EPA-600/4-79-012
February 1979
EFFECTS OF POLLUTANTS AND URBAN
PARAMETERS ON ATMOSPHERIC
DISPERSION AND TEMPERATURE
by
R. Viskanta and T. L. Weirich
School of Mechanical Engineering
Purdue University
West Lafayette, Indiana 47907
Grant No. R803514
Project Officer
Francis S. Binkowski
Meteorology and Assessment Division
Environmental Sciences and Assessment Laboratory
Research Triangle Park, North Carolina 27711
ENVIRONMENTAL SCIENCES RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
RESEARCH TRIANGLE PARK, NORTH CAROLINA 27711
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DISCLAIMER
This report has been reviewed by Environmental Sciences and
Assessment Laboratory, U. S. Environmental Protection Agency and
approved for publication. Approval does not signify that the
contents necessarily reflect the views and policies of the U. S.
Environmental Protection Agency, nor does mention of trade names
or commercial products constitute endorsement or recommendation
for use.
11
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ABSTRACT
A study of the effects of anthropogenic pollutants and urban-
ization on the thermal structure and pollutant dispersal in the
planetary boundary layer showed that urbanization had a greater
influence on the surface temperature excess between urban and
rural locations than the radiatively active pollutants. The net
effect of gaseous and particulate pollutants was to decrease the
surface temperature around the noon hours and to increase the
temperature during the rest of the diurnal cycle. The increase
in the surface temperature was most significant for winter simu-
lations with snow covered ground. The maximum temperature at the
urban center for a simulation with radiatively active pollutants was
about 1 K warmer than for a corresponding simulation without the
radiatively active pollutants. As a result of warmer surface
temperature, pollutant dispersal near the ground was improved.
The feedback between radiatively active pollutants, temperature
structure and pollutant dispersal was significant and resulted in
a maximum of 25 percent reduction in pollutant concentrations for
the winter simulations.
During wintertime the assumed rates of anthropogenic heat
release in the city were found to play a more important role in
the formation of the urban heat island than the radiatively ac-
tive pollutants. Increase in heat release raised the surface
temperature and caused the surface layer to become less stable
which improved pollutant dispersal. Changes in such interface
parameters as the surface roughness, moisture availability and
solar albedo were found to have significant effect near-surface
temperatures in the city and on the urban-rural temperature
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differences. The findings indicate that a change in land use is
a very important factor in climate and weather modification by
urbanization and industrialization.
This report was submitted in fulfillment of Grant Number
R 803514 by the School of Mechanical Engineering, Purdue Univer-
sity, West Lafayette, Indiana, under a partial sponsorship of the
U. S. Environmental Protection Agency. This phase of the work
was completed as of December 1977.
IV
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CONTENTS
Abstract iii
Figures vi
Tables xi
Abbreviations and symbols xii
Acknowledgement xv
1. Introduction 1
Local weather modificatior 1
Objectives and scope of study 3
Surface parameters 6
Radiation characteristics of air pollutants • • 7
2. Conclusions 10
3. Recommendations 14
4. The Urban PEL Model 18
Dynamic two-dimensional transport model .... 18
Radiative transfer model 22
Turbulent eddy exchange coefficients 29
Pollutant, water vapor, and heat emission sources 30
5. Case Study and Numerical Model 32
6. Results and Discussion 38
Energy balance components along the urban surface 38
Radiative transfer 43
Turbulent eddy diffusivi^ies 53
Diurnal variation of the surface energy flux
components 59
Surface temperatures . 77
Temperature distribution 87
Pollutant concentrations 100
Urban heat island Ill
References 117
Appendix 125
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FIGURES
Number
1. Schematic representation of urban environment and 18
coordinate system
2. Fractional pollutant emissions during the diurnal
cycle 35
3. Comparison of energy flux components at the surface
between simulations Wl and W2 39
4. Comparison of energy flux components at the surface
between simulations W6 and W7 40
5. Comparison of energy flux components at the surface
between simulations SI and S2 42
6. Schematic solar radiant energy balance diagram on
the PEL at the urban center: a) simulation SI with
radiatively nonparticipating aerosol and b) simu-
lation S2 with radiatively participating aerosol • • 44
7. Schematic solar radiant energy balance diagram on
the PEL at the urban center: a) simulation Wl with
radiatively nonparticipating aerosol, b) simulation
W2 with radiatively participating aerosol, c) simu-
lation W6 with radiatively nonparticipating aerosol,
and d) simulation W7 with radiatively participating
aerosol 46
8. Diurnal variation of the effective PEL albedo ... 47
9. Schematic thermal radiant energy balance diagram
(in W/m^) on the PEL at the urban center for the
winter simulations Wl and W2 at the urban center at
midnight 48
10. Isopleths of solar flux divergence perturbation (in
W/m3 x 103) in the PEL for simulation Wl (a) iso-
pleths of solar flux divergence difference (in W/m3
x 103) in the PEL between simulation W2 and Wl (b)
vi
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at noon (The abscissa represents the horizontal
grid point along the urban area and the ordinate
denotes the vertical grid point) 51
11. Isopleths of solar flux divergence perturbation
(in W/m3 x 103) in the PEL for simulation SI (a)
and isopleths of solar flux divergence between
(in W/m-5 x 103) in the PEL between simulation S2
and SI at noon 52
12. Vertical turbulent eddy diffusivity profiles for
momentum transport (K^) for simulation SI 54
13. Variation of turbulent eddy diffusivity for momen-
tum transport at z = 2 m with time and distance
along the city for simulation SI 56
14. Comparison of turbulent eddy diffusivities for
momentum transport between simulations SI and S2 . . 57
15. Comparison of turbulent eddy diffusivities for
momentum transport between simulations Wl and W2 • • 58
16. Comparison of solar incident flux along the
urban area at noon between simulations Wl and W2 • • 60
17. Comparison of solar incident flux along the urban
area at noon between simulations SI and S2 60
18. Diurnal variation of solar incident flux at urban
center for winter simulations Wl, W2, W6 and W7 . . . 62
19. Diurnal variation of solar incident flux at urban
center for summer simulations SI and S2 62
20. Comparison of thermal incident fluxes at upwind
rural location and urban center for simulations
Wl and W2 63
21. Comparison of thermal incident fluxes at upwind
rural location and urban center for simulations
W6 and W7 65
22. Comparison of thermal incident fluxes at upwind
rural location and urban center for simulations
SI and S2 66
23. Diurnal variation of turbulent heat flux at upwind
rural (x = 0), urban center (x = 17.5 km) and down-
wind urban (x = 30 km) locations for simulation Wl . 6«
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24. Diurnal variation of turbulent heat flux at
upwind rural, urban center and downwind urban
locations for simulation W6 68
25. Diurnal variation of turbulent heat flux at upwind
rural, urban center and downwind urban locations
for simulation SI 69
26. Diurnal variation of latent heat flux at upwind
rural, urban center and downwind urban locations
for simulation Wl 69
27. Diurnal variation of latent heat flux at upwind
rural, urban center and downwind urban locations
for simulation W6 71
28. Diurnal variation of latent heat flux at upwind
rural, urban center and downwind urban locations
for simulation SI 71
29. Diurnal variation of soil conductive heat flux
at upwind rural, urban center and downwind urban
locations for simulation Wl 73
30. Diurnal variation of soil conductive heat flux
at upwind rural, urban center and downwind urban
locations for simulation W6 73
31. Diurnal variation of soil conductive heat flux
at upwind rural, urban center and downwind urban
locations for simulation SI 74
32. Surface temperature variation along the urban area
for simulation Wl 78
33. Surface temperature variation along the urban
area for simulation si 78
34. Surface temperature variation along the urban
area for simulation W6 79
35. Diurnal surface temperature variation at upwind
rural, urban center and downwind urban locations
for simulation Wl 80
36. Diurnal surface temperature variation at upwind
rural, urban center and downwind urban locations
for simulation SI 80
37. Diurnal variation of surface temperature differences
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between simulations W2 and Wl at upwind rural, urban
center and downwind urban locations 82
38. Diurnal variation of surface temperature differences
between simulations S2 and SI at upwind rural, urban
center and downwind urban locations 82
39. Surface temperature differences between simulations
W2 and W8 along the urban area 84
40. Surface temperature differences between simulation
W3 and W2 along the urban area 84
41. Surface temperature differences between similations
S2 and S4 along the urban area 86
42. Surface temperature differences between simulations
W2 and W9 along the urban area 86
43. Surface temperature differences between simulations
S5 and S2 along the urban area 88
44. Surface temperature differences between simulations
S6 and S2 along the urban area 88
45. Isopleths of potential temperature perturbation in
the urban atmosphere for simulation Wl at noon (a)
and at midnight (b); the abscissa and the ordinate
denote the grid points in the horizontal and verti-
cal directions, respectively 90
46. Isopleths of potential temperature perturbation in
the urban atmosphere for simulation S6 at noon (a)
and at midnight (b) 91
47. Isopleths of potential temperature perturbation
in the urban atmosphere for simulation SI at noon
(a) and at midnight (b) 92
48. Isopleths of potential temperature difference in
the urban atmosphere between simulations W2 and
Wl at noon (a) and at midnight (b) 94
49. Isopleths of potential temperature difference in
the urban atmosphere between simulations S2 and SI
at noon (a) and at midnight (b) 95
50. Potential temperature differences in the urban
atmosphere between simulations W7 and W7 at the
upwind rural, urban center and downwind urban ....
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locations 96
51. Potential temperature perturbation at the urban
center for simulation Wl 98
52. Potential temperature perturbation at the urban
center for simulation SI 98
53. Comparison of potential temperature perturbations
at the urban center for some winter simulations
at noon 99
54. Isopleths of pollutant concentration perturbation
in the urban atmosphere for simulation Wl at noon
(a) and at midnight (b) 101
55. Isopleths of pollutant concentration perturbation
in the urban atmosphere for simulation SI at noon
(a) and at midnight (b) 102
56. Pollutant concentration variation along the urban
area during the diurnal cycle at a height of 2 m
for simulation Wl 104
57. Pollutant concentration variation along the urban
area during the diurnal cycle at a height of 2 m
for simulation SI 105
58. Three dimensional representation of pollutant con-
centration at a height of 2 m along the urban area
with time for simulation SI 107
59. Pollutant concentration difference between simu-
lation W2 and Wl at the urban center 108
60. Pollutant concentration difference between simu-
lation S2 and SI at the urban center 108
61. Maximum urban minus upwind rural surface tempera-
ture difference variation with time for some
winter simulations 112
62. Maximum urban minus upwind rural surface tempera-
ture differences for some summer simulations .... 112
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TABLES
Number Page
1. Grid spacing in the vertical (z) direction 32
2. Summary of interface parameters for the "base"
numerical summer and winter simulations at the
upwind rural (UR) and urban center (UC) locations • • 34
3. Summary of numerical simulations: Ug = 4.0 m/s,
Vg = 3.0 m/s, = 38.5° North latitude. Variations
from these and other parameters given in Table 2
are indicated below 36
4. Summary of net upward thermal fluxes (in W/m2) at
the urban center 49
5. Net radiative flux divergence differences (in
W/m3 x 102) for representative simulations at
the urban center 53
6. Turbulent heat flux differences (in W/m2) at the
urban center for different simulations 75
7. Latent heat flux differences (in W/m2) at the
urban center for different simulations 75
8. Conductive heat flux differences (in W/m2) at
the urban center for different simulations 76
9. Pollutant concentration differences (in yg/m3)
at the height of 2 m 11°
10. Diurnal variation of the urban heat island
intensity (in K) 114
XI
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LIST OF SYMBOLS
a Absorptance of soil, a = 1 - r
O o ^
C Concentration of species n
C Volumetric rate of production of species n
Concentration of water vapor at saturated conditions
c Specific heat at constant pressure
D Diffusion coefficient of species n
E Exponential integral function
ef Emittance (emissivity) of the air-soil interface in the
thermal part of the spectrum
f Net radiative flux defined by Eq. (22)
F Radiative flux in the positive z -direction
F" Radiative flux in the negative z-direction
f Coriolis parameter
g Gravitational constant
H Turbulent (sensible) heat flux at the air-soil interface,
see Eq. (10)
Ah Latent heat of vaporization of water
&
I, Intensity of radiation
I, , Planck's function
DA
K Turbulent eddy diffusivity in the z-direction
f-t
k Thermal conductivity
t Mixing length, see Eq. (20)
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L Latent heat flux at the air-soil interface, see Eq. (11)
M Halstead's moisture availability parameter, see Eq. (13)
m Surface source of pollutant emissions, see Eq. (14)
NP Refers to radiatively nonparticipating
p Pressure
P Refers to radiatively participating
p. Scattering distribution function, see Eq. (17)
q Anthropogenic heat emission source at the surface, see
an Eq. (10)
q Volumetric rate of heat generation
r Albedo (reflectance) of the air-soil interface in the
solar part of the spectrum
R Relative humidity of soil or gas constant
Ri Richardson number
T Thermodynamic temperature
T Temperature of the soil
^
t Time
u Horizontal north velocity component
v Horizontal west velocity component
w Vertical velocity component
x Horizontal coordinate, see Figure 1
y Horizontal coordinate
z Vertical coordinate, see Figure 1
z Surface roughness
a Thermal diffusivity of soil
j
R/c
0 Potential temperature defined as 0 = T(p /p) p
K Absorption coefficient or the ratio of specific heat
at constant pressure to specific heat at constant
xiii
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volume
A Wavelength
y Direction cosine
v Frequency
p Density
a Scattering coefficient or Stefan-Boltzmann constant
Azimuthal angle
Subscripts
n ' Refers to species n
p Refers to pollutants both aerosols and gases
w Refers to water vapor
A Refers to the bottom of the soil layer
6 Refers to the edge of the planetary boundary layer
v Refers to frequency or per unit frequency
1 Refers to aerosol
2 Refers to pollutant gas
00 Refers to top of the free atmosphere
Superscripts
M Refers to turbulent eddy diffusivity of momentum
0 Refers to turbulent eddy diffusivity of heat
C Refers to turbulent eddy diffusivity of mass of species
n
xiv
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ACKNOWLEDGMENTS
This work was supported by th'e Meteorology and Assessment
Division, U. S. Environmental Protection Agency, under Grant No.
R803514. The interest and support of Drs. James T. Peterson and
Francis S. Binkowski, the Grant Project Officers, is acknowledged
with sincere thanks. Computer facilities were made available by
the National Center for Atmospheric Research which is supported
by the National Science Foundation.
The authors also wish to acknowledge fruitful discussions
with and contributions of Drs. R. W. Bergstrom, Jr., and A.
Venkatram to this effort. Mr. Rodney A. Daniel has assisted
with numerical calculations and data reduction. One of the
authors (R. V.) held a Humbolt Award from the Alexander von
Humbolt Foundation, Bonn, Federal Republic of Germany during
1976-1977. He also gratefully acknowledges the hospitality ex-
tended to him by the Technical University of Munich during his
stay in the Federal Republic of Germany.
xv
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SECTION 1
INTRODUCTION
LOCAL WEATHER MODIFICATION
The rapid increase in industrialization over the past few
decades has been accompanied by relatively significant changes
in the local climate (Peterson, 1969; Landberg, 1975 and 1977).
There is also considerable measure of agreement among investi-
gators as to the nature and magnitude of inadvertant weather and
climate modifications particularly on urban and meso scales. The
modifications are manifested by observations of urban-rural dif-
ferences in horizontal and vertical temperature, relative humid-
ity, wind speeds, radiation, visibility, precipitation and other
alterations. The differences in the various meteorological para-
meters are well documented and recent reviews are available
(Garstang et al., 1975; Landsberg, 1977).
The temperature difference between urban and rural areas,
known as the urban heat island effect, is satisfactorily under-
stood (Landsberg, 1974). It is generally accepted that, in ad-
dition to the earth-surface properties and topography differences
between urban and rural environs there are a number of physical
processes which are known to contribute to the effects of urbani-
zation on temperature, relative humidity and wind flow patterns.
For example, the physical processes which are known to influence
the urban heat island formation include: 1) reduction of low
level winds due to buildings, 2) reduced evaporation cooling due
to lower moisture availability in the city, 3) increased heat
storage in concrete and buildings, 4) anthropogenic heat emis-
sions due to human activity, and 5) alteration of the radiation
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balance due to the presence of air pollutants in the urban
atmosphere. The physics of the development and decay of the
urban heat island is understood and the relative importance of
these processes has been examined (Garstang et al., 1975).
The urban heat island is created by man's activity; there-
fore, phenomena associated with that activity accompany the heat
island and may contribute to its unintended effects on weather
and climate. A number of feedback mechanisms can also come into
play as a result of anthropogenic effects. For example, radi-
ative interaction with air pollutants in the planetary boundary
layer (PEL) can affect the energy balance and the temperature
structure in the boundary layer and, through this change, the pol-
lutants can alter the mixed layer growth and turbulent diffusi-
vities. In turn, these changes may modify the concentration and
dispersion rate of pollutants near the ground. In addition,
cumulative and synergistic effects may also arise as a result of
changes in the pattern of land use, anthropogenic air pollutants,
water vapor and heat emissions and radiative transfer. It is
important to understand these mechanisms so that their contri-
bution to the magnitude of the inadvertant weather modification
in urban areas can be assessed.
The modification of the thermal structure by air pollutants
is just one of the many effects of urbanization on the weather
and climate of a city and its rural surroundings. Although ob-
servational data can provide, for example, information on the
urban environment, it cannot explain the effects of various
factors influencing the urban atmosphere. An understanding of
the different effects of urbanization can be obtained only by
isolating possible causative factors and studying their contri-
butions one at a time. Such a procedure is not practical from
the view of an observational program as it is not possible to
manipulate environmental and meteorological conditions. Mathe-
matical models, however, can be employed to advantage to help
understand and extend observational data by numerically simu-
lating the transport process in the atmosphere. To the extent
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that the mathematical model simulates observations, it can become
valuable. For example, the model can aid local weather predic-
tion, interpretation of field data, forecasting of pollution
episodes in urban areas, urban planning, estimation of air pol-
lution dispersion, and identification of pollutants using remote
sensing methods. In addition to the above, numerical modeling
can also be a valuable guide to on-going observational programs
such as METROMEX, SURE, MAP3S, MISTT and others. The main util-
ity of numerical modeling lies in its ability to predict what
will happen for any given set of changes in the urban parameters,
meteorological, surface and/or initial conditions.
OBJECTIVES AND SCOPE OF STUDY
The primary effects of particulate and gaseous pollutants on
urban climate is through their radiative interaction. Under-
standing the alteration of the visibility, insolation, ground and
near ground temperature and wind patterns by aerosols and of the
ground and of the near ground temperature and wind patterns by
gaseous pollutants are of importance in being able to assess the
impact of air pollutants on the anthropogenic contributions to
the changes in the urban environment. Apart from the precise
details by which gaseous and particulate matter might influence,
for example, the energy balance in an urban atmosphere, an impor-
tant aspect in understanding local climate modification and pol-
lutant dispersion involves feedback mechanisms which could ampli-
fy the influence of variations in factors such as atmospheric
stability, lapse rate, surface temperature, effective PEL albedo,
mixed layer growth and others.
