but usually increases one-hour ozone by less than 10 ppb.

 The weekend effect sensitivity analysis show s that peak eight-hour ozone is generally sensitive to NO X reductions in the base case. For a few days (e.g., June 23, 2005, August 16, 2006), however, a pocket of NO X disbenefit appears. When the model is run for those days with additional VOC from flares, the model responds vigorously, generating much larger ozone concentrations in the area of NO X disbenefit. This behavior indicates that the NO X disbenefit area has conditions suitable for VOC-sensitive ozone formation. The implication of these results is that both NO X and VOC emission controls will be needed to suppress ozone formation.

 The retrospective analysis showed that the model responds strongly to the emission changes that are believed to have occurred between 2000 and 2006. One of the areas of greatest change in ozone is in northeast Harris County, which was not well monitored in

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### 75 The model’ s response to emission changes is so metimes greatest in areas outside the current (2006) ozone monitoring network.



## Future 2018 design values base d upon 2005 baseline modeling

Future eight-hour ozone design values were calculat ed with 2005 baseline emissions (bl05.reg1). The emissions used in the modeling scenario and th e future design values are described below.

For the 2018 future estimate, the point source NO X emissions were estimated as follows:

 For the region outside Texas, the CENRAP/R PO regional haze 2018 emission levels (e.g. IPM, CAIR2 trading, controls) were used;

 For the attainment region of Texas, CAIR Phase 2 allocations were used for existing EGUs, new EGUs were limited to the 9.5% set -aside; all controls included in the recent DFW and BPA SIPs (e.g., East Texas Engine Rule) were applied; and growth was projected to 2018 with Economy.com factors;

 For the DFW and BPA nonattainment areas, CAIR Phase 2 and Chapter 117 ESAD were used; and

 For the HGB nonattainment area, MECT a nd HECT were used, as well as controls included in the recent HGB SIP (e.g., VOC rules for tanks and degassing). The point source NO X emissions within the HGB eight-count y nonattainment area were reduced by about 20% from a 2005 baseline of 194 tpd to 156 tpd in 2018.

The on-road mobile source NO X emissions were estimated as follows:

 For areas outside of Texas, county-level emission derived from NMIM were used and projected to 2018;

 For Texas counties outside of BPA and HGB, county-level hourly emissions by summer “day type” from TTI based on projected traffic count data from TxDOT were used; and

 For both HGB and BPA, link-based hourly emissions by summer “day type” from TTI based on projected travel demand modeling were used; The on-road mobile source NO X emissions within the HGB eight-county nonattainment area were reduced by about 78% from a 2005 base line of 222 tpd to 49.8 tpd in 2018.

The non-road and off-road (i.e., marine, locomotive and aircraft) mobile source NO X emissions were estimated as follows:

 For the region outside Texas, EPA’s NMIM was used for non-road categories, and 2002 NEI with EGAS growth was used for all o ff-road categories with national controls on locomotives and marine vessels;

 For the non-road categories within Texas the Tex-N model was used;

 For the off-road categories within Texas

o 2005 TexAER was used with REMI-EGAS growth with national controls for locomotives and marine vessels, except locomotives in HGB and DFW, and marine vessels in HGB and BPA

 2018 emissions for locomotives in HGB and DFW were provided from a contract with ERG (now in TexAER)

 2018 emissions for marine vessels in HGB and BPA were developed using emission trends provided by the HGB and BPA Port Authorities and 2007 and 2000 emission projections, respectively, provided from a contract with Starcrest

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### 76o For aircraft within Texas, except for HGB and DFW, 2002 TexAER was used with REMI-EGAS growth. For HGB and DFW, 200 5 emission estimates from AQP were used with REMI-EGAS growth. The non-road and off-road mobile source NO X emissions within the HGB eight-county nonattainment area were reduced by about 22% from a 2005 baseline of 148 tpd to 116 tpd in 2018.

The area source NO X emissions were estimated as follows:

 For the region outside Texas, 2002 NEI with EGAS growth was used; and

 For the region within Texas, 2005 TexA ER with REMI-EGAS growth was used. The area source NO X emissions within the HGB eight-count y nonattainment area were increased slightly from a 2005 baseline of 36. 2 tpd to 42.0 tpd, in 2018.

Note that no ERCs or DERCs have been included in this modeling scenario.

In total, the NO X emissions within the HGB eight-county nonattainment area were reduced by about 39% from a 2005 baseline of 600 tpd to 364 tpd in 2018 (Figure 1).

2005 Baseline vs 2018 Future Year NOx Emissions

194.02 155.93 221.65

49.82 148.31

115.85 36.18

42.04

0 100 200 300 400 500 600 700

2005 - BASELINE 2018 - FUTURENOx Emissions in tons per day "Area"
"Non-Road"
"On-Road"
"Points"

Figure 1. Baseline 2005 and future 2018 NO X emissions in the eight-county HGB area

.

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### 77The 2018 eig h t-hour ozone concentrations mode led at the various monitoring sites were compared to the 2005 baseline modeled concen trations, per the EPA Guidance to develop a Relative Response Factor (RRF) for each of the monitoring sites (Table 1). These RRFs were then multiplied by the 2005 b aseline ozone Design Values (DVb 05 s) to estimate the 2018 future ozone Design Values (DV 18 s). Of the 20 HGB monitoring sites with a DVb 05 greater than 85 ppb, only four are projected to have a DV 18 of 85 ppb or greater. The future DVs for those four sites are presented in Table 1. The future DVs fo r those four sites are presented in Table 1.

Table 1. Design values and relative reduction factors for 2005 baseline runs.
Monitoring

Site Code 2005 Baseline 8-Hour Design

Value (ppb) RRFs 2018 Future 8-Hour Design

Value (ppb)

DRPK – C35 96.3 0.921 88.7

BAYP – C53 100.7 0.862 86.8

HSMA –C406 95.3 0.896 85.4

WALV – C617* 94.0 0.924 86.8 *Note: WALV is not a regulatory monitor.


## Supplemental Corroborative Anal ysis and Weight-of-Evidence

Effects of Meteorological Conditions on 2007-2009 Ozone Monitoring Data

In Chapter 5 of the HGB attainment SIP, the TC EQ presented an analysis of ozone trends that accounted for the effects of meteorological cond itions (i.e., Sullivan, 2009). Since that time, questions have arisen about whether the Sulliv an analysis was sufficient to show that meteorological conditions were unable to explain th e substantial decrease in ozone concentrations since 2000. The EPA argued that higher than average wind speeds occurred in both 2007 and 2008, leading to lower ozone concentrations.

The TCEQ agrees that meteorological factors may ha ve played some role in the recently observed ozone trends, but disagrees that the meteorological factors are the primary reason for decreasing ozone. Analyses from the TCEQ and others show that ozone trends are still decreasing in the Houston area, even when the effects of meteorology have been accounted for.

