9.2 NEIGHBORHOOD SCALE AIR QUALITY MODELING IN HOUSTON USING URBAN
CANOPY PARAMETERS IN MM5 AND CMAQ WITH IMPROVED CHARACTERIZATION OF
MESOSCALE LAKE-LAND BREEZE CIRCULATION
Jason Ching(1), Sylvain Dupont(3)' Rob Gilliam (1)Steven Burian(2) and Ruen Tang(4)
(1) Atmospheric Sciences Modeling Division, Air Resources Laboratory, NOAA, RTP, NC
(2) Department of Civil and Environmental Engineering, University of Utah, Salt Lake City Utah
(3) INRA-EPHYSE, Bordeaux, France
(4) Computer Science Corporation, RTP, NC
EPA/600/A-04/086
1. INTRODUCTION
Advanced capability of air quality simulation
models towards accurate performance at finer
scales will be needed for such models to serve as
tools for performing exposure and risk assessments
in urban areas (Ching et al., 2004). It is recognized
that the impact of urban features such as street and
tree canopies on air quality simulations will become
more pronounced as grid sizes decrease. This paper
will focus on (a) methods to introduce urban
features into the MM5, the predictive model to
provide accurate, temporally and spatially resolved
meteorological fields and as a preprocessor for (b)
running the Community Multiscale Air Quality
(CMAQ) (Byun and Ching, 1999) modeling system
run at neighborhood scales (order 1 km grid
horizontal resolution) (see also
http://www.epa.gov/asmdnerl/models3/doc/science/
science.html)
The difficulty of performing predictions of air
quality and pollutant dispersion at high spatial
resolution is exacerbated by the need for high
quality, high definition of the meteorological fields
that govern transport and turbulence in urban areas.
Air quality fields are now being modeled at finer
spatial resolution to reveal "pollutant hot spots" in
urban areas. These fine resolution mesh
simulations will need to be driven by meteorology
at commensurate mesh sizes. The presence of
urban street and tree canopies can affect the
emission dispersion and transport, and play a major
role in defining the spatial variability of the air
quality fields. Preliminary results (Ching et al.,
2003) using a set of urban canopy parameters for
Philadelphia based on simple surveys of urban
building geometries (Otte et al., 2004) have shown
that the resulting MM5 and CMAQ fields are
The corresponding author is on assignment to the
National Exposure Research Laboratory of the
United States Environmental Protection Agency.
Email address: ching.jason@epa.gov
significantly impacted by the introduction of urban
canopy parameters (UCPs) of buildings at 1.3 km
mesh size. Given the sensitivity of the meteorology
prediction to this set of UCPs, it is important to
further examine the predictive consequence with
data on buildings and vegetation at high spatial
definition and accuracy. The basis for this study is
the implementation of the DA-SM2-U/MM5
system (Dupont, et al., 2004). Further, as Houston,
Texas will be the area for this study, the mesoscale
circulation associated with its lake-land breeze may
exert an important influence and has implications
for the fine scale modeling and so this subject will
also be part of our study.
2. STUDY APPROACH
A set of urban canopy parameters (UCP) has
been derived for a 1 km grid mesh from a high
definition building and vegetation database from
airborne lidar measurements, ancillary data from
satellites, high altitude photography, as well as
detailed residential, commercial and industrial
maps for a modeling domain encompassing Harris
County and surrounding areas (Burian et al.,
2004a,b). A total of 23 UCP (combination of
vertical profiles and surface values are shown in
Table 1 listed according to the following categories,
canopy, building, vegetation, and other.
These gridded UCPs were specifically
developed for the DA-SM2-U/MM5 system
(Dupont et al., 2004), which incorporated a canopy
drag approach into an advanced urbanized surface
layer model (SM2-U) that was further implemented
into the NCAR-Penn State Mesoscale
Meteorological Model, Version 5 (MM5). Our
effort provides the first implementation of this
detailed set of gridded UCPs into the DA-SM2-
U/MM5 system. We chose to simulate a case study
for August 30, 2000 for a domain encompassing the
greater Houston-Galveston area. The period of

