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, 20042 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 oz one 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 sour ces) 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 ge nerally 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, demons trates that while some types of emissions of volatile organic compounds from non-EGU point s ources are constant, others are not. The evidence includes emission event reports, air po llutant 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 fac ility 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 highl y 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 HR VOC 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 emi ssions and discrete emission events 1

. Roughly 3 times per month in 2003, reported emission events caused si ngle 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-Gal veston region is approximately 5,000 - 10,000 lb/hr). Roughly 3 times per week in 2003, reported emi ssion 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 preliminar y modeling indicates that variations in continuous (as opposed to discrete) emissions c ould cause emissions of total VOCs, averaged over areas larger than 100 km 2 , 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 forma tion 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 (concentr ations averaged over 1-hour) that would not have occurred in the absence of the emissi on 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 vari ations in emissions might lead to ozone NAAQS exceedances directly, more frequent, smaller va riations in emissions have the potential to marginally increase the magnitude of ozone con centrations. If the HRVOC emission variability occurs at critical times and locations, it can ma rginally increase the peak ozone concentration that might be expected in the Houston-Galves ton area. Air quality modeling analyses were performed to assess the changes in peak, regi on-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 a rea, can increase peak, region-wide ozone concentrations, if the emission variability occurs in reg ions 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, re leases 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 variab ility in the range of 100 to 1000 pounds per hour can be expected daily, at some time and some lo cation 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 a dditional 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 sel ected 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 documente d 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 frequen cy 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 Administrativ e 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 magnitudes6 

### II. Introduction The Houston-Galveston area (HGA) is designated as a severe ozone non-attainment region, and the State of Texas, through the Texas Commi ssion 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 de veloping 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 emi ssions inventories, and emission inventories are used for a variety of purposes. For example, em ission inventories are used to establish statewide and nation-wide trends in air quality. Th ey are used to determine which categories of emission sources are most important in specific ge ographical 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 th e 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 emissi on 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 concentra tions, 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 emissi ons. 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 specifi c day or days, the temperatures and sunlight intensities for those specific days are used. Em ission 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). Fo r electricity generating units (EGUs) that emit largely NOx and sulfur oxides (SOx), emissions i nventories are used in defining rules that limit acid precipitation. These inventories are based on a nnual or seasonal emission rates. In contrast, when emissions from EGUs are needed to ev aluate ozone formation on specific days, hourly emission rates on those days are used in models designed to predict air quality.

For petroleum refineries, chemical manufacturi ng 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 consta nt 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 prev alance 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 oz one formation in the HGA have been continually updated and reviewed for decades (for a revi ew, see Allen, et al., 2003), and the emission inventories developed by the Stat e 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 emissi ons, as a function of time of day, shown in the lower right portion of the Figur e, 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 emi ssions 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 info rmation 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 (calle d 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 i nventories are the “speciation profiles” that characterize the composition and therefore the at mospheric 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 obs erved in the Houston-Galveston area, a group of highly reactive VOCs (HRVOCs) in the inventor y merits special attention. These HRVOCs include ethene, propene, butenes, and 1,3-but adiene, and they are among the compounds11 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 poi nt source HRVOC emissions, the emissions of highly reactive volatile organic compounds (HRVOC s, 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. Ev en within major source categories, such as chemical manufacturing, a small number of unit pr ocesses 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 emi ssions of HRVOCs from point sources in the Houston-Galveston area.12Table 1.Fractions of the ethylene point source inventory assignedto source categories (based on historical inventory data)Industrial Processes Chemical Manufacturing Plastics Production 0.244Industrial Processes Chemical Manufacturing Fugitive Emissions 0.137Industrial Processes Chemical Manufacturing Butylene, Ethylene, Propylene, Olefin Production 0.126Industrial Processes Chemical Manufacturing General Processes 0.108Industrial Processes Chemical Manufacturing Other Not Classified 0.057Industrial Processes Chemical Manufacturing Fuel Fired Equipment 0.044Industrial Processes Petroleum Industry Fugitive Emissions 0.041Industrial Processes Cooling Tower Process Cooling 0.032Industrial Processes PetroleumIndustry Cooling Towers 0.030Industrial Processes Petroleum Industry Flares 0.021Industrial Processes Oil and Gas Production Natural Gas Production 0.019Industrial Processes Petroleum IndustryPetroleum Products - Not Classified 0.013Internal Combustion Engines Industrial Natural Gas 0.012Petroleum and Solvent Evaporation Petroleum Liquids Storage (non-Refinery) Bulk Terminals 0.011Petroleum and Solvent Evaporation Organic Solvent Evaporation Miscellaneous Volatile Organic Compound Evaporation 0.011Industrial Processes ChemicalManufacturing Vinyl Acetate 0.011Industrial Processes PetroleumIndustry Blowdown Systems 0.010Industrial Processes Miscellaneous Manufacturing Industries Miscellaneous Manufacturing Industries 0.009Petroleum and Solvent Evaporation Petroleum Liquids Storage (non-Refinery) Petroleum Products - Underground Tanks 0.007Industrial Processes Chemical Manufacturing Ethylene Glycol 0.007Industrial Processes Oil and Gas Production Fugitive Emissions 0.007Industrial Processes ChemicalManufacturing Ethylene Oxide 0.00513 Figure 5. Sources of emissions of highly reactiv e 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 la rge 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, Figur e 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 incl ude significant quantities of HRVOCs emitted by flares, cooling towers and as fugitives, and a sm all 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, indicat es 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 emissions15 

### 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 th e 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 oz one formation, and if so how they are most effectively addressed in photochemical modeling and policy development.

