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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
DISCLAIMER
Publication of this report 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.
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TABLE OF CONTENTS
Page
LIST OF ILLUSTRATIONS vii
LIST OF TABLES ix
1. INTRODUCTION 1-1
1.1 Background 1-1
1.2 The Mesoscale Plume Segment Model (MESOPLUME) 1-2
1.3 Integrated Mesoscale Modeling System 1-4
1.4 Organization of the Report 1-4
2. MESOPLUME TECHNICAL DISCUSSION 2-1
2.1 Basic Mass Conservation Equations 2-1
2.2 The Grid System 2-2
2.3 Specification of Model Inputs 2-4
2.4 Plume Trajectory, Dispersion and Sampling
Algorithms 2-4
2.4.1 Lagrangian Trajectory Function 2-4
2.4.2 The Plume Dispersion Function 2-9
2.4.3 The Plume Sampling Function 2-11
2.5 Conversion of Sulfur Dioxide to Sulfate 2-15
2.6 Dry Deposition of Sulfur Dioxide and Sulfate 2-16
2.7 Plume Rise 2-17
2.8 Treatment of Plume Fumigation 2-18
2.9 Comparison to the Conventional Gaussian Plume Model 2-21
2.10 The Computer Program 2-24
3. MESOPLUME USER INSTRUCTIONS 3-1
3.1 General 3-1
3.2 Description of Card-Image Input 3-1
3.3 Other Considerations 3-9
3.3.1 Meteorological Considerations and MESOPAC
Input 3-9
3.3.2 Array Size Considerations 3-10
3.4 MESOPLUME Model Output 3-10
3.4.1 Line Printer Output 3-10
3.4.2 Direct Access Disk Output 3-11
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ENVIRONMENTAL RESEARCH S TECHNOLOGY INC
Table of Contents (Continued)
3.5 Execution Time and Core Requirements 3-14
4. TEST CASE FOR MESOPLUME 4-1
REFERENCES
APPENDIX A TEST CASE MESOPAC INPUT AND OUTPUT
APPENDIX B TEST CASE MESOPLUME OUTPUT
APPENDIX C TEST CASE NAMELIST FILE
ABSTRACT
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
LIST OF ILLUSTRATIONS
Figure Page
1-1 Schematic Representation of Segmented Plume Approach 1-3
1-2 Integrated Modeling System 1-5
2-1 One Possible Arrangement of Sampling and Basic
Computational Grids 2-3
2-2 Calculation of the Trajectory of a Plume Segment Endpoint 2-7
2-3 Calculation of the Concentration C(i,j) by the Sampling
Function 2-12
2-4 A Schematic Representation of Adjacent Plume Segments
in Rapidly Decelerating Flow 2-14
2-5 A Schematic Representation of Adjacent Plume Segments
in Highly Curvilinear Flow 2-14
2-6 Response of Two Plume Elements to Changes in Mixing Depth 2-20
2-7 MESOPLUME Versus the Conventional Gaussian Plume 2-23
2-8 MESOPLUME Computer Program Flowchart 2-25
4-1 MESOPLUME Test Case Parameter and Emission Source
Inventory Input 4-2
4-2 Test Case Library File (MESOFILE) 4-3
4-3 MESOPLUME 24-Hour Average SO Concentration Calcomp Plot,
16 June, 1978 (from MESOFILEJ 4-4
4-4 MESOPLUME 24-Hour Average SO- Concentration Line Printer
Plot, 16 June, 1978 (from MESOFILE) 4-5
4-5 Bulk Statistical Comparison of MESOPUFF to MESOPLUME,
16 June, 1978 (from MESOFILE) 4-6
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
LIST OF TABLES
Table Page
2-1 Coefficients for Dispersion Parameter Formulas 2-10
2-2 Comparison of Cu/Q Values for MESOPLUME and Turner
Workbook - Computed Values 2-22
3-1 Logical Unit File Structure 3-12
IX
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ENVIRONMENTAL RESEARCH 8 TECHNOLOGY INC
1. INTRODUCTION
1.1 Background
In response to a growing national commitment to the use of indigenous
coal reserves to meet energy generation demands, several regions of the
country will see greatly expanded use of coal and oil shale resources
for steam electric power plants and other coal-based energy resource
development. But, as mandated by federal National Ambient Air Quality
Standards, and by the PSD major source review procedures, additional
coal-based energy resource development (ERD) will only be permitted
where consistent with the maintenance of human health, welfare, and
environmental quality. Accordingly, the siting and generation capacity
of such ERD facilities may be constrained by their potential impacts on
regional ambient air quality.
Various plans have been proposed for regional development of
multiple major new facilities, and suitable air quality simulation tools
are needed to assess the impacts of different energy development
scenarios on regional-scale air quality.
To meet this need in the public interest, the National Oceanic and
Atmospheric Administration (NOAA) has sponsored a study by Environmental
Research § Technology, Inc. (ERT) to develop, evaluate and exercise a
number of alternative approaches to regional-scale ambient air quality
modeling. A major objective of this study is to provide a suite of air
quality simulation models that are both technically sound and compu-
tationally practical for assessing regional-scale impacts of energy
development scenarios.
As described in the companion report, Volume 1 (Bass et al. 1979),
three different air quality transport-diffusion models have been
developed, implemented and compared for simulation of point-source plume
dispersion on the mesoscale [e.g., dispersion at ranges of 100 to
1,000 kilometers (km)]. The models are optimized for regional-scale
impacts; in Volume 1, the ambient air concentrations calculated by
MESOPLUME in the near field of sources, that is, less than 100 km, are
not realistic.* Both worst-case and average dispersion situations are
treated, with meteorological inputs constructed from rawinsonde data
that is readily available for the region. The models are computation-
ally practical for simulating the impact of multiple point sources over
periods of several days to several weeks and are easy to use for a
variety of decision-making and regulatory applications. Finally, these
models are intended to serve as flexible testbeds for further research,
development, and simulation tasks.
^Subsequently, the MESOPUFF model has been augmented to provide realistic
near-field impacts (see the companion report, Volume 3).
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The three new models are, respectively:
• MESOPLUME,. a mesoscale variable-trajectory Gaussian "plume-
segment" model, adapted by ERT from the plume segment approach
taken in the STRAM model (Hales et al. 1977),
• MESOPUFF, a mesoscale variable-trajectory Gaussian "puff"
superposition model (Benkley and Bass 1979a), adapted by ERT
from the puff approach taken in the MESODIF model (Start and
Wendell 1974) ; and
• MESOGRID, a mesoscale numerical grid model (Morris et al.
1979), adapted from the method of moments approach taken in
the SULFA3D model (Egan et al. 1976),
This report, the second in a series entitled "Development of
Mesoscale Air Quality Simulation Models", describes the MESOPLUME model
in technical detail; illustrates its application; and provides a User's
Guide to the model. However, to obtain a fuller understanding of the
model's capabilities, limitations and recommended usage, the user should
be familiar with the relevant materials contained in the following
related reports:
• the companion report (Volume 1) describing the extensive
series of comparison and model sensitivity analyses performed
with these models (Bass et al. 1979);
• the companion report (Volume 6) describing the specially
designed MESOSCALE meteorological preprocessor program,
MESOPAC (Benkley and Bass 1979b);
• the companion report (Volume 5) describing the specially
designed postprocessing and analysis system, MESOFILE (Scire
et al. 1979) .
1.2 The Mesoscale Plume Segment Model (MESOPLUME)
MESOPLUME is a regional-scale variable-trajectory Gaussian plume
segment model. The modeling method used in MESOPLUME, adapted from that
proposed by Hales et al. (1977), differs from the conventional Gaussian
plume approach in that MESOPLUME allows for deformation of the con-
tinuous plume by a temporally-varying vertically-uniform, horizontal
wind field. The plume is treated as divided into contiguous segments;
each segment describes a portion of plume behavior between successive
time periods; and the end points of each segment are advected in a
Lagrangian sense. The representation of a continuous plume by the
segmented-plume approach is depicted schematically in Figure 1-1.
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Figure 1-1 Schematic Representation of Segmented Plume Approach
o
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MESOPLUME accommodates multiple point sources, and includes modules
for plume rise, plume growth^ fumigation, linear conversion of sulfur
dioxide (802) to sulfate (SO^), and dry deposition of 862 and SO^.
1.3 Integrated Mesoscale Modeling System
The MESOPLUME model has been incorporated as an independent model
element within an efficient, easy to use integrated mesoscale modeling
system. This integrated system depicted in Figure 1-2, comprises
components for meteorological preprocessing, mesoscale transport-
diffusion, and post-processing. Standardizing model input/output
functions in this system permits easy combination of results from two or
more model runs, or direct and cost-effective comparison of simulations
performed with two or more different models (see, for example, Bass et
al. 1979). The meteorology preprocessor MESOPAC drives identically any
of the three mesoscale transport-diffusion models. In turn, each of
these models identically communicates its results to the MESOFILE post-
processing system - responsible for file management, display, and
statistical analysis of all model output fields.
1.4 Organization of the Report
Section 2 of this report contains detailed technical description of
the MESOPLUME model; specific user instructions are described in
Section 3; a test case for the MESOPLUME model is presented in Section 4.
A Fortran microfiche listing of the MESOPLUME model is appended.
