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Microscale evaluation of the urban heat island in Phoenix, Arizona

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Microscale evaluation of the urban heat island in Phoenix, Arizona
by
Brent Hedquist
A Dissertation Presented in Partial Fulfillment
of the Requirements for the Degree
Doctor of Philosophy
ARIZONA STATE UNIVERSITY
August 2010
UM! Number: 3425755
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Microscale evaluation of the urban heat island in Phoenix, Arizona
by
Brent Hedquist
has been approved
March 2010
Graduate Supervisory Committee:
Anthony J. Brazel, Chair
Harindra J.S. Fernando
Jay S. Golden
Elizabeth A. Wentz
ACCEPTED BY THE GRADUATE COLLEGE
ABSTRACT
This dissertation analyzed the urban heat island (UHI) in Phoenix, Arizona at a fine
scale using both field-based measurements and numerical modeling. A 24 hr field
campaign was conducted within central Phoenix 4-5 April 2008 and included mobile,
ground-based, and helicopter-based measurements of temperatures along an 18 km route
and at three specific locations within the city. Model simulations were then run with the
numerical microclimate model, environmental meteorology (ENVI-met), to compare with
field observations. Research questions focused on (1) testing, refining, and validating
ENVI-met for the hot arid city of Phoenix, 2) evaluating thermal comfort outputs from
ENVI-met for various seasons within central Phoenix, and 3) evaluating the effect of
downtown Phoenix building facade and street canyon temperatures on flow with
computational fluid dynamics (CFD) modeling and analysis of thermal infrared imagery
from the 24 hr April 2008 field experiment.
ENVI-met simulation results over the 24 hr experimental period generally predicted
afternoon maximum temperatures well, but over predicted minimum temperatures, with a
smaller than observed diurnal temperature range. Surface temperature predictions closely
matched observations at night, with a slight over-prediction of temperature during the
daytime hours. Seasonal comfort maps indicated that the higher density downtown has
more comfortable afternoon temperatures during most seasons, with lower density areas
with higher vegetation being more comfortable in the early evening. CFD simulations
found a distinct temperature change at 30 m along certain building facades measured by
hand-held IR thermography during the 24 hr field day. This distinct vertical temperature
gradient found along building facades in downtown correlates with similar findings in
iii
physical models investigating buoyancy and thermal stratification within street canyons.
This dissertation aids planners and civic leaders in gaining a better understanding of the
fine scale effects of the built environment on the UHI within different landscapes in the
city and devising heat mitigation strategies to increase the quality of life in Phoenix and
other cities in hot and arid climates.
IV
ACKNOWLEDGMENTS
This project was funded through Science Foundation Arizona as well as the Arizona
Department of Environmental Quality (ADEQ). I would like to thank all of the volunteers
who helped out during the April 2008 field campaign and Phoenix KPNX Channel 12
News for allowing us to utilize their helicopter for acquiring thermal IR camera images.
Field data collection over a 24 hour period was made possible from the many volunteers,
including, Dr. Silvana Di Sabatino, Winston Carter, Peter Hyde, Susan Bliss, Dan
Hedquist, Kasun Perera, Suhas Pol, Abhilasha Anna, Donna Hartz, and George Hartz. I
would also like to thank Joby Carlson, who made the helicopter thermography possible
with his expertise and previous experience. Weather station data and assistance with
calibration of equipment was obtained from Dr. Nancy Selover, State Climatologist of
Arizona at the Office of Climatology, Arizona State University. I am also sincerely
grateful to Dr.'s Joe Fernando, Tony Brazel, Jay Golden, and Libby Wentz for direction,
scientific advice, and counsel for the dissertation. Finally, this dissertation would not
have been possible without the support and patience of my wonderful wife Amy, and
from my family.
v
TABLE OF CONTENTS
Page
LIST OF TABLES
ix
LIST OF FIGURES
x
CHAPTER
1
2
3
INTRODUCTION
1
1.1. Background
1
1.2. Objectives and Scope of Research
3
1.3. Research Questions
4
1.4. Report Organization
5
LITERATURE REVIEW
6
2.1. Urban Heat Island
6
2.2. Field-based Measurements of the UHI
7
2.3. Remote Sensing and the Surface Heat Island
10
2.4. Infrared Thermography
12
2.5 ENVI-met
16
2.6. CFD Modeling of Temperature in Urban Street Canyons
20
FIELD DATA COLLECTION
22
3.1. Experimental Design and Study Area
22
3.2. Scientific Basis for UHI Field Experiment Measurements
24
3.3. Mobile Temperature Sampling
34
3.4. IR Thermography Calibration and Testing
37
3.5. Ground-based Thermography
38
vi
3.6. Helicopter-based Thermography
4
5
6
ENVI-MET SIMULATIONS
42
45
4.1. Introduction
45
4.2. Model Set-up and Testing
49
4.3. Model Input Refinement
55
4.4. Land Cover Comparisons
61
4.5. S imulation Results
63
4.6. Comparisons between Default and Refined Simulations
68
4.7. Model Validation
71
4.8. Conclusions
75
SEASONAL SIMULATIONS AND IMPLICATIONS
77
5.1. Introduction
77
5.2. Methods
78
5.3. Results
80
5.4. Implications for Phoenix, Arizona
87
DOWNTOWN THERMOGRAPHY AND CFD SIMULATIONS
90
6.1. Introduction
90
6.2. Details of Downtown Thermography
91
6.3. CFD Model Set-up
93
6.4. Diurnal Variation of Wall Temperatures
96
6.5. CFD Simulation Results and Discussion
103
6.6. Conclusions
107
vii
7
DISCUSSION AND CONCLUSIONS
108
7.1. Comparison of Helicopter Thermography with Modile Sampling.... 108
7.2. ENVI-met Limitations and Future Improvements
110
7.3. Summary of Findings and Research Implications
112
7.4. Further Research
114
LITERATURE CITED
115
APPENDIX
122
A. FIELD INSTRUMENTATION AND CALIBRATION
122
B. SPATIAL DATABASES FOR ENVI-MET INPUT FILES
130
C. DETAILS OF APRIL 2008 HELICOPTER THERMOGRAPHY
IMAGES
186
D. SELECT APRIL 2008 HELICOPTER THERMOGRAPHY IMAGES
201
E. SEASONAL OUTDOOR HUMAN COMFORT (PMV) OUTPUTS
208
viii
LIST OF TABLES
Table
Page
1.
Land cover and building comparisons between the three model domains
2.
Default surfance thermal properties and building materials compared with
refined values
3.
69
RMSE values between mobile points and model receptors for air temperature
(1.5 m)
4.
62
72
RMSE values between mobile points and model receptors for surface
temperature
72
5.
Seasonal PMV values (1.5 m) at 14:00 and 22:00 LST
6.
Simulations of wall heating in the street canyon
ix
86
104
LIST OF FIGURES
Figure
Page
1.
Field data collection area within Greater Phoenix indicated by red rectangle
overlaid in Google Maps™
23
2.
Mobile temperature sampling equipment mounted on a vehicle
3.
Mobile temperature sampling route indicated by blue line overlaid in Google
Maps™
4.
36
Downtown thermography locations indicated by yellow pushpins in Google
Earth™. Dark blue lines indicate A and B mobile routes
5.
35
40
Images captured of the downtown thermography crew obtaining thermal
images of building facades and street canyons via pedal cab at 14:00 LST
41
6.
FLIR thermal IR camera position in helicopter (left). KPNX Channel 12 News
helicopter (right)
7.
44
Model domains as seen from bird's eye view (520 m a.g.l.) in Google Earth™.
Red rectangle indicates (a) 24thSt (b) 1 st Ave and (c) 43 rd Ave domain
boundaries
48
8.
Data flow in ENVI-met simulations (http://www.envi-met.com)
9.
ENVI-met domain locations along the 4 April 2008 east-west
50
mobile/helicopter route (blue line) and overlaid in Google Earth™. Distance
is indicated by mobile sampling points every 1 km
10.
52
Final area input files indicated by (a) 24th St (b) 1st Ave and (c) 43rd Ave.
Mobile sampling points are shown at 100 m intervals
x
60
11.
Comparison between (a) final 1st Ave area input file and (b) area input used
in Emmanuel & Fernando (2007). Red arrows indicate the location on
Central & Adams
12.
61
Simulation comparisons for air temperature (1.5 m) overlaid in Google
Earth™ at 22:00 LST. (a) 1st Ave (b) 24th St and (c) 43rd Ave
13.
Simulation comparisons for air temperature (1.5 m) at 14:00 LST. (a) 1st Ave
(b) 24th St and (c) 43rd Ave
14.
65
Simulation comparisons for air temperature (1.5 m) at 22:00 LST. (a) 1st Ave
(b) 24th St and (c) 43rd Ave
15.
66
Simulation comparisons for surface temperature at 1st Ave. (a) 14:00 (b)
22:00 LST
16.
67
Simulation differences between Refined and Default (R-D) inputs from 12:00
to 24:00 LST 4-5 Apr 2008
17.
64
70
Final simulation comparisons from 12:00 to 24:00 LST (1.5 m) 4-5 Apr 2008
73
18.
Temperature predictions at mobile and receptor points (1.5 m) 4 Apr 2008.
Air temperatures on left, with surface temperatures on right, (a) 14:00 (b)
19:00 (c) 22:00
19.
74
ENVI-met domains and receptor points used in human comfort analyses
Points on roadways are 100 m apart, (a) 24th St (b) 1st Ave (c) 43 rd Ave
20.
80
Seasonal simulations for air temperature (left) and surface temperature (right)
along roadways, (a) January (b) April (c) June and (d) October
xi
82
21.
ENVI-met domains and receptor points used in human comfort analyses.
Points on roadwats are 100 m apart, (a) 24th St (b) 1st Ave (c) 43rd Ave .... 83
22.
PMV output (1.5 m) at 14:00 (left) and 22:00 (right) 4 Apr 2008. (a) 24th St
(b) 1st Ave (c) 43 rd Ave
23.
84
PMV output (1.5 m) at 14:00 (left) and 22:00 (right) 27 Jun 2008. (a) 24th St
(b) 1st Ave (c) 43rd Ave
24.
85
Flow patterns at z = 30 m without temperature forcing for Downtown
Phoenix using a simplified geometry
25.
94
Sketch of the street canyon geometry (left graph) with the height of each
building indicated on the roof and details fo the computational domain (right
graph) used for street canyon CFD simulations
26.
95
Building layout of the study area and with colors indicating qualitative
temperatureof buildings with the same temperature
27.
97
Comparison of diurnal mean radiant temperatures related to different western
(top) and eastern (bottom) building facades. Profiles of air temperature are
also reported
28.
100
Comparison of diurnal MRT related to different portion of the fagadc for both
a western (up) and an eastern (bottom) building. Profiles of air temperature
are also reported
29.
101
Comparison of MRT related to dark glass and concrete constituting the
fa?ade of the A1W building
30.
102
Vectors of x-velocity. (a) y = 50 m (b) y = -110 m (c) z = 30 m (d) z = 70 m
105
xiv
31.
Vertical z-velocity profiles, (a) z = 5 m (b) z = -50 m (c) y = 115 m (d) y =
-110 m
32.
106
Surface temperature comparisons at 14:00 LST for mobile sampling point 54
109
33.
Illustration showing the capabilities of the future ENVI-met v.4.0 area input
editor. Images ©Michael Bruse 2009
Xlll
112
1. INTRODUCTION
1.1. Background
With more than half of the world's population now living in urban areas, climate issues
directly relating to the city such as the urban heat island (UHI) have recently gained the
attention of those involved with shaping the future development of cities. This is
especially true in light of recent findings on climate change by the latest IPCC report
(IPCC 2007), which states that "warming of the climate system is unequivocal." While
the report finds that the UHI does not directly impact global temperatures as a whole, a
slight rise in the background temperatures will exacerbate the effects of heating within
the core of cities already warmer in the nighttime hours by the UHI. Impacts from the
UHI, when combined with effects of global climate change, threaten to undermine the
economic vitality and livability within built-up areas of the cities.
The UHI has been researched extensively in recent times, but a greater understanding
of its dynamics, including its causal factors has only recently emerged (Arnfield 2003,
Oke 2005). Within the field of urban climatology, the UHI is one of the most studied
phenomena mainly due to its impact on energy costs, human outdoor comfort, and air and
water quality issues, including the increase of low-level ozone. In one recent study, the
UHI has been estimated to be responsible for 5-10% of peak electricity demand for
buildings in cities (Akbari 2005). Within the urban canopy layer (UCL) of the city, the
principal reason for the formation of the night-time UHI has been found to be directly
related to the following factors:
2
• A decrease in sky view factor (SVF, ^sky) from taller buildings and an increase in
building density (which, in turn, decreases cooling from long wave radiation)
• Changes in land cover characteristics within a city as it expands and grows,
which includes an increase in impervious surfaces, such as concrete and asphalt
and other engineered materials, as well as a decrease in vegetation, leading to
an increase in sensible heat and heat storage at the surface and a lowering of
latent heat and evapotranspiration from vegetation and pervious surfaces (Oke
1982).
Other factors contributing to the UHI include a decrease in ventilation through narrow
street canyons within higher density building structures, which leads to an accumulation
of waste heat in the core of the city from anthropogenic factors such as automobiles, air
conditioning, and industrial factories (Oke 1982).
Of particular importance to city planners and policy makers is the need for a better
understanding of the dynamics of the UHI and ways to mitigate heat in hot, arid climates,
such as Phoenix, Arizona to make the city more comfortable and livable. From 1960 to
2000, Phoenix experienced a minimum temperature rise of 0.46°C per decade, which is
one of the highest rates in the world for a city of this size (Golden 2004). Much of this
warming can be attributed to the change in land cover characteristics of the city including
the increase of impervious pavements and replacement of dense, mesic landscapes in
older neighborhoods to less vegetated xeric landscapes within newer neighborhoods
(Stabler et al. 2005). In order to understand the dynamics of the UHI phenomenon in
Phoenix and other large cities in hot arid climates, there needs to be a better
3
understanding of the fine scale complexities within metropolitan areas as a whole.
Analysis of temperatures at the micro-local scales within the regional or mesoscale urban
environment is critical in order to understand why these areas show up as distinct hot
spots in the region at night as indicated by remote sensing.
Recent innovations and improvements in geospatial technologies and climate modeling
now allow for a better representation and interpretation of climate and other smoothlychanging phenomenon such as the UHI. Spatial analysis using geographical tools like
GIS and virtual globe technology (Google Earth™ and Microsoft's Bing Maps™) have
allowed for a better visual representation and understanding of the UHI (Szymanowski &
Kryza 2009).
1.2. Objectives and Scope of Research
Within this context, this dissertation takes an experimental approach to analyzing the
UHI within the central core of Phoenix, Arizona. Data was collected over a 24 hr period
in April 2008 with a comprehensive field experiment within the UCL, including
measurements at both the surface and pedestrian level. Within the field experiment, the
UHI was measured at two key scales: the local (or neighborhood scale) and the micro
(including the street canyon down to the building scale). In measuring temperatures
within the core of Phoenix, both traditional field-based approaches, including mobile
sampling and remote sensing are incorporated, as well as numerical modeling techniques,
in order to gain a better understanding of both spatial temperature pattern variation at the
surface and pedestrian level, and verification and identification of thermal-induced flow
4
patterns along buildings and along street canyons in within a small section of Downtown
Phoenix.
The UHI field experiment investigated and recorded diurnal temperatures at several key
locations within the central core of Phoenix, which typically has the highest and most
uncomfortable temperature readings at night throughout the warmer months of the year.
Details of field experiment techniques can be found in Chapter 3 of this dissertation.
Results from this dissertation will aid in a better understanding of fine scale complexities
of temperature differences, resulting from varying land covers and building densities,
within the core of a large hot arid city.
1.3. Research Questions
Given the growing concern about recent increases in night time temperatures and the
UHI in Phoenix, especially for residents located in areas experiencing the highest impacts
from the UHI (higher energy usage, discomfort, and health-related problems), the
following research questions will be addressed:
•
To what degree can the numerical microclimate model ENVI-met simulate
surface and air temperatures diurnally, especially at the time of maximum UHI,
across three unique landscapes within central Phoenix, Arizona?
•
What do seasonal simulations conducted in central Phoenix with ENVI-met tell us
about the ideal urban design for optimal human comfort and lowering the effects
of the UHI within a hot and arid climate?
5
•
What effect do the building materials, street canyon design, and building heights
have on building fapade temperatures and flow diurnally within the central
business district (CBD) of Phoenix, Arizona?
1.4. Report Organization
In order to address these research questions, this dissertation is divided into six
sequential chapters following this introduction: Chapter 2, containing background
material and a review of relevant literature; Chapter 3, describing field data collection
methods and scientific basis for the 4-5 April 2008 UHI field experiment; Chapter 4,
which addresses the first research question regarding the testing and validation of ENVImet for Phoenix, Arizona; Chapter 5, that addresses the second research question
regarding the seasonality of ENVI-met simulations and the implications for comfort and
UHI mitigation in Phoenix, Arizona; Chapter 6, which addresses the third question
regarding thermal effects on surface temperatures and flow within the CBD of Phoenix;
and Chapter 7, containing further discussion and conclusions. Finally, appendices A-E
contain useful information that is not included in the main body of the dissertation.
2. LITERATURE REVIEW
2.1. Urban Heat Island
Although the UHI has been researched extensively during the past century precise
definitions of causal factors have emerged only in the last thirty to forty years (Goward
1981, Landsberg 1981, Oke 1973, 1982, 1995). It is now well known that there is a
strong relationship between city size and the magnitude of the UHI (Oke 1973). In the
1970's another key distinction was made with regards to the boundary layer differences
in the UHI within a city. Oke (1976) defined the UHI as having a distinct urban boundary
layer (UBL) and urban canopy layer (UCL) closer to the surface, as well as further
refined the causation of the UHI in later papers published in 1980's and 1990's,
respectively (Oke 1982, 1995). However, the most significant advance made in the last
twenty years has been the idea that there are many heat islands depending on the spatial
scale (Arnfield 2003). Different physical processes take place on a continuum from the
micro to global scales, with interaction between the elements throughout all scales
(Meentemeyer 1989, Schmid & Oke 1992). This would include a possible interaction
between mesoscale warming from the UHI within a city with warming on a global scale,
associated with climate change.
Within the UCL, which is measured from the ground level to the top of buildings and
trees within a city, building geometry and surface thermal properties have been shown to
have the largest effect on the magnitude of the UHI (Oke 1982, 1987). Measurement of
building geometry includes (1) building height/canyon width (H/W) ratio, (2) SVF,
which is the quantity of visible sky seen from an outdoor point in space (Grimmond et al.
7
2001), and (3) a compactness index, which is the ratio of building surface area to the
surface area of a cube which has the same volume as the building (Unger 2004,
Emmanuel & Fernando 2007). Other causal factors of the UHI include anthropogenic
heat release from buildings and vehicles on the roadways, loss of evapotranspiration due
to reduced vegetation and latent heat transfer, and the loss of wind within the built
environment to transport heat out of the city (Oke 1988).
2.2. Field-based Measurements of the UHI
Early studies of the UHI were primarily conducted in temperate climates with a recent
emergence of studies in arid, tropical, as well as colder regions (Arnfield 2003). In
Sweden, Barring et al. (1985) wrote one of the first papers detailing the role of street
canyon geometry and surface temperatures on the UHI. They came up with an innovative
technique to measure SVF in street canyons using a fish-eye camera lens, which is still
used today. Later studies in Sweden further refined the role of urban canyons on the
micro-local scale UHI (Eliasson 1990/91, 94, 96). Eliasson (1990/91) utilized one of the
first real-time thermal infrared radiometers to measure the surface temperature within
urban street canyons in Malmo, Sweden. While there were strong correlations between
SVF and surface temperatures, little variation was found with air temperatures. Eliasson
(1996) found that air temperature variation within urban canyons was small, but there
were much greater variations between various land use types away from the core of the
city.
In arid regions, there have been several recent studies done on measuring and/or
modeling UHI intensities in various cities across the world, including Europe (Vague et
8
al. 1991, Moreno-Garcia 1994, Alonso et al. 2003), the Middle East (Nasrallah et al.
1990, Saaroni et al. 2000), and the United States (Balling & Brazel 1987, Hedquist 2002,
2006, Fast et al. 2005, Stabler et al. 2005). Cities in hot and arid climates, because of the
typical atmospherically stable conditions with clear skies and light winds, often have high
UHI magnitudes, even when measured in smaller cities of 30,000 people (Hedquist
2005), or measured at the microscale within a metropolitan area (Hawkins et al. 2004).
In Phoenix, Gordon (1921) was the first to document the UHI in the area with his paper
on the local climate in relation to minimum temperatures and agriculture. Hand-drawn
contour maps were created of areas containing similar minimum temperatures (isothermal
maps), based on a small weather station network existing at the time. These isothermal
maps created more than eighty years ago serve as a marked contrast to what exists today
in the area with a geographically large and high magnitude UHI at times throughout the
year. This paper also serves as a valuable historical baseline with which rapid
urbanization effects on temperature can be compared to today.
Fast et al. (2005) used a large weather station network to measure the intensity of the
UHI in Greater Phoenix over the summer of 2001. On one evening, they measured an
intensity of over 10°C with an east/west urban to rural gradient across forty in situ
weather stations across the valley. The use of a large weather station network makes
more sense to gain a better spatial representation of temperature across a city, but was not
as common prior to 1985 in most field studies. Early UHI studies in Phoenix (and other
large cities) would typically make a comparison of one urban station with a rural station
and subtract the minimum values to obtain the UHI intensity. This does not account for
9
the large variability between the urban and rural stations and is not representative of the
temperature across the city spatially. However, it allows for long term UHI analyses to be
conducted in an urban area where there may be a dearth of weather stations with an
extensive history online.
Mobile sampling of temperature along specific urban-rural transect routes has
continued as a popular means to measure UHI intensities in gradients across land uses in
a city. Sampling platforms have included diverse data collection methods, including the
use of tram-cars (Yamashita 1996), or walking along pre-defined paths within small
microclimate areas using a baby stroller (Hartz 2004). Another common measurement
method is to include the use of in situ stations in addition to mobile sampling, which
provides a longer temporal picture of temperature patterns in a city, whereas mobile
sampling offers a snapshot in time, similar to remote sensing (Hedquist 2002, 2006).
Mobile measurement techniques vary widely from city to city, and the downside is that
there has yet to be a set of standards or rules concerning the method of data collection
within the field of urban climatology, including field measurements of the UHI (Oke
2005).
With the advent of geospatial technology and remote sensing, field-based studies of the
UHI at the surface, or (SUHI), have emerged (Lo et al. 1997, Voogt & Oke 1997, 2003).
While studies prior to the 1980's focused on more descriptive aspects of the UHI within
the UCL, more recent research has explored the actual process of UHI development, and
have begun to utilize geospatial technology, including the use of GIS, satellites, and
10
infrared thermography, as well as the use of mobile and in situ weather station
monitoring sites (Saaroni et al. 2000).
2.3. Remote Sensing and the Surface Heat Island
In the last twenty years there has been a large increase of imagery from earth land
observation satellites, recently becoming more widely available to the general public and
researchers through the USGS and NASA (https://lpdaac.usgs.gov/). Early SUHI studies
in Phoenix tended to use satellite imagery such as the Advanced Very High Resolution
Radiometer (AVHRR), which had a thermal infrared band resolution of 1 km (Balling &
Brazel 1988, 1989). Gallo et al. (1993) compared imagery from the Normalized
Difference Vegetation Index (NDVI) to AVHRR temperature data for cities across the
United States and found that minimum temperatures in cities may rise not only by
population growth but also by change in vegetation at the periphery of the city.
AVHRR imagery may have had slightly better resolution than Landsat imagery being
used for temperature analyses at the city-wide scale, but with coarser resolution than what
became available by the late 1990's (Hubble 1993, Gallo et al. 1995, Owen et al. 1998).
By 1999, satellites sensors were able to scan the surfaces of urbanized areas with the
thermal infrared band at a 90 m resolution, such as NASA's Advanced Space-borne
Thermal Emission and Reflection Radiometer (ASTER) sensor on the Terra satellite
(http://asterweb.jpl.nasa.gov/). This allowed for a detailed analysis of surface
temperatures at much finer scales, such as individual buildings and street canyons within
downtown areas, which were previously indistinguishable with the coarse resolution of
11
older satellite sensors. Higher resolution satellite sensors made it possible to detect
hotspots within city, enhancing UHI studies.
While satellites launched in late 1990's offered increased resolution, other instruments
began to emerge around this time that allowed for surface temperatures to be analyzed at
the micro and local scales. This included airborne thermal radiometry (Ben-Dor &
Saaroni 1997), and thermal infrared cameras mounted on top of buildings or vehicles
during mobile sampling (Saaroni et al. 2000). Infrared sensors mounted on automobiles
during mobile transects had been done by earlier researchers, often to compare surface to
air temperatures (Barring et al. 1985, Stoll & Brazel 1992).
Voogt & Oke (1997) used a combination of both ground and airborne thermal sensors,
creating an innovative "complete" surface temperature measurement of buildings and
structures in downtown sections of Vancouver, British Columbia. This addressed the
issue in previous studies of the SUHI, which relied on surface temperatures
measurements taken solely from satellite sensors, which only "see" the roof of buildings
but not the sides or facets. In addition, ground-based surface measurements in previous
studies, either from in situ stations or mobile sampling, didn't account for temperatures
vertically between the ground and top of the UCL, such as those on trees and the sides of
buildings in urban canyons (Voogt & Oke 1997). However, recent studies have begun to
incorporate the use of thermal infrared imaging cameras with higher resolution or
"thermography," to capture the surfaces of all objects in view (Chudnovsky et al. 2004,
Hartz et al. 2006). Through the use of a high-resolution camera on top of a 103 m
building, Chudnovsky et al. (2004) were able to detect distinct microscale diurnal
12
patterns of temperature on various building and street surfaces. They found that the
optimal time to measure surfaces with infrared thermography was the early morning
hours before sunrise and the mid-day hours.
Voogt & Oke (2003) provide a good overview and synopsis of remote sensing advances
that have taken place sense the study by Roth et al. (1989). They mention that while
improvements have been made in this field, much more needs to be done to understand
the role of surface materials and energetics, which may include using technology such as
Forward Looking Infrared (FLIR) cameras, which can capture the "skin" of all objects
being analyzed in addition to birds-eye views from satellites and other remotely-sensed
technology.
2.4. Infrared Thermography
Infrared (IR) was discovered by Sir William Herschel, an astronomer, in 1800. He
wanted to find out which color(s) were responsible for heating objects. Herschel observed
an increase in temperature as he moved a thermometer, which he had created with
blackened bulbs, from violet to red in the rainbow created by light passing through a
prism. The hottest temperatures were actually beyond red light, with radiation causing the
heating invisible. He called this invisible radiation "calorific rays" and it is now known as
IR (http://www.flirthermography.com/).
Herschel's discovery of IR within the electromagnetic spectrum (EM) was more than a
century before modern technology existed to capture this wavelength emitting from the
Earth's surface in the form of images or film. In the field of remote sensing, the longer
wavelengths of IR were not captured in imagery until the mid-20 th century, when suitable
13
instruments where created to support applications such as the detection of diseased crops
from aerial reconnaissance. Prior to the 1940's, aerial imagery and remotely sensed data
consisted of the visible bands of the EM spectrum, due to lack of instruments that could
capture beyond these wavelengths (Campbell 2007).
More recently, thermal radiometers and IR thermography have been used from both the
ground and from aerial platforms (Ben-Dor & Saaroni 1997). IR thermography, which
captures IR imagery at high resolutions from a compact camera design, has become
common in several different applied fields. Since the emergence of the first full-featured
camcorder style came out in 1995, applications have included electrical inspection and
maintenance in the utilities industry, screening for cancer in the medical field, laboratory
experiments which have conducted investigations of environmental fluids and supersonic
flight, and wildlife management by field biologists (http://www.flirthermography.com/).
The principal advantage to using IR thermography in urban research lies in the sensors
ability to capture imagery at a significantly higher resolution than from a satellite sensor
(ASTER at 90 m). While satellite sensors are good at capturing and detecting city-wide
surface temperature patterns, thermal IR cameras offer the advantage of distinguishing
fine details, such as anthropogenic heat signatures from automobiles, HVAC systems on
buildings, etc. that can't be captured at the coarse resolution of satellite sensors. This is
especially useful for analyses of temperatures at a fine horizontal scale, from local
(neighborhood) scales down to the building scale. Another significant advantage of
thermal IR cameras is that they can capture images of building fa9ades, which are not
seen from an overhead orbiting satellite sensor (Voogt and Oke 1997). IR thermography
14
also allows for almost instantaneous capture and processing of surface temperature at a
target area of interest, whereas images from a satellite sensor are limited to a certain
snapshot in time determined by the orbit of the satellite and the priorities of the NASA
mission or private remote sensing organizations (Golden & Kaloush 2006).
IR thermography, while offering some advantages to satellite sensors, has some
limitations. Bias in temperature readings from handheld IR thermography can be
introduced if the user does not correctly adjust for emissivity. Emissivity is defined as the
ratio of the emittance of a given surface at a specified wavelength and temperature to the
emittance of an ideal blackbody at the same wavelength and temperature (Oke 1987). The
issue lies in the fact that some surfaces in the urban environment have low emissivities,
such as rooftops composed of smooth metal, whereas the surrounding surface materials
have high emissivities closer to unity, such as glass, concrete, and asphalt. This
discrepancy can cause unrealistically low temperature readings of metal rooftops in
comparison to the surrounding objects if not calibrated and adjusted for prior to
measurements. A second issue from handheld IR thermography that can arise is from
changing the measurement angle from the source to target during acquisition of images.
Previous research by Carlson (2006) found that an image taken perpendicular, or close to
90° from a surface, is more likely to capture the maximum emissive power coming from
the surface. If the angle is decreased, the emissive power may also decrease resulting in a
lower observed temperature when using the handheld thermal IR camera.
While IR thermography has been done from both ground and airborne-based platforms
in previous studies (Eliasson 1992, Voogt & Oke 1997, Ben-Dor & Saaroni 1997), the
use of handheld IR thermography for microscale UHI studies is a more recent technique.
This is most likely due to recent improvement in resolution and portability of thermal IR
imaging cameras, such as the hi-definition FLIR ThermaCAM™ SC640
(http://www.flirthermography.com/high-def/).
In Phoenix, several recent studies utilizing handheld IR thermography have been
carried out by researchers at Arizona State University (Carlson 2006, Hartz et al. 2006,
Golden & Kaloush, 2006, Golden et al. 2007). Two of these studies focused on the
diurnal thermal behavior of pavement materials in a hot arid climate (Carlson 2006,
Golden & Kaloush 2006), with the other two studies focusing on the comparison of
microscale (IR thermography) imagery to mesoscale (ASTER) within several
neighborhoods in Scottsdale, Arizona (Hartz et al. 2006) to the comparison of thermal
and radiative impacts of photovoltaic canopies on pavement surface temperatures
(Golden et al. 2007).
Carlson (2006) used thermography to analyze the thermal properties of surfaces,
principally pavements, at Sky Harbor International Airport. The author used the FLIR
ThermaCAM™ S60 camera to measure diurnal temperature patterns throughout the
airport property, using GIS software to compare surface properties to image
temperatures. Thermal images, when compared to land cover type percentages derived
from GIS shapefiles, identified pavement types that had the most significant contributing
factors to highest diurnal temperatures recorded at the airport. The author found that
during the day, darker surfaces, such as asphalt had the highest temperatures, while
higher density and thicker pavements, such as portland concrete cement (PCC) had the
16
highest temperatures in the evening and overnight hours, due to the high amount of heat
storage and thermal inertia.
The FLIR ThermaCAM™ S60 camera was also used in a comprehensive study of
surface pavement impacts on UHI effects in Phoenix by Golden & Kaloush (2006). In
addition to utilizing mesoscale remote sensing (ASTER) to analyze the impact of
pavement (by type) on the UHI on a citywide scale, the authors tested handheld
thermography to examine the role of surface pavements at the microscale. The accuracy
of handheld thermography was tested in comparison to in situ thermocouples, which were
embedded in various test sections of pavement located in a controlled outdoor research
laboratory in Phoenix. The authors also tested modified pavement materials for their
ability to reduce surface temperatures. They found that handheld thermography was
effective in evaluating homogenous surfaces such as parking lots and the influence tree
canopies and urban forestry as a UHI mitigation strategy. In areas of more complex
geometry in an urbanized area, care must taken to adjust for various parameters of
different influences, such as the emissivity for each material. Although albedo has a
strong influence on regulating temperature during the day, SVF and material type tend to
have the highest impact on cooling after sunset (Golden & Kaloush 2006).
2.5. ENVI-met
ENVI-met is a three-dimensional microclimate numerical model, which simulates the
surface-plant-air interactions in the urban environment. It was first introduced by Michael
Bruse in the late 1990's for his Doctoral Thesis (Bruse 1998). Various versions have been
released, with the latest fully operational version during the time of this writing version
17
3.1, containing slight modifications from version 3.0 (Bruse 2004). A fully capable "true"
3-D version (4.0) is planned on being released in 2010 (http://www.envi-met.com/).
ENVI-met, a freeware program available for download on the internet, is used in a
variety of fields, including urban climatology, architecture, building design, and
environmental planning. It is especially advantageous for research conducted at a fine
scale resolution, with typical grid cell resolutions between 0.5 and 10 m in space, and 10
sec in time. Model domain size can vary up to a maximum 250x250 m horizontal grid.
The model is prognostic and based on the fundamental laws of fluid mechanics and
thermodynamics. Several variables can be simulated, including flow around and between
buildings, exchange processes of heat and vapor at the ground surface and at the walls,
turbulence, exchange of vegetation and vegetation parameters, bioclimatology, and
particle dispersion (http://www.envi-met.com/).
In order to run the model, the user must have detailed soils, buildings, vegetation, and
initial atmospheric conditions for the model domain of interest. The file used to input
spatial information has a user-friendly graphical interface. However, correct use and set
up of the model for a specific area requires a thorough background in meteorology and a
good knowledge of the study area domain to be simulated. It is also helpful to have some
background in numerical modeling techniques, including parameterization.
As an urban microclimate model, ENVI-met may be the only currently available microscale computational fluid dynamic model capable of computing the thermal comfort
regime within a street canyon at a fine resolution (Emmanuel & Fernando 2007).
However, there are limitations to the model, especially in surface and potential
18
temperature outputs. The current version of the model does not allow for different albedo
(a) and heat transmission (U-value) values for individual building walls or roofs within
the modeling domain. Albedo and heat transmission for walls and rooftops, as well as
indoor temperature for all buildings within the domain are averaged for each simulation
at the time of initialization. The next version release (4.0) promises to allow for separate
definitions of these parameters for individual building fagades, roofs, and building
interiors, which will improve the accuracy of simulations to real world values
(http://www.envi-met.com/).
In literature, ENVI-met has been primarily used for urban planning purposes,
principally to obtain bioclimatic information on the built environment for either an
existing or proposed building project within a city. Studies incorporating ENVI-met often
test several heat mitigation scenarios, including the addition of trees, grass and other
greenery to the built-up and impervious surfaces, green roofs, and increasing the albedo
of roof and walls materials (Yu & Hien 2006, Hien & Jusuf 2008).
In hot and dry climates, ENVI-met has been tested recently in Ghardaia, Algeria and
Phoenix, Arizona (Ali-Toudert & Mayer 2006, Emmanuel & Fernando 2007). With the
use of ENVI-met simulations to test various urban canyon aspect ratios and orientation
effects on outdoor thermal comfort, Ali-Toudert & Mayer (2006) were able to confirm
the strong correlation between canyon ratios and orientations and the amount of human
comfort within these canyons. They concluded that although thermal comfort is very
difficult to reach passively in extremely hot and dry climate regions, slightly lower air
temperature decreases are possible with an increase of the aspect ratio. Emmanuel &
19
Fernando (2007) tested various heat mitigation scenarios for the CBD of Phoenix and
found that increasing the density of buildings offered the best solution for lowering the
Mean Radiant Temperature (MRT) and increasing outdoor thermal comfort. These
studies confirm earlier findings by Pearlmutter et al. (1999), that find that a dense and
compact building design in the CBD within a hot and arid city leads to cooler and more
comfortable day-time temperatures throughout the year. The only drawback to this
compact and dense design would be the retention of heat on summer nights, leading to
slightly higher discomfort later in the evening for pedestrians.
Recently, Chow et al. (2010) utilized ENVI-met in examining the horizontal and
vertical nocturnal influences of a park cool island (PCI) within the urbanized campus of
Arizona State University in Tempe, Arizona. Their study area was - 1 4 km from
Downtown Phoenix. They collected temperature data from 0.01 to 3 m heights via a
bicycle transect and then compared these observations with ENVI-met simulations within
a 23 ha study domain. The authors found that ENVI-met, in general, simulated mean
temperature fields well. Higher accuracy of temperature outputs were attributed to the
authors making accurate changes to local vegetation, surface, and building materials in
the model input. However, the model had a more difficult time in simulating a strong
near-surface inversion over non-urban surfaces found in observations. This could be
attributed to the lack of regional exchange processes accounted for in model (Chow et al.
2010).
