Application of a Lagrangian Process Analysis tool to characterize ozone formation in Southeast Texas

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Atmospheric Environment 42 (2008) 5743–5759 www.elsevier.com/locate/atmosenv

Application of a Lagrangian Process Analysis tool to characterize ozone formation in Southeast Texas Yosuke Kimuraa, Elena McDonald-Bullera, William Vizueteb, David T. Allena, a

Center for Energy and Environmental Resources (R7100), The University of Texas at Austin, 10100 Burnet Road, Austin, TX 78758, USA b Department of Environmental Science and Engineering, University of North Carolina at Chapel Hill, 113 Rosenau Hall CB#7431, Chapel Hill, NC 27599, USA Received 31 May 2007; received in revised form 4 November 2007; accepted 9 November 2007

Abstract Ozone formation in the Houston area is more rapid and efficient than in many other urban areas; these features are due to the interaction of urban emissions with industrial plumes which are associated with both continuous and episodic industrial emissions. To examine the chemistry of interactions of the industrial plumes with urban emissions, a Lagrangian Process Analysis tool was embedded in a gridded photochemical model. The tool successfully isolated the plume so that, within the Process Analysis volume, the dominant process affecting ozone concentrations was chemical production. The analyses showed that the chemistry of the industrial plumes is dependent on emissions encountered downwind, and the extent of radical availability in the morning. For one episode, morning stagnation over the industrial region resulted in enhanced morning radical formation due to aldehyde photolysis. The enhanced morning reactivity led to high ozone concentrations as the plume interacted with urban emissions later in the day. In contrast, during a second episode, the dominant factor influencing ozone concentrations was high volatile organic carbon (VOC)/NOx ratios late in the afternoon, as the plume advected over wooded areas. For emission events, the main perturbation of the ozone formation chemistry occurred during the first few hours of the event due to enhanced production of free radicals from aldehyde photolysis, and more efficient utilization of free radicals due to increased reactive hydrocarbon concentrations. r 2007 Elsevier Ltd. All rights reserved. Keywords: Photochemical grid model; Atmospheric chemistry; Ozone; Lagrangian Process Analysis

1. Introduction The Houston–Galveston area of southeast Texas records some of the highest ozone concentrations observed in the United States. Unlike other cities in the United States, where high ozone concentrations can occur over a relatively broad region and are Corresponding author. Tel.: +1 512 475 7842; fax: +1 512 471 1720. E-mail address: [email protected] (D.T. Allen).

1352-2310/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2007.11.027

frequently multi-day events, high ozone concentrations in Houston are often localized and transient. In addition, ozone formation in the Houston–Galveston area is often rapid and efficient. Ozone formation rates of up to 200 ppb h1 have been observed in Houston; most other cities in the United States observe maximum rates of ozone formation of o40 ppb h1 (Kleinman, et al., 2003). Moles of ozone produced per mole of nitrogen oxides converted to nitric acid, sometimes referred to as the efficiency of ozone production, can be 410 in

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Houston; in most other cities in the United States, this efficiency is 5 or less (Ryerson et al., 2003). These unique features of air pollution events in the Houston area are associated with plumes of highly reactive hydrocarbons over or near the industrial Ship Channel area (Kleinman et al., 2003; Ryerson et al., 2003). A number of photochemical modeling studies have been conducted for the Houston area with industrial emissions assumed to be either relatively constant or to be variable. The Texas Commission on Environmental Quality and its contractors have performed photochemical modeling for air quality planning purposes (TCEQ, 2004a) which have generally assumed that industrial emissions from facilities such as refineries and chemical manufacturing operations are constant. The analyses suggest that industrial emissions, particularly of low molecular weight alkenes referred to as highly reactive volatile organic compounds (HRVOCs), defined in Texas regulations as ethylene, propylene, butenes, and 1,3-butadiene), are substantial contributors to ozone formation. Murphy and Allen (2005) have documented, however, that not all HRVOC emissions are continuous; some are due to discrete emission events. Nam et al. (2006) performed photochemical modeling of hundreds of discrete emission events, and concluded that only a small percentage of the thousands of industrial emission events reported annually in the Houston area lead to large increases in ozone concentrations. Approximately 1.5% of emission events produced 410 ppb of additional ozone, and 0.5% of emission events produced 470 ppb of additional ozone, compared to base case photochemical modeling simulations with no emission events. However, some events that lead to substantial ozone formation can involve very high ozone concentrations that are important in designing strategies to meet federal air quality standards. Although a substantial amount of photochemical modeling has quantified ozone formation in Houston due to industrial emissions, using both constant and variable emission models, the chemistry of the evolution of industrial plumes is not well understood. In particular, explaining the high ozone production efficiencies observed in some of the plumes, and explaining the reasons why some emission events, but not others, lead to high ozone concentrations remains a challenge. The goal of this work will be to examine, in detail, the ozone formation chemistry of industrial plumes in the Houston area. This chemistry will be examined for