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MESOSCALE
METEOROLOGY
MESOPAC
MESOSCALE
TRANSPORT-
DIFFUSION
MODELS
MESOPLUME
MESOPUFF
MESOGRID
ANALYSIS
MESOFILE
o
r-t
O
Figure 1-2 Integrated Modeling System
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2. MESOPLUME TECHNICAL DISCUSSION
2.1 Basic Mass Conservation Equations
MESOPLUME, a variable-trajectory version of the conventional
straight-line Gaussian plume model, is designed to take into account the
spatial and temporal variations in the horizontal advection, diffusion,
transformation, and removal mechanisms governing the dispersion of a
plume on regional transport scales. In MESOPLUME, a continuous plume is
modeled by subdividing the plume into a number of contiguous 'plume
segment' elements. The conservation of pollutant mass over a plume
segment of length As is expressed by the mass balance equation:
AQ = / / G(s,r,z) dr dz As
(2-1)
/ / u C dr dz
/ u C dr dz
s+As
where s, r, and z are the_longitudinal , lateral, and vertical plume
directions, G(s,r,z) (g m~3s 0 is the rate of change (gain-loss) of
pollutant concentration C(s,r,z) (g m 3) by conversion and removal
processes, AQ (g s -1) is the rate of change of pollutant mass flux from
s to s + As, and u (m s"1) is the wind speed. In MESOPLUME, G(s,r,z)
and u are constant over the plume segment, and s is the current distance
of a plume segment endpoint from the emitting source, measured along the
plume axis.
MESOPLUME permits the user to specify two possible vertical
distribution functions; (1) a vertical Gaussian profile, ignoring any
effects of the mixing lid H; or (2) a uniform vertical distribution
below the mixing lid.
For Case 1, the ground-level axial plume concentration C(s,r,0) is
defined at the upwind edge of a plume segment by the expression:
C(s,r,0)
Q(s)
IT u a^(s) a (s)
exp
-r
2a
exp
-z
2a
(2-2)
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where Q(s) is the pollutant mass flux and cy(s), az(s) are the lateral
and vertical Gaussian plume dispersion coefficient distributions at
downwind distance s. Full reflection from the ground is assumed.
For Case 2, if the plume altitude (z) lies below the mixed lid H,
the ground-level axial concentration is expressed at the upwind edge of
the plume segment by the expression for uniform vertical mixing:
C(s,r,0) =
Q(s)
/2~n~ u H o (s)
m yk J
exp
-r
2o
(2-3)
where H is the maximum mixing depth encountered by the plume segment
(see Section 2.8). If, rather, the plume centerline lies above the
mixing lid, no ground-level concentrations are calculated. At the
downwind edge (s+As) of the plume segment, the ground-level axial con-
centration (s+As,r,0) is expressed as:
C(s+ds,r,0) =
{Q(s)+(dQ/dt)}At
•/2-v u H a (s + As)
my
exp
-r
20
(2-4)
The MESOPLUME model solves the mass conservation Equation 2-1
independently for both sulfur dioxide (802) and sulfate (SOiJ . The gain
functions include terms for the loss (gain) of 862 (SO^) by linear decay
of S02 to SCV and terms for dry deposition of either species (see
Sections 2.5 and 2.6).
2.2 The Grid System
The coordinate system used in the STRAM model has been replaced in
MESOPLUME by a simple Cartesian coordinate system. All spatial model
input data (emission source inventories and meteorological fields) are
referenced to the same grid, called the "basic computational" grid. To
improve the resolution of the plume sampling function (see Section 2.4.3),
MESOPLUME uses a sampling grid that is a subset of the basic computa-
tional grid. The origin of the sampling grid may be placed anywhere on
the basic computational grid (but not on the northern or eastern grid
boundaries). The resolution of the sampling grid is a multiple of the
resolution of the basic computational grid. Currently, the maximum
allowable size of both the basic computational and sampling grids is 40
by 40 horizontal grid points. Figure 2-1 illustrates one possible
arrangement of basic computational and sampling grids. [More detailed
instructions for defining the dimensions and resolutions of the two
grids are contained in Section 3.2.] Note that the grid index of the
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y = 5.0
y = 4.0
j = 5
y = 3.0
i = 4
y = 2.0
y=1.0
i = 2 "
Ay = Ad
y = 0.0
x = 0.0
i = 1
1 1
I I
-H+ +
-H ++
4-+ + --
1 1
x = 1.0
i = 2
x = 2.0
i=3
x = 3.0
i=4
x = 4.0
i=5
x = 5.0
i = 6
Figure 2-1 One Possible Arrangement of Sampling and Basic Computational
Grids
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origin (1,1) is assigned Cartesian coordinates x = 0, y = 0. The
sampling grid in this example originates at (i = 2, j = 2) or (x = 1,
y = 1) on the basic computational grid, and has a spatial resolution
four times finer than the basic computational grid.
2.3 Specification of Model Inputs
MESOPLUME is, at present, driven by the meteorological fields
produced by MESOPAC, a MESOscale meteorology PACkage (Benkley and Bass
1979b), but an alternative meteorological preprocessing system with
appropriate grid resolution could be substituted by the user. MESOPLUME
requires the following fields (usually at hourly intervals) interpolated
to each model grid point:
• horizontal (u,v) wind components,
• mixing depth, and
• Pasquill-Gifford-Turner (PGT) stability class.
Examples of the MESOPAC meteorological input used by MESOPLUME are shown
in Appendix A. Use of MESOPAC model output by the MESOPLUME model is
straightforward - the Cartesian coordinate system is identical in both
models. Note that the horizontal (u,v) wind components are considered
invariant with height. Vertical wind components are not used. The
fields of PGT stability class are used by MESOPLUME in doing plume rise
and plume growth calculations.
2.4 Plume Trajectory, Dispersion and Sampling Algorithms
The computational scheme of the MESOPLUME model has three distinct
functional elements: (1) a Lagrangian plume trajectory function, (2) a
plume dispersion function, and (3) a plume sampling function. The
Lagrangian trajectory function is used to advect the endpoints of each
plume segment during a basic time step; the resultant distance between
consecutive endpoints defines the length of each plume segment. The
widths at the upwind and downwind ends of each plume segment are deter-
mined by the plume dispersion function. Given the size and location of
each plume segment, the plume sampling function computes the concen-
tration exposure received during the time interval at each grid point
that lies within the plume segment.
2.4.1 Lagrangian Trajectory Function
This section describes how the endpoints of a plume segment are
advected during a time step; it has been adapted (but largely verbatim)
from Hales et al. (1977, pages 15-18).
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V
Let the components of the wind in the x and y grid directions along
the trajectory be designated by u[t;x(t),y(t)] and v[t;x(t),y(t)],
respectively,
where
t
x(t) = -^ I u[t';x(t'),y(t')]dt'
o
, t
Y(t) = •— / v[t';x(t'),y(t')]df
O
x(0) = XQ ; y(0) = yQ
u(0;x(0),y(0)) = u(xQ,y0)
v(0;x(0),y(0)) = v(x,y)
and XQ,yg are the initial coordinates (origin) of a trajectory of total
length s*. (The grid space unit Ad is used in the program to convert to
nondimensional spatial units on the computational grid.) The vectors
u(t;...) and v(t;...) provide a Lagrangian description of the velocity
field. Increments of advection over a further time interval At are
therefore given by
t + At
Ax = i- / u[t';x(t'),y(t')] dt' ; (2-5a)
t+At
Ay = ^ / v[t';x(t'),y(t')] dt' . (2-5b)
After the new coordinates x(t+At) and y(t+At) are calculated for each
incremented endpoint of a given plume, a check is made to see if any of
these values are off the grid. If so, both the affected plume increment
and all plume increments emitted from the same source previously are
deleted from further consideration.
In practice, however, the exact definition of s in MESOPLUME is the
current along-the-variable-plume-axis distance of a segment endpoint
from a source. In time-varying flows, it may be somewhat different
from the actual segment endpoint trajectory length.
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The integration of the wind components over the trajectory is
approximated by a two-step iteration method involving bilinear inter-
polation in space and in time (see Figure 2-2) .
The various positions in Figure 2-2 are best defined by equations:
xx = x(t) + u[t;x(t),y(t)] At ; (2-6a)
+ v[t;x(t),y(t)] At ; (2-6b)
+ u[t+At;x1,/1] At ; (2-bc)
x^/j] At ; (2-6d)
x(t+At) = 0.5[x(t) + x2] ; (2-6e)
yCt+At) = 0.5[y(t) + y] . (2-6f)
Thus, the new position (x(t+At) ,y(t+At)) is found by adding the
vector average of two advection increments to the former position
(x(t),y(t)). The first advection increment is calculated by advecting
the plume increment for the entire interval At using the wind effective
at [x(t),y(t)] at time t. The addition of this increment to the posi-
tion [x(t),y(t)] yields the position (x y ) . However, (x^y..) is not
assumed to be the actual end of the advection increment because the wind
may change along the trajectory. A second advection increment is cal-
culated using (x ,y1) as the starting point and the wind at that point
effective at the end of the time increment. The addition of this advec-
tion increment to position (x, ,y,) yields position (x?,y7). Then the
new plume increment position tx(t+At) ,y (t+At)) is taken to 1
be the point
halfway along the line from (x(t),y(t)) to (x ,y?) .