Effective September 12, 2002, per Texas Admini strative 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 reportabl e emissions event as “Any emissions event which, in any 24-hour period, results in an unauthor ized 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 inci dents be filed electronically and be posted in a publicly accessible database (TCEQ, 2002; Texa s 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 organi c 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 (NO X ), 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 Even t Reports, which can be downloaded from the Internet through an on-line reporting system (T CEQ, 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 enacti ng 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 su mmary of these events is presented in Table 2. The release of HRVOCs was involved in appr oximately 40% (711) of these events. Significant HRVOC event emissions, both in terms of frequency a nd 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 geographi c 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 c onstitute about 12% of the total. However, events are extremely limited in time and space and thus event emissions have the potential to be17 extremely concentrated. This characteristic is critical to development of an accurate HGA emissions inventory and consequently to unde rstanding 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 te mporal pattern of the emissions becomes clearer (Methods used in developing the time series are de scribed in the Appendix). Figure 7 presents a time series of NO X 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 facilitie s in the 4 county region has been calculated from the 2001 TCEQ Point Source Database (TCE Q, 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 NO X 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 NO X emissions, it appears that individual events do not significantly add to the magnitude of the inventory. Furthermore the to tal mass of NOx contributed by events is only 140 tons (280,954 lbs) per year or 0.1% of th e 105,482 tons per year emitted by point sources located within the four counties. Thus the magnitude of NO X 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 even t emissions in the same format as the NO X event emissions. The average annual flow rate for rou tine emissions for all of the facilities in the 4 county region, 10,359 lbs/hr based on historical inve ntories, appears as a horizontal line. In contrast to NO X , there are 14 times during the eleven-month period in which VOC event emissions exceed the annual average. The time invol ved 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 m illions 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 r eaction rates than the four identified HRVOC species may be of interest because of their larg e total mass and persistence. However, a larger examination of VOCs is out of the scope of the current effort.18 Figure 7 . NO X 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. NO X 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 HourEmissions (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 HourEmissions (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 lbs19 A time series for HRVOC emissions is shown in Figure 9. The annual average of routine HRVOC emissions is calculated from the 2000 sp ecial inventory (TCEQ, 2003d), which is the only generally available TCEQ inventory that c ontains speciated HRVOC emissions. The flow of HRVOC event emissions exceeds the 1500 lb/h r annual average 29 times during the elevenmonth period (almost 3 times per month), impacti ng 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 ethe ne (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) ma ss 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 HourEmissions (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 lbs20 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 ex ceed 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 av erage 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 HourEmissions (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 HourEmissions (lbs/hr) Event Emissions 2000 SpEI Annual Avg

237 lbs/hr

Jan 1 Dec 31

Total Event Emissions = 105,089 lbs21 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 a nd 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 annua l average of 586 lbs/hr. Ev ent 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 HourEmissions (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 HourEmissions (lbs/hr) Event Emissions 2000 SpEI Annual Avg

586 lbs/hr

Jan 1 Dec 31

Total Event Emissions = 954,418 lbs 12,239 lbs/hr22 Ethene (ethylene) exhibits the most significan t frequency and magnitude of event emissions. Over an 11-month period there are 58 times (aff ecting 395 hours) when ethylene event emissions exceed the 2000 annual average for all point sour ces 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 emissi ons 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 but ene) 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 (F igure 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 unschedul ed. More than half (17) last less than 10 minutes. The average release during these la rge events (greater than 1000 lbs) lasting 10 minutes or less was 2588 pounds of HRVOC. The av erage 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

Scheduled23 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

Scheduled24 Figures 14-16 differentiate between scheduled a nd unscheduled events. Scheduled events are due to scheduled start-up, shut-down and maintena nce 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 unde r 20% (13) entities have the primary SIC 2821, Plastics Materials. 10 entities are petroleum refi neries (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 (Vulcaniz able Elastomers) none listed* *includes some pipelines 9999 Nonclassifiable Establishments 4613 Refined Petroleum Pipelines 4212 Local Trucking Without Storage 4226 Special Warehousing and Storage 2899 C hemicals and Chemical Preparations 2865 Cyclic Organic Crudes and Intermediates 1311 Crude Petroleum and Natural Gas

