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ENGINEERING URBAN TRANSPORATION INFRASTRUCTURE TO MITIGATE THERMAL POLLUTION IN STORMWATER RAINFALL-RUNOFF USING SOURCE
CONTROL METHODS
By
RUBEN ALEXANDER KERTESZ
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2011
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© 2011 Ruben Kertesz
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To everybody who has encouraged me and supported my desire to explore our relationship in the global environment and to God for giving me the chance to share it
with others.
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ACKNOWLEDGMENTS
I thank my family for supporting my move into engineering. I thank Dr. Lindner for
bringing me to the University of Florida and I thank Dr. Heaney for encouraging me to
build my understanding of water conservation and computational techniques. I thank Dr.
Sansalone for allowing me to take classes to become a licensed engineer and for
encouraging me to pursue thermal pollution. I thank Dr. Huber for his guidance and
flexibility. I thank Dr. Bloomquist for his instruction and his enlightening comments. I
thank John Mocko for giving me access to campus weather data and to Demetris
Athienitis for assistance in statistical analysis. I thank the Florida Education Fund for
providing financial support. I thank my lab mates, my friends, and my significant other
who have listened to me share my findings.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 9
LIST OF ABBREVIATIONS ........................................................................................... 12
ABSTRACT ................................................................................................................... 13
CHAPTER
1 GLOBAL INTRODUCTION ..................................................................................... 15
2 HYDROLOGIC TRANSPORT AND FIRST FLUSH OF THERMAL LOAD FROM ASPHALTIC PAVEMENT ....................................................................................... 17
Background ............................................................................................................. 17
Objectives ............................................................................................................... 19 Methodology ........................................................................................................... 19
Data Collection Methods .................................................................................. 20
Calculation Methods for Temporal Distribution of Heat Transfer to Runoff During Event ................................................................................................. 21
Method Components of Heat Balance Models ................................................. 22 Radiation .................................................................................................... 22 Heat loss by evaporation............................................................................ 24
Sensible heat loss ...................................................................................... 25 Heat loss by convection ............................................................................. 25
Substitution of Runoff Temperature for Pavement Surface Temperature ......... 26 Results and Discussion........................................................................................... 26
Heat Transfer to Runoff during an Event .......................................................... 26 Impact of hydrologic parameters on heat transfer ...................................... 27 Relationship between antecedent pavement temperature and heat
transfer ................................................................................................... 28 Impact of event date and start time on heat transfer .................................. 29
Heat Balance Model Comparison ..................................................................... 29 Discussion .............................................................................................................. 31 Summary ................................................................................................................ 33
3 CYCLIC TEMPERATURE PROFILES FOR ASPHALTIC PAVEMENT AS A FUNCTION OF TREE CANOPY SHADING AND VEHICULAR PARKING FREQUENCY ......................................................................................................... 49
Background ............................................................................................................. 49
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Objective ................................................................................................................. 51 Methodology ........................................................................................................... 51
Parking Stall Data Collection Methods ............................................................. 52
Simulated Driving Activity Data Collection ........................................................ 54 Tree Canopy Shade Data Collection Methods ................................................. 55
Results and Discussion........................................................................................... 57 Thermal Results of Parking Stall Shade Treatments ........................................ 57 Pavement Temperature Shift Under Simulated Parking Activity ....................... 58
Thermal Trends on Shaded Roadway .............................................................. 61 Summary ................................................................................................................ 64
4 MITIGATING URBAN HEAT: TEMPORAL TEMPERATURE PROFILES FOR PAVEMENT MATERIALS ....................................................................................... 81
Background ............................................................................................................. 81 Objective ................................................................................................................. 83
Methodology ........................................................................................................... 84 Data Collection Methods .................................................................................. 84
CFD Model Components of Heat Transfer with Solar Radiation ...................... 86 Simulation Methods for Temporal Distribution of Heat Transfer Under Solar
Radiation ....................................................................................................... 89
Results and discussion ........................................................................................... 90 Measured Heat Balance on Pavement ............................................................. 90
Heat Balance Simulation Model ....................................................................... 97 Summary ................................................................................................................ 98
5 COMPUTATIONAL MODELING OF OVERLAND FLOW AND HEAT TRANSFER IN ASPHALTIC PAVEMENTS .......................................................... 116
Background ........................................................................................................... 116
Objective ............................................................................................................... 120 Methodology ......................................................................................................... 120
Physical Experiments ..................................................................................... 121 Modeling Methodology ................................................................................... 123
Heat Transfer Calculation of Flow Over a Flat Plate ...................................... 128 Results and Discussion......................................................................................... 130 Summary .............................................................................................................. 135
6 GLOBAL CONCLUSION ....................................................................................... 146
LIST OF REFERENCES ............................................................................................. 149
BIOGRAPHICAL SKETCH .......................................................................................... 159
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LIST OF TABLES
Table page 2-1 Selected properties of asphalt pavement from various studies .......................... 35
2-2 Storm event data for measured rainfall events and Kolomogorov-Smirnov test for goodness of fit ........................................................................................ 36
2-3 Correlations between storm event parameters. .................................................. 37
2-4 Tabular pavement and subgrade temperature profiles at beginning and end of storm. ............................................................................................................. 38
2-5 Total NHT for various modeling methods compared to measured values. Negative values represent heat gain by pavement. ............................................ 38
3-1 Weather conditions during 18 September and 19 September calibration days. . 65
3-2 Weather data during parking experiment performed on 4 October, 2010. .......... 65
3-3 Parametric statistics for hysteretic loop equations for 19 October, 2010 experiment. ......................................................................................................... 66
3-4 Parametric statistics for hysteretic loops equations for 28 October, 2010 experiment. ......................................................................................................... 66
3-5 Hourly asphalt pavement temperatures across east-west transect. ................... 67
3-6 Daily solar radiation, air temperature, wind, and shadow patterns. .................... 68
3-7 Shadow patterns over transect, measured from west curb ................................. 69
3-8 Average annual benefits of four tree sizes over 40 year period. ......................... 69
4-1 Thermal and physical properties of pavement .................................................. 100
4-2 Model parameters for computational simulation ............................................... 100
4-3 Properties of air and expanded polystyrene (EPS) ........................................... 100
4-4 Median values of pavement heat cycle for all measured days. ........................ 101
4-5 Integration of pavement heat cycle heat for 8 September to 10 September. .... 101
5-1 Thermal and physical properties of pavement .................................................. 136
5-2 Material parameters used in computational fluid dynamics simulation ............. 137
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5-3 Model parameters for computational simulation ............................................... 138
5-4 Analysis of error between modeled and measured results. .............................. 139
5-5 Analysis of error between modeled and measured results with implicit body force and specified operating density. .............................................................. 139
5-6 Analysis of error between modeled and measured results with 50% evaporation/condensation threshold. ................................................................ 140
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LIST OF FIGURES
Figure page 2-1 Historical monthly distribution of weather data for Gainesville, FL and
Portland, OR ....................................................................................................... 39
2-2 Lake Alice watershed including subject catchment (~450 m2). ........................... 39
2-3 Plan and cross-sectional view of thermocouples (TC) for catchment pavement system in Lake Alice watershed. ........................................................ 40
2-4 Conceptual pavement heat balance model with nominal thermocouple installation depths. .............................................................................................. 40
2-5 Low flow rate storm event data recorded on June 23, 2008. .............................. 41
2-6 Moderate flow rate storm event data recorded on June 30, 2008 ...................... 42
2-7 Storm event data recorded on August 21, 2008 (Tropical Storm Fay). ............... 43
2-8 Distributions of cumulative heat and cumulative flow for 12 storms that are similar according to K-S tests ............................................................................. 44
2-9 Modeled storm event data showing only best fit models for A) 14 July 2008 and B) 12 August 2008. ...................................................................................... 45
2-10 Modeled storm event data showing only best fit models for A) 21 August 2008 and B) September 10 2008 ........................................................................ 46
2-11 Residual values for four models.. ....................................................................... 47
2-12 Median temperature at two depths in a 38mm asphalt pavement with a forced wind velocity of 2.2 m/s over the pavement surface. ............................... 48
3-1 Lake Alice watershed including parking lot catchment, transect, and parking spaces investigated herein. ................................................................................ 70
3-2 Vehicle body and asphalt surface thermocouple installation diagram.. .............. 71
3-3 Vehicular surface temperatures measured in direct sunlight for the A) roof, B) hood, and C) trunk during calibration period. ...................................................... 72
3-4 Pavement surface temperatures beneath engine (front) and gas tank (rear) of vehicles A and B exposed to direct sunlight during calibration period. ............... 73
3-5 Comparison of average surface and pavement temperatures between shaded and unshaded vehicles between the hours of 10:00 and 17:00. ............ 74
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3-6 Pavement temperature A) before, B) during, and C) after driving test vehicle to observe effect of warm engine on 4 October, 2010. ....................................... 75
3-7 Pavement surface temperature under frequent parking A) on 19 October and B) on 28 October ................................................................................................ 76
3-8 Pavement surface temperature hysteretic loops on 19 October 2010 beneath front and rear of vehicle. Three cycles are shown. ............................................. 77
3-9 Pavement surface temperature hysteretic loops on 28 October 2010 beneath front and rear of vehicle. Three cycles are shown. ............................................. 78
3-10 Graphic analysis of shadow patterns over pavement surface for daytime hours. ................................................................................................................. 79
3-11 Plot of heat transfer to runoff compared to pavement temperature before storm. ................................................................................................................. 80
4-1 Comparison of rainfall pattern frequency by hour from 10 years of hourly rainfall data collected in two climates in the United States. .............................. 102
4-2 Schematic of simulation geometry.. .................................................................. 103
4-3 Comparison of temperatures at surface and interior of pavements, 15 September, 2010. ............................................................................................. 104
4-4 Relative distribution of rainfall event occurrence and total rainfall depth by day-hour during the rainy season in Gainesville, FL. ........................................ 105
4-5 Mean hourly temperature and heat absorption with standard deviation. KJ are per unit area 1m2. ....................................................................................... 106
4-6 Relative impact index (RII) for pavement heat storage reduction in Gainesville, FL (negative is better). .................................................................. 107
4-7 Comparison of cumulative heat storage in pavement and atmospheric conditions between 8 September and 11 September, 2010.. ........................... 108
4-8 Comparison of pavement temperature before, during, and after two rain events of differing intensity and time of day. ..................................................... 109
4-9 Comparison of thermal heating pattern on two dry days of differing radiation on A) 17 September and B) 10 September ...................................................... 110
4-10 Concrete temperature and asphalt temperature at A) east side of road and B) west side of road; C) difference between concrete and asphalt at both locations ........................................................................................................... 111
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4-11 Modeled pavement temperature for control asphalt and white asphalt pavements on 18 August, 2010. ....................................................................... 112
4-12 Comparison of modeled pavement temperature results under for current, low, and high thermal conductivity (k) values for reflective asphalt simulation. ........ 113
4-13 Measured vs. modeled asphalt temperatures for two days in August, 2010. .... 114
4-14 A comparison of measured and modeled asphalt and concrete temperatures on 6 September, 2010. ..................................................................................... 115
5-1 Installation of thermocouples in pavement specimen ....................................... 141
5-2 CFD mesh dimensions and statistics. ............................................................... 142
5-3 Measured and modeled asphalt specimen temperature and effluent temperature. ..................................................................................................... 143
5-4 Measured and modeled concrete specimen temperature and effluent temperature.. .................................................................................................... 144
5-5 Effluent temperature modeled using flat plate method. .................................... 145
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LIST OF ABBREVIATIONS
BMP best management practice
CDF cumulative distribution function
CFD computational fluid dynamics
EPS expanded polystyrene
EST eastern standard time
FDA functional data analysis
FEA finite element analysis
HRIC high resolution interface capturing
HSPF hydrologic simulation program in fortran
LID Low Impact Development
NHT Net Heat Transfer
PIP Peak Insolation Period
PISO pressure-implicit with splitting operators
PRESTO pressure staggering option
QUICK quadratic upwind interpolation
RHT relative heat transfer
RMSE root mean squared error
RPD relative percent difference
RPE relative percent error
TC thermocouple
TMDL total maximum daily load
TRMPAVE thermal runoff model for pavement
TURM thermal urban runoff model
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
ENGINEERING URBAN TRANSPORATION INFRASTRUCTURE TO MITIGATE
THERMAL POLLUTION IN STORMWATER RAINFALL-RUNOFF USING SOURCE CONTROL METHODS
By
Ruben A. Kertesz
May 2011
Chair: Sansalone Major: Environmental Engineering Sciences
Research in the field of thermal pollution in urban areas has traditionally been
relegated to studies on the urban heat island effect or global climate change. Little
research has been performed to test for the effect of pavement temperature on
stormwater runoff. The research presented herein focuses on the measurement and
simulation of heat transfer to pavement by radiation and of heat transfer from the
pavement to rainfall-runoff. Four studies are performed to provide an understanding of
the mechanisms to limit thermal pollution.
The first study involves the measurement and simulation of heat transfer to
rainfall-runoff from an in-situ parking lot surface. Results from applying a series of
published heat balance models indicate that evaporation and long wave radiation are
important runoff event-based heat transfer mechanisms. The second study is designed
to determine the effect of shading and vehicular activity on pavement surface
temperature in an asphaltic parking lot. Results show that pavement temperature does
not differ significantly beneath a shaded and an unshaded vehicle, that there is a
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demonstrable effect of vehicle operation on pavement temperature, and that it is most
critical to shade pavement during the daily peak insolation period.
The third study provides a thermal comparison between the daytime temperatures
of three pavement specimens of differing material selection and surface treatments. A
computational analysis is compared to measured data. CFD model results are not
statistically significantly different from measured data for each pavement material.
Results indicate that adding a reflective coating to asphalt or utilizing concrete in lieu of
asphalt results in a 20% reduction in pavement heat load through the day. Concrete
pavement stores up to 55% less heat than asphalt between 12:00 and 19:00.
The fourth study investigates the applicability of a computational fluid dynamics
simulation to model heat transfer to overland flow from two pavement surfaces with the
intent of enhancing knowledge of the rainfall-runoff heat transfer relationships for
various pavement mix designs. Results from 300 seconds of simulation are compared
to measured results. Findings indicate that evaporation may only be critical within the
first seconds of runoff. The best CFD result is exhibited by the turbulent concrete
simulation with a 50% air/water threshold for evaporation/condensation to occur.
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CHAPTER 1 GLOBAL INTRODUCTION
The series of investigations herein are developed as an exploration into the
contribution of urban rainfall-runoff pollution from urban surfaces. Akbari et al. (2003)
reported that pavement covers 29% of Houston and 45% of Sacramento with 60% and
29% of these areas attributed to parking, respectively. Converting vegetated areas to
impervious areas reduces groundwater-fed streamflow, compounding thermal impacts
(Janke et al. 2009; Ferguson and Suckling 1990; Leith and Whitfield 2000; Horner et al.
1994).
Much research has already been performed on nutrient, metal, and hydrocarbon
pollution sources. Various treatment mechanisms have been proposed, some of which
are commonly used today. The most commonplace mechanisms involve temporarily or
permanently impounding water, allowing various physical and chemical processes to
remove pollution from receiving waters. However, in many parts of the United States,
stormwater is still discharged directly to receiving waters, whether they be lakes,
streams, the ocean, or, to a lesser extent, direct discharge to groundwater.
This dissertation focuses on a novel pollutant: heat. Heat pollution is novel for two
reasons. Most importantly, the effects of heat pollution on receiving water biota are only
recently being documented but construction practices have not yet advanced in
accordance with these findings. Secondly, heat is a transient property rather than a
persistent pollutant. In fact, many of the traditional methods of impoundment that
remove persistent pollutants can actually increase exposure to sunlight and therefore
heat content of the water. The transient nature of thermal pollution also makes it difficult
to determine the magnitude and timing of pollution discharge in urban areas without
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having intimate knowledge of the contributing source areas as well as surface and
subsurface flow routing connectivity.
Many low impact development methods have been proposed to minimize the
energy and land area required for traditional treatment, such as the use of bioretention
areas, subsurface exfiltration basins, both of which are often coupled with filter media,
using porous building materials, or simply disconnecting source areas from conduit
networks. By focusing on the source area, stormwater pollution, and particularly heat
pollution can be controlled systematically and successfully mitigated. It is even possible
to additionally treat more well understood pollutants while controlling for thermal
pollution. It is within the context of Low Impact Development (LID) that the following
chapters are written.
The testing sites are located in North-Central Florida. As a heat-conductive
interface, impervious asphalt pavement serves as a thermal reservoir for climates with
diverse conditions such as annual rainfall distributions. For example, Florida’s climate is
unique from Wisconsin (Roa-Espinosa et al. 2003), Ontario, CA (Van Buren et al. 2000;
James and Verspagen 1995), or Oregon (Haq and James 2002); locations of previous
thermal runoff studies. The predominance of Florida’s precipitation is coincident with the
warmest months; illustrating an inverted pattern to that of Oregon. Florida storms
typically occur during the mid-afternoon when pavement temperature is hottest but
rainwater is at dew point temperature. Hence, the studies benefit by a high signal to
noise ratio due to the very high pavement temperatures that are reached in the sunlight.
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CHAPTER 2 HYDROLOGIC TRANSPORT AND FIRST FLUSH OF THERMAL LOAD FROM
ASPHALTIC PAVEMENT
Background
Since the Industrial Revolution, thermal loads from urban environs have increased
(Sansalone 2002). Recently, impacts of imperviousness on thermal load and causal
mechanisms have been identified (Oke 1982; Mestayer and Anquetin 1994; Langford
1990). Akbari et al. (2003) reported that pavement covers 29% of Houston and 45% of
Sacramento with 60% and 29% of these areas attributed to parking, respectively.
Converting vegetated areas to impervious areas reduces groundwater-fed streamflow,
compounding thermal impacts (Janke et al. 2009; Ferguson and Suckling 1990; Leith
and Whitfield 2000; Horner et al. 1994). Asphalt can emit 130 W/m2 of radiation and
200 W/m2 sensible heat at mid-day, significantly above vegetated cover levels (Thanh
Ca et al. 1997). Asaeda et al. (1996) reported that asphalt temperatures can exceed
65°C. As a heat-conductive interface, impervious asphalt pavement serves as a
thermal reservoir even for diverse climates. For example, as shown in Figure 2-1, the
predominance of Florida’s precipitation is coincident with the warmest months; an
inverted pattern to that of Oregon.
Thermal load is a concern due to impacts on water chemistry and ecosystem
integrity of receiving waters such as increases in cold water stream temperatures
(Langford 1990; Galli 1990) and fish distress (Coutant 1987; Nakatani 1969; Paul and
Meyer, 2001). Urbanization and increased receiving water temperature are related
(Langford 1990). Galli (1990) reported that a 1% increase in imperviousness is related
to a 0.09°C increase in cold-water stream temperature with local extinction of trout and
stoneflies. Trout and salmon stressed by water above 21°C will change habitat
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(Coutant 1987). From 1979-1999, an increase of 0.83°C had a deleterious impact on
the Upper Rhone River based on indicator species (Daufresne et al. 2004). Armour
(1991) found increased Escherichia coli. levels due to thermal load. Thermal load can
reduce dissolved oxygen needed for fish and plant survival (Nakatani 1969; James and
Xie 1998; Paul and Meyer 2001) and can lead to increased metal toxicity (Davies 1986).
Few studies have measured pavement and runoff temperature during uncontrolled
transient event loadings. Studies focused on pavement temperature (Minhoto et al.
2005; Asaeda et al. 1996; Yavuzturk et al., 2005), thermal load of pavement runoff
(Krause et al. 2004; Haq and James 2002), and heat fluxes to and from pavement
surfaces (Anandakumar 1999; Than Ca et al. 1997; Herb et al. 2008). While steady
loadings have the advantage of a controlled load-response, the response to
uncontrolled transient loadings is also required. However, researchers reported that
study of actual rainfall-runoff events can be challenged by spatial, temporal, event-
frequency and number constraints (Roa-Espinosa et al. 2003, Janke et al. 2009, Van
Buren 2000).
In my study it is hypothesized that the transport of temperature and thermal load
by source area pavement runoff has analogs to the transport of constituent
concentration and mass, respectively. It has been shown that transport concepts such
as the first flush commonly utilized for design, regulation and control can be distilled
from many previous studies into either concentration or mass definitions (Sansalone
and Cristina 2004). Specifically, with respect to the transport of pollutant load, Sheng et
al. (2008) demonstrated by categorical analysis that the limiting transport classes for
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dissolved or particulate matter mass are mass limited (first-order mass or heat
transport) or flow limited (zero-order mass or heat transport).
Objectives
The primary objective of my study is to measure and model the intra-event
distribution of temperature and transport of thermal load in runoff from an asphaltic
pavement source area. The study hypothesizes that (1) thermal load delivery is
controlled by hydrology and can be primarily flow limited; (2) for a rainfall-runoff event,
the seasonal event date, event duration, antecedent weather parameters, and
pavement temperature are correlated with net heat transfer (NHT) to runoff; (3) for a
rainfall-runoff event, the subgrade temperature and intra-event weather conditions are
correlated with NHT. A second objective is to reproduce measured results utilizing heat
balance models. As part of this second objective, the study hypothesizes that: (1)
pavement heat conduction is a surrogate for overall heat transfer to runoff; and (2) that
runoff temperature is an appropriate substitute for pavement surface temperature. The
study combines measurement and modeling to illustrate the transport and potential of a
first-flush of thermal load for an asphalt-paved source area, illustrating the coupling of
hydrology and heat transfer.
Methodology
In my study, an outfall appurtenance located at 29.644098° N, 82.348404° W
drains an asphalt-paved catchment used for surface parking as shown in Figure 2-2.
The catchment is loaded by approximately 708 vehicles per weekday and 84 vehicles
per weekend day. The contributing drainage area is approximately 450 to 500 m2,
determined using light detection and ranging (LIDAR) data and onsite surveying, and is
dependent on rainfall intensity. The hot-mix asphalt pavement has a concrete curb and
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gutter. Trees surround the catchment, with two dense foliage trees on the west side of
the catchment and magnolia trees immediately east of the catchment.
Data Collection Methods
Thermal Thermal measurements are made using type-T Omega Inc. {5TC-PVC}
thermocouples (TCs). The catchment primary flow path is ground-truthed and a 5.6 m
transect of TCs is installed in the path of the sheet flow. Measurements are taken at
0.1m, 1.2m, 2.6m, 4.1m, and 5.3m from the east end (headwater) of the transect, and
concrete-gutter measurements at 0m and 5.6m from the east end of the transect for
“East Concrete” (EC) and “West Concrete” (WC), respectively. Figures 2-3 and 2-4
illustrate the spatial and depth locations of the TCs. Surface temperature is
approximated as a function of subsurface pavement temperature as shown in Equation
2-1.
(2-1)
In this equation, is the mean surface temperature (oC), is the temperature in the
pavement at 13mm (oC), is the temperature at location A5 and depth of 1mm,
and is the temperature at location A5 and depth of 13mm. Runoff temperature is
measured with two TCs placed at the invert of a 150mm PVC pipe conveying pavement
flows at the catchment outfall.
Tipping bucket rain data (increments of 0.254mm) are collected at 29.642891° N,
82.34864° W. At 29.639461° N, 82.345293° W a Texas Weather Instruments WRL-25
records solar radiation, ambient temperature, cloud cover, and wind. An AM25T
multiplexer measures TC data and a Campbell Scientific CR800 logs data. A calibration
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curve is generated for each TCs by logging temperature of boiled water as it cools as
represented in Equation 2-2.