The bilinear interpolation by which the effective wind components
u(t), and v(t) are calculated works as follows. Let t and t be the
effective times of the two gridded wind fields closestnto time t. Time
interpolation weights t and t? are defined by:
t - t
t < t < t . ; (2-7a)
— ' ^ J
.
t . - t n — n+1
n+1 n
t2. (2-7b)
i
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Figure 2-2 Calculation of the Trajectory of a Plume Segment Endpoint
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
Next, the location of (x(t),y(t)) is noted on Figure 2-2. The
coordinate values are between 3 and 4 for x(t) and between 2 and 3 for
y(t) . Accordingly:
Xq = x(t) - 3 ; (2-8a)
Xp = 1 - Xq ; (2-8b)
Y = y(t) - 2 ; (2-8c)
Y = 1 - Y . (2-8d)
p q
This yields
u(t) = t. X Y u(t ;3,2) + t- X Y u(t ^3,2)
1 p p ^ n' 2 p p n+1'
+ t, X Y u(t ;4,2) + t~ X Y u(t ^4,2) (2-9)
1 q p n' 2 q p ^ n+1' '
t, X Y u(t ;4,3) + t0 X Y u(t ,;4,3) .
1 q q "• n' ' -" 2 q q *• n+1' ' J
Similar equations hold for v(t), u(t+At), and v(t+At). Therefore, the
advection equation 2.5 is calculated from
Ax = {u(t) + u(t+At)}At; (2-10a)
Ay = —• {v(t) + v(t + At)}At. (2-10b)
The subsequent positions are given by
x(t+At) = x(t) + Ax ; (2-lla)
y(t+At) = y(t) + Ay . (2-llb)
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2.4.2 The Plume Dispersion Function
This section, describing the evaluation of the plume dispersion
parameters, has been taken largely verbatim, with modifications, from
Hales et al. (1977, pp. 23-24). The plume dispersion parameters ov and
az are calculated for distances out to 100 km using plume growth formulas
fitted to the curves of Turner (1970). For distances greater than
100 km the plume growth rates given by Heffter (1965) are used. The
growth of a plume with travel time t or along the plume trajectory
distance s from the source is represented by
do
(2-12)
a (s+As) = a (s) + As
7 y ~, s+As/2
da
a (t+At) = a (t) + At ,„.
y y^ dt
y
t+At/2
(2-13)
Similar equations for oz are used. These terms allow for spatial and
temporal changes in stability class to be included, without violating
the entropy principle (centerline concentrations cannot increase with
downwind distance) .
The integral formulas for ay and a for travel distances less than
100 km are of the following forms
a (s,a) = Ya s°'9 (meters); (2-14)
o (s,a) = Z s (meters). (2-15)
Z Ot
Here a is a stability index designated as A, B, C, D, E, or F for the PGT
stability categories. The coefficients Y , Z , and b are given in
Table 2-1 as a function of stability index a,aand yiefd values of Oy and
az in meters when s is specified in meters. Because the integral
formulas are not valid if the stability class changes over the travel
distance s, the derivative forms are actually used to carry out the
computations:
do
- °'9 Ya S
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TABLE 2-1
COEFFICIENTS FOR DISPERSION PARAMETER FORMULAS
Plume Growth Coefficients
Stability
Index a
A
B
C
D
E
F
Y
a
0.36
0.25
0.19
0.13
0.096
0.063
Z
a
0.00023
0.058
0.11
0.57
0.85
0.77
b
a
2.10
1.09
0.91
0.58
0.47
0.42
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do (b -1)
-j-- = b Z s . (2-17)
ds a a
MESOPLUME assumes that a , az = 0 at the origin (s = 0) .
The derivative formulas used to grow Oy and az for travel distances
greater than 100 km are those given by Heffter (1965) :
da
(m s"1) = 0.5 (2-18)
j~ (m s"1) = 0.5(2Kz)1/2 t"1/2 (2-19)
2.4.3 The Plume Sampling Function
Each plume segment resident on the grid (at the end of every time
step At) is sampled, using the plume sampling function, to evaluate the
average concentration experienced at each sampling and intersection
during the previous time step. For example, consider the hypothetical
plume segment depicted in Figure 2-3. The plume segment centerline at
time t+At extends from (x,y) to (x+Ax,y+Ay), the positions of the two
consecutive plume segment endpoints at t+At. The plume segment length
is As = (Ax + Ay2)1/2. The lateral extent of a plume segment is
considered to be truncated at ±3a . This is a reasonable simplification
much less than 1% of the area under the Gaussian distribution function
lies beyond ±3a from its center. In this example, plume segment radii
of size 3o (s) at the upwind edge (x,y) of the segment and of size
3a (s+As) ^t the downwind edge (x+Ax,y+Ay) are indicated. At time t+At,
the' grid point intersections (21,12), (22,12), (22,13), (23,13), and
(22,14) are each impacted by the hypothetical plume segment; each grid
point is assigned a certain average concentration C(i,j) resulting from
the presence of the plume segment over At. This evaluation is illus-
trated as follows: Suppose the grid point concentration C(i,j) is to be
calculated at (22,12). First, the ground-level concentrations C(x,y)
and C(x+Ax, y+Ay) are computed using Equations 2-3 and 2-4 for the case
of uniform vertical distribution. Next, a point (x',y') is found such
that the line segment of length r constructed from (x',y') to the grid
point (i,j) is perpendicular to the plume segment centerline. The
effective source strength C(x',y') is then computed by linear inter-
polation:
C(x,y) As + C(x+Ax, y+Ay) As
C(x',y') = L <2
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j = 15
= 14
= 13
= 12
i = 20
^-
i = 21
(x+Ax,y+Ay)
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The lateral plume dispersion coefficient a (x,y) is similarly inter-
polated from:
a (x,y) As2 + a (x+Ax, y+Ay)
As +
(2-21)
Finally, the grid point concentration C(22,12) is computed as:
C(22,12) = C(x',y') exp
-r
(2-22)
If a grid point lies within more than one plume segment at the end of a
time step (for example, because plumes of multiple sources overlap), the
total concentration at the grid point CT(i,j) is computed as the sum of
the individual contributions:
m,
T
CT(i.j) = I Cm(
m=l
(2-23)
where m is the total number of segments impacting the grid intersection
(i,j) during the time step of interest. Note that a plume segment is
only sampled beginning with the second time step of its history and at
every time step thereafter.
The MESOPLUME model has two peculiarities intrinsic to the basic
advective-diffusive scheme of the model, which can present problems when
the sampling function is applied. These problems arise from (1) the
inverse-wind speed dependence of the concentration algorithm (Equation 2-2)
and (2) the way in which adjacent plume segments are actually juxtaposed
in a curvilinear flow. As an example of the inverse-wind speed depen-
dence problem, consider the case illustrated in Figure 2-4, for which
the wind flow along the plume axis rapidly decelerates, and suppose u.,
the average wind speed over segment A, is twice u , the average wind
speed over segment B.* Neglecting removal processes, MESOPLUME evalu-
ates the concentration C(x,,y ) at the downwind edge of segment A accord-
ing to Equation 2-4:
*This does not imply that the two-dimensional wind field is significantly
divergent--the local flow about segments A and B may be exactly non-
divergent if the mass fluxes through the lateral boundaries are "correct."
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ENVIRONMENTAL RESEARCH 5 TECHNOLOGY INC
(x0,yo)
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
/2-n" u, a (x. ,y,) H
A y 1 1 :
(2-24)
m
but can also evaluate the concentration C(x, ,y,)L at the same point
using Equation 2-3 as appropriate to the upwind edge of segment B using
u instead. Since u = 2u
B AD
B = 2C&i>yJ
(2-25)
If the grid resolution is such that severe discontinuities in the wind
field occur, the MESOPLUME model will indeed predict plume centerline
concentrations increasing with downwind distance. This prediction, a
violation of the second law of thermodynamics, represents a major short-
coming of the plume segment model - it cannot treat stagnation flows
correctly.
To address the second problem, that of juxtaposing adjacent plume
segments, consider the situation illustrated in Figure 2-5. In this
figure two adjacent plume segments are advected in a highly curvilinear
flow. In this case, for each plume segment, the lateral lines that
define the plume segment widths (at the upwind and downwind end points)
are constructed to be perpendicular to the local plume segment axes.
Therefore, if the average wind direction used to advect segment B is
very different from the average wind direction used to advect segment A,
the plume segments will not be perfectly contiguous, as shown. In the
example, there is a shaded region in which grid points will receive two
concentration doses during one time step and also a region where the
plume may actually "pass over" a grid point without impacting it at all.
This illustrates a second potentially serious shortcoming of the model -
it cannot treat strongly sheared flows (e.g., recirculating flows)
correctly.
Both of the limitations identified here are intrinsic to the basic
plume-segment approach, rather than to its present implementation. The
effects of these limitations on MESOPLUME model performance are dis-
cussed in greater detail in the companion model sensitivity document
(Bass et al. 1979) .
2.5 Conversion of Sulfur Dioxide to Sulfate
MESOPLUME represents the conversion of S02 to SO^ as a simple
linear rate function—the changes in mass of each pollutant in one time
step At are given by the time-discretized equations:
AQn(S02) = kj Qn(S02) At (2-26a)
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ENVIRONMENTAL RESEARCH&TECHNOLOGY INC
AQn(S04) = -1.5 k Qn(S02) At (2-26b)
where Q (802) is the mass of S02 at the beginning of the nth time step
and kifs"1) is the conversion rate constant. MESOPLUME uses the nominal
value kl = -5.56 x 10~6, suggested by Hales et al. (1977), unless
otherwise specified by the user.
Note that the conversion of S02 to 80^ represented by Equation 2-26
is (1) independent of the vertical or horizontal distribution of S02
within a plume and (2) independent of whether a plume lies above or
below the mixing height.
