JUNE 1985
USER'S GUIDE FOR THE ADVANCED STATISTICAL TRAJECTORY
REGIONAL AIR POLLUTION (ASTRAP) MODEL
ATMOSPHERIC SCIENCES RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
ENVIRONMENTAL PROTECTION AGENCY
RESEARCH TRIANGLE PARK, NORTH CAROLINA 27711
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USER'S GUIDE FOR THE ADVANCED STATISTICAL TRAJECTORY
REGIONAL AIR POLLUTION (ASTRAP) MODEL
by
Jack D. Shannon
Environmental Research Division
Argonne National Laboratory
Argonne, IL 60439
Interagency Agreement Number
DW930060-01
Project Officer
Terry L. Clark
Meteorology and Assessment Division
Atmospheric Sciences Research Laboratory
Research Triangle Park, NC 27711
ATMOSPHERIC SCIENCES RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
ENVIRONMENTAL PROTECTION AGENCY
RESEARCH TRIANGLE PARK, NORTH CAROLINA 27711
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NOTICE
The information in this document has been funded by the United States
Environmental Protection Agency under Interagency Agreement Number DW930060-
01 to Argonne National Laboratory. It has been subject to the Agency's
peer and administrative review, and it has been approved for publication
as an EPA document. Mention of trade names or commercial products does
not constitute endorsement or recommendation for use.
ii
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ABSTRACT
The Advanced Statistical Trajectory Regional Air Pollution (ASTRAP)
model simulates long-range, long-term transport and deposition of air
pollutants, primarily oxides of sulfur and nitrogen. The ASTRAP model is
designed to combine ease of exercise with an appropriate detail of physical
processes for assessment applications related to acid deposition.
The theoretical basis and the computational structure of the ASTRAP
model are described. The model is evaluated with observed data, and an
illustrative exercise is provided. Computer codes of the model subpro-
grams are provided in appendices.
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CONTENTS
Abstract i i i
Figures vi
Tables vii
Symbols and Abbreviations viii
Acronyms ix
Acknowledgements x
Executive Summary 1
1. Introduction 3
2. Data Requirements 5
3. Features and Limitations 7
4. Technical Description 8
Horizontal Dispersion 8
Vertical Dispersion. 12
Concentration and Deposition 21
Preprocessors and Postprocessors 23
Evaluation 32
5. Computer Aspects 32
6. Test Application 39
References 45
Appendix A: Program HORZ listing 48
Appendix B: Program VERT listing 59
Appendix C: Program CONCDEP listing 70
v
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FIGURES
Number Page
1 Grid systems for ASTRAP input and output 5
2 Time series of horizontal dry puff densities 10
3 Time series of horizontal wet puff densities. 11
4 Vertical eddy diffusivity for summer conditions in ASTRAP 113
5 Diurnal patterns of SCU dry deposition velocities in ASTRAP 14
6 Diurnal patterns of SO^ dry deposition velocities in ASTRAP 15
7 Diurnal patterns of N0/N02 dry deposition velocities in ASTRAP... 16
8 Diurnal patterns of NO-j dry deposition velocities in ASTRAP 17
9 Diurnal patterns of the rate of transformation of SCU to
S04 in ASTRAP 18
10 Diurnal patterns of the rate of transformation of NO/NC^ to
N03 in ASTRAP 19
11 Comparison of ASTRAP simulations with observed annual wet
sulfate deposition during 1980 26
12 Comparison of ASTRAP simulations with observed annual wet
nitrate deposition during 1980 27
13 Comparison of ASTRAP simulations with observed average SOo
concentrations for January and July 1978. 28
14 Comparison of ASTRAP simulations with observed average SO^
concentrations for January and July 1978 29
15 Comparison of ASTRAP simulations with observed wet sulfate
deposition for January and July 1978 30
16 ASTRAP program structure 33
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TABLES
Number Page
1 Early dispersion parameterization limits 21
2 ASTRAP wet deposition verification statistics 31
3 ASTRAP verification statistics for 1978 simulations 31
4 ASTRAP input 34
5 ASTRAP input/output files 38
6 ASTRAP test case S02 calculations 40
7 ASTRAP test case SO^ calculations 41
8 ASTRAP test case dry deposition calculations 42
9 ASTRAP test case wet deposition calculations 43
10 ASTRAP test case integrated deposition budget 44
VII
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SYMBOLS AND ABBREVIATIONS
V
H (x,y)
Hw (x,y)
R
P
N
M
b
L
E
c
C
n
m
d
D
W
S.D.
Ob
Mod
Res
Pom
z
mean position of a puff on the horizontal grid
one standard deviation spread of puff in x and y
correlation between x and y in the puff
two-dimensional density function of horizontal dry puff
two-dimensional density function of wet-deposited puff
fractional bulk removal by precipitation
precipitation rate in cm/6 hr
number of source cells
fraction of unit emission still aloft
M in the case of no previous wet deposition
number of emission layers
emission rate in tonnes per six hours
one-dimensional surface concentration (yg m~ )
three-dimensional surface concentration (ug m )
number of trajectories contributing to a dry deposition statistic
number of trajectories contributing to a wet deposition statistic
dry deposition increment per time step
_o
cumulative dry deposition (g m )
_o
cumulative wet deposition (g m )
standard deviation
observed value
modeled value
residual (Ob-Mod)
correlation between observations and modeled values
vertical eddy diffusivity in meters squared per second
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ACRONYMS
ASTRAP Advanced Statistical Trajectory Regional Air Pollution model
VERT VERTical dispersion subprogram
HORZ HORZontal dispersion subprogram
CONCDEP CONCentration and DEPosition subprogram
NMC National Meteorological Center
AES Atmospheric Environment Service of Canada
SURE SUlfur Regional Experiment
MOI Memorandum of Intent between the U. S. and Canada on trans boundary
air pollution
UTM : Universal Transverse Mercator map projection
LRT : Long-Range Transport and deposition
NAPAP : National Acid Precipitation Assessment Program
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ACKNOWLEDGMENTS
This research has been funded as part of the National Acid Precipitation
Assessment Program by the U. S. Environmental Protection Agency, under
Interagency Agreement DW 89930060-01 between the U. S. Environmental
Protection Agency and the U. S. Department of Energy.
Dr. Barry Lesht has provided many helpful suggestions. The assistance of
Mr. Steve Fine in reorganizing the coding was extremely valuable. Typing of
the user's guide by Mrs. Rebecca Spencer and Ms. Anh Tran is gratefully
acknowledged. Critical reviews by Mr. Terry Clark and Ms. Joan Novak have
been quite useful.
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EXECUTIVE SUMMARY
The Advanced Statistical Trajectory Regional Air Pollution (ASTRAP) model
calculates atmospheric concentrations and deposition of oxides of sulfur or
nitrogen over spatial scales of 100 km to continental, and over temporal
scales of one to three months.
The model assumes linearity between emissions and deposition, although
the linear rate itself varies diurnally and seasonally and can be changed as a
user's option. ASTRAP is not an appropriate model for short-term or episodic
conditions, nor for near-source air quality. Yearly simulations can be made
by averaging atmospheric concentrations and adding deposition from seasonal
simulations.
ASTRAP is structured here to treat as many as 730 source cells with up to
six emission layers and to make calculations for a 51 x 45 grid across the
United States and Canada, with grid spacing 100-130 km, depending upon
latitude. Preprocessed emission fields and time series of continental wind
and precipitation analyses are required in order to exercise the model. If
the user desires to change the size of the emission grid or the
concentration/deposition grid, simple coding changes would be necessary.
The deposition fields are integrated across each state and province in
turn, but in this version of the code all sources are in a single inventory.
If the concentration and deposition from individual sources or source areas
are to be produced separately as in a source-receptor transfer matrix, some
coding changes would be required.
The subprograms in ASTRAP do not contain simple options to select the
modeling alternatives discussed above, but guidance as to particular changes
that might be necessary is given in the section on applications.
Among the features of ASTRAP are the following:
efficient long-term simulations of continental scale.
diurnal and seasonal variations of dry deposition velocities,
chemical transformation rate, and vertical stability profile.
layers in the vertical to allow for the effect of different
effective emission heights.
use of analyzed synoptic meteorology, to allow examination of
meteorologically induced variability.
integrated deposition fields.
leakage to the free troposphere.
The ASTRAP model consists of three major subprograms, VERT, HORZ, and
CONCDEP, which calculate one-dimensional vertical profiles, two-dimensional
horizontal dispersion, and concentrations and deposition, respectively. On an
IBM 3033, VERT requires 210K (bytes) storage and 5-10 minutes of computation
time, plus one output file, for a seasonal simulation. HORZ requires 500K
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storage and 10 minutes computation time, two input tape drives, and an output
file for a seasonal simulation. CONCDEP requires 500K storage and 10 minutes
computation time, an emission file, the output files from the other two
programs, a file used to identify each receptor cell as to state or province,
and an output file for a seasonal simulation. If only the emission inventory
or the receptor grid is changed, only CONCDEP need be rerun. If the year of
meteorology is changed, both CONCDEP and HORZ must be rerun. If the season of
meteorology is changed, all subprograms must be rerun.
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1. INTRODUCTION
Acid rain, as the atmospheric deposition of acidifying substances is
commonly termed, is only one of many potential environmental and health
problems involving long-range transport and deposition of airborne
pollutants. Investigations of the transport and deposition of many different
substances over a wide range of spatial and temporal scales eventually may be
necessary, in order to develop and improve assessments of potential actions
and regulations. A common approach in such investigations is numerical
simulation of the atmospheric pathways. The Advanced Statistical Trajectory
Regional Air Pollution (ASTRAP) model described here has been developed to
simulate the long-term (monthly to yearly), regional-scale (resolution about
100 km) deposition of oxides of sulfur and nitrogen, the major contributors to
acid deposition. Many of the modeling techniques in ASTRAP are based upon
previous work by Taylor (1921), Durst et al. (1959), Bolin and Persson (1975),
and Sheih (1977). The ASTRAP techniques can be extended to other pollutants,
provided that linear parameterizations of chemical transformation and removal
processes are suitable, and can be extended to other spatial scales, if
appropriate meteorological data can be obtained. Unmodified extension of the
modeling techniques used in ASTRAP to shorter, episodic temporal scales is not
recommended, because of certain statistical features of the model, which are
discussed in following sections.
The ASTRAP model consists of three submodels and various preprocessors
and postprocessors. Use of particular preprocessors and postprocessors
depends upon application and data availability. The submodels are the
vertical diffusion program (VERT), the horizontal dispersion program (HORZ)
and the concentration and deposition program (CONCDEP). The processes of
vertical diffusion, chemical transformation, and dry deposition are simulated
in VERT through parameterizations that are independent of horizontal location
or particular meteorological conditions. In HORZ, the processes of horizontal
advection, horizontal diffusion, and wet deposition are simulated with the use
of time series of wind and precipitation analyses. CONCDEP combines the
statistics produced by the first two programs with emission inventories to
produce fields of average atmospheric concentration and cumulative deposition.
Data requirements for ASTRAP simulations are presented in Section 2,
while the features and limitations of ASTRAP are sketched in Section 3. The
theoretical basis of the ASTRAP model is outlined in Section 4. The primary
subprograms are described, preprocessors and postprocessors are discussed, and
model verification statistics are presented. Specific computer aspects are
discussed in Section 5 and a test simulation is presented in Section 6. The
computer codes of the subprograms, included in appendices, are written to be
exercised with meteorological data from 1980, as described in Section 4.
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2. DATA REQUIREMENTS
The data requirements for ASTRAP simulations are monthly to seasonal time
series of transport wind and precipitation analyses, and a source inventory.
Ideally, wind and precipitation analyses should be at intervals of six hours,
should have a spatial resolution of about 300 and 100 km, respectively, and
should extend well beyond the region of interest in order to allow accurate
calculations of recurving trajectories. Unfortunately, these desirable
features are not always available. Rawinsonde upper-air wind measurements are
made routinely at intervals of 12 hours rather than six hours, and the spatial
coverage of rawinsonde stations, while adequate over the United States and
southern Canada, is sparse over northern Canada and essentially missing over
oceanic areas. This can cause serious difficulties in obtaining wind fields
suitable for deposition simulations for Florida or the Canadian Maritime
Provinces, for instance. A similar difficulty can arise from inadequate
spatial coverage of precipitation fields. The temporal resolution of
precipitation data is usually better than for upper air winds, since many
stations report hourly precipitation.
Analysis of a one-layer transport wind field requires definition of the
depth of the field. There is no universally accepted transport layer, since
over a single location fresh pollutants might be transported in a growing
planetary boundary layer while aged pollutants might be transported in a
deeper layer defined by the previous day's maximum mixing depth upstream.
Definition of a mixed layer depth is adversely affected also by the practice
of making routine soundings only twice a day, as there is a typical diurnal
cycle of planetary boundary growth and decay.
The objective analysis of observed winds to produce a gridded analysis
does filter small-scale wind variations from the field, but that does not
appear to be a major cause of uncertainty for long-term simulations. In
gridding precipitation observations, however, frequency of precipitation
occurrence is increased as the grid increment increases, since a cell average
is non-zero if any one of perhaps several observation stations within the cell
is non-zero. Without adjustment by a threshold value, this could lead to
overprediction of wet deposition by ASTRAP, since the wet removal
parameterization was developed from observations at a point, not across a
grid, and is a function of both precipitation frequency and amount.
Initial preparation of wind and precipitation fields for ASTRAP
simulations has been performed at the University of Michigan by Professor
Perry Samson. His wind fields, produced every 12 hours, are for 500 m layers
to 3000 m MSL for a 17 x 19 horizontal grid of National Meteorological Center
(NMC) spacing. (The NMC polar stereographic projection, aligned along 80° W
and true at 60° N with 381 km spacing there, reduces to about 306 km spacing
at 30° N.) The wind grid is the coarser grid shown in Figure 1, with values
of west-east and south-north components at the cell centroids (i.e.,
components not aligned along grid system). The corners of the grid, too far
from rawinsonde stations for the analysis method, are coded as missing values,
as are layers that were in effect below the elevation of the terrain. Speed
is given in meters per second. Further processing has been necessary at
Argonne before the analyses could be used in ASTRAP simulations. For spring
and summer, the bottom 3 non-missing values have been averaged at each
gridpoint; for fall and winter the bottom 2 non-missing values have been
-4.
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Figure 1. Grid system for ASTRAP input and output: wind field analyses
(centers of large cells); precipitation analyses (centered about
intersections of small cells with complete coverage within dashed
rectangle); trajectory virtual sources (); emission field and
concentration/deposition fields (centers of small cells; not all
cells contain emissions). North Pole (large dot) is the (0,0)
point of the grid system, with X increasing to right and Y
increasing downward.
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averaged. For grid points still with missing value indicators, extrapolation
has been used to produce wind values. Finally, linear temporal interpolation
has created fields at 6-hr intervals, and the wind analyses have been combined
into files three months long (Dec - Feb, Mar - May, Jun - Aug, and Sep -
Nov). The wind components are converted to components along the NMC axes
internally in the HORZ subprogram.
The precipitation analyses produced at the University of Michigan are on
a 50 x 45 grid with 1/3 NMC spacing, as outlined in Figure 1. The original
analyses are hourly, with a special code for missing values. Precipitation
amount is in millimeters. At Argonne the hourly fields have been added to
produce 6-hrly fields, with missing values filled in by interpolation and
extrapolation. The precipitation data are organized in monthly files. Any
missing fields of either wind or precipitation have been replaced by temporal
interpolations in the processing at Argonne, so there are no date-time
indicators written with the fields.
For the version of ASTRAP in this user's guide, an SOo inventory has been
created in which the seasonal emissions in kilotonnes are given by effective
emission layer (0-100 m, 100-200 m, 200-300 m, 300-400 m, 400-600 m, and 600-
800 m) and NMC position of the lower left corner of the grid cell (the NMC
coordinate system is 0,0 at the north pole). No separate SO/ emission
inventory is used, so primary sulfate emissions factors are applied in
CONCDEP. The primary sulfate emission factor for sources in the bottom layer
is assumed to be 0.05 (i.e., one unit of SO equivalent emission is treated as
0.95 units of S02 and 1.5(0.05) = 0.075 units of primary sulfate, where the
1.5 factor arises because the ratio of the molecular weight of sulfate to that
of SOn is 96/64); the primary sulfate emission factor for elevated sources is
assumed to be 0.03 in Florida and in the Northeast, and 0.015 elsewhere. The
emission grid is the same spacing as the precipitation grid, but is irregular
because the inventory is organized by state and province. The emission
information has been derived from a preliminary version of the National Acid
Precipitation Assessment Program (NAPAP) inventory for 1980 produced for the
1985 Assessment. Wind, precipitation, and emission data are all written in
binary format for efficiency.
A different emission inventory for ASTRAP would need to be preprocessed
to the form described above, or some of the model code would require changes.
If an NO inventory were to be used, this version of ASTRAP would expect the
inventory to be expressed as equivalent kilotonnes of N0~, and it would
require a different output from program VERT (described in the Vertical
Dispersion subsection of the Technical Description section).
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3. FEATURES AND LIMITATIONS
The ASTRAP model can be applied in emission policy assessments, for which
the model can be exercised to predict how deposition fields would change in
response to changes in the pollutant emission field (Streets et al., 1983).
This assumes linearity between emissions and deposition. In assessment of
future actions, the specific meteorological conditions and short-term emission
variations that will occur are not known, and thus an expected or long-term
average deposition field, such as provided by ASTRAP, is generally
appropriate. The model can be used to estimate concentration and deposition
at specific receptor locations, and can estimate the individual contribution
from different sources or source regions.
The following major simplifications and assumptions have been
incorporated into the ASTRAP model:
1. Long-term horizontal dispersion and long-term vertical dispersion can
be simulated independently.
2. Long-term horizontal diffusion can be approximated by the spread of
plume centerlines, with small-scale diffusion about individual plumes
ignored.
3. Chemical transformation can be parameterized as a linear first-order
process.
4. Wet removal is a function of the half-power of the precipitation
amount over 6 or 12 hours.
5. Transport is two-dimensional.
6. The dry deposition parameterization is horizontally uniform (not
intrinsic to the ASTRAP approach but a part of the ASTRAP version
described here).
The separation of calculations of horizontal and vertical dispersion in
ASTRAP into two-dimensional horizontal processes and one-dimensional vertical
processes is much lower in computational requirements than three-dimensional
Eulerian numerical modeling, while the assumption of independence of
horizontal and vertical dispersion processes over periods of a month or longer
appears to give acceptable results. Because of the separation of the
calculations of horizontal and vertical dispersion, however, any
parameterization occurring solely in VERT has no horizontal variation, and any
parameterization occurring entirely in HORT is independent of the height of
release.
The "statistical trajectory" in ASTRAP refers to the treatment of
horizontal dispersion. Rather than diffusion of individual plumes, the
distribution of the plume centerlines resulting from synoptic scale variations
in transport wind fields is calculated. For monthly and longer-term
dispersion over the regional scale, the latter process is dominant. For
episodic and shorter time scales, however, ASTRAP should not be used without
modification to account for plume spreading.