The primary objective of the research program was to en-
hance understanding of the effects of pollutants on the urban
environment. To this end, the specific aims of the project were:
1. To perform short-term numerical experiments and sensi-
tivity studies using an unsteady, two-dimensional trans-
port model simulating transport processes in an urban
PEL by accounting for anthropogenic pollutant and heat
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emissions in the atmosphere as well as surface para-
meter variations along an urban area.
2. To determine the role of pollutants in modifying the
temperature structure in the urban atmosphere.
3. To search for primary feedback mechanisms between
pollutants, temperature structure and pollutant dis-
persion.
First, before proceeding with details of the model used and
the discussion of results, some background material is presented
which is relevant to the model. The available literature on the
urban PEL models, surface characteristics, radiation character-
istics of pollutants and anthropogenic pollutant, water vapor and
heat emissions in the atmosphere are reviewed.
In the report emphasis is placed on the results and their
discussion. The development of the model used for the numerical
simulations is discussed adequately elsewhere (Viskanta et al.,
1976) and changes made in the model since its initial develop-
ment will be highlighted. Only results of numerical experiments
which have not yet been published will be discussed in the report.
Some phases of the overall project have been completed some time
ago and the results are available in the published literature.
The publications which have resulted from the work under the
research grant are listed in APPENDIX.
URBAN PEL MODELS
During the past decade there has been considerable interest
in developing improved understanding of the urban environment and
a number of urban-PBL models have been developed. These models
can be roughly divided into three groups based on the approaches
taken. These groups are: 1) unsteady energy-budget studies of
the urban surface (e.g. Myrup, 1969; Gertis and Wolseher, 1977)
and for the surface plus mixing depth (e. g. Leahey and Friend,
1971; Oke and East, 1971), 2) dynamic models simulating air flow
over heated surfaces but ignoring pollutants and surface charac-
teristics (e. g. Estoque and Bhumralker, 1969; Olfe and Lee, 1971;
4
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Vukovich, 1973; Bernstein, 1975) anc those models which ignore
pollutants but account for surface characteristics (e. g. Yu and
Wagner, 1975; Gutman and Torrance, ]975) and 3) dynamic PEL
models incorporating the radiative effects of anthropogenic air
pollutant, water vapor and heat emissions. The third group of
models is most relevant to this work and will be summarized in
the following paragraphs.
The urban-PBL models which account for anthropogenic effects
of radiatively interacting air pollbtants, heat emissions and sur-
face characteristics can be grouped into one-, two- and three-
dimensional models. Some dynamic models which account for the
presence of radiatively active pollutants in the urban PEL but
ignore horizontal variations have been developed (Atwater, 1971a,
1971b, Zdunkowski and McQuage, 1972; Bergstrom and Viskanta,
1973a, 1973b; Zdunkowski et al., 1976; Venkatram and Viskanta,
1976). The earliest models (Atwater, 1971a, 1971b) neglected
both convection and advection and specified the surface tempera-
ture independently of the boundary layer. In some later models,
which could be considered as extensions of earlier work of
Atwater, other investigators (Zdunkcwski and McQuage, 1972;
Bergstrom and Viskanta, 1973a; Venkatram and Viskanta, 1976;
Zdunkowski et al., 1976) have incorporated radiative transfer and
convective transport by eddy diffusion into time-dependent, one-
dimensional models which produced vertical velocity, temperature,
water vapor and pollutant concentration profiles. In these
models radiative transfer is treated with different levels of
detail, but the K-theory is used to model turbulent eddy dif-
fusion.
More detailed time-dependent tvo-dimensional transport mod-
els in the urban PEL have also been developed and used to perform
numerical simulations (Atwater, 1974; Viskanta et al., 1976;
Venkatram and Viskanta, 1976; Viskanta et al., 1977a). In these
models the variation of the surface characteristics and anthro-
pogenic air pollution and heat emission parameters were accounted
for. Perhaps the most detailed (e. g. as far as the geometry is
concerned) three-dimensional PEL models developed to date are
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i
those of Atwater (1975, 1977). The model accounts for changes in
topography and incorporates radiation, convection, advection and
diffusive transport as well as source emissions. The model para-
metrizes surface-atmosphere interactions for rural and urban land
surfaces and calculates the surface temperature from an energy
balance equation. Anthropogenic heat emission sources are ac-
counted in the surface energy balance. Vertical transport is
described by turbulent eddy diffusivity approach while the radi-
ative transfer calculated using a simple approximate model
(Atwater, 1971a, 1971b). A relatively crude (5 x 5 at 12.8 km
intervals over Los Angeles) (Atwater, 1975) grid is used for the
numerical calculations, apparently because of computer time re-
quirements .
The sophistication of the urban PEL models for numerical
simulations has increased in the past few years. However, the
results are still ambiguous and must be treated with some skepti-
cism. This is due, in large part, to several factors: 1) vari-
ous surface parameters which enter the models are virtually un-
known in urban area, 2) radiation absorption and scattering char-
acteristics of anthropogenic aerosol and absorption coefficients
of air pollutants are uncertain, 3) air pollution and heat emis-
sion during the diurnal cycle cannot be modeled with sufficient
accuracy, 4) turbulence models are inadequate, and 5) the results
of the models have received little verification because of the
lack of comparison to actual urban data. The next few subsec-
tions will address some of these problem areas in greater detail.
SURFACE PARAMETERS
A rural surface is a combination of natural cover such as
grass, vegetation, trees, bare dirt, water and some impervious
cover such as roads. An urban surface is of course an amalga-
mation of impervious cover such as streets and buildings and
natural cover, predominantly grass, vegetation and trees. The
physical properties and the radiation characteristics of the
substrate must be determined as an average of these types of
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materials. However, the properties needed have not been estab-
lished for common type soils (Eagleson, 1970) not to mention the
large variety of materials encountered in different urban areas.
•
The amount of impervious cover in urban areas is a question
of some importance to hydrologists and few studies have been car-
ried out (Bartholomew, 1955; Abrams, 1966; Clawson, 1972). These
studies indicate that at least 50 percent of the average large
city is impervious cover. The studies of Bartholomew (1955) and
Clawson (1972) are, however, biased towards high cover by their
consideration of large cities with highly developed core and by
predominantly eastern cities with high polulation densities. A
statistical study (Stankowski, 1972) has developed a relationship
between impervious cover and population density.
Model calculations have indicated that urban surface char-
acteristics may play an important role in the formation of heat
islands (Atwater, 1972). However, little has been done (Dabbert
and Davis, 1974) to determine values for the surface parameters
which are to be used in the numerical models. The values used by
Atwater (1972) are taken, in large part, from the work of Myrup
(1969) and represent educated guesses rather than actual experi-
mental data. Other investigators (Yu and Wagner, 1975; Gutman
and Torrance, 1975; Viskanta et al., 1977a) who have modelled the
temperature structure used similar values.
RADIATION CHARACTERISTICS OF AIR POLLUTANTS
Particulate Pollutants
The atmospheric aerosols and their radiation absorption and
scattering properties are characterized by their size distribu-
tion, chemical composition, and physical properties such as the
density and the complex index of refraction. The problem of char-
acterizing the aerosols is a difficult one and consequently any
model of the radiation characteristics will be open to some
criticism over the parameters used. An extensive (over several
hundred literature citations) and up to date review of the tropo-
spheric and stratospheric aerosols is available (Kondratayev,
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1977) and will not be repeated. A few of the relevant topics
will be discussed here.
Recent studies in St. Louis (Bhardwaja et al., 1974; Charlson
et al., 1974; Dzuby and Stevens, 1975) suggest that the urban
aerosols may be separated into two distinct distributions based
on the size and the chemical composition of the aerosol particles.
The first of these distributions consists generally of particles
with radii greater than 2 ym. These particles are composed pri-
marily of silcon oxides with substantial amounts of calcium and
lesser amounts of iron oxides and metal chlorides. The second
of these two distributions consists generally of particles which
have radii less than 2 ym. These particles are chiefly anthro-
pogenic in origin and are either produced directly as aerosol or
by heterogeneous nucleation of gas-phase pollutants. Chemical
analysis of these particles shows large concentrations of sulfur
and carbon compounds with varying amounts of lead, zinc, arsenic
and other metals. Chemical analysis of organic constituents
(Kondratayev, 1977) suggests that organic acids are also present
in subrr.icron particles. Because of the hygroscopic nature of
many of the chemical compounds present in the aerosol and the high
humidities present in the urban areas, the particles are often
composed largely of liquid water. The solution of ivater-soluble
pollutants favors the formation of many pollutant species such as
sulfuric acid, bisulfates, ammonium sulfate and nitrate as well
as a host of other salts and acids (Kondratayev, 1977).
Measurements of size distributions in cities (Butcher and
Charlson, 1972) indicate that plots of volume distributions
versus particle diameter generally exhibit a bimodal structure
with the two peaks occuring about 0.4 ym and 10 ym and the minimum
between the peaks around 2 ym. The measurements also show a
large variation in the relative magnitude of the two peaks. While
the power law distributions are quite satisfactory for number
density, they are quite unsatisfactory for volume distributions.
Experimental data show variation in the aerosol particle size
distribution with altitude in the troposphere as is-ell as with the
-------
type of the aerosol (Kunkel et al., 1975; Kondratayev, 1977).
The chemical complexity of the aerosol makes it extremely
difficult to specify the spectral index of refraction, n^ = n,+
ik,, which together with the size distribution and density is
A
needed to calculate the spectral absorption and scattering char-
acteristics of the aerosol (Bergstrom, 1972). Measurements have,
however, been reported for the larger, more homogeneous natural
aerosol particles (Kondratayev, 1977). Because of the extreme
chemical complexity of anthropogenic aerosol, it is not possible
to relate its index of refraction to that of particular chemical
compound as was done with natural aerosol. Measurements of urban
aerosols (Hanel, 1968; Fischer, 1970; Charlson, et al., 1974;
Kondratayev, 1977) suggest that the imaginary part of the complex
index of refraction is small but not zero. However, there is a
great deal of uncertainty associated with these measurements.
Caseous Pollutants
There are hundreds of different waste gases emitted into
the atmosphere in urban areas as a result of human activity.
Leighton (1961) has presented the ab -orption coefficient in the
solar part of the spectrum of some g, ses associated with photo-
chemical reactions, including nitrogen dioxide, sulfur dioxide,
ozone, nitric acid, ethyl nitrate, biacetyl, methyl propenyl
keytone and hydrogen peroxide. Of tlese gases nitrogen dioxide
has the greatest effect on the radia" ive transfer and energy
budget in the atmosphere. The gas i: a strong absorber in the
short wave part of the spectrum and . ts concentrations in the
urban atmospheres are sufficiently h:gh (Kunkel et al., 1975).
The absorption coefficients of gaseoi s pollutants in the long-
v/ave part of the spectrum of such ga.< es as sulfur dioxide, me-
thane, ammonia, carbon monoxide, peroxyacetyl nitrate, ethylene,
benzene, nitric oxide, nitrogen dioxide and ozone has been pre-
sented by Ludwig et al. (1969). A more recent survey of the
gaseous pollutants and the measured range of the concentrations
in the atmosphere is available (Kunkel et al., 1975).
-------
SECTION 2
CONCLUSIONS
The conclusions of this study deal with the results of the
numerical simulations and with the model itself. These two topics
are related since the results obtained from the model can only be
applied to the urban PEL in so far as the model represents the
physical processes which occur in the urban atmosphere.
The results of the various numerical simulations performed
for the assumed, meteorological conditions and urban parameters
can be summarized into the following main points:
1. In general, urbanization has greater influence on the
ground and near-ground temperature than radiatively
active pollutants. The net effect of aerosols is
to decrease the daytime surface temperature. The
net effect of gaseous pollutants is to increase the
nighttime surface temperature. The increase in the
surface temperature is most significant during winter
nights with snow covered ground (~ 1 K at urban center).
2. Radiatively active air pollutants in the PEL accen-
tuate the nighttime and moderate the daytime urban
heat island. During wintertime the assumed rate of
anthropogenic heat releases are found to play a more
important role in the formation of the urban heat
island than radiatively active pollutants. Heat re-
leases in the city increase the temperature excess
between urban and rural environs.
3. Radiatively participating aerosols in the city gen-
erally give rise to cooling of the ground and warming
10
-------
of the PEL. Whether there is a net cooling or
warming of the earth-PBL system depends upon the
surface albedo, the radiation absorption and scat-
tering characteristics of the aerosol, aerosol load-
ings and vertical distribution and zenith angle
(latitude, season and time of day). Because of the
incomplete knowledge of aerosol radiative properties
(e. g. primarily absorption) the conclusions about
the effects on the radiation balance of the earth-
atmosphere system have to be carefully tempered by
statements about these properties.
From the results obtained it is clear that urban
interface parameters such as the roughness height,
moisture availability and solar albedo are more
important in forming the urban heat island than
radiatively active pollutanr.s. This is particu-
larly true during the day when solar irradiation
and surface temperature are at their maximum.
The findings indicate that urbanization and in-
dustrialization have a significant impact on the
urban microclimate. It is possible that these
microclimatic perturbations might lead to changes
in large scale weather. This, it is evident that
the modeling of atmospheric transport processes
is a crucial step in urban planning.
Radiatively active air pollttants alter the local
energy balance and through it modify the vertical
temperature structure, stability and heat fluxes
at the ground. The altered atmospheric stability
produces feedback between pollutant concentrations,
temperature structure and pollutant dispersion.
The feedback is particularly significant during
winter nights downwind of the urban center. It
results in a reduction of a maximum of 251 in pol-
lutant concentrations at a 2 m height for simulations
11
-------
with radiatively active pollutants in comparison
with radiatively inactive pollutants. The magni-
tude of the feedback correlates with the magnitude
of the near-ground temperature difference between
the two simulations.
6. Aerosol particles by absorbing and scattering solar
radiation have a number of direct and indirect ef-
fects. They affect air quality and by modifying
solar radiation they also influence the energy avail-
ability for solar energy utilization systems particu-
larly in urban areas. Aerosols not only reduce di-
rect solar flux reaching ground but also change the
proportion of direct and diffuse radiation.
7, The urban heat island formed by urbanization and
anthropogenic heat and pollutant emissions into
the atmosphere may have some social and economic
impact if it exceeds a few degrees Celsius. For
example, the energy demand for space heating would
be decreased. Taken over a diurnal cycle in summer,
the net effect may be nullified. The energy demand
for space cooling would be decreased but it may be
increased at night because of warmer air temperatures.
8. A more complete understanding to what extent air pol-
lutants modify the thermal structure and their own
dispersion in an urban PEL will most likely come from
better turbulence models, from more complete under-
standing of the absorption characteristics of anthro-
pogenic aerosols and gases, and from more complete
data on the properties of the urban interface. Ob-
servational data in urban atmospheres is essential
for verifying the various models.
Before concluding it is worthwhile to emphasize the short-
comings of the model used in this study. The model is two-
dimensional and cannot realistically simulate three-dimensional
transport processes in the urban PEL. However, a truly detailed
12
-------
and accurate treatment of the present problem is extremely dif-
ficult and beyond the capability of most present-day computers.
Since the primary objective of the research is to simulate ther-
mal structure, pollutant dispersion and radiative transfer, some
details of the complicated urban flow field have been ignored.
For example, the model cannot simulate diffusion within or between
tall roughness elements such as buildings. In addition, the model
of this study does not account for chemical and photochemical
reactions either in the PEL or at the surface. Finally, the
transport of pollutants by cumulus convection and interaction
between could processes and the radiation field have been neg-
lected by assuming that the atmosphere is cloudless. The two-
dimensional model, however, should provide a more realistic des-
cription of transport processes in the urban PEL than one-
dimensional models.
13
-------
SECTION 3
RECOMMENDATIONS
This investigation involved modeling o£ a large number of
physical processes. Although every attempt was made to model
the physical processes as realistically as possible, the wide
scope of this study made it impossible to give equal attention
to all the processes. Two types of specific recommendations are
made here for future work. First, problem areas are identified
which require additional research effort for the purpose of im-
proving the transport model. Second, specific research topics
are suggested which could be investigated using the model.
Some of the problem areas which should receive attention in
connection with the development of a dynamic two-dimensional
transport model are the following:
1. An improved turbulence model is needed. This is par-
ticularly critical in the simulation of the outer PEL
where the production-dissipation equilibrium assumption,
which forms the basis for eddy diffusivity correlations,
is not valid. A first step towards an improvement of
turbulence modeling would be the incorporation of a
two-equation (Launder and Spalding, 1974) model.
2. A radiative transfer model which accounts for the pre-
sence of clouds as well as for air pollutants in an
urban atmosphere should be developed and incorporated
in the two-dimensional transport model. In this model
the shadowing caused by buildings should be considered.
Some research effort in this direction has been re-
ported at a recent symposium (e. g. Avaste and Vainikko,
14
-------
1977; Davies and Weinman, 1977). Models which con-
sider clouds and radiation interaction with them are
needed for examining numerically various feedback
mechanisms and attempting to understand the higher
order nonlinear interactions of the various atmo-
spheric constituents with the radiation field.
3. An improved procedure should be developed to account
for the absorption and emission of thermal radiation
of a combination of pollutant gases. A realistic pol-
lutant model should include the most important pollu-
tants found in an urban atmosphere. Such a model
should also provide a procedure to account for the
overlap between the pollutant gas bands. As narrow
band models are too time consuming for dynamic cal-
culations, it is necessary to modify the wide band
models to account for overlap.
4. Realistic procedures for modeling the spatial and
temporal variation of anthropogenic pollutant, heat
and water vapor emissions in the urban atmosphere
must be developed. For example, it is important to
model the variation of the pollutant sources along
the urban area with time of the day since the loca-
tion of pollutant sources determines whether radi-
ative interaction by pollutants increases or de-
creases pollutant concentrations.
5. Simple operational urban PEL models incorporating
direct and indirect radiation effects (e. g. in a
parametrized manner) should be developed for per-
forming studies on urban and meso scales. The mod-
els should be sufficiently flexible to allow, for
example, modeling in urban areas with large vari-
ations in topography and/or near large bodies of
water.
While a number of problem areas remain, the dynamic two-
dimensional transport model can simulate time-dependent
15
-------
distributions of meteorological variables in the urban PEL. The
model can be used to study various feedback mechanisms and syner-
gistic effects which may be important in understanding inadver-
tent weather modification on micro and macro scales. Specifi-
cally, it is recommended that the following topics be investi-
gated using the model.
1. The limited results of this study indicate the impor-
tant role of the surface parameters in determining the
effect of radiatively active pollutants on the tem-
perature in the urban atmosphere. Systematic sensi-
tivity studies should be conducted to focus attention
on the interface parameters and their variation along
the urban area.
2. The cumulative and synergistic effects of changes in
the pattern of land use and anthropogenic heat and
pollutant emissions within an urban area should be
investigated. For example, the model can be used as
a component of a study evaluating the total environ-
mental change resulting from a proposed siting in an
existing urban area of a new large scale industrial
and/or residential development on weather, climate
and air quality. This type of input would be useful
to those developing strategies for land use, economic
and social development of an urban area.
3. Urbanization and industrialization, including heat
and pollutant emissions, modifies temperature and winds
near the ground. This may affect energy demand for
space heating and/or cooling over a diurnal cycle or
for a season. The net effect of modification of en-
vironment on the energy demand is by no means clear,
and more complete investigation appears to be warranted.
