Impacts of Emission Variability and Flare Combustion Efficiency on

Apr 5, 2012 - Recent studies in the Houston–Galveston–Brazoria (HGB) area of Texas have suggested that industrial flares exhibit high temporal emi...
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Impacts of Emission Variability and Flare Combustion Efficiency on Ozone Formation in the Houston−Galveston−Brazoria Area Radovan T. Pavlovic, Fahad M. Al-Fadhli, Yosuke Kimura, David T. Allen,* and Elena C. McDonald-Buller Center for Energy and Environmental Resources, The University of Texas at Austin, 10100 Burnet Road, Building 133, M.S. R7100, Austin, Texas 78759, United States S Supporting Information *

ABSTRACT: Recent studies in the Houston−Galveston−Brazoria (HGB) area of Texas have suggested that industrial flares exhibit high temporal emissions variability and that flare combustion efficiencies could vary with air and steam assist rates, particularly at lower flow rates, and when low heating value gases are combusted. This work examined the difference in ozone formation potential associated with accounting for temporal variability in flaring emissions, as opposed to assuming the same amount of mass was emitted at a constant, average flow rate. The temporal variability in flare emissions was found to lead to differences in ozone concentrations of as much as 27 ppb in the HGB area. This work also examined the potential ozone formation impacts of flare combustion efficiencies of less than 98−99%, applied to 25 flares throughout the HGB region. Deterioration in combustion efficiency (CE) was found to affect ozone concentrations by a few to more than 50 ppb, depending on the level of the assumed CE. While the ozone impacts associated with temporal variability in emissions typically lasted a few hours, consistent with the length of large flaring events, lowering of the CE significantly increased emissions and ozone concentrations over periods ranging from several hours to several days for some flare types. Thus, changes in CE may affect ozone concentrations for longer durations and over larger spatial extents than episodic emissions events.

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al.15,17 classified flare emissions variability from refineries and olefin production facilities based on industrial sector, process type, and chemical composition of the flared gas. Flare groups were further segregated based on average emission rates, number of emission events, and relative emission variability.15,17 Using an approach introduced by Webster,18 Pavlovic et al.15,17 modeled emissions using normal or log-normal distribution functions to produce a unique probability density function (PDF) for each category of refinery and olefin manufacturing flares. The resulting stochastic model used a Markov transition matrix to simulate emission histories for each flare category. Stochastic representations of refinery and chemical manufacturing (olefin production) flares showed very good agreement with the actual time-series data from the 2006 SI and as such provided more comprehensive input for air quality modeling.15,17 Preliminary air quality modeling analyses of the 2006 SI hourly emissions data indicated that impacts on ozone concentrations due to reported emissions variability tended to be localized and were highest during high emissions excursions.16 An objective of the work reported here was to more thoroughly examine the role of the variability of emissions from industrial facilities on ozone formation in the HGB area using the 2006 hourly SI.17 In addition to uncertainties

INTRODUCTION Recent air quality studies in the Houston−Galveston−Brazoria (HGB) area, the Texas Air Quality Study (TexAQS) 2000 and the Second Texas Air Quality Study (TexAQS II), have indicated that ozone formation in the region can be rapid, at rates that at times exceed 200 ppb/hour.1−5 Rapid ozone formation has been associated with plumes of highly reactive volatile organic compounds (HRVOCs) originating from the Houston industrial area.1,3,6−11 These industrial sites often include large emission sources of HRVOCs together with nitrogen oxides (NOx), which under conducive meteorological conditions may provide sufficient ozone precursors to rapidly form ozone. In addition, measured ambient concentrations of HRVOCs in the HGB area were often found to be many times larger than reported emissions inventories.12 Previous studies have found that emissions of hydrocarbons from petrochemical industry sources can exhibit considerable temporal variability, which may lead to high HRVOC to NOx ratios, accelerating ozone formation.3,7−9,13 These findings motivated the collection of hourly emissions data from more than 140 industrial locations during August 15 through September 15, 2006, as a part of the second Texas Air Quality Study (TexAQS II). The database is known as 2006 Special Inventory (SI) and is the largest database of industrial emissions variability from non-EGU sources.13 Analysis of the 2006 SI reinforced previous findings that suggested that flare emissions are highly variable in nature.14−17 A common assumption in the emission inventory input data used in regulatory air quality modeling is that industrial emissions are nearly constant, since many facilities operate continuously at a constant rate of output. Using 2006 SI hourly data, Pavlovic et © 2012 American Chemical Society