The TCEQ disagrees with the EPA that meteor ological conditions in Houston during 2006-2009 have been unfavorable for high ozone, and therefor e the decline in ozone concentrations is not due to local emission reductions. They base their position on a finding from an analysis by Sather and Snyder, which shows that higher wind speeds occurred more frequently during 2008 than in 2000-2007. Figure 1: Wind data from Houston Aldine shows the graph of interest.

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### 78Figure 1: Wind data from Houston Aldine (Source: The data are from June-Aug, 0500-1900 LST, with 65% minimum data capture. Winds of greater than 10 mph were more frequent during 2008 than any year since 1998. This figure is copied from Sather and Snyder, Region 6 “State of the Ozone” Report for 2008 , presented at the Southeast Texas Photochemical Modeling Technical Committee Meeting, April 28, 2009.)

Sather and Snyder assert that the higher winds in 2008 are the primary cause of lower ozone in 2008. As they have noted, the frequency of high winds in 2008 is comparable to the frequency observed in 1998. The maximum 4 th -high 8-hour ozone concentra tions in 1998 ranged from 90 to 117 ppb at regulatory monitors throughout Houston, but the maximum 4 th -high 8-hour ozone concentrations in 2008 ranged from 68 to 83 ppb at Houston regulatory monitors. This represents a 29% decrease in maximum 8-hour ozone from 1998 to 2008. Since the frequency of high winds is similar in 1998 and 2008, the frequency of hi gh winds cannot explain the difference between ozone concentrations during these two years.

The TCEQ contends that four extensive anal yses of meteorological conditions presented below (each of which includes more than just the frequency of high winds) indicate meteorological factors cannot explain the recent decrease in ozone. In addition, an analysis of the frequency of wind speed and direction will show that the peri od of high winds in 2008 noted by Sather and Snyder occurred at a time of year when high ozone usually doesn’t occur, because of the direction of the wind flow at that time of year. It will also show that the wind conditions were in fact more suitable for high ozone formation in August and September of 2008 and 2009 than in 2000 and 2004, but the number of exceedance days were far fewer in 2008 and 2009.

Chapter 5 of the HGB SIP contains an analysis from the University of Texas at Austin (UTAustin), which show the ozone trends adjusted for meteorology. Figures 2 and 3, and Table 1 illustrate the observed trends, and trends with th e meteorological effects removed. Those trends show that the ozone through 2007 is decreasing even with meteorological influences removed from the trends. Much of the work from UT-Aus tin was based upon a variation of the analysis technique developed by Camalier, Cox, and Do lwick, which is the technique recommended by the EPA for removing the influences of meteor ology from trend data. UT-Austin’s analysis shows that when the portion of the ozone variation associated with meteorological factors is removed from the observed ozone trend, the resi dual trend is still downward. The presence of a strong downward trend after the removal of me teorological effects strongly suggests that the overall downward trend is related to f actors other than meteorology.

A second analysis of meteorologically-adjusted oz one trends can be found on the EPA website,

www. epa .gov/air trends /weather/region06.p d f . The EPA trends are shown in Figure 4:

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### 79Me teorologically Adjusted Ozone Trends in the HGB Area from 1997 through 2008 , which illustrates the observed seasonal average ozone trend, and the seasonal average ozone trend adjusted for weather. The ozone trend adjusted for weather represents the expected ozone levels for typical weather conditions. According to th e EPA analysis, the downward trend in Houston’s ozone is unrelated to meteorological variations, beginning in 2002 and continuing through the end of the analysis period in 2008.

Figure 2: Actual Mean Eight-Hour Ozone Concentrations (Blue Line) and Mean EightHour Predicted Concentrations with the No n-Meteorological Trend Removed (Red Line)

Figure 3: Residual Meteorologically Adjusted Eight-Hour Ozone Concentrations Trend

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### 80Table 1: Su m m ary Statistics for Meteorologically Adjusted Ozone Trends from 1996 to 2007 Parameter Value Intercept 6.438 Slope -0.86 *
t-statistic -4.20 p-value 0.00 Lag 1 Coefficient -0.20 t-statistic -0.63 * Value is statistically significant. (Sullivan, 2009)

Figure 4: Meteorologically Adjusted Ozone Trends in the HGB Area from 1997 through 2008 (Source: EPA)

URS Corporation performed a third analysis of the effects of meteorology on ozone trends, which is included in comments from the Coalition. A presentation that discusses this analysis can be found at http://www.tceq.state.tx.us/assets/public/impleme ntati on/air/am /committees/p mt_set/20071212/2 0071212-setpmtc-hgb_ozone_trend.pdf . In this analysis, the author used the Classification and

I- 

### 81Regression Tree (CART) method, a mu ltivariate stat istical analysis technique, to classify ozone season days according to the meteorological cond itions, and whether the conditions are conducive to high ozone in Houston. From this analys is, URS calculated trends of the number of ozoneconducive days per year. Figure 5: Number of Ozone Conducive Days and Number of Ozone Conducive Days with Ozone Greater Than 84 ppb shows that the number of ozone conducive days in 2007, 2008, and 2009 is similar to or greater than previous years, indicating that the past three ozone seasons have been just as conducive to high ozone formation as previous years. Since 2005, there appear to be more ozone conduc ive days than average, but fewer eight-hour ozone exceedance days. Although 2005 had the largest number of ozone conducive days since 1990, there were fewer exceedance days in 2005 th an during than previous years with a lower number of ozone conducive days, such as 1995, 2000, or 2003.

Figure 5: Number of Ozone Conducive Days and Number of Ozone Conducive Days with Ozone Greater Than 84 ppb (Source: URS Corporation)

A fourth analysis is presented as a response to the comment by ED/GHASP about evaluating the trends in forecasted high ozone days. A possible surrogate for the number of ozone conducive days during each ozone season is the number of TCEQ-forecasted eight-hour ozone exceedance days. Figure 6: Forecasted and Actual Eight-Hour Ozone Exceedance Days (1997 NAAQS) in the HGB Area shows that the number of forecasted eight-hour ozone exceedance days is typically greater than the number of actual eight-hour ozone exceedances to avoid missing potentially high ozone days. To adjust the forecasted eight-hour ozone exceedances to co mpensate for the bias, a bias-adjusted forecast was developed by accoun ting for the average rates of false positives (predicted exceedances that di d not occur) and false negativ es (predicted non-exceedances on days with high ozone). The average percent of false negative days was added to the number of forecasted ozone exceedance days, and the average percent of false positive days was subtracted. The bias-adjusted forecasted ozone exceedance da ys approximately match the actual number of

I- 

### 82eight-hour ozone exceedance day s , with the exception of 2004 and 2008. In 2004, the biasadjusted ozone exceedance forecast was much lower than the actual number of eight-hour ozone exceedance days. However, in 2008, the bias- adjusted forecast of ozone exceedance days was much higher than the actual number of eight-h our ozone exceedance days, indicating decreases due to factors other than meteorology. There are uncertainties in this analysis, because TCEQ meteorologists adjust their forecast techniques as they learn more about which conditions are most conducive to ozone formation, accumulation, and transport in Houston. In other words, the criteria used for developing forecasts in 2000 may have differed somewhat from the criteria used in 2008. The results, however, are fairly consistent with the three previous analyses, in that the peak number of ozone-conducive days in recent y ears appears to be in 2005, with the number decreasing slightly in 2006-2007. The number of forecast ozone days in 2008 approximately matched the number in 2006, but there were about half as many actual exceedance days in 2008 as in 2006.



