Final report
Ozone Modeling System and Emission Control Strategies
for the Lake Tahoe Basin
Darko Koracin, Julide Kahyaoglu Koracin, Alan Gertler, Travis McCord, Amela Jericevic, John
Mejia, Eric Wilcox
Darko.Koracin@dri.edu; 775 674 7091
Division of Atmospheric Sciences
Desert Research Institute
2215 Raggio Parkway, Reno, NV 89512
Prepared for:
Tahoe Research Program Manager
USDA Forest Service
Pacific Southwest Research Station
This Round 12 SNLPMA research grant was supported by an agreement with the USDA Forest
Service Pacific Southwest Research Station. Funding for this research was provided by the
Bureau of Land Management through the sale of public lands as authorized by the Southern
Nevada Public Land Management Act (SNLPMA). The views in this report are those of the
authors and do not necessary reflect those of the USDA Forest Service Pacific Southwest
Research Station or the Bureau of Land Management.
1. Introduction
The Lake Tahoe basin area is characterized by highly developed topography in the Sierras with the
Sierra slopes and the California Central Valley to the west and the Sierra slopes and the Washoe and Reno
Basins to the east. Consequently, its meteorology is fairly complex with strong synoptic and seasonal
variations. During the warm season, the winds are generally strong and frequently from the southwest or
west, while in the winter the winds are mainly weak (except during occasional storms) and of variable
directions. Another important difference is that the depth of the atmospheric boundary layer is much
larger in the summer due to surface heating and development of convection, while the depth is much
smaller in the cold season. This has a strong impact on the transport and dispersion of the pollutants.
During the periods of weak pressure gradients, local meteorological circulations develop in the basin.
During the nighttime, downslope winds develop and drain cold air from the mountain slopes into the
center of the basin, i.e., over the lake. During the daytime, the air over the upper parts of the slopes is
heated at a faster rate than the surrounding air at the same elevation over the center of the basin. This
induces development of the upslope flows that ventilate the air from the center of the basin over the
slopes.
The climatology of the winds in the Lake Tahoe area is poorly known. One of the rare documented
wind climatologies for California was written by Hayes et al. (1992). Although they analyzed 176
stations, the Lake Tahoe area is represented only by the South Lake Tahoe station which shows dominant
1
southwest and southerly flows with secondary occurrence of northerly winds. This information shows the
limitations of using surface wind statistics in complex terrain, i.e., surface winds are dominantly
channeled by the surrounding topography and do not represent the broader flow field. Valuable
information on meteorology in the Tahoe area is presented by Zaremba and Carroll (1999). They
performed aircraft measurements over the Sierra slopes east of Sacramento and analyzed flow fields.
They concluded that the westerly upslope flows occur in some cases during the daytime with weak
synoptic pressure gradients. Tarnay et al. (2002) performed measurements of the ambient concentrations
of nitric acid and ammonia in the Lake Tahoe basin and indicated that the source of the nitric acid could
be outside of the basin, while the ammonia appears to be of local origin.
A comprehensive study of three-dimensional flows in the basin and their interaction with regional
flows is presented by Koracin et al. (2004). They concluded that the situations with dominant local flows
in the basin induce significant increases of the pollutant concentrations in the basin. Based on the
modeling and chemical measurements, Koracin et al. (2004) also found that the local emission sources
may have a dominant role during the episodes of high pollutant concentrations in the basin. A number of
observational studies and monitoring have been conducted in the Tahoe area, including the CARB
managed LTADS program for one year monitoring of gaseous and particulate matter at five sites as well
as IMPROVE monitoring of particulate matter at two sites.
The Lake Tahoe area is situated downwind of major urban areas with major emission sources in
California’s Central Valley and pollution including ozone and ozone pre-cursors may be transported into
the basin from the western slopes of the Sierra Nevada where biogenic organic compounds add to the
ozone pre-cursors. Gertler et al. (2006) mentioned that the highest 2-week and whole-season average
ozone and nitric acid concentrations occurred over the Sacramento foothills, west of the Lake Tahoe
Basin. Concentrations of these pollutants were much lower near the lake, especially near the west shore.
It appears that the mountain range west of the Lake Tahoe Basin (Desolation Wilderness) impedes the
westerly flow of low-layer polluted air masses from the Sacramento metropolitan area and the Sierra
Nevada foothills, limiting pollutant transport into the basin. In addition, later in the day, downslope
winds can carry the plume back to the Central Valley. Dolislager et al. (2012) argued that the intact
transport from the Central Valley was quite infrequent in 2013 according to their analysis of measured
ozone concentrations, but can occur during summer time. This is mainly due to blocking of the surface
flows from the Central Valley. However, they emphasized that the major upwind urban areas generate a
large regional pool of pollution which can on occasions impact the Tahoe area. They suggest that one of
the major mechanisms to impact the Tahoe area is vertical mixing that brings polluted air which was
transported aloft during the accumulation of the regional pool of pollution. In that case, even a small
amount of locally-emitted pre-cursors can cause ozone exceedances in the Tahoe basin. Although South
Lake Tahoe is the largest populated area, observed exceedances are relatively low due to possibly fresh
local emissions of NO suppress ozone concentrations by titration (Dolislager et al., 2012).
There is growing concern about the effects of ozone on air quality in the Lake Tahoe basin. Ozone
levels in the Tahoe air basin have been increasing and have led to exceedances of current California air
quality standards (ozone levels exceeded the 8-hr California standard 5 times in both 2007 and 2008, 2
times in 2006, and once in 2009). This trend can have serious implications with respect to human and
ecosystem health. This area is currently designated as a nonattainment-transitional zone for ozone (O3)
by the California Air Resources Board. A robust emission control strategy is needed and can only be
achieved if we have a detailed understanding of the processes affecting the local and regional O3
formation, transport and dispersion patterns by using measurements and modeling. In response to
Subtheme 3b: Managing air pollutants, we have conducted a comprehensive modeling study. The main
objectives were completed as follows.
 We have developed a complex modeling system (Lake Tahoe Air Quality Modeling System
/LTAQMS/ consisting of a meteorological model (WRF), emission models (SMOKE and
MOVES), and a photochemical model CMAQ (Fig. 1). We used the EPA National Emission
Inventory (EPA NEI) for 2005 which has been fully evaluated.
 We have validated the ozone modeling system on an ozone episode recorded in August 2009 at
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
the Lake Tahoe station.
We have performed sensitivity simulations for various emission scenarios and determined what
aspects of the planned cost effective control strategies might be cost effective for the Lake Tahoe
basin to control ozone levels.
Fig. 1. Flow diagram of the proposed ozone modeling system to be incorporated into the LTAQMS
framework.
2. Meteorological Modeling
Due to the topographic complexity of the Lake Tahoe Basin, only advanced meteorological models such
as the Weather and Research Forecasting (WRF) model (Skamarock et al. 2008) can provide sufficiently
accurate information to be input to the photochemical models. The WRF model is a next-generation
mesoscale numerical weather prediction system designed to serve both operational forecasting and
atmospheric research needs. It features multiple dynamical cores, a 3-dimensional variational (3DVAR)
data assimilation system, and a software architecture allowing for computational parallelism and system
extensibility. WRF is suitable for a broad spectrum of applications across scales ranging from meters to
thousands of kilometers (Skamarock et al. 2008). WRF is the successor of the widely used community
Mesoscale Model 5 (MM5) and, in terms of both computational and physics parameters, the most suitable
mesoscale meteorological model for this study that can provide input to the photochemical model CMAQ.
The selected case studies were simulated using WRF with coarse domains (12 and 4 km resolutions) to
capture synoptic processes and a nested domain centered on Lake Tahoe to account for detailed local
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circulations with a resolution of 1.33 km. The model results were evaluated using available
meteorological data in the basin and vicinity. The model outputs were stored in hourly intervals and
processed as inputs to the CMAQ photochemical model (see further sections). The entire modeling
process was done on DRI’s computers. In a possible future study, the modeling system could be ported to
a TRPA or another management agency computer.
The WRF map setup has 3 nested grids (see Fig. 2):
•
Grid 1: 86 x 62 points, resolution 12 km.
•
Grid 2: 76 x 58 points, resolution 4 km. This is the resolution that we processed the EPA's
National Emission Inventories. (http://www.epa.gov/ttn/chief/net/2005inventory.html)
•
Grid 3: 106 x 82 points, resolution 1+1/3 km.
Figure 2. Setup of all meteorological modeling WRF domains, shown in Google Earth.
Details of the WRF represented nested domain with a resolution of 1.33 km are shown in Fig. 3.
4
Figure 3. Topography of WRF domain 3.
The grids were set up so that the finest grid was centered just west of the lake itself. These resolutions
were chosen partially because the input geographic data that comes with WRF by default has as its
highest resolution 30 arc-seconds, which is roughly 0.9 km. The WRF model uses data from NARR
(North American Regional Reanalysis) as input for the initial and boundary conditions; this data covers
all of North America at a resolution of 32 km.
This setup was used for two runs. The first run was a test run, which covered the period 20-28 July 2012.
For the test run we used WRF's default 28 vertical levels; comparisons with station data for that time are
below.
The second run covered the period June 13-September 30, 2010, using 42 vertical levels chosen to get
higher-resolution data of the atmosphere near the ground.
To examine whether WRF can reproduce various synoptic conditions in different years with similar
success, we ran WRF meteorological simulations for:
1. 16-20 Jun 2006
2. 11-15 Sep 2006
3. 7-15 May 2007
4. 17-21 Jun 2007
5. 6-10 Jul 2007
6. 10-26 Jun 2008
7. 8-24 Jul 2008
8. 9-13 Aug 2009
9. 13 Jun - 30 Sep 2010
10. 20-28 Jul 2012
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Table 1. Basic statistics for the weather simulation comparison with measurements for the first run 20-28
July 2012.
Table 1 shows that the model reproduced temperature fairly well, with the correlation coefficient greater
than 0.755 considering all three locations. Note that the temperature variability in terms of the standard
deviation is similar for the model and measurements. The simulated winds show lower correlation
compared to temperature, mainly due to microlocation topographic variability.