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4. TECHNICAL DESCRIPTION
HORIZONTAL DISPERSION
In order that HORZ need be exercised only once for a particular
meteorological period, regardless of the number of emission scenarios
examined, horizontal dispersion statistics are calculated in HORZ for a
virtual source grid covering all of the contiguous states and the Canadian
provinces, and subsequently interpolated when concentrations and deposition
are calculated. The grid is of National Meteorological Center (NMC) spacing,
i.e., 381 km at 60° N, with 123 virtual sources as indicated in Fig. 1.
The meteorological data are time series of horizontal transport wind and
precipitation fields, as described in the data requirements section. The wind
fields are organized in quarterly files, while the precipitation data are
organized in monthly files.
Since similar calculations are made for each virtual source, this
discussion focuses on a single virtual source. For a one- to three-month
sequence of meteorological analyses, simulated tracers of unit mass are
released from the source at intervals of six hours. Calculations in HORZ are
independent of the height of release. The tracers are tracked for 28 time
steps (seven days), unless they leave the wind grid. At each successive
tracer position, the precipitation field is checked to see whether there
should be wet removal.
Positions are determined for the midpoints of the six-hour segments, and
thus the first trajectory segment is three hours long, while all subsequent
segments are six hours long. The statistics calculated in HORZ are ensemble
statistics, where each ensemble represents all trajectory positions
(endpoints) of a particular plume age (time since release) for each source for
the length of the meteorological analysis.
The statistics generated for a puff describing the density of the
ensemble of equal age trajectory endpoints are the coordinates y and y of
the mean position, the standard deviations o and
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concentration and deposition are calculated. For efficiency, however,
extremely low contributions outside approximately 2.0 - 2.5 standard
deviations of plume spread are skipped. The contributions at the other points
are scaled upward by 1.046 to compensate for the mass lying outside the
threshold puff density.
The wet deposition parameterization is based upon the work of Hicks and
Shannon (1979), but the coefficients have been modified to take into account a
different analysis method for the precipitation fields. The parameterization
is
min
o
0.5 P..1/2 M .
0.5 M '
±.
o.i
where R. . is the fractional pollutant deposition at time step (plume age) j of
the tracer mass from virtual source i, P^. is the six-hour precipitation in
centimeters encountered by the tracer during time step j , and M. . is the
initial normalized mass less the accumulated wet removal before time step j.
A mass equal to the wet deposition (or 1/2 of the removal from the mixed
layer) is shifted to the free troposphere, where it is subject to subsequent
wet deposition processes but no longer contributes to dry deposition nor to
atmospheric surface concentrations. Because of the "min" function,
precipitation amounts greater than 1 cm have the same effect as a 1-cm amount,
while because of the threshold, amounts less than or equal to 1 mm have no
removal effect.
As previously mentioned, there are separate sets of statistics for wet
and dry tracer ensembles. The same trajectory endpoints are used in
calculation for each corresponding pair of wet and dry ensembles, but the
weights are different (most of the wet ensemble have zero weight) , and thus
the wet and dry ensemble puffs differ. The ensemble puffs for dry deposition
and surface concentration tend to be more regular in the trend of their
overlapping positions than do the wet deposition puffs. This is because wet
removal is a highly irregular process and thus exhibits more statistical
variation for a single season. The mathematical description representing each
puff j from source i is a bivariant normal density function (Mood and
Graybill, 1963) :
, ^
H(x'y)
I
a ,, 2
x y /l-pxy
where x and y are any receptor coordinates and the terms y , y , a , a , and
P are functions of source and plume age. Examples of sequences of 28 puffs
are shown in Figs. 2 and 3 for dry and wet processes, respectively, from a
hypothetical Oklahoma source during summer of 1980. Weighting of the mass
associated with each puff is not shown here; older puffs have less mass
because of wet and dry removal enroute. Sometimes an apparent curl in the
sequence of mean dry puff centroids during the last time steps is produced as
a result of tracers beginning to exit the boundaries of the wind fields. The
endpoint ensembles thus become "slow" subsets of preceeding ensembles. It is
clear that the dry puffs describe the mean dry plume better than the wet puffs
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DRY DEPOSITION PUFFS SUMMER 1980
'-|N
I \
\
/»
ft
x'l
> J>
/
X
x >
/ -s
/N
/ V
^/^
I
\A-
x '
/ / * i '
* i "^ '
N' ' ^<.
/
> /
\ > /
\ l
» »'x
'
/ >
/ /
/
/
'>..
/ 7--*
Y--J '
/
/- ^
\ - ' "
^ .--* ;<
" -' !'
/*^0.- i .
/ *
' ~*
!'*.*-- '-,
>, -. V r -. , >
X* ' " V--'
# ' \ ,. ,
^S ' ,
\
\ t
\ t
N |
Figure 2. Time series of horizontal dry puff densities from a hypothetical
source in Oklahoma, contoured at one standard deviation. Puff
centroids are indicated by ().
-10-
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WET DEPOSITION PUFFS SUMMER 1980
Figure 3. Time series of horizontal wet puff densities from a hypothetical
source in Oklahoma, contoured at one standard deviation. Puff
centroids are indicated by (o).
-11-
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describe the wet deposition "plume". This is a statistical feature arising
because dry conditions are much more frequent. The average of a number of
summers would produce a smoother wet plume.
As in many atmospheric dispersion problems, plume resolution (here formed
by overlapping puffs) is most critical near the source. The basic model
calculations extend seven days in six-hour time increments, which means that
one of the major computational loops in the concentration and deposition
program would normally be exercised 28 times. Postprocessing statistical
trajectory and vertical integration results within CONCDEP before making
concentration and deposition calculations combines the 28 six-hour steps into
4 six-hour steps, 4 12-hour steps, and 4 24-hour steps, extending through the
same seven days of dispersion but reducing computation time by more than a
factor of two.
VERTICAL DISPERSION
The processes of turbulent vertical diffusion in the mixed layer, leakage
to the free troposphere, chemical transformation, and dry deposition are
calculated in program VERT of ASTRAP with a one-dimensional version of the
Gaussian Moment Conservation (QIC) technique (Shannon, 1979). The CMC version
here has nine layers plus a storage layer representing the free troposphere.
The diurnal variation or "breathing" of the planetary boundary layer,
i.e., the periodic decoupling and coupling of the surface and elevated
portions of the mixing layer by the cycle of nocturnal inversion formation,
intensification, lifting, and destruction by convective mixing from below, has
been characterized in field investigations (Hicks et al., 1981) and is
parameterized in VERT through a seasonally and diurnally varying stability
profile (vertical eddy diffusivity K~ specified for each layer). The vertical
resolution is that of the layer thicknesses (0-100, 100-200, 200-300, 300-400,
400-600, 600-800, 800-1000, 1000-1400, and 1400-1800 m). An example of input
KZ profiles (here two-hourly rather than hourly as in VERT) is provided in
Figure 4. The K values are roughly similar to the eddy viscosity
calculations of Yamada (1977), given the discrete layered structure of ASTRAP,
which acts as a filter for some turbulent eddies, and given that the ASTRAP
parameterizations represent the mix of sunny, well-mixed conditions with
cloudy, more stable conditions.
The variations in dry deposition associated with typical diurnal and
seasonal patterns of both atmospheric stability and surface resistances are
parameterized through average diurnal values of dry deposition velocities for
each season (Wesely and Hicks, 1977; Wesely and Shannon, 1984; Wesely et al.,
1984). The primary uncertainty arises in extending the measurements taken at
a few high quality, well characterized research sites to the continent as a
whole. The seasonal patterns currently used in ASTRAP for SOX and NOX species
are plotted in Figs. 5 through 8.
A diurnal variation in the linear first-order transformation rate of the
primary pollutant (S02 or NO/NO.,) to a secondary pollutant (SO, or NO.,"),
directly or indirectly due to photochemical activity patterns, is also
included (Gillani et al., 1981). The seasonal patterns, illustrated in Figs.
9 and 10 for the SO and NO pollutant systems as parameterized in ASTRAP, are
intended to include all chemical transformation pathways except those
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SUMMER VERTICRL EDDY DIFFUSIVITY
8.
£8.
-C CD
CD
OJ
CD
10
n q
300 600
900
1200
TIME
1500 1800 2100 2400
2 1
Figure 4. Time-height cross section of vertical eddy diffusivity in m s for
summer conditions in ASTRAP.
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SQ2 DEPOSITION VELOCITIES
oo
CJ
Q
CD
o
CM
O
O
o
0
8 12 16
LOCfll TIME
20
24
Figure 5. Diurnal patterns of SCU dry deposition velocities of ASTRAP for
winter (A), spring (+), summer (n), and autumn (o).
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SP4 DEPOSITION VELOCITIES
oo
CJ
CD
O
CM
CD
O
O
0
8 12 16
LOCRL TIME
20
Figure 6. Diurnal patterns of SO^ dry deposition velocities in ASTRAP for
winter (A), spring (+), summer (D), and autumn (o).
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NO/^02 DEPOSITION VELOCITIES
CO
z:
CJ
CD
O
^r
O
o
o
0
i
4
r
8
r
12
i
16
LOCRL TIME
20
I
24
Figure 7. Diurnal patterns of NO/NCU dry deposition velocities in ASTRAP
for winter (A), spring (+), summer (D), and autumn (o).
-16-
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ND3
o
GO
CJ
Q
CO
o
o
OJ
O
O
o
DEPOSITION
e-e-
VELOCITIES
0
8 12 16
LOCRL TIME
20
24
Figure 8. Diurnal patterns of NO-, dry deposition velocities in ASTRAP for
winter (A) spring and autumn (©) and summer (D).
-17-
-------
502-504 TRRNSFORMflTION RRTE
5
00-
CD-I
in-
CL.
8 12 16
LOCflL TIhE
Figure 9. Diurnal patterns of the rate of transformation of S02 to SO,
in ASTRAP for winter (A), spring (+), summer (Q), and autumn (o)
-18-
-------
MOX-
NITRRTE
TRRNSFORMRTION RRTES
en -
oo-
z: C£>H
r. i in
O_
o
0
8 12 16
LOCRL TIME
20
24
Figure 10.
Diurnal patterns of the rate of transformation of N0/N02 to NO., in
ASTRAP for winter (A), spring (+), summer (a), and autumn (o).
-19-
-------
associated with precipitating clouds. The rates of SOj transformation, for
example, are greater than normally found in clear-air experiments (Meagher and
Bailey, 1983) because the rates include the effects of chemical processing in
non-precipitating clouds (Hegg et al., 1984.)
Since most emissions from minor low-level sources are concentrated in
urban areas, where polluted air masses typically are more reactive chemically
(Alkezweeny and Powell, 1977) than in the less urbanized surroundings of many
tall-stack major sources, the chemical transformation rate in VERT is
increased during initial time steps for near-surface sources,
ATxr = 0.5 B(3 - A) (4 - t)/(2 - 0.5A); K3, t<4 (3)
0, otherwise
where ATxr is the increase in transformation rate in % hr , B is a constant
defined by the pollutant system (1.0 for S02, 0.5 for N02), I is the model
layer, t is the plume age in hours, and A is a seasonal constant (2 for
summer, 1 for spring and fall, 0 for winter).
The dry deposition velocities and low-level eddy diffusivities during the
first 2.5 hours of dispersion are not allowed to decline below arbitrarily
specified seasonal thresholds (Table 1), as urban areas are less prone to
strong nocturnal inversions than rural areas because of the local heat island
and aerodynamic roughness effects in the urban areas. Likewise, the dry
deposition velocity for SO^ during the first 2.5 hours of dispersion is not
allowed to rise above a specified seasonal maximum, since urban surfaces are
generally drier than rural surfaces. Elevated releases are less affected by
these changes than are low-level releases, because the diurnal stability
cycles in layers aloft are not modified, and the pollutant must diffuse
downward before dry deposition can take place. The dry deposition of primary
particles is set to 1.0 cm s during the first 2.5 hours to simulate some
gravitational settling of larger particles from small sources, presumed to be
inefficiently controlled.
Most modelers and field experimentalists agree that there must be
"leakage" from the mixed layer to the free troposphere through exchange
mechanisms such as fair-weather cumuli, but no definitive field experiments
quantifying such vertical transport mechanisms for pollutants on a regional
basis have been completed and analyzed. A parameterization for such a loss is
now included in ASTRAP, but the rates have been set at relatively low values
until such time as more is learned about the long-term regional significance
of the mechanism. (Loss to the free troposphere from the mixed layer via
precipitation-related processes is accomplished in subprogram HORZ by
"depositing" in the free troposphere 1/2 of the bulk wet removal from the
mixed layer, where it is subject to subsequent wet removal but not dry
removal.)
Since time evolution of incremental dry deposition and of the vertical
profiles of concentrations varies with the time of release, simulated releases
are made and diffused for seven days from each hour of the day in turn and
then averaged separately for emissions in each of the first six layers, and
stored in that form. Calculations could be made also for the top three model
layers, but normally no emissions start that high (800 m). In an earlier
version of ASTRAP (Shannon, 1981), a diurnal emission variation function was
-20-
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Table 1. EARLY DISPERSION PARAMETERIZATION LIMITS
Season
Summer
Autumn
Winter
Spring
Vd2 (max) '
0.50
0.40
0.40
0.40
7d2 (min)
0.30
0.15
0.15
0.20
Vd4 (min)
0.20
0.20
0.20
0.20
Kz (min)
3.0
3.0
3.0
3.0
7^2 and Vd4 are tne dry deposition velocities of S02 and SO^, respectively, in
cm s . KZ is the vertical eddy diffusity within the bottom 200 m in m2 s"1.
used to weight the average, but experience indicated that such detailed
emission information was almost never available with desired accuracy in
emission inventories or scenarios, and so that variation was removed from the
model. The coding here includes the parameterization, but there is no
variation in the rate.
The calculated and stored normalized statistics from the vertical
dispersion program for a seasonal simulation include the one-dimensional
surface concentration, the pollutant mass remaining aloft, and the pollutant
mass deposited by dry processes during the time increment (six hours except
the initial three-hour step). Separate sets of statistics are maintained for
S02, primary SO?" and secondary SO?" or for N0/N02, primary NOZ, and
secondary NOo-
The eddy diffusivity, chemical conversion, and dry deposition
parameterizations for a particular season are applied everywhere, regardless
of latitude or surface vegetation. This is an obvious limitation of the
ASTRAP algorithms, which include some oversimplifications in order to achieve
computational simplicity. A possible improvement would be to calculate the
vertical dispersion program with different input parameterization values for
distinct source regions, such as the Deep South or New England, with different
typical surface and meteorological conditions. This still would be imprecise,
however, since the trajectories eventually pass out of the source region.
CONCENTRATION AND DEPOSITION
The program CONCDEP combines the statistics produced by programs HORZ and
VERT with an emission inventory to calculate primary and secondary pollutant
surface atmospheric concentrations and dry and wet deposition. The version
described here produces those calculations for a regularly spaced grid.
Each emission source or cell is nested within the coarser trajectory
virtual source grid. The emission cells overlie the smaller cells in Fig. 1,
although there are no man-made emissions in the oceanic cells nor in some of
the continental cells. The bias between the emission location and the
-21-
-------
appropriate trajectory starting point is added to all ensemble puff mean
positions as calculated from that trajectory virtual source. The standard
deviations of each ensemble puff are increased by a term accounting for the
emission and deposition grid resolution and, for wet deposition puffs, the
precipitation analysis grid resolution. In the version of ASTRAP provided
here, all of the grid increments are 1/3 NMC, and the assumed rectangular
distribution across a cell has a standard deviation of 1/3 NMC unit//TI.
A portion of the coding in CONCDEP is designed to decrease computation
requirements by ignoring the effect of puffs outside their 2.5 a range. The
mass associated with the puff is adjusted such that there is no loss of mass.
The concentration and deposition fields are calculated by overlaying
puffs (N source cells x 12 telescoped time steps) and adding their densities
over the receptor grid (illustrated by the smaller cells in Fig. 1). Puff
superposition avoids computational difficulties often encountered with
continuous plume models in complex flows (Johnson, 1983) . For this the puffs
must be weighted by both the emission rate per 6 hours for the source and the
number of normalized tracer masses contributing to the puff:
N L 12 n
C(x,y) = E I E E J-J H(x.y) c , (4a)
i=l A=l ^ j=l o 3 i J J
N L 12
D(x,y) =» I E E E n H(x,y) d , and (4b)
1=1 ji=i 1X> j=i XJ *J
N L 12
W(x,y) = E I E E m H (x,y) b . (4c)
1=1 £=i ix- j=i ^ w x-3
where C is the long-term average concentration of either gaseous or
particulate pollutant, c.,. is the corresponding one-dimensional surface
concentration of the pollutrant as a function of emission layer and time step,
L is the number of layers, E^ is the emission rate to layer H, from source i,
n.f . is the number of normalized emission units of age j from source i
contributing to the statistic, n is the number of simulated tracers released,
D is the cumulative dry deposition of pollutant, d^, is the deposition
increment of the corresponding pollutant during time step j from layer Si, W is
the cumulative wet deposition of pollutant, rn^j is the number of normalized
emission units from source i deposited during time step j, HW is the wet
analogue to H, and b^, is the fraction of the original pollutant mass from
layer i still airborne at time step j if there had been no previous wet
deposition.
The NMC polar stereographic map projection defining the various ASTRAP
input and output fields is not an equal area projection. In order to
integrate the deposition field, therefore, the area represented by a grid cell
must be computed. This requires a transformation of the NMC cell centroid
coordinates to latitude and then calculation of the map scale factor at that
latitude. The map scale factor is then multiplied by the grid increment at
60° N, where the map projection is true.
-22-
-------
Provided that deposition gradients are not too steep over the region of
interest relative to the deposition grid resolution, the simplest way to
define an irregular area of deposition integration is with an integer
indicator array, which classifies each cell. The integrated deposition can
then be scaled by the ratio of the actual area of the region of interest to
the sum of the areas of the cells designated as being within the region of
interest.
PREPROCESSORS AND POSTPROCESSORS
For ASTRAP use, horizontal locations of emission sources must be defined
in NMC coordinates, or in units convertible to NMC coordinates, such as
latitude and longitude or UTM coordinates. Effective plume height, or stack
parameters (height, diameter, exhaust speed, and exhaust temperature)
sufficient to calculate the effective plume height according to a plume rise
formula such as that of Briggs (1971) must be provided; ambient wind speed and
temperature can be estimated from climatology. Since information on fuel type
is often lost by the time an emission inventory is compiled, the primary SO^ ~
emission factor is estimated here through a parameterization that varies with
location (fuel oil combustion, most common in the Northeast, emits a higher
fraction of sulfur oxides as primary sulfate than does coal combustion) and
layer (surface sources, as in residential heating, are more likely to be oil-
fired). If many sources are included in the scenario, it is usually more
convenient to apportion emissions to a three-dimensional grid, expressed in
polar stereographic coordinates horizontally and expanding coordinates
vertically.