multiple meteorologies, which lead to varying ozone production efficiencies, and for emission events. Characterizing the chemistry of industrial plumes in Houston has been difficult because, as shown later in this paper, the plumes are often substantially influenced by downwind emission sources. An industrial plume that advects over downtown Houston has a very different chemistry than a plume that advects over Galveston Bay or the wooded areas north of the city. Characterizing this situation requires a mix of plume based (Lagrangian) and grid based (Eulerian) modeling. Therefore, in this work a new analysis approach, referred to as a Lagrangian Process Analysis (PA) tool, was developed. The goal of the Lagrangian PA tool is to diagnose the causes of the chemical behavior of plumes in a regional photochemical model. Like other PA tools (for example, Jang et al., 1995; Jiang et al., 2003), the Lagrangian PA tool tracks the sources and fates of modeled species by tracking the rates of individual chemical and physical processes, such as emissions, advection, diffusion, deposition, and chemical consumption. The information is postprocessed from model simulation outputs to analyze the contributions of each of these processes to changes in the concentrations of the key species in the atmosphere, such as ozone, oxides of nitrogen (NOx) and volatile organic carbons (VOC). Various forms of PA have been implemented in Eulerian models including the Comprehensive Air quality Model with extensions (CAMx) (Environ International, 2007), and the Community Multiscale Air Quality Model (CMAQ) (Community Modeling and Analysis System, 2007). Despite the success of PA, its application has been limited to problems that involve phenomena in spatially fixed frameworks, such as examining concentrations modeled at one or a few grid cells near monitoring sites, or in an entire urban area. A notable exception is the work of Jiang et al. (2003), in which a back trajectory was calculated for an air parcel that reached a monitoring site that recorded the area wide maximum in ozone concentration in Seattle, WA. PA information of grid cells along the trajectory was then gathered and analyzed as an ensemble. In modeling the fate of emissions from a source region, such as the industrial source region in Houston, it is desirable to diagnose the contributions of modeled processes in a plume. Therefore, this work develops a Lagrangian PA tool that is

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implemented within the Eulerian framework of CAMx. The process metrics are spatially aggregated or averaged for the grid cells that track the movement of an air mass (hence Lagrangian). An inert tracer is released into the host model at a time and location specified by the user, and the movement of the tracer is modeled using the same algorithms and meteorological fields as the modeled species. The Process Analysis volume (‘‘PA volume’’), which is defined in this work as the cells for which the PA metrics are aggregated, is created by selecting cells that capture a specified fraction of the inert tracer, as outlined in the methods section. The location of the PA volume is updated at each time step of the advection/dispersion operation in the host program. As a result, the process metrics are integrated for the moving air mass at the finest spatial and temporal resolution of the simulation. The Lagrangian PA tool could be applied for a variety of source types and geographic areas. This study applied the tool to a case study of ozone formation chemistry of industrial plumes in the Houston area during August and September 2000. The development of the Lagrangian tool and examples of its applications are described in detail below. 2. Methods 2.1. Model formulation As with the conventional PA method, the Lagrangian PA will work within any Eulerian photochemical grid model and uses operator splitting to separate the contributions of different processes. Elements of Lagrangian PA that do not exist in conventional PA are (1) the introduction of inert tracer that tracks the movement of an air mass, (2) an algorithm to define the location and extent of the PA volume given the instantaneous vertical and horizontal spatial distribution of the tracer, (3) integration of the contribution of processes at the model’s forward marching time step, and (4) handling of pseudo processes caused by the movement of the PA volume. For this study, CAMx version 4.20 was chosen as the host model. The program was modified to allow the release of an inert tracer into a user specified volume. The user can specify either instantaneous release of tracer at a specific time, location and 3-D spatial extent, or continuous release of tracer from ground level cells for a specific duration. Advection and dispersion

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algorithms are applied to the tracer in the same order and manner as the regular modeled species, but without the surface deposition sink. At the end of each operation, the location of the PA volume is redefined by ranking all of the grid cells in the model according to the concentration of the tracer, and then selecting cells with the highest concentration of tracers, until the aggregate of cells meet a predefined threshold. One such threshold is that the total volume of the cells equals the initial PA volume. Another example is to make the PA volume capture 90%, or some other percentage, of the tracer released. In either case, the PA volume changes its shape and location. When the PA volume is defined by capturing a specified fraction of the tracer, the size of the PA volume also changes. Process contributions at each grid cell are calculated as they are in a conventional PA; concentration changes in individual grid cells at incremental time steps are attributed to each of the modeled processes. These values are then aggregated into a single value for each process for the PA volume. For example, the contribution of chemical reactions are summed over all grid cells within the PA volume at a given time and then divided by the PA volume (volume weighted average). P 1 i DC chemi  V P Pchem ¼ Dt iV i where Pchem represents process rate by chemistry (ppb h1), DCchem is the concentration change in one grid cell due to chemistry (mmol m3), V is the volume of a cell (m3), and Dt is the time step for the operation (s). The index i refers to all grid cells within the PA volume at a given time. Conversion from concentration (mmol m3) to mixing ratio was done after the summation using volumetric average pressure and temperature within the PA volume. Net advection at the PA volume boundary is determined as the sum of the net mass of species that flow into the PA volume from one direction divided by the total PA volume. P 1 j DC j V j P Padv ¼ Dt iV i where Padv is the process contribution by net advection to one of six directions (ppb h1), DCj is the concentration change due to advection from a selected direction, (positive for influx, negative for efflux) (mol m2 s), and V is the volume of a cell (m3). Summation of j encompasses all of the grid cells whose face is at the external boundary of the