The energy use in an urban area has a direct effect
on air quality and emission control planning.
4. The role of the turbulence model in determining pollu-
tant induced changes of pollutant dispersal should be
16
-------
investigated. This can be accomplished by per-
forming a series of simulations with identical con-
ditions and sources but with different turbulence
models. Use of different models on the sensitivity
of the vertical temperature' structure have been in-
vestigated CZdunkowski et al., 1976). Atmospheric
turbulence is still incompletely understood and
conclusions based on results of simulations which
examine the effect of the ciioice of the turbulence
model will find acceptance.
17
-------
SECTION 4
THE URBAN PBL MODEL
DYNAMIC TWO-DIMENSIONAL TRANSPORT MODEL
The dynamic two-dimensional transport model of the urban
PBL utilized for the numerical simulations is described else-
where (Viskanta et al., 1976) and is only highlighted here. On-
ly changes of substance in that model will be discussed below.
A schematic representation of the urban environment and
coordinate system is illustrated in Figure 1. In the model the
earth-atmosphere system is assumed to be composed of four lay-
ers: (1) the "free (natural) atmosphere" which is not affected
ATMOSF
'
1
FREE
Afr-,OSPNEP.E
o
HERE
U.V(B.pfCw.Ci.C2 SPECIFIED
•; ,'*'.-/ •,"'".'-'• ' -.". - '.-. '" -, • • '"*.' ' '••',•''•' '•.-.
PLANETARY ' '••'•- '• ' . ;-.''• '"• -'.•'-.. , '•• '•'" ''' '
BOUNDARY
LAYER j Z n H
| t IH mi J,
Z=0i
A
! SOIL U
i tx nnniirninm-,
\\VX\-rx\\ \ \ |\ \ \ \ \ \ \ \\\|\ \ \ '\ 's \ V V"V\ \-r
^Ych "T UHKir.U KUhAu *~"~ URBAN AREA , 'T '? DOhNVIND \URAL , . •?
. . -k \, S \ 1,, S >. i % \ V. '> > > i J * ;>_-»-.>' > > , > > > > .>.^.-A.A. N
EARTH ^ ^A |
(LITHOSPHERE) CONSTA
TEMPERATU
REGION
y
Figure 1.
Schematic representation of urban environment and
coordinate system.
18
-------
greatly by surface processes and where meteorological variables
are considered to be time independent; (2) the "polluted" atmo-
sphere (the PEL) where the meteorological variables such as the
horizontal, vertical, and lateral wind velocities, temperature,
water vapor and pollutant concentrations are functions of height,
time, and distance along the urban area; (3) the soil layer where
the energy transport is one-dimensional Cwith depth only), but
the soil properties and the radiation characteristics are con-
sidered to vary in the horizontal direction; and (4) the litho-
sphere where the temperature below the "soil layer" is assumed to
be constant during the simulation period. Variation in the topo-
graphy of the urban area is neglected, and the atmosphere is
assumed to be cloud free.
Model Equations
In the polluted transition layer, the boundary-layer equa-
tions expressing mass, momentum, energy, and species* conser-
vation are:
Mass :
9(pu) + 9(pw) = 0
Momentum (x-direction):
3u 9u _,_ 9u ,., , 9 ,VM 9u,
9t + U 37 + W 97 = fO - V + 97 (Kz 97)
Momentum (y-direction):
9u . 3v . 9v ff N 9 f,M 3v,
•g-£ + u -^TT + w TTT - ~ ± r~
Momentum (z-direction):
0 - + |f + Pg (4)
Energy:
*
The specific humidity is not conserved but the water vapor con
centration, partial pressure and density are conservable quan-
tities .
19
-------
Species:
oL "L oL_ « L oCi .-,- -.
1 - u " + w -^ - 4r CK_ n -*£) + C. (6)
__TU___TW_____^z .^^ T ^
In the soil layer the energy equation is
9T
• «, C7)
Boundary Conditions
At the top of the PEL, the dependent meteorological vari-
ables are specified and held constant during the simulation. This
assumes that the large scale flow (i.e., synoptic scale weather
pattern) remains constant during the simulation period. The
temperature at the lower sub-soil boundary is specified and is in-
dependent of time. Temperature, humidity, and pressure are
specified above the PEL in the "free atmosphere" for radiative
transfer calculations.
At the atmosphere-land interface (z = 0) the velocities are
assumed to vanish,
u(t,x,z) = v(t,x,z) = w(t,x,z) = 0 (8)
The temperatures in the air and the soil are coupled through
an energy balance at the interface (z = 0). This energy balance
requires that the sum of the heat fluxes at any horizontal posi-
tion x along the interface vanish, i.e.,
a F" + a F" - e.aT4 - H - L + S = 0 (9)
^> i> L L L-
where a (= 1 - r , with r being the solar albedo) is the solar
C* O O
absorptance, a is the thermal absorptance and e is the thermal
emittance. In this equation, the first two terms account for
absorption of solar (short-wave, F") and thermal (long-wave, F")
£5 L
radiation, the third term represents thermal emission, and the
fourth and fifth terms account for sensible and latent heat
transfer by turbulent diffusion, and the sixth term represents
heat conduction into the ground. The anthropogenic heat source
20
-------
has not been included in the energy balance at the interface
since it is considered to be released directly into the air
above and not at the surface. The turbulent sensible (H) and
latent (L) heat fluxes are given by
H - - pcpK° |f (10)
and
C 9C
L = - Ah K W —^ (11)
S z 3z
where Ahais the latent heat of vaporization. The sensible heat
flux in the soil is determined from
S - - ks-^ (12)
where k is the thermal conductivity of the soil (substrate).
The water vapor concentration at the surface is prescribed
by Halstead's moisture availability parameter M (Halstead et al.,
1957) using the expression
Cw = MRC + (1 - M)C (z-.) (13)
W W,bdL W -L
where R is the relative humidity of 1he interface, and C . is
W j S 3. t
the water vapor concentration at a saturation condition. The
values of the parameter R range from unity for water to zero for
dry soil.
The interface boundary conditiors for the pollutant con-
centration is written as
CP 3CP
Physically, this condition implies that there is a source of
pollutants at the earth surface. Pollutant emissions will also
be accounted in the volumetric source t included in Eq. f6") .
n -i ^ j
The horizontal variation of the urban interface parameters
such as ground solar albedo, thermal emittance, surface rough-
ness, thermal diffusivity and conductivity of the soil, moisture
21
-------
availability and relative humidity parameters are prescribed
functions of position along the urban area. There is lack of
data for these parameters and their variation along urban areas.
For example, the moisture parameter M, the fraction of surface
area which is covered with vegetation depends on soil type, root
distribution, water table depth, and other variables (Halstead
et al. , 1957). Also, the soil in urban areas is partly covered
by buildings, pavement, etc., and this fraction varies from one
area urban area to another and cannot be readily estimated.
More detailed models than the one utilized in this study for
predicting temperature distribution in the soil and evaporation
from the earth's surface are available (Sasamori, 1970). Unfor-
tunately, hydraulic and physical properties of soil such as
moisture potential, effective permeability (hydraulic conduc-
tivity) , and moisture content as well as thermal diffusivities
are not known for the soil types and textures encountered in
urban areas (Eagelson, 1970).
At the upwind rural boundary, the meteorological variables
are predicted from the one-dimensional model of Bergstrom and
Viskanta (1973a). This assumes that the flow into the urban area
is fully developed, parallel, and possesses no vertical velocity.
Downwind of the city (i.e., in the rural area) it is assumed that
all the meteorological and air pollution variables change slow-
ly, or,
II = 0 at x = L (15)
This condition implies that the downwind rural area is far ax^ay
from the city center and that nearly fully developed conditions
have been reached. At the initial tJme the variables are speci-
fied everywhere and are assumed to be independent of the hori-
zontal coordinate x.
RADIATIVE TRANSFER MODEL
The radiative transfer model used is an extension of the
earlier non-dynamical model of Bergstrom and Viskanta (1973c).
22
-------
Since multidimensional radiative transfer is quite complex and
the absorption and scattering characteristics of aerosols are
not known to sufficient accuracy (Coulson and Fraser, 1975) , it
is assumed that the transport of radiation can be approximated by
a quasi-two-dimensional field based on the vertical temperature,
water vapor and pollutant distributions at several predetermined
horizontal positions along the urban area. The radiative fluxes
and flux divergences can then be evaluated at these prescribed
horizontal locations. Suitable interpolation is then used to
determine the desired quantities between these locations. This
approach appears to be justifiable because the radiation prop-
erties, the ground albedo and pollutant emission sources along
the urban area are not known presently with sufficient accuracy.
The computational effort, which would be needed for a more sophis-
ticated treatment, does not warrant multidimensional radiative
transfer analysis.
The urban atmosphere is considered to be cloudless, plane-
parellel, and consist of two layers: (1) the free atmosphere and
(2) the urban PEL where most pollutants are concentrated. From
below, the polluted PEL is bounded by an opaque earth's surface
which not only emits but also reflects the incident radiation dif-
fusely. The emission characteristics and albedo of the ground
are arbitrary but prescribed functions of wavelength. The radi-
ative transfer between the free atmosphere and the polluted PEL
are coupled. The solar radiation incident on the free atmosphere
is scattered by gases (Rayleigh scattering) and absorbed by such
natural constituents as water vapor, carbon dioxide, ozone and
other trace gases. Solar energy absorption by NCU is generally
small, except during certain times in urban areas like Los
Angeles and will therefore be neglected. The solar radiation is
also scattered by the natural aerosols in the free atmosphere.
In the polluted PEL the solar radiation is absorbed and scat-
tered by gases and aerosols, both na ural and pollutant. In the
model the natural aerosol is considered nonabsorbing and the
absorption is accounted by the pollution aerosol. Thermal
23
-------
radiation is emitted and absorbed by natural atmospheric gases
in both layers and by pollutant gases in the lower layer. Ad-
ditional computational details can be found in the literature
(Viskanta et al., 3977b).
The solution of the radiative transfer equation was accom-
plished by dividing the entire electromagnetic spectrum into the
short wave solar part (0.3 <_ A £ 4 ym) and the long wave thermal
part (4 £ A <_ 50 ym) . This separation is natural and allows con-
siderable simplification in the analysis and evaluation of the
radiative fluxes and flux divergences.
It should be emphasized that the radiation transfer model
utilized is not as detailed as the more recent models which are
available (Ackerman et al., 1976; Welch and Zdunkowski, 1976).
Effects of multiple scattering by aerosols in the window region
(Balkan and Quenzel, 1976) do not appear to be sufficiently sig-
nificant to warrant inclusion in the radiative transfer calcu-
lations even for highly polluted atmospheres. In addition, since
the absorption and scattering characteristics of pollution aero-
sols are not known with sufficient accuracy (Coulson and Fraser,
1975), little data is available for the variation of the solar
albedo and thermal emittance along the urban area, and the fact
that radiative transfer in the urban PEL is not one-dimensional
are the primary reasons justifying the use of a less detailed
model for dynamic calculations.
Solar Spectrum
In the solar part of the spectrum emission by atmospheric
constituents is neglected, and following Chandrasekhar (1960)
the intensity I^(T^,y,
can be expressed as a sum of directly transmitted part I, and
of scattered part 1^ such that I = l£ + 1^. The directly trans-
mitted intensity can be immediately calculated from
-(Too,A - V^o^ - %)6(* ' V
(16)
24
-------
where F , is the spectral flux at the top of the free atmo-
00 , A
sphere, T , is the optical thickness of the entire (free +
00 , A
polluted) atmosphere, T, is the optical depth measured from the
surface of the earth, and 6 is the Dirac delta function. The
equation for scattered intensity I, when emission is neglected
becomes
to
n TT i
-1 J0
r27r s
J ix (Tx,a','+y,)dy'd'
where y (= cosG) is the direction cosine, w , is the albedo for
single scattering, and p, is the scattering distribution function.
The advantages of separating and then solving the radiative trans-
fer equation in this manner are that the scattered intensity
1^ may be orders of magnitude smaller than the directly trans-
mitted 1^ and that the latter is assumed to be a collimated
beam (plane wave) which does not have a continuous angular vari-
ation.
It is assumed that the scattering distribution function p,
can be expanded in a series of Legen«;re polynomials (P») and can
be written as
N
p, (cosG) = Z p pff(cos0) (18)
A £=0 , I
where 0 is the angle between the incident (y ',') and scattered
(U,) beams. If Eq. (18) is substiti ted into Eq. (17), the ad-
dition theorem of spherical harmonica (Irving and Mullineaux,
1959) is employed, the resulting equ£ tion is integrated over
from 0 to 2ir, and there results
s
dl, * OK ,-+1
25
-------
+ IT iJtVV /
-c=U
where
I^(TA,y) = | IA(TA,y,4>)d4> (20)
Assuming a solution for lf(TA,y) in the form
"s N
IA(rA,y) = Z a, (tJP, (y) (21)
A A , _ _ K A K.
substituting into Eq. (19), integrating over y from -1 to 1,
and manipulating yields a system of N + 1 (n = 0, 1, 2, ... N)
ordinary differential equations for the coefficients a . These
equations together with the boundary conditions are solved ana-
lytically and the details can be found elsewhere (Bergstrom and
Viskanta, 1973c).
Once the radiation field IA has been determined, the radi-
ative flux FA and its divergence follow immediately from the
definitions
F,(z) = [ f I (z,y,cj>)ydydcj> (22)
A J0 J-1 A
and
IA(z,y,)dyd (23)
respectively, where KA is the spectral absorption coefficient.
The spectral absorption and scattering coefficients and
the scattering distribution function of the aerosols in the pol-
luted PEL were predicted using Mie electromagnetic theory by
specifying the size distribution (Deirmendijian's Haze L distri-
bution) . The complex indices of refraction of the urban air
pollution aerosols were taken from the model proposed by
Bergstrom (1972). The resulting absorption and extinction
26
-------
coefficients agreed well with the values based on the bulk mean
indices of refraction of a dry aerosol found over the industrial
sector of Mainz, West Germany (Fischer, 1973).
The Pj-approximation of the spherical harmonics method was
used in the numerical computations. The predictions of radiative
transfer in a polluted atmosphere using this approximation have
been .previously made (Bergstrom and Viskanta, 1974) and found to
agree well with the results based on much more detailed and time
consuming calculations. The solar fluxes predicted using this
method (but somewhat different aerosol properties and spectral
integration) were also in good agreement with observations in
the St. Louis, Missouri metropolitan area for relatively clean
atmosphere (Bergstrom and Peterson, 1977). The fact that the
radiative transfer calculations were not too time consuming was
an important consideration because of the diurnal nature of the
transport processes to be modeled in the atmosphere. The inte-
gration over the wavelengths was carried out by dividing the
spectrum into twelve bands.
Thermal Spectrum
In the thermal part of the spectrum scattering is neglected
in comparison to absorption. As a first approximation this simp-
lification appears to be justifiable. The presence of scatter-
ing would increase only slightly the importance of absorption
by increasing the optical path. Assuming that emission and re-
flection from the earth's surface is isotropic (diffuse), the
total radiative flux F(z) can be expressed as a difference be-
tween the upward F (z), and downward F~(z), directed fluxes,
F(z) = F+(z) - F'(z) (24)
where
(25)
27
-------
and
F-(z) = 2u ICC)E(? - T)d£dA (26)
where e and r are the emittance and reflectance of the earth
surface, Ib^(T) is Planck's function, E (£) is the exponential
integral function (Chandrasekhar , 1960), and the optical depth
and thickness are defined respectively as
T, = K,dz and T , = «,dz (27)
A J0 A OOA JQ A
The radiative flux divergence follows immediately from Eq. (24)
and becomes
- |I = H(z) = 2\ K^Tre,!,, (0) + r, F~ (0)E? (T, ) +
Oi. JrtAAuA AA £ A.
A (28)
The thermal radiative flux, Eq. (24), and its divergence,
Eq. (28) , were evaluated with the help of the emissivity data
for the absorbing atmospheric gases. The emissivity concept is
well established in the meteorology literature (Goody, 1964;
Kondratayev, 1969) and is the most efficient method for predict-
ing the radiative fluxes and flux divergences. For water vapor
the emissivity data from Kuhn (1963) were used. This data simp-
lify the means of accounting for the overlap of the water and
carbon dioxide bands since the carbon dioxide contribution has
been subtracted out. For carbon dioxide the emissivity data of
Shekter (Kondratayev, 1969) was used. In the calculations mul-
tiple scattering was neglected, and it was assumed that the in-
fluence of gaseous pollutants was confined to the 8-12 ym spec-
tral region due to the relatively large opacity of the H-O and
CC>2 bands. Ethylene was chosen to simulate the net radiative
effects of the gaseous pollutants since Ludwig et al. (1969)
28
-------
considered it to be representative of a typical hydrocarbon and
since it has strong absorption in the atmospheric window (8-12
ym). Obviously, other gases such as ammonia or gas mixtures
could have been used to simulate the actual polluted conditions;
however, a more detailed description of gaseous pollutants was
considered unwarranted.
TURBULENT EDDY EXCHANGE COEFFICIENTS
Specification of turbulent eddy exchange coefficients in
connection with numerical modeling of the PEL is a very diffi-
cult task and has been the subject of a recent review (Shir and
Bornstein, 1977). The semi-empirical eddy exchange coefficient
correlations employed (Viskanta et al., 1977a) were not com-
pletely satisfactory because different correlations for K had to
z
be used which depended on the flow regime and stability condi-
tions. This has sometimes led to sharp discontinuities in the
M
values of K under some conditions which was undesirable. For
h
this reason an attempt was made to use the turbulent kinetic
energy model to calculate the eddy exchange coefficient which was
successfully employed in the numerical simulations of the one-
dimensional PEL (Venkatram and Viskanta, 1976). However, the
approach was not successful for two-dimensional simulations be-
cause of the stability and time step restrictions involved in
the numerical solution of the turbulent kinetic energy equation
and was therefore abandoned.
The turbulent eddy coefficients for momentum exchange were
predicted from the stability-dependert correlations (Estoque and
Bhunu-alkar, 1969)
rM -
Kz ~
I « f "*\ "\ "
8zJ I3z
1/2
(1 - 3Ri) for Ri <_ 0
(1 - SRi)"1 for Ri > 0
(29)
where the mixing length £ is given by Blackadaar (1962)
I = K(Z + zQ)/[l + K(Z + zo)/A] (30)
29
-------
Here X represents a constant mixing length. To avoid unrea-
sonably low values of K under stable conditions, an average
Richardson number, RT, is used for the lowest 50 m. The eddy
diffusion coefficients for heat and species transport are as-
sumed to be given by
K^K'" -„< C31)
M
where a (= K /K ) is taken to be the neutral value of 1.35
LI Z
(Businger, 1972). Equations (29) and (30) for the turbulent eddy
exchange coefficients are not completely satisfactory. However,
the sensitivity studies of Zdunkowski et al. (1976) (on the
choice of a turbulence model) show that the choice of the model
on the computed temperature, concentration, and wind profiles is
not critical but has some effect on the model computations.
These findings provide a partial justification for using Eqs.
(16) and (18) until more appropriate correlations become avail-
able to model turbulence.