Figure 17 . There were 79 reporting entities representing 14 diffe rent primary standard industrial codes (SICs) reporting HRVOC event emissions in th e 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 SICNumber of Reporting Entities Number of Reporting Entities w ith HRVOC Events by Primary SIC25 Reporting Entity Primary SIC SIC Description Total HRVOC lbs A Dow Texas Operations Freeport 2869 Industrial Organic Chemicals 467,138 B Equistar Chemicals Channelview Comp lex 2869 Industrial Organic Chemicals 186,813 C Equistar Chemicals Chocolate Bayou Co mplex 2869 Industrial Organic Chemicals 117,498 D BP Amoco Chemical Chocolate Bayou Pl ant 2869 Industrial Organic Chemicals 90,366 E Equistar Chemicals La Porte Comple x 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 Operati ons 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 to tal 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 lb s and thus contributed approximately 60% of the four county total.
While only 35% of the reporting entities had a prim ary SIC code of 2869, this industrial sector accounts for more than 1 million of the 1.6 m illion pounds of HRVOC event emissions reported for the last eleven months of 2003. In additi on, the top six facilities in terms of total HRVOC emission mass all fall within this SIC classifica tion (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 duri ng 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
FacilityHRVOC (lbs) Total HRVOC Event Emission Mass by Reporting Entity26 Figure 19. The amount of HRVOCs emitted during any single ev ent, 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 isomer s 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 mass27 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 epis odic 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, Fi gure 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-m ol/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). Appr oximately weekly, episodic emissions lead to daily mass flows that are double the annual averag e (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 fo r emissions) to a flare at an olefins facility28 Data related to VOC emissions from cooling towers also exhibit variability. Figure 21 shows the concentrations of VOC in cooling tower wate r (a surrogate for emissions) over roughly nine months of operation. In this case, constant c ontinuous 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 cont ributions 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 ra tes 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 de scribes the fraction of the time that the source emits at a particular rate. For example, the PD F on the left hand side of Figure 22 indicates that the probability of the flare having a mass fl ow rate within the range labeled as δ f (roughly 4.9 to 5.1 thous. lb/hr) is the probability density (0.03) multiplied by δ f.29 Figure 22. Probability distribution function (PDF) of mass fl ow 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 2030 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 char acterized by a group of PD Fs, 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 va riable emissions using PDFs. PDFs describe the full range of possible emission scenarios or em ission 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 con centrations. 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 ha ve 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 si ngle PDF. For example, if all of the mass flow to a flare were due to venting of ta nks due to diurnal cycling, then a single PDF representing the range of observed temperature cycli ng should be sufficient to describe the flow.

Figure 26 shows an attempt to model mass flow ra tes 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 10Probability 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 8000Flare 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 comp ared 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 indicat es 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 ep isodic and emission event. Work is currently underway to link physical causes to these diffe rent 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 di fferent 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 proba bility 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 30Probability 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 pr ovide a reasonable representation of emission variability from emission sources such as flares. Th e next step is to apply the variable emissions models to the entire Houston-Galveston area, rath er than to just a single emission source. This involves some uncertainties, since the available da ta 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 va riability will be affected by a large number of independently variable sources.35 The analysis, described in detail in the Appendi x, assumes that total annual emissions remain fixed, but that individual sources exhibit indepe ndent variability; so, if one source is in an allowable episodic emission mode, another source mi ght 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 regi on 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 “instan ce” 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 si ngle 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 to tal 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 5000VOC 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 exampl e, Figure 31 shows an estimated inventory for a region south of the ship channel and including D eer Park. Figure 31 also shows the estimated variability in the emissions in the subregion (ignor ing 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 Simulation0500100015002000250030003500400045005000Ethylene Emissions (tons/hour)

0.05 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.13

Hour of Simulation0500100015002000250030003500400045005000VOC 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 th e subregion, with an area of approximately 100 km 2 , is comparable to the variability in total emissions for the entire region, but for specific compounds, such as ethylene, the variability incr eases because a smaller number of sources is being considered. For ethylene, Figure 31 sugge sts 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: Variabilit y in HRVOC emissions from point sources is si g nificant and is due to both variability in continuous emissions and discrete emission events. R oughly 3 times per month in 2003, reported emission events caused single facilitie s to have HRVOC emissions that were greate r than 10,000 lb/hr (the total annual average emissions of HRVOCs, from all industrial point sources in the Houston-Galveston region is approximat ely 5,000 - 10,000 lb/hr). Roughly 3 times per wee k in 2003, reported emission events caused single facilities to have HRVOC emissions that were
g

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

acilities to have HRVOC emissions that were greater than 100 lb/hr. Variabilityin continuous emissions is more difficult to quantify than emi ssion variability due to reported emission events bu t
p reliminary modeling indicates that variations in continuous (as opposed to discrete) HRVO C emissions could cause emissions of total VOCs, averaged over areas larger than 100 km 2 , to vary b y 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. Gr ound monitors, that record the hourly average concentrations of more than 50 individual hydr ocarbons at several sites in the HGA, show significant variability in VOC concentrations. Fi gure 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 reactiv e 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 measurem ent 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 r ecord of variable, and at times very elevated concentrations of highly reactive hydrocar bons. Figure 33 (Main, et al, 2001) report concentrations of ethene as a f unction 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 tim es, 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 meas urements in excess of 40 ppbC is much lower than at Deer Park, suggesting that many of the ex treme 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 variab ility in emissions and variability in atmospheric conditions. The data in Figure 33 show that th e 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 300ETHYL

0 10 20 30 HOUR 0 10 20 30 40 50 60 70 80 90ETHYL

Figures 33 and 34 provide summaries of obser ved concentration distributions through lines that contain boxes and whiskers, as s hown in the diagram. .The box shows the 25 th , 50 th (median), and 75 th percentiles. The whiskers have a maximum length equal to 1.5 times the length of the b ox (the interquartile range). If there are data outside this range, the points are shown on the plot. Asterisks represent poin ts that fall within three times the interquartile range from the end of th e 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 20Measure

