United States Information August 1982
Environmental Protection Clearinghouse
Agency Washington DC 20460
<>EPA Environmental
Modeling
Catalogue
Abstracts
of Environmental Models
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EPA ENVIRONMENTAL MODELING CATALOGUE
Prepared for
EPA Information Clearinghouse (PM-211A)
Library Systems and Services Staff
U.S. Environmental Protection Agency
401 M Street, SW
Washington, DC 20460
August 1982
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FOREWARD
A substantial amount of current environmental analysis
employs mathematical modeling techniques. This publication,
EPA Environmental Modeling Catalogue, represents a part of
EPA's effort to establish communication and share information
among researchers interested in modeling applications and
techniques.
The EPA has made a commitment to support environmental
modeling in various forms. Two significant efforts have been
made in the areas of water quality and air quality. The Center
for Water Quality Modeling was established by the Office of
Research and Development as a central service activity where
users can obtain models and instructions in their use*. Tom
Barnwell is the chief of this center. Air quality models are
developed and supported through the Environmental Operations
Branch located at the Research Triangle Park in North
Carolina. D. Bruce Turner is the chief of this Branch and has
primary responsibility for air quality modeling efforts (He can
be reached at FTS 629-4564 or 919/541-4564). Other attempts to
promote mathematical modeling involve EPA sponsorship of
modeling conferences. These include "Environmetrics '81'", a
conference on statistical and mathematical methodologies
applied to problems in environmental quality, and "EPA
Conference on Environmental Modeling and Simulation," held in
Cincinnati, Ohio in April of 1976.
This document was developed by Systems Architects, Inc.
under Task No. 12 to EPA Contract No. 68-01-4723. The EPA
Project Officer was Elijah Poole of the Management Information
and Data Systems Division.
^Services are available by dialing (404)546-3585, FTS 250-3585
or writing the Center for Water Quality Modeling, Environmental
Research Laboratory, USEPA, College Station Road, Athens, GA
30613.
11
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ACKNOWLEDGEMENTS
The major support for this second edition of the EPA
Environmental Modeling Catalogue came from the numerous sources
both within and outside of EPA who contributed abstracts of
environmental models. As this catalogue contains abstracts of
some 103 models, there are too many names to acknowledge
individually in this section. However, we want to give special
thanks to Martha McDonald, former Director of the Information
Clearinghouse and members of her staff, Harvey Karch and
Deborah Ranciato, for conducting a survey of models. This
survey provided information on numerous additional models.
Finally, we want to thank Tom Barnwell and Bruce Turner for
performing peer review and providing editorial comments.
111
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TABLE OF CONTENTS
INTRODUCTION
IX
AIR QUALITY MODELS
Air Pollution Research Advisory
Committee Model 1A
Air Pollution Research Advisory
Committee Model 2
Air Quality Display Model
Averaging Time Model
Climatological Dispersion Models
Gaussian Plume Dispersion Algorithm
Gaussian Plume Multiple Source Air
Quality Algorithm
HIWAY Model
HIWAY-2
Industrial Source Complex Model
Kinetic Model and Ozone Isopleth
Plotting Package
Lagrangian Photochemical Air Quality
Simulation Model
Livermore Regional Air Quality Model
Modified Rollback Model
Multiple Point Gaussian Dispersion
Algorithm with Optional Terrain
Adjustment
Multi-Source Model
Nonlinear Rollback/Roll forward
Model
The Plume Visibility Model
Point, Area, Line Source Algorithm
Point Source Gaussian Plume Model
Point Source Models
APRAC-1A 1
APRAC-2 6
AQDM 13
AVGTIME 16
CDMQC, COM 20
VALLEY 25
RAM 31
HIWAY 38
HIWAY-2 42
ISC 46
OZIPP 50
LPAQSM 54
LIRAQ 56
ROLLBACK 60
MPTER 63
CRSTER-2 66
68
PLUVUE 7Q
PAL 74
PTPLU 78
PTMAX, PTDIS 81
& PTMTP
IV
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AIR QUALITY MODELS (Continued)
Reactive Plume Model
Regional Emissions Projection System
SAI Airshed Model
Simulation of Human Air Pollution
Exposures
Single Source Model
Systems Applications, Inc.
Model
Texas Climatological Model Version 2
Texas Episodic Model Version 8
RPM-II
REPS
SAIASP
SHAPE
CRSTER
SAI
86
90
93
97
103
108
TCM-2
TEM-8
111
116
ECONOMIC MODELS
Abatement and Residual Forecasting Model
Air Test Model
Automobile Demand Model
Construction Model
Section 120 Noncompliance Penalty Model
Steel Industry Model
Strategic Environmental Assessment System
U.S. Copper Industry Model
ABTRES
AIRTEST
CARMOD
CONMOD
PENALTY
PTM
SEAS
COPMOD1
120
123
125
132
135
138
144
151
NOISE MODELS
Construction Site Health and Welfare
National Roadway Traffic Noise
Exposure Model
Railroad Health and Welfare Model
CSM
RMEA 79N3
154
157
159
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OTHER MODELS
A Mathematical Model for Fast- Screening
Procedure for Testing the Effects of
Pollutants in Mammals
Premixed One-Dimensional Flame Code
Waste Resource Allocation Program
PROF
WRAP
161
165
168
RADIATION MODELS
Atmospheric Dispersion of Radionuclides
Great Lakes Dose/Concentration
High Level Radioactive Waste Repository
Risk Model
Maximum Individual Dose Model
Nonionizing Radiation Models
Plutonium Air Inhalation Dose
Radionuclide Dose Rate/Risk
AIRDOS-EPA
GLA-1
REPRISK
MAXDOSE
PAID
RADRISK
173
177
179
182
184
189
191
TOXIC SUBSTANCES MODELS
A Computer Program For The Risk Assessment
of Toxic Substances
Environmental Partitioning Model
Exposure Analysis Modeling System
A FORTRAN Program for Risk Assessment
Using Dose-Response Data Time-To
Occurence
A FORTRAN Program to Extrapolate Dichotomous
Animal Carcinogenicity Data to Low Doses
Mantel-Bryan Low-Dose Extrapolation Model
One-Hit Low-Dose Extrapolation Model
Seasonal Soil Model
MULTI80G 195
ENPART 198
EXAMS 201
RANK TIME 212
GLOBAL 79 215
MANTELAN 218
ONE HIT MD 221
SESOIL 224
VI
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TOXIC SUBSTANCES MODELS (Continued)
Statistical Methodology for
Toxicological Research
Unified Transport Model - Toxics
Urban Wastewater Toxics Flow Model
MRST
UTM-TOX
TOXFLO
227
230
233
WATER QUALITY MODELS
Centralized Treatment of Industrial Wastewater
Computer Program for Chemical Equilibria
in Aqueous Systems
Computer Program for Chemical Equilibria
in Aqueous Systems
Dissolved Oxygen Sag Model
Dynamic Estuary Model
Enhanced Hydrodynamical - Numerical Model
for Near Shore Processes
Estuarine Water Quality Model
EXEC/OP Version 1.2
Georgia Dosag
Hydrological Simulation Program-FORTRAN
LAKE-I Ecologic Model
Level III - Receiving Water Quality
Modeling for Urban Stormwater Management
M.I.T. Transient Water Quality Network
Multi- Segment Comprehensive Lake Ecosystem
Analyzer for Environmental Resources
National Residuals Discharge Inventory
Outfall Plume Model
Receiving Water Model
Receiving Water Model
Receiving Water Quality Model
River Basin Model
236
REDEQL.DWRD 241
REDEQL.EPAK 245
DOSAG-I
DEM
HN
ES001
EXEC/OP
GADOSAG
HSPF
LAKE-I
250
254
259
263
266
270
272
282
285
289
MINI.CLEANER 295
NRDI
PLUME
DIURNAL
RECEIV-II
RWQM
RIBAM
302
307
311
314
318
321
VII
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WATER QUALITY MODELS (Continued)
River Temperature Simulation Model
Simplified Estuary Model
Simplified Stream Model
Stream 7B
Stream Network Simulation Program
Stream Quality Model
Tidal Temperature Model
Time-Dependent, Three-Dimensional Transport
Model
Time-Dependent, Three-Dimensional, Variable-
Density Hydrodynamic Model
Water Quality Assessment Methodology
for Toxic and Conventional Pollutants
Water Quality Feedback Model
Water Quality for River - Reservoir Systems
Water Quality Model
Water Quality Model
Water Quality Modeling System for the Great
Lakes
TEMPSTAT
SEM
SSM
STREAM 7B
SNSIM
QUAL-II
TTM
WQAM
FEDBAK03
WQRRS
EXPLORE-I
HAR03
WQMSGL
324
327
330
333
335
338
342
345
348
353
356
358
363
367
371
WATER RUNOFF MODELS
Agricultural Runoff Model Version II
Agricultural Watershed Runoff Model
Non Point Source Pollutant Loading Model
One-Dimensional Groundwater Mass
Transport Model
Storage, Treatment, Overflow, Runoff Model
Storm Water Management Model (Version III)
Two-Dimensional Groundwater Mass
Transport Model
ARM II
AGRUN
NPS
GWMTM1
377
383
386
391
STORM
SWMM
GWMTM2
393
398
408
Vlll
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INTRODUCTION
The Environmental Protection Agency was established in 1970
to provide a focal point at the national level for monitoring
and improving the Nation's environment. An important element
of this mission is the use of analysis and research aimed at
identifying, quantifying, and providing solutions to
environmental problems. Modeling techniques are a cost
effective and widely applicable tool in the performance of EPA
research. Computers assist immeasurably in this area since
they are an effective means for applying most modeling
techniques.
The objective of this catalogue is to provide a reference
to existing modeling applications in a variety of subject
areas. These subjects include water quality, water runoff, air
quality, toxic substances, noise, radiation and economics. It
is hoped that this reference will promote communication and
resource sharing among the various groups conducting
environmental research,.
This publication is the second in a series of Environmental
Modeling Catalogues. Each model is described-by an abstract
that was provided by the technical contact. Each abstract
contains the following components: Model Overview, Functional
Capabilities, Basic Assumptions, Input and Output, System
Resource Requirements, Applications, Technical Contact(s), and
References. Each abstract will permit an evaluation of the
model's applicability to the reviewer's research area.
Additional models or enhancements to the descriptions
contained in this catalogue should be forwarded to the
Environmental Protection Agency, Management Information and
Data Systems Division, PM-218, 401 M Street, S.W., Washington,
D.C. 20460.
IX
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AIR POLLUTION RESEARCH ADVISORY COMMITTEE
MODEL 1A (APRAC-1A)
1. Model Overview: APRAC, Stanford Research Institute's urban
carbon monoxide model, computes hourly averages for any urban
location. The model requires an extensive traffic inventory for
the city of interest, and its requirements and technical details
are documented in User's Manual for the APRAC-IA Urban Diffusion
Model Computer Program, which is available from NTIS.
2. Functional Capabilities: The computer program can be used to
make calculations of the following types:
o Synoptic model: hourly concentrations as a function
of time, for comparison and verification with observed
concentrations and for operational application.
o Climatological model: the frequency distribution of
concentrations, for statistical prediction of the
frequency of occurence of specified high concentrations
in connection with planning activities.
o Grid-point model: concentrations at various locations
in a geographical grid, providing detailed horizontal
concentration patterns for operational or planning
purposes.
Roadway link information is limited to 1200 sources.
3. Basic Assumptions:
A. Source-Receptor Relationship. The user specifies the set
of traffic links (line sources) by providing link endpoints, road
type and daily traffic volume. The traffic links may have arbitrary
length and orientation. Off-link traffic is allocated to a 2 x 2. mi
grid. Link traffic emissions are aggregated into a receptor orien-
ted area source array. The boundaries of the area sources actually
treated are*1) arcs at radial distances from the receptor which
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increase in geometric progression; 2) the sides of a 22.5 sector
oriented upwind for distances greater than 1000 m.; and 3) the
sides of a 45° sector oriented upwind, for distances less than
1000 m. A similar area source array is established for each
receptor. Sources are assumed to be at ground level, and up to
ten receptors are allowed in the model. Receptors are at ground
level and their locations can be arbitrary. Four internally
defined receptor locations on each user-designated street are used
in a special street canyon sub-model.
B. Emission Rate. Daily traffic volume for each link and
off-link grid square is input and modified by various factors to
produce hour-by-hour emissions from each link. Link emissions are
aggregated as described above: sector area source contributions
are obtained analytically. Off-link traffic emissions on a two
mile grid square are added into the sector area sources. In the
street canyon sub-model, a separate hourly emission rate is provided
by the user for the link in question.
C. Plume Behavior. The model does not treat plume rise,
and it does not treat fumigation or downwash except in the street
canyon sub-model. In the street canyon sub-model, a helical
circulation pattern is assumed.
D. Horizontal Wind Field. Input for the model is hourly
wind speed and direction in tens of degrees. No variation of
wind speed or direction with height is allowed. A constant,
uniform (steady-state) wind is assumed within each hour.
E. Vertical Wind Speed. This is assumed to be equal to
zero except in the street canyon sub-model, where a helical
circulation pattern is assumed.
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F- Horizontal Dispersion. Section averaging has a uniform
distribution within sectors. Each section larger than 1 km, is
divided into sectors of 22.5°; sections within 1 km. of size are
divided into sectors of 45°.
G. Vertical Dispersion. The model utilizes a semi-empirical/
Gaussian plume. There are six stability classes, and each stability
class is determined internally from user-supplied meteorological
data (modified by Turner, 1964). Dispersion coefficients from
McElroy and Pooles (1968) have been modified using information in
Leighton and Ditmar (1953) . No adjustments are made for variations
in surface roughness, and the downwind distance variation of o~2 is
assumed to ax for purposes of doing analytic integration. In the
street canyon sub-model, an empirical function of wind speed and
street width and direction is used.
H. Chemistry/Reaction Mechanism. This is not treated.
I. Physical Removal. This is not treated.
J. Background. The box model used to estimate contributions
from upwind sources beyond 32 km. is based on wind speed, mixing
height, and annual fuel consumption. In the street canyon
sub-model, contributions from other streets are included in the
background.
4. Input and Output: Emission and meteorological information are
needed for the model. Emissions are a function of the hour of the
day and the day of the week, and meteorological parameters are
functions of the hour of the day. Output from the model includes
hourly concentration values at each receptor and frequency
distribution based on hourly values.
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APRAC
—INDAT
— BASIC
— LINKS
STORE
— RAOBHM
—SFCOB1
—SFCOB2
—EXTURB
CALXOQ
CALQUE
-HOUR LOOP
—MINWIN
— STABLE
— DEPTH
—LOCXOQ
— CALXOQ
—LOCQUE
—CALQUE
—CALCON
-< >STREET
\f I
PPDATA
END
RAOBHM
SFCOB1
CALLOC
FLOWCHART FOR APRAC 1A
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5. System Resource Requirements: The dispersion model is written
in FORTRAN V. The program does not require any special software or
utilities. Approximately 32K of core memory is required to execute
on the Univac 1110.
6. Applications: The source program for this dispersion model
is available as part of UNAMAP (Version 3), PB 277 193, for $420
from Computer Products, NTIS, Springfield, VA 22161.
7. Technical Contact
D. Bruce Turner
U.S. Environmental Protection Agency
Mail Drop 80
Environmental Applications Branch
Research Triangle Park
NC 27711
FTS 629-4564 COM 919/541-4564
8. References
Dabbert, W.F., Ludwig, F.L., and Johnson, W.B., Jr., "Validation
and Applications of an Urban Diffusion Model for Vehicular
Pollutants", Atmos. Environ., 7, 603-618, 1973.
Evaluation of the APRAC-1A Urban Diffusion Model for Carbon
Monoxide, NTIS Accession Number PB 210-813.
Field Study for Initial Evaluation of an Urban
Diffusion Model for Carbon Monoxide. NTIS Accession Number
PB 203-469.
Johnson, W.B., Ludwig, F.L., Dabbert, W.F., and Allen, R.J.,
"An Urban Diffusion Simulation Model for Carbon Monoxide",
Journal of the Air Pollution Control Association, 23, 6, pp.
490-498, 1973.
A Practical, Multipurpose Urban Diffusion Model for Carbon
Monoxide, NTIS Accession Number 196-003.
User's Manual for the APRAC-1A Urban Diffusion Model Computer
Program, NTIS Accession Number PB 213-091 (@ $5.25 per paper
copy, $2.25 for microfiche).
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AIR POLLUTION RESEARCH ADVISORY COMMITTEE MODEL 2 (APRAC-2)
1. Model Overview: The APRAC-2 model is a revised version of
the APRAC-1A diffusion model. It maintains basically the same
approach to the simulation of atmospheric diffusion, but it
incorporates recent advances in the estimation of vehicular
emission and in the dissemination of traffic information. One
of the most important characteristics of the APRAC-2 model is
its ability to make full use of the historic records and the
projections available from the Federal Highway Administratiqn"s
(FHWA) battery of computer programs. Mixing depth information
from alternative sources can be used. The model now can
provide as outputs the amount of pollutant emitted in grid
squares through the area. The APRAC-2 model uses EPA^s
emissions calculation methodology from Supplement No. 5 to
AP-42.
2- Functional Capabilities: The model has two major
components: A diffusion module (DIFMOD), and an emission
module (EMOD). The emissions module can operate without the
diffusion module, but the diffusion model requires the outputs
from the emissions module as its inputs. Each of the two
modules has several major components. The emissions module has
components to calculate tables of emissions, a component to
determine emissions on each roadway link, and a component that
estimates the emissions within each grid square.
The three major functions of the diffusion model are to:
(1) calculate diffusion; (2) derive, from conventional
meterorological information the stability, mixing depth, and
wind parameters used by the model; and (3) simulate
small-scale effects near the receptor. Diffusion calculations
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can be made for as many as 625 locations for a single hour and
as many as 10 locations for a single day or for a year at a
single station. There are two subroutines in the small-scale
effects category: one treats canyon conditions and the other
simulates traffic and dispersion in the vicinity of an
intersection.
ARPAC-2 can treat hydrocarbons, carbon monoxide, or oxides
of nitrogen. Diffusion calculations make use of a
receptor-oriented Gaussian plume model. Local winds at the
receptor can be used , and they are interpolated from multiple
wind inputs. Mixing heights may be calculated from sounding
data, or input directly. A small program is included from
decoding Federal Highway Administration data tapes.
3. Basic Assumptions: The method utilized by APRAC-2 for
computing emission factors has been described in detail by
Kircher and Williams (1975). Percentages of vehicles operating
in cold, hot transient, and hot stabilized modes are assumed to
vary with time of day and from one part of a city to another.
If land use categories are not specified, the model assumes
that all central business district area types correspond to the
same locale type. Core city areas are assumed to be
commercial. Suburban streets are assumed to be commercial if
their average weekday traffic exceeds 10,000; otherwise the
locale is taken to be residential. The locale for areas that
do not fit specified categories is taken to be rural or
unclassified.
A Gaussian-plume diffusion formulation is used for
diffusion calculations. The model uses an atmospheric
stability algorithm derived by Ludwig and Dabbert (1976) from
the basic method proposed by Pasquill (1961). Daytime
stability categories are based on wind speed and the strength
of the incoming solar radiation.
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A. Input and Output: If FHWA traffic data are to be used,
they must first be converted to a format compatible with the
rest of the program. For IBM machines this is done with the
program COMSIS, which will read and unpack the aata and then
create a file for subsequent use by the APRAC-2 program.
The input required to operate the EMOD module is as
follows: the first 17 cards are all required to identify which
options are to be used during the run and the other parameters
that define the nature of the run. The next 72 cards define
the diurnal traffic cycles appropriate to different kinds of
roadway, areas of the city, and days of the week.
At least 22 cards are required to operate the DIFMOD
model. The first six cards are required to define the region,
the types of calculations to be made, and the coordinates of
the receptors in kilometers with the origin at the same place
as the emissions grid. Cards D-6 through D-9 define the length
of the run, street canyon features, intersection link features
and coordinates, holidays, and pollutants to be treated. Cards
D-10 through D-15b define upwind background concentrations;
mixing depth input type; station, date, maximum and minimum
temperatures, and daylight savings time; radiosonde data,
weather data, and wind data for up to 100 sites; intersection
traffic parameters; and intersection signalization parameters.
Output provided by the model includes computer printouts of
ambient air concentration for hydrocarbons, carbon monoxide, or
oxides of nitrogen as amounts of pollutant emitted in grid
squares throughout the area; and a listing of the input data.
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Read Information Describing
Run Type
If FHWA Traffic Data
are Used, Extract
Necessary Information
and Create Traffic
File (Program COM5IS)
Read Basic Traffic Information:
• Speed Cycles
• Volume Cycles
• Seasonal Volume Corrections,
and So Forth
Read Traffic Data from
Cards, or Card Image
Tape
Read Data from File
Created by Preprocessing
Program
Write Link Data File
on Disk (Logical Unit
No. = TFILE1)
Distribute Secondary
Traffic Among Grid
Squares
Call Emissions Module
to Calculate and Print
Gridded Emissions
Is
Diffusion
Model to
Be Used
Call Diffusion Module,
which in Turn Calls
Emissions Module, Reads
Meteorological Data,
Calculates and Prints
Results
SIMPLIFIED FLOWCHART FOR APRAC II
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5. System Resource Requirements: APRAC-2 is written in
FORTRAN and is run on the CDC 6400 mainframe. Core storage is
55,000 words or less. A programmer with background in
environmental engineering and knowledge of computer simulation
is helpful in using this model.
6. Applications: The APRAC-2 model can be used to assess
ambient concentrations of hydrocarbons, carbon monoxide, or
oxides or nitrogen emitted by traffic in five typ,es of
locales. Local source models are available for treating
pollutant behavior in a street canyon or the vehicle and
pollutant effects at a signalized intersection.
7- Technical Contact
Linda Larson
U.S. Environmental Protection Agency
Air and Hazardous Materials Division
215 Fremont Street
San Francisco, CA 94105
COM 415/556-2004 FTS 556-2004
8. References
Heffter, J.L., and Taylor, A.D., A Regional Continental
Scale Transport, Diffusion, and Deposition_Model . Part I
Trajectory Model , National Oceanic and Atmospheric
Administration Technical Memoranda, ERL ARL-50, pp. 1-16,
1975.
10
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Johnson, W.B., Dabberdt, W.F., Ludwig, F.L., and Allen,
R.J., Field Study for Initial Evaluation of an Urban
Diffusion Model for Carbon Monoxide, Comprehensive Report
CRC and Environmental Protection Agency, Contract CAPA-3-68
(1-69), 1971-
Kircher, D.S. and Williams, M.E., Supplement No. 5 for
Compilation of Air Pollutant Emission Factors (AP-42) ,
Chapter 3 (second edition), U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards,
(OAQPS), 1975.
Kunselman, R., McAdams, H.T., Domke, C.J., and Williams,
M., Automobile Exhaust Emission Modal Analysis Model, EPA
Contract 68-01-0438, Calspan Corporation, Buffalo, New
York, 1974.
Ludwig, F.L., "Urban Air Temperatures and Their Relation to
Extra-Urban Meteorological Measurements," Proceedings _of
the Semi-annual Meeting^ of the American Society of Heating,
Refrigeration, and Air Conditioning Engineers, (Survival
Shelter Problems, Part II) San Francisco, pp. 40-45,
January 1970.
Ludwig, F.L. and Dabberdt, W.F., Evaluation of the APRAC1A
Urban Diffusion Model for Carbon Dioxide. Final Report,
CRC and EPA Contract CAPA-3-68 (1-69), 1972.
Ludwig, F.L. and Dabberdt, W.F., "Comparison of Two
Atmospheric Stability Classification Schemes in an Urban
Application," Journal of Applied Meteorology, 15, 11721176,
1976.
11
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Ludwig, F.L., Johson, W.B., Moon, A.E., and Mancuso, R.L.,
A Practical, Multipurpose Urban Diffusion Model for Carbon
Monoxide. Final Report, Coordinating Research Council
(CRC), Contract CAPA-3-68 and National Air Pollution
Control Administration Contract CPA 22-69-64, 1970.
Ludwig, F.L. and Kealoha, J.H.S., Selecting Sites for
Carbon^Monoxide Monitor ing, Final Report, EPA Contract
68-02-1471, Stanford Research Institute, Menlo Park,
Califonia, 1975.
Mancuso, R.L. and Ludwig, F.L., User' s Manual for the
APRAC-1A Diffusion_ Model Computer Program, CRC and EPA,
Contract CAPA-3-68(1-69), 1972.
Sagi, G. and Campbell, L., "Vehicle Delay at Signalized
Intersections," Traffic Engineering, 1969.
Sandys, R.C., Buder, P.A., and Dabberdt, W.F., ISMAP: A
Traffic/Emissions/Dispersion Model for Mobile Pollution
Sources , prepared for the California Business Properties
Association, Hawthorne, Califonia, by the Stanford Research
Institution, Menlo Park, California, 1975-
U.S. Department of Tr ansporation, Federal Highway
Administration, Urban^Transporation Planning, General
Information, 1972.
12
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AIR QUALITY DISPLAY MODEL (AQDM)
1. Model Overview: The Air Quality Display Model (AQDM) is a
three-dimensional, steady-state air model used in the
evaluation of area sources in "rough" urban areas. The AQDM
treats the physical processes of both transport and diffusion.
The model is appropriate for examining areas ranging in size
from small localized vicinities to whole urban areas, and it
has a long-term application for the evaluation of seasonal or
annual air quality variations.
2. Functional Capabilities: The AQDM model does not simulate
chemical processes, but it does treat the physical processes of
transport and diffusion in "rough" urban areas. It uses a one
layer discretization and a user-specified 14 x 14 grid. A 225
grid receptor with 12 additional receptor points is also
user-specified. The fixed-point meteorological data does not
describe micrometeorological variations within the city, nor
does it describe "urban heat island" air circulations. The
model has a sensitivity to effective stack height, wind speed,
and wind stability. It is limited to SCU and suspended
particulates, and it is designed for annual average and
seasonal applications.
3. Basic Assumptions: The AQDM is a deterministic model that
uses an analytically integrated solution technique. It assumes
a steady state for air quality constituents, Gaussian
diffusion, and homogeneous, discrete atmospheric conditions.
13
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4. Input and Output: Input to the model for initial set-up
and calibration include: point and area residual discharges
and stack parameters ,./,ich consist of height, diameter,
temperature, and exit velocity; meteorological data containing
wind speed and direction, stability, and mixing height; and
several ambient air concentration measurements. Model data
requirements for verification incorporate the above
meteorological data and ambient air concentration measurements.
Output for the model includes ambient concentration values
given at grid locations, ground level, or other user-selected
points. These values are given in the form of tabular
printouts or card decks for use with CALCOMP or SYMAP plot
programs. Some of the special features of the AQDM output are
its statistical output routines, receptor contribution
analysis, and calibration subroutines.
5- System Resource Requirements: AQDM is coded in FORTRAN and
is run on an IBM 360/40 or an equivalent system. It requires
300K bytes of core memory for execution. A background in
programming and engineering is useful.
6. Applications: AQDM can be used in the evaluation of area
sources in "rough" urban areas for seasonal or annual air
quality variations. This model has been superceded by such
models as CDM/CDMQC and RAM; thus AQDM is primarily of
historical interest.
14
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7. Technical Contact
Joe Tikvart
U.S. Environmental Protection Agency
Monitoring and Data Analysis Division
Mutual Building
411 W. Chapel Hill St.
Durham, N.C. 28801
COM 919/541-5561 FTS 629-5561
8. References
Croke, I.E., et al., "Regional Implementation Plan
Evaluation Process," ANC/ES-DA-001, Argonne National
Laboratory, Argonne, Illinois, (July 1970).
National Air Pollution Control Administration, "Air Quality
Display Model," PB 189 194, Washington, D.C., (November
1969).
15
-------
AVERAGING TIME MODEL (AVGTIME)
1. Model Overview: AVGTIME is a mathematical model based on
two characteristics that are often demonstrated by air-quality
data: (1) air pollutant concentrations tend to be lognormally
distributed (Figure 1) for all averaging times and (2) median
(50 percentile) concentrations tend to be proportional to
averaging time raised to an exponent and thus plot as a
straight line on logarithmic graph paper (Figure 2). Two
percentile concentrations (at the same or at different
averaging times) are read into the model and concentrations for
the maxima or any percentiles can then be calculated for any
other averaging times.
50 80 99 9999
FREQUENCY % Of TIME CONC EICE EDET ST1TED VALUE
SECOND
AVERAGING TIME
MINUTE HOUR DAY MONTH YEAR
i 5 15 I Z 4 8 1247 U 1 23 6 I 3.
—I 1 M i | i I | i [ I i I i | I I I | 1 ;
3412 135906350428 0128 O.O66 I
EXPECTED ANNUAL MAXIMUM CONC IN PPM i
2
0.
0.
•2
o
§
OOOOI 0.001 0.01 01 I 10 K)0
AVERAGING 'HKE, HOURS
10OO K3.0OO
' O.OOOI
Figure 1. Frequency that various
1-hr-averaging-time nitrogen
oxides concentrations exceeded
various values in Washington
D.C. from 12/1/61 to 12/1/64
Figure 2. Concentration
vs. averaging time and
frequency for nitrogen
oxides in Washington
from 12/1/61 to 12/1/64
16
-------
2. Functional Capabilities: The detailed characteristics of
the model are described by a dozen equations on pp. 51-52 in
Ref. 2. Two input concentrations are entered into the proper
equation to calculate two output parameters: the geometric
mean and standard geometric deviation for one averaging time.
The other equations are then used to calculate these two output
parameters, the maxima, and the concentrations for any desired
percentiles for any other averaging times.
3- Basic Assumptions: Analyses of air pollutant concentration
data suggest that urban concentrations often tend to fit a
general mathematical model having the following three
characteristics:
1) Pollutant concentrations are lognormally distributed
for all averaging times.
2) Median concentrations are proportional to averaging
time raised to an exponent.
3) Maximum concentrations are approximately inversely
proportional to averaging time raised to an exponent.
A 2-parameter averaging time model with the above three
characteristics has been developed.
Air pollutant concentrations measured near isolated point
sources often do not fit a 2-parameter lognormal distribution
very well. Such data often do fit a 3-parameter lognormal
distribution fairly well. A 3-parameter averaging time model
has therefore been developed to model such data.
4. Input and Output: The user inputs any two air quality
measurements for the 2-parameter model. These two input
parameters might be, for instance, the concentrations exceeding
17
-------
0.1% and 30% of the time for 1-hour average concentrations.
The two input concentrations can be at the same or at different
averaging times. The user inputs any three air quality
measurements into the 3-parameter model, at either the same or
at different averaging times.
The equations mentioned under "Functional Capabilities" are
used to calculate expected concentrations. Expected highest
and second highest concentrations for various averaging times
(1, 3, 8, and 24 hr, and 1 year) can be easily determined by
using Table II in Ref. 3.
The 3-parameter averaging time model is more difficult to
use than is the 2-parameter model. Trial and error techniques
can be used to calculate the third parameter (a constant that
is added or subtracted from each of the three input
concentration measurements) needed to fit the data to a
2-parameter lognormal distribution.
5- System Resource Requirements: AVGTIME is coded in FORTRAN
and is run on an Univac 1100 or any equivalent mainframe
computer. It requires 40K bytes of core memory for execution.
Operation needs include a background in environmental
engineering and air quality. A 500 card FORTRAN job deck is
available that will calculate expected maxima and percentile
concentrations for several averaging times based on three
concentration measurements input to the model. The job deck is
available on request from the Technical Contact .
6. Applications: As the title of Ref. 2 implies, the
averaging time model has been used to relate air quality
measurements to air quality standards to determine overall
average percent emission reductions needed to achieve air
quality standards. The input air quality data can either be
measured or dispersion-modeled.
Air quality data for one averaging time has been used to
calculate percentiles and expected maxima for other averaging
times for which air quality standards have been written. Air
18
-------
quality measurements might be available for 24-hour average
concentrations of sulfur dioxide for instance. The model could
be used to calculate expected maximum concentrations for 3-hour
averages and these maxima could then be compared with the 3-
hour sulfur dioxide National Ambient Air Quality Standard
(NAAQS).
7• Technical Contact
Ralph I. Larson
U.S. Environmental Protection Agency
Environmental Science Research Laboratory
Mail Drop 80
Research Triangle Park
N.C. 27711
COM 919/541-4564 FTS 629-4564
8. References
Larsen, R.I., "A New Mathematical Model of Air Pollutant
Concentration, Averaging Time, and Frequency", Journal of
the Air Pollution Control Assoc., 19 (1), 24-30, 1969-
Larsen, R.I., A Mathematical Model for Relating Air Quality
Measurements to Air Quality Standards, Publ. AP-89, U.S.
Environmental Protection Agency, Research Triangle Park,
NC, 56 pp., 1971.
Larsen, R.I., "An Air Quality Data Analysis System for
Interrelating Effects, Standards, and Needed Source
Reductions: Part 4. A Three-Parameter Averaging-Time
Model," Journal of the Air Pollution Control Assoc., 27
(5), 454-459, 1977-
19
-------
CLIMATOLOGICAL DISPERSION MODELS (CDMQC, COM)
1. Model Overview: The Climatological Dispersion Models
(CDMQC, CDM) determine long-term (seasonal or annual) quasi
stable pollutant concentrations at any ground level receptor
using average emission rates from point and area sources and a
joint frequency distribution of wind direction, wind speed, and
stability for the same period. The User's Guide for the Climato-
logical Dispersion Model describing tbe CDM is available from NTIS
(accession number PB 227-346-AS). The Addendum to User's Guide
for Climatological Dispersion Model describing the enhancements
available in the CDMQC is available from EPA as EPA-45013-77-015
and from NTIS (PB-274-040).
2. Functional Capabilities: Long-term concentrations corresponding
to the period of joint frequency distribution of meteorological data
(usually seasonal or annual) are produced for each receptor, assuming
that the emission inventory is valid for the same period. The two
contributions to the concentration, those due to points and those
due to areas, are output. Receptor locations are specified by the
user. An option is available to produce a pollutant rose-contribution
to the total concentration from each direction.
3. Basic Assumptions:
A. Source-Receptor Relationship. CDMQC and CDM use an
arbitrary location for each point source, and area sources are
represented in uniform grid squares. Receptor locations are arbitrary,
as are release heights for point and area sources. Receptors are
assumed to be at ground level. The model assumes that there are no
terrain differences between the sources and receptors.
B- Emission Rate. A single rate is allowed for each point
and area source. For area sources, area integrations are done
20
-------
numerically, one 22.5° sector at a time; sampling at discrete
points is defined by specific radial and angular intervals on a
polar grid centered on the receptor.
C. Chemical Composition. CDMQC and CDM treat one or two
pollutants simultaneously.
D. Plume Behavior. Only Briggs (1971) neutral/unstable
formula is used by the model. If the stack height plus the plume
rise is greater than the mixing height, then the ground level
concentrations are assumed to be equal to zero. As an alternate
to the Briggs formula, the input value of the plume rise times
the wind speed for each point source can be used. No plume rise
is calculated for area sources. CDMQC and CDM do not treat fumigation
or downwash.
E. Horizontal Wind Field. The models use a climatological
approach, and utilizes 16 wind directions and 6 wind speed classes.
The wind speed is corrected for the release height based on the
power law variation exponents from DeMarrais (3-959). A constant,
uniform (steady-state) wind is assumed.
F. Vertical Wind Speed. This is assumed to be equal to
zero.
G. Horizontal Dispersion. The models -use a climatological
approach, and assume a uniform distribution within each of 16
sectors (narrow-plume approximation). Averaging time for the model
is one month to one year.
H. Vertical Dispersion. The models use a semi -.empirical/
Gaussian plume with five stability classes as defined by Turner
21
-------
(1964). Neutral stability is split into day/night cases on input,
and dispersion coefficients are taken from Turner 0-970) . The
stability classes for area sources are decreased by one category
from the input values (to account for urban effects). Neutral
dispersion coefficients are used for all neutral and stable classes
No provision is made for variations in surface roughness.
I. Chemistry/Reaction Mechanism. The models use exponential
decay and a user-input half-life.
J. Physical Removal. The model utilizes exponential decay
and a user-input half-life. The same rate constant is always
applied.
K. Background. A single constant background value is input
for each pollutant.
4. Input and Output: Point and area source data, as well as
meteorological data must be input to the model. Output from the
model includes: input data; one month to one year averaging time
simulated (arithmetic mean only); arbitrary averaging time by the
Larsen (1969) procedure (typically 1 24 hours) which assumes a
lognormal concentration distribution and a power law dependence
of median and maximum concentrations on the averaging time; an
arbitrary number and location of receptors; an individual point
and area source culpability list for each receptor; and a point
and area concentration rose for each receptor.
5- System Resource Requirements: The models are written in
FORTRAN V. The programs do not require any special software or
utilities. Both models require approximately 22K words of core
storage for execution on the Univac 1110.
-------
CDMQC, CDM
end
Block Data
Clint
Calq
Area
Point
FLOWCHART FOR CI3WQC AND CDM
23
-------
6- Applications : The source programs for these dispersion
models is available as part of UNAMAP (Version 3), PB, 227-193
for $420 from Computer Products, NTIS, Springfield, VA 22161.
7. Technical Contact
D. Bruce Turner
U.S. Environmental Protection Agency
Environmental Applications Branch
Mail Drop 80
Research Triangle Park
N.C. 27711
FTS 629-4564 COM 919/541-4564
8. References
Busse, A.D. and Zimmerman, J.R., User's Guide for the
Climatological Dispersion Model, U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina,
Environmental Monitoring Series, EAP-R4-73-024, 131 p.
(NTIS accession number PB 227-346/AS, $4.75 paper copy),
1973.
Brubaker, K.L., Brown, P., and Cirillo, R.R., Addendum to
User's Guide for Climatological Dispersion Models, U.S.
EnvironmentalProtection Agency,Research Triangle Park,
North Carolina, EPA-450/3-77-015, 134p. (NTIS accession
number PB-274-040, $7.25 paper copy), 1977.
24
-------
GAUSSIAN PLUME DISPERSION ALGORITHM (VALLEY)
!• Model Overview: VALLEY is a steady-state, univariate
Gaussian Plume dispersion algorithm designed for estimating either
24-hour or annual concentrations resulting from emissions from up
to 50 (total) point and area sources. Calculations of ground-level
pollutant concentrations are made for each frequency designed in an
array defined by six stabilities, 16 wind directions, and six
speeds for 112 program-designed receptor sites on a radial grid of
variable scale. Empirical dispersion coefficients are used and
include adjustments for plume rise and limited mixing. Plume
height is adjusted according to terrain elevations and stability
classes.
2. Functional Capabilities; This dispersion model is capable
of estimating concentrations resulting from emissions from up to
50 point and area sources for a time frame of either 24 hours or
on an annual basis. The model performs calculations of
ground-level pollutant concentrations in an array defined by six
stabilities, 16 wind directions, and six wind speeds for 112
program-designed receptor sites on a radial grid of variable scale.
The model accounts for plume rise and limited mixing.
3. Basic Assumptions:
A. Source-Receptor Relationship. Each point source is
assigned an arbitrary location. Each area source is given an
arbitrary location and size. The model provides 112 receptors on
a radial grid for 16 directions; relative radial distances are
internally fixed, and the overall scale may be modified by the
user. The location of the grid center is defined by the user.
A unique release height for each point and area source is given by
VALLEY. Receptors are at ground level, and ground level elevations
25
-------
above mean sea level are defined by the user. The total number of
sources for the model is less than or equal to 50.
B. Emission Rate. A single rate is utilized by each point
and area source. Each source is treated by an effective point
source approximation, and no temporal variation is allowed.
C. Chemical Composition. This is not applicable to VALLEY.
D. Plume Behavior. The model uses Briggs (1971, 1972)
plume rise formula for both point and area sources. Alternately,
a single constant plume rise value may be input for any or all
sources. VALLEY does not treat fumigation or downwash. If the
plume height exceeds the mixing height: 1) for long-term
calculations, the ground level concentrations are assumed to be
equal to zero, 2) for short-term calculations, the maximum plume
height is limited to the mixing height.
E. Horizontal Wind Field. For long-term calculations, the
model utilizes the following: climatological approach, 16 wind
directions, 6 wind speed classes, no variation in wind speed with
height, constant uniform (steady-state) wind assumed, and the user
must specify the wind speeds representative of each class (these
are not internally defined). For short-term calculations,
specifically to predict the second highest 24-hour concentration
expected in one year, a Class F stability and a 2.5 m/sec. wind
speed with user-defined direction are assumed. These conditions
are assumed to exist for 25% of the 24-hour period, and an internal
adjustment is made for this. In stable conditions, in complex
terrain, concentrations for receptors located above the point of
impingement are obtained by linear interpolation between the value
obtained at the point of impingement and a value of zero at a
26
-------
height of 400 meters above that point. The value at the point of
impingment is taken to be equal to the value 10 meters below the
plume centerline. For receptors located below the point of
impingement, the effective plume height is equal to the height of
the plume above the receptor elevation or 10 meters, whichever is
larger. The plume is assumed to remain at a constant elevation
following the initial rise. In neutral or unstable conditions, in
complex terrain, the plume is assumed to remain at a constant
height above the topography, following the initial rise. The
model assumes that there is no variation of wind speed with
height, and that there is a constant, uniform (steady-state) wind.
F. Vertical Wind Speed. In stable conditions, this is
assumed to be equal to zero. In neutral and unstable conditions,
the vertical wind speed is assumed such that the plume remains at
a fixed height above the terrain.
G. Horizontal Dispersion. VALLEY uses a climatological
approach, with sector averaging (narrow plume approximation) for
calculating the center values of each of the 16 sectors. The
model uses linear interpolation between centerlines, as does the
Air Quality Display Model (AQDM). Averaging time for VALLEY is
one month to one year for long-term calculations.
H. Vertical Dispersion. The model uses a semi-empirical/
Gaussian plume. In the urban mode, the model assumes the following
five stability classes (Turner, 1964); neutral stability split
internally into 601 day and 40.% night; dispersion coefficients
from Pasquill (1961) and Gifford (1961); neutral dispersion
coefficients used for all neutral and stable classes; no provision
for variations in surface roughness; and stable cases are never
dealt with. In the rural mode, the model assumes: six stability
27
-------
classes (Turner, 1964); dispersion coefficients from Pasquill (1961)
and Gifford (1961); neutral stability split internally into 60%
day and 40% night (has no effect on dispersion coefficients);
long term mode only; and no adjustments are made for variation in
surface roughness.
I. Chemistry/Reaction Mechanism. VALLEY uses exponential
decay and a user-input half-life.
J. Physical Removal. The model uses exponential decay and
a user-input half-life.
K. Background. VALLEY does not treat this in any mode.
4. Input and Output: Input to the model includes: point and
area residual discharges and stack parameters; meteorological data;
and ambient air concentration measurements. Output from the model
in the long-term mode includes long-term arithmetic means and a
source contribution list for each receptor. Output for the short-
term mode includes the second highest 24-hour concentration and a
source contribution list for each receptor.
5. System Resource Requirements: VALLEY is written in FORTRAN
V. The program does not require any special software or utilities.
Approximately 13K words of core memory are required to execute on
the Univac UIO.
6. Applications: The source program for this dispersion model
is available as part of UNAMAP (Version 3), PB 277 193 for $420
from Computer Products, NTIS, Springfield, VA 22161.
28
-------
VALLEY
•LOOP ON WIND DIRECTION
LOOP ON STABILITY
LOOP ON WIND SPEED
BEH072
•EBPLTI
END
VALLEY FLOWCHART
29
-------
7. Technical Contact
D. Bruce Turner
U.S. Environmental Protection Agency
Mail Drop 80, EPA
Environmental Applications Branch
Research Triangle Park
N.C. 27711
FTS 629-4564 COM 919/541-4564
8. References
Burt, E,, Valley Model User's Guide, Publication No.
EPA-450/2-77-018, Environmental Protection Agency,
Research Triangle Park, North Carolina 27711, September
1977.
30
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GAUSSIAN PLUME MULTIPLE SOURCE AIR
QUALITY ALGORITHM (RAM)
1. Model Overview: This short-term Gaussian steady-
state algorithm estimates concentrations of stable pollutants from
urban point and atea sources. Hourly meteorological data are
used, and hourly concentrations and averages over a number of hours
can be estimated. The Briggs plume rise and the Pasquill-Gifford
dispersion equations with dispersion parameters thought to be
valid for urban areas are used in the model. Concentrations from
area sources are determined using the method of Hanna, that is,
sources directly upwind are considered representative of area
source emissions affecting the receptor. Special features include
determination of receptor locations downwind of significant
sources and determination of locations of uniformly spaced
receptors to ensure good area coverage with a minimum number of
receptors.
2. Functional Capabilities: Concentrations are estimated
hourly and for a longer averaging time (less than 24 hours) for
a limit of 150 receptor locations (all at the same height above
ground) from no more that 250 point sources and 100- area sources.
3. Basic Assumptions:
A. Source-Receptor Relationship. The model assumes an
arbitary location for point sources, and the receptors may be:
1) arbitrarily located, 2) internally located near individual source
maxima, or 3) on a program-generated hexagonal grid to give good
coverage to a user-specified portion of the region of interest.
Receptors are all assumed to be at the same height above (or at)
ground, and a flat terrain assumed. The model uses a unique stack
height for each point source. The model user may specify up to
three effective release heights for area sources, each assumed
31
-------
appropriate for a 5 m/sec wind speed. The value used for any given
area source must be one of these three. A unique separation for each
source-receptor pair is used.
B. Emission Rate. The model assumes a unique constant
emission rate for each point and area source. Area source treatment
encompasses: narrow plume approximation; area source used as input
(not subdivided into uniform elements]; arbitrary emission heights
input by user; areas must be squares (side length = integer multi-
ples of a basic unit); effective emission height equals that
appropriate for a 5 m/sec wind; and the area source contributions
are obtained by numerical integration along upwind distance of
narrow-plume approximation formulae for contribution from area
sources with given effective release heights.
C. Chemical Composition. This is treated as a single
inert pollutant.
D. Plume Behavior. The model uses Briggs ' ' plume
rise formulas and does not treat fumigations or downwash. If the
plume height exceeds the mixing height, the ground level con-
centration is assumed to be zero.
E. Horizontal ¥ind Field. The model uses user-supplied
hourly wind speeds and user-supplied hourly wind directions
(nearest 10 ), internally modified by addition of a random integer
value between -4° and +5°. Wind speeds are corrected for release
height based on power law variation, exponents from DeMarrais^;
different exponents for different stability classes are used, and
the reference height is equal to 10 meters. A constant, uniform
(steady-state) wind is assumed within each hour.
F. Vertical Wind Speed, This is assumed to be equal to
zero.
G. Horizontal Dispersion. Th£ model uses- a semi-empirical/
Gaussian plume. Hourly stability class is determined internally by
Turner procedure (six classes are used). Dispersion coefficients
are from McElroy and Pooler4 (urban) or Turner3 (rural) . No furth.er
32
-------
adjustments are made for variations in surface roughness or trans-
port time.
H- Vertical Dispersion. A semi-empirical/Gaussian plume
is used. Hourly stability class is determined internally. Dispersion
coefficients are from McElroy and Pooler (urban) or Turner (rural).
No further adjustments are made for variations in surface roughness.
I. Chemistry/Reaction Mechanism. The model assumes an
exponential decay with a user-input half-life.
J. Physical Removal. Exponential decay and a user-input
half-life are used.
K. Background. This is not treated.
4. Input and Output: Meteorological data must be input to
the model. Output produced by the model includes: hourly and average
(up to 24 hours) concentrations at each receptor; a limited individual
source contribution list; and cumulative frequency distribution
based on 24-hour averages and up to one year of data at a limited
number of receptors.
5. System Resource Requirements: This dispersion model is
written in FORTRAN V. The program does not require any special
software or utilities. Approximately 41K words of core are
required to execute on the Univac 1110,
6. Applications: The source program for this dispersion
model is available as part of UNAMAP (Version 3), PB 277 193,
$420, from Computer Products, NTIS, Springfield, VA 22161.
7. Technical Contact
D. Bruce Turner
U.S. Environmental Protection Agency
Mail Drop 80, EPA
Environmental Applications Branch
Research Triangle Park
N.C. 27711
FTS 629-4564 COM 919/541-4564
33
-------
EMISSION DATA
HOURLY DATA
FOR 1 YEAR
FROM NATIONAL
CLIMATIC CENTER
DISK
DATA
V V
R A M Q
V
FILE OF EMIS<
, AREA SOURCE
ARRAY, ETC.
y
R A M B
5ION Bl
MAP
f
HOURLY
MET
CARDS
\/
1 N
.0
>
\
RAMoRRAMR
L K
i
CK DATA
\
V
r >
NK
R A M M E T
DISK FIL!
OF MET. D/
s \^
t \
\TA
/
RAMFoRRAMFR
Y
)UR
HOURLY
AND
D'AILY
CONCENTRATIONS
365 OR 366
DISK RECORDS
OF 24-HOUR
CONCENTRATIONS
\f
MAGNETIC TAPE
WITH HOURLY
CONCENTRATIONS
FOR 1 YEAR
C U M F
T
PRINT
CUMULATIVE
FREQUENCY
DISTRIBUTIONS
PLOT
CUMULATIVE
FREQUENCY
DISTRIBUTIONS
Interrelationships of RAM System Main Programs
34
-------
RAM (RAF1R)
BLOCK (BLOCKR)
(PREPARED BY RAF1BLK)
-READ DATA FROM DISK FILES
(PREPARED BY RAMQ)
-READ DATA FROM CARDS
-LOOP ON DAYS
-READ MET DATA FROM CARDS
(OR FROM DISK PREPARED BY» RAfflET)
-ANGARC
-JMHREC
-JMHHON
-LOOP ON HOURS
-READ HOURLY EMISSIONS
- JF1H54U (JMH54R)
I JMHCZU (JMHCZR)
' BRSZ (PGSZ)
-JMHARE
I JMHPOL
-JMHPTU (JMHPTR)
I DBTRCU (DBTRCR)
I BRSYSZ (PGSYSZ)
-JFIHOUR
-JMHFIN
EXIT
Subroutine Structure of RAM and RAMR
35
-------
RAMF (RAMFR)
BLOCK (BLOCKR)
(PREPARED BY RAMBLK)
-READ DATA FROM DISK FILES
(PREPARED BY RAMQ)
-READ DATA FROM CARDS
-LOOP ON DAYS
-READ MET DATA FROM DISK
(PREPARED BY RAWED
-ANGARC
-LOOP ON HOURS
-READ HOURLY EMISSIONS
- JMH54U (JMH54R)
I JMHCZU (JMHCZR)
I BRSZ (PGSZ)
-JMHARE
1 JMHPOL
-JMHPTU (JMHPTR)
1 DBTRCU (DBTRCR)
I BRSYSZ (PGSYSZ)
-JMHFD
EXIT
Subroutine_S_tructure of RAMF and RAMFR
36
-------
8. References
Novak, J.H. and Turner, D.B., "An Efficient Gaussian
Plume Multiple-Source Air Quality Algorithm," Journal
of the Air Pollution Control Association, 26 (6),560'
575, 1976.
37
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HIWAY MODEL (HIWAY)
1. Model Overview: HIWAY is an interactive program which
computes the hourly concentrations of non-reactive pollutants
downwind of roadways. It is applicable for uniform wind conditions
and level terrain. Although best suited for at-grade highways,
it can also be applied to depressed highways (cut sections) . The
"User's Guide for HIWAY: A Highway Air Pollution Model," is avail
able for EPA as EPA-650/4-74-008 and from NTIS (accession number
PB 239-944/AS).
2. Functional Capabilities: Hourly estimates of concentrations
for receptors off roadways are determined resulting from a single
at-grade or cut-section roadway segment. The user specifies geometry
and emissions of roadway segment, meteorological conditions to be
simulated, and receptor coordinates and height of receptor above
ground.
3. Basic Assumptions:
A. Source-Receptor Relationship: The model uses a
horizontal finite line with multiple line sources (up to 24 lines).
These are straight lines, arbitrary in orientation and length.
One road or highway segment is run at a time. Receptors are
arbitrarily located, downwind of the sources, with a unique source-
receptor distance defined. Arbitrary receptor heights and arbitrary
release heights are used. In the cut section mode receptors cannot
be located in the cut, and emissions are treated as coming from 10
equal uniform line sources at the top of the cut. A flat terrain is
assumed, and line sources are treated as a sequence of point sources;
the number is such that convergence to within 21 is achieved.
B. Emission Rate: A constant uniform emission rate for
each lane is assumed.
38
-------
C. Chemical Composition: This is not applicable to the
model.
D. Plume Behavior: This is not treated.
E. Horizontal Wind Field. The user specifies arbitrary
wind speed and direction. No variation of wind speed and direction
with height is allowed, and a uniform, constant (steady-state) wind
is assumed.
F. Vertical Wind Speed. This is assumed to be equal to
zero.
G. Horizontal Dispersion. The model uses a semi-empirical/
Gaussian plume, and the user specifies which of 6 stability classes
to be used: Turner (1964). Dispersion coefficients used are from
Turner (1969) ; for distances less than 100m, dispersion coefficients
from Zimmerman and Thompson (1975) are used. In the level grade
mode, the initial value of the dispersion coefficient is 3 meters.
In the cut section mode, the initial value of the dispersion
coefficient is approximated as a function of the wind speed. No
further adjustments to the dispersion coefficients are made.
H. Vertical Dispersion. The model uses a semi-empirical/
Gaussian plume in which the user specifies stability class. Dis-
persion coefficients used are from Turner (1969) : for distances less
than 100m, dispersion coefficients from Zimmerman and Thompson
(1975) are used. In the level grade mode, the initial o~z is equal
to 1.5 meters. In the cut section mode, the initial
-------
input or they may be requested from program; (2) meteorological
data: wind speed, wind direction, stability class, mixing height;
(3) ambient air concentration measurements. For verification of
the model, meteorological date and ambient air concentrations are
needed. Output from the model includes a printout of the 1-hour
average concentration at each receptor.
5. System Resource Requirements: This dispersion model is
written in FORTRAN V. The program does not require any special
software or utiliities. Approximately 12K words of core are
required to execute on the TJnrvac 1110.
6. Applications: The source program for this dispersion model
is available as part of UNAMAP (Version 3), PB 277 193 for $420 from
Computer Products, NTIS, Springfield, VA 22161.
7. Technical Contact
D. Bruce Turner
U.S. Environmental Protection Agency
Mail Drop 80
Environmental Applications Branch
Research Triangle Park, NC 27711
FTS 629-4564 COM 919/541-4564
8. References :
Zimmerman, J.R. and Thompson, R.S., 1975: User's Guide
for HIWAY: A Highway Air Pollution "Mo'delT U.S. Environ-
mental Protection Agency, Research Triangle Park, NC. En-
vironmental Monitoring Series, EPA-650/4-74-008 , 59 p.
(NTIS accession number PB 239-944/AS).
40
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HIWAY
DBTLNE
XVY
xvz
DBTRCX
DBTSIG
END
.FLOWCHART FOR HIWAY
41
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HIWAY-2
1. Model Overview: HIWAY-2 is a batch and interactive program
which computes the hourly concentrations of non-reactive
pollutants downwind of roadways. It is applicable for uniform
wind conditions and level terrain. Although best suited for
at-grade highways, it can also be applied to depressed highways
(cut section s) .
2. Functional Capabilities: Hourly estimates of
concentrations for receptors off roadways are determined
resulting from a single at-grade or cut-section roadway
segment. The user specifies geometry and emissions of roadway
segment, meteorological conditions to be simulated, and
receptor coordinates and height of receptor above ground.
3• Basic Assumptions:
A. Source-Receptor Relationship: The model uses a
horizontal finite line with multiple line sources (up
to 24 lines). These are straight lines, arbitrary in
orientation and length. Receptors are arbitrarily
located, downwind of the sources, with a unique
source-receptor distance defined. Arbitrary receptor
heights and arbitrary release heights are used. In the
cut-section mode the receptors cannot be located in the
cut, and emissions are treated as coming from 10 equal
uniform line sources at the top of the cut. A flat
terrain is assumed, and line sources are treated as a
sequence of point sources; the number is such that
convergence to within 2% is achieved.
B. Emission Rate: A constant uniform emission rate for
each lane is assumed.
42
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C. Chemical Composition: This is not applicable to the
model.
D. Plume Behavior: This is not treated.
E. Horizontal Wind Field: The user specifies arbitrary
wind speed and direction. No variation of wind speed
and direction with height is allowed, and a uniform,
constant (steady-state) wind is assumed.
F. Vertical Wind Speed: This is assumed to be equal to
zero.
G. Horizontal Dispersion: The model uses a
semi-empirical/Gaussian plume, and the user specifies
which of 6 stability classes are to be used (Turner,
1964). For distances less than 300 m, empirically
derived dispersion parameters are used (Rao, et al. ,
1980). In the level grade mode, the initial value of
the dispersion coefficient is twice the value for the
initial vertical dispersion coefficient. In the
cut-section mode, the initial value of the dispersion
coefficient is approximated as a function of the wind
speed .
H. Vertical Dispersion: The model uses a
semi-empirical/Gaussian plume in which the user
specifies stability class. The dispersion coefficients
used are from Turner (1969); for distances less than
300 m dispersion coefficients (Rao, et al. , 1980) are
used. In the level grade mode, the initial
-------
I. Chemistry/Reaction Mechanism. This is not treated.
J. Physical Removal. This is not treated.
K. Background. This is not treated.
4. Input and Output: Initial set-up and calibration needs
are: (1) in both batch and interactive mode, discharges for
vehicular line sources are input into the program; (2)
meteorological data: wind speed, wind direction, stability
class, mixing height; and (3) ambient air concentration
measurements. For verification of the model, meteorological
data and ambient air concentrations are needed.
Output from the model includes a printout of the one-hour
average concentration at each receptor.
5. System Resource Requirements: HIWAY-2 is written in
FORTRAN and is run on the Univac 1110. It requires 6K bytes of
core storage for execution. An operator with knowledge in
engineering will be useful.
6. Applications: The source program for this dispersion model
is available as a part of UNAMAP (Version 3), PB 277-193 for
$420 from Computer Products, NTIS, Springfield, VA 22161.
7. Technical Contact
William B. Petersen
U.S. Environmental Protection Agency
Environmental Science Research Laboratory
Research Triangle Park
N.C. 27711
COM 919/541-4564 FTS 629-4564
44
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8. References
Petersen, W.B., 1980. Userv s Guide for HIWAY-2: A Highway
Air Pollution Model . U.S. Environmental Protection Agency,
Research Triangle Park, N.C., EPA-600/8-80-018, 70 p. (Also
available from NTIS as PB 80-227-576).
Rao, S.T. and Keenan, M.T., 1980: Suggestions for
Improvement of the EPA-HIWAY Model. JAPCA, 30, 6, pp.
247-256.
45
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INDUSTRIAL SOURCE COMPLEX MODEL (ISC)
1. Model Overview: The Industrial Source Complex Dispersion
Model is a Gaussian plume model used to evaluate the air
quality impact of emissions from industrial source complexes.
The ISC Model consists of two computer programs, one for
short-term analyses and one for long-term analyses. The
short-term model program, ISCST, uses sequential hourly
meteorological data to estimate the concentration or deposition
patterns from one hour to one year. The long-term model
program, ISCLT, uses statistical wind summaries to estimate the
seasonal and annual concentration or deposition patterns. The
ISC Model has rural and urban options. Major features of the
ISC Model program are: (1) the effects of aerodynamic
building wakes and stack tip downwash; (2) the effects of
variations in terrain height; (3) the plume rise due to
momentum and bouyancy as a function of downwind distance; (4)
the dispersion of emissions from stack area, line, and volume
sources where line sources are simulated by multiple volume
sources; (5) the physical separation of multiple sources;
(6) the time-dependent exponential decay of pollutants; and
(7) the effects of gravitational settling and dry deposition.
2. Functional Capabilities: The number of sources and
receptors are interdependent. However, 300 is the maximum
number of sources accepted, arbitrarily located. Receptors can
be specified on a polar or rectangular grid and Briggs" early
plume rise formulations, including the momentum terms, are
used. Deposition can be calculated or allowed for over flat
terrain only. The short-term program calculates values of
average concentration or deposition for time periods of 1 2
3, 4, 6, 8, 12, and 24 hours. Additionally, the ISCST may be
46
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used to calculate N-day concentration or deposition values
where the maximum value of N is 366 days. The units option
allows the user to specify the input emissions units and/or
output concentration or deposition units. Applications that do
not require at least one of the ISC Model features should
utilize a less comprehensive computer model.
3- Basic Assumptions: Meteorological homogeneity is assumed
following the conversion of surface wind speed to that at plume
height. All plumes remain level, regardless of terrain
elevation, unless significant terminal fall velocity is
specified. -Emission rates can be varied according to specified
meteorological classes or as a function of time (hour of day,
season or month, or both). A simple time dependent exponential
decay of the pollutant is optional.
4. Input and Output: The input of the meteorological data
required for ISCST are mean wind speed and measurement height,
average random flow vector, wind profile exponents*, ambient
air temperature, height of mixing layer, Pasquill stability,
and vertical potential temperature gradient*. These data may
be input directly using the same preprocessed meteorological
data tape as the CRSTER Model or alternatively input by card
deck. For ISCLT, joint frequencies of occurrence of wind
speed, direction, and stability are required. Source data
consists of emission rate (total emissions for deposition);
dimensions of stack, building area, or volume source; effluent
characteristics; surface reflection coefficients for each
settling-velocity category; receptor data; and receptor terrain
elevation data.
*Default values are available.