20
2.6. CFD Modeling of Temperatures in Urban Street Canyons
During the last thirty years, the field of urban climatology has benefited a great deal
from conceptual advances made in microclimatology and boundary-layer climatology in
general. Within applied urban climatology research, physical-mathematical modeling has
become one of the most important tools in analyzing fine scale meteorological processes,
although the suitability of process-response models is limited by the extent to which the
physics of the processes are understood (Buccolieri 2008). Some progress has been made
in this direction, in regards to understanding the role of scale, heterogeneity, dynamic
source areas for turbulent fluxes, and the complexity introduced by the roughness
sublayer over tall and rigid roughness elements within cities (Buccolieri 2008). The effect
of buildings on flow and temperature fields within the UCL has been addressed by recent
studies (Belcher et al. 2003, Lei et al. 2007). Several urban flow and energy
parameterizations are also now available for numerical modeling in CFD using FLUENT
(http://www.fluent.com). However, most recent studies have failed to address the thermal
aspects of the urban environment and the spatial temperature distribution both
horizontally and vertically within urban street canyons. Due to flow separation from
buildings and the consequent asymmetric pressure field around them, the exchange of
momentum in the urban environment is more efficient than the exchange of scalar
quantities, such as temperature or moisture. Due to the very high mechanical forcing
induced by the buildings, attention to flow modification-induced hot surfaces in real
conditions have only rarely been addressed. Solazzo & Britter (2007) have outlined that
in most real scenarios buoyancy effects are confined to near the wall and therefore a little
contribution to the overall flow structure is to be expected.
Recent field research and numerical modeling conducted in the CBD of Phoenix,
Arizona shows that large differences in thermal heating of building facades may
contribute to vertical flow differences (Di Sabatino et al. 2009). There is a need for
further field experimentation within street canyons to validate both physical and
numerical modeling efforts which have tested the effects of surface radiant temperatures
of ground and building facades on the surrounding air temperatures and flow within street
canyons (Dimitrova et al. 2009).
3. FIELD DATA COLLECTION
3.1. Experimental Design and Study Area
In order to create a baseline of temperature measurements for comparison,
parameterization, and validation of model simulations, data were collected during a 24 hr
period beginning 06:00 LST 4 April 2008. The selected day for field measurements had
relatively clear skies, calm winds, and lower humidity, making it ideal for observing
temperature differences due to various land cover surfaces within Phoenix. Multiple field
measurement techniques were utilized to collect both surface and air temperature over a
complete diurnal period. Field measurements of temperature were both on the ground at a
fixed location (Downtown Phoenix), with mobile sampling by automobile, and aerially
by helicopter within the urban core of Greater Phoenix, centered in the central business
district (CBD) of Downtown Phoenix (33°27'N, 112°04'W, 338 m above sea level, a.s.l.).
Fig. 1 illustrates the approximate area of data collection within Greater Phoenix.
This section of Phoenix was selected to investigate spatial variability of the UHI due to
land cover and built elements within some of the highest magnitude areas (CBD and Sky
Harbor Airport), and to compare with previous microscale modeling efforts in Phoenix
by Emmanuel & Fernando (2007). Preliminary analysis of 90 m resolution surface kinetic
temperatures from an Advanced Spaceborne Thermal Emission and Reflection
Radiometer (ASTER) (http://www.asterweb.jpl.nasa.gov) image captured over Greater
Phoenix at 22:00 LST 3 October 2003, indicated that the highest heat retention at the
surface after sunset was in and around Sky Harbor Airport and portions of the CBD in
Phoenix. Thus, these two areas were initially selected for field data collection and
23
microscale modeling. Later, when selecting the final areas for data sampling and
modeling, the immediate vicinity in and around Sky Harbor Airport was dropped due to
the complications that would arise from the need to obtain permissions to collect thermal
IR images within FAA restricted airspace in this area. The final area selected for field
data collection includes the area to the immediate northwest of Sky Harbor Airport on the
east, outside of FAA restricted airspace, extending westward through the CBD, and then
ending approximately 9 km southwest of the CBD on the western boundary, for a total
areal coverage of ~ 100 sq km.
Fig. 1. Field data collection area within Greater Phoenix indicated by red rectangle
overlaid in Google Maps™
24
3.2. Scientific Basis for UHI Field Experiment Measurements
Analyzing the thermal behavior of the urban environment requires an understanding of
the thermophysical properties of surface materials and physical laws that govern the
nature of diurnal temperature variation and creation of a distinct microclimate at a given
location in the city. This section will discuss some of the key scientific reasons for
temperature differences within the urban environment and how they apply to temperature
measurements obtained from the 24 hr UHI field experiment in April 2008. This section
emphasizes the surface energetics (heat transfer and energy exchange) of urban materials
(the engineered built environment) and impervious land cover surfaces, which have been
found to play a major role in the development, magnitude, and spatial variation of the
urban heat island at the mico-local scales of a city, especially in the absence of cloud
cover and high winds (Oke 1982). Other factors leading to the development of the UHI,
including SVF, anthropogenic heat flux, and (lack of) regional exchange processes with
light or non-existent winds within the city, will not be discussed in this section.
Thermophysical properties of matter fall into two distinct categories: 1) those related to
transport of energy through a system, and 2) those related to the thermodynamic, or
equilibrium state of a system. Transport of energy through a system, also called heat
transfer, occurs by radiation, conduction, and convection. In the urban environment,
transport properties related to radiation heat transfer commonly include emissivity and
albedo. Heat transfer via conduction and convection in the urban environment involve
terms such as thermal conductivity and the convection coefficient. Thermodynamic
properties of urban materials differ from transport properties because they are concerned
25
with the equilibrium state of a system. These properties include density (p) and specific
heat (c), which form the basis for the terms volumetric heat capacity and thermal
diffusivity (Oke 1987, Carlson 2006). These heat transfer and thermodynamic properties
and terms will be discussed in more detail below.
Emissivity
A portion of the thermal energy contained within materials at the Earth's surface is
being emitted constantly as (long wave) radiation back into the atmosphere. The StefanBoltzmann law governs the maximum emissive power of any surface is illustrated by the
following (Oke 1987):
Energy emitted = aT 0 4
(1)
Where,
CT = Stefan-Boltzmann Constant = 5.67 x 10"8 W m"2 K"4
T 0 = surface temperature of the body (K)
However, this equation makes the assumption that the radiator is ideal or a blackbody. In
reality, a real surface does not completely radiate all of its energy like that of a blackbody
and radiates as a real body. A blackbody has a surface emissivity (s) equal to unity,
whereas a real body has an s < unity (1.0). The power emitted from a real body is
determined by the following equation (Oke 1987):
Energy emitted = e T 0 4
(2)
26
Where,
s = the emissivity of the surface
In the urban environment, knowledge of emissivity values is very useful for estimating
how efficient a surface can emit energy relative to a blackbody. Values greatly depend on
the surface material and its finish. For example, in the central Phoenix study area, one
would expect to find smooth metallic rooftops (i.e. aluminum, copper) in the downtown
area to have very low emissivities of 0.1, while rough surfaces, such as asphalt roads,
concrete sidewalks, or building facades to have much higher emissivities of 0.9 or higher
(Oke 1987). Emissivity is an important parameter in calibrating handheld IR thermal
cameras, which were used in the Phoenix UHI field experiment discussed in this chapter.
Thermal imagery from a handheld IR camera can have unrealistically low temperature
readings for rooftops and/or building facades that contain smooth metallic surfaces if the
camera has been previously set-up to capture surfaces with higher emissivity values.
Albedo
Radiation of wavelength (X) incident upon a given surface must be either transmitted
through it, be reflected from its surface, or be absorbed. This is a statement of the
conservation of energy. If we express the proportions transmitted, reflected, and absorbed
as ratios of this incident energy, we can define these values as the following:
^
+ a, + & = 1
(3)
27
Where,
Tx = transmissivity
ax = reflectivity
C,x = absorptivity
The values in this equation are the radiative properties of the substance, which are
expressed as dimensionless numbers between zero and unity (Oke 1987). It is also true
that absorptivity
= emissivity (s x), which is Kirchhoff s Law. This law states that at
the same temperature and wavelength, good absorbers are good emitters. For a full
radiator (and blackbody), ^ = £ \ = 1. On the surface of the Earth, opaque surfaces
(transmittance = 0) have an absorptivity < 1, which means that some of the incoming
radiation to the surface is reflected. The rate at which energy is both emitted and reflected
by the surface is known as albedo (a) of the surface. This is shown by the following
equation:
a = 1 - £ (absorptivity)
(4)
In other words, albedo is the ratio of electromagnetic radiation reflected to the radiation
incident to a surface. In most cases, high albedo is related to lighter shades of color. For
example, fresh snow has an albedo (white) 0.85, whereas fresh asphalt (near black) has
an albedo of 0.05 (Oke 1987). However, the smoothness (higher albedo) or roughness
(lower albedo) of a surface can also determine its reflectivity or albedo. Increasing the
albedo of low reflective surfaces has been shown to be the most important factor in
mitigating the effects of the UHI in large urban areas (Akbari 2005). It was expected that
28
during the Phoenix UHI field campaign, albedo values of various surface materials would
play a large role in determining where the peak temperatures would be found during the
day. For example, previous research has found that lighter colored pavements, such as
concrete, would be cooler during the day, whereas darker colored pavements, such as
asphalt, would be hotter during the peak hours of insolation during the day. Lighter
colored rooftops would also be expected to cooler during the mid-day than darker colored
(tar) rooftops. However, after sundown, lighter colored surfaces may retain heat and be
warmer than darker colored surfaces if the following factors come in to play: (1) they are
in an area of the city that has a low SVF, (2) the material has a high thermal inertia and
takes longer to cool down, or (3) the material is very dense and thick, adding to extra heat
storage that could potentially be released over the course of the night.
Thermal Conductivity
Conductive heat transfer involves the molecule to molecule transfer of heat energy
from more energetic particles of a substance to the less energetic particles due to the
random interactions of an atom. In general, an area of warmer temperatures in a
substance will flow through materials to an area of cooler temperatures. In soils and the
substrate in general, this would mean that heat would be conducted downward during the
day and upward at night (Oke 1987). This sensible heat flux density (Qg) can be written
as Fourier's law. This conduction equation is expressed as:
(5)
Where, the subscripts refer to levels in the soil and the sign indicates the flux is in
direction of decreasing temperature. k s refers to the thermal conductivity (W m"1 K"1),
which is a measure of the ability to conduct heat (Oke 1987).
Thermal conductivity is the rate constant that governs the heat flux through a body. It is
a transport property that is characteristic of the material. For soils, conductivity tends to
vary with type of particle, porosity, and moisture. Adding moisture to an initially dry soil
tends to increase its conductivity. In pavements, the thermal conductivity value depends
on the type of mix, the aggregates used, the amount of each component in the mix, and its
level of compaction (Oke 1987, Carlson 2006). Thermal conductivity values of materials
found in an urban area can be found in a few select sources. A sample of typical values
found in urban materials can be found in Oke (1987): asphalt (0.75), aerated concrete
(0.08), dense concrete (1.51), average brick (0.83), light wood (0.09), steel (53.3), and
gypsum plaster (0.46). Some of these materials are typically found in Downtown Phoenix
along roadways and/or in the walls and roofs of buildings and were used in part for
calculating the heat transmission (U-value), or overall heat transfer coefficient, of
building materials contained with certain grid cells in the ENVI-met modeling of diurnal
temperature in Phoenix discussed in Chapter 4 of this dissertation. Heat transmission is
calculated by dividing the thermal conductivity of a material by the thickness of the wall
(or roof) in m. Lower heat transmission values are better and indicate a more insulted
wall or roof in the city (http://www.envi-met.com/).
Convection Coefficient
Convection heat transfer describes energy transfer due to random molecular motion
(conduction) and bulk fluid motion (Carlson 2006). With this study, this mode of heat
transfer is associated the most with the transfer of heat between warmer surfaces in the
city (such as heated pavements during the day) and the ambient air as it passes over it.
Convective heat transfer processes follow this rate equation:
h
Where,
Q h = the convective heat flux (W m"2)
Ts = surface temperature (K)
Tf = fluid temperature (K)
h = convective heat transfer coefficient (W m"2 K"1)
The convective heat transfer coefficient, h, is dependent on several boundary layer
conditions, which include surface geometry, the type of fluid motion (laminar or
turbulent), and several other thermodynamic and transport properties (Oke 1987, Carlson
2006).
The rates of heat transfer above the surfaces where temperatures measurements were
made during the Phoenix UHI field campaign would have been most affected by velocity
and direction of wind. Even during light wind conditions, a change in wind direction can
transport warmer air from hot surfaces to cooler areas or vice versa. Since the field
campaign was conducted under calm or light wind conditions, it is hypothesized that
31
advection across large horizontal areas within the study area in central Phoenix would be
negligible.
Density
Density (p), is the measure of mass per volume of a substance. It is expressed as:
p = m/V
(7)
Where,
m = the mass of the material
V = volume of the material
Density is typically given in units of kg m"3 or lbm ft"3.
Specific Heat
Specific heat (c) is an important property used in thermodynamics. It is defined as the
amount of heat energy required to raise the temperature of one gram of a substance by
1°C. Specific heat is typically given in units of J kg"1 * K and Btu lb^F" 1 (Oke 2007).
Volumetric Heat Capacity
Volumetric heat capacity is formed when the density and specific heat of a substance
are multiplied together (p * c in J m"3K"', Carlson 2006). This thermal property quantifies
the ability of a material to store thermal energy. For example, water, with a high value,
can store four times the amount of heat than a typical land surface, which means it takes
much longer to heat up and cool down. Substances that have large densities tend to have
small specific heat capacities, which counter acts each other (Carlson 2006). In the urban
environment, it is ideal to have a substance that has both a low density and low specific
32
heat, which would allow the material to cool at a faster rate and lower the UHI impacts
after sundown. In central Phoenix, it has been found that dense pavement, such as
Portland concrete cement (PCC), tends to have a higher density and higher heat capacity,
which leads to the material retaining heat (hysteresis lag) late into the evening, versus
pavements containing a thin layer of asphalt nearby (Golden 2004, Golden & Kaloush
2006).
Thermal Diffusivity
Thermal diffusivity is the ratio of thermal conductivity to the volumetric heat capacity
in a substance. This property measures the ability of a material to conduct heat relative to
its ability to store energy (Carlson 2006). It is expressed mathematically as the following:
K
= k / (p * c)
(8)
Where,
K = thermal diffusivity
k = thermal conductivity
p = density
c = specific heat
2 1
Thermal diffusivity is typically measured in m" s" (Oke 1987). With this ratio, one can
estimate how a surface material will behave under certain conditions. For example, a
material having a very large K will respond very quickly to thermal changes versus a
material with a small K. With a smaller K, it will take a material longer to reach an
equilibrium state. In the urban environment, typical values of materials are: asphalt
(0.38), brick (0.61), steel (13.6), wood (0.2), and glass (0.44) (Oke 1987, Carlson 2006).
Permeability
In the urban environment, permeability of ground surfaces plays a large role in
determining the surface energy balance, including the amount of heat present on the
surface during the day and night. Impermeable surfaces in the city, such as concrete or
asphalt streets and sidewalks, absorb and store radiation during the day and slowly
release this heat energy during the night. Whereas, permeable surfaces, such as empty
lots, containing open soil and/or grasses and other vegetation within the city, as well as
farm fields close to the downtown, tend to be cooler during the day and at night. Much of
this cooling is due to the increase in latent heat and a decrease in the sensible heat
component of the surface energy balance. Impermeable pavements within the city, which
cover an average of ~ 30-35% of most large cities (Golden & Kaloush 2006), make a
substantial contribution to the UHI. Satellite remote sensing of the surface UHI indicate
much of the warmest temperatures a few hours after sunset to occur along the grid-like
structure of asphalt and concrete roadways in the city. In this study, it was expected that
sections of the central Phoenix study area containing the highest density of impervious
surfaces would also have some of the highest temperatures diurnally. Exceptions to this
would be when there is either a very high (cooler at night, warmer during the day) or very
low (cooler during the day, warmer at night) SVF.
34
3.3. Mobile Temperature Sampling
Mobile sampling of surface and air temperatures occurred approximately every two
hours beginning 06:00 4 April 2008 and ending 23:00 4 April 2008 (ending earlier than
24 hr due to power supply issues). A total of 13 one hr mobile samples were conducted
by two separate vehicles traveling in opposite direction along an east-west route across
central Phoenix. The use of two vehicles made it possible to cover more area over time,
including measurements on both sides of the street during the same hours of data
collection for a better spatial coverage of temperatures along the roadways. The use of
two vehicles also served as a back-up in case of equipment failure.
Equipment mounted on the two vehicles consisted of an air temperature and relative
humidity (RH) sensor, an IR temperature sensor (IRT), and a GPS to record location.
Measurements were recorded every second with a consistent average vehicle speed of 5060 km hr"1, giving a reading of temperature approximately every 7-8 m along the route.
The route itself was 18.1 km in total length from east to west. Information obtained while
the vehicle was not moving, whether from traffic congestion and/or traffic lights, was
eliminated from the record after data collection was completed. Equipment used in
mobile sampling of the UHI through central Phoenix included two of each of the
following:
•
•
•
•
•
•
•
Campbell Scientific CR10X Datalogger
IRTS-P Apogee Precision Infrared Thermocouple Sensor
Vaisala HMP45C Temperature and RH Probe
GPS 16-HVS Garmin GPS Receiver (WAAS-enabled, 12 channel)
Campbell Scientific UT12VA 12-Plate Gill Radiation Shield
Laptop computer, used to monitor and download sensor data
Other equipment, including cables, mounting equipment, and power adapter
35
Fig. 2 illustrates the instrumentation set-up mounted on a vehicle. Fig. 3 illustrates the
mobile route, starting near the northwest portion of Sky Harbor Airport at 24 th Street and
Jackson (33°26'43"N, 112°01'47"W, 338 m a.s.l.) and ending at 51st Avenue and
Buckeye, (33°25'27"N, 112°10T'W, 313 m a.s.l.). In order to keep things simple and to
organize the data collected for later analysis, the westward moving vehicle and route was
named "A", with the eastward moving vehicle and route named "B."
Ventilated
Solar Shield
Air Temperature
& Relative Humidity
Sensor
\
Infrared Temperature
Sensor (IRT)
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Fig. 2. Mobile temperature sampling equipment mounted on a vehicle
36
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Fig. 3. Mobile temperature sampling route indicated by blue line overlaid in Google
TM
Maps
Calibration and testing of instrumentation was done in the month prior to the April field
day using sensors and other equipment borrowed from the Arizona State Climate Office
at Arizona State University. Through testing and calibration one of the two Vaisala
HMP45C temperature and RH probes was found to be faulty, requiring the use of
replacement sensor, borrowed from CAP LTER at Arizona State University. IRT sensors
were found to be within 0.2°C of accuracy, with temperature and RH sensors within
0.1°C of accuracy. GPS units did not require calibration, per manufacturer
recommendation, and were found to have reasonable location output within the nearest
37
sec., when compared to locations in Google Earth™ (GE). Further details on equipment
specification and calibrations which were used during the 4-5 April 2008 field day,
including both mobile and thermal IR cameras can be found in Appendix A.
Post processing of mobile data immediately following the field study day included a
clean-up of stationary records (while sitting at traffic lights), and plotting of measurement
points spatially. In order to make spatial and temporal comparisons with modeling and
other data collected during the field campaign, mobile data points were plotted in GE at
10 m intervals for the entire 18.1 km east-west route, for a total of 181 points. These
spatially referenced points, containing both surface and air temperatures from the
roadway, proved invaluable for later use as model receptor and validation points within
ENVI-met model domains (see Chapter 4 for more detail).
3.4. IR Thermography Calibration and Testing
Testing and calibration of handheld thermal IR cameras utilized in the 24 hr field
campaign was conducted by Joby Carlson, of Arizona State University, during the two
days prior to the experiment at Arizona State University as well as in Phoenix KPNX
Channel 12 News hanger at the Scottsdale Air Park, in Scottsdale, Arizona. The
challenge with this field campaign was to record simultaneous images of temperature
from diverse urban materials emitting and reflecting radiation at different angles from
two separate thermal IR cameras: the FLIR ThermaCAM™ S60 (ground based in
Downtown Phoenix) and the FLIR ThermaCAM™ SC640 (from a helicopter).
The first task was to make sure that both cameras had their timestamps calibrated to the
same time as each other as well as the data logger used in recording temperature
38
information from mobile sampling. The second task was to make sure that each of the
camera's settings were adjusted to match each other during field data acquisition on 4
April 2008. Both cameras automatically adjust for ambient temperature and angle, with
the main adjustment required being that of emissivity. Tests of temperature readings,
done in conjunction with a handheld IR thermometer, revealed a temperature difference
between sensors to be less than 0.1°C. Previous calibrations of emissivity with a FLIR IR
camera by Carlson (2006) found that for the majority of engineered surface materials in
Phoenix, the emissivity was ~ = 0.96. After testing various surfaces at Arizona State
University and Scottsdale Air Park, it was decided that this value would best represent
the majority of emissivity values encountered during the 24 hr UHI field campaign and
used for the experiment (Carlson 2008, personal communication). The drawback to this
approach (leaving the emissivity at 0.96 for all measurements instead of continuously
changing for each image) is that post-processing of imagery would contain erroneously
low temperature readings for objects that have very low emissivities, such as smooth
metallic roof-tops (helicopter) or metallic building facades (Downtown Phoenix).
3.5. Ground-based Thermography
Thermal images of surface temperature were captured with a FLIR ThermaCAM™ S60
at key locations within a four block area of Downtown Phoenix, centered at Central and
Adams streets. The FLIR ThermaCAM™ S60 has a built-in 24° lens, a visual color
camera, a laser pointer, and a 4 in color LCD on a removable remote control with a
spectral range of 7.5 to 13 (j.m, a thermal sensitivity of 0.06°C at 30°C, and a focal plane
array of 320 x 240 pixels (http://www.flirthermography.com/media/S60_datasheet.pdf).
Further details of camera specifications can be found in Appendix A. Images of three
separate north-south street canyons and various building facades were acquired over the
space of one hr, every two hours during the 24 hr field day. Acquisition times
corresponded to mobile sampling measurements, beginning 06:00 4 April 2008 and
ending 07:00 5 April 2008. Careful consideration was taken by the field crew at this
location to attempt to acquire imagery down the north-south 1st Ave street canyon at
approximately the same time as the mobile vehicle and helicopter were traveling through
the canyon during the 14:00, 19:00, and 22:00 times. Fig. 4 illustrates the final locations
chosen to acquire individual street canyon and building fagade images, with letters and
numbering corresponding to the respective street canyons and directions of interest.
40
Fig. 4. Downtown thermography locations indicated by yellow pushpins in Google
Earth™. Dark blue lines indicate A and B mobile routes
An innovative method was used for transportation between image acquisition sites with
the use of a "pedal cab," a type of bicycle taxi. The downtown field crew was able to
acquire a much greater number of thermal images with this type of transportation within
the street canyons than would have been possible with a vehicle or by walking between
sites. Fig. 5 illustrates the field crew in Downtown Phoenix, Silvana Di Sabatino &
Winston Carter, acquiring images from the peddle cab (photos obtained by permission
courtesy of Phoenix KPNX Channel 12 news). Greater details of image acquisition and
41
focus on the north-south 1st Ave street canyon for CFD modeling can be found in Chapter
5.
Fig. 5. Images captured of the downtown thermography crew obtaining thermal images of
building facades and street canyons via pedal cab at 14:00 LST
42
3.6. Helicopter-based Thermography
Thermal images were captured via a FLIR ThermaCAM™ SC640 camera from a
Phoenix KPNX Channel 12 News helicopter at approximately 305 m above ground level,
and along the mobile sampling route (Fig. 3) from east to west at three separate times of
the day on 4 April 2008. The FLIR ThermaCAM™ SC640 has a built-in 24° lens, a
visual color camera, a laser pointer, built-in 5.6 in color LCD (1024 x 600 pixels), a
spectral range of 7.5 to 13 (am, thermal sensitivity of 0.06°C, and a high definition focal
plane array of 640 x 480 pixels (http://www.flirthermography.com/). Further details of
camera specifications can be found in Appendix A.
It was decided that 14:00, 19:00, and 22:00 LST were the best times to follow the
mobile vehicle and route and acquire imagery during the field campaign. These times
corresponded to the maximum daily temperature, the temperatures immediately following
sunset, and at the time of maximum UHI development, several hours after sunset.
Thermal imagery captured from the helicopter allowed for very detailed surface radiant
temperature measurements to be made across a large area of central Phoenix, at a much
finer scale and coverage than would be possible by measurements on the ground.
The IR camera was held, at 45°, out the left passenger side of the helicopter. Previous
research has shown that this angle, in addition to nadir (90°), are optimal angles for
detecting differences in the ground, wall, and roof temperatures (Voogt & Oke 2003).
Since it was not possible to mount the camera to the bottom of the helicopter for nadir
measurements (due to FAA regulations and time constraints to obtain permission to do
so), the camera was pointed toward the ground at the 45° angle throughout the duration of
43
each one hour flight. The camera captured both visible and infrared images
simultaneously along the mobile route, with multiple images captured per minute in
flight. The photo in Fig. 6 illustrates the camera location held out the left passenger-side
window and the approximate 45° used to capture imagery during flights. The helicopter
used for image acquisition is also shown (Photos obtained by permission courtesy of Joby
Carlson).
Preliminary testing of thermal images captured by the FLIR ThermaCAM™ SC640
illustrated the dramatically improved difference of high definition over lower resolution
cameras used in previous studies capturing thermal images from the air, such as images
captured in a Chicago UHI study (personal communication with Joby Carlson 2008). The
SC640 has a much higher resolution of 640 x 480 pixels versus 320 x 240 pixels for the
FLIR ThermaCAM™ S60 used by the ground thermography team in Downtown Phoenix
(http://www.flirthermography.com). The higher resolution of images from the helicopter
camera (4 times greater than the ground camera) allowed for a greater ability to
distinguish microscale differences in radiative temperatures from various ground and
building surfaces, especially valuable for comparing with surface temperatures from
mobile, ground (downtown) and model simulation results from the April 2008 field study
day.
A recently published peer-reviewed article in a special issue of Physics of Fluids,
(Fernando et al. 2010) provides more detail of the April 2008 Phoenix UHI field
experiment, including some interesting findings and implications. Further details of
44
model simulations, Downtown Phoenix thermography and CFD modeling, and
significant results can be found in subsequent chapters of this dissertation.
Fig. 6. FLIR camera position in helicopter (left). KPNX Channel 12 News helicopter
(right)
4. ENVI-MET SIMULATIONS
4.1. Introduction
The 3D microclimate numerical simulation model ENVI-met v.3.0 was selected to both
test field data collected on 4 April 2008 and model three unique landscapes within central
Phoenix over a complete diurnal period beginning shortly before sunrise at 06:00 LST.
Since the primary research emphasis is on understanding the micro-local scale
complexities of the UHI due to land cover and surface thermal properties and materials,
the ability of ENVI-met to simulate temperatures on a very fine scale within the urban
area was well suited for these objectives.
Previous research utilizing ENVI-met in hot arid cities has primarily focused on
optimizing building orientation and street canyon design for outdoor human comfort, as
well as UHI mitigation strategies (Ali-Toudert & Mayer 2006, Emmanuel & Fernando
2007). A search made within peer-reviewed literature reveals two recent case studies
(Emmanuel & Fernando 2007, Chow et. al 2010) utilizing ENVI-met to test various
aspects of temperatures and the UHI in the hot arid climate of Phoenix, Arizona area.
Emmanuel & Fernando (2007) investigated and modeled a four-block area of downtown
Phoenix, determining the impact of building and surface materials and density on human
comfort. Chow et al. (2010) modeled the vertical and horizontal diurnal effects of park
cool island (PCI) located in an urbanized setting at Arizona State University's main
campus in Tempe, Arizona. In addition, a recent professional project in urban planning
thesis published by Love (2009) focused on heat mitigation during the early afternoon
(12:00-14:00) in a small portion of CBD of Phoenix in July 2008. ENVI-met modeling
46
has also been utilized to test the "urban form" of buildings in Downtown Phoenix on heat
mitigation and ventilation and flow in future developments
(http://phoenix.gov/urbanformproject/, Rosheidat et al. 2008). Due to this relative dearth
of literature on microclimate modeling of temperature in Phoenix, the use of ENVI-met
to simulate temperatures as well as compare with field measurements over a complete
diurnal period in Phoenix promised to bring some insightful results on understanding the
complexities of various landscape impacts on temperature within a large, hot arid city.
While most studies utilizing ENVI-met focus on one particular area within a city
(typically in the CBD), this study investigates three distinct microclimate areas, each
possessing unique characteristics of building density, ground surfaces, and vegetation
characteristics. The location and extent of model domains were selected to represent
typical land covers along the 18 km east-west study area across central Phoenix. Fig. 7
contains images of each ENVI-met domain. The inset photograph contained in Fig. 7c
shows the approximate location of a 10 m tall commercial building that was constructed
after the Google Earth™ image was acquired November 2005 and before the 4 April
2008 field study. The names of the three modeling domains simply correspond to the
nearest major north/south cross street: 24 th and Jackson Street (24th St), 1st Avenue and
Adams Street (1st Ave), and 43 rd Avenue and Lower Buckeye Street (43 rd Ave).
Images shown in Fig. 7 illustrate the marked difference in surface and building density
characteristics between the three model domains. 24 th St area is characterized by low-rise
light industrial, commercial, and a few residential buildings, as well as some empty lots
with pervious soils and a low percentage of vegetation. 1st Ave area, located in the heart
47
of Downtown Phoenix, is characterized by mid to high-rise buildings and predominately
impervious ground cover and low percentage of vegetation. The final area, 43 Ave, is
much different than the prior areas, in that it contains large amounts of vegetation and
pervious surfaces, principally on the southern 40% of the domain containing an irrigated
(corn) field. Detailed land cover comparisons, which were derived from methods used to
refine model input parameters, are discussed in more detail in the land cover comparison
section.
Fig. 7. Model domains as seen from bird's eye view (520 m a.g.l.) in Google Earth™.
Red rectangle indicates (a) 24 th St (b) 1st Ave and (c) 43 rd Ave domain boundaries
4.2. Model Set-Up and Testing
One of the most practical and time saving aspects of ENVI-met is the ability to run the
simulations on an average PC, containing at least 1 GB of RAM. Recent drops in
memory costs have allowed even lower priced consumer desktops and laptops to come
equipped with ample amounts of RAM. Another aspect of the model that is ideal is the
relatively few inputs required to generate a simulation. The model consists of four submodels, which form, when combined with user input configurations, the full
microclimate within the defined domain. The four sub-models consist of the following:
•
the atmosphere model (wind field, temperature, vapor, humidity, pollutants)
•
the soil model (temperature and moisture inside the soil, water bodies)
•
the surface model (fluxes on horizontal and vertical surfaces, pavements, roofs
and walls of buildings)
•
the vegetation model (foliage temperature, heat, water, and vapor exchange
with in-canopy air)
Details of the internal workings of each of the four sub-models can be found on the
ENVI-met website, under the "Model architecture" link (http://www.envi-met.com/). Fig.
8 illustrates the flow diagram of ENVI-met. While other simulation models perform
similar calculations of surface energy-balance and/or flow and dispersion of pollutants,
ENVI-met includes a very complex vegetation model, which is often overlooked in other
models because of the complexity of these interactions. The governing equations used to
calculate atmospheric turbulence, flow, and surface, building, and radiative fluxes are not
covered in this dissertation, but can be found in detail in Bruse (2004).
50
selected biput
fester
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h.'ioi^b}
v{t>uiputfolder;^sutifoidersi
General Data Flow in ENVI-met
Fig. 8. Data flow in the current ENVI-met model version (http://www.envi-met.com/)
Each model domain required two main input files to begin initialization: a
configuration file (.CF) and an area input file (.IN). Area input files contain spatial
attributes associated within a specific area chosen, such as building size and heights,
pervious or impervious soils and surfaces, type and heights of vegetation canopies, and
the 3D dimensions horizontally and vertically. The area input files are edited in a
graphical user interface (GUI) called ENVI-met Eddi or ENVIeddi. ENVIeddi contains a
user-friendly interface that allows the user to digitize spatial information from an
imported bitmap image of the model domain area. It contains two principal layers that the
users can toggle between to edit, based on the underlying bitmap. The first layer is the
buildings and vegetation layer, where the user can create the building footprints and
heights (m), as well as the type of vegetation and its respective height (m). The second
51
layer is the soils, where the user defines whether the surfaces contain pervious soils, or
impervious surfaces such as concrete or asphalt.
The freely available web-based virtual globe visualization and mapping program,
Google Earth™ (GE), was found to be ideal for obtaining base-map bitmaps for the three
model domains. Precise measurements of building footprints, street widths, and street
canyons could be done with a simple measurement tool in GE. The 3D buildings feature
was especially useful for detecting exist building footprints in the 1st Ave domain, since
shadows made it difficult to measure exact horizontal dimensions. However, building
heights could not be accurately measured from the images and were thus obtained from
Michael Brown at Los Alamos National Laboratory. Since some of the areas within the
domains had changed since GE images were acquired (principally from November 2005),
visible images taken from the FLIR ThermaCAM™ SC640 camera via helicopter within
the 14:00 hr on 4 April 2008 were utilized to make corrections in areas where changes
had occurred. While the majority of surface characteristic information could be digitized
into grid cells from GE imagery, several new buildings, as well as the Phoenix Metro
Light Rail, had been constructed since photos were acquired (mainly from November
2005), requiring the use of more recent visible helicopter images obtained from the
thermal IR camera for spatial reference.
After careful consideration of the optimal areal coverage of each model domain versus
the highest horizontal and vertical resolution possible without making the model unstable,
the grid cells were set at 5 m for the horizontal resolution within each of the three
domains. This resolution was also used for the Phoenix CBD in Emmanuel & Fernando
52
(2007), and allowed for spatial comparisons between the three domains. The vertical
resolution was set at 2 m for the 43 rd Ave and 24th St domains, with 3 m set for 1st Ave,
due to the greater complexity and heights of the buildings. The final domains consisted of
the following dimensions: (a) 43 rd Ave: 200 x 200 m (b) 1st Ave: 300 x 300 m and (c) 24 th
St: 220 x 210 m. Fig 9 illustrates the location of the three model domains along the 18 km
east-west mobile & helicopter route.
Due to the proximity of buildings to the edge of the model domains, nesting grids were
added to the 1st Ave and 24 th St domains. 1 st Ave contained three nesting grids, with the
greatest amount of building, heights, and complexity, while one nesting grid was added to
the 24th St domain to help stabilize this domain.
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Fig. 9. ENVI-met domain locations along the 4 April 2008 east-west mobile/helicopter
route (blue line) and overlaid in Google Earth™. Distance is indicated by mobile
sampling points shown every 1 km
After setting up the area input files, configuration files were populated with the proper
input parameters. Configuration files contain information such as the physical location of
53
the domain, initial atmosphere conditions near the ground, internal building temperature,
2
1
mean heat transmission (U-value) of walls and roofs (W m" K" ), mean albedo of walls
and roofs, and heat and moisture fluxes of soils. Heat transmission values are derived
from estimating the thermal conductivity of building material (W m"1 K"1) divided by the
mean wall thickness (m). Additional sections that can be defined include cloud cover,
pollution sources, dynamic time-steps, turbulence, receptors, lateral boundary conditions
(LBC), nesting area options, bio-meteorological parameters (Predicted Mean Vote, or
PMV), type of plant model, and the definition of local databases including pollution
sources or additional vegetation information not found in default settings and databases.
Atmospheric conditions near the ground where obtained from two weather stations: Sky
Harbor Airport NWS ASOS (33°25'40"N, 112°00'13"W, 339 m above sea level, a.s.l.)
and Kay PRISMS (33°25'12"N, 112°09'13"W, 314 m a.s.l.). Sky Harbor Airport was
chosen as the most representative weather station for the 24 th St and 1st Ave domains (at a
distance of 3.1 and 7.1 km, respectively) while Kay PRISMS was used for the 43 rd Ave
domain (at a distance of 300 m) due to close proximity and similar land cover
characteristics. The Phoenix Real-time Instrumentation for Surface Meteorological
Studies Network (PRISMS) is a network of predominately urban sites within Greater
Phoenix and operated by Salt River Project (SRP). Soil temperature and moisture
estimates (depth = 0-50 cm) were obtained from a representative urban weather station
within the Arizona Meteorological Network (AZMET), Mesa AZMET
(http://ag.arizona.edu/azmet/29.htm). The Mesa AZMET station is located about 20 km
southeast of Downtown Phoenix and has similar land cover characteristics, with a large
percentage of impervious surfaces and low vegetation cover, as the study area domains
(33°23'12"N, 111°52'03"W, 366 m a.s.l.). Another input parameter that was needed for
model initialization was specific humidity at the top of the model domain at 2500 m or
- 7 0 0 mb. This value was obtained from the NCEP/NCAR Reanalysis Daily Composite
website (http://www.cdc.noaa.gov/data/composites/day/).
The first series of tests to run ENVI-met for the three study domains occurred shortly
after completion of populating area input and configuration files with approximate values
within the three study domains. Some trial and error occurred over a series of model runs
to find the optimal configuration for predicting the most accurate output for each domain.
Since the goal was to compare model output with measured field data over a 24 hr period
for validation, initially the model was set to run 24 hr from 06:00 4 April 2008 until
06:00 5 April 2008. Initialization from this pre-sunrise time resulted in an output that was
much too cold for surface and air temperatures as compared to field data from the mobile
sampling. A second initialization time was tested for 24 hr beginning at 12:00 4 April
2008. This time period was found to be much too warm, with a gross over-prediction of
temperatures. It was found that the optimal initialization time for 24 hr model runs was
09:00 for 43 rd Ave and 24 th St domains, with 10:00 for the 1st Ave domain. When the
initialization time for 1st Ave was begun at 09:00, temperature output was found to be
under-predicted over the 24 hr period when compared with mobile field data within the
domain.
Within area input files, it was also discovered through trial and error that building
heights needed to match the vertical grid resolution set within the domain in order to
55
reduce instability in the turbulence calculations of the model. While this mainly applied
to the 1st Ave domain, with its complex pattern of mid-high rise buildings at 3 m
increments, building heights were also adjusted (at 2 m increments) for the 43rd Ave and
24th St domains.