For the solutions to be adequate representations of the continuous
exponential equations (e.g., Q(S02) = kiQ(S02)), Q should not change
appreciably over the time interval At. Since this is frequently not the
case, the time interval At is automatically subdivided into as many
subintervals At1 as are required to ensure that no more than a specified
fraction AQf of the mass Q is removed by decay during a subinterval At'.
This process is described further in the next section.
2.6 Dry Deposition of Sulfur Dioxide and Sulfate
The changes of mass
deposition are given by:
The changes of mass AQ (g) of each pollutant resulting from dry
AQn(S02) = -(vd(S02) Qn(S02) At) / Hm (2-27a)
AQn(SO=) - -Cvd(SOp Qn(SO=) At) / Hm (2-27b)
where Q (802) and Q (SO^) are the masses of 862 and 804, respectively,
in the plume segment at the beginning of the time step, Vd(S02)
v^fSO^) are the deposition velocities of each pollutant, and H is the
vertical depth of the plume segment (see Section 2.8). MESOPLUME uses
the nominal values vd(S02) = 0.01 (m/s) and vd(SCQ = 0.001 (m/s), as
suggested by Hales et al . (1977), unless otherwise specified by the
user.
As described before, MESOPLUME automatically ensures that the mass
Q does not change within one subinterval At' by more than a user-
specified fraction AQ.p. Taking into account both S02 removal mechanisms
(decay and deposition), the expression used by MESOPLUME to specify the
subinterval At' is
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ENVIRONMENTAL RESEARCH S TECHNOLOGY INC
AQ (SO ) AQ (SO ) H
t2-28'
In other words, the subinterval size At' is chosen automatically by
MESOPLUME at the beginning of each basic interval At such that no more
than a specified fraction AQf of S02 mass is removed by either mechanism
(or, no more than 2AQf by both mechanisms combined) within At'. It
should be emphasized that the conversion and removal rates are not
affected by this restriction. Also, individual puff trajectories are
not updated at each subinterval At'; they remain unchanged over the
basic interval At.
2.7 Plume Rise
The effective emission height he of each plume segment is computed
as he = hs + hp, where hs is the stack height (m) and hp is the plume
rise (m]. The plume rise equations used by the MESOPLUME model are
those described by Briggs (1975) for equilibrium (final) plume rise.
For unstable and neutral conditions with h' <_ H (i.e., the plume does
not rise into an elevated stable layer)
h = h' = 1.6 F1/3 (3.5 x*)2/3 u'1 ; (2-29a)
For unstable and neutral conditions with h' > H (i.e., the plume pene-
trates into an elevated stable layer)
, , , 1/3
h = MIN (h1 , (1.8 z£ + 18.75 F uffl S ) }; (2-29b)
For stable conditions when u >_ 1.37 m/s
h = 2.6 F1/3 S~1/3 u"1/3 ; (2-29c)
For stable conditions when u < 1.37 m/s
h = 5.0 F1/4 S 3/8 . (2-29d)
where:
F = buoyancy flux (m /s )
c* = 34.49 F°'4 for F > 55
= 14.0 F°'625 for F < 55
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
S = (g/T)O0/9z)
_2
g = 9.8 m s
T = 290°K
z = 0.0137°K m"1
H = mixing depth (m)
zb = H - hs (m)
u = wind speed (m s )
u = MAX {u, 1.37}
m
2.8 Treatment of Plume Fumigation
When the "uniform vertical distribution" option is elected,
MESOPLUME takes the spatial and temporal variations in the mixing
height into consideration to determine the extent of ground level
pollutant impact:
(a) For a plume element centerline less than the mixing height,
MESOPLUME arbitrarily assigns the element a height of zero and
immediately mixes the mass of the plume element uniformly
through the mixing depth with the mixing lid acting as a
perfect reflector. Equations 2-3 and 2-4 are then applied to
compute ground level concentrations.
(b) For a plume element centerline greater than the mixing height,
no impact of the element is felt at the ground. However, if
subsequently the mixing height becomes greater than the height
of the plume element centerline, the entire plume element is
immediately mixed uniformly through the mixing depth, and
Equations 2-3 and 2-4 again apply.
The mixing depth encountered by a plume element is likely to change
over time and space. MESOPLUME assumes that a plume element residing in
the mixed layer is always uniformly mixed throughout the maximum mixing
depth H encountered by the element in its progression through the
computational space-time grid.
The restriction of a uniform plume element through a character-
izable depth such as H is imperative for a one-layer model such as
MESOPLUME to ensure that parcels of material, once entrained, are not
bifurcated in the vertical when the height of the mixing lid changes.
Therefore, in the MESOPLUME model a plume segment, once entrained, is
uniformly mixed through a realistic height, consistent with the follow-
ing assumptions:
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
(a) When convection ceases in the late afternoon, turbulent
energy in the afternoon mixed layer dissipates. Once the
energy of the large convective cells is completely damped, the
remaining small scale energy is not sufficient to yield a
vertical entrainment rate comparable to the daytime entrain-
ment rate produced by large convective eddies.
(b) The stable lid capping the convective layer most likely
maintains some integrity after convection ceases, and acts to
prohibit any additional vertical entrainment of plume material.
(c) A modeling approach that redistributes mass into a shallower
layer when the mixing depth decreases will violate the second
law of thermodynamics.
Since no mechanism remains for turbulent transfer of pollutant
material either upwards or downwards once the mixing depth decreases, it
is asserted that the height most appropriate to define the extent of
vertical mixing of a segment is the maximum mixing height H that the
segment encounters during any portion of its travel.
A hypothetical example is given in Figure 2-6 to schematically
illustrate the interaction of the MESOPLUME vertical distribution
algorithm and the mixing depth progression algorithm used by MESOPAC
[Benkley and Bass (1979b), Benkley and Schulman (1979)]. Here, consider
two instantaneous parcels of material released at different times from a
source with an effective stack height of 250 m; the first release is
made at 0300 GMT on the first day; the second release is made at
0300 GMT on the second day. Assume that the hypothetical source is
located in the western United States - so that 0000 GMT corresponds to
late afternoon - the usual time of maximum mixing depth.
The first parcel is entrained at 0600 GMT on Day 1 as the nocturnal
boundary layer oscillates slightly upward in height. MESOPLUME immedi-
ately mixes the parcel uniformly throughout this mixing depth. This
parcel is mixed uniformly through 900 m by 0000 GMT on Day 2, and
remains mixed through 900 m subsequent to that time, even though the
height of the mixed layer collapses. The second parcel, emitted at
0300 GMT on Day 2 is fumigated at 1500 GMT and is mixed uniformly
through 700 m by the end of the afternoon. Therefore, the first parcel,
emitted at 0300 GMT on Day 1, remains uniformly mixed through a deeper
vertical layer than the second parcel; this is consistent with the
greater extent of mixing on the first day as compared to the second day.
This mixing depth/fumigation scheme exhibits at least two short-
comings :
• When a plume segment becomes bifurcated in the vertical by
collapse of the mixing lid, how should the plume segment
lateral dispersion rate be characterized? Because MESOPLUME
is a one-layer model, the same set of horizontal dispersion
2-19
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
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2-20
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
coefficients must be applied to both the above-mixing-lid and
the below-mixing-lid portions of the bifurcated segment.
• The dry deposition algorithm (see Section 2.6) removes S02 and
SO^ at a rate proportional to the surface concentration—this
loss is then redistributed throughout the entire vertical
extent of the plume. It would be more realistic to distribute
the loss only through the current mixing depth, but this is
not possible in a one-layer plume segment model.
It is felt that these limitations of the scheme are necessary to a
method that is both computationally practical and yet realistic for use
with a one-layer model. The inclusion of a fumigation cycle is very
important; the marked effects of plume fumigation on the distribution of
regional scale ground level concentration patterns are illustrated in
Bass et al. (1979).
2.9 Comparison to the Conventional Gaussian Plume Model
From the point of view of possible regulatory applications, one of
the most attractive features of MESOPLUME is its ability to recreate the
results of the conventional Turner Workbook plume model (using PGT
coefficients). When MESOPLUME is run with appropriate uniform, steady-
state meteorology and fine spatial resolution, it can reproduce the
Turner Workbook results to a high degree. This can be demonstrated, for
example, under the following test conditions:
• The horizontal grid spacing of the model is reduced, e.g., to
5 km.
• The meteorological fields (wind direction, wind speed, mixing
height and PGT stability class) used to drive MESOPLUME are
ttaken to be spatially uniform and constant in time.
• MESOPLUME is run with one source until quasi-steady-state
conditions are well established; that is, plume segments are
created at the same uniform rate at which they disappear off
the edge of the grid.
Under these special test conditions, MESOPLUME simulates very well
the Turner Workbook plume concentrations out to distances of 100 km from
an emission source. (The PGT a and az curves, represented in the model
by piecewise linear power law fits, are only defined to 100 km.)