## Forecasting 8-hour ozone exceedances, Houston

0 20 40 60 80 100

2000 2001 2002 2003 2004 2005 2006 2007 2008Count Bias-adjusted forecasts
Mean forecast
Actual forecast
Actual exceedances

Figure 6: Forecasted and Actual Eight-Hour Oz on e Exceedance Days (1997 NAAQS) in the HGB Area

These four analyses of the effects of inter-annual meteorological variations on ozone trends all imply that meteorological factors alone cannot expl ain the decrease in ozone concentrations in the Houston area.

The TCEQ has also analyzed the frequency of wi nd speeds and directions by month and by year, to show in greater detail the wind data that the EPA had originally cited in their comment. The winds in the HGB area were examined for each month of the ozone season from 2000 to 2009 to determine if higher wind speeds in 2008 and 2009 caused decreases in ozone concentrations. Daytime wind speeds were grouped into six leve ls and the frequencies of those levels were examined by wind direction for each month from Ma y to September, which represents the peak of the ozone season in the HGB area. Figure 7: Frequency of Morning Wind Direction and Wind Speed by Month in the HGB Area for 2000, 2004, 2008, and 2009 compares years with similar

I- 

### 83winds and shows the number of eight-hour ozone exceedances that occurred in each m onth (1997 NAAQS). Winds from 0 to 90 degrees are from the north to east, and, as shown in Chapter 5 of the SIP, winds from that direction typically re present higher background ozone concentrations, as well as winds that can interact with the bay br eeze and Gulf breeze to create stagnant conditions (Darby, 2005; Pakalapati et al., 2008; Sullivan, 2009; Ngan and Byun, 2008; Rappenglueck et a 2008). Winds from 90 to 225 degrees are from the east to southwest and represent cleaner air blowing in from the Gulf of Mexico (Sullivan , 2009; Rappenglueck et al., 2008). These winds accentuate the bay and Gulf breezes rather than oppos ing them (Darby, 2005; Banta et al., 2005). The findings of these previous studies have indicated that low winds are more favorable for high ozone, whereas high winds are less favorable. The findings also show that northerly wind l.,

s are ore favorable for high ozone in Houston, and that southerly winds are less favorable.

he wind analysis by the TCEQ found that:

formation based upon wind speeds, but had more exceedances than May 2008 and 2009.

ore ive based upon wind direction, yet ha d fewer exceedances than May 2000 or 2004.

ed by r of

winds in June 2008 was unlikely to affect the number of exceedances in that month.

e only two exceedance days in July 2009 compared to ten exceedance days in July 2000.

8

high ozone than August 2000, based upon wi nd speeds, but had far fewer exceedances.

re conducive to high ozone, but has far fewer exceedances than September 2000 or 2004.

08 and 2009. The greater frequency of high winds in 2008 noted by Sather and Snyder occurred m

T

 Wind speeds in May 2000 and May 2004 were higher than those observed in May 2008 and May 2009, but the number of eight-hour ozone exceedance days was higher in 2000 compared to 2008 and 2009. May 2000 and 2004 were less conducive to ozone

 May 2009 also had more winds from the north to northeast direction, which typically indicates higher background ozone, but only th ree exceedance days. May 2009 was m conduc

 June 2008 and 2009 had higher wind speeds than those observed in June of 2000 and 2004; however, winds in June are mainly out of the south, which brings in clean air from the Gulf of Mexico. There were only three exce edance days in June of 2000, five in June of 2005, and two in both June 2008 and June 200 9. For all years, June was dominat southerly winds, which are not conducive to ozone formation. The low numbe exceedances each year reflects how unfavorable southerly winds are to ozone exceedances; therefore, the slightly greater frequency of high

 July winds were very similar in 2000 and 2009, but there wer

 Winds in August 2008 were slower than those observed in August 2000, yet August 200 had one exceedance day compared to 15 in 2000. August 2008 was more conducive to

 September 2009 had slightly lower winds in the northeast direction compared to September 2000 and 2004, but only three exceedance days compared to 10 in September 2000 and 13 in September 2004. September 2009 was mo

In summary, Figure 7: Frequency of Morning Wind Direction and Wind Speed by Month in the HGB Area for 2000, 2004, 2008, and 2009 shows that the wind speed and direction were actually more conducive to ozone formation during the peak of the ozone season in August and September in 2008 and 2009 than in 2000 and 2004, but 2000 and 2004 had far more exceedances than 20

I- 

### 84I- 

### 85 during June, when winds were s outherly, and therefore not conducive to high ozone, during all four years.

The TCEQ believes that evidence provided in th e SIP, along with additional analyses presented here, show that favorable meteorology is not adequate to explain the decreases in ozone observed in 2007, 2008, and 2009. Evidence presented in Chapter 5 shows that decreases in ozone precursor emissions played a significant role in the recent decreases in ozone.MayJuneJulyAugustSeptember2000 HGB Winds (0500 – 1900 LST)8-Hr OzoneExceedance Days53101510MayJuneJulyAugustSeptember2000 HGB Winds (0500 – 1900 LST)MayJuneJulyAugustSeptember2000 HGB Winds (0500 – 1900 LST)8-Hr OzoneExceedance Days531015108-Hr OzoneExceedance Days531015102004 HGB Winds (0500 – 1900 LST)8-Hr OzoneExceedance Days1581213MayJuneJulyAugustSeptember2004 HGB Winds (0500 – 1900 LST)8-Hr OzoneExceedance Days15812132004 HGB Winds (0500 – 1900 LST)MayJuneJulyAugustSeptember8-Hr OzoneExceedance Days15812138-Hr OzoneExceedance Days1581213MayJuneJulyAugustSeptember*2008 HGB Winds (0500 – 1900 LST)8-Hr OzoneExceedance Days02114MayJuneJulyAugustSeptember*2008 HGB Winds (0500 – 1900 LST)* Incomplete data due to thepassage of Hurricane Ike8-Hr OzoneExceedance Days021148-Hr OzoneExceedance Days02114MayJuneJulyAugustSeptember2009 HGB Winds (0500 – 1900 LST)8-Hr OzoneExceedance Days322338-Hr OzoneExceedance Days322338-Hr OzoneExceedance Days32233MayJuneJulyAugustSeptember2009 HGB Winds (0500 – 1900 LST)W nd Speed, mphi< 55 - 1010 - 1515 - 20W nd Speed, mphi< 55 - 1020 - 25> 2510 - 15Figure 7: Frequency of Morning Wind Direction and Wind Speed by Month in the HGB Area for 2000,2004, 2008, and 2009I-86Effects of Economic Downturn on 200 7 -2009 Ozone Monitoring Data

Detailed analysis of actual economic data from the HGB area, Texas, and the refining industry along the Gulf Coast does not support the assertion that the decrease in ozone concentrations in the HGB area is primarily due to the national economic downturn.