4A: Comparison of air temperature at Homewood.
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4B: Wind speed at Homewood.
4C: Air temperature at TRPA.
7
4D: Wind speed at TRPA.
4E: Air temperature at South Lake Tahoe Airport.
8
4F: Wind speed at the South Lake Tahoe Airport.
Figure 4. Comparisons of WRF vs. station winds and temperature for the three stations for the
first test run (20-28 July 2012).
In this configuration, the model underestimates the maximum temperature and overestimates the
minimum temperature. This might imply the photochemical model underestimates ozone peaks
due to input of lower temperature maxima. Large jumps in the wind speed rapidly dropping to
zero indicate possible problems with the wind speed measurements.
9
5A: Air temperature at Homewood.
5B: Wind speed at Homewood.
10
5C: Air temperature at South Lake Tahoe Airport.
5D: Wind speed at South Lake Tahoe Airport.
11
Figure 5. Comparison of simulated and measured air temperature at Homewood and the South
Lake Tahoe Airport for the second run 13 June – 30 September, 2010.
Table 2. Statistics for the second run 13 June – 30 September, 2010.
Location
Variable
WRF mean
WRF St.
Station mean
Dev.
Homewood
Air
16.7
4.95
13.7
temperature
Wind speed 2.5
1.66
4.3
SLT Airport Air
15.6
7.81
13.6
temperature
Wind speed 2.0
2.11
3.4
Station St.
Dev.
4.64
Correlation
Bia
0.921
2.99
2.49
6.21
0.420
0.875
-1.8
1.95
1.81
0.525
-1.3
The second test run with increased vertical resolution showed the same high correlation with
respect to the temperature in spite of underestimation of the maximum and minimum
temperatures, and expected correlation between 0.4 and 0.5 for the wind speed. Similarly to the
previous case, the station measurements show a large number of cases when winds were
suddenly reduced to zero indicating possible instrumentation problems.
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(a)
(b)
(d)
(c)
Fig. 6. Comparison for 16-20 June 2006 between WRF and measurements at the South Lake
Tahoe airport (38.898º N, 119.995º W)—(a) Temperature (º C), (b) Wind speed (m s-1), (c) U
component (m s-1), (d) V component (m s-1). “Loc. 2” is the point 38.87º N, 119.995º W, roughly
2 miles south of the SLTA site, included for comparison.
We also examined how different the model results are if we consider surrounding points in the
proximity of the nearest grid point (shown as Loc. 2 in Fig. 6). Most of the variability between
the model results for different grid points is in the U and V wind components which are highly
variable in time and space, especially for low wind conditions during stagnant synoptic
conditions causing degradation of air quality.
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(a)
(b)
(c)
(d)
Fig. 7. As Fig. 6, but for 11-15 September 2006.
This simulation (Fig. 7) shows that in some cases the model predicts more uniform temperature
structure and the temperature results are fairly similar for neighboring points, but the maximum
temperature is underestimated at both grid points. The wind speed measurements had quite a few
data points lost, but the model generally overestimates westerly advection.
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(a)
(b)
(c)
(d)
Fig. 8. As Fig. 6, but for 7-15 May 2007.
The main issue with this simulation is that the model significantly overestimates wind speed and
consequently advection and mixing that might have an impact on the photochemical modeling
predicting lower concentrations. The underestimation of the maximum temperature also favors
reduction of simulated ozone peaks due to lower simulated temperature.
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(a)
(b)
(c)
(d)
Fig. 9. As Fig. 6, but for 17-21 June 2007.
The results from Fig. 9 are similar to the results from Fig. 8 with respect to underestimation of
the maximum temperature and overestimation of the wind speed. The wind speed shows a
significant number of missing data points.
16
(a)
(b)
(c)
(d)
Fig. 10. As Fig. 6, but for 6-10 July 2007.
Figure 10 confirms the previous results of underestimated maximum temperatures and problems
with wind speed measurements.
17
(a)
(b)
(c)
(d)
Figure 11. As Fig. 6, but for 10-26 June 2008.
This longer period shows much smaller simulated amplitudes between the maximum and
minimum temperatures and significant overestimation of the wind speed, especially for westerly
and northwesterly winds relevant to regional transport from the Sacramento valley. It should be
noted that numerical models generally do not predict zero wind speed.
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(a)
(b)
(c)
(d)
Fig. 12. As Fig. 6, but for 8-24 July 2008.
While the temperature was generally simulated well, the winds are again overestimated,
especially from westerly and northwesterly directions (Fig. 12).
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(a)
(b)
(c)
(d)
Figure 13. As Fig. 6, but for 9-13 August 2009.
This case shows relatively good agreement between the simulated and measured maximum
temperature with a high correlation coefficient (0.90) and, in spite of the overestimation of
measured winds with low reliability seen in the previous cases, we selected this case for
photochemical modeling and subsequent sensitivity tests for the purpose of estimating the
effectiveness of particular control strategies. Table 3 confirms this selection. For example, the
bias of the simulated temperature is less than 1°C and the bias in the wind speed is less than 1 m
s-1. Table 3 also summarizes the statistics for all meteorological simulations. The main result is
that the correlation coefficient for the temperature is generally high (0.77-0.90 with a median of
0.86) with the bias down to -1.65°C. The wind speed shows much lower correlation (0.41-0.85
with a median of 0.47 and an absolute bias up to 1 m s-1.
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Table 3. Statistics for the comparisons between the South Lake Tahoe station and WRF. Each
run includes one or more dates of the CA state exceedances.
16-20 June 2006
Location
Variable
SLT Airpt Temperature (º C)
Scalar wind speed (m/s)
U component (m/s)
V component (m/s)
Vector wind speed (m/s)
Loc. 2
Temperature
Scalar wind speed
U component
V component
Vector wind speed
R
Station mean Station St. Dev. WRF mean WRF St. Dev. Correlation bias
14.89
7.37
14.97
4.44
0.86
-0.09
3.34
1.52
4.34
1.96
0.44
-1.00
0.44
1.64
1.89
1.88
0.34
-1.46
1.55
2.88
2.91
2.69
0.53
-1.35
1.61
3.47
14.89
7.37
14.93
4.41
0.85
-0.04
3.34
1.52
3.90
1.59
0.47
-0.56
0.44
1.64
1.86
1.99
0.30
-1.42
1.55
2.88
2.42
2.14
0.47
-0.87
1.61
3.05
11-15 September 2006
Location
Variable
SLT Airpt Temperature
Scalar wind speed
U component
V component
Vector wind speed
Loc. 2
Temperature
Scalar wind speed
U component
V component
Vector wind speed
R
Station mean Station St. Dev. WRF mean WRF St. Dev. Correlation bias
13.22
6.79
14.87
4.03
0.77
-1.65
3.43
2.27
3.75
1.84
0.71
-0.31
0.55
1.47
0.95
2.55
0.04
-0.40
2.47
2.91
2.67
1.75
0.74
-0.20
2.53
2.83
13.22
6.79
14.72
3.96
0.78
-1.50
3.43
2.27
3.10
2.31
0.85
0.34
0.55
1.47
0.36
2.63
0.10
0.19
2.47
2.91
2.12
1.86
0.66
0.34
2.53
2.15
7-15 May 2007
Location
Variable
SLT Airpt Temperature
Scalar wind speed
U component
V component
Vector wind speed
Loc. 2
Temperature
Scalar wind speed
U component
V component
Vector wind speed
R
Station mean Station St. Dev. WRF mean WRF St. Dev. Correlation bias
9.53
6.37
9.57
4.34
0.88
-0.03
3.30
1.55
3.45
1.59
0.57
-0.15
0.17
1.58
1.15
2.01
0.47
-0.97
1.16
3.10
1.56
2.60
0.69
-0.40
1.17
1.94
9.53
6.37
9.44
4.36
0.87
0.09
3.30
1.55
3.27
1.71
0.67
0.03
0.17
1.58
1.35
2.18
0.45
-1.18
1.16
3.10
1.28
2.35
0.67
-0.12
1.17
1.86
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Table 3, continued.
8-24 July 2008
Location Variable
Station mean Station St. Dev. WRF mean WRF St. Dev. Correlation bias
RMSE
SLT Airpt Temperature
17.07
7.10
18.69
5.01
0.86
-1.62
4.08
Scalar wind speed
3.18
1.63
3.59
1.29
0.34
-0.41
1.74
U component
0.15
1.19
1.27
1.83
-0.09
-1.12
2.52
V component
1.72
2.90
2.54
1.79
0.31
-0.81
3.00
Vector wind speed
1.73
2.84
Loc. 2
Temperature
17.07
7.10
18.49
4.88
0.86
-1.43
4.09
Wind speed
3.18
1.63
3.47
1.58
0.45
-0.28
1.70
U component
0.15
1.19
1.54
1.98
-0.10
-1.39
2.77
V component
1.72
2.90
2.22
1.83
0.32
-0.50
2.92
Vector wind speed
1.73
2.70
9-13 August 2009
Location Variable
Station mean Station St. Dev. WRF mean WRF St. Dev. Correlation bias
RMSE
SLT Airpt Temperature
15.56
7.46
16.27
4.99
0.90
-0.70
3.75
Wind speed
2.59
1.20
3.12
0.97
0.25
-0.53
1.44
U component
-0.43
1.12
1.78
1.27
-0.08
-2.21
2.81
V component
-0.20
2.61
2.12
1.21
0.04
-2.32
3.64
Vector wind speed
0.48
2.76
Loc. 2
Temperature
15.56
7.46
16.04
4.90
0.90
-0.48
3.75
Wind speed
2.59
1.20
2.95
1.20
0.56
-0.36
1.18
U component
-0.43
1.12
1.72
1.68
-0.45
-2.15
3.20
V component
-0.20
2.61
1.71
1.24
-0.06
-1.92
3.50
Vector wind speed
0.48
2.42
We also evaluated meteorological predictions in a broader region. One of the important areas is
the Sacramento valley which is one of the main potential sources of ozone and ozone pre-cursors
that could impact the Tahoe basin. Figure 14 shows measured and simulated temperature and
wind speed at the location of the Sacramento airport meteorological station KSAC (38.512528°
N; -121.493472° E; elevation 7 m).