Table 2-2 and Figure 2-7 provide a representative illustration of how
closely MESOPLUME results compare to those obtained for the conventional
Gaussian plume model using the PGT "D" curve for oy, and a uniform
vertical distribution. Overall, the comparison is excellent—the very
small fractional differences (typically less than 2%) are attributable
mostly to inexact fitting of the PGT ay curve by the power law function
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
TABLE 2-2
COMPARISON OF Cu/Q VALUES FOR MESOPLUME AND
TURNER WORKBOOK-COMPUTED VALUES
(u = 2.78 m s"1, PGT Class = D, H = 1000 m,
uniform vertical distribution)
Distance
(km)
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
PGT
a (m)
y
550
780
1,000
1,220
1,420
1,620
1,820
2,020
2,200
2,400
2,600
2,775
2,975
3,200
3,375
3,550
3,700
3,850
4,000
Turner Workbook
(C u Q"1)
7.25xlO"7
5.11
3.99
3.27
2.80
2.46
2.19
1.97
1.81
1.66
1.53
1.44
1.34
1.25
1.18
1.12
1.08
1.04
1.00
MESOPLUME
(C u Q"1)
7.77xlO~7
5.94
4.11
3.47
2.83
2.50
2.19
1.97
1.78
1.64
1.50
1.42
1.31
1.25
1.17
1.11
1.06
1.00
0.95
Fractional
Deviation
0.07
0.16
0.03
0.06
0.01
0.02
0.00
0.00
-0.02
-0.01
-0.02
-0.01
-0.02
0.00
-0.01
-0.01
-0.02
-0.04
-0.05
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
= Gaussian ~ P6T
= MESOPLUME
40 60
Dtstance (km)
00
Figure 2-7 MESOPLUME vs. the Conventional Gaussian Plume
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
used in MESOPLUME. The somewhat larger deviations near the source are
expected - the results shown here were obtained with a wind velocity and
time step corresponding to plume segments about 5 km in length - so,
near the source, the linear averaging done by the sampling function does
not approximate accurately the power law behavior of the plume center-
line concentrations. It is expected that with use of smaller time steps
and correspondingly shorter plume segments, the comparison of MESOPLUME
to the Turner Workbook plume in the very near field would be even closer.
In its present version MESOPLUME does not include an option for a
reflected Gaussian vertical distribution, so that at^ distances where the
plume does not yet approach a uniform vertical distribution, MESOPLUME
will not give correct results. Under stable flow conditions, for
example, plumes may not approach a uniform vertical distribution for
many kilometers downwind. This version of MESOPLUME should therefore
only be used at and beyond distances for which the assumption of uniform
vertical mixing is appropriate - often only at distances of 100 km or
more. [It would be very simple to modify the present version, however,
to incorporate an optional Gaussian reflected vertical distribution so
as to make MESOPLUME suitable also for near-field computations.]
2.10 The Computer Program
MESOPLUME is a highly modular computer program which shares with
MESOPUFF and MESOGRID standardized input/output features and, where
possible, identical program modules. The computer program flow chart
(Figure 2-8) outlines the order of execution of the individual modules
described at length in the 'MESOPLUME Technical Discussion'.
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
Update hour, day, year
(Subroutine KALEND)
NLIMIT=NLIMIT+1
Initialize input parameters
(Subroutines INPARM, BLOCK DATA)
Initialize direct access files
(Subroutine FILMAN)
I
NINCV(K)=0 ; NLIMIT=0
Update meteorology from disk or tape
(Subroutine READER)
If (MOD(NLIMIT,IPRINF))=0,
output fields to line printer
(Subroutines WRITER, RANGE, DISP, PAGE)
I
If (MOD(NLIMIT,ISAVEF))=0,
output fields to direct access files
(Subroutine PUTOUT)
Calculate 'effective stack height' of new segment
{VIRTH(K,NINCV(K»} (Subroutine PRISE)
NP=0
MOD (NLIMIT,IAVG)=0?
Release new plume segment
{NINCV(K)=NINCV(K)+1}
Lagrangian trajectory function
Delete segments that fell off grid edges
{NINCV(K)=NINCV(K)-NFALL}
Dispersion function
(Subroutines BSTRAC.SIGMA)
Calculate removal from decay and dry deposition
Q(K,NP)=Q(K,NP)-dQ/dt
(Subroutine BSTRAC)
Sampling function
(Subroutines SAMPLE, LEIN)
NP=NP+1
Figure 2-8 MESOPLUME Computer Program Flow Chart
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
3. MESOPLUME USER INSTRUCTIONS
3.1 General
A MESOPLUME model simulation requires the following two input data
sets:
• meteorological fields from MESOPAC (unformatted disk or tape
input via logical unit 2) and
• model simulation control parameters and emission source
inventory (cards or card-image disk input via logical unit 5)
A complete description of model simulation control parameter and
emission source inventory input is contained in Section 3.2.
For most convenient operation, MESOPLUME should interface with the
output of the MESOPAC meteorological preprocessor (see Section 3.3).
Output of a MESOPLUME simulation can be routed to either (or both) of
the following:
• line printer (logical unit 6) and
• direct-access disk storage system (logical units 13, 15, 21,
22, 23, and 24).
The direct access storage system allows for automatic storage, cataloging,
and easy retrieval of all output data files from MESOPLUME. Avail-
ability of on-line direct-access disk storage enables the user to invoke
the powerful MESOFILE file-management system, designed especially for
interface with the regional-scale models. The MESOFILE system has file
management components that can produce a record of the date of the run,
the run characteristics, the disk file locations of all the concen-
tration data output, and the values of all the input parameters for the
run. The MESOFILE program allows this information to be retrieved for
all the previous runs made on a particular set of disk files. The
MESOPLUME program contains a file management subroutine (FILMAN) which
determines without further user input the proper locations for all of
the various MESOPLUME disk output files. MESOFILE can then be used for
flexible time averaging of any set of fields, summation of different
model simulations (particularly useful when more than 10 sources are to
be simulated), statistical comparison of model results, and graphical
contour display.
3.2 Description of Card-Image Input
This section provides a detailed description of all the card-image
input requirements of the MESOPLUME model, model simulation control
parameters, and emission inventory. The input package has been designed
for use in common by all three mesoscale models (MESOPLUME, MESOPUFF,
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ENVIRONMENTAL RESEARCH 8, TECHNOLOGY INC
and MESOGRID) especially to facilitate intermodel comparison tests. The
common input subroutine (INPARM) reads in all model simulation control
parameters in Fortran NAMELIST format so that input parameters common to
all three models can be included in standardized NAMELIST blocks. A few
model-specific NAMELIST sets, however, are required because of certain
features peculiar to each model algorithm. There are thirteen NAMELIST
blocks included within the INPARM subroutine package, as follows:
(1) MODEL, (2) CONTR, (3) GRID, (4) SIGMA, (5) METD, (6] PUFF1,
(7) PUFF2, (8) PLUM1, (9) PLUM2, (10) GRIDY, (11) REMOV, (12) OUTPT, and
(13) SOURC. NAMELIST blocks PUFF1, PUFF2, and GRIDY are not used by
MESOPLUME and must^ be_ omitted from the input data runstream.
The remainder of this section provides detailed descriptions of the
parameters in each NAMELIST set.* These NAMELIST sets are to be included
in the input run stream in the identical sequence described above.
NAMELIST TITLE—MODEL
MODEL defines which model is to be run.
for a MESOPLUME run.
Initialize LPLUME = .TRUE.
Parameter
LPLUME
LPUFF
LGRID
LOGICAL
LOGICAL
NAMELIST TITLE—CONTR
Definition
If .TRUE., MESOPLUME is
to be run
If .TRUE., MESOPUFF is
to be run
If .TRUE., MESOGRID is
to be run
CONTR initializes computational control variables.
Parameter Type Definition
REAL
DTIME
NADVTS
INTEGER
Length of the basic time step
(hours)
Length of the simulation in
terms of basic time steps
Default
.FALSE.
.FALSE.
.FALSE.
Default
1.0
24
*The description of certain parameters that were retained from the STRAM
model but not otherwise used or that were included only to perform
various model sensitivity tests, are omitted from this discussion--these
parameters would not be activated by the average user. However, for a
complete list of possible input parameters, the interested reader should
consult the documentation included as comment cards directly in sub-
routine INPARM.
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ENVIRONMENTAL RESEARCH STECHNOLOGY INC
Parameter Type
MTFREQ INTEGER
IAVG
INTEGER
Definition Default
Rate [in terms of 'DTIME') 1
with which wind, mixing
depth, and stability
fields are updated, in terms
of the basic time step.
'MTFREQ'*'DTIME' must be equal
to or an even multiple of the
MESOPAC time step 'ISTEP' (hours)
Number of basic time steps over 24
which output concentration arrays
are to be averaged
NAMELIST TITLE —GRID
GRID initializes parameters defining the three major grids:
meteorological, basic computational, and sampling. The basic
computational grid must be a subset of the meteorological grid and
also must have the same grid spacing. The sampling grid must be a
subset of the basic computational grid; it may be the same as the
basic computation grid; or denser by an integer multiple. Cur-
rently, the maximum allowable size of both the basic computational
and sampling grids is 40 by 40 horizontal grid elements.
Parameter
IELMET
JELMET
DELTMT
IASTAR
IASTOP
INTEGER
INTEGER
REAL
INTEGER
INTEGER
Definition Default
Number of elements in the x- 26
direction of the meteorological
grid--must be the same as 'IMAX'
used by MESOPAC (must be <_ 40) .
Number of elements in the y- 26
direction of the meteorological
grid--must be the same as 'JMAX'
used by MESOPAC (must be <_ 40) .
Basic computational grid spacing 40,000.
(meters)--must be equal to the
grid spacing, DX, used by the
MESOPAC meteorological grid.
Element number of the meteoro- 1
logical grid where the basic
computational grid starts
(x-direction).
Element number of the meteoro- 26
logical grid where the basic
computational grid stops
(x-direction).
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
Parameter Type
JASTAR INTEGER
JASTOP
ISASTR
ISASTP
JSASTR
JSASTP
MESHDN
INTEGER
INTEGER
INTEGER
INTEGER
INTEGER
INTEGER
NAMELIST TITLE —SIGMA
Definition
Element number of the meteoro-
logical grid where the basic
computational grid starts
(y-direction).