4.3. Model Input Refinement
Since initial ENVI-met runs included default input parameters and approximated land
cover within each model domain, an attempt was made to identify methods which could
increase the accuracy of the model output to the point of replicating the real-world as
much as possible. While searching for ideas on how to increase the accuracy land cover
within the 5 m grids, a free online tool was found called KML-GRID
(http://www.zonums.com/gmaps/kml_grid.html). This tool allowed for the creation of a 5
m grid, which could then be overlaid in GE within the model domains, and then brought
into the ENVIeddi area input file editor for refinement of building footprint boundaries,
streets, and vegetation. When populated by the 3D buildings layer in GE, the KML-GRID
files proved especially useful in defining the building footprints when overlaid onto the
base GE imagery within the 1 st Ave domain.
ENVI-met was designed in Germany for a typical humid continental Central European
city. Due to this fact, default area input parameters, such as trees, vegetation, and
building materials were inaccurate for the urbanized arid climate of Phoenix, Arizona.
Therefore, identification of specific building materials and vegetation native to the area
within each model domain was undertaken to increase the accuracy of model output.
Identification of individual trees and other vegetation within model domains was made
56
possible with the aid of free online tools such as "Street View™" in GE and Google
Maps™ and field observations (including photographs of trees, vegetation, and buildings
not viewable with online tools). Expert identification of specific tree species and types
within domains was made possible with aid in the field by Steve Priebe, the principal
arborist for the City of Phoenix. Google Street View™ proved especially valuable for
building material and vegetation identification since the pedestrian-level photographs (2.5
m) were acquired much more recently in October 2007 (six months before the field
experiment) than satellite images used in GE, which were primarily acquired in
November 2005 as listed online by Google and in the software program
(http://en.wikipedia.org/wiki/Google_Street_View). Through these extensive
observations it was discovered that trees planted along roadways within the three model
domains were generally found to have heights of 5-10 m and had lower density canopies
than the default trees available to use for input in ENVI-met.
A spatial database was created of individual trees and vegetation types for each 5 m
grid cell within each of the three study domains after proper identification of height,
density, and type. Two other spatial databases linking grid cell location with (a) building
material and height, and (b) impervious and pervious surface material information, were
created to improve the accuracy of the model inputs. The detailed spatial databases for
buildings, vegetation, and impervious surfaces can be found in Appendix A.
Identification of building wall and roof material type and its associated heat
transmission (U-value) and albedo (a) within each grid cell were calculated primarily
from estimates made in literature (Evans 1980, Goward 1981, BRE 1984, Statens
57
planverk 1985, Oke 1987, Carroll 1992). The color and material type of wall and roofs
were also identified from field photos, as well as from images found in GE and Street
View™. Building materials were found to vary considerably both within (24th St) and
between model domains (24th St & 1st Ave). 43 rd Ave only contains one building structure,
a 10 m tall commercial warehouse, requiring the least amount of time to estimate
individual grid cell heat transmission and albedo parameters for the domain. Upon
completion of grid cell estimates within the buildings database, building heat
transmission and albedo values were then averaged within each domain to obtain the final
value for input into the configuration files. Final values can be found in Table 2 (in
section: Comparisons between Default and Refined Simulations).
Tree, shrub, and grass identification within model domains yielded approximately 24
new arid-type plant id's that were not found in the default plants database. The new
plants not only represented the actual species, but an accurate leaf area density in the
canopy and proper height that was not obtainable from the existing default plant database
values. For example, for tree heights, there is no representation between the highest
shrubs at 2 m and the lowest trees at 10 m, whereas the majority of trees within the 24 th St
and 43 rd Ave domains are ~5 m tall. New species identified and included in the edited
plants database included trees such as Mexican Fan Palm, California Date Palm, several
species of Palo Verde, Sweet Acacia, Honey and Velvet Mesquite, Ficus (Laurel Fig),
and several other varieties. Due to the same species identified at various heights, new tree
id's were placed in lower case alphanumeric lettering to identify a lower height, such as 5
m, and placed in uppercase lettering to identify higher canopies, typically 10 m.
58
Comprehensive tables of vegetation, including the scientific name, type, and height for
each grid cell in each of the three domains can be found in Appendix B.
An additional consideration with model inputs was to decide the representative type of
pervious soils within each domain. The two choices offered in ENVI-met are (a) loam
and (b) sand. The standard default value is loam (depth = 0-2 m). Since all three domains
are in close proximity to the Salt River, there was the possibility that there was a
percentage of alluvial (sandy) soil. Through the use of the USGS Natural Resources
Conversation Service Web Soil Survey (http://websoilsurvey.nrcs.usda.gov/app/), soils in
all three domains were found to consist of loamy type of soil, with the most common soil
identified at each site as Avondale clay loam (24th St), Laveen loam (1st Ave), and
Gadsen clay (43 rd Ave). All three soil types are consistent with floodplain-type soils.
The final area input files for each domain can be found in Fig. 10. The background
image obtained from GE and the KML-GRID used to digitize buildings, vegetation, and
surfaces are also shown for spatial reference. Mobile measurement points, shown every
100 m, and later used for model validation, are also shown.
Since the 1st Ave domain contains much of the same area simulated in Emmanuel &
Fernando (2007), and interesting spatial comparison can be made by comparing the area
input files side by side. The center of the later domain was shifted one block to the east of
the 1st Ave domain, over Central Ave, but contains the same areal size (300 x 300m) and
horizontal and vertical grid size (5m & 3m, respectively). The comparison between the
two domains is illustrated in Fig. 11.
After refining the 1st Ave domain, the increased complexity of building morphology
caused instability of the model, resulting in a failure of runs during initialization or
during the main loop. It was discovered that increasing the dynamic time-steps from 10,
5, and 2 sec to 5, 2, and 1 sec stabilized the flow in the model and allowed for the
increased complexity found within this domain. Greater details of the final configuration
files and area input files, including the initial atmospheric conditions and inlet direction,
can be found in Appendix C.
c
Fig. 10. Final area input files, (a) 1 st Ave (b) 24 th St and (c) 43 rd Ave. Mobile sampling
points are shown at 100 m intervals
61
Fig. 11. Comparison between (a) final 1st Ave area input file and (b) area input file used
in Emmanuel & Fernando (2007). Red arrows indicate the same location on Central &
Adams Street
4.4. Land Cover Comparisons
After refining the ENVI-met grid inputs and creating spatial databases of surface
characteristics, including soils, vegetation, and building characteristics, an accurate land
cover classification for a 5 m resolution was created for each of the three domains. Each
of the three study domains has a unique breakdown of building density, impervious
surfaces, and amount of vegetation. Examination of aerial imagery clearly indicates that
the 1st Ave domain has the largest percentage of impervious surfaces, such as concrete
and asphalt, while the 43 rd Ave site, predominantly consisting of agriculture, has the
largest percentage of pervious soils. 1st Ave also has the highest density and percentage
of buildings occupying grid cells at 34%, followed by the much less dense area of 24th St
at 8% coverage. Table 1 compares the land cover characteristics for each of the three
modeling domains. It should be noted that the pervious soil class listed in the table
62
contains only the number of grid cells where there is open soil, with no overlying
vegetation.
Table 1. Land cover and building comparisons between the three model domains
24th St
2
Total # of grids:
Buildings
a) 2-10 m
b ) > 10 m
Impervious surfaces
a) Asphalt
b) Concrete or Brick
Pervious soil
Vegetation
a) Canopy (5-15 m)
b) Shrub (1-4 m)
c) Grass
m
grids
1,848 46,200
158
3,950
158
3,950
0
0
1,047 26,175
879 21,975
168
4,200
613 15,325
750
30
20
500
1
25
225
9
1st Ave
Total # of grids:
Buildings
a) 2-10 m
b) 11-20 m
c) 21-30 m
d) 31-40 m
e) 41-50 m
f) >50 m
Impervious surfaces
a) Asphalt
b) Concrete or Brick
Pervious soil
Vegetation
a) Canopy (5-15 m)
b) Shrub (1-4 m)
c) Grass
m
grids
3,600 90,000
1,226 30,650
271
6,775
175
4,375
338
8,450
59
1,475
14
350
9,225
369
2,191 54,775
1,044 26,100
1,147 28,675
1,200
48
135
3,350
121
3,025
1
25
12
300
2
%
100
8.5
8.5
0
56.7
47.6
9.1
33.2
1.6
1.1
0.1
0.5
43 rd Ave
Total # of grids:
Buildings
a) 2-10 m
b ) > 10 m
Impervious surfaces
a) Asphalt
b) Concrete or Brick
Pervious soil
Vegetation
a) Canopy (5-15 m)
b) Shrub (1-4 m)
c) Grass
d) Corn
2
m
grids
1,600 40,000
48
1,200
48
1,200
0
0
290
7,250
255
6,375
35
875
653 16,325
609 15,225
1
25
22
550
25
625
561 14,025
%
100
3
3
0
18.1
15.9
2.2
40.8
38.1
0.1
1.4
1.6
35.1
%
100
34.1
7.5
4.9
9.4
1.6
0.4
10.3
60.9
29
31.9
1.3
3.7
3.4
0.03
0.3
It is well known in literature that land cover characteristics play a dominant role in
determining the surface and near-surface temperature and UHI during stable atmospheric
conditions, with clear skies and light winds (Goward 1981, Oke 1982). ENVI-met is able
to replicate this to a certain extent with its output, including the fine scale land cover
characteristics into the final surface and atmospheric outputs three-dimensionally.
Building density is not shown in Table 1, but building heights are illustrated, with the
percentages of heights every 10 m. It is clear from the table that 1 st Ave has the greatest
density of buildings, with the majority of buildings over 10 m, whereas 43 rd Ave and
th
24 St are much less dense, with all building structures at 10 m or less.
4.5. Simulation Results
ENVI-met output files consisted of several sets of hourly files (atmosphere, soil,
surface fluxes, etc.), which could then be visualized in the free software package
associated with ENVI-met, Leonardo v. 3.5. While outputs produced in Leonardo were
helpful to understand spatial patterns, overlaying output in the 3D view of GE proved
uniquely valuable in visualizing temperature patterns spatially. Due to the large amount
of output results generated from 24 hr of simulations, this section focuses on graphical
output from the 14:00 and 22:00 times within the three domains. These times are chosen
to illustrate the spatial variability of surface and near-surface temperatures near the time
of maximum daytime temperature and early nighttime UHI maximum within central
Phoenix. More detailed information on simulations, including input parameters for each
domain, can be found in Appendix C. Fig. 12 illustrates air temperature output at 22:00
overlaid in GE. The maps illustrate 3D buildings in the 1st Ave domain, while the other
two domains are in shown in 2D. Fig. 13 illustrates air temperature output at 14:00, while
Fig. 14 has output for 22:00 at the three respective domains. Finally, Fig. 15 illustrates
surface temperature output at 1st Ave for 14:00 and 22:00.
Air Temp. (°q
below 22.61
--•65
to 22.71
to 22.11
-2.84 to 22.90
---90
L
to 22.96
1 22.9 6 to
23.02
02 to 23.08
-3 OS to 23,14
above
23.14
l<r Temp. (°c)
below 2! SS
-i 88 to 21.93
-' 93 to 21.99
,
»
t J
-' 99 to 22.04
-- 04 to 22.10
- >0 to 22. IS
-2.15 to 22 ~>l
3
—J
:
12.21 to 22.27
"
— J
«o 22.32
above 22.3 ">
Temp. (°q
befow 20.91
-0 91 to 20.98
-0.98 to 21.05
J
"
*
21
to 21.12
2
"-to2l.l9
f = = l
19 to 21.26
21 26 to 21.33
- 1 . 3 3 to
Fi
S• 12. Simulat,
H B
-'-^Oto21.47
—above 21.41
Ave (b) 24 th St a n d (c) 43S™*
Goo
S , e Earth™
65
Air Temp. (°C)
H H I
25.23 to 25.26
25.26 to 25.29
25.29 to 25.32
2 5 3 2
to
35
to
a
m
Air Temp. (°C)
H m
i b i
25.93 to 26.00
26.00 to 26.0"
26.0" to 26.14
26.1-1 to 26.21
26.21 to 26.29
26.29 to 26.36
above 26.36
Air Temp. (°C)
below 24.91
24.91 to 25,00
25.00 to 25.08
25. OS to 25.1"
2 5 . 1 " to 25.26
25.26 to 25.35
25.35 to 25.44
25.44 to 25.53
25.53 to 25.61
above 25.61
Fig. 13. Simulation comparisons for air temperature (1.5 m) at 14:00 LST. (a) 1 st Ave (b)
24 t h St and (c) 43 r d Ave
66
Air Temp. (°C)
below 22.65
22.65 to 22. "1
22."1 to 2 2 . "
2 2 . " " to 22.84
22.84 to 22.90
22.90 to 22.96
22.96 to 23.02
23.02 to 23.08
23.08 to 23.14
above 23.14
a
Air Temp. (°C)
below 21 88
21.88 to 21.93
21.93 to 21.99
21.99 to 22.04
22.04 to 22.10
22.10 to 22.15
22.15 to 22.21
22.21 to 22.27
2 2 . 2 7 to 22.32
above 22.32
Air Temp. (°C)
below 20.91
20.91 to 20.98
20.98 to 21.05
21.05 to 21.12
21.12 to 21.19
21 1 9 t o 2 1 . 2 6
21.26 to 21.33
21.33 to 21.40
21.40 to 21.47
above 2 1 . 4 "
Fig. 13. Simulation comparisons for air temperature (1.5 m) at 14:00 LST. (a) 1 st Ave (b)
24 th St and (c) 43 r d Ave
67
Surface Temp. (°C)
below 2~.0
27,0 to 29.-1
29.4 to 3 1 . "
31.7 to 34.0
34.0 to 36.4
3 6 . 4 to 38.7
38 7 to 41.1
41.1 to 43.4
43.4 to 45.8
above 4 5 . 8
a
Surface Temp. (°C)
below 15.3
15.3 to 16.5
16.5 to 1" 6
1 7 . 6 t o 18.8
18.8 to 19.9
19.9 to 21 0
21.0 to 22.2
22.2 to 23.3
23.3 to 24.5
above 24.5
Fig. 15. Simulation comparisons for surface temperature at 1st Ave. (a) 14:00 and (b)
22:00 LST
The positive effect of building shading and evapotranspiration (ET) on lowering
temperatures can be found at 1st Ave (and to a lesser extent at 24 th St and 43rd Ave)
during the 14:00 time, while the negative impacts of large amounts of impervious
surfaces, such as asphalt and concrete, on nighttime temperatures is evident in all three
domains at the 22:00 time. Additionally, while wind speeds were light in all three
domains (1.5 to 2 m s"1), the direction of flow is seen to have a positive effect on
transporting cooler air from pervious surfaces and/or vegetated surfaces from the East
(24th St and 1st Ave), and from the South (43rd Ave).
4.6. Comparisons between Default and Refined Simulations
After completing refined model simulations, comparisons were made with the original
model runs, which contained default surface thermal properties, building material, and
vegetation settings. This testing was done in order to see how much improvement was
made from refinements to model inputs, especially in regard to surface and near-surface
temperatures at pedestrian level within model domains. Initial conditions, found in the
configuration files, were kept the same between default and refined runs, with changes
occurring in the area input files to reflect the more accurate building and vegetation
parameters. Table 2 illustrates the default inputs for surface thermal properties and
building materials as compared to the refined inputs used after identification and
classification within each of the three domains. Most model domain input parameters for
buildings were vastly different than the default values, with 1st Ave and 43 rd Ave domains
exhibiting the greatest difference.
When comparing earlier default model outputs with refined outputs, the difference
between average air temperatures at receptor points (1.5 m) over the 24 hr period was 0.3
to 0.4°C. Refined outputs were warmer consistently throughout the diurnal period at all
three sites, with differences ranging from 0.1 to 0.6°C. An explanation of these slightly
warmer temperatures would most likely be explained by the lower density canopies
associated with arid-type trees, allowing higher amounts of insolation reaching the
ground surfaces below the canopies with less shading provided by the canopy, as well as
69
a decreased amount of ET throughout the model domains, leading to less cooling during
the daytime. However, in the real-world, a denser tree canopy would also lead to a
decrease of long-wave radiation in these areas, leading to slightly warmer temperatures at
night, which the model does not seem to account for. Fig. 16 plots the differences
between default and refined model outputs over a 12 hr period during the April 4 th field
study.
Table 2. Default surface thermal properties and building materials compared with refined
values
Building materials
Surface thermal properties
Default
24th St
Uw = 1.94 W m"2 K.
Walls
Brick, mortar, wood, and other
Ut- 6.00 W m"2 K
materials typical for a Central
a w = 0.20
European city
a r = 0.30
Roofs
Wood, shingles
Uw= 1.84 Wm" 2 K
Walls
Brick, plaster, cinderblock
Ur = 4.46 W m"2 K
wood, glass
a w = 0.27
a r = 0.30
1st Ave
C/w = 3.00 W m"2 K
Roofs
Wood, shingles, plasterboard
Walls
Reinforced concrete, glass,
2
Ur = 4.00 W m" K
steel, brick, stone
a w = 0.20
43rd Ave
a r = 0.30
Roofs
Concrete, brick, plaster
Uw= 1.38 Wm" 2 K
Walls
Cinderblock, plaster
Roofs
Cinderblock, plaster
t/ r = 1.81 W m"2 K
a w = 0.30
a r = 0.30
70
0.7
0.6 /
T
\
/
0.0
12
14
16
18
20
22
24
Local time (hr)
Fig. 16. Simulation differences between Refined and Default (R-D) inputs from 12:00 to
24:00 LST 4-5 Apr 2008
Overall, refined simulations made the most difference in afternoon temperatures at all
three sites, with as much as 0.6°C change in air temperature at receptor points within the
43 rd Ave domain. This large change could be accounted for by the less dense tree
canopies, as well as the increased accuracy of impervious surfaces within the domain,
which would cause the positive temperature change. Another interesting finding is the
steady increase in temperatures in the early evening at the 1 st Ave site, while the other
two sites have a decrease in temperature change. This may be explained by the change in
building footprints (decrease) from the default to refined area input files, which would
result in larger areas of impervious surfaces.
71
4.7. Model Validation
In order to test how well the model was able to simulate temperature within the
domains output were compared to mobile measurements for three times (14:00, 19:00,
22:00). These times correspond to the times associated with helicopter thermography
acquisition as well as the times of most accurate and thorough mobile measurements for
the 24 hr field day. Receptor locations used in model validation correspond to 10 m
mobile sampling points, previously plotted in GE (refer to Chapter 3: Field Data
Collection). Air temperature measurements were taken from receptor points at 1.5 m,
which was approximately the same height as air temperature measurements from the
mobile vehicle sensor. Root Mean Square Error (RMSE) statistics were then used to test
mobile observation values versus model output at receptor locations (Willmott et al.
1985).
RMSE values were best for the 22:00 time, at just under a 1°C difference between
modeled and observed. The systematic error was high, most likely due to the limited
amount of receptor points (11) used in the validation. Further testing with ten extra
random "dummy" values revealed a lower systematic error, confirming this. Despite
higher systematic error, index of agreement values were over 0.5 for all surface
temperature comparisons and the 22:00 time for air temperature, indicating the model
performed reasonably well. Correlations were also high, with R values close to 1 for all
three times. Table 3 illustrates the differences in RMSE statistics for the three mobile
sampling times (air temperature). Table 4 illustrates RMSE values for surface
temperatures at the same receptor points.
72
Table 3. RMSE values between mobile points and model receptors for air temperature
(1.5 m)
RMSE
RMSE (S)
RMSE (U)
d
R2
14:00
1.74
1.7
0.38
0.27
19:00
2.1
2.09
0.14
0.34
0.52
0.95
22:00 All Times
0.89
1.66
1.52
0.88
0.13
0.66
0.68
0.86
0.96
0.83
Table 4. RMSE values between mobile points and model receptors for surface
temperature
RMSE
RMSE (S)
RMSE (U)
d
R2
14:00
5.29
5.07
1.49
0.76
19:00
1.36
1.11
0.79
0.55
0.76
0.09
22:00 All Times
3.21
1.06
0.97
2.9
0.44
1.38
0.75
0.97
0.7
0.97
In order to justify the use of Sky Harbor Airport for the observations used to compare
and validate simulations conducted at 1st Ave and 24 th St, simulations were run for a 300
x 300 m domain centered on the weather station location at the airport. Initializations
matched those used at 24 th St, and times were made the same in order to be able to make
comparisons. A second simulation was conducted for a 200 x 200 m domain centered
around the Kay PRISMS weather station to test the validity of using the station for 43 rd
Ave, using the same initializations as those used at 43 rd Ave. More details of the initial
conditions as well as aerial images of the model domain boundaries and land cover can
be found in Appendix C.
Simulation output from the Sky Harbor Airport site most closely matched output from
the 24th St domain, with values coming within 0.2°C throughout most of the diurnal
73
period. 1st Ave simulations had much higher evening and minimum temperatures than
those at the Sky Harbor Airport domain, which is most likely due to the heat storage
found in the high building density and lack of SVF at night. The slower cooling rate at 1st
Ave is illustrated in Fig. 17. At the Kay PRISMS site, which was totally void of
impervious surfaces and contained a mix of various vegetative and pervious soil surfaces,
temperatures were slightly cooler than 43 rd Ave, but generally were within 0.2°C over the
24 hr simulation period, which one would expect to find in the real-world.
28
26
O
^ 24
d
a<D
4-a
u 22
—IstAve
0---24tlfit
43rdAve
IS
"i—i—i—i—i—i—i—r
12
14
16
18
20
20 -
22
00
02
04
06
Local time (hr)
Fig. 17. Final simulation comparisons from 12:00 to 24:00 LST 4-5 April 2008
Figure 18 illustrates the simulation output at each receptor point plotted against mobile
observations for surface and air temperature graphically. Strong correlations of
temperature can be found at 22:00, corresponding to the time of maximum UHI. Surface
temperatures during the day illustrate much more prediction variance, due to large
amounts of surface heterogeneity within a very fine horizontal scale.
74
55
0
O
•
1 st Ave
24th St
43 rd Ave
•
U
•
o.
e
B
utJ
1 stAve
24thSt
43rdAve
o
50
/
/
/
45 -
l-i
C3/5
J-l
o
H.
ua
u
at
40 35 •
•
30
24
25
26
27
28
29
30
30
Mobile Air temp. (°C)
35
40
45
50
55
Mobile Surface temp. (°C)
a
25
U
24
•
23
o
22
3
on
kH
O
a,
uo
u
U,
1st Ave
24th St
43 rd Ave
21 -
•
o
20
T
1 stAve
24th St
43 rd Ave
19
19
20
21
22
23
24
Mobile Air temp. (°C)
25
19
20
21
22
23
24
25
Mobile Surface temp. (°C)
Fig. 18. Temperature predictions at mobile and receptor points (1.5 m) 4 April 2008. Air
temperatures on left and surface temperatures on right, (a) 14:00 (b) 19:00 and (c) 22:00
75
4.8. Conclusions
Results from ENVI-met simulations illustrate the importance of building density, land
cover characteristics, and atmospheric conditions, in determining temperature differences
within the UCL of Phoenix. When comparing the three distinct domains in central
Phoenix, simulations at the 1st Ave site accurately capture the warmest temperatures at
22:00 in the vicinity of the CBD, where heat is both trapped within the street canyons and
being released from the higher percentage of impervious surfaces. Simulation outputs
estimate the SVF along the 1st Ave street canyon to be ~ 0.66. At 43rd Ave, the
combination of higher amounts of vegetation and pervious surfaces, and overall lack of
buildings and street canyons (SVF = 1) allow the surface and air temperatures to cool
more rapidly, which matches the mobile sampling readings captured at 22:00. 24th St
domain, while containing a large amount of impervious surfaces like 1st Ave, has a much
lower building density and open lots containing pervious soils. The high SVF (T^y >
0.95) and low building density allows the temperature to cool more rapidly in the evening
than 1st Ave, but slower than 43rd Ave.
In addition to land cover, building density, and SVF, wind speed and direction were
found to alter the diurnal patterns of air temperature to some degree within the three
modeling domains. In testing the sensitivity of the model to an increase in wind speed
from the initial speed of 1.5 and 2 at the three sites, a higher speed of 5 m s"1 was found to
lower the temperature at 43 rd Ave by 0.5 to 1°C, while the other two sites only lowered
by less than 0.2°C over the 24 hr period. However, when the wind direction was shifted
to the East at 43 rd Ave, temperatures decreased at a much lower amount (< 0.2°C),
76
similar to the other two domains. Wind direction changes tested at 1st Ave and 24 th St
showed no significant change in diurnal temperature. The explanation of the large
decrease in temperature at the 43 rd Ave domain due to an increase in wind speed is most
likely due to high amount of cooling occurring from the prevailing wind and advection
occurring over highly vegetated fields, void of buildings and containing low amounts of
impervious land cover. On the other hand, at 24th St and 1st Ave, most of the domain area
contains a large amount of impervious surfaces and/or buildings and is largely devoid of
vegetation and pervious land cover surfaces that may act to cool the area throughout the
diurnal cycle. Through this and other tests, ENVI-met accurately simulates the effect of
land cover and building density on diurnal temperatures and the UHI in Phoenix.
5. SEASONAL SIMULATIONS AND IMPLICATIONS
5.1. Introduction
ENVI-met simulations have recently been successfully utilized to predict human
comfort in outdoor environments within urban areas (Ali-Toudert & Mayer 2006,
Emmanuel & Fernando 2007). In arid cities, it has been found that dense and compact
street canyons in the CBD, (as well as proper orientation to the sun) have lowered the
temperature during the warmest part of the day (Pearlmutter et al. 1999, Ali-Toudert &
Mayer 2006, Pearlmutter et al. 2007). This "cool island" experienced at mid-day within
the higher density CBD of a large arid city, including Phoenix, is due to several factors:
(1) higher shading of street canyons from tall buildings, especially in east-west canyons,
(2) higher thermal inertia and heat storage of impervious ground surfaces, as well as
engineered materials found in building facades and roof materials, which take longer heat
than rural surfaces after sunrise, and (3) higher amounts of vegetation (especially in arid
cities), which leads to greater cooling from evapotranspiration (ET) (Ali-Toudert &
Mayer 2007). Deeper street canyons have also been found to be more comfortable than
shallow canyons during the summer in hot and arid climates, mainly due to building
shading (Johansson 2006). Pearlmutter (1999) further, however, points out the
disadvantage of narrow canyon environments for the nighttime hours in desert areas of
high temperatures, with lower ventilation and the warming influence of the lowered SVF
on heat retention at night. The urban canyon environment exhibits potential for "thermal
moderation" of daily extremes in temperature.
78
An advantage of ENVI-met as a microclimate numerical model is its ability to calculate
outdoor human comfort at a fine scale (Emmanuel & Fernando 2007). Mean radiant
temperature (MRT) and predicted mean vote (PMV) are two outputs which aid in
interpreting comfort at precise locations within model domains at various heights above
ground. MRT has been found to be the most important variable in determining human
comfort within a street canyon (Ali-Toudert & Mayer 2007). PMV was originally created
to measure indoor human comfort, but adapted to outdoor spaces by Jendritzky (1990).
5.2. Methods
Simulations of temperature and thermal comfort were conducted using ENVI-met over
various seasonal periods in Central Phoenix, in order to see differences spatially and
temporally both between and within sites, as well as to test the dependence of
temperature and comfort on land cover and building density characteristics. Within
ENVI-met, area input files were kept the same as April 2008 simulations, as well as inlet
conditions, such as wind speed and direction, in order to be able to make spatial
comparisons between the various seasonal outputs (see Appendix C). An exception to
this was during the summer season, where slightly higher wind speeds of 2 m s~' were
recorded at Sky Harbor Airport instead of the typical 1.5 ms" 1 winds during the other
seasonal periods. Seasonal simulations consisted of five separate 24 hr diurnal periods,
including the previously tested 4-5 April 2008 period (see Chapter 4), covering all four
seasons of the year. Selection of seasonal periods were based on days which were similar
to the 24 hr April 2008 field experiment, consisting of light winds and clear skies.
Selecting days experiencing these stable atmospheric conditions allowed for a good
comparison across sites during the various seasons, in order to analyze the UHI
magnitude and temperature differences caused by changes in intra-urban land cover and
surface materials (Oke 1987). The "Weather Underground" website was used to find and
select days during the various seasons which had clear skies and lower wind speeds,
averaging < 2 m s~' (http://www.wunderground.com/). The selected (24 hr) seasonal
periods consisted of the following days:
•
Fall (15-16 October 2007)
•
•
•
Winter (11-12 January 2008)
Spring (4-5 April 2008)
Early Summer/Pre-Monsoon (27-28 June 2008)
•
Summer/Break in Monsoon (31 July-1 August 2008)
After simulations were conducted for each seasonal day, radiation, thermal, and
comfort outputs were analyzed and compared seasonally and spatially both across and
within the three domains. Outputs at eleven receptor points (previously used in model
validation from the 4-5 April 2008 field experiment), were analyzed, as well as several
"off-road" grid cells, which represented the typical land cover within each domain. While
all the original eleven receptor points are located over asphalt road, off-road grid points,
indicated by the "OR" designation, are either over asphalt parking lots or pervious loamy
soil, with the off-road grid site in 43 rd Ave also containing a fairly dense coverage of
vegetation in the middle of an irrigated corn field. The eleven receptor points and offroad points were numbered sequentially from east to west across domains, with the first
group located within 24 th St, second group within 1st Ave, and third group within 43 rd
Ave domains, respectively. Receptor point locations are illustrated by domain in Fig. 19.
80
Fig. 19. ENVI-met domains and receptor points used in human comfort analyses. Points
on roadways are 100 m apart, (a) 24th St (b) 1st Ave (c) 43rd Ave
5.3. Results
Simulation results for air temperatures along the roadway receptor points (labeled 0-10
in Fig. 19) were compared with the observations made at Sky Harbor Airport NWS
ASOS weather station (SH) for 1st Ave and 24th St. and Kay PRISMS weather station
(Kay) for 43rd Ave. Simulation temperature trends followed what one would expect to
81
find with an urban versus rural site. The 1st Ave site had a more gradual and delayed
cooling curve after sundown, versus a more rapid cool down after sundown at the 43 rd
Ave site during all seasons. Fig. 20 illustrates the diurnal temperature trends over the
various seasons between the three domains using model receptor points along roadways
at 1.5 m.
During most simulations, a temperature lag, or hysteresis lag effect, is present in the
CBD (Golden 2004). The 1st Ave domain takes much longer to cool down in the evening
than 43 rd Ave, and to a lesser extent than 24 th St, due to the high thermal inertia of
engineered materials (i.e., concrete, asphalt, and brick) and the slow release of stored heat
from these materials after sunset. This difference in cooling rates between domains
begins in late afternoon (17:00) and becomes most dramatic, with as much as 2°C
difference, typically by 20:00 LST during all seasons. Fig. 21-23 illustrate predicted
mean vote (PMV) outcomes from simulations for January, April, and June, respectively.
Receptor points, indicated by black points and labels, are shown in all figures for spatial
reference. Table 5 illustrates the PMV outcomes for all seasons simulated at 1400 and
2200 LST. Table column headings can be defined as follows:
•
•
•
•
•
•
Domain - ENVI-met simulation domain name
ID - Numbers 0-10 indicate "on-road" receptor points, with OR1-OR4 indicating
"off-road" receptor point locations
Surface - Surface material within the 5 m grid cell where the receptor point is
located
X,Y - ENVI-met grid location within each simulation domain
Dir - Direction of the street and/or street canyon where receptor point is located
SVF - sky view factor 0P S ky), including building and vegetation, from 1.5 m at
receptor point locations
82
12
14
16
18
20
22
00
02
04
06
12
14
16
18
12
14
16
18
20
22
00
Local time (In)
20
22
00
02
04
06
02
04
06
Local time (hr)
Local time (111 )
02
04
06
12
14
16
18
20
22
00
Local time (lu-)
Fig. 20. Seasonal simulations for air temperature (left) and surface temperature (right)
along roadways, (a) January (b) April (c) June and (d) October
83
Fig. 21. PMV output (1.5 m) at 14:00 (left) and 22:00 (right) 11 Jan 2008. (a) 24 th St (b)
1 st Ave (c) 43 rd Ave
Fig. 23. P M V output (1.5 m) at 14:00 (left) and 22:00 (right) 27 Jun 2008. (a) 24 th St (b)
1 st Ave (c) 43 rd Ave
Fig. 23. P M V output (1.5 m) at 14:00 (left) and 22:00 (right) 27 Jun 2008. (a) 24 th St (b)
1 st Ave (c) 43 rd Ave
Table 5. Seasonal PMV values (1.5 m) at 14:00 and 22:00 LST
14:00
Domain
24thSt
24thSt
24thSt
24thSt
24thSt
24thSt
24thSt
24thSt
1 stAve
1st Ave
1 stAve
1 stAve
43rdAve
43 rd Ave
43rdAve
ID
0
1
2
3
4
5
OR1
OR2
6
7
8
OR3
9
10
OR4
Surface
Asphalt Rd.
Asphalt Rd.
Asphalt Rd.
Asphalt Rd.
Asphalt Rd.
Asphalt Rd.
Soil
Asphalt
Asphalt Rd.
Asphalt Rd.
Asphalt Rd.
Asphalt
Asphalt Rd.
Asphalt Rd.
Soil, Ag.
X
9
29
43
38
18
2
22
9
31
31
31
14
30
10
16
Y
2
2
8
23
23
29
8
27
57
37
17
36
21
21
9
Dir
E/W
E/W
N/S
E/W
E/W
N/S
E/W
E/W
N/S
N/S
N/S
E/W
E/W
E/W
N/A
SVF
1.00
0.98
0.98
0.96
0.99
0.99
1.00
1.00
0.61
0.45
0.56
0.78
1.00
0.98
1.00
Jan
0.7
0.7
0.7
0.7
0.7
0.8
0.7
0.6
-0.4
-0.4
-0.3
0.5
0.5
0.7
0.5
Apr
3.3
3.3
3.4
3.3
3.3
3.6
3.3
3.3
3.3
3.0
0.9
3.0
3.0
3.2
3.0
Jun
9.5
9.4
9.7
9.4
9.4
10.3
9.7
9.4
9.9
9.1
4.4
9.2
9.6
10.1
9.5
Jul
10.6
10.6
10.8
10.6
10.5
11.4
10.6
10.5
11.3
10.3
5.4
10.4
11.0
11.4
10.7
Oct
3.1
3.1
3.1
3.1
3.0
3.3
3.0
3.0
0.2
0.4
0.3
2.8
2.9
3.2
2.8
22:00
Domain
24thSt
24thSt
24th St
24thSt
24thSt
24thSt
24thSt
24thSt
1 stAve
1 stAve
1 stAve
1 stAve
43rdAve
43rdAve
43rdAve
ID
0
1
2
3
4
5
OR1
OR2
6
7
8
OR3
9
10
OR4
Surface
Asphalt Rd.
Asphalt Rd.
Asphalt Rd.
Asphalt Rd.
Asphalt Rd.
Asphalt Rd.
Soil
Asphalt
Asphalt Rd.
Asphalt Rd.
Asphalt Rd.
Asphalt
Asphalt Rd.
Asphalt Rd.
Soil, Ag.
X
9
29
43
38
18
2
22
9
31
31
31
14
30
10
16
Y
2
2
8
23
23
29
8
27
57
37
17
36
21
21
9
Dir
E/W
E/W
N/S
E/W
E/W
N/S
E/W
E/W
N/S
N/S
N/S
E/W
E/W
E/W
N/A
SVF
1.00
0.98
0.98
0.96
0.99
0.99
1.00
1.00
0.61
0.45
0.56
0.78
1.00
0.98
1.00
Jan
-1.3
-1.3
-1.2
-1.3
-1.3
-1.3
-1.3
-1.3
-1.0
-1.0
-1.1
-1.1
-1.4
-1.4
-1.4
Apr
-0.9
-0.9
-0.8
-0.9
-0.9
-0.9
-1.0
-1.0
-0.3
-0.4
-0.5
-0.4
-1.3
-1.3
-1.3
Jun
2.6
2.7
2.6
2.6
2.6
2.5
2.4
2.5
2.1
2.0
2.3
2.2
2.2
2.3
2.1
Jul
3.6
3.7
3.5
3.7
3.6
3.4
3.3
3.5
3.1
2.9
3.4
3.1
3.2
3.3
3.1
Oct
-1.6
-1.6
-1.4
-1.6
-1.6
-1.6
-1.7
-1.7
-1.2
-1.2
-1.4
-1.2
-2.0
-1.9
-2.0
<-3.5
<-2.5
<-1.5
<-0.5
0
>0.5
>1.5
>2.5
>3.5
Key:
Very Cold
Cold
Cool
Slightly Cool
Neutral
Slightly Warm
Warm
Hot
Very Hot
87
5.4. Implications for Phoenix, Arizona
Results from these seasonal comfort simulations have interesting implications for the
City of Phoenix, especially in regards to future planning and UHI mitigation. As the
largest city within the Greater Phoenix Area, at 1.8 million residents, Phoenix has
recently taken measures to mitigate the UHI with the approval of the Downtown Phoenix
Urban Form Project (http://phoenix.gov/urbanformproject/). The Urban Form Project
(UFP) is a comprehensive city plan to incorporate UHI mitigation and increased thermal
comfort strategies into higher density downtown building codes by including items such
as building shading and shade structures, increased cooling from tree shading and
building ventilation, as well as permeable pavements and higher reflecting surface
materials (http://phoenix.gov/urbanformproject/dtplan.html). Love (2009) recently ran
ENVI-met to test various UHI mitigation scenarios for a small 50 x 50 m area in
Downtown Phoenix for the early afternoon period of peak air temperatures in July of over
40°C. The author found that by adding a modest amount of green-space and native trees
to the model domain net a decrease in air temperature of up to 5°C, which would also
increase human comfort significantly in this area during the warmest part of the day in
the summer (Love 2009).