POLLUTANT, WATER VAPOR, AND HEAT EMISSION SOURCES
Because it is not practical to consider individual sources
in the simulations, the pollutant emissions into the urban PEL
were modeled as surface sources (Viskanta et al., 1976). The
physical limitations of this type of formulation are recognized,
and therefore the volumetric pollutant emission sources in the
present calculations were approximated by
-O ~ zc J2/2%
Cp(x,z,t) = fp(t)gp(x)e e'p p ; p = 1, 2 (32)
In writing Eq. (32) it was assumed that individual pollutant
sources are distributed continuously. The functions f(t) and
g(x) approximating the variation of pollutant emissions with
time of the day and the distance along the city, as well as the
discharge height z of the various sources will be specified
e ,p
later by making reasonable assumptions.
There are anthropogenic water vapor emissions in the
30
-------
atmosphere due to combustion of hydrocarbons and other human ac-
tivities. However, because of lack of quantitative observa-
tional data these vapor sources have been neglected.
In the past the anthropogenic heat emission sources were
represented by surface fluxes (Yu an^ Wagner, 1975; Viskanta
et al., 1976). In this work the man-caused heat emissions are
approximated by a volumetric source given by the function
qCx,z,t) = FCt)G(x)e ' (Z/V C33)
According to this expression the highest heat emission occurs
at the surface and decreases exponentially. The specific func-
tions and parameters in Eq. (33) will be specified later.
31
-------
SECTION 5
CASE STUDY AND NUMERICAL MODEL
For specific numerical calculations it is necessary to pre-
scribe an urban geometry. An urban area 40 km in extent was
simulated. There were 17 equally spaced grid points: 3 in the
upwind, 4 in the downwind and 10 in the city. The city starts
at x = 5 km and ends at x = 30 km, with the center being at x =
17.5 km. The top of the PEL was arbitrarily chosen at a height
of 2000 m. For the meteorological conditions used in the simu-
lations the mixed layer height even for the summer days never
exceeded a height of 1600 m. This suggests that the top of the
PEL at 2000 m was adequate for the numerical experiments. There
were a total of 22 grid points in the vertical direction. Near
the ground they were distributed logarithmically to improve reso-
lution, and above 600 m the points were distributed linearly, see
Table 1. The bottom of the soil layer is at a depth of 0.5 m
TABLE 1. GRID SPACING IN THE VERTICAL (z) DIRECTION
J 2 (m) J z (m)
1 0 12 190.00
2 2.00 13 275.00
3 4.78 14 400.00
4 8.65 15 600.00
5 14.04 16 800.00
6 21.52 17 1000.00
7 31.94 18 1200.00
8 46.43 19 1400.00
9 65.58 20 1600.00
10 94.92 21 1800.00
11 133.62 22 2000.00
32
-------
v/hich is below the penetration depth of the daily temperature
wave. There were 11 equally spaced grid points in the soil.
In the numerical simulations the city of St. Louis, Missouri
(38.5° North Latitude) was taken as an example. Both a summer day
(solar declination 6 = 14°) and a winter day (solar declination
6 = -21°) were modeled. The declination angle of 14° corresponds
to mid-August and an angle of -21° refers to mid-January. These
times of year were chosen because most of the serious pollution
episodes occur then and because insolation is large or near its
minimum. Cloudiness was assumed to be absent as often experi-
enced in episodes of heavy pollution. _
Table 2 lists the values of the atmosphere-soil interface
parameters for the "base" winter simulations. There is a large
uncertainty in the parameters listed in Table 2. The horizontal
distribution of the surface characteristics was established by
selecting the values of the parameters at the rural and urban
center locations, and for the lack of better data or information,
a Gaussian distribution curve was then fitted between these two
points. The values of parameters given in Table 2 appear to be
reasonable and similar to data that have been used by other in-
vestigators (Atwater, 1975; Yu and Wagner, 1975).
The diurnal variation of pollutant emissions assumed is il-
lustrated in Figure 2. This approximation is based on observa-
tions in Cologne (Kunkel et al., 1975). In the numerical simu-
lations the anthropogenic heating was assumed to occur within
2 m (first node) of the surface, i.e., qOT,-/2mqdz. The variation
3. IT o
of the heat releases with time or air temperature above ground has
been ignored, and an average value was used throughout the day.
It is recognized (Torrance and Shum, 1976) that the heat re-
leases vary during the diurnal cycle, but the lack of knowledge
of the variations along the city did not warrant this more de-
tailed treatment. Based on the parameters given in Table 2 the
anthropogenic heat flux for the summer simulations corresponds
to an average value of 25 W/m2. This is in a range of values
that have been estimated for some cities such as Cincinnati, Ohio
(Torrance and Shum, 1976). The anthropogenic sources and sinks
33
-------
TABLE 2. SUMMARY OF INTERFACE PARAMETER FOR THE "BASE" NUMERICAL
SIMULATIONS AT THE UPWIND RURAL (UR) AND URBAN CENTER
(UC) LOCATIONS
Parameter Summer Winter
UR UC
Surface albedo, rs 0.25 0.15 0.25 0.125
Thermal emittance, et 0.96 Q.94 0.96 0.94
Soil thermal conductivity,
kg(W/m-K) 0.50 2.00 0.50 2.00
Soil thermal diffusivity,
x m2/s) 0_55 2_20 0>50 2<00
Surface roughness, ZO(IP) 0.10 i.QO 0.10 1.00
Halstead's moisture
parameter, M 0.75 Q.20 0.80 0.20
Relative humidity of
soil, R 0.20 0.15 0.20 0.20
Aerosol source,
s) 0.02 1.25 0.02 0.56
Gaseous source,
g2(yg/m2-s) 0.02 1.25 0.02 0.56
Effective emission height,
ze,l = ze,2 ^ 2'00 40.00 2.00 40.00
Pollutant emission variance,
cjj = CT2 (m) 2.00 40.00 2.00 40.00
Anthropogenic heat emissions,
C(W/m3) 0.28 28.00 0.56 56.00
Heat emissions variance,
aqOn) l-°° 1.00 1.00 1.00
Lower soil boundary
temperature, TA(K) 296.00 296.00 274.00 274.00
Thermodynamic temperature
at the top of the PEL,
T6(K) 283.36 283.38 268.00 268.00
34
-------
0.10
c:
fo.08
e
LU
§0.06
o0.04
.f2
12
i8 24
Solar Time (hr)
Figure 2. Fractional pollutant emissions
during the diurnal cycle.
of water vapor in the atmosphere were neglected because they
have been estimated to be insignificant for summer daytime meso-
scale influences on the atmospheric water balance of the urban
area (Sisterson and Dirks, 1977).
The simulations were started at 6:00 in the morning and
were continued (depending on interest) for a period of 30 hours
until noon of the second day. The initial conditions were
representative for the two seasons. For the summer the initial
temperature and water vapor concentiation profiles used were
based on the observations of Spangler and Dirks (1974) for the
metropolitan area of St. Louis, Missouri and were imposed over
the entire city (no x-variation), while for the winter the
initial temperature and water vapor concentration profiles were
similar to those employed by Atwater (1972). The initial wind
profiles were decreased by a factor 3 from those given by
Lettau and Davidson (1957) for the Great Plains Turbulence
35
-------
TABLE 3. SUMMARY OF NUMERICAL SIMULATIONS: ug = 4 m/s AND
v£ = 3.0 m/s, = 38.5°. VARIATIONS FROM THESE AND
OTHER PARAMETERS ARE GIVEN IN TABLE 2 ARE INDICATED
BELOW
Simula- Season Radiative
tion No. Interaction
Wl
W2
W3
W4
W5
W6
Winter
NP
P
P
P
NP
NP
Remarks
Base simulation
qan,UC=10°
qan,UR=1° w/»'
Exponentially distributed q
Snow covered ground: r TTD = 0.8,
ks UR=0<171 w/mK> ks UC=0>537
'
as,uR=0-41xl0~6
a
s,UC
0.82/10-6 m2/s, qan UR=10 W/m2
^an,UC=100 W/m2
As W5 except qail)UR=5 W/m2,
W7
W8
W9
SI
S2
S3
S4
S5
S6
S7
S8
" P
" P
it p
Summer NP
P
P
" P
P
P
NP
P
As W6 except P
qan,UR=qan,UC=0
ZQ UR=0.1 m, ZQ uc
Base simulation
n
10% weight carbon
qan,Uirqan,UC::=0
rs,UR=0'3> rs,UC=0
Mr,n = 0. 5 , MTT^, = 0
UR ' UC
rs UR=0'15' rs UC=
Same as S7 except
= 2. 0 m
aerosol
.1
0.1
P
36
-------
Study. The initial pollutant concentrations assumed had an
exponential decrease with height from the ground to the top of
the PEL and a Gaussian variation along the urban area. The ini-
tial aerosol and gaseous pollutant concentrations at the surface
•7 J
were taken as 20 ug/m and 100 ug/m at the rural area and the
urban center, respectively.
The numerical experiments are designed to examine:
1. The effects of urbanization on the planetary boundary
layer.
2. The effects of radiatively active pollutants in modi-
fying the temperature structure and dispersion in the
PEL.
3. The effects of anthropogenic heat releases in modi-
fying urban temperatures.
4. The modification in temperature and pollutant con-
centrations resulting from changes of interface para-
meters in the presence of radiatively active pollu-
tants.
For ease of reference the numerical experiments which have been
performed are listed in Table 3. The detailed discussion of
the results obtained is given in the next section of the report.
37
-------
SECTION 6
RESULTS AND DISCUSSION
ENERGY BALANCE COMPONENTS ALONG THE URBAN SURFACE
The surface temperature is a very important parameter in
"forcing" the model. Thus, a comparison of the energy budget
components along the urban area is of considerable interest.
For this reason the energy fluxes for the "base" winter simu-
lations Wl and W2 and the winter simulations W6 and W7 are pre-
sented in Figures 3 and 4, respectively. Note in each case the
simulation with radiatively nonparticipating pollutants is rep-
resented by a solid line. In each case the fluxes are presented
at noon (12:00) and midnight (24:00) along the urban area. Ac-
cording to the definition a positive heat flux indicates energy
input into the soil while a negative heat flux indicates a loss
to the atmosphere.
Referring first to Figure 3a (for simulation Wl and W2 at
noon) it is seen that the absorbed solar flux (q__) is the
3 S
largest component, while the emitted (q ), absorbed thermal
(q ), turbulent convection (q ), and latent (q») fluxes are all
cl t C X-
important components in the energy balance. The soil conduc-
tion flux (q ) is important at the urban center where it is ap-
j
proximately 70% larger than in the rural areas. This is due
primarily to the larger thermal conductivity of the soil com-
pared to the rural environs, see Table 2. The absorbed solar
flux q is a maximum at the urban center as the solar albedo
3. S
(r ) of the ground is a minimum there. The turbulent convec-
i>
tive flux q shows large variations along the urban area pri-
marily because of the changes in the surface roughness
38
-------
OJ
li
o
_ oj
£ .i M
OJ CO CO
II
O
O
>
xny
O
o
ro
O
a
O
o
8
o
O
O
ro
Q
O
o o o cr
I
V)
S
O
O
rt
4-1
rt
i
O
O
X
3
t>0
(D
a
o
ti
nS
ID
in
3
bO
39
-------
CD
I S
»- "•«- "^
-C jp O
all
„ CO CO
I 1
OJ
It
L
o
o
J_
L
(juu/M)
O
o
ro
O
o
CM
O
o
o
o
ro
o
o
o
PI
^3
4-J
+J
rt
t/5
+J
P!
O
p.
e
o
o
X
oo
fr -O
o PS
Pi rt
o
to
(4 P;
o o
10 -H
•H -P
SH 05
Oj rH
d< 3
e s
O -H
U to
to
•H
40
-------
parameter z which affects greatly the turbulent eddy diffusivity
near the ground.
At midnight the emitted (q ) and absorbed thermal (q.«J
c cl t
fluxes dominate the energy budget as the turbulent (qt), latent
(q»), and soil conductive (q ) fluxes are an order of magnitude
smaller. The anthropogenic heat flux q is also included in
the figure for the purpose of comparison even though in the
model this energy is not released at the surface but within the
first two meters above the ground. This flux is given by
|-2m.
qflri(x,t) = q(x,z,t)dz
an j n
0
Note that the turbulent (q ) and latent (q») energy fluxes change
their sign during the diurnal cycle.
For the winter simulations with snow ground cover (W6 and
W7) some important changes in the energy budget can be observed.
Referring to Figure 4a, the most apparent difference at noon is
that q is not the largest component in the energy budget. Com-
Si S
paring simulation W7 with simulation W2 (see Figure 2a) it is
2
seei) that q, ^ has decreased from about 425 to 200 W/m depending
cl S
upon the location in the urban area. This is due to the high
solar albedo of the snow, which was assumed to be freshly fallen.
The latent energy and soil conductive fluxes are also smaller
due to changes in the soil conductivity and Halstead's moisture
parameter. As expected, at midnight (Figure 4b), the fluxes are
comparable to those found for simulations Wl and W2.
Examination of Figure 5 reveals that, compared to the win-
ter simulations, the corresponding heat fluxes along the urban
area are larger for the summer simulations. During the day, the
latent heat flux (q») is of much greater importance due to the
higher temperatures than for the winter simulations. The latent
heat flux reaches a minimum in the urban area due to decreased
evaporation (reduction in Halstead's moisture availability para-
meter M) which is controlled by M. Note the decrease in the
41
-------
CVJ
II
L_
O
O
O
— CVJ
an (D
c c.
o
i i
en (/)
o
CT
I
cr
\
C\J
•s H
o
o
ro
O
O
J£
x
O
rO
O
CO
O
o
o
00
o
o
O
O
o
o
(NJ
O
O
o
4->
Ctj
to
4-1
rt
0
c
o
PH
e ^
O CO
o
X
rt
iH
M-l rH
CO
>>
bO 10
QJ
ol
•H C
JH
O (D
LO
bo
•H
42
-------
relative importance of the anthropogenic heat flux (q__) shown
All
in Figure 5a when compared to the winter simulations. This
indicates that q is a much less important contribution for the
an r
summer than for the winter.
All three figures (Figures 3, 4 and 5) indicate that radi-
atively interacting pollutants reduce the absorbed solar flux
(q ) while increasing the absorbed thermal flux (q0+.) . As an
3. S 3-1
example, radiatively active pollutants in simulation S2 de-
crease the absorbed solar flux at noon by approximately 9% at
the urban center while increasing the absorbed thermal flux by
about 71 when compared to the noninteracting pollutants. The
reason q is lower downwind of the urban center is because the
cL 5
pollutant concentration are highest. The turbulent and latent
heat fluxes are about 7 and 4% smaller at the urban center for
simulation S2 than for SI, respectively. The net flux heating
(or cooling) the surface is therefore not very much different
for the two simulations. As a result, the effect of radiatively
active pollutants on the surface temperature which is determined
from the energy balance Eq. (9) is not expected to be very large
either during the day or the night.
RADIATIVE TRANSFER
Understanding of radiative transfer in the atmosphere is
essential because solar radiation "drives" the PEL model during
the day and thermal radiation cools r.he atmosphere at night.
Therefore, before discussing the detailed model results it is
desirable to discuss the effects of Aerosols and gaseous pollu-
tants on the radiative fluxes in the solar and thermal parts of
the spectrum. Also, the radiative flux divergence 8F/3z [or
the net heating/cooling rate (3F/8Z), pc ] is an important
component of the local energy balance for determing the tem-
perature distribution in the atmosphere, and it is therefore of
interest to determine how it is modified by urbanization and
air pollution. In the urban PEL the vertical transport of
energy by radiation can be of the sai;e order of magnitude or
43
-------
larger than energy transport by turbulence (Viskanta et al.,
1976).
Solar Fluxes and Effective PBL Albedo
A schematic diagram illustrating the radiative fluxes for
simulations SI and S2 at noon are compared in Figures 6a and
6b, respectively. The diagram shows the effect of absorbing
aerosol particles on the fluxes and indicates increased atten-
uation of solar radiation in the PBL from 0.918 for simulation
SI with radiatively nonactive anthropogenic aerosols (Figure 6a)
to 0.861 for simulation S2 with radiatively active anthropo-
genic aerosols (Figure 6b). Absorbing aerosol causes an increase
in atmospheric absorption by a factor of two, from 0.054 to
0.102. There is no appreciable increase in the effective albedo
of the earth-PBL system. The effective albedos, rounded off to
a three significant figure accuracy, are the same for the two
1.000 0.166 I.COO OJ66
A V A
0.054 0.102
0.918 0.138 0.86!
O A
V T-P
777777
0.780
(a)
Figure 6. Schematic solar radiant energy balance diagram on
the PBL at the urban center: a) simulation SI with
radiatively nonparticipating aerosol and b) simu-
lation S2 with radiatively participating aerosol
44
-------
simulations. There is, however, a corresponding decrease in the
absorption of solar radiation by the ground. One can conclude
from the results that there is no difference in the energy loss
from the earth-PBL system for the two simulations, but there is
some redistribution of the absorbed solar energy.
The effects of the absorbing pollutant aerosol on the radi-
ative fluxes for the winter simulations Wl and W2 (Figures 7a
and 7b) are similar to those for the summer simulations (Figure
6). The major difference is seen to be the increase in the ef-
fective albedo of theground-PBL system for the simulation W2
with radiatively active aerosols. The effective albedo is
increased from 0.212 to 0.228 which represents an energy loss
from the ground-PBL system. This loss is at the expense of de-
creased absorption of solar radiation by the ground. On the
contrary, the loss in energy from the ground-PBL system is
smaller for simulations W7 in comparison to simulation W6, see
Figure 7d and 7c. This is due primarily to the larger solar al-
bedo of the snow covered ground. The results show that the
tendency of warming of the PEL increases due to multiple re-
flection effects. This is consistent with the conclusions
of Mitchell (1971).
The effective PEL albedo is defined as the ratio of the
upward solar flux to the incident solar flux at the top of the
PEL (2000 m) . The effective albedo is a result of complex
interactions of solar radiation with aerosols and gases in the
atmosphere. It also depends on the solar zenith angle, ground
albedo, as well as the concentration and distribution of pollu-
tants. The diurnal variation at three urban locations of the
effective PEL albedo for nonparticipating simulations SI and S7
are shown in Figure 8. The ground albedo r (see Table 3) is
i>
similar to values reported by Dabbert and Davis (1974) over
St. Louis. The results are in general agreement with those
measured by White and Eaton (1977). The effective PEL albedo
predicted for large solar zenith angles (i.e., early morning
and late evening) is too high as shading effects are not
: 45
-------
1.000 0.212 '. 1.000 0.228
V A ' V A
0.047 O.I 12
0.847 0.106 0.755 0.095
A
////////
0.660
(b)
1.000 0.587 I.COO 0.525
V A
0.051 0.154
0.904 0.542 0.803 0.482
A V A
0.362 0.321
(c) (d)
Figure 7. Schematic solar radiant energy balance diagram on the
PEL at the urban center: a) simulation Wl with radi-
atively nonparticipating aerosol, b) simulation W2
with radiatively participating aerosol, c) simula-
tion W6 with radiatively nonparticipating aerosol,
and d) simulation W7 with radiatively participating
aerosol.