47
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Output can be directed to a line printer and/or
magnetic tape- Five categories of printed output can be
acquired from ISCST: input source-receptor and hourly
meteorological data listings; concentration or deposition
values calculated for any combination of sources at all
receptors for any specified day(s) or time period; highest and
second-highest such values; and a maximum of 50 such values.
ISCLT output provides input source-receptor and meteorological
data listings; long-term mean concentration or deposition
values calculated at each receptor for each source and for
combined emission sources; and contributions of individual
sources to the maximum 10 such values calculated for the
combined emission sources or as contributed to user specified
receptors.
5- System Resource Requirements: The ISC Model programs are
written in FORTRAN IV and require approximately 65,000 Univac
1110 words. The programs may also be used on a medium to large
IBM or CDC computer system with little or no modification.
6. Applications: The ISC Model is recommended for use in air
quality assessments of stack, area, and volume industrial
complex sources in urban and rural areas where short-term,
seasonal, and/or annual air quality concentration estimates of
stable pollutants are required.
48
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7. Technical Contact
Joseph A. Tikvart
U.S. Environmental Protection Agency
Office of Air Quality Pollution Standards
Mail Drop 14
Research Triangle Park
N.C. 27711
COM 919/541-5251 FTS 629-5251
8. Reference
Industrial Source Complex (ISC) Dispersion Model User's
Guide. Volume I; NTIS # PB 80-133044.
Industrial Source Complex (ISC) Dispersion Model User's
Guide, Volume II (Appendices A through I) NTIS # PB
80-133051.
Magnetic Tape of Programs NTIS # PB 80-133036.
49
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KINETIC MODEL AND OZONE ISOPLETH PLOTTING PACKAGE (OZIPP)
1. Model Overview: The Kinetics Model and Ozone Isopleth
Plotting Package (OZIPP) computer program can be used to
simulate ozone formation in urban atmospheres. OZIPP
calculates the maximum one-hour average ozone concentrations
given a set of input assumptions about initial precursor
concentrations, light intensity, dilution, diurnal and spatial
emission patterns, transported pollutant concentrations, and
reactivity of the precursor mix. The results of multiple
simulations are used to produce an ozone isopleth diagram
tailored to particular cities. Such a diagram relates maximum
ozone concentrations to concentrations of non-methane organic
compounds and oxides of nitrogen, and can be used in the
Empirical Kinetic Modeling Approach (EKMA) to calculate
emission reductions necessary to achieve air quality standards
for photochemical oxidants.
2. Functional Capabilities: The major function of the OZIPP
Model is to generate an ozone isopleth diagram representative
of a particular city. The diagram explicitly depicts the
maximum, one-hour average concentrations of ozone occuring
within or downwind of a city as a function of precursor levels
existing within the city in the early morning. These diagrams
are based on mathematical simulations of ozone formation
occurring during a day. As such, the model is limited in
applicability to ozone problems within or immediately downwind
of urban areas and cannot consider the following: rural ozone
problems; ozone problems occurring in the early morning or at
night; and contributions of single or small groups of sources
to ozone problems. The OZIPP model is best used to study the
effectiveness of areawide control strategies in reducing peak,
one-hour average ozone concentrations within or downwind of a
city .
50
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3- Basic Assumption: -The model underlying OZIPP is similar in
concept to a trajectory-type photochemical model which
simulates the formation of ozone from precursors within a
migrating column of air. A column of uniformly mixed air
extends from the earth's surface throughout the mixed layer.
The height of the column rises according to the diurnal
variation in mixing height, resulting in dilution of pollutants
within the column and entrainment of pollutants which were
initially above the column. As the column moves, emissions of
fresh precursors are encountered. The model mathematically
calculates the formation of ozone within the column as a
function of time in accordance with a chemical kinetic
mechanism. The model employs a gear-type integration scheme to
numerically solve the set of differential evaluations which
describe the model assumptions.
To generate an ozone isopleth diagram, the model performs
repeated simulations with differing pollutant levels initially
within the column. Using the results of these simulations, a
diagram is constructed which expresses the calculated peak and
one-hour average ozone concentrations as a function of the
initial precursor concentrations. The program incorporates a
hyperbolic spline interpolation scheme to construct the graph.
4. Input and Output: Data are supplied to the model to make
an ozone isopleth diagram specific to a particular city. These
data include: latitude, longitude and time zone of the city;
the day, month and year; the minimum morning and maximum
afternoon mixing heights; sets of emission fractions which
reflect the effect of precursor emissions occuring throughout
the day; and the concentration of ozone and precursor
transported into the city. Additional input parameters are
supplied to control the generation of the ozone isopleth
51
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diagram (e.g., scales of the diagram, size of the diagram,
accuracy, interpolation smoothing, etc.). All input data are
processed in a simple manner, and no extensive computerized
data base is required.
The primary output of the model is the ozone isopleth
diagram. The diagram is depicted on a line printer plot, and
can be generated as an option on a Calcomp Plotter. A report
is also produced which summarizes the input data and results of
the simulations that were performed to generate the diagram.
Operation includes training in' computer programming and
engineering.
5. System Resource Requirements: OZIPP is coded in FORTRAN.
It can be run on a Univac 1100 mainframe with a 132 position
line printer and a card reader/punch. A Calcomp plotter may be
used as an option. Operator requirements include training in
computer programming and engineering.
6. Applications: The OZIPP Model has been used to generate
ozone isopleth diagrams to calculate emission reductions
necessary to achieve the ambient air quality standard for
ozone. The model was used by state/local air pollution control
agencies as the basis for estimating emission reductions for
the 1979 submittal of the State Implementation Plans.
7. Technical Contact
Gerald L. Gipson
U.S. Environmental Protection Agency
OAQPS/MDAD/AMTB/TDS
Mail Drop 14
Research Triangle Park
N.C. 27711
COM (919) 541-5522 FTS 629-5522
52
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8. References
Kinetics Model and Ozone Isopleth Plotting Package (OZIPP),
EPA-600/8/7700l4b, U.S. Environmental Protection Agency,
Research Triangle Park, N.C., July 1978.
Whitten, G.Z. and Hugo, H. User's Manual for Kinetics Model
and Ozone Isopleth Plotting Package, EPA-600/8-78-014a,
U.S. Environmental Protection Agency, Research Triangle
Park, N.C., July 1978.
Users, Limitation and Technical Basis of Procedures for
Quantifying Relationships Between Photochemical Oxidants
and Precursors, EPA-M50/2-77-021a, U.S. Environmental
Protection Agency, Research Triangle Park, N.C., November
1977.
53
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LAGRANGIAN PHOTOCHEMICAL AIR QUALITY SIMULATION MODEL (LPAQSM)
1. Model Overview: LPAQSM is designed to predict the
concentrations of ozone produced in an urban area by modeling
the emissions, transport, and transformations in the presence
of ultraviolet radiation.
2. Functional Capabilities: The model is designed for
simulation between sunrise and sunset on a single day. It has
five levels of vertical resolution but describes only one area
of an urban domain at a particular time. Concentrations are
output for each 30 minutes along the trajectory.
3. Basic Assumptions: The model assumes a Lagrangian parcel
of air of dimensions typically 5x5 km by 1.5 km high. The
parcel moves with the wind, entraining emissions which enter
into the photochemical reactions. The initial leading of
pollutants is specified and the parcel has a rigid upper
boundary and no lateral diffusion.
4. Input and Output: Input for this model includes the
following data:
1. Emissions inventory for hydrocarbons and nitrogen
ox ide .
2. Surface network air quality and meteorological
measur ements .
3. Upper air radiosonde data.
4. Solar radiation data.
54
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The Regional Air Pollution Study (RAPS-St. Louis) data base
is being used with LPAQBM.
Output is in the folrra of a computer printout.
Concentrations of ozone, carbon monoxide, sulfur dioxide,
hydrocarbons, and nitrogen oxides are supplied at 30 minute
intervals for 5 levels in the vertical.
5. System Resource Requirements: LPAQSM is coded in FORTRAN
and is run on an Univao 1110 mainframe. The model requires
60,000 words of core storage for execution. It is stored on 2
magnetic tapes and requires a line printer for output.
Operating skills for the model include a programmer.
6. Applications; The model builder, Environmental Research
and Technology, Inc., has tested the model against data in Los
Angeles as well as using it in environmental impact assessments
7. Technical Contact
Jack Shreffler
U.S. Environmental Protection Agency
Environmenal Sciences Research Laboratory
\
Research Triangle Park
N.C. 27711
COM 919/541-4524 FTS 629-4524
8. References
A Lagrangian Photochemical Air Quality, Simulation Model
Vol. I. Model Formulation Vol. II. User's Manual
EPA-600/8-79-015a,,t>.
55
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LIVERMORE REGIONAL AIR QUALITY MODEL (LIRAQ)
1. Model Overview: The Livermore Regional Air Quality Model
(LIRAQ) exists in two versions, LIRAQ-1 and LIRAQ-2. Both ver-
sions are two-dimensional (horizontal) Eulorian grid models de-
signed to predict regional distributions of air pollutants.
LIRAQ-1 can treat up to four noninteracting or simply inter-
acting species on up to a 45 x 50 grid. It uses the flux-cor-
rected algorithm to treat transport. LIRAQ-2 simulates evolu-
tion of the concentrations of 12 chemically interacting species
on a 20 x 20 grid. It uses an upstream differencing scheme to
represent horizontal transport, and the Gear package to carry
out time integration. A version with chemistry updated to 19-80
is now being tested.
2. Functional Capabilities: Both versions of the model pro-
vide graphical and tabular displays of selected species over the
entire grid, and graphical displays of the temporal variability
of selected species at up to 50 selected grid elements. Edit
intervals are as specified and can be varied at the user's con-
venience. Extensive graphical capabilities are built into the
code, and all input quantities are echoed in tabular output.
Temporal and spatial variations of emissions, mixing depth, winds,
solar flux, K , and spatial variations of terrain are treated.
z
3. Basic Assumptions: Both of the LIRAQ models are 2-D hori
zontal models bounded on the top by a temporally and spatially
varying inversion "lid." Both models assume a logarithmic con-
centration profile in the vertical based on a balance of fluxes
at the boundaries which can be different for each species. This
vertical profile is assumed to interact with the power law wind
profile in determining horizontal transport. LIRAQ-2 does not
compensate for the effects of the vertical distribution of pol-
lutants in calculating transformation by chemical reactions.
LIRAQ-2 uses a chemical reaction mechanism of some complexity
but uses an approximate "lumping" scheme in treating hydrocarbon
emissions and other reactive organic species. Although developed
with the intention of maintaining the maximum fidelity to real
56
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chemical data compatible with the model, the chemical mechanism
is, in part, a simulation mechanism. The present version of
LIRAQ-1 assumes no chemical interactions other than a deposition
velocity and/or exponential decay.
4. Input and Output: Inputs for the initial set-up and cali-
bration of the model include:
1) A file specifying the topographic elevation at every
grid point in the model domain, as well as any map in-
formation (rivers or shore outlines, city or station
locations) to be displayed on the output.
2) Files specifying the emissions in each grid element at
hourly intervals.
3) Files giving data fields on mass consistent vertical
(through the inversion) and horizontal fluxes, inversion
base heights (i.e. mixing depths), atmospheric trans-
missivity (based on cloud extent), and horizontal and
vertical eddy diffusivities. These files are normally
supplied by a meteorological data processing code, MAS-
CON, but could be provided by other processing routines.
4) A file giving photodissociation rates as a function of
solar zenith angle for a clear sky (LIRAQ-2 only).
5) A file giving observed species concentrations at measur-
ing stations to be used for initializing the problem.
6) A file defining the particular problem to be run (i.e.
title, start time, stop time, species and locations for
graphical output, boundary conditions, molecular weights,
and specific emissions factors for various species).
Outputs provided by the model include the following:
1) Voluminous printer files echoing all input and providing
species concentrations at the surface and averages for
the mixed layer at all grid locations at every edit in-
terval.
2) A file containing concentrations for selected species at
selected locations as a function of time.
3) A file containing information about the numerical inte-
gration scheme.
57
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4) Voluminous graphical output as described above.
5. System Resource Requirements: The LIRAQ models are coded
in FORTRAN IV and require a CDC 7600 or equivalent with random
access disks. Typical problems take 0.1-0.3 hours for LIRAQ-1
and 0.5-1.0 hours for LIRAQ-2, thus pointing to the need for ad-
equate resources to fund computer time needs. The program pres-
ently exists only at the Lawrence Berkeley and Lawrence Liver-
more Laboratories. A computer progra™cr, a research assistant,
and an experienced modeler with expertise in coding, diffusion
models, and advanced mathematical solution techniques are useful
6. Applications: Both LIRAQ models have been used by the San
Francisco Bay Area Air Pollution Control District and the Asso-
ciation of Bay Area Governments in the preparation of the long-
term Air Quality Maintenance Plan for the San Francisco Bay area,
The U.S. Environmental Protection Agency has included LIRAQ-2 as
part of their model validation exercise gathered during the RAPS
program. Processors necessary to make the EPA data base LIRAQ-
compatible have been developed. C.D. Craig of Oregon State
University is currently involved in a program to use LIRAQ-1 to
model the air quality impact of agricultural burning in the
Willamette Valley.
7. Technical Contacts
Dr. Jack Shreffler
U.S. Environmental Protection Agency
Environmental Sciences Research Laboratory
Research Triangle Park
N.C. 27711 COM 919/541-3660 FTS 629-3660
M.C. MacCracken, J.J. Walton, and J.E. Penner
Lawrence Livermore Laboratory
Livermore, California 94550
COM 415/422-1800
FTS 422-1800
58
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8- References
ABAC, et al., Application of Photochemical Models: Volume I,
The Use of Photochemical Models in Urban Ozone Studies, EPA
Report 450/4-79-025, 1979.
Duewer, W.H., et al. "Livermore Regional Air Quality Model
(LIRAQ) Transfer to EPA, "Lawrence Livermore Laboratory Report
UCRL-52864, 1980 (available from NTIS). Also to be published
by EPA.
Duewer, W.H. et al.,"The Livermore Regional Air Quality Model
II., Verification and Sample Application in the San Fransisco
Bay Area," J. Appl. Meteorol.,'J7, 273-311, 1978.
MacCracken, M.C., et al., The Livermore Regional Air Quality Model;
Concept and Development," J. Appl. Meteorol., 17, 254-272, 1978.
MacCracken, M.C., et al., User's Guide to the LIRAQ Model; An Air
Pollution Model for the San Fransisco Bay Area,Lawrence Livermore
Laboratory, Livermore, California, December 1975.
fc
59
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MODIFIED ROLLBACK MODEL (ROLLBACK)
1 Model Overview; The Modified Rollback Model is a
computerized air quality simulation model that has been used
for assessing the relative air quality impacts of alternative
control strategies which are national in scope. Air quality
projections for carbon monoxide and nitrogen oxides are made
using the Morris-deNevers modified rollback equations. Ozone
projections are made using the Empirical Kinetic Modeling
Approach (EKMA) standard isopleth diagram. Emission inventory
projections are made using data on mobile and stationary source
emission factors, VMT growth rates, stationary source
retirement rates, growth rates, and control efficiencies.
2. Functional Capabilities; Modified Rollback can be used to
estimate changes in CO and annual average N0? levels due to
assumed changes in CO and NO emissions, respectively.
A
Changes in ozone air quality levels are estimated using the
standard isopleth diagram of EKMA. These procedures are best
used to compare the relative air quality impacts of alternative
area source control strategies. The model requires county
level, or larger, emissions inventories by major source
category .
3. Basic Assumptions; The simple rollback model is based on
the assumption that the air quality concentration of a
pollutant at a point is equal to the background concentration
of that pollutant and some linear function of the total
emission rate of that pollutant which influences the
concentration at that point. Modified Rollback uses the
60
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Morris-deNevers equations to account for differing rates of
growth/reduction in emissions from a number of source
categories. The model assumes that the spatial and temporal
distributions of emissions and the meteorological conditions
remain constant between the base year and the projection year
However, for ozone projections, the model uses the standard
EKMA isopleths described in Reference 3.
**• Input and Output: For each study area, the user must
furnish data on the base year air quality level; the background
concentration; the emissions, growth and retirement rates; and
the control efficiencies for each major mobile and stationary
source category and strategy scenario. The air quality data is
typically obtained from SAROAD and the emissions data from NEDS,
Output reports consist of individual source area emissions
inventories for the base year, each projection year/strategy
combination, and air quality summary reports. The air quality
summary reports, grouped by strategy, display the base year air
quality concentration, projection year air quality levels, and
expected number of violations of the NAAQS for each study area.
5. System Resource Requirements: ROLLBACK is coded in
FORTRAN. It is run on an IBM 360 or Univac 1108 mainframe
computer. The model also uses a card reader/punch. It uses a
regular line printer for the output. An engineering and
computer programming background are useful.
6. Applications: The Modified Rollback Model has been used by
EPA to evaluate the relative air quality impacts of revisions
to the automotive emission standards. Other applications
include the regulatory analyses conducted in association with
the review of the ambient air quality standards.
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7. Technial Contact
Warren P. Freas
U.S Environmental Protection Agency
Monitoring and Data Anaysis Division
Mail Drop 14
Research Triangle Park
N.C. 27711
COM (919) 541-5522 FTS 629-5522
8. References
deNevers, N. and Morris, J.R. "Rollback Modeling: Basic
and Modified," Journal of the Air Pollution Control
Association, Vol. 25, September 1975.
Wilson, J.H., Jr., "Methodologies for Projecting the
Relative Air Quality Impacts of Emission Control
Strategies," Presented at the 71st Annual APCA Meeting,
Houston, Texas, June 25-29, 1978.
Uses, Limitations and Technical Basis of Procedures for
Quantifying Relationships Between Photochemical Oxidants
and Precursors, EPA-450/2-77-021a, U.S. EPA, Research
Triangle Park, N.C. November 1977.
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MULTIPLE POINT GAUSSIAN DISPERSION ALGORITHM WITH OPTIONAL
TERRAIN ADJUSTMENT (MPTER)
1- Model Overivew: MPTER is a multiple point-source Gaussian
model with optional terrain adjustments. MPTER estimates
concentrations on an hour-by-hour basis for relatively inert
pollutants (i.e., S02 and TSP) . MPTER uses Pasquill-Gifford
dispersion parameters and Briggs plume rise methods to
calculate the spreading and the rise of plumes. The model is
most applicable for source-receptor distances less than 10
kilometers and for locations with level or gently rolling
terrain. Terrain adjustments are restricted to receptors whose
elevation is no higher than the lowest stack top. In addition
terrain adjustments options are also available for wind profile
exponents, buoyancy induced dispersion, gradual plume rise,
stack downwash, and plume half-life.
2. Functional Capabilities: MPTER computes hour-by-hour
concentrations for relatively inert pollutants for each
source-receptor pair. MPTER can handle up to 250 point sources
and 180 receptors. Model users have the option of specifying
the elevations and location coordinates in either metric or
English units. Hourly met data can be read either off cards or
off disk/tape. MPTER can calculate concentrations for
averaging periods of 1, 3, 8 and 24 hours for up to a year's
data. Annual concentrations can also be computed.
3. Basic Assumptions: MPTER is based upon Gaussian dispersion
theory using mean meteorology conditions on an hour-by-hour
basis. Dispersion coefficients used to calculate both vertical
and horizontal spreading are those of Pasquill and Gifford.
The rising plume is assumed to reflect completely off the top
of the mixing height in neutral and unstable conditions. The
plume rise is based on Briggs. MPTER can also optionally
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consider stack downwash, buoyancy induced dispersion, and
gradual plume rise. MPTER can either utilize constant emission
rates, or hourly emission rates for each point source. The
emitted pollutants should be relatively inert chemically since
MPTER does not consider complex physical removal or chemical
reaction processes. Users can approximate exponential decay of
a pollutant by supplying a half-life. Wind speeds are
extrapolated to the stack top using user supplied wind profile
exponents. The optional terrain adjustment reduces the plume
height relative to the ground. Additional terrain adjustment
factors can be entered which control the proportion of terrain
adjustment according to stability class.
4. Input and Output: Input for MPTER includes: control data,
emission data, receptor information, and hourly met data. The
hourly met data can be read either off cards or from a
disk/tape preprocessed from surface/upper-air observations.
Hourly emission data can optionally be input from disk/tape.
The variety of MPTER options allow the user to output to a
printer or to write to tape information required for a
multitude of applications. Tape/disk files can be written
containing hourly concentrations for each receptor for each
source, hourly concentrations for each receptor for all
sources, concentrations for user specified averaging periods
and highest five concentrations for each receptor for each
averaging period. MPTER allows even more flexibility on
printed output. The range of options include printouts for the
highest five concentrations, for each receptor to printout for
hourly contributions from each source at each receptor.
5. System Resource Requirements: MPTER is written in FORTRAN
and is run on an IBM 360, CDC 6600, or Univac 1100/82. A 132
position line printer, card reader/punch, and/or tape/disk are
required. A computer programming background is helpful to run
the model.
64
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6. Applications: The frequent use of MPTER is to assess air
quality from multiple point sources to compare with National
Ambient Air Quality Standards for S02 or TSP. MPTER can
estimate concentrations for a single source at one or more
receptors for one hour, or it can simulate concentrations on an
hour-by-hour basis for as many as 250 point sources at up to
1,180 receptors for up to a year. The types of multiple
applications for which MPTER is suited include stack design
studies, combustion source permit applications, regulatory
variances evaluation, monitoring network design, control
strategy evaluation, coal conversion studies, control
technology evaluation, new source review, and the prevention of
significant deterioration (within 10 km).
7. Technical Contacts
D. Bruce Turner and Tom Pierce
U.S. Environmental Protection Agency
Meteorology and Assessment Division
Davis Drive
Research Triangle Park
N.C. 27711
FTS 629-4564 COM 919/541-4564
8. References
Pierce, T.E. and Turner, D.B., 1980: User's Guide for
MPTER: A Multiple Point Gaussian Dispersion Algorithm with
Optional Terrain Adjustment. EPA-600/8-80-016, U.S.
Environmental Protection Agency, Research Triangle Park,
N.C. 239 pp.
U.S. Environmental Protection Agency, 1980: MPTER tape.
(Computer programs on tape containing programs, and PTPLU
screening model) NTIS PB 80-168156, National Technical
Information Service, Springfield, VA.
65
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MULTI-SOURCE MODEL (CRSTER-2)
1. Model Overview: This model is basically the same as EPA's
Single Source CRSTER.
2. Functional Capabilities: While essentially the same in
function as CRSTER, CRSTER-2 will allow separation of multiple
emission points.
3. Basic Assumptions: Same as CRSTER
4. Input and Output: Differs from CRSTER in that distinct
spacial coordinates can be assigned to each point of
emissions. Also, this model can handle an increased number of
sources and receptors, and stack data can be input in English
or metric units.
The output is basically the same as CRSTER, but stack and
receptor coordinates can be output in a format for use by the
CALCOMP plotter.
5. System Resource Requirements: This dispersion model is
written FORTRAN V. The program does not require any special
software or utilities. Approximately 28K words of core memory
is required to execute the model on the Univac 1110.
66
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6. Applications: Same as CRSTER, except for added flexibility
of allowing emission source separation.
7. Technical Contact
Lewis H. Nagler
Environmental Protection Agency
NOAA-Air Facilities Branch
EPA Region 4
Atlanta, GA 30365
COM 404/881-4901 FTS 257-4901
8. References
"User Information for the Modified CRSTER Program" (EPA
Information Clearinghouse Files).
Environmental Protection Agency, "User's Manual for Single
Source (CRSTER) Model". Publication No. EPA-450/2-77-013
(NTIS PB 271360), Office of Air Quality Planning and
Standards, Research Triangle Park, North Carolina 27711,
July 1977.
67
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NONLINEAR ROLLBACK/ROLLFORWARD MODEL
1. Model Overview: The Nonlinear Rollback/Rollforward
Model considers the relationship between ozone/oxidants and
single precursor hydrocarbons. It is'uncoded and requires no
equipment. The model can be used to provide "first cut"
approximations of ambient concentrations and reductions in
residual discharges in "rough" urban areas.
2. Functional Capabilities: The rollback/rollforward
model considers only the relationship between ozone/oxidants
and single precursor hydrocarbons. The model cannot consider
combined changes in hydrocarbons and nitrogen oxides. This
model can best be used to study "area wide" strategies or changes
such as vehicular discharge limitations, number of automobiles,
vehicle miles traveled projections. The rollback/rollforward
model might be used as a "screening technique" to decide which
strategies to further evaluate in a spatial and temporal
photochemical model. Several variations of the model are
possible: the nonlinear relationships can be developed for
specific cities and specific monitoring locations within a
city, a national relationship could be used, or relationships
could be developed to be used for both hydrocarbons and nitrogen
oxides.
3. Basic Assumptions: The Nonlinear Rollback/Rollforward
Model is a stochastic model that treats ozone/oxidants as a
function of hydrocarbons only. The model does not consider
changing spatial and temporal patterns of residual discharges,
and it assumes the same meteorological conditions as for a
base case.
68
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4- Input and Output: Input to the rollback portion of
the model include ambient concentration measurements (generally
hydrocarbons and oxidant/ozone) to determine nonlinear
relationships. Data requirements for the rollforward portion of
the model include ambient concentration measurements and projected
changes in residual discharges.
Output from the model is in the form of hourly reductions
in residual discharges from the rollback portion, and hourly
ambient concentrations from the rollforward portion.
5. System Resource Requirements: The Nonlinear Rollback/
Rollforward Model is uncoded and requires no equipment. The
model user should have an understanding of the model's
principles and limitations. The rollback/rollforward model
itself can be developed within a few man-days from ambient
monitoring data.
6. Applications: The Nonlinear Rollback/Rollforward
model can be used to provide "first cut" approximations of
ambient concentrations and reductions in residual discharges
in "rough" ruban areas.
7. Technical Contact
Joseph Tikvart
U.S. Environmental Protection Agency
Monitoring and Data Analysis Division
Mutual Building
411 W. Chapel Hill Street
Durham, N.C.28801
FTS 629-5561 COM 919/541-5561
8. References
Record, "Photochemical Oxidant Modeling," Vol. I, pp.32-44,
U.S. Environmental Protection Agency, Guidelines for Air
Quality Maintenance, Vol. XII, pp. 21-22.
69
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THE PLUME VISIBILITY MODEL (PLUVUE)
1. Model Overview: The design objective of the model is to
calculate visual range reduction and atmospheric discoloration
caused by the plumes consisting of primary particulates,
nitrogen oxides, and sulfur oxides emitted by a single
emissions source. The model is designed to predict the impacts
of a single emissions source on visibility in Federal Class I
areas. The model is a refinement of the plume model developed
in the 1978 publications EPA450/378 110a, b, and c (available
from NTIS) and PB 293118 set. PLUVUE predicts the transport,
atmospheric diffusion, chemical conversion, optical effects,
and surface deposition of point source emissions. The model
uses the Gaussian formulation for transport and dispersion.
The spectral radiance (intensity of light) at 39 visible
wavelengths is calculated for views with and without the
plume. The changes in the spectrum are used to calculate
various parameters that predict the perceptibility of the plume
and contrast reduction caused by the plume. PLUVUE performs
plume optics calculations in a plume-based mode and an
observer-based mode.
2. Functional Capabilities: The model calculates four
perception parameters useful for predicting visual impact:
reduction in visual range, contrast of the plume against a
viewing background at the 0.55 micrometer wavelength, the
blue-red ratio (color shift) of the plume, and the color change
perception parameter triangle E (L*a*b*). Visibility
impairment is caused by changes in light intensity as a result
of light scattering and absorption in the atmosphere.
Impairment can be qualified once the spectral light intensities
or radiance has been calculated for the specific lines of sight
of an observer at a given location in an atmosphere with known
aerosol and pollutant concentrations. PLUVUE is a near-source
plume visibility model, e.g., within 200 km of the source.
70
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3- Basic Assumptions; PLUVUE is based on Gaussian atmospheric
dispersion assumptions, contains Briggs' plume rise equations,
allows for surface deposition during the day and contains
atmospheric chemistry modules that allow for conversion of
nitric oxide to nitrogen dioxide and sulfur dioxide to sulfate
aerosol. Scattering and absorption properties are calculated
for particles and gases. For nitrog.en dioxide, the absorption
at a particular wavelength is a tabulated function multiplied
by the concentration. The effect of particle size on the
wavelength dependence of the scattering coefficient and the
phase function is calculated and the Mie equations are also
solved. Calculation of light intensity follows from the
radiative transfer equation.
4. Input and Output; The input data required for PLUVUE
include: wind speed aloft, stability category, lapse rate
mixing depth, relative humidity, sulfur dioxide, nitrous oxides
and particulate emission rates, stack gas parameters, stack gas
oxygen content, ambient temperature, ambient nitrous oxides,
nitrogen dioxide, ozone and sulfur dioxide concentrations,
properties of background and emitted aerosols in two size
modes, background visual range, deposition velocities for
sulfur dioxide, nitrous oxides, coarse mode and accumulation
mode aerosol, UTM coordinates and elevation of the source, and
UTM coordinates and elevation of the observer location.
The principal PLUVUE run output is the print 'file written
on logical unit six. All runs have the data tables for the
emissions source, meteorological and ambient air quality, and
background radiative transfer. Plot files can also be written
by PLUVUE. If a PLUVUE run is for either observer-based or
plume-based calculations, either an observer-based or a
plume-based plot file will be centered. These files are
written on disk storage units.
71
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5. System Resource Requirements: PLUVUE is written in
FORTRAN. It is run on an Univac 1110 mainframe. It requires
25K bytes of core storage for execution. The model uses a
printer system line printer for output. It also requires a
card reader. An engineer with programming background is
helpful.
6. Applications: PLUVUE has been used by EPA primarily in a
research mode and to provide estimates for hypothetical
scenarios such as power plant siting impact.
7. Technical Contact
James Dicke
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park
North Carolina 27711
COM 919/541-5681 FTS 629-5681
8. References
EPA, User's Manual for the Plume Visibility Model (PLUVUE),
November 1980, EPA-450/4-80-032.
Latimer, D.A., et. al., The Development of Mathematical
Models for the Prediction of Anthropogenic Visibility
Impairment. 1978, EPA 450/3-78-110a, b, c.
72
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Latimer, D.A., Power Plant Impacts on Visibility in the
West; Siting and Emissions Control Implications . JA PC A,
Vol. 30, pp. 142-146, 1980.
Bergstron, R. W., et al., "Comparison of the Observed and
Predicted Visual Effects Caused by Power Plant Plumes
Symposium on Plumes and Visibility," November 10-14, 1980,
to be published in Atmospheric Environment, 1981.
73
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POINT, AREA, LINE SOURCE ALGORITHM (PALJ
1. Model Overview. The Point, Area, Line Source Algorithm
is a short-term Gaussian steady state algorithm that estimates
concentrations of stable pollutants from point, area, and line
sources. Computations from area sources include effects of the
edge of the source. Line source computations can include effects
from a variable emission rate along the source. The algorithm is
not intended for application to entire urban areas, but for smaller
scale analysis of such sources as shopping centers, airports, and
single plants. Hourly concentrations are estimated and average
concentrations for 1 hour to 24 hours can be obtained.
2. Functional Capabilities. Concentration estimates are
given for up to 99 receptor locations, and there are concentration
estimates for 6 different source types, as many as 99 sources of
each type. The model provides concentration averages for 1 to
24 hours for each source type.
3. Basic Assumptions:
A. Source-Receptor Relationship. Arbitary locations are
given for each point, area, and line source. Area source sizes are
specified by an X and Y dimension. A horizontal finite line or
curved path is utilized, as is a slant finite line. Receptor
location is arbitrary. Release heights for point, area, and line
sources are arbitary, as are receptor heights. A flat terrain is
assumed, and the model provides a unique separation for each
source-receptor pair.
B. Emission Rate. Point sources use a single rate for
each source. Area sources also use a single rate for each source.
Area integrations are done by numerical intergration in the up-
wind direction of the concentration from an infinite crosswind line
source corrected for finite length. Horizontal finite line and
curved path sources assume a constant uniform emission rate for
74
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each line. Slant line and special path sources assume a variable
emission rate along line or path.
C. Chemical Composition. The model does not treat this.
D. Plume Behavior. The model uses Briggs (1969, 1971,
1972) neutral/unstable formulas. If stack height plus plume rise
is greater than the mixing height, ground level concentrations are
assumed to be equal to zero. No plume rise calculation is performed
for area or line sources, and the model does not treat fumigation
or downwash.
E. Horizontal Wind Field. Arbitary wind speed and
direction is user-specified. Variation of wind speed with height
is optional, and a constant, uniform (steady-state) wind is assumed,
F. Vertical Wind Speed. This is assumed to be equal to
zero.
G. Horizontal Dispersion. This model uses a semi-empirical/
Gaussian plume. The user specifies which of the six stability
classes are to be used: Turner (1964). Dispersion coefficients
are from Turner (1969). For distances less than 100m, dispersion
coefficients for line sources are from Zimmerman and Thompson (1975) .
The initial value of the dispersion coefficient is specified by the
user, and no further adjustments to the dispersion coefficients are
made.
H. Chemistry/Reaction Mechanism. This is not treated.
I. Physical Removal. The model does not treat this.
4. Input and Output: The user must specify the source
types and provide meteorological data. Output from the model
includes hourly and average (up to 24 hour) concentrations at each
receptor.
5. System Resource Requirements. This dispersion model is
written in FORTRAN V. The program does not require any special
software or utilities. Approximately 51K w,ords of core are
to execute on -the Univ.ac 1110,
75
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PAL
Point
XVY
XVZ
FPLUME
- XPLUME
- RCONCP
L PGSIG
Area
t
PGSIG
RCONCA
HRZLN
XVY
\- XVZ
L RCONCP
L
CRVLN
PGSIQ
XVY
XVZ
CURLIfl
- ANGARC
- DIFANG
I RCONCP
L
PGSIG
SPCLN
- XVY
- XVZ
L RCONCP
PGSIG
SPCCR
• XVY
• XVZ
- ANGARC
- DIFANG
- CURLIN
I RCONCP
L
PGSIG
INTEGL
LFUNCTION
H
RCONCP
DIFANG
'INTEGL
^FUNCTION
IRCONCP
IDIFANG
-INTEGL
(^FUNCTION
RCONCP
DIFANG
H
.INTEGL
| FUNCTION
IRCONCP
ID I FANG
FLOWCHART FOR PAL
76
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6. Applications. The source program for this dispersion
model is available as part of UNAMAP (Version 3), PB 277 193 for
$420 from Computer Products, NTIS, Springfield, VA 22161.
7. Technical Contact
D. Bruce Turner
U.S. Environmental Protection Agency
Environmental Applications Branch
Mail Drop 80
Research Triangle Park
N.C. 27711
FTS 629-4564 COM 919/541-4564
8. References
Petersen, W.B., User's Guide for PAL, A Gaussian Plume
Algorithm for Point, Area, and Line Sources, U7S~!
Environmental Protection Agency, Research Triangle Park,
North Carolina, Environmental Monitoring Series,
EPA-600/4-78-013, 1975.
Turner, D.B., and PetersenJ W.B., A Gaussian Plume
Algorithm for Point, Area, and Line Sources, NATO/CCMS
Sixth International Technical Meeting on Air Pollution
Modeling and Its Application, V 42: 185-228, 1975.
77
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POINT SOURCE GAUSSIAN PLUME MODEL (PTPLU)
1. Mojel Overview: PTPLU is a point source dispersion
Gaussian screening model for estimating maximum surface
concentrations for 1-hour concentrations. PTPLU is based upon
Briggs plume rise methods and Pasquill-Gi f ford dispersion
coefficients as outlined in the Workbook of Atmospheric
Dispersion Estimates. PTPLU is an adaptation and improvement
of PTMAX which allows for wind profile exponents and other
optional calculations such as bouyancy induced dispersion,
stack downwash, and gradual plume rise.
PTPLU produces an analysis of concentration as a function of
wind speed and stability class for both wind speeds constant
with height and wind speeds increasing with height. Use of the
extrapolated wind speeds and the options allows the model user
a more accurate selection of distances to maximum concentration
2. Functional Capabilities: PTPLU estimates the maximum
ground-level concentration and the distance to the maximum for
both wind speeds constant with height and wind speeds
increasing with height for each stability class. The user has
the option of selecting anemometer height, receptor height,
wind profile exponents, stack downwash, buoyancy inducted
dispersion, gradual plume rise, and mixing height. Output
consists of 2 two-dimensional tables listing maximum
concentrations, the distance to maximum concentrations, and the
effective plume heights for a range of surface wind speeds and
extrapolated wind speeds in each stability class.
3. Basic Assumptions: PTPLU calculates the source-receptor
distance to the point of maximum concentration for each wind
speed and stability class. Relatively inert pollutants are
modeled and emissions are assumed to be constant. The plume is
spread horizontally and vertically using P-G dispersion
78
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coefficients. Briggs plume rise computations are employed with
options available for buoyancy induced dispersion, stack
downwash, and gradual plume rise. PTPLU does not allow for any
depletion of the plume by physical removal or chemical
reactions. Eddy reflection with the ground is assumed. If the
effective plume height is calculated to be below the mixing
height in neutral and unstable conditions, then multiple
reflections of the plume between the ground and the mixing
height are computed. But if the effective plume height is
above the mixing height in neutral and unstable conditions then
no calculations are made for ground-level concentrations.
Also, ground-level concentrations are not calculated if the
distance to maximum concentration extends beyond 100 kilometers
from the source. Cautionary messages are printed for plume
heights greater than 200 meters and plume resident times
greater than that expected under normal atmospheric conditions.
U. Input and Output: PTPLU is extremely convenient since only
nominal effort is needed to supply the necessary input. Four
data cards are needed for a single run, however, additional
separate point soures can be analyzed by input of two data
cards for every source. Information required to run PTPLU
includes selection of options, anemometer height, wind profile
exponents, stack parameters (emission rate, stack height, exit
velocity, stack gas temperature, and stack diameter), receptor
height, and mixing height.
PTPLU is a screening model and its output results can be
helpful in more detailed modeling. In particular, the tables
of concentration and distance to maximum concentration can be
examined for selection of receptor distances for use in
detailed models.
79
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5. System Resource Requirements: PTPLU is written in FORTRAN
and can be run on IBM 360, CDC 6600, or Univac 1100/82.
Approximately 12K bytes of core memory are needed for execution
on the Univac 1100/82. A 132 position line printer is
required. Manpower needs include an operator with knowledge of
computer programming.
6. Applications: PTPLU is being used to screen maximum hourly
concentrations of various meteorological conditions.
7. Technical Contact
Tom Pierce
U.S. Environmental Protection Agency
Environmental Operations Branch
Mail Drop 80
Research Triangle Park
N.C. 27711
COM (919) 541-4564 FTS 629-4564
8. References
The PTPLU source program is presently available on the
MPTER tape from COMPUTER PRODUCTS, NTIS, Springfield, VA.
22161. Ask for PB80-168156; the price is $420. The PTPLU
program is also available on UNAMAP (version 4).
Preparation of a user's guide is underway, and the user's
guide should be available by October 1981.
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POINT SOURCE MODELS (PTMAX, PTDIS, £ PTMTP)
1. Model Overview: These point source models use Briggs plume
rise methods and Pasquill-Gifford dispersion methods as given in
EPA's AP-26, "Workbook of Atmospheric Dispersion Estimates,"
to estimate hourly concentrations for stable pollutants.
PTMAX is an interactive program that performs an analysis
of the maximum short-term concentrations from a single point
source as a function of stability and wind speed. The final
plume height is used for each computation.
PTDIS is an interactive program .that estimates short-term con-
centrations directly downwind of a point source at distances speci-
fied by the user. The effect of limiting vertical dispersion by
a mixing height can be included and gradual plume rise to the
point of final rise is also considered. An option allows the
calculation of isopleth half-widths for specific concentrations
at each downwind distance.
PTMTP is an interactive program that estimates for a number
of arbitrarily located receptor points at or above ground-level,
the concentration from a number of point sources. Plume rise
is determined for each source. Downwind and crosswind distances
are determined for each source-receptor pair. Concentrations at
a receptor from various sources are assumed additive. Hourly
meteorological data are used; both hourly concentrations and
averages over any averaging time from one to 24 hours can be
obtained.
2. Functional Capabilities: PTMAX estimates maximum ground-
level concentration and distance to maximum for combinations
of stability class and wind speed (internal to program).
81
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Output from the model is a two-dimensional table giving maximum
concentration, distance to maximum, and height of final rise for
each stability-wind speed combination.
PTDIS estimates the ground-level concentrations directly
downwind of source for each downwind distance specified (maximum
number of distances is equal to 50) for one set of specified
meteorological conditions. A table is produced with distance,
height of plume, and ground-level concentration given for each
distance. The program may reiterate for additional meteorological
conditions in the same run, or for additional source and
meteorology in the same run.
PTMTP estimates concentrations at arbitrary heights above
ground for multiple sources at multiple receptors for multiple
hourly time periods. Input data is repeated in tabular form.
Total concentrations at each receptor and concentration
contribution from each source may be tabulated hourly. Average
concentrations (maximum 24 hours) are given for each receptor.
The concentration contribution from each source for averaging time
is optional.
3. Basic Assumptions:
A. Source-Receptor Relationship. For PTMAX all receptor
locations are determined internally for a point of maximum. For
PTDIS all receptors are considered directly downwind of the plume.
For PTMTP each source and receptor is arbitrarily located by
coordinates in a kilometer grid scheme.
B. Emission Rate. A single constant emission rate for each
source is assumed.
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C. Chemical Composition. The models treat chemical
composition as a single inert pollutant.
D. Plume Behavior. Gaussian spreading is both horizontal
and vertical. Briggs (1969, 1971, 1972) plume rise formulas are
used. Eddy reflection from the ground in all three models is
assumed, and multiple eddy reflections from the ground and mixing
height are used in PTDIS and PTMTP. If the plume height exceeds
the mixing height, the ground-level concentration is assumed to
be zero.
E. Horizontal Wind Field. The wind speed is internal in
PTMAX, and it is user-specified in PTDIS. The wind speed and
direction are user-specified in PTMTP.
F. Vertical Wind Speed. This is assumed to be equal to zero,
G. Horizontal Dispersion. A Gaussian horizontal plume
shape is assumed. Dispersion parameters of Pasquill-Gifford are
dependent upon stability class.
H. Vertical Dispersion. A Gaussian plume shape is assumed.
Dispersion parameters of Pasquill-Gifford are dependent upon
stability class.
I. Chemistry/Reaction Mechanism. This is not treated.
J. Physical Removal. This is not treated by the models.
K. Background. This is not treated by the models.
83
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PTMAX
PTMTP
END
BEH072
DBTMCX
END
SOURCE LOOP
RECEPTOR LOOP
BEH072
DBTRCX
L
DBTSIG
PTDIS
BEH072
DBTRCX
DBTSIG
L
END
SIMPLIFIED FLOWCHARTS FOR PTMAX, PTDIS, AND PTMTP
84
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4. Input and Output: PTMAX has the ability to run additional
sources in the same run. PTDIS can run additional meteorological
or additional sources and meteorology or other distances, additional
sources and meteorology in the same run. PTMTP has optional output
for hourly periods and for concentration contribution from each
source for averaging time. It can run for additional meteorology
or additional receptors, and meteorology in the same run. This
model, which gives estimates for a single point, would not normally
be calibrated or verified in actual application, but rather would
be used for planning or design purposes to find a "worst case"
impact.
5. System Resource Requirements: This dispersion model is
written in FORTRAN V. The program does not require any special
software or utilities. Approximately 12K .words of core memory
are required to execute on the TTnivac 1110.
6. Applications: The source program for this dispersion model
is available as part of UNAMAP (Version 3), PB 277 193, for $420
from Computer Products, NTIS, Springfield, VA 22161.
7. Technical Contact
D. Bruce Turner
U.S. Environmental Protection Agency
Mail Drop 80
Environmental Appplications Branch
Research Triangle Park, NC 27711
FTS 629-4564 COM 919/541-4564
8. Reference
Turner, D.B., and Busse, A.D., Users' Guides to the
Interactive Versions of Three Point Source Dispersion
Programs: PTMAX, PTDI5, and PTMTP, Preliminary Draft,
Meteorology Laboratory, U.S. Environmental Protection
Agency. Research Triangle Park, NC 27711, 1973.
85
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REACTIVE PLUME MODEL (RPM-II)
1. Model Overview: The Reactive Plume Model (RPM-II) is an air
quality simulation model that provides a time history of
pollutant concentrations within a chemically reactive point
source plume. Its purpose is to estimate the concentration
levels these species will attain within the plume downwind of
the source by simulating in the model the physical and chemical
processes responsible for the plume's evolution. These include
the emissions of primary pollutants from the source, their
transport and dispersion downwind, their chemical
transformation into secondary products, and the entrainment of
background ambient air into the plume. Simulated species of
particular interest would include NO, N02, and 0-,. RPM-II
was developed and tested by Systems Applicators, Inc. (SAI) of
San Rafael, California, for the Environmental Protection Agency,
2. Functional Capabilities: RPM-II simulates the reactive
system of NO -HC-0-, species with the Carbon Bond-II
x _j
generalized kinetic mechanism. This includes a set of 68
chemical reactions with 35 separate species, including NO,
M02, do, six classes of organics, PAN, CO, and other
intermediate products. The model is constructed within a
Lagrangian framework. The plume parcel is followed downwind
from the source as it is advected by the wind. The frame of
reference moves with the parcel. The plume model is composed
of a fixed number of cross-wind cells, typically from 2 to 10,
that can expand as they move downwind. The rate of horizontal
dispersion is determined by Fickian diffusion considerations.
The rectangular cells comprising the plume are considered well
mixed reactors. The model is applicable under limited mixing
atmospheric conditions and ground level computed concentrations
are relevant only after the plume touch-down point has been
86
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reached. While RPM-II is primarily designed for use as a point
source plume model, an urban area plume may also be modeled by
considering the source area as a virtual upwind point source.
The model simulates the evolving concentrations using time
steps of less than a minute, but the model inputs and outputs
are hourly averages. Concentration units are in parts per
mill ion.
The model's limitations include the requirement for valid
ambient concentration estimates of reactants along the plume
trajectory, and the specification of valid wind speeds and
dispersion rates, especially in complex terrain applications.
3- Basic Assumptions: The plume is assumed to advect downwind
of the source according to the specified hour averaged wind
speed and direction. Fickian dispersion is assumed to govern
the diffusion between adjacent cells in the model and all cells
are assumed to be well mixed. The numerical solution of the
set of chemical reactions is by a modified version of the GEAR
routine, a predictor - corrector method for stiff systems of
differential equations. It is implicitly assumed that the
Carbon Bond-II mechanism is an accurate description of the
chemical transformations of NO -HC-Oo in the real
x j
atmosphere .
4. Input and Output: Inputs to the model include: wind speed
and dispersion rates as a function of time and downwind
distance respectively; average initial concentrations for all
species and the time varying ambient concentrations (an
option); hourly source emission rates; and the time varying
photolysis rates for the photolysis chemical reactions. The
reactions comprising the chemical kinetic mechanism are also a
set of inputs .
87
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Outputs from RPM-II include: A printout of all input data;
a printout of the program control variables; a printout of
plume concentrations, plume widths, plume depths, wind speed,
and photolysis factors at various downwind distances; and
printer plots of average plume and ambient concentrations
versus time. Average concentrations are printed for each
species within each plume cell as well as average
concentrations for the entire plume.
5. System Resource Requirements: RPM-II is coded in FORTRAN.
It can be run on an Univac 1110 mainframe or an equivalent
computer. It requires about 260K bytes (65,000 words) of core
for execution. The model uses any regular printer for output.
Skills in programming, environmental engineering and
meteorology (with a background in atmospheric chemistry) are
useful.
6. Applications: This model aids in the analysis of reactive
plumes from point sources. A limited data base from such
sources was collected as part of the Midwest Interstate Sulfur
Transport and Transformation (MISTT) project in and around St.
Louis in 1976. SAI analyzed this data base for use with the
RPM-II and applied the model to 10 test cases for the EPA.
Despite problems with the ambient HC measurements in the data
base, model results are encouraging.
7. Technial Contact
Kenneth L. Schere
U. S. Environmental Protection Agency
Environmental Sciences Research Laboratory
Mail Drop 80
Research Triangle Park
N.C. 27711
COM 919/541-3795 FTS 629-3795
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8. References
Yocke, M.S., Stewart, D.A., Liu, M.K., and Burton, C. S.
1980: Evaluation of RPM-II and Simple Short-Term NOg
Model Predictions Using MISTT Data. Proc. of Second Joint
Conference on Applications of Air Pollution Meterology, New
Orleans, LA, March 1980.
Liu, M., Stewart, D. A., and Roth, P.M. 1978: An Improved
Version of the Reactive Plume Model (RPM-II). Paper
presented at the Ninth NATO/CCMS International Technical
Meeting on Air Pollution Modeling, Toronto, Canada, August
1978.
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REGIONAL EMISSIONS PROJECTION SYSTEM (REPS)
1. Model Overview: The REPS model is a series of FORTRAN and PL/1
programs that utilize data pn existing air pollution sources and
projected energy use to project emissions (and proxy air
quality measures) for five of the criteria pollutants faMdants
and lead are excluded) for the 243 Air Quality Control Regions
(AQCR's) in the United States. The model was originally developed
by the U.S. Environmental Protection Agency; the revised model is
available from the Energy Information Administration of the U.S.
Department of Energy.
2. Functional Capabilities: REPS permits the examination of
projected emissions under changes in (1) environmental control
policy, (2) projected energy use, or (3) patterns of economic
growth. Ambient proxy measures of air quality are based on
emission density and population exposure. Outputs can be gen-
erated in tabular form or map displays. Forecasts can be made
for any year from 1980-2000.
3. Basic Assumptions: The REPS model relies on the National
Emissions Data System (NEDS) for base year (1975) and estimates
of future emissions from present sources. A constant annual
retirement rate is applied to all AQCR's and fuel-burning source
categories. Regionalization of expected growth is based on the
Department of Commerce OBERS projections, the Energy Information
Administration's fuel use projections, and assumptions concerning
the expected patterns of fuel switching.
4. Input and Output: Major inputs are the NEDS file, OBERS
projections by AQCR and fuel use projections by Federal Region.
The user may also provide information on specific synthetic fuel
facilities and/or information on the desired environmental con-
trol policy to develop projected regional emissions in map or
tabular form.
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5. System Resource Requirements: The REPS is written in FORTRAN
and PL/1 and is currently operating on IBM 370 system. The
programs require approximately 200K bytes and a full run can
be made for less than $100. Required operator skills include
programming and engineering.
6. Applications: REPS results have been used for the following:
1) The Department of Energy report, 1985 Air Pollution Emis-
sions , a study of the regional air emission impacts of
the National Energy Plan.
2) The environmental chapter in the Energy Information Ad-
ministrator's Annual Report to Congress, 1978.
3) Environmental analysis of elements of the proposed Na-
tional Energy Supply Strategy under development by the
Department of Energy.
7. Technical Contacts
William Weygandt
U.S. Department of Energy
Washington. D.C. 20461
References
Booz, Allen, and Hamilton. Regional Emission Projection
System--System Documentation. January 1977.
Energy Information Administration. 1977 Annual Report to
Congress. Volume II, April 1978.
Pechan, E.H. An Air Emissions Analysis of Energy Projections
for the Annual Report to Congress, in preparation.
Pechan, E.H. 1985 Air Pollution Emissions. Department of
Energy DOE/PE-0001, Government Printing Office, December 1977
U.S. Environmental Protection Agency. AEROS Manual Series
Volume I: AEROS Overview. EPA-450/2-76-001, February 1976.
U.S. Environmental Protection Agency. AEROS Manual Series
Volume II: AEROS User's Manual. EPA-450/2-76-029,
December 1976.
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U.S. Environmental Protection Agency. AEROS Manual Series
Volume V: AEROS Manual of Codes. EPA-450/2- 76-005, April
1976.
U.S. Environmental Protection Agency. Compilation of Air
Pollutant Emission Factors. Second Edition, AP-42, Parts A
and B, February 1976.
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SAI AIRSHED MODEL (SAIASP)
1. Model Overview: The SAI Airshed Model is a grid-type
photochemical air quality simulation model. Its primary
purpose is to estimate the evolution of concentrations of urban
atmospheric smog-related pollutants, including ozone. These
concentration estimates are based on simulating the physical
and chemical processes occurring in the ambient atmosphere that
are responsible for ozone production. These include the
emissions of organics and NO , chemical reactions of these
A.
precursors, advection and dispersion among grid cells, and
transport of ozone and its precursors into the model region
from upwind areas. The precursor include N02, NO, and six
classes of organics. Typically, a model simulation period is
on the order of one day. This model is quite complex and is
rather input data-intensive. Nevertheless, it is useful for
providing spatial and temporal resolution of ozone
concentration estimates based on a detailed consideration of
the underlying physical and chemical processes.
2. Functional Capabilties: The model considers emissions, the
atmospheric chemistry of ozone formation, advection, and
dispersion. The chemistry embedded in the model includes 71
reactions and 35 species including N02, NO, six classes of
organics, ozone, PAN, CO, and several intermediate products.
Advection is simulated by estimating the transfer between
neighboring cells, and dispersion is estimated by using
horizontal and vertical diffusivity coefficients. Wind shear
can also be considered. The model considers these processes in
each cell of a three dimensional grid system. This grid system
typically includes four to six vertical layers of between 15 x
15 and 30 x 30 cells, each cell being between two and ten
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kilometers square. The model simulates the evolving
concentrations using time steps of less than a minute, but the
model inputs and outputs are hourly averages. The principal
limitations of the model are its complexity and the substantial
amount of data required.
3. Basic Assumptions: The SAI Airshed Model uses a finite
difference method to calculate the progression of pollutant
concentrations through a series of time steps. The model
assumes flat terrain in estimating concentrations, although the
influence of the terrain on the wind field can be considered if
the user is able to do so. All emissions and concentrations
are assumed uniformly mixed thoughout each grid cell. It is
assumed that turbulent fluxes are linearly related to the
gradients in the mean concentrations so that eddy d if f usiv ities
are used in the diffusion calculations.
4. Input and Output: The SAI Airshed Model requires various
emissions, and meteorological and air quality data for each
grid cell in the grid system. The emissions inventory must be
gridded hourly, and must include N02, NO, and five classes of
organics. The meteorological and air quality input data are
interpolated from the values measured by a relatively dense
monitoring network. The meteorological data include wind
speed; wind direction; mixing height; atmospheric stability and
photolysis rate constant; and air quality data including
concentrations of N0x, organics, and ozone at the beginning
of the simulation and at the upwind boundary. If an inert
pollutant is being simulated, only data for that pollutant is
n ecessar y.
The principal output of the model is a printed array of
concentrations at ground-level or any level aloft throughout
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the grid for each hour for each major pollutant. This array of
concentrations is also put into disk storage in case the user
wishes to develop programs to analyze the data further. In
addition, the model provides the option of estimating
concentrations at specific sites by interpolating among the
concentrations in the surrounding grid cells.
5. System Resource Requirements: SAIASP is coded in FORTRAN.
It can be run on an Univac 1110 mainframe or an equivalent
computer. The model requires about 70,000 words of core
storage for execution. It uses any regular printer for
output. Useful operation skills include a background in
programming, engineering, and meteorology with experience in
atmospheric chemistry.
6. Applications; The SAI Airshed Model has been used by EPA
and some state agencies to estimate the impact of emission
controls on urban ozone concentrations. The model is currently
undergoing evaluation and verification as part of the EPA
Regional Air Pollution Study (RAPS) model validation program.
The urban area modeled in this study is St. Louis, MO.
7. Technical Contact
Kenneth L. Schere
U.S. Environmental Protection Agency
Environmental Sciences Research Laboratory
Mail Drop 80
Research Triangle Park
N.C. 27711
COM 919/541-3795 FTS 629-3795
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8. References
Only draft documentation is available at this time
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SIMULATION OF HUMAN AIR POLUTION EXPOSURES (SHAPE)
1. Model Overview: The Simulation of Human Air Pollution
Exposures (SHAPE) model was developed at Stanford University's
Department of Statistics in 1980-81 under an Innovative
Research Program research grant (Ott, 1981). Its purpose was
to provide environmental descision makers and scientists with a
methodology for generating frequency distributions of human
exposures to air pollution either for an urban area or for the
nation as a whole- Version I of the SHAPE program, completed
in May 1981, generates a frequency distribution of the
population's exposure to carbon monoxide (CO) for the U.S.
population as a whole on any particular day (24-hour period) of
the year that is specified by the user. It combines activity
patterns of the population with statistical descriptions of
pollutants concentrations in specified locations
( "microenvironments") in order to calculate the diurnal
pollutant exposure concentration of each person comprising the
population. As each person's diurnal exposure is computed, the
program stores the maximum hourly exposure, the maximum 8-hour
running average exposure, and the highest hourly blood
carboxyhemoglobin (COHb) level. Then a frequency distribution
is generated for each of these three variables, as well as
statistical results (mean, standard deviation, minimum,
maximum, etc.), and the line printer displays these results
graphically. The program can be run for a variety of different
assumptions about regulatory actions and changes in population
activity patterns, and the impact of different control
strategies or travel habits on the frequency distribution of
exposures can be examined.
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The program was developed as an outgrowth of earlier
research that showed that fixed air monitoring stations do not
accurately reflect the exposures of the population, due largely
to the fact that concentrations indoors (where the public
spends over 80$ of its time) and in traffic (where
concentrations are known to be very high) are different from
those observed at fixed monitoring stations. The program will
be validated with a large-scale field study using personal
exposure monitors at some time in the future. At present, it
provides the only means currently available for estimating the
frequency distribution of thee exposures of a population,
either on a national basis or an urban basis.
2. Functional Capabilities: The SHAPE program is written in
FORTRAN and uses Monte Carlo simulation to calculate carbon
monoxide (CO) exposures of the population. During a 24-hour
period, the program assigns each person to specific
"microenvironments" (for example, home, automobile, office,
garage) based on probability distributions derived from
activity pattern studies. The program then combines activity
profiles with data on CO concentrations in specific
microenvironments and urban background concentrations in order
to calculate integrated exposures of a large number of persons
over a 21-hour period. Instead of a Markov chain approach,
both the time spent engaged in a specific activity and the
transitions from one activity to another are treated as random
variables that are sampled from distributions specified by the
user. The time resolution of SHAPE is on a minute-by-minute
basis, and activities (and concentrations) are coded into an
activity vector consisting of 24 x 60 = 1440 minutes per
24-hour period. The "inverse transformation method" for
generating random variables is used, so any arbitrary frequency
distribution can be employed as an input to the program.
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3. Basic Assumptions: The SHAPE computer program assumes that
all persons are located in their homes (Microenv ironment No. 1)
at the first minute past midnight (12:01 a.m.), and the first
time computed probabilistically is the work arrival time.
Next, the home-to-work trip is computed. Both the work arrival
time and the home-to-work trip time are sampled from
probability distributions specified by the user. The mode by
which the trip is taken (automobile, truck, bus, street car,
subway, train, walk) also is determined by sampling from a
probability distribution specified by the user. For all
persons at work, it is assumed that lunch begins a a time that
is uniformly distributed between 11:00 a.m. and 4 hours (240
minutes) after the time of arrival at work. The evening work
departure time is assumed to be 9 hours (540 minutes) after the
work arrival time. The person must return to home from work by
the same transportation mode that they used to travel to work,
and the travel time must be the same. A total of 14
microenv ironment s are used and the microenvironmental component
of concentration is added to the background component of
concentration, which comes from data from an actual air
monitoring station. Within each microenvironment, pollutant
concentrations are assumed to be lognormally distributed. The
COHb blood levels are computed using the model of Ott and Mage
(1978) .
The program can be used to generate the frequency
distribution of exposures of any number of persons, and many
runs thus far have been for n = 400 persons. Increasing the
number of persons in the computer simulation tends to increase
the computing cost, so care should be exercised in determining
how many people should be included in each computer simulation.
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4. Input and Output: The user is required to specify the
number of persons in the computer simulation and the date on
which the simulation takes place. The date must correspond to
one of the dates contained in the ambient CO data file provided
by the user. The program contains demographic data (sex, age
distributions, weight, distribution, employment categories), as
well as activity pattern data (home-work travel mode
probabilities, home-work trip time distribution, lunch travel
mode probabilities, lunch trip time distribution, probability
of parking in an indoor garage, and probabilities for shopping
trips) which currently is based on U.S. Census data but which
can be altered by the user. The user also must specify the
geometric mean and geometric standard deviation of the
lognormally distributed concentrations in each of 14
micorenvironments. The output from each run displays all the
assumed parameters, and prints out the minute-by minute
activity profiles and exposure profiles for the first 10
persons. Then three statistics and histograms are printed out
for the highest 1-hour average CO exposure, highest 8-hour
running average exposure, and highest COHb level for all people
in the simulation.
5. System Resource Requirements: The SHAPE program is coded
in FORTRAN and compiled on a FORTRAN IV (level G) computer. It
has been run on an IBM 370 series system and requires 300K
bytes of core storage, or less. About 5 man-days are required
for model set-up, and calibration and verification can be done
in about 2 man-days. The manpower needs include a computer
programmer and an environmental engineer or statistician
familiar with exposure estimation methodology, activity pattern
field studies, and computer simulation. The model user must
have an understanding of the sources of data for human activity
pattern and time budget studies and the error present in these
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studies. The user also must have an understanding of the
concepts behind microenvironmental modeling of human exposure
to air pollution (Duan, 1981) and the stochastic properties of
pollutant concentrations in selected in microenvironments.