( ) ⁄ (2-2)
In this equation, TC is the thermocouple reading (°C) and Tt is the temperature (°C)
recorded using an alcohol thermometer. Runoff is measured using a 25.4mm (1 inch)
calibrated Parshall flume. Flow depth is measured using a 24 volt ultrasonic sensor and
recorded. From the calibration the relationship between flow (Q) and depth in the flume
is given in Equation 2-3, for Q (L/s) and D, depth in the flume (inches). Intra-event TC
data are logged at five second intervals.
(2-3)
Calculation Methods for Temporal Distribution of Heat Transfer to Runoff During Event
NHT from the pavement to the runoff is calculated by the convection equation (Herb et
al. 2008) as shown in Equation 2-4 where qc is the pavement net heat export to runoff
(W/m2), is the runoff temperature ( ), is the dewpoint temperature ( ), as a
surrogate for rainfall temperature (U.S. Army Corps of Engineers, 1956), is the flow
(m3/s), is the specific heat of runoff (J/kg-K), is the runoff density (kg/m3), and As
is the contributing area (m2). The Kolomogorov-Smirnov (K-S) test is performed for
goodness of fit between cumulative runoff volume and cumulative NHT to the runoff.
This test is chosen due to the non-normal distribution of intra-event flows.
( ) (2-4)
A heat-based first flush is defined as an event where there is a disproportionate
heat transfer as NHT (analogous to mass) in relation to runoff volume early in the event.
In contrast, a flow limited event is an event in which NHT is proportional to flow; heat
22
transferred to runoff is linearly proportional to flow volume. A temperature-based first
flush is defined where there is a disproportionate increase in runoff temperature
(analogous to concentration) in relationship to runoff volume early in the event, followed
by a rapid decline in runoff temperature.
Method Components of Heat Balance Models
Simulation using heat balance models requires pavement characterization, atmospheric
data, and pavement and runoff temperature data during a storm event. The models are
validated by comparing intra-event modeled results to measured NHT. Heat balance
model components are utilized from Janke et al. (2009), Herb et al. (2008), Van Buren
et al. (2000), Kim et al. (2008), Thompson et al. (2008), and Sansalone and Teng
(2005). Models incorporating these components are compared with a heat budget on
rainfall-runoff generated from measured rainfall and runoff temperatures. The governing
heat balance equations used in this study are shown in Equation 2-5 for the Van Buren
et al. method (2000) and in Equation 2-6 for the other methods. In these equations, qt is
the total heat stored in the pavement. Thompson et al. (2008) further includes
pavement-subgrade conduction (qsub) as a loss term. All balances are in W/m2. Table
2-1 presents thermal properties based on published results.
– , ( )- (2-5)
(2-6)
Radiation
Net radiation qrad may be calculated as shown in Equation 2-7 where qr,s is net
direct and diffuse solar radiation where qr,lw is net longwave radiation (W/m2). Solar
radiation is calculated in the same manner for each method, shown in Equation 2-8.
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(2-7)
qr,s = rs(1-α) (2-8)
In Equation 2-8, rs is the total incoming solar radiation at the surface (W/m2) and α is the
albedo. In contrast to solar radiation, methods for net long wave radiation are more
variable. Janke et al. (2009) calculates net longwave radiation as summarized in
Equations 2-9 and 2-10.
(
) (2-9)
(
) (2-10)
In these equations is amospheric emissivity, is cloud cover fraction, is surface
emissivity, Ta,k is air temperature (K), Ts,k is surface temperature (K), es,kPa is saturated
vapor pressure (kPa), and is the Stefan-Boltzmann constant (J1K-4m-2sec-1). Net
longwave radiation from Herb et al. (2008) is summarized in Equation 2-11 where ea,Pa
is surface vapor pressure (Pa). Kim’s longwave radiation is shown in Equation 2-12
where ea,Hg is surface pressure (mm Hg).
( ( )
) (2-11)
( √ ) (2-12)
Equation 2-13 shows the calculation method for Sanalone and Teng (2005) where
atmospheric emissivity is calculated as shown in Equation 2-14, where is the
vapor pressure at 2 meters (mbar).
( )( )
(2-13)
. (2-14)
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Heat loss by evaporation
Evaporative heat loss model components vary across studies. Van Buren’s method is
summarized in Equations 2-15, 2-16, and 2-17. In these equations, r is runoff water
density (kg/m3), and Dv are the latent heat of vaporization (J/kg) and evaporation rate
(m/s), Tr is runoff temperature (°C), is wind speed (m/s), and RH is relative humidity.
Herb et al. (2008) utilizes Equation 2-18.
(2-15)
, ( )- (2-16)
( ) ( ) (2-17)
( )( ) (2-18)
In Equation 2-18, is the air density (kg/m3), and are published without
reference to units, is the difference in virtual temperature between the surface and
air (°C) (Ryan et al. 1974), and q is specific humidity (kg/kg). Virtual temperature is the
equivalent dry air temperature if pressure and density equal measured moist air
conditions. Specific humidity is shown in Equation 2-19.
.
/ (2-19)
In this expression qx is either the saturated or surface specific humidity, is saturated
or surface vapor pressure and p is atmospheric pressure, all of the same units. Kim et
al. (2008) report heat loss by evaporation to be a function of wind speed and vapor
pressure. The heat loss equation is derived from the form discussed in Edinger (1974)
as shown in Equation 2-20.
( )( ) (2-20)
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Kim et. al. present the following values for wind function coefficients: a0 = 57; a2 = 2.85.
Thompson et al. (2008) publish a similar expression shown in Equation 2-21.
( )( ) (2-21)
In this equation ao = [7.2 to 13.6], a1 = [3.1 to 4.9], a2 = [0.0 to 0.66], and es,Hg is in mm
Hg. An alternative method (Sansalone and Teng 2005) is based on Penman-Monteith
(Monteith 1980).
Sensible heat loss
Sensible heat loss is explicitly added to the heat balance by Van Buren et al., Herb
et al., and Kim et al. Van Buren et al. calculate sensible heat as a function of
evaporation by multiplying by the Bowen ratio as shown in Equation 2-22.
[ (
( ))] (2-22)
In this expression is atmospheric pressure in kPa, and temperature is recorded in
°C. This ratio is also used to calculate sensible heat loss as a function of qevap using the
Sansalone and Teng method (2005). Herb et al. utilize Equation 2-23 to calculate heat
transfer by sensible heat.
( )( ) (2-23)
In this expression is the specific heat of the air (1.005 J/kg-K) and Ts is surface
temperature (°C). Kim et al. use a similar method shown in Equation 2-24.
( )( ) (2-24)
In this expression, c1 is Bowen’s coefficient, equal to 0.47mm Hg/°C.
Heat loss by convection
Convection is calculated as the remainder of the heat balance equation and does
not include heat loss of evaporation or sensible heat; hence it is defined as net heat
26
transfer (NHT). Results are compared to values calculated explicitly using the rainfall-
runoff temperature differential method described previously. Figure 2-4 demonstrates
the heat balance. The measured NHT response is adjusted by the storm’s average
pavement residence time to better correlate runoff temperature readings with NHT
calculated by pavement response. The methodology by which convection is solved for
in the heat budget is as shown in Equations 2-25 and 2-26, written to express heat gain
by radiation and heat loss by other terms.
(2-25)
Tpavi+1 = Tpavi + ( )*
( ) (2-26)
Substitution of Runoff Temperature for Pavement Surface Temperature
Herb et al. and Janke et al. indicate that turbulence generate a uniform runoff
temperature equal to pavement temperature at the start of a given time step. Therefore
this study examines if substituting runoff temperature for pavement surface temperature
impacts model predictions. Results from the substitution of runoff temperature for
pavement surface temperature are compared to results from the same events where the
models do not substitute runoff temperature for surface temperature.
Results and Discussion
Heat Transfer to Runoff during an Event
Table 2-2 summarizes event data while Table 2-3 summarizes correlation
coefficients between storm event parameters. There is a positive correlation (r = 0.96)
between peak flow and NHT. The correlation with NHT for rainfall is 0.64; for initial air
temperature is 0.14; and for continuous flow duration is 0.24. Table 2-2 illustrates the
27
positive correlation between peak flow and NHT reflected by the K-S test for similarity
between cumulative flow and NHT in 12 of 17 events.
Impact of hydrologic parameters on heat transfer
Figures 2-5 and 2-6 illustrate relationships between NHT and runoff volume for low
and medium flow storms as defined in Table 2-4. K-S tests between cumulative runoff
volume and cumulative NHT indicate a statistically significant difference (p > = 0.05).
While these events illustrate a temperature first-flush, with respect to NHT both events
are flow limited with respect to thermal load. There is a linear relationship between
cumulative NHT and volume. The net flux of heat to runoff continues throughout each
event and dilution occurs during peak flows. Instantaneous NHT and instantaneous
flow follow similar temporal patterns, suggesting lack of a distinct heat based first flush.
In contrast, Figure 2-7 illustrates the only heat limited event (Tropical Storm, TS Fay) in
the database, where cumulative heat transfer proceeds cumulative flow. The maximum
difference between cumulative runoff and cumulative NHT is 33.2% (p < = 0.05). All
other events are flow limited where heat is not exhausted from the pavement.
Of the 17 storms, only five produce a significant difference in trajectories between
cumulative flow and NHT as shown in Table 2-2. For the remaining 12 storms,
cumulative NHT shows an approximate linear trajectory when plotted against
cumulative flow as shown in Figure 2-8. Results indicate that hydrology drives NHT for
a given pavement source area. Relative heat transfer (RHT, defined as NHT divided by
rainfall depth) is conceptually similar (ignoring losses) to an event mean concentration
(EMC); in this case, dividing NHT by rainfall depth is similar to dividing constituent load
by runoff volume. Results in Figure 2-8 indicate for high intensity events, there is a
28
lower RHT and by proxy a lower unit heat transfer as compared to the short duration,
lower flow events. The negative correlation between MPRT and NHT indicates that
events with longer pavement residence time have lower NHT from pavement to runoff.
Parameters other than hydrologic parameters have the potential to influence NHT
and RHT. Correlations for RHT are defined as follows: no correlation, r ≤ 0.2; weak
correlation, r ≤ 0.5; and correlated, r > 0.5. Based upon analysis of the 17 measured
events, tabulated in Table 2-3, initial radiation levels show no correlation with NHT (r =
0.05). However, Figure 2-7 is an example where solar radiation between rainfall bands
of TS Fay results in pavement temperature increasing despite moderate wind during the
storm. Wind speed before the onset of rainfall is observed to have no correlation with
RHT (r = 0.08) but does have a moderate negative correlation with NHT (r = -0.48). In a
separate experiment, air flow over the surface of 38mm thick asphalt at 2.2 m/s resulted
in 6% drop in surface temperature but 11% in the pavement interior, after 8 minutes of
airflow as shown in Figure 2-12. This suggests that wind does affect surface
temperature, however with a corresponding slow rate of interior heat loss, supporting
the moderate correlation with NHT measured in-situ. Results illustrate that antecedent
air temperature (immediately before rainfall) exhibits a weak correlation with RHT (r =
0.42).
Relationship between antecedent pavement temperature and heat transfer
Antecedent asphalt temperature correlates with RHT (r = 0.74) more strongly than
with NHT (r = 0.45) and has the greatest correlation of any non-hydrologic factor for
NHT and RHT. Antecedent subgrade temperature has a weak correlation with NHT (r =
0.25) and RHT (0.28), noting that subgrade is buffered from surface temperature and
hydrologic parameters. Results indicate that initial concrete temperatures are lower
29
than asphalt and subgrade. As a reflective surface, concrete does not correlate
strongly with either NHT or RHT.
Impact of event date and start time on heat transfer
There is a weak correlation between event date and heat transfer, as between
event date and other initial conditions (air, subgrade, and pavement temperature).
Similarity of the intra-event phenomena at different seasonal points suggests a lack of
seasonal correlation. Event start time has little correlation with NHT (r = -0.16) or RHT (r
= 0.04). Results shown in Table 2-4 suggest that shading of locations A1 and A5
confounds any correlation between event date and pavement temperature patterns.
This may also cause the difference in East Concrete and West Concrete pavement
temperatures shown in Figures 2-5 through 2-7.
Heat Balance Model Comparison
Table 2-5 summarizes results of cumulative net heat transfer (KJ/m2) measured
directly by heat gain in runoff as well as modeled using the heat transfer components
from Sansalone and Teng, Herb as modified to use Janke’s qlw (hereafter modified
Herb), Van Buren, Kim, Kim as modified to use Sansalone and Teng’s qlw (hereafter
modified Kim), Kim modified to use Thompson’s qv (hereafter Thompson), and Kim
modified both to use Thompson’s qv and Sansalone and Teng’s qr,lw (hereafter modified
Thompson). Additionally, all events are modeled with the substitution of runoff
temperature for pavement surface temperature.
Figures 2-9 and 2-10 summarize modeling results for four storms where pavement
surface temperature is measured. The two closest fitting models are shown. In
addition, these figures also summarize the mean differential produced by the two
30
closest models when runoff temperature (Tro) is substituted for pavement surface
temperature (Tsurf) in each model; assuming Tro at the discharge location equals Tsurf.
The rationale for applying net longwave radiation from Janke et al (2009) in the
Herb model is two-fold: (1) when applying qlw as calculated by Herb, the net flux of
longwave radiation away from the pavement is lower under clear sky conditions than
under cloudy conditions; (2) the Boltzmann constant is reported in non-standard units in
the Herb model, possibly leading to modeling error. The Janke method for calculating
qlw is of similar origin to the Herb method and provides results consistent with
Sansalone and Teng (2005).
The Kim et al. (2008) method for calculation of net longwave radiation has been
modified to substitute Sansalone and Teng’s qlw because Kim et al. refer to longwave
radiation leaving the water surface but provide no equation for calculation. Results
calculated without this term are opposite in sign and 10x the magnitude of the
Sansalone and Teng (2005) and the Janke et al. (2009) methods as shown in Table 2-
5. The Thompson model is very similar to the Kim model but presents a different
calculation method for evaporative heat transfer. The same longwave radiation
modification made to the Kim model is applied to the Thompson model.
The distribution of residuals in Figure 2-11 illustrate that both the modified Kim and
the Sansalone and Teng methods represent measured data (mean normalized
residuals closest to 0). For example, the 14 July event is best represented using the
modified Kim method. This method is also closest to measured total NHT for the 12
August event, followed by the Sansalone and Teng method. The 10 September event
is also best predicted using the same methods. In contrast, the modified Herb method
31
over-predicts NHT and the Thompson model under-predicts NHT for the measured
events. During the 12 August event, all models generate a greater magnitude increase
in heat transfer during peak flow (5 L/s) than measured values. The 21 August event
has a very low instantaneous NHT and all models perform poorly. There is a difference
in calculated NHT when substituting runoff temperature for asphalt surface temperature
as shown in Figure 2-9 however the difference is relatively small. The maximum
differences for each of the four events are -16.7, -19.9, 4.1, and -41.8 W/m2 for the 14
July, the 12 August, the 21 August and the 10 September events, respectively. The
mean differences for the same storm events are -0.7, -2.1, 2.3, and -4.52 W/m2.
Discussion
Results of this thermal pollution study for an asphalt-paved source area illustrate a
temperature first-flush and lack of a heat-based first flush. This finding suggests that
thermal pollutant transport can be analogous to particulate or solute transport from
urban source areas (Sheng et al. 2008). Sheng et al. also suggest that there is a need
to capture and treat the entire event rather than a first flush or water quality volume
(WQV) that is designated a-priori. This link between hydrology and pollutant transport is
also supported by the correlation between NHT and rainfall-runoff flow volume and by
the statistical analysis of the same.
Results demonstrate that pavement temperature exhibits a strong correlation with
NHT. For the same ambient conditions, low rainfall depth events can exhibit a more
significant temperature increase in runoff than high rainfall depth events for asphalt-
paved source areas. However, for the same ambient conditions the NHT for a high
rainfall depth event will be greater than a low rainfall depth event. In contrast to
capturing a first-flush or WQV, a more effective management strategy may be to
32
minimize the storage of heat in the pavement through design and material changes.
This strategy also remedies the disproportionate impact of thermal pollution on
perennial, low volume, or ephemeral systems compared to streams with significant base
flow. Radiation is the dominant mechanism by which the pavement warms; hence,
although a low correlation is measured between radiation and NHT/RHT, it is
particularly useful to minimize radiation that reaches or is absorbed by pavement. For
example, the uses of shading and concrete pavement have well-known thermal benefits
and are passive strategies.
The thermal discontinuity between the subgrade (composed largely of sand) and
the asphalt is shown clearly in Figure 2-6. The implications of a thermal disconnect are
multi-fold. It suggests that models do not need to focus on sub pavement heat content;
at the same time, it implies that better coupling may be achieved by using engineered
pavement and ground media to enhance thermal connectivity between the pavement
and the subgrade.
There are multiple mechanisms that impact the temperature of receiving waters
due to urbanization. The critical component of thermal pollution in urban streams is
direct discharge. While there are deviations between the Sansalone and Teng,
modified Kim, and modified Herb models, all of the aforementioned models are
observed to approximate measured NHT following the same temporal pattern. Results
suggest that existing models may benefit by performing more tests under real storm
events, validating parameters such as longwave radiation with measured values, and
focusing more discretely on evaporation early in the storm event.
33
Substitution of runoff temperature for pavement surface temperature provides
cumulative NHT values that compare nearly as closely to measured surface
temperature as non-substituted NHT calculation. However, initial runoff temperature
misrepresents initial pavement temperature because it is cooler than the asphalt
pavement (Figures 2-5 through 2-7). It is important to accurately model initial heat
transfer because of the rapid convection and evaporation processes unique to event
beginnings.
Summary
Thermal load transport in runoff from urban asphalt pavement is measured for 17
events at a Gainesville, FL catchment and results are simulated with a series of
published models. Hypothesizing that thermal load delivery is driven by hydrology and
is primarily flow limited, a K-S statistical analysis is performed that demonstrates that for
12 out of 17 storms normalized cumulative runoff is an appropriate surrogate for
normalized cumulative NHT. Correlation results between these parameters also
support this conclusion. The thermal load transport is predominately flow limited with no
first-flush in relation to NHT. While pavement temperature is strongly correlated to
NHT, results indicate that seasonal event date, event duration, and antecedent weather
parameters are not correlated to NHT.
Results do not support the hypothesis that pavement heat conduction is an
appropriate estimation of heat transfer to and from the pavement based on measured
pavement and pavement subgrade temperatures during runoff events. Governing
equations for pavement heat balance models described by Herb et al. (2008) and Kim
et al. (2009) are applied in this study and evaluated with measured NHT. These models
are modified to include heat balance components from Janke et al. (2009), Sansalone
34
and Teng (2005), Thompson et al. (2009) and Van Buren et al. (2009). Results indicate
heat transfer is modeled equally well with more than one model but that the heat
transfer predicted by each model early in an event requires further refinement.
Utilization of runoff temperature as a surrogate for asphalt surface temperature has little
effect on simulated NHT based on models presented but provides a lower NHT early in
the event.
35
Table 2-1. Selected properties of asphalt pavement from various studies
Study Density
(kg/m
3)
Thermal Conductivity
(W/m-oC)
Specific Heat
(J/kg-oC)
Thermal Diffusivity
(m2/s)
Albedo Emissivity
Van Buren et al. (2000)
2250 (1760)
1.21 (1.3)
921 (837)
5.86x10-7
(8.79x10-7
) NR NR
Janke et al. (2009)
2100-2400 (1300-1500)
1.4-1.8 (0.4-1.2)
1120-1370 (900-1400)
NR 0.12 0.94
Herb et al. (2008)
NR NR NR 4x10
-7
(6x10-7
) 0.12 0.94
Kim et al. (2008)
NR NR NR 6.98x10-7
0.05 NR
This Study 1850 1.3 (0.6) 1050 6.69 x10-7
0.12 0.94
Note: Values in parentheses are for pavement subgrade. NR: not reported
36
Table 2-2. Storm event data for measured rainfall events and Kolomogorov-Smirnov (K-S) test for goodness of fit between normalized cumulative heat and time and normalized cumulative flow and time
Event D
ate
(200
8)
(MM
-DD
)
Sta
rt T
ime
of R
ain
fall
(HH
:mm
) (t
o)
Dura
tion (
H:m
m)
Rain
fall
(mm
)
Peak F
low
(L/s
)
Initia
l A
ir
Tem
pera
ture
(oC
)
Initia
l P
avem
ent
Tem
pera
ture
(oC
)
Initia
l te
mpera
ture
of soil(
oC
)
Runoff
Tm
ax (
oC
)
Continu
ous F
low
Dura
tion (
H:m
m)*
Pre
vio
us D
ry H
ours
Net H
eat T
ransfe
r to
Runoff
(K
J)
Rela
tive H
eat
Tra
nsfe
r
(KJ/m
m o
f ra
infa
ll)
MP
RT
** (
min
)
D (K-S test), P
++
7-31 10:59 0:42 1.27 .15 30.6 33.6 29.1 32.5 0:04 37 2,035 1,602 4 0.044,1 7-14 22:11 1:19 2.03 .15 27.2 31.2 28.7 27.5 0:28 75 3,785 1,865 6 0.033,1 10-23 14:58 0:51 3.56 1.6++ 25.6 28 24.9 26.5 0:15 340 19,216 5,398 3 0.3, 0.043 (n) 6-22 14:38 2:25 1.78 0.07 31.7 33.2 28.3 31.0 0:06 25 2,248 1,263 5 0.283, ~0.0 6-3 15:26 0:55 2.03 0.82 33.9 39.3 29.9 34.2 0:15 600 14,814 7,298 4 0.l22, 0.832 9-20 13:44 0:47 3.30 1.01 27.8 36.5 28.4 30.3 0:16 45 15,055 4,562 3 0.0857, 0.99 8-21** 12:34 7:09 54.6 5.94
++ 26.1 27.2 28.1 27.8 2:47 2 74,700 1,368 2 0.332, ~0.0
10-09 14:08 1:41 20.8 9.2++
29.4 31.6 26.7 26.8 0:26 20 131,048 6,300 3 0.40, ~0.0 8-12 14:29 1:30 16.8 4.6 27.8 31.3 30.3 28.7 1:10 2 45,771 2,724 5 0.0737, 0.951 6-30 14:42 0:31 5.58 3.17 30.0 38.9 27.4 32 0:13 45 39,277 7,039 4 0.111, 0.994 6-11 13:22 1:54 21.6 11
++ 29.4 41.7 29.4 33.4 0:30 12 218,622 10,121 0.5 0.351, ~0.015
7-15 13:08 1:40 62.2 13.2++
29.4 35.7 28.6 31.1 0:54 12 170,047 2,734 1 0.180, 0.514 9-10 16:13 0:58 6.10 1.96++ 32.8 37.4 29.8 31.1 0:42 120 38,022 6,233 3 0.204, 0.19 6-10 14:02 1:21 22.6 10.7++ 32.8 42.2 31.5 31.7 1:00 600 195,427 8,647 4.5 0.0405, 1.0 7-29 11:43 0:43 5.08 3.64++ 31.1 37.8 30.6 32.8 0:25 330 45,930 9,041 5 0.18, 0.51 6-21 11:45 1:10 13.7 3.8 30.0 27.3 28.1 26.4 0:10 61 35,808 2,614 3 0.0465, 1 (n) 6-23 10:35 2:27 7.87 0.52 25.6 28.2 28.1 0.52 1:30 18 26,022 3,306 3 0.0417, 1
* Excludes gutter flow; **MPRT (Median Pavement Residence Time); ++
(n) = normally distributed; D is maximum difference, P is p-value for test of significant difference where α = 0.05.