Element number of the meteoro-
logical grid where the basic
computational grid stops
(y-direction).
Element number of the meteoro-
logical grid where the
sampling grid starts
(x-direction)--make sure that
'ISASTR1 >_ 'IASTAR' .
Element number of the meteoro-
logical grid where the
sampling grid stops
(x-direction)--make sure that
'ISASTP' <_ 'IASTOP'.
Element number of the meteoro-
logical grid where the
sampling grid starts
(y-direction)--make sure that
'JSASTR' >_ 'JASTAR1 .
Element number of the meteoro-
logical grid where the
sampling grid stops
(y-direction)--make sure that
'JSASTP' <_ 'JASTOP' .
A factor by which the basic
computational grid spacing
'DELTMT' is divided to produce
the sampling grid spacing.
Default
26
26
26
SIGMA initializes PGT stability-class-dependent coefficients that
describe the differential forms of a and a . (a is not appli-
cable when 'LLID' = .TRUE.) y z z
3a
a (s + As) = a (s) + -~- As
y J y^ J 9s
where
= ay(s
As/2)
3-4
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
oz(s + As) = oz(s)
9a
As
where 3s
- = a (s + As/2) Z
Parameter
AY (6)
BY (6)
AZ(6)
BZ(6)
Type
REAL ARRAY
REAL ARRAY
REAL ARRAY
REAL ARRAY
Definition
a for PGT classes A-F,
respectively
b for PGT classes A-F,
respectively
a for PGT classes A-F,
respectively
b for PGT classes A-F,
2
respectively
NAMELIST TITLE--METD
Defaults
(.33, .22, .17, .12,
.086, .057)
(-.1, -.1, -.1, -.1,
-.1, -.1)
(.00048, .063, .1,
.33, .41, .32)
(1.10, .09, -.09,
-.42, -.53, -.58)
METD initializes parameters that identify the input meteorological
data base.
Parameter
METSRT
Type
INTEGER
METCOD
INTEGER
Definition Default
Year (2 digits), Julian day 7700101
(3 digits), and hour (2 digits)
in the MESOPAC output data file
at which the MESOPLUME run
begins.
4-digit serial number to 1001
identify the meteorological
data used (previously assigned
by the user to the MESOPAC
simulation run that generated
the input meteorology fields).
NAMELIST TITLE —PLUM1
For MESOPLUME only, PLUM1 initializes computational parameters.
Parameter Type Definition Default
MAXSEG
INTEGER
Maximum number of plume segments
per source allowed on the grid
at any time (must be < 100).
100
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
Parameter
LLID
Type
LOGICAL
Definition
If .TRUE., plumes below the
mixing lid are assumed to be
uniformly vertically distributed.
If .FALSE., plumes are assumed
to have a Gaussian distribution
in the vertical, with no
constraining lid.
Default
.TRUE.
NAMELIST TITLE —PLUM2
For MESOPLUME only, PLUM2 initializes parameters allowing the use
of nongridded receptors.
Parameter
NRE
XRES(IO)
YRES(IO)
Type
INTEGER
REAL ARRAY
REAL ARRAY
Definition
Number of nongridded receptors
(must be <_ 10) .
x-coordinates of the nongridded
receptors (meteorological grid
units).
y-coordinates of the nongridded
receptors (meteorological grid
units).
NAMELIST TITLE —REMOV
REMOV assigns values to the removal rate parameters.
Parameter Type
CONVFR REAL
LDCAY
EXTNCT
LDEPOT
LOGICAL
REAL
LOGICAL
DEPVEL(2) REAL ARRAY
Default
0
10 * 0.
10
0.
Default
.02
.TRUE.
Definition
Maximum fractional amount of
conversion or removal of S02 by
either decay or dry deposition
in any one sampling step (limits
the sampling step size, not the
conversion rate).
If .TRUE., exponential decay of
S02 to SOIJ is simulated; If
.FALSE., no decay is simulated.
If 'LDCAY' is .TRUE., conversion
rate of S02 to SO^ (negative,
for loss of S02).
If .TRUE, dry deposition is
simulated for both species.
If .FALSE., dry deposition is
not simulated for either specie.
If 'LDEPOT' is .TRUE., 'DEPVEL(l)' (.01,.001)
and, 'DEPVEL(2V are deposition
velocities (m s"1) for S02 and
SOiJ, respectively.
-5.56x10
-6
.TRUE.
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
NAMELIST TITLE—OUTPT
OUTPT initializes the output control parameters.
Parameter Type Definition
LPRINT
IPRINF
LOGICAL
INTEGER
Is .TRUE, if line printer out-
put of gridded concentration
arrays is desired.
If 'LPRINT1 is .TRUE., specifies
the rate (in terms of 'DTIME')
of gridded concentration
array output (to line printer)
in terms of the basic time step.
Must be equal to or an even
"lAVG1
Default
.TRUE.
24
multiple of
CONTR).
(see NAMELIST
LSAVE
ISAVEF
LOGICAL
INTEGER
If .TRUE., concentration arrays
are to be saved on disk or tape.
If 'LSAVE' is .TRUE., rate (in
terms of 'DTIME') of concentra-
tion array output to tape or
disk. Must be an_ even multiple
of ' lAVG^Tsee NAMELIST CONTR) .
.FALSE.
24
NAMELIST TITLE —SOURC
Default
1
.FALSE.
SOURC assigns values to the parameters associated with source
characteristics.
Parameter Type Definition
NSOURC INTEGER Number of sources (up to 10).
LMISS LOGICAL If .TRUE., a 24 hour cycle of
emission rate multipliers is to
be read in. If .FALSE., tem-
porally constant emission rates
are assumed.
LFLUX LOGICAL If .TRUE., a 24 hour cycle of
buoyancy flux multipliers is to
be read in. If .FALSE., tempor-
ally constant buoyancy fluxes
are assumed.
The following formatted (non-NAMELISTED) input follows the NAMELISTED
input described above: For each source (there are a total of 'NSOURC'
sources) the formatted input consists of the following sequence of
cards.
.FALSE.
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
STACK PARAMETERS (1 card per source)
Columns Format Parameter
Definition
1-10 F10.2 STAKHT Stack height (m) above ground
11-20 F10.2 XXSTAK x-coordinate of source (meteorological
grid units)
21-50 F10.2 YYSTAK y-coordinate of source (meteorological
grid units)
31-40 F10.2 EMISS(l) Emission rate (g s'1) for SC>2
41-50 F10.2 EMISS(2) Emission rate (g s'1) for S(JT
4-3
51-60 F10.2 BFLUX Buoyancy flux (m s ) for plume rise
EMISSION CYCLE (2 cards per source; needed only if LMISS = .TRUE.)
The emission rate Qu(P,I) for hour h, source I, and pollutant P
(1 = S02, 2 = SO 4) is computed as Qh(P,I) = 'ECYCLE(h,I)' * 'EMISS(P,I)'.
Two emission cycle input cards are required for each source; the mul-
tipliers for hours 1-12 are specified on Card #1 and hours 13-24 are on
Card #2. Card #1 is for each source set up as follows.
Columns Format
1-6
7-12
13-18
19-24
25-30
31-36
37-42
43-48
49-54
55-60
61-66
67-72
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
Parameter
ECYCLE (1,N)
ECYCLE (2,N)
ECYCLE (3,N)
ECYCLE (4,N)
ECYCLE (5,N)
ECYCLE (6,N)
ECYCLE (7,N)
ECYCLE (8,N)
ECYCLE (9,N)
ECYCLE (10,N)
ECYCLE (11,N)
ECYCLE (12,N)
Definition
Emission multiplier for hour
Emission multiplier for hour
Emission multiplier for hour
Emission multiplier for hour
Emission multiplier for hour
Emission multiplier for hour
Emission multiplier for hour
Emission multiplier for hour
Emission multiplier for hour
Emission multiplier for hour
Emission multiplier for hour
Emission multiplier for hour
1, source N
2, source N
3, source N
4, source N
5, source N
6, source N
7, source N
8, source N
9, source N
10, source N
11, source N
12, source N
The format for Card #2 (hours 13-24) is identical.
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ENVIRONMENTAL RESEARCH*TECHNOLOGY INC
BUOYANCY FLUX CYCLE (2 cards per source, needed only if_ LFLUX = .TRUE.).
The buoyancy flux F, (P,I) for hour h, source I, and pollutant P
(1 = S02, 2 = SO^) is computed as Fh(P,I) = 'BCYCLE(h,I)' * 'BFLUX(P,I)'.
Two emission cycle input cards are required for each source; the mul-
tipliers for hours 1-12 are on Card #1, and hours 13-24 are on Card #2.
Card #1 is for each source is set up as follows.
Columns Format
1-6
7-12
13-18
19-24
25-30
31-36
37-42
43-48
49-54
55-60
61-66
67-72
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
F6.2
Parameter
BCYCLE (1,N)
BCYCLE (2,N)
BCYCLE (3,N)
BCYCLE (4,N)
BCYCLE (5,N)
BCYCLE (6,N)
BCYCLE (7,N)
BCYCLE (8,N)
BCYCLE (9,N)
BCYCLE (10,N)
BCYCLE (11,N)
BCYCLE (12,N)
Definition
Buoyancy flux multiplier for hour
Buoyancy flux multiplier for hour
Buoyancy flux multiplier for hour
Buoyancy flux multiplier for hour
Buoyancy flux multiplier for hour
Buoyancy flux multiplier for hour
Buoyancy flux multiplier for hour
Buoyancy flux multiplier for hour
Buoyancy flux multiplier for hour
Buoyancy flux multiplier for hour
Buoyancy flux multiplier for hour
Buoyancy flux multiplier for hour
1, source N
2, source N
3, source N
4, source N
5, source N
6, source N
7, source N
8, source N
9, source N
10, source N
11, source N
12, source N
The format for Card #2 (hours 13-24) is identical.