Though the national economy entered a recession be ginning in December of 2007 (well past the peak ozone season), both Texas and the HGB area continued to experience economic expansion during 2007 and 2008. Only in early 2009 did either Texas or the HGB area begin to exhibit flat or slightly reduced economic activity. While Te xas Gross Domestic Product (GDP) increased 7.3 percent in nominal terms from 2006 to 2007 (see Figure 8: Gross Domestic Product of Texas ), and 6.5 percent from 2007 to 2008, the economy of the Houston-Sugarland-Baytown area grew even faster, at 8.4 percent and 7.4 percent over those years, respectively (see Bureau of Economic Analysis, Regional Economic Accounts: www.bea.gov/regional/gsp/ and Gross Domestic Product by Metropolitan Area: www.bea.gov/regional/gdpmetro/ ). Though these figures are not corrected for inflation, inflation was exceptionally low over this period. “Houston-Sugarland-Baytown” is the definition of the HGB metropolitan area used by the U.S. Department of Commerce.

$- $200 $400 $600 $800 $1,000 $1,200 $1,400

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Source: Bureau of Economic Analysis, U.S. Department of Commerce http://www.bea.gov/regional/gsp/ $ billions

Figure 8: Gross Domestic Product of Texas

Another measure of economic activity for the Houston-Sugarland-Baytown metro area, the business cycle index computed by the Federal Reserve Bank of Dallas ( www.dallasfed.org/data/data/houcoini.htm ), exhibits a downward trend beginning only in early 2009 (Figure 9: Metro Area Business Cycle Index, Houston-Sugarland-Baytown ). The index, which incorporates measures of employment, real wages, and retail commerce, grew 5.6 percent from December 2006 to December 2007, and 2. 5 percent from December 2007 to December 2008. Through September 2009, the index is dow n -2.7 percent from the end of 2008. It is

I- 

### 87especially not eworthy that this index, available through the first three quarters of 2009, shows that during each of the years 2007, 2008, and 2009 to date, the HGB area generated greater economic activity than in any previous year on r ecord, exceeding an index value of 245 in every month. Previous to 2007, the index never exceeded that value. Despite this burgeoning economic activity, the HGB area continued to exhi bit reductions in both pr ecursor emissions and resultant ozone over those three years.

150 175 200 225 250 275 300

Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 October 1980 = 100

Source: Federal Reserve Bank of Dallas Figure 9: Metro Area Business Cycle Index, Houston-Sugarland-Baytown

While the overall economies of Texas and the HGB area were not impacted by the economic downturn until early 2009, industries linked to pe trochemicals and refining, which are key to HGB area air quality, also failed to exhibit the claimed downward trend (see Figure 10: Gross Domestic Product of Selected Petroleum Related Industries ). From 2006 to 2007, the most recent annual period for which sub-indus try GDP data are available, oil and gas extraction increased 7.6 percent. Similar patterns emerge for other segme nts of the petroleum industry over that period. Manufacturing of chemicals grew 9.9 percent, while manufacturing of plastics and rubber products increased 2.7 percent. Only manufacturing of petroleum and coal products declined (at a rate of -1.9 percent) from 2006 to 2007.

I- 

### 88$- $10 $20 $30 $40 $50 $60 $70 $80 $90

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 $- $5 $10 $15 $20 $25 $30 $35 $40 $45

Oil and gas extraction (left axis) Petroleum and coal products mfg (right axis) Chemical mfg (right axis) Plastics and rubber products mfg (right axis) $ billions $ billions

Source: Bureau of Economic Analysis, U.S. Department of Commerce http://www.bea.gov/regional/gsp/ Figure 10: Gross Domestic Product of Selected Petroleum Related Industries

While more recent data on GDP trends in the pe trochemical and refining industries are not yet available, other recent information on activity in these industries corroborates the above findings. The Energy Information Administration of the U.S. Department of Energy reports refining activity along the Gulf Coast, termed Petroleum Administration for Defense District (PADD) 3, which includes the HGB area ( tonto.eia.doe.gov/dnav/pe t/pet_sum_sndw_dcus_ r 30_w.htm ). As evident in Figure 11: Weekly Gulf Coast (PADD 3) Crude Oil Inputs to Refineries , reported weekly inputs are highly variable. A thirteen-week, or roughly quarterly, moving average is super-imposed on the weekly data to smooth out so me of this variability. This measure reveals the seasonal cycles in the industry, as refineries ra mp up production for pe riods of high product demand. Though inputs to refineries appear to be on a slight downward trend since 2007, they have actually been increasing since early 2009, th ough this is likely a cyclical phenomenon. In fact, neither high nor low values of the quarterly average since 2007 have strayed beyond the highest or lowest values observed over the previous decade, indicating the industry as a whole has neither grown nor shrunk markedly.

A quadratic fit to the weekly data reveals a very slight long-term downward trend in the refining industry, beginning around 2004. Since 2004, PADD 3 inputs to refineries have generally dropped, at a rate of about seven-tenths of one percent per year, even when hurricane-affected periods in 2005 (Katrina, Rita) and 2008 (Ike) are excluded.

I- 

### 893,000 4,000 5,000 6,000 7,000 8,000

S ep -9 2 Mar-93 A ug- 9 3
F

e b-94 A ug- 9 4
F

e b-95 A ug -95
F

e b- 9 6 A

ug -96
F

eb

- 97 A

ug -97
F

eb-98 A ug -98 Jan-99
J u l-99
J

an-00
J u l-00
J

an-01 Jul-01
J an-0

2 Jul-02
J a n- 0 3 Jul-03
J a n- 0 4 Jul-04 D ec -0 4

J

un-05
D ec- 0 5

J

u n- 0 6
D

ec- 0 6

J

u n- 0 7 Dec-07

J u n- 0 8 De

c -08 Jun -09

Source: U.S. Dept. of Energy, Energy Information Admin., http://tonto.eia.doe.gov/dnav/pet/hist/w crrip32w .htm Katrina Ike

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 EIA reports inputs by Petroleum Administration for Defense District (PADD). PADD 3 includes refining operations Reported weekly inputs (light blue) 13-week moving average (dark blue) Quadratic regression fit to weekly data

upper bound

lower bound 1998-2009 range

Figure 11: Weekly Gulf Coast (PADD 3) Crude Oil Inputs to Refineries

This may suggest factors other than an economic downturn in 2007 or 2008 were influencing the industry. Average weekly inputs to refineri es in 2007 were 7.35 million barrels, 1.4 percent above the average of the previous year, and near ly as high as in 2004, when they averaged 7.37 million barrels per week. Though inputs to refineries in 2008 fell about 3 percent after six weeks of hurricane-affected data were removed, they we re back on trend again by 2009, dropping -0.6 percent.