37
Table 2-3. Correlations between storm event parameters. Note that correlation coefficients for wind velocity and radiation are not shown.
Eve
nt
Da
te
Sta
rt o
f R
ain
fall
(t o
)
Dura
tio
n o
f E
ve
nt
Rain
fall
De
pth
Pe
ak F
low
An
tece
den
t A
ir T
em
pera
ture
An
tece
den
t A
sp
ha
lt
Te
mp
era
ture
An
tece
den
t S
ubg
rad
e
Te
mp
era
ture
Ma
xim
um
Ru
no
ff T
em
pe
ratu
re
(Tm
ax)
Con
tin
uo
us F
low
Dura
tion
(CF
D)
Pre
vio
us D
ry H
ou
rs (P
DH
)
NH
T (K
J)
RH
T (
J/m
m r
un
off
)
MP
RT
(m
inu
tes)
Event Date 1.00 Start of Rainfall (to) 0.08 1.00
Duration of Event 0.02 -0.17 1.00 Rainfall Depth 0.00 -0.23 0.62 1.00
Peak Flow -0.09 -0.19 0.18 0.77 1.00 Air T (to) -0.41 0.03 -0.39 -0.20 0.09 1.00
Asphalt T (to) -0.37 0.09 -0.42 -0.09 0.33 0.69 1.00 Subgrade T (to) -0.52 -0.03 -0.10 0.02 0.17 0.58 0.59 1.00
Runoff Tmax 0.01 0.25 -0.22 0.04 0.22 0.58 0.56 0.29 1.00 CFD 0.04 -0.19 0.85 0.66 0.27 -0.43 -0.32 0.11 -0.37 1.00
PDH -0.17 0.09 -0.10 -0.11 0.02 0.43 0.36 0.33 0.25 0.00 1.00 NHT -0.18 -0.16 0.15 0.64 0.96 0.14 0.45 0.25 0.19 0.24 0.08 1.00
RHT -0.10 0.04 -0.56 -0.16 0.36 0.42 0.74 0.28 0.29 -0.24 0.49 0.49 1.00 MPRT -0.13 0.44 -0.33 -0.53 -0.50 0.22 -0.01 0.28 0.09 -0.27 0.23 -0.53 -0.19 1.00
Note: Units are as defined in the previous table. MPRT = Mean Pavement Residence Time; Tmax Runoff = Maximum Runoff Temperature
38
Table 2-4. Tabular pavement and subgrade temperature profiles at beginning and end of storm.
Event Date (2008) (MM-DD)
Initial Pavement
Profile
Final Pavement
Profile
Initial Subgrade
Profile
Final Subgrade
Profile
Start Time
(HH:mm)
Runoff Volume
(L) Q50
(L/s) Percentile
(%)
6-10 3>5>1 3>5>1 3>5>1 3>5>1 14:00 8000 1.195 75-100 6-21 3>5>1 3>5>1 3>5>1 3>5>1 11:40 3568 0.391 0-25 7-29 3>5>1 3>5>1 3>5>1 3>5>1 11:42 1406 0.656 75-100 7-31 3>5>1 3>5>1 3>5>1 3>5>1 10:56 66 0.061 0-25 7-15 3>5>1 3>5>1 3>5>1 3>5>1 13:03 22380 3.451 75-100 7-14 3>5>1 3>5>1 3>1>5 3>1>5 21:25 248 0.005 0-25 8-21 3>5>1 3>1>5 3>5>1 3>5>1 11:05 20409 0.310 50-75 6-23 3>5>1 3>1>5 3>1>5 3>1>5 10:35 1373 0.184 25-50 6-11 3>1>5 3>5>1 3>5>1 3>5>1 13:11 6678 1.560 75-100 6-22 3>1>5 3>5>1 3>1>5 3>1>5 14:33 29 0.006 0-25 9-20 3>1>5 3>5>1 3>1>5 3>1>5 13:36 502 0.200 25-50 10-23 3>1>5 3>5>1 3>1>5 3>1>5 14:50 916 0.194 25-50 6-3 3>1>5 3>1>5 3>1>5 3>1>5 15:25 293 0.073 0-25 6-30 3>1>5 3>1>5 3>1>5 3>1>5 14:38 1028 0.359 50-75 8-12 3>1>5 3>1>5 3>1>5 3>1>5 14:24 3861 0.216 25-50 10-9 3>1>5 3>1>5 3>1>5 3>1>5 13:56 8467 0.707 75-100 9-10 1>3>5 3>1>5 1>3>5 3>1>5 16:07 1540 0.217 50-75
Note: Thermal profiles are in order from hot to cold. Thermal profile symbols are 1=A1, 2=A2, 3=A3 as illustrated in Figure 2-3. The 25, 50, 75th percentile = 0.184, 0.217, 0.656 L/s, respectively. Flow less than 25% is defined as low flow; less than 75% is moderate flow; greater than or equal to 75% is high flow.
Table 2-5. Total NHT for various modeling methods compared to measured values.
Negative values represent heat gain by pavement.
Event Date (Day/Month/2008) 6/10 6/23 7/14 8/12 8/21 9/10 Model Components Heat Transfer to Runoff (KJ)
Sansalone and Teng -28 54 34 104 -209 101
Modified Herb 63 214 94 134 157 122
Van Buren -377 -411 -122 -69 -290 -198 Kim -909 2014 684 653 3770 941 Thompson -1025 2021 662 610 3497 895
Modified Kim -77 -54 19 99 -151 83 Modified Thompson -192 -46 -4 56 -425 37
Measured 258 68 9 83 51 76
39
Month
Januar
y
Feb
ruar
y
Mar
ch
Apri
l
May
June
Mea
n M
onth
ly P
reci
pit
atio
n (
in)
0
2
4
6
8
Mea
n M
onth
ly A
ir T
emper
atu
re (
oC
)
0
4
8
12
16
20
24
28
Month
January
Febru
ary
Marc
h
April
May
June
Mea
n N
um
ber
of
Even
ts p
er M
onth
0
10
20
30
40
50# Events / Month
Mean Monthly Precipitation
Air T
Portland, ORGainesville, FL
Figure 2-1. Historical monthly distribution of weather data for Gainesville, FL from
August 1998 to July 2008 (NCDC, 2009) and for Portland, OR (Oregon Climate Service, 2010) from January 1998 to December 2008.
Figure 2-2. Lake Alice watershed including subject catchment (~450 m2).
40
Figure 2-3. Plan and cross-sectional view of thermocouples (TC) for catchment
pavement system in Lake Alice watershed.
Figure 2-4. Conceptual pavement heat balance model with nominal thermocouple
installation depths. Tdew represents rainfall temperature (oC); TR.O. is runoff temperature (oC); qr,lw is net longwave radiation; qr,s is net shortwave radiation; qconv is convective heat transfer; qv is evaporative heat transfer; qs is sensible heat transfer; Tsurf is surface temperature (oC); T13 is asphalt temperature (oC) measured at ~13mm depth; T38 is asphalt temperature (oC) measured at ~38mm depth; Tsub is subgrade temperature (oC) measured at ~76mm depth; Tpav is average pavement temperature.
41
Q, L
/s
0.0
0.2
0.4
0.6
Tem
per
atu
re (
oC
)
22
24
26
28
Win
d (
m/s
)
0
2
4
6
8Q
TQ
Air
Wind
23 June 2008to = 10:35:00
Incr
emen
tal
Hea
t T
ran
sfer
(W
/m2
)
0
10
20
30
40
50
% l
ess
than
, fo
r (H
eat,
V) n
0.0
0.2
0.4
0.6
0.8
1.0
Rad
iati
on
(W
/m2
)
0
100
200
300
400
500
Heat
V
Heat
Radiation
Elapsed Time, HH
00:0
0
00:3
0
01:0
0
01:3
0
02:0
0
Tem
per
atu
re (
oC
)
23
24
25
26
27
28
29Mean Pavement T
Mean Subgrade T
E. Concrete T
Runoff
Figure 2-5. Low flow rate storm event data recorded on June 23, 2008. Q: Flow; V:
Volume; T: Temperature OC; Heatn: normalized heat; Vn: normalized volume
42
Q (
L/s
)
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(o
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to = 14:38
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(o
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45Mean Pavement T
Mean Subgrade T
E. Concrete T
Runoff
Figure 2-6. Moderate flow rate storm event data recorded on June 30, 2008. Q: Flow; V:
Volume; T: Temperature OC; Heatn: normalized heat; Vn: normalized volume
43
Q, L
/s
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Radiation
21 Aug 2008t0 = 11:05:00
Figure 2-7. Storm event data recorded on August 21, 2008 (Tropical Storm Fay). Q:
Flow; V: Volume; T: Temperature OC; Heatn: normalized heat; Vn: normalized volume
44
Cumulative Flow Volume (L)
0 5000 10000 15000 20000
Cum
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tive
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t T
ransp
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KJ)
0.0
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6-30
6-23
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7-15
7-14
9-10
6-10
6-03
9-20
6-21
Figure 2-8. Distributions of cumulative heat and cumulative flow for 12 storms that are
similar according to K-S tests of difference between normalized values of the former. The heat response is stronger during small storms and shallow under larger events, with the exception of the 6-10 event.
45
Storm Duration (HH:mm)
00:10:00 00:20:00 00:30:00 00:40:00
Flo
w (
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del
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ence-20
02040
Flow
Measured
Modified Kim
Modified Thompson
Radiation
DNHT
14 July, 2008
A
Storm Duration (HH:mm)
00:00 00:10 00:20 00:30 00:40 00:50F
low
(L
/s)
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50Flow
Measured
Sansalone
Modified Kim
Radiation
DNHT
B Figure 2-9. Modeled storm event data showing only best fit models for A) 14 July 2008,
B) 12 August 2008. ΔNHT is the difference between heat transfer modeled using a substitution of runoff temperature for pavement surface temperature.
46
Storm Duration (HH:mm)
01:00 03:00 05:00 07:00
Flo
w (
L/s
)
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6N
et H
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Rad
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(W
/m2
)M
od
el D
iver
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ce0204060
Flow
Measured
Sansalone
Modified Herb
Radiation
NHT
21 August, 2008
A
Storm Duration (HH:mm)
00:00 00:10 00:20 00:30 00:40 00:50
Flo
w (
L/s
)0
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nd (
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Mod
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iver
gen
ce-40-2002040
Flow
Measured
Sansalone
Modified Kim
Radiation
DNHT
September 10, 2008
B Figure 2-10. Modeled storm event data showing only best fit models for A) 21 August
2008, and B) September 10 2008. ΔNHT is the difference between heat transfer modeled using a substitution of runoff temperature for pavement surface temperature.
47
No
rmal
ized
Res
idu
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-4
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Elapsed Time (HH:mm)
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Elapsed Time (HH:mm)
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Norm
aliz
ed R
esid
ual
s
-6
-4
-2
0
2
Modified Herb et al.
Mean Modified Herb
Sansalone and Teng
Mean Sansalone
Modified Kim et al.
Mean Modified Kim
Modified Thompson et al.
Mean Modified ThompsonRunoff Temperature Measurements
Measured Pavement Surface TemperatureMeasured Pavement Surface Temperature
Measured Pavement Surface TemperatureMeasured Pavement Surface Temperature
Figure 2-11. Residual values for four models. Kim and Thompson models are corrected
to use qr,lw from Sansalone and Teng model. Herb is modified to use qr,lw from Janke model. A mean of 0 with a normal distribution about the mean indicates a close estimation of total heat transfer with a good fit to the measured NHT. Use of runoff temperature in place of pavement surface temperature for NHT model calculations results in trending similar mean residual values.
48
Time (minutes)
0 2 4 6 8 10
Pav
emen
t T
emper
ature
(o
C)
50
55
60
65
70
Interior Pavement (19mm depth)
Pavement Surface
Figure 2-12. Median temperature at two depths in a 38mm asphalt pavement with a
forced wind velocity of 2.2 m/s over the pavement surface. There is an 11% reduction in surface temperature and 6% reduction in the interior temperature. 95% confidence interval is shown in light-gray for surface measurements and dark gray for interior measurements.
49
CHAPTER 3 CYCLIC TEMPERATURE PROFILES FOR ASPHALTIC PAVEMENT AS A FUNCTION
OF TREE CANOPY SHADING AND VEHICULAR PARKING FREQUENCY
Background
The temperature of urban runoff is fast becoming a concern in many locations
throughout the United States, most of which have sensitive cold-water habitats
(Langford 1990; Galli 1990) and some of which exhibit fish distress (Coutant 1987;
Nakatani 1969; Paul and Meyer 2001). If not mitigated, runoff temperature can have an
impact on the ecology of receiving waters (Daufresne et al. 2004; James and Xie 1998).
The clean water act, as amended by the water quality act of 1987, has established total
maximum daily loads whereby states must identify locations where controls on thermal
discharges to waters cannot assure protection of biota in those waters. Thermal TMDLs
have been established in states ranging from the Northwest (Oregon DEQ 2008) to the
Southeast (Louisiana DEQ 2001).
Parking lot surfaces dominate the urban landscape in urban environments, making
up more than 29% of paved area in Houston and Sacramento (Akbari et al. 2003) and
between 39% and 64% of commercial areas in Olympia, Washington (City of Olympia
1994). Asaeda (1996), Celestian, and Martin (2004), and Grimmond and Oke (1999)
have demonstrated a contribution to the urban heat island effect from parking lots.
Urban drainage areas used for parking generate a thermal input into stormwater run-off
that is comparable with roadways with high speed and high intensity traffic (Hanh and
Pfeifer 1994).
Low impact development best management practices (BMP) mitigate thermal load
to receiving waters in addition to meeting other stormwater criteria or ancillary benefits
such as metal, nutrient, or volumetric reduction, or even energy production (Golden
50
2007). One such BMP is to reduce the area dedicated to parking. Most municipalities
maintain minimum parking space requirements, such as 2 spaces per single family
home, 0.25 spaces per movie theater seat or 6.8 spaces per 100m2 of health spa
leasable area (Davidson and Dolnick 2002). Some requirements vary wildly between
regions or municipalities. A pool hall may vary between 1 space per billiard table in
North Ogden, Utah to 4 spaces per table in Platte County, Missouri (Litman 2006).
There also is a very complex relationship between available parking and
patronage (Shoup 1997) and few definitive numbers are available of typical parking lot
patronage (Institue for Traportation Engineers 1987). Wilson (1995) found that peak
parking demand is only 56% of total capacity at 10 office buildings in CA. According to
the Urban Land Institute, shopping malls only receive 100% parking space patronage
for 19 hours/year (Shoup, 1997). Litman (2006) produced a table from data gathered by
Gould (2003) that finds an average occupancy of <50% across a wide cross-section of
land uses with a maximum occupancy of 82.5%.
Thermal pollution mitigation strategies include multi-level parking structures,
alternative pavement materials, treatment or infiltration of runoff, and the
implementation of shade structures on parking lots (McPherson, 2001; Noguera,2005;
Laverne and Winson-Geideman 2003). While McPherson and Muchnick (2005) and
Heisler & Grant (2000) found that that tree shade is partially responsible for reduced
pavement fatigue and increased lifetime, few studies have previously compared
pavement temperatures beneath vehicles when shaded and unshaded, but vehicles are
parked for up to 23 hours of the day, as determined by a study of approximately 11,000
persons in Atlanta (Frank et al. 2004) and may serve to lower pavement temperature.
51
Scott et al. (1999) measured a 2.1-3.7°C drop in vehicle chassis temperature when
parked in shaded parking lot in Sacramento, CA, however they did not document
pavement temperature.
Objective
My study first investigates the relative impact of tree canopy shade on pavement
temperature beneath parked vehicles; the hypothesis put forth is that there exists a
demonstrable and statistically significant difference in day time pavement surface
temperature beneath a vehicle that is shaded by tree canopy and beneath a vehicle that
is not shaded. The second objective is to determine the cumulative impact of parking
activity on pavement surface temperature in a parking space under varied initial
conditions; the hypothesis put forth is that pavement exposed to insolation for 8 hours
before treatment will cool when repeatedly parking and removing vehicles over the
space while pavement that is shaded before the experiment will warm instead.
In cases where a parking lot is not filled to capacity, multiple parking spaces may
be exposed to direct solar radiation unless another form of shade is provided. The third
objective of the study is to investigate the relative influence of tree shading on roadway
temperature at the surface parking facility. The study hypothesizes that the presence of
medium to large foliage trees (as defined in McPherson et al. 2005) east and west of a
N-S road lowers peak pavement temperature and that the thermal disconnect between
asphalt and subgrade is visible as a difference in the gradient of temperature response
in the two materials.
Methodology
In my study, a student union parking lot on the University of Florida campus
located at 29.644098° N, 82.348404° W is composed of hot-mix asphaltic concrete
52
(density=1850 kg/m3, conductivity=1.3 W/m-oC, specific heat=1050 J/kg-oC, and
albedo=0.12) and is used for surface parking as shown in Figure 3-1, receiving
approximately 708 vehicles per weekday and 84 vehicles per weekend day. Two dense
foliage trees of canopy diameters > 9.1m (30ft) are located directly west and one
Magnolia Grandiflora tree (diameter >6.1m (20ft)) is located directly east of a
catchbasin that drains a 450m catchment shown in Figure 3-1. Due to the N-S
orientation of the parking spaces, most automobiles receive little to no shade from
nearby foliage. A parking stall 6m northeast of the catchbasin is shaded by the
magnolia and is used for the vehicular shade experiment.
Parking Stall Data Collection Methods
A central component of my investigation is the analysis of pavement temperature
beneath vehicles. A vehicle shade experiment is performed to determine the relative
impact of tree canopy shade on the pavement temperature beneath the vehicle.
Temperature data collection methods include point measurements of temperature taken
on the exteriors of two vehicles (on the hood, roof, and trunk) and on the pavement
beneath the vehicles as shown in Figure 3-2, on the parking space centerline, 1.22m (4
ft) interior of the front and rear of the vehicle. Parking space dimensions are measured
to be 2.74m wide by 6.1m long (9x20 ft). Type-T Omega {5TC-TT-T-30} thermocouples
(TC) are used to measure vehicle and pavement surface temperatures. TCs are
calibrated by heating water in a beaker over 30 minutes until boiling. Water
temperature is measured simultaneously using an alcohol thermometer every minute
while a datalogger measures water temperature via TCs to generate a calibration curve
for the TCs. All experimental temperature data are logged at 2 minute intervals using a
Campbell Scientific CR10x logger with AM25T multiplexer. Tests for significant
53
difference are performed using the Mann-Whitney rank sum test due to the non-normal
nature of the data.
Vehicle models used in the investigation are a 2005 Lexus RX300 (burnished gold
metallic), denoted Vehicle A, and a 2001 Toyota Corolla (silverstream opalescent),
denoted Vehicle B. Vehicles are not modified from factory condition. Temperature data
collected on 18 September and 19 September, 2010 are used to calibrate temperature
measurements including the hood, roof, trunk, and front and rear pavement
temperatures. The calibration method involves placing both vehicles in parking spaces
unobstructed from sunlight, with the front end of the vehicle facing south (same direction
as in the experimental trials), over a two day weekend period, separated by 10m to
prevent interference. Afterwards, the thermocouple readings measured on the warmer
vehicle are calibrated to the cooler readings on the other by a coefficient of
multiplication, normalizing temperatures recorded at vehicle B to those at vehicle A.
The converse method is used to normalize the cooler asphalt temperature
measurements (vehicle A) to those measured beneath the other vehicle (vehicle B).
Each of the five measurements locations is independently calibrated.
Two parking stalls are included in the shade investigation. One stall is partially
shaded from the southwest by the aforementioned magnolia tree, leaving the rear 33%
of the parking space exposed to solar radiation. An unshaded stall is located 14 meters
directly east of the shaded stall. Vehicle A is parked in the unshaded stall and vehicle B
in the shaded stall between 4 September, 2010 and 16 September, 2010. Temperature
measurements are made between 10:00 and 17:00. Upon parking the vehicle, the
54
thermocouples used to measure pavement surface temperature are affixed to the
pavement surface using thermal paste.
Simulated Driving Activity Data Collection
Three driving experiments are performed to determine the effect of engine and
drivetrain use on the pavement temperature. The first experiment is designed to
measure the impact of vehicle operation on parking space surface temperature after
being parked and shut-off. The second experiment is designed to measure the
cumulative impact on pavement temperature from parking, removing, and reparking
vehicles over a parking space not exposed to radiation before the experiment. The third
experiment is similar to the second experiment but over a parking space previously
under insolation.
The first test, performed on 4 October, 2010, simulates typical workweek parking
lot driving activity by parking both vehicles in sunlit spaces until 13:30 then removing
and driving a test vehicle (vehicle B) for 30 minutes, measuring the temperature
increase of the pavement while the pavement is exposed to sunlight. The vehicle is
driven with maximum air conditioning for 10 minutes and then the air conditioning is
shut off for the remainder of the vehicle operation. A control vehicle (vehicle A) is
simultaneously removed from its parking space, driven to a location 6m outside of the
experimental area and then turned off for the duration of the test-vehicle’s excursion
while the surface temperature of the exposed parking space is measured. The vehicles
are then re-parked and all temperature measurements are continued for ½ hour.
The second driving experiment is set up by first parking vehicle A in its space in
the morning of 19 October, 2010. The vehicle is then temporarily removed to secure
the pavement surface measurement TCs at the same locations used in the previous
55
experiments and then reparked, marking the beginning of the experiment at 15:43. The
experimental procedure involves cycling the vehicles through the space. While vehicle
A is parked in the stall and turned off for approximately 10 minutes, vehicle B is driven
on university roads. Vehicle A is then started, immediately removed, and left running
nearby but outside the experimental area. The space is empty for approximately 4
minutes, after which vehicle B is parked in the space and turned off. Vehicle A is then
driven while vehicle B is parked. This cycle continues until the end of the experiment.
Air conditioning is used as needed to maintain a comfortable cabin temperature. In
order to draw comparisons between the hysteretic cycle of a cool pavement to a warm
pavement, the third driving experiment is the same as the second experiment but is
performed on a parking space that is exposed to sunlight for 8 hours, warming it until
the beginning of the experiment at 14:15 on 28 October, 2010.
Tree Canopy Shade Data Collection Methods
The tree canopy shade investigation is performed prior to the other experiments in
this publication but serves to address temperature phenomena of pavement that is
constantly exposed, a phenomenon that is not observed at the university parking lot but
one that may be more typical of retail locations described by sources in the introduction
of this publication. Roadway transect thermal measurements are made using Omega
{5TC-PVC-24} type-T TCs calibrated in the same manner described in the first
experiment. A 5.6m transect of TCs is installed as shown in in Figure 3-1 which
illustrates the horizontal and vertical placement of TCs in the pavement. TCs are
installed at various depths by drilling a vertical shaft through the asphalt using a carbide
tip 6.3mm (1/4in) bit. After placement, thermocouples are sealed into the pavement
using elastic crack filler followed by a coal-tar emulsion sealcoat.
