FY12-13 Phase III, Task 1
CAMx Modeling of the June 2006 Ozone
Episode for the HOTCOG Area
Prepared for:
The Heart of Texas Council of
Governments
1514 S. New Road
Waco, TX
Prepared by:
Sue Kemball-Cook, Jeremiah Johnson,
Michele Jimenez and Greg Yarwood
ENVIRON International Corporation
773 San Marin Drive, Suite 2115
Novato, California, 94998
www.environcorp.com
P-415-899-0700
F-415-899-0707
January 2014
January 2014
CONTENTS
EXECUTIVE SUMMARY ....................................................................................................... 10
1.0 INTRODUCTION ........................................................................................................... 14
1.1 Ozone Modeling of the June 2006 Episode ...................................................................17
1.1.1 Using the 2006 Ozone Episode for Control Strategy Development ...................19
1.2 Report Outline ...............................................................................................................19
2.0 DEVELOPMENT OF A JUNE 2006 OZONE MODEL FOR THE HOTCOG AREA ..................... 20
2.1 Meteorological Data ......................................................................................................20
2.1.1 WRF Model Configuration ..................................................................................21
2.1.2 WRF Model Performance Evaluation..................................................................24
2.2 Emission Inventory Development .................................................................................32
2.3 CAMx Model Configuration ...........................................................................................33
2.3.1 CAMx Modeling Domain .....................................................................................33
2.3.2 Other Inputs ........................................................................................................35
3.0 CAMX MODEL INITIAL RUN MODEL PERFORMANCE EVALUATION ................................ 36
3.1 Model Performance Metrics .........................................................................................36
3.2 Model Performance Evaluation Results ........................................................................37
3.2.1 Base Case Model Run 36 km Grid Evaluation .....................................................37
3.2.2 Texas Border Monitor Evaluation .......................................................................38
3.2.3 Base Case Run Model Performance in the Vicinity of the HOTCOG
Area .....................................................................................................................41
3.2.4 Summary of Base Case Model Performance Evaluation ....................................45
4.0 MODEL UPDATES AND EVALUATION ............................................................................ 50
4.1 New CAMx Version ........................................................................................................50
4.2 CB6r1 Chemical Mechanism ..........................................................................................50
4.3 Updated TCEQ EI ...........................................................................................................51
4.3.1 Day-Specific Wildfire Emissions ..........................................................................52
4.4 Revised CAMx Model Performance Evaluation .............................................................53
4.5 Summary of Model Performance Evaluation ................................................................54
5.0 2006 OZONE SOURCE APPORTIONMENT MODELING .................................................... 61
5.1 Description of the CAMx APCA Source Apportionment Tool ........................................61
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5.2 APCA Results ..................................................................................................................62
5.2.1 Source Apportionment Results for the Location of the Waco Mazanec
Monitor ...............................................................................................................63
6.0 REVISED 2006 OZONE MODEL ...................................................................................... 73
6.1 Model Configuration .....................................................................................................73
6.1.1 Additional Days of Modeling Inputs ...................................................................73
6.1.2 New TCEQ 36 km Modeling Grid ........................................................................73
6.1.3 Cloud Kv Patch ....................................................................................................74
6.1.4 Boundary Condition Patch ..................................................................................75
6.1.5 New CAMx Version .............................................................................................76
6.1.6 CB6r2 Chemical Mechanism ...............................................................................76
6.1.7 Emission Inventory..............................................................................................77
6.2 Model Performance Evaluation.....................................................................................80
7.0 SENSITIVITY TESTS WITH TCEQ 2012 EMISSION INVENTORY ......................................... 88
7.1 Model Configuration .....................................................................................................88
7.2 Emissions .......................................................................................................................88
8.0 EMISSIONS SENSITIVITY TESTING WITH 2012 EMISSION INVENTORY ............................ 98
8.1 Gas Compressor Engine NOx Emissions Sensitivity Test ...............................................98
8.2 HDDV NOx Emissions Sensitivity Test............................................................................99
9.0 CONCLUSIONS AND FUTURE WORK ............................................................................ 102
10.0
REFERENCES........................................................................................................ 106
TABLES
Table 2-1. CAMx meteorological input data requirements. ......................................................20
Table 2-2. Rider 8 WRF and CAMx model layer structure. TCEQ table from
http://www.tceq.texas.gov/airquality/airmod/rider8/modeling/domai
n. ............................................................................................................................23
Table 2-3. Physics Parameterizations used in the initial TCEQ WRF Run. .................................24
Table 2-4. TCEQ May 31 – July 2, 2006 model ready emission files. .........................................32
Table 2-5. Emission component files for May 31 – July 2, 2006 provided by the
TCEQ. .....................................................................................................................32
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Table 6-1. Maximum concentration limits for ozone precursors applied to the 36 km
boundary condition grid cells across the Gulf of Mexico, Caribbean
Sea, and Atlantic Ocean south of Cape Hatteras. .................................................75
Table 7-1. The TCEQ 2012 emission inventory files. A leading arrow (->) indicates
that the TCEQ file was not directly used in the final 2012 modeling
inventory. ..............................................................................................................89
FIGURES
Figure 1-1. Waco Mazanec CAMS monitor location. Adaptation of TCEQ figure from
http://gis3.tceq.state.tx.us/geotam/index.html, accessed December
15, 2013. Blue circles indicate the locations of ozone monitors. ........................15
Figure 1-2. Trends in annual 4th highest 8-hour ozone values (upper panel) and
design values (lower panel) at the Waco Mazanec and Killeen monitors
in central Texas. The Temple Georgia monitor does not yet have full
year of data and is not shown. The dashed red line indicates the 1996
84 ppb standard and the solid red line shows the 2008 75 ppb ozone
standard. All data have been validated by the TCEQ. ...........................................16
Figure 1-3. Time series of daily max 8-hour average ozone averaged over all
monitors in each Near Non-Attainment Area and Non-Attainment Area
for the period June 1-July 2, 2006. TCEQ figure from Breitenbach
(2010). ...................................................................................................................17
Figure 1-4. May 31-July 2, 2006 daily maximum 8-hour average ozone at central
Texas monitors. .....................................................................................................18
Figure 2-1. TCEQ’s WRF modeling 36/12/4 km grid system for regional scale
modeling on the RPO projection. Figure from Breitenbach (2010). ....................22
Figure 2-2. Time series of observed (black) and WRF model (blue) near-surface
wind speed at Temple CAMS 651. ........................................................................25
Figure 2-3. Time series of WRF model near-surface wind speed bias at Temple
CAMS 651. .............................................................................................................25
Figure 2-4. Wind speed scatterplot for night (left panel) and day (right panel) hours
for the Temple CAMS 651 monitor. ......................................................................26
Figure 2-5. Time series of observed (black) and WRF model (blue) near-surface
wind direction at Temple CAMS 651. ....................................................................27
Figure 2-6. Time series of observed (black) and WRF model (blue) near-surface
temperature at Temple CAMS 651. ......................................................................27
Figure 2-7. Time series of WRF model near-surface temperature bias at Temple
CAMS 651. .............................................................................................................28
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Figure 2-8. Time series of observed (black) and WRF YSU run (blue) near-surface
wind speed at Italy CAMS 650...............................................................................28
Figure 2-9. Time series of WRF YSU run near-surface wind speed bias at Italy CAMS
650. ........................................................................................................................29
Figure 2-10. Wind speed scatterplot for night (left panel) and day (right panel) hours
for the Italy H.S. CAMS 650 monitor. ....................................................................29
Figure 2-11. Time series of observed (black) and WRF model (blue) near-surface
wind direction at Italy CAMS 650. .........................................................................30
Figure 2-12. Time series of observed (black) and WRF model (blue) near-surface
temperature at Italy CAMS 650.............................................................................30
Figure 2-13. Time series of WRF YSU run near-surface temperature bias at Italy
CAMS 650. .............................................................................................................30
Figure 2-14. Cleburne RWP-derived mixed layer heights and WRF modeled PBL
heights. ..................................................................................................................31
Figure 3-1. Episode MNB for rural ozone monitors in the southeastern U.S. and the
Ohio River Valley and Texas and adjacent states..................................................38
Figure 3-2. Texas monitors used in model performance evaluation on the 4 km grid.
Texas border monitors evaluated in Section 3.3.2 are circled in red and
monitors used for the HOTCOG area evaluation are circled in green. .................39
Figure 3-3. Mean normalized bias for ozone during episode 1 for monitoring sites
near the northern and eastern Texas borders. .....................................................40
Figure 3-4. Mean normalized bias for ozone during episode 2 for monitoring sites
near the northern and eastern Texas borders. .....................................................41
Figure 3-5. Upper panel: observed 1-hour ozone (red) at the Temple CAMS 651
monitor versus modeled 1-hour average surface layer ozone (blue,
HOTCOG) during the May 31-June 15, 2006 period for the Base Case
run 06_base_01. Lower panel: mean normalized bias (MNB) for the
Temple CAMS 651 monitor. Red lines show ±15% EPA (1991)
benchmarks. ..........................................................................................................43
Figure 3-6. Upper panel: observed 1-hour ozone (red) at the Temple CAMS 651
monitor versus modeled 1-hour average surface layer ozone (blue,
HOTCOG) during the June 23-July 2, 2006 period for the Base Case run
06_base_01. Lower panel: mean normalized bias (MNB) for the
Temple CAMS 651 monitor. Red lines show ±15% EPA (1991)
benchmarks. ..........................................................................................................44
Figure 3-7. Upper panel: observed 1-hour ozone (red) at the Italy H.S. CAMS 650
monitor versus modeled 1-hour average surface layer ozone (blue,
HOTCOG) during the May 31-June 15, 2006 period for the Base Case
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run 06_base_01. Lower panel: mean normalized bias (MNB) for the
Italy H.S. CAMS 650 monitor. Red lines show ±15% EPA (1991)
benchmarks. ..........................................................................................................46
Figure 3-8. Upper panel: observed 1-hour ozone (red) at the Italy H.S. CAMS 650
monitor versus modeled 1-hour average surface layer ozone (blue,
HOTCOG) during the June 23-July 2, 2006 period for the Base Case run
06_base_01. Lower panel: mean normalized bias (MNB) for the Italy
H.S. CAMS 650 monitor. Red lines show ±15% EPA (1991)
benchmarks. ..........................................................................................................47
Figure 3-9. Upper panel: observed 1-hour ozone (red) at the Palestine CAMS 647
monitor versus modeled 1-hour average surface layer ozone (blue,
HOTCOG) during the May 31-June 15, 2006 period for the Base Case
run 06_base_01. Lower panel: mean normalized bias (MNB) for the
Palestine CAMS 647 monitor. Red lines show ±15% EPA (1991)
benchmarks. ..........................................................................................................48
Figure 3-10. Upper panel: observed 1-hour ozone (red) at the Palestine CAMS 647
monitor versus modeled 1-hour average surface layer ozone (blue,
HOTCOG) during the June 23-July 2, 2006 period for the Base Case run
06_base_01. Lower panel: mean normalized bias (MNB) for the
Palestine CAMS 647 monitor. Red lines show ±15% EPA (1991)
benchmarks. ..........................................................................................................49
Figure 4-1. NOx emission inventory comparison by region for June 8, 2006
(weekday) for 06_base_01 (red; denoted Base_06) and
06_wildfires_07 (blue; denoted revTCEQ_EI_06) simulations. ............................51
Figure 4-2. Geographic regions used in the emissions comparison shown in Figure
4-1..........................................................................................................................52
Figure 4-3. Upper panel: observed 1-hour ozone (black) at the Temple CAMS 651
monitor versus modeled 1-hour average surface layer ozone
(06_base_01 [blue] and 06_wildfires_07 [green]) during May 31-June
15, 2006. Lower panel: mean normalized bias (MNB) for the Temple
CAMS 651 monitor. Bar colors are as in upper panel. Red lines show
±15% EPA (1991) benchmarks...............................................................................55
Figure 4-4. Upper panel: observed 1-hour ozone (black) at the Temple CAMS 651
monitor versus modeled 1-hour average surface layer ozone
(06_base_01 [blue] and 06_wildfires_07 [green]) during June 23-July 2,
2006. Lower panel: mean normalized bias (MNB) for the Temple
CAMS 651 monitor. Bar colors are as in upper panel. Red lines show
±15% EPA (1991) benchmarks...............................................................................56
Figure 4-5. Upper panel: observed 1-hour ozone (black) at the Italy H.S. CAMS 650
monitor versus modeled 1-hour average surface layer ozone
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(06_base_01 [blue] and 06_wildfires_07 [green]) during May 31-June
15, 2006. Lower panel: mean normalized bias (MNB) for the Italy H.S.
CAMS 650 monitor. Bar colors are as in upper panel. Red lines show
±15% EPA (1991) benchmarks...............................................................................57
Figure 4-6 Upper panel: observed 1-hour ozone (red) at the Italy H.S. CAMS 650
monitor versus modeled 1-hour average surface layer ozone
(06_base_01 [blue] and 06_wildfires_07 [green]) during June 23-July 2,
2006. Lower panel: mean normalized bias (MNB) for the Italy H.S.
CAMS 650 monitor. Bar colors are as in upper panel. Red lines show
±15% EPA (1991) benchmarks...............................................................................58
Figure 4-7. Upper panel: observed 1-hour ozone (black) at the Palestine CAMS 647
monitor versus modeled 1-hour average surface layer ozone
(06_base_01 [blue] and 06_wildfires_07 [green]) during May 31-June
15, 2006. Lower panel: mean normalized bias (MNB) for the Palestine
CAMS 647 monitor. Bar colors are as in upper panel. Red lines show
±15% EPA (1991) benchmarks...............................................................................59
Figure 4-8. Upper panel: observed 1-hour ozone (black) at the Palestine CAMS 647
monitor versus modeled 1-hour average surface layer ozone
(06_base_01 [blue] and 06_wildfires_07 [green]) during June 23-July 2,
2006. Lower panel: mean normalized bias (MNB) for the Palestine
CAMS 647 monitor. Bar colors are as in upper panel. Red lines show
±15% EPA (1991) benchmarks...............................................................................60
Figure 5-1. 4 km grid APCA source region map. ........................................................................63
Figure 5-2. Contribution to the episode maximum 8-hour ozone at the Temple and
Italy H.S. monitors and at the location of the Waco Mazanec monitor
during the June 2006 episode. ..............................................................................64
Figure 5-3. Contribution to the episode average 8-hour ozone at the Temple and
Italy H.S. monitors and at the location of the Waco Mazanec monitor
during the June 2006 episode. ..............................................................................64
Figure 5-4. 2006 NOx (upper panel) and VOC (lower panel) emissions for the 6county HOTCOG area from the TCEQ emission inventory. ...................................65
Figure 5-5. Contribution to daily maximum 8-hour ozone by source region for the
location of the CAMS 1037 Waco Mazanec monitor. ...........................................67
Figure 5-6. Waco monitor location ozone source apportionment by emissions
category for the local contribution shown in green in Figure 5-5. .......................67
Figure 5-7. Episode maximum contribution to the Waco Mazanec monitor location
ozone from HOTCOG 6-county Area emissions. ...................................................69
Figure 5-8. Episode average contribution to the Waco Mazanec monitor location
ozone from HOTCOG 6-County area emissions. ...................................................69
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Figure 5-9. Location point sources of NOx in the HOTCOG 6-county area in the 2006
TCEQ emission inventory. .....................................................................................70
Figure 5-10. Contribution of gas compressor engines to oil and gas NOx in the 2006
TCEQ emission inventory for the HOTCOG area. ..................................................71
Figure 5-11. Trends in Natural Gas Well Productivity in the HOTCOG Area. .............................71
Figure 5-12. Episode average contributions to MDA8 ozone at the Waco Mazanec
monitor location from local 6-county area emissions sources, the sum
of initial conditions and boundary conditions (IC+BC), sources within
Texas but outside the 6-county area (Texas), and sources within the 36
km grid but outside of Texas (outside TX).............................................................72
Figure 6-1. TCEQ 36/12/4 km CAMx nested modeling grids for the Texas ozone
modeling of June 2006. 36 km grid is outlined in black. The 12 km grid
outlined in blue, and the 4 km grid is outlined in green. TCEQ figure
from
http://www.tceq.texas.gov/airquality/airmod/rider8/modeling/domai
n. ............................................................................................................................73
Figure 6-2. NOx emission inventory comparison by region for June 8, 2006
(weekday) for new and old base cases. ................................................................77
Figure 6-3. Geographic regions used in the emissions comparison shown in Figure
6-2..........................................................................................................................77
Figure 6-4. Upper panel: observed 1-hour ozone (black) at the Temple CAMS 651
monitor versus modeled 1-hour average surface layer ozone
(06_base_01 [blue], 06_wildfires_07 [green] and 06_newbase_10
[red]) during May 31-June 15, 2006. Lower panel: mean normalized
bias (MNB) for the Temple CAMS 651 monitor. Bar colors are as in
upper panel. Red lines show ±15% EPA (1991) benchmarks. ..............................82
Figure 6-5. Upper panel: observed 1-hour ozone (black) at the Temple CAMS 651
monitor versus modeled 1-hour average surface layer ozone
(06_base_01 [blue], 06_wildfires_07 [green] and 06_newbase_10
[red]) during the June 23-July 2, 2006 period. Lower panel: mean
normalized bias (MNB) for the Temple CAMS 651 monitor. Bar colors
are as in upper panel. Red lines show ±15% EPA (1991) benchmarks. ...............83
Figure 6-6. Upper panel: observed 1-hour ozone (black) at the Italy H.S. CAMS 650
monitor versus modeled 1-hour average surface layer ozone
(06_base_01 [blue], 06_wildfires_07 [green] and 06_newbase_10
[red]) during May 31-June 15, 2006. Lower panel: mean normalized
bias (MNB) for the Italy H.S. CAMS 650 monitor. Bar colors are as in
upper panel. Red lines show ±15% EPA (1991) benchmarks. ..............................84
Figure 6-7. Upper panel: observed 1-hour ozone (black) at the Italy H.S. CAMS 650
monitor versus modeled 1-hour average surface layer ozone
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(06_base_01 [blue], 06_wildfires_07 [green] and 06_newbase_10
[red]) during June 23-July 2, 2006. Lower panel: mean normalized bias
(MNB) for the Italy H.S. CAMS 650 monitor. Bar colors are as in upper
panel. Red lines show ±15% EPA (1991) benchmarks..........................................85
Figure 6-8. Upper panel: observed 1-hour ozone (black) at the Palestine CAMS 647
monitor versus modeled 1-hour average surface layer ozone
(06_base_01 [blue], 06_wildfires_07 [green] and 06_newbase_10
[red]) during May 31-June 15, 2006. Lower panel: mean normalized
bias (MNB) for the Palestine CAMS 647 monitor. Bar colors are as in
upper panel. Red lines show ±15% EPA (1991) benchmarks. ..............................86
Figure 6-9. Upper panel: observed 1-hour ozone (black) at the Palestine CAMS 647
monitor versus modeled 1-hour average surface layer ozone
(06_base_01 [blue], 06_wildfires_07 [green] and 06_newbase_10
[red]) during June 23-July 2, 2006. Lower panel: mean normalized bias
(MNB) for the Palestine CAMS 647 monitor. Bar colors are as in upper
panel. Red lines show ±15% EPA (1991) benchmarks.. ........................................87
Figure 7-1. TCEQ HOTCOG 6-county area NOx emissions comparison for 2006 (left
panel) and 2012 (right panel). ...............................................................................92
Figure 7-2. TCEQ HOTCOG 6-county area VOC emissions comparison for 2006 (left
panel) and 2012 (right panel). ...............................................................................92
Figure 7-3. Contribution to daily maximum 8-hour ozone by source region for the
location of the CAMS 1037 Waco Mazanec monitor for 2006 (upper
panel) and 2012 (lower panel). .............................................................................94
Figure 7-4. Episode average 8-hour ozone contribution to the location of the Waco
Mazanec monitor. .................................................................................................95
Figure 7-5. Episode average 8-hour ozone contribution to the location of the Waco
Mazanec monitor. .................................................................................................95
Figure 7-6. Episode maximum contribution to the Waco Mazanec monitor location
ozone from HOTCOG 6-county area emissions.....................................................97
Figure 7-7. Episode average contribution to the Waco Mazanec monitor location
ozone from HOTCOG 6-county area emissions.....................................................97
Figure 8-1. Change in 8-hour average surface layer ozone for compressor engines
NOx emissions reductions. Left hand panel: episode maximum
difference. Right hand panel: episode average. Differences were
calculated only for times when surface layer ozone concentration was
> 60 ppb. Gray shading denotes grid cells that do not have any days
where MDA8 > 60 ppb. .........................................................................................99
Figure 8-2. Temporal allocation of HDDV NOx emissions. ......................................................100
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Figure 8-3. Change in 8-hour average surface layer ozone for HDDV NOx emissions
reductions. Left hand panel: episode maximum difference. Right
hand panel: episode average. Differences calculated only for times
when surface layer ozone concentration was > 60 ppb. Gray shading
denotes grid cells that do not have any days where MDA8 > 60 ppb. ...............100
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EXECUTIVE SUMMARY
In this report, we summarize the development of a June 2006 ozone model for the Heart of
Texas Council of Governments (HOTCOG) 6-county area, and report on application of the model
to evaluate: (1) the relative importance of local emissions sources and transport in determining
ozone levels in the HOTCOG area; (2) the local emissions source categories that make the
largest contribution to ozone at the location of the Waco Mazanec monitor; and (3) the ozone
impacts of changes in emissions between 2006 and 2012.