When output is in the form of gridded concentration and deposition
fields, the normal mode of graphic production is to draw isopleths over a
background map of the region of interest. Since the available graphics
packages vary considerably from one computer system to another, these program
codes are not included here. The output grid is defined in NMC coordinates
which are (0,0) at the pole, X increasing to the right, Y increasing downward,
and oriented along 80° W longitude; conversions of NMC coordinates to latitude
and longitude, and latitude/longitude to NMC coordinates are given below:
A - 6397/381
B = 3.1416/2
D = 57.29
C - A(l + sin(60,/D)l
LAT - D(B - 2 tan~if(X/ -I- YZ)i/
-------
air quality networks are not routinely operated, although the past Sulfur
Regional Experiment (SURE) (Environmental Research & Technology, 1982)
provided a valuable network for much of 1977 and 1978. Unfortunately, wet
deposition networks were very limited then. Current observations of wet
deposition measure the combined effect of all emissions, including natural
emissions, and do not directly evaluate the individual source-receptor
relationships actually calculated by ASTRAP. Observations also contain
"noise", in terms of both regional unrepresentativeness produced by local
source effects and errors associated with precipitation undercatch or
windblown dust contamination. A model of transport and deposition represents
a relationship between emissions and deposition. Model performance statistics
can be adversely affected by errors in emission inventories, as well as by
errors or unrepresentativeness of observations. Thus, even a "perfect" model
of source-receptor relationships could never explain all observed variance.
There are at least two approaches to "correcting" observation data sets
that contain apparent local effects before comparison with model output.
Detailed analysis of local sources near each observation site may allow one to
eliminate sites from the set subjectively, or the raw observations may be used
to produce an objectively analyzed field on the same scale as model output,
with local effects and error presumably filtered to some extent.
Verification statistics recommended by the American Meteorological
Society (Fox, 1981, 1984) apply best to plume models, i.e., near the source
and with negligible travel time for removal or transformation processes to
become significant. The techniques offered are most useful for individual
locations where a sensor is affected by only one or a few sources. Also, a
measurement is considered both accurate and representative of an ensemble of
possible measurements for the particular conditions. For long-range transport
(LRT) modeling the verification techniques available are not so direct. Dry
deposition is clearly important in the sulfur budget and is likely important
in pollutant loading to ecological systems, but it is not monitored.
Horizontal net mass fluxes could provide an important check for model
consistency, but are not measured. Interpretation of monitored wet deposition
is hampered by the fact that various monitoring networks have different
sampling and analysis protocols. Hence, proper LRT model validation is a
contentious and complicated matter.
A qualitative evaluation of three models, including ASTRAP, developed as
part of the Multistate Atmospheric Power Production Pollution Study (MAP3S)
program (Shannon et al., 1982) gave a subjective comparison of observed and
modeled isopleth patterns of atmospheric concentrations, and presented
concurrent simulations of wet deposition without comparison with
observations. ASTRAP and seven other models were evaluated in studies related
to the Memorandum of Intent (MOI) on Transboundary Mr Pollution between the
United States and Canada (Schiermeier and Misra, 1982; Ferguson and Machta,
1982) . However, verification statistics were inconclusive because large
discrepancies in different emission inventories for 1978 were never resolved
and because the period of comparison was rather limitedtwo months in the
case of ASTRAP. A comparison of ASTRAP simulations with selected high-quality
Canadian wet sulfate deposition observations was given by Voldner and Shannon
(1983).
-24-
-------
More complete ASTKAP wet deposition verification is given here. Several
precipitation chemistry networks in the United States and Canada provide data
on annual wet deposition of sulfate and nitrate at about 100 sites in 1980
(Niemann, 1982). In this comparison, the contribution from natural sources
(not included in the emission inventory) is estimated in terms of the observed
rainfall at the sites by subtracting assumed concentrations resulting from
natural sources from the observations. Thus, the comparison is between
simulations of wet deposition of anthropogenic sulfate and nitrate and
adjusted observations of the same species. Comparisons of ASTRAP simulations
with observations are shown in Fig. 11 for wet sulfate deposition and in Fig.
12 for wet nitrate deposition. Statistics associated with these comparisons
are listed in Table 2. About 69% to 73% of the observed variance of sulfate
or nitrate wet deposition over the continent is explained by the model. An
emission inventory for 1980, a preliminary version of the NAPAP inventory, has
been used.
In a further evaluation of ASTRAP, the 1980 SOX emission inventory has
also been used with meteorological data from 1978 in recalculation of S02 and
SOA ~ average atmospheric concentrations and wet sulfate deposition for
January and July 1978 and for the entire year as examined in the MOI
studies. The SURE January air quality observations were for 10-31 January.
The appropriate 22 days of meteorological data were used, but that is probably
shorter than desirable for ASTRAP simulations. Comparisons are plotted in
Figs. 13 through 15 and are summarized in Table 3. The arbitrary adjustment
for the effects of natural sources at wet deposition receptors (all in the
east) is larger than that for the 1980 data; the 1978 observations were mostly
from monthly collection, whereas almost all of the 1980 were collected daily
or weekly in higher quality networks. Model performance is generally better
than that presented for ASTRAP in the MOI model comparisons, even though
emissions are for a different year. The ASTRAP simulations in the MOI work
for annual concentrations and deposition were actually an average or sum of
January and July results, since those were the only months of meteorological
data analysed in model-usuable form for ASTRAP at the time. Here the 12
months of meteorological data are December 1977 through November of 1978, so
there still is not a complete temporal overlap.
-25-
-------
WET SULFRTE RNNUflL DEPOSITION 1980
o-
03
o
,, o
CJ
^ ^
in °.
\ o
rr *
O
en
CD
O
LJ
LJ
CO
LJ
E
CO
CD °.
tr o-
%
'/ '
o
o.
0.0
10.0
20.0 30.0 40.0
hODELED (KG S04/HECTRRE)
50.0
60.0
Figure 11. Comparison of ASTRAP simulations with observed annual wet sulfate
deposition during 1980. (Dashed lines are for a perfect fit and
for factor of two range about a perfect fit.)
-26-
-------
WET NITRRTE flNNURL DEPOSITION 1980
10.0 15.0 20.0 25.0 30.0 35.0
MODELED (KG N03/HECTRRE)
Figure 12. Comparison of ASTRAP simulations with observed annual wet nitrate
deposition during 1980. (Dashed lines are for a perfect fit and
for factor of two range about a perfect fit.)
-27-
-------
502 CONCENTRflTION JflNURRY RND JULY 1978
o-
en
<-* o
E
LJ
z:
'"""^ * ~r "
' CD
O
LJ
CL.
cn °.
21 to-
ol "*
CD
O
CJ
i« o
2Z o.
ro
Q
LJ
LJ o
CD
CD L0'
O ""
a
o
/
/
/
/
/
/
/
/
/
/
/
/
/
/
I
I
I
%
i
i
i
i
i
+ +
/ 4-
/+
0.0 15.0 30.0 45.0 60.0 75.0 90.0
MODELED (MICROGRRMS PER CUBIC METER)
Figure 13. Comparison of ASTRAP simulations with observed average S02
concentrations for January () and July (+) 1978. (Dashed lines
are for a perfect fit and for factor of two range about a perfect
fit.)
-28-
-------
504 CONCENTRRTION JflNURRY RND JULY 1978
o
rvj
25 «.
E cjD'
QJ
O
CD
ID
LJ ~
Q_
CO
21
(H
01
CD
O o
a
LJ
>
ce o
LJ ,,:
en
CD
a
o
o
/
/
/
/
/
.+
/
/
I
I
I
I
+
I f
I '
+
/
/
/ +
/ +
0.0 4.0 3.0 12.0 16.0 20.0
MODELED (MICROGRflMS PER CUBIC METER)
Figure 14. Comparison of ASTRAP simulations with observed average SO,
concentrations for January () and July (+) 1978. (Dashed lines
are for a perfect fit and for factor of two range about a perfect
fit.)
-29-
-------
SULFRTE DEPOSITION JflNURRY RND JULY 197E
o
CO'
LJ
C£
cn o
«-" co-
CJ
LJ
x:
O
cn
CD
O
LJ
LJ
cn
CD
O
a
LJ
E o
a
az
a
a
£
0.0
2.0 4.0 6.0
MODELED (KG 504/HECTflRE)
8.0
Figure 15. Comparison of ASTRAP simulations with observed wet sulfate
deposition for January (o) and July (+) 1978. (Dashed lines are
for a perfect fit and for factor of two range about a perfect
fit.)
-30-
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TABLE 2. ASTRAP ANNUAL WET DEPOSITION VERIFICATION STATISTICS.4
1980
1980
1980
1980
Caseb
sulfate
sulfate
nitrate
nitrate
#0bs .
95
95
95
95
OF
20.2
17.7
11.8
11.0
Mod
17.6
17.6
10.5
10.5
Res
2.5
0.1
1.2
0.4
S.D.
12.
12.
7.
7.
o
4
9
5
7
S.D
15
15
8
8
'm
.3
.3
.4
.4
S.D.r
8.3
8.0
4.3
4.2
O
om
0.83
0.84
0.85
0.86
aWet deposition units are kilograms/hectare.
An asterisk (*) indicates that observations have been reduced by estimated
natural source contribution (SO^: 2 micromoles/liter east of the Mississippi
or Manitoba and 7 micromoles/liter west; NOg: 1 micromole/liter east and 3.5
micromoles/liter west).
TABLE 3. ASTRAP VERIFICATION STATISTICS FOR 1978.a
Caseb
Jan S02
Jul S02
Annual
Jan SO,
July SO
Annual
Jan wet
Jul wet
so2
,
\
sulf ate^
ft
sulfate
#0bs
17
20
3
29
47
9
7
13
Ob
40.7
27.3
30.3
6.8
11.5
9.1
1.5
3.1
Mod
35.5
21.8
32.8
6.7
10.0
9.2
1.1
2.1
Res
5
5
-2
0
1
-0
0
0
.2
.5
.4
.1
.5
.1
.4
.9
S.D
17
13
15
1
2
1
2
1
o
.7
.9
.9
.7
.8
.9
.8
.8
«».
16.5
13.5
13.2
1.6
3.4
1.5
0.9
1.7
S.D.r
17.6
13.5
5.0
2.3
2.5
1.9
2.2
0.8
Pom
0.44
0.49
0.64
0.06
0.69
0.35
0.67
0.82
Annual wet sulfate 8 28.0 14.7 13.2 15.6 13.1 6.8 0.79
aUnits are micrograms/cubic meter for air concentrations and kilograms/hectare
for wet deposition.
An asterisk (*) indicates that observations have been reduced by estimated
natural source contribution (5 micromoles/liter).
-31-
-------
5. COMPUTER ASPECTS
As currently structured, ASTRAP consists of three programs, HORZ, VERT,
and CONCDEP, the first two of which must be run to create data files required
by CONCDEP. There is no prescribed order in running HORZ and VERT, and they
could be run simultaneously. The job control language is appropriate for the
IBM 3033 mainframe at Argonne National Laboratory, and would require some
modifications for use on other systems. While programming is in standard
FORTRAN, input and output algorithms may require coding modification for other
computer systems. A simplied flow chart for the overall model is shown in
Fig. 16. Obviously there is preprocessing of raw wind and precipitation
observations in order to produce the gridded fields, but as the fields were
initially processed elsewhere as discussed in the Data Requirements section no
discussion of their preprocessing is provided here.
On an IBM 3033, VERT requires 210K (bytes) storage and 5-10 minutes of
CPU time, plus one output file, for a seasonal simulation. HORZ requires 500K
storage and 10 minutes CPU time, two input tape drives, and an output file for
a seasonal simulation. CONCDEP requires 500K storage, 10-15 minutes CPU time,
an emission input file, the output files from the other two subprograms, a
file used to identify each receptor cell as to state or province, and an
output file for a seasonal simulation. The meteorological fields on tape
input to HORZ are written in binary, as are the output files from HORZ and
VERT, the emission file, and the output file from CONCDEP.
Quarterly sets of wind analyses and monthly sets of precipitation
analyses are written on two separate tapes. The internal file number of the
nonlabeled wind file (file 10 in HORZ), and the names and internal file
numbers of three corresponding precipitation data sets (files 20-22 in HORZ),
as well as the tape number for different years must be changed whenever a
different season is simulated. The names of data sets created by the three
programs should be changed every time a different period is simulated.
In normal application ASTRAP is structured so as to read an emission file
from disk storage. Since the emission inventory is likely to be the first
data set that a user would change, however, the appropriate code is "commented
out" for the test example and an algorithm defining a single hypothetical
source is added. The write file is unit 9 for each program; the input files
in CONCDEP must be renamed when different meteorological periods have been
used in HORZ or VERT.
The output file from CONCDEP is normally stored on disk, and
postprocessors are later used to display the results graphically. Since
available graphics libraries will vary greatly from one computer system to
another and are often partly proprietary, the postprocessor is not included
here. Input parameters for the ASTRAP programs are listed in Table 4, while
input/output files are summarized in Table 5. Some changes which a
prospective user of ASTRAP might wish to make are discussed in the following
section on the test application of ASTRAP.
-32-
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TABLE 5. ASTRAP INPUT/OUTPUT FILES
INPUT
UNIT # OUTPUT PROGRAM
PARAMETER NAMES
COMMENTS
OUTPUT HORZ
OUTPUT CONCDEP
10
10
INPUT HORZ
INPUT CONCDEP
11
OUTPUT VERT
20
INPUT
ITP INPUT
(20,21,22)
CONCDEP
HORZ
30
50
INPUT
CONC(6,28,123),
WD(6,28,123),
LANDE(19,16), NF,
NXEM, NYEM, XEMLL,
YEMLL, DXEM, DYEM,
NTS
S02(51,45),
S04(51,45),
DDEP(51,45),
WDEP(51,45),
SUMM, ADRY(60),
AWET(60)
, V(17,19)
C0(6,28,123),
WD(6,28,123),
LANDE(19,16),
NF
AVSFC(28,3)
AVDD(28,3)
AVBGT(28,3)
SOX(28,3,6),
DEPT(28,3,6),
SBUD(28,3,6)
PRECIP(50,45)
CONCDEP EM(720,6),
XS(720) YS(720),
NS, NZ, DX, DY
INPUT CONCDEP INDD(51,45)
Output from HORZ read into
CONCDEP containing season-
al simulation of horizon-
tal statistics
Final output file from
CONCDEP containing all
concentration and
deposition data, usually
stored on disk to be used
later by postprocessors
Nonlabeled seasonal 6-hour
wind file, requires input
tape drive.
Results of HORZ, contain-
ing seasonal simulation of
horizontal trajectory
statistics
Output file from VERT
read into CONCDEP
containing 1-D surface
concentration data, dry
deposition increment and
total airborne mass for
each emission layer
Results of VERT for all
emission layers
Three monthly 6-hour
precipitation files,
require input tape drive
Emissions in kT S0x as SO
by source cell and layer
for each season
Input file which contains
state and province
geographical information
-38-
-------
6. TEST APPLICATION
The computer codes attached as appendices to this report simulate the
ASTRAP exercise described here. In order to make the test example simple, a
section of code defining a single source is inserted. In more typical
simulations a preprocessed emission inventory, gridded on a 1/3 NMC scale,
provides seasonal SO- emissions for layered virtual sources. This more
relevant code remains in the program, but is skipped by a GO TO 4444
statement. In normal ASTRAP simulations certain randomizations are done to
avoid biases associated with discrete source and deposition grids. These are
temporarily commented out for this test example (see statements containing
RANF in CONCDEP). An emission of 1000 kT SOX as SO, per year from a
hypothetical source in Oklahoma (same location as source of ensemble puffs in
Figs. 2 and 3) is combined with summer vertical dispersion statistics and with
trajectory statistics for summer (June through August) of 1980 in CONCDEP to
produce the concentration and deposition fields printed in Tables 6 through
9. The printed fields begin in the llth small cell column of Fig. 1. The
integrated deposition amounts for each state and province are shown in Table
10. (Deposition to the Great Lakes is totaled without regard to state or
province.)
Obviously, a user of ASTRAP would wish to make changes from this test
case of model coding. The most likely changes would be in either the emission
field, the concentration and deposition field, or both, in CONCDEP. The key
to defining successfully a different emission field is to describe the field
horizontally in terms of NMC coordinates and NMC spacing, if gridded, and
vertically according to the layers in VERT. A change in the
concentration/deposition grid can be easily accomplished by changing the
variables describing the dimension, spacing, and lower left corner. To change
to an irregular set of n receptors, however, the two-dimensional receptor
grids would need to be changed to (n x 1) fields, and the X and Y arrays would
need to be described in NMC coordinates and dimensioned to (n). The
integration of the deposition field should then be skipped.
For the more sophisticated user who might wish to change a
parameterization, the default values included in the code as input data and
illustrated in Figs. 4 through 10 could be replaced, as well as some default
values (such as minimum deposition velocities during initial dispersion) which
are part of the program code. A change in the input meteorological fields to
different spatial scales can be fairly straightforward, as long as the
dimension, spacing, and corner of the various arrays are changed in a
consistent fashion. A change in the temporal scale would involve more
complicated changes.
This user's guide is incomplete without the accompanying program codes,
which have been augmented by numerous comment statements. It is suggested
that any potential user read the code in detail.
-39-
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REFERENCES:
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Durst, C. S., A. F. Crossley and N. E. Davies, 1959: Horizontal diffusion in
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Evolution of the Planetary Boundary Layer Over Land. Report ANL/RER/81-1,
Argonne National Laboratory.
Hicks, B. B. and J. D. Shannon, 1979: A method for modeling the deposition of
sulfur by precipitation over regional scales. J. Appl. Meteor. 18,
1415-1420.
-45-
-------
Johnson, W. B., 1983: Interregional exchanges of air pollution: model types
and applications. J. Air Poll. Assoc. 33, 563-574.
Meagher, J. F. and E. M. Bailey, 1983: The seasonal variation of the
atmospheric SC^ to S0^2~ conversion rate. J. Geophys. Res. 88, 1525-1527.
Mood, A. M. and F. A. Graybill, 1963: Introduction to the Theory of
Statistics (2nd ed.). McGraw-Hill, p. 198.
Niemann, B. L., 1982: The 1980 data set for further evaluation of regional
air quality/acid deposition simulation models. Informal report to Acid
Rain Staff, U.S. Environmental Protection Agency.
Schiermeier, F. A. and P. K. Misra, 1982: Regional Modeling Subgroup Report
2F-M. Submitted to Work Group 2: Atmospheric Sciences and Analysis.
Shannon, J. D., 1979: A Gaussian moment-conservation diffusion model. J.
Appl. Meteor. 18, 1406-1414.
Shannon, J. D., 1981: A model of regional long-term average sulfur
atmospheric pollution, surface removal, and net horizontal flux. Atmos.
Environ. 15, 689-701.
Shannon, J. D., L. Kleinman, C. Benkovitz, and C. Berkowitz, 1982:
Intercomparison of MAP3S models of long-range transport and deposition.
Preprints, 3rd Joint Conf. on Applications of Air Pollution Meteorology,
American Meteorological Society, Boston, pp. 29-32.
Sheih, C. M., 1977: Application of a statistical trajectory model to the
simulation of sulfur pollution over northeastern United States. Atmos.
Environ. 11, 173-178.