56
Pavement thickness is measured to be 0.0381m (nominally 1.5in). Pavement
subgrade is identified as a clean sand backfill. At 29.639461° N, 82.345293° W, directly
west of the catchment, a Texas Weather Instruments WRL-25 is installed to collect solar
radiation using its included pyranometer, ambient temperature using both wet and dry
bulb thermometers, and wind velocity using an anemometer. A Campbell Scientific
AM25T thermocouple multiplexer and a CR800 datalogger are used to log TC
temperatures. Dry period weather and pavement temperature data are recorded
discontinuously from 16 May, 2008 to 6 September, 2008, along the transect shown in
Figure 3-1. Measurements are made and recorded at 5 minute intervals, grouped by
daytime hour, and tabulated. Horizontal and vertical temperature profiles are analyzed
for trends in time. The aforementioned pyranometer has dimensions of 305x102x61mm
(12x4x2in) and a spectral range of 300-1100nm from 0 W/m2 up to 1500 W/m2 radiation.
This spectral range allows for the capture of energy associated from the near-UV range
to part of the near infrared range.
Shade coverage is measured by photographing the test site hourly, from 07:00 to
19:00 between 7 June and 9 June, 2008 and retaining photos that most clearly illustrate
shading. Photographs are taken with a tripod mounted 7.2 megapixel digital camera
from a point due south from the crown of the road. All photos are taken from the same
vantage point and viewing angle. Visual editing software is then used to quantify the
areal coverage of shadows with the included pixel area measurement algorithm which is
then normalized to the maximum extents of the asphalt pavement (maximum width
equal to the distance between concrete curbs and maximum length equal to the
northernmost and southernmost records of pavement shade). Results are then entered
57
into the spreadsheet and are presented along with the hourly pavement temperature
and radiation records in the results section.
Results and Discussion
Thermal Results of Parking Stall Shade Treatments
Calibration results are shown in Figure 3-3 and Figure 3-4. Table 3-1 shows the
weather data during the calibration period. Thirteen days of experimental results are
shown in Figure 3-5. There is > 20°C difference between treatment and control peak
hood and roof temperatures but not trunk temperatures. Visual observation confirms
that the trunk of the shaded vehicle is only partially shaded during the early afternoon
and evening. The maximum temperature difference between treatment methods is
observed at the vehicle hoods (23.4°C), followed by roofs (22.5°C) and trunks (14.8°C)
at 14:00. The difference in pavement surface temperatures is < 0.7°C.
Results also show that there is minimal difference between pavement temperature
measured at the front and rear of the vehicle. Interestingly, sub-vehicular pavement
temperature continues to climb after 15:00 when the temperature of continuously
exposed asphalt drops. The pavement surface most likely continues to heat beneath
the vehicle after 15:00 because it is influenced by conduction from nearby exposed
pavement and by sensible heat while the exposed asphalt is strongly controlled by
radiation. The ambient air temperature peaks at 17:00 as shown in Table 3-6, which
supports this posit.
The non-normal data are statistically analyzed using the Mann-Whitney rank sum
test; results indicate a significant difference between the shaded and control roofs,
trunks, and hoods (P < = 0.05), but not the asphalt surface temperatures (P > =
0.05). The results suggest that vehicles parked without shade during the PIP should be
58
parked in high density such as by using fewer roadways, sizing lots for typical usage
rather than peak patronage, or if possible by double parking vehicles using a valet
system. Hot vehicle surfaces would still contribute to a first flush, however, the duration
of which is a function of material heat capacity.
Pavement Temperature Shift Under Simulated Parking Activity
While the results of the shading experiment show little difference in sub-vehicular
pavement temperature due to canopy shade, three parking experiments do show
significant influence on pavement temperature (p < a = 0.05). Exposure to solar
radiation increases pavement surface temperature while parking a vehicle over the
pavement cools pavement surface temperature. The first experiment documents a
stronger thermal influence from a warm engine and drivetrain on pavement temperature
than the later experiments do, likely because the vehicle is operated for 3 times as long
before reparking in the first experiment as it is in the second and third experiments.
Results from the first experiment are shown in Figure 3-6 and Table 3-2. The
average pavement temperature when first exposed to sunlight a 14:14 is 26.2°C,
increasing to 45.6°C for both test and control vehicles at 15:00. The vehicles are
reparked at 15:04, two minutes after which the pavement surface temperature drops by
2.2°C in the front of the test vehicle and 9.6°C in the rear, suggesting an exponential
decay in surface temperature and that heat does not penetrate deeply into the
pavement within 30 minutes of insolation.
The front and rear pavement temperatures beneath the control vehicle differ by
0.2°C after 15:26 but the front of the test vehicle is approximately 5°C hotter than the
rear of the test vehicle in the same period. In addition, both front and rear temperatures
of test vehicle are hotter than beneath the control vehicle at 15:28, after which the test
59
vehicle remains under monitor for 10 hours. The front and rear pavement temperatures
beneath the test vehicle do not reach within 0.5 degrees of each other (not shown),
suggesting a thermal influence from the vehicle engine or drivetrain. Results suggest
that the benefits of shade outweigh the costs of a hot drivetrain on pavement heat
balance during sunlight hours.
Results from the second vehicle parking experiment are shown in Figure 3-7A,
which illustrates the difference between temperatures measured front and rear of the
vehicle over three cycles. After 16:32, without direct insolation, removing vehicle B
drops the pavement surface temperature by more than 2°C in < 4 minutes and when
vehicle A is returned to the parking space the temperature increases in both the front
and rear of the vehicle for 2 minutes before cooling off. Hence drivetrain temperature
produces a measured but transient impact on pavement surface temperature.
Figure 3-8 represents the hysteretic heating and cooling cycles observed before
16:32 in the 19 October experiment, recorded on a 2 minute timestep, generated from
data shown in Figure 3-7A, with fit statistics shown in Table 3-3. In the first cycle, the
front pavement temperature rises exponentially to 38.7°C (+6.7°C) when exposed to
solar radiation while the pavement at the rear rises in an near linear manner to 36°C
(+5°C). Pavement temperatures sharply plateau in the second cycle when reaching
approximately 40°C. The third cycle exhibits a linear temperature increase and
exponential temperature decay in both the front and the rear. While the observed
exposure/shade cycle does not result in a systematic increase in peak pavement
temperatures, base temperatures do show a consistent increase. The very large
increase in front pavement temperature during the first heat phase lays credence to the
60
influence of engine temperature. After radiation diminishes due to shade from nearby
vehicles, the influence of drivetrain temperature is strong enough to show a warming
effect on pavement surface temperature. The cumulative effect of this phenomenon on
stormwater runoff during evening events a parking lot is unknown and only of possible
concern in parking lots serving high frequency afternoon and evening traffic.
The third parking scenario is designed to determine if exposure of a pavement
surface to solar radiation before parking and reparking vehicles would dampen the
trends observed in the previous experiment. Results are shown in Figure 3-7B. Initial
pavement temperature is 52°C, 18°C higher than the previous experiment. The
pavement surface temperature increases during insolation and cools when covered by a
vehicle. All three heating cycles observed between 14:53 and 15:38 exhibit less change
in pavement temperature, however, than the previous experiment. As shade begins to
cover the site at 15:30, the temperature data exhibit noise for three cycles (possibly due
to patchy shade) after which the heat/cool patterns shift at the front of the vehicle in the
same manner as the previous experiment.
The three cycles shown in Figure 3-9 are generated from data shown in Figure 3-
7B, recorded on a 0.5 minute timestep to better capture rapid pavement temperature
decay; statistics are shown in Table 3-4. In comparison to the previous experiment,
there are two distinct components to the cooling cycle during this experiment: a rapid
decrease for between 1 minutes and 2 minutes, followed by a slower loss rate. The
cooling patterns are modeled using a 5 parameter exponential function. The heating
patterns follow power law relationships rather than the linear or exponential increases
observed in the previous experiment. Conclusions that can be drawn from the
61
reparking experiments are that exposure of a shaded pavement to sunlight results in a
rapid increase in temperature whose shape may not be known a priori (ranging from a
power function to exponential).
In comparison to Figure 3-8, Figure 3-9 shows very sharp contrasts between
pavement temperature measured at the front and rear of the vehicle. While the patterns
at the rear are consistent and repeated, base temperatures are inconsistent at the front
of the vehicle but still higher in the front of the vehicle than at the rear, by >1°C. The
heat already stored in the pavement before the third experiment is a likely cause for the
stable base temperature at the pavement rear. Note that water is observed on the
pavement in close proximity to the thermocouple when removing the vehicle after the
third cycle, indicating that condensation may have cooled the pavement. In general, the
heat cycles at the front of the vehicle consistently exhibit a slower rate of heat gain
(lower slope) than at the rear but the heat cycles also begin at warmer temperatures
when compared to the rear. Figure 3-8 exhibits a rise in the base temperature during
consecutive cycles, both in the front and rear graphs. Figure 3-9 shows a consistent
base temperature at the rear while results in the front are influenced by condensation.
Thermal Trends on Shaded Roadway
A roadway on the parking lot is instrumented so as to monitor subsurface
temperatures and determine the impact of shade on the temperatures measured.
Results from measurements made beneath the pavement surface between 16 May
2008 and 6 September, 2008 are summarized in Table 3-5. Surface temperatures
indicate a distinct peak and trough in daily temperatures occurring between 12:00 to
15:00 and 6:00 to 7:00, respectively. Peak temperatures occur at different times for
different locations along the transect. For example, peak temperature at location A1s
62
occurs between 14:00 and 15:00 but the peaks for A4s and A5s occur between 12:00
and 13:00. The subscript s denotes surface, b indicates bottom of pavement, and sub
indicates subgrade. The rate of change with respect to time is most strongly positive
between 10:00 and 13:00 and most negative between 14:00 and 17:00. Note that the
positive rate of change at the surface occurrs sharply at locations A1s and A2s as well
as A4s and A5s but more gradually at A3s, located in the center of the transect and
furthest from shade trees. Results at 38mm of depth also show rapid heating and
cooling. Both peak gradient and maximum temperatures are lower in A4b and A5b, as
compared to the surface, and they occur later than at the surface. A1b, A2 b, and A3 b
temperatures all peak when their respective surface temperatures peak.
In the subgrade, peak gradient occurs 1 hour later than peak temperature, except
at A3b. The magnitude of peak temperatures and peak gradients are notably lower in
the subgrade than at the surface. Still, the observation that subgrade temperatures do
change according to diurnal patterns indicates some thermal connectivity between the
pavement and the subgrade while simultaneously highlighting the lack of thermal
conductivity between the subgrade and the thermal mass of the ground beneath.
Table 3-6 shows atmospheric conditions as a function of time as well peak
gradient and peak temperature location vs. time. Shade coverage shifts from A1 and
A2 at 11:00 to A3, A4, and A5 at 13:00 as the sun is blocked by east tree in the morning
and the west tree in the afternoon. It appears that if a region of the transect is shaded
during the peak insolation period (PIP), the thermal peak also occur at this time.
Table 3-7 and Figure 3-10 show shadow extension over the more than 5m wide
pavement surface as a function of time. Shading from the west provides shadow
63
coverage during the PIP. A2 and A3 peak in temperature gradient from 11:00 to 12:00,
during the PIP, but A4 and A5 peak long before the time of maximum solar radiation
shown in Table 3-5. The exposure of shaded TC locations to radiation after the PIP
does not result in a thermal peak, strongly suggesting that it is critical to shade
pavement during the peak period of insolation to minimize pavement heat storage. It is
recommended that surface parking lots in the North Florida region be oriented with rows
in the N-S direction with trees planted west of the parked vehicles. This would be most
advantageous when combined with angled parking and 1 lane roads such that every car
may receive maximum benefit from canopy shade. This also has a positive impact on
aesthetics, vehicle surface temperature, and likely on vehicle interior temperature.
As illustrated in Kertesz and Sansalone (2011), shown in Figure 3-11, there is a
demonstrable difference in the heat transfer potential of a pavement at 40°C and a
pavement at 35°C. In addition to the previously mentioned methods to improve
pavement shade, alternative methods include but are not limited to parking garages,
alternative pavement material, application of reflective pavement coatings, and runoff
retention. Parking garages provide vehicle shading and increase the effective water
quality of urban runoff per parking space. Alternative material parking lots such as
porous concrete effectively treat runoff by increasing onsite infiltration but may introduce
pollutants into the ground if not properly engineered to treat pollutants. Some solutions
also provide ulterior benefits. Sansalone et al. (2009) redesigned a University of Florida
parking lot to mitigate stormwater pollution while also minimizing phosphorus and
nitrogen runoff, demonstrating that LID designs and retrofits are potentially more cost
effective than mitigating contaminated roadway runoff.
64
Given that soft BMPs such as tree canopy shade are context sensitive, it is critical
to select plant species that maintain coverage during the hot season and to properly
maintain them. Scott et al (1999) found that 41% of the shaded lot trees in their study
site are Chinese elm which are defoliating due to drought stress. Kjelgren and
Montague (1998) found that two tree species, Green- and Norway maple, exhibited
reduced transpiration over asphalt surfaces while flowering pear showed increased
transpiration. If installed correctly, the benefits of trees can outweigh their costs as
shown in Table 8 (McPherson et al. 2005). However, improper vegetation maintenance
may result in increased biogenic material entering the urban drainage network.
Summary
This research illustrates the impact of shade on pavement surface temperature,
whether provided by a vehicle, tree canopy, or both. Results from the first experiment
reject the hypothesis that tree-canopy shading of vehicles provides a significant
decrease in pavement temperature beneath the vehicle compared to pavement
temperature beneath an unshaded vehicle. Results do not reject the hypothesis that
pavement temperature increases during repeated reparking cycles over an initially cool
(31oC) pavement while pavement temperatures decrease under repeated cycles when
the pavement is initially warm (43oC). The investigation does not reject the hypothesis
that shade from the west lowers peak pavement temperature more than shade from the
east, on a N-S roadway. The hypothesis that temperature gradients can be used to
illustrate a thermal disconnect between pavement and subgrade material cannot be
rejected, however it is not strongly supported.
65
Table 3-1. Weather conditions during 18 September and 19 September calibration days.
Date 9/18/2010
9/19/2010
Statistic Range Mean Median
Range Mean Median
Temperature (oC) 33.9 22.8
32.8 22.8
Time 14:48 6:00
15:59 5:04
Humidity (%) 51.0 63.3 62.0
56.0 69.1 73.0
Pressure (Pa) 339 101592 101592
339 101592 101592
Rainfall (mm) 0.0 0.0 0.0
0.0 0.0 0.0
Wind speed (m/s) 4.02 0.72 0.0
4.92 0.54 0.0
Solar radiation (W/m2) 650.0 208.4 140.0
660.0 144.2 10.0
Table 3-2. Weather data during parking experiment performed on 4 October, 2010. Time (HH:mm)
Temperature (oC)
Humidity (%)
Pressure (Pa)
Rainfall (mm)
Wind speed (m/s)
Solar radiation (W/m2)
13:30 25.0 48 101659 0 3.13 540
13:40 25.6 47 101626 0 1.34 540
13:50 25.6 42 101626 0 0.89 540
14:00 25.6 42 101626 0 2.68 530
14:10 26.1 42 101626 0 0.00 530
14:20 26.1 42 101592 0 2.24 530
14:30 26.1 39 101592 0 0.00 520
14:40 26.1 39 101592 0 0.00 510
14:50 26.1 40 101592 0 0.89 500
15:00 26.1 39 101592 0 1.79 490
15:10 26.1 38 101592 0 1.79 480
15:20 25.6 41 101558 0 0.45 470
15:30 26.7 40 101558 0 2.24 450
66
Table 3-3. Parametric statistics for hysteretic loop equations for 19 October, 2010 experiment.
Location Time Description Equation Formulation r2 Standard Error
Fro
nt
15:48 Rising Limb Equation f=-0.743+0.744*exp(0.377*x) 1 0.004
Falling Limb Equation f=2.951+0.614*exp(0.282*x) 0.939 0.391
16:04 Rising Limb Equation f=3.495*x^0.21 1 0
Falling Limb Equation f=-0.921+1.833*exp(0.287*x) 0.998 0.12
16:18 Rising Limb Equation f=0.008+1.213*x 1 0.02
Falling Limb Equation f=1.161+1.258*exp(0.265*x) 0.991 0.159
Rear
15:48 Rising Limb Equation f=-5.51+5.37Eexp(0.105*x) 0.979 0.512
Falling Limb Equation f=0.321+1.272*exp(0.196*x) 0.984 0.223
16:04 Rising Limb Equation f=5.706*x^0.214 1 0
Falling Limb Equation f=0.566+1.162*exp(0.452*x) 0.996 0.22
16:18 Rising Limb Equation f=0.008+1.538*x 1 0.02
Falling Limb Equation f=0.737+2.658*exp(0.176*x) 0.737 0.375
Equations are developed to model temperature difference from the base temperature measured at the beginning of the rising limb. The independent axis is net exposure time in minutes. The rising limb moves forward in net exposure time while the falling limb reduces net exposure time. Table 3-4. Parametric statistics for hysteretic loop equations for 28 October, 2010
experiment. Location Time Description Value r
2 Standard Error
Fro
nt
14:58 Rising Limb f=1.288*x^0.737 0.992 0.104 Falling Limb f=-19.238+20.46*exp(0.012*x) 0.993 0.085
15:12 Rising Limb f=1.241x^0.915 0.986 0.159
Falling Limb f=-3.666+4.936*exp(0.025*x)+0.007 *exp(1.832*x) 0.973 0.124
15:24 Rising Limb f=0.969*x^0.749 0.948 0.212
Falling Limb f=-1E8+1.5E8*exp(0)+0.107 *exp(0.731*x) 0.985 0.161
Rear
14:58 Rising Limb f=2.36*x^0.562 0.988 0.188
Falling Limb f=-2.425+3.487*exp(0.071*x) 0.995 0.092
15:12
Rising Limb f=2.579*x^0.457 0.797 0.991
Falling Limb f=-0.753+2.044*exp(0.124*x)+0.001 *exp(2.709*x) 0.793 0.112
15:24 Rising Limb f=2.073*x^0.525 0.952 0.993
Falling Limb f=-1.881+2.858*exp(0.086*x) 0.326 0.081
Equations are developed to model temperature difference from the base temperature measured at the beginning of the rising limb. The independent axis is net exposure time in minutes. The rising limb moves forward in net exposure time while the falling limb reduces net exposure time.
67
Table 3-5. Hourly asphalt pavement temperatures across east-west transect.
Time
Temperatures under Pavement Surface
(°C)
Temperatures at Pavement Bottom
(°C)
Subgrade Temperatures
(°C) Scale
(HH) A1s A2s A3s A4s A5s A1b A2b A3b A4b A5b A1sub A2sub A3sub (°C)
00-01 28.2 29.3 29.7 28.7 28.2 28.6 29.3 29.9 30.0 28.7 30.1 31.4 29.5 7.0
01-02 27.6 28.6 29.0 28.1 27.6 27.8 28.6 29.2 29.3 28.2 29.5 30.7 28.9 6.4
02-03 27.2 28.2 28.6 27.7 27.2 27.4 28.1 28.8 28.9 27.8 29.1 30.3 28.5 5.9
03-04 26.8 27.8 28.2 27.4 26.8 27.0 27.8 28.4 28.6 27.4 28.7 29.9 28.2 5.3
04-05 26.5 27.5 27.8 27.1 26.5 26.7 27.4 28.1 28.2 27.1 28.4 29.5 27.8 4.7
05-06 26.3 27.2 27.5 26.8 26.2 26.4 27.1 27.8 27.9 26.8 28.1 29.2 27.6 4.2
06-07 26.1 26.9 27.3 26.6 26.0 26.2 26.9 27.6 27.7 26.6 27.8 28.9 27.3 3.6
07-08 26.5 27.5 27.7 27.2 26.2 26.5 27.4 27.8 27.8 26.6 27.7 28.8 27.2 3.0
08-09 28.0 28.9 28.9 28.5 27.2 27.9 28.9 28.9 28.5 27.3 27.9 29.1 27.4 2.5
09-10 29.6 30.1 30.2 30.5 29.0 29.4 30.1 30.0 29.6 28.4 28.5 29.6 28.0 1.9
10-11 31.2 32.3 34.0 36.6 34.0 30.8 32.0 33.1 32.8 31.6 29.4 30.8 29.7 1.4
11-12 33.2 38.5 39.3 41.6 38.9 33.0 38.0 38.1 36.8 35.0 30.3 33.4 32.2 0.8
12-13 39.5 44.1 43.7 46.0 43.3 38.2 44.1 42.4 40.6 38.3 32.1 36.7 34.9 0.2
13-14 44.6 46.0 45.4 45.5 41.2 45.4 46.0 44.3 42.1 38.0 35.2 39.1 36.0 -0.3
14-15 46.6 47.1 44.1 40.8 38.3 47.4 47.2 43.6 40.0 36.3 37.5 40.1 35.2 -0.9
15-16 45.5 43.7 41.0 40.0 37.7 46.0 44.2 40.8 38.6 35.7 38.5 39.1 34.7 -1.5
16-17 40.2 40.9 39.6 38.0 36.1 40.2 41.1 39.6 37.7 34.8 37.9 38.5 34.3 -2.0
17-18 36.8 38.0 37.6 36.4 35.2 37.1 38.0 37.6 36.6 34.3 36.4 37.6 34.0 -2.6
18-19 34.5 35.8 35.7 34.7 33.8 35.0 35.8 35.9 35.3 33.4 35.0 36.4 33.4 -3.2
19-20 33.0 34.4 34.5 33.4 32.6 33.4 34.3 34.7 34.2 32.5 34.2 35.5 32.8 -3.7
20-21 31.3 32.6 32.9 31.7 31.1 31.4 32.5 33.1 33.0 31.4 33.0 34.4 31.9 -4.3
21-22 29.9 31.3 31.6 30.5 29.9 30.2 31.2 31.8 31.8 30.3 31.8 33.3 31.0 -4.9
22-23 29.1 30.4 30.7 29.7 29.1 29.2 30.3 30.9 30.9 29.6 31.1 32.4 30.3 -5.4
23-24 28.5 29.7 30.0 29.0 28.4 28.7 29.6 30.2 30.3 29.0 30.4 31.7 29.7 -6.0
68
Table 3-6. Daily solar radiation, air temperature, wind, and shadow patterns. 10:00 to 14:00 is the peak insolation period.
Time (<= HH)
Wind (m/s)
Air Temp
(°C)
Solar Radiation
(W/m2)
% Shadow
Coverage
Gradient Peak
Location
Thermal Peak
Location Shaded TCs
01:00 0.44 25.8 0.0 100 All
02:00 0.10 25.0 0.0 100 All
03:00 0.16 24.6 0.0 100 All
04:00 0.08 24.3 0.0 100 All
05:00 0.07 24.0 0.0 100 All
06:00 0.04 23.7 0.0 100 All
07:00 0.03 23.5 7.3 20.4 A1
08:00 0.03 23.4 90.9 82.4 A2-A5
09:00 0.11 24.1 214.1 93.7 All TCs
10:00 0.21 25.6 318.1 33.6 None None A1, A2
11:00 0.38 27.3 410.9 10.8 A4, A5 None A1, (A2)
12:00 0.69 28.6 487.1 0.7 A2, A3 None A1
13:00 0.96 29.8 549.1 15.4 A1 A4, A5 A5, A4, (A3)
14:00 1.24 30.3 540.9 47.6 None A4, A3 A5-A3, (A2)
15:00 1.53 30.6 510.4 76.6 None A1, A2 A5, A3, A2
16:00 1.47 30.8 415.8 71.3 A1, A2-A5
17:00 1.27 31.3 318.7 90.7 All
18:00 1.57 31.2 246.9 95 All
19:00 1.86 30.7 148.3 69.1 A3-A5
20:00 1.40 29.8 66.0 100 All
21:00 1.17 29.2 9.1 100 All
22:00 0.61 27.6 0.0 100 All
23:00 0.32 26.8 0.0 100 All
24:00 0.19 26.1 0.0 100 All
An explanation for the migration of peak temperature from A5 to A1 throughout the PIP is due to shade patterns, as shown in the far-right column. Shade patterns reverse from A1 and A2 to A5-A3 from 11:00 to 13:00, at same time as peak gradient shifts from A5 to A1 (column 5). There is a relationship between shading of a TC and time of thermal peak (two rightmost columns). TC: thermocouple
69
Table 3-7. Shadow patterns over transect, measured from west curb Time of Day Covered Portion of Asphalt Pavement Transect (m (ft))
07:00 5.5-6.1 (18-20) 08:00 0.3-0.9 (1-3) 09:00 0.9-6.1 (3-20) 10:00 4.9-6.1 (16-20) 11:00 >5.8 (>19) 12:00 <0.3 (<1) 13:00 0-1.8 (0-6) *{large coverage area 0-11ft directly south of transect} 14:00 0-1.2, 1.8-5.5 (0-4, 6-18) 15:00 0.3-1.2, 3.7-6.1 (1-4, 12-20) 16:00 Full coverage 17:00 Spotty full coverage 18:00 Full coverage 19:00 3.7-4.0 (12-13) 20:00 5.2-5.8 (17-19)
Distances mentioned are perpendicular to the concrete curb shown in Figure 3-10.