An additional perspective on trends in the petr ochemical industry is provided in Figure 12: Weekly Percent Utilization of Refinery Operable Capacity, PADD 3 , which summarizes utilization of available refining capacity, also reported by the Energy Information Administration of the U.S. Department of Energy ( tonto.eia.doe.gov/dnav/pet/hist/wpuleus3w.htm ). Capacity utilization, like inputs to refineries, is highly variable from week-to-week, but is clearly on a long term downward trend, having peaked in 1998 at 100 .5 percent of operable capacity. Since then, despite considerable seasonal variation, it has fa llen in most years, as evident from the quadratic regression fitted to the series. When considered on an annualized basis to smooth out short term fluctuations, average capacity utilization decreased -0.7 percent from 2006 to 2007, -4.3 percent from 2007 to 2008, and -1.1 percent from 2008 to 2009. The decade-long duration of this downward trend corroborates the earlier conclusi on that the petroleum refining industry may likely be experiencing phenomena unrelated to th e national recession that began in 2007. In summary, the data presented here indicate that the decreasing ozone in the HGB area is not likely primarily due to the recent economic downturn.

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### 9060 65 70 75 80 85 90 95 100

Jan91 Jan92 Jan93 Jan94 Jan95 Jan96 Jan97 Jan98 Jan99 Jan00 Jan01 Jan02 Jan03 Jan04 Jan05 Jan06 Jan07 Jan08 Jan09 Source: U.S. Dept. of Energy, Energy Information Admin. Katrina

Ike %

Reported weekly capacity utilization (light olive) Quadratic regression fit to weekly data (dark olive)

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 12: Weekly Percent Utilization of Refinery Operable Capacity, PADD 3

Emission Trend Analysis

As the EPA correctly stated on pages 102-103 of its modeling guidance, “a simpler (and more uncertain) way to qualitatively assess progress toward attainment is to examine recently observed air quality and emissions trends.” Refer to Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze , EPA - 454/B-07-002, April 2007. An emission trend analys is is less sophisticated, less certain, and less quantitative than other analyses, and for that reas on, no emission trend data were included in the original proposal. The TCEQ contends that ambient NO X and VOC data are a more reliable indicator for historical emission trends than th e emissions inventory da ta, which have known and well-documented problems for industrial emissions. Chapter 5 of the Attainment Demonstration SIP contains extensive historical ozone precursor trend analyses that are based upon monitoring data. These ozone precursor concentration trends are not subject to the underestimates observed in the industrial emissions inventories.

However, the EPA’s request for a trend analysis for past and future emissions by major source category can be satisfied with the Reasonable Further Progress (RFP) emission estimates that are included in Section 2.7 of the HGB RFP SIP (pages 2-16 through 2-18). For more detail on the development of these emission inventories, th e RFP SIP along with its accompanying appendices can be consulted. Figure 13: RFP NO X Emission Trends from 2002-2018 and Figure 14: RFP VOC Emission Trends from 2002-2018 present bar chart summaries of the controlled NO X and VOC emissions compared with uncontrolled emission totals. Tables 2-5 show the data used to make these graphs.

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### 91

### HGB 8-County RFP Controlled Ozone Season Weekday NOx emissions

0 200 400 600 800 1000 1200 1400

2002 2008 2011 2014 2017 2018tons per day reductions
onroad
nonroad

area point

Figure 13: RFP NO X Emission Trends from 2002-2018 Includes each source category, based upon weekday emissions within the eight-county HGB area. The reductions shown here represent the mass of weekday NO X emissions eliminated by controls each year.



### HGB 8-County RFP Controlled Ozone Season Weekday VOC emissions

0 200 400 600 800 1000 1200 1400

2002 2008 2011 2014 2017 2018tons per day reductions
onroad
nonroad

area point

Figure 14: RFP VOC Emission Trends from 2002-2018 Includes each source category, based upon weekday emissions within the eight-county HGB area. The reductions shown here represent the mass of weekday VOC emissions eliminated by controls each year.

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### 92Table 2: HGB Eight-County RFP Uncontrolled Oz one Sea son Weekday NO X Emissions
Calendar Year Point Sources Area Sources Non-Road Mobile On-Road Mobile Grand Total

2002 339.29 89.11 166.98 387.09 982.47

2008 375.56 89.63 202.72 363.05 1,030.96

2011 383.85 93.90 210.01 372.46 1,060.22

2014 401.05 96.01 220.48 407.05 1,124.59

2017 421.57 98.23 230.99 432.04 1,182.83

2018 428.69 99.38 237.25 443.70 1,209.02

Table 3: HGB Eight-County RFP Controlled Ozone Season Weekday NO X Emissions Calendar Year Point Sources Area Sources Non-Road Mobile On-Road Mobile Grand Total
2002 339.29 89.11 156.98 326.72 912.10

2008 155.73 89.63 149.44 175.80 570.60

2011 156.21 93.90 138.83 123.41 512.35

2014 157.17 96.01 128.45 86.61 468.24

2017 158.33 98.23 121.15 61.78 439.49

2018 158.75 99.38 119.88 55.39 433.40

Table 4: HGB Eight-County RFP Uncontrolled Ozone Season Weekday VOC Emissions Calendar Year Point Sources Area Sources Non-Road Mobile On-Road Mobile Grand Total
2002 316.62 407.61 100.15 189.08 1,013.46

2008 333.14 371.82 113.68 194.89 1,013.53

2011 338.75 382.72 120.10 205.03 1,046.60

2014 347.70 393.07 126.28 222.41 1,089.46

2017 358.79 404.84 132.38 237.86 1,133.87

2018 362.90 408.82 134.29 242.84 1,148.85

Table 5: HGB Eight-County RFP Controlled Ozone Season Weekday VOC Emissions Calendar Year Point Sources Area Sources Non-Road Mobile On-Road Mobile Grand Total
2002 316.62 407.61 84.32 132.64 941.19

2008 170.05 371.82 69.23 86.15 697.25

2011 164.33 379.04 64.03 68.35 675.75

2014 172.31 383.42 61.42 56.21 673.36

2017 181.86 394.74 60.05 48.39 685.04

2018 185.38 398.57 59.84 46.68 690.47

I- 

### 93Additional Description of Retros pecti v e Modeling for 2000 and 2006

In accordance with EPA’s modeling guidance, the TCEQ performed a retrospective modeling analysis. The model’s response to emission changes between 2000 and 2006 was compared to the actual ozone design value changes between 2000 and 2006. The initial description of this analysis is presented in Appendix C: 

### Photochemical Modeling for the HGB Attainment Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard of the current HGB Attainment Demonstration SIP. The purpose of th is section is to describe more completely the modeled ozone response to the emission cha nges, and to compare the response in the retrospective analysis to other studies of modeled ozone response.