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Fig. 14. Simulated and measured temperature (upper panel) and wind speed (lower panel) at the
Sacramento airport station (KSAC) for the ozone episode 9-13 Aug 2009.
Figure 14 shows that the model was able to quite accurately represent the temperature values and
variations and to follow the general trends of the wind speed with some over-estimation of the
maximum and minimum values. It is important to mention that the model was able to predict
almost exact values of the maximum temperatures (except for 12 Aug) and fairly good values for
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the minimum temperatures. Favorable predictions of the maximum temperatures will be highly
relevant to simulations of the peak ozone values (see the photochemical section).
In summary, as expected, the model shows some success in this complex terrain. Some of the
difficulties include problems of the station microlocation due to proximity of buildings, forest,
and other obstacles that are common in this type of area where in many cases stations are located
by convenience. Additional problems are that the winds are quite low for the stagnant synoptic
situations when ozone episodes usually occur. The conditions with low and variable winds are
very difficult for any model to accurately represent due to a delicate balance among very small
physical forcings and the limitations of the numerical schemes. The time series show that WRF
generally predicts lower maximum temperatures and higher minimum temperatures compared to
the station data, which can be an important contribution to underestimation of daytime ozone
peaks. However, the simulated temperatures agreed very well at the Sacramento airport station
outside of the highly complex topography of the Lake Tahoe basin.
3. Emission Processing Using the SMOKE Modeling System
After the meteorological modeling, we used the SMOKE modeling system to process emissions
and to evaluate control strategies. The Sparse Matrix Operator Kernel Emissions (SMOKE)
Modeling System (The University of North Carolina at Chapel Hill, 2010) was developed by the
MCNC Environmental Modeling Center (EMC) to allow emissions data processing methods to
integrate high-performance computing (HPC) sparse-matrix algorithms. The SMOKE system is a
significant addition to the available resources for decision-making about emissions controls for
both urban and regional applications. It provides a mechanism for preparing specialized inputs
for air quality modeling research. The purpose of SMOKE, as an emissions processor, is to
convert the resolution of the emission inventory data to the resolution needed by an air quality
model. Emission inventories are typically available with an annual-total emissions value for each
emissions source, or in some cases with an average-day emissions value.
SMOKE can process gaseous pollutants such as O3, CO, NOx, volatile organic compounds
(VOCs), NH3, SO2, and particulate matter (PM) pollutants such as PM 2.5 and PM10; as well as a
large array of toxic pollutants, such as mercury, cadmium, benzene, and formaldehyde.
Currently, SMOKE supports area-, mobile-, and point-source emissions processing and also
includes biogenic emissions modeling through the Biogenic Emission Inventory System, version
2 (BEIS2) and the BEIS3 system. The major components of the SMOKE system are shown in
Figure 15. Note that the meteorological modeling results are needed as input for the SMOKE
processing. The outputs of the SMOKE system are used as inputs to the CMAQ model, and also
for subsequent sensitivity studies and examination of the efficiency of various emission control
strategies for particular emission components.
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Fig. 15. Schematic of the SMOKE-CMAQ modeling system.
Emission inventories and source categories
Emission inventories are divided into several source categories. These divisions stem from both
differing methods for preparing the inventories and from different characteristics and attributes
of the categories. Generally, emission inventories are divided into the following source
categories:
•
Stationary area/Nonpoint sources: Sources that are treated as being spread over a
spatial extent (usually a county or air district). Because it is not possible to collect emissions data
at each point of emission, they are estimated over larger regions. Examples of nonpoint or
stationary area sources are residential heating and architectural coatings. Numerous sources, such
as dry cleaning facilities, may be treated either as stationary area/nonpoint sources or as point
sources. In particular, the toxics inventory contains numerous small sources (based on emissions)
that are not inventoried as nonpoint sources because their locations are known and are provided.
•
Non-road mobile sources: Vehicular and other movable sources that do not include
vehicles that travel on roadways. These sources are also computed as being spread over a spatial
extent (again, a county or air district). Examples of non-road mobile sources include
locomotives, lawn and garden equipment, construction vehicles, and boating emissions.
•
On-road mobile sources: Vehicular sources that travel on roadways. These sources can
be computed either as being spread over a spatial extent or as being assigned to a line location
(called a link). Data in on-road inventories can be either emissions or activity data. Activity data
consists of vehicle miles traveled (VMT) and, optionally, vehicle speed. Activity data are used
25
when SMOKE will be computing emission factors via another model such as MOVES.
Examples of on-road mobile sources include light-duty gasoline vehicles and heavy-duty diesel
vehicles.
•
Point sources: These are sources that are identified by point locations, typically because
they are regulated and their locations are available in regulatory reports. Point sources are often
further subdivided into electric generating utilities (EGUs) and non-EGU sources, particularly in
criteria inventories in which EGUs are a primary source of NOx and SO2. Examples of non-EGU
point sources include chemical manufacturers and furniture refinishers.
•
Wildfire sources: Traditionally, wildfire emissions have been treated as stationary area
sources. More recently, data have also been developed for point locations, with day-specific
emissions and hour-specific plume rise (vertical distribution of emissions). In this case, the
wildfire emissions are processed by SMOKE as point sources.
•
Biogenic land use data: Biogenic land use data characterize the type of vegetation that
exists in either county total or grid cell values. The biogenic land use data in North America is
available using two different sets of land use categories: the Biogenic Emissions Landcover
Database (BELD) version 2 (BELD2), and the BELD version 3 (BELD3).
Emission processing in SMOKE is divided into four processing categories: area (nonpoint or
stationary area, non-road mobile), biogenic (biogenic land use), mobile (on-road mobile), and
point (EGU, non-EGU, wildfires with pre-computed plume rise, wildfires with internal plume
rise calculation). SMOKE distinguishes the inventory source categories, the types of inventories
(activity data, criteria, particulates, and toxics) and the temporal resolution. Each inventory
source category has source characteristics, source attributes, data values, and data attributes.
Source characteristics are unique to each inventory source category and also distinguish one
source in the inventory from another. Source attributes further describe the sources with other
information that is useful for emissions processing, such as point-source gas exit height and
temperature. The data values are either emissions values or activity values. The data attributes
are additional information about the data values. For all typical inventories, the source
characteristics that identify these sources are country/state/county code and Source Classification
Code (SCC).
SMOKE uses a 6-digit integer code to identify a country, state (or province), and county (or
other region) for a particular source. Most U.S. inventories input to SMOKE have the 5-digit
U.S. Federal Implementation Planning Standards (FIPS) state and county codes. All inventory
input formats have been adapted to include a special header record with which you can specify
the country, effectively allowing the inventories to be provided with the 6-digit code that
SMOKE uses. The 6-digit system was designed for use in the United States with states and
counties, as well as Canada and Mexico, but it can be adapted for other uses.
EPA uses Source Classification Codes (SCCs) and area and mobile source (AMS) codes to
classify different types of anthropogenic emission activities. SCCs have 8 digits for point
sources, while AMS codes have 10 digits. SMOKE imports the inventory, assigns temporal and
spatial allocation (prescribed resolution), performs chemical speciation, calculates growth
factors, and determines some specific parameters for mobile sources. In principle, SMOKE uses
county level emissions to allocate emission categories for a given grid resolution. SMOKE
outputs are direct inputs to the photochemical model CMAQ.
26
The input data for SMOKE was the 2005 EPA National Emission Inventory
(http://www.epa.gov/ttn/chief/net/2005inventory.html), which is a comprehensive collection of
emissions data collected from state, tribal and local sources focusing on emissions that are
potentially harmful to people such as carbon monoxide and volatile organic compounds (the
latter being the chief source of ozone formation).
Figure 16 shows the counties included in our modeling domains.
Fig. 16. Domains 2 and 3 for the WRF simulations, with county names indicated. Note that
Carson City is its own county.
One of the source types tracked by CMAQ is mobile emissions (i.e., vehicles). For these, in
addition to SMOKE, we used an emission pre-processor called MOVES (Motor Vehicle
Emission Simulator). MOVES reads in survey data for vehicle emissions—including those that
occur while a vehicle is idling or parked. It also adjusts its calculations to take into account time
of day, weekends, and holidays, as well as differentiating between the emissions at various
vehicle speeds. Because of the extent of these calculations, setting up and using MOVES is timeconsuming, as is reading the resultant data into SMOKE. The input to MOVES and SMOKE is
27
separated by county. In total, the second domain includes pieces of 31 counties, which
complicates the work.
The following figures illustrate some of the SMOKE outputs for emissions of some of the main
pollutants that are input to the CMAQ model.
Figure 17. Emission rates of carbon monoxide from area sources (upper panel) and from
biogenic sources (lower panel) for 13 June 2010 at 00 UTC as estimated by SMOKE for the run
13-16 June 2010.
28
Fig.18. Emission rates for isoprene for 15 June 2010 at 00 UTC (upper panel) and methanol for
14 June 2010 at 00 UTC (lower panel) from the biogenic sources.
Note that some of the figures show maximum values over CA urban areas such as Sacramento
valley (e.g., methanol) and most of them show maximum values over the western ridges as a
29
superposition of urban regional transport and biogenic sources (e.g., isoprene, carbon monoxide).