Table 3-8. Average annual benefits of four tree sizes over 40 year period.
Tree Size Representative Species
Stormwater Retention
(gal) [$]
Cooling Energy Saving
(kWh) [$]
Heating Energy Saving
(kWh) [$] CO2 offset
(lbs)
Increased Property
Value ($)
Small Cornus florida 1,265
[$12.52] 44 [$3.36] 278 [$2.91] 168 [$1.26] $7.29
Medium Magnolia grandiflora
2,566 [$25.40] 53 [$3.99] 298 [$3.12] 128 [$0.96] $13.44
Large Deciduous Acer rubrum
4,778 [$47.30] 89 [$6.74] 415 [$4.34] 340 [$2.55] $41.02
Large Conifer Pinus taeda
3,888 [$38.49] 66 [$4.98] 337 [$3.53] 227 [$1.71] $23.08
Note: Values are generated from data presented by McPherson et al. (2005).
70
Figure 3-1. Lake Alice watershed including parking lot catchment, transect, and parking
spaces investigated herein.
71
Figure 3-2. Vehicle body and asphalt surface thermocouple installation diagram. Vehicle
length, width, and height (from ground) is 4.58m, 1.816m, and 1.669m for vehicle A and 4.768m, 1.76m, and 1.466m for vehicle B. Pavement temperature is measured 1.2 m inward from the front and rear for the respective measurements. Parking space dimensions are measured to be 6.1m long and 2.74m wide (9x20 ft).
72
Date and Time (HH:mm)
0
6:0
0
1
2:0
0
1
8:0
0
0
0:0
0
0
6:0
0
1
2:0
0
1
8:0
0
0
0:0
0
Veh
icle
Tem
per
atu
re (
oC
)
10
20
30
40
50
60
10
20
30
40
50
60
(Vehicle A)
(Vehicle B)(Trunk Temperature)
Veh
icle
Tem
per
atu
re (
oC
)
10
20
30
40
50
60
10
20
30
40
50
60
(Vehicle A)
(Vehicle B)
(Roof Temperature)
Veh
icle
Tem
per
atu
re (
oC
)
10
20
30
40
50
60
10
20
30
40
50
60
(Vehicle A)
(Vehicle B)(Hood Temperature)
18 September 19 September (2010)
18 September 19 September (2010)
18 September 19 September (2010)
Figure 3-3. Vehicular surface temperatures measured in direct sunlight for A) the roof,
B) the hood, and C) the trunk during calibration period.
A
B
C
73
Date and Time
0
6:0
0
1
2:0
0
1
8:0
0
0
0:0
0
0
6:0
0
1
2:0
0
1
8:0
0
0
0:0
0
Pav
emen
t S
urf
ace
Tem
per
atu
re (
oC
)
26
28
30
32
34
26
28
30
32
34
Front B
Rear B
Front A
Rear A
18 September 19 September (2010)
Asphalt temperature beneath vehicle
Figure 3-4. Pavement surface temperatures beneath engine (front) and gas tank (rear)
of vehicles A and B exposed to direct sunlight during calibration period. These results are used to calibrate the pavement measurements between vehicles, performing a temperature correction for the front of the vehicles, and a separate correction for the rear.
74
Veh
icle
Tem
per
atu
re (
oC
)
20
30
40
50
60
70Shaded
Control
Shaded
Control
Time (HH:mm)
09:00 11:00 13:00 15:00 17:00
Veh
icle
or
Pav
emen
t T
emp
erat
ure
(o
C)
30
40
50
60Shaded
Control
Time (HH:mm)
09:00 11:00 13:00 15:00 17:00
Shaded
Control
Exposed Asphalt
(Hood) (Roof)
(Trunk)
(Vehicular Shaded Asphalt Temperature)
Figure 3-5. Comparison of average surface (A-C) and D) pavement temperatures between shaded and unshaded vehicles
between the hours of 10:00 and 17:00. Error bars represent 1 standard deviation from mean.
A
C
B
D
75
Time (HH:mm)
14:1
5
14:2
5
14:3
5
14:4
5
14:5
5
Test
Control
Time (HH:mm)
13:3
0
13:4
0
13:5
0
14:0
0
14:1
0
Pav
emen
t T
emper
ature
(o
C)
24
30
36
42
48
54
Test
Control
Exposedto Sun
Time (HH:mm)
15:0
5
15:1
5
15:2
5
15:3
5
15:4
5
15:5
5
Front Test
Rear Test
Rear Control
Front Control
Exposed to Sun
A (Vehicles Parked) B (Exposed Asphalt) C (Re-parked Vehicles)
N
Figure 3-6. Pavement temperature A) before, B) during, and C) after driving test vehicle to observe effect of warm engine
on 4 October, 2010.
76
Time (HH:mm)
14:15 14:35 14:55 15:15 15:35 15:55 16:15 16:35 16:55 17:15
Pav
emen
t T
emp
erat
ure
(oC
)
36
42
48
54
60
Beneath Front of Vehicle
Beneath Rear of Vehicle
Exposed Pavement Surface
Pav
emen
t T
emp
erat
ure
(oC
)
30
36
42
Beneath Front of Vehicle
Beneath Rear of Vehicle
(Cycled Parking Asphalt Temperature: 19 October, 2010)
(Cycled Parking Asphalt Temperature: 28 October, 2010)
B ABA AB B AB BA ABA
AB B BA AB BA AB B A A B B A ABA
B
Figure 3-7. Pavement surface temperature under frequent parking on A) 19 October and B) 28 October. The symbol [A] denotes vehicle A (Lexus); [B] denotes vehicle B (Toyota). Up arrows denote removing a vehicle; down arrows denote placing a vehicle in the space.
A
B
77
Duration of insolation (+) and shade (-) (minutes)
-20 -15 -10 -5 0 5 10
Pav
emen
t T
emp
erat
ure
(o
C)
30
32
34
36
38
40
(Front, Initially Cool) P
avem
ent
Tem
per
atu
re (
oC
)
30
32
34
36
38
40
Heat Phase (Exposed)
Cool Phase (Shaded)
Measured
Net Heat Loss
(0:00)
(0:06)
(0:16)
(0:20)
(0:30)
(0:34)
(0:44)
(0:00)
(0:06)
(0:16)
(0:20)
(0:30)
(0:34)
(0:44)
(Rear, Initially Cool)
Figure 3-8. Pavement surface temperature hysteretic loops on 19 October 2010
beneath front and rear of vehicle. Three cycles are shown. Parenthetical time is duration since start of experiment (H:mm). Arrows show the cycle trajectory from 0:00. Experiment is started at 15:48 (H:mm) EST. Note the use of the x-axis for net duration of insolation and shade. While the experiment has progressed for 44 minutes, there have been 16 minutes more shade than exposure as shown by the x-axis at time 0:44.
A
B
78
Pav
emen
t T
emp
erat
ure
(o
C)
42
43
44
45
46
47
48
Heat Phase (Exposed)
Cool Phase (Shaded)
Measured
Duration of insolation (+) and shade (-) (minutes)
-20 -15 -10 -5 0 5
Pav
emen
t T
emp
erat
ure
(oC
)
42
43
44
45
46
47
48
(0:00:00)
(0:03:30)
(0:10:30)
(0:14:30)
(0:24:30)
(0:28:00)
(0:37:00)
(0:00:00)
(0:03:30)
(0:10:30)
(0:14:30)
(0:24:30)
(0:28:00)
(0:37:00)
(Front, Initially Warm)
(Rear, Initially Warm)
Figure 3-9. Pavement surface temperature hysteretic loops on 28 October 2010
beneath front and rear of vehicle. Three cycles are shown. Experiment commences at 14:58 EST (H:mm:ss). Parenthetical time is duration since start of experiment (H:mm:ss). Arrows show the cycle trajectory from 0:00. The x-axis is used for net duration of insolation and shade.
A
B
79
A
B Figure 3-10. Graphic analysis of shadow patterns over pavement surface. A) Purple
07:00, green 08:00, blue 09:00, yellow 10:00, orange 11:00, red 12:00; B) Red 13:00, orange 14:00, yellow 15:00, blue 16:00, purple 17:00, white 18:00, green 19:00; time is in HH:mm.
80
Pavement Temperature at Event Onset (oC)
26 28 30 32 34 36 38 40 42 44
KJ
0
2000
4000
6000
8000
10000
12000
Heat Load to Runoff *
Polynomial Regression
95% Confidence Band
Figure 3-11: Plot of heat transfer to runoff compared to pavement temperature before
storm. A 2.5mm precipitation event over the hotter pavement can release a net (after evaporation, convection, etc.) 12500 KJ more heat to runoff. Polynomial regression y = 49.2x2-2805x+44479. *Heat load is per unit depth of rainfall = 1mm.
81
CHAPTER 4 MITIGATING URBAN HEAT: TEMPORAL TEMPERATURE PROFILES FOR
PAVEMENT MATERIALS
Background
As part of demographic changes across North America, the constructed
environment continues to undergo expansion and reconstruction. For example, in
Florida, 1.54 million hectares of land have been converted into the constructed
environment from 1960 to 1997 (Reynolds 2001). Paved parking and roadways are
prominent features of the constructed environment and can dominate the urban
landscape. Parking lots alone represent approximately 30% of the paved area in
Houston and Sacramento (Akbari et al. 2003). Pavement surface area in urban
environments has been documented to cause the urban heat island (UHI) effect (Akbari
et al. 2003; Thanh Ca et al. 1997; Pomerantz et al. 2002; Chudnovsky et al. 2004).
Asphalt pavement is the predominant pavement surface type in the United States.
Approximately 94-95% of paved roads in America are asphalt (Takamura 2002;
Anderson et al. 2009). While many formulations have been made in asphalt design such
as the incorporation of recycled rubber (Choubane et al. 1999), and while asphalt
properties provide for a quiet and smooth vehicle ride, asphalt’s physico-thermal
properties make it a heat sink for solar radiation during periods of insolation and a heat
source at night and during rainfall-runoff events. Asphalt thermally-augments rainfall-
runoff during precipitation events (Hanh and Pfeifer 1994).
The environmental effect of transient and long-term temperature changes in
receiving waters have been thoroughly documented (Langford 1990; Galli 1990;
Coutant 1987; Nakatani 1969; Paul and Meyer 2001; Daufresne et al. 2004; James and
Xie 1998). There are more recent examples of thermal total maximum daily load
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(TMDL) values for receiving waters (Oregon DEQ 2008), indicating a regulatory
recognition of the issue. Thermal augmentation of runoff is a nascent concern but is a
growing issue in light of global climate change and TMDLs.
The transfer of heat to rainfall-runoff depends on the seasonal and daily
distribution of rainfall as illustrated in Figure 4-1 for Portland, OR and Gainesville, FL.
Whereas a rainfall event is likely to occur in the afternoon during the wet season
(summer) in Gainesville, there is an approximately equal probability of a rain event
occurring at any hour of the day in Portland. The potential for thermal loadings based
on daily distribution of rainfall alone would be greater for an asphalt pavement in
Gainesville in the summer as compared to Portland.
Pavement heat gain can be mitigated by changing physical properties or surface
reflectivity (albedo). Previous studies have examined the relationship between albedo
and building temperature (Oleson et al. 2010; Akbari and Taha 1992; Bretz et al. 1998;
Synnefa et al. 2006) as well as the effect of reflective coatings on pavement
temperature (Akbari et al. 2001, Levinson and Akbari 2002; Kinouchi et al. 2004).
Leadership in Energy and Environmental Design (LEED) credits (Haselbach 2008; U.S.
Green Building Council 2009) are available for coatings under the sustainability (credit
7.1) and the green neighborhood development rating system (credit 9). Santero and
Horvath (2009) concluded that changing surface reflectivity can be an effective method
to lower the environmental impact of parking lots. Their study also suggested that
roadways with higher average daily vehicle traffic (ADT) may not heat up as much as
those with lower traffic; hence it may be more beneficial to treat low ADT areas such as
parking as compared to highways. Surface reflective treatments such as reflective
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paints have been utilized for decades in building rooftop applications (Oleson et al.
2010) where there is little abrasion. When applied to pavements, surface coating can
have the undesirable impact of reducing depression storage or infiltration for permeable
pavements.
The results of modifying parameters such as pavement reflectivity, heat storage
and heat transfer properties can be examined with physical models but such
parameters can also be examined using computational resources under conditions and
variability that can be for more challenging with a physical model. For example, finite
element models (FEM) have been developed by Hermansson (2001) and Gui et al.
(2007) for pavement temperature as a function of energy flux. In addition, computational
fluid dynamics (CFD), while similar to FEM (Onate and Idelsohn 1992), is well-suited for
modeling fluid and energy fluxes for dry and wet weather scenarios. CFD can be
expanded into a 3-D environment with a solar radiation routine and user defined
functions (UDF). This current study is designed to simulate pavement heat flux.
Objective
The objective of my study is to physically measure and model the temporal
temperature gradients of concrete pavement as well as asphalt pavement materials of
differing surface treatments subject to wet and dry ambient atmospheric conditions. A
primary hypothesis is that changing the pavement material from asphalt to concrete
reduces cumulative pavement energy in North-Central Florida summer weather
conditions during the daytime period of maximum potential rainfall. The secondary
hypothesis is that changing pavement surface reflectivity mitigates potential storage of
solar radiation in the pavement matrix, therefore reducing the heat storage while
maintaining land use function. Another objective is to measure and compare pavement
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responses under rainfall loads if a rainfall event occurs during the investigation. A final
objective is development of a CFD model based on physical model data to simulate
pavement temperature.
Methodology
The methodology consists of two experiments, an uncontrolled physical
experiment where pavement specimens are exposed to ambient weather conditions,
and an experiment where a computational model is used to approximate measured
data. Physical experiments consist of investigating and comparing pavement surface
temperatures to interior temperatures, assessing hourly rainfall frequency and
pavement temperature to create a thermal relative impact index (RII), investigating
pavement temperature during a storm event, and comparing pavement response rates
under changing weather conditions. The computational model is used to simulate
pavement temperature under simulated weather conditions reproduced from measured
weather data.
Data Collection Methods
Physical experiments are performed at an urban environment in Gainesville
located at coordinates 29.643006° N, 82.34902° W. There are existing buildings 10 m to
the south, 20 m to the east and 100 m to the southeast. Trees are located 7m to the
north and 10m to the west. The asphalt specimens are taken from an asphalt pavement
wearing course that was in service for three years on the Center Drive roadway in
Gainesville FL. The Portland cement concrete specimen is taken from a concrete slab
that has been in service for two years. The properties of the pavement materials are
shown in Table 4-1.
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Pavement specimen mass and volume are measured to the nearest 10 mg and
10cm3 respectively utilizing a Mettler Toledo type electronic balance and volumetric
displacement in a 10 L rectangular polycarbonate vessel that was volumetrically
calibrated to the nearest 10 mL. Bulk density is calculated based on measured dry mass
and volume. Specific heat capacity is measured using a calibrated low heat capacity,
low density expanded polystyrene calorimeter. Calorimetric tests of the specimens are
performed by measuring the temperature change of a known volume of liquid water
(initially at ambient temperature) after placing a pavement specimen uniformly heated to
60ºC in the calorimeter. Conductivity values are estimated from pavement thermal
diffusivity as measured by the time to reach equilibrium in the calorimeter, based upon
the methods of Army Corps of Engineers (1949). Due to the uncertainty in measuring
this model parameter, a sensitivity analysis is also performed to determine the impact of
conductivity on simulated pavement temperature, as discussed in the results.
Of the two instrumented asphalt specimens, one is used as a control (control) with
no reflective coating, and another specimen (reflective asphalt) is painted with two coats
of reflective white paint. This reflective paint is applied as an aerosol from a distance of
250mm for 3 seconds (3 passes, 1 second per pass). This painting method was used to
minimize the effect that multiple or thick layers of paint may have on the heat transfer to
the pavement and also to minimize the infill of depression storage. No surface
treatments are applied to the Portland cement concrete (concrete). A third asphalt
sample cut from the same asphalt roadway is instrumented and measured but not
modeled. The specimen (sealed asphalt) is sealed with a commercially-available
86
petroleum asphalt-silica crystalline filler/sealant. The sealant is applied in two coats to
visibly seal the pavement surface cavities.
Calibrated Omega {5TC-TT-T-30-ST} type T thermocouples (TC) are inserted into
4mm diameter boreholes drilled into the asphalt specimen in duplicate at both 5 and
30mm depths from the pavement surface. TCs are then encapsulated in a
cyanoacrylate bonding agent (k = 0.2 W/m-K) and fully inserted into the drilled
boreholes, securing the TC to the pavement and sealing each borehole. Temperature
is monitored every 5 minutes using a Campbell Scientific CR5000 datalogger and
AM25T thermocouple multiplexer. Local atmospheric data are monitored through
proximate weather stations. Solar radiation is measured only at the weather station
located at 29.6395° N, 82.3453° W, north of the urban location where the specimens
are monitored, using a 305mm x 102mm x 51mm pyranometer (CdS photocell
manufactured by Advanced Phototnix Inc.) with a spectral range of 300-1100 nm (up to
1500 W/m2 radiation), affixed to a Texas Weather Instruments WRL-25 weather station.
This spectral range allows for the capture of energy associated from the near-ultraviolet
(UV) range (300-400 nm) to part of the near-infrared range (750-1400 nm).
CFD Model Components of Heat Transfer with Solar Radiation
Modeling of environmental phenomena requires validation (Thacker et al. 2004)
and in this study CFD simulations are validated with physical model data. In order to
simulate heat transfer under simulated weather conditions by the CFD model, an
unsteady pressure-based solver is used with an absolute velocity formulation and
Green-Gauss cell based gradient solution under compressible air flow. Fundamental
equations used for compressible air flow in this study are those of mass continuity and
87
momentum. The continuity or conservation of mass equation is as shown in Equation 4-
1 (Patankar 1980).
( ) (4-1)
In this equation is the velocity field under laminar conditions (m/s),
is the rate of
change of density per unit volume, where density (kg/m3) can be related back to mass
fraction and temperature (K) via an equation of state. Sm is the mass (kg) added to the
continuous phase from a secondary phase (in this case, Sm = 0). Conservation of
momentum is written for an inertial (0 acceleration) frame of reference (Batchelor 2000)
as shown in Equation 4-2.
( ) ( ) (4-2)
In this equation, p is static pressure (Pa), is the gravitational force (kg/m2s2), is an
external body force (in this case = 0), and is the stress tensor (Equation 4-3), where
is molecular viscosity, I is the unit tensor, volume dilation is accounted for by the loss
term on the right hand side.
0( )
1 (4-3)
A fundamental equation of energy conservation is used because the employed
model also simulates conduction, convection, and radiation. The equation for the
conservation of energy can be written as shown in Equation 4-4.
( ) ( ( )) . ∑ ( )/ (4-4)
In this equation k is laminar conductivity (W/m-°C), Jj is diffusion flux of species j (kg/m2-
s), and hj is enthalpy (J/kg); where T is temperature (K) and Sh is the heat of chemical
88
reaction plus radiation. The first term on the right hand side of the equation is energy
transfer due to conduction. The second term is species diffusion. The third term is
viscous dissipation. Viscous heating is important when the Brinkman number shown in
Equation 4-5 is 1 or greater.
( ) (4-5)
In this equation, is the fluid dynamic viscosity, is the velocity, k is thermal
conductivity, T0 is the bulk fluid temperature, T is the wall temperature. In this case, the
viscosity of the air (nominal 1.8E-5 Kg/m-s) is too low to necessitate the inclusion of this
term and asphalt can be considered to have zero velocity and thus negligible viscous
heating in the simulated time duration. In Equation 4-4, E is defined as shown in
Equation 4-6. In the same equation, h is enthalpy (J/kg) defined for compressible fluids
as shown in Equation 4-7.
(4-6)
∑ (4-7)
In Equation 4-7, Yj is the mass fraction of species j. Specific enthalpy is defined as
shown in Equation 4-8. In this equation Tref is 298.15 K.
∫
(4-8)
Equations 1-8 can be solved simultaneously for compressible flow when coupled with
equations of state (Versteeg and Malalasekara 1995; Batchelor, 2000).
89
Simulation Methods for Temporal Distribution of Heat Transfer Under Solar Radiation
The 3-D numerical model requires a model domain and a mesh composition
illustrated in Figure 4-2. The cubic enclosure is designed as a large air domain such that
surrounding air is circulated within the domain. A velocity inlet is used to specify wind
magnitude according to measured values using a transient profile while a transient
temperature profile is used to specify the measured air temperature data, recorded on
one minute intervals. An outlet zone boundary is added downstream of the inlet. The
model incorporates a simplification where wind direction is constant. However, because
temperature measurements are numerically measured along a linear transect
perpendicular to the direction of wind, bisecting the pavement, this is a reasonable
simplification.
In addition to the upstream inlet and downstream outlet, the domain enclosure
consists of shear-free boundaries at the top and side walls, a no-lip wall boundary at all
other walls to simulate interaction with the pavement surface and insulation chamber.
Wall temperatures are specified using the transient profile specified for upstream air
temperature. The pavement top surface participates in solar ray tracing.
Simulations are performed using solar ray tracing in CFD where a user defined
function (UDF) is generated to lookup measured radiation (W/m2) from an array using a
binary search algorithm (Knuth 1997) and apply the radiation to participating surfaces at
1 minute increments between 07:00 and 19:00. The methods of Michalsky (1988a;
1988b), Iqbal (1983), and Spencer (1971) are used to track solar inclination. Ray tracing
methods are discussed in Cook et al. (1984) and Weghorst et al. (1984). Simulation
parameters are shown in Table 4-2. Air and insulating materials are specified in Table
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4-3. The pavement is simulated as a dense liquid of high dynamic viscosity (50000
kg/m-s), much higher than a semi-solid mixture (Barbe et al. 2000).
While the statistical method used to assess significant difference between different
measured data is the Mann-Whitney rank sum test, the statistical methods used to
determine significant difference between measured and modeled results are part of a
Functional Data Analysis (FDA). Functional data are often data that can be represented
as a curve over a continuum such as time. FDA allow for a comparison of nonfunctional
or partially functional data (Ramsay and Silverman 2005). It achieves this by
approximating measured and modeled data using piecewise curves. While there are
many aspects to FDA, the basic process applied here is to transform the measured and
modeled data to a normal distribution using a box-cox transformation, divide the data
into 27 segments, fit a piecewise continuous curve to the data, perform a principle
component analysis to determine proper of design matrix, group the measured data by
forcing their matrix vectors to sum to zero, then perform an F-test between the
measured an modeled curves with =0.05.