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interest also correspond to the TEXAS 2000
photochemical oxidant and PM study. Simulations
were made at grid sizes of 36 km, 12 km, and 4 km
using 30 sigma layers in the vertical. For the UCP
driven version run at 1 km grid size, six (6)
additional sigma layers were introduced near the
surface to simulate the flows within the building
and vegetative canopy region. Subsequently, the
impact of introducing urban canopy features into
the MM5 for the simulation of air quality using the
CMAQ modeling system is examined. The MM5
and CMAQ were run in standard one-way nesting
mode (Byun and Ching, 1999) and the system
applied at 36, 12, 4, and 1 km grid mesh sizes. The
meteorological output from MM5 was applied to
CMAQ by invoking the MCIP, a Meteorology-
Chemistry Interface Processor. Emissions for
CMAQ were obtained using the Model-3 SMOKE
processor, which produces gridded, hourly
emissions outputs at the different grid mesh sizes
and chemically speciated for the chemical
mechanism used in the CMAQ modeling system.
For this study, we used CBIV-AT, an advanced
research version of the Carbon Bond-IV
mechanism (CBIV) modified to predict gaseous air
toxics species such as formaldehyde, acetaldehyde,
acrolein, and others.
Table 1: Urban Canopy Parameters (UCP) for Houston Texas
Canopy UCPs:
Building UCPs:
Vegetation, Other UCPs:
Mean canopy height
Canopy plan area density
Canopy top area density
Canopy frontal area
density
Roughness length
Displacement height
Sky view factor
Mean building height
Standard deviation of building
height
Building height histograms
Building wall-to-plan area ratio
Building height-to-width ratio
Building plan area density
Building rooftop area density
Building frontal area density
Mean vegetation height
Vegetation plan area density
Vegetation top area density
Vegetation frontal area density
Mean orientation of streets
Plan area fraction surface covers
Percent directly connected impervious
area
Building material fraction
3. RESULTS
We first present results from the 1 km grid
simulations using DA-SM2-U/MM5 with the
gridded UCP for Houston area. The figures 1-6 are
for August 30, 2000 at 2000GMT (3pm local time).
Figure 1 presents planetary boundary layer (PBL)
parameters simulated by this model. The patterns
show complex but highly resolved spatial patterns.
The northern edge of Galveston Bay appears on the
far right hand side of each of the figures; the model
predicted reduced heat and momentum fluxes and
mixing heights as expected.
Figure 2 shows simulations of formaldehyde
(HCHO) from the CMAQ-AT modeling system for
both 4 km and the 1 km grid sizes. The panel on
the top right is the result of the 1 km grid
simulations driven by the DA-SM2-U version of
MM5. Regions of high concentration are exhibited
in the results. These are the so-called "hot spots"
that can be associated with increased exposure and
an increased probability of health risk. The panel on
the top left is a 4 km simulation reconstructed by
aggregating 16 of the 1 km outputs per 4 km cell.
The magnitude of the concentration for the hot
spots from the 1 km results are reduced in this
display. The CMAQ run performed at a native 4
km (Parent) resolution is shown in the bottom left
panel. While the general pattern is similar to that
from the aggregated results, the hot spot features
are considerably diminished. This is not an
unexpected result; the modeled concentration as
impacted by atmospheric chemistry, transport,
deposition processes that operate at a 1 km
resolution yield results that are not expected to be
reproducible with coarser resolution modeling and
its inherent artificial dilution effects associated with
representing emission at coarse resolution. Finally,
the bottom right hand panel displays resulting
differences between the reconstructed 4 km set
from aggregation of the 1 km results minus the
native 4 km simulation and normalized using the
aggregated mean results. The figure displays both
positive and negative differences of several tens of
percentage magnitude. We will see other pollutants
exhibiting similar behavior but somewhat different
degrees of differences in magnitude and pattern (cf
Figure 4 and 6 below).

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PBL Parameters (2000 GMT)
Figure 1. PBL parameters from 1 km grid simulations of DA-SM2U/MM5 (with high resolution UCPs) at
2000 GMT as follows: Top left. Sensible heat Flux (w/m2) ; Top right. Latent Heat Flux (w/m2); Bottom
left. Mixing height (m); Bottom right, ustar (m/s).
o.ooo
I 2000.00
Another example showing differences as well
as value using higher resolution modeling is
illustrated in Figure 3. This example reflects two
(of many other) ways to depict the degree of sub
grid variability associated with the 4 km resolution
concentration variations that are possible using the
finer 1 km predictions. The results are obtained by
sampling the 16 1-km grid values for the maximum
(peak) and the range (maximum -minimum) values
in each 4 km cell, and for all cells in the modeling
domain. On the left hand side, we see normalized
peak-to-mean values exceeding 50% throughout the
model domain and several areas which exceed
factor-of-two values. The normalized range-of-
values also exceeding 50% applies throughout the
entire modeling domain. Several grid cells have sub
grid variabilities exceeding factor of two or more.
Such results are not possible with purely
interpolation-based methodologies. Figures 4-5
and 6-7 present results for ozone and NOx,
respectively. They are set up in identical manner to
Figures 2 and 3. The notable feature for the ozone
results is the ability of the 1 km grid mesh
simulations to resolve the titrating effect of high
NOx along highway corridors and industrial areas
especially along the ship channel region (Top right
hand panel of Figure 4). The results of aggregating
the 1 km results to 4 km grid size shown in the top
left hand panel also show evidence of the titration
effect, but greatly filtered. The native 4 km
simulation mutes this effect even further. Even
with such filtering, differences shown in the bottom
right hand panel shows differences exceed 50% and
are mostly negative near highways and industrial
areas.