A June 2006 Comprehensive Air Quality Model with Extensions (CAMx; ENVIRON, 2013) ozone
model was developed from inputs provided by the Texas Commission on Environmental Quality
(TCEQ) to the Texas Near Nonattainment Areas. ENVIRON evaluated this base case ozone
model at ozone monitors in the HOTCOG area, at rural monitors along the Texas border with
Louisiana and Oklahoma, and at rural monitors in the Southeastern U.S. and Ohio River Valley;
previous ozone transport studies (e.g. ENVIRON, 2010) have shown that these two latter
regions can be sources of ozone and precursors transported into Texas. The ozone model
performance evaluation showed that ozone was generally overestimated at most Texas
monitors throughout the episode, including central Texas ozone monitors active in 2006 that
were located in the vicinity of the HOTCOG area: Temple, Italy H.S., and Palestine. The high
bias in modeled ozone occurred during periods of stagnant air as well as transport periods. A
high bias was present at rural monitors within the Southeastern U.S. and Ohio River Valley as
well as at the rural Texas border monitors. Because a high bias in the model can affect the
attribution of the local versus transported ozone contribution in the HOTCOG area, efforts were
made to improve model performance.
A series of changes were made to the model received from TCEQ. The CAMx model version
was updated from version 5.40 to version 6.00 with minimal effect on predicted surface layer
ozone. A new version of the CB6 chemical mechanism (CB6r1) was incorporated into the model
and was found to significantly increase ozone throughout Texas. The addition of an updated
TCEQ emission inventory with finer detail in the categorization of emissions and a day-specific
wildfire emission inventory generally increased ozone slightly. Of all of these changes, the test
that increased the ozone high bias the most was the CB6r1 chemical mechanism update. One
difference between CB6r1 and CB6 is the extent to which compounds formed from nitrogen
oxide (NOx) emissions can react in the atmosphere to re-form NOx, a process called NOx
recycling. NOx recycling was increased in CB6r1 based on experimental data from Texas Air
Quality Research Program (AQRP) Project 10-042 (Yarwood et al., 2012a). Although this change
made the chemical mechanism consistent with the best available science at the time, ozone
increased regionally and model performance was degraded.
Ozone source apportionment modeling was carried out with the 2006 model in the updated
configuration and with the TCEQ’s new emission inventory. The source apportionment results
showed that ozone formation in the HOTCOG area is limited by the amount of available NOx.
This finding is consistent with the emission inventory, which shows that the VOC emission
inventory for the 6-county area is dominated by biogenic VOCs. The abundance of biogenic
VOC ensures that there is always enough VOC available to form ozone so that the amount of
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ozone formed is determined by the amount of NOx emissions. This finding means that
emission control strategy development in the HOTCOG area should focus on controlling NOx
emission sources rather than VOC sources.
The ozone source apportionment results showed that, on average, transport contributed far
more to HOTCOG area ozone than local sources during the June 2006 episode. Emissions within
the 6-county area accounted for 10 ppb of the episode average 8-hour average ozone at the
Waco monitor location, while transport accounted for 65 ppb. The local HOTCOG contribution
to the daily maximum 8-hour average ozone varied from day to day depending on the wind
direction, but reached a maximum of 24 ppb. The magnitude of this impact indicates that local
emissions control measures can be effective in reducing ozone in the HOTCOG area.
The ozone source apportionment results were analyzed to determine which HOTCOG area
emissions source categories make the largest contributions to HOTCOG area ozone levels. The
categories with the largest ozone impacts were on-road and off-road mobile sources, elevated
point sources, and oil and gas sources. On-road mobile sources made the largest episode
maximum and episode average contribution to ozone at the Waco monitor location. The next
largest episode maximum contribution was made by elevated point sources, followed by oil and
gas sources. The elevated point source NOx emission inventory is dominated by power plant
emissions; there are two large power plants located in the vicinity of the Waco monitor. The
largest contributor to oil and gas ozone impacts was NOx emissions from wellhead compressor
engines.
During 2013, several updates were made to the CAMx model as well as to the TCEQ’s Rider 8
modeling platform. The TCEQ revised the emission inventory, expanded the outer modeling
domain to encompass the entire continental U.S., and provided modeling inputs for several
additional days prior to the start of the episode to allow for model spinup. ENVIRON altered the
model boundary conditions to compensate for known bias in the global chemistry-transport
model used to develop the CAMx model outer boundary conditions, and modified the CAMx
vertical diffusivity inputs to enhance transport of chemical species out of the boundary layer
and into the free troposphere when clouds are present in the model. New emission inventories
for lightning NOx, wildfires and aircraft were added to the TCEQ SIP modeling inventory. A new
version of CAMx was used that contained an important update to the Plume-in-Grid model that
treats plumes emissions from large point sources of NOx. The most important change was the
use of a new chemical mechanism, CB6r2 (Hildebrandt Ruiz and Yarwood, 2013).
The treatment of NOx recycling in CB6r1 (discussed above) is further revised in CB6r2. CB6r2
differentiates organic nitrates (ONs) between simple alkyl nitrates that remain in the gas-phase
and multi-functional ONs that can partition into organic aerosols. Uptake of multi-functional
ONs by OA was added to CAMx for CB6r2. ONs present in aerosols are then assumed to
undergo hydrolysis to nitric acid with a lifetime of approximately 6 hours based on laboratory
experiments and ambient data. These changes tend to reduce regional concentrations ozone
and ONs, and increase nitric acid. Regional modeling simulations using CAMx with CB6r2
showed that accounting for ON hydrolysis in aerosols improved performance for ozone and in
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simulating the partitioning of oxidized nitrogen species (NOy) between ONs and nitric acid
(Hildebrandt Ruiz and Yarwood, 2013). Evaluation of the updated model showed that the high
bias in modeled ozone in the vicinity of the HOTCOG area and throughout Texas was reduced in
the June 2006 episode. Model performance improved significantly relative to the previous
runs, although a high bias persisted.
During the summer of 2013, the TCEQ made a 2012 anthropogenic emission inventory available
to ENVIRON for use in the development of an ozone forecasting system for the State of Texas
(Johnson et al., 2013). We developed a 2012 typical day emission inventory for the June 2006
episode and performed an assessment of how emissions changes from 2006 to 2012 affect
HOTCOG area ozone under the meteorological conditions of June 2006.
Going from the 2006 emissions scenario to 2012 emissions, there was an overall decrease of
NOx emissions in the HOTCOG 6-county area from 182 tpd to 137 tpd. The largest declines
among the NOx emission source categories were for point sources (13 tpd) and on-road mobile
sources (20 tpd). The relative proportion of each source category to the total NOx emission
inventory did not change significantly from 2006 to 2012. Point source and on-road mobile
were the two largest NOx emissions source categories in both the 2006 and 2012 inventories.
Total anthropogenic VOC emissions declined from 85 tpd in 2006 to 62 tpd in 2012. The CAMx
run using 2012 emissions in the 2006 Rider 8 modeling platform showed decreases in HOTCOG
area ozone throughout the modeling episode relative to 2006 emissions. There were six days in
which the daily maximum 8-hour average ozone exceeded 75 ppb in the 2006 emissions run,
but no days over 75 ppb in the 2012 emissions run. The episode average HOTCOG contribution
dropped from 10 ppb in the 2006 emissions run to 5 ppb in the 2012 emissions run.
The relative contributions of transported ozone and local ozone due to emissions sources
within the 6-county HOTCOG area were similar in nature in both 2006 and 2012. In the 2006
emissions run, transport contributed far more (65 ppb) to ozone at the Waco monitor than did
HOTCOG area emissions sources (10 ppb). This was also true in the 2012 emissions run, in
which transport contributed 53 ppb and the HOTCOG area sources contributed 5 ppb.
The HOTCOG Air Quality Advisory Committee is interested in understanding the effectiveness of
NOx emissions reductions for heavy duty diesel vehicles (HDDV) and gas compressor engines
used in natural gas production. Emissions from these two source categories have different
spatial and temporal distributions which may affect their propensity to cause ozone formation.
For both source categories, a 5 tpd NOx emissions reduction was made to the 2012 emission
inventory. The magnitude of the emissions reduction was arbitrary and selected to produce a
response in the ozone model that was large enough to illustrate potential differences in ozone
impacts due to reductions in these two source categories. Maximum ozone impacts were
smaller in the HDDV case than in the gas compressor engine case. This is because the NOx
emissions reductions in the gas compressor engine case were made only in the two HOTCOG
counties with the largest number of natural gas wells, while in the HDDV case, the emissions
reductions were made across the entire 6-county area. This difference in the spatial
distribution of the emissions reduction caused the ozone decreases to be more evenly
12
January 2014
distributed and smaller in the HDDV case. This shows that for a given NOx reduction, the
magnitude of the local ozone impact is larger if it is applied to a source that is more
geographically concentrated near the Waco monitor than to one that is dispersed across the
HOTCOG area.
In summary, a 2006 ozone model was developed for the HOTCOG area . Significant revisions to
the model occurred during FY12-13, and performance for ground level ozone in the HOTCOG
area improved to the point where it is reasonably good. However, a high bias persists in the
HOTCOG area and is present and more pronounced at the Texas border monitors. This
indicates that transport of ozone into Texas may be overestimated. While it is unlikely that a
model with a lower bias would contradict the finding that transport has a stronger influence on
HOTCOG area ozone than local emissions sources, the high bias introduces uncertainty into the
source apportionment results.
The change in anthropogenic emission inventory from 2006 to 2012 in the June 2006 modeling
platform produced large decreases in modeled ozone in the HOTCOG area. This is not
consistent with the relatively flat observed design value trend in recent years at the Waco
monitor; however, interannual variability in meteorological conditions as well as emissions
changes can play a role in observed ozone trends. A full ozone model for the year 2012 must be
developed to fully evaluate the effects of emissions changes between 2006 and 2012 on
HOTCOG area ozone levels. A 2012 ozone model that uses 2012 emissions and 2012
meteorology (as well as other model inputs) would be beneficial to HOTCOG because it is clear
that there have been large changes in emissions since 2006. Development of a full 2012 model
would allow evaluation of emission control strategies in a more recent episode and would
account for the ozone impacts of changes such as declines in power plant emissions,
development of the Barnett Shale and the implementation of the East Texas Combustion Rule,
among others.
13
January 2014
1.0 INTRODUCTION
The Heart of Texas Council of Government (HOTCOG) 6-county area consists of McLennan,
Bosque, Hill, Falls, Limestone and Freestone Counties. The HOTCOG Air Quality Advisory
Committee (AQAC) oversees ozone air quality planning for the 6-county area. The focus of the
AQAC’s efforts is maintaining the areas’s compliance with Federal ozone air quality regulations.
The U.S. EPA sets a National Ambient Air Quality Standard (NAAQS) for ozone in order to
protect public health and the environment. Ozone monitoring data are used to determine
whether a given area is in compliance with the National Ambient Air Quality Standard (NAAQS)
for ozone. The NAAQS for ozone is violated at a monitor if the annual fourth highest daily
maximum 8-hour average concentration averaged over three consecutive years exceeds a
threshold value. This threshold is currently 0.075 ppm (75 ppb). A single year of data is not
considered sufficient to demonstrate attainment; instead, the fourth highest value in a given
year is used in the calculation of the indicator of attainment status. Consequently, this statistic
is referred to as the annual 8-hour design value. A design value of 75 ppb attains the NAAQS,
while a design value of 76 ppb violates the NAAQS.
The Texas Commission on Environmental Quality (TCEQ) operates a Continuous Air Monitoring
Stations (CAMS)at the Waco Airport in McLennan County. The location of this station, known
as the Waco Mazanec monitor or CAMS 1037, is shown in Figure 1-1. Nearby ozone monitors
active in 2013 are also shown in Figure 1-1: Killeen, Temple, Italy, and Corsicana. Ozone data
from the Waco Mazanec monitor determine whether McLennan County is in compliance with
the NAAQS for ozone. Currently, the monitor has a design value of 74 ppb, which is in
compliance with the NAAQS.
Under the Clean Air Act, the EPA is required to review the NAAQS periodically. EPA’s next
review of the ozone standard is scheduled to be finalized in late 2014. During its previous
review in 2010, EPA announced its intention to reconsider the 75 ppb 2008 ozone standard and
proposed to set the new standard in the range 60-70 ppb. In July 2011, the EPA completed its
reconsideration of the standard, but did not release a final rule. In September 2011, President
Obama announced his decision to let the 2008 ozone standard remain in effect. If the EPA
decides to lower the NAAQS to the 60-70 ppb range following its current review, then the
McLennan County monitor will no longer comply with the NAAQS. Failure to comply with the
NAAQS carries adverse public health impacts and significant economic penalties.
Figure 1-2 shows recent trends in 4th highest ozone and design values at the Waco Mazanec
monitor. Ozone data for the nearby Killeen monitor is also shown for comparison. The Waco
Mazanec monitor’s design value has remained fairly constant since 2007-2009, ranging
between 70-72 ppb until the 2011-2013 period, when the design value rose to 74 ppb. The lack
of a pronounced downward trend in the HOTCOG area design value and the potential for a
more stringent ozone standard in the near future underscore the importance of air quality
planning in the HOTCOG area.
14
January 2014
Figure 1-1. Waco Mazanec CAMS monitor location. Adaptation of TCEQ figure from
http://gis3.tceq.state.tx.us/geotam/index.html, accessed December 15, 2013. Blue circles
indicate the locations of ozone monitors.
Development of an ozone model for the HOTCOG area is a critical step in the development of
an appropriate State Implementation Plan (SIP), should this become necessary. The ozone
model is a tool for understanding the formation, transport and fate of ozone in the area and is
also used in developing local emission control strategies. An ozone model can be used to
perform a modeled attainment demonstration, which the EPA (EPA, 2007) defines to be:
(a) analyses which estimate whether selected emissions reductions will result in ambient
concentrations that meet the NAAQS and
(b) an identified set of control measures which will result in the required emissions
reductions.
In this report, we describe the development and application of an ozone model for the HOTCOG
area for a June 2006 high ozone episode.
15
January 2014
Figure 1-2. Trends in annual 4th highest 8-hour ozone values (upper panel) and design values
(lower panel) at the Waco Mazanec and Killeen monitors in central Texas. The Temple
Georgia monitor does not yet have full year of data and is not shown. The dashed red line
16
January 2014
indicates the 1996 84 ppb standard and the solid red line shows the 2008 75 ppb ozone
standard. All data have been validated by the TCEQ.
1.1 Ozone Modeling of the June 2006 Episode
In response to the EPA’s January 6, 2010 proposal to strengthen the NAAQS for ground-level
ozone, the TCEQ began preparing for SIP development by planning for ozone modeling that
would coordinate the efforts of the TCEQ and all of the Texas Near Non-Attainment Areas
(NNAs). The purpose of the ozone modeling was to develop and test emissions control
strategies that would ensure that each NNA would attain the new ozone standard. Although
the NAAQS were not changed from the 2008 NAAQS of 75 ppb, HOTCOG is using the new ozone
modeling episode for conceptual model development and control strategy development and
evaluation.
The TCEQ selected a June, 2006 modeling episode in which ozone was high across most of East
Texas for extended periods of the month. Figure 1-3 shows the time series of daily maximum 8hour average ozone (MDA8) for Texas non-attainment areas and NNAs.
Daily Max 8-Hour Ozone in Texas
Jun 1- July 2, 2006
140
120
8-Hr Max Ozone (ppb)
100
80
60
40
20
1Ju
n
2Ju
n
3Ju
n
4Ju
n
5Ju
n
6Ju
n
7Ju
n
8Ju
n
9Ju
n
10
-J
un
11
-J
un
12
-J
un
13
-J
un
14
-J
un
15
-J
un
16
-J
un
17
-J
un
18
-J
un
19
-J
un
20
-J
un
21
-J
un
22
-J
un
23
-J
un
24
-J
un
25
-J
un
26
-J
un
27
-J
un
28
-J
un
29
-J
un
30
-J
un
1Ju
l
2Ju
l
0
Dallas
SATx
Victoria
NE Texas
BPA
Corpus Christi
Aus-SM
Houston
El Paso
Figure 1-3. Time series of daily max 8-hour average ozone averaged over all monitors in each
Near Non-Attainment Area and Non-Attainment Area for the period June 1-July 2, 2006. TCEQ
figure from Breitenbach (2010).
Each time series represents an average over all ozone monitors in that area. Except for the El
Paso area, the time series show two multi-day periods where ozone was high and a cleaner
period in between them. The TCEQ elected to model this episode because (Breitenbach, 2010):

It includes a wide variety of meteorological conditions, wind speeds and transport
directions;
17
January 2014

It has numerous high ozone days, more than any other period between 2005 and 2010,
when the modeling episode was selected;

It is a regional episode, and affected all the cities in Texas that would have been vulnerable
under a standard in the range 60-70 ppb; and

It has an extensive database of observations of ambient data from the TexAQS II study
performed in 2005-2006 and has proven to be a reliable episode for both the Houston and
Dallas Nonattainment Areas.
Analysis of ozone data from the central Texas monitors that were in operation during the June
episode (McGaughey et al., 2010; ENVIRON, 2011a) shows the same overall variability at central
Texas monitors as at monitors in other areas of Texas. Figure 1-4 shows a time series of MDA8
at central Texas monitors for the June 2006 episode. Comparison of Figure 1-3 and Figure 1-4
shows the similar patterns at central Texas monitors as at monitors in other areas of Texas.
There is a sustained period of high (MDA8 ≥ 60 ppb) ozone values during the first half of June
with peaks exceeding 75 ppb, followed by a period of lower ozone from approximately June 1622, and concluding with another high ozone period during the final week of the episode. Early
June 2006 was dominated by high pressure at the surface and aloft over Texas. The high ozone
days during late June 2006 occurred following the passage of a relatively strong cold front
through Texas. The passage of the cold front was marked by generally cleaner air during the
June 16-June 24 period.
Figure 1-4. May 31-July 2, 2006 daily maximum 8-hour average ozone at central Texas
monitors.
18
January 2014
1.1.1 Using the 2006 Ozone Episode for Control Strategy Development
The June 2006 episode meets EPA’s criteria for episode selection for use in ozone modeling for
attainment demonstrations (McGaughey et al., 2010; ENVIRON, 2011a). The episode contains a
mix of meteorological conditions that can lead to high ozone in central Texas, and has periods
favorable for ozone transport as well as longer periods of stagnant winds that can enhance the
effects of local emissions sources. The episode has sufficient high ozone days to carry out a
modeled attainment test according to EPA guidelines. The June 2006 episode has
meteorological and emissions databases developed by the TCEQ available for use in modeling
as well as extensive routine ambient monitoring data collected by the TCEQ. TexAQS II field
study observations are available the entire June 2006 episode and provide a wealth of data for
model evaluation.
1.2 Report Outline
In Section 2, we describe the development of the 2006 ozone model, including the evaluation
of the meteorological modeling database for central Texas. The evaluation of the base case
ozone modeling results is presented in Section 3. In Section 4, we describe changes made to the
model in order to improve its performance in replicating measured ozone at central Texas
monitors. With this revised modeling platform, the model’s ozone source apportionment
capability was used to determine which emissions source categories made the largest
contributions to ozone at the location of central Texas ozone monitors during the 2006 episode.
This analysis is discussed in Section 5. Section 6 describes addiitonal modifications made to the
model during the 2013 calendar year and provides a revised model performance evaluation.
Section 7 describes a new TCEQ emission inventory for 2012 which was used in the 2006
modeling platform along with the ozone source apportionment capability to analyze the ozone
effects of local and regional emissions changes from 2006 to 2012. Finally, in Section 8, we
summarize the findings of this study, and make recommendations for further work.
19
January 2014
2.0 DEVELOPMENT OF A JUNE 2006 OZONE MODEL FOR THE HOTCOG AREA
In this section, we discuss the development of the meteorological database and the emission
inventory as well as the configuration of the photochemical grid model for modeling the
HOTCOG area during the period May 31-July 2, 2006. The photochemical grid model used for
this application was the Comprehensive Air Quality Model with Extensions (CAMx; ENVIRON,
2013). The rationale for the model selection is discussed in the Modeling Protocol (ENVIRON,
2011a).
The TCEQ prepared modeling inputs for the June 2006 episode for use by the NNAs. The TCEQ
ran the Weather Research and Forecasting meteorological model (WRF; Skamarock et al., 2005)
for the June 2006 episode to prepare meteorological inputs for CAMx, developed an emission
inventory, and made available other inputs such as lateral boundary conditions for the outer
modeling grid. The period for which modeling inputs were available was May 31-July 2, 2006.
In Section 2.1, we discuss the meteorological model performance evaluation. In Section 2.2, we
describe the processing of the emission inventory and in Section 2.3, we describe the ozone
model configuration and other model inputs.
2.1 Meteorological Data
In 2010, TCEQ made a WRF run to be used in the Rider 8 modeling of the June 2006 episode.
The TCEQ performed an initial evaluation of this run in which performance was averaged over
all monitors in each NNA (Breitenbach, 2010). This initial evaluation indicated that the average
WRF run performance was reasonably good within central Texas. Therefore, all subsequent
ozone modeling was performed using the TCEQ WRF run. In Section 2.1, we describe the model
configuration of the TCEQ’s original WRF run and present the results of the model performance
evaluation at the two central Texas CAMS sites that were the closest sites in 2006 to the
current location of the Waco Mazanec monitor; these CAMS sites are Temple and Italy H.S.
CAMx requires meteorological input data for the parameters shown in Table 2-1. For the Rider
8 episode, the TCEQ developed meteorological input data for CAMx were developed using the
WRF Model version 3.2 and then processed the WRF outputs using the WRFCAMx preprocessor
to generate model-ready meteorological files containing each field in Table 2-1.
Table 2-1. CAMx meteorological input data requirements.
