1 

Final Report 

Texas Environmental Research Consortium Project H-13 

Variable Industrial VOC Emissions and their impact on 

ozone formation 

in the Houston Galveston Area 

Submitted by: 

David Allen, Cynthia Murphy, Yosuke Kimura, William Vizuete, and Thomas Edgar 

Center for Energy and Environmental Resources 

The University of Texas 

Austin, Texas 

and 

Harvey Jeffries, Byeong-Uk Kim, Mort Webster and Michael Symons 

Department of Environmental Sciences and Engineering 

The University of North Carolina 

Chapel Hill, North Carolina

Submitted to: 

Texas Environmental Research Consortium 

April 16, 2004
 2 

I. Executive Summary 

The Houston-Galveston area (HGA) is designated as a severe ozone non-attainment region, and 

the State of Texas is charged with developing a State Implementation Plan (SIP) for reducing 

emissions that lead to ozone formation. A first step in developing the SIP is to characterize and 

quantify the emissions that lead to high ozone concentrations. In the HGA, developing 

inventories of emissions that lead to high ozone concentrations is more complicated than in many 

other urban areas because of the extensive industrial operations in the region. 

Emissions from industrial facilities (point sources) are generally assumed, for SIP development 

purposes, to be continuous and at a nearly constant level. Emissions from Electricity Generating 

Units (EGUs) are the exception, and the State of Texas, and most other regions of the United 

States, use continuously collected data on emissions to characterize the role of EGUs in ozone 

formation. For petroleum refineries, chemical manufacturing facilities, and other industrial 

operations (non-EGUs), however, SIP analyses generally assume emissions are constant and 

continuous. This assumption is made because many non-EGUs operate 24 hours per day, 7 days 

per week, and their material throughput is nearly constant. 

Recent evidence, from a variety of sources, demonstrates that while some types of emissions of 

volatile organic compounds from non-EGU point sources are constant, others are not. The 

evidence includes emission event reports, air pollutant measurements made by aircraft, air 

pollutant measurements made by ground monitors, and industrial process measurements. Daily 

emissions from a single facility can vary from annual average emissions by a factor of 10-1000. 

Variations of this magnitude at any single facility typically occur only a few times per year, but 

because there are so many facilities in the HGA, on many days, there is likely to be a facility 

experiencing significant emission variability. 

Air quality measurements taken in recent field studies have also found evidence of localized 

regions with elevated concentrations of highly reactive volatile organic compounds (HRVOC). 

These regions with elevated HRVOC concentrations are frequently associated with very rapid 

ozone formation, leading to exceedances of the ozone air quality standard. 

This report documents the evidence for HRVOC emission variability from non-EGU point 

sources, characterizes the nature of the variability and assesses the impact of variability on ozone 

formation processes in the HGA. The analyses presented in the report are summarized in a series 

of Findings. 
 3 

Finding 1: Variability in HRVOC emissions from point sources is significant and is due to 

both variability in continuous emissions and discrete emission events1. Roughly 3 times per 

month in 2003, reported emission events caused single facilities to have HRVOC emissions that 

were greater than 10,000 lb/hr (the total annual average emissions of HRVOCs, from all

industrial point sources in the Houston-Galveston region is approximately 5,000 - 10,000 lb/hr). 

Roughly 3 times per week in 2003, reported emission events caused single facilities to have 

HRVOC emissions that were greater than 1,000 lb/hr. Roughly once a day in 2003, reported 

emission events caused single facilities to have HRVOC emissions that were greater than 100 

lb/hr. Variability in continuous emissions is more difficult to quantify than emission variability 

due to reported emission events, but preliminary modeling indicates that variations in 

continuous (as opposed to discrete) emissions could cause emissions of total VOCs, averaged 

over areas larger than 100 km2, to vary by 5-10%. 

2-3 times per month HRVOC emissions variability > 10,000 lb/hr 

2-3 times per week HRVOC emissions variability > 1,000 lb/hr 

daily HRVOC emissions variability > 100 lb/hr

The impact of emission variability on ozone formation can take multiple forms. If the emission 

variability is large enough, and the meteorological conditions are sufficiently ozone conducive, 

the variability in emissions may be sufficient to cause an exceedance of the National Ambient 

Air Quality Standard (NAAQS) for ozone (concentrations averaged over 1-hour) that would not 

have occurred in the absence of the emission event. Documentation, from the Texas 

Commission on Environmental Quality (TCEQ), is provided in the report for a 6700 pound 

release of ethylene that caused ozone NAAQS exceedances at multiple monitors. 

As noted in Finding 1, however, very large variations in emissions are less common than smaller 

variations in emissions. While very large variations in emissions might lead to ozone NAAQS 

exceedances directly, more frequent, smaller variations in emissions have the potential to 

marginally increase the magnitude of ozone concentrations. If the HRVOC emission variability 

occurs at critical times and locations, it can marginally increase the peak ozone concentration 

that might be expected in the Houston-Galveston area. Air quality modeling analyses were 

performed to assess the changes in peak, region-wide ozone concentrations that might be 

expected from HRVOC emission variability in the range of 100-5000 lb/hr. 

Finding 2: HRVOC emission variability in the range of 100-1000 lb/hr, which has been 

reported daily in the Houston-Galveston area, can increase peak, region-wide ozone 

concentrations, if the emission variability occurs in regions upwind of the location of the 

peak, region-wide ozone concentration. The magnitude of the increase in ozone concentration 

depends on the location of the emission variability, the time of day when the emission variability 

occurs and the magnitude of the non-variable ozone precursor emissions. At the most sensitive 

locations, at the most sensitive times of day, releases over approximately a two to three hour 

period can lead to increases of 2-3 ppb in peak ozone concentration per 1000 lb of additional 

HRVOC emissions. This sensitivity may increase as non-variable HRVOC emissions decrease 

and NOx emissions increase. 
 4 

Finding 1 suggests that HRVOC emission variability in the range of 100 to 1000 pounds per hour 

can be expected daily, at some time and some location in the Houston-Galveston area. Finding 2 

suggests that if the emission variability occurs at a sensitive time and a sensitive location, that 

peak ozone concentration could be increased by 2-3 ppb for every 1000 lb of HRVOC emissions 

released. In order to link these two findings, it was necessary to assess how frequently emission 

variability might occur at sensitive times and sensitive locations. 

The bases for the frequency analysis were a TCEQ database on event emissions, described in the 

report, and meteorological assessment of the frequency of ozone-conducive conditions. The 

frequency analysis suggests that, if the emission variability is not large enough to directly cause 

an ozone exceedance, then the probability that additional emissions will occur at the right time 

and at the right location to marginally enhance ozone peak concentration is small. The exact 

probability has significant uncertainty, but is most likely of order 1 in 20 to 1 in 200. Stated 

differently, only 1 in 20 to 1 in 200 randomly selected instances of emission variability is likely 

to be at the right time and in the right location to influence peak ozone concentration. However, 

because the regulatory goal is not to exceed the national ambient air quality standard for ozone 

more than once per year, even if the probability of an event being in the right place at the right 

time is 1 in 100, the frequency analysis documented in this report shows that emission variability 

of roughly 1000 lb/hr should be expected upwind of peak, region wide ozone concentrations. 

This would suggest that, if no actions were taken to reduce emission variability, it would be 

necessary to plan for an event of 1000 pounds in an attainment demonstration, at a location that 

would influence peak ozone concentrations. If actions are taken to reduce emission variability, 

however, the magnitude of the emission variability that should be expected upwind of peak, 

region wide ozone concentrations, could be decreased. 

 

Finding 3: Based on current data, emission variability of roughly 1000 lb/hr should be 

expected in the regions upwind of peak, region wide ozone concentration at least once per 

year in the Houston-Galveston area. This finding is based on estimates of the frequency of 

ozone conducive conditions and the frequency and magnitude of HRVOC emission events 

reported through a TCEQ database. This expected value could potentially be decreased by 

imposing short term limits on HRVOC emission variability. 

1

Reportable emission events are defined by Texas Administrative Code (TAC) Title 30 Chapter 101. Section 101.1, paragraph 

(83) defines a reportable emissions event as “Any emissions event which, in any 24-hour period, results in an unauthorized 

emission equal to or in excess of the reportable quantity...”. The reportable quantity for HRVOCs is 100 lb. Emission 

variability, either discrete, or routine, may not in all cases result in a reportable emission event. 

 
 5 

Contents 

I. Executive Summary 2 

II. Introduction 6 

III. Emission Inventories for air quality modeling in southeast Texas 7 

IV. Variability in emissions from industrial sources 15 

a.) Event emissions 

b.) Continuous emissions 

V. Ozone formation potential of variable emissions 37 

a.) Observational data 

b.) Air quality modeling analyses 

VI. Modeling tools for characterizing the 

ozone formation potential of variable emissions 52 

a.) Computationally efficient sub-domain models 

b.) Combining 3-D photochemical and sub-domain models 

VII. Characterizing the impact of variable emissions 

on attaining the NAAQS for ozone 88 

a.) Estimating the probability of emission events and impacts on peak ozone 

concentrations 

b.) Impact of short term limits and other control scenarios on emission event 

magnitudes 
 6 

II. Introduction 

The Houston-Galveston area (HGA) is designated as a severe ozone non-attainment region, and 

the State of Texas, through the Texas Commission on Environmental Quality (TCEQ), is 

charged with developing a State Implementation Plan (SIP) for reducing emissions that lead to 

ozone formation in the HGA. A first step in developing the SIP is to characterize and quantify 

emissions of volatile organic compounds (VOCs) and oxides of nitrogen (NOx) that lead to 

ozone formation, especially the emissions of ozone precursors that produce the highest ozone 

concentrations. 

Emissions are characterized and quantified in emissions inventories, and emission inventories are 

used for a variety of purposes. For example, emission inventories are used to establish statewide and nation-wide trends in air quality. They are used to determine which categories of 

emission sources are most important in specific geographical areas. They are also used as inputs 

to models that attempt to predict air quality on specific days. The type of information that is 

required in an emission inventory depends on the way in which the inventory will be used. 

Emission inventories that are used to establish air quality trends at regional or national scales 

need only have information about average emission rates. However, emission inventories that 

will be used in models that predict air quality on specific days, or that are used to predict the 

likelihood of extremes in air pollutant concentrations, must consider both average emission rates 

and the daily variability in emissions. 

The concept that daily, even hourly, variability in emissions must be accounted for in air quality 

modeling of specific days, or analyses used to predict extremes in air pollutant concentrations, is 

well established for certain categories of emissions. For example, when considering biogenic 

emissions (compounds released by vegetation) or the vaporization of fuel from vehicles, 

inventories used to establish national trends report emissions developed for average “ozone 

season days”. Temperatures and the intensity of sunlight, which influence these emissions, are 

based on average data or data from representative days. In contrast, when an emission inventory 

is to be used for modeling air quality on a specific day or days, the temperatures and sunlight 

intensities for those specific days are used. Emission rates on specific days can be significantly 

different than on average days. 

This same concept is also applied to some, but not all, emissions from large industrial facilities 

(commonly referred to as point sources). For electricity generating units (EGUs) that emit 

largely NOx and sulfur oxides (SOx), emissions inventories are used in defining rules that limit 

acid precipitation. These inventories are based on annual or seasonal emission rates. In contrast, 

when emissions from EGUs are needed to evaluate ozone formation on specific days, hourly 

emission rates on those days are used in models designed to predict air quality. 

For petroleum refineries, chemical manufacturing facilities, and other industrial operations (nonEGUs), however, emissions, even those used to predict air quality on specific days, are generally 

assumed to be continuous and at a nearly constant level. A large body of observational evidence 

from the Houston-Galveston area indicates that these emissions are not constant and can have 

variability that has a significant impact on the prevalance of extreme ozone concentrations. This 

report documents the evidence for emission variability from non-EGU point sources, 
 7 

characterizies the nature of the variability and assesses the impact of variability on ozone 

formation processes in the HGA. 

III. Emission inventories for air quality modeling in southeast Texas 

Inventories of the emissions that lead to ozone formation in the HGA have been continually 

updated and reviewed for decades (for a review, see Allen, et al., 2003), and the emission 

inventories developed by the State of Texas for air quality modeling are among the most detailed 

that have ever been assembled. In the summer of 2000, however, a large air quality field 

program was undertaken in southeast Texas and the unique measurements that were deployed 

during that field program provided new insights into the magnitude and variability of ozone 

precursor emissions, especially the emissions of VOCs from industrial facilities (point sources). 

Subsequent measurements and analyses, especially at industrial facilities, have further defined 

the features of point source emissions in the HGA. 

Figure 1 summarizes one of several sets of point source VOC emissions that have been used to 

simulate air quality for August 25, 2000. The emissions, as a function of time of day, shown in 

the lower right portion of the Figure, clearly indicate that for this emission inventory point source 

VOC emissions are treated as nearly constant. Figure 2 shows that for this, and other point 

source VOC emission inventories, most of the emissions are calculated or estimated, rather than 

directly measured. 
 8 

Figure 1. Example of one set of point source VOC emissions used in TCEQ’s air quality model for 

August 25, 2000. Note the nearly constant diurnal profile to apportion the daily emission to 

hour of the day. 
 9 

Figure 2. The Houston-Galveston Year 2000 VOC Point Source Emissions by method used to determine 

the emission magnitudes. (Cantu, 2004) 

The data in Figures 1 and 2 summarize information on point source VOC emissions from 

approximately 30,000 distinct sources, called FINs, in the Houston-Galveston area. These FINs 

are connected to atmospheric emission points (called EPNs) that are classified by “point type”. 

The point source emission database has historically had three major types of EPNs, but others are 

being added. The types of EPNs used historically are: 

• FL – flares 

• FU – fugitive emissions 

• ST – stacks 

Figure 3 shows the contribution of these emission point types to the HGA inventory. 
 10 

Figure 3. The contribution of emission point types to Houston-Galveston Year 2000 VOC Point Source 

Emissions (Cantu, 2004) 

Work is underway to add additional point types such as 

• CT – cooling towers 

• PV – polymer vents 

• TK – tanks 

• ELF – equipment leak fugitives 

• COM – combustion device 

• CD – control device 

• WW – waste water fugitives 

Another important component of VOC emission inventories are the “speciation profiles” that 

characterize the composition and therefore the atmospheric reactivity of the VOC emissions. 

Each of the tens of thousands of FINs in the inventory is assigned a profile, resulting in 

approximately 10,000 distinct speciation profiles. The overall composition of the the VOC 

inventory (in a historically used inventory) is summarized in Figure 4. 

Because certain hydrocarbons are highly reactive in the atmosphere, and can play a significant 

role in the rapid ozone formation that is observed in the Houston-Galveston area, a group of 

highly reactive VOCs (HRVOCs) in the inventory merits special attention. These HRVOCs 

include ethene, propene, butenes, and 1,3-butadiene, and they are among the compounds 
 11 

identified in Figure 4. Ambient observations from a variety of sources are being used to revise 

the estimated emissions of these compounds, so the emissions of HRVOCs shown in Figure are 

subject to considerable adjustment. 