Results and discussion
Measured Heat Balance on Pavement
Measured densities, specific heat, albedo and conductivities for the asphalt and
concrete pavements are shown in Table 4-1 alongside published values for asphalt and
concrete. Due to variation in specific heat, comparison between concrete and the other
materials are conducted using heat storage as the dependent variable.
The specific heat for the asphalt and concrete used in this study are below the
range of typical asphalt and concrete. In the case of asphalt the limestone aggregate
and asphalt is oxidized and the higher air content (approximately 10%) in the concrete
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the measured concrete density, specific heat, and conductivity are all lower than
published values for concrete (Bentz et al. 2010; Newman and Choo 2003). The
concrete unconfined compressive strength is 18.6 ± 0.14 MPa.
Temperatures measured at the pavement surface are not statistically significantly
different (p < = 0.05) from the interior of the pavement according to the Mann-Whitney
rank sum test as shown in Figure 4-3. The relative percent difference (RPD) between
the interior and surface temperature measurements are 1.96, 1.24, 1.91, and 1.89 for
the control (conventional asphalt), reflective asphalt, concrete, and sealed asphalt,
respectively.
A test of significant difference with respect to the control is performed on the data
presented in Figure 4-3 after transforming the temperature data into energy storage
(W/m2) where cumulative heat flux = Tt * Cp * * V) for t = 0 to 720 minutes at a 5
minute timestep (t0 = 7:00). The results of the Mann-Whitney rank sum test indicate that
interior pavement temperatures for the reflective asphalt treatment is significantly
different (p < = 0.05), the concrete is significantly different (p < = 0.05), and the
sealed asphalt treatment is not significantly different (p > = 0.05) from the control.
It is hypothesized that changing the pavement material from asphalt to concrete
reduces heat storage during the highest probability of hourly summer rainfall in
Gainesville. Analyzing historical rainfall data collected at the Gainesville regional airport
between 1998 and 2008 (inclusive) for daily trends yields slightly different results for
peak event frequency hour and time of peak precipitation within the wet season (June to
September) as shown in Figure 4-4. Events are defined as rainfall greater than 0.01
inches (the minimum rain gage sensitivity). The probability of a rainfall event occurring
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between midnight and 12:00 (noon) is low, a total of approximately 19.7%. The chance
of an event occurring between 12:00 and 13:00 is almost double that of the previous
hour, increasing every hour until after 15:00 and finally dropping below 4% at 21:00.
The frequency distribution of rainfall is similarly higher during the same time interval but
more normally distributed about 17:00. Events occurring after 16:00 tend to generate
higher precipitation depth per event than events before 16:00. A cumulative distribution
function (CDF) of precipitation probability is shown in Figure 4-4, indicating that after
19:00 approximately 80% distribution of rainfall has been accounted for. After 20:00, the
CDF reaches 90%. According to the distribution of heat in Figure 4-5, after 19:00 there
is <18% difference between reflective asphalt and the control (dropping to 16% at
20:00) and <3% between the concrete and the control. The low chance of a rainfall
event before noon combined with the diminishing temperature differential between
treatment methods after 19:00 indicates that this is a critical period in which to minimize
the potential for heat pollution in rainfall-runoff.
Temperature data collected for the control, reflective asphalt, coated asphalt, and
concrete are analyzed for temperature and heat flux trends as shown in Figure 4-5 and
summarized Table 4-4. Results are utilized to (1) test the hypothesis that changing
pavement reflectivity significantly mitigates potential storage of solar radiation in the
pavement matrix and (2) test the hypothesis that concrete reduces potential heat
transfer to runoff when compared to the control asphalt specimen during hours of peak
rainfall frequency. The Mann-Whitney rank sum test of significant difference indicates
that both the reflective asphalt and concrete (but not the sealed asphalt) are significantly
different lower than the control (p < = 0.05), supporting hypothesis (1) above. The
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performance of the reflective asphalt between 12:00 and 19:00 is 80% of the control
while the concrete is 87% of the control as shown in Table 4-4 (lower is better),
supporting hypothesis (2) above. Results from an analysis of three consecutive days of
temperature data (8 September – 11 September) are summarized in Table 4-5 and
Figure 4-7. Results support aforementioned hypotheses. Concrete and reflective
asphalt perform equally between 12:00 and 19:00, achieving 78% - 81% of the control.
The daily concrete heat pattern is significantly different from the control over the three
days shown (p < = 0.05). Reflective asphalt is not significantly different from the
control on 8 September (p > = 0.05).
By observation, Figure 4-7 also shows a delay in the rising limb of the concrete,
30-35 minutes later than the reflective asphalt curve (also observed in Figure 4-6). The
falling limb is similarly shifted, however the peak for the concrete is observed to occur at
the same time as the reflective asphalt. In two of the three days, heat is not lost from the
concrete after the peak as fast as it is for the reflective asphalt (similar to Figure 4-5).
The exception is 8 September, where the light concrete performs similarly to the
reflective asphalt, during concurrent wind gusts. In general, the daily results support the
findings based upon averaged temperature data.
Multiplying the probability of rainfall by the difference in heat storage for various
pavement treatment methods provides the relative impact index (RII) for each hour of
the day where 0 indicates no improvement and -1 indicates 100% mitigation of heat.
This result is normalized to a maximum potential heat loss from the pavement that can
occur during the maximum frequency of precipitation (600 KJ/m2 x 11.75%) as shown in
Figure 4-6A, or maximum probability of a rainfall event (600 KJ/m2 x 12.0%) as shown in
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Figure 4-6B. Results indicate that concrete minimizes heat storage as compared to
asphalt. Median performance of the concrete is -0.58 on the volumetric RII and -0.60 on
the event frequency RII between the hours of 12:00 and 19:00 (Figure 4-6). Reflective
asphalt performs both 5% and 6% better than concrete during the same time period for
the volumetric and event frequency based RII, respectively. Neither the daily
performance nor the performance between 12:00 and 19:00 is statistically significantly
different according to the Mann-Whitney rank sum test and the t-test respectively (p <
= 0.05).
Figure 4-8 illustrates pavement heat loss during two rainfall events, on the 5
September and the 24 August. Figure 4-8 shows a heat loss of 773 KJ/m2, 941 KJ/m2,
and 1062 KJ/m2 from the reflective asphalt, concrete, and control specimens between
the onset of rainfall at 15:40 and the end of the event at 17:15. Figure 4-8B shows a
heat loss between the start of the event at 10:45 and 14:00 of 550 KJ/m2 and 670
KJ/m2 for the reflective asphalt and control, respectively. In comparison to the control,
the reflective asphalt performs 9.3% better during an afternoon rain event (Figure 4-9A)
than a morning event where the initial pavement heat storage is half that of the
afternoon (Figure 4-9B). The higher heat loss from concrete compared to asphalt in
(Figure 4-8A) may suggest that reflective asphalt performs better than concrete,
however the rapid and strong response of the control and reflective asphalt to a change
in solar insolation after 16:00 as well as the stronger heat loss from the control and
reflective asphalt due to wind and radiation between 15:15 and 15:40 (600 KJ/m2, 499
KJ/m2) compared to concrete (273 KJ/m2) suggests that at parking lot surfaces where
pre-event wind was previously found not to be correlated to heat transfer to runoff, as
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shown in Chapter 2, concrete and reflective asphalt would perform comparably. This
also emphasizes a benefit of concrete, that it is observed to release heat more slowly
due to convection than asphalt does.
Figure 4-9 presents a narrow focus on pavement energy under two different
radiation patterns, on the 10 September and 17 September. The pavement interiors are
observed to respond to changes in radiation within 5-10 minutes of the change in solar
radiation. One interesting finding is that the smoother solar radiation curve observed in
Figure 4-9B corresponds to a higher peak pavement temperature for all specimens
compared to results observed during the less steady insolation in Figure 4-9A. The
reflective asphalt specimen more rapidly loses heat following its thermal peak when
compared to the concrete, an observation that is consistent with Figure 4-5 and Figure
4-7.
In a separate experiment, concrete curb temperature was previously measured at
a University of Florida parking lot, along a transect described in the second chapter.
Concrete temperature was measured in a 6.1 m wide north-south asphalt road and
300mm wide concrete curb next to the road. Measurements were made 15cm on either
side of the asphalt-concrete seam for the asphalt and concrete measurements,
respectively, and 15mm below the pavement surfaces. This experiment was performed
in duplicate, with one location at the east side of the road, and the other at the west side
of the road. A graphical comparison of concrete to asphalt pavement temperature is
shown in Figure 4-10. Note that the temperatures of the concrete readings on the East
and the West are similar, only 0.87 °C difference on average (3.3% different during
peak hours) with the west curb higher in temperature than the east curb. The difference
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in temperature between concrete and asphalt is similar to the difference between
concrete and the control in Figure 4-5, suggesting that the application of concrete may
provide similar thermal mitigation without the need to alter existing mix designs or apply
special paints or coatings.
Heat, rather than temperature, is the continual focus of this investigation because
it allows for comparison of specimens of differing material composition and because
peak it is useful to consider the potential heat transfer to rainfall-runoff. The RII heat
results stress the importance of accounting for regional rainfall patterns in mitigation
techniques. While the asphalt performs better than concrete as shown in Figure 4-6,
because asphalt and concrete perform similarly over a 24 hour period as shown in
Table 4-4, locations such as Portland OR with a consistent probability of rainfall, shown
in Figure 4-1, may achieve better performance using concrete than Gainesville, FL.
While many observations are made from the 24 August and 5 September rainfall
events, one of the most interesting results is that changes in wind and radiation appear
to affect asphalt specimens more than concrete. This may be a function of pavement
thickness or a function of the reflective paint increasing convection, which is also
supported by sealed asphalt results presented in Figure 4-8A. The second chapter
found that wind before a rain event had little effect on heat transfer to runoff, suggesting
that, in parking lots, more stored energy may be transferred to rainfall-runoff than
presented here. The use of concrete thus would likely result in reduced heat transfer to
runoff t, thereby increasing the performance of concrete relative to asphalt.
During data collection for the roadway experiment, shadows were present at
various points in the day which will have affected pavement temperature in both asphalt
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and concrete results due to the close proximity of the measurements. Future studies
would benefit by analyzing when shading is critical to pavement heat content in order to
make recommendations as to proper placement of shade trees in parking lots.
Heat Balance Simulation Model
Utilizing the physical model data, a heat balance CFD model is created. Figure 4-
11 illustrates the model fit of measured data for the control (r2 = 0.986) and reflective
asphalt (r2 = 0.982) pavements for 18 August 2010. The F-test indicates no significant
difference (p < = 0.05) between measured and modeled results for both the control
and reflective asphalt. A second series of simulations illustrates the effect of changing
thermal conductivity within the ranges presented for asphalt and concrete in Table 4-1.
Results are shown in Figure 4-12. The difference in models after varying conductivity
from 1.2 to 1.8 W/m-K is not statistically significant (p < = 0.05) and resulting curves
overlap for the entire simulation. Given the small variation observed when changing
conductivity, a second simulation of asphalt temperature is performed to test model
performance subject to dry weather conditions using 19 August data. Results are
summarized in Figure 4-13 illustrating the overall model fit (r2 = 0.987 control, r2 = 0.99
reflective asphalt). A third series of measured and modeled comparisons are made
using 6 September weather data. The fit (r2) between measured and modeled for each
pavement treatment is greater than 0.95, and modeled and measured results are not
significantly different (p ≤ = 0.05).
The falling limb of the model deviates from measured results in the August
simulations while the rising limb deviates in the September simulation. Deviation in the
falling limb may occur because heat stored in the rooftop itself is radiating up to the
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specimens. It may also be a function of the higher pyranometer elevation such that it
receives more shading at 17:00 than the specimens. A likely cause for the need to
utilize reduced wind speed in the simulation is because of local obstructions. Previous
publications (Touma 1977, Blackadar 1962) have also measured lower wind speeds at
pavement surface than at >10 m.
Simulation results show that it is possible to use 2006 computational technology to
simulate pavement temperature. Run times are 3.3 times as fast as a physical
measurement experiment. Performing simulations offers the opportunity to rapidly test
multiple pavement types, such as warm-mix asphalt and pavement with softeners or
various surface treatments in a under constrained or unconstrained weather scenarios.
The model presented herein can also be used to enhance existing models such as
the NCAR urban canyon model (Oleson et al. 2010) or to build on work by Wu et al.
(2008) who augmented heat transfer in asphalt pavement by adding graphite. This
model can also be expanded to simulate thermal connectivity with the subgrade and
optimize thermal properties of an engineered subgrade. It can additionally be used to
assess the impact of various surface treatments and mix designs on ice formation in
cold climates under varying conditions of humidity and temperature.
Summary
Through experimental research, three major hypotheses are investigated. The
first, that changing pavement composition from asphalt to low density concrete reduces
pavement heat storage during the period of maximum daily rainfall frequency, cannot be
rejected. In North-Central Florida, the precipitation frequency is highest time period
between 12:00 and 19:00. Results show a statistically significant difference between
control asphalt and concrete heat storage patterns during these hours. Results from a
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separate roadway experiment indicate that traditional concrete may provide similar
thermal mitigation to the low density concrete measured here without altering mix
designs or applying special paints or coatings. A thermal relative impact index (RII) is
created to better compare the relative performance of concrete and reflective asphalt.
According to the RII, reflective asphalt performs 5% to 6% better than concrete.
The second hypothesis, that pavement albedo can be changed to reduce heat
storage in pavements, cannot be rejected. Results show a statistically significant
difference in heat storage patterns by analyzing the entire population of gathered data
for concrete and reflective asphalt. The third hypothesis, that CFD can be used to
simulate heat storage using physical pavement properties and weather data as model
inputs, is not rejected. Simulations successfully model measured weather conditions
and generate pavement temperature results that are not statistically significantly
different from measured data for three example days. Two of the three simulations do,
however, begin to depart from measured pavement temperature data after 17:00 and
possible causes of this departure are discussed.
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Table 4-1. Thermal and physical properties of pavement.
Item Control Reflective
Asphalt Concrete Asphalt** Concrete+, ‡
Density (kg/m3) 2203 2203 2079 2100-2400 1600-2000 Cp (J/kg-K) 950 950 915 1000-1400 920-1004 K (W/m-K) 1.7 1.7 1.0 1.2-1.8 0.2-1.0
Viscosity (kg/m-s)* 50000 50000 50000 NA NA Albedo .22 .6 0.5 0.1-0.2 0.4-0.5
Thickness (mm) 55 55 65 NA NA Area (m2) 0.02076 0.02581 0.01761 NA NA
*Specified for purposes of model functionality; +Bentz et al. (2010); ‡Newman and Choo (2003); **Van Buren (2000) and Janke et al. (2009) Table 4-2. Model parameters for computational simulation
Item Value
Solver Transient Gravity -9.81 m/s2 on x-axis Equations used Energy, flow Models used Solar load, energy Pressure-velocity coupling Simple scheme Gradient discretization Green-Gauss node Pressure discretization Body force Momentum discretization 1st order upwind Energy discretization 2nd order upwind Transient formulation 1st order implicit
Table 4-3. Properties of air and expanded polystyrene (EPS)
Item Air EPS*,‡
Density (kg/m3) 1.18 15 Cp (J/kg-K) 1006.4 1300 K (W/m-K) 0.0242 0.038 Viscosity (kg/m-s) 1.789E-05 NA * Glicksman et al. (1987); ‡Yajnik and Roux (1990)
101
Table 4-4. Median values of pavement heat cycle for all measured days.
Energy stored in pavement Daily Median 12:00-19:00
Control (KJ/m2) 668 2617
Reflective asphalt (KJ/m2) 574 2089
Sealed asphalt (KJ/m2) 623 2539
Concrete (KJ/m2) 572 2279
Reflective asphalt (% of control) 0.86 0.80
Sealed asphalt (% of control) 0.93 0.97
Concrete (% of control) 0.86 0.87
Note: Data are not normally distributed Table 4-5. Integration of pavement heat cycle heat for 8 September to 10 September.
Daily Median 12:00-19:00 Median Energy stored in pavement (KJ/m2) 8-Sept 9-Sept 10-Sept 8-Sept 9-Sept 10-Sept
Control (KJ/m2) 813 935 1153 3070 3373 3771 Reflective asphalt (KJ/m2) 699 804 992 2399 2740 3083 Sealed asphalt (KJ/m2) 730 826 1039 3075 3397 3864 Concrete (KJ/m2) 621 743 933 2404 2720 3033 Reflective asphalt (% of control) 0.86 0.86 0.86 0.78 0.81 0.82 Sealed asphalt (% of control) 0.90 0.88 0.90 1.00 1.01 1.02 Concrete (% of control) 0.76 0.80 0.81 0.78 0.81 0.80
Sealed asphalt results in less daily heat storage when compared to the control asphalt but slightly higher storage during peak hours. Reflective asphalt reduces heat storage by 18% or more and concrete reduces heat storage by 19% or more during the critical hours of 12:00 to 19:00.
102
Time (HH:mm)
0:00 4:00 8:00 12:00 16:00 20:00
Fre
qu
ency
of
Ev
ent
Occ
urr
ence
(%
)
0
2
4
6
8
10
Event Onset Hour
Precipiration During Hour
Time (HH:mm)
0:00 4:00 8:00 12:00 16:00 20:00
Fre
qu
ency
of
Pre
cip
itat
ion
(%
)
0
2
4
6
8
10
Event Onset Hour
Precipiration During Hour
(Gainesville, FL) (Portland, OR)
Figure 4-1. Comparison of rainfall pattern frequency by hour from 10 years of hourly rainfall data collected in two climates
in the United States.
A B
103
Figure 4-2. Schematic of simulation geometry. A) plan view, B) side view of cropped mesh, C) side view geometry.
*Nominal representation of value (see Table 4-1 for dimensions per specimen). The outer box (air enclosure) represents the domain extent for the simulation. The mesh contains 165,123 elements (average skewness = 0.240 + 027). A curvature size function with medium smoothing and a slow transition setting was used with a smooth transition inflation function, a transition ratio of 0.272, 3 layer maximum, and growth rate of 1.2 to produce cells between 4.3E-4 meters and 8.6E-2 meters.
B
A C
104
7:00 11:00 15:00 19:00T
emper
ature
(oC
)
20
30
40
50
Interior
Surface
7:00 11:00 15:00 19:00
20
30
40
50
Interior
Surface
Time (HH:mm) (15 Sept 2010)
7:00 11:00 15:00 19:00
Tem
per
ature
(oC
)
20
30
40
50
Interior
Surface
Time (HH:mm) (15 Sept 2010)
7:00 11:00 15:00 19:00
20
30
40
50
Interior
Surface
(Control) (Reflective Asphalt)
(Sealed Asphalt) (Concrete)
Figure 4-3. Comparison of temperatures at surface and interior of pavements, 15
September, 2010. Average relative percent difference between interior and surface temperature measurements for the A) control, B) reflective asphalt, C) sealed asphalt, and D) concrete are 1.96%, 1.24%, 1.91%, and 1.89%, respectively.
C
A B
D
105
00:00 04:00 08:00 12:00 16:00 20:00
Fre
quen
cy o
f E
ven
t O
ccurr
ence
(%
)
0
2
4
6
8
10
12
Cum
ula
tive
% o
f T
ota
l P
robab
ilit
y
0.0
0.2
0.4
0.6
0.8
1.0
Event Onset Hour
Precipitation During Hour
CDF Precipitation
CDF Event Onset
Figure 4-4. Relative distribution of rainfall event occurrence and total rainfall depth by
day-hour during the rainy season (June – September, inclusive) from 10 years of historical data collected in Gainesville, FL. The distribution of events based upon the onset time of rainfall is denoted event onset hour and the distribution of rainfall depth by hour is denoted precipitation during hour.
106
00:00 04:00 08:00 12:00 16:00 20:00
Hea
t (K
J)
-1000
0
1000
2000
3000
4000
-1000
0
1000
2000
3000
4000Control
Reflective Asphalt
Sealed Asphalt
Time (HH:mm)
00:00 04:00 08:00 12:00 16:00 20:00
Hea
t (K
J)
-1000
0
1000
2000
3000
4000
-1000
0
1000
2000
3000
4000
Control
Reflective Asphalt
Sealed Asphalt
Concrete
Time (HH:mm)
00:00 04:00 08:00 12:00 16:00 20:00
Tem
per
atu
re (
oC
)
20
30
40
50
60
20
30
40
50
60
Control
Reflective Asphalt
Sealed Asphalt
Concrete
00:00 04:00 08:00 12:00 16:00 20:00T
emp
erat
ure
(o
C)
20
30
40
50
60
20
30
40
50
60
Control
Reflective Asphalt
Sealed Asphalt
(Average Hourly Temperature 17 Aug - 22 Sept)
(Average Hourly Temperature 4 Sept - 22 Sept)
(Cumulative Heat Storage 17 Aug - 22 Sept)
(Cumulative Heat Storage 4 Sept - 22 Sept)
Figure 4-5. Mean hourly temperature and heat absorption with standard deviation. KJ are per unit area 1m2. Control,
reflective, and sealed asphalt data collection period is between 17 August and 22 September. Concrete data collection period is between 4 September and 22 September. Standard error bars are shown.
C
A B
D
107
Time (HH:mm)
00:00 04:00 08:00 12:00 16:00 20:00
Dif
fere
nti
al f
rom
Contr
ol
(norm
aliz
ed)
-1.4
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
Reflective Asphalt
Concrete
Reflective Asphalt(late season)
Time (HH:mm)
00:00 04:00 08:00 12:00 16:00 20:00
-1.4
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
Reflective Asphalt
Concrete
Reflective Asphalt(late season)
(Volume Frequency) A (Event Frequency) B
Figure 4-6. Relative impact index (RII) for pavement heat storage reduction in Gainesville, FL (negative is better). Results
are normalized to the product of maximum difference from control heat content and either A) rainfall depth or B) event frequency. Median concrete performance is -0.58 on the volumetric RII between 12:00 and 19:00 and -0.60 on the event frequency RII. The reflective asphalt performs 5% and 6% better than the concrete for the volumetric and event frequency based RII, respectively. Standard error bars are shown.
108
08-Sep 09-Sep 10-Sep 11-Sep H
eat
(KJ)
0
1000
2000
3000
4000
5000
0
1000
2000
3000
4000
5000 Control
Reflective Asphalt
Sealed Asphalt
Concrete
Date (M/D/2010)
08-Sep 09-Sep 10-Sep 11-Sep
Rad
iati
on
(W
/m2
)
0
200
400
600
800
1000
Tem
per
atu
re (
oC
)
15
20
25
30
35
40
Sp
eed
(m
/s)
0
2
4
6
8
10Solar Radiation
Air Temperature
Wind Speed
(Heat Storage)
(Atmospheric Conditions)
Figure 4-7. Continuous measurement of A) Cumulative heat storage in pavement and
B) atmospheric conditions between 8 September and 11 September, 2010. KJ are per unit are 1m2. The areas under the curve illustrate that there is a hysteretic cycle of heat gain in the pavement where a minimum cumulative heat level is reached between 7:00 and 7:30 each day, after all the heat is exhausted.