Uncertainty does exist in both the 2000 and 200 6 emission inventories, as it does for any set of emission estimates. The TCEQ has reconciled both of these inventories with observational data, so that the emission inputs used in the retrosp ective modeling analyses reflect available adjusted inventories. The TCEQ asserts that the retr ospective analysis included in this modeling demonstration is appropriate, and is in acco rdance with EPA modeling and analyses guidance.

The EPA’s modeling and analyses guidance does not precisely specify how a retrospective analysis should be done, but rather provides flex ibility to states in how to carry out this kind of analysis. The TCEQ decided to use the same methodology as the EPA prescribes for an attainment demonstration, which includes m odifying the observed design values by the RRF developed from the modeling. In this instance, the relative change between 2000 and 2006 was used in the same way that the relative change between 2006 and 2018 was used in the RRF calculation.

Figure 15: Modeled and Observed Response to Em ission Changes Between 2000 and 2006 shows how the model and observations respond to the emission changes between 2000 and 2006. The model predicts ozone values 15 percent lo wer with 2006 emissions compared to 2000 emissions. But the observations show that th e ozone design values actually decreased by 28 percent from 2000 to 2006. Figure 16: Modeled and Observed Response to Weekend Emission Reductions shows how the model and observations respond to emission changes that occur between weekdays and weekends. For the weekend effect, the model response is also less than the observed response. Other studies have also found weaker responses by the model than observed. Another example of how photochemical grid models have underestimated the response to emission reductions can be found in a stud y by EPA scientist Alice Gilliland and colleagues, published in 2008 in Atmospheric Environment . In this study, the authors show that both relative and absolute model response to the NO X SIP call emission reductions is less than the observed responses. For the CB05 chemical mechanism (w hich is used by the TCEQ), the relative response in modeled ozone concentrations from 2002 to 2005 is about 73 percent of the observed relative response. The retrospective analysis presented in Appendix C: 

### Photochemical Modeling for the HGB Attainment Demonstr ation SIP Revision for the 1997 Eight-Hour Ozone Standard is in agreement with other studies s howing that modeled ozone response to emission controls is underestimated.

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### 94

### Retrospective analysis, response to emission changes from 2000 to 2006

y = 0.7206x + 12.662
R 2 

### = 0.3995

75 80 85 90 95 100 105 110

75 80 85 90 95 100 105 110
2000 Ozone DV, ppb2006 Ozone DV, ppb 

### y = 0. 8501x + 2.6019
R 2 

### = 0.9026

Modeled
Observed Linear (Observed) Linear (Modeled)

Figure 15: Model and Observed Response to Emission Changes Between 2000 and 2006 Each point represents the modeled or observed eight-hour ozone values for a particular monitoring site. The slopes of the response lin es show the degree of ozone decrease from 2000 to 2006.

I- 

### 95Observed and Modeled weekend effect, 8-hour ozone

30 40 50 60 70 80

30 40 50 60 70 80
Wednesday 8-hr O3Sun 8-hr O3 Mod Sunday Obs Sunday Obs Sun >50ppb
1:1 line
-10%
-20%

Observed and Modeled weekend effect, 1-hour ozone

30 40 50 60 70 80 90

30 50 70 90
Wednesday 1-hr O3Sun 1-hr O3 Mod Sunday Obs Sunday Obs Sun >50ppb
1:1 line
-10%
-20% Observed and Modeled weekend effect, 8-hour ozone

30 40 50 60 70 80

30 40 50 60 70 80
Wednesday 8-hr O3Sat 8-hr O3 Mod Saturday Obs Saturday Obs Sat >50ppb
1:1 line
-10%
-20%

Observed and Modeled weekend effect, 1-hour ozone

30 40 50 60 70 80 90

30 50 70 90
Wednesday 1-hr O3Sat 1-hr O3 Mod Saturday Obs Saturday Obs Sat >50ppb
1:1 line
-10%
-20%

Figure 16: Modeled and Observed Response to Weekend Emission Reductions Blue squares show average daily maximum m odeled ozone concentrations on Wednesday and Saturday or Sunday; green triangles show the average daily maximum observed ozone concentrations on Wednesday and Saturday or Sunday for days with maximum area-wide peak eight-hour ozone over 50 ppb. The latter values ar e more comparable to the modeled days, since the modeled days are usually high ozone days. The green triangles show greater degree of decrease during the weekends than the blue squar es, indicating that the model has less response to emission decreases than the observations.

I- 

### 96Trends in the reactivity of volatile organic compounds in the HGB area Since 2000, t h e TCEQ and its resear ch partners ha ve routinely and systematically examined the reactivity of VOCs observed in Texas. Studies to date have shown that light alkenes (ethylene, propylene, butenes, and 1,3-butadiene) were the VOCs most likely to contribute to ozone formation in Houston; these studies helped form the basis for the HRVOC regulations currently in place in Houston. The purposes of this analysis are to track the progress in reducing HRVOC emissions in Houston by examining how HRVO C reactivity has changed over time, and to determine whether the official TCEQ HRVOCs still comprise the largest portion of the VOC reactivity. The analysis will also show how ove rall VOC reactivity has changed in recent years. The analysis provides weight of evidence that concentrations of ambient HRVOCs, and the emissions that put them into the air, are d ecreasing, and the reduced reactivity of the VOC mixture in Houston correlates with decreasing ozone concentrations.

Background and methods The ability of VOCs to participate in ozone forma tion can be estimated by two methods. The first method is to calculate OH reactivity, i.e., to determine from chemical kinetics how often each VOC can react with the OH radical, which is the first step towards participating in the ozone formation process. The second method is to calcu late the Maximum Incremental Reactivity, i.e., to determine the maximum amount of additional ozone that can be formed when adding an incremental amount of VOC to an urban air mixtur e rich in NOx. Both of these methods involve multiplying the observed concentration of each VOC by its distinctive reactivity factor. OH reactivity is determined by multiplying the con centration by the OH reaction rate coefficient; MIR reactivity is determined by multiplying the concentration by the Maximum Incremental Reactivity coefficient (Carter, 2008), and by a unit conversion factor that converts the MIR coefficient to molar units. The units for OH reactivity are second -1 . This can be interpreted as the number of OH reactions per second (at standard temperature and pressure), but the calculated OH reactivities are only used in a relative sense in this application, not to calculate the number of reactions occurring. The units for MIR reactivity are ppbv of ozone formed per incremental addition of ppbv VOC, but since these units are pr imarily applicable for smog chamber or box model calculations, they are presented as unitless in this context. The calculated MIR reactivity is only used in a relative sense anyway, to id entify which VOCs are likely to be more reactive than others under ideal reaction conditions, not to estimate how much ozone is formed from each VOC.

The OH reactivity indicates how quickly a compound is consumed by reactions with the OH radical, and thus how quickly the VOC can enter the ozone formation process. OH reactivity is therefore only indirectly related to the amount of ozone formed by each particular VOC, but it can indicate how likely it is for an emitted VOC mo lecule to react in the hours immediately after it is emitted, and thus whether it is likel y to create ozone on the day it is emitted.