Input Parameter
Layer interface height (m)
Winds (m/s)
Temperature (K)
Pressure (mb)
Vertical Diffusivity (m2/s)
Water Vapor (ppm)
Clouds and Rainfall (g/m3)
Description
3-D gridded time-varying layer heights for the start and end of each hour
3-D gridded wind vectors (u,v) for the start and end of each hour
3-D gridded temperature and 2-D gridded surface temperature for the start
and end of each hour
3-D gridded pressure for the start and end of each hour
3-D gridded vertical exchange coefficients for each hour
3-D gridded water vapor mixing ratio for each hour
3-D gridded cloud and rain liquid water content for each hour
20
January 2014
2.1.1 WRF Model Configuration
2.1.1.1 Modeling Domain
The WRF coarse and nested grids defined by the TCEQ are shown in Figure 2-1. The modeling
domains are defined on a Lambert Conformal Conic map projection which is identical to that
used in the Regional Planning Organization (RPO) modeling1. The RPO projection is defined to
have true latitudes of 33°N and 45°N and central latitude and longitude point (97°W, 40°N).
The 36 km WRF modeling domain encompasses the continental U.S. and parts of Canada and
Mexico. The 12 km grid includes Texas and adjacent states, and the 4 km grid is centered on
East Texas. The TCEQ suggested that some NNAs may wish to define a smaller 4 km grid
focused on their local area, but we have chosen not to do this in order to simulate the
formation and transport of ozone within all of East Texas at the highest possible spatial
resolution (ENVIRON 2011a). The WRF 36, 12 and 4 km grids are slightly larger than the
corresponding CAMx grids to remove any artifacts (i.e., numerical noise) that can arise in WRF
adjacent to fine grid boundaries.
1
http://www.epa.gov/visibility/regional.html
21
January 2014
Figure 2-1. TCEQ’s WRF modeling 36/12/4 km grid system for regional scale modeling on the
RPO projection. Figure from Breitenbach (2010).
2.1.1.2 Vertical layer Structure
EPA’s current guidance on applying models for 8-hr ozone (EPA, 2007) includes the following
information on vertical layer structure:
•
•
•
•
There is no current recommended number of vertical layers, however, EPA notes that
recent applications have used 12-21 vertical layers and 8-15 layers within the planetary
boundary layer (PBL);
The surface layer should be no thicker than 50 m;
Excessively thick layers within the PBL are to be avoided; and
The top of the modeling domain should be set at the 100 mb (~16,000 meters) level for
modeled periods that include meteorological conditions that are not dominated by
synoptic high pressure systems and are not free of clouds and precipitation.
The TCEQ modeling system’s vertical structure satisfies all of the above criteria. Table 2-2
shows the layer heights and centers for the WRF and CAMx modeling.
22
January 2014
Table 2-2. Rider 8 WRF and CAMx model layer structure. TCEQ table from
http://www.tceq.texas.gov/airquality/airmod/rider8/modeling/domain.
23
January 2014
2.1.1.3 Column Physics and Data Assimilation
The WRF model physics options selected by the TCEQ for the WRF run are shown in Table 2-3.
Table 2-3. Physics Parameterizations used in the initial TCEQ WRF Run.
Process
Cumulus Parameterization
Radiation (LW/SW)
Cloud Microphysics
PBL/Surface Physics
Physics Parameterization
36/12 km Grid
4 km Grid
Kain-Fritsch
None
RRTM/Dudhia
RRTM/Dudhia
WSM5
WSM6
YSU/5-layer
YSU/5-layer
2.1.1.4 WRFCAMx Configuration
The TCEQ used the WRFCAMx v3.2 preprocessor to convert the raw WRF output files into
model-ready input files formatted for CAMx. WRFCAMx was used to calculate the vertical
turbulent exchange coefficients (Kv), which are derived from meteorological data supplied to
CAMx by the WRF meteorological model. The YSU Kv method was used. The CAMx preprocessor KVPATCH was then used to adjust the Kv to improve the turbulent coupling between
the surface and the lower boundary layer. The Kv 100 patch was applied to the Kv calculated
within WRFCAMx. In the Kv 100 patch, the minimum Kv for all layers within the lowest 100
meters (defined to be the stable boundary layer) is set to the maximum Kv value found within
the lowest 100 meters.
2.1.2 WRF Model Performance Evaluation
ENVIRON evaluated the performance of the WRF model on the 4 km grid shown in Figure 2-1
with a focus on model performance at central Texas monitors near the HOTCOG area. The
model performance evaluation addressed the following question: is the performance of the
June 2006 WRF run sufficiently good to allow CAMx to accurately characterize pollutant
transport, chemistry, and removal processes and accurately simulate ozone concentrations in
the HOTCOG area? In this evaluation, output from WRF was compared against meteorological
observations of winds, temperature, mixed layer height and precipitation.
Figure 2-2 shows the observed and modeled wind speed at the CAMS 651 monitor in Temple
and Figure 2-3 shows the bias in the wind speed, defined to be the difference between the
predicted and observed values of the wind speed. During the first high ozone episode (May 31June 15), periods of low wind speeds that coincided with high ozone days at Temple occurred
during June 2-3, June 8-9 and June 13. Winds were generally light from June 2-14 and then the
wind speed increased sharply on June 15-16 as a week-long period of clouds, rain and low
ozone began. During the second episode of high ozone (June 23-July 2), low wind speeds
occurred together with high ozone from June 28-30.
24
January 2014
Observed/Predicted Windspeed
12
ObsWndSpd
PrdWndSpd.YSU
10
m/s
8
6
4
2
07/03
07/02
07/01
06/30
06/29
06/28
06/27
06/26
06/25
06/24
06/23
06/22
06/21
06/20
06/19
06/18
06/17
06/16
06/15
06/14
06/13
06/12
06/11
06/10
06/09
06/08
06/07
06/06
06/05
06/04
06/03
06/02
06/01
05/31
05/30
05/29
05/28
0
Figure 2-2. Time series of observed (black) and WRF model (blue) near-surface wind speed at
Temple CAMS 651.
Bias Windspeed
8
BiasWndSpd
6
m/s
4
2
0
-2
-4
07/03
07/02
07/01
06/30
06/29
06/28
06/27
06/26
06/25
06/24
06/23
06/22
06/21
06/20
06/19
06/18
06/17
06/16
06/15
06/14
06/13
06/12
06/11
06/10
06/09
06/08
06/07
06/06
06/05
06/04
06/03
06/02
06/01
05/31
05/30
05/29
05/28
-6
Figure 2-3. Time series of WRF model near-surface wind speed bias at Temple CAMS 651.
The model does a reasonably good job of replicating the low frequency (weekly) variability in
the wind speed, and the value of the wind speed bias is generally between ±2 m s-1. The
episode average wind speed bias is -0.2 m s-1, which is within the ±0.5 m s-1 wind bias
benchmarks of Emery et al. (2001).
Figure 2-4 shows scatter plots of observed versus modeled wind speed for the nighttime hours
(7 pm-5 am) and the daytime hours (6 am-6 pm). The model does not have a strong tendency
toward low or high bias at night but shows evidence of a low bias in wind speed during the day.
Underestimation of wind speeds can contribute to modeled overprediction of ozone by
underestimating the dispersion of ozone and its precursors.
25
January 2014
Figure 2-4. Wind speed scatterplot for night (left panel) and day (right panel) hours for the
Temple CAMS 651 monitor.
Time series of observed and predicted wind direction are shown in Figure 2-5. The model
replicates the gross features of the observed wind time series. During periods in which the wind
blows fairly steadily, the model captures much of the variability in the wind direction (e.g. June
4-7, June 14-17, and June 19-23). The rapid shifts in wind direction that occurred during the
period of light, stagnant winds during June 2-3 and during the June 26-27 period were much
more difficult for the model to simulate. During these periods, there are several changes in
wind direction that the model either does not capture or mistimes. On June 13, one of the four
highest 8-hour ozone days during 2006 at Temple, the model mistimes the first wind shift of the
day, but simulates the second wind shift well.
26
January 2014
Observed/Predicted Wind Direction
ObsWndDir
PrdWndDir.YSU
360
300
deg
240
180
120
60
07/03
07/02
07/01
06/30
06/29
06/28
06/27
06/26
06/25
06/24
06/23
06/22
06/21
06/20
06/19
06/18
06/17
06/16
06/15
06/14
06/13
06/12
06/11
06/10
06/09
06/08
06/07
06/06
06/05
06/04
06/03
06/02
06/01
05/31
05/30
05/29
05/28
0
Figure 2-5. Time series of observed (black) and WRF model (blue) near-surface wind direction
at Temple CAMS 651.
The observed and modeled temperatures at Temple are shown in Figure 2-6. The model tracks
the diurnal cycle of observed temperatures reasonably well, although it doesn’t accurately
simulate the observed maxima and minima. In general, the modeled daily temperature maxima
are too high and the modeled daily minima are too low (Figure 2-7). The episode average
temperature bias is positive, indicating that the model is generally too warm. The warm bias is
more pronounced during the first half of the episode, when the modeled temperature minima
are generally well simulated, but the daily temperature maxima are always overestimated. The
episode average temperature bias is 0.7 m s-1, which is higher than the ±0.5 K temperature bias
benchmark of Emery et al. (2001).
315
Observed/Predicted Temperature
ObsTemp
PrdTemp.YSU
310
K
305
300
295
290
05/28
05/29
05/30
05/31
06/01
06/02
06/03
06/04
06/05
06/06
06/07
06/08
06/09
06/10
06/11
06/12
06/13
06/14
06/15
06/16
06/17
06/18
06/19
06/20
06/21
06/22
06/23
06/24
06/25
06/26
06/27
06/28
06/29
06/30
07/01
07/02
07/03
285
Figure 2-6. Time series of observed (black) and WRF model (blue) near-surface temperature
at Temple CAMS 651.
27
January 2014
12
Bias Temperature
BiasTemp
8
K
4
0
-4
-8
05/28
05/29
05/30
05/31
06/01
06/02
06/03
06/04
06/05
06/06
06/07
06/08
06/09
06/10
06/11
06/12
06/13
06/14
06/15
06/16
06/17
06/18
06/19
06/20
06/21
06/22
06/23
06/24
06/25
06/26
06/27
06/28
06/29
06/30
07/01
07/02
07/03
-12
Figure 2-7. Time series of WRF model near-surface temperature bias at Temple CAMS 651.
The Italy H.S. monitor wind speed and wind speed bias are shown in Error! Reference source
not found. and Figure 2-9, respectively. As at the Temple monitor, the WRF model simulates
the low-frequency variability in the observations reasonably well at Italy H.S. However, at Italy
H.S., the wind speed has a persistent positive bias which is large and persistent on June 10-12.
Figure 2-9 shows that the wind speeds are, on average, too fast during the June 2006 episode,
while Figure 2-10 indicates that this bias occurs during both day and night. The high bias is less
pronounced during the second high ozone episode (June 23-July 2) than during the first
episode. During the second episode, there are a number of days when the modeled wind
speed drops to near zero and the negative bias becomes large. The reason for these spurious
modeled calm periods is not clear. Figure 2-10 indicates that modeled wind speeds tend to be
too fast at night but that there is less evidence of wind speed bias during the day. The episode
average wind speed bias is 0.5 m s-1, which is within the ±0.5 m s-1 wind bias benchmarks of
Emery et al. (2001).
14
Observed/Predicted Windspeed
ObsWndSpd
PrdWndSpd
12
8
6
4
2
0
05/28
05/29
05/30
05/31
06/01
06/02
06/03
06/04
06/05
06/06
06/07
06/08
06/09
06/10
06/11
06/12
06/13
06/14
06/15
06/16
06/17
06/18
06/19
06/20
06/21
06/22
06/23
06/24
06/25
06/26
06/27
06/28
06/29
06/30
07/01
07/02
07/03
m/s
10
28
January 2014
Figure 2-8. Time series of observed (black) and WRF YSU run (blue) near-surface wind speed
at Italy CAMS 650.
5
Bias Windspeed
BiasWndSpd
4
3
2
m/s
1
0
-1
-2
-3
-4
05/28
05/29
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05/31
06/01
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06/24
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06/27
06/28
06/29
06/30
07/01
07/02
07/03
-5
Figure 2-9. Time series of WRF YSU run near-surface wind speed bias at Italy CAMS 650.
Figure 2-10. Wind speed scatterplot for night (left panel) and day (right panel) hours for the
Italy H.S. CAMS 650 monitor.
Figure 2-11 shows the observed and modeled wind direction at the Italy H.S. monitor. As at
Temple, the wind direction is well simulated at Italy H.S during periods where the wind
direction is not changing rapidly. From June 3-June 12, the wind direction varies slowly and the
model reproduces the observed wind direction well. During the periods where the air is
stagnant with light winds and rapidly shifting wind direction (May 31-June 2; June 18-19), the
model does not perform as well.
29
January 2014
Figure 2-12 shows the time series of modeled and observed temperature at Italy H.S. As at
Temple, an overall high bias in modeled temperature is apparent. The model consistently
overestimates the daily temperature maxima and frequently underestimates the temperature
minima. The episode average temperature bias is 0.3 K, which is within the ±0.5 K temperature
30
05/28
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deg
January 2014
360
315
10
8
6
4
2
0
-2
-4
-6
-8
-10
Observed/Predicted Wind Direction
ObsWndDir.TEMF
Observed/Predicted Temperature
ObsTemp
Temperature Bias
31
PrdWndDir
300
240
180
120
60
0
Figure 2-11. Time series of observed (black) and WRF model (blue) near-surface wind
direction at Italy CAMS 650.
PrdTemp
310
305
300
295
290
285
Figure 2-12. Time series of observed (black) and WRF model (blue) near-surface temperature
at Italy CAMS 650.
BiasTemp
Figure 2-13. Time series of WRF YSU run near-surface temperature bias at Italy CAMS 650.
January 2014
bias benchmark of Emery et al., (2001). Overall, the amplitude of the diurnal cycle in
temperature is larger in the model than in the observations. This suggests errors may exist in
the modeled surface energy budget, which is beyond the scope of the present work to
diagnose.
During the TexAQS II experiment, a number of radar wind profilers (RWP) were deployed in
Texas. Radar wind profilers transmit pulses of radiation upward in several different directions.
Some of this emitted energy is reflected or backscattered to the radar. The returned energy is
undergoes a Doppler shift due to the motion of the air and this information can be used to
diagnose the winds aloft and the height of the mixed layer; the mixed layer height is a critical
parameter for the model to simulate accurately, as it determines the depth through which
emissions are mixed.
The TCEQ analyzed mixed layer depths at a number of stations during the first high ozone
period of the June 2006 episode and provided this data to ENVIRON (Doug Boyer, personal
communication, 2009). The Cleburne CAMS station in Johnson County was the closest RWP site
to the HOTCOG area, and we compared WRF modeled planetary boundary layer (PBL) heights
to the RWP-derived mixed layer heights for the June episode (Figure 2-14). The WRF model
succeeds in modeling the daily growth of the mixed layer on some days. For example, on June
3, June 10, and June 14, the model accurately simulates the vertical growth of the mixed layer
as the sun rises and heats the earth, increasing atmospheric mixing aloft and driving up the
height of the mixed layer. On these days, the magnitude of the modeled mixed layer is within
500 m of the mixed layer heights derived from the RWP observations. Although there are a
number of days when the model is less successful at reproducing the RWP-derived mixed layer
heights (e.g. June 1-2), the model does a reasonably good job of simulating the daily rise and
fall of the mixed layer during this period, and shows no evidence of a large bias that would
significantly affect the ozone model. Note that the results for Cleburne may not be
representative of the model’s performance in the HOTCOG area.
Figure 2-14. Cleburne RWP-derived mixed layer heights and WRF modeled PBL heights.
The precipitation fields over the 4 km East Texas grid are presented in Appendix A and showed
reasonable agreement with observed precipitation. In general, the periods of high ozone did
32
January 2014
not coincide with periods of precipitation, so we do not include the evaluation in the main body
of the report. The model accurately reproduced the lack of precipitation during the first high
ozone episode and simulated the spatial pattern of precipitation over central Texas during the
second episode reasonably well.
2.2 Emission Inventory Development
The TCEQ provided CAMx-ready emission inventory files for the June 2006 modeling episode in
January 2012. The 2006 final merged model-ready emission files were downloaded from the
TCEQ web site Rider 8 State and Local Air Quality Planning Program: Modeling Files and
Information (the website address is shown in Table 2-4). All surface and elevated day-specific
files which were downloaded from the Rider 8 website in January 2012 are listed in Table 2-4.
There is a surface emissions file for each modeling grid (36 km, 12 km and 4 km grids) and a
single elevated point source for the entire modeling domain. The files listed in Table 2-4
constitute the emission inventory for the base case modeling described in Section 3.2. All
surface emissions source categories (e.g. on-road mobile, areas sources) are grouped together
into a single file.
Table 2-4. TCEQ May 31 – July 2, 2006 model ready emission files.
Component
Filename
http://www.tceq.texas.gov/airquality/airmod/rider8/rider8Modeling
Surface
Elevated
camx_cb6_ei_lo.2006{MMDD}.rider8.bl06_06jun.reg1.tx_4km
camx_cb6_ei_lo.2006{MMDD}.rider8.bl06_06jun.reg1.tx_12km
camx_cb6_ei_lo.2006{MMDD}.rider8.bl06_06jun.reg1.us_36km
camx_cb6_ei_el.2006{MMDD}.rider8.bc06_06jun.reg1a
In February, 2012, the TCEQ made the component files for the surface emission inventory
available via the Rider 8 website. The purpose of this additional detail in the emission inventory
was to allow source apportionment modeling to assess the ozone impacts of each emissions
source category. The components of the surface emission inventory are listed in Table 2-5 and
include area, non-road, on-road mobile, stationary points, and biogenics.
Table 2-5. Emission component files for May 31 – July 2, 2006 provided by the TCEQ.
Component
Filename
ftp://amdaftp.tceq.texas.gov/pub/Rider8/camx/basecase/bc06_06jun.reg1c.2006ep0ext_5layer_YSU_WSM6_3
dsfc_fddats/input/ei/Component/
Area
camx_cb6_ei_area.{wkd,sat,sun}.bl06.reg1c. {grid}
Nonroad
camx_cb6_ei_noroad.{wkd,sat,sun}.bl06.reg1c. {grid}
On-road
camx_cb6_ei_mobile.{mon,wkd,fri,sat,sun}.bl06.reg1c. {grid}
Surface Points
camx_cb6_ei_lo_point.wkd.bl06.reg1c. {grid}
Biogenics
camx_cb6_ei_bio.2006{MMDD}..tamu3_11Oct19. {grid}
33
January 2014
2.3 CAMx Model Configuration
In addition to the emission inventory and the WRF meteorological modeling files, the TCEQ
provided to the NNAs information on the CAMx air quality modeling domains and all additional
inputs required to run the model.
2.3.1 CAMx Modeling Domain
For the June 2006 episode, the modeling domain for the WRF meteorological modeling and the
domain for the CAMx ozone model were both specified. There is necessarily a close
relationship between the CAMx and WRF grids to ensure that meteorological information is
transferred accurately from WRF to CAMx. In order to minimize interpolation of the
meteorological variables from WRF to CAMx and the resulting potential for disruption of mass
consistency, the TCEQ defined the CAMx modeling grids to use the same LCC projection as the
WRF modeling.
EPA’s guidance on applying models for 8-hour ozone (EPA, 2007) states that the most
important factors that determine the horizontal extent of the domain are the nature of the
ozone problem and the spatial scale of the emissions which affect the region of interest. The
overall strategy in defining a nested modeling grid system is that a fine grid provides higher
resolution in the area of interest, while the coarse grid provides computational efficiency over
the larger modeling region. The TCEQ nested grid air quality modeling system for the June 2006
episode is shown in Figure 2-15. In accordance with EPA (2007) guidance, the outer 36 km
domain shown in red in Figure 2-15 was designed to be large enough to encompass all
important upwind sources of emissions and to allow the use of clean or relatively clean
boundary conditions. Back trajectory analyses performed by the TCEQ have suggested that air
mass transport times from the Ohio Valley/Midwest to Texas may be 2-3 days, so the 36 km
modeling domain is consistent with EPA’s guidance that regional domains should account for
potential transport distances of about 2 days upwind (EPA, 2007).
The 12 km grid (shown in blue in Figure 2-15) includes all of the areas in eastern Texas that are
conducting ozone modeling so that a consistent 12 km grid can be used in all studies. In
addition, the 12 km grid includes a substantial area that would typically be upwind of Texas
during an ozone episode with easterly or northeasterly winds. This is important to accurately
represent any influence of ozone transport since ozone formation is modeled more accurately
by a 12 km grid than a 36 km grid. The intention is to accurately model potential transport of
ozone from areas at a distance upwind of about one state.
The TCEQ specified a set of 3 nested modeling grids (36/12/4 km) designed to be suitable for
use by all of the NNAs. The TCEQ supplied emissions and meteorological inputs for the June
2006 episode on these grids; the NNAs then had the option to window out portions of the TCEQ
input data for use on their local 4 km CAMx modeling grid. TCEQ also defined a local 4 km
CAMx modeling grid for each NNA. As discussed in Section 2.1, HOTCOG has elected to carry
out the CAMx modeling on the full 4 km grid in order to simulate ozone formation and intrastate transport as accurately as possible. The following factors were considered in defining the
4 km CAMx air quality modeling grid for the HOTCOG area:
34
January 2014



Compatibility with TCEQ modeling efforts.
Placing a high resolution (4 km) grid over the key central Texas monitors as well as local
emissions sources
The high resolution 4 km domain must extend far enough upwind to include all sources
that might contribute substantially to elevated ozone levels in the HOTCOG area so that
ozone formation in these areas and subsequent transport to the HOTCOG area is
simulated as accurately as possible.
Figure 2-15. TCEQ 36/12/4 km CAMx nested modeling grids for the Texas ozone modeling of
June 2006. TCEQ figure from
http://www.tceq.texas.gov/airquality/airmod/rider8/modeling/domain.