4. Vehicle emission model – MOVES
(http://www.epa.gov/otaq/models/moves/#generalinfo )
In order to process mobile emission sources and perform subsequent sensitivity tests, we had to
install and run the Motor Vehicle Emission Simulator (MOVES) emission model using the EPA
emission inventory as input. The input to MOVES is the MOVES default database created by
the U.S. EPA (updated 10/30/2012). It includes data from several studies of vehicle emissions
into an extensive database that takes several factors into account, including vehicle age and tire
wear. The purpose of this tool is to provide a reasonable estimate of emissions from mobile
sources under a wide range of user-defined conditions. In the modeling process, the user
specifies vehicle types, time periods, geographical areas, pollutants, vehicle operating
characteristics, and road types to be modeled. The model then performs a series of calculations,
which have been developed to accurately reflect vehicle operating processes, such as cold start or
extended idle, and provide estimates of bulk emissions or emission rates. Specific scenarios are
defined in a Run Specification or RunSpec file. The current version (2010) can easily work with
data bases and new data can be incorporated into the model. The MOVES model includes a
default data base that summarizes emission-relevant information for the entire U. S. The data for
this data base comes from many sources including EPA research studies, Census Bureau vehicle
surveys, Federal Highway Administration travel data, and other federal, state, local, industry, and
academic sources. MOVES can be used to estimate national, state, and county level inventories
of criteria pollutants, greenhouse gas emissions, and mobile source air toxics from highway
vehicles. Tables 4 and 5 demonstrate the complexity of vehicle emission information for a
selected county.
Table 4. Example of mobile-source data for SMOKE. This particular part is a subset of the
computed mobile emissions for Placer County, CA (FIPS=6061) for running exhaust
(smokeProcID=EXR) for various gasoline vehicles. The data for July is taken as representative
for the period.
MOVESScenarioID
RD_06061_2009_7_T35_105
RD_06061_2009_7_T35_105
RD_06061_2009_7_T35_105
RD_06061_2009_7_T35_105
RD_06061_2009_7_T35_105
RD_06061_2009_7_T35_105
RD_06061_2009_7_T35_105
RD_06061_2009_7_T35_105
RD_06061_2009_7_T35_105
RD_06061_2009_7_T35_105
RD_06061_2009_7_T35_105
RD_06061_2009_7_T35_105
RD_06061_2009_7_T35_105
yearID monthID FIPS SCCsmoke smokeProcID
2009
7 6061 2201001110 EXR
2009
7 6061 2201001110 EXR
2009
7 6061 2201001110 EXR
2009
7 6061 2201001110 EXR
2009
7 6061 2201001110 EXR
2009
7 6061 2201001110 EXR
2009
7 6061 2201001110 EXR
2009
7 6061 2201001110 EXR
2009
7 6061 2201001110 EXR
2009
7 6061 2201001110 EXR
2009
7 6061 2201001110 EXR
2009
7 6061 2201001110 EXR
2009
7 6061 2201001110 EXR
temperature relHumidity THC
CO
NOX
CH4
N2O
BENZENE
35
30.400 2.304 32.172
2.093 1.11E-01 1.19E-01 1.01E-01
35
30.400 1.247 18.489
1.454 6.07E-02 5.95E-02 5.45E-02
35
30.400 0.711 11.418
1.099 3.48E-02 2.98E-02 3.11E-02
35
30.400 0.521 8.723
0.928 2.55E-02 1.98E-02 2.27E-02
35
30.400 0.406 7.353
0.819 2.00E-02 1.49E-02 1.77E-02
35
30.400 0.349 6.626
0.803 1.72E-02 1.19E-02 1.52E-02
35
30.400 0.316 6.369
0.808 1.57E-02 9.92E-03 1.38E-02
35
30.400 0.296 6.552
0.846 1.50E-02 8.50E-03 1.29E-02
35
30.400 0.281 6.667
0.873 1.44E-02 7.44E-03 1.22E-02
35
30.400 0.268 6.713
0.891 1.39E-02 6.61E-03 1.17E-02
35
30.400 0.254 6.553
0.890 1.32E-02 5.95E-03 1.11E-02
35
30.400 0.240 6.312
0.884 1.26E-02 5.41E-03 1.05E-02
35
30.400 0.230 6.156
0.885 1.21E-02 4.96E-03 1.00E-02
30
Table 5. More example data. Evaporative permeation data (smokeProcID=EVP) for Carson City, NV
(FIPS=32510). (Only non-zero values shown.) The “avgSpeedBinID” divides the vehicle speeds into 5
mph intervals.
MOVESScenayearID monthID FIPS SCCsmoke smokeProcID avgSpeedBinIDtemperature relHumidity THC
BENZENE TRMEPN224 ETHYLBENZ HEXANE TOLUENE XYLS NMHC NMOG TOG
VOC_INV
RD_32510_200 2009
7 32510 2201001110 EVP
1
35
27.5
2.533
0.023
0.050
0.064
0.056
0.244 0.203 2.533
2.533 2.533
2.533
7 32510 2201001110 EVP
2
35
27.5
1.266
0.011
0.025
0.032
0.028
0.122 0.101 1.266
1.266 1.266
1.266
RD_32510_200 2009
RD_32510_200 2009
7 32510 2201001110 EVP
3
35
27.5
0.633
0.006
0.013
0.016
0.014
0.061 0.051 0.633
0.633 0.633
0.633
RD_32510_200 2009
7 32510 2201001110 EVP
4
35
27.5
0.422
0.004
0.008
0.011
0.009
0.041 0.034 0.422
0.422 0.422
0.422
7 32510 2201001110 EVP
5
35
27.5
0.317
0.003
0.006
0.008
0.007
0.031 0.025 0.317
0.317 0.317
0.317
RD_32510_200 2009
RD_32510_200 2009
7 32510 2201001110 EVP
6
35
27.5
0.253
0.002
0.005
0.006
0.006
0.024 0.020 0.253
0.253 0.253
0.253
7 32510 2201001110 EVP
7
35
27.5
0.211
0.002
0.004
0.005
0.005
0.020 0.017 0.211
0.211 0.211
0.211
RD_32510_200 2009
RD_32510_200 2009
7 32510 2201001110 EVP
8
35
27.5
0.181
0.002
0.004
0.005
0.004
0.017 0.014 0.181
0.181 0.181
0.181
RD_32510_200 2009
7 32510 2201001110 EVP
9
35
27.5
0.158
0.001
0.003
0.004
0.004
0.015 0.013 0.158
0.158 0.158
0.158
7 32510 2201001110 EVP
10
35
27.5
0.141
0.001
0.003
0.004
0.003
0.014 0.011 0.141
0.141 0.141
0.141
RD_32510_200 2009
RD_32510_200 2009
7 32510 2201001110 EVP
11
35
27.5
0.127
0.001
0.003
0.003
0.003
0.012 0.010 0.127
0.127 0.127
0.127
7 32510 2201001110 EVP
12
35
27.5
0.115
0.001
0.002
0.003
0.003
0.011 0.009 0.115
0.115 0.115
0.115
RD_32510_200 2009
RD_32510_200 2009
7 32510 2201001110 EVP
13
35
27.5
0.106
0.001
0.002
0.003
0.002
0.010 0.008 0.106
0.106 0.106
0.106
RD_32510_200 2009
7 32510 2201001110 EVP
14
35
27.5
0.097
0.001
0.002
0.002
0.002
0.009 0.008 0.097
0.097 0.097
0.097
7 32510 2201001110 EVP
15
35
27.5
0.090
0.001
0.002
0.002
0.002
0.009 0.007 0.090
0.090 0.090
0.090
RD_32510_200 2009
RD_32510_200 2009
7 32510 2201001110 EVP
16
35
27.5
0.084
0.001
0.002
0.002
0.002
0.008 0.007 0.084
0.084 0.084
0.084
5. Photochemical modeling using CMAQ
The grid used for CMAQ was based on the domain 2 WRF grid (see Figs. 2 and 16). This grid
corresponds to the resolution of the EPA NEI and also includes a broader area that allows for
examining the regional characteristics of ozone and ozone pre-cursors. The program MCIP
(Meteorology-Chemistry Interface Processor; Otte and Pleim, 2009) reads in the output from
WRF and creates the files that CMAQ needs. In this case, the CMAQ grid used the same vertical
layers as the WRF grid (42 levels). The horizontal grid for CMAQ was the one for WRF with the
resolution of 4 km, except six points on each side were removed to eliminate boundary problems.
31
Fig. 19. Mean CMAQ simulated ozone (ppmV) for the entire episode 9-13 (upper panel) and the
peak of the episode 11-12 (lower panel) August 2009.
Figure 19 shows a spatial distribution of the mean ozone for the entire episode from 9 to 13 Aug
2009 as well as for the sub-period of highest ozone 11-12 August 2009. Note that the ozone
levels were elevated in the Lake Tahoe basin, while the urban areas of the Sacramento valley and
the Reno basin have lower mean values at that time, possibly due to large NOx concentrations
causing low minimum values of ozone during nighttime. See detailed discussion in Sections 811.
32
Fig. 20. CMAQ-simulated ozone at time of peak reading at the South Lake Tahoe Airport station (August
12, 0100 UTC).
Figure 20 shows the simulated ozone at the time of the peak. Note the colorbar scale adjustments
comparing Figs. 19 and 20. The maximum ozone concentrations are over the western ridges
where there are significant biogenic emissions and also where ozone and ozone pre-cursors
possibly accumulate from the Central Valley. We will see from the subsequent text that this area
is also characterized by significant biogenic emission sources.
Figure 21 shows a time series of the simulated vs. measured ozone at the South Lake Tahoe
station. The measurements show a gradual increase of ozone peaks with the maximum on 12
Aug 2009.
33
Fig. 21. Time series of measured and modeled ozone at the South Lake Tahoe station for the
episode 9-13 Aug 2009 (a baseline run).
The evaluation shows that the model cannot fully reproduce the amplitude of the ozone extremes
at this location, but it shows a correct order of magnitude and even the gradual trend of increased
ozone in time. The measured values range from 0.01 to 0.08 ppmV, while the model has a
narrower range from 0.03 to 0.05 ppmV. Obviously the model’s input emissions have much less
diurnal variation and/or the model is not able to reproduce the diurnal variation of the chemistry.