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PA volume, whereas i encompasses all grid cells within the PA volume. Entrainment and dilution, and advective correction are two processes that do not have counterparts in conventional PA. These terms approach zero when the PA volume is constant (by user specification). When the PA volume is defined by capturing a specified mass fraction of tracer, the PA volume can grow over time, and entrainment and dilution terms can be significant. Entrainment is the contribution of mass in the grid cells that is newly introduced within the PA volume; dilution is the concentration decrease due to the dilution of pre-existing mass by the expansion of volume. Calculations for entrainment and dilution are: Pent ¼

1 ðMass added to PA box by entrainmentÞ Dt ðVolume after entrainmentÞ 1 ðMass before entrainmentÞ Dt ðVolume after entrainmentÞ   Volume added by entrainment  Volume before entrainment

Pdil ¼ 

where Pent is the concentration change due to entrainment into the PA volume (ppb h1), Pdil is the concentration change due to dilution of species pre-existing in the PA volume. Pent is always positive when PA volume is growing, whereas Pdil is always negative. ‘‘Advective correction’’ is a pseudo-process that is a nudging factor for the Lagrangian PA that allows it to operate within the Eulerian grid model. By its nature, the Lagrangian PA should not have changes in tracer concentrations within the PA volume due to advection. In contrast, the host Eulerian model explicitly calculates changes in species concentrations in each grid cell due to advection. In order for the two different modeling frameworks to coexist

within a single system, an advective correction was developed. The advective correction was calculated as the difference in concentration within the PA volume before and after the shift of location at each time step. C new_loc  C old_loc Dt where Padv is the pseudo-process contribution of advection to the concentration in the PA volume, Cnew_loc is the average concentration of species in the new location of the PA volume, using the concentration field after the advection operation (ppb), and Cold_loc is the average concentration with prior PA volume location and prior concentration field (ppb). If the advection algorithm of the Eulerian model purely operates as advection, this correction should have a value of zero. However, this is not always the case for two reasons. One reason is that the location of the PA volume is discrete in nature and defined by selecting an integer number of grid cells. Advection is calculated using wind fields that are real, non-integer numbers, so there is a mismatch between the distance traveled by the modeled species (real number) and the shift of the PA volume (integers). Another factor is that the advection operation in the Eulerian model includes the effect of numerical diffusion. Even if the placement of PA volume was ideal, the Padv term may not be zero because of numerical diffusion. Table 1 summarizes calculations performed for each of the processes.

Padv ¼

2.2. Modeling episode The Texas Commission on Environmental Quality (TCEQ) has developed a photochemical model for an 22 August to 6 September 2000 episode, in order to develop the State Implementation Plan

Table 1 Summary of process aggregation methods Process

Summation

Emission Diffusion

Total mass emitted to PA volume Total PA volume Surface integrate mass that flows into PA volume from faces Total PA volume facing each direction Total mass lost to the ground Total PA volume Change in average concentration before and after diffusion of tracer (if volume of PA volume grows, and cells of new PA volume is superset of the old PA volume) Total mass of species gained/lost by chemistry in PA volume Total PA volume Change in average concentration before and after redefining location of PA volume (PA volume is approximately constant)

Deposition Entrainment/dilution Chemistry Advective correction (pseudo process)

Division

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#

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Conroe

#

Crawford La Porte #

36km domain 12km domain 4km domain 1km domain

Fig. 1. Modeling domain: (a) rectangles show nested grids: 36 km, 12 km domain, 4 and 1 km horizontal resolution; (b) portion of 4 km domain incorporating the Houston–Galveston region; monitoring site locations that exhibited maximum ozone concentrations are shown.

(TCEQ, 2004a) for the region. Figs. 1a and b show the modeling domain. Three nested domains (36, 12, and 4 km) were used for most simulations. For event simulations, an additional 1 km resolution domain was added. Within the period modeled, three days with distinct meteorological conditions were chosen for this study: 25, 28, and 30 August. Meteorological conditions and observed ozone concentrations on these days are described in detail by MacDonald and Roberts (2002) and are summarized briefly here. The peak predicted ozone concentration averaged over 1-h on 25 August was 194 ppb ozone observed in downtown Houston (Crawford monitor, see Fig. 1b) at 13:00 h. Winds were stagnant during the morning and in the afternoon, the seabreeze advected air from the industrial source region east of Houston (Ship Channel) into the downtown Houston area. August 28th had a maximum daily 1-h ozone concentration of 112 ppb observed at the Conroe monitoring site, 50 km north of downtown Houston (see Fig. 1b), at 15:00 h. Southerly winds dominated the region throughout this day. August 30th had an observed high ozone concentration of 202 ppb, at the La Porte monitoring site (see Fig. 1b) at 17:00 h, 30 km east of downtown Houston and close to Galveston Bay. Winds were southwesterly to northwesterly in the morning, and then became stagnant in the early afternoon. In the late afternoon, the southerly Bay breeze strengthened. This day was also characterized by relatively low mixing heights. Figs. 2–4 show spatial distributions of 1-h average ground level