3.3 Other Considerations
3.3.1 Meteorological Considerations and MESOPAC Input
The meteorological input for the MESOPLUME model is specified as a
time sequence of spatially variable, gridded fields of horizontal (u,v)
wind components, mixing depth, and PGT stability class. These fields
are generated and written to off-line storage (disk or tape) by the
MESOPAC* Meteorological Preprocessing Program (see Benkley and Bass
1979b). As mentioned in Section 3.1, MESOPLUME retrieves the meteoro-
logical data set from logical unit 2.
Direct compatibility of MESOPLUME input with MESOPAC output is
ensured if the successive runs of MESOPAC and MESOPLUME jointly satisfy
the following constraints on respective run parameters.
• The four-digit serial number 'METCOD' used to identify the
meteorological data sets requested by MESOPLUME must match the
'METCOD' assigned when MESOPAC created and tagged its output
meteorological data set.
*If desired, another meteorological preprocessing routine can be
substituted to drive the MESOPLUME model.
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
• The grid spacing 'DELTMT' used by MESOPLUME must be identical
to the grid spacing 'DX1 used by MESOPAC; of course, the
number of horizontal grid elements 'IELMET' and 'JELMET' used
to specify the met data array sizes in both models must be
identical.
• The geographical area covered by the MESOPLUME basic computa-
tional grid must be identical to, or a subset of the area
covered by the MESOPAC meteorological grid.
• The chronological time period over which the MESOPLUME simu-
lation run is performed must be a subset of the time period
used by MESOPAC to define the meteorological fields.
• The interval at which the meteorological fields are updated by
MESOPLUME is specified by the product 'DTIME' * 'MTFREQ'
(hours). This time interval must be equal to or an even
multiple of the time interval 'ISTEP' (hours) used by MESOPAC
to generate and output successive sets of meteorological
fields.
3.3.2 Array Size Considerations
Currently, MESOPLUME allows for the basic computational grid to be
as large as 40 elements in each horizontal direction. However, since
most of the large arrays in MESOPLUME are related to numbers of emission
sources and plume increments, the size of the grid could be increased
without a substantial increase in the core required by MESOPLUME. The
user can specify up to 10 sources; if a larger source inventory is
needed, the user will run MESOPLUME with up to 10 sources at a time and
then aggregate results with MESOFILE. MESOPLUME allows for up to 100
plume segments per source to be resident on the grid at any time; thus,
for example, for a 1-hour basic time step, the MESOPLUME simulation is
prematurely terminated if any segment from any source remains on the
grid for longer than 100 hours.
3.4 MESOPLUME Model Output
MESOPLUME output can be specified in either (or both) of two media
depending on further user needs. The results can be output directly to
the line printer, and/or routed for storage on the direct-access disk
storage system for subsequent postprocessing. This section describes
the types of output data that can be routed through each system. In
most cases, the user may specify whether a certain type of output data
will be received.
3.4.1 Line Printer Output
The following is a complete list of the line printer output options
available from a MESOPLUME simulation:
• a table that lists the values of all input parameters used in
the run (this output is always generated);
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
• a brief description of the model run time, concentration
averaging time, source data, and grid characteristics of the
model run (this output is always generated) ;
• arrays of ground-level concentrations for S02 and SO^ block
averaged over time intervals of 'IAVG' * 'DTIME' hours and
output at intervals of 'IPRINF' * 'DTIME' hours during the run
(these fields are printed only if 'LPRINT' = .TRUE.);
• tables of S02 and S04 ground level concentrations averaged and
output as above, for a small number of receptor locations
specified arbitrarily within the computational area (these
tables are printed only if 'NRE' > 0 and 'LPRINT' = .TRUE.);
• for each pollutant, arrays of the maximum grid point concen-
tration values (averaged as above) during the run span (these
are printed only if 'LPRINT' = .TRUE.);
• for each pollutant, arrays of the grid point concentration
values for SCL and S0~ averaged over the entire run span
(these are printed only if 'LPRINT' = .TRUE.); and
• a table listing the time when the initial plume segment from
each source first reached the edge of the basic computational
grid. This table is a useful adjunct to choosing an "initial-
ization time" for the simulation; that is, the period of time
during which the model concentration results are influenced by
source "startup," and so are not to be considered representa-
tive of average concentrations for a continuously emitting
source (or sources) (this table is always generated).
3.4.2 Direct Access Disk Output
Table 3-1 describes the logical unit file structure of the direct
access disk storage system expected by MESOPLUME. (Model outputs are
only directed to direct access disk storage when 'LSAVE' = .TRUE.) As
indicated, six distinct direct access files are used, each with indepen-
dent size characteristics; these file characteristics are currently
frozen within the MESOPLUME code itself via Fortran "Define File" state-
ments and^ may require specific modification for adaptation t£ the user's
host system.
Concentration Files 21 and 22 contain the S02 and SO^ concentration
fields for each output time step. An identifying header record iden-
tical to the format used for File 15 (see File 15 description below)
precedes the concentration fields. Files 21 and 22 can accommodate a
maximum of 1,800 records each; therefore, for example, the results of 10
independent model simulations, each of 179 hours duration with hourly
output, will fit exactly within the 1,800 records allotted.
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ENVIRONMENTAL RESEARCH S TECHNOLOGY INC
TABLE 3-1
LOGICAL UNIT FILE STRUCTURE
Device Logical
Unit Number
13
15
21
22
23
24
File Name
Library
NAMELIST
SO Concentration
SO. Concentration
Run Number
Pointer
Record Structure
25 records,
14 words/record
25 records,
800 words/record
1,800 records,
1,610 words/record
1,800 records,
1,610 words/record
1 record,
2 words
1 record,
4 words
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
The library file (13) contains a single-record descriptor for each
model run; File 13 can accommodate a maximum of 25 independent model
runs. A hard copy printout of the library file produced by MESOFILE is
shown in Figure 4-2 in the next section.
The parameters defining each model simulation run are as follows:
INUMB - a model run number which is automatically assigned
by the direct access disk output subroutine.
DATFLD - The date of execution (day, month, and year) of the
model run [not the day (or days) simulated] .
MODEL
YR/DAY/HR
NGRIDS
IAVG
ISAVEF
The particular mesoscale dispersion model chosen
from the integrated modeling mesoscale system--in this
case, MESOPLUME.
The year, Julian day, and hour on which the model
simulation begins (Note that the first gridded
concentration field is not output until ' ISAVEF'* 'DTIME'
hours subsequently, where 'DTIME' is the basic
MESOPLUME time step (hours) and 'ISAVEF' is defined
below) .
The total number of concentration arrays output for
each pollutant during the model simulation
('NGRIDS' = 'NADVTS'/' ISAVEF1 where 'NADVTS' is the
total number of time steps in the model simulation.)
The concentration array averaging frequency in terms
of the basic time step, 'DTIME'.
The concentration array direct-access disk output
frequency in terms of the basic model time step,
'DTIME'.
I SAFE
The direct access address of the duplicate File 15
record.
IBEGIN - The direct access address of the first of the 'NGRIDS'
number of arrays output to File 21 for SO and
File 22 for SO".
ISTOP - The direct access address of the last of the 'NGRIDS'
number of arrays to File 21 for SO and File 22 for
S0=.
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ENVIRONMENTAL RESEARCH S. TECHNOLOGY INC
ICHECK - The run termination status indicator 'ICHECK' = 1
indicates the model simulation terminated normally;
'ICHECK' = 0 indicates it terminated abnormally. In
the latter case, the results of this run must be
deleted from the file management system using the
program BACKUP01 (see Scire et al. 1979) before
another run is made.
NAMELIST File 15 contains a one-record detailed description of the
parameters used for each model run. Each record in File 15 contains:
(1) a duplicate of the corresponding FILE 13 record for the run and
(2) the NAMELIST parameters used to make the run (also output to the
line printer).
Finally, the Run Number and Pointer Files (23 and 24) preserve
information between runs necessary to maintain proper archival sequen-
cing of run outputs — they are invisible to the user.
3.5 Execution Time and Core Requirements
The time required for a MESOPLUME simulation is directly propor-
tional to the number of time steps N and varies linearly with the number
of sources S. Various meteorological factors—for example, lower trans-
port wind speeds--cause an increase in the average number of plume
segments resident on the grid and therefore an increase in running
costs. For typical meteorological conditions within the Four Corners
grid, and for the spatial extent and grid resolution used for these
experiments, MESOPLUME model run times on an IBM 370/158 can be esti-
mated by:
t(sec) = 0.17(S)(N) + 0.40 (N) (3-1)
On the same installation, MESOPLUME required 230k bytes of core during
execution.
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
4. TEST CASE FOR MESOPLUME
A test case is provided in this section to familiarize the user
with the operation of the MESOPLUME model and to verify that the model
is running properly on the host system. The test case included here is
a 3-day sequence of MESOPLUME model output for the time period 0000
Greenwich Mean Time (GMT) June 14, 1978 through 0000 GMT June 17, 1978
in the Four Corners area of the southwestern United States [see Bass et
al. (1979)]. A 10-source partial emission inventory was selected from
the full set of energy-related major point sources of S02 in the Four
Corners area.