Streets, D. G., D. A. Knudson and J. D. Shannon, 1983: Selected strategies to
reduce acidic deposition in the U.S. Environ. Sci. Technol. 17, 474A-485A.
Taylor, G. I., 1921: Diffusion by continuous movements. Proc. Math. SL20,
196-212.
Voldner, E. C. and J. D. Shannon, 1983: Evaluation of predicted wet
deposition fields of sulfur in eastern Canada. Transactions, Air
Pollution Control Association Specialty Conference on The Meteorology of
Acid Deposition, 387-399.
Wesely, M. L. and B. B. Hicks, 1977: Some factors that affect the deposition
rates of sulfur dioxide and similar gases on vegetation. J. Air Poll.
Control Assoc. 27, 1110-1116.
Wesely, M. L. and J. D. Shannon, 1984: Improved estimates of sulfate dry
deposition in eastern North America. Environ. Progress _3_, 78-81.
Wesely, M. L., D. R. Cook, R. L. Hart and R. E. Spear, 1984: Measurements and
parameterization of particulate sulfur dry deposition over grass. J.
Geophys. Res. 90, 2131-2143.
-46-
-------
Yaraada, T., 1977: Analyses of the Sangamon data. Argonne National Laboratory
Radiological and Environmental Research Division Annual Report ANL-77-65,
Part IV, 49-52.
-47-
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Appendix A: Program HORZ Listing
48
-------
1. //HORZ 003
2. //*MAIN ORC
3. // EXEC FGICLG
i.. //SYSIN DD *
5. C ... USING WIND AND PRECIP ANALYSES FROM U MICH, THIS' PROGRAM WILL
5. C PRODUCE THE TRAJECTORY STATISTICS REQUIRED BY ASTRAP.
7. C
8. C VARIABLES USED IN MORE THAN ONE SUBROUTINE
9. C
l.'J. C CONC : STATISTICS DESCRIBING THE AIRBORNE PORTION OF THE
11. C TRACERS.
12. C CONCd,*,*) AND CONC(2,*,*> : USED TO CALCULATE THE MEAN
13. C LOCATION IN THE X AND Y DIRECTIONS, RESPECTIVELY,
14. C OF THE AIRBORNE TRACER FOR EACH TIME STEP/SOURCE
15. C COMBINATION.
IS. C CONC(3,*,*) AND CONCU,*,*) : USED TO CALCULATE THE STANDARD
17. C DEVIATION IN THE X AND Y DIRECTIONS OF THE AIRBORNE
13. C TRACER FOR EACH TIME STEP/SOURCE COMBINATION.
19. C CONC<5,*,*> : THE SUM OF THE AMOUNTS OF AIRBORNE TRACER FOR
2.'j . C THE TIME STEP/SOURCE COMBINATIONS.
21. C CONC(S,*,*> : USED TO DETERMINE THE CORRELATION BETWEEN THE X
22. C AND Y POSITIONS IN THE ENSEMBLE PUFFS.
23. C DC : THE NUMBER OF MEASUREMENTS CONTRIBUTING TO THE
24. C RESULTS FOR EACH TIME STEP/SOURCE COMBINATION.
25. C DELT : THE NUMBER OF SECONDS IN A TIME STEP.
26. C DELX AND DELY : LENGTH OF AN NMC GRID INCREMENT IN METERS
27. C AT 6£f DEGREES NORTH LATITUDE.
25. C DXP AND DYP : THE LENGTHS IN NMC UNITS OF A PRECIPITATION GRID
29. C CELL IN THE X AND Y DIRECTIONS.
30. C DXWI AND DYWI : THE LENGTH IN NMC UNITS OF A WIND GRID CELL
31. C IN THE X AND Y DIRECTIONS, RESPECTIVELY.
32. C FAC3 : A SEASONAL FACTOR USED TO DETERMINE THE SPEED OF THE
33. C NOCTURNAL JET.
34. C INCPR : THE TIME INCREMENT FOR V/IND/PRECIP DATA.
35. C IPER : A NUMBER FROM 1 TO 4 REPRESENTING THE SIX-HOUR PERIOD
33. C OF THE DAY (GMT) TO WHICH THE WIND DATA APPLY.
37. C LANDE: REPRESENTS THE TRAJECTORY CALCULATION GRID. CELLS WHICH
33. C HAVE NONZERO VALUES WILL BE USED AS VIRTUAL SOURCES.
39. C THESE CELLS WILL BE ASSIGNED SEQUENTIAL IDENTIFICATION
4J. C NUMBERS PROCEEDING ROW-BY-ROW FROM THE LOWER LEFT CORNER.
'-:. C MEAS : THE NUMBER OF MEASUREMENTS READ.
^2. C NDAY : THE NUMBER OF DAYS THE PROGRAM SHOULD MODEL.
43. C NF : THE NUMBER OF DATA SETS FOR THE DESIRED PERIOD.
44. C NS : THE NUMBER OF SOURCE (LAND-CONTAINING) CELLS.
45. C NTS : THE NUMBER OF TIME STEPS TO BE CALCULATED.
iJ. C NX? AND MYP : THE NUMBER OF CELLS IN THE X AND Y DIRECTIONS IN
47. C THE PRECIPITATION GRID.
43. C NXWI AND NYWI : THE NUMBER OF CELLS IN THE X AND Y DIRECTIONS
49. C IN THE WIND GRID.
5.-J. C PRECIP : 24-HOUR PRECIPITATION TOTALS.
51. C ?Z : SET TO PI DIVIDED BY 2.
52. C RO : A CONSTANT USED TO CONVERT LATITUDE AND LONGITUDE TO NMC
£3. C UNITS.
E-,. C TR : THE FRACTION OF THE TRACER LEFT FOR EACH TIME STEP/SOURCE
55. C COMBINATION.
5.:. C U AND V : X AND Y COMPONENTS Or THE TRANSPORT WIND.
57. C ALONG 30 DEGREES WEST LONGITUDE X IS WEST-EAST AND V IS
53. C NORTH-SOUTH.
59. C UTROP : THE FRACTION OF A TRACER LOST TO THE UPPER TROPOSPHERE
c.I . C QY CONVECTION. '
C WC : THE NUMBER OF WET DEPOSITION EVENTS FOR EACH TIME STEP/
C SOURCE COMB I NATION.
C WD : WET DEPOSITION DATA
49
-------
54.
So.
67.
63.
59.
72.
11 .
72 .
73 .
74.
75 .
73 .
77.
73.
79.
a .y .
21 .
o o .
SS.
57.
S3 .
S3.
50.
92.
93 .
94 ,
95.
96 ,
97 .
C £
99.
1
l.C'3 .
l/i'9 .
1 1 £',
Hi .
112.
113.
11'.
115,
116.
1 17.
11C.
C
r
C
C
C
C
C
c
C
c
c
c
c
c
c
c
C
C
c
c
c
c
c
c
c
c
c
c
WD(1,*,*) AND WD(2,*,*) : USED TO COMPUTE THE MEAN
LOCATION IN THE X AND Y DIRECTIONS, RESPECTIVELY,
OF WET DEPOSITION FOR EACH TIME STEP/SOURCE COMBINATION.
WD(3,*,*) AND WD<4,*,*> : USED TO COMPUTE THE STANDARD
DEVIATION IN THE X AND Y DIRECTIONS OF WET DEPOSITION FOR
EACH TIME STEP/SOURCE COMBINATION.
WD<5,*,*> : THE AMOUNT OF MASS REMOVED BY WET DEPOSITION FOR
EACH TIME STEP/SOURCE COMBINATION.
WD(6,*,*) : USED TO DETERMINE THE CORRELATION BETWEEN THE X AND
Y POSITIONS IN THE ENSEMBLE PUFFS.
. READ PARAMETERS AND INITIALIZE VARIABLES
CALL I NIT
, READ DATA AND COMPUTE TRAJECTORIES
CALL MODEL
, COMPUTE STATISTICS AND PRINT RESULTS.
CALL RESULT
STOP
END
SUBROUTINE INIT
. READ USER-SPECIFIED PARAMETERS AND INITIALIZE VARIABLES.
VARIABLES USED ONLY IN INIT
DXEM AND DYEM : NMC LENGTH OF AM TRAJECTORY GRID CELL IN NMC
UNITS IN THE X AND Y DIRECTIONS, RESPECTIVELY.
NXEM AND NYEM : NUMBER OF CELLS IN THE X AND Y DIRECTIONS,
RESPECTIVELY, ON THE TRAJECTORY GRID.
COMMON/WIND/IK 17,19),V<17,19),DXWI,DYWI,XWILL,YWILL,DELT,X( 17),
X Y( 19),DELX,DELY,NXWI,NYWI,MEAS -
COMMON/PREC/PRECIP(5#,45),WD<6,23,123),DXP,DYP,XPLL,
% YPLL,UTROP,WC<23,123>,NXP,NYP
COMMON/STAT/ XC(29,123 ) ,YC(29,123 ) ,TR(29,123>,CONC(6,28,123),
Y, F ACS, DC ( 28, 1 23 ) , P2 , RO , XEMLL , TRL < 29 , 123 >,
% YEMLL,DXEM,DYEM,NS,NTS,IPER,LANDE(19,16),NXEM,NYEM
COMMON /CONTRL/ NDAY,NF,ITP, IOUTST,IOUTEN,IDD(4)
. DEFINE MISCELLANEOUS CONSTANTS
P2=3.14159/2.
R0=6397./381
CALCULATE NMC COORDINATES FOR SOURCE
125 .
125.
127.
... READ DATA FOR LANDE
CELLS.
NXEM=19
NYEM=16
XEMLL = -11 .5
YEMLL=20.5
DXEM=1 .
DYEM=1.
READ 2023,( ,!=!,NXEM),0=1,NYEM)
2023 FORMAT(1913)
NS=0
DO 1030 0 = 1 , NYEM
DO 1030 1 = 1, NXEM
IF (LANDE(I,0>.EQ.0> GO TO 1030
NS = MS + 1
IF (NS.GT.123) GO TO 40
LANDE = MS
XC< 1 , NS ) = XEi1LL > DXEX"< 1-1 )
50
-------
128. YC< 1,NS)=YEMLL - D YEM* ( 0- 1 >- 1 6 .3
129. 1030 CONTINUE
_;4 . GO TO 60
131 . 42 PRINT 2050
132. 2050 FORMAT* /'^'WARNING: TOO MANY SOURCE CELLS. ONLY 123 USED #*'/)
103. NS=123
134. 60 PRINT 2070, NS
135. 2070 FORMAT* 15,' SOURCE CEILS WERE READ1)
135. PRINT 2080, ( ')
1~3. PRINT 2100,XPLL,YPLL
174. PRINT 2110,DXP,DYP
175. XPLL=XPLL-DXP/2.0
173. YPLL=YPLL+DYP/2.0
177. YPLL=YPLL-15.0
173. C
179. C ... INPUT MISCELLANEOUS PARAMETERS.
135. READ 2140,UTROP
1C: . 2140 FORMAT(F5.0)
132. PRINT 2150,UTROP
133. 2150 FORMAT*/1 FRACTION OF SULFUR LOST TO THE" UPPER TROPOSPHERE BY CONV
XECTIVE PRECIPITATION = ',F6.3>
C
C ... READ DAY/TIME INFORMATION.
READ 2220, I DO , MDAY , NTS
2220 FORMAT* 4* IX, 12), 14. IX, 12 )
INCPR=6
240 NF=4rtNDAY
51
-------
192. ITP=20
1-Z. 2250 FORMAT(4(IX, 12) )
1?-' . PRINT 2250,17?
Ii5. 2260 FORMAT = 9999.
210. WC<1,0)=0.0
211. DC(I,0)=0.0
212. TRLCI+i,0)=0.
213. TR(1 + 1,0 )=0.
21-;. DO 1330 K=l ,o
215. CONC,NXP,NYP
2-!C. COMMON/STAT/ XC ( 29 , 1 23 ) , YC< 29 , 1 23 ) , TR( 29 , 1 23 ) , CONC( 6 , 28 , 123),
2-; 4. % FAC3,DC(28, 1 23 ) , P 2 , RO , XEMLL , TRL ( 29 , 123),
245. % YEMLL,DXEM,DYEM,NS,NTS,IPER,LAilDE(19,16),NXEM,NYEM
2 5. COMMON /CONTRL/ NDAY,NF,ITP,IOUTGT,IOUTEN,IDD(4 )
Z-\l. DIMENSION IDPU)
C
NEWMON=1
MEAS=0
NF=0
C
253. C ... COMPUTE TRAOECTORIES
254. DO 1222 ITI = I ,,IDAY
255. DO 1222 IPER=1,4
52
-------
256. C
257. C ... SET SEASONAL VARIABLES IF A NEW MONTH WILL BE STARTED.
258. IF (NEWMON.NE.i> GO TO S3
259. NEWMON=0
263-. ITEMP=IDD(1>/3+l
251. GO T0(40,10,20,30,40),ITEMP
252. C ... SPRING (MARCH-MAY)
253. 10 FAC3=1.I5
254. GO TO 50
255. C ... SUMMER (JUNE-AUGUST )
235. 20 FAC3=1.25
257. GO TO 50
258. C ... FALL (SEPTEMBER-NOVEMBER)
259. 30 FAC3=1.15
270. GO TO 50
271. C ... WINTER (DECEMBER-FEBRUARY )
272. 40 FAC3=1.05
273. 50 MEAS=MEAS+1
274. C
275. C . . . READ WIND DATA.
275. READ <10,END=996) U,V
277. C
27G. C ... READ PRECIPITATION DATA.
279. 998 READ (ITP,END=999> PRECIP
23.0. C
231. GO TO 1220
282. 999 REWIND ITP
283. ITP=ITP+1
2C4. IF (ITP.GT.22) GO TO 996
255. GO TO 998
215. 1220 CALL TRAJ
237. 1222 NF=NF+1
288. 995 CONTINUE
289. RETURN
290. END
291.
292.
293. SUBROUTINE TRAJ
294. C
295. C ... TRAJ TRANSPORTS TRACERS FOR A TIME STEP AND COLLECTS
295. C STATISTICS.
297. C
298. C VARIABLES USED ONLY IN TRAJ
299. C
300. C DA , DB , DD , AND DE : RECIPROCALS OF THE. DISTANCES FROM
3^1. C THE TRACER'S LOCATION TO THE FOUR CLOSEST WIND MEASUREMENTS.
3.S'2. C Dl , D2 , D3 , AND D4 : PERPENDICULAR DISTANCES FROM THE
303. C TRACER'S LOCATION TO THE FOUR CLOSEST WIND CELLS.
304. C DT : NUMBER OF SECONDS IN A CENTER-DIFFERENCED TIME STEP.
305. C FRMOV : THE FRACTION OF A TRACER REMOVED--BY WET DEPOSITION
306. C AND CLOUD CONVECTIVE EXCHANGE.
307. C FRMAI1 : THE FRACTION OF THE TRACER REMAINING AFTER WET
300. C DEPOSITION.
309. C FRMOV1 : THE FRACTION OF THE TRACER DEPOSITED 3Y WET DEPOSITION.
310. C IM : THE TIME STEP BEING PROCESSED.
311. C I : THE NEXT TIME STEP.
312. C IQ AND JQ : THE COLUMN AND ROW, RESPECTIVELY, OF THE TRACER'S
313. C LOCATION ON THE TRAJECTORY CALCULATION GRID.
314. C IX : COLUMN Of THE WIND GRID TO THE LEFT OF THE POINT OF
315. C INTEREST.
315. C IY : ROW OF THE WIND GRID BELOW THE POINT OF INTEREST.
317. C IXX : COLUMN OF THE WIND GRID TO THE RIGHT OF THE POINT OF
318. C INTEREST.
319. C IYY : ROW OF THE WIND GRID ABOVE THE POINT OF INTEREST.
53
-------
320. C JX AND JY : COLUMN AND ROW ON THE PRECIPITATION GRID FOR THE
321. C TRACER'S NSW LOCATION.
322. C MM : INITIAL VALUE OF THE INNER LOOP INDEX SO ONLY TIME
323. C STEPS THAT HAVE VALUES WILL BE PROCESSED.
324. C PP : PRECIPITATION IN THE TRACER'S CELL.
325. C UU AMD VV : THE ESTIMATIONS OF THE U AND V COMPONENTS OF
326. C THE WIND IN THE TRANSPORT LAYER.
327. C
328. COMMON/WIND/U<17,19),V(17,1 9 ) , DXWI,DYW!,XWILL,YWILL,DELT,X{17>,
329. % Y<19),DELX,DELY,NXWI,NYWI,MEAS
330. COMMON/PREC/PRECIP(50,45),WD<5,23,123>,DXP,DYP,XPLL ,
331. % YPLL.UTROP,WC(23,123),NXP,NYP
332. COMMON/STAT/ XC<29,123),YC(29,1Z3),TR<29,123),COMC(5,28,123),
333. % FAC3,DC(28,123),P2,RO,XEMLL,TRL(29,123),
334. % YEMLL,DXEM,DYEM,NS,NTS,IPER,LANDE<19,16 ) ,NXEM,NYEM
335. C
336. C ... COMPUTE TRAJECTORIES FOR THE TIME STEP/SOURCE COMBINATIONS.
337. C
333. C ... UNTIL THERE ARE DATA FOR EACH TIME STEP, ONLY LOOK AT
339. C THE TRAJECTORIES FOR WHICH THERE ARE DATA.
340. MM=MAX0(1,(NTS+1-MEAS) )
341 . DO 1040 ITS = MM,NTS
342. DO 1040 J = l ,MS
343. IM=NTS + 1 -ITS
344. I=IM+1
3-15. C
346. DT=DELT
347. C IF THIS IS THE FIRST TIME STEP, FORWARD DIFFERENCING IS DONE
340. C SO DT IS DIVIDED BY 2.
349. IF (IM.EQ.l ) DT=DELT*.5
350. C ... CHECK TO SEE IF THE TRACER IS OUTSIDE THE WIND GRID ( XC AND
351. C YC ARE SET TO 9999 WHEN A TRACER IS OUTSIDE THE WIND GRID).
352. IF (XC(IM.J>.GE.1000.> GO TO 30
353. C
354. C ... USE BILINEAR INTERPOLATION TO ESTIMATE THE WIND AT THE CURRENT
355. C TRACER LOCATION.
356. C
357. C FIRST OBTAIN THE FOUR CLOSEST GRIDDED WIND VALUES.
358. IX = -XWILL>/DXWI + 1.
359. IY = (YWILL - YC(IM,J ) )/DYWI + 1.
360. IXX=IX+1
3S1. IYY=!Y+1
362. IF (IXX.LT.l) IXX=1
363. IF UYY.LT.l) IYY=1
364. IF -V( IY )
374. D3 = XC( IM,0 )-)(< IXX >
375. D4 = YC(IM,0>-Y< IYY)
375. C
377. C ... OBTAIN WEIGHTINGS.
378. DA=1 ./< SCRT(D1*DH-D2*D2) + 1 .E-10)
379. DB=1 ./< SQRT*1 . E-10)
381 . DD=1 ./( SQRT(D3*D3+D4*D4)+1 . E-1.9)
382. DENOM=1./
-------
334. C ... COMPUTE SPEEDS.
385. UU=(DA*U< IX,1Y)-H)B*U( IX , I YY )+DE*U( I XX , IY >
386. % +DD*U< IXX,IYY) )*DENOM
387. VV=(DA*V< IX, IY>+DB*V< IX, IYY)+DE*V< IXX,IY>
388. % +DD*V
405. UMAP=UMAP*SIG/DELX
405. VMA?=VMAP-SIG/DELY
A 07 . C
403. C ... TRANSPORT THE TRACER FOR A TIME STEP.
409. XC(I,J)=XC(IM,J>+UMAP"DT
410. YC( I ,J ) = YC( IM,J >+VMAP*DT
411. TR(I,J )=TR(IM,J )
.112. TRL(I.J)=TRL(IM,J)
413. C ... HAVE TRAJECTORIES DEPARTED THE WIND GRID?
4U. IF GO TO 30
416. IF (YC( I,J).GT.{Y(1 ) + 2. » GO TO 30
417. IF (YC< I,J ).LT.(Y(NYWI )-2. >> GO TO 30
418. C
419. FRMAIl=1.0
420. C
421. C ... IF THE TRACER IS NOT WITHIN THE PRECIPITATION ANALYSIS,
422. C EXTEND THE LAST CELL ON THE ROW OUTWARD.
"23. JX = (XC( I ,0 5-XPLD/DXP + 1.