Figure 4. The fractional contribution of major VOC species to Houston-Galveston Year 2000 VOC Point 

Source Emissions. (Cantu, 2004) 

Regardless of the absolute magnitude of the point source HRVOC emissions, the emissions of 

highly reactive volatile organic compounds (HRVOCs, which include ethene, propene, butenes 

and 1,3-butadiene) are dominated by a small number of source categories. Shown in Table 1 is a 

list of the major emission source categories for ethene. A small number of source categories 

account for the majority of the emissions. Even within major source categories, such as 

chemical manufacturing, a small number of unit processes can lead to the majority of the 

emissions. Figure 5 shows that fugitive emissions, and emissions from flares, vents and cooling 

towers dominate the inventory of routine emissions of HRVOCs from point sources in the 

Houston-Galveston area. 
 12 

Table 1. Fractions of the ethylene point source inventory assigned to source categories (based on historical inventory data) 

Industrial Processes Chemical Manufacturing Plastics Production Industrial Processes Chemical Manufacturing Fugitive Emissions Industrial Processes Chemical Manufacturing Butylene, Ethylene, Propylene, Olefin Produ

Industrial Processes Chemical Manufacturing General Processes Industrial Processes Chemical Manufacturing Other Not Classified Industrial Processes Chemical Manufacturing Fuel Fired Equipment Industrial Processes Petroleum Industry Fugitive Emissions Industrial Processes Cooling Tower Process Cooling Industrial Processes Petroleum Industry Cooling Towers Industrial Processes Petroleum Industry Flares Industrial Processes Oil and Gas Production Natural Gas Production Industrial Processes Petroleum Industry Petroleum Products - Not Classified Internal Combustion Engines Industrial Natural Gas Petroleum and Solvent Evaporation Petroleum Liquids Storage (non-Refinery) Bulk Terminals Petroleum and Solvent Evaporation Organic Solvent Evaporation Miscellaneous Volatile Organic Compound E

Industrial Processes Chemical Manufacturing Vinyl Acetate Industrial Processes Petroleum Industry Blowdown Systems Industrial Processes Miscellaneous Manufacturing Industries Miscellaneous Manufacturing Industries Petroleum and Solvent Evaporation Petroleum Liquids Storage (non-Refinery) Petroleum Products - Underground Tanks Industrial Processes Chemical Manufacturing Ethylene Glycol Industrial Processes Oil and Gas Production Fugitive Emissions Industrial Processes Chemical Manufacturing Ethylene Oxide 
 13 

Figure 5. Sources of emissions of highly reactive volatile organic compounds (ethene, propene, 

butanes and 1,3-butadiene) from point sources in the Houston-Galveston area 

Finally, even among the source categories with large emissions and the unit operations within 

those source categories with large emissions, a small number of individual units account for a 

large fraction of the emissions. For example, Figure 6 shows that less than 5% of the total flares 

in the HGA account for 50% of the total flare emissions. 
 14 

To summarize, there are tens of thousands of point sources of VOC emissions in the HoustonGalveston area. These VOC emissions include significant quantities of HRVOCs emitted by 

flares, cooling towers and as fugitives, and a small number of sources contribute the bulk of the 

HRVOC emissions. Point source VOC and HRVOC emissions have generally been assumed to 

be continuous and constant, but data, summarized in the next section, indicates that the emissions 

can have substantial variability. 

Figure 6. Emissions from flares sorted by emission rate; 19 flares (3.6% of 527) account for the majority 

of the total flare emissions 
 15 

IV. Variability in emissions from industrial sources 

Emissions from industrial sources can be divided into four major categories: 

• Emissions released at a constant rate due to continuous process operation (nearly constant 

emissions) 

• Emissions released at a variable rate due to fluctuations in process operations (routinely 

variable emissions) 

• Episodic emissions that lead to a significant increase in daily emission rate, yet are below 

the maximum daily permit level (allowable episodic emission events) 

• Emergency releases and other event driven emissions that lead to daily emissions greater 

than permitted levels (large episodic emission events) 

This section describes data available on each of these potential sources of variability in 

emissions, beginning with large, episodic emission events. 

 

IV a. Event Emissions 

Prior to late 2002, emission events above permitted levels were not recorded on a regular basis 

unless a reportable emission of greater than 5000 pounds in a 24-hour period was released. Both 

lack of data and the lack of easy access to the data has made it difficult to quantitatively and 

accurately determine whether the magnitude and or frequency of event emissions appreciably 

affects ozone concentrations and rates of ozone formation, and if so how they are most 

effectively addressed in photochemical modeling and policy development. 

Effective September 12, 2002, per Texas Administrative Code (TAC) Title 30 Chapter 101 

(TAC, 2002), reporting requirements were changed and reportable quantities were reduced from 

5000 lbs. to 100 lbs. for most compounds in the Houston/Galveston ozone non-attainment area. 

Section 101.1, paragraph (83) defines a reportable emissions event as “Any emissions event 

which, in any 24-hour period, results in an unauthorized emission equal to or in excess of the 

reportable quantity...”. Alkanes remain at the 5000 lb limit provided they contain less than 

0.02% of ethene (ethylene), propene (propylene), butene (butylenes), toluene, acetaldehyde, or 

oxides of nitrogen, and less than 2.0% of any other reportable compound. In addition, Texas 

House Bill (HB) 2912 requires that air emission incidents be filed electronically and be posted in 

a publicly accessible database (TCEQ, 2002; Texas House, 2003). This has resulted in the 

availability of on-line data for events beginning January 31, 2003 (2003b). These newly 

available data provide new insight into the magnitude and variability of emission events, 

especially those of highly reactive volatile organic compounds. The data will be used to address 

two key questions: 

Question 1: Are the magnitudes of emission events, singularly and collectively, significant 

relative to that of continuous emissions? The effect of individual events is examined by 

calculating flow rates (pounds per hour) of oxides of nitrogen (NOX), volatile organic 

compounds (VOC), and HRVOC during events and comparing these against annual average flow 

rates. 
 16 

Question 2. What are the characteristics of the events in terms of time, space, and composition? 

The facilities, types of facilities, and/or facility locations that have the largest (greatest mass), 

most frequent, and most persistent events are identified. 

In order to answer these questions, data have been collected from the Texas Commission on 

Environmental Quality (TCEQ) Air Emission Event Reports, which can be downloaded from the 

Internet through an on-line reporting system (TCEQ, 2003b). The data were imported into a 

relational Microsoft Access® database developed by the University of Texas at Austin and data 

for analysis are selected and aggregated by enacting queries within the database. The database is 

described in the Appendix. 

As of December 31, 2003, a total of 1727 events occurring in TCEQ Region 12 (which includes 

the Houston-Galveston area) and beginning on or after January 31, 2003 had been posted on the 

TCEQ web site (TCEQ, 2003b). A summary of these events is presented in Table 2. The release 

of HRVOCs was involved in approximately 40% (711) of these events. Significant HRVOC 

event emissions, both in terms of frequency and mass are limited to four counties, Harris, 

Brazoria, Galveston, and Chambers; 709 out of 711 (99.72%) events and 1,666,540 lbs out of 

1,667,009 lbs (99.97%) occur in these four counties. 

Table 2. TCEQ Region 12 events posted between January 31, 2003 and December 31, 2003 

Event HRVOCs County All Events HRVOC Events 

lbs tons 

Point Source 

HRVOCs TPY 

(2000)** 

Point Source VOCs 

TPY (2001)* 

Harris 934 423 816,961 408 4,736 28,992 

Brazoria 331 187 759,853 380 1,433 6,251 

Galveston 329 86 69,229 35 515 8,342 

Chambers 53 13 20,497 10 112 1,788 

4 County Total 1,647 709 1,666,540 833 6,796 45,373 

 

Fort Bend 40 798 

Montgomery 19 730 

Liberty 1 1 558 <1 475 

Waller 2 205 

HGA Total 1,709 710 1,667,098 833 6,796 47,581 

 

Matagorda 11 1 1 <1 290 

Colorado 4 170 

Wharton 2 525 

Walker 1 16 

Austin 0 458 

Region 12 Total 1,727 711 1,667,099 833 6,796 49,040 

*TCEQ Point Source Database (TCEQ, 2003c) 

**TCEQ Special Inventory (TCEQ, 2003d) 

It can be seen from Table 2 that the mass of emitted HRVOCs is relatively low when considered 

on an annual time-scale and over a broad geographic region (i.e., at the county level). The mass 

of HRVOCs emitted as events, relative to annual VOC emissions, is less than 2%; relative to 

annual HRVOC emissions HRVOC event emissions constitute about 12% of the total. However, 

events are extremely limited in time and space and thus event emissions have the potential to be 
 17 

extremely concentrated. This characteristic is critical to development of an accurate HGA 

emissions inventory and consequently to understanding and modeling the formation of groundlevel ozone. 

 When the data summarized in Table 2 are presented as emission rates for each hour, over the 

course of a year (an annual time series), the temporal pattern of the emissions becomes clearer 

(Methods used in developing the time series are described in the Appendix). Figure 7 presents a 

time series of NOX event emissions facilities in a 4 county region (Brazoria, Harris, Galveston 

and Chambers counties). To provide a point of comparison for the event emissions, the average 

annual flow rate in pounds per hour for all facilities in the 4 county region has been calculated 

from the 2001 TCEQ Point Source Database (TCEQ, 2003c) and is graphed as a horizontal line 

at 24,083 lbs/hr. As can be seen in the figure, only one event exceeds a NOX flow rate of 1000 

lbs/hr and that event, at 7665 lbs/hr is less than a third of the annual average, routine emissions. 

In the case of NOX emissions, it appears that individual events do not significantly add to the 

magnitude of the inventory. Furthermore the total mass of NOx contributed by events is only 

140 tons (280,954 lbs) per year or 0.1% of the 105,482 tons per year emitted by point sources 

located within the four counties. Thus the magnitude of NOX from events appears not to be 

significant relative to that of routine emissions, either singularly or collectively. 

Figure 8 presents a time series of VOC event emissions in the same format as the NOX event 

emissions. The average annual flow rate for routine emissions for all of the facilities in the 4 

county region, 10,359 lbs/hr based on historical inventories, appears as a horizontal line. In 

contrast to NOX, there are 14 times during the eleven-month period in which VOC event 

emissions exceed the annual average. The time involved is 18 hours. In four instances, the flow 

rate of event emissions is more than five times the annual average with a maximum of 86,557 

lbs/hr. The total mass of greater than 4 millions pounds (2018 tons) contributes 4% to the 45,373 

tons of VOC emitted during a single year from point sources in the four counties. Therefore, if 

event reports are complete and reasonably accurate, individual VOC events may have an impact 

on the magnitude (total mass) of the inventory when considered locally and over limited amounts 

of time, but collectively they do not add significantly to the annual, regional inventory. 

The frequency of these events warrants further investigation. Many of the events involve 

unspeciated VOCs and it is unknown whether these many involve significant amounts of 

HRVOCs. In addition, VOCs that have lower reaction rates than the four identified HRVOC 

species may be of interest because of their large total mass and persistence. However, a larger 

examination of VOCs is out of the scope of the current effort. 
 18 

Figure 7. NOX emissions (lbs/hr) from events and as an annual average (from 2001) are presented in a time series 

using 8760 one-hour time blocks for a single year. NOX event emissions exceed 1000 lbs/hr only once and never 

exceed the total 2001 annual average for all facilities in the 4 county area. Event emission data are from TCEQ 

(2003b) and point source data are from TCEQ (2003c). 

Figure 8. VOC emissions (lbs/hr) from events and as an annual average (from 2001) are presented in a time series 

using 8760 one-hour time blocks for a single year. There are 14 times during a roughly eleven-month time period 

when VOC event emissions exceed the total 2001 annual average of 10,359 lbs/hr for all facilities in the 4 county 

area. Event emission data are from TCEQ (2003b) and point source data are from TCEQ (2003c). 

Harris, Galveston, Chambers, and Brazoria Counties

NOX Event Emissions

as Reported Jan 31 - Dec 31, 2003

0

5,000

10,000

15,000

20,000

25,000

0 730 1460 2190 2920 3650 4380 5110 5840 6570 7300 8030 8760

Yearly Hour

Emissions (lbs/hr)

Event Emissions

2001 Annual Avg

7665 lbs/hr

24,083 lbs/hr

Jan 1 Dec 31

Total Event Emissions = 280,954 lbs

Harris, Galveston, Chambers, and Brazoria Counties

VOC Event Emissions

as Reported Jan 31 - Dec 31, 2003

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

0 730 1460 2190 2920 3650 4380 5110 5840 6570 7300 8030 8760

Yearly Hour

Emissions (lbs/hr)

Event Emissions

2001 Annual Avg

10,359 lbs/hr

Jan 1 Dec 31

64,860 

lbs/hr

53,983 

lbs/hr

86,557 

lbs/hr

64,539 

lbs/hr

Total Event Emissions = 4,035,322 lbs
 19 

A time series for HRVOC emissions is shown in Figure 9. The annual average of routine 

HRVOC emissions is calculated from the 2000 special inventory (TCEQ, 2003d), which is the 

only generally available TCEQ inventory that contains speciated HRVOC emissions. The flow 

of HRVOC event emissions exceeds the 1500 lb/hr annual average 29 times during the elevenmonth period (almost 3 times per month), impacting a total of 115 hours. There are 7 times (8 

hours) when the flow exceeds 5 times that of the annual average, with a maximum of 39,340 

lbs/hr. HRVOC event emissions also account for an estimated 12% of the total HRVOC mass 

emitted over the year based on the 828 tons (1.6 million pounds) emitted in 2003. Therefore, if 

event reports are complete and reasonably accurate, HRVOC events have a significant impact on 

the magnitude (total mass) of the inventory when considered both individually and collectively. 

In addition, the frequency is such that they have a marked effect on the temporal profile. 

Figure 9. HRVOC emissions (lbs/hr) from events and as an annual average (from 2001) are presented in a time 

series using 8760 one-hour time blocks for a single year. There are 29 times during a roughly eleven-month time 

period when HRVOC event emissions exceed the 2000 annual average of 1,552 lbs/hr. Event emission data are 

from TCEQ (2003b) and point source data are from TCEQ (2003d). 

Four species, or groups of species have been designated as HRVOCs; these are 1,3-butadiene, all 

isomers of butene, propene (propylene), and ethene (ethylene). For events beginning January 31, 

2003 and posted through December 31, 2003, the total 1,3-butadiene event mass is 53,383 

pounds, the total butene event mass is 105,089 pounds, the total propene (propylene) mass is 

543,783, and the total ethene (ethylene) mass is 954,418 pounds. Time series for event 

emissions for each of these have been developed and are given in Figures 10, 11, 12, and 13. 

Harris, Galveston, Chambers, and Brazoria Counties

all HRVOC Event Emissions

as Reported Jan 31 - Dec 31, 2003

0

5,000

10,000

15,000

20,000

25,000

0 730 1460 2190 2920 3650 4380 5110 5840 6570 7300 8030 8760

Yearly Hour

Emissions (lbs/hr)

Event Emissions

2000 SpEI Annual Avg

1,552 lbs/hr

Jan 1 Dec 31

39,340 

lbs/hr

Total Event Emissions = 1,656,672 lbs
 20 

Figure 10. 1,3-butadiene emissions (lbs/hr) from events and as an annual average are presented in a time series 

using 8760 one-hour time blocks for a single year. Over an 11-month period there are 17 times (affecting 206 

hours) when 1,3-butadiene emissions event emissions exceed the 2000 annual average of 97 lbs/hr. Event emission 

data are from TCEQ (2003b) and point source data are from TCEQ (2003d). 

Figure 11. Butene emissions (lbs/hr) from events and as an annual average are presented in a time series using 8760 

one-hour time blocks for a single year. Over an 11-month period there are 10 times (affecting 64 hours) when 

butene emissions event emissions exceed the 2000 annual average of 237 lbs/hr. Event emission data are from 

TCEQ (2003b) and point source data are from TCEQ (2003d). 

Harris, Galveston, Chambers, and Brazoria Counties

all 1,3-Butadiene Event Emissions

as Reported Jan 31 - Dec 31, 2003

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 730 1460 2190 2920 3650 4380 5110 5840 6570 7300 8030 8760

Yearly Hour

Emissions (lbs/hr)

Event Emissions

2000 SpEI Annual Avg

97

lbs/hr

Jan 1 Dec 31

Total Event Emissions = 53,383 lbs

Harris, Galveston, Chambers, and Brazoria Counties

all Butene Event Emissions

as Reported Jan 31 - Dec 31, 2003

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 730 1460 2190 2920 3650 4380 5110 5840 6570 7300 8030 8760

Yearly Hour

Emissions (lbs/hr)

Event Emissions

2000 SpEI Annual Avg

237

lbs/hr

Jan 1 Dec 31

Total Event Emissions = 105,089 lbs
 21 

Figure 12. Propene emissions (lbs/hr) from events and as an annual average are presented in a time series using 

8760 one-hour time blocks for a single year. Over an 11-month period there are 21 times (affecting 87 hours) when 

propene emissions event emissions exceed the 2000 annual average of 551 lbs/hr. Event emission data are from 

TCEQ (2003b) and point source data are from TCEQ (2003d). 

Figure 13. Ethene (ethylene) emissions (lbs/hr) from events and as an annual average are presented in a time series 

using 8760 one-hour time blocks for a single year. Over an 11-month period there are 58 times (affecting 395 

hours) when ethylene event emissions exceed the 2000 annual average of 586 lbs/hr. Event emission data are from 

TCEQ (2003b) and point source data are from TCEQ (2003d). 