Special Issue: Industrial Flares Received: Revised: Accepted: Published: 12593

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associated with the temporal variability of flare emissions, changes in their combustion efficiencies could also impact ozone formation, as the amount of compounds in unburned flared gas depends on a flare’s combustion efficiency (CE). In this work CE is defined as the percentage of gases fed to a flare that are completely combusted, typically to carbon dioxide and water; it is assumed no products of incomplete combustion are formed. Many factors can affect flare CE such as the heating value of flared gases, oversteaming (high steam to flared gas ratio), overaerating (high air to flared gas ratio), wind speed, gas exit velocity, and chemical composition of flared gases.19,20 Although the flare CE is often specified based on its design value, previous studies have indicated that CE changes (deteriorates), especially for low heating value gases, as inlet gas mass flow rates approach low values, and could lead to an underestimation of ozone precursor emissions (such as HRVOCs) by an order of magnitude or more.19−21 Therefore, another objective of this study was to evaluate the effects of flare CE on predicted ozone concentrations in the HGB area by using recently developed stochastic models that simulate the temporal patterns of flare emissions.15,17

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METHODS The effects of industrial emissions variability were evaluated by comparing ozone concentrations obtained using hourly emission rates reported in the 2006 SI with those obtained using daily average 2006 emissions during an August 15− September 15, 2006 episode12,13,16 The impacts of changes in flare CE were investigated by developing modified hourly emission rates based on a range of different CE scenarios. To examine numerous emissions scenarios, simulations were conducted using a more computationally efficient subdomain photochemical model,6,7,11 described by Nam et al.6 and Webster et al.,7 developed to support the State Implementation Plan (SIP) for the HGB area.22 The geographical region and subdomain for the modeling are shown in Figure 1. Details of the modeling are described by Nam et al.6 Photochemical modeling was performed using the Comprehensive Air Quality Model with extensions, version 4.53.23 Additional details regarding the photochemical modeling are provided in Supporting Information. Emissions Variability Analysis. The emissions variability analysis consisted of two scenarios. The first, designated as the “Hourly EI” scenario, used reported hourly emission profiles from the 2006 SI. The second, designated as the “OSD Average EI” scenario, used ozone season daily (OSD) average emissions rates developed from 2006 SI data.13,14 Both scenarios assume constant CE of 98−99% for all flares with reported emissions. Although the Texas Commission on Environmental Quality (TCEQ) eventually included 32 days during the period between August 15 and September 15, 2006,14 at the time of this study, episode development was still in progress and only 22 modeling days were available (i.e., August 15−22, August 29−September 7, and September 12−14). Evaluation of the relative air quality impacts of variable emissions sources focused on a set of modeling days that satisfied some or all of the following criteria: days for which the 2006 SI data showed significant variability and greater emissions, days that were likely to be more sensitive to changes in emissions because of high measured ozone concentrations, and, of special interest, days with meteorological conditions such that plumes from the Houston industrial area/Houston Ship Channel could strongly effect regional ozone formation.24 Selection of modeling days

Figure 1. Full domain and subdomain (dark gray) regions. The horizontal resolutions of the Regional, East Texas, Houston− Galveston−Beaumont−Port Arthur (HGBPA), and Houston−Galveston−Brazoria (HGB) (dark gray region) nested domains are 36, 12, 4, and 1 km, respectively.22.