The MIR reactivity indicates how much ozone each VOC is capable of creating, given ideal ozone formation conditions. However, it does not tell how quickly the ozone could be formed.

These calculations only give the approximate reac tivity of VOCs, because they do not account for availability of radicals for reaction with the VOCs, or for competition among VOCs and other compounds for the available radicals.

Geometric mean reactivities were calculated separ ately for each VOC. VOCs were classified into groups, and the calculated reactivity of all VOCs in the group were added together to get a cumulative reactivity for each group. Official TCEQ HRVOCs comprised the “light alkenes” group. Table A: Classification of measured VOCs shows how the classifications were made for

I- 

### 97the canister and auto-GC sa m ples. Geometric m ean reactivities were calculated for each year, for each monitoring site, for both every-sixth-day, 24-hour canister samples and continuous 1-hour automated gas chromatography measurements, fo r both OH and MIR reactivities. All available data were used in the analysis. OH reaction rate coefficients were obtained from Gilman et al., 2009 and from Atkinson, 2002; MIR reactivity co efficients were obtained from Carter, 2008.

Results and Discussion Figures 17-64 present the reactivity trends for can ister and auto-GC data, for both the OH and MIR reactivity calculations, at each monitoring site. This analysis includes data from sites with at least four years of data, though many of the canister sites have more than 10 years of

measurements.

In general, the OH reactivity and MIR reactivity me asures tell a similar story: the official TCEQ HRVOCs (ethylene, propylene, butene isomers, and 1,3-butadiene) comprise a larger portion of both the total OH and MIR reactivity than any of the other groups of compounds. The higher OH reactivity associated with light alkenes indicates th at they can form ozone more quickly than the other compound groups, and the higher MIR reactivity indicates they are capable of creating more ozone than the other groups.

 There is an apparent downward trend in to tal VOC reactivity at all sites, for both reactivity scales. The downward trends have not yet been tested for statistical significance. The downward trends in total r eactivity due to substantial decreases in the concentrations of light alkenes (i.e., HRVOC s), though in some cases, decreases in heavy alkenes also play a role.

 Monitoring sites in and near the industrial areas (Baytown, Galena Park, Clinton, HRM3, Deer Park, Nessler Pool) consistently observe higher VOC reactivity than sites in urban and suburban areas (Aldine, Bayland Park, Northwest Harris County). They also tend to show the greatest differences in proportion of total reactivity between the light alkenes and the other classes of compounds.

 The greatest differences between TCEQ HRVOCs and other classes of compounds in MIR reactivity are observed at sites located near large ethylene and propylene sources: Baytown, Clute, Deer Park.

 Although the sites far downwind of the i ndustrial areas have considerably lower reactivities than sites near industrial areas, they still have a substantial portion of their reactivity composed of HRVOCs, and they s till show decreasing trends in the reactivity of these HRVOCs over the period of study.

 The reactivity calculations do not take into account reactions with unusual radicals such as chlorine. The MIR reactivity is based upon box modeling of t ypical urban mixtures using the SAPRC07 chemical mechanism. Ther e is evidence to suggest that all of the major chemical mechanisms are missing an important radical formation pathway, and therefore the reactivity data presented here may not fully represent what is actually occurring in Houston’s atmosphere. Since th ere is no scientific consensus on the missing radical formation pathways, however, this assessment represents the best reactivity assessment currently available.

 Studies of TexAQS 2000 data have shown that the 90 th percentile of MIR reactivity values for aircraft canister samples was 166, and 90 th percentile OH reactivity values was 10. The TexAQS aircraft samples were taken throughout the HGB area, and therefore represent a wide geographic area. Subseque nt TCEQ reactivity studies at individual monitoring sites have used these criteria to identify samples with high reactivity. In 1998, the geometric mean MIR reactivity valu es exceeded 166 at the canister monitoring

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### 98-1 ; Galena Park dropped below the high reactivity threshold in 2007.

 Of the 46 trend analyses shown below, 45 show the lowest calculated reactivity occurring during the final year of the trend. The sole exception is the Galveston Airport MIR trend (Figure 12), for which the final year of 2007 is slightly higher than the lowest year of 2005, though both years are at or below 30, i.e., less than 20% of the high reactivity threshold of 166.

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### 99Figures 17 and 18: Geometric mean maximum in cremental reactivity for VOCs measured in 24- hour canisters at Baytown and Gale na Park from 1998 through 2008.

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### 100 Maximum Incremental Reactivity (MIR) of VOCs BAYTOWN- 482010058

1998

De toiled c;:!QS::> 1999 2000 2001



### - c2c3

- cydos

- liqhl o\kenes 2002 2003 2004

- Qlk.c;mes

- ho!oganotes

- O)tygenoles 2005 2006 2007

- cromclic.:s

- l"leo\ly o!kenes

Maximum Incremental Reactivity (MIR) of VOCs GALENA PARK- 48201 005 7

1998

De toiled c;:IQS::> 1999 2000 2001



### - c2c3

- cydos

- li9hl o!kenes 2002 2003 2004

- Qlk.c;mes

- ho!oganotes

- O)tygenoles 2005 2006 2007

- cromclic;::;

- t'leovy o!kenes 2008

2008Figures 19 and 20: Geometric mean maximum in cremental reactivity for VOCs measured in 24- hour canisters at Haden Road/HRM3 a nd Deer Park from 1998 through 2008.

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### 101Figures 21 and 22: Geometric mean maximum in cremental reactivity for VOCs measured in 24- hour canisters at Clute and Aldine from 1998 through 2008.

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### 102Figures 23 and 24: Geometric mean maximum in cremental reactivity for VOCs measured in 24- hour canisters at Bayland Park and No rthwest Harris County from 1998 through 2008.

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### 103 Max·rrum ncrementc Recct"v"ty 

### (rv R) of VOCs HOUSTON BAYLAND PARK- 4820 ' 0055

10

60

!>()

JO

20

10

0 1998

Del oiled elou 1999 '000 ?001



### - c2c3

- cydos

- light olkenef-1 2002 2003 2004

-**olkon~s**

- hologenotes

- O)tygenoles 2005 2006 200 /

- Qromotic, hoovy olkones

Maximum Incremento Recct;v·ty (MIR) of VOCs NORTHW[ST HARRIS COUNTY- 482010029

1995

Crlo' ed c:IOn I 999 ?000 ?001



### - c2c3

- t)'d09

- 'ql'lt**o~<.enes** 2002 2003 200<

-**co~.ore$**

- l'!oog6f"'otes

- O)t)'geno1es 2005 2006 2007

- oromot.t:'"i

- heovy 0 II;QI"CS 2006

2006Figures 25 and 26: Geometric mean maximum in cremental reactivity for VOCs measured in 24- hour canisters at Shore Acres (1998-2008) and Clinton (1998-2006).

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### 104Figures 27 and 28: Geometric mean maximum incremental reactivity for VOCs measured in 24- hour canisters at Nessler Pool (Texas City ) and Galveston Airport from 1998 through 2007.