Harris, Galveston, Chambers, and Brazoria Counties

all Propene Event Emissions

as Reported Jan 31 - Dec 31, 2003

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 730 1460 2190 2920 3650 4380 5110 5840 6570 7300 8030 8760

Yearly Hour

Emissions (lbs/hr)

Event Emissions

2000 SpEI Annual Avg

551

lbs/hr

Jan 1 Dec 31

Total Event Emissions = 543,783 lbs

38,734 

lbs/hr

16,338 

lbs/hr

Harris, Galveston, Chambers, and Brazoria Counties

all Ethene Event Emissions

as Reported Jan 31 - Dec 31, 2003

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 730 1460 2190 2920 3650 4380 5110 5840 6570 7300 8030 8760

Yearly Hour

Emissions (lbs/hr)

Event Emissions

2000 SpEI Annual Avg

586

lbs/hr

Jan 1 Dec 31

Total Event Emissions = 954,418 lbs

12,239 lbs/hr
 22 

Ethene (ethylene) exhibits the most significant frequency and magnitude of event emissions. 

Over an 11-month period there are 58 times (affecting 395 hours) when ethylene event emissions 

exceed the 2000 annual average for all point sources in the region of 586 lbs/hr and 7 times 

(affecting 44 hours) when event emissions exceed 5 times the annual average. Next most 

significant is propene (propylene). Over the same period of time there were 21 instances 

(affecting 87 hours) where event emissions exceeded the 2000 annual average of 551 lbs/hr. In 4 

cases (affecting 8 hours), the amount was 5 times the annual average. 1,3-butadiene contributes 

only about half the total mass to event emissions when compared to butene, however it has 

nearly double the number of instances where event emissions exceed the 2000 annual average 

(17 for 1,3-butadiene vs. 10 for butene) and four times the number of events where the amount is 

5 times the annual average (4 for 1,3-butadiene vs. 1 for butene). 

Visual analysis of the time series plots (Figures 9 –13) suggests that the length of the events is 

relatively short. Quantitative evaluation reveals that of the 711 events involving the release of 

HRVOCs, 523 events (74%) last 24 hours or less (Figure 14). 175 events (25%) last one hour or 

less and 82 events (12%) last 10 minutes or less. The distribution of event durations for events 

of up to one hour is shown in Figures 15 and 16 for all HRVOC events and for those emitting 

greater than 1000 pounds of HRVOC. All 27 of the events lasting less than one hour and 

emitting more than 1000 pounds of HRVOC are unscheduled. More than half (17) last less than 

10 minutes. The average release during these large events (greater than 1000 lbs) lasting 10 

minutes or less was 2588 pounds of HRVOC. The average release of large events lasting 60 

minutes or less was 3771 pounds of HRVOC. 

Figure 14. Most events, both scheduled and unscheduled, last less than 24 hours. More than 50% of events that last 

more than two days (48 hours) are scheduled. 

All HRVOC Events

as Reported Jan 31 - Dec 31, 2003

0

100

200

300

400

500

600

0-24 24-48 48-72 72-96 96-120 120-144 144-168 >168

Event Duration (Hours)

Number of Reported Events

Unscheduled

Scheduled
 23 

Figure 15. Of the 711 HRVOC events 175 (25%) last one hour or less and 82 (12%) last ten minutes or less. 

Increased frequency in the 30, 45, and 60-minute bins is probably an artifact of reporting estimates. 

Figure 16. All 27 of the events lasting less than one hour and emitting more than 1000 pounds of HRVOC are 

unscheduled. More than half (17) last less than 10 minutes. 

All HRVOC events

as Reported Jan 31 - Dec 31, 2003

0

10

20

30

40

50

60

70

1-5 6-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60

Event Duration (minutes)

Number of Reported Events

Unscheduled

Scheduled

Events Lasting One Hour or Less with

Greater than 1000 lbs Total HRVOCs

as Reported Jan 31 - Dec 31, 2003 

0

2

4

6

8

10

12

14

1-5 6-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60

Event Duration (minutes)

Number of Reported Events

Unscheduled

Scheduled
 24 

Figures 14-16 differentiate between scheduled and unscheduled events. Scheduled events are 

due to scheduled start-up, shut-down and maintenance activities. Unscheduled events are due to 

a wide range of causes and may result in an unplanned shut-down. The data in Figures 14-16 

show that unscheduled event emissions tend to dominate. 

As of December 31, 2003 a total of 79 entities had reported events with HRVOC emissions 

beginning on or after January 31, 2003. Approximately one third of these entities are identified 

in the TCEQ Central Registry (TCEQ, 2003e) with a primary standard industrial code (SIC) of 

2869, Industrial Organic Chemicals. Just under 20% (13) entities have the primary SIC 2821, 

Plastics Materials. 10 entities are petroleum refineries (SIC 2911) and at least 8 are pipelines 

(SICs 4613 and 4619 plus some of the entities for which no SIC is listed). The distribution of 

HRVOC event emitting entities by primary SIC is shown in Figure 17. 

SIC SIC Description SIC SIC Description 

2869 Industrial Organic Chemicals 1321 Natural Gas Liquids 

2821 Plastics Materials 4619 Pipelines 

2911 Petroleum Refining 2822 Synthetic Rubber (Vulcanizable Elastomers) 

none listed* *includes some pipelines 9999 Nonclassifiable Establishments 

4613 Refined Petroleum Pipelines 4212 Local Trucking Without Storage 

4226 Special Warehousing and Storage 2899 Chemicals and Chemical Preparations 

2865 Cyclic Organic Crudes and Intermediates 1311 Crude Petroleum and Natural Gas 

Figure 17. There were 79 reporting entities representing 14 different primary standard industrial codes (SICs) 

reporting HRVOC event emissions in the last 11 months of 2003. 28 of the entities (35%) had a primary SIC of 

2869, Industrial Organic Chemicals. 

As Reported Jan 31 - Dec 31, 2003

28

13

10

7

4 333 2 2 1111

0

5

10

15

20

25

30

35

2869 2821 2911 none

listed

4613 4226 2865 1321 4619 2822 9999 4212 2899 1311

Primary SIC

Number of Reporting Entities

Number of Reporting Entities w ith HRVOC Events by Primary SIC
 25 

 Reporting Entity Primary SIC SIC Description Total HRVOC lbs

A Dow Texas Operations Freeport 2869 Industrial Organic Chemicals 467,138 

B Equistar Chemicals Channelview Complex 2869 Industrial Organic Chemicals 186,813 

C Equistar Chemicals Chocolate Bayou Complex 2869 Industrial Organic Chemicals 117,498 

D BP Amoco Chemical Chocolate Bayou Plant 2869 Industrial Organic Chemicals 90,366 

E Equistar Chemicals La Porte Complex 2869 Industrial Organic Chemicals 89,763 

F Lyondell Chemical Channelview 2869 Industrial Organic Chemicals 83,596 

G Lyondell-Citgo Refining 2911 Petroleum Refining 83,017 

H Sunoco Inc R & M Bayport Polypropylene 2821 Plastics Materials 67,148 

I Chevron Phillips Chemical Sweeny Complex 2911 Petroleum Refining 54,451 

J Chevron Cedar Bayou Chemical Plant 2869 Industrial Organic Chemicals 51,583 

K Exxon Mobil Baytown Facility 2911 Petroleum Refining 36,045 

L Union Carbide Texas City Operations 2869 Industrial Organic Chemicals 31,034 

M Shell Oil Deer Park 2911 Petroleum Refining 25,059 

N BP Solvay Polyethylene NA 2821 Plastics Materials 23,319 

O BP Products North America Texas City 2911 Petroleum Refining 22,414 

Figure 18. The top 15 reporting entities for 2003 in terms of total HRVOC event emissions mass account for nearly 

90% of the total HRVOC event emissions mass in Harris, Brazoria, Galveston, and Chambers Counties. The top 6, 

all with a primary SIC of 2869 (Industrial Organic Chemicals) emitted 951,579 lbs and thus contributed 

approximately 60% of the four county total. 

While only 35% of the reporting entities had a primary SIC code of 2869, this industrial sector 

accounts for more than 1 million of the 1.6 million pounds of HRVOC event emissions reported 

for the last eleven months of 2003. In addition, the top six facilities in terms of total HRVOC 

emission mass all fall within this SIC classification (Figure 18). The total mass for these six 

reporting entities is 951,579 lbs. Figure 18 shows the total mass and corresponding SIC codes 

for the 15 entities with the largest amounts of HRVOC event emissions. Together these 15 

facilities released a total of 1,429,244 lbs or almost 90% of all 79 reporting entities. 

The amount of HRVOCs reported as emitted during any single event ranges from one pound to 

203,000 pounds. More than half of the events (375 out of 711) emitted between 100 and 1000 

lbs (Figure 19). However, the actual number of events with less than 100 lbs of HRVOC is 

likely to be higher than the reported 160, since 100 lbs is the reporting threshold. Data on these 

events are available only because a reportable quantity of one or more compounds other than an 

HRVOC was emitted during the same event. 

As Reported Jan 31 - Dec 31, 2003

0

100,000

200,000

300,000

400,000

500,000

A B C D E F G H I J K L M NO

Facility

HRVOC (lbs)

Total HRVOC Event Emission Mass by Reporting Entity
 26 

Figure 19. The amount of HRVOCs emitted during any single event, as reported in 2003, ranges from one pound to 

203,000 pounds. More than half of the events (375 out of 711) are reported to have emitted between 100 and 1000 

lbs. 

To summarize, if event reports are complete and reasonably accurate, HRVOC events contribute 

approximately 12% to the total annual HRVOC mass emitted, and large events (those that 

exceed the annual average) occur on the order of almost three times a month (29 in an 11 month 

period). More than half of the mass is attributable to ethene and almost one third is due to 

propene. The remaining 10% consists of isomers of butene and 1,3-butadiene. In addition to 

dominating the mass, ethene has the most frequent events, with emissions from events exceeding 

the annual average flow rate for this compound more than once per week. 

The length of the events is relatively short. Of the 711 HRVOC events, 523 (74%) last 24 hours 

or less, 175 (25%) last one hour or less, and 82 (12%) last 10 minutes or less. Despite their short 

duration, however, the magnitude of the emissions can still be quite large. 27 of the events 

lasting one hour or less release more than 1000 pounds of HRVOCs. 

Roughly two-thirds of the mass is attributable to reporting entities with a primary SIC code of 

2869 (Industrial Organic Chemicals) and 90% of the reported mass in 2003 can be assigned to 15 

reporting entities. Although the amount of HRVOC event emissions per event in 2003 ranged 

from one pound to 203,000 pounds, more than half of the events (375 out of 711) emitted 

between 100 and 1000 lbs. 

As Reported Jan 31 - Dec 31, 2003

160

375

142

33

1

0

100

200

300

400

500

0-100 100-1000 1,000-10,000 10,000-100,000 >100,000

Mass of HRVOC per Event (lbs)

Number of Events

Frequency of HRVOC events by HRVOC mass
 27 

IV b. Continuous emissions 

As noted in the introduction to this section, industrial emissions fall into several categories: 

• Emissions released at a constant rate due to continuous process operation (nearly constant 

emissions) 

• Emissions released at a variable rate due to fluctuations in process operations (routinely 

variable emissions) 

• Episodic emissions that lead to a significant increase in daily emission rate, yet are below 

the maximum daily permit level (allowable episodic emission events) 

• Emergency releases and other event driven emissions that lead to daily emissions greater 

than permitted levels (large episodic emission events) 

The data in Section IV a. described large episodic emission events; the variability in emissions 

introduced by routinely variable and allowable episodic emissions are described here and in 

more detail in the Appendix. 

To more clearly define the emission categories, Figure 20 reports the mass flow rate to a flare at 

an olefins facility, over approximately a year of operation. The flare has constant continuous 

emissions associated with a mass flow rate of approximately 2000 lb-mol/hr. Variable 

continuous mass flows add approximately 1000 lb-mol/hr, leading to the actual annual average 

mass flow rate of 2930 lb-mol/hr (blue line). This is below the average annual permitted mass 

flow rate of 3430 lb-mol/hr (purple line). Approximately weekly, episodic emissions lead to 

daily mass flows that are double the annual average (6000 lb-mol-hr). This is well below the 

daily allowable maximum flow to the flare of 34,700 lb-mol/hr. Twice during the year, large 

episodic emission events led to exceedances of the daily allowable maximum flow. 

Figure 20. Variability in mass flow rates (a surrogate for emissions) to a flare at an olefins facility 
 28 

Data related to VOC emissions from cooling towers also exhibit variability. Figure 21 shows the 

concentrations of VOC in cooling tower water (a surrogate for emissions) over roughly nine 

months of operation. In this case, constant continuous emissions are very small. Variable 

continuous emissions are also relatively small, but episodic emissions can be significant. 

Figure 21. Variability in concentrations of hydrocarbons detected at a cooling tower (a surrogate for 

emissions) 

To adequately describe emissions from units such as the flare of Figure 20 or the cooling tower 

of Figure 21, each of the four contributions to the total emissions must be accounted for. The 

first contributor, constant continuous emissions, is straightforward and can be characterized 

using existing emission estimation techniques. The remaining 3 components are not constant 

emissions. Quantifying these variable emission rates requires a new approach to characterizing 

point source emissions, illustrated in Figure 22. 

Figure 22 illustrates how the flare flow data of Figure 20 can be characterized using a probability 

distribution function (PDF). The distribution describes the fraction of the time that the source 

emits at a particular rate. For example, the PDF on the left hand side of Figure 22 indicates that 

the probability of the flare having a mass flow rate within the range labeled as δf (roughly 4.9 to 

5.1 thous. lb/hr) is the probability density (0.03) multiplied by δf. 
 29 

Figure 22. Probability distribution function (PDF) of mass flow rate for a flare at an olefins facility 

Figures 20 and 21 illustrate variability for specific flares and cooling towers. The limited data 

that are available indicate that not all flares, and not all cooling towers, have the same PDFs. For 

example, Figure 23 shows mass flow data for a different flare than in Figure 20 and Figure 24 

shows hydrocarbon concentrations for a different cooling tower than in Figure 21 (additional 

data are available in the Appendix). 

Figure 23. Variability in mass flow rates (a surrogate for emissions) in a flare at a different plant than 

shown in Figure 20 
 30 

Figure 24. Variability in concentrations of hydrocarbons detected at a cooling tower (a surrogate for 

emissions) at a different plant than shown in Figure 21 

Comparison of Figures 20 and 21 and Figures 23 and 24 indicate that there will be some 

differences in PDFs of emissions for specific flares, specific cooling towers and other unit 

operations. Examination of the emerging data on these unit operations, however, suggest that a 

limited number of PDFs could characterize the major types of emission sources. Thus, the 

variable emissions from a facility might be characterized by a group of PDFs, as shown in Figure 

25. 

Figure 25. Variable emission sources within a facility could be characterized by a set of PDFs, each representing a 

particular class of emission sources. 
 31 

There are a number of advantages to describing variable emissions using PDFs. PDFs describe 

the full range of possible emission scenarios or emission snapshots. Examining the full range of 

these snapshots allows for a more comprehensive assessment of the variable emissions that can 

lead to rapid ozone formation and high ozone concentrations. The PDFs can also be used to 

forecast future emissions and control strategies can be modeled by varying the PDFs; for 

example, a strategy designed to reduce large emission could be expressed as a reduced 

probability of a high emission. 

Because of these, and other advantages, PDFs have been used in other types of environmental 

analyses (e.g., estimation of risks) and there are sophisticated analysis tools for describing and 

manipulating PDFs. Balancing these advantages are the challenges of assessing the accuracy of 

PDFs describing emissions, using the PDFs to develop a region wide inventory, and accounting 

for the ozone formation implications of variable emissions in air quality models. 

PDFs describing variability in point source emissions have been based on analyses of historical 

data of emission surrogates (e.g., mass flow rates to flares). The simplest approach in modeling 

historical data is to assume that the emission rate or the emission surrogate is due to one type of 

phenomenon, and therefore can be represented by a single PDF. For example, if all of the mass 

flow to a flare were due to venting of tanks due to diurnal cycling, then a single PDF 

representing the range of observed temperature cycling should be sufficient to describe the flow. 

Figure 26 shows an attempt to model mass flow rates to a flare using a single PDF. The PDF 

and a simulated mass flow rate time series are shown. The mass flow rate time series can be 

compared to the original data in Figure 20. In this case it is clear that a single PDF is not able to 

characterize the flare flow. 