was conducted using the hourly 2006 SI, National Weather Service archived weather data (EDAS 40 km), in combination with the NOAA Air Research Lab (ARL) HYSPLIT model, as well as extensive data from the Houston area ozone monitoring network (available at http://www.tceq.state.tx.us).12,13,24 Flare Combustion Efficiency Analysis. Flare emissions categorization and stochastic models were developed by Pavlovic et al.13,15,17 for petroleum and olefin production flares in the HGB area. This work simulated hourly emissions profiles for 25 high VOC emission rate industrial flares reported in the 2006 SI.13−15,17 Locations and emission rates for the flares are provided in Supporting Information. Stochastically simulated hourly emissions for modeled flares were obtained by multiplying the flare’s 2006 monthly average hourly flow rate by the hourly temporal allocation factors for each of the 25 flares, generated by the stochastic model.15,17 In this work, 240 hourly temporal allocation factors (10 simulation days, using meteorological data from an August 2000 episode analyzed by Nam e al.6) were generated for each flare. The inlet gas hourly mass flow was calculated as follows: EA × 100 (1) 100 − CEA A where M represented the hourly inlet flow rate from a representative flare (designated flare A); EA represented flare A’s hourly emissions rate; and CEA was flare A’s combustion efficiency value (a constant value, typically 98% or 99% for the flares considered here; CE values were obtained from the TCEQ Control Equipment Identification Number (CIN) database, at ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/ Modeling/TexAQS/EI/Point/). Stochastic flare emission scenarios were generated by applying various CE efficiencies to each flare’s inlet mass flow MA =

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Finally, the modified hourly emissions for each flare were obtained as follows:

rate with several assumptions. First, based on previous findings that a flare’s CE could change as a result of variable inlet gas mass flows, particularly at lower flow rates,20,21 it was assumed that the flare’s CE will start to deteriorate as inlet mass flow rate goes below a specific turndown ratio value (designated here as the TRv). The turndown ratio (TR) is defined as the ratio of the actual flare waste gas streamflow rate to maximum design flow rate. However, because of the lack of maximum design capacity data for the selected flares, the maximum flow rate observed over the month-long special inventory was used instead of maximum design flow rate. Ten values of TRv between 0 and 1.0 with increments of 0.1 were investigated. Second, the deterioration of the flare’s CE was assumed to have a linear relationship with TR below TRv. Finally, the CE efficiency value at the zero mass flow intercept (designated here as CE0) was assumed to have ten possible values between 0% and the default CE (i.e., CEA in eq 1; default CE value was either 98% or 99% for all modeled flares), with increments of 10% (i.e., 0%, 10%, 20%,...,90%). For each flare, the CE as a function of TR was calculated as follows:

E A′ = M A ×

100% − CEA(TR ) 100%

(3)

where E ′ represented the modified emission rate of flare A; CEA(TR) was the variable combustion efficiency of flare A that was a function of turndown ratio (eq 2); and MA was the inlet mass flow rate of flare A (eq 1). Figure 3 shows a surface plot of the average daily VOC emissions for a representative flare as an example. A

⎧CEA (i.e., 98% or 99%) TR > TR V ⎪ ⎪ A CEA(TR) = ⎨ CE − CE0 ⎪ × TR TR < TR V ⎪CE0 + TR V ⎩ Figure 3. Simulated values of average daily VOC emissions (tpd) for 100 modeled scenarios (i.e., ten TRv scenarios × ten CE0 scenarios) for a representative flare.

(2)

where CEA(TR) was the variable hourly combustion efficiency; TR was the flare’s turndown ratio; TRv was the turndown ratio value at which the flare CE starts to decline; CE0 was the combustion efficiency at the zero mass flow intercept; and CEA was flare A’s constant default combustion efficiency value. Note that CEA(TR) acted as a parametric function with two parameters (i.e., TRv and CE0) and one independent variable (i.e., TR). Figure 2 shows an example of ten possible CE scenarios for the case where the parameter TRv is equal to 0.5. Because both TRv and CE0 can have ten values, a total of one hundred CE scenarios were developed for each of the 25 flares.

The Base Case emissions scenario assumed a constant and time invariant flare CE,16 whereas the sensitivity scenarios for the 25 flares assumed a variable CE. CAMx simulations were conducted for ten consecutive days (240 h), generated by stochastic models, with identical meteorological and emissions inventory input data as the Base Case scenario except for emissions from the 25 flares.