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Formaldehyde
¦ 0.008
0.006
0.002
u 0.000
ppmV
Figure 2. Formaldehyde at 2000GMT (3 pm local time) as simulated using CMAQ as follows: Top left- 4
km grid means from aggregate of 1 km grid values; Top right, 1 km gridded field; Bottom left. Parent 4 km
grid results; Bottom right, difference of 4 km aggregated mean from 4 km Parent normalized to the 4 km
aggregated mean.
0.004
In Figure 5, results for ozone are similar to that
for formaldehyde in that the range-to-mean values
exceed 50% throughout the modeling domain.
Also, the peak values are comparable or greatly
exceed their respective cell mean values throughout
the modeling domain. The results for NOx are
shown in Figures 6 and 7. While these results are
qualitatively similar to that of ozone, some
additional features are noteworthy. First, the 1 km
grid size simulation shows the areal coverage of
high NOx to be considerably larger than that
simulated at 4 km grid sizes. The normalized
difference in the bottom right panel shows a much
larger areal extent of positive differences.
exceeding 50% throughout most of the modeling
domain and not limited to the highways and
industrial areas. Likewise, the sub-grid variability
indicators in Figure 7 show peak-to-mean values
exceeding 50% throughout the modeling domain,
but with ratios higher and more extensive than for
ozone. This is to be expected because NOx
gradients are sharper and more localized. The same
conclusion is reached for the range-to-mean
display. It appears that photochemical modeling at
1 km produces results that yield both a high degree
of spatial variability as well as predicting important
differences as compared to coarser grid simulations.

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Formaldehyde
LHS: Peak-to-mean	RHS Range-to-mean
n
I
2.000
1.750
1.500
1.250
1.000
0.750
0.500
p.
¦






Figure 3. Formaldehyde simulations : August 30, 2000GMT: Left side: Gridded values are the peak
values 1 km grid cell value within each 4 km grid cells divided by the mean for each such the cell. Right
side, range of concentrations from the 16 1 km grid cell within each 4 km grid cell also normalized to the
mean value for each such cell.
n
0.008
0.006
0.004
0.002
u 0.000
ppmV
Ozone
H
— _n ^nn I—M
Figure 4. Same
as 2 but for ozone

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Ozone
LHS: Peak-to-mean	RHS Range-to-mean
i

2.000
1.750
1.500
1.250
1.000
0.750
0.500
Figure 5. Same as 3 but for ozone
NOx
n
0.100
0.075
0.050
0.025
I
0.000
ppmV

m


Wk
I

Figure 5. Same as 2 but for NOx

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NOx
LHS: Peak-to-mean RHS Range-to-mean
2.000
1.750
1.500
1.250
1.000
0.750
0.500