424. JY=(YPLL - YC(I,J))/DYP + 1.
425. IF JX = NXP
428. IF (JX.LT.1 ) JX=1
A29. C
430. C ... CALCULATE WET DEPOSITION IF THERE IS PRECIPITATION.
431. PP=PRECIP(JX,JY>
432. IF < PP.LE.1.0) GO TO 20
433. FRMOV = SQRT( . 1»PP )
434. C ... DURING THE WINTER { FAC3 =1.05 IN THE WINTER) IN NORTHERN
435. C AREAS, WET DEPOSITION IS REDUCED BECAUSE THERE IS SNOW
436. C INSTEAD OF RAIN.
437. IF (FAC3.EQ.1.05 .AND. YC(I,J).LT.0) FRMOV=FRMOV*.5
430. IF !FRMOV.GT.1.0) FRHOV=1.0
'139 . FRMOV1 = < 1 .0-UTROP )*FRMOV
^'40. FRMAI1 = 1 .0-FRMOV1
TRL( I,J )=TRL< !M,J >*( 1.-FRHOV)
TR( I,J >=TR( IM,J )-FRt1AIl
C
"-M. C ... GATHER STATISTICS IF ENOUGH STEPS HAVE ELAPSED.
'- J 5 . WTF RAC = TR ! IM , J ) '- F RMOV 1
446. V/D < 5 , If j, J ) =\/D { 5 , I M , J ) +WTFRAC
447. WD< 1 , IM.J )=>/D{ 1 , IF1,J ) + XC( I ,0 --WTFRAC
55
-------
448.
449.
450.
451.
452.
453.
454.
455.
456.
457.
458.
459.
460.
461 .
462.
453.
464 .
465.
456.
467.
463 .
469.
''-70.
471.
472.
473.
474.
475.
475.
477.
478.
479.
480.
481 .
482.
433.
484.
1- i i. ,
AS3.
4 £ 4 .
=WD(2,IM,
WD(3,IM,0)=WD(3,IM,
WD<4,IM,0 )=WD(4,IM,
WD(5,IM,0 )=WD<5,IM,
WC(IM,0)=WC
0)+YC{I,J}*WTFRAC
0)+XC(I,0)*XC + YC + XC< I,J >*YC=CONC(2,
>=CONC(3,
>=CONC(4,
CONC<6,IM,0)=CONC<6
DC( IM,0 ) = DC( IM.O> + l
GO TO 1040
IM,
IM,
IM,
J )=CONC(5, IM,
,IM,
.0
0 ) » XC( I,0)*DRFRAC
0) + YC(1,0 >*DRFRAC
0)*XC(I,0 )*XC( I ,
0)+YC(1,0 >*YC( I,
0)+DRFRAC
0) + YC*DRFRAC
0)*DRFRAC
0>*DRFRAC
c
c
c
c
c
c
c
: ... TAG TRACERS OUTSIDE GRID
33 XC(I,0 ) = 9999.
YC< I,J) = 9999.
TR(1,0)=0.0
TRL( I,0 )=0.0
1040 CONTINUE
RETURN
END
SUBROUTINE RESULT
. FINISH CALCULATION. OF STATISTICS AND OUTPUT RESULTS.
VARIABLES USED ONLY IN RESULT
TITLE , WDLBL , AND REMLBL : LABELS FOR OUTPUT.
COMMON/PREC/PRECIP<50,45),WD(6,28,123),DXP,DYP,XPLL,
% YPLL.UTROP,WC(28,123),NXP,NYP
COMMON/STAT/ XC ( 29 , 1 23 >, YC ( 29 , 12.3 ), TR{ 29 , 1 23 ), CONC( 6 , 28 ,123 ),
% FAC3,DC<20,123),P2,RO,XEMLL,TRL(29,123),
% YEMLL,DXEM,DYEM,NS,NTS,IPER,LANDE(19,15),NXEM,NYEM
COMMON /CONTRL/ NDAY , TJF , ITP , IOUTST, IOUTEN , I DD( 4 )
DOUBLE PRECISION TITLEt7,6 ) ,WDL3L(5 ) ,REMLBL(5>,NUL3L(7)
DATA
, ' MEAN LO1
1 MEAN LO1,
S.D. I ' ,
S.D. I'.
SUM 0',
, ' X-Y COR'
OSITION ','
'/. REMLBL/1 RZMAINI ' , 'NG TRACE1,
% NULBL/1 NUMBER1,' OF MEAS','
FOR'/
TITLE/3*'
3* .
4*'
4*'
4*.
4*'
WDLBL/' WET DEP:
REMLBL/1 REMAIN I ' , '
NULBL/' NUMBER','
1 THE RES','ULTS
, 'CATION I
'CATION I '
1 N X DIRE '
1 N Y DIRE '
'F THE MA'
, 'RELATION
EXTRACT I ' ,
'R FRACTI '
UREMENTS' ,
1 , 'N X DIRE
, ' N Y DIRE1
, 'CTION OF '
, 'CTION OF '
, 'SSES FOR'
'ON
, 'ONS EXTR1
1 CONTRIB1 ,
1 , 'CTION OF1 ,
, 'CTION OF ' ,
' / ,
'/,
, 'ACTION '/,
'UTING TO1 ,
COMPLETE THE CALCULATIONS FOR CONC ,
DO 1030 1=1,NTS
DO 1030 0 = 1 ,NS
IF (CONC(5,I,0J.LE.0.) GO TO 10
COMC( 1 , I .0 )=CONC< 1,1,0 )/COt!C(5, I
CONC(2, I , J >=COMC(2, I ,0 )/COi\!C<5, I
IF (DC( I ,J ).LT.3. ) GO TO 21
DD = DCiI.0)/(DC(I,0 >-l.0)
0 )
0 )
DQ = DC' I ,0 ;/'( DC( I ,0 )-2.
CONC(3, I ,0 > = DD«((CONC(
CONC' 1,1,0))
I,0 )/CONC<5,1,0) )-CONC(1,I ,0 >*
56
-------
512,
513.
514.
515.
515.
517.
518.
519.
520.
521 .
522.
523.
524.
525.
525.
527.
523 .
32S.
-.
5 -' 4 .
545 .
.
525 .
5"£G.
£57 .
5£1 .
5- o
j t..
T- ~ O
O SO .
3 O 4 .
5 5 o .
572 ,
573 .
574 ,
575 .
CONG (4, !,J) = DD*« CONG (4
CONC( 2, ! ,0 ) )
CONCC6, I,0)=DQ*((CONC<6
CONC<2, 1,0 )>
IF =CCNC< 2,1,0)+ 15.0
CONC( 3,1,0 >=SQRT(CONC<3, 1,0))
CONC( 4,1,0 )=SQRT(CONC<4, 1,0))
GO TO 1030
1 ,0 }/CONC( 5 , I ,0 ) )-CONC( 2 , I ,0)*
I,J)/CONC<5, I,0))-CONC< 1 ,1 .0)*
»
) )
GO
GO
TO
TO
20
20
10
21
20
CONCt1,
CONC(2,
CONC<6,
CONC(3.
CONC(4,
CONC(2,
0 )=0.
0)=0.
0)=0.0
0)=DKP/SQRT(12,
0)=DYP/SQRT(12.
0 )=CONC(2,I,0 ) + 16.0
1030
CONTINUE
C
C
COMPLETE THE CALCULATIONS
DO 1050 1=1,NTS
DO 1060 0=1,NS
FOR WD
IF (WD<5,I ,0).LE.£T. ) GO TO 40
WD(1,I,0)=WD(1,1,0)/WD(5,1,0)
WD(2,I,0)=WD(2,I,0)/WD(5,I,0 )
IF (WC( I,0 ).LT.3. > GO TO 51
WW=WC(I,0)/(WC(I,0)-1.0)
WQ=WC(I,0 )/(WC(I,0)-2. )
WD < 3,I,0)=WW* « WD(3,I,0)/WD(5,
)=WW*((WD(4,I,0 >/WD(5,
>=WQ*((WD(6,I,0 )/WD(5,
,I,0 ).LE.(DXP*DXP/12. !
,I,0 ).LE.(DYP*DYP/12. )
WD(4,1 ,0
WD(5 , I , 0
IF (WD(3
I r ( '.;D ( 4
WD(3,I,0
WD(4,I
WD(2, I
GO TO
I,0»-WD( 1
I ,0 »-WD(2
1,0)>-WD(1
> GO TO 50
) GO TO 50
I,0)*WD(1
I,0>*WD(2
I,0)*WD(2
,I,J
!i!o
))
»
»
40
51
50
WD( 1 .
WD(2,
WD(6,
WD(3,
WD(4,
WD(2,
I ,0
I ,0
I ,0
1,0
I ,0
I ,0
>=SQRT(WD(3,I ,0) )
,0 )=SQRT(WD(4, 1,0))
,0 )=WD(2, I ,0 ) + 16.0
1060
)=0.
}=3.
)=0.0
) = DXP/SQRT( 12. )
) = DYP/SQRT( 12
>
)=WD(2, I ,0 ) + 16.0
1060
CONTINUE
1080
2093
:>,L=1,7),R£MLBL,(J,
-------
577.
573 .
579.
530.
531 .
532.
583.
584.
535 .
585.
587.
588.
539.
590.
591.
592.
593.
534.
595 .
59sl
597.
59C.
599.
6."' rj .
5 C' 1 .
6.: 2.
END
//GO.FT09F001 DD DSN = B254S8 .TSUM80HC , UNI7=ALLPFRM,
// DISP=(NEW,CATLG),DCB=(LRECL=X,BLKSIZE=6144,RECFM=VBS),
// SPACE=(TRK,(10,5),RISE)
//GO.FT10F001 DD LABEL = ( 19,NL, ,IN ),UNIT=READ5250,OISP = , UN IT=READ625£T,
// DISP=,DCB=,
// VOL=SER=201436
//GO.?T21FJr01 DD DSN=OUL80. PF8HR , LABEL = { 8 , SL , , I N> , UNIT = READ6250,
// DISP = (OLD,KEEP > , DCB = (LRECL = X,BLKSIZE = 6136,RECFM=VBS),
// VOL=SER=201435
//GO.FT22F001 DD DSN=AUG80. PF6HR , LABEL = { 9 , SL., , IN >, UN IT = READ6250,
// DISP=(OLD,KEEP),DCB=,
// VOL=
'SER=201436
//GO.SYSIN DD *
0
0
0
0
0
0
0
0
0
0
0
vj
0
0
0
0
0
0
0
0
0000000
0 0123 0000
0 0120121122 0 0
0114115116117118119
01041051061071081091101
87
71
55
42
0
0
0
0
2T
0
0
88 89 90 91 92 93 94
72 73 74 75 76 77 78
57 58 59 50 61 62 63
43 44 45 45 47 48 49
30 31 32 33 34 35 35
0 19 20 21 22 23 24
0 0 3 11 12 13 14
0004567
0000200
0000000
0000000
0.500
6
MO
/*
1
DA
80 0 92 28
YR TI NDY NT
0 SS
0 0
0 0
0 0
11 0
95 95
79 80
64 65
50 51
37 38
25 25
15 16
8 9
0 0
0 0
0 0
UTROP
MO , DA
0
0
0
0
0
97
81
56
52
39
27
17
10
3
1
0
,YR
0
0
0
0
0
98
32
57
53
40
28
18
3
0
0
0
,HR
0
0
0
0
0
99
83
68
54
41
29
0
0
0
0
0
0 0
0 0
0 0
0 0
01.121
0
0
0
0
13
0
0
0
0
0
100101102103.
84 85
69 70
55 0
0 0
0 5
0 0
0 0
0 0
0 0
0 0
86
0
0
0
0
0
0
0
0
0
OF START, #
0
0
0
0
0
0
0
0
0
0
DAYS
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
*
INDICATOR
ARRAY FOR
VIRTUAL
SOURCES OF
TRAJECTORY
STATISTICS
# TIMESTETS
58
-------
Appendix B: Program VERT Listing
59
-------
1. //VERT JOB REGION»ZU3rK,TIME=ljer.,CLASS-X
2. //"MAIN ORG=RM080
3. /'/ EXEC FGICLG
4. //SYSIN DD *
5. C THIS PROGRAM CALCULATES THE HORIZONTALLY INTEGRATED VERTICAL
5. C PROFILES OF GAS, PRIMARY AND SECONDARY PARTICLES
7. C AS A FUNCTION OF TIME AFTE3. RELEASE (PLUME AGE).
3. C
9. C DIURNAL AND VERTICAL VARIATIONS OF PARAMETERS ARE SIMULATED,
10. C BUT NOT HORIZONTAL VARIATIONS. TYPICAL PATTERNS OF VERTICAL EDDY
11. C DIFFUSIVITY ARE SPECIFIED BY LAYER AND TIME OF DAY. THE RATE OF
12. C TRANSFORMATION FROM GAS TO PARTICLE IS. A FUNCTION OF TIME OF DAY.
13. C DEPOSITION VELOCITIES FOR S02 OR NO/N02 AND S04 OR N03
14. C ARE SPECIFIED BY TIME OF DAY.
15. C
15. C THE TYPICAL STABILITY CYCLE IMPLIED 3Y THE EDDY DIFFUSIVITIES IS
17. C A NOCTURNAL INVERSION, FORMING AROUND SUNSET, DEEPENING DURING
13. C THE NIGHT, AND RISING AND ERODING FROM BELOW DURING THE MORNING.
19. C DURING THE AFTERNOON THE ATMOSPHERE IS UNSTABLE UP TO THE
23. C SYNOPTIC-SCALE MIXING HEIGHT.
21. C
22. C THE MODEL CAM HAVE A MAXIMUM OF 9 LAYERS WITHOUT REDIMENSIONING,
23. C WITH THE NUM3ER OF LAYERS AND THE LAYER THICKNESSES BEING INPUT.
24. C CONCENTRATIONS ARE. ALSO CALCULATED AT THE SURFACE.
25. C
2o. C THROUGHOUT THE PROGRAM, S02/N02 IS POLLUTANT 1, PRIMARY S04/N03
27, C IS POLLUTANT 2, AND SECONDARY S04/N03 IS POLLUTANT 3.
23. C
29. C
3~. C VARIABLES IN LABELLED COMMON1
31. C
32. C ABVML : AVERAGE MASS OF S OR N ABOVE THE MIXING LAYER
33. C FOR EACH TIME STEP AND POLLUTANT.
34. C A123 ,. A? , AND AS : PARTIAL CALCULATIONS FOR EACH LAYER.
35. C CONV : FRACTION OF A MOLECULE COMPOSED OF S OR N (BY MASS) FOR
~3G. C EACH POLLUTANT.
37. C DCOR : AIR DENSITY AT EACH LEVEL AS A FRACTION. OF SURFACE
C DENSITY.
C DEPT : DRY DEPOSITION MASS INCREMENT FOR EACH TIME STEP AND
C POLLUTANT, FOR A UNIT EMISSION.
4i. C 02 : THICKNESS OF EACH LAYER IN METERS.
.12. C DZD , DZU , AND DZZ : PARTIAL CALCULATIONS FOR EACH LAYER.
43. C EMR : RELATIVE DIURNAL EMISSION RATE.
4-1. C FAC : SEASONAL FACTOR AFFECTING THE TRANSFORMATION RATE.
45. C KZ : VERTICAL EDDY DIFFUSIVITY BY LAYER AMD HOUR.
45. C NTS : NUMBER OF TIME STEPS.
47, C NZ : NUMBER OF LAYERS.
4S. C NZ1 : ONE LESS THAN THE NUMBER OF LAYERS.
49. C SBUD : REMAINING AIRBORNE MASS FROM A UNIT EMISSION OF
53. C ATMOSPHERIC S OR N, BY TIME STEP AND POLLUTANT.
51. C SOXS : AVERAGE ONE-DIMENSIONAL SURFACE CONCENTRATION OF
52. C S OR N FOR A UNIT EMISSION.
53. C SRCLVL : SOURCE LEVEL POINTER.
54. C TR : HOURLY S02-S04 OR N02-N03 TRANSFORMATION RATE.
E5. C USTAR : FRICTION VELOCITY IM M/SEC.
55. C VD : DEPOSITION VELOCITIES 3Y AG£ CF THE PLUME, HOUR OF
57. C THE DAY, AND POLLUTANT.
Sa. C VK : VON KARMAN CONSTANT.
59. C Zl : HEIGHT FOR SURFACE CALCULATIONS.
£j.j. C 22 : HEIGHT OF THE CONSTANT FLU!! LAYER.
6:. C IPOL : POLLUTANT SYSTEM INDICATOR <1=SOX, 2=t!OX)
5i!. C
53. C
60
-------
54. C VARIABLES ONLY IN MAIM
55. C
56. C AVADML : AVERAGE MASS CF SOX OR NOX AB'OVI THE MIXING LAYER
67. C CORRESPONDING TO TRAJECTORY TIME STEPS.
GG. C A'-'BGT : AVERAGE MASS OF AIR30RME SOX OR NOX COERTSPOND! NG TO
53. C TRAJECTORY TIME STEPS.
73. C AVDD : AVERAGE DRY DEPOSITION CORRESPONDING TO TRAJECTORY
71. C TIME STEPS.
72. C AVSFC : AVERAGE SURFACE CONCENTRATIONS CORRESPONDING TO
73. C TRAJECTORY TIME STEPS.
74. C NSEASN : CODE FOR THE SEASON <1=SUMMER, 2-AUTUMN, 3=WINTER,
73. C 4=SPRING>.
7,5. C NVI : NUMBER OF VICTUAL SOURCE LEVELS.