Seasonal comfort simulation results reveal a strong correlation between building
shading during the day and increased human comfort when experiencing warm to hot air
temperatures, as indicated by PMV values for 1st Ave in April and June 2008, illustrated
in Fig. 22 and 23. The tall and dense building structure in 1st Ave acts as shield against
direct insolation during much of the peak hours of the day, creating shadows and
increased human comfort in the lee of the taller buildings facing the direct rays of the sun
during these peak radiation times. 24th St and 43 rd domains, with both low building
density and heights, experience the direct impacts of insolation during the peak hours of
the day during warm to hot times of the year, with virtually no relief from the sun due to
building shading (as illustrated in Fig 23). An exception to this would be during times of
low sun angles close to sunset at 24 th St, with low buildings providing shading on the east
side of the few buildings in the domain. These findings of a cooler and more comfortable
CBD and warmer and less comfortable areas of lower building density in the city are
consistent with studies done in other hot and arid cities, such as in Israel's Negev desert
(Pearlmutter et al. 1999, Pearlmutter et al. 2006). This "cool island" experienced in the
CBD of Phoenix is then followed later in the evening by a "heat island," caused in part by
the slow release of heat storage from the same buildings that provided shade earlier in the
day. This cooling in the mid-day and warming at night in the downtown area of Phoenix
causes a thermal moderation of temperature extremes, as discussed by Pearlmutter
(1999).
Another interesting aspect of human comfort results for the seasonal simulations is
found in the orientation of street canyons and roadways, especially for 1 st Ave, and to a
lesser extent 24 th St. During the mid-day in the summer months, with the sun almost
directly overhead, portions of the N/S oriented street canyons are shaded by the buildings
directly to the West, with heights mainly over 100 m. This small amount of shading
provides a large amount of relief from the heat, as indicated by the slightly warm PMV
value at receptor #8 at 14:00 LST in June. This comfort level, though still uncomfortable,
is in stark contrast to the "very hot" PMV values experienced for the same time period at
all other receptor points in direct sunlight (Table 5). The N/S street canyons also provide
the most comfort in the later afternoon hours at both 1st Ave and 24th St, with the lower
sun angles blocked by building as low as 4 m. E/W oriented street canyons tend to absorb
the highest amounts of solar radiation from the mid-afternoon until sunset, maintaining a
higher radiant temperature and discomfort level well after sunset. This is in contrast to
the N/S street canyon areas that have been shaded since the early afternoon, which
contain surfaces near pedestrian level that have had a chance to cool down for several
hours prior to sunset. This higher discomfort can be seen for 22:00 LST in Fig. 23.
While heat mitigation is a principal goal of the City of Phoenix UFP, seasonal comfort
simulations for the winter period (January) reveal another pattern that needs to be kept in
mind with future planning in the city. 1st Ave, with a high building density and narrow
street canyons, was found to be slightly less comfortable than the more open landscapes
found in 24th and 43 rd Ave domains. This is due to the fact that areas in the shade during
the day tend to be less comfortable on a cool or cold day, while an area in the open
sunlight is more comfortable. A graphical illustration of this can be found by looking at
the spatial patterns of human comfort in January at 14:00 LST across the three modeling
domains. However, Pearlmutter (1998) has indicated that compact street canyon design,
while not providing much sunlight for warmth, offers the protection from cold winds in
the winter season, as well as positive heat retention during the cooler evening hours. The
positive effect from heat retention in buildings and impervious surfaces at 1st Ave can be
seen in the January maps of PMV at 22:00, illustrated in Fig. 23.
6. DOWNTOWN THERMOGRAPHY AND CFD SIMULATIONS
6.1. Introduction
Although the microclimate simulation model ENVI-met can generally capture the
effect of building density, land cover, and seasonal weather conditions on the magnitude
of the UHI, the effect of individual buildings on the surrounding microclimate, including
the flow through the street canyons and vertical and horizontal temperature fields, are
difficult to simulate. This is mainly due to the simplification of building facades within
the domain, including the use of a mean domain value for heat transmission (U-value)
and albedo for walls and roofs, instead of breaking these values down into individual
building components. One way to analyze these effects in greater detail is through
Computational Fluid Dynamics (CFD) modeling.
In this framework a 24 hr UHI field campaign was performed in Phoenix, Arizona 4-5
April 2008. Innovative aspects of this campaign included the tracking of changes in
surface and air temperatures at several fluid dynamics relevant scales, including the
neighborhood, street canyon, and building scale, with an emphasis on the CBD of
Phoenix using high definition thermal IR cameras. The goal was that to create a detailed
record of surface and near-surface temperatures within the urban canopy layer (UCL)
over a complete diurnal period, which could then be used to compare with numerical
modeling outputs to gain a better understanding of the causes of the (UHI) within the
central core of Phoenix.
A four block area in the CBD of Phoenix, centered at Central and Adams Street was
chosen as the area interest for acquisition of handheld thermal IR images of building
91
fagade and street canyon temperatures over the 24 hr period. The four-block Downtown
Phoenix study area is characterized by the presence of several high-rise buildings, some
of them exceeding 100 m. The tallest buildings in the immediate vicinity of the study area
and ordered by tallest first include (1) Chase Tower: 147 m, (2) US Bank Center: 124 m,
(3) 44 Monroe: 116 m, (4) Wells Fargo Plaza: 113 m, (5) Two Renaissance Square: 113
m, and (6) Bank of America Tower: 110 m
(http://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Phoenix). Building fagades
have a wide variety of materials ranging from concrete to glass, often being present in
combination to form a grid-like structure in most cases (See Appendix B, Buildings
material classification: 1st Ave).
6.2. Details of Downtown Thermography
In total, about two thousand images using the FLIR ThermaCAM™ S60 camera
(http://www.flirthermography.com), were taken during the 24 hr measurement period.
Images were taken at regular intervals of every two hours beginning at 06:00 LST. This
was possible using the handheld thermal IR camera while being transported on a special
bicycle platform called a pedal-cab, or "pedi-cab" (a type of bicycle taxi). Various riders
from the Arizona Pedal Cab Co. rotated throughout the 24 hr measurement period to
transport the field crew acquiring thermal images (http://www.azpedicab.com/). The
pedi-cab allowed for quick transport through normal working day traffic from one
location to the other, significantly cutting back on image acquisition time. Approximately
twenty building fagades and several street canyons were surveyed during each
measurement interval. Images were taken using a standard camera set-up while standing
92
immediately outside the pedi-cab while at a complete stop. The image acquisition
distance was set at 100 m, with an average 50 to 200 m corresponding to the distance
between the object and the camera position. Maximum distance was limited by the street
canyon width where images were acquired, generally between 25 and 40 m across. To be
able to maintain a similar resolution for all images, several shots of individual building
fa9ades at several vertical levels were taken, starting at ground level and ending at the top
of the building. These sequential images of individual building fa9ades were then
assembled or 'stitched' into a single infrared image using the FLIR ThermaCAM Image
Builder™ Software. The use of ThermaCAM Researcher™ software allowed for a
preliminary analysis of the overall images as well as an in-depth analysis of mean radiant
temperature.
A sensitivity analysis was carried out to verify camera settings which were kept the
same during the entire experiment. The most important object parameter is its emissivity
because it is a measure of how much radiation is emitted from an object in comparison
with that from a perfect blackbody at the same temperature. During the measurement
campaign we set an emissivity value of 0.96. Sensitivity analysis confirmed that this
value was appropriate for material such as concrete and dark glass, due to these materials
being the main constituents of CBD building facades. A review of literature also confirms
that emissivity values at this range are appropriate for these types of urban materials
(Goward 1981, Oke 1987).
93
6.3. CFD Model Set-Up
In order to test field measurements of temperatures on various surfaces and within
street canyons of the CBD in Phoenix, a general numerical CFD model was employed.
Simulations were used to aid measurement interpretation. In particular, we were
interested in possible flow modifications created by surface temperature gradients which
were not directly measured during the experiment as well as identifying temperature
distribution (vertically and horizontally) within the street canyons of the CBD.
CFD simulations were carried out by considering an approaching boundary layer flow.
Reynolds averaged equations with a standard k-s turbulence closure (Launder & Spalding
1974) together with the Fourier equation were considered for flow and temperature,
respectively. The Boussinesq approximation was assumed.
Wind speed U, specified as an inlet boundary condition, was assumed to follow a
logarithmic profile:
z+z0
u = —In
K
V
z
0
(9)
J
where U,= 0.25 m s"1 is the friction velocity, K the von Karman constant (0.40) and ZQ =
0.4 m the roughness height. Turbulent kinetic energy and dissipation rate profiles were
specified as follows:
k =
± L ( l &
J c ,
*
s=^ ( \ - - )
KZ
(10)
5
where d is the boundary layer depth and Cfl = 0.09. Symmetry boundary conditions were
specified on the top and lateral sides of the computational domain. A termination
criterion of 10~5 was used for all field variables. Several computations were made using
94
both a simplified geometry of individual buildings shown in Fig. 24 and one street
canyon. The computational domain used for flow simulations without heated walls was a
parallelepiped with dimensions of 2625 m (in the x direction, parallel to the flow
direction) by 854 m (in the y direction) by 620 m (vertical z direction). This simulation
was used to obtain some qualitative information about flow patterns in the area
investigated. The effect of temperature for this geometry is left to future investigations.
Fig. 24 shows results of the simplified geometry simulation.
Fig. 24. Flow patterns at z = 30 m without temperature forcing for Downtown Phoenix
using a simplified geometry
With the simplified geometry simulation illustrated in Fig. 24, complex flow patterns
developed within buildings, where vortices behind tall buildings, flow separations and
accelerated flow at the building top and along the canyons are present. For example, a
two canyon-vortex structure is visible between the tallest buildings (>100 m)
characterized by high turbulence, affecting among others, diffusion of heat and other
95
scalars. As we were mainly interested on flow modifications occurring at the street
canyon and building scale, a single street canyon was chosen for detailed numerical
investigations. In particular, we chose the street canyon of 1st Ave, as temperature
measurements were available for almost all buildings forming the canyon walls. A
similar choice was made for the selection of the main north-south canyon within the
downtown domain using the numerical simulation model ENVI-met (discussed in more
detail in Chapter 4).
Fig. 25 illustrates the street canyon geometry used in the 1st Ave CFD simulations.
Building shapes were partially simplified with respect to the real ones, but building
heights and relative distances between buildings are accurate to the nearest 10 m
increment of height as found in reality along the street canyon. The total area enclosed by
the buildings is 128 x 254 m.
Fig. 25. Sketch of the street canyon geometry (left graph) with the height of each building
indicated on the roof and details of the computational domain (right graph) used for street
canyon CFD simulations
96
Overall the street canyon is asymmetric as buildings at both sides do not have the same
height. The heights of buildings range from 10 m to 120 m, while the width W is equal to
49 m. The "average" height of buildings at both sides is about 60 m leading to an aspect
ratio W/H equal to about 1.2 OPsky -0.66). However, it is clear that the flow which
developed inside the canyon was strongly affected by the relative heights of each
individual building. Moreover, an intersection is also present in the geometry (Adams St)
affecting the flow substantially.
The computational domain was built using hexahedral elements (about two million
cells total) with a finer resolution in the volume occupied by the buildings (inner
domain), where the smallest dimensions of the elements were Sxmin = 8ymin = 3 m and
Szmin = 0.5 m. Outside the inner domain, the expansion rate between two consecutive
cells was below 1.3. Several tests were performed to verify grid size independence. The
distance from the inlet plane to the first building of the street canyon was 5Hmax, the
distance from the top of the domain to the ground was 6Hmax and the distance from the
outflow plane to the downstream building was 20Hmax. This was done following
suggestions from relevant CFD literature containing similar problems, such as Di
Sabatino et al. (2007).
6.4. Diurnal Variation of Wall Temperatures
Fig. 26 is a schematic representation of the building layout within the four block
downtown Phoenix study area. In particular, the figure illustrates labels used to identify
those buildings that we have surveyed. Colors qualitatively indicate buildings having
similar maximum temperature (-14:00 to 16:00 LST). As we are mainly interested in
97
studying temperature distribution within the air layer most crucial for outdoor human
comfort, these values, extracted from thermal IR images, represent surface building
temperatures close to the ground (0 to 3 m a.g.l.).
Fig. 26. Building layout of the study area and with colors indicating qualitative
temperature of buildings with the same temperature
Based on the analysis of thermal IR images, Fig. 27 reports the main features of
warming/cooling diurnal cycle experienced by the Phoenix CBD building fapades. The
figure illustrates the diurnal variation of the mean radiant temperature for six buildings,
each of which is representative of a different portion of the study area. The mean radiant
98
temperature (MRT) value shown is the average of all temperature readings over an entire
building fafade. In particular, we consider two buildings for each of the three street
canyons oriented along the north-south direction. These street canyons are denominated
as A, B, and C in a west-east direction. Buildings denoted with E are those facing east
and representative of west side of the canyon, while those denoted with W have fa9ades
facing west and therefore are representative of east side of the canyon.
Looking at the top graph of Fig. 27, it is evident that the north-south canyon orientation
results in a maximum MRT of about 34°C on west walls about 2 hours before solar noon.
However, some exceptions exist, such as the C3E building which experiences a
maximum of 40°C at nearly 08:00. This is most likely due to the absence of high
buildings to the east in front of C3E, which normally shadow building to the west.
Instead, shadowing buildings are present in front of A3E and B3E, lowering temperatures
significantly at this time of day. Analogous considerations can be made for the east side
of canyon, but now the temperature peak is shifted about 2 hours after 12:00, with the
exception of the Al W fafade. In fact, A1W is higher than the building in front of it while
the situation is reversed for B3W and C3W facades.
Some insight on environmental conditions being affected by these hot surfaces
throughout the day can be inferred by comparing these readings with air temperatures
obtained from nearby weather stations. Fig. 27 illustrates air temperatures measured at
- 1 . 5 m within the downtown study area (via non-calibrated handheld sensor) and at 2 m
from the Phoenix Sky Harbor Airport ASOS weather station. The handheld sensor was a
unit manufactured by Reed Instruments (http://www.testequipmentdepot.com/reed/air-
99
velocity/lm-8000.htm). Analysis of these curves suggests: (1) an abrupt excursion
experienced by building fagades during the day, and (2) a maximum air temperature
difference between two sites of about 7°C during the day around 13:00 and about 6°C in
the early evening after 23:00. This air temperature difference is maintained throughout
most of the night. Furthermore, it appears that air temperature profile correlates roughly
with surface temperature of buildings facing the West. Air temperature rise within the
CBD is accompanied by a rapid cooling of building fagades, suggesting warm air
previously heated being transported from adjacent areas.
100
Air Temperature-Sky Harbour Airport
—e— Air Temperature-Downtown
—•—Mean Radiant Temperature-A3E
* Mean Radiant Temperature-B3E
—•—Mean Radiant Temperature-C3E
—e— Air Temperature-Sky Harbour Airport
a Air Temperature-Downtown
* Mean RadiantTemperature-A1W
•
•
Mean RadiantTemperature-B3W
Mean RadiantTemperature-C3W
Fig. 27. Comparison of diurnal mean radiant temperatures related to different western
(top) and eastern (bottom) building facades. Profiles of air temperature are also reported
101
- • - A i r Temperature-Sky Harbour Airport
— » - Air Te m pe ratu re-Downtown
— ^ M e a n Radiant Temperature-A3E-BOTTOM
• Mean Radiant Temperature-A3E -MIDDLE-down
—e— Mean Radiant Temperature-A3E-MIDDLE-up
• Mean Radiant Temperature-A3E-up
- • - A i r Temperature-Sky Harbour Airport
- • - A i r Temperature-Downtown
—b— Mean Radiant Temperature-A1 W-BOTTOM
—b— Mean Radiant Temperature-A1W -MIDDLE-down
- a — Mean Radiant Temperature- A1W-MIDDLE-up
-HB— Mean Radiant Temperature- A1W-up
Fig. 28. Comparison of diurnal MRT related to different portion of the facade for both a
western (top) and an eastern (bottom) building. Profiles of air temperature are also
reported
102
Fig. 29. Comparison of MRT related to dark glass and concrete constituting the fagade of
the A1W building
103
Fig. 28 illustrates as an example of diurnal MRT related to different portions of the
building facade for both a western and eastern building. It can be observed that the
cooling/warming processes of building fagades are not homogeneous, showing a specific
dependence with elevation and building exposure. In particular, the A3E fagade shows an
intense temperature peak near the street level of the building, which decreases linearly
with elevation toward the top of the building. The A1W fagade shows a different
behavior with the near-street level and highest fagade section being warmer through the
day than the middle portion of the building. In order to emphasize the role played by the
different materials in determining overall fagade MRT, profiles of temperature
corresponding to dark glass and concrete have been reported in Fig. 29. These curves
suggest the major contribution to heat storage due to concrete material which remains
nearly the same temperature throughout day.
6.5. CFD Simulation Results and Discussion
Buoyancy effects are studied by means of the dimensionless Richardson number
defined as:
Ri= E * " " 7 ^ -
(11)
T U
c, H~
where g is the gravitational acceleration, Tw is the wall temperature, Ta = 15°C is the air
temperature, L is a characteristic length, and uh is a reference velocity set at the building
height in the incoming flow. We also fixed the same air temperature for building walls
and roofs (15°C) except for the leeward and windward side. Different cases with and
104
without heating at ground level, leeward and windward sides of the street canyon were
analyzed (see Table 6). Temperatures are those measured at 22:00.
Table 6. Simulations of wall heating in the street canyon
Case
1 (reference case)
2
3
4
Tground ( C)
Tleeward/windward ( C)
NO HEAT
20 (measured
Measured temperatures
temperature)
(gradient)
23 (average of all temperature measurements)
20 (measured
Average temperature for
temperature)
each building
In particular, four cases were simulated. Casel is the isothermal reference case with no
extra wall heating being specified. Case2 and Case4 are both characterized by the same
temperature at the ground. They only differ from each other by building wall
temperature. This is constant in Case4 while an average temperature profile derived from
thermal IR images of individual building fa9ades forming the street canyon was imposed
in Case2. Case3 has the same average temperature for the ground, leeward, and the
windward sides of all buildings.
Fig. 30 illustrates, as an example, vectors of the wind velocity at several planes for the
reference case. In particular, the horizontal planes refer to z = 30 m and z = 70 m. The
vertical plane y = 50 m cuts the portion of the canyon characterized by H = 30 m at both
sides. The vertical plane y = -110 m cuts the portion of the canyon characterized by H =
110 m at the leeward and H = 10 m at the windward.
105
Fig. 30. Vectors of x-velocity, (a) y = 50 m (b) y = -110 m (c) z = 30 m (d) z = 70 m
The analysis of simulation results for cases with walls heated (not shown) reveals a
weak flow dependence of the heating within the canyon. In fact, overall the qualitative
behavior of the flow is similar in every case considered. This implies that for the
considered canyon geometry and boundary conditions, heating leeward, windward and
ground differently do not significantly affect the general flow patterns. Most likely, the
particular geometry of the street canyon, which is quite wide, makes the buoyancy effect
much less significant with respect to the mechanical one, which, in turn, is the dominant
effect.
106
Fig. 31. Vertical z-velocity profiles, (a) z = 5 m (b) z = -50 m (c) y = 115 m (d) y = -110
m
From horizontal and vertical z-velocity profiles shown in Fig. 31, it is possible to
appreciate a low effect of buoyancy both in the center and at sides of the entire street
canyon. Vertical profiles refer to the last sections of the canyon at both sides (see Fig.
19). In particular, we note that near the ground, the buoyancy effect is significant in the
vicinity of the middle of the intersection, while above the effect is visible at sides of the
canyon. This is due to stagnant conditions behind tall buildings which enhance the effect
107
of buoyancy with respect to the mechanical ones. Fig. 31 also illustrates that no
significant flow modifications are visible at the street canyon scale due to different wall
heating (Case2, 3 & 4). However, flow patterns within the canyons result to be more
sensitive when a wall temperature gradient (the one corresponding to temperature
measurements) is imposed (Case2).
6.6. Conclusions
Overall, results showed that flow and turbulence developed within the canyon produced
a temperature distribution spatially uniform (apart from a relatively thin near-wall
thermal boundary layer), as already found by Solazzo & Britter (2007). Moreover, we
should note that the maximum difference in temperature between air and wall is about
14°C and that the undisturbed velocity at 10 m is 2 m s"1. In these conditions, we would
expect a significant influence of buoyancy at least in those portions of the canyon
characterized by stagnant conditions and large temperature differences. However, the air
approaching the canyon most likely had been already heated and consequently its
temperature was not so different from that imposed at the canyon walls.
7. DISCUSSION AND CONCLUSIONS
7.1. Comparison of Helicopter Thermography with Mobile Sampling
Analysis of surface temperatures within the study area in central Phoenix from thermal
IR imagery acquired by helicopter on 4 April 2008 reveals a much higher level of detail
not found in mobile measurement or microscale modeling results found in previous
chapters. However, due to the lack of a GPS to record location in the helicopter, only
approximations could be made when comparing helicopter imagery with field recorded
surface temperature observations throughout the study area in central Phoenix. In spite of
this, there were some very interesting findings relating to mobile sampling measurements
taken along roadways and thermal imagery taken in the immediate vicinity of these
measurements along roadways. Google Earth™ (GE) was used to tilt the aerial view of
imagery to the approximate 45° angle of selected thermal IR imagery from the helicopter
during the field experiment. This was done in order to interpret thermal IR images from
the helicopter with visible satellite imagery in GE. Comparisons made at specific mobile
point locations within the three ENVI-met domains revealed that surface temperatures
from the roadway surfaces varied by no more than 0.2 to 0.3°C between that recorded by
the mobile IRT sensor and from the helicopter thermal IR camera. This accuracy allowed
for qualitative interpretation of specific points of interest along the mobile/helicopter
route from east to west during the 14:00, 19:00, and 22:00 flight times on 4 Apr 2008
(See Chapter 3 for an illustration of the route).
Roadway temperatures at mobile point locations were estimated using FLIR
ThermaCAM Researcher™ software. Using the software, point-specific temperature
109
readings could be acquired and displayed with accuracy to the nearest 0.1 °C. Fig. 32
illustrates a comparison of surface temperature readings for a specific point along the 1 st
Ave street canyon between helicopter thermography, mobile IRT, and ENVI-met
simulation output at 14:00 LST 4 Apr 2008. Using the mobile IRT measurements as the
baseline for observations, a point-specific thermal IR measurement from the helicopter
within the 1st Ave street canyon revealed a surface temperature measurement 0.5°C
cooler, while the ENVI-met simulation receptor point measuring 3.9°C cooler. The effect
of tall buildings, shading, and angle of highest solar radiation are illustrated in Fig. 32.
Helicopter thermography
Mobile sampling
(viewed in Google
Street View™)
Fig. 32. Surface temperature comparisons at 14:00 LST for mobile sampling point 54
110
7.2. ENVI-met Limitations & Future Improvements
While ENVI-met has impressive capabilities of replicating the urban microclimate near
the ground, there are a few important features that are missing. The following list
illustrates these key missing features in the current version (3.x):
•
•
Thermal properties of buildings are averaged for the entire domain
Absence of regional exchange processes, with initial boundary conditions kept
constant through entire simulation
•
Failure to capture large diurnal temperature range found in arid climates
The most important piece that is missing in the current version of the model is to take
into consideration heat transmission (U-value) and albedo (a) values for individual
building grid cells (walls and roof). Building values for the whole domain need to be
averaged, as was the case in this study and all other studies interpreting potential
temperature output (Spangenberg 2004). A second limitation of the model is in the static
nature of the inlet conditions during the duration of the simulation, with regards to the
wind direction and speed. While the model could theoretically be initialized and run for
separate simulations for every change in wind direction and/or speed recorded by nearby
observations, this would not only be impractical but computationally problematic. It is
recommended to run the model for at least 12 hr durations (with no change in inlet
conditions) to get accurate results, with at least several hours needed for the model to get
set-up properly (www.envi-met.com). Running the model for only 2 to 3 hr at a time to
change the wind speed and direction would make it impossible to link and compare these
separate simulations together for a 24 hr time period, due to the model set-up and
initialization time.
126
A third issue with the current version of the model is the that boundary conditions are
limited to the area within the domain, and it does not allow "forced" conditions from
outside the domain during the simulation run, such as using atmospheric profiles from a
mesoscale model or weather balloon. This makes it difficult to compare simulation
results inside the domain with observations immediately outside the domain.
ENVI-met v. 4.0, expected to be released in 2010, will for the most part, resolve the
previously mentioned issues and limitations with v. 3.x. According to Bruse and
colleagues in a recent post on the ENVI-met bulletin board (http://www.envi-met.com),
the following improvements will be made with v. 4.0:
•
•
•
•
•
Allow for different thermal properties values for individual building cells with
a new fapade model
Boundary conditions values can be updated with external data forcing during
simulation
Account for uneven terrain with elevation model input (DEM)
Improved 3D vegetation inputs and parameters in area input file
Improved 3D visualization due to change to OpenGL standard, resulting in a
'true' 3D representation of data, versus an existing 2.5D
Fig. 33 illustrates some of the projected changes that will come with v. 4.0, including the
input of individual building facade inputs (material type, thickness, heat transmission,
and albedo), and the ability to represent close to real-world 3D shapes and types of trees
and vegetation within the model domain.
112
Fig. 33. Illustration showing the capabilities of the future ENVI-met v.4.0 area input
editor. Images ©Michael Bruse 2009
7.3. Summary of Findings and Implications
Findings from this study revealed that the microclimate simulation model ENVI-met
can be a useful tool to both interpret and predict the UHI in a large hot and arid city, such
as Phoenix, Arizona. While diurnal temperatures ranges were under-predicted by the
simulations, model validations revealed a strong correlation to the overall temperature
trends over time and space within the three unique Central Phoenix model domains for
the 4-5 April 2008 field study.
Refinement of model input from default settings using free web-based tools as GE 3D
and Street View™ resulted in a change in accuracy to pedestrian-level air temperature
within the domains to as much as 0.6°C. Techniques for refining ENVI-met area input
files can be applied to other areas of interest in Phoenix or other cities, increasing the
accuracy of model outputs and aiding planners, designers and policy makers in making
decisions that will aid in heat mitigation and sustainable practices in urban areas.
Further testing of ENVI-met for different seasons within central Phoenix revealed that
the model performs well in predicting real-world cooling rates and the UHI based on land
113
cover characteristics. In all of the various seasonal simulations, 1 st Ave warms and cools
at a much slower rate after the maximum afternoon temperature while 43 rd Ave warms
and cools more rapidly. These results closely match observations at Sky Harbor Airport
and Kay PRISMS weather stations, with Sky Harbor representing an urban site
(impervious surfaces, lack of vegetation), and Kay representing a rural site (pervious
surfaces, higher amounts of vegetation). ENVI-met simulations conducted in the study
confirm the need for finding various solutions to mitigating heat in urbanized areas by
implementing things such as pervious pavements and shading structures in order to lower
the effects of the UHI in Phoenix and other hot arid cities around the world.
Detailed IR thermography measurements of building fa9ades over a complete diurnal
period in the CBD of Phoenix brought important insights to understanding the spatial
patterns of temperatures within urban street canyons. Diurnal radiative surface
temperature measurements recorded by thermal IR cameras within street canyons as well
as along canyon walls illustrated a complex interaction between the angle of the sun, the
width and height of the canyon, as well as the height of buildings toward the direction of
the sun in determining the time of maximum temperature on a particular surface. Cooling
rates of various building fa9ades after sunset are strongly determined by the type of
material, with concrete maintaining a steady temperature for the entire 24 hr, while glass
cools and warms at a much higher rate diurnally. Findings from CFD modeling revealed
a distinct vertical separation of temperature along 1st Ave canyon walls at height ~
=canyon width, confirming previous research conducted on the effect of thermally driven
stratification of flows within street canyons.
114
7.4. Further Research
Further research related to this study could include several items of importance. One of
the first items that would be of benefit would be a follow-up study conducted during the
summer season in Phoenix. This would allow for a detailed record of temperatures within
central Phoenix during the hottest and most uncomfortable time of the year, as well as an
additional 24 hr period to validate ENVI-met. Ideally, this follow-up study conducted in
the summer would include the use of ENVI-met v. 4.0, where previously created spatial
databases for buildings and vegetation could be applied as much more accurate inputs for
the three domains. Additionally, the use of GPS would be incorporated with both ground
and airborne thermography in order to track exact spatial coordinates in addition to the
time stamp for each image.
A related future research project could also include a classification of mean surface
radiant temperatures from helicopter thermography within 1 km (or finer) pixels along
the mobile/helicopter route on 4 April 2008, or along a future summer day route (with
GPS). This would allow for a testing of surface temperature output from lower resolution
remote sensing products such as Advanced Very-High Resolution Radiometer (AVHRR)
at 1km, and/or ASTER (90 m). A classification of surface temperatures from helicopter
thermography would also aid in the testing, validation, and parameterization of mesoscale
models, such as an urbanized version of MM5 (MM5-Urban) recently tested in Phoenix
at a fine scale for surface temperatures and prediction of the UHI (Dupont et al. 2004,
Park & Fernando 2006, Fernando 2008).
115
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APPENDIX A
FIELD INSTRUMENTATION AND CALIBRATION
123
MOBILE SAMPLING INSTRUMENTATION
Campbell Scientific CR10X Datalogger
(http://www.campbellsci.com/crlOx)
Features:
•
•
•
•
•
Consists of a measurement and control module and the CR10XWP, a detachable
wiring panel
Stores 62000 data points (non-volatile)
32 kbytes for active and user-stored programs (non-volatile)
Data format options are mixed array (default) or table
Software support offered in LoggerNet or *PC400 (full-featured) or Shortcut
(programming).
*PC400 software used in this project
Specifications:
•
Analog inputs: 12 single-ended or 6 differential, individually configured
•
•
•
•
•
•
•
•
•
•
•
Pulse counters: 2
Switched voltage excitations: 3
Control/digital ports: 8
Serial I/O port: 1
Scan rate: 64 Hz
Burst mode: 750 Hz
Analog volt, resolution: to 0.33 uV
A/D bits: 13
Programming: Edlog
Data Storage: Mixed Array, Table
Telecommunications: PakBus, Modbus, Alert
124
IRTS-P Apogee Infrared Thermocouple Sensor Precision Model
(http://www.campbellsci.com/documents/product-brochures/b_irts-p.pdf)
Specifications:
Power supply requirements: none, self-powered
Accuracy:
±0.2°C from 15° to 35°C
+0.3°C from 5° to 45°C
+0.1 °C when sensor body and target are at the same
temperature
Repeatability:
0.05°C from 15°to35°C
Response Time:
<1 s to changes in target temperature
Target Temperature
Output Signal:
type-K thermocouple
Body Temperature
Output Signal:
type-K thermocouple
Optics:
Silicon lens
Wavelength Range:
6 to 14 m
Field of View:
3:1 (At 3 m from sensor, the FOV is a 1 m diameter circle,
as calculated from the geometry of the sensor and lens.
Under typical conditions, 80 to 90% of the IR signal is
from the FOV and 10 to 20% is from the area surrounding
the FOV).
Weight:
<3.5 oz. (100 g)
Dimensions:
2.3 cm diameter, 6 cm length
125
Operating Temperature:
0° to 50°C
Calibration:
The Apogee IR Sensors were factory calibrated by Campbell Scientific in 2005.
Testing of the two sensors used in the 4-5 April 2008 field study was done on 15 March
2008 at the same time as the Vaisala Temp/RH probes. The two sensors used in the study
came within 0.1°C of each other, giving a reasonable reading for the test.
Vaisala HMP45C Temperature and RH Probe
(http://www.campbellsci.com/documents/product-brochures/b_hmp45c.pdf)
Specifications:
Physical specs:
Probe Dimensions:
25.4 cm length, 2.5 cm diameter
Filter:
0.2 m Teflon membrane
Filter Diameter:
1.9 cm
Operating Temperature:
-40° to +60°C
Temperature:
Sensor:
1000 ohmPRT
Measurement Range:
-39.2° to +60°C
Output signal range:
0.008 to 1.0 V
Accuracy:
see graph below
126
<ISr
HA •
13 •
<12 •
0.1 •
Accuracy 'Z
Uil •
•0.1 .
0
14
T«ragMnfeB* "C
Calibration:
The Vaisala Temp/RH probes were factory calibrated by Campbell Scientific in 2004.
Testing of the two sensors used in the 4-5 April 2008 field study was done on 15 March
2008 at the same time as the Apogee IR sensors. Through testing, it was found that one of
the probes was defective, thus requiring the use of a sensor from CAP LTER and the
Global Institute of Sustainability at Arizona State University. The replaced sensor from
CAP LTER was found to be in great working order, with the temperature and humidity
specs within factory range.
GPS16-HVS Garmin GPS Receiver (WAAS-enabled, 12 channel)
http://www.campbellsci.com/documents/product-brochures/b_gpsl6-hvs.pdf
Specifications:
Reciever:
WAAS enabled; 12 parallel GPS receiver continuously
tracks and uses up to 12 satellites (up to 11 with PPS
active) to compute and update your position
Update Rate:
Factory set to 1 sec between updates; programmable from 1
to 900 sec
PPS Output:
1 Hz pulse, 1 microsecond accuracy, width factory set to 80
milliseconds; pulse width is programmable
Reacquisition:
<2 sec
Baud Rate:
Factory set to 1200 bps; 300, 600, 2400, 4800, 9600, and
19200 baud rates also available
127
Temperature Range:
-30° to 80°C operating, -40° to 80°C storage
Operating Voltage:
6 to 40 Vdc
Current Drain:
65 mA active @ 12 Vdc
Dimensions:
8.6 cm diameter, 4.2 cm height
Weight:
332 g with 5 m cable
Accuracy:
<15 m with GPS Standard Positioning Service (SPS); 3 to 5
m with DGPS (USCG/RTCM) correction; *<3 m with
DGPS (WAAS) correction
*WAAS correction used in this project
Position (95% typical):
Velocity:
0.1 knot RMS steady state
Acquisition Times:
Cold:
- 4 5 sec (initial position, time and almanac known,
ephemeris unknown)
Warm:
~15 sec (all data known)
SkySearch:
- 5 min (no data known)
AutoLocate™:
~5 min (almanac known, initial position and time
unknown)
Calibration:
The two Garmin GPS receivers were tested on 15 Mar 2008, along with the Vaisala
and Apogee sensors. Testing revealed that the receivers gave reasonable location
readings, locking on to at least ten satellites and giving coordinates within 3 m of the
correct location (as verified by hand-held GPS owned by author).
128
DOWNTOWN PHOENIX MEASUREMENTS
FLIR ThermaCAM® S60
(http://www.flirthermography.com/media/S60_datasheet.pdf)
Specs:
Thermal Imaging Performance:
Field of view/min focus distance:
Spatial resolution (IFOV):
Image frequency:
Themal sensitivity @ 50/60 Hz:
Electronic zoom function:
Focus:
Digital image enhancement:
Detector type:
Spectral range:
24° x 1870.3 m
1.3 mrad
60 Hz
0.06°C at 30°C
2,4,8, interpolating
Automatic or manual
Normal and enhanced
Focal plane array (FPA) uncooled
microbolometer 320 x 240 pixels
7.5 to 13 |im
Visual:
Built-in digital video:
640 x 480 pixels, full color
Calibration:
The FLIR ThermaCAM S60 was calibrated by Joby Carlson in the field and in the EPA
NCE SMART lab at Arizona State University during the week of 30 March-3 April 2008
Calibrations were done in conjunction with a previously calibrated handheld IRT sensor
and tested on various surfaces, as well as tested with the FLIR ThermaCAM SC640 on 3
April 2008. Temperature readings were found to agree within 0.1 °C between all sensors.
129
HELICOPTER THERMOGRAPHY
FLIR ThermaCAM® SC640
(http ://www. flirthermography.com/cameras/camera/1101/)
Features:
•
•
•
•
•
•
•
•
•
•
•
•
•
•
640 X 480 Resolution
Visual and Thermal Interchangeable Lenses
CompactFlash® Memory Card
USB and Fire Wire Connectivity
QuickView Reporting Software
Auto & Manual Focus
8-to-l Digital Zoom with Pan
1.2 Megapixel Visual Camera
Large 5.6" Swivel/Color LCD
Real-time Picture-and-Picture (PaP)
Auto Hot Spot on Thermal/Visual
Built-in Laser LocallR
Target Illuminator
Bluetooth® Enabled
Calibration:
The FLIR ThermaCAM SC640 was factory calibrated by FLIR in March 2008, prior to
obtaining the camera on 3 April 2008 for the field experiment. In addition to the factory
calibrations, testing and settings were adjusted to match those of the FLIR ThermaCAM
S60 by Joby Carlson on 3 April 2008. Additional testing of the camera was done by
Carlson at the KPNX Channel 12 News helicopter hanger at the Scottsdale Air Park in
Scottsdale, Arizona prior to the 14:00 and 22:00 flight acquisition times on 4 April 2008
Temperature readings were found to agree within 0.1 °C between all sensors.