The TCEQ’s 4 km grid encompasses all of the HOTCOG 6-county area and includes Dallas-Fort
Worth, nearby EGUs, and oil and gas development in the Barnett Shale. The HOTCOG
Conceptual Model (McGaughey et al. 2010, 2012) also indicates that transport from the
Houston area can play a role in determining ozone levels in The HOTCOG area. The HoustonGalveston-Brazoria-Port Arthur nonattainment area is the largest urban area to the south. The
TCEQ’s previous Houston modeling (e.g. TCEQ, 2010) has shown that accurate simulation of
ozone formation in the area requires high-resolution 4 km modeling in order to reproduce the
effects of the sea breeze circulation as well as the effects of numerous point sources of
emissions on ozone production and transport. In order to accurately model ozone formation in
the Houston area and its possible transport into the HOTCOG area, it is necessary to model the
Houston area at 4 km resolution in this study. Running CAMx on the 4 km grid shown in green
35
January 2014
in Figure 2-15 allows the best balance of computational efficiency and accuracy in simulating
processes that determine ozone levels in the HOTCOG area.
2.3.2 Other Inputs
Lateral boundary conditions for the 36 km modeling domain were provided by the TCEQ and
were generated from a GEOS-Chem global chemistry-transport model run. Acetone boundary
conditions are missing from this file. This may affect the NOy budget in the upper troposphere,
but is not expected to significantly bias the CAMx simulation of ground level ozone. All TCEQ
input files begin on May 31, 2006. No inputs for days preceding May 31 were provided, so no
spinup period was run before beginning the simulation of the Rider 8 episode. The
consequences of this for the source apportionment analysis are discussed in Section 5.2. TCEQ
also provided other required inputs to the NNAs such as photolysis rates, chemistry
parameters, land use input files and the albedo-haze-ozone files.
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January 2014
3.0 CAMX MODEL INITIAL RUN MODEL PERFORMANCE EVALUATION
The base case run was performed in the spring of 2012 with TCEQ’s original emission inventory
that was released in January 2012 and described in Section 2.2. ENVIRON ran the June 2006
episode using this emission inventory and TCEQ’s WRF modeling run, as well as all of the other
TCEQ modeling inputs supplied on the Rider 8 modeling site2 as described in Section 2.
ENVIRON used an updated version of CAMx that contained a corrected version of the LVISPiG
plume-in-grid module. This base case run is referred to later in this report as the 06_base_01
run. The modeled ozone results were compared to results obtained by the TCEQ with the same
modeling inputs, and were determined to be consistent, given the differences introduced by
the use of a different version of CAMx. Once it had been verified that the model results were
consistent with those obtained by the TCEQ, we evaluated the model’s performance in
simulating ozone and precursors with emphasis on the model’s performance at monitors in the
vicinity of the HOTCOG area.
3.1 Model Performance Metrics
The metrics used in this model performance evaluation include the mean normalized bias
(MNB) and the Mean Normalized Error (MNE). The Mean Normalized Bias (MNB), is defined as
1 N Pi  Oi 

N i 1 Oi
MNB 
where Pi and Oi are the predicted and observed values (Oi,Pi) in a data pair and N is the number
of observed/modeled data pairs. The MNB shows whether a modeled quantity such as ozone is
under- or over-predicted on average, compared with observations.
The MNE is defined as
MNE =
1
N
N
Pi  Oi
i 1
Oi

In 1991, EPA established model performance goals for ozone State Implementation Plan (SIP)
modeling that bias should be within ±15% (EPA, 1991). EPA’s most recent 8-hour ozone
guidance document (EPA, 2007) recommends against the use of bright line tests to evaluate
model performance. However, these benchmarks can serve to place the present model runs
within the context of previous modeling efforts. We therefore compare the results of the
simulation to the benchmarks with the intention of investigating performance rather than using
the benchmarks as a pass/fail test for either simulation.
The EPA 1991 ozone bias and error performance goals were based on the MNB and MNE using
predicted and observed hourly ozone pairs for which the observed value was greater than a 60
2
http://www.tceq.texas.gov/airquality/airmod/rider8/rider8Modeling
37
January 2014
ppb ozone concentration threshold. Since this analysis includes regions with relatively low
ozone (including rural regions), a 40 ppb ozone concentration threshold was used. Although
MNE was calculated along with the MNB, the MNE results are not presented in this report for
the sake of brevity.
3.2 Model Performance Evaluation Results
We begin by looking at performance in the 36 km grid and then focus on performance in the 4
km grid and, specifically, at three ozone monitors in the vicinity of the HOTCOG area that were
operating during June 2006.
3.2.1 Base Case Model Run 36 km Grid Evaluation
As part of a separate project for the TCEQ, ENVIRON carried out an analysis of Rider 8 June
2006 episode model performance in the 36 km grid over the southeastern U.S. and the Ohio
River Valley (Kemball-Cook et al., 2012). We summarize the results of this evaluation here
because of its relevance to the accuracy of simulated ozone transport into the HOTCOG area.
The monitoring data used for the 36 km model performance evaluation come from the EPA Air
Quality Station (AQS) network, the Clean Air Status Trends Network (CASTNet) monitoring
networks and the SEARCH sites, which are located in the southeastern U.S. CASTNet sites are
all located in rural areas and the AQS sites were determined to be rural or urban based on their
site description. The SEARCH network contains urban, suburban, and rural sites, but only the
suburban and rural sites were used in this study because this region is modeled using a 36 km
grid, which cannot be expected to accurately simulate variations in urban ozone.
Figure 3-1 shows the episode average bias at selected monitors within Texas and at rural
monitors outside Texas and within the 36 km grid. There is a high bias throughout the domain,
and there are large modeled overestimates of ozone in the southeastern U.S. and the Ohio
River Valley region. The model performance evaluation for ozone in the 36 km grid showed
large overestimates of ozone similar to the results of previous ozone transport modeling
studies that evaluated other 2005 and 2006 TCEQ modeling databases (ENVIRON 2010 and
2011a). The cause of modeled ozone overestimates in the southeastern U.S. is an area of
current research (e.g. Herwehe et al., 2011).
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January 2014
Figure 3-1. Episode MNB for rural ozone monitors in the southeastern U.S. and the Ohio
River Valley and Texas and adjacent states.
3.2.2 Texas Border Monitor Evaluation
We turn now to model performance at monitors located near Texas’ borders with Louisiana and
Oklahoma. Rural sites and sites along the state’s borders and coast were chosen in order to
focus the evaluation on monitors that would be most affected by background ozone and ozone
transport (Figure 3-2). Figure 3-3 shows the mean normalized bias for episode 1 (May 31-June
15) for sites along the Texas border. There is a high bias that is consistent across all sites. The
daily average MNB is positive for nearly all sites for all days of the episode. The largest
overestimates of ozone occur at the Galveston monitor (GALC). For episode 2, the results are
similar. Figure 3-4 shows that there is a high bias at all sites that becomes particularly
pronounced during the final three days of the second episode. The fact that ozone is
overestimated at Texas border monitors indicates that ozone transport into Texas may be
exaggerated. Additional detailed analysis would be required to assess the accuracy of the
transported ozone contribution at each monitor, but the results suggest that caution is required
when interpreting source apportionment results that parse ozone at a monitor into transported
and local contributions.
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January 2014
Figure 3-2. Texas monitors used in model performance evaluation on the 4 km grid. Texas
border monitors evaluated in Section 3.3.2 are circled in red and monitors used for the
HOTCOG area evaluation are circled in green.
40
January 2014
Figure 3-3. Mean normalized bias for ozone during episode 1 for monitoring sites near the
northern and eastern Texas borders.
41
January 2014
Figure 3-4. Mean normalized bias for ozone during episode 2 for monitoring sites near the
northern and eastern Texas borders.
3.2.3 Base Case Run Model Performance in the Vicinity of the HOTCOG Area
Next, we focused on model performance in the vicinity of the HOTCOG 6-county area. The
Waco Mazanec monitor was not yet operational in 2006, and there was no other monitor in the
HOTCOG area during the June 2006 episode. The closest ozone monitors to the HOTCOG area
in 2006 were the Temple and Italy H.S. monitors (Figure 3-2). We evaluated model
performance at these two monitors and also at the Palestine monitor, which can be upwind of
the HOTCOG area on high ozone days (e.g. McGaughey et al., 2010; 2012). For all three of these
monitors, ozone data are available but precursor data (NOx, VOCs) are not. Therefore, we
compared modeled and observed ozone only for these three monitors.
Figure 3-5 shows the measured and modeled time series for 1-hour ozone (upper panel) and
the mean normalized bias for 8-hour ozone (MNB; lower panel) at the Temple monitor during
the first high ozone period of the June 2006 episode. High ozone episode 1 extends from May
31-June 15. In the analysis of the model performance, we consider the large-scale
meteorological conditions for each day. To determine whether a particular day had stagnant
conditions or conditions conducive to long-range transport of ozone and precursors, we drew
on a back trajectory analysis performed by the TCEQ. In 2010, the TCEQ generated back
trajectories for the entire June 2006 episode and provided them to ENVIRON. The back
trajectories were prepared using on‐line tools provided by the National Oceanic and
Atmospheric Administration (NOAA) at http://www.arl.noaa.gov/ready/hysplit4.html. These
tools are based on application of NOAA’s HYSPLIT model (Draxler et al., 1997) with archived
weather forecast model data from the National Center for Environmental Prediction’s EDAS
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January 2014
forecast model. The EDAS data have a horizontal resolution of 40 km. Note that back
trajectories are a qualitative tool subject to theoretical and data limitations and can only
provide approximate information regarding possible source regions for pollutants transported
to a monitor. During the development of the Modeling Protocol for this study (ENVIRON,
2011a), the HYSPLIT back trajectories were examined to determine periods of possible
stagnation and transport. No back trajectories for the HOTCOG area were available, so we used
the TCEQ back trajectories for the Dallas-Fort Worth (DFW) area as a general indicator of
whether air was stagnant over central Texas or whether transport of ozone from outside
central Texas was likely to have occurred.
Analysis of daily back trajectories ending in midafternoon at DFW as well as wind data from
local monitors (Section 2.1.2) indicate that air over the HOTCOG area was relatively stagnant
during June 2-13. Stagnant air tends to enhance the contribution of local emissions sources, as
ozone precursors remain in the area rather than being transported away by the winds. HYSPLIT
back trajectories indicate that May 31-June 1 and June 14-15 were periods when central Texas
was likely affected by long-range transport from the Ohio River Valley region.
Figure 3-5 shows that at the Temple CAMS monitor, the CAMx model has an overall high bias
during the first episode. The mean normalized bias shown in the lower panel of Figure 4-3 is
greater than zero for 14 out of 15 days. The model bias was within the ±15% bias benchmark
on 10 of 15 days. Two of the Temple monitor’s 4 highest values of the MDA8 during 2006
occurred during this episode on June 9 and June 13. Wind speeds at Temple were very low on
both days (Figure 2-2). On June 9, the model simulates the peak 1-hour ozone value reasonably
well, but the ozone buildup to the peak value does not match the timing of the observations.
On June 13, the model overestimates ozone throughout the day, and the peak value is higher in
the model than in the observations. The overestimation continues through the end of the
episode, with an extremely high positive model bias on the June 15 transport day. In general,
the model performs better during the stagnant period in the middle of this episode, and has a
larger positive bias during the transport periods at the beginning and end of the episode. This
is consistent with the analysis presented in the previous section which indicated that transport
of ozone into Texas may be overestimated in the model.
CAMx frequently overestimates observed night time ozone minima. This problem has been
noted in previous CAMx simulations in suburban/rural areas (e.g. Kemball-Cook et al., 2012).
This problem is likely related to problems with modeled nighttime vertical mixing. Vertical
mixing in CAMx is controlled by the vertical turbulent exchange coefficients (Kv), which are
calculated based on meteorological data supplied to CAMx by WRF. It is difficult for
meteorological models to simulate nighttime mixing accurately, as the characterization of
turbulence in the nocturnal boundary layer is not well-understood. The addition of ad hoc Kv
patches can reduce this effect somewhat but does not eliminate it entirely. The Kv100 patch
applied in this modeling does not address this nighttime mixing issue.
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January 2014
Figure 3-5. Upper panel: observed 1-hour ozone (red) at the Temple CAMS 651 monitor
versus modeled 1-hour average surface layer ozone (blue, HOTCOG) during the May 31-June
15, 2006 period for the Base Case run 06_base_01. Lower panel: mean normalized bias
(MNB) for the Temple CAMS 651 monitor. Red lines show ±15% EPA (1991) benchmarks.
The model simulates ozone reasonably well during the first few days of the second part of the
June episode (Figure 3-6). During the last few days of the episode, the model develops a strong
positive bias for ozone at Temple, with the model bias exceeding the benchmark from June 29July 1.
Performance at the Italy monitor was similar to that at Temple in that the model had an overall
high bias and was generally within the bias benchmark (13 of out of 16 days) during the first
high ozone episode and also tended to overestimate the nighttime ozone minima. June 9, 13
and 15 were among the 4 highest MDA8 days at Italy in 2006. The model is within the
benchmark on all three of these days and simulates the highest 1-hour ozone peak (June 13)
well. During the second episode (Figure 3-8), the model does a good job of reproducing the rise
and fall of ozone on June 27, the other
44
January 2014
Figure 3-6. Upper panel: observed 1-hour ozone (red) at the Temple CAMS 651 monitor
versus modeled 1-hour average surface layer ozone (blue, HOTCOG) during the June 23-July 2,
2006 period for the Base Case run 06_base_01. Lower panel: mean normalized bias (MNB)
for the Temple CAMS 651 monitor. Red lines show ±15% EPA (1991) benchmarks.
of the 4 highest MDA8 days at Italy in 2006. However, the model develops a very large high
bias at the end of the period, as was seen at the Temple monitor.
At the Palestine monitor performance is not as good as at the Italy and Temple monitors during
the first part of the June episode (Figure 3-9). The high bias is more pronounced at Palestine
than at the other two monitors, and the model exceeds the bias benchmark on 9 of 16 days and
was within the benchmark on 7 of 16 days. During the second part of the June episode (Figure
3-10), the Palestine also develops a large positive bias during the last few days of the episode,
similar to Temple and Italy. Unlike the other two monitors, the Palestine monitor does not
show good performance during the first few days of the second episode. The daily variability in
ozone is much less pronounced in the model than in the observations. The nighttime minima in
45
January 2014
the measurements are not replicated in the model and neither the shape nor the magnitude of
the peaks is well-simulated.
3.2.4 Summary of Base Case Model Performance Evaluation
The model performance evaluation of the base case run showed that the model has an overall
high bias. This was true for monitors in both the 36 km grid and the 4 km grid. The fact that
the CAMS monitors in rural areas on the Texas border consistently overestimate ozone suggests
that transport into Texas may be overestimated. A model performance evaluation of upwind
areas (Ohio River Valley, southeast U.S.) showed that simulated ozone was too high in these
ozone source regions. However, the model overestimates ozone at monitors in the vicinity of
the HOTCOG area and other Texas monitors (not shown) even during the stagnant period of the
first high ozone episode, so that ozone overpredictions cannot be solely due to overestimated
transport into Texas. In the HOTCOG area, the high bias is largest at Palestine and smaller at
Temple and Italy.
It was determined that additional work was needed to improve model performance and to
address the high bias. Sensitivity tests aimed at improving model performance were carried
out during 2012 and are described in Section 4.
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January 2014
Figure 3-7. Upper panel: observed 1-hour ozone (red) at the Italy H.S. CAMS 650 monitor
versus modeled 1-hour average surface layer ozone (blue, HOTCOG) during the May 31-June
15, 2006 period for the Base Case run 06_base_01. Lower panel: mean normalized bias
(MNB) for the Italy H.S. CAMS 650 monitor. Red lines show ±15% EPA (1991) benchmarks.
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January 2014
Figure 3-8. Upper panel: observed 1-hour ozone (red) at the Italy H.S. CAMS 650 monitor
versus modeled 1-hour average surface layer ozone (blue, HOTCOG) during the June 23-July 2,
2006 period for the Base Case run 06_base_01. Lower panel: mean normalized bias (MNB)
for the Italy H.S. CAMS 650 monitor. Red lines show ±15% EPA (1991) benchmarks.
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January 2014
Figure 3-9. Upper panel: observed 1-hour ozone (red) at the Palestine CAMS 647 monitor
versus modeled 1-hour average surface layer ozone (blue, HOTCOG) during the May 31-June
15, 2006 period for the Base Case run 06_base_01. Lower panel: mean normalized bias
(MNB) for the Palestine CAMS 647 monitor. Red lines show ±15% EPA (1991) benchmarks.
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January 2014
Figure 3-10. Upper panel: observed 1-hour ozone (red) at the Palestine CAMS 647 monitor
versus modeled 1-hour average surface layer ozone (blue, HOTCOG) during the June 23-July 2,
2006 period for the Base Case run 06_base_01. Lower panel: mean normalized bias (MNB)
for the Palestine CAMS 647 monitor. Red lines show ±15% EPA (1991) benchmarks.
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January 2014
4.0 MODEL UPDATES AND EVALUATION
By the time the base case modeling (run 06_base_01) was completed in late spring 2012, the
TCEQ had updated the June 2006 episode emission inventory and had provided additional
emissions component files so that detailed ozone source apportionment analyses could be
performed by the Texas NNAs. Other updated model components had become available, as
well. A new version of CAMx was released, and this version contained the capability to run an
updated version of the CB6 chemical mechanism (Yarwood et al., 2012a). For the Northeast
Texas NNA, ENVIRON developed day-specific wildfire emissions for the June 2006 episode, and
these emissions were incorporated into HOTCOG’s ozone model as well. ENVIRON carried out a
series of sensitivity tests for the Northeast Texas NNA; the tests were aimed at evaluating the
effect of each of these changes and at arriving at an optimal model configuration for the June
2006 episode. Once the model configuration had been determined based on this sensitivity
testing (Kemball-Cook et al., 2013), a new model run was made for HOTCOG in the updated
configuration. This run is referred to hereafter as the 06_wildfires_07 run. In this section, we
describe the updates to the model, the new configuration, and the model performance
evaluation that was carried out following the model run.
4.1 New CAMx Version
The base case CAMx modeling run 06_base_01 was carried out with CAMx version v5.40 with a
modification made to correct an error in the LVISPIG module of CAMx. A new version of CAMx,
version 5.41, was released in November, 2012. Relevant changes in v5.41 relative to v5.40 are:
1) Two updated versions of the CB6 chemical mechanism were added to the model:

CB6 with iodine chemistry (Yarwood et al., 2012b). Iodine chemistry is intended for
modeling ozone in marine environments and was not used for HOTCOG’s modeling.

CB6r1 was the first revision to CB6. It contained extensive revisions to the chemistry of
isoprene and aromatics, and has additional NOx recycling from organic nitrates. It is
documented in Yarwood et al. (2012a); however, not all of the updates in this reference
were implemented in the version of CB6r1 implemented in v5.41.
2) Photolysis rates for NO2, O3, HCHO and CH3CHO are now adjusted for temperature and
pressure.
3) Ozone dry deposition velocity over oceans is modified to be a function of temperature and
wind speed.
4) Reduced minimum thresholds for cloud/rain water and cloud temperature.
4.2 CB6r1 Chemical Mechanism
The CB6r1 chemical mechanism differs from its predecessor, CB6, in that NOx recycling was
increased in based on experimental data from AQRP project 10-042 (Yarwood et al., 2012a).
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January 2014
Organic nitrates are produced in reactions between VOCs and NOx, e.g.
RO2 + NO -> α RONO2 + (1- α) (RO + NO2)
The nitrate yield (α) depends on RO, and is typically 1 – 10%. NOx may re-form when organic
nitrates react by photolysis and with OH; this is called NOx recycling. Although most nitrates
react slowly, nitrates from isoprene react rapidly with OH because they contain a double bond.
Yarwood et al. (2012a) noted, “CB6r1 tentatively includes NOx recycling from the reaction of
OH with NTR (a change from CB6) but we recommend conducting photochemical model
sensitivity tests to evaluate the impacts of this change. Additional experiments to test for the
occurrence of NOx recycling from alkyl nitrates larger than isopropyl and isobutyl are needed
because OH reaction is more important relative to photolysis and the nitrogen-containing
products could be different for larger alkyl nitrates than for the compounds studied here.”
4.3 Updated TCEQ EI
The TCEQ updated the emission inventory for the June 2006 files with component emission
inventory files in February, 20123. Figure 4-1 is a comparison by geographic region of the NOx
emission inventory used in the 06_base_01 run and the updated NOx emission inventory. The
emission inventory is for a June weekday. The definition of the regions is given in Figure 4-2.
For most regions, NOx emissions were slightly larger in the new TCEQ emission inventory.
Figure 4-1. NOx emission inventory comparison by region for June 8, 2006 (weekday) for
06_base_01 (red; denoted Base_06) and 06_wildfires_07 (blue; denoted revTCEQ_EI_06)
simulations.
3
ftp://amdaftp.tceq.texas.gov/pub/Rider8/camx/basecase/bc06_06jun.reg1c.2006ep0ext_5layer_YSU_WSM6_3dsf
c_fddats/input/ei/Component/
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Figure 4-2. Geographic regions used in the emissions comparison shown in Figure 4-1.
4.3.1 Day-Specific Wildfire Emissions
Wildfires can emit large quantities of trace gases and aerosols into the atmosphere, and these
emissions undergo chemical and physical changes as they are transported away from the active
fire region. Primary emitted species are depleted as they are deposited and/or chemically
processed and secondary species such as ozone and secondary organic aerosols form within the
plume. Both primary and secondary species can influence air quality at local and regional scales
and can affect human health (e.g., Junquera et al., 2005; Jaffe et al., 2008, Hu et al., 2008.)