ozone concentrations at selected times for each episode day. Figs. 2c, 3c and 4c corresponds to each episode day’s time of maximum ozone concentration. The Comprehensive Air quality Model with extensions (CAMx) (Environ International, 2007) was selected for this study because it is currently being used by the State of Texas for attainment demonstrations in Houston and other areas in Texas that have violated the National Ambient Air Quality Standards for ozone. Meteorological inputs required by the model were based on results from the Mesoscale Meteorological Model, version 5 (MM5) (Grell et al., 1994). The Carbon Bond mechanism (version IV, CB-IV) was used as the atmospheric chemical mechanism in this study (Gery et al., 1989; Adelman, 1999; Environ International, 2007). Emission inventories for VOCs and NOx used as input for the modeling episode have been developed and described by the TCEQ, and include additions of industrial emissions of light olefins, based on ambient measurements (TCEQ, 2004a). Harris County, which includes the Houston urban core, accounts for more than half of the NOx and VOC anthropogenic emissions in the 8-County non-attainment area, with NOx emissions dominated by mobile and industrial point sources and VOC emissions dominated by industrial point sources and area sources. Elevated NOx and VOC emissions and low-level VOC emissions are concentrated in the Houston Ship Channel industrial complex. Low-level NOx emissions arise mainly from mobile sources concentrated in the downtown

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Fig. 2. Ground level ozone concentration and the location of the PA volume; reported ozone concentrations are 1-h averages starting from the time shown below the map. Dark blue and dark red boxes show surface footprint of the PA volume at the beginning and the end of hour, respectively. Data are for 25 August.

Houston urban core and spreading outwards along major transportation corridors. Biogenic VOC emissions extend from Harris County to northeast Texas. This episode includes the period of the Texas Air Quality Study 2000, which provided an extensive database of surface and airborne measurements for model performance evaluation. The overall performance of the model in predicting ozone concentrations during the episode period has been evaluated by the TCEQ (2004b). A summary of the performance evaluation has been published by Wang et al. (2007). On the days of interest that are the focus of this study, the normalized bias of the model when observed ozone concentrations were above 60 ppb

ranged from approximately 10% on 25 and 30 August to 20% on 28 August. Similarly, the normalized gross error of the model, when observed ozone concentrations were above 60 ppb, ranged from 20% to 25% for the three days under evaluation. 3. Results 3.1. Temporal evolution of Process Analysis volumes The initial time and location of the release of tracer was chosen so that the PA volume moves to the location of the predicted daily maximum 1-h average ozone concentration. It is also desirable to start the

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Fig. 3. Ground level ozone concentration and the location of the PA volume; reported ozone concentrations are 1-h averages starting from the time shown below the map. Dark blue and dark red boxes show surface footprint of the PA volume at the beginning and the end of hour, respectively. Data are for 28 August.

analysis before sunrise, which was approximately 6:00 LST, so that evolution of photochemistry throughout the day can be analyzed. To meet these two requirements, a back trajectory analysis was performed using HYSPLIT4 model (Draxler and Rolph, 2003) to determine an approximate initial location (at 6 a.m.) for the PA volume. The location of the tracer release was fine-tuned after running CAMx with the tracer so that the PA volume captures the time and location of the daily maximum of ozone concentration. For all three episode days, the initial volume was 28  8  0.25 km (7 easting grid cell  2 northing grid cells, including the first four vertical layers above ground level); the PA volume footprint is approximately equivalent to the

size of the industrial source region (Ship Channel). The initial vertical depth was chosen as the approximate mixing height at 6 a.m. Figs. 2–4 show the locations of the PA volume at different times for the various episode days, along with the 1-h average ground level ozone concentrations at the same times. Regions surrounded by red and blue lines represent the location of PA volume at the beginning and the end of the 1-h period for which ozone concentrations were shown. The location of the tracer release for the three episode days were at the Ship Channel for 25 August (Fig. 2a), south of Houston for 28 August (Fig. 3a), and west of Houston for 30 August (Fig. 4a). In all cases, the PA volume passes over the Ship Channel area around

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Fig. 4. Ground level ozone concentration and the location of the PA volume; reported ozone concentrations are 1-h averages starting from the time shown below the map. Dark blue and dark red boxes show surface footprint of the PA volume at the beginning and the end of hour, respectively. Data are for 30 August.