77. C
73. C
79. COMMON /DP/ DZ<10),A123(9),A7(9>,A3(9 ) ,DZD<9 ), DZU(9 ), DZZC 9 )
ZiJ. COMMON /R/ DEPT( 163,3), 30XS( 158,3), SBUD( 168,3), ABVML( 168,3), Z1..Z2,
31. . y. VK,USTAR,KZ(2,9,24),VD{2,24,2>,TR(24),DCOR{ 10 ) , COMV( 4 ) , EMR< 9 , 24 )
32. COMMON /INT/ NTS , fiZ, NZ1 , SRCLVL, FAC , I POL
T3. DOU3LE PRECISION DZ, Al 23 , A7 , A8 , DZD , DZU., DZZ
2-\. DOU3LE PRECISION A1,A2,A3
Co. DOUBLE PRECISION SEASON(4 ) ,IP0(2 )
e3. REAL AVSFC (28, 3), AVDD (23, 3) , AV3GT< 28 , 3.) , AVABML ( 28 ,3 ) , KZ
37. INTEGER SRCLVL
S3. DATA SEASON/' SUMMER ',' AUTUMN ',' WINTER ',' SPRING '/
89. DATA IPO/' SULFUR ' ,'NITROGEN ' /
90. C
91 . C ... INPUT DATA
32. READ 2010,NSEASN,IPOL
93. 2010 FORWAT(2I3)
34. NZ=9
95. NTS-158
i'S. NVI =6
37. Z1 = 2.0
30. 22=35.0
39. PRINT 2021,IPO(IPOL)
!£?. 2J021 FORMATS CALCULATIONS FOR QXIDTS OF ' ,A8 )
131. PRINT 2020,NZ,NVI,SEASCfU NSEASN>, NTS,Zl,Z2
132. 2020 FORMAT(I2,' LAYERS WITH THE BOTTOM1,12,' BEING SOURCE LEVELS.1/
103. % ' MODEL',AS,'WITH',14,' TIME STEPS.'/' SURFACE CONCENTRATIONS',
104. X ' CALCULATED AT',F6.1,'M AND CONSTANT FLUX LAYER AT',
1J5, % 76 . 1 , 'M. ' >
10S. C
I.i7. C
153. C ... SET CONSTANTS.
USTAR=0.4
i.l, CONV<1)=32./54.
i;2. COMV(2>=32./96.
13. CONV<3>=32./96.
114. CONV<4)=96./64.
115. IF (IPOL.EQ. 1 ) GO TO 10
iio. COMV<1 ) = 14./46.
117. CCMV(2)=14./G2.
ll^J. CO;IV(4> = 62./46.
125. 10 CONTINUE
'.2;.. C ... CHECK PARAMETERS.
122. IF riZ.LE.9 .AND. NVL.LE.9 .AND. NSEASJH ^L£. 4) GO TO 50
12?. PRINT 2.750
:24. 2JET50 FORMATi'1 A USEP.-SUPPL.I^D PARAffETUR IS TOO LARG.E AND MAKES'/
125. % ' EXECUTION IMPOSSIBLE. EXECUTION HALTED.')
::ro. STOP
127, 50 IF (iiZ.ST.l .AMD. NVI.GT.-Cf .AND. MSIASti. GT .3 .AND. Z1.GT.0)
61
-------
128.
129.
135.
131.
132.
133.
134.
135.
135.
137.
133.
139.
1 40 .
141.
142.
143.
144.
145.
145.
147.
143.
1-19.
i30.
I 51 .
152.
153.
154.
155.
156.
157.
1 58 .
1 = 3.
160.
161 .
162.
153.
134.
1G5.
166.
167.
153.
i i 3 .
1-0.
1 - [
1-2 .
1 7C .
1-4.
175.
176.
177.
178.
179.
130.
1 G 1 ,
132.
133.
1S4.
105.
135.
i'37.
183.
139.
Isfl.
191.
?
2070
2
C
80
C
C . . .
2090
2100
2110
C
C . . .
2121
2120
2122
2123
2130
2124
2125
1140
C
C
9934
C
r*
c
c
c . . .
c
c
c
c . . .
150
C . . .
160
C . . .
170
GO TO 80
PRINT 2070
FORMAT* ' A USER-SUPP1_X£D PARAMETER IS TOO SMALL ACT MAKES1/
1 EXECUTION IMPOSSIBLE. EXECUTION HALTED.')
STOP
NZL=NZ-1
AMD DENSITY
,I=1,MZ)
READ THICKNESSES
READ 2090,
FORMAT*//1 LAYER THICKNESSES
READ 2090,,1=1,NZ)
FORMAT, 0 = 1 , 24), I =-1, NZ )
1 )
IPOL
1> GO TO 2125
READ
READ
READ
READ
FORMAT* 12F5
DO 2124 KQ-1
READ 2120, TR
IF (IPOL.EQ
GO TO 1140
READ 2 120, WASTE
READ 2120, WASTE
CONTINUE
INPUT DUMMY VALUES FOR DIURNAL EMISSION VARIATION.
DO 9934 1=1 ,9
DO 9934 0 = 1 ,24
EMR( 1,0 )=1.0
SET DEPOSITION VELOCITIES FOR THE FIRST 2.5 HOURS EQUAL TO
THOSE FOR AFTER 2.5 HOURS, UNLESS THEY ARE LE.SS THAN A
SEASONAL MINIMUM.
IN SUBROUTINE INTGRT THE PRIMARY SULFATE. DEPOSITION VELOCITY
IS SET TO 1.0 CM PER SECOND DURING THE FIRST 2.5 HOURS TO
ALLOW FOR GRAVITATIONAL SETTLING OF LARGE PARTICLES.
GO TO < 150, 160,170,1S0),NSEASM
SUMMER
S02MIN=.0030
S02MAX= .0050
GO TO 190
FALL
S02MIN=.0015
S02 MAX =.0040
S04MIM=..a020
GO TO 190
WINTER
S02MIM-.
62
-------
192. S04M!N=
193. GO TO 190
194. C ... SPRING
195. 130 S02MIN=.002S
196. S02MAX=.0040
197. S04MIN=.0020
133. 190 DO 1200 1=1,24
199. VD!2,I,1 )=VD(1,1,1)
200. IF ;VD(2,I,1 >.LT.S:02MIN> VDC2-,-! , 1 )=S02M1N
251. IF ;VD(2,I ,1 ).GT.S02MAX> VD( 2,-L, 1 ) = S02MAX
202. VD;2,I,2)=VD;1,1,2)
253. 1200 IF ; VD(2., I , 2 ) . LT . S04MIN > VD< 2,1, 2 ) = S04MI N
254. C
205. C ... PRINT DEPOSITION VELOCITIES.
255. PRINT 2210
207. 2210 FORMAT;- FIRST 2.5 HOURS OF DISPERSION')
208. PRINT 2220,>
2:5. PRINT 2220,,I=1,24)
211. PRINT 2210
2:2. PRINT 2230,(VD;2,I,2),1=1,24>
213. 2230 FORMAT;' PARTICLE DEPOSITION VELOCITY IN M/S',/,z(5X,i2F7.4,/>>
21.;. PRINT 2230,;VD( 1,1,2),1 = 1,24)
215. C
216. C ... SET EDDY DIFFUSIVITIES FOR THE FIRST 2.5 HOURS EQUAL TO THOSE
217. C FOR AFTER THE FIRST 2.5 HOURS, EXCEPT FOU THE FIRST
210. C AND SECOND LAYERS.
219. DO 1240 1=1,NZ
225. DO 1240 J=l,24
221. 1240 KZ<2,1,0)=KZ(1,1,0)
222. DO 1250 J = l ,24
223. 1250 IF ;:
235. PRINT 2260,<
231 . PRINT 2270,TR
232. 2270 FORMAT;' HOURLY TRANSFORMATION RATE',/,2;5X,i2F7.4,/)>
233. C
234. C ... CALCULATE, FOR LATER USE, FACTORS RELATED TO THE THICKNESSES
235. C OF THE LAYERS.
235. DO 2280 K=l,NZ
237. !*
245. A2 = *DZ< K )*DZ< K >/( 4.*DZD(K) )
245. A3=*DZU/4. )
Z-".7. A123;X)=A1-A2->A3
243. A7<;<) = ( DZD
2-'9. 2280 AS-vK) = DZ( K)*DZ{ K )/( 4 . *DZD( K ) )
205. FAC=2-.5rt:ABS;NSEASN-3)
251 . C
2B2. C ... SET DUMMY VALUES FOR USE IN COMPUTING DIFFUSION UP ACROSS THE
252. C TOP OF THE MIXING LAYER.
25-1. oz(NZ+i ) = DZ;NZ>
DCCR
-------
256.
257.
253.
259.
230.
231 .
252.
253.
254.
2S5.
265.
267.
253.
259.
270.
271.
272.
273.
274.
275.
275.
277.
278.
279.
280.
281 .
232.
233.
234.
2S5.
233.
287.
283.
289.
290.
231.
292.
293.
294.
293.
295.
297.
298.
299.
300 .
301 .
302.
303.
304 .
305.
305.
307.
3.V8.
309.
310.
311 .
312.
313.
314.
315.
316 .
317.
313 .
313.
C
C
C
C
C
C
C
C
C
C
C
C
C
. DO FOR EACH SOURCE. LEVEL.
DO 1390 SRCLVL=1,NVI
DO 1290 1=1,168
DO 1290 0=1,3
ASVMLtI,J)=0.
S3UD< I,0)=0.
SOXS(I,0)=0.
12.90 DEPT=AVSFC( 11,0 ) + St>XS( 1,0 5/6.JET
AVDD( II,0)=AVDD< II,0)+DEPT< 12.2 ,0 >/Z.0
IF (II.ME.l) AVDD< II ,0 >=AVDD( II ,0)-DEPT< 11,0 5/2.JET
AVBGT( II,0)=, ' PRIMARY PARTICLE', 2< T21_ 14-( 1
XX,F6.5>/>,' SECONDARY PARTICLE',2(T21,14(1X,F5.5)/))
PRINT 2350
2360 FCRMATi ' MASS OF DRY CrEPOSITTON: ' >
PRINT 2350,AVDD
PRINT 2370
2370 FORMAT< ' MASS OF S OR N ABOVE THE MIXING LAYER'.1)
PRINT 2350,AVABML
PRINT 2380
2380 FORMAT(' MASS OF AIRBORNE S OR N:1)
PRINT 2350,AVBGT
WRITE (11) AVSFC,AVDD,AVBGT
1390 CONTINUE
STOP
END
SUBROUTINE INTGRT
C ... DIFFUSE UNIT ZMISSIONS OT SOX OR XOX ORIG1NATTN-G ' IN A GIVEN
64
-------
223. C SOURCE LAYER.
321 . C
322. C
323. C VARIABLE'S ONLY IN INTCRT
324. C
325. C AML : AMOUNT ABOVE THE MI X ING...LAYER FOtt A 1-HOUR
326. C TIME STEP.
327. C CP : THE DISPLACEMENT IN METETtS OF THE CENTER OF MASS OF A
328. C PUFF AFTER DIFFUSION.
329. C DEP : AMOUNT OF DEPOSITION IN A 1-HOUR TIME STEP.
330. C DTT : LENGTH OF A SU3STEP LN SECONDS.
331. C FL2 AND FL4 : FLUXES OF GAS AND PARTICLE.
332. C ID : EQUALS 1 AFTER THE FIRST 2.5 HOURS. EQUALS 2
333. C DURING THE FIRST 2.5 HOURS.
334. C L : HOUR OF DAY SEING MODELLED.
335. C Ml , M2 , AND M3 : THE FRACTION OF THE MASS ORIGINALLY1 IN
336. C THE CURRENT LAYER WHICH HAS DIFFUSED TO THE LOWER
337. C ADJACENT, THE CURRENT, AND THE UPPER ADJACENT LAYERS,
333. C RESPECTIVELY.
339. C NTI : NUMBER OF SUBSTEPS IN AN HOUR.
340. C ORIGHR : HOUR OF THE DAY THE PLUME ORIGINATED.
341. C P : FRACTIONAL ALLOTMENT OF POLLUTANT PUFFS FROM EACH LAYER
342. C TO THE LOWER, CURRENT, AND UPPER LAYERS FOR EACH POLLUTANT.
343. C RATIO : RATIO BETWEEN THE CONCENTRATIONS AT THE TOP OF THE
344. c CONSTANT FLUX LAYER AND THE SURFACE.
345. C SOX : VERTICAL PROFILES OF SOX OR NOX.
346. C TRR : FRACTION OF GAS NOT TRANSFORMED TO PARTICLE.
347. C SX : HOLDS NEW PROFILE OF SOX OR NOX.
343. C TX : LENGTH OF A TIME STEP IN SECONDS.
349. C VAR : THE SPATIAL VARIANCE OF THE POLLUTANTS' CONCENTRATIONS.
350. C
351 . C
352. COMMON /DP/ DZ< 113), A 1.23 ( 9 ), A7< 9 > ,A8( 9 ), DZD< 9 ), DZU( 9 >, DZZ< 9 )
353. COMMON /R/ DEPT< 153 , 3 ), SOXS< 168, 3 ), SBUD< 1 S3 , 3 ), ABVML ( 1 63 , 3 ) ,21,22.,
354. X VK,USTAR,KZ{2,9,24>,VD(2,24,2),TR<24>,DCOR<10),CONV(4>,EMR<9,24>
355. COMMON /INT/ NTS,NZ,NZi,SRCLVL,FAC,I POL
355. DOUBLE PRECISION DZ, A123 ,A7 ,AS , DZD , DZU., DZZ
337. DOUBLE PRECISION SOX ( \S3, 3 ), F 1 < 9 ), F3( 9 ) , AA , AAA , TRR
358. DOUBLE PRECISION A12,A21,P(9,3,3 ) ,A1,A2,A3,AB,D,DD
3£9. DOUBLE PRECISION RATIO*6),SX(9,3>,VDD,VDU,CP,VAR
3 0.0. DOUBLE PRECISION Ml,M2,M3,DEP<3 ) ,AML<3>
361. REAL KZ
362. INTEGER SRCLVL,ORIGHR
3S3. C
354. C ... SET DUMMY VALUES FOR USE IN CALCULATING DIFFUSION ACROSS
365. C THE TOP OF THE MIXING LAYER ASSOCIATED. WITH CLOUD PENETRATION.
366. SOX(NZ+1,1>=0.
357. SOX(NZ+1,2)=0.
358. SOX(NZ+1,3)=0.
369. C
370. DO 1190 ORIGHR=1,24
371. AML(1)=0.
372. AML(2>=0.
373. AML(3)=0.
374. C
375. C ... KIPUT UNIT EMISSIONS OF GAS AND PRIMARY PARTICLE INTO
375. C THE SOURCE LAYERS. PUT A VERY SMALL AMOUNT OF SOX OR NOX INTO
377. C THE OTHER LAYERS TO ELIMINATE DIVISION BY ZERO.
373. SOX< SRCLVL,1 ) = !.*EMR(SRCLVL,ORIGHR)
379. SOX{SRCLVL,25 = 1.*EMR(SRCLVL,ORIGHR )
20,'j. DO 1U10 0 = 1 ,3
331. DO 1010 1 = 1, NZ
332. 17 ( I.EQ.SRCLVL .AND. J.LT.3) GO TO 101.3
333. SCX! I , J ) = OZ( I )*1 . E-ljCT
65
-------
384.
385.
385.
387.
388.
339.
390.
391 .
392.
393.
394.
395.
395.
397.
398.
399.
400.
401 .
402 .
403.
1013
407 ,
4,19
412.
413.
414.
415.
415.
417.
413.
419.
420.
421 .
422.
423.
424.
42S.
428.
423.
429.
430.
431 .
'132.
433.
434.
435.
435.
437.
433.
439.
44£f,
441 .
442.
443 .
444.
445.
445.
4 4 7 ,
C
C
C
C
C
C
1020
C
C
C
1021
C
C
C
1030
1040
C
C . . .
50
CONTINUE
PRODUCE INTEGRATION TO CORRESPOND TO THE TRAJECTORY
CALCULATIONS.
DO 1190 ITT=1,NTS
ID=1
IF (ITT.LT.4) ID=2
DEP(1>=0.
DEP<2)=0.
DEP(3)=0.
L=MOD((ITT+ORIGftR-2),24)+l
COMPUTE RATIOS BETWEEN CONCENTRATIONS AT THE'TOP OF THE
CONSTANT FLUX LAYER AND AT THE SURFACE HEIGHT.
DO 1020 0=1,2
RR=VK*USTAR/VD=RR/(RR+ALOG = RATIO(5)
COMPUTE THE LARGEST ALLOWABLE TIME INCREMENT OF INTEGRATION
FOR A SUBSTEP.
TX=3600.
DTT=TX
RS=10000.
DO 1030 1 = 1,NZ.
RR=DZ/KZ(ID,I,L)
IF (RR.LT.RS) RS=RR
AAA=0.
DO 1040 0=1,3
DO 1040 I = l,NZ.l
SS = SOX+A2*DZ(I >
AA=A12/A21
IF (AA.GT.AAA) AAA=AA
CONTINUE
DTT=0. i250*RS/AAA
IF (DTT.GT.TX) DTT=TX
NTI=TX/DTT + 0.9999
DTT=TX/NTI
CHECK FLUXES. IF EITHER IS TOO LARGE, REDUCE THE TIME STEP,
FL2 = VD(!D,L,1}*DTT/DZ<1 )
FL4=VD
-------
448. C ... IF FIRST TOUR, REDUCE THE TIME STEP SINCE DOING FORWARD
449. C INSTEAD OF CENTERED FINITE DIFFERENCING.
450. 60 IF UTT.EQ.l) DTT=DTT/2.
451. C
452. C ... MODIFY TRANSFORMATION RATE FOR SOURCES IN EOTTOM TWO LAYERS
453. C DURING THE FIRST 2.5 HOURS.
454. AFAC=0.
455. IF (ITT.GT.3 .OR. SRCLVL.GT.2) GO TO 70
456. AFAC=0..25*(3.-SRCLVL)'-<4.-FLOAT( ITT) )/( FAC*FLOAT( IPOD)
457. 70 TRR=1 .0-(TR< L )+AFAC* .01 )
458. C
459. C ... MAKE A PORTION OF THE DIFFUSIVITV CALCULATIONS.
450. DO 1080 K=2,NZ.
451. F1(IO=2D0*KZ< ID.K-1 ,L)*DTT
462. 1080 F3(K)=2D0*KZ< ID,K,L)*DTT
453. Fl(l>=0.0
464. F3(1)=F1(2>
455. F3(NZ)=F3*DCOR< KU ) )
434. A3=SOX(KD,I >/< DZ( KD >*DCOR< KD ) )
405. C
435. VDU=F3+A3*DZ )
433. CP=(VDU-VDD>*.5
',09. VAR=(DZZC<) -i- 5D-r-'/A123 M3=0.
436. C ... FOR BOTTOM LAYER
497. IF (K.EQ. 1 ) Ml=0.
493. M2=1.-M1-M3
499. P=0.
1110 CONTINUE
C
5J9. C ... REALLOCATE DISPERSED PUFFS.
51J. DO 1120 0=1 ,3
511. SX
-------
512.
513.
514.
515.
515.
517.
513.
519.
520.
521.
522.
523.
524.
525.
526.
527.
528.
529.
530.
531.
532.
533.
534.
535.
536.
537.
533.
539.
540.