APPENDIX B
SPATIAL DATABASES FOR ENVI-MET AREA INPUT FILES
131
VEGETATION CLASSIFICATION
24th St
X
Y
ID
Name
Species
1
6
10
mp
Mexican Palo Verde
Parkinsonia
2
23
12
mp
Mexican Palo Verde
Parkinsonia
3
27
14
se
White popinac
Leucaena
4
29
14
se
Fruitless Olive
Olea europaea
5
32
14
mp
Mexican Palo Verde
Parkinsonia
6
37
14
wp
Willow pittosporum
Pittosporum
7
38
14
mp
Mexican Palo Verde
Parkinsonia
8
26
19
Bermuda grass
Cynodon
Bermuda grass
Cynodon
Origin
9
28
10
11
g
Type
Ht (m)
Density
low
aculeata
Semi-Evergreen
5.0
aculeata
Semi-Evergreen
5.0
low
Evergreen
5.0
high
Evergreen
5.0
average
Semi-Evergreen
5.0
average
Evergreen
5.0
average
Semi-Evergreen
5.0
average
dactylon
Semi-Evergreen
0.5
average
dactylon
Semi-Evergreen
0.5
average
leiicocephala
wilsoni
aculeata
phyllyraeoids
aculeata
19
g
29
19
sd
Honey Locust
Gleditisia
Decidious
5.0
average
26
20
g
Bermuda grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
12
28
20
g
Bermuda grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
13
29
20
g
Bermuda grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
14
26
21
mp
Mexican Palo Verde
Parkinsonia
Semi-Evergreen
5.0
average
triacanthos
aculeata
15
24
25
g
Bermuda grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
16
25
25
g
Bermuda grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
17
23
26
H2
Bougainvillea
Bougainvillea
Evergreen
2.0
high
18
24
26
g
Bermuda grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
19
25
26
g
Bermuda grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
Mexican Palo Verde
Parkinsonia
Semi-Evergreen
5.0
average
spectabilis
aculeata
20
26
26
mp
21
35
26
dp
Phoenix Date Palm
Phoenix dactyl if era
Evergreen
5.0
average
22
38
26
F2
California Fan Palm
Washingtonia
Evergreen
10.0
average
23
26
27
mp
Mexican Palo Verde
Parkinsonia
24
38
27
dp
Phoenix Date Palm
Phoenix
25
23
29
mp
Mexican Palo Verde
Parkinsonia
ftlifera
aculeata
dactylifera
aculeata
Evergreen
5.0
average
Evergreen
5.0
average
Semi-Evergreen
5.0
average
low
26
34
32
mq
Velvet Mesquite
Prosopis
velutina
Decidious
5.0
27
35
32
mq
Velvet Mesquite
Prosopis
velutina
Decidious
5.0
low
28
15
33
mp
Mexican Palo Verde
Parkinsonia
aculeata
Semi-Evergreen
5.0
average
29
21
33
mp
Mexican Palo Verde
Parkinsonia
aculeata
Semi-Evergreen
5.0
average
30
28
33
mp
Mexican Palo Verde
Parkinsonia
aculeata
Semi-Evergreen
5.0
average
132
1 st Ave
Origin
X
Y
2
1
2
5
1
3
9
1
4
18
1
5
22
1
6
41
1
7
46
1
8
1
2
1
Type
ID
Name
Species
LF
Indian Laurel Fig
Ficus microcarpa
pb
Palo Brea
Parkinsonia
mq
Honey Mesquite
Prosopis
pb
Palo Brea
Parkinsonia
pb
Palo Brea
Parkinsonia
g
Bermuda Grass
Cynodon
g
Bermuda Grass
Cynodon
mq
Honey Mesquite
Prosopis
Ht (m)
Density
Evergreen
10.0
high
Semi-Evergreen
5.0
low
Deciduous
5.0
low
praecox
Semi-Evergreen
5.0
low
praecox
Semi-Evergreen
5.0
low
dactylon
Semi-Evergreen
0.5
average
dactylon
Semi-Evergreen
0.5
average
glandulosa
Deciduous
5.0
low
5.0
average
v. n'ltida
praecox
glandulosa
9
4
2
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
10
6
2
Pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
11
23
2
Pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
average
praecox
12
28
2
Pb
Palo Brea
Parkinsonia
Semi-Evergreen
5.0
low
13
38
2
g
Bermuda Grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
14
39
2
g
Bermuda Grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
15
48
2
g
Bermuda Grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
16
49
2
g
Bermuda Grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
17
1
3
M2
Honey Mesquite
Prosopis
glandulosa
Deciduous
10.0
average
18
2
3
M2
Honey Mesquite
Prosopis
glandulosa
Deciduous
10.0
average
19
6
3
B2
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
10.0
average
20
8
3
B2
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
10.0
average
21
10
3
V2
Blue Palo Verde
Parkinsonia
florida
Semi-Evergreen
10.0
low
22
16
3
B2
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
10.0
low
23
17
3
B2
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
10.0
low
24
23
3
B2
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
10.0
low
25
24
3
B2
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
10.0
low
praecox
Semi-Evergreen
10.0
low
Semi-Evergreen
0.5
average
26
27
3
B2
Palo Brea
Parkinsonia
27
49
3
g
Bermuda Grass
Cynodon
dactylon
28
42
4
sd
Gregg's Ash
Fraxinus
greggii
Deciduous
5.0
low
29
45
4
sd
Gregg's Ash
Fraxinus
greggii
Deciduous
5.0
low
30
52
4
sd
Evergreen Elm
Ulmus
Semi-Evergreen
5.0
low
Semi-Evergreen
0.5
average
parvifolia
31
6
9
g
Bermuda Grass
Cynodon
dactylon
32
7
9
g
Bermuda Grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
33
12
9
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
34
15
9
Pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
35
19
9
Pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
praecox
Semi-Evergreen
5.0
low
36
25
9
pb
Palo Brea
Parkinsonia
37
5
10
38
6
10
g
Bermuda Grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
g
Bermuda Grass
Cynodon
dactylon
Semi-Evergreen
0.5
39
4
11
average
M2
Velvet Mesquite
Prosopis
velutina
Deciduous
10.0
40
5
11
average
g
Bermuda Grass
Cynodon
dactylon
Semi-Evergreen
0.5
41
8
11
average
ME
Mexican Ebony
Pithecellobium
Deciduous
10.0
low
42
4
43
8
12
M2
Velvet Mesquite
Prosopis
velutina
Deciduous
10.0
average
12
M2
Velvet Mesquite
Prosopis
velutina
Deciduous
10.0
44
average
5
13
M2
Honey Mesquite
Prosopis
glandulosa
Deciduous
10.0
45
average
8
14
mq
Honey Mesquite
Prosopis
glandulosa
Deciduous
5.0
average
mexicanum
133
46
7
15
M2
Honey Mesquite
Prosopis
glandulosa
Deciduous
10.0
average
47
8
15
M2
Honey Mesquite
Prosopis
glandulosa
Deciduous
10.0
average
48
34
15
ac
Sweet Acacia
Acacia
Evergreen
5.0
low
49
7
17
M2
Honey Mesquite
Prosopis
Deciduous
10.0
average
farnesiana
glandulosa
50
34
17
ac
Sweet Acacia
Acacia
51
9
20
M2
Honey Mesquite
Prosopis
farnesiana
Evergreen
5.0
low
Deciduous
10.0
average
52
52
20
LF
Indian Laurel Fig
Ficus microcarpa
53
7
21
M2
Honey Mesquite
Prosopis
Evergreen
10.0
average
Deciduous
10.0
54
47
21
LF
Indian Laurel Fig
Ficus microcarpa
average
v. nitida
Evergreen
10.0
average
55
48
21
LF
Indian Laurel Fig
56
49
21
LF
Indian Laurel Fig
Ficus microcarpa
v. nitida
Evergreen
10.0
average
Ficus microcarpa
v. nitida
Evergreen
10.0
57
50
21
LF
average
Indian Laurel Fig
Ficus microcarpa
v. nitida
Evergreen
10.0
average
58
51
21
59
34
22
LF
Indian Laurel Fig
Ficus microcarpa
v. nitida
average
sd
Evergreen Elm
Ulmus
60
46
22
LF
Indian Laurel Fig
Ficus microcarpa
glandulosa
v. nitida
glandulosa
parvifolia
v. nitida
Evergreen
10.0
Semi-Evergreen
5.0
low
Evergreen
10.0
average
61
7
23
M2
Honey Mesquite
Prosopis
glandulosa
Deciduous
10.0
average
62
9
23
M2
Honey Mesquite
Prosopis
glandulosa
Deciduous
10.0
average
63
34
24
sd
Evergreen Elm
Ulmus
Semi-Evergreen
5.0
low
64
46
24
LF
Indian Laurel Fig
Ficus microcarpa
Evergreen
10.0
average
65
9
25
M2
Honey Mesquite
Prosopis
Deciduous
10.0
average
66
46
25
LF
Indian Laurel Fig
Ficus microcarpa
Evergreen
10.0
average
67
34
26
sd
Evergreen Elm
Ulmus
Semi-Evergreen
5.0
low
68
45
26
h
Hedge
Default, unknown
Evergreen
2.0
high
parvifolia
v. nitida
glandulosa
v. nitida
parvifolia
69
11
28
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
70
14
28
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
71
17
28
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
72
20
28
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
praecox
73
23
28
pb
Palo Brea
Parkinsonia
Semi-Evergreen
5.0
low
74
35
28
sd
Evergreen Elm
Ulmus
parvifolia
Semi-Evergreen
5.0
low
75
37
28
sd
Evergreen Elm
Ulmus
parvifolia
Semi-Evergreen
5.0
low
76
39
28
sd
Evergreen Elm
Ulmus
parvifolia
Semi-Evergreen
5.0
low
77
41
28
sd
Evergreen Elm
Ulmus
parvifolia
Semi-Evergreen
5.0
low
78
47
28
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
79
59
28
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
80
9
31
B2
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
10.0
low
81
11
31
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
82
22
31
B2
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
10.0
low
praecox
Semi-Evergreen
5.0
low
praecox
Semi-Evergreen
5.0
low
Evergreen
15.0
average
31
59
84
34
32
pb
Palo Brea
Parkinsonia
85
36
32
P2
Phoenix Date Palm
Phoenix
86
38
32
P2
Phoenix Date Palm
Phoenix
dactylifera
Evergreen
15.0
average
87
40
32
P2
Phoenix Date Palm
Phoenix
dactylifera
Evergreen
15.0
average
88
42
32
P2
Phoenix Date Palm
Phoenix
dactylifera
Evergreen
15.0
average
89
8
34
pb
Palo Brea
Parkinsonia
90
34
35
P2
Phoenix Date Palm
Phoenix
91
5
36
M2
Honey Mesquite
Prosopis
92
8
36
pb
Palo Brea
Parkinsonia
93
34
P2
Phoenix Date Palm
Phoenix
37
pb
Palo Brea
Parkinsonia
83
dactylifera
praecox
dactylifera
glandulosa
praecox
dactylifera
Semi-Evergreen
5.0
low
Evergreen
15.0
average
Deciduous
10.0
average
Semi-Evergreen
5.0
low
Evergreen
15.0
average
134
94
5
38
ac
Sweet Acacia
Acacia
95
8
38
pb
Palo Brea
Parkinsonia
farnesiana
96
34
39
P2
Phoenix Date Palm
Phoenix
97
10
40
B2
Palo Brea
Parkinsonia
praecox
dactylifera
98
34
41
P2
Phoenix Date Palm
Phoenix
99
5
43
ac
Sweet Acacia
Acacia
100
34
43
P2
Phoenix Date Palm
Phoenix
101
5
45
A2
Sweet Acacia
Acacia
102
8
45
B2
Palo Brea
Parkinsonia
103
34
45
P2
Phoenix Date Palm
Phoenix
104
5
47
ac
Sweet Acacia
Acacia
105
8
47
pb
Palo Brea
Parkinsonia
106
34
47
P2
Phoenix Date Palm
Phoenix
praecox
dactylifera
farnesiana
dactylifera
farnesiana
praecox
dactylifera
farnesiana
praecox
dactylifera
Evergreen
5.0
Semi-Evergreen
5.0
average
low
Evergreen
15.0
average
Semi-Evergreen
10.0
low
Evergreen
15.0
average
Evergreen
5.0
average
Evergreen
15.0
average
Evergreen
10.0
average
Semi-Evergreen
10.0
low
Evergreen
15.0
average
average
Evergreen
5.0
Semi-Evergreen
5.0
low
Evergreen
15.0
average
107
8
49
pb
Palo Brea
Parkinsonia
Semi-Evergreen
5.0
low
108
34
49
P2
Phoenix Date Palm
Phoenix
dactylifera
Evergreen
15.0
average
109
35
51
P2
Phoenix Date Palm
Phoenix
dactylifera
Evergreen
15.0
average
110
37
51
P2
Phoenix Date Palm
Phoenix
dactylifera
Evergreen
15.0
average
111
39
51
P2
Phoenix Date Palm
Phoenix
dactylifera
Evergreen
15.0
average
112
41
51
P2
Phoenix Date Palm
Phoenix
dactylifera
Evergreen
15.0
average
113
1
52
pv
Blue Palo Verde
Parkinsonia
florida
Semi-Evergreen
5.0
low
114
3
52
V2
Blue Palo Verde
Parkinsonia
florida
Semi-Evergreen
10.0
low
115
10
52
V2
Blue Palo Verde
Parkinsonia
florida
Semi-Evergreen
10.0
low
116
13
52
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
10.0
low
117
16
52
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
118
19
52
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
119
47
52
V2
Blue Palo Verde
Parkinsonia
florida
Semi-Evergreen
10.0
low
10.0
low
120
51
52
V2
praecox
Blue Palo Verde
Parkinsonia
florida
Semi-Evergreen
121
1
54
V2
Blue Palo Verde
Parkinsonia
florida
Semi-Evergreen
10.0
low
122
3
54
V2
Blue Palo Verde
Parkinsonia
florida
Semi-Evergreen
10.0
low
123
10
54
B2
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
10.0
low
124
13
54
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
125
20
54
B2
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
10.0
low
126
22
54
pb
Palo Brea
Parkinsonia
praecox
Semi-Evergreen
5.0
low
127
1
55
F2
California Fan Palm
Washingtonia
filifera
Evergreen
15.0
average
128
3
55
F2
California Fan Palm
Washingtonia
filifera
Evergreen
15.0
average
129
19
55
mq
Chilean Mesquite
Proposis
Deciduous
5.0
low
130
27
56
pb
Palo Brea
Parkinsonia
Semi-Evergreen
5.0
low
131
4
57
F2
California Fan Palm
Washingtonia
filifera
Evergreen
15.0
average
132
9
57
F2
California Fan Palm
Washingtonia
filifera
Evergreen
15.0
average
133
27
57
pb
Palo Brea
Parkinsonia
Semi-Evergreen
5.0
low
134
9
59
F2
California Fan Palm
Washingtonia
Evergreen
15.0
average
chilensis
praecox
praecox
filifera
135
43 rd Ave
Origin
X
Y
1
ID
1
1
c
Name
Species
Com
Zea mays var.
Type
riigosa
H t (m)
Density
Evergreen
1.5
high
2
2
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
3
3
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
4
4
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
5
5
1
c
Corn
Zea mays var.
rugosa
, Evergreen
1.5
high
6
6
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
7
7
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
8
8
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
9
9
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
10
10
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
high
11
11
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
12
12
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
13
13
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
14
14
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
15
15
1
c
Cora
Zea mays var.
rugosa
Evergreen
1.5
high
16
16
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
17
17
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
18
18
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
19
19
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
20
20
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
21
21
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
22
22
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
23
23
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
24
24
1
c
Cora
Zea mays var.
rugosa
Evergreen
1.5
high
25
25
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
26
26
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
27
27
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
28
28
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
29
29
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
30
30
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
31
31
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
32
32
1
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
33
33
1
c
Corn
Zea mays var.
rugosa
34
34
1
g
Bermuda grass
Cynodon
dactylon
Evergreen
1.5
high
Semi-Evergreen
0.5
average
high
35
1
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
36
2
2
c
Cora
Zea mays var.
rugosa
Evergreen
1.5
high
37
3
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
high
38
4
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
39
5
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
40
6
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
41
7
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
42
8
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
43
9
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
44
10
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
45
11
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
46
12
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
47
13
2
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
48
14
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
49
15
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
high
50
16
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
51
17
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
52
18
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
53
19
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
54
20
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
55
21
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
56
22
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
57
23
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
58
24
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
59
25
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
60
26
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
61
27
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
62
28
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
63
29
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
64
30
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
65
31
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
66
32
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
67
33
2
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
68
34
2
g
Bermuda grass
Cynodon
Semi-Evergreen
0.5
average
69
1
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
70
2
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
71
3
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
72
4
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
73
5
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
74
6
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
75
7
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
76
8
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
77
9
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
78
10
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
79
11
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
80
12
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
81
13
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
82
14
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
83
15
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
84
16
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
dactylon
85
17
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
86
18
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
87
19
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
88
20
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
89
21
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
90
22
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
91
23
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
92
24
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
93
25
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
94
26
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
95
27
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
96
28
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
97
29
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
98
30
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
99
31
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
100
32
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
101
33
3
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
102
34
3
g
Bermuda grass
Cynodon
Semi-Evergreen
0.5
average
103
1
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
104
2
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
105
3
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
106
4
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
107
5
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
108
6
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
109
7
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
110
8
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
111
9
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
dactylon
112
10
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
113
11
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
114
12
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
115
13
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
116
14
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
117
15
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
118
16
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
119
17
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
120
18
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
high
121
19
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
122
20
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
123
21
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
124
22
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
125
23
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
126
24
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
127
25
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
128
26
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
129
27
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
130
28
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
131
29
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
132
30
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
133
31
4
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
134
32
4
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
rugosa
Evergreen
1.5
high
Semi-Evergreen
0.5
average
135
33
4
c
Com
Zea mays var.
136
34
4
g
Bermuda grass
Cynodon
137
1
5
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
138
2
5
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
139
3
5
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
140
4
5
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
dactylon
141
5
5
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
142
6
5
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
143
7
5
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
144
8
5
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
145
9
5
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
146
10
5
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
147
11
5
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
148
12
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
149
13
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
high
150
14
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
151
15
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
152
16
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
153
17
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
154
18
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
155
19
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
156
20
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
157
21
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
158
22
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
159
23
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
160
24
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
161
25
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
162
26
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
163
27
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
164
28
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
165
29
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
166
30
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
167
31
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
168
32
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
169
33
5
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
170
34
5
g
Bermuda grass
Cynodon
Semi-Evergreen
0.5
average
171
1
6
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
172
2
6
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
173
3
6
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
174
4
6
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
dactylon
175
5
6
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
176
6
6
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
177
7
6
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
178
8
6
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
179
9
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
180
10
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
181
11
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
182
12
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
183
13
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
184
14
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
185
15
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
186
16
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
187
17
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
188
18
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
189
19
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
190
20
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
191
21
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
192
22
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
193
23
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
194
24
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
195
25
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
196
26
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
197
27
6
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
198
28
6
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
199
29
6
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
200
30
6
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
Zea mays var.
rugosa
Evergreen
1.5
high
Evergreen
1.5
high
201
31
6
c
Corn
202
32
6
c
Corn
Zea mays var.
rugosa
203
33
6
c
Corn
Zea mays var.
rugosa
204
34
6
g
Bermuda grass
Cynodon
205
1
7
c
Corn
Zea mays var.
206
2
7
c
Corn
Zea mays var.
207
3
7
c
Corn
208
4
7
c
209
5
7
210
6
211
Evergreen
1.5
high
Semi-Evergreen
0.5
average
rugosa
Evergreen
1.5
high
rugosa
Evergreen
1.5
high
Zea mays var.
rugosa
Evergreen
1.5
high
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
7
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
212
8
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
213
9
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
1.5
high
dactylon
214
10
7
c
Corn
Zea mays var.
rugosa
Evergreen
215
11
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
216
12
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
217
13
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
218
14
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
219
15
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
220
16
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
221
17
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
222
18
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
223
19
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
224
20
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
225
21
7
e
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
rugosa
Evergreen
1.5
high
226
22
7
c
Corn
Zea mays var.
227
23
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
228
24
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
229
25
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
230
26
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
231
27
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
232
28
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
233
29
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
234
30
7
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
235
31
7
c
Cora
Zea mays var.
rugosa
Evergreen
1.5
high
236
32
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
237
33
7
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
238
34
7
g
Bermuda grass
Cynodon
Semi-Evergreen
0.5
average
239
1
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
240
2
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
241
3
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
242
4
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
243
5
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
244
6
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
245
7
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
246
8
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
247
9
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
dactylon
248
10
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
249
11
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
250
12
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
251
13
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
252
14
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
253
15
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
254
16
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
255
17
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
256
18
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
257
19
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
258
20
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
259
21
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
260
22
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
261
23
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
262
24
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
1.5
high
263
25
8
c
Corn
Zea mays var.
rugosa
Evergreen
264
26
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
265
27
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
266
28
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
267
29
8
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
268
30
8
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
269
31
8
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
270
32
8
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
271
33
8
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
272
34
8
g
Bermuda grass
Cynodon
Semi-Evergreen
0.5
average
273
1
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
274
2
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
dactylon
275
3
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
276
4
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
277
5
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
278
6
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
279
7
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
280
8
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
281
9
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
282
10
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
283
11
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
284
12
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
285
13
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
286
14
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
287
15
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
288
16
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
289
17
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
290
18
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
291
19
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
292
20
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
293
21
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
294
22
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
295
23
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
296
24
9
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
Com
Zea mays var.
rugosa
Evergreen
1.5
high
297
25
9
c
298
26
9
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
299
27
9
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
300
28
9
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
1.5
high
301
29
9
c
Corn
Zea mays var.
rugosa
Evergreen
302
30
9
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
303
31
9
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
304
32
9
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
305
33
9
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
306
34
9
g
Bermuda grass
Cynodon
Semi-Evergreen
0.5
average
307
1
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
308
2
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
309
3
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
310
4
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
311
5
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
312
6
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
313
7
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
dactylon
314
8
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
315
9
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
316
10
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
317
11
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
318
12
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
319
13
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
320
14
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
321
15
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
322
16
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
323
17
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
324
18
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
325
19
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
326
20
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
327
21
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
328
22
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
329
23
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
330
24
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
331
25
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
332
26
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
333
27
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
334
28
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
335
29
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
336
30
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
Zea mays var.
rugosa
Evergreen
1.5
high
337
31
10
c
Corn
338
32
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
339
33
10
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
340
34
10
g
Bermuda grass
Cynodon
Semi-Evergreen
0.5
average
341
1
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
342
2
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
343
3
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
344
4
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
345
5
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
346
6
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
347
7
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
dactylon
348
8
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
349
9
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
rugosa
Evergreen
1.5
high
350
10
11
c
Corn
Zea mays var.
351
11
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
352
12
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
353
13
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
354
14
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
355
15
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
356
16
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
357
17
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
358
18
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
359
19
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
360
20
11
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
361
21
11
362
22
11
363
23
11
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
364
24
11
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
365
25
11
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
366
26
11
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
367
27
11
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
368
28
11
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
369
29
11
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
370
30
11
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
371
31
11
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
372
32
11
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
373
33
11
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
374
34
11
g
Bermuda grass
Cynodon
Semi-Evergreen
0.5
average
375
1
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
376
2
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
377
3
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
378
4
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
379
5
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
380
6
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
381
7
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
382
8
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
383
9
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
384
10
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
385
11
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
386
12
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
387
13
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
388
14
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
389
15
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
390
16
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
391
17
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
392
18
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
393
19
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
394
20
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
395
21
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
396
22
12
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
Com
Zea mays var.
rugosa
Evergreen
1.5
high
397
23
12
c
dactylon
398
24
12
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
399
25
12
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
400
26
12
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
401
27
12
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
402
28
12
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
403
29
12
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
404
30
12
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
405
31
12
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
406
32
12
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
407
33
12
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
408
34
12
g
Bermuda grass
Cynodon
Semi-Evergreen
0.5
average
409
1
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
410
2
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
411
3
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
412
4
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
413
5
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
414
6
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
415
7
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
416
8
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
417
9
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
418
10
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
419
11
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
420
12
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
421
13
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
422
14
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
423
15
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
424
16
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
425
17
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
426
18
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
dactylon
high
427
19
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
428
20
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
429
21
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
430
22
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
431
23
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
432
24
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
433
25
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
434
26
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
435
27
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
436
28
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
437
29
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
438
30
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
439
31
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
440
32
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
441
33
13
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
442
34
13
g
Bermuda grass
Cynodon
Semi-Evergreen
0.5
average
443
1
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
444
2
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
445
3
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
446
4
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
447
5
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
dactylon
448
6
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
449
7
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
450
8
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
451
9
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
452
10
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
453
11
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
454
12
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
455
13
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
456
14
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
457
15
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
458
16
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
459
17
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
460
18
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
461
19
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
462
20
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
463
21
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
464
22
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
465
23
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
466
24
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
467
25
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
468
26
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
469
27
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
470
28
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
471
29
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
472
30
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
473
31
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
474
32
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
475
33
14
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
476
34
14
g
Bermuda grass
Cynodon
Semi-Evergreen
0.5
average
477
1
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
478
2
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
479
3
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
480
4
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
481
5
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
482
6
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
483
7
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
484
8
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
485
9
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
dactylon
486
10
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
487
11
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
488
12
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
489
13
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
490
14
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
491
15
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
492
16
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
493
17
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
494
18
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
495
19
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
496
20
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
497
21
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
498
22
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
499
23
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
500
24
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
501
25
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
502
26
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
503
27
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
504
28
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
Zea mays var.
rugosa
Evergreen
1.5
high
high
505
29
15
c
Corn
506
30
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
507
31
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
508
32
15
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
rugosa
509
33
15
c
Corn
Zea mays var.
510
34
15
g
Bermuda grass
Cynodon
511
1
16
c
Corn
Zea mays var.
512
2
16
c
Corn
513
3
16
c
514
4
16
515
5
516
6
517
7
518
Evergreen
1.5
high
Semi-Evergreen
0.5
average
rugosa
Evergreen
1.5
high
Zea mays var.
rugosa
Evergreen
1.5
high
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
1.5
high
dactylon
Corn
Zea mays var.
rugosa
Evergreen
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
12
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
8
16
c
519
9
16
520
10
16
521
11
522
523
13
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
524
14
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
525
15
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
526
16
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
527
17
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
528
18
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
529
19
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
530
20
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
531
21
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
532
22
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
533
23
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
534
24
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
535
25
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
536
26
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
537
27
16
c
Corn
Zea mays var.
rugosa
Evergreen
1.5
high
538
28
16
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
539
29
16
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
540
30
16
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
541
31
16
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
542
32
16
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
543
33
16
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
544
34
16
g
B e r m u d a grass
Cynodon
Semi-Evergreen
0.5
average
545
1
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
546
2
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
547
3
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
dactylon
548
4
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
549
5
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
550
6
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
551
7
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
552
8
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
553
9
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
554
10
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
555
11
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
556
12
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
557
13
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
558
14
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
559
15
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
560
16
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
561
17
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
562
18
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
563
19
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
564
20
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
565
21
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
566
22
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
567
23
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
568
24
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
569
25
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
570
26
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
571
27
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
572
28
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
573
29
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
574
30
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
575
31
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
17
c
Com
Zea mays var.
rugosa
Evergreen
1.5
high
17
c
Com
Zea mays var.
rugosa
576
32
577
33
578
1
18 mh Velvet Mesquite
579
2
18
580
3
18 H3 Velvet Mesquite
581
4
18
582
5
18 H3 Velvet Mesquite
583
6
18
584
7
18 d b
g
g
g
Bermuda grass
Prosopis
Evergreen
1.5
high
velutina
Decidious
5.0
high
Cynodon
dactylon
Semi-Evergreen
0.5
average
Prosopis
velutina
Decidious
3.0
average
Cynodon
dactylon
Semi-Evergreen
0.5
average
Prosopis
velutina
Decidious
3.0
average
Bermuda grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
Desert Broom
Baccharis
hedge
2.0
average
Semi-Evergreen
0.5
average
hedge
2.0
average
Bermuda grass
sarothroides
585
8
18
Bermuda grass
Cynodon
586
9
18 db
Wolf Berry
Lycium
587
10
18
Bermuda grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
Cynodon
dactylon
Semi-Evergreen
0.5
average
588
13
589
590
g
g
dactylon
sp.
18
g
Bermuda grass
16
18
g
Bermuda grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
17
18
g
Bermuda grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
591
21
18
g
Bermuda grass
Cynodon
dactylon
Semi-Evergreen
0.5
average
592
4
22
th
Hybrid Palo Verde
Cercidium
hybrid
Semi-Evergreen
3.0
average
593
6
22
dg
Deer Grass
Muhlenbergia
594
8
22
th
Hybrid Palo Verde
Cercidium
595
3
23
th
Heritage Live Oak
596
5
23
597
7
23
Evergreen
1.0
high
hybrid
Semi-Evergreen
3.0
average
Quercus
virginiana
Semi-Evergreen
3.0
average
g
Gold M o u n d Lantana
Lantana
camara
Evergreen
0.2
high
th
Heritage Live Oak
Quercus
virginiana
Semi-Evergreen
3.0
average
rigens
598
12
24
th
Gregg's Ash
Fraximts
greggii
Decidious
3.0
average
599
1
25
th
Honey Mesquite
Prosopis
glandulosa
Decidious
3.0
average
600
3
25
th
Gregg's Ash
Fraxinus
greggii
Decidious
3.0
average
601
9
25
th
Honey Mesquite
Prosopis
glandulosa
Decidious
3.0
average
602
12
26
th
Gregg's Ash
Fraxinus
greggii
Decidious
3.0
average
603
12
28
th
Gregg's Ash
Fraxinus
greggii
Decidious
3.0
average
604
12
30
th
Gregg's Ash
Fraxinus
greggii
Decidious
3.0
average
605
12
32
th
Gregg's Ash
Fraxinus
greggii
Decidious
3.0
average
606
12
34
th
Gregg's Ash
Fraxinus
greggii
Decidious
3.0
average
607
2
35
th
Honey Mesquite
Prosopis
glandulosa
Decidious
3.0
average
608
5
35
th
Honey Mesquite
Prosopis
glandulosa
Decidious
3.0
average
609
8
35
th
Honey Mesquite
Prosopis
glandulosa
Decidious
3.0
average
610
11
35
th
Honey Mesquite
Prosopis
glandulosa
Decidious
3.0
average
148
BUILDING MATERIAL CLASSIFICATION
Key
Origin
Ht
N_U
F_ M
R_M
W_C
W_T
Values begin from domain origin, at x,y = 0,0
Building height in meters
Name of building and/or type
Building facade material type and coloring
Building roof material type and coloring
Building wall thermal conductivity or k-value (W/mK)
Building wall thickness (m)
W_U
R C
R T
Building wall thermal conductance or U-value (W/m2K)
Building roof thermal conductivity or k-value (W/mK)
Building roof thickness (m)
R U
W_A
R A
Building roof thermal conductance or U-value (W/m2K)
Building wall albedo
Building roof albedo
Word Abbreviations
AC
a.g.l.
Apart
Bldg
Bk
CH
Com
Cone
Fed
G1
Grn
Ind
Lt
Mbe
OH
Pers
Phx
PI
Reinf
Ren
Res
Rest
Sh
SI
Sto
Th
Wd
Wh
Air Conditioning
Above ground level
Apartments
Building
Brick
Courthouse
Commercial
Concrete
Federal
Glass
Green
Industrial
Light
Marble
Overhang
Personnel
Phoenix
Plaster
Reinforced
Renaissance
Residential
Restaurant
Shade
Steel
Stone
Theater
Wood
White
149
24 th St
ID
X
W C
W T
W U
R C
R T
R U
W A
R A
F IVI
R M
5
2 Storage Shed
Lt. Pl./Bk.
Lt. Gypsum
0.52
0.203
2.56 0.27
0.203
1.33
0.30
0.40
Y
Ht N a m e Use
1
15
2
16
5
2 Storage Shed
Lt. Pl./Bk.
Lt. Gypsum
0.52
0.203
2.56 0.27
0.203
1.33
0.30
0.40
3
15
6
2 Storage Shed
Dark Bk.
Tar/Wd
0.56
0.203
2.76 0.40
0.203
1.97
0.10
0.15
0.203
2.76 0.40
0.203
1.97
0.10
0.15
4
16
6
2 Storage Shed
Dark Bk.
Tar/Wd
0.56
5
30
6
4 Res./Com.
Wd siding
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.15
0.22
6
31
6
4 Res./Com.
Wd siding
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.15
0.22
7
33
6
4 Res./Com.
Wd
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.15
0.22
8
34
6
4 Res./Com.
Wd
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.15
0.22
9
36
6
6 Lt. Ind./Com.
Lt. Cb.
Lt. G y p s u m
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
10
37
6
6 Lt. Ind./Com.
Lt. Cb.
Lt. G y p s u m
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
11
30
7
4 Res./Com.
Wd siding
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.15
0.22
Wd siding
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.15
0.22
12
31
7
4 Res./Com.
13
33
7
4 Res./Com.
Wd
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.15
0.22
14
34
7
4 Res./Com.
Wd
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.15
0.22
Lt. Cb.
Lt. Gypsum
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
15
36
7
6 Lt. Ind./Com.
16
37
7
6 Lt. Ind./Com.
Lt. Cb.
Lt. Gypsum
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
17
7
8
4 Commercial
W h Adobe/Pi.
Lt. Gypsum
0.55
0.203
2.71
0.27
0.203
1.33
0.45
0.40
18
8
8
4 Commercial
W h Adobe/Pi.
Lt. Gypsum
0.55
0.203
2.71
0.27
0.203
1.33
0.45
0.40
19
30
8
4 Res./Com.
Wd siding
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.15
0.22
20
31
8
4 Res./Com.
Wd siding
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.15
0.22
21
33
8
4 Res./Com.
Wd
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.15
0.22
22
34
8
4 Res./Com.
Wd
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.15
0.22
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
23
36
8
6 Lt. Ind./Com.
Lt. Cb.
Lt. Gypsum
24
37
8
6 Lt. Ind./Com.
Lt. Cb.
Lt. Gypsum
25
39
8
6 Overhang
Posts 4 m a.g.l. Aluminum
26
40
8
4 Overhang
Posts 2 m a.g.l. Grey shingles
N/A
N/A
N/A
N/A
N/A
N/A
N/A
27
7
9
4 Commercial
Wh Adobe/Pi.
Lt. G y p s u m
0.55
0.203
2.71
0.27
0.203
1.33
0.45
0.40
28
8
9
4 Commercial
Wh Adobe/Pi.
Lt. G y p s u m
0.55
0.203
2.71
0.27
0.203
1.33
0.45
0.40
29
36
9
4 Lt. Ind./Com.
Red Wd.
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.20
0.22
30
37
9
4 Lt. Ind./Com.
Red W d .
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.20
0.22
Lt. Pl./Bk.
Grey Shingles
0.50
0.203
2.46 0.40
0.203
1.97
0.30
0.22
31
39
9
4 Res./Com.
32
40
9
N/A
4 Res./Com.
Lt. Pl./Bk.
Grey Shingles
0.50
0.203
2.46 0.40
0.203
1.97
0.30
0.22
7 10
4 Commercial
Wh Adobe/Pi.
Lt. Gypsum
0.55
0.203
2.71 0.27
0.203
1.33
0.45
0.40
34
8 10
4 Commercial
W h Adobe/PI.
Lt. Gypsum
0.55
0.203
2.71 0.27
0.203
1.33
0.45
0.40
35
30 10
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
36
31
10
4 Overhang
Posts 2 m a.g.l.
Rusted Metal
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
37
32
10
4 Overhang
Posts 2 m a.g.l.
Rusted Metal
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
38
36 10
4 Lt. Ind./Com.
Red W d .
Grey Shingles
0.14
0.152
0.92 0.40
0.203
1.97
0.20
0.22
0.203
2.46 0.40
0.203
1.97
0.30
0.22
2.46 0.40
33
39
39
10
4 Res./Com.
Lt. Pl./Bk.
Grey Shingles
0.50
40
40
10
4 Res./Com.
Lt. Pl./Bk.
Grey Shingles
0.50
0.203
0.203
1.97
0.30
0.22
41
30
11
4 Overhang
Posts 2m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
42
31
11
4 Overhang
Posts 2 m a.g.l.
Rusted Metal
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
150
32
11
4 Overhang
Posts 2 m a.g.l.
Rusted Metal
N/A
44
39
11
4 Res./Com.
Lt. Pl./Bk.
G r e y Shingles
0.50
0.203
45
40
11
4 Res./Com.
Lt. Pl./Bk.
G r e y Shingles
0.50
0.203
46
30
12
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
43
N/A
N/A
N/A
N/A
N/A
N/A
N/A
2.46 0.40
0.203
1.97
0.30
0.22
2.46
0.40
0.203
1.97
0.30
0.22
N/A
N/A
N/A
N/A
N/A
47
31
12
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
48
32
12
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
49
39
12
4 Com. Garage
W h B k . / M o r t a r White metal
0.50
0.203
2.46 10.00
0.152
65.79
0.45
0.55
50
5
14
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46 0.27
0.203
1.33
0.20
0.40
51
6
14
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46
0.27
0.203
1.33
0.20
0.40
52
7
14
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46
0.27
0.203
1.33
0.20
0.40
53
8
14
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46 0.27
0.203
1.33
0.20
0.40
54
9
14
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46 0.27
0.203
1.33
0.20
0.40
55
10 14
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46 0.27
0.203
1.33
0.20
0.40
56
11
14
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2 . 4 6 0.27
0.203
1.33
0.20
0.40
57
12
14
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46
0.27
0.203
1.33
0.20
0.40
58
13
14
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46
0.27
0.203
1.33
0.20
0.40
59
14
14
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46 0.27
0.203
1.33
0.20
0.40
60
17 14
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
61
18 14
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
62
19 14
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
63
5
15
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46
0.27
0.203
1.33
0.20
0.40
64
6
15
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46
0.27
0.203
1.33
0.20
0.40
65
7
15
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46
0.27
0.203
1.33
0.20
0.40
66
8
15
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46
0.27
0.203
1.33
0.20
0.40
67
9
15
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46 0.27
0.203
1.33
0.20
0.40
10 15
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46 0.27
0.203
1.33
0.20
0.40
69
11
15
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2 . 4 6 0.27
0.203
1.33
0.20
0.40
70
12
15
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46
0.27
0.203
1.33
0.20
0.40
71
19
15
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
72
27
15
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48 0.40
0.203
1.97
0.25
0.22
73
28
15
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48 0 . 4 0
0.203
1.97
0.25
0.22
68
74
30
15
4 Lt. Ind./Com.
Cinderblock
G r e y Shingles
0.21
0.203
1.03 0 . 4 0
0.203
1.97
0.15
0.22
75
31
15
4 Lt. Ind./Com.
Cinderblock
G r e y Shingles
0.21
0.203
1.03 0.40
0.203
1.97
0.15
0.22
76
36
15
4 Res./Com.
W h A d o b e / W d G r e y Shingles
0.30
0.203
1.48 0.40
0.203
1.97
0.45
0.22
77
37
15
4 Res./Com.
W h A d o b e / W d G r e y Shingles
0.30
0.203
1.48 0.40
0.203
1.97
0.45
0.22
78
38
15
4 Res./Com.
W h A d o b e / W d G r e y Shingles
0.30
0.203
1.48 0 . 4 0
0.203
1.97
0.45
0.22
79
5
16
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46 0.27
0.203
1.33
0.20
0.40
80
6
16
4 F o x Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46 0.27
0.203
1.33
0.20
0.40
81
7
16
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2 . 4 6 0.27
0.203
1.33
0.20
0.40
82
8
16
4 F o x Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46
0.27
0.203
1.33
0.20
0.40
83
9
16
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46
0.27
0.203
1.33
0.20
0.40
84
10
16
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46 0.27
0.203
1.33
0.20
0.40
85
11
16
4 Fox Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2.46 0.27
0.203
1.33
0.20
0.40
86
12
16
4 F o x Rental
Br. Bk./Pl.