A
B
109
(5 September 2010)
Time (HH:mm) (5 September 2010)
00:00 04:00 08:00 12:00 16:00 20:00
Inte
nsi
ty (
mm
/hr)0
4
8
12
Rad
iati
on
(W
/m2)
0
100
200
300
400
500
600
700
800
900
1000
Win
d V
elo
city
(m
/s)
0
4
8
Hea
t S
tora
ge
(KJ)
-500
0
500
1000
1500
2000
2500
3000
Rainfall
Radiation
Wind
Control
ReflectiveAsphalt
Concrete
(24 August 2010)
Time (HH:mm) (24 August 2010)
00:00 04:00 08:00 12:00 16:00 20:00
Inte
nsi
ty (
mm
/hr)0
4
8
Rad
iati
on
(W
/m2)
0
100
200
300
400
500
600
700
800
900
1000
Win
d V
elo
city
(m
/s)
0
4
8
Hea
t S
tora
ge
(KJ)
-500
0
500
1000
1500
2000
2500
3000
Rainfall
Radiation
Wind
Control
Reflective Asphalt
Figure 4-8. Comparison of pavement temperature before, during, and after two rain
events of differing intensity and time of day. It is observed that wind cools the pavements before the storm onset. The rate of thermal recovery in the pavements is not proportional to the rate of change of solar radiation, following the event, suggesting evaporation is mitigating heat gain.
A
B
110
Time (HH:mm) (10 September, 2010)
06:00 10:00 14:00 18:00
Hea
t T
ran
sfer
to
Pav
emen
t (K
J)
0
1000
2000
3000
4000
5000
Rad
iati
on
(W
/m2)
0
200
400
600
Control
Reflective Asphalt
Sealed Asphalt
Concrete
Solar Radiation
Time (HH:mm) (17 September, 2010)
6:00 10:00 14:00 18:00
Hea
t T
ran
sfer
to
Pav
emen
t (K
J)
0
1000
2000
3000
4000
5000
Rad
iati
on
(W
/m2)
0
200
400
600
Control
Reflective Asphalt
Sealed Asphalt
Concrete
Solar Radiation
Figure 4-9. Comparison of thermal heating pattern on two dry days of differing radiation
A) on the 17 September and B) on the 10 September.
A
B
111
Time (HH:mm)
0:00 4:00 8:00 12:00 16:00 20:00 0:00
Tem
epra
ture
(oC
)
-6
-5
-4
-3
-2
-1
0
-6
-5
-4
-3
-2
-1
0
East
West
Tem
per
ature
(oC
)
25
30
35
40
45
50
25
30
35
40
45
50T
emper
ature
(oC
)
25
30
35
40
45
50
25
30
35
40
45
50
Asphalt
Concrete
Asphalt
Concrete
(Surface Temperature - East)
(Surface Temperature - West)
(Difference From Asphalt TC)
Figure 4-10. Concrete temperature and asphalt temperature at A) east side of road, B)
west side of road, and C) difference from asphalt thermocouple (TC) temperature at both locations. There is a reduction in temperature of >5 degrees between 12:00 and 16:00. These results are similar to the 4 ºC to 6ºC difference during these hours shown in Figure 4-4.
A
B
C
112
S
ola
r R
adia
tio
n (
W/m
2)
0
200
400
600
Win
d (
m/s
)
0
2
4
6
8
10
Radiation
Wind
Time (HH:mm) (18 August, 2010)
07:00 09:00 11:00 13:00 15:00 17:00 19:00
Tem
per
atu
re (
oC
)
20
30
40
50
60
20
30
40
50
60
Measured Control
Measured Reflective Asphalt
Modeled Control
Modeled Reflective Asphalt
Figure 4-11. Modeled pavement temperature for control asphalt and white asphalt
pavements on 18 August, 2010. Tests for goodness of fit: r2=0.986, 0.981 for control and white asphalt, respectively. No significant difference is found between measured and modeled results using functional data analysis (p <
=0).
113
Time (HH:mm) (18 August, 2010)
07:00 09:00 11:00 13:00 15:00 17:00 19:00
Tem
per
ature
(oC
)
20
25
30
35
40
45
50
20
25
30
35
40
45
50
Modeled Low k Reflective Asphalt
Modeled Reflective Asphalt
Modeled High k Reflective Asphalt
Figure 4-12. Comparison of modeled pavement temperature results under for current,
low, and high thermal conductivity (k) values (k=1.7, 1.2, 1.8 W/m-K, respectively) for white asphalt simulation. Results indicate r2 = 0.999 and no
significant difference using functional data analysis (p < =0)
114
So
lar
Rad
iati
on (
W/m
2)
0
200
400
600
Win
d (
m/s
)
0
2
4
6
8
10
Radiation
Wind
Time (HH:mm) (19 August, 2010)
07:00 09:00 11:00 13:00 15:00 17:00 19:00
Tem
per
atu
re (
oC
)
20
30
40
50
60
20
30
40
50
60
Measured Control
Measured Reflective Asphalt
Modeled Control
Modeled Reflective Asphalt
Figure 4-13. Measured vs. modeled asphalt temperatures for two days in August, 2010.
The rising limb deviates more during this event than during the 18 August. Note that it also shows a drop in radiation at approximately the same time of day. The model fit statistics are r2=0.987, 0.99 for control and white asphalt, respectively. No significant difference is found between measured and
modeled results using functional data analysis (p < =0)
115
Sola
r R
adia
tion (
W/m
2)
0
200
400
600
Win
d (
m/s
)
0
2
4
6
8
10
Radiation
Wind
Time (HH:mm) (6 September, 2010)
07:00 09:00 11:00 13:00 15:00 17:00 19:00
Tem
per
ature
(oC
)
20
30
40
50
60
20
30
40
50
60
Measured Control
Measured ReflectiveAsphalt
Measured Concrete
Modeled Control
Modeled Reflective Asphalt
Modeled Concrete
Figure 4-14. A comparison of measured and modeled asphalt and concrete
temperatures on 6 September, 2010. In comparison to the aforementioned simulations, the model fit is better at the end of the day than before 12:00. Model fit statistics r2=0.954, 0.944, 0.980 with for control, white asphalt, and light concrete, respectively. No significant difference is found between
measured and modeled results using functional data analysis (p < =0).
116
CHAPTER 5 COMPUTATIONAL MODELING OF OVERLAND FLOW AND HEAT TRANSFER IN
ASPHALTIC PAVEMENTS
Background
Asphalt concrete is the predominant pavement surface type in the United States.
Approximately 94% of roads in the United States are asphalt (Takamura 2002;
Anderson et al. 2000). However, asphalt has been found to contribute to increased
stormwater runoff temperature in Florida in Chapter 2. The rate at which heat is gained
or lost from an asphalt pavement is a function of mix design, additives, and/or coatings,
as discussed in Chapter 4. A commonly used alternative paving material to asphalt is
Portland-cement concrete. North America has used using Portland-cement concrete to
construct roads since 1881 (Snell and Snell 2002).
The most direct method to measure heat transfer potential to stormwater runoff is
by performing highly controlled ex-situ physical experiments but it is time consuming
and challenging to compare thermal responses from the variety of pavement materials
to stormwater runon. However, with knowledge of basic material properties, a
computational model can rapidly prototype the thermal response of different pavements
by digitally altering material properties. Previous studies have performed simulations of
asphalt pavement temperature as a function of overland flow (Janke et al. 2009; Van
Buren et al. 2000; Roa-Espinosa et al. 2003; Minhoto et al. 2005; Yavuzturk et al. 2005;
Krause et al. 2004). Most of them accounted for evaporation using empirical methods.
Janke used an unsteady 1-D model that required coefficients of convection. Van Buren
used a 1-D finite difference model called the thermal runoff model for pavement
(TRMPAVE). Roa-Espinosa’s application of the thermal urban runoff model (TURM)
was on a large watershed-scale. Krause used a hydrologic simulation program fortran
117
(HSPF) which was applied to a large watershed and focused on stream temperature.
Minhoto used a custom 3-D finite element method for calculating asphalt temperature.
Yavuzturk used a 2-D finite difference model and calculated a convection heat transfer
coefficient using the flat-plate method (Incropera and DeWitt 2002). The models were
designed and calibrated for use at large scales.
Some but not all of the models accounted for evaporation. Janke used the heat
flux equation put forth by Stefan et. al (1980) to account for thermal transfer and did not
account for mass transfer. Van Buren used Myer’s equation for evaporation rate (Chow
1964) and an equation by Linsley et al. (1975) for heat flux. Roa Espinosa did not
provide a method used to calculate evaporation. Minhoto, Yavuzturk, and Krause also
did not discuss evaporation. Part of the reason evaporation was not included in all the
aforementioned investigations is because evaporation has historically been a
challenging phenomenon to model. Fundamental models of evaporation/condensation
stem from kinetic and statistical rate theory (Rahimi and Ward 2005).
The equations applied in this study are kinetic. A widely known kinetic equation is
by Schrage (1953), who focused on the interface between water liquid and vapor
phases by applying the first approximation Maxwell’s velocity distribution of gas: that
simple motion prevails near the interface. The rate of mass transfer at the interface is
the sum of condensation and vaporization, each of which are calculated separately as
shown in Equation 5-1 (Schrage 1953; Marek and Straub 2001).
√
2
3 (5-1)
In this equation, w is the mass flux vector (kg/m2-sec) s is the evaporation coefficient, M
is the molar mass (kg/mol), R is the universal gas constant (J/mol-K), is the Schrage
118
correction factor which accounts for net velocity of vapor molecules under non-steady
conditions, pv is the vapor pressure (N/m2), pl is the liquid vapor pressure (N/m2), Tv is
the liquid temperature (K) and Tl is the vapor temperature (K). This equation is a
modification of the Hertz-Knudsen formula that allows for a non-stationary flow of vapor
(Barrett and Clement 1991) (Equation 5-2), which assumes a Maxwellian distribution of
molecules at the interface.
√
2
3 (5-2)
In this equation, e is an evaporation coefficient, e is the condensation coefficient, p∞ is
the vapor pressure far away from the interface (N/m2), T∞ is the temperature far away
from the interface (K). Assuming only minor departure from equilibrium conditions, the
equation can be written as shown in Equation 5-3 (Eames et al 1997; Schrage 1953;
Kucherov and Rikenglaz 1960).
√
2
3 (5-3)
Assuming the temperature of the gas is approximately the same as the
temperature of the liquid, the H-K equation (Equation 5-2) can be further simplified and
rearranged as Equation 5-4, which has been used in numerous investigations (Alty and
Mckay 1933; Alty and Mckay 1935; Bowman and Briant 1947; Carman 1948; Eames et
al.1997).
√
* + (5-4)
In this equation, 1 is the evaporation coefficient, Ts is the temperature at the surface of
the liquid/air interface (K), Ps is the saturated vapor pressure (N/m2), and P0 is the
119
current vapor pressure (N/m2). This is only valid as w/ws+ approaches zero (far away
from the interface). This approximation gives rise to Alty’s equation (Equation 5-5).
√
* + ∫
(5-5)
In this equation W is mass transfer (kg/sec) and A is interface area (m2). The derivation
of this equation is well described in Nabavian and Bromley (1963). Eames et al. (1997)
applied the modified correction factor from Schrage to Equation 5-4, resulting in
Equation 5-6.
√
* + (5-6)
Marek and Straub (2001) reviewed a number of published coefficients and
proposed that evaporation and condensation coefficients are higher for moving thin
films ( >0.1) than they are for quasi-static surfaces ( <0.1). Through experimentation,
Nabavian and Bromley (1963) found that >0.35 for water. Hardt and Wondra (2008)
used = 1 because they stated that it creates a more numerically challenging situation
to model. Eames et al. (1997) reviewed the literature and concluded data suggest
>0.5 with little deviation in evaporation rate when 0.5 < < 1.
Computational fluid dynamics (CFD) have been previously developed to model
evaporation using Kinetic theory (Hardt and Wondra 2008; Welch and Wilson 2000).
Both Hardt and Wondra and Welch and Wilson used CFD with a volume of fluid (VOF)
scheme to model the multiphase water liquid/vapor interface. Hardt and Wandra applied
their own user defined functions to a commercially available CFD package to simulate
inter-phase mass and energy flux. Both studies claimed good agreement with measured
results. Both studies focused on the micro scale. Computational power is such that
120
increasingly complex phenomena can be simulated in domains of increasing size. My
study endeavors to apply a user defined function to a commercially available CFD
package in order to simulate the effects of heat transfer from asphaltic and Portland-
cement concrete pavement under controlled benchtop-scale conditions while
accounting for the generation of turbulence and the effects of evaporation/condensation.
Objective
The goal of my research is to simulate heat transfer from two pavement surfaces
under constant rate overland flow of water and validate each model against ex-situ
pavement tests. I seek to demonstrate that the flow of stormwater over a pavement
surface can be modeled using computational fluid dynamics (CFD). A secondary
hypothesis is that evaporation measurably affects pavement and runoff temperature. It
is hypothesized that flow can be modeled using a laminar regime for travel lengths of 2
feet (0.61m). A fourth hypothesis is that the flat plate model for forced convection does
not provide as accurate of an approximation of measured heat transfer to stormwater
runoff as does the CFD evaluation.
Methodology
This study is completed in two phases, beginning with physical testing and ending
with simulations of the experiment. Physical testing consists of calibrating
thermocouples (TC), obtaining pavement materials, instrumenting the pavement with
TCs, performing the overland flow experiment, and then measuring the thermal
properties of the pavement after the completion of the overland flow experiments.
Simulations consist of developing CFD models (with appropriate material properties and
experimental conditions) for both asphalt and concrete tests, followed by a comparison
with the classical flat-plate model for heat transfer in overland flow.
121
Physical Experiments
Omega {TC-PVC-T-24} 0.5mm TCs are used to instrument the asphalt specimen.
Omega {TC-TT-T-30} 0.25mm TCs are used to instrument the concrete specimen. TCs
are calibrated by simultaneously recording the temperature of water using TCs and an
alcohol thermometer every minute as the water is heated. Changes in temperature are
recorded with a timestamp that is used to generate a calibration curve for the TCs. All
thermal data are logged at 2 minute intervals using a Campbell Scientific CR10x logger.
The asphalt experiment is constructed by compacting asphalt (FDOT FC-5) into a
38mm x 305mm x 610mm wooden form by hand tamping, allowing the pavement to
cure for 10 weeks. Calibrated TCs are then placed into the pavement by drilling a hole
of 3/16” diameter into the bottom of the pavement at each of 8 locations as shown in
Figure 5-1, towards the pavement surface so as not to disturb the surface of the
pavement. Drill depth for a surface TC is 2mm while interior TC borings reach 19mm
beneath the pavement surface. One TC is installed in each boring by inserting the tip
upwards from the pavement bottom, and backfilling the boring with pavement filler
(<30% silica crystalline, <25% petroleum asphalt, <15% latex polymer). The asphalt
bottom and sides are then filled with the pavement filler and smoothed. The TC wires
are pressed into the curing filler material. The cured specimen is then placed into a
37mm x 305mm x 610mm, 18-gage steel tray before testing.
The concrete experiment is constructed by troweling a 3000 psi Type-II cement
with a 4-5 in. slump, 3% air, and size 67 coarse aggregate into an 18 gage steel tray of
internal dimensions 38mm x 305mm x 610mm, then placing the TCs into the wet
concrete by burying them to the same locations as the TCs in the asphalt specimen,
also shown in Figure 5-1. Surface TCs are buried 2mm beneath the concrete surface
122
and interior TCs are buried to a depth of 19mm. The pavement is troweled to a smooth
surface and the concrete is allowed to hydrate for 8 weeks before performing
experiments.
Pavement properties are determined as follows, with results shown in Table 5-1:
Mass is measured using a Melner scale, and bulk density is calculated as mass per
volume. Specific heat capacity is measured for both pavements using a calibrated, low
heat capacity expanded polystyrene calorimeter. Calorimetric tests of the specimens
are performed by measuring temperature change of a known volume of liquid water at
ambient temperature after placing a pavement specimen heated to 60ºC in the
calorimeter. Conductivity is estimated from pavement thermal diffusivity as measured by
the time to reach equilibrium in the calorimeter, based upon the methods of Army Corps
of Engineers (1949).
After heating either the asphalt or concrete specimen in an oven at 65oC, the tray
with pavement is placed into a 51mm thick expanded polystyrene (EPS) insulation bed
(R-value = 7) which is also heated to reduce the heat gradient between the pavement
and the insulation bed. The insulation bed height is previously trimmed at the entrance
to fit a 3mm thick polyvinyl chloride splash plate (not shown) at the pavement entrance
to transition water flow from the influent pipe to the chamber to smoothly transition flow
across the entire width of the pavement. The downstream end of the insulation bed is
also trimmed to channel flow to a central outlet. An EPS cover with closed top and sides
(interior width = 310mm) is placed atop the insulation bed and pavement, creating a flow
chamber. The assembly (including insulation bed) is then placed on a 2% downslope.
123
Before water flow is turned on, Omega {TC-PVC-T-24} TCs are placed in duplicate
at the both water influent pipe and the water effluent channel (not shown). A Campbell
Scientific thermocouple multiplexer (AM25T) and a Campbell Scientific logging device
(CR10x) are used to record temporal data on a 5 second intervals. Air temperature is
also measured in the lab using an Onset HOBO U12 logger. Influent flow rate is
volumetrically calibrated by measuring time to fill a flask calibrated to 4L +10mL and
determined to be 0.485 L/s (kg/s). With the datalogging equipment operational, flow is
turned on and the start time is recorded. After flowing through the experimental domain,
flow is discharged into a floor drain. The experiment is conducted for >5 minutes. Data
are then uploaded to a computer and analyzed for thermal patterns.
Modeling Methodology
The hydrodynamics and heat transfer dynamics of the pavement-runoff system
can be approximated using a 2-dimensional (2-D) spatial environment. A 2-D CFD
analysis is developed in Fluent using a k-kl-ω transitional turbulence model. Two User
Defined Functions (UDF) are incorporated into the model to assist in simulating physical
phenomena. An initialization UDF is used to produce an x-y distribution of the initial
temperatures created from the pavement TC measurements made at time zero of the
physical experiment. This is necessary because initial temperature gradients within the
pavement are unavoidable. The UDF is invoked during initialization of the simulation.
Using a cell loop, it steps through a [3,256] array of the x-position, y-position, and
pavement temperature (K). For threads in the solid domain, Equation 5-7 holds true
where the measured temperature at the index of the array with the shortest linear path
from the cell location at the current position in the loop is applied to the cell at the
current position.
124
{ { √, -
, -
, - (5-7)
In this equation i is the current cell in the cell loop, j is the current index of the array, xi is
the current x coordinate in the cell loop, yi is the current y coordinate in the cell loop, Xj
is the X location at the current index in the array, Yj is the Y location at the current index
in the array. D is the linear distance between the current array X,Y location and the
current x,y cell coordinate, Tj is the temperature at index j of the array and ti is the
temperature of the current cell in the cell loop.
After initialization, a different UDF is called during each iteration to make
adjustments before the next iteration commences. This is used to calculate
evaporation/condensation of H2O using a mass transfer mechanism. The simulation is
performed under compressible flow, utilizing fundamental equations of mass continuity
and momentum. The continuity or conservation of mass equation is shown in Equation
5-8 (Patankar 1980).
( ) (5-8)
In this equation
is the rate of change of density per unit volume, where density
(kg/m3) can be related back to mass fraction and temperature (K) via an equation of
state, is the velocity field (m/s). Sm is the mass (kg) added to the continuous phase
from a secondary phase. Conservation of momentum is written for an inertial frame of
reference as shown in Equation 5-9 for the x-direction.
( ) ( ) ( )
(5-9)
125
In this equation, u is directional velocity, is the viscosity (Pa-s), p is static pressure
(Pa), Bx is the directional body force per unit volume, and Vx represents additional
viscous terms (Patankar 1980).
Heat transfer is physically modeled using energy conservation as shown in
Equation 5-10. Note that in the solid zone, heat transfer can be simplified as a function
of conductivity and radiation.
( ) ( ( )) . ∑ ( )/ (5-10)
The first term on the right hand side of the equation is energy transfer due to
conduction. The second term is species diffusion; the third term is viscous dissipation.
In this equation k is laminar conductivity (W/m-°C), Jj is diffusion flux of species j (kg/m2-
s), hj is enthalpy (J/kg), and Sh is the heat of chemical reaction plus radiation. is the
viscous stress, shown in Equation 5-11 where I is the unit tensor. E is the total energy,
defined as shown in Equation 5-12.
0( )
1 (5-11)
(5-12)
In this equation, h is defined for compressible fluids as shown in Equation 5-13 where Yj
is the mass fraction of species j. Specific enthalpy is defined as shown in Equation 5-14.
∑ (5-13)
∫
(5-14)
126
In this equation Tref is 298.15 K. These equations can be solved simultaneously for
compressible flow when coupled with the equation of state for gases (Versteeg and
Malalasekara 1995; Batchelor, 2000).
The mass transfer UDF operates in the fluid domain, where the gradient surface
area of the liquid/air interface is reconstructed at each timestep when the UDF is
invoked (Rider and Kothe 1998). Molar fraction of the water vapor (yvap) is calculated
using Equation 5-15 if the mass fraction of vapor is greater than zero, otherwise yvap is
zero.
4 .
/5
(5-15)
In this equation, MWair = 28.0 kg/kmol, MWvap = 18.0 kg/kmol, Xvap is mass fraction of
vapor (kg vapor/kg air). Saturation pressure (Psat, Pa) is calculated based on the local
cell temperature following an eight-term polynomial equation published by Reynolds
(1979). Vapor pressure is calculated as shown in Equation 5-16.
(5-16)
In this equation, Pvap is vapor pressure (Pa), gas is the gas density (kg/m3), R is the
universal gas constant (J/kmole-K), Tc is the cell temperature (K). Evaporation occurs if
Psat > Pvap but if the volume of gas is less than 10% of the cell, then evaporation is not
expected to occur and is set equal to zero. This is necessary to reduce numerical
instability. Evaporation flux is calculated according to Equation 5-17, reproduced below.
[
√
{ }
⁄
] (5-17)
127
In this equation Ai (m-1) is calculated as shown in Equation 5-18, l is the density of the
water liquid (kg/m3), Vl is the volume of liquid in the cell (m3) and Vc is the volume of the
cell (m3), TS is the timestep (seconds), and W is the interfacial mass transfer rate per
unit of volume (kg/m3-s).
| | (5-18)
If the volume of liquid is not less than 10% of the cell volume, and if Psat < Pvap,
then condensation occurs as shown in Equation 5-19, where xvap is the mass fraction of
vapor in the gas, Vg is the volume of gas in the cell (m3), and Vc is the volume of the cell
(m3). The total mass transfer to or from the liquid phase (M) at the interface between
liquid and gas during each timestep is shown in Equation 5-20.
[
√
{ }
⁄
] (5-19)
(5-20)
The CFD model is shown in Figure 5-2. Properties for the air mixture, water vapor,
air, water liquid, and steel used in the simulation are shown in Table 5-2. Model
parameters are shown in Table 5-3. The following boundary conditions are specified in
the simulation: The tray is simulated as a solid boundary along the bottom and sides of
the pavement with a wall thickness of 1.024mm. Pressure boundaries are defined to
have 1E-6 m2/s2 laminar kinetic energy, no turbulent kinetic energy, a 1s-1 specific
dissipation rate, and a vapor mass fraction of 0.0143 kg H2O/kg dry air. The water inlet
is defined to have a water flow rate of 0.485 kg/s (l/s), a hydraulic diameter of 0.002m,
and no turbulence. Liquid temperature is specified using measured data every second
at the mass-flow influent boundary using a transient profile and air temperature is
128
specified every second at the pressure boundaries. The water-air surface tension is
specified as 0.04 N/m. The geometry is tested for grid convergence. Simulation
monitors are used to export runoff temperature and pavement temperature over time.