Additionally, Fig. 13 shows that the simulated winds were overestimated which can cause more
mixing and lower concentrations, especially during the ozone peak hours. The temperature was
relatively well simulated, but still the underestimated temperature maximum during the peak
ozone hours generally reduces the simulated ozone maximum. Note that the sensitivity tests for
South Lake Tahoe are shown separately because of scale (see Figs. 24 and 33). Similar
maximum concentrations at similar times at South Lake Tahoe and Echo Summit can be due to
downward vertical mixing during the greatest temperatures and solar radiation. However, one
should note that the maximum ozone concentrations upwind were a day earlier and that could
correspond to the 1-day transport from the upwind sources to the Tahoe area. Since the
concentrations at South Lake Tahoe are not reduced by effects of local emissions if fresh NO and
considering a one-day delay in the maximum concentrations in the Tahoe basin compared to
Sacramento, the simulations suggest that the cause for the elevated ozone concentrations in the
34
Tahoe basin is mainly regional transport of ozone and ozone pre-cursors from the upwind areas.
This will be further elaborated in the next sections.
We also evaluated the CMAQ results in a broader region where there were meteorological and
ozone measurements conducted. Figure 22 shows a time series of measured and simulated ozone
at the southern ridge of the Tahoe basin (Echo Summit) and at an urban location (Sacramento, Tstreet), respectively. The Echo Summit location (2235 m elevation) represents the southern
Sierra crest basin boundary.
Fig. 22. Time series of measured and modeled ozone at the Echo Summit (upper panel) and
Sacramento T-street (lower panel) stations for the episode 9-13 Aug 2009.
35
In addition to the baseline run, sensitivity tests are also shown which will be discussed in the
next section.
The main result is that the CMAQ model underestimates peak ozone concentrations and also
concentration amplitudes at the ridge and near the lake; however, the simulations are much more
accurate with respect to ozone concentrations and diurnal variation in the Sacramento urban area.
The peak ozone values are much less underestimated, and the nighttime minimum values are
reproduced well. Note that a similar magnitude of ozone concentrations and their variations over
all three areas indicate a regional pollution plume.
Note an important result from Fig. 22: the stations in the Tahoe basin show modest, but
distinguishable reduction in ozone concentrations with reducing mobile emissions. In contrast,
the Sacramento T-street station shows generally the opposite effect: reducing mobile emissions
does not generally provide improvements in reducing ozone concentrations, moreover it actually
increases the ozone concentrations. Note that double emissions generally yield lower
concentrations at the T-street station. All of these findings indicate that control strategies cannot
be uniform over the entire area. The urban area appears to have the characteristics of a VOC
limited region, while the Tahoe station shows characteristics of a weakly NOx limited area.
In summary, considering a relatively favorable comparison of the model vs. measurements with
respect to a correct order of magnitude of ozone concentrations and the increasing trend in time
together with the satisfactory simulations of the maximum temperatures, we have selected this
case for sensitivity tests and possible control strategies.
6. Sensitivity tests and control strategies
For the sensitivity runs, the main strategy was to reduce by half each component of the emission
inputs (mobile, biogenic, point, and area sources) and then to compare the results with the base
run (control run) with the original emissions. We also simultaneously reduced all emission
inputs for an additional sensitivity run. These tests show how the model responds to overall
emissions reduction.
Figure 23 shows the spatial distribution of the differences between the run in which all emissions
were reduced by half and the control run with full emissions and also the differences between the
run with half mobile emissions and the control run.
36
Fig. 23. Mean differences in ozone (August 11-12, as simulated by CMAQ) for the sensitivity
run in which all emissions (upper panel) and only mobile emissions (lower panel) were reduced
by half vs. the baseline run.
A first inspection of Fig. 23 indicates that the main impact on the ozone production is related to
the mobile emissions. The strongest impact of the reduction of all emissions occurs on the
western ridges of the Tahoe basin where a large pool of ozone and ozone pre-cursors
accumulates. This is also an area with significant biogenic emissions (Fig. 18). Note that with
reduced emissions, CMAQ simulates on average higher ozone in the cities and lower ozone over
the Sierra Nevada Mountains, possibly due to reduction of NOx which likely increases ozone
due to a less intense titration. Consequently, it is extremely important to know how accurate the
emission inventory is and how well the photochemical model simulates the evolution of NOx.
37
Figure 24 shows time series of NOx for the South Lake, Echo Summit, and Sacramento T-street
stations. Various sensitivity tests are also included in the figure.
38
Fig. 24. Time series of simulated NOx for the baseline run and sensitivity tests for South Lake
Tahoe (top), Echo Summit (middle), and Sacramento T-street (bottom panel). Note that NOx
measurements exist for the T-street station (dashed red line).
It is important to notice that the NOx at T-street is almost two orders of magnitude greater than at
Echo Summit and South Lake Tahoe. The model responds well to reductions in the input
emissions and shows reduction of NOx. Regarding T-street where there were NOx
measurements, the NOx appears to be generally well simulated, except during 10 August.
Depending on whether the area is NOx or VOC limited, the changes in NOx concentration will
have a different effect on the ozone production. If the area is VOC limited, then even with the
reduction of NOx, ozone production can increase. The relationship between the NOx and
simulated ozone was examined for the whole episode 9-13 August. The locations inside the
Tahoe basin show two NOx maxima corresponding to morning and afternoon peak traffic hours,
while the morning peak is more pronounced at T-street. The afternoon peak might be lower due
to much more intense mixing causing lower concentrations in the Central Valley.
This is further refined by separating the whole period into nighttime (2100-0500 PST) and
daytime (1000-1800 PST) parts.
Figure 25 shows a scatter plot of NO2 vs. ozone concentrations for the entire episode.
39
Fig. 25. Scatter plot of CMAQ simulated ozone vs. NO2 concentrations for the episode 9-13 Aug 2009.
Figure 25 shows an expected general trend of decreasing ozone by increasing NO2 concentrations.
Although the split into daytime and nighttime results does not clearly provide separation, it still shows
that the nighttime high NO2 is mainly associated with low ozone concentrations. The figure indicates that
the area is possibly VOC limited since the increase of NOx was associated with lower ozone
concentrations.
40
Fig. 26. Simulated average ozone (upper panel) and ozone concentration differences for the
sensitivity run with all emissions reduced by half (lower panel) for the peak episode 11-12 Aug
2009.
Figure 26 shows that the main reduction of ozone by halving emissions is simulated over the
western ridges of the Tahoe basin due to reduction of biogenic sources. There is also some
reduction in the Tahoe basin itself, but it is less pronounced possibly due to lower NOx that
cannot inhibit ozone formation.
We further analyzed the mobile emission effect by separately reducing off-road and on-road
emissions by half, respectively. Figure 27 shows the results of these sensitivity tests.
41
Fig. 27. Sensitivity runs for simulated ozone for half off-road emissions (upper panel) and half
on-road emissions (lower panel) for the peak ozone episode 11-12 Aug 2009.
Figure 27 shows that the reduction of on-road emissions has a much stronger impact over the
entire Tahoe basin and the western regions, while the reduction of the off-road emissions causes
approximately an order of magnitude less of an effect on ozone reduction. So, the smaller ozone
concentrations over the Lake Tahoe basin of about 1-2 ppbV in the sensitivity run are mainly due
to the on-road emissions.
42
One of the most important questions is how much of an effect the emission reduction may have
on ozone peaks. In the following figures, we show results from the sensitivity tests for the 11-12
Aug period when the episode was most intense (according to the SLT station ozone
measurements) and the effect of the reduction on the ozone peak on 12 Aug at 0100 UTC.
Figure 28 shows the differences of the simulated ozone for reduction of on-road emissions by
half.
Fig. 28. Simulated average ozone concentrations for the sensitivity run with on-road emissions
reduced by half for 11-12 Aug period (upper panel) and for the peak episode on 12 Aug 2009 at
0100 UTC (lower panel).
43
Figure 28 shows that the spatial distribution is similar in general characteristics, but the reduced
values are generally twice as large for the peak (up to 4 ppbV) compared to the two-day event of
elevated ozone concentrations (up to 4 ppbV). As discussed earlier, emission reductions in a
large urban area (Sacramento) cause lower NOx concentrations which inhibit ozone production
and the sensitivity runs show increases in ozone concentrations compared to the baseline run.
The maximum increase is again twice as large for the peak time (6 ppbV) compared to the 11-12
Aug period (3 ppbV).
Figure 29 shows the differences due to sensitivity runs with half biogenic emissions for the 1112 Aug period and for the peak ozone concentration hour.
Fig. 29. Sensitivity runs for simulated ozone with half biogenic emissions for the 11-12 Aug
period (upper panel) and for the peak ozone hour on 12 Aug 2009 at 0100 UTC (lower panel).
44
Most of the Lake Tahoe area shows a small ozone reduction (1-2 ppbV) for the peak ozone time
and up to 1 ppBV for the 11-12 Aug period. The reduction has its major impact over the Sierras’
western ridges up to 9 ppbV.
Figure 30 compares the effect on simulated ozone by reducing off-road emissions.
Fig. 30. Sensitivity runs for simulated ozone with half off-road emissions for the 11-12 Aug
period (upper panel) and the peak ozone hour on 12 Aug 2009 at 0100 UTC (lower panel).
It is important to note that the figure shows an order of magnitude lower differences compared to
the sensitivity test with reducing on-road emissions.
45
The analyzed figures confirm that the major impacts on the simulated ozone reduction are due to
mobile and biogenic emissions. The major reduction occurs over the western ridges of the Tahoe
basin and to a lesser extent over the basin itself. Note that the ozone reduction is uniform for
reduction of the biogenic sources (negative values on the colorbar), while the reduction of
mobile emissions causes smaller NOx and less effect on the ozone concentration reduction.
Although one would expect the mobile emissions to have a larger effect, both reductions are on
the order of 1-2 ppbV on average due to the NOx effect.
Since the strongest impact is from the mobile sources, we also performed a sensitivity test with
doubled mobile emissions to examine if the model correctly responds to increased emissions and
also to see how much the model’s nonlinear dynamics and chemistry can cause an ozone
increase in the case of increasing mobile emissions. As a reference, the baseline ozone
simulation result is also shown.
Fig. 31. Differences of simulated average ozone (upper panel) and ozone concentrations for the
sensitivity run with all emissions doubled (lower panel) for the peak episode 11-12 Aug 2009.
Doubling mobile emissions induces an increase of average ozone of 1-2 ppbV in the basin
46
considering the average over the whole episode (Fig. 31).