75th percentile

Median
25th percentile Box ( Interquartile

ran g e ) 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 observe d at ground monitors is to examine time series. Figure 35 shows time series of concentrations, obs erved 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 4000Jan-99May-99Sep-99Jan-00May-00Sep-00Jan-01May-01Sep-01Total VOC Concentrataion(ppbC) 50%

75%

90%

95%

98%

99%

Percentiles Values of Ethene Concentration:

Hourly AutoGC Measurements at Clinton

0 50 100 150 200

J

an-

99

May-99 Sep- 99

Jan-00 May - 00

Sep-00 Jan-01 May - 01

Sep-01Ethene Concentrataion (ppbC)

50%

75%

90%

95%

98%

99%

Percentiles Values of Propene Concentration:

Hourly AutoGC Measurements at Clinton

0 50 100 150 200Jan-99Apr-99Jul-99Oct-99Jan-00Apr-00Jul-00Oct-00Jan-01Apr-01Jul-01Oct-01Propene 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 99 th 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. N either 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 coas t 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

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

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 0:00PPM

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 0:00PPM

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 co llected over a 40-minute period – followed by a 20 minute analysis/purge sequence. The IR mon itor 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 th e 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 0:00Duration 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 0:00Duration 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 0:00PPM

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 0:00PPM

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 se ries shows that the detection of very high (more than 1 ppmC) concentrations of some co mpounds can occur as isolated instances, while other, similar magnitude instances, can occur as pa rt 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-Ga lveston 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 r each 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 variab ility. 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 ma de 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/measuremen t system. The Houston data reported in Figure 41 are based on the hydrocarbon concentrations obs erved by the aircraft, during the August and September 2000 sampling program, when ozone wa s 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 concentrati on 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. HoustonReactivity (s-1)

0 5 10 15 20 25 Methane CO HCHO Biogenic Anthropogenic

NO x (ppb) 13 4.3 21 35 25 Figure 41. VOC reactivity for 5 cities, averaged over a set of samples wit h ozone formation rate equal or above the 90 th percentile values. One Houston and one Phoenix VOC sample have been removed as outliers with biogenic reactivity (primaril y from terpenes) an order of magnitude greater than the 2 nd highest value. N Ox concentration is averaged ove r near-source samples as defined b y 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 re sults of a principal component analysis of the aircraft data collected by Brookhaven National La boratory. The analysis in Figure 43 indicates that high ozone productivity is most highly corre lated with high concentrations of ethene, propene, acetylene, ethane, propane and butane, in the presence of NOy (Factor 1). These compounds are commonly associated with emissi ons 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 distri bution is consistent with the gr ound measurements, showing significant differences between median and high values of reactiv ity. b.) compounds that contri bute 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 99OH 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(O 3 ))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 th e observed and predicted concentrations of paraffins (PAR) ethylene (ETH) the sum of highe r 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 sp ecies. 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 a nd higher aldehydes (both represented by ALD2) observed by aircraft on August 30, 2000, during the Texas Air Quality Study, and concen trations 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 underpredi ction of the highest observed concentrations.48 V a. Observational data Ozone measurements

As will be demonstrated in later sections of th is 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 emissi on 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 su fficient sources of other emissions (principally nitrogen oxides) that will lead to significant reactions of the emissions.

Later sections of this report will examine the oz one 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 th is 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 ai r quality episodes selected are, of necessity, extreme examples of the impact of emission va riability on ozone concentrations, since these are the instances that are most evident in the obser vational 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 doc umented 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 approxi mately 6700 pounds occurred between 11 AM and noon, lasting for approximately 30 minutes. This re lease was advected initially to the west, then to the northwest, passing over significant highway sources of NO x . 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 plum e 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 lo cated 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 con centrations 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 12000:001:002:003:004:005:006:007:008:009:0010:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

TimeMIR Reactivity

0 50 100 150 200 250 300Ozone 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 event51 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-G C 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 e ither 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. Th e 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 o n 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 vari ability on peak ozone concentrations. The computational requirements of this approach, however, are extremely demanding. It would be difficult, if not impossible, to consider thousa nds 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 photoche mical grid models. Yet, consideration of many different emission “snapshots” or scenar ios is necessary, because of the emission variability outlined in previous sections of this repor t. 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 forma tion 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 computati onally 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 th e Houston-Galveston area. The Comprehensive Air Quality Model with extensions (CAMx) was us ed 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 pr ojected to future year 2007 (FY 07), reduced using the suite of controls labeled as C ontrol 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 c oncentrations 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 Augus t 25 and August 30, two of the days that had high ozone concentrations for this simulation (upper plots), a nd 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 con centration, 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-s outhwest 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 th is 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 e fficient 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-dom ain 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 5056 show these two-sub-domains for the region upwi nd 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 th e locations of peak ozone concentrations. They were selected to provide a contrast to th e 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 becau se they are in regions that are not near the locations of peak ozone concentrations.