6. Application: SHAPE can be used for air pollution risk
analysis by generating exposure frequency distributions for the
population. It also can be used to compare the impact on the
population of different vehicular emission control strategies.
Finally, it can be used for planning personal monitoring field
studies.
7. Technical Contact:
Wayne Ott
U.S. Environmental Protection Agency
Office of Research and Development
Office of Monitoring Systems and Quality Assurance
401 M Street, S.W.
Washington, D.C. 20460
COM 202/426-4153 FTS 426-4153
8. References:
Duan, Naihua "Micro-environment types: A Model for Human
Exposure to Air Pollution," Department of Health and Human
Services, Report No. WD-9Q8-1-HHS, RAND, Santa Monica, CA,
1981.
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Ott, Wayne "Exposure Estimates Based on Computer Generated
Activity Patterns," Paper No. 81-57.6 presented at the 74th
Annual Meeting of the Air Pollution Control Association,
Philadelphia, PA, 1981.
Ott, Wayne and Mage,David, "Interpreting Urban Carbon
Monoxide Concentrations by Means of a Computerized Blood
COHb Model," Journal of the Air Pollution Control
Association. Vol. 28, No. 9, pp. 911-916, 1978.
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SINGLE SOURCE MODEL (CRSTER)
1- Model Overview: This algorithm estimates ground-level
concentrations resulting from up to 19 colocated elevated stack
emissions for an entire year and prints out the highest and
second-highest 1-hour, 3-hour, and 24-hour concentrations as
well as the annual mean concentrations at a set of 180
receptors (5 distances by 36 azimuth). The algorithm is based
on a modified form of the steady-state Gaussian plume equation
which uses empirical dispersion coefficients and includes
adjustments for plume rise and limited mixing. Terrain
adjustments are made as long as the surrounding terrain is
physically lower than the lowest stack height input. Pollutant
concentrations for each averaging time are computed for
discrete, non-overlapping time periods (no running averages are
computed) using measured hourly values of wind speed and
direction, and estimated hourly values of atmospheric stability
and mixing height.
2. Functional Capabilities: Concentrations are estimated for
each hour of a one-year period of record at a 180-receptor grid
laid out in polar coordinate form (36 directions by 5
distances) resulting from a single plant which may consist of
from one to eighteen individual stacks. Sources are all
colocated, that is, distances between sources are not
considered. It is assumed that this algorithm will be applied
to elevated sources and distances far enough away (near maximum
concentrations) so that final plume rise is applicable and
distances between sources will be unimportant. Output consists
of tables giving concentration estimates for each receptor.
Annual concentrations, highest and second highest 1-hour,
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3-hour, and 24-hour concentrations are standard output.
Calculations for highest and second highest for a selected
averaging time of 2, 4, 6, 8, or 12 hours can be optionally
selected.
3- Basic Assumptions:
Source Receptor Relationship. Up to 19 point sources, but
no area sources can be run. All point sources are assumed to
be at the same location, and a unique stack height is assumed
to each source. Receptor locations are restricted to 36
azimuths (every 10 degrees) and 5 user-specified radial
distances. There is a unique topographic elevation for each
receptor which must be below the top of the stack.
Emission Rate. The model assumes a unique average emission
rate for each source, and monthly variations in the emission
rate are allowed.
Chemical Composition. This is treated as a single inert
pollutant.
Plume Behavior. The model uses Briggs (8,) (9,) (10) final
plume rise formulas, and does not treat fumigation or
downwash. If the plume height exceeds the mixing height,
concentrations further downwind are assumed to be equal to zero
Horizontal Wind Field. The model uses user-supplied hourly
wind direction (nearest 10 degrees), internally modified by the
addition of a random integer value between -4 degrees and +5
degrees. Wind speeds are corrected for release height based on
power law variations and exponents from DeMarrais(6) .
Different exponents are used for different stability classes,
and the reference height is equal to 10 meters. A constant,
uniform (steady-state) wind is assumed within each hour.
Vertical Wind Speed . This is assumed to be equal to zero.
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Horizontal Dispersion. The model assumes a
semiempirical/Gaussian plume. Seven stability classes are
used: Turner Class 7 is an extremely stable, elevated plume
assumed not to touch the ground. Dispersion coefficients are
from Turner, and no further adjustments are made for variations
in surface roughness, transport, or averaging time.
Vertical Dispersion. A semi-empirical/Gaussian plume is
used, and the model utilizes seven stabiltiy classes.
Dispersion coefficients are from Turner, and no further
adjustments are made-
Ch em is try/Re act ion Mechanism . This is not treated.
Physical Removal. This is not treated.
4. Input and Output; Meteorological data must be input to the
model .
Output produced by the model includes highest and second
highest concentrations for the year at each receptor for
averaging times of 1, 3, and 24-hours, plus a user selected
averaging time which may be 2, 4, 6, 8, or 12-hours. An annual
arithmetic average at each receptor is given, and the model
provides the highest 1-hour and 24-hour concentrations over the
receptor field for each day, and hourly concentrations for each
receptor on magnetic tape .
5. Systems Resource Requirements:
This model is coded in FORTRAN V and is run on an Univac
1110 mainframe. The model uses 28K words of core memory for
execution.
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CRSTER PREPROCESSOR
— BLOCK DATA
—RANDU (RANDOM NO. GENERATOR)
END
CRSTER
-CRS
-SIGMA
1—BEH072
END
CRSTER FLOWCHART
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6. Applications: The source program for this dispersion model
is available as part of UNAMAP (Version 3), PB 277 193, for
$420 from Computer Products, NTIS, Springfield, Va. 22161.
7. Technical Contact
D. Bruce Turner
U.S. Environmental Protection Agency
Environmental Applications Branch
Mail Drop 80
Research Triangle Park
N.C. 27711
COM 919/541-4564 FTS 629-4564
8. References
Environmental Protection Agency, "User's Manual for Single
Source (CRSTER) Model, " Publication No. EPA-450/2-77-013
(NTIS PB 271360), Office of Air Quality Planning and
Standards, Research Triangle Park, North Carolina 27711,
July, 1977.
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SYSTEMS APPLICATIONS, INC. MODEL (SAI}
1. Model Overview: The Systems Applications, Inc. (SAI) Model
is a three-dimensional, dynamic air model used in the evaluation
of area sources in urban areas of all terrain types. The model
was designed to treat the physical processes of both transport and
diffusion, and to simulate photochemical oxidation reactions. The
daytime build-up of photochemical oxidants, hourly variations in
ozone, and their precursors is handled by a short term application
of the SAI model. The model is appropriate for examining areas
ranging from very localized vicinities to whole urban areas.
2. Functional Capabilities: The model is capable of simulating
photochemical oxidation reactions and the physical processes of
both transport and diffusion. It incorporates user-specified
multiple layer discretization and a 25 x 25 grid spacing, which is
also user-specified. A fixed horizontal diffusion coefficient
detracts from the actuality of the model, but the input variations
in wind speed and direction add realism to the representiveness of
the prototype system. The SAI model has a sensitivity to residual
discharges, initial conditions, and boundary conditions, but it
does provide a detailed treatment of both chemical reactions and
area-wide meteorological conditions. The model is limited by its
complexity, data requirements, and the modifications that are
required to eliminate its "site specific" features.
3. Basic Assumptions: The SAI model is a deterministic model
that is numerically integrated by the finite difference method.
Constituents are assumed to be in a dynamic state.
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4. Input and Output: Input for the initial set-up and
calibration of the model include: activity data and residual
discharge data for point and area sources; meteorological data at
multiple points (wind speed, wind direction, and mixing heights);
and multiple point ambient concentration data. Data requirements
for verification of the model include the above meteorological
data and ambient concentration data.
Output from the SAI model include ambient concentration
values given at grid areas and interpolated points. The output is
given in the following forms: printed hourly-averaged ground level
concentration maps; printed summaries of hourly-averaged
concentrations at each monitoring station; printed instantaneous
ground level concentration maps; and printed summaries of
instantaneous vertical concentration distributions above each
monitoring station.
5. System Resource Requirements: The SAI model is coded in
FORTRAN IV and run on an IBM 370/155, or equivalent. Manpower
needs for the model include: a computer programmer, a research
assistant, and an experienced modeler with knowledge of FORTRAN
IV, the diffusion model, photochemistry, and advanced mathematical
and solution techniques.
6. Applications: The SAI model can be used in the evaluation of
area sources in urban areas of all terrain types.
7. Technical Contact
Joseph Tikvart
U.S. Environmental Protection Agency
Monitoring and Data Analysis Division
Mutual Building
411 W. Chapel Hill Street
Durham,N.C. 28801
FTS 629-5561 COM 919/541-5561
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References
Record, "Photochemical Oxidant Modeling," Vol. I, pp. 92-100.
Reynolds, J.D., ejt aJ. , "Mathematical Modeling of
Photochemical Air~Pollution I, Formulation of the Model,"
Atmospheric Environment, Vol. VII (1973), pp. 1033-1061.
Reynolds, J.D., e_t aJ. , "Mathematical Modeling of
Photochemical Air Pollution - III, Evaluation of the Model,"
Atmospheric Environment, Vol. VIII (1974), pp. 563-596.
Roth, P.M., "Summary of Results of the Denver Air Quality
Simulation Study: Model Evaluation and Analysis," U.S.
Environmental Protection Agency, Region VIII, (February 1977)
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TEXAS CLIMATOLOGICAL MODEL VERSION 2 (TCM-2)
1. Model Overiview: The Texas Climatological Model Version 2
(TCM-2) uses the steady-state Gaussian plume hypothesis. It is
a relatively fast FORTRAN computer program used to predict
ground level, long-term concentrations of atmospheric
pollutants. The Briggs plume rise, the Pasquill-Gifford-Turner
dispersion equations, and sector averaging are used in this
model. Contributions from area sources are determined by a
modification of the method developed by Gifford-Hanna. An
emissions inventory and a set of meteorological conditions are
input to the model by the user. The TCM was developed by the
Texas Air Control Board, Austin, Texas.
2. Functional Capabilities; Concentrations for one or two
pollutants may be determined for long averaging times. Any
number of area and point sources may be analyzed .
Concentrations are calculated for up to 2500 locations in a
user-defined rectilinear array of receptors. Up to 5 sets of
meteorological conditions in the form of a meteorlogical joint
frequency function and average ambient temperature may be input
to the model. Important user options are exponential pollutant
decay, use of only final plume rise, choice of urban or rural
dispersion, and calibration with observed concentrations. A
variety of other input and output options are available to
enhance the utility of the model.
3. Basic Assumptions: The basic assumptions are:
a) The Emission Rate - is constant for each set of
meteorological conditions.
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b) Wind Speed - The pollutants are transported downwind
at an appropriate average wind speed. Wind speed is
adjusted to physical stack height.
c) Wind Shear - There is no directional wind shear in the
vertical .
d) Plume Behavior - The plume is infinite with no plume
history. The plume is reflected at the earth's
surface with no pollutant losses due to reaction or
deposition at the surface.
e) Chemistry/Reaction Mechanisms - The pollutants are
non-reactive gases or aerosols and remain suspended in
the air following the turbulent movement of the
atmosphere. There is an option to use exponential
decay of pollutant concentration based upon a user
input half life .
f) Horizontal and Vertical Dispersion - The concentration
in the vertical direction is described by a Gaussian
distribution about the plume centerline. Dispersion
coefficients are from Pasquill-Gifford-Turner with no
additional adjustments being made for variations in
surface roughness. Horizontal dispersion is described
by sector averaging instead of by a Guassian
distribution. A meteorological joint frequency
function is used to describe dispersion in the
horizontal .
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4. Input and Output:
A. Inputs to the TCM-2 are as follows:
1) Control parameters cards specify the input and output
options, grid spacing, and orientation, etc.
2) Calibration factor cards
3) Meteorological joint frequency function value cards
4) Area source inventory cards
5) Point source inventory cards
6) Monitoring data cards
B. Input options
1) Point source inventory parameters may be in metric or
English units.
2) Point source inventory may be read from cards or disk
file.
3) Meteorological joint frequency function may be read
from cards or disk file.
TCM2-S output options are:
1) A list of coordinates and concentration at each grid
receptor
2) An array map of grid coordinates and concentration
3) A culpability list identifying the highest five major
concentration contributors and respective contributions
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4) A list of the point of maximum concentration for each
scenario
5) Card punch output for input to contour plotting
programs
5. System Resource Requirements: TCM-2 is coded in FORTRAN
and is run on a Burroughs 6810/11 mainframe computer. It
requires 1?K words of core memory for execution. It uses a
Burroughs B 9247-15 printer for output and a card reader/punch
for input. A background in engineering, meteorology, and air
pollution is useful .
6. Applications: TCM-2 is used by state air pollution control
agencies, meteorological consultants, and industry for:
1) Stack parameter design studies
2) Evaluation of the impact of new sources or source
modification for permit application review
3) Fuel conversion studies
4) Monitoring network design
5) Control technology evaluation
6) Control strategy evaluation for SIP
7) Prevention of significant deterioration
114
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7. Technical Contacts
Bruce Turner
U.S. Environmental Protection Agency
Mail Drop 80
Research Triangle Park
N.C. 27711
COM 919/541-4564 FTS 629-4564
Cyril Durrenberger and James Bryant
Texas Air Control Board
Permits Section
6330 Highway 290 East
Austin, Texas 78723
COM 512/451-5711
8. References
Texas Air Control Board. "User's Guide to the Texas
Climatological Model." Austin, Texas, August 1980.
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TEXAS EPISODIC MODEL VERSION 8 (TEM-8)
1. Model Overview: The Texas Episodic Model Version 8 (TEM-8)
uses the steady state Gaussian plume hypothesis in a FORTRAN
computer program designed to predict ground level, short-term
concentrations of atmospheric pollutants. The Briggs plume
rise and the Pasquill-Gifford-Turner dispersion equations are
used in the model. Concentrations from area sources are
determined using the method developed by Gifford-Hanna. An
emissions inventory and a set of meteorological conditions are
input to the model by the user. The TEM was developed by the
Texas Air Control Board, Austin, Texas.
2. Functional Capabilities: Concentrations for one or two
pollutants may be determined for time periods from 10 minutes
to 24 hours. The model, as supplied, may analyze up to 300
individual point sources and up to 50 area sources, but these
size limits are easily expanded. Concentrations are calculated
at up to 2500 locations in a user-defined rectilinear array of
receptors. An automatic grid feature in the program may be
used to define a grid that encompasses the point of maximum
concentration. A variety of input and output options are
available to enhance the utility of the model. Up to 24 sets
of meteorological conditions may be input to the model.
Exponential decay of pollutant concentration may be calculated
as a user option.
3. Basic Assumptions:
a) Emission Rate: The emission rate is constant.
b) Wind Speed: The pollutants are transported downwind
at an appropriate average wind speed. Wind speed is
adjusted according to the physical stack height.
116
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c) Wind Shear: There is no directional wind shear in the
vertical.
d) Plume Behavior: The plume is infinite with no plume
history. The plume is reflected at the earth's
surface with no pollutant losses due to reaction or
deposition at the surface.
e) Chemistry/Reaction Mechanism: The pollutants are
non-reactive gases or aerosols and remain suspended in
the air following the turbulent movement of the
atmosphere. There is an option to use exponential
decay of pollutants concentration based upon a user
input half life.
f) Horizontal and Vertical Dispersion: Dispersion
occurring in the downwind direction is negligible
compared to advection. The concentrations in both the
crosswind and the vertical directions are described by
the Gaussian distribution about the plume centerline.
Dispersion coefficients are from Pasquill-Gifford
-Turner with no additional adjustments being made for
variations in surface roughness. Horizontal
coefficients (sigma-y) are assumed to represent
dispersion over a 10 minute averaging period and are
increased for longer averaging times to represent the
greater horizontal plume meander due to fluctuations
in wind direction.
4. Input and Output: Input to the TEM-8 consists of 4 types
of parameter cards. Control parameter cards indicate the input
and output options, the grid spacing, and orientation, etc.
Scenario parameter cards indicate meterological conditions.
The third and fourth cards are point and area source inventory
cards. Additionally, two options are available for point
source inventory cards. Parameters may be either metric or
English units and they may be read from cards or disk file.
117
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Output options include the following:
1) A list of coordinates and concentrations at each grid
receptor
2) An array map of grid coordinates and concentrations
3) A culpability list identifying the five highest major
concentrations contributors and their respective
contributions
4) A list of the point of maximum concentrations for each
scenar io
5) Card punch output for input to a contour plotting
program
5 • System Resource Requirements:
This model is written in FORTRAN. It requires
approximately 26K bytes on a Burroughs B6810/11. An
engineering background with knowledge of meteorology and air
pollution is helpful.
6. Applic ations: This model is used by state air pollution
control agencies, meteorological consultants and industry for:
1) Stack parameter design studies
2) Evaluation of the impact of new source or source
modifications for permit applications review
3) Fuel conversion studies
4) Monitoring network design
118
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5. Control technology evaluation
6. Control strategy evaluation for SIP
7. Prevention of significant deterioration
7. Technical Contacts
Bruce Turner
U.S. Environmental Protection Agency
Environmental Applications Branch
Mail Drop 80
Research Triangle Park
N.C. 27711
COM 919/541-4564 FTS 629-4564
Keith Zimmerman and James Bryant
Texas Air Control Board
Permits Section
6330 Highway 290 East
Austin, Texas 78723
512/451-5711
8. References
Texas Air Control Board, "User's Guide to the Texas
Episodic Model", Austin, Texas, October 1979.
Dames and Moore, "Final Report Phase I Bay Area Sulphur
Oxides Study for Bay Area Air Quality Management
District." October 1979.
119
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ABATEMENT AND RESIDUAL FORECASTING MODEL (ABTRES)
1. Model Overview: The Abatement and Residual Forecasting
Model (ABTRES) forecasts and reports the costs associated with
pollution control systems, and the concomitant residual levels.
The system is based upon "sectors"; that is, processes or
technologies that have identifiable pollution control costs.
These sectors are aggregated to "chapters" for reporting
purposes. Chapters are industrial segments, organized in a
manner determined by the analyst. This aggregation is useful
since there are often several sequential operations within an
industry, each with separate pollution control systems, or an
industry may be defined in a general manner, to include several
different end products, such as "Organic Chemicals."
2. Functional Capabilities: The ABTRES model allows the user
to compute costs associated with meeting the pollution control
standards in effect through internal calculations based upon
certain input parameters, or the user may enter these costs
exogenously. In conjunction with these cost forecasts, the
model projects estimated residual levels associated with the
treatment methods of each abatement technology sector. There are
two standards which apply to existing industries to meet Federal
guidelines for water pollution control, and these are the Best
Practicable Technology (BPT) and the Best Available Technology
(BAT). There are separate standards promulgated for plants
established after a particular date (which varies by industry) ,
and the set of records is referred to as New Source Performance
Standards (NSPS). Sectors dealing with air pollution have a
single standard to implement, which is based upon state
implementation plans (SIP). There are also more stringent
120
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regulations dealing w.ith new plants. Types of pollution
considered by the model include; particulates, sulfur oxides,
nitrogen oxides, hydrocarbons, carbon monoxides, vinyl chloride,
other gases and mists, biological oxygen demand, chemical
oxygen demand, suspended solids, dissolved solids, acids, bases,
and oils and greases.
3. Basic Assumptions: ABTRES is an accounting model that sub-
categorizes industries and computes costs associated with meeting the
pollution control standards in effect through internal calculations
based upon certain input parameters. Costs may also be entered
exogenously. A straight line interpolation method is used to find
the growth rates for years not specified as corresponding to these
rates. Growth is held constant for the intervals between interpolation
years. The conceptual growth curves are smooth; for computational
purposes, the step curve is used, allocating all growth to the begin-
ning of the fiscal year.
4. Input and Output: Input to the model is in card image form
and the following types of information are included: abatement
technology description, number of residuals, equipment life,
interest rates, exogenous costs, loading factors, capacity
utilization, number of plants, average capacity, growth percentage
by interpolation year, percentage of capacity pretreating wastes
prior to municipal treatment by interpolation year, residual codes
for pollution types, base residual coefficients (to yield total
pollutant level generated without any treatment), and fraction
of waste treated.
Once forecasts of costs and residuals have been generated
by the computational program of ABTRES, a report generator is
implemented using the output files. The costs for several
abatement technology sectors are aggregated as a "chapter level".
121
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Different reports are issued for air and water treatment systems
5. System Resource Requirements: Two programs must be
implemented in the ABTRES system. The first is a forecasting
model, and the second a report generator. The model requires
100K bytes of core storage. A knowledge of programming and an
awareness of the model's theory and limitations is helpful.
6. Applications: ABTRES can be used to forecast and report the
cost associated with pollution control systems and the concomitant
residual levels. It has been applied to manufacturing plants and
the levels of water pollution associated with these plants.
7. Technical Contact
James Titus
U.S. Environmental Protection Agency
Economic Analysis Division
PM-220
401 M Street, S.W.
Washington, D.C. 20460
COM 202/382-2774 FTS 382-2774
8. References
Wing, B.J.Abatement and Residual Forecasting Model (ABTRES),
Prepared by the Professional Services Division, Control Data
Corporation, Rockville, Maryland, for the Office of Planning
and Evaluation, U. S. Environmental Protection Agency,
Washington, D.C., April 1977.
122
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AIR TEST MODEL (AIRTEST)
1. Model Overview: The Air Test Model is a preprocessor to
the Utility Simulation Model, which can also be used as a stand
alone model. Using actual fuel and specified generation for
each power plant or generating unit, it calculates the
controlled and uncontrolled emission of S09, NO , and
L A.
particulates for one year. In addition, the model selects the
least levelized cost fuel and pollution control option to meet
unit specific emissions standards.
2. Functional Capabilities: The options to meet the
applicable S02, NOX , and particulate standards currently
include: Actual 1979 data for fuels burned in the generating
unit, coal washing on a coal specific basis, low sulfur coal
options for each unit, coal-blending to meet unit specific
standards, wet and dry F.D.G., E.S.P.'s fabric filters, low
excess air, staged combustion, fuel gas recirculation ,
limestone injection burners, and oil hydrodesulf ur ization . The
Air Test Model passes each unit's low cost and fuel
characteristics on to the Utility Simulation Model.
3. Basic Assumptions: AIRTEST assumes minimization of
levelized cost of fuel and pollution control vs. the decision
factor in selection of fuel and technology.
4. Input and Output : Input is the actual fuel and specified
generation for each power plant or generating unit to be
considered .
The output is the controlled and uncontrolled emissions of
SOp, NO and particulates; pollution c
and fuel type; and cost for each unit.
NO and particulates; pollution control option; cost
123
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5. System Resource Requirements: AIRIEST is written in
FORTRAN and can be run on either a CDC 7600 or IBM mainframe.
Any model line printer can be used. The operator's background
should include programming and engineering.
6. Application: AIRTEST is currently being used in the Acid
Rain Mitigation Strategies research program.
7. Technical Contacts
Dr. Andrew Van Horn
Teknekron Research, Inc.
2118 Milvia Street
Berkeley, California 94704
415/548-4100
Paul Schwengels
U.S. Environmental Protection Agency
Office of Research and Development
401 M Street, S.W.
Washington, D.C. 20460
COM 202/382-2577 FTS 382-2577
8. References
Bowan, Carol, Clements, Don, Moffet, Michale, and Van Horn,
Andy. AIRTEST USER'S GUIDE, Nov. 1980. Teknekron Report
No. (RM-60-DOE80).
124
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AUTOMOBILE DEMAND MODEL (CARMOD)
1. Model Overview: CARMOD is a 400 equation simultaneous econo-
metric model. The model is concerned primarily with estimating
long»run levels of automobile demand. CARMQD was developed by
Wharton Econometric Forecasting Associates (WEFA). The model is
long-run and movements toward equilibrium are governed by a stock
adjustment mechanism. CARMOD resides on the IPS/TROLL system at
the Massachusetts Institute of Technology.
2. Functional Capabilities: The TROLL system allows for direct
user interaction with Carmod. Simulation of any equation or the
production of graphs is accomplished through simple conversation
with TROLL. This system also provides for a variety of output
options in addition to the printing of standard regression sta-
tistics . Econometric techniques ranging from OLS to 2SLS with
autocorrelation correction procedures are readily available.
CARMOD's equations were estimated using ordinary least squares.
TROLL's unique file system allows all relevant files asso-
ciated with a given model to be accessed automatically by ref-
erencing the model itself. The user doesn't need to be concerned
with loading data or parameters. TROLL also contains a file
editor and provides for off line printing.
3. Basic Assumptions: The assumptions concerning model forecasts
(baseline projection) fall into three categories; demographic
trends, the economic environment, and automobile characteristics.
1. The major demographic assumptions are:
Slow population growth: the growth-rate changes from 0.17%
per annum for 1976-1985 to just over 0.3% for 1995-2000.
Family formation outspaces population: the number of family
units rises from 75.3 million in 1975 to 87.4 million in
1985 (a 1.5% per annum rate) to 100.7 million by 2000 (a
0.9% per annum rate).
125
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Families become smaller: the proportion with five or more
members falls sharply, while that for three or four remains
constant.
An aging population: the percentage between 20 and 29
years of age falls, especially after 1980.
2. The key economic assumptions are:
Strong real income growth: real GNP growth in excess of 5%
per annum through 1978, slowing to 2% for 1979-1980, stabi-
lizing at around 3% per annum thereafter.
Slowing inflation: the overall GNP deflator rises at around
5.51 per annum through 1980, slowing towards 4% by 1985, and
reaching 3% per annum by 2000.
Declining unemployment-rate: unemployment falls towards a
5% rate by the mid-1980's, then slowly trends towards 3% by
2000.
Slowly increasing 'real' automobile costs: operating costs
are expected to outpace the overall consumer price index,
especially the price of gasoline projected to increase
over 20% in 1972 prices by 1985; however, 'real' purchase
prices are expected to be quite stable.
3. The auto characteristics assumptions are:
Sharply reduced weights and displacements: a major domestic
downsizing program, applied to each size-class in succession,
reducing curb-weights about 30%, and engine displacements
about 40%, by 1990.
Efficiency improvements: technological developments are
projected to yield increases in fuel efficiency totalling
111 for 1976-80; thereafter these gains are held to 1% per
annum on the assumption of more stringent pollution standards
126
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4. Input and Output; Once CARMOD has been accessed, the TROLL
system automatically provides the required data input. The user
can then specify the equation technique, simulation period, re-
quired statistics, and any graphic output desired. After the model
has been simulated once, TROLL will automatically create a 'data
set' file which includes the model, data, and estimated coefficient
values. In this way performing a variety of simulation experiments
at a later time is a simple task.
5. System Resource Requirements: There are no specific system
resources requirements needed to run CARMOD on TROLL. The system
is conversational and costs approximately $9.00 per CPU minute and
$2.00 per hour connect time to operate.
6. Applications: CARMOD has been used by the Department of Trans
portation to forecast the long run size and composition of U.S, auto
demand and stock. More recently, the model has been employed by the
Environmental Protection Agency in forecasting impacts on the U.S.
automobile industry resulting from environmental regulations.
7. Technical Contacts
Mahesh Podar
U.S. Environmental Protection Agency
Economic Analysis Division
PM-220
401 M Street, S.W.
Washington, B.C. 20460
COM 202/382-2770 FTS 382-2770
127
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FOREIGN AUTO INDUSTRIES
K)
00
AUTO PRICES § COSTS
PURCHASE PRICES
GASOLINE COSTS
TAXES, ETC.
GOVERNMENT POLICY
EXOGENOUS AND MACROECONOMIC INPUTS
INCOME
PRICES
DEMOGRAPHICS
TRANSPORTATION SYSTEM CHARACTERISTICS
7\
U.S. AUTO INDUSTRY
V
\/
LONG-RUN EQUILIBRIUM
DESIRED TOTAL STOCK
DESIRED STOCK
ADJUSTMENT TO EQUILIBRIUM
ACTUAL STOCK, AUTO SALES
VEHICLE MILES, USED CAR MARKET
SIMPLIFIED SCHEMATIC OF THE WEFA AUTO MODEL
-------
TRANSPORTATION
SYSTEM CHARACTERISTICS
CHARACTERISTICS OF
NEW AUTOS
n
NEW CAR SUPPLY CONSTRAINTS
.DEMOGRAPHIC
VARIABLES
DESIRED AUTO
STOCK
COST PER MILE OF
NEW CARS AND OF
THE DESIRED STOCK
7\
NEW CAR PRICES
TAXES AND TARIFFS
INCOME AND ACTIVITY
VARIABLES
SIZE CLASS SHARE OF
DESIRED AUTO STOCK
A
PRICES AND TAXES
ASSOCIATED WITH
OPERATION
NEW CAR PRODUCTION COSTS
DETAIL OF THE LONG RUN AUTO MODEL
-------
NEW CAR
SUPPLY CONSTRAINTS
INCOME AND ACTIVITY
VARIABLES
ACTUAL VEHICLE
MILES
u-i
o
UNIT SCRAPPAGE
7
SCRAP PRICES
USED CAR PRICES
DESIRED AUTO
STOCK
\/ \/
SIZE CLASS SHARE OF DESIRED
AUTO STOCK
ACTUAL NEW CAR
SALES
1
V
SIZE CLASS SHARE OF AUTO
SALES
7K
END OF YEAR AUTO
STOCK
FLEET OPERATION AND OWNERSHIP
COSTS PER MILE
NEW CAR PRICES
\ /
\
CHARACTERISTICS OF AUTOS
INPUT AND OUTPUT DETAIL OF THE LONG RUN AUTO MODEL:
ADJUSTMENTS TO THE DESIRED VALUES OF THE ACTIVITIES OVER TIME
-------
References
Wharton Econometric Forecasting Associations, "An Analysis
of the Automobile Market: Modeling the Long Run Determinants
of the Demand for Automobiles" Philadelphia^ PA» February 1977
National Bureau of Economic Research, "TROLL/1 User's Guide"
Cambridge, Massachusetts, June 1972.
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CONSTRUCTION MODEL (CONMOD)
1. Model Overview: The construction model is an econometric
model designed for the evaluation of sewer line and treatment
plant expenditures by the Environmental Protection Agency. The
model estimates impacts on labor supply, investment, and prices
from EPA sewer-related expenditures. CONMOD was developed by
the Center for Naval Analyses (CNA). CONMOD resides on the IPS/
TROLL system at the Massachusetts Institute of Technology.
2. Functional Capabilities: The TROLL system allows for direct
user interaction with CONMOD. Simulation of any equation or the
production of graphs is accomplished through simple conversation
with TROLL. This system also provides for a variety of output
options in addition to the printing of standard regression sta-
tistics. Econometric techniques ranging from OLS to 2SLS with
autocorrelation correction procedures are readily available.
CONMOD utilizes GLS because of the small sample sizes.
TROLL's unique file system allows all relevant files associ
ated with a given model to be accessed automatically by referencing
the model itself. The user doesn't need to be concerned with
loading data or parameters. TROLL also contains a file editor
and provides for off line printing.
3. Basic Assumptions: CONMOD was developed assuming the con-
struction industry to be competitive. For any type of construc-
tion, the composition of construction between trades remains
fixed.
Stock of structures equations imply a stock adjustment mech-
anism where actual stocks adjust to desired stocks at a constant
rate (estimated by regression analysis). Lastly, the labor sup-
ply schedule is derived from a constant elasticity of substitu-
tion production function (CES).
132
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4. Input and Output: Once CONMOD has been accessed, the TROLL
system automatically provides the required data input. The user
can then specify the equation technique, simulation period, re-
quired statistics, and any graphic output desired. After the
model has been simulated once, TROLL will automatically create
a 'data set' file which includes the model, data, and estimated
coefficient values. In this way performing a variety of simula-
tion experiments at a later time is a simple task. Data can be
printed when desired and results from the output stream can be
stored for later use.
5. System Resource Requirements: There are no specific system
resource requirements needed to run CONMOD on TROLL. The system
is conversational and costs approximately $9.00 per CPU minute
and $2.00 per hour connect time to operate.
6. Applications: CONMOD is used for estimating the economic
impact resulting from the EPA's massive sewer line and sewer treat
ment plant expenditure program. The model may also be used for
other applications involving the construction industry.
7. Technical Contacts
James Titus
U.S. Environmental Protection Agency
Economic Analysis Division
PM-220
401 M Street, S.W.
Washington, D.C. 20460
COM 202/382-2774 FTS 382-2774
133
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References
Center for Naval Analyses, March 1978. The Economic Effects
of Environmental Regulations on the Construction Industry"
Arlington, Virginia.
National Bureau of Economic Research, June 1972. "TROLL/1
Users Guide1' Cambridge, Massachusetts.
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SECTION 120 NONCOMPLIANCE PENALTY MODEL (PENALTY)
1. Model Overview; The Section 120 Noncompliance Penalty
Model (PENALTY) is an economic model used to calculate the
economic benefit of delayed compliance with the requirements of
the Clean Air Act as amended, August 1977. The noncompliance
penalty is based on the concept that it is usually in a
source's best economic interest to delay the commitment of
funds for pollution control equipment, and that incentive
should be eliminated. The program was completed in February
1979, by Putnam, Hayes & Bartlett, Inc. of Newton,
Massachusetts, for the U.S. EPA, Office of Planning and
Management.
2. Functional Capabilities: PENALTY compares two cash flows:
that which the source would have experienced had it achieved
compliance on the date it received a notice of noncompliance,
and that which it is estimated it will experience as a result
of its delay. Because these cash flows occur at different
times, a basis of comparison is provided by discounting them to
their present value equivalents. The model then calculates the
difference between these two cash flows and the appropriate
quarterly payment schedule that the source should follow. It
can also make a final adjusted penalty calculation when the
source has achieved compliance. The capital investment portion
of the penalty is calculated using standard and rapid
amortization. Under both types of amortization, the program
calculates the depreciation tax savings using straight line,
sum-of-the-years-digits, and double declining balance
depreciation methods. The program will automatically choose
the method which will result in the lowest penalty.
135
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3. Basic Assumptions: Assumptions of the penalty follow:
1) The relative mix of debt, preferred stock, and common
equity allocated to pollution control equipment is the
same as that found in the firm's capital structure as
shown on its balance sheet.
2) Cash flows are discounted using the equity method.
3) The non-compliance penalty is computed as a non-tax
deductible expense to .the firm.
4) Cash flows take place at the end of each month.
5) The rate of inflation of pollution control operating
and maintenance expenditures is the same as that for
pollution control capital costs.
6) The noncompliance penalty is calculated using a
thirty-year time horizon.
7) The salvage value of any equipment with useful life
remaining at the end of the thirty year time horizon
is zero .
8) The discount rate is not less than the inflation rate.
4. Input and Output: Input to the model includes
source-related data: facility life, months of noncompliance
income tax rate, discount rate, and preferred stock dividend
rate; equipment-related data, capital expenditures, operating
and maintenance costs, financing (industrial bonds; equity
share, preferred stock share, and debt share of investment),
equipment useful life and depreciation life; and a forecasted
inflation rate. This information may come from the firm itself
as well as the Internal Revenue Service, Chemical Engineering
Plant Cost Inflation Index, the Federal Trade Commission, and
Moody" s Bond Record.
136
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Output consists of two user-selected formats; a lump sum
settlement, or a schedule of quarterly payments, both expressed
in thousands of dollars.
5. System Resource Requirements; PENALTY is coded in FORTRAN.
It is run on an IBM 360/370 mainframe. A background in
economics and finance is helpful.
6. Applications: It is used by HQ and regional offices as
well as sources and contractors to compute noncompliance
penalties.
7. Technical Contact
Howard F. Wright
U.S. Environmental Protection Agency
Division of Stationary Source Enforcement
401 M Street, S.W.
Washington, D.C. 20460
COM 202/382-2833 FTS 382-2833
9. References
Federal Register. Monday, July 18, 1980. Part II - EPA -
Assessment and collection of Non-Compliance Penalties by
EPA and approval of State Non-Compliance Penalty Program.
Appendix A - Technical Support Document
Appendix B - CCA Section 120 Non-Compliance Penalties
Instruction Manual
137
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STEEL INDUSTRY MODEL (PTM)
1. Model Overview: PTM was developed by Temple, Barker,
and Sloane (TBS) for the purpose of systematically analyzing the
effects on the steel industry resulting from environmental regula-
tions, input price changes, or from other cost variations. The
model partially relies on a modeling effort previously done by
Arthur D. Little in Cambridge, Massachusetts. PTM con-
tains three modular components: production, pollution control,
and finance. The two later components depend upon the production
and capacity data from the production component in order to exe-
cute. Exogenous variable values for simulation were obtained
through Chase Econometrics.
2. Functional Capabilities: PTM has the capability of
performing many different sensitivity analyses by altering data
inputs such as the rate of return on equity, degree of cost pass
through, cost of capital, etc. In addition, effects on energy
usage, employment, and the balance of trade stemming from environ-
mental regulations can be estimated. Cost impacts of the Clean
Air Act and other air pollution regulations can be calculated
utilizing different engineering cost estimates. The resulting
revenue requirements and price effects are also computed by the
model.
3. Basic Assumptions: In establishing a baseline forecast for
the steel industry, TBS has assumed that domestic steel shipments
will rebound from 1975 recession levels. This adjustment is as-
sumed to be completed by 1977 and, thereafter, steel shipments
are assumed to follow the long run trend to 1983. The baseline
forecast for steel shipments by 1980 is 120 million tons. The
other baseline indicators needed to simulate the baseline forecast
are capital expenditures, external financing needs, operations and
138
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maintenance expenses, revenue requirements, and the average price
of steel per ton. TBS has calculated the following numbers for
the baseline forecast.
Short Run Long Run
1975-1977 1975-1985
Capital Expenditures $ 8.7 $ 27.5
External Financing Needs 3.8 13.0
0§M Expenses 78.2 272.9
Revenue Requirements 96.1 338.6
Average Price 345.26 365.21
(in 1975 dollars per ton)
The theoretical assumptions used in constructing PTM
were not available as of this writing.
4. Input and Output: PTM requires many cost inputs.
These consist of production costs and pollution control costs.
Under these two headings there are several subdivisions. Pollu-
tion control costs can be broken down into water pollution and
air pollution control costs. Each type of pollution control cost
has two (main) cost categories; operations and maintenance ex-
penditures and capital costs. Production costs include capital
expenditures, operations and maintenance cost, raw materials cost,
and 'other costs'.
PTM (Steel) produces the following outputs:
1. Income Statement
2-. Flow of funds summary
3. Balance sheet
These outputs contain all the information necessary to analyze the
impacts on the industry. All output figures are in current dollars
139
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PRODUCTION §
CAPACITY
REQUIREMENTS
o STEEL SHIPMENTS
o PRODUCTION FLOWS
o CAPACITY
DETERMINATION
FINANCIAL
RESULTS
o INCOME STATEMENT
o SOURCES AND USES
OF FUNDS
o BALANCE SHEET
ENVIRONMENTAL
COSTS
0 WATER POLLUTION
CONTROL COSTS
o AIR POLLUTION
CONTROL COSTS
o OTHER POLLUTION
CONTROL COSTS
PTM MAJOR COMPONENTS
140
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MODEL INPUT
DATA INPUT
REQUIRED REVENUES
7^~
|~ SALES TAXES
0 § M EXPENSE
OPERATIONS § MAINTENANCE
EXPENSE
BOOK VALUE
PROPERTY TAXES
/\
1972 REPLACEMENT VALUE
1972 BOOK VALUE
CAPITAL EXPENDITURES
LONG-TERM DEBT
STOCKHOLDERS EQUITY
^
DEPRECIATION CHARGES
A
EARNINGS BEFORE INTEREST AND
TAXES
A A
INTEREST
EXPENSE
INCOME
A
REQUIRED NET
INCOME
SALES TAX RATE
PROPERTY TAX RATE
J
LIFETIMES
REINVESTMENT RATES
PCE SCALE FACTORS
INTEREST RATES
STATE TAX RATE
FEDERAL TAX RATE
RETURN ON EQUITY
REQUIRED
INCOME STATEMENT LOGIC
141
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MODEL INPUT
DATA INPUT
CAPITAL EXPENDITURES
CAPITAL EXPENDITURES
PREVIOUS YEAR'S REVENUE
AND
WORKING CAPITAL
CHANGES IN WORKING
CAPITAL
WORKING CAPITAL AS PERCENT
OF
PREVIOUS YEAR'S INCOME
1971 LONG-TERM DEBT
T
LONG-TERM DEBT
REFUNDINGS
REFUNDING PERCENTAGE
V
SHORT-TERM INVESTMENTS
(IF REQUIRED)
V
TOTAL USES OF FUNDS
TOTAL SOURCES OF FUNDS
A
A
EQUITY ISSUED
A
A
LONG-TERM DEBT
TOTAL INTERNAL SOURCES
A
SHORT-TERM INVESTMENTS FROM
PREVIOUS YEAR (IF ANY)
DEPRECIATION CHARGES
DEPRECIATION CHARGES
REQUIRED NET INCOME
RETAINED EARNINGS
CAPITALIZATION
RATIO
DIVIDEND PAYOUT RATIO
FLOW OF FUNDS LOGIC
142
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5. System Resource Requirements: PTM is coded in FORTRAN V
and can be run on the IBM 370/158 or Univac 1110 computers. Unless
alterations to the baseline or scenario forecasts are desired, the
model can be run immediately after being loaded into the machine.
Direct alterations to the program would require a working knowledge
of FORTRAN and econometrics.
6. Applications: PTM Steel has been primarily used for environ-
mental impact/sensitivity analyses by the U.S. Environmental Pro-
tection Agency. The model could also be used in forecasting im-
pacts on the steel industry resulting from changes in factors of
production, factor prices, or technological advancement.
7. Technical Contacts
Robert Greene
U.S. Environmental Protection Agency
Economic Analysis Division
PM-220
401 M. Street, S.W.
Washington, D.C. 20460
COM 202/382=5480 FTS 382-5480
References
Temple, Barker, and Sloane (TBS) July 1977. "Analysis of
Economic Effects of Environmental Regulation on the Inte-
grated Iron and Steel Industry71 Volumes 1 and 2. Welles-
ley Hills, Massachusetts.
143
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STRATEGIC ENVIRONMENTAL ASSESSMENT SYSTEM
(SEAS)
1. Model Overview: The Strategic Environmental Assessment
System (SEAS) is a comprehensive forecasting system which can
be used to determine the environmental, economic,and energy ef-
fects of differing growth patterns and policies. Depending upon
its use, the forecast horizon for SEAS can be from one to fifty
years, with the range of 15-25 years for the most common projec-
tions. The SEAS model was developed at EPA and operating ver-
sions are in existence at EPA.
2. Functional Capabilities: The SEAS system is modular in na-
ture in that any program may be executed independently of the
others provided that all mandatory input files have been created
previously. Each program produces a detailed printed output re-
port and an output file for processing by other programs. In ad-
dition, general purpose report generators are available for the
production of system summary reports.
All programs are autonomous in the sense that they do not
require mandatory user-supplied information. A "default scenario"
run reflects forecasts based on the best data available from
government sources, excluding any external input information.
Each SEAS program will accept two types of user-supplied infor-
mation:
1) Execution Options. The selection of years to be pro-
cessed is an option common to all programs except IN-
FO RUM. In addition, optional scenario conditions may
be applied to all programs. Each scenario corresponds
to a certain set of parameter values and conditions.
By selecting a given scenario, these data values are
automatically set by the program.
2) Override of Default Data. The user must specify each
item of the substitute data.
The SEAS programs may be grouped and categorized according to
functional capability. Each functional unit of the SEAS system
144
-------
is referred to in this report as a "module". The following is
a description of most of the modules in the system:
1) INFORUM: An expanded version of the University of Mary-
land's INFORUM interindustry input-output model, which
may be driven by alternate national macroeconomic fore-
casts .
2) Sector Disaggregation (INSIDE): Disaggregation of IN-
FORUM economic sectors to represent subsector growth and
technological change in greater detail.
3) Abatement Costs (ABATE): Capitol costs, and operating
and maintenance (0{|M) costs associated with the control
of pollution for each economic sector and subsector.
4) Relative Commodity Prices: Adjusts industrial forecasts
to account for the impact on relative prices due to pol-
lution control activity or stock shortage.
5) Stock Reserves and Prices (STOCKS): Accounts on domestic
and worldwide reserves of critical resources and materials
as determined by relative production price, investment
requirements, and net import levels.
6) Solid Waste/Recycle (SOLRECYC): Annual tonnage, disposal
methods, and costs for non-industrial and solid waste
sources, and estimated levels of recycling.
7) National Residuals (RESGEN): Estimates of annual tonnage
of air, water, and land pollution from stationary sources
on a nation-wide basis.
8) The Energy Demand Modules (Residential/Commercial, In-
dustrial, Transportation): Calculates the energy demand
due to activities in these sectors. The level of activ-
ity for each of the sectors depends on the economic sce-
nario currently being utilized.
9) Energy System Network Simulator (ESNS): An adaptation of
the ESNS network was developed at Brookhaveii National Labora
tories. For each set of energy demands the network traces
end use through the processes of extraction, transporta-
tion and conversion. Residuals produced during these pro-
cesses are calculated in ESNS.
10) Regionalization (REGION): Regionalization of national
economic, pollutant, and abatement cost data.
145
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11) Land Use: Forecasts of urban, rural and agricultural
land use and associated noiipoint residuals.
12) Ambients (AIRAME, WATERQUAL): Surrogate measures of am-
bient air and water effluent discharge levels (experi
mental).
A module flowchart illustrating the standard module configura-
tion is presented. A brief explanation of the flow of informa-
tion is given in the following narrative.
National economic forecasts are generated by a common pro-
gram consisting of INFORUM, Sector Disaggregation, and Abatement
Cost modules. Economic output forecasts from INFORUM are first
input to Sector Disaggregation for more detailed industrial fore-
casts. The Sector Disaggregation may require feedback from IN-
FORUM to adjust the output forecasts of certain related sectors.
Sector growth rates, at the disaggregated level, are then used
in the computation of abatement cost estimates for predefined
control technologies within each sector. Internal feedbacks
from these computations automatically adjust the coefficients in
the various matrices in INFORUM. The integrated economic model
iterates through this solution procedure twice for each forecast
year. (The user, however, may at his option, shut off either
the Abatement Cost module or the Sector Disaggregation module,
or both.)
Nationwide solid waste residuals and recycling levels are
calculated in the Solid Waste/Recycling module. Levels of de-
pletable resources are calculated from consumption levels and
recycling levels. Residential/commercial, industrial, and trans-
portation energy demands are calculated in the demand modules
based upon the national economic forecasts. The Energy System
Network Simulator calculates the amounts of extraction, trans-
portation and conversion necessary to meet the energy demands.
Both the Transportation Energy Demand module and the ESNS calcu-
late residuals resulting from their activity levels.
Residuals and economic abatement costs forecasts are region-
alized to various regional classification schemes in the Region-
alization module. Regional transportation emissions, computed
within the transportation module, may be added to the regional
146
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residuals data file. Regional land use activity, residuals, and
ambient pollutant concentrations can also be calculated.
The levels of resources and materials cause changes in
prices. Significant changes in prices may necessitate feedback
to INFORUM to adjust the national economic forecasts.
3. Basic Assumptions: Most of the parameters in the system
can be changed by the user of the system. Economic input/output
coefficients are based on historical time series analysis. Most
of the direct relationships are linear, but resource constraints,
boundary conditions, and iterative feedback loops produce non-
linear effects.
4. Input and Output Options: Many of the input parameters in
SEAS exist as default values to be changed by the user should he
elect to do so. Assumptions about population growth, per capita
disposable income, unemployment rates, years for abatement com-
pliance, price of gasoline, miles per gallon fleet mixes for
automobiles, logistics for new technological process mixes in in-
dustry, changing product mixes, increasing or shortening the viable
lifetime of durable goods, the recycling rate for industry, the
composition of demand for energy,and the mixture of supply tech-
nologies to meet the demand, are some examples of the types of in-
put changes that can be made to the system.
The selection and format of output reports is an option of
the user. Each module in the system is capable of producing a
variety of outputs; the user may specify the points in the execu-
tion and categorization of that output.
In addition, four general report generators are available
to support the assessment of scenario results:
1) POSTCOMP, which provides annual values and annualized
percentage changes for significant parameters from every
SEAS module and comparative indices for pollutant resid-
uals from a maximum of four scenarios.
2) INFRPT, which provides comparative rankings and percent-
age differences for sector and subsector economic fore-
casts from selected scenario pairs,
3) RANKER, which ranks sectors and subsectors based on the
147
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Scenario. Data
Inputs
r
Kational
Economic
Forecants
oo
Pollution Abatement
Cost Module
(ABATE)
Residential/
Commercial
Energy Demand
Module
(RES/COM)
Transportation
Energy Demand
Module
(TRANS)
Industrial Energy
Demand Module
(I DM)
1
1-
1
1
/
1
L_ _ J
Energy Supply
Network
Simulator
(ESNS)
f
r
\.
r
V
f
—
Enerpy Investment
Module
(EIM)
— — — — *— — —
National
Energy
Forecasts
I
Economic
Input/Output
Module
(INFORUM)
Sector Disagregation
Module
(INSIDE)
T~ <
x* *\ /^" *\
Energy \ f Trans-\ 1
Supply \ / portationV^
Residuals J 1 Residuals Y"\
File J V File /
Industrial Residual
Generation Module
(RESOEN)
\_
f
\
V V
Regionalization
Module
(REGION)
[reRional
RESGEN for
selected
industries]
s^
f
Air Quality
Module
(AIRAMD)
Water Quality
Module
(WATERQUAL)
Stock Reserves
and Trices
Module
(STOCKS)
4—f-
Solid Waste/
Recycling
Module
(SOLRECYC)
National
Residuals.
Forecasts
Land Use
Module
(non-point source
(I.ANDUSE)
SEAS System Flowchart
-------
annual amount of net residuals for each pollutant in a
specified medium, at either national or regional levels-
4) CLEANSUM, which provides annual pollution control costs
and residuals associated with predefined processes and
abatement technologies.
5. System Resources Requirements: Most of the SEAS module al-
gorithms are written in FORTRAN IV-G1, and the system is installed
on an IBM 370/158 computer. The programs do not require any
special software packages and are available on both OS and VS IBM
operating systems. SEAS modules require from 70K to 650K bytes
of main storage depending upon the modules being run, and most
modules require 250K of storage or less.
6. Applications: The Strategic Environmental Assessment System
has been used for both comprehensive and issue-specific applica-
tions within EPA and within other public and private institutions
and government agencies. Within EPA, SEAS has been used to sup-
port several ORD Integrated Technology Assessment (.ITA) projects
including the Western Energy Development ITA and the Ohio River
Basin Energy Study ITA. SEAS is also used to provide environmen-
tal trends projections to support the production of ORD's annual
Research Outlook. SEAS was also used within EPA to produce the
1976 Cost of Clean Air and Cost of Clean Water reports.
The Department of Energy (DOE) uses SEAS to analyze the im-
plications of alternative patterns of energy and economic develop-
ment in the National Environmental Impact Projection Series (NEIP).
DOE also uses SEAS to support internal planning and budgeting
activities and to produce the Annual Environmental Analysis Report.
In addition SEAS forms a major part of the DOE Transportation
Energy Conservation Network (.TECNET) , which forecasts impacts of
transportation energy conservation initiatives,
Resources for the future has used SEAS to forecast implica-
tions of population growth through the year 2025. The Solar Energy
Rese?^ch Institute has used SEAS to forecast the environmental and
149
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economic implications of alternative penetrations of solar tech-
nologies. There are and have been numerous additional applications
of the system.
7 . Technical Contacts^:
John J. Coleman
U.S. Environmental Protection Agency
Office of Research and Development
401 M St. S.W.
Washington, D.C. 20460
COM 202/382-2608 FTS 382-2608
Basil Manns
U.S. Environmental Protection Agency
Office of Research and Development
401 M St. S.W.
Washington, D.C. 20460
COM 202/245-3026 FTS 245-3026
References
House, P.W. Trading-off Environment, Economics, and Energy:
A Case Study~bf EPA's Strategic Environmental Assessment
System.Lexington, Massachusetts:D.C.Heath § Company, 1977
Control Data Corporation. AEAR/TAMP User's Guides. 2 vols.
Prepared for the Department of Energy, Washington, B.C., by
Control Data Corporation, Rockville, MD , November 1977.
Control Data Corporation. AEAR/TAMP Data Specifications.
Prepared for the Department of Energy, Washington, B.C., by
Control Data Corporation, Rockville, MD , November 1977.
Control Data Corporation. AEAR/TAMP Program Specifications.
2 vols. Prepared for the Department of Energy, Washington,
D.C., by Control Data Corporation, Rockville, MD , November
1977.
150
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U.S. COPPER INDUSTRY MODEL (COPMOD1)
1. Model Overview. The financial-econometric simulation model of
the U.S. Copper Industry has been developed to assess the industry-
wide economic impact of compliance with, the air and water pollution
abatement legislation. The model consists of a market clearing
module and a dynamic investment module programmed in FORTRAN. COPMOD1
was constructed by Arthur D. Little, Cambridge, Massachusetts.
2. Functional Capabilities. COPMOD1 incorporates two versions of
the U.S. Copper model, linear and nonlinear. The model identifies
three alternative modes of pricing behavior for the primary producers
in the linear and nonlinear cases:
1. Price = Average Variable Cost (slack demand)
2. Price = Average Total Cost (normal demand)
3. Marginal Revenue = Marginal Cost (demand 'crunch')
The nonlinear version permits the introduction of capacity constraints
in supply and cost curves, whereas the linear version yields uncon-
strained production estimates. Plant capacity could be exceeded in
the linear simulation experiment.
In addition, subroutines are included to plot average total cost,
average variable cost, average fixed cost,and compare historical
period simulation results with actual data.
3. Basic Assumptions. The model explicitly assumes the following
market classifications:
1. Primary producers
2. Secondary refiners
3. Producers of non-refined scrap
These classifications are based upon pricing behavior and production
technology. More importantly, primary producers are analyzed as
behaving oligopolistically while secondary refiners and producers
of non-refined scrap are treated as behaving competitively.
151
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4. Input and Output. Alteration of the scenario file in COPMOD1
can take two forms:
1. Card input
2. File editor
Due to the structure of the program, modification of the scenario
file is most easily accomplished using the file editor. Modification
through card input can only occur if the entire program subroutine
with the new cards is reloaded into the computer. As of this writing,
program modification remains non-interactive.
COPMOD1 provides for the following input options:
1. Specification of model version:
a. Linear
b. Nonlinear
c. Both
2. Printing of all endogenous and exogenous variables.
3. Printing of various diagnostic variables.
4. Which of the three parametric solutions (P = ATC, P = AVC,
MR = MC) is most probable.
Depending upon the version chosen, COPMOD1 will generate a wide
range of subsidiary calculations such as production estimates, sales
estimates, and payroll estimates. Financial estimates (pollution
abatement investment, depreciation, dividends, etc.) derived from
primary producer's estimated fixed costs are printed in constant and
current dollars.
5. System Resource Requirements. COPMOD1 is coded in (ASCII)
FORTRAN V and requires approximately 65K words of core storage for
execution. The program is compatible with the Univac 1110 and
a moderate amount of computer skills and a working knowledge of
econometrics is useful.
152
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6. Applications: COPMOD1 was developed primarily for estimating
the impact on copper producer's costs from pollution abatement ex-
penditure. The model may also be utilized in evaluating the effects
of noncompliance fees on the producers of copper and non-refined scrap,
Technical Contacts
James Titus
U.S. Environmental Protection Agency
Economic Analysis Division
PM-220
401 M Street, S.W.
Washington, B.C. 20460
COM 202/382-2774 FTS 382-2774
8. References
Arthur D. Little, October 1976. 'Economic Impact of Environmental
Regulation on the U.S. Copper Industry". Draft report.
Arthur D. Little, April 1978. " COPMOD1 Program, Documentation".
Cambridge, Massachusetts.
Raymond S. Hartman, January 1977. "An Oligopolistic Model of
the U.S. Copper Industry" Ph.D thesis, M.lTT.
153
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CONSTRUCTION SITE HEALTH AND WELFARE MODEL (CSM)
1. Model Overview: The model computes noise impacts on the
population surrounding more than two million construction sites
that are active every year in the U.S. In addition, the model
computes the relief accruing to the various populations
affected by construction site noise as a result of individual
and combined regulations for one or more of the operational
types of equipment.
2. Functional Capabilities: The complete model contains the
following:
1) Time stream
2) Curve
3) Output of impact reduction
4) Distribution of Level Weighted Population (LWP) and
population exposed with respect to 1 decible (1 db)
level of noise day-night average (Ldn) intervals
5) Usage factors
6) Duration of construction site activity
7) Daytime population density shifts
3- Basic Assumptions: There are no basic assumptions.
154
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4. Input and Output; Inputs to the CSM are:
1) Noise levels of construction equipment
2) Equipment usage factors
3) Number of construction sites by the type of site and
by population density category
4) Population density
5) Duration of construction activity by phase of
construction
Outputs to the CSM are:
1) Yearly Ldn
2) Equivalent sound level
3) Population exposed
4) Level Weighted Population (LWP)
5) Sound propagation distance to criteria levels
6) Relative change in impact
5.. System Resource Requirements: CSM is coded in FORTRAN and
is run on an IBM 370/168 mainframe. It uses any 132 position
printer. Manpower needs include a background in engineering.
6. Applications: No outside use is allowed unless designated
by Office of Noise Abatement and Control.
155
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7. Technical Contact
Ken Feith
U.S. Environmental Protection Agency
Office of Noise Abatement and Control
Crystal Mall #2, Room 1101
1921 Jefferson Davis Highway
Arlington, VA 22202
COM 703/557-2710 FTS 557-2710
8. References
No references are available.
156
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NATIONAL ROADWAY TRAFFIC NOISE EXPOSURE MODEL
1 • Model Overview: The model deterministically estimates
extent and severity of noise exposure in the U.S. due to motor
vehicles operating on the national roadway network. The model
is used primarily for assessing the national impact of various
noise control strategies, including source control. The
structure of the model is based on extensive roadway data,
demographic data, and vehicle use and noise data. Time series
projections are also calculated on all time dependent variables
where data are available.
2. Functional Capabilties: The accuracy of the model is
indeterminate since the basic output is noise impacts on a
national scale.
3. Basic Assumptions: The model is based on the equivalent
sound level methodology and calculates various impact metrics.
The model calculates general adverse response impacts (energy
summation) as well as single event impact (independent sources
assumption) . Propagation .losses are on the usual excess
attenuation approach with values assigned by site type. Site
types are hard, soft, or built up urban site types.
4. Input and Output: Inputs are vehicle, roadway, and
population data; years for scenario; vehicle noise levels by
vehicle type, speed, and mode of driving; population
projection; and vehicle fleet forecast.
157
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Outputs of the model are plots of exposure contours, health
and welfare metrics for general adverse responses, single event
responses. Health ana welfare metrics include number of
level-weighted people, relative exposure in percent, noise
impact index, number of people exposed by area and roadway type
in dB bands, LWP by roadway type, and area type in dB bands per
year of time-stream.
5. System Resource Requirements: The model is written in
FORTRAN IV. It is run on an IBM 370 mainframe. Operation
requires a computer programming background.
6. Applications: This model was used for the health and
welfare analyses to examine the effectiveness of selected noise
regulatory options.
7. Technical Contact
Fred Mintz
U.S. Environmental Protection Agency
Standards and Regulations Division
Crystal Mall #2, Room 1105
1921 Jefferson Davis Highway
Arlington, VA 22202
703/557-2710
Re ferences
Model documentation is being prepared
158
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RAILROAD HEALTH AND WELFARE MODEL (RMEA 79N3)
1. Model Overview: The model estimates national noise
exposure due to railroad operations. It calculates the noise
emissions from individual railyard' operations of selected
yards. These railyards are selected statistically to represent
the national distribution of railyard type and classification.
The noise levels are calculated at the boundary of the
railyards and propagated into occupied land use areas. Thus,
the results are noise exposures due to railyard operations.
From the statistically defined sample of railyards, the
resultant exposures are extrapolated to the national level by
yard types and then summed.
2. Functional Capabilities: The model calculates the present
exposure due to railyard operations. It can be used to
calculate the total exposure from one railyard to any number of
railyards, knowing the basic characteristics, size, type, and
activity level of the railyard. The population distribution
around the yard must also be known.
3- Basic Assumptions; Refer to "Description of Railroad
Health and Welfare Model" for basic assumptions of the model.
4. Input and Output: Input to the model consists of:
1) Engine noise from locomotives and switch engines
2) Retarder squeal noise
3) Refrigerator car noise
4) Car-coupling noise
5) Load cell testing, repair facilities and locomotive
service area noise
6) Wheel/rail noise
7) Horns and address systems
8) Regulatory scenarios
159
-------
The output consists of exposure descriptors for any
aggregated set of railyards, total LWP, benefits due to a
regulatory schedule, and the number of people actually exposed
to various levels of noise.
5. System Resource Requirements: RMEA 79N3 is coded in
FORTRAN and is run on an IBM 370.
6. Applications; The model has been used for internal
EPA-Office of Noise Abatement and Control decision-making
purposes.
7. Technical Contact
Basil H. Manns
U.S. EPA
Office of Research and Development
401 M Street, S.W.
Washington, D.C. 20460
COM 202/245-3026 FTS 245-3026
8. Re ferences
1. Description of Railroad Health and Welfare Model
2. User's Manual for the Railroad Health and Welfare Model
3. Programmer's Manual for the Railroad Health and
Welfare Model
160
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A MATHEMATICAL MODEL FOR FAST-SCREENING PROCEDURE
FOR TESTING THE EFFECTS OF POLLUTANTS IN MAMMALS
1. Model Overview: The model offers an "on-line" method for
measuring the effect of pollutants on respiratory efficiency in
mammals, and it applies to any biological system in which the
transport of matter is through well-defined compartments.