10 a.m. (Figs. 2b, 3b and 4b). In the afternoon, the PA volumes move to the location of the domain wide daily maximum ozone. In all three days, the daily maximum occurs in late afternoon although the location is very different for the three episode days (Figs. 2c, 3c and 4c). Fig. 5 shows the evolution of the height and depth of the PA volume. For each day, two lines are shown: one is for the average height of the top of the PA volume, and the other is for the bottom. For 25 and 28 August, the top of the PA volume grew higher during the day, due to vertical mixing within the boundary layer, while the bottom of the PA volume contacted the ground. For 30 August, the height of the top of the volume starts dropping at

noon, which is the time when the PA volume advects over Galveston Bay. The PA volume spreads horizontally in the afternoon and eventually breaks into two parts, as seen in Fig. 4. When the PA volume breaks into two parts, the air parcels can exhibit two different types of chemical conditions, and so the analysis of results from the Langrangian PA, which averages conditions over the entire PA volume, is terminated for this day when the PA volume splits. 3.2. Process Analysis for three episode days Fig. 6 shows the process rates contributing to the temporal evolution of ozone concentrations for

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1800 8/25/2000 8/28/2000 8/30/2000

Height Above Ground (m)

1600 1400 1200 1000

Box Top

800 600 400 200

Box Bottom

0 6:00:00

8:00:00

10:00:00

12:00:00 Time (LST)

14:00:00

16:00:00

18:00:00

200 08/25/00 180 O3 160 140 120 100 80 60 40 20 0 -20 -40 6:00 8:00

10:00

12:00

14:00

16:00

18:00

Concentration (ppb) or Conc Change Rate (ppb/hr).

Concentration (ppb) or Conc Change Rate (ppb/hr).

Fig. 5. Height of top and bottom of the PA volume. Two lines are shown for each day of simulation. The upper line represents the height of the top of the volume, the lower line shows the height of the bottom.

200 08/28/00 180 O3 160 140 120 100 80 60 40 20 0 -20 -40 6:00 8:00

Time of Day (LST)

Concentration (ppb) or Conc Change Rate (ppb/hr).

Time of Day (LST)

200 180 08/30/00 O3 160 140 120 100 80 60 40 20 0 -20 -40 6:00 8:00

10:00 12:00 14:00 16:00 18:00

Concentration Chemistry Emission Advective Correction Horiz. Diffusion Vert. Diffusion Deposition

10:00 12:00 14:00 16:00 18:00 Time of Day (LST)

Fig. 6. Time series for rates of processes contributing to O3 concentrations; note that in this, and subsequent figures, data for 30 August terminate at 15:00 h because the PA volume splits at that time.

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each of three simulation days. Black lines represent instantaneous concentrations, spatially averaged within the PA volume (in ppb). Colored lines show

the process rates that contribute to the changes in concentration in the PA volume (ppb h1). A feature that is immediately apparent is the dominance of chemical processes, which is by design. Compared to PA performed using a fixed grid, the Lagrangian PA tool minimizes the contributions of advection so that the chemical evolution of a plume can be isolated. Table 2 compares the changes in ozone concentrations in the PA volume, integrated from 06:00 to 18:00 h over each of the days, to the contributions from other processes. Since chemical processes dominate the evolution of ozone in the PA volumes, and since understanding the chemistry of ozone formation in petrochemical plumes is the focus of this work, the remainder of this paper will examine the chemical processes within the plumes.

Table 2 Summary of daily ozone budget for each day being studied (units are in ppb)

Initial Chemistry (including change by PiG) Advective correction Horizontal diffusion Vertical diffusion Deposition Final

25/8/2000

28/8/2000

30/8/2000

10.2 187.9

39.5 124.8

28.1 99.2

27.0 16.9 30.8 19.3 165.7

5.0 1.3 4.3 32.9 120.8

17.3 4.5 17.5 18.6 104.4

70

70

08/28/00 60

50

50

NOx Throughput Rate (ppb/hr).

NOx Throughput Rate (ppb/hr).

08/25/00 60

40 30 20 10 0 -10 Physical Loss Chemical Termination Rate NO oxidation w/o O3 consumption

-20 -30 6:00:00

8:00:00

10:00:00

12:00:00

14:00:00

16:00:00

40 30 20 10 0 -10 Physical Loss Chemical Termination Rate NO oxidation w/o O3 consumption

-20

18:00:00

-30 6:00:00

8:00:00

10:00:00

Time (LST)

12:00:00

14:00:00

16:00:00

18:00:00

Time (LST)

70 08/30/00 NOx Throughput Rate (ppb/hr).

60 50 40 30 20 10 0 -10 Physical Loss Chemical Termination Rate NO oxidation w/o O3 consumption

-20 -30 6:00:00

8:00:00

10:00:00

12:00:00

14:00:00

16:00:00

18:00:00

Time (LST)

Fig. 7. Comparison of NOx recirculation rate and termination rate. Rate of NO to NO2 conversion without O3 consumption (NOx recirculation) is shown as a positive value; chemical termination (primarily NO2 oxidation to HNO3) and physical loss (primarily diffusion to aloft) are shown as negative values.