Primary Flare Flow (klb/hr)

0 1 2 3 4 5 6 7 8 9 10

Probability Density 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

LogNormal(0.98, 0.26)

Simulated Flare Flow

Hour of Simulation

0 1000 2000 3000 4000 5000 6000 7000 8000

Flare Flow (klbs/hr)

0

2

4

6

8

10

12

Figure 26. Mass flow rates to a flare modeled using a single PDF. The PDF and a simulated mass flow rate time 

series are shown. The mass flow rate time series can be compared to the original data in Figure 20. In this case it is 

clear that a single PDF is not able to characterize the flare flow. 
 32 

Figure 27. Four major types of phenomena that may lead to emission variability 

A closer examination of the original data indicates that it is unlikely that a single phenomenon is 

responsible for the flare flow. Figure 27 shows that there are at least four major types of 

phenomena that may lead to very different flare flow rates. These are labeled in the Figure as 

nearly constant, routinely variable, allowable episodic and emission event. Work is currently 

underway to link physical causes to these different types of phenomena; for example, an 

emission event may be caused by the failure of a compressor that feeds hydrocarbons to a 

reactor. This type of event leads to a very different PDF than the diurnal cycling of tanks (a 

nearly constant or routinely variable emission). Therefore, it should not be surprising that 

multiple PDFs might be required to represent an emission source. Figure 28 shows the multiple 

PDFs used to model a flare, and Figure 29 shows a simulated time series of emissions predicted 

using the multiple PDF model and an actual time series. Data on additional flares and cooling 

towers are provided in the Appendix. 

 
 33 

Figure 28. Three PDFs are used to model the emissions from a flare. To simulate an hourly emission, the emission 

model first randomly selects which flow mode the flare is in, nearly constant, routinely variable or allowable 

episodic (emission events are handled separately). Then an emission flow rate is selected for that hour, based on the 

PDF in Figure a. The combination of time in mode and probability of emission rate in each mode can be plotted as a 

composite PDF, shown in Figure b. For the next hour of emissions, the mode is selected based on the probability 

that the flare operates in the same mode as the previous hour, or transitions to a new mode – this probability is 

shown in Figure c. 

Hours Within Mixture Component before next Transition

0 5 10 15 20 25 30

Probability Density

0.0

0.2

0.4

0.6

0.8

1.0

Nearly Constant Component

Routinely Variable Component

Allowable Episodic Component

a.) b.) 

c.)
 34 

Figure 29. Simulated and actual time series of flare flow simulated using the model of Figure 28. Note that the 

goal is not to represent the exact time series, but to represent the variability in the time series. 

Thus, with multiple PDFs, it is possible to provide a reasonable representation of emission 

variability from emission sources such as flares. The next step is to apply the variable emissions 

models to the entire Houston-Galveston area, rather than to just a single emission source. This 

involves some uncertainties, since the available data indicate that individual sources like flares 

may have very different emission variabilities, and there is not yet a reasonable basis for 

assigning specific emission types for every source in the region. 

Nevertheless, some preliminary results are shown. The point of presenting these preliminary 

results is to assess how the overall emission variability will be affected by a large number of 

independently variable sources. 
 35 

The analysis, described in detail in the Appendix, assumes that total annual emissions remain 

fixed, but that individual sources exhibit independent variability; so, if one source is in an 

allowable episodic emission mode, another source might be in a routine variability mode. Using 

these constraints, and eliminating large episodic events which were described in Section IVa., a 

time series of the emissions for the entire region can be estimated. Figure 30 shows one possible 

hourly profile of total VOC emissions for 200 days (not including large emission events 

described in Section IVa). This is an “instance” or random sample for the aggregate of all point 

source VOC emissions over all of the Houston Galveston area. Many other instances are 

possible. Note that the total variability in emissions, summed over the entire domain, is much 

less than the variability in any single source. The estimates suggest that the mass associated with 

this combined variability is about 5-10% of the inventory, comparable to the mass due to 

emission events (which are not included in this time series). 

Figure 30. One possible hourly profile of total VOC emissions for 200 days. This is an “instance” or random 

sample for the aggregate of all point source VOC emissions over all of the Houston Galveston area. 

Hour of Simulation

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

VOC Emissions (tons/hr)

6.0

6.1

6.2

6.3

6.4

6.5

6.6

To better characterize local impacts of variability in emissions, it is useful to focus on a specific 

geographic sub-region of interest. As an example, Figure 31 shows an estimated inventory for a 

region south of the ship channel and including Deer Park. Figure 31 also shows the estimated 

variability in the emissions in the subregion (ignoring the effects of transport, i.e., considering 

only emissions released in the subregion; details of the analysis are available in the Appendix). 
 36 

Figure 31. Geographical subregion and estimated variability in the emissions in that subregion 

Hour of Simulation

0

500

100015002000250030003500400045005000

Ethylene Emissions (tons/hour)

0.05

0.06

0.07

0.08

0.09

0.10

0.11

0.12

0.13

Hour of Simulation

0

500

100015002000250030003500400045005000

VOC Emissions (tons/hour)

1.10

1.15

1.20

1.25

1.30

1.35

1.40

The 5-10% variability of total emissions in the subregion, with an area of approximately 100 

km2

, is comparable to the variability in total emissions for the entire region, but for specific 

compounds, such as ethylene, the variability increases because a smaller number of sources is 

being considered. For ethylene, Figure 31 suggests that variability in emissions could cause 

emission rates in localized sub-regions to double for specific compounds roughly once per 

month. 

Figure 13: 

Finding 1: Variability in HRVOC emissions from point sources is significant and is due to both

variability in continuous emissions and discrete emission events. Roughly 3 times per month in

2003, reported emission events caused single facilities to have HRVOC emissions that were greater

than 10,000 lb/hr (the total annual average emissions of HRVOCs, from all industrial point sources

in the Houston-Galveston region is approximately 5,000 - 10,000 lb/hr). Roughly 3 times per week

in 2003, reported emission events caused single facilities to have HRVOC emissions that were

greater than 1,000 lb/hr. Roughly once a day in 2003, reported emission events caused single

facilities to have HRVOC emissions that were greater than 100 lb/hr. Variabilityin continuous

emissions is more difficult to quantify than emission variability due to reported emission events but

preliminary modeling indicates that variations in continuous (as opposed to discrete) HRVOC

emissions could cause emissions of total VOCs, averaged over areas larger than 100 km2, to vary by

5-10%. 
 37 

V. Ozone formation potential of variable emissions 

V a. Observational data Ground VOC Monitors 

The emission variability documented in the previous section is qualitatively apparent in ambient 

measurements of hydrocarbon concentrations. Ground monitors, that record the hourly average 

concentrations of more than 50 individual hydrocarbons at several sites in the HGA, show 

significant variability in VOC concentrations. Figure 32 shows time series of the hourly average 

concentrations of ethene, propene and other organic compounds at two sampling sites located 

near the heavily industrialized Ship Channel. The data are for August 2000, and demonstrate 

that total hourly averaged non-methane hydrocarbon concentrations can exceed 2000 ppb and 

concentrations of individual highly reactive hydrocarbons can exceed 100 ppbC on a weekly 

basis. 

Figure 32. Selected ambient 

reactive VOCs measured 

hourly for August 2000 at 

two sites in Houston near the 

Ship Channel. (Sum-CH4 

represents non-methane 

hydrocarbons) 
 38 

The distributions of concentrations documented in Figure 32 are described in Table 3. Note that 

the data are hourly measurements, so a measurement at the 99.5 percentile level (equivalent to 1 

in 200) occurs approximately weekly (or once every 168 hours). 

Table 3. Statistical summary of hydrocarbon concentration distributions 

The time period of August 2000 is not unique in its record of variable, and at times very elevated 

concentrations of highly reactive hydrocarbons. Figure 33 (Main, et al, 2001) report 

concentrations of ethene as a function of time of day at the Deer Park monitoring site (dominated 

by industrial sources) for the entire year of 2000. Similar data analyses are available for 1,3 

butadiene, propene, and other reactive hydrocarbons. Figure 33 indicates that the median ethene 

hourly concentration is less than about 10 ppbC for all hours and that 75 percent of the 

concentrations fall below 20 ppbC. However, there are a number of hours when ethene 

concentrations are significantly greater than 50 ppbC and some approach 300 ppbC. 

The data from Deer Park can be contrasted with a similar data set collected at the Aldine 

monitoring site (largely residential, but at times, downwind of the industrial source region). The 

Aldine data are shown in Figure 34. For Aldine, ethene concentrations did not exceed 100 ppbC 

and the frequency of ethylene concentration measurements in excess of 40 ppbC is much lower 

than at Deer Park, suggesting that many of the extreme values in ambient concentrations are due 

to proximity to industrial sources. 

While Figures 33 and 34 suggest that there is variability in VOC and HRVOC concentrations, 

the variability can be attributed to both variability in emissions and variability in atmospheric 

conditions. The data in Figure 33 show that the highest concentrations are observed in the 

morning hours when mixing heights are low and when hydrocarbons have had the little 

opportunity to react, indicating that at least some of the ambient concentration variability is due 

to meteorology. 
 39 

0 10 20 30

HOUR

0

100

200

300

ETHYL

0 10 20 30

HOUR

0

10

20

30

40

50

60

70

80

90

ETHYL

Figures 33 and 34 provide summaries of observed concentration distributions through lines that contain boxes and whiskers, as shown in the

diagram. .The box shows the 25th, 50th (median), and 75th percentiles. The whiskers have a maximum length equal to 1.5 times the length of the

box (the interquartile range). If there are data outside this range, the points are shown on the plot. Asterisks represent points that fall within three

times the interquartile range from the end of the box and circles representing points beyond this. 

0

100

200

300

Outliers

Whisker

Notch

around

median

Median

95 % C.I.

95 % C.I.

0

5

10

15

20

Measure

75th percentile

Median

25th percentile

Box

(Interquartile

range)

Whisker

Outliers

Data within 1.5*IR

Data within 3*IR

Figure 33. Notched box plots of ethene concentrations 

(ppbC) by time of day in 2000 at the Deer Park 

monitoring site (Main, et al, 2001). 

Figure 34. Notched box plots of ethene concentrations 

(ppbC) by time of day in 2000 at the Aldine monitoring 

site (Main, et al, 2001) 
 40 

One qualitative method for attempting to separate the effects of meteorology from emissions 

variability in the concentrations of VOCs observed at ground monitors is to examine time series. 

Figure 35 shows time series of concentrations, observed at the Clinton monitor, of total VOCs, 

ethene, and propene. The Figure shows the hourly averaged concentrations at various percentile 

levels. If meteorology were the sole cause of concentration variability, consistent annual cycles 

would be expected due to the seasonality of wind direction, and similar time series patterns 

might be expected for individual pollutants (such as ethene and propene) due to the effects of 

low mixing heights on certain days. Neither of these patterns is evident in the data. 

Percentiles Values of Total VOC Concentration:

Hourly AutoGC Measurements at Clinton

0

1000

2000

3000

4000

Jan-99

May-99

Sep-99

Jan-00

May-00

Sep-00

Jan-01

May-01

Sep-01

Total VOC Concentrataion

(ppbC)

50%

75%

90%

95%

98%

99%

Percentiles Values of Ethene Concentration:

Hourly AutoGC Measurements at Clinton

0

50

100

150

200

Jan-99

May-99

Sep-99

Jan-00

May-00

Sep-00

Jan-01

May-01

Sep-01

Ethene Concentrataion (ppbC)

50%

75%

90%

95%

98%

99%

 

Percentiles Values of Propene Concentration:

Hourly AutoGC Measurements at Clinton

0

50

100

150

200

Jan-99

Apr-99

Jul-99

Oct-99

Jan-00

Apr-00

Jul-00

Oct-00

Jan-01

Apr-01

Jul-01

Oct-01

Propene Concentrataion (ppbC)

50%

75%

90%

95%

98%

99%

Figure 35. Concentration time series, 

observed at the Clinton monitor, for total 

VOCs, ethene, and propene. The Figure 

shows the concentrations at various 

percentile levels. For example, the line 

labeled 99% represents the 99th

percentile or the highest 1% of 

concentrations recorded each month. If 

meteorology were the sole cause of 

concentration variability, consistent 

annual cycles would be expected due to 

the seasonality of wind direction, and 

similar time series patterns might be 

expected for individual pollutants (such 

as ethene and propene) due to the effects 

of low mixing heights on certain days. 

Neither of these patterns is evident in the 

data. 
 41 

Further evidence from ground monitors on the role of emissions variability as a factor in driving 

variability in ambient concentrations comes from FTIR based monitoring. An FTIR monitor is 

located in the Bayport region on the western coast of Galveston Bay. Shown in Figure 36 are 

time series of ethene and propene concentrations observed at the monitor in 2003. The data 

show concentrations above 50 ppb (100 and 150 ppbC for ethylene and propylene, respectively) 

on a roughly monthly basis. 

Figure 36. Time series of ethene and propene concentrations observed with an FTIR monitor at the Seabrook site 

(near Bayport) 

Ethylene

9/15/2003 1:00

2/21/2003 1:42 6/7/2003 1:28 8/10/2003 14:48

0

0.05

0.1

0.15

0.2

0.25

0.3

1/1/2003 0:00 2/20/2003

0:00

4/11/2003

0:00

5/31/2003

0:00

7/20/2003

0:00

9/8/2003 0:00 10/28/2003

0:00

12/17/2003

0:00

PPM

Propylene

6/16/2003 23:05

8/9/2003 11:14

0

0.05

0.1

0.15

0.2

0.25

1/1/2003 0:00 2/20/2003

0:00

4/11/2003

0:00

5/31/2003

0:00

7/20/2003

0:00

9/8/2003 0:00 10/28/2003

0:00

12/17/2003

0:00

PPM

These data are consistent with measurements made at other monitoring sites that rely on 

automatic gas chromatographs (auto-GCs). The new information that this monitor provides is 

sub-hour time resolution. The auto-GCs provide an hourly average measurement of hydrocarbon 

concentrations that is based on a sample collected over a 40-minute period – followed by a 20 

minute analysis/purge sequence. The IR monitor collects spectra continuously and currently 

reports 13-14 minute average concentrations. Therefore, the data from the IR monitor provide 

data on a finer time scale. Those measurements are summarized in Figure 37. 
 42 

Figure 37. Duration of individual events hydrocarbon detection events for ethylene and propene, where the 

duration is defined as the length of time between the start of detection and the return to a no-detect level (10 ppb for 

ethylene and 20 ppb for propylene). 

Ethylene

0

20

40

60

80

100

120

26 52 78 104 130 156 182 208 234 260 286

Duration of a event (minute)

Frequency (#)

Propylene

0

10

20

30

40

50

60

26 52 78 104 130 156 182 208 234

Duration of a event (minute)

Frequency (#)

Figure 37 shows, for ethylene and propylene, the duration of individual events, where the 

duration is defined as the length of time between the start of detection and the return to a nodetect level (10 ppb for ethylene and 20 ppb for propylene). The data clearly show that some 

events persisted for up to half a day, and some events were very short, but most lasted 

approximately an hour (4-6 measurement periods). These data are qualitatively consistent with 

the durations of emission events reported in Section IVa. 
 43 

Figure 38. Time series pattern of the duration of individual hydrocarbon detection events for ethylene and propene, 

where the duration is defined as the length of time between the start of detection and the return to a no-detect level 

(10 ppb for ethylene and 20 ppb for propylene). 

Ethylene

0

50

100

150

200

250

300

350

1/1/03 0:00 2/20/03 0:00 4/11/03 0:00 5/31/03 0:00 7/20/03 0:00 9/8/03 0:00 10/28/03 0:00 12/17/03 0:00

Duration of a event (minute)

Propylene

0

50

100

150

200

250

1/1/03 0:00 2/20/03 0:00 4/11/03 0:00 5/31/03 0:00 7/20/03 0:00 9/8/03 0:00 10/28/03 0:00 12/17/03 0:00

Duration of a event (minute)
 44 

Figure 39. Time series of hexane concentrations (in this case note that a concentration of 50 ppb corresponds to 300 

ppbC) detected at the Seabrook FTIR monitor. 