46
-------
-80.5
g, 0.4
T3
ft 0.3
0.2
O.I
0
Simulation SI
Simulation S7
^Upwind
Rural
Downwind Urban
Urban Center^*
I
8
10 12
14
16
J I
18 20
Time (hr)
Figure 8. Diurnal variation of the effective PEL albedo.
considered in the model. Higher values for surface albedo were
assumed for simulation SI (see Table 2), as well as a greater
difference between the rural and urban values. The results show
a higher effective PEL albedo as well as a greater difference
between urban locations than measured (White and Eaton, 1977) .
The addition of radiatively participating pollutants has
some effect on the effective albedo. For example, the solar
albedo at the urban center at noon for participating simulation
S8 is 4% greater than for nonparticipating simulation S7.
While aerosol, water vapor, and carbon dioxide absorption tend
to decrease the albedo, aerosol and Rayleigh scattering tend to
increase the albedo. The results show that the effective albedo
is most sensitive to the zenith angle and ground albedo and to
a smaller degree by the presence of participating pollutants.
This agrees with the recent results of Liou and Sasamori (1975).
On the basis of the results obtained, the effect of radi-
atively active aerosols in the city atmosphere is to cool the
ground and warm the PEL. The net effect of the aerosols on the
ground-PEL system is greater during the winter than the summer.
47
-------
Conclusions about aerosol effects have to be carefully tem-
pered by statements about solar albedo, aerosol concentrations,
solar zenith angle (time of the day and year) and other factors.
Thermal Fluxes
A schematic diagram illustrating the effect of radiatively
interacting gaseous pollutants on the net upward thermal flux at
the ground and at the top of the PEL are illustrated in Figure 9
for the winter simulations W2 and Wl at midnight. Note that
there is a decrease in the net flux (emitted minus absorbed) at
the ground in the presence of the radiatively active gaseous
pollutants (e.g. compare results for simulations W2 and Wl),
while there is an increase in the net radiant energy loss from
the PEL for simulation W2 as compared to Wl. The net effect of
the radiatively active pollutants is to decrease the radiant
energy loss from the ground-PBL system. The net modification of
the radiant energy balance by pollutants at night is to reduce
the cooling of the ground and of the system but to increase the
139.6 144.5 -4.9
A A A -
697 _ 61.7 - 8.0
82.8 -12.9
A A
W2 Wl W2-WI
Figure 9. Schematic thermal radiant energy balance diagram (in
W/m^) on the PEL at the u ban center for the winter
simulations Wl and W2 at - he urban center at mid-
night .
48
-------
cooling of the PEL.
A summary of results for other simulations and both midnight
and noon is given in Table 4. The effects indicated are seen to
be identical to those already discussed. Again, it should be
strongly emphasized that the conclusions reached regarding the
modifications of the radiative fluxes by the pollutant gases are
valid, strictly speaking, for the particular conditions consid-
ered. This is because the thermal radiative fluxes depend not
TABLE 4. SUMMARY OF NET UPWARD THERMAL RADIATIVE
FLUXES (IN W/m2) AT THE URBAN CENTER
Net flux
at ground
Net loss
from PBL
Net flux at
top of PBL
Wl
97.8
54.3
152.1
W2
78.8
69.4
147.6
a) Noon, 12:00
W2-W1
SI
-19.4 92.3
15.1 76.9
S2
75.6
89.4
-4.5 169.2 156.0
S2-S1
-17.7
12.5
-4.2
b) Midnight, 24:00
Net flux
at ground 82.8
Net loss
from PBL
61.7
Net flux at
top of PBL 144.5
69.9
69.7
139.6
-12.9 67.1 55.9
.0 89.6 96.7
-4.9 156.7 152.6
-11.2
7.1
-4.1
only by pollutant gas and water vapor concentrations and their
distributions but also on the vertical temperature structure in
the PBL which is affected by urbanization.
Radiative Flux Divergences
The isopleths of solar flux divergence perturbations
49
-------
(divergence in the upwind rural location) at noon for simulations
Wl and SI are presented in Figures lOa and lla, respectively. In
the absence of radiatively active pollutants the divergences over
the city are smaller than in the rural area. The variations
shown in the figures are due to urbanization, e.g., smaller solar
albedo and evapotranspiration at the surface in the city compared
to that in the upwind rural area. As expected, the perturbation
is greater for the summer than for the winter simulation be-
cause of larger insolation during the summer.
The isopleths of the solar flux divergence differences be-
tween simulations W2 and Wl and between S2 and SI are depicted
in Figures lOb and lib, respectively. The divergences are larg-
er (more negative) for simulations with radiatively active pollu-
tans. The greatest differences occur near the surface and down-
wind of the urban center where the aerosols are concentrated.
The isopleths shown in the figures reflect the aerosol plume
which develops downwind of the city center as a result of pollu-
tant emissions near the ground and their transport by turbulent
diffusion and advection. The results obtain are consistent
with those reported in the literature (Viskanta et al, 1977b)
under unstable meteorological conditions.
The net (solar plus thermal) flux divergences for some rep-
resentative simulations are summarized in Table 5. The results
show that the radiatively active pollutants increase the heat-
ing rate in the PEL, while during the night the gaseous pollu-
tants increase the cooling rate, except in the immediate vicin-
ity of the ground where there is an increase. The higher solar
albedo for simulatjons W7 and W6 with snow covered ground than
for simulations W2 and Wl. with the bare ground result in larger
differences during the day because of the larger solar albedo
for the former two cases. For particular meteorological condi-
tions, urban surface parameters, pollutant concentrations, heat
and pollutant the net effect of radiatively active pollutants
is to warm the PEL during the day and cool the PEL at night.
50
-------
CD
— CsJ
-Q
00
. I , ! . I , I , I , I , I i I , I
CVJ
00
CVJ
GO
fl
0 -H
0
fi
•H
0 H
0
•P
ri
0
to 0 O
o m o
rH m fi
•H
X T3 -P
05
to 0
P* t J y-"~"\
\ PJ^3
S= 0^
•H 0 &
•rH rc3
o ri
•H X
•p rs CM
0
JH
ri
PS
ri
^ p!
3 5-i O
+J ri -H
0 O ri
P, V) rH
3
0 Mn g
O O -H
P!
0
•P
bo
o
ri
P!
•H
O
«H
O
Pn
^H -(-> 0)
-p
?H O
P.P!
0 0
cd 0
to P
w cd
•H p!
O -H
10 T3
bo
•H
PH
51
-------
JL
I . I , ! .I.I. I . I . I , I J"
O CD CVJ 00
C\J — —
CD
_ CVJ
00
CVJ
o
00
H-l
PQ
PH
•HTd
PI
Oi
CO
P!
O <-H
•H p|
-P 5-i O
,0 rH P
?H O 03
-P 3
CD O -H
PH tf)
0 rd Pi
U -P CD
P) CD CD
CD rH S
5n O CD
0) W ,0
> -H
•H J
T3 TJ PQ
Pi PH
X rt
M-l
-P
CO
O O
<+H -H rH
O -P
03 X
to i— I
rH C/) ^
PH
O H S
in o -H
i— i M-t "• — '
CD
5-H
3
M
•H
PH
52
-------
TABLE 5. NET RADIATIVE FLUX DIVERGENCE DIFFERENCES (W/m3 x i()2)
FOR REPRESENTATIVE SIMULATIONS AT THE URBAN CENTER
z(m) W2-W1 W7-W6 S2-S1
0 -11.97 -13.60 -17.14
5 -3.01 -7.53 -5.18
25 -2.72 -7.48 -4.99
50 -2.59 -7.22 -4.61
100 -2.14 -6.51 -3.89
500 -0.75 -0.45 -2.11
2000 -0.02 -0.02 -0.01
W2-W1
11.97
-3.01
-2.72
-2.59
-2.14
-0.75
-0.02
b)
-4.64
0.50
0.72
0.64
1.10
0.30
0.62
a) Noon, 12:00
W7-W6
-13.60
-7.53
-7.48
-7.22
-6.51
-0.45
-0.02
Midnight, 24:00
-1.37
1.26
1.29
1.19
1.59
0.26
0.59
0 -4.64 -1.37 -6.47
5 0.50 1.26 0.45
25 0.72 1.29 0.69
50 0.64 1.19 0.49
100 1.10 1.59 0.94
500 0.30 0.26 0.05
2000 0.62 0.59 0.49
TURBULENT EDDY DIFFUSIVITIES
The vertical distribution of turbulent eddy diffusivities
for simulation S] without active pollutants is shown in Figure
12. Inspection of the figure reveals large variations in K
z
during the diurnal cycle particularly for z > 50 m. For meteor-
ologically very stable conditions when the vertical potential
gradient 30/8z exceeded 4 K/km, the turbulent eddy diffusivity
M 2
KZ was arbitrarily set to a small value of 0.01 m /s. The dif-
fusivities become maximum early in the afternoon and reach a
minimum during the night. The magnitude of K^ of about 100 m /s
LJ
is in agreement with the values reported by other investigators
(Orlanski et al., 1974). From about 14:00 hours the
53
-------
0}
O
c
a
.a
Q.
=)
Ilill ! I I I
Ifll M I I
CVJ -
O %
Q
°0
O
°0
8
o
o
o
in
O
C/)
CD
m co
o
H a
PL, O
•H
•P as
•H rH
> 3
l-H fn
•H O
-P •!->
C !H
CD O
rH PL,
d to
rQ P!
^ cti
rH g
Cti 3
U P
P CD
JH S
CD O
> E
txO
•H
UH
54
-------
diffusivities decay rapidly and there is a rapid decrease in
the mixed layer height. By about 18:00 hours turbulence is
practically extinguished in a large portion of the PEL. The K
t*
profile for 18:00 hours is not drawn as it is virtually identi-
cal with the profile at 24:00 hours. This type of behavior of
PEL has been observed by Kaimal et al. (1975). Zdunkowski et
al. (1976) has found that there is considerable variation in the
magnitude of the eddy diffusivity particularly at greater heights
from one formula to another but there are only minor variations
in the vertical temperature profiles. The reason for this be-
havior, according to Zdunkowski et al., is that the temperature
field is strongly controlled by the heat fluxes near the earth's
surface where the diffusivities do not differ greatly.
The variation of the eddy diffusivity at the first grid
point (z = 2 m) with time and position along the urban area is
shown in Figure 13. An initial transient is noted during the
early part (from 06:00 to about 09:00 hours) of the simulation.
This is due to the fact that the assumed initial velocity, tem-
perature and water vapor concentrations were too "far" away from
the quasi-steady solutions induced by the diurnal cycle, and it
took the system about 2 to 3 hours to adjust. The diffusivity
is largest at the urban center (x = 17.5 km) where the surface
roughness is maximum.
The diurnal variation of the eddy diffusivity at 2 m and
100 m for the summer simulations SI and S2 and for the winter
simulations Wl and W2 are shown in Figures 14 and 15, respec-
tively. At the upwind rural location separate curves could not
be drawn for either SI and S2 or Wl and W2. At the urban cen-
ter the radiatively active pollutants modify the diffusivities
by altering the atmospheric stability through the temperature
structure near the ground. However, the net effect is rela-
tively modest. Due to the opposing influences the trends indi-
cated are quite complex.
55
-------
Time (hr)
24
x (km)
Figure 13.
Variation of turbulent eddy diffusivity for momentum
transport at z = 2 m with time and distance along
the city for simulation S] .
56
-------
CM.
10'
CO
c
JD
ZJ
•
•Simulation SI at Urban Center
•Simulation S2at Urban Center
•Simulation SI at Upwind Rural
10'
,o
10
cl
r
I
5 12
•*i ..
J L
18 24
! 1
6 12
Solar Time (hr)
Figure 14.
Comparison of turbulent eddy diffusivities for
momentum transport between simulations SI and S2
57
-------
10'
•>*
'>
c
JD
13
_
(2
—Simulation W! at Urban Center
—Simulation W2 at Urban Center
—Simulation Wl at Upwind Rural
10'
•"•^^tal^^^
1
3 12
1 1
18 24
1 1
6 12
Solar Time (hr)
Figure 15.
Comparison of turbulent jddy diffusivities for
momentum transport between simulations Wl and W2
58
-------
DIURNAL VARIATION OF THE SURFACE ENERGY FLUX COMPONENTS
The diurnal variation of the energy flux components at the
surface is important in understanding the surface temperature be-
havior during the daily cycle and in the dynamics of the PEL.
For this reason some typical results which shed light on the
salient nature of the transport processes at the ground are pre-
sented and discussed in this subsection.
Incident Solar and Thermal Fluxes
Figure 16 illustrates the variation of the solar flux inci-
dent on the surface q . at noon for simulations Wl and W2. The
o J-
radiatively participating simulation W2 shows a marked decrease
in q . (approximately 12% at the urban center) compared to the
radiatively nonparticipating simulation Wl due to the atten-
uation of the incident solar flux by aerosols. Note that the
effect becomes greater along the urban area due to the build-up
in aerosol concentrations. The solar flux is attenuated by
about 14% at x = 40 km for simulation W2 as compared to simu-
lation Wl. The solar albedo of the ground and the attenuation
of solar radiation by aerosols in the atmosphere are factors in
determining the solar flux incident along the urban area inci-
dent solar flux. Note that q . is slightly decreased at the
urban center compared to the rural areas for simulation Wl due
to the lower solar albedo assumed (see Table 2). A fraction of
the incident solar flux reflected from the ground is then scat-
tered back by the natural (nonabsorbing) aerosols in the plane-
tary boundary layer. However, this is only a second order ef-
fect. An examination of Figure 17 shows that, for simulations
SI and S2, the effect of radiatively active pollutants is simi-
lar. At noon, the solar flux incident on the ground has been
reduced by about 6%. This is well within the range of observed
reduction of solar fluxes in an urban area (Landsberg, 1977).
It should be noted that carbon dioxide and water vapor in both
P and NP simulations absorb solar radiation. For example, for
59
-------
500
X
3
LL.
,_ 460
c
420
Simufotion Wi
Simulation W2
380
0
10
J L
20
30
40
x (km)
Figure 16. Comparison of solar incident flux along the urban
area at noon between simulations Wl and W2.
X" !000 p=
S60
"C3
fc~
o
S20
880
0
10
20
30
40
Figure 17. Comparison of solar incident flux along the urban
area at noon between simulations SI and S2.
60
-------
the NP simulation SI, the downward solar flux has been atten-
uated by 7% from 2000 meters to the ground level. This is at-
tributed to scattering by the natural aerosols and absorption by
the carbon dioxide and water vapor in the PEL.
Figure 18 shows the diurnal variation of the solar incident
flux at the urban center for some typical winter simulations.
Comparing the radiatively participating (W2 and W7) with the non-
participating (Wl and W6) simulations, the attenuation of solar
flux by the radiatively active pollutants is apparent. Figure
19 illustrates the diurnal variation of q • for simulations SI
and S2. The trends are similar to those for the winter simula-
tions. The attenuation of solar radiation by pollutants is
greatest for large zenith angles such as early morning and late
afternoon as a result of an increase in the optical path. For
instance, comparing simulations SI and S2 at 7:00, the solar
incident flux is attenuated 141 by participating pollutants, while
at noon the attenuation is 6%.
Insolation measured in such cities as London and Vienna
showed typical deficits of 10 and 20 percent below rural areas
(Robinson, 1962). An 11% daily average decrease in the global
insolation compared to rural areas was observed (Peterson and
Flowers, 1977) during the more recent measurements (autumn 1973)
in the Los Angeles area. In St. Louis area, however, smaller
urban-rural differences were observed (Bergstrom and Peterson,
1977). The difference in behavior among St. Louis and Los
Angeles and European urban areas appears to involve a decreased
urban and increased rural attenuation. St. Louis may be atypi-
cal in this respect in that neither the city of St. Louis nor
its surroundings over a wide area modify solar radiation in a
manner typical of other locations.
Examination of Figure 20 shows a substantial increase in
incident thermal flux for simulation W2 with radiatively active
pollutants when compared to the simulation Wl with radiatively
noninteracting ones. The average increase is about 7 percent.
61
-------
Figure 18.
Simulation W6
Simulation Wl
Simulation W2
16 18 20
Solar Time (hr)
Diurnal variation of solar incident flux at urban
center for winter simulations Wl, W2, W6 and W7.
16 20
Solar Time (hr)
Figure 19.
Diurnal variation of solar incident flux at urban
center for summer simulations SI and S2.
62
-------
280
X
P
UI
_
o
260
220
200
Urban Center
Upwind Rural
Simulation W2
Emulation Wl
I
I
12
18
24 6 12
Solar Time (hr)
•'igure 20. Comparison of thermal incident fluxes at upwind
rural location and urban center for simulations
Wl and W2.
63
-------
Also note that, for both simulations, the incident thermal flux
is greater at the urban center. This is due to higher tempera-
tures and pollutant concentrations in the PEL than at the upwind
rural location. Referring to participating simulation W2, the
incident thermal flux is 4.4% greater at the urban center than
at the upwind rural location. At midnight, this increase is
only 1.3%. These trends are consistent with measurements of
Rouse, et al. (1973). However, a meaningful comparison between
the fluxes predicted in this study and those observed is not
possible without a knowledge of the pollutant concentrations in
the urban atmosphere. Inspection of Figures 20 and 21 reveals
that the difference caused by radiatively active pollutants is
relatively greater between simulations W6 and W7 than for Wl
and W2. The variation of the thermal flux incident on the sur-
face for the summer simulations SI and S2, shown in Figure 22,
is seen to be similar in trends to the winter simulations. The
magnitudes are greater due to warmer PEL temperatures. The ir-
regular trends in the incident thermal flux near the start (from
06:00 to about 09:00 hours) of the simulations are a result of
the initial transient and will be explained when discussing the
surface temperatures.
At night, the model predicts that the thermal radiation
flux incident on the surface is slightly greater at the urban
center than in the surrounding rural areas. However, this in-
crease is slightly more than compensated for by an increase in
radiation emitted by the surface. Thus, the surface net long-
wave radiation balance for the urban area is slightly more nega-
tive than for its rural surroundings. The difference is gener-
2
ally less than 10 W/m . These results are in agreement with
measurements made by Oke and Fuggle (1972) in Montreal. This
finding suggests that radiative exchange should play a relative-
ly small role in the urban/rural surface energy balance dif-
ferences at night.
It should be noted that the decrease in the solar fluxes
64
-------
CVJ
260
X
Ll_
_240
o
-
1220
200
180
Simulation W7
Simulation W6
Urban Center
Upwind Rural
I
I
12
18
I
I
24 6 12
Solar Time (hr)
Figure 21.
Comparison of thermal incident fluxes at upwind
rural location and urban center for simulations
W6 and W7.
65
-------
340
Urban Center
Upwind Rural
6 12
Solar Time (hr)
Figure 22.
Comparison of thermal incident fluxes at upwind
rural location and urban center for simulations
SI and S2.