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18 8/25/00 8/28/00 8/30/00

16

NOx Chain Length

14 12 10 8 6 4 2 0 6:00:00

8:00:00

10:00:00

12:00:00 Time (LST)

14:00:00

16:00:00

18:00:00

Fig. 8. Comparison of NOx chain length, calculated as NOx recirculation divided by sum of physical/chemical losses of NOx.

45

45 08/28/00 40

35

35 New OH rate (ppb/hr)

OH rate (ppb/hr)

08/25/00 40

30 25 20 15

30 25 20 15

10

10

5

5

0 6:00:00

8:00:00

10:00:00

12:00:00

14:00:00

16:00:00

0 6:00:00

18:00:00

8:00:00

10:00:00

Time (LST)

12:00:00

14:00:00

16:00:00

18:00:00

Time (LST) 45 40

08/30/00

35 new OH from Organics + Ox new OH from Organics + NO3 new OH from O3 and H2O2 phtolysis Total new OH Total OH reacted

New OH rate (ppb/hr)

new OH from Aldehyde Photolysis 30 25 20 15 10 5 0 6:00:00

8:00:00

10:00:00

12:00:00

14:00:00

16:00:00

18:00:00

Time (LST)

Fig. 9. Rate of total OH reacted (broken line) and new OH created (solid black line). The new OH is further attributed to the source reaction (aldehyde photolysis, organics+Ox or NO3 reaction and O3 or H2O2 photolysis).

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PA allows tracking of not just the overall concentrations of stable chemical species, such as ozone, but also tracks the rates of individual reactions and groups of reactions. In this work, several parameters characterizing ozone formation pathways will be examined, specifically NO to NO2 oxidation by peroxy radicals, NOx cycle termination, NOx chain length, OH reactions, new OH formation, and OH chain length. As noted in the introduction, one of the unique features of ozone formation chemistry in Houston is its efficiency, where efficiency is characterized as the number of moles of ozone formed per mole of nitrogen oxides converted to nitric acid, or otherwise removed from the NOx cycle. Thus, the efficiency is related to the ratio of NO oxidation rate to the NOx termination rate. The overall rates of NO oxidation, and loss, are shown in Fig. 7. NO to NO2 oxidation by peroxy radicals was determined by taking the summation of the rates of four reactions in the CB-IV chemical mechanism (Gery et al., 1989; Adelman, 1999; Environ International, 2007) (NO oxidation by model species HO2, C2O3, XO2, or TO2). Chemical loss rate is determined as net formation rate of NOz, and was primarily HNO3 formation (other reactions being organonitrate formation, net PAN, HONO, and NxOy formation). Physical processes are the summation of vertical diffusion (typically the dominating term), horizontal diffusion, advective correction, and deposition terms.

NOx chain length is calculated as follows: ðNOx chain lengthÞ ðNO oxidation by peroxy radicalsÞ ¼P ððChemical NOx terminationÞ þ ðPhysical NOx lossÞÞ

The chain length reported in Fig. 8 shows the evolution of NOx chemistry along the trajectory of the PA volume. The PA volume for 25 August has the shortest NOx chain length, as the volume passes over urban areas and consequently continuously receives fresh NOx emissions. The NOx efficiency during the afternoon of 28 August, 14:00–16:00 h, is particularly high, as the volume begins to pass over heavily wooded areas with extensive emissions of isoprene. The early morning hours of 28 and 30 August also showed large values of NOx chain length, due to high VOC to NOx ratios. All three episode days have short NOx chain lengths in the later morning, about 7.30–9 a.m., as emissions from morning traffic begin. The ozone formation process is radical driven, and therefore understanding sources and sinks of free radicals is important in characterizing ozone formation. Fig. 9 shows the total rate of hydroxyl radical reaction, formation rate of new hydroxyl radicals, and the new hydroxyl radical attributed to its origin. The total rate of hydroxyl radical reaction is defined as OH reacted with any molecule and expressed as ppb h1. New hydroxyl radical refers to formation of radicals from sources other than radicals. This includes photolysis of non-radical

6

OH Chain Length

5

4

3

2

8/25/00 8/28/00 8/30/00

1

0 6:00:00

8:00:00

10:00:00

12:00:00 Time (LST)

14:00:00

Fig. 10. OH chain length.

16:00:00

18:00:00

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↑ Peak Value = 48 @15:15, 8/28

8/25/00 8/28/00 8/30/00 VOC to NOx Ratio (ppbC/ppbN)

5755

20

15

10

5 ↑ Bottom Value = 2.6 @7:02, 8/30

0 6:00

8:00

10:00

12:00

14:00

16:00

18:00

Time (LST) Fig. 11. VOC to NOx ratio in the PA volume.

species (O3, aldehydes), oxidation of non-radical species by odd oxygen (O3 and O(3P)) and by NO3. Radicals created as species other than hydroxyl radical were counted as new OH only for the fraction which propagates to form OH. Of the three episode days, 25 August has the largest radical reaction rates. Peak total OH reaction rate was 43 ppb h1 which is 50% and 80% higher than the values for 28 August (28 ppb h1) and 30 August (23 ppb h1). On 25 August (Fig. 9a), new OH from aldehydes sets the day apart from the other two days. Aldehyde photolysis is the primary source of radicals until noon, the time when the PA volume leaves the Ship Channel region (Fig. 1b). Fig. 10 compares radical chain lengths for the three episode days. The chain length is defined as follows: ðOH chain lengthÞ ¼