Hexanes

0

0.05

0.1

0.15

0.2

0.25

0.3

1/1/03 0:00 2/20/03

0:00

4/11/03

0:00

5/31/03

0:00

7/20/03

0:00

9/8/03 0:00 10/28/03

0:00

12/17/03

0:00

PPM

Figure 40. Time series of formaldehyde concentrations detected at the Seabrook FTIR monitor. 

Formaldehyde

0

0.005

0.01

0.015

0.02

0.025

1/1/03 0:00 2/20/03

0:00

4/11/03

0:00

5/31/03

0:00

7/20/03

0:00

9/8/03 0:00 10/28/03

0:00

12/17/03

0:00

PPM

Figures 39 and 40 provide some additional insights from the Seabrook site FTIR dataset. Figure 

39 shows a time series of hexane concentrations (in this case note that a concentration of 50 ppb, 

or 0.05 ppm, corresponds to 300 ppbC). This time series shows that the detection of very high 

(more than 1 ppmC) concentrations of some compounds can occur as isolated instances, while 

other, similar magnitude instances, can occur as part of a recurring pattern of lower concentration 

measurements. Figure 40 shows a time series of formaldehyde concentrations. While there are 

some formaldehyde emissions in the Houston-Galveston area, most is formed due to the 

reactions of hydrocarbons. Thus, the formaldehyde concentration can be viewed as a qualitative 

surrogate for total hydrocarbons reacted. The data in Figure 40 show that concentrations of 

formaldehyde exceed the detection limit and reach 5-10 ppb on a roughly weekly basis during 

the ozone season at this site, with roughly monthly excursions to 15 ppb or above. 
 45 

To summarize, while the ambient ground data are suggestive of emission variability, it is 

difficult, based only on ambient ground data, to separate out the effects of atmospheric 

conditions from the effects of emission variability. Nevertheless, the combined evidence of 

industrial process upsets and variability in ambient concentrations is consistent and indicative of 

significant variability in point source VOC and HRVOC emissions. Aircraft data provide 

additional supporting evidence. 

V a. Observational data Aircraft VOC Measurements 

Figure 41 provides a summary of measurements made by one of the aircraft operating during the 

Texas Air Quality Study during the summer of 2000. The data are compared to data collected in 

other U.S. cities by the same aircraft/measurement system. The Houston data reported in Figure 

41 are based on the hydrocarbon concentrations observed by the aircraft, during the August and 

September 2000 sampling program, when ozone was forming very rapidly (ozone formation 

rates in the top 10% of those observed during the study). The hydrocarbon concentrations are 

weighted by their atmospheric reactivity (for details, see Kleinman, et al., 2002), so the data are 

reported in reactivity units. The aircraft data clearly show that the high hydrocarbon 

concentrations are due to human activities and that Houston has more extreme values of 

hydrocarbon reactivity (caused by elevated highly reactive hydrocarbon concentrations) than 

cities that do not have Houston’s concentration of industrial facilities. If the elevated 

concentrations were due strictly to meteorology instead of emission variability, elevated 

concentrations carbon monoxide would also be expected. 

Nash. NYC Phoenix Phil. Houston

Reactivity (s-1)

0

5

10

15

20

25

Methane

CO 

HCHO 

Biogenic

Anthropogenic 

NOx (ppb) 13 4.3 21 35 25

Figure 41. VOC reactivity for 5 cities,

averaged over a set of samples with

ozone formation rate equal or above

the 90th percentile values. One

Houston and one Phoenix VOC

sample have been removed as outliers

with biogenic reactivity (primarily

from terpenes) an order of magnitude

greater than the 2nd highest value.

NOx concentration is averaged over

near-source samples as defined by

Kleinman, et al. (2002) 
 46 

Additional evidence, supporting the conclusion that the high reactivity of some air samples is 

caused by industrial point source emissions is shown in Figures 42 and 43. Figure 42a shows 

that a small percentage of the samples had much higher reactivity than the bulk of the samples 

collected by aircraft (all of these samples were collected during mid-morning to late afternoon). 

Figure 42b shows the average composition of the samples that had high reactivity (hydrocarbon 

concentrations are again weighted by reactivity). The dominant contributors are ethene, propene, 

and other light alkenes. Figure 43 presents the results of a principal component analysis of the 

aircraft data collected by Brookhaven National Laboratory. The analysis in Figure 43 indicates 

that high ozone productivity is most highly correlated with high concentrations of ethene, 

propene, acetylene, ethane, propane and butane, in the presence of NOy (Factor 1). These 

compounds are commonly associated with emissions from industrial facilities processing light 

olefins. 

Figure 42. a.) Distribution of reactivity observed in the samples collected by an aircraft that operated during the 

Texas Air Quality Study; the distribution is consistent with the ground measurements, showing significant 

differences between median and high values of reactivity. b.) compounds that contribute to high reactivity in 

samples collected during rapid ozone formation. 

Propene

Butenes

Ethene

Aromatic

>C4 olefins

Alkanes

C2-C6

>C6

Isoprene

&Terpenes

0

50

100

150

.01 1 5 50 90 99

OH Reactivity (s -1)

Percent of Samples

a.) b.)

Figure 43. Principal component 

analysis of data collected by 

Brookhaven aircraft during August 

and September 2000. Factor 1 is 

most highly correlated with high 

ozone formation rate (P(O3)) 
 47 

Aircraft data from specific days provide additional supporting evidence for variability in 

emissions and hydrocarbon concentrations. Hydrocarbon concentrations, measured on 

sequential days at the same locations, show large variations. 

Shown in Figure 44 are comparisons between the observed and predicted concentrations of 

paraffins (PAR) ethylene (ETH) the sum of higher olefins (OLE, propylene and other terminal 

olefins), and the sum of internal olefins and higher aldehydes (both represented as ALD2 in the 

Carbon Bond IV mechanism). The measurements, for August 30, 2000, were taken from aircraft 

operated by NCAR and NOAA during the Texas Air Quality Study, and the data have been 

processed to be comparable with the modeled species. The predicted values are from TCEQ’s 

gridded photochemical model. Each vertical line in the Figure represents one point of 

comparison. The modeled values include enhanced ethylene and olefin emissions (emission 

approximately 600% greater than those reported in the Special Inventory for 2000), but these 

emissions are assumed to have a uniform temporal distribution. 

Figure 44. Comparisons between the concentrations of ethylene (ETH), sum of higher olefins (OLE, propylene and 

other terminal olefins) and sum of internal olefins and higher aldehydes (both represented by ALD2) observed by 

aircraft on August 30, 2000, during the Texas Air Quality Study, and concentrations predicted by a gridded 

photochemical model. Each vertical line in the Figure represents one point of comparison. The modeled values 

include enhanced ethylene and olefin emissions, but these emissions are assumed to have a uniform temporal 

distribution. 

The comparison shows a systemic overprediction of hydrocarbon concentrations in the model at 

low observed concentration levels, but underprediction of the highest observed concentrations. 
 48 

V a. Observational data Ozone measurements

As will be demonstrated in later sections of this report, only a small fraction of instances where 

emissions increase substantially above annual averages will lead to changes in the peak ozone 

concentrations in Houston. In order for emission events and other sources of emission variability 

to influence peak ozone concentrations, they must occur during times that are conducive to 

ozone formation and at locations that provide sufficient sources of other emissions (principally 

nitrogen oxides) that will lead to significant reactions of the emissions. 

Later sections of this report will examine the ozone formation consequences of the full range of 

emission variability, using computationally efficient tools. The goal of that analysis will be to 

identify the characteristics of the small fraction of instances of emission variability that lead to 

increases in peak ozone concentrations. In this section, the goal is to document the potential 

magnitude of changes in peak ozone concentrations, due to emission variability, using data from 

the ozone monitoring network in Houston. The air quality episodes selected are, of necessity, 

extreme examples of the impact of emission variability on ozone concentrations, since these are 

the instances that are most evident in the observational record. While they should be recognized 

as extreme instances, they are useful to examine since they define the magnitudes of changes in 

ozone concentrations that air quality models should be able to replicate. 

One extreme episode that has been well documented by TCEQ staff occurred on March 27, 

2002. In LaPorte, a series of ethylene releases totaling more than 10,000 pounds were released 

over several hours. One release of approximately 6700 pounds occurred between 11 AM and 

noon, lasting for approximately 30 minutes. This release was advected initially to the west, then 

to the northwest, passing over significant highway sources of NOx . Most of the monitors in the 

Houston area showed peak concentrations of roughly 60 ppb on this day, however, several of the 

the monitors that were in the path of the plume reached peak ozone concentrations in excess of 

160 ppb. The estimated air parcel plume and the data from the ozone monitors are shown in 

Figure 45. 
 49 

Figure 45. a.) Estimated trajectory of a 10,000+ lb ethylene release at LaPorte, (6700 lb between 11 and 11:25 AM) 

on 3/27/2002. b.) Multiple ozone monitors were in the path of the plume; other monitors were outside of the path. 

a.) 

b.) 
 50 

Another ozone episode that appears to be related to a emission variability occurred on October 

23, 2003. On this date, ozone monitors detected a large plume of ozone originating in the area 

near the Clinton Drive monitor, which is located just east of the East 610 Loop where it 

intersects the 10 Freeway. As shown in Figure 46, peak ozone concentrations eventually reached 

more than 200 ppb at sites as distant as Aldine (near Bush Intercontinental Airport). Also 

detected at the Clinton site, were very high concentrations of hydrocarbons. These data are also 

shown in Figure 46. 

Contour Maps Showing the Progression of High 

Ozone on 10-23-03

10 am 11 am 12 noon 1 pm

2 pm 3 pm 4 pm 5 pm

Ozone Concentration (5 Min. Data) and VOC Reactivity Vs. Time 

at Clinton C403 for 10-23-03

0

200

400

600

800

1000

1200

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Time

MIR Reactivity

0

50

100

150

200

250

300

Ozone Concentration (ppbC

Ethylene Propylene Isobutane n-Butane

1-Butene c-2-Butene Cyclopentane Isopentane

n-Pentane 1_3-Butadiene Isoprene Benzene

Toluene o-Xylene 1_2_4-Trimethylbenzene Ozone

Figure 46. Data from an 

ozone episode that 

occurred on October 23, 

2003. 

a.) high ozone 

concentrations, including 

concentrations in excess 

of 200 ppb were detected 

initially near the Clinton 

monitor at 11AM; high 

concentrations 

subsequently spread over 

a large region 

b.) high hydrocarbon 

concentrations 

(represented in units of 

reactivity) were detected 

at the Clinton site at the 

start of the event 
 51 

A systematic analysis has also been done of all days during 2003 when ozone concentrations 

exceeded 125 ppb (1-hour average) at sites that also recorded hourly hydrocarbon concentrations 

using an auto-GC (URS, 2004; see Appendix). The analysis identified 31 exceedances of the 

ozone standard (1-hour average) at the auto-GC sites during 2003. Nine of these exceedances 

occurred when VOC reactivity exceeded the levels TCEQ considers high; 26 of the exceedances 

occurred on days when reactivity was high either during or preceding the exceedance. 

Additional information concerning these analyses is provided in the Appendix. 

V b. Air quality modeling analyses 

Air quality models can be used to assess the effect of instances of emission variability on peak 

ozone concentrations. One such analysis is shown in Figure 47, which shows the impact of a 

10,000 lb/hr, 2 hour release in the region near Deer Park. This modeling was done using a 1-km 

grid resolution in the region of the release. The difference in peak ozone concentration between 

the simulation with the release and without the release is more than 50 ppb. 

Figure 47. 3-D photochemical grid 

model simulation of an ozone episode on

August 30, 2000, performed using the 

Comprehensive Air Quality Model with 

extensions (CAMx) at a 1-km grid 

resolution; the upper plot shows the base 

case simulation (peak ozone 

concentration of 150 ppb) with no 

emission event; the lower plot shows the 

ozone concentrations predicted if a 

10,000 lb/hr, 2 hour reactive olefin 

release is added to the base case. The 

peak ozone concentration in the 

simulation with the release is in excess 

of 200 ppb, more than 50 ppb higher 

than in the base case. 
 52 

Simulations of the type shown in Figure 47 could, in principle, be used to simulate the impacts of 

a wide variety on instances of emission variability on peak ozone concentrations. The 

computational requirements of this approach, however, are extremely demanding. It would be 

difficult, if not impossible, to consider thousands of potential emission variability scenarios in 

full 3-D photochemical grid models. Therefore, in this work, an alternative modeling approach 

was employed, which is described in the next section. 

VI. Modeling tools for characterizing the ozone formation potential of ozone 

formation 

As outlined in Section V, it would be difficult, if not impossible, to consider thousands of 

instances of emission variability in full 3-D photochemical grid models. Yet, consideration of 

many different emission “snapshots” or scenarios is necessary, because of the emission 

variability outlined in previous sections of this report. Therefore, in this work, an alternative to 

full 3-D modeling was employed, which will be referred to as sub-domain modeling. 

The sub-domain modeling approach recognizes that the emission variability occurs in the Ship 

Channel and other industrialized regions of the Houston Galveston area and that much of the 

photochemistry leading to increased ozone formation will be confined to relatively small subdomains near the industrial source regions. Computationally efficient sub-domain models can be 

built to examine the effect of emission variability in these sub-domains. The sub-domain models 

can be used as screening tools to identify the types of emission variability (time, location and 

magnitude of emission) that would have the greatest impact on ozone formation. Once a small 

number of emission scenarios are identified, they can be more precisely simulated in a full 3-D 

photochemical model. 

VI a.) Computationally efficient sub-domain models 

The steps involved in developing computationally efficient sub-domain models are: 

• Identify sensitive sub-domains based on full 3-D photochemical model simulations

• Match 3-D photochemical model to computationally efficient sub-domain model using 

Process Analysis

• Run scenarios in computationally efficient sub-domain model

• Evaluate sensitive scenarios in full 3-D photochemical model

Each of these steps is described in more detail below. 

Identify sensitive sub-domains based on full 3-D photochemical model simulations The 3-D 

gridded photochemical model that was used in this work is the same model that is being used to 

develop revisions to the air quality plan for the Houston-Galveston area. The Comprehensive 

Air Quality Model with extensions (CAMx) was used in the simulations and the historical ozone 

episode used in the analysis spanned the period from August 22-September 1, 2000. 
 53 

The first step in the analysis was to use the full 3-D photochemical model to identify the 

locations of critical sub-domains. This was done by identifying areas upwind of peak ozone 

concentrations in the base case episode and in a number of future year, control strategy 

simulations. Figure 48 shows results from the simulation labeled FY07i CS03_harCap GOES2. 

This simulation has the meteorology for the August 25-September 1, 2000 historical episode 

modified by GOES satellite data, and emissions projected to future year 2007 (FY 07), reduced 

using the suite of controls labeled as Control Strategy 3 (CS03) with a cap on HRVOC 

emissions. Figure 48 shows the locations of peak ozone concentrations on August 25 and 

August 30, two of the days that had high ozone concentrations for this simulation. Also shown 

in Figure 48 are industrialized sub-domains upwind of the locations where peak ozone 

concentrations were predicted. 

Figure 48. Locations of peak ozone concentrations on August 25 and August 30, two of the days that had high 

ozone concentrations for this simulation (upper plots), and industrialized sub-domains upwind of the locations where 

peak ozone concentrations were predicted (lower plots). 

 
 54 

Figure 49 shows, in more detail, the sub-domain for August 25. Wind trajectories on this date, 

during the time period preceding the peak ozone concentration, were out of the east. Therefore, 

the peak ozone concentration is most sensitive to emissions beginning near Deer Park and 

advecting to the west-southwest over industrial and urban NOx sources. The area outlined in 

Figure 49 captures this source region. 

Figure 49. Sub-domains for August 25. Wind trajectories on this date, during the time period preceding the peak 

ozone concentration, were out of the east. Therefore, the peak ozone concentration is most sensitive to emissions 

beginning near Deer Park and advecting to the west-southwest over industrial and urban NOx sources. For an 

explanation of the two areas outlined, see text. 
 55 

Figure 50 shows, in more detail, the sub-domain for August 30. Wind trajectories on this date, 

during the time period preceding the peak ozone concentration, were out of the northwest. 

Therefore, the peak ozone concentration is most sensitive to emissions beginning near Deer Park 

and advecting to the southeast over industrial NOx sources, and out over Galveston Bay. The 

areas outlined in Figure 50 capture this source region. 