Lt. G y p s u m
0.50
0.203
2 . 4 6 0.27
0.203
1.33
0.20
0.40
87
13 16
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
151
88
14 16
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
89
15 16
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
90
19 16
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
91
27
16
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48 0 . 4 0
0.203
1.97
0.25
0.22
92
28
16
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48 0.40
0.203
1.97
0.25
0.22
93
30
16
4 Lt. Ind./Com.
Cinderblock
G r e y Shingles
0.21
0.203
1.03 0.40
0.203
1.97
0.15
0.22
Cinderblock
G r e y Shingles
0.21
0.203
1.03 0.40
0.203
1.97
0.15
0.22
94
31
16
4 Lt. Ind./Com.
95
33
16
6 Lt. Ind./Com.
Lt. C b .
Lt. G y p s u m
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
96
34
16
6 Lt. Ind./Com.
Lt. Cb.
Lt. G y p s u m
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
97
35
16
6 Lt. Ind./Com.
Lt. Cb.
Lt. G y p s u m
98
36
16
4 Res./Com.
W h A d o b e / W d G r e y Shingles
0.30
0.203
1.48 0.40
0.203
1.97
0.45
0.22
99
37
16
4 Res./Com.
W h A d o b e / W d G r e y Shingles
0.30
0.203
1.48 0 . 4 0
0.203
1.97
0.45
0.22
100
38
16
4 Res./Com.
W h A d o b e / W d G r e y Shingles
0.30
0.203
1.48 0 . 4 0
0.203
1.97
0.45
0.22
101
5
17
4 Lt. Ind./Com.
Plaster/Brick
G r e y Plaster
0.50
0.203
2.46 0 . 4 0
0.203
1.97
0.25
0.25
102
6
17
4 Lt. Ind./Com.
Plaster/Brick
G r e y Plaster
0.50
0.203
2.46 0.40
0.203
1.97
0.25
0.25
103
7
17
4 Lt. Ind./Com.
Plaster/Brick
G r e y Plaster
0.50
0.203
2.46
0.40
0.203
1.97
0.25
0.25
104
13
17
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
105
14
17
4 Overhang
106
15 17
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
107
19 17
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
108
27
17
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48 0.40
0.203
1.97
0.25
0.22
109
28
17
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48 0 . 4 0
0.203
1.97
0.25
0.22
110
33
17
6 Lt. Ind./Com.
Lt. C b .
Lt. G y p s u m
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
111
34
17
6 Lt. Ind./Com.
Lt. Cb.
Lt. G y p s u m
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
112
35
17
6 Lt. Ind./Com.
Lt. C b .
Lt. G y p s u m
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
113
37
17
2 Storage Shed
Wd./Metal
frame
Shingles/Wd.
0.15
0.203
0.74 0.40
0.152
2.63
0.15
0.22
114
5
18
4 Lt. Ind./Com.
Plaster/Brick
G r e y Shingles
0.50
0.203
2.46 0.40
0.203
1.97
0.25
0.22
115
6
18
4 Lt. Ind./Com.
Plaster/Brick
G r e y Shingles
0.50
0.203
2.46
0.40
0.203
1.97
0.25
0.22
116
7
18
4 Lt. Ind./Com.
Plaster/Brick
G r e y Shingles
0.50
0.203
2.46
0.40
0.203
1.97
0.25
0.22
N/A
N/A
N/A
N/A
N/A
N/A
117
19 18
4 Overhang
Posts 2 m a.g.l.
Aluminum
N/A
N/A
118
27
18
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48 0.40
0.203
1.97
0.25
0.22
119
28
18
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48 0 . 4 0
0.203
1.97
0.25
0.22
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
120
33
18
6 Lt. Ind./Com.
Lt. C b .
Lt. G y p s u m
121
34
18
6 Lt. Ind./Com.
Lt. Cb.
Lt. G y p s u m
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
122
35
18
6 Lt. Ind./Com.
Lt. C b .
Lt. G y p s u m
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
123
36
18
2 Storage Shed
Wd./
Metal f r a m e
Shingles/Wd.
0.15
0.203
0 . 7 4 0.40
0.152
2.63
0.15
0.22
Shingles/Wd.
0.15
0.203
0.74
0.40
0.152
2.63
0.15
0.22
G r e y Plaster
0.50
0.203
2.46
0.30
0.152
1.97
0.25
0.25
0.30
0.152
1.97
0.25
0.25
124
38
18
2 Storage Shed
w a./
Metal f r a m e
125
5
19
4 Lt. Ind./Com.
Plaster/Brick
6
19
4 Lt. Ind./Com.
Plaster/Brick
G r e y Plaster
0.50
0.203
2.46
127
7
19
4 Lt. Ind./Com.
Plaster/Brick
G r e y Plaster
0.50
0.203
2.46 0 . 3 0
0.152
1.97
0.25
0.25
128
33
19
6 Lt. Ind./Com.
Lt. Cb.
Lt. G y p s u m
0.21
0.203
1.03 0 . 2 7
0.203
1.33
0.25
0.40
129
34
19
6 Lt. Ind./Com.
Lt. Cb.
Lt. G y p s u m
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
130
35
19
6 Lt. Ind./Com.
Lt. Cb.
Lt. G y p s u m
0.21
0.203
1.03 0.27
0.203
1.33
0.25
0.40
131
36
19
2 Storage Shed
Wd./
Metal
Shingles/Wd.
0.15
0.203
0.74
0.152
2.63
0.15
0.22
126
frame
0.40
152
132
5 20
4 Lt. Ind./Com.
Plaster/Brick
G r e y Plaster
0.50
0.203
2.46
0.30
0.152
1.97
0.25
0.25
133
6 20
4 Lt. Ind./Com.
Plaster/Brick
G r e y Plaster
0.50
0.203
2.46
0.30
0.152
1.97
0.25
0.25
134
7 20
4 Lt. Ind./Com.
Plaster/Brick
G r e y Plaster
0.50
0.203
2.46
0.30
0.152
1.97
0.25
0.25
135
5 21
4 Lt. Ind./Com.
Plaster/Brick
G r e y Plaster
0.50
0.203
2.46
0.30
0.152
1.97
0.25
0.25
136
6 21
4 Lt. Ind./Com.
Plaster/Brick
G r e y Plaster
0.50
0.203
2.46
0.30
0.152
1.97
0.25
0.25
137
7 21
4 Lt. Ind./Com.
Plaster/Brick
G r e y Plaster
0.50
0.203
2.46
0.30
0.152
1.97
0.25
0.25
138
2 4 27
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
139
25 2 7
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
140
30 27
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.25
141
31 2 7
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.25
142
34 27
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.25
143
35 27
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.25
144
39 2 7
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
145
40 27
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
146
2 4 28
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
147
25 28
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
148
31 28
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
149
32 28
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
150
34 28
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
151
35 28
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
152
39 28
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
153
4 0 28
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
154
24 29
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
155
25 2 9
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
156
31 29
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
157
32 29
4 Res. H o m e
Adobe/Wd
G r e y Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
158
39 29
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
159
40 29
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
160
7 30
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46
0.30
0.203
1.48
0.35
0.40
161
8 30
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46
0.30
0.203
1.48
0.35
0.40
162
9 30
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46
0.30
0.203
1.48
0.35
0.40
0.20
163
36 3 0
4 Warehouse
Lt. C b .
Wd/Metal
0.21
0.203
1.03
5.00
0.152
32.89
0.25
164
3 7 30
4 Warehouse
Lt. C b .
Wd/Metal
0.21
0.203
1.03
5.00
0.152
32.89
0.25
0.20
165
38 30
4 Warehouse
Lt. C b .
Wd/Metal
0.21
0.203
1.03
5.00
0.152
32.89
0.25
0.20
166
39 3 0
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
167
40 30
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
168
7 31
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46
0.30
0.203
1.48
0.35
0.40
169
8 31
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46
0.30
0.203
1.48
0.35
0.40
170
9 31
4 Commercial
W h e / B l u e PI.
Light Plaster
0.50
0.203
2.46
0.30
0.203
1.48
0.35
0.40
171
10 31
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46
0.30
0.203
1.48
0.35
0.40
172
11 31
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46
0.30
0.203
1.48
0.35
0.40
173
12 31
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46
0.30
0.203
1.48
0.35
0.40
174
13 31
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46
0.30
0.203
1.48
0.35
0.40
175
2 5 31
4 Residential
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
176
2 6 31
4 Residential
Adobe/Wd
Br. Shingles
0.30
0.203
1.48
0.40
0.203
1.97
0.25
0.22
153
177
36 31
4 Warehouse
Lt. Cb.
Wd/Metal
0.21
0.203
1.03 5.00
0.152
32.89
0.25
0.20
178
37 31
4 Warehouse
Lt. Cb.
Wd/Metal
0.21
0.203
1.03 5.00
0.152
32.89
0.25
0.20
179
38 31
4 Warehouse
Lt. Cb.
Wd/Metal
0.21
0.203
1.03 5.00
0.152
32.89
0.25
0.20
180
4 0 31
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48 0 . 4 0
0.203
1.97
0.25
0.22
181
5 32
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46 0.30
0.203
1.48
0.35
0.40
182
6 32
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46 0.30
0.203
1.48
0.35
0.40
183
7 32
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46 0.30
0.203
1.48
0.35
0.40
184
8 32
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46
0.30
0.203
1.48
0.35
0.40
185
9 32
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46 0 . 3 0
0.203
1.48
0.35
0.40
186
10 32
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46 0.30
0.203
1.48
0.35
0.40
187
11 32
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46 0.30
0.203
1.48
0.35
0.40
188
12 32
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46
0.30
0.203
1.48
0.35
0.40
0.40
189
13 32
4 Commercial
W h / B l u e PI.
Light Plaster
0.50
0.203
2.46
0.30
0.203
1.48
0.35
190
2 4 32
2 Residential
W d - decaying Wd./shingles
0.10
0.203
0.49 0 . 1 0
0.152
0.66
0.15
0.20
191
2 5 32
4 Residential
Adobe/Wd
Br. Shingles
0.30
0.203
1.48 0.40
0.203
1.97
0.25
0.22
192
2 6 32
4 Residential
Adobe/Wd
Br. Shingles
0.30
0.203
1.48 0.40
0.203
1.97
0.25
0.22
193
4 0 32
4 Res. H o m e
Adobe/Wd
Br. Shingles
0.30
0.203
1.48 0.40
0.203
1.97
0.25
0.22
194
8 35
4 L&J Tire
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
195
9 35
4 L&J Tire
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
196
10 3 5
4 L&J Tire
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
197
13 3 5
2 C o m m . Storage W h B k . / M o r t a r Light Plaster
0.50
0.203
2.46 0.30
0.203
1.48
0.45
0.40
198
8 36
4 L&J Tire
W h Bk./Pl.
Steel/Wd
0.50
0.203
2.46
1.50
0.100
15.00
0.45
0.17
199
9 36
4 L&J Tire
W h Bk./Pl.
Steel/Wd
0.50
0.203
2.46
1.50
0.100
15.00
0.45
0.17
200
18 36
4 Trailer
Wh Aluminum Aluminum
5.00
0.050
0 . 0 5 0 100.00
0.45
0.50
201
25 36
4 Lt. Ind./Com.
Beige Pl./Bk.
Beige Plaster
0.50
0.203
100.00 5.00
2.46
0.30
0.203
1.48
0.25
0.25
202
26 36
4 Lt. Ind./Com.
Beige Pl./Bk.
Beige Plaster
0.50
0.203
2.46 0 . 3 0
0.203
1.48
0.25
0.25
203
8 37
4 L & J Tire
W h Bk./Pl.
Steel/Wd
0.50
0.203
2.46
1.50
0.100
15.00
0.45
0.17
204
9 37
4 L & J Tire
W h Bk./Pl.
Steel/Wd
0.50
0.203
2.46
1.50
0.100
15.00
0.45
0.17
0 . 0 5 0 100.00
205
18 37
4 Trailer
Wh Aluminum Aluminum
5.00
0.050
100.00 5.00
0.45
0.50
206
25 37
4 Lt. Ind./Com.
Tan Pl./Bk.
Tan Plaster
0.50
0.203
2.46 0 . 3 0
0.203
1.48
0.25
0.25
207
2 6 37
4 Lt. Ind./Com.
Tan Pl./Bk.
Tan Plaster
0.50
0.203
2.46 0 . 3 0
0.203
1.48
0.25
0.25
208
6 38
4 L&J Tire
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
209
7 38
4 L&J Tire
Posts 2 m a.g.l.
Aluminum
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
210
8 38
4 L & J Tire
W h Bk./Gl.
Light Plaster
0.60
0.203
2.96
0.30
0.203
1.48
0.45
0.40
211
9 38
4 L & J Tire
W h Bk./Gl.
Light Plaster
0.60
0.203
2.96
0.30
0.203
1.48
0.45
0.40
4.46
0.27
0.30
2.92 w / m e t a l
Final input averages:
1.84 w o / m e t a l
154
1 st Ave
ID
F M
R M
1
43
3
3 Patriots Sq.
Red Brick/GI.
Lt. Pl./Bk.
0.75 0.203
3.69
0.80
0.150
5.33
0.20
0.25
2
44
3
3 Patriots Sq.
Red Brick/GI.
Lt. Pl./Bk.
0.75 0.203
3.69
0.80
0.150
5.33
0.20
0.25
3
11
10
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90 0.305
2.95
1.20
0.305
3.93
0.20
0.35
4
12
10
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90 0.305
2.95
1.20
0.305
3.93
0.20
0.35
5
13
10
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90 0.305
2.95
1.20
0.305
3.93
0.20
0.35
14
10
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90 0.305
2.95
1.20
0.305
3.93
0.20
0.35
Ht
6
N U
WC
W T W U
RC
RT
RU
WA
RA
7
15
10
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90 0.305
2.95
1.20
0.305
3.93
0.20
0.35
8
16
10
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90 0.305
2.95
1.20
0.305
3.93
0.20
0.35
9
17
10
111 Wells Fargo
Rnf. Conc./GI.
Cone. PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
10
18
10
111 Wells Fargo
Rnf. Conc./GI.
Cone. PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
11
19
10
111 Wells Fargo
Rnf. Conc./GI.
Cone. PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
12
20
10
111 Wells Fargo
Rnf. Conc./GI.
Cone. PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
13
21
10
111 Wells Fargo
Rnf. Conc./GI.
Cone. PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
14
22
10
111 Wells Fargo
Rnf. Conc./GI.
Cone. PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
15
23
10
111 Wells Fargo
Rnf. Conc./GI.
Cone. PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
16
24
10
111 Wells Fargo
Rnf. Conc./GI.
Cone. PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
17
25
10
111 Wells Fargo
Rnf. Conc./GI.
Cone. PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
18
26
10
111 Wells Fargo
Rnf. Conc./GI.
Cone. PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
19
27
10
111 Wells Fargo
Rnf. Conc./GI.
Cone. PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
20
35
10
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
36
10
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
22
37
10
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.25
23
38
10
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
24
39
10
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
25
40
10
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.25
26
41
10
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.25
27
42
10
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.25
28
43
10
93 Ernst&Young Mbe./Gl./Steel
Tan Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.25
29
44
10
93 E m s t & Y o u n g Mbe./Gl./Steel
Tan Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.25
30
45
10
93 Ernst&Young Mbe./Gl./Steel
Tan Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.25
46
10
93 Ernst&Young Mbe./Gl./Steel
Tan Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.25
47
10
105 Ernst&Young Mbe./Gl./Steel
White Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.50
48
10
105 Ernst&Young Mbe./Gl./Steel
White Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
49
10
105 Ernst&Young Mbe./Gl./Steel
White Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
50
10
105 Ernst&Young Mbe./Gl./Steel
White Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.50
51
10
105 Ernst&Young Mbe./Gl./Steel
White Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.50
37
52
10
105 Ernst&Young Mbe./GI./Steel
White Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.50
38
59
10
21 Phelps Dodge Stone/Gl./Steel
Cone. PI.
0.80 0.305
2.62
0.80
0.305
2.62
0.20
0.25
10
21 Phelps Dodge Stone/Gl./Steel
Cone. PI.
0.80 0.305
2.62
0.80
0.305
2.62
0.20
0.25
11
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90 0.305
2.95
1.20
0.305
3.93
0.20
0.35
11
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90 0.305
2.95
1.20
0.305
3.93
0.20
0.35
11
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
11
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90 0.305
2.95
1.20
0.305
3.93
0.20
0.35
11
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90 0.305
2.95
1.20
0.305
3.93
0.20
0.35
11
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90 0.305
2.95
1.20
0.305
3.93
0.20
0.35
11
111 Wells Fargo
Rnf. Conc./GI.
Cone. PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
21
31
32
33
34
35
36
39
60
40
11
41
12
42
43
44
45
46
13
14
15
16
17
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 W e l l s Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 W e l l s Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 W e l l s Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 W e l l s Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
0.305
3.93
0.20
0.25
0.305
4.59
0.20
0.25
0.20
0.25
0.25
111 W e l l s F a r g o
111 W e l l s Fargo
9 Ren. Square
Rnf. Conc./Gl.
Rnf. Conc./Gl.
Mbe./Gl./Steel
C o n e . PI.
Green C o n e .
1.10
0.80
0.305
0.305
3.61
2.62
1.20
1.40
9 Ren. S q u a r e
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
9 Ren. S q u a r e
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
9 Ren. S q u a r e
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. S q u a r e
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. S q u a r e
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. S q u a r e
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. S q u a r e
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
93 E r n s t & Y o u n g
Mbe./GI./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
93 E r n s t & Y o u n g
Mbe./GI./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
93 E r n s t & Y o u n g
Mbe./GI./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
White C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
0.305
2.62
0.305
4.59
0.20
0.50
0.305
2.62
0.305
4.59
0.20
0.50
0.305
2.62
0.305
4.59
0.20
0.50
0.305
2.62
0.305
4.59
0.20
0.50
0.305
4.59
0.20
0.50
0.305
4.59
0.20
0.50
0.305
2.62
0.20
0.25
105 E r n s t & Y o u n g
105 E r n s t & Y o u n g
105 E r n s t & Y o u n g
105 E r n s t & Y o u n g
105 E r n s t & Y o u n g
105 E r n s t & Y o u n g
105 E r n s t & Y o u n g
21 Phelps D o d g e
Mbe./GI./Steel
Mbe./GI./Steel
Mbe./Gl./Steel
Mbe./Gl./Steel
Mbe./Gl./Steel
Mbe./Gl./Steel
Mbe./Gl./Steel
Stone/GI./Steel
White Cone.
White C o n e .
White C o n e .
White Cone.
White Cone.
White Cone.
C o n e . PI.
0.80
0.80
0.80
0.80
0.80
0.80
0.80
0.305
0.305
0.305
2.62
2.62
2.62
1.40
1.40
1.40
1.40
1.40
1.40
0.80
21 Phelps D o d g e
Stone/GI./Steel
C o n e . PI.
0.80
0.305
2.62
0.80
0.305
2.62
0.20
0.25
21 Phelps D o d g e
Stone/GI./Steel
C o n e . PI.
0.80
0.305
2.62
0.80
0.305
2.62
0.20
0.25
15 W e l l s F a r g o
Rnf. Conc./Gl.
Lt. Conc./Pl.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
15 W e l l s F a r g o
R n f . Conc./Gl.
Lt. Conc./Pl.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
15 W e l l s F a r g o
Rnf. Conc./Gl.
Lt. Conc./Pl,
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
0.305
2.95
0.305
3.93
0.20
0.35
0.305
2.95
0.305
3.93
0.20
0.35
0.305
2.95
0.305
3.93
0.20
0.35
0.305
3.61
0.305
3.93
0.20
0.25
0.305
3.61
0.305
3.93
0.20
0.25
1.20
0.305
3.93
0.20
0.25
1.20
0.305
3.93
0.20
0.25
3.93
0.20
0.25
3.93
0.20
0.25
3.93
0.20
0.25
3.93
0.20
0.25
3.93
0.20
0.25
3.93
0.20
0.25
3.93
0.20
0.25
4.59
0.20
0.25
4.59
0.20
0.25
15 W e l l s F a r g o
15 Wells F a r g o
15 Wells Fargo
11 W e l l s F a r g o
11 W e l l s Fargo
Rnf. Conc./Gl.
Rnf. Conc./Gl.
Rnf. Conc./Gl.
Rnf. Conc./Gl.
Rnf. Conc./Gl.
Lt. Conc./Pl.
Lt. Conc./Pl.
Lt. Conc./Pl.
C o n e . PI.
C o n e . PI.
0.90
0.90
0.90
1.10
1.10
11 W e l l s F a r g o
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
11 W e l l s F a r g o
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
0.305
3.61
11 W e l l s F a r g o
11 W e l l s F a r g o
11 Wells F a r g o
R n f . Conc./Gl.
R n f . Conc./Gl.
R n f . Conc./Gl.
C o n e . PI.
C o n e . PI.
C o n e . PI.
1.10
1.10
1.10
0.305
0.305
3.61
3.61
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.20
0.305
0.305
0.305
11 Wells F a r g o
R n f . Conc./GI.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
11 W e l l s F a r g o
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
11 Wells F a r g o
R n f . Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
11 W e l l s F a r g o
9 Ren. Square
9 Ren. Square
R n f . Conc./Gl.
Mbe./Gl./Steel
Mbe./Gl./Steel
C o n e . PI.
Green Cone.
Green Cone.
1.10
0.80
0.80
0.305
0.305
0.305
3.61
2.62
2.62
1.20
1.40
1.40
0.305
0.305
0.305
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. Square
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. Square
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. Square
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. Square
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 3 E r n s t & Y o u n g Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 3 E r n s t & Y o u n g Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
0.50
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
105 E m s t & Y o u n g Mbe./Gl./Steel
Wh C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
21 Phelps D o d g e Stone/Gl./Steel
C o n e . PI.
0.80
0.305
2.62
0.80
0.305
2.62
0.20
0.25
21 Phelps D o d g e Stone/Gl./Steel
C o n e . PI.
0.80
0.305
2.62
0.80
0.305
2.62
0.20
0.25
21 Phelps D o d g e Stone/Gl./Steel
C o n e . PI.
0.80
0.305
2.62
0.80
0.305
2.62
0.20
0.25
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
111 Wells Fargo
Rnf. Conc./GI.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 W e l l s Fargo
Rnf. Conc./GI.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 W e l l s Fargo
Rnf. Conc./GI.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 W e l l s F a r g o
Rnf. Conc./GI.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 W e l l s Fargo
Rnf. Conc./GI.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 W e l l s Fargo
Rnf. Conc./GI.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 W e l l s F a r g o
Rnf. Conc./GI.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 W e l l s Fargo
Rnf. Conc./GI.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 Wells F a r g o
Rnf. Conc./GI.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 W e l l s F a r g o
Rnf. Conc./GI.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
111 W e l l s F a r g o
Rnf. Conc./GI.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. S q u a r e
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
9 Ren. Square
9 Ren. Square
9 Ren. Square
9 Ren. Square
93 E r n s t & Y o u n g
105 E r n s t & Y o u n g
105 E r n s t & Y o u n g
105 E r n s t & Y o u n g
105 E r n s t & Y o u n g
105 E m s t & Y o u n g
157
147
49
13
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
148
50
13
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
149
51
13
105 E r n s t & Y o u n g Mbe./GI./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
150
52
13
105 E r n s t & Y o u n g Mbe./GI./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
151
59
13
21 Phelps D o d g e Stone/GI./Steel
C o n e . PI.
0.80
0.305
2.62
0.80
0.305
2.62
0.20
0.25
152
60
13
87 Phelps D o d g e Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.35
153
1
14
15 City Hall
Lt. Red
Cone.
Lt. Conc./Pl.
0.90
0.305
2.95
1.40
0.305
4.59
0.20
0.25
154
11
14
15 Wells Fargo
Tan
Con./GI./Sl.
Rnf. Conc./Gl.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
155
12
14
15 Wells Fargo
Rnf. Conc./Gl.
Lt. Conc./Pl.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
156
13
14
15 Wells Fargo
Rnf. Conc./Gl.
Lt. Conc./Pl.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
157
14
14
15 Wells Fargo
Rnf. Conc./Gl.
Lt. Conc./Pl.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
158
15
14
15 Wells Fargo
Rnf. Conc./Gl.
Lt. Conc./Pl.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
159
16
14
15 Wells Fargo
Rnf. Conc./Gl.
Lt. Conc./Pl.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
160
17
14
111 Wells Fargo
161
18
14
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
162
19
14
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
163
20
14
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
164
21
14
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
165
22
14
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
166
23
14
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
167
24
14
111 W e l l s Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
168
25
14
111 Wells F a r g o
Rnf. Conc./Gl.
C o n e . PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
169
26
14
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
170
27
14
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
171
35
14
9 Ren. Square
Mbe./Gl./Steel
Green C o n e .
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.25
172
36
14
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Green Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.25
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
0.305
2.62
1.40
0.305
4.59
0.20
0.25
2.62
1.40
0.305
4.59
0.20
0.25
173
174
175
37
38
39
14
14
14
9 Ren. S q u a r e
9 Ren. Square
9 Ren. Square
Mbe./Gl./Steel
Mbe./GI./Steel
Mbe./GI ./Steel
176
40
14
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
177
41
14
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
178
42
14
9 Ren. Square
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
179
43
14
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
180
44
14
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
181
45
14
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
182
46
14
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
183
47
14
105 E r n s t & Y o u n g Mbe./GI./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
184
48
14
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
0.305
2.62
1.40
0.305
4.59
0.20
0.50
185
186
49
50
14
14
105 E r n s t & Y o u n g Mbe./Gl./Steel
105 E r n s t & Y o u n g Mbe./Gl./Steel
187
51
14
105 E r n s t & Y o u n g Mbe./Gl./Steel
Wh Cone.
0.80
188
52
14
9 3 E r n s t & Y o u n g Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.35
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.35
0.90
0.305
2.95
1.40
0.305
4.59
0.22
0.25
0.90
0.305
2.95
1.40
0.305
4.59
0.22
0.25
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
189
190
58
59
14
14
18 Phelps D o d g e Stone/Glass
87 Phelps D o d g e Glass/Steel
191
60
14
87 Phelps D o d g e Glass/Steel
192
1
15
15 City Hall
193
2
15
15 City Hall
194
11
15
15 W e l l s Fargo
Tan
Con./GI./Sl.
Tan
Con./GI./Sl.
Rnf. Conc./Gl.
Concrete
Lt. C o n e .
Lt. C o n e .
Lt. R e d
Cone.
Lt. R e d
Cone.
Lt. Conc./Pl.
158
195
12
15
15 Wells Fargo
Rnf. Conc./GI
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
196
13
15
15 Wells F a r g o
Rnf. Conc./GI
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
197
14
15
15 Wells F a r g o
Rnf. Conc./GI
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
198
15
15
15 Wells F a r g o
Rnf. Conc./GI
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
15
15 Wells F a r g o
Rnf. Conc./GI
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
3.93
0.20
0.25
199
16
200
17
15
111 Wells Fargo
Rnf. Conc./GI
C o n e . PI.
1.10 0.305
3.61
1.20
0.305
201
18
15
111 Wells Fargo
Rnf. Conc./GI
C o n e . PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
202
19
15
111 Wells Fargo
Rnf. Conc./GI
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
203
20
15
111 W e l l s Fargo
Rnf. Conc./GI
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
204
21
15
111 W e l l s Fargo
Rnf. Conc./GI
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
205
22
15
111 W e l l s Fargo
Rnf. Conc./GI
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
206
23
15
111 W e l l s Fargo
Rnf. Conc./GI
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
207
24
15
111 Wells Fargo
Rnf. Conc./GI
C o n e . PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
208
25
15
111 Wells Fargo
Rnf. Conc./GI
C o n e . PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
209
26
15
111 W e l l s F a r g o
Rnf. Conc./GI
C o n e . PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
210
27
15
111 Wells F a r g o
Rnf. Conc./GI
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
211
35
15
9 Ren. S q u a r e
Mbe./Gl./Stee
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. S q u a r e
Mbe./Gl./Stee
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. S q u a r e
Mbe./Gl./Stee
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 Ren. S q u a r e
Mbe./Gl./Stee
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Green C o n e .
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.25
Green C o n e .
0.80
2.62
1.40
0.305
4.59
0.20
0.25
4.59
0.20
0.25
0.25
212
213
214
36
37
38
15
15
15
215
39
15
9 Ren. S q u a r e
Mbe./Gl./Stee
216
40
15
9 Ren. S q u a r e
Mbe./Gl./Stee
0.305
217
41
15
9 Ren. S q u a r e
Mbe./Gl./Stee
Green Cone.
0.80 0.305
2.62
1.40
0.305
218
42
15
9 Ren. S q u a r e
Mbe./Gl./Stee
Green Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
219
43
15
105 E r n s t & Y o u n g Mbe./Gl./Stee
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
220
44
15
105 E r n s t & Y o u n g Mbe./Gl./Stee
Wh Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.50
221
45
15
105 E r n s t & Y o u n g Mbe./Gl./Stee
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
222
46
15
105 E r n s t & Y o u n g Mbe./Gl./Stee
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
223
47
15
105 E r n s t & Y o u n g Mbe./Gl./Stee
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
15
105 E r n s t & Y o u n g Mbe./Gl./Stee
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
15
105 E r n s t & Y o u n g Mbe./Gl./Stee
Wh Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.50
15
105 E r n s t & Y o u n g Mbe./Gl./Stee
Wh Cone.
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.50
15
93 E r n s t & Y o u n g Mbe./Gl./Stee
Tan C o n e .
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.30
15
93 E r n s t & Y o u n g Mbe./Gl./Steel
Tan C o n e .
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.30
Lt. C o n e .
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.35
Lt. C o n e .
0.80 0.305
2.62
1.40
0.305
4.59
0.20
0.35
0.305
4.59
0.20
0.35
224
225
226
227
228
48
49
50
51
52
229
58
15
87 Phelps D o d g e Glass/Steel
230
59
15
87 Phelps D o d g e Glass/Steel
15
232
1
16
233
2
16
Lt. C o n e .
87 Phelps D o d g e Glass/Steel
Tan
Con/Gl./SI
Lt
Red C o n e
15 City Hall
Tan Con/GI./Sl Lt Red C o n e
15 City Hall
234
11
16
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
235
12
16
15 Wells Fargo
Rnf. Conc./GI.
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
Rnf. Conc./GI.
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
R n f . Conc./GI.
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
R n f . Conc./GI.
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
R n f . Conc./GI.
Lt. Conc./PI.
0.90
0.305
2.95
1.20
0.305
3.93
0.20
0.35
R n f . Conc./GI.
C o n e . PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
R n f . Conc./GI.
C o n e . PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
Rnf. Conc./GI.
C o n e . PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
Rnf. Conc./GI.
Cone. PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
Rnf. Conc./GI.
C o n e . PI.
1.10 0.305
3.61
1.20
0.305
3.93
0.20
0.25
231
236
237
238
60
13
14
15
16
16
16
15 W e l l s F a r g o
15 W e l l s F a r g o
15 W e l l s F a r g o
239
16
16
15 W e l l s F a r g o
240
17
16
111 W e l l s F a r g o
241
242
243
244
18
19
20
21
16
16
16
16
111 W e l l s F a r g o
111 W e l l s F a r g o
111 Wells F a r g o
111 W e l l s F a r g o
0.80 0.305
2.62
1.40
0.90
0.305
2.95
1.40
0.305
4.59
0.22
0.25
0.90
0.305
2.95
1.40
0.305
4.59
0.22
0.25
159
245
22
16
111 W e l l s Fargo
Rnf. Conc./Gl.
Cone. PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
246
23
16
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
247
24
16
111 Wells Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
248
25
16
111 W e l l s Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
249
26
16
111 W e l l s Fargo
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
250
27
16
111 W e l l s F a r g o
Rnf. Conc./Gl.
C o n e . PI.
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
251
35
16
9 Ren. Square
Mbe./GI./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
252
36
16
9 Ren. Square
Mbe./Gl ./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
253
37
16
9 Ren. Square
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
254
38
16
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
255
39
16
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
256
40
16
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
257
41
16
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
258
42
16
9 Ren. Square
Mbe./GI./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
259
43
16
105 E r n s t & Y o u n g Mbe./GI./Steel
White C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
260
44
16
105 E r n s t & Y o u n g Mbe./GI./Steel
White C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
261
45
16
105 E r n s t & Y o u n g Mbe./GI./Steel
White Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
262
46
16
105 E r n s t & Y o u n g Mbe./GI./Steel
White Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
263
47
16
105 E r n s t & Y o u n g Mbe./Gl./Steel
White Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
264
48
16
105 E r n s t & Y o u n g Mbe./Gl./Steel
White Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
265
49
16
105 E r n s t & Y o u n g Mbe./Gl./Steel
White C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
266
50
16
93 E r n s t & Y o u n g Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
267
51
16
93 E r n s t & Y o u n g Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
0.25
268
52
16
93 E r n s t & Y o u n g Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
269
58
16
18 Phelps D o d g e Stone/Glass
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
270
59
16
87 Phelps D o d g e Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.35
271
60
16
87 Phelps D o d g e Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.35
272
1
17
15 City Hall
Glass/Steel
Glass/Steel
0.80
0.305
2.62
0.80
0.305
2.62
0.18
0.22
273
2
17
15 City Hall
Glass/Steel
Glass/Steel
0.80
0.305
2.62
0.80
0.305
2.62
0.18
0.22
274
13
17
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
275
14
17
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
276
20
17
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
277
21
17
6 Wells Fargo
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
278
35
17
9 Ren. S q u a r e
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
279
36
17
9 Ren. S q u a r e
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
280
37
17
9 Ren. S q u a r e
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
281
38
17
9 Ren. Square
Mbe./Gl./Steel
Green Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
282
39
17
9 Ren. Square
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
283
40
17
9 Ren. Square
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
284
41
17
9 Ren. Square
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
285
42
17
9 Ren. Square
Mbe./Gl./Steel
Green C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
286
43
17
105 E r n s t & Y o u n g Mbe./Gl./Steel
White Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
287
44
17
105 E r n s t & Y o u n g Mbe./Gl./Steel
White Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
288
45
17
105 E r n s t & Y o u n g Mbe./Gl./Steel
White Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
289
46
17
105 E r n s t & Y o u n g Mbe./Gl./Steel
White Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
290
47
17
105 E r n s t & Y o u n g Mbe./Gl./Steel
White C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
291
48
17
105 E r n s t & Y o u n g Mbe./GI./Steel
White Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
292
49
17
9 3 E r n s t & Y o u n g Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
160
293
50
17
9 3 E r n s t & Y o u n g Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
294
51
17
9 3 E r n s t & Y o u n g Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
295
52
17
93 E r n s t & Y o u n g Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
296
58
17
18 Phelps D o d g e Stone/Glass
Concrete
297
59
17
18 Phelps D o d g e Stone/Glass
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
298
60
17
18 Phelps D o d g e Stone/Glass
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
299
1
18
15 City Hall
Glass/Steel
Glass/Steel
0.80
0.305
2.62
0.80
0.305
2.62
0.18
0.22
300
2
18
15 City Hall
Glass/Steel
Glass/Steel
0.80
0.305
2.62
0.80
0.305
2.62
0.18
0.22
301
12
18
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
302
13
18
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
303
14
18
6 W e l l s Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
304
15
18
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
305
16
18
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
306
18
18
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
307
19
18
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
308
20
18
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
309
21
18
6 W e l l s Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
310
22
18
6 W e l l s Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
311
23
18
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
312
24
18
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
313
35
18
9 Ren. S q u a r e
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
314
36
18
9 Ren. S q u a r e
Mbe./GI./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
315
37
18
9 Ren. S q u a r e
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
316
38
18
9 Ren. Square
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
317
39
18
9 Ren. Square
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
318
40
18
9 Ren. Square
Mbe./GI./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1.40
0.305
4.59
0.20
0.25
0.25
319
41
18
9 Ren. Square
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
9 Ren. Square
Mbe./Gl./Steel
320
42
18
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
321
58
18
18 Phelps D o d g e Stone/Glass
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
322
59
18
18 Phelps D o d g e Stone/Glass
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
323
60
18
18 Phelps D o d g e Stone/Glass
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
324
1
19
9 City Hall
Glass/Steel
Glass/Steel
0.80
0.305
2.62
0.80
0.305
2.62
0.18
0.22
325
2
19
9 City Hall
Glass/Steel
Glass/Steel
0.80
0.305
2.62
0.80
0.305
2.62
0.18
0.22
326
15
19
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
327
16
19
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
328
18
19
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
329
19
19
6 Wells Fargo
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
330
35
19
9 Ren. Square
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
O H 3m/Lt.