Since CO^ excretion from the lungs (a measure of
efficiency of respiratory function) has a well-defined
distribution with time, it can be used for the prediction of
effects by pollutants entering the body system. In this
particular case, the model was derived for the prediction of
the effect of ingested methylmercury chloride (11) on the
14
excretion of COp from the lungs. This method reduces the
observation period from several hours to only a few minutes.
It is suggested that this model or a similar one can be
used for measuring the efficiency of other body functions,
provided that there exists a measurable parameter that has a
well-defined distribution with time.
2. Functional Capabilities: The model is in the form of a
fourth order differential equation requiring a solution of
eight equations. Using mathematical methods of approximation,
the model can be fitted precisely to a two-parameter model of
the form: R = B..t exp (-B~t), where R is the rate of
14
excretion of CO-. In this form, only two measurements at
the beginning of the experiment are required in order to
predict the effects of the pollutant on respiratory function.
161
-------
The measure of effects is the difference of cumulated
14C02 excreted [R (+) - \\k dtj between the control
animals and the exposed animals.
3. Basic Assumptions: It is assumed that a two-pool open
system exists (Shipley and Clark, 1972) in which the blood pool
is the central compartment while the second pool is a
conglomerate of peripherals such as the kidneys, lungs, and
liver. Peripheral pools can communicate only through the
central compartment. If we ignore the dead space in the
respiratory tract, then the lung can be considered as composed
of two classical compartments (Riley, 1965): the gas-exchange
compartment, and the anatomical dead space in the alveoli. The
model is based on the fact that the blood is the vehicle by
which the effect of an ingested toxicant, such as CH^HgCl, is
superimposed on all other peripherals, thus influencing the
14
C09 pattern. Each component is assumed to follow
1 4
first-order kinetics in that the CO- loss rate is taken
1 4
to be proportional to the number of moles of the CO „
within a compartment. Actually, excretion from the blood pool
is not linear (Piotrowski, 1971). But, as we assume, when
steady-state kinetics apply, the blood pool can also be treated
as a classical compartment (Aris, 1966).
4. Input and Output: The model requires only two measurments
.j-jj -> j
of C02 from Gary vibrating reed electrometers in
conjuction with ionization chambers. Output of the model is
1 4
the total cumulative value of C excreted and the percent of
C ex creted .
162
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5. System Resource Requirements: Solutions to the model can
be obtained on a DEC 10 computer, but the model is simple
enough that it can be solved on any other computer system or
calculator.
6. Applications: The model has been used for a series of
experiments by the Health Effects Research Laboratory of the
EPA, and it can be used in biological investigations where
there is a need for a fast screening of pollutants (e.g., heavy
metals or chemical compounds). The effects on respiratory
efficiency or cardiac output can be predicted in a short time,
thus saving time, animals, and personnel. This same approach
can be developed from any other body function with a well
defined time distribution via a compartmental analysis.
7. Technical Contacts
Rumult Iltis and Robert L. Miller
U.S. Environmental Protection Agency
Health Effects Research Laboratory
26 W. St. Clair Street
Cincinnati, OH 45268
FTS 684-7417 COM 513/684-7417
8. Re ferences
Iltis, R. and Miller, R.L. "A Fast-Screening Procedure for
Testing the Effects of Pollutants in Mammals." Journal of
Toxicology and Environmental Health, 3:683-689, 1977-
Itis, R. "Mathematical Model for the Excretion of ^CO^
During Radio Respirornetric Studies." Proceedings of the
Conference on Environmental Modeling and Simulation. U.S.
EPA publication EPA 600/9-76-016, July 1976.
163
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Aris, R. Compartmental Analysis and the Tneory of Residence
Time Distribution in Intercellular Transport, ed . K.B.
Warren. New York: Academic Progess, 1966.
Piotrowski, J. The Application of Metabolic and Excretion
Kinetics to Problems of Industrial Technnology. Washington,
D.C.: Department of Health, Education, and Welfare, 1971-
Riely, R.L. "Gas Exchange Tr ansporation," Physiology and
Biophysics, eds. T.C. Ruch and H.D. Patton. London:
Saunders, 1965.
Shipley, R.A. and Clark, R.E. Tracer Methods for in Vivo
Kinetics. New York: Academic Press, 1972.
164
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PREMIXED ONE-DIMENSIONAL FLAME CODE (PROF)
1. Model Overview: The PROF code can be used to predict
the detailed chemical kinetic combustion and/or pollutant
formation events which occur in a wide variety of experimental
and practical combustion devices. Both steady, free and
confined premixed flames, where gaseous diffusion is important,
can be treated by the code. Also, well-stirred reactor,
plug-flow reactor, and fixed mass time-evolution chemical
kinetic problems, where diffusion is not explicitly treated,
can be modeled by the code. The code was completed in February
1978 by the Acurex Corporation/Energy & Environmental Division
of Mountain View, California. (Previously called: Modeling
Studies in Combustion Aerodynamics/Chemistry.)
2. Functional Capabilities: The PROF code was developed to
accurately model the detailed combustion and pollutant
formation processes occurring in premixed one-dimensional
flames. Previous plug-flow models applied to premixed flame
combustion and pollutant formation processes did not
incorporate axial diffusion in the formulation. Since ignition
processes require upstream diffusion, these plug-flow models
could not be directly applied to flames without making some
gross assumptions as to the upstream ignition zone starting
conditions. In addition, the accuracy of these nondiffusive
models is very poor in the flame zone, where diffusion is
important. Since the PROF code includes axial diffusion,
predictions of combustion and pollutant formation processes can
be achieved in the flame zone as well as downstream of this
zone. The accuracy of these predictions is dependent only on
the adequacy of elementary kinetic reaction and transport
data. Thus, PROF predictions, combined with experimental data,
can provide valuable insights into the complex chemical events
taking place within and downstream of the flame zone.
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3. Basic Assumptions: The key program element in the PROF
code is a stable and reliable kinetic chemistry routine. This
routine can be applied to any chemical system for which kinetic
reaction data are available. Flame and reactor-type problems
are modeled by linking appropriate drive routines to the
general chemistry routine. The flame model includes axial gas
phase diffusion and is, mathematically, a multivariable
boundary value problem. This problem requires a coupled grid
solution procedure for all variables. This grid problem is
solved in PROF by using a predictor-linearized corrector
iterative matrix procedure. The reactor type models do not
have explicit diffusion terms. These models are initial value
problems solved by simple time or space matching in the PROF
code.
U. Input and Output: Input to the code include: solution
type (e.g., well-stirred reactor), names and number of chemical
species, thermochemical data for chemical species, chemical
reactions and associated forward rate constants, third body
efficiencies, initial mole fractions, temperature, pressure,
and flow rates .
The PROF code output gives complete summary information or
flame, well-stirred and plug-flow reactor, and time-evolution
chemical kinetic problems. If called for, it can also provide
information on intermediate iterations and chemistry routine
solutions. For each iteration during a flame solution the code
always prints out a line of output that gives the flame speed
parameters, its error, the maximum error in concentration, and
the constraint (i.e., damping) applied to the corrector step
correction vector. In addition, all of the input data is
output along with the title of the run.
5- System Resource Requirements: The PROF is written in
FORTRAN and can be run on an Univac 1108, IBM 360 or CDC 6600
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mainframes. Disk storage requirements include 15,355 words of
decimal storage for the model and 31,978 words of decimal
storage for data. Additional and peripheral equipment required
includes magnetic tape or disk storage, a 132 line printer and
a card reader/punch or tape/disk (input).
6. Applications: The PROF code has been used widely by Acurex
and the Environmental Protection Agency to predict the
pollutant formations that occur in a wide variety of
experimental and practical combustion devices. The code has
been applied to a variety of gas turbine, furnace and catalytic
combustion, and pollutant formation problems. The PROF code
has also been used to treat the reaction of a fixed mass of gas
in time as the pressure and temperature change. Chemical
evolution inside internal combustion engines, combustion bombs,
and other time-dependent combustion systems have been predicted
by this option. Of course, the option assumes uniformly mixed
and reacting mixtures within the system. Therefore, applying
this option to spatially non-uniform systems represents only an
approximation modeling of the system.
7. Technical Contact
W. Steve Lanier
U.S. Environmental Protection Agency
Industrial Environmental Research Laboratory
Research Triangle Park, N.C. 27711
COM 919/541-2432 FTS 629-2432
8. References
Kendall, R.M. and Kelly, J.T. Premixed One-Dimensional
Flame (PROF) Code User's Manual, EPA-600/7-78-172a, August
1978.
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WASTE RESOURCES ALLOCATION PROGRAM (WRAP)
1. Model Overview: WRAP is a waste resources allocation
program which is coded in FORTRAN. This modeling program consists
of a series of equations which consider the sources of solid waste
generation, a set of sites, and processes to be considered at those
sites, as well as various site and process capacity constraints.
WRAP sorts out the various allocation options specified by the user
and indicates a preferred allocation solution which is the minimum
cost plan that meets all the user-supplied constraints. Use of
the model enables users to study and analyze the costs and
implications of all available alternatives under consideration.
WRAP has been used for decisions regarding solid waste management
in Massachusetts, in Illinois, and in St. Louis, Missouri.
2. Functional Capabilities: WRAP is an optimizing model which
selects, sizes, and locates solid waste processing and disposal
facilities. Costs for the solid waste systems are determined by a
specialized fixed charge linear programming algorithm. There are
two operational modes available: static and dynamic. The dynamic
operating mode allows for two to four planning periods. Planning
periods are expressed in years, and, in the dynamic mode, are
consecutive over the total planning period.
The model consists of a series of equations which consider
the sources of solid waste generation, a set of sites, and
processes to be considered at those sites, as well as various site
and process capacity constraints. The processes can be transfer
stations, resource recovery processes (including the extraction of
recoverable resources to be marketed), secondary processes (which
receive the residue of primary processes as input) and various
disposal processes. WRAP further considers many transportation
route alternatives from sources of waste generation to sites, and
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from sites to sites, with due allowance for site traffic
constraints.
3. Basic Assumptions: WRAP is a fixed-charge linear programming
model, using as the optimizer an algorithm developed by Dr. Warren
Walker of Cornell University in Ithaca, New York. The fixed-charge
capability of the model permits the representation of economies of
scale in process costs. Since the model is cost-minimizing, it
will seek out the lowest cost segment at any level of tonnage.
Thus the capability of treating cost in two parameters (fixed and
variable, or intercept and slope) permits the model to represent
economies of scale at any level of accuracy desired. In the
actual model applications, three segment representations have
been used for nearly all processes.
4. Input and Output: Program execution data is input from a
sequential data set, normally the card reader. There are eight
types of inputs to be prepared. Five of the input types are
required by every program. Three input types are optional. All
data for a particular input type is input sequentially. There is
no special ordering required within an input type. However, a
special ordering is required for the types of user-supplied inputs.
The required input sequence follows:
1) Control Records: four control record inputs are possible.
Two records are always required and two records are
optional.
2) Source Identification Records: an identification record
for each original solid waste source must be supplied.
Each record must have a unique source identification
code as well as the record code.
3) Site Identification Records: there must be one record
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for each intermediate and ultimate site. All site
records are input in one group.
4) Process Identification Records: there are five types
of records possible for each unique process. Though the
processes need not be in a particular order, the records
associated with each process must be in a specific
sequence.
5) Site Process Identification Records: there is one
record for each process at each site, and there may be a
maximum of 125 records. These records do not need to be
in any particular order.
6) Transportation Activity Records: these records are
optional, but generally part of the input file. Each
record must have an activity type code which describes
the transportation links.
7) Truck Constraint Identification Records: these
records are optional and are required only when the
user's control data indicates that sites are to be subject
to truck contraints. There must be one record for each
truck constraint, and each record may have a maximum of
three site identification codes.
8) Starting Basis Records: these records are optional
and are not considered part of the problem identification
records. These records are input only when the user has
indicated their availability. The number of records is
determined by the number of rows (equations) in the matrix.
There are six types of output generated in the WRAP
program: (1) optional debugging variables and tables, (2) error
messages and codes, (3) input data reports, (4) punched transporta-
tion and matrix decks, (5) intermediate phase solution tables, and
(6) final solution punchout and reports.
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5. System Resource Requirements: The WRAP program does not
require any specialized computer environment. The program,
however, is very large and does require special handling at
execution time. Memory requirements, for the 90 [equations] x
360 (coefficients) matrix WRAP model are 270K bytes of storage.
This requirement must be modified' if the model dimensions are
altered to accomodate a larger matrix which uses more memory or to
process a smaller matrix which uses less computer memory. The
input-output file requirements are: a card reader, a card punch,
at least one pr-inter, and a disk with storage for at least eight
files. An environmental engineer or programmer familiar with
computer modeling is needed to fulfill the manpower requirements
for the WRAP program.
6. Applications: WRAP has. been used in several locations by
decision-makers who are considering regional solid waste management,
In Massachusetts, the model was used to identify the most efficient
regional system design for that state's first regional resource
recovery system. WRAP was used in St. Louis, Missouri, to
determine the advantages of community participation in a proposed
regional solid waste management plan. In suburban Chicago, WRAP
was used to evaluate the economic feasibility of various solid
waste management and recovery options facing the decision-makers.
7. Technical Contact
Frank McAlister (WH-563)
U.S. Environmental Protection Agency
Office of Solid Waste
401 M Street, S.W.
Washington, B.C. 20460
FTS: 382-2223 COM: 202/382-2223
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References
Herman, Edward B., et al. "WRAP: A Model for Regional Solid
Waste Management Planning User's Guide." Publication No.
EPA/530/SW-574, prepared by the Mitre Corporation, Bedford,
Massachusetts, under Contract No. 68-01-2976, February 1977.
Hensey, Verniece, et al. "WRAP: A Model for Regional Solid
Waste Management Planning Programmer's Manual."
Publication No. EPA/530/SW-573, prepared by the Mitre
Corporation, Bedford, Massachusetts, under Contract No.
68-01 2976, February 1977.
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ATMOSPHERIC DISPERSION OF RADIONUCLIDES (AIRDOS-EPA)
1. Model Overview: AIRDOS-EPA is a model for estimating
annual intakes and exposures from the atmospheric release of
radionuclides. The purpose of the program is to provide these
quantities as input to a companion program (DARTAB) to assess
the individual or collective doses and risks associated with
chronic releases of radionuclides. The model is a revision of
AIRDOS-II (Mo77). Atmospheric dispersion, wet and dry
deposition, and food pathway models are included. Provision is
made for radionuclide chain ingrowth and decay as well as
environmental removal in the terrestrial portion of the model.
2. Functional Capabilities: AIRDOS-EPA calculates
radionuclide concentrations in air and on the ground surface
and intake rates for inhalation and ingestion. Calculations
can be performed for an individual at the grid locations or for
a population distributed over the grid. Releases of up to 36
radionuclides from as many as 6 (point or area) sources can be
considered. All sources are located at the origin of the
calculational grid. Up to 320 or 400 locations may be
described by the circular and square grid options
respectively. All quantities calculated are long-term
averages. Source terms are specified in curies/year. Air and
ground surface concentrations are in curies/cubic meter and
curies/square meter respectively. Ingestion and inhalation
intake rates are in pico curies/year. The concentration of the
short-lived progeny of radon-222 is in working levels.
Concentration and intake rate calculations are performed after
a user specified period of operation at the specified release
rates.
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3. Basic Assumptions: Dispersion is calculated by a
straight line, long-term average, Gaussian model. Momentum or
ouc/ant plane rise can oe calculated or assigned a value for
each staoility class. A dry deposition velocity nnd a
precipitation scavenging rate can pe specified for each
radionuclide. A source depletion model accounts for plume
Depletion oue to deposition. The terrestrial moael includes
environmental removal as well as a radiological decay. The
food pathway moael (vegetable, meat, ana milk) is consistent
witn that in Reg. Guide 1.109 (NRC77). Ingrowth for
radionuclioe chains subsequent to deposition can be calculated
by providing a set of ingrowth factors. Air concentrations of
short-lived radon-222 progeny are calculated in working level
units for a specified value of equilibrium. Output for DARTAB
is in an unformatted file. The basic calculational methodology
is from AIRDOS-II (Mo77) with modifications for area sources,
radon progeny concentrations, terrestrial ingrowth for
radionuclide chains and an updated food pathway model.
- Input and Output: Model inputs include: grid size
alues; wind data; stack or area source data; radionuclide
release rates, deposition ana settling velocities, scavenging
rates, and decay constants; arrays of meat animals, dairy
cattle, crop areas, ana population data for each grid location;
the fraction of each food category consumed from outside the
assessment area; the fraction of that consumed food which is
produced within the assessment area which is in the grid
location; ingestion; agricultural model parameters; ingestion
rates by food category; inhalation rate; radionuclide decay and
environmental removal rate constants of soil to vegetation;
intake to meat and intake to milk conversion factors;
raoionuclide chain ingrowth factors; clearance class; and
gastro intestinal aosorption fraction.
v
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Printed outputs available include: predicted air
concentrations; dry and wet deposition rates for each location
and radionuclide; ground-level Chi/Q for each location by
radionuclide; agricultural and population data for each grid
location; list of nuclide independent variables; list of
computer totals of population, food production, and food
consumption for the assessment area; a list of nuclide
dependent data for each nuclide; individual or population
weighted concentration and intake rates for each location by
nuclide, radon-222 progency concentration for each location;
and dose summaries (supplementary - not used for
AIRDOS-EPA/DART.AB assessments). An unformatted file is created
of concentration and intake for each location to be used with
DARTAB for a dose and risk assessment.
5. Computational System Requirements:
This system may be run on an IBM 360 or 370 series or
an equivalent. It is coded in FORTRAN IV (H extended)
6. Applications: This model provides a means for the
radiological assessment of radionuclides released to the
atmosphere. It has been used by EPA and the Oak Ridge National
Laboratory for this purpose. The model is generally used in
conjunction with DARTAB for dose and risk assessments.
7. Contact:
David Fields
Oak Ridge National Laboratory
P.O. Box X
Oak Ridge, TN 37830
COM: (615) 576-2131
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8. References:
Begovich, C.L., E.G. Schlatter, S.Y. Ohr, K.R.
Eckerman, "DARTAB: A Program to Combine Airborne
Radionuclide Environmental Exposure Data with
Dosimetric and Health Effects Data to Generate
Tabulations of Predicted Impacts." ORNL-5692, 1980.
Mo77 Moore, R.E. "The AIRDOS-II Computer Code for
Estimating Radiation Dose to Man from Radio-nuclides in
Areas Surrounding Nuclear Facilities." ORNL-5245,
1977.
Mo79 Moore, R.E. "AIRDOS-EPA: A Computerized
Methodology for Estimating Environmental
Concentrations and Dose to Man from Airborne Releases
of Radionuclides." EPA 520/1-79-009, ORNL-5532, 1979.
NRC77 U.S. Nuclear Regulatory Commission, Regulatory
Guide 1.109, Calculation of Annual Doses to Man from
Routine Releases of Reactor Effluents for the Purpose
of Evaluating Compliance with 10 CFR Part 50, Appendix
_!. (Revision 1). Office of Standards Development,
1977.
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GREAT LAKES DOSE/CONCENTRATION (GLA-1)
1. Model Overview: This model uses a simplified
representation of the Great Lakes along with the time dependent
dose equations of International Commission on Radiological
Protection (ICRP) IDA to predict ambient lake concentrations on
the dose rates resulting from chronic ingestion of
radioactivity in lake waters. The program is also applicable
to other pollutants.
2. Functional Capabilities: The model comprises a simplified
physical representation of the Great Lakes chain which
considers only total volume of each lake, assumes annual mixing
but allows for changes in dilution volume required by
thermoclines, and corrects for sedimentation and equilibration
where required. Dose rates due to chronic ingestion of 2.2
liters of water per day are calculated according to ICRP 10 and
ICRP 10A.
3. Basic Assumptions: GLA-1 assumes constant total volume,
constant outflow and inflow, and constant surface area. It
assumes that thermocline exists for 1/2 year at a depth of 17
meters and that inflow and outflow are from the epilimnion
during this period but that perfect mixing occurs during the
balance of the year. Concentration equations are convoluted
with the ICRP equation for organ burden and solved in closed
form. Only six isotopes, Tritium (H-3), Cobalt-60 (Co-60),
Strontium-90 (Sr-90), Cesium-134 (Cs-134), and Cesium-137
(Cs-137) are treated at present.
4. Input and Output: Major inputs to the GLA-1 include source
terms, time results desired, initial concentrations, and lake
volumes and outflows. As currently programmed, individual
sources discharging into lakes can be used or parameters for
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sets of nuclear power plants having differing types of rad
waste systems may be substituted.
Outputs are lake concentrations for the five isotopes at
each time specified along with dose rate and dose equivalents.
Concentrations as a function of time for other pollutants can
be found without altering the program. Degradable pollutants
can be treated like radionuclides by selection of an
appropriate decay constant.
5. System Resource Requirements: All programs are written in
FORTRAN for an IBM 360/370 system. The model requires 150K
bytes of core memory. A oackground in computer programming is
useful in using this model.
6. Applications: GLA-1 has been used to predict concen-
trations and doses from Great Lakes waters due to nuclear fuel
cycle activities projected through the year 2050.
7. Technical Contact
R. E. Sullivan
U.S. Environmental Protection Agency
Criteria and Standards Division
Crystal Mall #2
1921 Jefferson Davis Highway
Arlington, VA 22202
COM 703/557-9380 FTS 557-9380
8. Re ferences
Sullivan, R.E. and Ellett, W.H. The Effect of Nuclear Power
Generation on Water Quality in the Great Lakes.
ORP/CSD-77-5, 1977.
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HIGH LEVEL RADIOACTIVE WASTE REPOSITORY RISK MODEL (REPRISK)
1- Model Overview: This computer code calculates the expected
genetic and somatic health effects at a generic high level
radioactive waste geologic repository. The code calculates
radionuclide releases to air, land surface, and rivers or lakes
from a repository as a result of expected events and accident
events. The accidents are human intrusion (drilling), breccia
pipes, faults, meteorites and volcanoes. The expected events
are shaft and borehole leakage and bulk rock transport. The
releases result either from destruction of waste packages or
disturbance of the contaminated repository backfilled tunnels.
The concentration of radioactivity in the backfilled tunnels
depends on availability of water in the tunnels, the
dissolution of radionuclides (solubility), and the
characteristics of the waste matrix and canisters. Movement of
contaminated water in the tunnels is either directly to land
surface or to aquifers overlying the repository. Movement of
the radioactivity in the aquifer is governed by groundwater
flow and retardation of radionuclides.
2. Functional Capabilities: The model calculates the total
release of radionuclides over a time period and converts these
releases to health effects. To calculate releases the flow
rate of radioactivity in curies per year is integrated either
analytically or numerically over the time period of interest.
The numerical integrator is 90% accurate. Flow in the aquifer
is 1-dimensional nondispersive. The tunnel mixing volume is
assumed homogeneous. Parameters are constant over all time,
but flow rates of water from the repository are time
dependent. The health effects are combined with event
probabilities to calculate probability consequence curves and
overall risk .
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3. Basic Assumptions:
1) One-dimensional non-dispersive aquifer
2) Homogeneous mixing volumes whose radionuclide
concentrations can be described by first order differential
equations.
3) Input parameters are con-stant over all time.
4) Probabilities of accident events are constant over
various time bands. A reasonable number of time bands can be
input .
4. Input and Output: Representative input data will be
contained in the two EPA documents listed in the references
section.
Two types of output are available for somatic health
effects, genetic health effects, or release limit ratios:
1) Integrated risk or release limit ratios and 2) Probability
consequence curves.
5- System Resource Requirements: REPRISK can be run on an IBM
370. The model is currently stored on two magnetic tapes and
requires a line printer for output.
6. Applications: EPA/ORP is using the code to conservatively
evaluate 5 generic high level waste repositories - bedded salt,
granite, shale, and basalt. This effort supports the EPA/ORP
EIS and standard for high level waste repositories.
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7. Technical Contact
Daniel J. Egan
U.S. Environmental Protection Agency
Criteria and Standards Division
Crystal Mall #2
1921 Jefferson Davis Highway
Arlington, Va. 22202
COM 703/557-8610 FTS 557-8610
8. References
User's Manual to be published.
Smith, C. B.; Egan, D. J.; Williams, W. A.; Gruhlke, J. M.;
Hung, C-Y; and Serini, B. Population Risk from Disposal of
High-Level Radioactive Wastes in Geologic Repositories.
EPA/520/3-80-006.
Smith, J.M., Fowler, T.W., and Goldin, A.S. Environmental
Pathway Models for Evaluating Population Risks from
Disposal of High-Level Radioactive Wastes in Geologic
Repositories. EPA 520/5-80-002.
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MAXIMUM INDIVIDUAL DOSE MODEL (MAXDOSE)
1. Model Overview: The Maxdose code is a Gaussian dispersion
model that calculates accidental releases from a nuclear waste
repository. Both geological and human events are modelled.
Each event produces a given set of dose rates at different
times and distances. A second set of tables estimates
contaminated areas and individual risk. Both leaching and
dissolution remove wastes from the matrix into the accessible
environment. The releases are used to calculate the dose table
2. Functional Capabilities: The code can calculate the dose
for up to 10 distances, 13 dose times, and 20 nuclides per
run. All transport models are 2-d imensional, yielding the
highest dose along the centerline. Error on numerical
integration is less than 10% using cautious adaptive Romberg
extrapolat ion .
3- Basic Assumptions: For atmospheric release, MAXDOSE uses
AIRDOSE equations and no direction is specified for the wind.
Water releases are calculated along the centerline, where the
maximum concentration occurs. Area calculations assume
parabolic distribution for contaminants in the groundwater and
a circular distribution for air release.
*J- Input and Output: Input to the model includes initial
inventories of waste, their halflives, retardation factors, 3
sets of dose conversion factors, solubilities, bioaccumulation
factors, permeability and its rate of change, numerical
constants for approximating the gradient, the canister life,
leach rate, groundwater velocity, size of tank, porosities,
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dose times, and distances. The boreholes and the flow through
the boreholes are modeled .
Output consists of an echo check of the input data in a
standard format. A table of dose rates at various dose times
and distances, and areas contaminated by a given event are
presented .
5- System Resource Requirements: MAXDOSE is coded in FORTRAN
and is run on an IBM 360 mainframe. A subroutine of this model
calls a routine, TIMER, that is coded in machine language.
Engineering and programming backgrounds, and knowledge of-the
job control language of the International Mathematics and
Statistics Library (IMSL) are useful.
6. Applications: Two routines, CTIME and DCADRE, are not in
the code. They are in the linkage step of the job control
language. DCADRE is a numerical integration from the
International Mathematics and Statistics Library. CTIME
returns the time of day and date the job is run. The code has
been used to estimate risks to individual and in IDAR
(Individual Dose Assessment Report).
7. Technical Contact
Barry L. Serini
U.S. Environmental Protection Agency
Office of Air Noise and Radiation
Criteria Standards Division
Crystal Mall #2
1921 Jefferson Davis Highway
Arlington, VA_ 22202
8. References
MAXDOSE - EPA User" s Manual
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NONIONI ZING RADIATION MODELS
1. Model Overview: Certain mathematical models have been
developed to assist researchers involved in the biological effects
of nonionizing electromagnetic energy in the RF-microwave frequency
spectrum. Such models have provided some insight into the inter-
section of RF energy with biologica-1 objects (e.g., experimental
animals, human subjects, etc.) and have provided significant data
on average and localized energy absorption rates in such objects.
These data enable researchers to predict approximately the thermal
load to which an animal is being subjected during experimental
irradiation and to extrapolate research findings to humans. This
summary primarily discusses modeling work performed in-house within
EPA or under EPA grants; similar work sponsored by other government
agencies has been recently summarized in the form of a dosimetry
handbook. XSECT and ECOMP programs were developed at the RAND
Corporation, Santa Monica, Calif.; EPA obtained copies of these
in 1972. The variations on ECOMP were developed in-house at
Research Triangle Park, N.C. EBCO was developed under EPA grant
at Oregon State University.
2. Functional Capabilities: The EPA models examine the inter
action between a plane electromagnetic wave and biological objects
composed of dissipative (lossy) dielectric materials which possess
a simple spherical or prolate spheriodal shape. The model employing
a spherical object will be discussed first.
The spherical model actually consists of a core of brain-
like material surround by thin concentric layers of other tissues
including cerebro-spinal fluid, dura, bone, fat and skin. The
model is a crude approximation to isolated animal and human heads.
Because of the chosen spherical boundaries of the dielectric object,
the mathematical solution to the electro-magnetic problem is readily
formulated using the Mie theory. The basic solution involves
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expanding the incident and secondary (scattered and internally
induced) fields into vector spherical harmonics involving spherical
Bessel functions of complex argument. The problem has been pro-
grammed in FORTRAN for computational solution . on both IBM 360/50
and UNIVAC 1110 machines. Two basic programs have been written for
this problem: XSECT computes the absorption efficiency (electro-
magnetic absorption cross section divided by geometric cross section)
of a given sized sphere over a given range of incident frequencies,
as well as the overall specific absorption rate (SAR) or dose rate,
averaged throughout the model. The other program, ECOMP, computes
the local internal electric field strength as well as the local
SAR value at various defined locations within the sphere, for a
given incident frequency. Two additional programs both employing
ECOMP have been developed: PLOT is a graphical routine giving
plots of local SAR against radial distance for different azi-
muthal angles. ECOMX searches for the peak internal SAR value
over a given range of RF frequencies for a given sized sphere.
Convergence tests are employed in these programs in order to
ensure that there is rapid convergence of the infinite series
used in the Bessel function expressions. Convergence is generally
obtained within 12-15 terms. Valid solutions are obtainable for
all combinations of sphere size and wave length including the
important resonant region where energy absorption reaches a peak.
The prolate spheriodal model is composed of homogeneous
muscle-like dielectric only and simlulates large-scale objects
such as humans or primates. Solutions to the problem are based
on the extended boundary condition method (EBCM) which has
been found to be the most successful of the various methods avail
able for dealing with lossy prolate spheroid objects. However,
for spheroids having high eccentricity ratios equivalent to that
of standard man (a/b = 6.3), the method breaks down in the resonant
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frequency region owing to problems of ill-conditioned matrices
which the computer cannot handle. The computer program, EBCO,
is written in FORTRAN and has been run on a Control Data CYBER
70, Model 73 machine.
3. Basic Assumptions'. The electromagnetic radiation is
assumed to be a simple plan wave, as exists in the far-field of
an antenna. The biological objects are of simple spherical or
prolate spheroid shape and are composed of dissipative dielectric
materials that are linear, homogeneous and isotropic. The heat
transfer or conduction aspects of the problem have not been
considered.
4 . Input and Output'.
a. Inputs for sphere model.
1) Radius of core.
2) Number of outer layers and layer thicknesses.
3) Dielectric data (relative permittivity and
conductivity) of all tissue equivalent materials
as a function of frequency.
4) Frequency or frequency range of incident wave.
5) Internal spherical co-ordinates, (/z. ,9,0) at
which internal fields are to be computed (or
range of samej.
6) Maximum allowable number of terms in series
and tolerance for convergence of series.
b. Inputs for prolate spheroid model.
1) Major and minor axis values.
2) Dielectric data of muscle-equivalent material
as a function of frequency.
3) Frequency range of incident wave.
4) Orientation of major axis of spheriod with respect
to E-field vector.
5) Angle of incident wave with respect to major axis.
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c. Outputs for both models.
1) Absorption cross section and absorption efficiency
2) Total absorbed power.
3) Average SAR.
d. Outputs for sphere model only.
1) Local internal E-field values (AJ , Q ,j> components).
2) Local SAR or dose rate value.
5. System Resource Requirements: All programs are coded in
FORTRAN IV or V and require a high-speed computer with approximately
75-80 thousand word capacity. Heavy use is made of double
precision arithmetic. Data preparation requires one to two hours,
while output analysis may require a day or two, depending on the
form of data presentation desired, A person with some aacjcground
in electro-magnetic theory and scientific programming is help.ful,
6. Applications: These models provide insight into the effect
of nonionizing electro-magnetic energy in the RF-microwave
frequency, and allow researchers to predict approximately the
thermal load to which an animal is being subjected to during
experimental irradiation. These results can be extrapolated and
applied to humans.
7. Technical Contact
Dr. Claude M. Weil
Experimental Biology Division (MD-74)
Health Effects Research Laboratory
Environmental Protection Agency
Research Triangle Park
N.C. 27711
FTS 629-7740 COM 919/541-7740
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8. References
Durney, C.H. et. al., "Radio Frequency Radiation Dpsimetry
Handbook, Second Edition " Report SAM-TR-78-ZZ, published
by USAF School of Aerospace Medicine, Brooks Air Force
Base, Texas 78235, May 1978.
Weil, C.M., "Absorption Characteristics of Multilayered
Sphere Models Exposed to UHF/Microwave Radiation", IEEE
Trans, on Biomed. Eng., BME-22, pp. 468-476, November 1975.
Tripathi, V.K. andLee, H., Electromagnetic Power Absorption
by Prolate Spheroid Models of Man and Animals at and near
Resonance, Final Report under EPA Grant No. R-804697-01 1,
December 1978.
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PLUTONIUM AIR INHALATION DOSE (PAID)
1. Model Overview: The model is designed to calculate dose
rates and doses resulting from the acute or chronic lifetime
inhalation or ingestion of transuranic radioisotopes.
2. Functional Capabilities: The model is designed for
long-lived parents or daughters. Only one daughter is
permitted. Gastro-intestinal tract (GIT) and blood transfer
fractions are used, but no time delay for either is included.
3. Basic Assumptions: The model is, basically, a combination
of the International Commission on Radiological Protection
(ICRP) lung model and a standard organ model using exponential
retention functions. The resulting solutions are analytical,
require no numerical integration, and are obtained rapidly and
exactly for the times desired.
4. Input and Output: Input is the acute or chronic intake,
deposition fractions for the lung compartments, the mass for
post blood organs and physical and biological halflives,
transfer fractions, and average energies for the parent and
daughter isotopes.
Output is for all input times, the dose rates and doses for
each lung compartment, including lymph nodes, and for the
reference organs. The values for the trachiobronchial
compartment due to clearance from the pulmonary are also given
explicitly.
5. System Resource Requirements: PAID is coded in FORTRAN and
is run on an IBM 360/370 system. Any standard model printer
can be used. Operation requires a computer programmer.
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6. Applications: The model has been used to calculate lung
and organ doses for transuranic and other radioisotopes. It is
used primarily by ORP/EPA.
7. Technical Contact
R. E. Sullivan
U.S. Environmental Protection Agency
Criteria and Standards Division
Crystal Mall #2
1921 Jefferson Davis Highway
Arlington, VA 22202
703/557-9380
8. Re ferences
Sullivan, R.E. PAID; A Code for Calculating Organ Doses
Due to the Inhalation and Ingestion of Radioactive
Aerosols. ORP/CSD-77-4, 1977.
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RADIONUCLIDE DOSE RATE/RISK (RADRISK)
1. Model Overview: RADRISK is a model designed to estimate
the health risk due to inhalation or ingestion of radionuclides
for arbitrary exposure periods. The end result of the system
is a set of values relating fatal cancers and genetically
significant radiation doses to a unit intake of radionuclides.
The model is a greatly revised combination of two previously
existing programs, INREM II and CAIRO. The health risk from
external exposures is also estimated by1 the CAIRO model using
dose rates from a separate model, DOSFACTER.
2. Functional Capabilities: RADRISK calculates the radiation
dose rates and estimated fatal cancers resulting from the
chronic inhalation or ingestion of one pico curie/yr of
radioisotope. All radioactive decay products of the parent
isotope are also considered. Dose rates are calculated over a
110-year period for eighteen organs. Cross irradiation dose
rates are incorporated using Monte Carlo results from the
S-factor model. These dose rates are then combined in a life
table, using U.S. population mortality rates, to compensate for
competing risks in estimating radiation health effects.
External dose rates, taken from DOSFACTER, are treated
similarly in the life table analysis. An integration of the
gonadal dose rate is also performed to obtain the 30-year
genetically significant dose. Input units are pico curies/yr,
pico curies, or squared centimeter pico curies/cubed
centimeter. Dose rates are given in mrad/yr for both high-LET
and low-LET radiation and the life table returns estimated
premature deaths to a cohort of 100,000 for each cancer.
3. Basic Assumptions: The dose rate calculational model
incorporates the International Commission on Radiological
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Protection (ICRP) lung and gastro-intestinal tract models; and
uses exponential retention functions and standard metabolic
parameters for the post blood organs. Nonexponential retention
functions are fitted, by means of an auxiliary program, to an
exponential series of up to five terms. The life table
calculation is based on a cohort of 100,000 persons whose
mortality rate is that of the U.S. 1969-1971 population. The
additional risk, either absolute- or relative, from radiation is
then followed from birth (0 years) to death (110 years) of the
cohort. At present, no age dependence is allowed in the dose
rate calculation (a reference man is assumed) although the life
table dose rate or risk may be age adjusted.
4. Input and Output: Inputs required for the dose rate
portion of the code include the physical (half life, energy)
and metabolic (transfer fractions, retention functions) data
for the parent and each daughter product. In addition, a
library of cross-irradiation terms must be supplied. The life
table calculation, in addition to the time dependent dose,
requires specification of the risk, including latency and
plateau periods, associated with the radiation. For relative
risk cases, mortality rates must be supplied for each cancer to
be considered.
Outputs comprise the total dose rate, for both high-LET and
low-LET radiation, to each of 18 organs at the midpoint of
specified time intervals. Options are available for printing
out each daughter contribution as well as the cross-irradiation
terms. The integrated genetically significant dose to the
gonads, along with an average value, is also output. The life
table calculation outputs the number of premature deaths, the
average years of life lost for each, and the decrease in
overall life expectancy for each cancer type as well as the
totals.
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5. System Resource Requirements: All programs are written in
FORTRAN for an IBM 360/370 system. The model requires 500K
bytes of core memory. Any standard 132 column printer can be
used. Operation requires a background in programming,
engineering, and health/medical physics.
6. Applications: This model has been used to produce a set of
dose/risk values for a unit intake, or unit exposure, of most
common radioisotopes. It has been run extensively at Oak Ridge
National Laboratory. The major external link is S-Factor
output.
7. Technical Contact
R. E. Sullivan
U.S. Environmental Protection Agency
Criteria and Standards Division
Crystal Mall #2
1921 Jefferson Davis Highway
Arlington, VA 22202
COM 703/557-9380 FTS 557-9380
8. Re ferences
RADRISK (to be published)
Dunning, D.E., Jr., e_t a_l. 5-FACTOR: A Computer Code for
Calculating Dose Equivalent to a Target Organ per
Microcurie-Day Residence of a Radionuclide in a Source
Organ, 1977.
Cook, J.R., et_ aJL_. CAIRO: A Computer Code for Cohort
Analysis of Increased Risks of Death. EPA 520/5-78-012,
1978.
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Kocher, D.C. DOSFACTER; Dose-Rate Conversion Factors for
External Exposure to Photon and Electron Radiation from
Radionuclides Occurring in Routine Releases from Nuclear
Fuel Facilities. ORNL/NUREG/TM-283, 1979.
Killough, G.G., et_ al_. INREM-II: A Computer
Implementation of Recent Models for Estimating the Dose
Equivalent to Organs of Man from an Inhaled or Ingested
Radionuclide. ORNL/NUREG/TM-84, 1978.
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A COMPUTER PROGRAM FOR THE RISK ASSESSMENT
OF TOXIC SUBSTANCES (MULTI80G)
1- Model Overview: This program was developed for generating
low-dose carcinogenic risk assessments of toxic substances
based on the generalized multi-hit and one-hit dose-response
functions applied to animal response data derived from lifetime
feeding studies.
2. Functional Capabilities: The limitations of the model are:
1) There must be at least two positive (non-zero) dose
level.
2) There may be no more than 14 positive dose levels.
3) The average run time may vary for the same job by as
much as +20%.
3- Basic Assumptions: This model is based on a gamma
distribution. Individuals interested in the underlying
assumptions are referred to the technical contact for copies of
theoretical papers underlying the development of this model.
4. Input and Output: The inputs to the model are:
1) The number of positive (non-zero) dose levels in the
bioassay
2) Magnitude of each dose level
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3) Total numbers of animals on test at each dose level
4) Total numbers of animals with tumor types of interest at
each dose level
The principal outputs of interest from the model are:
1 ) A chi-square goodness of fit test
2) An estimate of the number of "hits" required to initiate
a carcinogenic response
3) Point estimates and 90, 95, 97-5, 99, and 99-5 lower
confidence limits on "virtually safe dose" for risks for
1 in 10 to 1 in 100,000,000
5. System Resource Requirements: This model is coded in
FORTRAN G and is run on an IBM 370/168 mainframe. It requires
less than 300k bytes of core memory for execution. It uses any
132 position line printer for output.
6. Applications: The model, in conjucntion with a variety of
other models, is used to estimate lifetime carcinogenic risks
associated with various levels of suspected human carcinogens.
The estimates are then included in risk assessments supporting
regulatory actions under Section 5 and 6 of the Toxic
Substances Control Act.
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7. Technical Contact
Gary Grindstaff
U.S. Environmental Protection Agency
Health and Environmental Review Division
401 M Street, S.W.
Washington, D.C. 20460
COM 202/382-3459 FTS 382-3459
8. References
Rai, K. and Van Ryzin, J. MULTI80: A Computer Program for
Risk Assessment of Toxic Substances. Technical Report No.
N-1512-NIEHS, Rand Corporation, Santa Monica, 1980.
Van Ryzin, J. and Rai, K. The Use of Quantal Response Data
to Make Predictions. In The Scientific Basis of Toxicity
Assessment. H. Witschi (ed.) Elsevier/North Holland, New
York, 273-290, 1980.
Rai, K. and Van Ryzin, J. A Generalized Multihit Dose
Response Model for Low-dose Extrapolation. Biometrics 36
(to appear), 1980).
A user's manual is available from the technical contact.
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ENVIRONMENTAL PARTITIONING MODEL (ENPART)
1. Model Overview: This generalized partitioning model
integrates information about a chemical's production, use, and
disposal with laboratory data describing its physiochemical
properties in order to provide insight into the dominant
processes responsible for that substance's transport and
degradation in the environment. It is intended to be used in
early stages of chemical risk assessments to identify
environmental media through which exposure may occur, and to
provide a guide for further assessment by indicating the media
with the highest exposure potential. The methodology
explicitly treats transfer between and transformation within
environmental media, and it ranks media as to their exposure
potential and transformation processes as to the relative
importance in controlling the level of exposure. The analysis
can also be applied in the design of a cost-effective testing
approach to yield data on interrelated transport and
transformation processes which, when considered together,
present a clear picture of a substance's environmental fate.
2- Functional Capabilities: The equilibrium partitioning
segment of the model combines information on chemical releases
and intermedia transport processes to determine the
partitioning of the chemical between air, water, soil,
sediment, and biota. The dynamic partitioning segment sums the
first-order degradation rates to yield the overall
transformation half-life for each medium. This is then
compared with the previously calculated intermedia transfer
half life to determine if the chemical is degraded before it
can be transferred from the media to which it is released.
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3. Basic Assumptions; The approach used in the equilibrium
partitioning analysis assumes that each medium compartment is
homogeneously well mixed, and that all compartments are in
equilibrium. The dynamic partitioning portion of the model
assumes that inter-compartmental transfer is at a steady state
with the transformation processes such as photolysis,
hydrolysis, oxidation, and biodegradation. The concentration
ratios are determined using fugacity constants describing
tendencies to transfer between compartments which are valid for
use at low environmental concentrations.
4- Input and Output; The equilibrium partitioning of the
model requires data on the substance's vapor pressure, water
solubility, soil and/or sediment adsorption coefficients, and
the octanol/water partition coefficient, or data for chemical
surrogates. The dynamic partitioning portion requires first
order or pseudo first order rate constants for major chemical
transformation processes. These include ozone and hydroxyl
radical oxidation, direct photolysis, aqueous photolysis,
hydrolysis in surface water and soil water, and biodegradation
in water and soil. The Office of Toxic Substances is in the
process of compiling some of this data for major organic
chemicals to interface with the computerized version of the
model.
The model provides the ratios of chemical concentrations
between media compartments, rather than absolute
concentrations. This avoids the problem of selecting media
volumes for the compartments, but allows medias to be ranked in
order of exposure potential. Overall environmental persistence
of the substances is calculated based upon the degradation and
intermedia transfer rates.
5. System Resource Requirements: ENPART is coded in FORTRAN.
It can be run using a calculator or VAX 11/780 mainframe. It
would be useful for the operator to have basic scientific
skills.
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6. Applications: The environmental partitioning model is
still under development at this writing by the EPA Office of
Toxic Substances, Exposure Evaluation Division. It has not yet
been used except in test runs. Reference 1 includes a user
workbook which allows users to perform model operations using a
hand held calculator. A computerized version will become fully
operational in 1981, and will be linked to computerized
techniques for estimating some input parameters based on data
for the chemical of interest or similar ones.
7. Technical Contact
William P. Wood
U.S. Environmental Protection Agency
Exposure Evaluation Division
401 M Street, S.W.
Washington, D.C. 20460
COM 202/382-3928 FTS 382-3828
8. References
Chemical Fate Branch Modeling Team, "An Environmental
Partitioning Model (draft)", EPA Office of Toxic
Substances, April 1980.
Mackay, Donald, "Finding Fugacity Feasible", Environmental
Science and Technology, Vol. 13, No. 10, October 1979, pp.
1218-1223.
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EXPOSURE ANALYSIS MODELING SYSTEM (EXAMS)
1. Model Overview: EXAMS is designed for rapid screening and
evaluation of the behavior of synthetic organic chemicals in
aquatic ecosystems. Starting from a description of the
chemistry of a compound, and the relevant transport and
physical/chemical characteristics of the ecosystem, EXAMS
computes:
1) Exposure: The ultimate (steady-state) expected
environmental concentrations (EECs) resulting from a
specified pattern of (long term, time-invariant)
chemical loadings.
2) Fate: The distribution of the chemical in the system,
and the fraction of the loadings consumed by each
transport and transformation process.
3) Persistence: The time required for effective
purification of the system (via export/transformation
processes) should the chemical loadings terminate.
The EXAMS program is an interactive modeling system that
allows a user to specify and store the properties of chemicals
and ecosystems, modify the characteristics of either, via
simple English-like commands, and conduct efficient, rapid
evaluations and error analyses of the probable aquatic fate of
synthetic organic chemicals.
EXAMS combines the loadings, transport, and transformations
of a chemical into a set of differential equations by using the
law of conservation of mass as an accounting principle. This
law accounts for all the chemical mass entering and leaving a
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system as the algebraic sum of (1) external loadings,
(2) transport processes that export the compound from the
system, and (3) transformation processes within the system that
convert the chemical to daughter products. The equations used
in the program describe the rate of change in chemical
concentrations as a balance between increases, originating from
external and internally recycled loadings, and decreases
resulting from transport and transformation processes.
EXAMS environmental data are contained in a file composed
of concise ("canonical") descriptions of the aquatic systems of
interest to a user. Each water-body is represented via a set
of N segments or distinct zones in the system. The program is
based on a series of mass balances that give rise to a single
differential equation for each segment. Working from the
transport and transformation process equations, EXAMS compiles
an equation for the net rate of change of chemical
concentration in each segment. The resulting system of N
differential equations describes a mass balance for the entire
system. EXAMS has been designed to accept standard
water-quality parameters and system characteristics that are
commonly measured by limnologists throughtout the world. EXAMS
also includes a descriptor language that simplifies the
specification of system geometry and connectedness. The EXAMS
code has been written in a general (N-compartment) form. The
software is available in 10-, 50-, and 100- segment versions.
2. Functional Capabilities: The set of unit process equations
used to compute the kinetics of chemicals is the central core
of E-XAMS. These unit models are all "second-order" or
system-independent equations. Each includes a direct statement
of the interactions between the chemistry of a compound and the
environmental forces that shape its behavior in aquatic
systems. Most of the process equations are based on standard
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theoretical constructs or accepted empirical relationships.
For example, the light intensity in the water column of the
system is computed using the Beer-Lambert law, and temperature
corrections for rate constants are computed using Arrhenius
functions.
lonization of organic acids and bases, and sorption of the
compound with sediments and biota, are treated as thermodynamic
properties or (local) equilibria that constrain the operation
of the kinetic processes. For example, an organic base in the
water column may occur in a number of molecular species (as
dissolved ions, sorbed with sediments, etc.), but only the
uncharged, dissolved species can be volatilized across the
air-water interface. EXAMS allows for the simultaneous
treatment of up to 15 molecular species of a compound. These
include the parent uncharged molecule, and singly or doubly
charged cations and anions, each of which can occur in a
dissolved, sediment-sorbed, or biosorbed form. The program
computes the fraction of the total concentration of a compound
that is present in each of the 15 molecular structures (the
"distribution coefficents," ALPHA). These values enter the
kinetic equations as multipliers on the rate constants. The
program thus completely accounts for differences in reactivity
that depend on the molecular form of the chemical. EXAMS makes
no intrinsic assumptions about the relative transformation
reactivities of the 15 molecular species. These assumptions
are under direct user control through the way the user
structures the chemical input data.
EXAMS computes the kinetics of transformations due to
direct photolysis, hydrolysis, biolysis, and oxidation
reactions. The input chemical data for hydrolytic, biolytic,
and oxidative reactions can be entered either as single valued,
second-order rate constants, or as pairs of values defining the
rate constant as a function of the environmental temperature
specified for each segment.
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EXAMS includes two algorithms for computing the rate of
photolytic transformation of a synthetic organic chemical.
These algorithms were structured to accommodate the two more
common kinds of laboratory data and chemical parameters
available to describe photolysis reactions. The simpler of the
algorithms (subroutine PHOT01) requires only an average
pseudo-first-order rate constant (KDPG) applicable to
near-surface waters under cloudless conditions at a specified
reference latitude (RFLATG). In order to give the user control
of reactivity assumptions, KDPG is coupled to user-supplied
(normally unit-valued) reaction quantum yields (QUANTG) for
each molecular species of the compound. The more complex
algorithm (subroutine PHOT02) computes photolysis rates >
directly from the absorption spectra (molar extinction
coefficients, ABSG) of the compound and its ions, measured
values of the reaction quantum yields, and the environmental
concentrations of competing light absorbers (chlorophyll,
suspended sediments, dissolved organic carbon, and water).
The total rate of hydrolytic transformation of a chemical
is computed by EXAMS as the sum of three contributing
processes. Each of these processes can be entered via simple
rate constants, or as Arrhenius functions of temperature. The
rate of specific-acid catalyzed reactions is computed from the
pH of each sector of the ecosystem, and specific-base catalysis
is computed from the environmental pOH data. The rate data for
neutral hydrolysis of the compound is entered as a set of
pseudo-first-order rate coefficients (or Arrhenius functions)
for reaction of the 15 (potential) molecular species with the
water molecule.
EXAMS allows the user to compute biotransformation of the
chemical in the water column, and in the bottom sediments, of
the system as entirely separate functions. Both functions are
second-order equations that relate the rate of
biotransformation to the size of the bacterial population
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actively degrading the compound. The second-order rate
constants (KBACWG for the water column, KBACSG for benthic
sediments) can be entered either as single-valued constants or
as functions of temperature. When a non-zero value is entered
for the Q-10 of a biotransformation (parameters QTBAWG and
QTBASG respectively), KBAC is interpreted as the rate constant
at 20 degrees C., and the biolysis rate in each sector of the
ecosystem is adjusted for the local temperature (BIOTMG).
Oxidation reactions are computed from the chemical input
data and the total environmental concentrations of reactive
oxidizing species (alkylperoxy and alkoxyl radicals, etc.)
specified by the user. The chemical data can again be entered
either as simple second-order rate constants, or as Arrhenius
functions.
Internal transport, and export, of a chemical occur in
EXAMS via advective and dispersive movement of dissolved,
sediment-sorbed, and biosorbed materials, and by volatilization
losses at the air-water interface. EXAMS provides a set of
vectors (JFRADG, etc.) that allows the user to specify the
location and strength of both advective and dispersive
transport pathways. Advection of water through the system is
then computed from the water balance, using hydrologic data
(rainfall, evaporation rates, streamflows, groundwater
seepages, etc.) supplied as part of the definition of each
environment. Dispersive interchanges within the system, and
across system boundaries, are computed from the characteristic
length (CHARLG), cross-sectional area (XSTURG), and dispersion
coefficient (DSPG) specified for each active exchange pathway.
EXAMS can compute transport of a chemical via whole-sediment
bedloads, suspended sediment wash-loads, ground-water
infiltration, transport through the thermocline of a lake,
losses in effluent streams, etc. Volatilization losses are
computed using a two-resistance model. This computation treats
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the total resistance to transport across the air-water
interface as the sum of resistances in the liquid and vapor
phases immediately adjacent to the interface.
External loadings of a chemical can enter the ecosystem via
point sources (STRLDG), non-point sources (NPSLDG), dry fallout
or aerial drift (DRFLDG), atmospheric wash-out (PCPLDG), and
via ground-water seepage (IFLLDG) entering the system. Any
type of chemical load can be entered for any system segement,
but the program will not implement a loading that is
inconsistent with the system definition. For example, the
program will automatically cancel a PCPLD entered for the
hypolimnion or benthic sediments of a lake ecosystem. When
this type of corrective action is executed, the change is
reported to the user via an error message.
3. Basic Assumptions: EXAMS has been designed to evaluate the
consequences of long-term, time-averaged toxicant loadings that
ultimately result in trace-level contamination of aquatic
systems. EXAMS generates a steady-state, average flow field
for the ecosystem. The program thus cannot evaluate the
transient concentrated EECs that arise, for example, from
chemicals spills. This limitation derives from two factors.
First, a steady flow field is not always appropriate for
evaluating the spread and decay of a major pulse (spill)
input. Second, the assumption of trace-level EECs, which can
be violated by spills, has been used to design the process
equations used in EXAMS. The following assumptions were used
to build the model:
(1) A first-order evaluation can be executed independently
of the chemical actual effects on the system. In
other words, the chemical does not itself radically
change the environmental variables that drive its
transformations. Thus, for example, an organic acid
or base is assumed not to change the pH of the system;
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the chemical is assumed to not itself absorb a
significant fraction of the light entering the system;
bacterial populations do not grow (or decline) simply
due to the presence of the chemical.
(2) EXAMS uses linear sorption isotherms, and second-order
(rather than Michaelis-Menten-Monod) expressions for
biotransformation Kinetics. This approach is known to
be valid for low concentrations of pollutants; its
validity at high concentrations is less certain.
EXAMS controls its computational range to ensure that
the assumption of trace-level concentrations is not
grossly violated. This control is keyed to
aqueous-phase (dissolved) residual concentrations of
the compound; EXAMS aborts any analysis generating
EECs that exceed 50% of the compound's aqueous
solubility or l.E-05 in an unionized molecular
species. This restraint incidentally allows the
program to ignore precipitation of the compound from
solution, and precludes inputs of solid particles of
the chemical.
(3) Sorption is treated as a thermodynamic or constitutive
property of each segment of in the system, that is,
sorption/desorption kinetics are assumed to be rapid
compared to other processes. The adequacy of this
assumption is partially controlled by properties of
the chemical and system being evaluated. Extensively
sorbed chemicals tend to be sorbed and desorbed more
slowly than weakly sorbed compounds; desorption
half-lives may approach 40 days for the most
extensively bound compounds. Experience with the
program has indicated, however, that strongly sorbed
chemicals tend to be captured by benthic sediments,
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where their release to the water column is controlled
by benthic exchange processes. This phenomenon
overwhelms any accentuation of the speed of processes
in the water column that may be caused by the
assumption of local equilibrium.
A. Input and Output: Input parameters include:
(1) A set of chemical loadings on each sector of the
ecosystem.
(2) Molecular weight, solubility, and ionization constants
of the compound.
(3) Sediment-sorption and biosorption parameters: Kp, Koc
or Kow, biomasses, benthic water contents and bulk
densities, suspended sediment concentrations, sediment
organic carbon, and ion exchange capacities.
(4) Volatilization parameters: Henry's Law constant or
vapor pressure data, windspeeds, and reaeration rates.
(5) Photolysis parameters: reaction quantum yields,
absorption spectra, surface scalar irradiance,
cloudiness, scattering parameters, suspended
sediments, chlorophyll, and dissolved organic carbon.
(6) Hydrolysis: 2nd-order rate constants or Arrhenius
functions for the relevant molecular species; pH, pOH,
and temperatures.
(7) Oxidation: rate constants, temperature, and oxidant
concentrations.
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(8) Biotransformation: rate constants, temperature, total
and active bacterial population densities.
(9) Parameters defining strength and direction of
advective and dispersive transport pathways.
(10) System geometry and hydrology: volumes, areas,
depths, rainfall, evaporation rates, entering stream
and non-point-source flows and sediment loads, and
ground water flows.
Although EXAMS allows for the entry of extensive
environmental data, the program can be run with a much reduced
data set when the chemistry of a compound of interest precludes
some of the transformation processes. For example, pH and pOH
data can be omitted in the case of neutral organics that are
not subject to acid or alkaline hydrolysis.
The 17 output tables include an echo of the input data, and
tabulations giving the exposure, fate, and persistence of the
chemical. The program prints a summary report of the results
obtained. Printer-plots of longitudinal and vertical
concentration profiles can be invoked by the interactive user.
5. System Resource Requirements; EXAMS has been implemented
in FORTRAN IV as defined by the ANSI FORTRAN X.39-1966 report.
The DEFINE FILE extension of the standard is used for file
manipulation, but standard unformatted I/O can be substituted
with some sacrifice in speed of execution. An overlay
capability is required to implement EXAMS on small computers
such as the PDP-11 or HP 3000. EXAMS is available from the EPA
Athens Environmental Research Laboratory in either a batch or
an interactive version. The batch version requires 64K bytes
(overlaid) of memory (for aquatic systems of up to 17
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segments); this version does not require mass storage
capabilities. The interactive version also requires 64K bytes
(overlaid) of memory, plus an additional mass storage
capability. The interactive version of EXAMS requires 100K
bytes of mass storage for utility files, 2K bytes for each
chemical in the active files, and 2.5K bytes for each active
defined environment. Execution times range from a few seconds
to several minutes depending on the problem to be solved. The
software is distributed on magnetic tape; the source code
consists of about 16,000 card images.
6. Applications: EXAMS can be used to assess the fate,
exposure, and persistence of synthetic organic chemicals in
aquatic ecosystems in which the chemical loadings can be
time-averaged and residuals are at trace levels. The program
has been used, for example, by EPA to evaluate the behavior of
relatively field-persistent herbicides. EXAMS has been
successfully used to model volatilization of organics in
specific field situations, and for a general assessment of the
behavior of phthalate esters in aquatic systems. EXAMS has
been implemented by a number of manufacturing firms for
environmental evaluations of newly synthesized materials and
has been used in an academic setting for both teaching and
research.
7. Technical Contacts
Lawrence A. Burns
U.S. Environmental Protection Agency
Environmental Systems Branch
Environmental Research Laboratory
College Station Road
Athens, Georgia 30613
FTS 250-3123 COM 404/546-3123
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Davia M. Cline
U.S. Environmental Protection Agency
Automatic Data Processing
Environmental Research Laboratory
College Station Road
Athens, Georgia 30613
FTS 250-3123 COM 404/546-3123
8. References
Baughman, G.L. and Burns, L.A. Transport and Transformation
of Chemicals in the Environment: A Perspectve. pp. 1-17.
In: 0. Hutzinger (ed.) Handbook of Environmental
Chemistry., Vol. 2, Part A - Reactions and Processes.
Springer-Verlag, 1980.
Burns, L.A., Cline, D.M., and Lassiter, R.R. Exposure
Analysis Modeling System (EXAMS): User Manual and System
Documentation. (443 pp. In process; supplied with
software.), 1982.
Lassiter, R.R., Baughman, G.L. and Burns, L.A. "Fate of
Toxic Organic Substances in the Aquatic Environment." pp.
219-246 In: S.E. Jorgensen (ed) State-of-the-Art in
Ecological Modeling. Proceedings of the Conference on
Ecological Modeling, Copenhagen, Denmark 28 Auguest - 2
September 1978. International Society for Ecological
Modelling, Copenhagen, 1978.
Wolfe, N.L. Burns, L.A. and Steen, W.C. Use of Linear Free
Energy Relationships and an Evaluative Model to Assess the
Fate and Transport of Phthalate Esters in the Aquatic
Environment. Chemosphere 9:393-402, 1980.
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A FORTRAN PROGRAM FOR RISK ASSESSMENT USING DOSE-RESPONSE
DATA TIME-TO-OCCURRENCE (RANK TIME)
1. Model Overview: The program RANK TIME implements the
theory developed in a 1980 paper, by Daffer, Crump, and
Masterman entitled, "Asymptotic Theory for Analyzing
Dose-Response Surivial Data with Application to the Low-Dose
Extrapolation Problem" (to appear in Mathematical
Biosciences). It is used for analyzing dose-response
time-to-occurence data and for estimating low-dose risks from
such data. This method is based on the multi-stage model. The
data are derived from lifetime feeding studies with animals,
usually rodents.
2. Functional Capabilties: The limitations to the model are:
1) The number of dose groups must be greater than 2 and
less than 10.
2) The number of animals must not exceed 1000.
3) The animal death times must not exceed 1000.
4) The degree of the polynomial must not exceed 11.