Ozone and particulates formed in wildfire plumes can be transported to populated regions and
can influence measured concentrations at air quality monitors.
Wildfire emissions are a function of the fuel type and moisture, fire characteristics, and
atmospheric conditions, and can have significant spatial and temporal variability. It is important
that HOTCOG’s model represent emissions from wildfires as accurately as possible so that fire
impacts are accounted for correctly. In the TCEQ emission inventory for June 2006, emissions
from wildfires were taken from the U.S. EPA’s National Emission Inventory (NEI), which contains
annual average wildfire emissions totals that are allocated evenly among the days of the year.
Modeling fire emissions in this way does not accurately represent the variability of actual
wildfire emissions, which are highly intermittent in time and space. Therefore, a day-specific
wildfire emission inventory was added to the June 2006 emission inventory. A detailed
description of the fire emissions processing is available in Kemball-Cook et al., (2013b), and we
provide a summary here.
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January 2014
We replaced the EPA NEI average emissions in the TCEQ inventory with day-specific fire
emissions in the emission inventories for the June 2006 Rider 8 modeling episode. The fire
emissions are based on satellite fire detections, and are modeled so that fire characteristics
such as fuel loading and plume height are taken into account.
For each day of the 2006 ozone modeling episode, ENVIRON obtained estimates of fire
emissions from the National Center for Atmospheric Research (NCAR) (Wiedinmyer, personal
communication, 2008). These emission estimates are derived from analysis of fire locations
determined by satellite-borne detectors. The MODerate-resolution Imaging Spectroradiometer
(MODIS) instruments fly aboard two polar-orbiting satellites, Terra, and Aqua. These two
satellites orbit the Earth, traveling from pole to pole while the earth rotates beneath them; a
given area of the Earth will have an overpass from Terra and Aqua approximately twice a day.
MODIS instruments detect fires as thermal anomalies (i.e. hot spots seen against a cooler
background) at a spatial resolution of about 1 kilometer. The NCAR fire emissions inventory
development is described by Wiedinmyer et al. (2006). The NCAR satellite-derived fire dataset
contains emissions of NOx, CO, VOC, SO2 and PM species, emissions location, acreage burned,
and fuel loading at a resolution of 1 km2, which represents the size of each satellite pixel.
The chemical speciation profile used in the NCAR inventory was derived from a study on
tropical biomass burning (Karl et al., 2007). The study integrated laboratory experiments and
field measurements conducted as part of the TROFFEE (Tropical Fire and Forest Emissions
Experiment) campaign. NCAR fire emissions data for NOx, CO, VOC and SO2 both episodes were
extracted for the full extent of the TCEQ’s 36/12/4 km nested grid system. Fire locations were
mapped from latitude/longitude coordinates to the Lambert Conformal Projection of the
TCEQ’s modeling grids. Any fire location within 5 km of another fire was assumed to be part of
the same fire event. A fire event is therefore defined as a cluster of points meeting this
criterion.
The NCAR fire emissions for the two episodes were then reformatted into the Emissions
Processing System version 3 (EPS3) input format. The daily fire emissions were processed using
version 3.20 of EPS3. Each fire event was treated as a point source. A plume profile was
calculated for each point in order to distribute the emissions vertically using the methodology
developed by the Western Regional Air Partnership (WRAP) and documented in WRAP (2004;
2005). The WRAP method requires as input the acreage burned and the fuel loading.
4.4 Revised CAMx Model Performance Evaluation
The 06_wildfires_07 run used CAMx v5.41 and the CB6r1 chemical mechanism and the updated
TCEQ emission inventory with day-specific wildfires. Figure 4-3 through Figure 4-8 show the
model performance evaluation for run 06_wildifres_07 and a comparison with the base case
run 06_base_01. For the Temple, Italy H.S and Palestine monitors, ozone model performance is
degraded in the 06_wildfires_07 run relative to the base case. The results are similar for the
second episode at Temple and for both episodes at the other two monitors. The changes in the
model result in a larger high bias for ozone in the model in 06_wildfires_7 run than in the base
case run.
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January 2014
4.5 Summary of Model Performance Evaluation
A June 2006 ozone model was developed from inputs provided by the TCEQ and its
performance in simulating observed surface layer ozone within the modeling domain was
evaluated, with a focus on the HOTCOG area. The base case model performance evaluation
showed that the model overestimates ozone at rural monitors in the southeastern U.S., the
Ohio River Valley, and at rural monitors along the Texas borders with Louisiana and Oklahoma.
It is therefore possible that the model overestimates transport into Texas. The model had a
high bias at three monitors in the vicinity of the HOTCOG area. This bias occurred during
periods of stagnation as well as during transport episodes.
A series of updates were made to the model during 2012; these updates were aimed at
improving model performance by using the most recent versions of the model and its chemical
mechanism as well as the most up-to-date and detailed TCEQ emission inventory and a new
day-specific wildfire emission inventory. The net result of the updates was to increase the high
bias for ozone at central Texas monitors in the vicinity of the HOTCOG area as well as at other
monitors in Texas. Of all of the model changes, the introduction of the CB6r1 chemical
mechanism caused the largest increase to modeled ozone in the HOTCOG area (Kemball-Cook
et al. 2013a). NOx recycling was increased in CB6r1 based on experimental data from AQRP
project 10-042 (Yarwood et al., 2012a). Although this change made the mechanism consistent
with the best available science at the time, modeled ozone increased region-wide and model
performance degraded.
Following the 06_wildfires_07 run described in this section, further development of the CB6
chemical mechanism took place under AQRP project 12-012 in 2012-13 (Hildebrandt Ruiz and
Yarwood, 2013). During that time, the source apportionment modeling described in Section 5
was carried out with the 06_wildfires_07 run. Once AQRP Project 12-012 was completed in the
fall of 2013, the improvements to the CB6 chemical mechanism that resulted from the project
were integrated into HOTCOG’s ozone model. A new model run employing the revised
chemical mechanism was made and the results of this run are described in Section 6.
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January 2014
Figure 4-3. Upper panel: observed 1-hour ozone (black) at the Temple CAMS 651 monitor
versus modeled 1-hour average surface layer ozone (06_base_01 [blue] and 06_wildfires_07
[green]) during May 31-June 15, 2006. Lower panel: mean normalized bias (MNB) for the
Temple CAMS 651 monitor. Bar colors are as in upper panel. Red lines show ±15% EPA
(1991) benchmarks.
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January 2014
Figure 4-4. Upper panel: observed 1-hour ozone (black) at the Temple CAMS 651 monitor
versus modeled 1-hour average surface layer ozone (06_base_01 [blue] and 06_wildfires_07
[green]) during June 23-July 2, 2006. Lower panel: mean normalized bias (MNB) for the
Temple CAMS 651 monitor. Bar colors are as in upper panel. Red lines show ±15% EPA
(1991) benchmarks.
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January 2014
Figure 4-5. Upper panel: observed 1-hour ozone (black) at the Italy H.S. CAMS 650 monitor
versus modeled 1-hour average surface layer ozone (06_base_01 [blue] and 06_wildfires_07
[green]) during May 31-June 15, 2006. Lower panel: mean normalized bias (MNB) for the
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January 2014
Italy H.S. CAMS 650 monitor. Bar colors are as in upper panel. Red lines show ±15% EPA
(1991) benchmarks.
Figure 4-6 Upper panel: observed 1-hour ozone (red) at the Italy H.S. CAMS 650 monitor
versus modeled 1-hour average surface layer ozone (06_base_01 [blue] and 06_wildfires_07
[green]) during June 23-July 2, 2006. Lower panel: mean normalized bias (MNB) for the Italy
H.S. CAMS 650 monitor. Bar colors are as in upper panel. Red lines show ±15% EPA (1991)
benchmarks.
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January 2014
Figure 4-7. Upper panel: observed 1-hour ozone (black) at the Palestine CAMS 647 monitor
versus modeled 1-hour average surface layer ozone (06_base_01 [blue] and 06_wildfires_07
[green]) during May 31-June 15, 2006. Lower panel: mean normalized bias (MNB) for the
Palestine CAMS 647 monitor. Bar colors are as in upper panel. Red lines show ±15% EPA
(1991) benchmarks.
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January 2014
Figure 4-8. Upper panel: observed 1-hour ozone (black) at the Palestine CAMS 647 monitor
versus modeled 1-hour average surface layer ozone (06_base_01 [blue] and 06_wildfires_07
[green]) during June 23-July 2, 2006. Lower panel: mean normalized bias (MNB) for the
Palestine CAMS 647 monitor. Bar colors are as in upper panel. Red lines show ±15% EPA
(1991) benchmarks.
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January 2014
5.0 2006 OZONE SOURCE APPORTIONMENT MODELING
Following the model performance evaluation described in Section 4, the June 2006 ozone
model was used to investigate ozone formation in the HOTCOG area as well as the transport of
ozone and precursors into central Texas. The goal of this supplemental analysis was to provide
the HOTCOG AQAC and the TCEQ with preliminary information regarding emissions control
strategies that can reduce ozone levels in the HOTCOG area. The CAMx run that used dayspecific wildfires, CAMx v5.41, CB6r1 and the most recent TCEQ emission inventory was used in
the source apportionment analysis (run 06_wildfires_07). The run 06_wildfires_07 model run
had a strong high bias, as noted in Section 4, but was the CAMx run containing the best
available science at the time the source apportionment analysis described in this section was
performed in the spring of 2012. During 2013, the CAMx model was updated and model
performance improved relative to run 06_wildfires_07; this is described in Section 6. The
source apportionment analysis presented in Section 5 below can be repeated with the new
CAMx model configuration described in Section 6, but results are not expected to be
qualitatively different from those presented here.
Using run 06_wildfires_07, the CAMx model’s source apportionment capability was used to
determine the relative importance of transport and local emissions in causing high ozone in the
HOTCOG area. Source apportionment was also used to determine which categories of local
emissions sources make the largest contribution to HOTCOG area ozone. The CAMx source
apportionment tool used in this analysis is described in Section 5.1, and the results of the
source apportionment analysis are given in Section 5.2.
5.1 Description of the CAMx APCA Source Apportionment Tool
The CAMx Anthropogenic Precursor Culpability Assessment (APCA) tool uses multiple tracer
species to track the fate of ozone precursor emissions (VOC and NOx) and the ozone formation
caused by these emissions within a simulation. The tracers operate as spectators to the normal
CAMx calculations so that the underlying CAMx-predicted relationships between emission
groups (sources) and ozone concentrations at specific locations (receptors) are not perturbed.
Tracers of this type are conventionally referred to as “passive tracers,” however it is important
to realize that the tracers in the APCA tool track the effects of chemical reaction, transport,
diffusion, emissions and deposition within CAMx. In recognition of this, they are described as
“ozone reaction tracers.” The ozone reaction tracers allow ozone formation from multiple
“source groupings” to be tracked simultaneously within a single simulation. A source grouping
can be defined in terms of geographical area and/or emission category. So that all sources of
ozone precursors are accounted for, the CAMx boundary conditions and initial conditions are
always tracked as separate source groupings. This allows an assessment of the role of
transported ozone and precursors in contributing to high ozone episodes within the HOTCOG
area.
The methodology is designed so that all ozone and precursor concentrations are attributed
among the selected source groupings at all times. Thus, for all receptor locations and times,
the ozone (and ozone precursor concentrations) predicted by CAMx are attributed among the
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source groupings. The methodology also estimates the fractions of ozone arriving at the
receptor that were formed en-route under VOC- or NOx-limited conditions. This information
suggests whether ozone concentrations at the receptor may be responsive to reductions in VOC
and NOx precursor emissions and can guide the development of additional sensitivity analyses.
APCA differs from the standard CAMx Ozone Source Apportionment Tool (OSAT) in recognizing
that certain emission groups are not controllable (e.g., biogenic emissions) and that
apportioning ozone production to these groups does not provide information that is relevant to
development of control strategies. To address this, in situations where OSAT would attribute
ozone production to non-controllable (i.e., biogenic) emissions, APCA re-allocates that ozone
production to the controllable portion of precursors that participated in ozone formation with
the non-controllable precursor. For example, when ozone formation is due to biogenic VOC
and anthropogenic NOx under VOC-limited conditions (a situation in which OSAT would
attribute ozone production to biogenic VOC), APCA re-directs that attribution to the
anthropogenic NOx precursors present. The use of APCA instead of OSAT results in more ozone
formation attributed to anthropogenic NOx sources and less ozone formation attributed to
biogenic VOC sources, but generally does not change the partitioning of ozone attributed to
local sources and the transported background for a given receptor.
5.2 APCA Results
In this section, we describe the local versus transported contribution and the breakdown by
emissions source category for the Waco Mazanec monitor location. Although the Waco
monitor was not active during June 2006, a model receptor was placed at the current monitor
location and the CAMx source apportionment tool therefore treats the Waco monitor location
exactly as it would any monitor that was actually active in June 2006. Figure 5-1 shows the
source region map used in the APCA analysis. The map covers the 4 km grid only. All areas
outside the 4 km grid are defined to be part of the “Other” source region that includes all parts
of the modeling domain that are outside Texas. The HTCG region consists of the HOTCOG 6county area. The contribution to HOTCOG area ozone from local emissions sources is reckoned
using this source region boundary. Other NNAs such as the Austin and San Antonio areas are
broken out as separate source regions, as are the Houston-Galveston-Brazoria (HGB) and
Dallas-Fort Worth (DFW) non-attainment areas. The remaining areas of East Texas are grouped
into source regions as follows:
AACG: San Antonio Area (AACOG)
CPCG: Austin Area (CAPCOG)
VCCC: Victoria/Corpus Christi
HGB: Houston-Galveston-Brazoria Area
BPA: Beaumont-Port Arthur Area
CTX: Central Texas
DFW: Dallas-Fort Worth Area
NETX: The HOTCOG area (Tyler-Waco-Marshall NNA)
NNTEX: Area north of NETX
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Figure 5-1. 4 km grid APCA source region map.
5.2.1 Source Apportionment Results for the Location of the Waco Mazanec Monitor
In order to develop emission control strategies for the HOTCOG area that will reduce the local
contribution to ozone, it is necessary to understand how ozone formation in the area depends
on the amount of available NOx and VOC. Figure 5-2 and Figure 5-3 show the episode
maximum and average contributions to ozone at the Temple and Italy H.S. monitors and at the
location of the Waco Mazanec monitor. The Temple and Italy H.S. monitors are shown because
they are near the HOTCOG area and provide an indication of whether the Waco monitor results
are consistent with those of the larger region. The ozone contributions for each monitor are
broken down into contributions from elevated point source emissions and surface emissions
and into contributions from ozone formed under NOx-limited and VOC-limited conditions. The
results for all three monitors show that ozone formation in the area is strongly NOx-limited,
Ozone formation is limited by the amount of available NOx because there are abundant
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January 2014
Figure 5-2. Contribution to the episode maximum 8-hour ozone at the Temple and Italy H.S.
monitors and at the location of the Waco Mazanec monitor during the June 2006 episode.
Figure 5-3. Contribution to the episode average 8-hour ozone at the Temple and Italy H.S.
monitors and at the location of the Waco Mazanec monitor during the June 2006 episode.
biogenic VOCs in the area. Figure 5-4 shows that the VOC emission inventory for the HOTCOG
area is dominated by biogenic VOCs. Therefore, there is always enough VOC available and
anthropogenic NOx is required for ozone to form. Both point sources and surface sources of
NOx contribute to ozone formation at all three monitors. In the HOTCOG area, the contribution
from the sum of the surface sources of NOx emissions (on-road and off-road mobile, area
sources and oil and gas and low points) is larger than the contribution from the elevated point
sources of NOx. This is reasonable given that the sum of NOx emissions from surface sources is
larger than the elevated point source NOx emissions (Figure 5-4).
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January 2014
Figure 5-4. 2006 NOx (upper panel) and VOC (lower panel) emissions for the 6-county
HOTCOG area from the TCEQ emission inventory.
In summary, the source apportionment results show that ozone formation in the HOTCOG area
depends on anthropogenic NOx and that reduction of ozone in the HOTCOG area requires
reduction of NOx emissions through emission control measures.
Figure 5-5 shows the contribution from local sources and transport to the MDA8 at the location
of the Waco Mazanec monitor during the two periods of high ozone during the June 2006
episode. Source apportionment results were not calculated for the low ozone period between
the two episodes. The height of the bar shows the modeled MDA8 for each day. The green
portion of the bar shows the contribution to the Waco Mazanec MDA8 from emissions sources
within the 6-county HOTCOG area. The contribution from emissions sources within Texas but
outside the 6-county area is shown in gray. The contribution from regions outside Texas but
within the 36 km modeling domain shown in Figure 2-15 is shown in light blue. The contribution
of the model initial conditions is shown in purple. The initial conditions contribute 28 ppb on
June 2, and this contribution decreases to less than 2 ppb by June 9. The modeling episode
begins on May 31, and May 31 is the first day for which TCEQ had provided modeling inputs to
the NNAs at the time the 06_wildfires_07 run was made. Typically, a photochemical model is
initialized with a value for all chemical species in all grid cells, and the model is run for a period
during which the influence of the initial conditions declines. This period is known as model
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January 2014
spinup, and the simulation of the period of interest is begun at the end of the spinup period,
when the influence of the initial conditions has decreased to near zero. While a spinup period
is desirable, the lack of spinup does not affect the source apportionment results for the
HOTCOG area unduly, because days with the highest ozone occur after the influence of the
initial conditions has decreased to relatively small levels. The contribution of the boundary
conditions is shown in dark blue. The boundary conditions represent the contributions from
emissions sources outside the U.S. and the contribution from the stratosphere. Once the
contribution from the initial conditions has declined to near zero, the boundary condition
contribution ranges from 16-29 ppb; this is a reasonable range of values for the boundary
condition contribution.
The contribution from local (HOTCOG area) sources varies from day to day during the episode,
ranging from 3 ppb on June 26 to 24 ppb on June 8. The total contribution from sources
outside the HOTCOG area (i.e. transport) is always larger than the local contribution. June 13 is
the day with the highest value of the modeled MDA8 at the Waco monitor location during the
June 2006 episode. On June 13, the Waco Mazanec monitor is brought to an exceedance of the
75 ppb 8-hour standard through the contribution of transport alone. However, as noted above,
the local contribution can be significant. It is this local contribution from HOTCOG emissions
that is amenable to reduction through local emissions controls.
Figure 5-6 shows the breakdown of the local contribution (the green part of the bar in Figure
5-5 by contribution from each emissions source category. The magnitude of the contribution
from each category of emissions fluctuates from day to day. For example, the contribution
from elevated point sources is 9 ppb on June 12, but drops to 2 ppb the following day. The
impact of point sources on the monitor is highly variable because whether the plume from the
point source reaches the monitor depends on the wind direction and the altitude of the plume.
The contributions from source categories that are distributed in space, such as non-road
mobile, are less variable in time.
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January 2014
Figure 5-5. Contribution to daily maximum 8-hour ozone by source region for the location of
the CAMS 1037 Waco Mazanec monitor.
Figure 5-6. Waco monitor location ozone source apportionment by emissions category for
the local contribution shown in green in Figure 5-5.
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The Waco monitor episode maximum and episode average ozone contributions from each
emissions source category are shown in Figure 5-7 and Figure 5-8, respectively. The largest
value of the maximum contribution comes from on-road mobile source category (~10 ppb) and
occurs on June 9. On-road mobile sources also make the largest episode average contribution
to ozone at the Waco monitor location (Figure 5-8). The next largest value of the maximum
contribution comes from elevated point sources. It is reasonable that the Waco monitor
location should have a large maximum contribution from point sources, because there are two
large EGUs, the Limestone and Big Brown facilities located nearby (Figure 5-9). Elevated point
sources are the largest component of the 6-county area’s NOx emission inventory. The episode
average contribution from elevated point sources is lower than that of on-road and off-road
mobiles sources, despite the fact that its NOx emissions are larger. This is because
contributions from elevated points are more dependent on the wind direction, while on-road
and off-road mobile sources are distributed more evenly across the 6-county area and so make
a more consistent contribution to ozone at the Waco monitor.
Oil and gas sources make the third largest maximum contribution to ozone at the Waco
monitor (Figure 5-7) and make the 4th largest contribution to the episode average value. Total
NOx emissions from oil and gas are 4th largest source category, following elevated points and
on-road and off-road mobile, which is consistent with the episode average ozone contribution
results. The episode maximum contribution for oil and gas is larger than that of off-road
sources, which has larger total emissions, but is a more evenly distributed across the HOTCOG
area. In the 6-county area, natural gas production is concentrated in Limestone and Freestone
Counties (ENVIRON, 2012). Therefore, the contribution of oil and gas sources to ozone at the
Waco monitor location depends on whether the wind direction is favorable for transport from
Limestone and Freestone to the monitor. When the ozone plume from this smaller but more
concentrated source affects the Waco monitor location, it has a higher maximum impact than
the larger off-road mobile emissions source, which is more evenly distributed and has a more
diffuse plume.
Figure 5-7 shows that oil and gas sources can have a significant effect on ozone at the Waco
monitor. In Figure 5-10, the contribution from gas compressor engines to the 2006 HOTCOG
area NOx emission inventory is shown. In Freestone and Limestone Counties, gas compressor
engines are the largest component of the oil and gas NOx emission inventory. Gas compressor
engines are used to extract natural has from a well when reservoir pressures alone are
insufficient to bring the gas to the surface. Compressor engines are also used to transmit
natural gas along pipelines from the well to gas processing plants and then to the consumer. In
a mature gas field, such as those found in Freestone and Limestone Counties, the need for
compression to produce the gas increases over time as the subsurface gas reservoir is drained
and reservoir pressures drop. Figure 5-11 shows the estimated average natural gas production
per gas well in the three HOTCOG counties with significant natural gas production: Hill,
Limestone and Freestone. The estimate was derived by dividing the natural gas production in
each county for a given year by the number of active gas wells in that county during that year.