Figure 32 shows the effect of doubling mobile emissions on the peak ozone concentrations.
Fig. 32. Simulated ozone concentrations for the sensitivity run with all emissions doubled (lower
panel) for the peak ozone concentrations on 12 Aug 2009 at 0100 UTC.
Doubling the mobile emissions affects the ozone peak concentrations more; their increase is
about 5 ppbV over the Tahoe area.
7. EPA statistical measures
EPA recommends several statistical measures for model evaluation as follows.
The recommended statistical parameters for the bias are the mean bias (MB) and the normalized
mean bias (MNB). The measures for the error are the root mean square error (RMSE) and the
normalized mean error (NME). The formulas for MB, NMB, RMSE and NME are as follows
(Yu et al., 2005 and Zhang et al. 2006):
47
Table 6. Results of the comparison between the model results (“model”) and measurements
(“obs”) at the South Lake Tahoe station.
Mean Bias (ppmV)
Norm Mean Bias (%)
RMSE (ppmV)
NME (%)
Baseline Half‐Area Half‐bio Half‐mobile Half‐onroad Half‐offroad Half‐point Half‐all Double‐mobile
‐0.0023 ‐0.0023 ‐0.0027
‐0.0032
‐0.003
‐0.0024
‐0.0023 ‐0.0033
‐0.0015
‐4.93
‐4.93
‐5.65
‐6.65
‐6.38
‐5.13
‐4.93
‐6.92
‐3.14
0.0159
0.0159
0.0162
0.0171
0.017
0.0161
0.0159
0.0173
0.0143
29.56
29.56
30.1
31.85
31.55
29.82
29.56
32.05
26.44
For NMB, EPA recommends absolute values less than or equal to 15% and for NME values
recommends less than or equal to 30%. Firstly, one can see that the model conforms to the EPA
recommended values for NMB for all runs, while the values for NME are right at or above the
recommended value. Note that emissions which do not have significant impact on the simulated
ozone (area, point, and to some extent off-road) show all parameters similar to the baseline. The
impact is significant for mobile emissions (except off-road emissions) and biogenic emissions.
Halving of the emissions generally worsens the statistics, which indicates that the emissions are
most likely not overestimated. Doubling the emissions, however, produces smaller errors which
indicate that the emissions, at least in the southern part of the Lake Tahoe basin, are possibly
underestimated. The reduction of emissions for the whole period yields a change in the bias of
only 1 ppbV, but the main effect of the emission reduction can be examined only by checking the
change of the ozone peaks due to the emission reduction.
There are some interesting features observed while examining the hourly values of the
measurements and simulations for various sensitivity tests at the South Lake Tahoe station (Fig.
33). Note that Fig. 21 shows a comparison between the model and measurements.
48
49
Fig. 33. Time series of baseline and sensitivity test simulations for various reductions and
doubling of the emission categories at the SLT station for 9-13 Aug 2009.
The first impression from Fig. 33 is that there are large changes in the values among the baseline
and sensitivity tests, especially having in mind Fig. 21 where the model was able to represent a
correct order of magnitude and increasing trends of ozone concentrations, but highly
underestimated the maxima and overestimated the minima.
An average value of measured ozone concentrations for the whole 11-12 Aug period was 47
ppbV and the model showed a fairly good average value of 45 ppbV with a reasonable
correlation coefficient of 0.49. However, the maximum measured ozone concentration during
the peak period 11-12 Aug was 77 ppbV on 12 Aug at 0100 UTC, while the baseline model at
that time showed only 47 ppbV. Note that the state 1-hr ambient air quality standard for ozone
concentrations is not to exceed 90 ppbV, while the local TRPA standard is not to exceed 80
ppbV. Also, due to small variations of the simulations the model keeps a gradual increase after
the measurements actually show a decrease and the measurement maximum of 52 ppbV occurred
at the end of the period. Doubling the emissions increases the maximum to 51 ppbV and
significantly increases the correlation coefficient to 0.70, while for the various reductions the
average and maximum values drop for a relatively small difference. The difference is quite
insignificant for emission reduction runs with a low impact on the simulated concentrations (half
area, half off-road, and half point tests). For other tests (half bio, half mobile, and half on-road)
the differences in the average and maximum are rather small – only about 1 ppbV. The peak
values were reduced only by 1 ppbV for half-biogenic and by 3 ppbV for half-mobile emission
reductions. As expected, with the emission reduction the correlation worsens; however, it
improves with doubling the emissions. All of this suggests that the model has problems with
variability of the ozone concentrations and that significant reductions do not induce significant
50
effects in the model’s results. Assuming that the model responds well to the variations in the
emission inputs, the tests also indicate that the emissions are most likely underestimated and
even doubling does not produce sufficient peak values.
For Echo Summit (Fig. 22), the results are similar to the results for South Lake Tahoe. A broad
ozone maximum occurred between 2000 UTC on 11 Aug to 0100 UTC on 12 Aug. Although the
correlation coefficient between the measurements and the model was quite high (0.60), the
average value of measured ozone concentrations was 61 ppbV, while the model showed 44
ppbV. The measured peak was 78 ppbV, while the model showed only 47 ppbV. As in the SLT
case, the reduction with half biogenic emission led to only a 1 ppbV reduction in ozone and with
half mobile, it led to a reduction of 3 ppbV of the ozone peak. Again, correlation worsened for
emission reductions, but reached 0.68 for doubling the emissions.
For Sacramento T-street (Fig. 22), the results were different compared to South Lake Tahoe and
Echo Summit. The average values for the 11-12 Aug period were 24 ppbV (measured) and 29
ppbV (model) with a high correlation coefficient of 0.82. The maximum ozone concentration of
76 ppbV was measured at 2000 UTC on 10 Aug, while the model showed 41 ppbV. The
reduction with half biogenic emission led to a 4 ppbV reduction in ozone and with half mobile, it
led to an increase of 7 ppbV of the ozone peak. According to the present study, it is suggested
that the efficient method in this urban area is not to reduce mobile emissions, but to reduce
VOCs, while in the Lake Tahoe area, reductions in both mobile and biogenic emissions can lead
to reductions of ozone concentrations peaks. However, the reductions in ozone peaks are quite
small for large reductions of the emissions.
In addition to examining the effects of sensitivity tests for particular locations (South Lake
Tahoe, Echo Summit, and Sacramento T-street), we computed differences in ozone
concentrations between the each sensitivity run and the baseline run for each grid point of the
domain. By this process, we can estimate the effect of reduced (increased emissions) for the
entire area. Table 7 shows the average differences calculated over the entire domain (ppbV).
Table 7. Differences in simulated ozone (ppbV) between the sensitivity runs and the baseline
run as averaged from all model grid points in the entire domain.
Tests
Half-all
Half-mobile
Double mobile
Half point
Half area
Half bio
Half onroad
Half offroad
Aug. 9-12
Aug. 11-12
-1.200000
-1.600000
-1.100000
-1.400000
1.200000
1.400000
-0.003470
-0.001130
-0.003330
-0.000955
-0.421970
-0.580000
-0.833850
-1.100000
-0.178190
-0.220000
Table 7 shows an overall effect of simulated ozone concentrations of the sensitivity runs
compared to the baseline run. Negative values indicate that a sensitivity run overall produces
smaller ozone concentrations. In contrast, doubling the mobile emission produces greater ozone
concentrations overall. As expected, the effects of reducing point and area emissions are about
51
3-4 orders of magnitude smaller than the effects of mobile and biogenic emissions.
The spatial plots of the sensitivity tests as well as time series plots indicate that the urban area of
the Sacramento Valley exhibits different characteristics compared to the Tahoe area stations, i.e.,
the reduction of mobile emissions does not necessarily reduce ozone concentrations and their
peak values at the Sacramento station.
8. Regional transport of ozone and ozone pre-cursors: Synoptic conditions
In order to investigate the regional aspects of pollution, it is necessary to examine the
characteristics of synoptic conditions relevant to the ozone episode.
Surface weather maps (http://www.noaa.gov) indicate the presence of a high pressure ridge
developing after the frontal passage on the northern part of the West Coast and a trough with
weak pressure gradients on the southern part of the West Coast that supported the northwesterly
air flow on 9 August 2009 (Fig. 34). At the beginning of the ozone episode there was a
significant change in the surface pressure fields, with movement of the ridge and the high
pressure system from the north toward inland and the propagation of the trough from the south to
the north. This setup created an area of weak pressure gradients over the majority of the West
Coast. This synoptic situation supported the advection of ozone and ozone pre-cursor rich air
from the west toward the Lake Tahoe area. Surface maximum and minimum temperature fields
showed the advection of southern warm air toward the north during the episode and a significant
change in surface maximum temperatures at the northern part of the West Coast (Fig. 34). A
large ridge of high pressure at 500 hPa height extending from south to north supported the
surface conditions over the analyzed area during the episode (Fig 35).
52
Fig. 34. Surface pressure fields at 7 AM in (EST) for the period from 9 to 13 of August 2009.
53
54
Fig. 35. Surface maximum and minimum temperatures in the period from 9 to 13 August 2009.
The highest and lowest temperature chart shows the maximum temperature for a period from
0700 am through 0700 pm EST the previous day and the minimum temperature for the period
from 0700 pm EST the previous day through 0800 am EST.
55
Fig. 36. 500 hPa height contours over the U.S. at 0700 am EST in the period from 9 to 13 Aug
56
2009.
9. Regional transport of ozone and ozone pre-cursors: Trajectory analysis
To examine the effects of regional emission sources and possible regional transport of ozone and
ozone pre-cursors, we used the HYSPLIT (HYbrid Single-Particle Lagrangian Integrated
Trajectory) model (HYSPLIT, 2009). HYSPLIT is a complete system for computing simple air
parcel trajectories based on complex dispersion and deposition simulations and was originally
developed jointly by NOAA and Australia's Bureau of Meteorology. Without the additional
dispersion modules, HYSPLIT computes the advection of a single pollutant particle, or simply
its trajectory, and has been used in many studies (e.g., Koracin et al., 2010). The dispersion of a
pollutant is calculated by assuming either puff or particle dispersion. In the puff model, puffs
expand until they exceed the size of the meteorological grid cell (either horizontally or vertically)
and then split into several new puffs, each with it's share of the pollutant mass. In the particle
model, a fixed number of particles are advected about the model domain by the mean wind field
and spread by a turbulent component. The model's default configuration assumes a 3dimensional particle distribution (horizontal and vertical).