5) The number of animals that die of cancer must be less
than 300.
3- Basic Assumptions: This model is a variant of the
multi-stage model, with death time included as an additional
parameter. The model is mathematically quite complex, so
individuals interested in the underlying assumptions are
referred to the technical contact for copies of theoretical
papers underlying the development of this model.
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4. Input and Output: The inputs to the model include:
1) Number of dose groups
2) Number of animals in each treatment group
3) Dosages administered to each treatment group
4) Times of death for which confidence limits are to be
computed
5) Time of death of each animal
6) Degree of polynomial of multi-stage model
7) Cutoff time for ignoring cancer deaths
8) Method of ties among cancer deaths
Based on a number of estimates and the asymptotic theory
developed in the 1980 Daffer, et al. paper, estimates and
confidence limits are calculated for a number of quantities.
These include:
1) The risk P(t,d) at time t from dose d
2) The extra risk P(t,d) - P(t,o) at time t from dose d
3) The safe dose corresponding to time t and additional
risk
4) The expected fraction of life-shortening by the t from
dose d
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5. System Resource Requirements: This model is coded in
FORTRAN G and is run on an IBM 370/168 mainframe. It requires
less than 300k bytes of core memory for execution. It uses any
132 positon line printer for execution.
6. Application: This model is used to estimate lifetime
carcinogenic risks associated with various suspected human
carcinogens. The model is only used when time-to occurrence
data is available from an animal bioassay. The estimates of
carcinogenic risk are used in risk assessments supporting
regulatory actions under Section 5 and 6 of the Toxic
Substances Control Act.
7. Technical Contact
Gary Grindstaff
U.S. Environmental Protection Agency
Office of Pesticides and Toxic Substances
401 M Street, S.W.
Washington D.C. 20460
COM 202/382-3459 FTS 382-3459
8. Re ferences
Crump, K.S. Daffer, D.Z. and Masterman, M.D.; (1980)
Low-Dose Extrapolation Utilizing Time-to-occurrence Cancer
Data. In: Final Report, National Institute of
Environmental Health Sciences, Contract No. N01-ES-23133.
Daffer, D.Z., Crump, K.C., and Masterman, M.C. (1980)
"Asymptotic Theory for Analyzing Dose-response Survival
Data With Application to the Low-dose Extrapolation
Problem." Mathematical Biosciences 50, pp. 204-230.
A user's manual is available from the technical contact.
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A FORTRAN PROGRAM TO EXTRAPOLATE DICHOTOMOUS ANIMAL
CARCINOGENICITY DATA TO LOW DOSES (GLOBAL 79)
1- Model Overview: GLOBAL 79 is a program to analyze
dichotomous animal carcinogenicity data. It is assumed that at
each dose level, animals have been exposed to a constant dose
rate of the agent under test and that some positive responses
have occurred. The program calculates maximum likelihood
estimates of a multistage dose response function. The user may
allow the program to set the degree of the polynomial function
to be one less than the number of dose groups, or force the
degree of polynomial, or globally maximize the likelihood over
polynomials of arbitrary degree. A likelihood ratio test is
then performed on the linear polynomial coefficient. Next,
lower statistical confidence limits on dose and upper
statistical confidence limits on risk are calculated for risk
levels of 10 (-1)....10(-8) and other dose levels input by the
user. Finally, if requested by the user, the program will
conduct a Monte-Carlo goodness of fit test of the model to the
experimental data.
2. Functional Capabilities: Limitations of the model are:
the number of dose levels must not exceed 19; the number of
animals must not exceed 2,000; the number of environmental
doses input by the user must not exceed 50; and the number of
data sets which may be analyzed in one run must not exceed
1,000.
3. Basic Assumptions: This is a multistage model, the
parameters of which are estimated by the method of maximum
likelihood. However, the model is mathematically complex, thus
rather than list them here, individuals interested in the
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assumptions and theory of this model may refer to the technical
contact for copies of theoretical papers underlying the
development of this model.
4. Input and Output: Inputs to the model include:
1) The number of dose levels
2) Goodness of fit option
3) Number of animals at risk at each dose level
4) Number of animals showing a positive response at each
dose level
5) Magnitude of each dose level
6) Model option (multistage, forced stage, global
optimization)
7) Degree of polynomial (for forced stage option)
8) Number and level of environmental doses for which risks
are to be computed
The principal outputs of the model are:
1) Lower statistical confidence limits for the dose
producing extra risks of 10(-1), 10(-2) 10(-8)
(virtually safe dose)
2) Upper confidence limits on extra risk for maximum
likelihood estimated doses (or other doses which are
input by the user) corresponding to increased risks of
10C-1) 10 (-8)
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5. System Resource Requirements: GLOBAL 79 is coded in
FORTRAN G and is run on an IBM 370/168 mainframe. It requires
less than 300K bytes of core memory for execution. It uses any
132 position line printer.
6. Applications: This model is used to estimate lifetime
carcinogenic risks associated with various suspected human
carcinogens. The model is only used with dichotomous data from
animal bioassays. The estimates of carcinogenic are used in
risk assessments supporting regulatory actions under Sections 5
and 6 of the Toxic Substances Control Act.
7. Technical Contact
Gary Grindstaff
U.S. Environmental Protection Agency
Office of Pesticides and Toxic Substances
401 M Street, S.W.
Washington, D.C. 20460
COM 202/382-3459 FTS 382-3459
8. References
Crump, K.S. "An Improved Procedure for Low-dose
Carcinogenic Assessment for Animal Data. Journal of
Environmental Pathology and Toxicology (to appear), 1980.
A user's manual is available from the technical contact.
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MANTEL-BRYAN LOW-DOSE EXTRAPOLATION MODEL (MANTELAN)
1. Model Overview: This computer model is an implementation
of the technique for low-dose extrapolation developed by
Mantel, Bohidar, Brown, Ciminera, and Tukey in a 1975 paper
entitled "An Improved Mantel-Bryan Procedure for "Safety*
Testing of Carcinogens" (This paper is available from the
technical contact).
2. Functional Capabilities: The limitations of this model are
not well known. The documentation is somewhat sparse.
3- Basic Assumptions: The Mantel-Bryan model is a special
case of the well-known probit model. Mantel-Bryan, however,
assumes a slope of 1.0. The methods used to estimate the
parameters and to place confidence limits on a dose are
explained fully in a number of theoretical background papers
available from the technical contact.
4. Input and Output: Inputs to the model include:
1) The assumed slope of the dose-response curve (usually
1.0)
2) The number of experimental groups (e.g. males, females)
3) The number of dose levels
4) The number of confidence limits
5) The chi-square values for desired confidence limits
6) The dose levels
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7) The titles of experimental groups
8) The number responders, the number at risk for each
control and treated group in each experimental group
The principal outputs of this model include lower
confidence bounds for the dose at specified attributable risks
of 10(-1) to 10(-8). These estimates are presented for all
dose groups first and then for successively smaller dose group
combinations, eliminating the highest dose on each iteration.
5. System Resource Requirements: MANTELAN is coded in FORTRAN
G and is run on an IBM 370/168 mainframe. It requires less
than 300k bytes of core memory for execution. It uses any 132
position line printer for output.
6. Appl ications: This model is used to estimate lifetime
carcinogenic risks associated with various suspected human
carcinogens. The model is only used with dichotomous data from
animal bioassays. The estimates of carcinogenic risk are used
in risk assessments supporting regulatory actions under Section
5 and 6 of the Toxic Substances Control Act.
7. Technical Contact
Gary Grindstaff
U.S. Environmental Protection Agency
Health and Environmental Review Division
401 M Street, S.W.
Washington, D.C. 20460
COM 202/755-6841 FTS 755-6841
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8. References
Mantel, N., Bohidar, N., Brown C., Ciminera, J., and
Tukey, J. "An Improved Mantel-Bryan Procedure for "Safety"
Testing of Carcinogens." Cancer Research 34, 865-872, 1975,
A user's manual is available from the technical contact.
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ONE-HIT LOW-DOSE EXTRAPOLATION MODEL (ONE HIT MD)
1- Model Overview: This program computes maximum likelihood
estimates of the parameters of the one-hit model. Abbott's
connection is incorporated so that estimates of increased risk
may be generated. The parameters generated by the model are
used in the assessment of lifetime carcinogenic risks at low
environmental doses.
2. Functional Capabilities; The limitations of the model are:
1) The numbers of experimental clauses must not exceed 10
2) The numbers of dose levels must not exceed 20
3) Other limitations, if any, are unknown
3. Basic Assumptions: According to the theory of the one-hit
model, there is some risk of cancer from even a slight exposure
to a carcinogen. The probability that a carcinogen at a given
dosage will induce cancer in a laboratory animal is stated in
the experimental probability law. The detailed, mathematical
assumptions underlying this model are provided in the program
documentation, available from the technical contact.
4. Input and Output: Inputs to the model include:
1) Number of experimental groups (e.g., males, females)
2) Number of dose levels
3) Chi-square values for derived confidence limits
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4) Dose levels
5) Titles of experimental groups
6) Number of responders, number at risk for each control
and treated group in each experimental group
The principal outputs of this model include:
1) Lower confidence limits for dose at specified
attributable risks of 10(-1) and 10(-8)
2) Lower confidence limits on the one-hit parameter
These estimates are presented for all dose groups first and
then for successively smaller dose group combinations,
eliminating the highest dose on each iteration.
5. System Resource Requirements: This model is coded in
FORTIRAN G and is run on an IBM 370/168 mainframe. It requires
less; than 300k bytes of core memory for execution. It uses any
132 position line printer for output.
6. Applications: This model is used to estimate lifetime
carcinogenic risks associated with various suspected human
carcinogens. The model is only used with dichotomous data from
animal bioassays. The estimates of carcinogenic risk are used
in risk assessments supporting regulatory actions under
Sections 5 and 6 of the Toxic Substances Control Act.
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7. Technical Contacts
Gary Grindstaff
U.S. Environmental Protection Agency
Office of Pesticides and Toxic Substances
401 M Street, S.W.
Washington D.C. 20460
COM 202/382-3459 FTS 382-3459
8. References
General statistics text like:
Neter, J. and Wasserman, W.f Applied Linear Statistical
Models, Irwin, Inc., 1974.
A user's manual is available from the technical contact
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SEASONAL SOIL MODEL (SESOIL)
1. Model Overview: SESOIL is a statistical mathematical
model designed for long-term environmental pollutant fate
simulations that can describe: water transport (quality/quantity);
pollutant transport/transformation; and soil quantity. Simulations
are performed for a user specified soil column (designated as
compartment), extending between the ground surface and the lower
part of the saturated soil zone of a region. The simulation is
based upon a three-cycle rationale, each cycle being associated
with a number of processes. The three cycles are the: (1) water
cycle which takes account of rainfall, infiltration, exfiltration,
surface runoff, evapotranspiration, and groundwater runoff;
(2) sediment cycle (not currently operational) which takes
account of sediment resuspension (because of wind) and sediment
washload (because of rain storms); and (3) pollutant cycle which
takes account of convection, volatilization, adsorption/desorption,
chemical degradation/decay, biological transformation/uptake,
hydrolysis, complexation of metals by organics, and cation exchange.
2. Functional Capabilities: The model calculates
seasonal (either annual or monthly) concentrations of pollutants
in the soil air, soil water, and sorbed to soil solids in up to
three layers of a soil column. The model is potentially useful
in studying leaching from waste disposal sites and environmental
assessment of chemicals in soil environments.
3. Basic Assumptions: The hydrologic cycle is based on
Eagleson's theory, which is a statistical dynamic formulation
of vertical water budget at a land-atmosphere interface. The
fundamental water balance equation sets infiltration equal to
precipitation minus surface runoff which is in turn equal to
net evapotranspiration and groundwater recharge and loss.
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The pollutant cycle is based on a layer-by-layer mass-
balance equation, which is solved for the pollutant concentration
in each phase.
4. Input and Output: SESOIL does not require calibra-
tion because it uses mainly theoretically derived equations.
Input can be input for either annual or monthly simulations.
Hydrologic input includes climatic (e.g., average precipitation,
depth, average storm duration, number of storms), soil (e.g.,
density, porosity), and chemical data (e.g., degradation rates,
molecular weight, adsorption coefficients).
5. System Resource Requirements; The model has been
implemented on a IBM 370 and a VAX 11/780. It is coded in FORTRAN
and requires several disk input files.
6. Applications: The SESOIL model predictions have
been compared with laboratory determined concentrations in the
upper unsaturated zone at two land-treatment sites. Concentra-
tions were found to agree within expected limits.
The model has also been employed as an exposure
assessment model for screening, analyzing, and prioritizing
pollutant behaviors in a number of soil disposal systems.
The model is still under development and is still
being extended under contracts with the Office of Toxic Substances,
7. Technical Contact:
Annett Nold
U.S. Environmental Protection Agency
Office of Toxic Substances
Exposure Evaluation Division (TS-798)
401 M Street, S.W.
Washington, DC 20460
COM 202/382-3926
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References:
Bonazountas, M. and J. Wagner (1981); "SESOIL:
A Seasonal Soil Compartment Model," Arthur D.
Little, Inc., Cambridge, Massachusetts 02140.
Prepared for U.S. Environmental Protection
Agency, Office of Toxic Substances, Contract
No. 68-01-6271.
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STATISTICAL METHODOLOGY FOR TOXICOLOGICAL
RESEARCH (MRST)
1 • Model Overview: MRST is a program designed to use both as
a powerful research tool and a statistical analyzer of
experimental data from tox icological studies. The program will
provide a statistical analysis of real experimental data. In
addition, the program has the capability of simulating
experimental data. This capability is particularly useful in
evaluating the effectiveness of the risk estimation procedures
and alternative experimental designs, especially with regard to
a design* s number and spacing of dose levels and the number of
test animals assigned to each dose level.
2. Functional Capabilities: The limitations of the model are
tied to the input specifications. Given the extent of the
required program input, interested users are referred to the
technical contact for the program documentation which will
cover these specifications in detail.
3- Basic Assumptions: The program is based, to some extent,
on the simulation of time-to-death for an experimental animal
by a Weibull distribution. However, the theory underlying this
model is quite involved. Interested users are referred to the
technical contact for copies of the theoretical papers upon
which this model is based.
4. Input and Output; The input requirements for this program
are quite large, too many to list here in detail. They include
detailed information from the bioassay, such as individual
animal death times. Interested users are urged to obtain a
copy of the input specifications from the technical contact.
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The outputs from this model are extremely voluminous and
too numerous to list here. In general, estimates of increased
risk and life-shortening are provided. Interested individuals
are urged to obtain a sample output listing from the technical
contact.
5. System Resource Requirements: This model is coded in
FORTRAN G and is run on an IBM 370/168 mainframe. It requires
less than 300K bytes of core memory for execution. It uses any
132 position line printer for output.
6. Applications: This model is used in conjunction with a
variety of other models to estimate lifetime carcinogenic risks
associated with various levels of suspected human carcinogens.
However, this model is used only with time-to-occurrence dose
response data. These estimates are then included in risk
assessments supporting regulatory actions under Sections 5 and
6 of the Toxic Substances Control Act.
7. Technical Contact
Gary Grindstaff
U.S. Environmental Protection Agency
Office of Pesticides and Toxic Substances
401 M Street, S.W.
Washington, O.C. 20460
COM 202/382-3459 FTS 382-3459
8. References
Hartley, H.O., and Sielken, R.L. "Estimation of "Safe Dose"
in Carcinogenic Experiments".
Biometrics 33 p. 1-30, 1977.
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Hartley, H.O. and Sielken, R.L., Jr. "Development of
Statistical Methodology for Risk Examination." Final
report. National Center for Toxicological Research,
Contract No. 222-77-2001 April, 1978.
A users's manual is available from the technical contact
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UNIFIED TRANSPORT MODEL - TOXICS (UTM-TOX)
1. Model Overview: The Unified Transport Model is a
multimedia model that simulates the movement of a chemical
through an inland watershed. The model calculates the
concentration of organic and inorganic chemicals in air, water,
soil, sediment and biota. The UTM consists of the Atmospheric
Transport Model (ATM), the Wisconsin Hydrologic Transport Model
(WHTM), the Terrestrial Ecosystem Hydrology Model (TEHM), and a
suite of associated submodels. The model was originally
developed by the Oak Ridge National Laboratory to simulate
trace element transport through a forested ecosystem. The
model was modified by Oak Ridge in 1980-81 for the
Environmental Protection Agency to incorporate the transport
and transformation processes associated with organic chemicals.
2. Functional Capabilities: The model is applicable to
small watersheds consisting of up to 3 land segments and 7
reaches. The concentration of the chemical in air is
determined on a monthly basis. Movement of the chemical
through the terrestrial and aquatic environment is simulated at
15 minute intervals. The average monthly and annual
concentrations can be calculated with an accuracy of better
than an order of magnitude. The hydrologic submodel requires
calibration.
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3. Basic Assumptions: The chemical (organic or
inorganic) is assumed to be released from point, line or area
sources into air, deposited onto land and subsequently
transported to ground water and surface water. The ATM
consists of a steady state Gaussian algorithm. The terrestrial
model is a simulation model. The ecological submodels are
mechanistic in character.
4. Input and Output: The input data includes monthly
wind roses, hourly precipitation, solar radiation, daily
maximum and minimum temperatures, soil characteristics,
topographic information, surface water characteristics,
sediment characteristics, and the physiochemical properties and
transformation rates associated with the chemical.
The output consists of plots and tables summarizing
the average monthly and annual chemical concentrations in 8
wind sectors, in saturated and unsaturated soil layers, in
runoff, out of each reach, and in the stems, leaves, roots and
fruits of vegetation.
5. System Resource Requirements: UTM may be run on an
IBM 370 series computer or a VAX 11/780 machine. Core storage
on an IBM amounts to 540 Kbytes. The model is written FORTRAN
IV. Programming experience is helpful in using this model.
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6. Applications: The original UTM has been used by the
Environmental Sciences Division of Oak Ridge National
Laboratory to study the accumulation of trace metals in the
soil and biota of two forested ecosystems.
7. Technical Contact:
Joan Lefler (TS-798)
U.S. Environmental Protection Agency
Office of Toxic Substances
401 M Street
Washington, D.C. 20460
COM 202/382-3930 FTS 382-3930
References:
Culkowski, W.M., and Patterson, M.R. "A Comprehensive
Atmospheric Transport and Diffusion Model." Oak Ridge
National Laboratory Report CRNL/NSF/EATC-17 1976.
Patterson, M.R., et al."A User's Manual for the
FORTRAN IV Version of The Wisconsin Hydrologic
Transport Model. Oak Ridge National Laboratory Report
CRNL/NSF/EATC-7, 1974.
Huff, D.D., et al. "TEHM: A Terrestrial Ecosystem
Hydrology Model." Oak Ridge National Laboratory
Report CRN/NSF/EATC-27, 1977.
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URBAN WASTEWATER TOXICS FLOW MODEL (TOXFLO)
1- Model Overview: The Urban Wastewater Toxics Flow Model
provides statistical estimation of the generation and fate of
toxic pollutants entering into a given municipal sewage
treatment system. Quantities computed by the model include
flow and concentration values from each controllable industrial
discharger and the domestic/commercial sector. It also
computes the quality of the influent, effluent, and sludge
values from the municipal sewage treatment plant and receiving
water quality from the domestic/commercial source. The model
can be run to compute either statistical confidence limits for
the mean values of these quantities or to predict the frequency
distributrion of the daily performance of a system (e.g., how
often will water quality criteria be violated. ) The model can
aid in developing industrial pretreatment programs by
indicating which industrial dischargers and toxic pollutants
may be problematic under existing levels of treatment and what
impact alternative industrial pretreatment/municipal treatment
technologies may have in controlling toxic pollutants. The
program can be run in a time-sharing mode over an interactive
terminal.
2. Functional Capabilities: The model computes a mean,
variance, skewness, and kurtosis for flows and concentrations
of toxic pollutants at various points within a municipal sewer
and sewage treatment system. Depending on the nature of the
input data employed, these statistics can be used to estimate
confidence intervals for mean values of system performance or
to characterize the daily statistical behavior of the system.
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3. Basic Assumptions: The model assumes statistical
independence between all industrial discharges and between the
performance of the municipal treatment plant and the flow in
the receiving stream. The frequency distributions of all input
quantities must be either normal, log normal, or beta
distributed. Serial correlations in time of these quantities
is not considered. Municipal treatment plan removal
capabilities may be described as a deterministic function of
influent concentration coupled to a random error term.
4. Input and Output: Input to the model consists of means and
standard deviations for the flow and concentration of each
pollutant of interest from each industrial discharger and for
the concentrations attributed to domestic/commercial sources.
A set of pollutant removal functions and their standard errors
is required for the municipal treatment plant. The mean and
standard deviation of the receiving stream flow is also
needed. There is a possibility that at some future date an
internal data base will be added to the model so that the input
can be reduced to specifying industrial sub-category types,
pretreatment technologies, and municipal treatment technologies,
Standard output from TOXFLO consists of the estimated means of
the concentration of each toxic pollutant in the influent,
effluent, and sludge of the municipal plant, and of the
receiving water. Also, reported is the possibility or
frequency with which water quality and sludge quality criteria
are violated. The user can request that a more detailed
inventory report be printed for a specific pollutant. This
report will contain the first four months of the flow,
concentration, and mass loading from each discharge source and
similar statistics for the concentrations at the municipal
treatment plant and in the receiving stream. External to the
program, the user may then use this information to assume
distribution types for the quantities of interest and then
develop confidence limits or determine percentile values.
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5. System Resource Requirements: TOXFLO is coded in FORTRAN
and is run on an Univac 1100 mainframe. Operation requirements
include a background in engineering.
6. Applications: TOXFLO was developed to aid in the planning
of industrial pretreatment programs. It can also be used to
assist in the analysis of municipal treatment facility
discharge permit requirements.
7 • Technical Contact
Lewis Rossman
U.S. Environmental Protection Agency
Municipal Environmental Research Laboratory
26 West St. Clair Street
Cincinnati, Ohio 45268
COM 513/684-7636 FTS 684-7636
8. References
A User's Manual is being prepared.
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CENTRALIZED TREATMENT OF INDUSTRIAL WASTEWATER
1. Model Overview: The objective of the Centralized Treatment
model is to produce an optimal geographic pattern of treatment
facilities for industrial wastewater in a metropolitan area.
An optimal pattern is one whose annual costs of transporting
and treating industrial wastewater- is minimal. Treatment
facilities may be located at wastewater-producing industrial
plants or at one or more candidate sites for centralized
treatment. Both capital and operating costs are considered.
The fundamental tradeoff resolved by the model is between
the costs of transportation and the economies of scale inherent
in centralized treatment.
The model is formulated as a mixed integer program. It is
supported by a matrix generator and reports programs.
The model was developed by CENTEC Corporation during 1979
and 1980 in connection with an analysis of centralized
treatment as an option for meeting pretreatment regulations
published by EPA in 1979. Antecedents include a model for
optimizing municipal sludge handling and disposal systems
developed by Yakir Hasit in his PhD dissertation at Duke
University in 1978, and WRAP, Waste Resource Allocation
Program, a model developed for DPA by Mitre Corporation in 1977,
2. Functional Capabilities: The model views the original
wastewater workload as 1, 2, or 3 streams emanating from each
industrial plant. The 3 stream types are chromium, cyanide,
and acid/alkali. Each stream is characterized by a flow rate;
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the concentrations of Cr+3, Cr+6, Cn, Zn, Fe, Ni, Cu , Cd
and Pb; through its plant or identification number; and a
geographic location. Treatment processes considered are chrome
reduction, cyanide oxidation, neutral ization/precipitation ,
flocculation, clarification, thickening, and filtration. Each
process may be located at each industrial plant and at each
candidate site for central treatment.
Each wastewater source stream is required to undergo full
treatment in the sequence neutralization-precipitation,
flocculation, clarification, thickening, and filtration. In
addition, chrome and cyanide streams must undergo chrome
reduction and cyanide destruction, respectively, before
entering neutrali zation/precipitation . Overflows from the
clarifier and thickener go to the sanitary sewer. Sludge from
the filtration unit goes to a landfill. When wastewater is
transported from one site to another, storage tanks with
attendant costs are required at each site. Capital and
operating costs for each treatment process reflect economies of
scale. In addition to wastewater treatment processes, the
model considers the economics of wastewater reduction measures
applied in the plant production processes to reduce the size of
source streams. The model determines the optimal pattern of
wastewater reduction, the optimal size of each treatment
process at each plant and central site, and transporation flows
among sites. Plants and central sites may perform full
treatment, partial treatment, or no treatment. A typical
problem size is 50-100 plants, 1-3 candidate central sites, and
1-2 landfills. That translates to a typical model size of 4000
constraint equations with 500-1500 integer variables, about
half of the latter for representing increasing returns to scale
as piecewise linear equations. For mixed integer programs of
this size, it is not feasible to run the solution algorithm
long enough to prove optimality, but experience indicates that
an optimal or near-optimal solution is obtained on the 2nd or
3rd integer-feasible solution when the search criteria are
chosen to support that objective.
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3. Basic Assumptions: The treatment processes modeled are
assumed adequate to meet treatment regulations when properly
operated. The performance of clarifiers, thickeners, and
filtration units is modeled in terms of fixed efficiencies of
solids captured, and fixed ratios of solid to liquid weights in
the underflow. Process sizes include a 20% safety factor.
Transportation cost is assumed to be a function of payload and
distance. It is further.assumed that liquid waste is moved in
5500-gallon tank trucks only, filled to capacity. Dewatered
sludge is assumed moved in trucks of 30-cubic yard capacity,
fully loaded.
Separate capital recovery factors are used for in-plant and
central facilities, to permit inclusion of rate-for-return
requirements on capital investments by industrial plants. The
capital costs of central facilities include a component for
site acquisition and construction. Every plant and central
site is assumed large enough to accommodate treatment equipment
of sizes chosen by the model.
4. Input and Output: The model requires four kinds of input.
Technological Data is the cost and performance characterstics
of treatment technologies and transporation. With the
exception of occasional adjustments to costs, which is provided
for in the model via appropriate standard cost indices, this is
a reasonably stable set of data that need not be developed anew
for each model run. Plant and Source Stream Data is the
identification and location, for a region, of each industrial
plant whose wastewater requires treatment, and the flow rate
and chemical composition of the chrome, cyanide and acid/alkali
waste streams at each plant. Input also includes the number of
production lines where wastewater reduction has been applied
and for whicn it is feasible. Central Site and Landfill Data
is the location of candidate sites for central treatment, and
the location of landfills suitable for receiving filtercake
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containing precipitated metals. Economic Factors are the
interest rates, equipment lifetime, Wholesale Price Index for
Industrial Chemicals, Chemical Engineering Plant Cost Index,
Chemical Engineering Manpower Cost Index, sewer fee, and
landfill fee.
Three principle output reports are produced: plant report,
central facility report, and transportation report. The plant
and central facility reports show whether'a flow reduction or
treatment process is present at a site, and, when it is, its
size, capital cost, and operating cost. Operating cost is
broken out into labor, utilities, and chemicals. When
transportation is used, the costs of storage are shown. The
transportation report shows the workloads and costs of
transporting liquid wastes from plants to central facilities,
and transportation among central facilities. The costs of
transporting sludge from plants to central facilities and to
landfills is also shown. Regional totals are reported by
process and resource. Subsidiary outputs include the standard
linear programming report of the model solution, and reports of
intermediate factors generated during input preparation.
5. System Resource Requirements: The computer program has
been written for the Univac 1110 Series using the FMPS and
GAMMA language. Additional requirements include short term
disk storage for 100 million bytes, and long term storage for 1
million bytes. Any 132 character per line printer may be
used. An operator with a background in programming,
engineering, and mathematical programming would be helpful.
6. Applications: The model was used by CENTEC Corporation to
analyze centralized treatment in the Milwaukee region. The
analysis was performed in the project under whose aegis the
model was developed.
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Technical Contact
Alfred Craig
U.S. Environmental Protection Agency
Industrial and Environmental Research Laboratory
Cincinnati, Ohio 45268
COM 513/684-4491 FTS 684-4491
Howard Markham
CENTEC Corporation
11800 Sunrise Valley Drive
Reston, VA 22091
COM 703/471-6300 FTS 471-6300
References
CENTEC Corporation, Centralized Treatment Model .
User' s Manual, August 1980.
CENTEC Corporation, Centralized Waste Treatment Mixed
Integer Programming Mod el -- Milwaukee Results, July
1980.
Yakir Hasit, Optimization of Municipal Sludge Handling
and Disposal System, Ph.D. Dissertation at Duke
University, Department of Civil Engineering, 1978.
CENTEC Corporation, Centralized Treatment _of Metal
Finishing Wastejs, September 1980.
WRAP, _A Model for Regional Solid Waste Management
Planning - User" s Guide, EPA/530/SW574, U.S.
Environmental Protection Agency, February 1977.
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COMPUTER PROGRAM FOR CHEMICAL EQUILIBRIA
IN AQUEOUS SYSTEMS (REDEQL.DWRD)
1. Model Overview; This program can calculate equilibrium
aqueous speciation, saturation states of solids, and dissolved
and solid concentrations following precipitation reactions.
The program is useful for aquatic toxicology studies, titration
experiment modeling (determination of pH or complexation
changes), water chemical evaluation for corrosion control
treatments, and determination of solubility controls on
constituents in natural or drinking waters.
2. Functional Capabilities; The model can include up to 20
metals and 30 ligands at any one time, many of which can be
added by the user. The model allows temperature and ionic
strength corrections to equilibrium constants. The model
allows the user to impose solids in contact with the solution,
to prohibit precipitation of supersaturated solids, or to allow
precipitation. The program calculates aqueous and solid
speciation, interaction intensities and capacities, and can be
used to calculate pH. Several other options are noted in
References 1 and 2. The accuracy depends on the quality of the
thermochemical data, the accuracy of the species chosen to
represent the real system, the nearness to equilibrium, and the
quality of the anaytical data input.
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3. Basic Assumptions: The major assumption is that the system
in question is at equilibrium (or can be considered to be in a
metastable state that can be treated as equilibrium).
Temperature corrections to the equilibrium constants are done
by the Van't Hoff relation. Three solids and six complexes are
allowed for each metal-ligand pair. Equilibrium constants can
contain no more than one decimal place. Mathematical and
computational limitations are given in References 1 and 2.
Charge balance is not required.
4. Input and Output: A program header card with input and
output selections is included with each run. Ten cases can be
considered in each run. Concentrations of each metal and
ligand can be given in mg/L or - log (molarity). Cards are
added for carbon dioxide partial pressure, redox reactions and
solids to check saturation indices for precipitation. A test
data set is given in Reference 2.
The model can output solid and aqueous speciation in units
of "-log (molarity)", and give a table summarizing forms by
percentage. Interaction capacities and intensities can be
given, and the ionic strength, saturation indices for numerous
solids, the pH, and several other parameters can be calculated.
5. System Resource Requirements: This model is coded in
FORTRAN Gl and is run on an IBM 370/168 mainframe. It is
stored on magnetic tape. It uses any 132 position line printer
for output, and a card reader/punch or terminal for input. The
manpower needs include a background in water chemistry and
ability to input data via cards or terminal.
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6. Applications: The model has been used extensively by
Drinking Water Research Division, U.S. EPA to determine water
quality adjustments to protect asbestos-cement, lead, and
galvanized pipe. The program has been used to gain a
comprehensive understanding of the naturally-occurring chemical
factors involved in preventing the deterioration of
asbestos-cement pipe. (see ref 3)
7. Technical Contact
Michael R. Schock
U.S. Environmental Protection Agency
Drinking Water Research Division
26 W. St. Clair Street
Cincinnati, Ohio 45268
513/684-7236
8. References
Ingle, S.E. et al., A User's Guide for FEDEQL.EPA, A
Computer Program for Chemical Equilibria in Aqueous
Systems, EPA-600/3-78-024 (1978).
Ingle, S.E. et al., REDEQL.EPAK Aqueous Chemical
Equilibrium Computer Program. Marine and Freshwater
Ecology Branch, Corvallis Environmental Research
Laboratory, Corvallis, Oregon (In press, 1980).
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Schock, Michael R. and Buelow, R. W., The Behavior of
Asbestos-Cement Pipe Under Various Water Quality
Conditions, A Progress Report. Part 2 - Theoretical
Considerations. Submitted manuscript (1980).
Schock, Michael R., Computer Modeling of Solid Solubilities
as a Guide to Treatment Techniques. A paper given at the
seminar "Corrosion Control Water Distribution Systems, "
Cincinnati, Ohio, May 20-22, (1980).
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COMPUTER PROGRAM FQR CHEMICAL EQUILIBRIA
IN AQUEOUS SYSTEMS (REDEQL. EPAK)
1. Model Overview: The REDEQL.EPAK program computes aqueous
equilibria for up to 20 metals and 30 ligands (anions) in a system.
The metals and ligands are selected from a list of 35 metals and
59 ligands for which thermodynamic data for complexes and solids
have been stored in a data file. More data may be added by the
user. The equilibria which the program considers include
complexation, precipitation, oxidation-reduction, and pH-dependent
phenomena. REDEQL.EPAK is a modification of REDEQL2 which was
developed over a number of years at the California Institute of
Technology by Morgan, Morel, and McDuff under grants initially
from Gulf Oil Corporation, the Environmental Quality Laboratory
of Cal Tech, and the Rockefeller Foundation, and, since 1972,
under grants from the Environmental Protection Agency.
2. Functional Capabilities: The REDEQL.EPAK program is designed
to compute chemical equilibria involving solids, complexes,
oxidation-reduction, and mixed solids in an aqueous system. Ionic
strength may be calculated or specified and formation constants
for solids and complexes are computed from the thermodynamic data
for infinite dilution and from iqnic strength. Interaction
capacities and intensities as described by Morel et al. can
also be computed.
Up to ten cases of different total concentrations for a set
of metals and ligands selected from those available can be treated
by the program in one run. A maximum of 20 metals and 30 ligands
may be included in any one system considered. The results of the
program are the speciation of the metals and ligands in various
forms and combinations. A large amount of complexation and
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solubility data is stored for up to three solids and six complexes
for each metal ligand pair. It is possible to allow for
supersaturation with respect to any of the possible solids.
The program may be used with either laboratory, field, or
hypothetical input data. The results of the program are based on
thermodynamic data, which have been previously measured in less
complex systems, applied to the input data. If the formation
constant for a solid or complex is not in the thermodynamic data
file, that solid or complex will not be predicted to form in the
aqueous system being studied, even though it may indeed be present.
If a chemical (metal or ligand) present in the system is omitted
from the input data for the program, then the results will be in
error. If the pH of the system is not known or if the redox
potential is not known when oxidation or reduction might occur,
then results will be in error. The pH is not required if the
chemical form of all species introduced to the system is known.
3. Basic Assumptions: Assumptions and limitations of REDEQL.EPAK
follow:
1) The system being modeled is assumed to be at equilibrium.
Kinetics of dissolution, precipitation, and
oxidation/reduction are not considered.
2) The program does not consider surface effects. Neither
the variation of surface properties of solids from bulk
properties nor adsorption is included.
3) Errors in analytical or thermodynamic data will be
propagated by the program.
4) The ionic strength corrections of equilibrium constants
used in the program are not accurate above approximately
0.1 M ionic strength and should not be used above 1.0 M.
5) The program can violate electroneutrality. Nowhere in
the computational scheme of the program is the number of
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positive charges in a unit volume required to equal the
number of negative charges. The "charge" on the
equilibrium solution is calculated.
6) Very small concentrations in the output may be inaccurate.
7) Atmospheric concentrations of CO- and N must be fixed
/ 2
and cannot vary during a computation except to disappear
entirely.
8) No pressure variation is allowed for the thermodynamic
data in the program which is given for 25°C and one
atmosphere. Temperature data are available for a few
thermodynamic constants.
9) At low concentrations, organic matter which is undetected
and uncharacterized may strongly affect equilibria.
4. Input and Output: The primary inputs for the program are
the total concentrations of metals and ligands in the system,
including quantities in solid and gas phases if these are allowed
to interact with the aqueous system. Concentration may be in
molar units or milligrams/liter. The thermodynamic data file
contains thermodynamic data for metals, ligands, solids, complexes,
redox reactions, and mixed solids. It presently includes the
thermodynamic data for 35 metals and 59 ligands. Input to the
program is in card image form.
Output from the model is in the form of computer printouts.
Normal output from REDEQL.EPAK include thermodynamic input for
verification, input data for the program calculation, case progress
for each case, concentration of complexes, speciation of the metals
and ligands, primary distribution of metals and ligands, interaction
capacities and/or intensities, and error message output.
5. System Resource Requirements: REDEQL.EPAK is available frojn
the EPA IBM cconputer system in Research Triangle Park, N.C. The
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program is run from a load module and requires 200K bytes of core
storage. A background in environmental chemistry with familiarity
with the model's theory and limitations are helpful.
6. Applications: The model is useful for situations such as
1) mixing compounds together in a beaker of water and predicting
what would happen after equilibrium is reached, 2) determining
the fate of a chemical introduced into a well-characterized body
of water (assuming no further mixing or dilution), 3) predicting
removal of chemicals through precipitation under varying
conditions of pH, redox potential, and ionic strength,
4) confirming laboratory results for pH or precipitation,
5) predicting pH or metal concentration in aquatic media for
biological experiments, and 6) examining mixing and dilution
effects in sequential cases with varying initial concentrations.
7. Technical Contact
Don Schultz
U.S. Environmental Protection Agency
Marine and Freshwater Ecology Branch
Corvallis Environmental Research Laboratory
200 S.W. 35th Street
Corvallis, Oregon '97330
COM 503/867-4039 FTS 867-4039
8. References
Ingle, S.E., Schuldt, M.D., and Schults, D.W., "A User's
Guide for REDEQL.EPA: A Computer Program for Chemical
Equilibria in Aqueous Systems," Corvallis Environmental
Research Laboratory, Office of Research and Development,
U.S. Environmental Protection Agency, Corvallis, Oregon,
• EPA-600/3-78-024 , February, 1978 .
McDuff, R.E. and Morel, P.M., Technical Report EQ-73-02:
Description and Use of the Chemical Equilibrium Program
REDEQL2. Iv.M. Keck Laboratory of Environmental
Engineering Science, California Institute of Technology,
Pasadena, 1975.
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Morel, F. and Morgan, J.J., "A Numerical Method for
Computing Equilibria in Aqueous Chemical Systems,"
Environmental Science Technology, 6:58-57, 1972.
Morel, F., McDuff, R.E., and Morgan, J.J., "Interactions
and Chemostasis in Aquatic Chemical Systems - The Role
of pH, pE, Solubility and Complexation," in P. C. Singer
(ed.)> Trace Metals and Metal-Organic Interactions in
Natural Waters, Ann Arbor Science Publishers, Ann Arbor,
1973.
Stumm, W. and Morgan, J.J., Aquatic Chemistry, Wiley-
Interscience, New York, 1970.
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DISSOLVED OXYGEN SAG MODEL (DOSAG-I)
1. Model Overview: DOSAG-I is a mathematical model developed
to predict the steady state dissolved oxygen concentrations in
streams and canals resulting from a specified set of streamflow,
wasteload, and temperature conditions. The model will determine
the streamflow required to maintain a specified dissolved oxygen
goal and will search the system for available storage to achieve
the goal. The model can be used to estimate mean monthly dis-
solved oxygen levels over a full year. Both carbonaceous and
nitrogenous oxygen demands are included,and up to five degrees
of treatment for both can be specified. It is one of two pro-
grams that the Texas Water Quality Development Board and the EPA
has for use in stream quality simulation studies. The other
program, QUAL-I, is designed to be used as a complement to DOSAG-I.
2. Functional Capabilities: The purpose of this model is to
calculate the biochemical oxygen demand and the minimum dissolved
oxygen concentration in a particular stream system. If desired,
the minimum dissolved oxygen concentration in the stream system
may be checked against a prespecified target level dissolved oxy-
gen concentration. If the minimum dissolved oxygen level is be-
low the target dissolved oxygen level, the program will compute
the required amount of flow augmentation to bring the dissolved
oxygen level up to the target level in the entire system. The
program is designed to be run for varying climatic and hydrologic
conditions during a twelve month period. Thus, it is possible
to enter up to twelve different temperatures and corresponding
discharges to each of the headwaters within the stream system be-
ing modeled.
The DOSAG-I model is a one-dimensional, horizontal plane
model for streams, rivers, manmade canals and other water convey-
ance systems. Large impoundments such as reservoirs cannot be
considered by this program. A list of restrictions follows:
1) Maximum of 10 headwater stretches
2) Maximum of 20 junctions
3) Maximum of SO reaches
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4) Maximum of 20 stretches
5) Maximum of twelve months of routing for temperature and
headwater flows; a minimum of one month must be used
6) Maximum of four dissolved oxygen targets. A minimum of
one dissolved oxygen target must be specified. This
dissolved oxygen target may be entered as a negative
number if no flow augmentation is desired.
7) Maximum of five degrees of treatment for both carbona-
ceous and nitrogenous wastes; a minimum of one degree
of treatment may be specified. If the user does not
wish for the degree of treatment calculations to be used
in the modeling process, a number less than one should
be entered as the treatment factor for both types of
wastes.
DOSAG-I has a high sensitivity for residual loads and velocities,
and a moderate estimated sensitivity for flow and decay coeffi-
cients .
3. Basic Assumptions: The model assumes constant stream
velocity throughout each reach and assumes first order decay
only. The Streeter-Phelps equation is used to calculate dis-
solved oxygen concentration, and the computation of atmospheric
reaeration is based on the Fickian law of diffusion, A Lagrang-
ian solution technique is used to solve the dissolved oxygen
equations.
4. Input and Output: The following are required for input
and calibration needs:
1) Reach length
2) Mean velocity
3) Mean discharge
4) Mean depth (per reach)
5) Average reach temperature
6) Residuals discharge inflows
7) Withdrawals and groundwater inflows
8) Residuals inputs (as BOD)
9) Dissolved oxygen concentration in each reach
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For verification of the model, streamflows, stream velocity, and
observed constituent concentrations throughout the modeled area
are required.
Output from the model consists of a tabular printout of
the concentration of dissolved oxygen for each reach, BOD (car-
bonaceous and nitrogenous) at the start and end of each reach,
an echo of all input data, and a final summary.
5. System Resource Requirements: DOSAG-I is coded in FORTRAN
IV (G level) and requires a digital computer with at least a
27,000 word storage capacity. Data preparation requires 2-6
man-weeks and output analysis takes less than one hour. Actual
computation costs run from $1-5. A Background in- engineering
with, some "Baste programming experience is--useful.
6. Applications: DOSAG-I was developed by Water Resources
Engineers, Inc. and the Texas Water Development Board. The model
has been used by the Texas Water Development Board for use in
the San Antonio River Basin, and it has been used for a variety
of applications by the EPA. DOSAG I has been replaced in recent
years by the nore flexible QUAL-II Model.
7. Technical Contact
Tom Barnwell
U.S. Environmental Protection Agency
Center for Water Quality Modeling
College Station Road
Athens, Georgia 30605
COM 404/546-3585 FTS 546-3585
8. References
Finnemore, E.J., Grimsrud, G.P. , and Owen, H.J. Evaluation
of Water Quality Models: A Management Guide for Planners,
prepared tor the Environmental Protection Agency, Office of
Research and Development, Washington, D.C. , under Contract
No. 68-01-2641, July 1976.
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Texas Water Development Board, "DOSAG-I - Simulation of
Water Quality in Streams and Canals: Program Documentation
and User's Manual." Report by TWDB Systems Engineering
Division, Austin, Texas, 1972.
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DYNAMIC ESTUARY MODEL (DEM)
1. Model Overview: The DEM is a "real time" link-node model
that simulates the unsteady tidal flow and dispersional charac-
teristics of an estuary. The model can be applied to estuaries
in which vertical stratification is either absent or limited to
relatively small areas. It can accommodate both conservative and
non-conservative constituents. Constituents which have been
modeled include salinity, tracer dye, dissolved solids, DO, BOD,
total nitrogen, NH3, N03> phosphates, chlorophyll a, and coli-
form bacteria. Given the necessary kinetics and rates, the model
could also treat parameters such as pesticides, heavy metals, and
organic compounds. The DEM is often linked to the Tidal Tempera-
ture Model (TTM) for heat budgets. Ecological processes such as
algal dynamics, nutrient transport, sediment oxygen demand, coli
form die-off, and first order kinetics have been expressed in the
model. Higher order kinetics are available on some versions.
2. Functional Capabilities: The DEM represents the two-dimen-
sional (lateral and longitudinal) tidal flow pattern, basic
transport processes (advection and dispersion), and the accretion
or depletion of pollutants within an estuary [provided that ver-
tical stratification is either absent or insignificant). The es-
tuary is represented by a network of channels (or links) and
junctions Cor nodes). A channel is viewed as a flow conduit with
a length, width, time varying depth, time varying velocity, time
varying cross - sectional area, and frictional resistance associated
with it. A junction acts as a receptacle for mass and volume. It
is described by a constant surface area, time varying head, and
time varying volume.
The DEM is composed of two separate, but interrelated compon-
ents. The first component is a hydraulic model which uses a step-
forward explicit finite difference scheme to solve the equations
of motion and continuity for channels and junctions, respectively.
The result is a "dynamic steady-state" solution of the hydrodynamic
behavior of the estuary applicable to a specific set of flow inputs
and boundary tidal conditions,
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The second component, a quality model, is closely tied to
the hydraulic model. The quality model and the hydraulic model
are referenced to the same network of channels and junctions.
The tidally fluctuating velocities, flows, and heads predicted
by the hydraulic model are stored on tape or disk and are the
basis of the hydraulic inputs to the quality program. Constit-
uents in the quality program are subject to the processes of
advection, dispersion (including both eddy diffusion and disper-
sion due to density currents), biological and/or chemical decay,
transfer between water and the atmosphere, and transfer between
water and the bottom sediments. A mass balance for each con-
stituent is performed at each junction for each time step. The
quality model predicts the dynamic (time varying) constituent
concentrations in each junction which result from a specified
set of boundary conditions, inflows, waste discharges, and di
versions. It is important that the time and space scales used
in the DEM approximate as nearly as possible the physical, tid-
al, and climatic characteristics of the estuary. Special atten-
tion should be paid to the correspondence of model network fea-
tures with existing sampling stations and wastewater inputs.
The DEM is sensitive to (1) the time step in the hydraulic
program (stability reasons), (2) net flows, (3) residuals loading
rates, (4) frictional resistance coefficient, (5) initial condi-
tions if "real time" solutions are desired, and (6) the specified
reaction kinetics and rates.
The Dynamic Estuary Model has been run for networks with as
many as 1300 channels and 840 junctions. Some versions have mod-
eled up to 15 constituents.
3. Basic Assumptions: The model assumes that vertical strati-
fication is either absent or limited to relatively small areas,
and it does not handle wind stress or tidal flats exposed at low
tide. Other hydrodynamic processes assumed negligible include
longitudinal density gradients, Coriolis acceleration, and bottom
slope. The instantaneous mixing of residuals discharge throughout
junctions is also assumed.
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4. Input and Output: The DEM requires a large input data base
on disk, tape, and/or cards. Parameters which need to be speci-
fied include headwater and tributary flows, wastewater flows and
loadings, water withdrawals, seaward tidal conditions, channel
and junction geometry, bottom roughness of each channel, constit-
uent concentrations at boundaries, and decay rates for non-con-
servative constituents. Physical data pertaining to channels and
junctions can be obtained from navigational charts since direct
measurements are seldom available.
The model is capable of producing a wide variety of outputs.
Output options available are: (1) maximum and minimum flows,
heads, and velocities, as well as net flows, over a tidal cycle
for the model network, (2) maximum, minimum, and average constit-
uent concentrations for each junction over a complete tidal cycle
(or other specified averaging interval), (3) "slack water" and
"snapshot" tables of constituent concentrations at desired time
intervals throughout the simulation, and (4) line-printer plots
of both spatial and temporal concentration profiles.
5. System Resource Requirements: The DEM is written in FORTRAN
IV. The hydraulic component of the model requires 2 files, either
on disk or tape. For a network of 129 junctions and 131 channels,
the hydraulic program can be run on a digital computer with 130K
of main storage. The cost of a 50 hour (4 tidal cycles) hydraulic
simulation on an IBM 370/168 is approximately $40. The quality
component of DEM requires from 4 to 7 files, depending of the out-
put options desired. A quality program with 6 constituents can be
run on a digital computer with 200K to 400K of main storage. The
cost of a 1000 hour (80 tidal cycles) quality simulation for 6
constituents and 129 junctions can range from $40 to $75. The DEM
requires 5 to 20 manweeks of effort for data preparation and out-
put interpretation, A background in programming and environmental
engineering with experience in water quality modeling is useful.
6. Applications: The Dynamic Estuary Model has been used by
the EPA for the Pearl Harbor Water Quality Model development pro-
ject. It was originally developed by the Water Resources Engin-
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eers for the Division of Water Supply and Pollution Control of
the Public Health Service, and it has been used by the Federal
Water Pollution Control Administration (FWPCA) and by the State
of California. The DEM was used by the Federal Water Quality Ad-
ministration (FWQA) for water studies of the San Francisco and
San Diego Bay estuaries, and by the EPA for water quality studies
of the Delaware and Potomac estuaries. There have been other
users and applications of this model.
7. Technical Contacts
Leo J. Clark and Stephen E. Roesch (Potomac application)
U. S. Environmental Protection Agency
Annapolis Field Office
Annapolis Science Center
Annapolis, Maryland 21401
FTS 922-3752 COM 301/224-2740
Robert B. Ambrose (Delaware application)
U. S. Environmental Protection Agency
College Station Road
Athens, Georgia 30605
FTS 250-3546 COM 404/546-3546
8. References
Ambrose, R.B. and Prather, T.L. "Linkage of RECEIV Hydrodynamics
with Dynamic Estuary Model Water Quality." From proceedings of
the Storm Water Management Model User's Group Meetings, January
19-20, 1981. Department of Civil Engineering. McMaster Univer-
sity, Hamilton, Ontario, Canada.
Ambrose, R.B. and Roesch, S.E. "Dynamic Estuary Model Performance.
Journal Environmental Engineering Division ASCE, Paper No. 16847,
pp. 51-71, February 1982.
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California Water Resources Control Board. "Final Report -
San Francisco Bay - Delta Water Quality Control Program."
Preliminary Abridged Edition, 1969-
Feigner, K.D., and Harris, H.S. "Documentation Report, FWQA
Dynamic Estuary Model." Report to U. S. Department of the
Interior, Federal Water Quality Administration, 1970.
Finnemore, E.J.,Grimsrud,G.P. , and Owen,a.J . "Evaluation
of Water Quality Models: A Management Guide for Planners.
Report to Environmental Protection Agency, Office of Research
and Development, Washington, D.C., 1976.
Genet, L.A., Smith, D.J., and Sonnen,M.B. "Computer Program
Documentation for the Dynamic Estuary Model." Report by
Water Resources. Engineers, Inc., Walnut Creek, California, to
U.S. Environmental Protection Agency, Systems Development
Branch, Washington, D.C., 1974.
Water Resources Engineers, Inc. "A Hydraulic Water Quality
Model of Suisun and San Pablo Bays." Report to U.S. Depart-
ment of the Interior, Federal Water Pollution Control Adminis-
tration, Southwest Region.
Water Resources Engineers, Inc. "A Water Quality Model of the
Sacramento San Joaquin Delta." Report by WRE to U.S. Public
Health Service, Region IX, 1965.
Water Resources Engineers, Inc. "Computer Program Documenta-
tion for the Dynamic Estuary Model." Report by WRE to Florida
Department of Pollution Control, Tallahassee, Florida, 1974.
Water Resources Engineers, Inc. "Validation and Sensitivity
Analyses of Stream and Estuary Models Applied to Pearl Harbor
Hawaii." Reoort bv WRE. Walnut Creek. California, to U S En-
vironmental Protection Agency, Systems Development Branch
Washington, D.C., 1974.
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ENHANCED HYDRODYNAMICAL-NUMERICAL MODEL
FOR NEAR SHORE PROCESSES (HN)
1. Model Overview: The Hansen type multilayer Hydrodynamical-
Numerical (HN) model described by Bauer has been used success-
fully to study the dynamics of numerous coastal areas. The opti
mized version of the HN model combines the vertically integrated
single layger HN model originally developed by Professor W. Hansen,
University of Hamburg, Germany, and the multilayer multiple-open
boundary HN model proposed by Hansen and developed by Dr. T.
Laevastu.
2. Functional Capabilities: The enhanced HN model simulates
near-shore currents and exchange processes. Enhancements to the
multilayer Hansen type Hydrodynamical-Numerical model include:
non-linear term extension to facilitate small-mesh studies of the
near-shore, including river dynamics; layer disappearance exten-
sion to enable appropriate procedures in tidal flat and marshy
regions, as well as some down/upwelling cases; thermal advection
enhancement for treatment of thermal pollution cases by method
of moments coupled with heat budget procedures for dynamic plume
development experiments; and Monte Carlo diffusion enhancement
to deal with dispersion via statistical methods and comparison
to the method of moments experiments.
3. Basic Assumptions: The Hydrodynamical-Numerical model is
an explicit numerical difference scheme based on leap-frog inte-
gration of the two dimensional Eulerian form of the hydrodynamical
equations through time to obtain a dynamical boundary-value solu-
tion of tidal order. Advection is simulated by the method of
moments, a quasi-Lagrangian method which maintains information
on the ieroth, first, and second order moments of the concentra-
tion in each cell of the grid mesh. In order to introduce the
random element for utilization of Monte Carlo methods, the total
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velocity for a particular fluid particle is assumed to be composed
of a mean flow velocity component and a turbulent flux velocity
component. The HN model provides the mean flow velocity and the
Monte Carlo scheme provides the turbulent flux velocity. Disper-
sion is thus modeled by simulating the diffusion process stochas-
tically within the background fluid in motion. The Pedersen-Prahm
thermal advection scheme has been chosen since it is a conservative
scheme without the pseudo-diffusion of Eulerian difference methods.
In order to model the heat budget effects on the thermal discharge
as it is transported by the currents throughout the region, the
Laevastu thermal techniques were selected.
4. Input and Output: Input to the HN model include grid
and mesh size; system geometry, bathymetry, and boundaries;
average latitude; Coriolis factor; tidal data; wind values; outfall
sources and sinks; storm surge, and river inflow. Control cards
and library directives are necessary for the selection of sub-
routines. Output provided by the model include computer print-
outs of the input variables and contour plots.
5. System Resource Requirements: Various portions of the
HN model are run on Ore-gon State's CDC 3300 computer, and the Bonne-
ville Power Authority's CBPA1 CDC 65QO_, Experience in environmental
modeling and an understanding of th£ jnodel'-s theory and limitatigns
are useful for anyone wishing to use the enhanced HN model.
6- Applications: The enhanced HN model has been applied
by the EPA to Prudhoe Bay, a section of the coastal area of the
Beaufort Sea, Alaska, and the San Onofre outfall in California,
The model can be used to evaluate the adveetion and dispersion of
constituents in near shore coastal waters.
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7. Technical Contact
Richard J. Callaway
U.S. Environmental Research Laboratory
Corvallis Environmental Research Laboratory
200 Southwest 35th Street
Corvallis, Oregon 97330
FTS 420-4703 COM 503/757-4703
References
Bauer, R.A. and Stroud, A.D. Enhanced Hydrodynamical-Numerical
Model for Near Shore Processes"! In Press. Prepared by Compass
Systems, Inc., San Diego, California, for Corvallis Environ-
mental Research Laboratory, Office of Research and Development,
U.S. Environmental Protection Agency, Corvallis, Oregon. EPA
Contract 68-03-2225.
Bauer, R.A. Description of the Optimized DPRF Multi-Layer
Hydjodynamical-Numerical Model"ENVPREDRSCHFAC Tech. Paper
No. 15-74, Environmental Prediction Research Facility, Mon-
terey, California, 1974.
Hansen, W. Hydrodynamical Methods Applied to Oceanographic
Problems. In: Proceedings of the Symposium on Mathematical-
Hydrodynamical Methods of Physical Oceanography^Institute Fur
Meereskunde der Universitat Hamburg, Hamburg, West Germany,
1962. pp. 25-34.
Laevastu, T. and Stevens, P., Applications of Numerical-Hydro -
dynamical Models in Ocean Analysis/Forecasting.FNWC Tech.
Note No.51,Fleet Numerical Weather Central, Monterey,
California, 1969.
Laevastu, T. and Rabe, K., A Description of the EPRF Hydro-
dynamical Models in Ocean Analysis/Forecasting. FNWC Tech.
Note No.51,Fleet Numerical Weather Central, Monterey,
California, 1969.
Laevastu, T. A Vertically Integrated Hydrodynamical-Numerical
Model CW. Hansen Type), Model Description and Qpera^:J-J1g/R^nn^g
Instructions. Part 1 of a series of' four reports-. ENVPREDRSCHFAC
Technical Note No. 2-74, Environmental Prediction Research
Facility, Monterey, California, 1974.
Laevastu, T. A Multilayer Hydrodynamical-Numerical Model
(W. Hansen Type), Model Description and Operation/Running
Instructions.Part 2 of a series of four reports.
ENVPREDRSCHFAC Technical Note No. 2-74, Environmental
Prediction Research Facility, Monterey, California, 1974.
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Laevastu, T. in collaboration with M. Clancy and A. Stroud.
Computation of Tides, Currents and Dispersal of Pollutants
in Lower Bay and Approaches to New York with Fine and Medium
Grid Size Hydrodynamical-Numerical Models.Part 3 of a series
of four reports. ENVPREDRSCHFAC Technical Note No. 3-74,
Environmental Prediction Research Facility, Monterey, California,
1974 .
Laevastu, T. and Callaway, R. ,in collaboration with A. Stroud
and M. Clancy. Computation of Tides, Currents and Dispersal
of Pollutants in New York Bight from Block Island to Atantic
City with Large Grid Size, Single and Two Layer Hydrodynamical
Numerical Models. Part 4 of a series of four reports.
ENVPREDRSCHFAC Technical Note No. 4-74, Environmental Pre-
diction Research Facility, Monterey, California, 1974.
Laevastu, T. and Hamilton, G.D., Computations of Real-Time
Currents Off Southern California With Multilayer Hydrodynamical-
Numerical Models with'Several Open Boundaries.ENVPREDRSCHFAC
Technical Paper No. 10- 74 .
Pedersen, L.B. and Prahm, L.P., A Method for Numerical Solution
of the Advection Equation 3C/ 3t=-V-VC, Meteorological Insti-
tute of Denmark, as submitted to TELLUS, August, 1973.
Laevastu, T. and Harding, J.M., "Numerical Analysis and Fore-
casting of Surface Air Temperature and Water Vapor Pressure."
Journal of Geophysical Research. 79(30) :4478-4480 , 1974.
Maier-Reimer, E. Numerical Treatment of Horizontal Diffusion
and Transport Phenomena in Marine Basins of Large Size, Institut
fur Meereskunde der Universitat Hamburg, Hamburg, West Germany,
as presented at IAMPA/IAPSO Assembly, Melbourne, Australia,
January 1974.
Stroud, A. D. and Bauer, R. A. User Guide for the Enhanced
Hvdrodvnamical-Numerical Model. In Press. Prepared by
Compass Systems, Inc., San Diego, California, for Corvallis
Environmental Research Laboratory, Office of Research and
Development, U.S. Environmental Protection Agency Corvallis
Oregon, under EPA Contract No. 68-03-2225. '
Thompson, R. Numerical Calculation of Turbulent Diffusion.
Quart. J. R. Met. Soc. 97(411) : 93-98, 1971.
Young, Chen-Shyong. Thermal Discharges into the Coastal
Waters of Southern California. Southern California Coastal
Water Research Project (SCCWRP), Los Angeles, California,
1971 .
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ESTUARINE WATER QUALITY MODEL (ESOOlj
1. Model Overview: ES001 is a steady-state, one-dimensional,
estuarine water quality model which simulates BOD and DO varia-
tions. It was prepared by the EPA to improve upon and document
some water quality models developed for the EPA by Hydroscience,
Inc., and it is particularly useful for the rapid evaluation of
a number of varying estuary and wasteload conditions. Based on
the law of conservation of mass, the program is designed to
model the BOD-DO deficit system, but it is capable of modeling
analogous systems of sequential reactions of two substances
having first order kinetics, like that of a nitrogen reaction
with ammonia and nitrate. The model is assumed to be at steady
state and to be tidally averaged.
2. Functional Capabilities: The program segments the system
being modeled into sections within which the various geomorpho-
logical, physical, and hydrological parameters of the estuary
are constant. For each segment the estuarine steady-state advec-
tive-dispersive equation with constant coefficients is defined,
the junction points of the segments being boundary points where
these parameters can change. Several types of junctions are al-
lowed, including triple junctions, dams, etc., which in combina-
tion allow the modeling of numerous types of configurations. A
maximum of 100 junctions and 50-100 sections can be accommodated.
Each section can have a length of up to 20 miles. Physical pro-
cesses that can be simulated include: dilution, advection, longi-
tudinal dispersion, temperature effects, and the processes of
first order decay and reaeration. The model handles riverine es-
tuaries well, especially when the net velocity is less than one
foot per second. The ES001 is sensitive to reaeration coeffi-
cients, dispersion coefficients, net flow velocities, and deoxy-
genation coefficients. A number of output options are provided.
3. Basic Assumptions: ES001 handles only steady-state flows
and discharges, and it does not consider flow velocity or qual-
ity variations with depth or within stream cross sections. The
model assumes only first order kinetics for BOD and DO, and it
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utilizes a matrix inversion technique for the solution of simul-
taneous differential equations which are derived from the Law of
conservation of mass.