541.
542.
543.
544.
545.
5 -IS.
547.
540.
549.
550.
551 .
532 .
553!
534.
555.
555.
557 .
553.
559.
550.
561 .
562.
563.
554.
C '^ C
566.
567.
568.
559.
570.
571 .
572.
573.
574.
575.
1120
t
/
C
C . . .
1
c
c
1130
C
C . . .
1140
1150
C
C . . .
1150
C
C . . .
1170
1130
1190
C
C . . .
c
1210
//GO.:
// UN
// SP-
//GO.:
1
SX< 1,0)=P( U2.,0)*SOX(UO)+4>(2,1,J)*SOX{2,0)
AML<0)=-AML.<0)+P*SOX(NZ,0 )
DO 1120 1=2,NZ1
11=1-1
12=1+1
SX 00=2
IF 0*DTT*RATIO{ 0 + 3)*SXC1,0)
/DZ(1 )
ALLOW NO MORE THAN 3% OF THE S OR N TO BE DEPOSITED IN
ONE SUBSTEP.
IF (D.GT.(SX( 1 ,0 )*.03) ) D=SX( 1 ,0 )*0.,03.00
DEP<0)=DE?<0)+D
SX( 1,0) = SX<1 ,0 )-D
CONTINUE
TRANSFER NEW, VALUES TO ARRAY SOX .
DO 1140 1 = 1,NZ
DO 1140 0=1,3
SOX<1,0 > = SX(1,0)
CONTINUE
CALCULATE CHEMICAL TRANSFORMATION FROM GAS TO PARTICLE.
DO 1160 I=1,NZ
SOX=TRR*SOX<1,1)
AML(3)=AML< 3)+CONV(4)*(1.0-TRR >*AML(1)
ABVML(ITT,3)=A3VML(ITT,3>+AML<3>
ABVML
SUM FIELDS.
DO 1180 0=1,3
SOXS
-------
575.
577.
570.
573.
530.
531 .
532.
533.
5C4.
505 .
535.
507.
533.
589.
590.
591 .
592.
593.
534.
595 .
596.
597.
590.
599.
60,0' .
601 .
602.
603 .
60- .
5^5.
60'6.
657 .
60S ,
509.
610.
611.
612.
613.
614.
615.
613.
617.
613.
619.
620.
62 i .
622,
623.
624.
625 .
625.
627.
623.
629.
630.
631 .
632.
633.
634.
635.
636.
6 C 7 .
630.
639.
100
1 .
10
90
5
45
3
50
16
120
1 .
10.
1 .
10.
1.
10.
0.5"
10.
1 .
10.
1 .
10.
1 .
10.
1.
10.
1.
10.
40
400
100
1000
10
65
5
35
3
40
8
50
1 .
10.
1 .
10.
1.
10.
.75
10.
1.
10.
1 .
10.
1.
10.
1 .
1 .
1 ,
1 .
24
200
50
485 .
10
70
100 100
.988 .975 .
10 10 10
85 30 75
555
45 45 45
333
55 50 45
16 16 16
110 100 90
1 . 1.
10. 10.
1. 1.
10. 10.
1. 1.
10. 10.
0.5 0.5
10. 10.
1. 1 .
10. 10.
1 . 1 .
10. 10.
1 . 1 .
10. 10.
1 . 1 .
10. 10.
1 . 1 .
10. 10.
40 40 40
380 360 300
100 100 100
950 900 750
10 10 10
55 50 45
555
36 36 30
388
35 30 25
888
55 50 45
1. 1.
10. 10.
1 . 1 .
10. 10.
1 . 1 .
10. 10.
.75 .75
10. 10.
1 . 1 .
10. 10.
1 . 1 .
10. 10.
1 . 1 .
10. 10.
1 . 1.
1 . 1 .
1 . 1 .
1 . 1 .
24 24 24
180 L60 140
50 50 60
''50 400 350
10 10 10
60 55 50
100
.965
15
70
5
40
10
40
20
80
1.
10.
1 .
10.
1.
10.
0.5
10.
1.
10.
1 .
10.
1 .
10.
1 .
10.
1 .
10.
40
220
100
550
10
40
5
25
8
20
3
40
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10.
1 .
10.
1 .
10.
.75
10.
1 .
10.
1 .
10.
1 .
10.
1 .
1 .
1 .
1 .
24
112
60
280
13
40
200 200
.950 .930 .
25
55
10
30
15
35
30
70
2.
10.
1 .
10.
1.
10.
45 55
30 15
15 25
20 10
30 45
25 10
60 90
50 20
4.
10.
1 .
10.
1 .
10.
0.S 0.5
10.
1 .
10.
i
10!
i .
10.
i .
10.
i.
10.
60
140
150
350
20
35
8
20
15
15
15
35
1 .
10.
1 .
10.
1 .
10.
.75
10.
1 .
5.
1 .
5.
1 .
3.
1 .
1 .
1 .
1 .
24
30
50
200
10
35
10.
1 .
5.
1 .
5 .
1 .
5.
1 .
5.
1 .
5.
80 140
80 60
200 3B0
200 150
30 40
20 10
12 16
10 5
25 30
10 a
30 45
20 B
2.
5.
1 .
5.
1 .
5 .
.75
5.
1 .
1 .
1 .
1 .
1 .
1 .
1.
1 .
1 .
1 .
5-6 80
55 24
140 200
1 .10 30
10 30
10 10
200
910 .
80
10
35
5
55
8
110
16
8.
6.
3.
5.
1 .
5.
0.5
5.
1 .
1.
1 .
1.
1 .
1.
1 .
1 .
1 .
1.
220
40
550
100
50
10
20
5
35
8
55
8
5.
1.
2.
1.
1 .
1 .
.75"
.75
1 .
1 .
1 .
1 .
1 .
1 .
1.
1 .
1 .
1 .
112
24
280
50
50
10
400
.830
90
10
40
5
60
8
120
15
10.
2.
7.
i .
i!
i .
3.
0.5
1.
1 .
1 .
1.
1 .
1 .
1 .
1 .
1 .
1.
300
40
T50
100
60
10
25
5
40
8
60
8
3.
1 .
5.
1.
3.
1 .
.75
.75
1 .
1 .
i .
1 .
1 .
1 .
i .
1 .
1 .
1 .
U0
24
350
60
60
10
400 LAYER TWICKNFSS
.340 DENSITY CORRECTION
90 90
10 10
45 45
5 5
60 60
8 8
120 120
16 16
10. 10
1. 1
10. 10
1 . 1
13. 10
1 . 1
10. 10
0.5 0.
5. 10
1. 1
1. 5
1. 1
1. 5
1 . 1
1 . 1
1 . 1
1. 1
1. 1
360 3SJ0T
40 40
900 9-50
100 100
65 65
10 10
30 36
5 5
40 40
8 3
60 60
8 8
10. 10
1. 1
8. 10
1. 1
7. 10
1. 1
3. 10
S02 DRY
*
S04 DRY
NO/N02
DEP
100
DEP
S.UMMFR
DRY DEP
NITRATE/HNO3
10.
1.
. 10.
1 .
. 10.
1 .
13.
5 0.5
. 10.
1 .
. 10.
1 .
. 10.
1.
5.
1 .
1 .
1 .
S02 OXID
TIMES
10.
1.
10.
1 .
10.
1 .
10.
0.5
10.
1.
10.
1 .
10.
1 .
10.
1 .
5.
1 .
DRY DEP
DIURNAL
PATTERN OF
VERTICAL
EDDY
DIFFUSIV.
IN METERS
SQUARED
PER S
BY HR AND
LAYER
. RATE IN X/ttR
100
NOX OXLDATION
. 10.
1.
10.
1 .
. 10.
1.
10.
.75 .75 .75
1 . 3
1 . 1
1 . 1
1 . 1
1 . 1
1 . 1
1. 1
1 . 1
1 . 1
1 . 1
160 150
24 24
400 450
50 60
73 70
10 10
10.
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
10.
1 .
10.
1 .
10.
1 .
10.
.75
10.
1 .
10.
1 .
10.
1 .
1 .
1 .
1 .
1 .
RATE
FALL
69
-------
Appendix C: Program CONCDEP Listing
70
-------
640. ;3 5 5 5 5 5 5 12 15 18 21 24 WINTER
641.
642.
643.
644.
5-5.
645 .
647.
643.
649.
650.
651 .
652.
653.
654.
655.
655 .
657.
658.
659.
663.
651 .
632.
663.
654 .
6G5 .
66S.
567.
5S3. 10 10 10 10 15 25 35 55 70 80 80 80 STRING
G59 .
67.2?.
671.
672.
673.
6 7 -I.
675.
675.
6 7 7 .
673.
679.
63.1?.
o» 1 .
632.
633.
684.
6S5.
6S6.
637.
683.
639.
695.
691 .
6S2.
693.
694.
6'J5.
653.
697.
693.
;3
25
9
45
15
3£?
1.
10.
1.
10.
1 .
ISS.
.75
10.
i .
12.
I.
10.
1.
1.
1 .
1 .
1 .
1 .
16
112
40
280
10
80
5
40
a
50
a
60
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10.
1.
10.
1 .
10.
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10.
1 .
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1 .
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1 .
10.
i .
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32
325
35
800
/*
5
25
9
35
15
80
1
i *
10.
1 .
10.
1 .
10.
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1 .
1 .
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16
96
40
5 5
25 25
9 9
30 25
15 15
75 70
1.
10.
1 .
10.
1 .
10.
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10.
1 .
10.
1.
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1.
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1 .
1 .
1 .
1.
15 16
30 64
40 40
240 Z00 1S0
10
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5
40
3
45
3
55
1 .
10.
1 .
10.
1 .
10.
.75
IS.
1 .
10.
1 .
10.
1 .
10.
1 .
10.
1 .
10.
32
230 2
80
10 10
70 65
5 5
40 40
8 8
40 35
8 B
50 45
1.
10.
1 .
li?.
1 .
10.
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10.
1 .
10.
1 .
10.
1.
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1 .
15.
1 .
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32 32
40 200
80 80
750 500 500
5
16
9
20
15
65
1 .
10.
1 .
10.
1 .
10.
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10.
1 .
10.
1 .
10.
1.
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1 .
1 .
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16
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40
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15
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5
35
3
30
10
40
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80
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5
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9
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15
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1.
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5.
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5.
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1 .
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1.
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16
32
40
80
25
45
10
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15
25
15
35
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10.
1 .
10.
1 .
10.
.75'
10.
1 .
10.
1 .
10.
1 .
10.
1 .
10.
1 .
5 .
56
1.25
140
300
5 12
10 5
9 30
9 9
15 45
20 15
1 .
1 .
1.
1.
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1.
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.75 .
1 .
1 .
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1.
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16 32
16 16
40 80
40 40
35 55
30 15
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15 10
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30 45
25 10
3.
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10.
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10.
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80 12.B
80 56
200 300
200 140
15
5
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9
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15
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1.
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75
75
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48
16
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40
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10
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5
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3
55
3
7.
5.
4.
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75
5.
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5.
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64
16
L60
40
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5
50
8
60
8
1.0.
1 .
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6.
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5.
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1 .
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1 .
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1 60'. 200
32
400
80
32
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80
21
5
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9
80
15
10.
1.
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3.
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1.
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80
16
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40
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60
3
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240
32
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80
24
5
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15
10.
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10.
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1 .
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9-6
16
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40
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10
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5
50
3
60
8
10.
1 .
10.
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1 .
10.
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10.
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5.
1 .
5.
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28J2T
32
7'00
80
10.
1.
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1 .
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1.
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10.
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1.
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10.
l .
10.
.75
10.
1 .
10.
1 .
10.
1.
5.
1.
i[
I .
10.
1.
10.
1 .
10.
1 .
10.
.75
10.
1 .
10.
1 .
1 .
1.
1 .
1 .
1.
1 .
10.
1 .
10.
1.
10.
i *
10.
.75
10.
1 .
10.
1 .
10.
1 .
10.
1.
5.
1 .
71
-------
3.
9.
10.
11 .
12.
13.
14.
15.
15.
17.
33,
O *v ,
C 5
3 5 ,
33.
42 .
44
//CONCDEP JOB
//*VAIN ORG=RM080
// EXEC FGICLG
//SYS IN DD *
DIMENSION
DInZMSIOM
DIMENSION
DIMENSION
DIME MS I ON
DIMENSION
DIMENSION
REAL MAXM
R0 = 5397./381 .
R018=l.866*RO
P2=3.14159/2.
= 1,REGION=5J2TJ3TK,CLASS=W
C
C
C
C
C
C
C
C
C
C
,45)
CO(6,28,123),WD(6,28,123),LANDE< 19,16)
S02<51 ,45), 204(51 ,45>,DDEP(51,45),WDEP(51,
X(51 ),Y(45), INDO(51 ,45)
SOX<28,3,6),DEPT{28,3,6),S8UD(28,3,6),MAXN( 12)
EM(720,6),XS{720),YS(720),WF(6>,EW( 12)
ISTPR<60>, IND(51 ,45)
AREA(60),AREAA(60),ADRY(60),AWET(60)
NSP
MSP =
333:
IS
12
THE NUMBER OF TELESCOPED TIME STEPS,
SBXX AMD SBYY ARE THE VARIANCES OF A UNIFORM DISTRIBUTION ACROSS
A 1/3 NMC
SBXX=(1,/3.
SBYY = ( 1 ./3
CELL
)*( 1 ,
)*( 1
AND ARE USED IN RESOLUTION LIMITATIONS,
C!<1 AND OUTSID ARE USED IN REDISTRIBUTING PUFF MASS BEYOND .
2.5 STANDARD DEVIATIONS.
OUTSID=1.045
CK1=0.0175*0.3939*2.0*3.14159
CK1=ALOG(CK1 )
FAC1 AMD FAC2 ARE USED IN CALCULATING CONCENTRATION AND DEPOSITION
FIELDS.
FAC1=1.E06*OUTSID
FAC2=365."OUTSID
UTROP IS THE FRACTION OF SULFUR MASS REMOVED
THE PBL 3Y PRECIPITATION TRANSFERRED TO THE
TROPOSPHERE.
UTROP=l./2.
UT = UTROP/(1.-UTROP )
FROM
FREE
INPUT PARAMETERS
NXC=51
NYC = .-t5
X<1)=-10.83
DXC=1 ./3.
DESCRIBING CONCENTRATION/DEPOSITION GRIDS.
V ( 1 ) = 1 9 . S 3 3
DYC=1./3.
YS=Y< 1 ) + DYC/2.0
DO 2333 1=2,NXC
X( I > = X! 1-1 J + DXC
DO 3332 0=2,NYC
YCO ) = Y(0-1 )-DYC
FACX=38l.*381.*2.*3.1416/(1.866*1.366)
FACXX = 381 .-'381 .*DXC*DYC/( 1 .366*1 .865)
DEFINE PARAMETERS DESCRIBING TRAJECTORY PROGRAM FIELDS.
NG=19
MG=>5
X L = - 1 1 . 5
YL=20.5
72
-------
64. GX=1.0
S 5 . G Y = 1 .0
IS. NTS=2S
63. DO 901 1=1,NXC
69. DO 901 0=1,NYC
70. S02*I,0)=0.0
71. S04(I,0)=0.0
72. DDEP
84. 7003 FORMAT*1812)
S5. 7002 FORMAT*33I2)
CJ. C
S7. C ... INDD IDENTIFIES EACH STATE AND PROVINCE.
£3. DO 34.J 00 = 1 ,45
S3. 0=46-00
20. 940 READ (50,7005) *1.51*1.61*10.0
C
iZ'7. C ... MAXN IS USED IN CALCULATING THE MAXIMUM POTENTIAL TRAJECTORIES
103. C CONTRIBUTING TO A STATISTIC IN THE TELESCOPED TIME STEPS.
l.-:9. READ 8020, MAXN
lit/. 3020 FORMAT* 12F6. 1 )
111. C
112. C ... EW IS USED TO WEIGHT PUFFS IN THE TELESCOPED TIME STEPS.
113. READ 8021,EW
114. 8021 FORMAT*12F2.0)
115. C
ue. c
117. C ... READ RESULTS OF TRAOECTORY PROGRAM.
113. C IN THE TRAOECTORY PROGRAM ARRAY CO IS CALLED CONC.
113. READ <10) CO,WD,LANDE,NF
12.0'. XNF = NF
12.. DO 700 0 = 1, 123
122. DO 701 1=1,23
i:3. CO*6, I ,0)=CO<5,I,0)/*CO*3,1,0)*CO(d,!,J) )
124. 701 WD(5,I,0)=WO(6,I,0)/*WD(3,I,0 )*WD*4,I,0) )
125. C
125. C
127. C
73
-------
12S. C
129. C ... POST-PROCE'SSING OF TRAJECTORY STATISTICS TO COMPRESS TIMESTEPS,
1?7. DO 500 K=3,4
131. DO 502 1=1,4
132. IA=2*I+4
133. IB=IA-1
134. IC=I+4
135. W1=CO<5,IA,0 )
135. W2 = CO<5,IB,J )
137. WA=W1+W2
133. IF (WA.LE.0. ) WA=1.
139. X1=UD(5,IA,J)
140. X2=WD<5,IB,J)
141. XA=X1+X2
142. IF (XA.LE.0.) XA=1.
143. Y1=CO(K-2,IA,0)
1A4. Y2 = CO(K-2,IB,0 )
145. Z1=WD(K-2,IA,0)
145. Z2=WD(K-2,IB,J)
147. YY=\W1*Y1+W2*Y2)/WA
140. ZZ=(X1*ZUX2«Z2)/XA
143. CC=(W1*CO(K,IA.O>*CO(K,IA,0)+W2*CO(K,IB,0)*CO* + X2*(Z2-ZZ>*(Z2-ZZ»/XA
153. C0(K,IC,0 ) = SQRT(CC>
154. WD(K,IC,0>=SQRT(DD)
155. 5J?2 CONTINUE
156. DO 503 1=1,4
137. IA=4*I+12
lie. IB=IA-1
IC=IA-2
ID=IA-3
IE=I+8
W1=CO(5,IA,0 )
153. W2 = CO<5,18,0 )
164. W3=CO(5,IC,J)
165. W4 = CO(5,ID,J )
166. WA=W1+W2+W3+W4
157. IF (WA.LE.0. ) WA=1.
153. X1=WD<5,IA,0>
:.-;. X2=WD(5, IB.O )
i7,. X3=WD(5,IC,J)
1"'.. X4=WD<5, ID,0 )
1/2. XA=:;i-x2-;:3-M4
173. IF < XA.LZ.Cf. ) XA=1 .
174. Yl=CO(K-2,IA,0)
175. Y2 = CO(!<-2, IB,0 )
175. Y3 = CO(K-2,IC,0 )
177. Y4=CO(K-2,ID,0>
178. Zl=WD(K-2,IA,0>
179. Z2=WD(K-2,IB,J>
180. Z3=WD
181. Z4=WD( !<-2, ID,J >
102. YY = (Wl*Yl+W2*Y2-s-W3*Y3-t-W4*Y4)/WA
133. ZZ=< X1*Z1+X2*Z2 + X3*Z3 + X4*Z4>/XA
1S4. CC = (Wl-*CO(K,IA,0 ) + ( Yl-YY )*( Y 1-YY » +
1G5. 1 W2-«CO + ( Y2-YY >*( Y2-YY ) ) +
1SS. 2 W3*(CO(I<:, IC,0 )?;CO(K, IC,0 > + ( Y3-YY)*( Y3-YY) > +
187. 3 W4*/WA
133. DD = < Xla(WD*(Zl-ZZ ) )+
1S9. 1 X2~(V/D(K, IB,0 )*WD(K, IB,0 ) + ( 22-ZZ )'-; { Z2-ZZ ) ) +
190. 2 X3*(WD(K,IC,0)-WD(K,IC,0)-K Z3-ZZ)*(Z3-ZZ ) ) +
191 . 3 X4-'/XA
74
-------
192.