Match 3-D photochemical model to computationa lly efficient sub-domain model using Process Analysis Once the sub-domain locations are selecte d, a computationally efficient sub-domain model is constructed. The goal of the sub-domain model is to mimic the predictions of the full57 3-D simulation, as accurately as possible, within the sub-domain. The sub-domain model achieves its computational efficiency by assuming th at 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 si mulation is not the goal, however. The goal is to create a computationally efficient sub-dom ain 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 scr eening 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 calcula te the rates of atmospheric processes that control air pollutant concentrations. These pr ocesses include chemical formation, chemical consumption, advection, diffusion, and deposition. In the photochemical model, these processes are coupled into a system of mass continuity equa tions 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 ma gnitudes of the individual processes (advection, diffusion, deposition, and chemical formation and de struction) that lead to these changes in species concentrations are not typically recorded.58 Figure 52. Conceptual model explaining processes that c ontribute 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 ar e determined by a set of processes that describe the physical and chemical changes that are o ccurring. Various physical and chemical processes that affect the species concentrations are repr esented 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 ar e 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, th e rates of all these processes for each cell are added together and the combined effect is integr ated 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 ro les of individual processes that contribute to ozone formation in a complex model. These met hods 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 An alysis tools were modified and expanded at the University of Texas by Jeffries, Kimura and Vi zuete to function with the existing output of CAMx and additional post processing codes we re written. (Tesche, et al., 2004; Vizuete et al. 2004). These were needed to permit a dynamic verti cal analysis box that wa s a new feature of the University of Texas version of the Process Analys is code. The process analysis tool produces a significant amount of information that ultimatel y 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 speci es 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 pa thways to the net change. A different type of analysis detailed in the cycle spreadsheet is requi red to characterize the magnitudes of particular reaction pathways.

Figure 55. Example Process Analysis time series plot with obs erved hourly averaged observations. The change in the red line (predicted mixing ratio or concentration) is th e 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 cy cles spreadsheet, which describes reaction cycles in the atmosphere responsible for generating ozone, it is useful to briefly review ozone formation pathways. Ozone (O 3 ) 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 NO 2 in the ambient air. In sunlight, the NO 2 is photolyzed to produce NO and atomic oxygen, O( 3 P). The atomic oxygen then reacts with molecular oxygen to form O 3 . Therefore, the photolysis of NO 2 is the major source of O 3 in the troposphere. This ozone can immediately react with NO to produce NO 2 again. In the absence of competing reactions, NO, NO 2 , and O 3 reach equilibrium and the amount of O 3 never exceeds the amount of NOx (NO+NO 2 ). However, if volatile organic compounds (VOCs) are present in the system then free radicals, generated from the oxidative degradation of VOCs, oxidize NO to NO 2 . This oxidation results in accumulation of the O 3 formed in the NO 2 photolysis, causing a net increase in O 3 . The NO can be reused several times under these conditions (NO is oxidized by a hydrocarbon radical, converted to NO 2 ; the NO 2 photolyzes to form NO and O( 3 P); the O( 3 P) reacts to form ozone and the NO starts the cycle again). Thus, the amount of O 3 can now be many times the amount of NOx. The hydrocarbon radicals are products of the reacti ons involving hydroxyl radi cals, OH, with VOC and CO. Each time a radical oxidizes an NO, an O 3 accumulates and another NO 2 is generated. This NO 2 can again photolyze to create yet another O 3 . Eventually the reaction of NO 2 with several of the radicals forms nitrogen products such as nitric acid (HNO 3 ) and organic nitrates terminating the production of O 3 from that particular NO mo lecule. 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, VOC s are oxidized and NO-to-NO 2 conversion occurs in radical propagation steps. The following reaction step s illustrate a propagation chain where a VOC molecule is converted to one or more car bonyl molecules and two molecules of NO are62 converted to two molecules of NO 2 . Note that the free radical emer ges 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 ra dicals 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 NO 2 to make HNO 3 . Similarly, the term Pn represents the fraction of NO 2 that is photolyzed, and 1-Pn symbolizes the fraction of NO 2 that is converted to termination products (HNO 3 , RNO 3 ) or is lost by physical processes like deposition and horizontal or vertical transport.64 An example of an application of a cycle diag ram 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 fi gure 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 O 3 is explained by the following: new O 3 = E*Pn*f where E = (new NO) [1/(1- Pn)] = total NO oxidized to NO 2 Pn = fraction of all NO 2 that is photolyzed f = fraction of all NO 2 photolysis reactions that make ozone

Using the values in Figure 57, new NO = 49.0 ppb, Pn = 0.731, f = 0.937. Therefore (new O 3 )/(new NO) = 2.54

For radical limited conditions, new O 3 is explained by the following: new O 3 = (VOC reacted)*R*Pn*f where (VOC reacted) = (new OH)*[1/(1- Pr)] + (VOC photolyzed) R = (NO → NO 2 )/( VOC reacted) essentially a type of “mixture reactivity” Pn = fraction of all NO 2 that photolyzes f = fraction of all NO 2 photolysis reactions that make ozone

Using the values in Figure 57, new OH = 27.9 ppb, Pr = 0.757, R = 1.26 (new O 3 )/(new OH) = 4.4665 These two values are the “gain” or “chemical amp lification” 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 tr ansport, vertical transport and chemical production/destruction of ozone for the August 25 sub-domain (source regi on). The results shown in Figure 58 are for the full 3-D simulation. From the full 3-D simula tion, 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 i ndividual chemical species entering the sub-domain, horizontally and vertically