4. Input and Output: The ES001 requires a large amount of
input data in card-image form. Initial input/calibration needs
include: estuary cross-sectional data, segment length, water
depth, net flow, reservoir outflows, estuary volume, tidal ex-
change coefficient, dispersion coefficient, constituent concen-
tration for all system inflows, temperatures, benthic oxygen de-
mand, algal photosynthesis, respiration, and other rate coeffi
cients, residual inputs from point sources, uniform waste input,
salinities at seaward boundaries, tidal exchange coefficients,
and temperature correction factors. Constituent concentrations
throughout the system and observed salinity patterns are needed
for verification.
Output information provided by the model includes a tabular
print-out of the input data, BOD concentration and DO deficits at
ten equidistant points per segment, and a number of matrices (DO
deficit matrix, BOD matrix, and inverted DO deficit matrix).
5. System Resource Requirements: ES001 is written in FORTRAN
IV and may be installed on an IBM System/370 or equivalent. A
region size of 245K is utilized by the program, and 2-6 manweeks
are needed for data preparation and programming. A background
in programming or environmental engineering with familiarity in
computerized modeling is useful. A later version of the ES001,
the ES002, is designed to run on an IBM 1130 system.
6. Applications: ES001 was developed by Hydroscience , Inc., for
the Federal Water Pollution Control Administration (FWPCA) in the
Water Quality for the Hudson-Champlain and Metropolitan Coastal
Water Pollution Control Project in 1968. The model was one of sev-
eral simulation programs used to evaluate water quality for the New
York Harbor Complex and parts of the Raritan and Hudson Rivers.
Since then, ES001 has found other applications.
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Technical Contacts
George A. Nossa and Laura Livingston
U.S. Environmental Protection Agency
Information Systems Branch 2PM-IS
2o Federal Plaza
New York, New York 10278
FTS 264-9850 COM 212/264-9850
Steven C. Chapra
NOAA Great Lakes Environmental Research Laboratory
2300 Washtenaw Avenue
Ann Arbor, Michigan 48104
FTS 378-225C COM 313/668-2250
References
Chapra, S.C., and Gordimer, S., Documentation of ES001, A
Steady-state, One Dimensional, Estuarine Water Quality Model,
U.S.Environmental Protection Agency,Region II, 26 Federal
Plaza, New York, New York, September 1973.
Finnemore, E.J., Grimsrud, G.P., and Owen, H.J., Evaluation
of Water Quality Models: A Management Guide for Planners,
prepared for the Environmental Protection Agency, Office of
Research and Development, Washington, B.C., under contract
No. 68-01-2641, July 1976.
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EXEC/OP VERSION 1.2 (EXEC/OP)
1. Model Overview: EXEC/OP is a FORTRAN computer program that
synthesizes municipal wastewater treatment and sludge disposal
systems from a specified list of unit treatment process types.
It can be thought of as an optimization version of a previously
developed treatment system evaluation program known as
EXECUTIVE. EXEC/OP will select the combination of unit
processes that approximately best meets a stipulated set of
criteria on cost, energy, land utilization, a subjective index
of system desireability, and effluent quality. It also has the
capability to identify up to 40 next-best designs.
2- Functional Capabilities: The model features the following
capabilities :
1) The design of the wastewater and sludge treatment
sub-system is done in an integrated fashion.
2) Principles of mass balance and reaction kinetics are
employed.
3) Nineteen species of pollutants are accounted for.
4) Twenty-two different types of unit processes are
includ ed .
5) Eight types of design criteria, plus effluent quality,
can be considered.
6) Up to 50 unique process options spread among up to 20
treatment stages, with no more than 10 per stage can
be hand led.
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7) Problems with 15 to 20 thousand design configurations
can be solved in several minutes of processing time.
3- Basic Assumptions: EXEC/OP is designed on the basis of
steady state mass balance and pollutant transformation
relationships. Unit process performance consists of predicting
effluent quality, sludge or sidestream quality, equipment
sizing, cost, land energy consumption, and production as a
function of influent waste quality. A set of design parameters
are specified as input by the user. Cost and energy
relationships are parameterized on specific design variables
and will be accurate to within _+ 30 percent. The opimization
methodology employed consists of implicit enumeration (a form
of integer programming) coupled to a heuristic penalty method
that accounts for the impact of return sidestreams from sludge
processing.
4. Input and Output: Input specifies the types of unit
processes to be considered at each stage of a multi-stage
liquid waste treatment train, a secondary sludge treatment
train, and a primary or mixed primary-secondary sludge
treatment train. A set of input design parameters (e.g.,
loading rates, kinetic constants, and required effluent
quality) is also supplied for each unit process. Unit wage
rates and cost indices are used to adjust costs to current
prices. Input also includes the quality of the influent waste
stream to the system, the required final effluent quality, and
a stiulation of the type of design criteria to be used in the
optimization (e.g., minimize total cost, minimize energy usage
subject to cost not exceeding a certain value, etc.).
Output includes a listing of the unit process choices for
each stage of the system in the best design and the M next-best
designs, where M is specified by the user. Another output
option permits a detailed system performance report to be
generated for a given user-specified design.
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5. System Resource Requirements: EXEC/OP is coded in
FORTRAN IV and is run on an Univac 1100 or PDF 11/70. A
background in engineering is useful.
6. Applications: EXEC/OP is an optimization version of a
previously EPA developed treatment system evaluation program
known as EXECUTIVE. Its purpose is to enable a designer to
efficiently synthesize and analyze large numbers of alternative
criteria. The program has been used by several wastewater
engineering design firms for preparing facilities plans and by
universities as a teaching and research aid.
7. Technical Contact
Lewis Rossman
U.S. Environmental Protection Agency
Environmental Research Laboratory
26 West St. Clair Street
Cincinnati, Ohio 45268
COM 513/684-7636 FTS 684-7636
8. References
Rossman, L.A., "Computer Aided Synthesis of Wastewater and
Sludge Disposal Systems",: EPA-600/2/79-158, U.S.
Environmental Protection Agency, Cincinnati, Ohio, December
1979 (available from NTIS as PB80174220).
Rossman, L.A., "Synthesis of Waste Treatment Systems by
Implicit Enumeration", Journal of Water Pollution Control
Federation, Vol. 52, No. 1, January 1980.
Rossman, L.A., "EXEC/OP Reference Manual: Version 1.2",
EPA-600/2-80-l82a, U.S. Environmental Protection Agency,
Cincinnati, Ohio, June 1980 (available from NTIS as
PB81104176) .
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Magnetic Tape of EXEC/OP Version 1.2 FORTRAN Source Code,
EPA-600/2-80-182b, available from NTIS as PB81104168.
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GEORGIA DOSAG (GADOSAG)
1. Model Overview: GADOSAG is a dissolved oxygen model based
on the modified Streeter Phelps equation, with options for
incremental runoff, an extra non-conservative variable, a
converge to set dissolved oxygen routine ,and numerous waste
inputs .
2. Functional Capabilities: GADOSAG is a steady state, one
dimensional, and one stretch model.
3. Basic Assumptions: The model is based on the modified
Streeter Phelps equation which predicts dissolved oxygen
deficits based on instream concentration of carbonaceous and
nitrogenous BOD and their respective reaction rates and the
reaeration characteristics of the stream. Inputs are entered
by the modeler, and there are no default values.
4. Input and Output: The inputs for this model are: waste
treatment facility effluent flow; carbonaceous biological
oxygen demand; nitrogenous biological oxygen demand and
dissolved oxygen; instream concentrations of CBOD, NBOD, and
D.O. ; and stream velocity and flow measurements.
The output consists of: dissolved oxygen concentrations
along the modeled stream segment, the carbonaceous biological
oxygen demand and nitrogenous biological oxygen demand
predicted concentrations, and graphs of any of the variables in
the model .
5. System Resource Requirements: GADOSAG is coded in BASIC
and is run on a HP 9845 or 9831. It uses a 80 character
thermal printer. Knowledge of water modeling is useful.
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6. Applications; GADOSAG was used by the State of Georgia to
set effluent limits for the waste dischargers.
7. Technical Contact
James Greenfield
U.S. Environmental Protection Agency
Water Quality Standards
Atlanta, GA 30365
COM 404/881-4913 FTS 257-2913
8. References
Georgia DOSAGE User's Manual
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HYDROLOGICAL SIMULATION PROGRAM — FORTRAN (H5PF)
1. Model Overview: HSPF is a comprehensive package for
simulation of watershed hydrology and water quality developed
for the U.S. Environmental Protection Agency (EPA). The
simulation model uses information such as the time history of
rainfall, temperature, and solar radiation; characteristic land
use patterns and soil types; and agricultural practices to
simulate the processes that occur in a watershed. The result
of this simulation is a time history of the quantity and
quality of the runoff. Flow rate, sediment load, and nutrient
and pesticide concentrations are predicted. The model then
takes these results and information about the stream channels
in the watershed and simulates the processes that occur in
these streams. This part of the simulation produces a time
history of water quantity and quality at any point in the
watershed—the inflow to a lake, for example. HSPF includes a
data management system to process the large amounts of input
and output required for the simulations. Computer routines are
provided to statistically analyze the data for ease of
presentation and interpretation. HSPF can be applied to a wide
range of water resource problems. The key attribute that makes
it applicable to such a wide variety of water resource problems
is its ability to simulate the continuous behavior of
time-varying physical processes and provide statistical
summaries of the results.
2. Functional Capabilities: HSPF is built on a systematic
framework in which a variety of process modules can fit. The
system consists of a set of modules arranged in a hierarchical
structure that permits the continuous simulation of a
comprehensive range of hydrologic and water quality processes.
HSPF currently contains three application modules; PERLND,
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IMPLND, and RCHRES: and five utility modules; COPY, PLTGEN,
DISPLY, DURANL, and GENER. Each of these modules is briefly
discussed below.
Module PERLND simulates a pervious land segment with
homogeneous hydrologic and climatic characteristics. The
simulation of snow accumulation and melt is based on an energy
balance approach. Water movement is modeled along three flow
paths—overland flow, interflow, and groundwater flow — in the
manner of the Stanford Watershed Model. Erosion processes
include sediment detachment by rainfall splash and man's
influence and transport by overland flow. Scour in rills and
gullies is also considered. Water quality constituents may be
simulated in the fashion of the NPS model using simple
relationships with sediment and water yield or by using the
detailed algorithms for pesticides and nutrients as in the ARM
model.
Module IMPLND is designed to simulate impervious land
segments where little or no infiltration occurs. Algorithms
are similar to PERLND except that no water movement occurs by
interflow or groundwater flow. Solids are simulated using
accumulation and removal relationships in the manner of urban
models such as SWMM and STORM. Water quality constituents are
simulated using empirical relationships with solids and water
yield.
Module RCHRES simulates the processes that occur in a
single reach of an open channel or a completely mixed lake.
Hydraulic behavior is modeled using the kinematic wave
assumption. The outflow of an element may be distributed
across several targets that might represent normal outflows,
diversions, and multiple gates on a reservoir. Temperature is
simulated using a heat balance approach. Sediments are
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simulated as three size fractions-sand, silt, and clay. Sand
transport may be simulated using a power function of velocity,
or the Toffaleti or Colby methods. Bed shear stress
relationships are used for cohesive sediments (silt and clay.)
Both suspended and bed sediment balances are maintained.
Conservative and nonconservative constitutents are simulated in
a manner that allows maximum user flexibility. The constituent
being simulated may be dissolved or associated with any size
fraction of sediment. Dissolved constituents may undergo
hydrolysis oxidation, pholotysis, volatilization, biodegration
or a generalized first-order decay rate may be specified.
Daughter products may be simulated in the same fashion as the
parent compound. The primary dissolved oxygen and biochemical
oxygen demand balances are simulated in the traditional manner,
with provisions for decay, settling, benthal sources,
reaeration, etc. The primary nitrogen balance is modeled as
sequential reactions from ammonia through nitrate.
Denitrification is also considered. Both nitrogen and
phosphorus are considered in modeling three types of
plankton--phytoplankton, zooplankton, and attached algae.
Dissolved oxygen is considered in modeling plankton and the
nitrogen cycle. Hydrogen ion activity (pH) is calculated
considering carbon dioxide, total inorganic carbon, and
alkalinity.
HSPF's utility modules are designed to give the user
maximum flexibility in managing simulation input and output.
COPY is used to manipulate time series. The user can change
the form of the time series during the COPY operation. A
5-minute rainfall record may be aggregated to an hourly time
interval, for example. The PLTGEN module creates a specially
formatted sequential file for later access by a stand-alone
plot program. DISPLY takes a time series and summarizes the
data in a neatly formatted table. Aggregation of the basic
data is also possible. DURANL performs a duration and
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excursion analysis on a time series and computes some
elementarty statistics. It can answer questions like: "How
often does dissolved oxygen stay below 4 mg/1 for 4 consecutive
hours?" The GENER module is used to transform a time series
(A) to produce a new series (C) or to combine two time series
(A+B) to create a new one (C). For example, this module is
useful if one wants to compute the mass outflow of a
constituent from the flow and concentration.
3. Basic Assumptions: In a model as comprehensive as HSPF, it
is difficult to list all the assumptions made in its
development. The watershed hydrologic algorithms generally
follow the assumptions made in the Stanford Watershed Model.
The agricultural chemical algorithms were derived from the ARM
model and the "simple" land surface washoff algorithms are from
the NFS model. Both ARM and NFS are described elsewhere in
this catalogue. Stream routing uses the kinematic wave
approximation and the water quality algorithms use first order
kinetics except in the plankton algorithms, where Monod growth
kinetics are incorporated. The general quality constituent
algorithms used second order kinetics for specific processes
and allow kinetic sorption/desorption between dissolved and
sediment-associated phases.
4. Input and Output: Data requirements to run HSPF can be
quite extensive and depend on the state variables selected for
simulation. As a minimum, precipitation and evaporation
records are required for simulations. Many parameters can be
defaulted but defaults are not provided for the more sensitive,
site-specific parameters. System output can be obtained at
several levels, from a detailed printout of system state
variables and parameter values at every time step to yearly
summaries. Printout formats compatible with output interval
are provided. An interface file for plotters is provided and a
stand-alone program for CALCOMP plotters is available.
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5. System Resource Requirements: HSPF requires a FORTRAN
compiler that supports direct access I/O. Twelve (12) external
files are required. The system requires 128K bytes of
instruction and data storage on virtual memory machines, or
about 250K with extensive overlaying on overlay-type machines.
The system was developed on a Hewlett-Packard 3000 minicomputer
and has been used on IBM 370 series computer. It has also been
installed on PRIME, HARRIS, and Burroughs minicomputers, and on
Univac and CDC mainframes.
Because of its comprehensive nature, HSPF requires
individuals with several different backgrounds to implement
it. As a minimum, an implementation team should consist of a
systems programmer, a hydrologist, and a water quality expert.
Personnel requirements for data preparation and output
interpretation will vary with the system being modeled.
6. Applications: Although HSPF is a relatively new product,
having been publically released by EPA in April 1980, it has
undergone some testing through applications to inhouse projects
by the developer (Hydrocomp, Inc.). It has also been used in
projects sponsored by EPA. Some of these applications are
described below.
In a 208 study, a prototype of the HSPF system was applied
to the Occoquan River Basin by the Northern Virginia Planning
District Commission. The Occoquan Basin Computer Model
2
consists of 15 sub-basins (39 mi average) linked by a
network consisting of 12 stream channels and 3 reservoirs and
was based on a linkage of the NPS model and HSP. Considerable
effort went into data collection to calibrate the runoff and
stream quality models, resulting in one of the better general
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nonpoint source data bases. The model was used to project
long-term receiving water quality impacts of existing and
future (year 2005) land use patterns and to compare the
benefits of alternative "best management practice" (BMP)
levels. The HSPF software was tested largely using the
simulations of the Occoquan basin as a reference.
One of the early applications of HSPF was in a hydropower
study for the Dominican Republic. Hydropower is a major source
of electricity in this developing country, which is
experiencing an 11% annual increase in demand. Twenty
potential hydropower sites were identified and 10 potential
network configurations were hypothesized. The analysis
procedure consisted of the generation of 99 years of synthetic
hourly precipitation, calculation of land surface runoff, and
calculation of natural stream flow at 21 sites. Power
generation was simulated by routing the streamflow through the
10 different hydropower configurations. The time series for
depth of flow (head) and flow rate were then analyzed using the
GENER module to estimate the most efficient configuration.
Another water resources study using HSPF has been conducted
in the Clinton River Basin, Michigan. The purpose of the study
was to evaluate a proposed Corps of Engineers floodway,
estimate the impact of developing wet lands, investigate better
lake operating procedures, and simulate the effect of retention
ponds. The basin is located north of Detroit and lies in
Macomb and Oakland counties. It is largely urban in the south
and agricultural in the north. The HSPF network consisted of
four land segments for each of nine rain gauges and 128 channel
reaches in six sub-basins. The model was calibrated on a
ten-year record (1965-1975). Simulated annual peak flows were
then compared with 14 USGS stream gauges for the period
1927-1975. The longer time period was then used to evaluate
water resource management options.
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EPA is currently applying HSPF, through a contractor,
merged with the Chemical Migration and Risk Assessment (CMRA)
methodology, to demonstrate its application as a planning tool
for the evaluation of agricultural BMP's. This demonstration
2
is being done at two scales—the 20 mi Four Mile Creek
watershed in east central Iowa and the 1200 mi Iowa River
Basin above Coralville Reservoir. The small scale
demonstration serves as a calibration site for scale-up to the
larger basin. A number of potential BMP implementation
scenarios will be investigated relative to their impact on
water quality. Different scenarios will focus on a mix of
structural, non-structural, and input-management related
practices. Schemes aimed at receiving water quality targets
for pesticides, phosphorus, and nitrates will be simulated.
HPSF is also being used by the State of Nebraska to
investigate the effect of groundwater pumping for agriculture
in the Big Blue Basin. The University of Nebraska is using the
system as a data management tool for the Dee Creek Research
Watershed. A project sponsored through the University of North
Dakota is using HSPF to investigate the effect on wildlife
habitat of proposed lake drainage in the basin of the Red River
of the North.
An ongoing maintenance and training program is sponsored by
the center for Water Quality Modeling.
7. Technical Contact
Thomas 0. Barnwell, Jr.
U.S. Environmental Protection Agency
Center for Water Quality Modeling
Environmental Research Laboratory
College Station Road
Athens, Ga. 30613
FTS 250-3585 404/546-3585
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Hydrocomp, Inc.
201 San Antonio Circle
Mountain View, CA 94046
COM 415/948-3919
8. References
Anderson, E.A. "Development and Testing of Snow Pack Energy
Balance Equations." Water Resources Research, 4(l):19-37,
1968.
Barnwell, T.O. and Johanson, R.C. "HSPF: A Comprehensive
Package for Simulation of Watershed Hydrology and Water
Quality." Presented at: Nonpoint Pollution Control:
Tools and Techniques for the Future, Gettysburg, PA, June
1980.
Crawford, N.H. and Donigian, A.S. Jr. Pesticide Transport
and Runoff Model for Agricultural Lands.
EPA-600/2-74-013. Environmental Research Laboratory,
Athens, GA 30613, 1973.
Crawford, N.H. and Lindsey, R.K. Digital Simulation in
Hydrology; Stanford Watershed Model IV. Stanford Univ.
Tech. Rep. No. 39, Stanford Univ., Palo Alto, CA, 1966.
Donigian, A.S., Jr., Beyerlain, D.C., Davis, H.H., Jr., and
Crawford, N. H. Agricultural Runoff Management (ARM) Model
Version II; Refinement and Testing. EPA-600/3-77-098.
Environmental Research Laboratory, Athens, GA 30613, 1977,
Donigian, A.S., Jr. and Crawford, N. H. Modeling
Pesticides and Nutrients on Agricultural Lands.
EPA-600/2-76-043. Environmental Research Laboratory,
Athens, GA 30613, 1976.
279
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Donigian, A.S., Jr. and Crawford, N.H. Modeling Nonpoint
Pollution From The Land Surface. EPA-600/3-76-083.
Environmental Research Laboratory, Athens, GA 30613, 1976,
Donigian, A.S., Jr. and Crawford, N.H. Simulation o°f
Nutrient Loadings in Surface Runoff with the NFS Model.
EPA-600/3-77-065. Environmental Research Laboratory,
Athens, GA 30613, 1977.
Donigian, A.S., Jr. and Crawford, N.H. User's Manual for
the Nonpoint Source (NP5) Model. Unpublished Report.
Environmental Research Laboratory, Athens, GA 30613, 1979.
Donigian, A.S., Jr. and Crawford, N.H. User's Manual for
Agricultural Runoff Management (ARM) Model.
EPA-600/3-78-080. Environmental Research Laboratory,
Athens, GA 30613, August 1978.
Grimsrud, G.P., Franz, D.D., Johanson, R.C., Crawford,
N.H. Executive Summary for the Hydrologic Simulation
Program -- FORTRAN (HSPF). In Press. Environmental
Research Laboratory, Athens, GA 30613, 1980.
Hydrocomp, Inc. Hyd.rocomp Simulation Programming:
Operations Manual, 2nd Edition, Hydrocomp, Inc., Palo
Alto, CA, 1969.
Johanson, R.C., Imhoff, J.C., and Davis, H.H. User's
Manual for the Hydrologic Simulation Program -- FORTRAN
(HSPF). Environmental Research Laboratory, Athens, GA
30613, 1980.
280
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Leytham, K. M. and Johanson, R.C. Watershed Erosion and
Sediment Transport Model. EPA-600/3-79-028. Environmental
Research Laboratory, Athens, GA 30613, 1979.
Negev, M. A Sediment Model on a Digital Computer.
Stanford Univ. Tech. Rep. No. 76. Stanford Univ., Palo
Alto, CA, 1967.
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LAKE-I ECOLOGIC MODEL (LAKE-I)
1. Model Overview: The LAKE-I Ecologic Model is a water
quality model used in the evaluation of fresh water lakes. It
is a two-dimensional, time variable model that includes temperature,
nitrogen and phosphorous in a three layer system. The model
simulates the ecological process of photosynthesis, growth, decay,
and respiration. It considers algal-nutrient relationships, algal
zooplankton dynamics, and relatively simple higher consumer rela-
tionships. The principle driving forces of the Lake-I model include
vertical dispersion between layers, lake inflows-lake outflow, and
inflows split proportionally between layers. The model was orig-
inally developed by Thomann, DiToro, Winfield, and O'Connor at
Manhatten College, Bronx, New York for the EPA Large Lakes Research
Station in Grosse lie, Michigan.
2. Functional Capabilities: LAKE-I is a stratified three
layer system. The epilimnion extends from the surface to a 17
meter depth, the hypolimnion occurs between 17 meters and a 90
meter depth. A sediment layer is included as a material sink.
The physical transport of advection and dispersion are considered
only for exchange between the epilimnion and hypolimnion. A
thermocline is established by setting vertical dispersion between
layers to near zero, and seasonal overturns are simulated by
variable mixing rates.
Constituents modeled by LAKE-I include nitrogen,(organic
N, ammonia, and nitrate), phosphorous (organic P and ortho P) ,
organic carbon (zooplankton), and phytoplankton chlorophyll. The
Lake-I model adequately represents nutrients and phytoplankton
in an open lake. A later revision, LAKE-III can handle horizontal
transport and simulations of phytoplankton, zooplankton, and
nutrients. Model coefficients include variable vertical mixing
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rates, phytoplankton settling velocity, nutrient half-saturated
values and herbivore grazing rates.
3. Basic Assumptions: The LAKE-I Ecologic Model is based
on deterministic assumptions and is numerically integrated by the
finite difference method. Mixed horizontal dimensions with vertical
gradients are assumed.
4. Input and Output: Input to the model for initial set-up
and calibration include: segment depth; average segment water
temperature variations with time, solar radiation and photo-period;
water clarity, nutrient inputs as mass loadings (mass/unit time)
or boundary conditions (mass/unit volume); and initial conditions
for phytoplankton, zooplankton, and consumer biomass. Model
date requirements for verification incorporate observed concen-
trations of chlorophyll a, primary production (mg C/M /day),
2
total zooplankton carbon, bottom deposition rate (mg C/M /year),
total phosphorous, orthophosphorous, and total Kjedahl nitrogen,
ammonia, and nitrate.
Output from LAKE-I include the phytoplankton chlorophyll
for a one year simulation, nutrient pools, and zooplankton biomass.
Output at 0.5 day time steps are also possible. Output formats
are given as printed tables which can yield digital overplots, or
as tape outputs which can yield 3-D plots or visual projections.
5. System Resource Requirements: The model is coded and
operational in FORTRAN IV, and it is currently being run on a CDC
6600 system. Newer versions of the model will be available on
IBM and DEC equipment. Total CPU time is equal to 30 CPU seconds
for a one year simulation, with an actual execution time of 7 CPU
seconds. At least 8-16 annual runs are needed to reach a steady
state.
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Manpower needs include a computer programmer or an
environmental engineer with programming experience. Some exper-
ience in water quality models based on FORTRAN IV is helpful.
Data collection programs would involve limnologists, biologists,
and chemists.
6. Applications: The LAKE-I Model has been used by the EPA
at its Large Lakes Research Station at Grosse lie, Michigan for the
modeling of Lake Ontario as part of its Great Lakes Modeling Pro-
gram and has been applied to Saginaw Bay.
7. Technical Contact
William L Richardson
U.S. Environmental Protection Agency
Large Lakes Research Station
9311 Groh Road
Grosse He, Michigan 48138
FTS 226-7811 COM 313/226-7811
8. References
Thomann, R.V.,DiToro, D.M., Winfield, R.P., and O'Connor,
D.J. , Mathematical Modeling of Phytoplankton in
Lake Ontario, Vol. 1: Model Development and Verification.
U.S.Environmental Protection Agency,Corvallis,Oregon,
660/3-75-005, 'March 1975.
Thomann, R.V., Winfield, R.P.,DiToro, D.M., and O'Connor,D.J
Mathematical Modeling of Phytoplankton in Lake Ontario.
Vol. 2: Simulations Using LAKE-I Model.U.S. Environmen-
tal Protection Agency, Duluth, Minnesota. In press.
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LEVEL III - RECEIVING WATER QUALITY MODELING
FOR URBAN STORMWATER MANAGEMENT
1. Model Overview: Level Ill-Receiving is a simplified
continuous receiving water quality model that can be used as a
planning guide to permit preliminary screening of area wide
wastewater treatment strategies. The model was designed to
interface with hourly continuous urban catchment hydrologic
simulation models such as STORM or SWMM (both described
elsewhere in this catalogue). A large number of urban
pollution control alternatives can be simulated and evaluated
in terms of their impact on receiving water quality.
Evaluation is accomplished by evaluating either classical
dissolved oxygen sag curves or cumulative frequency curves of
dissolved oxygen concentration. The model computes a minimum
interevent time to define statistically independent storm
events.
2. Functional Capabilities: Level III - Receiving can
accommodate a large number of inflow combinations of receiving
water flow, dry-weather flow, and wet-weather flow. Oxygen
concentration is considered the key to the quality of natural
water bodies, although it is certainly not the only viable
water quality indicator. Urban runoff quantity and quality
must be computed with a model like STORM or SWMM. Receiving
water effects are computed using a simplified modeling
approach. Advective and dispersive transport is modeled. The
dissolved oxygen balance includes carbonaceous BOD and
reaeration. Nitrogenous oxygen demand is neglected.
3. Basic Assumptions: Assumptions typical of models limited
to interim planning are made, including:
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1) Temporal steady-state conditions prevail, i.e., all
system parameters and inputs (other than stormwater
inputs) are constant with respect to time.
2) Natural system parameters (such as flow, velocity,
depth, deoxygenation and reaeration rates, and
longitudinal dispersion) are spatially constant
throughout each time step.
3) All waste inflows occur at one point.
4) The effects of various natural biological processes
(algal photosynthesis and respiration, benthal
stabilization) are incorporated into a background
quality reflected by an upstream D. 0. deficit. Any
benthic buildup is incorporated in the BOD decay rate.
5) Waste treatment facilities operate at constant
efficiency, independent of hydraulic and organic
loadings, for the entire period of simulation.
4. Input and Output; Program input consists of STORM/SWMM
output and data cards organized into 5 major card groups. Card
group I controls the execution of the three major subprograms.
Card group II controls the autocorrelation analysis of
hydrologic time series. Card group III contains input data
common to both the wet weather and dry weather flow models.
Card -group IV contains the wet weather flow model input. Card
group V is specific to the dry-weather flow model.
Program output consists of tables and plots describing the
system response, including correlograms of time series,
frequency histograms or cumulative frequency curves of D.O.
concentrations, and tables of D.O. concentrations at specified
1ocations.
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5. System Resource Requirements; The program has been tested
on the IBM 370/165 and Amdahl 470-V6/II computers. It may be
compiled under either the G or H-level FORTRAN compilers. Core
requirements are approximately 100K bytes on the IBM 370/165.
Typical costs for the example problem described in the
documentation range from $5 to $25 for one year of simulation.
6. Applications: Level-Ill Receiving may be applied to the
surface drainage of most urban catchments. There is no
limitation to the size of catchment or number of storm events
modeled (other than computer time and costs). Data
requirements are common to engineering analysis of nonpoints
source problems and complete instructions on data preparation
are provided. Field measurements are necessary to calibrate
model parameters and verify predicted values. The methodology
is not applicable to systems requiring multi-dimensional
transient analysis. Complex water quality conditions such as
eutrophication, non-linear kinetic interactions, sedimentation,
and sediment exchange are not represented.
7. Technical Contacts
Richard Field
U.S. Environmental Protection Agency
Storm and Combined Sewer Section
Edison, N.J. 08837
FTS 340-6674 COM 201/321-6674
Dr. Miguel A. Medina
Department of Civil' Engineering
Duke University
Durham, N.C. 27706
919/684-2434
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References
Medina, M.A., Jr., Level-Ill: Receiving Water Quality
Modeling for Urban Stormwater Management.
EPA-600/2-79-100. U.S. Environmental Protection Agency,
Cincinnati, OH 45268, 1979-
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M.I.T. TRANSIENT WATER QUALITY NETWORK MODEL
1. Model Overview: The M.I.T. Transient Water Quality Network
Model is a one-dimensional, real-time, nitrogen cycle model
which can be used for nitrogen-limited, aerobic estuarine sys-
tems. The model solves one-dimensional continuity and momentum
equations to generate the temporal and spatial variations in the
tidal discharges and elevations. This information is used in
the solution of the conservation of mass equations for the water
quality variables, which include salinity, temperature, carbona-
ceous BOD, nitrogen cycle variables, DO, and fecal coliform. The
model combines the work of many investigators and has undergone
a great deal of modification. It was originally developed at the
Ralph M. Parsons Laboratory for Water Resources and Hydrodynamics
at the Massachusetts Institute of Technology, and its broadest
application has been the St. Lawrence River and Estuary- The
model is intended to be used in engineering decisions regarding
the degree of eutrophication due to distributed and point source
loadings in estuaries.
2. Functional Capabilities: The model was developed for aero-
bic estuarine ecosystems and includes seven storage variables and
twelve transformations of nitrogen between those variables. The
storage variables include: 1) N*, Ammonia-N, 2) N^, Nitrite-N,
3) N3, Nitrate-N, 4) N4, Phytoplankton-N, 5) NS, Zooplankton-N,
6) N6, Particulate Organic-N, and 7) Ny, Dissolved Organic-N.
The transformations include: 1) nitrification, 2) uptake of inor-
ganic nitrogen by phytoplankton, 3) grazing of herbivores, 4)
ammonia regeneration in living cells, 5) release of organic matter
from living cells, 6) natural death of living organisms, and 7)
ammonification of organic nitrogen. The user can specify a branch
ing and or looping network of channels called reaches where each
reach can be of variable cross-section along its longitudinal
axis. Storage volumes are provided for along the reach and any
number of concentrated or distributed water quality loadings can
be specified along each reach. The flow regime can be that of an
estuarine system with an unsteady tidal elevation driving the
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circulation at the ocean boundaries in combination with the up-
stream flow.
For subcritical flow, three possible boundary conditions can
be specified, and these are: 1) the Discharge Q, 2) the surface
elevation Z, and 3] a relationship between Z and Q. The model
can simulate control structures within the network itself instead
of only at the boundaries, and the user is permitted to specify
a boundary condition at the upstream side of the control struc-
ture. Three possible boundary conditions (concentration, dis-
persion flux, and total flux) and a special ocean boundary pro-
cedure are provided.
3. Basic Assumptions: The M.I.T. Transient Water Quality Net-
work Model defines the geometry of the water body along a par-
ticular reach by interpolation between the cross-sectional data
submitted by the user. The model pays strict adherence to the
mass conservation principle as applied to the element nitrogen,
and its ecosystem model is coupled with a real-time hydrodynamic
transport system as opposed to a tidal-average or slack-tide
approximation. The structure of the model was formulated such
that the level of complexity would not be too complex to the
point of diminishing returns, nor too simplified to the point
where rate-governing parameters must be determined by curve fit-
ting the available field data. Carbonaceous BOD is handled as a
first order decaying substance in the classical manner.
4. Input and Output: Input data is divided into nine groups.
Card Group A includes information regarding solution options.
Here it is stipulated which solutions (hydraulic and water qual-
ity) will be executed and which water quality parameters will be
modeled. Time parameters stipulating the duration of the run
and the time step of integration, and the network topology (iden-
tification and sequence of reaches) is also provided.
Card Group B provides the geometric information (i.e., the
physical properties of the channel), and the computational mesh
spacing and initial conditions required for the hydraulic solu-
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tion. This group is repeated for each reach as given in Group A,
Card Group C provides values of rate coefficients for those
water quality parameters being modeled. The coefficients may be
specified for the entire network or may be specified for each
individual reach. If the user does not wish to specify values,
the program will automatically use default values. In this
card group, the computational mesh spacing for water quality
calculations and initial conditions for water quality parameters
are also specified.
Card Group D describes the location, magnitude and quality
of any lateral inflows being considered. Lateral inflows are
considered for both the hydraulic and water quality solutions.
Card Group E describes the same information for any injections
(e.g. sewerage treatment plant or waste heat discharge) of water
quality parameters. Injections are considered only in the water
quality solution. For hydraulic purposes they are considered
passive, that is, they have no effect on the flow field in the
receiving water. Card Group F stipulates the hydraulic boundary
conditions to be applied at each node.
Card Group G allows the user to selectively view output
from the hydraulic solution. Card Group H stipulates the water
quality boundary conditions to be applied at each node in the
network, and Card Group I allows the user to selectively view
output for the water quality solution. The sequence of the
input cards is important to note. Certain card groups (D,E,F,
G,H,I) for particular cases must be repeated several times cor-
responding to the number of periods for which the solution is
executed.
The large volume of numerical information generated by the
computer is conveniently represented in graphical form. A plot-
ting program is available for use on an incremental drum plotter.
The output for the hydraulic solution can be requested in two
forms: 1) a hydrograph which displays the parameters at a given
mesh point as a function of time, and 2) a hydraulic profile
which displays the parameters at a given time as a function of
distance. The hydraulic parameters displayed are surface ele-
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vation, depth, discharge, and velocity. Output for the water
quality solution also may be displayed in two forms: 1) water
quality graphs, i.e., parameters as a function of time, and
2) water quality profiles, i.e., parameters as a function of
distance. Special features permit the user to plot several
variables on the same frame and also to plot user-supplied
data points as special symbols.
5. System Resource Requirements: The model is coded in FOR-
TRAN IV (H), and storage space allocation is as follows: 256K
for compilation, 2.26K for execution, and 92K for the plotting
program. The program utilizes 4, 445 source statements (not
counting comments) and consists of 47 routines. The time
required for data preparation and output interpretation will
vary with the system being modeled. A background in environmental
engineering with experience in computer modeling is useful.
6. Applications: The model has had a number of applications,
the broadest application being to the St. Lawrence River and
Estuary, a study sponsored by the Canadian Departments of the
Environment and Transport and Quebec Service de Protection de
1'Environment and Ministere des Richesses Naturelles. Thatcher,
Pearson,and Mayor-Mora have described the application to both
riverine and estuarine portions of the St. Lawrence River from
Cornwall to Montmagny, a distance of 275 miles. The need for
a published user's manual was recognized by the National Envi
ronmental Research Center, U.S. EPA, Corvallis, Oregon, and
their support has enabled documentation of the model at this
stage of its development. The EPA has used the model for test
purposes.
7. Technical Contacts
Richard J. Callaway
U.S. Environmental Protection Agency
Corvallis Environmental Research Laboratory
200 S.W. 35th Street
Corvallis, Oregon 20036
FTS 420-4703 COM 503/757-4703
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8. References
Chow, V.T., Open Channel Hydraulics, McGraw Hill, N.Y., 1959.
Dailey, J.E. and Harleman, D.R.F., "Numerical Model for the
Prediction of Transient Water Quality in Estuary Networks",
Technical Report No. 158, R.M. Parsons Laboratory for Water
Resources and Hydrodynamics, Department of Civil Engineer-
ing, M.I.T., October 1972.
Gunaratnum, D.J. and Perkins, F.E., "Numerical Solution of
Unsteady Flows in Open Channels", Technical Report No. 127,
R.M. Parsons Laboratory for Water Resources and Hydrodyna-
mics, Department of Civil Engineering, M.I.T., July 1970.
Harleman, D.R.F-, Brocard, D.N. Najarian, T.O., "A Predic-
tive Model for Transient Temperature Distributions in Un-
steady Flows", Technical Report No. 175, R.M. Parsons
Laboratory for Water Resources and Hydrodynamics, Depart-
ment of Civil Engineering, M.I.T., November 1973.
Harleman, D.R.F. and Thatcher, M.L., "Longitudinal Disper-
sion and Unsteady Salinity Intrusion in Estuaries", La Houille
Blanche/No. 1/2 - 1974.
Harleman, D.R.F., Dailey, J.E., Thatcher,M.L., Najarian,
T.O., Brocard, D.N., and Ferrara, R.A. "User's Manual for
the M.I.T. Transient Water Quality Network Model", Report
for U.S. Environmental Protection Agency, Office of Research
and Development, Corvallis Environmental Research Laboratory,
Corvallis, Oregon. U.S. EPA Publication EPA-600/3-77-010,
January 1977.
Henderson, F.M. Open Channel Flow, MacMillan Co. N.Y., 1966.
Larsen, P.A., "Hydraulic Roughness of Ice Covers", JHD, ASCE
99, HYI, January 1973.
Najarian, T.O. and Harleman, D.R.F., "A Real Time Model of
Nitrogen-Cycle Dynamics in an Estuarine System", Technical
Report No. 204, R.M. Parsons Laboratory for Water Resources
and Hydrodynamics, Department of Civil Engineering, M.I.T.,
July 1975.
Surveyor, Nenniger § Chenevert, Inc. and Carrier, Trottier,
Aubin, "Hydrodynamic and Water Quality Simulation Model:
Cornwall-Montmagny Section", Report to Department of Envi-
ronment, Canada, March 1973.
Surveyer, Nenniger § Chenevert, Inc. and Carrier, Trottier,
Aubin; (in French) "Hydrodynamic and Water Quality Simula-
tion Model: Cornwall-Montmagny Section", Report to Service
de Protection de 1'Environment Quebec, March 1974.
Thatcher, M.L. and Harleman, D.R.F., "Mathematical Model
for the Prediction of Unsteady Salinity Intrusion in
Estuaries", Technical Report No. 144, R.M. Parsons Laboratory
-------
References (Continued)
for Water Resources and Hydrodynamics, Department of Civil
Engineering, M.I.T., February 1972.
Thatcher, M.L., Pearson, H.W., and Mayor-Mora, R.E., "Appli
cation of a Dynamic Network Model to Hydraulic and Water
Quality Studies of the St. Lawrence River", 2nd Annual
Symposium of the Waterways, Harbours and Coastal Engineering
Division, ASCE, San Francisco, September 1975.
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MULTI-SEGMENT COMPREHENSIVE LAKE ECOSYSTEM
ANALYZER FOR ENVIRONMENTAL RESOURCES (MINI. CLEANER)
1. Model Overview; Th.e Multi-Segment Comprehensive Lake
system Analyzer for Environmental Resources (MINI, CLEANER), is one
of the more biologically realistic aquatic ecosystem models, Because
of the attention to general process-level constructs, the inodel is
appropriate for application to diverse types of lakes and reservoirs
and is capable of addressing .many environmental problems.
The inodel represents over 30 ^man-years of development by a
multidisciplinary team. The precursor, CLEANER, was formulated b.y
25 investigators from several institutions under the aegis of the
International Biological Program, Eastern Deciduous Forest Biome,
The model is structured to simulate up to 20 biotic and 20 abiotic
state-variables in each of 10 spatial segments simultaneously.
Current development includes adaptation to coastal environments and
coupling to a model for bioaccumulation of toxic substances. MINI,
CLEANER was developed b.y Dr. Richard A. Park of the Center for
Ecological Modeling, RensseJaer Polytechnic Institute, Troy, New York,
for the U.S. Environmental Protection Agency,
2. Functional Capabilities; MINI. CLEANER is an ecological model
capable of addressing environmental problems such as nutrient en-
richment, thermal pollution, siltation, impoundment, and fish re-
moval. The model can simulate a variety of biotic variables,
including four types of ph.ytoplank.ton (/two with, internal storage
of nitrogen and phosphorous}; up to four types of submerged aquatic
vegetation Cmacrophytes); five types of zooplankton^ two or more
types of fish with as many as four life stages; two kinds of b.ottom-
dwelling animals Czoobenthos}•; and three groups of decomposers
(bacteria and fungi). An equal number of abiotic variables can be
simulated, including seven types of dissolved organic matter; four
types of particulate organic matter; five types of inorganic
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nutrients in the water column and sediments; and four compartments
for dissolved oxygen and inorganic carbon. This potential com-
plexity is seldom fully utilized, however. Normal applications
require a subset of perhaps 20 state-variables. The model has
been run with as few as two dynamic state-variables. Several
external variables and all the biotic state variables can be
used as loadings. External variables include water temperature,
temperature and discharge rate of inflowing water, wind speed,
light, dissolved inorganic nitrogen, orthophosphate, dissolved
silica, dissolved organic material, and particulate organic material.
As a user-oriented onodel, MINI, CLEANER features a machine-.
independent namelist editor that enables the user to list and make
changes in parameter values while running the model; to plot state-
variable concentrations, rates for various processes, and loadings
to the model; and to transform the state-^variable values into
"environmental perception" characteristics such as turbidity, fish
catch, or concentrations of noxious algae.
Multiple segments can be simulated, each having different
physical-chemical and biotic characteristics. Movement of materials
across the boundaries of the segments is specified by a linking
language. The movement can either be intersegmental or be treated
as a loss from the system, as specified by the user.
The loadings of nutrients, temperature, and light can be
perturbed using the editing capability of MINI. CLEANER to set at the
perturbation parameters. The perturbations can be either additive
or multiplicative, and either constant or as a pulse of user--
specified duration and timing.
An algorithm has been developed to facilitate analysis of
the sensitivity of MINI, CLEANER to changes in values of parameters
and driving variables. Using a random number generator, values
with a normal or uniform distribution are used to vary loadings
or parameters within a specified range. The simulation is repeated
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a given number of times with different perturbations, and the
results are summarized. The parameters are perturbed at the
beginning of each simulation; the loadings are perturbed at each
step.
3. Basic Assumptions; Th.e realist of MINI, CLEANER £5-
by disaggregation of state-syaTiaBles and By- -use of detailed
process equations. Adaptive constructs are used for light and
temperature response in phytoplankton. These constructs have
permitted application to a wide range of lakes without changing
parameters .
Unlike most ecosystem .models, MINI , CLEANER can treat phyto-
plankton growth as a function of stored nutrients. This uncoupling
of growth and external nutrient concentrations has resulted in
more realistic simulations with avoidance of the time lags between
modeled and observed phytoplankton peaks that are a persistent
problem with many models.
Likewise, process-level realism in simulating zooplankton
has been achieved by recognizing variations in modes of food con-
sumptions. In some zooplankton the rate of consumption is independ-
ent of the concentration of food; in others a minimum food level
is necessary before feeding will occur. The rate of consumption
follows saturation kinetics.
In order to use MINI. CLEANER in studying bioacc-umulation of
toxic substances, the fish compartments have been disaggregated
to represent age classes. At the time of promotion, all, or some
fraction, of the fish in one age class is transferred to the next
age class at a rate that follows a normal distribution. The shedding
of gonadal products (and associated pesticides) by adults is handled
in the same manner.
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By modeling decomposers explicitly, more realistic simula-
tions of decomposition have been obtained. This is particularly
important in representing the differential degradation of different
types of organic material [including oil spills), the cycling of
nutrients, and—through the production of microbial biomass--
formulation of a high-quality food source for detritus feeding
animals.
Phosphorus, silicon, carbon and soluble inorganic nitrogen
are the only nutrients that can be limiting. The temperature,
nutrient, and biomass are considered to be uniformly distributed
in a segment.
4. Input and Output: The data required to calibrate and use
the model fall into several categories:
1) Driving Variables a time-series of data for the period
of the simulation (which may be from a few days to years),
including water temperature, incident radiation, loadings
of dissolved and particulates, organic matter, biomass,
and nutrients.
2) Site Constants average water depth and light extinction
coefficient.
3) Initial Conditions - for all variables.
4) Parameter Values - a default set of values for the exten-
sive parameter list may be used or values that are known
may be input.
5) Calibration Data observed values for some state variables
are necessay to fine-tune the model.
6) Pertubations and Sensitivity Analysis sensitivity to
changes in parameters and driving variables may be
examined using a built-in algorithm.
All of the input data and parameter values used can be output as well
as the state variables simulated. A plotting routine is available.
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5. System Resource Requirements: MINI. CLEANER requires
approximately 64K bytes of core. Ten files are required for operation
of the program, plus an additional number of files equal to the number
of segments being simulated (up to 10 segments). Execution times
depend on the complexity and length of the simulation. The model is
coded in FORTRAN IV. Current work includes adaptation to minicomputers,
The model has run on the PDF 11/03 computer although execution times
are excessive. It should be adaptable to any 16-bit scientific mini
computer with 64K bytes of memory.
6. Applications: The model has been calibrated and verified
with data from a number of lakes of diverse types. It was originally
applied to Lake George, New York. Subsequent versions have been
calibrated for Lock Leven, Scotland; Slapy Reservoir, Czechoslovakia;
Balaton Lake, Hungary; Lakes d'Endine and Mergozzo, Italy; Lake Esrum,
Denmark; Lake Paijanne, Finland; and DeGray Reservoir, Arkansas. A
version that incorporates all the latest improvements, including
storage of internal nutrients in phytoplankton, has been calibrated
for subalpine Ovre Heimdalsvatn, Norway, and verified with data from
Vorderer Finstertaler See, Austria, without changing parameter
values. With only minor changes, it gives reasonable results for
Lake Mergozzo, a mesotrophic, stratified lake.
Because of the complexity of the program, application requires
a sophisticated user familiar with ecologic and computer -modeling
concepts. A short residency at Rensselaer Polytechnic Institute is
recommended for those wh_o wish to use the -model,
7. Technical Contacts,
Thomas 0. Bamwell, Jr.
U.S. Environmental Protection Agency
Environmental Research. Laboratory"
College Station Road
Athens, Georgia 3Q6Q5
FTS 250-3585 COM 404/546-3585
Dr. Richard A. Park
Center for Ecologic Modeling
Renssselaer Polytechnic Institute
Troy, New York 12181
COM 518/270-649.4
299
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8, References
Bloomfield, J, A., Park, R. A., Scavia, D. , and Zahorcak, D. S.
"Aquatic Modeling in the Eastern Deciduous Forest Biome, U.S.
International Biological Program," In: Middlebrooks, E. J, , D.
H. Falkenborg, and T. E. Maloney CEditors), Modeling the Eutro-
phication Process, Utah State University, Logan, Utah, pp.
139-158, 1973.
Clesceri, L.S.,Park, R. A0 and Bloomfield, J. A. "General
Model o£ Microbial Growth and Decomposition in Aquatic Eco-
systems." Appl. Environ. Microbiol., _33 (5) : 1047-1058 , 1977.
deCaprariis, P-, Park, R.A., Haimes , R. , Albanese ,-J . , Collins,
C. , Desormeau, Groden,T., Leung, D., and Youngberg, B. "Utility
of the Complex Ecosystem Model MS.CLEANER." In: Proceedings of
the International Conference on Cybernetics and Society, pp.
87-89, 1977.
Desormeau, C. J. "Mathematical Modeling of Phytoplankton
Kinetics with Application to Two Alpine Lakes." Report #4,
Center for Ecological Modeling, Rensselaer Polytechnic
Institute, Troy, New York, 21 pp., 1978.
Groden, T. W. "Modeling Temperature and Light Adaptation of
Phytoplankton." Report #2, Center for Ecological Model,
Rensselaer Polytechnic Institute, Troy, New York, 17 pp., 1977.
Leung, D. K. "Modeling the Bioaccumulation of Pesticides in
Fish." Report #5, Center for Ecological Model, Rensselaer Poly-
technic Institute, Troy, New York, 18 pp., 1978.
Leung, D. K., Park, R. A., Desormeau, C. J., and Albanese, J.
"MS.CLEANER: An Overview." In: Proceedings of Pittsburg
Modeling and Simulation Conference, Pittsburg, Pennsylvania,
1978.
Park, R. A. "Theoretical Implications of Models of Aquatic
Systems." Presented at AAAS, Biological Sciences Meeting, New
York, New York 1975.
Park, R. A. "A Model for Simulating Lake Ecosystems." Report
#3, Center for Ecological Modling, Rensselaer Polytechnic
Institute, Troy, New York, 19pp., 1978.
Park, R. A. "Predicting the Impact of Man on Lake Ecosystems."
(Abstract) In: Biro, P. (Editor), Human Effects on Life in
Fresh Water, Hungarian Academy of Sciences, Tihany, Hungary,
300
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Park, R, A., O'Neil, R. V., Bloomfield, J. A., Shugart, H. H.,
Booth, R. S., Goldstein, R. A., Mankin, J. B., Koonce, J. F.,
Scavia, D., Adams, M. S., Clesceri, L. S., Colon, E. M.,
Dettmann, E. H., Hoopes, J., Huff, D. D., Katz, S., Kitchell,
J. F., Kohberger, R. G., LaRow, E. J., McNaught, D. C., Peterson,
J., Titus, J., Weiler, P. R., Wilkinson, J. W., Zahorcak, C. S.
"A Generalized Model for Simulating Lake Ecosystems." Simulation,
23_ (2) : 33-50, 1974.
Park, R. A., Scavia, D., and Clesceri, N. L."CLEANER, the Lake
George Model." In: Russell, C. S. (Editor), Ecological
Modeling of a Resource Management Framework. Resources for the
Future, Inc., Washington, D. C. pp. 49-82, 1975.
Park, R. A., Grddin, T. W., and Desormeau, C. J."Modifications
to the Model CLEANER Requiring Further Research." In: Scavia,
D. and A. Robertson (Editors), Perspectives on Aquatic Ecosystem
Modeling, Ann Arbor Science Publishers, Inc., 1978.
Park, R. A., Collins, C. D. , Leung., D. K. , Boylen, C. W. ,
Albanese, J., deCaprariis, P0 , an
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NATIONAL RESIDUALS DISCHARGE INVENTORY (NRDI)
1. Model Overview: The NRDI is a quantitative assessment of
residual generation and discharges (total suspended solids, BOD,
nitrogen, phosphorous, and nutrients) and of residual reduction
technology costs in each of 3,111 counties or county approxima-
tions in the contiguous U.S. Data for industrial, municipal,
urban runoff, and non-irrigated agriculture sources are avail-
able for each county. However, the data are not displayed at
the county level, but rather are aggregated for purposes of anal-
ysis by the Water Resources Council's 99 aggregated sub-areas
(ASAs), the 18 Water Resource Regions (WRRs) and by the nation.
The model was developed under the auspices of the National Acad-
emy of Sciences, Washington, D.C.
2. Functional Capabilities: The NRDI is a series of FORTRAN
programs and data bases which provide estimates of current and
projected discharges of wastes (total suspended solids, BOD, ni-
trogen and phosphorus) to surface waters, and capital and opera-
tion and maintenance costs of treatment facilities. The model
estimates pollutant discharges and costs by industrial or munici-
pal facility for approximately 40,000 point-source discharges,
and by county for urban storm runoff and non-irrigated agricul-
ture. These detailed estimates are then aggregated as desired.
Discharges and costs are estimated for conditions of (1) no con-
trol, (2) controls in place in 1973, (3) 1977 standards, and
(4) 1983 standards.
The NRDI allows for an evaluation of policy alternatives to
the unifo-rm application of residual reduction technologies to
legislatively defined (P.L. 92-500) point sources. These policies
reflect alternatives where in a given ASA or WRR, achievement of
the 1983 effluent limitations would not make a significant im-
provement in total residual reductions and ambient water quality,
and where a given level of residual reduction could be achieved
at a lower cost without the uniform application of residual reduc-
tion technology to point sources.
302
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3. Basic Assumptions: The model consists of (1) inventories
of production and consumption activities which generate and dis-
charge residuals, (2) a system for analyzing the effects of in-
creased industrial and population growth, (3) an index of poten-
tial water quality changes, and (4) residual discharge reduction
policies which include the BPT/ST ( best practicable control
technology currently available/secondary treatment ) and BAT/
BPWTT ( best available technology economically achievable/best
practicable waste treatment technology ) technology goals in
the Federal Water Pollution Control Act (P.L. 92-500).
For point-source discharges, discharge and cost estimates
are based on simulation of specific end-of-pipe technologies for
discharge reduction. Discharge conditions in 1973 as well as
1977 and 1983 standards are estimated for specific industrial
facilities or industry subgroups. For municipal facilities, the
1974 EPA Needs Survey is used. For urban runoff, required treat-
ment is estimated based on work done for the National Commission
on Water Quality. For non-irrigated agriculture, information from
the 1967 Conservation Needs Inventory is used.
4. Input and Output: Major inputs to the NRDI include the
EPA Needs Survey, the Conservation Needs Inventory, County Busi-
ness Patterns, City-County Data Book, and Census data, A detailed
industrial source Inventory was developed for the model. Thus, the
model contains information on identifiable point and areal source
residual generating activities which cover most waterborne residual
generating activities. Information included about these activi-
ties, where appropriate and available, are location of activity,
measures of production (physical output, employees, land area,
or population), type of production process, and current residual
reduction technologies being used.
Outputs are produced by county-aggregate unit, and source
category or subcategory. A variety of alternative policies can
be selected for solution in the NRDI. These policies include
both uniform and non-uniform abatement policies and can simulate
controls on areal as well as point sources. The outputs from
303
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each policy alternative are: residual generation, residual dis-
charge, abatement costs, and residuals dilution index. The basic
policies used to date are discussed below:
1) No control This policy estimates residuals discharge
if no control technology is used.
2) 1973 controls This policy estimates discharge and
costs based on control technology in place in 1973.
3) BPT/ST - This policy estimates effects of the 1977
standards of the P.L. 9'2-500: Best Practicable Treat-
ment for industry and Secondary Treatment for munici-
palities .
4) BAT/BPWTT This policy estimates the effects of the
1983 standards for industry and secondary treatment
for municipalities supplemented with tertiary facilities
when requested in the EPA Needs Survey.
5) BAT/BPWTT+ - This policy is identical to (3) for indus-
trial sources but includes filtration for all municipal
ities not requesting treatment more stringent than sec-
ondary in the EPA Needs Survey.
6) Non-irrigated agricultural control Costs and residual
implications of implementing practices outlined in the
1967 Conservation Needs Inventory are included.
7) Urban storm control Costs and residual implications
of one of five urban storm control strategies (combined,
separate storm, and unsewered) is simulated.
8) Ocean discharges Effects of discharge and costs for
<3cean counties are excluded. This function is used to
simulate lower levels of treatment for ocean discharges
based on using a specified set of counties.
9) New source performance standards - In this policy, re-
sidual discharges and costs for industrial growth are
based on new source performance standards (approximated
by BAT).
10) Limited technology - Simulation of stringent effluent
limitation policies can be limited to ASA with relative-
ly bad water quality.
11) Cost effective strategy - This policy used data on cost
304
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per quantity of residuals removed to identify cost-
effective solutions in each ASA.
Combinations of these policy components can be combined in a
single run if desired.
5. System Resource Requirements: All programs are written in
FORTRAN for an IBM 360/370 system. Programs run in 200K bytes'
or less. A full run can be made for under $100. Manpower re-
quirements include a computer programmer or an environmental
engineer with experience in environmental modeling.
6. Applications: NRDI results have been used for the following:
1) The book: Water Pollution Control: Assessing the Impacts
and Costs of Environmental Standards (Praeger, 1977).
2) The report: The National Residuals Discharge Inventory
(National Academy of Sciences, 1976).
3) The 1976 Annual Report of the Council on Environmental
Quality.
4) The Water Resources Council's 1975 National Assessment.
5) The National Commission on Water Quality's Environmental
Technical report.
7. Technical Contact
Ralph A. Luken
U.S. Environmental Protection Agency
Economic Analyses Division
401 M, Street,. S.W.
Washington, B.C. 20460
FTS 382-5475 COM 202/382-5475
8. References
Black, Crow, and Eidsness, Study and Assessment of Capabil-
ities and Costs of Technology for Control of Pollutant. Dis-
charges from Urban Runofff NCWQ Contract, November 1975.
Luken, Basta, and Pechan, The National Residuals Discharge
Inventoryj National Research Council, Washington, D.C.,
January 1976.
305
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Midwest Research Institute. Cost and Effectiveness of Con-
trol of Pollution from Selected Non-point Sources, NCWQ
Contract, July 1975.
Pechan, E.H., and Luken, R.A., "A Water Residuals Inventory
for National Policy Analysis.", Proceedings of the Conference
on Environmental Modeling and Simulation, U.S. EPA publica-
tion EPA 600/9-76-016, July 1976.
U.S. Department of Commerce, Bureau of the Census, 1972
County Business Patterns, Washington, B.C.
U.S. Department of Commerce, Bureau of the Census, 1972
Census of Manufacturerst Water Use in Manufacturing, Wash-
ington, D.C.
U.S. Department of Commerce, Bureau of the Census, 1972
City County Data Book, Washington, D.C.
U.S. Environmental Protection Agency, Joint State-EPA
Survey of Needs for Municipal Waste Water_Tre a t m e n t F a c i 1 -
ities, computer tape, March 1975.
U.S. Water Resources Council, 1972 PEERS Projections. April
1974.
Wharton Econometric Forecasting Associates, Wharton Econo-
metric Forecasting Estimates, Mark IV, Solution of March
4, 1975.
306
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OUTFALL PEUME MODEL (PLUME)
1. Model Overview: PLUME is a computer program which can be
used to evaluate coastal waters, lakes, or estuaries under
consideration as disposal sites. It is designed to evaluate
and/or predict the length of outfall needed to adequately dilute
a proposed discharge in order to provide compliance with water
quality standards. The model was developed by the U.S. EPA En-
vironmental Research Laboratory in Corvallis, Oregon, and it has
been used by the San Juan Field Office of the U.S. EPA to aid in
the location and analysis of ocean outfalls.
2. Functional Capabilities: PLUME simulates the three dimen-
sional (two dimensions in the horizontal plane and one dimension
in the vertical plane) initial dilution of the effluent plume of
residuals in lakes, coastal waters, and estuaries. Some of the
factors which can be evaluated by PLUME include the effects of
onshore currents, tides, density and salinity gradients, ambient
surface and hypolimnetic velocities, the initial jet velocity,
the quantity of discharge, the slope of the ocean bottom, and
the rates of coliform die-off in the vicinity of outfall locations.
The model simulates a stratified aquatic environment, and up to
50 layers are permitted.
PLUME is sensitive to discharged fluid density and flow rate,
and also to extent of stratification. The model is also sensitive
to the physical features of the outfall - the diameter of openings,
the number of ports, and the port depth.
3. Basic Assumptions: The model simulates the behavior of a
buoyant, round effluent plume being discharged into a non-flowing
water body where it considers the density differences between
freshwater (with residuals) and saltwater masses. The model does
not simulate the transport of discharged residuals by mechanisms
other than mixing and dilution of fluids with different densities,
and it assumes no water flow other than the plume-induced movement.
A steady-state, stratified aquatic environment is assumed.
307
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4. Input and Output: Input to the PLUME program should be in
card-image form. Initial setup/calibration needs include: (1)
water temperature profile (with depth), (2) salinity or density
profile (with depth), (3) effluent flow rate, (4) effluent den-
sity, and (5) the outfall features such as the port diameter,
number of discharge points, depth of discharge points, and the
angle of the discharge points to the horizontal plane. The in-
itial constituent concentration throughout the plume is needed
for verification.
The model provides a tabular printout of the following out-
put information: (1) labeled input values, (2) constituent dilu-
tion along the plume centerline, and (3) the depth at which the
plume stabilizes.
5. System Resource Requirements: PLUME is coded in FORTRAN IV
and requires a FORTRAN IV compiler. The program is compatible
with the IBM 370/155 and requires 10,000 words of core storage.
Up to one man-week is necessary for data preparation, and onlv
one or two man-hours for output interpretation. A background in
computer programming or in hydraulics engineering with environmental
modeling experience is useful.
6. Applications: Outfall plume was developed by the U.S. EPA
Environmental Research Laboratory in Corvallis, Oregon, and the
model has been used by the San Juan Field Office of the U.S. EPA
to aid in the location and analysis of ocean outfalls. PLUME
has also found other applications and other users.