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RESULTS Effects of Flare Emissions Variability on Ozone Formation. Comparisons of the “Hourly EI” and “OSD Average EI” scenarios indicated that the relative impacts from a variable hourly emissions inventory could be significant, resulting in differences in ozone mixing ratios of as much as +18 ppb and −27 ppb at HGB area ozone monitors.16 However, the largest ozone concentration differences between the two scenarios were localized and tended to be associated with relatively low ozone concentrations in the base case. Thus, while the amounts of additional ozone formed could be high, the ozone was added to a relatively low base concentration. This finding is based on assumed flare CEs of 98−99%. The two scenarios exhibited similar performance in predicting daily maximum, region-wide, 1-h averaged ozone concentration in the HGB area. This finding suggested that differences in emissions between the two scenarios due to different temporal profiles of the 2006 SI industrial sources may not affect regionwide maximum daily ozone concentrations; for most of the episode days the location of the predicted maximum was different from the location where the maximum differences between the two scenarios occurred.16 As an example, results for August 20, 2006 were explored in more detail.

Figure 2. Ten combustion efficiency functions, CE(TR), for TRv = 0.5. Each CE(TR) scenario is determined by one of ten CE0 values and one of ten turndown ratio values (TRv). CE is combustion efficiency; TR is turndown ratio; TRv is the turndown ratio value at which the flare CE starts to decline; CE0 is the combustion efficiency at the zero mass flow intercept. A total of 100 CE scenarios were developed for each of the 25 flares. 12595

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Figure 5. Maximum differences in 1-h ozone concentrations in the HGB area between scenarios with a combustion efficiency of 80% at zero flow rate (CE0 = 80%) and the Base Case for ten simulated days.

Figure 6. Differences in predicted ozone concentrations on August 25 at 13:00 between a scenario with TRv = 0.6 and CE0 = 30% and the Base Case during the 7th day of the stochastic emission inventory (HGB 1-km grid; 72 × 72 km).

during the morning hours (i.e., 8:00 a.m.). It was preceded by an emission peak, shown in Figure 4c, that occurred at a nearby petrochemical plant in La Porte at 6:00 a.m. Hence, the impacts from variable source emissions are expected to be influenced by the location, timing, and magnitude of peak emissions. Effects of Flare Combustion Efficiency on Ozone Formation. Although 100 scenarios of CE were examined for the 25 flares, the results and discussion presented here will examine a few representative scenarios. A detailed presentation of all of the scenarios is provided by Al-Fadhli.20 Maximum predicted differences in ozone concentrations in the HGB area between scenarios with a CE of 80% at zero flow rate (CE0 = 80%) and the Base Case for ten simulated days are shown in Figure 5. Maximum differences in hourly ozone concentrations from 25 flares under conditions where the CE varies between 98% or 99% and 80% as a function of the flare’s inlet mass flow were as much as 18 ppb. Predicted ozone impacts due to CE deterioration were driven by flaring conditions on particular days, i.e. by the relationship between TR and TRv. This was evident, for example, during days 2 and 7, and for the CE scenarios at TRv above 0.5. Extremes in predicted ozone differences during certain days arise from the variable nature of the flare emissions, as the changes in CE could lead to higher

Figure 4. (a) Time series of ozone concentrations for the “Hourly EI” and “OSD Average EI” scenarios at the grid cell where the largest difference occurred on August 20, 2006; (b) location and hour when the maximum ozone difference occurred (“Hourly EI” − “OSD Average EI”); (c) temporal profiles from the “Hourly EI” and “OSD Average EI” inventories for the nearby petrochemical plant in LaPorte.

The difference between the daily maximum, region-wide, hourly ozone concentrations from the “Hourly EI” and “OSD Average EI” scenarios on August 20, 2006 was 0.5 ppb (i.e., 118.8 ppb with the “Hourly EI” as opposed to 118.3 ppb using the “OSD Average EI”). Although the predicted daily maximum ozone concentration was only minimally affected by the variability in emissions, large differences still occurred at locations and times of the day different from the location and time of the region-wide daily maximum. Figure 4a and b show the maximum positive ozone concentration difference between the two emissions scenarios of more than 25 ppb occurred 12596