Cone.
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
331
36
19
9 Ren. Square
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
332
37
19
9 Ren. Square
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
333
38
19
9 Ren. S q u a r e
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
334
39
19
9 Ren. S q u a r e
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
161
335
336
40
41
337
42
19
9 Ren. Square
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
19
9 Ren. Square
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1.40
0.305
4.59
0.20
0.25
19
9 Ren. Square
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
338
58
19
18 Phelps D o d g e Stone/Glass
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
339
59
19
18 Phelps D o d g e Stone/Glass
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
340
60
19
18 Phelps D o d g e Stone/Glass
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
341
1
20
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
342
2
20
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
343
3
20
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
344
11
20
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
345
12
20
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
346
13
20
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
347
14
20
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
348
15
20
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
3 0 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
0.305
3.93
0.20
0.25
349
16
20
350
17
20
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
351
18
20
30 W e l l s F a r g o
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
352
19
20
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
353
20
20
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
354
21
20
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
355
22
20
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
356
23
20
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
357
24
20
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
358
25
20
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
359
26
20
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
360
27
20
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
361
35
20
9 Ren. Square
Mbe./Gl./Steel
Tan Cone.
362
36
20
9 Ren. Square
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
363
37
20
9 Ren. Square
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
364
38
20
9 Ren. Square
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
365
39
20
9 Ren. Square
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
366
40
20
9 Ren. Square
Mbe./GI./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
367
41
20
9 Ren. Square
Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
368
42
20
9 Ren. Square
Mbe./GI./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
369
60
20
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
370
1
21
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
371
2
21
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
372
3
21
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
18 Phelps D o d g e Stone/Glass
373
11
21
3 0 W e l l s Fargo
R n f . Conc./Gl.
374
12
21
3 0 W e l l s Fargo
R n f . Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
375
13
21
3 0 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
376
14
21
30 W e l l s F a r g o
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
377
15
21
30 W e l l s F a r g o
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
378
16
21
30 W e l l s F a r g o
R n f . Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
379
17
21
30 Wells Fargo
R n f . Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
380
18
21
30 W e l l s F a r g o
R n f . Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
381
19
21
30 W e l l s F a r g o
R n f . Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
382
20
21
30 W e l l s F a r g o
R n f . Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
383
21
21
30 W e l l s F a r g o
R n f . Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
384
22
21
30 Wells Fargo
R n f . Conc./Gl.
162
385
23
21
30 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
386
24
21
30 W e l l s F a r g o
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
0.25
387
25
21
30 W e l l s F a r g o
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
388
26
21
30 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
389
27
21
3 0 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
390
35
21
9 9 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
391
36
21
99 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
392
37
21
99 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
393
38
21
99 MidFirst B a n k Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
394
39
21
9 9 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
395
40
21
9 9 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
396
41
21
9 9 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
397
42
21
9 9 MidFirst Bank Mbe./Gl./Steel
Tan Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
398
43
21
111 MidFirst Bank Mbe./Gl./Steel
White Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.55
399
1
22
9 O r p h e u m Th.
Cone. Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
400
2
22
9 O r p h e u m Th.
Cone. Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
401
3
22
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
402
11
22
3 0 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
403
12
22
3 0 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
404
13
22
3 0 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
405
14
22
30 W e l l s Fargo
R n f . Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
406
15
22
30 W e l l s F a r g o
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
407
16
22
30 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
408
17
22
30 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
409
18
22
3 0 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
410
19
22
3 0 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
411
20
22
3 0 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
412
21
22
3 0 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
413
22
22
30 Wells F a r g o
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
414
23
22
3 0 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
415
24
22
30 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
416
25
22
3 0 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
417
26
22
3 0 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
418
27
22
3 0 Wells F a r g o
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
419
35
22
99 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
420
36
22
9 9 MidFirst B a n k Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
421
37
22
105 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
422
38
22
105 MidFirst B a n k Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
423
39
22
105 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
424
40
22
105 MidFirst B a n k Mbe./Gl./Steel
Wh Cone.
425
41
22
105 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
426
42
22
111 MidFirst B a n k Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
427
43
22
111 M i d F i r s t B a n k Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
428
1
23
9 O r p h e u m Th.
Cone. Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
429
2
23
9 O r p h e u m Th.
Cone. Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
430
3
23
9 O r p h e u m Th.
Cone. Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
431
11
23
30 Wells Fargo
R n f . Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
432
12
23
30 Wells Fargo
R n f . Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
433
13
23
30 W e l l s F a r g o
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
434
14
23
30 W e l l s F a r g o
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
163
435
15
23
3 0 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
436
16
23
3 0 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
0.305
3.93
0.20
0.25
0.20
0.25
437
17
23
3 0 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
438
18
23
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
439
19
23
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
440
20
23
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
441
21
23
30 Wells F a r g o
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
442
22
23
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
443
23
23
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
444
24
23
3 0 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
445
25
23
3 0 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
446
26
23
3 0 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
447
27
23
3 0 Wells F a r g o
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
448
35
23
9 9 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
449
36
23
111 MidFirst Bank Mbe./Gl./Steel
Wh C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
450
37
23
111 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
451
38
23
111 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
452
39
23
111 MidFirst B a n k Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
453
40
23
111 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
454
41
23
105 MidFirst Bank Mbe./GI./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
455
42
23
111 MidFirst Bank Mbe./GI./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
456
43
23
111 MidFirst Bank Mbe./GI./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
457
51
23
6 Baja Fresh
Stone/Conc./Gl
Lt. Red PI.
0.60
0.203
2.96
0.40
0.203
1.97
0.20
0.25
458
52
23
6 Baja Fresh
Stone/Conc./GI Lt. R e d PI.
0.60
0.203
2.96
0.40
0.203
1.97
0.20
0.25
459
1
24
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
460
2
24
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
461
3
24
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
0.305
3.61
1.20
0.305
3.93
0.20
0.25
462
11
24
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
463
12
24
3 0 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
464
13
24
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
465
14
24
30 W e l l s F a r g o
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
466
15
24
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
467
16
24
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
468
17
24
3 0 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
469
18
24
3 0 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
470
19
24
3 0 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
471
20
24
3 0 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
472
21
24
30 W e l l s F a r g o
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
473
22
24
30 W e l l s F a r g o
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
23
24
30 W e l l s F a r g o
Rnf. Conc./Gl.
475
24
24
30 W e l l s F a r g o
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
476
25
24
30 W e l l s F a r g o
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
477
26
24
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
478
27
24
30 Wells Fargo
R n f . Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
479
35
24
99 MidFirst B a n k Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
480
36
24
111 MidFirst B a n k Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
481
37
24
111 MidFirst B a n k Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
482
38
24
111 MidFirst B a n k Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
483
39
24
111 M i d F i r s t B a n k Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
474
164
484
40
24
111 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
485
41
24
111 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
0.50
486
42
24
111 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
487
43
24
9 9 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
488
50
24
6 B a j a Fresh
Stone/Conc./Gl
Lt. Red PI.
0.60
0.203
2.96
0.40
0.203
1.97
0.20
0.25
489
51
24
6 B a j a Fresh
Stone/Conc./GI Lt. Red PI.
0.60
0.203
2.96
0.40
0.203
1.97
0.20
0.25
490
52
24
6 Baja Fresh
Stone/Conc./GI
Lt. Red PI.
0.60
0.203
2.96
0.40
0.203
1.97
0.20
0.25
491
1
25
9 O r p h e u m Th.
Cone. B l o c k s
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
492
2
25
9 O r p h e u m Th.
Cone. Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
493
3
25
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
494
11
25
3 0 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
495
12
25
30 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
496
13
25
30 W e l l s F a r g o
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
497
14
25
30 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
498
15
25
30 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
499
16
25
3 0 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
500
17
25
3 0 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
501
18
25
3 0 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
502
19
25
30 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
503
20
25
30 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
504
21
25
30 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
505
22
25
30 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
506
23
25
3 0 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
507
24
25
3 0 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
508
25
25
3 0 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
509
26
25
30 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
510
27
25
30 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
511
35
25
111 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
512
36
25
111 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
513
37
25
111 MidFirst B a n k Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
514
38
25
105 MidFirst Bank Mbe./GI./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
515
39
25
111 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
516
40
25
111 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
517
41
25
111 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
518
42
25
111 MidFirst Bank Mbe./GI ./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
519
43
25
9 9 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
520
49
25
6 B a j a Fresh
Stone/Conc./GI Lt. R e d PI.
0.60
0.203
2.96
0.40
0.203
1.97
0.20
0.25
521
50
25
6 B a j a Fresh
Stone/Conc./GI Lt. Red PI.
0.60
0.203
2.96
0.40
0.203
1.97
0.20
0.25
522
51
25
6 B a j a Fresh
Stone/Conc./GI Lt. R e d PI.
0.60
0.203
2.96
0.40
0.203
1.97
0.20
0.25
523
52
25
6 B a j a Fresh
Mbe./Conc./Gl. Lt. R e d PI.
0.80
0.203
3.94
0.70
0.203
3.45
0.20
0.25
524
1
26
9 O r p h e u m Th.
Cone. Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
525
2
26
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
526
3
26
9 O r p h e u m Th.
Cone. Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
527
11
26
30 W e l l s F a r g o
R n f . Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
528
12
26
30 W e l l s F a r g o
R n f . Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
529
13
26
30 Wells Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
530
14
26
3 0 W e l l s Fargo
R n f . Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
165
531
15
26
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
532
16
26
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
533
17
26
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
534
18
26
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
0.305
3.61
1.20
0.305
3.93
0.20
0.25
0.20
0.25
0.25
535
19
26
3 0 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
536
20
26
3 0 W e l l s Fargo
Rnf. Conc./GI.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
537
21
26
3 0 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
538
22
26
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
539
23
26
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
540
24
26
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
541
25
26
30 W e l l s F a r g o
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
542
26
26
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
543
27
26
30 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
544
35
26
111 MidFirst Bank Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
545
36
26
111 MidFirst Bank Mbe./Gl./Steel
W h Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
546
37
26
99 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
26
105 MidFirst Bank Mbe./GI./Steel
W h Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
1.40
0.305
4.59
0.20
0.50
0.50
547
38
Wh Cone.
0.80
0.305
2.62
105 MidFirst Bank Mbe./GI./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
105 MidFirst Bank Mbe./GI./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
26
99 MidFirst Bank Mbe./GI./Steel
Tan Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
43
26
9 9 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
48
26
6 Baja Fresh
Stone/Conc/Gl
Lt. Red PI.
0.60
0.203
2.96
0.40
0.203
1.97
0.20
0.25
554
49
26
6 B a j a Fresh
Stone/Conc/GI
Lt. Red PI.
0.60
0.203
2.96
0.40
0.203
1.97
0.20
0.25
555
50
26
6 B a j a Fresh
Stone/Conc/Gl
Lt. Red PI.
0.60
0.203
2.96
0.40
0.203
1.97
0.20
0.25
556
51
26
6 B a j a Fresh
Mbe./Conc/GI.
Lt. Red PI.
0.80
0.203
3.94
0.70
0.203
3.45
0.20
0.25
557
1
27
9 O r p h e u m Th.
Cone. Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
558
2
27
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
559
3
27
9 O r p h e u m Th.
C o n e . Blocks
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
560
11
27
R n f . Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
3.61
1.20
0.305
3.93
0.20
0.25
0.25
548
39
26
105 MidFirst Bank Mbe./GI./Steel
549
40
26
550
41
26
551
42
552
553
30 Wells Fargo
561
12
27
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
562
13
27
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
563
14
27
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
564
15
27
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
565
16
27
3 0 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
566
17
27
3 0 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
567
18
27
3 0 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
568
19
27
3 0 Wells Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
569
20
27
3 0 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
570
21
27
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
571
22
27
30 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
30 W e l l s F a r g o
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
0.20
0.25
572
23
27
573
24
27
3 0 W e l l s Fargo
Rnf. Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
574
25
27
30 Wells Fargo
R n f . Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
575
26
27
3 0 W e l l s Fargo
R n f . Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
576
27
27
30 Wells Fargo
R n f . Conc./Gl.
Concrete
1.10
0.305
3.61
1.20
0.305
3.93
0.20
0.25
577
35
27
111 MidFirst B a n k Mbe./Gl./Steel
Wh Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.50
578
36
27
99 MidFirst B a n k Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
579
37
27
99 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
580
38
27
99 MidFirst B a n k Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
166
581
39
27
9 9 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
582
40
27
9 9 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
583
41
27
9 9 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 9 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
9 9 MidFirst Bank Mbe./Gl./Steel
Tan C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1.97
0.20
0.25
584
585
42
43
27
27
0.60
0.203
2.96
0.40
0.203
Stone/Conc/GI. Lt. Red PI.
0.60
0.203
2.96
0.40
0.203
1.97
0.20
0.25
Mbe./Conc/GI.
Lt. Red PI.
0.80
0.203
3.94
0.70
0.203
3.45
0.20
0.25
12 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
32
12 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
60
32
12 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
592
1
33
51 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
593
2
33
51 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
594
3
33
51 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
595
19
33
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
20
33
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
597
21
33
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
598
22
33
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
599
23
33
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
600
24
33
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
601
25
33
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
602
26
33
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
603
27
33
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
604
35
33
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
605
36
33
6
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
606
37
33
6
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
607
38
33
6
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
608
39
33
6
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
609
40
33
6
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
610
41
33
6
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
611
42
33
6
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
612
43
33
6
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
613
44
33
6
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
O H 3m/Lt.
Cone.
US Bank
O H 3m/Lt.
Cone.
O H 3m/Lt.
US Bank
Cone.
O H 3m/Lt.
US Bank
Cone.
O H 3m/Lt.
U S Bank
Cone.
O H 3m/Lt.
US Bank
Cone.
O H 3m/Lt.
US Bank
Cone.
O H 3m/Lt.
US Bank
Cone.
O H 3m/Lt.
US Bank
Cone.
Thai Elephant B e i g e Pl./Bk.
Lt. Br. PI.
596
9 Orpheum
Lofts
9 Orpheum
Lofts
9 Orpheum
Lofts
36 O r p h e u m
Lofts
36 O r p h e u m
Lofts
36 O r p h e u m
Lofts
36 O r p h e u m
Lofts
36 O r p h e u m
Lofts
36 Orpheum
Lofts
6 US Bank
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
614
45
33
6 Thai Elephant B e i g e Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
615
46
33
6 Comm./Rest.
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
616
47
33
6 Comm./Rest.
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
617
48
33
9 Comm./loft
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
618
49
33
9 Comm./loft
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
3.45
0.17
0.35
586
48
27
6 B a j a Fresh
Stone/Cone/Gl. Lt. Red PI.
587
49
27
6 B a j a Fresh
588
50
27
6 B a j a Fresh
589
58
32
590
59
591
619
50
33
9 Quiznos/loft
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
620
51
33
9 Quiznos/loft
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
621
52
33
9 Quiznos/loft
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
167
4.59
1.40
0.305
4.59
0.17
0.25
0.305
2.95
1.40
0.305
4.59
0.17
0.25
0.305
2.95
1.40
0.305
4.59
0.17
0.25
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
Wh Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
White
Conc./St.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Rnf. C o n e .
Wh C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
3.93
0.27
0.35
Beige C o n e .
1.40
0.305
51 W y n d h a m
OH 3m/Tan
Cone.
Tan C o n c r e t e
Beige C o n e .
0.90
51 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
33
51 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
1
34
51 A T & T
Wh Conc./St.
627
2
34
51 A T & T
628
3
34
51 A T & T
629
20
34
630
21
34
631
22
34
632
23
34
633
24
34
634
25
34
635
26
34
636
27
34
637
35
34
15 O r p h e u m
Lofts
15 O r p h e u m
Lofts
39 O r p h e u m
Lofts
39 O r p h e u m
Lofts
36 O r p h e u m
Lofts
36 O r p h e u m
Lofts
36 O r p h e u m
Lofts
36 Orpheum
Lofts
27 U S B a n k
622
57
623
58
33
624
59
33
625
60
626
638
36
33
6 Wyndham
34
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
639
37
34
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
640
38
34
27 U S B a n k
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
641
39
34
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
642
40
34
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
643
41
34
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
644
42
34
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
645
43
34
6 U S Bank
Lt. C o n e .
1.51
0.305
4.95
1.51
0.305
4.95
0.27
0.35
646
44
34
O H 3m/Lt.
Cone.
6 Thai Elephant Beige Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
647
45
34
6 Thai Elephant Beige Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
648
46
34
6 Comm./Rest.
Brown Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
649
47
34
6 Comm./Rest.
Brown Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
650
48
34
9 Comm./loft
Brown Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
651
49
34
9 Comm./loft
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
652
50
34
9 Quiznos/loft
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
653
51
34
9 Quiznos/loft
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
52
34
9 Quiznos/loft
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
Beige C o n e .
1.40
0.305
4.59
1.40
0.305
4.59
0.17
0.25
654
656
58
34
51 W y n d h a m
OH 3m/Tan
Cone.
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
657
59
34
51 W y n d h a m
Tan C o n c r e t e
Beige Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
658
60
34
51 W y n d h a m
Tan C o n c r e t e
Beige Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
35
30 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
0.50
655
659
660
57
34
6 Wyndham
1
2
35
30 A T & T
W h Conc./St.
661
3
35
30 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
662
21
35
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
22
35
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
664
23
35
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Lt. Br. PI.
663
15 O r p h e u m
Lofts
42 Orpheum
Lofts
42 Orpheum
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
168
Lofts
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Rnf. C o n e .
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
35
36 O r p h e u m
Lofts
36 Orpheum
Lofts
36 Orpheum
Lofts
36 O r p h e u m
Lofts
27 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
36
35
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
37
35
27 U S B a n k
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
672
38
35
2 7 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
673
39
35
2 7 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
674
40
35
27 US Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
675
41
35
2 7 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. Rnf. C o n e .
665
24
35
666
25
35
667
26
35
668
27
35
669
35
670
671
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
35
O H 3m/Lt.
Cone.
6 Thai Elephant Beige Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.20
0.35
35
6 T h a i Elephant Beige Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.20
0.35
46
35
6 Comm./Rest.
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.20
0.35
681
47
35
6 Comm./Rest.
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.20
0.35
682
48
35
6 Commercial
Brown Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.20
0.35
683
49
35
6 Commercial
Brown Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.20
0.35
684
50
35
6 Commercial
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.20
0.35
685
51
35
6 Cafe Roma
Brown Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.20
0.35
6 Cafe Roma
B r o w n Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.20
0.35
4.59
0.17
0.25
676
42
35
27 U S B a n k
677
43
35
6 U S Bank
678
44
679
45
680
686
52
35
Beige C o n e .
1.40
0.305
4.59
1.40
51 W y n d h a m
OH 3m/Tan
Cone.
Tan C o n c r e t e
0.305
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
51 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
35
51 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
36
30 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
0.50
687
57
35
6 Wyndham
688
58
35
689
59
35
690
60
691
1
692
2
36
30 A T & T
693
3
36
30 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
694
22
36
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
23
36
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
696
24
36
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
697
25
36
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
698
26
36
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
699
27
36
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
700
35
36
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Rnf. C o n e .
Lt. Br. PI.
695
39 O r p h e u m
Lofts
39 O r p h e u m
Lofts
39 O r p h e u m
Lofts
9 Orpheum
Lofts
9 Orpheum
Lofts
9 Orpheum
Lofts
27 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
701
36
36
27 U S B a n k
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
27 U S B a n k
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
3.93
0.27
0.35
702
37
36
1.10
0.305
3.61
1.20
0.305
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. R n f . C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. R n f . C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
38
36
27 US Bank
Lt. R n f . C o n e .
Lt. C o n e .
704
39
36
27 US Bank
Lt. R n f . C o n e .
705
40
36
27 US Bank
Lt. R n f . C o n e .
706
41
36
27 U S Bank
707
42
36
27 US Bank
703
169
708
47
36
6 Comm./Rest.
Brown Pl./Bk.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.17
0.35
709
48
36
6 Commercial
Plaster/Brick
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.22
0.35
710
49
36
6 Commercial
Plaster/Brick
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.22
0.35
711
50
36
6 Commercial
Plaster/Brick
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.22
0.35
712
51
36
6 Cafe Roma
Gr. Pl./Gl.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.18
0.35
713
52
36
6 Cafe Roma
Gr. Pl./Gl.
Lt. PI.
0.50
0.203
2.50
0.70
0.203
3.45
0.18
0.35
Beige C o n e .
1.40
0.305
4.59
1.40
0.305
4.59
0.17
0.25
714
57
36
6 Wyndham
715
58
36
51 W y n d h a m
O H 3m/Tan
Cone.
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
716
59
36
51 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
717
60
36
51 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
718
1
37
30 A T & T
W h Conc./St.
Wh C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
719
2
37
30 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
720
3
37
30 A T & T
W h Conc./St.
Wh Cone.
721
22
37
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
23
37
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
723
24
37
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
724
25
37
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
725
26
37
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
726
27
37
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
727
35
37
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Rnf. C o n e .
Lt. Br. PI.
722
39 O r p h e u m
Lofts
39 O r p h e u m
Lofts
36 O r p h e u m
Lofts
9 Orpheum
Lofts
9 Orpheum
Lofts
9 Orpheum
Lofts
27 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
728
36
37
27 U S B a n k
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
729
37
37
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
730
38
37
27 US Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
731
39
37
27 US Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
732
40
37
27 US Bank
Lt. Rnf. C o n e .
733
41
37
27 US Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
734
42
37
27 U S B a n k
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
735
44
37
2 7 Heard Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
736
45
37
27 H e a r d Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
737
46
37
27 Heard Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
738
47
37
9 Commercial
Plaster/Brick
Lt. PI.
0.60
0.203
2.96
0.70
0.203
3.45
0.22
0.35
739
48
37
10 C o m m e r c i a l
Plaster/Brick
Lt. PI.
0.60
0.203
2.96
0.70
0.203
3.45
0.22
0.35
740
49
37
10 C o m m e r c i a l
Plaster/Brick
Lt. PI.
0.60
0.203
2.96
0.70
0.203
3.45
0.22
0.35
741
50
37
10 C o m m e r c i a l
Plaster/Brick
Lt. PI.
0.60
0.203
2.96
0.70
0.203
3.45
0.22
0.35
742
51
37
2 7 Heard Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
743
52
37
2 7 Heard Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
4.59
1.40
0.305
4.59
0.17
0.25
Beige C o n e .
1.40
12 W y n d h a m
OH 3m/Tan
Cone.
Tan C o n c r e t e
0.305
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
12 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
37
12 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
38
30 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
1.40
0.305
4.59
0.24
0.50
744
57
37
6 Wyndham
745
58
37
746
59
37
747
60
1
748
Lt. C o n e .
2
38
30 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
750
3
38
30 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
751
22
38
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
23
38
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br. PI.
752
36 Orpheum
Lofts
36 Orpheum
Lofts
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
749
170
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Rnf. C o n e .
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.12
0.22
38
36 O r p h e u m
Lofts
36 O r p h e u m
Lofts
36 O r p h e u m
Lofts
36 O r p h e u m
Lofts
27 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
38
2 7 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
37
38
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
760
38
38
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
761
39
38
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
762
40
38
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
763
41
38
27 U S B a n k
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
764
42
38
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
765
44
38
2 7 Heard Bldg
Lt. Conc./GI.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
766
45
38
2 7 Heard Bldg
Lt. Conc./GI.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
2 7 Heard Bldg
Lt. Conc./GI.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
0.305
2.95
1.20
0.305
3.93
0.27
0.35
753
24
38
754
25
38
755
26
38
756
27
38
757
35
758
36
759
767
46
38
768
47
38
2 7 Heard Bldg
Lt. Conc./GI.
Lt. C o n e .
0.90
769
48
38
27 Heard Bldg
Lt. Conc./GI.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
770
49
38
27 Heard Bldg
Lt. Conc./GI.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
771
50
38
27 Heard Bldg
Lt. Conc./GI.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
772
51
38
27 Heard Bldg
Lt. Conc./GI.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
773
52
38
27 Heard B l d g
Lt. Conc./GI.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
774
57
38
6 Wyndham
Beige C o n e .
1.40
0.305
4.59
1.40
0.305
4.59
0.17
0.25
775
58
38
12 W y n d h a m
O H 3m/Tan
Cone.
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
776
59
38
12 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
777
60
38
12 W y n d h a m
Tan Concrete
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
778
1
39
30 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
779
2
39
30 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
780
3
39
30 A T & T
W h Conc./St.
W h Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
781
22
39
0.75
0.305
2.46
0.80
0.305
2.62
0.17
0.22
23
39
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.17
0.22
783
24
39
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.17
0.22
784
25
39
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.17
0.22
785
26
39
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.17
0.22
786
27
39
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.17
0.22
787
35
39
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Rnf. C o n e .
Lt. Br. PI.
782
36 Orpheum
Lofts
36 Orpheum
Lofts
36 Orpheum
Lofts
36 Orpheum
Lofts
36 O r p h e u m
Lofts
36 O r p h e u m
Lofts
27 US Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
788
36
39
27 US Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
789
37
39
27 U S B a n k
Lt. R n f . C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
790
38
39
27 U S B a n k
Lt. R n f . C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
791
39
39
27 U S B a n k
Lt. R n f . C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
792
40
39
27 US Bank
Lt. R n f . C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
793
41
39
27 US Bank
Lt. R n f . C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
794
42
39
27 US Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
0.305
2.95
1.20
0.305
3.93
0.27
0.35
0.305
2.95
1.20
0.305
3.93
0.27
0.35
795
44
39
2 7 Heard B l d g
Lt. Conc./GI.
Lt. C o n e .
0.90
796
45
39
2 7 Heard B l d g
Lt. Conc./GI.
Lt. C o n e .
0.90
171
797
46
39
27 Heard Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
798
47
39
27 H e a r d Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
48
39
27 Heard Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
2.95
1.20
0.305
3.93
0.27
0.35
799
27 H e a r d Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
39
27 Heard Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
39
2 7 H e a r d Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
52
39
2 7 Heard Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
804
57
39
6 Wyndham
Beige C o n e .
1.40
0.305
4.59
1.40
0.305
4.59
0.17
0.25
805
58
39
12 W y n d h a m
OH 3m/Tan
Cone.
Tan C o n c r e t e
Beige Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
0.17
0.25
800
49
39
801
50
802
51
803
12 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
39
12 W y n d h a m
Tan Concrete
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
40
30 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
40
30 A T & T
W h Conc./St.
Wh C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
3
40
30 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
811
22
40
0.75
0.305
2.46
0.80
0.305
2.62
0.17
0.22
23
40
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.17
0.22
813
24
40
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.17
0.22
814
25
40
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.17
0.22
815
26
40
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.17
0.22
816
27
40
Lt. Br. PI.
0.75
0.305
2.46
0.80
0.305
2.62
0.17
0.22
817
35
40
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
818
36
40
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
819
37
40
120 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
820
38
40
120 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
821
39
40
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
822
40
40
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
823
41
40
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
824
42
40
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
825
44
40
2 7 Heard Bldg
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Br.
Bk./Mbe.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Conc./Gl.
Lt. Br. PI.
812
36 Orpheum
Lofts
36 Orpheum
Lofts
36 Orpheum
Lofts
36 Orpheum
Lofts
36 Orpheum
Lofts
36 O r p h e u m
Lofts
120 U S Bank
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
826
45
40
2 7 Heard Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
827
46
40
2 7 Heard Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
828
47
40
2 7 H e a r d Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
1.20
0.305
3.93
0.27
0.35
806
59
807
60
808
1
809
2
810
39
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
27 Heard Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
27 H e a r d Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
40
2 7 H e a r d Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
52
40
27 Heard Bldg
Lt. Conc./Gl.
Lt. C o n e .
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.35
834
57
40
6 Wyndham
Beige C o n e .
1.40
0.305
4.59
1.40
0.305
4.59
0.17
0.25
835
58
40
12 W y n d h a m
O H 3m/Tan
Cone.
Tan Concrete
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
836
59
40
12 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
837
60
40
12 W y n d h a m
Tan C o n c r e t e
Beige C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.17
0.25
838
1
41
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
829
48
40
27 Heard Bldg
830
831
49
40
50
40
832
51
833
172
839
2
41
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
840
3
41
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
841
35
41
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
842
36
41
120 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
843
37
41
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
844
38
41
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
845
39
41
120 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
846
40
41
120 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
847
41
41
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
848
42
41
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
849
1
42
15 A T & T
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.20
0.305
3.93
0.24
0.50
850
2
42
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.20
0.305
3.93
0.24
0.50
851
3
42
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.20
0.305
3.93
0.24
0.50
852
35
42
120 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
853
36
42
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
854
37
42
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
855
38
42
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
856
39
42
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
857
40
42
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
858
41
42
120 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
859
42
42
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
0.50
860
1
43
15 A T & T
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
861
2
43
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
862
3
43
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
863
11
43
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
864
12
43
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
865
13
43
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
866
14
43
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
867
15
43
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
868
16
43
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
869
17
43
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
870
18
43
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
871
19
43
Lt. Br. B k .
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
872
20
43
Lt. Br. B k .
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
873
21
43
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
874
22
43
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o r a
m.
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
173
875
23
43
876
24
43
877
25
43
878
26
43
879
35
43
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
120 U S B a n k
880
36
43
120 U S Bank
881
37
43
120 U S Bank
882
38
43
120 U S Bank
883
39
43
120 U S Bank
884
40
43
120 U S Bank
885
41
43
120 U S Bank
886
42
43
120 U S Bank
887
46
43
888
47
43
889
48
43
890
49
43
891
50
43
892
51
43
893
52
43
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Mixed Cone.
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Conc./Art Deco Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
3.93
0.22
0.25
Apart.
894
58
43
9 Hotel M o n r o e
Conc./Art Deco Concrete
0.90
0.305
2.95
1.20
0.305
Conc./Art Deco Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
44
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
11
44
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
901
12
44
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
902
13
44
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
903
14
44
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
904
15
44
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
905
16
44
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
906
17
44
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
907
18
44
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
908
19
44
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
909
20
44
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
910
21
44
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
81 Elevator s h a f t
D a r k steel
White Cone.
1.60
0.254
6.30
1.00
0.254
3.94
0.12
0.50
895
59
43
896
60
43
897
1
44
15 A T & T
898
2
44
899
3
900
9 Hotel M o n r o e
9 Hotel M o n r o e
174
White C o n e .
1.60
0.254
6.30
1.00
0.254
3.94
0.12
0.50
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
Lt. Br. Bk.
Lt. Br. PI.
0.80
0.254
3.15
0.80
0.254
3.15
0.17
0.22
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Mixed Cone.
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Conc./Art Deco C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
9 Hotel M o n r o e Cone./Art Deco C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
9 Hotel M o n r o e Conc./Art Deco C o n c r e t e
911
22
44
912
23
44
913
24
44
914
25
44
915
26
44
916
35
44
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
12 P a r k i n g / C o m
m.
120 U S B a n k
917
36
44
120 U S B a n k
918
37
44
120 U S B a n k
919
38
44
120 U S Bank
920
39
44
120 U S Bank
921
40
44
120 U S B a n k
922
41
44
120 U S B a n k
923
42
44
120 U S B a n k
924
46
44
925
47
44
926
48
44
927
49
44
928
50
44
929
51
44
930
52
44
931
58
44
932
59
44
81 Elevator s h a f t Dark steel
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
9 Hotel M o n r o e
933
60
44
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
934
1
45
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
935
2
45
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
936
3
45
15 A T & T
W h Conc./St.
Wh C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
937
10
45
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
938
11
45
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
939
12
45
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
940
13
45
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
941
16
45
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
942
17
45
Glass/Steel
Br. Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
943
18
45
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
944
19
45
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
945
20
45
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
946
21
45
2 4 Phx Pers.
Bldg
2 4 Phx Pers.
Bldg
2 4 P h x Pers.
Bldg
2 5 Phx Pers.
Bldg
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
81 E l e v a t o r
D a r k steel
Wh Cone.
1.60
0.254
6.30
1.00
0.254
3.94
0.12
0.50
175
22
45
81 Elevator
Dark steel
Wh Cone.
1.60
0.254
6.30
1.00
0.254
3.94
0.12
0.50
948
23
45
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
949
24
45
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
950
25
45
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
951
26
45
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
952
27
45
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
953
35
45
72 111 W.
Monroe
72 111 W.
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
954
36
45
120 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
955
37
45
120 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
956
38
45
120 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
957
39
45
120 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
958
40
45
120 U S Bank
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
959
41
45
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
960
42
45
120 U S B a n k
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
961
46
45
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
962
47
45
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
963
48
45
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
964
49
45
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
965
50
45
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
966
51
45
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
967
52
45
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
968
58
45
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
4 5 Hotel M o n r o e
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Lt. Rnf.
Conc./Gl.
Mixed Cone.
Conc./Art D e c o C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
969
59
45
4 5 Hotel M o n r o e Conc./Art D e c o C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
45
4 5 Hotel M o n r o e Conc./Art D e c o C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
0.305
2.95
1.40
0.305
4.59
0.24
0.55
947
970
60
971
1
46
15 A T & T
W h Conc./St.
Wh Cone.
0.90
972
2
46
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
973
3
46
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
974
10
46
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
975
11
46
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
976
12
46
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
977
13
46
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
978
16
46
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
979
17
46
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
980
18
46
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
981
19
46
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
982
20
46
2 4 Phx Pers.
Bldg
2 4 P h x Pers.
Bldg
2 4 Phx Pers.
Bldg
24 P h x Pers.
Bldg
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 A C / E l e v a t o r
D a r k steel
Wh Cone.
1.60
0.254
6.30
1.00
0.254
3.94
0.12
0.50
176
983
21
46
81 Elevator shaft Dark steel
Wh Cone.
1.60
0.254
6.30
1.00
0.254
3.94
0.12
0.50
984
22
46
82 Elevator shaft Dark steel
Wh Cone.
1.60
0.254
6.30
1.00
0.254
3.94
0.12
0.50
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Lt. Rnf.
Conc./GI.
Mixed Cone.
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Mixed Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
985
23
46
986
24
46
987
25
46
988
26
46
989
27
46
990
35
46
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W.
Monroe
72 111 W.
Monroe
123 U S B a n k
991
36
46
120 U S Bank
992
37
46
120 U S B a n k
993
38
46
120 US B a n k
994
39
46
120 U S B a n k
995
40
46
120 U S Bank
996
41
46
120 U S Bank
997
42
46
123 U S B a n k
998
46
46
999
47
46
1000
48
46
1001
49
46
1002
50
46
1003
51
46
1004
52
46
1005
58
46
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
12 T h e H u b
Apart.
4 5 Hotel M o n r o e
Conc./Art D e c o C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
1006
59
46
4 5 Hotel M o n r o e Conc./Art D e c o C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
1007
60
46
4 5 Hotel M o n r o e Conc./Art D e c o C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
0.50
1008
1
47
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
1009
2
47
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
1010
3
47
15 A T & T
W h Conc./St.
Wh C o n e .
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
1011
10
47
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
1012
11
47
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
1013
12
47
Light Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.17
0.35
1014
13
47
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
1015
14
47
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
1016
15
47
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
1017
16
47
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
1018
17
47
24 Phx Pers.
Bldg
2 4 Phx Pers.
Bldg
2 4 P h x Pers.
Bldg
2 4 P h x Pers.
Bldg
24 P h x Pers.
Bldg
24 Phx Pers.
Bldg
72 111 W .
Monroe
72 111 W .
Monroe
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
177
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Lt. Rnf. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.30
Lt. Rnf. C o n e .
Lt. C o n e . /
Grn.
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
47
27 US Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
40
47
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
1035
41
47
2 7 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
1036
42
47
27 US Bank
Lt. Rnf. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.30
1037
45
47
6 Comm./Rest.
Tan Pl./Gl.
Lt. C o n e . /
Grn.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
1038
46
47
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
1039
47
47
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
1040
48
47
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
1041
49
47
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
1042
50
47
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
47
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W.
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
27 U S B a n k
36
47
27 U S B a n k
37
47
27 U S B a n k
1032
38
47
1033
39
1034
1019
18
47
1020
19
47
1021
20
47
1022
21
47
1023
22
47
1024
23
47
1025
24
47
1026
25
47
1027
26
47
1028
27
47
1029
35
1030
1031
1043
1044
51
47
6 Comm./Rest.
Tan Pl./Gl.
Plaster
52
47
1045
58
47
4 5 Hotel M o n r o e Conc./Art Deco C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
1046
59
47
4 5 Hotel M o n r o e Conc./Art D e c o C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
1047
60
47
4 5 Hotel M o n r o e Conc./Art Deco C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
1048
1
48
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
1049
2
48
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
1050
3
48
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
0.50
1051
10
48
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
1052
11
48
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
1053
12
48
Light Steel
Lt. C o n e .
1.60
0.254
6.30
1.40
0.305
4.59
0.18
0.35
1054
13
48
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
1055
14
48
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
1056
15
48
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
1057
16
48
Glass/Steel
Br. Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
1058
17
48
2 4 P h x Pers.
Bldg
2 4 Phx Pers.
Bldg
2 4 P h x Pers.
Bldg
2 4 P h x Pers.
Bldg
2 4 P h x Pers.
Bldg
2 4 P h x Pers.
Bldg
72 111 W .
Monroe
72 111 W .
Monroe
Glass/Steel
Br. Cone.
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
178
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Lt. Rnf. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.30
Lt. Rnf. C o n e .
Lt. C o n e . /
Gm.
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
2 7 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
48
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
40
48
27 U S B a n k
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
1075
41
48
27 U S B a n k
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
1076
42
48
27 U S B a n k
Lt. Rnf. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.30
1077
45
48
6 Comm./Rest.
Tan Pl./Gl.