Figure 50. Sub-domain for August 30. Wind trajectories on this date, during the time period preceding the peak 

ozone concentration, were out of the northwest. Therefore, the peak ozone concentration is most sensitive to 

emissions beginning near Deer Park and advecting to the southeast over industrial NOx sources, and out over 

Galveston Bay. The areas outlined in capture this source region. For an explanation of the two areas outlined, see 

text. 

Figures 49 and 50 identify two sub-domains, one in the upwind region that contains the added 

emission source, and a second, downwind area. To understand why two sub-domains are used, it 

is necessary to understand the conflicting goals that are involved in selecting sub-domains. 

One goal in sub-domain selection is to make the area of the sub-domain sufficiently large so that 

the area includes both the source and the location of the peak ozone concentration. A conflicting 

goal is imposed because the computationally efficient sub-domain model assumes that the subdomain is well mixed, as described later in this section. Since the sub-domain is assumed to 

have homogeneous concentrations (the sub-domain is well-mixed), the sub-domain should be 

kept as small as possible to preserve information about localized peak ozone concentrations. 

A method for dealing with these conflicting demands on the spatial scale of the sub-domain is to 

create two sub-domains – a source sub-domain and a down-wind sub-domain. Figures 49 and 50 
 56 

show these two-sub-domains for the region upwind of the peak ozone concentration on August 

25 and 30. 

Figure 51 shows additional sub-domains used in this analysis. These sub-domains were selected 

because they are in regions that are not near the locations of peak ozone concentrations. They 

were selected to provide a contrast to the sub-domains shown in Figures 49 and 50. 

Figure 51. Sub-domains originating near Chocolate Bayou and Baytown. The lower boxes are the source regions; 

the upper boxes are the downwind regions. These sub-domains were selected because they are in regions that are 

not near the locations of peak ozone concentrations. 

Match 3-D photochemical model to computationally efficient sub-domain model using Process 

Analysis Once the sub-domain locations are selected, a computationally efficient sub-domain 

model is constructed. The goal of the sub-domain model is to mimic the predictions of the full 
 57 

3-D simulation, as accurately as possible, within the sub-domain. The sub-domain model 

achieves its computational efficiency by assuming that all of the concentrations within the subdomain are homogeneous (the sub-domain is well mixed). Clearly, this is an approximation, 

therefore, the sub-domain model will not precisely replicate the spatial sensitivity of the 3-D 

simulation. Completely replicating the full 3-D simulation is not the goal, however. The goal is 

to create a computationally efficient sub-domain model that will show the same relative 

sensitivity to added emissions as the full 3-D model. The sub-domain model is used as a 

screening tool to identify emission times and locations that will most affect peak ozone 

concentrations in the full 3-D model. 

To make the sub-domain model an effective screening tool for evaluating the impact of added 

emissions on ozone formation, the atmospheric processes used in the sub-domain model to 

calculate ozone and other pollutant concentrations are matched to the predictions of the full 3-D 

model using a tool referred to as Process Analysis. 

Gridded, 3-D photochemical air quality models calculate the rates of atmospheric processes that 

control air pollutant concentrations. These processes include chemical formation, chemical 

consumption, advection, diffusion, and deposition. In the photochemical model, these processes 

are coupled into a system of mass continuity equations used to predict the species concentrations 

in each grid cell (Russell and Dennis, 2000). Many models output only the spatial and temporal 

distribution of species concentrations. The magnitudes of the individual processes (advection, 

diffusion, deposition, and chemical formation and destruction) that lead to these changes in 

species concentrations are not typically recorded. 
 58 

Figure 52. Conceptual model explaining processes that contribute to changes in pollutant concentrations. 

Figure 52 illustrates the conceptual basis of a “process-based” photochemical grid model. 

Concentrations of pollutants at every time step are determined by a set of processes that describe 

the physical and chemical changes that are occurring. Various physical and chemical processes 

that affect the species concentrations are represented in the model by fluxes, flows of mass, 

momentum, and energy. When these processes are allowed to operate simultaneously for a small 

amount of time, changes in species concentrations over a time step are predicted. This “marching 

in time” technique can be used to advance a given initial concentration to a future time by 

repeating many incremental steps. 

This conceptual model is implemented in a photochemical grid model as “well-mixed” 

environmental volumes, or grid cells as shown in Figure 53. These grid cells are subjected to a 

mass balance at each time step. The air quality model utilized in the analyses reported here 

applied grid cells of different sizes, ranging from 1 km by 1 km to 4 km by 4 km, or larger, in 

horizontal resolution. The grid cells can extend vertically from 20 to 1000 meters. 

Figure 53 Models are formulated 

using “well-mixed” chemical reactor 

cells that hold concentration 

information and are subject to a 

collection of processes that modify 

those concentrations over short time 

steps.
 59 

Figure 54 shows that many well-mixed grid cells are coupled together via cell-to-cell transport 

through all faces in common with other adjacent cells. This cell-to-cell transport can either be by 

advection or by diffusion. In air quality models, the rates of all these processes for each cell are 

added together and the combined effect is integrated and added to the initial state to predict the 

subsequent state. In most 3-D gridded models, only the resulting species concentrations are 

saved and all information concerning how these concentrations were achieved is lost. 

Process analysis techniques permit displaying the roles of individual processes that contribute to 

ozone formation in a complex model. These methods were developed at the University of North 

Carolina by Jeffries and his graduate students beginning in the 1990s (Crouse, 1990; Jeffries, 

1993, Tonnesen and Jeffries, 1994; Jeffries and Tonnesen, 1994; Jang et. al, 1995; Jeffries, 

Keating, Wang, 1997; Jeffries and Wang, 1997). The Comprehensive Air quality Model with 

Figure 54 Conceptual 

drawing of the CAMx 

grid cell network of 

many “well-mixed cells” 

coupled together via cell 

to cell transport through 

all faces in common with 

other adjacent cells. This 

cell-to-cell transport can 

either be by advection or 

by diffusion. 
 60 

extensions (CAMx) photochemical air quality model includes the option to output process 

analysis data (Environ 2004).

As part of this project, the existing Process Analysis tools were modified and expanded at the 

University of Texas by Jeffries, Kimura and Vizuete to function with the existing output of 

CAMx and additional post processing codes were written. (Tesche, et al., 2004; Vizuete et al.

2004). These were needed to permit a dynamic vertical analysis box that was a new feature of the 

University of Texas version of the Process Analysis code. The process analysis tool produces a 

significant amount of information that ultimately leads to the creation of spreadsheets entitled 

“time series” and “cycles”. In the time series, a series of graphs show the magnitudes of the 

various processes that are used to determine species concentrations, as a function of time of day. 

In the cycles spreadsheet, the initialization, propagation, and termination processes in the 

chemistry are illustrated and distributions of competitive pathways in the chemistry are shown. 

Figure 55 shows an example of a time series plot that illustrates the process rates and model 

concentrations versus time (Jeffries, et al., 1997). This graph closely follows the concepts shown 

in Figure 53, where the model concentration is given for each hour and the magnitudes of the 

various processes are shown as different colored lines. In these graphs the chemistry change is 

the net change due to multiple reaction pathways, so time series plots alone are not capable of 

explaining the contributions of specific reaction pathways to the net change. A different type of 

analysis detailed in the cycle spreadsheet is required to characterize the magnitudes of particular 

reaction pathways. 

Figure 55. Example Process Analysis time series plot with observed hourly averaged observations. The change in 

the red line (predicted mixing ratio or concentration) is the sum of all of the individual processes represented by the 

green, blue and yellow lines (∆Ozone mixing ratio in any hour = Σ net vertical transport + net horizontal transport + 

net chemical production) 
 61 

In order to describe the data provided by the cycles spreadsheet, which describes reaction cycles 

in the atmosphere responsible for generating ozone, it is useful to briefly review ozone formation 

pathways. Ozone (O3) is not emitted directly from sources, but is formed by chemical reactions 

in the atmosphere (Seinfeld 1995). Combustion processes emit mostly NO, which is rapidly 

oxidized to NO2 in the ambient air. In sunlight, the NO2 is photolyzed to produce NO and atomic 

oxygen, O(3P). The atomic oxygen then reacts with molecular oxygen to form O3. Therefore, the 

photolysis of NO2 is the major source of O3 in the troposphere. This ozone can immediately react 

with NO to produce NO2 again. In the absence of competing reactions, NO, NO2, and O3 reach 

equilibrium and the amount of O3 never exceeds the amount of NOx (NO+NO2). However, if 

volatile organic compounds (VOCs) are present in the system then free radicals, generated from 

the oxidative degradation of VOCs, oxidize NO to NO2. This oxidation results in accumulation 

of the O3 formed in the NO2 photolysis, causing a net increase in O3. The NO can be reused 

several times under these conditions (NO is oxidized by a hydrocarbon radical, converted to 

NO2; the NO2 photolyzes to form NO and O(3

P); the O(3P) reacts to form ozone and the NO 

starts the cycle again). Thus, the amount of O3 can now be many times the amount of NOx. The 

hydrocarbon radicals are products of the reactions involving hydroxyl radicals, OH, with VOC 

and CO. Each time a radical oxidizes an NO, an O3 accumulates and another NO2 is generated. 

This NO2 can again photolyze to create yet another O3. Eventually the reaction of NO2 with 

several of the radicals forms nitrogen products such as nitric acid (HNO3) and organic nitrates 

terminating the production of O3 from that particular NO molecule. In the Carbon Bond IV 

mechanism used by the model in this study, new OH radicals are produced directly and indirectly 

by the following reactions (Jeffries, 1995): 

Following radical initiation, VOCs are oxidized and NO-to-NO2 conversion occurs in radical 

propagation steps. The following reaction steps illustrate a propagation chain where a VOC 

molecule is converted to one or more carbonyl molecules and two molecules of NO are 
 62 

converted to two molecules of NO2. Note that the free radical emerges as OH again at the end of 

the chain. In these chains, the initial OH could be either a new OH or a re-created OH. 

The radical propagation stops when the reacting radicals are incorporated into stable products. 

The following are the radical termination steps.
 63 

Figure 56 shows a schematic in which the OH initiation, propagation, and termination and the 

NO emissions, oxidation, and photolysis cycles are combined. Here the NOx and radical cycles 

which show the re-creation of OH and of NO, are made explicit. 

Figure 56 Conceptual model of the radical and nitrogen oxides chemical cycles resulting in ozone production 

(Jeffries, 1995) 

The named quantities in the figure are: 

The term Pr represents the fraction of OH that propagates via reactions with VOC, and 1-Pr is 

essentially the fraction of OH that reacts with NO2 to make HNO3. Similarly, the term Pn 

represents the fraction of NO2 that is photolyzed, and 1-Pn symbolizes the fraction of NO2 that is 

converted to termination products (HNO3, RNO3) or is lost by physical processes like deposition 

and horizontal or vertical transport. 
 64 

An example of an application of a cycle diagram is shown in Figure 57, which is for a UAM 

application in Charlotte, NC over a 6 x 6, 5-km aggregated cell area covering the downtown 

region (Jeffries et al. 1997). The numerical values in this figure are typical of conditions that led 

to ozone near the one-hour standard. Charlotte is in general NOx limited, but the core city are 

near the optimum ozone production conditions. This is evident in that the NOx is nearly 

completely consumed by the end of the daylight period. 

Figure 57 Example of the Process Analysis Radical and Nitrogen Cycles diagram (Jeffries, 1995)

For NOx limited conditions, new O3 is explained by the following: 

new O3 = E*Pn*f 

where 

E = (new NO) [1/(1- Pn)] = total NO oxidized to NO2

Pn = fraction of all NO2 that is photolyzed 

f = fraction of all NO2 photolysis reactions that make ozone 

Using the values in Figure 57, new NO = 49.0 ppb, Pn = 0.731, f = 0.937. Therefore (new 

O3)/(new NO) = 2.54 

For radical limited conditions, new O3 is explained by the following: 

new O3 = (VOC reacted)*R*Pn*f 

where 

(VOC reacted) = (new OH)*[1/(1- Pr)] + (VOC photolyzed) 

R = (NO→NO2)/( VOC reacted) essentially a type of “mixture reactivity” 

Pn = fraction of all NO2 that photolyzes 

f = fraction of all NO2 photolysis reactions that make ozone 

Using the values in Figure 57, new OH = 27.9 ppb, Pr = 0.757, R = 1.26 

(new O3)/(new OH) = 4.46 
 65 

These two values are the “gain” or “chemical amplification” of the system. If there is sufficient 

NOx, then for 1 ppb of new OH radical created, there will be 4.4 ppb ozone created. From the 

NOx viewpoint, if there are sufficient sources of peroxy radicals, then for every 1 ppb of NO 

emitted, there will be 2.5 ppb ozone created. 

Returning to the sub-domains of Figures 49 to 51, time series and cycle analyses can be extracted 

from the full 3-D model for these subdomains, as shown in Figure 58. Figure 58 (analogous to 

Figure 55) reports the horizontal transport, vertical transport and chemical production/destruction 

of ozone for the August 25 sub-domain (source region). The results shown in Figure 58 are for 

the full 3-D simulation. From the full 3-D simulation, the following data are extracted for use in 

the computationally efficient sub-domain model: 

• Hour by hour emissions into the sub-domain 

• Hour by hour concentrations of individual chemical species entering the sub-domain, 

horizontally and vertically 

• Hour by hour wind fields causing vertical and horizontal advection 

• Hour by hour changes in mixing heights, based on changes in vertical diffusivity as a 

function of elevation 

• Hour by hour deposition rates and photolysis rates 

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Figure 58. Time series process analysis from the full CAMx simulation for the sub-domain shown in Figure 49 

(source region) 

Based on these inputs, the sub-domain model calculates: 

• Hour by hour chemical formation and destruction of species in the sub-domain 

• Hour by hour concentrations of individual chemical species leaving the sub-domain due 

to advection and diffusion 

• Hour by hour net (entering – leaving) vertical and horizontal advection 

• Hour by hour concentrations of chemical species 
 66 

These quantities, calculated by the sub-domain model, can be represented in time series and 

compared to the time series of the same quantities calculated by the full gridded simulation. If 

the two time series match, then the computationally efficient sub-domain model is an effective 

tool for characterizing the spatially averaged performance of the full 3-D simulation. Figures 59, 

60 and 61 compare time series generated by the full 3-D simulation, and the computationally 

efficient sub-domain model. Many more comparisons could be presented, but all show 

comparable performance. More complete documentation is available in the Appendix. 

Figure 59. Comparison of process analysis time series (See Figure 55 for definitions) for ozone in the sub-domain 

defined in Figure 49 (8/25, source region); sub-domain processes (lower diagram) match the analogous processes 

for the full 3-D, gridded model (upper diagram) in the sub-domain. 

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 67 

Figure 60. Comparison of process analysis time series (See Figure 55 for definitions) for NO in the sub-domain 

defined in Figure 49 (8/25, source region); sub-domain processes (lower diagram) match the analogous processes 

for the full 3-D, gridded model (upper diagram) in the sub-domain. 

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 68 

Figure 61. Comparison of process analysis time series (See Figure 55 for definitions) for ozone in the sub-domain 

defined in Figure 49 (8/25, source region); sub-domain processes (lower diagram) match the analogous processes 

for the full 3-D, gridded model (upper diagram) in the sub-domain. 

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 69 

Figures 59-61 have demonstrated that the sub-domain model can be matched to the full 3-D 

simulation. Since, the next step in the use of the sub-domain model will be to evaluate the impact 

of many emission scenarios in the sub-domain model, it is also useful to demonstrate that the 

sub-domain model will generate the same response to added emissions as the full 3-D model. To 

perform this comparison, the 8/25 subdomain with a source region (Figure 62) was used. The 

sub-domain model was created using the full 3-D simulation, without added emissions. A 2900 

lb release of a reactive olefin in the Deer Park region was then simulated independently in the 

sub-domain model and in a full 3-D simulation with a 1 km by 1 km horizontal grid cell 

dimension in the region of the event (Simulation FY 07 CS04). The results from those 

independent analyses were compared using the Process Analysis tool. Figures 63, 64 and 65 

show time series comparisons for selected species before and after addition of the emissions. 

Figure 65 shows a comparison of reaction cycles analyses before and after the addition of the 

emissions. Overall, the results indicate that the sub-domain model shows the same response to 

added emissions as the full 3-D model, averaged over the same spatial area. 