9.1 Input meteorology
a. Global Data Assimilation System (GDAS)
(ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1/)
The National Weather Service's National Centers for Environmental Prediction (NCEP) runs a
series of computer analyses and forecasts operationally. One of the operational systems is the
GDAS (Global Data Assimilation System). At NOAA's Air Resources Laboratory (ARL), NCEP
model outputs are used for air quality transport and dispersion modeling. ARL archives both
EDAS (Eta Data Assimilation System) and GDAS outputs. Both archives contain basic fields
such as the u- and v-wind components, temperature, and humidity. However, the archives differ
from each other because of the horizontal and vertical resolution, as well as in the specific fields
provided by NCEP.
The 3-hourly archive data come from NCEP's GDAS. The GDAS is run 4 times a day, i.e., at 00,
06, 12, and 18 UTC. Model output is for the analysis time and 3, 6, and 9-hour forecasts. NCEP
post-processing of the GDAS converts the data from spectral coefficient form to 1 degree
latitude-longitude (360 by 181) grids and from sigma levels to mandatory pressure levels. Model
output is in the GRIB format. ARL saves the successive analyses and 3-hour forecasts, four
times each day to produce a continuous data archive. ARL processing converts NCEP's 1-degree
GRIB output and produces a 3 hourly, global, 1 degree latitude and longitude dataset on pressure
surfaces. If processing for a cycle fails, the data from the 6-h and 9-h forecast will fill in missing
data.
b. North America Mesoscale (NAM) fields
(http://nomads.ncdc.noaa.gov/data.php#hires_weather_datasets)
57
NOAA ARL has started a 31-day rotational archive of the first 3 hours of NAM 12-km
resolution meteorological data from NOAA NCEP. An extraction procedure was implemented
to concatenate the initial field (+0 h forecast) and the +3 h forecast for each cycle, four times
daily, into daily files that can be accessed as a pseudo analysis archive.
9.2 HYSPLIT back trajectories
HYSPLIT back trajectories were created using both NAM and GDAS input meteorological
fields. Figure 36 shows HYSPLIT back trajectories for the 3-day period ending on 13 Aug at
0000 UTC at the South Lake Tahoe location.
Fig. 37. Three-day backward trajectories arriving at South Lake Tahoe Airport in the period from
11 to 13 August 2009, at 10m (red), 500 m (blue) heights.
Figure 37 shows that there was a favourable west-southwest advection from the Sacramento
valley pollutants toward the Lake Tahoe area. The transport was vertically uniform with almost
the same wind direction at the surface and 500 m height, especially approaching the Sierras and
the Lake Tahoe area. The winds appear to be weak, and subsidence was capping boundary layer
pollution mixed within the first 500 m or so. All this suggests favourable conditions for a
regional pool of ozone and ozone pre-cursors affecting the Lake Tahoe area.
To investigate the time evolution of advection and its effects on the transport of ozone and precursors, we produced HYSPLIT back trajectories ending at each day of the episode. We also
extended the back trajectories for two days before (7 and 8 Aug) and after (14 and 15 Aug) to
58
examine possible advection characteristics for the formation and end of the episode. Figure 38
shows this sequence of back trajectories ending at the South Lake Tahoe location from 7 to 15
Aug.
59
60
61
62
Fig. 38. Two-day backward trajectories arriving at South Lake Tahoe Airport in the period from
7 to 15 August 2009, at 10m (red), and 500 m (blue) heights calculated with NAM
meteorological archived files on 12 km horizontal resolution (left) and with GDAS calculated
with global GDAS meteorological archived files on 111 km (1°) horizontal resolution (right).
The times for NAM and GDAS are quite similar; however, there are some differences between
their results such as for 7 and 15 Aug, but there is pretty good general resemblance (note the
differences in the horizontal resolution of these fields). Prior to the elevated ozone concentrations
at South Lake Tahoe (7 and 8 Aug), the advection had significant components from the north
(relatively clean air coming), in some cases bringing a clean air mass from the ocean. On 9 Aug,
the surface winds started curling around the Lake Tahoe area indicating stagnant conditions.
During 11-13 Aug, the surface winds had a dominantly westerly direction, which favors regional
transport of pollutants from the Sacramento valley toward the Tahoe area. On 14 Aug the winds
were getting onshore components bringing a maritime air mass. On 15 Aug the NAM back
trajectories show onshore components on both levels while the GDAS results turned to northerly
directions. In both cases these conditions are less favorable to maintaining elevated
concentrations and consequently contributed to the end of the episode.
9.3 HYSPLIT forward trajectories
To further clarify the effects of the possible regional transport (correlation between a source and
a receptor), forward trajectories were generated for the period of the ozone episode. This
sequence of figures shows 24-hr forward trajectories from the Sacramento T-street station for
every day from 9 to 13 Aug.
63
Fig. 39. Forward 24-hr trajectories starting at 00 UTC from Sacramento calculated for 10 m (red)
and 50 m (blue) heights with NAM and GDAS meteorology for period from 9 to 10 August
2009.
64
Fig. 40. Forward 24-hr trajectories from Sacramento calculated at 10 m (red) and 50 m (blue)
heights with NAM and GDAS meteorology starting at 00 UTC for period from 11 to 12 of
August 2009.
65
Fig. 41. Forward 24-hr trajectories from Sacramento calculated at 10 m (red) and 50 m (blue)
height with NAM and GDAS meteorology starting at 00 UTC for 13 of August 2009.
In the beginning of the episode (9-10 Aug) the forward trajectories indicate northerly regional
flows along the coast channeled by the Sierras. On 11 and 12 Aug there was a significant change
of advection with westerly regional flow across the mountains toward the Lake Tahoe area. On
13 Aug the flow again turned to northerly which is associated with the decrease of the ozone
concentrations. These flow characteristics further suggest that the level of ozone concentrations
in the Tahoe area is impacted by the transport of regional pollution.
10. Discussion
A comprehensive meteorological and air quality modeling study was conducted to understand
the spatial and temporal characteristics of ozone concentrations in the Lake Tahoe area.
Meteorological conditions were simulated during ten episodes of elevated ozone concentrations,
selected based on ozone measurements at the referent station South Lake Tahoe. Both the
meteorological and photochemical models were on two domains with 12 and 4 km resolutions,
respectively. The meteorological simulations were evaluated using stations at South Lake Tahoe,
Homewood, TRPA, and Sacramento locations. Unfortunately, analysis of the wind data showed
that there were problems with measurements at all locations in terms of erratic sudden changes
and frequent reset to the zero level. However, the temperature measurements show consistency
and expected behavior. WRF was able to follow the diurnal variation of the temperature, but
symptomatically underestimated maxima and overestimated minima, i.e., simulated temperatures
are in a narrower range compared to the measurements. The simulated wind speed is
significantly overestimated, which favors higher mixing and lower concentrations. This
66
overestimation appears to be reported for WRF quite often (e.g., Horvath et al., 2012).
Based on the favorable temperature evaluation (especially the maximum temperature), an
episode 9-13 Aug 2009 was selected for detailed emission and photochemical modeling. The air
quality modeling was conducted on the 4 km resolution grid using the EPA NEI. The first steps
were to use SMOKE emission modeling with the NEI and also MOVES model processing for
separating categories of the mobile emissions that were necessary for sensitivity studies. The
obtained inputs were used for CMAQ photochemical modeling of the 9-13 August episode. The
CMAQ results were evaluated using ozone measurements at South Lake Tahoe (the CARB
monitoring station near the lake), Echo Summit (southern ridge of the basin), and Sacramento Tstreet locations.
The evaluation shows that the model cannot fully reproduce the amplitude of the ozone extremes
at the complex terrain locations (South Lake Tahoe and Echo Summit), however, it shows a
correct order of magnitude and even the gradual trend of increased ozone in time. The measured
values range from 0.01 to 0.08 ppmV, while the model has a narrower range from 0.03 to 0.05
ppmV. Obviously the model’s input emissions have much less diurnal variation and/or the
model is not able to reproduce the diurnal variation of the chemistry. Some of the problems are
caused by overestimation of simulated winds (Fig. 13). This causes more mixing and lower
concentrations, which is emphasized during the ozone peak hours. The underestimation of the
maximum temperatures also contributes to the reduced simulated ozone maximum.
CMAQ results were also evaluated in the Sacramento valley where there were meteorological
and ozone measurements conducted. Although the CMAQ model underestimates peak ozone
concentrations and also concentration amplitudes in the ridge and near the lake, the simulations
are more accurate with respect to ozone concentrations and diurnal variation in the Sacramento
urban area. The peak ozone values are still underestimated (about 15 ppbV or so), but the
nighttime minimum values are reproduced well. Note the underestimation of the ozone peaks for
the South Lake Tahoe and Echo Summit stations, where the differences were up to about 35
ppbV.
Careful examination of the model and measurements time series shows interesting features. The
ozone peaks at the Sacramento station were recorded on the afternoons of 10 and 11 Aug while
at the Tahoe area (South Lake Tahoe and Echo Summit) the ozone peaked at 11 and 12 Aug also
in the afternoon hours. The behavior of the peaks was also similar – they gradually increased in
time (the peaks were greater on the second day compared to the previous day). Another
interesting point is that the magnitude of the measured peaks was similar at all stations – 69 and
76 ppbV at Sacramento, 68 and 78 ppbV at Echo Summit, and 60 and 77 ppbV at South Lake
Tahoe. Considering that Fig. 13 shows that the surface winds and general regional transport
were westerly and southwesterly,
All of these characteristics indicate that there was a regional ozone plume in the area with less
influence from the local emissions. The sensitivity test with emissions reduced by half showed
that the reduction of the simulated ozone peaks was rather small in the model’s results. This also
suggests that the reduction of emissions including NOx in the major area of the regional pool of
ozone was not effective to reduce ozone peaks since the lower NOx inhibits ozone reduction.