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the area with ozone increases of 5 ppb or more due to episodic emissions peaks, depicted in Figure 4, extended to only a few 4km × 4-km grid cells. Figure 7 shows the average difference in maximum daily 1-h ozone concentrations in the HGB 1-km grid for the scenario with CE0 = 80%. Daily ozone maximum concentrations increased between 0.2 and 1.5 ± 0.6 ppb. These results suggested that variability in CE could have a larger influence on daily maximum hourly ozone concentration than episodic emissions events. Table 1 summarizes results from all 100 CE scenarios by showing ten-day average maximum impacts on ozone concentrations in the HGB subdomain. The deterioration in CE for the 25 flares increased predicted average 1-h ozone concentrations in the HGB subdomain from a few ppb to more than 50 ppb. Similarly, Table 2 shows the ten-day average impact on daily maximum ozone concentrations in the subdomain area. Changes in flare CE led to an average increase of 17 ± 18 ppb in predicted maximum hourly ozone concentrations. The standard deviation, shown in Tables 1 and 2, increased significantly for scenarios where the TRv was above 0.5; in particular, these increases were driven by two simulated days (2 and 7) which represented flaring conditions characterized by high CE deterioration. Impacts on 8-h ozone averages are discussed in the Supporting Information.

Figure 7. Average differences in maximum daily 1-h ozone concentrations in the HGB area for scenarios with a combustion efficiency at zero flow rate of 80% (CE0 = 80%). Box encloses lower and upper quartile of the data (25th and 75th percentiles), while whiskers represent 10th and 90th percentiles.

VOC and HRVOC emissions especially if applied to flares operating at low flows. The spatial area affected by changes in flare CE was, for some scenarios, greater than the area impacted by emission peaks. This was most notable for scenarios where TRv was 0.5 or higher where at least two simulated days (i.e., Days 2 and 7) were characterized by significant CE deterioration, and hence resulted in emission increases that were orders of magnitude higher than the Base Case for several flares across the domain simultaneously (Figure 3; Depending on nature of the flare variability, total emissions can increase by more than an order of magnitude due to deteriorating CE). In contrast, episodic increases in emissions, driven only by changes in flared gas flow rate and not by changes in CE, occurred sporadically and almost always as isolated events.13,16 As an example, Figure 6 shows the affected area in the ozone difference plot between a scenario with TRv = 0.6 and CE0 = 30% and the Base Case at 13:00 h during day 7 of the emission simulation; in particular, the area with a predicted ozone increase of 5 ppb or more in Figure 6 is almost 50 km long and 10−15 km wide. In contrast,

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CONCLUSIONS The effects of industrial flare emissions variability on predicted ozone concentrations in the HGB area were explored by comparing two emission scenarios from the 2006 SI, the first with hourly emission rates and the second with average ozone season day emission rates. Impacts on predicted ozone concentrations in the region were primarily associated with episodic emissions periods (i.e., high emissions excursions, lasting only a couple of hours) that led to increases of as much as 27 ppb, but were often short in duration and small in spatial extent. Increases in ozone concentrations were also frequently associated with the emissions variability of a single source. The magnitude, location, and timing of reported emissions in the 2006 SI had only negligible effects on predicted daily ozone maximum concentrations. Changes in flare combustion efficiencies had a larger impact on ozone concentrations than episodic emissions. While episodic emissions events typically lasted a few hours,

Table 1. Ten-Day Average of the Maximum Differences in Predicted Ozone Concentrations (ppb) in the HGB Sub-Domain Area between 100 CE Scenarios and the Base Case turndown ratio value (TRv) CE at zero mass flow 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