• Hour by hour wind fields causing ve rtical and horizontal advection

• Hour by hour changes in mixing heights, base d on changes in vertical diffusivity as a function of elevation

• Hour by hour deposition rates and photolysis rates

-40.0 -20.0

0.0 20.0 40.0 60.0 80.0 100.0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hours, LDTppb or ppb/hr

Conc_I Tot_Emis Chem H_trans V_trans Cell_VChg Depo monitor 35 monitor 608 03harCap.1k.825.srce.box.goes2

O3 camx403.20000825.fy07i.UT.cs03_harCap.1km.goes2.ipr

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 i ndividual 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 species66 These quantities, calculated by the sub-domain model, can be represented in time series and compared to the time series of the same quan tities calculated by the full gridded simulation. If the two time series match, then the computationa lly efficient sub-domain model is an effective tool for characterizing the spatially averaged pe rformance 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 comp arisons 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 pro cesses (lower diagram) match the analogous processes for the full 3-D, gridded model (upper diagram) in the sub-domain.

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O3 camx403.20000825.fy07i.UT.cs03_harCap.4km.ipr

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O3 camx403.20000825.fy07i.UT.cs03_harCap.4km.ipr67 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 pro cesses (lower diagram) match the analogous processes for the full 3-D, gridded model (upper diagram) in the sub-domain.

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NO camx403.20000825.fy07i.UT.cs03_harCap.4km.ipr

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NO camx403.20000825.fy07i.UT.cs03_harCap.4km.ipr68 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 pro cesses (lower diagram) match the analogous processes for the full 3-D, gridded model (upper diagram) in the sub-domain.

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OLE camx403.20000825.fy07i.UT.cs03_harCap.4km.ipr

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OLE camx403.20000825.fy07i.UT.cs03_harCap.4km.ipr69 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 th e 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 (Simul ation FY 07 CS04). The results from those independent analyses were compared using th e 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 an alyses 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 cells70 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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O3 camx403.20000825.fy07i.UT.cs03_harCap.1km.goes2.ipr71 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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O3 camx403.20000825.fy07i.UT.cs03_harCap.shell.025ETH025OLE.1km.goes2.ipr

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O3 camx403.20000825.fy07i.UT.cs03_harCap.shell.025ETH025OLE.1km.goes2.ipr72 Figure 64a. Comparison of process analysis time series (See Figure 55 for definitions) for NO y 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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NOY camx403.20000825.fy07i.UT.cs03_harCap.1km.goes2.ipr

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NOY camx403.20000825.fy07i.UT.cs03_harCap.1km.goes2.ipr73 Figure 64b. Comparison of process analysis time series (See Figure 55 for definitions) for NO y 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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NOY camx403.20000825.fy07i.UT.cs03_harCap.shell.025ETH025OLE.1km.goes2.ipr

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NOY camx403.20000825.fy07i.UT.cs03_harCap.shell.025ETH025OLE.1km.goes2.ipr74 Figure 65a. Comparison of process analysis time series (See Fi gure 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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OLE camx403.20000825.fy07i.UT.cs03_harCap.1km.goes2.ipr

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OLE camx403.20000825.fy07i.UT.cs03_harCap.1km.goes2.ipr75 Figure 65b. Comparison of process analysis time series (See Fi gure 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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OLE camx403.20000825.fy07i.UT.cs03_harCap.shell.025ETH025OLE.1km.goes2.ipr

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OLE camx403.20000825.fy07i.UT.cs03_harCap.shell.025ETH025OLE.1km.goes2.ipr76 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

O 3 + h υ 3.158 OH cycles 21.06 ppb

Propagation and Termination new [OH] [OH] reacted P OH = 0.683 [OH] recreated 31.51 ppb 99.51 ppb [VOC] reacted = 90.56 ppbV 68.00 ppb

aldehydes + h υ (NO -> NO 2 ) / VOC= 1.5076

10.45 ppb RO 2 + NO -> NO 2 + RO; r(dt) = 136.53 ppb

+H2O old [O 3 ] aloft O 3 + NO -> NO 2 + O 2 ; r(dt) = 1.57 ppb 10.53 ppb

1.57 ppb

NO 2 from Other Paths NO -> NO 2 [O 3 ]p / [NO 2 ]h υ = 0.902 new [NO2] 0 ppb 138.24 ppb 9.54 ppb 10.79 ppb +org/HO2 Propagation and Termination new [NO] [NO] reacted P NO = 0.726 [NO2] + hv [O 3 ] produced 38.45 ppb 140.49 ppb [NO Z ] 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 NO 2 31.41 ppb 7.04 ppb 0.11 ppb 87.76

[O 3 ] h trans [O 3 ] v trans -93.23 ppb 13.81 ppb [O 3 ] init [O 3 ] final 35.11 ppb 44.45 ppb old O3 + NO [O 3 ] h trans [O 3 ] v trans [O 3 ] depo 1.57 ppb 93.225 ppb -13.81 ppb 3.91 ppb77 Radical Cycle/ NO Cycle/ Ozone 825.source.1km.goes2.box.cycles