66
-------
is greater than the increase in thermal fluxes for simulations
with radiatively participating pollutants during the daylight
hours. For example, at noon the total (solar plus thermal) radi-
ant energy flux incident on the surface for simulations W2, W7
and S2 is about 4, 5 and 3 percent, respectively, lower at the
urban center as compared to the upwind rural location. The re-
sults disagree with observation by Rouse et al. (1973) in and
around Hamilton, Ontario. At noon, a 2% increase in total radi-
ant energy was observed when the industrial areas were compared
to a relatively clean control site. Solar flux measurements
in combination with information about the aerosol and gaseous
pollutant concentration profiles, temperature structure and sur-
face properties are not readily available. Thus, it is not
possible to check the validity of the radiation model by com-
paring predictions with observed data.
Turbulent Fluxes
The variation of the turbulent heat flux for simulations
Wl, W6 and SI with radiatively nonactive pollutants are pre-
sented in Figures 23, 24 and 25, respectively. Except for the
magnitude of the fluxes, the trends indicated in the figures are
similar. During the daylight hours the turbulent flux is nega-
tive (directed way from the surface) and indicates that turbu-
lent convection tends to cool the surface. The reason the flux
is largest near the noon hours is because the surface temperature
and the turbulent diffusivity are highest at that time of the
diurnal cycle. Depending on the location along the urban area
considered, there may be reversal in the sign of the turbulent
heat flux before the sunset and after the sunrise. Comparison
of the results in Figure 25 with those reported elsewhere
(Viskanta et al., 1976) indicates that the turbulent heat flux
is quite sensitive to the meteorological conditions (particu-
larly the wind speed) and is also influenced by the choice of
the turbulence model used.
Comparison of the turbulence fluxes, particularly at the
67
-------
Downwind Urban
Upwind Rura!
Urban Center
-400
12
18
24 6 12
Solar Time (hr)
Figure 23.
Diurnal variation of turbulent heat flux at upwind
rural (x = 0), urban center (x = 17.5 km) and down-
wind urban (x = 30 km) locations for simulation Wl,
§200
O
03
X
0
c
o
-200
-400
Downwind Urban
Upwind Rural
Urban Center
12 18 24 6 12
Soior Time (hr)
Figure 24.
Diurnal variation of turbulent heat flux at upwind
rural, urban center and downwind urban locations
for simulation W6.
68
-------
100
I -100
b
42
g-300
|2
-500
Downwind Urban
Upwind Rural
Urban Center
i
1
1
6 12 18 24 6 12
Solar Time (hr)
Figure 25.
Diurnal variation of turbulent heat flux at upwind
rural, urban center and downwind urban locations
for simulation SI.
-120
12 18 24 6 12
Solar Time (hr)
Figure 26.
Diurnal variation of latent heat flux at upwind
rural, urban center and downwind urban locations
for simulation Wl.
69
-------
urban center during the day, for simulations SI and Wl shows a
39% greater flux for the summer conditions. This is due to
higher temperatures near the ground as a result of solar input
and greater tubulent diffusivities for the summer than the winter
simulations.
Latent Fluxes
Figure 26 illustrates the variation of the latent heat flux
for simulation Wl with radiatively nonactive pollutants. Note
that latent heat flux is greatest (shortly after noon) at the
downwind urban center, in spite of the lower turbulent eddy dif-
fusivity, due to the relative warmth (0.53 K greater than the
upwind rural location) and the fact that Halstead's moisture
availability parameter M is larger than at the urban center. At
2
night the loss at the urban center is approximately 10 W/m or
2
less compared with 94 W/m at noon. This is attributed to the
cooler temperatures and smaller turbulent diffusivities. At the
upwind rural and downwind urban locations the flux changes signs
at night, indicating condensation at the surface. The value at
the upwind rural node is slightly higher (on the order of 2
2
W/m ) due to the cooler surface temperature. The variation of
the latent heat flux for the simulation W6 with snow covered
ground is shown in Figure 27. Note that the magnitude of the
flux is more than 50" smaller during the day than that for simu-
lation Wl with bare ground. This is attributable to the cooler
surface temperatures for simulation W6.
Because of varroer surface temperatures and hence much
larger water vapor concentrations at the ground RC . ,._t, the
Vv j S fi L
latent energy fluxes during the day for the summer simulation
SI are about a factor of four larger than for the corresponding
winter simulation Wl, see Figures 26 and 28. This suggests that
the surface temperatures should be much more sensitive to the
urban interface parameters, such as M and R, affecting q,> for
the summer than for the winter seasons.
70
-------
Figure 27.
40 r-
Upwind Rural
•Downwind Urban
18
24 6 12
Solar Time (hr)
Diurnal variation of latent heat flux at upwind
rural, urban center and downwind urban locations
for simulation W6.
12
18
24 6 12
Solar Tims (hr)
Figure 28. Diurnal variation of latent heat flux at upwind
rural, urban center and downwind urban locations
for simulation SI.
71
-------
Conductive Fluxes
The conductive heat flux variations during the diurnal cycle
for simulations Wl, W6 and SI are shown in Figures 29, 30 and 31,
respectively. The conductive flux for simulation W6 is the smal-
lest because the thermal conductivity of the snow was assumed to
be about 25% lower than for the bare ground. Comparison of the
results in these figures with those of Figures 23 through 28
shows that the conductive flux is the smallest component of the
energy budget at the surface. Except for the magnitude and dir-
ection, the diurnal trends indicated are the same for the three
numerical experiments discussed.
The conductive heat flux is largest at the urban center
because the thermal conductivity os the bare ground is larger in
the city than the rural environs. For these similations the
maximum conductive heat flux occurs before noon and changes
direction (sign) in the afternoon about 16:00 hours. The trends
for the summer simulation SI indicated in Figure 31 for upwind
rural agree with results of other investigators (Myrup, 1969;
Gertis and Wolfseher, 1977) and with observations (Sasamori,
1970).
Sensitivity of Turbulent, Latent and Conductive Fluxes to
Changes in Urban Parameters
The sensitivity of the turbulent, latent and conductive heat
fluxes in the city to changes in the urban parameters can be
determined from Tables 6, 7 and 8, respectively. The difference
in fluxes between corresponding simulations, with only a single
parameter changed, are presented. The differences W2-W1, W7-
W6, S2-S1 and S7-S6 show the effect of radiatively participating
pollutants. The differences W3-W2, W8-W2 and S4-S2 indicate
the effects of anthropogenic heat releases. The results for
W9-W2 and S6-S2 illustrate the sensitivity to the surface
roughness 2. and moisture availability parameter M changes, res-
pectively.
72
-------
Downwind Urban
Upwind Rural
Urban Center
-80
18
24 6 12
Solar Time (hr)
Figure 29.
Diurnal variation of soil conductive heat flux at
upwind rural, urban center and downwind urban
locations for simulation Wl.
•Urban Center
-Upwind Rural
24 6 12
Solar Time (hr)
F.ij'.ure 30.
Diurnal variation of soil conductive heat flux at
upwind rural, urban center and downwind urban loca-
tions for simulation W6.
73
-------
CM
20
X
iZ
"o
0>
"o
-20
-60
O
O
-100
Downwind Urban
Upwind Rural
Urban Center
6
I
12
18
24 6 12
Solar Time (hr)
Figure 31.
Diurnal variation of soi* conductive heat flux at
upwind rural, urban center and downwind urban loca-
tions for simulation SI.
74
-------
TABLE 6. TURBULENT HEAT FLUX DIFFERENCES (IN W/m2) AT THE URBAN
CENTER FOR DIFFERENT SIMULATIONS
Time W2-W1 W7-W6
(hr)
S2-S1 W3-W2 W8-W2 S4-S2 W9-W2 S6-S2
06
08
10
12
14
16
18
20
22
24
02
04
06
08
10
12
0
5.8
-12.4
-20.9
-12.9
3.4
7.6
5.8
6.9
6.6
7.8
7.0
6.8
4.9
-19.6
-26.2
0
9.3
-2.2
-5.8
-2.6
7.0
9.5
8.6
9.0
9.2
9.8
10.1
8.9
0
-15.7
-28.1
-25.4
-21.1
-36.3
-1.2
5.2
4.6
4.0
4.2
9.7
1.1
-23.7
-37.0
-23.8
0
66.5
71.2
77.1
75.9
73.2
69.9
66.5
67.7
71.0
72.5
75.2
73.7
0
-59.1
-75.5
-85.2
-78.1
-59.3
-49.3
-54.8
-59.9
-62.8
-65.0
-66.3
-67.1
0
-28.6
-33.0
-30.4
-30.3
-9.1
-21.0
-21.9
-22.4
-22.4
-22.6
-23.4
-25.9
0
-7.2
-14.7
-26.4
-31.1
-22.5
-8.7
-6.8
-5.3
-3.5
-2.3
-1.4
-0.9
0
120.6
226.2
310.7
251.5
258.2
92.1
35.8
25.2
19.0
15.4
11.6
14.5
TABLE 7. LATENT HEAT FLUX DIFFERENCES (IN W/m2) AT THE URBAN
CENTER FOR DIFFERENT SIMULATIONS
Time W2-W1 W7-W6
(hr)
S2-S1 W3-W2 W8-W2 S4-S2 W9-W2 S6-S2
06
08
10
12
14
16
18
20
22
24
02
04
06
08
10
12
0
1.9
-1.5
-3.6
27.7
1.7
4.5
3.9
2.4
2.2
1.3
1.8
1.6
1.4
-1.1
-1.7
0
2.5
1.5
3.3
2.0
2.9
3.5
2.9
2.3
2.3
2.0
1.8
2.1
2.2
1.7
1.9
0
-6.0
-10.6
-8.8
-7.2
-6.8
0.0
2.8
3.2
4.0
3.9
3.7
2.7
-5.9
-7.8
-8.6
0
16.6
20.6
19.6
20.2
18.9
20.8
24.2
17.5
16.0
14.7
12.5
13.2
0
-15.4
-20.8
-23.4
-20.2
-31.5
-27.5
-20.9
-18.3
-16.6
-15.3
-14.8
-13.8
0
-15.4
-19.1
-18.0
-17.7
-17.9
-18.3
-15.1
-15.5
-16.4
-17.1
-16.8
-17.3
0
10.8
22.1
29.6
32.3
20.5
6.4
5.9
4.5
3.2
2.1
1.2
1.3
-212.9
-150.2
-261.8
-333.9
-343.7
-248.7
-89.4
-41.2
-26.9
-18.7
-13.4
-8.9
-13.9
75
-------
TABLE 8. CONDUCTIVE HEAT FLUX DIFFERENCES (IN W/m2) AT THE
URBAN CENTER FOR DIFFERENT SIMULATIONS
Time W2-W1 W7-W6 S2-S1 W3-W2 W8-W2 S4-S2 W9-W2 S6-S2
(hr)
06
08
10
12
14
16
18
20
22
24
02
04
06
08
10
12
0
2.1
-6.2
-2.2
-0.2
2.6
3.2
3.5
4.6
4.0
3.5
3.3
3.9
2.5
-3.4
-3.2
0
1.3
-0.5
-0.8
1.0
2.1
2.4
2.3
2.2
2.0
1.9
2.0
2.9
1.9
1.0
1.5
0
-3.1
-2.7
-0.6
-0.5
-0.1
2.9
3.7
3.3
3.1
2.7
2.7
1.1
-5.2
-5.3
-1.4
0
14.4
6.9
4.4
3.8
5.9
6.6
6.2
11.4
9.4
9.5
8.9
9.9
0
-22.9
-8.8
-3.9
-2.9
-7.9
-17.9
-17.9
-16.1
-15.4
-14.8
-13.7
-13.8 '
0
-5.5
1.2
-1.5
-1.7
-2.4
-9.3
-10.6
-9.7
-8.8
-7.6
-7.0
-4.7
0
-3.7
-9.3
-7.3
-2.2
2.0
2.4
0.6
0.3
-0.4
-0.2
0.0
-0.1
0
25.0
29.7
13.5
2.9
0.4
-9.0
-0.9
-2.5
-3.6
-4.4
-4.3
-1.7
The results how that the radiatively active pollutants de-
crease the daytime and increase the nighttime turbulent fluxes.
The decrease of the turbulent flux during the sunlight hours is
due primarily to the decrease of the surface temperature for
simulations with radiatively participating versus the nonparti-
cipating pollutants. The opposite is true at night. Generally
the latent and conductive fluxes follow the same diurnal trends
but their differences are smaller.
Examination of the tables shows that turbulent fluxes are
quite sensitive to the anthropogenic heat releases. The larger
the q the greater is q . The results also reveal, that q is
3-11 C C
the most sensitive and q is the least sensitive flux to the
o
increase in qo . The flux differences between simulations W9
till
and W2 indicate that increasing the surface roughness height z
decreases the turbulent and increasef the latent flux. Of all
the changes in the urban parameters considered, the turbulent
and latent fluxes are most sensitive to the moisture availabil-
ity parameter M. The differences S6-S2 indicate that a decrease
in M results in a decrease in q« and an almost, equal increase
76
-------
in q . The parameter M affects the latent heat flux and through
the partitioning of the energy balance at the surface changes the
turbulent flux. As a consequence of these compensating trends
there is only a relatively small increase in conductive heat
flux into the soil.
SURFACE TEMPERATURES
Surface Temperature Variation Along Urban Area
The surface temperature variation along the urban area for
the base winter simulation Wl and the base summer simulation SI
with radiatively inactive pollutants are presented in Figures 32
and 33, respectively. For the particular surface parameters and
anthropogenic heat emissions assumed in the simulations, the re-
sults show that urbanization increases the surface temperature
in the city in comparison with rural environs. The temperature
downwind of the city is higher than that upwind because of the
heating of the air as it flows over the warm city. The differ-
ence is more pronounced at night than during the day. As ex-
pected, because of the larger anthropogenic heat releases into
the atmosphere the difference is larger for the winter than for
the summer simulation.
The difference in the surface temperatures between the
urban and upwind rural locations is an indication of the inten-
sity of the urban heat island. Comparison of the results in
Figures 32 and 34 shows that the anomaly is greater for the win-
ter simulation W6 with snow covered ground than for simulation
Wl with bare ground. This is due to the fact that the anthropo-
genic heat releases in the city are a relatively more important
contribution to the surface energy balance for simulation W6
because of decreased total radiative flux incident on the sur-
face than for simulation Wl.
Figures 35 and 36 depict the daily course of the tempera-
ture at the earth's surface at three different locations along
the urban area for simulations Wl and SI, respectively. The
77
-------
:278 r-
276
§274
Q.
272
|270
c?5268
266
,!2hr
18 hr
10
20
40
x(km)
Figure 32. Surface temperature variation along the urban area
for simulation Wl.
Figure 33.
g304
12 hr
290
30 40
x(km)
Surface temperature variation along the urban area
for simulation SI.
78
-------
g 278 r-
12 hr
30
40
x(km)
Figure 34. Surface temperature variation along the urban
area for simulation W6.
79
-------
270
268
Upwind rural
_ Urban Center
Downwind Urban
12
18
24
1
6 12
Time (hr)
Figure 35.
Diurnal surface temperature variation at upwind
rural, urban center and downwind urban locations
for simulation Wl.
Figure 36.
£304 r-
S302
§300
Q.
E
^298
•8296
A
294
292 -
290
- Upwind rural
- Urban Center
Downwind Urban
6
12
18
24
6 12
Time (hr)
Diurnal surface temperature variation at upwind
rural, urban center and downwind urban locations
for simulation SI.
80
-------
modifications of temperature show up very distinctly. The re-
sults indicate that after a 30 hour simulation period a steady
diurnal cycle has not yet been established, and the earth-atmo-
sphere continues to cool. For simulation SI, however, there is
only about 0.1 K temperature difference at the urban center be-
tween noon on the first and the second days of the simulation
period. This suggests that the time is too short for complete
stabilization. The trends indicated in the figures agree with
those reported in the literature (McElroy, 1973; Torrance and
Shum, 1976) and are consistent with observations during the
winter at Montreal (Oke and East, 1971) and during the summer at
Columbus (McElroy, 1971). Various simulations have been per-
formed to determine the sensitivity of the model to a change in
a single parameter or group of parameters. The results are pre-
sented as surface temperature differences between two comparable
simulations.
Effects of Radiatively Active Pollutants
The effects of the radiatively active pollutants on the sur-
face temperature difference (temperature for simulation with ra-
diatively participating pollutants minus temperature for simu-
lation with nonparticipating pollutants) along the urban area
for the base winter (W2-W1) and summer (S2-S1) simulations are
presented in Figures 37 and 38, respectively. Examination of
the figures shows that during the night the differences are posi-
tive and during the day they are negative. The results indicate
that the surface temperatures are relatively more sensitive to
pollutants for the winter than for the summer simulations. This
is due to several factors. First, the thermal flux incident on
the surface, which is the dominant term in the surface energy
balance at night, is relatively larger during the winter than
during the summer. Second, the warmer surface temperatures de-
crease stability of the atmosphere near the ground and in doing
so increase the turbulent energy transport to the surfacdfc This
w^
feedback is more significant in the rural areas and downwind of
81
-------
Figure 37
Solar Time (hr)
Diurnal variation of surface temperature differences
between simulations W2 and Wl at upwind rural, urban
center and downwind urban locations.
0.6 r-
13
24
6 12
Solar Time (hr)
Figure 38. Diurnal variation of surface temperature differences
between simulations S2 and SI at upwind rural, urban
center and downwind urban locations.
80
L
-------
the city where the anthropogenic heat emissions into the atmo-
sphere are relatively small in comparison to the absorbed ther-
mal flux. The surface temperatures during the day are lower for
the simulations with radiatively interacting pollutants primarily
because of the attenuation of the solar radiation by anthropogenic
aerosols.
The feedback between radiatively active pollutants and sur-
face temperatures is greater for the simulations with snow cov-
ered ground (W7 and W6) than with bare ground (W2 and Wl) winter
simulations. Inspection of Figure 37 shows that even at noon
AT Np is positive at the upwind rural location. This indicates
that there was an increase in the net flux heating the surface
resulting from the radiatively active pollutants instead
of a net decrease of the flux. Thus, the greatest feedback be-
tween pollutants and the surface temperature is expected to occur
when the net flux at the surface is smallest. This is certainly
the case for the winter simulations with snow covered ground.
Effects of Anthropogenic Heat Releases
Comparison of surface temperatures for simulation W2 with
W3 and W2 with W8 show that the temperatures are very sensitive
to the anthropogenic heat releases. Figure 39 illustrates the
surface temperature difference along the urban area between simu-
lations W2 and WS with radiatively active pollutants. For simu-
lation W2 the average anthropogenic heat releases in the city
7
wereSO W/m while for W8 there were no heat releases (see Table 3).
As a result of the decrease in this parameter the nighttime surface
temperature at the urban center is approximately 3.1 K lower for
simulation W8 than W2. However, during the day the difference is
only 1.0 K as the anthropogenic heat release is a less signifi-
cant component of the total energy balance near the surface.
Because of the strong coupling of the near-surface temperature
to the turbulent eddy diffusivity through the stability para-
meter (Richardson number Ri) the increase in q does not result
3.H
in a proportional increase in the surface temperatures. This
83
-------
6hr(2nd day)
hr
Figure 39. Surface temperature differences between simulation
W2 and W8 along the urban area..
Figure 40.
g 2.4
o>
H
£ 2.0
.£>
b
1.6
o
g. 1-2
E
0.8
£ 0.4
0
-0.4
0
10
20
6hr(2nd day)
30
40
x(km)
Surface temperature differences between simulation
W3 and W2 along the urban area.