Table 3 Summary of major photochemical parameters for each day (units are in ppb for 12-h period, except chain length, which is dimensionless)

Available VOC New OH OH reacted OH chain length NO to NO2 conversion Available NOx NOx terminated (physical+chemical) NOx chain length O3 formed

25/8/2000

28/8/2000

30/8/2000

1104.6 66.6 230.9 3.47 333.5 231.9 222.2

630.0 44.8 143.7 3.21 205.7 85.7 84.6

310.4 36.4 122.2 3.35 167.8 79.1 77.2

1.50 217.5

2.43 138.2

2.17 118.9

ðTotal OH reduction rateÞ ðdirect new OH formationÞ þ ðOH formation from other new radicalsÞ

For all three episode days, radical chain length in early morning and late afternoon tend to be higher than at mid day. The exceptions are the morning of 30 August and the afternoon of 28 August. During the morning of 30 August, the VOC to NOx ratio (Fig. 11) in the PA volume is lowered by NOx emissions from urban sources, and the radical chain length is short. During the afternoon of 28 August, the VOC to NOx ratio is high due to rural isoprene sources, and the

radical chain length is increased. With these exceptions noted, the range of variation in radical chain length is modest, implying that once a radical is formed, the extent of its reactions until its termination is relatively constant. Consequently, radical formation plays a controlling role in oxidant formation. Table 3 summarizes the photochemistry of the three PA volumes, with process rates integrated over daylight hours. The more extensive ozone

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formation on 25 August can largely be attributed to the increased availability of free radicals from aldehyde photolysis during morning hours. The aldehydes are produced photochemically by highly reactive hydrocarbon species in the Ship Channel region.

events. To characterize the chemistry during emission events, the Lagrangian PA tool was applied to a simulated event on 25 August. An emission of 2268 kg h1 (5000 lbs h1) of ethylene for a 1-h period, 10:00–11:00 h, was introduced to one ground layer cell in the Ship Channel region. The event emissions within the hour were constant. In order to accurately characterize the event, the Lagrangian PA was modified in two ways. The first change was to modify the criteria for defining PA volume. The algorithm used was to capture 90% of

3.3. Effect of episodic release

260 240 220 200 180 160 140 120 100 80 60 40 20 0 -209:00 -40 -60

08/25/00 4km O3

11:00

13:00

15:00

Concentration (ppb) or Conc Change Rate (ppb/hr).

Concentration (ppb) or Conc Change Rate (ppb/hr).

As noted in the introduction, one of the causes of rapid ozone formation in Houston is emission

17:00

260 240 220 200 180 160 140 120 100 80 60 40 20 0 -209:00 -40 -60

08/25/00 4km O3

11:00

13:00

15:00

17:00

Time of Day (LST)

Time of Day (LST) Concentration

Chemistry

Emission

Advective Correction

Horiz. Diffusion

Vert. Diffusion

Deposition

260 240 220 200 180 160 140 120 100 80 60 40 20 0 -20 -40 -60

Concentration (ppb) or Conc Change Rate (ppb/hr).

Concentration (ppb) or Conc Change Rate (ppb/hr).

Fig. 12. Comparison of ozone process rates for base case simulations of 25 August performed at 4 km resolution in the smallest nested grid (a) and at 1 km resolution in the smallest nested grid (b).

08/25/00 O3

9:00

11:00

13:00

15:00

17:00

260 240 220 200 180 160 140 120 100 80 60 40 20 0 -20 -40 -60

08/25/00 ethene event O3

9:00

11:00

13:00

15:00

Time of Day (LST)

Time of Day (LST) Concentration

Chemistry

Advective Correction

Horiz. Diffusion

Emission Vert. Diffusion

Deposition

Fig. 13. Ozone process time series for 1 km simulation with and without ethylene episodic release.

17:00

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tracer mass until 11:00 h (while the tracer/event emission continues) and to fix the size of the PA volume from 11:00 h on. The second change was to add an extra nested grid of 1 km resolution, shown in Fig. 1. Modeling at a 1-km resolution was not performed for the previous simulations since the meteorological modeling was not designed to operate at that resolution (TCEQ, 2004a), however, when emission events are evaluated, large concen-

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tration gradients, over length scales approaching 1 km arise. Previous modeling analyses (Murphy and Allen, 2005; Nam et al., 2006) have shown that modeling at a spatial resolution of 1 km is necessary for emission events, even if meteorological model inputs are not designed to operate at this scale. Thus, the emission event analyses presented here differ from the previous simulations in spatial resolution (1 km vs. 4 km in the finest nested grid)

120 08/25/00

NOx Throughput Rate (ppb/hr).