O 3 + h υ 3.362 OH cycles 22.33 ppb

Propagation and Termination new [OH] [OH] reacted P OH = 0.703 [OH] recreated 33.11 ppb 111.31 ppb [VOC] reacted = 101.82 ppbV 78.20 ppb

aldehydes + h υ (NO -> NO 2 ) / VOC= 1.498

10.78 ppb RO 2 + NO -> NO 2 + RO; r(dt) = 152.53 ppb

+H2O old [O 3 ] aloft O 3 + NO -> NO 2 + O 2 ; r(dt) = 1.65 ppb 11.16 ppb

1.65 ppb

NO 2 from Other Paths NO -> NO 2 [O 3 ]p / [NO 2 ]h υ = 0.912 new [NO2] 0 ppb 154.87 ppb 7.81 ppb 11.39 ppb +org/HO2 Propagation and Termination new [NO] [NO] reacted P NO = 0.765 [NO2] + hv [O 3 ] produced 37.07 ppb 157.63 ppb [NO Z ] 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 NO 2 30.99 ppb 6.08 ppb -0.78 ppb 104.09

[O 3 ] h trans [O 3 ] v trans -105.45 ppb 10.11 ppb [O 3 ] init [O 3 ] final 35.98 ppb 45.70 ppb old O3 + NO [O 3 ] h trans [O 3 ] v trans [O 3 ] depo 1.65 ppb 105.45 ppb -10.11 ppb 4.04 ppb78 Figure 66b. Comparison of process analysis time series (See Fi gure 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

O 3 + h υ 3.246 OH cycles 21.63 ppb

Propagation and Termination new [OH] [OH] reacted P OH = 0.692 [OH] recreated 33.36 ppb 108.28 ppb [VOC] reacted = 101.45 ppbV 74.92 ppb

aldehydes + h υ (NO -> NO 2 ) / VOC= 1.4918

11.73 ppb RO 2 + NO -> NO 2 + RO; r(dt) = 151.34 ppb

+H2O old [O 3 ] aloft O 3 + NO -> NO 2 + O 2 ; r(dt) = 1.57 ppb 10.82 ppb

1.57 ppb

NO 2 from Other Paths NO -> NO 2 [O 3 ]p / [NO 2 ]h υ = 0.911 new [NO2] 0 ppb 153 ppb 9.53 ppb 11.13 ppb +org/HO2 Propagation and Termination new [NO] [NO] reacted P NO = 0.748 [NO2] + hv [O 3 ] produced 39.19 ppb 155.26 ppb [NO Z ] 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 NO 2 31.97 ppb 7.23 ppb 0.12 ppb 101.34

[O 3 ] h trans [O 3 ] v trans -105.50 ppb 12.85 ppb [O 3 ] init [O 3 ] final 35.11 ppb 44.45 ppb old O3 + NO [O 3 ] h trans [O 3 ] v trans [O 3 ] depo 1.57 ppb 105.5 ppb -12.85 ppb 4.00 ppb79 Radical Cycle/ NO Cycle/ Ozone 825.source.1km.goes2.2910.box.cycles

O 3 + h υ 3.423 OH cycles 22.92 ppb

Propagation and Termination new [OH] [OH] reacted P OH = 0.708 [OH] recreated 34.74 ppb 118.93 ppb [VOC] reacted = 111.19 ppbV 84.19 ppb

aldehydes + h υ (NO -> NO 2 ) / VOC= 1.4885

11.82 ppb RO 2 + NO -> NO 2 + RO; r(dt) = 165.51 ppb

+H2O old [O 3 ] aloft O 3 + NO -> NO 2 + O 2 ; r(dt) = 1.66 ppb 11.46 ppb

1.66 ppb

NO 2 from Other Paths NO -> NO 2 [O 3 ]p / [NO 2 ]h υ = 0.917 new [NO2] 0 ppb 167.72 ppb 7.84 ppb 11.72 ppb +org/HO2 Propagation and Termination new [NO] [NO] reacted P NO = 0.779 [NO2] + hv [O 3 ] produced 37.60 ppb 170.45 ppb [NO Z ] 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 NO 2 31.39 ppb 6.21 ppb -0.38 ppb 115.88

[O 3 ] h trans [O 3 ] v trans -116.16 ppb 9.40 ppb [O 3 ] init [O 3 ] final 35.98 ppb 45.70 ppb old O3 + NO [O 3 ] h trans [O 3 ] v trans [O 3 ] depo 1.66 ppb 116.16 ppb -9.40 ppb 4.12 ppb80 Run scenarios in computationally efficient sub-domain model Figures 59-66 have demonstrated that computa tionally efficient sub-domain models can be constructed that replicate the be havior 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. Specificall y, the sub-domain modeling tool was used to address the following questions:

1. What is the response of peak ozone concen tration to different magnitudes of HRVOC added emissions? 2. Does the time of day when the emission o ccurs 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, howev er, 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 th e 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, si mulation 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. Upwi nd 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 referre d 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-domai ns 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, simulati on at 4 km horizontal resolution). These subdomains will be referred to as FY 07i CS 03_harcap GOES2 Deer Park 8/25, and FY 07 CS03_harcap GOES2 Deer Park 8/30. Upwind a nd downwind sub-domains in each of these areas will be denoted by the suffix source and pl ume, 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 correcti on, 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 si gnificantly 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 Y ear 2007, Control Strategy 3 with additional highly reactive VOC reductions, with meteorol ogy 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 demonstra tion. 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 D eer Park 8/25 scenario. The sub-domain model was used to systematically examine the imp act, on peak ozone concentrations, of added emissions of different magnitudes. Emission a dditions 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 consider ed 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