7. Technical Contacts
George A. Nossa and Laura ^ivingston
U.S. Environmental Protection Agency
Information Systems Branch- 2PM'-IS
26 Federal Plaza
New York, New York 10278
FTS 264-9850 COM 212/264-9850
Richard J. Callaway
U.S. Environmental Protection Agency
Corvallis Environmental Research Laboratory
200 SW 35th Street
Corvallis, Oregon 97330
FTS 420-4703 COM 503/757-4703
308
-------
START
l
/
I
READ
X
"CITY DATA"
"PHYSICAL DATA"
"PLUME DATA"
I
/
/
\
CALL SIGMA!
(RETURN WITH SICHAT-f (RHO, SAL»
j.
/WRITE "CITY DATA", "PHYSICAL DATA"/
/ WRITE "PLUME DATA" /
jC
L NN - 0 J
NO
YES
ASSIGN COLI VALUES
FOR TREATMENT w/Clj
ASSIGN COLI VALUES
FOR TREATMENT w/o Clg
1 "'-o I
YES
| CLASS - SB |
| CLASS - SC |
/WRITE "CITY DATA'^y
ASSIGN T90 VALUES
JJ - 0
ASSIGN DT VALUES 1
~ ''I
. X - 0. 1
I x - x + looo.l
I
CALL EQN (IF SB AND X>1320.. RETUSH
WITH DTI - Dl (CALL PLUME (RETURN
WITH DTI - Dl (CALL SDERIV (RETURH
WITH FK, FM))) * D2)) * D3. OTHERWISE
RETURN WITH DTI - Dl (CALL PLUME
(RETURN WITH DTI - Dl (CALL SDERIV
(RETURN WITH FK, FM)))))
I ITERATIOH~REPEATED WITH X-AO.',2., .25 ]
Flowchart for OUTFALL PLUME
309
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8. References
Baumgartner, D.J. and Trent, D.S,, "Ocean Outfall Design:
Part I, Literature Review and Theoretical Development."
Report by FWPCA, Washington, D. C., 1970.
Baumgartner, D.J. and Trent, D.S., "User's Guide and Documenta-
tion for Outfall Plume Model." Working Paper #80 by U.S.
EPA Pacific Northwest Water Laboratory, Corvallis, Oregon,
1971.
Burchett, M.E., Tchobanglous, G . , and Burdoin, A.J ., "A Practi-
cal Approach to Submarine Outfall Calculations". Public Works.
5, 95, 19&7.
Callaway, R.J., "Computer Program to Calculate ERF". EPA
Pacific Northwest Environmental Research Laboratory, Corvallis,
Oregon, 1973.
Guthrie, D.L., "Documentation for Outfall: A Computer Program
for the Calculation of Outfall Lengths Based upon Dilution
Requirements." U.S. Environmental Protection Agency, San
Juan Field Office, Santurce, Puerto Rico, 1975.
310
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RECEIVING WATER MODEL (DIURNAL)
1. Model Overview; DIURNAL is a one-dimensional (hori-
zational plane) receiving water quality model. The model represents
the physical processes of advection and dilution and simulates
receiving water quality changes in dissolved oxygen. Coupled chemical
reactions can be simulated, and dissolved oxygen, CBOD and NOD can
be modeled. The primary function of DIURNAL is to predict the
diurnal fluctuations during periodic steady-state conditions. The
model was developed by Hydroscience, Inc. of Westwood, New Jersey.
2. Functional Capabilities: The DIURNAL model can be applied
to streams and rivers under one-dimensional, steady-state conditions.
It allows a stream length to be analyzed with any number of functional
segments; the number being dependent only on the frequency of in-
stream characteristics changes, waste discharge (or withdrawal)
locations, and accuracy desired. EAch segment, likewise, can be
divided into any number of functional elements, again dependent on
desired accuracy. The following effects have not been included in
the solution; the time variation of flow, the time variation of
the temperature and wasterwater discharges and the effect of dispersion,
The sources and sinks of dissolved oxygen considered in the model
include reaeration, biochemical oxygen demand, nitrogenous oxygen
demand, benthic oxygen demand, photosynthesis,and respiration. The
model is best used in conjunction with a steady state water quality
model to fix all parameters except respiration and photosynthesis.
DIURNAL would then be used to determine these two parameters on
a periodic steady-state basis.
3. Basic Assumptions: The solution analysis is an extension
of the technique based on the continuity equation for dissolved oxygen
which includes the diurnal time-variable effect of photosynthetic
oxygen production. The analysis considers the temporal as well as
spatial distributions. The periodic extension of the photosynthetic
oxygen production is expressed as a Fourier series.
311
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4. Input and Output: Input information is basically of
three types; initial conditions definition, individual segment
characteristics definition and discharger information. Initial
conditions data include system description (number of sections,
element length, river mile at the head of the -system) and quality
description (upstream BOD, upstream NBOD , and Fourier coefficients
of upstream DO). The individual segment data include the following;
section length, velocity, temperature, reaeration rate, and decay
rates (carbonaceous and nitrogenous). Other information includes
bottom demand, hours of daylight, maximum photosynthetic rate,
respiration, time of sunrise, stream flow, and segment elevation.
Discharger information includes, DO, flow, BOD , and NOD. Segment
characteristics and discharger information is repeated for each
segment modeled.
DIURNAL produces a tabular printout of section parameters
and dissolved oxygen response. The section parameters include;
length, velocity, temperature, flow, reaeration* and decay rate,
benthic rates and photosynthesis and respiration rates. The DO
response table includes hourly dissolved oxygen values, for 24
hours, for the beginning and the end of the segment, and any inter-
mediate point designated by the print interval.
5- System Resource Requirements: DIURNAL is coded in FORTRAN
IV (G) and can be run on a digital computer with a 40,000 word core
storage capability; FORTRAN IV (G) compiler. Application of the
model results in minimal costs, in the range of $1 $2 per run,
including on the number of segments. Approximately 1-man week is
required for model setup and data preparation. Initial runs on a
companion model is necessary to establish appropriate velocities
and rates. A background in environmental engineering with a
programming background is helpful.
6- Applications: DIURNAL can be used on any stream where it
is assumed that the primary cause of the diurnal variation of the
dissolved oxygen is the algal oxygen production.
312
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Technical Contact
Thomas Henry
U.S. Environmental Protection Agency
Region III Water Division
Curtis Building
6th and Walnut Street
Philadelphia, Pa. 19106
FTS 597-8048 COM 215/597^8048
References
Di Toro, Dominic, and O'Connor, Donald J.
"Photosynthesis and Oxygen Balance in Streams",
Journal of the Sanitary Engineering Division,
ASCE, April 1970, PP 547-571.
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RECEIVING WATER MODEL (RECEIV-II)
1. Model Overview: RECEIV-II is a two dimensional receiving
water model for streams, rivers, estuaries, lakes, and reservoirs.
The model represents the physical processes of advection,
dispersion, and dilution, and it can simulate flows, tidal
movements, and water surface changes in a link-node network.
Coupled and non-coupled chemical reactions can be simulated, and
dissolved oxygen, BOD, coliforms, nutrients, salinity, conservative
constituents, chlorophyll a, and non-conservative constituents
with first order decay can be modeled. RECEIV-II is a modification
of the receiving water module of the Storm Water Management Model
(SWMM) developed by Water Resources Engineers, Metcalf and Eddy,
and the University of Florida.
2. Functional Capabilities: The RECEIV-II model can be
applied to streams, rivers, estuaries, lakes, and reservoirs.
Dynamic conditions are represented, and an option is available for
steady state conditions. The model is two dimensional and permits
up to 225 channels and up to 100 junctions in a link-node network.
RECEIV-II can simulate estuarine flats at low tide by varying
cross-sectional area, and it can handle multiple tidal inlets,
upstream dams, and unsteady inflows such as residual discharges,
storm runoff, and tides. The physical processes of advection,
dispersion, and dilution are represented. Chemical processes
represented include coupled and uncoupled reactions, DO, BOD,
coliforms, nutrients, salinity, conservative constituents,
chlorophyll a, NH^, N02, and non-conservative constituents with
first order decay. The model does not consider stratified systems,
but it does consider ocean tide exchange at a single input point.
RECEIV-II lacks the ability to simulate temperature, but it does
consider wind stress and direct rainfall inputs. The model is
314
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sensitive to quality time step size and decay coefficients.
Lateral and vertical velocity variation within the channels can
be broken-up laterally for a two dimensional effect.
3. Basic Assumptions: The model is based on deterministic
assumptions and uses a finite difference methods as a solution
technique. RECEIV-II assumes instantaneous mixing throughout each
junction, and it uses a two dimensional channel network to
simulate two dimensional flow and transport.
4. Input and Output: Input to the model for initial setup
and calibration includes: constant headwater inflow rates; flow
rate for each inflow (discharge, tributary, etc.) or withdrawalj
tidal cycles and heights at the seaward boundary; widths and
depths of each channel; initial flow velocities and water surface
elevations throughout the system; initial constituent
concentrations throughout the system; residual loading rates from
discharges, tributaries, and headwaters; tidal exchange coefficient;
meteorological data (wind speed, rainfall, and daily solar
radiation); and first order decay rates for constituents. Input
for verification of the model includes: net flow and velocities for
each channel; data record of constituent concentration throughout
the modeled system; and salinity data to establish concentration
inputs at the seaward boundary.
The model produces a tabular printout of: maximum, minimum,
and net flows for each tidal cycle; maximum, minimum, and average
constituent concentrations in each channel at specified time
intervals; and depth at each junction at specified time intervals.
Hydrodynamic output (especially channel velocities) can be written
onto magnetic tape or disk.
5. System Resource Requirements: RECEIV-II is coded in
315
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FORTRAN IV CG), and can be run on a digital computer with 400K
bytes of core storage,a Fortran IV (G) compiler, and two magnetic
tapes or disks. Application of the hydrodynamic module costs
between $15-$100 per run, and application of the quality module
ranges from $10-$50 per simulation run. The actual expenses of
using RECEIV-II is dependent upon the extent of discretization,
time step size, and length of simulation. Depending on the model
complexity, 1-5 man-months are needed for model set-up and data
preparation. Several manhours are necessary for output analysis.
A background in computer programming and hydraulics engineering
with a basic programming background are useful. The model may be
obtained from the Planning Assistance Branch of the Environmental
Protection Agency.
6. Applications: RECEIV-II can be linked to the terrestrial
flow routing module of the Storm Water Management Model (SWMM),
or it can be utilized by itself to simulate dynamic conditions.
RECEIV-II has been used by the U.S. Army Corps of Engineers, and
it is currently being utilized by the EPA in Regions III and IV.
7. Technical Contact
Tom,Barnwell
Center For Water Quality :Model-i-n-g
TJ.S\ Environmental Protection Agency
Environmental Research Laboratory
College Station Road
Athens, GA 30613
FTS 250-3585 COM 404 / 546-^585
8. References
Metcalf and Eddy, Inc., University of Florida, and Water
Resources Engineers, Inc., Storm Water Management Model,
Volumes 1-4, Report to U.S. Environmental Protectinn
Agency, Washington, D.C., 1971 (EPA Report No. 11024DOC).
316
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"Stormwater Management Model User's Guide, Version II",
For the U.S. Environmental Protection Agency - Report No
EPA-670/2-75-017, March 1975.
317
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RECEIVING WATER QUALITY MODEL (RWQM)
1. Model Overview: The Receiving Water Quality Model (RWQM)
was developed by Resource Analysis, Inc. for the U.S. Army
Corps of Engineers 'Hydrologic Engineering Center to interface
with STORM (Storage Treatment Overflow Runoff Model). RWQM
(HEC, 1979), when linked with the time history of storm and
dry-weather flows generated by STORM, provides the capability
to predict the impact and land surface runoff on stream water
quality.
2. Functional Capability: RWQM simulates the one-dimensional
transport and transformation of six water quality parameters
(temperature, DO, carbonaceous BOD, nitrogenous BOD,
orthophospha te , coliforms) in freshwater streams.
3- Basic Assumption: RWQM simulates time-varying hydraulic
and water quality conditions by utilizing the law of mass
conservation for water and pollutant volumes. Stream routing
uses the kinematic wave assumption. Water quality parameters
are modeled using first order kinetics.
4. Input and Output: RWQM input is on cards and tape files.
Input consists of, for each stream reach: Stream reach and
segmentation, simulation controls, I/O controls, percentile
curve limits and location, atmospheric temperature input,
monthly stream equilibrium temperature, monthly heat transfer
rate coefficients, reaction rate coefficients, reaction rate
temperature modification bases, gauged baseflow information,
318
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stream hydraulics information, boundary conditions, tributary
flows, point sources, nonpoint sources, volumetric sources and
sinks, STORM runoff and dry weather flow, combined sewer
overflows, storage releases and treated outflow, and initial
conditions.
RWQM offers a wide range of statistical output to both
summarize and provide "snapshots" of simulated instream
conditions. Long term average, maximum and minimum pollutant
concentrations, temperatures, and flows are tabulated; and
frequency (percentile) curves can be computed. In addition,
parameter profiles can be printed for any day or
"pollutagraphs" for chosen locations.
5. System Resource Requirements: The RWQM is designed to
operate on a high speed digital computer having a FORTRAN IV
compiler. It has been tested on both an IBM 370 and a CDC
7600. It makes extensive use of computer tape and/or disk
storage for temporary and permanent saving of input data and
simulation output.
6. Applications: The RWQM can be used in conjunction with the
STORM land runoff model. It has been applied to Pennypack
Creek (Abbott and Willey, 1979) in the Philadelphia area.
Computer program documentation costs $4 from the Hydrologic
Engineering Center, Davis, California. The source deck can be
obtained from HEC for $120.
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7. Technical Contact
R. G. Willey
Hydrologic Engineering Center
Corps of Engineers
609 Second St.
Davis, CA 95616
FTS 448-3292 COM 916/440-3292
8. References
HEC. Receiving Water Quality Model, Hydrologic Engineering
Center, U.S. Army Corps of Engineers, Davis, CA. 95616,
1979.
Abbott, Jess and Willey, R. G. Pennypack Creek Water
Quality Study, Hydrologic Engineering Center, U.S. Army
Corps of Engineers, Uavis, CA. 95616, 1979.
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RIVER BASIN MODEL (RIBAM)
!• Model Overview: RIBAM is a modification of the DOSAG
water quality prepared by the Texas Water Development Board.
The DOSAG model has been expanded to model 17 stream water
quality parameters, sulfates, manganese, iron, total nitrogen,
dissolved solids, lead, chlorides, phosphorus, ammonia,
nitrite, nitrate, cyanide, phenols, BOD, chlorpohyll A,
dissolved oxygen, and coliforms. The user supplies streamflow,
waste discharge flows and concentrations, and stream physical
characteristics. The model calculates the water quality
profile for each water quality management alternative.
2. Functional Capabilities: RIBAM is a steady state, one
dimensional stream model. For modeling, the river system is
sub-divided into segments with uniform physical and hydrologic
characteristics. Advection is considered the dominant
transport mechanism. Tributaries and waste water discharges
are added at segment boundaries. Stream concentrations are
computed at the head and end of each segment.
3. Basic Assumptions: RIBAM assumes stream
concentrations are either conservative within a segment or
behave according to first order reaction kinectics. Point
source discharges are assumed to mix completely and are
instantaneously minimal. Within each segment concentrations
are calculated using an exact solution to the differential
equation.
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4. Input and Output; RIBAM requires the following
inputs: Stream physical characteristics (width, depth and
velocity for each segment), upstream conditions (flow and
concentrations), effluent flows and concentrations, stream
reaction rates for each segment and segment temperatures.
RIBAM provides a copy of all input data and a table
summarizing computed stream concentrations for each constituent
modeled. Stream concentrations are reported for upstream and
downstream ends of each segment for all constituents except
dissolved oxygen for which maximum and minimum concentrations
and their location are reported in addition to the above.
5. System Resource Requirements: RIBAM may be run on a
mainframe similar to the IBM 360 or Univac 1110 series. Core
memory requirements are less than 100K bytes and the model is
coded in FORTRAN IV. An engineering background is useful in
the model's application.
6. Applications: RIBAM was used to determine effluent
limitations for municipal and industrial dischargers on the
Mahoning River (eastern Ohio). In this study the program
option to assess the sensitivity of computed concentrations to
various inputs was used in addition to the subroutine which
determines dissolved oxygen reaeration at channel dams.
7. Technical Contact:
Donald Schregardus
U.S. Environmental Protection Agency
Region 5, Eastern District Office
25089 Center Ridge Road
Westlake, OH 44145
COM 216/835-5200
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8. References:
Raytheon Co., Oceanographic and Environmental
Services, Documentation Report Beaver River Basin
Model Project, March 1973, Contract No. 68-01-0746.
Raytheon Co., Oceanographic and Environmental
Services, Expanded Development of BEBAM-A Mathematical
Model of Water Quality for the Beaver River Basin, May
1974^Contract No. 68-01-1836.
Amendola, G.A., Schregardus, D.R., Harris, W.H.,
Moloney, M.E., Mahoning River Waste Load Allocation
Study, September 1977.
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RIVER TEMPERATURE SIMULATION MODEL (TEMPSTAT)
1. Model Overview; The computer program TEMPSTAT is a
river temperature simulation model developed for assessing
numerous thermal loading alternatives for the Mahoning River
(eastern border of Ohio, flows through Warren and Youngstown).
The Edinger and Geyer temperature decay equations were used for
the program with statistically varying inputs to compute the
statistical distribution of temperatures at designated points
along the river.
2. Functional Capabilities: The model uses a one
dimensional steady state temperature calculation procedure
developed by Edinger and Geyer. Thermal loadings are input as
a mean and standard deviation. TEMPSTAT uses a normal
distribution random number generator to determine specific
inputs for each calculation. Numerous repetitive stream
calculations are made to determine the distribution of
temperature in the river.
3. Basic Assumptions: The model assumes that the
temperature of heated water discharged to the river decays
exponentially to an equilibrium temperature. Complete mixing
of the effluent into the receiving stream is assumed as well as
a non-stratified uniform temperature distribution at each point
in the river. Input loadings, equilibrium temperatures, heat
exchange coefficients and stream flows are assumed to be
independent variables.
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4. Input and Output; TEMPSTAT requires the mean and
standard deviation of thermal effluent loadings, flow duration
data, stream surface area and the mean and standard deviation
of the equilibrium temperature (E), and heat exchange
coefficient (K). E and K may be calculated by a separate
program requiring hourly meteorological data (air temperature,
wind speed, relative humidity and cloud cover).
TEMPSTAT outputs the statistical distribution of
stream temperatures at selected points in the river. Outputs
include maximum, minimum, mean and standard deviations of the
calculated temperatures. Also reported are the temperatures
exceeded 1, 5, 10 and 20 percent of the time.
5. System Resource Requirements: This model may be run
on a Univac 1110 mainframe with core requirements of less than
100K bytes. It is coded in FORTRAN IV- A programming and an
engineering background is useful in this model's application.
6. Applications; TEMPSTAT was used to evaluate thermal
loading alternatives on the Mahoning River. With some
modification it was also used to compute temperatures in the
low Black River (tributary of Lake Erie, West of Cleveland).
Equilibrium temperatures and heat exchange coefficients were
determined for both rivers using a model developed oy the U.S.
Army Corps of Engineers and modified to fit program
requirements.
7. Technical Contact:
Donald Schregardus
U.S. Environmental Protection Agency
Region 5, Surveillance & Analysis Division
Eastern District Office
25089 Center Ridge Road
Westlake, OH 44145
COM 216/835-5200 325
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8. References:
U.S. Environmental Protection Agency, Eastern District
Office, Mahoning River Waste Load Allocation Study,
September 1977.
U.S. Environmental Protection Agency, Eastern District
Office, Black River Waste Load Allocation Report,
September 1980.
User's Manual not presently available-
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SIMPLIFIED ESTUARY MODEL (SEM)
1. Model Overview: The Simplifed Estuary Model (SEM) is a
one-dimensional, steady state water quality model for the eval-
uation of uncoupled chemical reactions and BOD-DO deficits in
tidal streams and rivers, and non-stratified estuaries. Con-
stituents that can be modeled include BOD, DO, unspecified con-
servatives, and uncoupled non-conservatives with first order
decay (i.e. some nutrients). SEM was developed by Hydroscience,
Inc. for the EPA as a "first cut" water quality planning tool to
achieve estimates of ambient DO concentrations in estuaries down-
stream from point source residual inputs. The model requires no
computer equipment except for a hand calculator which is used
to compute logs and exponentials.
2. Functional Capabilities: SEM is a far-field, one-dimen-
sional (horizontal plane) model that considers only longitudinal
variations and handles only point source residual inputs. The
model can be applied to tidal streams and rivers, and to non-
stratified estuaries. Physical processes that can be represented
include advection, longitudinal dispersion, dilution, reaeration,
and temperature effects. The model can simulate uncoupled chemi-
cal reactions and coupled BOD-DO deficit reactions, and it can
represent the following constituents: BOD, DO, unspecified con-
servatives, and uncoupled non-conservatives with first order decay.
No biochemical processes are represented directly, although the
modeling of first order decay for coliform bacteria is possible.
The simplified nature of SEM restricts its usage when complicated
prototype systems or complex water quality problems are involved.
SEM has a moderate estimated sensitivity to residual inputs,
stream flow and velocity, and decay coefficients.
3. Basic Assumptions: The model considers only longitudinal
variations and handles only point source residual inputs. It
assumes a constant velocity for each reach, and assumes first-
order decay rates for quality constituents.
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4. Input and Output: The following input data is required for
initial setup/calibration of the model:
1J Net river flow exclusive of tidal effects
2) Flow velocities
3) Average depths
4) Distance from point source discharges
5) Dispersion coefficients
6) Cross - sectional area
7) Constituent concentration, temperature and background
DO for all stream inflows
8) Loading rate for ultimate oxygen demand
9) Deoxygenation coefficients
11) Reaeration coefficients
12) Salinities at the seaward boundary
Constituent concentrations throughout the modeled area are needed
for verification of the model.
Output from the model (through hand calculation) includes
constituent concentrations, maximum DO deficit, and minimum DO
concentrations.
5. System Resource Requirements: SEM is uncoded and requires
no computer equipment except for a hand calculator which is used
to compute logs and exponentials. Data preparation and model
setup (including model familiarization) requires 1-2 man-weeks,
and the actual computation time may take from several man-hours
to several man-days depending on the complexity of the application
and the mathematical skill of the personnel involved. A back-
ground in engineering with a mathematical orientation would
be Useful. No programming experience is necessary.
6. Applications: SEM has been used for various applications
7. Technical Contact
Robert B. Ambrose
U.S. Environmental Protection Agency
Environmental Research Laboratory
College Station Road
Athens. Georgia 30605
FTS 250-3546 COM 404/546-3546
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8. References
Hydroscience, Inc. "Simplified Mathematical Modeling of
Water Quality." Report to Office of Water Programs, U.S.
Environmental Protection Agency, Washington B.C. (TF. S.
Government Printing Office No. 1971-44-367/392), 1971.
Hydroscience, Inc. "Addendum to Simplified Mathematical
Modeling of Water Quality." Report to Office of Water
Programs, U.S. Environmental Protection Agency, Washing-
ton7 D.C. (U.S. Government Printing Office No. 1972-484-
486/291), 1972.
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SIMPLIFIED STREAM MODEL (SSM)
1. Model Overview: The Simplified Stream Model is a one-
dimensional, steady state water quality model for the evaluation
of conservatives, singular non-conservatives with first order
decay, and coupled BOD-DO deficits in streams, rivers, and shal-
low non-stratified lakes. It was developed by Hydroscience, Inc.
for the EPA as a "first cut" planning tool to achieve estimates
of ambient DO downstream from point sources. SSM requires no
computer equipment except for an electric hand calculator which
is used to compute logs and exponentials.
2. Functional Capabilities: SSM is capable of simulating the
physical processes of dilution, advection, reaeration, and tem-
perature effects in streams, rivers, and shallow non-stratified
lakes. Uncoupled chemical reactions can be represented, as well
as coupled BOD-DO deficit reactions. Constituents that can be
represented by the model include BOD, DO, unspecified conserva-
tives, and un-coupled non-conservatives with first order decay.
The model handles only point source residual inputs, and its
simplified nature restricts its usage when complicated prototype
systems or complex water quality problems are involved. No bio-
chemical processes are represented directly, although first order
decay for coliform bacteria may be possible.
SSM has a high estimated sensitivity to residual loading
rates and stream velocities, and a moderate estimated sensitiv-
ity to decay coefficients and total streamflow.
3. Basic Assumptions: The model considers only longitudinal
variations and handles only point source residual inputs. It as-
sumes a constant stream velocity for each reach, and assumes first
order decay rates for quality constituents.
4- Input and Output: For intial set-up/calibration needs,
the following input data is required:
1) Net river flow
2) Flow velocity
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3) Dep'th
4) Distance from point source discharges
5) Constituent concentration, temperature, and background
DO deficit for all stream inflows
6) Loading rate for ultimate oxygen demand
7) Deoxygenation coefficients
Constituent concentrations throughout the modeled area are re-
quired for model verification.
Output from the model (through hand calculation) includes
constituent concentrations and DO deficits.
5. System Resource Requirements: The model is uncoded and re-
quires no computer equipment except for an electronic hand cal-
culator which is used to compute logs and exponentials. Data
preparation and model set-up may require 1-2 man-weelcs, and actual
computation time may take from several man-hours to several man-
days, depending on the complexity of the application and the math-
ematical skill of the personnel involved. A background in engineering
with, a mathematical orientation is useful. No programming
experience i.s: necessary.
6, Applicationsi SSM can be applied to rivers, streams, and
shallow, non-stratified lakes for the evaluation of conservatives,
non-conservatives with first-order decay, and coupled BOD-DO deficits.
7. Technical Contact
Robert B. Ambrose
U.S. Environmental Protection Agency
Environmental Research Laboratory
College Station Road
Athens. Georgia 30605
FTS 250-3546 COM 404/546-3546
8. References
Hydroscience, Inc. "Simplified Mathematical Modeling of Water
Quality." Report to Office of Water Programs, U.S, Environ-
mental Protection Agency, Washington, D.C. (U~. S. Government
Printing Office No. 1971-444-367/392), 1971.
331
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Hydroscience, Inc. "Addendum to Simplified Mathematical
Modeling of Water Quality." Report to Office of Water Pro-
grams, U.S. Environmental Protection Agency, Washington
DT^7-(U.S. Government Printing Office No. 1972-484-486/291),
1972.
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STREAM 7B
1- Model Overview; STREAM 7B is a one-dimensional steady -
state model, characterized by first-order or coupled first
order reaction kinetics. It is primarily intended for use in
the analysis of biochemical oxygen demand (BOD) and dissolved
oxygen (DO), although it may be applied for any other parameter
assumed to follow first-order reaction kinetics.
2. Functional Capabilities: The purpose of this model is to
calculate NBOD, CBOD, DO and fecal coliforms as well as an
optional constituent at various points within a river system.
Average algal photosynthesis and respiration can be modeled .
Model can produce plots if desired.
3- Basic Assumptions: The basic assumptions are: constant
stream velocity, steady state, first order decay, and
reaeration by COVAR* s method.
4. Input and Output: The inputs for this model are: river
NBOD oxidation rates, bottom sludge deoxygenation rates, dam
reaeration rates, rapids reaeration rate, stream reaeration
rates, NBOD settling rate, fecal coliform decay rate, optional
constituent decay rate, time of travels, reach lengths, flows,
distributed inflow, temperatures, waste discharges, algal
production, respiration rates, and mean reach depths.
The outputs for this model are: concentrations of NBOD,
CBOD, DO, fecal coliform and an optional constituent at various
points within a river system. It also provides plots of algal
photosynthesis and respiration over time.
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5. System Resource Requirements: STREAM ?B is coded in
FORTRAN IV and can be run on the IBM 370/168 or any equivalent
mainframe computer. It requires less than 300K bytes of core
storage for execution. It uses any 132 position line printer
for output. A background in programming and engineering is
helpful.
6. Applications: STREAM 7B is used in waste load allocations.
7. Technical Contact
Douglas A. Little
U.S. Environmental Protection Agency
JFK Federal Building
Boston, MA 02203
COM 617/223-5885 FTS 223-5885
8. References
Resource Analysis, Inc. STREAM 7A User* s Manual, March 1978.
Resource Analysis Addendum to STREAM 7A User's Manual -
STREAM 7B, June 1980.
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STREAM NETWORK SIMULATION PROGRAM (SNSIM)
1. Model Overview: SNSIM is a computer program for the
steady-state water quality simulation of a stream network. Its
basis is an expanded form of the Streeter-Phelps equation, and
it is designed to evaluate and/or predict the DO and the car-
bonaceous and nitrogenous BOD profiles in a river or stream
where the effects of dispersion can be assumed to be insignifi-
cant. This environmental model is ideal for the evaluation of
various water treatment schemes, as its basic control variable
is waste input.
2. Functional Capabilities: SNSIM can be used to formulate a
steady-state, one-dimensional simulation model of a stream net-
work. The stream network consists of a river and its tributaries
which are segmented into sections of constant hydrologic, physical,
chemical, and biological parameters. Loads may be applied point-
wise at the ends of the section or as distributed sources along
its length. BOD loads include carbonaceous and nitrogenous
point loads and distributed loads, while DO deficit loads include
the distributed loads of benthai demand and photosynthetic demand.
Point sources of both BOD and DO deficit from minor tributaries
can be input at the ends of a section, and background loads of
BOD and DO deficit can be introduced at the system's upstream
ends. SNSIM is limited to combining a maximum of 4 tributaries
at one confluence, and the number of reaches that may be stored
at one time is 10, but these limits may be expanded by changing
the dimension statements for these variables.
3. Basic Assumptions: This model typifies the sanitary
engineering approach in that its emphasis is on relating man's
waste inputs to the aquatic environment with the express purpose
of managing the inputs and thus the water quality. An expanded
form of the Streeter-Phelps equation is the basis of the SNSIM
computer program, and the model is designed to evaluate and/or
predict the dissolved oxygen, and the carbonaceous and nitro-
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genous BOD profiles in a river or stream where the effects of
dispersion can be assumed to be insignificant.
4. Input and Output: SNSIM requires a large input data base
which must be in card-image form. For each reach the following
information should be provided: Ihstream flow, instream carbon-
aceous demand, instream nitrogenous demand, instream DO deficit,
increment size for a section output, an integer representing the
reach number of the starting mile-point, the number of sections
in the reach, the number of tributaries or reaches to be com-
bined, and the indicator which designates if the reaeration rate
is to be input or computed. A control variable which indicates
if the stream depth, flow; and velocity are to be computed by ex
ponential correlation equations may also be used.
The section length, stream depth, stream velocity, waste or
effluent flow at the head of the section, effluent COD, effluent
NOD, effluent DO deficit, tributary flow at the head of the sec-
tion, and the ratio of ultimate to 5-day BOD are needed. In
addition, the tributary COD, tributary NOD, the tributary DO
deficit, water temperature, carbonaceous BOD deoxygenation rate,
carbonaceous BOD decay rate, nitrogenous BOD decay rate,
reaeration rate, algal oxygen rate, benthic oxygen demand, the
carbonaceous and nitrogenous bank loads, and the altitude above
sea level are required.
Reports produced by the SNSIM program include the input
parameters for each reach, as well as converted reaction rates,
section numbers, section names, distance downstream, CBOD, NBOD,
DO, flow, deficit components, and the total deficit for each
reach.
5- System Resource Requirements: The SNSIM/1 is written in
FORTRAN IV for use on the IBM 370/155 in a 16K area of core
storage. It may also be modified for compatibility with the IBM
1130 (SNSIM/2). A background in programming or environmental
engineering with experience in water quality modeling are
helpful for the model.
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6- Applications: SNSIM has been used for various applications
within the Environmental Protection Agency.
7. Technical Contacts
George A. Nossa and Laura Livingston
U,S. Environmental Protection Agency
Information Systems Branch 2PM-IS
26 Federal Plaza
New York, New York 10278
FTS 264-9850 COM 212/264-9850
Steven C. Chapra
NOAA Great Lakes Environmental Research Laboratory
2300 Washtenaw Avenue
Ann Arbor, Michigan 48104
FTS 378-2250 COM 313/668-2250
8. References
Braster, R.E., Chapra, S.C., and Nossa, G.A., Documentation
for SNSIM1/2. A Computer Program for the Steadv-State Water
Quality Simulation of a Stream Network. U.S. Environmental
Protection Agency, Region II, 26 Federal Plaza, New York,
New York, March 1978.
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STREAM QUALITY MODEL (QUAL-II)
1. Model Overview: QUAL-II is designed to simulate the
dispersion and advection of conservative and reacting
constituents in branching stream system and rivers.
Constituents modeled include conservative minerals,
temperature, BOD, cnlorophyll a, phosphorus, ammonia, nitrate,
nitrite, DO, coliform bacteria, radioactive material, and an
arbitrary nonconservative material. It also considers nutrient
cycles and algal growth. The program simulates the dynamic
behavior of these constituents by numerical integration of the
one-dimensional form of the advection-dispersion transport
equation. A steady-state solution of the equations is also
available in the program. Any branching stream system can be
simulated.
2. Functional Capabilities: QUAL-II represents the stream
simulated through the use of reaches. A reach is defined as a
stretch of the stream with uniform hydraulic characteristics.
Each reach is divided into computational elements. A maximum
of 75 reaches, each with up to 20 computational elements with
no more than 500 in the system, can be accommodated in the
standard version of the program. In addition, there can be a
total of 15 headwater elements, 15 junction elements, and 90
input and withdrawal elements. All input is in relation to
each reach, and the hydraulics equations are solved by
incorporating advection and dispersion through a finite
difference implicit solution technique. The results on the
quality constituents are obtained by numerical integration of
the one dimensional form of the advection-dispersion mass
transport equation for each constituent. Tne model proceeds to
solve the relevant equations of each constituent until a steady
state equilibrium is achieved. QUAL-II is written in a modular
fashion so as to facilitate the incorporation of additional
processes.
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3- Basic Assumptions: QUAL-II assumes first order kinetics
and it utilizes a simplified nutrient-algae cycle with Monod
kinetics. Only constant inflows and point source discharges
are considered, and each computational element is considered
completely mixed. The model does not consider variations in
depth or within stream cross-section.
4. Input and Output: QUAL-II requires an input data base in
card-image form. Aside from the printed report, no additional
requirements are imposed. The data required are varied and
includes evaporation coefficients, oxygen uptake per unit of
nitrogen and unit of algae, algal growth rates, nitrogen and
phosphorus half saturation constants, and reaction rate
constants. Further input required is the identification of the
computational elements and their hydraulic characteristics, and
the initial conditions of the system.
The printed output includes a complete history of every
quality parameter and temperature at each computational
element. The hydraulic information provided includes the flow,
velocity, and depth of each reach, as well as at the head of
the system. Water quality information provided includes the
concentration of each quality component, the temperature, and
the reaction rates at each computational element in the system.
5. System Resource Requirements: The model is written in
FORTRAN IV (G) and may be installed on any digital computer
with at least 45,000 words of core storage and a FORTRAN IV
compiler. A version of the program with reduced capabilities
is available that will execute on EPA s PDF 11/70 scientific
computers and may be used on other minicomputers. It requires
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4-10 weeks of effort for the data preparation and report
interpretation. An operator with computer programming and
environmental engineering experience is useful.
6. Applications: QUAL-II can be used to simulate the
dispersionary and flow characteristics of conservative and
non-conservative constituents in branching stream systems and
rivers. It is a modification of the QUAL-I Model that was
developed by the Texas Water Development Board. The version
currently (May 82) distributed by EPA was modified for the
Southeast Michigan Council of Goverments and has been
extensively reviewed and tested. Training in the use of the
model is available through the center for Water Quality
Modeling. It is widely used in Wasteland Allocation studies.
7. Technical Contact
Tom Barnwell
U.S. Environmental Protection Agency
Center for Water Quality Modeling
Athens, GA. 30613
COM 404/546-3585 FTS 250-3585
8. Reference
Brown, L.C. "A Review of the Mathematical Water Quality
Model QUAL-II and Guidance for Its Use." Technical
Bulletin 338, National Council of the Paper Industry for
Air and Stream Improvement, Inc., 260 Madison Ave., New
York, Oct. 1980.
Roesner, L.A., Giguere, P.R., and Evenson, D.E. Computer
Program Documentation for the Stream Quality Model QUAL-II,
EPA-600/9-81-014, USEPA, Athens, GA, Feb. 1981.
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Roesner, L.A., Giguere, P.R., and Evenson, D.E. "Users
Manual for the Stream Water Quality Model QUAL-II,"
EPA-600/9-81-015, USEPA, Athens, GA» Feb. 1981.
Whittemore, R.C. and Hovis, J. "A Study of the Selection,
Calibration and Verification of Mathematical Water Quality
Models." Technical Bulletin 367, National Council of the
Paper Industry for Air and Stream Improvement, Inc., 260
Madison Ave., New York, March 1982.
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TIDAL TEMPERATURE MODEL (TTM)
1. Model Overview: The Tidal Temperature Model (TTM) is a
derivative of the Dynamic Estuary Model (DEM) that can simulate
the heat budget and dispersional characteristics of streams,
well-mixed shallow impoundments, estuaries, and coastal waters.
The model can accomodate constituents which may be conservative
or non-conservative, have coupled or non-coupled reactions, and
which undergo first order decay. The TTM was developed by the
EPA Pacific Northwest Laboratory and has been applied to the
Columbia River below the Bonneville Dam.
2. Functional Capabilities: The TTM represents the one dimen-
sional tidal and net river flow, transport processes of advection
and diffusion, the heat budget and the dilution of up to four
constituents within an estuary, stream, well-mixed shallow im-
poundment, or coastal waters. Coupled BOD-DO reactions may be
modeled, as well as up to four constituents which may be con-
servative or non-conservative, have coupled or non-coupled re-
actions, and undergo first order decay. The model can simulate
systems with up to 300 channels and as many as 300 junctions.
The TTM primarily links hydrodynamic and temperature/heat budget
components, but additional water quality constituents may be
possible. A standard heat budget approach and a thermal equilib-
rium approach are used to figure tenperatures.
3. Basic Assumptions: The TTM assumes that all inflows or
withdrawals are constant, and it utilizes a simplified form of
evaporation. One dimensional channels are used to represent two
dimensional flows and transports. The model neglects wind stress
and disregards lateral and vertical variation in channel cross-
sectional area with tidal elevation change. It handles constant
residual input rates which can be put in variable form, and it
cannot simulate tidal flats that go dry.
4- Input and Output: The Tidal Temperature Model allows for a
large input data base, written in card-image form. Parameters
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which can be specified include headwater flows, tributary flows,
groundwater flows, water withdrawals, seaward tides, channel
depths and widths, bottom roughness, constituents in freshwater
inflows and at seaward boundaries, constituent concentrations
throughout the modeled area, the quality and quantity of point-
source residuals discharges, net solar radiation, and wet and dry
bulb temperatures.
Output formats include tabular printouts and velocities
written by the hydrodynamic module. Output information provided
by the model includes summarized data (maximum and minimum val-
ues) for tidal cycles, flows, velocities, water elevation, con-
stituent concentrations at each junction, channel velocities,
junction depths, and constituents at user specified periods.
5. System Resource Requirements: The TTM is written in FOR-
TRAN IV and can be run on any digital computer with at least
50,000 words of main storage and a FORTRAN IV compiler.
Operator skills required include experience in hydrodynamic
and water quality modeling and programming. The model requires
5-20 manweeks for data preparation and several manhours for
output interpretation.
6. Applications: The Tidal Temperature Model has been used
by the EPA Pacific Northwest Water Laboratory and has been ap-
plied to the Columbia River below the Bonneville Dam. The model
has also been used by the EPA in Massachusetts, South Carolina,
Florida, Oregon, and Washington.
7. Technical Contact
Richard J. Callaway
U.S. Environmental Protection Agency
Corvallis Environmental Research Laboratory
200 S.W. 35th Street
Corvallis, Oregon 97330
FTS 420-4703 COM 503/757-4703
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8. References
Callaway, R.J. and Byxam, K.V . "Mathematical Model of the
Columbia River from the Pacific Ocean to Bonneville Dam, Part
II: Input-Output and Initial Verification Procedures." Re-
port by U.S. EPA Pacific Northwest Water Laboratory, Corvallis,
Oregon, 1971.
Callaway, R.J., Byram, K.V., and Ditsworth, G.R. "Mathematical
Model of the Columbia River from the Pacific Ocean to Bonneville
Dam, Part I. Theory, Program-Notes and Programs." Report by
FWQA Pacific Northest Water Laboratory, Corvallis, Oregon, 1969.
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TIME-DEPENDENT, THREE-DIMENSIONAL TRANSPORT MODEL
1. Model Overview: The time-dependent, three-dimensional
transport model was developed to calculate the hydrodynamic
transport of conservative and non-conservative substances in
various water bodies. The model calculates the time-dependent
concentration of the desired substance. Input to this model
for velocities are results from the separately described
hydrodynamic model. Various user specified options permit
application to conservative substances such as chloride and
non-conservative substances such as suspended solids.
2. Functional Capabilities: The model is fully time-dependent
and three-dimensional. The spatial resolution is up to the
discretion of the modeler but is usually the same as used in
the hydrodynamic model. The time step restrictions are
dependent on the particular application. The vertical
diffusion term in the equation is calculated implicitly in time
so there is no time step stability restriction for this term.
The other terms in the equations are treated explicitly.
Various combinations of boundary conditions can be used. For
suspended solids calculations, flux at the water-sediment
interface is parameterized by 2 coefficients determined by
laboratory experiments. Conservation-perserving finite
difference techniques are used.
3. Basic Assumptions: The equations are derived from the
time-dependent, three-dimensional equation for conservation of
material. The main assumption is that eddy coefficients are
used to account for turbulent diffusion effects. The program
for the model is modular in form so this condition can be
changed to incorporate various turbulence modeling schemes.
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A. Input and Output: Input to the model includes: complete
specification of geometry and grid layout (which can be
obtained from hydrodynamic model), topography, and forcing.
The latter includes velocities (from hydrodynamic model),
inputs/outputs, and other things such as wind depending on what
is being modeled. The initial conditions can be user specified
or from results of previous calculation.
The basic output of the program is a printed record of
concentrations, as desired. If results are stored (disk or
tape), separate programs are available to produce graphic
output on either Tektronix or Calcomp equipment. The plots
available include time series and horizontal and vertical
sections.
5. System Resource Requirements: This model is coded in
FORTRAN and can be run on an IBM 370/4300, Univac 1100, VAX
11/780 and Cray mainframe. It requires between 50k and 1.5 m
words core storage. It uses a high speed line printer for
output. It requires a card reader/punch if disk input is not
used. A background in scientific computer programming and
hydrodynamic modeling is helpful.
6. Applications: The model has been applied to the
following: Lake Erie, entire oasins of Lake Erie, Saginaw Bay,
Sea of Azov, Lake Baikal and Waukegan Harbor. Organization
that have used the model include various federal agencies and
universities in this country and in Europe.
7. Technical Contact
Dr. John F. Paul
U.S. Environmental Protection Agency
Environmental Research Laboratory
South Ferry Road
Narragansett, RI 02882
COM 401/789-1071 FTS 838-4843
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8. References
Lick, W.J., Paul, J., and Sheng, Y.P., The Dispersion of
Contaminants in the Near-Shore Region. In: Modeling
Biochemical Processes in Aquatic Ecosystems (R.P. Canale,
ed.). Ann Arbor Science Publishers, Inc. pp. 93-112, 1976.
Paul, J.F., and Patterson, R.L., Hydrodynamic Simulation
of Movement of Larval Fishes in Western Lake Erie and their
Vulnerability to Power Plant Entrainment. Proc. of the
1977 Winter Simulation Conf. (H.J. Highland, R.G. Sargent
and J.W. Schmidt, ed.), WSC Executive Committee, pp.
305-316, 1977.
Paul, J.F., Richardson, W.L., Gorstko, A.B., and Matveyev,
A.A., Results of a Joint USA/USSR Hydrodynamic and
Transport Modeling Project. EPA-600/3-79-015, 1979-
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TIME-DEPENDENT, THREE-DIMENSIONAL, VARIABLE-DENSITY
HYDRODYNAMIC MODEL
1. Model Overview: The time-dependent, three-dimensional,
variable density hydrodynamic model was developed to describe
the motion in thermal discharges in-harbors, bays, lake basins,
entire lakes, estuaries, etc. The model calculates velocities
and temperature (salinity, also, if required) as a coupled set
of time-dependent, non-linear partial differential equations.
The results of the model can be used as input to a transport
model (described separately). The model has various versions
(user specified) such that the calculations can be performed
with the water surface treated rigid-lid or as a free-surface
and with the bottom boundary condition specified either as
no-slip or slip.
2. Functional Capabilities: The model is fully time-dependent
and three-dimensional. The spatial resolution is up to the
discretion of the modeler and the time step restrictions are
dependent on the individual application. The Coriolis,
pressure, and vertical diffusion terms in the equations are
calculated implicitly in time, so no time step stability
restiction applies to them. The other terms are calculated
explicitly. The momentum and energy (and salinity) equations
are coupled. Various combinations of boundary conditions can
be used at the discretion of the modeler.
Conservation-preserving finite difference techniques are used.
3. Basic Assumptions: The equations are derived from the
time-dependent, three-dimensional equations for conservation of
mass, momentum, energy, and salinity. The principal
assumptions are: 1. hydrostatic pressure variations 2.
rigid-lid or linearized free-surface approximation and 3. eddy
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coefficients to account for turbulent diffusion effects. The
program for the model is modular in form so the last condition
can be modified to account for various turbulence modeling
schemes. The solution procedure is a modification of the
simplified marker and cell technique.
4. Input and Output: Input to the model includes: complete
specification of geometry and grid layout, topography, and
forcing functions. The latter includes wind (constant or
spatial and temporal varying), in flows/out flows , and heat
specification at water surface. The initial conditions can be
quiescent conditions, some user specified form, or results from
previous calculation.
The basic outputs of the program are a printed record of
velocities, temperature, salinity and pressure, as desired. If
results are stored (disk or tape), separate programs are
available to produce grahic output on either Tektronix or
Calcomp equipment. The plots available include time series of
variables, and horizontal and vertical section plots of the
variables.
5. System Resource Requirements; This model is coded in
FORTRAN and can be run on an IBM 370/4300, Univac 1100, VAX
11/780, or Cray mainframe. It requires between 50k and 1.5 M
words core storage. It uses a high speed line printer for
output. It requires a card reader/punch if disk input is not
used. A background in scientific computer programming and
hydrodynamic modeling is helpful.
6. Applications: The model has been applied to the
following: Lake Huron; Lake Erie; separate basins of Lake
Erie; the area of Lake Erie for proposed jetport; Cleveland
harbor; vicinity of Monroe, Michigan for proposed dredged spoil
sites; Saginaw Bay; numerous thermal discharges in the Great
Lakes and the Baltic Sea; Sea of Azov; Lake Baikal; and
Waukegan Harbor .
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Organizations that have used the model or results of the
model include Corps of Engineers, Swedish Meteorological and
Hydrological Institute, Argonne National Laboratory, Rudjer
Boskovic Institue (Yugoslavia), Hydrometeorological Institute
(USSR), Ohio State University, University of Arizona, NASA,
NOAA, University of California, and U.S. EPA.
The output of the model can be used directly in a separate
transport model.
7. Technical Contact
Dr. John F. Paul
U.S. Environmental Protection Agency
Environmental Research Laboratory - Duluth
South Ferry Road
Narragansett, RI 02882
COM 401/789-1071 FTS 838-4843
8. References
Lick, W.J., Paul J., and Sheng, Y.P. The disperison of
contaminants in the near-shore region. In: Modeling
Biochemical Processes in Aquatic Ecosystems (R.P. Canale,
ed. ) Ann Arbor Science Publishers, Inc. pp.93-112, 1976.
Paul, J.F. Modeling the Hydrodynamic Effects of Large
Man-Made Modifications to Lakes. Proc. of the EPA Conf. on
Environmental Modeling and Simulaton (W.R. Ott, ed.).
EPA-600/9-76-016, pp. 171-175, 1976.
Paul, J.F. and Lick, W.J. A Numerical Model For a
Three-Dimensional, Variable-Density Jet. Report No.
FTAS/TR73-92, School of Engineering, C.W.R.U., Cleveland,
Ohio, 1973.
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Paul, J.F. and Lick W.J., A Numerical Model For a
Three-Dimensional, Variable-Density Jet. Proc. 16th Conf.
Great Lakes Res., IAGLR, pp. 818-830, 1973.
Paul, J.F. and Lick, W.J., A Numerical Model for Thermal
Plumes and River Discharges. Proc. 17th Conf. Great Lakes
Res., IAGLR, pp. 445-455, 1974.
Paul, J.F. and Lick, W.J., Report to Argonne National
Laboratory of the Application of the Paul-Lick Model to
Point Bench Unit 1 Outfall. Appears in appendix of Surface
Thermal Plumes; Evaluation of Mathematical Medals for the
Near and Complete Field (W.E. Dunn, A.J. Policastro and
R.A. Paddock), ANL/WR-75-3, pp. 484-511, 1975.
Paul, J.F. and Lick, W.J., Application of Three
Dimensional Hydrodynamic Model to Study Effects of Proposed
Jetport Island on Thermocline Structure in Lake Erie.
Report 17-6 of Lake Erie International Jetport Model
Feasibility Investigation. U.S. Army Engineer Waterways
Experimental Stations Contract Report H-75-1, 1976.
Paul, J.F. and Lick, W.J., An Efficient, Implicit Method
for Calculating Time-Dependent, Free-Surface, Hydrodynamic
Flows. Presented at the 22nd Conference on Great Lakes
Research, Rochester, New York, 1979.
Paul, J.F. and Lick W.J., Numerical Model for Three
Dimensional, Variable-Density Rigid-Lid Hydrodynamic
Flows: Volume 1. Details of the numerical model. In
preparaton, 1980.
Paul, J.F., Richardson, W.L., Gorstko, A.B. and Matveyou,
A.A., Results of a Joint USA/USSR Hydrodynamic and
Transport Modeling Project EPA-600/3-79-015, 1979.
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Vasseur, B., Funkquist, L., and Paul, J.F., Verification
of a Numerical Model for Thermal Plumes. SMHI Hydrology
and Oceanography Report No. 24, 1980.
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WATER QUALITY ASSESSMENT METHODOLOGY
FOR TOXIC AND CONVENTIONAL POLLUTANTS (WQAM)
1. Model Overview: The Water Quality Assessment Methodology
is a collection of formulas, tables, and graphs which planners
can use to perform a preliminary (screening) assessment of
surface water quality in large basins. Analyses require little
data, and in most cases, can be accomplished with the
assistance of a desk top calculator. Desk top calculation
procedures are provided for the following subject categories:
Environmental chemistry, including equilibrium and
transformation process.
Wasteload estimation, including point and non-point source
pollutants.
Stream analyses for toxic organic chemicals, priority
pollutants, water temperature, biochemical oxygen demand,
dissolved oxygen, total suspended solids, coliform
bacteria, plant nutrients, and conservative constituents.
Lake analyses for toxic organic chemicals, priority
pollutants, thermal stratification, sediment accumulation,
phosphorus budget, eutrophication potential, and
hypolimnion DO.
Estuarine analyses for estaurine classification, toxic
organic chemicals, priority pollutants, temperature, BOD,
DO, turbidity, sediment accumulation, and conservative
constituents.
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2. Functional Capabilities: This methodology predicts
far-field, average steady-state conditions in streams, lakes,
and estauries as a function of long term average maximum and
minimum nonpoint source loads and point source loads.
Longitudinal concentration variations are predicted for streams
and estuaries. The accuracy is sufficient to bracket expected
conditions in a "screening" exercise.
3. Basic Assumptions; The nonpoint source loading section is
based on the modified Universal Soil Loss Equation. The stream
section is based on steady-state, plug flow solutions to the
conservation of mass equation. The lake section is based on
empirical stratification relationships and mass balance. The
estuary section is based on the modified tidal prism and/or
fraction of fresh water formulas.
4. Input and Output: The methodology is designed to operate
with minimum data, recognizing that the more data available,
the more accurate the analysis. Basic information needed
includes: land use, stream lengths and net flows, reservoir
depths and volumes, and estuary salinity distributions. Point
source loading data is also needed, as well as pollutant
properites.
Output from the model includes predicted stream
concentrations of BOD, DO, total N, total P, temperature, toxic
organic polutants, priority pollutants, and conservative
pollutants by reach; total lake nutrient, toxic organic,
priority pollutant concentrations, eutrophic status, and
hypolimnion DO deficit; and estuary concentrations of BOD, DO,
total N, total P, toxic organic, priority and conservative
pollutants by reach. Calculations are done by hand calculator
and can be arranged to the user's convenience.
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5. System Resource Requirements; This model uses a
calculator. An engineering background is useful.
6. Applications: The methodology has been applied and tested
on the Sandusky River Basin and four Chesapeake Bay
sub-basins: the Patuxent, Chester, Ware, and Occoquan. This
work was done by the Midwest Research Institute and Tetra Tech
on a grant from EPA. The methodology is linked to the Midwest
Research Institue loading functions.
The Environmental Protection Agency sponsors training in
the use of the methodology from time to time. Further
information is available from the Technical Contact.
7. Technical Contact
Thomas 0. Barnell, Jr.
U.S. Environmental Protection Agency
EPA Athens Environmental Research Laboratory
Center for Water Quality Modeling
Athens, GA 30613
COM 404/546-3585 FTS 250-3585
8. References
Water Quality Assessment: A Screening Method for
Nondesignated 208 Areas. EPA 600/9-77-023, August 1977.
Available from NTIS (PB277161/AS for $29).
"Water Quality Assessment Methodology for Toxic and
Conventional Pollutants." (in Press), Center for Water
Quality Modeling, USEPA, Athens, GA 30613
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WATER QUALITY FEEDBACK MODEL (FEDEAK05)
1. Model Overview: FEDBAK03 is used to compute the steady-
state distribution of water quality variables undergoing con-
secutive reactions with feedback and following first order
kinetics. The program has been developed in a general form
but is specifically applicable 'to the reactions observed by
nitrogenous species and the associated dissolved oxygen uptake
in the natural environment. The basis for this model is the
theory of conservation of mass. The approach used to solve
the equations is a finite difference scheme developed by Tho-
mann, which has been shown to be a very effective tool in the
field of water quality management.
2. Functional Capabilities: FEDBAK03 has been developed to
help predict water quality parameters which react under first
order kinetics and as a system of consecutive reactions, where
any parameter can react in a feedback fashion. The problem
setting assumes an aquatic environment in which steady-state
conditions can be applied. The Thomann solution of solving
the general estuarine advection/dispersion equation by re-
placing the derivatives with finite-difference approximations
is the approach followed in FEDBAK03. Optionally, the program
can also perform system sensitivity analysis by varying the
waste vector and re-multiplying by [A] 1 and/or changing the
reaction rate constants for any reactants and repeating several
steps. A second option is the computation of dissolved oxygen
deficit and the corresponding dissolved oxygen concentration
by selecting the reaction schemes producing the deficit and the
associated stochiometric coefficients.
As presently written, the program can accomodate a multi
dimensional system of up to 60 sections and each section can
have a maximum of six interfaces. The maximum number of reac-
tants is such that when multiplied by the number of sections,
it cannot exceed 120. This present limitation can easily be
expanded.
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3. Basic Assumptions: The model assumes steady-state condi-
tions in an aquatic environment. It is based on the theory of
conservation of mass and utilizes a finite difference scheme
for the solution of the general estuarine advection/dispersion
equation. Reactants are assumed to undergo consecutive reac-
tions with feedback and first order kinetics.
4. Input and Output: FEDBAK03 requires the input of the phys-
ical characteristics of the system to be evaluated; namely,
the geometry, temperature, hydrologic characteristics, reaction
schemes, and corresponding reaction rates.
5. System Resource Requirements: The computer program has
been written for the IBM 370 with a FORTRAN IV (G or H) level
compiler. The program occupies 140 K of core to execute and
takes 35 CPU seconds to solve a 10 segment, 8 component system.
A background in computer programming or environmental engineering
with experience in computer modeling is useful.
6. Applications: FEDBAK03 can be used for the calculation of
BOD deficit and nitrification.
7. Technical Contacts
George A. Nossa and Laura Livingston
U.S. Environmental Protection Agency
Information Systems Branch
26 Federal Plaza
New York, New York 10278
FTS 264-9850 COM 212/264-9850
8. References
Nossa, G.A., "FEDBAK03 A Computer Program for the Modeling
of First Order Consecutive Reactions with Feedback Under a
Steady State Multidimensional Natural Aquatic System".
Environmental Modeling and Simulation, USEPA Office of Re-
search and Development, Washington, B.C. July 1976.
Nossa, G.A., "FEDBAK03 - Program Documentation and User's
Guide" USEPA, Region II, New York, New York, November 1978.
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WATER QUALITY FOR RIVER-RESERVOIR SYSTEMS (WQRRS)
1. Model Overview: WQRRS was developed and is supported by
the U.S. Army Corps of Engineers' Hydrologic Engineering
Center. The WQRRS model (HEC, 1978a) consists of three
separate but integrable modules: the reservoir module, the
stream hydraulic module, and the stream quality module. The
three computer programs may be integrated for a complete river
basin water quality analysis through automatic storage of
results. An output tape may be generated for input to a
separate plotting and statistical program called the
Statistical and Graphical Analysis of Stream Water Quality Data
(HEC, 1978b).
The system is based on a comprehensive lake ecological
simulation program originally developed by Chen and Orlob
(1972) and a river simulation model developed by Norton
(1972). The original river routines only simulated steady
hydraulic conditions so the capability to dynamically route
streamflow using either the St. Venant equations, Kinematic
Wave, Muskingum, or Modified Pulse method was added.
Subsequent updating of the system added the capability to
analyze branched and looped stream systems and added additional
water quality and biological constituents (King, 1976; Smith,
1978). A separate program for statistical and graphical
analyses of stream water quality data is available (HEC, I978b),
2. Functional Capabilities: The methodology in the reservoir
section is applicable to aerobic impoundments that can be
represented as one-dimensional systems with horizontal
isotherms. The stream hydraulic module includes six hydraulic
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computation options capable of handling hydraulic behavior
within both the "gradually varied" steady and unsteady flow
regimes. The stream quality module simulates transport of
quality parameters in aerobic streams.
Physical processes modeled in WQRRS include advection,
diffusion, dilution, and the heat budget. Temperature effects
on water density and various kinetic parameters are
considered. State variables include 18 highly interconnected
physical, chemical and biological parameters: BOD, organic
detritus, organic sediment, coliform, total carbon, phosphate,
ammonia, nitrites, nitrate, DO, pH, total alkalinity, total
dissolved solids, carbon dioxide, two algae species,
zooplankton, fish, and benthic animals. The river module also
includes aquatic insects, benthic algae, suspended solids, and
inorganic sediment.
3. Basic Assumptions; WQRRS simulates reservoirs as a
one-dimensional vertical plane, and stream segments as a
one-dimensional horizontal plane.
4. Input and Output: The initial setup/calibration needs
are: flowrate for all lake and stream inflows; lake and stream
hydrogeometric data; lake outflow elevations and locations;
latitude and longitude of prototype systems; Secchi disc depth
for light extinction; concentrations of quality constituents,
biological parameters, and temperatures in all lake inflows and
river segments throughout the simulation period; initial
conditions of quality constituents, biological parameters, and
temperatures in each lake layer and river segment; all kinetic
parameters, including growth rates, decay rates, respiration
rates, settling velocities, mortality rates, and other
chemical-ecologic reaction rates; temperature stability
coefficients; meteorological data-air temperature (wet and dry
bulb); and atmospheric pressure, wind speed, and sky cover.
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Verification needs are: the conditions of quality
constituents, biological parameters, and temperatures during
the simulation period (the vertical profile for lakes, the
horizontal profile for river sediments); the constituent
concentrations, biological parameters, and temperatures at the
lake outflow for specified time periods; and the time history
of the lake water surface elevations during simulation.
Output information includes: the time history of quality
constituents, temperatures, and biological parameters in each
lake layer and river segment; the time history of quality
constituents, temperatures, and biological parameters in lake
outflows; and all the input values specified. The output
format is a tabular printout. Reservoir outflows to river
system may be recorded on cards or magnetic tape.
5. System Resource Requirements: The model is coded in
FORTRAN IV. Requirements are for 50,000 words storage capacity
on Univac 1108. WQRRS can be successfully run on Univac 1108,
IBM 360/50, CDC 7600, and Honeywell 600 series.
Pesonnel requirements include computer programming and
experience in water quality engineering. Additional experience
and expertise in biology and water chemistry are needed to
establish proper ecological relationships. Previous experience
in detailed water quality models is useful because of the
potentially large data base needed for WQRRS.
6. Application: The documentation report costs $14 and can be
obtained from the Hydrologic Engineering Center in Davis,
California. The complete computer programs can be obtained
from HEC for $120. Model setup and data preparation require
about 6-18 weeks, depending upon the data base size. Actual
computer cost ranges from $30-100 per run, depending upon the
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system size and extent of discretization, length of simulation,
and number of constituents considered. Output analysis
requires several manhours to evaluate each simulation run.
7. Technical Contact
R. G. Willey
Hydrologic Engineering Center
Corps of Engineers
609 Second Street
Davis, California 95616
COM 916/440-3292 FTS 448-3292
8. References
Chen, C. W. and Orlob, G. T. Ecologic Simulation of
Aquatic Environments, Office of Water Resources Research,
U.S. Dept. of Interior, Washington, D.C, 1972.