100 80 60 40 20 0 9:00:00

11:00:00

13:00:00

15:00:00

17:00:00

-20 Physical Loss Chemical Termination Rate NO oxidation w/o O3 consumption

-40 -60 Time (LST) 120 08/25/00 Ethene episode

NOx Throughput Rate (ppb/hr).

100 80 60 40 20 0 -20 Physical Loss Chemical Termination Rate NO oxidation w/o O3 consumption

-40 -60 9:00:00

11:00:00

13:00:00 Time (LST)

15:00:00

17:00:00

Fig. 14. Comparison of NOx termination and recirculation rates for 25 August, with and without the ethylene emission event.

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8/25/00

9

8/25/2000 ethene episode

8

OH Chain Length

7 6 5 4 3 2 1 0 9:00:00

11:00:00

13:00:00

15:00:00

17:00:00

Time (LST)

Figs. 15. Comparison of OH chain length for 25 August with and without ethylene emission event.

and by including the emission event. Fig. 12 shows a comparison of the 25 August simulation, without the emission event, modeled at 1 km and at 4 km resolution in the finest nested grid daily maximum ozone within the PA volume was 183 and 223 ppb, for the 4 and 1 km simulations, respectively. This difference was due to both changing the grid resolution, from 4 to 1 km, and changing the size and location of the PA volume over which concentrations were averaged. The PA volume in the 4 km simulation is considerably larger than the PA volume of the 1 km simulation. Because of these differences in the base case at 4 and 1 km resolution, all simulations of the emission event are compared to the base case simulation performed with a 1 km resolution nested grid. Fig. 13 shows the evolution of process rates contributing to ozone concentrations, with and without the emission event. The results are similar to those shown in Fig. 6; chemistry is the dominant factor explaining overall concentration changes (except for a very short time period when initializing the PA volume). When the ethylene event was introduced, chemical production of ozone and formaldehyde (not shown) was considerably enhanced for up to 4 h, increasing the maximum ozone concentration from 223 to 250 ppb. The features of the enhanced ozone formation in the event plume can be characterized as in the previous section. Fig. 14 reports parameters de-

scribing NOx cycling, with and without the emission event. NO to NO2 oxidation rate was greatly enhanced in first 2 h of the event, and there is some increase in NOx termination. The NOx chain length increases modestly. Overall, the main perturbation of the ozone formation chemistry due to the event is due to enhanced production of free radicals from aldehyde photolysis and more efficient utilization of free radicals due to increased reactive hydrocarbon concentrations, shown in Fig. 15, both during the first few hours of the event.

4. Conclusions A Lagrangian PA tool was developed and used in characterizing the chemistry of ozone formation in industrial plumes in Houston, Texas. The tool successfully isolated the plume so that, within the PA volume, the dominant process affecting ozone concentrations was chemical production. Some challenges remain in the implementation of this tool, such as addressing plume splitting and addressing convective corrections that are necessitated when mapping an advecting plume onto a fixed grid. Nevertheless, this Lagrangian PA tool, applied here in CAMx, proved useful in characterizing the chemistry of plumes and could be adapted for use in other Eulerian models.

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The plume analyses, performed for Houston, examined the differences in chemical pathways associated with different plume trajectories for Houston. Ryerson et al. (2003) had reported very different ozone productivities for urban and industrial plumes in Houston. The analyses presented here demonstrated the critical role of morning radical production and the availability of afternoon VOC and NOx emissions in explaining these differences in ozone productivity. Nam et al. (2006) had shown that some emission events can lead to very large enhancements of ozone concentrations, and the analyses presented here again pointed out the importance of morning radical production. Acknowledgment The authors thank Harvey Jeffries from the University of North Carolina for his contributions on understanding of Process Analysis. References Adelman, Z.E., 1999. A reevaluation of the carbon bond-iv photochemical mechanism. M.S. Thesis, University of North Carolina at Chapel Hill. Community Modeling and Analysis System, 2007. Science algorithms of the EPA models-3 Community Multiscale Air Quality (CMAQ) modeling system. Available at: /http:// www.epa.gov/asmdnerl/CMAQ/CMAQscienceDoc.htmlS (accessed May 2007). Draxler, R.R., Rolph, G.D., 2003. HYSPLIT (HYbrid SingleParticle Lagrangian Integrated Trajectory) model access via NOAA ARL READY Website. Available at: /http:// www.arl.noaa.gov/ready/hysplit4.htmlS (accessed May 2007). Environ International, 2007. CAMx User Guide. Version 4.40. Available at: /http://www.camx.com/S (accessed May 2007). Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 1989. A photochemical kinetics mechanism for urban and regional scale computer modeling. Journal of Geophysical Research 94, 925–956. Grell, G.A., Dudhia, J., Stauffer, D., 1994. A description of the fifth-generation Penn State/NCAR mesoscale model. NCAR Technical Note NCAR/TN-398+STR. Jang, J.C., Jeffries, H.E., Tonnesen, S., 1995. Sensitivity of ozone to model grid resolution—II. Detailed process analysis for ozone chemistry. Atmospheric Environment 29, 3101–3114.

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