Lt. C o n e . /
Grn.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
1078
46
48
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
1079
47
48
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
1080
48
48
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
1081
49
48
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
1082
50
48
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
1083
51
48
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
1084
52
48
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.22
0.25
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
3.93
0.22
0.25
1059
18
48
1060
19
48
1061
20
48
1062
21
48
1063
22
48
1064
23
48
1065
24
48
1066
25
48
1067
26
48
1068
27
48
1069
35
48
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W.
Monroe
72 111 W.
Monroe
72 111 W.
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W .
Monroe
72 111 W.
Monroe
2 7 U S Bank
1070
36
48
27 U S B a n k
1071
37
48
27 U S B a n k
1072
38
48
1073
39
1074
1085
58
48
4 5 Hotel M o n r o e Conc./Art D e c o C o n c r e t e
1086
59
48
4 5 Hotel M o n r o e Conc./Art Deco C o n c r e t e
0.90
0.305
2.95
1.20
0.305
1087
60
48
4 5 Hotel M o n r o e Conc./Art D e c o C o n c r e t e
0.90
1088
1
49
15 A T & T
W h Conc./St.
Wh Cone.
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
0.305
2.95
1.40
0.305
4.59
0.24
1089
2
49
15 A T & T
W h Conc./St.
Wh Cone.
0.50
0.90
0.305
2.95
1.40
0.305
4.59
0.24
1090
3
49
15 A T & T
W h Conc./St.
0.50
Wh Cone.
0.90
0.305
2.95
1.40
0.305
4.59
0.24
1091
10
49
1092
11
49
1093
12
49
1094
13
49
1095
14
49
1096
15
49
1097
16
49
1098
17
49
2 4 P h x Pers.
Bldg
2 4 Phx Pers.
Bldg
2 4 P h x Pers.
Bldg
2 4 Phx Pers.
Bldg
2 4 P h x Pers.
Bldg
24 P h x Pers.
Bldg
6 111 W .
Monroe
6 111 W .
Monroe
0.50
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
179
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Glass/Steel
Br. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.22
Lt. Rnf. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.30
Lt. Rnf. C o n e .
Lt.
Conc./Grn.
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
27 U S B a n k
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
27 U S B a n k
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
1.20
0.305
3.93
0.27
0.35
1099
18
49
1100
19
49
1101
20
49
1102
21
49
1103
22
49
1104
23
49
1105
24
49
1106
25
49
1107
26
49
1108
35
49
6 111 W .
Monroe
6 111 W.
Monroe
6 111 W.
Monroe
6 111 W.
Monroe
6 111 W.
Monroe
6 111 W.
Monroe
6 111 W .
Monroe
6 111 W .
Monroe
6 111 W .
Monroe
27 US Bank
1109
36
49
27 U S B a n k
1110
37
49
27 U S Bank
1111
38
49
1112
39
49
1113
40
49
1114
41
49
27 U S Bank
27 U S Bank
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1115
42
49
Lt. Rnf. C o n e .
Lt. C o n e .
1.10
0.305
3.61
1.20
0.305
3.93
0.27
0.35
1116
45
49
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1117
46
49
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1118
47
49
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1119
48
49
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1120
49
49
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1121
50
49
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1122
51
49
6 Comm./Rest.
T a n Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1123
52
49
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1124
58
49
9 Hotel M o n r o e Conc./Art D e c o C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
9 Hotel M o n r o e Conc./Art Deco C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
0.25
1125
59
49
1126
60
49
1127
10
50
1128
11
50
1129
12
50
1130
13
50
1131
14
50
1132
15
50
1133
16
50
1134
17
50
1135
18
50
1136
19
50
1137
20
50
0.90
0.305
2.95
1.20
0.305
3.93
0.22
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
Lt. Glass/Steel
Lt. C o n e .
0.80
0.305
2.62
1.40
0.305
4.59
0.18
0.35
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
9 Hotel M o n r o e Conc./Art Deco C o n c r e t e
2 4 Phx Pers.
Bldg
24 Phx Pers.
Bldg
24 Phx Pers.
Bldg
2 4 Phx Pers.
Bldg
2 4 Phx Pers.
Bldg
2 4 Phx Pers.
Bldg
6 111 W.
Monroe
6 111 W .
Monroe
6 111 W .
Monroe
6 111 W .
Monroe
6 111 W .
Monroe
180
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
OH. 3m/Cone.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
50
6 111 W .
Monroe
6 111 W.
Monroe
6 111 W .
Monroe
6 111 W .
Monroe
6 111 W .
Monroe
6 111 W .
Monroe
6 U S Bank
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
50
6 U S Bank
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
37
50
6 U S Bank
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
38
50
6 U S Bank
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
1138
21
50
1139
22
50
1140
23
50
1141
24
50
1142
25
50
1143
26
50
1144
35
1145
36
1146
1147
1148
39
50
6 US Bank
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
1149
40
50
6 U S Bank
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
1150
41
50
6 US Bank
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
1151
42
50
6 U S Bank
OH. 3m/Conc.
Concrete
1.20
0.305
3.93
1.40
0.305
4.59
0.20
0.25
1152
45
50
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
50
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1.97
0.50
0.203
2.46
0.17
0.25
1153
46
1154
47
50
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1155
48
50
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1156
49
50
6 Comm./Rest.
T a n Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1157
50
50
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1158
51
50
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1159
52
50
6 Comm./Rest.
Tan Pl./Gl.
Plaster
0.50
0.254
1.97
0.50
0.203
2.46
0.17
0.25
1160
58
50
9 Hotel M o n r o e Co nc. /Ar t Deco C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
1161
59
50
9 Hotel M o n r o e Co nc. /Ar t Deco C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
1162
60
50
9 Hotel M o n r o e Co nc. /Ar t Deco C o n c r e t e
0.90
0.305
2.95
1.20
0.305
3.93
0.22
0.25
1163
21
56
1.164
22
56
1165
23
56
1166
24
56
1167
35
56
1168
36
56
1169
37
56
1170
38
56
1171
39
56
1172
40
56
1173
41
56
1174
42
56
1175
43
56
1176
44
56
1177
45
56
33 U S C H Fed
Bld g
33 U S C H Fed
Bldg
33 U S C H Fed
Bldg
33 U S C H Fed
Bld g
24 44 Monroe
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
24 44 Monroe
Res.
9 San C a r l o s
9 San Carlos
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.22
0.25
Tan Cone.
Concrete/
pool
Concrete/
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Tan Cone.
181
pool
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
Tan C o n e .
Concrete/
pool
Wh Cone.
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.50
Tan C o n e .
Wh Cone.
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.50
21 San C a r l o s
Tan C o n e .
Wh Cone.
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.50
56
21 San C a r l o s
Tan C o n e .
Wh Cone.
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.50
51
56
2 4 San C a r l o s
Tan C o n e .
Conc./Wd
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.22
1184
52
56
2 4 San C a r l o s
Tan C o n e .
Conc./Wd
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.22
1185
19
57
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1186
20
57
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1187
21
57
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1188
22
57
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1189
23
57
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1190
24
57
Lt. Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1191
25
57
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1192
26
57
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1193
35
57
Conc./Sl./Gl.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1194
36
57
Conc./Sl./Gl.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1195
37
57
Conc./Sl./Gl.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1196
38
57
Conc./Sl./Gl.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1197
39
57
Conc./Sl./Gl.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1198
40
57
Conc./SI./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1199
41
57
Conc./Sl./Gl.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1200
42
57
Conc./Sl./Gl.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1201
44
57
27 U S C H Fed
Bldg
27 U S C H Fed
Bldg
33 U S C H Fed
Bldg
33 U S C H Fed
Bldg
33 U S C H Fed
Bldg
33 U S C H Fed
Bldg
27 U S C H Fed
Bldg
2 7 U S C H Fed
Bldg
24 4 4 Monroe
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
24 44 Monroe
Res.
9 San C a r l o s
1202
45
57
1203
46
1204
47
1205
1178
46
56
9 San Carlos
Tan Cone.
1179
47
56
21 San C a r l o s
1180
48
56
21 San C a r l o s
1181
49
56
1182
50
1183
Tan C o n e .
Cone/pool
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
9 San Carlos
Tan C o n e .
Conc/pool
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
57
9 San Carlos
Tan C o n e .
Cone/pool
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
57
18 San C a r l o s
Tan Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
48
57
21 San C a r l o s
Tan Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1206
49
57
21 San C a r l o s
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1207
50
57
21 San C a r l o s
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1208
51
57
21 San Carlos
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1209
52
57
2 4 San Carlos
Tan Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1210
19
58
Fed
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1211
20
58
Fed
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1212
21
58
Fed
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1213
22
58
Fed
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1214
23
58
Fed
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1215
24
58
27 US CH
Bldg
27 U S C H
Bldg
33 U S C H
Bldg
33 U S C H
Bldg
33 U S C H
Bldg
33 U S C H
Fed
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
182
Bld g
1216
25
58
1217
26
58
1218
35
58
1219
36
58
1220
37
58
1221
38
58
1222
39
58
1223
40
58
1224
41
58
1225
42
58
1226
44
58
1227
45
58
1228
46
58
2 7 U S C H Fed
Bld g
2 7 U S C H Fed
Bld g
24 44 Monroe
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
24 44 Monroe
Res.
9 San Carlos
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
Tan C o n e .
Cone/Pool
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
9 San Carlos
Tan C o n e .
Cone/Pool
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
9 San Carlos
Tan C o n e .
Cone/Pool
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
0.70
0.254
2.76
0.17
0.25
1229
47
58
18 San Carlos
Tan C o n e .
Concrete
0.70
0.305
2.30
1230
48
58
18 San Carlos
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1231
49
58
18 San Carlos
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1232
50
58
21 San Carlos
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1233
51
58
18 San Carlos
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1234
52
58
2 4 San Carlos
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1235
19
59
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1236
20
59
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1237
21
59
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1238
22
59
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1239
23
59
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1240
24
59
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1241
25
59
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1242
26
59
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1243
35
59
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1244
36
59
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1245
37
59
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1246
38
59
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1247
39
59
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1248
40
59
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1249
41
59
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1250
42
59
Conc./Sl./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1251
44
59
27 U S C H Fed
Bldg
27 U S C H Fed
Bld g
33 U S C H Fed
Bld g
33 U S C H Fed
Bld g
33 U S C H Fed
Bld g
33 U S C H Fed
Bldg
2 7 U S C H Fed
Bld g
2 7 U S C H Fed
Bld g
24 44 Monroe
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
24 44 Monroe
Res.
9 San Carlos
Tan C o n e .
G r n . Sh./
Cone.
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1252
45
59
9 San Carlos
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1253
46
59
9 San Carlos
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1254
47
59
3 San Carlos
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1255
48
59
3 San C a r l o s
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1256
49
59
3 San C a r l o s
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1257
50
59
18 San C a r l o s
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
.0.17
0.25
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1258
51
59
18 San Carlos
Tan Cone.
1259
52
59
2 4 San Carlos
Tan Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1260
19
60
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1261
20
60
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1262
21
60
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1263
22
60
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1264
23
60
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1265
24
60
Lt. C o n e .
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.27
0.25
1266
25
60
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1267
26
60
Metal/Cone.
Concrete
0.90
0.305
2.95
1.20
0.305
3.93
0.12
0.25
1268
35
60
Conc./Sl./Gl.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1269
36
60
Conc./Sl./Gl.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1270
37
60
Conc./Sl./Gl.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1271
38
60
Conc./SI./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1272
39
60
Conc./SI./GI.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1273
40
60
Conc./Sl./Gl.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1274
41
60
Conc./Sl./Gl.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1275
42
60
Conc./Sl./Gl.
Concrete
0.80
0.305
2.62
1.40
0.305
4.59
0.20
0.25
1276
44
60
27 U S C H Fed
Bldg
27 U S C H Fed
Bldg
33 U S C H Fed
Bldg
33 U S C H Fed
Bldg
33 U S C H Fed
Bldg
33 U S C H Fed
Bldg
27 U S C H Fed
Bldg
27 U S C H Fed
Bldg
24 44 Monroe
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
114 4 4 M o n r o e
Res.
24 44 Monroe
Res.
9 San C a r l o s
Tan C o n e .
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1277
45
60
9 San C a r l o s
Tan C o n e .
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1278
46
60
9 San C a r l o s
Tan C o n e .
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1279
47
60
3 San Carlos
Tan Cone.
Grn. Sh./
Cone.
Grn. Sh./
Cone.
Grn. Sh./
Cone.
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1280
48
60
3 San C a r l o s
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1281
49
60
3 San C a r l o s
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1282
50
60
18 San C a r l o s
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1283
51
60
18 San C a r l o s
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
1284
52
60
18 San C a r l o s
Tan C o n e .
Concrete
0.70
0.305
2.30
0.70
0.254
2.76
0.17
0.25
0.90
0.298
3.02
1.17
0.293
3.96
0.20
0.30
4.00
0.20
0.30
Final i n p u t ; averages:
3.00
184
43 rd Ave
ID
X
Y
Ht
N U
F M
R M
T
W U
R C
R T
R U
W A
R A
1
1
26
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
2
2
26
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
3
3
26
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
1.38
0.46
0.254
1.8
0.3
0.3
W
C
W
4
26
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
5
5
26
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
6
6
26
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
7
1
27
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
8
2
27
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
9
3
27
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
1.8
0.3
0.3
4
10
4
27
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
11
5
27
10
A t o Z Tire
Lt. Pl./CB
Lt P L
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
12
6
27
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
13
1
28
10
A t o Z Tire
Lt. Pl./CB
Lt P L
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
14
2
28
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
15
3
28
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
16
4
28
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
17
5
28
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
18
6
28
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
19
1
29
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
20
2
29
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
0.254
1.8
0.3
0.3
21
3
29
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
22
4
29
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
23
5
29
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
24
6
29
10
A t o Z Tire
Lt. Pl./CB
Lt P L
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
25
1
30
10
A t o Z Tire
Lt. Pl./CB
Lt P L
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
26
2
30
10
A t o Z Tire
Lt. Pl./CB
Lt P L
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
27
3
30
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
28
4
30
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
29
5
30
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
30
6
30
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
31
1
31
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
32
2
31
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
33
3
31
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
34
4
31
10
A t o Z Tire
Lt. Pl./CB
Lt P L
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
35
5
31
10
A t o Z Tire
Lt. Pl./CB
Lt P L
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
36
6
31
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
37
1
32
10
A t o Z Tire
Lt. P l . / C B
Lt PL
38
2
32
10
A t o Z Tire
Lt. P l . / C B
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
39
3
32
10
A t o Z Tire
Lt. P l . / C B
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
40
4
32
10
A t o Z Tire
Lt. Pl./CB
Lt P L
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
41
5
32
10
A t o Z Tire
Lt. P l . / C B
Lt P L
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
6
32
10
A t o Z Tire
Lt. P l . / C B
Lt P L
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
43
1
33
10
A t o Z Tire
Lt. Pl./CB
Lt P L
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
44
2
33
10
A t o Z Tire
Lt. P l . / C B
Lt P L
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
45
3
33
10
A t o Z Tire
Lt. Pl./CB
Lt P L
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
42
185
46
4
33
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
47
5
33
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
48
6
33
10
A t o Z Tire
Lt. Pl./CB
Lt PL
0.35
0.254
1.38
0.46
0.254
1.8
0.3
0.3
1.8
0.3
0.3
Final input averages:
1.38
APPENDIX C
DETAILS OF APRIL 2008 ENVI-MET SIMULATIONS
187
FINAL AREA INPUT FILES
24th St
Buildings and vegetation
9
10
II
12
13
14
15
16
17
IS
19
20
21
22
23
24
25
26
27
20
29
30
31
32
33
34
35
36
3?
38
B
40
41
«
43
44
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Building heights (m) and footprints are illustrated
Buildings in light grey indicate overhangs
Vegetation id's in green, with detailed information found in Appendix B
Red points and numbers indicate mobile sampling locations, as well as receptor
points, used in validation of the model
188
24 th St
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189
1 st Ave
Buildings and vegetation
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Key
Building heights (m) and footprints are illustrated
Buildings in light grey indicate overhangs
Vegetation id's in green, with detailed information found in Appendix B
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190
1 st Ave
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191
43 rd Ave
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Building heights (m) and footprints are illustrated
Buildings in light grey indicate overhangs
Vegetation id's in green, with detailed information found in Appendix B
Red points and numbers indicate mobile sampling locations, as well as receptor
points, used in validation of the model
192
43 rd Ave
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FINAL CONFIGURATION FILES
24th St
%
ENVI-met Configuration File Vl.l
%
%MAIN-DATA
—
Name for Simulation (Text):
=Sky Harbor AprNew final
Input file Model Area
=[INPUT]\NewSHfinal.in
Filebase name for Output (Text):
=SkyHarbor_AprNew_final
Output Directory:
=[OUTPUT]
Start Simulation at Day (DD.MM.YYYY):
=04.04.2008
Start Simulation at Time (HH:MM:SS):
=09:00:00
Total Simulation Time in Hours:
=24.00
Save Model State each ? min
=60
Wind Speed in 10 m ab. Ground [m/s]
=1.5
Wind Direction (0:N..90:E..180:S..270:W..)
=90
Roughness Length zO at Reference Point
=0.1
Initial Temperature Atmosphere [K]
=293.7
Specific Humidity in 2500 m [g Water/kg air]
=1.7
Relative Humidity in 2m [%]
=24
Database Plants
=Plants.dat
( End of Basic Data )
( Following: Optional data. The order of sections is free.)
( Missing Sections will keep default data.)
[POSITION]
Longitude (+:east -:west) in dec. deg:
Latitude (+:northern ^southern) in dec.deg:
Longitude Time Zone Definition:
Where the area is located on earth
=-112.0
=33.4
=-112.0
[SOILDATA]
Initial Temperature Upper Layer (0-20 cm) [K]
Initial Temperature Middle Layer (20-50 cm) [K]
Initial Temperature Deep Layer (below 50 cm) [K]
Relative Humidity Upper Layer (0-20 cm)
Relative Humidity Middle Layer (20-50 cm)
Relative Humidity Deep Layer (below 50 cm)
=292
=294
=293
=24
=35
=40
[TIMESTEPS]
Sun height for switching dt(0) -> dt( 1)
Sun height for switching dt( 1) -> dt(2)
Time step (s) for interval 1 dt(0)
Time step (s) for interval 2 dt(l)
Time step (s) for interval 3 dt(2)
=25
=30
=10.0
=5.0
=2.0
[TURBULENCE]
Turbulence Closure ABL (0:diagn.,l:prognos.)
Turbulence Closure 3D Model (0:diag.,l:prog)
Upper Boundary for e-epsilon (0:clsd., Lop.)
=1
=1
=0
Settings for Soil
Dynamical Timesteps
Options Turbulence Model
[BUILDING]
Inside Temperature [K]
Heat Transmission Walls [W/m 2 K]
Heat Transmission Roofs [W/m 2 K]
Albedo Walls
Albedo Roofs
Building properties
= 297
= 1.84
=4.46
=0.27
=0.3
[NESTINGAREA]
Use aver, solar input in nesting area (0:n, l:y)
Include Nesting Grids in Output (0:n, 1 :y)
= 1
[PMV]
Walking Speed (m/s)
Energy-Exchange (Col. 2 M/A)
Mech. Factor
Heat transfer resistance cloths
[PLANTMODEL]
Stomata res. approach (1 =Deardorff, 2=A-gs)
Background C 0 2 concentration [ppm]
[CLOUDS]
Fraction of LOW clouds (x/8)
Fraction of MEDIUM clouds (x/8)
Fraction of HIGH clouds (x/8)
[TIMING]
Update Surface Data each ? sec
Update Wind field each ? sec
Update Radiation and Shadows each ? sec
Update Plant Data each ? sec
Settings for nesting
=0
Settings for PMV-Calculation
=0.3
= 116
=0.0
=0.5
Settings for plant model
=2
=350
=0
=0
=0
Update & Save Intervals
=30.0
=900
=600
=600
[RECEPTORS]
% —remove line above if your receptors are in the area input fileSave Receptors each ? min
=60.0
195
1 st Ave
%
ENVI-met Configuration File VI. 1
%
%MAIN-DATA
Name for Simulation (Text):
=lst Ave April New Soils
Input file Model Area
=[INPUT]\Newl stAveSoils.in
Filebase name for Output (Text):
=lstAve_April_New_Soils
Output Directory:
=[OUTPUT]
Start Simulation at Day (DD.MM.YYYY):
=04.04.2008
Start Simulation at Time (HH:MM:SS):
=10:00:00
Total Simulation Time in Hours:
=24.00
Save Model State each ? min
=60
Wind Speed in 10 m ab. Ground [m/s]
=1.5
Wind Direction (0:N..90:E..180:S..270:W..)
=90
Roughness Length zO at Reference Point
=0.1
Initial Temperature Atmosphere [K]
=295.9
Specific Humidity in 2500 m [g Water/kg air]
=1.7
Relative Humidity in 2m [%]
=17
Database Plants
=Plants.dat
( End of Basic Data )
( Following: Optional data. The order of sections is free.)
( Missing Sections will keep default data.)
[POSITION]
Longitude (+:east -:west) in dec. deg:
Latitude (+:northern -southern) in dec.deg:
Longitude Time Zone Definition:
[SOILDATA]
Initial Temperature Upper Layer (0-20 cm) [K]
Initial Temperature Middle Layer (20-50 cm) [K]
Initial Temperature Deep Layer (below 50 cm) [K]
Relative Humidity Upper Layer (0-20 cm)
Relative Humidity Middle Layer (20-50 cm)
Relative Humidity Deep Layer (below 50 cm)
[TIMESTEPS]
Sun height for switching dt(0) -> dt( 1)
Sun height for switching dt( 1) -> dt(2)
Time step (s) for interval 1 dt(0)
Time step (s) for interval 2 dt(l)
Time step (s) for interval 3 dt(2)
[TURBULENCE]
Turbulence Closure ABL (0:diagn., 1 :prognos.)
Turbulence Closure 3D Model (0:diag.,l :prog)
Upper Boundary for e-epsilon (0:clsd., Lop.)
Where the area is located on earth
=-112.00
=33.4
=-112.0
Settings for Soil
=295
=294
=293
= 17
=30
=40
Dynamical Timesteps
=25
=30
=5.0
=2.0
= 1.0
Options Turbulence Model
=1
=1
=0
[BUILDING]
Inside Temperature [K]
Heat Transmission Walls [W/m 2 K]
Heat Transmission Roofs [W/m 2 K]
Albedo Walls
Albedo Roofs
Building properties
= 297
=3
=4
=0.2
=0.3
[NESTINGAREA]
Use aver, solar input in nesting area (0:n, 1 :y)
Include Nesting Grids in Output (0:n,l :y)
= 1
[PMV]
Walking Speed (m/s)
Energy-Exchange (Col. 2 M/A)
Mech. Factor
Heat transfer resistance cloths
[PLANTMODEL]
Stomata res. approach (l=Deardorff, 2=A-gs)
Background C 0 2 concentration [ppm]
[CLOUDS]
Fraction of LOW clouds (x/8)
Fraction of MEDIUM clouds (x/8)
Fraction of HIGH clouds (x/8)
[TIMING]
Update Surface Data each ? sec
Update Wind field each ? sec
Update Radiation and Shadows each ? sec
Update Plant Data each ? sec
Settings for nesting
=0
Settings for PMV-Calculation
=0.3
= 116
=0.0
=0.5
Settings for plant model
=2
=350
=0
=0
=0
Update & Save Intervals
=30.0
=900
=600
=600
[RECEPTORS]
% —remove line above if your receptors are in the area input fileSave Receptors each ? min
=60.0
197
43 rd Ave
%
ENVI-met Configuration File VI. 1
%
%MAIN-DATA
—
Name for Simulation (Text):
=43rd Ave AprNew finalv5
Input file Model Area
=[INPUT]\New43rdAvefinalv3.in
Filebase name for Output (Text):
=43rdAve_AprNew_finalv5
Output Directory:
=[OUTPUT]
Start Simulation at Day (DD.MM.YYYY):
=04.04.2008
Start Simulation at Time (HH:MM:SS):
=09:00:00
Total Simulation Time in Hours:
=24.00
Save Model State each ? min
=60
Wind Speed in 10 m ab. Ground [m/s]
=2.0
Wind Direction (0:N..90:E..180:S..270:W..)
=180
Roughness Length zO at Reference Point
=0.1
Initial Temperature Atmosphere [K]
=293.7
Specific Humidity in 2500 m [g Water/kg air]
=1.7
Relative Humidity in 2m [%]
=26
Database Plants
=Plants.dat
( End of Basic Data )
( Following: Optional data. The order of sections is free.)
( Missing Sections will keep default data.)
[POSITION]
Longitude (+:east -:west) in dec. deg:
Latitude (+:northern ^southern) in dec.deg:
Longitude Time Zone Definition:
[SOILDATA]
Initial Temperature Upper Layer (0-20 cm) [K]
Initial Temperature Middle Layer (20-50 cm) [K]
Initial Temperature Deep Layer (below 50 cm) [K]
Relative Humidity Upper Layer (0-20 cm)
Relative Humidity Middle Layer (20-50 cm)
Relative Humidity Deep Layer (below 50 cm)
[TIMESTEPS]
Sun height for switching dt(0) -> dt(l)
Sun height for switching dt(l) -> dt(2)
Time step (s) for interval 1 dt(0)
Time step (s) for interval 2 dt(l)
Time step (s) for interval 3 dt(2)
[TURBULENCE]
Turbulence Closure ABL (0:diagn.,l :prognos.)
Turbulence Closure 3D Model (0:diag.,l:prog)
Upper Boundary for e-epsilon (0:clsd., 1 :op.)
Where the area is located on earth
=-112.2
=33.4
=-112.0
Settings for Soil
=292
=294
=293
=24
=35
=40
Dynamical Timesteps
=25
=30
=10.0
=5.0
=2.0
Options Turbulence Model
=1
=1
=0
[BUILDING]
Inside Temperature [K]
Heat Transmission Walls [W/m 2 K]
Heat Transmission Roofs [W/m 2 K]
Albedo Walls
Albedo Roofs
[NESTINGAREA]
Use aver, solar input in nesting area (0:n, 1 :y)
Include Nesting Grids in Output (0:n,l :y)
[PMV]
Walking Speed (m/s)
Energy-Exchange (Col. 2 M/A)
Mech. Factor
Heat transfer resistance cloths
[PLANTMODEL]
Stomata res. approach (l=Deardorff, 2=A-gs)
Background C 0 2 concentration [ppm]
[CLOUDS]
Fraction of LOW clouds (x/8)
Fraction of MEDIUM clouds (x/8)
Fraction of HIGH clouds (x/8)
[TIMING]
Update Surface Data each ? sec
Update Wind field each ? sec
Update Radiation and Shadows each ? sec
Update Plant Data each ? sec
Building properties
= 297
=1.38
= 1.81
=0.3
=0.3
Settings for nesting
=1
=0
Settings for PMV-Calculation
=0.3
= 116
=0.0
=0.5
Settings for plant model
=2
=350
=0
=0
=0
Update & Save Intervals
=30.0
=900
=600
=600
[RECEPTORS]
% —remove line above if your receptors are in the area input f i l e Save Receptors each ? min
=60.0
199
WEATHER STATION SITES USED FOR MODEL INITIALIZATIONS
Sky Harbor Airport NWS ASOS
This image from Google Earth illustrates the approximate location of the Phoenix Sky
Harbor NWS ASOS weather station (indicated by black & red point), as well as the 300 x
300 m ENVI-met domain (indicated by red rectangle) used to test and validate
initializations for simulations run at both 24th St and 1st Ave during the 24 hr 4-5 April
2008 study
200
Kay PRISMS
This image from Google Earth illustrates the approximate location of the Kay PRISMS
weather station (indicated by black & red point), as well as the 200 x 200 m ENVI-met
domain (indicated by red rectangle) used to test and validate initializations for
simulations run at 43 rd Ave during the 24 hr 4-5 April 2008 study
APPENDIX D
SELECT APRIL 2008 HELICOPTER THERMOGRAPHY IMAGES
202
IR thermal imagery and temperature readings within ENVI-met domains (4 April 2008)
14:00 LST
6t.9°C
IU
*
24th St - centered on mobile sampling point #4
1st Ave - centered on mobile sampling point #54
19:00 LST
1st Ave - centered on mobile sampling point #54
206
22:00 LST
28.0°C
.r 28
24 St - centered on mobile sampling point #4
1st Ave - centered on mobile sampling point #54
APPENDIX E
SEASONAL OUTDOOR HUMAN COMFORT (PMV) OUTPUTS
209
11 January 2008 - 14:00 LST
below -0.53
- 0 . 5 3 to -0.38
-0.38 to - 0 . 2 3
- 0 . 2 3 to - 0 . 0 8
- 0 . 0 8 to 0 . 0 7
0 . 0 - to 0.22
0.22 to 0 . 3 "
0.37 to 0.52
0.52 to 0.66
above 0.66
below -0.58
- 0 . 5 8 to - 0 . 4 4
- 0 . 4 4 to - 0 . 2 9
- 0 . 2 9 to - 0 . 1 5
- 0 . 1 5 to - 0 . 0 0
- 0 . 0 0 to 0 . 1 4
0.14 to 0.28
0 . 2 8 to 0 . 4 3
0 . 4 3 to 0.57
above 0.5"
below -0.55
- 0 . 5 5 to - 0 . 4 1
- 0 . 4 1 to - 0 . 2 6
-0.26 to-0.12
- 0 . 1 2 to 0 . 0 3
0.03 to 0.1"
0.1" to 0.32
0 32 to 0 46
0 46 to 0.61
a b o v e 0.61
210
11 January 2008 - 14:00 LST
below -1,22
ft
. 1
- 1 . 2 2 to - 1 . 1 9
i
I
•
-
«l
- 1 1 9 to - 1 . 1 6
#
- 1 . 1 6 to - 1 . 1 2
-1.12 t o - 1 . 0 9
I
I
I
I
-1.09 to-1,06
-1.06 to-1.03
- 1 . 0 3 to - 0 . 9 9
- 0 . 9 9 to - 0 . 9 6
above -0.96
I
E
C
U
Z
- 1 2 1 to - 1 . 1 8
- 1 . 1 8 to - 1 . 1 5
J
-1.15 t o - 1 . 1 2
211
11 January 2008 - 14:00 LST
below -1.34
- 1 . 3 4 to -1 31
| . .(J
- 1 . 3 1 to - 1 . 2 8
- 1 . 2 8 t o -1 2 5
- 1 . 2 5 to - 1 . 2 3
- 1 . 2 3 to -1.20
- 1 . 2 0 to - 1 . 1 T
- 1 . 1 " t o - 1 14
TL
-1.14 to-1.11
above - I II
below -1.3"
- 1 . 3 " to - 1 . 3 2
-1.32 t o - 1 . 2 6
-1.26 to-1.21
-1.21 t o - 1 . 1 6
- 1 . 1 6 to - 1 . 1 1
- 1 . 1 1 to -1 0 6
-1.06 to-1.01
- 1 . 0 1 to - 0 . 9 6
above -0.96
212
4 April 2008 - 14:00 LST
below 0.96
0 . 9 6 to 1 . 2 6
1.26 to 1 . 5 6
1 . 5 6 to 1.87
1 . 8 - to 2 . 1 7
2.17 to 2 . 4 8
2.48 to 2.78
2 . 7 8 to 3 . 0 8
3 . 0 8 to 3 . 3 9
above 3.39
below 0.65
0 . 6 5 to 0 . 9 6
0 . 9 6 to 1 . 2 "
1.27 to 1 . 5 9
1.59 to 1.90
1 . 9 0 t o 2.21
2.21 t o 2 . 5 2
2.52 to 2.84
2.84 to 3.15
above 3.15
below 0.65
0 . 6 5 to 0 . 9 6
0 . 9 6 to 1.27
1 . 2 " to 1 . 5 8
1.58 to 1.88
1 . 8 8 t o 2 19
2 . 1 9 to 2 . 5 0
2.50 to 2.81
2.81 t o 3.11
a b o v e 3.11
213
4 April 2008 - 14:00 LST
214
4 April 2008 - 14:00 LST
b e l o w -1 01
-1.01 t o - 0 . 9 6
- 0 . 9 6 to - 0 91
- 0 . 9 1 to - 0 . 8 6
- 0 . 8 6 to - 0 . 8 0
- 0 . 8 0 to - 0 . 7 5
-0.75 t o - 0 . 7 0
- 0 . 7 0 to - 0 . 6 5
- 0 . 6 5 to - 0 . 6 0
above -0.60
below -0.9"
- 0 . 9 7 to - 0 . 8 9
-0.89 to-0.81
- 0 . 8 1 to - 0 . 7 4
- 0 . 7 4 to - 0 . 6 6
- 0 . 6 6 to - 0 . 5 8
- 0 . 5 8 to - 0 . 5 1
-0.51 to - 0 . 4 3
- 0 . 4 3 to - 0 . 3 5
above - 0 . 3 5
1
below -1.35
-1.35 t o - 1 . 2 9
-1.29 to-1.24
- 1 . 2 4 to - 1 . 1 8
-1.18 t o - 1 . 1 2
-1.12 t o - 1 . 0 7
-1.07 t o - 1 . 0 1
- 1 . 0 1 to - 0 . 9 5
- 0 . 9 5 to - 0 . 9 0
above - 0 . 9 0
June 2008 -14:00
LST
5.5-4 to ft 2 o
r==•]
6
-0'O6.S-
6 . 8 - to
5 3
8
-.53
to 8 . 1 9
19 to 8 . 8 6
8 6 to 9 . 5 2
a b o v e 9.5-1
5 3 to 9 1 0
9
9
10 to
9.68
68 to 10.26
above 10.26
216
27 June 2 0 0 8 - 19:00 LST
217
27 June 2 0 0 8 - 2 2 : 0 0 LST
218
31 July 2 0 0 8 - 14:00 LST
below 6.04
6 . 0 4 to 6 . 7 4
6.74 to 7.44
7.44 to 8.13
8 13 t o 8 . 8 3
8.83 to 9.52
9.52 to 10.22
10.22 to 10.92
1 0 . 9 2 t o 11.61
a b o v e 11.61
b e l o w 4.97
4.97 to 5 . 7 0
5 . 7 0 to 6 . 4 4
6 . 4 4 to 7 . 1 8
7.18 to 7 91
7.91 to 8 . 6 5
8.65 to 9.39
9 3 9 to 10 12
10.12 to 10.86
above 10.86
below 6.60
6 . 6 0 to 7 . 1 9
" 19 t o 7 . 7 8
7.78 to 8 . 3 6
8 36 to 8.95
8.95 to 9.53
9 . 5 3 to 1 0 . 1 2
10 1 2 t o 1 0 . 7 1
10.71 t o 1 1 . 2 9
above 11.29
219
31 July 2 0 0 8 - 19:00 LST
below 3.29
3.29 to 3.42
3 . 4 2 to 3 . 5 5
3.55 to 3.68
3 . 6 8 t o 3.81
3.81 t o 3 . 9 4
3.94 to 4.06
4.06 to 4.19
4.19 to 4.32
above 4.32
below 3.14
3.14 to 3.34
3.34 to 3.54
3 . 5 4 to 3. " 3
3.73 to 3.93
3.93 to 4.12
4.12 to 4.32
4.32 to 4.52
4 . 5 2 to 4 . " 1
above 4
T
1
b e l o w 3 19
3.19 to 3.31
3.31 to 3.44
3.44 to 3.57
3 . 5 " to 3 . 6 9
3.69 to 3.82
3.82 to 3.95
3.95 to 4.0"
4 . 0 - to 4.20
above 4.20
220
31 July 2 0 0 8 - 2 2 : 0 0 LST
below 2 . 7 0
2."0to2,81
2.81 t o 2 . 9 2
2 . 9 2 to 3 . 0 3
3 . 0 3 to 3 . 1 4
3.14 to 3.25
3.25 to 3.36
3 . 3 6 10 3 . 4 7
3.4" to 3.58
above 3.58
b e l o w 2. " 3
2.73 to 2.90
2.90 to 3 . 0 7
3.07 to 3.23
3.23 to 3.40
3.40 to 3.57
3.57 to 3 . " 4
3 . 7 4 t o 3.91
3.91 t o 4 . 0 7
above 4.0"
below 2.50
2.50 to 2.62
2.62 to 2.73
2. " 3 t o 2 . 8 4
2.84 to 2.95
2.95 to 3.07
3.07 to 3.18
3.18 to 3.29
3 . 2 9 to 3.41
above 3.41
221
15 October 2008 - 19:00 LST
1
below 0.2"
0 . 2 7 to 0 . 6 3
TH
0 . 6 3 to 0 . 9 9
"I.
0 . 9 9 to 1 . 3 4
1.34 t o 1.70
1.70 to 2 . 0 5
I
1 "1
T1
1
I T -
1
2 . 0 5 t o 2.41
l
2.41 t o 2 . 7 6
2 . 7 6 to 3 . 1 2
above 3.12
below -0.09
- 0 . 0 9 to 0 . 2 9
0.29 to 0.66
0 . 6 6 to 1 . 0 3
1.03 t o 1 . 4 0
1.40 to 1.78
1.78 to 2.15
2.15 to 2.52
2.52 to 2.89
above 2.89
below -0.05
- 0 . 0 5 to 0 . 3 3
0.33 to 0.70
0 . 7 0 to 1.07
1.07 to 1.44
1.44 to 1.82
1.82 to 2.19
2.19 to 2.56
2.56 to 2.93
above 2.93
222
15 October 2008 - 19:00 LST
below -1.75
-1.75 to -1.68
-1.68 to -1.61
-1.61 to -1.54
-1.54 t o - 1 . 4 "
- 1 . 4 " to -1.40
-1.40 t o - 1 . 3 3
-1.33 to-1.26
-1.26 t o - 1 . 1 9
above - 1 . 1 9
223
15 October 2008 - 19:00 LST
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