King, I. P. Flow Routing for Branched River Systems,
prepared for Hydrologic Engineering Center, U.S. Army Corps
of Engineers, Davis, CA 95616, 1976.
Norton, W. R. An Assessment of Water Quality in the Lower
American River: Past, Present, and Future, County of
Sacramento, Dept. of Public Works, Sacramento, CA 95616,
1972.
HEC. Water Quality for River Reservoir Systems, Hydrologic
Engineering Center, U.S. Army Corps of Engineers, Davis,
CA 95616, 1978.
HEC. Statistical and Graphical Analyses of Stream Water
Quality Data, Hydrologic Engineering Center, U.S. Army
Corps of Engineers, Davis, CA 95616, 1978.
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Smith, D. J. Revised Water Quality for River-Reservoir
Systems Model, prepared for Hydrologic Engineering Center,
U.S. Army Corps of Engineers, Davis, CA 95616, 1978.
Willey, R. G. and Huff, D. Chattahoochee River Water
Quality Analysis, Hydrologic Engineering Center, U.S. Army
Corps of Engineers, Davis, CA. 95616, 1978.
Willey, R. G., Abbott, J. and Gee, M. Oconee River Water
Quality and Sediment Analysis, Hydrologic Engineering
Center, U.S. Army Corps of Engineers, Davis, CA. 95616,
1977.
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WATER QUALITY MODEL (EXPLORE-I)
1. Model Overview: EXPLORE-I is a comprehensive mathematical
water quality model to be used in river basin planning and water
resource studies. This generalized river basin water quality
model can predict the hydrodynamics and water quality dynamics
for rivers and well mixed estuaries. The EXPLORE-I model is an
extended and modified version of the Storm Water Management Model,
receiving water component, which was developed for studies of DO/BOD
dynamics. The model is capable of simulating a number of hydraulic
regimes in either a dynamic or steady state mode, and it has been
set up, calibrated, and verified on a portion of the Williamette
River Basin, consisting of major tributaries. EXPLORE-I was
developed by Battelle-Northwest'Laboratories for the EPA.
2. Functional Capabilities: EXPLORE-I can be used to study
the effects of various flow conditions, waste discharge and/or
treatment schemes on the water quality levels in the river basin.
EXPLORE-I is capable of simulating a number of hydraulic regimes
in either a dynamic or steady state mode. These are: 1) streams
and rivers, 2) shallow lakes, and 3) estuaries or tidally influenced
rivers. In addition, the behavior of the following water quality
parameters can be studied:
1) Carbonaceous Biochemical Demand (BOD)
2) Nitrogenous BOD
3) Benthic BOD
4) Total Organic Carbon (TOG)
5) Refractory Organic Carbon
6) Sedimentary Phosphorous
7) Soluble Phosphorous
8) Organic Phosphorous
9) Ammonia Nitrogen
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10) Nitrite Nitrogen
11) Nitrate Nitrogen
12) Organic Nitrogen
13) Toxic Compounds
14) Phytoplankton
15) Zooplankton
16) Dissolved Oxygen
EXPLORE-I is composed of a river basin program which is capable of
modeling one-dimensional open channel flow in streams and rivers,
and two-dimensional flow in shallow lakes and estuaries. The
program consists of a hydraulic code which calculates the required
water velocities, depths, and flows, and a quality code which
evaluates the quality parameter reactions and routes the
constituents through the system. The model is capable of
simulating diurnal or long-term periods, and it can handle constant
and/or time-varying point or diffuse sources.
3. Basic Assumptions: The overall model formulation is
partitioned into two basic modules which can be operated
sequentially: a hydrodynamics module and a mass transport and
water quality submodels module. The hydrodynamics module is
formulated on conservation of mass and momentum principles. The
mass transport and water quality submodels module is formulated
from the expressions for specie continuity, i.e., mass balance of
a particular constituent or specie. For any biotic or abiotic
substance the general mass transfer expression is the sum of the
individual forms of mass transfers. Diffusion is assumed to be
negligible, and mass transfer is partitioned into simple transport
and water quality kinetics.
4. Input and Output: Junction and channel data are required
for the hydrodynamics module. Junction input data include:
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1) The average water surface elevation for the junction
point
2) The water surface area associated with the junction
point
3) Any significant inflows to the junction point from
small streams, tributaries, or other sources
4) Any significant outflows from the junction point
5) The average elevation of the bottom of the river or
estuary for the junction point
6) The cartesian coordinates of the junction point
(necessary only if the effect of wind stress on channel
flow is being calculated)
Required input channel data include:
1) Channel length
2) Channel width
3) Average elevation of the channel bottom
4) Manning coefficient for the channel
5) Initial velocity in the channel
Input for the water quality program include: upstream node
specification, reach boundaries, source node specification, BOD
constants, benthic BOD constants, TOG constants, toxic constants,
DO production by phytoplankton, DO production by benthic plants,
reaeration constants, phosphorous constants, nitrogen constants,
algae constants, number of constant sources, constant source
values, time-varying source values, reach temperatures, constant
upstream node concentrations, and time-varying upstream node
concentrations.
Output produced by the model includes an echo of the input
data and BOD and loading rates for each of the constituents
modeled.
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5. System Resource Requirements: EXPLORE-I is coded in
FORTRAN and can be run on either IBM or Univac mainframes. The
model requires 44K words of core on a Univac 1110 and 220K bytes
on an IBM 370. A background in computer programming, engineering
and environmental modeling are useful.
6. Applications: EXPLORE-I has been used by the Environmental
Protection Agency for studies of hydrodynamics of the Willamette
River Basin, and it has been tested in the Detroit Reservoir.
Application of EXPLORE-I is relatively inexpensive, and it can be
efficiently used to find sound and economical solutions to com-
plex water pollution problems in many different types of water
systems including estuaries and bays, streams and river networks,
lakes and reservoirs, and combinations of these.
7. Technical Contacts
Robert B. Ambrose, Jr.
U.S. Environmental Protection Agency
Environmental Research Laboratory
College Station Road
Athens, Georgia 30605
FTS 250-3546 COM 404/546-3546
8. References
Callaway, R. J., Byram, K.V.,and Ditsworth, G.R • "Mathe-
matical Model of the Columbia River from the Pacific Ocean
to Bonneville Dam - Part I." Federal Water Pollution
Control Administration, Pacific Northwest Water Laboratory,
pp. 155, November 1969.
Feigner, K. D. and Harris, U.S., "Documentation Report:
FWQA Dynamic Estuary Model. " U.S. Department of the
Interior, Federal Water Quality Administration, July 1970.
Metcalf § Eddy, Inc. "Storm Water Management Model."
Vol. 1-4. Palo Alto, California; University of Florida,
Gainesville, Florida; and Water Resources Engineers, Inc.
Walnut Creek, California.
Thackston, E. L. and Krenkel, P.A., "Reaeration Predictions
in Natural Streams." ASCE, Proc. Journal of the Sanitary
Engineering Division, Vol. 95, No. SAI, Paper 6407,
pp. 65-94, February 1969.
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WATER QUALITY MODEL (HAR05)
1. Model Overview: HAR03 is a computer program for the mod-
eling of water quality parameters in steady-state multi-dimen-
sional natural aquatic systems. The technique underlying the
program is based on the law of conservation of mass, and the
program can handle up to two variables reacting in a feed for-
ward fashion with first order kinetics. The computer program
from which HAR03 evolved was developed by Hydroscience, Inc.,
for the Massachusetts Water Resources Commission. HAR03 uti-
lizes a numerical solution technique to a convective-diffusion
equation for mass transport including decay and source terms.
2. Functional Capabilities: HAR03 is designed to model a num-
ber of water quality parameters in a steady-state, multi-dimen-
sional natural aquatic system. The program has been constructed
with the BOD-DO deficit system in mind, but with minor modifica-
tions the program may be used to model other variables which are
analogous to the BOD-DO deficit system such as chlorides, coli-
form bacteria, polyphosphate-orthophosphate, etc. HAR03 is cap-
able of modeling conservative substances, single reactive sub-
stances, coupled reactive substances, additive coupled substances,
estuarine coupled reactive substances, and estuarine additive
coupled systems. At present the program is limited to simulating
a system of up to 200 sections, where each section may have up to
six interfaces. A section may have only one interface to act as
a boundary.
3. Basic Assumptions: In an application of HAR03, it is assumed
that the variables and parameters inputted do not vary from tidal
cycle to tidal cycle. The reaction coefficients are assumed to
follow first order kinetics, and each individual segment of the
system is assumed to be completely mixed. An orthogonal system
segmentation for multi-dimensional systems is used.
4, Input and Output: HAR03 requires a large input data base
367
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in card image form. JCL cards are required, as are cards to des-
cribe the general system being modeled. The general system cards
include data for the interface parameters, the length, depth,
temperature, and volume of the system. These must be followed
by specific constituent cards which include data on: the number
of boundaries, B.C. concentrations, photosysnthetic rates, ben-
thai rates and loads, and other parameters for the constituents
being modeled. The geometric configuration for each section is
also required, as well as CBOD and NBOD removal rates, deoxygen-
ation rates and loads.
Output produced by the model includes: printouts of the in-
put system parameters, section temperatures, volumes and depths,
chloride boundary load, BOD rates and loads, correction factors,
deoxygenation and reaeration rates, and BOD-DO deficits for each
section in the system.
5. System Resource Requirements: HAR03 has been designed for
an IBM System/370 computer, and it is written for a FORTRAN IV
G or H level compiler. The program requires approximately 184K words
of storage, but in order to save core storage for small systems,
HAR03 has been compiled in three versions. The first version
handles a system of up to 50 segments and is designated HAR50;
similarly, HAR100 and HAR200 can handle a maximum of 100 and 200
segments correspondingly. The only difference between these
versions is in the size of the arrays defined in the programs.
A background in programming, environmental engineering and a
familiarity with water quality modeling are helpful.
6. Applications: HAR03 has been used by the EPA for various
applications.
7. Technical Contacts
George A. Nossa and Laura Livingston
U.S. Environmental Protection Agency
Information Systems Branch 2PM-IS
26 Federal Plaza
New York, New York 10278
FTS 264-9850 COM 212/264-9850
368
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START
SUBROUTINE DATA
Input General System Parameters which do not depend on the
constituent being modeled
| SUBROUTINE REVI (optional)
| Used to revise the parameters E and/or Q without altering
iany other elements of the input
I
SUBROUTINE FLOW
Computes a flow balance around each section to insure
continuity of flow
SUBROUTINE SETUP
Primarily sets up the System Matrix, £AB]
READ INDIC & IEXIT
INDIC Is used to designate the type
of constitaent which will be
described by the subsequent set of
specific constituent cards
IEXIT is used to terminate the run
if so desired
STOP
SUBROUTINE BCC
Primarily introduces the boundary conditions into the algorithm
SUBROUTINE RATE
Reads in rates, constructs the specific constituent system
matrix [AC] and the forcing function
SUBROUTINE WRITE (optional)
I Writes matrix [AC]
SUBROUTINE MATN
Inverts [AC] and multiplies it by the forcing function
I SUBROUTINE WRITE (optional)
I Writes matrix [AC]**-1
I
I
SUBROUTINE STORE
Primarily writes final results and retains output from
this particular iteration which will be used in subsequent
iterations
FLOWCHART OF THE MAIN PROGRAM OF HARD3
369
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Steven C. Chapra
NOAA Great Lakes Environmental Research Laboratory
2300 Washtenaw Avenue
Ann Arbor, Michigan 48104
References
Chapra, S.C., and Nossa, G.A., Documentation for HAR05: A
Computer Program for the Modeling of Water Quality Parameters
in Steady State Multi-dimensional Natural Aquatic Systems"
U.S.Environmental Protection Agency,Region II,26 Federal
Plaza, New York, New York, October 1974.
370
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WATER QUALITY MODELING SYSTEM FOR THE GREAT LAKES (WQMSGL)
1. Model Overview: The Water Quality Modeling System for the
Great Lakes consists of three subsystems that allow the user to
develop, calibrate, and verify water quality models for aquatic
systems. Although the system was developed to serve EPA's
research mandates for the Great Lakes, it has general
applicability to any water system, (i.e., rivers, estuaries,
small lakes, and coastal). The system can also be applied to
many other water quality problems constituents, or their
interactions.
2. Functional Capabilities: The functional capabilities
follow:
a) WASP - The kinetic/biochemical structures of the model
are written by the user or he can apply any number of
kinetic subroutines that have been developed by
previous users. The transport characteristics of the
water system to be modeled are assumed to be known or
determined by current meter studies or hydrodynamic
models. Another method of determining transport is to
use WASP to trace a conservative substance through the
system and adjust the transport parameters until the
calculated concentration matches the measured one.
WASP then numerically integrates the system mass
balance equations in space and time, and calculates
time variable concentrations of the substances in each
special segment. A table of available kinetic
subroutines, authors, capabilities, applications, and
references follow. EPA and LLRS continue to support
research to refine and verify these models and to
incorporate new substances and processes as they are
needed.
371
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b. DASA - DASA consists of a small, working data base
that is inputed directly or obtained from STORE!
retrievals and maintained on a DEC-PDF-1 1/45. A
series of sub-programs are available which access the
parameters under study and compute means, standard
deviations, standard errors, etc. for input to MVP or
for graphical display subroutines available in WASP.
c. MVP - MVP is used to make statistical comparisons
between calculated concentrations and those measured.
It is used during calibration to provide an efficient
means to determine the difference between results of
two or more "runs" which have been made varying one of
the model coefficients. Finally, it can be used to
judge the accuracy of the model compared to the data
and to compare results of two or more models.
3. Basic Assumptions: The basic assumptions are:
a. WASP - The transport structure is known or can be
determined by tracing a conservative substance. The
water system can be divided into large, spacial
compartments- which can be assumed to be completely
mixed. The time scale of the problem is on the order
of weeks, seasons or decades (although smaller time
scales could be considered).
b. DASA - It is assumed that data are available through a
research or surveillance program over the period of
time corresponding to the scale of the problem and
recorded by geographical locations (station, depth),
time (year, month, day, hour), and collected and
analyzed according to prescribed quality control
programs.
372
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c. MVP - It is assumed that enough data exists to
statistically characterize the model state variables
in many of the water segments over the time scale of
the problem.
Input and Output: The inputs for WQMSGL are:
a. WASP - The physical characteristics of the water body,
material loads, environmental factors affecting the
system variables (i.e. sunlight intensity and
temperature), boundary conditions, initial conditions,
and model coefficients (i.e. growth rates, degradation
rates, etc.)
b. DASA - The concentration or other appropriate units
connected with measurements made are input directly
from terminal or cards or from direct link to STORET.
c. MVP - The mean, standard deviation, and number for
each model constituent-segment-cruise combination are
received from DASA or input directly to output files
from WASP of the calculated concentrations-
segment-cruise combination corresponding to field
measurements.
The output for WQMSGL are :
a. Output -^ The concentration of each state variable in
each model segment at any time increment desired can
be specified by the user.
b. Output - Output includes data sets in WASP graphics or
MVP input formats; printouts of seasonal or cruise
statistics; spatial plots of concentrations at
measurement points; histograms; raw data versus time;
mean and standard deviation versus time; and
regression of two parameters.
373
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c. Output: 1) Relative error model compared to data
2) T-statistics for model versus data
3) Regression statistics for model versus
data
5. System Resource Requirements: WQMSGL is written in
FORTRAN. It is run on DEC-PDP-11/45 or IBM 370/168. The model
uses 2000 blocks of disk storage for execution. It uses a
regular printer for output. A background in programming,
engineering, biochemistry, and limnology is useful.
6. Applications:
WASP kinetic subroutines and applications:
Name
Summary
LAKE 1 An Eutrophication Model, developed for Lake
Ontario for three vertical layers, and also
applied to Lake Michigan and Saginaw Bay. State
variables include phytoplankton, chlorophyll,
zooplankton, carbon, three forms of nitrogen,
sampling organics, and available phosphorus.
References:
Thomann, R.V., DiToro, D. M., Winfield, R. P.,
and O'Connor, D. J. Mathematical Modeling of
Phytoplankton in Lake Ontario, Vol. 1: Model
Development and Verification. U.S. Environmental
Protection Agency, Corvallis, Oregon,
660/3-75-005, March 1975.
374
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LAKE 1 Thomann, R. V., Winfield, R. P., DiToro, D. M.,
and O'Connor, D. J. Mathematical Modeling of
Phytoplankton in Lake Ontario, Vol. 2:
Simulations Using LAKE-I Model. U.S.
Environmental Protection Agency, Duluth,
Minnesota. In press.
LAKE III A three dimensional, 67-segment version of LAKE-1
used for Lake Ontario.
HUR01 A refined version of LAKE-1 applied to five
segments of Lake Huron with improved nutrient
recycle kinetics. References:
DiToro, D.M. ejt al. 1980. Mathematical Models of
Water Quality in Large Lakes; Part 1: Lake Huron
and Saginaw Bay.
press .
EPA Ecological Series. In
ER01
A major revision made to Lake 1 and applied to 10
segments in Lake Erie. Improved kinetics include:
1. Division of chlorophyll between diatoms and
non-diatoms
2. Silica as a limiting nutrient
3. Carbon cycle and alkalinity
4. Dissolved oxygen
DiToro, D.M. and Connolly, John P. 1980.
Mathematical Models of Water Quality in Large
Lakes. EPA Ecological Research Series. In press
375
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CONS A simple subroutine for tracing a conservative or
first order reacting substance in a system which
was applied to Saginaw Bay.
7. Technical Contact
William L. Richardson
U.S. Environmental Protection Agency
Environmental Research Laboratory
931 1 Groh Road
Grosse He, Michigan 48138
COM 313/226-7811 FTS 226-7811
8. Re fer ences
DiToro, D.M., Fitzpatrick, James J. , and Thomann, R.V.
Water Quality Analysis Simulation Program (WASP) and Model
Verification Program (MVP) Documentation. Hydroscience ,
Inc. In preparation .
Richardson, W.L., and McGunagle, K. Data Analysis and
Storage (DASA) User's Manual. EPA, Large Lakes Research
Station. In preparation.
376
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AGRICULTURAL RUNOFF MODEL (VERSION II)
(ARM II)
1- Model Overview: The Agricultural Runoff Management Model -
Version II (ARM II) is a continuous simulation model that estimates
the movement and degradation of pollutants on agricultural land sur-
faces. The model can be used to study pollutants including pesti-
cides, nutrients, and sediments. ARM II is an improved version of ARM
I and the Pesticide Transport and Runoff (PTR) Model published in
1973. All of these models build upon the Stanford Watershed Model.
The ARM II model has been tested on agricultural plots at Watkins-
ville, Georgia, and is currently being tested at other sites through-
out the United States. The model is recommended for use to estimate
pollutant loads from agricultural fields. Applications include basin
planning and evaluation of pesticides being considered for registra-
tion. ARM II was developed by Hydrocomp, Incorporated, for the U. S.
Environmental Protection Agency.
2. Functional Capabilities: ARM II can be used to study the impact
of various land use practices on pollutant loading to streams in agri-
cultural areas. It can also be used to evaluate loadings of pesti-
cides based on the physio-chemical properties of pesticides, recom-
mended application rates, and topographical, soil, and meteorological
properties of an area in which the pesticides are to be used.
For the best results, land areas simulated should not be larger
2
then 2 mi . Channel processes affect timing of loadings for areas
2
larger than this. Consequently, for areas larger than 2 mi , it is
recommended that ARM II be used with a compatible channel routing
model to insure accurate representation of significant transport
processes.
377
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The model outputs include a continuous recording of the
following parameters:
1) Volume flow rate of surface runoff
2) Volume flow rate of interflow
3) Volume flow rate of base flow
4) Volume of water contained in interception storage
5) Volume of water in upper zone soils
6) Volume o,f water in lower zone soil
7) Volume of water in active groundwater storage
8) Rate of evapotranspiration water from land surface
9) Soil surface temperature
10) Upper zone soil temperature
11) Mass of pesticide on the watershed
12) Mass loading of pesticide
13) Concentration of dissolved pesticide in runoff
14) Concentration of adsorbed pesticide in runoff
15) Amount of pesticide degraded and volatilized
16) Amount of organic nitrogen in the soil
17) Amount of dissolved ammonia in the soil
18) Amount of adsorbed ammonia in the soil
19) Amount of dissolved nitrite plus nitrate in the soil
20) Amount of nitrogen incorporated into plant material
21) Load of organic nitrogen in runoff
22) Load of adsorbed ammonia in runoff
23) Load of dissolved ammonia in runoff
24) Load of nitrite plus "nitrate jn -roinp^f
25) Concentration of dissolved inorganic phosphorus in soil
26) Concentration of adsorbed inorganic phosphorus in soil
27) Concentration of organic phosphorus in soil and
28) Amount of phosphorus incorporated into plant material
ARM II is composed of six subroutines: MAIN, LANDS, SEDT,
378
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ADSRB, DEGRAD, and NUTRNT. MAIN calls and executes the other sub-
routines and establishes input and output files, LANDS performs the
moisture balance in the soil and generates runoff. SEDT generates and
transports sediment loads. ADSRB partitions pesticides between ad-
sorbed and dissolved phases. DEGRAD degrades pesticides contained on
the soil surface. NUTRNT transforms nutrients to various forms and
also establishes the equilibrium between adsorbed and dissolved
phases. The model is capable of simulating changes in any parameter
on 5- and 15-minute intervals. Several output formats can be selected
to display results on 5-minute, 15-minute, hourly, daily, or monthly
intervals. Simulations can be run for any number of years desired.
3. Basic Assumptions: Both water and pollutant transport and
transformation descriptions used in the model are based on the prin-
ciple of conservation of mass. Movement of water through the soil
from surface to groundwater is modeled empirically. A key assumption
in the model is that runoff water form the watershed simulated is
derived from all locations within the watershed. Thus, it is impos-
sible to identify the specific location as the source of a fractional
load of the watershed. Degradation kinetics are also modeled empiri-
cally. All pollutant transformations are approximated by a series of
first-order rate expressions. Volatilization is lumped with chemical
and microbial degradation to form one rate expression for the removal
and degradation of pesticides in the soil. Arrhenius equation cor-
rections of specific transformation rate coefficients are assumed to
adequately approximate temperature effects on nutrient cycling in
soils. Detachment of sediment particles from the soil and transport
of these particles across the watershed are empirically modeled.
Pollutants are assumed to partition between adsorbed and dissolved
phases instanteously.
379
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4. Input and Output; Rainfall records.
Inputs include;
1) Control parameters that specify amount of information
printed as output, operations as to whether nutrient cycles
and pesticide dynamics.or just pesticide dynamics are to be
simulated and definition of the period to be simulated.
2) Hydrology parameters that specify rates of water movement
and nominal soil moisture storage capacity parameters.
3) Snowmelt parameters that specify snowmelt rates and snow-
pack characteristics.
4) Sediment parameters that define soil properties which
determine sediment load response to rainfall events.
5) Pesticide parameters that determine rates of degradation of
pesticides in soil, and adsorption partition between soil
and dissolved phases.
6) Soil parameters that define topographical characteristics
of the land surface and soil properties.
7) Nutrient parameters that define the timing and amount of
fertilizer application and calculation time intervals for
nutrient transformations.
8) Nitrogen and phosphorus parameters that define reaction
rates and nutrient storage.
9) Chloride storage parameter.
Outputs produced by the model include an echo of the input data
set, the concentration on pestides and nutrients in the soil and run-
off, and loads of nutrients, pesticides, and sediment in runoff.
Nutrient concentrations are reported in terms of the various forms
present in the cycle. For both pesticides and nutrients, concentra-
tions and loads for dissolved and suspended materials are reported
separately. Some selection is available as to the frequency of print-
out. Monthly and yearly summaries for loads are also provided.
380
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5. Systems Resource Requirements: On the IBM 370/168, using the
FORTRAN H compiler, tile program requires approximately 360K bytes
C9 0,000 vrords) of storage for compilation of the largest subroutine.
Program execution requires up to 230K bytes (57,500 words) of storage
depending on the model operation selected. Thus, a computer with
relatively large storage capability is usually needed for use of the
ARM model. However, Version II of the ARM model has been adapted and
run on a Hewlett-Packard 3000 Series II computer, which is a
relatively small computer. The effort and model changes needed to
adapt the ARM model to other computers will depend on the
specific computer installation.
The ARM model requires no special external storage devices
(tape, disc, etc.) other than the standard card reader input and line
printer output. However, the model includes an option to output sim-
ulated runoff and sediment values to an external storage device as
unformatted FORTRAN records.
6. Applications: ARM II has been used by the U. S. Environmental
Protection Agency to evaluate potential runoff of a pesticide from
agricultural lands. Application of ARM includes evaluation of best
management practices for agricultural lands in relation to basin plan-
ning. It can also be used to evaluate pesticide loading in relation
to registration of pesticides.
7. Technical Contacts
Lee A. Mulkey
U.S. Environmental Protection Agency
Environmental Research Laboratory
College Station Road
Athens, Georgia 30605
FTS 250-3581 COM 404/546-3581
381
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8. References
Crawford, N. H. and Donigian, A. S.,Jr., "Pesticide Transport
and Runoff Model for Agricultural Lands." Office of Research
and Development, U, S. Environmental Protection Agency, Washing
ton, DC, EPA-660/2-74-013, 1973.
Donigian, A. S., Jr. and Crawford, N. H. "Modeling Pesticides
and Nutrients on Agricultural Lands." Environmental Research
Laboratory, U. S. Environmental Protection Agency, Athens.
Georgia, 'EPA-600/2-76-043, 1976.
Donigian, A. S., Jr. , Beyerlein, D. C., Davis, H.H.,Jr., and
Crawford, N.H."Agricultural Runoff Management (ARM) Model -
Version II, Refinement and Testing ." Environmental Research
Laboratory, U. S. Environmental Protection Agency, Athens,
Georgia, EPA-600/3-77-098, 1977.
Donigian, A.S. Jr., and Davis, H.H., Jr. "Agricultural Runoff
Management (ARM) Model User's Manual: Versions I and II."
Environmental Research Laboratory, U. S. Environmental Protec-
tion Agency, Athens, Georgia, EPA-600/3-78-080, 1978.
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AGRICULTURAL WATERSHED RUNOFF MODEL (AGRUN)
1. Model Overview: The AGRUN model is a modification of the
EPA Stormwater Management Model (SWMM) that dynamically simulates
hydrology and channel pollutant loads for agricultural watersheds.
This model can simulate storm runoff hydrographs and pollutographs
for conservative water quality constituents which include: total
suspended solids, non-settleable suspended solids, TDS, BOD, COD,
chlorides, SO^, grease, total coliforms, fecal coliforms, NH,,
organic nitrogen, nitrite and nitrate, phosphate, orthophosphate,
mercury, copper, zinc, lead, chromium, cadmium, and arsenic.
AGRUN is one module of a larger set of compatible programs which
include a runoff model (AGRUN), a transport model, and a receiving
model, and AGRUN has an interface subroutine to connect it with
the other two. This model uses the Universal Soil Loss Equation
to compute the suspended solids source loading rates, and it as-
sumes that there is no decay of BOD or conversion of nitrogen
forms.
2. Functional Capabilities: The Agricultural Watershed Runoff
Model has the capability to simulate storm runoff hydrographs and
pollutographs for up to 22 water quality parameters from agricul-
tural watersheds. The watershed may be subdivided into as many
as 200 subareas, and up to two crop types from a list of five may
be specified for each subarea. Crop types include corn, beans,
pasture, oats, and hay.
The tributary drainage system may be subdivided into as many
as 200 channels, and the system must be dendritic in form. Cross
sections may be triangular, trapezoidal, or rectangular in shape.
The user has the option of representing infiltration by the Horton
equation alone, in which case interflow computations are neglected,
or he may specify the additional data which will be used to compute
the contribution of interflow to storm runoff.
Computations of water quality can be made for up to 22 con-
servative constituents whose number is specified by the user.
Constituents modeled include: total suspended solids, non-settleable
383
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suspended solids, IDS, BOD, COD, chlorides, SO., grease, total
coliforms, fecal coliforms, NH3, organic nitrogen, nitrite and
nitrate, phosphate, orthophosphate, mercury, copper, zinc, lead,
chromium, cadmium, and arsenic. Only total suspended solids,
BOD, and fecal coliforms have been calibrated for this model.
AGRUN has been used to simulate the surface runoff hydrograph
for both urban and agricultural watersheds, and because it
assumes no decay of BOD or conversion of nitrogen forms, the
model should be considered for relatively short-term storm
episodes only.
3. Basic Assumptions: The AGRUN model uses the Universal Soil
Loss Equation to compute the suspended solids source loading
rates, and Morton's equation to compute infiltration rates. An
iterative Newton-Raphson technique is the basis for the
determination of water depth and outflow rates. Velocity
computations are made on th.e basis that flow only occurs wh.en th.e
soil is above field capacity. The model assumes that there is
no decay of BOD and that there is no conversion of nitrogen forms,
4. Input and Output: The Agricultural Watershed Runoff Model
requires a large card-image input data base. Input data for the
model fall into seven categories, and these are: 1) input and
program control data, 2) precipitation data, 3) drainage channel
specifications, 4) land use hydrogeometric data, 5) watershed
specifications, 6) soil characteristics, and 7) output control
information.
For each watershed subarea, the surface area, width, and
slope must be specified . For each land use type specified for
each subarea, the Manning n, surface depression storage, and
Horton infiltration coefficients must be entered as input data.
Drainage channel specifications must include the length, invert
slope, and Manning n, plus the appropriate cross section data.
If the user wishes to specify the additional data which will
be used to compute the contribution of interflow to storm runoff,
rather than by representing infiltration by the Horton equation
384
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alone, the following data are required for each subarea:
1) number of soil layers above the grdundwater table, 2) depth
of each soil layer, 3) soil permeability coefficient, 4) soil
field capacity, 5) soil saturation level, 6) present field
capacity available at the beginning of the storm, and 7) constant
baseflow from the watershed. Constituents must be specified by
the user.
Output produced by the model includes a print-out of the
input data, rainfall hyetographs, runoff hyetographs, and a
variety of charts representing the concentrations of the
constituents.
5. System Resource Requirements: AGRUN is written in FORTRAN,
and currently resides on an Univac 1108. It requires 520K bytes
of core memory and a 120 character per line printer. Operators with
skills in programming and engineering are useful.
6. Applications: The Agricultural Watershed Runoff Model has
been used by the EPA for the Iowa and Cedar River Basins Model
Project. The model was applied to the 2800-acre Buffalo Bill
Watershed for four storms monitored in 1973, and it was
calibrated for three parameters (suspended solids, BOD, and fecal
coliforms). All subsequent simulations were performed with no
adjustments in model coefficients. The model has found other
applications by other users.
7. Technical Contact
Dr. Larry Roesner
Camp, Dresser § TtfcK.ee, Inc.
7620 Little River Turnpike
Annandale, VA 22003
COM 703/642-550.0
8. References
Roesner, L.A., Zison, S.W., Monser, J.R., and Lyons, T.C.,
Agricultural Watershed Runoff Model for the Iowa-Cedar
River Basins, prepared for the Environmental Protection
Agency, Systems Development Branch, Washington, D.C., by
Water Resources Engineers, Inc., under contract No.
68-01-0742, November 1975.
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NONPOINT SOURCE POLLUTANT LOADING MODEL (NFS)
1. Model Overview: The Nonpoint Source Pollutant Loading
(NFS) Model is a continuous simulation model which estimates the
movement of pollutants on land surfaces. The model can be used
to study pollutants that are conserved or which degrade slowly.
The NFS model is one of a series, including the Agricultural
Runoff Management Model (ARM) and the Pesticide Transport and
Runoff Model (PTR), which are based on the Standford Watershed
Model. The model was initially tested on three predominantly
urban sites and has been used in relation to planning required
under Section 208 of Public Law 92-500.
The model is recommended for use to estimate nonpoint
source pollutant loads in urban and rural areas. Applications are
primarily waste allocation in relation to basin planning. NFS
was developed by Hydrocomp, Incorporated, for the U.S Environmental
Protection Agency.
2. Functional Capabilities: NFS can be used to study the
impact of various land management strategies on pollutant loading
to streams in a planning area. For best results, land areas
2
simulated should not be larger than 2 mi . Since channel processes
2
affect timing of loadings for areas larger than 2 mi , it is re-
commended that NFS be used with a compatible channel routing model
to insure accurate representation of significant transport processes
2
for areas of application larger than 2 mi .
The model outputs include a continuous recording of the
following parameters:
1) Runoff flow rate.
2) Runoff temperature.
3) Dissolved oxygen concentration of runoff.
386
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4) Amount pf pollutant load in a given time interval for
up to five pollutants.
5) Concentration of up to five pollutants in runoff.
6) Amount of sediment load in a given time interval.
7) Concentration of sediment in runoff.
NFS is composed of three major components: MAIN, LANDS, and
QUAL. MAIN calls and executes the two major subroutines in the
program and establishes input and output files. LANDS performs the
moisture balance and generates runoff. QUAL executes erosion cal
culations that generate sediment loads and sediment concentration in
runoff. It also calculates pollutant loads and concentrations by
relating these parameters to sediment load and concentration. The
model is capable of simulating changes in any parameter on 15-minute
or hourly intervals. Several output formats can be selected to
display 15-minute, hourly, daily, or monthly intervals. Simulations
can be run for any number of years desired.
3. Basic Assumptions: Both water and pollutant transport descrip
tions are based on the principle of conservation of mass. The
overall model is based on the Stanford Watershed Model. Water
transported out of the watershed is assumed to be drawn from
every portion of the watershed. Consequently, water and chemical
constituents in runoff cannot be identified with any particular
location within the watershed. The water balance calculations
assumes that there are five water storage zones, namely, intercep-
tion storage, upper zone storage, lower zone storage, active ground-
water storage, and inactive ground-water storage. During a storm
event, rainfall is partitioned between these storages and also
exported from the watershed by overland flow, interflow and baseflow.
Between storm events, export continues along with transfers between
compartments. In addition, water is lost by evapotranspiration.
Snow accumulation and melt is also mo-deled.
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The rates of water export and intercompartment transfers
are governed by empirical equations which contain constants re-
quiring calibration for each application of the model. Where pos-
sible, guidance has been provided to show reasonable parameter
estimates in various parts of the country. Calibration requires
simultaneous rainfall and runoff records.
Sediment loss from pervious surfaces are modeled in identi
cal fashion as the ARM Version I model. Two processes are described:
detachment of fines and transport of fines. For impervious surfaces,
sediment particulate accumulates and is removed according to im-
pirical equations during dry weather periods. During storms, trans-
port of sediment particles are defined by the same relationship
as for sediment fines from pervious surfaces.
Conservative constituent transport is assumed to move at
a rate proportional to the rate of movement of sediment.
4. Input and Output: The Input parameter list includes the following
1) A set of control parameter values that defines frequency
of printing of output, dates of simulation, and whether
snowmelt calculations are to be performed.
2) A set of hydrology parameter values that specify the
nominal capacity of the storage zone and specific rates
of water transport between zones and the rates of export
of water by runoff and evapotranspiration.
3) A set of parameter values that define snow pack characteris-
tics .
4) A set of parameter values that define sediment transport
characteristics.
5) A set of parameters that defines land use characteristics
and for impervious surfaces, sediment accumulation, and
removal rates during dry weather periods.
6) Precipitation date for period of simulation.
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Outputs commonly displayed are hydrographs for each storm
as well as base flow projected for dry weather periods, sediment
loads and concentrations as a function of time, pollutant loads
and concentrations as a function of time , dissolved oxygen concentra-
tion and temperature as a function of time. An echo of the input
data set is also printed along with storm, monthly, and annual
summaries of the output data sets.
5. System Resource Requirements: The NFS Model is written.in the
IBM FORTRAN IV language. The "handy minimal language" concept was
adopted to the extent possible to produce a reasonably compatible
computer code for at least the following computer systems: IBM
360, Univac 1108, CDC 6000, and Honeywell Series 32. However, at
the present time, model operation has been limited to the IBM
systems. The NFS model operates most effeciently in a two-step
procedure. The first step involves the compilation of the program
and the storage of the compiled version on disk or magnetic tape.
In the second step, the compiled model is provided the necessary
input data and is executed. Thus, the model can operate a number
of types of different input data with a single compilation.
6. Applications: NFS has been applied to small urban water-
sheds in Durham, North Carolina; Madison, Wisconsin; and Seattle,
Washington. It is also being used in conjunction with implementa-
tion of several 208 plans, including the plan being developed by
the Northern Virginia Planning Commission.
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Technical Contact
Lee A. Mulkey
U.S. Environmental Protection Agency
Environmental Research Laboratory-
College Station Road
Athens, Georgia 30605
FTS 250-3581 COM 404/546-3581
References
Donigian, A.' S. , Jr., and Crawford, N. FT . "Modeling Pesticides
and Nutrients on Agricultural Lands." U. S. Environmental
Protection Agency, Environmental Research Laboratory,Athens ,
Georgia, EPA-600/2-76-043, 317 p., 1976.
Donigian, A, S., Jr., and Crawford, N. H. "Modeling Nonpoint
Pollution from the Land Surface," U. S. Environmental Pro-
tection Agency, Environmental Research Laboratory, Athens,
Georgia, EPA-600/3-76-083, 280 pt, 1976.
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ONE-DIMENSIONAL GROUNDWATER MASS TRANSPORT MODEL (GWMTM1)
1. Model Overview: GWMTM1 is a deterministic, one-dimen-
sional, unsteady-state, analytical model which simulates consti-
tuent concentrations in groundwater systems. It is based on the
convective-dispersive mass transport equation modified for first
order decay. The analytical solution is based on a semi-infinite
medium with the following surface boundary condition: C = CQexp
(-^t) ; this allows the surface concentration to be constant or
exponentially varying (e.g., through dilution processes). It is
typically applied in cases of vertical infiltration of wastewaters.
The soil may be saturated or unsaturated (provided the moisture
content is constant); the vertical seepage velocity must be con-
stant.
2. Functional Capabilities: The program is user-oriented
requiring no previous FORTRAN experience. Its digital output is
in matrix form giving concentration versus distance at given times
or concentration versus time at given distances. It accounts for
advection, dispersion and first order decay.
3. Basic Assumptions: The model assumes a homogeneous
soil and a constant seepage velocity. The constant seepage velo-
city requirement is met under steady, saturated conditions or
steady, constant moisture content, unsaturated conditions.
4. Input and Output: Data is inputted as FORTRAN state-
ments. The model requires only four pieces of data: the dispersion
coefficient, the kinetic decay constant, the seepage velocity, and
the surface constant (if the surface concentration is not constant),
Concentrations are printed out at any number of specified (read
in as data cards) space and time positions. It is a very simple
model to operate.
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5. System Resource Requirements: The model is written in
standard FORTRAN IV and has been run on a S/360/91; it should run
on any standard digital computer. The pr-ogram requires a region
size of approximately 100 K on the S/360/91. One can learn to run
the model in less than a half hour and only four FORTRAN state-
ments need to be punched (space and time positions are specified
as data cards). Set up time is insignificant and FORTRAN program-
ming knowledge is unnecessary. It has also been run on minicomputers
using less than 1QQK of core.
6. Applications: The model was developed for the Nassau-
Suffolk Regional Planning Board (Lee Koppelman, Executive Director)
as part of a large 208 project. It was applied to wastewater
recharge basins where the depth to water was about 30 feet. It
has been distributed widely through short courses dealing with
groundwater pollution and has found similar applications throughout
the country.
7. Technical Contacts:
Professor Robert W. Cleary*
P.O. Box 2010
Princeton, New Jersey 0"8540
8. References
Cleary, R.W., Final 208 Report to the Naussau-Suffolk
Regional Planning Board, Hauppauge, New York, December
1977.
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STORAGE, TREATMENT, OVERFLOW. RUNOFF MODEL (STORM)
1. Model Overview: The Storage, Treatment, Overflow, Runoff
Model(STORM) is a continuous simulation model that provides an
analysis of the quantity and quality of runoff from urban or
nonurban watersheds. STORM computes loads and concentrations
of six basic water quality parameters and land surface erosion.
The purpose of the program is to aid in the sizing of storage and
treatment facilities so that the quantity and quality of storm
water runoff and land surface erosion may be controlled. The
original version of the model was completed in January 1973 by
Water Resources Engineers, Inc. of Walnut Creek, California, for
the Hydrologic Engineering Center (HEC) and the Environmental
Protection Agency. Major additions* since then include the ability
to compute (or specify) the quantity and quality of dry weather
flow. The program and its usage are described "in the current HEC
STORM User's Manual dated August 19., 198Q.
2. Functional Capabilities: STORM provides a means for analysis
of the quantity and quality of runoff from urban and nonurban water-
sheds. The purpose of this analysis is to aid in the sizing of
storage and treatment facilities so that the quantity and quality
of storm water runoff and land surface erosion may be controlled.
The model considers the interaction of seven storm water elements
(rainfall/snowmelt runoff, dry weather flow, pollutant accumulation,
washoff, land surface erosion, treatment rates, and detention
reservoir storage). STORM computes land surface erosion and loads
and concentrations of six basic water quality parameters (.suspended
and settleable solids, biochemical oocygen demand, total nitrogen,
orthophosphate, and total coliform bacteria).
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The program is designed for period of record analysis using
continuous hourly precipitation data. It is a continuous
simulation model that may also be used for single events. The
HEC revised the input and output formats of the program to con-
form to standardized methods • It made program modifications
which include a soil conservation service runoff curve number
technique, the use of hydrographs to define runoff, pollutant
accumulation in terms of pounds/acre/day, the ability to compute
or specify quantity and quality of dry weather flow, specification
of up to twenty land uses, and the choice of English or Metric
units.
3. Basic Assumptions: The model assumes that precipitation
cannot be considered without the system, and a design storm can
not be defined by itself, but must be defined in the light of
the characteristics of the storm water facilities. The approach
used in the STORM model recognizes not only the properties of
storm duration and intensity, but also storm spacing and the
storage capacity of the storm water system. In this approach,
rainfall washes dust, dirt, and the associated pollutants off
the watershed. The resulting runoff is routed to the treatment-
storage facilities where runoff greater than the treatment rate
is stored for treatment at a later time.
If storage is exceeded, the untreated excess is wasted
through overflow directly into the receiving waters. The
magnitude and frequency of these overflows are important in a
storm water study, so STORM provides statistical information on
washoff, as well as overflows. The quantity, quality, and number
of overflows are treated as functions of hydrologic characteris-
tics, land use, treatment rate, and storage capacity.
4. Input and Output: Input to the model include: job spec-
ifications, hourly precipitation record, daily temperature record,
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land use data including runoff parameters, pollutant accumulation
and washoff data, and land surface erosion data.. The hourly
precipitation record and the daily temperature record are available
on magnetic tape from the National Weather Service, Asheville,
North Carolina.
The two main types of output are statistical information
on the quantity and quality of washoff and overflow, and polluto-
graphs for selected individual events. The STORM program produces
four output reports: quantity analysis, quality analysis, polluto-
graph analysis and land surface erosion analysis. Input variables
allow control of the level of printout which may be summary only,
all events, and/or detailed analysis of selected events. The
quantity and quality reports also include average annual statis-
tics of the rainfall/snowmelt; runoff; pollutant washoff; and the
quantity, quality, and frequency of overflows to the receiving
water. The land surface erosion report shows average annual
values for sediment production and delivery to the receiving
system.
5. System Resource Requirements: The STORM program is operable
on the CDC, Univac, IBM, and certain other computer systems. It
requires about 50,000 words of core storage. Input is accomplished
by card reader and/or a tape/disk. Output is accomplished by a
132 position line printer. Five additional tape/disk units are
required for temporary storage during processing, although all five
may not be used during any given run depending on input/output
options. The only non-standard features of the three computer
systems required by this model are those due to end of file 'checks
and the way in which multiple output files are handled. Up to three
output files are generated on tape/disk which are automatically
printed at the conclusion of the job. A computer programmer to
implement the program on a computer and a hydrologic and/or environ-
mental engineer with experience in computer simulation to perform
the analysis are helpful.
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6. Applications: This model provides a means for analysis
of the quantity and quality of runoff from urban and nonurban
watersheds. The purpose of this analysis is to aid in the sizing
of storage and treatment facilities so that the quantity and
quality of storm water runoff and land surface erosion may be
controlled. STORM has been widely used by the Hydrologic Engin-
eering Center of the U.S. Army Corps of Engineers and by the
Environmental Protection Agency.
7. Technical Contacts
Arlen Feldman
Hydrologic Engineer Center
U.S. Army Corps of Engineers
609 Second Street
Davis, CA 95616
FTS 448-2329 COM 916/440-2329
Tom Barnwell
U.S. Environmental Protection Agency
Environmental Research Laboratory
College Station Road
Athens, GA 30605
COM 404/546-3585 FTS 250-3585
8. References
roi'lV-' "Guidelines for Calibration and Application
of the STORM Model," The Hydrologic Engineering Center,
1976 7 °rps °£ Engineers, Davis, California, March
American Public Works Association, "Water Pollution
Aspects of Urban Runoff," Water Pollution Control
Research Series, Federal Wlter Pollution Control
Administration, Report No. WP-20-15, January 1969.
American Society of Civil Engineers, Design and
Construction of Sanitary and Storm Sewers, New York
1970. - —
Brandt, G.H., et al., "An Economic Analysis of Erosion
and Sediment Control Measures for Watersheds Undergoing
Urbanization," The Dow Chemical Company, Contract No
14-31-0001 3392, February 1972, p. 85.
Huber W.C. et al., Storm Water Management Mn^i
Users Manual Version H, LiiicimiaLi, Ohio, National
Environmental Research Center, March 1975.
396
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Hydrologic Engineering Center; "Pennypark Creek Water
Quality Study", Special Projects Report, No. 79-S, U.S.
Army Corps of Engineers, Davis, California; November 1979
nydrologic Engineering Center; "Storage, Treatment,
Overflow, Runoff Model (STORM)", Program User's Manual;
U.S. Army Corps of Engineers, Davis, California;
August 1977.
Kramer, Chin, and Mayo; Water Resources Engineers,
Yoder, Trotter, Orlob, and Associates, for the Seattle
District U.S. Army Corps of Engineers, "Environmental
Planning for the Metropolitan Area, Cedar Green River
Basin, Washington, Urban Drainage Study," Appendix C
Stjorm_Water Monitoring Program, December 1974.
Metcalf § Eddy, Inc., University of Florida, Water
Resources Engineers, Inc., "Storm Water Management
Model," Water Pollution Control Research Series, EPA
Report Nos. 11024-DOC-07/71 through 11024-DOC-10/71,
July 1971.
Mockus, V., et al. , U.S. Soil Conservation Service,
"National Engineering Handbook, Section 4, Hydrology,"
1964, with revisions of 1969.
Renfro, G.W., "Present and Prospective Technology for
Predicting Sediment Yields and Sources: Use of Erosion
Equations and Sediment Delivery Ratios for Predicting
Sediment Yield," Proceedings of the Sediment Yield
Workshop, USDA Sedimentation Laboratory, Oxford,
Mississippi, November 28-30, 1972.
Resource Analysis, Inc., Modifications to the STORM
Program, January 1975.
U.S. Soil Conservation Service, Urban Hydrology for
Small Watersheds, TR No. 55, January 1975.
Wischmeier, W.H., and Smith, D.D., "Rainfall Energy and
Its Relationship to Soil Loss," Transactions, American
Geophysical Union, Vol. 39, No. 2, April 1958.
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STORM WATER MANAGEMENT MODEL (Version III) (SWMM)
1. Model Overview: The SWMM is a large FORTRAN program which
models the complete urban rainfall/runoff cycle in an extremely
comprehensive manner. It includes flow overland and in the
sewerage system, in-line and off-line storage treatment
(including costs) of stormwater flows. It also includes a
receiving water module to assess water quality impacts.
Program outputs consist of tables, hydrographs, and
"pollutographs", which can be displayed at points within the
system as well as in the receiving waters. The SWMM has had
limited application to non-urban areas as well. An updated
release of the Model (denoted Version III) became available in
Jan. 1982.
The original SWMM was designed for single event simulation,
producing detailed (i.e., short time increment) hydrographs and
pollutographs for individual storm events. This capability
remains, and the model has been modified so that it may run for
an unlimited number of time steps, i.e., continuously. In this
mode it may be used in a planning context, that is, for an
overall assessment of urban runoff problems and estimates of
the effectiveness and costs of abatement procedures. Tradeoffs
among various control options, e.g., storage, treatment, and
street sweeping, may be evaluated. Complex interactions
between the meteorology, e.g., precipitation patterns, and the
hydrology of an area may be simulated without resorting to
average .values or very simplified methods. In this manner,
critical events from the long period of simulation may be
selected for detailed analysis. In addition, return periods
for intensity, duration, and volume (mass) of runoff (pollutant
loads) may be assigned on the basis of the simulated record
instead of equating them (unjustifiably) to the same statistics
of rainfall record. In this manner, the critical events chosen
for study may be substituted for hypothetical "design storms,"
the latter often being synthesized from intensity-duration-
frequency curves on the basis of questionable statistical
assumptions.
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SWMM is run continuously using only the Runoff and
Storage/Treatment blocks. Routing in TRANSPORT, EXTRAN, or
RECEIV is avoided and is unnecessary for the planning purposes
to which the model is applied. However, there is no limitation
on the number of time steps for either EXTRAN or RECEIV- A
receiving water model that will couple with either continous
SWMM or STORM has been developed and documented.
2. Functional Capabilities: The SWMM consists of 7 blocks of
subroutines. They are:
a. The Executive block assigns logical units
(disk/tape/drum), determines the block or sequence of
blocks to be executed, and, on call, produces graphs
of selected results on the line printer. Thus, this
Block does no computation as such, while each of the
other six blocks are set up to carry through a major
step in the quantity and quality computations. All
access to the computational blocks and transfers
between them pass through subroutine MAIN of the
Executive Block. Transfers are accomplished on
offline devices (disk/tape/drum) which may be saved
for multiple trails or permanent record.
b. The Combine block allows manipulation of data sets
(files stored on offline de.vices) in order to
aggregate the results of previous runs for input into
subsequent blocks. In this manner, large complex
drainage systems may be partitioned for simulation in
smaller segements.
c. The Runoff block computes the stormwater runoff and
its associated pollution loadings for a given storm
for each subcatchment, and stores the results in the
form of hydrographs and pollutographs at the inlets to
the main sewer system. Overland flow simulation is
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accomplished by a storage routing method using
Manning's equation and the continuity equation.
Overland flow does not begin until depression storages
are full. Infiltration on pervious areas is computed
by Morton's exponential function, and is subtracted
from water depth existing on the subcatchment. Gutter
flows are treated as a succession of steady-state
flows, with routing accomplished using Manning's
equation and the continuity equation. To use this
block the user must input the rainfall hydrograph and
a discretization of the drainage basin into sub-basins
of constant land form characteristics. The location
and characteristics of the gutters and pipes also have
to be described. In addition, the user must input
street cleaning frequency and catchbasin data as well
as the land use and other features of the different
areas of the basin.
d. The Transport block routes flow through the sewer
system. Pre-storm conditions in the sewers are set up
by computing dry-weather flow and infiltration, and
distributing them throughout the conveyance system.
The Transport Block then routes the storm runoff (as
determined by the RUNOFF Block), the dry weather flow
(DWF), and the water that has infiltrated into the
system through the main sewer pipes, and through a
maximum of two optional "internal" storage tanks.
The routing scheme is based on an implicit
finite-difference solution of the Kinematic Wave
equations, in which normalized values of the flow and
conduit cross-sectional area are used. Wnen a pipe is
flowing full and inflow exceeds outflow, the excess
(surcharge) is stored at the upstream manhole. The
flows are routed to a maximum of five outlet points.
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This block requires that the sewer system be
discretized into pipe segments of constant size,
slope, and type joined by either manhole, control
structures such as flow dividers, or "internal"
storage tanks. An "internal" storage tank is
described by its size, shape, outlet device, and unit
cost. The outlet device can be either a pump
specified to go on or off at a specified tank depth, a
weir, or an orifice. The outlet device is used to
specify the operation policy of the storage tank.
The DWF quality and quantity entering the sewer system
are calculated by inputting to the model such
parameters as daily and hourly pollution correction
factors, land use population of the subareas, and
average market value of the dwellings in a subarea.
If more exact data is available, such as average BOD
of flows, this can be used in place of some of the
other data.
Infiltration is calculated by estimates of base dry
weather, groundwater, and rainwater infiltration, and
such parameters as average joint distance. The use of
subroutines calculating DWF quality and infiltration
is optional.
e. The EXTRAN block is an alternative to the TRANSPORT
Block. It provides the user the capability to model
sewer systems with extensive surcharging, backwater,
flow reversal, looped sewers, and a variety of flow
control devices. EXTRAN performs the same basic
functions as the TRANSPORT block; however, there are
three major differences:
1. EXTRAN does not route water quality parameters.
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2. EXTRAN uses a link-node conceptual representation
of the transport system, totally unlike the
TRANSPORT block.
3. EXTRAN includes the inertial terms of the
Navier-Stokes equations in the solution, whereas
TRANSPORT is based on a kinematic wave assumption.
Like TRANSPORT, EXTRAN sets up pre-storm conditions by
computing DWF and infiltration and distributing them
throughout the conveyance systems. It then performs
flow routing, picking up the runoff results, and
producing combined flow hydrographs for the total
drainage basin and at selected intermediate points.
EXTRAN may also be used strictly for stormwater
routing with neither DWF nor infiltration.
The two programs are approximately the same length.
The order of operations in both cases is similar,
although the software itself is quite different.
(EXTRAN is not a direct derivative of the original
TRANSPORT block).
f. The Storage/Treatment block simulates the changes in
the hydrographs and pollutographs of the runoff as it
flows through a dry-or wet-weather storage/treatment
plant containing up to five unit operations. Each
unit operation may have detention on non-detention
characteristics. The various units may be linked in a
variety of configurations. Sludge handling may also
be modeled using one or more units. Also, capital
cost and O&M may be estimated for each unit.
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g. The RECEIV block takes output from runoff, TRANSPORT,
EXTRAN, or STORAGE/TREATMENT and computes the impact
of the discharges upon the quality of the receiving
water. The receiving body of water is discretized by
the user to consist of a network of nodes connected by
channels. An option in the program allows two
parallel channels to be used between junctions to aid
in simulating receiving bodies such as marshes. Each
channel is of contant surface and cross-sectional
area. Boundary conditions can be specified as a weir
(outfall from a lake) or some tidal condition. RECEIV
in Version III is uncharged from Version II.
3. Basic Assumptions: The model incorporates numerous
assumptions, some implicit in the formulation chosen, such as
in the Morton infiltration function, and some more explicit,
such as the kinematic wave flow routing assumptions. The
potential model users would be well advised to thoroughly
understand the implications of these assumptions, as they
strongly affect the model results, and will greatly influence
the correct interpretation of model output. The assumptions
are too numerous to be described here, and the user is referred
to model documentation.
4. Input and Output: SWMM requires a large amount of input
data, as described above. Typically, the collection and
preparation of input data can consume 50% or more of modeling
project resources. The various blocks of SWMM will accept
input data in card-image form, or from disk or tape drives,
particularly when output from one blocks input to the next.
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The SWMM is designed as a "deterministic" model, in that if
all input parameters are accurate, the physics of the processes
are simulated sufficiently well to produce accurate results
without calibration. This concept may fail in practice because
the input data or the numerical methods may not be accurate
enough for most real applications. Furthermore, many
computational procedures within the Model are based upon
limited data themselves. For instance, surface quality
predictions are based almost totally on data from Chicago, and
are unlikely to be of universal applicability.
As result it is essential that some local
verification/calibration data be available at specific
application sites to lend credibility to the prediction of any
urban runoff model. These data are usually in the form of
measured flows and concentrations at outfalls or combined sewer
overflow locations. Note that the quality measurements without
accompanying flows are of little value. The SWMM has
sufficient parameters that may be "adjusted," particularly in
the Runoff block, so that calibrating the Model against
measured data is usually readily accomplished.
SWMM output is in the form of tables and various graphs of
rainfall intensity, flow and pollutant concentration, or
loads. Output can be provided for selected points within the
system, as well as in the receiving waters. As implied above,
successful use of the SWMM requires careful evaluation and
interpertation of model results.
5- System Resource Requirements: SWMM is written in FORTRAN
and can be run on any computer with more than 350 K bytes of
core memory. Disk and magnetic tape storage vary with the
application. The operator must have some knowledge of computer
programming and engineering to successfully use this model.
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6. Applications; The SWMM is perhaps the world's most widely
used hydrologic model, having been extensively applied in the
U.S., Canada, and 18 other foreign countries to such problems
as:
a. Analysis and design of storm and combined sewer
overflow pollution abatement facilities
b. Drainage design (urban area, subdivision, airports)
c. Analysis of storage/treatment alternatives
d. Evaluation of the effects of changes in population and
land use
e. Design of systems to relieve surcharging and/or
basement flooding
f. Analysis of sewer system performance
An active SWMM Users Group meets semi-annually, and
publishes meeting proceedings which document a wide variety of
applications.
SWMM is a complex model both computationally and
theoretically, and a successful user must have a thorough
knowledge of hydraulics, hydrology, and water pollution,
together with some experience in water quality modeling.
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7. Technical Contact
Tom Barnwell
U.S. Envirnomental Protection Agency
Environmental Research Laboratory
College Station Road
Athens, GA. 30613
COM 404/546-3585 FTS 250-3585
Dr. Wayne C. Huber
Department of Environmental Engineering
Sciences
College of Engineering
A.P. Black Hall-Room 420
Gainsville, FL 32611
COM 904/392-0846 or 392-0840
8. References
Huber, W.C., et al., "Storm Water Management User's Manual,
Version II," EPA-670/2-75-017, Enviromental Protection
Agency, Cincinnati, OH, March 1975.
Huber, W.C., et al., "Storm Water Management User's Manual,
Version III," (In Press), Environmental Protection Agency,
Cincinnati, OH, 1982.
Medina, M.A. Jr., "Level III: Receiving Water Quality
Modeling for Urban Stormwater Management," EPA
600/2-79-100, Environmental Protection Agency, Washington,
D.C., August 1979.
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Metcalf and Eddy, Inc., University of Florida, Water
Resources Engineers, Inc., "Storm Water Management Model,
Vol. I Final Report," Report 110 24 DOC 07/71, (NTIS PB 203
289), Environmental Proctection Agency, Washington, D.C.,
July 1971.
Roesner, L.A., Shubinski, R.P., and Aldrich, J.A., "Storm
Water Management Model User's Manual, Version III:
Addendum I EXTRAN." (In Press). Environmental Protection
Agency, Cincinnati, OH, 1982.
Torno, H.C. (Editor), "Proceedings, Stormwater Management
Model (SWMM) Users Group Meeting, January 10-11, 1980," EPA
600/9-80-017, Environmental Protection Agency, Washington,
D.C., March 1980.
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TWO-DIMENSIONAL GROUNDWATER MASS TRANSPORT MODEL (GWMTM2)
1. Model Overview. GWMTM2 is based on an analytical solut-
ion to the unsteady-state, convective-dispersive mass transport
equation which describes the concentration distribution in two-
dimensional groundwater systems. The model accounts for advection,
dispersion in two dimensions, first order decay, amd an exponentially
decaying, Gaussian boundary condition. The model can be used as
an excellent test of available two-dimensional, unsteady-state,
numerical mo'dels; the degree of numerical models'; and the degree of
numerical dispersion and oscillations for different numerical
solution schemes can be easily determined. In addition to exactly
checking numerical models, this Gaussian boundary condition model
is a valuable tool, in itself, for estimating the two-dimensional
(areal or vertical cross-section) concentration pattern down-
gradient from sanitary landfills, wastewater lagoons, or other
groundwater pollution sources.
2. Functional Capabilities: The time-varying Gaussian
boundary condition is general allowing any variance, peak concen-
tration and center location. The exponential decay multiplier
may be used or omitted. The groundwater aquifer can be any size.
3. Basic Assumptions: The model is applicable where there
is a uni-directional, constant seepage velocity and the dispersion
coefficients(longitudinal and lateral) are constants. This pre-
sumes steady, horizontal flow in the homogeneous aquifer.
4. Input and Output: System parameters are inputted as
FORTRAN statements in the main program. Space and time positions
where concentration predictions are desired are inputted as data
cards. The program is user-oriented requiring the punching of
less than 10 cards for parametric information.
40!
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5. System Resource Requirements: The model is written in
standard FORTRAN IV and has been run on the IBM 360/91 computer;
it will run on any standard digital computer. It requires a region
size on the IBM machine of approximately 100K. One can learn to
operate the model in less than a half hour. Set up time is insig-
nificant and programming experience is necessary. It has also been
run. on minicomputers using less than 100K of core.
6. Applications: The model was developed for the Nassau-
Suffolk Regional Planning Board (Lee Koppleman, Executive Director)
as part of a large 208 project. It was applied to simulate the
two-dimensional chloride distribution downgradient from the Babylon
sanitary landfill. It was also used to check the numerical accuracy
of several solution schemes of two-dimensional, numerical models
of groundwater quality. It has been distributed widely through
short courses dealing with groundwater pollution and has been used
principally to simulate leachate plumes from landfills and check
the accuracy of two-dimensional, numerical models.
7. Technical Contacts.
Professor Robert W, deary
P.O. Eox 2010
Princeton, New Jersey 08540
8. References
Cleary, R.W., Final 208 Report to the Nassau-Suffolk
Regional Planning Board, Hauppauge, New York, December
1977.
409
* U.S. GOVERNMENT PRINTING OFFICE: 1982 361-082/32U
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