Heat Transfer Calculation of Flow Over a Flat Plate
Incropera and DeWitt (2002) provided a rigorous method for the calculation of
convection over a flat plate. The method is applied to stormwater flow over a pavement
surface shown below and model results are compared to measured results. The basic
convection equation is shown in Equation 5-21.
( ) (5-21)
In this equation, T∞ is the free stream temperature (ᵒC), and the average heat transfer
coefficient is calculated as shown in Equation 5-22.
(5-22)
In this equation, k is pavement conductivity (W/m-C) and L is the length of pavement
flow (m). The Nusselt number (unitless) is calculated differently under laminar and
transitional flow. Under laminar flow [Rex < 5X105] and Equation 5-23 is applied. Under
transitional flow [5X105 <= Rex < 1X107] and Equation 5-24 is applied.
⁄ ⁄ (5-23)
( ⁄ ) ⁄ (5-24)
In the previous two equations, Re is Reynold’s number (unitless) and Pr is the Prandtl
number (unitless). Reynold’s number is calculated as shown in Equation 5-25.
(5-25)
129
In this equation, V is the cross sectional runoff velocity (m/s), L is the length of the
pavement transect (m), and is the kinematic viscosity (m2/s). Velocity is calculated as
shown in Equation 5-26.
(5-26)
In this expression, Vt is the runoff cross sectional velocity at time t (m/s), Qt is the flow at
time t (m3/s), B is the path width (m), and Ht is the depth of water over the pavement at
time t (m). Given a known flow rate (Q), Ht (depth of flow) is calculated using Manning’s
equation with substitution as shown in Equations 5-27 to 5-32.
(5-27)
0
1
, -
(5-28)
( )4
0
1
, -
5 (5-29)
( )4
0
1
, -
5, width perpendicular to flow (5-30)
, -
( ) ., -
/ , -
(5-31)
0 .
/ , -
1
(5-32)
In these equations, n is Manning’s coefficient of roughness (no units), S is the slope of
the water surface (m/m), V is the cross-sectional average velocity (ft/s m/s), k is a
conversion constant equal to 1.486 for U.S. customary units or 1.0 for SI units, and A is
the cross-sectional area of the flow.
130
The water runoff temperature is calculated as a function of heat transfer on a 10
second timesteps where the flat plate method described above is used to calculate the
heat transfer during the timestep. The heat capacity of water is used to calculate the
temperature change in water as a function of volumetric flow rate of runoff in that
timestep as shown in Equation 5-33.
(5-33)
In this equation Ti is the influent water temperature and To is the effluent water
temperature (°C), Ql is the liquid flow rate, and Cp is the specific heat capacity of water
(4200 J/kg-°C).
Results and Discussion
CFD model performance for 300 seconds of flow time is shown in Table 5-4.
Simulation time for the most complicated simulations (evaporation, turbulent flow)
ranges between 60-70 hours on an 8-core workstation with Intel Nehalem design.
Results indicate that laminar simulations perform more poorly than their respective
turbulent simulations. This is most strongly shown in the difference between measured
and modeled runoff temperature. There is little difference in the model RMSE or RPE
between evaporation coefficients of 0.5 and 0.1 for the turbulent simulations. The
turbulent concrete simulation with =0.5 performs better than =0.1 except for the
upstream internal temperature. The turbulent asphalt simulation with =0.1 shows less
error in runoff temperature than =0.5 but almost identical error in pavement
temperature. Interestingly, the turbulent models that do not include evaporation provide
a better estimation of runoff temperature than the simulations with evaporation.
131
In multiphase simulations, body forces and pressure gradients can dominate
convection and viscosity, causing poor convergence. The CFD software utilized in this
study allows for the use of an implicit body force that provides better pressure field
stability during initial iterations. It requires the specification of operating density for VOF
simulations. Due to the compressibility of the water vapor species, operating density is
set to zero for simulations that perform evaporation, otherwise operating density is
specified as 1.225 kg/m3. Results of asphalt simulations that incorporate the
aforementioned changes are shown in Table 5-5. They indicate that the model fit is
worse for all the simulations with respect to pavement temperature but better with
respect to error in runoff temperature. Convergence also occurs as a much faster rate
(approximately 3 times as fast) and with 1 order of magnitude better continuity residual
error.
A third analysis was performed to determine the effect of changing the threshold
for evaporation or condensation to take place from 10% to 50%. Hence, for
condensation to occur in an interface cell, the cell must have at least 50% water
present; for evaporation to occur the cell must have at least 50% air present. The
analysis performed for asphalt and concrete simulations with =0.1. Results show that
error in runoff temperature is the lowest of all simulations for concrete and asphalt as
shown in Table 5-6. In addition, in the asphalt simulation the RPE and RMSE for the
upstream and downstream internal pavement temperature are lower than in the
turbulent asphalt simulation shown at the top of Table 5-4 but error in surface
temperature is worse. Graphical representations of the results for the third analysis are
shown in Figure 5-3 and Figure 5-4.
132
Results from the simulation of pavement runoff show that the simulated effluent
temperature approaches the measured effluent temperature after 240 seconds. In
addition, the first 20 seconds of the simulation follow measured effluent results (Figure
5-1). However, the interim simulation results are up to 1.4 degrees higher than
measured at the point of largest difference. The measured upstream interior
temperature of the asphalt experiment cools off more rapidly than downstream until it
meets the interior temperature measured downstream. At the asphalt surface,
downstream simulation results also do not decay as quickly as upstream simulation
results. The concrete simulation differs in the following manner: the upstream interior
temperature is observed to cool much more rapidly than downstream and the shape of
the temperature decay curve is similar to those shown for the surface temperatures.
The specimen was examined and it was determined that both the upstream and
downstream interior TCs floated closer to the surface while the specimen was hydrating.
The upstream TC rose approximately 9mm closer to the surface while the downstream
TC rose approximately 4mm. These corrections were made in the CFD models before
simulations were performed.
The results of the flat plate model are shown in Figure 5-5. While the CFD model
tends to over predict runoff temperature, the flat plate method tends to under predict
runoff temperature. The flat plate model does not fit as well as the CFD model results in
Table 5-6 for the concrete simulation, while the opposite is true for asphalt.
Previous research in the second chapter describes a more rapid cooling of
upstream pavement temperature with a slower decay in downstream pavement
temperature. This is observed in both measured and simulated results in this
133
experiment. The poor fit of the laminar simulations suggests that a laminar simulation is
not appropriate, contrary to the hypothesis. However, the poor results are only present
during the first 100 seconds of simulation time, after which the pressure field stabilizes
and simulated runoff temperature approaches measured values (not shown). The
asphalt simulation with the specified operating density and implicit body force stabilizes
much earlier in the simulation (after 16 seconds of simulation). The use of implicit body
force is very effective at improving results for simulations without turbulence.
The similarity in performance between turbulent simulations that incorporate
evaporation and those that do not suggests that evaporation is not critical to measuring
runoff temperature during the first 5 minutes of runon over a hot pavement. It is even
less important as time progresses due to the cooler pavement surface. Interestingly, the
concrete (zero evaporation) simulation effluent starts approximately 1°C warmer than
the measured value but in 2 seconds it drops by approximately 1°C and follows the
measured curve closely. Effluent temperature in the zero evaporation asphalt is
approximately 1°C warmer for 8 seconds before following measured results for 10
seconds. In both of the simulations shown in Figure 5-3 and Figure 5-4, effluent
temperature starts at the measured temperature, suggesting that evaporation may only
be critical within the first seconds of a runoff event.
It is possible that the experimental design is minimizing observed evaporation,
leading to the very small difference between accounting for- and not accounting for
evaporation. The closed top design was thought to be necessary at the outlay of the
experiment because it is used to block extra-experimental air currents and to allow for a
smaller modeling domain. Evaporation was observed to occur because following the
134
experiment, droplets were observed on the underside of the cover. There is no forced
airflow in the domain and it is likely that evaporation may have a larger effect over
pavement surfaces in-situ.
The shorter time to runoff from the asphalt (Figure 5-3 and Figure 5-4) may be due
to increased surface depression storage in the asphalt, whereas the concrete is
smoother than the asphalt. Water was initially observed to flow through channels along
the asphalt pavement surface due to surface tension. It took approximately 30-60
seconds for the liquid to cover the entire pavement surface. This may also be a factor in
the increased heat loss observed from the measured internal asphalt temperature
compared to the modeled temperature during the initial 200 seconds of the simulation.
Depression storage increases the effective surface area of the asphalt, increasing heat
transfer. However, this would likely also lead to increased runoff temperature.
The flat plate method is observed to under predict concrete runoff temperature for
the entire duration of the 300 seconds shown in Figure 5-5B but it simulated asphalt
pavement temperature much better than any of the CFD simulations between 50-300
seconds. The flat plate method requires knowledge of the pavement surface
temperature as a function of time in order to calculate runoff temperature, which limits
its utility, however if those data are available, these results suggest that it may be a
good approximation of temperature from a pavement surface.
Previous research, shown in Chapter 4, has successfully modeled ex-situ
pavement temperature over varied weather conditions using CFD. It did not include the
simulation of runoff temperature during rain events. The results presented herein
suggest that the previously designed model and the model presented herein can be
135
combined into a unified model to simulate heat exchange from concrete pavements
under both wet and dry periods.
Summary
A model is successfully created to simulate the flow of stormwater over heated
concrete for 5 minutes (300 seconds) within less than 0.5% RPE of measured
pavement and runoff temperature. A model is also created for asphalt flow with less
than 0.3% RPE of measured pavement temperature and runoff temperature but asphalt
model performance decreases after 40 seconds. Conversely, the flat plate method is
observed to perform well after 100 seconds of flow over the asphalt pavement, which
does not support the hypothesis that CFD modeling is more accurate than the flat-plate
method for measuring runoff temperature from an asphalt pavement. This hypothesis is
supported, however, by the concrete results. Results for both concrete and asphalt
simulations are not strongly improved by accounting for evaporation, however models
that incorporate turbulence (via the k-kl- transitional model) are observed to perform
better than those that don’t.
Results suggest that there is potential for comparison between concrete pavement
of different mix designs to help identify the effect of mix design on pavement runoff
temperature. The tools presented herein can be used in part to evaluate the efficacy of
these solutions and possibly provide future TMDL BMPs and better understand heat
transfer during the early period of rainfall-runoff events.
136
Table 5-1. Thermal and physical properties of pavement.
Property Asphalt Concrete
Density (kg/m3) 2393 2252 Cp (J/kg-K) 1008 1104 K (W/m-K) 1.8 2.19
137
Table 5-2. Material parameters used in computational fluid dynamics simulation.
Species Density (kg/m3)
Specific heat
(j/kg-k)
Thermal conductivity
(w/m-k) Viscosity (kg.m-s)
Mass diffusivity
(m2/s)
Molecular weight
(kg/kgmol) Enthalpy (j/kgmol)
Air-vapor mixture Ideal-gas Mixing law Mass weighted Mass weighted 2.88E-05 -- -- Water-vapor Ideal gas 2014 0.026 1.34E-05 -- 18.02 -4.07E+7 Water-liquid* UDF 4182 0.600 0.0018 -- 18.02 0 Air 1.225 1006.43 0.024 1.79E-05 -- 28.97 0 Steel 8030 502.38 16.270 -- -- -- --
*When applying the UDF, water density and speed of sound are defined using the Tait equations (Dymond et al. 1988).
138
Table 5-3. Model parameters for computational simulation
Item Value*
Solver Transient, pressure based Gravity -9.81 m/s2 on y-axis Models used VOF multiphase, k-kl-turbulence, species
transport Pressure-velocity coupling PISO Gradient discretization Least squares cell based Pressure discretization PRESTO! Momentum discretization QUICK Density discretization QUICK Volume fraction Modified HRIC Turbulent kinetic energy QUICK Laminar kinetic energy Second order Specific dissipation rate QUICK Energy discretization 2nd order upwind Gas Phase Water Vapor QUICK
*PISO: pressure-implicit with splitting of operators; PRESTO!: pressure staggering option; QUICK: quadratic upwind interpolation; HRIC: high resolution interface capturing
139
Table 5-4. Analysis of error between modeled and measured results.
Pavement Turbulence Model
Evap Model Cevap
Upstream Surface
Downstream Surface
Upstream Interior
Downstream Interior Effluent Water
RMSE RPE RMSE RPE RMSE RPE RMSE RPE RMSE RPE
Asphalt Turbulent None NA 0.882 0.19% 1.319 0.27% 0.118 0.03% 0.262 0.07% 0.402 0.11%
Asphalt Turbulent UDF 0.1 0.941 0.20% 1.458 0.31% 0.113 0.03% 0.329 0.09% 0.853 0.18%
Asphalt Turbulent UDF 0.5 0.924 0.20% 1.394 0.29% 0.116 0.03% 0.290 0.08% 0.534 0.16%
Asphalt Laminar None NA 1.668 0.43% 2.340 0.65% 0.169 0.05% 0.410 0.11% 1.689 0.44%
Asphalt Laminar UDF 0.1 1.681 0.44% 2.290 0.62% 0.288 0.08% 0.568 0.15% 2.342 0.64%
Asphalt Laminar UDF 0.5 1.717 0.44% 2.470 0.68% 0.274 0.08% 0.577 0.16% 1.926 0.50%
Concrete Turbulent None NA 3.482 0.35% 3.304 0.50% 0.654 0.12% 0.833 0.22% 0.166 0.04%
Concrete Turbulent UDF 0.1 3.449 0.40% 2.942 0.50% 0.738 0.15% 0.727 0.19% 0.278 0.05%
Concrete Turbulent UDF 0.5 3.429 0.39% 3.003 0.50% 0.742 0.15% 0.681 0.18% 0.355 0.10%
Concrete Laminar None NA 3.802 0.87% 4.353 1.18% 1.623 0.46% 0.733 0.18% 3.289 0.93%
Concrete Laminar UDF 0.1 3.363 0.81% 4.100 1.13% 1.731 0.49% 0.738 0.20% 1.216 0.25%
Concrete Laminar UDF 0.5 3.512 0.93% 3.875 1.11% 1.742 0.53% 0.522 0.14% 3.014 0.91%
Table 5-5. Analysis of error between modeled and measured results with implicit body force and specified operating
density.
Pavement Turbulence Model
Evap Model Cevap
Upstream Surface
Downstream Surface
Upstream Interior
Downstream Interior Effluent Water
RMSE RPE RMSE RPE RMSE RPE RMSE RPE RMSE RPE
Asphalt Turbulent None NA 2.165 0.57% 2.757 0.77% 1.138 0.30% 1.324 0.35% 0.383 0.11%
Asphalt Turbulent UDF 0.1 2.305 0.68% 2.999 0.90% 1.702 0.45% 1.870 0.49% 0.538 0.16%
Asphalt Turbulent UDF 0.5 2.157 0.65% 3.251 0.97% 1.426 0.37% 1.627 0.42% 0.511 0.15%
Asphalt Laminar None NA 2.186 0.58% 2.860 0.81% 0.719 0.19% 1.139 0.30% 0.509 0.10%
140
Table 5-6. Analysis of error between modeled and measured results with 50% evaporation/condensation threshold.
Pavement Turbulence Model
Evaporation Model Cevap
Upstream Surface
Downstream Surface
Upstream Interior
Downstream Interior
Effluent Water
RMSE RPE RMSE RPE RMSE RPE RMSE RPE RMSE RPE
Asphalt Turbulent UDF 0.1 0.865 0.19% 1.307 0.28% 0.125 0.03% 0.225 0.06% 0.368 0.10%
Concrete Turbulent UDF 0.1 3.314 0.39% 3.073 0.49% 0.775 0.16% 0.301 0.08% 0.117 0.03%
141
Figure 5-1. Installation of thermocouples in pavement specimen. A) Top view and side
view and B) front view. Note that air cavity chamber and EPS insulation bed are not shown in top or side view.
142
Figure 5-2. CFD mesh dimensions and statistics. Mesh Type: MapPave; element Nodes: 19925; cell thickness at water
inlet = 0.5mm, 4 cell thickness; cell thickness above water inlet = geometric growth edge sizing (bias factor = 2), 8 cell thickness; solid domain cell size = 4mm; fluid domain cell width = 2mm at pressure inlet and bottom wall at water inlet, 4mm in main domain; cell thickness at pressure inlet = 2mm; wedge cell size = 4mm
143
Tem
per
ature
(o
C)
27
28
29
30
31
32
33
34
Measured Effluent
Simulated Effluent
Measured Influent
Tem
per
ature
(o
C)
30
40
50
60
70
Interior Measured
Interior Simulated
Surface Measured
Surface Simulated
Flow duration (seconds)
0 50 100 150 200 250 300
Tem
per
ature
(o
C)
30
40
50
60
70
Interior Measured
Interior Simulated
Surface Measured
Surface Simulated
(Upstream Pavement Temperature)
(Downstream Pavement Temperature)
(Water Temperature)
Figure 5-3. Measured and modeled asphalt specimen temperature and effluent
temperature. Model shown incorporated 50% evaporation/condensation
threshold, turbulent, and =0.1.
144
T
emp
erat
ure
(o
C)
27
28
29
30
31
32
33
34
Measured Effluent
Simulated Effluent
Measured Influent
Tem
per
atu
re (
oC
)
30
40
50
60
70Interior Measured
Interior Simulated
Surface Measured
Surface Simulated
Flow duration (seconds)
0 50 100 150 200 250 300
Tem
per
atu
re (
oC
)
30
40
50
60
70Interior Measured
Interior Simulated
Surface Measured
Surface Simulated
(Upstream Pavement Temperature)
(Downstream Pavement Temperature)
(Water Temperature)
Figure 5-4. Measured and modeled concrete specimen temperature and effluent
temperature. Model shown incorporated 50% evaporation/condensation
threshold, turbulent, and =0.l.
145
Flow duration (seconds)
0 50 100 150 200 250 300
Tem
per
atu
re (
oC
)
25
30
35
Measured
Modeled
Tem
per
atu
re (
oC
)
25
30
35
Measured
Modeled
(Asphalt Pavement Temperature)
(Concrete Pavement Temperature)
RMSE = 0.89oC, RPE = 2.7%
RMSE = 0.37oC, RPE = 0.7%
Figure 5-5. Effluent temperature modeled using flat plate method for both A) asphalt
and B) concrete.
.
A
B
146
CHAPTER 6 GLOBAL CONCLUSION
Four investigations are performed to better understand the heat transfer
phenomena to and from pavement bodies, before and during storm events. The first two
investigations focus on in-situ temperature measurement. Research findings from an
investigation of 17 rainfall events at a University of Florida faculty parking lot indicate
that event heat transfer from an asphalt pavement surface during the rainy season in
North-Central Florida is flow limited, with cumulative flow as an appropriate surrogate
for cumulative heat transfer to the rainfall-runoff for 12 of 17 storms. It is also found that
the average pavement temperature before a rain event is very strongly correlated with
heat transfer and that concrete temperatures before an event are lower than asphalt
temperatures.
Heat balance models are able to approximate measured data. Results from the
first investigation suggest that stormwater runoff temperature is not equal to pavement
surface temperature when sampling flow from a large contributing area. It is posited that
the flow regime may diminish heat transfer, creating an insulating boundary layer if flow
is laminar or increasing air entrainment if turbulent. It is also observed that there is a
sharp difference between subgrade thermal response and pavement temperature.
The second investigation consists of a series of sub-experiments performed to
determine the impact of shade on pavement temperature. Pavement surface
temperature is measured when exposed to sunlight, shaded by a parked vehicle, and
shaded both by a vehicle and by tree canopy. There is a peak difference of more than
16oC between shaded and unshaded surface temperature but no significant difference
between the pavement temperature beneath vehicles. It is also determined that vehicle
147
surface temperatures reach more than 60°C. A first flush may occur on full parking lot
due to the potential energy release from vehicle surfaces during an event, however this
investigation is not performed. Radiation from the chassis of a recently operated vehicle
(before parking) is observed to dampen the cooling capacity of vehicular shading and it
is suggested that frequent removal and replacement of a vehicle from a cool parking
space may lead to a gradual increased in pavement temperature over time. An
investigation of the temperature along a temporally shaded transect illustrates the effect
of shadows on pavement temperature but also illustrates that localized horizontal
conduction to cool pavements has a demonstrable effect on surface and subsurface
temperatures. It is recommended to orient to face east with shade trees reducing
radiation on empty and vehicle occupied parking spaces during a peak insolation
period.
The third and fourth investigations are performed ex-situ to aid in the creation of
computational fluid dynamics models. In addition, the third investigation compares the
relative performance of pavements of different material composition and surface
reflectivity. Results of the third investigation show that concrete performs comparably to
an asphalt to which a reflective coating is applied. The CFD model is found to simulate
measured internal pavement temperature with no significant difference between
measured and modeled results. The model also performs this successfully using
weather data input and pavement material composition properties.
The fourth chapter details an investigation in the relationship between pavement
temperature and runoff temperature. Similar to in-situ results, a temperature first flush is
seen. A model is created to simulate the measured results and it is concluded that flow
148
can be simulated as turbulent over the surface. The CFD model performs better when
simulating concrete than asphalt. Theories are postulated as to why there is higher error
in simulating asphalt temperature. An interesting finding from the investigation is that
there is little improvement by modeling evaporation, suggesting that, for the conditions
of the physical experiment, evaporation may not be significant.
These results support the future investigation of engineered systems that can
achieve multiple goals, such as porous concrete which allows for groundwater recharge,
runoff reduction, and reduced thermal storage. Studies have shown that using a BMP
such as permeable paving provides reductions in runoff temperatures of 2°C to 4°C in
comparison to asphalt streets (Haq and James 2002). Simply changing the pavement
color can also have profound impacts outside the field of stormwater such as the urban
heat island effect (Akbari et al. 2009) for a lower cost while addressing the issue of
runoff temperature, however it does nothing to minimize peak flow. Shading by natural
foliage allows for the reduction in pavement temperature and peak runoff volume,
critical to maintaining cool stream temperatures (Roy 2005), however if not incorporated
correctly, it can cause increased nutrient loading to receiving waters. The tools
presented herein can be used in part to evaluate the efficacy of these solutions and
possibly provide future BMPs. More advanced tools can be used to evaluate the more
intricate mechanisms occurring during rainfall-runoff to better understand heat transfer
mechanisms during the early period of rainfall-runoff events.
149
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BIOGRAPHICAL SKETCH
Ruben Kertesz was born in the Pacific Northwest, and grew an affinity to know
more about why he exists, how things work, and nature. Encouraged to carefully and
cautiously explore, he ventured into environmental subjects throughout his scholastic
career. While attending a high school in the Pacific Northwest, Ruben was invited to join
an environmental science club which afforded him an opportunity to perform hands on
research on artificial reefs. Ruben realized the joy of building and testing, recording
data, and presenting his findings.
Throughout college, Ruben has participated in numerous environmental action
and recreation groups while obtaining a bachelor’s degree in biology. He obtained a
master’s degree from the Department of Environmental Engineering Sciences at the
University of Florida in 2005, focusing on water resources conservation and stormwater
mitigation by low impact development. Ruben is an engineering intern and his interests
have focused on sustainable construction practices. Ruben still carries a passion for
integrating research, social awareness, and technology. He reminds himself every day
that the wellbeing of him and humankind depend on sound and conscious
environmental and social thought.