Figure 62. Sub-domain used to compare full CAMx simulation (FY 07i CS03_harcap GOES2, 1 km grid resolution 

in source area) to sub-domain model; data for 8/25 used in the comparison; source region is to the right (including 

Deer Park); downwind region is to the left. 

8-25_00 Process Analysis Box outlined in black. Black Dots represent lower left hand corner of 1 km grid cells
 70 

Figure 63a. Comparison of process analysis time series (See Figure 55 for definitions) for ozone in the sub-domain 

defined in Figure 62 (8/25, source region) before emissions are added; sub-domain processes (lower diagram) 

match the analogous processes for the full 3-D, gridded model (upper diagram) in the sub-domain. 

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 71 

Figure 63b. Comparison of process analysis time series (See Figure 55 for definitions) for ozone in the sub-domain 

defined in Figure 62 (8/25, source region) after emissions are added; sub-domain processes (lower diagram) match 

the analogous processes for the full 3-D, gridded model (upper diagram) in the sub-domain. 

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 72 

Figure 64a. Comparison of process analysis time series (See Figure 55 for definitions) for NOy in the sub-domain 

defined in Figure 62 (8/25, source region) before emissions are added; sub-domain processes (lower diagram) 

match the analogous processes for the full 3-D, gridded model (upper diagram) in the sub-domain. 

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 73 

Figure 64b. Comparison of process analysis time series (See Figure 55 for definitions) for NOy in the sub-domain 

defined in Figure 62 (8/25, source region) after emissions are added; sub-domain processes (lower diagram) match 

the analogous processes for the full 3-D, gridded model (upper diagram) in the sub-domain. 

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 74 

Figure 65a. Comparison of process analysis time series (See Figure 55 for definitions) for reactive olefins in the 

sub-domain defined in Figure 62 (8/25, source region) before emissions are added; sub-domain processes (lower 

diagram) match the analogous processes for the full 3-D, gridded model (upper diagram) in the sub-domain. 

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 75 

Figure 65b. Comparison of process analysis time series (See Figure 55 for definitions) for reactive olefins in the 

sub-domain defined in Figure 62 (8/25, source region) after emissions are added; sub-domain processes (lower 

diagram) match the analogous processes for the full 3-D, gridded model (upper diagram) in the sub-domain. 

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Figure 66a. Comparison of process analysis reaction cycle diagram (See Figures 56 and 57 for definitions) in the 

sub-domain defined in Figure 62 (8/25, source region) before emissions are added; sub-domain processes (next 

page) match the analogous processes for the full 3-D, gridded model (this page) in the sub-domain. 

camx403.20000825.fy07i.UT.cs03_harCap.1km.goes2.ipr 

camx403.20000825.fy07i.UT.cs03_harCap.1km.goes2.irr Radical Cycle/ NO Cycle/ Ozone 

825.source.1km.goes2.cycles

O3 + hυ 3.158 OH cycles

21.06 ppb

Propagation and Termination

new [OH] [OH] reacted POH = 0.683 [OH] recreated

31.51 ppb 99.51 ppb [VOC] reacted = 90.56 ppbV 68.00 ppb

aldehydes + hυ (NO -> NO2) / VOC= 1.5076

10.45 ppb

RO2 + NO -> NO2 + RO; r(dt) = 136.53 ppb

+H2O

old [O3] aloft O3 + NO -> NO2 + O2; r(dt) = 1.57 ppb 10.53 ppb

1.57 ppb

 NO2 from Other Paths NO -> NO2 [O3]p / [NO2]hυ = 0.902

new [NO2] 0 ppb 138.24 ppb

9.54 ppb 10.79 ppb

+org/HO2

Propagation and Termination

new [NO] [NO] reacted PNO = 0.726 [NO2] + hv [O3] produced

38.45 ppb 140.49 ppb [NOZ] reacted = 24.34 ppbV 109.08 ppb 98.29 ppb

[NO] recreated [NO] fm new NO2

102.04 ppb 7.04 ppb

[NO] from [NO] from [NO] bal 3.653 NO cycles

new NO new NO2

31.41 ppb 7.04 ppb 0.11 ppb

87.76

[O3] h trans [O3] v trans

-93.23 ppb 13.81 ppb

[O3] init [O3] final

35.11 ppb 44.45 ppb

old O3 + NO [O3] h trans [O3] v trans [O3] depo

1.57 ppb 93.225 ppb -13.81 ppb 3.91 ppb
 77 

 

 Radical Cycle/ NO Cycle/ Ozone 

825.source.1km.goes2.box.cycles

O3 + hυ 3.362 OH cycles

22.33 ppb

Propagation and Termination

new [OH] [OH] reacted POH = 0.703 [OH] recreated

33.11 ppb 111.31 ppb [VOC] reacted = 101.82 ppbV 78.20 ppb

aldehydes + hυ (NO -> NO2) / VOC= 1.498

10.78 ppb

RO2 + NO -> NO2 + RO; r(dt) = 152.53 ppb

+H2O

old [O3] aloft O3 + NO -> NO2 + O2; r(dt) = 1.65 ppb 11.16 ppb

1.65 ppb

 NO2 from Other Paths NO -> NO2 [O3]p / [NO2]hυ = 0.912

new [NO2] 0 ppb 154.87 ppb

7.81 ppb 11.39 ppb

+org/HO2

Propagation and Termination

new [NO] [NO] reacted PNO = 0.765 [NO2] + hv [O3] produced

37.07 ppb 157.63 ppb [NOZ] reacted = 28.74 ppbV 126.64 ppb 115.25 ppb

[NO] recreated [NO] fm new NO2

120.56 ppb 6.08 ppb

[NO] from [NO] from [NO] bal 4.253 NO cycles

new NO new NO2

30.99 ppb 6.08 ppb -0.78 ppb

104.09

[O3] h trans [O3] v trans

-105.45 ppb 10.11 ppb

[O3] init [O3] final

35.98 ppb 45.70 ppb

old O3 + NO [O3] h trans [O3] v trans [O3] depo

1.65 ppb 105.45 ppb -10.11 ppb 4.04 ppb
 78 

Figure 66b. Comparison of process analysis time series (See Figure 55 for definitions) for reactive olefins in the 

sub-domain defined in Figure 62 (8/25, source region) after emissions are added; sub-domain processes (next page) 

match the analogous processes for the full 3-D, gridded model (this page) in the sub-domain.

camx403.20000825.fy07i.UT.cs03_harCap.shell.025ETH025OLE.1km.goes2.ipr 

camx403.20000825.fy07i.UT.cs03_harCap.shell.025ETH025OLE.1 Radical Cycle/ NO Cycle/ Ozone 

825.source.1km.goes2.2910.cycles

O3 + hυ 3.246 OH cycles

21.63 ppb

Propagation and Termination

new [OH] [OH] reacted POH = 0.692 [OH] recreated

33.36 ppb 108.28 ppb [VOC] reacted = 101.45 ppbV 74.92 ppb

aldehydes + hυ (NO -> NO2) / VOC= 1.4918

11.73 ppb

RO2 + NO -> NO2 + RO; r(dt) = 151.34 ppb

+H2O

old [O3] aloft O3 + NO -> NO2 + O2; r(dt) = 1.57 ppb 10.82 ppb

1.57 ppb

 NO2 from Other Paths NO -> NO2 [O3]p / [NO2]hυ = 0.911

new [NO2] 0 ppb 153 ppb

9.53 ppb 11.13 ppb

+org/HO2

Propagation and Termination

new [NO] [NO] reacted PNO = 0.748 [NO2] + hv [O3] produced

39.19 ppb 155.26 ppb [NOZ] reacted = 25.82 ppbV 123.29 ppb 112.16 ppb

[NO] recreated [NO] fm new NO2

116.06 ppb 7.23 ppb

[NO] from [NO] from [NO] bal 3.961 NO cycles

new NO new NO2

31.97 ppb 7.23 ppb 0.12 ppb

101.34

[O3] h trans [O3] v trans

-105.50 ppb 12.85 ppb

[O3] init [O3] final

35.11 ppb 44.45 ppb

old O3 + NO [O3] h trans [O3] v trans [O3] depo

1.57 ppb 105.5 ppb -12.85 ppb 4.00 ppb
 79 

 

 Radical Cycle/ NO Cycle/ Ozone 

825.source.1km.goes2.2910.box.cycles

O3 + hυ 3.423 OH cycles

22.92 ppb

Propagation and Termination

new [OH] [OH] reacted POH = 0.708 [OH] recreated

34.74 ppb 118.93 ppb [VOC] reacted = 111.19 ppbV 84.19 ppb

aldehydes + hυ (NO -> NO2) / VOC= 1.4885

11.82 ppb

RO2 + NO -> NO2 + RO; r(dt) = 165.51 ppb

+H2O

old [O3] aloft O3 + NO -> NO2 + O2; r(dt) = 1.66 ppb 11.46 ppb

1.66 ppb

 NO2 from Other Paths NO -> NO2 [O3]p / [NO2]hυ = 0.917

new [NO2] 0 ppb 167.72 ppb

7.84 ppb 11.72 ppb

+org/HO2

Propagation and Termination

new [NO] [NO] reacted PNO = 0.779 [NO2] + hv [O3] produced

37.60 ppb 170.45 ppb [NOZ] reacted = 29.61 ppbV 139.06 ppb 127.34 ppb

[NO] recreated [NO] fm new NO2

132.85 ppb 6.21 ppb

[NO] from [NO] from [NO] bal 4.533 NO cycles

new NO new NO2

31.39 ppb 6.21 ppb -0.38 ppb

115.88

[O3] h trans [O3] v trans

-116.16 ppb 9.40 ppb

[O3] init [O3] final

35.98 ppb 45.70 ppb

old O3 + NO [O3] h trans [O3] v trans [O3] depo

1.66 ppb 116.16 ppb -9.40 ppb 4.12 ppb
 80 

Run scenarios in computationally efficient sub-domain model 

Figures 59-66 have demonstrated that computationally efficient sub-domain models can be 

constructed that replicate the behavior of a full 3-D model and that replicate the 3-D model’s 

response to added emissions. 

The sub-domains defined in Figures 49-51 were used in evaluating the ozone formation 

implications of added emissions. Specifically, the sub-domain modeling tool was used to 

address the following questions: 

1. What is the response of peak ozone concentration to different magnitudes of HRVOC 

added emissions?

2. Does the time of day when the emission occurs influence the response of peak ozone 

concentration?

3. Does the length of the added emission influence peak ozone concentration?

4. Does the location of the added emission influence peak ozone concentrations?

Each of these questions is addressed below, however, before describing the results it is useful to 

present a brief summary of the sub-domains that were used in the analysis. 

One set of sub-domains (corresponding to the areas shown in Figures 49, 50 and 51) was 

matched to CAMx simulation FY07i CS03_harCap GOES1 (Future Year 2007, Control Strategy 

3 with additional highly reactive VOC reductions, with meteorology modified using GOES data, 

but not including the mixing height correction, simulation at 4 km horizontal resolution). These 

sub-domains will be referred to as FY 07i CS03_harcap GOES1 Deer Park 8/25, FY 07 

CS03_harcap GOES1 Deer Park 8/30, FY 07 CS03_harcap GOES1 Baytown 8/28 and FY 07 

CS03_harcap GOES1 Chocolate Bayou 8/28. Upwind and downwind sub-domains in each of 

these areas will be denoted by the suffix source and plume, respectively. 

Another set of sub-domains (corresponding to the areas shown in Figures 49, 50 and 51) was 

matched to CAMx simulation FY07i CS03_harCap GOES1 (1 km) (Future Year 2007, Control 

Strategy 3 with additional highly reactive VOC reductions, with meteorology modified using 

GOES data, but not including the mixing height correction, simulation at 1 km horizontal 

resolution). These sub-domains will be referred to as FY 07i CS03_harcap GOES1 1km Deer 

Park 8/25, FY 07 CS03_harcap GOES1 1km Deer Park 8/30, and FY 07 CS03_harcap GOES1 

1km Baytown 8/28. Upwind and downwind sub-domains in each of these areas will be denoted 

by the suffix source and plume, respectively. 

A third set of sub-domains (corresponding to the areas shown in Figures 49, 50 and 51) was 

matched to CAMx simulation FY07i CS03_harCap GOES2 (Future Year 2007, Control Strategy 

3 with additional highly reactive VOC reductions, with meteorology modified using GOES data, 

including the mixing height correction, simulation at 4 km horizontal resolution). These subdomains will be referred to as FY 07i CS03_harcap GOES2 Deer Park 8/25, and FY 07 

CS03_harcap GOES2 Deer Park 8/30. Upwind and downwind sub-domains in each of these 

areas will be denoted by the suffix source and plume, respectively. This is the set of subdomains most consistent with the attainment demonstration.
 81 

A fourth set of sub-domains (corresponding to the areas shown in Figures 49, 50 and 51) was 

matched to CAMx simulation FY07i CS03_harCap GOES2 (1 km) (Future Year 2007, Control 

Strategy 3 with additional highly reactive VOC reductions, with meteorology modified using 

GOES data, including the mixing height correction, simulation at 1 km horizontal resolution). 

These sub-domains will be referred to as FY 07i CS03_harcap GOES2 1km Deer Park 8/25, and 

FY 07 CS03_harcap GOES2 1km Deer Park 8/30. Upwind and downwind sub-domains in each 

of these areas will be denoted by the suffix source and plume, respectively. 

None of these different simulations led to significantly different relative sensitivities of source 

areas and time of day. Therefore, the focus will be on the simulations matched to CAMx 

simulation FY07i CS03_harCap GOES2 (Future Year 2007, Control Strategy 3 with additional 

highly reactive VOC reductions, with meteorology modified using GOES data, including the 

mixing height correction, simulation at 4 km horizontal resolution), since this is the case most 

closely related to the attainment demonstration. Full results, including some additional subdomains not described above, are presented in the Appendix, for the sake of completeness. 
 82 

Consider first the FY 07i CS03_harcap GOES2 Deer Park 8/25 scenario. The sub-domain model 

was used to systematically examine the impact, on peak ozone concentrations, of added 

emissions of different magnitudes. Emission additions beginning at 100 lbs were considered, in 

100 pound increments, ranging up to 5000 pounds. Additions of ethylene emissions and 

additions of propylene emissions were considered separately. All emission additions were 

assumed to occur over a 1-hour period, beginning at 10 AM. The results are shown in Figure 67.

8/25 Deer Park source region

77.5

78

78.5

79

79.5

80

80.5

81

81.5

82

0

100200300400500600700800900

10001100120013001400150016001700180019002000210022002300240025002600270028002900300031003200330034003500360037003800390040004100420043004400450046004700480049005000

Total Event Release (lbs)

Daily Max O3 (ppb)

ethene

propene

nhour 1 bhr 11

Sum of daymax

mass

spc

8_25_00 Process Analysis Box outlined in black. Black Dots represent lower left hand corner of 4 km grid cells

As shown in Figure 67, the peak ozone concentration in the sub-domain was a linear function of 

the size of the added emission. A one thousand pound added emission led to an increase in peak 

ozone concentration of approximately 0.5 ppb. As shown in the Appendix, similar sensitivities 

of peak ozone concentration to event size were seen for the 8/30 Deer Park subdomain. 

Figure 67. Sensitivity 

of ozone formation in 

the FY 07i 

CS03_harcap GOES2 

8/25 Deer Park 

domain (shown) to 

emission events of 

ethylene and 

propylene, ranging 

from 100 to 5000 lbs. 

The events were 

assumed to occur at 11 

AM and to last for an 

hour. The peak ozone 

concentration in the 

sub-domain was a 

linear function of the 

size of the event. A 

one thousand pound 

event led to an 

increase in peak ozone 

concentration of 

approximately 0.5 ppb.
 83 

Since the goal of the sub-domain modeling is to determine relative response of peak ozone 

concentrations to various emission addition magnitudes, the key feature of Figure 67 is that the 

responses are linear. As described in the Appendix, many other sub-domain scenarios were 

examined and the sub-domain models consistently yielded a linear relationship between peak 

ozone concentration and emission magnitude. Details of the other sub-domain results are 

provided in the Appendix. 