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### 105Figures 29 and 30: Geometric mean OH reactivity for VOCs measured in 24-hour canisters at Baytown and Galena Park from 1998 through 2008.

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### 106Figures 31 and 32: Geometric mean OH reactivity for VOCs measured in 24-hour canisters at Haden Road/HRM3 and Deer Park from 1998 through 2008.

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### 107Figures 33 and 34: Geometric mean OH reactivity for VOCs measured in 24-hour canisters at Clute and Aldine from 1998 through 2008.

I- 

### 108Figures 35 and 36: Geometric mean OH reactivity for VOCs measured in 24-hour canisters at Bayland Park and Northwest Harris County from 1998 through 2008.

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### 109Figures 37 and 38: Geometric mean OH reactivity for VOCs measured in 24-hour canisters at Shore Acres (1998-2008) and Clinton (1998-2006).

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### 110Figures 39 and 40: Geometric mean OH reactivity for VOCs measured in 24-hour canisters at Nessler Pool (Texas City) and Galveston Airport from 1998-2007.

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### 111Figures 41 and 42: Geometric mean maximum incremental reactivity for VOCs measured by automated gas chromatography at Wallisville Road (2003-2008) and Cesar Chavez High School (2004-2008).

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### 112Figures 43 and 44: Geometric mean maximum incremental reactivity for VOCs measured by automated gas chromatography at Channelview (2001-2008) and Clinton (1998-2008).

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### 113Figures 45 and 46: Geometric mean maximum incremental reactivity for VOCs measured by automated gas chromatography at Dancig er (2003-2008) and Deer Park (1998-2008).

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### 114Figures 47 and 48: Geometric mean maximum incremental reactivity for VOCs measured by automated gas chromatography at Milby Park (2005-2008) and Haden Road/HRM3 (2001, 2003- 2008).

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### 115Figures 49 and 50: Geometric mean maximum incremental reactivity for VOCs measured by automated gas chromatography at Lake Jacks on (2003-2008) and Lync hburg Ferry (2003-2008).

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### 116Figures 51 and 52: Geometric mean maximum incremental reactivity for VOCs measured by automated gas chromatography at Mustang Bayou (2003-2008) and Texas City (2003-2008).

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### 117Figures 53 and 54: Geometric mean OH reactivity for VOCs measured by automated gas chromatography at Wallisville Road (2003-20 08) and Cesar Chavez High School ( 2004-2008).

I- 

### 118Figures 55 and 56: Geometric mean OH reactivity for VOCs measured by automated gas chromatography at Channelview (2001-2008) and Clinton (1998-2008).

I- 

### 119Figures 57 and 58: Geometric mean OH reactivity for VOCs measured by automated gas chromatography at Danciger (2003-200 8) and Deer Park (1998-2008).

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### 120Figures 59 and 60: Geometric mean OH reactivity for VOCs measured by automated gas chromatography at Milby Park (2005-2008) and Haden Road/HRM3 (2001, 2003-2008).

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### 121Figures 61 and 62: Geometric mean OH reactivity for VOCs measured by automated gas chromatography at Lake Jackson (2003- 2008) and Lynchburg Fe rry (2003-2008).

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### 122Figures 63 and 64: Geometric mean OH reactivity for VOCs measured by automated gas chromatography at Mustang Bayou (2003-2008) and Texas City (2003-2008).

Table A: Classification of measured VOCs Class Canister Compounds Auto-GC Compounds Alkanes isobutane isobutane

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### 123n-Butane isopentane n-Pentane 2,3-Dim e thy l Butane 2-Methyl Pentane 2,2-Dimethyl Butane 3-Methylpentane 2-Methyl Pentane n-Hexane 2,3-Dimethyl Pentane 2,4-Dimethyl Pentane 2-Methyl Hexane 3-Methyl Hexane n-Heptane 2,2,4-Trimethyl Pentane 2,3,4-Trimethyl Pentane 2-Methyl Heptane 3-Methyl Heptane n-Octane n-Nonane n-Decane n-Undecane n-butane isopentane n-pentane 2,2-dimethyl butane 2,3-dimethyl butane 2-methyl pentane 3-methyl pentane n-hexane 2,3-dimethyl pentane 2,4-dimethyl pentane 2-methyl hexane 3-methyl hexane n-heptane 2,2,4-trimethyl pentane 2,3,4-trimethyl pentane 2-methyl heptane 3-methyl heptane

n-octane

n-nonane n-decane

Aromatics Benzene Toluene Ethyl Benzene o-Xylene m-Xylene Styrene 1,2,3-Trimethyl Benzene 1,2,4-Trimethyl Benzene 1,3,5-Trimethyl Benzene Isopropyl Benzene m-Ethyl Toluene n-Propyl Benzene o-Ethyl Toluene p-Ethyl Toluene m-Diethyl Benzene Naphthalene p-Diethyl Benzene t_Butylbenzene benzene toluene ethyl benzene o-xylene p-xylene styrene m-xylene 1,2,3-trimethyl benzene 1,2,4-trimethyl benzene 1,3,5-trimethyl benzene isopropyl benzene m-ethyl toluene n-propyl benzene o-ethyl toluene p-ethyl toluene m-diethyl benzene p-diethyl benzene

C2C3 Methanol Acetylene Ethane Acetone Propane acetylene ethane
propane

Cyclos Cyclopentane Cyclopentene Cyclohexane Methylcyclopentane Methylcyclohexane cyclopentane cyclopentene cyclohexane methyl cyclopentane methyl cyclohexane

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### 124Halogenates Chloroprene Cis-1,3-dichl o ropro pylene Methylene chloride Trans-1,3-dichloropropylene Trichlorofluoromethane Methyl Bromide Carbon Tetrachloride Chloroform 1,1,2-Trichloroethane 1,1-Dichloroethane 1,2-Dibromoethane 1,2-Dichloroethane 1,1,1-Trichloroethane Perchloroethylene Trichloroethylene Vinyl Chloride Monochlorobenzene 1,1,2,2-tetrachloroethane 1,1-Dichloroethylene 1,2-Dichloropropane 2-Chloropentane none

Heavy alkenes Isoprene trans-2-butene 1-Pentene 2-Methyl-2-Butene 3-Methyl-1-Butene cis-2-Pentene trans-2-Pentene 1-Hexene 2-Methyl-1-Pentene 4-Methyl-1-Pentene Cis-2-Hexene Trans-2-Hexene 1-Heptene 1-Octene 1-pentene 2-methyl-2-butene 3-methyl-1-butene cis-2-pentene isoprene trans-2-pentene 2-methyl-1-pentene

Light alkenes ethene
propene 1,3-butadiene 1-butene cis-2-butene ethene
propene 1,3-butadiene 1-butene cis-2-butene trans-2-butene Oxygenates Acetaldehyde n-Butyl Alcohol Methyl Ethyl Ketone Vinyl Acetate Methyl t-Butyl Ether Hexanal n-butyl acrylate None

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### 125

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