Reductions of local emissions in the Tahoe area were not sufficiently effective to significantly
67
reduce the ozone peaks.
Runs with doubled emissions showed interesting features. The Lake Tahoe area stations show
directly proportional relationships between the emission increase and the ozone concentration
increase; however, the peaks are even well underestimated with doubling the emissions. The
urban station (Sacramento) shows that the model with reduced emissions can lead to an increase
in simulated ozone concentrations due to the reduced effect of NOx on ozone by titration.
All these results suggest that the emissions are most likely underestimated and that the model
does not have enough sensitivity to fully respond to large changes in the emission inputs.
Backward and forward trajectories have been used to further clarify the effects of the regional
pool of pollution affecting the Lake Tahoe area. The results show that the period of the highest
ozone concentrations is associated with the cross-mountain westerly transport from the
Sacramento area toward the Lake Tahoe region. The period of the highest ozone concentrations
was also associated with low wind conditions, strong subsidence, and well mixed lower portion
of the atmospheric boundary layer.
11. Conclusions
A comprehensive meteorological and air quality modeling study has been completed to
investigate the characteristics and causes of the episodes of elevated ozone. Meteorological
modeling was conducted using the WRF model, while the air quality modeling consisted of
emission inventory modeling using the SMOKE model, detailed mobile emission modeling using
the MOVES model, and photochemical modeling using CMAQ. Regional transport processes
were examined using the HYSPLIT model with backward and forward trajectories.
A summary of the main accomplishments and conclusions is as follows.



This project developed a comprehensive modeling system consisting of meteorological
(WRF), emission (SMOKE and MOVES), and photochemical (CMAQ) modeling as well
as backward and forward trajectory analysis (HYSPLIT). Input emissions were obtained
from the EPA National Emissions Inventory (2005). The modeling system was applied
to an episode of elevated ozone concentrations (9-13 Aug 2009) for which WRF provided
sufficiently accurate simulations, especially for the maximum temperature.
The meteorological model had some problems in simulating atmospheric characteristics
in complex terrain. Mainly, the maximum temperature was underestimated (resulting in
reduction of the simulated ozone peaks) while the minimum temperature was
overestimated. The surface winds were overestimated (resulting in stronger mixing and
reduction of the ozone peaks). Note that other studies in the literature also indicate this
type of WRF behavior. An analysis of the measured time series indicates possible
problems with wind measurements.
The CMAQ results with inputs from SMOKE and MOVES were evaluated using ozone
measurements at the Lake Tahoe area (South Lake Tahoe and Echo Summit) and in
Sacramento (T-street). At South Lake Tahoe and Echo Summit the model shows a correct
order of magnitude and gradual increasing trend during the episode, but underestimates
68






the ozone concentration extremes. It is encouraging that the model at the Sacramento
station showed favorable comparison with respect to the ozone peaks and diurnal
variation.
The CMAQ model results were evaluated for the South Lake Tahoe station using EPA
recommended statistical measures: the mean bias (MB), the normalized mean bias
(NMB), the root mean square error (RMSE), and the normalized mean error (NME). The
model statistics were within the EPA recommended values for NMB for all runs, while
the values for NME are right at or above the recommended value. The emissions which
do not have significant impact on the simulated ozone (area, point, and to some extent
off-road) all have parameters similar to the baseline results. The impact can be seen for
the mobile emissions (except for off-road emissions) and biogenic emissions. Halving of
the emissions generally worsens the statistics, which indicates that the emissions are most
likely not overestimated. Doubling the emissions, however, produces smaller errors
which indicate that the emissions, at least in the southern part of the Lake Tahoe basin,
are possibly underestimated. The reduction of emissions for the whole period yields a
change in the bias of only 1 ppbV. Obviously, even large reductions in the emissions
would not significantly impact ozone peak reductions in the simulations.
It is important to note that the magnitude of the ozone peaks was quite similar for all
three stations in the region. The fact that the maximum in Sacramento occurred on 10
and 11 Aug and in the Tahoe area on 11 and 12 Aug during conditions of westerly crossmountain regional flows indicates the possible influence of the regional transport of
ozone and ozone pre-cursors.
Spatial distribution of the simulated ozone shows that the whole region was characterized
by elevated ozone concentrations, especially over the western Sierra ridges (also found in
previous studies such as Gertler et al. 2008) and the Tahoe area.
A number of sensitivity tests were conducted to examine the effect of variable emissions
on the simulated ozone concentrations. The strategy was to reduce by half each of the
main emission categories (area, point, mobile, biogenic) and analyze the effect of reduced
emissions on the ozone concentrations simulated by the CMAQ model. These results can
provide guidance on deciding on the efficiency of emission control strategies to eliminate
exceedances or elevated ozone concentrations in the Lake Tahoe area.
The main result from the sensitivity runs was that only the reduction of mobile and
biogenic emissions had a noticeable impact on the simulated concentrations. We further
separated on-road and off-road emissions using MOVES and found out that the main
effect is due to on-road emissions. However, even for reduction by half of the mobile
(on-road) and biogenic emissions, differences with respect to the control run were only
several ppbV considering the entire domain, which is very small. A question remains
whether the emissions are underestimated or the model does not respond well to the
emission changes, or both. According to the time series at all three stations, the
emissions and their temporal variation appear to be underestimated. The simulated ozone
peaks were lower by 30, 31, and 35 ppbV for the South Lake Tahoe, Echo Summit and
Sacramento stations, respectively. The correlation coefficients were 0.49, 0.60, and 0.82
for the South Lake Tahoe, Echo Summit and Sacramento stations, respectively. It appears
that the model was not sensitive enough to large changes in the emission reductions.
Model sensitivity to input emissions was further investigated by doubling all input
emissions. Even the double emissions did not produce same magnitude of the ozone
69




peaks. Due to reduced NOx, urban areas such as Sacramento showed increased ozone
concentrations due to lack of ozone titration by NOx.
A general conclusion from the sensitivity tests is that the main features of the ozone
episode in the Tahoe area have to be considered within the regional pool of ozone and
ozone pre-cursors and that the local effects might have a minor role. It appears that the
efficient method in this urban area is not to reduce mobile emissions, but to reduce
VOCs, while in the Lake Tahoe area, reductions in both mobile and biogenic emissions
can lead to reductions of ozone concentration peaks. However, the reductions in ozone
peaks were quite small for large reductions of the emissions.
It is important to note that stations in the Tahoe basin show modest reduction in ozone
concentrations in the sensitivity runs where the mobile emissions were reduced.
However, the mobile emission reductions show no improvement in reducing ozone
concentrations at the Sacramento T-street station and even increase them. The tests with
double emissions generally show even lower concentrations at the T-street station
compared to the baseline run. Consequently, control strategies must be region specific
since the urban area appears to have the characteristics of a VOC limited region, while
the Tahoe station shows characteristics of a weakly NOx limited area. However, as
discussed by Dolislager et al. (2012), during some conditions, input of locally-emitted
NO in the basin can contribute to lowering ozone peaks.
To understand possible regional characteristics and transport, an analysis of forward and
backward trajectories was performed using the HYSPLIT model. The trajectory analysis
shows that the period of the highest ozone concentrations was associated with the crossmountain westerly transport from the Sacramento valley toward the Lake Tahoe region.
The period of the highest ozone concentrations was also associated with low wind
conditions, strong subsidence, and a well-mixed lower portion of the atmospheric
boundary layer.
An analysis of synoptic conditions supports the conclusions from the trajectory analysis.
In the beginning of the episode, the ridge from the north and the trough from the south
developed a pressure field with weak gradients over the central portion of the West
Coast. This pressure system setup supported weak winds dominantly from the west
during the period of peak ozone concentrations in the Tahoe area on 11 and 12 August.
Based on all results from the measurements, models, and synoptic and trajectory analyses, it
appears that the ozone episode 9-13 Aug 2009 had strong regional characteristics. The models
showed satisfactory characteristics with respect to order of magnitude and trends in ozone
concentrations, but underestimated the peaks and overestimated the minimum ozone
concentrations. A significant reduction in emissions by 50% did not result in a significant
response of the model with simulated concentrations. One part of the possible problems lies in
the model insensitivity, another in underestimated emissions, and the third in the nonlinear effect
of reduced NOx to inhibit ozone destruction.
Even after doubling the emissions the simulated ozone peaks did not reach the measured peaks.
This part of the sensitivity tests indicates that the emissions are generally underestimated and/or
the model is not sensitive enough to changes in emissions.
Consequently, based on this study, control strategies need to consider measures for the non70
uniform reduction of regional emissions in order to improve air quality in the Tahoe area and
selective measures for particular emission categories. The control should focus on on-road
emissions and biogenic sources as the major components impacting ozone concentrations in the
Tahoe area. The reduction in the surrounding areas should be focused on VOCs, while in the
Tahoe area, reduction of both mobile (primary) and VOCs (secondary effect) should be
considered.
This study emphasized the need for a more accurate emission inventory for the Lake Tahoe area
and the broader region – on both regional and local scales. Continuous monitoring should be
extended to other sides of Lake Tahoe in addition to the Salt Lake Tahoe station. Special
attention should be placed on the western ridges of the Tahoe basin where the model predicts
significant concentrations during ozone episodes. The continuous monitoring should include
major ozone pre-cursors including VOCs and NOx.
Moreover, good spatial coverage of meteorological monitoring (surface and upper air) is also
needed as a crucial element to provide accurate inputs for the models and models’ evaluation.
Future studies should include ensemble simulations for both meteorological and photochemical
models. Additional air quality models such as WRF/Chem and CAMx should be applied
together with CMAQ leading to a new set of multi-model ensembles which, in principle, would
have more weight for understanding all processes relevant to the formation, evolution, and
destruction of ozone episodes and their variability in the Lake Tahoe basin.
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