Figure 6. Same as 3 but for NOx
4. DISCUSSION
The results described in this paper should be
considered a work-in-progress. The presentation at
the Symposium will provide additional details
regarding: (a) sensitivity studies with comparisons
between results of the "urbanized" MM5 system
and the standard version of MM5 run at 1 km
resolution and its impact on the prediction with the
CMAQ system; (b) a sensitivity study to examine
the degree of accuracy of the input boundary
condition of the flow field from the coarser 4 km
grid nest. In this regard, it is important to recognize
that pollutant transport in the Houston area is
strongly affected by breezes induced by the Gulf of
Mexico, and the close proximity of Galveston Bay.
In an effort to resolve the bay/sea breeze evolution,
high resolution (~1 km) sea surface temperature
observations taken from the Polar-orbiting
Operational Enviromnental Satellites (POES)
Advanced Very-High Resolution Radiometer
(AVHRR/2) sensor are used in a sensitivity run. A
comparison of Bay temperatures shows a difference
as large as 4° C wanner in the sensitivity as
compared to the base or control simulation) (Figure
7a). The results show the Bay water temperature
warms up to 4 degrees while remaining constant in
the control simulation. Figure 7b shows the
resulting difference in the near surface wind
direction at Site C608 (of the Texas 2000 study)
which is ~6 km west of the northwestern part of
Galveston Bay. As a result, the accuracy of the
near surface land-bay breeze circulation simulations
at 4 km grid resolution in the MM5 predictions was
greatly improved. The sensitivity run clearly
reproduces the observed wind directions and the
wind shift at the time of the Bay breeze passage,
while the control run does not capture the details of
the Bay breeze passage. Consequently, this
provides a much more reasonable set of IC/BC for
the nested, 1 km grid predictions. The presentation
at the Symposium will further allow us to
demonstrate the degree to which the flow and air
quality prediction will depend on the introduction
of more accurate temporally resolved sea surface
temperatures (of the proximate Galveston Bay) and
the subsequent improvement in representation of
the modeled land-sea (lake) breeze features.

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August 30, 1 OOP at T O Z
Figure 7a: Difference in the sea surface temperature (°C) between the 4 km grid control using standard
MM5 sea surface temperature vs. the Sensitivity simulation using POES-AVHRR/2 data
360
315
270
en
® 225
c
o
o ISO
"O
c
135
90
45
Observed
Control
Sensitivity
0
0700
1000	1300
1600	1900
Time (UTC)
2200	0100 0400
Figure 7b: Same as 7a showing modeled wind direction and observations at site C108. The passage of the
Bay breeze front is indicated by the shift in wind direction.

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5. SUMMARY
We have successfully implemented the DA-
SM2-U/MM5 system using a sophisticated set of
gridded UCPs based on high resolution building
and vegetation data. We have now achieved the
development of modeling tools that can resolve
physically, flows in urban areas that are impacted
by the presence of canopy features at 1 km grid
sizes. This method reduces the problems or
uncertainties associated with simple interpolation
schemes that cannot be expected to accurately
represent the flow in urban areas. Also, we have
demonstrated that the CMAQ system can be
successfully driven using these meteorology fields
as inputs for simulating air quality (and air toxics
species) at relatively high spatial resolution. We
have further shown that by employing a finer grid
resolution mesh, that areas of enhanced pollutant
concentration become evident, a situation that will
permit the resolution of pollution "hot spots" for
more accurate human exposure and risk
assessments. Clearly, efforts to evaluate all these
findings will be necessary.
Thus, the combination of UCP-driven
meteorology for fine scale modeling and more
accurately modeled lake-land breeze circulations
will, in our opinion, provide a strong scientific
basis for advancing the simulations of the flow and
air quality for Houston and other urban areas with
similar climatic features.
Disclaimer: This paper has been reviewed in
accordance with United States Environmental
Protection Agency's peer and administrative review
policies and approved for presentation and
publication.
6. REFERENCES
Burian, S., W. Han, S.Velugubantla, and S.
Maddula, 2004a: Development of Gridded Fields of
Urban Canopy Parameters for Models-
3/CMAQ/MM5. Unpublished EPA Internal Report
Burian S., S. Stetson, W. Han, J. Ching, and D.
Byun, 2004b: High resolution dataset of urban
canopy parameters for Houston, Texas, Extended
Abstract: Fifth Urban Environment Symposium,
Am Meteorol Soc., Vancouver BC, CA, August 23-
26, 2004.
Byun, D., and J. Ching, 1999: Science algorithms
of the EPA Models-3 Community Multiscale Air
Quality (CMAQ) Modeling System EPA/600/R-
99/030. See also (http://www.epa.eov/asmdnerl/
models3/doc/science/science.html)
Ching, J., S. Dupont , J. Herwehe, T. Otte, A.
Lacser, D.Byun, and R. Tang, 2004: Air quality
modeling at coarse-to-fine scales in urban areas.
AMS Urban Environment Extended Abstract,
J2.18, Jan 2004
Dupont, S., Otte, T., and J.K.S. Ching, 2004:
Simulation of meteorological fields within and
above urban and rural canopies with a mesoscale
model (MM5). Boundary layer Meteorology, in
press
Otte, T., A. Lacser, S. Dupont, and J. Ching, 2004:
Implementation of an urban canopy
parameterization in a mesoscale meteorological
model. Journal of Applied Meteorology, accepted.

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