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Figure 5-7. Episode maximum contribution to the Waco Mazanec monitor location ozone
from HOTCOG 6-county Area emissions.
Figure 5-8. Episode average contribution to the Waco Mazanec monitor location ozone from
HOTCOG 6-County area emissions.
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Figure 5-9. Location point sources of NOx in the HOTCOG 6-county area in the 2006 TCEQ
emission inventory.
Figure 5-11 indicates that the natural gas production per well is declining over time in all three
counties, which may indicate an increasing need for compression. The potential effect of
decreasing per well production on gas compressor NOx emissions is unclear.
In March, 2010, the East Texas Combustion Rule went into effect. The East Texas Combustion
Rule requires owners and operators of stationary, rich-burn gas-fired, reciprocating internal
combustion engines greater than or equal to 240 HP in 33 East Texas counties (including
Limestone and Freestone Counties) to meet NOx emission limits and follow specified reporting
requirements.
The TCEQ has developed an oil and gas emission inventory for the year 2011. This inventory
accounts for the East Texas Combustion Rule, but applies engine population data for the
Barnett Shale to all 33 East Texas counties affected by the East Texas Combustion Rule. Given
differences in the field ages, operational differences, formation depths and gas/liquid
composition, it is unlikely that the engine population distributions, NOx emissions factors and
load factors for the Barnett Shale are applicable to the to conventional production in the
HOTCOG area. Comprehensive, recent data on engine population are critical for the
development of an accurate effect of the East Texas Combustion Rule on emissions or
accurately estimate emissions of ozone precursors from compressor engines in the HOTCOG
area.
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January 2014
Figure 5-10. Contribution of gas compressor engines to oil and gas NOx in the 2006 TCEQ
emission inventory for the HOTCOG area.
Figure 5-11. Trends in Natural Gas Well Productivity in the HOTCOG Area.
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Figure 5-12 shows the episode average contributions to the Waco Mazanec monitor location
from local sources (emissions sources within the 6-county area), sources within Texas but
outside the HOTCOG area, sources outside Texas, and the sum of contributions from initial and
boundary conditions. The sum of contributions from initial and boundary conditions may be
taken as an estimate of the contribution to HOTCOG area ozone from sources outside the U.S.
and from the stratosphere. This contribution is on average about 20 ppb. For the Waco
monitor location, the sum of contributions from IC+BC and sources within and outside of Texas
far exceeds the local contribution from 6-county area emissions. The model results therefore
show the area to be strongly affected by transport. While the model performance evaluation in
Section 3 indicates that the effects of ozone transport into Texas may be overstated in the
model, the results overwhelmingly suggest that the contribution from transported is larger than
the local contribution. The local contribution, which is the portion that is amenable to
reduction through local emissions control measures, is approximately 10 ppb. The daily source
apportionment results for the 2006 episode indicate that on average, transport dominates
Waco ozone, but that on a given day, the local HOTCOG contribution can exceed 20 ppb (Figure
5-5). Therefore, local controls may affect the area’s attainment status by reducing the HOTCOG
contribution to ozone at the Waco monitor location.
Figure 5-12. Episode average contributions to MDA8 ozone at the Waco Mazanec monitor
location from local 6-county area emissions sources, the sum of initial conditions and
boundary conditions (IC+BC), sources within Texas but outside the 6-county area (Texas), and
sources within the 36 km grid but outside of Texas (outside TX).
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6.0 REVISED 2006 OZONE MODEL
During 2013, several updates were made to the CAMx model as well as to the TCEQ’s Rider 8
modeling platform. We ran the model in its new configuration (run 06_newbase_10) and reevaluated model performance at the same central Texas monitors used in the evaluation of the
base case (Section 3) and updated (Section 4) versions of the model. In this section, we
describe the updates to the model and the model performance evaluation of the run.
6.1 Model Configuration
6.1.1 Additional Days of Modeling Inputs
The June 2006 episode runs from May 31-July 2. In 2012, the TCEQ supplied input files for
running CAMx (e.g. emissions, meteorology) for these dates. This did not allow for the model
to spin up before the May 31 start date, which caused the initial conditions to influence the
results during the first few days of the simulation (see Section 5.2.1). In 2013, the TCEQ
provided model inputs for May 28-May 30 to allow for model spin up. Accordingly, the revised
HOTCOG model run began on May 28 and we analyzed the model results from May -31-July 2
as in the two previous runs.
6.1.2 New TCEQ 36 km Modeling Grid
During 2013, the TCEQ revised their modeling grid system. The 36 km grid was expanded from
the grid shown in Figure 2-15 to that shown in Figure 6-1. The new grid matches the definition
of the RPO modeling grid. The TCEQ also supplied revised boundary conditions for the new 36
km grid.
Figure 6-1. TCEQ 36/12/4 km CAMx nested modeling grids for the Texas ozone modeling of
June 2006. 36 km grid is outlined in black. The 12 km grid outlined in blue, and the 4 km grid
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is outlined in green. TCEQ figure from
http://www.tceq.texas.gov/airquality/airmod/rider8/modeling/domain.
6.1.3 Cloud Kv Patch
In run 06_newbase_10, we applied the Kv100 patch as in the two previous runs (06_base_01
and 06_wildfires_07); however, in 06_newbase_10, we applied an additional adjustment to the
vertical diffusivity coefficients that are an input for CAMx.
Where clouds are active, deep cumulus convection mixes chemical species through the rapid
transport of air parcels from the boundary layer to the upper troposphere. The upward
transport of air parcels occurs in narrow “hot towers” that have a horizontal spatial scale of 1
km or less. Parcel buoyancies and vertical velocities are large (flow is non-hydrostatic) within
the cumulus tower. When the atmosphere becomes unstable, convection acts to reduce the
vertical gradient of moist static energy, thereby destroying the instability. Convective mixing
also acts to minimize the vertical gradient of tracer species as near-surface air is lofted into the
free troposphere and upper tropospheric air is brought rapidly toward the surface through the
action of convective downdrafts or more slowly through the effects of large-scale subsidence in
the region surrounding convective towers. Deep convection is difficult to simulate in a grid
model because of the small horizontal scale of the cumulus towers.
The 4 km resolution of the fine grid in the June 2006 modeling platform is too coarse a
resolution to accurately characterize transport within convective updrafts and downdrafts and
is too fine a resolution for the use of a cumulus parameterization. In the June 2006 modeling,
the WRF model meteorological model simulates convection explicitly on the 4 km grid (i.e. no
cumulus parameterization is used) and on the 12 km and 36 km grids, the Kain-Fritsch cumulus
parameterization is used. Sub-grid scale convective mass fluxes may be calculated within the
WRF model, depending on which cumulus parameterization is used, but the mass fluxes are not
passed to CAMx. Although the presence of convection in a given grid cell can be diagnosed
from the large-scale meteorological fields or from the presence of convective precipitation,
there is no direct simulation of convective transport in CAMx; therefore this source of tracer
species to the upper troposphere is missing in the model. This can cause ozone and precursor
species to be overestimated in the boundary layer.
The application of a cloud vertical diffusivity (Kv) patch developed in a recent ENVIRON-TCEQ
project (ENVIRON, 2010) increases transport of air from the planetary boundary layer into the
free troposphere and up to the cloud top within cloudy grid cells. The Kv cloud patch extends
diffusive mixing upward beyond the planetary boundary layer to the cloud top using the OB70
technique. The OB70 mixing depth is set to the top of the cloud and the Kv at the top is set to 1
m2s-1. At night, the PBL drops to the stable boundary layer and separates from remaining cloud
bases, and switches the patch off. This patch attempts to approximate the effects of
convection by increasing vertical diffusion and was shown to improve surface layer ozone in a
recent project performed by ENVIRON and the TCEQ (Kemball-Cook et al., 2013). Therefore,
the Cloud Kv patch was applied in HOTCOG’s ozone modeling.
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6.1.4 Boundary Condition Patch
A global chemistry-transport model was run to provide boundary conditions for the 36 km grid
in the June 2006 model. Global- and regional-scale models typically have too much ozone over
the ocean. Several theories have been proposed for why this overabundance of ozone exists
(e.g. McFiggans et al., 2000 and Read et al., 2008), but since no scientific consensus currently
exists, we use an ad hoc adjustment to the 36 km grid boundary conditions to minimize the
effect of global model bias on the HOTCOG CAMx simulation. Therefore, we perform a flat 10
ppb ozone reduction and apply various caps to ozone precursors (Table 6-1) in all 36 km grid
cells located over the Gulf of Mexico and Atlantic Ocean in order to deplete the ozone coming
onshore.
Table 6-1. Maximum concentration limits for ozone precursors applied to the 36 km boundary
condition grid cells across the Gulf of Mexico, Caribbean Sea, and Atlantic Ocean south of
Cape Hatteras.
Species
NO2
Nitrogen dioxide
Max.
Concentration
(ppb)
0.05
CO
Carbon monoxide
150.0
N2O5
Dinitrogen pentoxide
0.001
HNO3
Nitric acid
0.25
PNA
Peroxynitric acid
0.001
H2O2
Hydrogen peroxide
0.5
NTR
Organic nitrates
0.01
FORM
Formaldehyde
0.25
ALD2
Acetaldehyde
0.05
ALDX
Propionaldehyde and higher aldehydes
0.02
PAR
Paraffin carbon bond (C-C)
1.0
OLE
Terminal olefin carbon bond (R-C=C)
0.01
ETHA
Ethane
1.0
MEPX
Methylhydroperoxide
0.1
PAN
Peroxyacetyl Nitrate
0.01
PANX
C3 and higher peroxyacyl nitrate
0.001
INTR
Organic nitrates from ISO2 reaction with NO
0.001
ISOP
Isoprene
0.1
ISPD
Isoprene product (lumped methacrolein, methyl vinyl ketone, etc.)
0.1
TERP
Monoterpenes
0.05
ISP
Isoprene (SOA chemistry)
0.1
TRP
Monoterpenes (SOA chemistry)
0.05
TOL
Toluene and other monoalkyl aromatics
0.02
XYL
Xylene and other polyalkyl aromatics
0.01
SO2
Sulfur dioxide
0.1
PRPA
Propane
0.5
Description
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ACET
Acetone
0.25
KET
Ketone carbon bond (C=O)
0.05
BENZ
Benzene
0.1
6.1.5 New CAMx Version
CAMx v6.10 was used in the 06_newbase_10 modeling. The changes in v6.10 relative to v5.41
(used in 06_wildfires_07) that are directly relevant to this modeling are (ENVIRON, 2013):
1. Gas-phase chemistry mechanism 2 has been updated from CB6r1 to CB6r2. A
description of the CB6r2 mechanism is given in Section 6.1.2 below.
2. The Plume-in-Grid (PiG) submodel has been extensively updated to include a condensed
gas-phase chemical mechanism for GREASD PiG, reduced nocturnal puff growth rates,
and other minor updates to improve performance and diagnostic output. Reduced
nocturnal growth rates lead to puff sizes in better agreement with in situ aircraft
measurements, and prolonged puff life for both GREASD and IRON PiG. These updates
can result in a wide range of ozone, precursor, and PM impacts on the grid, especially
for coarser grids that allow for extended puff lifetimes.
3. Revised Fortran binary I/O file format. All major gridded input/output fields are now in
a common format; this includes all input meteorology, landuse, IC/BCs, emissions, and
output concentration/deposition fields.
4. Revised the process to generate photolysis rates and their use in CAMx.
6.1.6 CB6r2 Chemical Mechanism
Organic nitrates (ONs) are formed when VOCs degrade in the presence of NOx and are
important in the atmosphere because they sequester NOx and can contribute to organic
aerosol (OA). NO2 is released when ONs degrade by photolysis in the gas-phase, returning NOx
to the atmosphere where it may contribute to ozone production. Revision 2 of Carbon Bond 6
(CB6r2) differentiates organic nitrates between simple alkyl nitrates that remain in the gasphase and multi-functional ONs that can partition into OA. Uptake of multi-functional ONs by
OA was added to CAMx for CB6r2. ONs present in aerosols are then assumed to undergo
hydrolysis to nitric acid with a lifetime of approximately 6 hours based on laboratory
experiments and ambient data. These changes tend to reduce regional concentrations ozone
and ONs, and increase nitric acid. Regional modeling simulations using CAMx with CB6r2
showed that accounting for ON hydrolysis in aerosols improved performance for ozone and in
simulating the partitioning of NOy between ONs and nitric acid (Hildebrandt Ruiz and Yarwood,
2013). Sensitivity testing done under the AQRP project showed that the high bias in modeled
ozone was reduced in the June 2006 episode as result of the use of CB6r2, so this change was
made in HOTCOG’s ozone model.
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6.1.7 Emission Inventory
In this section, we describe several changes to the emission inventory used in the
06_wildfires_07 run. These changes were incorporated into the emission inventory used in the
06_newbase_10 run.
6.1.7.1 Revised TCEQ Emission Inventory
The TCEQ updated the emission inventory for the June 2006 files during the spring of 2013. The
changes to the NOx emission inventory were relatively small and are shown in Figure 6-2.
NOx Emissions (tpd) for June 8, 2006
12,000
Emissions (tpd)
10,000
8,000
6,000
Old Base Case
4,000
New Base Case
2,000
0
Figure 6-2. NOx emission inventory comparison by region for June 8, 2006 (weekday) for new
and old base cases.
Figure 6-3. Geographic regions used in the emissions comparison shown in Figure 6-2.
The TCEQ also replaced the biogenic emission inventory. The TCEQ used the GloBEIS model
(Yarwood et al., 2008) to calculate the biogenic emissions in the 06_base_01 and
06_wildfires_07 runs. In 2013, the TCEQ elected to use the MEGAN model v2.10 (Guenther et
al., 2006) to calculate biogenic emissions for the June 2006 episode. Photosynthetically active
radiation (PAR) was derived using EPA’s Meteorology-Chemistry Interface Processor (MCIP)
with a 0.45 multiplicative factor applied to the downward radiation at the earth’s surface. The
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Leaf Area Index (LAI) was derived from the MODIS MCD15A2 product (Doug Boyer, TCEQ,
personal communication, 2013).
6.1.7.2 Lightning NOx Emission Inventory
As part of a project performed by ENVIRON for the TCEQ, ENVIRON added two additional
sources of NOx emissions to TCEQ’s SIP modeling emission inventory: lightning NOx (LNOx)
emissions and aircraft cruise emissions. These sources are not included in the SIP modeling
emission inventory because they do not have a large impact on ground level ozone, but were
necessary for the project because model comparison against aloft aircraft measurements and
satellite NO2 column data were performed. The new emission inventory components were
included in the HOTCOG model. Although they do not have a large effect on surface ozone
(Kemball-Cook et al., 2012), they allow a more complete characterization of the NOx emission
inventory. We describe the LNOx emission inventory in this section and the aircraft emission
inventory in the next section.
NOx is formed in lightning channels as the heat released by the electrical discharge causes the
conversion of N2 and O2 to NO. Lightning NOx emissions (LNOx) can be estimated directly
based on the number of lightning flashes, the intensity of each flash, the lightning type (cloudto-ground vs. cloud-to-cloud), and the amount of NOx emitted per flash. ENVIRON incorporated
LNOx emissions that were developed using the parameterization of Koo et al. (2010) and
distributed in the vertical using the profiles of Ott et al. (2010). Koo et al. (2010) estimated
annual total LNOx emissions for North America using National Lightning Detector Network flash
data from Orville et al. (2002) and (Boccippio et al., 2001). The NO emissions factor that
determines the amount of NO generated per flash of lightning is taken from the EULINOX study
(Holler and Schumann, 2000) and is 9.3 kg N per flash. Using these data, Koo et al. estimate the
total LNOx emissions for North America to be 1.06 Tg N year-1 . Lightning emissions are then
allocated to grid cells where modeled convection occurred using convective precipitation as a
proxy for lightning activity. The hourly and gridded 3-D lightning NO emissions are calculated
as follows:
𝐸(𝑥, 𝑡) = 𝑅𝑁𝑂 𝑃𝐶 (𝑥, 𝑡)𝐷(𝑥, 𝑡)𝑝(𝑥, 𝑡)
where E(x,t) is the NO emission rate (mol hr-1) at time t and grid location x; RNO is the NO
emission factor; PC is the convective precipitation (m hr-1) at time t and grid location x; D(x,t) is
the convective cloud depth (m) at time t and grid location x; and p(x,t) is the pressure (Pa) at
time t and grid location x. Constraining the total emissions within North America to 1.06 Tg N
year-1 requires that RNO be equal to 3.9x10-12.
This parameterization was used to generate emissions for the May 31-July 2, 2006 episode.
Because the TCEQ 4 km WRF simulation for this episode did not employ a cumulus
parameterization, the results from the 12 km grid, which did use a cumulus scheme, were used
on the 4 km grid. Lightning NOx emissions were distributed in the vertical using the profiles of
Ott et al. (2010). A description of the Ott et al. (2010) profiles is given in Kemball-Cook et al.
(2012). The emissions were modeled as point sources injected into each model grid cell with
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zero plume rise. Relatively little LNOx is generated over Texas during June 1-15 and June 24-July
2; far more LNOx is emitted during the low ozone period from June 16-June 22.
6.1.7.3 TCEQ Aircraft Emission Inventory
The TCEQ recently developed a new single-day aircraft emission inventory for use in the June
2006 modeling episode (Doug Boyer, TCEQ, personal communication, 2013). The emission
inventory was developed using the Advanced Emission Model (AEM3) developed at the
EUROCONTROL Experimental Centre (http://www.eurocontrol.int/services/advanced-emissionmodel). For climb, cruise and descent phases of flight at altitudes greater than 3,000 feet,
AEM3 estimates fuel burn and aircraft emissions based on user-specified aircraft and engine
types and aircraft flight profiles (the aircraft’s path in time and space). AEM3 calculates
emissions for a set of pollutants, including NOx, for each flight segment for each aircraft, and
uses an updated version of the Boeing Method 2 (EEC-BM2). Boeing Method 2 is used to
compute emission factors, fuel flow and emissions with consideration for atmospheric and
flight conditions. For altitudes less than 3,000 feet, AEM3 does not use flight trajectory data to
calculate fuel burn and emissions with Boeing Method 2, but instead uses the landing and takeoff (LTO) cycle defined by the International Civil Aviation Organization (ICAO). The LTO cycle
consists of four modes of aircraft operation (takeoff, climb, approach, and taxi) with specific
thrust settings and time in flight mode defined for each part of the cycle. Emissions from AEM3
for altitudes < 3,000 feet were not used because these emissions are accounted for in the
airport emission inventories in the TCEQ’s June 2006 SIP modeling inventory.
The TCEQ used NASA’s Flight Track Database (http://www-angler.larc.nasa.gov/flighttracks/) to
supply the flight profile data required for input to AEM3. This database contains detailed flight
information for every commercial flight in the U.S. on the June, 2006 day for which the
inventory was developed. AEM3 was then used to calculate the emissions for all flights and
flight segments. The TCEQ then ran the EPS3 emissions model for each unique flight segment
for each hour using the PRESHP/PSTSHP link-based module to preserve the spatial detail
inherent in the inventory (Doug Boyer, TCEQ, personal communication, 2013).
The addition of the aircraft NOx emissions and lightning NOx emissions did not have a large
effect on the modeled ozone, NO2 or NOx at ground level (Kemball-Cook et al., 2013). Changes
in surface layer ozone due to the additional NOx emissions are relatively small overall and in
the expected direction (increased ozone), and the spatial pattern of ozone changes is consistent
with the distribution of the LNOx and aircraft emissions. The addition of aircraft and lightning
NOx emissions to the model allows for comparison with aircraft flight measurements in the free
troposphere. It also allows the model to produce NO2 column amounts that can be compared
with observed satellite NO2 columns. Following the addition of the aircraft and lightning NOx
emissions, the model continued to produce reasonable results at the surface, so these emission
inventories were integrated into HOTCOG’s ozone model. These sensitivity tests are
documented in Kemball-Cook et al. (2012).
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6.1.7.4 Fire Emission Inventory
The fire emission inventory was updated from the NCAR fire emission inventory described in
section 4.3.1 to the FINN (Fire Inventory from NCAR; Wiedinmyer et al., 2011) version 1
dataset. The FINN inventory improves upon the original NCAR fire emission inventory by
providing more detailed speciation profiles that are specific to the landuse type at each fire
location (savanna/grasslands, shrubs, tropical forest, temperate forest, boreal forest, and
agriculture), rather than using the tropical forest speciation profile of Karl et al. (2007) for all
fires.
No fuel loading data were provided in the FINN inventory. However, ENVIRON found a strong
linear relationship (96 % correlation) between the daily NOx emissions and fuel loading in the
original 2006 NCAR fire dataset; therefore, a regression was used to estimate the fuel loading in
the FINN inventory based on the NOx emissions so that the vertical and temporal emissions
allocations could be computed using the same WRAP method that was used for the NCAR
emission inventory (section 4.4.3).