193.
194.
135.
1S6.
197.
193.
199.
200.
201 .
202.
203.
204.
205.
206.
207.
208.
259.
210.
21 1 .
212.
213.
214.
215.
216.
217.
213.
21S.
220.
221 .
222.
223.
224.
225.
226.
227.
223.
229.
230.
231 .
232.
233 .
234.
235.
235.
237.
233.
239 .
240.
241.
242.
243.
2 -' 4 .
245 .
246.
247.
243.
249.
250.
251 .
252.
253.
254.
255.
CO(K, 11, J )=SQRT
503 CONTINUE
500 CONTINUE
DO 710 !<=! ,5
IF (K.EQ.3) GO TO 7
IF (K.EQ.4) GO TO 7
DO 702 1=1,4
IA=2*I+4
IB=IA-1
IC=I+4
W1=CO(5, IA,0 )
W2=CO<5, IB,0 )
WA=W1+W2
X1=WD(5, IA,0 )
X2=WD(5, IB,0 )
XA=X1+X2
IF (WA.LE.0. ) WA=1 .
IF ( XA.LE .0. ) XA=1 .
C0( K, IC,0 )=(W1*CO
W4 = CO(5, ID,0 )
WA=W1+W2+W3+W4
X1=WD(5, IA,0 )
X2=WD(5, IB,0 )
X3=WD<5, IC,0)
X4=WD<5, ID,0 >
XA=X1+X2+X3+X4
IF ( XA.LE. 0. ) XA=1 .
IF (WA.LE.0. ) WA=1 .
CO(K, IE,0 >= C0<5, I ,0 ) = -0.99
, I ,0 >*CO(3, I ,0 )+2.*SBXX)
, I ,0 )*CO< 4,1,0 )+2.*SBYY)
, I ,0 )*WD< 3,1,0 )+3.*SBXX )
, I ,0 )*WD< 4,1,0 ) + 3.*SBYY>
C ... READ RESULTS OF VERTICAL DISPERSION PROGRAM.
C NVI IS THE NUMBE
C VERTICAL INTEGRA
DO 10 0=1,5
R OF EFFECTIVE EMISSION LEVELS FOR WHICH
TIONS ARE MADE.
75
-------
256.
257.
258.
259.
260.
261.
262.
263.
254.
265.
266.
267.
268.
269.
270.
271 .
272.
273.
274.
275.
276.
277 .
278.
279.
230.
281.
282.
283.
284.
285.
286.
287.
288.
289.
290.
291 .
292.
293.
294.
295.
296.
297.
298.
299.
300.
301 .
302.
303.
304.
305.
306.
307.
308.
309.
310.
311 .
312.
313.
314.
315.
316.
317.
318.
319.
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
READ (20) «SOX(I,K,0),I = 1,NTS>,K=1,3),«DEPT(I,K,0),I = 1,NTS>,
# K=1,3),«SBUD,K=1 ,3)
10 CONTINUE
REWIND 22
... POST-PROCESSING OF VERTICAL INTEGRATION STATISTICS TO COMPRESS
TIMESTEPS.
DO 706 K=1,NVI
DO 706 J=l,3
DO 707 1=1 ,4
IA=2*I+4
IB=IA-1
IC=I+4
SOX( IC,J,K)=(SQX< IA,J,K)+SOX< IB,J,K)>/2.
DEPT +DEPT( IB,J,K) >/2.
707 SBUD< IC,0,K) = (SGUD< IA ,0 , K)+SBUD< IB,J,K) 5/2.
DO 708 1=1 ,4
IA=4*I+12
IB=IA-1
IC=IA-2
ID=IA-3
IE=I+8
SOX( IE,J,K>=(SOX( IA,0,IO + SOX< I2,J,K)+SOX( 1C , J , K >+SOX( ID ,0 , K ) )/4.
DEPT+DEPT< 1C , 0 , K >+DEPT< ID,J,K»
S /4 .
708 SBUD + SBUD(ID,0,!<))
# /4 .
706 CONTINUE
GO TO 4444
... IS DEFINES THE APPROPRIATE SEASONAL EMISSION FIELD.
DO 4460 L = l , IS
READ (30) NS,NZ,( (EM< I ,0 >, 1 = 1 ,NS),0 = 1 ,NZ),(XS( I ),
1 I=1,NS),(YS(I),I=1,MS),DX,DY
4460 CONTINUE
DO 4488 1=1, MS
XS( I ) = XS< I ) + DX
4488 YS< I ) = YS( I )-DY
... THE GO TO 4444 STATEMENT AND THE FOLLOWING 11 STATEMENTS
... AND COMMENT CARDS SHOULD BE DELETED IN REAL SIMULATIONS.
4444 NS=1
XS( 1 ) = -4.5
YS< 1 ) = 15.5
EM< 1,5)=250.0
EM< 1 , 1 )=0.0
EM< 1 ,2)=0.ff
EM( 1 ,3)=0.0
EM( 1 ,4 >=0.0
EM( 1 ,6)=0.0
... EMISSION SOURCE IS LOCATED IN EASTERN OKLAHOMA.
... EMISSION RATE IS 250 KT S02 PER SEASON.
76
-------
320. C
321 . C
322. SUMM=0.0
323. DO 771 1=1,NS
324. DO 771 0=1,NVI
325. SUMM=SUMM+EM<1,0 )/365.
326. 771 EM(I,0>=1.E03*EM- GO TO 9999
343. IF (Il.GT.MG) GO TO 9999
344. IF (Ol.LT.l ) GO TO 9999
345. IF (Ol.GT.MG) GO TO 9999
346. IF (LANDE(II,015.EQ.0) GO TO 779
347. GO TO 773
348. 779 13=11-1
349. 14=11+1
350. 03=01-1
351. 04=01+1
352. IF ( I3.LT.1 ) 13 = 1
353. IF (I4.GT.NG) I4=NG
354. IF (03.LT.1 ) 03=1
355. IF (04.GT.MG) 04=MG
356. DO 777 i<=13, 14
357. DO 777 L=03,04
358. IF .GT.0) GO TO 7708
359. 777 CONTINUE
360. GO TO 9999
361. 7708 I1=K
362. 01=L
363. 778 CONTINUE
364. XBI=XS- )
355. YBI=YS(IO)-
356. I=LANDE( II ,J1 )
337. 8000 FORMAT(' TOTAL EMISSIONS = ',F8.1,' KT S PER SIMULATION1)
o < p p
369. C ... PMF IS THE FRACTION OF TOTAL SULFUR EMITTED AS
370. C PRIMARY SULFATE. IT IS HIGHER IN THE NORTHEAST, FLORIDA, AND
371. C IN THE FIRST EMISSION LAYER, UNDER THE ASSUMPTION
372. C THAT MOST EMISSIONS THERE ARE FROM OIL COMBUSTION.
373. PMF=0.015
374. IF (XS( 10 KGT.0.5) PMF = 1.33
375. IF (YS< 10 ).GT. 17.5 .AnO. XS( I 0>.GT.-3.0 ) PMF=0.03
376 . C
377. DO 86 M=l,NVI
373. 86 WF(M)=0.£f
379. C
3S0. C ... DO-LOOP FOR THE TELESCOPED TIME STEPS.
381. DO 100 0=1,NSP
382. 00=0-1
383. IF (O.EQ.1 ) 00 = 1
77
-------
334. C
385. C ... CALCULATE MASS WEIGHTING OF DRY AND WET PUFFS.
325. TW=CO(5,0,I>/ GO TO 95
396. PMQ=PMF
397. IF (M.EQ.l) PMQ=0.05
398. EMQ=1.0-PMQ
393. PMQ=1.5*PMQ
4.;/0. V2=V2+EW(0)*EM(IJ,M)*SOX(0,1,M)*EMQ
401 . V4=V4+EW< J )*EM< 10 , M ) * < SOX < 0 , 2 , M ) *PMCH-SOX ( 0 , 3 , M >*EMQ )
4.J2. IF (O.EQ. 1 ) GO TO 995
403. DO 85 L=l,00
4.74. 85 WF(M>=WF(M>+(UT*WD(5,L,I) *(EMQ*(DEPT(0J,1,M>
^75. 1 + DEPT(00,3,M)) + PMQ-/( UT*WD < 5 , L , I )+CO(5,L, I ) >>
<;75. WETF=WETF + EW< J )'£;!( 10 , M )*< WF < M )-M SBUD< J J , 1 , M > + SBUD< 0 0 . 3 , M > >*EMQ
407. # + PMQ*SBUD(00,2,M>)
458. WQ = UTi'WD(5,JO, I >+CO(5,00, I >
409. IF (WQ.LE.0.> GO TO 996
410. WF < M)=WF < M)*(1.0-WD(5,0,1)/WQ >
411. GO TO 996
412. 995 WETF=WETF+0.5*EW<0)*EM*EM
416. 95 CONTINUE
417. C
418. C ... ADD BIAS BETWEEN TRAJECTORY VIRTUAL SOURCE AND EMISSION CELL TO
419. C PUFF CENTROID, AND DETERMINE RECEPTOR GRID RECTANGLE CONTAINING
420. C 2.5 STANDARD DEVIATION PUFF FOR CONCENTRATIONS AMD DRY DEP.
421 . IF (TV/.LE.0. > GO TO 601
422. XB = XBI+CO< 1,0,1 )
423. YA = YBI-i-CO( 2,0,1)
''2-\. SCALE=CO< 3,0,1 )-vCO(4,0, I )*FACX
!i5. R01=SG!rf{ 1 .-C0< 5,0,1 )-CO( 5,0,1))
425. R02 = -2.'-'R01"ROi
427. ROW=-2.*CO<6,0,I>
423. R01=R01*SCALE
429. TWR=TW/R01
43,0'. I1 = (XB-2.5"CO< 3,0, I )-X0)/DXC + 1.
431. !2 = (XB + 2.5*CO(3,0,I )-X0)/DXC + 1.
432. IF { II .LT.1 ) 11 = 1
433. IF ( I2.GT.NXO I2 = NXC
434. 01 = ( Y0-YA-2.5*CO<4,0, I ) >/DYC + 1.
435. 02=
-------
443. PH!=P2-2.*A7AN-t-Y(L)*Y )/R013)
449. SIG=1.0+SIN-(RANFt-1 5-0.5 )*DYC
434. DLX=ABS(X5-XB )/CO(3,J,I )
455. DLY=ABS-X0)/DXC + 1.0
491. IF (II.LT.1 ) 11 = 1
<192. IF (I2.GT.NXC) I2 = NXC
493. 01 = (Y0-B-2.5*WD(4,0,I) )/DYC + 1.0
434. 02=(Y0-B+2.5*WD(4,0,I))/DYC + 1.0
495. IF (01.LT.1> 01=1
495. IF (02.GT.NYC) 02=NYC
497. DO 92 K=I1 , 12
49S. DO 91 L=01 .02
4C9. IF (IND(K,L).EQ.0) GO TO 91
5.-0. MI = 1
5 " 1 . F I = 1 .0
5,:2. IF (O.GT.l ) GO TO 54
53. N I = 13
5.-'. FI=0.1
5.5. 64 CONTINUE
: .:. ?HI=?2-2.*ATAN(SQRT(X
-------
512. C
513. C
5.4. C
515. C ... FOLLOWING 2 STATEMENTS SHOULD Be DELETED AND THE 2 STATEMENTS
516. C ... FOLLOWING THEM SHOULD BE ACTIVATED IN REAL SIMULATIONS.
517. X5=X(K>
518 . Y5 = Y( L )
519. C X5 = X< K)-(RANF(-1 )-0.5)*DXC
520. C Y5 = Y(L )-(RANF(-l )-0.5)*DYC
521. WLX=ABS(X5-A)/WD(3,0,I )
522. WLY=ABS< V5-B >/WD( 4 , J , I )
523. B B = < WL X * WL X +WRCW*WL X *WL Y + WL Y * WL Y > /WR2
524. IF (BB.LT.CK1) GO TO 93
525. WCON=FI*WTWW*EXP=WDEP(K,L >+WCON*WETF
527. 93 CONTINUE
5:3. 91 CONTINUE
529. 92 CONTINUE
S3£T. 600 CONTINUE
531 . 100 CONTINUE
532. 9999 CONTINUE
533. DO 9230 1=1, NXC
534. DO 9233 0 = 1 ,NYC
535. S02( I ,0 ) = S02( I ,0 )*FAC1
536. S04( I ,0 )=S04< I ,0 )*FAC1
537. DDEP< 1,0 )=DDEP( 1,0 >*FAC2
53S. 9200 WDEPf I ,0 )=WDEP( I ,0 )*FAC2
329. PRINT 20S0
2 ::. PRKIT 2373
5,;. 2053 FORMATi i H 1 ,//. ' TABLE 6: AVERAGE CONCENTRATION OF'
1 , ' S02 IN MICROGRAMS PER CUBIC METER1)
DO 173 00 = 1 ,MYC
170 PRINT 20S1 ,0,(S02( 1,0 ), 1=1 1,50)
2061 FORMAT( !3, IX, 40F3.0)
G<7. 2070 FORMAT;' 11 12 13 14 15 16 17 13 19 23 21 22 23 24 25 26 27 23
543. S29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
549. % ' >
550. PRINT 2062
551. PRINT 2070
552. 2062 FORMAT; i HI ,//,' TASLE T. AVERAGE CONCENTRATION OF
553. 2 ,' S04 IN MICROGRAMS PER CUBIC METER')
5=J. DO 171 00=!,NVC
dc5, 0 = M\'C + 1-J3
5 = 0. 1/1 PRI;;T 2051 ,o , i S04( i ,o ) , 1 = 11 ,50)
5S7. PRI-IT 2063
5?8. PRINT 2070
5E9. 2063 FORMAT( 1H1 ,/, ' TABLE 8: CUMULATIVE DRY DEPOSITION OF'
3 ,' TOTAL SULFUR IN GRAMS PER SQUARE METER SIMULATION1)
DO 172 00 = 1 ,NYC
0=NYC+1-00
553. 172 PRINT 2064 , 0 , < DDEP < I , 0 > , I = 1 1 , 50 )
5G4. 2064 FORMAT< 13, 1X.40F3.2)
555. PRINT 2065
555. PRINT 2070
557. 2065 FORMAT; ii-ii ,/,' TABLE 9: CUMULATIVE WET DEPOSITION OF'
5S3. 4 ,' TOTAL SULFUR IN GRAMS PER SQUARE METER SIMULATION1)
Ec9. DO 173 00=1 ,NYC
570. 0=NYC+1-00
571. 173 PRINT 2064 . 0 , ( WDEP ( I , 0 ) , I = 1 1 , 50 )
572. COMDRY=0.0
573. CONWET=0.0
574. DO 200 1 = 1 , NXC
573. DO 200 0 = 1 , MYC
80
-------
S7S. IF ( INDD< !,OKIE.JEM GO TO 283
577. PHI=P2-2.*ATAN< < XU >*X< I > + Y< 0 >*Y< 0 »**0. 5/R018 >
578. SIG=1.+SIN(PHI>
579. SF=SIG*SIG*FACXX
5C0. IQ=INDD(I,J)
531. IF ( IQ.EQ.0) GO TO 2.882
582. AREAA.LE.0.) GO TO 921
590. ADRY*1.E-3*AREA(I)/AREAA(I>
591. AWET*1.E-3*AREA,AWET(I>,! = !,60)
613. 2044 FORMAT(10X,A4,2F12.3 )
314. C
315. STOP
615. END
j.T'. //GO.FT10F7J1 DD DSN = B2545S . TSUMS0HC , D I SP=OLD
£i3. //GO.FT50F001 DD DSN=B25458.ITC.DATA,DISP=OLD
3:9. //GO.FT20F001 DD DSN=B2546S.VSUMMER,DISP=OLD
S:.J. //GC.FT30F001 DD DSN = 325468 . EMI 980SU . AU84 , D I SP =OLD
62i. //GO.FT£r9F;j.ai DD D3n = B25468 . CONDTE ST , D I SP = < NEW, CATLG ),
622. // UNIT=LONGSHRD,DC3=,
523. // SPACE = (TRK,{10,5),RLSE )
624. //GO.SYSIN DD *
325. 3 IS IDENTIFIES EMISSION SEASON ( 1=W,2 = SP,3 = SU,4 = F )
626.
627.
628.
629 .
o.. o ,
c"o
537
623.
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(2) INDICATES WESTERN U.S.A.
(3> INDICATES NEAR-SHORE REGION
NECESSARY FOR PLOTTING
206 07
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11050 930
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2420 5610
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GA ID IL
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OR
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255
83
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
REPORT NO.
2.
3. RECIPIENT'S ACCESSION NO.
TITLE AND SUBTITLE
5. REPORT DATE
USER'S GUIDE FOR THE ADVANCED STATISTICAL TRAJECTORY
AIR POLLUTION (ASTRAP) MODEL
6. PERFORMING ORGANIZATION CODE
AUTHOR(S)
Jack D. Shannon
8. PERFORMING ORGANIZATION REPORT NO.
PERFORMING ORGANIZATION NAME AND ADDRESS
Environmental Research Division
Argonne National Laboratory
Argonne, IL 60439
10. PROGRAM ELEMENT NO.
CCVN1A/05-3102 (FY-85)
11. CONTRACT/GRANT NO.
DW930060-01
2. SPONSORING AGENCY NAME ANO ADDRESS
Atmospheric Sciences Research Laboratory - RTF ,NC
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
13. TYPE OF REPORT AND PERIOD COVERED
Final (10/82 - 6/85)
14. SPONSORING AGENCY CODE
EPA/600/09
5. SUPPLEMENTARY NOTES
6. ABSTRACT
The Advanced Statistical Trajectory Regional Air Pollution (ASTRAP) model
simulates long-range, long-term transport and deposition of air pollutants, primarily
oxides of sulfur and nitrogen. The ASTRAP model is designed to combine ease of
exercise with an appropriate detail of physical processes for assessment applications
related to acid deposition.
The theoretical basis and the computational structure of the ASTRAP model are
described. The model is evaluated with observed data, and an illustrative exercise
is provided. Computer codes of the model subprograms are provided in appendices.
7.
KEY WORDS ANO DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
COSATI Field/Group
18. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (This Report I
UNCLASSIFIED
21. NO. OF PAGES
20. SECURITY CLASS (This page)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (R«v. 4-77) PREVIOUS EDITION is OBSOLETE
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