82010020030040050060070080090010001100120013001400150016001700180019002000210022002300240025002600270028002900300031003200330034003500360037003800390040004100420043004400450046004700480049005000

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 concentra tion 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 magnit udes, 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 s hown 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, la sting one hour, have maximum impact on peak ozone concentration if they occur at 11 AM. Imp act falls off either before or after this time window. Figure 68b shows that for propylene rel eases 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 con centration 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 82010020030040050060070080090010001100120013001400150016001700180019002000210022002300240025002600270028002900300031003200330034003500360037003800390040004100420043004400450046004700480049005000

Total Event Release (lbs)Daily Max O3 (ppb) 9
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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 82010020030040050060070080090010001100120013001400150016001700180019002000210022002300240025002600270028002900300031003200330034003500360037003800390040004100420043004400450046004700480049005000

Total Event Release (lbs)Daily Max O3 (ppb) 9
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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 ozoneconcentration 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 respons e of peak ozone concentration was somewhat dependent on the duration of the emission additi on, but the sensitivity could have largely been predicted from the results shown in Figure 68, wh ich indicated that the response of peak ozone concentration was sensitive to the time of day of th e 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 ma gnitude. 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 relations hip 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 82010020030040050060070080090010001100120013001400150016001700180019002000210022002300240025002600270028002900300031003200330034003500360037003800390040004100420043004400450046004700480049005000

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 81.5010020030040050060070080090010001100120013001400150016001700180019002000210022002300240025002600270028002900300031003200330034003500360037003800390040004100420043004400450046004700480049005000

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 81.5010020030040050060070080090010001100120013001400150016001700180019002000210022002300240025002600270028002900300031003200330034003500360037003800390040004100420043004400450046004700480049005000

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 o f day, emission events spread over one, two, or even three hours have comparable impacts on peak ozone concentrations

, if the y have86 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 hour s 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 th e 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 conduc ted. The results are summarized in Figure 70. The CAMx simulations confirm that the respons e 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 add ition is linear up to emission event magnitudes much larger than 5000 pounds

The full 3-D CAMx simulations provide an asse ssment 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 ra nges 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 a rea, can increase peak, region-wide ozone concentrations, if the emission variability occurs in reg ions 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, re leases 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 var iable emissions on attaining the NAAQS for ozone

VIIa. Estimating the probability of emissi on events and impacts on peak ozone concentrations

In order to characterize the impact of variab le 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 combin e 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 da y 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 m onths of August, September and October are ozone conducive in the Houston-Galveston area. The fr equency of ozone conducive days is expected to be lower in other months, leading to a ve ry 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 exceedan ces 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 im pact of event location, but for the moment assume that events in Harris County are most likel y 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 in stance 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 databa se 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 Harri s 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 av ailable (roughly 8000 hours), randomly select 80 hours

2. Identify the worst case emission event. Th is 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 th is distribution of worst case emission events select the median (an event of this magnitude is more likely than not to occur) or the 90 th 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 ar e 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, ha s the potential for an emission event to impact peak ozone concentration. The median value is approximately 1000 pounds and the 90 th 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 w ould influence peak ozone concentrations. As noted in Section VI, this level of emissi on 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 90 th percentile value is roughly 3000 pounds.91 There are multiple assumptions inherent in th e 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 an alysis could be examined. Figure 73 shows the results of an analysis using an alternative to c onsidering the worst case event in any year. Since the NAAQS for ozone is based on the 4 th highest ozone concentration observed over 3 years, an alternative is to examine the 4 th highest ozone concentration over 320 randomly selected hours (equivalent to 3 years). The median valu e is again 1000 pounds, but in this case the 90 th 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 concentrati ons 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 90 th percentile value is roughly 1500 pounds.93 VII b. Impact of short-term limits and ot her control scenarios on emission event magnitudes

The worst case emission event distributions s hown in Figures 72 and 73 are based on current event magnitudes and frequencies. If contro l practices are put in place to reduce event emissions, then expected worst case emission even ts 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 possi ble 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 limite d 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 ther e 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 ev ent emission database). In effect, the number of events, or frequency, is reduced because even ts in excess of the limit are zeroed-out and therefore are assumed to no longer occur. The re sults of this analysis are shown in Figure 75. The new median values of worst case emi ssion events are 97, 360 and 747 pounds, respectively, for emission limits of 100, 500 and 1000 pounds.

Finding 3: Based on current pr actices, 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 frequen cy 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 w ould 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 90 th 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 90 th percentile value is 500 pounds96 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 90 th percentile value is roughly 900 pounds97 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 90 th percentile value is roughly 450 pounds98 

### References

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Jeffries, H. and Z. Wang, 1997. Applications of Integrated Process Rate Analysis to the SARMAP Air Quality Model, prepared for the California Air Resources Board, prepared by the Department of Environmental Sciences and Engi neering, University of North Carolina, Chapel Hill, NC.

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.101 

## 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