0.1 5.2 4.7 4.1 3.6 3 2.5 2 1.5 0.9 0.4

± ± ± ± ± ± ± ± ± ±

0.2 1.4 1.3 1.1 1 0.8 0.7 0.5 0.4 0.2 0.1

9.2 8.2 7.2 6.2 5.3 4.3 3.4 2.5 1.6 0.7

± ± ± ± ± ± ± ± ± ±

0.3 2.1 1.9 1.7 1.4 1.2 1 0.8 0.5 0.4 0.2

12.7 11.3 9.9 8.5 7.2 5.9 4.6 3.3 2.1 0.9

± ± ± ± ± ± ± ± ± ±

0.4 3.3 2.9 2.5 2.2 1.8 1.4 1.1 0.8 0.5 0.2

16.4 14.5 12.7 10.9 9.1 7.5 5.8 4.2 2.7 1.2

± ± ± ± ± ± ± ± ± ±

0.5 3.9 3.5 3 2.6 2.1 1.7 1.3 0.9 0.6 0.3

21.1 18.7 16.3 13.9 11.7 9.5 7.3 5.3 3.3 1.5

± ± ± ± ± ± ± ± ± ±

0.6 4.7 4.1 3.5 3 2.5 2 1.5 1.1 0.7 0.3

12597

29.3 25.8 22.4 19.2 16 13 10 7.2 4.6 2

± ± ± ± ± ± ± ± ± ±

0.7 7.4 6.5 5.6 4.8 4 3.3 2.5 1.8 1.1 0.5

37.1 32.7 28.4 24.2 20.2 16.3 12.6 9.1 5.7 2.5

± ± ± ± ± ± ± ± ± ±

0.8 13.8 12.3 10.8 9.3 7.7 6.2 4.8 3.4 2.1 0.9

43.2 38.2 33.2 28.4 23.7 19.1 14.7 10.5 6.6 2.9

± ± ± ± ± ± ± ± ± ±

0.9 19.4 17.3 15.2 13.1 11 8.8 6.8 4.8 2.9 1.2

48.4 42.8 37.2 31.8 26.5 21.4 16.5 11.7 7.3 3.2

± ± ± ± ± ± ± ± ± ±

1.0 23.6 21.2 18.7 16.2 13.6 11 8.4 5.9 3.6 1.5

53.1 46.7 40.5 34.6 28.8 23.3 17.9 12.8 7.9 3.4

± ± ± ± ± ± ± ± ± ±

26.7 24.3 21.6 18.7 15.7 12.7 9.7 6.9 4.2 1.7

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Table 2. Ten-Day Average Differences in Predicted Daily Maximum Ozone Concentrations (ppb) in the HGB Sub-Domain Area between 100 CE Scenarios and the Base Case turndown ratio value (TRv) CE at zero mass flow 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

0.1 0.8 0.7 0.6 0.5 0.5 0.4 0.3 0.2 0.1 0.1

± ± ± ± ± ± ± ± ± ±

0.2 0.4 0.4 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0

1.3 1.2 1.1 0.9 0.8 0.7 0.5 0.4 0.2 0.1

± ± ± ± ± ± ± ± ± ±

0.3 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0

2 1.8 1.5 1.3 1.1 0.9 0.7 0.5 0.3 0.2

± ± ± ± ± ± ± ± ± ±

0.4 0.7 0.6 0.5 0.4 0.3 0.3 0.2 0.2 0.1 0

2.8 2.5 2.1 1.8 1.5 1.2 1 0.7 0.5 0.2

± ± ± ± ± ± ± ± ± ±

0.5 0.8 0.7 0.6 0.5 0.4 0.3 0.3 0.2 0.1 0.1

4.2 3.6 3.1 2.6 2.1 1.7 1.3 1 0.6 0.3

± ± ± ± ± ± ± ± ± ±

0.6 0.7 0.6 0.5 0.4 0.4 0.3 0.2 0.2 0.1 0

reductions in flare CE increased VOC and HRVOC emissions associated with routine flaring operations over periods ranging from several hours to several days for some flare types. Impacts on predicted ozone concentrations in the HGB area ranged from a few to more than 50 ppb.

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ASSOCIATED CONTENT

Additional data on the flares and the air quality modeling methods and results. This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported through a grant from the Texas Air Research Center (TARC). We express our gratitude to Dr. Greg Yarwood for his insights and review of the study. We acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing computational resources that have contributed to the research results reported within this paper.