6.2 Model Performance Evaluation
The June 2006 episode was run with the model in the configuration described above. A model
performance evaluation was performed for the updated run, 06_newbase_10, in the same
manner as the two previous runs. Figure 6-4 shows the observed and modeled 1-hour ozone
time series and the normalized bias for the 06_base_01, 06_wildfires_07 and 06_newbase_10
runs for the Temple monitor during episode 1. The 06_newbase_10 run shows a reduction in
bias relative to the 06_wildfires_07 run on all episode days when ozone was high enough for
the bias to be calculated and shows a reduction in bias relative to the 06_base_01 on 11 of 15
days. The time series shows that the daily ozone maxima are better simulated in the
06_newbase_10 run than in the two previous runs in that the peak ozone values are generally
lower and closer to the observed maxima. The normalized bias in the 06_newbase_10 run is
within the benchmark on 11 of 15 episode days. The 06_newbase_10 nighttime values are
often lower and closer to the observations than the 06_wildfires_07 run, but are often not as
low as the 06_base _01 run. The reason for this is not clear. For the first episode, the
06_newbase_10 represents a clear improvement in performance relative to the two previous
runs. This is not true for the second episode at the Temple monitor (Figure 6-5). On 7 of 8
episode days for which bias was calculated, the 06_newbase_10 run had a smaller bias than the
06_wildfires _07 run; however the bias in the 06_newbase_10 run is smaller than the bias in
the 06_base_01 run on only one day and the bias in the 06_newbase_10 run lies within the
benchmark on only 4 of 8 days. Simulation of the nighttime minima is most accurate in the
06_base_01 run.
At the Italy H.S. monitor (Figure 6-6), the 06_newbase_10 run shows a reduction in bias relative
to the 06_wildfires_07 and 06_base_01 runs on 13 of 16 episode days. The largest reduction in
bias occurs during the period June 7-14. The normalized bias in the 06_newbase_10 run is
within the benchmark on 14 of 16 episode days. During the second episode (Figure 6-7), the
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January 2014
normalized bias in the 06_newbase_10 run is less than in the 06_wildfires_07 run on all days
and is within the benchmark on 4 of 9 episode days.
In the 06_newbase_10 run, performance at the Palestine monitor is not as good as at the
Temple and Italy H.S. monitors, as was true for the other two model runs. At the Palestine
monitor, the normalized bias in the 06_newbase_10 run is within the benchmark on 6 of 16
episode days during the first episode. During the second episode, the normalized bias in the
06_newbase_10 run is within the benchmark on 4 of 9 episode days. However, for both
episodes, the 06_newbase_10 run has smaller bias than the 06_wildfires_07 run on nearly all
days.
6.2.1.1 Summary of Model Performance Evaluation
The 06_newbase_10 run performs better than the 06_wildfires_07 run in simulating observed
ground level ozone; the high bias that was so pronounced in the 06_wildfires_07 run is
significantly reduced in the 06_newbase_10 run. Testing done as part of the AQRP Project
(Hildebrandt Ruiz and Yarwood, 2013) indicates that this improvement is due to the
introduction of the CB6r2 chemical mechanism that replaces the CB6r1 mechanism used in the
06_wildifres_07 run. The reduction of NOx recycling in CB6r2 relative to CB6r1 reduces ozone
region-wide and improves performance at the monitors in the vicinity of the HOTCOG area.
Although model performance improved significantly, and the model is within the performance
benchmark at these monitors on most episode days, a high bias is still present in the model in
the HOTCOG area and beyond.
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Figure 6-4. Upper panel: observed 1-hour ozone (black) at the Temple CAMS 651 monitor
versus modeled 1-hour average surface layer ozone (06_base_01 [blue], 06_wildfires_07
[green] and 06_newbase_10 [red]) during May 31-June 15, 2006. Lower panel: mean
normalized bias (MNB) for the Temple CAMS 651 monitor. Bar colors are as in upper panel.
Red lines show ±15% EPA (1991) benchmarks.
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January 2014
Figure 6-5. Upper panel: observed 1-hour ozone (black) at the Temple CAMS 651 monitor
versus modeled 1-hour average surface layer ozone (06_base_01 [blue], 06_wildfires_07
[green] and 06_newbase_10 [red]) during the June 23-July 2, 2006 period. Lower panel:
mean normalized bias (MNB) for the Temple CAMS 651 monitor. Bar colors are as in upper
panel. Red lines show ±15% EPA (1991) benchmarks.
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January 2014
Figure 6-6. Upper panel: observed 1-hour ozone (black) at the Italy H.S. CAMS 650 monitor
versus modeled 1-hour average surface layer ozone (06_base_01 [blue], 06_wildfires_07
[green] and 06_newbase_10 [red]) during May 31-June 15, 2006. Lower panel: mean
normalized bias (MNB) for the Italy H.S. CAMS 650 monitor. Bar colors are as in upper panel.
Red lines show ±15% EPA (1991) benchmarks.
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January 2014
Figure 6-7. Upper panel: observed 1-hour ozone (black) at the Italy H.S. CAMS 650 monitor
versus modeled 1-hour average surface layer ozone (06_base_01 [blue], 06_wildfires_07
[green] and 06_newbase_10 [red]) during June 23-July 2, 2006. Lower panel: mean
normalized bias (MNB) for the Italy H.S. CAMS 650 monitor. Bar colors are as in upper panel.
Red lines show ±15% EPA (1991) benchmarks.
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January 2014
Figure 6-8. Upper panel: observed 1-hour ozone (black) at the Palestine CAMS 647 monitor
versus modeled 1-hour average surface layer ozone (06_base_01 [blue], 06_wildfires_07
[green] and 06_newbase_10 [red]) during May 31-June 15, 2006. Lower panel: mean
normalized bias (MNB) for the Palestine CAMS 647 monitor. Bar colors are as in upper panel.
Red lines show ±15% EPA (1991) benchmarks.
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January 2014
Figure 6-9. Upper panel: observed 1-hour ozone (black) at the Palestine CAMS 647 monitor
versus modeled 1-hour average surface layer ozone (06_base_01 [blue], 06_wildfires_07
[green] and 06_newbase_10 [red]) during June 23-July 2, 2006. Lower panel: mean
normalized bias (MNB) for the Palestine CAMS 647 monitor. Bar colors are as in upper panel.
Red lines show ±15% EPA (1991) benchmarks..
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7.0 SENSITIVITY TESTS WITH TCEQ 2012 EMISSION INVENTORY
During the summer of 2013, the TCEQ made a 2012 anthropogenic emission inventory available
to ENVIRON for use in the development of an ozone forecasting system for the State of Texas
(Johnson et al., 2013). In this section, we describe the use of this 2012 emission inventory in
the 2006 modeling system described in the previous sections of this report. Using the 2012
inventory in the 2006 modeling platform allowed us to assess how emissions changes from
2006 to 2012 affect Texas ozone under the meteorological conditions of June 2006, and to test
the sensitivity of modeled ozone to changes in HOTCOG area emissions using the 2012
emissions scenario.
7.1 Model Configuration
We developed a typical day ozone model for the June 2006 episode conditions using the
06_newbase_10 modeling platform together with the TCEQ 2012 anthropogenic emissions.
Note that this is different from development of a 2012 ozone model. In order to develop a true
2012 episode, a meteorological model such as WRF would be run for the 2012 period of
interest and day-specific biogenic, wildfire and EGU emission inventories for 2012 would be
required. Instead, we adapted the existing June 2006 modeling platform used for the
06_newbase_10 run by removing the 2006 anthropogenic emissions for the 36/12/4 km grids
and substituting TCEQ’s 2012 anthropogenic emissions. We continued to use the day-specific
2006 biogenic and wildfire emissions and the same June 2006 boundary conditions for the 36
km grid and albedo-haze-ozone input file. This simulation is referred to below as the
12_baseline_16 run,
7.2 Emissions
The TCEQ has developed an anthropogenic emission inventory for June 2012 that is specific to
the day of the week. The TCEQ inventory uses 2012 summer quarter hourly average emissions
for power plants that report day-specific hourly emissions to EPA’s Acid Rain Program Clean Air
Markets Database (CAMD). For Texas point sources that do not report to the CAMD, emissions
were extracted from the State of Texas Air Reporting System (STARS) data base. Emissions for
point sources outside of Texas were taken from the EPA’s 2008 National Emission Inventory
(NEI). The on-road portion of the inventory was developed using the Motor Vehicle Emission
Simulator emissions model (MOVES; www.epa.gov/otaq/models/moves/). The EPA NONROAD
model (www.epa.gov/otaq/nonrdmdl.htm) was the basis for developing much of the non-road
inventory.
Table 7-1 lists the files provided by the TCEQ for use in modeling 2012. The files are grouped by
emissions source type. In Table 7-1, {GRID} represents either the 4 km, 12 km or 36 km
modeling domain (tx_4km, tx_12km and rpo_36km) and {DOW} represents the day of the
week: weekday, Friday, Saturday, and Sunday (wkd, fri, sat, sun). Oil and gas, area, off-road and
point sources have no day of week variation. Only on-road sources distinguish between
weekday and Fridays.
89
January 2014
The TCEQ provided the 2012 anthropogenic emission inventory component files in model-ready
format. Subsets of these files were merged by source type for APCA modeling. The files in
Table 7-1 that are preceded with an arrow () were adjusted by ENVIRON in order to
incorporate a new emission inventory for natural gas development in the Haynesville Shale in
Northeast Texas and Louisiana. These emissions changes will not affect source apportionment
results for ozone impacts of HOTCOG area emissions, but may have a very small effect on ozone
transported into the HOTCOG area during periods of northeasterly winds. More information on
the Haynesville Shale (HS) emission inventory and how the inventory was prepared for
photochemical modeling may be found in Bar-Ilan et al. (2013) and Kemball-Cook et al. (2014).
Table 7-1. The TCEQ 2012 emission inventory files. A leading arrow (->) indicates that the
TCEQ file was not directly used in the final 2012 modeling inventory.
Area Sources
lo_ar.grdem.cb6p.{DOW}.{GRID}.area12_v1.13Jun12
lo_ar.grdem.cb6p.{DOW}.{GRID}.area2012_nei2011v1_noTX.13Oct10
lo_ei.anthro.cb6p.{DOW}.{GRID}.base12.mexico
lo_ei.anthro.cb6p.{DOW}.{GRID}.base2012.canada
lo_area.grdem.cb6.area_tx_bline_fires.{GRID}.TX.wkd
Oil & Gas Production Sources
lo_ar.grdem.cb6p.wkd.{GRID}.area12a_oilgasp.BtS.13Aug08
lo_ar.grdem.cb6p.wkd.{GRID}.area12a_oilgasp.EFS.13Aug08
lo_ar.grdem.cb6p.wkd.{GRID}.area12a_oilgasp.HS.13Aug081
lo_ar.grdem.cb6p.wkd.{GRID}.area12a_oilgasp.PB.13Sep06
lo_ar.grdem.cb6p.wkd.{GRID}.area12a_oilgasp.tx_other.13Aug08
lo_ar.grdem.cb6p.wkd.{GRID}.oilgasp2012_nei2011v1_noTX.13Oct21
lo_ar.grdem.cb6p.wkd.{GRID}.offshore_oilgasp08.13Feb19
Off-road Sources
lo_nr.grdem.cb6p.{DOW}.{GRID}.NONROAD_12_b12b.13Jun10
lo_nr.grdem.cb6p.{DOW}.{GRID}.nmim08a.2012_noTX.12Sep11
lo_nr.grdem.cb6p.wkd.{GRID}.NONROAD_12_b12_Drill_Rigs.13Apr102
lo_nr.grdem.cb6p.wkd.{GRID}.airports12_b8_dfw10co.13Jun13
lo_nr.grdem.cb6p.wkd.{GRID}.by12a_nei2008v2_noTX_airports.13Sep10
lo_nr.grdem.cb6p.wkd.{GRID}.by2012_nei2008v2a_noTX_harbor_vessel_inport_limited.13Sep04
lo_nr.grdem.cb6p.wkd.{GRID}.by2012_nei2008v2a_noTX_harbor_vessel_underway_limited.13Sep04
lo_nr.grdem.cb6p.wkd.{GRID}.by2012_nei2011v1_noTX_loco.13Oct01
lo_nr.grdem.cb6p.wkd.{GRID}.fy12_nei2008v2_noTX_switchers.13Jun05
lo_nr.grdem.cb6p.wkd.{GRID}.OFFR12_b8a_att_ships.13Sep05
lo_nr.grdem.cb6p.wkd.{GRID}.OFFR12_b9a_linehaul_c1.13Jan02
lo_nr.grdem.cb6p.wkd.{GRID}.OFFR12_b9a_linehaul_c2.13Jan02
lo_nr.grdem.cb6p.wkd.{GRID}.OFFR12_b9a_switcher.13Jan02
lo_nr.grdem.cb6p.wkd.{GRID}.att_airport12_b8b.13Mar25
lo_nr.grdem.cb6p.wkd.{GRID}.HGairport12_b8b_8co.13Mar25
lo_nr.grdem.cb6.wkd.{GRID}.GWEInonplatform2005noCM.11Jul18
90
January 2014
On-road Sources
lo_mv.grdem.cb6.{GRID}.mvs10a_link.dfw_10co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6.{GRID}.mvs10a_link.hh_2co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6p.{GRID}.mvs10a_hpms.dfw_10co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6p.{GRID}.mvs10a_hpms.hh_2co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6p.{GRID}.mvs10a_idle.dfw_10co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6p.{GRID}.mvs10a_idle.hh_2co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6p.{GRID}.mvs10a_offn.dfw_10co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6p.{GRID}.mvs10a_offn.hh_2co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6p.{GRID}.mvs10a_hpms.etx_98co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6p.{GRID}.mvs10a_hpms.wtx_144co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6p.{GRID}.mvs10a_idle.etx_98co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6p.{GRID}.mvs10a_idle.wtx_144co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6p.{GRID}.mvs10a_offn.etx_98co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6p.{GRID}.mvs10a_offn.wtx_144co_2012_sum_$DOW.13May02
lo_mv.grdem.cb6p.{GRID}.mvs10b_dflt.notx_alo_2012_jul_$DOW.13May07
lo_mv.grdem.cb6p.{GRID}.mvs10b_dflt.notx_ctz_2012_jul_$DOW.13May07
lo_mv.grdem.cb6p.{GRID}.mvs10b_dflt.notx_mtz_2012_jul_$DOW.13May07
lo_mv.grdem.cb6p.{GRID}.mvs10b_dflt.notx_etz_2012_jul_$DOW.13May07
lo_mv.grdem.cb6p.{GRID}.mvs10b_dflt.notx_ptz_2012_jul_$DOW.13May07
Stationary Point Sources - Surface
lo_pt.grdem.cb6p.080603.{GRID}.gwei2008a.13Jun19
lo_pt.grdem.cb6p.080611.{GRID}.ar_osd_b2008a.13Oct16
lo_pt.grdem.cb6p.080611.{GRID}.la_osd_b2008a.13Oct16
lo_pt.grdem.cb6p.080611.{GRID}.ok_osd_b2008a.13Oct16
lo_pt.grdem.cb6p.120606.{GRID}.tx_osd_bcl12a.mard_dfw_10co.13May15
lo_pt.grdem.cb6p.120606.{GRID}.tx_osd_bcl12a.mard_hgb_8co.13May15
lo_pt.grdem.cb6p.120606.{GRID}.tx_osd_bcl12a.mard_no_h_d_18co.13May15 3
lo_pt.grdem.cb6p.080611.{GRID}.no_westarlaoktx_osd_b2008a.13Oct16
lo_pt.grdem.cb6p.080611.{GRID}.west_azcaorwa_osd_b2008a.13Oct16
Stationary Point Sources - Elevated
pstpnt.out.cb6p.120815.conus_36km.tx_ard_bl12a_jun2sep_avg.hgb_8co.13Sep23
pstpnt.out.cb6p.120815.conus_36km.tx_ard_bl12a_jun2sep_avg.dfw_10co.13Sep23
pstpnt.out.cb6p.120815.conus_36km.tx_ard_bl12a_jun2sep_avg.no_h_d_18co.13Sep23
pstpnt.out.cb6p.120606.conus_36km.tx_osd_bcl12a.mard_hgb_8co.13May15
pstpnt.out.cb6p.120606.conus_36km.tx_osd_bcl12a.mard_dfw_10co.13May15
 pstpnt.out.cb6p.120606.conus_36km.tx_osd_bcl12a.mard_no_h_d_18co.13May15 3
pstpnt.out.cb6p.120606.conus_36km.tx_osd_bcl12a.mard_fl_hgb_8co.13May15
pstpnt.out.cb6p.120606.conus_36km.tx_osd_bcl12a.mard_fl_dfw_10co.13May15
 pstpnt.out.cb6p.120606.conus_36km.tx_osd_bcl12a.mard_fl_no_h_d_18co.13May15 3
pstpnt.out.cb6p.120815.conus_36km.reg_ard_bl12a_jun2sep_avg.13Sep23
pstpnt.out.cb6p.080611.conus_36km.ar_osd_b2008a.13Oct16
pstpnt.out.cb6p.080611.conus_36km.la_osd_b2008a.13Oct16
91
January 2014
pstpnt.out.cb6p.080611.conus_36km.ok_osd_b2008a.13Oct16
pstpnt.out.cb6p.080611.conus_36km.no_westarlaoktx_osd_b2008a.13Oct16
pstpnt.out.cb6p.080611.conus_36km.west_azcaorwa_osd_b2008a.13Oct16
pstpnt.out.cb6p.080603.conus_36km.gwei2008a.13Jun19
pstpnt.out.cb6.990703.conus_36km.spcems_mex_pt_v4.12Jan17
pstpnt.out.cb05.060606.conus_36km.canada_2006.11Oct12
pstpnt.out.cb05.060801.conus_36km.bcl06_pscfv3_hgb_8co.11Jul28
el_pstshp.out.cb6p.hg_1km.HGB_2007.fy2012a.13Aug20
el_pstshp.out.cb6p.tx_4km.HGB_2007.fy2012a.13Aug20
el_pstshp.out.cb6p.tx_4km.BPA_2011.by2012.13Aug20
el_pstshp.out.cb6p.tx_4km.epa_nearport_TX_CRUSM.fy2012.13Aug29
el_pstshp.out.cb6p.tx_12km.epa_nearport_TX_CRUSM.fy2012.13Aug29
el_pstshp.out.cb6p.tx_4km.epa_nearport_noTX.fy2012.13Aug29
el_pstshp.out.cb6p.tx_12km.epa_nearport_noTX.fy2012.13Aug29
el_pstshp.out.cb6p.us_36km.epa_nearport_noTX.fy2012.13Aug29
el_pstshp.out.cb6p.rpo_36km.epa_nearport_noTX.fy2012.13Aug29
el_pstshp.out.cb6p.tx_4km.STEEMships.fy2012.13Aug20
el_pstshp.out.cb6p.tx_12km.STEEMships.fy2012.13Aug20
el_pstshp.out.cb6p.us_36km.STEEMships.fy2012.13Aug20
el_pstshp.out.cb6p.rpo_36km.STEEMships.fy2012.13Aug21
1. TCEQ HS oil & gas file is replaced by ENVIRON conventional oil & gas and HS 2012 Update Inventory.
2. Texas drill rigs were adjusted to remove HS counties as these sources are included in ENVIRON’s oil & gas inventories.
3. The Texas point source files were adjusted to back out Haynesville Shale midstream sources emissions.
Figure 7-1 and Figure 7-2 compare the 2006 and 2012 6-county HOTCOG area anthropogenic
NOx and VOC emissions by source group. Going from 2006 to 2012, there is an overall decrease
in NOx emissions in the 6-county area from 182 tpd to 137 tpd. Point source NOx emissions
decreased by 13 tpd and on-road mobile emissions decreased by 20 tpd. Smaller declines
occurred for oil and gas (3 tpd) and non-road mobile sources (9 tpd), while area source NOx
emissions remained constant. The relative proportion of each source category to the total NOx
emission inventory does not change significantly from 2006 to 2012. Point source and on-road
mobile source NOx are the two largest source categories in both 2006 and 2012.
Total anthropogenic VOC emissions declined from 84 tpd in 2006 to 62 tpd in 2012. Note that
anthropogenic VOC emissions are small relative to biogenic VOC emissions. In 2006, biogenic
VOC emissions totaled 1261 tpd compared to the anthropogenic VOC inventory of 85 tpd.
Biogenic emissions were not available for 2012, but are expected to be far larger than the 2012
anthropogenic emission inventory. Oil and gas VOC emissions decreased by a factor of two.
Area source VOC emissions increased from 24 to 27 tpd while point source VOC emissions
increased by approximately 1 tpd.
Fleet turnover to cleaner burning engines is likely responsible for the decrease in on-road and
non-road mobile emissions. The Tradinghouse and Lake Creek EGUs operated in 2006 but not
in 2012 (ENVIRON, 2012). The reasons for the decreases in oil and gas emissions is not clear,
since the number of both oil and gas wells in the 6-county area has grown from 2006 to 2012
92
January 2014
(http://www.rrc.state.tx.us/data/wells/wellcount/gaswellct_092013.pdf). Documentation of
TCEQ oil and gas emissions estimation methods was not available at the time this document
was written, however, the oil and gas emission inventory for the HOTCOG area will be reviewed
in 2014.
2012 NOx Emissions (tpd)
2006 NOx Emissions (tpd)
Area, 2
Area, 2
Oil &
Gas, 21
Oil &
Gas, 24
Points,
76
Points,
64
Offroad, 32
Onroad, 47
Offroad, 23
On-road,
27
TOTAL EMISSIONS: 137 TPD
TOTAL EMISSIONS: 182 TPD
Figure 7-1. TCEQ HOTCOG 6-county area NOx emissions comparison for 2006 (left panel) and
2012 (right panel).
2012 VOC Emissions (tpd)
2006 VOC Emissions (tpd)
Points, 4
On-road,
11
Points, 5
On-road,
7
Area, 24
Area, 27
Off-road,
6
Offroad, 9
Oil &
Gas, 17
Oil &
Gas, 35
TOTAL EMISSIONS: 84 TPD
TOTAL EMISSIONS: 62 TPD
Figure 7-2. TCEQ HOTCOG 6-county area VOC emissions comparison for 2006 (left panel) and
2012 (right panel).