84
-------
can be seen by comparing the results in Figure 40, where the
2
anthropogenic heat releases are increased from 50 W/m (simu-
2
lation W2) to 100 W/m (simulation W3) in the city, with those
presented in Figure 39. For example, the maximum surface temper-
ature difference at 6:00 in the center of the city for W3-W2
is about 2.1 K while for W2-W8 the maximum difference is approxi-
mately 3.1 K. Figure 41 shows that the anthropogenic heat re-
lease is also of importance for summer simulations. For simu-
lation S2 q =20 W/m in the city while for simulation S4
qan = 0 W/m2n
Effects of Other Surface Parameters
Figure 42 shows the effect of increased surface roughness
on the temperature difference. This effect is most significant
during the day and is caused by the increase in the upward turb-
ulent heat flux at the surface. Doubling z at the urban cen-
ter from 1.0 to 2.0 m increases the surface temperature at noon
by about 1 K. The trend is consistent with available results
(Venkatram and Viskanta, 1976) but the magnitude of the effect
predicted is smaller primarily because of the larger mois-
ture availability parameter used for the simulations reported
here.
The sensitivity of the surface temperature to the solar
albedo along the urban area can be determined by examining the
surface temperatures for simulations S2, S5 and S8. As expected,
an increase in the ratio r /r increases the surface tem-
s >UR s >UC
porature in the city relative to that in the rural areas. This
is clearly indicated in Figure 43 where ATqc_c2 are presented
along the urban area. A decrease in the solar albedo from
0.125 to 0.1 results in a surface temperature increase of about
0.2 K between the two summer simulations S5 and S2 at the urban
center. At night the temperature is approximately 0.1 to 0.2 K
lo\\rer for simulation S5 due to the altered temperature structure
in the atmosphere and the soil during the previous day. Note
that increasing the rural-urban albedo difference increases the
85
-------
Figure 41.
Figure 42.
-0.2
0
10
Surface temperature differences between simulations
S2 and S4 along the urban area.
-0.4
Surface temperature differences between simulations
W2 and W9 along the urban area.
86
-------
surface temperature difference during the day, but has little
effect at night. Comparing temperatures of simulation S8 to
simulation S2 shows that a decrease in albedo at both the rural
and urban locations causes the temperature to be higher all along
the urban area. The effect is greatest at noon in the upwind
rural area where the temperature is 0.77 K higher and at the ur-
ban center the temperature is 0.39 K higher for simulation S8
than for S2. At night the temperature along the urban area is
warmer by 0.3 to 0.4 K due to the altered temperature structure
during the day.
Halstead's moisture availability parameter is also very
important in determining the surface temperature. Figure 44
shows that reducing M from 0.75 to 0.5 at the rural location and
from 0.2 and 0.0 at the urban center causes a relatively large
difference in the surface temperature. As the latent energy
flux is reduced, the surface temperature must rise to increase
the turbulent heat flux and emission to compensate for the re-
duction in latent energy flux. The effect is the greatest at
noon, when the temperature increase at the urban center is ap-
proximately 4.2 K.
In summary of surface property effects, it can be stated
that increasing the solar albedo or decreasing the moisture
availability parameter decreases the sensitivity of the surface
temperature to the solar flux changes and hence the effects of
racliatively interacting aerosols. The greater sensitivity of
the earth-PBL system to the radiative participation by the aero-
sols when the solar albedo is increased is a well know result
(Russell and Grams, 1975) which is confirmed by this study in
the context of surface temperature changes.
TEMPERATURE DISTRIBUTION
Temperature Perturbation
Isopleths of potential temperature perturbation 0' [temp-
erature in the city minus the upwind rural temperature) are
87
-------
Figure 43.
-0.6
0
Surface temperature differences between simulations
S5 and S2 along the urban area.
Figure 44.
Surface temperature differences between simulations
S6 and S2 along the urban area.
88
-------
illustrated for the radiatively noninteracting simulations Wl,
W6 and SI in Figures 45, 46 and 47, respectively. The left panel
(part a) gives the temperature at noon and the right panel (part
b) gives the temperature at midnight. It is seen from the fig-
ures that the air temperature in the city and downwind are
warmer than upwind of the city. In agreement with observations
(DeMarais, 1975) the perturbations are greater at midnight than
at noon. The trends indicated in all three figures are the
same, except the magnitudes of 0T are different. In the upper
part of PEL the potential temperatures over the city and for
some distance downwind are cooler than upwind of the city. This
is referred to as the potential temperature "crossover effect"
and has been observed by some investigators (Bornstein, 1968;
Oke and East, 1971). The magnitude of the effect and the height
at which the crossover occurs depends at least in part on the
particular interface parameters, meteorological and boundary condi-
tions chosen for the simulations and is greater for the winter
(Figure 46) than for the summer (Figure 47) seasons.
Comparison of results in Figures 45 and 46 shows that the
potential temperature excess is considerably greater at noon
and midnight for simulation W6 compared to simulation Wl. The
maximum potential temperature excess occurs at the surface and
is 4.65 K and 5.26 K at noon and midnight, respectively. The
maximum potential temperature excess at noon for simulation SI
(Figure 47) occurs at some distance above the surface, and is
in this respect, similar to simulation Wl. Notice the crossover
effect is relatively small (a maximum of 0.27 K) and occurs at
a higher altitude than for the winter simulations. At midnight,
the maximum potential temperature excess of 1.80 K for the simu-
lation is rougly half of that for simulation Wl, which is 3.44 K.
The results presented indicate that urbanization and human
activity (even without the radiatively active pollutants in the
atmosphere) increase the temperature near the ground above that
in the rural environs. This finding is in agreement with the
89
-------
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results reported by other investigators for different meteorolog-
ical conditions (Garstang et al., 1975; Yu and Wagner, 1975).
Effect of Radiatively Active Pollutants on Temperature
The effects of the radiatively active pollutants on the tem-
perature structure in the urban atmosphere are presented in
Figures 48 and 49 as isopleths of the potential temperature dif-
ference A0 (temperature for simulation with radiatively inter-
acting pollutants minus temperature for simulation with noninter-
acting pollutants) for simulation Wl and W2, respectively, at
both noon and midnight. The presence of active pollutants
changes the radiative energy balance which alters the temperature
structure. As expected, the effect is greater during the winter
than during the summer. During the day, the potential air temp-
erature near the surface is generally lower for simulations with
radiatively participating pollutants than for the simulations
with nonparticipating pollutants. The largest difference (less
than 0.4 K) is downwind of the city center where the pollutant
concentration is highest. The nighttime temperature differences
are positive and somewhat higher in magnitude near the surface.
Near the top of the PEL, the radiatively participating pollutants
tend to decrease the potential temperatures slightly at both
noon and midnight.
A small surface inversion upwind of the city was predicted
by the model at night, but no surface inversion developed near
the city center. This is in agreement with the observations of
Clarke and McElroy (1974) in Columbus, Ohio and St. Louis,
Missouri. The warmer near-surface temperatures in the city de-
crease the stability of the atmosphere and increase turbulent
heat flux to the surface, see Table 4. The results clearly show
that the pollutants have not only a direct influence on the tem-
perature near the ground through the reduction of the infrared
cooling rate, see Table 5, but also indirectly through the feed-
back between temperature, stability and turbulent heat transport
to the surface. Near-surface temperatures at noon for
93
-------
O CD CJ
o
o
f-t
3
+J
rt
QJ a
-t-> Oj
r-l CSI
rt^:
•H
4-> V)
a c
0 O
•P -H
O -P
<4H 3
o e
• H
(/> t/)
^1
+-> c
o
-------
, . , I , i , ! , , . .
CD
rC!
ft
o
6 •
+-> (— V
rt ,0
> — /
C
oj -P
,Q,c!
S-i M
3 -H
Ci
R -t->
•H nS
H rH
0 CO
(!) Pi
4J cd
•H
+-> t/)
rt Pi
(U O
•M -H
O +->
ft oJ
o a
•H
10 10
0> V
rH
-------
simulation Wl in and downwind of the city primarily because of
the attenuation of solar radiation by the aerosols in the PEL.
The net effect of radiatively active pollutants on the
temperature structure is more clearly illustrated in Figure 50
where the temperature differences between simulations W7 and W6
with snow covered ground are plotted. The temperature differ-
ences between these two simulations are rather significant.
Clearly, the air pollutants increase the temperature in the sur-
face region and decrease it slightly near the top of the PEL.
The most dramatic increase in the temperature is at night down-
wind of the city where the gaseous pollutant concentrations are
largest. Results presented in Figures 45 and 50 allow us to con-
clude that under certain meteorological conditions, surface and
800
600 -4
200
r
!2hr
24 hr
400H\I\\
\ \ . Urban Center
\ \ ^i Downwind Urban
U
0
Z_^
-0.4 0 0.4 0.8 1.2 1.6 2.0 24
A® (K)
2.8 3.2 3.6
Figure 50.
Potential temperature differences in the urban atmo
sphere between simulations W7 and W6 at the upwind
rural, urban center and downwind urban locations.
96
-------
urban parameters, the effects of radiatively active pollutants
on the temperature structure near the surface may be of the same
order of magnitude as effects of urbanization.
The Crossover Effect
The temperature crossover effect is associated with the
existence of lower temperatures around 400 m over the city
(Bornstein, 1968). Below 400 m city temperatures are generally
higher than those over the countryside. Two physical mechanisms
have been suggested to explain the potential temperature cross-
over effect. Lee and Olfe (1974) believe that the effect is
caused by the lifting of stable air over the city. Lee and Olfe
have conducted numerical experiments using a constant turbulent
eddy diffusivity. They obtain crossover temperatures as great
as 0.7 K. It should be noted that the results of Bornstein
(1974), which are based on a more sophisticated numerical model,
do not show the crossover effect. A second possible explanation
is radiative cooling due to pollutants. However, the results of
this study and those of Atwater (1975) and Venkatram and Viskanta
(1976) indicate that the effect cannot be explained by radiative
cooling due to the gaseous pollutants as nonparticipating simu-
lations SI and Wl both exhibit the crossover effect, see Figures
51 and 52. It appears that the prediction of the effect is de-
pendent on the particular turbulence model used in the numerical
calculations.
The diurnal variation of the vertical potential temperature
perturbation 0' for simulation Wl and SI are shown in Figures 51
and 52, respectively. The height at which the crossover occurs
depends on the time during the diurnal cycle but is never lower
than 200 m. The magnitude of the temperature deficit increases
as the night progresses. The summer simulation SI (Figure 52)
does not exhibit formation of the temperature crossover during
the day.
The effects of radiative participation on the daytime tem-
perature crossover is shown in Fig. 53. Simulation W2 with
97
-------
-:.6 -0.8
0.8 1.6 2.4
®' (K)
Figure 51. Potential temperature perturbation at the urban
center for simulation Wl.
200 -
0
-i.6 -0.8
IB 2.4
©' (K)
Figure 52.
Potential temperature perturbation at the urban
center for simulation SI.
98
-------
Simulation WI
W3
-W4
Figure 53.
Comparison of potential temperature perturbations
at the urban center for some winter simulations
at the urban center.
99
-------
radiatively participating pollutants shows a decrease in the
crossover effect of approximately 0.2 K compared to simulation
Wl. Also note that the temperature deficit first occurs at about
500 m as compared to 600 m for nonparticipating simulation Wl.
The effect is due to lower surface temperature and turbulent dif-
fusivity. At night the crossover effect is also reduced. This
is caused by the altered temperature structure during the day.
Increasing the anthropogenic heat emissions as in simulation W3
is seen to have an effect of less than 0.1 K when compared to
simulation W2. The warmer surface temperature causes the de-
crease in the crossover effect. The effect of volumetrically
distributing the anthropogenic heat (simulation ¥4) is seen to
have an even smaller effect. At night, the effects are similarly
small. Increasing the surface roughness from 1.0 m (simulation
W2) to 2.0 m (simulation W9) is seen to have the greatest influ-
ence. The temperature deficit is partially due to the reduced
surface temperature.
The maximum temperature crossover of about 1 K predicted in
this study for the base simulations agrees fairly well with ob-
servations made by Clarke and McElroy (1974) over Columbus, Ohio
and St. Louis, Missouri. The measurements were made in September
and March and direct comparisons are not possible because of
different meteorological conditions used in the numerical experi-
ments. The observations are consistent with the predictions of
this study.
POLLUTANT CONCENTRATIONS
Concentration Distributions
As indicated in Table 2 the initial aerosol and gaseous
pollutant concentration distribution? as well as the emissions
were arbitrarily assumed to be the same in order to reduce the
number of independent parameters. Therefore, the concentrations
of the two pollutants are identical, and hence only the gaseous
pollutant concentrations will be discussed. Isopleths of the
100
-------
. I , I , I , I I , I . I . I .
I.._L_1_J ..1,1.1.1,1,1.
0) n)
o PJ
O O
o
-p PI
aj
cx> sj-
rt
O
P< a
o
M-l -H
O P
rt
P 6
0) -H
r-( tfl
cx
O Ki
(/) O
M m
0)
>-i
t>0
•H
ti.
101
-------
f-i
0>
rt
(H
rt
3
0)
•P
e
OX)
•H \— '
4->
03 -P
X3X!
M bo
3-H
•p rt
JH TJ
0) -H
o A
•rt
•P Tj
CS C
(H Ct
-p
C'-^
-------
concentration perturbations are illustrated for the radiatively
noninteracting simulations Wl and SI in Figures 54 and 55, res-
pectively. The left panel (part a) gives the concentration at
noon and the right panel (part b) the concentration at midnight.
The background pollutant concentration at the upwind countryside
was assumed to decrease exponentially with height from 20 yg/m
at. the surface to about 10 yg/m at the top of the PEL.
Inspection of Figures 54 and 55 reveals a pollutant plume
formation downwind of the urban center. This finding agrees
with that of Atwater (1975) who also predicted higher concentra-
tions downwind of the city center. The diurnal variation of the
pollutant emissions and mixed layer height influence very strongly
pollutant concentrations during the daily cycle. During the day
the pollutants disperse more effectively throughout the PEL for
the summer simulation SI than for the winter simulation Wl. This
is due primarily to the fact that the mixed layer height is
larger for SI than Wl. Even though the emission sources were
assumed to be larger for SI (see Table 2) the concentrations for
Wl are higher due to more limited vertical mixing. During the
night the mixed layer height decreases more drastically for
simulation SI than for Wl. As a consequence the concentrations
increase for SI and decrease for Wl in comparison to those at
noon. The decrease in mixed layer height and the decrease in pol-
lutant releases in the evening, for example, have opposite ef-
fects on pollutant concentrations. Their net influence is to
dampen the concentration variations during the diurnal cycle.
The pollutant concentration near the ground (say, at z = 2m)
is important to human health and comfort. For this reason, the
diurnal variation of pollutant concentrations along the city are
given for simulations Wl and SI in Figures 56 and 57, respective-
ly, at a height of z = 2m. It is seen from the figures that the
concentrations reach a maximum late in the afternoon when the
emissions are still high (see Figure 2) and the turbulent eddy
diffusivities have decreased to a minimum (see Figure 14). The
collapse of the mixed layer at this time contributes also to
103
-------
18 hr
6 hr (2nd day)
40
x(km)
Figure 56.
Pollutant concentration variation along the urban
area during the diurnal cycle at a height of 2 m
for simulation Wl.
104
-------
ML 280
>••
o
4-
O
o
o
c
o
240
200
160
120
80
40
0
18 hr
6 hr (2nd day)
10
20
30
40
x(km)
Figure 57. Pollutant concentration variation along the urban
area during the diurnal cycle at a height of 2 m
for simulation SI.
105
-------
increased pollutant concentrations.
The two worst times for pollutant dispersion are early in
the morning just after sunrise and before sunset. This is clearly
seen from Figure 58 where the pollutant concentrations at a
height of z = 2m are presented along the city as a function of
time. The concentrations near the ground are most sensitive to
the turbulent diffusivities at the first few grid points above
the ground. The diurnal trends in pollutant concentrations can
be explained on the basis of the diurnal variation of turbulent
diffusivity near the surface. For example, as the stability of
the atmosphere near the ground decreases the pollutants are dis-
persed more effectively by vertical mixing. The concentrations
are seen to decrease and reach a minimum after noon. The trends
predicted agree with observations. The worst times for disper-
sion are at night while the middle of the day is generally the
best (Stern et al., 1972).
Concentration Differences
While the presence of radiatively interacting pollutants
alters the radiant and total energy balances and therefore the
temperature distribution directly, the pollutant concentration
is only indirectly modified through the turbulent eddy diffusi-
vity. By modifying the potential temperature and velocity
(through the eddy diffusivity) gradients, radiatively active pol-
lutants alter the eddy diffusivity, vhich in turn affects the
pollutant concentrations. Thus, the effect of pollutants on
their own dispersal is expected to be rather sensitive to the
turbulence model used.
The effect of radiatively active pollutants on their own
dispersion can be most clearly indicated by examining the dif-
ferences between simulations with participating and nonpartici-
pating pollutants. Such differences between simulations W2 and
Wl and between simulations S2 and SI are depicted in Figures 59
and 60, respectively. Examination of the figures reveals that
the concentration differences are larger for the winter than for
106
-------
Time (hr)
x(km)
Figure 58.
Three dimensional representation of pollutant con-
centration at a height of 2 m along the urban area
with time for simulation SI.
107
-------
12 hr (2nd day)
Figure 59.
Pollutant concentration difference between
simulation W2 and Wl at the urban center.
Figure 60.
Pollutant concentration difference between simu-
lation S2 and SI at the urban center.
108
-------
the summer simulations. This is not surprising and is due to the
greater sensitivity of the temperature structure to the radi-
atively active pollutants in the PEL for the winter simulations.
The temperature structure in turn modifies turbulent diffusivi-
ties and pollutant concentrations.
The sensitivity of the pollutant concentrations to the urban
parameter changes is highlighted in Table 9. The results clearly
show that radiatively active pollutants reduce significantly
(about 151) pollutant concentrations near the surface at the
urban center for the winter simulations (e.g. W2-W1, W7-W6). The
maximum percent reduction occurs in the evening at 18:00 hours.
For the summer simulations (S2-S1) the modification of pollutant
concentrations by the radiatively active pollutants is not as
significant. Examination of Table 9a and 9b reveals that, in
general, the decrease in pollutant concentration downwind of the
city center is substantially greater than at the urban center.
This is primarily due to greater turbulent diffusivities which
result from decreased atmospheric stability caused by warmer
ground temperatures (see Figure 37). The feedback between pol-
lutant concentrations, temperature structure and pollutant dis-
persion is greatest downwind of the city where the concentrations
are largest.
The increased anthropogenic heat emissions (W3-W2) are seen
to decrease pollutant concentrations whereas decreased emissions
(W8-W2, S4-S2) cause a very substantial increase in pollutant
concentrations, particularly for the winter simulations when q
r J nan
represents a substantial contribution to the energy balance near
the ground. Greater surface roughness height and associated
cooler surface temperatures (simulations W9-W2) increase the
pollutant concentrations near the ground both at the urban cen-
ter and downwind of the city.
The results obtained cannot be compared directly with those
reported earlier (Viskanta et al., 1977a) for different meteoro-
logical conditions with time independent pollutant emissions.