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± ± ± ± ± ± ± ± ± ±

0.7 1.4 1.2 1 0.9 0.8 0.6 0.4 0.3 0.2 0.1

7.8 6.4 5.5 4.6 3.8 3.1 2.4 1.7 1.1 0.5

± ± ± ± ± ± ± ± ± ±

0.8 3.2 2 1.7 1.5 1.3 1.1 0.8 0.5 0.3 0.1

11.5 9 7 5.4 4.4 3.6 2.7 1.9 1.2 0.6

± ± ± ± ± ± ± ± ± ±

0.9 9.4 6.4 3.8 2 1.7 1.4 1.1 0.7 0.5 0.2

14.5 11.7 9 6.8 5 3.9 3 2.2 1.3 0.6

± ± ± ± ± ± ± ± ± ±

1.0 14.3 10.9 7.5 4.4 2.1 1.7 1.3 0.9 0.5 0.2

17 13.9 10.8 8.2 5.9 4.3 3.3 2.3 1.4 0.7

± ± ± ± ± ± ± ± ± ±

17.9 14.4 10.6 7.1 3.7 1.9 1.5 1.1 0.6 0.3

(4) Emery, C.; Kemball-Cook, S.; Tai, E.; Morris, R.; Yarwood, G. Final Report HARC Project H-1, “SENSITIVITY MODELING STUDY OF RAPID OZONE FORMATION EVENTS IN THE HOUSTONGALVESTON AIRSHED”; Environ Corp., 2004. (Available at www. harc.edu). (5) Gilliani, N. V.; Doty , K. TERC Project H85, “Top-down Emissions Verification (TDEV) of Industrial Emissions in Houston Based on TexAQS II Data”; University of Alabama-Huntsville, 2008. (Available at www.harc.edu). (6) Nam, J.; Kimura, Y.; Vizuete, W.; Murphy, C.; Allen, D. T. Modeling the impact of emission events on ozone formation in Houston, Texas. Atmos. Environ. 2006, 40 (28), 5329−5341. (7) Webster, M.; Nam, J.; Kimura, Y.; Jeffries, H.; Vizuete, W.; Allen, D. T. The Effect of Variability in Industrial Emissions on Ozone Formation in Houston, Texas. Atmos. Environ. 2007, 41, 9580−9593. (8) Allen, D. T.; Murphy, C. F.; Kimura, Y.; Vizuete, W.; Edgar, T.; Jeffries, H.; Kim, B.; Webster, M.; Symons, M. Variable Industrial VOC Emissions and Their Impact on Ozone Formation in the Houston Galveston Area; Project H-13; Houston Advanced Research Consortium (HARC), 2004. (9) Vizuete, W.; Kim, B.; Jeffries, H. E.; Kimura, Y.; Allen, D. T.; Kioumourtzoglou, M.; Biton, L.; et al. Modeling ozone formation from industrial emission events in Houston, Texas. Atmos. Environ. 2008, 42 (33), 7641−7650, DOI: 10.1016/j.atmosenv.2008.05.063. (10) Olaguer, E. P.; Rappenglueck, B.; lefer, B.; Stutz, J.; Dibb, J.; Griffin, R.; Brune, W. H.; et al. Deciphering the Role of Radical Precursors during the Second Texas Air Quality Study. J. Air Waste Manage. Assoc. 2009, 59 (11), 1258−1277, DOI: 10.3155/10473289.59.11.1258. (11) Henderson, B. H.; Jeffries, H. E.; Kim, B.-U.; Vizuete, W. G. The Influence of Model Resolution on Ozone in Industrial Volatile Organic Compound Plumes. J. Air Waste Manage. Assoc. 2010, 60 (9), 1105− 1117, DOI: 10.3155/1047-3289.60.9.1105. (12) Thomas, R.; Smith, J.; Jones, M.; MacKay, J.; Jarvie, J. Emissions Modeling of Specific Highly Reactive Volatile Organic Compounds (HRVOC) in the Houston-Galveston-Brazoria Ozone Nonattainment Area. 17th Annual International Emission Inventory Conference, US EPA, Portland, Oregon, June 2−5, 2008. (13) Pavlovic, R. T.; McDonald-Buller, E.; Allen, D. T. TERC Project No. H-95: Estimating Future Year Emissions and Control Strategy Effectiveness based on Hourly Industrial Emissions; submitted to the Houston Advanced Research Consortium (HARC), Project No. H-95, 2009. (14) Texas Commission on Environmental Quality (TCEQ). TexAQS II Emissions Inventory Files Modeled for Intensive Period of August 15 through September 15, 2006; 2008. Accessed Jan 2008, ftp:// ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/HGB8H2/ei/point/ 2006Aug15-Sept15/. (15) Pavlovic, R. T.; McDonald-Buller, E.; Allen, D. T. Flare Emission Characterization of Refineries in Houston, Texas; (2009-A-485-AWMA)

S Supporting Information *

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6 5.2 4.4 3.7 3.1 2.5 1.9 1.4 0.9 0.4

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