The upper panel of Figure 7-3 is identical to Figure 5-5 and shows the contribution from local
sources and transport to the MDA8 at the location of the Waco Mazanec monitor during the
93
January 2014
June 2006 episode. The lower panel of Figure 7-3 is identical to the upper panel except that the
data shown are for the CAMx run in which the 2006 anthropogenic emission inventory was
replaced with the TCEQ 2012 anthropogenic emission inventory.
For each day of the June episode shown in Figure 7-3, the MDA8 ozone is lower in the
12_baseline_16 run with 2012 emissions than in the 06_wildires_07 run with 2006 emissions.
There are six days in which the MDA8>75 ppb in the 06_wildires_07 run, and there are no days
with MDA8>75 ppb in the 12_baseline_16 run. The HOTCOG area (local) emission contribution
to Waco Mazanec ozone is shown in green in both the upper and lower panels of Figure 7-3 and
is smaller on each day in the 2012 emissions run than in the 2006 emissions run. This is
consistent with the reduction in ozone precursor emissions in the HOTCOG 6-county area
shown in Figure 7-1 and Figure 7-2. Another factor that may contribute to the higher ozone in
the 2006 emissions scenario is the fact that model output from the 06_wildfires_07 run was
used to develop Figure 7-1. The 06_wildfires_07 run used the CB6r1 chemical mechanism,
which leads to higher modeled ozone levels than the CB6r2 chemical mechanism used in the
12_baseline_16 run. Changes in ozone caused by the difference in chemical mechanism are
likely of lesser importance than the ozone decreases caused by the emissions reductions from
2006 to 2012.
Figure 7-4 shows the episode average contribution for local and transported ozone at the Waco
Mazanec monitor location. The episode average contribution from emissions sources outside
Texas is 3 ppb lower for the 2012 run than in the 2006 run. The episode average contribution to
Waco ozone from Texas sources outside the HOTCOG area falls by 10 ppb from the 2006 run to
the 2012 run. The episode average HOTCOG contribution drops from 10 ppb in the
06_wildfires_07 run to 5 ppb in the 12_baseline_16 run. June 13 had the highest modeled
ozone among all days in the June 2006 episode at the Waco monitor location. In the
06_wildfires_07 run with 2006 emissions, June 13 had a 16 ppb contribution from the DFW area
at the Waco monitor, and this contribution was reduced to 10 ppb in the 12_baseline_16 run
with 2012 emissions. The June 13 HOTCOG contribution went from 12 ppb with 2006 emissions
to 6 ppb with 2012 emissions and the non-Texas contribution went from 16 ppb with 2006
emissions to 15 ppb with 2012 emissions.
In the 12_baseline_16 run with 2012 emissions, the contribution from the initial conditions is
smaller than in the 06_wildfire_07 run. This is because the TCEQ had provided model inputs for
May 28-May 30 by the time the 12_baseline_16 run was made, so that the model had three
days to spin up before the episode start date of May 31; during the spin up period, the
influence of model initial conditions declines. For a grid system like the one used in the Rider 8
modeling, about a week is required for the influence of initial conditions to wane. At the time
the 06_wildfires_07 run shown in Figure 7-3 was made, no model inputs prior to May 31 were
available; this is why the initial condition contribution is higher in the 06_wildfires_07 run. In
both the 06_wildfires_07 and 12_baseline_16 runs, the contribution from the sum of initial and
boundary conditions is similar, ranging between 15-20 ppb. The contribution from the
boundary conditions is slightly larger in the run with 2012 emissions. Figure 7-4 shows the
episode average contribution from the sum of initial and boundary conditions, and indicates
94
January 2014
Figure 7-3. Contribution to daily maximum 8-hour ozone by source region for the location of
the CAMS 1037 Waco Mazanec monitor for 2006 (upper panel) and 2012 (lower panel).
95
January 2014
Figure 7-4. Episode average 8-hour ozone contribution to the location of the Waco Mazanec
monitor.
Figure 7-5. Episode average 8-hour ozone contribution to the location of the Waco Mazanec
monitor.
that the contribution grows by 1 ppb in the 12_baseline_16 run relative to the 06_wildfires_07
run. Figure 7-5 shows the relative contributions of transported ozone and local ozone due to
emissions sources within the 6-county HOTCOG area. In the 2006 run, transport contributes far
more (65 ppb) to ozone at the Waco monitor than do HOTCOG area emissions sources (10 ppb).
This is also true in the 2012 run, in which transport contributes 53 ppb and the HOTCOG area
96
January 2014
sources contribute 5 ppb. The local contribution decreases by 5 ppb in the 12_baseline_16 run
and the transported contribution decreases by 12 ppb.
Figure 7-6 and Figure 7-7 are similar to Figure 5-6, but show the changes in ozone contribution
from 2006 to 2012 for each source category of emissions in the HOTCOG 6-county area
inventory. All source categories except low points sources and non-oil and gas area sources
show decreases in ozone contribution of a ppb or more in 2012 relative to 2006. On-road
mobile sources show the largest decrease in ozone contribution of all source categories,
followed by oil and gas and non-road sources. These changes are consistent with the NOx
emissions decreases shown in Figure 7-1.
The change in anthropogenic emission inventory from 2006 to 2012 in the June 2006 episode
produces large decreases in modeled ozone in the HOTCOG area. This is not consistent with
the flat design value and 4th high 8-hour ozone value trends shown in Figure 1-2, however,
meteorological as well as emissions changes can play a role in observed ozone trends and a full
ozone model for the year 2012 must be developed to fully evaluate the effects of emissions
changes between 2006 and 2012 on HOTCOG area ozone levels.
97
January 2014
Figure 7-6. Episode maximum contribution to the Waco Mazanec monitor location ozone
from HOTCOG 6-county area emissions.
Figure 7-7. Episode average contribution to the Waco Mazanec monitor location ozone from
HOTCOG 6-county area emissions
98
January 2014
8.0 EMISSIONS SENSITIVITY TESTING WITH 2012 EMISSION INVENTORY
The HOTCOG AQAC is considering local NOx emissions control strategies designed to reduce
ozone in the 6-county area. Different emissions source categories have different spatial and
temporal distributions which can affect the magnitude of their ozone impacts. For example, an
emissions reduction in a NOx source category that is broadly distributed across the HOTCOG
area may have a different ozone impacts than the same NOx emission reduction made at a
point source of emissions such as an EGU or industrial facility. The HOTCOG AQAC is
considering NOx emissions reductions for heavy duty diesel vehicles (HDDV) and gas
compressor engines used in natural gas production. These two source categories have different
spatial and temporal distributions. For both source categories, a 5 tpd NOx emissions reduction
was made to the 2012 emission inventory. The magnitude of the emissions reduction is
arbitrary and is designed to produce a response in the ozone model that is large enough to
illustrate potential differences in ozone due to reductions in these two source categories. A
CAMx run was made that was identical to the 2012 baseline run 12_baseline_16 except for the
5 tpd NOx emission reduction for gas compressor engines. Then, a CAMx run was made that
identical to the 2012 baseline run except for the 5 tpd NOx emission reduction for HDDV. These
runs and their results are described in Sections 8.1 and 8.2, respectively.
8.1 Gas Compressor Engine NOx Emissions Sensitivity Test
A 5 tpd NOx emission reduction was made for rich-burn gas compressor engines <500 HP in
Limestone and Freestone Counties. These counties were selected because, taken together,
they contained most of the natural gas wells in the 6-county area during 2006 and 2012
(ENVIRON, 2012). The reduction was evenly distributed between the two counties so that a 2.5
tpd reduction was made for Freestone and a 2.5 tpd reduction for Limestone. Gas compressor
engines typically run 24 hours a day and the reduction was distributed evenly across all hours.
This was the only change to the model relative to the 12_baseline_16 run. Once the model run
was completed, we took the episode average and episode maximum difference in MDA8
between the two simulations (Figure 8-1).
Figure 8-1 shows that the 5 tpd NOx emission reduction results in a maximum reduction in 8hour ozone of 1 ppb. The area with the largest ozone decrease is located within and to the
north/northwest of Freestone and Limestone Counties, where the emissions reductions were
made. The maximum plot suggests the influence of south/southeasterly and
north/northeasterly winds during many of the high ozone periods during the June episode. At
the location of the Waco monitor, the maximum ozone reduction was approximately 0.6 ppb.
The average ozone impact plot indicates that the ozone reduction was largest within Limestone
and Freestone and adjacent counties, and reached a peak value of 0.3 ppb.
99
January 2014
Figure 8-1. Change in 8-hour average surface layer ozone for compressor engines NOx
emissions reductions. Left hand panel: episode maximum difference. Right hand panel:
episode average. Differences were calculated only for times when surface layer ozone
concentration was > 60 ppb. Gray shading denotes grid cells that do not have any days where
MDA8 > 60 ppb.
8.2 HDDV NOx Emissions Sensitivity Test
A 5 tpd NOx emission reduction was applied to non-long haul HDDV in the 6-county HOTCOG
area. These vehicles were assumed to be locally-based, whereas long-haul HDDV are more
likely to be based outside the HOTCOG area and to be passing through the area en route to
their destination. Unlike the gas compressor emissions reduction, which was made only in
Limestone and Freestone Counties, the HDDV NOx reduction was applied equally to the 6
HOTCOG counties. While the gas compressor engines are assumed operate 24 hours a day and
7 days a week, a different temporal profile is used to allocate the HDDV emissions in time. This
profile, developed by the TCEQ, is shown in Figure 8-2. The profile indicates that HDDV activity
is at a minimum during the nighttime hours, ramps up sharply in the early morning during the
commute hours, and increases during the day until it reaches a maximum during the evening
commute. The emissions reductions were applied evenly across all hours of the day, according
to the temporal profile.
Figure 8-3 shows the ozone impacts of the HDDV sensitivity test. The impacts were calculated
in the same way as for the gas compressor test. The episode average plot indicates that the
largest average impacts occurred within and to the north of the Waco metropolitan area. The
maximum impact plots is similar to Figure 8-1 in that the presence of south-southeasterly and
100
January 2014
Figure 8-2. Temporal allocation of HDDV NOx emissions.
Figure 8-3. Change in 8-hour average surface layer ozone for HDDV NOx emissions
reductions. Left hand panel: episode maximum difference. Right hand panel: episode
average. Differences calculated only for times when surface layer ozone concentration was >
60 ppb. Gray shading denotes grid cells that do not have any days where MDA8 > 60 ppb.
north-northeasterly winds during high ozone periods may be inferred from the location of the
impacts. Maximum ozone impacts are smaller in the HDDV test than in the gas compressor test.
This is because the NOx emissions reductions in the gas compressor case were made only in
101
January 2014
two counties, while in the HDDV case, the emissions reductions were more spread out causing
the ozone reductions to be more evenly distributed. The temporal allocation profile for HDDV
is such that the emissions reductions occur mainly during the day, when ozone is formed in the
presence of sunlight. For compressors, the emissions reduction was taken equally across all
hours of the day because compressor engines typically run continuously. Therefore, half of the
compressor emissions reduction occurred at night, while the much of the HDDV reduction
occurred during the day. This result indicates the importance of the spatial distribution of
emissions for ozone reductions. The maximum ozone impact of the HDDV reduction was 0.8
ppb while the peak value of the average ozone reduction was 0.2 ppb.
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January 2014
9.0 CONCLUSIONS AND FUTURE WORK
In this report, we summarized the development and application of an ozone model for the
HOTCOG area. In early 2012, a June 2006 ozone model was developed from inputs provided by
the TCEQ to the Texas NNAs. This base case CAMx ozone model was evaluated at monitors
operating in the vicinity of the HOTCOG area during 2006 as well as within the 36 km grid and
for monitors along the Texas border with Louisiana and Oklahoma. The ozone model
performance evaluation showed that ozone was generally overestimated at most Texas
monitors throughout the episode, including Temple, Italy H.S. and Palestine, which are located
near the HOTCOG area. This high bias occurred during periods of stagnant air as well as
transport periods. A high bias was present at rural monitors within the 36 km grid as well as
Texas border monitors and may affect the attribution of local versus transported ozone
contribution in the HOTCOG area. It was determined that model performance had to be
improved before the model could be applied for emissions control strategy development.
Later in 2012, a series of changes were made to the model with the goal of updating the CAMx
model with the best available science and improving ozone performance. An updated TCEQ
emission inventory with finer detail in the categorization of emissions was added, along with
day-specific wildfire emissions. The CAMx model was updated from version 5.40 to version
5.41, which used new version of the CB6 chemical mechanism. NOx recycling was increased in
CB6r1 based on experimental data from AQRP project 10-042. Although this change made the
CB6r1 mechanism consistent with the best available science at the time, modeled ozone
increased regionally and model performance was degraded.
Source apportionment modeling was carried out with the 2006 model in the updated
configuration and with the TCEQ’s new emission inventory. The source apportionment results
showed that ozone formation in the HOTCOG area is limited by the amount of available NOx.
This finding is consistent with the emission inventory, which shows that the VOC emission
inventory for the 6-county area is dominated by biogenic VOCs. The abundance of biogenic
VOC means there is always enough VOC available to form ozone so that the amount ozone
formed is determined by the amount of anthropogenic NOx available. This finding means that
emission control strategy development in the HOTCOG area should focus on controlling NOx
emissions sources rather than VOC sources.
The ozone contribution from regions within and outside Texas to ozone at the location of the
Waco Mazanec monitor was quantified for each day of the June 2006 episode. The
contribution of local 6-county area emissions to ozone at the Waco monitor was also evaluated,
and was broken down into contributions from the different emissions source categories. The
source apportionment results showed that, on average, transport contributes far more to
HOTCOG area ozone than local emissions sources, although the contribution of emissions from
within the 6-county area accounted for 10 ppb of the episode average 8-hour ozone at the
Waco monitor location. On a day-to-day basis, the local contribution was as high as 24 ppb.
This indicates that local emissions control measures can be effective in reducing ozone in the
HOTCOG area.
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The source apportionment results showed the breakdown of ozone impacts by HOTCOG area
emissions source categories and indicate the source categories that make the largest
contributions to HOTCOG area ozone levels. The categories with the largest ozone impacts are
on-road and off-road mobile sources, elevated point sources, and oil and gas sources. On-road
mobile sources make the largest episode maximum and episode average contribution to ozone
at the Waco monitor location. The next largest episode maximum contribution is made by
elevated point sources, followed by oil and gas sources. The elevated point source NOx
emission inventory is dominated by EGU emissions; there are two large EGUs located in the
vicinity of the Waco monitor. The largest contributor to oil and gas ozone impacts was
wellhead compression.
During 2013, several updates were made to the CAMx model as well as to the TCEQ’s Rider 8
modeling platform. The TCEQ revised the emission inventory, expanded the 36 km domain, and
provided modeling inputs for several additional days prior to the start of the episode to allow
for model spinup. The model boundary conditions were altered to compensate for known bias
in the global model used to develop the boundary conditions, and a cloud Kv patch was applied
to enhance transport of chemical species out of the boundary layer and into the free
troposphere. New emission inventories for lightning NOx, wildfires and aircraft were added to
the TCEQ SIP modeling inventory. A new version of CAMx was used that contained an
important update to the Plume-in-Grid model that treats plumes from large point sources. The
most critical change was the use of a new chemical mechanism, CB6r2.
CB6r2 differentiates organic nitrates between simple alkyl nitrates that remain in the gas-phase
and multi-functional ONs that can partition into OA. Uptake of multi-functional ONs by OA was
added to CAMx for CB6r2. ONs present in aerosols are then assumed to undergo hydrolysis to
nitric acid with a lifetime of approximately 6 hours based on laboratory experiments and
ambient data. These changes tend to reduce regional concentrations ozone and ONs, and
increase nitric acid. Regional modeling simulations using CAMx with CB6r2 showed that
accounting for ON hydrolysis in aerosols improved performance for ozone and in simulating the
partitioning of NOy between ONs and nitric acid (Hildebrandt Ruiz and Yarwood, 2013).
Evaluation of the updated model showed that the high bias in modeled ozone in the vicinity of
the HOTCOG area and throughout Texas was reduced in the June 2006 episode. Model
performance improved significantly, although a high bias persists. For the three monitors in the
vicinity of the HOTCOG area the mean normalized bias is within the benchmarks for nearly all
days of the first episode and about half of the days in the second episode. This is the bestperforming run of the three runs that were done as part of this study.
During the summer of 2013, the TCEQ made a 2012 anthropogenic emission inventory available
to ENVIRON for use in the development of an ozone forecasting system for the State of Texas.
We developed a typical day ozone model for the June 2006 episode conditions using the TCEQ
2012 anthropogenic emissions and performed an assessment of how emissions changes from
2006 to 2012 affect HOTCOG area ozone under the meteorological conditions of June 2006.
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There was an overall decrease in NOx emissions in the 6-county area from 182 tpd to 137 tpd.
The largest declines among the NOx emission source categories were for point sources (13 tpd)
and on-road mobile (20 tpd). The relative proportion of each source category to the total NOx
emission inventory did not change significantly from 2006 to 2012. Point source and on-road
mobile source NOx were the two largest source categories in both 2006 and 2012. Total
anthropogenic VOC emissions declined from 85 tpd in 2006 to 62 tpd in 2012.
Due in large part to the emissions reductions, the 12_baseline_16 run showed decreases in
HOTCOG area ozone throughout the June episode. There were six days in which the MDA8>75
ppb in the 2006 run, and there were no days with MDA8>75 ppb in the 2012 run. The episode
average HOTCOG contribution dropped from 10 ppb in the 2006 run to 5 ppb in the 2012 run.
The relative contributions of transported ozone and local ozone due to emissions sources
within the 6-county HOTCOG area were similar in nature in both 2006 and 2012. In the 2006
emissions run, transport contributed far more (65 ppb) to ozone at the Waco monitor than did
HOTCOG area emissions sources (10 ppb). This was also true in the 2012 emissions run, in
which transport contributed 53 ppb and the HOTCOG area sources contributed 5 ppb. The local
contribution decreased by 5 ppb in the 2012 emissions run and the transported contribution
decreased by 12 ppb.
Because the HOTCOG AQAC is considering local NOx emissions reductions for heavy duty diesel
vehicles (HDDV) and gas compressor engines, we performed 5 tpd emissions reductions to
these two source categories to compare the effect on local ozone. These two source categories
have different spatial distributions and different temporal allocations, and this affected the
ozone impacts. Maximum ozone impacts were smaller in the HDDV test than in the gas
compressor test. This is because the NOx emissions reductions in the gas compressor case
were made only in two counties, while in the HDDV case, the emissions reductions were made
across all HOTCOG counties, causing the ozone reductions to be more evenly distributed. This
shows that for a given NOx reduction, the local ozone impact is larger if it is applied to a source
that is more geographically concentrated than to one that is dispersed across the HOTCOG
area.
In summary, a 2006 ozone model was developed. Significant revision to the model occurred
during FY12-13, and performance in Texas improved to the point where it is reasonably good.
However, a high bias persists in the HOTCOG area and is present and more pronounced at the
Texas border monitors. This indicates that transport of ozone into Texas may be
overestimated. While it is unlikely that the model with a lower bias would contradict the
finding that transport has a stronger influence on HOTCOG area ozone than local emissions
source, the high bias introduces uncertainty into the source apportionment results.
The change in anthropogenic emission inventory from 2006 to 2012 in the June 2006 episode
produces large decreases in modeled ozone in the HOTCOG area. This is not consistent with
the flat design value and 4th high 8-hour ozone value trends shown in Figure 1-2, however,
meteorological as well as emissions changes can play a role in observed ozone trends and a full
ozone model for the year 2012 must be developed to fully evaluate the effects of emissions
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changes between 2006 and 2012 on HOTCOG area ozone levels. A 2012 ozone model that uses
2012 emissions and 2012 meteorology (as well as other model inputs) would be beneficial to
HOTCOG because it is clear that there have been large changes in emissions since 2006.
Development of a full 2012 model would allow evaluation of emission control strategies in a
more recent episode and would account for the ozone impacts of changes such as declines in
EGU emissions, development of the Barnett Shale and the implementation of the East Texas
Combustion Rule, among others.
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Appendix A: WRF Precipitation Evaluation
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A-1
Precipitation Evaluation
A comparison of observed and modeled precipitation over the 4 km modeling domain was
performed for each day of the simulation, including the low ozone period from June 16-June 23.
Observed precipitation data were obtained from the Advanced Hydrologic Prediction Service
(AHPS) (http://water.weather.gov/about.php). Daily AHPS precipitation totals are derived from
rain gauge measurements and radar data and are gridded at a resolution of approximately 4
km. The data are given as precipitation totals for the 24 hour period ending at 12Z each day.
The AHPS data were re-projected onto the TCEQ WRF 4 km modeling domain so that the
observed and modeled precipitation can be compared by inspection. The AHPS precipitation
data are derived from data that are collected only over land and areas immediately offshore, so
the modeled precipitation cannot be evaluated over the Gulf of Mexico.
The sum of the hourly precipitation amounts in each WRF grid cell was taken over each day to
obtain a daily precipitation total for each grid cell. The day was defined to be a 24-hour total
ending at 12Z to be consistent with the AHPS dataset. The daily model precipitation totals were
then compared with daily observed precipitation totals.
During the first HOTCOG area high ozone episode, (June 4-15) there is little observed
precipitation over Texas. WRF does a reasonably good job of simulating the precipitation
pattern on dry days. During the low ozone period extending from June 16-June 23, there was
widespread precipitation across Texas and WRF generally underestimates the precipitation.
Since this is a period of low ozone, the model’s failure to simulate precipitation accurately is not
critical. From June 29-July 1, the WRF run is closer to the observed precipitation patterns and
amounts. Overall, the WRF model simulation of precipitation is not expected to introduce
significant bias into the CAMx simulation of ozone in the HOTCOG area during the high ozone
periods of the June 2006 episode.
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AHPS Observed Precipiation
YSU WRF Run Precipitation
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