The effects of anthropogenic heat releases on pollutant
109
-------
TABLE 9. POLLUTANT CONCENTRATION DIFFERENCES (IN yg/nT) AT
THE HEIGHT OF 2 m
a) Urban Center, x = 17.5 km
Time W2-W1 W7-W6 S2-S1 S8-S7 W3-W2 W8-W2 S4-S2 W9-W2
(hr)
06
08
10
12
14
16
18
20
22
24
02
04
06
08
10
12
Time
(hr)
06
08
10
12
14
16
18
20
22
24
02
04
06
08
10
12
0
-6.2
-5.0
-0.4
0.3
-1.6
-18.7
-8.1
-5.3
-3.9
-7.1
-5.2
-6.2
-7.1
-7.2
-14.8
W2-W1
0
-0.4
-4.7
-4.2
-0.8
0.7
-1.5
-8.2
-22.5
-16.4
-6.5
-5.6
-6.9
-7.1
-6.5
-9.0
0
-7.6
-21.6
-22.7
-6.8
-7.9
-15.8
-13.9
-9.4
-10.4
-9.7
-6.1
-7.1
-27.7
-39.1
-10.8
b)
W7-W6
0
-0.6
-1.9
-14.8
-34.4
-42.7
-48.8
-52.5
-58.8
-48.3
-44.7
-40.5
-42.5
-32.8
-27.3
-29.5
0
1.7
0.5
0.5
0.2
1.5
3.8
1.7
0.8
-1.2
-11.7
-7.6
-5.7
-2.9
-6.6
-0.6
0
20.5
1.6
0.4
0.8
1.5
0.6
1.6
1.6
0.2
-14.9
-7.9
-6.3
0
-34.0
-46.8
-13.1
-8.1
-8.5
-17.5
-22.3
-17.5
-12.8
-19.8
-9.2
-6.1
Downwind Urban, x
S2-S1
0
-8.5
0.6
-1.3
-0.1
1.4
2.8
3.0
1.8
0.5
0.6
1.0
-0.1
-1.8
-15.4
-2.0
S8-S7
0
0.6
1.6
-0.6
0.1
0.6
3.1
3.6
2.5
0.8
0.6
1.2
0.6
W3-W2
0
-2.4
-17.5
-35.9
-10.4
-7.4
-14.8
-41.3
-53.0
-31.7
-21.3
-14.1
-12.4
0
77.8
99.8
45.7
9.0
73.8
129.5
84.8
49.9
37.6
36.7
38.0
42.3
= 30 km
W8-W2
0
-0.7
13.9
46.4
45.7
14.6
28.9
39.5
45.2
33.0
18.9
14.8
14.5
0
9.7
9.0
2.3
2.0
3.4
3.7
10.0
4.3
4.7
26.9
38.5
52.4
S4-S2
0
2.5
5.1
5.0
0.7
0.3
5.4
10.4
12.1
5.8
2.0
-0.7
-1.8
0
3.6
-4.2
11.6
1.3
3.5
25.6
5.2
1.1
-1.4
0.2
2.2
5.0
W9-W2
0
-2.5
-2.7
8.1
19.8
7.5
9.0
10.6
26.6
23.3
14.7
10.5
9.6
110
-------
concentrations, however, agree in trend with those found in the
present numerical experiments. Atwater (1975) predicted an in-
crease in the daytime pollutant concentrations of about 20% near
the surface due to the radiative active pollutants in the PEL.
Our results do not show such large changes probably because the
pollutant concentrations were considerably smaller as compared to
those used by Atwater.
URBAN HEAT ISLAND
One of the better known features of urban climatology is the
existence of the urban heat island. The heat island intensity
(the maximum difference between the upwind rural and the highest
urban surface temperature, AT, _ mov) is shown as a function of
U ~ T y m3.X
time for some representative numerical simulations in Figures 61
and 62. Results of numerical modeling efforts (Myrup, 1969;
Atwater, 1972; McElroy, 1973; Yu and Wagner, 1975; Torrance and
Shum, 1976) show that the urban heat island is caused primarily
by the surface parameters and anthropogenic heat release dif-
ferences between urban and rural areas. Comparison of AT
values for simulations W2 and Wl shows that the radiatively
interacting pollutants decrease the urban heat island intensity.
For example, just before sunrise the intensity is 3.59 K for
simulation Wl while it is 3.38 K for simulation W2. In the simu-
lation with radiatively participating pollutants the presence of
background pollutants increased the downward thermal flux inci-
dent upon the surface, see Figure 14. This causes slightly
higher surface temperatures at the upwind rural area. Background
pollutants were introduced in the upwind rural area as the atmo-
sphere is not free of pollutants a few kilometers upwind or down-
wind of the city. Note that for the summer simulations (Figure
62) the peak intensity occurs at sunset, and then decreases dur-
ing the night. Similar behavior has been observed for Johannes-
burg, South Africa (Garstang et al., 1975) during July, 1971.
This behavior is inarkedly different than that for the winter
simulations and is due to the different mechanisms causing the
111
-------
Simulation W3
6 12
Time (hr)
Figure 61. Maximum urban minus upwind rural surface temperature
difference variation with time for some winter
simulations.
S? 3.0
I
j
go i Simulation SI
18
24
6 12
Time (hr)
Figure 62.
Maximum urban minus upwind rural surface temperature
differences for some summer simulations.
112
-------
heat island. In the summer during the day, the city absorbs and
stores more solar energy than the surrounding countryside. This
happens because the city itself possesses a higher thermal admit-
tance than the vegetation and soil characteristic of the surround-
ing countryside. This stored energy causes the temperature in
the city to decrease more slowly than in the rural area toward
sunset. At night, the temperature difference decreases as the
city cools by emission of thermal radiation. In the winter, the
insolation is considerably smaller. Hence, man-made heat emis-
sion becomes a more significant factor in the formation of the
urban heat island during the winter. As these emissions are as-
sumed constant throughout the night, the heat island remains
relatively constant.
The urban heat island intensity depends on the extent of
urbanization and is seen from Figures 61 and 62 to be a positive
temperature anomaly. The magnitude of the effect depends on
meteorological conditions and is partly caused by changes in the
responses of the earth's surface to solar and thermal radiation,
the storage of sensible heat, moisture transfer and anthropogenic
heat releases. The effect therefore exhibits considerable di-
urnal variation as indicated in the figures. Because of the
greater admittance of the surface materials in the city than in
the surroundings, the temperature anomaly may be negative after
sunrise, but it will be warmer in the evening and at night. If
the moisture availability parameter is relatively large,
AT,, -n mov may be negative (Venkatram and Viskanta, 1976). The
Li "" L y Hid JC
sensitivity of the urban heat island to different mechanisms
which contribute to its formation by making reference to Table
10.
Comparing values of AT for simulation W2 (with q =
« u. ~ r j II13.X clU
50 W/m as an average emission in the city) to simulation W3
2
(with qan = 100 W/m ) shows the importance of anthropogenic heat
releases in the city. A more dramatic comparison is between
simulation W2 and W8 for which qori = 0. For simulations W5,
cin
113
-------
W6 and W7 \vith snow covered ground, the urban heat island inten-
sities are considerably higher than those with bare ground. The
reason is the heat island intensity continues to increase through-
out the simulation period. This is felt to be due to the initial
conditions as the rural temperature continues to fall throughout
the cycle. The heat island intensity predicted is not unrealisti-
cal]y high, however, since values of this magnitude have been
observed (DeMarrais, 1975). Again, results of simulations W7 and
W6 show that the radiatively active pollutants decrease the magni-
tude of AT . For all simulations except simulation W4, the
u ~r,max
anthropogenic heat is assumed to be emitted into the atmosphere
within the first two meters of the surface. This approach is
oversimplified, as in reality most of this heat is released into
the atmosphere through chimneys and through the sides of build-
ings. Comparison of results presented in Table 10 shows the ef-
fect of distibuting q,, through the lower 50 or so meters of
an
atmosphere. A relatively small difference in the heat island
intensity (less than 0.5 K at any time) is noted when the results
of simulation W4 are compared with those of W2. Other urban PEL
models (Yu and Wagner, 1975; Atwater, 1975) have assumed the
TABLE 10. DIURNAL VARIATION OF THE URBAN HEAT ISLAND INTENSITY (IN K)
Time
(hr)
06
08
10
12
14
16
18
20
22
24
02
04
06
08
10
12
V/l
0
3.2
1.6
1.0
1.2
2.0
3.1
3.4
3.4
3.4
3.5
3.6
3.6
3.3
1.9
0.9
W2
0
3.1
1.4
0.7
1.1
1.9
2.9
3.1
3.1
3.3
3.3
3.3
3.4
3.1
1.6
0.6
W3
0
4.5
2.8
1.9
2.1
3.0
4.1
4.4
4.7
4.8
5.0
5.1
5.3
W4
0
2.8
1.3
0.6
C.9
1.9
2.8
3.2
3.3
3.3
3.4
3.4
3.6
3.5
l.S
0.6
W6
0
3.9
4.5
4.6
4.6
4.4
4.2
4.5
4.8
5.7
5.7
W7
0
3.8
4-2
4.4
4.2
'4.1
3.9
4.2
4.5
4.8
5 . 2
5.9
6.1
o . 2
6.5
6.7
W8
0
1.2
0.4
0.0
.0
0.7
0.9
0.9
0.8
0.7
0.7
5.5
5.7
5.8
6.0
6.0
W9
0
2.8
0.8
0.2
0.3
1.5
2.8
3.1
3.1
3.2
3.2
0.7
0.7
SI
0
0.9
0.9
1.2
1.4
1.6
1.9
2.1
2.0
1.8
1.7
1.6
1.4
0.8
C.6
1.0
S2
0
0.8
0.7
1.0
1.2
1.5
1.8
2.1
2.0
1.8
1.7
1.6
1.4
0.6
0.3
0.8
S4
0
0.3
0.4
0.7
0.9
1.1
1.3
1.1
0.9
0.7
0.5
0.4
0.2
SS
0
1.1
1.2
1.6
1.8
1.8
2.0
2.2
2.0
1.9
1.8
1.6
1.5
S6
0
2.0
2.3
4.6
5.7
3.7
3.4
3.8
3.3
3.0
2.6
2.3
1.9
S7
0
0.7
0.5
0.8
1.1
1.4
1.8
2.1
1.9
1.8
1.6
1.5
1.3
S8
0
0.5
0.3
0.6
1.0
1.3
1.7
2.1
1.9
1.8
1.6
1.5
1.3
114
-------
anthropogenic heat sources to enter the surface energy balance.
Even when the anthropogenic heat emissions are neglected
(simulation S4) a heat island is formed due to the urban-rural
parameter differences discussed above. The anthropogenic heat
emissions assumed for simulation SI increase the heat island in-
tensity, but the importance of the heat releases is less than that
for the winter simulations. Note that simulation S2 slightly neg-
ative heat island intensity is predicted in the morning of the
second day. This phenomenon has been observed during the sum-
mer (Mitchell, 1961).
The effect of the surface roughness on the heat island in-
tensity can be determined by comparing the results of simulation
W2 with those of simulation W9. Increasing the surface roughness
from 1 m to 2 m at the urban center decreases the urban heat is-
land intensity by a maximum of about 0.5 K at noon. In general,
the winter simulations have their minimum heat island intensity
around noon. There is a rapid increase in heat island intensity
toward evening, then a small increase during the night. A maxi-
mum is reached just before sunrise, with the heat island decreas-
ing rapidly thereafter. This variation in heat island intensity
is consistent with experimental observations of Oke and Maxwell
(1974) over Montreal and Vancouver, Canada.
Results show (compare AT for simulations S2 and S6)
11 ~ ± y HI 3.X
that the magnitude of the heat island intensity is quite sensi-
tive to the decrease in the moisture availability parameter M
in the city, particularly during the noon hours. It is seen
that the reduced evaporation in the urban area leads to a heat
island intensity of 5.72 K. It is noted that this temperature
excess occurs in the early afternoon rather than after sunset.
Although this situation has not been observed, it has been pre-
dicted theoretically (Myrup, 1969).
The solar albedo is also seen to have an effect on the heat
island intensity during the day. An increase in the urban-rural
albedo difference (simulation S5, see Table 10 for values)
115
-------
causes an increase in heat island intensity. A decrease in the
urban-rural albedo difference (simulations 57 and S8) is seen to
have the opposite effect.
The urban heat island is just one measure of the inadvertent
climate and weather modification. The intensities predicted are
of sufficient magnitude to be of social and economic significance.
The results obtained indicate that urbanization together with
anthropogenic heat and pollutant emissions into the atmosphere
may affect energy demand for space heating and cooling. The prac-
tical implications do not appear to have been considered previous-
ly. There may be a saving in the energy demand for heating if the
heat island intensity is sufficienty large (a few degrees Kelvin
as for some simulations predicted) during the winter, but this
benefit would be nullified in summer if energy demand for space
cooling is increased. The net effect of pollutants on the energy
demand for space heating and cooling in an urban area during a
diurnal or a yearly cycle are by no means clear, and a more
complete investigation appears to be warranted. There are also
secondary effects and impacts of the urban heat island. For
example, one would mention lower costs for snow removal and in-
creased human mobility during winter months in urban areas.
116
-------
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spheres. EPA-600/4-76-002, U. S. Environmental Protection
Agency, Research Triangle Park, NC, 1976. 106 pp.
Viskanta, R., R. W. Bergstrom, and R. 0. Johnson. Effects of
Air Pollution on Thermal Structure and Dispersion in an
Urban Planetary Boundary Layer. Contributions to Atmo-
spheric Physics 50: 419-440, 1977a.
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124
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APPENDIX
PUBLICATIONS
Venkatram, A., and R. Viskanta. Radiative Effects of Pollutants
on the Planetary Boundary Layer. EPA-600-4-76-039, U. S.
Environmental Protection Agency, Research Triangle Park,
NC, 1976. 243 pp.
Venkatram, A. and R. Viskanta. Effect of Elevated Pollutant
Layers on Mixed Layer Growth. In: Preprints of the Third
Symposium on Atmospheric Turbulence, Diffusion and Air
Quality, American Meteorological Society, Boston, MA, 1976,
pp. 528-532.
Venkatram, A. and R. Viskanta. The Contribution of Pollutants to
the Urban Heat Island 'Crossover' Effects. In: Preprints
of the Third Symposium on Atmospheric Turbulence, Diffusion
and Air Quality, American Meteorological Society, Boston,
MA, 1976, pp. 536-542.
Viskanta, R., R. 0. Johnson and R. W. Bergstrom. Effect of
Urbanization on the Thermal Structure in the Atmosphere.
In: Proceedings of the Conference on METROPOLITAN PHYSICAL
ENVIRONMENT, USDA Forest Service General Technical Report
NE-25, Upper Darby, PA, 1977, pp. 62-76. (Paper presented
at The Conference on METROPOLITAN PHYSICAL ENVIRONMENT;
Syracuse, New York, 25-29 August 1975).
Viskanta, R., R. W. Bergstrom, and R. 0. Johnson, Effects of
Pollutants on Radiative Transfer in an Urban Boundary
Layer. In: Proceedings of the SYMPOSIUM ON RADIATIVE
TRANSFER IN THE ATMOSPHERE, Science Press, Princeton, NJ,
1977, pp. 460-462. (Paper presented at the Symposium on
the Radiative Transfer in the Atmosphere, Garmisch-Parten-
kirchen, Federal Republic of Germany, 19-28 August 1976).
Viskanta, R., R. W. Bergstrom, and R. 0. Johnson. Radiative
Transfer in a Polluted Urban Planetary Boundary Layer. J.
Atm. Sci., 34 (7): 1991-1103, 1977.
Viskanta, R., R. W. Bergstrom, and R. 0. Johnson. Effects of
125
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Air Pollution on Thermal Structure and Dispersion in an
Urban Planetary Boundary Layer. Cont. Atm. Phys., 50 (7):
419-440, 1977.
Venkatram, A., and R. Viskanta. Effects of Aerosol-Induced Heat-
ing on the Convective Boundary Layer. J. Atm. Sci., 34 (12):
1918-1933, 1977.
Venkatram, A., and R. Viskanta. Radiative Effects of Elevated
Pollutant Layers. J. Appl. Meteor., 16 (12): 1256-1272,
1977.
Viskanta, R., T. L. Weirich, and R. A. Daniel. Effects of Pollu-
tant and Heat Emissions on Temperature in an Urban Area. In:
Environmental Effects of Atmospheric Heat/Moisture Releases,
American Society of Mechanical Engineers, New York, 1978,
pp. 107-117.
Viskanta, R., and T. L. Weirich. Feedback Between Radiatively
Interacting Pollutants and Their Dispersion in the Urban
Planetary Boundary Layer. In: Papers Presented at the
WHO Symposium on Boundary Layer Physics Applied to Specific
Problems of Air Pollution, WMO-No. 510, World Meteorologi-
cal Organization, Geneva, 1978, pp. 31-38.
Viskanta, R., and R. A. Daniel. Radiative Effects of Elevated
Pollutant Layers on Temperature Structure and Dispersion
in an Urban Atmosphere. J. Appl. Meteor, (submitted for
publication).
126
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/4-79-012
3. RECIPIENT'S ACCESSIOI»NO.
4 TITLE AND SUBTITLE
EFFECTS OF POLLUTANTS AND URBAN PARAMETERS ON
ATMOSPHERIC DISPERSION AMD TEMPERATURE
6. PERFORMING ORGANIZATION CODE
5. REPORT DATE
February 1979
7 AUTHOR(S)
R. Viskanta and T. L. Weirich
8. PERFORMING ORGANIZATION REPORT NO
9. PERFORMING ORGANIZATION NAME AND ADDRESS
School of Mechanical Engineering
Purdue University
West Lafayette, IN 47907
1O. PROGRAM ELEMENT NO.
1AA603A AG-02(FY-77)
11. CONTRACT/GRANT NO.
.R803514
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Sciences Research Laboratory - RTP,NC
Office of Research and Development
U. S, Environmental Protection Agency
Research Triangle Park. NC 27711
13. TYPE OF REPORT AND PERIOD COVERED
Final 1/75-12/77
14. SPONSORING AGENCY CODE
EPA/600/09
15. SUPPLEMENTARY NOTES
16. ABSTRACT
Two dimensional numerical simulations of planetary boundary layer flow indicate
that urbanization (increase of surface roughness, changes in surface albedo, and
the addition of anthropogenic heat sources) has a greater influence on the urban
heat island than the addition of pollutants (gaseous and particulate) which are
active in the radiative transfer processes of the heat budget. Although mid-day
surface temperatures are decreased by adding such pollutants, temperatures at
other times of day are increased. These simulations also indicate that heat
islands are most pronounced with a snow cover on the ground. The warmer surface
temperatures enhance low level pollutant dispersal with a consequent 25%
concentration reduction. These results indicate that a change in land use is an
important factor in local climate and weather modification.
7.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COS AT I Field/Group
*Air pollution
*Mathematical models
*Boundary layer flow
Urbanization
*Diffusion
*Surface temperature
13B
12A
20D
05J
20M
119. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (ThisReport)
UNCLASSIFIED
21. NO. OF PAGES
143
20. SECURITY CLASS (Thispage)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (9-73)
127
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