Figure 4-1 contains the run control parameters and emission source
inventory (input on logical unit 5) for this test case. Note that most
of the model input variables assume their default values. Appendix A
contains the entire input data set used by the MESOPAC preprocessor to
generate the meteorological fields that were subsequently read in by
MESOPLUME (on logical unit 2). Appendix A also contains a very small
portion of the MESOPAC model output — included to aid the user in veri-
fying the full test case.
The full set of line printer output for this MESOPLUME test case is
included in Appendix B. Descriptions of the model output fields
assigned to the direct access disk storage system (as echoed in hard
copy form by the MESOFILE system) are shown in Figure 4-2 (the library
file) and in Appendix C (the NAMELIST file). In this example, the
results from the MESOPLUME test case are stored together with the
results from the MESOPUFF and MESOGRID test cases as discussed in the
MESOPUFF user's manual (Benkley and Bass 1979) and the MESOGRID user's
manual (Morris et al. 1979). Appendix C also shows the NAMELIST file;
there, the library record NAMELIST input parameters and emission source
data are stored for future reference.
The MESOFILE postprocessing system (Scire et al. 1979) operates on
the direct access disk output files produced by MESOPLUME, to produce
various forms of result analyses. Figure 4-3 illustrates one such
option—a Calcomp contour plot of the 24-hour average ground-level S02
concentration field for June 16, 1978 as obtained from the test case.
(Concentration isopleth values are not printed directly on the contour
plot, but the user can generate an equivalent line printer plot, as
illustrated in Figure 4-4, which does include the actual isopleth
values.) Figure 4-5 illustrates the bulk statistical descriptors
(described by Bass et al. 1979) used to compare, for example, two time
sequences of model result fields. In this example, the base case 24-hour
average ground-level S02 concentration fields for June 16 computed by
the MESOPUFF model are compared to those computed by the MESOPLUME
model.
4-1
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
&MUDEL LPLUME=.1RUE. KENU
&CUNTK NAUVTSS7C1 K.EM)
*GkIU aENU
4SIGMA SEND
6METO METSWT=7816500,METCOD=10U3
&PLUM1 8.END
SEND
AEND
&OUTPT LSAVE=.
&SOUKC NSUURC=1U
8.8
SEND
NPLUM2
236.
152.
7b.ci
183.
175.
152.
23.a
17.2
23.7
13.5
10.0
ft.O
9.7
0.9
1.5
KENU
b.8
14.0
Iti (j
O 0 7
13.0
1.2
13.1
19.b
15.2
8.0
2560.
343.H
633.4
4«8.
53.7
320.
146.9
1310.8
268.8
1011.
0
0
0
0
0
0
0
0
0,
0
6397.
1503.
2847.
3353.
3016.
6222.
3016.
1047.
11144,
Figure 4-1 MESOPLUME Test Case Parameter and Emission Source
Inventory Input
4-2
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ENVIRONMENTAL RESEARCH* TECHNOLOGY INC
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4-3
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ENVIRONMENTAL RESEARCH 8 TECHNOLOGY INC
Day 167 MESOPLUME
Figure 4-3 MESOPLUME 24-Hour Average S02 Concentration Calcomp
Plot, 16 June, 1978 (from MESOFILE)
4-4
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
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ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
REFERENCES
Bass, A., C. W. Benkley, J. S. Scire, and C. S. Morris. 1979.
Development of Mesoscale Air Quality Simulation Models. Volume I.
Comparative Sensitivity Studies of Puff, Plume, and Grid Models
for Long-Distance Dispersion. EPA 600/7-79-XXX, Environmental
Protection Agency, Research Triangle Park, NC, 185 pp.
Benkley, C. W. and A. Bass 1979a. Development of Mesoscale Air Quality
Simulation Models. Volume 3. User's Guide to MESOPUFF (Mesoscale
Puff) Model. EPA 600/7-79-XXX, Environmental Protection Agency,
Research Triangle Park, NC, 124 pp.
Benkley, C. W. and A. Bass. 1979b. Development of Mesoscale Air
Quality Simulation Models. Volume 6. Users Guide to MESOPAC
(Mesoscale Meteorology Package). EPA 600/7-79-XXX, Environmental
Protection Agency, Research Triangle Park, NC, 60 pp.
Benkley, C. W. and L. L. Schulman. 1979. Estimating Hourly Mixing
Depths from Historical Meteorological Data. J. Appl. Meteor.
18:772-780.
Briggs, G. S. 1975. Plume Rise Predictions. Lectures on Air Pollution
and Environmental Impact Analyses. American Meteorological Society,
Boston, MA, p. 59-111.
Egan, B. A., K. S. Rao, and A. Bass. 1976. A Three-Dimensional
Advective-Diffusive Model for Long Range Sulfate Transport and
Transformation. Seventh International Technical Meeting on Air
Pollution Modeling and its Application, Airlie, VA, September 7-10,
1976, p. 697-714.
Hales, J. M., D. C. Powell and T. D. Fox. 1977. STRAM-An Air Pollution
Model Incorporating Non-linear Chemistry, Variable Trajectories,
and Plume Segment Diffusion. EPA 450/3-77-012, Environmental
Protection Agency, Research Triangle Park, NC, 147 pp.
Heffter, J. L. 1965. The Variations of Horizontal Diffusion Parameters
with Time for Travel Periods of One Hour or Longer. J. Appl.
Meteor. 4:153-156.
Morris, C. S., C. W. Benkley and A. Bass. 1976. Development of Meso-
scale Air Quality Simulation Models. Volume 4. User's Guide to
MESOGRID (Mesoscale Grid) Model. EPA 600/7-79-XXX, Environmental
Protection Agency, Research Triangle Park, NC, 85 pp.
Scire, J. S., J. E. Beebe, C. W. Benkley and A. Bass. 1979. Develop-
ment of Mesoscale Air Quality Simulation Models. Volume 5. User's
Guide to the MESOFILE Postprocessing Package. EPA 600/7-79-XXX,
Environmental Protection Agency, Research Triangle Park, NC, 67 pp.
-------
ENVIRONMENTAL RESEARCH & TECHNOLOGY INC
REFERENCES (Continued)
Start, G. E. and L. L. Wendell. 1974. Regional Effluent Dispersion
Calculations Considering Spatial and Temporal Meteorological
Conditions. NOAA Tech. Memo. ERL-ARL-44, National Oceanic and
Atmospheric Administration, Washington, DC, 63 pp.
Turner, D. B. 1970. Workbook of Atmospheric Dispersion Estimates. U.S.
Dept. of H.E.W, Public Health Service, Publ. 999-AP-26, 88 pp.
-------
APPENDIX A
TEST CASE MESOPAC INPUT AND OUTPUT
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-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO
EPA-600/7-79-XXX
3. RECIPIENT'S ACCESSIOWNO.
4 TITLE AND SUBTITLE
Development of Mesoscale Air Quality Simulation Models.
5. REPORT DATE
September 1979
Volume 2.
Model
User's Guide to MESOPLUME (Mesoscale Plume)
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Carl W. Benkley
Arthur Bass
8. PERFORMING ORGANIZATION REPORT NO
9 PERFORMING ORGANIZATION NAME AND ADDRESS
Environmental Research £ Technology, Inc.
696 Virginia Road
Concord, MA 01742
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
03-6-022-35254/NOAA Contract
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Sciences Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park. NC 27711
13. TYPE OF REPORT AND PERIOD COVERED
Contract Report
14. SPONSORING AGENCY CODE
EPA-600/7
15. SUPPLEMENTARY NOTES
Performed under contract to the National Oceanic and Atmospheric Administration
16. ABSTRACT
MESOPLUME is a variable-trajectory regional-scale Gaussian segmented plume
model especially designed to simulate the air quality impacts of multiple point
sources at long transport distances. It has been developed to answer the need for a
simple, computationally practical, easy to use and flexible mesoscale point source
model - suitable for decision-making and regulatory applications - particularly at
transport distances beyond the range of applicability of the conventional (Turner
Workbook) straight-line Gaussian plume model. MESOPLUME is a natural generalization
of the conventional Gaussian plume model to situations in which plume transport and
diffusion may be dominated by spatial and temporal variations in mesoscale meteorology
Highly user-oriented,MESOPLUME provides a range of flexible options, and its
clean, modular structure permits further modifications with ease. It is designed to
be driven by user-specified meteorological scenarios, of arbitrary duration, con-
structed by a suitable meteorological preprocessor model (e.g., MESOPAC). It out-
puts spatially-gridded concentration arrays averaged over arbitrary time intervals
of one hour or more and is designed to be coupled to a postprocessor model (e.g.,
MESOFILE) to provide additional graphical and statistical analyses. Routines are
provided for: plume rise; plume growth; fumigation; linear conversion of S00 to
SO • anH H-ry rlppngi t i rvn nf ?f\ pnrl ^fl 2
17.
CEY WORDS^AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
* Air Pollution
* Algorithms
Atmospheric Models
Atmospheric Diffusion
Transport Properties
13B
12A
04A
07D
14B
18. DISTRIBUTION STATEMENT
Release to Public
19. SECURITY CLASS (ThisReport)
Unclassified
21. NO. OF PAGES
141
20. SECURITY CLASS (Thispage)
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
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significant bibliography or literature survey, mention it here.
17. KEY WORDS AND DOCUMENT ANALYSIS
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