The sub-domain models were also used to examine the sensitivity of peak ozone formation to 

time of day of the added emissions. As shown in Figure 68, the response of peak ozone 

concentration to time of day was strongly dependent on the time of day of the release. As 

shown in Figure 68a, releases of ethylene, lasting one hour, have maximum impact on peak 

ozone concentration if they occur at 11 AM. Impact falls off either before or after this time 

window. Figure 68b shows that for propylene releases lasting one hour, the maximum changes 

in peak ozone concentration result if the emissions are added at 11AM. Note that in all cases, the 

relationship between change in peak ozone concentration and magnitude of the release is linear, 

as was observed in Figure 67. 

8/25 Deer Park source region

77.5

78

78.5

79

79.5

80

80.5

81

81.5

82

0

100200300400500600700800900

10001100120013001400150016001700180019002000210022002300240025002600270028002900300031003200330034003500360037003800390040004100420043004400450046004700480049005000

Total Event Release (lbs)

Daily Max O3 (ppb)

9

10

11

12

13

14

15

spc ethene nhour 1

Sum of daymax

mass

bhr

 

8/25 Deer Park source region

77.5

78

78.5

79

79.5

80

80.5

81

81.5

82

0

100200300400500600700800900

10001100120013001400150016001700180019002000210022002300240025002600270028002900300031003200330034003500360037003800390040004100420043004400450046004700480049005000

Total Event Release (lbs)

Daily Max O3 (ppb)

9

10

11

12

13

14

15

spc propene nhour 1

Sum of daymax

mass

bhr

Figure 68. Sensitivity 

of ozone formation in the

FY 07i CS03_harcap 

GOES2 8/25 Deer Park 

domain, source region to 

emission events of 

ethylene (a.) and 

propylene (b.). The 

events ranged from 100 

to 5000 lbs. The events 

were assumed to last for 

an hour, beginning at 

various times of day 

The peak ozone 

concentration in the subdomain was a linear 

function of the size of 

the event, and was 

sensitive to the time of 

day of the event. 

a.) 

b.) 
 84 

The sub-domain models can be further used to examine the sensitivity of peak ozone formation 

to the duration of the emission addition. To examine the sensitivity to added emission duration, 

each added emission was divided over 1, 2 or 3 hours. Thus, a one thousand pound emission 

addition was evaluated as a 1 hour/1000 lb/hr addition, a 2 hr/500 lb/hr addition and a 3 hour/333 

lb/hr addition. As shown in Figure 69, the response of peak ozone concentration was somewhat 

dependent on the duration of the emission addition, but the sensitivity could have largely been 

predicted from the results shown in Figure 68, which indicated that the response of peak ozone 

concentration was sensitive to the time of day of the release. At the most sensitive times of day, 

added emissions spread over one, two, or even three hours have comparable impacts on peak 

ozone concentrations, if they have the same magnitude. If the added emissions begin to fall 

outside of the time window of peak sensitivity, the impact on peak ozone concentration 

decreases. Note that in all cases, the relationship between change in peak ozone concentration 

and magnitude of the release is linear, as was observed in Figures 67 and 68. 

Scenarios similar to those reported in Figures 67-69 were examined for other sub-domains, with 

analogous results. The results from those analyses are provided in the Appendix. 
 85 

8/25 Deer Park source region

77.5

78

78.5

79

79.5

80

80.5

81

81.5

82

0

100200300400500600700800900

10001100120013001400150016001700180019002000210022002300240025002600270028002900300031003200330034003500360037003800390040004100420043004400450046004700480049005000

Total Event Release (lbs)

Daily Max O3 (ppb)

9

10

11

12

13

14

15

spc propene nhour 1

Sum of daymax

mass

bhr

8/25 Deer Park source region

77.5

78

78.5

79

79.5

80

80.5

81

81.5

0

100200300400500600700800900

10001100120013001400150016001700180019002000210022002300240025002600270028002900300031003200330034003500360037003800390040004100420043004400450046004700480049005000

Total Event Release (lbs)

Daily Max O3 (ppb)

9

10

11

12

13

14

15

spc propene nhour 2

Sum of daymax

mass

bhr

8/25 Deer Park source region

77.5

78

78.5

79

79.5

80

80.5

81

81.5

0

100200300400500600700800900

10001100120013001400150016001700180019002000210022002300240025002600270028002900300031003200330034003500360037003800390040004100420043004400450046004700480049005000

Total Event Release (lbs)

Daily Max O3 (ppb)

9

10

11

12

13

14

15

spc propene nhour 3

Sum of daymax

mass

bhr

Figure 69. Sensitivity of 

ozone formation in the FY 

07i CS03_harcap GOES2 

8/25 Deer Park domain, 

source region to emission 

events of propylene. The 

events ranging from 100 to 

5000 lbs. The events were 

assumed to last for one (top 

diagram), two (middle 

diagram) or three hours 

(bottom diagram), beginning 

at various times of day. For 

multiple hour events, the 

emissions were distributed 

evenly over the length of the 

event (e.g., a 1000 lb event 

over 2 hours had an emission 

rate of 500 lb/hr). The peak 

ozone concentration in the 

sub-domain was a linear 

function of the size of the 

event, and was sensitive to 

the time of day of the event. 

At the most sensitive times of

day, emission events spread 

over one, two, or even three 

hours have comparable 

impacts on peak ozone 

concentrations, if they have 
 86 

VI b. Combining 3-D photochemical and sub-domain models 

Evaluate sensitive scenarios in full 3-D photochemical model 

The results from the sub-domain models indicated that the peak ozone concentration is a linear

function of the magnitude (mass) of the emission addition up to emission addition masses of at 

least 5000 pounds; the peak ozone concentration response to emission additions is greatest 

during a time window of approximately 2-4 hours on ozone conducive days, and the length of 

the emission addition (e.g., 1000 lbs over one hour or 500 lbs/hr over 2-4 hours) does not impact 

peak ozone concentration, as long as all of the releases occur during the sensitive time window 

of approximately 2 hours. 

Using these findings as a starting point, multiple full 3-D CAMx simulations of the FY 07i 

CS03_harcap GOES2 (1 km) simulation were conducted. The results are summarized in Figure 

70. The CAMx simulations confirm that the response of change in peak ozone concentration to 

the magnitude of the emission addition is linear up to emission addition magnitudes much larger 

than 5000 pounds. 

0

10

20

30

40

50

60

0 5000 10000 15000 20000 25000

25-Aug

30-Aug

31-Aug

Figure 70. Response of full CAMx simulations (FY 07i CS03_harcap GOES2 (1 km) ) to emission additions at the 

time of day identified as most sensitive in the sub-domain simulations. The CAMx simulations confirm that the 

response of change in peak ozone concentration to the magnitude of the emission addition is linear up to emission 

event magnitudes much larger than 5000 pounds 

The full 3-D CAMx simulations provide an assessment of the magnitude of the impact of 

emission additions on peak ozone concentration. At the most sensitive locations, the magnitude 

of change in peak ozone concentration ranges from 2-3 ppb per 1000 lbs of HRVOC emission 

addition, depending on the date of the release. 
 87 

Finding 2: HRVOC emission variability in the range of 100-1000 lb/hr, which has been 

reported daily in the Houston-Galveston area, can increase peak, region-wide ozone 

concentrations, if the emission variability occurs in regions upwind of the location of the 

peak, region-wide ozone concentration. The magnitude of the increase in ozone concentration 

depends on the location of the emission variability, the time of day when the emission variability 

occurs and the magnitude of the non-variable ozone precursor emissions. At the most sensitive 

locations, at the most sensitive times of day, releases over approximately a two to three hour 

period can lead to increases of 2-3 ppb in peak ozone concentration per 1000 lb of additional 

HRVOC emissions. This sensitivity may increase as non-variable HRVOC emissions decrease 

and NOx emissions increase. 
 88 

VII. Characterizing the impact of variable emissions on attaining the NAAQS 

for ozone 

VIIa. Estimating the probability of emission events and impacts on peak ozone 

concentrations 

In order to characterize the impact of variable emissions, particularly emission events, on 

attainment of the National Ambient Air Quality Standard (NAAQS) for ozone (concentrations 

averaged over 1-hour), it is necessary to combine the information presented in the previous 

section with additional analyses. 

Section VI demonstrated that the effect of emission additions on peak ozone concentration 

depends on the magnitude, location and time of day of the release. Stated differently, for 

emission variability to have an impact on peak ozone concentration, added emissions must occur 

at a particular time and a particular location. 

Consider first the issue of the time window for added emissions to impact peak ozone 

concentration. For peak ozone concentration to be impacted, the added emissions must occur on 

an ozone conducive day, during a time window of approximately 2-4 hours. Figure 71 shows 

that approximately half of all days in the months of August, September and October are ozone 

conducive in the Houston-Galveston area. The frequency of ozone conducive days is expected 

to be lower in other months, leading to a very rough estimate of one day in 4 being ozone 

conducive, on an annual basis. 

Figure 71. Analysis of the degree to which flow circulations (rotating wind directions) occur that are conducive to 

ozone formation occur during August-October. Roughly half of the days during this period have wind fields that 

rotate through 3 or 4 quadrants and virtually all of the exceedances occur during these periods. 
 89 

If one day in 4, and 2-4 hours of each of those days are conducive to emission events impacting 

peak ozone concentration, then roughly 1 hour in 25-50 has the potential for an added emissions 

to impact peak ozone concentration if the emission occurs in the correct location. 

Analyses are still underway for assessing the impact of event location, but for the moment 

assume that events in Harris County are most likely to impact peak ozone concentration, and that 

within Harris County, roughly one in two to one in four significant additions of emissions will 

occur at a location that will impact peak ozone concentration. 

Taken together, this suggests that only one instance of added emissions in one hundred will 

occur at the right location and the right time to impact peak ozone concentration. A key question 

to answer then, is what is the expected magnitude of added emissions that would impact peak 

ozone concentration. 

To answer this question, the emission event database examined in Section IV was analyzed using 

a Monte Carlo simulation. The Monte Carlo simulation followed the following process: 

1. For any emission event occurring in Harris County over a one hour period, roughly one 

event in one hundred (one hour in one hundred) has the potential to impact peak ozone; 

therefore for the 11 months of data available (roughly 8000 hours), randomly select 80 

hours 

2. Identify the worst case emission event. This event corresponds to the worst emission 

event that would impact peak ozone concentration in a hypothetical year. 

3. Repeat steps 1 and 2 ten thousand times to generate a distribution of worst case 

emission events in potential years. From this distribution of worst case emission events 

select the median (an event of this magnitude is more likely than not to occur) or the 

90th percentile emission event (only for one in 10 hypothetical years would an event 

larger than this, impacting peak ozone concentration, occur) 

The results of the Monte Carlo analysis are shown in Figure 72. Figure 72 shows the 

distribution of worst case emission events in Harris County for total HRVOCs that would be 

expected if 1 hour in one hundred, on average, has the potential for an emission event to impact 

peak ozone concentration. The median value is approximately 1000 pounds and the 90th

percentile value is roughly 3000 pounds. This would suggest that, if no actions were taken to 

reduce event emissions, it would be necessary to plan for an event of 1000 to 3000 pounds in the 

attainment demonstration, at a location that would influence peak ozone concentrations. As 

noted in Section VI, this level of emission event, would likely increase peak ozone 

concentrations by 2-3 ppb. 
 90 

Figure 72 Distribution of annual worst case emission events in Harris County for total HRVOCs that would be 

expected if 1 hour in one hundred, on average, has the potential for an emission event to impact peak ozone 

concentration. The median value is approximately 1000 pounds and the 90th percentile value is roughly 3000 

pounds. 
 91 

There are multiple assumptions inherent in the Monte Carlo analysis. These include the 

probability that an emission event will lead to a change in peak ozone concentration (currently 

set at 1 in 100), the assumption that only one hour of emissions should be considered (instead of 

one, hour, two hour, and three hour blocks), and the assumption that the worst case event should 

be chosen in any given year. 

The impacts of all of these assumptions on the analysis could be examined. Figure 73 shows the 

results of an analysis using an alternative to considering the worst case event in any year. Since 

the NAAQS for ozone is based on the 4th highest ozone concentration observed over 3 years, an 

alternative is to examine the 4th highest ozone concentration over 320 randomly selected hours 

(equivalent to 3 years). The median value is again 1000 pounds, but in this case the 90th

percentile value for a worst case emission event is 1500 pounds. 

It is important to note that this finding assumes that emission events are merely adding a 

marginal enhancement to peak ozone concentrations and are not the sole cause of the emission 

event. 
 92 

Figure 73 Distribution of fourth highest HRVOC emission event magnitudes, over 3 years, in Harris County, that 

would be expected if 1 hour in one hundred, on average, has the potential for an emission event to impact peak 

ozone concentration. The median value is approximately 1000 pounds and the 90th percentile value is roughly 

1500 pounds. 
 93 

VII b. Impact of short-term limits and other control scenarios on emission event 

magnitudes 

The worst case emission event distributions shown in Figures 72 and 73 are based on current 

event magnitudes and frequencies. If control practices are put in place to reduce event 

emissions, then expected worst case emission events in future years would be reduced. For 

example, if a short term limit of 500 lb/hr were placed on HRVOC emissions, then the expected 

value of the worst case emission event would decrease from 1000 pounds/hr to less than 500 

pounds/hr. 

The goal of this section is to apply several possible control strategies to the event emissions to 

determine the approximate impact on expected worst case emission event magnitudes. 

The first control strategy assumes that there is a short term limit such that the maximum event 

magnitude is 100, 500 or 1000 pounds/hr. Note that there is no assumed effect on event 

frequency – all events still occur but are limited in maximum magnitude. The results of this 

analysis are shown in Figure 74. The new median values of worst case emission events are 100, 

500 and 1000 pounds/hr, respectively , for emission limits of 100, 500 and 1000 pounds/hr. 

The second control strategy assumes that there is a short term limit of 100, 500 or 1000 pounds 

and that the control strategy has the effect of preventing events over 100, 500 or 1000 pounds 

(these events are set to zero in the controlled event emission database). In effect, the number of 

events, or frequency, is reduced because events in excess of the limit are zeroed-out and 

therefore are assumed to no longer occur. The results of this analysis are shown in Figure 75. 

The new median values of worst case emission events are 97, 360 and 747 pounds, respectively, 

for emission limits of 100, 500 and 1000 pounds. 

 

Finding 3: Based on current practices, emission variability of roughly 1000 lb/hr should be 

expected in the regions upwind of peak, region wide ozone concentration at least once per 

year in the Houston-Galveston area. This finding is based on estimates of the frequency of 

ozone conducive conditions and the frequency and magnitude of HRVOC emission events 

reported through a TCEQ database. This expected value could potentially be decreased by 

imposing short term limits on HRVOC emission variability. . 
 94 

Figure 74a Distribution of annual worst case emission events in Harris County for total HRVOCs (with a 1000 lb/hr 

short term limit, but no change in event frequency) that would be expected if 1 hour in one hundred, on average, has 

the potential for an emission event to impact peak ozone concentration. The median value is 1000 pounds and the 

90th percentile value is 1000 pounds. 
 95 

Figure 74b Distribution of annual worst case emission events in Harris County for total HRVOCs (with a 500 lb/hr 

short term limit, , but no change in event frequency) that would be expected if 1 hour in one hundred, on average, 

has the potential for an emission event to impact peak ozone concentration. The median value is 500 pounds and 

the 90th percentile value is 500 pounds 
 96 

Figure 75a Distribution of annual worst case emission events in Harris County for total HRVOCs (with a 1000 lb/hr 

short term limit, which causes events larger than 1000 lb to be prevented) that would be expected if 1 hour in one 

hundred, on average, has the potential for an emission event to impact peak ozone concentration. The median 

value is approximately 750 pounds and the 90th percentile value is roughly 900 pounds 
 97 

Figure 75b Distribution of annual worst case emission events in Harris County for total HRVOCs (with a 500 lb/hr 

short term limit, which causes events larger than 500 lb to be prevented) that would be expected if 1 hour in one 

hundred, on average, has the potential for an emission event to impact peak ozone concentration. The median 

value is approximately 350 pounds and the 90th percentile value is roughly 450 pounds 
 98 

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Appendices 

A. Event Emissions in the Houston-Galveston Area 

B. Stochastic Emission Inventories of Continuous Emissions 

C. Case Studies of 2003 Ozone Events Using HRM and TCEQ 

PAMS Monitors, URS, February 19, 2003 

D. Sub-domain modeling case studies